---------------------------------------------------------------------------------------------------- name: log: C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationF > iles\All.log log type: text opened on: 2 Jul 2021, 16:38:33 . . // Baseline set of controls . global controls wave gender prior democrat indep otherpol midwest south west age1 age2 age3 age4 a > nychildren loghhinc associatemore fulltime parttime selfemp unemp student . . . *ØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØ* . ***************** Generate Treatment Values ****************************************** . *ØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØ* . . do "02_Generate_Treatment_Values.do" . *** Baseline wage statistic: Gender wage gap among those age 45 who work 40 hours per week and hol > d a Bachelor degree . . // Based on the American Community Survey: . clear all . set more off . . use "$path\data\usa_00025.dta", clear . . // Latest available wave as of January 2018: Year 2016 . keep if year==2016 (60,038,334 observations deleted) . . keep if age>17&age<66 (1,211,541 observations deleted) . . // Set missing wages to missing if applicable . replace incwage=. if incwage==9999999 (0 real changes made) . replace incwage=. if incwage==999999 (0 real changes made) . . gen female=0 if sex!=. . replace female=1 if sex==2 (983,904 real changes made) . . gen GWG_ACS_45_Bachelor=. (1,944,946 missing values generated) . . gen e_sample=. (1,944,946 missing values generated) . . * uhrswork = weekly hours worked . * educd==101 -> Bachelor's degree . * classwkr=2 -> Employee . . // Calculate women's wages as a share of male wages in the group of 45-year-old employees with a B > achelors degree who work 40 hours per week . reg incwage female if age==45&empstat==1&uhrswork==40&educd==101&classwkr==2 [pweight=perwt], robu > st (sum of wgt is 3.4085e+05) Linear regression Number of obs = 3,152 F(1, 3150) = 86.67 Prob > F = 0.0000 R-squared = 0.0403 Root MSE = 55750 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -22856.08 2455.082 -9.31 0.000 -27669.8 -18042.35 _cons | 86339.27 2135.552 40.43 0.000 82152.06 90526.48 ------------------------------------------------------------------------------ . mat beta = e(b) . sca feml = beta[1,1] . disp feml -22856.076 . replace e_sample=0 (1,944,946 real changes made) . replace e_sample=1 if e(sample)==1 (3,152 real changes made) . mean incwage if female==0&age==45&empstat==1&uhrswork==40&educd==101&classwkr==2 [pweight=perwt] Mean estimation Number of obs = 1,520 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 86339.27 2135.577 82150.28 90528.26 -------------------------------------------------------------- . matrix mean=e(b) . mat list mean symmetric mean[1,1] incwage y1 86339.269 . sca const=mean[1,1] . replace GWG_ACS_45_Bachelor = 1+(feml/const) if educd==101&age==45&empstat==1&uhrswork==40&classwk > r==2 (3,152 real changes made) . . tab GWG_ACS_45_Bachelor GWG_ACS_45_ | Bachelor | Freq. Percent Cum. ------------+----------------------------------- .735276 | 3,152 100.00 100.00 ------------+----------------------------------- Total | 3,152 100.00 . . ******* . . // Based on the Current Population Survey: . . use "$path\data\cps_00004.dta", clear . . // Latest available wave as of January 2018: October 2017 . keep if year==2017 (0 observations deleted) . keep if month==10 (1,148,130 observations deleted) . . //keep if eligible for outgoing rotation group (otherwise labor market earnings not elicited) . keep if eligorg==1 (114,316 observations deleted) . . gen inc_male=. (13,789 missing values generated) . gen inc_female=. (13,789 missing values generated) . . // Calculate women's wages as a share of male wages in the group of 45-year-old employees with a B > achelors degree who work 40 hours per week . mean earnweek [pweight=earnwt] if sex==1&uhrswork1==40&age==45&educ==111 Mean estimation Number of obs = 20 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ earnweek | 1390.621 158.3602 1059.169 1722.073 -------------------------------------------------------------- . matrix mean1=e(b) . replace inc_male=mean1[1,1] if uhrswork1==40&age==45&educ==111 (48 real changes made) . mean earnweek [pweight=earnwt] if sex==2&uhrswork1==40&age==45&educ==111 Mean estimation Number of obs = 28 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ earnweek | 1301.907 168.9566 955.2371 1648.578 -------------------------------------------------------------- . matrix mean2=e(b) . replace inc_female=mean2[1,1] if uhrswork1==40&age==45&educ==111 (48 real changes made) . gen GWG_45_Bachelor_month_10=inc_female/inc_male (13,741 missing values generated) . . tab GWG_45_Bachelor_month_10 GWG_45_Bach | elor_month_ | 10 | Freq. Percent Cum. ------------+----------------------------------- .9362057 | 48 100.00 100.00 ------------+----------------------------------- Total | 48 100.00 . . . end of do-file . . *ØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØ* . ***************** Clean Survey Data ****************************************** . *ØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØ* . . // Wave A, main survey, prepare data . do "03_SurveyStageIA_cleaning.do" . . *********************************************************************************** . // Replication Files . ********************************************************************************** . /* > HOW DO BELIEFS ABOUT THE GENDER WAGE GAP AFFECT THE DEMAND FOR PUBLIC POLICY? > Sonja Settele > AEJ:pol > */ . ********************************************************************************** . . *********************************************************************************** . **** Cleaning file Survey Wave A, Stage I (main survey): . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_WaveA_raw.dta", clear . // Variables not included for confidentiality: zip code and final thoughts/comments by respondent . . keep submitdate lastpage startdate panelID interviewtime > /// > groupTime499 groupTime742 > /// > region agebracket gender employment hhincbracket demrep demrepother > /// demographics > RAND0 RAND RAND12 RAND6 RAND13 RAND2 RAND3 RAND4 RAND5 RAND7 RAND8 RAND9 RAND10 RANDINST > /// randomization > elicitbgendeMarc elicitbgendernoinMar > /// > manicheckSQ001 manicheckSQ002 manicheckSQ003 womenwages > /// > legislationanchor AAanchor transparencyanchor quotaanchor childcare > /// > AAanchorder transpanchorder legisanchorder childcareorder quotaanchorder > /// > petition1 AAUW fblike > /// > reasonsSQ003 reasonsSQ004 reasonsSQ005 reasonsSQ006 reasonsSQ001 reasonsSQ002 // > / > reasons2SQ012 reasons2SQ011 reasons2SQ010 reasons2SQ009 reasons2SQ008 reasons2SQ007 // > / > reasons3SQ012 reasons3SQ011 reasons3SQ010 reasons3SQ009 reasons3SQ008 reasons3SQ007 // > / > reasons4SQ007 reasons4SQ008 reasons4SQ009 reasons4SQ010 reasons4SQ011 reasons4SQ012 // > / > reasons5SQ007 reasons5SQ008 reasons5SQ009 reasons5SQ010 reasons5SQ011 reasons5SQ012 // > / > Scenario01 Scenario02 Scenario03 Scenario04 Scenario05 Scenario06 > /// > Scenario0B1 Scenario0B2 Scenario0B3 Scenario0B4 Scenario0B5 Scenario0B6 > /// > extrayoung extraHSS extraoccuu > /// > genderroleattSQ001 genderroleattSQ002 > /// > tenyears wageexpectation fairown demo1 demo2 demo2other demo3 ind1 ind1other ind2 occ /// > companysize labinc hhincome hrsweek maritalst maritalstother > /// > childrenSQ001 childrenSQ002 birthyear fblikesettings biased relevant read // > / > trustworthy trustcensus interviewtime . . . ******************************************************************************** . //// Screening questions ***************************************************** > ******************************************************************************** . . //employment . gen employ=. (2,510 missing values generated) . replace employ= 1 if employment=="A1" (1,321 real changes made) . replace employ=2 if employment=="A2" (252 real changes made) . replace employ=3 if employment=="A3" (209 real changes made) . replace employ=4 if employment=="A4" (157 real changes made) . replace employ=5 if employment=="A5" (83 real changes made) . replace employ=6 if employment=="A6" (237 real changes made) . replace employ=7 if employment=="A7" (251 real changes made) . . label define empl 1 "full-time" 2 "part-time" 3 "self-empl" 4 "unempl" 5 "student" 6 "retired" 7 " > out of LF" . label values employ empl . . gen employee=(employ==1|employ==2) if employ!=. . lab var employee "Full- or part-time employee" . drop employment . . gen employed=(employ==1|employ==2|employ==3) if employ!=. . lab var employed "Employed (full- or part-time or self-emp.)" . . gen nonemployed=(employ==4|employ==5|employ==6|employ==7) if employ!=. . lab var nonemployed "Not employed (unempl., student, out of labor force)" . . gen fulltime=(employ==1) . label var fulltime "Full-time employee" . gen parttime=(employ==2) . label var parttime "Part-time employee" . gen selfemp=(employ==3) . label var selfemp "Self-employed" . gen unemployed=(employ==4) . lab var unemployed "Unemployed" . gen student=(employ==5) . lab var student "Student" . gen oolf=(employ==6|employ==7) . label var oolf "Out of labor force" . . gen oolf2=student + oolf . label var oolf2 "Out of LF (incl. student and retired)" . . //region . rename region regionstring . gen region=. (2,510 missing values generated) . replace region = 1 if regionstring=="A1" (446 real changes made) . replace region = 2 if regionstring=="A2" (527 real changes made) . replace region = 3 if regionstring=="A3" (943 real changes made) . replace region = 4 if regionstring=="A4" (594 real changes made) . drop regionstring . . gen northeast = (region==1) . gen midwest = (region==2) . gen south = (region==3) . gen west = (region==4) . . label var northeast "Northeast" . label var midwest "Midwest" . label var south "South" . label var west "West" . . //age group . gen age=. (2,510 missing values generated) . replace age = 1 if agebracket=="A1" (215 real changes made) . replace age = 2 if agebracket=="A2" (657 real changes made) . replace age = 3 if agebracket=="A3" (558 real changes made) . replace age = 4 if agebracket=="A4" (526 real changes made) . replace age = 5 if agebracket=="A5" (554 real changes made) . . label define age 1 "18-24" 2 "25-34" 3 "35-44" 4 "45-54" 5 "55-65" . drop agebracket . tab age, gen(age) age | Freq. Percent Cum. ------------+----------------------------------- 1 | 215 8.57 8.57 2 | 657 26.18 34.74 3 | 558 22.23 56.97 4 | 526 20.96 77.93 5 | 554 22.07 100.00 ------------+----------------------------------- Total | 2,510 100.00 . . label var age1 "Age 18-24" . label var age2 "Age 25-34" . label var age3 "Age 35-44" . label var age4 "Age 45-54" . label var age5 "Age 55-65" . . //household income . gen hhinc=. (2,510 missing values generated) . forvalues i=1/8{ 2. replace hhinc=`i' if hhincbracket=="A`i'" 3. } (186 real changes made) (207 real changes made) (584 real changes made) (557 real changes made) (404 real changes made) (368 real changes made) (126 real changes made) (78 real changes made) . . . label define hhinc 1 "< \textdollar 15,000" 2 "\textdollar 15,000 - \textdollar 25,000" 3 "\textdo > llar 25,000 - \textdollar 50,000" 4 "\textdollar 50,000 - \textdollar 75,000" 5 "\textdollar 75,00 > 0 - \textdollar 100,000" 6 "\textdollar 100,000 - \textdollar 150,000" 7 "\textdollar 150,000 - \t > extdollar 200,000" 8 "> \textdollar 200,000" . label values hhinc hhinc . . tab hhinc, gen (hhinc) hhinc | Freq. Percent Cum. ----------------------------------------+----------------------------------- < \textdollar 15,000 | 186 7.41 7.41 \textdollar 15,000 - \textdollar 25,000 | 207 8.25 15.66 \textdollar 25,000 - \textdollar 50,000 | 584 23.27 38.92 \textdollar 50,000 - \textdollar 75,000 | 557 22.19 61.12 \textdollar 75,000 - \textdollar 100,00 | 404 16.10 77.21 \textdollar 100,000 - \textdollar 150,0 | 368 14.66 91.87 \textdollar 150,000 - \textdollar 200,0 | 126 5.02 96.89 > \textdollar 200,000 | 78 3.11 100.00 ----------------------------------------+----------------------------------- Total | 2,510 100.00 . . label var hhinc1 "Yearly household inc. < \textdollar 15,000" . label var hhinc2 "Yearly household inc. \textdollar 15,000 - \textdollar 25,000" . label var hhinc3 "Yearly household inc. \textdollar 25,000 - \textdollar 50,000" . label var hhinc4 "Yearly household inc. \textdollar 50,000 - \textdollar 75,000" . label var hhinc5 "Yearly household inc. \textdollar 75,000 - \textdollar 100,000" . label var hhinc6 "Yearly household inc. \textdollar 100,000 - \textdollar 150,000" . label var hhinc7 "Yearly household inc. \textdollar 150,000 - \textdollar 200,000" . label var hhinc8 "Yearly household inc. > \textdollar 200,000" . . gen lowinc=hhinc1+hhinc2+hhinc3 . gen highinc=hhinc4+hhinc5+hhinc6+hhinc7+hhinc8 . . label var lowinc "Household inc $\leq$ \textdollar 50,000" . label var highinc "Household inc. > \textdollar 50,000" . . . //gender . rename gender gendstring . gen gender = . (2,510 missing values generated) . replace gender = 0 if gendstring== "A1" (1,250 real changes made) . replace gender = 1 if gendstring== "A2" (1,260 real changes made) . . label define gender 0 "male" 1 "female" . label values gender gender . drop gendstring . . gen female=gender . label values female gender . label var female "Female" . . gen male=0 if female==1 (1,250 missing values generated) . replace male=1 if female==0 (1,250 real changes made) . lab var male "Male" . . // political orientation . gen pol=. (2,510 missing values generated) . replace pol=-2 if demrep=="A1" (682 real changes made) . replace pol=-1 if demrep=="A2" (228 real changes made) . replace pol=0 if demrep=="A3" (424 real changes made) . replace pol=1 if demrep=="A4" (301 real changes made) . replace pol=2 if demrep=="A5" (826 real changes made) . . gen otherpol=(demrep=="-oth-") . label var otherpol "Other pol. orientation" . . drop demrep . . label define demrep -2 "Republican" -1 "Indep (Repub)" 0 "Independent" 1 "Indep (Demo)" 2 "Democra > t" . label values pol demrep . . gen repubindep=(pol<1) if pol!=. (49 missing values generated) . . gen republican= (pol<0) . gen democrat= (pol>0&pol!=.) . gen indep = (pol==0) . replace republican=0 if otherpol==1 (0 real changes made) . replace democrat=0 if otherpol==1 (0 real changes made) . replace indep=0 if otherpol==1 (0 real changes made) . . label var republican "Republican (incl. indep leaning repub.)" . label var democrat "Democrat (incl. indep. leaning dem.)" . label var indep "Independent" . . gen repubonly=(pol==-2) . gen indeprepub=(pol==-1) . gen indeponly=(pol==0) . gen indepdem=(pol==1) . gen demonly=(pol==2) . . lab var repubonly "Republican" . lab var indeprepub "Indep. leaning Republican" . lab var indeponly "Independent" . lab var indepdem "Indep. leaning Democrat" . lab var demonly "Democrat" . . gen testpol=democrat+indep+republican+otherpol . . tab testpol testpol | Freq. Percent Cum. ------------+----------------------------------- 1 | 2,510 100.00 100.00 ------------+----------------------------------- Total | 2,510 100.00 . drop testpol . . . ******************************************************************************** . //// Treatment conditions ********************************************** > ******************************************************************************** . . // Randomized treatment groups . * T1: High wage gap treatment (74%) . * T2: Low wage gap treatment (94%) . gen T2=(RAND==2) if RAND!=. . gen T1=(RAND==1) if RAND!=. . . . ******************************************************************************** . //// Prior beliefs ***************************************************** > ******************************************************************************** . . // Prior incentivized or not . gen prior1=(RAND12==1) . lab var prior1 "Incentive" . . // prior belief . gen prior = elicitbgendeMarc if RAND12==1 (1,012 missing values generated) . replace prior = elicitbgender if RAND12==0 (1,012 real changes made) . . label var prior "Prior belief" . . . // Time spent on prior belief: . *groupTime499: time spent on prior estimate in incentivized condition . *groupTime742: time spent on prior estimate in unincentivized condition . rename groupTime499 timepriorinc . rename groupTime742 timepriornoinc . gen timeprior=timepriorinc (1,012 missing values generated) . replace timeprior=timepriornoinc if timeprior==. (1,012 real changes made) . . . ******************************************************************************** . ******************************************************************************** . //// Outcome variables ***************************************************** > ******************************************************************************** . ******************************************************************************** . . ******************************************************************************** . //// Self-reported main outcomes ***************************************************** > ******************************************************************************** . . // Posterior belief about females' relative wages . * RAND4 determines which posterior belief statistic respondent was assigned to . gen posterior = extrayoung (1,676 missing values generated) . replace posterior = extraHS if RAND4==10 (825 real changes made) . replace posterior = extraoccu if RAND4==11 (838 real changes made) . . label var extrayoung "Posterior belief (age 25)" . label var extraHS "Posterior belief (HS degree)" . label var extraoccu "Posterior belief (same occu.)" . . tab posterior,m posterior | Freq. Percent Cum. ------------+----------------------------------- 0 | 3 0.12 0.12 1 | 2 0.08 0.20 3 | 1 0.04 0.24 5 | 1 0.04 0.28 6 | 2 0.08 0.36 9 | 1 0.04 0.40 14 | 1 0.04 0.44 15 | 1 0.04 0.48 18 | 2 0.08 0.56 19 | 2 0.08 0.64 21 | 2 0.08 0.72 22 | 1 0.04 0.76 24 | 1 0.04 0.80 25 | 5 0.20 1.00 26 | 1 0.04 1.04 27 | 1 0.04 1.08 28 | 1 0.04 1.12 29 | 1 0.04 1.16 30 | 2 0.08 1.24 31 | 2 0.08 1.31 32 | 1 0.04 1.35 33 | 1 0.04 1.39 34 | 1 0.04 1.43 35 | 1 0.04 1.47 36 | 4 0.16 1.63 37 | 1 0.04 1.67 39 | 2 0.08 1.75 40 | 6 0.24 1.99 41 | 2 0.08 2.07 42 | 3 0.12 2.19 43 | 2 0.08 2.27 44 | 1 0.04 2.31 45 | 3 0.12 2.43 46 | 1 0.04 2.47 47 | 4 0.16 2.63 48 | 8 0.32 2.95 49 | 5 0.20 3.15 50 | 31 1.24 4.38 51 | 8 0.32 4.70 52 | 3 0.12 4.82 53 | 3 0.12 4.94 54 | 8 0.32 5.26 55 | 15 0.60 5.86 56 | 7 0.28 6.14 57 | 8 0.32 6.45 58 | 12 0.48 6.93 59 | 7 0.28 7.21 60 | 32 1.27 8.49 61 | 6 0.24 8.73 62 | 16 0.64 9.36 63 | 15 0.60 9.96 64 | 15 0.60 10.56 65 | 47 1.87 12.43 66 | 17 0.68 13.11 67 | 24 0.96 14.06 68 | 32 1.27 15.34 69 | 20 0.80 16.14 70 | 76 3.03 19.16 71 | 24 0.96 20.12 72 | 40 1.59 21.71 73 | 30 1.20 22.91 74 | 149 5.94 28.84 75 | 144 5.74 34.58 76 | 49 1.95 36.53 77 | 39 1.55 38.09 78 | 64 2.55 40.64 79 | 34 1.35 41.99 80 | 119 4.74 46.73 81 | 30 1.20 47.93 82 | 39 1.55 49.48 83 | 33 1.31 50.80 84 | 31 1.24 52.03 85 | 102 4.06 56.10 86 | 27 1.08 57.17 87 | 36 1.43 58.61 88 | 61 2.43 61.04 89 | 42 1.67 62.71 90 | 120 4.78 67.49 91 | 39 1.55 69.04 92 | 56 2.23 71.27 93 | 40 1.59 72.87 94 | 158 6.29 79.16 95 | 91 3.63 82.79 96 | 60 2.39 85.18 97 | 34 1.35 86.53 98 | 42 1.67 88.21 99 | 22 0.88 89.08 100 | 109 4.34 93.43 101 | 8 0.32 93.75 102 | 6 0.24 93.98 103 | 3 0.12 94.10 104 | 5 0.20 94.30 105 | 3 0.12 94.42 106 | 5 0.20 94.62 107 | 2 0.08 94.70 108 | 6 0.24 94.94 109 | 1 0.04 94.98 110 | 7 0.28 95.26 111 | 5 0.20 95.46 112 | 3 0.12 95.58 113 | 3 0.12 95.70 114 | 4 0.16 95.86 115 | 2 0.08 95.94 116 | 1 0.04 95.98 117 | 1 0.04 96.02 118 | 1 0.04 96.06 119 | 2 0.08 96.14 120 | 3 0.12 96.25 121 | 1 0.04 96.29 122 | 1 0.04 96.33 123 | 3 0.12 96.45 124 | 2 0.08 96.53 125 | 5 0.20 96.73 126 | 3 0.12 96.85 127 | 2 0.08 96.93 128 | 3 0.12 97.05 129 | 2 0.08 97.13 130 | 3 0.12 97.25 133 | 1 0.04 97.29 134 | 2 0.08 97.37 135 | 1 0.04 97.41 137 | 2 0.08 97.49 141 | 1 0.04 97.53 142 | 1 0.04 97.57 144 | 1 0.04 97.61 145 | 3 0.12 97.73 147 | 3 0.12 97.85 149 | 1 0.04 97.89 150 | 3 0.12 98.01 151 | 1 0.04 98.05 152 | 3 0.12 98.17 154 | 2 0.08 98.25 156 | 1 0.04 98.29 157 | 1 0.04 98.33 158 | 1 0.04 98.37 162 | 1 0.04 98.41 163 | 1 0.04 98.45 164 | 1 0.04 98.49 167 | 1 0.04 98.53 168 | 1 0.04 98.57 170 | 2 0.08 98.65 176 | 2 0.08 98.73 177 | 2 0.08 98.80 180 | 1 0.04 98.84 181 | 1 0.04 98.88 183 | 1 0.04 98.92 185 | 3 0.12 99.04 186 | 2 0.08 99.12 190 | 1 0.04 99.16 199 | 1 0.04 99.20 200 | 7 0.28 99.48 . | 13 0.52 100.00 ------------+----------------------------------- Total | 2,510 100.00 . * -> 13 missing observations, not recorded due to bug in survey . . . //Manipulation check/ Perceptions related to wage gap . rename manicheckSQ001 large . rename manicheckSQ002 problem . rename manicheckSQ003 govmore . . . foreach var in large problem govmore womenwages fairown{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/10{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. } (2,510 missing values generated) (94 real changes made) (84 real changes made) (142 real changes made) (126 real changes made) (189 real changes made) (194 real changes made) (455 real changes made) (491 real changes made) (257 real changes made) (478 real changes made) (2,510 missing values generated) (115 real changes made) (90 real changes made) (92 real changes made) (86 real changes made) (149 real changes made) (183 real changes made) (305 real changes made) (425 real changes made) (373 real changes made) (692 real changes made) (2,510 missing values generated) (161 real changes made) (70 real changes made) (72 real changes made) (65 real changes made) (181 real changes made) (139 real changes made) (246 real changes made) (350 real changes made) (356 real changes made) (870 real changes made) (2,510 missing values generated) (509 real changes made) (1,382 real changes made) (451 real changes made) (106 real changes made) (62 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (2,510 missing values generated) (169 real changes made) (623 real changes made) (1,213 real changes made) (280 real changes made) (155 real changes made) (70 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) . . * Set perceived fairness of own most recent or current wage to missing for those who have never wo > rked . replace fairown=. if fairown==6 (70 real changes made, 70 to missing) . . label var large "GWG large" . label var problem "GWG problem" . label var govmore "Gvmt. should do more" . label var womenwages "Women's wages are fair" . label var fairown "My own wage is fair" . . . //Demand for specific policies . foreach var in quotaanchor AAanchor transparencyanchor legislationanchor childcare{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/5{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. } (2,510 missing values generated) (324 real changes made) (519 real changes made) (670 real changes made) (684 real changes made) (313 real changes made) (2,510 missing values generated) (179 real changes made) (293 real changes made) (857 real changes made) (815 real changes made) (366 real changes made) (2,510 missing values generated) (152 real changes made) (239 real changes made) (517 real changes made) (905 real changes made) (697 real changes made) (2,510 missing values generated) (98 real changes made) (248 real changes made) (808 real changes made) (979 real changes made) (377 real changes made) (2,510 missing values generated) (91 real changes made) (145 real changes made) (601 real changes made) (946 real changes made) (727 real changes made) . . . label var quotaanchor "Gender quotas" . label var AAanchor "Aff. action" . label var legislationanchor "Equal pay legislation" . label var transparencyanchor "Wage transparency" . label var childcare "Public child care" . . . ******************************************************************************** . //// Behavioral outcomes ***************************************************** > ******************************************************************************** . . //Intentions to sign petition . gen petition=-1 if petition1=="A2" (2,282 missing values generated) . replace petition=0 if petition1=="A3" (988 real changes made) . replace petition=1 if petition1=="A1" (1,294 real changes made) . . gen petitionI=0 if petition<1 (1,294 missing values generated) . replace petitionI=1 if petition==1 (1,294 real changes made) . . gen petitionII=0 if petition>-1 &petition!=. (228 missing values generated) . replace petitionII=1 if petition==-1 (228 real changes made) . . label var petitionI "Petition I (increase reporting)" . label var petitionII "Petition II (abolish reporting)" . . drop petition1 . . . //Donation decision . label var AAUW "Donation for NGO" . . . // Facebook like . rename fblike fblikestring . gen fblike=0 if fblikestring=="not clicked" (281 missing values generated) . replace fblike=1 if fblikestring=="clicked" (278 real changes made) . . label var fblike "Facebook like for NGO" . . drop fblikestring . . gen fblike2=fblike (3 missing values generated) . replace fblike2=. if fblikesettings=="A3" (563 real changes made, 563 to missing) . . . // Decision to acquire additional information . replace Scenario0B3="A2" if Scenario0B3=="A3" (1,043 real changes made) . . * RANDINST==1: First three choices were about information from progressive source, last three abou > t conservative source . * RANDINST==2: First three choices were about information from conservative source, last three fro > m progressive source . gen scenario1support=0 . replace scenario1support=1 if (Scenario01=="A1"&RANDINST==1)|(Scenario0B4=="A1"&RANDINST==2) (1,381 real changes made) . gen scenario2support=0 . replace scenario2support=1 if (Scenario02=="A1"&RANDINST==1)|(Scenario0B5=="A1"&RANDINST==2) (1,167 real changes made) . gen scenario3support=0 . replace scenario3support=1 if (Scenario03=="A1"&RANDINST==1)|(Scenario0B6=="A1"&RANDINST==2) (1,050 real changes made) . . gen scenario1oppose=0 . replace scenario1oppose=1 if (Scenario04=="A1"&RANDINST==1)|(Scenario0B1=="A1"&RANDINST==2) (687 real changes made) . gen scenario2oppose=0 . replace scenario2oppose=1 if (Scenario05=="A1"&RANDINST==1)|(Scenario0B2=="A1"&RANDINST==2) (538 real changes made) . gen scenario3oppose=0 . replace scenario3oppose=1 if (Scenario06=="A1"&RANDINST==1)|(Scenario0B3=="A1"&RANDINST==2) (473 real changes made) . . * Number of times respondent chose info over money: . gen infopaysupport=scenario1support + scenario2support + scenario3support . gen infopayoppose=scenario1oppose + scenario2oppose + scenario3oppose . . tab infopaysupport infopaysupp | ort | Freq. Percent Cum. ------------+----------------------------------- 0 | 1,009 40.20 40.20 1 | 300 11.95 52.15 2 | 305 12.15 64.30 3 | 896 35.70 100.00 ------------+----------------------------------- Total | 2,510 100.00 . tab infopayoppose infopayoppo | se | Freq. Percent Cum. ------------+----------------------------------- 0 | 1,701 67.77 67.77 1 | 259 10.32 78.09 2 | 211 8.41 86.49 3 | 339 13.51 100.00 ------------+----------------------------------- Total | 2,510 100.00 . . label var infopaysupport "Will. to pay for progressive info" . label var infopayoppose "Will. to pay for traditional info" . . drop Scenario01-Scenario06 . drop Scenario0B1-Scenario0B6 . . . ******************************************************************************** . //// Additional outcomes/mechanisms ********************************************* > ******************************************************************************** . . // Beliefs about prevalence of underlying factors . local reasons reasonsSQ001 reasonsSQ002 reasonsSQ003 reasonsSQ004 reasonsSQ005 reasonsSQ006 > /// reasons 1 > reasons2SQ012 reasons2SQ011 reasons2SQ010 reasons2SQ009 reasons2SQ008 reasons2SQ007 // > / reasons 2 > reasons3SQ012 reasons3SQ011 reasons3SQ010 reasons3SQ009 reasons3SQ008 reasons3SQ007 // > / reasons 3 > reasons4SQ007 reasons4SQ008 reasons4SQ009 reasons4SQ010 reasons4SQ011 reasons4SQ012 /// reason > s 4 > reasons5SQ007 reasons5SQ008 reasons5SQ009 reasons5SQ010 reasons5SQ011 reasons5SQ012 . . . foreach var in `reasons'{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/5{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. } (2,510 missing values generated) (65 real changes made) (121 real changes made) (133 real changes made) (164 real changes made) (52 real changes made) (2,510 missing values generated) (45 real changes made) (97 real changes made) (107 real changes made) (211 real changes made) (75 real changes made) (2,510 missing values generated) (14 real changes made) (53 real changes made) (110 real changes made) (215 real changes made) (143 real changes made) (2,510 missing values generated) (15 real changes made) (50 real changes made) (120 real changes made) (258 real changes made) (92 real changes made) (2,510 missing values generated) (147 real changes made) (166 real changes made) (126 real changes made) (69 real changes made) (27 real changes made) (2,510 missing values generated) (193 real changes made) (164 real changes made) (110 real changes made) (40 real changes made) (28 real changes made) (2,510 missing values generated) (205 real changes made) (139 real changes made) (104 real changes made) (53 real changes made) (28 real changes made) (2,510 missing values generated) (175 real changes made) (157 real changes made) (109 real changes made) (64 real changes made) (24 real changes made) (2,510 missing values generated) (18 real changes made) (43 real changes made) (78 real changes made) (262 real changes made) (128 real changes made) (2,510 missing values generated) (34 real changes made) (51 real changes made) (101 real changes made) (211 real changes made) (132 real changes made) (2,510 missing values generated) (44 real changes made) (71 real changes made) (108 real changes made) (197 real changes made) (109 real changes made) (2,510 missing values generated) (84 real changes made) (131 real changes made) (149 real changes made) (113 real changes made) (52 real changes made) (2,510 missing values generated) (21 real changes made) (49 real changes made) (99 real changes made) (219 real changes made) (131 real changes made) (2,510 missing values generated) (31 real changes made) (57 real changes made) (97 real changes made) (225 real changes made) (109 real changes made) (2,510 missing values generated) (79 real changes made) (123 real changes made) (137 real changes made) (133 real changes made) (47 real changes made) (2,510 missing values generated) (203 real changes made) (132 real changes made) (100 real changes made) (65 real changes made) (19 real changes made) (2,510 missing values generated) (179 real changes made) (156 real changes made) (99 real changes made) (59 real changes made) (26 real changes made) (2,510 missing values generated) (19 real changes made) (35 real changes made) (87 real changes made) (223 real changes made) (155 real changes made) (2,510 missing values generated) (16 real changes made) (39 real changes made) (71 real changes made) (214 real changes made) (105 real changes made) (2,510 missing values generated) (56 real changes made) (109 real changes made) (121 real changes made) (120 real changes made) (39 real changes made) (2,510 missing values generated) (161 real changes made) (127 real changes made) (92 real changes made) (43 real changes made) (22 real changes made) (2,510 missing values generated) (15 real changes made) (39 real changes made) (106 real changes made) (184 real changes made) (101 real changes made) (2,510 missing values generated) (34 real changes made) (61 real changes made) (99 real changes made) (171 real changes made) (80 real changes made) (2,510 missing values generated) (143 real changes made) (127 real changes made) (105 real changes made) (50 real changes made) (20 real changes made) (2,510 missing values generated) (24 real changes made) (56 real changes made) (76 real changes made) (229 real changes made) (97 real changes made) (2,510 missing values generated) (12 real changes made) (34 real changes made) (71 real changes made) (216 real changes made) (149 real changes made) (2,510 missing values generated) (134 real changes made) (149 real changes made) (102 real changes made) (61 real changes made) (36 real changes made) (2,510 missing values generated) (70 real changes made) (97 real changes made) (143 real changes made) (130 real changes made) (42 real changes made) (2,510 missing values generated) (23 real changes made) (38 real changes made) (78 real changes made) (224 real changes made) (119 real changes made) (2,510 missing values generated) (203 real changes made) (134 real changes made) (75 real changes made) (47 real changes made) (23 real changes made) . . * RAND 3 indicates which out of 5 randomized orders was chosen . . gen interested = . (2,510 missing values generated) . replace interested = reasonsSQ001 if RAND3==6 (535 real changes made) . replace interested = reasons2SQ007 if RAND3==7 (529 real changes made) . replace interested = reasons3SQ010 if RAND3==8 (519 real changes made) . replace interested = reasons4SQ008 if RAND3==9 (445 real changes made) . replace interested = reasons5SQ010 if RAND3==10 (482 real changes made) . label var interested "Diff. interests" . . gen society=. (2,510 missing values generated) . replace society = reasonsSQ002 if RAND3==6 (535 real changes made) . replace society = reasons2SQ008 if RAND3==7 (529 real changes made) . replace society = reasons3SQ011 if RAND3==8 (519 real changes made) . replace society = reasons4SQ011 if RAND3==9 (445 real changes made) . replace society = reasons5SQ007 if RAND3==10 (482 real changes made) . label var society "Work vs. family" . . gen boys=. (2,510 missing values generated) . replace boys = reasonsSQ003 if RAND3==6 (535 real changes made) . replace boys = reasons2SQ009 if RAND3==7 (529 real changes made) . replace boys = reasons3SQ012 if RAND3==8 (519 real changes made) . replace boys = reasons4SQ010 if RAND3==9 (445 real changes made) . replace boys = reasons5SQ008 if RAND3==10 (482 real changes made) . label var boys "Sozialisation" . . gen discrimination=. (2,510 missing values generated) . replace discrimination = reasonsSQ004 if RAND3==6 (535 real changes made) . replace discrimination = reasons2SQ010 if RAND3==7 (529 real changes made) . replace discrimination = reasons3SQ007 if RAND3==8 (519 real changes made) . replace discrimination = reasons4SQ007 if RAND3==9 (445 real changes made) . replace discrimination = reasons5SQ011 if RAND3==10 (482 real changes made) . label var discrimination "Discrimination" . . gen ambitious=. (2,510 missing values generated) . replace ambitious = reasonsSQ005 if RAND3==6 (535 real changes made) . replace ambitious = reasons2SQ011 if RAND3==7 (529 real changes made) . replace ambitious = reasons3SQ008 if RAND3==8 (519 real changes made) . replace ambitious = reasons4SQ012 if RAND3==9 (445 real changes made) . replace ambitious = reasons5SQ009 if RAND3==10 (482 real changes made) . label var ambitious "Diff. ambitions" . . gen talented=. (2,510 missing values generated) . replace talented = reasonsSQ006 if RAND3==6 (535 real changes made) . replace talented = reasons2SQ012 if RAND3==7 (529 real changes made) . replace talented = reasons3SQ009 if RAND3==8 (519 real changes made) . replace talented = reasons4SQ009 if RAND3==9 (445 real changes made) . replace talented = reasons5SQ012 if RAND3==10 (482 real changes made) . label var talented "Diff. talents" . . local reasons ambitious talented interested boys discrimination society . . drop reasonsSQ001 reasonsSQ002 reasonsSQ003 reasonsSQ004 reasonsSQ005 reasonsSQ006 // > / reasons 1 > reasons2SQ012 reasons2SQ011 reasons2SQ010 reasons2SQ009 reasons2SQ008 reasons2SQ007 // > / reasons 2 > reasons3SQ012 reasons3SQ011 reasons3SQ010 reasons3SQ009 reasons3SQ008 reasons3SQ007 // > / reasons 3 > reasons4SQ007 reasons4SQ008 reasons4SQ009 reasons4SQ010 reasons4SQ011 reasons4SQ012 /// reason > s 4 > reasons5SQ007 reasons5SQ008 reasons5SQ009 reasons5SQ010 reasons5SQ011 reasons5SQ012 . . gen orderint=1 if RAND3==6 (1,975 missing values generated) . replace orderint=6 if RAND3==7 (529 real changes made) . replace orderint=3 if RAND3==8 (519 real changes made) . replace orderint=2 if RAND3==9 (445 real changes made) . replace orderint=4 if RAND3==10 (482 real changes made) . . gen ordersociety=2 if RAND3==6 (1,975 missing values generated) . replace ordersociety=5 if RAND3==7 (529 real changes made) . replace ordersociety=2 if RAND3==8 (519 real changes made) . replace ordersociety=5 if RAND3==9 (445 real changes made) . replace ordersociety=1 if RAND3==10 (482 real changes made) . . gen orderboys=3 if RAND3==6 (1,975 missing values generated) . replace orderboys=4 if RAND3==7 (529 real changes made) . replace orderboys=1 if RAND3==8 (519 real changes made) . replace orderboys=4 if RAND3==9 (445 real changes made) . replace orderboys=2 if RAND3==10 (482 real changes made) . . gen orderdisc=4 if RAND3==6 (1,975 missing values generated) . replace orderdisc=3 if RAND3==7 (529 real changes made) . replace orderdisc=6 if RAND3==8 (519 real changes made) . replace orderdisc=1 if RAND3==9 (445 real changes made) . replace orderdisc=5 if RAND3==10 (482 real changes made) . . gen orderambitious=5 if RAND3==6 (1,975 missing values generated) . replace orderambitious=2 if RAND3==7 (529 real changes made) . replace orderambitious=5 if RAND3==8 (519 real changes made) . replace orderambitious=6 if RAND3==9 (445 real changes made) . replace orderambitious=3 if RAND3==10 (482 real changes made) . . gen ordertalent=6 if RAND3==6 (1,975 missing values generated) . replace ordertalent=1 if RAND3==7 (529 real changes made) . replace ordertalent=4 if RAND3==8 (519 real changes made) . replace ordertalent=3 if RAND3==9 (445 real changes made) . replace ordertalent=6 if RAND3==10 (482 real changes made) . . . //Treatment info perceived as relevant . rename relevant relevantstring . gen relevant=. (2,510 missing values generated) . forvalues i=1/5{ 2. replace relevant=`i' if relevantstring=="A`i'" 3. } (239 real changes made) (231 real changes made) (884 real changes made) (658 real changes made) (0 real changes made) . . . // Info perceived as trustworthy . rename trustworthy trustw . gen trustworthy=. (2,510 missing values generated) . forvalues i=1/5{ 2. replace trustworthy=`i' if trustw=="A`i'" 3. } (12 real changes made) (46 real changes made) (232 real changes made) (208 real changes made) (0 real changes made) . drop trustw . . //Trust in survey data provided by census . rename trustcensus trustcensusstring . gen trustcensus=. (2,510 missing values generated) . forvalues i=1/6{ 2. replace trustcensus=`i' if trustcensusstring=="A`i'" 3. } (8 real changes made) (47 real changes made) (181 real changes made) (169 real changes made) (69 real changes made) (24 real changes made) . . replace trustcensus=. if trustcensus==6 (24 real changes made, 24 to missing) . . . // Survey perceived as biased . rename biased biasedstring . gen biased = 0 if biasedstring=="A3" (477 missing values generated) . replace biased = -1 if biasedstring=="A1" (350 real changes made) . replace biased = 1 if biasedstring=="A2" (127 real changes made) . drop biasedstring . . . // Personal wage expectations (ten years from now) . rename tenyears tenyearsstring . gen tenyears=. (2,510 missing values generated) . forvalues i=1/3{ 2. replace tenyears=`i' if tenyearsstring=="A`i'" 3. } (1,487 real changes made) (331 real changes made) (692 real changes made) . . replace tenyears=-tenyears (2,510 real changes made) . drop tenyearsstring . . gen wageexp=. (2,510 missing values generated) . forvalues i=1/5{ 2. replace wageexp=`i' if wageexpectation=="A`i'" 3. } (133 real changes made) (158 real changes made) (580 real changes made) (1,037 real changes made) (602 real changes made) . drop wageexpectation . . . // Views/attitudes of control group . rename genderroleattSQ001 GRA . rename genderroleattSQ002 zerosum . . foreach var in GRA zerosum{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/5{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. } (2,510 missing values generated) (179 real changes made) (123 real changes made) (116 real changes made) (56 real changes made) (24 real changes made) (2,510 missing values generated) (111 real changes made) (151 real changes made) (129 real changes made) (79 real changes made) (28 real changes made) . . . . ******************************************************************************** . ***********Additional Controls/Background info ****************************** . ******************************************************************************** . . . // education . rename demo1 education . gen educ=. (2,510 missing values generated) . . forvalues i=1/9{ 2. replace educ=`i' if education=="A`i'" 3. } (0 real changes made) (44 real changes made) (398 real changes made) (542 real changes made) (305 real changes made) (802 real changes made) (314 real changes made) (48 real changes made) (57 real changes made) . . label define edu 1 "8th degree" 2 "some HS" 3 "HS" 4 "some college" 5 "2-year college" 6 "4-year c > ollege" 7 "Master" 8 "Dr." 9 "Prof. Degree" . label values educ edu . . gen bachelormore=(educ>5) if educ!=. . gen associatemore=(educ>4) if educ!=. . drop education . . label var bachelormore "Bachelor's degree or more" . label var associatemore "2-year College degree +" . . tab educ, gen(educ) educ | Freq. Percent Cum. ---------------+----------------------------------- some HS | 44 1.75 1.75 HS | 398 15.86 17.61 some college | 542 21.59 39.20 2-year college | 305 12.15 51.35 4-year college | 802 31.95 83.31 Master | 314 12.51 95.82 Dr. | 48 1.91 97.73 Prof. Degree | 57 2.27 100.00 ---------------+----------------------------------- Total | 2,510 100.00 . . gen educ0= (educ==1) . . lab var educ0 "8th degree" . lab var educ1 "Some HS" . lab var educ2 "HS" . lab var educ3 "Some college" . lab var educ4 "2-year college" . lab var educ5 "4-year college" . lab var educ6 "Master" . lab var educ7 "PhD" . lab var educ8 "Prof. Degree (JD, MD, MBA)" . . . // race . gen white = (demo2=="A1") . gen black = (demo2=="A2") . gen asian = (demo2=="A3") . gen otherrace= (demo2=="-oth-") . . label var white "White/Caucasian" . label var black "Black/African American" . label var asian "Asian American" . . label var otherrace "Other race" . . // hispanic . gen hispanic = (demo3=="A1") . label var hispanic "Hispanic or latino origin" . . // Continuous measure of hh income . tab hhinc hhinc | Freq. Percent Cum. ----------------------------------------+----------------------------------- < \textdollar 15,000 | 186 7.41 7.41 \textdollar 15,000 - \textdollar 25,000 | 207 8.25 15.66 \textdollar 25,000 - \textdollar 50,000 | 584 23.27 38.92 \textdollar 50,000 - \textdollar 75,000 | 557 22.19 61.12 \textdollar 75,000 - \textdollar 100,00 | 404 16.10 77.21 \textdollar 100,000 - \textdollar 150,0 | 368 14.66 91.87 \textdollar 150,000 - \textdollar 200,0 | 126 5.02 96.89 > \textdollar 200,000 | 78 3.11 100.00 ----------------------------------------+----------------------------------- Total | 2,510 100.00 . gen hhinccont=. (2,510 missing values generated) . replace hhinccont=6735 if hhinc==1 (186 real changes made) . replace hhinccont=19742 if hhinc==2 (207 real changes made) . replace hhinccont=36701 if hhinc==3 (584 real changes made) . replace hhinccont=61275 if hhinc==4 (557 real changes made) . replace hhinccont=86204 if hhinc==5 (404 real changes made) . replace hhinccont=120686 if hhinc==6 (368 real changes made) . replace hhinccont=170381 if hhinc==7 (126 real changes made) . replace hhinccont=327261 if hhinc==8 (78 real changes made) . . gen loghhinc=log(hhinccont) . . label var loghhinc "Log household income" . label var hhinccon "Household income" . . // own labor market income . * Some respondents used a dot to indicate "thousands" -> Correct manually . replace labinc=9936 if labinc==9.936 (1 real change made) . replace labinc=14122 if labinc==14.411 (1 real change made) . replace labinc=16945 if labinc==16.945 (1 real change made) . replace labinc=28999 if labinc==28.999 (1 real change made) . replace labinc=29999 if labinc==29.999 (1 real change made) . replace labinc=34674 if labinc==34.674 (1 real change made) . replace labinc=37042 if labinc==37.042 (1 real change made) . replace labinc=45169 if labinc==45.169 (1 real change made) . replace labinc=45812 if labinc==45.812 (1 real change made) . replace labinc=46125 if labinc==46.125 (1 real change made) . replace labinc=64999 if labinc==64.999 (1 real change made) . replace labinc=93855 if labinc==93.855 (1 real change made) . . . winsor labinc, gen (labincome) p(0.005) . . // company size . rename companysize companysizestring . gen companysize=1 if companysizestring=="A1" (2,358 missing values generated) . replace companysize=2 if companysizestring=="A2" (391 real changes made) . replace companysize=3 if companysizestring=="A3" (382 real changes made) . drop companysizestring . . . // hours worked per week . gen hours=. (2,510 missing values generated) . . forvalues i=1/12{ 2. replace hours=`i' if hrsweek=="A`i'" 3. } (558 real changes made) (107 real changes made) (76 real changes made) (120 real changes made) (138 real changes made) (801 real changes made) (347 real changes made) (159 real changes made) (57 real changes made) (31 real changes made) (43 real changes made) (73 real changes made) . . . . // civil status . rename maritalst married1 . gen married=. (2,510 missing values generated) . replace married=1 if married1=="A1" (911 real changes made) . replace married=2 if married1=="A2" (1,220 real changes made) . replace married=3 if married1=="A3" (224 real changes made) . replace married=4 if married1=="A4" (63 real changes made) . drop married1 . . rename married civil . . gen single = (civil==1) if civil!=. (92 missing values generated) . label var single "Single" . gen married = (civil==2) if civil!=. (92 missing values generated) . label var married "Married" . gen divorced = (civil==3) if civil!=. (92 missing values generated) . label var divorced "Divorced" . gen widowed = (civil==4) if civil!=. (92 missing values generated) . label var widowed "Widowed" . . // number of children . . rename childrenSQ001 boy . rename childrenSQ002 girl . . foreach var in boy girl{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/6{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. replace `var'=`var'-1 9. } (2,510 missing values generated) (1,517 real changes made) (666 real changes made) (243 real changes made) (56 real changes made) (18 real changes made) (10 real changes made) (2,510 real changes made) (2,510 missing values generated) (1,552 real changes made) (637 real changes made) (235 real changes made) (66 real changes made) (12 real changes made) (8 real changes made) (2,510 real changes made) . . gen children=boy+girl . gen anychildren=(children>0&children!=.) . label var anychildren "Has children" . . tab children children | Freq. Percent Cum. ------------+----------------------------------- 0 | 1,151 45.86 45.86 1 | 452 18.01 63.86 2 | 566 22.55 86.41 3 | 219 8.73 95.14 4 | 72 2.87 98.01 5 | 22 0.88 98.88 6 | 13 0.52 99.40 7 | 8 0.32 99.72 8 | 3 0.12 99.84 9 | 2 0.08 99.92 10 | 2 0.08 100.00 ------------+----------------------------------- Total | 2,510 100.00 . . // Birthyear . rename birthyear by . gen birthyear=. (2,510 missing values generated) . replace birthyear=1951 if by=="A1" (0 real changes made) . replace birthyear=1952 if by=="A2" (25 real changes made) . replace birthyear=1953 if by=="A3" (34 real changes made) . replace birthyear=1954 if by=="A4" (37 real changes made) . replace birthyear=1955 if by=="A5" (55 real changes made) . replace birthyear=1956 if by=="A6" (54 real changes made) . replace birthyear=1957 if by=="A7" (60 real changes made) . replace birthyear=1958 if by=="A8" (59 real changes made) . replace birthyear=1959 if by=="A9" (51 real changes made) . replace birthyear=1960 if by=="A10" (53 real changes made) . replace birthyear=1961 if by=="A11" (46 real changes made) . replace birthyear=1962 if by=="A12" (56 real changes made) . replace birthyear=1963 if by=="A13" (51 real changes made) . . gen birthyearhelp=. (2,510 missing values generated) . . forvalues i=1/37{ 2. replace birthyearhelp=`i' if by=="`i'" 3. } (52 real changes made) (53 real changes made) (48 real changes made) (48 real changes made) (55 real changes made) (58 real changes made) (59 real changes made) (55 real changes made) (44 real changes made) (43 real changes made) (47 real changes made) (60 real changes made) (56 real changes made) (59 real changes made) (48 real changes made) (48 real changes made) (65 real changes made) (66 real changes made) (65 real changes made) (57 real changes made) (73 real changes made) (69 real changes made) (60 real changes made) (61 real changes made) (76 real changes made) (52 real changes made) (83 real changes made) (58 real changes made) (57 real changes made) (62 real changes made) (43 real changes made) (45 real changes made) (38 real changes made) (21 real changes made) (19 real changes made) (21 real changes made) (5 real changes made) . . replace birthyear=birthyearhelp+1963 if birthyearhelp!=. (1,929 real changes made) . . drop by . . gen age_det=2018-birthyear . label var age_det "Age" . . // fblikesettings . gen fbset=. (2,510 missing values generated) . forvalues i=1/3{ 2. replace fbset=`i' if fblikesettings=="A`i'" 3. } (1,106 real changes made) (841 real changes made) (563 real changes made) . drop fblikesettings . . label define fbset 1 "visible" 2 "private" 3 "no facebook" . label values fbset fbset . . . //Have you read about the topic in the past 3 weeks? . rename read readcat . gen read=. (2,510 missing values generated) . replace read=0 if readcat=="A2" (1,419 real changes made) . replace read=1 if readcat=="A1" (421 real changes made) . replace read=2 if readcat=="A3" (172 real changes made) . . drop readcat . . rename RAND rand . . save "$path\data\SurveyStageIA_beforezscore.dta", replace file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\dat > a\SurveyStageIA_beforezscore.dta saved . . . exit end of do-file . . // Wave B, main survey, prepare data . do "04_SurveyStageIB_cleaning.do" . . *********************************************************************************** . // Replication Files . ********************************************************************************** . /* > HOW DO BELIEFS ABOUT THE GENDER WAGE GAP AFFECT THE DEMAND FOR PUBLIC POLICY? > Sonja Settele > AEJ:pol > */ . ********************************************************************************** . . *********************************************************************************** . **** Cleaning file Survey Wave B, Stage I (main survey): . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_WaveB_raw.dta", clear . // Variables not included for confidentiality: zip code and final thoughts/comments by respondent . . . keep submitdate lastpage startdate panelID interviewtime > /// > groupTime843 groupTime883 > /// > region agebracket gender employment hhincbracket demrep demrepother > /// > RAND0 RAND RAND12 RAND6 RAND13 RAND2 RAND3 RAND4 RAND5 RAND6 RAND7 RAND8 RAND9 RAND10 RANDINST > /// > RAND14 RAND15 RAND16 RAND17 > /// > elicitbgendeMarc elicitbgendernoinMar > /// > manicheckSQ001 manicheckSQ002 manicheckSQ003 womenwages > /// > legislationanchor AAanchor transparencyanchor quotaanchor childcare > /// > AAanchorder transpanchorder legisanchorder childcareorder quotaanchorder > /// > petition1 AAUW fblike > /// > Scenario01 Scenario02 Scenario03 Scenario04 Scenario05 Scenario06 > /// > Scenario0B1 Scenario0B2 Scenario0B3 Scenario0B4 Scenario0B5 Scenario0B6 > /// > effectivenessSQ001 effectivenessSQ003 effectivenessSQ004 > /// > effectiveness2SQ001 effectiveness2SQ003 effectiveness2SQ004 > /// > effectiveness3SQ001 effectiveness3SQ003 effectiveness3SQ004 > /// > trustgov costsSQ002 costsSQ003 costsSQ004 costs2SQ002 costs2SQ003 costs2SQ004 // > / > costs3SQ002 costs3SQ003 costs3SQ004 > /// > extrasame extrachild > /// > genderroleattSQ001 genderroleattSQ002 genderroleattSQ004 genderroleattSQ005 > /// > genderroleattSQ006 genderroleattSQ007 > /// > attitudes2SQ001 attitudes2SQ002 attitudes2SQ004 attitudes2SQ005 > /// > attitudes2SQ006 attitudes2SQ007 > /// > attitudes3SQ001 attitudes3SQ002 attitudes3SQ004 attitudes3SQ005 attitudes3SQ006 // > / > attitudes3SQ007 > /// > attitudes4SQ001 attitudes4SQ002 attitudes4SQ004 attitudes4SQ005 attitudes4SQ006 // > / > attitudes4SQ007 > /// > attitudes5SQ001 attitudes5SQ002 attitudes5SQ004 attitudes5SQ005 attitudes5SQ006 // > / > attitudes5SQ007 > /// > attitudes6SQ001 attitudes6SQ002 attitudes6SQ004 attitudes6SQ005 attitudes6SQ006 // > / > attitudes6SQ007 > /// > tenyears wageexpectation > /// > importanceSQ001 importanceSQ002 importanceSQ003 importanceSQ004 > /// > importance2SQ001 importance2SQ002 importance2SQ003 importance2SQ004 > /// > importance3SQ001 importance3SQ002 importance3SQ003 importance3SQ004 > /// > importance4SQ001 importance4SQ002 importance4SQ003 importance4SQ004 > /// > vote voteother > /// > tenyears wageexpectation fairown demo1 demo2 demo2other demo3 ind1 ind1other ind2 occ /// > companysize labinc hhincome hrsweek maritalst maritalstother > /// > childrenSQ001 childrenSQ002 birthyear fblikesettings biased relevant read // > / > trustworthy trustcensus interviewtime payoffinfo131 . . . ******************************************************************************** . //// Screening questions ***************************************************** > ******************************************************************************** . . //employment . gen employ=. (1,555 missing values generated) . replace employ=1 if employment=="A1" (826 real changes made) . replace employ=2 if employment=="A2" (182 real changes made) . replace employ=3 if employment=="A3" (98 real changes made) . replace employ=4 if employment=="A4" (78 real changes made) . replace employ=5 if employment=="A5" (108 real changes made) . replace employ=6 if employment=="A6" (115 real changes made) . replace employ=7 if employment=="A7" (148 real changes made) . . label define empl 1 "full-time" 2 "part-time" 3 "self-empl" 4 "unempl" 5 "student" 6 "retired" 7 " > out of LF" . label values employ empl . . gen employee=(employ==1|employ==2) if employ!=. . lab var employee "Full- or part-time employee" . drop employment . . gen employed=(employ==1|employ==2|employ==3) if employ!=. . lab var employed "Employed (full- or part-time or self-emp.)" . . gen nonemployed=(employ==4|employ==5|employ==6|employ==7) if employ!=. . lab var nonemployed "Not employed (unempl., student, out of labor force)" . . gen fulltime=(employ==1) . label var fulltime "Full-time employee" . gen parttime=(employ==2) . label var parttime "Part-time employee" . gen selfemp=(employ==3) . label var selfemp "Self-employed" . gen unemployed=(employ==4) . lab var unemployed "Unemployed" . gen student=(employ==5) . lab var student "Student" . gen oolf=(employ==6|employ==7) . label var oolf "Out of labor force" . . gen oolf2=student + oolf . label var oolf2 "Out of LF (incl. student and retired)" . . //region . rename region regionstring . gen region=. (1,555 missing values generated) . replace region = 1 if regionstring=="A1" (276 real changes made) . replace region = 2 if regionstring=="A2" (329 real changes made) . replace region = 3 if regionstring=="A3" (578 real changes made) . replace region = 4 if regionstring=="A4" (372 real changes made) . drop regionstring . . gen northeast = (region==1) . gen midwest = (region==2) . gen south = (region==3) . gen west = (region==4) . . label var northeast "Northeast" . label var midwest "Midwest" . label var south "South" . label var west "West" . . *agegroup . gen age=. (1,555 missing values generated) . replace age = 1 if agebracket=="A1" (259 real changes made) . replace age = 2 if agebracket=="A2" (326 real changes made) . replace age = 3 if agebracket=="A3" (301 real changes made) . replace age = 4 if agebracket=="A4" (315 real changes made) . replace age = 5 if agebracket=="A5" (354 real changes made) . . label define age 1 "18-24" 2 "25-34" 3 "35-44" 4 "45-54" 5 "55-65" . drop agebracket . . //hhinc . gen hhinc=. (1,555 missing values generated) . forvalues i=1/8{ 2. replace hhinc=`i' if hhincbracket=="A`i'" 3. } (124 real changes made) (124 real changes made) (355 real changes made) (346 real changes made) (254 real changes made) (234 real changes made) (70 real changes made) (48 real changes made) . . . label define hhinc 1 "< \textdollar 15,000" 2 "\textdollar 15,000 - \textdollar 25,000" 3 "\textdo > llar 25,000 - \textdollar 50,000" 4 "\textdollar 50,000 - \textdollar 75,000" 5 "\textdollar 75,00 > 0 - \textdollar 100,000" 6 "\textdollar 100,000 - \textdollar 150,000" 7 "\textdollar 150,000 - \t > extdollar 200,000" 8 "> \textdollar 200,000" . label values hhinc hhinc . . . //gender . rename gender gendstring . gen gender = . (1,555 missing values generated) . replace gender = 0 if gendstring== "A1" (707 real changes made) . replace gender = 1 if gendstring== "A2" (848 real changes made) . . label define gender 0 "male" 1 "female" . label values gender gender . drop gendstring . . gen female=gender . label values female gender . label var female "Female" . . gen male=0 if female==1 (707 missing values generated) . replace male=1 if female==0 (707 real changes made) . lab var male "Male" . . // political orientation . gen pol=. (1,555 missing values generated) . replace pol=-2 if demrep=="A1" (401 real changes made) . replace pol=-1 if demrep=="A2" (151 real changes made) . replace pol=0 if demrep=="A3" (295 real changes made) . replace pol=1 if demrep=="A4" (171 real changes made) . replace pol=2 if demrep=="A5" (507 real changes made) . . gen otherpol=(demrep=="-oth-") . label var otherpol "Other pol. orientation" . . drop demrep . . label define demrep -2 "Republican" -1 "Indep (Repub)" 0 "Independent" 1 "Indep (Demo)" 2 "Democra > t" . label values pol demrep . . gen repubindep=(pol<1) if pol!=. (30 missing values generated) . . gen republican= (pol<0) . gen democrat= (pol>0&pol!=.) . gen indep = (pol==0) . replace republican=0 if otherpol==1 (0 real changes made) . replace democrat=0 if otherpol==1 (0 real changes made) . replace indep=0 if otherpol==1 (0 real changes made) . . label var republican "Republican (incl. indep leaning repub.)" . label var democrat "Democrat (incl. indep. leaning dem.)" . label var indep "Independent" . . gen repubonly=(pol==-2) . gen indeprepub=(pol==-1) . gen indeponly=(pol==0) . gen indepdem=(pol==1) . gen demonly=(pol==2) . . lab var repubonly "Republican" . lab var indeprepub "Indep. leaning Republican" . lab var indeponly "Independent" . lab var indepdem "Indep. leaning Democrat" . lab var demonly "Democrat" . . gen testpol=democrat+indep+republican+otherpol . . tab testpol testpol | Freq. Percent Cum. ------------+----------------------------------- 1 | 1,555 100.00 100.00 ------------+----------------------------------- Total | 1,555 100.00 . drop testpol . . . ******************************************************************************** . //// Treatment conditions ********************************************** > ******************************************************************************** . . // Randomized treatment groups . * T1: High wage gap treatment (74%) . * T2: Low wage gap treatment (94%) . gen T2=(RAND==2) if RAND!=. . gen T1=(RAND==1) if RAND!=. . . ******************************************************************************** . //// Prior beliefs ***************************************************** > ******************************************************************************** . . // Prior incentivized or not . gen prior1=(RAND12==1) . lab var prior1 "Incentive" . . // prior belief . gen prior = elicitbgendeMarc if RAND12==1 (760 missing values generated) . replace prior = elicitbgender if RAND12==0 (760 real changes made) . . label var prior "Prior belief" . . // Time for prior guess: . *groupTime843: time spent on prior estimate in incentivized condition . *groupTime883: time spent on prior estimate in unincentivized condition . rename groupTime843 timepriorinc . rename groupTime883 timepriornoinc . gen timeprior=timepriorinc (760 missing values generated) . replace timeprior=timepriornoinc if timeprior==. (760 real changes made) . . . ******************************************************************************** . ******************************************************************************** . //// Outcome variables ***************************************************** > ******************************************************************************** . ******************************************************************************** . . // posterior belief . gen posterior = extrachild if RAND4==11 (790 missing values generated) . replace posterior = extrasame if RAND4==10 (790 real changes made) . . label var extrachild "Posterior belief (with child)" . label var extrasame "Posterior belief (same job)" . . . tab posterior,m posterior | Freq. Percent Cum. ------------+----------------------------------- 5 | 1 0.06 0.06 7 | 1 0.06 0.13 11 | 1 0.06 0.19 14 | 1 0.06 0.26 19 | 1 0.06 0.32 23 | 1 0.06 0.39 25 | 2 0.13 0.51 26 | 1 0.06 0.58 29 | 1 0.06 0.64 30 | 3 0.19 0.84 31 | 1 0.06 0.90 32 | 1 0.06 0.96 38 | 1 0.06 1.03 41 | 1 0.06 1.09 44 | 1 0.06 1.16 45 | 2 0.13 1.29 46 | 1 0.06 1.35 47 | 2 0.13 1.48 48 | 1 0.06 1.54 49 | 2 0.13 1.67 50 | 20 1.29 2.96 51 | 3 0.19 3.15 52 | 3 0.19 3.34 53 | 5 0.32 3.67 54 | 2 0.13 3.79 55 | 3 0.19 3.99 56 | 3 0.19 4.18 57 | 3 0.19 4.37 58 | 3 0.19 4.57 59 | 2 0.13 4.69 60 | 24 1.54 6.24 61 | 7 0.45 6.69 62 | 10 0.64 7.33 63 | 5 0.32 7.65 64 | 6 0.39 8.04 65 | 18 1.16 9.20 66 | 6 0.39 9.58 67 | 10 0.64 10.23 68 | 10 0.64 10.87 69 | 6 0.39 11.25 70 | 41 2.64 13.89 71 | 9 0.58 14.47 72 | 16 1.03 15.50 73 | 22 1.41 16.91 74 | 116 7.46 24.37 75 | 85 5.47 29.84 76 | 24 1.54 31.38 77 | 25 1.61 32.99 78 | 32 2.06 35.05 79 | 21 1.35 36.40 80 | 84 5.40 41.80 81 | 12 0.77 42.57 82 | 33 2.12 44.69 83 | 22 1.41 46.11 84 | 16 1.03 47.14 85 | 54 3.47 50.61 86 | 37 2.38 52.99 87 | 23 1.48 54.47 88 | 37 2.38 56.85 89 | 29 1.86 58.71 90 | 78 5.02 63.73 91 | 16 1.03 64.76 92 | 29 1.86 66.62 93 | 41 2.64 69.26 94 | 106 6.82 76.08 95 | 54 3.47 79.55 96 | 27 1.74 81.29 97 | 23 1.48 82.77 98 | 22 1.41 84.18 99 | 15 0.96 85.14 100 | 131 8.42 93.57 101 | 3 0.19 93.76 102 | 4 0.26 94.02 103 | 2 0.13 94.15 104 | 3 0.19 94.34 105 | 2 0.13 94.47 106 | 2 0.13 94.60 107 | 1 0.06 94.66 108 | 1 0.06 94.73 109 | 4 0.26 94.98 110 | 2 0.13 95.11 111 | 1 0.06 95.18 112 | 3 0.19 95.37 113 | 1 0.06 95.43 114 | 2 0.13 95.56 116 | 3 0.19 95.76 117 | 2 0.13 95.88 118 | 2 0.13 96.01 119 | 1 0.06 96.08 120 | 3 0.19 96.27 121 | 2 0.13 96.40 124 | 1 0.06 96.46 125 | 6 0.39 96.85 128 | 1 0.06 96.91 129 | 1 0.06 96.98 131 | 2 0.13 97.11 133 | 1 0.06 97.17 136 | 1 0.06 97.23 137 | 2 0.13 97.36 139 | 2 0.13 97.49 140 | 2 0.13 97.62 141 | 1 0.06 97.68 144 | 1 0.06 97.75 145 | 2 0.13 97.88 146 | 1 0.06 97.94 148 | 2 0.13 98.07 149 | 2 0.13 98.20 150 | 2 0.13 98.33 153 | 1 0.06 98.39 154 | 1 0.06 98.46 156 | 1 0.06 98.52 158 | 1 0.06 98.59 159 | 3 0.19 98.78 160 | 1 0.06 98.84 163 | 1 0.06 98.91 165 | 1 0.06 98.97 167 | 1 0.06 99.04 172 | 3 0.19 99.23 177 | 1 0.06 99.29 178 | 1 0.06 99.36 180 | 1 0.06 99.42 182 | 1 0.06 99.49 184 | 1 0.06 99.55 185 | 2 0.13 99.68 200 | 5 0.32 100.00 ------------+----------------------------------- Total | 1,555 100.00 . . . //Manipulation check . rename manicheckSQ001 large . rename manicheckSQ002 problem . rename manicheckSQ003 govmore . . . foreach var in large problem govmore womenwages fairown{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/10{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. } (1,555 missing values generated) (72 real changes made) (50 real changes made) (83 real changes made) (73 real changes made) (124 real changes made) (126 real changes made) (259 real changes made) (304 real changes made) (156 real changes made) (308 real changes made) (1,555 missing values generated) (76 real changes made) (48 real changes made) (51 real changes made) (51 real changes made) (114 real changes made) (124 real changes made) (200 real changes made) (251 real changes made) (205 real changes made) (435 real changes made) (1,555 missing values generated) (97 real changes made) (41 real changes made) (39 real changes made) (30 real changes made) (149 real changes made) (92 real changes made) (152 real changes made) (200 real changes made) (211 real changes made) (544 real changes made) (1,555 missing values generated) (292 real changes made) (817 real changes made) (334 real changes made) (64 real changes made) (48 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (1,555 missing values generated) (70 real changes made) (208 real changes made) (512 real changes made) (139 real changes made) (55 real changes made) (35 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) (0 real changes made) . . replace fairown=. if fairown==6 (35 real changes made, 35 to missing) . . label var large "GWG large" . label var problem "GWG problem" . label var govmore "Gvmt. should do more" . label var womenwages "Women's wages are fair" . label var fairown "My own wage is fair" . . . //Anchored policies . foreach var in quotaanchor AAanchor transparencyanchor legislationanchor childcare{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/5{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. } (1,555 missing values generated) (209 real changes made) (248 real changes made) (405 real changes made) (448 real changes made) (245 real changes made) (1,555 missing values generated) (113 real changes made) (181 real changes made) (510 real changes made) (507 real changes made) (244 real changes made) (1,555 missing values generated) (91 real changes made) (134 real changes made) (263 real changes made) (480 real changes made) (587 real changes made) (1,555 missing values generated) (77 real changes made) (128 real changes made) (534 real changes made) (559 real changes made) (257 real changes made) (1,555 missing values generated) (73 real changes made) (81 real changes made) (425 real changes made) (555 real changes made) (421 real changes made) . . foreach var in quotaanchor AAanchor transparencyanchor legislationanchor childcare{ 2. tab `var' if RAND==0 3. } quotaanchor | Freq. Percent Cum. ------------+----------------------------------- 1 | 77 14.37 14.37 2 | 88 16.42 30.78 3 | 148 27.61 58.40 4 | 152 28.36 86.75 5 | 71 13.25 100.00 ------------+----------------------------------- Total | 536 100.00 AAanchor | Freq. Percent Cum. ------------+----------------------------------- 1 | 39 7.28 7.28 2 | 56 10.45 17.72 3 | 187 34.89 52.61 4 | 179 33.40 86.01 5 | 75 13.99 100.00 ------------+----------------------------------- Total | 536 100.00 transparenc | yanchor | Freq. Percent Cum. ------------+----------------------------------- 1 | 33 6.16 6.16 2 | 38 7.09 13.25 3 | 103 19.22 32.46 4 | 164 30.60 63.06 5 | 198 36.94 100.00 ------------+----------------------------------- Total | 536 100.00 legislation | anchor | Freq. Percent Cum. ------------+----------------------------------- 1 | 25 4.66 4.66 2 | 42 7.84 12.50 3 | 198 36.94 49.44 4 | 189 35.26 84.70 5 | 82 15.30 100.00 ------------+----------------------------------- Total | 536 100.00 childcare | Freq. Percent Cum. ------------+----------------------------------- 1 | 24 4.48 4.48 2 | 19 3.54 8.02 3 | 145 27.05 35.07 4 | 195 36.38 71.46 5 | 153 28.54 100.00 ------------+----------------------------------- Total | 536 100.00 . . * The variable called transparencyanchor contained a different question in Wave B . * Question in Wave B is about demand for a reporting tool similar to the one in the UK -> rename . gen UKtool=transparencyanchor . replace transparencyanchor = . (1,555 real changes made, 1,555 to missing) . . label var UKtool "Public website" . . label var quotaanchor "Gender quotas" . label var AAanchor "Aff. action" . label var legislationanchor "Equal pay legislation" . label var childcare "Public child care" . . . ******************************************************************************** . //// Behavioral outcomes ***************************************************** > ******************************************************************************** . . //Petition decision . gen petition=-1 if petition1=="A2" (1,413 missing values generated) . replace petition=0 if petition1=="A3" (597 real changes made) . replace petition=1 if petition1=="A1" (816 real changes made) . . gen petitionI=0 if petition<1 (816 missing values generated) . replace petitionI=1 if petition==1 (816 real changes made) . . gen petitionII=0 if petition>-1 &petition!=. (142 missing values generated) . replace petitionII=1 if petition==-1 (142 real changes made) . . label var petitionI "Petition I (increase reporting)" . label var petitionII "Petition II (abolish reporting)" . . . //donation decision . label var AAUW "Donation for NGO" . . . // facebook like . rename fblike fblikestring . gen fblike=0 if fblikestring=="not clicked" (213 missing values generated) . replace fblike=1 if fblikestring=="clicked left"|fblikestring=="clicked right" (206 real changes made) . . label var fblike "Facebook like for NGO" . . drop fblikestring . . gen fblike2=fblike (7 missing values generated) . replace fblike2=. if fblikesettings=="A3" (402 real changes made, 402 to missing) . . . //Info decision . replace Scenario0B3="A2" if Scenario0B3=="A3" (410 real changes made) . . * RANDINST==1: First three choices were about information from progressive source, last three abou > t conservative source . * RANDINST==2: First three choices were about information from conservative source, last three fro > m progressive source . . gen scenario1support=0 if RAND!=0 (536 missing values generated) . replace scenario1support=1 if (Scenario01=="A1"&RANDINST==1)|(Scenario0B4=="A1"&RANDINST==2) (538 real changes made) . gen scenario2support=0 . replace scenario2support=1 if (Scenario02=="A1"&RANDINST==1)|(Scenario0B5=="A1"&RANDINST==2) (468 real changes made) . gen scenario3support=0 . replace scenario3support=1 if (Scenario03=="A1"&RANDINST==1)|(Scenario0B6=="A1"&RANDINST==2) (428 real changes made) . . gen scenario1oppose=0 if RAND!=0 (536 missing values generated) . replace scenario1oppose=1 if (Scenario04=="A1"&RANDINST==1)|(Scenario0B1=="A1"&RANDINST==2) (316 real changes made) . gen scenario2oppose=0 . replace scenario2oppose=1 if (Scenario05=="A1"&RANDINST==1)|(Scenario0B2=="A1"&RANDINST==2) (273 real changes made) . gen scenario3oppose=0 . replace scenario3oppose=1 if (Scenario06=="A1"&RANDINST==1)|(Scenario0B3=="A1"&RANDINST==2) (239 real changes made) . . gen infopaysupport=scenario1support + scenario2support + scenario3support (536 missing values generated) . gen infopayoppose=scenario1oppose + scenario2oppose + scenario3oppose (536 missing values generated) . . tab infopaysupport infopaysupp | ort | Freq. Percent Cum. ------------+----------------------------------- 0 | 438 42.98 42.98 1 | 91 8.93 51.91 2 | 127 12.46 64.38 3 | 363 35.62 100.00 ------------+----------------------------------- Total | 1,019 100.00 . tab infopayoppose infopayoppo | se | Freq. Percent Cum. ------------+----------------------------------- 0 | 647 63.49 63.49 1 | 97 9.52 73.01 2 | 94 9.22 82.24 3 | 181 17.76 100.00 ------------+----------------------------------- Total | 1,019 100.00 . . label var infopaysupport "Will. to pay for progressive info" . label var infopayoppose "Will. to pay for traditional info" . . drop Scenario01-Scenario06 . drop Scenario0B1-Scenario0B6 . . . . ******************************************************************************** . //// Additional outcomes/mechanisms ********************************************* > ******************************************************************************** . . . * Perceived Effectiveness . . local effective effectivenessSQ001 effectivenessSQ003 effectivenessSQ004 /// > effectiveness2SQ001 effectiveness2SQ003 effectiveness2SQ004 /// > effectiveness3SQ001 effectiveness3SQ003 effectiveness3SQ004 . . . foreach var in `effective'{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/5{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. } (1,555 missing values generated) (21 real changes made) (131 real changes made) (229 real changes made) (96 real changes made) (52 real changes made) (1,555 missing values generated) (13 real changes made) (162 real changes made) (197 real changes made) (103 real changes made) (54 real changes made) (1,555 missing values generated) (19 real changes made) (147 real changes made) (187 real changes made) (130 real changes made) (46 real changes made) (1,555 missing values generated) (20 real changes made) (122 real changes made) (180 real changes made) (124 real changes made) (50 real changes made) (1,555 missing values generated) (24 real changes made) (112 real changes made) (183 real changes made) (137 real changes made) (40 real changes made) (1,555 missing values generated) (21 real changes made) (127 real changes made) (182 real changes made) (123 real changes made) (43 real changes made) (1,555 missing values generated) (32 real changes made) (128 real changes made) (230 real changes made) (98 real changes made) (42 real changes made) (1,555 missing values generated) (29 real changes made) (117 real changes made) (182 real changes made) (159 real changes made) (43 real changes made) (1,555 missing values generated) (18 real changes made) (108 real changes made) (201 real changes made) (157 real changes made) (46 real changes made) . . . . gen effdis = . (1,555 missing values generated) . replace effdis = effectivenessSQ001 if RAND14==1 (529 real changes made) . replace effdis = effectiveness2SQ003 if RAND14==2 (496 real changes made) . replace effdis = effectiveness3SQ004 if RAND14==3 (530 real changes made) . label var effdis "Effective Anti-Disc." . . gen effAA=. (1,555 missing values generated) . replace effAA = effectivenessSQ003 if RAND14==1 (529 real changes made) . replace effAA = effectiveness2SQ004 if RAND14==2 (496 real changes made) . replace effAA = effectiveness3SQ001 if RAND14==3 (530 real changes made) . label var effAA "Effective AA" . . gen effworkfam=. (1,555 missing values generated) . replace effworkfam = effectivenessSQ004 if RAND14==1 (529 real changes made) . replace effworkfam = effectiveness2SQ001 if RAND14==2 (496 real changes made) . replace effworkfam = effectiveness3SQ003 if RAND14==3 (530 real changes made) . label var effworkfam "Effective work-fam" . . local effective effdis effAA effworkfam . . . drop effectivenessSQ001 effectivenessSQ003 effectivenessSQ004 /// > effectiveness2SQ001 effectiveness2SQ003 effectiveness2SQ004 /// > effectiveness3SQ001 effectiveness3SQ003 effectiveness3SQ004 . . . gen ordereffdis=1 if RAND14==1 (1,026 missing values generated) . replace ordereffdis=2 if RAND14==2 (496 real changes made) . replace ordereffdis=3 if RAND14==3 (530 real changes made) . . gen ordereffAA = 2 if RAND14==1 (1,026 missing values generated) . replace ordereffAA = 3 if RAND14==2 (496 real changes made) . replace ordereffAA = 1 if RAND14==3 (530 real changes made) . . gen ordereffworkfam=3 if RAND14==1 (1,026 missing values generated) . replace ordereffworkfam=1 if RAND14==2 (496 real changes made) . replace ordereffworkfam=2 if RAND14==3 (530 real changes made) . . **Trust in government . foreach var in trustgov{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/5{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. } (1,555 missing values generated) (244 real changes made) (486 real changes made) (387 real changes made) (349 real changes made) (89 real changes made) . . * Wage expectations . rename tenyears tenyearsstring . gen tenyears=. (1,555 missing values generated) . forvalues i=1/3{ 2. replace tenyears=`i' if tenyearsstring=="A`i'" 3. } (613 real changes made) (138 real changes made) (268 real changes made) . . replace tenyears=-tenyears (1,019 real changes made) . drop tenyearsstring . . gen wageexp=. (1,555 missing values generated) . forvalues i=1/5{ 2. replace wageexp=`i' if wageexpectation=="A`i'" 3. } (62 real changes made) (68 real changes made) (256 real changes made) (388 real changes made) (245 real changes made) . drop wageexpectation . . . . ******************************************************************************** . ***********Additional Controls ************************************************* . ******************************************************************************** . . // Views/attitudes of control group: . . local attitudes > /// > genderroleattSQ001 genderroleattSQ002 genderroleattSQ004 genderroleattSQ005 > /// > genderroleattSQ006 genderroleattSQ007 > /// > attitudes2SQ001 attitudes2SQ002 attitudes2SQ004 attitudes2SQ005 > /// > attitudes2SQ006 attitudes2SQ007 > /// > attitudes3SQ001 attitudes3SQ002 attitudes3SQ004 attitudes3SQ005 attitudes3SQ006 // > / > attitudes3SQ007 > /// > attitudes4SQ001 attitudes4SQ002 attitudes4SQ004 attitudes4SQ005 attitudes4SQ006 // > / > attitudes4SQ007 > /// > attitudes5SQ001 attitudes5SQ002 attitudes5SQ004 attitudes5SQ005 attitudes5SQ006 // > / > attitudes5SQ007 > /// > attitudes6SQ001 attitudes6SQ002 attitudes6SQ004 attitudes6SQ005 attitudes6SQ006 // > / > attitudes6SQ007 > /// > . foreach var in `attitudes'{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/5{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. } (1,555 missing values generated) (33 real changes made) (24 real changes made) (20 real changes made) (15 real changes made) (13 real changes made) (1,555 missing values generated) (20 real changes made) (29 real changes made) (28 real changes made) (19 real changes made) (9 real changes made) (1,555 missing values generated) (0 real changes made) (9 real changes made) (24 real changes made) (35 real changes made) (37 real changes made) (1,555 missing values generated) (3 real changes made) (10 real changes made) (24 real changes made) (41 real changes made) (27 real changes made) (1,555 missing values generated) (16 real changes made) (17 real changes made) (28 real changes made) (26 real changes made) (18 real changes made) (1,555 missing values generated) (27 real changes made) (16 real changes made) (24 real changes made) (23 real changes made) (15 real changes made) (1,555 missing values generated) (7 real changes made) (13 real changes made) (24 real changes made) (37 real changes made) (10 real changes made) (1,555 missing values generated) (6 real changes made) (14 real changes made) (18 real changes made) (33 real changes made) (20 real changes made) (1,555 missing values generated) (38 real changes made) (19 real changes made) (21 real changes made) (9 real changes made) (4 real changes made) (1,555 missing values generated) (4 real changes made) (9 real changes made) (17 real changes made) (30 real changes made) (31 real changes made) (1,555 missing values generated) (16 real changes made) (14 real changes made) (26 real changes made) (27 real changes made) (8 real changes made) (1,555 missing values generated) (15 real changes made) (24 real changes made) (27 real changes made) (20 real changes made) (5 real changes made) (1,555 missing values generated) (14 real changes made) (25 real changes made) (15 real changes made) (18 real changes made) (10 real changes made) (1,555 missing values generated) (5 real changes made) (8 real changes made) (17 real changes made) (30 real changes made) (22 real changes made) (1,555 missing values generated) (3 real changes made) (12 real changes made) (20 real changes made) (30 real changes made) (17 real changes made) (1,555 missing values generated) (34 real changes made) (17 real changes made) (16 real changes made) (9 real changes made) (6 real changes made) (1,555 missing values generated) (15 real changes made) (23 real changes made) (21 real changes made) (13 real changes made) (10 real changes made) (1,555 missing values generated) (14 real changes made) (11 real changes made) (18 real changes made) (21 real changes made) (18 real changes made) (1,555 missing values generated) (3 real changes made) (10 real changes made) (17 real changes made) (31 real changes made) (31 real changes made) (1,555 missing values generated) (14 real changes made) (23 real changes made) (14 real changes made) (27 real changes made) (14 real changes made) (1,555 missing values generated) (9 real changes made) (18 real changes made) (28 real changes made) (23 real changes made) (14 real changes made) (1,555 missing values generated) (13 real changes made) (28 real changes made) (29 real changes made) (17 real changes made) (5 real changes made) (1,555 missing values generated) (12 real changes made) (6 real changes made) (27 real changes made) (33 real changes made) (14 real changes made) (1,555 missing values generated) (42 real changes made) (17 real changes made) (13 real changes made) (17 real changes made) (3 real changes made) (1,555 missing values generated) (7 real changes made) (8 real changes made) (13 real changes made) (28 real changes made) (12 real changes made) (1,555 missing values generated) (31 real changes made) (11 real changes made) (14 real changes made) (5 real changes made) (7 real changes made) (1,555 missing values generated) (14 real changes made) (10 real changes made) (22 real changes made) (13 real changes made) (9 real changes made) (1,555 missing values generated) (16 real changes made) (10 real changes made) (17 real changes made) (18 real changes made) (7 real changes made) (1,555 missing values generated) (3 real changes made) (8 real changes made) (14 real changes made) (23 real changes made) (20 real changes made) (1,555 missing values generated) (5 real changes made) (10 real changes made) (22 real changes made) (19 real changes made) (12 real changes made) (1,555 missing values generated) (17 real changes made) (27 real changes made) (29 real changes made) (16 real changes made) (9 real changes made) (1,555 missing values generated) (5 real changes made) (19 real changes made) (29 real changes made) (30 real changes made) (15 real changes made) (1,555 missing values generated) (16 real changes made) (24 real changes made) (29 real changes made) (15 real changes made) (14 real changes made) (1,555 missing values generated) (11 real changes made) (20 real changes made) (24 real changes made) (24 real changes made) (19 real changes made) (1,555 missing values generated) (41 real changes made) (20 real changes made) (22 real changes made) (8 real changes made) (7 real changes made) (1,555 missing values generated) (4 real changes made) (9 real changes made) (25 real changes made) (28 real changes made) (32 real changes made) . . gen GRA=. (1,555 missing values generated) . replace GRA=genderroleattSQ001 if RAND16==1 (105 real changes made) . replace GRA=attitudes2SQ004 if RAND16==2 (91 real changes made) . replace GRA=attitudes3SQ005 if RAND16==3 (82 real changes made) . replace GRA=attitudes4SQ007 if RAND16==4 (92 real changes made) . replace GRA=attitudes5SQ002 if RAND16==5 (68 real changes made) . replace GRA=attitudes6SQ006 if RAND16==6 (98 real changes made) . . gen zerosum=. (1,555 missing values generated) . replace zerosum=genderroleattSQ002 if RAND16==1 (105 real changes made) . replace zerosum= attitudes2SQ007 if RAND16==2 (91 real changes made) . replace zerosum=attitudes3SQ006 if RAND16==3 (82 real changes made) . replace zerosum=attitudes4SQ005 if RAND16==4 (92 real changes made) . replace zerosum=attitudes5SQ004 if RAND16==5 (68 real changes made) . replace zerosum=attitudes6SQ001 if RAND16==6 (98 real changes made) . . gen meritocracy=. (1,555 missing values generated) . replace meritocracy= genderroleattSQ004 if RAND16==1 (105 real changes made) . replace meritocracy= attitudes2SQ005 if RAND16==2 (91 real changes made) . replace meritocracy= attitudes3SQ002 if RAND16==3 (82 real changes made) . replace meritocracy= attitudes4SQ001 if RAND16==4 (92 real changes made) . replace meritocracy= attitudes5SQ006 if RAND16==5 (68 real changes made) . replace meritocracy= attitudes6SQ007 if RAND16==6 (98 real changes made) . . gen priority=. (1,555 missing values generated) . replace priority=genderroleattSQ005 if RAND16==1 (105 real changes made) . replace priority=attitudes2SQ001 if RAND16==2 (91 real changes made) . replace priority=attitudes3SQ004 if RAND16==3 (82 real changes made) . replace priority=attitudes4SQ006 if RAND16==4 (92 real changes made) . replace priority=attitudes5SQ007 if RAND16==5 (68 real changes made) . replace priority=attitudes6SQ002 if RAND16==6 (98 real changes made) . . gen tallshort=. (1,555 missing values generated) . replace tallshort=genderroleattSQ006 if RAND16==1 (105 real changes made) . replace tallshort=attitudes2SQ002 if RAND16==2 (91 real changes made) . replace tallshort=attitudes3SQ007 if RAND16==3 (82 real changes made) . replace tallshort=attitudes4SQ004 if RAND16==4 (92 real changes made) . replace tallshort=attitudes5SQ001 if RAND16==5 (68 real changes made) . replace tallshort=attitudes6SQ005 if RAND16==6 (98 real changes made) . . gen reversedisc=. (1,555 missing values generated) . replace reversedisc=genderroleattSQ007 if RAND16==1 (105 real changes made) . replace reversedisc=attitudes2SQ006 if RAND16==2 (91 real changes made) . replace reversedisc=attitudes3SQ001 if RAND16==3 (82 real changes made) . replace reversedisc=attitudes4SQ002 if RAND16==4 (92 real changes made) . replace reversedisc=attitudes5SQ005 if RAND16==5 (68 real changes made) . replace reversedisc=attitudes6SQ004 if RAND16==6 (98 real changes made) . . drop genderroleattSQ001 genderroleattSQ002 genderroleattSQ004 genderroleattSQ005 // > / > genderroleattSQ006 genderroleattSQ007 > /// > attitudes2SQ001 attitudes2SQ002 attitudes2SQ004 attitudes2SQ005 > /// > attitudes2SQ006 attitudes2SQ007 > /// > attitudes3SQ001 attitudes3SQ002 attitudes3SQ004 attitudes3SQ005 attitudes3SQ006 // > / > attitudes3SQ007 > /// > attitudes4SQ001 attitudes4SQ002 attitudes4SQ004 attitudes4SQ005 attitudes4SQ006 // > / > attitudes4SQ007 > /// > attitudes5SQ001 attitudes5SQ002 attitudes5SQ004 attitudes5SQ005 attitudes5SQ006 // > / > attitudes5SQ007 > /// > attitudes6SQ001 attitudes6SQ002 attitudes6SQ004 attitudes6SQ005 attitudes6SQ006 // > / > attitudes6SQ007 . . // Perceived Costs of policy intervention . . local costs costsSQ002 costsSQ003 costsSQ004 costs2SQ002 costs2SQ003 costs2SQ004 /// > costs3SQ002 costs3SQ003 costs3SQ004 . . foreach var in `costs'{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/5{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. } (1,555 missing values generated) (53 real changes made) (98 real changes made) (179 real changes made) (124 real changes made) (65 real changes made) (1,555 missing values generated) (69 real changes made) (114 real changes made) (160 real changes made) (107 real changes made) (69 real changes made) (1,555 missing values generated) (82 real changes made) (83 real changes made) (165 real changes made) (107 real changes made) (82 real changes made) (1,555 missing values generated) (90 real changes made) (111 real changes made) (144 real changes made) (114 real changes made) (61 real changes made) (1,555 missing values generated) (80 real changes made) (103 real changes made) (142 real changes made) (123 real changes made) (72 real changes made) (1,555 missing values generated) (87 real changes made) (85 real changes made) (150 real changes made) (123 real changes made) (75 real changes made) (1,555 missing values generated) (61 real changes made) (94 real changes made) (144 real changes made) (152 real changes made) (65 real changes made) (1,555 missing values generated) (74 real changes made) (82 real changes made) (158 real changes made) (136 real changes made) (66 real changes made) (1,555 missing values generated) (64 real changes made) (95 real changes made) (163 real changes made) (118 real changes made) (76 real changes made) . . gen monetary=. (1,555 missing values generated) . replace monetary = costsSQ002 if RAND15==1 (519 real changes made) . replace monetary = costs2SQ004 if RAND15==2 (520 real changes made) . replace monetary = costs3SQ003 if RAND15==3 (516 real changes made) . . gen bureaucracy=. (1,555 missing values generated) . replace bureaucracy = costsSQ003 if RAND15==1 (519 real changes made) . replace bureaucracy = costs2SQ002 if RAND15==2 (520 real changes made) . replace bureaucracy = costs3SQ004 if RAND15==3 (516 real changes made) . . gen distortion=. (1,555 missing values generated) . replace distortion = costsSQ004 if RAND15==1 (519 real changes made) . replace distortion = costs2SQ003 if RAND15==2 (520 real changes made) . replace distortion = costs3SQ002 if RAND15==3 (516 real changes made) . . . ***** . *Importance of the topic (Control group only)*** . **** . . local importance importanceSQ001 importanceSQ002 importanceSQ003 importanceSQ004 /// > importance2SQ001 importance2SQ002 importance2SQ003 importance2SQ004 /// > importance3SQ001 importance3SQ002 importance3SQ003 importance3SQ004 > /// > importance4SQ001 importance4SQ002 importance4SQ003 importance4SQ004 > /// > . foreach var in `importance'{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/5{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. } (1,555 missing values generated) (13 real changes made) (13 real changes made) (20 real changes made) (40 real changes made) (50 real changes made) (1,555 missing values generated) (8 real changes made) (10 real changes made) (28 real changes made) (53 real changes made) (37 real changes made) (1,555 missing values generated) (1 real change made) (3 real changes made) (13 real changes made) (49 real changes made) (70 real changes made) (1,555 missing values generated) (6 real changes made) (10 real changes made) (21 real changes made) (48 real changes made) (51 real changes made) (1,555 missing values generated) (5 real changes made) (10 real changes made) (33 real changes made) (56 real changes made) (47 real changes made) (1,555 missing values generated) (3 real changes made) (4 real changes made) (18 real changes made) (30 real changes made) (96 real changes made) (1,555 missing values generated) (11 real changes made) (22 real changes made) (35 real changes made) (39 real changes made) (44 real changes made) (1,555 missing values generated) (20 real changes made) (14 real changes made) (26 real changes made) (42 real changes made) (49 real changes made) (1,555 missing values generated) (4 real changes made) (5 real changes made) (10 real changes made) (44 real changes made) (64 real changes made) (1,555 missing values generated) (14 real changes made) (10 real changes made) (23 real changes made) (31 real changes made) (49 real changes made) (1,555 missing values generated) (5 real changes made) (11 real changes made) (23 real changes made) (39 real changes made) (49 real changes made) (1,555 missing values generated) (7 real changes made) (15 real changes made) (27 real changes made) (47 real changes made) (31 real changes made) (1,555 missing values generated) (7 real changes made) (4 real changes made) (31 real changes made) (53 real changes made) (27 real changes made) (1,555 missing values generated) (4 real changes made) (5 real changes made) (32 real changes made) (37 real changes made) (44 real changes made) (1,555 missing values generated) (8 real changes made) (5 real changes made) (26 real changes made) (39 real changes made) (44 real changes made) (1,555 missing values generated) (1 real change made) (2 real changes made) (15 real changes made) (41 real changes made) (63 real changes made) . . . gen impclimate=importanceSQ001 if RAND17==1 (1,419 missing values generated) . replace impclimate=importance2SQ004 if RAND17==2 (151 real changes made) . replace impclimate=importance3SQ002 if RAND17==3 (127 real changes made) . replace impclimate=importance4SQ003 if RAND17==4 (122 real changes made) . . gen impgender=importanceSQ002 if RAND17==1 (1,419 missing values generated) . replace impgender=importance2SQ003 if RAND17==2 (151 real changes made) . replace impgender=importance3SQ004 if RAND17==3 (127 real changes made) . replace impgender=importance4SQ001 if RAND17==4 (122 real changes made) . . gen imphealth=importanceSQ003 if RAND17==1 (1,419 missing values generated) . replace imphealth=importance2SQ002 if RAND17==2 (151 real changes made) . replace imphealth=importance3SQ001 if RAND17==3 (127 real changes made) . replace imphealth=importance4SQ004 if RAND17==4 (122 real changes made) . . gen impimmi=importanceSQ004 if RAND17==1 (1,419 missing values generated) . replace impimmi=importance2SQ001 if RAND17==2 (151 real changes made) . replace impimmi=importance3SQ003 if RAND17==3 (127 real changes made) . replace impimmi=importance4SQ002 if RAND17==4 (122 real changes made) . . . . // race . gen white = (demo2=="A1") . gen black = (demo2=="A2") . gen asian = (demo2=="A3") . gen otherrace= (demo2=="-oth-") . . label var white "White/Caucasian" . label var black "Black/African American" . label var asian "Asian American" . . label var otherrace "Other race" . . // hispanic . gen hispanic = (demo3=="A1") . label var hispanic "Hispanic or latino origin" . . // hh income . tab hhinc hhinc | Freq. Percent Cum. ----------------------------------------+----------------------------------- < \textdollar 15,000 | 124 7.97 7.97 \textdollar 15,000 - \textdollar 25,000 | 124 7.97 15.95 \textdollar 25,000 - \textdollar 50,000 | 355 22.83 38.78 \textdollar 50,000 - \textdollar 75,000 | 346 22.25 61.03 \textdollar 75,000 - \textdollar 100,00 | 254 16.33 77.36 \textdollar 100,000 - \textdollar 150,0 | 234 15.05 92.41 \textdollar 150,000 - \textdollar 200,0 | 70 4.50 96.91 > \textdollar 200,000 | 48 3.09 100.00 ----------------------------------------+----------------------------------- Total | 1,555 100.00 . gen hhinccont=. (1,555 missing values generated) . replace hhinccont=6735 if hhinc==1 (124 real changes made) . replace hhinccont=19742 if hhinc==2 (124 real changes made) . replace hhinccont=36701 if hhinc==3 (355 real changes made) . replace hhinccont=61275 if hhinc==4 (346 real changes made) . replace hhinccont=86204 if hhinc==5 (254 real changes made) . replace hhinccont=120686 if hhinc==6 (234 real changes made) . replace hhinccont=170381 if hhinc==7 (70 real changes made) . replace hhinccont=327261 if hhinc==8 (48 real changes made) . . gen loghhinc=log(hhinccont) . . label var loghhinc "Log household income" . label var hhinccon "Household income" . . . *company size . rename companysize companysizestring . . gen companysize=1 if companysizestring=="A1" (1,464 missing values generated) . replace companysize=2 if companysizestring=="A2" (267 real changes made) . replace companysize=3 if companysizestring=="A3" (247 real changes made) . drop companysizestring . . . //hours worked per week . gen hours=. (1,555 missing values generated) . . forvalues i=1/12{ 2. replace hours=`i' if hrsweek=="A`i'" 3. } (340 real changes made) (75 real changes made) (55 real changes made) (90 real changes made) (115 real changes made) (479 real changes made) (216 real changes made) (60 real changes made) (41 real changes made) (22 real changes made) (17 real changes made) (45 real changes made) . . . *education . rename demo1 education . gen educ=. (1,555 missing values generated) . . forvalues i=1/9{ 2. replace educ=`i' if education=="A`i'" 3. } (1 real change made) (11 real changes made) (263 real changes made) (325 real changes made) (188 real changes made) (534 real changes made) (175 real changes made) (25 real changes made) (33 real changes made) . . label define edu 1 "8th degree" 2 "some HS" 3 "HS" 4 "some college" 5 "2-year college" 6 "4-year c > ollege" 7 "Master" 8 "Dr." 9 "Prof. Degree" . label values educ edu . . gen bachelormore=(educ>5) if educ!=. . gen associatemore=(educ>4) if educ!=. . drop education . . label var bachelormore "Bachelor's degree or more" . label var associatemore "2-year College degree +" . . . *civil status . rename maritalst married1 . gen married=. (1,555 missing values generated) . replace married=1 if married1=="A1" (608 real changes made) . replace married=2 if married1=="A2" (748 real changes made) . replace married=3 if married1=="A3" (132 real changes made) . replace married=4 if married1=="A4" (31 real changes made) . drop married1 . . rename married civil . . gen single = (civil==1) if civil!=. (36 missing values generated) . label var single "Single" . gen married = (civil==2) if civil!=. (36 missing values generated) . label var married "Married" . gen divorced = (civil==3) if civil!=. (36 missing values generated) . label var divorced "Divorced" . gen widowed = (civil==4) if civil!=. (36 missing values generated) . label var widowed "Widowed" . . * children . rename childrenSQ001 boy . rename childrenSQ002 girl . . foreach var in boy girl{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/6{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. replace `var'=`var'-1 9. } (1,555 missing values generated) (960 real changes made) (405 real changes made) (145 real changes made) (31 real changes made) (10 real changes made) (4 real changes made) (1,555 real changes made) (1,555 missing values generated) (993 real changes made) (375 real changes made) (138 real changes made) (36 real changes made) (10 real changes made) (3 real changes made) (1,555 real changes made) . . gen children=boy+girl . gen anychildren=(children>0&children!=.) . label var anychildren "Has children" . . tab children children | Freq. Percent Cum. ------------+----------------------------------- 0 | 762 49.00 49.00 1 | 275 17.68 66.69 2 | 306 19.68 86.37 3 | 132 8.49 94.86 4 | 49 3.15 98.01 5 | 16 1.03 99.04 6 | 8 0.51 99.55 7 | 3 0.19 99.74 8 | 3 0.19 99.94 10 | 1 0.06 100.00 ------------+----------------------------------- Total | 1,555 100.00 . . . * birthyear . rename birthyear by . gen birthyear=. (1,555 missing values generated) . replace birthyear=1951 if by=="A1" (0 real changes made) . replace birthyear=1952 if by=="A2" (2 real changes made) . replace birthyear=1953 if by=="A3" (8 real changes made) . replace birthyear=1954 if by=="A4" (32 real changes made) . replace birthyear=1955 if by=="A5" (35 real changes made) . replace birthyear=1956 if by=="A6" (45 real changes made) . replace birthyear=1957 if by=="A7" (38 real changes made) . replace birthyear=1958 if by=="A8" (45 real changes made) . replace birthyear=1959 if by=="A9" (41 real changes made) . replace birthyear=1960 if by=="A10" (33 real changes made) . replace birthyear=1961 if by=="A11" (27 real changes made) . replace birthyear=1962 if by=="A12" (33 real changes made) . replace birthyear=1963 if by=="A13" (17 real changes made) . . gen birthyearhelp=. (1,555 missing values generated) . . forvalues i=1/37{ 2. replace birthyearhelp=`i' if by=="`i'" 3. } (43 real changes made) (32 real changes made) (31 real changes made) (23 real changes made) (26 real changes made) (38 real changes made) (36 real changes made) (39 real changes made) (23 real changes made) (26 real changes made) (30 real changes made) (30 real changes made) (27 real changes made) (25 real changes made) (46 real changes made) (31 real changes made) (37 real changes made) (18 real changes made) (25 real changes made) (30 real changes made) (37 real changes made) (47 real changes made) (33 real changes made) (38 real changes made) (33 real changes made) (32 real changes made) (34 real changes made) (21 real changes made) (24 real changes made) (20 real changes made) (39 real changes made) (52 real changes made) (42 real changes made) (37 real changes made) (31 real changes made) (34 real changes made) (22 real changes made) . . replace birthyear=birthyearhelp+1963 if birthyearhelp!=. (1,192 real changes made) . . drop by . . gen age_det=2018-birthyear (7 missing values generated) . label var age_det "Age" . . *fblikesettings . . gen fbset=. (1,555 missing values generated) . forvalues i=1/3{ 2. replace fbset=`i' if fblikesettings=="A`i'" 3. } (712 real changes made) (440 real changes made) (403 real changes made) . drop fblikesettings . . label define fbset 1 "visible" 2 "private" 3 "no facebook" . label values fbset fbset . . . //Have you read about the topic in the past 3 weeks? . rename read readcat . gen read=. (1,555 missing values generated) . replace read=0 if readcat=="A2" (730 real changes made) . replace read=1 if readcat=="A1" (226 real changes made) . replace read=2 if readcat=="A3" (63 real changes made) . . drop readcat . . rename RAND rand . . . . // own labor market income . replace labinc=4452 if labinc==4.452 (1 real change made) . replace labinc=7904 if labinc==7.904 (1 real change made) . replace labinc=9999 if labinc==9.999 (1 real change made) . replace labinc=19999 if labinc==19.999 (1 real change made) . replace labinc=20101 if labinc==20.101 (1 real change made) . . winsor labinc, gen (labincome) p(0.005) . . . //generate dummies . . // age . tab age, gen(age) age | Freq. Percent Cum. ------------+----------------------------------- 1 | 259 16.66 16.66 2 | 326 20.96 37.62 3 | 301 19.36 56.98 4 | 315 20.26 77.23 5 | 354 22.77 100.00 ------------+----------------------------------- Total | 1,555 100.00 . . label var age1 "Age 18-24" . label var age2 "Age 25-34" . label var age3 "Age 35-44" . label var age4 "Age 45-54" . label var age5 "Age 55-65" . . // hh inc . tab hhinc, gen (hhinc) hhinc | Freq. Percent Cum. ----------------------------------------+----------------------------------- < \textdollar 15,000 | 124 7.97 7.97 \textdollar 15,000 - \textdollar 25,000 | 124 7.97 15.95 \textdollar 25,000 - \textdollar 50,000 | 355 22.83 38.78 \textdollar 50,000 - \textdollar 75,000 | 346 22.25 61.03 \textdollar 75,000 - \textdollar 100,00 | 254 16.33 77.36 \textdollar 100,000 - \textdollar 150,0 | 234 15.05 92.41 \textdollar 150,000 - \textdollar 200,0 | 70 4.50 96.91 > \textdollar 200,000 | 48 3.09 100.00 ----------------------------------------+----------------------------------- Total | 1,555 100.00 . . label var hhinc1 "Yearly household inc. < \textdollar 15,000" . label var hhinc2 "Yearly household inc. \textdollar 15,000 - \textdollar 25,000" . label var hhinc3 "Yearly household inc. \textdollar 25,000 - \textdollar 50,000" . label var hhinc4 "Yearly household inc. \textdollar 50,000 - \textdollar 75,000" . label var hhinc5 "Yearly household inc. \textdollar 75,000 - \textdollar 100,000" . label var hhinc6 "Yearly household inc. \textdollar 100,000 - \textdollar 150,000" . label var hhinc7 "Yearly household inc. \textdollar 150,000 - \textdollar 200,000" . label var hhinc8 "Yearly household inc. > \textdollar 200,000" . . gen lowinc=hhinc1+hhinc2+hhinc3 . gen highinc=hhinc4+hhinc5+hhinc6+hhinc7+hhinc8 . . label var lowinc "Household inc $\leq$ \textdollar 50,000" . label var highinc "Household inc. > \textdollar 50,000" . . gen wave=2 . . save "$path\data\SurveyStageIB_beforezscore.dta", replace file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\dat > a\SurveyStageIB_beforezscore.dta saved . . . . . end of do-file . . // Wave A + B, main survey, append waves . do "05_SurveyStageIAB_append.do" . . *********************************************************************************** . // Replication Files . ********************************************************************************** . /* > HOW DO BELIEFS ABOUT THE GENDER WAGE GAP AFFECT THE DEMAND FOR PUBLIC POLICY? > Sonja Settele > AEJ:pol > */ . ********************************************************************************** . . ************************************************************************************************** > *** . **** Append Waves A and B, Stage I (main survey), do z-scoring, generate interaction terms and ind > ices: . ************************************************************************************************** > *** . **** . . * Start with Wave A: . use "$path\data\SurveyStageIA_beforezscore.dta", clear . . * Everybody has weight 1 in wave A . gen pweight=1 . gen wave=1 . . append using "$path\data\SurveyStageIB_beforezscore.dta" (note: variable ind1other was str74, now str76 to accommodate using data's values) (note: variable ind2 was str728, now str794 to accommodate using data's values) (note: variable maritalstother was str37, now str40 to accommodate using data's values) (label fbset already defined) (label edu already defined) (label demrep already defined) (label gender already defined) (label hhinc already defined) (label empl already defined) . . // Adjust for the fact that women in age groups 1 and 5 were accidentally oversampled . cap drop pweight . gen pweight=1 . replace pweight=1.4615 if wave==2&gender==0&age1==1 (78 real changes made) . replace pweight=0.6298 if wave==2&gender==1&age1==1 (181 real changes made) . replace pweight=1.0184 if wave==2&gender==0&age5==1 (163 real changes made) . replace pweight=0.8691 if wave==2&gender==1&age5==1 (191 real changes made) . . // z-score Variables based on mean and standard deviation in the pure control group . . *z-score manipulation check: . foreach var of varlist large problem govmore{ 2. egen mean_`var'=mean(`var') if rand==0 3. egen max_mean_`var'=min(mean_`var') 4. replace mean_`var'=max_mean_`var' 5. drop max_mean_`var' 6. egen sd_`var'=sd(`var') if rand==0 7. egen min_sd_`var'=min(sd_`var') 8. replace sd_`var'=min_sd_`var' 9. drop min_sd_`var' 10. replace `var'=(`var'-mean_`var')/sd_`var' 11. drop mean_`var' sd_`var' 12. } (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) . . foreach var of varlist large problem govmore{ 2. gen `var'raw=`var' 3. } . . . gen donation=AAUW . gen petIraw=petitionI . gen petIIraw=petitionII . gen fblikeraw=fblike (10 missing values generated) . gen fblike2raw=fblike2 (975 missing values generated) . . *z-score behavioral measures and other: . . foreach var of varlist AAUW petition petitionI petitionII womenwages fblike fblike2{ 2. egen mean_`var'=mean(`var') if rand==0 3. egen max_mean_`var'=min(mean_`var') 4. replace mean_`var'=max_mean_`var' 5. drop max_mean_`var' 6. egen sd_`var'=sd(`var') if rand==0 7. egen min_sd_`var'=min(sd_`var') 8. replace sd_`var'=min_sd_`var' 9. drop min_sd_`var' 10. replace `var'=(`var'-mean_`var')/sd_`var' 11. drop mean_`var' sd_`var' 12. . } (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) variable AAUW was int now float (4,065 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,055 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (3,090 real changes made) . . . foreach var of varlist quotaanchor AAanchor transparencyanchor UKtool legislationanchor childcare{ 2. gen `var'raw=`var' 3. } (1,555 missing values generated) (2,510 missing values generated) . . *z-score anchored policy preferences: . foreach var of varlist quotaanchor AAanchor legislationanchor transparencyanchor UKtool childcare{ 2. egen mean_`var'=mean(`var') if rand==0 3. egen max_mean_`var'=min(mean_`var') 4. replace mean_`var'=max_mean_`var' 5. drop max_mean_`var' 6. egen sd_`var'=sd(`var') if rand==0 7. egen min_sd_`var'=min(sd_`var') 8. replace sd_`var'=min_sd_`var' 9. drop min_sd_`var' 10. replace `var'=(`var'-mean_`var')/sd_`var' 11. drop mean_`var' sd_`var' 12. . } (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (2,510 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (1,555 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) . . . . *z-score perceived reasons: . foreach var of varlist interested society boys discrimination ambitious talented{ 2. gen `var'raw=`var' 3. egen mean_`var'=mean(`var') if rand==0 4. egen max_mean_`var'=min(mean_`var') 5. replace mean_`var'=max_mean_`var' 6. drop max_mean_`var' 7. egen sd_`var'=sd(`var') if rand==0 8. egen min_sd_`var'=min(sd_`var') 9. replace sd_`var'=min_sd_`var' 10. drop min_sd_`var' 11. replace `var'=(`var'-mean_`var')/sd_`var' 12. drop mean_`var' sd_`var' 13. } (1,555 missing values generated) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (2,510 real changes made) (1,555 missing values generated) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (2,510 real changes made) (1,555 missing values generated) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (2,510 real changes made) (1,555 missing values generated) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (2,510 real changes made) (1,555 missing values generated) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (2,510 real changes made) (1,555 missing values generated) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (2,510 real changes made) . . . . * z-score effectiveness . foreach var of varlist effdis effAA effworkfam{ 2. gen `var'raw=`var' 3. egen mean_`var'=mean(`var') if rand==0 4. egen max_mean_`var'=min(mean_`var') 5. replace mean_`var'=max_mean_`var' 6. drop max_mean_`var' 7. egen sd_`var'=sd(`var') if rand==0 8. egen min_sd_`var'=min(sd_`var') 9. replace sd_`var'=min_sd_`var' 10. drop min_sd_`var' 11. replace `var'=(`var'-mean_`var')/sd_`var' 12. drop mean_`var' sd_`var' 13. . } (2,510 missing values generated) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (1,555 real changes made) (2,510 missing values generated) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (1,555 real changes made) (2,510 missing values generated) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (1,555 real changes made) . . * z-score perceived costs . foreach var of varlist monetary distortion bureaucracy{ 2. egen mean_`var'=mean(`var') if rand==0 3. egen max_mean_`var'=min(mean_`var') 4. replace mean_`var'=max_mean_`var' 5. drop max_mean_`var' 6. egen sd_`var'=sd(`var') if rand==0 7. egen min_sd_`var'=min(sd_`var') 8. replace sd_`var'=min_sd_`var' 9. drop min_sd_`var' 10. replace `var'=(`var'-mean_`var')/sd_`var' 11. drop mean_`var' sd_`var' 12. . } (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (1,555 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (1,555 real changes made) (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (1,555 real changes made) . . . * z-score ambitions and future expectations . // not asked in control group, use full sample . . foreach var of varlist tenyears wageexp fairown { 2. egen mean_`var'=mean(`var') 3. egen max_mean_`var'=min(mean_`var') 4. replace mean_`var'=max_mean_`var' 5. drop max_mean_`var' 6. egen sd_`var'=sd(`var') 7. egen min_sd_`var'=min(sd_`var') 8. replace sd_`var'=min_sd_`var' 9. drop min_sd_`var' 10. replace `var'=(`var'-mean_`var')/sd_`var' 11. drop mean_`var' sd_`var' 12. . } (0 real changes made) (0 real changes made) (3,529 real changes made) (0 real changes made) (0 real changes made) (3,529 real changes made) (0 real changes made) (0 real changes made) (3,424 real changes made) . . . . ******************************************************************************** . ******************************************************************************** . //// Interaction terms ***************************************************** > ******************************************************************************** . ******************************************************************************** . . // For treatments . . replace T2=(rand==2) if rand!=. (0 real changes made) . gen T2female=T2*female . label var T2female "T2 x female" . . gen T2democrat=T2*democrat . label var T2democrat "T2 x democrat" . . gen T2republican=T2*republican . label var T2repub "T2 x republican" . . replace T1=(rand==1) if rand!=. (0 real changes made) . gen T1female = T1*female . label var T1female "T1 x female" . . gen T1male = T1*male . label var T1male "T1 x male" . . gen T1democrat=T1*democrat . label var T1democrat "T1 x democrat" . . gen T1republican=T1*republican . label var T1repub "T1 x republican" . . gen T1indep=T1*indep . label var T1indep "T1 x independent" . . gen malerepub=male*republican . lab var malerepub "Male x Republican" . . gen maleindep=male*indep . lab var maleindep "Male x Independent" . . gen maleotherpol=male*otherpol . lab var maleotherpol "Male x other pol." . . gen femdem=female*democrat . lab var femdem "Female x Democrat" . . gen femindep=female*indep . lab var femindep "Female x Indep." . . gen femotherpol=female*otherpol . lab var femotherpol "Female x Other pol." . . gen incmale=0 . replace incmale=male if prior1==1 (1,116 real changes made) . lab var incmale "Incentive x male" . . gen increpub=0 . replace increpub=republican if prior1==1 (828 real changes made) . lab var increpub "Incentive x Republican" . . gen incindep=prior1*indep . lab var incindep "Incentive x Independent" . . gen incmaleindep= prior1*maleindep . lab var incmaleindep "Incentive x Male x Indep." . . gen incmaleotherpol=prior1*maleotherpol . lab var incmaleotherpol "Incentive x Male x Other pol." . . gen incotherpol=prior1*otherpol . lab var incotherpol "Incentive x other pol." . . gen incmalerepub=0 . replace incmalerepub=malerepub if prior1==1 (433 real changes made) . lab var incmalerepub "Inc. x male x Repub." . . gen fememployee=employee*female . lab var fememployee "Female x employee" . . gen femassociate=associatemore* female . lab var femassociate "Female x associate degree +" . . . * Interaction terms with time spent on prior belief elicitation . gen repubtime=republican*timeprior . lab var repubtime "Republican x time spent on prior" . . gen indeptime=indep*timeprior . lab var indeptime "Independent x time spent on prior" . . gen otherpoltime=otherpol*timeprior . lab var otherpoltime "Other pol. x time spent on prior" . . gen maletime=male*timeprior . lab var maletime "Male x time spent on prior" . . gen malerepubtime=malerepub*timeprior . lab var malerepubtime "Male x Repub. x time spent on prior" . . gen maleindeptime=maleindep*timeprior . lab var maleindeptime "Male x Indep. x time spent on prior" . . gen maleotherpoltime=maleotherpol*timeprior . lab var maleotherpoltime "Male x other pol. x time spent on prior" . . . ******************************************************************************** . ************** * Create Summary Indices ****************************************** . ******************************************************************************** . . // Build index for manipulation check: . . gen dm_y1 = large . gen dm_y2 = problem . gen dm_y3 = govmore . . corr dm_y1 dm_y2 dm_y3 (obs=4,065) | dm_y1 dm_y2 dm_y3 -------------+--------------------------- dm_y1 | 1.0000 dm_y2 | 0.8228 1.0000 dm_y3 | 0.6994 0.7905 1.0000 . . matrix varcov_dmsimple = r(C) . matrix varcov_inv_dmsimple = invsym(varcov_dmsimple) . . matrix list varcov_dmsimple symmetric varcov_dmsimple[3,3] dm_y1 dm_y2 dm_y3 dm_y1 1 dm_y2 .82278152 1 dm_y3 .69940995 .79045617 1 . matrix list varcov_inv_dmsimple symmetric varcov_inv_dmsimple[3,3] dm_y1 dm_y2 dm_y3 dm_y1 3.1583478 dm_y2 -2.2723242 4.3002544 dm_y3 -.41280721 -1.8098764 2.7193495 . . // Sum of row elements give us the weight . forvalues nvars = 1/3{ // > Do this for 4 Indicators (rows) 2. scalar weight_var`nvars' = 0 // Plac > eholder for the weight. Start at 0 3. local totrowscols = rowsof(varcov_inv_dmsimple) // Tell me the size of > a symetric matrix 4. // Loop for each column . forvalues n=1/`totrowscols' { > 5. qui local ele`n' = varcov_inv_dmsimple[`nvars',`n'] // Get for each > indicator (row) each column (element) 6. qui scalar weight_var`nvars' = weight_var`nvars' + `ele`n'' // For each ind > icator (row) summ the elements. This is the weight 7. } 8. scalar list weight_var`nvars' // Show weight 9. } weight_var1 = .47321641 weight_var2 = .21805371 weight_var3 = .49666583 . . // Get SUM n=1 to n=N of (WEIGHT_n X INDICATOR_n) . gen numerator = (dm_y1 * weight_var1) . // We loop to get the sum . forvalues h=2/3{ 2. replace numerator = numerator + (dm_y`h' * weight_var`h') 3. } (4,065 real changes made) (4,065 real changes made) . . // The final step is to get the SUM of the weights . . scalar denominator = 0 . forvalues nvars = 1/3{ 2. scalar denominator = denominator + weight_var`nvars' 3. } . . gen z_mani_index = numerator / denominator . . label var z_mani_index "index (mani.-check)" . . drop dm_y1 dm_y2 dm_y3 numerator . . . ************************************************************************************* . // Build index for LM policies: . . gen dm_y1 = quotaanchor . gen dm_y2 = AAanchor . gen dm_y3 = legislationanchor . gen dm_y4 = transparencyanchor (1,555 missing values generated) . gen dm_y5 = childcare . . replace dm_y4=UKtool if dm_y4==. (1,555 real changes made) . . corr dm_y1 dm_y2 dm_y3 dm_y4 dm_y5 (obs=4,065) | dm_y1 dm_y2 dm_y3 dm_y4 dm_y5 -------------+--------------------------------------------- dm_y1 | 1.0000 dm_y2 | 0.5752 1.0000 dm_y3 | 0.3935 0.4796 1.0000 dm_y4 | 0.4160 0.4462 0.4842 1.0000 dm_y5 | 0.4056 0.4640 0.4385 0.4135 1.0000 . . matrix varcov_dmsimple = r(C) . matrix varcov_inv_dmsimple = invsym(varcov_dmsimple) . . matrix list varcov_dmsimple symmetric varcov_dmsimple[5,5] dm_y1 dm_y2 dm_y3 dm_y4 dm_y5 dm_y1 1 dm_y2 .57521735 1 dm_y3 .39347302 .47960902 1 dm_y4 .41601438 .44618607 .48415962 1 dm_y5 .40557822 .46397963 .43849451 .41348232 1 . matrix list varcov_inv_dmsimple symmetric varcov_inv_dmsimple[5,5] dm_y1 dm_y2 dm_y3 dm_y4 dm_y5 dm_y1 1.6093496 dm_y2 -.67944365 1.8053977 dm_y3 -.10978875 -.35283179 1.5469588 dm_y4 -.23348574 -.22147679 -.42398265 1.49899 dm_y5 -.19278513 -.31580869 -.29478893 -.23643435 1.451743 . . // Sum of row elements give us the weight . forvalues nvars = 1/5{ // > Do this for n Indicators (rows) 2. scalar weight_var`nvars' = 0 // Plac > eholder for the weight. Start at 0 3. local totrowscols = rowsof(varcov_inv_dmsimple) // Tell me the size of > a symetric matrix 4. // Loop for each column . forvalues n=1/`totrowscols' { > 5. qui local ele`n' = varcov_inv_dmsimple[`nvars',`n'] // Get for each > indicator (row) each column (element) 6. qui scalar weight_var`nvars' = weight_var`nvars' + `ele`n'' // For each ind > icator (row) summ the elements. This is the weight 7. } 8. scalar list weight_var`nvars' // Show weight 9. } weight_var1 = .39384629 weight_var2 = .23583682 weight_var3 = .3655667 weight_var4 = .38361046 weight_var5 = .4119259 . . // Get SUM n=1 to n=N of (WEIGHT_n X INDICATOR_n) . gen numerator = (dm_y1 * weight_var1) . // We loop to get the sum . forvalues h=2/5{ 2. replace numerator = numerator + (dm_y`h' * weight_var`h') 3. } (4,065 real changes made) (4,065 real changes made) (4,065 real changes made) (4,065 real changes made) . . // The final step is to get the SUM of the weights . . scalar denominator = 0 . forvalues nvars = 1/5{ 2. scalar denominator = denominator + weight_var`nvars' 3. } . . gen z_lmpolicy_index = numerator / denominator . . label var z_lmpolicy_index "index (pol. demand)" . . drop dm_y1 dm_y2 dm_y3 dm_y4 dm_y5 numerator . . . ************************************************************************************* . // Build index for perceived external reasons . . gen dm_y1 = discrimination (1,555 missing values generated) . gen dm_y2 = society (1,555 missing values generated) . gen dm_y3 = boys (1,555 missing values generated) . . corr dm_y1 dm_y2 dm_y3 (obs=2,510) | dm_y1 dm_y2 dm_y3 -------------+--------------------------- dm_y1 | 1.0000 dm_y2 | 0.2536 1.0000 dm_y3 | 0.3651 0.3519 1.0000 . . matrix varcov_dmsimple = r(C) . matrix varcov_inv_dmsimple = invsym(varcov_dmsimple) . . matrix list varcov_dmsimple symmetric varcov_dmsimple[3,3] dm_y1 dm_y2 dm_y3 dm_y1 1 dm_y2 .25364636 1 dm_y3 .36510301 .3518548 1 . matrix list varcov_inv_dmsimple symmetric varcov_inv_dmsimple[3,3] dm_y1 dm_y2 dm_y3 dm_y1 1.1781133 dm_y2 -.16831797 1.165342 dm_y3 -.37090924 -.34857778 1.2580688 . . // Sum of row elements give us the weight . forvalues nvars = 1/3{ // > Do this for n Indicators (rows) 2. scalar weight_var`nvars' = 0 // Plac > eholder for the weight. Start at 0 3. local totrowscols = rowsof(varcov_inv_dmsimple) // Tell me the size of > a symetric matrix 4. // Loop for each column . forvalues n=1/`totrowscols' { > 5. qui local ele`n' = varcov_inv_dmsimple[`nvars',`n'] // Get for each > indicator (row) each column (element) 6. qui scalar weight_var`nvars' = weight_var`nvars' + `ele`n'' // For each ind > icator (row) summ the elements. This is the weight 7. } 8. scalar list weight_var`nvars' // Show weight 9. } weight_var1 = .63888612 weight_var2 = .64844626 weight_var3 = .53858183 . . // Get SUM n=1 to n=N of (WEIGHT_n X INDICATOR_n) . gen numerator = (dm_y1 * weight_var1) (1,555 missing values generated) . // We loop to get the sum . forvalues h=2/3{ 2. replace numerator = numerator + (dm_y`h' * weight_var`h') 3. } (2,510 real changes made) (2,510 real changes made) . . // The final step is to get the SUM of the weights . . scalar denominator = 0 . forvalues nvars = 1/3{ 2. scalar denominator = denominator + weight_var`nvars' 3. } . . gen z_extreasons_index = numerator / denominator (1,555 missing values generated) . . label var z_extreasons_index "index (perc. ext reasons)" . . drop dm_y1 dm_y2 dm_y3 numerator . . . // Build index for perceived personal reasons . . gen dm_y1 = ambitious (1,555 missing values generated) . gen dm_y2 = talented (1,555 missing values generated) . gen dm_y3 = interested (1,555 missing values generated) . . corr dm_y1 dm_y2 dm_y3 (obs=2,510) | dm_y1 dm_y2 dm_y3 -------------+--------------------------- dm_y1 | 1.0000 dm_y2 | 0.7071 1.0000 dm_y3 | 0.5263 0.5618 1.0000 . . matrix varcov_dmsimple = r(C) . matrix varcov_inv_dmsimple = invsym(varcov_dmsimple) . . matrix list varcov_dmsimple symmetric varcov_dmsimple[3,3] dm_y1 dm_y2 dm_y3 dm_y1 1 dm_y2 .7070855 1 dm_y3 .52626731 .56180067 1 . matrix list varcov_inv_dmsimple symmetric varcov_inv_dmsimple[3,3] dm_y1 dm_y2 dm_y3 dm_y1 2.1021441 dm_y2 -1.2637443 2.2209006 dm_y3 -.39631736 -.58263618 1.5358943 . . // Sum of row elements give us the weight . forvalues nvars = 1/3{ // > Do this for n Indicators (rows) 2. scalar weight_var`nvars' = 0 // Plac > eholder for the weight. Start at 0 3. local totrowscols = rowsof(varcov_inv_dmsimple) // Tell me the size of > a symetric matrix 4. // Loop for each column . forvalues n=1/`totrowscols' { > 5. qui local ele`n' = varcov_inv_dmsimple[`nvars',`n'] // Get for each > indicator (row) each column (element) 6. qui scalar weight_var`nvars' = weight_var`nvars' + `ele`n'' // For each ind > icator (row) summ the elements. This is the weight 7. } 8. scalar list weight_var`nvars' // Show weight 9. } weight_var1 = .44208249 weight_var2 = .3745202 weight_var3 = .55694073 . . // Get SUM n=1 to n=N of (WEIGHT_n X INDICATOR_n) . gen numerator = (dm_y1 * weight_var1) (1,555 missing values generated) . // We loop to get the sum . forvalues h=2/3{ 2. replace numerator = numerator + (dm_y`h' * weight_var`h') 3. } (2,510 real changes made) (2,510 real changes made) . . // The final step is to get the SUM of the weights . . scalar denominator = 0 . forvalues nvars = 1/3{ 2. scalar denominator = denominator + weight_var`nvars' 3. } . . gen z_personalreasons_index = numerator / denominator (1,555 missing values generated) . . label var z_personalreasons_index "index (perc. personal reasons)" . . drop dm_y1 dm_y2 dm_y3 numerator . . . // Build index for perceived effectiveness . . gen dm_y1 = effdis (2,510 missing values generated) . gen dm_y2 = effAA (2,510 missing values generated) . gen dm_y3 = effworkfam (2,510 missing values generated) . . corr dm_y1 dm_y2 dm_y3 (obs=1,555) | dm_y1 dm_y2 dm_y3 -------------+--------------------------- dm_y1 | 1.0000 dm_y2 | 0.3814 1.0000 dm_y3 | 0.3339 0.3809 1.0000 . . matrix varcov_dmsimple = r(C) . matrix varcov_inv_dmsimple = invsym(varcov_dmsimple) . . matrix list varcov_dmsimple symmetric varcov_dmsimple[3,3] dm_y1 dm_y2 dm_y3 dm_y1 1 dm_y2 .38140774 1 dm_y3 .33389701 .38090051 1 . matrix list varcov_inv_dmsimple symmetric varcov_inv_dmsimple[3,3] dm_y1 dm_y2 dm_y3 dm_y1 1.2301432 dm_y2 -.36580796 1.2784876 dm_y3 -.2714047 -.3648344 1.2295868 . . // Sum of row elements give us the weight . forvalues nvars = 1/3{ // > Do this for n Indicators (rows) 2. scalar weight_var`nvars' = 0 // Plac > eholder for the weight. Start at 0 3. local totrowscols = rowsof(varcov_inv_dmsimple) // Tell me the size of > a symetric matrix 4. // Loop for each column . forvalues n=1/`totrowscols' { > 5. qui local ele`n' = varcov_inv_dmsimple[`nvars',`n'] // Get for each > indicator (row) each column (element) 6. qui scalar weight_var`nvars' = weight_var`nvars' + `ele`n'' // For each ind > icator (row) summ the elements. This is the weight 7. } 8. scalar list weight_var`nvars' //Show weight 9. } weight_var1 = .59293055 weight_var2 = .54784524 weight_var3 = .59334773 . . // Get SUM n=1 to n=N of (WEIGHT_n X INDICATOR_n) . gen numerator = (dm_y1 * weight_var1) (2,510 missing values generated) . // We loop to get the sum . forvalues h=2/3{ 2. replace numerator = numerator + (dm_y`h' * weight_var`h') 3. } (1,555 real changes made) (1,555 real changes made) . . // The final step is to get the SUM of the weights . . scalar denominator = 0 . forvalues nvars = 1/3{ 2. scalar denominator = denominator + weight_var`nvars' 3. } . . gen z_eff_index = numerator / denominator (2,510 missing values generated) . . label var z_eff_index "index (perc. eff.)" . . drop dm_y1 dm_y2 dm_y3 numerator . . . save "$path\data\SurveyStageI_AB_final.dta" , replace file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\dat > a\SurveyStageI_AB_final.dta saved . . end of do-file . . // Wave A + B, follow-up, append waves and prepare data . do "06_SurveyStageIIAB_append_clean.do" . . . *********************************************************************************** . // Replication Files . ********************************************************************************** . /* > HOW DO BELIEFS ABOUT THE GENDER WAGE GAP AFFECT THE DEMAND FOR PUBLIC POLICY? > Sonja Settele > AEJ:pol > */ . ********************************************************************************** . . *********************************************************************************** . **** Cleaning file Survey Waves A and B, Stage II (follow-up survey): . *********************************************************************************** . . // Append Waves A and B: . . use "$path\data\SurveyStageII_WaveA_raw.dta", clear . append using "$path\data\SurveyStageII_WaveB_raw.dta" (label gender already defined) . . . // Prepare Data . // Demographics . * Age . rename ageII agecatII . . gen ageII=1 if agecatII=="A1" //18-24 (1,051 missing values generated) . replace ageII=2 if agecatII=="A2" //25-34 (214 real changes made) . replace ageII=3 if agecatII=="A3" //35-54 (457 real changes made) . replace ageII=4 if agecatII=="A4" //55-70 (376 real changes made) . replace ageII=5 if agecatII=="A5" //71+ (4 real changes made) . . drop agecatII . tab ageII ageII | Freq. Percent Cum. ------------+----------------------------------- 1 | 54 4.89 4.89 2 | 214 19.37 24.25 3 | 457 41.36 65.61 4 | 376 34.03 99.64 5 | 4 0.36 100.00 ------------+----------------------------------- Total | 1,105 100.00 . . * Gender . rename genderII gendercatII . . gen genderII=0 if gendercatII=="A1" //male (551 missing values generated) . replace genderII=1 if gendercatII=="A2" //female (551 real changes made) . . drop gendercatII . . * Employment . rename employmentII employmentcatII . tab employmentcatII employment | Freq. Percent Cum. ------------+----------------------------------- A1 | 666 60.27 60.27 A2 | 110 9.95 70.23 A3 | 329 29.77 100.00 ------------+----------------------------------- Total | 1,105 100.00 . . gen employmentII=1 if employmentcatII=="A1" //working (439 missing values generated) . replace employmentII=2 if employmentcatII=="A2" //business owner (110 real changes made) . replace employmentII=3 if employmentcatII=="A3" //oolf (329 real changes made) . . . // Outcome variables: Perceptions and policy demand . * (Order of subquestions was randomized in Wave B, not in Wave A) . . * "Wage differences between high- and low-skilled are a problem" . gen problemskill=. (1,105 missing values generated) . . forvalues i=1/6{ 2. replace problemskill=`i' if wave=="B"&wageproblemSQ001=="A`i'" & randorder2==1 3. replace problemskil=`i' if wave=="B"&wageproblem2SQ001=="A`i'" & randorder2==2 4. replace problemskill=`i' if wave=="A"&wageproblemSQ001=="A`i'" 5. } (57 real changes made) (43 real changes made) (66 real changes made) (64 real changes made) (70 real changes made) (80 real changes made) (76 real changes made) (52 real changes made) (117 real changes made) (50 real changes made) (50 real changes made) (110 real changes made) (36 real changes made) (45 real changes made) (75 real changes made) (33 real changes made) (30 real changes made) (51 real changes made) . . drop wageproblemSQ001 wageproblem2SQ001 . . * "Wage differences between men and women are a problem" . gen problemII=. (1,105 missing values generated) . . forvalues i=1/6{ 2. replace problemII=`i' if wave=="B"&wageproblemSQ002=="A`i'" & randorder2==1 3. replace problemII=`i' if wave=="B"&wageproblem2SQ002=="A`i'" & randorder2==2 4. replace problemII=`i' if wave=="A"&wageproblemSQ002=="A`i'" 5. } (48 real changes made) (42 real changes made) (64 real changes made) (48 real changes made) (46 real changes made) (68 real changes made) (67 real changes made) (71 real changes made) (98 real changes made) (57 real changes made) (61 real changes made) (109 real changes made) (51 real changes made) (34 real changes made) (90 real changes made) (45 real changes made) (36 real changes made) (70 real changes made) . . drop wageproblemSQ002 wageproblem2SQ002 . . * "Wage differences between high-skilled men and women are a problem" . gen problemIIHS=. (1,105 missing values generated) . . forvalues i=1/6{ 2. replace problemIIHS=`i' if wave=="B"&wageproblemSQ003=="A`i'" & randorder2==1 3. replace problemIIHS=`i' if wave=="B"&wageproblem2SQ004=="A`i'" & randorder2==2 4. replace problemIIHS=`i' if wave=="A"&wageproblemSQ003=="A`i'" 5. } (61 real changes made) (51 real changes made) (72 real changes made) (42 real changes made) (57 real changes made) (68 real changes made) (60 real changes made) (49 real changes made) (108 real changes made) (56 real changes made) (49 real changes made) (85 real changes made) (56 real changes made) (48 real changes made) (93 real changes made) (41 real changes made) (36 real changes made) (73 real changes made) . . drop wageproblemSQ003 wageproblem2SQ004 . . . * "Wage differences between low-skilled men and women are a problem" . . gen problemIILS=. (1,105 missing values generated) . . forvalues i=1/6{ 2. replace problemIILS=`i' if wave=="B"&wageproblemSQ004=="A`i'" & randorder2==1 3. replace problemIILS=`i' if wave=="B"&wageproblem2SQ003=="A`i'" & randorder2==2 4. replace problemIILS=`i' if wave=="A"&wageproblemSQ004=="A`i'" 5. } (46 real changes made) (29 real changes made) (72 real changes made) (63 real changes made) (66 real changes made) (83 real changes made) (69 real changes made) (71 real changes made) (99 real changes made) (53 real changes made) (53 real changes made) (104 real changes made) (43 real changes made) (40 real changes made) (71 real changes made) (42 real changes made) (31 real changes made) (70 real changes made) . . drop wageproblemSQ004 wageproblem2SQ003 . . . * Specific policy demand (Wave B only) . foreach var in AAanchorII legislationanchorII{ 2. rename `var' `var'string 3. gen `var'=. 4. forvalues i=1/5{ 5. replace `var'=`i' if `var'string=="A`i'" 6. } 7. drop `var'string 8. } (1,105 missing values generated) (57 real changes made) (85 real changes made) (207 real changes made) (162 real changes made) (95 real changes made) (1,105 missing values generated) (37 real changes made) (68 real changes made) (226 real changes made) (190 real changes made) (85 real changes made) . . * How fair are wages of low-skilled workers/ of women? . gen fairskill=. (1,105 missing values generated) . gen fairII=. (1,105 missing values generated) . forvalues i=1/5{ 2. replace fairskill=`i' if fairnessSQ001=="A`i'" 3. replace fairII=`i' if fairnessSQ002=="A`i'" 4. } (149 real changes made) (126 real changes made) (395 real changes made) (424 real changes made) (436 real changes made) (407 real changes made) (80 real changes made) (98 real changes made) (45 real changes made) (50 real changes made) . . tab fairskill if submitdateII!="" fairskill | Freq. Percent Cum. ------------+----------------------------------- 1 | 149 13.48 13.48 2 | 395 35.75 49.23 3 | 436 39.46 88.69 4 | 80 7.24 95.93 5 | 45 4.07 100.00 ------------+----------------------------------- Total | 1,105 100.00 . tab fairII if submitdateII!="" fairII | Freq. Percent Cum. ------------+----------------------------------- 1 | 126 11.40 11.40 2 | 424 38.37 49.77 3 | 407 36.83 86.61 4 | 98 8.87 95.48 5 | 50 4.52 100.00 ------------+----------------------------------- Total | 1,105 100.00 . drop fairnessSQ001 fairnessSQ002 . . label define fair 1 "much less than fair" 2 "less than fair" 3 "fair" 4 "more than fair" 5 "much m > ore than fair" . label values fairII fair . . . * Government should increase efforts to support low-skilled workers/women in the labor market . gen govmoreII=. (1,105 missing values generated) . gen govmoreLS=. (1,105 missing values generated) . . forvalues i=1/7{ 2. replace govmoreII=`i' if govwomen=="A`i'" 3. replace govmoreLS=`i' if govlowskilled=="A`i'" 4. } (25 real changes made) (18 real changes made) (12 real changes made) (16 real changes made) (28 real changes made) (41 real changes made) (327 real changes made) (312 real changes made) (275 real changes made) (320 real changes made) (222 real changes made) (208 real changes made) (216 real changes made) (190 real changes made) . . drop govwomen govlowskilled . . . * Government should strengthen anti-disc policies/policies to help women combine work and family . gen antidiscII=. (1,105 missing values generated) . gen fampolII=. (1,105 missing values generated) . forvalues i=1/5{ 2. replace antidiscII=`i' if antidiscr=="A`i'" 3. replace fampolII=`i' if fampolicy=="A`i'" 4. } (46 real changes made) (29 real changes made) (56 real changes made) (65 real changes made) (362 real changes made) (379 real changes made) (338 real changes made) (399 real changes made) (303 real changes made) (233 real changes made) . . drop antidiscr fampolicy . . . * Read and discuss about topic since last survey . rename readII readIIcat . gen readII = 1 if readIIcat == "A1" (936 missing values generated) . replace readII=0 if readIIcat=="A2"|readIIcat=="A3" (695 real changes made) . . rename discussII discussIIcat . gen discussII = 1 if discussIIcat=="A1" (925 missing values generated) . replace discussII=0 if discussIIcat=="A2"|discussIIcat=="A3" (693 real changes made) . . * Never taken survey on the topic before . gen firsttime=(readIIcat=="A4"&discussIIcat=="A4") . drop readIIcat discussIIcat . . . //z-score outcomes . local outcomesII AAanchorII legislationanchorII problemskill problemII problemIIHS problemIILS fai > rskill fairII govmoreII govmoreLS antidiscII fampolII readII discussII . foreach var in `outcomesII'{ 2. egen mean_`var'=mean(`var') 3. egen sd_`var'=sd(`var') 4. replace `var'=(`var'-mean_`var')/sd_`var' 5. drop mean_`var' sd_`var' 6. } (606 real changes made) (606 real changes made) (1,105 real changes made) (1,105 real changes made) (1,105 real changes made) (1,105 real changes made) (1,105 real changes made) (1,105 real changes made) (1,105 real changes made) (1,105 real changes made) (1,105 real changes made) (1,105 real changes made) (864 real changes made) (873 real changes made) . . // Survey wave . rename wave wavecat . gen wave=1 if wavecat=="A" (606 missing values generated) . replace wave=2 if wavecat=="B" (606 real changes made) . . //Treatment dummy T1 corresponding to T74 . gen T1=(rand==1) . . . // Generate additional outcomes, such as summary indices . . // z-scored version of posterior belief . egen zposteriorII=std(posteriorII) (16 missing values generated) . . . // Build index for perceptions . gen dm_y1 = problemII . gen dm_y2 = govmoreII . gen dm_y3 = problemIIHS . gen dm_y4 = problemIILS . . . corr dm_y1 dm_y2 dm_y3 dm_y4 (obs=1,105) | dm_y1 dm_y2 dm_y3 dm_y4 -------------+------------------------------------ dm_y1 | 1.0000 dm_y2 | 0.4560 1.0000 dm_y3 | 0.8254 0.4095 1.0000 dm_y4 | 0.6650 0.3895 0.6174 1.0000 . . matrix varcov_dmsimple = r(C) . matrix varcov_inv_dmsimple = invsym(varcov_dmsimple) . . matrix list varcov_dmsimple symmetric varcov_dmsimple[4,4] dm_y1 dm_y2 dm_y3 dm_y4 dm_y1 1 dm_y2 .45595138 1 dm_y3 .82543556 .40945519 1 dm_y4 .6650343 .3895309 .61741181 1 . matrix list varcov_inv_dmsimple symmetric varcov_inv_dmsimple[4,4] dm_y1 dm_y2 dm_y3 dm_y4 dm_y1 3.6912655 dm_y2 -.3848748 1.2868018 dm_y3 -2.3694821 -.09332675 3.2299689 dm_y4 -.84195133 -.18767306 -.38208034 1.8689319 . . // Sum of row elements give us the weight . forvalues nvars = 1/4{ // > Do this for n Indicators (rows) 2. scalar weight_var`nvars' = 0 // Plac > eholder for the weight. Start at 0 3. local totrowscols = rowsof(varcov_inv_dmsimple) // Tell me the size of > a symetric matrix 4. // Loop for each column . forvalues n=1/`totrowscols' { > 5. qui local ele`n' = varcov_inv_dmsimple[`nvars',`n'] // Get for each > indicator (row) each column (element) 6. qui scalar weight_var`nvars' = weight_var`nvars' + `ele`n'' // For each ind > icator (row) summ the elements. This is the weight 7. } 8. scalar list weight_var`nvars' // Display weight 9. } weight_var1 = .09495726 weight_var2 = .62092716 weight_var3 = .38507961 weight_var4 = .45722716 . . // Get SUM n=1 to n=N of (WEIGHT_n X INDICATOR_n) (In this case n=6 indicators) . gen numerator = (dm_y1 * weight_var1) . // We loop to get the sum . forvalues h=2/4{ 2. replace numerator = numerator + (dm_y`h' * weight_var`h') 3. } (1,105 real changes made) (1,105 real changes made) (1,105 real changes made) . . // The final step is to get the SUM of the weights . scalar denominator = 0 . forvalues nvars = 1/4{ 2. scalar denominator = denominator + weight_var`nvars' 3. } . . gen z_maniII_index2 = numerator / denominator . . label var z_maniII_index2 "Perception Index" . . drop dm_y1 dm_y2 dm_y3 dm_y4 numerator . . . // Build index for demand for two newly elicited types of policies . gen dm_y1 = antidiscII . gen dm_y2 = fampolII . . . corr dm_y1 dm_y2 (obs=1,105) | dm_y1 dm_y2 -------------+------------------ dm_y1 | 1.0000 dm_y2 | 0.6911 1.0000 . . matrix varcov_dmsimple = r(C) . matrix varcov_inv_dmsimple = invsym(varcov_dmsimple) . . matrix list varcov_dmsimple symmetric varcov_dmsimple[2,2] dm_y1 dm_y2 dm_y1 1 dm_y2 .69109094 1 . matrix list varcov_inv_dmsimple symmetric varcov_inv_dmsimple[2,2] dm_y1 dm_y2 dm_y1 1.9142664 dm_y2 -1.3229322 1.9142664 . . // Sum of row elements give us the weight . forvalues nvars = 1/2{ // > Do this for n Indicators (rows) 2. scalar weight_var`nvars' = 0 // Plac > eholder for the weight. Start at 0 3. local totrowscols = rowsof(varcov_inv_dmsimple) // Tell me the size of > a symetric matrix, here 4 4. // Loop for each column . forvalues n=1/`totrowscols' { > 5. qui local ele`n' = varcov_inv_dmsimple[`nvars',`n'] // Get for each > indicator (row) each column (element) 6. qui scalar weight_var`nvars' = weight_var`nvars' + `ele`n'' // For each ind > icator (row) summ the elements. This is the weight 7. } 8. scalar list weight_var`nvars' // Display weight 9. } weight_var1 = .59133426 weight_var2 = .59133426 . . // Get SUM n=1 to n=N of (WEIGHT_n X INDICATOR_n) . gen numerator = (dm_y1 * weight_var1) . // We loop to get the sum . forvalues h=2/2{ 2. replace numerator = numerator + (dm_y`h' * weight_var`h') 3. } (1,105 real changes made) . . // The final step is to get the SUM of the weights . scalar denominator = 0 . forvalues nvars = 1/2{ 2. scalar denominator = denominator + weight_var`nvars' 3. } . . gen z_lmpolicyII_types_index = numerator / denominator . . label var z_lmpolicyII_types_index "index (labor market policy types)" . . drop dm_y1 dm_y2 numerator . . . // Build index for specific (re-elicited) policy demand . gen dm_y1 = legislationanchorII (499 missing values generated) . gen dm_y2 = AAanchorII (499 missing values generated) . . . corr dm_y1 dm_y2 (obs=606) | dm_y1 dm_y2 -------------+------------------ dm_y1 | 1.0000 dm_y2 | 0.5973 1.0000 . . matrix varcov_dmsimple = r(C) . matrix varcov_inv_dmsimple = invsym(varcov_dmsimple) . . matrix list varcov_dmsimple symmetric varcov_dmsimple[2,2] dm_y1 dm_y2 dm_y1 1 dm_y2 .59731698 1 . matrix list varcov_inv_dmsimple symmetric varcov_inv_dmsimple[2,2] dm_y1 dm_y2 dm_y1 1.5546963 dm_y2 -.92864653 1.5546963 . . // Sum of row elements give us the weight . forvalues nvars = 1/2{ // > Do this for n Indicators (rows) 2. scalar weight_var`nvars' = 0 // Plac > eholder for the weight. Start at 0 3. local totrowscols = rowsof(varcov_inv_dmsimple) // Tell me the size of > a symetric matrix, here 4 4. // Loop for each column . forvalues n=1/`totrowscols' { > 5. qui local ele`n' = varcov_inv_dmsimple[`nvars',`n'] // Get for each > indicator (row) each column (element) 6. qui scalar weight_var`nvars' = weight_var`nvars' + `ele`n'' // For each ind > icator (row) summ the elements. This is the weight 7. } 8. scalar list weight_var`nvars' // Display weight 9. } weight_var1 = .62604981 weight_var2 = .62604981 . . // Get SUM n=1 to n=N of (WEIGHT_n X INDICATOR_n) . gen numerator = (dm_y1 * weight_var1) (499 missing values generated) . // We loop to get the sum . forvalues h=2/2{ 2. replace numerator = numerator + (dm_y`h' * weight_var`h') 3. } (606 real changes made) . . // The final step is to get the SUM of the weights . scalar denominator = 0 . forvalues nvars = 1/2{ 2. scalar denominator = denominator + weight_var`nvars' 3. } . . gen z_lmpolicyII_specific_index = numerator / denominator (499 missing values generated) . . label var z_lmpolicyII_specific_index "index (labor market policy types)" . . drop dm_y1 dm_y2 numerator . . . save "$path\data\SurveyStageIIAB_final.dta", replace file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\dat > a\SurveyStageIIAB_final.dta saved . . . ********************************************************************************* . ** ** ** ** ** ** ** ** ** ** ** ** ** ** > ** ** ** ** ** ** . . . exit end of do-file . . . *ØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØ* . ***************** Replicate Tables and Figures *********************************** . *ØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØØ* . . // Main Figures . do "07_MainFigures.do" . . *********************************************************************************** . // Replication Files . ********************************************************************************** . /* > HOW DO BELIEFS ABOUT THE GENDER WAGE GAP AFFECT THE DEMAND FOR PUBLIC POLICY? > Sonja Settele > AEJ:pol > */ . ********************************************************************************** . . *********************************************************************************** . **** Generate Figures 2-3 in main paper . *********************************************************************************** . . *********************************************************************************** . // Figure 2: Treatment effect on signatures for real online petitions . *********************************************************************************** . . /* Number of potential signatures for Petitions I and II per treatment group correspond to the num > ber of respondents assigned to either treatment group > The numbers of actual signatures for Petitions I and II are all "manually" retrieved from the Whit > e House Petition Website. > */ . . // PETITION I . /* Run prtesti for a two-sided proportion test for Petition I. > Input: Total number of potential signatures in T74 (1531) > Number of actual signatures in T74 (259) > Total number of potentila signatures in T94 (1500) > Number of actual signatures in T94 (220) > Output: Proportion of signatures in T74 (incl. 95% CI) > Proportion of signatures in T94 (incl. 95% CI) > P-value of two-sided proportion test > --> Output entered manually below > */ . . prtesti 1531 259 1500 220, count Two-sample test of proportions x: Number of obs = 1531 y: Number of obs = 1500 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .1691705 .0095814 .1503912 .1879497 y | .1466667 .0091344 .1287636 .1645697 -------------+---------------------------------------------------------------- diff | .0225038 .0132379 -.0034419 .0484495 | under Ho: .013252 1.70 0.089 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 1.6981 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.9553 Pr(|Z| < |z|) = 0.0895 Pr(Z > z) = 0.0447 . . clear all . set scheme s2mono . . global legend = `"label(1 "T{sup:74}") label(2 "T{sup:94}") order(1 2) size(large)"' . . . **** Set numbers for bar graph in a matrix . . * Petition I: . . mat R=J(2,6,.) . . local pvalue1 = 0.09 //2-sided test . . * Means . mat R[1,1] = 0.16917 // All T74 . mat R[2,1] = 0.14667 // All T94 . . * Lower bounds . mat R[1,2] = 0.1504 // All T74 . mat R[2,2] = 0.1288 // All T94 . . * Upper bounds . mat R[1,3] = 0.1879 // All T74 . mat R[2,3] = 0.1646 // All T94 . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 1 . mat R[2,5] = 1 . . mat R[1,6] = 1 . mat R[2,6] = 1 . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile Pet1 . save `Pet1' file C:\Users\gxf271\AppData\Local\Temp\ST_00000002.tmp saved . restore . . . . // PETITION II . /* Run prtesti for a two-sided proportion test for Petition II. > Input: Total number of potential signatures in T74 (1531) > Number of actual signatures in T74 (19) > Total number of potentila signatures in T94 (1500) > Number of actual signatures in T94 (35) > Output: Proportion of signatures in T74 (incl. 95% CI) > Proportion of signatures in T94 (incl. 95% CI) > P-value of two-sided proportion test > --> Output entered manually below > */ . . prtesti 1531 19 1500 35, count Two-sample test of proportions x: Number of obs = 1531 y: Number of obs = 1500 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0124102 .0028294 .0068647 .0179557 y | .0233333 .0038978 .0156938 .0309728 -------------+---------------------------------------------------------------- diff | -.0109231 .0048164 -.0203632 -.0014831 | under Ho: .0048057 -2.27 0.023 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -2.2729 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.0115 Pr(|Z| < |z|) = 0.0230 Pr(Z > z) = 0.9885 . . . ********************************** . * Petition II . . . local pvalue2 = 0.02 //2-sided test . . * Means . mat R[1,1] = 0.01241 // All T74 . mat R[2,1] = 0.02333 // All T94 . . * Lower bounds . mat R[1,2] = 0.0069 // All T74 . mat R[2,2] = 0.0157 // All T94 . . * Upper bounds . mat R[1,3] = 0.01796 // All T74 . mat R[2,3] = 0.031 // All T94 . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 1 . mat R[2,5] = 1 . . mat R[1,6] = 2 . mat R[2,6] = 2 . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile Pet2 . save `Pet2' file C:\Users\gxf271\AppData\Local\Temp\ST_00000004.tmp saved . restore . . . * Save bar graph matrix as dataset . clear . . local numcats = "1 2 " . foreach a of local numcats { 2. append using `Pet`a'' 3. } . . . * For alignment along the x-axis . gen s1 = R6 . gen s2 = . (4 missing values generated) . replace s2 = s1 - 0.2 if R6 == 1 (2 real changes made) . replace s2 = s1 - 0.6 if R6 == 2 (2 real changes made) . replace s2 = s1 - 1.0 if R6 == 3 (0 real changes made) . replace s2 = s1 - 1.4 if R6 == 4 (0 real changes made) . replace s2 = s1 - 1.8 if R6 == 5 (0 real changes made) . gen p1 = (s2 - 0.1) - .6 . gen p2 = s2 + 0.1 - .6 . . . * This recovers the group means with which to label each bar. . local i = 0 . foreach pgrade of local numcats { 2. forval rel = 1/2 { 3. local ++i 4. sum R1 if R4 == `rel' & R6 == `pgrade' 5. local barval`i' = trim("`: di %9.2f r(mean)'") 6. } 7. } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .16917 . .16917 .16917 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .14667 . .14667 .14667 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .01241 . .01241 .01241 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .02333 . .02333 .02333 . . * Set positions of elements in graph . *if "$grades" == "separate" { . global barlabels `" " 0.8 " " "' . global pvalues `"0.22 0.2 "p-value = `pvalue1'" 0.22 0.8 "p-value = `pvalue2'" "' . global grouplabels `"0.27 0.2 "Petition I:" 0.27 0.8 "Petition II:""' . global grouplabels2 `"0.258 0.2 "Increase rep. requirements" 0.258 0.8 "Decrease rep. requ > irements""' . global barvalues = `"0.04 0.1 "`barval1'" 0.04 0.3 "`barval2'" 0.04 0.7 "`barval3'" 0.04 0 > .9 "`barval4'" "' . *} . . . twoway (bar R1 p1 if R4 == 1, barw(0.18) fi(inten50) lc(black) lw(medium)) (bar R1 p2 if R4 == 2, > barw(0.18) fi(inten20) lc(black) lw(medium)) /// > (rcap R3 R2 p1 if R4 == 1, lc(gs5)) (rcap R3 R2 p2 if R4 == 2, lc(gs5)), legend(${legend}) > graphregion(color(white)) /// > yscale(range(0.3)) yla(0(0.05)0.3) xla(none) text($pvalues, size(4.0)) text($grouplabels, > size(4.0)) text($grouplabels2, size(4.0)) text($barvalues, size(4.0)) /// > ytitle("Fraction of respondents who signed", height(5) size(4.2)) . graph export "$output\fig_petitions_combined.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\fig_petitions_combined.pdf written in PDF format) . . . . *********************************************************************************** . // Figure 3: Incentivized vs. non-incentivized prior beliefs. . *********************************************************************************** . . set scheme s2mono . . use "$path\data\SurveyStageI_AB_final.dta", clear . . keep prior prior1 pweight wave democrat republican indep otherpol gender midwest south west age1 a > ge2 age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemp student . . gen priormen=prior if gender==0 (2,108 missing values generated) . gen priorwoman=prior if gender==1 (1,957 missing values generated) . gen priorrepub=prior if republican==1 (2,603 missing values generated) . gen priordem = prior if democrat==1 (2,260 missing values generated) . . lab var priormen "Men" . lab var priorwoman "Women" . lab var priorrepub "Republican" . lab var priordem "Democrat" . . rename prior1 incentive . . global legend = `"label(1 "No incentive") label(2 "Incentive") order(1 2) size(small)"' . . gen notincentive = 1-incentive . . local gender = "$gender" . if "$gender" == "male" local genval = 0 . if "$gender" == "female" local genval = 1 . . local group1 = "priormen" . local group2 = "priorwoman" . local group3 = "priorrepub" . local group4 = "priordem" . local numcats = "1 2 3 4" . . . **** Calculate numbers for bar graph matrix . . * Group 1: Men . . * Set up matrix . mat R=J(2,5,.) . . * Store means by incentive condition . local row=1 . foreach X in notincentive incentive{ 2. mean `group1' if `X' == 1 [pweight=pweight] 3. mat R[`row',1] = e(b) 4. local ++row 5. } Mean estimation Number of obs = 841 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ priormen | 88.05205 .8109416 86.46034 89.64376 -------------------------------------------------------------- Mean estimation Number of obs = 1,116 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ priormen | 85.67494 .6443394 84.41069 86.9392 -------------------------------------------------------------- . . . * Calculate and store mean belief in no-incentive condition . mean `group1' if notincentive == 1 [pweight=pweight] Mean estimation Number of obs = 841 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ priormen | 88.05205 .8109416 86.46034 89.64376 -------------------------------------------------------------- . matrix meannoin=e(b) . . * Calculate and store incentive coeficient and p-value . reg `group1' incentive [pweight=pweight], robust (sum of wgt is 1.9960e+03) Linear regression Number of obs = 1,957 F(1, 1955) = 5.27 Prob > F = 0.0218 R-squared = 0.0028 Root MSE = 22.318 ------------------------------------------------------------------------------ | Robust priormen | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- incentive | -2.377112 1.035733 -2.30 0.022 -4.408368 -.3458554 _cons | 88.05205 .8108738 108.59 0.000 86.46179 89.64232 ------------------------------------------------------------------------------ . local pvalue1 = trim("`: di %9.3f 2*ttail(e(df_r), abs(_b[incentive]/_se[incentive]))'") . . . local row=1 . foreach X in notincentive incentive { 2. mat R[`row',2]= meannoin[1,1] + _b[incentive]-1.96*_se[incentive] 3. mat R[`row',3]= meannoin[1,1] + _b[incentive]+1.96*_se[incentive] 4. mat R[`row',4]=`row' 5. mat R[`row',5] = 1 6. local ++row 7. } . . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat1 . save `cat1' file C:\Users\gxf271\AppData\Local\Temp\ST_00000006.tmp saved . restore . . * Group 2: Women . . * Set up matrix . mat R=J(2,5,.) . . * Store means by incentive condition . local row=1 . foreach X in notincentive incentive{ 2. mean `group2' if `X' == 1 [pweight=pweight] 3. mat R[`row',1] = e(b) 4. local ++row 5. } Mean estimation Number of obs = 931 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ priorwoman | 79.37533 .6645989 78.07104 80.67961 -------------------------------------------------------------- Mean estimation Number of obs = 1,177 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ priorwoman | 81.07164 .6158674 79.86332 82.27996 -------------------------------------------------------------- . . * Calculate and store mean belief in no-incentive condition . mean `group2' if notincentive == 1 [pweight=pweight] Mean estimation Number of obs = 931 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ priorwoman | 79.37533 .6645989 78.07104 80.67961 -------------------------------------------------------------- . matrix meannoin=e(b) . . * Calculate and store incentive coeficient and p-value . reg `group2' incentive [pweight=pweight], robust (sum of wgt is 2.0160e+03) Linear regression Number of obs = 2,108 F(1, 2106) = 3.50 Prob > F = 0.0613 R-squared = 0.0017 Root MSE = 20.595 ------------------------------------------------------------------------------ | Robust priorwoman | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- incentive | 1.696315 .9060721 1.87 0.061 -.0805746 3.473205 _cons | 79.37533 .6645572 119.44 0.000 78.07207 80.67858 ------------------------------------------------------------------------------ . local pvalue2 = trim("`: di %9.3f 2*ttail(e(df_r), abs(_b[incentive]/_se[incentive]))'") . . . local row=1 . foreach X in notincentive incentive { 2. mat R[`row',2]= meannoin[1,1] + _b[incentive]-1.96*_se[incentive] 3. mat R[`row',3]= meannoin[1,1] + _b[incentive]+1.96*_se[incentive] 4. mat R[`row',4]=`row' 5. mat R[`row',5] = 2 6. local ++row 7. } . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat2 . save `cat2' file C:\Users\gxf271\AppData\Local\Temp\ST_00000008.tmp saved . restore . . * Group 3: Republicans . . * Set up matrix . mat R=J(2,5,.) . . * Store means by incentive condition . local row=1 . foreach X in notincentive incentive{ 2. mean `group3' if `X' == 1 [pweight=pweight] 3. mat R[`row',1] = e(b) 4. local ++row 5. } Mean estimation Number of obs = 634 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ priorrepub | 85.92992 .8905775 84.18107 87.67876 -------------------------------------------------------------- Mean estimation Number of obs = 828 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ priorrepub | 85.91644 .803113 84.34007 87.49282 -------------------------------------------------------------- . . . * Calculate and store mean belief in no-incentive condition . mean `group3' if notincentive == 1 [pweight=pweight] Mean estimation Number of obs = 634 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ priorrepub | 85.92992 .8905775 84.18107 87.67876 -------------------------------------------------------------- . matrix meannoin=e(b) . . * Calculate and store incentive coeficient and p-value . reg `group3' incentive [pweight=pweight], robust (sum of wgt is 1.4462e+03) Linear regression Number of obs = 1,462 F(1, 1460) = 0.00 Prob > F = 0.9910 R-squared = 0.0000 Root MSE = 22.49 ------------------------------------------------------------------------------ | Robust priorrepub | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- incentive | -.0134719 1.19919 -0.01 0.991 -2.365791 2.338847 _cons | 85.92992 .8904842 96.50 0.000 84.18315 87.67668 ------------------------------------------------------------------------------ . local pvalue3 = trim("`: di %9.3f 2*ttail(e(df_r), abs(_b[incentive]/_se[incentive]))'") . . local row=1 . foreach X in notincentive incentive { 2. mat R[`row',2]= meannoin[1,1] + _b[incentive]-1.96*_se[incentive] 3. mat R[`row',3]= meannoin[1,1] + _b[incentive]+1.96*_se[incentive] 4. mat R[`row',4]=`row' 5. mat R[`row',5] = 3 // group 6. local ++row 7. } . . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat3 . save `cat3' file C:\Users\gxf271\AppData\Local\Temp\ST_0000000a.tmp saved . restore . . * Group4: Democrats . . * Set up matrix . mat R=J(2,5,.) . . * Store means by incentive condition . local row=1 . foreach X in notincentive incentive{ 2. mean `group4' if `X' == 1 [pweight=pweight] 3. mat R[`row',1] = e(b) 4. local ++row 5. } Mean estimation Number of obs = 773 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ priordem | 81.50275 .7747331 79.98192 83.02358 -------------------------------------------------------------- Mean estimation Number of obs = 1,032 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ priordem | 81.28295 .6568122 79.99411 82.57179 -------------------------------------------------------------- . . . * Calculate and store mean belief in no-incentive condition . mean `group4' if notincentive == 1 [pweight=pweight] Mean estimation Number of obs = 773 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ priordem | 81.50275 .7747331 79.98192 83.02358 -------------------------------------------------------------- . matrix meannoin=e(b) . . * Calculate and store incentive coeficient and p-value . reg `group4' incentive [pweight=pweight], robust (sum of wgt is 1.7737e+03) Linear regression Number of obs = 1,805 F(1, 1803) = 0.05 Prob > F = 0.8287 R-squared = 0.0000 Root MSE = 21.155 ------------------------------------------------------------------------------ | Robust priordem | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- incentive | -.2197957 1.015658 -0.22 0.829 -2.211787 1.772196 _cons | 81.50275 .7746611 105.21 0.000 79.98342 83.02208 ------------------------------------------------------------------------------ . local pvalue4 = trim("`: di %9.3f 2*ttail(e(df_r), abs(_b[incentive]/_se[incentive]))'") . . local row=1 . foreach X in notincentive incentive { 2. mat R[`row',2]= meannoin[1,1] + _b[incentive]-1.96*_se[incentive] 3. mat R[`row',3]= meannoin[1,1] + _b[incentive]+1.96*_se[incentive] 4. mat R[`row',4]=`row' 5. mat R[`row',5] = 4 // group 6. local ++row 7. } . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat4 . save `cat4' file C:\Users\gxf271\AppData\Local\Temp\ST_0000000c.tmp saved . restore . . . * Save bar graph matrix as dataset . clear . foreach a of local numcats { 2. append using `cat`a'' 3. } . . . * For alignment along the x-axis . gen s1 = R5 . gen s2 = . (8 missing values generated) . replace s2 = s1 - 0.2 if R5 == 1 (2 real changes made) . replace s2 = s1 - 0.6 if R5 == 2 (2 real changes made) . replace s2 = s1 - 1.0 if R5 == 3 (2 real changes made) . replace s2 = s1 - 1.4 if R5 == 4 (2 real changes made) . gen pgrade1 = (s2 - 0.1) - .6 . gen pgrade2 = s2 + 0.1 - .6 . . * This recovers the group means with which to label each bar. . local i = 0 . foreach pgroup of local numcats { 2. forval rel = 1/2 { 3. local ++i 4. sum R1 if R4 == `rel' & R5 == `pgroup' 5. local barval`i' = trim("`: di %9.2f r(mean)'") 6. } 7. } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 88.05206 . 88.05206 88.05206 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 85.67494 . 85.67494 85.67494 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 79.37533 . 79.37533 79.37533 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 81.07164 . 81.07164 81.07164 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 85.92992 . 85.92992 85.92992 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 85.91644 . 85.91644 85.91644 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 81.50275 . 81.50275 81.50275 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 81.28295 . 81.28295 81.28295 . . global barlabels `"0.2 "Men" 0.8 "Women" 1.4 "Republicans" 2.0 "Democrats""' . global pvalues `"95 0.2 "p-value = `pvalue1'" 95 0.8 "p-value = `pvalue2'" 95 1.4 "p-value = `pval > ue3'" 95 2.0 "p-value = `pvalue4'""' . global barvalues = `"65 0.1 "`barval1'" 65 0.3 "`barval2'" 65 0.7 "`barval3'" 65 0.9 "`barval4'" 6 > 5 1.3 "`barval5'" 65 1.5 "`barval6'" 65 1.9 "`barval7'" 65 2.1 "`barval8'""' . . . twoway (bar R1 pgrade1 if R4 == 1, barw(0.18) fi(inten50) lc(black) lw(medium)) (bar R1 pgrade2 if > R4 == 2, barw(0.18) fi(inten20) lc(black) lw(medium)) /// > (rcap R3 R2 pgrade2 if R4 == 2, lc(gs5)), legend(${legend}) graphregion(color(white)) /// > yscale(range(98)) yla(60(20)100) xla($barlabels, labsize(3.5)) text($pvalues, size(3.5)) t > ext($barvalues, size(3.5)) /// > ytitle("Prior belief", size (4) height(5)) . graph export "$output\fig_motbeliefs1_truncABcont.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\fig_motbeliefs1_truncABcont.pdf written in PDF format) . . . . . . end of do-file . . //Main Tables . do "08_MainTables.do" . . *********************************************************************************** . // Replication Files . ********************************************************************************** . /* > HOW DO BELIEFS ABOUT THE GENDER WAGE GAP AFFECT THE DEMAND FOR PUBLIC POLICY? > Sonja Settele > AEJ:pol > */ . ********************************************************************************** . . *********************************************************************************** . **** Generate Tables 2-8 in main paper . ********************************************************************************** . . *********************************************************************************** . // Table 2: Correlates of prior beliefs about gender differences in wages . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . * Keep only incentivized priors . keep if prior1==1 (1,772 observations deleted) . . loc experiments "1 2 3 4 5 6 7 8" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . gen cons=1 . . . reg prior gender [pweight=pweight], vce(r) (sum of wgt is 2.2644e+03) Linear regression Number of obs = 2,293 F(1, 2291) = 26.67 Prob > F = 0.0000 R-squared = 0.0116 Root MSE = 21.233 ------------------------------------------------------------------------------ | Robust prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | -4.6033 .8913275 -5.16 0.000 -6.351193 -2.855407 _cons | 85.67494 .6443318 132.97 0.000 84.41141 86.93848 ------------------------------------------------------------------------------ . sigstar gender, prec(1) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . . sigstar _cons, prec(1) . estadd loc thisstat29 = "`r(bstar)'": col`colnum' . estadd loc thisstat30 = "`r(sestar)'": col`colnum' . qui sum prior . estadd loc thisstat32 = r(N): col`colnum' . . loc ++colnum . . reg prior democrat indep otherpol [pweight=pweight], vce(r) (sum of wgt is 2.2644e+03) Linear regression Number of obs = 2,293 F(3, 2289) = 7.18 Prob > F = 0.0001 R-squared = 0.0102 Root MSE = 21.258 ------------------------------------------------------------------------------ | Robust prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- democrat | -4.633492 1.037822 -4.46 0.000 -6.668662 -2.598322 indep | -1.99201 1.194651 -1.67 0.096 -4.334722 .3507012 otherpol | -6.736169 4.195446 -1.61 0.109 -14.96344 1.491104 _cons | 85.91644 .8033289 106.95 0.000 84.34112 87.49177 ------------------------------------------------------------------------------ . . sigstar democrat, prec(1) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar indep, prec(1) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . . sigstar _cons, prec(1) . estadd loc thisstat29 = "`r(bstar)'": col`colnum' . estadd loc thisstat30 = "`r(sestar)'": col`colnum' . qui sum prior . estadd loc thisstat32 = r(N): col`colnum' . . loc ++colnum . . reg prior gender democrat indep otherpol [pweight=pweight], vce(r) (sum of wgt is 2.2644e+03) Linear regression Number of obs = 2,293 F(4, 2288) = 11.18 Prob > F = 0.0000 R-squared = 0.0208 Root MSE = 21.148 ------------------------------------------------------------------------------ | Robust prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | -4.406256 .8864571 -4.97 0.000 -6.1446 -2.667913 democrat | -4.392452 1.033577 -4.25 0.000 -6.419298 -2.365605 indep | -1.788643 1.183622 -1.51 0.131 -4.109726 .5324411 otherpol | -6.413449 4.103676 -1.56 0.118 -14.46076 1.633865 _cons | 87.95982 .9206688 95.54 0.000 86.15438 89.76525 ------------------------------------------------------------------------------ . sigstar gender, prec(1) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar democrat, prec(1) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar indep, prec(1) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . . sigstar _cons, prec(1) . estadd loc thisstat29 = "`r(bstar)'": col`colnum' . estadd loc thisstat30 = "`r(sestar)'": col`colnum' . qui sum prior . estadd loc thisstat32 = r(N): col`colnum' . . loc ++colnum . . reg prior gender democrat indep otherpol femdem femindep femotherpol [pweight=pweight], vc > e(r) (sum of wgt is 2.2644e+03) Linear regression Number of obs = 2,293 F(7, 2285) = 6.98 Prob > F = 0.0000 R-squared = 0.0220 Root MSE = 21.15 ------------------------------------------------------------------------------ | Robust prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | -4.28028 1.595354 -2.68 0.007 -7.408773 -1.151786 democrat | -4.710364 1.490336 -3.16 0.002 -7.632917 -1.78781 indep | -1.124308 1.696447 -0.66 0.508 -4.451045 2.202429 otherpol | -.9305548 3.800115 -0.24 0.807 -8.382591 6.521482 femdem | .5999083 2.065906 0.29 0.772 -3.451339 4.651156 femindep | -1.314282 2.365811 -0.56 0.579 -5.953643 3.325079 femotherpol | -10.22771 7.743882 -1.32 0.187 -25.41349 4.958063 _cons | 87.90139 1.137842 77.25 0.000 85.67008 90.13271 ------------------------------------------------------------------------------ . sigstar gender, prec(1) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar democrat, prec(1) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar indep, prec(1) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . sigstar femdem, prec(1) . estadd loc thisstat11 = "`r(bstar)'": col`colnum' . estadd loc thisstat12 = "`r(sestar)'": col`colnum' . sigstar femindep, prec(1) . estadd loc thisstat14 = "`r(bstar)'": col`colnum' . estadd loc thisstat15 = "`r(sestar)'": col`colnum' . . sigstar _cons, prec(1) . estadd loc thisstat29 = "`r(bstar)'": col`colnum' . estadd loc thisstat30 = "`r(sestar)'": col`colnum' . qui sum prior . estadd loc thisstat32 = r(N): col`colnum' . . loc ++colnum . . qui reg prior gender democrat indep otherpol employee [pweight=pweight], vce(r) . sigstar gender, prec(1) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar democrat, prec(1) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar indep, prec(1) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . sigstar employee, prec(1) . estadd loc thisstat17 = "`r(bstar)'": col`colnum' . estadd loc thisstat18 = "`r(sestar)'": col`colnum' . . sigstar _cons, prec(1) . estadd loc thisstat29 = "`r(bstar)'": col`colnum' . estadd loc thisstat30 = "`r(sestar)'": col`colnum' . qui sum prior . estadd loc thisstat32 = r(N): col`colnum' . . loc ++colnum . . reg prior gender democrat indep otherpol employee fememp [pweight=pweight], vce(r) (sum of wgt is 2.2644e+03) Linear regression Number of obs = 2,293 F(6, 2286) = 8.25 Prob > F = 0.0000 R-squared = 0.0229 Root MSE = 21.135 ------------------------------------------------------------------------------ | Robust prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | -3.299709 1.300036 -2.54 0.011 -5.849083 -.7503343 democrat | -4.409239 1.031395 -4.28 0.000 -6.431806 -2.386671 indep | -1.702904 1.179858 -1.44 0.149 -4.016609 .6107997 otherpol | -6.075869 4.095326 -1.48 0.138 -14.10681 1.955074 employee | 2.656586 1.26561 2.10 0.036 .1747217 5.13845 fememployee | -1.382535 1.752764 -0.79 0.430 -4.819709 2.054639 _cons | 86.10828 1.157776 74.37 0.000 83.83787 88.37868 ------------------------------------------------------------------------------ . sigstar gender, prec(1) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar democrat, prec(1) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar indep, prec(1) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . sigstar employee, prec(1) . estadd loc thisstat17 = "`r(bstar)'": col`colnum' . estadd loc thisstat18 = "`r(sestar)'": col`colnum' . sigstar fememp, prec(1) . estadd loc thisstat20 = "`r(bstar)'": col`colnum' . estadd loc thisstat21 = "`r(sestar)'": col`colnum' . . sigstar _cons, prec(1) . estadd loc thisstat29 = "`r(bstar)'": col`colnum' . estadd loc thisstat30 = "`r(sestar)'": col`colnum' . qui sum prior . estadd loc thisstat32 = r(N): col`colnum' . . loc ++colnum . . . qui reg prior gender democrat indep otherpol associatemore femassociate [pweight=pweight], vce(r) . sigstar gender, prec(1) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar democrat, prec(1) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar indep, prec(1) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . sigstar associatemore, prec(1) . estadd loc thisstat23 = "`r(bstar)'": col`colnum' . estadd loc thisstat24 = "`r(sestar)'": col`colnum' . sigstar femassociate, prec(1) . estadd loc thisstat26 = "`r(bstar)'": col`colnum' . estadd loc thisstat27 = "`r(sestar)'": col`colnum' . . sigstar _cons, prec(1) . estadd loc thisstat29 = "`r(bstar)'": col`colnum' . estadd loc thisstat30 = "`r(sestar)'": col`colnum' . qui sum prior . estadd loc thisstat32 = r(N): col`colnum' . . loc ++colnum . . . reg prior gender democrat indep otherpol employee fememp associatemore femassociate [pweig > ht=pweight], vce(r) (sum of wgt is 2.2644e+03) Linear regression Number of obs = 2,293 F(8, 2284) = 6.51 Prob > F = 0.0000 R-squared = 0.0236 Root MSE = 21.137 ------------------------------------------------------------------------------- | Robust prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- gender | -2.275654 1.606712 -1.42 0.157 -5.426421 .8751132 democrat | -4.395571 1.032527 -4.26 0.000 -6.420359 -2.370782 indep | -1.604517 1.186165 -1.35 0.176 -3.930591 .7215565 otherpol | -6.148675 4.091221 -1.50 0.133 -14.17157 1.874222 employee | 2.252743 1.259835 1.79 0.074 -.2177978 4.723283 fememployee | -.8021308 1.79054 -0.45 0.654 -4.313386 2.709124 associatemore | 1.64863 1.316091 1.25 0.210 -.9322277 4.229489 femassociate | -2.238702 1.858749 -1.20 0.229 -5.883716 1.406311 _cons | 85.30226 1.383426 61.66 0.000 82.58936 88.01517 ------------------------------------------------------------------------------- . sigstar gender, prec(1) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar democrat, prec(1) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar indep, prec(1) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . sigstar employee, prec(1) . estadd loc thisstat17 = "`r(bstar)'": col`colnum' . estadd loc thisstat18 = "`r(sestar)'": col`colnum' . sigstar fememp, prec(1) . estadd loc thisstat20 = "`r(bstar)'": col`colnum' . estadd loc thisstat21 = "`r(sestar)'": col`colnum' . sigstar associatemore, prec(1) . estadd loc thisstat23 = "`r(bstar)'": col`colnum' . estadd loc thisstat24 = "`r(sestar)'": col`colnum' . sigstar femassociate, prec(1) . estadd loc thisstat26 = "`r(bstar)'": col`colnum' . estadd loc thisstat27 = "`r(sestar)'": col`colnum' . . sigstar _cons, prec(1) . estadd loc thisstat29 = "`r(bstar)'": col`colnum' . estadd loc thisstat30 = "`r(sestar)'": col`colnum' . qui sum prior . estadd loc thisstat32 = r(N): col`colnum' . . loc ++colnum . . . loc rowlabels " " " "Female" " " " " "Democrat" " " " " "Independent" " " " " "Female x Democrat" > " " " " "Female x Independent" " " " " "Employee" " " " " "Female x Employee" " " " " "Associate D > egree +" " " " " "Female x Ass. Degree +" " " " " "Constant" " " " " "Observations" " . loc rowstats "" . . forval i = 1/32 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . esttab * using "$output\priorcorrAB.tex", replace cells(none) booktabs nonotes nonum compress alig > nment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mtitle("(1)" "(2)" "(3)" "(4)" "(5)" "(6)" "(7)" "(8)") /// > mgroups("Outcome variable: Incentivized prior belief", pattern(1 0 0 0 0 0) prefix(\multicolumn{@ > span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\priorcorrAB.tex) . . . . *********************************************************************************** . // Table 3: Correlates of perceptions . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . loc experiments "large problem govmore z_mani_index" . . *Keep only control group respondents . keep if rand==0 (3,031 observations deleted) . . **z-score prior beliefs: . foreach var of varlist prior{ 2. egen mean_`var'=mean(`var') 3. egen sd_`var'=sd(`var') 4. replace `var'=(`var'-mean_`var')/sd_`var' 5. drop mean_`var' sd_`var' 6. } (1,034 real changes made) . . . *Keep only those with prior beliefs above the 5th and below the 95th percentile of the distributio > n . sum prior,d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% -2.432051 -3.873509 5% -1.548576 -3.780512 10% -.9905922 -3.780512 Obs 1,034 25% -.3861097 -3.362024 Sum of Wgt. 1,034 50% -.130367 Mean 1.04e-07 Largest Std. Dev. 1 75% .3113702 5.193729 90% .7763568 5.426223 Variance 1 95% 1.380839 5.426223 Skewness 1.506369 99% 4.263756 5.426223 Kurtosis 10.77207 . keep if prior > r(p5) & prior < r(p95) (113 observations deleted) . . . // Build Panels A-C . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experiments' { 2. . . ***Panel A: Main correlates: Dem, Female . . qui reg `choice' i.wave democrat indep otherpol gender [pweight=pweight],r 3. . sigstar democrat, prec(3) 4. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 6. . sigstar gender, prec(3) 7. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat7 = "`r(sestar)'": col`colnum' 9. . . ***Panel B: Raw correlation: z-scored prior . . qui reg `choice' i.wave prior [pweight=pweight],r 10. . sigstar prior, prec(3) 11. estadd loc thisstat10 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat11 = "`r(sestar)'": col`colnum' 13. . . ***Panel C: Prior and main correlates: Dem, Rep, Female . . qui reg `choice' prior i.wave democrat indep otherpol gender [pweight=pweight],r 14. . sigstar prior, prec(3) 15. estadd loc thisstat14 = "`r(bstar)'": col`colnum' 16. estadd loc thisstat15 = "`r(sestar)'": col`colnum' 17. . sigstar democrat, prec(3) 18. estadd loc thisstat17 = "`r(bstar)'": col`colnum' 19. estadd loc thisstat18= "`r(sestar)'": col`colnum' 20. . sigstar gender, prec(3) 21. estadd loc thisstat20 = "`r(bstar)'": col`colnum' 22. estadd loc thisstat21 = "`r(sestar)'": col`colnum' 23. . qui sum `choice' 24. estadd loc thisstat22 = r(N): col`colnum' 25. . loc ++colnum 26. loc colnames "`colnames' `"`: var la `choice''"'" 27. . } . . . loc rowlabels " "{\bf Panel A}" " " "Democrat" " " " " "Female" " " "\hline {\bf Panel B}" " " "P > rior (z-scored)" " " "\hline {\bf Panel C}" " " "Prior (z-scored)" " " " " "Democrat" " " " " "Fem > ale" " " "\hline Observations" "\hline" " . loc rowstats "" . . . loc rowstats "" . . forval i = 1/22 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . esttab * using "$output\tab_correlates_AB_5_95_PanelsA_C.tex", replace cells(none) booktabs nonote > s nomtitles compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabe > ls')) /// > mgroups("\shortstack{Gender diff.\\ in wages\\are large}" "\shortstack{Gender diff.\\ in w > ages\\are a problem}" /// > "\shortstack{Government\\should mitigate\\gender wage gap}" "\shortstack{Perception\\Index}", pa > ttern(1 1 1 1) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\tab_correlates_AB_5_95_PanelsA_C.tex) . . eststo clear . . . . *********************************************************************************** . // Table 4: Correlates of demand for specific policies . *********************************************************************************** . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . loc experiments "quotaanchor AAanchor legislationanchor transparencyanchor UKtool childcare z_lmpo > licy_index" . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experiments' { 2. . . ***Panel A: Main correlates: Dem, Female . . qui reg `choice' i.wave democrat indep otherpol gender [pweight=pweight],r 3. . sigstar democrat, prec(3) 4. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 6. . sigstar gender, prec(3) 7. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat7 = "`r(sestar)'": col`colnum' 9. . . ***Panel B: Raw correlation: z-scored prior . . qui reg `choice' i.wave prior [pweight=pweight],r 10. . sigstar prior, prec(3) 11. estadd loc thisstat10 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat11 = "`r(sestar)'": col`colnum' 13. . . ***Panel C: Prior and main correlates: Dem, Rep, Female . . qui reg `choice' prior i.wave democrat indep otherpol gender [pweight=pweight],r 14. . sigstar prior, prec(3) 15. estadd loc thisstat14 = "`r(bstar)'": col`colnum' 16. estadd loc thisstat15 = "`r(sestar)'": col`colnum' 17. . sigstar democrat, prec(3) 18. estadd loc thisstat17 = "`r(bstar)'": col`colnum' 19. estadd loc thisstat18= "`r(sestar)'": col`colnum' 20. . sigstar gender, prec(3) 21. estadd loc thisstat20 = "`r(bstar)'": col`colnum' 22. estadd loc thisstat21 = "`r(sestar)'": col`colnum' 23. . qui sum `choice' 24. estadd loc thisstat22 = r(N): col`colnum' 25. . loc ++colnum 26. loc colnames "`colnames' `"`: var la `choice''"'" 27. . } . . . . loc rowlabels " "{\bf Panel A}" " " "Democrat" " " " " "Female" " " "\hline {\bf Panel B}" " " "P > rior (z-scored)" " " "\hline {\bf Panel C}" " " "Prior (z-scored)" " " " " "Democrat" " " " " "Fem > ale" " " "\hline Observations" "\hline" " . loc rowstats "" . . . loc rowstats "" . . forval i = 1/22 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\tab_correlates_AB_5_95_PanelsD_F.tex", replace cells(none) booktabs nonote > s nomtitles compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabe > ls')) /// > mgroups("\shortstack{Introduce\\gender\\quotas}" "\shortstack{Statutory\\affirmative\\acti > on}" /// > "\shortstack{Stricter\\equal pay\\legislation}" "\shortstack{Wage transp.\\within\\ companies}" > "\shortstack{Introduce\\reporting\\website}" "\shortstack{Increase\\subsidies\\to child care}" /// > "\shortstack{Policy\\demand\\index}", pattern(1 1 1 1 1 1 1 ) prefix(\multicolumn{@span}{c}{) s > uffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\tab_correlates_AB_5_95_PanelsD_F.tex) . . . *********************************************************************************** . // Table 5: Treatment effect on perceptions and demand for specific policies . *********************************************************************************** . . // Note: The following code produces one table for Panel A and another table for Panels B-C. Stack > manually to obtain final table. . . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . * Drop pure control group . drop if rand==0 (1,034 observations deleted) . . // Build Panel A . zscore posterior [aweight=pweight], stub(z) zposterior created with 9 missing values . . . loc experiments "posterior zposterior large problem govmore z_mani_index" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . . foreach choice in `experiments' { 2. . reg `choice' T1 $controls [pweight=pweight], vce(r) 3. . local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 7. . estadd loc thisstat7 = "`n'": col`colnum' 8. . loc ++colnum 9. } (sum of wgt is 2.9862e+03) Linear regression Number of obs = 3,022 F(22, 2999) = 41.26 Prob > F = 0.0000 R-squared = 0.3151 Root MSE = 16.341 ------------------------------------------------------------------------------- | Robust posterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -12.95017 .5944266 -21.79 0.000 -14.1157 -11.78465 wave | 2.479558 .6445038 3.85 0.000 1.215844 3.743272 gender | -1.627223 .615404 -2.64 0.008 -2.83388 -.4205665 prior | .3829316 .0297417 12.88 0.000 .3246155 .4412478 democrat | .0455357 .7050197 0.06 0.949 -1.336835 1.427907 indep | .4954273 .8474422 0.58 0.559 -1.1662 2.157054 otherpol | .9130897 3.045069 0.30 0.764 -5.057545 6.883724 midwest | -.128744 .9595199 -0.13 0.893 -2.010128 1.75264 south | .1123306 .8757661 0.13 0.898 -1.604832 1.829494 west | -.3839532 .9510489 -0.40 0.686 -2.248727 1.480821 age1 | 2.786798 1.304821 2.14 0.033 .228363 5.345232 age2 | 2.833446 .9491474 2.99 0.003 .9723999 4.694491 age3 | .6263516 .8837923 0.71 0.479 -1.106549 2.359252 age4 | -.3270036 .7936086 -0.41 0.680 -1.883076 1.229069 anychildren | 1.36046 .6732131 2.02 0.043 .0404542 2.680467 loghhinc | -.3195568 .4244794 -0.75 0.452 -1.151857 .5127434 associatemore | 1.127363 .6614084 1.70 0.088 -.1694968 2.424223 fulltime | .7059229 .822411 0.86 0.391 -.9066238 2.31847 parttime | -.6429914 1.137014 -0.57 0.572 -2.872397 1.586415 selfemp | -.4426654 1.188221 -0.37 0.710 -2.772476 1.887145 unemployed | -2.766373 1.201853 -2.30 0.021 -5.122912 -.4098338 student | -.4273227 1.664225 -0.26 0.797 -3.69046 2.835815 _cons | 57.31349 5.416622 10.58 0.000 46.69282 67.93416 ------------------------------------------------------------------------------- (sum of wgt is 2.9862e+03) Linear regression Number of obs = 3,022 F(22, 2999) = 41.26 Prob > F = 0.0000 R-squared = 0.3151 Root MSE = .83059 ------------------------------------------------------------------------------- | Robust zposterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.6582479 .0302143 -21.79 0.000 -.7174907 -.5990051 wave | .1260341 .0327597 3.85 0.000 .0618005 .1902678 gender | -.0827106 .0312805 -2.64 0.008 -.1440441 -.0213771 prior | .0194641 .0015117 12.88 0.000 .0165 .0224283 democrat | .0023145 .0358356 0.06 0.949 -.0679504 .0725795 indep | .0251822 .0430749 0.58 0.559 -.0592771 .1096415 otherpol | .0464117 .1547786 0.30 0.764 -.2570714 .3498947 midwest | -.006544 .0487717 -0.13 0.893 -.1021733 .0890854 south | .0057097 .0445146 0.13 0.898 -.0815725 .0929918 west | -.0195161 .0483411 -0.40 0.686 -.1143012 .0752691 age1 | .1416509 .0663231 2.14 0.033 .0116075 .2716943 age2 | .144022 .0482445 2.99 0.003 .0494264 .2386176 age3 | .031837 .0449225 0.71 0.479 -.0562451 .1199191 age4 | -.0166214 .0403386 -0.41 0.680 -.0957154 .0624727 anychildren | .0691512 .0342189 2.02 0.043 .0020563 .1362462 loghhinc | -.0162428 .021576 -0.75 0.452 -.0585481 .0260624 associatemore | .0573031 .0336189 1.70 0.088 -.0086154 .1232215 fulltime | .0358815 .0418026 0.86 0.391 -.046083 .1178461 parttime | -.0326828 .0577936 -0.57 0.572 -.1460019 .0806363 selfemp | -.0225004 .0603964 -0.37 0.710 -.140923 .0959222 unemployed | -.1406127 .0610893 -2.30 0.021 -.2603939 -.0208316 student | -.0217205 .0845913 -0.26 0.797 -.1875834 .1441424 _cons | -1.373675 .275323 -4.99 0.000 -1.913516 -.8338337 ------------------------------------------------------------------------------- (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 28.60 Prob > F = 0.0000 R-squared = 0.1745 Root MSE = .96735 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .5972595 .0356733 16.74 0.000 .5273129 .6672061 wave | -.0269099 .0382786 -0.70 0.482 -.1019648 .048145 gender | .2354511 .036212 6.50 0.000 .1644483 .3064539 prior | -.0057459 .0009906 -5.80 0.000 -.0076882 -.0038036 democrat | .5251511 .0405832 12.94 0.000 .4455776 .6047247 indep | .2014438 .0549588 3.67 0.000 .0936831 .3092045 otherpol | .1360499 .1555581 0.87 0.382 -.1689611 .441061 midwest | -.0463282 .0571594 -0.81 0.418 -.1584037 .0657473 south | .0559538 .0518565 1.08 0.281 -.0457239 .1576315 west | -.0351877 .0561643 -0.63 0.531 -.145312 .0749366 age1 | .0210497 .0824246 0.26 0.798 -.1405645 .182664 age2 | .0410061 .0557812 0.74 0.462 -.068367 .1503793 age3 | -.007434 .0551204 -0.13 0.893 -.1155115 .1006435 age4 | -.1330057 .0532157 -2.50 0.012 -.2373486 -.0286629 anychildren | .1281935 .0394718 3.25 0.001 .050799 .205588 loghhinc | .0382895 .0247828 1.55 0.122 -.0103035 .0868826 associatemore | .0061067 .0400086 0.15 0.879 -.0723404 .0845538 fulltime | .0912619 .0539965 1.69 0.091 -.0146119 .1971356 parttime | -.1105413 .07271 -1.52 0.129 -.2531076 .032025 selfemp | .0609548 .0753707 0.81 0.419 -.0868285 .2087382 unemployed | .0541613 .087939 0.62 0.538 -.1182653 .2265878 student | .0485873 .1105169 0.44 0.660 -.1681092 .2652837 _cons | -.7538024 .2863028 -2.63 0.009 -1.315171 -.1924334 ------------------------------------------------------------------------------- (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 28.42 Prob > F = 0.0000 R-squared = 0.1775 Root MSE = .95226 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4215353 .0351138 12.00 0.000 .3526858 .4903849 wave | -.0162456 .0374007 -0.43 0.664 -.0895791 .0570878 gender | .2970653 .0356542 8.33 0.000 .2271563 .3669743 prior | -.0061382 .0009184 -6.68 0.000 -.007939 -.0043374 democrat | .6560668 .0403051 16.28 0.000 .5770385 .7350951 indep | .2531112 .0553512 4.57 0.000 .1445812 .3616413 otherpol | .2965088 .1414328 2.10 0.036 .0191939 .5738237 midwest | -.1166761 .0567613 -2.06 0.040 -.227971 -.0053813 south | -.0312535 .0503927 -0.62 0.535 -.1300611 .0675541 west | -.0710708 .0545453 -1.30 0.193 -.1780206 .035879 age1 | .0597321 .079137 0.75 0.450 -.0954361 .2149002 age2 | .0223707 .0561827 0.40 0.691 -.0877896 .132531 age3 | .0067231 .054451 0.12 0.902 -.100042 .1134881 age4 | -.1110718 .0532144 -2.09 0.037 -.2154121 -.0067315 anychildren | .0898165 .0392609 2.29 0.022 .0128357 .1667974 loghhinc | .0179323 .0243871 0.74 0.462 -.0298847 .0657493 associatemore | .0221103 .0400605 0.55 0.581 -.0564384 .1006589 fulltime | .0578635 .0531468 1.09 0.276 -.0463443 .1620712 parttime | -.1076561 .0717129 -1.50 0.133 -.2482674 .0329552 selfemp | -.0018603 .0750208 -0.02 0.980 -.1489577 .145237 unemployed | .0524455 .0901715 0.58 0.561 -.1243585 .2292496 student | .0620267 .1116342 0.56 0.579 -.1568603 .2809137 _cons | -.4256575 .2795707 -1.52 0.128 -.9738266 .1225116 ------------------------------------------------------------------------------- (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 30.21 Prob > F = 0.0000 R-squared = 0.1861 Root MSE = .95956 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2433785 .0352867 6.90 0.000 .17419 .312567 wave | -.0096391 .0377105 -0.26 0.798 -.08358 .0643019 gender | .3085263 .0359816 8.57 0.000 .2379752 .3790773 prior | -.0045144 .0008754 -5.16 0.000 -.0062308 -.002798 democrat | .8030348 .0405338 19.81 0.000 .723558 .8825116 indep | .2993824 .0575167 5.21 0.000 .1866064 .4121584 otherpol | .3205847 .1439486 2.23 0.026 .038337 .6028325 midwest | -.1937205 .0578454 -3.35 0.001 -.307141 -.0802999 south | -.0569456 .0500487 -1.14 0.255 -.1550787 .0411875 west | -.1076544 .0546978 -1.97 0.049 -.2149032 -.0004056 age1 | .1921427 .0786191 2.44 0.015 .0379901 .3462953 age2 | .2088134 .0569403 3.67 0.000 .0971676 .3204593 age3 | .146825 .0574529 2.56 0.011 .0341742 .2594759 age4 | .0641893 .0561595 1.14 0.253 -.0459256 .1743042 anychildren | .1511771 .0385274 3.92 0.000 .0756343 .2267199 loghhinc | -.0219385 .0248728 -0.88 0.378 -.070708 .0268309 associatemore | -.0324966 .0405884 -0.80 0.423 -.1120804 .0470873 fulltime | .0173079 .0565705 0.31 0.760 -.0936129 .1282288 parttime | -.0848721 .0713493 -1.19 0.234 -.2247704 .0550262 selfemp | -.1157398 .0778424 -1.49 0.137 -.2683696 .0368899 unemployed | .0127901 .0882162 0.14 0.885 -.1601801 .1857602 student | -.0213595 .1083587 -0.20 0.844 -.2338242 .1911052 _cons | -.1987263 .2832389 -0.70 0.483 -.7540879 .3566352 ------------------------------------------------------------------------------- (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 32.19 Prob > F = 0.0000 R-squared = 0.1988 Root MSE = .87434 ------------------------------------------------------------------------------- | Robust z_mani_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4170495 .0322181 12.94 0.000 .3538779 .4802212 wave | -.0177316 .0343865 -0.52 0.606 -.0851551 .0496919 gender | .2773129 .0327356 8.47 0.000 .2131265 .3414992 prior | -.005303 .0008692 -6.10 0.000 -.0070072 -.0035988 democrat | .6653624 .0368133 18.07 0.000 .5931805 .7375442 indep | .251875 .0513566 4.90 0.000 .1511774 .3525726 otherpol | .2426557 .1382573 1.76 0.079 -.0284328 .5137441 midwest | -.1208645 .0519175 -2.33 0.020 -.2226618 -.0190672 south | -.007256 .0462316 -0.16 0.875 -.0979048 .0833928 west | -.072072 .0499037 -1.44 0.149 -.1699209 .0257769 age1 | .0996826 .0719346 1.39 0.166 -.0413634 .2407286 age2 | .1077443 .0510046 2.11 0.035 .0077368 .2077517 age3 | .059659 .0507031 1.18 0.239 -.0397572 .1590752 age4 | -.046534 .0491284 -0.95 0.344 -.1428627 .0497946 anychildren | .1307584 .0354388 3.69 0.000 .0612716 .2002452 loghhinc | .009372 .0224739 0.42 0.677 -.0346938 .0534378 associatemore | -.0070954 .0365091 -0.19 0.846 -.0786808 .0644899 fulltime | .0542119 .0497542 1.09 0.276 -.0433438 .1517675 parttime | -.0992796 .0651583 -1.52 0.128 -.2270389 .0284797 selfemp | -.0244499 .0692565 -0.35 0.724 -.1602447 .111345 unemployed | .0365494 .0802278 0.46 0.649 -.1207575 .1938563 student | .02181 .1024313 0.21 0.831 -.1790325 .2226525 _cons | -.4614966 .2596682 -1.78 0.076 -.9706419 .0476486 ------------------------------------------------------------------------------- . . . * Calculate sharpened q-values for columns 3-5 . mat def P = J(5, 1, .) . reg large T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 28.60 Prob > F = 0.0000 R-squared = 0.1745 Root MSE = .96735 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .5972595 .0356733 16.74 0.000 .5273129 .6672061 wave | -.0269099 .0382786 -0.70 0.482 -.1019648 .048145 gender | .2354511 .036212 6.50 0.000 .1644483 .3064539 prior | -.0057459 .0009906 -5.80 0.000 -.0076882 -.0038036 democrat | .5251511 .0405832 12.94 0.000 .4455776 .6047247 indep | .2014438 .0549588 3.67 0.000 .0936831 .3092045 otherpol | .1360499 .1555581 0.87 0.382 -.1689611 .441061 midwest | -.0463282 .0571594 -0.81 0.418 -.1584037 .0657473 south | .0559538 .0518565 1.08 0.281 -.0457239 .1576315 west | -.0351877 .0561643 -0.63 0.531 -.145312 .0749366 age1 | .0210497 .0824246 0.26 0.798 -.1405645 .182664 age2 | .0410061 .0557812 0.74 0.462 -.068367 .1503793 age3 | -.007434 .0551204 -0.13 0.893 -.1155115 .1006435 age4 | -.1330057 .0532157 -2.50 0.012 -.2373486 -.0286629 anychildren | .1281935 .0394718 3.25 0.001 .050799 .205588 loghhinc | .0382895 .0247828 1.55 0.122 -.0103035 .0868826 associatemore | .0061067 .0400086 0.15 0.879 -.0723404 .0845538 fulltime | .0912619 .0539965 1.69 0.091 -.0146119 .1971356 parttime | -.1105413 .07271 -1.52 0.129 -.2531076 .032025 selfemp | .0609548 .0753707 0.81 0.419 -.0868285 .2087382 unemployed | .0541613 .087939 0.62 0.538 -.1182653 .2265878 student | .0485873 .1105169 0.44 0.660 -.1681092 .2652837 _cons | -.7538024 .2863028 -2.63 0.009 -1.315171 -.1924334 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg problem T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 28.42 Prob > F = 0.0000 R-squared = 0.1775 Root MSE = .95226 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4215353 .0351138 12.00 0.000 .3526858 .4903849 wave | -.0162456 .0374007 -0.43 0.664 -.0895791 .0570878 gender | .2970653 .0356542 8.33 0.000 .2271563 .3669743 prior | -.0061382 .0009184 -6.68 0.000 -.007939 -.0043374 democrat | .6560668 .0403051 16.28 0.000 .5770385 .7350951 indep | .2531112 .0553512 4.57 0.000 .1445812 .3616413 otherpol | .2965088 .1414328 2.10 0.036 .0191939 .5738237 midwest | -.1166761 .0567613 -2.06 0.040 -.227971 -.0053813 south | -.0312535 .0503927 -0.62 0.535 -.1300611 .0675541 west | -.0710708 .0545453 -1.30 0.193 -.1780206 .035879 age1 | .0597321 .079137 0.75 0.450 -.0954361 .2149002 age2 | .0223707 .0561827 0.40 0.691 -.0877896 .132531 age3 | .0067231 .054451 0.12 0.902 -.100042 .1134881 age4 | -.1110718 .0532144 -2.09 0.037 -.2154121 -.0067315 anychildren | .0898165 .0392609 2.29 0.022 .0128357 .1667974 loghhinc | .0179323 .0243871 0.74 0.462 -.0298847 .0657493 associatemore | .0221103 .0400605 0.55 0.581 -.0564384 .1006589 fulltime | .0578635 .0531468 1.09 0.276 -.0463443 .1620712 parttime | -.1076561 .0717129 -1.50 0.133 -.2482674 .0329552 selfemp | -.0018603 .0750208 -0.02 0.980 -.1489577 .145237 unemployed | .0524455 .0901715 0.58 0.561 -.1243585 .2292496 student | .0620267 .1116342 0.56 0.579 -.1568603 .2809137 _cons | -.4256575 .2795707 -1.52 0.128 -.9738266 .1225116 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg govmore T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 30.21 Prob > F = 0.0000 R-squared = 0.1861 Root MSE = .95956 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2433785 .0352867 6.90 0.000 .17419 .312567 wave | -.0096391 .0377105 -0.26 0.798 -.08358 .0643019 gender | .3085263 .0359816 8.57 0.000 .2379752 .3790773 prior | -.0045144 .0008754 -5.16 0.000 -.0062308 -.002798 democrat | .8030348 .0405338 19.81 0.000 .723558 .8825116 indep | .2993824 .0575167 5.21 0.000 .1866064 .4121584 otherpol | .3205847 .1439486 2.23 0.026 .038337 .6028325 midwest | -.1937205 .0578454 -3.35 0.001 -.307141 -.0802999 south | -.0569456 .0500487 -1.14 0.255 -.1550787 .0411875 west | -.1076544 .0546978 -1.97 0.049 -.2149032 -.0004056 age1 | .1921427 .0786191 2.44 0.015 .0379901 .3462953 age2 | .2088134 .0569403 3.67 0.000 .0971676 .3204593 age3 | .146825 .0574529 2.56 0.011 .0341742 .2594759 age4 | .0641893 .0561595 1.14 0.253 -.0459256 .1743042 anychildren | .1511771 .0385274 3.92 0.000 .0756343 .2267199 loghhinc | -.0219385 .0248728 -0.88 0.378 -.070708 .0268309 associatemore | -.0324966 .0405884 -0.80 0.423 -.1120804 .0470873 fulltime | .0173079 .0565705 0.31 0.760 -.0936129 .1282288 parttime | -.0848721 .0713493 -1.19 0.234 -.2247704 .0550262 selfemp | -.1157398 .0778424 -1.49 0.137 -.2683696 .0368899 unemployed | .0127901 .0882162 0.14 0.885 -.1601801 .1857602 student | -.0213595 .1083587 -0.20 0.844 -.2338242 .1911052 _cons | -.1987263 .2832389 -0.70 0.483 -.7540879 .3566352 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . . . minq P, q("Q") step(0.001) number of observations will be reset to 5 Press any key to continue, or Break to abort number of observations (_N) was 0, now 5 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = .7109999999999997 Correction with q = .7099999999999997 Correction with q = .7089999999999997 Correction with q = .7079999999999997 Correction with q = .7069999999999997 Correction with q = .7059999999999997 Correction with q = .7049999999999997 Correction with q = .7039999999999997 Correction with q = .7029999999999997 Correction with q = .7019999999999997 Correction with q = .7009999999999997 Correction with q = .6999999999999997 Correction with q = .6989999999999997 Correction with q = .6979999999999997 Correction with q = .6969999999999997 Correction with q = .6959999999999997 Correction with q = .6949999999999997 Correction with q = .6939999999999997 Correction with q = .6929999999999997 Correction with q = .6919999999999997 Correction with q = .6909999999999997 Correction with q = .6899999999999997 Correction with q = .6889999999999997 Correction with q = .6879999999999997 Correction with q = .6869999999999997 Correction with q = .6859999999999997 Correction with q = .6849999999999997 Correction with q = .6839999999999997 Correction with q = .6829999999999997 Correction with q = .6819999999999997 Correction with q = .6809999999999997 Correction with q = .6799999999999997 Correction with q = .6789999999999997 Correction with q = .6779999999999997 Correction with q = .6769999999999997 Correction with q = .6759999999999997 Correction with q = .6749999999999997 Correction with q = .6739999999999997 Correction with q = .6729999999999997 Correction with q = .6719999999999997 Correction with q = .6709999999999997 Correction with q = .6699999999999997 Correction with q = .6689999999999997 Correction with q = .6679999999999997 Correction with q = .6669999999999997 Correction with q = .6659999999999997 Correction with q = .6649999999999997 Correction with q = .6639999999999997 Correction with q = .6629999999999997 Correction with q = .6619999999999997 Correction with q = .6609999999999997 Correction with q = .6599999999999997 Correction with q = .6589999999999997 Correction with q = .6579999999999997 Correction with q = .6569999999999997 Correction with q = .6559999999999997 Correction with q = .6549999999999997 Correction with q = .6539999999999997 Correction with q = .6529999999999997 Correction with q = .6519999999999997 Correction with q = .6509999999999997 Correction with q = .6499999999999997 Correction with q = .6489999999999997 Correction with q = .6479999999999997 Correction with q = .6469999999999997 Correction with q = .6459999999999997 Correction with q = .6449999999999997 Correction with q = .6439999999999997 Correction with q = .6429999999999997 Correction with q = .6419999999999997 Correction with q = .6409999999999997 Correction with q = .6399999999999997 Correction with q = .6389999999999997 Correction with q = .6379999999999997 Correction with q = .6369999999999997 Correction with q = .6359999999999997 Correction with q = .6349999999999997 Correction with q = .6339999999999997 Correction with q = .6329999999999997 Correction with q = .6319999999999997 Correction with q = .6309999999999997 Correction with q = .6299999999999997 Correction with q = .6289999999999997 Correction with q = .6279999999999997 Correction with q = .6269999999999997 Correction with q = .6259999999999997 Correction with q = .6249999999999997 Correction with q = .6239999999999997 Correction with q = .6229999999999997 Correction with q = .6219999999999997 Correction with q = .6209999999999997 Correction with q = .6199999999999997 Correction with q = .6189999999999997 Correction with q = .6179999999999997 Correction with q = .6169999999999997 Correction with q = .6159999999999997 Correction with q = .6149999999999997 Correction with q = .6139999999999997 Correction with q = .6129999999999997 Correction with q = .6119999999999997 Correction with q = .6109999999999997 Correction with q = .6099999999999997 Correction with q = .6089999999999997 Correction with q = .6079999999999997 Correction with q = .6069999999999997 Correction with q = .6059999999999997 Correction with q = .6049999999999997 Correction with q = .6039999999999997 Correction with q = .6029999999999997 Correction with q = .6019999999999997 Correction with q = .6009999999999997 Correction with q = .5999999999999996 Correction with q = .5989999999999996 Correction with q = .5979999999999996 Correction with q = .5969999999999996 Correction with q = .5959999999999996 Correction with q = .5949999999999996 Correction with q = .5939999999999996 Correction with q = .5929999999999996 Correction with q = .5919999999999996 Correction with q = .5909999999999996 Correction with q = .5899999999999996 Correction with q = .5889999999999996 Correction with q = .5879999999999996 Correction with q = .5869999999999996 Correction with q = .5859999999999996 Correction with q = .5849999999999996 Correction with q = .5839999999999996 Correction with q = .5829999999999996 Correction with q = .5819999999999996 Correction with q = .5809999999999996 Correction with q = .5799999999999996 Correction with q = .5789999999999996 Correction with q = .5779999999999996 Correction with q = .5769999999999996 Correction with q = .5759999999999996 Correction with q = .5749999999999996 Correction with q = .5739999999999996 Correction with q = .5729999999999996 Correction with q = .5719999999999996 Correction with q = .5709999999999996 Correction with q = .5699999999999996 Correction with q = .5689999999999996 Correction with q = .5679999999999996 Correction with q = .5669999999999996 Correction with q = .5659999999999996 Correction with q = .5649999999999996 Correction with q = .5639999999999996 Correction with q = .5629999999999996 Correction with q = .5619999999999996 Correction with q = .5609999999999996 Correction with q = .5599999999999996 Correction with q = .5589999999999996 Correction with q = .5579999999999996 Correction with q = .5569999999999996 Correction with q = .5559999999999996 Correction with q = .5549999999999996 Correction with q = .5539999999999996 Correction with q = .5529999999999996 Correction with q = .5519999999999996 Correction with q = .5509999999999996 Correction with q = .5499999999999996 Correction with q = .5489999999999996 Correction with q = .5479999999999996 Correction with q = .5469999999999996 Correction with q = .5459999999999996 Correction with q = .5449999999999996 Correction with q = .5439999999999996 Correction with q = .5429999999999996 Correction with q = .5419999999999996 Correction with q = .5409999999999996 Correction with q = .5399999999999996 Correction with q = .5389999999999996 Correction with q = .5379999999999996 Correction with q = .5369999999999996 Correction with q = .5359999999999996 Correction with q = .5349999999999996 Correction with q = .5339999999999996 Correction with q = .5329999999999996 Correction with q = .5319999999999996 Correction with q = .5309999999999996 Correction with q = .5299999999999996 Correction with q = .5289999999999996 Correction with q = .5279999999999996 Correction with q = .5269999999999996 Correction with q = .5259999999999996 Correction with q = .5249999999999996 Correction with q = .5239999999999996 Correction with q = .5229999999999996 Correction with q = .5219999999999996 Correction with q = .5209999999999996 Correction with q = .5199999999999996 Correction with q = .5189999999999996 Correction with q = .5179999999999996 Correction with q = .5169999999999996 Correction with q = .5159999999999996 Correction with q = .5149999999999996 Correction with q = .5139999999999996 Correction with q = .5129999999999996 Correction with q = .5119999999999996 Correction with q = .5109999999999996 Correction with q = .5099999999999996 Correction with q = .5089999999999996 Correction with q = .5079999999999996 Correction with q = .5069999999999996 Correction with q = .5059999999999996 Correction with q = .5049999999999996 Correction with q = .5039999999999996 Correction with q = .5029999999999996 Correction with q = .5019999999999996 Correction with q = .5009999999999996 Correction with q = .4999999999999996 Correction with q = .4989999999999996 Correction with q = .4979999999999996 Correction with q = .4969999999999996 Correction with q = .4959999999999996 Correction with q = .4949999999999996 Correction with q = .4939999999999996 Correction with q = .4929999999999996 Correction with q = .4919999999999996 Correction with q = .4909999999999996 Correction with q = .4899999999999996 Correction with q = .4889999999999996 Correction with q = .4879999999999996 Correction with q = .4869999999999995 Correction with q = .4859999999999995 Correction with q = .4849999999999995 Correction with q = .4839999999999995 Correction with q = .4829999999999995 Correction with q = .4819999999999995 Correction with q = .4809999999999995 Correction with q = .4799999999999995 Correction with q = .4789999999999995 Correction with q = .4779999999999995 Correction with q = .4769999999999995 Correction with q = .4759999999999995 Correction with q = .4749999999999995 Correction with q = .4739999999999995 Correction with q = .4729999999999995 Correction with q = .4719999999999995 Correction with q = .4709999999999995 Correction with q = .4699999999999995 Correction with q = .4689999999999995 Correction with q = .4679999999999995 Correction with q = .4669999999999995 Correction with q = .4659999999999995 Correction with q = .4649999999999995 Correction with q = .4639999999999995 Correction with q = .4629999999999995 Correction with q = .4619999999999995 Correction with q = .4609999999999995 Correction with q = .4599999999999995 Correction with q = .4589999999999995 Correction with q = .4579999999999995 Correction with q = .4569999999999995 Correction with q = .4559999999999995 Correction with q = .4549999999999995 Correction with q = .4539999999999995 Correction with q = .4529999999999995 Correction with q = .4519999999999995 Correction with q = .4509999999999995 Correction with q = .4499999999999995 Correction with q = .4489999999999995 Correction with q = .4479999999999995 Correction with q = .4469999999999995 Correction with q = .4459999999999995 Correction with q = .4449999999999995 Correction with q = .4439999999999995 Correction with q = .4429999999999995 Correction with q = .4419999999999995 Correction with q = .4409999999999995 Correction with q = .4399999999999995 Correction with q = .4389999999999995 Correction with q = .4379999999999995 Correction with q = .4369999999999995 Correction with q = .4359999999999995 Correction with q = .4349999999999995 Correction with q = .4339999999999995 Correction with q = .4329999999999995 Correction with q = .4319999999999995 Correction with q = .4309999999999995 Correction with q = .4299999999999995 Correction with q = .4289999999999995 Correction with q = .4279999999999995 Correction with q = .4269999999999995 Correction with q = .4259999999999995 Correction with q = .4249999999999995 Correction with q = .4239999999999995 Correction with q = .4229999999999995 Correction with q = .4219999999999995 Correction with q = .4209999999999995 Correction with q = .4199999999999995 Correction with q = .4189999999999995 Correction with q = .4179999999999995 Correction with q = .4169999999999995 Correction with q = .4159999999999995 Correction with q = .4149999999999995 Correction with q = .4139999999999995 Correction with q = .4129999999999995 Correction with q = .4119999999999995 Correction with q = .4109999999999995 Correction with q = .4099999999999995 Correction with q = .4089999999999995 Correction with q = .4079999999999995 Correction with q = .4069999999999995 Correction with q = .4059999999999995 Correction with q = .4049999999999995 Correction with q = .4039999999999995 Correction with q = .4029999999999995 Correction with q = .4019999999999995 Correction with q = .4009999999999995 Correction with q = .3999999999999995 Correction with q = .3989999999999995 Correction with q = .3979999999999995 Correction with q = .3969999999999995 Correction with q = .3959999999999995 Correction with q = .3949999999999995 Correction with q = .3939999999999995 Correction with q = .3929999999999995 Correction with q = .3919999999999995 Correction with q = .3909999999999995 Correction with q = .3899999999999995 Correction with q = .3889999999999995 Correction with q = .3879999999999995 Correction with q = .3869999999999995 Correction with q = .3859999999999995 Correction with q = .3849999999999995 Correction with q = .3839999999999995 Correction with q = .3829999999999995 Correction with q = .3819999999999995 Correction with q = .3809999999999995 Correction with q = .3799999999999995 Correction with q = .3789999999999995 Correction with q = .3779999999999995 Correction with q = .3769999999999995 Correction with q = .3759999999999995 Correction with q = .3749999999999994 Correction with q = .3739999999999994 Correction with q = .3729999999999994 Correction with q = .3719999999999994 Correction with q = .3709999999999994 Correction with q = .3699999999999994 Correction with q = .3689999999999994 Correction with q = .3679999999999994 Correction with q = .3669999999999994 Correction with q = .3659999999999994 Correction with q = .3649999999999994 Correction with q = .3639999999999994 Correction with q = .3629999999999994 Correction with q = .3619999999999994 Correction with q = .3609999999999994 Correction with q = .3599999999999994 Correction with q = .3589999999999994 Correction with q = .3579999999999994 Correction with q = .3569999999999994 Correction with q = .3559999999999994 Correction with q = .3549999999999994 Correction with q = .3539999999999994 Correction with q = .3529999999999994 Correction with q = .3519999999999994 Correction with q = .3509999999999994 Correction with q = .3499999999999994 Correction with q = .3489999999999994 Correction with q = .3479999999999994 Correction with q = .3469999999999994 Correction with q = .3459999999999994 Correction with q = .3449999999999994 Correction with q = .3439999999999994 Correction with q = .3429999999999994 Correction with q = .3419999999999994 Correction with q = .3409999999999994 Correction with q = .3399999999999994 Correction with q = .3389999999999994 Correction with q = .3379999999999994 Correction with q = .3369999999999994 Correction with q = .3359999999999994 Correction with q = .3349999999999994 Correction with q = .3339999999999994 Correction with q = .3329999999999994 Correction with q = .3319999999999994 Correction with q = .3309999999999994 Correction with q = .3299999999999994 Correction with q = .3289999999999994 Correction with q = .3279999999999994 Correction with q = .3269999999999994 Correction with q = .3259999999999994 Correction with q = .3249999999999994 Correction with q = .3239999999999994 Correction with q = .3229999999999994 Correction with q = .3219999999999994 Correction with q = .3209999999999994 Correction with q = .3199999999999994 Correction with q = .3189999999999994 Correction with q = .3179999999999994 Correction with q = .3169999999999994 Correction with q = .3159999999999994 Correction with q = .3149999999999994 Correction with q = .3139999999999994 Correction with q = .3129999999999994 Correction with q = .3119999999999994 Correction with q = .3109999999999994 Correction with q = .3099999999999994 Correction with q = .3089999999999994 Correction with q = .3079999999999994 Correction with q = .3069999999999994 Correction with q = .3059999999999994 Correction with q = .3049999999999994 Correction with q = .3039999999999994 Correction with q = .3029999999999994 Correction with q = .3019999999999994 Correction with q = .3009999999999994 Correction with q = .2999999999999994 Correction with q = .2989999999999994 Correction with q = .2979999999999994 Correction with q = .2969999999999994 Correction with q = .2959999999999994 Correction with q = .2949999999999994 Correction with q = .2939999999999994 Correction with q = .2929999999999994 Correction with q = .2919999999999994 Correction with q = .2909999999999994 Correction with q = .2899999999999994 Correction with q = .2889999999999994 Correction with q = .2879999999999994 Correction with q = .2869999999999994 Correction with q = .2859999999999994 Correction with q = .2849999999999994 Correction with q = .2839999999999994 Correction with q = .2829999999999994 Correction with q = .2819999999999994 Correction with q = .2809999999999994 Correction with q = .2799999999999994 Correction with q = .2789999999999994 Correction with q = .2779999999999994 Correction with q = .2769999999999994 Correction with q = .2759999999999994 Correction with q = .2749999999999994 Correction with q = .2739999999999994 Correction with q = .2729999999999994 Correction with q = .2719999999999994 Correction with q = .2709999999999994 Correction with q = .2699999999999994 Correction with q = .2689999999999994 Correction with q = .2679999999999994 Correction with q = .2669999999999994 Correction with q = .2659999999999994 Correction with q = .2649999999999994 Correction with q = .2639999999999994 Correction with q = .2629999999999994 Correction with q = .2619999999999993 Correction with q = .2609999999999993 Correction with q = .2599999999999993 Correction with q = .2589999999999993 Correction with q = .2579999999999993 Correction with q = .2569999999999993 Correction with q = .2559999999999993 Correction with q = .2549999999999993 Correction with q = .2539999999999993 Correction with q = .2529999999999993 Correction with q = .2519999999999993 Correction with q = .2509999999999993 Correction with q = .2499999999999993 Correction with q = .2489999999999993 Correction with q = .2479999999999993 Correction with q = .2469999999999993 Correction with q = .2459999999999993 Correction with q = .2449999999999993 Correction with q = .2439999999999993 Correction with q = .2429999999999993 Correction with q = .2419999999999993 Correction with q = .2409999999999993 Correction with q = .2399999999999993 Correction with q = .2389999999999993 Correction with q = .2379999999999993 Correction with q = .2369999999999993 Correction with q = .2359999999999993 Correction with q = .2349999999999993 Correction with q = .2339999999999993 Correction with q = .2329999999999993 Correction with q = .2319999999999993 Correction with q = .2309999999999993 Correction with q = .2299999999999993 Correction with q = .2289999999999993 Correction with q = .2279999999999993 Correction with q = .2269999999999993 Correction with q = .2259999999999993 Correction with q = .2249999999999993 Correction with q = .2239999999999993 Correction with q = .2229999999999993 Correction with q = .2219999999999993 Correction with q = .2209999999999993 Correction with q = .2199999999999993 Correction with q = .2189999999999993 Correction with q = .2179999999999993 Correction with q = .2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = .1909999999999993 Correction with q = .1899999999999993 Correction with q = .1889999999999993 Correction with q = .1879999999999993 Correction with q = .1869999999999993 Correction with q = .1859999999999993 Correction with q = .1849999999999993 Correction with q = .1839999999999993 Correction with q = .1829999999999993 Correction with q = .1819999999999993 Correction with q = .1809999999999993 Correction with q = .1799999999999993 Correction with q = .1789999999999993 Correction with q = .1779999999999993 Correction with q = .1769999999999993 Correction with q = .1759999999999993 Correction with q = .1749999999999993 Correction with q = .1739999999999993 Correction with q = .1729999999999993 Correction with q = .1719999999999993 Correction with q = .1709999999999993 Correction with q = .1699999999999993 Correction with q = .1689999999999993 Correction with q = .1679999999999993 Correction with q = .1669999999999993 Correction with q = .1659999999999993 Correction with q = .1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 Correction with q = .0069999999999991 Correction with q = .0059999999999991 Correction with q = .0049999999999991 Correction with q = .0039999999999991 Correction with q = .0029999999999991 Correction with q = .0019999999999991 (5 real changes made) (2 real changes made, 2 to missing) . . estadd loc thisstat5 = "[" + string(Q[1, 1], "%9.3f") +"]" : col3 . estadd loc thisstat5 = "[" +string(Q[2, 1], "%9.3f")+"]": col4 . estadd loc thisstat5 = "[" + string(Q[3, 1], "%9.3f")+"]": col5 . . . loc rowlabels " "\multicolumn{2}{l}{{\bf Panel A: First Stage}}" " " "T$^{74}$" " " "Sharpened q-v > alue" " " "Observations" " . . loc rowstats "" . . forval i = 1/7 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output/tab_treatment_main_PanelA.tex", replace cells(none) booktabs nonotes nomti > tles compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) / > // > mgroups("\shortstack{Post. belief about\\fem. rel. wage\\(0-200)}" "\shortstack{Post. beli > ef about\\fem. rel. wage\\(z-scored)}" "\shortstack{Gender diff.\\ in wages\\are large}" "\shortst > ack{Gender diff.\\ in wages\\are a problem}" /// > "\shortstack{Government\\should mitigate\\gender wage gap}" "\shortstack{Perception\\Index\\((2) > -(4))}", pattern(1 1 1 1 1 1) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr) > {@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output/tab_treatment_main_PanelA.tex) . . eststo clear . . . // Build Panels B-C . . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . loc experiments "quotaanchor AAanchor legislationanchor transparencyanchor UKtool childcare z_lmpo > licy_index" . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . . /* Statistics */ . . loc colnum = 1 . . . foreach choice in `experiments' { 2. . ***Panel B . . reg `choice' T1 $controls [pweight=pweight], vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 7. . estadd loc thisstat7 = "`n'": col`colnum' 8. . . ***Panel C . . ivregress 2sls `choice' $controls (zposterior = T1) [pweight=pweight], vce(r) first 9. local n = round(e(N)) 10. . sigstar zposterior, prec(3) 11. estadd loc thisstat10 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat11 = "`r(sestar)'": col`colnum' 13. . estadd loc thisstat13 = "`n'": col`colnum' 14. . . loc ++colnum 15. . } (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 17.54 Prob > F = 0.0000 R-squared = 0.1149 Root MSE = .97022 ------------------------------------------------------------------------------- | Robust quotaanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0555262 .0355592 1.56 0.119 -.0141966 .1252491 wave | .135324 .0384106 3.52 0.000 .0600104 .2106376 gender | .2540003 .0365454 6.95 0.000 .1823439 .3256567 prior | -.0037742 .0009395 -4.02 0.000 -.0056164 -.001932 democrat | .5591765 .0409111 13.67 0.000 .4789598 .6393931 indep | .1584659 .0539054 2.94 0.003 .0527707 .2641611 otherpol | .1682778 .1265152 1.33 0.184 -.0797874 .4163429 midwest | -.1216167 .0589867 -2.06 0.039 -.2372751 -.0059583 south | .0183978 .0525728 0.35 0.726 -.0846844 .1214801 west | -.0313563 .0564172 -0.56 0.578 -.1419765 .079264 age1 | .2485652 .0795284 3.13 0.002 .0926296 .4045007 age2 | .2953435 .0578768 5.10 0.000 .1818614 .4088256 age3 | .2047762 .0563071 3.64 0.000 .0943719 .3151806 age4 | .0512901 .0550873 0.93 0.352 -.0567225 .1593027 anychildren | .1317022 .0392475 3.36 0.001 .0547476 .2086568 loghhinc | -.0397111 .0247723 -1.60 0.109 -.0882835 .0088612 associatemore | -.0639229 .0404276 -1.58 0.114 -.1431914 .0153456 fulltime | .058038 .054327 1.07 0.285 -.0484839 .1645599 parttime | .0364979 .0720797 0.51 0.613 -.1048326 .1778284 selfemp | .0849985 .0780653 1.09 0.276 -.0680683 .2380652 unemployed | .1133252 .0851681 1.33 0.183 -.0536684 .2803189 student | -.0747328 .111563 -0.67 0.503 -.2934803 .1440148 _cons | -.0734587 .2908389 -0.25 0.801 -.643722 .4968046 ------------------------------------------------------------------------------- (sum of wgt is 2.9862e+03) First-stage regressions ----------------------- Number of obs = 3,022 F( 22, 2999) = 41.26 Prob > F = 0.0000 R-squared = 0.3151 Adj R-squared = 0.3101 Root MSE = 0.8306 ------------------------------------------------------------------------------- | Robust zposterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- wave | .1260341 .0327597 3.85 0.000 .0618005 .1902678 gender | -.0827106 .0312805 -2.64 0.008 -.1440441 -.0213771 prior | .0194641 .0015117 12.88 0.000 .0165 .0224283 democrat | .0023145 .0358356 0.06 0.949 -.0679504 .0725795 indep | .0251822 .0430749 0.58 0.559 -.0592771 .1096415 otherpol | .0464117 .1547786 0.30 0.764 -.2570714 .3498947 midwest | -.006544 .0487717 -0.13 0.893 -.1021733 .0890854 south | .0057097 .0445146 0.13 0.898 -.0815725 .0929918 west | -.0195161 .0483411 -0.40 0.686 -.1143012 .0752691 age1 | .1416509 .0663231 2.14 0.033 .0116075 .2716943 age2 | .144022 .0482445 2.99 0.003 .0494264 .2386176 age3 | .031837 .0449225 0.71 0.479 -.0562451 .1199191 age4 | -.0166214 .0403386 -0.41 0.680 -.0957154 .0624727 anychildren | .0691512 .0342189 2.02 0.043 .0020563 .1362462 loghhinc | -.0162428 .021576 -0.75 0.452 -.0585481 .0260624 associatemore | .0573031 .0336189 1.70 0.088 -.0086154 .1232215 fulltime | .0358815 .0418026 0.86 0.391 -.046083 .1178461 parttime | -.0326828 .0577936 -0.57 0.572 -.1460019 .0806363 selfemp | -.0225004 .0603964 -0.37 0.710 -.140923 .0959222 unemployed | -.1406127 .0610893 -2.30 0.021 -.2603939 -.0208316 student | -.0217205 .0845913 -0.26 0.797 -.1875834 .1441424 T1 | -.6582479 .0302143 -21.79 0.000 -.7174907 -.5990051 _cons | -1.373675 .275323 -4.99 0.000 -1.913516 -.8338337 ------------------------------------------------------------------------------- Instrumental variables (2SLS) regression Number of obs = 3,022 Wald chi2(22) = 379.61 Prob > chi2 = 0.0000 R-squared = 0.1044 Root MSE = .97164 ------------------------------------------------------------------------------- | Robust quotaanchor | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- zposterior | -.0847812 .0541509 -1.57 0.117 -.190915 .0213526 wave | .1460525 .0392101 3.72 0.000 .0692021 .222903 gender | .2447435 .036924 6.63 0.000 .1723739 .3171131 prior | -.0021802 .001401 -1.56 0.120 -.0049262 .0005657 democrat | .5571868 .0410108 13.59 0.000 .4768072 .6375665 indep | .1617053 .0539425 3.00 0.003 .05598 .2674306 otherpol | .1718024 .1263957 1.36 0.174 -.0759286 .4195334 midwest | -.1166386 .0592295 -1.97 0.049 -.2327264 -.0005509 south | .0229955 .0527883 0.44 0.663 -.0804677 .1264586 west | -.030006 .0565099 -0.53 0.595 -.1407633 .0807514 age1 | .2710225 .0802627 3.38 0.001 .1137104 .4283345 age2 | .3034438 .0585332 5.18 0.000 .1887208 .4181668 age3 | .2067801 .0563914 3.67 0.000 .096255 .3173052 age4 | .0495431 .0550257 0.90 0.368 -.0583054 .1573915 anychildren | .1382897 .0396028 3.49 0.000 .0606696 .2159099 loghhinc | -.0403406 .0247953 -1.63 0.104 -.0889386 .0082574 associatemore | -.0594576 .0406606 -1.46 0.144 -.139151 .0202358 fulltime | .0598029 .0543485 1.10 0.271 -.0467182 .1663241 parttime | .0286466 .0722156 0.40 0.692 -.1128934 .1701867 selfemp | .0823955 .0777501 1.06 0.289 -.069992 .2347829 unemployed | .1090777 .0852419 1.28 0.201 -.0579933 .2761488 student | -.0829884 .1111837 -0.75 0.455 -.3009044 .1349276 _cons | -.1945116 .3080468 -0.63 0.528 -.7982722 .409249 ------------------------------------------------------------------------------- Instrumented: zposterior Instruments: wave gender prior democrat indep otherpol midwest south west age1 age2 age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student T1 (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 20.80 Prob > F = 0.0000 R-squared = 0.1332 Root MSE = .93684 ------------------------------------------------------------------------------- | Robust AAanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1124561 .0342902 3.28 0.001 .0452216 .1796907 wave | .0223997 .0374271 0.60 0.550 -.0509856 .095785 gender | .1792126 .0350613 5.11 0.000 .1104659 .2479592 prior | -.0043927 .0009723 -4.52 0.000 -.006299 -.0024863 democrat | .6690384 .0397297 16.84 0.000 .5911382 .7469385 indep | .2537192 .0523123 4.85 0.000 .1511478 .3562907 otherpol | .1193334 .1249381 0.96 0.340 -.1256392 .3643061 midwest | -.0915092 .0564249 -1.62 0.105 -.2021444 .0191261 south | .0705323 .0507837 1.39 0.165 -.029042 .1701067 west | -.0527099 .0547876 -0.96 0.336 -.1601348 .054715 age1 | .145654 .0740603 1.97 0.049 .0004401 .290868 age2 | .1579825 .0555227 2.85 0.004 .0491161 .2668489 age3 | .0576528 .0546985 1.05 0.292 -.0495975 .1649031 age4 | .0124483 .0521028 0.24 0.811 -.0897124 .114609 anychildren | .1346613 .0380059 3.54 0.000 .0601411 .2091815 loghhinc | -.0112497 .023842 -0.47 0.637 -.0579981 .0354986 associatemore | .0594325 .0381496 1.56 0.119 -.0153694 .1342343 fulltime | -.0205963 .0515 -0.40 0.689 -.1215751 .0803825 parttime | -.0489328 .0684958 -0.71 0.475 -.1832362 .0853706 selfemp | -.0188682 .0759922 -0.25 0.804 -.1678701 .1301336 unemployed | -.013886 .083436 -0.17 0.868 -.1774833 .1497113 student | .1036868 .1046431 0.99 0.322 -.1014925 .3088661 _cons | -.228448 .2858848 -0.80 0.424 -.7889976 .3321015 ------------------------------------------------------------------------------- (sum of wgt is 2.9862e+03) First-stage regressions ----------------------- Number of obs = 3,022 F( 22, 2999) = 41.26 Prob > F = 0.0000 R-squared = 0.3151 Adj R-squared = 0.3101 Root MSE = 0.8306 ------------------------------------------------------------------------------- | Robust zposterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- wave | .1260341 .0327597 3.85 0.000 .0618005 .1902678 gender | -.0827106 .0312805 -2.64 0.008 -.1440441 -.0213771 prior | .0194641 .0015117 12.88 0.000 .0165 .0224283 democrat | .0023145 .0358356 0.06 0.949 -.0679504 .0725795 indep | .0251822 .0430749 0.58 0.559 -.0592771 .1096415 otherpol | .0464117 .1547786 0.30 0.764 -.2570714 .3498947 midwest | -.006544 .0487717 -0.13 0.893 -.1021733 .0890854 south | .0057097 .0445146 0.13 0.898 -.0815725 .0929918 west | -.0195161 .0483411 -0.40 0.686 -.1143012 .0752691 age1 | .1416509 .0663231 2.14 0.033 .0116075 .2716943 age2 | .144022 .0482445 2.99 0.003 .0494264 .2386176 age3 | .031837 .0449225 0.71 0.479 -.0562451 .1199191 age4 | -.0166214 .0403386 -0.41 0.680 -.0957154 .0624727 anychildren | .0691512 .0342189 2.02 0.043 .0020563 .1362462 loghhinc | -.0162428 .021576 -0.75 0.452 -.0585481 .0260624 associatemore | .0573031 .0336189 1.70 0.088 -.0086154 .1232215 fulltime | .0358815 .0418026 0.86 0.391 -.046083 .1178461 parttime | -.0326828 .0577936 -0.57 0.572 -.1460019 .0806363 selfemp | -.0225004 .0603964 -0.37 0.710 -.140923 .0959222 unemployed | -.1406127 .0610893 -2.30 0.021 -.2603939 -.0208316 student | -.0217205 .0845913 -0.26 0.797 -.1875834 .1441424 T1 | -.6582479 .0302143 -21.79 0.000 -.7174907 -.5990051 _cons | -1.373675 .275323 -4.99 0.000 -1.913516 -.8338337 ------------------------------------------------------------------------------- Instrumental variables (2SLS) regression Number of obs = 3,022 Wald chi2(22) = 442.96 Prob > chi2 = 0.0000 R-squared = 0.1095 Root MSE = .94586 ------------------------------------------------------------------------------- | Robust AAanchor | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- zposterior | -.1710345 .0525989 -3.25 0.001 -.2741264 -.0679426 wave | .0430203 .0386269 1.11 0.265 -.032687 .1187275 gender | .1624854 .0356222 4.56 0.000 .0926671 .2323037 prior | -.0010688 .0014545 -0.73 0.462 -.0039195 .0017819 democrat | .6657954 .0400398 16.63 0.000 .5873189 .7442719 indep | .2559276 .0529664 4.83 0.000 .1521154 .3597398 otherpol | .1246857 .1185158 1.05 0.293 -.107601 .3569723 midwest | -.0929281 .0571495 -1.63 0.104 -.204939 .0190828 south | .0705506 .0515473 1.37 0.171 -.0304802 .1715814 west | -.0571382 .0552316 -1.03 0.301 -.1653903 .0511138 age1 | .1776558 .0751215 2.36 0.018 .0304204 .3248912 age2 | .1807458 .0565199 3.20 0.001 .0699689 .2915227 age3 | .0622262 .0549275 1.13 0.257 -.0454297 .1698821 age4 | .0089149 .0523444 0.17 0.865 -.0936783 .1115082 anychildren | .1468554 .0384795 3.82 0.000 .0714369 .2222739 loghhinc | -.0155536 .0240778 -0.65 0.518 -.0627453 .031638 associatemore | .0700996 .0384101 1.83 0.068 -.0051828 .145382 fulltime | -.0158019 .0516656 -0.31 0.760 -.1170646 .0854608 parttime | -.0578542 .0693501 -0.83 0.404 -.1937779 .0780696 selfemp | -.0240531 .0755509 -0.32 0.750 -.1721301 .1240239 unemployed | -.0310917 .0828444 -0.38 0.707 -.1934637 .1312804 student | .0935304 .1053075 0.89 0.374 -.1128685 .2999292 _cons | -.4404713 .3101329 -1.42 0.156 -1.048321 .167378 ------------------------------------------------------------------------------- Instrumented: zposterior Instruments: wave gender prior democrat indep otherpol midwest south west age1 age2 age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student T1 (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 18.37 Prob > F = 0.0000 R-squared = 0.1192 Root MSE = .94848 ------------------------------------------------------------------------------- | Robust legislation~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1152908 .0348747 3.31 0.001 .0469102 .1836714 wave | .0169243 .0380401 0.44 0.656 -.057663 .0915116 gender | .237316 .0357167 6.64 0.000 .1672843 .3073477 prior | -.004017 .0010618 -3.78 0.000 -.006099 -.0019351 democrat | .6182478 .0402208 15.37 0.000 .5393847 .6971108 indep | .2353013 .0509727 4.62 0.000 .1353564 .3352462 otherpol | .4703131 .1283571 3.66 0.000 .2186365 .7219897 midwest | -.069528 .0573761 -1.21 0.226 -.1820284 .0429724 south | -.0282264 .0513194 -0.55 0.582 -.128851 .0723982 west | -.059146 .0551212 -1.07 0.283 -.1672251 .048933 age1 | -.1681727 .0752971 -2.23 0.026 -.3158117 -.0205337 age2 | -.1143301 .0561517 -2.04 0.042 -.2244298 -.0042304 age3 | -.1067755 .0555956 -1.92 0.055 -.2157846 .0022337 age4 | -.0552778 .0533647 -1.04 0.300 -.1599128 .0493572 anychildren | .0378855 .0387236 0.98 0.328 -.0380419 .1138128 loghhinc | .0033672 .023876 0.14 0.888 -.0434478 .0501822 associatemore | .0011689 .0380079 0.03 0.975 -.0733553 .075693 fulltime | .0452467 .0542015 0.83 0.404 -.0610291 .1515225 parttime | -.0198583 .073063 -0.27 0.786 -.1631169 .1234002 selfemp | .1711308 .0744418 2.30 0.022 .0251689 .3170928 unemployed | .2739314 .0840264 3.26 0.001 .1091764 .4386864 student | .1661792 .1074768 1.55 0.122 -.0445562 .3769147 _cons | -.1935231 .285267 -0.68 0.498 -.7528613 .365815 ------------------------------------------------------------------------------- (sum of wgt is 2.9862e+03) First-stage regressions ----------------------- Number of obs = 3,022 F( 22, 2999) = 41.26 Prob > F = 0.0000 R-squared = 0.3151 Adj R-squared = 0.3101 Root MSE = 0.8306 ------------------------------------------------------------------------------- | Robust zposterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- wave | .1260341 .0327597 3.85 0.000 .0618005 .1902678 gender | -.0827106 .0312805 -2.64 0.008 -.1440441 -.0213771 prior | .0194641 .0015117 12.88 0.000 .0165 .0224283 democrat | .0023145 .0358356 0.06 0.949 -.0679504 .0725795 indep | .0251822 .0430749 0.58 0.559 -.0592771 .1096415 otherpol | .0464117 .1547786 0.30 0.764 -.2570714 .3498947 midwest | -.006544 .0487717 -0.13 0.893 -.1021733 .0890854 south | .0057097 .0445146 0.13 0.898 -.0815725 .0929918 west | -.0195161 .0483411 -0.40 0.686 -.1143012 .0752691 age1 | .1416509 .0663231 2.14 0.033 .0116075 .2716943 age2 | .144022 .0482445 2.99 0.003 .0494264 .2386176 age3 | .031837 .0449225 0.71 0.479 -.0562451 .1199191 age4 | -.0166214 .0403386 -0.41 0.680 -.0957154 .0624727 anychildren | .0691512 .0342189 2.02 0.043 .0020563 .1362462 loghhinc | -.0162428 .021576 -0.75 0.452 -.0585481 .0260624 associatemore | .0573031 .0336189 1.70 0.088 -.0086154 .1232215 fulltime | .0358815 .0418026 0.86 0.391 -.046083 .1178461 parttime | -.0326828 .0577936 -0.57 0.572 -.1460019 .0806363 selfemp | -.0225004 .0603964 -0.37 0.710 -.140923 .0959222 unemployed | -.1406127 .0610893 -2.30 0.021 -.2603939 -.0208316 student | -.0217205 .0845913 -0.26 0.797 -.1875834 .1441424 T1 | -.6582479 .0302143 -21.79 0.000 -.7174907 -.5990051 _cons | -1.373675 .275323 -4.99 0.000 -1.913516 -.8338337 ------------------------------------------------------------------------------- Instrumental variables (2SLS) regression Number of obs = 3,022 Wald chi2(22) = 397.71 Prob > chi2 = 0.0000 R-squared = 0.1091 Root MSE = .95106 ------------------------------------------------------------------------------- | Robust legislation~r | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- zposterior | -.1768863 .0531235 -3.33 0.001 -.2810064 -.0727662 wave | .0399912 .0386933 1.03 0.301 -.0358464 .1158287 gender | .2220452 .0361288 6.15 0.000 .1512341 .2928563 prior | -.0005867 .0015114 -0.39 0.698 -.003549 .0023756 democrat | .6178396 .0403949 15.29 0.000 .538667 .6970121 indep | .2410704 .0512754 4.70 0.000 .1405725 .3415684 otherpol | .4793467 .1252561 3.83 0.000 .2338492 .7248443 midwest | -.070272 .0576118 -1.22 0.223 -.183189 .0426451 south | -.0297383 .0517523 -0.57 0.566 -.131171 .0716944 west | -.0629474 .0555169 -1.13 0.257 -.1717585 .0458638 age1 | -.1409776 .0760852 -1.85 0.064 -.2901019 .0081468 age2 | -.0907303 .0565289 -1.61 0.108 -.2015249 .0200642 age3 | -.1030185 .0556689 -1.85 0.064 -.2121275 .0060904 age4 | -.0583444 .0531373 -1.10 0.272 -.1624916 .0458029 anychildren | .0506106 .0388148 1.30 0.192 -.025465 .1266862 loghhinc | -.0005043 .0238613 -0.02 0.983 -.0472717 .046263 associatemore | .0133973 .0378919 0.35 0.724 -.0608695 .087664 fulltime | .0506403 .0538061 0.94 0.347 -.0548176 .1560983 parttime | -.0275398 .0731074 -0.38 0.706 -.1708276 .115748 selfemp | .1668333 .0741452 2.25 0.024 .0215115 .3121552 unemployed | .2489749 .08474 2.94 0.003 .0828876 .4150622 student | .1606534 .1096738 1.46 0.143 -.0543032 .37561 _cons | -.425604 .3067871 -1.39 0.165 -1.026896 .1756876 ------------------------------------------------------------------------------- Instrumented: zposterior Instruments: wave gender prior democrat indep otherpol midwest south west age1 age2 age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student T1 (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 10.39 Prob > F = 0.0000 R-squared = 0.0957 Root MSE = .94476 ------------------------------------------------------------------------------- | Robust transparenc~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0145207 .0421926 -0.34 0.731 -.097267 .0682256 wave | 0 (omitted) gender | .1968569 .0436751 4.51 0.000 .1112032 .2825107 prior | -.0026412 .0011873 -2.22 0.026 -.0049697 -.0003127 democrat | .5648746 .048392 11.67 0.000 .4699703 .6597789 indep | .2426681 .0645124 3.76 0.000 .1161491 .3691871 otherpol | .1372274 .1916125 0.72 0.474 -.2385547 .5130095 midwest | -.1229332 .0686787 -1.79 0.074 -.257623 .0117565 south | -.0270683 .0603186 -0.45 0.654 -.1453626 .0912259 west | -.048422 .0656441 -0.74 0.461 -.1771604 .0803163 age1 | .0478043 .1037241 0.46 0.645 -.1556149 .2512235 age2 | .0581052 .0678145 0.86 0.392 -.0748897 .1911001 age3 | .0554398 .0677336 0.82 0.413 -.0773964 .188276 age4 | .0267632 .0655902 0.41 0.683 -.1018694 .1553958 anychildren | .0940054 .0484718 1.94 0.053 -.0010554 .1890661 loghhinc | -.0266598 .0288938 -0.92 0.356 -.083325 .0300055 associatemore | .1015891 .0476979 2.13 0.033 .0080459 .1951322 fulltime | .0090151 .0654756 0.14 0.891 -.1193927 .137423 parttime | -.1014771 .0838587 -1.21 0.226 -.2659372 .062983 selfemp | .0372718 .0988473 0.38 0.706 -.1565833 .2311269 unemployed | .0970394 .0974852 1.00 0.320 -.0941443 .2882232 student | .3350384 .129311 2.59 0.010 .0814392 .5886376 _cons | -.0332594 .3314474 -0.10 0.920 -.6832798 .6167609 ------------------------------------------------------------------------------- (sum of wgt is 2.0030e+03) note: wave omitted because of collinearity First-stage regressions ----------------------- Number of obs = 2,003 F( 21, 1981) = 28.51 Prob > F = 0.0000 R-squared = 0.3219 Adj R-squared = 0.3147 Root MSE = 0.8377 ------------------------------------------------------------------------------- | Robust zposterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- wave | 0 (omitted) gender | -.0544777 .040017 -1.36 0.174 -.1329575 .0240021 prior | .0208339 .0018999 10.97 0.000 .0171079 .02456 democrat | .0238558 .0440303 0.54 0.588 -.0624948 .1102064 indep | .0318796 .0535923 0.59 0.552 -.0732235 .1369827 otherpol | .2025092 .2036445 0.99 0.320 -.1968707 .6018892 midwest | .0151798 .0638961 0.24 0.812 -.1101308 .1404904 south | -.0499889 .0560495 -0.89 0.373 -.159911 .0599332 west | -.0369329 .0600194 -0.62 0.538 -.1546407 .080775 age1 | .1160422 .0996786 1.16 0.244 -.0794436 .311528 age2 | .1663956 .0583041 2.85 0.004 .0520517 .2807394 age3 | .0019002 .0534765 0.04 0.972 -.1029758 .1067762 age4 | -.0088487 .050013 -0.18 0.860 -.1069323 .0892348 anychildren | .071085 .0422187 1.68 0.092 -.0117127 .1538827 loghhinc | -.0264572 .02556 -1.04 0.301 -.0765846 .0236701 associatemore | .0332075 .0438253 0.76 0.449 -.052741 .1191561 fulltime | .0627065 .052383 1.20 0.231 -.0400251 .1654382 parttime | -.0111634 .0729282 -0.15 0.878 -.1541875 .1318607 selfemp | .0024145 .0749821 0.03 0.974 -.1446376 .1494665 unemployed | -.1169611 .0731428 -1.60 0.110 -.260406 .0264837 student | -.0700707 .1062345 -0.66 0.510 -.2784137 .1382723 T1 | -.6524723 .0371216 -17.58 0.000 -.7252737 -.5796709 _cons | -1.264817 .3068054 -4.12 0.000 -1.866512 -.6631215 ------------------------------------------------------------------------------- Instrumental variables (2SLS) regression Number of obs = 2,003 Wald chi2(21) = 217.06 Prob > chi2 = 0.0000 R-squared = 0.0938 Root MSE = .94076 ------------------------------------------------------------------------------- | Robust transparenc~r | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- zposterior | .0257453 .0645284 0.40 0.690 -.1007281 .1522187 wave | 0 (omitted) gender | .1966492 .043845 4.49 0.000 .1107146 .2825838 prior | -.0032912 .0018127 -1.82 0.069 -.0068441 .0002616 democrat | .5595867 .0484729 11.54 0.000 .4645816 .6545919 indep | .243653 .0642221 3.79 0.000 .11778 .3695259 otherpol | .1304002 .1908026 0.68 0.494 -.2435661 .5043665 midwest | -.1227927 .0683978 -1.80 0.073 -.25685 .0112645 south | -.032037 .0603848 -0.53 0.596 -.150389 .086315 west | -.0498672 .0655775 -0.76 0.447 -.1783969 .0786624 age1 | .0604629 .1042205 0.58 0.562 -.1438055 .2647313 age2 | .0519832 .0686365 0.76 0.449 -.082542 .1865083 age3 | .0552939 .0675819 0.82 0.413 -.0771642 .1877519 age4 | .025978 .0653051 0.40 0.691 -.1020177 .1539737 anychildren | .0924908 .0484113 1.91 0.056 -.0023937 .1873752 loghhinc | -.0277556 .0289677 -0.96 0.338 -.0845313 .0290201 associatemore | .1049215 .0476529 2.20 0.028 .0115234 .1983195 fulltime | .0080867 .0653566 0.12 0.902 -.1200098 .1361833 parttime | -.1092722 .0835987 -1.31 0.191 -.2731227 .0545783 selfemp | .0357852 .0982895 0.36 0.716 -.1568587 .2284291 unemployed | .0988136 .097809 1.01 0.312 -.0928886 .2905159 student | .3273598 .1288336 2.54 0.011 .0748505 .5798691 _cons | .0330921 .3465942 0.10 0.924 -.64622 .7124043 ------------------------------------------------------------------------------- Instrumented: zposterior Instruments: gender prior democrat indep otherpol midwest south west age1 age2 age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student T1 (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 7.10 Prob > F = 0.0000 R-squared = 0.1242 Root MSE = .96536 ------------------------------------------------------------------------------- | Robust UKtool | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0976392 .0627325 1.56 0.120 -.0254636 .2207421 wave | 0 (omitted) gender | .3101848 .0625144 4.96 0.000 .18751 .4328596 prior | -.0036998 .0017424 -2.12 0.034 -.007119 -.0002807 democrat | .5962684 .0739746 8.06 0.000 .4511045 .7414322 indep | .2463499 .0943696 2.61 0.009 .061164 .4315357 otherpol | .2376436 .2738113 0.87 0.386 -.299669 .7749563 midwest | -.1507862 .101069 -1.49 0.136 -.3491186 .0475462 south | -.0618433 .086354 -0.72 0.474 -.2312998 .1076132 west | -.1117538 .097955 -1.14 0.254 -.3039755 .0804679 age1 | -.0525978 .1305981 -0.40 0.687 -.3088765 .2036809 age2 | -.0586592 .1048296 -0.56 0.576 -.2643711 .1470527 age3 | -.0298531 .0985224 -0.30 0.762 -.2231882 .163482 age4 | .086665 .0958668 0.90 0.366 -.1014588 .2747888 anychildren | -.0297168 .070903 -0.42 0.675 -.1688532 .1094195 loghhinc | .057605 .0460921 1.25 0.212 -.0328436 .1480537 associatemore | -.0051252 .0710553 -0.07 0.943 -.1445604 .13431 fulltime | .0581511 .0943182 0.62 0.538 -.126934 .2432361 parttime | .0347192 .1274041 0.27 0.785 -.2152917 .2847301 selfemp | .2747962 .1322147 2.08 0.038 .0153452 .5342472 unemployed | .1769462 .1682875 1.05 0.293 -.1532921 .5071845 student | .2265534 .166965 1.36 0.175 -.1010897 .5541965 _cons | -.811541 .5433414 -1.49 0.136 -1.877765 .254683 ------------------------------------------------------------------------------- (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity First-stage regressions ----------------------- Number of obs = 1,019 F( 21, 997) = 16.42 Prob > F = 0.0000 R-squared = 0.3155 Adj R-squared = 0.3011 Root MSE = 0.8123 ------------------------------------------------------------------------------- | Robust zposterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- wave | 0 (omitted) gender | -.1474178 .0499282 -2.95 0.003 -.2453943 -.0494414 prior | .0163834 .0023505 6.97 0.000 .011771 .0209958 democrat | -.0491233 .0603995 -0.81 0.416 -.1676481 .0694015 indep | .0145476 .0715422 0.20 0.839 -.1258431 .1549382 otherpol | -.3150698 .1757874 -1.79 0.073 -.6600254 .0298859 midwest | -.043072 .0692702 -0.62 0.534 -.179004 .0928601 south | .1304369 .0707943 1.84 0.066 -.008486 .2693598 west | .0238148 .078873 0.30 0.763 -.1309612 .1785909 age1 | .1799476 .0848609 2.12 0.034 .013421 .3464741 age2 | .0790408 .0886473 0.89 0.373 -.094916 .2529976 age3 | .0774551 .0831839 0.93 0.352 -.0857805 .2406907 age4 | -.0390767 .0672063 -0.58 0.561 -.1709588 .0928053 anychildren | .0565311 .058467 0.97 0.334 -.0582014 .1712636 loghhinc | .0062266 .038427 0.16 0.871 -.0691804 .0816337 associatemore | .1053486 .0501164 2.10 0.036 .0070029 .2036944 fulltime | -.0070557 .0684607 -0.10 0.918 -.1413992 .1272879 parttime | -.0738886 .0947 -0.78 0.435 -.2597227 .1119455 selfemp | -.0794346 .0981194 -0.81 0.418 -.2719789 .1131097 unemployed | -.2045361 .1078029 -1.90 0.058 -.4160827 .0070105 student | .0028821 .1287532 0.02 0.982 -.2497763 .2555404 T1 | -.677441 .0510589 -13.27 0.000 -.7776363 -.5772457 _cons | -1.075281 .52632 -2.04 0.041 -2.108104 -.0424593 ------------------------------------------------------------------------------- Instrumental variables (2SLS) regression Number of obs = 1,019 Wald chi2(21) = 149.91 Prob > chi2 = 0.0000 R-squared = 0.1165 Root MSE = .95904 ------------------------------------------------------------------------------- | Robust UKtool | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- zposterior | -.1441295 .0921037 -1.56 0.118 -.3246493 .0363903 wave | 0 (omitted) gender | .2889375 .0633162 4.56 0.000 .16484 .4130351 prior | -.0013385 .0024122 -0.55 0.579 -.0060664 .0033894 democrat | .5891883 .0736187 8.00 0.000 .4448982 .7334783 indep | .2484466 .0935517 2.66 0.008 .0650887 .4318045 otherpol | .1922328 .278709 0.69 0.490 -.3540267 .7384923 midwest | -.1569942 .1006096 -1.56 0.119 -.3541853 .040197 south | -.0430435 .0866777 -0.50 0.619 -.2129287 .1268417 west | -.1083214 .097682 -1.11 0.267 -.2997745 .0831317 age1 | -.026662 .1323247 -0.20 0.840 -.2860137 .2326896 age2 | -.0472671 .1056386 -0.45 0.655 -.254315 .1597808 age3 | -.0186896 .0985191 -0.19 0.850 -.2117834 .1744043 age4 | .0810329 .0951467 0.85 0.394 -.1054512 .2675169 anychildren | -.021569 .0702902 -0.31 0.759 -.1593352 .1161971 loghhinc | .0585025 .0454198 1.29 0.198 -.0305187 .1475237 associatemore | .0100587 .0701963 0.14 0.886 -.1275235 .1476408 fulltime | .0571342 .0934118 0.61 0.541 -.1259497 .240218 parttime | .0240697 .1284433 0.19 0.851 -.2276747 .275814 selfemp | .2633473 .1317606 2.00 0.046 .0051012 .5215935 unemployed | .1474665 .1676325 0.88 0.379 -.1810872 .4760202 student | .2269688 .1634377 1.39 0.165 -.0933633 .5473008 _cons | -.9665208 .5635005 -1.72 0.086 -2.070961 .13792 ------------------------------------------------------------------------------- Instrumented: zposterior Instruments: gender prior democrat indep otherpol midwest south west age1 age2 age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student T1 (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 16.85 Prob > F = 0.0000 R-squared = 0.1142 Root MSE = .94481 ------------------------------------------------------------------------------- | Robust childcare | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0026486 .0346768 0.08 0.939 -.0653441 .0706412 wave | -.099568 .0374546 -2.66 0.008 -.1730073 -.0261287 gender | .1118256 .0361868 3.09 0.002 .0408722 .1827789 prior | -.0038869 .0009617 -4.04 0.000 -.0057725 -.0020014 democrat | .5781269 .0401769 14.39 0.000 .4993498 .6569039 indep | .1065453 .0524179 2.03 0.042 .0037668 .2093238 otherpol | .2258622 .1341441 1.68 0.092 -.0371613 .4888857 midwest | -.0919717 .0562669 -1.63 0.102 -.2022972 .0183539 south | .0059153 .0502006 0.12 0.906 -.0925156 .1043462 west | -.146718 .0564412 -2.60 0.009 -.2573853 -.0360507 age1 | .2577494 .0806588 3.20 0.001 .0995974 .4159014 age2 | .3202499 .0565913 5.66 0.000 .2092884 .4312114 age3 | .2364555 .0564334 4.19 0.000 .1258036 .3471074 age4 | .0964648 .0541442 1.78 0.075 -.0096985 .2026282 anychildren | .2043124 .0388232 5.26 0.000 .1281896 .2804352 loghhinc | -.0321861 .0244382 -1.32 0.188 -.0801032 .0157311 associatemore | -.0264179 .0383545 -0.69 0.491 -.1016215 .0487858 fulltime | -.0064537 .055003 -0.12 0.907 -.1143011 .1013937 parttime | .0186614 .0715453 0.26 0.794 -.1216214 .1589441 selfemp | .0714849 .0774456 0.92 0.356 -.0803668 .2233366 unemployed | .0308187 .0857939 0.36 0.719 -.137402 .1990394 student | .0482532 .1013599 0.48 0.634 -.1504884 .2469948 _cons | .217017 .2832473 0.77 0.444 -.338361 .772395 ------------------------------------------------------------------------------- (sum of wgt is 2.9862e+03) First-stage regressions ----------------------- Number of obs = 3,022 F( 22, 2999) = 41.26 Prob > F = 0.0000 R-squared = 0.3151 Adj R-squared = 0.3101 Root MSE = 0.8306 ------------------------------------------------------------------------------- | Robust zposterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- wave | .1260341 .0327597 3.85 0.000 .0618005 .1902678 gender | -.0827106 .0312805 -2.64 0.008 -.1440441 -.0213771 prior | .0194641 .0015117 12.88 0.000 .0165 .0224283 democrat | .0023145 .0358356 0.06 0.949 -.0679504 .0725795 indep | .0251822 .0430749 0.58 0.559 -.0592771 .1096415 otherpol | .0464117 .1547786 0.30 0.764 -.2570714 .3498947 midwest | -.006544 .0487717 -0.13 0.893 -.1021733 .0890854 south | .0057097 .0445146 0.13 0.898 -.0815725 .0929918 west | -.0195161 .0483411 -0.40 0.686 -.1143012 .0752691 age1 | .1416509 .0663231 2.14 0.033 .0116075 .2716943 age2 | .144022 .0482445 2.99 0.003 .0494264 .2386176 age3 | .031837 .0449225 0.71 0.479 -.0562451 .1199191 age4 | -.0166214 .0403386 -0.41 0.680 -.0957154 .0624727 anychildren | .0691512 .0342189 2.02 0.043 .0020563 .1362462 loghhinc | -.0162428 .021576 -0.75 0.452 -.0585481 .0260624 associatemore | .0573031 .0336189 1.70 0.088 -.0086154 .1232215 fulltime | .0358815 .0418026 0.86 0.391 -.046083 .1178461 parttime | -.0326828 .0577936 -0.57 0.572 -.1460019 .0806363 selfemp | -.0225004 .0603964 -0.37 0.710 -.140923 .0959222 unemployed | -.1406127 .0610893 -2.30 0.021 -.2603939 -.0208316 student | -.0217205 .0845913 -0.26 0.797 -.1875834 .1441424 T1 | -.6582479 .0302143 -21.79 0.000 -.7174907 -.5990051 _cons | -1.373675 .275323 -4.99 0.000 -1.913516 -.8338337 ------------------------------------------------------------------------------- Instrumental variables (2SLS) regression Number of obs = 3,022 Wald chi2(22) = 372.14 Prob > chi2 = 0.0000 R-squared = 0.1143 Root MSE = .94151 ------------------------------------------------------------------------------- | Robust childcare | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- zposterior | -.0085365 .0525552 -0.16 0.871 -.1115429 .0944699 wave | -.0971295 .0377079 -2.58 0.010 -.1710356 -.0232234 gender | .1127275 .036376 3.10 0.002 .0414317 .1840232 prior | -.0036812 .0013909 -2.65 0.008 -.0064073 -.0009551 democrat | .5800571 .0401561 14.45 0.000 .5013526 .6587617 indep | .1110283 .0522845 2.12 0.034 .0085527 .213504 otherpol | .2292314 .1335232 1.72 0.086 -.0324693 .4909321 midwest | -.0903686 .0561947 -1.61 0.108 -.2005083 .019771 south | .0033251 .0502035 0.07 0.947 -.0950719 .1017221 west | -.1472321 .0564224 -2.61 0.009 -.2578181 -.0366462 age1 | .2593556 .081455 3.18 0.001 .0997068 .4190044 age2 | .3182915 .0569925 5.58 0.000 .2065883 .4299947 age3 | .2333155 .0562847 4.15 0.000 .1229996 .3436314 age4 | .0959649 .0539435 1.78 0.075 -.0097624 .2016922 anychildren | .2045241 .0387288 5.28 0.000 .128617 .2804312 loghhinc | -.0325236 .0244094 -1.33 0.183 -.0803653 .015318 associatemore | -.0243945 .0383178 -0.64 0.524 -.099496 .050707 fulltime | -.0079208 .0548343 -0.14 0.885 -.1153942 .0995525 parttime | .0184335 .0715284 0.26 0.797 -.1217596 .1586266 selfemp | .070952 .0771626 0.92 0.358 -.080284 .2221879 unemployed | .0268833 .0861053 0.31 0.755 -.14188 .1956466 student | .0463486 .1012411 0.46 0.647 -.1520803 .2447775 _cons | .1999153 .2969788 0.67 0.501 -.3821525 .7819831 ------------------------------------------------------------------------------- Instrumented: zposterior Instruments: wave gender prior democrat indep otherpol midwest south west age1 age2 age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student T1 (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 30.19 Prob > F = 0.0000 R-squared = 0.1867 Root MSE = .68334 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0562661 .0251019 2.24 0.025 .0070476 .1054847 wave | .0197672 .0276836 0.71 0.475 -.0345136 .0740479 gender | .2029651 .0255524 7.94 0.000 .1528633 .253067 prior | -.0037725 .0007382 -5.11 0.000 -.0052199 -.0023251 democrat | .5935843 .0289701 20.49 0.000 .5367811 .6503875 indep | .1922975 .0382281 5.03 0.000 .1173417 .2672533 otherpol | .2365913 .0989824 2.39 0.017 .0425112 .4306714 midwest | -.1029934 .0414268 -2.49 0.013 -.184221 -.0217657 south | -.0001163 .0365547 -0.00 0.997 -.0717909 .0715584 west | -.0744621 .040303 -1.85 0.065 -.1534864 .0045622 age1 | .1008579 .0547726 1.84 0.066 -.0065376 .2082534 age2 | .1402544 .040765 3.44 0.001 .0603244 .2201845 age3 | .0901361 .0408989 2.20 0.028 .0099433 .1703288 age4 | .0323242 .0389596 0.83 0.407 -.0440659 .1087144 anychildren | .1131292 .0277134 4.08 0.000 .0587901 .1674683 loghhinc | -.0172534 .017781 -0.97 0.332 -.0521175 .0176107 associatemore | .0023243 .0280954 0.08 0.934 -.0527639 .0574125 fulltime | .0236588 .0396963 0.60 0.551 -.0541758 .1014934 parttime | -.0092615 .0510459 -0.18 0.856 -.1093498 .0908268 selfemp | .0897213 .0563452 1.59 0.111 -.0207577 .2002003 unemployed | .1115593 .0592556 1.88 0.060 -.0046262 .2277449 student | .1018654 .0751883 1.35 0.176 -.0455603 .2492912 _cons | -.0983964 .2090348 -0.47 0.638 -.508262 .3114692 ------------------------------------------------------------------------------- (sum of wgt is 2.9862e+03) First-stage regressions ----------------------- Number of obs = 3,022 F( 22, 2999) = 41.26 Prob > F = 0.0000 R-squared = 0.3151 Adj R-squared = 0.3101 Root MSE = 0.8306 ------------------------------------------------------------------------------- | Robust zposterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- wave | .1260341 .0327597 3.85 0.000 .0618005 .1902678 gender | -.0827106 .0312805 -2.64 0.008 -.1440441 -.0213771 prior | .0194641 .0015117 12.88 0.000 .0165 .0224283 democrat | .0023145 .0358356 0.06 0.949 -.0679504 .0725795 indep | .0251822 .0430749 0.58 0.559 -.0592771 .1096415 otherpol | .0464117 .1547786 0.30 0.764 -.2570714 .3498947 midwest | -.006544 .0487717 -0.13 0.893 -.1021733 .0890854 south | .0057097 .0445146 0.13 0.898 -.0815725 .0929918 west | -.0195161 .0483411 -0.40 0.686 -.1143012 .0752691 age1 | .1416509 .0663231 2.14 0.033 .0116075 .2716943 age2 | .144022 .0482445 2.99 0.003 .0494264 .2386176 age3 | .031837 .0449225 0.71 0.479 -.0562451 .1199191 age4 | -.0166214 .0403386 -0.41 0.680 -.0957154 .0624727 anychildren | .0691512 .0342189 2.02 0.043 .0020563 .1362462 loghhinc | -.0162428 .021576 -0.75 0.452 -.0585481 .0260624 associatemore | .0573031 .0336189 1.70 0.088 -.0086154 .1232215 fulltime | .0358815 .0418026 0.86 0.391 -.046083 .1178461 parttime | -.0326828 .0577936 -0.57 0.572 -.1460019 .0806363 selfemp | -.0225004 .0603964 -0.37 0.710 -.140923 .0959222 unemployed | -.1406127 .0610893 -2.30 0.021 -.2603939 -.0208316 student | -.0217205 .0845913 -0.26 0.797 -.1875834 .1441424 T1 | -.6582479 .0302143 -21.79 0.000 -.7174907 -.5990051 _cons | -1.373675 .275323 -4.99 0.000 -1.913516 -.8338337 ------------------------------------------------------------------------------- Instrumental variables (2SLS) regression Number of obs = 3,022 Wald chi2(22) = 657.07 Prob > chi2 = 0.0000 R-squared = 0.1792 Root MSE = .68394 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- zposterior | -.0865306 .0381897 -2.27 0.023 -.161381 -.0116802 wave | .0307853 .0281398 1.09 0.274 -.0243677 .0859382 gender | .1950235 .0257245 7.58 0.000 .1446043 .2454426 prior | -.0021106 .0010506 -2.01 0.045 -.0041697 -.0000515 democrat | .5924771 .0290286 20.41 0.000 .5355821 .649372 indep | .1960109 .0382774 5.12 0.000 .1209885 .2710333 otherpol | .2407771 .0974469 2.47 0.013 .0497846 .4317696 midwest | -.1018954 .0415904 -2.45 0.014 -.1834112 -.0203797 south | -.0008762 .0368001 -0.02 0.981 -.073003 .0712506 west | -.0761695 .0403928 -1.89 0.059 -.155338 .0029991 age1 | .1187183 .0552088 2.15 0.032 .0105111 .2269255 age2 | .1501187 .0411242 3.65 0.000 .0695167 .2307206 age3 | .0914162 .0408607 2.24 0.025 .0113307 .1715017 age4 | .0304688 .0388946 0.78 0.433 -.0457633 .1067008 anychildren | .1193677 .0277545 4.30 0.000 .0649699 .1737655 loghhinc | -.0191739 .0177265 -1.08 0.279 -.0539171 .0155693 associatemore | .0086775 .0280398 0.31 0.757 -.0462795 .0636346 fulltime | .025843 .0396142 0.65 0.514 -.0517993 .1034854 parttime | -.0150624 .0512211 -0.29 0.769 -.115454 .0853291 selfemp | .0870944 .0561208 1.55 0.121 -.0229004 .1970893 unemployed | .1011194 .0595856 1.70 0.090 -.0156662 .217905 student | .0958891 .0753427 1.27 0.203 -.0517799 .2435581 _cons | -.2094885 .2223357 -0.94 0.346 -.6452584 .2262815 ------------------------------------------------------------------------------- Instrumented: zposterior Instruments: wave gender prior democrat indep otherpol midwest south west age1 age2 age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student T1 . . * Calculate sharpened q-values for Panel B, columns 1-6 . mat def P = J(6, 1, .) . reg quotaanchor T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 17.54 Prob > F = 0.0000 R-squared = 0.1149 Root MSE = .97022 ------------------------------------------------------------------------------- | Robust quotaanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0555262 .0355592 1.56 0.119 -.0141966 .1252491 wave | .135324 .0384106 3.52 0.000 .0600104 .2106376 gender | .2540003 .0365454 6.95 0.000 .1823439 .3256567 prior | -.0037742 .0009395 -4.02 0.000 -.0056164 -.001932 democrat | .5591765 .0409111 13.67 0.000 .4789598 .6393931 indep | .1584659 .0539054 2.94 0.003 .0527707 .2641611 otherpol | .1682778 .1265152 1.33 0.184 -.0797874 .4163429 midwest | -.1216167 .0589867 -2.06 0.039 -.2372751 -.0059583 south | .0183978 .0525728 0.35 0.726 -.0846844 .1214801 west | -.0313563 .0564172 -0.56 0.578 -.1419765 .079264 age1 | .2485652 .0795284 3.13 0.002 .0926296 .4045007 age2 | .2953435 .0578768 5.10 0.000 .1818614 .4088256 age3 | .2047762 .0563071 3.64 0.000 .0943719 .3151806 age4 | .0512901 .0550873 0.93 0.352 -.0567225 .1593027 anychildren | .1317022 .0392475 3.36 0.001 .0547476 .2086568 loghhinc | -.0397111 .0247723 -1.60 0.109 -.0882835 .0088612 associatemore | -.0639229 .0404276 -1.58 0.114 -.1431914 .0153456 fulltime | .058038 .054327 1.07 0.285 -.0484839 .1645599 parttime | .0364979 .0720797 0.51 0.613 -.1048326 .1778284 selfemp | .0849985 .0780653 1.09 0.276 -.0680683 .2380652 unemployed | .1133252 .0851681 1.33 0.183 -.0536684 .2803189 student | -.0747328 .111563 -0.67 0.503 -.2934803 .1440148 _cons | -.0734587 .2908389 -0.25 0.801 -.643722 .4968046 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg AAanchor T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 20.80 Prob > F = 0.0000 R-squared = 0.1332 Root MSE = .93684 ------------------------------------------------------------------------------- | Robust AAanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1124561 .0342902 3.28 0.001 .0452216 .1796907 wave | .0223997 .0374271 0.60 0.550 -.0509856 .095785 gender | .1792126 .0350613 5.11 0.000 .1104659 .2479592 prior | -.0043927 .0009723 -4.52 0.000 -.006299 -.0024863 democrat | .6690384 .0397297 16.84 0.000 .5911382 .7469385 indep | .2537192 .0523123 4.85 0.000 .1511478 .3562907 otherpol | .1193334 .1249381 0.96 0.340 -.1256392 .3643061 midwest | -.0915092 .0564249 -1.62 0.105 -.2021444 .0191261 south | .0705323 .0507837 1.39 0.165 -.029042 .1701067 west | -.0527099 .0547876 -0.96 0.336 -.1601348 .054715 age1 | .145654 .0740603 1.97 0.049 .0004401 .290868 age2 | .1579825 .0555227 2.85 0.004 .0491161 .2668489 age3 | .0576528 .0546985 1.05 0.292 -.0495975 .1649031 age4 | .0124483 .0521028 0.24 0.811 -.0897124 .114609 anychildren | .1346613 .0380059 3.54 0.000 .0601411 .2091815 loghhinc | -.0112497 .023842 -0.47 0.637 -.0579981 .0354986 associatemore | .0594325 .0381496 1.56 0.119 -.0153694 .1342343 fulltime | -.0205963 .0515 -0.40 0.689 -.1215751 .0803825 parttime | -.0489328 .0684958 -0.71 0.475 -.1832362 .0853706 selfemp | -.0188682 .0759922 -0.25 0.804 -.1678701 .1301336 unemployed | -.013886 .083436 -0.17 0.868 -.1774833 .1497113 student | .1036868 .1046431 0.99 0.322 -.1014925 .3088661 _cons | -.228448 .2858848 -0.80 0.424 -.7889976 .3321015 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg legislationanchor T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 18.37 Prob > F = 0.0000 R-squared = 0.1192 Root MSE = .94848 ------------------------------------------------------------------------------- | Robust legislation~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1152908 .0348747 3.31 0.001 .0469102 .1836714 wave | .0169243 .0380401 0.44 0.656 -.057663 .0915116 gender | .237316 .0357167 6.64 0.000 .1672843 .3073477 prior | -.004017 .0010618 -3.78 0.000 -.006099 -.0019351 democrat | .6182478 .0402208 15.37 0.000 .5393847 .6971108 indep | .2353013 .0509727 4.62 0.000 .1353564 .3352462 otherpol | .4703131 .1283571 3.66 0.000 .2186365 .7219897 midwest | -.069528 .0573761 -1.21 0.226 -.1820284 .0429724 south | -.0282264 .0513194 -0.55 0.582 -.128851 .0723982 west | -.059146 .0551212 -1.07 0.283 -.1672251 .048933 age1 | -.1681727 .0752971 -2.23 0.026 -.3158117 -.0205337 age2 | -.1143301 .0561517 -2.04 0.042 -.2244298 -.0042304 age3 | -.1067755 .0555956 -1.92 0.055 -.2157846 .0022337 age4 | -.0552778 .0533647 -1.04 0.300 -.1599128 .0493572 anychildren | .0378855 .0387236 0.98 0.328 -.0380419 .1138128 loghhinc | .0033672 .023876 0.14 0.888 -.0434478 .0501822 associatemore | .0011689 .0380079 0.03 0.975 -.0733553 .075693 fulltime | .0452467 .0542015 0.83 0.404 -.0610291 .1515225 parttime | -.0198583 .073063 -0.27 0.786 -.1631169 .1234002 selfemp | .1711308 .0744418 2.30 0.022 .0251689 .3170928 unemployed | .2739314 .0840264 3.26 0.001 .1091764 .4386864 student | .1661792 .1074768 1.55 0.122 -.0445562 .3769147 _cons | -.1935231 .285267 -0.68 0.498 -.7528613 .365815 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . reg transparencyanchor T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 10.39 Prob > F = 0.0000 R-squared = 0.0957 Root MSE = .94476 ------------------------------------------------------------------------------- | Robust transparenc~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0145207 .0421926 -0.34 0.731 -.097267 .0682256 wave | 0 (omitted) gender | .1968569 .0436751 4.51 0.000 .1112032 .2825107 prior | -.0026412 .0011873 -2.22 0.026 -.0049697 -.0003127 democrat | .5648746 .048392 11.67 0.000 .4699703 .6597789 indep | .2426681 .0645124 3.76 0.000 .1161491 .3691871 otherpol | .1372274 .1916125 0.72 0.474 -.2385547 .5130095 midwest | -.1229332 .0686787 -1.79 0.074 -.257623 .0117565 south | -.0270683 .0603186 -0.45 0.654 -.1453626 .0912259 west | -.048422 .0656441 -0.74 0.461 -.1771604 .0803163 age1 | .0478043 .1037241 0.46 0.645 -.1556149 .2512235 age2 | .0581052 .0678145 0.86 0.392 -.0748897 .1911001 age3 | .0554398 .0677336 0.82 0.413 -.0773964 .188276 age4 | .0267632 .0655902 0.41 0.683 -.1018694 .1553958 anychildren | .0940054 .0484718 1.94 0.053 -.0010554 .1890661 loghhinc | -.0266598 .0288938 -0.92 0.356 -.083325 .0300055 associatemore | .1015891 .0476979 2.13 0.033 .0080459 .1951322 fulltime | .0090151 .0654756 0.14 0.891 -.1193927 .137423 parttime | -.1014771 .0838587 -1.21 0.226 -.2659372 .062983 selfemp | .0372718 .0988473 0.38 0.706 -.1565833 .2311269 unemployed | .0970394 .0974852 1.00 0.320 -.0941443 .2882232 student | .3350384 .129311 2.59 0.010 .0814392 .5886376 _cons | -.0332594 .3314474 -0.10 0.920 -.6832798 .6167609 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[4, 1] = r(p) . reg UKtool T1 $controls [pweight=pweight], vce(r) (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 7.10 Prob > F = 0.0000 R-squared = 0.1242 Root MSE = .96536 ------------------------------------------------------------------------------- | Robust UKtool | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0976392 .0627325 1.56 0.120 -.0254636 .2207421 wave | 0 (omitted) gender | .3101848 .0625144 4.96 0.000 .18751 .4328596 prior | -.0036998 .0017424 -2.12 0.034 -.007119 -.0002807 democrat | .5962684 .0739746 8.06 0.000 .4511045 .7414322 indep | .2463499 .0943696 2.61 0.009 .061164 .4315357 otherpol | .2376436 .2738113 0.87 0.386 -.299669 .7749563 midwest | -.1507862 .101069 -1.49 0.136 -.3491186 .0475462 south | -.0618433 .086354 -0.72 0.474 -.2312998 .1076132 west | -.1117538 .097955 -1.14 0.254 -.3039755 .0804679 age1 | -.0525978 .1305981 -0.40 0.687 -.3088765 .2036809 age2 | -.0586592 .1048296 -0.56 0.576 -.2643711 .1470527 age3 | -.0298531 .0985224 -0.30 0.762 -.2231882 .163482 age4 | .086665 .0958668 0.90 0.366 -.1014588 .2747888 anychildren | -.0297168 .070903 -0.42 0.675 -.1688532 .1094195 loghhinc | .057605 .0460921 1.25 0.212 -.0328436 .1480537 associatemore | -.0051252 .0710553 -0.07 0.943 -.1445604 .13431 fulltime | .0581511 .0943182 0.62 0.538 -.126934 .2432361 parttime | .0347192 .1274041 0.27 0.785 -.2152917 .2847301 selfemp | .2747962 .1322147 2.08 0.038 .0153452 .5342472 unemployed | .1769462 .1682875 1.05 0.293 -.1532921 .5071845 student | .2265534 .166965 1.36 0.175 -.1010897 .5541965 _cons | -.811541 .5433414 -1.49 0.136 -1.877765 .254683 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[5, 1] = r(p) . reg childcare T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 16.85 Prob > F = 0.0000 R-squared = 0.1142 Root MSE = .94481 ------------------------------------------------------------------------------- | Robust childcare | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0026486 .0346768 0.08 0.939 -.0653441 .0706412 wave | -.099568 .0374546 -2.66 0.008 -.1730073 -.0261287 gender | .1118256 .0361868 3.09 0.002 .0408722 .1827789 prior | -.0038869 .0009617 -4.04 0.000 -.0057725 -.0020014 democrat | .5781269 .0401769 14.39 0.000 .4993498 .6569039 indep | .1065453 .0524179 2.03 0.042 .0037668 .2093238 otherpol | .2258622 .1341441 1.68 0.092 -.0371613 .4888857 midwest | -.0919717 .0562669 -1.63 0.102 -.2022972 .0183539 south | .0059153 .0502006 0.12 0.906 -.0925156 .1043462 west | -.146718 .0564412 -2.60 0.009 -.2573853 -.0360507 age1 | .2577494 .0806588 3.20 0.001 .0995974 .4159014 age2 | .3202499 .0565913 5.66 0.000 .2092884 .4312114 age3 | .2364555 .0564334 4.19 0.000 .1258036 .3471074 age4 | .0964648 .0541442 1.78 0.075 -.0096985 .2026282 anychildren | .2043124 .0388232 5.26 0.000 .1281896 .2804352 loghhinc | -.0321861 .0244382 -1.32 0.188 -.0801032 .0157311 associatemore | -.0264179 .0383545 -0.69 0.491 -.1016215 .0487858 fulltime | -.0064537 .055003 -0.12 0.907 -.1143011 .1013937 parttime | .0186614 .0715453 0.26 0.794 -.1216214 .1589441 selfemp | .0714849 .0774456 0.92 0.356 -.0803668 .2233366 unemployed | .0308187 .0857939 0.36 0.719 -.137402 .1990394 student | .0482532 .1013599 0.48 0.634 -.1504884 .2469948 _cons | .217017 .2832473 0.77 0.444 -.338361 .772395 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[6, 1] = r(p) . . . minq P, q("Q") step(0.001) number of observations will be reset to 6 Press any key to continue, or Break to abort number of observations (_N) was 0, now 6 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = 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.1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 Correction with q = .0069999999999991 Correction with q = .0059999999999991 Correction with q = .0049999999999991 Correction with q = .0039999999999991 Correction with q = .0029999999999991 (6 real changes made) (0 real changes made) . . estadd loc thisstat5 = "[" + string(Q[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat5 = "[" +string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat5 = "[" + string(Q[3, 1], "%9.3f")+"]": col3 . estadd loc thisstat5 = "["+ string(Q[4, 1], "%9.3f")+"]": col4 . estadd loc thisstat5 = "["+ string(Q[5, 1], "%9.3f")+"]": col5 . estadd loc thisstat5 = "["+ string(Q[6, 1], "%9.3f")+"]": col6 . . . loc rowlabels " "\multicolumn{2}{l}{{\bf Panel B: Reduced Form}}" " " "T$^{74}$" " " "Sharpened q- > value" " " "Observations" "\hline \multicolumn{2}{l}{{\bf Panel C: 2SLS}}" " " "$\widehat{\text{Po > sterior belief about}}$" "fem. rel. wage (z-scored)" " " "Observations" " . loc rowstats "" . . forval i = 1/13 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . esttab * using "$output\tab_treatment_main_PanelBC.tex", replace cells(none) booktabs nonotes nomt > itles compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) > /// > mgroups("\shortstack{Introduce\\gender\\quotas}" "\shortstack{Statutory\\affirmative\\acti > on}" /// > "\shortstack{Stricter\\equal pay\\legislation}" "\shortstack{Wage transp.\\within\\ companies}" > "\shortstack{Introduce\\reporting\\website}" "\shortstack{Increase\\subsidies\\to child care}" /// > "\shortstack{Policy\\demand\\index}", pattern(1 1 1 1 1 1 1 ) prefix(\multicolumn{@span}{c}{) s > uffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\tab_treatment_main_PanelBC.tex) . . . . *********************************************************************************** . // Table 6: Persistence of the treatment effect in obfuscated follow-up survey . *********************************************************************************** . . // Note: Code produces one table for Panel A and another table for Panel B. Stack manually to obta > in final table. . . clear all . . use "$path\data\SurveyStageIIAB_final.dta" . . drop if rand==0 (0 observations deleted) . . . // Build Panel A . . loc experimentsII "posteriorII zposteriorII problemII govmoreII problemIIHS problemIILS z_maniII_i > ndex2" . . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experimentsII' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experimentsII' { 2. . ***Panel A . . reg `choice' T1 $controls [pweight=pweight], vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 7. sigstar gender, prec(3) 8. estadd loc thisstat7 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat8 = "`r(sestar)'": col`colnum' 10. sigstar democrat, prec(3) 11. estadd loc thisstat10 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat11 = "`r(sestar)'": col`colnum' 13. . estadd loc thisstat13 = "`n'": col`colnum' 14. . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. . } (sum of wgt is 1.0757e+03) Linear regression Number of obs = 1,089 F(22, 1066) = 7.81 Prob > F = 0.0000 R-squared = 0.1848 Root MSE = 19.273 ------------------------------------------------------------------------------- | Robust posteriorII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -10.66813 1.177305 -9.06 0.000 -12.97823 -8.358033 wave | 2.565484 1.19466 2.15 0.032 .2213316 4.909636 gender | -2.291954 1.247802 -1.84 0.067 -4.740382 .1564731 prior | .2598492 .0467084 5.56 0.000 .1681983 .3515001 democrat | .5542626 1.318671 0.42 0.674 -2.033222 3.141748 indep | 1.432808 1.77421 0.81 0.420 -2.048532 4.914149 otherpol | -5.537553 4.53255 -1.22 0.222 -14.43129 3.35618 midwest | .2694683 1.890907 0.14 0.887 -3.440855 3.979791 south | .7499282 1.537377 0.49 0.626 -2.2667 3.766556 west | 1.718348 1.595265 1.08 0.282 -1.411868 4.848564 age1 | -.4316977 2.585749 -0.17 0.867 -5.505433 4.642037 age2 | 3.403712 1.954737 1.74 0.082 -.431857 7.239281 age3 | 1.520966 1.820882 0.84 0.404 -2.051953 5.093885 age4 | 1.434861 1.431952 1.00 0.317 -1.374904 4.244626 anychildren | 1.937104 1.327437 1.46 0.145 -.6675814 4.541789 loghhinc | 1.335197 .7920496 1.69 0.092 -.2189566 2.88935 associatemore | -.179255 1.264446 -0.14 0.887 -2.66034 2.30183 fulltime | 2.885366 1.52653 1.89 0.059 -.1099785 5.88071 parttime | 1.822827 2.047744 0.89 0.374 -2.19524 5.840894 selfemp | 4.592572 2.331022 1.97 0.049 .0186591 9.166484 unemployed | 1.432388 2.408224 0.59 0.552 -3.293009 6.157785 student | -.0900716 3.462428 -0.03 0.979 -6.88402 6.703877 _cons | 45.56059 10.08021 4.52 0.000 25.78128 65.33991 ------------------------------------------------------------------------------- (sum of wgt is 1.0757e+03) Linear regression Number of obs = 1,089 F(22, 1066) = 7.81 Prob > F = 0.0000 R-squared = 0.1848 Root MSE = .90958 ------------------------------------------------------------------------------- | Robust zposteriorII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.5034716 .0555617 -9.06 0.000 -.6124944 -.3944489 wave | .1210754 .0563808 2.15 0.032 .0104455 .2317053 gender | -.1081665 .0588888 -1.84 0.067 -.2237175 .0073846 prior | .0122633 .0022044 5.56 0.000 .0079379 .0165887 democrat | .0261579 .0622333 0.42 0.674 -.0959559 .1482716 indep | .0676199 .083732 0.81 0.420 -.0966784 .2319183 otherpol | -.2613392 .2139091 -1.22 0.222 -.6810698 .1583915 midwest | .0127173 .0892394 0.14 0.887 -.1623876 .1878222 south | .0353921 .0725549 0.49 0.626 -.1069746 .1777588 west | .0810957 .0752869 1.08 0.282 -.0666317 .228823 age1 | -.0203735 .1220318 -0.17 0.867 -.2598233 .2190762 age2 | .1606347 .0922518 1.74 0.082 -.0203811 .3416505 age3 | .0717804 .0859347 0.84 0.404 -.0968398 .2404007 age4 | .0677168 .0675795 1.00 0.317 -.0648872 .2003208 anychildren | .0914197 .062647 1.46 0.145 -.0315058 .2143451 loghhinc | .0630132 .03738 1.69 0.092 -.0103334 .1363599 associatemore | -.0084598 .0596742 -0.14 0.887 -.125552 .1086325 fulltime | .1361719 .072043 1.89 0.059 -.0051903 .2775341 parttime | .0860265 .0966412 0.89 0.374 -.1036021 .2756551 selfemp | .2167418 .1100102 1.97 0.049 .0008806 .4326029 unemployed | .0676001 .1136537 0.59 0.552 -.1554102 .2906104 student | -.0042508 .1634058 -0.03 0.979 -.3248843 .3163826 _cons | -1.848995 .4757254 -3.89 0.000 -2.78246 -.9155304 ------------------------------------------------------------------------------- (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 7.43 Prob > F = 0.0000 R-squared = 0.1233 Root MSE = .94732 ------------------------------------------------------------------------------- | Robust problemII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1862455 .0574768 3.24 0.001 .0734668 .2990241 wave | -.1162432 .0605539 -1.92 0.055 -.2350596 .0025732 gender | .2715331 .0600688 4.52 0.000 .1536685 .3893977 prior | -.0034779 .0014995 -2.32 0.021 -.0064201 -.0005357 democrat | .5466579 .0647114 8.45 0.000 .4196838 .6736319 indep | .1757318 .0837438 2.10 0.036 .0114132 .3400504 otherpol | .4599919 .2244909 2.05 0.041 .019505 .9004787 midwest | -.0773175 .0903467 -0.86 0.392 -.2545921 .0999571 south | -.044038 .084007 -0.52 0.600 -.2088731 .120797 west | -.0335816 .0891099 -0.38 0.706 -.2084294 .1412662 age1 | -.1611792 .1601315 -1.01 0.314 -.4753826 .1530243 age2 | -.0147312 .0881425 -0.17 0.867 -.1876808 .1582184 age3 | .0392122 .0877694 0.45 0.655 -.1330053 .2114297 age4 | -.1247621 .0786027 -1.59 0.113 -.2789931 .0294688 anychildren | .003066 .0619042 0.05 0.961 -.1183999 .124532 loghhinc | -.0384099 .044325 -0.87 0.386 -.1253825 .0485627 associatemore | .1244386 .0629237 1.98 0.048 .0009724 .2479049 fulltime | -.053542 .0815721 -0.66 0.512 -.2135994 .1065154 parttime | -.2879121 .1111861 -2.59 0.010 -.5060769 -.0697473 selfemp | -.0957633 .1236903 -0.77 0.439 -.3384633 .1469368 unemployed | .0793364 .1366261 0.58 0.562 -.1887457 .3474184 student | .3973115 .2587663 1.54 0.125 -.1104291 .9050521 _cons | .4357183 .5056997 0.86 0.389 -.5565449 1.427982 ------------------------------------------------------------------------------- (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 8.64 Prob > F = 0.0000 R-squared = 0.1558 Root MSE = .93015 ------------------------------------------------------------------------------- | Robust govmoreII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1828748 .0565973 3.23 0.001 .071822 .2939276 wave | -.1903932 .0604385 -3.15 0.002 -.3089831 -.0718033 gender | .1737051 .058018 2.99 0.003 .0598645 .2875456 prior | -.0034117 .0014704 -2.32 0.021 -.0062969 -.0005264 democrat | .6861387 .0630777 10.88 0.000 .5623703 .8099072 indep | .2210611 .0873482 2.53 0.012 .0496701 .392452 otherpol | .3937247 .2034672 1.94 0.053 -.0055102 .7929596 midwest | -.1477592 .0919095 -1.61 0.108 -.3281003 .0325819 south | -.0192244 .0809033 -0.24 0.812 -.1779696 .1395207 west | -.0663522 .0866272 -0.77 0.444 -.2363285 .1036242 age1 | .1584649 .1375694 1.15 0.250 -.111468 .4283979 age2 | .2111594 .0882318 2.39 0.017 .0380346 .3842841 age3 | .2439942 .0862619 2.83 0.005 .0747346 .4132538 age4 | .0010924 .0754631 0.01 0.988 -.1469783 .149163 anychildren | .0562098 .059316 0.95 0.344 -.0601777 .1725972 loghhinc | .0386549 .0395151 0.98 0.328 -.03888 .1161898 associatemore | -.0588073 .0599907 -0.98 0.327 -.1765185 .0589039 fulltime | -.0954094 .0763063 -1.25 0.211 -.2451345 .0543157 parttime | .0062921 .1023859 0.06 0.951 -.1946053 .2071894 selfemp | -.1242601 .1079909 -1.15 0.250 -.3361554 .0876351 unemployed | .0394383 .1302257 0.30 0.762 -.2160852 .2949617 student | .0092176 .2153666 0.04 0.966 -.413366 .4318012 _cons | -.3421826 .4394858 -0.78 0.436 -1.204524 .5201584 ------------------------------------------------------------------------------- (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 5.18 Prob > F = 0.0000 R-squared = 0.0948 Root MSE = .96073 ------------------------------------------------------------------------------- | Robust problemIIHS | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1242225 .0582051 2.13 0.033 .0100148 .2384303 wave | -.1215149 .0614693 -1.98 0.048 -.2421275 -.0009024 gender | .1882041 .0605107 3.11 0.002 .0694726 .3069357 prior | -.003244 .0014464 -2.24 0.025 -.0060821 -.000406 democrat | .5058463 .0661692 7.64 0.000 .3760117 .6356808 indep | .1826822 .0837734 2.18 0.029 .0183055 .3470589 otherpol | .4721287 .2185769 2.16 0.031 .0432462 .9010113 midwest | -.0441137 .0938029 -0.47 0.638 -.2281698 .1399425 south | -.0030214 .0845212 -0.04 0.971 -.1688655 .1628227 west | .0371499 .0905634 0.41 0.682 -.1405498 .2148496 age1 | .0163062 .1619461 0.10 0.920 -.3014577 .3340701 age2 | .0377367 .0908265 0.42 0.678 -.1404793 .2159527 age3 | .0404914 .0890981 0.45 0.650 -.1343333 .215316 age4 | -.0995257 .0801076 -1.24 0.214 -.2567095 .0576581 anychildren | .0552264 .0624038 0.88 0.376 -.0672198 .1776726 loghhinc | -.0113903 .0441229 -0.26 0.796 -.0979664 .0751857 associatemore | .1275981 .0637431 2.00 0.046 .0025239 .2526722 fulltime | -.10029 .0855835 -1.17 0.242 -.2682185 .0676384 parttime | -.2805653 .1139617 -2.46 0.014 -.5041764 -.0569543 selfemp | -.0183834 .1229212 -0.15 0.881 -.2595743 .2228074 unemployed | .0079603 .1359794 0.06 0.953 -.2588529 .2747735 student | .3067732 .2641769 1.16 0.246 -.2115838 .8251301 _cons | .1404287 .5019663 0.28 0.780 -.844509 1.125366 ------------------------------------------------------------------------------- (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 4.33 Prob > F = 0.0000 R-squared = 0.0757 Root MSE = .97145 ------------------------------------------------------------------------------- | Robust problemIILS | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1386613 .0584397 2.37 0.018 .0239934 .2533292 wave | -.0642286 .0622869 -1.03 0.303 -.1864455 .0579882 gender | .1974916 .0615134 3.21 0.001 .0767925 .3181906 prior | .0000495 .0014301 0.03 0.972 -.0027566 .0028557 democrat | .3924081 .0663427 5.91 0.000 .2622331 .5225831 indep | -.0153198 .0853073 -0.18 0.858 -.1827064 .1520667 otherpol | .475383 .2199372 2.16 0.031 .0438312 .9069348 midwest | -.137135 .0930387 -1.47 0.141 -.3196918 .0454217 south | -.099849 .0853163 -1.17 0.242 -.2672531 .0675551 west | -.14244 .0905101 -1.57 0.116 -.3200353 .0351553 age1 | -.1280459 .1644528 -0.78 0.436 -.4507284 .1946367 age2 | -.0299117 .0918827 -0.33 0.745 -.2102002 .1503767 age3 | .1566163 .0881445 1.78 0.076 -.0163371 .3295698 age4 | -.1471654 .0828083 -1.78 0.076 -.3096484 .0153177 anychildren | .0843655 .0640172 1.32 0.188 -.0412465 .2099774 loghhinc | -.0447743 .0439957 -1.02 0.309 -.1311009 .0415523 associatemore | .0806905 .0650248 1.24 0.215 -.0468985 .2082795 fulltime | -.0475454 .0866433 -0.55 0.583 -.2175533 .1224626 parttime | -.1452873 .110675 -1.31 0.190 -.3624492 .0718745 selfemp | -.0461344 .1249446 -0.37 0.712 -.2912955 .1990267 unemployed | .1555316 .140336 1.11 0.268 -.11983 .4308932 student | .1769654 .2482017 0.71 0.476 -.3100457 .6639765 _cons | .2840862 .4975363 0.57 0.568 -.6921591 1.260332 ------------------------------------------------------------------------------- (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 9.73 Prob > F = 0.0000 R-squared = 0.1591 Root MSE = .743 ------------------------------------------------------------------------------- | Robust z_maniII_in~2 | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1556116 .0448762 3.47 0.001 .0675573 .2436659 wave | -.1318313 .0483194 -2.73 0.006 -.2266417 -.037021 gender | .1902297 .0462945 4.11 0.000 .0993926 .2810669 prior | -.0023586 .0010642 -2.22 0.027 -.0044468 -.0002705 democrat | .5468918 .0502079 10.89 0.000 .4483759 .6454078 indep | .1394517 .0672927 2.07 0.038 .0074127 .2714907 otherpol | .4411006 .1877698 2.35 0.019 .0726665 .8095347 midwest | -.1147348 .0697722 -1.64 0.100 -.251639 .0221694 south | -.0403903 .0637858 -0.63 0.527 -.1655483 .0847676 west | -.0611032 .0673292 -0.91 0.364 -.1932138 .0710074 age1 | .0197814 .1207156 0.16 0.870 -.2170818 .2566446 age2 | .0837965 .0720284 1.16 0.245 -.0575347 .2251276 age3 | .1555828 .0681133 2.28 0.023 .0219337 .2892319 age4 | -.0749472 .0627795 -1.19 0.233 -.1981306 .0482362 anychildren | .06099 .0482766 1.26 0.207 -.0337364 .1557164 loghhinc | -.0028903 .0330975 -0.09 0.930 -.0678328 .0620522 associatemore | .0393601 .0489013 0.80 0.421 -.056592 .1353123 fulltime | -.0800192 .0665951 -1.20 0.230 -.2106894 .0506511 parttime | -.1270073 .0866612 -1.47 0.143 -.2970503 .0430356 selfemp | -.0734332 .0988364 -0.74 0.458 -.2673659 .1204996 unemployed | .0681563 .0978998 0.70 0.486 -.1239388 .2602513 student | .155627 .2086899 0.75 0.456 -.2538558 .5651097 _cons | .0082611 .3787954 0.02 0.983 -.7349957 .7515179 ------------------------------------------------------------------------------- . . . // FDR control . . mat def P = J(6, 1, .) . reg posteriorII T1 $controls [pweight=pweight], vce(r) (sum of wgt is 1.0757e+03) Linear regression Number of obs = 1,089 F(22, 1066) = 7.81 Prob > F = 0.0000 R-squared = 0.1848 Root MSE = 19.273 ------------------------------------------------------------------------------- | Robust posteriorII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -10.66813 1.177305 -9.06 0.000 -12.97823 -8.358033 wave | 2.565484 1.19466 2.15 0.032 .2213316 4.909636 gender | -2.291954 1.247802 -1.84 0.067 -4.740382 .1564731 prior | .2598492 .0467084 5.56 0.000 .1681983 .3515001 democrat | .5542626 1.318671 0.42 0.674 -2.033222 3.141748 indep | 1.432808 1.77421 0.81 0.420 -2.048532 4.914149 otherpol | -5.537553 4.53255 -1.22 0.222 -14.43129 3.35618 midwest | .2694683 1.890907 0.14 0.887 -3.440855 3.979791 south | .7499282 1.537377 0.49 0.626 -2.2667 3.766556 west | 1.718348 1.595265 1.08 0.282 -1.411868 4.848564 age1 | -.4316977 2.585749 -0.17 0.867 -5.505433 4.642037 age2 | 3.403712 1.954737 1.74 0.082 -.431857 7.239281 age3 | 1.520966 1.820882 0.84 0.404 -2.051953 5.093885 age4 | 1.434861 1.431952 1.00 0.317 -1.374904 4.244626 anychildren | 1.937104 1.327437 1.46 0.145 -.6675814 4.541789 loghhinc | 1.335197 .7920496 1.69 0.092 -.2189566 2.88935 associatemore | -.179255 1.264446 -0.14 0.887 -2.66034 2.30183 fulltime | 2.885366 1.52653 1.89 0.059 -.1099785 5.88071 parttime | 1.822827 2.047744 0.89 0.374 -2.19524 5.840894 selfemp | 4.592572 2.331022 1.97 0.049 .0186591 9.166484 unemployed | 1.432388 2.408224 0.59 0.552 -3.293009 6.157785 student | -.0900716 3.462428 -0.03 0.979 -6.88402 6.703877 _cons | 45.56059 10.08021 4.52 0.000 25.78128 65.33991 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg zposteriorII T1 $controls [pweight=pweight], vce(r) (sum of wgt is 1.0757e+03) Linear regression Number of obs = 1,089 F(22, 1066) = 7.81 Prob > F = 0.0000 R-squared = 0.1848 Root MSE = .90958 ------------------------------------------------------------------------------- | Robust zposteriorII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.5034716 .0555617 -9.06 0.000 -.6124944 -.3944489 wave | .1210754 .0563808 2.15 0.032 .0104455 .2317053 gender | -.1081665 .0588888 -1.84 0.067 -.2237175 .0073846 prior | .0122633 .0022044 5.56 0.000 .0079379 .0165887 democrat | .0261579 .0622333 0.42 0.674 -.0959559 .1482716 indep | .0676199 .083732 0.81 0.420 -.0966784 .2319183 otherpol | -.2613392 .2139091 -1.22 0.222 -.6810698 .1583915 midwest | .0127173 .0892394 0.14 0.887 -.1623876 .1878222 south | .0353921 .0725549 0.49 0.626 -.1069746 .1777588 west | .0810957 .0752869 1.08 0.282 -.0666317 .228823 age1 | -.0203735 .1220318 -0.17 0.867 -.2598233 .2190762 age2 | .1606347 .0922518 1.74 0.082 -.0203811 .3416505 age3 | .0717804 .0859347 0.84 0.404 -.0968398 .2404007 age4 | .0677168 .0675795 1.00 0.317 -.0648872 .2003208 anychildren | .0914197 .062647 1.46 0.145 -.0315058 .2143451 loghhinc | .0630132 .03738 1.69 0.092 -.0103334 .1363599 associatemore | -.0084598 .0596742 -0.14 0.887 -.125552 .1086325 fulltime | .1361719 .072043 1.89 0.059 -.0051903 .2775341 parttime | .0860265 .0966412 0.89 0.374 -.1036021 .2756551 selfemp | .2167418 .1100102 1.97 0.049 .0008806 .4326029 unemployed | .0676001 .1136537 0.59 0.552 -.1554102 .2906104 student | -.0042508 .1634058 -0.03 0.979 -.3248843 .3163826 _cons | -1.848995 .4757254 -3.89 0.000 -2.78246 -.9155304 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg problemII T1 $controls [pweight=pweight], vce(r) (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 7.43 Prob > F = 0.0000 R-squared = 0.1233 Root MSE = .94732 ------------------------------------------------------------------------------- | Robust problemII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1862455 .0574768 3.24 0.001 .0734668 .2990241 wave | -.1162432 .0605539 -1.92 0.055 -.2350596 .0025732 gender | .2715331 .0600688 4.52 0.000 .1536685 .3893977 prior | -.0034779 .0014995 -2.32 0.021 -.0064201 -.0005357 democrat | .5466579 .0647114 8.45 0.000 .4196838 .6736319 indep | .1757318 .0837438 2.10 0.036 .0114132 .3400504 otherpol | .4599919 .2244909 2.05 0.041 .019505 .9004787 midwest | -.0773175 .0903467 -0.86 0.392 -.2545921 .0999571 south | -.044038 .084007 -0.52 0.600 -.2088731 .120797 west | -.0335816 .0891099 -0.38 0.706 -.2084294 .1412662 age1 | -.1611792 .1601315 -1.01 0.314 -.4753826 .1530243 age2 | -.0147312 .0881425 -0.17 0.867 -.1876808 .1582184 age3 | .0392122 .0877694 0.45 0.655 -.1330053 .2114297 age4 | -.1247621 .0786027 -1.59 0.113 -.2789931 .0294688 anychildren | .003066 .0619042 0.05 0.961 -.1183999 .124532 loghhinc | -.0384099 .044325 -0.87 0.386 -.1253825 .0485627 associatemore | .1244386 .0629237 1.98 0.048 .0009724 .2479049 fulltime | -.053542 .0815721 -0.66 0.512 -.2135994 .1065154 parttime | -.2879121 .1111861 -2.59 0.010 -.5060769 -.0697473 selfemp | -.0957633 .1236903 -0.77 0.439 -.3384633 .1469368 unemployed | .0793364 .1366261 0.58 0.562 -.1887457 .3474184 student | .3973115 .2587663 1.54 0.125 -.1104291 .9050521 _cons | .4357183 .5056997 0.86 0.389 -.5565449 1.427982 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . reg govmoreII T1 $controls [pweight=pweight], vce(r) (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 8.64 Prob > F = 0.0000 R-squared = 0.1558 Root MSE = .93015 ------------------------------------------------------------------------------- | Robust govmoreII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1828748 .0565973 3.23 0.001 .071822 .2939276 wave | -.1903932 .0604385 -3.15 0.002 -.3089831 -.0718033 gender | .1737051 .058018 2.99 0.003 .0598645 .2875456 prior | -.0034117 .0014704 -2.32 0.021 -.0062969 -.0005264 democrat | .6861387 .0630777 10.88 0.000 .5623703 .8099072 indep | .2210611 .0873482 2.53 0.012 .0496701 .392452 otherpol | .3937247 .2034672 1.94 0.053 -.0055102 .7929596 midwest | -.1477592 .0919095 -1.61 0.108 -.3281003 .0325819 south | -.0192244 .0809033 -0.24 0.812 -.1779696 .1395207 west | -.0663522 .0866272 -0.77 0.444 -.2363285 .1036242 age1 | .1584649 .1375694 1.15 0.250 -.111468 .4283979 age2 | .2111594 .0882318 2.39 0.017 .0380346 .3842841 age3 | .2439942 .0862619 2.83 0.005 .0747346 .4132538 age4 | .0010924 .0754631 0.01 0.988 -.1469783 .149163 anychildren | .0562098 .059316 0.95 0.344 -.0601777 .1725972 loghhinc | .0386549 .0395151 0.98 0.328 -.03888 .1161898 associatemore | -.0588073 .0599907 -0.98 0.327 -.1765185 .0589039 fulltime | -.0954094 .0763063 -1.25 0.211 -.2451345 .0543157 parttime | .0062921 .1023859 0.06 0.951 -.1946053 .2071894 selfemp | -.1242601 .1079909 -1.15 0.250 -.3361554 .0876351 unemployed | .0394383 .1302257 0.30 0.762 -.2160852 .2949617 student | .0092176 .2153666 0.04 0.966 -.413366 .4318012 _cons | -.3421826 .4394858 -0.78 0.436 -1.204524 .5201584 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[4, 1] = r(p) . reg problemIIHS T1 $controls [pweight=pweight], vce(r) (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 5.18 Prob > F = 0.0000 R-squared = 0.0948 Root MSE = .96073 ------------------------------------------------------------------------------- | Robust problemIIHS | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1242225 .0582051 2.13 0.033 .0100148 .2384303 wave | -.1215149 .0614693 -1.98 0.048 -.2421275 -.0009024 gender | .1882041 .0605107 3.11 0.002 .0694726 .3069357 prior | -.003244 .0014464 -2.24 0.025 -.0060821 -.000406 democrat | .5058463 .0661692 7.64 0.000 .3760117 .6356808 indep | .1826822 .0837734 2.18 0.029 .0183055 .3470589 otherpol | .4721287 .2185769 2.16 0.031 .0432462 .9010113 midwest | -.0441137 .0938029 -0.47 0.638 -.2281698 .1399425 south | -.0030214 .0845212 -0.04 0.971 -.1688655 .1628227 west | .0371499 .0905634 0.41 0.682 -.1405498 .2148496 age1 | .0163062 .1619461 0.10 0.920 -.3014577 .3340701 age2 | .0377367 .0908265 0.42 0.678 -.1404793 .2159527 age3 | .0404914 .0890981 0.45 0.650 -.1343333 .215316 age4 | -.0995257 .0801076 -1.24 0.214 -.2567095 .0576581 anychildren | .0552264 .0624038 0.88 0.376 -.0672198 .1776726 loghhinc | -.0113903 .0441229 -0.26 0.796 -.0979664 .0751857 associatemore | .1275981 .0637431 2.00 0.046 .0025239 .2526722 fulltime | -.10029 .0855835 -1.17 0.242 -.2682185 .0676384 parttime | -.2805653 .1139617 -2.46 0.014 -.5041764 -.0569543 selfemp | -.0183834 .1229212 -0.15 0.881 -.2595743 .2228074 unemployed | .0079603 .1359794 0.06 0.953 -.2588529 .2747735 student | .3067732 .2641769 1.16 0.246 -.2115838 .8251301 _cons | .1404287 .5019663 0.28 0.780 -.844509 1.125366 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[5, 1] = r(p) . reg problemIILS T1 $controls [pweight=pweight], vce(r) (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 4.33 Prob > F = 0.0000 R-squared = 0.0757 Root MSE = .97145 ------------------------------------------------------------------------------- | Robust problemIILS | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1386613 .0584397 2.37 0.018 .0239934 .2533292 wave | -.0642286 .0622869 -1.03 0.303 -.1864455 .0579882 gender | .1974916 .0615134 3.21 0.001 .0767925 .3181906 prior | .0000495 .0014301 0.03 0.972 -.0027566 .0028557 democrat | .3924081 .0663427 5.91 0.000 .2622331 .5225831 indep | -.0153198 .0853073 -0.18 0.858 -.1827064 .1520667 otherpol | .475383 .2199372 2.16 0.031 .0438312 .9069348 midwest | -.137135 .0930387 -1.47 0.141 -.3196918 .0454217 south | -.099849 .0853163 -1.17 0.242 -.2672531 .0675551 west | -.14244 .0905101 -1.57 0.116 -.3200353 .0351553 age1 | -.1280459 .1644528 -0.78 0.436 -.4507284 .1946367 age2 | -.0299117 .0918827 -0.33 0.745 -.2102002 .1503767 age3 | .1566163 .0881445 1.78 0.076 -.0163371 .3295698 age4 | -.1471654 .0828083 -1.78 0.076 -.3096484 .0153177 anychildren | .0843655 .0640172 1.32 0.188 -.0412465 .2099774 loghhinc | -.0447743 .0439957 -1.02 0.309 -.1311009 .0415523 associatemore | .0806905 .0650248 1.24 0.215 -.0468985 .2082795 fulltime | -.0475454 .0866433 -0.55 0.583 -.2175533 .1224626 parttime | -.1452873 .110675 -1.31 0.190 -.3624492 .0718745 selfemp | -.0461344 .1249446 -0.37 0.712 -.2912955 .1990267 unemployed | .1555316 .140336 1.11 0.268 -.11983 .4308932 student | .1769654 .2482017 0.71 0.476 -.3100457 .6639765 _cons | .2840862 .4975363 0.57 0.568 -.6921591 1.260332 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[6, 1] = r(p) . . minq P, q("Q") step(0.001) number of observations will be reset to 6 Press any key to continue, or Break to abort number of observations (_N) was 0, now 6 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = .7109999999999997 Correction with q = .7099999999999997 Correction with q = .7089999999999997 Correction with q = .7079999999999997 Correction with q = .7069999999999997 Correction with q = .7059999999999997 Correction with q = .7049999999999997 Correction with q = .7039999999999997 Correction with q = .7029999999999997 Correction with q = .7019999999999997 Correction with q = .7009999999999997 Correction with q = .6999999999999997 Correction with q = .6989999999999997 Correction with q = .6979999999999997 Correction with q = .6969999999999997 Correction with q = .6959999999999997 Correction with q = .6949999999999997 Correction with q = .6939999999999997 Correction with q = .6929999999999997 Correction with q = .6919999999999997 Correction with q = .6909999999999997 Correction with q = .6899999999999997 Correction with q = .6889999999999997 Correction with q = .6879999999999997 Correction with q = .6869999999999997 Correction with q = .6859999999999997 Correction with q = .6849999999999997 Correction with q = .6839999999999997 Correction with q = .6829999999999997 Correction with q = .6819999999999997 Correction with q = .6809999999999997 Correction with q = .6799999999999997 Correction with q = .6789999999999997 Correction with q = .6779999999999997 Correction with q = .6769999999999997 Correction with q = .6759999999999997 Correction with q = .6749999999999997 Correction with q = .6739999999999997 Correction with q = .6729999999999997 Correction with q = .6719999999999997 Correction with q = .6709999999999997 Correction with q = .6699999999999997 Correction with q = .6689999999999997 Correction with q = .6679999999999997 Correction with q = .6669999999999997 Correction with q = .6659999999999997 Correction with q = .6649999999999997 Correction with q = .6639999999999997 Correction with q = .6629999999999997 Correction with q = .6619999999999997 Correction with q = .6609999999999997 Correction with q = .6599999999999997 Correction with q = .6589999999999997 Correction with q = .6579999999999997 Correction with q = .6569999999999997 Correction with q = .6559999999999997 Correction with q = .6549999999999997 Correction with q = .6539999999999997 Correction with q = .6529999999999997 Correction with q = .6519999999999997 Correction with q = .6509999999999997 Correction with q = .6499999999999997 Correction with q = .6489999999999997 Correction with q = .6479999999999997 Correction with q = .6469999999999997 Correction with q = .6459999999999997 Correction with q = .6449999999999997 Correction with q = .6439999999999997 Correction with q = .6429999999999997 Correction with q = .6419999999999997 Correction with q = .6409999999999997 Correction with q = .6399999999999997 Correction with q = .6389999999999997 Correction with q = .6379999999999997 Correction with q = .6369999999999997 Correction with q = .6359999999999997 Correction with q = .6349999999999997 Correction with q = .6339999999999997 Correction with q = .6329999999999997 Correction with q = .6319999999999997 Correction with q = .6309999999999997 Correction with q = .6299999999999997 Correction with q = .6289999999999997 Correction with q = .6279999999999997 Correction with q = .6269999999999997 Correction with q = .6259999999999997 Correction with q = .6249999999999997 Correction with q = .6239999999999997 Correction with q = .6229999999999997 Correction with q = .6219999999999997 Correction with q = .6209999999999997 Correction with q = .6199999999999997 Correction with q = .6189999999999997 Correction with q = .6179999999999997 Correction with q = .6169999999999997 Correction with q = .6159999999999997 Correction with q = .6149999999999997 Correction with q = .6139999999999997 Correction with q = .6129999999999997 Correction with q = .6119999999999997 Correction with q = .6109999999999997 Correction with q = .6099999999999997 Correction with q = .6089999999999997 Correction with q = .6079999999999997 Correction with q = .6069999999999997 Correction with q = .6059999999999997 Correction with q = .6049999999999997 Correction with q = .6039999999999997 Correction with q = .6029999999999997 Correction with q = .6019999999999997 Correction with q = .6009999999999997 Correction with q = .5999999999999996 Correction with q = .5989999999999996 Correction with q = .5979999999999996 Correction with q = .5969999999999996 Correction with q = .5959999999999996 Correction with q = .5949999999999996 Correction with q = .5939999999999996 Correction with q = .5929999999999996 Correction with q = .5919999999999996 Correction with q = .5909999999999996 Correction with q = .5899999999999996 Correction with q = .5889999999999996 Correction with q = .5879999999999996 Correction with q = .5869999999999996 Correction with q = .5859999999999996 Correction with q = .5849999999999996 Correction with q = .5839999999999996 Correction with q = .5829999999999996 Correction with q = .5819999999999996 Correction with q = .5809999999999996 Correction with q = .5799999999999996 Correction with q = .5789999999999996 Correction with q = .5779999999999996 Correction with q = .5769999999999996 Correction with q = .5759999999999996 Correction with q = .5749999999999996 Correction with q = .5739999999999996 Correction with q = .5729999999999996 Correction with q = .5719999999999996 Correction with q = .5709999999999996 Correction with q = .5699999999999996 Correction with q = .5689999999999996 Correction with q = .5679999999999996 Correction with q = .5669999999999996 Correction with q = .5659999999999996 Correction with q = .5649999999999996 Correction with q = .5639999999999996 Correction with q = .5629999999999996 Correction with q = .5619999999999996 Correction with q = .5609999999999996 Correction with q = .5599999999999996 Correction with q = .5589999999999996 Correction with q = .5579999999999996 Correction with q = .5569999999999996 Correction with q = .5559999999999996 Correction with q = .5549999999999996 Correction with q = .5539999999999996 Correction with q = .5529999999999996 Correction with q = .5519999999999996 Correction with q = .5509999999999996 Correction with q = .5499999999999996 Correction with q = .5489999999999996 Correction with q = .5479999999999996 Correction with q = .5469999999999996 Correction with q = .5459999999999996 Correction with q = .5449999999999996 Correction with q = .5439999999999996 Correction with q = .5429999999999996 Correction with q = .5419999999999996 Correction with q = .5409999999999996 Correction with q = .5399999999999996 Correction with q = .5389999999999996 Correction with q = .5379999999999996 Correction with q = .5369999999999996 Correction with q = .5359999999999996 Correction with q = .5349999999999996 Correction with q = .5339999999999996 Correction with q = .5329999999999996 Correction with q = .5319999999999996 Correction with q = .5309999999999996 Correction with q = .5299999999999996 Correction with q = .5289999999999996 Correction with q = .5279999999999996 Correction with q = .5269999999999996 Correction with q = .5259999999999996 Correction with q = .5249999999999996 Correction with q = .5239999999999996 Correction with q = .5229999999999996 Correction with q = .5219999999999996 Correction with q = .5209999999999996 Correction with q = .5199999999999996 Correction with q = .5189999999999996 Correction with q = .5179999999999996 Correction with q = .5169999999999996 Correction with q = .5159999999999996 Correction with q = .5149999999999996 Correction with q = .5139999999999996 Correction with q = .5129999999999996 Correction with q = .5119999999999996 Correction with q = .5109999999999996 Correction with q = .5099999999999996 Correction with q = .5089999999999996 Correction with q = .5079999999999996 Correction with q = .5069999999999996 Correction with q = .5059999999999996 Correction with q = .5049999999999996 Correction with q = .5039999999999996 Correction with q = .5029999999999996 Correction with q = .5019999999999996 Correction with q = .5009999999999996 Correction with q = .4999999999999996 Correction with q = .4989999999999996 Correction with q = .4979999999999996 Correction with q = .4969999999999996 Correction with q = .4959999999999996 Correction with q = .4949999999999996 Correction with q = .4939999999999996 Correction with q = .4929999999999996 Correction with q = .4919999999999996 Correction with q = .4909999999999996 Correction with q = .4899999999999996 Correction with q = .4889999999999996 Correction with q = .4879999999999996 Correction with q = .4869999999999995 Correction with q = .4859999999999995 Correction with q = .4849999999999995 Correction with q = .4839999999999995 Correction with q = .4829999999999995 Correction with q = .4819999999999995 Correction with q = .4809999999999995 Correction with q = .4799999999999995 Correction with q = .4789999999999995 Correction with q = .4779999999999995 Correction with q = .4769999999999995 Correction with q = .4759999999999995 Correction with q = .4749999999999995 Correction with q = .4739999999999995 Correction with q = .4729999999999995 Correction with q = .4719999999999995 Correction with q = .4709999999999995 Correction with q = .4699999999999995 Correction with q = .4689999999999995 Correction with q = .4679999999999995 Correction with q = .4669999999999995 Correction with q = .4659999999999995 Correction with q = .4649999999999995 Correction with q = .4639999999999995 Correction with q = .4629999999999995 Correction with q = .4619999999999995 Correction with q = .4609999999999995 Correction with q = .4599999999999995 Correction with q = .4589999999999995 Correction with q = .4579999999999995 Correction with q = .4569999999999995 Correction with q = .4559999999999995 Correction with q = .4549999999999995 Correction with q = .4539999999999995 Correction with q = .4529999999999995 Correction with q = .4519999999999995 Correction with q = .4509999999999995 Correction with q = .4499999999999995 Correction with q = .4489999999999995 Correction with q = .4479999999999995 Correction with q = .4469999999999995 Correction with q = .4459999999999995 Correction with q = .4449999999999995 Correction with q = .4439999999999995 Correction with q = .4429999999999995 Correction with q = .4419999999999995 Correction with q = .4409999999999995 Correction with q = .4399999999999995 Correction with q = .4389999999999995 Correction with q = .4379999999999995 Correction with q = .4369999999999995 Correction with q = .4359999999999995 Correction with q = .4349999999999995 Correction with q = .4339999999999995 Correction with q = .4329999999999995 Correction with q = .4319999999999995 Correction with q = .4309999999999995 Correction with q = .4299999999999995 Correction with q = .4289999999999995 Correction with q = .4279999999999995 Correction with q = .4269999999999995 Correction with q = .4259999999999995 Correction with q = .4249999999999995 Correction with q = .4239999999999995 Correction with q = .4229999999999995 Correction with q = .4219999999999995 Correction with q = .4209999999999995 Correction with q = .4199999999999995 Correction with q = .4189999999999995 Correction with q = .4179999999999995 Correction with q = .4169999999999995 Correction with q = .4159999999999995 Correction with q = .4149999999999995 Correction with q = .4139999999999995 Correction with q = .4129999999999995 Correction with q = .4119999999999995 Correction with q = .4109999999999995 Correction with q = .4099999999999995 Correction with q = .4089999999999995 Correction with q = .4079999999999995 Correction with q = .4069999999999995 Correction with q = .4059999999999995 Correction with q = .4049999999999995 Correction with q = .4039999999999995 Correction with q = .4029999999999995 Correction with q = .4019999999999995 Correction with q = .4009999999999995 Correction with q = .3999999999999995 Correction with q = 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.3209999999999994 Correction with q = .3199999999999994 Correction with q = .3189999999999994 Correction with q = .3179999999999994 Correction with q = .3169999999999994 Correction with q = .3159999999999994 Correction with q = .3149999999999994 Correction with q = .3139999999999994 Correction with q = .3129999999999994 Correction with q = .3119999999999994 Correction with q = .3109999999999994 Correction with q = .3099999999999994 Correction with q = .3089999999999994 Correction with q = .3079999999999994 Correction with q = .3069999999999994 Correction with q = .3059999999999994 Correction with q = .3049999999999994 Correction with q = .3039999999999994 Correction with q = .3029999999999994 Correction with q = .3019999999999994 Correction with q = .3009999999999994 Correction with q = .2999999999999994 Correction with q = .2989999999999994 Correction with q = .2979999999999994 Correction with q = .2969999999999994 Correction with q = .2959999999999994 Correction with q = 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.2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = 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.1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 Correction with q = .0069999999999991 Correction with q = .0059999999999991 Correction with q = .0049999999999991 Correction with q = .0039999999999991 Correction with q = .0029999999999991 Correction with q = .0019999999999991 (6 real changes made) (0 real changes made) . . estadd loc thisstat5 = "[" + string(Q[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat5 = "[" +string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat5 = "[" + string(Q[3, 1], "%9.3f")+"]": col3 . estadd loc thisstat5 = "["+ string(Q[4, 1], "%9.3f")+"]": col4 . estadd loc thisstat5 = "["+ string(Q[5, 1], "%9.3f")+"]": col5 . estadd loc thisstat5 = "["+ string(Q[6, 1], "%9.3f")+"]": col6 . . . . loc rowlabels " "{\bf Panel A: Perceptions}" " " "T$^{74}$" " " "Sharpened q-value" " " "Female" " > " " " "Democrat" " " " " "Observations" " . loc rowstats "" . . forval i = 1/13 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . . esttab * using "$output\StageIIABmain_PanelA.tex", replace cells(none) booktabs nonotes compress a > lignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mtitle("\shortstack{Post. belief \\about fem. \\rel. wage\\(0-200)}" "\shortstack{Post. belief\\ > about fem.\\ rel. wage\\(z-scored)}" "\shortstack{Gender diff.\\ in wages\\are a \\problem}" /// > "\shortstack{Government\\should mitigate \\ gender \\ wage gap}" "\shortstack{Gender diff. in\\wa > ges are a\\problem among\\high-skilled}" /// > "\shortstack{Gender diff. in\\wages are a\\problem among\\low-skilled}" "\shortstack{Perception\ > \Index\\((3)-(6))}") /// > mgroups("Re-elicited Outcomes" "Newly elicited Outcomes" "Summary Index", pattern(1 0 0 0 1 0 > 1) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\StageIIABmain_PanelA.tex) . . eststo clear . . . . // Build Panel B . . loc experimentsIII "AAanchorII legislationanchorII fampolII antidiscII z_lmpolicyII_specific_index > z_lmpolicyII_types_index fairII" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experimentsIII' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experimentsIII' { 2. . ***Panel A . . reg `choice' T1 $controls [pweight=pweight], vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 7. sigstar gender, prec(3) 8. estadd loc thisstat7 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat8 = "`r(sestar)'": col`colnum' 10. sigstar democrat, prec(3) 11. estadd loc thisstat10 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat11 = "`r(sestar)'": col`colnum' 13. . estadd loc thisstat13 = "`n'": col`colnum' 14. . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. . } (sum of wgt is 5.9260e+02) note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 3.88 Prob > F = 0.0000 R-squared = 0.1250 Root MSE = .95179 ------------------------------------------------------------------------------- | Robust AAanchorII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0089001 .0781448 0.11 0.909 -.144579 .1623792 wave | 0 (omitted) gender | .1498878 .0800418 1.87 0.062 -.0073171 .3070927 prior | -.0013343 .002229 -0.60 0.550 -.0057121 .0030434 democrat | .5830079 .0914901 6.37 0.000 .4033182 .7626976 indep | .1785287 .1089496 1.64 0.102 -.035452 .3925094 otherpol | .2292134 .2976949 0.77 0.442 -.3554697 .8138965 midwest | -.1344534 .1239589 -1.08 0.279 -.377913 .1090062 south | -.1062846 .1128784 -0.94 0.347 -.3279818 .1154125 west | -.1333294 .1118277 -1.19 0.234 -.3529629 .086304 age1 | .4890332 .2283838 2.14 0.033 .0404797 .9375868 age2 | .3803372 .1289624 2.95 0.003 .1270506 .6336238 age3 | .3637203 .1149816 3.16 0.002 .1378924 .5895481 age4 | .2137002 .1026468 2.08 0.038 .0120984 .4153021 anychildren | .1036842 .0833331 1.24 0.214 -.0599849 .2673533 loghhinc | -.0174578 .0610393 -0.29 0.775 -.1373411 .1024255 associatemore | -.0634021 .0866087 -0.73 0.464 -.2335045 .1067003 fulltime | -.0269664 .1064979 -0.25 0.800 -.236132 .1821992 parttime | .182043 .1461773 1.25 0.213 -.1050541 .4691402 selfemp | .066374 .1739631 0.38 0.703 -.2752955 .4080434 unemployed | -.2786128 .2253217 -1.24 0.217 -.7211523 .1639268 student | .0381993 .2561315 0.15 0.881 -.4648518 .5412504 _cons | -.1781802 .6976993 -0.26 0.799 -1.548486 1.192125 ------------------------------------------------------------------------------- (sum of wgt is 5.9260e+02) note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 3.91 Prob > F = 0.0000 R-squared = 0.1212 Root MSE = .95714 ------------------------------------------------------------------------------- | Robust legislation~I | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0960297 .0790875 1.21 0.225 -.0593009 .2513603 wave | 0 (omitted) gender | .1974441 .0832618 2.37 0.018 .0339151 .3609731 prior | -.0000512 .0025113 -0.02 0.984 -.0049835 .0048811 democrat | .6415791 .0905637 7.08 0.000 .4637088 .8194493 indep | .0635866 .112058 0.57 0.571 -.1564991 .2836724 otherpol | .6588273 .363019 1.81 0.070 -.0541546 1.371809 midwest | -.1437006 .1226844 -1.17 0.242 -.384657 .0972559 south | -.137414 .1145276 -1.20 0.231 -.3623502 .0875222 west | -.1325884 .1141376 -1.16 0.246 -.3567586 .0915819 age1 | -.1541154 .254164 -0.61 0.545 -.6533021 .3450714 age2 | .1223774 .1269558 0.96 0.335 -.1269682 .3717229 age3 | .1643467 .1100414 1.49 0.136 -.0517784 .3804718 age4 | .0301334 .1062233 0.28 0.777 -.1784929 .2387597 anychildren | .0160008 .082434 0.19 0.846 -.1459025 .1779041 loghhinc | -.012623 .0594664 -0.21 0.832 -.1294171 .1041711 associatemore | .0322715 .0885371 0.36 0.716 -.1416185 .2061615 fulltime | -.0889138 .1071599 -0.83 0.407 -.2993794 .1215519 parttime | -.0564062 .136518 -0.41 0.680 -.3245323 .2117199 selfemp | .0515579 .1710974 0.30 0.763 -.2844832 .387599 unemployed | .1532315 .257608 0.59 0.552 -.3527195 .6591825 student | .3035541 .30002 1.01 0.312 -.2856956 .8928038 _cons | -.2258347 .6909103 -0.33 0.744 -1.582806 1.131137 ------------------------------------------------------------------------------- (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 8.10 Prob > F = 0.0000 R-squared = 0.1448 Root MSE = .93582 ------------------------------------------------------------------------------- | Robust fampolII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1518715 .0569003 2.67 0.008 .040224 .263519 wave | -.1550275 .0617998 -2.51 0.012 -.2762886 -.0337664 gender | .1882747 .0586516 3.21 0.001 .073191 .3033584 prior | -.0014721 .0014012 -1.05 0.294 -.0042214 .0012773 democrat | .6752189 .063353 10.66 0.000 .5509102 .7995276 indep | .1876026 .0864202 2.17 0.030 .0180325 .3571727 otherpol | .2982685 .2099717 1.42 0.156 -.1137293 .7102663 midwest | -.2290569 .0904649 -2.53 0.011 -.4065635 -.0515503 south | -.1490133 .0817396 -1.82 0.069 -.3093993 .0113727 west | -.1744886 .0880876 -1.98 0.048 -.3473306 -.0016467 age1 | .0346704 .1631084 0.21 0.832 -.2853741 .354715 age2 | .1555931 .090234 1.72 0.085 -.0214604 .3326466 age3 | .1744102 .0847031 2.06 0.040 .0082093 .3406111 age4 | -.011569 .0779173 -0.15 0.882 -.164455 .1413171 anychildren | .1113091 .0614318 1.81 0.070 -.0092299 .2318481 loghhinc | .0268074 .0401186 0.67 0.504 -.0519116 .1055264 associatemore | -.0516929 .0628466 -0.82 0.411 -.1750078 .0716221 fulltime | -.1518334 .0797037 -1.90 0.057 -.3082247 .0045579 parttime | -.1087152 .1191883 -0.91 0.362 -.3425816 .1251512 selfemp | -.1405241 .1067433 -1.32 0.188 -.3499714 .0689232 unemployed | .0548224 .1337368 0.41 0.682 -.2075905 .3172353 student | .2371248 .2329636 1.02 0.309 -.2199868 .6942365 _cons | -.2872076 .4460619 -0.64 0.520 -1.162452 .5880368 ------------------------------------------------------------------------------- (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 7.56 Prob > F = 0.0000 R-squared = 0.1398 Root MSE = .93796 ------------------------------------------------------------------------------- | Robust antidiscII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0941124 .0571267 1.65 0.100 -.0179792 .206204 wave | -.0692723 .0604023 -1.15 0.252 -.1877912 .0492466 gender | .2213737 .058542 3.78 0.000 .106505 .3362424 prior | -.0025304 .0013792 -1.83 0.067 -.0052367 .0001758 democrat | .678339 .0631887 10.74 0.000 .5543528 .8023252 indep | .193234 .0877949 2.20 0.028 .0209664 .3655015 otherpol | .571742 .2145504 2.66 0.008 .15076 .9927241 midwest | -.2138275 .0925094 -2.31 0.021 -.3953456 -.0323093 south | -.0841875 .0805979 -1.04 0.296 -.2423333 .0739584 west | -.1253039 .0855152 -1.47 0.143 -.2930983 .0424905 age1 | .0714831 .1595265 0.45 0.654 -.2415332 .3844993 age2 | .1367427 .0877816 1.56 0.120 -.0354988 .3089841 age3 | .1576038 .0862159 1.83 0.068 -.0115654 .326773 age4 | -.017222 .0781746 -0.22 0.826 -.170613 .1361689 anychildren | -.0250573 .0592377 -0.42 0.672 -.1412911 .0911765 loghhinc | .0942273 .0424426 2.22 0.027 .0109482 .1775063 associatemore | -.0138951 .0606256 -0.23 0.819 -.1328522 .105062 fulltime | -.0524082 .0801419 -0.65 0.513 -.2096593 .104843 parttime | .002756 .1118206 0.02 0.980 -.2166539 .2221658 selfemp | .05098 .1141037 0.45 0.655 -.1729096 .2748697 unemployed | .1937088 .1293398 1.50 0.135 -.0600763 .447494 student | .1584144 .2159776 0.73 0.463 -.2653678 .5821967 _cons | -1.12238 .4817705 -2.33 0.020 -2.06769 -.1770695 ------------------------------------------------------------------------------- (sum of wgt is 5.9260e+02) note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 4.72 Prob > F = 0.0000 R-squared = 0.1412 Root MSE = .8433 ------------------------------------------------------------------------------- | Robust z_lmp~c_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0524649 .0692534 0.76 0.449 -.0835511 .1884809 wave | 0 (omitted) gender | .173666 .071036 2.44 0.015 .0341487 .3131832 prior | -.0006928 .001994 -0.35 0.728 -.004609 .0032234 democrat | .6122935 .0773448 7.92 0.000 .4603857 .7642013 indep | .1210577 .0989518 1.22 0.222 -.0732871 .3154025 otherpol | .4440203 .3010152 1.48 0.141 -.1471839 1.035225 midwest | -.139077 .1086949 -1.28 0.201 -.3525576 .0744036 south | -.1218493 .0968042 -1.26 0.209 -.311976 .0682774 west | -.1329589 .0982536 -1.35 0.177 -.3259323 .0600145 age1 | .1674589 .1830217 0.91 0.361 -.192002 .5269199 age2 | .2513573 .112293 2.24 0.026 .0308101 .4719045 age3 | .2640335 .1004723 2.63 0.009 .0667023 .4613646 age4 | .1219168 .0929843 1.31 0.190 -.0607076 .3045412 anychildren | .0598425 .0739313 0.81 0.419 -.0853611 .2050461 loghhinc | -.0150404 .0552009 -0.27 0.785 -.123457 .0933761 associatemore | -.0155653 .0773387 -0.20 0.841 -.1674612 .1363306 fulltime | -.0579401 .0955999 -0.61 0.545 -.2457015 .1298214 parttime | .0628184 .1191614 0.53 0.598 -.1712186 .2968555 selfemp | .0589659 .1542259 0.38 0.702 -.243939 .3618709 unemployed | -.0626906 .2170543 -0.29 0.773 -.4889927 .3636114 student | .1708767 .2159806 0.79 0.429 -.2533166 .59507 _cons | -.2020074 .6273788 -0.32 0.748 -1.434201 1.030186 ------------------------------------------------------------------------------- (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 9.04 Prob > F = 0.0000 R-squared = 0.1634 Root MSE = .85096 ------------------------------------------------------------------------------- | Robust z_lmp~s_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .122992 .0518717 2.37 0.018 .0212115 .2247724 wave | -.1121499 .0555978 -2.02 0.044 -.2212417 -.0030582 gender | .2048242 .0531778 3.85 0.000 .1004808 .3091676 prior | -.0020012 .0013026 -1.54 0.125 -.0045572 .0005547 democrat | .6767789 .0573594 11.80 0.000 .5642307 .7893271 indep | .1904183 .0804139 2.37 0.018 .0326335 .348203 otherpol | .4350053 .1991749 2.18 0.029 .0441925 .825818 midwest | -.2214422 .0830502 -2.67 0.008 -.3843998 -.0584845 south | -.1166004 .0733908 -1.59 0.112 -.2606048 .027404 west | -.1498963 .0790471 -1.90 0.058 -.3049993 .0052068 age1 | .0530768 .1444572 0.37 0.713 -.2303713 .3365248 age2 | .1461679 .0804898 1.82 0.070 -.0117659 .3041016 age3 | .166007 .0782034 2.12 0.034 .0125594 .3194546 age4 | -.0143955 .0693549 -0.21 0.836 -.1504808 .1216898 anychildren | .0431259 .0547022 0.79 0.431 -.0642085 .1504603 loghhinc | .0605173 .0368268 1.64 0.101 -.0117428 .1327775 associatemore | -.032794 .0563321 -0.58 0.561 -.1433265 .0777386 fulltime | -.1021208 .0720487 -1.42 0.157 -.2434919 .0392503 parttime | -.0529796 .1024799 -0.52 0.605 -.2540615 .1481022 selfemp | -.044772 .0970356 -0.46 0.645 -.2351712 .1456271 unemployed | .1242656 .1210611 1.03 0.305 -.1132755 .3618067 student | .1977696 .2151836 0.92 0.358 -.2244548 .619994 _cons | -.7047937 .4089592 -1.72 0.085 -1.507237 .0976491 ------------------------------------------------------------------------------- (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 11.67 Prob > F = 0.0000 R-squared = 0.1914 Root MSE = .90842 ------------------------------------------------------------------------------- | Robust fairII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.1097739 .0551285 -1.99 0.047 -.2179448 -.001603 wave | .1909377 .0581807 3.28 0.001 .0767779 .3050975 gender | -.1205513 .057796 -2.09 0.037 -.2339562 -.0071464 prior | .0091594 .0014201 6.45 0.000 .006373 .0119458 democrat | -.4298205 .0645207 -6.66 0.000 -.5564203 -.3032207 indep | -.3414993 .0767006 -4.45 0.000 -.491998 -.1910006 otherpol | -.4188169 .1558718 -2.69 0.007 -.7246621 -.1129717 midwest | -.1030418 .0883223 -1.17 0.244 -.2763442 .0702607 south | .0237766 .0811438 0.29 0.770 -.1354405 .1829936 west | -.1246635 .083302 -1.50 0.135 -.2881153 .0387883 age1 | .2185058 .1303842 1.68 0.094 -.0373288 .4743403 age2 | .4699578 .0908287 5.17 0.000 .2917374 .6481781 age3 | .2277369 .0863138 2.64 0.008 .0583756 .3970982 age4 | .0665438 .0655659 1.01 0.310 -.062107 .1951946 anychildren | .1100983 .0599282 1.84 0.066 -.0074904 .2276869 loghhinc | .0407074 .0403886 1.01 0.314 -.0385415 .1199562 associatemore | .004367 .0602619 0.07 0.942 -.1138764 .1226104 fulltime | .1571716 .0763046 2.06 0.040 .0074498 .3068935 parttime | .0680314 .1018638 0.67 0.504 -.1318415 .2679042 selfemp | .1119624 .1167311 0.96 0.338 -.1170827 .3410074 unemployed | -.0410833 .1077674 -0.38 0.703 -.25254 .1703734 student | -.6765308 .1893676 -3.57 0.000 -1.0481 -.3049614 _cons | -1.409347 .4596632 -3.07 0.002 -2.311279 -.5074146 ------------------------------------------------------------------------------- . . // FDR control . . mat def P = J(2, 1, .) . reg AAanchorII T1 $controls [pweight=pweight], vce(r) (sum of wgt is 5.9260e+02) note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 3.88 Prob > F = 0.0000 R-squared = 0.1250 Root MSE = .95179 ------------------------------------------------------------------------------- | Robust AAanchorII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0089001 .0781448 0.11 0.909 -.144579 .1623792 wave | 0 (omitted) gender | .1498878 .0800418 1.87 0.062 -.0073171 .3070927 prior | -.0013343 .002229 -0.60 0.550 -.0057121 .0030434 democrat | .5830079 .0914901 6.37 0.000 .4033182 .7626976 indep | .1785287 .1089496 1.64 0.102 -.035452 .3925094 otherpol | .2292134 .2976949 0.77 0.442 -.3554697 .8138965 midwest | -.1344534 .1239589 -1.08 0.279 -.377913 .1090062 south | -.1062846 .1128784 -0.94 0.347 -.3279818 .1154125 west | -.1333294 .1118277 -1.19 0.234 -.3529629 .086304 age1 | .4890332 .2283838 2.14 0.033 .0404797 .9375868 age2 | .3803372 .1289624 2.95 0.003 .1270506 .6336238 age3 | .3637203 .1149816 3.16 0.002 .1378924 .5895481 age4 | .2137002 .1026468 2.08 0.038 .0120984 .4153021 anychildren | .1036842 .0833331 1.24 0.214 -.0599849 .2673533 loghhinc | -.0174578 .0610393 -0.29 0.775 -.1373411 .1024255 associatemore | -.0634021 .0866087 -0.73 0.464 -.2335045 .1067003 fulltime | -.0269664 .1064979 -0.25 0.800 -.236132 .1821992 parttime | .182043 .1461773 1.25 0.213 -.1050541 .4691402 selfemp | .066374 .1739631 0.38 0.703 -.2752955 .4080434 unemployed | -.2786128 .2253217 -1.24 0.217 -.7211523 .1639268 student | .0381993 .2561315 0.15 0.881 -.4648518 .5412504 _cons | -.1781802 .6976993 -0.26 0.799 -1.548486 1.192125 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg legislationanchorII T1 $controls [pweight=pweight], vce(r) (sum of wgt is 5.9260e+02) note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 3.91 Prob > F = 0.0000 R-squared = 0.1212 Root MSE = .95714 ------------------------------------------------------------------------------- | Robust legislation~I | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0960297 .0790875 1.21 0.225 -.0593009 .2513603 wave | 0 (omitted) gender | .1974441 .0832618 2.37 0.018 .0339151 .3609731 prior | -.0000512 .0025113 -0.02 0.984 -.0049835 .0048811 democrat | .6415791 .0905637 7.08 0.000 .4637088 .8194493 indep | .0635866 .112058 0.57 0.571 -.1564991 .2836724 otherpol | .6588273 .363019 1.81 0.070 -.0541546 1.371809 midwest | -.1437006 .1226844 -1.17 0.242 -.384657 .0972559 south | -.137414 .1145276 -1.20 0.231 -.3623502 .0875222 west | -.1325884 .1141376 -1.16 0.246 -.3567586 .0915819 age1 | -.1541154 .254164 -0.61 0.545 -.6533021 .3450714 age2 | .1223774 .1269558 0.96 0.335 -.1269682 .3717229 age3 | .1643467 .1100414 1.49 0.136 -.0517784 .3804718 age4 | .0301334 .1062233 0.28 0.777 -.1784929 .2387597 anychildren | .0160008 .082434 0.19 0.846 -.1459025 .1779041 loghhinc | -.012623 .0594664 -0.21 0.832 -.1294171 .1041711 associatemore | .0322715 .0885371 0.36 0.716 -.1416185 .2061615 fulltime | -.0889138 .1071599 -0.83 0.407 -.2993794 .1215519 parttime | -.0564062 .136518 -0.41 0.680 -.3245323 .2117199 selfemp | .0515579 .1710974 0.30 0.763 -.2844832 .387599 unemployed | .1532315 .257608 0.59 0.552 -.3527195 .6591825 student | .3035541 .30002 1.01 0.312 -.2856956 .8928038 _cons | -.2258347 .6909103 -0.33 0.744 -1.582806 1.131137 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . . minq P, q("Q") step(0.001) number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 (2 real changes made) (0 real changes made) . estadd loc thisstat5 = "["+ string(Q[1, 1], "%9.3f")+"]": col1 . estadd loc thisstat5 = "["+ string(Q[2, 1], "%9.3f")+"]": col2 . . mat def P = J(2, 1, .) . . reg fampolII T1 $controls [pweight=pweight], vce(r) (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 8.10 Prob > F = 0.0000 R-squared = 0.1448 Root MSE = .93582 ------------------------------------------------------------------------------- | Robust fampolII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1518715 .0569003 2.67 0.008 .040224 .263519 wave | -.1550275 .0617998 -2.51 0.012 -.2762886 -.0337664 gender | .1882747 .0586516 3.21 0.001 .073191 .3033584 prior | -.0014721 .0014012 -1.05 0.294 -.0042214 .0012773 democrat | .6752189 .063353 10.66 0.000 .5509102 .7995276 indep | .1876026 .0864202 2.17 0.030 .0180325 .3571727 otherpol | .2982685 .2099717 1.42 0.156 -.1137293 .7102663 midwest | -.2290569 .0904649 -2.53 0.011 -.4065635 -.0515503 south | -.1490133 .0817396 -1.82 0.069 -.3093993 .0113727 west | -.1744886 .0880876 -1.98 0.048 -.3473306 -.0016467 age1 | .0346704 .1631084 0.21 0.832 -.2853741 .354715 age2 | .1555931 .090234 1.72 0.085 -.0214604 .3326466 age3 | .1744102 .0847031 2.06 0.040 .0082093 .3406111 age4 | -.011569 .0779173 -0.15 0.882 -.164455 .1413171 anychildren | .1113091 .0614318 1.81 0.070 -.0092299 .2318481 loghhinc | .0268074 .0401186 0.67 0.504 -.0519116 .1055264 associatemore | -.0516929 .0628466 -0.82 0.411 -.1750078 .0716221 fulltime | -.1518334 .0797037 -1.90 0.057 -.3082247 .0045579 parttime | -.1087152 .1191883 -0.91 0.362 -.3425816 .1251512 selfemp | -.1405241 .1067433 -1.32 0.188 -.3499714 .0689232 unemployed | .0548224 .1337368 0.41 0.682 -.2075905 .3172353 student | .2371248 .2329636 1.02 0.309 -.2199868 .6942365 _cons | -.2872076 .4460619 -0.64 0.520 -1.162452 .5880368 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg antidiscII T1 $controls [pweight=pweight], vce(r) (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 7.56 Prob > F = 0.0000 R-squared = 0.1398 Root MSE = .93796 ------------------------------------------------------------------------------- | Robust antidiscII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0941124 .0571267 1.65 0.100 -.0179792 .206204 wave | -.0692723 .0604023 -1.15 0.252 -.1877912 .0492466 gender | .2213737 .058542 3.78 0.000 .106505 .3362424 prior | -.0025304 .0013792 -1.83 0.067 -.0052367 .0001758 democrat | .678339 .0631887 10.74 0.000 .5543528 .8023252 indep | .193234 .0877949 2.20 0.028 .0209664 .3655015 otherpol | .571742 .2145504 2.66 0.008 .15076 .9927241 midwest | -.2138275 .0925094 -2.31 0.021 -.3953456 -.0323093 south | -.0841875 .0805979 -1.04 0.296 -.2423333 .0739584 west | -.1253039 .0855152 -1.47 0.143 -.2930983 .0424905 age1 | .0714831 .1595265 0.45 0.654 -.2415332 .3844993 age2 | .1367427 .0877816 1.56 0.120 -.0354988 .3089841 age3 | .1576038 .0862159 1.83 0.068 -.0115654 .326773 age4 | -.017222 .0781746 -0.22 0.826 -.170613 .1361689 anychildren | -.0250573 .0592377 -0.42 0.672 -.1412911 .0911765 loghhinc | .0942273 .0424426 2.22 0.027 .0109482 .1775063 associatemore | -.0138951 .0606256 -0.23 0.819 -.1328522 .105062 fulltime | -.0524082 .0801419 -0.65 0.513 -.2096593 .104843 parttime | .002756 .1118206 0.02 0.980 -.2166539 .2221658 selfemp | .05098 .1141037 0.45 0.655 -.1729096 .2748697 unemployed | .1937088 .1293398 1.50 0.135 -.0600763 .447494 student | .1584144 .2159776 0.73 0.463 -.2653678 .5821967 _cons | -1.12238 .4817705 -2.33 0.020 -2.06769 -.1770695 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . . minq P, q("Q") step(0.001) number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = .7109999999999997 Correction with q = .7099999999999997 Correction with q = .7089999999999997 Correction with q = .7079999999999997 Correction with q = .7069999999999997 Correction with q = .7059999999999997 Correction with q = .7049999999999997 Correction with q = .7039999999999997 Correction with q = .7029999999999997 Correction with q = .7019999999999997 Correction with q = .7009999999999997 Correction with q = .6999999999999997 Correction with q = .6989999999999997 Correction with q = .6979999999999997 Correction with q = .6969999999999997 Correction with q = .6959999999999997 Correction with q = .6949999999999997 Correction with q = .6939999999999997 Correction with q = .6929999999999997 Correction with q = .6919999999999997 Correction with q = .6909999999999997 Correction with q = .6899999999999997 Correction with q = .6889999999999997 Correction with q = .6879999999999997 Correction with q = .6869999999999997 Correction with q = .6859999999999997 Correction with q = .6849999999999997 Correction with q = .6839999999999997 Correction with q = .6829999999999997 Correction with q = .6819999999999997 Correction with q = .6809999999999997 Correction with q = .6799999999999997 Correction with q = .6789999999999997 Correction with q = .6779999999999997 Correction with q = .6769999999999997 Correction with q = .6759999999999997 Correction with q = .6749999999999997 Correction with q = .6739999999999997 Correction with q = .6729999999999997 Correction with q = .6719999999999997 Correction with q = .6709999999999997 Correction with q = .6699999999999997 Correction with q = .6689999999999997 Correction with q = .6679999999999997 Correction with q = .6669999999999997 Correction with q = .6659999999999997 Correction with q = .6649999999999997 Correction with q = .6639999999999997 Correction with q = .6629999999999997 Correction with q = .6619999999999997 Correction with q = .6609999999999997 Correction with q = .6599999999999997 Correction with q = .6589999999999997 Correction with q = .6579999999999997 Correction with q = .6569999999999997 Correction with q = .6559999999999997 Correction with q = .6549999999999997 Correction with q = .6539999999999997 Correction with q = .6529999999999997 Correction with q = .6519999999999997 Correction with q = .6509999999999997 Correction with q = .6499999999999997 Correction with q = .6489999999999997 Correction with q = .6479999999999997 Correction with q = .6469999999999997 Correction with q = .6459999999999997 Correction with q = .6449999999999997 Correction with q = .6439999999999997 Correction with q = .6429999999999997 Correction with q = .6419999999999997 Correction with q = .6409999999999997 Correction with q = .6399999999999997 Correction with q = .6389999999999997 Correction with q = .6379999999999997 Correction with q = .6369999999999997 Correction with q = .6359999999999997 Correction with q = .6349999999999997 Correction with q = .6339999999999997 Correction with q = .6329999999999997 Correction with q = .6319999999999997 Correction with q = .6309999999999997 Correction with q = .6299999999999997 Correction with q = .6289999999999997 Correction with q = .6279999999999997 Correction with q = .6269999999999997 Correction with q = .6259999999999997 Correction with q = .6249999999999997 Correction with q = .6239999999999997 Correction with q = .6229999999999997 Correction with q = .6219999999999997 Correction with q = .6209999999999997 Correction with q = .6199999999999997 Correction with q = .6189999999999997 Correction with q = .6179999999999997 Correction with q = .6169999999999997 Correction with q = .6159999999999997 Correction with q = .6149999999999997 Correction with q = .6139999999999997 Correction with q = .6129999999999997 Correction with q = .6119999999999997 Correction with q = .6109999999999997 Correction with q = .6099999999999997 Correction with q = .6089999999999997 Correction with q = .6079999999999997 Correction with q = .6069999999999997 Correction with q = .6059999999999997 Correction with q = .6049999999999997 Correction with q = .6039999999999997 Correction with q = .6029999999999997 Correction with q = .6019999999999997 Correction with q = .6009999999999997 Correction with q = .5999999999999996 Correction with q = .5989999999999996 Correction with q = .5979999999999996 Correction with q = .5969999999999996 Correction with q = .5959999999999996 Correction with q = .5949999999999996 Correction with q = .5939999999999996 Correction with q = .5929999999999996 Correction with q = .5919999999999996 Correction with q = .5909999999999996 Correction with q = .5899999999999996 Correction with q = .5889999999999996 Correction with q = .5879999999999996 Correction with q = .5869999999999996 Correction with q = .5859999999999996 Correction with q = .5849999999999996 Correction with q = .5839999999999996 Correction with q = .5829999999999996 Correction with q = .5819999999999996 Correction with q = .5809999999999996 Correction with q = .5799999999999996 Correction with q = .5789999999999996 Correction with q = .5779999999999996 Correction with q = .5769999999999996 Correction with q = .5759999999999996 Correction with q = .5749999999999996 Correction with q = .5739999999999996 Correction with q = .5729999999999996 Correction with q = .5719999999999996 Correction with q = .5709999999999996 Correction with q = .5699999999999996 Correction with q = .5689999999999996 Correction with q = .5679999999999996 Correction with q = .5669999999999996 Correction with q = .5659999999999996 Correction with q = .5649999999999996 Correction with q = .5639999999999996 Correction with q = .5629999999999996 Correction with q = .5619999999999996 Correction with q = .5609999999999996 Correction with q = .5599999999999996 Correction with q = .5589999999999996 Correction with q = .5579999999999996 Correction with q = .5569999999999996 Correction with q = .5559999999999996 Correction with q = .5549999999999996 Correction with q = .5539999999999996 Correction with q = .5529999999999996 Correction with q = .5519999999999996 Correction with q = .5509999999999996 Correction with q = .5499999999999996 Correction with q = .5489999999999996 Correction with q = .5479999999999996 Correction with q = .5469999999999996 Correction with q = .5459999999999996 Correction with q = .5449999999999996 Correction with q = .5439999999999996 Correction with q = .5429999999999996 Correction with q = .5419999999999996 Correction with q = .5409999999999996 Correction with q = .5399999999999996 Correction with q = .5389999999999996 Correction with q = .5379999999999996 Correction with q = .5369999999999996 Correction with q = .5359999999999996 Correction with q = .5349999999999996 Correction with q = .5339999999999996 Correction with q = .5329999999999996 Correction with q = .5319999999999996 Correction with q = .5309999999999996 Correction with q = .5299999999999996 Correction with q = .5289999999999996 Correction with q = .5279999999999996 Correction with q = .5269999999999996 Correction with q = .5259999999999996 Correction with q = .5249999999999996 Correction with q = .5239999999999996 Correction with q = .5229999999999996 Correction with q = .5219999999999996 Correction with q = .5209999999999996 Correction with q = .5199999999999996 Correction with q = .5189999999999996 Correction with q = .5179999999999996 Correction with q = .5169999999999996 Correction with q = .5159999999999996 Correction with q = .5149999999999996 Correction with q = .5139999999999996 Correction with q = .5129999999999996 Correction with q = .5119999999999996 Correction with q = .5109999999999996 Correction with q = .5099999999999996 Correction with q = .5089999999999996 Correction with q = .5079999999999996 Correction with q = .5069999999999996 Correction with q = .5059999999999996 Correction with q = .5049999999999996 Correction with q = .5039999999999996 Correction with q = .5029999999999996 Correction with q = .5019999999999996 Correction with q = .5009999999999996 Correction with q = .4999999999999996 Correction with q = .4989999999999996 Correction with q = .4979999999999996 Correction with q = .4969999999999996 Correction with q = .4959999999999996 Correction with q = .4949999999999996 Correction with q = .4939999999999996 Correction with q = .4929999999999996 Correction with q = .4919999999999996 Correction with q = .4909999999999996 Correction with q = .4899999999999996 Correction with q = .4889999999999996 Correction with q = .4879999999999996 Correction with q = .4869999999999995 Correction with q = .4859999999999995 Correction with q = .4849999999999995 Correction with q = .4839999999999995 Correction with q = .4829999999999995 Correction with q = .4819999999999995 Correction with q = .4809999999999995 Correction with q = .4799999999999995 Correction with q = .4789999999999995 Correction with q = .4779999999999995 Correction with q = .4769999999999995 Correction with q = .4759999999999995 Correction with q = .4749999999999995 Correction with q = .4739999999999995 Correction with q = .4729999999999995 Correction with q = .4719999999999995 Correction with q = .4709999999999995 Correction with q = .4699999999999995 Correction with q = .4689999999999995 Correction with q = .4679999999999995 Correction with q = .4669999999999995 Correction with q = .4659999999999995 Correction with q = .4649999999999995 Correction with q = .4639999999999995 Correction with q = .4629999999999995 Correction with q = .4619999999999995 Correction with q = .4609999999999995 Correction with q = .4599999999999995 Correction with q = .4589999999999995 Correction with q = .4579999999999995 Correction with q = .4569999999999995 Correction with q = .4559999999999995 Correction with q = .4549999999999995 Correction with q = .4539999999999995 Correction with q = .4529999999999995 Correction with q = .4519999999999995 Correction with q = .4509999999999995 Correction with q = .4499999999999995 Correction with q = .4489999999999995 Correction with q = .4479999999999995 Correction with q = .4469999999999995 Correction with q = .4459999999999995 Correction with q = .4449999999999995 Correction with q = .4439999999999995 Correction with q = .4429999999999995 Correction with q = .4419999999999995 Correction with q = .4409999999999995 Correction with q = .4399999999999995 Correction with q = .4389999999999995 Correction with q = .4379999999999995 Correction with q = .4369999999999995 Correction with q = .4359999999999995 Correction with q = .4349999999999995 Correction with q = .4339999999999995 Correction with q = .4329999999999995 Correction with q = .4319999999999995 Correction with q = .4309999999999995 Correction with q = .4299999999999995 Correction with q = .4289999999999995 Correction with q = .4279999999999995 Correction with q = .4269999999999995 Correction with q = .4259999999999995 Correction with q = .4249999999999995 Correction with q = .4239999999999995 Correction with q = .4229999999999995 Correction with q = .4219999999999995 Correction with q = .4209999999999995 Correction with q = .4199999999999995 Correction with q = .4189999999999995 Correction with q = .4179999999999995 Correction with q = .4169999999999995 Correction with q = .4159999999999995 Correction with q = .4149999999999995 Correction with q = .4139999999999995 Correction with q = .4129999999999995 Correction with q = .4119999999999995 Correction with q = .4109999999999995 Correction with q = .4099999999999995 Correction with q = .4089999999999995 Correction with q = .4079999999999995 Correction with q = .4069999999999995 Correction with q = .4059999999999995 Correction with q = .4049999999999995 Correction with q = .4039999999999995 Correction with q = .4029999999999995 Correction with q = .4019999999999995 Correction with q = .4009999999999995 Correction with q = .3999999999999995 Correction with q = .3989999999999995 Correction with q = .3979999999999995 Correction with q = .3969999999999995 Correction with q = .3959999999999995 Correction with q = .3949999999999995 Correction with q = .3939999999999995 Correction with q = .3929999999999995 Correction with q = .3919999999999995 Correction with q = .3909999999999995 Correction with q = .3899999999999995 Correction with q = .3889999999999995 Correction with q = .3879999999999995 Correction with q = .3869999999999995 Correction with q = .3859999999999995 Correction with q = .3849999999999995 Correction with q = .3839999999999995 Correction with q = .3829999999999995 Correction with q = .3819999999999995 Correction with q = .3809999999999995 Correction with q = .3799999999999995 Correction with q = .3789999999999995 Correction with q = .3779999999999995 Correction with q = .3769999999999995 Correction with q = .3759999999999995 Correction with q = .3749999999999994 Correction with q = .3739999999999994 Correction with q = .3729999999999994 Correction with q = .3719999999999994 Correction with q = .3709999999999994 Correction with q = .3699999999999994 Correction with q = .3689999999999994 Correction with q = .3679999999999994 Correction with q = .3669999999999994 Correction with q = .3659999999999994 Correction with q = .3649999999999994 Correction with q = .3639999999999994 Correction with q = .3629999999999994 Correction with q = .3619999999999994 Correction with q = .3609999999999994 Correction with q = .3599999999999994 Correction with q = .3589999999999994 Correction with q = .3579999999999994 Correction with q = .3569999999999994 Correction with q = .3559999999999994 Correction with q = .3549999999999994 Correction with q = .3539999999999994 Correction with q = .3529999999999994 Correction with q = .3519999999999994 Correction with q = .3509999999999994 Correction with q = .3499999999999994 Correction with q = .3489999999999994 Correction with q = .3479999999999994 Correction with q = .3469999999999994 Correction with q = .3459999999999994 Correction with q = .3449999999999994 Correction with q = .3439999999999994 Correction with q = .3429999999999994 Correction with q = .3419999999999994 Correction with q = .3409999999999994 Correction with q = .3399999999999994 Correction with q = .3389999999999994 Correction with q = .3379999999999994 Correction with q = .3369999999999994 Correction with q = .3359999999999994 Correction with q = .3349999999999994 Correction with q = .3339999999999994 Correction with q = .3329999999999994 Correction with q = .3319999999999994 Correction with q = .3309999999999994 Correction with q = .3299999999999994 Correction with q = .3289999999999994 Correction with q = .3279999999999994 Correction with q = .3269999999999994 Correction with q = .3259999999999994 Correction with q = .3249999999999994 Correction with q = .3239999999999994 Correction with q = .3229999999999994 Correction with q = .3219999999999994 Correction with q = .3209999999999994 Correction with q = .3199999999999994 Correction with q = .3189999999999994 Correction with q = .3179999999999994 Correction with q = .3169999999999994 Correction with q = .3159999999999994 Correction with q = .3149999999999994 Correction with q = .3139999999999994 Correction with q = .3129999999999994 Correction with q = .3119999999999994 Correction with q = .3109999999999994 Correction with q = .3099999999999994 Correction with q = .3089999999999994 Correction with q = .3079999999999994 Correction with q = .3069999999999994 Correction with q = .3059999999999994 Correction with q = .3049999999999994 Correction with q = .3039999999999994 Correction with q = .3029999999999994 Correction with q = .3019999999999994 Correction with q = .3009999999999994 Correction with q = .2999999999999994 Correction with q = .2989999999999994 Correction with q = .2979999999999994 Correction with q = .2969999999999994 Correction with q = .2959999999999994 Correction with q = .2949999999999994 Correction with q = .2939999999999994 Correction with q = .2929999999999994 Correction with q = .2919999999999994 Correction with q = .2909999999999994 Correction with q = .2899999999999994 Correction with q = .2889999999999994 Correction with q = .2879999999999994 Correction with q = .2869999999999994 Correction with q = .2859999999999994 Correction with q = .2849999999999994 Correction with q = .2839999999999994 Correction with q = .2829999999999994 Correction with q = .2819999999999994 Correction with q = .2809999999999994 Correction with q = .2799999999999994 Correction with q = .2789999999999994 Correction with q = .2779999999999994 Correction with q = .2769999999999994 Correction with q = .2759999999999994 Correction with q = .2749999999999994 Correction with q = .2739999999999994 Correction with q = .2729999999999994 Correction with q = .2719999999999994 Correction with q = .2709999999999994 Correction with q = .2699999999999994 Correction with q = .2689999999999994 Correction with q = .2679999999999994 Correction with q = .2669999999999994 Correction with q = .2659999999999994 Correction with q = .2649999999999994 Correction with q = .2639999999999994 Correction with q = .2629999999999994 Correction with q = .2619999999999993 Correction with q = .2609999999999993 Correction with q = .2599999999999993 Correction with q = .2589999999999993 Correction with q = .2579999999999993 Correction with q = .2569999999999993 Correction with q = .2559999999999993 Correction with q = .2549999999999993 Correction with q = .2539999999999993 Correction with q = .2529999999999993 Correction with q = .2519999999999993 Correction with q = .2509999999999993 Correction with q = .2499999999999993 Correction with q = .2489999999999993 Correction with q = .2479999999999993 Correction with q = .2469999999999993 Correction with q = .2459999999999993 Correction with q = .2449999999999993 Correction with q = .2439999999999993 Correction with q = .2429999999999993 Correction with q = .2419999999999993 Correction with q = .2409999999999993 Correction with q = .2399999999999993 Correction with q = .2389999999999993 Correction with q = .2379999999999993 Correction with q = .2369999999999993 Correction with q = .2359999999999993 Correction with q = .2349999999999993 Correction with q = .2339999999999993 Correction with q = .2329999999999993 Correction with q = .2319999999999993 Correction with q = .2309999999999993 Correction with q = .2299999999999993 Correction with q = .2289999999999993 Correction with q = .2279999999999993 Correction with q = .2269999999999993 Correction with q = .2259999999999993 Correction with q = .2249999999999993 Correction with q = .2239999999999993 Correction with q = .2229999999999993 Correction with q = .2219999999999993 Correction with q = .2209999999999993 Correction with q = .2199999999999993 Correction with q = .2189999999999993 Correction with q = .2179999999999993 Correction with q = .2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = .1909999999999993 Correction with q = .1899999999999993 Correction with q = .1889999999999993 Correction with q = .1879999999999993 Correction with q = .1869999999999993 Correction with q = .1859999999999993 Correction with q = .1849999999999993 Correction with q = .1839999999999993 Correction with q = .1829999999999993 Correction with q = .1819999999999993 Correction with q = .1809999999999993 Correction with q = .1799999999999993 Correction with q = .1789999999999993 Correction with q = .1779999999999993 Correction with q = .1769999999999993 Correction with q = .1759999999999993 Correction with q = .1749999999999993 Correction with q = .1739999999999993 Correction with q = .1729999999999993 Correction with q = .1719999999999993 Correction with q = .1709999999999993 Correction with q = .1699999999999993 Correction with q = .1689999999999993 Correction with q = .1679999999999993 Correction with q = .1669999999999993 Correction with q = .1659999999999993 Correction with q = .1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 (2 real changes made) (0 real changes made) . estadd loc thisstat5 = "["+ string(Q[1, 1], "%9.3f")+"]": col3 . estadd loc thisstat5 = "["+ string(Q[2, 1], "%9.3f")+"]": col4 . . . . loc rowlabels " "{\bf Panel B: Policy Demand}" " " "T$^{74}$" " " "Sharpened q-value" " " "Female" > " " " " "Democrat" " " " " "Observations" " . loc rowstats "" . . forval i = 1/13 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\StageIIABmain_PanelB.tex", replace cells(none) booktabs nonotes compress a > lignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mtitle("\shortstack{Statutory\\affirmative\\action}" "\shortstack{Stricter\\equal pay\\legislati > on}" /// > "\shortstack{Supportive\\ policy}" "\shortstack{Anti- \\discrimination \\policy}" /// > "\shortstack{Policy\\demand\\index\\((1)-(2))}" "\shortstack{Policy\\demand\\index\\((3)-(4))}" > "\shortstack{Women's \\ wages\\are\\ fair}") /// > mgroups("Re-elicited Outcomes" "Newly elicited Outcomes" "Summary Indices" "Mechanism", patter > n(1 0 1 0 1 0 1) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\StageIIABmain_PanelB.tex) . . . . *********************************************************************************** . // Table 7: Heterogeneity in the treatment effect by gender and political orientation . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . . // Generate variable that indicates which posterior belief statistic was elicited . gen postgroup =. (4,065 missing values generated) . replace postgroup =1 if wave==1&RAND4== 9 (839 real changes made) . replace postgroup = 2 if wave==1&RAND4==10 (829 real changes made) . replace postgroup= 3 if wave==1&RAND4==11 (842 real changes made) . replace postgroup = 4 if wave==2&RAND4==10 (790 real changes made) . replace postgroup=5 if wave==2&RAND4==11 (765 real changes made) . . . loc experiments1 "posterior quotaanchor AAanchor legislationanchor transparencyanchor UKtool child > care z_lmpolicy_index" . loc experiments "quotaanchor AAanchor legislationanchor transparencyanchor UKtool childcare z_lmpo > licy_index" . . . // Set up table . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments1' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . // Panel A: Heterogeneity by Gender . . /* Column 1 */ . . reg posterior T1 T1female $controls i.postgroup [pweight=pweight] if rand!=0, vce(r) (sum of wgt is 2.9862e+03) note: 5.postgroup omitted because of collinearity Linear regression Number of obs = 3,022 F(26, 2995) = 41.54 Prob > F = 0.0000 R-squared = 0.3429 Root MSE = 16.018 ------------------------------------------------------------------------------- | Robust posterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -12.07611 .8684364 -13.91 0.000 -13.7789 -10.37332 T1female | -1.60668 1.192579 -1.35 0.178 -3.945036 .7316768 wave | -2.380387 .9283335 -2.56 0.010 -4.200623 -.560151 gender | -.8229055 .782757 -1.05 0.293 -2.357701 .7118904 prior | .3804295 .0295975 12.85 0.000 .3223961 .438463 democrat | .0665148 .6929225 0.10 0.924 -1.292137 1.425167 indep | .4225038 .8366176 0.51 0.614 -1.2179 2.062907 otherpol | 1.004266 3.002778 0.33 0.738 -4.883449 6.891982 midwest | -.3242488 .9326438 -0.35 0.728 -2.152936 1.504439 south | .0161309 .8526962 0.02 0.985 -1.655799 1.68806 west | -.5547404 .9237729 -0.60 0.548 -2.366034 1.256553 age1 | 3.014456 1.298903 2.32 0.020 .4676243 5.561287 age2 | 3.09126 .9267685 3.34 0.001 1.274093 4.908427 age3 | .9229856 .865953 1.07 0.287 -.7749373 2.620909 age4 | -.0278248 .7719419 -0.04 0.971 -1.541415 1.485765 anychildren | 1.329366 .6607258 2.01 0.044 .0338437 2.624889 loghhinc | -.2619721 .4203934 -0.62 0.533 -1.086261 .5623169 associatemore | 1.056464 .6482714 1.63 0.103 -.2146382 2.327566 fulltime | .6365087 .796787 0.80 0.424 -.9257966 2.198814 parttime | -.473926 1.113021 -0.43 0.670 -2.65629 1.708438 selfemp | -.4666154 1.179911 -0.40 0.693 -2.780133 1.846902 unemployed | -3.029679 1.182 -2.56 0.010 -5.347294 -.7120639 student | -.3506673 1.668185 -0.21 0.834 -3.621571 2.920237 | postgroup | 2 | -7.637294 .9024025 -8.46 0.000 -9.406685 -5.867902 3 | .1175117 .8230461 0.14 0.886 -1.496281 1.731305 4 | 4.502374 1.019216 4.42 0.000 2.50394 6.500808 5 | 0 (omitted) | _cons | 63.85771 5.536132 11.53 0.000 53.00271 74.71272 ------------------------------------------------------------------------------- . local n = round(e(N)) . . sigstar T1, prec(3) . estadd loc thisstat3 = "`r(bstar)'": col1 . estadd loc thisstat4 = "`r(sestar)'": col1 . sigstar T1female, prec(3) . estadd loc thisstat6 = "`r(bstar)'": col1 . estadd loc thisstat7 = "`r(sestar)'": col1 . test T1 + T1female = 0 ( 1) T1 + T1female = 0 F( 1, 2995) = 293.47 Prob > F = 0.0000 . estadd loc thisstat8 = string(r(p), "%9.3f"): col1 . . sigstar gender, prec(3) . estadd loc thisstat10 = "`r(bstar)'": col1 . estadd loc thisstat11 = "`r(sestar)'": col1 . . estadd loc thisstat13 = "`n'": col1 . . . . /* Columns 2-9 */ . loc colnum = 2 . loc colnames "" . . foreach choice in `experiments' { 2. . qui reg `choice' T1 T1female $controls [pweight=pweight] if rand!=0, vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 7. sigstar T1female, prec(3) 8. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat7 = "`r(sestar)'": col`colnum' 10. test T1 + T1female = 0 11. estadd loc thisstat8 = string(r(p), "%9.3f"): col`colnum' 12. . sigstar gender, prec(3) 13. estadd loc thisstat10 = "`r(bstar)'": col`colnum' 14. estadd loc thisstat11 = "`r(sestar)'": col`colnum' 15. . estadd loc thisstat13 = "`n'": col`colnum' 16. . loc ++colnum 17. loc colnames "`colnames' `"`: var la `choice''"'" 18. . } ( 1) T1 + T1female = 0 F( 1, 3007) = 0.01 Prob > F = 0.9346 ( 1) T1 + T1female = 0 F( 1, 3007) = 6.32 Prob > F = 0.0120 ( 1) T1 + T1female = 0 F( 1, 3007) = 6.48 Prob > F = 0.0110 ( 1) T1 + T1female = 0 F( 1, 1989) = 0.20 Prob > F = 0.6576 ( 1) T1 + T1female = 0 F( 1, 996) = 1.62 Prob > F = 0.2028 ( 1) T1 + T1female = 0 F( 1, 3007) = 0.18 Prob > F = 0.6730 ( 1) T1 + T1female = 0 F( 1, 3007) = 1.97 Prob > F = 0.1606 . . // Panel B: Heterogeneity by political orientation . . /* Column 1 */ . . reg posterior T1 T1democrat T1indep $controls i.postgroup [pweight=pweight] if rand!=0&otherpo > l!=1, vce(r) (sum of wgt is 2.9301e+03) note: otherpol omitted because of collinearity note: 5.postgroup omitted because of collinearity Linear regression Number of obs = 2,965 F(26, 2938) = 39.68 Prob > F = 0.0000 R-squared = 0.3392 Root MSE = 15.883 ------------------------------------------------------------------------------- | Robust posterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -13.7545 .9619803 -14.30 0.000 -15.64073 -11.86828 T1democrat | 1.610058 1.31289 1.23 0.220 -.96422 4.184337 T1indep | 1.326933 1.63378 0.81 0.417 -1.876537 4.530403 wave | -2.263139 .936273 -2.42 0.016 -4.098957 -.4273212 gender | -1.659056 .6060737 -2.74 0.006 -2.847428 -.470684 prior | .3730941 .029464 12.66 0.000 .3153219 .4308662 democrat | -.7436481 .8757691 -0.85 0.396 -2.460831 .9735351 indep | -.2972387 1.122331 -0.26 0.791 -2.497873 1.903396 otherpol | 0 (omitted) midwest | -.1731012 .9034521 -0.19 0.848 -1.944565 1.598362 south | .3628869 .8214385 0.44 0.659 -1.247767 1.97354 west | -.2791327 .889156 -0.31 0.754 -2.022565 1.464299 age1 | 3.14967 1.325425 2.38 0.018 .5508135 5.748526 age2 | 2.943586 .9288626 3.17 0.002 1.122299 4.764874 age3 | 1.193658 .8677491 1.38 0.169 -.5078003 2.895115 age4 | .0574846 .7793817 0.07 0.941 -1.470705 1.585674 anychildren | 1.345135 .6639603 2.03 0.043 .0432601 2.647009 loghhinc | -.1857525 .4216973 -0.44 0.660 -1.012605 .6410997 associatemore | .8695365 .6446024 1.35 0.177 -.3943818 2.133455 fulltime | .5266299 .8012182 0.66 0.511 -1.044376 2.097636 parttime | -.6141188 1.126883 -0.54 0.586 -2.82368 1.595442 selfemp | -.5118625 1.132405 -0.45 0.651 -2.732251 1.708526 unemployed | -3.007098 1.175936 -2.56 0.011 -5.312841 -.7013554 student | -.5125004 1.69238 -0.30 0.762 -3.830872 2.805871 | postgroup | 2 | -7.763975 .9160303 -8.48 0.000 -9.560101 -5.967848 3 | -.0677127 .8179295 -0.08 0.934 -1.671486 1.53606 4 | 4.39873 1.025176 4.29 0.000 2.388595 6.408866 5 | 0 (omitted) | _cons | 64.33904 5.551397 11.59 0.000 53.45402 75.22406 ------------------------------------------------------------------------------- . local n = round(e(N)) . . sigstar T1, prec(3) . estadd loc thisstat16 = "`r(bstar)'": col1 . estadd loc thisstat17 = "`r(sestar)'": col1 . sigstar T1democrat, prec(3) . estadd loc thisstat19 = "`r(bstar)'": col1 . estadd loc thisstat20 = "`r(sestar)'": col1 . . test T1 + T1democrat = 0 ( 1) T1 + T1democrat = 0 F( 1, 2938) = 187.77 Prob > F = 0.0000 . estadd loc thisstat21 = string(r(p), "%9.3f"): col1 . . sigstar T1indep, prec(3) . estadd loc thisstat23 = "`r(bstar)'": col1 . estadd loc thisstat24 = "`r(sestar)'": col1 . test T1 + T1indep = 0 ( 1) T1 + T1indep = 0 F( 1, 2938) = 87.94 Prob > F = 0.0000 . estadd loc thisstat25 = string(r(p), "%9.3f"): col1 . . sigstar democrat, prec(3) . estadd loc thisstat27 = "`r(bstar)'": col1 . estadd loc thisstat28 = "`r(sestar)'": col1 . sigstar indep, prec(3) . estadd loc thisstat30 = "`r(bstar)'": col1 . estadd loc thisstat31 = "`r(sestar)'": col1 . . estadd loc thisstat33 = "`n'": col1 . . /* Columns 2-9 */ . . loc colnum = 2 . loc colnames "" . . foreach choice in `experiments' { 2. . . reg `choice' T1 T1democrat T1indep $controls [pweight=pweight] if rand!=0&otherpol!=1, vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat16 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat17 = "`r(sestar)'": col`colnum' 7. sigstar T1democrat, prec(3) 8. estadd loc thisstat19 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat20 = "`r(sestar)'": col`colnum' 10. . test T1 + T1democrat = 0 11. estadd loc thisstat21 = string(r(p), "%9.3f"): col`colnum' 12. . sigstar T1indep, prec(3) 13. estadd loc thisstat23 = "`r(bstar)'": col`colnum' 14. estadd loc thisstat24 = "`r(sestar)'": col`colnum' 15. test T1 + T1indep = 0 16. estadd loc thisstat25 = string(r(p), "%9.3f"): col`colnum' 17. . sigstar democrat, prec(3) 18. estadd loc thisstat27 = "`r(bstar)'": col`colnum' 19. estadd loc thisstat28 = "`r(sestar)'": col`colnum' 20. sigstar indep, prec(3) 21. estadd loc thisstat30 = "`r(bstar)'": col`colnum' 22. estadd loc thisstat31 = "`r(sestar)'": col`colnum' 23. . estadd loc thisstat33 = "`n'": col`colnum' 24. . loc ++colnum 25. loc colnames "`colnames' `"`: var la `choice''"'" 26. . } (sum of wgt is 2.9391e+03) note: otherpol omitted because of collinearity Linear regression Number of obs = 2,974 F(23, 2950) = 16.67 Prob > F = 0.0000 R-squared = 0.1154 Root MSE = .9711 ------------------------------------------------------------------------------- | Robust quotaanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1074621 .0613732 1.75 0.080 -.0128766 .2278007 T1democrat | -.0555312 .0796855 -0.70 0.486 -.211776 .1007136 T1indep | -.1318383 .1069442 -1.23 0.218 -.3415311 .0778546 wave | .1340325 .0388805 3.45 0.001 .0577968 .2102682 gender | .2520565 .0368488 6.84 0.000 .1798044 .3243085 prior | -.0036786 .0009571 -3.84 0.000 -.0055552 -.0018021 democrat | .5870132 .0574318 10.22 0.000 .4744027 .6996237 indep | .2240171 .0790209 2.83 0.005 .0690753 .3789588 otherpol | 0 (omitted) midwest | -.1209034 .0595489 -2.03 0.042 -.237665 -.0041417 south | .0231511 .0530979 0.44 0.663 -.0809616 .1272639 west | -.0395877 .0570605 -0.69 0.488 -.1514701 .0722947 age1 | .2300875 .0811076 2.84 0.005 .0710543 .3891206 age2 | .2954456 .058317 5.07 0.000 .1810994 .4097918 age3 | .2138522 .0568809 3.76 0.000 .1023221 .3253824 age4 | .0593527 .0555619 1.07 0.286 -.0495914 .1682968 anychildren | .1254624 .0396012 3.17 0.002 .0478137 .2031111 loghhinc | -.0377241 .0249441 -1.51 0.131 -.0866338 .0111856 associatemore | -.0623485 .0406945 -1.53 0.126 -.142141 .017444 fulltime | .0717917 .0550798 1.30 0.193 -.0362071 .1797905 parttime | .054975 .0731372 0.75 0.452 -.0884301 .1983802 selfemp | .082912 .0790196 1.05 0.294 -.0720272 .2378511 unemployed | .13674 .0862917 1.58 0.113 -.0324579 .305938 student | -.030875 .1133598 -0.27 0.785 -.2531473 .1913972 _cons | -.1380999 .2949554 -0.47 0.640 -.7164391 .4402392 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 2950) = 1.03 Prob > F = 0.3092 ( 1) T1 + T1indep = 0 F( 1, 2950) = 0.08 Prob > F = 0.7807 (sum of wgt is 2.9391e+03) note: otherpol omitted because of collinearity Linear regression Number of obs = 2,974 F(23, 2950) = 19.42 Prob > F = 0.0000 R-squared = 0.1321 Root MSE = .93745 ------------------------------------------------------------------------------- | Robust AAanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0752509 .0611111 1.23 0.218 -.0445738 .1950757 T1democrat | .0478333 .0775812 0.62 0.538 -.1042856 .1999521 T1indep | .072395 .1033732 0.70 0.484 -.1302959 .2750858 wave | .0250508 .0379116 0.66 0.509 -.049285 .0993866 gender | .1747557 .0354242 4.93 0.000 .1052971 .2442142 prior | -.0040977 .0009841 -4.16 0.000 -.0060272 -.0021682 democrat | .6455415 .055442 11.64 0.000 .5368326 .7542503 indep | .2179295 .0762553 2.86 0.004 .0684106 .3674485 otherpol | 0 (omitted) midwest | -.0855383 .0567471 -1.51 0.132 -.1968062 .0257295 south | .0734612 .0511214 1.44 0.151 -.026776 .1736985 west | -.0517103 .0553484 -0.93 0.350 -.1602357 .0568151 age1 | .1238686 .0751803 1.65 0.100 -.0235425 .2712797 age2 | .1655438 .0560448 2.95 0.003 .0556529 .2754347 age3 | .0632171 .0551105 1.15 0.251 -.0448418 .1712759 age4 | .016035 .0526263 0.30 0.761 -.087153 .119223 anychildren | .129023 .0383752 3.36 0.001 .0537781 .2042679 loghhinc | -.0089033 .0239544 -0.37 0.710 -.0558723 .0380657 associatemore | .0579066 .0384543 1.51 0.132 -.0174934 .1333067 fulltime | -.0128174 .052456 -0.24 0.807 -.1156714 .0900367 parttime | -.0479644 .0697442 -0.69 0.492 -.1847166 .0887879 selfemp | -.0298135 .0761662 -0.39 0.696 -.1791577 .1195308 unemployed | .0021274 .0840197 0.03 0.980 -.1626158 .1668707 student | .1444177 .1051012 1.37 0.170 -.0616614 .3504968 _cons | -.2680125 .2882874 -0.93 0.353 -.8332774 .2972524 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 2950) = 6.60 Prob > F = 0.0102 ( 1) T1 + T1indep = 0 F( 1, 2950) = 3.14 Prob > F = 0.0767 (sum of wgt is 2.9391e+03) note: otherpol omitted because of collinearity Linear regression Number of obs = 2,974 F(23, 2950) = 17.81 Prob > F = 0.0000 R-squared = 0.1224 Root MSE = .94728 ------------------------------------------------------------------------------- | Robust legislation~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0268014 .0594967 -0.45 0.652 -.1434605 .0898578 T1democrat | .2471671 .0784356 3.15 0.002 .0933731 .400961 T1indep | .1977979 .1001203 1.98 0.048 .0014852 .3941106 wave | .0169919 .0384816 0.44 0.659 -.0584617 .0924455 gender | .2349616 .036042 6.52 0.000 .1642915 .3056317 prior | -.0036944 .001071 -3.45 0.001 -.0057944 -.0015944 democrat | .4939824 .0541018 9.13 0.000 .3879014 .6000635 indep | .1355719 .073644 1.84 0.066 -.008827 .2799708 otherpol | 0 (omitted) midwest | -.0684749 .0577513 -1.19 0.236 -.181712 .0447621 south | -.0280397 .0516424 -0.54 0.587 -.1292984 .073219 west | -.0708902 .0555418 -1.28 0.202 -.1797949 .0380145 age1 | -.1737533 .0768362 -2.26 0.024 -.3244114 -.0230952 age2 | -.1051698 .0566089 -1.86 0.063 -.2161668 .0058272 age3 | -.1035268 .0561269 -1.84 0.065 -.2135787 .006525 age4 | -.0484769 .0536324 -0.90 0.366 -.1536375 .0566838 anychildren | .0337377 .0389846 0.87 0.387 -.0427021 .1101775 loghhinc | .002368 .0241262 0.10 0.922 -.0449379 .0496739 associatemore | .0047557 .0383174 0.12 0.901 -.0703758 .0798873 fulltime | .0472316 .0549397 0.86 0.390 -.0604923 .1549556 parttime | -.0154621 .0741957 -0.21 0.835 -.1609428 .1300185 selfemp | .1748559 .0747843 2.34 0.019 .0282213 .3214905 unemployed | .2825179 .0830821 3.40 0.001 .1196132 .4454227 student | .1764305 .1098435 1.61 0.108 -.0389473 .3918082 _cons | -.1415567 .2882175 -0.49 0.623 -.7066845 .4235711 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 2950) = 18.37 Prob > F = 0.0000 ( 1) T1 + T1indep = 0 F( 1, 2950) = 4.49 Prob > F = 0.0341 (sum of wgt is 1.9740e+03) note: wave omitted because of collinearity note: otherpol omitted because of collinearity Linear regression Number of obs = 1,974 F(22, 1951) = 9.91 Prob > F = 0.0000 R-squared = 0.0974 Root MSE = .94029 ------------------------------------------------------------------------------- | Robust transparenc~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0236809 .0738391 -0.32 0.748 -.1684928 .1211309 T1democrat | -.015054 .0947977 -0.16 0.874 -.2009693 .1708613 T1indep | .0814034 .1256573 0.65 0.517 -.1650332 .3278401 wave | 0 (omitted) gender | .1996861 .043931 4.55 0.000 .1135295 .2858426 prior | -.0023878 .0011742 -2.03 0.042 -.0046908 -.0000849 democrat | .5735736 .0663054 8.65 0.000 .4435368 .7036104 indep | .1988907 .0943959 2.11 0.035 .0137632 .3840181 otherpol | 0 (omitted) midwest | -.1258826 .069398 -1.81 0.070 -.2619847 .0102194 south | -.0146963 .0607894 -0.24 0.809 -.1339154 .1045228 west | -.0542637 .0664654 -0.82 0.414 -.1846144 .0760869 age1 | .0453046 .1057443 0.43 0.668 -.162079 .2526882 age2 | .0663184 .0680667 0.97 0.330 -.0671728 .1998095 age3 | .0707297 .0678393 1.04 0.297 -.0623154 .2037747 age4 | .026314 .066054 0.40 0.690 -.1032298 .1558578 anychildren | .0894448 .0486217 1.84 0.066 -.0059111 .1848007 loghhinc | -.0247424 .0289741 -0.85 0.393 -.0815658 .0320809 associatemore | .0901477 .0478472 1.88 0.060 -.0036893 .1839846 fulltime | .0260939 .0661917 0.39 0.693 -.1037199 .1559077 parttime | -.0940913 .0849815 -1.11 0.268 -.2607554 .0725728 selfemp | .0755688 .0980023 0.77 0.441 -.1166315 .2677691 unemployed | .1135145 .0971185 1.17 0.243 -.0769525 .3039814 student | .3528979 .1338391 2.64 0.008 .0904153 .6153804 _cons | -.0852833 .3326843 -0.26 0.798 -.7377373 .5671708 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 1951) = 0.42 Prob > F = 0.5157 ( 1) T1 + T1indep = 0 F( 1, 1951) = 0.32 Prob > F = 0.5704 (sum of wgt is 9.6510e+02) note: wave omitted because of collinearity note: otherpol omitted because of collinearity Linear regression Number of obs = 1,000 F(22, 977) = 6.75 Prob > F = 0.0000 R-squared = 0.1254 Root MSE = .96365 ------------------------------------------------------------------------------- | Robust UKtool | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .114551 .1140828 1.00 0.316 -.1093245 .3384265 T1democrat | -.0056344 .1420657 -0.04 0.968 -.2844235 .2731547 T1indep | .0079955 .1864444 0.04 0.966 -.357882 .373873 wave | 0 (omitted) gender | .3007744 .0627768 4.79 0.000 .1775815 .4239673 prior | -.0034398 .0017637 -1.95 0.051 -.0069008 .0000212 democrat | .6012953 .1072941 5.60 0.000 .3907418 .8118488 indep | .2445384 .1345546 1.82 0.069 -.0195109 .5085877 otherpol | 0 (omitted) midwest | -.1507572 .1017349 -1.48 0.139 -.3504012 .0488867 south | -.0565932 .086706 -0.65 0.514 -.2267446 .1135583 west | -.1047404 .0979583 -1.07 0.285 -.2969734 .0874925 age1 | -.0072517 .1302647 -0.06 0.956 -.2628826 .2483792 age2 | -.0388848 .105041 -0.37 0.711 -.2450167 .167247 age3 | .0031761 .0992863 0.03 0.974 -.1916629 .1980151 age4 | .1283142 .0964276 1.33 0.184 -.0609149 .3175434 anychildren | -.0324858 .0715526 -0.45 0.650 -.1729002 .1079287 loghhinc | .0611881 .0459925 1.33 0.184 -.0290674 .1514435 associatemore | .0039757 .0712683 0.06 0.956 -.1358809 .1438323 fulltime | .0220052 .0934531 0.24 0.814 -.1613868 .2053971 parttime | .0101262 .127625 0.08 0.937 -.2403244 .2605768 selfemp | .2513869 .1326332 1.90 0.058 -.0088917 .5116656 unemployed | .1617126 .1719787 0.94 0.347 -.1757776 .4992029 student | .1826535 .1655492 1.10 0.270 -.1422194 .5075264 _cons | -.8847493 .5451106 -1.62 0.105 -1.954472 .1849731 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 977) = 1.66 Prob > F = 0.1980 ( 1) T1 + T1indep = 0 F( 1, 977) = 0.69 Prob > F = 0.4077 (sum of wgt is 2.9391e+03) note: otherpol omitted because of collinearity Linear regression Number of obs = 2,974 F(23, 2950) = 16.23 Prob > F = 0.0000 R-squared = 0.1178 Root MSE = .94302 ------------------------------------------------------------------------------- | Robust childcare | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.1137899 .0608013 -1.87 0.061 -.2330072 .0054274 T1democrat | .1451014 .0777541 1.87 0.062 -.0073565 .2975592 T1indep | .2896996 .1027186 2.82 0.005 .0882923 .491107 wave | -.1016865 .0377479 -2.69 0.007 -.1757015 -.0276716 gender | .1079464 .0363986 2.97 0.003 .0365772 .1793156 prior | -.0037107 .0009799 -3.79 0.000 -.0056321 -.0017894 democrat | .5053602 .0557336 9.07 0.000 .3960795 .6146409 indep | -.0383125 .0751836 -0.51 0.610 -.1857302 .1091052 otherpol | 0 (omitted) midwest | -.0973756 .0565493 -1.72 0.085 -.2082557 .0135046 south | .0077735 .0502476 0.15 0.877 -.0907505 .1062975 west | -.1525141 .0566882 -2.69 0.007 -.2636666 -.0413617 age1 | .2581292 .0820726 3.15 0.002 .0972039 .4190545 age2 | .3306344 .05715 5.79 0.000 .2185765 .4426922 age3 | .2417686 .0568856 4.25 0.000 .1302292 .353308 age4 | .1050042 .0542558 1.94 0.053 -.0013789 .2113873 anychildren | .1984581 .0390357 5.08 0.000 .1219181 .274998 loghhinc | -.0329528 .0246265 -1.34 0.181 -.0812398 .0153341 associatemore | -.022529 .0383829 -0.59 0.557 -.097789 .0527309 fulltime | -.0136211 .0556451 -0.24 0.807 -.1227283 .095486 parttime | .0067118 .0724429 0.09 0.926 -.135332 .1487555 selfemp | .0578472 .0783054 0.74 0.460 -.0956916 .2113859 unemployed | .0153042 .0863867 0.18 0.859 -.15408 .1846885 student | .0491039 .1033424 0.48 0.635 -.1535267 .2517344 _cons | .2775292 .2864581 0.97 0.333 -.2841489 .8392072 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 2950) = 0.41 Prob > F = 0.5221 ( 1) T1 + T1indep = 0 F( 1, 2950) = 4.52 Prob > F = 0.0336 (sum of wgt is 2.9391e+03) note: otherpol omitted because of collinearity Linear regression Number of obs = 2,974 F(23, 2950) = 28.66 Prob > F = 0.0000 R-squared = 0.1885 Root MSE = .68247 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0082125 .0452422 0.18 0.856 -.0804971 .096922 T1democrat | .0731409 .0566596 1.29 0.197 -.0379554 .1842372 T1indep | .0967321 .0754933 1.28 0.200 -.0512928 .2447571 wave | .0191266 .027968 0.68 0.494 -.0357122 .0739654 gender | .2005202 .0256963 7.80 0.000 .1501357 .2509047 prior | -.0035523 .0007484 -4.75 0.000 -.0050198 -.0020847 democrat | .557111 .0401895 13.86 0.000 .4783086 .6359133 indep | .1433696 .0563556 2.54 0.011 .0328693 .2538699 otherpol | 0 (omitted) midwest | -.1037232 .0417503 -2.48 0.013 -.1855859 -.0218606 south | .0037527 .0367836 0.10 0.919 -.0683714 .0758767 west | -.0802343 .0406266 -1.97 0.048 -.1598936 -.000575 age1 | .0961255 .055538 1.73 0.084 -.0127717 .2050226 age2 | .1479504 .0410501 3.60 0.000 .0674607 .22844 age3 | .0993781 .0412356 2.41 0.016 .0185247 .1802315 age4 | .0405977 .0391538 1.04 0.300 -.0361738 .1173693 anychildren | .1077774 .0278873 3.86 0.000 .0530968 .162458 loghhinc | -.0162852 .0178512 -0.91 0.362 -.0512872 .0187169 associatemore | .0030326 .0281531 0.11 0.914 -.052169 .0582343 fulltime | .0263653 .0400865 0.66 0.511 -.052235 .1049655 parttime | -.0070778 .0518604 -0.14 0.891 -.1087641 .0946084 selfemp | .0898338 .0564099 1.59 0.111 -.0207729 .2004406 unemployed | .1181732 .059664 1.98 0.048 .0011858 .2351605 student | .1182444 .0763857 1.55 0.122 -.0315302 .268019 _cons | -.1067445 .2100551 -0.51 0.611 -.5186139 .305125 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 2950) = 5.61 Prob > F = 0.0179 ( 1) T1 + T1indep = 0 F( 1, 2950) = 3.00 Prob > F = 0.0834 . . . loc rowlabels " "\multicolumn{2}{l}{{\bf Panel A: Het. by gender}}" " " "T$^{74}$" " " " " "T$^{74 > }$ x Female" " " "p-value [T$^{74}$ + T$^{74}$ x female]" " " "Female" " " " " "Observations" "\hl > ine \multicolumn{2}{l}{{\bf Panel B: Het. by pol. orientation}}" " " "T$^{74}$" " " " " "T$^{74}$ > x Democrat" " " "p-value [T$^{74}$ + T$^{74}$ x Dem.]" " " "T$^{74}$ x Independent" " " "p-value [ > T$^{74}$ + T$^{74}$ x Indep.]" " " "Democrat" " " " " "Independent" " " " " "Observations" " . loc rowstats "" . . forval i = 1/33 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\HetTreatment_femdemindAB.tex", replace cells(none) booktabs nonotes compre > ss alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mtitle("\shortstack{Posterior\\belief about\\fem. rel. wage}" /// > "\shortstack{Introduce\\gender\\quotas}" "\shortstack{Statutory\\affirmative\\action}" // > / > "\shortstack{Stricter\\equal pay\\legislation}" "\shortstack{Wage transp.\\within\\ companies}" > "\shortstack{Introduce\\reporting\\website}" "\shortstack{Increase\\subsidies\\to child care}" /// > "\shortstack{Policy\\demand\\index}" ) /// > mgroups("First Stage" "Policy Demand", pattern(1 1 0 0 0 0 0 0) prefix(\multicolumn{@span}{c}{) > suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\HetTreatment_femdemindAB.tex) . . eststo clear . . . . *********************************************************************************** . // Table 8: Treatment effect on beliefs about underlying factors and fairness perceptions . *********************************************************************************** . . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . // Code produces two panels. Stack manually to obtain final table . . // Drop pure control group . drop if rand==0 (1,034 observations deleted) . . loc experimentsA "ambitious talented interested z_personalreasons_index" . loc experimentsB "discrimination boys society z_extreasons_index womenwages" . . . // Panel A: . . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experimentsB' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experimentsA' { 2. . . qui reg `choice' T1 $controls [pweight=pweight] if rand!=0, vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 7. sigstar gender, prec(3) 8. estadd loc thisstat7 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat8 = "`r(sestar)'": col`colnum' 10. sigstar democrat, prec(3) 11. estadd loc thisstat10 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat11 = "`r(sestar)'": col`colnum' 13. . estadd loc thisstat13 = "`n'": col`colnum' 14. . . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. . } . . . // FDR control . mat def P = J(3, 1, .) . . reg ambitious T1 $controls if rand!=0, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 16.74 Prob > F = 0.0000 R-squared = 0.1439 Root MSE = .99189 ------------------------------------------------------------------------------- | Robust ambitious | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .031637 .0446251 0.71 0.478 -.0558799 .1191538 wave | 0 (omitted) gender | -.4670399 .0466562 -10.01 0.000 -.55854 -.3755398 prior | .0087269 .0011681 7.47 0.000 .0064362 .0110177 democrat | -.2758837 .0510315 -5.41 0.000 -.3759644 -.175803 indep | .006136 .0660675 0.09 0.926 -.1234327 .1357048 otherpol | -.1429522 .1389902 -1.03 0.304 -.4155338 .1296293 midwest | .1036013 .0689051 1.50 0.133 -.0315323 .238735 south | .1469123 .0616564 2.38 0.017 .0259944 .2678302 west | .2280431 .0696421 3.27 0.001 .091464 .3646222 age1 | .2779156 .1113559 2.50 0.013 .0595292 .4963021 age2 | .2472956 .0697019 3.55 0.000 .1105992 .3839921 age3 | .1235114 .0657624 1.88 0.061 -.0054589 .2524817 age4 | .0591717 .0637423 0.93 0.353 -.065837 .1841804 anychildren | .0428698 .0490791 0.87 0.383 -.053382 .1391216 loghhinc | .0474007 .0297085 1.60 0.111 -.0108623 .1056637 associatemore | -.0055662 .0503243 -0.11 0.912 -.1042601 .0931277 fulltime | .0758884 .0634529 1.20 0.232 -.0485527 .2003295 parttime | .0663839 .0838124 0.79 0.428 -.0979854 .2307532 selfemp | -.0202652 .0891414 -0.23 0.820 -.1950855 .154555 unemployed | .1220479 .0985104 1.24 0.216 -.0711465 .3152424 student | -.2907137 .1431889 -2.03 0.042 -.5715296 -.0098978 _cons | -1.204057 .3320892 -3.63 0.000 -1.855336 -.5527783 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg talented T1 $controls if rand!=0, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 17.48 Prob > F = 0.0000 R-squared = 0.1485 Root MSE = .94065 ------------------------------------------------------------------------------- | Robust talented | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .015874 .0422124 0.38 0.707 -.0669112 .0986591 wave | 0 (omitted) gender | -.419098 .0439539 -9.53 0.000 -.5052985 -.3328975 prior | .0085966 .0011356 7.57 0.000 .0063695 .0108236 democrat | -.2811486 .0487518 -5.77 0.000 -.3767584 -.1855387 indep | -.0499155 .0634572 -0.79 0.432 -.1743649 .074534 otherpol | -.3400399 .1243836 -2.73 0.006 -.5839757 -.0961041 midwest | .0386172 .0660292 0.58 0.559 -.0908763 .1681107 south | .0531085 .0578823 0.92 0.359 -.0604078 .1666247 west | .1400354 .0653001 2.14 0.032 .0119717 .268099 age1 | .3404559 .0987247 3.45 0.001 .1468412 .5340705 age2 | .3675451 .0658232 5.58 0.000 .2384555 .4966346 age3 | .210071 .0623838 3.37 0.001 .0877264 .3324155 age4 | .1114774 .0598394 1.86 0.063 -.005877 .2288319 anychildren | .1365211 .0472684 2.89 0.004 .0438203 .2292219 loghhinc | -.0324974 .028306 -1.15 0.251 -.0880099 .0230151 associatemore | -.0880297 .0483425 -1.82 0.069 -.1828369 .0067775 fulltime | .0447995 .0615896 0.73 0.467 -.0759874 .1655863 parttime | .0252761 .0813639 0.31 0.756 -.1342912 .1848434 selfemp | -.0975495 .0844857 -1.15 0.248 -.2632391 .0681401 unemployed | -.0075722 .0936596 -0.08 0.936 -.1912533 .1761089 student | -.3770227 .1267974 -2.97 0.003 -.6256923 -.1283531 _cons | -.3065622 .3249033 -0.94 0.346 -.9437486 .3306242 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg interested T1 $controls if rand!=0, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 18.20 Prob > F = 0.0000 R-squared = 0.1461 Root MSE = .96987 ------------------------------------------------------------------------------- | Robust interested | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0501951 .0433191 1.16 0.247 -.0347605 .1351507 wave | 0 (omitted) gender | -.3780604 .0454496 -8.32 0.000 -.4671942 -.2889267 prior | .0075593 .0010153 7.45 0.000 .0055681 .0095506 democrat | -.4308658 .0495164 -8.70 0.000 -.5279752 -.3337565 indep | -.190827 .0635736 -3.00 0.003 -.3155049 -.0661491 otherpol | -.5354522 .1494109 -3.58 0.000 -.8284704 -.2424339 midwest | .0694467 .0703994 0.99 0.324 -.0686176 .2075111 south | .1340804 .0626651 2.14 0.033 .0111843 .2569765 west | .2582269 .068039 3.80 0.000 .1247917 .391662 age1 | .2623082 .1074905 2.44 0.015 .0515025 .4731139 age2 | .2047531 .0698133 2.93 0.003 .0678384 .3416679 age3 | .1447512 .0671237 2.16 0.031 .013111 .2763913 age4 | .088007 .0673019 1.31 0.191 -.0439825 .2199965 anychildren | .1175936 .0477414 2.46 0.014 .0239653 .211222 loghhinc | -.0149622 .0304779 -0.49 0.624 -.0747342 .0448098 associatemore | -.1857942 .0483302 -3.84 0.000 -.2805772 -.0910111 fulltime | .0949398 .0682009 1.39 0.164 -.038813 .2286925 parttime | .0281135 .0877176 0.32 0.749 -.1439145 .2001415 selfemp | -.0571547 .0924056 -0.62 0.536 -.2383765 .1240672 unemployed | -.0711984 .1044542 -0.68 0.496 -.2760494 .1336526 student | -.4673958 .1485924 -3.15 0.002 -.7588088 -.1759828 _cons | -.3375407 .3409772 -0.99 0.322 -1.00625 .3311691 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . . minq P, q("Q") step(0.001) number of observations will be reset to 3 Press any key to continue, or Break to abort number of observations (_N) was 0, now 3 Correction with q = 1 (3 real changes made) (0 real changes made) . . estadd loc thisstat5 = "["+ string(Q[1, 1], "%9.3f")+"]": col1 . estadd loc thisstat5 = "["+ string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat5 = "["+ string(Q[3, 1], "%9.3f")+"]": col3 . . . loc rowlabels " "{\bf Panel A}" " " "T$^{74}$" " " "Sharpened q-value" " " "Female" " " " " "Democ > rat" " " " " "Observations" " . loc rowstats "" . . forval i = 1/13 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\reasonsmainA.tex", replace cells(none) booktabs nonotes compress alignment > (c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mtitle("Ambitions" "Talent" "Preferences" "Index") /// > mgroups("Personal Factors", pattern(1 0 0 0 ) prefix(\multicolumn{@span}{c}{) suffix(}) span e > repeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\reasonsmainA.tex) . . eststo clear . . . . // Panel B: . . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experimentsB' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experimentsB' { 2. . . qui reg `choice' T1 $controls [pweight=pweight] if rand!=0, vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 7. sigstar gender, prec(3) 8. estadd loc thisstat7 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat8 = "`r(sestar)'": col`colnum' 10. sigstar democrat, prec(3) 11. estadd loc thisstat10 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat11 = "`r(sestar)'": col`colnum' 13. . estadd loc thisstat13 = "`n'": col`colnum' 14. . . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. . } . . . mat def P = J(3, 1, .) . . reg discrimination T1 $controls if rand!=0, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 15.71 Prob > F = 0.0000 R-squared = 0.1531 Root MSE = .93186 ------------------------------------------------------------------------------- | Robust discriminat~n | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2274584 .0415737 5.47 0.000 .1459258 .308991 wave | 0 (omitted) gender | .2399906 .0427561 5.61 0.000 .1561392 .3238421 prior | -.0052218 .00116 -4.50 0.000 -.0074966 -.0029469 democrat | .6929086 .0486083 14.25 0.000 .5975802 .7882371 indep | .4067714 .0645112 6.31 0.000 .2802549 .5332879 otherpol | .1760567 .1912559 0.92 0.357 -.199026 .5511394 midwest | -.0414081 .0681514 -0.61 0.544 -.1750637 .0922474 south | .0162056 .0595112 0.27 0.785 -.1005052 .1329164 west | -.0070745 .0657409 -0.11 0.914 -.1360028 .1218538 age1 | .0910942 .103886 0.88 0.381 -.1126425 .2948309 age2 | .0710605 .0662629 1.07 0.284 -.0588915 .2010125 age3 | -.0069268 .0639648 -0.11 0.914 -.1323718 .1185183 age4 | -.1021303 .0636243 -1.61 0.109 -.2269076 .022647 anychildren | .0692027 .0457798 1.51 0.131 -.0205787 .158984 loghhinc | -.0011042 .0291176 -0.04 0.970 -.0582084 .0560001 associatemore | .0291884 .0476259 0.61 0.540 -.0642136 .1225903 fulltime | -.0196119 .0622037 -0.32 0.753 -.1416031 .1023793 parttime | -.0870396 .0847513 -1.03 0.305 -.2532503 .079171 selfemp | -.0092685 .0886316 -0.10 0.917 -.1830889 .1645519 unemployed | -.0240291 .1015413 -0.24 0.813 -.2231676 .1751093 student | -.0701561 .1387565 -0.51 0.613 -.3422792 .2019671 _cons | -.2268811 .3314066 -0.68 0.494 -.8768214 .4230591 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg boys T1 $controls if rand!=0, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 8.07 Prob > F = 0.0000 R-squared = 0.0760 Root MSE = 1.0186 ------------------------------------------------------------------------------- | Robust boys | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0140718 .0455805 0.31 0.758 -.0753187 .1034624 wave | 0 (omitted) gender | .2683724 .0474308 5.66 0.000 .1753533 .3613916 prior | -.0042359 .0012081 -3.51 0.000 -.0066051 -.0018666 democrat | .4125695 .052571 7.85 0.000 .3094696 .5156694 indep | .1780657 .0702367 2.54 0.011 .0403205 .3158109 otherpol | .0195485 .1986084 0.10 0.922 -.3699536 .4090506 midwest | .0696903 .0721272 0.97 0.334 -.0717625 .2111432 south | .068344 .0651856 1.05 0.295 -.0594952 .1961831 west | .1518719 .0694738 2.19 0.029 .0156229 .288121 age1 | .0050075 .1127661 0.04 0.965 -.2161446 .2261596 age2 | -.0542084 .0714482 -0.76 0.448 -.1943295 .0859127 age3 | -.0532855 .0696549 -0.76 0.444 -.1898898 .0833187 age4 | -.1466723 .0687792 -2.13 0.033 -.2815591 -.0117855 anychildren | -.0505841 .0504503 -1.00 0.316 -.1495252 .0483569 loghhinc | .0187107 .0318953 0.59 0.558 -.043841 .0812624 associatemore | .1549808 .0505044 3.07 0.002 .0559337 .2540278 fulltime | .0761424 .0696015 1.09 0.274 -.060357 .2126418 parttime | .0511799 .0923144 0.55 0.579 -.1298632 .232223 selfemp | .0207782 .1034415 0.20 0.841 -.1820867 .2236432 unemployed | .1208437 .1035887 1.17 0.244 -.08231 .3239974 student | .2712232 .1456971 1.86 0.063 -.0145117 .5569582 _cons | -.3893253 .3622047 -1.07 0.283 -1.099665 .3210148 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg society T1 $controls if rand!=0, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 4.76 Prob > F = 0.0000 R-squared = 0.0460 Root MSE = 1.0081 ------------------------------------------------------------------------------- | Robust society | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0756639 .0453031 1.67 0.095 -.0131826 .1645105 wave | 0 (omitted) gender | .2466364 .0466296 5.29 0.000 .1551884 .3380844 prior | -.0014201 .0011549 -1.23 0.219 -.0036849 .0008448 democrat | .2190132 .0514858 4.25 0.000 .1180415 .3199849 indep | -.0154882 .0659278 -0.23 0.814 -.1447829 .1138065 otherpol | -.1781188 .2017211 -0.88 0.377 -.5737255 .2174879 midwest | .1403176 .0740628 1.89 0.058 -.0049312 .2855664 south | .0730197 .0660764 1.11 0.269 -.0565665 .202606 west | .0826177 .0725196 1.14 0.255 -.0596046 .2248401 age1 | -.2175024 .1091624 -1.99 0.046 -.4315869 -.0034179 age2 | -.1888142 .0713256 -2.65 0.008 -.328695 -.0489335 age3 | -.1800787 .070098 -2.57 0.010 -.3175519 -.0426054 age4 | -.1400442 .0676251 -2.07 0.038 -.2726677 -.0074206 anychildren | -.0384746 .0496887 -0.77 0.439 -.135922 .0589728 loghhinc | .0742292 .031183 2.38 0.017 .0130745 .1353838 associatemore | .0992709 .051703 1.92 0.055 -.0021267 .2006686 fulltime | .0705512 .0706611 1.00 0.318 -.0680264 .2091287 parttime | .032474 .0890408 0.36 0.715 -.1421491 .2070971 selfemp | .1128184 .1010773 1.12 0.264 -.0854099 .3110467 unemployed | .0108111 .099819 0.11 0.914 -.1849497 .2065718 student | .1286066 .1573231 0.82 0.414 -.1799287 .437142 _cons | -.9824176 .3536644 -2.78 0.006 -1.676009 -.2888264 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . . . minq P, q("Q") step(0.001) number of observations will be reset to 3 Press any key to continue, or Break to abort number of observations (_N) was 0, now 3 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = 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.6579999999999997 Correction with q = .6569999999999997 Correction with q = .6559999999999997 Correction with q = .6549999999999997 Correction with q = .6539999999999997 Correction with q = .6529999999999997 Correction with q = .6519999999999997 Correction with q = .6509999999999997 Correction with q = .6499999999999997 Correction with q = .6489999999999997 Correction with q = .6479999999999997 Correction with q = .6469999999999997 Correction with q = .6459999999999997 Correction with q = .6449999999999997 Correction with q = .6439999999999997 Correction with q = .6429999999999997 Correction with q = .6419999999999997 Correction with q = .6409999999999997 Correction with q = .6399999999999997 Correction with q = .6389999999999997 Correction with q = .6379999999999997 Correction with q = .6369999999999997 Correction with q = .6359999999999997 Correction with q = .6349999999999997 Correction with q = .6339999999999997 Correction with q = .6329999999999997 Correction with q = .6319999999999997 Correction with q = .6309999999999997 Correction with q = .6299999999999997 Correction with q = .6289999999999997 Correction with q = .6279999999999997 Correction with q = .6269999999999997 Correction with q = .6259999999999997 Correction with q = .6249999999999997 Correction with q = .6239999999999997 Correction with q = .6229999999999997 Correction with q = .6219999999999997 Correction with q = .6209999999999997 Correction with q = .6199999999999997 Correction with q = .6189999999999997 Correction with q = .6179999999999997 Correction with q = .6169999999999997 Correction with q = .6159999999999997 Correction with q = .6149999999999997 Correction with q = .6139999999999997 Correction with q = .6129999999999997 Correction with q = .6119999999999997 Correction with q = .6109999999999997 Correction with q = .6099999999999997 Correction with q = .6089999999999997 Correction with q = .6079999999999997 Correction with q = .6069999999999997 Correction with q = .6059999999999997 Correction with q = .6049999999999997 Correction with q = .6039999999999997 Correction with q = .6029999999999997 Correction with q = .6019999999999997 Correction with q = .6009999999999997 Correction with q = .5999999999999996 Correction with q = .5989999999999996 Correction with q = .5979999999999996 Correction with q = .5969999999999996 Correction with q = .5959999999999996 Correction with q = .5949999999999996 Correction with q = .5939999999999996 Correction with q = .5929999999999996 Correction with q = .5919999999999996 Correction with q = .5909999999999996 Correction with q = .5899999999999996 Correction with q = .5889999999999996 Correction with q = .5879999999999996 Correction with q = .5869999999999996 Correction with q = .5859999999999996 Correction with q = .5849999999999996 Correction with q = .5839999999999996 Correction with q = .5829999999999996 Correction with q = .5819999999999996 Correction with q = .5809999999999996 Correction with q = .5799999999999996 Correction with q = .5789999999999996 Correction with q = .5779999999999996 Correction with q = .5769999999999996 Correction with q = .5759999999999996 Correction with q = .5749999999999996 Correction with q = .5739999999999996 Correction with q = .5729999999999996 Correction with q = .5719999999999996 Correction with q = .5709999999999996 Correction with q = .5699999999999996 Correction with q = .5689999999999996 Correction with q = .5679999999999996 Correction with q = .5669999999999996 Correction with q = .5659999999999996 Correction with q = .5649999999999996 Correction with q = .5639999999999996 Correction with q = .5629999999999996 Correction with q = .5619999999999996 Correction with q = .5609999999999996 Correction with q = .5599999999999996 Correction with q = .5589999999999996 Correction with q = .5579999999999996 Correction with q = .5569999999999996 Correction with q = .5559999999999996 Correction with q = .5549999999999996 Correction with q = .5539999999999996 Correction with q = .5529999999999996 Correction with q = .5519999999999996 Correction with q = .5509999999999996 Correction with q = .5499999999999996 Correction with q = .5489999999999996 Correction with q = .5479999999999996 Correction with q = .5469999999999996 Correction with q = .5459999999999996 Correction with q = .5449999999999996 Correction with q = .5439999999999996 Correction with q = .5429999999999996 Correction with q = .5419999999999996 Correction with q = .5409999999999996 Correction with q = .5399999999999996 Correction with q = .5389999999999996 Correction with q = .5379999999999996 Correction with q = .5369999999999996 Correction with q = .5359999999999996 Correction with q = .5349999999999996 Correction with q = .5339999999999996 Correction with q = .5329999999999996 Correction with q = .5319999999999996 Correction with q = .5309999999999996 Correction with q = .5299999999999996 Correction with q = .5289999999999996 Correction with q = 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.0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 Correction with q = .0069999999999991 Correction with q = .0059999999999991 Correction with q = .0049999999999991 Correction with q = .0039999999999991 Correction with q = .0029999999999991 Correction with q = .0019999999999991 (3 real changes made) (0 real changes made) . . estadd loc thisstat5 = "[" + string(Q[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat5 = "[" +string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat5 = "[" + string(Q[3, 1], "%9.3f")+"]": col3 . . loc rowlabels " "{\bf Panel B}" " " "T$^{74}$" " " "Sharpened q-value" " " "Female" " " " " "Democ > rat" " " " " "Observations" " . loc rowstats "" . . forval i = 1/13 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\reasonsmainB.tex", replace cells(none) booktabs nonotes compress alignment > (c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mtitle("Discrimination" "Socialization" "Work-Family" "Index" "of Women's Wages" ) /// > mgroups("Impersonal Factors" "Fairness", pattern(1 0 0 0 1) prefix(\multicolumn{@span}{c}{) su > ffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\reasonsmainB.tex) . . eststo clear . . . . *********************************************************************************** . // Table 9: The role of beliefs about the effectiveness of policies . *********************************************************************************** . . *Note: Code produces three separate tables for Panels A,B and C. Stack panels manually to obtain f > inal table . . use "$path\data\SurveyStageI_AB_final.dta", clear . . // Keep treatment groups of Wave B . drop if rand==0 (1,034 observations deleted) . keep if wave==2 (2,012 observations deleted) . . // Construct indicator for above median perceived effectiveness based on summary index . egen median_eff=median(z_eff_index) . gen abovemedeff=z_eff_index>median_eff . replace abovemedeff=. if z_eff_index==. (0 real changes made) . gen T1abovemedeff= T1*abovemedeff . . . // Build Panel A . . loc experiments "effdis effAA effworkfam z_eff_index" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . foreach choice in `experiments' { 2. . . ***Panel A . . qui reg `choice' T1 $controls [pweight=pweight], vce(r) 3. sigstar T1, prec(3) 4. estadd loc thisstat2 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat3 = "`r(sestar)'": col`colnum' 6. sigstar gender, prec(3) 7. estadd loc thisstat5 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat6 = "`r(sestar)'": col`colnum' 9. sigstar democrat, prec(3) 10. estadd loc thisstat8 = "`r(bstar)'": col`colnum' 11. estadd loc thisstat9 = "`r(sestar)'": col`colnum' 12. . qui sum `choice' 13. estadd loc thisstat11 = r(N): col`colnum' 14. . . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. . } . . . loc rowlabels " "{\bf Panel A: Treatment Effect}" "T$^{74}$" " " " " "Female" " " " " "Democrat" " > " " " "Observations" " " " . loc rowstats "" . . forval i = 1/11 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . . esttab * using "$output\effectivenessAB_PanelA.tex", replace cells(none) booktabs nonotes nomtitle > s compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mgroups("\shortstack{Perceived\\effectiveness\\of anti-disc. \\policies}" "\shortstack{Per > ceived\\effectiveness \\of affirmative\\ action }" /// > "\shortstack{Perceived\\effectiveness\\of work-family\\policies}" "\shortstack{Perceived\\effect > iveness\\index\\((1)-(3))}" /// > , pattern(1 1 1 1 ) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span}) > ) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\effectivenessAB_PanelA.tex) . . eststo clear . . . // Build Panel B . . loc experiments "quotaanchor AAanchor legislationanchor UKtool childcare z_lmpolicy_index" . . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . loc experiments "posterior large problem govmore z_mani_index" . . foreach choice in `experiments' { 2. . reg `choice' T1 T1abovemedeff abovemedeff $controls [pweight=pweight], vce(r) 3. . local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 7. . sigstar T1abovemedeff, prec(3) 8. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat7 = "`r(sestar)'": col`colnum' 10. . sigstar abovemedeff, prec(3) 11. estadd loc thisstat9 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat10 = "`r(sestar)'": col`colnum' 13. . test T1 + T1abovemedeff =0 14. estadd loc thisstat12 = string(r(p), "%9.3f"): col`colnum' 15. . *estadd loc thisstat13 = "`n'": col`colnum' . . loc ++colnum 16. } (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(23, 995) = 14.98 Prob > F = 0.0000 R-squared = 0.3161 Root MSE = 15.99 ------------------------------------------------------------------------------- | Robust posterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -14.28178 1.381761 -10.34 0.000 -16.99328 -11.57028 T1abovemedeff | 1.91904 2.034503 0.94 0.346 -2.073369 5.911448 abovemedeff | -.8106408 1.434052 -0.57 0.572 -3.624753 2.003472 wave | 0 (omitted) gender | -2.886923 .9817281 -2.94 0.003 -4.813418 -.9604274 prior | .3221803 .0461176 6.99 0.000 .2316813 .4126792 democrat | -.9790489 1.190884 -0.82 0.411 -3.315981 1.357883 indep | .3101735 1.409104 0.22 0.826 -2.454983 3.07533 otherpol | -6.219727 3.413828 -1.82 0.069 -12.91885 .479401 midwest | -.8783267 1.373147 -0.64 0.523 -3.572923 1.81627 south | 2.617923 1.401038 1.87 0.062 -.1314064 5.367251 west | .5479878 1.556529 0.35 0.725 -2.506469 3.602445 age1 | 3.613512 1.68938 2.14 0.033 .2983547 6.92867 age2 | 1.520511 1.74746 0.87 0.384 -1.90862 4.949642 age3 | 1.479992 1.624319 0.91 0.362 -1.707492 4.667476 age4 | -.8218913 1.320562 -0.62 0.534 -3.413298 1.769515 anychildren | 1.136833 1.147315 0.99 0.322 -1.114601 3.388268 loghhinc | .1433484 .760262 0.19 0.850 -1.348553 1.635249 associatemore | 2.054945 .9823298 2.09 0.037 .1272693 3.982621 fulltime | -.0798783 1.33449 -0.06 0.952 -2.698616 2.538859 parttime | -1.44848 1.862457 -0.78 0.437 -5.103276 2.206315 selfemp | -1.41168 1.917659 -0.74 0.462 -5.174799 2.351439 unemployed | -4.047238 2.138558 -1.89 0.059 -8.243838 .1493629 student | .0501432 2.541006 0.02 0.984 -4.936203 5.036489 _cons | 63.29428 10.49079 6.03 0.000 42.70767 83.88089 ------------------------------------------------------------------------------- ( 1) T1 + T1abovemedeff = 0 F( 1, 995) = 70.24 Prob > F = 0.0000 (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(23, 995) = 13.80 Prob > F = 0.0000 R-squared = 0.2273 Root MSE = .96105 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .5604039 .0894358 6.27 0.000 .3848995 .7359082 T1abovemedeff | .1243746 .1237652 1.00 0.315 -.1184962 .3672453 abovemedeff | .2291806 .0977178 2.35 0.019 .0374239 .4209373 wave | 0 (omitted) gender | .2770597 .0626409 4.42 0.000 .1541364 .3999831 prior | -.0050213 .0017367 -2.89 0.004 -.0084292 -.0016133 democrat | .5169666 .0727176 7.11 0.000 .3742692 .659664 indep | .1311287 .0946159 1.39 0.166 -.054541 .3167984 otherpol | .3211209 .2400816 1.34 0.181 -.1500034 .7922452 midwest | -.0464574 .093712 -0.50 0.620 -.2303533 .1374384 south | .1470849 .0866529 1.70 0.090 -.0229586 .3171283 west | -.0745761 .0978725 -0.76 0.446 -.2666362 .117484 age1 | -.0899744 .1341976 -0.67 0.503 -.3533171 .1733683 age2 | .1544634 .1001368 1.54 0.123 -.0420401 .3509669 age3 | .1332412 .0969038 1.37 0.169 -.0569181 .3234005 age4 | -.0696255 .0956559 -0.73 0.467 -.2573359 .1180849 anychildren | .0324952 .0717349 0.45 0.651 -.1082738 .1732643 loghhinc | .0790071 .0460524 1.72 0.087 -.0113639 .1693782 associatemore | -.0119079 .0683793 -0.17 0.862 -.146092 .1222762 fulltime | .1280452 .0951926 1.35 0.179 -.0587562 .3148466 parttime | -.1250342 .1309671 -0.95 0.340 -.3820377 .1319693 selfemp | .1237077 .1388381 0.89 0.373 -.1487413 .3961568 unemployed | .1456581 .1816462 0.80 0.423 -.2107956 .5021118 student | .1817231 .1675598 1.08 0.278 -.147088 .5105342 _cons | -1.493028 .5390161 -2.77 0.006 -2.550767 -.4352891 ------------------------------------------------------------------------------- ( 1) T1 + T1abovemedeff = 0 F( 1, 995) = 61.90 Prob > F = 0.0000 (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(23, 995) = 12.20 Prob > F = 0.0000 R-squared = 0.2192 Root MSE = .93198 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4866756 .0870219 5.59 0.000 .3159081 .657443 T1abovemedeff | .0226509 .1213598 0.19 0.852 -.2154997 .2608014 abovemedeff | .2855169 .0982931 2.90 0.004 .0926312 .4784025 wave | 0 (omitted) gender | .2616153 .0611683 4.28 0.000 .1415815 .381649 prior | -.005552 .0015017 -3.70 0.000 -.0084989 -.0026052 democrat | .606513 .0696804 8.70 0.000 .4697757 .7432503 indep | .2010958 .0962997 2.09 0.037 .012122 .3900696 otherpol | .4544627 .1600909 2.84 0.005 .1403082 .7686172 midwest | -.1185768 .0951037 -1.25 0.213 -.3052035 .06805 south | -.0154574 .083684 -0.18 0.853 -.1796747 .14876 west | -.1012344 .0913352 -1.11 0.268 -.2804662 .0779973 age1 | .0974438 .1206959 0.81 0.420 -.1394039 .3342916 age2 | .142888 .0998464 1.43 0.153 -.0530457 .3388216 age3 | .1081388 .0948514 1.14 0.255 -.077993 .2942706 age4 | -.0089563 .0958426 -0.09 0.926 -.1970332 .1791206 anychildren | .0583922 .0708531 0.82 0.410 -.0806464 .1974309 loghhinc | .0384095 .0440847 0.87 0.384 -.0481001 .124919 associatemore | .085654 .0673225 1.27 0.204 -.0464564 .2177644 fulltime | .1040116 .0935051 1.11 0.266 -.0794782 .2875014 parttime | -.097946 .1285435 -0.76 0.446 -.3501934 .1543015 selfemp | .0821352 .1314963 0.62 0.532 -.1759067 .3401771 unemployed | .0828762 .1860185 0.45 0.656 -.2821575 .4479099 student | .1132629 .1620003 0.70 0.485 -.2046386 .4311645 _cons | -.995593 .5089546 -1.96 0.051 -1.994341 .0031546 ------------------------------------------------------------------------------- ( 1) T1 + T1abovemedeff = 0 F( 1, 995) = 36.14 Prob > F = 0.0000 (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(23, 995) = 12.38 Prob > F = 0.0000 R-squared = 0.2184 Root MSE = .94619 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .228938 .0909792 2.52 0.012 .050405 .4074711 T1abovemedeff | .0908784 .1219076 0.75 0.456 -.148347 .3301039 abovemedeff | .2617361 .0939882 2.78 0.005 .0772982 .4461739 wave | 0 (omitted) gender | .2951993 .0617564 4.78 0.000 .1740114 .4163871 prior | -.0035083 .0013643 -2.57 0.010 -.0061856 -.0008311 democrat | .7296882 .0700526 10.42 0.000 .5922204 .8671561 indep | .1869658 .0988026 1.89 0.059 -.0069196 .3808511 otherpol | .3777284 .2440539 1.55 0.122 -.1011911 .8566478 midwest | -.2483025 .0984783 -2.52 0.012 -.4415514 -.0550536 south | -.0000429 .0849164 -0.00 1.000 -.1666787 .166593 west | -.173723 .0960596 -1.81 0.071 -.3622257 .0147798 age1 | .1337322 .1282889 1.04 0.297 -.1180157 .3854801 age2 | .3110823 .1018498 3.05 0.002 .1112173 .5109473 age3 | .2523321 .0993291 2.54 0.011 .0574136 .4472506 age4 | .1653486 .101361 1.63 0.103 -.0335573 .3642545 anychildren | .0845144 .0692318 1.22 0.222 -.0513427 .2203716 loghhinc | .0070371 .0429331 0.16 0.870 -.0772126 .0912869 associatemore | -.000897 .0688883 -0.01 0.990 -.1360801 .134286 fulltime | .0096983 .0995616 0.10 0.922 -.1856766 .2050731 parttime | -.0090479 .1261951 -0.07 0.943 -.2566871 .2385912 selfemp | .0623808 .1339291 0.47 0.641 -.2004351 .3251968 unemployed | .0407496 .1704569 0.24 0.811 -.2937467 .375246 student | .1164409 .1628071 0.72 0.475 -.2030438 .4359256 _cons | -.7665758 .5068894 -1.51 0.131 -1.761271 .2281191 ------------------------------------------------------------------------------- ( 1) T1 + T1abovemedeff = 0 F( 1, 995) = 15.00 Prob > F = 0.0001 (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(23, 995) = 14.28 Prob > F = 0.0000 R-squared = 0.2479 Root MSE = .85897 ------------------------------------------------------------------------------- | Robust z_mani_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4082875 .0823242 4.96 0.000 .2467386 .5698365 T1abovemedeff | .091698 .1111132 0.83 0.409 -.1263451 .3097412 abovemedeff | .2531327 .0882941 2.87 0.004 .0798687 .4263967 wave | 0 (omitted) gender | .2818088 .056124 5.02 0.000 .1716738 .3919438 prior | -.0044861 .0014162 -3.17 0.002 -.0072652 -.0017071 democrat | .6223406 .0638339 9.75 0.000 .4970761 .747605 indep | .1673166 .0891807 1.88 0.061 -.0076871 .3423204 otherpol | .3692638 .2090988 1.77 0.078 -.0410614 .779589 midwest | -.1440851 .0854365 -1.69 0.092 -.3117415 .0235712 south | .0557363 .0771904 0.72 0.470 -.0957384 .207211 west | -.1209219 .0858363 -1.41 0.159 -.2893628 .0475191 age1 | .0379573 .115346 0.33 0.742 -.1883921 .2643067 age2 | .2178197 .0909763 2.39 0.017 .0392922 .3963472 age3 | .1784244 .0880608 2.03 0.043 .0056182 .3512305 age4 | .0397514 .0885648 0.45 0.654 -.1340438 .2135466 anychildren | .0589976 .0633428 0.93 0.352 -.0653033 .1832985 loghhinc | .0414651 .0404647 1.02 0.306 -.0379408 .120871 associatemore | .0106038 .0613516 0.17 0.863 -.1097896 .1309972 fulltime | .0741538 .0875415 0.85 0.397 -.0976332 .2459409 parttime | -.0715691 .1159325 -0.62 0.537 -.2990695 .1559312 selfemp | .0904366 .1231929 0.73 0.463 -.1513112 .3321843 unemployed | .0902727 .1667186 0.54 0.588 -.2368876 .4174331 student | .1418629 .1509306 0.94 0.347 -.1543159 .4380416 _cons | -1.097997 .4772109 -2.30 0.022 -2.034452 -.1615416 ------------------------------------------------------------------------------- ( 1) T1 + T1abovemedeff = 0 F( 1, 995) = 43.62 Prob > F = 0.0000 . . . loc rowlabels " "\midline" "{\bf Panel B: First Stage}" "T$^{74}$ (a)" " " " " "T$^{74}$ x $\textb > f{1}$ (Perceived" "effectiveness > p50) (b) " " " "$\textbf{1}$ (Perceived" "effectiveness > p50)" > " " "p-value [(a) + (b) = 0]" " . loc rowstats "" . . forval i = 1/12 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\TreatmentFirst_hetbyeff_PanelB.tex", replace cells(none) booktabs nonotes > nomtitles compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels > ')) /// > mgroups("\shortstack{Posterior \\ belief about \\fem. rel. wage}" "\shortstack{Gender diff.\\ in w > ages\\are large}" "\shortstack{Gender diff.\\ in wages\\are a problem}" /// > "\shortstack{Government\\should mitigate\\gender wage gap}" "\shortstack{Perception\\index\\((2) > -(4))}", pattern(1 1 1 1 1) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@ > span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\TreatmentFirst_hetbyeff_PanelB.tex) . . eststo clear . . . // Build Panel C . . loc colnum = 1 . . loc experiments "quotaanchor AAanchor legislationanchor UKtool childcare z_lmpolicy_index" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . foreach choice in `experiments' { 2. . qui reg `choice' T1 T1abovemedeff abovemedeff $controls [pweight=pweight], vce(r) 3. . local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 7. sigstar T1abovemedeff, prec(3) 8. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat7 = "`r(sestar)'": col`colnum' 10. . sigstar abovemedeff, prec(3) 11. estadd loc thisstat9 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat10 = "`r(sestar)'": col`colnum' 13. . test T1 + T1abovemedeff = 0 14. estadd loc thisstat12 = string(r(p), "%9.3f"): col`colnum' 15. . . estadd loc thisstat13 = "`n'": col`colnum' 16. . . loc ++colnum 17. . } ( 1) T1 + T1abovemedeff = 0 F( 1, 995) = 4.52 Prob > F = 0.0338 ( 1) T1 + T1abovemedeff = 0 F( 1, 995) = 0.89 Prob > F = 0.3447 ( 1) T1 + T1abovemedeff = 0 F( 1, 995) = 4.63 Prob > F = 0.0316 ( 1) T1 + T1abovemedeff = 0 F( 1, 995) = 1.71 Prob > F = 0.1913 ( 1) T1 + T1abovemedeff = 0 F( 1, 995) = 0.00 Prob > F = 0.9468 ( 1) T1 + T1abovemedeff = 0 F( 1, 995) = 3.02 Prob > F = 0.0824 . . . . loc rowlabels " " " "{\bf Panel C: Reduced Form}" "T$^{74}$ (a)" " " " " "T$^{74}$ x $\textbf{1}$ > (perceived" "effectiveness > p50) (b)" " " "$\textbf{1}$ (perceived" "effectiveness > p50" " " "p- > value [(a) + (b) = 0]" "\hline Observations" " " " . loc rowstats "" . . forval i = 1/13 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\Treatmentpolicy_hetbyeff_PanelC.tex", replace cells(none) booktabs nonotes > nomtitles compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabel > s')) /// > mgroups("\shortstack{Introduce\\gender \\quotas}" "\shortstack{Statutory\\affirmative\\ ac > tion}" /// > "\shortstack{Stricter\\equal pay\\ legislation}" "\shortstack{Introduce\\reporting \\website}" " > \shortstack{Increase\\subsidies\\ to child care}" /// > "\shortstack{Policy\\demand\\Index}", pattern(1 1 1 1 1 1 ) prefix(\multicolumn{@span}{c}{) suf > fix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\Treatmentpolicy_hetbyeff_PanelC.tex) . . . eststo clear . . . . *********************************************************************************** . // Table 10: Correlates of willingness to pay for additional information . *********************************************************************************** . . use "$path\data\SurveyStageI_AB_final.dta", clear . . // Keep only control group . keep if rand==0 (3,031 observations deleted) . . // z-score willingness to pay for info . foreach var of varlist infopaysupport infopayoppose{ 2. egen mean_`var'=mean(`var') 3. egen sd_`var'=sd(`var') 4. replace `var'=(`var'-mean_`var')/sd_`var' 5. drop mean_`var' sd_`var' 6. } (498 real changes made) (498 real changes made) . . . loc experiments "infopaysupport infopaysupport infopaysupport infopaysupport infopaysupport infopa > ysupport infopaysupport infopaysupport" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach var in infopaysupport infopayoppose{ 2. . qui reg `var' democrat indep otherpol [pweight=pweight], vce(r) 3. local n = round(e(N)) 4. . sigstar democrat, prec(3) 5. estadd loc thisstat2 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat3 = "`r(sestar)'": col`colnum' 7. . estadd loc thisstat11 = "`n'": col`colnum' 8. . loc ++colnum 9. . reg `var' gender [pweight=pweight], vce(r) 10. local n = round(e(N)) 11. . sigstar gender, prec(3) 12. estadd loc thisstat5 = "`r(bstar)'": col`colnum' 13. estadd loc thisstat6 = "`r(sestar)'": col`colnum' 14. . estadd loc thisstat11 = "`n'": col`colnum' 15. . loc ++colnum 16. . . reg `var' z_lmpolicy_index [pweight=pweight], vce(r) 17. local n = round(e(N)) 18. . sigstar z_lmpolicy_index, prec(3) 19. estadd loc thisstat8 = "`r(bstar)'": col`colnum' 20. estadd loc thisstat9 = "`r(sestar)'": col`colnum' 21. . estadd loc thisstat11 = "`n'": col`colnum' 22. . loc ++colnum 23. . reg `var' z_lmpolicy_index democrat indep otherpol gender [pweight=pweight], vce(r) 24. local n = round(e(N)) 25. . sigstar democrat, prec(3) 26. estadd loc thisstat2 = "`r(bstar)'": col`colnum' 27. estadd loc thisstat3 = "`r(sestar)'": col`colnum' 28. . sigstar gender, prec(3) 29. estadd loc thisstat5 = "`r(bstar)'": col`colnum' 30. estadd loc thisstat6 = "`r(sestar)'": col`colnum' 31. . sigstar z_lmpolicy_index, prec(3) 32. estadd loc thisstat8 = "`r(bstar)'": col`colnum' 33. estadd loc thisstat9 = "`r(sestar)'": col`colnum' 34. . estadd loc thisstat11 = "`n'": col`colnum' 35. . loc ++colnum 36. . . } (sum of wgt is 4.9800e+02) Linear regression Number of obs = 498 F(1, 496) = 3.42 Prob > F = 0.0652 R-squared = 0.0068 Root MSE = .99758 ------------------------------------------------------------------------------ | Robust infopaysup~t | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | .1652087 .0893907 1.85 0.065 -.0104225 .3408398 _cons | -.0829361 .061982 -1.34 0.181 -.2047157 .0388435 ------------------------------------------------------------------------------ (sum of wgt is 4.9800e+02) Linear regression Number of obs = 498 F(1, 496) = 64.46 Prob > F = 0.0000 R-squared = 0.1110 Root MSE = .94382 ---------------------------------------------------------------------------------- | Robust infopaysupport | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- z_lmpolicy_index | .4534335 .0564745 8.03 0.000 .3424748 .5643921 _cons | -.0057823 .0422501 -0.14 0.891 -.0887935 .0772288 ---------------------------------------------------------------------------------- (sum of wgt is 4.9800e+02) Linear regression Number of obs = 498 F(5, 492) = 15.23 Prob > F = 0.0000 R-squared = 0.1206 Root MSE = .94254 ---------------------------------------------------------------------------------- | Robust infopaysupport | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- z_lmpolicy_index | .4093303 .0654496 6.25 0.000 .2807351 .5379254 democrat | .1213418 .1070758 1.13 0.258 -.0890404 .3317241 indep | .0267121 .128695 0.21 0.836 -.2261474 .2795717 otherpol | -.5130715 .2571037 -2.00 0.047 -1.018228 -.0079148 gender | .0295997 .0862364 0.34 0.732 -.1398372 .1990367 _cons | -.0690788 .0856629 -0.81 0.420 -.2373891 .0992315 ---------------------------------------------------------------------------------- (sum of wgt is 4.9800e+02) Linear regression Number of obs = 498 F(1, 496) = 2.88 Prob > F = 0.0904 R-squared = 0.0058 Root MSE = .99811 ------------------------------------------------------------------------------ | Robust infopayopp~e | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | -.1518304 .0894857 -1.70 0.090 -.3276482 .0239874 _cons | .0762201 .0661505 1.15 0.250 -.0537496 .2061898 ------------------------------------------------------------------------------ (sum of wgt is 4.9800e+02) Linear regression Number of obs = 498 F(1, 496) = 3.92 Prob > F = 0.0482 R-squared = 0.0094 Root MSE = .99631 ---------------------------------------------------------------------------------- | Robust infopayoppose | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- z_lmpolicy_index | -.1316638 .0664819 -1.98 0.048 -.2622848 -.0010429 _cons | .001679 .0447214 0.04 0.970 -.0861878 .0895459 ---------------------------------------------------------------------------------- (sum of wgt is 4.9800e+02) Linear regression Number of obs = 498 F(5, 492) = 1.85 Prob > F = 0.1020 R-squared = 0.0188 Root MSE = .99555 ---------------------------------------------------------------------------------- | Robust infopayoppose | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- z_lmpolicy_index | -.0694556 .0786427 -0.88 0.378 -.2239727 .0850615 democrat | -.1696706 .1172118 -1.45 0.148 -.3999681 .0606269 indep | -.1076388 .1360719 -0.79 0.429 -.3749924 .1597149 otherpol | -.3700148 .2635498 -1.40 0.161 -.8878367 .1478071 gender | -.1176235 .0918331 -1.28 0.201 -.2980569 .06281 _cons | .16376 .0995455 1.65 0.101 -.0318267 .3593466 ---------------------------------------------------------------------------------- . . . loc rowlabels " " " "Democrat" " " " " "Female" " " " " "Policy Demand (Index)" " " " " "Observati > ons" " " " . loc rowstats "" . . forval i = 1/11 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\infopay_corr.tex", replace cells(none) booktabs nonotes nonum compress ali > gnment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mtitle("(1)" "(2)" "(3)" "(4)" "(5)" "(6)" "(7)" "(8)") /// > mgroups("Willingness to pay for progressive info" "Willingness to pay for traditional info" , > pattern(1 0 0 0 1 0 0 0) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@spa > n})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\infopay_corr.tex) . . eststo clear . . . . . . end of do-file . . // Appendix Figures . do "09_AdditionalFigures.do" . . *********************************************************************************** . // Replication Files . ********************************************************************************** . /* > HOW DO BELIEFS ABOUT THE GENDER WAGE GAP AFFECT THE DEMAND FOR PUBLIC POLICY? > Sonja Settele > AEJ:pol > */ . ********************************************************************************** . . *********************************************************************************** . **** Generate Appendix Figures . *********************************************************************************** . . *********************************************************************************** . // Figure A.7: Sample balance in terms of prior belief distributions . *********************************************************************************** . . clear all . use "$path\data\SurveyStageI_AB_final.dta" . . // winsorize prior beliefs at 59 and 102 . replace prior=59 if prior<59 (330 real changes made) . replace prior=102 if prior>102&prior!=. (276 real changes made) . . * Treatment group T74 . sum prior if T1==1, d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% 59 59 5% 59 59 10% 60 59 Obs 1,531 25% 75 59 Sum of Wgt. 1,531 50% 81 Mean 81.89092 Largest Std. Dev. 12.58858 75% 90 102 90% 100 102 Variance 158.4724 95% 102 102 Skewness -.1371694 99% 102 102 Kurtosis 2.258736 . hist prior if T1==1, width(1) xaxis(1) /// > xtitle("Prior belief (T74)", axis(1)) xla(60 65 70 75 80 85 90 95 100, axis(1)) frac (bin=43, start=59, width=1) . graph save "$output\prior_hist_T74.gph", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\prior_hist_T74.gph saved) . . * Treatment group T94 . sum prior if T2==1, d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% 59 59 5% 59 59 10% 63 59 Obs 1,500 25% 75 59 Sum of Wgt. 1,500 50% 81 Mean 82.29933 Largest Std. Dev. 12.64032 75% 92 102 90% 100 102 Variance 159.7776 95% 102 102 Skewness -.1406846 99% 102 102 Kurtosis 2.18605 . hist prior if T2==1, width(1) xaxis(1) /// > xtitle("Prior belief (T94)", axis(1)) xla(60 65 70 75 80 85 90 95 100, axis(1)) frac (bin=43, start=59, width=1) . graph save "$output\prior_hist_T94.gph", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\prior_hist_T94.gph saved) . . * Control group . sum prior if rand==0, d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% 59 59 5% 59 59 10% 62 59 Obs 1,034 25% 75 59 Sum of Wgt. 1,034 50% 80.5 Mean 81.96905 Largest Std. Dev. 12.22027 75% 90 102 90% 100 102 Variance 149.335 95% 102 102 Skewness -.1273457 99% 102 102 Kurtosis 2.334627 . hist prior if rand==0, width(1) xaxis(1) /// > xtitle("Prior belief (Control group)", axis(1)) xla(60 65 70 75 80 85 90 95 100, axis(1)) frac (bin=43, start=59, width=1) . graph save "$output\prior_hist_cont.gph", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\prior_hist_cont.gph saved) . . . graph combine "$output\prior_hist_T74.gph" "$output\prior_hist_T94.gph" "$output\prior_hist_cont. > gph", cols(1) . graph export "$output\Priorhist_treatmentgroups.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\Priorhist_treatmentgroups.pdf written in PDF format) . . . // Information in Figure notes . * Median prior belief in the three conditions -> See sum commands above . * Mean prior beliefs . mean prior [pweight=pweight] if T1==1 Mean estimation Number of obs = 1,531 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ prior | 82.01385 .3250779 81.3762 82.65149 -------------------------------------------------------------- . mean prior [pweight=pweight] if T2==1 Mean estimation Number of obs = 1,500 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ prior | 82.37688 .3287078 81.7321 83.02166 -------------------------------------------------------------- . mean prior [pweight=pweight] if rand==0 Mean estimation Number of obs = 1,034 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ prior | 82.12469 .3825207 81.37409 82.8753 -------------------------------------------------------------- . . * Kolmogorov Smirnov test for similarity of distribution between T74 and T94 . ksmirnov prior if rand!=0, by(T1) Two-sample Kolmogorov-Smirnov test for equality of distribution functions Smaller group D P-value ----------------------------------- 0: 0.0074 0.921 1: -0.0267 0.340 Combined K-S: 0.0267 0.654 Note: Ties exist in combined dataset; there are 44 unique values out of 3031 observations. . . . *********************************************************************************** . // Figure A.8: Distribution of prior beliefs about women’s relative wages . *********************************************************************************** . . use "$path\data\SurveyStageI_AB_final.dta", clear . . * Winsorize prior beliefs at 59 and 102 . replace prior=59 if prior<59 (330 real changes made) . replace prior=102 if prior>102&prior!=. (276 real changes made) . . * a) Pooled: All observations . hist prior, width(1) xaxis(1) /// > xtitle("a) Prior beliefs (pooled)", axis(1)) xla(60 65 70 75 80 85 90 95 100, axis(1)) frac (bin=43, start=59, width=1) . graph save "$output\pooled.gph", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\pooled.gph saved) . . * b) Non-incentivized priors only . hist prior if prior1==0, width(1) xaxis(1) /// > xtitle("b) Prior beliefs (non-incentivized)", axis(1)) xla(60 65 70 75 80 85 90 95 100, axis(1) > ) frac (bin=43, start=59, width=1) . graph save "$output\noninc.gph", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\noninc.gph saved) . . * c) Beliefs incentivized on the ACS only . hist prior if prior1==1&(T1==1|RAND6==1), width(1) xaxis(1) /// > xtitle("c) Prior beliefs (incentivized based on ACS)", axis(1)) xla(60 65 70 75 80 85 90 95 100 > , axis(1)) frac (bin=43, start=59, width=1) . graph save "$output\inc_ACS.gph", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\inc_ACS.gph saved) . . * d) Beliefs incentivized on the CPS only . hist prior if prior1==1&(T2==1|RAND6==0), width(1) xaxis(1) /// > xtitle("d) Prior beliefs (incentivized based on CPS)", axis(1)) xla(60 65 70 75 80 85 90 95 100 > , axis(1)) frac (bin=43, start=59, width=1) . graph save "$output\inc_CPS.gph", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\inc_CPS.gph saved) . . graph combine "$output\pooled.gph" "$output\noninc.gph" "$output\inc_ACS.gph" "$output\inc_CPS.gph > ", cols(2) . graph export "$output\Priorhist_all4.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\Priorhist_all4.pdf written in PDF format) . . . // Information in Figure notes: . . use "$path\data\SurveyStageI_AB_final.dta", clear . . *Number of obs. and median . * Panel a) . sum prior,d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% 31 0 5% 50 1 10% 61 2 Obs 4,065 25% 75 2 Sum of Wgt. 4,065 50% 81 Mean 83.36531 Largest Std. Dev. 21.67554 75% 90 200 90% 100 200 Variance 469.8289 95% 116 200 Skewness 1.382362 99% 173 200 Kurtosis 10.03941 . *Panel b) . sum prior if prior1==0,d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% 35 1 5% 50 3 10% 60 9 Obs 1,772 25% 75 10 Sum of Wgt. 1,772 50% 81 Mean 83.51524 Largest Std. Dev. 22.12957 75% 91 200 90% 100 200 Variance 489.718 95% 117 200 Skewness 1.534594 99% 175 200 Kurtosis 10.32276 . * Panel c) . sum prior if prior1==1&(T1==1|RAND6==1),d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% 31 2 5% 50 2 10% 63 2 Obs 1,142 25% 75 7 Sum of Wgt. 1,142 50% 81 Mean 83.56305 Largest Std. Dev. 21.86668 75% 90 200 90% 100 200 Variance 478.1516 95% 115 200 Skewness 1.498269 99% 180 200 Kurtosis 10.69759 . * Panel d) . sum prior if prior1==1&(T2==1|RAND6==0),d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% 30 0 5% 50 2 10% 62 2 Obs 1,151 25% 75 9 Sum of Wgt. 1,151 50% 81 Mean 82.93831 Largest Std. Dev. 20.77265 75% 90 180 90% 100 186 Variance 431.5031 95% 116 192 Skewness .95265 99% 157 195 Kurtosis 8.498212 . . *Means . mean prior [pweight=pweight] Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ prior | 83.49412 .3432603 82.82114 84.1671 -------------------------------------------------------------- . mean prior [pweight=pweight] if prior1==0 Mean estimation Number of obs = 1,772 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ prior | 83.63559 .5326054 82.59099 84.68019 -------------------------------------------------------------- . mean prior [pweight=pweight] if prior1==1&(T1==1|RAND6==1) Mean estimation Number of obs = 1,142 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ prior | 83.73156 .655685 82.44507 85.01804 -------------------------------------------------------------- . mean prior [pweight=pweight] if prior1==1&(T2==1|RAND6==0) Mean estimation Number of obs = 1,151 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ prior | 83.04014 .6119458 81.83949 84.2408 -------------------------------------------------------------- . . *********************************************************************************** . // Figure A.9: Behavioral outcomes (pure control group) . *********************************************************************************** . . clear all . set scheme s2mono . . // Panel A: Propensity to sign a petition . . /* Number of potential signatures for Petitions I and II per treatment group correspond to the num > ber of > respondents assigned to either treatment group. > The numbers of actual signatures for Petitions I and II are all "manually" retrieved from the Whit > e House > Petition Website. > */ . . // PETITION I . /* Run prtesti for a two-sided proportion test for Petition I by gender and by pol. orientation > Input gender: Total number of potential signatures among women in control group (544) > Number of actual signatures among women in control group (101) > Total number of potential signatures among men in control group (4 > 90) > Number of actual signatures among men in control group (58) > Input pol. : Total number of potential signatures among Democrats in control group (230 > ) > Number of actual signatures among Democrats in control group (50) > Total number of potential signatures among Non-Democrats in contro > l group (268) > Number of actual signatures among Non-Democrats in control group ( > 24) > Output: Proportion of signatures by group (incl. 95% CI) > P-value of two-sided proportion test > --> Output entered manually below > */ . . * Women vs. men: . prtesti 544 101 490 58, count Two-sample test of proportions x: Number of obs = 544 y: Number of obs = 490 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .1856618 .0166711 .152987 .2183365 y | .1183673 .0145936 .0897645 .1469702 -------------+---------------------------------------------------------------- diff | .0672944 .0221562 .023869 .1107198 | under Ho: .022467 3.00 0.003 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 2.9953 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.9986 Pr(|Z| < |z|) = 0.0027 Pr(Z > z) = 0.0014 . . * Democrats vs. Non-Democrats: . prtesti 230 50 268 24, count Two-sample test of proportions x: Number of obs = 230 y: Number of obs = 268 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .2173913 .0271975 .1640851 .2706975 y | .0895522 .0174421 .0553664 .1237381 -------------+---------------------------------------------------------------- diff | .1278391 .0323099 .0645128 .1911654 | under Ho: .0319707 4.00 0.000 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 3.9986 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 1.0000 Pr(|Z| < |z|) = 0.0001 Pr(Z > z) = 0.0000 . . . // PETITION II . /* Run prtesti for a two-sided proportion test for Petition I by gender and by pol. orientation > Input gender: Total number of potential signatures among women in control group (544) > Number of actual signatures among women in control group (3) > Total number of potential signatures among men in control group (4 > 90) > Number of actual signatures among men in control group (17) > Input pol. : Total number of potential signatures among Democrats in control group (230 > ) > Number of actual signatures among Democrats in control group (2) > Total number of potential signatures among Non-Democrats in contro > l group (268) > Number of actual signatures among Non-Democrats in control group ( > 8) > Output: Proportion of signatures by group (incl. 95% CI) > P-value of two-sided proportion test > --> Output entered manually below > */ . . * Women vs. men: . prtesti 544 3 490 17, count Two-sample test of proportions x: Number of obs = 544 y: Number of obs = 490 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0055147 .0031751 -.0007084 .0117378 y | .0346939 .0082672 .0184904 .0508974 -------------+---------------------------------------------------------------- diff | -.0291792 .008856 -.0465366 -.0118217 | under Ho: .0085778 -3.40 0.001 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -3.4017 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.0003 Pr(|Z| < |z|) = 0.0007 Pr(Z > z) = 0.9997 . . * Democrats vs. Non-Democrats: . prtesti 230 2 268 8, count Two-sample test of proportions x: Number of obs = 230 y: Number of obs = 268 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0086957 .006122 -.0033032 .0206945 y | .0298507 .0103951 .0094767 .0502248 -------------+---------------------------------------------------------------- diff | -.0211551 .0120639 -.0447998 .0024897 | under Ho: .0126085 -1.68 0.093 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -1.6778 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.0467 Pr(|Z| < |z|) = 0.0934 Pr(Z > z) = 0.9533 . . . **** Insert numbers derived from prtesti to generate bar graphs . . // Petition I by gender: . . /* prtesti 544 101 490 58, count > > Two-sample test of proportions x: Number of obs = 544 > y: Number of obs = 490 > ------------------------------------------------------------------------------ > Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > x | .1856618 .0166711 .152987 .2183365 > y | .1183673 .0145936 .0897645 .1469702 > -------------+---------------------------------------------------------------- > diff | .0672944 .0221562 .023869 .1107198 > | under Ho: .022467 3.00 0.003 > ------------------------------------------------------------------------------ > diff = prop(x) - prop(y) z = 2.9953 > Ho: diff = 0 > > Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 > Pr(Z < z) = 0.9986 Pr(|Z| < |z|) = 0.0027 Pr(Z > z) = 0.0014 */ . . . mat R=J(2,5,.) // Set up matrix . . // Insert values: . local pvalue1 = 0.01 . . * Means . mat R[1,1] = 0.1857 // women Pet I . mat R[2,1] = 0.1184 // men Pet I . . * Lower bounds . mat R[1,2] = 0.1530 // women Pet I . mat R[2,2] = 0.0898 // men Pet I . . * Upper bounds . mat R[1,3] = 0.2183 // women Pet I . mat R[2,3] = 0.1470 // men Pet I . . // Define some coordinates . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 1 . mat R[2,5] = 1 . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat1 . save `cat1' // Save matrix as dataset file C:\Users\gxf271\AppData\Local\Temp\ST_00000002.tmp saved . restore . . ************************************* . // Petition I by dem-rep: . . /* prtesti 230 50 268 24, count > > Two-sample test of proportions x: Number of obs = 230 > y: Number of obs = 268 > ------------------------------------------------------------------------------ > Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > x | .2173913 .0271975 .1640851 .2706975 > y | .0895522 .0174421 .0553664 .1237381 > -------------+---------------------------------------------------------------- > diff | .1278391 .0323099 .0645128 .1911654 > | under Ho: .0319707 4.00 0.000 > ------------------------------------------------------------------------------ > diff = prop(x) - prop(y) z = 3.9986 > Ho: diff = 0 > > Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 > Pr(Z < z) = 1.0000 Pr(|Z| < |z|) = 0.0001 Pr(Z > z) = 0.0000*/ . . . // Insert values: . local pvalue2 = 0.001 . . * Means . mat R[1,1] = 0.2174 // dem Pet I . mat R[2,1] = 0.0896 // rep Pet I . . * Lower bounds . mat R[1,2] = 0.1641 // dem Pet I . mat R[2,2] = 0.0554 // rep Pet I . . * Upper bounds . mat R[1,3] = 0.2707 // dem Pet I . mat R[2,3] = 0.1237 // rep Pet I . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 2 . mat R[2,5] = 2 . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat2 . save `cat2' // Save matrix as dataset file C:\Users\gxf271\AppData\Local\Temp\ST_00000004.tmp saved . restore . . . ************************************************* . // Petition II by gender . . /* prtesti 544 3 490 17, count > > Two-sample test of proportions x: Number of obs = 544 > y: Number of obs = 490 > ------------------------------------------------------------------------------ > Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > x | .0055147 .0031751 -.0007084 .0117378 > y | .0346939 .0082672 .0184904 .0508974 > -------------+---------------------------------------------------------------- > diff | -.0291792 .008856 -.0465366 -.0118217 > | under Ho: .0085778 -3.40 0.001 > ------------------------------------------------------------------------------ > diff = prop(x) - prop(y) z = -3.4017 > Ho: diff = 0 > > Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 > Pr(Z < z) = 0.0003 Pr(|Z| < |z|) = 0.0007 Pr(Z > z) = 0.9997 > */ . . . // Insert values: . local pvalue3 = 0.001 . . * Means . mat R[1,1] = 0.0056 // women Pet II . mat R[2,1] = 0.0347 // men Pet II . . * Lower bounds . mat R[1,2] = -0.0007 // women Pet II . mat R[2,2] = 0.0185 // men Pet II . . * Upper bounds . mat R[1,3] = 0.0117 // women Pet II . mat R[2,3] = 0.0509 // men Pet II . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 3 . mat R[2,5] = 3 . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat3 . save `cat3' file C:\Users\gxf271\AppData\Local\Temp\ST_00000006.tmp saved . restore . . ************************************************' . // Petition II by dem-rep . . /*prtesti 230 2 268 8, count > > Two-sample test of proportions x: Number of obs = 230 > y: Number of obs = 268 > ------------------------------------------------------------------------------ > Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > x | .0086957 .006122 -.0033032 .0206945 > y | .0298507 .0103951 .0094767 .0502248 > -------------+---------------------------------------------------------------- > diff | -.0211551 .0120639 -.0447998 .0024897 > | under Ho: .0126085 -1.68 0.093 > ------------------------------------------------------------------------------ > diff = prop(x) - prop(y) z = -1.6778 > Ho: diff = 0 > > Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 > Pr(Z < z) = 0.0467 Pr(|Z| < |z|) = 0.0934 Pr(Z > z) = 0.9533*/ . . . local pvalue4 = 0.09 . . * Means . mat R[1,1] = 0.0087 // dem . mat R[2,1] = 0.0299 // rep . . * Lower bounds . mat R[1,2] = -0.0033 // dem . mat R[2,2] = 0.0095 // rep . . * Upper bounds . mat R[1,3] = 0.0201 // dem . mat R[2,3] = 0.0502 // rep . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 4 . mat R[2,5] = 4 . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat4 . save `cat4' file C:\Users\gxf271\AppData\Local\Temp\ST_00000008.tmp saved . restore . . . // Append 4 datasets . clear . . local numcats = "1 2 3 4" . foreach a of local numcats { 2. append using `cat`a'' 3. } . . . * For alignment along the x-axis . gen s1 = R5 . gen s2 = . (8 missing values generated) . replace s2 = s1 - 0.2 if R5 == 1 (2 real changes made) . replace s2 = s1 - 0.6 if R5 == 2 (2 real changes made) . replace s2 = s1 - 1.0 if R5 == 3 (2 real changes made) . replace s2 = s1 - 1.4 if R5 == 4 (2 real changes made) . gen p1 = (s2 - 0.1) - .6 . gen p2 = s2 + 0.1 - .6 . . . * This recovers the group means with which to label each bar. . local i = 0 . foreach cat of local numcats { 2. forval rel = 1/2 { 3. local ++i 4. sum R1 if R4 == `rel' & R5 == `cat' 5. local barval`i' = trim("`: di %9.2f r(mean)'") 6. } 7. } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .1857 . .1857 .1857 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .1184 . .1184 .1184 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .2174 . .2174 .2174 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .0896 . .0896 .0896 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .0056 . .0056 .0056 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .0347 . .0347 .0347 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .0087 . .0087 .0087 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .0299 . .0299 .0299 . . . global barlabels `"0.2 "Gender" 0.8 "Pol. orientation" 1.4 "Gender" 2.0 "Pol. orientation" > "' . global pvalues `"0.29 0.2 "p-value < `pvalue1'" 0.29 0.8 "p-value < `pvalue2'" 0.25 1.4 "p > -value < `pvalue3'" 0.25 2.0 "p-value = `pvalue4'""' . global grouplabels `"0.32 0.5 "Petition I (increase reporting)" 0.32 1.7 "Petition II (abo > lish reporting)""' . global bargroups = `"0.02 0.1 "Women" 0.02 0.3 "Men" 0.02 0.7 "Dem." 0.02 0.9 "Non-Dem." 0 > .02 1.3 "Women" 0.02 1.5 "Men" 0.02 1.9 "Dem." 0.02 2.1 "Non-Dem.""' . global barvalues = `"0.045 0.1 "`barval1'" 0.045 0.3 "`barval2'" 0.045 0.7 "`barval3'" 0.0 > 45 0.9 "`barval4'" 0.061 1.3 "`barval5'" 0.061 1.5 "`barval6'" 0.061 1.9 "`barval7'" 0.061 2.1 "`b > arval8'""' . . . twoway (bar R1 p1 if R4 == 1, barw(0.18) fi(inten50) lc(black) lw(medium)) (bar R1 p2 if R4 == 2, > barw(0.18) fi(inten20) lc(black) lw(medium)) /// > (rcap R3 R2 p1 if R4 == 1, lc(gs5)) (rcap R3 R2 p2 if R4 == 2, lc(gs5)), legend(off) graph > region(color(white)) /// > yscale(range(0.35)) yla(0(0.05)0.25) xla($barlabels, labsize(2.5)) text($pvalues, size(2.5 > )) text($grouplabels, size(2.8)) text($bargroups, size(2.5)) text($barvalues, size(2.5)) /// > ytitle("Fraction of respondents who signed", height(5)) . graph export "$output\fig_petitions0.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\fig_petitions0.pdf written in PDF format) . . . // Panel B: Donations made to an NGO . use "$path\data\SurveyStageI_AB_final.dta", clear . . keep rand wave donation democrat republican gender pweight . . * Keep only control group . keep if rand==0 (3,031 observations deleted) . . gen women=(gender==1) . gen men=(gender==0) . . gen donationmen=donation if gender==0 (544 missing values generated) . gen donationwoman=donation if gender==1 (490 missing values generated) . gen donationrepub=donation if republican==1 (673 missing values generated) . gen donationdem =donation if democrat==1 (564 missing values generated) . gen donationnondem=donation if democrat==0 (470 missing values generated) . . lab var donationmen "Men" . lab var donationwoman "Women" . lab var donationrepub "Republican" . lab var donationdem "Democrat" . lab var donationnondem "Non-Democrat" . . *drop gender . . local outcome = "donation" . . **** Calculate numbers for bar graph matrix . . * Set up matrix for bars by gender . mat R=J(2,5,.) . . * Store means by gender . local row=1 . foreach X in women men { 2. mean `outcome' if `X' == 1 [pweight=pweight] 3. mat R[`row',1] = e(b) 4. local ++row 5. } Mean estimation Number of obs = 544 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donation | 78.92025 3.731155 71.59098 86.24952 -------------------------------------------------------------- Mean estimation Number of obs = 490 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donation | 88.56383 4.430802 79.85807 97.26959 -------------------------------------------------------------- . . * Calculate and store mean belief for women . mean `outcome' if women == 1 [pweight=pweight] Mean estimation Number of obs = 544 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donation | 78.92025 3.731155 71.59098 86.24952 -------------------------------------------------------------- . matrix meanwomen=e(b) . . * Calculate and store gender coefficient and p-value . reg `outcome' men [pweight=pweight], robust (sum of wgt is 1.0167e+03) Linear regression Number of obs = 1,034 F(1, 1032) = 2.77 Prob > F = 0.0962 R-squared = 0.0027 Root MSE = 92.281 ------------------------------------------------------------------------------ | Robust donation | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- men | 9.643579 5.792476 1.66 0.096 -1.722796 21.00995 _cons | 78.92025 3.731335 21.15 0.000 71.59838 86.24212 ------------------------------------------------------------------------------ . local pvalue1 = trim("`: di %9.3f 2*ttail(e(df_r), abs(_b[men]/_se[men]))'") . . local row=1 . foreach X in women men { 2. mat R[`row',2]= meanwomen[1,1] + _b[men]-1.96*_se[men] 3. mat R[`row',3]= meanwomen[1,1] + _b[men]+1.96*_se[men] 4. mat R[`row',4]=`row' 5. mat R[`row',5] = 1 6. local ++row 7. } . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat1 . save `cat1' // Save matrix with numbers for gender bars as dataset file C:\Users\gxf271\AppData\Local\Temp\ST_0000000a.tmp saved . restore . . // Drop independents and those with "other" political orientation . keep if democrat==1|republican==1 (203 observations deleted) . . * Set up matrix for bars by Dem-Rep . mat R=J(2,5,.) . . * Store means by Dem-Rep . local row=1 . foreach X in democrat republican { 2. mean `outcome' if `X' == 1 [pweight=pweight] 3. mat R[`row',1] = e(b) 4. local ++row 5. } Mean estimation Number of obs = 470 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donation | 93.03085 4.296592 84.5879 101.4738 -------------------------------------------------------------- Mean estimation Number of obs = 361 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donation | 75.0664 4.899083 65.43199 84.70082 -------------------------------------------------------------- . . * Calculate and store mean belief for Democrats . mean `outcome' if democrat == 1 Mean estimation Number of obs = 470 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donation | 93.31489 4.249469 84.96454 101.6652 -------------------------------------------------------------- . matrix meandemocrat=e(b) . . * Calculate and store pol coefficient and p-value . reg `outcome' republican [pweight=pweight], robust (sum of wgt is 8.1437e+02) Linear regression Number of obs = 831 F(1, 829) = 7.60 Prob > F = 0.0060 R-squared = 0.0092 Root MSE = 92.426 ------------------------------------------------------------------------------ | Robust donation | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- republican | -17.96445 6.515991 -2.76 0.006 -30.75423 -5.174665 _cons | 93.03085 4.297193 21.65 0.000 84.59619 101.4655 ------------------------------------------------------------------------------ . local pvalue2 = trim("`: di %9.3f 2*ttail(e(df_r), abs(_b[republican]/_se[republican]))'") . . local row=1 . foreach X in women men { 2. mat R[`row',2]= meandemocrat[1,1] + _b[republican]-1.96*_se[republican] 3. mat R[`row',3]= meandemocrat[1,1] + _b[republican]+1.96*_se[republican] 4. mat R[`row',4]=`row' 5. mat R[`row',5] = 2 6. local ++row 7. } . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat2 . save `cat2' // Save matrix with numbers for pol orientation bars as dataset file C:\Users\gxf271\AppData\Local\Temp\ST_0000000c.tmp saved . restore . . . clear . . // Append the two data sets that contain the numbers for all four bars . local numcats = "1 2" . foreach a of local numcats { 2. append using `cat`a'' 3. } . . . * For alignment along the x-axis . gen s1 = R5 . gen s2 = . (4 missing values generated) . replace s2 = s1 - 0.2 if R5 == 1 (2 real changes made) . replace s2 = s1 - 0.6 if R5 == 2 (2 real changes made) . replace s2 = s1 - 1.0 if R5 == 3 (0 real changes made) . replace s2 = s1 - 1.4 if R5 == 4 (0 real changes made) . gen p1 = (s2 - 0.1) - .6 // Fix position for gender bars . gen p2 = s2 + 0.1 - .6 // Fix position for pol. orientation bars . . * This recovers the group means with which to label each bar. . local i = 0 . foreach p of local numcats { 2. forval rel = 1/2 { 3. local ++i 4. sum R1 if R4 == `rel' & R5 == `p' 5. local barval`i' = trim("`: di %9.2f r(mean)'") 6. } 7. } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 78.92025 . 78.92025 78.92025 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 88.56383 . 88.56383 88.56383 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 93.03085 . 93.03085 93.03085 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 75.06641 . 75.06641 75.06641 . . * Bar labels . global barlabels `"0.2 "Gender" 0.8 "Pol. orientation" "' . global pvalues `"108 0.2 "p-value = `pvalue1'" 108 0.8 "p-value < 0.01" "' . global grouplabels `"118 0.5 "Amount donated""' . global bargroups = `"10 0.1 "Women" 10 0.3 "Men" 10 0.7 "Dem." 10 0.9 "Repub." "' . global barvalues = `"20.045 0.1 "`barval1'" 20.045 0.3 "`barval2'" 20.045 0.7 "`barval3'" > 20.045 0.9 "`barval4'" "' . . . twoway (bar R1 p1 if R4 == 1, barw(0.18) fi(inten50) lc(black) lw(medium)) (bar R1 p2 if R4 == 2, > barw(0.18) fi(inten20) lc(black) lw(medium)) /// > (rcap R3 R2 p2 if R4 == 2, lc(gs5)), legend(off) graphregion(color(white)) /// > yscale(range(2)) yla(0(20)120) xla($barlabels, labsize(5.5)) text($pvalues, size(5.5)) tex > t($grouplabels, size(6.0)) text($bargroups, size(5.0)) text($barvalues, size(5.5)) /// > ytitle("Amount donated", size(5.5) height(5)) . graph export "$output\fig_donation0.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\fig_donation0.pdf written in PDF format) . . . . *********************************************************************************** . // Figure A.10: Distribution of posterior beliefs in both treatment groups . *********************************************************************************** . . use "$path\data\SurveyStageI_AB_final.dta", clear . . // Drop pure control group . drop if rand==0 (1,034 observations deleted) . . // Define a variable indicating which posterior belief the respondent was assigned to . * High wage gap treatment (T74) . gen randnew=0 if wave==1&RAND4==9&rand==1 // Age 25 (2,682 missing values generated) . replace randnew=1 if wave==1&RAND4==10&rand==1 // HS degree (348 real changes made) . replace randnew=2 if wave==1&RAND4==11&rand==1 // Same occu group (308 real changes made) . replace randnew=3 if wave==2&RAND4==10&rand==1 // Same job and employer (276 real changes made) . replace randnew=4 if wave==2&RAND4==11&rand==1 // Parent (250 real changes made) . . * Low wage gap treatment (T94) . replace randnew=5 if wave==1&RAND4==9&rand==2 // Age 25 (324 real changes made) . replace randnew=6 if wave==1&RAND4==10&rand==2 // HS degree (330 real changes made) . replace randnew=7 if wave==1&RAND4==11&rand==2 // Same occu group (353 real changes made) . replace randnew=8 if wave==2&RAND4==10&rand==2 // Same job and employer (247 real changes made) . replace randnew=9 if wave==2&RAND4==11&rand==2 // Parent (246 real changes made) . . * Assign labels . label define randnew 0 "T{sup:74} (Age 25)" 1 "T{sup:74} (HS degree)" 2 "T{sup:74} (Same occu. gro > up)" 3 "T{sup:74} (Same job)" 4 "T{sup:74} (Parent)" 5 "T{sup:94} (age 25)" 6 "T{sup:94} (HS degre > e)" 7 "T{sup:94} (same occu)" 8 "T{sup:94} (Same job)" 9 "T{sup:94} (Parent)" . label values randnew randnew . . //Winsorize posteriors at 49 and 101: . replace posterior=101 if posterior>101&posterior!=. (165 real changes made) . replace posterior=49 if posterior<49 (59 real changes made) . . // Generate individual histograms (top panel) . twoway histogram posterior if randnew==0, width(1) title("T{sup:74} (Age 25)") fraction width(5) x > line(74, lcolor(blue)) xla(50 75 95 100) yscale(range(0 0.52)) name(graph1, replace) . . twoway histogram posterior if randnew==1, width(1) title("T{sup:74} (HS degree)") fraction width(5 > ) xline(74, lcolor(blue)) xla(50 75 95 100) yscale(range(0 0.52)) name(graph2, replace) . . twoway histogram posterior if randnew==2, width(1) title("T{sup:74} (Same occu. group)") fraction > width(5) xline(74, lcolor(blue)) xla(50 75 95 100) yscale(range(0 0.52)) name(graph3, replace) . . twoway histogram posterior if randnew==3, width(1) title("T{sup:74} (Same job)") fraction width(5) > xline(74, lcolor(blue)) xla(50 75 95 100) yscale(range(0 0.52)) name(graph4, replace) . . twoway histogram posterior if randnew==4, width(1) title("T{sup:74} (Parent)") fraction width(5) x > line(74, lcolor(blue)) xla(50 75 95 100) yscale(range(0 0.52)) name(graph5, replace) . . // Combine top panel . graph combine graph1 graph2 graph3 graph4 graph5, rows(1) xsize(6) ysize(1.5) name(coefplot, repla > ce) . graph export "$output\posterior_histog_treat_row1_win1.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\posterior_histog_treat_row1_win1.pdf written in PDF format) . . . // Generate individual histograms (bottom panel) . twoway histogram posterior if randnew==5, width(1) title("T{sup:94} (Age 25)") fraction width(5) x > line(94, lcolor(green)) xla(50 75 95 100) yscale(range(0 0.52)) name(graph1, replace) . . twoway histogram posterior if randnew==6, width(1) title("T{sup:94} (HS degree)") fraction width(5 > ) xline(94, lcolor(green)) xla(50 75 95 100) yscale(range(0 0.52)) name(graph2, replace) . . twoway histogram posterior if randnew==7, width(1) title("T{sup:94} (Same occu. group)") fraction > width(5) xline(94, lcolor(green)) xla(50 75 95 100) yscale(range(0 0.52)) name(graph3, replace > ) . . twoway histogram posterior if randnew==8, width(1) title("T{sup:94} (Same job)") fraction width(5) > xline(94, lcolor(green)) xla(50 75 95 100) yscale(range(0 0.52)) name(graph4, replace) . . twoway histogram posterior if randnew==9, width(1) title("T{sup:94} (Parent)") fraction width(5) x > line(94, lcolor(green)) xla(50 75 95 100) yscale(range(0 0.52)) name(graph5, replace) . . graph combine graph1 graph2 graph3 graph4 graph5, rows(1) xsize(6) ysize(1.5) name(coefplot, repla > ce) . graph export "$output\posterior_histog_treat_row2_win1.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\posterior_histog_treat_row2_win1.pdf written in PDF format) . . . *********************************************************************************** . // Figure A.11: Donation Decision . *********************************************************************************** . . clear all . set scheme s2mono . . global legend = `"label(1 "T{sup:74}") label(2 "T{sup:94}") order(1 2) size(medium)"' . . use "$path\data\SurveyStageI_AB_final.dta", clear . . keep rand T1 T2 wave donation democrat republican indep otherpol gender pweight prior midwest sout > h west age1 age2 age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemp stud > ent . *global controls wave gender democrat republican otherpol midwest south west age1 age2 age3 age4 a > nychildren loghhinc associatemore fulltime parttime selfemp unemp student . . . * Drop control group, keep only treated respondents . drop if rand==0 (1,034 observations deleted) . . gen donationmen=donation if gender==0 (1,564 missing values generated) . gen donationwoman=donation if gender==1 (1,467 missing values generated) . gen donationrepub=donation if republican==1 (1,930 missing values generated) . gen donationdem =donation if democrat==1 (1,696 missing values generated) . gen donationnondem=donation if democrat==0 (1,335 missing values generated) . . local out1 = "donation" . local out2 = "donationmen" . local out3 = "donationwoman" . local out4 = "donationdem" . local out5 = "donationnondem" . local numcats = "1 2 3 4 5" . . . **** Calculate numbers for bar graph matrix . . forvalues a=1/5{ 2. . * Set up matrix . mat R=J(2,5,.) 3. . * Store mean of outcome by treatment condition . local row=1 4. foreach X in T1 T2{ 5. mean `out`a'' if `X' == 1 [pweight=pweight] 6. mat R[`row',1] = e(b) 7. local ++row 8. } 9. . . * Calculate and store treatment coeficient and p-value . reg `out`a'' T2 $controls [pweight=pweight], robust 10. local pvalue`a' = trim("`: di %9.3f 2*ttail(e(df_r), abs(_b[T2]/_se[T2]))'") 11. . * Fill output of above steps into matrix . local row=1 12. foreach X in T1 T2 { 13. mat R[`row',2]= R[1,1] + _b[T2]-1.96*_se[T2] // calculate upper bound for CI 14. mat R[`row',3]= R[1,1] + _b[T2]+1.96*_se[T2] // calculate lower bound for CI 15. mat R[`row',4]=`row' 16. mat R[`row',5] = `a' 17. local ++row 18. } 19. . . preserve 20. clear 21. svmat R 22. tempfile cat`a' 23. save `cat`a'' 24. restore 25. . } Mean estimation Number of obs = 1,531 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donation | 89.69321 2.439522 84.90805 94.47837 -------------------------------------------------------------- Mean estimation Number of obs = 1,500 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donation | 87.82939 2.454485 83.0148 92.64398 -------------------------------------------------------------- (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 7.17 Prob > F = 0.0000 R-squared = 0.0467 Root MSE = 92.566 ------------------------------------------------------------------------------- | Robust donation | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T2 | -2.392756 3.404181 -0.70 0.482 -9.067513 4.282002 wave | -7.256078 3.599304 -2.02 0.044 -14.31342 -.1987324 gender | -10.47757 3.524809 -2.97 0.003 -17.38885 -3.566295 prior | .1764424 .0800391 2.20 0.028 .0195055 .3333793 democrat | 28.80245 3.848012 7.49 0.000 21.25744 36.34745 indep | 12.14197 5.013233 2.42 0.015 2.31226 21.97168 otherpol | 8.0855 12.99897 0.62 0.534 -17.40226 33.57326 midwest | -4.753814 5.531027 -0.86 0.390 -15.59879 6.091163 south | -2.151573 5.01122 -0.43 0.668 -11.97734 7.674192 west | -2.205502 5.389712 -0.41 0.682 -12.7734 8.362391 age1 | 26.73146 7.551308 3.54 0.000 11.92521 41.5377 age2 | 25.08332 5.473783 4.58 0.000 14.35058 35.81605 age3 | 14.25737 5.431244 2.63 0.009 3.608048 24.9067 age4 | -.855226 4.868367 -0.18 0.861 -10.40089 8.690439 anychildren | 7.729726 3.727625 2.07 0.038 .4207754 15.03868 loghhinc | 6.56166 2.364772 2.77 0.006 1.924927 11.19839 associatemore | 6.167519 3.705495 1.66 0.096 -1.098042 13.43308 fulltime | -5.565094 5.258442 -1.06 0.290 -15.8756 4.745412 parttime | -7.575232 6.495687 -1.17 0.244 -20.31167 5.161206 selfemp | -4.130282 7.390852 -0.56 0.576 -18.62192 10.36135 unemployed | 2.115884 8.379999 0.25 0.801 -14.31522 18.54699 student | -11.14789 10.2155 -1.09 0.275 -31.17796 8.882177 _cons | -9.564605 26.39839 -0.36 0.717 -61.32533 42.19612 ------------------------------------------------------------------------------- number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 file C:\Users\gxf271\AppData\Local\Temp\ST_0000000e.tmp saved Mean estimation Number of obs = 733 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donationmen | 98.63861 3.674787 91.42423 105.853 -------------------------------------------------------------- Mean estimation Number of obs = 734 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donationmen | 90.73072 3.505761 83.84819 97.61325 -------------------------------------------------------------- (sum of wgt is 1.4941e+03) note: gender omitted because of collinearity Linear regression Number of obs = 1,467 F(21, 1445) = 5.01 Prob > F = 0.0000 R-squared = 0.0626 Root MSE = 94.455 ------------------------------------------------------------------------------- | Robust donationmen | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T2 | -9.065251 4.965294 -1.83 0.068 -18.80521 .6747042 wave | .688746 5.37237 0.13 0.898 -9.849733 11.22723 gender | 0 (omitted) prior | .325556 .1087357 2.99 0.003 .1122594 .5388527 democrat | 27.5225 5.531154 4.98 0.000 16.67255 38.37245 indep | 13.09812 7.573328 1.73 0.084 -1.757771 27.95402 otherpol | 2.650506 22.07406 0.12 0.904 -40.65013 45.95114 midwest | -6.975123 8.016095 -0.87 0.384 -22.69955 8.749304 south | -6.337934 7.059619 -0.90 0.369 -20.18613 7.510264 west | -6.962621 7.342156 -0.95 0.343 -21.36505 7.439804 age1 | 40.36453 11.61779 3.47 0.001 17.57499 63.15406 age2 | 29.29255 8.143504 3.60 0.000 13.3182 45.26691 age3 | 18.48063 8.097584 2.28 0.023 2.596351 34.36491 age4 | -2.707852 7.565494 -0.36 0.720 -17.54838 12.13267 anychildren | 14.06464 5.535278 2.54 0.011 3.206596 24.92268 loghhinc | 8.540989 3.694106 2.31 0.021 1.294604 15.78737 associatemore | .2948614 5.600528 0.05 0.958 -10.69117 11.2809 fulltime | -5.192084 8.736913 -0.59 0.552 -22.33047 11.94631 parttime | -12.5281 10.49255 -1.19 0.233 -33.11037 8.054165 selfemp | -15.68046 11.22828 -1.40 0.163 -37.70594 6.345017 unemployed | 12.18676 13.11824 0.93 0.353 -13.54607 37.91959 student | -26.12689 16.02747 -1.63 0.103 -57.56649 5.312701 _cons | -49.22747 39.62346 -1.24 0.214 -126.9531 28.49819 ------------------------------------------------------------------------------- number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 file C:\Users\gxf271\AppData\Local\Temp\ST_0000000g.tmp saved Mean estimation Number of obs = 798 --------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] --------------+------------------------------------------------ donationwoman | 80.95137 3.189489 74.69058 87.21216 --------------------------------------------------------------- Mean estimation Number of obs = 766 --------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] --------------+------------------------------------------------ donationwoman | 84.88741 3.43474 78.14477 91.63004 --------------------------------------------------------------- (sum of wgt is 1.5012e+03) note: gender omitted because of collinearity Linear regression Number of obs = 1,564 F(21, 1542) = 3.62 Prob > F = 0.0000 R-squared = 0.0468 Root MSE = 90.1 ------------------------------------------------------------------------------- | Robust donationwoman | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T2 | 4.484868 4.627207 0.97 0.333 -4.591416 13.56115 wave | -15.42497 4.851548 -3.18 0.002 -24.9413 -5.908641 gender | 0 (omitted) prior | -.0404309 .1152599 -0.35 0.726 -.2665136 .1856518 democrat | 29.73434 5.36422 5.54 0.000 19.21241 40.25628 indep | 11.78381 6.761116 1.74 0.082 -1.478144 25.04576 otherpol | 13.47446 15.94416 0.85 0.398 -17.80006 44.74897 midwest | 1.116414 7.709499 0.14 0.885 -14.0058 16.23862 south | 4.14525 7.08547 0.59 0.559 -9.752924 18.04342 west | 5.729267 7.886369 0.73 0.468 -9.739874 21.19841 age1 | 9.158854 9.293618 0.99 0.325 -9.070611 27.38832 age2 | 19.86813 7.451288 2.67 0.008 5.252399 34.48386 age3 | 11.92529 7.475926 1.60 0.111 -2.738769 26.58934 age4 | 2.513429 6.366291 0.39 0.693 -9.974075 15.00093 anychildren | -.3004424 5.196403 -0.06 0.954 -10.49321 9.89232 loghhinc | 3.990711 3.086132 1.29 0.196 -2.062748 10.04417 associatemore | 12.77392 4.951426 2.58 0.010 3.061682 22.48616 fulltime | -8.531045 6.666948 -1.28 0.201 -21.60829 4.546199 parttime | -5.842634 8.222706 -0.71 0.477 -21.9715 10.28623 selfemp | 6.852867 10.05581 0.68 0.496 -12.87164 26.57737 unemployed | -8.119872 10.99148 -0.74 0.460 -29.67971 13.43997 student | 9.596596 12.16058 0.79 0.430 -14.25643 33.44962 _cons | 30.31401 35.53966 0.85 0.394 -39.39716 100.0252 ------------------------------------------------------------------------------- number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 file C:\Users\gxf271\AppData\Local\Temp\ST_0000000i.tmp saved Mean estimation Number of obs = 681 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donationdem | 106.2953 3.777479 98.87833 113.7122 -------------------------------------------------------------- Mean estimation Number of obs = 654 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donationdem | 99.41073 3.625381 92.29192 106.5295 -------------------------------------------------------------- (sum of wgt is 1.3150e+03) note: democrat omitted because of collinearity note: indep omitted because of collinearity note: otherpol omitted because of collinearity Linear regression Number of obs = 1,335 F(19, 1315) = 1.57 Prob > F = 0.0547 R-squared = 0.0228 Root MSE = 94.287 ------------------------------------------------------------------------------- | Robust donationdem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T2 | -7.014867 5.244861 -1.34 0.181 -17.30408 3.274343 wave | -3.570574 5.693562 -0.63 0.531 -14.74003 7.598882 gender | -8.208181 5.445447 -1.51 0.132 -18.89089 2.47453 prior | .2444775 .1272313 1.92 0.055 -.005121 .494076 democrat | 0 (omitted) indep | 0 (omitted) otherpol | 0 (omitted) midwest | 1.325327 8.504662 0.16 0.876 -15.35886 18.00951 south | -3.222067 7.774165 -0.41 0.679 -18.47319 12.02905 west | -6.790277 8.01919 -0.85 0.397 -22.52208 8.941526 age1 | 23.50245 11.22858 2.09 0.037 1.474556 45.53033 age2 | 17.34804 8.25791 2.10 0.036 1.147918 33.54815 age3 | 3.987266 8.877388 0.45 0.653 -13.42812 21.40266 age4 | -2.442076 7.901757 -0.31 0.757 -17.9435 13.05935 anychildren | -5.107204 5.879432 -0.87 0.385 -16.64129 6.426887 loghhinc | 5.470571 3.704008 1.48 0.140 -1.79584 12.73698 associatemore | 8.979006 5.722462 1.57 0.117 -2.247145 20.20516 fulltime | -12.68577 8.664332 -1.46 0.143 -29.6832 4.31165 parttime | -8.09416 10.4493 -0.77 0.439 -28.59329 12.40497 selfemp | .2310074 11.7815 0.02 0.984 -22.88158 23.3436 unemployed | -2.317616 13.99346 -0.17 0.868 -29.76957 25.13434 student | -18.42317 14.38097 -1.28 0.200 -46.63532 9.788977 _cons | 35.67091 41.70218 0.86 0.393 -46.13916 117.481 ------------------------------------------------------------------------------- number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 file C:\Users\gxf271\AppData\Local\Temp\ST_0000000k.tmp saved Mean estimation Number of obs = 850 ---------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] ---------------+------------------------------------------------ donationnondem | 76.42523 3.105457 70.32996 82.52051 ---------------------------------------------------------------- Mean estimation Number of obs = 846 ---------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] ---------------+------------------------------------------------ donationnondem | 78.95656 3.297529 72.48425 85.42886 ---------------------------------------------------------------- (sum of wgt is 1.6802e+03) note: democrat omitted because of collinearity Linear regression Number of obs = 1,696 F(21, 1674) = 5.06 Prob > F = 0.0000 R-squared = 0.0528 Root MSE = 90.884 ------------------------------------------------------------------------------- | Robust donationnon~m | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T2 | 1.21222 4.472143 0.27 0.786 -7.559362 9.983802 wave | -10.74062 4.613646 -2.33 0.020 -19.78974 -1.691494 gender | -12.93819 4.640159 -2.79 0.005 -22.03931 -3.837064 prior | .1378118 .1027578 1.34 0.180 -.0637355 .339359 democrat | 0 (omitted) indep | 14.14505 5.099199 2.77 0.006 4.143572 24.14653 otherpol | 11.0974 13.15578 0.84 0.399 -14.7061 36.90091 midwest | -8.282152 7.247798 -1.14 0.253 -22.49785 5.93355 south | -.4169175 6.538353 -0.06 0.949 -13.24113 12.40729 west | 4.735571 7.234492 0.65 0.513 -9.454032 18.92517 age1 | 30.2967 10.27109 2.95 0.003 10.15116 50.44223 age2 | 32.25702 7.342282 4.39 0.000 17.856 46.65804 age3 | 21.73561 6.881516 3.16 0.002 8.238322 35.23289 age4 | 1.039263 6.179695 0.17 0.866 -11.08148 13.16001 anychildren | 18.56401 4.778483 3.88 0.000 9.191577 27.93644 loghhinc | 7.522546 3.055547 2.46 0.014 1.52945 13.51564 associatemore | 4.529664 4.892479 0.93 0.355 -5.066356 14.12569 fulltime | -.777617 6.545399 -0.12 0.905 -13.61564 12.06041 parttime | -9.021212 8.275927 -1.09 0.276 -25.25347 7.211043 selfemp | -8.554231 9.444162 -0.91 0.365 -27.07784 9.969379 unemployed | 3.500347 10.39864 0.34 0.736 -16.89537 23.89606 student | -10.10742 15.32136 -0.66 0.510 -40.15846 19.94362 _cons | -26.52458 33.32472 -0.80 0.426 -91.88709 38.83794 ------------------------------------------------------------------------------- number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 file C:\Users\gxf271\AppData\Local\Temp\ST_0000000m.tmp saved . . . * Save bar graph matrix as dataset . clear . foreach a of local numcats { 2. append using `cat`a'' 3. } . . . * For alignment along the x-axis . gen s1 = R5 . gen s2 = . (10 missing values generated) . replace s2 = s1 - 0.2 if R5 == 1 (2 real changes made) . replace s2 = s1 - 0.6 if R5 == 2 (2 real changes made) . replace s2 = s1 - 1.0 if R5 == 3 (2 real changes made) . replace s2 = s1 - 1.4 if R5 == 4 (2 real changes made) . replace s2 = s1 - 1.8 if R5 == 5 (2 real changes made) . *replace s2 = s1 - 2.2 if R5 == 6 . gen pos1 = (s2 - 0.1) - .6 . gen pos2 = s2 + 0.1 - .6 . . . * This recovers the group means with which to label each bar. . local i = 0 . foreach pos of local numcats { 2. forval rel = 1/2 { 3. local ++i 4. sum R1 if R4 == `rel' & R5 == `pos' 5. local barval`i' = trim("`: di %9.2f r(mean)'") 6. } 7. } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 89.69321 . 89.69321 89.69321 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 87.82938 . 87.82938 87.82938 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 98.63861 . 98.63861 98.63861 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 90.73072 . 90.73072 90.73072 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 80.95137 . 80.95137 80.95137 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 84.88741 . 84.88741 84.88741 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 106.2953 . 106.2953 106.2953 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 99.41074 . 99.41074 99.41074 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 76.42523 . 76.42523 76.42523 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 78.95656 . 78.95656 78.95656 . . global barlabels `"0.2 "All" 0.8 "Men" 1.4 "Women" 2.0 "Democrats" 2.6 "Non-Democrats" "' . global pvalues `"120 0.2 "p-value = `pvalue1'" 120 0.8 "p-value = `pvalue2'" 120 1.4 "p-va > lue = `pvalue3'" 120 2.0 "p-value = `pvalue4'" 120 2.6 "p-value = `pvalue5'" "' . global barvalues = `"60 0.1 "`barval1'" 60 0.3 "`barval2'" 60 0.7 "`barval3'" 60 0.9 "`bar > val4'" 60 1.3 "`barval5'" 60 1.5 "`barval6'" 60 1.9 "`barval7'" 60 2.1 "`barval8'" 60 2.5 "`barval > 9'" 60 2.7 "`barval10'" "' . . . twoway (bar R1 pos1 if R4 == 1, barw(0.18) fi(inten50) lc(black) lw(medium)) (bar R1 pos2 if R4 == > 2, barw(0.18) fi(inten20) lc(black) lw(medium)) /// > (rcap R3 R2 pos2 if R4 == 2, lc(gs5)), legend(${legend}) graphregion(color(white)) /// > yscale(range(103)) yla(50(25)125) xla($barlabels, labsize(2.5)) text($pvalues, size(2.5)) > text($barvalues, size(2.5)) /// > ytitle("Amount donated", height(5)) . . graph export "$output\fig_donation_AB_demnondem.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\fig_donation_AB_demnondem.pdf written in PDF format) . . . *********************************************************************************** . // Figure A.12: Signatures on real online petitions . *********************************************************************************** . . // Panel (a): Petition I (Increase reporting) . clear all . . /* Number of potential signatures for Petitions I and II per group correspond to the number of res > pondents assigned to either treatment group > The numbers of actual signatures for Petitions I and II are all "manually" retrieved from the Whit > e House Petition Website. > */ . . // All: See MainFigures.do -> Figure 2 for numbers of signatures . prtesti 1531 259 1500 220, count Two-sample test of proportions x: Number of obs = 1531 y: Number of obs = 1500 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .1691705 .0095814 .1503912 .1879497 y | .1466667 .0091344 .1287636 .1645697 -------------+---------------------------------------------------------------- diff | .0225038 .0132379 -.0034419 .0484495 | under Ho: .013252 1.70 0.089 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 1.6981 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.9553 Pr(|Z| < |z|) = 0.0895 Pr(Z > z) = 0.0447 . . /* Female respondents: > Input: Total number of potential signatures in T74 (798) > Number of actual signatures in T74 (161) > Total number of potential signatures in T94 (766) > Number of actual signatures in T94 (134) > Output: Proportion of signatures in T74 (incl. 95% CI) > Proportion of signatures in T94 (incl. 95% CI) > P-value of two-sided proportion test > --> Output entered manually below */ . prtesti 798 161 766 134, count Two-sample test of proportions x: Number of obs = 798 y: Number of obs = 766 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .2017544 .0142062 .1739107 .2295981 y | .1749347 .0137268 .1480308 .2018387 -------------+---------------------------------------------------------------- diff | .0268197 .0197545 -.0118984 .0655378 | under Ho: .0197883 1.36 0.175 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 1.3553 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.9123 Pr(|Z| < |z|) = 0.1753 Pr(Z > z) = 0.0877 . . /* Male respondents: > Input: Total number of potential signatures in T74 (733) > Number of actual signatures in T74 (98) > Total number of potential signatures in T94 (734) > Number of actual signatures in T94 (86) > Output: Proportion of signatures in T74 (incl. 95% CI) > Proportion of signatures in T94 (incl. 95% CI) > P-value of two-sided proportion test > --> Output entered manually below */ . prtesti 733 98 734 86, count Two-sample test of proportions x: Number of obs = 733 y: Number of obs = 734 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .1336971 .0125703 .1090599 .1583344 y | .1171662 .0118711 .0938992 .1404332 -------------+---------------------------------------------------------------- diff | .0165309 .0172897 -.0173564 .0504182 | under Ho: .0172945 0.96 0.339 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 0.9559 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.8304 Pr(|Z| < |z|) = 0.3391 Pr(Z > z) = 0.1696 . . /* Democrats: > Input: Total number of potential signatures in T74 (447) > Number of actual signatures in T74 (106) > Total number of potential signatures in T94 (450) > Number of actual signatures in T94 (99) > Output: Proportion of signatures in T74 (incl. 95% CI) > Proportion of signatures in T94 (incl. 95% CI) > P-value of two-sided proportion test > --> Output entered manually below */ . prtesti 447 106 450 99, count Two-sample test of proportions x: Number of obs = 447 y: Number of obs = 450 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .2371365 .0201173 .1977074 .2765656 y | .22 .0195278 .1817263 .2582737 -------------+---------------------------------------------------------------- diff | .0171365 .0280364 -.0378138 .0720867 | under Ho: .0280397 0.61 0.541 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 0.6111 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.7294 Pr(|Z| < |z|) = 0.5411 Pr(Z > z) = 0.2706 . . /* Non-Democrats: > Input: Total number of potential signatures in T74 (558) > Number of actual signatures in T74 (63) > Total number of potential signatures in T94 (557) > Number of actual signatures in T94 (60) > Output: Proportion of signatures in T74 (incl. 95% CI) > Proportion of signatures in T94 (incl. 95% CI) > P-value of two-sided proportion test > --> Output entered manually below */ . prtesti 558 63 557 60, count Two-sample test of proportions x: Number of obs = 558 y: Number of obs = 557 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .1129032 .0133974 .0866447 .1391617 y | .1077199 .0131362 .0819734 .1334665 -------------+---------------------------------------------------------------- diff | .0051833 .018763 -.0315916 .0419582 | under Ho: .018764 0.28 0.782 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 0.2762 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.6088 Pr(|Z| < |z|) = 0.7824 Pr(Z > z) = 0.3912 . . set scheme s2mono . . global legend = `"label(1 "T{sup:74}") label(2 "T{sup:94}") order(1 2) size(medium)"' . . **** Calculate numbers for bar graph matrix . . // All: . . mat R=J(2,5,.) . . local pvalue1 = 0.09 //2-sided test . . * Means . mat R[1,1] = 0.16917 // All T74 . mat R[2,1] = 0.14667 // All T94 . . * Lower bounds . mat R[1,2] = 0.1504 // All T74 . mat R[2,2] = 0.1288 // All T94 . . * Upper bounds . mat R[1,3] = 0.1879 // All T74 . mat R[2,3] = 0.1646 // All T94 . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 1 . mat R[2,5] = 1 . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat1 . save `cat1' file C:\Users\gxf271\AppData\Local\Temp\ST_0000000o.tmp saved . restore . . . ********************************** . . // Women: . /*prtesti 798 161 766 134, count > > Two-sample test of proportions x: Number of obs = 798 > y: Number of obs = 766 > ------------------------------------------------------------------------------ > Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > x | .2017544 .0142062 .1739107 .2295981 > y | .1749347 .0137268 .1480308 .2018387 > -------------+---------------------------------------------------------------- > diff | .0268197 .0197545 -.0118984 .0655378 > | under Ho: .0197883 1.36 0.175 > ------------------------------------------------------------------------------ > diff = prop(x) - prop(y) z = 1.3553 > Ho: diff = 0 > > Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 > Pr(Z < z) = 0.9123 Pr(|Z| < |z|) = 0.1753 Pr(Z > z) = 0.0877*/ . . . local pvalue2 = 0.18 //2-sided test . . * Means . mat R[1,1] = 0.2018 // Women T74 . mat R[2,1] = 0.1749 // Women T94 . . * Lower bounds . mat R[1,2] = .1739107 // Women T74 . mat R[2,2] = .1480308 // Women T94 . . * Upper bounds . mat R[1,3] = .2295981 // Women T74 . mat R[2,3] = .2018387 // Women T94 . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 2 . mat R[2,5] = 2 . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat2 . save `cat2' file C:\Users\gxf271\AppData\Local\Temp\ST_0000000q.tmp saved . restore . . ************************************************* . . // Men . /* prtesti 733 98 734 86, count > > Two-sample test of proportions x: Number of obs = 733 > y: Number of obs = 734 > ------------------------------------------------------------------------------ > Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > x | .1336971 .0125703 .1090599 .1583344 > y | .1171662 .0118711 .0938992 .1404332 > -------------+---------------------------------------------------------------- > diff | .0165309 .0172897 -.0173564 .0504182 > | under Ho: .0172945 0.96 0.339 > ------------------------------------------------------------------------------ > diff = prop(x) - prop(y) z = 0.9559 > Ho: diff = 0 > > Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 > Pr(Z < z) = 0.8304 Pr(|Z| < |z|) = 0.3391 Pr(Z > z) = 0.1696*/ . . local pvalue3 = 0.34 // 2-sided test . . * Means . mat R[1,1] = 0.1337 // Men T74 . mat R[2,1] = 0.1172 // Men T94 . . * Lower bounds . mat R[1,2] = 0.1091 // Men T74 . mat R[2,2] = 0.0939 // Men T94 . . * Upper bounds . mat R[1,3] = 0.1583 // Men T74 . mat R[2,3] = 0.1404 // Men T94 . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 3 . mat R[2,5] = 3 . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat3 . save `cat3' file C:\Users\gxf271\AppData\Local\Temp\ST_0000000s.tmp saved . restore . . ************************************************* . . // Democrats . /*prtesti 447 106 450 99, count > > Two-sample test of proportions x: Number of obs = 447 > y: Number of obs = 450 > ------------------------------------------------------------------------------ > Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > x | .2371365 .0201173 .1977074 .2765656 > y | .22 .0195278 .1817263 .2582737 > -------------+---------------------------------------------------------------- > diff | .0171365 .0280364 -.0378138 .0720867 > | under Ho: .0280397 0.61 0.541 > ------------------------------------------------------------------------------ > diff = prop(x) - prop(y) z = 0.6111 > Ho: diff = 0 > > Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 > Pr(Z < z) = 0.7294 Pr(|Z| < |z|) = 0.5411 Pr(Z > z) = 0.2706*/ . . local pvalue4 = 0.54 // 2-sided test . . * Means . mat R[1,1] = 0.2371 // Democrats T74 . mat R[2,1] = 0.22 // Democrats T94 . . * Lower bounds . mat R[1,2] = .1977074 // Democrats T74 . mat R[2,2] = .1817263 // Democrats T94 . . * Upper bounds . mat R[1,3] = .2765656 // Democrats T74 . mat R[2,3] = .2582737 // Democrats T94 . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 4 . mat R[2,5] = 4 . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat4 . save `cat4' file C:\Users\gxf271\AppData\Local\Temp\ST_0000000u.tmp saved . restore . . ************************************************* . . // Non-Democrats . /* prtesti 558 63 557 60, count > > Two-sample test of proportions x: Number of obs = 558 > y: Number of obs = 557 > ------------------------------------------------------------------------------ > Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > x | .1129032 .0133974 .0866447 .1391617 > y | .1077199 .0131362 .0819734 .1334665 > -------------+---------------------------------------------------------------- > diff | .0051833 .018763 -.0315916 .0419582 > | under Ho: .018764 0.28 0.782 > ------------------------------------------------------------------------------ > diff = prop(x) - prop(y) z = 0.2762 > Ho: diff = 0 > > Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 > Pr(Z < z) = 0.6088 Pr(|Z| < |z|) = 0.7824 Pr(Z > z) = 0.3912*/ . . local pvalue5 = 0.78 // 2-sided test . . * Means . mat R[1,1] = 0.1129 // Non-Democrats T74 . mat R[2,1] = 0.1077 // Non-Democrats T94 . . * Lower bounds . mat R[1,2] = .0866447 // Democrats T74 . mat R[2,2] = .0819734 // Democrats T94 . . * Upper bounds . mat R[1,3] = .1391617 // Democrats T74 . mat R[2,3] = .1334665 // Democrats T94 . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 5 . mat R[2,5] = 5 . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat5 . save `cat5' file C:\Users\gxf271\AppData\Local\Temp\ST_0000000w.tmp saved . restore . . * Save bar graph matrix as dataset . . clear . . local numcats = "1 2 3 4 5" . foreach a of local numcats { 2. append using `cat`a'' 3. } . . . . * For alignment along the x-axis . gen s1 = R5 . gen s2 = . (10 missing values generated) . replace s2 = s1 - 0.2 if R5 == 1 (2 real changes made) . replace s2 = s1 - 0.6 if R5 == 2 (2 real changes made) . replace s2 = s1 - 1.0 if R5 == 3 (2 real changes made) . replace s2 = s1 - 1.4 if R5 == 4 (2 real changes made) . replace s2 = s1 - 1.8 if R5 == 5 (2 real changes made) . gen pos1 = (s2 - 0.1) - .6 . gen pos2 = s2 + 0.1 - .6 . . * This recovers the group means with which to label each bar. . local i = 0 . foreach pos of local numcats { 2. forval rel = 1/2 { 3. local ++i 4. sum R1 if R4 == `rel' & R5 == `pos' 5. local barval`i' = trim("`: di %9.2f r(mean)'") 6. } 7. } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .16917 . .16917 .16917 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .14667 . .14667 .14667 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .2018 . .2018 .2018 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .1749 . .1749 .1749 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .1337 . .1337 .1337 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .1172 . .1172 .1172 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .2371 . .2371 .2371 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .22 . .22 .22 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .1129 . .1129 .1129 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .1077 . .1077 .1077 . . global barlabels `"0.2 "All" 0.8 "Women" 1.4 "Men" 2.0 "Democrats" 2.6 "Non-Democrats" " > ' . global pvalues `"0.29 0.2 "p-value = `pvalue1'" 0.29 0.8 "p-value = `pvalue2'" 0.29 1.4 "p > -value = `pvalue3'" 0.29 2.0 "p-value = `pvalue4'" 0.29 2.6 "p-value = `pvalue5'" "' . global barvalues = `"0.03 0.1 "`barval1'" 0.03 0.3 "`barval2'" 0.03 0.7 "`barval3'" 0.03 0 > .9 "`barval4'" 0.03 1.3 "`barval5'" 0.03 1.5 "`barval6'" 0.03 1.9 "`barval7'" 0.03 2.1 "`barval8'" > 0.03 2.5 "`barval9'" 0.03 2.7 "`barval9'" "' . . . twoway (bar R1 pos1 if R4 == 1, barw(0.18) fi(inten50) lc(black) lw(medium)) (bar R1 pos2 if R4 == > 2, barw(0.18) fi(inten20) lc(black) lw(medium)) /// > (rcap R3 R2 pos1 if R4 == 1, lc(gs5)) (rcap R3 R2 pos2 if R4 == 2, lc(gs5)), legend(${lege > nd}) graphregion(color(white)) /// > yscale(range(0.3)) yla(0(0.05)0.3) xla($barlabels, labsize(2.5)) text($pvalues, size(2.5)) > text($barvalues, size(2.5)) /// > ytitle("Fraction of respondents who signed", height(5)) . graph export "$output\fig_petitionI_2sided.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\fig_petitionI_2sided.pdf written in PDF format) . . . // Panel (b): Petition II (Decrease reporting) . clear all . set scheme s2mono . . /* Number of potential signatures for Petitions I and II per group correspond to the number of res > pondents assigned to either treatment group > The numbers of actual signatures for Petitions I and II are all "manually" retrieved from the Whit > e House Petition Website. > */ . . // All: See MainFigures.do -> Figure 2 for numbers of signatures . prtesti 1531 19 1500 35, count Two-sample test of proportions x: Number of obs = 1531 y: Number of obs = 1500 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0124102 .0028294 .0068647 .0179557 y | .0233333 .0038978 .0156938 .0309728 -------------+---------------------------------------------------------------- diff | -.0109231 .0048164 -.0203632 -.0014831 | under Ho: .0048057 -2.27 0.023 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -2.2729 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.0115 Pr(|Z| < |z|) = 0.0230 Pr(Z > z) = 0.9885 . . /* Female respondents: > Input: Total number of potential signatures in T74 (798) > Number of actual signatures in T74 (6) > Total number of potential signatures in T94 (766) > Number of actual signatures in T94 (16) > Output: Proportion of signatures in T74 (incl. 95% CI) > Proportion of signatures in T94 (incl. 95% CI) > P-value of two-sided proportion test > --> Output entered manually below */ . prtesti 798 6 766 16, count Two-sample test of proportions x: Number of obs = 798 y: Number of obs = 766 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0075188 .003058 .0015253 .0135123 y | .0208877 .0051671 .0107604 .0310151 -------------+---------------------------------------------------------------- diff | -.0133689 .0060042 -.0251369 -.001601 | under Ho: .0059569 -2.24 0.025 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -2.2443 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.0124 Pr(|Z| < |z|) = 0.0248 Pr(Z > z) = 0.9876 . . /* Male respondents: > Input: Total number of potential signatures in T74 (733) > Number of actual signatures in T74 (13) > Total number of potential signatures in T94 (734) > Number of actual signatures in T94 (19) > Output: Proportion of signatures in T74 (incl. 95% CI) > Proportion of signatures in T94 (incl. 95% CI) > P-value of two-sided proportion test > --> Output entered manually below */ . prtesti 733 13 734 19, count Two-sample test of proportions x: Number of obs = 733 y: Number of obs = 734 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0177353 .0048751 .0081803 .0272903 y | .0258856 .0058612 .0143978 .0373733 -------------+---------------------------------------------------------------- diff | -.0081502 .0076236 -.0230923 .0067918 | under Ho: .0076276 -1.07 0.285 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -1.0685 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.1426 Pr(|Z| < |z|) = 0.2853 Pr(Z > z) = 0.8574 . . /* Democrats: > Input: Total number of potential signatures in T74 (447) > Number of actual signatures in T74 (3) > Total number of potential signatures in T94 (450) > Number of actual signatures in T94 (2) > Output: Proportion of signatures in T74 (incl. 95% CI) > Proportion of signatures in T94 (incl. 95% CI) > P-value of two-sided proportion test > --> Output entered manually below */ . prtesti 447 3 450 2, count Two-sample test of proportions x: Number of obs = 447 y: Number of obs = 450 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0067114 .0038618 -.0008576 .0142804 y | .0044444 .0031357 -.0017014 .0105903 -------------+---------------------------------------------------------------- diff | .002267 .0049746 -.007483 .0120169 | under Ho: .0049718 0.46 0.648 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 0.4560 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.6758 Pr(|Z| < |z|) = 0.6484 Pr(Z > z) = 0.3242 . . /* Non-Democrats: > Input: Total number of potential signatures in T74 (558) > Number of actual signatures in T74 (10) > Total number of potential signatures in T94 (557) > Number of actual signatures in T94 (18) > Output: Proportion of signatures in T74 (incl. 95% CI) > Proportion of signatures in T94 (incl. 95% CI) > P-value of two-sided proportion test > --> Output entered manually below */ . prtesti 558 10 557 18, count Two-sample test of proportions x: Number of obs = 558 y: Number of obs = 557 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0179211 .0056162 .0069137 .0289286 y | .032316 .0074929 .0176302 .0470017 -------------+---------------------------------------------------------------- diff | -.0143948 .009364 -.0327479 .0039582 | under Ho: .0093716 -1.54 0.125 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -1.5360 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.0623 Pr(|Z| < |z|) = 0.1245 Pr(Z > z) = 0.9377 . . . // All: . . mat R=J(2,5,.) . . local pvalue1 = 0.02 //2-sided test . . * Means . mat R[1,1] = 0.01241 // All T74 . mat R[2,1] = 0.02333 // All T94 . . * Lower bounds . mat R[1,2] = 0.0069 // All T74 . mat R[2,2] = 0.0157 // All T94 . . * Upper bounds . mat R[1,3] = 0.01796 // All T74 . mat R[2,3] = 0.031 // All T94 . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 1 . mat R[2,5] = 1 . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat1 . save `cat1' file C:\Users\gxf271\AppData\Local\Temp\ST_00000010.tmp saved . restore . . ********************************** . . // Women: . /* prtesti 798 6 766 16, count > > Two-sample test of proportions x: Number of obs = 798 > y: Number of obs = 766 > ------------------------------------------------------------------------------ > Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > x | .0075188 .003058 .0015253 .0135123 > y | .0208877 .0051671 .0107604 .0310151 > -------------+---------------------------------------------------------------- > diff | -.0133689 .0060042 -.0251369 -.001601 > | under Ho: .0059569 -2.24 0.025 > ------------------------------------------------------------------------------ > diff = prop(x) - prop(y) z = -2.2443 > Ho: diff = 0 > > Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 > Pr(Z < z) = 0.0124 Pr(|Z| < |z|) = 0.0248 Pr(Z > z) = 0.9876*/ . . local pvalue2 = 0.02 //2-sided test . . * Means . mat R[1,1] = 0.007519 // Women T74 . mat R[2,1] = 0.02089 // Women T94 . . * Lower bounds . mat R[1,2] = .0015253 // Women T74 . mat R[2,2] = .0107604 // Women T94 . . * Upper bounds . mat R[1,3] = .0135123 // Women T74 . mat R[2,3] = .0310151 // Women T94 . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 2 . mat R[2,5] = 2 . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat2 . save `cat2' file C:\Users\gxf271\AppData\Local\Temp\ST_00000012.tmp saved . restore . . ************************************************* . . // Men . /*prtesti 733 13 734 19, count > > Two-sample test of proportions x: Number of obs = 733 > y: Number of obs = 734 > ------------------------------------------------------------------------------ > Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > x | .0177353 .0048751 .0081803 .0272903 > y | .0258856 .0058612 .0143978 .0373733 > -------------+---------------------------------------------------------------- > diff | -.0081502 .0076236 -.0230923 .0067918 > | under Ho: .0076276 -1.07 0.285 > ------------------------------------------------------------------------------ > diff = prop(x) - prop(y) z = -1.0685 > Ho: diff = 0 > > Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 > Pr(Z < z) = 0.1426 Pr(|Z| < |z|) = 0.2853 Pr(Z > z) = 0.8574*/ . . local pvalue3 = 0.29 // 2-sided test . . * Means . mat R[1,1] = 0.017735 // Men T74 . mat R[2,1] = 0.02589 // Men T94 . . * Lower bounds . mat R[1,2] = 0.0081803 // Men T74 . mat R[2,2] = .0143978 // Men T94 . . * Upper bounds . mat R[1,3] = 0.0272903 // Men T74 . mat R[2,3] = .0373733 // Men T94 . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 3 . mat R[2,5] = 3 . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat3 . save `cat3' file C:\Users\gxf271\AppData\Local\Temp\ST_00000014.tmp saved . restore . . . ************************************************* . . // Democrats . /* prtesti 447 3 450 2, count > > Two-sample test of proportions x: Number of obs = 447 > y: Number of obs = 450 > ------------------------------------------------------------------------------ > Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > x | .0067114 .0038618 -.0008576 .0142804 > y | .0044444 .0031357 -.0017014 .0105903 > -------------+---------------------------------------------------------------- > diff | .002267 .0049746 -.007483 .0120169 > | under Ho: .0049718 0.46 0.648 > ------------------------------------------------------------------------------ > diff = prop(x) - prop(y) z = 0.4560 > Ho: diff = 0 > > Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 > Pr(Z < z) = 0.6758 Pr(|Z| < |z|) = 0.6484 Pr(Z > z) = 0.3242*/ . . . local pvalue4 = 0.65 // 2-sided test . local pvalue4 = trim("`: di %9.2f 0.65'") . . * Means . mat R[1,1] = 0.0067 // Democrats T74 . mat R[2,1] = 0.00444 // Democrats T94 . . * Lower bounds . mat R[1,2] = -.0008576 // Democrats T74 . mat R[2,2] = -.0017014 // Democrats T94 . . * Upper bounds . mat R[1,3] = .0142804 // Democrats T74 . mat R[2,3] = .0105903 // Democrats T94 . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 4 . mat R[2,5] = 4 . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat4 . save `cat4' file C:\Users\gxf271\AppData\Local\Temp\ST_00000016.tmp saved . restore . . . ************************************************* . . // Non-Democrats . /*prtesti 558 10 557 18, count > > Two-sample test of proportions x: Number of obs = 558 > y: Number of obs = 557 > ------------------------------------------------------------------------------ > Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] > -------------+---------------------------------------------------------------- > x | .0179211 .0056162 .0069137 .0289286 > y | .032316 .0074929 .0176302 .0470017 > -------------+---------------------------------------------------------------- > diff | -.0143948 .009364 -.0327479 .0039582 > | under Ho: .0093716 -1.54 0.125 > ------------------------------------------------------------------------------ > diff = prop(x) - prop(y) z = -1.5360 > Ho: diff = 0 > > Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 > Pr(Z < z) = 0.0623 Pr(|Z| < |z|) = 0.1245 Pr(Z > z) = 0.9377*/ . . local pvalue5 = 0.12 // 2-sided test . . * Means . mat R[1,1] = 0.01792 // Non-Democrats T74 . mat R[2,1] = 0.0323 // Non-Democrats T94 . . * Lower bounds . mat R[1,2] = .0069137 // Democrats T74 . mat R[2,2] = .0176302 // Democrats T94 . . * Upper bounds . mat R[1,3] = .0289286 // Democrats T74 . mat R[2,3] = .0470017 // Democrats T94 . . mat R[1,4]=1 . mat R[2,4]=2 . . mat R[1,5] = 5 . mat R[2,5] = 5 . . . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat5 . save `cat5' file C:\Users\gxf271\AppData\Local\Temp\ST_00000018.tmp saved . restore . . . // Save bar graph matrix as dataset . clear . . local numcats = "1 2 3 4 5" . foreach a of local numcats { 2. append using `cat`a'' 3. } . . . * For alignment along the x-axis . gen s1 = R5 . gen s2 = . (10 missing values generated) . replace s2 = s1 - 0.2 if R5 == 1 (2 real changes made) . replace s2 = s1 - 0.6 if R5 == 2 (2 real changes made) . replace s2 = s1 - 1.0 if R5 == 3 (2 real changes made) . replace s2 = s1 - 1.4 if R5 == 4 (2 real changes made) . replace s2 = s1 - 1.8 if R5 == 5 (2 real changes made) . gen pos1 = (s2 - 0.1) - .6 . gen pos2 = s2 + 0.1 - .6 . . * This recovers the group means with which to label each bar. . local i = 0 . foreach pos of local numcats { 2. forval rel = 1/2 { 3. local ++i 4. sum R1 if R4 == `rel' & R5 == `pos' 5. local barval`i' = trim("`: di %9.2f r(mean)'") 6. } 7. } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .01241 . .01241 .01241 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .02333 . .02333 .02333 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .007519 . .007519 .007519 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .02089 . .02089 .02089 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .017735 . .017735 .017735 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .02589 . .02589 .02589 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .0067 . .0067 .0067 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .00444 . .00444 .00444 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .01792 . .01792 .01792 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .0323 . .0323 .0323 . . global barlabels `"0.2 "All" 0.8 "Women" 1.4 "Men" 2.0 "Democrats" 2.6 "Non-Democrats" " > ' . global pvalues `"0.08 0.2 "p-value = `pvalue1'" 0.08 0.8 "p-value = `pvalue2'" 0.08 1.4 "p > -value = `pvalue3'" 0.08 2.0 "p-value = `pvalue4'" 0.08 2.6 "p-value = `pvalue5'" "' . global barvalues = `"0.05 0.1 "`barval1'" 0.05 0.3 "`barval2'" 0.05 0.7 "`barval3'" 0.05 0 > .9 "`barval4'" 0.05 1.3 "`barval5'" 0.05 1.5 "`barval6'" 0.05 1.9 "`barval7'" 0.05 2.1 "`barval8'" > 0.05 2.5 "`barval9'" 0.05 2.7 "`barval10'" "' . . . twoway (bar R1 pos1 if R4 == 1, barw(0.18) fi(inten50) lc(black) lw(medium)) (bar R1 pos2 if R4 == > 2, barw(0.18) fi(inten20) lc(black) lw(medium)) /// > (rcap R3 R2 pos1 if R4 == 1, lc(gs5)) (rcap R3 R2 pos2 if R4 == 2, lc(gs5)), legend(${lege > nd}) graphregion(color(white)) /// > yscale(range(0.1)) yla(0(0.05)0.1) xla($barlabels, labsize(2.5)) text($pvalues, size(2.5)) > text($barvalues, size(2.5)) /// > ytitle("Fraction of respondents who signed", height(5)) . graph export "$output\fig_petitionII_2sided.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\fig_petitionII_2sided.pdf written in PDF format) . . . . *********************************************************************************** . // Figure A.13: Heterogeneity by gender x age . *********************************************************************************** . . clear all . . use "$path\data\SurveyStageI_AB_final.dta", clear . . *global controls wave democrat indep otherpol prior midwest south west anychildren loghhinc associ > atemore fulltime parttime selfemp unemp student . . . // Generate two matrices R1 and R2 for male and female, . * columns will contain point estimate of treatment effect, upper and lower bound of CI . * rows will contain 5 age groups . forv s=0/1{ 2. matrix R`s' = J(5, 3, .) 3. . matrix coln R`s' = coef lb95 ub95 4. . matrix rown R`s' = "18-24" "25-34" "35-44" "45-54" "55-65" 5. } . . // Estimate treatment effect by gender x age group, excluding the pure control group (rand=0) . forv s=0/1{ 2. forv i = 1/5 { 3. reg z_lmpolicy_index T1 $controls if rand!=0&gender==`s' & age==`i' 4. matrix reg = e(b) 5. matrix var = e(V) 6. matrix list reg 7. matrix list var 8. local coef=reg[1,1] 9. local var1=var[1,1] 10. local se= sqrt(`var1') 11. local lb=`coef' - 1.645*`se' 12. local ub=`coef' + 1.645*`se' 13. matrix R`s'[`i',1] = `coef' 14. matrix R`s'[`i',2] = `lb' 15. matrix R`s'[`i',3] = `ub' 16. } 17. } note: gender omitted because of collinearity note: age1 omitted because of collinearity note: age2 omitted because of collinearity note: age3 omitted because of collinearity note: age4 omitted because of collinearity Source | SS df MS Number of obs = 133 -------------+---------------------------------- F(17, 115) = 2.05 Model | 13.994853 17 .823226648 Prob > F = 0.0137 Residual | 46.243688 115 .402119026 R-squared = 0.2323 -------------+---------------------------------- Adj R-squared = 0.1188 Total | 60.238541 132 .456352583 Root MSE = .63413 ------------------------------------------------------------------------------- z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.1209655 .1167482 -1.04 0.302 -.3522211 .1102902 wave | -.0405205 .1206134 -0.34 0.738 -.2794324 .1983914 gender | 0 (omitted) prior | -.0032038 .0026324 -1.22 0.226 -.008418 .0020105 democrat | .592733 .1320234 4.49 0.000 .3312201 .8542459 indep | .082762 .1703825 0.49 0.628 -.254733 .4202569 otherpol | .439124 .8034809 0.55 0.586 -1.152417 2.030665 midwest | -.2364545 .191771 -1.23 0.220 -.616316 .143407 south | .0289933 .1435398 0.20 0.840 -.2553313 .3133179 west | -.0535833 .171788 -0.31 0.756 -.3938622 .2866957 age1 | 0 (omitted) age2 | 0 (omitted) age3 | 0 (omitted) age4 | 0 (omitted) anychildren | -.0721441 .1922461 -0.38 0.708 -.4529466 .3086583 loghhinc | -.0513168 .066198 -0.78 0.440 -.1824423 .0798087 associatemore | .0506201 .130438 0.39 0.699 -.2077524 .3089926 fulltime | .5582992 .4808821 1.16 0.248 -.3942357 1.510834 parttime | .5845191 .4736046 1.23 0.220 -.3536004 1.522639 selfemp | .6067778 .5318316 1.14 0.256 -.4466782 1.660234 unemployed | .6083415 .4915866 1.24 0.218 -.365397 1.58208 student | .7442519 .4700549 1.58 0.116 -.1868364 1.67534 _cons | -.1190205 .8832434 -0.13 0.893 -1.868556 1.630515 ------------------------------------------------------------------------------- reg[1,23] o. T1 wave gender prior democrat indep y1 -.12096547 -.04052048 0 -.00320378 .59273299 .08276198 o. o. otherpol midwest south west age1 age2 y1 .43912401 -.2364545 .02899332 -.05358328 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime y1 0 0 -.07214413 -.05131681 .05062011 .55829917 parttime selfemp unemployed student _cons y1 .58451909 .60677783 .60834148 .74425189 -.11902051 symmetric var[23,23] o. T1 wave gender prior democrat indep T1 .01363013 wave -.00169868 .01454759 o.gender 0 0 0 prior -.00002018 .00004801 0 6.929e-06 democrat -.00117397 -.00041024 0 -2.794e-06 .01743017 indep .00058389 -.00112486 0 -.00006917 .01081073 .0290302 otherpol -.00466986 -.00134536 0 -.00010391 .01472784 .01729326 midwest .00307616 .0002435 0 .00001151 .00053397 .00039878 south .00002336 .00134742 0 -.00003371 .00020394 -.00014918 west -.0019452 .00237347 0 .00004206 .00126694 .00204317 o.age1 0 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren -.00074103 -.00098499 0 -.00010119 -.00219522 .00101924 loghhinc -.0002959 -.00049117 0 -.00002179 -.00098053 .0005604 associatem~e -.00065003 -.00029283 0 -.00003557 .00179271 .00402345 fulltime -.00011335 .0111001 0 .00012276 .00194197 .0049665 parttime -.0006388 .00807002 0 .00005039 .00433146 .00650395 selfemp .00192147 .00479989 0 .00004741 -.00083196 .00387701 unemployed .00114088 .0078485 0 .00003003 .00024409 .00538813 student -.00088944 .00730021 0 .00006573 .00209138 .00617024 _cons .00196297 -.02771866 0 -.00048664 -.00223386 -.01696522 o. o. otherpol midwest south west age1 age2 otherpol .64558154 midwest -.00929515 .03677614 south -.00241216 .01219236 .02060366 west -.0204131 .01139352 .01127823 .02951112 o.age1 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00534169 -.00148467 .0013989 -.00405207 0 0 loghhinc -.00350733 .00239528 .00140337 -.00092797 0 0 associatem~e .00380749 -.00091888 -.00103377 .00137481 0 0 fulltime .20823341 -.00578537 -.00276419 -.00515985 0 0 parttime .20869345 -.00595955 -.00109066 -.00209976 0 0 selfemp .2096995 -.00929657 .00270608 -.00711321 0 0 unemployed .20161783 -.00239706 -.00153394 -.00071534 0 0 student .20886188 -.00470673 -.00010112 -.00474998 0 0 _cons -.16373907 -.03489675 -.02446584 -.00545798 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime o.age3 0 o.age4 0 0 anychildren 0 0 .03695855 loghhinc 0 0 -.00012596 .00438218 associatem~e 0 0 -.00123334 -.00187745 .01701407 fulltime 0 0 -.01137949 -.00057391 -.00767195 .23124759 parttime 0 0 -.00114017 -.00056281 -.0044673 .21382229 selfemp 0 0 .00243248 .00006712 -.00826523 .2135338 unemployed 0 0 -.00056638 .00121013 -.00147834 .21019254 student 0 0 -.00052674 .00087817 -.00358603 .21300203 _cons 0 0 .0135197 -.04327432 .02175594 -.23133497 parttime selfemp unemployed student _cons parttime .22430127 selfemp .21009669 .28284486 unemployed .20786927 .20665404 .24165739 student .20974591 .21040373 .20779194 .2209516 _cons -.22080084 -.21987089 -.2350532 -.23499959 .78011895 note: gender omitted because of collinearity note: age1 omitted because of collinearity note: age2 omitted because of collinearity note: age3 omitted because of collinearity note: age4 omitted because of collinearity Source | SS df MS Number of obs = 395 -------------+---------------------------------- F(17, 377) = 2.52 Model | 22.9300746 17 1.34882792 Prob > F = 0.0008 Residual | 201.403467 377 .534226704 R-squared = 0.1022 -------------+---------------------------------- Adj R-squared = 0.0617 Total | 224.333542 394 .569374472 Root MSE = .73091 ------------------------------------------------------------------------------- z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0594697 .0750315 0.79 0.429 -.088063 .2070025 wave | .0359878 .0845518 0.43 0.671 -.1302643 .20224 gender | 0 (omitted) prior | -.0026331 .0014196 -1.85 0.064 -.0054244 .0001582 democrat | .4005911 .0852928 4.70 0.000 .2328818 .5683004 indep | .0444587 .1178004 0.38 0.706 -.1871695 .2760868 otherpol | -.3335398 .2888107 -1.15 0.249 -.9014215 .2343418 midwest | -.1571585 .1305362 -1.20 0.229 -.4138287 .0995116 south | .0257273 .1159096 0.22 0.824 -.202183 .2536375 west | -.06656 .1139384 -0.58 0.559 -.2905945 .1574744 age1 | 0 (omitted) age2 | 0 (omitted) age3 | 0 (omitted) age4 | 0 (omitted) anychildren | .0877821 .0833342 1.05 0.293 -.076076 .2516401 loghhinc | .0616797 .0526438 1.17 0.242 -.0418326 .1651919 associatemore | .0729765 .0924011 0.79 0.430 -.1087097 .2546626 fulltime | -.4253519 .2328977 -1.83 0.069 -.8832931 .0325894 parttime | -.3833842 .2497216 -1.54 0.126 -.8744059 .1076375 selfemp | -.1511919 .2978014 -0.51 0.612 -.7367518 .434368 unemployed | -.3340796 .2553402 -1.31 0.192 -.8361491 .1679899 student | -.2975532 .2884136 -1.03 0.303 -.8646542 .2695477 _cons | -.4180269 .6040819 -0.69 0.489 -1.605819 .769765 ------------------------------------------------------------------------------- reg[1,23] o. T1 wave gender prior democrat indep y1 .05946975 .03598782 0 -.00263312 .40059109 .04445866 o. o. otherpol midwest south west age1 age2 y1 -.33353984 -.15715853 .02572727 -.06656005 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime y1 0 0 .08778208 .06167966 .07297646 -.42535188 parttime selfemp unemployed student _cons y1 -.3833842 -.15119193 -.33407962 -.29755325 -.41802691 symmetric var[23,23] o. T1 wave gender prior democrat indep T1 .00562973 wave .00012388 .007149 o.gender 0 0 0 prior -4.775e-06 -7.312e-06 0 2.015e-06 democrat -.00013932 .00039864 0 6.070e-06 .00727487 indep .00045347 .00017743 0 -3.755e-06 .00444818 .01387693 otherpol -.0001011 .00161658 0 -.00005746 .00526418 .00668003 midwest -.00049841 -.00107245 0 .00002086 .00003687 .00039068 south -.00040839 -.000321 0 .00001486 .00097166 .00026909 west .00014114 .00061305 0 3.309e-06 -.00001381 .00071717 o.age1 0 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren -.00003652 -.00016246 0 -.00001303 .00087048 .00109374 loghhinc .00032849 .00006109 0 -5.854e-06 .00038207 .00028236 associatem~e .00037208 .00013858 0 -2.299e-06 .00034146 .00162883 fulltime -.00142493 .00014911 0 .00001298 -.00036375 .00260462 parttime -.00168017 -.00017417 0 3.599e-06 .00009745 .0038686 selfemp -.00218983 -.00016947 0 7.859e-06 -.00006552 .00181516 unemployed -.00075177 .00112778 0 8.857e-06 .00027557 .00403982 student -.00137723 -.00008653 0 .00002718 -.00006189 .0024683 _cons -.00483903 -.00955046 0 -.00012174 -.01021427 -.01239984 o. o. otherpol midwest south west age1 age2 otherpol .08341161 midwest -.00191842 .01703969 south .00103604 .00915303 .01343503 west -.00025202 .00877736 .00878627 .01298196 o.age1 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00175009 .00045778 .00060531 -.00010805 0 0 loghhinc .00128665 .00072199 .00050311 .00036093 0 0 associatem~e .00395388 .00057533 .0001204 -.00008594 0 0 fulltime .0028776 -.00118013 -.00217696 -.00190276 0 0 parttime .00523772 .00014985 -.00076298 -.00006705 0 0 selfemp -.00724462 .00092328 -.00138897 -.00118379 0 0 unemployed .00190604 .00026162 -.00159714 -.00124675 0 0 student -.00490616 -9.493e-06 -.00099198 -.00219509 0 0 _cons -.02185048 -.01702491 -.01420777 -.01233877 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime o.age3 0 o.age4 0 0 anychildren 0 0 .00694459 loghhinc 0 0 -.00075923 .00277137 associatem~e 0 0 -.00012418 -.0010356 .00853797 fulltime 0 0 -.00206331 -.00245575 -.00298032 .05424134 parttime 0 0 -.00147162 -.00089484 -.00215116 .04986116 selfemp 0 0 -.00088098 -.00181519 -.00339486 .05029149 unemployed 0 0 .00002581 -.00025711 .00040765 .04670881 student 0 0 .00011202 -.00148077 -.00056032 .04795078 _cons 0 0 .00767706 -.02766804 .00724645 -.02083636 parttime selfemp unemployed student _cons parttime .06236088 selfemp .04837355 .08868568 unemployed .04658172 .04628588 .06519864 student .0467281 .04799601 .04631152 .08318242 _cons -.03710733 -.02582686 -.04586209 -.03189866 .36491489 note: gender omitted because of collinearity note: age1 omitted because of collinearity note: age2 omitted because of collinearity note: age3 omitted because of collinearity note: age4 omitted because of collinearity Source | SS df MS Number of obs = 346 -------------+---------------------------------- F(17, 328) = 4.24 Model | 39.1606416 17 2.30356716 Prob > F = 0.0000 Residual | 178.209803 328 .543322571 R-squared = 0.1802 -------------+---------------------------------- Adj R-squared = 0.1377 Total | 217.370445 345 .630059261 Root MSE = .7371 ------------------------------------------------------------------------------- z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2432591 .0807125 3.01 0.003 .0844796 .4020386 wave | -.0475034 .0898677 -0.53 0.597 -.2242932 .1292864 gender | 0 (omitted) prior | .0017603 .0016733 1.05 0.294 -.0015313 .005052 democrat | .6028958 .0885914 6.81 0.000 .4286167 .7771748 indep | .36153 .1215637 2.97 0.003 .1223871 .600673 otherpol | .1594551 .3429333 0.46 0.642 -.5151711 .8340814 midwest | -.2315488 .1274796 -1.82 0.070 -.4823296 .0192319 south | -.2487645 .1119289 -2.22 0.027 -.4689536 -.0285754 west | -.2064203 .1169422 -1.77 0.078 -.4364717 .0236311 age1 | 0 (omitted) age2 | 0 (omitted) age3 | 0 (omitted) age4 | 0 (omitted) anychildren | .21912 .090973 2.41 0.017 .040156 .3980841 loghhinc | .0548636 .0652017 0.84 0.401 -.0734026 .1831298 associatemore | .0064117 .102659 0.06 0.950 -.1955414 .2083648 fulltime | -.1587758 .2046532 -0.78 0.438 -.5613742 .2438225 parttime | -.2974884 .267877 -1.11 0.268 -.8244621 .2294853 selfemp | -.1378385 .2469038 -0.56 0.577 -.6235532 .3478763 unemployed | .2047288 .2878841 0.71 0.477 -.3616034 .771061 student | -.5190764 .3354439 -1.55 0.123 -1.178969 .1408165 _cons | -1.065756 .6976678 -1.53 0.128 -2.438224 .3067115 ------------------------------------------------------------------------------- reg[1,23] o. T1 wave gender prior democrat indep y1 .24325915 -.04750336 0 .00176033 .60289576 .36153001 o. o. otherpol midwest south west age1 age2 y1 .15945515 -.23154884 -.24876449 -.2064203 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime y1 0 0 .21912004 .05486362 .00641166 -.15877584 parttime selfemp unemployed student _cons y1 -.29748841 -.13783845 .20472877 -.51907637 -1.0657565 symmetric var[23,23] o. T1 wave gender prior democrat indep T1 .00651451 wave .00028006 .0080762 o.gender 0 0 0 prior 2.149e-06 3.756e-06 0 2.800e-06 democrat .00066226 -.00026713 0 .00001084 .00784844 indep .0001852 -.00116845 0 .0000131 .00416232 .01477774 otherpol .00109771 .00009106 0 .00001543 .00441099 .00487465 midwest .00069843 .0003768 0 .00001827 .0003302 .00074148 south -.00027733 .00051082 0 .00001313 -.00011499 -.00040605 west .00053834 .00122598 0 .0000106 .00011333 -.00017239 o.age1 0 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00028367 -.00047382 0 -2.018e-06 .00086402 .00136578 loghhinc -.00012254 -.00028827 0 2.672e-06 .00020344 .00044576 associatem~e -.00031076 -.00086854 0 -1.912e-07 -.00031587 .00084974 fulltime .00032476 .00028794 0 .00001355 .00020947 -.00010573 parttime -.00092604 -8.270e-07 0 .00003483 -.00041864 .00055259 selfemp .00024703 -.00002316 0 .00005783 .00112657 -.00003079 unemployed -.00068808 .00032858 0 .00004192 .00007814 .00193592 student -.0005334 -.00117061 0 -1.588e-06 .00040705 .00357914 _cons -.00308684 -.00719159 0 -.00031852 -.00784399 -.01035821 o. o. otherpol midwest south west age1 age2 otherpol .11760327 midwest -.00137571 .01625104 south -.00350544 .00762476 .01252808 west -.00109235 .00752053 .00744264 .01367549 o.age1 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00476024 .0000548 -.00023606 -.00004836 0 0 loghhinc -.00183497 .00057682 .00090328 .00008664 0 0 associatem~e .002118 .00127965 .00026783 .0003992 0 0 fulltime -.00112537 .0003781 .00109743 .00039192 0 0 parttime .00142777 .00062266 .00016425 -.00024548 0 0 selfemp .00266527 -.00006591 .00179574 .00052627 0 0 unemployed .00215463 .00201715 .001921 .00212484 0 0 student .00162847 .00145018 .00199659 -.00120574 0 0 _cons .01184873 -.01806112 -.02028062 -.01182945 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime o.age3 0 o.age4 0 0 anychildren 0 0 .00827608 loghhinc 0 0 -.00111848 .00425126 associatem~e 0 0 -.0005719 -.00172661 .01053887 fulltime 0 0 .00099722 -.00539738 -.00210191 .04188291 parttime 0 0 .00150774 -.00413679 .0000652 .03672876 selfemp 0 0 .00421748 -.00396972 -.00193796 .03747657 unemployed 0 0 .00349355 -.00169575 .00034639 .03284998 student 0 0 .0045324 .00097037 -.00137789 .02969738 _cons 0 0 .00659724 -.04104175 .01437239 .02009511 parttime selfemp unemployed student _cons parttime .07175808 selfemp .0357355 .06096147 unemployed .03300292 .03414888 .08287726 student .03032424 .03180045 .03217082 .1125226 _cons .00641655 -.00024628 -.02205326 -.04208827 .48674033 note: gender omitted because of collinearity note: age1 omitted because of collinearity note: age2 omitted because of collinearity note: age3 omitted because of collinearity note: age4 omitted because of collinearity Source | SS df MS Number of obs = 279 -------------+---------------------------------- F(17, 261) = 4.19 Model | 37.7415872 17 2.22009337 Prob > F = 0.0000 Residual | 138.26769 261 .529761266 R-squared = 0.2144 -------------+---------------------------------- Adj R-squared = 0.1633 Total | 176.009278 278 .633126898 Root MSE = .72785 ------------------------------------------------------------------------------- z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0956324 .0884706 1.08 0.281 -.0785746 .2698395 wave | .0316368 .0972515 0.33 0.745 -.1598606 .2231343 gender | 0 (omitted) prior | -.0052281 .0026485 -1.97 0.049 -.0104432 -.000013 democrat | .7181338 .1056572 6.80 0.000 .5100848 .9261828 indep | .234218 .1220027 1.92 0.056 -.006017 .4744529 otherpol | .1837928 .3153234 0.58 0.560 -.4371087 .8046944 midwest | -.039074 .1349734 -0.29 0.772 -.3048494 .2267014 south | .1482398 .123027 1.20 0.229 -.094012 .3904917 west | -.0577405 .1443928 -0.40 0.690 -.3420635 .2265825 age1 | 0 (omitted) age2 | 0 (omitted) age3 | 0 (omitted) age4 | 0 (omitted) anychildren | .0643583 .0963596 0.67 0.505 -.1253829 .2540995 loghhinc | .0587616 .0694228 0.85 0.398 -.0779385 .1954617 associatemore | -.1356695 .0992344 -1.37 0.173 -.3310715 .0597325 fulltime | -.0164926 .1329399 -0.12 0.901 -.2782638 .2452787 parttime | .2814243 .2138832 1.32 0.189 -.139732 .7025805 selfemp | .097111 .1733343 0.56 0.576 -.2442007 .4384227 unemployed | .005831 .2282787 0.03 0.980 -.4436715 .4553334 student | .1532298 .7492687 0.20 0.838 -1.322151 1.628611 _cons | -.8240365 .7705304 -1.07 0.286 -2.341284 .6932109 ------------------------------------------------------------------------------- reg[1,23] o. T1 wave gender prior democrat indep y1 .09563243 .03163681 0 -.00522811 .71813383 .23421798 o. o. otherpol midwest south west age1 age2 y1 .18379283 -.03907398 .14823984 -.05774048 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime y1 0 0 .06435827 .05876163 -.13566947 -.01649257 parttime selfemp unemployed student _cons y1 .28142425 .097111 .00583096 .15322978 -.82403648 symmetric var[23,23] o. T1 wave gender prior democrat indep T1 .00782706 wave .00006228 .00945786 o.gender 0 0 0 prior -.00001047 1.691e-06 0 7.014e-06 democrat .00053638 -.00046819 0 .00006525 .01116344 indep .00021342 -.00068916 0 5.792e-06 .00530471 .01488467 otherpol .00080432 -.00105481 0 .00001056 .00505681 .00386783 midwest -.00027209 .0010686 0 -1.665e-06 -.00099731 .00009836 south .00039587 .00069982 0 -1.886e-06 .00095998 .0016298 west -.00062659 .00101266 0 -3.623e-06 .00052283 .00304249 o.age1 0 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00008425 -.0006312 0 2.471e-06 .00077033 .00020586 loghhinc .00030794 -.00031966 0 -4.449e-06 .00004718 .00013613 associatem~e .00040385 -.0008416 0 5.346e-06 -.00084976 -.0002661 fulltime .00010992 -.00075659 0 .00002726 .00071693 .00061969 parttime .00039882 -.00038296 0 -.00001025 -.00148528 -.00099697 selfemp -.00004032 -.00012331 0 .00005634 -.00088862 -.00075838 unemployed .00046149 .00040239 0 .0000543 -.00044025 -.0013743 student -.00363102 .00267891 0 3.547e-06 -.00615728 -.00206166 _cons -.00704501 -.00804791 0 -.00058561 -.01129844 -.0076496 o. o. otherpol midwest south west age1 age2 otherpol .09942882 midwest .00164222 .01821782 south .00257509 .00949504 .01513565 west -.00606401 .00943594 .00961532 .02084927 o.age1 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00085081 -.00096671 .00029075 .00154163 0 0 loghhinc -.00025994 .00064827 -.00004955 .00080487 0 0 associatem~e .00177468 .00010454 .00022894 -.0017952 0 0 fulltime .00194741 .000679 .00092477 .00086024 0 0 parttime -.00027692 .00051934 .00090089 -.00282362 0 0 selfemp .00395739 .00207478 .00137737 -.00025354 0 0 unemployed .00417186 .00239554 .00092901 .00048849 0 0 student .00579759 .00276458 .00032473 -.00935919 0 0 _cons -.00528823 -.01781631 -.01184182 -.02031152 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime o.age3 0 o.age4 0 0 anychildren 0 0 .00928517 loghhinc 0 0 -.00141915 .00481953 associatem~e 0 0 .00063797 -.0018051 .00984747 fulltime 0 0 -.00090564 -.00311111 -.00112896 .01767301 parttime 0 0 .00123454 -.0011676 .00068862 .0113836 selfemp 0 0 -.00088165 -.00206221 -.00032164 .0133926 unemployed 0 0 .00067168 .0029072 -.00052208 .00893027 student 0 0 -.0076026 .00084002 -.00387423 .0119088 _cons 0 0 .01063036 -.0487517 .01501113 .01968657 parttime selfemp unemployed student _cons parttime .04574602 selfemp .01179353 .03004478 unemployed .01083088 .01052515 .05211118 student .01307737 .01401405 .0117586 .56140362 _cons .00239528 .00560654 -.04777454 -.01281339 .59371716 note: gender omitted because of collinearity note: age1 omitted because of collinearity note: age2 omitted because of collinearity note: age3 omitted because of collinearity note: age4 omitted because of collinearity note: student omitted because of collinearity Source | SS df MS Number of obs = 314 -------------+---------------------------------- F(16, 297) = 10.07 Model | 75.7064435 16 4.73165272 Prob > F = 0.0000 Residual | 139.54177 297 .469837611 R-squared = 0.3517 -------------+---------------------------------- Adj R-squared = 0.3168 Total | 215.248214 313 .687693974 Root MSE = .68545 ------------------------------------------------------------------------------- z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0150203 .0791998 0.19 0.850 -.1408435 .1708842 wave | .0554577 .0819653 0.68 0.499 -.1058486 .216764 gender | 0 (omitted) prior | -.0102923 .0023882 -4.31 0.000 -.0149922 -.0055924 democrat | .8611785 .0914218 9.42 0.000 .681262 1.041095 indep | -.0484132 .1147214 -0.42 0.673 -.2741829 .1773565 otherpol | .7979022 .2926848 2.73 0.007 .2219032 1.373901 midwest | .0459775 .1224767 0.38 0.708 -.1950546 .2870095 south | -.0314053 .1063763 -0.30 0.768 -.2407521 .1779415 west | -.1419696 .1203338 -1.18 0.239 -.3787845 .0948453 age1 | 0 (omitted) age2 | 0 (omitted) age3 | 0 (omitted) age4 | 0 (omitted) anychildren | .1066961 .0860958 1.24 0.216 -.0627389 .2761311 loghhinc | -.0574502 .053887 -1.07 0.287 -.163499 .0485985 associatemore | -.0897472 .0829092 -1.08 0.280 -.2529112 .0734167 fulltime | .1278898 .0948981 1.35 0.179 -.0588682 .3146477 parttime | .0861487 .1554661 0.55 0.580 -.2198061 .3921034 selfemp | .196291 .1324658 1.48 0.139 -.0643995 .4569814 unemployed | .1786869 .1966684 0.91 0.364 -.2083534 .5657271 student | 0 (omitted) _cons | .7880651 .6208066 1.27 0.205 -.433672 2.009802 ------------------------------------------------------------------------------- reg[1,23] o. T1 wave gender prior democrat indep y1 .01502032 .05545769 0 -.01029233 .86117853 -.04841321 o. o. otherpol midwest south west age1 age2 y1 .79790217 .04597748 -.03140528 -.14196961 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime y1 0 0 .10669613 -.05745024 -.08974722 .12788976 o. parttime selfemp unemployed student _cons y1 .08614866 .19629096 .17868686 0 .78806509 symmetric var[23,23] o. T1 wave gender prior democrat indep T1 .0062726 wave -.00020138 .0067183 o.gender 0 0 0 prior -.00001074 -3.003e-06 0 5.703e-06 democrat -.00023138 .00048889 0 .00003435 .00835794 indep .00020682 .00033659 0 1.320e-06 .00377739 .01316099 otherpol .00229782 -.00069324 0 .00005906 .00404274 .0034628 midwest .00036943 -.00108628 0 -6.777e-06 .00027314 -.00077776 south -.00023452 -.00040522 0 2.731e-06 .00108209 .00110411 west -.00018292 -.00102913 0 6.468e-06 .00122929 .0010108 o.age1 0 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00055748 .00066359 0 -.00001025 .0012994 .00176399 loghhinc -.00008324 -.00028124 0 5.053e-06 .00009944 -.00034561 associatem~e .00016508 -.00044489 0 -.00001018 .00036564 .00109427 fulltime .00011661 -.00011272 0 2.168e-06 .00013802 -.00039569 parttime -.00033821 -.00031203 0 -5.763e-06 -.00156089 -.00065638 selfemp -.00049641 -.00032519 0 -7.535e-06 -.00117358 -.00154347 unemployed .00167835 .00057387 0 .00001393 .0009895 -.00032988 o.student 0 0 0 0 0 0 _cons -.00152616 -.00545103 0 -.00052302 -.00956135 -.00214196 o. o. otherpol midwest south west age1 age2 otherpol .08566442 midwest .00043384 .01500053 south .0014479 .00691226 .01131592 west -.00101495 .00724794 .00722773 .01448022 o.age1 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00045792 -.00094506 -.00004728 -.0000828 0 0 loghhinc .00105375 .00081029 .00050683 .00101775 0 0 associatem~e .0004472 .00025669 .00012185 -.00011153 0 0 fulltime .00208053 -.00055851 .00016067 -.00040348 0 0 parttime -.00192913 -.00120156 -.0016367 -.00203849 0 0 selfemp -.00061749 -.00109645 .00007519 -.00107658 0 0 unemployed .00434295 .00184332 .00009588 .00161585 0 0 o.student 0 0 0 0 0 0 _cons -.02188476 -.01330339 -.01268681 -.0174789 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime o.age3 0 o.age4 0 0 anychildren 0 0 .00741248 loghhinc 0 0 -.00146126 .00290381 associatem~e 0 0 .00112523 -.00102066 .00687394 fulltime 0 0 -.00005867 -.00149795 -.00018901 .00900566 parttime 0 0 -.00012095 -.00099452 .00030821 .00415322 selfemp 0 0 -.0009861 -.0004992 -.00058956 .00425197 unemployed 0 0 .00015026 .00247183 .00060889 .00219346 o.student 0 0 0 0 0 0 _cons 0 0 .0104874 -.0303265 .00757241 .01242019 o. parttime selfemp unemployed student _cons parttime .02416971 selfemp .00419502 .01754718 unemployed .00244182 .00250807 .03867848 o.student 0 0 0 0 _cons .00986273 .00479877 -.03447843 0 .38540078 note: gender omitted because of collinearity note: age1 omitted because of collinearity note: age2 omitted because of collinearity note: age3 omitted because of collinearity note: age4 omitted because of collinearity Source | SS df MS Number of obs = 198 -------------+---------------------------------- F(17, 180) = 5.30 Model | 28.6234603 17 1.68373296 Prob > F = 0.0000 Residual | 57.198366 180 .3177687 R-squared = 0.3335 -------------+---------------------------------- Adj R-squared = 0.2706 Total | 85.8218262 197 .435643788 Root MSE = .56371 ------------------------------------------------------------------------------- z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2532911 .084811 2.99 0.003 .0859395 .4206427 wave | .0937434 .0884688 1.06 0.291 -.080826 .2683128 gender | 0 (omitted) prior | -.0074086 .0021885 -3.39 0.001 -.011727 -.0030902 democrat | .4567567 .1090023 4.19 0.000 .24167 .6718434 indep | .0969746 .1304219 0.74 0.458 -.1603779 .3543272 otherpol | .5018914 .253355 1.98 0.049 .0019634 1.001819 midwest | -.10059 .1322252 -0.76 0.448 -.3615008 .1603208 south | .0122709 .1126548 0.11 0.913 -.210023 .2345648 west | .0152232 .1348219 0.11 0.910 -.2508115 .2812579 age1 | 0 (omitted) age2 | 0 (omitted) age3 | 0 (omitted) age4 | 0 (omitted) anychildren | -.1666049 .1233828 -1.35 0.179 -.4100676 .0768578 loghhinc | .0494137 .0450688 1.10 0.274 -.0395176 .1383449 associatemore | .1719959 .0893956 1.92 0.056 -.0044023 .3483941 fulltime | .1858336 .2082209 0.89 0.373 -.2250343 .5967015 parttime | .3666705 .212999 1.72 0.087 -.0536258 .7869668 selfemp | .5443815 .2695792 2.02 0.045 .0124396 1.076323 unemployed | .6131641 .2304676 2.66 0.009 .1583982 1.06793 student | .4418825 .2071169 2.13 0.034 .033193 .8505721 _cons | -.6004939 .5817113 -1.03 0.303 -1.748345 .5473568 ------------------------------------------------------------------------------- reg[1,23] o. T1 wave gender prior democrat indep y1 .2532911 .09374341 0 -.00740857 .45675671 .09697464 o. o. otherpol midwest south west age1 age2 y1 .50189137 -.10059002 .01227088 .01522319 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime y1 0 0 -.16660487 .04941367 .17199591 .18583361 parttime selfemp unemployed student _cons y1 .36667054 .5443815 .61316408 .44188253 -.60049394 symmetric var[23,23] o. T1 wave gender prior democrat indep T1 .0071929 wave .00005762 .00782674 o.gender 0 0 0 prior .00001928 -1.307e-06 0 4.789e-06 democrat -.0001086 .00063324 0 9.536e-06 .0118815 indep -.0005421 .00173983 0 1.296e-06 .00783617 .01700987 otherpol -.00133389 .0019248 0 7.332e-06 .00679937 .00794753 midwest -.00132628 -.00153558 0 -.00004175 .00134271 -.00070707 south -.00115798 -.00084497 0 -.00001567 .00163929 .00128571 west -.00181782 -.00155154 0 -.0000102 -.00033698 -.00045252 o.age1 0 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren -.00025088 .00070429 0 -.00001149 .00086468 .00017293 loghhinc .00034261 .00023209 0 -3.550e-06 .0006305 .00004284 associatem~e -.0000393 -.00011916 0 -1.600e-06 -.00132361 -.0005112 fulltime -.00080353 .00252385 0 2.278e-06 -.00206333 .00020187 parttime -.0007373 .00253512 0 8.001e-06 -.00303919 -.00097522 selfemp -.00030088 .00303329 0 -.00003335 -.00068307 -.00353179 unemployed .00072303 .0024231 0 -.00003567 -.00403455 -.00099723 student -.00209113 .00122107 0 -.0000181 -.00454256 -.00151511 _cons -.00655951 -.01704136 0 -.00033313 -.01399708 -.01013051 o. o. otherpol midwest south west age1 age2 otherpol .06418878 midwest -.00061128 .0174835 south .00013383 .00875433 .0126911 west -.00439908 .00858133 .00806061 .01817694 o.age1 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00151502 -.00131071 -.00019394 .00069773 0 0 loghhinc -.00001936 .00114832 .00075239 .00020314 0 0 associatem~e .00189172 -.00016583 -.00046348 .00005454 0 0 fulltime -.00052055 -.00095147 -.00170702 .00420014 0 0 parttime .00082894 .0004388 -.00107151 .00527437 0 0 selfemp -.00383921 -.00121734 -.003998 .00320143 0 0 unemployed .00182782 -.00028742 -.00066689 .00396618 0 0 student .00253707 .00070466 -.00050192 .00526436 0 0 _cons -.01114291 -.01440466 -.01277421 -.01026533 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime o.age3 0 o.age4 0 0 anychildren 0 0 .01522331 loghhinc 0 0 .0002519 .0020312 associatem~e 0 0 -.00047591 -.00057226 .00799157 fulltime 0 0 .00613362 -.00077879 -.00372766 .04335595 parttime 0 0 .00626887 .00028664 -.00205335 .03636316 selfemp 0 0 .00410102 -.00088126 -.0025144 .03540995 unemployed 0 0 .00640591 .00003033 -.00256172 .0363048 student 0 0 .00793238 -.00052321 -.00045374 .03628798 _cons 0 0 -.01149941 -.02186571 .00604713 -.03004577 parttime selfemp unemployed student _cons parttime .0453686 selfemp .03453543 .07267293 unemployed .03636436 .03414625 .05311534 student .03674368 .03385952 .0366338 .04289743 _cons -.04236756 -.02443583 -.03573047 -.02973679 .33838805 note: gender omitted because of collinearity note: age1 omitted because of collinearity note: age2 omitted because of collinearity note: age3 omitted because of collinearity note: age4 omitted because of collinearity Source | SS df MS Number of obs = 342 -------------+---------------------------------- F(17, 324) = 3.26 Model | 23.3187525 17 1.37169132 Prob > F = 0.0000 Residual | 136.194821 324 .420354386 R-squared = 0.1462 -------------+---------------------------------- Adj R-squared = 0.1014 Total | 159.513574 341 .467781741 Root MSE = .64835 ------------------------------------------------------------------------------- z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0332919 .0722263 -0.46 0.645 -.1753837 .1087998 wave | .0870208 .0810535 1.07 0.284 -.0724367 .2464783 gender | 0 (omitted) prior | -.005895 .0015683 -3.76 0.000 -.0089804 -.0028096 democrat | .4198599 .0835512 5.03 0.000 .2554885 .5842313 indep | .2658153 .1154446 2.30 0.022 .0386997 .492931 otherpol | .2652218 .2782286 0.95 0.341 -.282141 .8125845 midwest | .0264866 .1375333 0.19 0.847 -.2440845 .2970577 south | .0724076 .1234342 0.59 0.558 -.1704261 .3152414 west | -.0332174 .1313177 -0.25 0.800 -.2915604 .2251255 age1 | 0 (omitted) age2 | 0 (omitted) age3 | 0 (omitted) age4 | 0 (omitted) anychildren | .0195467 .0755503 0.26 0.796 -.1290844 .1681777 loghhinc | .0612461 .0544857 1.12 0.262 -.0459444 .1684366 associatemore | .0416132 .0842697 0.49 0.622 -.1241715 .207398 fulltime | -.1534657 .1548767 -0.99 0.322 -.4581567 .1512253 parttime | -.0244554 .1710885 -0.14 0.886 -.36104 .3121292 selfemp | -.0064004 .1994638 -0.03 0.974 -.398808 .3860073 unemployed | -.3980327 .249399 -1.60 0.111 -.8886785 .0926132 student | .2858543 .2910741 0.98 0.327 -.2867796 .8584882 _cons | -.33852 .6120746 -0.55 0.581 -1.542662 .8656221 ------------------------------------------------------------------------------- reg[1,23] o. T1 wave gender prior democrat indep y1 -.03329193 .0870208 0 -.00589498 .4198599 .26581533 o. o. otherpol midwest south west age1 age2 y1 .26522176 .02648659 .07240763 -.03321742 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime y1 0 0 .01954667 .06124609 .04161324 -.15346572 parttime selfemp unemployed student _cons y1 -.02445544 -.00640037 -.39803267 .28585432 -.33852003 symmetric var[23,23] o. T1 wave gender prior democrat indep T1 .00521664 wave -.00031575 .00656966 o.gender 0 0 0 prior 8.961e-06 3.840e-06 0 2.460e-06 democrat -.00009316 -.00052105 0 .00001468 .00698081 indep .00043929 -.00160457 0 9.560e-06 .0047643 .01332745 otherpol .00075087 .00024232 0 .00003643 .00488606 .00498371 midwest -.00008574 -.00076103 0 -9.396e-06 -9.870e-06 -.00019735 south .00022932 -.0006786 0 -3.591e-06 .00020128 .00005935 west .0000773 -.00011091 0 1.857e-06 -.0000492 .00038632 o.age1 0 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren -.00070492 .00001229 0 -.00001132 .00060651 .00029935 loghhinc .00005545 -.00008933 0 -8.894e-06 .00006438 .0006245 associatem~e -.0005049 .00027009 0 -4.445e-06 -.00020195 6.829e-06 fulltime -.00109897 -.00046151 0 -.0000135 -.00061732 -.00041472 parttime -.00146025 -.00030688 0 -.00001715 -.00052401 -.00023958 selfemp -.00084228 .00047596 0 -.00001287 -.00095977 -.00127225 unemployed -.00063823 -.00076191 0 -8.962e-06 -.00049573 -.00199147 student -.00251359 .00071267 0 -.00001139 -.00093985 -.00227352 _cons -.00199868 -.00638056 0 -.0000989 -.00547698 -.01030557 o. o. otherpol midwest south west age1 age2 otherpol .07741117 midwest -.00118918 .01891542 south .00139381 .01230276 .01523601 west .00105874 .01210951 .01230648 .01724433 o.age1 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00179197 -.00050544 -.0010133 -.00076882 0 0 loghhinc .00046481 .00068951 .00036848 .00017314 0 0 associatem~e .00114516 .00035676 .00017185 .00023463 0 0 fulltime .00027322 .00071542 -.00153181 -.00155335 0 0 parttime .00043782 .0012118 -.00111686 -.00078351 0 0 selfemp .00167527 .00065883 -.00252863 -.00187973 0 0 unemployed .00231942 .003506 -.00064235 -.00065321 0 0 student .00220472 .00162779 -.00085527 -.00203885 0 0 _cons -.01632225 -.01890579 -.01381637 -.01273644 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime o.age3 0 o.age4 0 0 anychildren 0 0 .00570784 loghhinc 0 0 -.00036795 .0029687 associatem~e 0 0 .00074587 -.00104387 .00710137 fulltime 0 0 .00170686 -.00117633 -.00198789 .0239868 parttime 0 0 .00113327 .000444 -.002096 .02114656 selfemp 0 0 .00189132 -.0002031 -.00086501 .02130409 unemployed 0 0 .0032861 .00094822 -.0010425 .02128171 student 0 0 .00407812 -.00030447 -.00050646 .02205081 _cons 0 0 .00104413 -.03061239 .00792047 -.00451517 parttime selfemp unemployed student _cons parttime .02927128 selfemp .02094965 .03978581 unemployed .02141506 .02186576 .06219986 student .02154918 .02211025 .02331996 .08472416 _cons -.0215501 -.01605733 -.03004375 -.01720446 .37463528 note: gender omitted because of collinearity note: age1 omitted because of collinearity note: age2 omitted because of collinearity note: age3 omitted because of collinearity note: age4 omitted because of collinearity Source | SS df MS Number of obs = 316 -------------+---------------------------------- F(17, 298) = 4.14 Model | 32.4888989 17 1.9111117 Prob > F = 0.0000 Residual | 137.543163 298 .461554239 R-squared = 0.1911 -------------+---------------------------------- Adj R-squared = 0.1449 Total | 170.032062 315 .539784324 Root MSE = .67938 ------------------------------------------------------------------------------- z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0066434 .0788643 -0.08 0.933 -.1618449 .148558 wave | -.0565631 .0838664 -0.67 0.501 -.2216086 .1084824 gender | 0 (omitted) prior | -.0065081 .0017963 -3.62 0.000 -.0100432 -.0029731 democrat | .520893 .0945549 5.51 0.000 .3348132 .7069729 indep | .254744 .1024886 2.49 0.013 .0530508 .4564372 otherpol | -.0103881 .2325828 -0.04 0.964 -.4681008 .4473247 midwest | -.1122558 .123771 -0.91 0.365 -.3558317 .1313201 south | .1247217 .1154549 1.08 0.281 -.1024884 .3519319 west | .0740318 .1319451 0.56 0.575 -.1856304 .333694 age1 | 0 (omitted) age2 | 0 (omitted) age3 | 0 (omitted) age4 | 0 (omitted) anychildren | .0597364 .0909931 0.66 0.512 -.1193341 .2388069 loghhinc | -.1394626 .0560032 -2.49 0.013 -.2496744 -.0292508 associatemore | -.0003296 .0919423 -0.00 0.997 -.1812681 .1806089 fulltime | .0690648 .1021508 0.68 0.499 -.1319635 .2700932 parttime | -.1175978 .1504794 -0.78 0.435 -.4137346 .178539 selfemp | .1113767 .1759089 0.63 0.527 -.2348043 .4575578 unemployed | .2050931 .1822374 1.13 0.261 -.1535422 .5637283 student | -.8280739 .4941322 -1.68 0.095 -1.800505 .1443567 _cons | 1.852939 .6469562 2.86 0.004 .5797574 3.126121 ------------------------------------------------------------------------------- reg[1,23] o. T1 wave gender prior democrat indep y1 -.00664344 -.05656311 0 -.00650812 .52089303 .25474401 o. o. otherpol midwest south west age1 age2 y1 -.01038805 -.1122558 .12472172 .07403183 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime y1 0 0 .05973639 -.13946258 -.00032958 .06906483 parttime selfemp unemployed student _cons y1 -.11759781 .11137673 .20509307 -.82807389 1.8529391 symmetric var[23,23] o. T1 wave gender prior democrat indep T1 .00621957 wave .00031587 .00703358 o.gender 0 0 0 prior -.00001508 9.227e-06 0 3.227e-06 democrat -.00060787 -.00027213 0 .00001845 .00894062 indep -.00119763 .00021053 0 5.276e-06 .00442984 .01050392 otherpol -.00070013 -.00010916 0 .00002173 .00491451 .00464726 midwest .00058356 .00086439 0 -.00001068 .00082245 .00059174 south .00060223 -.0003796 0 5.428e-06 .00127917 .00040398 west .00021808 -.00070205 0 -6.281e-06 .00036431 .00037296 o.age1 0 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren -.00003894 -.00050029 0 -.00001282 .0012278 .00093244 loghhinc -.00026976 .00010588 0 4.881e-06 -.00005523 .00056404 associatem~e .00022938 -.00040393 0 -.0000128 -.00030507 .00013822 fulltime .00061482 -.00048601 0 -1.509e-06 -.00070408 -.00106056 parttime -.00057111 -.00056858 0 .00001953 .00054611 -.00092629 selfemp -.00003057 .00084524 0 -1.731e-06 .0001025 -.00032432 unemployed -.00038811 .00096556 0 -2.309e-06 -.00028363 -.001369 student .0004471 -.00156377 0 -.00004981 -.0047351 -.00066201 _cons .00041832 -.01051805 0 -.00031273 -.00570659 -.01107662 o. o. otherpol midwest south west age1 age2 otherpol .05409475 midwest -.00111351 .01531926 south -.00058881 .00927088 .01332982 west -.00170935 .00939299 .00936058 .0174095 o.age1 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00464302 -.0015142 -.00121436 -.00176152 0 0 loghhinc -.00080182 .00075251 .00053604 .00130164 0 0 associatem~e .00294267 .00020577 .00029366 -.00115343 0 0 fulltime -.00021285 -.00031317 -.00001874 .00064771 0 0 parttime .00218032 -.00123797 -.00062343 -.00145728 0 0 selfemp .00059425 .00010545 -.00141507 -.00163931 0 0 unemployed .00217234 .00224869 .00116615 .0015133 0 0 student .00155145 -.00370316 -.00097426 -.00401206 0 0 _cons -.00176516 -.01749701 -.01517916 -.02076982 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime o.age3 0 o.age4 0 0 anychildren 0 0 .00827975 loghhinc 0 0 -.00107038 .00313635 associatem~e 0 0 .00133596 -.00159676 .00845339 fulltime 0 0 .00031373 -.00090531 -.00114505 .01043479 parttime 0 0 .00021794 .00020752 -.00094177 .00708071 selfemp 0 0 .00175553 -.00062888 .00015075 .00710991 unemployed 0 0 .00152706 .00116971 -.00046666 .00674712 student 0 0 .00229841 -.00029881 .00164121 .0071781 _cons 0 0 .0068161 -.0335957 .01314272 .00408668 parttime selfemp unemployed student _cons parttime .02264404 selfemp .00705712 .03094394 unemployed .00722791 .00742355 .03321047 student .00688729 .00702987 .0068201 .24416661 _cons -.00867514 -.00163561 -.02257666 .00330517 .41855235 note: gender omitted because of collinearity note: age1 omitted because of collinearity note: age2 omitted because of collinearity note: age3 omitted because of collinearity note: age4 omitted because of collinearity Source | SS df MS Number of obs = 339 -------------+---------------------------------- F(17, 321) = 6.60 Model | 41.5905621 17 2.44650366 Prob > F = 0.0000 Residual | 118.968578 321 .370618624 R-squared = 0.2590 -------------+---------------------------------- Adj R-squared = 0.2198 Total | 160.55914 338 .475027042 Root MSE = .60878 ------------------------------------------------------------------------------- z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0522414 .0681508 0.77 0.444 -.0818373 .1863201 wave | .117619 .0732223 1.61 0.109 -.0264372 .2616752 gender | 0 (omitted) prior | -.0023757 .0017611 -1.35 0.178 -.0058404 .0010891 democrat | .6893536 .0774506 8.90 0.000 .5369786 .8417286 indep | .2367424 .0923904 2.56 0.011 .0549752 .4185096 otherpol | .365802 .3174454 1.15 0.250 -.2587342 .9903382 midwest | -.1937375 .1077054 -1.80 0.073 -.4056351 .01816 south | -.1566666 .0981656 -1.60 0.111 -.3497959 .0364627 west | -.1050199 .1157157 -0.91 0.365 -.3326768 .122637 age1 | 0 (omitted) age2 | 0 (omitted) age3 | 0 (omitted) age4 | 0 (omitted) anychildren | .2423283 .0765105 3.17 0.002 .0918029 .3928537 loghhinc | -.0322466 .0445406 -0.72 0.470 -.1198749 .0553818 associatemore | -.080978 .0788683 -1.03 0.305 -.2361421 .0741861 fulltime | .1109579 .0932278 1.19 0.235 -.0724568 .2943727 parttime | -.1779538 .1145473 -1.55 0.121 -.4033122 .0474045 selfemp | -.0531432 .147584 -0.36 0.719 -.3434972 .2372108 unemployed | .0999996 .1536002 0.65 0.515 -.2021906 .4021898 student | .2187823 .621361 0.35 0.725 -1.003672 1.441237 _cons | .0635537 .5393444 0.12 0.906 -.9975427 1.12465 ------------------------------------------------------------------------------- reg[1,23] o. T1 wave gender prior democrat indep y1 .05224138 .11761904 0 -.00237565 .68935358 .2367424 o. o. otherpol midwest south west age1 age2 y1 .36580198 -.19373755 -.15666662 -.10501992 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime y1 0 0 .24232831 -.03224657 -.08097799 .11095795 parttime selfemp unemployed student _cons y1 -.17795383 -.0531432 .0999996 .21878228 .06355366 symmetric var[23,23] o. T1 wave gender prior democrat indep T1 .00464454 wave -.00021624 .0053615 o.gender 0 0 0 prior .00001072 4.329e-06 0 3.101e-06 democrat .00029865 .00058399 0 .00001001 .0059986 indep -.00030767 .000653 0 .00001021 .0029932 .00853599 otherpol .0018781 -.0000926 0 .00005641 .00338174 .00330016 midwest .00031383 .00003385 0 1.351e-06 .00053887 .00048227 south .00009189 -.00005372 0 -4.026e-06 .00056088 .00092586 west .0002666 -.00042327 0 .00001034 .00033875 .00063379 o.age1 0 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00019368 .00041705 0 -1.516e-06 .00020678 -.00040848 loghhinc .0001374 .00045142 0 7.709e-07 .00055661 .00062318 associatem~e -.00046855 -.00003811 0 -9.584e-06 -.00051031 .00027474 fulltime .00049965 -.00011407 0 8.085e-06 -.00057827 -.00065013 parttime .00014404 -.00009964 0 1.499e-06 -.00016051 -.00034985 selfemp .00062321 .00077768 0 6.566e-06 -.00038394 -.00085718 unemployed -.00044992 .00048889 0 .00001543 -.00083259 -.00120267 student -.00194165 -.00332547 0 1.372e-06 -.00404423 -.00102614 _cons -.00490911 -.01278094 0 -.00026744 -.0105827 -.01120693 o. o. otherpol midwest south west age1 age2 otherpol .10077155 midwest .00116157 .01160045 south -.00131554 .00686568 .00963649 west -.00015554 .00718473 .00692881 .01339012 o.age1 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00084465 -.00055435 -.00010238 -.00032527 0 0 loghhinc .0015837 .00057948 .00025608 .00088939 0 0 associatem~e .00100205 .00084426 .00030481 .00027363 0 0 fulltime -.00117985 -.00077139 .00011904 -.00021554 0 0 parttime -.00164669 -.00060435 .00004024 -.00067871 0 0 selfemp .00021311 .00053701 .00056354 .00091132 0 0 unemployed -.00412855 -.00037282 -.00025691 .0011187 0 0 student .00097824 -.00038978 -.00268232 .0004683 0 0 _cons -.02561374 -.01358827 -.00988903 -.01727945 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime o.age3 0 o.age4 0 0 anychildren 0 0 .00585386 loghhinc 0 0 -.00037034 .00198386 associatem~e 0 0 .00052792 -.00084077 .00622021 fulltime 0 0 .00036404 -.0010593 -.00228424 .00869143 parttime 0 0 -.00009251 -.00033058 -.0006092 .00500414 selfemp 0 0 .00069814 -.00027949 -.00179823 .00571675 unemployed 0 0 .00155845 .0002203 -.00244358 .00562442 student 0 0 .0039686 -.00093273 .00294933 .00391366 _cons 0 0 -.00109456 -.02182953 .00735674 .00680151 parttime selfemp unemployed student _cons parttime .01312109 selfemp .00470406 .02178103 unemployed .00465731 .00565127 .02359302 student .00393495 .00381204 .00545237 .38608955 _cons -.00039735 -.00372504 -.00836318 .00954881 .29089243 note: gender omitted because of collinearity note: age1 omitted because of collinearity note: age2 omitted because of collinearity note: age3 omitted because of collinearity note: age4 omitted because of collinearity note: student omitted because of collinearity Source | SS df MS Number of obs = 369 -------------+---------------------------------- F(16, 352) = 7.79 Model | 49.6564132 16 3.10352583 Prob > F = 0.0000 Residual | 140.255432 352 .398452931 R-squared = 0.2615 -------------+---------------------------------- Adj R-squared = 0.2279 Total | 189.911845 368 .516064796 Root MSE = .63123 ------------------------------------------------------------------------------- z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1168485 .0672092 1.74 0.083 -.0153336 .2490306 wave | -.0135198 .0744176 -0.18 0.856 -.1598788 .1328393 gender | 0 (omitted) prior | -.0022953 .0019583 -1.17 0.242 -.0061468 .0015561 democrat | .6847901 .0735551 9.31 0.000 .5401274 .8294527 indep | .0879257 .1076578 0.82 0.415 -.1238078 .2996592 otherpol | .7398836 .2958023 2.50 0.013 .1581215 1.321646 midwest | .1545064 .1120695 1.38 0.169 -.0659036 .3749164 south | .1859729 .1037337 1.79 0.074 -.0180429 .3899887 west | .0881913 .1123867 0.78 0.433 -.1328426 .3092252 age1 | 0 (omitted) age2 | 0 (omitted) age3 | 0 (omitted) age4 | 0 (omitted) anychildren | .1130774 .0771315 1.47 0.144 -.0386192 .2647741 loghhinc | -.0839386 .0445627 -1.88 0.060 -.1715812 .0037041 associatemore | .0387828 .0695881 0.56 0.578 -.0980779 .1756435 fulltime | .0138132 .0812496 0.17 0.865 -.1459826 .1736091 parttime | -.1106264 .1229308 -0.90 0.369 -.3523977 .1311449 selfemp | .149484 .118714 1.26 0.209 -.0839939 .3829619 unemployed | .3567179 .1504033 2.37 0.018 .0609159 .6525199 student | 0 (omitted) _cons | .4853232 .5184739 0.94 0.350 -.5343731 1.50502 ------------------------------------------------------------------------------- reg[1,23] o. T1 wave gender prior democrat indep y1 .1168485 -.01351978 0 -.00229535 .68479006 .08792567 o. o. otherpol midwest south west age1 age2 y1 .73988364 .15450641 .1859729 .08819127 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime y1 0 0 .11307742 -.08393858 .0387828 .01381324 o. parttime selfemp unemployed student _cons y1 -.1106264 .14948404 .35671787 0 .48532322 symmetric var[23,23] o. T1 wave gender prior democrat indep T1 .00451707 wave -.00035147 .00553798 o.gender 0 0 0 prior 3.001e-06 -5.920e-06 0 3.835e-06 democrat -.00004718 .00029266 0 .0000205 .00541035 indep .000462 -.00079876 0 .00001931 .00288262 .01159021 otherpol -.00125716 -.00012418 0 .00007354 .00359608 .00330819 midwest .00060678 .00049017 0 .00001192 .00055868 -.00024142 south .00055935 .00063369 0 5.214e-06 .00038987 .00021618 west .00078428 -.00040889 0 -8.643e-07 .00087694 .00032214 o.age1 0 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren -7.448e-06 .00061264 0 3.888e-06 .00065664 .00009448 loghhinc .00024472 -.00051453 0 -6.608e-07 .00007928 .00045515 associatem~e 2.026e-06 .00003927 0 6.753e-06 .00050473 .00033573 fulltime -.00012942 -.00017374 0 -8.338e-06 .00010218 -.0002798 parttime -.00083357 -.00013696 0 -7.610e-06 -.0000328 .00050408 selfemp -.00012538 -.00023545 0 -3.220e-06 .00003828 -.00022688 unemployed .00046512 .00082912 0 .00001738 .00101852 -.00103531 o.student 0 0 0 0 0 0 _cons -.00509035 -.0018543 0 -.00030662 -.00706362 -.00868795 o. o. otherpol midwest south west age1 age2 otherpol .087499 midwest .00223812 .01255957 south .00170295 .00776809 .01076069 west -.00015487 .00775022 .0077678 .01263077 o.age1 0 0 0 0 0 o.age2 0 0 0 0 0 0 o.age3 0 0 0 0 0 0 o.age4 0 0 0 0 0 0 anychildren .00212606 .00050614 .00048549 .0014341 0 0 loghhinc .00090104 .00033924 .00039575 .00061257 0 0 associatem~e .00152654 .00031146 .00033348 -.00010927 0 0 fulltime .00070108 .00015107 .00038541 .00026868 0 0 parttime .00168315 -.00033723 .00068913 .00049769 0 0 selfemp .00213793 .00011983 .00025896 -.00079371 0 0 unemployed .00303891 .00139543 .00165176 .00176427 0 0 o.student 0 0 0 0 0 0 _cons -.02198484 -.01410619 -.01453236 -.01579322 0 0 o. o. age3 age4 anychildren loghhinc associatem~e fulltime o.age3 0 o.age4 0 0 anychildren 0 0 .00594928 loghhinc 0 0 -.00030778 .00198584 associatem~e 0 0 .00065435 -.00057925 .0048425 fulltime 0 0 .00048649 -.00063437 -.00033191 .00660151 parttime 0 0 .00011861 .00004349 -.00137517 .0024853 selfemp 0 0 .00055826 -.00020259 -.00060168 .00250269 unemployed 0 0 .00108856 .0008302 -.00022225 .0020719 o.student 0 0 0 0 0 0 _cons 0 0 -.00367008 -.02054874 .0026951 .00492076 o. parttime selfemp unemployed student _cons parttime .01511199 selfemp .002517 .01409301 unemployed .00221237 .00224581 .02262114 o.student 0 0 0 0 _cons -.00148739 .00037175 -.01628858 0 .26881523 . . . // Generate a set of matrices M1 and M2 for male and female responents . * 5 rows will contain control group means by age group . forv s=0/1{ 2. matrix M`s' = J(5, 1, .) 3. . matrix coln M`s' = mean 4. . matrix rown M`s' = "18-24" "25-34" "35-44" "45-54" "55-65" 5. } . . // calculate control group means by gender x age group . forv s=0/1{ 2. forv i = 1/5 { 3. mean z_lmpolicy_index if rand==0&gender==`s' & age==`i' 4. matrix mean = e(b) 5. matrix list mean 6. local mean=mean[1,1] 7. matrix M`s'[`i',1] = `mean' 8. } 9. } Mean estimation Number of obs = 60 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | .0163913 .0835373 -.1507664 .183549 ------------------------------------------------------------------ symmetric mean[1,1] z_lmpolicy~x y1 .0163913 Mean estimation Number of obs = 117 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | -.0677821 .0683504 -.2031588 .0675945 ------------------------------------------------------------------ symmetric mean[1,1] z_lmpolicy~x y1 -.06778213 Mean estimation Number of obs = 105 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | -.2025356 .0775708 -.3563614 -.0487099 ------------------------------------------------------------------ symmetric mean[1,1] z_lmpolicy~x y1 -.20253561 Mean estimation Number of obs = 103 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | -.29872 .0836836 -.4647059 -.132734 ------------------------------------------------------------------ symmetric mean[1,1] z_lmpolicy~x y1 -.29871995 Mean estimation Number of obs = 105 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | -.2340837 .0820596 -.3968109 -.0713565 ------------------------------------------------------------------ symmetric mean[1,1] z_lmpolicy~x y1 -.23408368 Mean estimation Number of obs = 83 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | .2712376 .0573958 .1570591 .3854161 ------------------------------------------------------------------ symmetric mean[1,1] z_lmpolicy~x y1 .27123755 Mean estimation Number of obs = 129 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | .1711939 .0562604 .0598731 .2825146 ------------------------------------------------------------------ symmetric mean[1,1] z_lmpolicy~x y1 .17119386 Mean estimation Number of obs = 92 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | .0776592 .0699867 -.0613607 .2166792 ------------------------------------------------------------------ symmetric mean[1,1] z_lmpolicy~x y1 .07765925 Mean estimation Number of obs = 120 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | .1600625 .059958 .0413397 .2787854 ------------------------------------------------------------------ symmetric mean[1,1] z_lmpolicy~x y1 .16006252 Mean estimation Number of obs = 120 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | .1050944 .0660986 -.0257874 .2359762 ------------------------------------------------------------------ symmetric mean[1,1] z_lmpolicy~x y1 .10509438 . . // Plot graph containing control group means and treatment effects for male resondents . coefplot (matrix(M0[,1]), weight(25) recast(bar) noci barwidth(0.4) color(ltblue)) /// > (matrix(R0[,1]), ci((2 3)) ciopts(recast(rcap))) /// > , vertical color(blue) nooffsets legend(off) /// > xtitle("Age group", size(4)) ytitle("Mean/Treatment effect T{sup:74}", size(4)) title("Male resp > ondents") name(graph190CI) . . // Plot graph containing control group means and treatment effects for female resondents . coefplot (matrix(M1[,1]), weight(25) recast(bar) noci barwidth(0.4) color(erose)) /// > (matrix(R1[,1]), ci((2 3)) ciopts(recast(rcap))) /// > ,vertical color(red) nooffsets legend(off) /// > xtitle("Age group", size(4)) ytitle("Mean/Treatment effect T{sup:74}", size(4)) title("Female re > spondents") name(graph290CI) . . graph combine graph190CI graph290CI, xsize(4) ysize(2.5) name(coefplot, replace) . . graph export "$output\T1_mean_bygenderbyage_90CI.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\T1_mean_bygenderbyage_90CI.pdf written in PDF format) . . . *********************************************************************************** . // Figure A.14: Willingness to pay for additional information . *********************************************************************************** . . set scheme s2mono . use "$path\data\SurveyStageI_AB_final.dta", clear . . keep if rand==0 // Keep only pure control group (3,031 observations deleted) . keep infopaysupport infopayoppose T1 T2 democrat republican gender pweight . . gen women=(gender==1) . gen men=(gender==0) . . local outcome1 = "infopaysupport" . local outcome2 = "infopayoppose" . . . **** Calculate numbers for bar graph matrix . * Note: pweights can be ignored for this graph because it is based on Wave A only, i.e. all pweigh > ts equal 1. . . mat R=J(2,6,.) . . *1) Supportive info, by gender . reg `outcome1' men, robust Linear regression Number of obs = 498 F(1, 496) = 3.42 Prob > F = 0.0652 R-squared = 0.0068 Root MSE = 1.3146 ------------------------------------------------------------------------------ | Robust infopaysup~t | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- men | -.2177097 .1177978 -1.85 0.065 -.4491539 .0137346 _cons | 1.504 .0848815 17.72 0.000 1.337228 1.670772 ------------------------------------------------------------------------------ . local pvalue1 = trim("`: di %9.3f 2*ttail(e(df_r), abs(_b[men]/_se[men]))'") . local row=1 . foreach X in women men { 2. sum `outcome1' if `X' == 1 3. mat R[`row',1] = r(mean) 4. mat R[`row',2]=_b[_cons] + _b[men]-1.96*_se[men] 5. mat R[`row',3]=_b[_cons] + _b[men]+1.96*_se[men] 6. mat R[`row',4]=`row' 7. mat R[`row',5] = 1 // category 8. local ++row 9. } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- infopaysup~t | 250 1.504 1.342084 0 3 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- infopaysup~t | 248 1.28629 1.286292 0 3 . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat1 . save `cat1' file C:\Users\gxf271\AppData\Local\Temp\ST_0000001a.tmp saved . restore . . *2) Supportive info, by pol. orientation . reg `outcome1' republican if democrat==1|republican==1, robust Linear regression Number of obs = 407 F(1, 405) = 19.27 Prob > F = 0.0000 R-squared = 0.0451 Root MSE = 1.2919 ------------------------------------------------------------------------------ | Robust infopaysup~t | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- republican | -.5652665 .128756 -4.39 0.000 -.8183801 -.312153 _cons | 1.678261 .0860955 19.49 0.000 1.509011 1.847511 ------------------------------------------------------------------------------ . local pvalue2 = trim("`: di %9.3f 2*ttail(e(df_r), abs(_b[republican]/_se[republican]))'") . local row=1 . foreach X in democrat republican { 2. sum `outcome1' if `X' == 1 3. mat R[`row',1] = r(mean) 4. mat R[`row',2]=_b[_cons] + _b[republican]-1.96*_se[republican] 5. mat R[`row',3]=_b[_cons] + _b[republican]+1.96*_se[republican] 6. mat R[`row',4]=`row' 7. mat R[`row',5] = 2 // category 8. local ++row 9. } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- infopaysup~t | 230 1.678261 1.305332 0 3 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- infopaysup~t | 177 1.112994 1.274176 0 3 . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat2 . save `cat2' file C:\Users\gxf271\AppData\Local\Temp\ST_0000001c.tmp saved . restore . . ************************************************* . . *3) Traditional info, by gender . reg `outcome2' men, robust Linear regression Number of obs = 498 F(1, 496) = 2.88 Prob > F = 0.0904 R-squared = 0.0058 Root MSE = 1.0366 ------------------------------------------------------------------------------ | Robust infopayopp~e | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- men | .1576774 .0929318 1.70 0.090 -.0249111 .340266 _cons | .552 .0625853 8.82 0.000 .4290351 .6749649 ------------------------------------------------------------------------------ . local pvalue3 = trim("`: di %9.3f 2*ttail(e(df_r), abs(_b[men]/_se[men]))'") . local row=1 . foreach X in women men{ 2. sum `outcome2' if `X' == 1 3. mat R[`row',1] = r(mean) 4. mat R[`row',2]=_b[_cons] + _b[men]-1.96*_se[men] 5. mat R[`row',3]=_b[_cons] + _b[men]+1.96*_se[men] 6. mat R[`row',4]=`row' 7. mat R[`row',5] = 3 // category 8. local ++row 9. } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- infopayopp~e | 250 .552 .9895518 0 3 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- infopayopp~e | 248 .7096774 1.081865 0 3 . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat3 . save `cat3' file C:\Users\gxf271\AppData\Local\Temp\ST_0000001e.tmp saved . restore . . . ************************************************ . . *4) Traditional info, by pol. orientation . reg `outcome2' republican if democrat==1|republican==1, robust Linear regression Number of obs = 407 F(1, 405) = 5.12 Prob > F = 0.0242 R-squared = 0.0128 Root MSE = 1.0449 ------------------------------------------------------------------------------ | Robust infopayopp~e | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- republican | .2392287 .1057621 2.26 0.024 .0313174 .44714 _cons | .5347826 .0659887 8.10 0.000 .4050594 .6645058 ------------------------------------------------------------------------------ . local pvalue4 = trim("`: di %9.3f 2*ttail(e(df_r), abs(_b[republican]/_se[republican]))'") . local row=1 . foreach X in democrat republican { 2. sum `outcome2' if `X' == 1 3. mat R[`row',1] = r(mean) 4. mat R[`row',2]=_b[_cons] + _b[republican]-1.96*_se[republican] 5. mat R[`row',3]=_b[_cons] + _b[republican]+1.96*_se[republican] 6. mat R[`row',4]=`row' 7. mat R[`row',5] = 4 // category 8. local ++row 9. } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- infopayopp~e | 230 .5347826 1.000484 0 3 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- infopayopp~e | 177 .7740113 1.100001 0 3 . preserve . clear . svmat R number of observations will be reset to 2 Press any key to continue, or Break to abort number of observations (_N) was 0, now 2 . tempfile cat4 . save `cat4' file C:\Users\gxf271\AppData\Local\Temp\ST_0000001g.tmp saved . restore . . . * Save bar graph matrix as dataset . clear . . local numcats = "1 2 3 4" . foreach a of local numcats { 2. append using `cat`a'' 3. } . . . * For alignment along the x-axis . gen s1 = R5 . gen s2 = . (8 missing values generated) . replace s2 = s1 - 0.2 if R5 == 1 (2 real changes made) . replace s2 = s1 - 0.6 if R5 == 2 (2 real changes made) . replace s2 = s1 - 1.0 if R5 == 3 (2 real changes made) . replace s2 = s1 - 1.4 if R5 == 4 (2 real changes made) . gen pos1 = (s2 - 0.1) - .6 . gen pos2 = s2 + 0.1 - .6 . . * This recovers the group means with which to label each bar. . local i = 0 . foreach cat of local numcats { 2. forval rel = 1/2 { 3. local ++i 4. sum R1 if R4 == `rel' & R5 == `cat' 5. local barval`i' = trim("`: di %9.2f r(mean)'") 6. } 7. } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 1.504 . 1.504 1.504 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 1.28629 . 1.28629 1.28629 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 1.678261 . 1.678261 1.678261 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 1.112994 . 1.112994 1.112994 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .552 . .552 .552 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .7096774 . .7096774 .7096774 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .5347826 . .5347826 .5347826 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- R1 | 1 .7740113 . .7740113 .7740113 . . global barlabels `"0.2 "Gender" 0.8 "Pol. orientation" 1.4 "Gender" 2.0 "Pol. orientation" > "' . global pvalues `"1.8 0.2 "p-value = `pvalue1'" 1.8 0.8 "p-value = `pvalue2'" 1.8 1.4 "p-va > lue = `pvalue3'" 1.8 2.0 "p-value = `pvalue4'""' . global grouplabels `"2.0 0.5 "Supportive Information" 2.0 1.7 "Traditional Information""' . global bargroups = `"0.3 0.1 "Women" 0.3 0.3 "Men" 0.3 0.7 "Dem." 0.3 0.9 "Repub." 0.3 1.3 > "Women" 0.3 1.5 "Men" 0.3 1.9 "Dem." 0.3 2.1 "Rep.""' . global barvalues = `"0.1 0.1 "`barval1'" 0.1 0.3 "`barval2'" 0.1 0.7 "`barval3'" 0.1 0.9 " > `barval4'" 0.1 1.3 "`barval5'" 0.1 1.5 "`barval6'" 0.1 1.9 "`barval7'" 0.1 2.1 "`barval8'""' . . . twoway (bar R1 pos1 if R4 == 1, barw(0.18) fi(inten50) lc(black) lw(medium)) (bar R1 pos2 if R4 == > 2, barw(0.18) fi(inten20) lc(black) lw(medium)) /// > (rcap R3 R2 pos2 if R4 == 2, lc(gs5)), legend(off) graphregion(color(white)) /// > yscale(range(2)) yla(0(0.5)2.1) xla($barlabels, labsize(3.5)) text($pvalues, size(3.5)) te > xt($grouplabels, size(4.0)) text($bargroups, size(3.0)) text($barvalues, size(3.5)) /// > ytitle("Willingness to pay for additional info", size(4.5) height(5)) . graph export "$output\fig_infopay0.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\fig_infopay0.pdf written in PDF format) . . *********************************************************************************** . // Figure A.15: Non-linearity in correlation between beliefs about the gender wage gap and outcome > s . *********************************************************************************** . . use "$path\data\SurveyStageI_AB_final.dta", clear . . binscatter large prior if rand==0 [aweight=pweight], nquantiles(15) rd(50, 116) xtitle("Prior beli > ef") ytitle("Gender diff. in wages are large") warning: nquantiles(15) was specified, but only 14 were generated. see help file under nquantiles() > for explanation. . graph export "$output\large_binscatter_discontinuity.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\large_binscatter_discontinuity.pdf written in PDF format) . . binscatter problem prior if rand==0 [aweight=pweight], nquantiles(15) rd(50, 116) xtitle("Prior be > lief") ytitle("Gender diff. in wages are a problem") warning: nquantiles(15) was specified, but only 14 were generated. see help file under nquantiles() > for explanation. . graph export "$output\problem_binscatter_discontinuity.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\problem_binscatter_discontinuity.pdf written in PDF format) . . binscatter govmore prior if rand==0 [aweight=pweight], nquantiles(15) rd(50, 116) xtitle("Prior be > lief") ytitle("Gov. should promote gender wage equality") warning: nquantiles(15) was specified, but only 14 were generated. see help file under nquantiles() > for explanation. . graph export "$output\govmore_binscatter_discontinuity.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\govmore_binscatter_discontinuity.pdf written in PDF format) . . binscatter quotaanchor prior if rand==0 [aweight=pweight], nquantiles(15) rd(50, 116) xtitle("Prio > r belief") ytitle("Introduce gender quotas") warning: nquantiles(15) was specified, but only 14 were generated. see help file under nquantiles() > for explanation. . graph export "$output\quota_binscatter_discontinuity.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\quota_binscatter_discontinuity.pdf written in PDF format) . . binscatter AAanchor prior if rand==0 [aweight=pweight], nquantiles(15) rd(50, 116) xtitle("Prior b > elief") ytitle("Statutory affirmative action") warning: nquantiles(15) was specified, but only 14 were generated. see help file under nquantiles() > for explanation. . graph export "$output\AA_binscatter_discontinuity.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\AA_binscatter_discontinuity.pdf written in PDF format) . . binscatter legislationanchor prior if rand==0 [aweight=pweight], nquantiles(25) rd(50, 116) xtitle > ("Prior belief") ytitle("Stricter equal pay legislation") warning: nquantiles(25) was specified, but only 21 were generated. see help file under nquantiles() > for explanation. . graph export "$output\legis_binscatter_discontinuity.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\legis_binscatter_discontinuity.pdf written in PDF format) . . binscatter transparencyanchor prior if rand==0 [aweight=pweight], nquantiles(15) rd(50, 116) xtitl > e("Prior belief") ytitle("Wage transparency within companies") . graph export "$output\transp_binscatter_discontinuity.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\transp_binscatter_discontinuity.pdf written in PDF format) . . binscatter UKtool prior if rand==0 [aweight=pweight], nquantiles(15) rd(50, 116) xtitle("Prior bel > ief") ytitle("Introduce reporting website") warning: nquantiles(15) was specified, but only 14 were generated. see help file under nquantiles() > for explanation. . graph export "$output\UKtool_binscatter_discontinuity.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\UKtool_binscatter_discontinuity.pdf written in PDF format) . . binscatter childcare prior if rand==0 [aweight=pweight], nquantiles(15) rd(50, 116) xtitle("Prior > belief") ytitle("Increase subsidies to child care") warning: nquantiles(15) was specified, but only 14 were generated. see help file under nquantiles() > for explanation. . graph export "$output\childcare_binscatter_discontinuity.pdf", replace (file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFiles\ou > tput\childcare_binscatter_discontinuity.pdf written in PDF format) . end of do-file . . // Appendix Tables . do "10_AdditionalTables.do" . . *********************************************************************************** . // Replication Files . ********************************************************************************** . /* > HOW DO BELIEFS ABOUT THE GENDER WAGE GAP AFFECT THE DEMAND FOR PUBLIC POLICY? > Sonja Settele > AEJ:pol > */ . ********************************************************************************** . . ***************************f******************************************************** . **** Generate Appendix Tables . *********************************************************************************** . . *********************************************************************************** . // Table B.1: Representativeness of the sample in terms of targeted variables . *********************************************************************************** . . * Column "Mean: U.S. population age 18-65" -> Calculate values based on ACS 2016 . . clear all . set more off . . use "$path\data\usa_00025.dta", clear . . keep if year==2016 (60,038,334 observations deleted) . keep if age>17&age<66 (1,211,541 observations deleted) . . //Census Region . gen northeast=(region==11|region==12) . gen midwest=(region==21|region==22) . gen south=(region==31|region==32|region==33) . gen west=(region==41|region==42) . . mean northeast [pweight=perwt] Mean estimation Number of obs = 1,944,946 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ northeast | .1765362 .0003473 .1758554 .1772169 -------------------------------------------------------------- . mean midwest [pweight=perwt] Mean estimation Number of obs = 1,944,946 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ midwest | .2088534 .0003809 .208107 .2095999 -------------------------------------------------------------- . mean south [pweight=perwt] Mean estimation Number of obs = 1,944,946 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ south | .3760818 .0004501 .3751996 .3769639 -------------------------------------------------------------- . mean west [pweight=perwt] Mean estimation Number of obs = 1,944,946 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ west | .2385286 .000388 .2377682 .2392891 -------------------------------------------------------------- . . // Age . mean age [pweight=perwt] Mean estimation Number of obs = 1,944,946 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ age | 41.04722 .0126087 41.0225 41.07193 -------------------------------------------------------------- . . // Sex . replace sex=sex-1 (1,944,946 real changes made) . mean sex [pweight=perwt] Mean estimation Number of obs = 1,944,946 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ sex | .5031741 .0004623 .502268 .5040803 -------------------------------------------------------------- . . // Employment . gen employed = (empstat==1) . mean employed [pweight=perwt] Mean estimation Number of obs = 1,944,946 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ employed | .7110642 .0004172 .7102465 .7118819 -------------------------------------------------------------- . . // Household income . replace ftotinc=. if ftotinc==9999999 (111,924 real changes made, 111,924 to missing) . gen lowinc=ftotinc<=50000 . mean lowinc [pweight=perwt] Mean estimation Number of obs = 1,944,946 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ lowinc | .3854519 .00046 .3845504 .3863534 -------------------------------------------------------------- . . . clear all . use "$path\data\SurveyStageI_AB_final.dta" . . * Define Democrats as Democrats (excluding Independents leaning Democrat) . replace democrat=0 (1,805 real changes made) . replace democrat=1 if pol==2 (1,333 real changes made) . . * Define Republicans as Republicans (excluding Independents leaning Republican) . replace republican=0 (1,462 real changes made) . replace republican=1 if pol==-2 (1,083 real changes made) . . * Define Independents as Independents and Independents leaning Democrat/Republican . replace indep=0 (719 real changes made) . replace indep=1 if pol==-1|pol==0|pol==1 (1,570 real changes made) . . lab var democrat "Democrat" . lab var republican "Republican" . lab var indep "Independent (including Indep. leaning Dem. or Rep.)" . . * Targeted quotas . *(Based on ACS 2016 for demographics -> See above) . *(Based on Pew Research 2018 for political orientation -> https://www.pewresearch.org/politics/201 > 8/03/20/wide-gender-gap-growing-educational-divide-in-voters-party-identification/) . . gen northeastACS=0.177 . gen midwestACS=0.209 . gen southACS=0.376 . gen westACS=0.238 . gen age_detACS=41.05 . gen femaleACS=0.50 . gen maleACS=0.50 . gen employedACS=0.711 . gen nonemployedACS=0.289 . gen lowincACS=0.39 . gen highincACS=0.61 . gen democratACS=0.33 . gen republicanACS=0.26 . gen indepACS=0.37 . . loc covars "northeast midwest south west age_det female male employed nonemployed lowinc highinc d > emocrat republican indep" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . forval i = 1/2 { 2. . qui eststo col`i': reg x y 3. . } . . restore . . . /* Statistics */ . . loc tabletitle "Representativeness of the sample in terms of targeted variables" . loc rowstats "" . loc rowlabels "" . loc colnames " "Mean: Sample" "Mean: U.S. population age 18-65" " . . loc varlength: list sizeof covars . loc varindex = 1 . . mat def P1 = J(`varlength', 1, .) . mat def P2 = J(`varlength', 1, .) . mat def P3 = J(`varlength', 1, .) . . . . foreach var in `covars' { 2. . . cap noi { 3. mean `var' [pweight=pweight] 4. matrix mean=e(b) 5. estadd loc `var'_mean = string(mean[1,1], "%9.2f"): col1 6. } 7. . cap noi { 8. sum `var'ACS 9. estadd loc `var'_mean = string(r(mean), "%9.2f"): col2 10. } 11. . . loc rowstats " `rowstats' `var'_mean " 12. loc rowlabels "`rowlabels' `"`: var la `var''"' " 13. . loc ++varindex 14. . } Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ northeast | .1786538 .0060624 .1667682 .1905395 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- northeastACS | 4,065 .177 0 .177 .177 Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ midwest | .2104126 .0064268 .1978126 .2230127 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- midwestACS | 4,065 .209 0 .209 .209 Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ south | .3746401 .0076362 .3596689 .3896114 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- southACS | 4,065 .376 0 .376 .376 Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ west | .2362934 .0066843 .2231885 .2493984 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- westACS | 4,065 .238 0 .238 .238 Mean estimation Number of obs = 4,058 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ age_det | 42.06331 .2085973 41.65434 42.47227 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age_detACS | 4,065 41.05 0 41.05 41.05 Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ female | .502492 .0078878 .4870276 .5179564 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- femaleACS | 4,065 .5 0 .5 .5 Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ male | .497508 .0078878 .4820436 .5129724 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- maleACS | 4,065 .5 0 .5 .5 Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ employed | .7147221 .0071087 .7007852 .7286589 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- employedACS | 4,065 .711 0 .711 .711 Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ nonemployed | .2852779 .0071087 .2713411 .2992148 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- nonemploye~S | 4,065 .289 0 .289 .289 Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ lowinc | .3853071 .0076723 .3702652 .400349 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- lowincACS | 4,065 .39 0 .39 .39 Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ highinc | .6146929 .0076723 .599651 .6297348 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- highincACS | 4,065 .61 0 .61 .61 Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ democrat | .3261288 .007386 .3116481 .3406094 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- democratACS | 4,065 .33 0 .33 .33 Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ republican | .2667495 .0069718 .253081 .280418 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- republican~S | 4,065 .26 0 .26 .26 Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ indep | .3878847 .0076936 .372801 .4029683 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- indepACS | 4,065 .37 0 .37 .37 . . esttab * using "$output\repACS2AB.tex", replace cells(none) booktabs nonotes nonum compres > s alignment(c) nogap noobs nobaselevels label mtitle(`colnames') stats(`rowstats', labels(`rowlabe > ls')) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\repACS2AB.tex) . eststo clear . . . . *********************************************************************************** . // Table B.2: Main survey: Integrity of randomization . *********************************************************************************** . . . use "$path\data\SurveyStageI_AB_final.dta", clear . . . * Generate indicators for any treatment group and for pure control group . gen T=T1+T2 . gen C=rand==0 . . . loc covars "female democrat republican indep otherpol prior northeast midwest south west age1 age2 > age3 age4 age5 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student oo > lf" . . . /* Balance table */ . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . . loc experiments "Full Treatment Control T1 T2 Incent Noincent pvalueTC pvalueT1T2 pvalueincent" . . . loc columns = 0 . . foreach choice in `experiments' { 2. loc ++columns 3. qui eststo col`columns': reg x y 4. } . . restore . . loc colnum = 1 . loc colnames "" . . . . ** Column 1: Full sample . . loc varindex = 2 . . foreach var in `covars' { 2. . summ `var' 3. local mean=round(r(mean),.001) 4. cap: estadd loc thisstat`varindex' = string(`mean', "%9.2f"): col1 5. . loc ++varindex 6. . } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- female | 4,065 .5185732 .4997164 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- democrat | 4,065 .4440344 .4969191 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- republican | 4,065 .3596556 .4799585 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- indep | 4,065 .1768758 .3816105 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- otherpol | 4,065 .0194342 .1380623 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 4,065 83.36531 21.67554 0 200 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- northeast | 4,065 .1776138 .3822343 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- midwest | 4,065 .2105781 .4077694 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- south | 4,065 .3741697 .4839673 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- west | 4,065 .2376384 .4256888 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age1 | 4,065 .1166052 .3209887 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age2 | 4,065 .2418204 .4282387 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age3 | 4,065 .2113161 .4082923 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age4 | 4,065 .2068881 .4051244 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age5 | 4,065 .2233702 .4165557 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- anychildren | 4,065 .5293973 .4991965 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- loghhinc | 4,065 10.90099 .8640326 8.815073 12.69851 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- associatem~e | 4,065 .6103321 .4877349 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- fulltime | 4,065 .5281673 .4992674 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- parttime | 4,065 .1067651 .3088523 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- selfemp | 4,065 .0755228 .2642655 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- unemployed | 4,065 .0578106 .2334136 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- student | 4,065 .0469865 .2116359 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- oolf | 4,065 .1847478 .3881406 0 1 . . . summ prior Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 4,065 83.36531 21.67554 0 200 . local mean=round(r(N),.001) . cap: estadd loc thisstat27 = string(`mean', "%9.0f"): col1 . . . ** Column 2: Treatment groups pooled . loc varindex = 2 . . foreach var in `covars' { 2. . summ `var' if T==1 3. local mean=round(r(mean),.001) 4. cap: estadd loc thisstat`varindex' = string(`mean', "%9.2f"): col2 5. . loc ++varindex 6. . } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- female | 3,031 .5160013 .4998264 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- democrat | 3,031 .4404487 .4965229 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- republican | 3,031 .3632465 .4810143 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- indep | 3,031 .1774992 .3821536 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- otherpol | 3,031 .0188057 .1358606 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 3,031 83.38634 21.73656 1 200 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- northeast | 3,031 .1788189 .3832638 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- midwest | 3,031 .2108215 .4079592 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- south | 3,031 .3721544 .483459 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- west | 3,031 .2382052 .4260556 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age1 | 3,031 .1092049 .3119476 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age2 | 3,031 .2431541 .4290582 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age3 | 3,031 .2184098 .4132351 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age4 | 3,031 .2038931 .4029569 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age5 | 3,031 .2253382 .4178738 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- anychildren | 3,031 .5364566 .4987514 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- loghhinc | 3,031 10.90881 .8555155 8.815073 12.69851 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- associatem~e | 3,031 .6126691 .4872207 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- fulltime | 3,031 .5291983 .4992291 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- parttime | 3,031 .1062356 .3081897 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- selfemp | 3,031 .0732432 .2605782 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- unemployed | 3,031 .0574068 .2326566 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- student | 3,031 .0458595 .2092147 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- oolf | 3,031 .1880567 .390822 0 1 . . summ prior if T==1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 3,031 83.38634 21.73656 1 200 . local mean=round(r(N),.001) . cap: estadd loc thisstat27 = string(`mean', "%9.0f"): col2 . . . ** Column 3: Control group . loc varindex = 2 . . foreach var in `covars' { 2. . summ `var' if T==0 3. local mean=round(r(mean),.001) 4. cap: estadd loc thisstat`varindex' = string(`mean', "%9.2f"): col3 5. . loc ++varindex 6. . } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- female | 1,034 .5261122 .4995593 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- democrat | 1,034 .4545455 .4981706 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- republican | 1,034 .3491296 .4769257 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- indep | 1,034 .1750484 .3801923 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- otherpol | 1,034 .0212766 .1443747 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 1,034 83.30368 21.506 0 200 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- northeast | 1,034 .1740812 .3793628 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- midwest | 1,034 .2098646 .4074089 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- south | 1,034 .3800774 .4856405 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- west | 1,034 .2359768 .4248132 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age1 | 1,034 .1382979 .3453794 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age2 | 1,034 .237911 .4260104 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age3 | 1,034 .1905222 .3929031 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age4 | 1,034 .2156673 .4114835 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age5 | 1,034 .2176015 .4128146 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- anychildren | 1,034 .5087041 .5001662 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- loghhinc | 1,034 10.87805 .8885535 8.815073 12.69851 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- associatem~e | 1,034 .6034816 .4894111 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- fulltime | 1,034 .5251451 .499609 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- parttime | 1,034 .1083172 .310931 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- selfemp | 1,034 .082205 .2748098 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- unemployed | 1,034 .0589942 .2357279 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- student | 1,034 .0502901 .2186487 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- oolf | 1,034 .1750484 .3801923 0 1 . . summ prior if T==0 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 1,034 83.30368 21.506 0 200 . local mean=round(r(N),.001) . cap: estadd loc thisstat27 = string(`mean', "%9.0f"): col3 . . . ** Column 4: T74 . loc varindex = 2 . . foreach var in `covars' { 2. . summ `var' if T1==1 3. local mean=round(r(mean),.001) 4. cap: estadd loc thisstat`varindex' = string(`mean', "%9.2f"): col4 5. . loc ++varindex 6. . } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- female | 1,531 .521228 .4997124 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- democrat | 1,531 .4448073 .4971068 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- republican | 1,531 .3598955 .4801263 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- indep | 1,531 .1770085 .3818006 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- otherpol | 1,531 .0182887 .1340372 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 1,531 83.34226 21.98872 1 200 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- northeast | 1,531 .1815807 .3856245 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- midwest | 1,531 .2031352 .4024638 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- south | 1,531 .3755715 .4844284 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- west | 1,531 .2397126 .4270475 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age1 | 1,531 .1071195 .3093662 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age2 | 1,531 .2442848 .4298027 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age3 | 1,531 .2161986 .4117857 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age4 | 1,531 .2083605 .4062687 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age5 | 1,531 .2240366 .4170825 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- anychildren | 1,531 .5316786 .4991585 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- loghhinc | 1,531 10.88476 .8585411 8.815073 12.69851 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- associatem~e | 1,531 .6133246 .4871473 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- fulltime | 1,531 .5107773 .5000472 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- parttime | 1,531 .1201829 .3252815 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- selfemp | 1,531 .073808 .2615436 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- unemployed | 1,531 .0529066 .2239202 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- student | 1,531 .0542129 .2265114 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- oolf | 1,531 .1881123 .3909295 0 1 . . summ prior if T1==1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 1,531 83.34226 21.98872 1 200 . local mean=round(r(N),.001) . cap: estadd loc thisstat27 = string(`mean', "%9.0f"): col4 . . . ** Column 5: T94 . loc varindex = 2 . . foreach var in `covars' { 2. . summ `var' if T2==1 3. local mean=round(r(mean),.001) 4. cap: estadd loc thisstat`varindex' = string(`mean', "%9.2f"): col5 5. . loc ++varindex 6. . } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- female | 1,500 .5106667 .5000529 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- democrat | 1,500 .436 .4960525 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- republican | 1,500 .3666667 .4820551 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- indep | 1,500 .178 .3826403 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- otherpol | 1,500 .0193333 .1377396 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 1,500 83.43133 21.48338 2 200 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- northeast | 1,500 .176 .3809472 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- midwest | 1,500 .2186667 .4134798 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- south | 1,500 .3686667 .4826042 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- west | 1,500 .2366667 .4251777 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age1 | 1,500 .1113333 .3146494 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age2 | 1,500 .242 .4284371 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age3 | 1,500 .2206667 .4148344 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age4 | 1,500 .1993333 .3996324 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age5 | 1,500 .2266667 .4188148 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- anychildren | 1,500 .5413333 .4984548 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- loghhinc | 1,500 10.93336 .8520021 8.815073 12.69851 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- associatem~e | 1,500 .612 .4874571 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- fulltime | 1,500 .548 .4978566 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- parttime | 1,500 .092 .2891223 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- selfemp | 1,500 .0726667 .2596751 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- unemployed | 1,500 .062 .241236 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- student | 1,500 .0373333 .1896405 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- oolf | 1,500 .188 .3908425 0 1 . . summ prior if T2==1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 1,500 83.43133 21.48338 2 200 . local mean=round(r(N),.001) . cap: estadd loc thisstat27 = string(`mean', "%9.0f"): col5 . . . * Column 6: Incentivized prior . loc varindex = 2 . . foreach var in `covars' { 2. . summ `var' if prior1==1 3. local mean=round(r(mean),.001) 4. cap: estadd loc thisstat`varindex' = string(`mean', "%9.2f"): col6 5. . loc ++varindex 6. . } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- female | 2,293 .5133014 .4999321 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- democrat | 2,293 .4500654 .4976088 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- republican | 2,293 .361099 .4804239 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- indep | 2,293 .1722634 .3776916 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- otherpol | 2,293 .0165722 .1276897 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 2,293 83.24945 21.32217 0 200 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- northeast | 2,293 .1761884 .3810635 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- midwest | 2,293 .2045355 .4034498 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- south | 2,293 .3785434 .4851298 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- west | 2,293 .2407327 .4276216 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age1 | 2,293 .1247274 .3304817 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age2 | 2,293 .2433493 .4291978 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age3 | 2,293 .2067161 .4050384 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age4 | 2,293 .2040994 .4031299 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age5 | 2,293 .2211077 .4150834 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- anychildren | 2,293 .5272569 .4993654 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- loghhinc | 2,293 10.90267 .8728094 8.815073 12.69851 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- associatem~e | 2,293 .6105539 .4877311 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- fulltime | 2,293 .5089403 .5000291 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- parttime | 2,293 .1203663 .3254604 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- selfemp | 2,293 .0806803 .272403 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- unemployed | 2,293 .0575665 .2329727 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- student | 2,293 .0497165 .2174061 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- oolf | 2,293 .18273 .3865293 0 1 . . summ prior if prior1==1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 2,293 83.24945 21.32217 0 200 . local mean=round(r(N),.001) . cap: estadd loc thisstat27 = string(`mean', "%9.0f"): col6 . . . . * Column 7: T94 . loc varindex = 2 . . foreach var in `covars' { 2. . summ `var' if prior1==0 3. local mean=round(r(mean),.001) 4. cap: estadd loc thisstat`varindex' = string(`mean', "%9.2f"): col7 5. . loc ++varindex 6. . } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- female | 1,772 .525395 .4994956 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- democrat | 1,772 .4362302 .4960567 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- republican | 1,772 .3577878 .4794846 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- indep | 1,772 .1828442 .3866479 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- otherpol | 1,772 .0231377 .1503832 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 1,772 83.51524 22.12957 1 200 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- northeast | 1,772 .1794582 .3838439 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- midwest | 1,772 .2183973 .4132751 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- south | 1,772 .3685102 .4825369 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- west | 1,772 .2336343 .4232616 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age1 | 1,772 .1060948 .3080459 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age2 | 1,772 .239842 .4271074 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age3 | 1,772 .2172686 .4125033 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age4 | 1,772 .2104966 .4077764 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age5 | 1,772 .226298 .4185523 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- anychildren | 1,772 .532167 .4991051 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- loghhinc | 1,772 10.8988 .8527821 8.815073 12.69851 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- associatem~e | 1,772 .6100451 .4878774 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- fulltime | 1,772 .5530474 .4973184 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- parttime | 1,772 .0891648 .2850619 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- selfemp | 1,772 .0688488 .2532682 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- unemployed | 1,772 .0581264 .2340484 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- student | 1,772 .0434537 .2039337 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- oolf | 1,772 .1873589 .3903095 0 1 . . summ prior if prior1==0 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 1,772 83.51524 22.12957 1 200 . local mean=round(r(N),.001) . cap: estadd loc thisstat27 = string(`mean', "%9.0f"): col7 . . . . ** Column 8: P-value for testing equality of means T-C . loc varindex = 2 . . foreach var in `covars' { 2. mvtest means `var', by(T) 3. local p=round(r(p_F),.001) 4. cap: estadd loc thisstat`varindex' = string(`p', "%9.3f"): col8 5. . . loc ++varindex 6. } Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 4063.0 0.32 0.5743 e Pillai's trace | 0.0001 1.0 4063.0 0.32 0.5743 e Lawley-Hotelling trace | 0.0001 1.0 4063.0 0.32 0.5743 e Roy's largest root | 0.0001 1.0 4063.0 0.32 0.5743 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 4063.0 0.62 0.4309 e Pillai's trace | 0.0002 1.0 4063.0 0.62 0.4309 e Lawley-Hotelling trace | 0.0002 1.0 4063.0 0.62 0.4309 e Roy's largest root | 0.0002 1.0 4063.0 0.62 0.4309 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 4063.0 0.67 0.4142 e Pillai's trace | 0.0002 1.0 4063.0 0.67 0.4142 e Lawley-Hotelling trace | 0.0002 1.0 4063.0 0.67 0.4142 e Roy's largest root | 0.0002 1.0 4063.0 0.67 0.4142 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.03 0.8585 e Pillai's trace | 0.0000 1.0 4063.0 0.03 0.8585 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.03 0.8585 e Roy's largest root | 0.0000 1.0 4063.0 0.03 0.8585 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 4063.0 0.25 0.6193 e Pillai's trace | 0.0001 1.0 4063.0 0.25 0.6193 e Lawley-Hotelling trace | 0.0001 1.0 4063.0 0.25 0.6193 e Roy's largest root | 0.0001 1.0 4063.0 0.25 0.6193 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.01 0.9157 e Pillai's trace | 0.0000 1.0 4063.0 0.01 0.9157 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.01 0.9157 e Roy's largest root | 0.0000 1.0 4063.0 0.01 0.9157 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.12 0.7308 e Pillai's trace | 0.0000 1.0 4063.0 0.12 0.7308 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.12 0.7308 e Roy's largest root | 0.0000 1.0 4063.0 0.12 0.7308 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.00 0.9481 e Pillai's trace | 0.0000 1.0 4063.0 0.00 0.9481 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.00 0.9481 e Roy's largest root | 0.0000 1.0 4063.0 0.00 0.9481 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 4063.0 0.21 0.6495 e Pillai's trace | 0.0001 1.0 4063.0 0.21 0.6495 e Lawley-Hotelling trace | 0.0001 1.0 4063.0 0.21 0.6495 e Roy's largest root | 0.0001 1.0 4063.0 0.21 0.6495 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.02 0.8845 e Pillai's trace | 0.0000 1.0 4063.0 0.02 0.8845 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.02 0.8845 e Roy's largest root | 0.0000 1.0 4063.0 0.02 0.8845 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9984 1.0 4063.0 6.34 0.0118 e Pillai's trace | 0.0016 1.0 4063.0 6.34 0.0118 e Lawley-Hotelling trace | 0.0016 1.0 4063.0 6.34 0.0118 e Roy's largest root | 0.0016 1.0 4063.0 6.34 0.0118 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.12 0.7339 e Pillai's trace | 0.0000 1.0 4063.0 0.12 0.7339 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.12 0.7339 e Roy's largest root | 0.0000 1.0 4063.0 0.12 0.7339 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9991 1.0 4063.0 3.60 0.0579 e Pillai's trace | 0.0009 1.0 4063.0 3.60 0.0579 e Lawley-Hotelling trace | 0.0009 1.0 4063.0 3.60 0.0579 e Roy's largest root | 0.0009 1.0 4063.0 3.60 0.0579 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 4063.0 0.65 0.4197 e Pillai's trace | 0.0002 1.0 4063.0 0.65 0.4197 e Lawley-Hotelling trace | 0.0002 1.0 4063.0 0.65 0.4197 e Roy's largest root | 0.0002 1.0 4063.0 0.65 0.4197 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 4063.0 0.27 0.6061 e Pillai's trace | 0.0001 1.0 4063.0 0.27 0.6061 e Lawley-Hotelling trace | 0.0001 1.0 4063.0 0.27 0.6061 e Roy's largest root | 0.0001 1.0 4063.0 0.27 0.6061 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9994 1.0 4063.0 2.38 0.1227 e Pillai's trace | 0.0006 1.0 4063.0 2.38 0.1227 e Lawley-Hotelling trace | 0.0006 1.0 4063.0 2.38 0.1227 e Roy's largest root | 0.0006 1.0 4063.0 2.38 0.1227 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 4063.0 0.98 0.3229 e Pillai's trace | 0.0002 1.0 4063.0 0.98 0.3229 e Lawley-Hotelling trace | 0.0002 1.0 4063.0 0.98 0.3229 e Roy's largest root | 0.0002 1.0 4063.0 0.98 0.3229 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 4063.0 0.27 0.6010 e Pillai's trace | 0.0001 1.0 4063.0 0.27 0.6010 e Lawley-Hotelling trace | 0.0001 1.0 4063.0 0.27 0.6010 e Roy's largest root | 0.0001 1.0 4063.0 0.27 0.6010 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.05 0.8217 e Pillai's trace | 0.0000 1.0 4063.0 0.05 0.8217 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.05 0.8217 e Roy's largest root | 0.0000 1.0 4063.0 0.05 0.8217 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.04 0.8516 e Pillai's trace | 0.0000 1.0 4063.0 0.04 0.8516 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.04 0.8516 e Roy's largest root | 0.0000 1.0 4063.0 0.04 0.8516 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 4063.0 0.89 0.3464 e Pillai's trace | 0.0002 1.0 4063.0 0.89 0.3464 e Lawley-Hotelling trace | 0.0002 1.0 4063.0 0.89 0.3464 e Roy's largest root | 0.0002 1.0 4063.0 0.89 0.3464 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.04 0.8502 e Pillai's trace | 0.0000 1.0 4063.0 0.04 0.8502 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.04 0.8502 e Roy's largest root | 0.0000 1.0 4063.0 0.04 0.8502 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 4063.0 0.34 0.5611 e Pillai's trace | 0.0001 1.0 4063.0 0.34 0.5611 e Lawley-Hotelling trace | 0.0001 1.0 4063.0 0.34 0.5611 e Roy's largest root | 0.0001 1.0 4063.0 0.34 0.5611 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 4063.0 0.87 0.3521 e Pillai's trace | 0.0002 1.0 4063.0 0.87 0.3521 e Lawley-Hotelling trace | 0.0002 1.0 4063.0 0.87 0.3521 e Roy's largest root | 0.0002 1.0 4063.0 0.87 0.3521 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F . . . ** Column 9: P-value for testing equality of means T74 - T94 . loc varindex = 2 . . foreach var in `covars' { 2. mvtest means `var' if rand!=0, by(T1) 3. local p=round(r(p_F),.001) 4. cap: estadd loc thisstat`varindex' = string(`p', "%9.3f"): col9 5. . loc ++varindex 6. } Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 3029.0 0.34 0.5609 e Pillai's trace | 0.0001 1.0 3029.0 0.34 0.5609 e Lawley-Hotelling trace | 0.0001 1.0 3029.0 0.34 0.5609 e Roy's largest root | 0.0001 1.0 3029.0 0.34 0.5609 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 3029.0 0.24 0.6254 e Pillai's trace | 0.0001 1.0 3029.0 0.24 0.6254 e Lawley-Hotelling trace | 0.0001 1.0 3029.0 0.24 0.6254 e Roy's largest root | 0.0001 1.0 3029.0 0.24 0.6254 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.15 0.6985 e Pillai's trace | 0.0000 1.0 3029.0 0.15 0.6985 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.15 0.6985 e Roy's largest root | 0.0000 1.0 3029.0 0.15 0.6985 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.01 0.9431 e Pillai's trace | 0.0000 1.0 3029.0 0.01 0.9431 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.01 0.9431 e Roy's largest root | 0.0000 1.0 3029.0 0.01 0.9431 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.04 0.8324 e Pillai's trace | 0.0000 1.0 3029.0 0.04 0.8324 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.04 0.8324 e Roy's largest root | 0.0000 1.0 3029.0 0.04 0.8324 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.01 0.9102 e Pillai's trace | 0.0000 1.0 3029.0 0.01 0.9102 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.01 0.9102 e Roy's largest root | 0.0000 1.0 3029.0 0.01 0.9102 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 3029.0 0.16 0.6886 e Pillai's trace | 0.0001 1.0 3029.0 0.16 0.6886 e Lawley-Hotelling trace | 0.0001 1.0 3029.0 0.16 0.6886 e Roy's largest root | 0.0001 1.0 3029.0 0.16 0.6886 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9996 1.0 3029.0 1.10 0.2947 e Pillai's trace | 0.0004 1.0 3029.0 1.10 0.2947 e Lawley-Hotelling trace | 0.0004 1.0 3029.0 1.10 0.2947 e Roy's largest root | 0.0004 1.0 3029.0 1.10 0.2947 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 3029.0 0.15 0.6943 e Pillai's trace | 0.0001 1.0 3029.0 0.15 0.6943 e Lawley-Hotelling trace | 0.0001 1.0 3029.0 0.15 0.6943 e Roy's largest root | 0.0001 1.0 3029.0 0.15 0.6943 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.04 0.8440 e Pillai's trace | 0.0000 1.0 3029.0 0.04 0.8440 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.04 0.8440 e Roy's largest root | 0.0000 1.0 3029.0 0.04 0.8440 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.14 0.7101 e Pillai's trace | 0.0000 1.0 3029.0 0.14 0.7101 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.14 0.7101 e Roy's largest root | 0.0000 1.0 3029.0 0.14 0.7101 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.02 0.8835 e Pillai's trace | 0.0000 1.0 3029.0 0.02 0.8835 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.02 0.8835 e Roy's largest root | 0.0000 1.0 3029.0 0.02 0.8835 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.09 0.7660 e Pillai's trace | 0.0000 1.0 3029.0 0.09 0.7660 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.09 0.7660 e Roy's largest root | 0.0000 1.0 3029.0 0.09 0.7660 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 3029.0 0.38 0.5376 e Pillai's trace | 0.0001 1.0 3029.0 0.38 0.5376 e Lawley-Hotelling trace | 0.0001 1.0 3029.0 0.38 0.5376 e Roy's largest root | 0.0001 1.0 3029.0 0.38 0.5376 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.03 0.8625 e Pillai's trace | 0.0000 1.0 3029.0 0.03 0.8625 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.03 0.8625 e Roy's largest root | 0.0000 1.0 3029.0 0.03 0.8625 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 3029.0 0.28 0.5942 e Pillai's trace | 0.0001 1.0 3029.0 0.28 0.5942 e Lawley-Hotelling trace | 0.0001 1.0 3029.0 0.28 0.5942 e Roy's largest root | 0.0001 1.0 3029.0 0.28 0.5942 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9992 1.0 3029.0 2.45 0.1179 e Pillai's trace | 0.0008 1.0 3029.0 2.45 0.1179 e Lawley-Hotelling trace | 0.0008 1.0 3029.0 2.45 0.1179 e Roy's largest root | 0.0008 1.0 3029.0 2.45 0.1179 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.01 0.9404 e Pillai's trace | 0.0000 1.0 3029.0 0.01 0.9404 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.01 0.9404 e Roy's largest root | 0.0000 1.0 3029.0 0.01 0.9404 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9986 1.0 3029.0 4.22 0.0401 e Pillai's trace | 0.0014 1.0 3029.0 4.22 0.0401 e Lawley-Hotelling trace | 0.0014 1.0 3029.0 4.22 0.0401 e Roy's largest root | 0.0014 1.0 3029.0 4.22 0.0401 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9979 1.0 3029.0 6.35 0.0118 e Pillai's trace | 0.0021 1.0 3029.0 6.35 0.0118 e Lawley-Hotelling trace | 0.0021 1.0 3029.0 6.35 0.0118 e Roy's largest root | 0.0021 1.0 3029.0 6.35 0.0118 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.01 0.9041 e Pillai's trace | 0.0000 1.0 3029.0 0.01 0.9041 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.01 0.9041 e Roy's largest root | 0.0000 1.0 3029.0 0.01 0.9041 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9996 1.0 3029.0 1.16 0.2821 e Pillai's trace | 0.0004 1.0 3029.0 1.16 0.2821 e Lawley-Hotelling trace | 0.0004 1.0 3029.0 1.16 0.2821 e Roy's largest root | 0.0004 1.0 3029.0 1.16 0.2821 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9984 1.0 3029.0 4.94 0.0263 e Pillai's trace | 0.0016 1.0 3029.0 4.94 0.0263 e Lawley-Hotelling trace | 0.0016 1.0 3029.0 4.94 0.0263 e Roy's largest root | 0.0016 1.0 3029.0 4.94 0.0263 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.00 0.9937 e Pillai's trace | 0.0000 1.0 3029.0 0.00 0.9937 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.00 0.9937 e Roy's largest root | 0.0000 1.0 3029.0 0.00 0.9937 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F . . . ** Column 10: P-value for testing equality of means inc - no inc . loc varindex = 2 . . foreach var in `covars' { 2. mvtest means `var', by(prior1) 3. local p=round(r(p_F),.001) 4. cap: estadd loc thisstat`varindex' = string(`p', "%9.3f"): col10 5. . loc ++varindex 6. . } Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 4063.0 0.59 0.4443 e Pillai's trace | 0.0001 1.0 4063.0 0.59 0.4443 e Lawley-Hotelling trace | 0.0001 1.0 4063.0 0.59 0.4443 e Roy's largest root | 0.0001 1.0 4063.0 0.59 0.4443 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 4063.0 0.77 0.3788 e Pillai's trace | 0.0002 1.0 4063.0 0.77 0.3788 e Lawley-Hotelling trace | 0.0002 1.0 4063.0 0.77 0.3788 e Roy's largest root | 0.0002 1.0 4063.0 0.77 0.3788 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.05 0.8274 e Pillai's trace | 0.0000 1.0 4063.0 0.05 0.8274 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.05 0.8274 e Roy's largest root | 0.0000 1.0 4063.0 0.05 0.8274 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 4063.0 0.77 0.3808 e Pillai's trace | 0.0002 1.0 4063.0 0.77 0.3808 e Lawley-Hotelling trace | 0.0002 1.0 4063.0 0.77 0.3808 e Roy's largest root | 0.0002 1.0 4063.0 0.77 0.3808 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9994 1.0 4063.0 2.26 0.1327 e Pillai's trace | 0.0006 1.0 4063.0 2.26 0.1327 e Lawley-Hotelling trace | 0.0006 1.0 4063.0 2.26 0.1327 e Roy's largest root | 0.0006 1.0 4063.0 2.26 0.1327 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.15 0.6983 e Pillai's trace | 0.0000 1.0 4063.0 0.15 0.6983 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.15 0.6983 e Roy's largest root | 0.0000 1.0 4063.0 0.15 0.6983 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.07 0.7868 e Pillai's trace | 0.0000 1.0 4063.0 0.07 0.7868 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.07 0.7868 e Roy's largest root | 0.0000 1.0 4063.0 0.07 0.7868 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9997 1.0 4063.0 1.16 0.2825 e Pillai's trace | 0.0003 1.0 4063.0 1.16 0.2825 e Lawley-Hotelling trace | 0.0003 1.0 4063.0 1.16 0.2825 e Roy's largest root | 0.0003 1.0 4063.0 1.16 0.2825 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 4063.0 0.43 0.5123 e Pillai's trace | 0.0001 1.0 4063.0 0.43 0.5123 e Lawley-Hotelling trace | 0.0001 1.0 4063.0 0.43 0.5123 e Roy's largest root | 0.0001 1.0 4063.0 0.43 0.5123 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 4063.0 0.28 0.5981 e Pillai's trace | 0.0001 1.0 4063.0 0.28 0.5981 e Lawley-Hotelling trace | 0.0001 1.0 4063.0 0.28 0.5981 e Roy's largest root | 0.0001 1.0 4063.0 0.28 0.5981 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9992 1.0 4063.0 3.37 0.0665 e Pillai's trace | 0.0008 1.0 4063.0 3.37 0.0665 e Lawley-Hotelling trace | 0.0008 1.0 4063.0 3.37 0.0665 e Roy's largest root | 0.0008 1.0 4063.0 3.37 0.0665 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.07 0.7957 e Pillai's trace | 0.0000 1.0 4063.0 0.07 0.7957 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.07 0.7957 e Roy's largest root | 0.0000 1.0 4063.0 0.07 0.7957 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 4063.0 0.67 0.4139 e Pillai's trace | 0.0002 1.0 4063.0 0.67 0.4139 e Lawley-Hotelling trace | 0.0002 1.0 4063.0 0.67 0.4139 e Roy's largest root | 0.0002 1.0 4063.0 0.67 0.4139 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 4063.0 0.25 0.6177 e Pillai's trace | 0.0001 1.0 4063.0 0.25 0.6177 e Lawley-Hotelling trace | 0.0001 1.0 4063.0 0.25 0.6177 e Roy's largest root | 0.0001 1.0 4063.0 0.25 0.6177 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.16 0.6937 e Pillai's trace | 0.0000 1.0 4063.0 0.16 0.6937 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.16 0.6937 e Roy's largest root | 0.0000 1.0 4063.0 0.16 0.6937 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.10 0.7559 e Pillai's trace | 0.0000 1.0 4063.0 0.10 0.7559 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.10 0.7559 e Roy's largest root | 0.0000 1.0 4063.0 0.10 0.7559 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.02 0.8874 e Pillai's trace | 0.0000 1.0 4063.0 0.02 0.8874 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.02 0.8874 e Roy's largest root | 0.0000 1.0 4063.0 0.02 0.8874 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.00 0.9737 e Pillai's trace | 0.0000 1.0 4063.0 0.00 0.9737 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.00 0.9737 e Roy's largest root | 0.0000 1.0 4063.0 0.00 0.9737 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9981 1.0 4063.0 7.81 0.0052 e Pillai's trace | 0.0019 1.0 4063.0 7.81 0.0052 e Lawley-Hotelling trace | 0.0019 1.0 4063.0 7.81 0.0052 e Roy's largest root | 0.0019 1.0 4063.0 7.81 0.0052 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9975 1.0 4063.0 10.22 0.0014 e Pillai's trace | 0.0025 1.0 4063.0 10.22 0.0014 e Lawley-Hotelling trace | 0.0025 1.0 4063.0 10.22 0.0014 e Roy's largest root | 0.0025 1.0 4063.0 10.22 0.0014 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9995 1.0 4063.0 2.00 0.1570 e Pillai's trace | 0.0005 1.0 4063.0 2.00 0.1570 e Lawley-Hotelling trace | 0.0005 1.0 4063.0 2.00 0.1570 e Roy's largest root | 0.0005 1.0 4063.0 2.00 0.1570 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.01 0.9396 e Pillai's trace | 0.0000 1.0 4063.0 0.01 0.9396 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.01 0.9396 e Roy's largest root | 0.0000 1.0 4063.0 0.01 0.9396 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 4063.0 0.88 0.3496 e Pillai's trace | 0.0002 1.0 4063.0 0.88 0.3496 e Lawley-Hotelling trace | 0.0002 1.0 4063.0 0.88 0.3496 e Roy's largest root | 0.0002 1.0 4063.0 0.88 0.3496 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 4063.0 0.14 0.7062 e Pillai's trace | 0.0000 1.0 4063.0 0.14 0.7062 e Lawley-Hotelling trace | 0.0000 1.0 4063.0 0.14 0.7062 e Roy's largest root | 0.0000 1.0 4063.0 0.14 0.7062 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F . . . loc rowlabels " "Female" "Democrat" "Republican" "Independent" "Other pol. orientation" "Prior bel > ief" "Northeast" "Midwest" "South" "West" "Age 18-24" "Age 25-34" "Age 35-44" "Age 45-54" "Age 55- > 65" "Has children" "Log household income" "Associate degree or more" "Full-time employee" "Part-ti > me employee" "Self-employed" "Unemployed" "Student" "Out of labor force" " " "Observations" " . . loc rowstats "" . . . forval i = 2/27 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\sumstats_balance_main.tex", replace cells(none) booktabs nonotes /*nonum*/ > /*nomtitles*/ compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowla > bels')) /// > mtitle("\shortstack{Full\\Sample}" "\shortstack{Treatment\\Groups}" "\shortstack{Control\\Gr > oup}" "\shortstack{T$^{74}$}" "\shortstack{T$^{94}$}" /// > "\shortstack{Prior\\incentivized}" "\shortstack{Prior not\\incentivized}" /// > "\shortstack{p-value \\ $(2)=(3)$}" "\shortstack{p-value \\ $(4)=(5)$}" "\shortstack{p-v > alue \\ $(6)=(7)$}") /// > mgroups("Main survey" "Follow-up survey", pattern(1 0 0 0 0 0 0 0 0 0) prefix(\multicolu > mn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\sumstats_balance_main.tex) . . . . // Joint F-tests mentioned in Table footnotes: . * Regress "any treatment group" on all covariats . reg T female democrat republican indep otherpol prior northeast midwest south west age1 age2 age3 > age4 age5 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student oolf, vc > e(r) note: indep omitted because of collinearity note: midwest omitted because of collinearity note: age1 omitted because of collinearity note: student omitted because of collinearity Linear regression Number of obs = 4,065 F(20, 4044) = 0.65 Prob > F = 0.8741 R-squared = 0.0033 Root MSE = .43592 ------------------------------------------------------------------------------- | Robust T | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- female | -.0069632 .0142203 -0.49 0.624 -.0348429 .0209164 democrat | -.00917 .0194341 -0.47 0.637 -.0472717 .0289316 republican | .0006607 .0199848 0.03 0.974 -.0385205 .0398418 indep | 0 (omitted) otherpol | -.0266337 .0535383 -0.50 0.619 -.1315982 .0783309 prior | -.0000506 .0003236 -0.16 0.876 -.0006849 .0005838 northeast | .0050616 .0222989 0.23 0.820 -.0386566 .0487798 midwest | 0 (omitted) south | -.0040819 .0186942 -0.22 0.827 -.0407329 .0325691 west | .0002268 .0206199 0.01 0.991 -.0401996 .0406532 age1 | 0 (omitted) age2 | .0566365 .0281368 2.01 0.044 .0014729 .1118001 age3 | .0730168 .0294187 2.48 0.013 .01534 .1306936 age4 | .0370455 .0298717 1.24 0.215 -.0215195 .0956105 age5 | .0511003 .0301645 1.69 0.090 -.0080388 .1102394 anychildren | .0106299 .0152703 0.70 0.486 -.0193082 .040568 loghhinc | .0051629 .0093134 0.55 0.579 -.0130965 .0234223 associatemore | .0004536 .0154839 0.03 0.977 -.0299033 .0308105 fulltime | -.0332196 .038469 -0.86 0.388 -.10864 .0422008 parttime | -.0218373 .0410142 -0.53 0.594 -.1022478 .0585732 selfemp | -.0507504 .0449726 -1.13 0.259 -.1389214 .0374207 unemployed | -.021323 .0455336 -0.47 0.640 -.1105939 .0679479 student | 0 (omitted) oolf | -.014599 .0411412 -0.35 0.723 -.0952583 .0660604 _cons | .6756377 .1051679 6.42 0.000 .4694507 .8818247 ------------------------------------------------------------------------------- . * Regress T74 dummy on all covariates, omitting pure control . reg T1 female democrat republican indep otherpol prior northeast midwest south west age1 age2 age3 > age4 age5 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student oolf if > rand!=0, vce(r) note: indep omitted because of collinearity note: northeast omitted because of collinearity note: age3 omitted because of collinearity note: student omitted because of collinearity Linear regression Number of obs = 3,031 F(20, 3010) = 1.09 Prob > F = 0.3534 R-squared = 0.0070 Root MSE = .49995 ------------------------------------------------------------------------------- | Robust T1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- female | .0075831 .0189094 0.40 0.688 -.0294935 .0446598 democrat | .0043609 .0258065 0.17 0.866 -.0462393 .054961 republican | -.0013916 .0266637 -0.05 0.958 -.0536726 .0508894 indep | 0 (omitted) otherpol | -.0103919 .071461 -0.15 0.884 -.1505091 .1297254 prior | .0000563 .000427 0.13 0.895 -.000781 .0008936 northeast | 0 (omitted) midwest | -.0319705 .0295419 -1.08 0.279 -.0898949 .0259539 south | -.0061646 .0264437 -0.23 0.816 -.0580142 .045685 west | -.0105857 .0287844 -0.37 0.713 -.0670248 .0458534 age1 | -.0684241 .0388528 -1.76 0.078 -.1446048 .0077566 age2 | -.0022973 .0274981 -0.08 0.933 -.0562142 .0516197 age3 | 0 (omitted) age4 | .0135949 .0283198 0.48 0.631 -.0419332 .069123 age5 | -.003023 .0289544 -0.10 0.917 -.0597955 .0537494 anychildren | -.0031982 .0203055 -0.16 0.875 -.0430122 .0366159 loghhinc | -.0165087 .0123205 -1.34 0.180 -.0406661 .0076487 associatemore | .014468 .0205508 0.70 0.481 -.025827 .0547631 fulltime | -.1476343 .0505982 -2.92 0.004 -.2468449 -.0484238 parttime | -.0613684 .0538724 -1.14 0.255 -.1669989 .0442621 selfemp | -.1304643 .0589434 -2.21 0.027 -.2460376 -.014891 unemployed | -.1774011 .0595285 -2.98 0.003 -.2941217 -.0606804 student | 0 (omitted) oolf | -.1376822 .0543752 -2.53 0.011 -.2442984 -.0310659 _cons | .8160003 .1465956 5.57 0.000 .5285627 1.103438 ------------------------------------------------------------------------------- . * Regress indicator for incentivized prior belief on all covariates . reg prior1 female democrat republican indep otherpol prior northeast midwest south west age1 age2 > age3 age4 age5 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student ool > f, vce(r) note: indep omitted because of collinearity note: midwest omitted because of collinearity note: age1 omitted because of collinearity note: student omitted because of collinearity Linear regression Number of obs = 4,065 F(20, 4044) = 1.33 Prob > F = 0.1464 R-squared = 0.0063 Root MSE = .49559 ------------------------------------------------------------------------------- | Robust prior1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- female | -.0211361 .0161491 -1.31 0.191 -.0527973 .010525 democrat | .0201917 .0221424 0.91 0.362 -.0232195 .063603 republican | .0168756 .0229591 0.74 0.462 -.0281369 .061888 indep | 0 (omitted) otherpol | -.0694187 .0593326 -1.17 0.242 -.1857431 .0469058 prior | -.0002137 .0003701 -0.58 0.564 -.0009394 .0005119 northeast | .0058776 .0254324 0.23 0.817 -.0439839 .0557391 midwest | 0 (omitted) south | .0202394 .0213122 0.95 0.342 -.0215443 .0620231 west | .0196595 .0235073 0.84 0.403 -.0264278 .0657467 age1 | 0 (omitted) age2 | -.0267421 .0308607 -0.87 0.386 -.0872461 .0337618 age3 | -.0433473 .0325293 -1.33 0.183 -.1071227 .0204281 age4 | -.0422499 .0325324 -1.30 0.194 -.1060313 .0215314 age5 | -.0468159 .0332768 -1.41 0.160 -.1120567 .0184249 anychildren | .0088454 .0174357 0.51 0.612 -.0253382 .0430289 loghhinc | .0102036 .0103851 0.98 0.326 -.0101569 .0305641 associatemore | .006045 .017579 0.34 0.731 -.0284195 .0405095 fulltime | -.0423692 .0432222 -0.98 0.327 -.1271084 .04237 parttime | .0576589 .0455317 1.27 0.205 -.0316082 .1469261 selfemp | .0265412 .049764 0.53 0.594 -.0710237 .1241062 unemployed | -.0098133 .0511849 -0.19 0.848 -.1101639 .0905373 student | 0 (omitted) oolf | -.0071821 .0466655 -0.15 0.878 -.0986721 .0843079 _cons | .4972229 .1187943 4.19 0.000 .2643206 .7301252 ------------------------------------------------------------------------------- . . . *********************************************************************************** . // Table B.3: Follow-up survey: Attrition and integrity of randomization . *********************************************************************************** . . use "$path\data\SurveyStageI_AB_final.dta", clear . . // Merge Follow-up Data . merge 1:1 panelID using "$path\data\SurveyStageIIAB_final.dta" (label gender already defined) Result # of obs. ----------------------------------------- not matched 2,960 from master 2,960 (_merge==1) from using 0 (_merge==2) matched 1,105 (_merge==3) ----------------------------------------- . . * Identifier for Follow-up participants . gen StageII=(_merge==3) . drop _merge . . . * Generate indicators for any treatment group and for pure control group . gen T=T1+T2 . gen C=rand==0 . . . loc covars "female democrat republican indep otherpol prior northeast midwest south west age1 age2 > age3 age4 age5 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student oo > lf" . . . /* Balance table */ . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . . loc experiments "StageII NoStageII T1follow T2follow pvalueStageII pvalueT1T2II" . . . loc columns = 0 . . foreach choice in `experiments' { 2. loc ++columns 3. qui eststo col`columns': reg x y 4. } . . . restore . . loc colnum = 1 . loc colnames "" . . . . ** Column 1: In follow-up (including control group) . loc varindex = 2 . . foreach var in `covars' { 2. . summ `var' if StageII==1 3. local mean=round(r(mean),.001) 4. cap: estadd loc thisstat`varindex' = string(`mean', "%9.2f"): col1 5. . loc ++varindex 6. . } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- female | 1,105 .4968326 .5002164 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- democrat | 1,105 .4180995 .49347 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- republican | 1,105 .3819005 .4860723 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- indep | 1,105 .1800905 .3844368 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- otherpol | 1,105 .0199095 .1397526 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 1,105 83.80181 23.6421 2 200 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- northeast | 1,105 .1927602 .394645 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- midwest | 1,105 .2099548 .4074604 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- south | 1,105 .3529412 .478101 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- west | 1,105 .2443439 .4298921 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age1 | 1,105 .0497738 .2175757 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age2 | 1,105 .19819 .3988166 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age3 | 1,105 .20181 .4015328 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age4 | 1,105 .2135747 .4100154 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age5 | 1,105 .3366516 .4727786 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- anychildren | 1,105 .5819005 .49347 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- loghhinc | 1,105 10.92397 .8145149 8.815073 12.69851 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- associatem~e | 1,105 .6063348 .4887833 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- fulltime | 1,105 .5004525 .5002262 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- parttime | 1,105 .1022624 .3031303 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- selfemp | 1,105 .0850679 .279109 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- unemployed | 1,105 .0579186 .2336951 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- student | 1,105 .0180995 .1333719 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- oolf | 1,105 .2361991 .4249382 0 1 . . summ prior if StageII==1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 1,105 83.80181 23.6421 2 200 . local mean=round(r(N),.001) . cap: estadd loc thisstat27 = string(`mean', "%9.0f"): col1 . . . ** Column 2: Not in follow-up (including control group) . loc varindex = 2 . . foreach var in `covars' { 2. . summ `var' if StageII==0&T==1 3. local mean=round(r(mean),.001) 4. cap: estadd loc thisstat`varindex' = string(`mean', "%9.2f"): col2 5. . loc ++varindex 6. . } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- female | 1,926 .526999 .4994002 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- democrat | 1,926 .453271 .4979409 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- republican | 1,926 .3525441 .4778863 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- indep | 1,926 .1760125 .3809297 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- otherpol | 1,926 .0181724 .1336092 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 1,926 83.14798 20.56638 1 200 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- northeast | 1,926 .1708204 .3764497 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- midwest | 1,926 .2113188 .40835 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- south | 1,926 .3831776 .4862873 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- west | 1,926 .2346833 .4239108 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age1 | 1,926 .1433022 .3504717 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age2 | 1,926 .2689512 .4435297 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age3 | 1,926 .2279335 .4196085 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age4 | 1,926 .1983385 .398852 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age5 | 1,926 .1614746 .3680637 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- anychildren | 1,926 .5103842 .500022 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- loghhinc | 1,926 10.90012 .8782647 8.815073 12.69851 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- associatem~e | 1,926 .6163032 .4864118 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- fulltime | 1,926 .5456906 .4980373 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- parttime | 1,926 .1085151 .3111106 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- selfemp | 1,926 .066459 .2491474 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- unemployed | 1,926 .0571132 .232119 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- student | 1,926 .0617861 .2408291 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- oolf | 1,926 .1604361 .3671054 0 1 . . summ prior if StageII==0&T==1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 1,926 83.14798 20.56638 1 200 . local mean=round(r(N),.001) . cap: estadd loc thisstat27 = string(`mean', "%9.0f"): col2 . . . ** Column 3: T74 in follow-up . loc varindex = 2 . . foreach var in `covars' { 2. . summ `var' if T1==1&StageII==1 3. local mean=round(r(mean),.001) 4. cap: estadd loc thisstat`varindex' = string(`mean', "%9.2f"): col3 5. . loc ++varindex 6. . } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- female | 554 .5072202 .5003997 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- democrat | 554 .4061372 .4915546 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- republican | 554 .3898917 .4881663 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- indep | 554 .1841155 .3879287 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- otherpol | 554 .0198556 .13963 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 554 83.94404 24.18005 2 200 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- northeast | 554 .1967509 .3979017 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- midwest | 554 .2148014 .4110557 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- south | 554 .3465704 .4763075 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- west | 554 .2418773 .4286073 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age1 | 554 .0505415 .2192575 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age2 | 554 .198556 .3992734 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age3 | 554 .1949458 .396517 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age4 | 554 .2220217 .4159812 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age5 | 554 .333935 .472043 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- anychildren | 554 .5794224 .4940979 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- loghhinc | 554 10.90744 .7925444 8.815073 12.69851 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- associatem~e | 554 .5956679 .4912059 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- fulltime | 554 .4891697 .5003345 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- parttime | 554 .1245487 .3305049 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- selfemp | 554 .0938628 .2919013 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- unemployed | 554 .0523466 .2229262 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- student | 554 .0162455 .1265325 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- oolf | 554 .2238267 .417184 0 1 . . summ prior if T1==1&StageII==1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 554 83.94404 24.18005 2 200 . local mean=round(r(N),.001) . cap: estadd loc thisstat27 = string(`mean', "%9.0f"): col3 . . . ** Column 4: T94 in follow-up . loc varindex = 2 . . foreach var in `covars' { 2. . summ `var' if T2==1&StageII==1 3. local mean=round(r(mean),.001) 4. cap: estadd loc thisstat`varindex' = string(`mean', "%9.2f"): col4 5. . loc ++varindex 6. . loc rowlabels "`rowlabels' `"`: var la `var''"' " 7. . } Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- female | 551 .4863884 .5002689 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- democrat | 551 .430127 .4955436 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- republican | 551 .3738657 .4842683 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- indep | 551 .1760436 .3812033 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- otherpol | 551 .0199637 .1400026 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 551 83.6588 23.10969 2 200 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- northeast | 551 .1887477 .3916637 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- midwest | 551 .2050817 .4041282 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- south | 551 .3593466 .4802449 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- west | 551 .246824 .4315552 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age1 | 551 .0490018 .216068 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age2 | 551 .1978221 .3987193 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age3 | 551 .2087114 .406757 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age4 | 551 .2050817 .4041282 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age5 | 551 .3393829 .4739302 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- anychildren | 551 .584392 .4932743 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- loghhinc | 551 10.94058 .8364149 8.815073 12.69851 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- associatem~e | 551 .6170599 .4865456 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- fulltime | 551 .5117967 .500315 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- parttime | 551 .0798548 .2713146 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- selfemp | 551 .076225 .265599 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- unemployed | 551 .0635209 .2441191 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- student | 551 .0199637 .1400026 0 1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- oolf | 551 .2486388 .4326167 0 1 . . summ prior if T2==1&StageII==1 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- prior | 551 83.6588 23.10969 2 200 . local mean=round(r(N),.001) . cap: estadd loc thisstat27 = string(`mean', "%9.0f"): col4 . . . ** Column 5: P-value for testing Stage II vs. not Stage II . loc varindex = 2 . . foreach var in `covars' { 2. mvtest means `var' if rand!=0, by(StageII) 3. local p=round(r(p_F),.001) 4. cap: estadd loc thisstat`varindex' = string(`p', "%9.3f"): col5 5. . loc ++varindex 6. } Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9992 1.0 3029.0 2.56 0.1098 e Pillai's trace | 0.0008 1.0 3029.0 2.56 0.1098 e Lawley-Hotelling trace | 0.0008 1.0 3029.0 2.56 0.1098 e Roy's largest root | 0.0008 1.0 3029.0 2.56 0.1098 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9988 1.0 3029.0 3.53 0.0605 e Pillai's trace | 0.0012 1.0 3029.0 3.53 0.0605 e Lawley-Hotelling trace | 0.0012 1.0 3029.0 3.53 0.0605 e Roy's largest root | 0.0012 1.0 3029.0 3.53 0.0605 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9991 1.0 3029.0 2.62 0.1058 e Pillai's trace | 0.0009 1.0 3029.0 2.62 0.1058 e Lawley-Hotelling trace | 0.0009 1.0 3029.0 2.62 0.1058 e Roy's largest root | 0.0009 1.0 3029.0 2.62 0.1058 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.08 0.7774 e Pillai's trace | 0.0000 1.0 3029.0 0.08 0.7774 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.08 0.7774 e Roy's largest root | 0.0000 1.0 3029.0 0.08 0.7774 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.11 0.7348 e Pillai's trace | 0.0000 1.0 3029.0 0.11 0.7348 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.11 0.7348 e Roy's largest root | 0.0000 1.0 3029.0 0.11 0.7348 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 3029.0 0.64 0.4255 e Pillai's trace | 0.0002 1.0 3029.0 0.64 0.4255 e Lawley-Hotelling trace | 0.0002 1.0 3029.0 0.64 0.4255 e Roy's largest root | 0.0002 1.0 3029.0 0.64 0.4255 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9992 1.0 3029.0 2.30 0.1293 e Pillai's trace | 0.0008 1.0 3029.0 2.30 0.1293 e Lawley-Hotelling trace | 0.0008 1.0 3029.0 2.30 0.1293 e Roy's largest root | 0.0008 1.0 3029.0 2.30 0.1293 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.01 0.9294 e Pillai's trace | 0.0000 1.0 3029.0 0.01 0.9294 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.01 0.9294 e Roy's largest root | 0.0000 1.0 3029.0 0.01 0.9294 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9991 1.0 3029.0 2.75 0.0975 e Pillai's trace | 0.0009 1.0 3029.0 2.75 0.0975 e Lawley-Hotelling trace | 0.0009 1.0 3029.0 2.75 0.0975 e Roy's largest root | 0.0009 1.0 3029.0 2.75 0.0975 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 3029.0 0.36 0.5480 e Pillai's trace | 0.0001 1.0 3029.0 0.36 0.5480 e Lawley-Hotelling trace | 0.0001 1.0 3029.0 0.36 0.5480 e Roy's largest root | 0.0001 1.0 3029.0 0.36 0.5480 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9792 1.0 3029.0 64.44 0.0000 e Pillai's trace | 0.0208 1.0 3029.0 64.44 0.0000 e Lawley-Hotelling trace | 0.0213 1.0 3029.0 64.44 0.0000 e Roy's largest root | 0.0213 1.0 3029.0 64.44 0.0000 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9937 1.0 3029.0 19.21 0.0000 e Pillai's trace | 0.0063 1.0 3029.0 19.21 0.0000 e Lawley-Hotelling trace | 0.0063 1.0 3029.0 19.21 0.0000 e Roy's largest root | 0.0063 1.0 3029.0 19.21 0.0000 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9991 1.0 3029.0 2.81 0.0939 e Pillai's trace | 0.0009 1.0 3029.0 2.81 0.0939 e Lawley-Hotelling trace | 0.0009 1.0 3029.0 2.81 0.0939 e Roy's largest root | 0.0009 1.0 3029.0 2.81 0.0939 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9997 1.0 3029.0 1.00 0.3165 e Pillai's trace | 0.0003 1.0 3029.0 1.00 0.3165 e Lawley-Hotelling trace | 0.0003 1.0 3029.0 1.00 0.3165 e Roy's largest root | 0.0003 1.0 3029.0 1.00 0.3165 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9593 1.0 3029.0 128.59 0.0000 e Pillai's trace | 0.0407 1.0 3029.0 128.59 0.0000 e Lawley-Hotelling trace | 0.0425 1.0 3029.0 128.59 0.0000 e Roy's largest root | 0.0425 1.0 3029.0 128.59 0.0000 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9952 1.0 3029.0 14.50 0.0001 e Pillai's trace | 0.0048 1.0 3029.0 14.50 0.0001 e Lawley-Hotelling trace | 0.0048 1.0 3029.0 14.50 0.0001 e Roy's largest root | 0.0048 1.0 3029.0 14.50 0.0001 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 3029.0 0.55 0.4602 e Pillai's trace | 0.0002 1.0 3029.0 0.55 0.4602 e Lawley-Hotelling trace | 0.0002 1.0 3029.0 0.55 0.4602 e Roy's largest root | 0.0002 1.0 3029.0 0.55 0.4602 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 3029.0 0.29 0.5878 e Pillai's trace | 0.0001 1.0 3029.0 0.29 0.5878 e Lawley-Hotelling trace | 0.0001 1.0 3029.0 0.29 0.5878 e Roy's largest root | 0.0001 1.0 3029.0 0.29 0.5878 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9981 1.0 3029.0 5.77 0.0163 e Pillai's trace | 0.0019 1.0 3029.0 5.77 0.0163 e Lawley-Hotelling trace | 0.0019 1.0 3029.0 5.77 0.0163 e Roy's largest root | 0.0019 1.0 3029.0 5.77 0.0163 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 3029.0 0.29 0.5909 e Pillai's trace | 0.0001 1.0 3029.0 0.29 0.5909 e Lawley-Hotelling trace | 0.0001 1.0 3029.0 0.29 0.5909 e Roy's largest root | 0.0001 1.0 3029.0 0.29 0.5909 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9988 1.0 3029.0 3.58 0.0584 e Pillai's trace | 0.0012 1.0 3029.0 3.58 0.0584 e Lawley-Hotelling trace | 0.0012 1.0 3029.0 3.58 0.0584 e Roy's largest root | 0.0012 1.0 3029.0 3.58 0.0584 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 3029.0 0.01 0.9269 e Pillai's trace | 0.0000 1.0 3029.0 0.01 0.9269 e Lawley-Hotelling trace | 0.0000 1.0 3029.0 0.01 0.9269 e Roy's largest root | 0.0000 1.0 3029.0 0.01 0.9269 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9899 1.0 3029.0 30.92 0.0000 e Pillai's trace | 0.0101 1.0 3029.0 30.92 0.0000 e Lawley-Hotelling trace | 0.0102 1.0 3029.0 30.92 0.0000 e Roy's largest root | 0.0102 1.0 3029.0 30.92 0.0000 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9913 1.0 3029.0 26.61 0.0000 e Pillai's trace | 0.0087 1.0 3029.0 26.61 0.0000 e Lawley-Hotelling trace | 0.0088 1.0 3029.0 26.61 0.0000 e Roy's largest root | 0.0088 1.0 3029.0 26.61 0.0000 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F . . . ** Column 6: P-value for testing equality of means T74 - T94 in follow-up . loc varindex = 2 . . foreach var in `covars' { 2. mvtest means `var' if rand!=0&StageII==1, by(T1) 3. local p=round(r(p_F),.001) 4. cap: estadd loc thisstat`varindex' = string(`p', "%9.3f"): col6 5. . loc ++varindex 6. } Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9996 1.0 1103.0 0.48 0.4891 e Pillai's trace | 0.0004 1.0 1103.0 0.48 0.4891 e Lawley-Hotelling trace | 0.0004 1.0 1103.0 0.48 0.4891 e Roy's largest root | 0.0004 1.0 1103.0 0.48 0.4891 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9994 1.0 1103.0 0.65 0.4193 e Pillai's trace | 0.0006 1.0 1103.0 0.65 0.4193 e Lawley-Hotelling trace | 0.0006 1.0 1103.0 0.65 0.4193 e Roy's largest root | 0.0006 1.0 1103.0 0.65 0.4193 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9997 1.0 1103.0 0.30 0.5839 e Pillai's trace | 0.0003 1.0 1103.0 0.30 0.5839 e Lawley-Hotelling trace | 0.0003 1.0 1103.0 0.30 0.5839 e Roy's largest root | 0.0003 1.0 1103.0 0.30 0.5839 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 1103.0 0.12 0.7273 e Pillai's trace | 0.0001 1.0 1103.0 0.12 0.7273 e Lawley-Hotelling trace | 0.0001 1.0 1103.0 0.12 0.7273 e Roy's largest root | 0.0001 1.0 1103.0 0.12 0.7273 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 1103.0 0.00 0.9897 e Pillai's trace | 0.0000 1.0 1103.0 0.00 0.9897 e Lawley-Hotelling trace | 0.0000 1.0 1103.0 0.00 0.9897 e Roy's largest root | 0.0000 1.0 1103.0 0.00 0.9897 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 1103.0 0.04 0.8412 e Pillai's trace | 0.0000 1.0 1103.0 0.04 0.8412 e Lawley-Hotelling trace | 0.0000 1.0 1103.0 0.04 0.8412 e Roy's largest root | 0.0000 1.0 1103.0 0.04 0.8412 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 1103.0 0.11 0.7362 e Pillai's trace | 0.0001 1.0 1103.0 0.11 0.7362 e Lawley-Hotelling trace | 0.0001 1.0 1103.0 0.11 0.7362 e Roy's largest root | 0.0001 1.0 1103.0 0.11 0.7362 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9999 1.0 1103.0 0.16 0.6919 e Pillai's trace | 0.0001 1.0 1103.0 0.16 0.6919 e Lawley-Hotelling trace | 0.0001 1.0 1103.0 0.16 0.6919 e Roy's largest root | 0.0001 1.0 1103.0 0.16 0.6919 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 1103.0 0.20 0.6571 e Pillai's trace | 0.0002 1.0 1103.0 0.20 0.6571 e Lawley-Hotelling trace | 0.0002 1.0 1103.0 0.20 0.6571 e Roy's largest root | 0.0002 1.0 1103.0 0.20 0.6571 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 1103.0 0.04 0.8484 e Pillai's trace | 0.0000 1.0 1103.0 0.04 0.8484 e Lawley-Hotelling trace | 0.0000 1.0 1103.0 0.04 0.8484 e Roy's largest root | 0.0000 1.0 1103.0 0.04 0.8484 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 1103.0 0.01 0.9064 e Pillai's trace | 0.0000 1.0 1103.0 0.01 0.9064 e Lawley-Hotelling trace | 0.0000 1.0 1103.0 0.01 0.9064 e Roy's largest root | 0.0000 1.0 1103.0 0.01 0.9064 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 1103.0 0.00 0.9756 e Pillai's trace | 0.0000 1.0 1103.0 0.00 0.9756 e Lawley-Hotelling trace | 0.0000 1.0 1103.0 0.00 0.9756 e Roy's largest root | 0.0000 1.0 1103.0 0.00 0.9756 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9997 1.0 1103.0 0.32 0.5690 e Pillai's trace | 0.0003 1.0 1103.0 0.32 0.5690 e Lawley-Hotelling trace | 0.0003 1.0 1103.0 0.32 0.5690 e Roy's largest root | 0.0003 1.0 1103.0 0.32 0.5690 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9996 1.0 1103.0 0.47 0.4925 e Pillai's trace | 0.0004 1.0 1103.0 0.47 0.4925 e Lawley-Hotelling trace | 0.0004 1.0 1103.0 0.47 0.4925 e Roy's largest root | 0.0004 1.0 1103.0 0.47 0.4925 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 1103.0 0.04 0.8482 e Pillai's trace | 0.0000 1.0 1103.0 0.04 0.8482 e Lawley-Hotelling trace | 0.0000 1.0 1103.0 0.04 0.8482 e Roy's largest root | 0.0000 1.0 1103.0 0.04 0.8482 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 1.0000 1.0 1103.0 0.03 0.8672 e Pillai's trace | 0.0000 1.0 1103.0 0.03 0.8672 e Lawley-Hotelling trace | 0.0000 1.0 1103.0 0.03 0.8672 e Roy's largest root | 0.0000 1.0 1103.0 0.03 0.8672 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9996 1.0 1103.0 0.46 0.4992 e Pillai's trace | 0.0004 1.0 1103.0 0.46 0.4992 e Lawley-Hotelling trace | 0.0004 1.0 1103.0 0.46 0.4992 e Roy's largest root | 0.0004 1.0 1103.0 0.46 0.4992 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9995 1.0 1103.0 0.53 0.4672 e Pillai's trace | 0.0005 1.0 1103.0 0.53 0.4672 e Lawley-Hotelling trace | 0.0005 1.0 1103.0 0.53 0.4672 e Roy's largest root | 0.0005 1.0 1103.0 0.53 0.4672 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9995 1.0 1103.0 0.57 0.4524 e Pillai's trace | 0.0005 1.0 1103.0 0.57 0.4524 e Lawley-Hotelling trace | 0.0005 1.0 1103.0 0.57 0.4524 e Roy's largest root | 0.0005 1.0 1103.0 0.57 0.4524 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9946 1.0 1103.0 6.03 0.0142 e Pillai's trace | 0.0054 1.0 1103.0 6.03 0.0142 e Lawley-Hotelling trace | 0.0055 1.0 1103.0 6.03 0.0142 e Roy's largest root | 0.0055 1.0 1103.0 6.03 0.0142 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9990 1.0 1103.0 1.10 0.2938 e Pillai's trace | 0.0010 1.0 1103.0 1.10 0.2938 e Lawley-Hotelling trace | 0.0010 1.0 1103.0 1.10 0.2938 e Roy's largest root | 0.0010 1.0 1103.0 1.10 0.2938 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9994 1.0 1103.0 0.63 0.4270 e Pillai's trace | 0.0006 1.0 1103.0 0.63 0.4270 e Lawley-Hotelling trace | 0.0006 1.0 1103.0 0.63 0.4270 e Roy's largest root | 0.0006 1.0 1103.0 0.63 0.4270 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9998 1.0 1103.0 0.21 0.6433 e Pillai's trace | 0.0002 1.0 1103.0 0.21 0.6433 e Lawley-Hotelling trace | 0.0002 1.0 1103.0 0.21 0.6433 e Roy's largest root | 0.0002 1.0 1103.0 0.21 0.6433 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F Test for equality of 2 group means, assuming homogeneity | Statistic F(df1, df2) = F Prob>F -----------------------+----------------------------------------------- Wilks' lambda | 0.9991 1.0 1103.0 0.94 0.3320 e Pillai's trace | 0.0009 1.0 1103.0 0.94 0.3320 e Lawley-Hotelling trace | 0.0009 1.0 1103.0 0.94 0.3320 e Roy's largest root | 0.0009 1.0 1103.0 0.94 0.3320 e ----------------------------------------------------------------------- e = exact, a = approximate, u = upper bound on F . . . loc rowlabels " "Female" "Democrat" "Republican" "Independent" "Other pol. orientation" "Prior bel > ief" "Northeast" "Midwest" "South" "West" "Age 18-24" "Age 25-34" "Age 35-44" "Age 45-54" "Age 55- > 65" "Has children" "Log household income" "Associate degree or more" "Full-time employee" "Part-ti > me employee" "Self-employed" "Unemployed" "Student" "Out of labor force" " " "Observations" " . . loc rowstats "" . . . forval i = 2/27 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\sumstats_balance_followup.tex", replace cells(none) booktabs nonotes /*non > um*//*nomtitles*/ compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`r > owlabels')) /// > mtitle("\shortstack{In\\Stage II sample}" "\shortstack{Not in\\Stage II sample}" "\shortstac > k{T$^{74}$\\(Stage II sample)}" "\shortstack{T$^{94}$\\(Stage II sample)}" /// > "\shortstack{p-value \\ $(1)=(2)$}" "\shortstack{p-value \\ $(3)=(4)$}") /// > mgroups("Follow-up survey (Eligible respondents only)", pattern(1 0 0 0 0 0) prefix(\mul > ticolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\sumstats_balance_followup.tex) . . // P-values from joint F-tests mentioned in Table footnotes . * Outcome = Participation in follow-up . reg StageII female democrat republican indep otherpol prior northeast midwest south west age1 age2 > age3 age4 age5 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student oo > lf, vce(r) note: indep omitted because of collinearity note: midwest omitted because of collinearity note: age1 omitted because of collinearity note: student omitted because of collinearity Linear regression Number of obs = 4,065 F(20, 4044) = 10.90 Prob > F = 0.0000 R-squared = 0.0424 Root MSE = .4365 ------------------------------------------------------------------------------- | Robust StageII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- female | -.0221089 .0142882 -1.55 0.122 -.0501216 .0059038 democrat | -.0180774 .0195961 -0.92 0.356 -.0564964 .0203417 republican | -.0099124 .0205585 -0.48 0.630 -.0502185 .0303937 indep | 0 (omitted) otherpol | -.0059068 .0533034 -0.11 0.912 -.1104108 .0985972 prior | .000419 .0003456 1.21 0.225 -.0002586 .0010965 northeast | .0256386 .0227708 1.13 0.260 -.0190047 .0702819 midwest | 0 (omitted) south | -.0073258 .0186029 -0.39 0.694 -.0437977 .0291461 west | .0128873 .0208594 0.62 0.537 -.0280087 .0537833 age1 | 0 (omitted) age2 | .0936835 .023494 3.99 0.000 .0476223 .1397447 age3 | .1232038 .0256753 4.80 0.000 .072866 .1735416 age4 | .1456284 .0258283 5.64 0.000 .0949907 .1962662 age5 | .2688443 .0275214 9.77 0.000 .2148872 .3228014 anychildren | .0137974 .0156756 0.88 0.379 -.0169353 .0445302 loghhinc | .0065503 .0086601 0.76 0.449 -.0104282 .0235288 associatemore | -.007986 .0157102 -0.51 0.611 -.0387865 .0228146 fulltime | .0321256 .0301317 1.07 0.286 -.0269491 .0912003 parttime | .0551765 .0318535 1.73 0.083 -.0072738 .1176268 selfemp | .0555826 .0374764 1.48 0.138 -.0178919 .129057 unemployed | .0642232 .0377546 1.70 0.089 -.0097966 .1382431 student | 0 (omitted) oolf | .0553193 .034024 1.63 0.104 -.0113866 .1220251 _cons | .0015076 .0947629 0.02 0.987 -.1842798 .1872951 ------------------------------------------------------------------------------- . * Outcome = T74, sample restricted to follow-up sample . reg T1 female democrat republican indep otherpol prior northeast midwest south west age1 age2 age3 > age4 age5 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student oolf if > StageII==1, vce(r) note: otherpol omitted because of collinearity note: midwest omitted because of collinearity note: age3 omitted because of collinearity note: student omitted because of collinearity Linear regression Number of obs = 1,105 F(20, 1084) = 0.61 Prob > F = 0.9107 R-squared = 0.0107 Root MSE = .50212 ------------------------------------------------------------------------------- | Robust T1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- female | .0227285 .0315327 0.72 0.471 -.0391434 .0846005 democrat | -.00165 .1089864 -0.02 0.988 -.2154982 .2121983 republican | .0300401 .1096124 0.27 0.784 -.1850364 .2451167 indep | .0252766 .1124289 0.22 0.822 -.1953263 .2458795 otherpol | 0 (omitted) prior | .0001288 .0006583 0.20 0.845 -.0011628 .0014205 northeast | .0154366 .0485091 0.32 0.750 -.0797458 .110619 midwest | 0 (omitted) south | -.0131619 .042141 -0.31 0.755 -.095849 .0695252 west | -.0155088 .0455026 -0.34 0.733 -.1047918 .0737743 age1 | .026102 .083495 0.31 0.755 -.1377281 .1899322 age2 | .018366 .0487283 0.38 0.706 -.0772465 .1139785 age3 | 0 (omitted) age4 | .0328851 .0476047 0.69 0.490 -.0605226 .1262928 age5 | .0173228 .0449987 0.38 0.700 -.0709716 .1056171 anychildren | -.0099855 .0331814 -0.30 0.764 -.0750925 .0551216 loghhinc | -.0143414 .0219189 -0.65 0.513 -.0573497 .0286669 associatemore | -.0216664 .0333756 -0.65 0.516 -.0871546 .0438217 fulltime | .0554274 .125939 0.44 0.660 -.1916844 .3025392 parttime | .1620878 .1324569 1.22 0.221 -.0978131 .4219887 selfemp | .1128531 .1353386 0.83 0.405 -.1527021 .3784083 unemployed | -.0104013 .139429 -0.07 0.941 -.2839826 .2631801 student | 0 (omitted) oolf | .0215328 .1311622 0.16 0.870 -.2358277 .2788934 _cons | .5688152 .2954775 1.93 0.054 -.0109575 1.148588 ------------------------------------------------------------------------------- . . . . *********************************************************************************** . // Table C.1: Gender and partisan differences in beliefs about the gender wage gap compared to oth > er politically relevant beliefs . *********************************************************************************** . . // Calculate fem-male difference and left-right difference in z-scored prior beliefs . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final", clear . . * Keep only incentivized priors . keep if prior1==1 (1,772 observations deleted) . . * z-score prior . egen z_prior=std(prior) . . reg z_prior gender democrat indep otherpol [pweight=pweight], vce(r) (sum of wgt is 2.2644e+03) Linear regression Number of obs = 2,293 F(4, 2288) = 11.18 Prob > F = 0.0000 R-squared = 0.0208 Root MSE = .99185 ------------------------------------------------------------------------------ | Robust z_prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | -.2066514 .0415744 -4.97 0.000 -.2881789 -.1251239 democrat | -.206004 .0484743 -4.25 0.000 -.3010621 -.1109458 indep | -.0838865 .0555113 -1.51 0.131 -.1927443 .0249712 otherpol | -.3007878 .1924605 -1.56 0.118 -.6782031 .0766275 _cons | .2209137 .0431789 5.12 0.000 .1362398 .3055877 ------------------------------------------------------------------------------ . . *********************************************************************************** . // Table C.2: Correlates of beahvioral proxies of demand for government intervention . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final", clear . . * Drop control group . keep if rand==0 (3,031 observations deleted) . . loc experiments "petIraw petIIraw donation fblike2raw" . . **z-score prior: . foreach var of varlist prior{ 2. egen mean_`var'=mean(`var') 3. egen sd_`var'=sd(`var') 4. replace `var'=(`var'-mean_`var')/sd_`var' 5. drop mean_`var' sd_`var' 6. } (1,034 real changes made) . . *Keep only those with prior beliefs above the 5th and below the 95th percentile of the distributio > n . sum prior,d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% -2.432051 -3.873509 5% -1.548576 -3.780512 10% -.9905922 -3.780512 Obs 1,034 25% -.3861097 -3.362024 Sum of Wgt. 1,034 50% -.130367 Mean 1.04e-07 Largest Std. Dev. 1 75% .3113702 5.193729 90% .7763568 5.426223 Variance 1 95% 1.380839 5.426223 Skewness 1.506369 99% 4.263756 5.426223 Kurtosis 10.77207 . keep if prior > r(p5) & prior < r(p95) (113 observations deleted) . . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experiments' { 2. . . ***Panel A: Main correlates: Dem, Female . . reg `choice' democrat indep otherpol gender [pweight=pweight],r 3. . sigstar democrat, prec(3) 4. estadd loc thisstat4 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat5 = "`r(sestar)'": col`colnum' 6. . sigstar gender, prec(3) 7. estadd loc thisstat7 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat8 = "`r(sestar)'": col`colnum' 9. . . . ***Panel B: Raw correlation: z-scored prior . . reg `choice' prior [pweight=pweight],r 10. . sigstar prior, prec(3) 11. estadd loc thisstat13 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat14 = "`r(sestar)'": col`colnum' 13. . . ***Panel C: Prior and main correlates: Dem, Rep, Female . . reg `choice' prior democrat indep otherpol gender [pweight=pweight],r 14. . sigstar prior, prec(3) 15. estadd loc thisstat19 = "`r(bstar)'": col`colnum' 16. estadd loc thisstat20 = "`r(sestar)'": col`colnum' 17. . sigstar democrat, prec(3) 18. estadd loc thisstat22 = "`r(bstar)'": col`colnum' 19. estadd loc thisstat23 = "`r(sestar)'": col`colnum' 20. . sigstar gender, prec(3) 21. estadd loc thisstat25 = "`r(bstar)'": col`colnum' 22. estadd loc thisstat26 = "`r(sestar)'": col`colnum' 23. . . ***Panel D: Correlates with prior, full set of controls . . reg `choice' prior $controls [pweight=pweight],r 24. . sigstar prior, prec(3) 25. estadd loc thisstat31 = "`r(bstar)'": col`colnum' 26. estadd loc thisstat32 = "`r(sestar)'": col`colnum' 27. . sigstar democrat, prec(3) 28. estadd loc thisstat34 = "`r(bstar)'": col`colnum' 29. estadd loc thisstat35 = "`r(sestar)'": col`colnum' 30. . sigstar gender, prec(3) 31. estadd loc thisstat37 = "`r(bstar)'": col`colnum' 32. estadd loc thisstat38 = "`r(sestar)'": col`colnum' 33. . . mean `choice' [pweight=pweight] 34. matrix mean=e(b) 35. matrix nobs=e(N) 36. estadd loc thisstat40 = string(mean[1,1], "%9.2f"): col`colnum' 37. estadd loc thisstat41 = string(nobs[1,1], "%9.0f"): col`colnum' 38. . . loc ++colnum 39. loc colnames "`colnames' `"`: var la `choice''"'" 40. . } (sum of wgt is 9.0487e+02) Linear regression Number of obs = 921 F(4, 916) = 25.89 Prob > F = 0.0000 R-squared = 0.0951 Root MSE = .47662 ------------------------------------------------------------------------------ | Robust petIraw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- democrat | .2972455 .0358194 8.30 0.000 .2269478 .3675431 indep | .0269015 .047544 0.57 0.572 -.0664064 .1202093 otherpol | -.2040422 .0875947 -2.33 0.020 -.3759517 -.0321327 gender | .0464056 .0316952 1.46 0.144 -.015798 .1086092 _cons | .3595587 .0308039 11.67 0.000 .2991043 .4200131 ------------------------------------------------------------------------------ (sum of wgt is 9.0487e+02) Linear regression Number of obs = 921 F(1, 919) = 20.01 Prob > F = 0.0000 R-squared = 0.0215 Root MSE = .49482 ------------------------------------------------------------------------------ | Robust petIraw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.1453462 .0324906 -4.47 0.000 -.2091106 -.0815818 _cons | .5117622 .016478 31.06 0.000 .4794233 .5441012 ------------------------------------------------------------------------------ (sum of wgt is 9.0487e+02) Linear regression Number of obs = 921 F(5, 915) = 23.64 Prob > F = 0.0000 R-squared = 0.1056 Root MSE = .47411 ------------------------------------------------------------------------------ | Robust petIraw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.1031491 .0323731 -3.19 0.001 -.1666832 -.039615 democrat | .2839852 .0360936 7.87 0.000 .2131493 .3548211 indep | .030589 .0469522 0.65 0.515 -.0615574 .1227354 otherpol | -.2090989 .0866202 -2.41 0.016 -.3790964 -.0391015 gender | .0358273 .0318028 1.13 0.260 -.0265876 .0982423 _cons | .3660016 .0309864 11.81 0.000 .3051889 .4268144 ------------------------------------------------------------------------------ (sum of wgt is 9.0487e+02) note: prior omitted because of collinearity Linear regression Number of obs = 921 F(21, 899) = 7.43 Prob > F = 0.0000 R-squared = 0.1213 Root MSE = .47409 ------------------------------------------------------------------------------- | Robust petIraw | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- prior | -.1112737 .0326239 -3.41 0.001 -.1753016 -.0472459 wave | .0512793 .0315794 1.62 0.105 -.0106985 .1132572 gender | .0423432 .0333209 1.27 0.204 -.0230526 .107739 prior | 0 (omitted) democrat | .2797274 .0370339 7.55 0.000 .2070445 .3524103 indep | .0295142 .0484498 0.61 0.543 -.0655737 .1246021 otherpol | -.1847571 .0850157 -2.17 0.030 -.3516095 -.0179047 midwest | -.0221545 .0499753 -0.44 0.658 -.1202364 .0759273 south | .0245617 .046298 0.53 0.596 -.066303 .1154265 west | .0519322 .0502907 1.03 0.302 -.0467686 .150633 age1 | .1184307 .0697172 1.70 0.090 -.0183967 .2552581 age2 | .0509386 .0527788 0.97 0.335 -.0526454 .1545227 age3 | .0379228 .0526094 0.72 0.471 -.0653287 .1411743 age4 | .0061088 .0485821 0.13 0.900 -.0892387 .1014564 anychildren | .0007417 .0353161 0.02 0.983 -.06857 .0700533 loghhinc | .0398357 .0207515 1.92 0.055 -.0008913 .0805627 associatemore | .0239625 .0357262 0.67 0.503 -.046154 .094079 fulltime | -.0127629 .0523551 -0.24 0.807 -.1155153 .0899895 parttime | -.003428 .0639463 -0.05 0.957 -.1289295 .1220735 selfemp | .03407 .0678842 0.50 0.616 -.0991599 .1672998 unemployed | .0078653 .0762971 0.10 0.918 -.1418758 .1576064 student | -.0527322 .1011671 -0.52 0.602 -.2512833 .1458189 _cons | -.2083533 .2233561 -0.93 0.351 -.6467133 .2300067 ------------------------------------------------------------------------------- Mean estimation Number of obs = 921 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ petIraw | .5178128 .0165805 .4852727 .5503529 -------------------------------------------------------------- (sum of wgt is 9.0487e+02) Linear regression Number of obs = 921 F(4, 916) = 8.91 Prob > F = 0.0000 R-squared = 0.0511 Root MSE = .28643 ------------------------------------------------------------------------------ | Robust petIIraw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- democrat | -.1181858 .0231357 -5.11 0.000 -.1635909 -.0727806 indep | -.0992677 .0287503 -3.45 0.001 -.1556918 -.0428436 otherpol | -.00978 .0838838 -0.12 0.907 -.1744068 .1548467 gender | -.0679466 .0187565 -3.62 0.000 -.1047574 -.0311359 _cons | .2006849 .0250036 8.03 0.000 .1516139 .2497559 ------------------------------------------------------------------------------ (sum of wgt is 9.0487e+02) Linear regression Number of obs = 921 F(1, 919) = 16.20 Prob > F = 0.0001 R-squared = 0.0224 Root MSE = .29025 ------------------------------------------------------------------------------ | Robust petIIraw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | .0871592 .0216531 4.03 0.000 .0446639 .1296545 _cons | .0986469 .0099973 9.87 0.000 .0790268 .1182671 ------------------------------------------------------------------------------ (sum of wgt is 9.0487e+02) Linear regression Number of obs = 921 F(5, 915) = 8.87 Prob > F = 0.0000 R-squared = 0.0657 Root MSE = .28437 ------------------------------------------------------------------------------ | Robust petIIraw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | .0715084 .0213132 3.36 0.001 .0296799 .1133369 democrat | -.108993 .0229385 -4.75 0.000 -.1540113 -.0639748 indep | -.1018241 .0286009 -3.56 0.000 -.1579552 -.0456931 otherpol | -.0062744 .0828707 -0.08 0.940 -.1689131 .1563643 gender | -.0606132 .0186621 -3.25 0.001 -.0972388 -.0239876 _cons | .1962183 .0246978 7.94 0.000 .1477474 .2446892 ------------------------------------------------------------------------------ (sum of wgt is 9.0487e+02) note: prior omitted because of collinearity Linear regression Number of obs = 921 F(21, 899) = 3.09 Prob > F = 0.0000 R-squared = 0.0866 Root MSE = .28366 ------------------------------------------------------------------------------- | Robust petIIraw | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- prior | .0774385 .0214308 3.61 0.000 .0353782 .1194987 wave | -.0373434 .0189929 -1.97 0.050 -.074619 -.0000678 gender | -.0621898 .0188051 -3.31 0.001 -.0990968 -.0252829 prior | 0 (omitted) democrat | -.0993005 .0230225 -4.31 0.000 -.1444847 -.0541164 indep | -.0846255 .0288029 -2.94 0.003 -.1411542 -.0280967 otherpol | .0032223 .0845492 0.04 0.970 -.1627145 .1691591 midwest | .0060628 .028762 0.21 0.833 -.0503856 .0625113 south | .0445223 .0277934 1.60 0.110 -.0100251 .0990698 west | .0123272 .0290734 0.42 0.672 -.0447325 .0693869 age1 | -.0751489 .0318666 -2.36 0.019 -.1376905 -.0126073 age2 | -.0335041 .0303998 -1.10 0.271 -.0931669 .0261587 age3 | -.0138429 .0331962 -0.42 0.677 -.0789941 .0513082 age4 | -.0365833 .0291289 -1.26 0.209 -.0937519 .0205853 anychildren | .023918 .020465 1.17 0.243 -.0162468 .0640828 loghhinc | -.0166444 .0109069 -1.53 0.127 -.0380504 .0047615 associatemore | .0119091 .0201783 0.59 0.555 -.0276929 .0515111 fulltime | .02734 .028208 0.97 0.333 -.0280212 .0827011 parttime | -.0082732 .0330847 -0.25 0.803 -.0732054 .0566591 selfemp | -.0435786 .0352846 -1.24 0.217 -.1128284 .0256712 unemployed | -.0223918 .0395359 -0.57 0.571 -.0999853 .0552016 student | .0235268 .0416443 0.56 0.572 -.0582046 .1052582 _cons | .4056421 .1285084 3.16 0.002 .1534307 .6578535 ------------------------------------------------------------------------------- Mean estimation Number of obs = 921 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ petIIraw | .0950186 .0097512 .0758815 .1141557 -------------------------------------------------------------- (sum of wgt is 9.0487e+02) Linear regression Number of obs = 921 F(4, 916) = 3.70 Prob > F = 0.0054 R-squared = 0.0157 Root MSE = 90.237 ------------------------------------------------------------------------------ | Robust donation | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- democrat | 21.37546 6.78767 3.15 0.002 8.054269 34.69665 indep | 3.178439 8.476845 0.37 0.708 -13.45785 19.81473 otherpol | -2.083325 22.85083 -0.09 0.927 -46.92938 42.76273 gender | -11.38447 5.99014 -1.90 0.058 -23.14046 .3715217 _cons | 77.50978 6.059583 12.79 0.000 65.6175 89.40205 ------------------------------------------------------------------------------ (sum of wgt is 9.0487e+02) Linear regression Number of obs = 921 F(1, 919) = 0.15 Prob > F = 0.6991 R-squared = 0.0002 Root MSE = 90.797 ------------------------------------------------------------------------------ | Robust donation | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -2.337287 6.045355 -0.39 0.699 -14.20159 9.527017 _cons | 81.92063 3.053921 26.82 0.000 75.92717 87.9141 ------------------------------------------------------------------------------ (sum of wgt is 9.0487e+02) Linear regression Number of obs = 921 F(5, 915) = 2.96 Prob > F = 0.0116 R-squared = 0.0157 Root MSE = 90.286 ------------------------------------------------------------------------------ | Robust donation | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.7782945 6.057876 -0.13 0.898 -12.66724 11.11065 democrat | 21.27541 6.815198 3.12 0.002 7.900171 34.65064 indep | 3.206263 8.487554 0.38 0.706 -13.45107 19.8636 otherpol | -2.121479 22.86938 -0.09 0.926 -47.004 42.76104 gender | -11.46429 5.995767 -1.91 0.056 -23.23134 .3027663 _cons | 77.55839 6.038062 12.84 0.000 65.70833 89.40845 ------------------------------------------------------------------------------ (sum of wgt is 9.0487e+02) note: prior omitted because of collinearity Linear regression Number of obs = 921 F(21, 899) = 1.92 Prob > F = 0.0077 R-squared = 0.0373 Root MSE = 90.079 ------------------------------------------------------------------------------- | Robust donation | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- prior | -1.159109 6.09731 -0.19 0.849 -13.12573 10.80751 wave | .0402207 6.04376 0.01 0.995 -11.8213 11.90174 gender | -9.392812 6.247721 -1.50 0.133 -21.65463 2.869005 prior | 0 (omitted) democrat | 22.56673 6.997699 3.22 0.001 8.833003 36.30046 indep | 6.074783 8.895429 0.68 0.495 -11.38344 23.53301 otherpol | 4.245018 22.11679 0.19 0.848 -39.16152 47.65156 midwest | 6.437763 9.764503 0.66 0.510 -12.72611 25.60164 south | -.4482387 8.831511 -0.05 0.960 -17.78102 16.88454 west | 5.371383 9.75614 0.55 0.582 -13.77608 24.51885 age1 | 4.360886 12.71917 0.34 0.732 -20.60183 29.3236 age2 | 12.20632 9.849418 1.24 0.216 -7.124204 31.53685 age3 | -2.524794 10.02432 -0.25 0.801 -22.19859 17.149 age4 | 1.296028 9.130029 0.14 0.887 -16.62262 19.21468 anychildren | 10.45511 6.759055 1.55 0.122 -2.810253 23.72047 loghhinc | 10.08979 3.853268 2.62 0.009 2.527346 17.65224 associatemore | 2.075078 6.718897 0.31 0.758 -11.11147 15.26163 fulltime | 3.533213 9.720435 0.36 0.716 -15.54417 22.6106 parttime | 2.555574 10.91453 0.23 0.815 -18.86535 23.9765 selfemp | 8.9277 13.56599 0.66 0.511 -17.69699 35.55239 unemployed | -5.631781 12.56342 -0.45 0.654 -30.28883 19.02527 student | 9.969544 18.46102 0.54 0.589 -26.26216 46.20125 _cons | -50.13782 41.8237 -1.20 0.231 -132.2213 31.94563 ------------------------------------------------------------------------------- Mean estimation Number of obs = 921 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ donation | 82.01793 3.009845 76.11097 87.92489 -------------------------------------------------------------- (sum of wgt is 6.9019e+02) Linear regression Number of obs = 702 F(4, 697) = 0.63 Prob > F = 0.6436 R-squared = 0.0027 Root MSE = .33856 ------------------------------------------------------------------------------ | Robust fblike2raw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- democrat | .0076571 .0293284 0.26 0.794 -.0499255 .0652396 indep | -.0326643 .0353987 -0.92 0.356 -.1021651 .0368365 otherpol | -.069465 .0661933 -1.05 0.294 -.1994273 .0604972 gender | -.0065423 .0255727 -0.26 0.798 -.056751 .0436664 _cons | .1384518 .0266746 5.19 0.000 .0860796 .1908239 ------------------------------------------------------------------------------ (sum of wgt is 6.9019e+02) Linear regression Number of obs = 702 F(1, 700) = 2.42 Prob > F = 0.1203 R-squared = 0.0038 Root MSE = .33766 ------------------------------------------------------------------------------ | Robust fblike2raw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.0416793 .0267931 -1.56 0.120 -.0942837 .0109251 _cons | .1303421 .0127372 10.23 0.000 .1053345 .1553498 ------------------------------------------------------------------------------ (sum of wgt is 6.9019e+02) Linear regression Number of obs = 702 F(5, 696) = 0.82 Prob > F = 0.5390 R-squared = 0.0063 Root MSE = .33819 ------------------------------------------------------------------------------ | Robust fblike2raw | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.0414216 .0282365 -1.47 0.143 -.0968605 .0140173 democrat | .0024013 .0297476 0.08 0.936 -.0560045 .0608071 indep | -.0308167 .0352666 -0.87 0.383 -.1000583 .0384249 otherpol | -.0781591 .0709043 -1.10 0.271 -.217371 .0610528 gender | -.0105666 .026234 -0.40 0.687 -.062074 .0409407 _cons | .1416658 .0270703 5.23 0.000 .0885166 .194815 ------------------------------------------------------------------------------ (sum of wgt is 6.9019e+02) note: prior omitted because of collinearity Linear regression Number of obs = 702 F(21, 680) = 1.33 Prob > F = 0.1448 R-squared = 0.0336 Root MSE = .33743 ------------------------------------------------------------------------------- | Robust fblike2raw | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- prior | -.0344968 .0288199 -1.20 0.232 -.0910836 .0220899 wave | -.0013334 .0264582 -0.05 0.960 -.053283 .0506162 gender | -.0170236 .0269837 -0.63 0.528 -.070005 .0359578 prior | 0 (omitted) democrat | .0200642 .0302023 0.66 0.507 -.0392369 .0793652 indep | -.0059991 .036743 -0.16 0.870 -.0781424 .0661442 otherpol | -.059964 .0725239 -0.83 0.409 -.2023618 .0824337 midwest | .0463707 .0380004 1.22 0.223 -.0282415 .1209829 south | .0571623 .0333805 1.71 0.087 -.0083789 .1227036 west | .0854031 .0395678 2.16 0.031 .0077134 .1630927 age1 | -.0940815 .0479324 -1.96 0.050 -.1881948 .0000317 age2 | -.0542768 .0444497 -1.22 0.222 -.141552 .0329985 age3 | -.0545919 .046369 -1.18 0.239 -.1456355 .0364518 age4 | -.0555052 .0429338 -1.29 0.197 -.1398041 .0287936 anychildren | .0711539 .0290962 2.45 0.015 .0140246 .1282831 loghhinc | -.0066812 .0153374 -0.44 0.663 -.0367956 .0234332 associatemore | -.0036082 .0298265 -0.12 0.904 -.0621713 .0549549 fulltime | .0191823 .0444409 0.43 0.666 -.0680755 .1064401 parttime | -.0347022 .0504408 -0.69 0.492 -.1337407 .0643362 selfemp | .0414162 .0609248 0.68 0.497 -.0782071 .1610395 unemployed | -.0069563 .0631408 -0.11 0.912 -.1309306 .1170181 student | .1102957 .0774466 1.42 0.155 -.0417676 .2623589 _cons | .1556511 .1744952 0.89 0.373 -.186963 .4982652 ------------------------------------------------------------------------------- Mean estimation Number of obs = 702 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ fblike2raw | .1313779 .012828 .1061921 .1565637 -------------------------------------------------------------- . . . . loc rowlabels " "\hline" "{\bf Panel A: Gender and political orientation}" " " "Democrat" " " " " > "Female" " " " " "\hline" "{\bf Panel B: Prior belief about wage gap}" " " "Prior (z-scored)" " " > " " "\hline" "{\bf Panel C: Prior, gender, pol. orientation}" " " "Prior (z-scored)" " " " " "De > mocrat" " " " " "Female" " " " " "\hline" "{\bf Panel D: Full set of controls}" " " "Prior (z-scor > ed)" " " " " "Democrat" " " " " "Female" " " " " "Mean outcome (control group)" "Observations" " > " "\hline" "\hline" " . loc rowstats "" . . . loc rowstats "" . . forval i = 1/42 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . esttab * using "$output\tab_correlates_behaviorAB_5_95.tex", replace cells(none) booktabs nonotes > nomtitles compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels > ')) /// > mgroups("\shortstack{Intention to sign\\Petition I}" "\shortstack{Intention to sign\\Petit > ion II}" /// > "\shortstack{Amount donated\\to supportive NGO}" "\shortstack{Facebook Like}", pattern(1 1 1 1) > prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\tab_correlates_behaviorAB_5_95.tex) . . eststo clear . . . *********************************************************************************** . // Table D.1: Treatment effect on posterior beliefs . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final", clear . . * Drop control group . drop if rand==0 (1,034 observations deleted) . . * Generate dummy indicating which posterior belief statistic respondent was assigned to . gen postgroup =. (3,031 missing values generated) . replace postgroup =1 if wave==1&RAND4== 9 (673 real changes made) . replace postgroup = 2 if wave==1&RAND4==10 (678 real changes made) . replace postgroup= 3 if wave==1&RAND4==11 (661 real changes made) . replace postgroup = 4 if wave==2&RAND4==10 (523 real changes made) . replace postgroup=5 if wave==2&RAND4==11 (496 real changes made) . . . loc experiments "extraHS extrayoung extraoccu extrasame extrachild posterior" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experiments' { 2. . ***Panel A . . reg `choice' T1 $controls i.postgroup [pweight=pweight], vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(1) 5. estadd loc thisstat4 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat5 = "`r(sestar)'": col`colnum' 7. sigstar gender, prec(1) 8. estadd loc thisstat7 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat8 = "`r(sestar)'": col`colnum' 10. sigstar democrat, prec(1) 11. estadd loc thisstat10 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat11 = "`r(sestar)'": col`colnum' 13. sigstar prior, prec(1) 14. estadd loc thisstat13 = "`r(bstar)'": col`colnum' 15. estadd loc thisstat14 = "`r(sestar)'": col`colnum' 16. estadd loc thisstat16 = "`n'": col`colnum' 17. . . ***Panel B : het by gender . . reg `choice' T1 T1female $controls i.postgroup [pweight=pweight], vce(r) 18. local n = round(e(N)) 19. . sigstar T1, prec(1) 20. estadd loc thisstat20 = "`r(bstar)'": col`colnum' 21. estadd loc thisstat21 = "`r(sestar)'": col`colnum' 22. sigstar T1female, prec(1) 23. estadd loc thisstat23 = "`r(bstar)'": col`colnum' 24. estadd loc thisstat24 = "`r(sestar)'": col`colnum' 25. sigstar gender, prec(1) 26. estadd loc thisstat27 = "`r(bstar)'": col`colnum' 27. estadd loc thisstat28 = "`r(sestar)'": col`colnum' 28. . test T1 + T1female = 0 29. estadd loc thisstat25 = string(r(p), "%9.3f"): col`colnum' 30. estadd loc thisstat30 = "`n'": col`colnum' 31. . . ***Panel C : het by political orientation . . reg `choice' T1 T1democrat T1indep $controls i.postgroup [pweight=pweight] if otherpol==0, vce > (r) 32. local n = round(e(N)) 33. . sigstar T1, prec(1) 34. estadd loc thisstat34 = "`r(bstar)'": col`colnum' 35. estadd loc thisstat35 = "`r(sestar)'": col`colnum' 36. sigstar T1democrat, prec(1) 37. estadd loc thisstat37 = "`r(bstar)'": col`colnum' 38. estadd loc thisstat38 = "`r(sestar)'": col`colnum' 39. sigstar democrat, prec(1) 40. estadd loc thisstat41 = "`r(bstar)'": col`colnum' 41. estadd loc thisstat42 = "`r(sestar)'": col`colnum' 42. sigstar T1indep, prec(1) 43. estadd loc thisstat44 = "`r(bstar)'": col`colnum' 44. estadd loc thisstat45 = "`r(sestar)'": col`colnum' 45. sigstar indep, prec(1) 46. estadd loc thisstat48 = "`r(bstar)'": col`colnum' 47. estadd loc thisstat49 = "`r(sestar)'": col`colnum' 48. . test T1 + T1democrat = 0 49. estadd loc thisstat39 = string(r(p), "%9.3f"): col`colnum' 50. test T1 + T1indep = 0 51. estadd loc thisstat46 = string(r(p), "%9.3f"): col`colnum' 52. . estadd loc thisstat51 = "`n'": col`colnum' 53. . loc ++colnum 54. loc colnames "`colnames' `"`: var la `choice''"'" 55. . } (sum of wgt is 6.7600e+02) note: wave omitted because of collinearity note: 2.postgroup omitted because of collinearity Linear regression Number of obs = 676 F(21, 654) = 10.79 Prob > F = 0.0000 R-squared = 0.3043 Root MSE = 18.098 ------------------------------------------------------------------------------- | Robust extraHSS | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -12.99309 1.404142 -9.25 0.000 -15.75026 -10.23592 wave | 0 (omitted) gender | -1.787075 1.532823 -1.17 0.244 -4.796922 1.222773 prior | .4138511 .0623807 6.63 0.000 .2913605 .5363417 democrat | -.0755152 1.724788 -0.04 0.965 -3.462306 3.311276 indep | 1.055981 2.144355 0.49 0.623 -3.154671 5.266632 otherpol | 2.948457 3.010123 0.98 0.328 -2.962214 8.859128 midwest | 1.931595 2.399965 0.80 0.421 -2.780973 6.644162 south | 2.252609 1.895059 1.19 0.235 -1.468525 5.973743 west | 1.565394 2.073521 0.75 0.451 -2.506168 5.636956 age1 | 4.119984 2.91402 1.41 0.158 -1.60198 9.841949 age2 | 3.236178 2.202445 1.47 0.142 -1.088539 7.560895 age3 | 1.228499 1.87043 0.66 0.512 -2.444273 4.90127 age4 | .4790915 1.976753 0.24 0.809 -3.402457 4.36064 anychildren | 1.469273 1.631053 0.90 0.368 -1.73346 4.672006 loghhinc | -1.167306 .9702604 -1.20 0.229 -3.072507 .7378954 associatemore | -.2605631 1.645923 -0.16 0.874 -3.492494 2.971368 fulltime | 1.311091 1.954417 0.67 0.503 -2.526599 5.14878 parttime | 1.406599 2.464993 0.57 0.568 -3.433656 6.246854 selfemp | 2.430886 2.799528 0.87 0.386 -3.066262 7.928034 unemployed | -.290216 2.85335 -0.10 0.919 -5.893049 5.312617 student | 2.382266 3.348191 0.71 0.477 -4.192235 8.956766 2.postgroup | 0 (omitted) _cons | 58.98701 11.53781 5.11 0.000 36.33139 81.64262 ------------------------------------------------------------------------------- (sum of wgt is 6.7600e+02) note: wave omitted because of collinearity note: 2.postgroup omitted because of collinearity Linear regression Number of obs = 676 F(22, 653) = 10.29 Prob > F = 0.0000 R-squared = 0.3043 Root MSE = 18.112 ------------------------------------------------------------------------------- | Robust extraHSS | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -12.95086 1.945009 -6.66 0.000 -16.77009 -9.131636 T1female | -.0847898 2.804116 -0.03 0.976 -5.590962 5.421383 wave | 0 (omitted) gender | -1.744033 1.912056 -0.91 0.362 -5.498552 2.010486 prior | .4138073 .0625457 6.62 0.000 .2909923 .5366223 democrat | -.0753467 1.72694 -0.04 0.965 -3.466373 3.315679 indep | 1.058998 2.167029 0.49 0.625 -3.196188 5.314185 otherpol | 2.940736 3.001614 0.98 0.328 -2.953245 8.834716 midwest | 1.932029 2.400917 0.80 0.421 -2.782419 6.646478 south | 2.253371 1.892423 1.19 0.234 -1.462596 5.969339 west | 1.567611 2.068189 0.76 0.449 -2.493491 5.628714 age1 | 4.119886 2.916703 1.41 0.158 -1.607363 9.847134 age2 | 3.239922 2.172449 1.49 0.136 -1.025906 7.50575 age3 | 1.230836 1.870913 0.66 0.511 -2.442895 4.904568 age4 | .4807638 1.977643 0.24 0.808 -3.402542 4.36407 anychildren | 1.469354 1.632063 0.90 0.368 -1.735372 4.674079 loghhinc | -1.166543 .9679697 -1.21 0.229 -3.067252 .7341653 associatemore | -.2596379 1.649275 -0.16 0.875 -3.498161 2.978885 fulltime | 1.30795 1.964273 0.67 0.506 -2.549103 5.165003 parttime | 1.407145 2.466633 0.57 0.569 -3.436344 6.250634 selfemp | 2.426632 2.802101 0.87 0.387 -3.075584 7.928848 unemployed | -.2907463 2.8562 -0.10 0.919 -5.899191 5.317698 student | 2.384348 3.350802 0.71 0.477 -4.195299 8.963995 2.postgroup | 0 (omitted) _cons | 58.9601 11.40306 5.17 0.000 36.56902 81.35118 ------------------------------------------------------------------------------- ( 1) T1 + T1female = 0 F( 1, 653) = 41.46 Prob > F = 0.0000 (sum of wgt is 6.6200e+02) note: wave omitted because of collinearity note: otherpol omitted because of collinearity note: 2.postgroup omitted because of collinearity Linear regression Number of obs = 662 F(22, 639) = 10.17 Prob > F = 0.0000 R-squared = 0.3027 Root MSE = 18.231 ------------------------------------------------------------------------------- | Robust extraHSS | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -14.18195 2.379724 -5.96 0.000 -18.85497 -9.508925 T1democrat | 3.1287 3.182953 0.98 0.326 -3.121613 9.379012 T1indep | -1.472461 4.283772 -0.34 0.731 -9.884434 6.939511 wave | 0 (omitted) gender | -1.714929 1.581446 -1.08 0.279 -4.820389 1.390531 prior | .4114138 .0626999 6.56 0.000 .288291 .5345366 democrat | -1.730311 2.081253 -0.83 0.406 -5.817232 2.356611 indep | 1.804943 3.205512 0.56 0.574 -4.489668 8.099553 otherpol | 0 (omitted) midwest | 1.816506 2.441156 0.74 0.457 -2.977152 6.610164 south | 2.099721 1.940294 1.08 0.280 -1.710402 5.909844 west | 1.403691 2.101562 0.67 0.504 -2.723113 5.530494 age1 | 3.709488 2.991861 1.24 0.215 -2.16558 9.584556 age2 | 3.332016 2.248095 1.48 0.139 -1.082531 7.746562 age3 | 1.345785 1.888553 0.71 0.476 -2.362735 5.054304 age4 | .4212144 2.0001 0.21 0.833 -3.506348 4.348777 anychildren | 1.374703 1.66165 0.83 0.408 -1.888252 4.637659 loghhinc | -1.158832 .9839011 -1.18 0.239 -3.090902 .7732387 associatemore | -.3489028 1.671137 -0.21 0.835 -3.630486 2.93268 fulltime | 1.293089 2.012995 0.64 0.521 -2.659795 5.245973 parttime | 1.398481 2.534746 0.55 0.581 -3.578958 6.375919 selfemp | 2.367193 2.860028 0.83 0.408 -3.248995 7.983382 unemployed | -.3598238 2.881006 -0.12 0.901 -6.017207 5.297559 student | 2.975048 3.400946 0.87 0.382 -3.703332 9.653429 2.postgroup | 0 (omitted) _cons | 59.90002 11.55229 5.19 0.000 37.21499 82.58505 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 639) = 28.07 Prob > F = 0.0000 ( 1) T1 + T1indep = 0 F( 1, 639) = 19.63 Prob > F = 0.0000 (sum of wgt is 6.7000e+02) note: wave omitted because of collinearity note: 1.postgroup omitted because of collinearity Linear regression Number of obs = 670 F(21, 648) = 12.35 Prob > F = 0.0000 R-squared = 0.3247 Root MSE = 15.088 ------------------------------------------------------------------------------- | Robust extrayoung | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -11.38606 1.148347 -9.92 0.000 -13.64099 -9.131133 wave | 0 (omitted) gender | -2.046031 1.206669 -1.70 0.090 -4.415483 .3234219 prior | .3787083 .0618645 6.12 0.000 .2572292 .5001874 democrat | .5337871 1.410971 0.38 0.705 -2.23684 3.304414 indep | -.1377801 1.712139 -0.08 0.936 -3.499791 3.224231 otherpol | -2.634214 3.052605 -0.86 0.388 -8.628406 3.359978 midwest | -1.326509 1.963409 -0.68 0.500 -5.18192 2.528903 south | -2.977956 1.676431 -1.78 0.076 -6.269848 .3139357 west | -3.274308 1.745371 -1.88 0.061 -6.701574 .1529571 age1 | 3.821156 4.295907 0.89 0.374 -4.614422 12.25673 age2 | 4.570124 1.8182 2.51 0.012 .9998489 8.140398 age3 | .982311 1.904424 0.52 0.606 -2.757277 4.721899 age4 | -.2567229 1.365582 -0.19 0.851 -2.938223 2.424777 anychildren | .9358537 1.356239 0.69 0.490 -1.7273 3.599007 loghhinc | .1106301 .8189485 0.14 0.893 -1.497483 1.718743 associatemore | .3493701 1.433384 0.24 0.808 -2.465269 3.164009 fulltime | .1716976 1.576769 0.11 0.913 -2.924496 3.267891 parttime | .3317842 2.812009 0.12 0.906 -5.189965 5.853533 selfemp | -2.883969 2.907128 -0.99 0.322 -8.592498 2.824561 unemployed | -3.398048 2.07795 -1.64 0.102 -7.478376 .6822796 student | -3.37551 4.275377 -0.79 0.430 -11.77078 5.019756 1.postgroup | 0 (omitted) _cons | 60.08452 8.951549 6.71 0.000 42.50698 77.66207 ------------------------------------------------------------------------------- (sum of wgt is 6.7000e+02) note: wave omitted because of collinearity note: 1.postgroup omitted because of collinearity Linear regression Number of obs = 670 F(22, 647) = 12.19 Prob > F = 0.0000 R-squared = 0.3269 Root MSE = 15.076 ------------------------------------------------------------------------------- | Robust extrayoung | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -9.746111 1.710678 -5.70 0.000 -13.10526 -6.38696 T1female | -3.365772 2.328539 -1.45 0.149 -7.938178 1.206635 wave | 0 (omitted) gender | -.3001731 1.471676 -0.20 0.838 -3.19001 2.589664 prior | .3792095 .0615502 6.16 0.000 .2583472 .5000719 democrat | .5619956 1.40994 0.40 0.690 -2.206615 3.330607 indep | -.0223731 1.702194 -0.01 0.990 -3.364866 3.320119 otherpol | -2.129001 3.053295 -0.70 0.486 -8.124564 3.866562 midwest | -1.452643 1.945322 -0.75 0.455 -5.272549 2.367264 south | -3.151775 1.686826 -1.87 0.062 -6.46409 .1605387 west | -3.451869 1.753939 -1.97 0.049 -6.895969 -.007769 age1 | 3.717242 4.265611 0.87 0.384 -4.65887 12.09335 age2 | 4.500734 1.829401 2.46 0.014 .9084539 8.093013 age3 | .9257122 1.902822 0.49 0.627 -2.81074 4.662165 age4 | -.2474686 1.370836 -0.18 0.857 -2.939293 2.444356 anychildren | .8983139 1.355625 0.66 0.508 -1.763642 3.56027 loghhinc | .1857684 .8144634 0.23 0.820 -1.413542 1.785079 associatemore | .395109 1.429492 0.28 0.782 -2.411894 3.202113 fulltime | .083557 1.563949 0.05 0.957 -2.987471 3.154585 parttime | .2947469 2.812975 0.10 0.917 -5.228915 5.818409 selfemp | -3.076173 2.905897 -1.06 0.290 -8.7823 2.629954 unemployed | -3.503061 2.069609 -1.69 0.091 -7.567022 .5609008 student | -3.114208 4.316875 -0.72 0.471 -11.59099 5.362569 1.postgroup | 0 (omitted) _cons | 58.54971 8.876033 6.60 0.000 41.1204 75.97902 ------------------------------------------------------------------------------- ( 1) T1 + T1female = 0 F( 1, 647) = 71.54 Prob > F = 0.0000 (sum of wgt is 6.6000e+02) note: wave omitted because of collinearity note: otherpol omitted because of collinearity note: 1.postgroup omitted because of collinearity Linear regression Number of obs = 660 F(22, 637) = 11.82 Prob > F = 0.0000 R-squared = 0.3258 Root MSE = 15.152 ------------------------------------------------------------------------------- | Robust extrayoung | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -10.80313 1.828243 -5.91 0.000 -14.39325 -7.213024 T1democrat | .4799687 2.64944 0.18 0.856 -4.722723 5.68266 T1indep | -4.625561 3.140979 -1.47 0.141 -10.79349 1.542363 wave | 0 (omitted) gender | -1.845905 1.214468 -1.52 0.129 -4.23075 .5389387 prior | .3782021 .0627298 6.03 0.000 .25502 .5013842 democrat | .3178138 1.725427 0.18 0.854 -3.070398 3.706025 indep | 2.45977 2.360114 1.04 0.298 -2.174775 7.094314 otherpol | 0 (omitted) midwest | -1.517684 1.972315 -0.77 0.442 -5.39071 2.355341 south | -3.109699 1.671646 -1.86 0.063 -6.392302 .1729041 west | -3.505383 1.756555 -2.00 0.046 -6.954722 -.0560438 age1 | 3.532337 4.407231 0.80 0.423 -5.12212 12.18679 age2 | 4.19825 1.856683 2.26 0.024 .5522911 7.844208 age3 | 1.282186 1.928639 0.66 0.506 -2.505073 5.069445 age4 | -.2058018 1.37234 -0.15 0.881 -2.900659 2.489056 anychildren | .8284723 1.383596 0.60 0.550 -1.888488 3.545432 loghhinc | .2005042 .8325089 0.24 0.810 -1.434289 1.835298 associatemore | .4242925 1.44096 0.29 0.769 -2.405313 3.253898 fulltime | .1155129 1.592259 0.07 0.942 -3.011199 3.242224 parttime | .5870915 2.840044 0.21 0.836 -4.989889 6.164072 selfemp | -2.573838 2.964279 -0.87 0.386 -8.394777 3.247102 unemployed | -3.651389 2.056508 -1.78 0.076 -7.689743 .3869648 student | -3.16165 4.383686 -0.72 0.471 -11.76987 5.446573 1.postgroup | 0 (omitted) _cons | 58.93407 9.270869 6.36 0.000 40.72891 77.13923 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 637) = 30.60 Prob > F = 0.0000 ( 1) T1 + T1indep = 0 F( 1, 637) = 38.22 Prob > F = 0.0000 (sum of wgt is 6.5700e+02) note: wave omitted because of collinearity note: 3.postgroup omitted because of collinearity Linear regression Number of obs = 657 F(21, 635) = 16.74 Prob > F = 0.0000 R-squared = 0.4066 Root MSE = 14.887 ------------------------------------------------------------------------------- | Robust extraoccuu | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -13.69859 1.147742 -11.94 0.000 -15.95242 -11.44476 wave | 0 (omitted) gender | .450845 1.278479 0.35 0.724 -2.059714 2.961404 prior | .438637 .0663847 6.61 0.000 .3082769 .5689971 democrat | .8235969 1.291833 0.64 0.524 -1.713185 3.360379 indep | .3007264 1.668593 0.18 0.857 -2.975901 3.577353 otherpol | 10.40895 9.623558 1.08 0.280 -8.488896 29.3068 midwest | -1.230214 1.919074 -0.64 0.522 -4.998713 2.538286 south | -2.776708 1.937158 -1.43 0.152 -6.580718 1.027302 west | -1.377623 2.025482 -0.68 0.497 -5.355077 2.59983 age1 | -.7952757 2.861836 -0.28 0.781 -6.415083 4.824531 age2 | 2.76114 1.678714 1.64 0.101 -.5353625 6.057643 age3 | -1.803745 1.532921 -1.18 0.240 -4.813953 1.206462 age4 | .8770695 1.548101 0.57 0.571 -2.162947 3.917086 anychildren | 1.730929 1.24881 1.39 0.166 -.721368 4.183225 loghhinc | -.2471345 .7741035 -0.32 0.750 -1.767247 1.272978 associatemore | .7144229 1.301291 0.55 0.583 -1.84093 3.269776 fulltime | 2.207047 1.57411 1.40 0.161 -.8840447 5.298138 parttime | -2.511865 1.728624 -1.45 0.147 -5.906375 .8826457 selfemp | -.060642 2.055156 -0.03 0.976 -4.096365 3.975081 unemployed | -3.690083 2.627723 -1.40 0.161 -8.85016 1.469995 student | -3.7775 3.520313 -1.07 0.284 -10.69036 3.135363 3.postgroup | 0 (omitted) _cons | 57.468 10.144 5.67 0.000 37.54814 77.38785 ------------------------------------------------------------------------------- (sum of wgt is 6.5700e+02) note: wave omitted because of collinearity note: 3.postgroup omitted because of collinearity Linear regression Number of obs = 657 F(22, 634) = 17.20 Prob > F = 0.0000 R-squared = 0.4068 Root MSE = 14.896 ------------------------------------------------------------------------------- | Robust extraoccuu | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -14.27604 1.716739 -8.32 0.000 -17.64722 -10.90486 T1female | 1.117902 2.461481 0.45 0.650 -3.715739 5.951544 wave | 0 (omitted) gender | -.0706241 1.553512 -0.05 0.964 -3.121276 2.980028 prior | .4386511 .0663875 6.61 0.000 .3082853 .569017 democrat | .790855 1.297156 0.61 0.542 -1.756386 3.338096 indep | .2575373 1.668304 0.15 0.877 -3.018533 3.533608 otherpol | 10.32342 9.682665 1.07 0.287 -8.690556 29.33739 midwest | -1.211169 1.924795 -0.63 0.529 -4.990914 2.568576 south | -2.748692 1.949079 -1.41 0.159 -6.576124 1.07874 west | -1.383059 2.026815 -0.68 0.495 -5.363143 2.597024 age1 | -.8624464 2.891356 -0.30 0.766 -6.540239 4.815346 age2 | 2.781947 1.670471 1.67 0.096 -.4983782 6.062272 age3 | -1.812043 1.536256 -1.18 0.239 -4.828809 1.204722 age4 | .8818731 1.54836 0.57 0.569 -2.158661 3.922407 anychildren | 1.704056 1.26189 1.35 0.177 -.7739328 4.182045 loghhinc | -.2445272 .775107 -0.32 0.753 -1.766615 1.27756 associatemore | .6913105 1.297078 0.53 0.594 -1.855779 3.2384 fulltime | 2.19667 1.575361 1.39 0.164 -.8968855 5.290226 parttime | -2.482774 1.731326 -1.43 0.152 -5.882601 .9170531 selfemp | -.0547581 2.052107 -0.03 0.979 -4.084506 3.97499 unemployed | -3.745055 2.644296 -1.42 0.157 -8.937692 1.447582 student | -3.800617 3.493661 -1.09 0.277 -10.66116 3.05993 3.postgroup | 0 (omitted) _cons | 57.76431 10.01314 5.77 0.000 38.10137 77.42724 ------------------------------------------------------------------------------- ( 1) T1 + T1female = 0 F( 1, 634) = 63.55 Prob > F = 0.0000 (sum of wgt is 6.4300e+02) note: wave omitted because of collinearity note: otherpol omitted because of collinearity note: 3.postgroup omitted because of collinearity Linear regression Number of obs = 643 F(22, 620) = 16.46 Prob > F = 0.0000 R-squared = 0.4067 Root MSE = 13.965 ------------------------------------------------------------------------------- | Robust extraoccuu | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -17.54825 1.812474 -9.68 0.000 -21.10758 -13.98892 T1democrat | 5.978207 2.410322 2.48 0.013 1.244823 10.71159 T1indep | 8.616741 3.596881 2.40 0.017 1.553194 15.68029 wave | 0 (omitted) gender | -.0032608 1.250525 -0.00 0.998 -2.459039 2.452517 prior | .414982 .0664264 6.25 0.000 .284534 .54543 democrat | -1.866919 1.515299 -1.23 0.218 -4.84266 1.108822 indep | -3.612537 1.803211 -2.00 0.046 -7.153678 -.0713951 otherpol | 0 (omitted) midwest | -1.405542 1.81314 -0.78 0.439 -4.966181 2.155097 south | -1.989888 1.811754 -1.10 0.272 -5.547806 1.568031 west | -.6596408 1.815522 -0.36 0.716 -4.224958 2.905676 age1 | .0639674 2.883997 0.02 0.982 -5.599619 5.727554 age2 | 2.255992 1.564647 1.44 0.150 -.816657 5.328642 age3 | -.8019448 1.436279 -0.56 0.577 -3.622506 2.018616 age4 | 1.047477 1.556686 0.67 0.501 -2.009539 4.104494 anychildren | 2.212571 1.189697 1.86 0.063 -.1237535 4.548896 loghhinc | .0160405 .7185095 0.02 0.982 -1.394967 1.427048 associatemore | .2046873 1.245382 0.16 0.870 -2.240991 2.650365 fulltime | 1.293992 1.474591 0.88 0.381 -1.601806 4.18979 parttime | -2.973323 1.733659 -1.72 0.087 -6.377878 .4312317 selfemp | -.9992881 1.650791 -0.61 0.545 -4.241108 2.242531 unemployed | -4.516334 2.551943 -1.77 0.077 -9.527834 .4951649 student | -4.688828 3.712326 -1.26 0.207 -11.97909 2.601429 3.postgroup | 0 (omitted) _cons | 58.77683 9.498317 6.19 0.000 40.12406 77.4296 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 620) = 55.29 Prob > F = 0.0000 ( 1) T1 + T1indep = 0 F( 1, 620) = 8.69 Prob > F = 0.0033 (sum of wgt is 5.0609e+02) note: wave omitted because of collinearity note: 4.postgroup omitted because of collinearity Linear regression Number of obs = 523 F(21, 501) = 9.87 Prob > F = 0.0000 R-squared = 0.2395 Root MSE = 15.984 ------------------------------------------------------------------------------- | Robust extrasame | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -11.88218 1.535044 -7.74 0.000 -14.89809 -8.866258 wave | 0 (omitted) gender | -1.402512 1.419319 -0.99 0.324 -4.191062 1.386039 prior | .2279378 .0638911 3.57 0.000 .1024103 .3534652 democrat | -.5975033 1.658555 -0.36 0.719 -3.856083 2.661077 indep | .6993539 2.068631 0.34 0.735 -3.364907 4.763615 otherpol | -3.329258 3.379958 -0.98 0.325 -9.969897 3.311381 midwest | -.9290445 1.906573 -0.49 0.626 -4.674909 2.81682 south | 3.260045 1.857435 1.76 0.080 -.3892756 6.909367 west | -.1267942 2.085762 -0.06 0.952 -4.224712 3.971123 age1 | .3584929 1.944331 0.18 0.854 -3.461555 4.178541 age2 | 3.366399 2.193909 1.53 0.126 -.9439959 7.676794 age3 | 2.191002 2.380418 0.92 0.358 -2.48583 6.867834 age4 | -.4839624 2.008206 -0.24 0.810 -4.429505 3.461581 anychildren | -1.673756 1.662196 -1.01 0.314 -4.93949 1.591979 loghhinc | -.7368665 .9583839 -0.77 0.442 -2.619813 1.14608 associatemore | 2.914589 1.262935 2.31 0.021 .4332868 5.39589 fulltime | .0155944 1.888396 0.01 0.993 -3.694556 3.725745 parttime | -.5055443 2.874611 -0.18 0.860 -6.153322 5.142234 selfemp | -1.303979 2.203078 -0.59 0.554 -5.63239 3.024432 unemployed | -4.901674 2.870815 -1.71 0.088 -10.54199 .7386461 student | 2.374662 3.194297 0.74 0.458 -3.901205 8.65053 4.postgroup | 0 (omitted) _cons | 81.28726 13.13586 6.19 0.000 55.4791 107.0954 ------------------------------------------------------------------------------- (sum of wgt is 5.0609e+02) note: wave omitted because of collinearity note: 4.postgroup omitted because of collinearity Linear regression Number of obs = 523 F(22, 500) = 9.48 Prob > F = 0.0000 R-squared = 0.2424 Root MSE = 15.969 ------------------------------------------------------------------------------- | Robust extrasame | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -9.942998 2.103777 -4.73 0.000 -14.07633 -5.809667 T1female | -3.966289 2.911824 -1.36 0.174 -9.687208 1.754631 wave | 0 (omitted) gender | .6466012 2.018867 0.32 0.749 -3.319907 4.613109 prior | .2261775 .0633532 3.57 0.000 .1017062 .3506487 democrat | -.723768 1.665136 -0.43 0.664 -3.995293 2.547757 indep | .6844406 2.04688 0.33 0.738 -3.337105 4.705986 otherpol | -3.573197 3.512218 -1.02 0.309 -10.47372 3.327328 midwest | -.8063304 1.920901 -0.42 0.675 -4.580362 2.967701 south | 3.201282 1.845123 1.73 0.083 -.4238681 6.826433 west | -.316781 2.058164 -0.15 0.878 -4.360496 3.726934 age1 | .6375096 1.965932 0.32 0.746 -3.224996 4.500015 age2 | 3.518832 2.214755 1.59 0.113 -.8325412 7.870205 age3 | 2.379135 2.393251 0.99 0.321 -2.322932 7.081203 age4 | -.3400117 2.031403 -0.17 0.867 -4.331149 3.651126 anychildren | -1.527105 1.661756 -0.92 0.359 -4.791989 1.737779 loghhinc | -.8168916 .9596754 -0.85 0.395 -2.702385 1.068602 associatemore | 2.946554 1.266321 2.33 0.020 .4585877 5.43452 fulltime | -.00211 1.886559 -0.00 0.999 -3.70867 3.70445 parttime | -.3803871 2.865138 -0.13 0.894 -6.00958 5.248806 selfemp | -1.273542 2.202232 -0.58 0.563 -5.600311 3.053226 unemployed | -4.65502 2.843509 -1.64 0.102 -10.24172 .9316796 student | 2.319516 3.193799 0.73 0.468 -3.955404 8.594436 4.postgroup | 0 (omitted) _cons | 81.22992 13.08125 6.21 0.000 55.52893 106.9309 ------------------------------------------------------------------------------- ( 1) T1 + T1female = 0 F( 1, 500) = 42.83 Prob > F = 0.0000 (sum of wgt is 4.9596e+02) note: wave omitted because of collinearity note: otherpol omitted because of collinearity note: 4.postgroup omitted because of collinearity Linear regression Number of obs = 513 F(22, 490) = 10.06 Prob > F = 0.0000 R-squared = 0.2437 Root MSE = 16.068 ------------------------------------------------------------------------------- | Robust extrasame | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -9.643052 2.056629 -4.69 0.000 -13.68395 -5.602153 T1democrat | -5.061004 3.141603 -1.61 0.108 -11.23368 1.111671 T1indep | .8855825 4.028032 0.22 0.826 -7.028764 8.799929 wave | 0 (omitted) gender | -1.583315 1.448041 -1.09 0.275 -4.42845 1.26182 prior | .2308573 .0634024 3.64 0.000 .1062833 .3554314 democrat | 2.097088 2.452477 0.86 0.393 -2.721582 6.915757 indep | .3046467 2.620089 0.12 0.907 -4.843349 5.452642 otherpol | 0 (omitted) midwest | -1.008693 1.90248 -0.53 0.596 -4.746718 2.729333 south | 3.329052 1.864346 1.79 0.075 -.3340466 6.992151 west | -.546716 2.112206 -0.26 0.796 -4.696815 3.603383 age1 | .7660657 2.054373 0.37 0.709 -3.270401 4.802533 age2 | 3.090326 2.236713 1.38 0.168 -1.304406 7.485058 age3 | 2.159835 2.383589 0.91 0.365 -2.52348 6.843151 age4 | -.8009618 2.026676 -0.40 0.693 -4.783009 3.181085 anychildren | -1.685506 1.664532 -1.01 0.312 -4.956007 1.584995 loghhinc | -.7636046 .9878713 -0.77 0.440 -2.704591 1.177382 associatemore | 3.153535 1.297715 2.43 0.015 .6037636 5.703307 fulltime | .0754657 1.947652 0.04 0.969 -3.751315 3.902247 parttime | -.5980311 2.973883 -0.20 0.841 -6.441168 5.245106 selfemp | -1.648156 2.311665 -0.71 0.476 -6.190155 2.893843 unemployed | -5.206359 3.007122 -1.73 0.084 -11.1148 .7020855 student | 1.753508 3.282962 0.53 0.593 -4.696911 8.203928 4.postgroup | 0 (omitted) _cons | 80.32435 13.54941 5.93 0.000 53.70224 106.9465 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 490) = 33.62 Prob > F = 0.0000 ( 1) T1 + T1indep = 0 F( 1, 490) = 6.03 Prob > F = 0.0144 (sum of wgt is 4.7715e+02) note: wave omitted because of collinearity note: 5.postgroup omitted because of collinearity Linear regression Number of obs = 496 F(21, 474) = 12.50 Prob > F = 0.0000 R-squared = 0.4469 Root MSE = 15.213 ------------------------------------------------------------------------------- | Robust extrachild | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -15.35412 1.340701 -11.45 0.000 -17.98857 -12.71967 wave | 0 (omitted) gender | -4.240141 1.309181 -3.24 0.001 -6.812657 -1.667624 prior | .4167164 .0635823 6.55 0.000 .2917783 .5416545 democrat | -.9070079 1.752067 -0.52 0.605 -4.349788 2.535772 indep | .2197893 1.78154 0.12 0.902 -3.280903 3.720482 otherpol | -7.36452 5.167765 -1.43 0.155 -17.51908 2.790043 midwest | -.5115558 1.939108 -0.26 0.792 -4.321867 3.298756 south | 2.126298 2.026629 1.05 0.295 -1.85599 6.108585 west | .6655138 2.129228 0.31 0.755 -3.51838 4.849407 age1 | 7.352967 2.660636 2.76 0.006 2.124866 12.58107 age2 | -.3016575 2.761005 -0.11 0.913 -5.726981 5.123666 age3 | .9125045 2.044815 0.45 0.656 -3.105519 4.930528 age4 | .6084857 1.709706 0.36 0.722 -2.751055 3.968026 anychildren | 3.991424 1.490192 2.68 0.008 1.063224 6.919624 loghhinc | 1.485375 1.167616 1.27 0.204 -.8089687 3.779719 associatemore | .6694453 1.394433 0.48 0.631 -2.07059 3.40948 fulltime | .4265601 1.884894 0.23 0.821 -3.277221 4.130341 parttime | -1.736952 2.157999 -0.80 0.421 -5.97738 2.503476 selfemp | .2873422 3.138186 0.09 0.927 -5.879134 6.453819 unemployed | -1.919767 2.926226 -0.66 0.512 -7.669747 3.830213 student | -.4126208 3.582094 -0.12 0.908 -7.451369 6.626127 5.postgroup | 0 (omitted) _cons | 38.40458 15.59207 2.46 0.014 7.766457 69.0427 ------------------------------------------------------------------------------- (sum of wgt is 4.7715e+02) note: wave omitted because of collinearity note: 5.postgroup omitted because of collinearity Linear regression Number of obs = 496 F(22, 473) = 13.83 Prob > F = 0.0000 R-squared = 0.4473 Root MSE = 15.224 ------------------------------------------------------------------------------- | Robust extrachild | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -14.58318 2.346918 -6.21 0.000 -19.19486 -9.971503 T1female | -1.550295 2.922815 -0.53 0.596 -7.293603 4.193013 wave | 0 (omitted) gender | -3.458104 1.937522 -1.78 0.075 -7.265319 .3491118 prior | .4159737 .0639648 6.50 0.000 .2902834 .541664 democrat | -.8748474 1.754292 -0.50 0.618 -4.322016 2.572322 indep | .2348533 1.785019 0.13 0.895 -3.272694 3.742401 otherpol | -7.211496 5.205885 -1.39 0.167 -17.44102 3.018028 midwest | -.5543023 1.915181 -0.29 0.772 -4.317617 3.209012 south | 2.099749 2.022824 1.04 0.300 -1.875084 6.074583 west | .708112 2.145953 0.33 0.742 -3.508669 4.924893 age1 | 7.238635 2.643114 2.74 0.006 2.044937 12.43233 age2 | -.3531599 2.778546 -0.13 0.899 -5.812981 5.106661 age3 | .7836817 2.103044 0.37 0.710 -3.348783 4.916147 age4 | .5965581 1.712276 0.35 0.728 -2.768051 3.961168 anychildren | 4.058803 1.486184 2.73 0.007 1.138463 6.979143 loghhinc | 1.429774 1.162189 1.23 0.219 -.8539183 3.713467 associatemore | .7349312 1.419792 0.52 0.605 -2.054949 3.524811 fulltime | .5390174 1.903307 0.28 0.777 -3.200966 4.279001 parttime | -1.613859 2.149883 -0.75 0.453 -5.838361 2.610643 selfemp | .3870175 3.14284 0.12 0.902 -5.788638 6.562673 unemployed | -1.989831 2.943058 -0.68 0.499 -7.772916 3.793254 student | -.2641024 3.617845 -0.07 0.942 -7.373138 6.844933 5.postgroup | 0 (omitted) _cons | 38.55997 15.55709 2.48 0.014 7.990415 69.12953 ------------------------------------------------------------------------------- ( 1) T1 + T1female = 0 F( 1, 473) = 111.15 Prob > F = 0.0000 (sum of wgt is 4.6913e+02) note: wave omitted because of collinearity note: otherpol omitted because of collinearity Linear regression Number of obs = 487 F(22, 463) = . Prob > F = . R-squared = 0.4353 Root MSE = 15.255 ------------------------------------------------------------------------------- | Robust extrachild | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -15.85957 2.693139 -5.89 0.000 -21.15186 -10.56728 T1democrat | .1234977 3.379317 0.04 0.971 -6.517201 6.764197 T1indep | 1.274019 3.78282 0.34 0.736 -6.159603 8.707641 wave | 0 (omitted) gender | -4.110814 1.333228 -3.08 0.002 -6.730741 -1.490886 prior | .4151477 .0649004 6.40 0.000 .2876118 .5426836 democrat | -1.084935 2.413889 -0.45 0.653 -5.828471 3.658601 indep | -.5129078 2.681742 -0.19 0.848 -5.782801 4.756985 otherpol | 0 (omitted) midwest | -.358511 1.951677 -0.18 0.854 -4.193752 3.47673 south | 2.300861 2.02478 1.14 0.256 -1.678036 6.279758 west | .2676492 2.157401 0.12 0.901 -3.971861 4.507159 age1 | 7.162695 2.739655 2.61 0.009 1.778997 12.54639 age2 | -.1405073 2.791825 -0.05 0.960 -5.626725 5.34571 age3 | 1.526067 2.063367 0.74 0.460 -2.528656 5.580791 age4 | .9962369 1.771899 0.56 0.574 -2.485724 4.478197 anychildren | 3.687132 1.513609 2.44 0.015 .7127385 6.661526 loghhinc | 1.46928 1.203179 1.22 0.223 -.8950884 3.833648 associatemore | .6623595 1.40611 0.47 0.638 -2.100789 3.425508 fulltime | .6887328 1.886111 0.37 0.715 -3.017665 4.395131 parttime | -1.379483 2.167264 -0.64 0.525 -5.638376 2.879409 selfemp | .6800857 3.210639 0.21 0.832 -5.629145 6.989316 unemployed | -.2232217 2.774389 -0.08 0.936 -5.675177 5.228733 student | .1620706 3.580384 0.05 0.964 -6.873746 7.197887 5.postgroup | 2.333447 3063087 0.00 1.000 -6019272 6019276 _cons | 36.27884 3063087 0.00 1.000 -6019238 6019310 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 463) = 64.04 Prob > F = 0.0000 ( 1) T1 + T1indep = 0 F( 1, 463) = 32.77 Prob > F = 0.0000 (sum of wgt is 2.9862e+03) note: 5.postgroup omitted because of collinearity Linear regression Number of obs = 3,022 F(25, 2996) = 42.05 Prob > F = 0.0000 R-squared = 0.3424 Root MSE = 16.02 ------------------------------------------------------------------------------- | Robust posterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -12.87937 .5835648 -22.07 0.000 -14.0236 -11.73514 wave | -2.371084 .9284259 -2.55 0.011 -4.1915 -.5506669 gender | -1.629386 .6028202 -2.70 0.007 -2.811369 -.4474023 prior | .3809219 .0295577 12.89 0.000 .3229664 .4388775 democrat | .0536486 .6924897 0.08 0.938 -1.304155 1.411452 indep | .3841582 .8363779 0.46 0.646 -1.255775 2.024091 otherpol | .9699728 3.000369 0.32 0.747 -4.913019 6.852965 midwest | -.3194316 .9333669 -0.34 0.732 -2.149537 1.510673 south | .0374975 .8523704 0.04 0.965 -1.633793 1.708788 west | -.5494237 .9244205 -0.59 0.552 -2.361987 1.26314 age1 | 3.002351 1.299926 2.31 0.021 .453513 5.55119 age2 | 3.082082 .9274165 3.32 0.001 1.263645 4.90052 age3 | .9183063 .8666152 1.06 0.289 -.7809148 2.617527 age4 | -.0506689 .7711933 -0.07 0.948 -1.562791 1.461453 anychildren | 1.304172 .6603282 1.98 0.048 .0094299 2.598915 loghhinc | -.2558697 .4205601 -0.61 0.543 -1.080485 .568746 associatemore | 1.031262 .6475147 1.59 0.111 -.2383565 2.300881 fulltime | .6360235 .7970402 0.80 0.425 -.9267778 2.198825 parttime | -.4908382 1.11466 -0.44 0.660 -2.676415 1.694738 selfemp | -.4454821 1.179428 -0.38 0.706 -2.758053 1.867089 unemployed | -3.05459 1.183421 -2.58 0.010 -5.374991 -.7341895 student | -.3999696 1.664168 -0.24 0.810 -3.662998 2.863059 | postgroup | 2 | -7.663844 .9032437 -8.48 0.000 -9.434885 -5.892804 3 | .1276747 .8222634 0.16 0.877 -1.484583 1.739933 4 | 4.462121 1.017204 4.39 0.000 2.467631 6.456611 5 | 0 (omitted) | _cons | 64.19311 5.562268 11.54 0.000 53.28686 75.09936 ------------------------------------------------------------------------------- (sum of wgt is 2.9862e+03) note: 5.postgroup omitted because of collinearity Linear regression Number of obs = 3,022 F(26, 2995) = 41.54 Prob > F = 0.0000 R-squared = 0.3429 Root MSE = 16.018 ------------------------------------------------------------------------------- | Robust posterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -12.07611 .8684364 -13.91 0.000 -13.7789 -10.37332 T1female | -1.60668 1.192579 -1.35 0.178 -3.945036 .7316768 wave | -2.380387 .9283335 -2.56 0.010 -4.200623 -.560151 gender | -.8229055 .782757 -1.05 0.293 -2.357701 .7118904 prior | .3804295 .0295975 12.85 0.000 .3223961 .438463 democrat | .0665148 .6929225 0.10 0.924 -1.292137 1.425167 indep | .4225038 .8366176 0.51 0.614 -1.2179 2.062907 otherpol | 1.004266 3.002778 0.33 0.738 -4.883449 6.891982 midwest | -.3242488 .9326438 -0.35 0.728 -2.152936 1.504439 south | .0161309 .8526962 0.02 0.985 -1.655799 1.68806 west | -.5547404 .9237729 -0.60 0.548 -2.366034 1.256553 age1 | 3.014456 1.298903 2.32 0.020 .4676243 5.561287 age2 | 3.09126 .9267685 3.34 0.001 1.274093 4.908427 age3 | .9229856 .865953 1.07 0.287 -.7749373 2.620909 age4 | -.0278248 .7719419 -0.04 0.971 -1.541415 1.485765 anychildren | 1.329366 .6607258 2.01 0.044 .0338437 2.624889 loghhinc | -.2619721 .4203934 -0.62 0.533 -1.086261 .5623169 associatemore | 1.056464 .6482714 1.63 0.103 -.2146382 2.327566 fulltime | .6365087 .796787 0.80 0.424 -.9257966 2.198814 parttime | -.473926 1.113021 -0.43 0.670 -2.65629 1.708438 selfemp | -.4666154 1.179911 -0.40 0.693 -2.780133 1.846902 unemployed | -3.029679 1.182 -2.56 0.010 -5.347294 -.7120639 student | -.3506673 1.668185 -0.21 0.834 -3.621571 2.920237 | postgroup | 2 | -7.637294 .9024025 -8.46 0.000 -9.406685 -5.867902 3 | .1175117 .8230461 0.14 0.886 -1.496281 1.731305 4 | 4.502374 1.019216 4.42 0.000 2.50394 6.500808 5 | 0 (omitted) | _cons | 63.85771 5.536132 11.53 0.000 53.00271 74.71272 ------------------------------------------------------------------------------- ( 1) T1 + T1female = 0 F( 1, 2995) = 293.47 Prob > F = 0.0000 (sum of wgt is 2.9301e+03) note: otherpol omitted because of collinearity note: 5.postgroup omitted because of collinearity Linear regression Number of obs = 2,965 F(26, 2938) = 39.68 Prob > F = 0.0000 R-squared = 0.3392 Root MSE = 15.883 ------------------------------------------------------------------------------- | Robust posterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -13.7545 .9619803 -14.30 0.000 -15.64073 -11.86828 T1democrat | 1.610058 1.31289 1.23 0.220 -.96422 4.184337 T1indep | 1.326933 1.63378 0.81 0.417 -1.876537 4.530403 wave | -2.263139 .936273 -2.42 0.016 -4.098957 -.4273212 gender | -1.659056 .6060737 -2.74 0.006 -2.847428 -.470684 prior | .3730941 .029464 12.66 0.000 .3153219 .4308662 democrat | -.7436481 .8757691 -0.85 0.396 -2.460831 .9735351 indep | -.2972387 1.122331 -0.26 0.791 -2.497873 1.903396 otherpol | 0 (omitted) midwest | -.1731012 .9034521 -0.19 0.848 -1.944565 1.598362 south | .3628869 .8214385 0.44 0.659 -1.247767 1.97354 west | -.2791327 .889156 -0.31 0.754 -2.022565 1.464299 age1 | 3.14967 1.325425 2.38 0.018 .5508135 5.748526 age2 | 2.943586 .9288626 3.17 0.002 1.122299 4.764874 age3 | 1.193658 .8677491 1.38 0.169 -.5078003 2.895115 age4 | .0574846 .7793817 0.07 0.941 -1.470705 1.585674 anychildren | 1.345135 .6639603 2.03 0.043 .0432601 2.647009 loghhinc | -.1857525 .4216973 -0.44 0.660 -1.012605 .6410997 associatemore | .8695365 .6446024 1.35 0.177 -.3943818 2.133455 fulltime | .5266299 .8012182 0.66 0.511 -1.044376 2.097636 parttime | -.6141188 1.126883 -0.54 0.586 -2.82368 1.595442 selfemp | -.5118625 1.132405 -0.45 0.651 -2.732251 1.708526 unemployed | -3.007098 1.175936 -2.56 0.011 -5.312841 -.7013554 student | -.5125004 1.69238 -0.30 0.762 -3.830872 2.805871 | postgroup | 2 | -7.763975 .9160303 -8.48 0.000 -9.560101 -5.967848 3 | -.0677127 .8179295 -0.08 0.934 -1.671486 1.53606 4 | 4.39873 1.025176 4.29 0.000 2.388595 6.408866 5 | 0 (omitted) | _cons | 64.33904 5.551397 11.59 0.000 53.45402 75.22406 ------------------------------------------------------------------------------- ( 1) T1 + T1democrat = 0 F( 1, 2938) = 187.77 Prob > F = 0.0000 ( 1) T1 + T1indep = 0 F( 1, 2938) = 87.94 Prob > F = 0.0000 . . . loc rowlabels " " " "{\bf Panel A: Avg. Treatment Effect}" " " "T$^{74}$" " " " " "Female" " " " " > "Democrat" " " " " "Prior" " " " " "Observations" "\hline" "{\bf Panel B: Het by Gender}" " " " > T$^{74}$" " " " " "T$^{74}$ * Female" " " "p-value [T$^{74}$ + T$^{74}$ x Female]" " " "Female" " > " " " "Observations" "\hline" "{\bf Panel C: Het by pol. attitude}" " " "T$^{74}$" " " " " "T$^{7 > 4}$ * Democrat" " " "p-value [T$^{74}$ + T$^{74}$ x Democrat]" " " "Democrat" " " " " "T$^{74}$ * > Independent" " " "p-value [T$^{74}$ + T$^{74}$ x Independent]" " " "Independent" " " " " "Observa > tions" " " " . loc rowstats "" . . forval i = 1/51 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output/beliefhet_fempolAB.tex", replace cells(none) booktabs nonotes nomtitles co > mpress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mgroups("\shortstack{High school\\Degree}" "\shortstack{Age 25}" /// > "\shortstack{Same\\occupation}" "\shortstack{Parent}" "\shortstack{Same\\job}" "\shortstack{Post > erior\\(pooled)}" /// > , pattern(1 1 1 1 1 1 ) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@sp > an})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output/beliefhet_fempolAB.tex) . . . eststo clear . . . *********************************************************************************** . // Table D.2: Correlates of beliefs and treatment effect on beliefs about the wage gap . *********************************************************************************** . . clear all . . use "$path\data\SurveyStageI_AB_final.dta", clear . . * Drop respondents assigned to the unincentivized posterior belief about the wage gap controlling > for job and employer . drop if extrasame!=. (790 observations deleted) . . loc experiments "extrayoung extraHS extraoccu extrachild posterior" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . loc experiments "extrayoung extraHS extraoccu extrachild" . . foreach choice in `experiments' { 2. . * Panel A: Correlations . . reg `choice' gender democrat indep otherpol [pweight=pweight] if rand==0, vce(r) 3. local n = round(e(N)) 4. . sigstar gender, prec(2) 5. estadd loc thisstat4 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat5 = "`r(sestar)'": col`colnum' 7. sigstar democrat, prec(2) 8. estadd loc thisstat7 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat8 = "`r(sestar)'": col`colnum' 10. . estadd loc thisstat10 = "`n'": col`colnum' 11. . . * Panel B: Treatment effect . . qui reg `choice' T1 $controls [pweight=pweight] if rand!=0, vce(r) 12. local n = round(e(N)) 13. . sigstar T1, prec(2) 14. estadd loc thisstat16 = "`r(bstar)'": col`colnum' 15. estadd loc thisstat17 = "`r(sestar)'": col`colnum' 16. . estadd loc thisstat19 = "`n'": col`colnum' 17. loc ++colnum 18. . } (sum of wgt is 1.6400e+02) Linear regression Number of obs = 164 F(4, 159) = 0.77 Prob > F = 0.5468 R-squared = 0.0324 Root MSE = 23.266 ------------------------------------------------------------------------------ | Robust extrayoung | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | -.9728639 3.530659 -0.28 0.783 -7.945903 6.000175 democrat | -5.015432 3.609389 -1.39 0.167 -12.14396 2.113098 indep | -.4388385 5.794896 -0.08 0.940 -11.88374 11.00606 otherpol | 15.50897 19.01332 0.82 0.416 -22.04226 53.0602 _cons | 84.63508 3.05197 27.73 0.000 78.60745 90.66271 ------------------------------------------------------------------------------ (sum of wgt is 1.4900e+02) Linear regression Number of obs = 149 F(3, 144) = . Prob > F = . R-squared = 0.0179 Root MSE = 26.098 ------------------------------------------------------------------------------ | Robust extraHSS | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | -.2016568 4.710488 -0.04 0.966 -9.512291 9.108977 democrat | -5.89374 5.394162 -1.09 0.276 -16.55571 4.768226 indep | -8.438551 5.290547 -1.60 0.113 -18.89571 2.018612 otherpol | -12.25535 3.533935 -3.47 0.001 -19.24043 -5.270259 _cons | 82.25535 3.533935 23.28 0.000 75.27026 89.24043 ------------------------------------------------------------------------------ (sum of wgt is 1.8100e+02) Linear regression Number of obs = 181 F(4, 176) = 1.12 Prob > F = 0.3500 R-squared = 0.0221 Root MSE = 18.446 ------------------------------------------------------------------------------ | Robust extraoccuu | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | -5.13387 2.748495 -1.87 0.063 -10.55812 .290379 democrat | -3.105674 3.362618 -0.92 0.357 -9.741917 3.530568 indep | -.6982962 3.289939 -0.21 0.832 -7.191104 5.794512 otherpol | -2.68869 6.593261 -0.41 0.684 -15.70072 10.32334 _cons | 87.22216 3.078474 28.33 0.000 81.14668 93.29763 ------------------------------------------------------------------------------ (sum of wgt is 2.6036e+02) Linear regression Number of obs = 269 F(4, 264) = 0.77 Prob > F = 0.5425 R-squared = 0.0127 Root MSE = 22.135 ------------------------------------------------------------------------------ | Robust extrachild | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- gender | -.8985562 2.733749 -0.33 0.743 -6.281281 4.484169 democrat | -4.017042 3.136684 -1.28 0.201 -10.19314 2.159059 indep | 1.008501 3.720243 0.27 0.787 -6.316623 8.333624 otherpol | 6.043144 12.9392 0.47 0.641 -19.43402 31.52031 _cons | 85.49839 2.942808 29.05 0.000 79.70403 91.29275 ------------------------------------------------------------------------------ . . // Add unweighted averages manually . . * Panel A: . estadd loc thisstat4 = "-1.80": col5 . estadd loc thisstat7 = "-4.50": col5 . estadd loc thisstat10 = "763": col5 . . * Panel B: . estadd loc thisstat16 = "-13.36": col5 . estadd loc thisstat19 = "2,499": col5 . . loc rowlabels " " " "{\bf Panel A: Correlations}" " " "Female" " " " " "Democrat" " " " " "Observa > tions" " " "\hline" " " "{\bf Panel B: Treatment effect}" " " "T$^{74}$" " " " " "Observations" > " " " . loc rowstats "" . . forval i = 1/20 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output/backofenvelope_beliefsAB.tex", replace cells(none) booktabs nonotes compre > ss alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mtitle("Age 25" "HS degree" "Same occu." "Parent" "Average") /// > mgroups("Outcome: (Incentivized) beliefs about the size of the wage gap", pattern(1 0 0 0 0) p > refix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output/backofenvelope_beliefsAB.tex) . . . *********************************************************************************** . // Table D.4: Follow-up survey: No treatment effect on placebo outcomes . *********************************************************************************** . . use "$path\data\SurveyStageIIAB_final.dta", clear . . drop if rand==0 (0 observations deleted) . . loc experimentsII "problemskill fairskill govmoreLS" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experimentsII' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experimentsII' { 2. . reg `choice' T1 $controls [pweight=pweight], vce(r) 3. sigstar T1, prec(3) 4. estadd loc thisstat2 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat3 = "`r(sestar)'": col`colnum' 6. sigstar gender, prec(3) 7. estadd loc thisstat5 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat6 = "`r(sestar)'": col`colnum' 9. sigstar democrat, prec(3) 10. estadd loc thisstat8 = "`r(bstar)'": col`colnum' 11. estadd loc thisstat9 = "`r(sestar)'": col`colnum' 12. qui sum `choice' 13. estadd loc thisstat11 = r(N): col`colnum' 14. . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. . } (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 3.83 Prob > F = 0.0000 R-squared = 0.0689 Root MSE = .97542 ------------------------------------------------------------------------------- | Robust problemskill | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0305762 .0589778 -0.52 0.604 -.1463001 .0851476 wave | -.0980301 .0625046 -1.57 0.117 -.220674 .0246139 gender | .1991803 .0615889 3.23 0.001 .0783332 .3200274 prior | .0014196 .0014263 1.00 0.320 -.001379 .0042181 democrat | .4108087 .0668662 6.14 0.000 .2796065 .5420109 indep | .1164451 .085927 1.36 0.176 -.0521573 .2850475 otherpol | -.0446728 .2258317 -0.20 0.843 -.4877906 .3984449 midwest | -.1592263 .0938705 -1.70 0.090 -.3434152 .0249626 south | -.0459411 .0860436 -0.53 0.594 -.2147724 .1228902 west | -.174058 .0920122 -1.89 0.059 -.3546006 .0064846 age1 | -.0537588 .1627162 -0.33 0.741 -.3730338 .2655162 age2 | .0378183 .0926714 0.41 0.683 -.1440178 .2196544 age3 | .0900974 .0891489 1.01 0.312 -.0848269 .2650218 age4 | -.0687715 .0827545 -0.83 0.406 -.231149 .0936059 anychildren | -.0142906 .0640294 -0.22 0.823 -.1399266 .1113453 loghhinc | -.091596 .0444182 -2.06 0.039 -.1787517 -.0044404 associatemore | .0001632 .0643576 0.00 0.998 -.1261166 .126443 fulltime | .0707819 .087713 0.81 0.420 -.101325 .2428887 parttime | -.1141153 .1110713 -1.03 0.304 -.3320548 .1038242 selfemp | -.1272267 .121181 -1.05 0.294 -.3650031 .1105497 unemployed | .0549677 .148306 0.37 0.711 -.2360323 .3459676 student | .1833629 .2453594 0.75 0.455 -.2980713 .664797 _cons | .8273483 .5096576 1.62 0.105 -.1726808 1.827377 ------------------------------------------------------------------------------- (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 9.35 Prob > F = 0.0000 R-squared = 0.1711 Root MSE = .92029 ------------------------------------------------------------------------------- | Robust fairskill | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0422278 .055694 -0.76 0.448 -.1515083 .0670528 wave | .1210702 .0591844 2.05 0.041 .004941 .2371994 gender | -.0504065 .0575978 -0.88 0.382 -.1634225 .0626095 prior | .0083071 .0015984 5.20 0.000 .0051707 .0114434 democrat | -.3328102 .0634644 -5.24 0.000 -.4573375 -.2082829 indep | -.1324393 .0790522 -1.68 0.094 -.2875523 .0226737 otherpol | -.3823654 .1626937 -2.35 0.019 -.7015962 -.0631346 midwest | -.0415033 .0897531 -0.46 0.644 -.2176132 .1346067 south | -.0221834 .084258 -0.26 0.792 -.187511 .1431442 west | -.0226183 .086337 -0.26 0.793 -.1920251 .1467885 age1 | .4572567 .1309461 3.49 0.000 .2003196 .7141938 age2 | .5553109 .0929095 5.98 0.000 .3730077 .7376141 age3 | .2630474 .0884126 2.98 0.003 .0895679 .436527 age4 | .1407497 .0700897 2.01 0.045 .0032226 .2782767 anychildren | .1195069 .0611338 1.95 0.051 -.0004474 .2394611 loghhinc | .0927885 .0405847 2.29 0.022 .0131549 .1724221 associatemore | .0519361 .0626017 0.83 0.407 -.0708983 .1747706 fulltime | .1637294 .0782725 2.09 0.037 .0101462 .3173126 parttime | .0306285 .1032508 0.30 0.767 -.1719659 .2332229 selfemp | .1716756 .1214365 1.41 0.158 -.0666022 .4099534 unemployed | -.0109544 .1200298 -0.09 0.927 -.2464719 .2245631 student | -.3867661 .1930164 -2.00 0.045 -.765495 -.0080373 _cons | -2.067763 .4644033 -4.45 0.000 -2.978996 -1.156529 ------------------------------------------------------------------------------- (sum of wgt is 1.0916e+03) Linear regression Number of obs = 1,105 F(22, 1082) = 6.94 Prob > F = 0.0000 R-squared = 0.1226 Root MSE = .94817 ------------------------------------------------------------------------------- | Robust govmoreLS | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0422471 .0574664 0.74 0.462 -.0705111 .1550054 wave | -.2521267 .0597463 -4.22 0.000 -.3693584 -.134895 gender | .0662982 .059487 1.11 0.265 -.0504247 .1830211 prior | -.0028617 .0014419 -1.98 0.047 -.005691 -.0000324 democrat | .5974079 .0656651 9.10 0.000 .4685625 .7262534 indep | .1839814 .0875368 2.10 0.036 .0122203 .3557425 otherpol | .2599073 .1737008 1.50 0.135 -.0809213 .6007359 midwest | -.0728951 .0918714 -0.79 0.428 -.2531613 .1073711 south | -.0178009 .0829553 -0.21 0.830 -.1805725 .1449706 west | -.0287065 .0877011 -0.33 0.743 -.2007901 .143377 age1 | -.0017566 .1447485 -0.01 0.990 -.2857763 .282263 age2 | .035288 .0905023 0.39 0.697 -.1422919 .2128679 age3 | .1792348 .0844928 2.12 0.034 .0134465 .3450232 age4 | .0649479 .0785695 0.83 0.409 -.0892179 .2191137 anychildren | .0049793 .0617138 0.08 0.936 -.1161131 .1260717 loghhinc | -.0377389 .0410131 -0.92 0.358 -.1182132 .0427354 associatemore | -.0095418 .0638685 -0.15 0.881 -.1348619 .1157784 fulltime | -.2177962 .0812042 -2.68 0.007 -.3771316 -.0584607 parttime | -.021017 .1000137 -0.21 0.834 -.2172598 .1752259 selfemp | -.054382 .1138515 -0.48 0.633 -.2777768 .1690128 unemployed | .0011331 .1336522 0.01 0.993 -.2611138 .2633799 student | -.1879863 .2119848 -0.89 0.375 -.6039341 .2279615 _cons | .792514 .4651393 1.70 0.089 -.1201632 1.705191 ------------------------------------------------------------------------------- . . . . loc rowlabels " " " "T$^{74}$" " " " " "Female" " " " " "Democrat" " " " " "Observations" " . loc rowstats "" . . forval i = 1/11 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output/StageIIAB_placebo.tex", replace cells(none) booktabs nonotes nomtitles com > press alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mgroups("\shortstack{Wage differences btw high-\\and low-skilled are a prob.}" "\shortstac > k{Low skilled workers's wages\\are fair}" /// > "\shortstack{Government should support\\low-skilled workers more}", pattern(1 1 1) prefix(\multi > column{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output/StageIIAB_placebo.tex) . . eststo clear . . . *********************************************************************************** . // Table D.5: Follow-up survey: No role for attrition . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final", clear . . // Merge Follow-up Data . merge 1:1 panelID using "$path\data\SurveyStageIIAB_final.dta" (label gender already defined) Result # of obs. ----------------------------------------- not matched 2,960 from master 2,960 (_merge==1) from using 0 (_merge==2) matched 1,105 (_merge==3) ----------------------------------------- . . * Identifier for Follow-up participants . gen StageII=(_merge==3) . drop _merge . . * Drop pure control . drop if rand==0 (1,034 observations deleted) . . // Generate table . loc experiments "posterior problem womenwages govmore AAanchor legislationanchor" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experiments' { 2. . ***Panel A: Stage I results . . qui reg `choice' T1 $controls [pweight=pweight], vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat4 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat5 = "`r(sestar)'": col`colnum' 7. sigstar gender, prec(3) 8. estadd loc thisstat7 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat8 = "`r(sestar)'": col`colnum' 10. sigstar democrat, prec(3) 11. estadd loc thisstat10 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat11 = "`r(sestar)'": col`colnum' 13. . estadd loc thisstat13 = "`n'": col`colnum' 14. . . ***Panel B: Stage I results restricted to Stage II sample . . qui reg `choice' T1 $controls [pweight=pweight] if StageII==1 , vce(r) 15. local n = round(e(N)) 16. . sigstar T1, prec(3) 17. estadd loc thisstat19 = "`r(bstar)'": col`colnum' 18. estadd loc thisstat20 = "`r(sestar)'": col`colnum' 19. sigstar gender, prec(3) 20. estadd loc thisstat22 = "`r(bstar)'": col`colnum' 21. estadd loc thisstat23 = "`r(sestar)'": col`colnum' 22. sigstar democrat, prec(3) 23. estadd loc thisstat25 = "`r(bstar)'": col`colnum' 24. estadd loc thisstat26 = "`r(sestar)'": col`colnum' 25. . estadd loc thisstat28 = "`n'": col`colnum' 26. . loc ++colnum 27. loc colnames "`colnames' `"`: var la `choice''"'" 28. . } . . . loc colnum = 1 . loc colnames "" . . loc experimentsII "posteriorII problemII fairII govmoreII AAanchorII legislationanchorII" . . foreach choice in `experimentsII' { 2. . ***Panel C: Stage II results . . qui reg `choice' T1 $controls [pweight=pweight], vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat34 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat35 = "`r(sestar)'": col`colnum' 7. sigstar gender, prec(3) 8. estadd loc thisstat37 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat38 = "`r(sestar)'": col`colnum' 10. sigstar democrat, prec(3) 11. estadd loc thisstat40 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat41 = "`r(sestar)'": col`colnum' 13. . estadd loc thisstat43 = "`n'": col`colnum' 14. . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. . } . . . loc rowlabels " " " "{\bf Panel A: Main results}" " " "T$^{74}$" " " " " "Female" " " " " "Democra > t" " " " " "Observations" " " "\hline" " " "{\bf Panel B: Main results (follow-up sample)}" " " "T > $^{74}$" " " " " "Female" " " " " "Democrat" " " " " "Observations" " " "\hline" " " "{\bf Panel > C: Follow-up results}" " " "T$^{74}$" " " " " "Female" " " " " "Democrat" " " " " "Observations" " > " " . loc rowstats "" . . . forval i = 1/43 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\StageI_II_selection.tex", replace cells(none) booktabs nonotes nomtitles c > ompress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mgroups("\shortstack{Posterior\\belief about\\fem.rel.wage\\(percent)}" "\shortstack{Gende > r\\differences\\in wages\\are a problem}" /// > "\shortstack{Women's\\wages\\are\\fair}" "\shortstack{Government\\should mitigate\\gender\\wage > gap}" /// > "\shortstack{Statutory\\affirmative\\action}" "\shortstack{Stricter\\equal pay\\legislation}", p > attern(1 1 1 1 1 1) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\StageI_II_selection.tex) . . eststo clear . . . *********************************************************************************** . // Table D.6: First stage, reduced form and 2SLS: Heterogeneity by interest in topic . *********************************************************************************** . . clear all . . // Note: The following code produces one table for Panels A and another table for Panel B. Stack m > anually to obtain final table. . . use "$path\data\SurveyStageI_AB_final.dta", clear . . // Panel A: . . drop if read==2 (235 observations deleted) . gen T1read=T1*read (1,034 missing values generated) . . gen posteriorread=posterior*read (1,042 missing values generated) . gen largeread=large*read (1,034 missing values generated) . gen problemread=problem*read (1,034 missing values generated) . . drop if rand==0 (1,034 observations deleted) . . gen postgroup =. (2,796 missing values generated) . replace postgroup =1 if wave==1&RAND4== 9 (614 real changes made) . replace postgroup = 2 if wave==1&RAND4==10 (624 real changes made) . replace postgroup= 3 if wave==1&RAND4==11 (602 real changes made) . replace postgroup = 4 if wave==2&RAND4==10 (486 real changes made) . replace postgroup=5 if wave==2&RAND4==11 (470 real changes made) . . loc experiments "posterior large problem z_lmpolicy_index" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . . foreach choice in `experiments' { 2. . reg `choice' T1 T1read read $controls [pweight=pweight], vce(r) 3. . local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 7. . sigstar T1read, prec(3) 8. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat7 = "`r(sestar)'": col`colnum' 10. . test T1 + T1read =0 11. estadd loc thisstat8 = string(r(p), "%9.3f"): col`colnum' 12. . estadd loc thisstat10 = "`n'": col`colnum' 13. . loc ++colnum 14. } (sum of wgt is 2.7555e+03) Linear regression Number of obs = 2,788 F(24, 2763) = 38.77 Prob > F = 0.0000 R-squared = 0.3273 Root MSE = 16.304 ------------------------------------------------------------------------------- | Robust posterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -13.92121 .6320284 -22.03 0.000 -15.16051 -12.68191 T1read | 4.160822 1.755645 2.37 0.018 .7183125 7.603332 read | 1.221058 1.178631 1.04 0.300 -1.09003 3.532146 wave | 2.655036 .6651413 3.99 0.000 1.350811 3.95926 gender | -1.440495 .6432702 -2.24 0.025 -2.701834 -.1791561 prior | .3830437 .0306048 12.52 0.000 .3230332 .4430542 democrat | .5163261 .7412109 0.70 0.486 -.9370572 1.969709 indep | .6933698 .8747371 0.79 0.428 -1.021835 2.408574 otherpol | -1.841583 2.432986 -0.76 0.449 -6.612239 2.929072 midwest | .3977285 .9715066 0.41 0.682 -1.507224 2.302681 south | .3535563 .8714871 0.41 0.685 -1.355276 2.062388 west | -.1243118 .9362333 -0.13 0.894 -1.9601 1.711476 age1 | 2.565148 1.378534 1.86 0.063 -.1379127 5.268208 age2 | 2.473477 .9892005 2.50 0.012 .5338296 4.413124 age3 | .5818146 .9247382 0.63 0.529 -1.231433 2.395063 age4 | -.1690584 .8186955 -0.21 0.836 -1.774375 1.436258 anychildren | .9894499 .6981876 1.42 0.157 -.3795723 2.358472 loghhinc | -.3575489 .4529008 -0.79 0.430 -1.245607 .5305095 associatemore | .6998193 .6904608 1.01 0.311 -.654052 2.053691 fulltime | .3191065 .8464798 0.38 0.706 -1.34069 1.978904 parttime | -.7235092 1.19699 -0.60 0.546 -3.070595 1.623577 selfemp | -.676415 1.254454 -0.54 0.590 -3.136177 1.783347 unemployed | -2.897729 1.263086 -2.29 0.022 -5.374418 -.4210408 student | -1.086691 1.727422 -0.63 0.529 -4.47386 2.300479 _cons | 57.44118 5.658158 10.15 0.000 46.34653 68.53582 ------------------------------------------------------------------------------- ( 1) T1 + T1read = 0 F( 1, 2763) = 35.46 Prob > F = 0.0000 (sum of wgt is 2.7635e+03) Linear regression Number of obs = 2,796 F(24, 2771) = 27.73 Prob > F = 0.0000 R-squared = 0.1896 Root MSE = .96984 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .6450318 .0419963 15.36 0.000 .5626846 .7273789 T1read | -.186626 .0898283 -2.08 0.038 -.3627632 -.0104888 read | .4352978 .0686677 6.34 0.000 .3006529 .5699428 wave | -.0201534 .0396817 -0.51 0.612 -.0979621 .0576553 gender | .2470416 .0376627 6.56 0.000 .1731919 .3208914 prior | -.006113 .0009903 -6.17 0.000 -.0080548 -.0041713 democrat | .5132542 .0425511 12.06 0.000 .4298192 .5966893 indep | .200915 .0569616 3.53 0.000 .0892235 .3126064 otherpol | .2022382 .1671072 1.21 0.226 -.1254289 .5299053 midwest | -.0366178 .0597731 -0.61 0.540 -.1538221 .0805866 south | .0757136 .0536163 1.41 0.158 -.0294184 .1808456 west | -.039119 .0589672 -0.66 0.507 -.1547431 .0765051 age1 | -.0415749 .0861608 -0.48 0.629 -.2105206 .1273709 age2 | -.008953 .059165 -0.15 0.880 -.1249649 .107059 age3 | -.0149208 .057743 -0.26 0.796 -.1281444 .0983028 age4 | -.1214387 .0557861 -2.18 0.030 -.2308251 -.0120522 anychildren | .1191326 .041264 2.89 0.004 .0382213 .2000439 loghhinc | .0207779 .0264852 0.78 0.433 -.0311548 .0727106 associatemore | -.0219965 .04185 -0.53 0.599 -.1040569 .0600638 fulltime | .0861652 .0567546 1.52 0.129 -.0251204 .1974508 parttime | -.115071 .0769406 -1.50 0.135 -.2659377 .0357957 selfemp | .0395503 .0789855 0.50 0.617 -.115326 .1944267 unemployed | .0383274 .0932668 0.41 0.681 -.1445519 .2212068 student | .0541499 .1168155 0.46 0.643 -.1749042 .2832041 _cons | -.6199092 .3047016 -2.03 0.042 -1.217374 -.0224442 ------------------------------------------------------------------------------- ( 1) T1 + T1read = 0 F( 1, 2771) = 33.15 Prob > F = 0.0000 (sum of wgt is 2.7635e+03) Linear regression Number of obs = 2,796 F(24, 2771) = 27.12 Prob > F = 0.0000 R-squared = 0.1889 Root MSE = .95875 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4915609 .0415038 11.84 0.000 .4101794 .5729423 T1read | -.2494142 .0885812 -2.82 0.005 -.4231059 -.0757224 read | .4070115 .0680754 5.98 0.000 .2735279 .5404951 wave | -.0068125 .0389274 -0.18 0.861 -.0831421 .0695172 gender | .3048921 .0372776 8.18 0.000 .2317975 .3779868 prior | -.0064408 .0009235 -6.97 0.000 -.0082516 -.00463 democrat | .6481591 .042285 15.33 0.000 .5652457 .7310725 indep | .2591878 .0575776 4.50 0.000 .1462884 .3720872 otherpol | .3386628 .1508862 2.24 0.025 .0428021 .6345235 midwest | -.1099049 .0596869 -1.84 0.066 -.2269402 .0071305 south | -.0200934 .0525382 -0.38 0.702 -.1231114 .0829246 west | -.0787964 .057725 -1.37 0.172 -.1919848 .034392 age1 | .0187416 .0823214 0.23 0.820 -.1426758 .1801591 age2 | .000013 .0593862 0.00 1.000 -.1164327 .1164587 age3 | .011252 .0567773 0.20 0.843 -.100078 .122582 age4 | -.0972914 .0557668 -1.74 0.081 -.2066401 .0120573 anychildren | .086208 .041157 2.09 0.036 .0055065 .1669095 loghhinc | -.0002267 .0261414 -0.01 0.993 -.0514852 .0510318 associatemore | -.0063388 .0422001 -0.15 0.881 -.0890856 .076408 fulltime | .052677 .0557278 0.95 0.345 -.0565952 .1619491 parttime | -.1127092 .0756172 -1.49 0.136 -.260981 .0355626 selfemp | -.0026915 .0786869 -0.03 0.973 -.1569824 .1515993 unemployed | .0168843 .0956274 0.18 0.860 -.1706239 .2043925 student | .0559016 .1192017 0.47 0.639 -.1778316 .2896347 _cons | -.3054538 .2980807 -1.02 0.306 -.8899365 .2790288 ------------------------------------------------------------------------------- ( 1) T1 + T1read = 0 F( 1, 2771) = 9.52 Prob > F = 0.0021 (sum of wgt is 2.7635e+03) Linear regression Number of obs = 2,796 F(24, 2771) = 25.91 Prob > F = 0.0000 R-squared = 0.1868 Root MSE = .68275 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0695981 .0284169 2.45 0.014 .0138777 .1253186 T1read | -.0393165 .0680053 -0.58 0.563 -.1726626 .0940296 read | .1749577 .0501745 3.49 0.000 .0765746 .2733409 wave | .0246677 .0287117 0.86 0.390 -.0316309 .0809663 gender | .2006091 .0264823 7.58 0.000 .1486821 .2525362 prior | -.0039072 .0007682 -5.09 0.000 -.0054136 -.0024009 democrat | .5750968 .0302059 19.04 0.000 .5158684 .6343252 indep | .194475 .0394492 4.93 0.000 .1171222 .2718278 otherpol | .265652 .1082646 2.45 0.014 .0533645 .4779395 midwest | -.08913 .0432742 -2.06 0.040 -.1739829 -.004277 south | .0103146 .0379625 0.27 0.786 -.064123 .0847521 west | -.0750236 .0422955 -1.77 0.076 -.1579575 .0079104 age1 | .0661929 .0560933 1.18 0.238 -.0437961 .1761818 age2 | .12054 .0426413 2.83 0.005 .0369281 .2041519 age3 | .0855788 .0424624 2.02 0.044 .0023176 .16884 age4 | .0356113 .040531 0.88 0.380 -.0438626 .1150852 anychildren | .112646 .0288476 3.90 0.000 .0560811 .169211 loghhinc | -.0278603 .0189207 -1.47 0.141 -.0649605 .0092399 associatemore | .0025911 .0292065 0.09 0.929 -.0546777 .0598598 fulltime | .0105341 .0413395 0.25 0.799 -.0705252 .0915935 parttime | -.0151637 .0531337 -0.29 0.775 -.1193493 .0890219 selfemp | .0869482 .0586188 1.48 0.138 -.0279928 .2018892 unemployed | .1181577 .0625422 1.89 0.059 -.0044764 .2407918 student | .1286149 .0785732 1.64 0.102 -.0254531 .2826828 _cons | -.0124144 .2212166 -0.06 0.955 -.4461804 .4213516 ------------------------------------------------------------------------------- ( 1) T1 + T1read = 0 F( 1, 2771) = 0.24 Prob > F = 0.6247 . . . loc rowlabels " "{\bf Panel A: First Stage/Reduced Form}" " " "T$^{74}$" " " " " "T$^{74}$ x read" > " " "p-value [T$^{74}$ + T$^{74}$ x read]" " " "Observations" " . . loc rowstats "" . . forval i = 1/10{ 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\TableCompliers_PanelA.tex", replace cells(none) booktabs nonotes nomtitles > compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mgroups("\shortstack{Posterior \\ belief about \\fem. rel. wage}" "\shortstack{Gender diff > .\\ in wages\\are large}" "\shortstack{Gender diff.\\ in wages\\are a problem}" /// > "\shortstack{Policy\\Demand\\Index}", pattern(1 1 1 1) prefix(\multicolumn{@span}{c}{) suffix(}) > span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\TableCompliers_PanelA.tex) . . . *** Panel B . . . loc experiments "1 2 3" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . ivregress 2sls z_lmpolicy_index read $controls (posterior posteriorread = T1 T1read ) [pweight=pwe > ight], vce(r) (sum of wgt is 2.7555e+03) Instrumental variables (2SLS) regression Number of obs = 2,788 Wald chi2(24) = 625.31 Prob > chi2 = 0.0000 R-squared = 0.1821 Root MSE = .68194 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- posterior | -.0050899 .0020421 -2.49 0.013 -.0090923 -.0010875 posteriorread | .0022112 .0065152 0.34 0.734 -.0105584 .0149807 read | -.0211558 .5673671 -0.04 0.970 -1.133175 1.090863 wave | .0354113 .0297021 1.19 0.233 -.0228038 .0936264 gender | .1933133 .02667 7.25 0.000 .141041 .2455855 prior | -.0023922 .0014698 -1.63 0.104 -.005273 .0004886 democrat | .577588 .0302203 19.11 0.000 .5183573 .6368186 indep | .1992565 .0394178 5.05 0.000 .1219991 .2765139 otherpol | .2542311 .1078455 2.36 0.018 .0428578 .4656043 midwest | -.0832452 .0435326 -1.91 0.056 -.1685675 .0020772 south | .0104227 .038372 0.27 0.786 -.0647851 .0856305 west | -.0731268 .0424625 -1.72 0.085 -.1563519 .0100982 age1 | .0829995 .0566426 1.47 0.143 -.0280179 .1940169 age2 | .1266279 .0442401 2.86 0.004 .0399189 .2133369 age3 | .0864163 .042313 2.04 0.041 .0034843 .1693484 age4 | .0335128 .0404958 0.83 0.408 -.0458575 .1128832 anychildren | .1136091 .0305361 3.72 0.000 .0537595 .1734587 loghhinc | -.0291402 .0188591 -1.55 0.122 -.0661034 .007823 associatemore | .0052465 .0300839 0.17 0.862 -.0537168 .0642098 fulltime | .0094429 .0415606 0.23 0.820 -.0720145 .0909002 parttime | -.0208124 .0532878 -0.39 0.696 -.1252546 .0836298 selfemp | .0832612 .0583443 1.43 0.154 -.0310915 .1976139 unemployed | .1057668 .0629557 1.68 0.093 -.0176241 .2291576 student | .1168191 .0784689 1.49 0.137 -.0369771 .2706154 _cons | .3179425 .276744 1.15 0.251 -.2244659 .8603508 ------------------------------------------------------------------------------- Instrumented: posterior posteriorread Instruments: read wave gender prior democrat indep otherpol midwest south west age1 age2 age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student T1 T1read . . local n = round(e(N)) . . sigstar posterior, prec(3) . estadd loc thisstat3 = "`r(bstar)'": col`colnum' . estadd loc thisstat4 = "`r(sestar)'": col`colnum' . . sigstar posteriorread, prec(3) . estadd loc thisstat6 = "`r(bstar)'": col`colnum' . estadd loc thisstat7 = "`r(sestar)'": col`colnum' . . test posterior + posteriorread =0 ( 1) posterior + posteriorread = 0 chi2( 1) = 0.22 Prob > chi2 = 0.6423 . estadd loc thisstat8 = string(r(p), "%9.3f"): col`colnum' . . . estadd loc thisstat10 = "Posterior": col`colnum' . estadd loc thisstat11 = "belief about": col`colnum' . estadd loc thisstat12 = "wage gap": col`colnum' . . estadd loc thisstat14 = "`n'": col`colnum' . . . loc ++colnum . . . ivregress 2sls z_lmpolicy_index read $controls (large largeread = T1 T1read ) [pweight=pweight], v > ce(r) (sum of wgt is 2.7635e+03) Instrumental variables (2SLS) regression Number of obs = 2,796 Wald chi2(24) = 696.82 Prob > chi2 = 0.0000 R-squared = 0.2595 Root MSE = .64862 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- large | .1079205 .0416237 2.59 0.010 .0263395 .1895014 largeread | -.0423426 .1380376 -0.31 0.759 -.3128913 .2282061 read | .1256942 .0419306 3.00 0.003 .0435117 .2078767 wave | .0257769 .0274159 0.94 0.347 -.0279573 .0795112 gender | .1767064 .0274759 6.43 0.000 .1228546 .2305581 prior | -.0032468 .0007689 -4.22 0.000 -.0047537 -.0017398 democrat | .523811 .0370284 14.15 0.000 .4512367 .5963854 indep | .1729323 .0386868 4.47 0.000 .0971076 .248757 otherpol | .2428154 .1012199 2.40 0.016 .0444281 .4412027 midwest | -.0860537 .0413394 -2.08 0.037 -.1670774 -.0050299 south | .0023288 .0359049 0.06 0.948 -.0680435 .072701 west | -.072254 .0404984 -1.78 0.074 -.1516293 .0071213 age1 | .0712735 .0539294 1.32 0.186 -.0344261 .1769732 age2 | .1210861 .0402885 3.01 0.003 .042122 .2000501 age3 | .0870518 .0399447 2.18 0.029 .0087617 .1653418 age4 | .0475619 .0385738 1.23 0.218 -.0280414 .1231652 anychildren | .1013498 .0280783 3.61 0.000 .0463174 .1563823 loghhinc | -.0299022 .0179174 -1.67 0.095 -.0650197 .0052153 associatemore | .0045271 .027783 0.16 0.871 -.0499266 .0589809 fulltime | .0013332 .0392088 0.03 0.973 -.0755147 .078181 parttime | -.0037014 .0507589 -0.07 0.942 -.103187 .0957842 selfemp | .082787 .0555822 1.49 0.136 -.0261521 .1917261 unemployed | .1141478 .0578519 1.97 0.048 .0007602 .2275354 student | .1214249 .0758299 1.60 0.109 -.027199 .2700488 _cons | .0507878 .2078408 0.24 0.807 -.3565727 .4581483 ------------------------------------------------------------------------------- Instrumented: large largeread Instruments: read wave gender prior democrat indep otherpol midwest south west age1 age2 age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student T1 T1read . . local n = round(e(N)) . . sigstar large, prec(3) . estadd loc thisstat3 = "`r(bstar)'": col`colnum' . estadd loc thisstat4 = "`r(sestar)'": col`colnum' . . sigstar largeread, prec(3) . estadd loc thisstat6 = "`r(bstar)'": col`colnum' . estadd loc thisstat7 = "`r(sestar)'": col`colnum' . . test large + largeread = 0 ( 1) large + largeread = 0 chi2( 1) = 0.25 Prob > chi2 = 0.6180 . estadd loc thisstat8 = string(r(p), "%9.3f"): col`colnum' . . estadd loc thisstat10 = "Gender diff.": col`colnum' . estadd loc thisstat11 = "in wages": col`colnum' . estadd loc thisstat12 = "are large": col`colnum' . . estadd loc thisstat14 = "`n'": col`colnum' . . . loc ++colnum . . . ivregress 2sls z_lmpolicy_index read $controls (problem problemread = T1 T1read ) [pweight=pweight > ], vce(r) (sum of wgt is 2.7635e+03) Instrumental variables (2SLS) regression Number of obs = 2,796 Wald chi2(24) = 742.23 Prob > chi2 = 0.0000 R-squared = 0.3035 Root MSE = .62905 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- problem | .1415191 .0528458 2.68 0.007 .0379432 .2450951 problemread | -.0170455 .2510992 -0.07 0.946 -.5091909 .4750998 read | .1177806 .049536 2.38 0.017 .0206919 .2148693 wave | .0256432 .0263724 0.97 0.331 -.0260458 .0773321 gender | .1587374 .0327083 4.85 0.000 .0946303 .2228445 prior | -.0030073 .0007956 -3.78 0.000 -.0045666 -.0014479 democrat | .485628 .0522914 9.29 0.000 .3831387 .5881173 indep | .1580509 .0391269 4.04 0.000 .0813636 .2347382 otherpol | .2185207 .1003617 2.18 0.029 .0218154 .415226 midwest | -.0736337 .0404297 -1.82 0.069 -.1528745 .005607 south | .0133886 .0347802 0.38 0.700 -.0547793 .0815565 west | -.0646124 .0407136 -1.59 0.113 -.1444097 .0151849 age1 | .0635678 .0520244 1.22 0.222 -.0383981 .1655338 age2 | .1200272 .0397551 3.02 0.003 .0421086 .1979458 age3 | .0836139 .0390751 2.14 0.032 .0070281 .1601997 age4 | .0487549 .0383544 1.27 0.204 -.0264183 .1239282 anychildren | .1010018 .0278431 3.63 0.000 .0464302 .1555733 loghhinc | -.0279904 .0175332 -1.60 0.110 -.0623549 .0063741 associatemore | .0036553 .0269631 0.14 0.892 -.0491915 .0565021 fulltime | .0029979 .0377883 0.08 0.937 -.0710658 .0770615 parttime | .00033 .0496003 0.01 0.995 -.0968849 .0975448 selfemp | .0872883 .0542095 1.61 0.107 -.0189604 .193537 unemployed | .115776 .0558468 2.07 0.038 .0063183 .2252337 student | .1203418 .0736266 1.63 0.102 -.0239637 .2646474 _cons | .0319833 .202696 0.16 0.875 -.3652935 .4292601 ------------------------------------------------------------------------------- Instrumented: problem problemread Instruments: read wave gender prior democrat indep otherpol midwest south west age1 age2 age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemployed student T1 T1read . . local n = round(e(N)) . . sigstar problem, prec(3) . estadd loc thisstat3 = "`r(bstar)'": col`colnum' . estadd loc thisstat4 = "`r(sestar)'": col`colnum' . . sigstar problemread, prec(3) . estadd loc thisstat6 = "`r(bstar)'": col`colnum' . estadd loc thisstat7 = "`r(sestar)'": col`colnum' . . test problem + problemread =0 ( 1) problem + problemread = 0 chi2( 1) = 0.26 Prob > chi2 = 0.6132 . estadd loc thisstat8 = string(r(p), "%9.3f"): col`colnum' . . estadd loc thisstat10 = "Gender diff.": col`colnum' . estadd loc thisstat11 = "in wages": col`colnum' . estadd loc thisstat12 = "are a problem": col`colnum' . . estadd loc thisstat14 = "`n'": col`colnum' . . loc ++colnum . . . loc rowlabels " "{\bf Panel B: 2SLS}" " " "\widehat{\text{Perception}}" " " " " "\widehat{\text{P > erception x read}}" " " "p-value [\widehat{\text{Perception}} + \widehat{\text{Perception x read}} > ]" " " "Perception measure" " " " " " " "Observations" " " " . . loc rowstats "" . . forval i = 1/14{ 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\TableCompliers_PanelB.tex", replace cells(none) booktabs nonotes nomtitles > compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mgroups("\shortstack{Policy Demand Index}", pattern(1 0 0) prefix(\multicolumn{@span}{c}{) > suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\TableCompliers_PanelB.tex) . . . *********************************************************************************** . // Table D.7: Treatment effect on beliefs about the wage gap and related perceptions . *********************************************************************************** . . clear all . . use "$path\data\SurveyStageI_AB_final.dta", clear . . gen priornorm=prior . . * Standardize prior beliefs based on control group . foreach var of varlist priornorm{ 2. egen mean_`var'=mean(`var') if rand==0 3. egen max_mean_`var'=min(mean_`var') 4. replace mean_`var'=max_mean_`var' 5. drop max_mean_`var' 6. egen sd_`var'=sd(`var') if rand==0 7. egen min_sd_`var'=min(sd_`var') 8. replace sd_`var'=min_sd_`var' 9. drop min_sd_`var' 10. replace `var'=(`var'-mean_`var')/sd_`var' 11. drop mean_`var' sd_`var' 12. } (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) . . * Generate measures of over- and underestimation compared to treatment signals . gen bias74=74-prior //underestimation before T1 . gen bias94=prior-94 //overestimated before T2 . . replace bias74=(bias74>0) //Dummy for prior belief below 74 (3,949 real changes made) . replace bias94=(bias94>0) //Dummy for prior belief above 94 (3,956 real changes made) . . * Generate dummy for moderate prior beliefs between 74 and 94 . gen normal=0 . replace normal=(bias74==0)&(bias94==0) (2,352 real changes made) . . * Generate independent variables of interest . gen T1normal=T1*normal //Dummy for assigned to T74 and moderate prior . gen T2normal=T2*normal //Dummy for assigned to T94 and moderate prior . . gen T1bias74=T1*bias74 //Dummy for assigned to T74 and prior below 74 . gen T2bias94=T2*bias94 //Dummy for assigned to T94 and prior below 74 . . gen T1bias94=T1*bias94 //Dummy for assigned to T74 and prior above 94 . gen T2bias74=T2*bias74 //Dummy for assigned to T94 and prior above 94 . . preserve . . clear all . eststo clear . estimates drop _all . . . loc experiments "posterior large problem govmore z_mani_index" . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . . loc colnum = 1 . . . foreach choice in `experiments' { 2. . reg `choice' T1normal T2normal T1bias74 T1bias94 T2bias74 T2bias94 bias74 bias94 normal $contr > ols prior1 [pweight=pweight], nocons vce(r) 3. sigstar T1normal, prec(3) 4. estadd loc thisstat2 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat3 = "`r(sestar)'": col`colnum' 6. sigstar T2normal, prec(3) 7. estadd loc thisstat5 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat6 = "`r(sestar)'": col`colnum' 9. . sigstar T1bias74, prec(3) 10. estadd loc thisstat9 = "`r(bstar)'": col`colnum' 11. estadd loc thisstat10 = "`r(sestar)'": col`colnum' 12. sigstar T2bias74, prec(3) 13. estadd loc thisstat12 = "`r(bstar)'": col`colnum' 14. estadd loc thisstat13 = "`r(sestar)'": col`colnum' 15. . . sigstar T1bias94, prec(3) 16. estadd loc thisstat16 = "`r(bstar)'": col`colnum' 17. estadd loc thisstat17 = "`r(sestar)'": col`colnum' 18. . sigstar T2bias94, prec(3) 19. estadd loc thisstat19 = "`r(bstar)'": col`colnum' 20. estadd loc thisstat20 = "`r(sestar)'": col`colnum' 21. . * p-values . test T1normal - T2normal = 0 22. estadd loc thisstat7 = string(r(p), "%9.2f"): col`colnum' 23. . test T1bias74 - T2bias74 = 0 24. estadd loc thisstat14 = string(r(p), "%9.2f"): col`colnum' 25. . test T1bias94 - T2bias94 = 0 26. estadd loc thisstat21 = string(r(p), "%9.2f"): col`colnum' 27. . . test T1normal - T1bias74 = 0 28. estadd loc thisstat23 = string(r(p), "%9.2f"): col`colnum' 29. . test T1normal - T1bias94 = 0 30. estadd loc thisstat24 = string(r(p), "%9.2f"): col`colnum' 31. . test T1bias74 - T1bias94 = 0 32. estadd loc thisstat25 = string(r(p), "%9.2f"): col`colnum' 33. . . test T2normal - T2bias74 = 0 34. estadd loc thisstat27 = string(r(p), "%9.2f"): col`colnum' 35. . test T2normal - T2bias94 = 0 36. estadd loc thisstat28 = string(r(p), "%9.2f"): col`colnum' 37. . test T2bias74 - T2bias94 = 0 38. estadd loc thisstat29 = string(r(p), "%9.2f"): col`colnum' 39. . * Control group mean and Nobs for each belief bracket: . mean `choice' [pweight=pweight] if normal == 1&rand==0 40. matrix mean=e(b) 41. matrix nobs=e(N) 42. estadd loc thisstat31 = string(mean[1,1], "%9.2f"): col`colnum' 43. estadd loc thisstat32 = string(nobs[1,1], "%9.0f"): col`colnum' 44. . mean `choice' [pweight=pweight] if bias74 == 1&rand==0 45. matrix mean=e(b) 46. matrix nobs=e(N) 47. estadd loc thisstat34 = string(mean[1,1], "%9.2f"): col`colnum' 48. estadd loc thisstat35 = string(nobs[1,1], "%9.0f"): col`colnum' 49. . mean `choice' [pweight=pweight] if bias94 == 1&rand==0 50. matrix mean=e(b) 51. matrix nobs=e(N) 52. estadd loc thisstat37 = string(mean[1,1], "%9.2f"): col`colnum' 53. estadd loc thisstat38 = string(nobs[1,1], "%9.0f"): col`colnum' 54. . * Total number of observations . sum `choice' 55. estadd loc thisstat40 = r(N): col`colnum' 56. . . loc ++colnum 57. loc colnames "`colnames' `"`: var la `choice''"'" 58. } (sum of wgt is 3.9990e+03) Linear regression Number of obs = 4,052 F(31, 4021) = 5402.59 Prob > F = 0.0000 R-squared = 0.9638 Root MSE = 16.539 ------------------------------------------------------------------------------- | Robust posterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1normal | -6.831227 .6638195 -10.29 0.000 -8.132681 -5.529773 T2normal | 7.66743 .6558273 11.69 0.000 6.381645 8.953215 T1bias74 | 4.598398 1.728255 2.66 0.008 1.21006 7.986736 T1bias94 | -10.60406 2.367201 -4.48 0.000 -15.24509 -5.963036 T2bias74 | 16.38209 1.798845 9.11 0.000 12.85536 19.90882 T2bias94 | -.6098072 2.014797 -0.30 0.762 -4.559927 3.340312 bias74 | 34.97414 4.902471 7.13 0.000 25.36258 44.5857 bias94 | 46.61574 5.915021 7.88 0.000 35.01902 58.21246 normal | 40.23713 5.271567 7.63 0.000 29.90194 50.57232 wave | 2.970377 .5538705 5.36 0.000 1.884484 4.05627 gender | -1.01788 .5410248 -1.88 0.060 -2.078588 .0428283 prior | .4525132 .0436988 10.36 0.000 .3668394 .5381871 democrat | -.1552231 .6216684 -0.25 0.803 -1.374038 1.063592 indep | .420016 .7214308 0.58 0.560 -.9943881 1.83442 otherpol | 2.509025 2.928071 0.86 0.392 -3.231617 8.249667 midwest | -.6488121 .8198866 -0.79 0.429 -2.256244 .95862 south | -.0030253 .748543 -0.00 0.997 -1.470584 1.464534 west | -1.057254 .8293908 -1.27 0.202 -2.683319 .5688117 age1 | 1.871767 1.178062 1.59 0.112 -.4378871 4.181421 age2 | 2.464576 .8406039 2.93 0.003 .8165265 4.112626 age3 | .5368153 .7785576 0.69 0.491 -.989589 2.06322 age4 | .0147672 .6831262 0.02 0.983 -1.324539 1.354073 anychildren | 1.214172 .585406 2.07 0.038 .0664513 2.361892 loghhinc | -.0788754 .3552859 -0.22 0.824 -.7754326 .6176819 associatemore | .9604524 .5856334 1.64 0.101 -.1877135 2.108618 fulltime | .3955515 .7167326 0.55 0.581 -1.009641 1.800745 parttime | .6813674 .9867059 0.69 0.490 -1.253123 2.615858 selfemp | -1.012218 1.027644 -0.98 0.325 -3.02697 1.002534 unemployed | -.3596829 1.141394 -0.32 0.753 -2.597447 1.878082 student | 1.256232 1.741727 0.72 0.471 -2.158518 4.670983 prior1 | .5365366 .544099 0.99 0.324 -.530199 1.603272 ------------------------------------------------------------------------------- ( 1) T1normal - T2normal = 0 F( 1, 4021) = 734.16 Prob > F = 0.0000 ( 1) T1bias74 - T2bias74 = 0 F( 1, 4021) = 67.83 Prob > F = 0.0000 ( 1) T1bias94 - T2bias94 = 0 F( 1, 4021) = 26.08 Prob > F = 0.0000 ( 1) T1normal - T1bias74 = 0 F( 1, 4021) = 39.00 Prob > F = 0.0000 ( 1) T1normal - T1bias94 = 0 F( 1, 4021) = 2.40 Prob > F = 0.1216 ( 1) T1bias74 - T1bias94 = 0 F( 1, 4021) = 27.16 Prob > F = 0.0000 ( 1) T2normal - T2bias74 = 0 F( 1, 4021) = 21.14 Prob > F = 0.0000 ( 1) T2normal - T2bias94 = 0 F( 1, 4021) = 15.42 Prob > F = 0.0001 ( 1) T2bias74 - T2bias94 = 0 F( 1, 4021) = 40.02 Prob > F = 0.0000 Mean estimation Number of obs = 605 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ posterior | 82.99638 .5431837 81.92962 84.06314 -------------------------------------------------------------- Mean estimation Number of obs = 225 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ posterior | 67.56744 1.57744 64.45892 70.67596 -------------------------------------------------------------- Mean estimation Number of obs = 200 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ posterior | 103.4459 1.910021 99.67939 107.2123 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- posterior | 4,052 84.13203 20.28366 0 200 (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(31, 4034) = 35.18 Prob > F = 0.0000 R-squared = 0.2077 Root MSE = .93706 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1normal | .298954 .04449 6.72 0.000 .2117291 .3861789 T2normal | -.4213283 .0504615 -8.35 0.000 -.5202608 -.3223958 T1bias74 | .0557155 .067078 0.83 0.406 -.0757944 .1872255 T1bias94 | .3439521 .1091565 3.15 0.002 .129945 .5579592 T2bias74 | -.3361367 .0714225 -4.71 0.000 -.4761643 -.1961091 T2bias94 | -.0676241 .1065535 -0.63 0.526 -.2765277 .1412795 bias74 | -.8692522 .2470375 -3.52 0.000 -1.353582 -.3849223 bias94 | -2.098079 .2821933 -7.43 0.000 -2.651334 -1.544824 normal | -1.300419 .2553182 -5.09 0.000 -1.800984 -.7998543 wave | .0009618 .0316161 0.03 0.976 -.0610232 .0629468 gender | .1827105 .0304306 6.00 0.000 .1230497 .2423714 prior | .0059979 .0012986 4.62 0.000 .0034519 .0085439 democrat | .4901099 .0340883 14.38 0.000 .4232779 .5569418 indep | .1678184 .0465074 3.61 0.000 .0766383 .2589986 otherpol | .0441716 .1254088 0.35 0.725 -.2016989 .2900422 midwest | -.0080854 .0483681 -0.17 0.867 -.1029136 .0867429 south | .1006401 .0439414 2.29 0.022 .0144906 .1867895 west | -.007893 .0475753 -0.17 0.868 -.1011668 .0853808 age1 | .0271621 .068254 0.40 0.691 -.1066534 .1609776 age2 | .0618664 .0479064 1.29 0.197 -.0320567 .1557895 age3 | .0369455 .0470059 0.79 0.432 -.0552121 .129103 age4 | -.0842145 .0449936 -1.87 0.061 -.1724268 .0039977 anychildren | .1226767 .0331891 3.70 0.000 .0576076 .1877457 loghhinc | .0338745 .0205638 1.65 0.100 -.0064418 .0741909 associatemore | -.0323106 .0337341 -0.96 0.338 -.0984481 .0338269 fulltime | .0777107 .0463094 1.68 0.093 -.0130814 .1685028 parttime | -.138253 .0596553 -2.32 0.021 -.2552103 -.0212957 selfemp | .081347 .0645511 1.26 0.208 -.0452087 .2079027 unemployed | .0461697 .0748272 0.62 0.537 -.1005329 .1928723 student | .012683 .0933442 0.14 0.892 -.1703232 .1956891 prior1 | .0286012 .0313013 0.91 0.361 -.0327666 .0899691 ------------------------------------------------------------------------------- ( 1) T1normal - T2normal = 0 F( 1, 4034) = 278.78 Prob > F = 0.0000 ( 1) T1bias74 - T2bias74 = 0 F( 1, 4034) = 33.29 Prob > F = 0.0000 ( 1) T1bias94 - T2bias94 = 0 F( 1, 4034) = 18.72 Prob > F = 0.0000 ( 1) T1normal - T1bias74 = 0 F( 1, 4034) = 9.45 Prob > F = 0.0021 ( 1) T1normal - T1bias94 = 0 F( 1, 4034) = 0.15 Prob > F = 0.7004 ( 1) T1bias74 - T1bias94 = 0 F( 1, 4034) = 5.12 Prob > F = 0.0238 ( 1) T2normal - T2bias74 = 0 F( 1, 4034) = 0.98 Prob > F = 0.3229 ( 1) T2normal - T2bias94 = 0 F( 1, 4034) = 9.09 Prob > F = 0.0026 ( 1) T2bias74 - T2bias94 = 0 F( 1, 4034) = 4.44 Prob > F = 0.0351 Mean estimation Number of obs = 607 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ large | .0545688 .0354117 -.0149758 .1241135 -------------------------------------------------------------- Mean estimation Number of obs = 226 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ large | .3959777 .052301 .2929153 .4990401 -------------------------------------------------------------- Mean estimation Number of obs = 201 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ large | -.6249062 .092716 -.8077325 -.44208 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- large | 4,065 -.0380913 1.043989 -2.510636 1.218055 (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(31, 4034) = 37.39 Prob > F = 0.0000 R-squared = 0.2146 Root MSE = .92209 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1normal | .1802087 .0440019 4.10 0.000 .0939407 .2664767 T2normal | -.314075 .0504572 -6.22 0.000 -.4129989 -.215151 T1bias74 | .0280618 .0625044 0.45 0.653 -.0944813 .150605 T1bias94 | .3468026 .1044109 3.32 0.001 .1420995 .5515056 T2bias74 | -.2790509 .0672145 -4.15 0.000 -.4108285 -.1472734 T2bias94 | .0751714 .105456 0.71 0.476 -.1315806 .2819234 bias74 | -.6291683 .2433113 -2.59 0.010 -1.106193 -.1521437 bias94 | -1.866256 .2764354 -6.75 0.000 -2.408222 -1.32429 normal | -.9559863 .2508663 -3.81 0.000 -1.447823 -.4641498 wave | -.0114295 .0310215 -0.37 0.713 -.0722488 .0493898 gender | .2463267 .0299095 8.24 0.000 .1876876 .3049659 prior | .0052157 .0012533 4.16 0.000 .0027585 .007673 democrat | .6064672 .0338755 17.90 0.000 .5400525 .6728819 indep | .2262541 .0464908 4.87 0.000 .1351065 .3174018 otherpol | .1831198 .1179763 1.55 0.121 -.048179 .4144186 midwest | -.080251 .0479955 -1.67 0.095 -.1743487 .0138467 south | -.0023595 .0431989 -0.05 0.956 -.0870532 .0823342 west | -.0585013 .0465952 -1.26 0.209 -.1498536 .032851 age1 | .0685137 .0670498 1.02 0.307 -.0629411 .1999684 age2 | .055169 .0471133 1.17 0.242 -.0371991 .147537 age3 | .0429212 .0463916 0.93 0.355 -.048032 .1338744 age4 | -.0597168 .0443094 -1.35 0.178 -.1465878 .0271542 anychildren | .0839036 .0328204 2.56 0.011 .0195575 .1482498 loghhinc | .0152345 .0203113 0.75 0.453 -.0245868 .0550558 associatemore | -.0067357 .0337931 -0.20 0.842 -.0729888 .0595174 fulltime | .0411641 .0451114 0.91 0.362 -.0472793 .1296074 parttime | -.1229799 .0580954 -2.12 0.034 -.2368789 -.0090809 selfemp | .0208312 .0639865 0.33 0.745 -.1046176 .14628 unemployed | .0230184 .0760248 0.30 0.762 -.1260322 .172069 student | .021449 .0934887 0.23 0.819 -.1618405 .2047385 prior1 | -.0106368 .0308871 -0.34 0.731 -.0711926 .0499189 ------------------------------------------------------------------------------- ( 1) T1normal - T2normal = 0 F( 1, 4034) = 128.87 Prob > F = 0.0000 ( 1) T1bias74 - T2bias74 = 0 F( 1, 4034) = 24.46 Prob > F = 0.0000 ( 1) T1bias94 - T2bias94 = 0 F( 1, 4034) = 8.40 Prob > F = 0.0038 ( 1) T1normal - T1bias74 = 0 F( 1, 4034) = 4.07 Prob > F = 0.0438 ( 1) T1normal - T1bias94 = 0 F( 1, 4034) = 2.19 Prob > F = 0.1387 ( 1) T1bias74 - T1bias94 = 0 F( 1, 4034) = 6.93 Prob > F = 0.0085 ( 1) T2normal - T2bias74 = 0 F( 1, 4034) = 0.18 Prob > F = 0.6724 ( 1) T2normal - T2bias94 = 0 F( 1, 4034) = 11.24 Prob > F = 0.0008 ( 1) T2bias74 - T2bias94 = 0 F( 1, 4034) = 8.15 Prob > F = 0.0043 Mean estimation Number of obs = 607 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ problem | .0976908 .0358084 .0273671 .1680144 -------------------------------------------------------------- Mean estimation Number of obs = 226 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ problem | .3647938 .0494064 .2674354 .4621523 -------------------------------------------------------------- Mean estimation Number of obs = 201 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ problem | -.734576 .0883769 -.9088462 -.5603059 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- problem | 4,065 -.0244405 1.032663 -2.547891 1.0096 (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(31, 4034) = 35.67 Prob > F = 0.0000 R-squared = 0.1959 Root MSE = .94231 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1normal | .100799 .0467452 2.16 0.031 .0091525 .1924454 T2normal | -.1929875 .0516832 -3.73 0.000 -.2943152 -.0916598 T1bias74 | .0062978 .0662607 0.10 0.924 -.1236098 .1362053 T1bias94 | .131645 .1056752 1.25 0.213 -.0755368 .3388268 T2bias74 | -.1800456 .0698225 -2.58 0.010 -.3169363 -.0431549 T2bias94 | -.0020245 .103103 -0.02 0.984 -.2041633 .2001142 bias74 | -.3758063 .2441213 -1.54 0.124 -.8544188 .1028062 bias94 | -1.191526 .2769429 -4.30 0.000 -1.734487 -.6485655 normal | -.6616374 .2506288 -2.64 0.008 -1.153008 -.1702666 wave | -.0073109 .0318016 -0.23 0.818 -.0696595 .0550378 gender | .2479468 .0305528 8.12 0.000 .1880464 .3078473 prior | .0037523 .0011906 3.15 0.002 .001418 .0060865 democrat | .7525304 .0346826 21.70 0.000 .6845333 .8205275 indep | .2836474 .0488678 5.80 0.000 .1878394 .3794554 otherpol | .2196462 .1247259 1.76 0.078 -.0248855 .4641779 midwest | -.1436276 .0493209 -2.91 0.004 -.2403237 -.0469315 south | -.0315675 .0432823 -0.73 0.466 -.1164247 .0532896 west | -.084481 .0471128 -1.79 0.073 -.1768481 .0078861 age1 | .2022267 .0659729 3.07 0.002 .0728834 .3315701 age2 | .197765 .0488376 4.05 0.000 .1020163 .2935137 age3 | .1434656 .049782 2.88 0.004 .0458654 .2410659 age4 | .0485128 .0483343 1.00 0.316 -.0462491 .1432748 anychildren | .1273774 .0326511 3.90 0.000 .0633633 .1913916 loghhinc | -.0191397 .0208214 -0.92 0.358 -.0599612 .0216818 associatemore | -.0512308 .0345446 -1.48 0.138 -.1189572 .0164956 fulltime | .0129249 .0486775 0.27 0.791 -.0825099 .1083597 parttime | -.1382435 .0599525 -2.31 0.021 -.2557834 -.0207035 selfemp | -.0664965 .0681974 -0.98 0.330 -.2002011 .0672082 unemployed | .017547 .0740416 0.24 0.813 -.1276154 .1627093 student | -.0306036 .0924285 -0.33 0.741 -.2118144 .1506073 prior1 | .0141657 .0314235 0.45 0.652 -.0474417 .0757731 ------------------------------------------------------------------------------- ( 1) T1normal - T2normal = 0 F( 1, 4034) = 43.03 Prob > F = 0.0000 ( 1) T1bias74 - T2bias74 = 0 F( 1, 4034) = 8.83 Prob > F = 0.0030 ( 1) T1bias94 - T2bias94 = 0 F( 1, 4034) = 2.03 Prob > F = 0.1539 ( 1) T1normal - T1bias74 = 0 F( 1, 4034) = 1.39 Prob > F = 0.2380 ( 1) T1normal - T1bias94 = 0 F( 1, 4034) = 0.07 Prob > F = 0.7878 ( 1) T1bias74 - T1bias94 = 0 F( 1, 4034) = 1.02 Prob > F = 0.3131 ( 1) T2normal - T2bias74 = 0 F( 1, 4034) = 0.02 Prob > F = 0.8801 ( 1) T2normal - T2bias94 = 0 F( 1, 4034) = 2.78 Prob > F = 0.0953 ( 1) T2bias74 - T2bias94 = 0 F( 1, 4034) = 2.07 Prob > F = 0.1500 Mean estimation Number of obs = 607 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ govmore | .0292695 .0385033 -.0463466 .1048855 -------------------------------------------------------------- Mean estimation Number of obs = 226 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ govmore | .3073535 .0538123 .2013129 .4133941 -------------------------------------------------------------- Mean estimation Number of obs = 201 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ govmore | -.4651623 .0858913 -.634531 -.2957935 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- govmore | 4,065 -.0355077 1.042504 -2.521133 .8984361 (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(31, 4034) = 41.18 Prob > F = 0.0000 R-squared = 0.2292 Root MSE = .8458 ------------------------------------------------------------------------------- | Robust z_mani_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1normal | .1943106 .0402311 4.83 0.000 .1154353 .2731858 T2normal | -.3061739 .0461623 -6.63 0.000 -.3966775 -.2156703 T1bias74 | .0299784 .056599 0.53 0.596 -.0809869 .1409436 T1bias94 | .2557115 .0973684 2.63 0.009 .0648158 .4466073 T2bias74 | -.2603979 .0615331 -4.23 0.000 -.3810368 -.1397589 T2bias94 | -.0139864 .0955667 -0.15 0.884 -.2013498 .1733771 bias74 | -.6188777 .2222256 -2.78 0.005 -1.054562 -.1831929 bias94 | -1.676504 .2549983 -6.57 0.000 -2.176442 -1.176567 normal | -.9701269 .2306692 -4.21 0.000 -1.422366 -.5178879 wave | -.0047714 .0284934 -0.17 0.867 -.0606342 .0510913 gender | .2216625 .0273824 8.10 0.000 .1679778 .2753472 prior | .0049154 .001156 4.25 0.000 .0026491 .0071818 democrat | .6211838 .0309287 20.08 0.000 .5605465 .6818212 indep | .2269718 .043288 5.24 0.000 .1421034 .3118402 otherpol | .1430409 .1120643 1.28 0.202 -.076667 .3627488 midwest | -.0780009 .0439984 -1.77 0.076 -.164262 .0082602 south | .0264589 .0393497 0.67 0.501 -.0506882 .103606 west | -.0492033 .0426211 -1.15 0.248 -.1327641 .0343576 age1 | .1079455 .0593983 1.82 0.069 -.008508 .224399 age2 | .1174551 .0433134 2.71 0.007 .032537 .2023732 age3 | .0825775 .0433368 1.91 0.057 -.0023866 .1675416 age4 | -.0242256 .0415973 -0.58 0.560 -.1057793 .057328 anychildren | .117525 .0296788 3.96 0.000 .0593381 .1757119 loghhinc | .0082882 .0186308 0.44 0.656 -.0282385 .0448149 associatemore | -.0355265 .0308283 -1.15 0.249 -.0959671 .024914 fulltime | .0439159 .0424636 1.03 0.301 -.0393362 .127168 parttime | -.1354455 .0534022 -2.54 0.011 -.2401433 -.0307478 selfemp | .0084268 .0593264 0.14 0.887 -.1078857 .1247394 unemployed | .0299532 .0672768 0.45 0.656 -.1019465 .1618529 student | -.0038057 .0858354 -0.04 0.965 -.1720904 .164479 prior1 | .0153634 .028256 0.54 0.587 -.040034 .0707608 ------------------------------------------------------------------------------- ( 1) T1normal - T2normal = 0 F( 1, 4034) = 158.22 Prob > F = 0.0000 ( 1) T1bias74 - T2bias74 = 0 F( 1, 4034) = 25.70 Prob > F = 0.0000 ( 1) T1bias94 - T2bias94 = 0 F( 1, 4034) = 9.58 Prob > F = 0.0020 ( 1) T1normal - T1bias74 = 0 F( 1, 4034) = 5.76 Prob > F = 0.0165 ( 1) T1normal - T1bias94 = 0 F( 1, 4034) = 0.35 Prob > F = 0.5570 ( 1) T1bias74 - T1bias94 = 0 F( 1, 4034) = 4.05 Prob > F = 0.0442 ( 1) T2normal - T2bias74 = 0 F( 1, 4034) = 0.37 Prob > F = 0.5453 ( 1) T2normal - T2bias94 = 0 F( 1, 4034) = 7.68 Prob > F = 0.0056 ( 1) T2bias74 - T2bias94 = 0 F( 1, 4034) = 4.77 Prob > F = 0.0290 Mean estimation Number of obs = 607 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ z_mani_index | .0519067 .0329877 -.0128774 .1166908 -------------------------------------------------------------- Mean estimation Number of obs = 226 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ z_mani_index | .3532007 .0456585 .2632277 .4431737 -------------------------------------------------------------- Mean estimation Number of obs = 201 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ z_mani_index | -.5782493 .0818938 -.7397354 -.4167632 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- z_mani_index | 4,065 -.0345054 .9553777 -2.521863 1.046162 . . . loc rowlabels " " " "T$^{74}$ x (74 $\leq$ prior $\leq$ 94)" " " " " "T$^{94}$ x (74 $\leq$ prior > $\leq$ 94)" " " "p-value [T$^{74}$ x (74 $\leq$ prior $\leq$ 94) = T$^{94}$ x (74 $\leq$ prior $\l > eq$ 94)]" " " "T$^{74}$ x (prior < 74)" " " " " "T$^{94}$ x (prior < 74)" " " "p-value [T$^{74}$ x > (prior < 74) = T$^{94}$ x (prior < 74)]" " " "T$^{74}$ x (prior > 94)" " " " " "T$^{94}$ x (prior > > 94)" " " "p-value [T$^{74}$ (prior > 94) = T$^{94}$ x (prior > 94)]" " " "p-value [T$^{74}$ x ( > 74 $\leq$ prior $\leq$ 94) = T$^{74}$ x (prior < 74)]" "p-value [T$^{74}$ x (74 $\leq$ prior $\leq > $ 94) = T$^{74}$ x (prior > 94)]" "p-value [T$^{74}$ x (prior < 74) = T$^{74}$ x (prior > 94)]" " > " "p-value [T$^{94}$ x (74 $\leq$ prior $\leq$ 94) = T$^{94}$ x (prior < 74)]" "p-value [T$^{94}$ > x (74 $\leq$ prior $\leq$ 94) = T$^{94}$ x (prior > 94)]" "p-value [T$^{94}$ x (prior < 74) = T$^{ > 94}$ x (prior > 94)]" " " "Control group mean (74 $\leq$ prior $\leq$ 94)" " " " " "Control group > mean (prior < 74)" " " " " "Control group mean (prior > 94)" " " " " "Observations" " " " . loc rowstats "" . . . . forval i = 1/40 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\manicheckaltspec_interact.tex", replace cells(none) booktabs nonotes nomti > tles compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) / > // > mgroups("\shortstack{Posterior\\belief}" "\shortstack{Gender diff. in wages\\are large}" " > \shortstack{Gender diff. in wages\\are a problem}" /// > "\shortstack{Government should\\promote gender wage equality}" "\shortstack{Index\\(2)-(4)}", pa > ttern(1 1 1 1 1) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\manicheckaltspec_interact.tex) . . . eststo clear . . . . *********************************************************************************** . // Table D.8: Treatment effect on demand for specific policies . *********************************************************************************** . . . preserve . . clear all . eststo clear . estimates drop _all . . loc experiments "quotaanchor AAanchor legislationanchor transparencyanchor UKtool childcare z_lmp > olicy_index" . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . . loc colnum = 1 . . . foreach choice in `experiments' { 2. . reg `choice' T1normal T2normal T1bias74 T1bias94 T2bias74 T2bias94 bias74 bias94 normal $contr > ols prior1 [pweight=pweight], nocons vce(r) 3. sigstar T1normal, prec(3) 4. estadd loc thisstat2 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat3 = "`r(sestar)'": col`colnum' 6. sigstar T2normal, prec(3) 7. estadd loc thisstat5 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat6 = "`r(sestar)'": col`colnum' 9. . sigstar T1bias74, prec(3) 10. estadd loc thisstat9 = "`r(bstar)'": col`colnum' 11. estadd loc thisstat10 = "`r(sestar)'": col`colnum' 12. sigstar T2bias74, prec(3) 13. estadd loc thisstat12 = "`r(bstar)'": col`colnum' 14. estadd loc thisstat13 = "`r(sestar)'": col`colnum' 15. . . sigstar T1bias94, prec(3) 16. estadd loc thisstat16 = "`r(bstar)'": col`colnum' 17. estadd loc thisstat17 = "`r(sestar)'": col`colnum' 18. . sigstar T2bias94, prec(3) 19. estadd loc thisstat19 = "`r(bstar)'": col`colnum' 20. estadd loc thisstat20 = "`r(sestar)'": col`colnum' 21. . * p-values . test T1normal - T2normal = 0 22. estadd loc thisstat7 = string(r(p), "%9.2f"): col`colnum' 23. . test T1bias74 - T2bias74 = 0 24. estadd loc thisstat14 = string(r(p), "%9.2f"): col`colnum' 25. . test T1bias94 - T2bias94 = 0 26. estadd loc thisstat21 = string(r(p), "%9.2f"): col`colnum' 27. . . test T1normal - T1bias74 = 0 28. estadd loc thisstat23 = string(r(p), "%9.2f"): col`colnum' 29. . test T1normal - T1bias94 = 0 30. estadd loc thisstat24 = string(r(p), "%9.2f"): col`colnum' 31. . test T1bias74 - T1bias94 = 0 32. estadd loc thisstat25 = string(r(p), "%9.2f"): col`colnum' 33. . . test T2normal - T2bias74 = 0 34. estadd loc thisstat27 = string(r(p), "%9.2f"): col`colnum' 35. . test T2normal - T2bias94 = 0 36. estadd loc thisstat28 = string(r(p), "%9.2f"): col`colnum' 37. . test T2bias74 - T2bias94 = 0 38. estadd loc thisstat29 = string(r(p), "%9.2f"): col`colnum' 39. . * Control group mean and Nobs for each belief bracket: . mean `choice' [pweight=pweight] if normal == 1&rand==0 40. matrix mean=e(b) 41. matrix nobs=e(N) 42. estadd loc thisstat31 = string(mean[1,1], "%9.2f"): col`colnum' 43. estadd loc thisstat32 = string(nobs[1,1], "%9.0f"): col`colnum' 44. . mean `choice' [pweight=pweight] if bias74 == 1&rand==0 45. matrix mean=e(b) 46. matrix nobs=e(N) 47. estadd loc thisstat34 = string(mean[1,1], "%9.2f"): col`colnum' 48. estadd loc thisstat35 = string(nobs[1,1], "%9.0f"): col`colnum' 49. . mean `choice' [pweight=pweight] if bias94 == 1&rand==0 50. matrix mean=e(b) 51. matrix nobs=e(N) 52. estadd loc thisstat37 = string(mean[1,1], "%9.2f"): col`colnum' 53. estadd loc thisstat38 = string(nobs[1,1], "%9.0f"): col`colnum' 54. . * Total number of observations . sum `choice' 55. estadd loc thisstat40 = r(N): col`colnum' 56. . . loc ++colnum 57. loc colnames "`colnames' `"`: var la `choice''"'" 58. } (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(31, 4034) = 18.66 Prob > F = 0.0000 R-squared = 0.1238 Root MSE = .95978 ------------------------------------------------------------------------------- | Robust quotaanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1normal | .0466866 .049679 0.94 0.347 -.0507117 .1440849 T2normal | -.0378815 .0513448 -0.74 0.461 -.1385456 .0627826 T1bias74 | .085316 .0804402 1.06 0.289 -.0723913 .2430232 T1bias94 | .0437863 .0986998 0.44 0.657 -.1497198 .2372924 T2bias74 | .0811529 .0797968 1.02 0.309 -.075293 .2375987 T2bias94 | .0130416 .0962123 0.14 0.892 -.1755877 .2016709 bias74 | -.1164729 .2551331 -0.46 0.648 -.6166747 .3837289 bias94 | -.4182528 .2860503 -1.46 0.144 -.9790694 .1425638 normal | -.3200844 .2628814 -1.22 0.223 -.8354772 .1953083 wave | .0968036 .0327826 2.95 0.003 .0325316 .1610755 gender | .2349529 .0314035 7.48 0.000 .1733847 .2965211 prior | .0007876 .0013013 0.61 0.545 -.0017636 .0033388 democrat | .5725358 .0352496 16.24 0.000 .5034271 .6416444 indep | .1412274 .0461228 3.06 0.002 .0508012 .2316537 otherpol | .0793472 .1098401 0.72 0.470 -.1359999 .2946944 midwest | -.087045 .0500552 -1.74 0.082 -.1851809 .011091 south | .0130732 .0450844 0.29 0.772 -.0753171 .1014636 west | -.0501144 .0484965 -1.03 0.301 -.1451944 .0449655 age1 | .2486329 .0672624 3.70 0.000 .1167614 .3805045 age2 | .2430709 .0498581 4.88 0.000 .1453214 .3408203 age3 | .1796899 .0484509 3.71 0.000 .0846995 .2746804 age4 | .0628591 .047331 1.33 0.184 -.0299357 .1556539 anychildren | .1049905 .0336919 3.12 0.002 .0389357 .1710453 loghhinc | -.0455543 .0208693 -2.18 0.029 -.0864697 -.0046389 associatemore | -.0907073 .0346169 -2.62 0.009 -.1585756 -.0228391 fulltime | .080296 .0472958 1.70 0.090 -.0124298 .1730219 parttime | .0554738 .0614744 0.90 0.367 -.0650501 .1759976 selfemp | .0931896 .0668542 1.39 0.163 -.0378815 .2242607 unemployed | .0881342 .0740762 1.19 0.234 -.0570961 .2333645 student | -.0647726 .0946327 -0.68 0.494 -.250305 .1207599 prior1 | -.0015134 .032418 -0.05 0.963 -.0650706 .0620438 ------------------------------------------------------------------------------- ( 1) T1normal - T2normal = 0 F( 1, 4034) = 3.35 Prob > F = 0.0672 ( 1) T1bias74 - T2bias74 = 0 F( 1, 4034) = 0.00 Prob > F = 0.9539 ( 1) T1bias94 - T2bias94 = 0 F( 1, 4034) = 0.13 Prob > F = 0.7174 ( 1) T1normal - T1bias74 = 0 F( 1, 4034) = 0.17 Prob > F = 0.6792 ( 1) T1normal - T1bias94 = 0 F( 1, 4034) = 0.00 Prob > F = 0.9787 ( 1) T1bias74 - T1bias94 = 0 F( 1, 4034) = 0.11 Prob > F = 0.7428 ( 1) T2normal - T2bias74 = 0 F( 1, 4034) = 1.62 Prob > F = 0.2033 ( 1) T2normal - T2bias94 = 0 F( 1, 4034) = 0.22 Prob > F = 0.6368 ( 1) T2bias74 - T2bias94 = 0 F( 1, 4034) = 0.30 Prob > F = 0.5828 Mean estimation Number of obs = 607 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ quotaanchor | -.0398163 .0390551 -.1165161 .0368835 -------------------------------------------------------------- Mean estimation Number of obs = 226 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ quotaanchor | .2265703 .0655923 .0973166 .3558241 -------------------------------------------------------------- Mean estimation Number of obs = 201 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ quotaanchor | -.1482045 .0808845 -.3077005 .0112914 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- quotaanchor | 4,065 .0020435 1.020349 -1.731601 1.567294 (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(31, 4034) = 20.50 Prob > F = 0.0000 R-squared = 0.1376 Root MSE = .93571 ------------------------------------------------------------------------------- | Robust AAanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1normal | -.0089667 .0484487 -0.19 0.853 -.1039528 .0860195 T2normal | -.10858 .0495184 -2.19 0.028 -.2056633 -.0114966 T1bias74 | .0695022 .0798332 0.87 0.384 -.0870149 .2260194 T1bias94 | .1153536 .0998336 1.16 0.248 -.0803753 .3110825 T2bias74 | -.0799634 .079975 -1.00 0.317 -.2367586 .0768318 T2bias94 | .024105 .0984512 0.24 0.807 -.1689137 .2171237 bias74 | -.1177786 .2522003 -0.47 0.641 -.6122304 .3766731 bias94 | -.6212307 .2853441 -2.18 0.030 -1.180663 -.0617987 normal | -.2827247 .2608016 -1.08 0.278 -.7940398 .2285904 wave | .0121386 .0319395 0.38 0.704 -.0504804 .0747577 gender | .1578104 .0306267 5.15 0.000 .0977651 .2178556 prior | .0012864 .0013764 0.93 0.350 -.0014122 .0039849 democrat | .6564635 .0346048 18.97 0.000 .588619 .7243081 indep | .2265038 .0453676 4.99 0.000 .1375582 .3154494 otherpol | .0709696 .1102323 0.64 0.520 -.1451466 .2870859 midwest | -.0807237 .0485382 -1.66 0.096 -.1758853 .014438 south | .0336393 .0442127 0.76 0.447 -.0530421 .1203206 west | -.0716064 .0474108 -1.51 0.131 -.1645578 .0213449 age1 | .1408713 .0644404 2.19 0.029 .0145326 .2672101 age2 | .129309 .0478362 2.70 0.007 .0355237 .2230943 age3 | .0406112 .0473077 0.86 0.391 -.052138 .1333604 age4 | .0229437 .0451023 0.51 0.611 -.0654817 .1113691 anychildren | .0743787 .0330682 2.25 0.025 .0095467 .1392106 loghhinc | -.0293935 .0202908 -1.45 0.148 -.0691746 .0103875 associatemore | .0179675 .0333807 0.54 0.590 -.0474771 .0834121 fulltime | -.0115768 .0446725 -0.26 0.796 -.0991595 .076006 parttime | -.0286043 .0575718 -0.50 0.619 -.1414768 .0842681 selfemp | .0200288 .0660722 0.30 0.762 -.1095093 .1495668 unemployed | -.0139743 .0710861 -0.20 0.844 -.1533424 .1253938 student | .0365282 .0909303 0.40 0.688 -.1417453 .2148018 prior1 | .0039021 .0313348 0.12 0.901 -.0575315 .0653356 ------------------------------------------------------------------------------- ( 1) T1normal - T2normal = 0 F( 1, 4034) = 5.29 Prob > F = 0.0214 ( 1) T1bias74 - T2bias74 = 0 F( 1, 4034) = 4.55 Prob > F = 0.0329 ( 1) T1bias94 - T2bias94 = 0 F( 1, 4034) = 1.12 Prob > F = 0.2897 ( 1) T1normal - T1bias74 = 0 F( 1, 4034) = 0.73 Prob > F = 0.3935 ( 1) T1normal - T1bias94 = 0 F( 1, 4034) = 1.29 Prob > F = 0.2565 ( 1) T1bias74 - T1bias94 = 0 F( 1, 4034) = 0.13 Prob > F = 0.7178 ( 1) T2normal - T2bias74 = 0 F( 1, 4034) = 0.09 Prob > F = 0.7581 ( 1) T2normal - T2bias94 = 0 F( 1, 4034) = 1.48 Prob > F = 0.2238 ( 1) T2bias74 - T2bias94 = 0 F( 1, 4034) = 0.68 Prob > F = 0.4090 Mean estimation Number of obs = 607 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ AAanchor | .0293626 .0383848 -.0460207 .1047459 -------------------------------------------------------------- Mean estimation Number of obs = 226 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ AAanchor | .2208828 .0654196 .0919694 .3497962 -------------------------------------------------------------- Mean estimation Number of obs = 201 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ AAanchor | -.3443186 .0819431 -.5059018 -.1827354 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- AAanchor | 4,065 -.0252506 1.001344 -2.190388 1.471473 (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(31, 4034) = 19.23 Prob > F = 0.0000 R-squared = 0.1274 Root MSE = .94359 ------------------------------------------------------------------------------- | Robust legislation~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1normal | .0498637 .0477889 1.04 0.297 -.0438288 .1435562 T2normal | -.1114032 .0481206 -2.32 0.021 -.2057462 -.0170603 T1bias74 | .1175418 .086453 1.36 0.174 -.0519538 .2870374 T1bias94 | -.0032679 .1003771 -0.03 0.974 -.2000624 .1935266 T2bias74 | .0188979 .0856315 0.22 0.825 -.1489873 .186783 T2bias94 | .0320371 .09655 0.33 0.740 -.1572542 .2213284 bias74 | -.562923 .2542217 -2.21 0.027 -1.061338 -.0645081 bias94 | -.8937442 .2873259 -3.11 0.002 -1.457062 -.3304269 normal | -.512553 .2635101 -1.95 0.052 -1.029178 .0040723 wave | -.003191 .0322512 -0.10 0.921 -.0664211 .0600391 gender | .2370815 .0311102 7.62 0.000 .1760884 .2980747 prior | .0012144 .001492 0.81 0.416 -.0017108 .0041395 democrat | .6075815 .0348854 17.42 0.000 .5391869 .675976 indep | .2580466 .0437008 5.90 0.000 .172369 .3437243 otherpol | .3611507 .1147526 3.15 0.002 .1361722 .5861292 midwest | -.028038 .0487865 -0.57 0.566 -.1236866 .0676105 south | -.0049756 .0443413 -0.11 0.911 -.0919091 .0819579 west | -.0318847 .0471419 -0.68 0.499 -.1243089 .0605395 age1 | -.0813214 .065758 -1.24 0.216 -.2102434 .0476006 age2 | -.0540916 .0483501 -1.12 0.263 -.1488845 .0407014 age3 | -.0776504 .0488659 -1.59 0.112 -.1734546 .0181538 age4 | -.0375222 .0461254 -0.81 0.416 -.1279536 .0529091 anychildren | .0288498 .0333591 0.86 0.387 -.0365524 .0942521 loghhinc | .010399 .0204561 0.51 0.611 -.0297062 .0505042 associatemore | -.0160801 .0331913 -0.48 0.628 -.0811533 .0489931 fulltime | -.0023924 .0470773 -0.05 0.959 -.0946898 .0899051 parttime | -.0576375 .0607224 -0.95 0.343 -.1766869 .0614118 selfemp | .1690297 .0649183 2.60 0.009 .041754 .2963053 unemployed | .111803 .0751691 1.49 0.137 -.0355699 .259176 student | .1058413 .0918037 1.15 0.249 -.0741446 .2858273 prior1 | -.0374812 .0313032 -1.20 0.231 -.0988529 .0238904 ------------------------------------------------------------------------------- ( 1) T1normal - T2normal = 0 F( 1, 4034) = 14.19 Prob > F = 0.0002 ( 1) T1bias74 - T2bias74 = 0 F( 1, 4034) = 1.81 Prob > F = 0.1782 ( 1) T1bias94 - T2bias94 = 0 F( 1, 4034) = 0.16 Prob > F = 0.6902 ( 1) T1normal - T1bias74 = 0 F( 1, 4034) = 0.48 Prob > F = 0.4894 ( 1) T1normal - T1bias94 = 0 F( 1, 4034) = 0.23 Prob > F = 0.6285 ( 1) T1bias74 - T1bias94 = 0 F( 1, 4034) = 0.84 Prob > F = 0.3596 ( 1) T2normal - T2bias74 = 0 F( 1, 4034) = 1.80 Prob > F = 0.1792 ( 1) T2normal - T2bias94 = 0 F( 1, 4034) = 1.81 Prob > F = 0.1789 ( 1) T2bias74 - T2bias94 = 0 F( 1, 4034) = 0.01 Prob > F = 0.9183 Mean estimation Number of obs = 607 ------------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] ------------------+------------------------------------------------ legislationanchor | .0816474 .0375925 .00782 .1554748 ------------------------------------------------------------------- Mean estimation Number of obs = 226 ------------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] ------------------+------------------------------------------------ legislationanchor | .0571502 .0732754 -.0872435 .201544 ------------------------------------------------------------------- Mean estimation Number of obs = 201 ------------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] ------------------+------------------------------------------------ legislationanchor | -.3504729 .0779442 -.5041709 -.196775 ------------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- legislatio~r | 4,065 -.0057364 1.00501 -2.523294 1.486058 (sum of wgt is 2.5100e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,510 F(30, 2480) = 11.46 Prob > F = 0.0000 R-squared = 0.1182 Root MSE = .93607 ------------------------------------------------------------------------------- | Robust transparenc~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1normal | -.0624182 .0666546 -0.94 0.349 -.1931226 .0682861 T2normal | -.0875072 .0673598 -1.30 0.194 -.2195944 .0445801 T1bias74 | -.1896771 .1034111 -1.83 0.067 -.392458 .0131038 T1bias94 | .0263153 .1379229 0.19 0.849 -.2441406 .2967713 T2bias74 | -.23195 .1041518 -2.23 0.026 -.4361833 -.0277166 T2bias94 | .2337431 .1353789 1.73 0.084 -.0317243 .4992105 bias74 | -.1360026 .2983828 -0.46 0.649 -.7211077 .4491024 bias94 | -.7242799 .3440137 -2.11 0.035 -1.398864 -.0496962 normal | -.252227 .311992 -0.81 0.419 -.8640188 .3595647 wave | 0 (omitted) gender | .226612 .0387347 5.85 0.000 .1506562 .3025678 prior | .0004684 .0016914 0.28 0.782 -.0028483 .0037851 democrat | .5648341 .0433165 13.04 0.000 .479894 .6497743 indep | .2515594 .0574317 4.38 0.000 .1389405 .3641783 otherpol | .0449046 .1657886 0.27 0.787 -.2801937 .370003 midwest | -.0709309 .0607599 -1.17 0.243 -.1900762 .0482144 south | -.0115116 .0541668 -0.21 0.832 -.1177285 .0947052 west | -.0032798 .058899 -0.06 0.956 -.1187761 .1122164 age1 | .0992179 .0890612 1.11 0.265 -.0754242 .2738599 age2 | .0805548 .0606283 1.33 0.184 -.0383324 .1994421 age3 | .0694453 .0607518 1.14 0.253 -.0496842 .1885748 age4 | .0693614 .0596804 1.16 0.245 -.0476672 .18639 anychildren | .0670576 .0428743 1.56 0.118 -.0170156 .1511307 loghhinc | -.0167961 .0253617 -0.66 0.508 -.0665285 .0329363 associatemore | .0923537 .0425867 2.17 0.030 .0088446 .1758628 fulltime | -.0178634 .0592503 -0.30 0.763 -.1340485 .0983217 parttime | -.1265405 .0745121 -1.70 0.090 -.2726529 .0195719 selfemp | .0256022 .0829829 0.31 0.758 -.1371208 .1883252 unemployed | .0287982 .0880751 0.33 0.744 -.1439102 .2015066 student | .2293998 .1182952 1.94 0.053 -.0025678 .4613674 prior1 | -.0856286 .0421708 -2.03 0.042 -.1683223 -.002935 ------------------------------------------------------------------------------- ( 1) T1normal - T2normal = 0 F( 1, 2480) = 0.22 Prob > F = 0.6365 ( 1) T1bias74 - T2bias74 = 0 F( 1, 2480) = 0.22 Prob > F = 0.6388 ( 1) T1bias94 - T2bias94 = 0 F( 1, 2480) = 3.99 Prob > F = 0.0459 ( 1) T1normal - T1bias74 = 0 F( 1, 2480) = 1.12 Prob > F = 0.2908 ( 1) T1normal - T1bias94 = 0 F( 1, 2480) = 0.35 Prob > F = 0.5550 ( 1) T1bias74 - T1bias94 = 0 F( 1, 2480) = 1.61 Prob > F = 0.2049 ( 1) T2normal - T2bias74 = 0 F( 1, 2480) = 1.44 Prob > F = 0.2307 ( 1) T2normal - T2bias94 = 0 F( 1, 2480) = 4.70 Prob > F = 0.0302 ( 1) T2bias74 - T2bias94 = 0 F( 1, 2480) = 7.68 Prob > F = 0.0056 Mean estimation Number of obs = 299 -------------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------------+------------------------------------------------ transparencyanchor | .0509041 .0561999 -.0596948 .161503 -------------------------------------------------------------------- Mean estimation Number of obs = 111 -------------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------------+------------------------------------------------ transparencyanchor | .226807 .0810902 .0661052 .3875088 -------------------------------------------------------------------- Mean estimation Number of obs = 88 -------------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------------+------------------------------------------------ transparencyanchor | -.4590441 .1200081 -.6975732 -.2205149 -------------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- transparen~r | 2,510 -.0322003 .9905502 -2.359831 1.08902 (sum of wgt is 1.5020e+03) note: bias94 omitted because of collinearity Linear regression Number of obs = 1,555 F(30, 1525) = 9.38 Prob > F = 0.0000 R-squared = 0.1385 Root MSE = .9496 ------------------------------------------------------------------------------- | Robust UKtool | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1normal | .0662188 .0719796 0.92 0.358 -.0749707 .2074083 T2normal | -.0677599 .0811521 -0.83 0.404 -.2269415 .0914217 T1bias74 | -.0117821 .1248236 -0.09 0.925 -.2566262 .2330621 T1bias94 | .0482534 .1552889 0.31 0.756 -.256349 .3528557 T2bias74 | .0586421 .1189223 0.49 0.622 -.1746264 .2919106 T2bias94 | -.0354048 .1404874 -0.25 0.801 -.3109736 .2401641 bias74 | .3258034 .1658766 1.96 0.050 .000433 .6511738 bias94 | 0 (omitted) normal | .383565 .1279687 3.00 0.003 .1325517 .6345782 wave | -.6304888 .2274521 -2.77 0.006 -1.076641 -.1843368 gender | .3128252 .0510667 6.13 0.000 .2126569 .4129935 prior | .0007541 .0022441 0.34 0.737 -.0036478 .0051561 democrat | .5437485 .0585556 9.29 0.000 .4288905 .6586066 indep | .219893 .0739215 2.97 0.003 .0748944 .3648916 otherpol | .0884785 .2088684 0.42 0.672 -.3212213 .4981783 midwest | -.1309372 .0796782 -1.64 0.101 -.2872276 .0253531 south | -.097438 .0709422 -1.37 0.170 -.2365925 .0417166 west | -.1422095 .0781343 -1.82 0.069 -.2954714 .0110525 age1 | .0867638 .1065846 0.81 0.416 -.1223041 .2958317 age2 | -.005875 .0826519 -0.07 0.943 -.1679985 .1562485 age3 | -.0088128 .0795906 -0.11 0.912 -.1649315 .1473059 age4 | .0434225 .075827 0.57 0.567 -.1053137 .1921586 anychildren | -.0298046 .0550805 -0.54 0.589 -.1378462 .0782369 loghhinc | .0547337 .0343125 1.60 0.111 -.0125711 .1220384 associatemore | .0096882 .0571065 0.17 0.865 -.1023273 .1217037 fulltime | -.0652893 .0757435 -0.86 0.389 -.2138618 .0832832 parttime | -.0335624 .0965534 -0.35 0.728 -.2229539 .1558291 selfemp | .1579107 .1131712 1.40 0.163 -.0640771 .3798984 unemployed | -.0641804 .1307065 -0.49 0.623 -.3205639 .1922031 student | .1128921 .1400814 0.81 0.420 -.1618805 .3876647 prior1 | -.0450613 .049604 -0.91 0.364 -.1423606 .0522379 ------------------------------------------------------------------------------- ( 1) T1normal - T2normal = 0 F( 1, 1525) = 2.88 Prob > F = 0.0901 ( 1) T1bias74 - T2bias74 = 0 F( 1, 1525) = 0.32 Prob > F = 0.5738 ( 1) T1bias94 - T2bias94 = 0 F( 1, 1525) = 0.29 Prob > F = 0.5875 ( 1) T1normal - T1bias74 = 0 F( 1, 1525) = 0.29 Prob > F = 0.5896 ( 1) T1normal - T1bias94 = 0 F( 1, 1525) = 0.01 Prob > F = 0.9162 ( 1) T1bias74 - T1bias94 = 0 F( 1, 1525) = 0.09 Prob > F = 0.7640 ( 1) T2normal - T2bias74 = 0 F( 1, 1525) = 0.77 Prob > F = 0.3808 ( 1) T2normal - T2bias94 = 0 F( 1, 1525) = 0.04 Prob > F = 0.8420 ( 1) T2bias74 - T2bias94 = 0 F( 1, 1525) = 0.26 Prob > F = 0.6102 Mean estimation Number of obs = 308 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ UKtool | .0988555 .0554465 -.0102478 .2079588 -------------------------------------------------------------- Mean estimation Number of obs = 115 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ UKtool | .0549673 .0907136 -.1247357 .2346702 -------------------------------------------------------------- Mean estimation Number of obs = 113 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ UKtool | -.3802973 .1010198 -.5804552 -.1801395 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- UKtool | 1,555 .0082619 1.00718 -2.427111 .9784691 (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(31, 4034) = 16.86 Prob > F = 0.0000 R-squared = 0.1165 Root MSE = .94388 ------------------------------------------------------------------------------- | Robust childcare | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1normal | -.0696776 .0495271 -1.41 0.160 -.1667782 .0274229 T2normal | -.0831474 .0504065 -1.65 0.099 -.181972 .0156773 T1bias74 | -.0246814 .0790865 -0.31 0.755 -.1797345 .1303718 T1bias94 | -.0621871 .0997051 -0.62 0.533 -.2576642 .13329 T2bias74 | -.0003172 .0794056 -0.00 0.997 -.1559961 .1553616 T2bias94 | -.0346565 .098011 -0.35 0.724 -.2268121 .1574991 bias74 | .0686228 .2495716 0.27 0.783 -.4206754 .5579209 bias94 | -.2074317 .2817721 -0.74 0.462 -.7598605 .3449972 normal | .0257729 .2558772 0.10 0.920 -.4758876 .5274335 wave | -.0843311 .0317879 -2.65 0.008 -.146653 -.0220092 gender | .1295888 .0314543 4.12 0.000 .0679209 .1912566 prior | .0004816 .0013092 0.37 0.713 -.0020852 .0030484 democrat | .568473 .0348218 16.33 0.000 .500203 .636743 indep | .1126421 .0448018 2.51 0.012 .0248058 .2004784 otherpol | .1185665 .1209534 0.98 0.327 -.118569 .355702 midwest | -.0941826 .0477397 -1.97 0.049 -.1877788 -.0005864 south | -.0386248 .0432257 -0.89 0.372 -.123371 .0461214 west | -.1377707 .0478085 -2.88 0.004 -.2315017 -.0440397 age1 | .2775519 .0657743 4.22 0.000 .1485979 .4065059 age2 | .2796541 .0491082 5.69 0.000 .183375 .3759333 age3 | .1880444 .0491327 3.83 0.000 .0917171 .2843717 age4 | .0657057 .0464107 1.42 0.157 -.0252849 .1566964 anychildren | .1718235 .0336981 5.10 0.000 .1057566 .2378904 loghhinc | -.0313091 .0206896 -1.51 0.130 -.0718721 .0092538 associatemore | -.0164421 .0335733 -0.49 0.624 -.0822643 .04938 fulltime | -.0159201 .0473618 -0.34 0.737 -.1087755 .0769352 parttime | .0271181 .0604798 0.45 0.654 -.0914557 .1456919 selfemp | .1019458 .0662964 1.54 0.124 -.0280317 .2319233 unemployed | .0318784 .0744763 0.43 0.669 -.1141362 .177893 student | .0224113 .0850077 0.26 0.792 -.1442506 .1890733 prior1 | -.1109227 .0314316 -3.53 0.000 -.172546 -.0492994 ------------------------------------------------------------------------------- ( 1) T1normal - T2normal = 0 F( 1, 4034) = 0.09 Prob > F = 0.7581 ( 1) T1bias74 - T2bias74 = 0 F( 1, 4034) = 0.11 Prob > F = 0.7352 ( 1) T1bias94 - T2bias94 = 0 F( 1, 4034) = 0.10 Prob > F = 0.7465 ( 1) T1normal - T1bias74 = 0 F( 1, 4034) = 0.24 Prob > F = 0.6248 ( 1) T1normal - T1bias94 = 0 F( 1, 4034) = 0.00 Prob > F = 0.9457 ( 1) T1bias74 - T1bias94 = 0 F( 1, 4034) = 0.09 Prob > F = 0.7663 ( 1) T2normal - T2bias74 = 0 F( 1, 4034) = 0.80 Prob > F = 0.3710 ( 1) T2normal - T2bias94 = 0 F( 1, 4034) = 0.20 Prob > F = 0.6562 ( 1) T2bias74 - T2bias94 = 0 F( 1, 4034) = 0.08 Prob > F = 0.7837 Mean estimation Number of obs = 607 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ childcare | .0240321 .0396285 -.0537939 .101858 -------------------------------------------------------------- Mean estimation Number of obs = 226 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ childcare | .1244614 .0618025 .0026756 .2462472 -------------------------------------------------------------- Mean estimation Number of obs = 201 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ childcare | -.2382844 .0794581 -.3949676 -.0816013 -------------------------------------------------------------- Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- childcare | 4,065 -.0187221 1.001278 -2.714746 1.139764 (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(31, 4034) = 32.09 Prob > F = 0.0000 R-squared = 0.2013 Root MSE = .67443 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1normal | .0022058 .0342144 0.06 0.949 -.0648733 .0692849 T2normal | -.078193 .0354756 -2.20 0.028 -.1477448 -.0086412 T1bias74 | .0268486 .0562147 0.48 0.633 -.0833633 .1370605 T1bias94 | .0178801 .0730097 0.24 0.807 -.1252591 .1610194 T2bias74 | -.0099759 .0554286 -0.18 0.857 -.1186465 .0986948 T2bias94 | .0332427 .070892 0.47 0.639 -.1057448 .1722302 bias74 | -.2493617 .1793312 -1.39 0.164 -.6009498 .1022265 bias94 | -.6105433 .2032374 -3.00 0.003 -1.009001 -.2120857 normal | -.3220238 .1861093 -1.73 0.084 -.6869008 .0428533 wave | .0071415 .0231834 0.31 0.758 -.0383107 .0525938 gender | .2052636 .0220761 9.30 0.000 .1619824 .2485449 prior | .0008005 .0010285 0.78 0.436 -.0012159 .0028168 democrat | .5868091 .0249449 23.52 0.000 .5379032 .635715 indep | .1901884 .0322294 5.90 0.000 .1270009 .2533759 otherpol | .141006 .0879919 1.60 0.109 -.0315067 .3135187 midwest | -.0768386 .0350855 -2.19 0.029 -.1456256 -.0080516 south | -.0124847 .0311906 -0.40 0.689 -.0736355 .048666 west | -.0695135 .0340273 -2.04 0.041 -.1362258 -.0028011 age1 | .1426753 .0457205 3.12 0.002 .0530378 .2323128 age2 | .1341023 .0351804 3.81 0.000 .0651294 .2030753 age3 | .0803781 .0355489 2.26 0.024 .0106826 .1500736 age4 | .0367321 .0335855 1.09 0.274 -.029114 .1025782 anychildren | .0845904 .0236626 3.57 0.000 .0381986 .1309821 loghhinc | -.016447 .0146745 -1.12 0.262 -.0452172 .0123232 associatemore | -.0114988 .0242296 -0.47 0.635 -.0590022 .0360047 fulltime | .0048103 .0339902 0.14 0.887 -.0618292 .0714497 parttime | -.0148513 .0423976 -0.35 0.726 -.097974 .0682714 selfemp | .0958065 .0484102 1.98 0.048 .0008959 .1907171 unemployed | .0483426 .0511953 0.94 0.345 -.0520284 .1487136 student | .0567577 .0626387 0.91 0.365 -.0660487 .1795642 prior1 | -.0465634 .0228068 -2.04 0.041 -.0912774 -.0018493 ------------------------------------------------------------------------------- ( 1) T1normal - T2normal = 0 F( 1, 4034) = 6.68 Prob > F = 0.0098 ( 1) T1bias74 - T2bias74 = 0 F( 1, 4034) = 0.49 Prob > F = 0.4822 ( 1) T1bias94 - T2bias94 = 0 F( 1, 4034) = 0.06 Prob > F = 0.8083 ( 1) T1normal - T1bias74 = 0 F( 1, 4034) = 0.14 Prob > F = 0.7045 ( 1) T1normal - T1bias94 = 0 F( 1, 4034) = 0.04 Prob > F = 0.8433 ( 1) T1bias74 - T1bias94 = 0 F( 1, 4034) = 0.01 Prob > F = 0.9220 ( 1) T2normal - T2bias74 = 0 F( 1, 4034) = 1.11 Prob > F = 0.2920 ( 1) T2normal - T2bias94 = 0 F( 1, 4034) = 2.03 Prob > F = 0.1546 ( 1) T2bias74 - T2bias94 = 0 F( 1, 4034) = 0.23 Prob > F = 0.6280 Mean estimation Number of obs = 607 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | .0333385 .0283953 -.0224266 .0891037 ------------------------------------------------------------------ Mean estimation Number of obs = 226 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | .1497922 .046459 .0582418 .2413426 ------------------------------------------------------------------ Mean estimation Number of obs = 201 ------------------------------------------------------------------ | Mean Std. Err. [95% Conf. Interval] -----------------+------------------------------------------------ z_lmpolicy_index | -.2930991 .0603533 -.4121097 -.1740886 ------------------------------------------------------------------ Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- z_lmpolicy~x | 4,065 -.0119356 .7512594 -2.32877 1.337296 . . . loc rowlabels " " " "T$^{74}$ x (74 $\leq$ prior $\leq$ 94)" " " " " "T$^{94}$ x (74 $\leq$ prior > $\leq$ 94)" " " "p-value [T$^{74}$ x (74 $\leq$ prior $\leq$ 94) = T$^{94}$ x (74 $\leq$ prior $\l > eq$ 94)]" " " "T$^{74}$ x (prior < 74)" " " " " "T$^{94}$ x (prior < 74)" " " "p-value [T$^{74}$ x > (prior < 74) = T$^{94}$ x (prior < 74)]" " " "T$^{74}$ x (prior > 94)" " " " " "T$^{94}$ x (prior > > 94)" " " "p-value [T$^{74}$ (prior > 94) = T$^{94}$ x (prior > 94)]" " " "p-value [T$^{74}$ x ( > 74 $\leq$ prior $\leq$ 94) = T$^{74}$ x (prior < 74)]" "p-value [T$^{74}$ x (74 $\leq$ prior $\leq > $ 94) = T$^{74}$ x (prior > 94)]" "p-value [T$^{74}$ x (prior < 74) = T$^{74}$ x (prior > 94)]" " > " "p-value [T$^{94}$ x (74 $\leq$ prior $\leq$ 94) = T$^{94}$ x (prior < 74)]" "p-value [T$^{94}$ > x (74 $\leq$ prior $\leq$ 94) = T$^{94}$ x (prior > 94)]" "p-value [T$^{94}$ x (prior < 74) = T$^{ > 94}$ x (prior > 94)]" " " "Control group mean (74 $\leq$ prior $\leq$ 94)" " " " " "Control group > mean (prior < 74)" " " " " "Control group mean (prior > 94)" " " " " "Observations" " " " . loc rowstats "" . . . . forval i = 1/40 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\policyaltspec_interact.tex", replace cells(none) booktabs nonotes nomtitle > s compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mgroups("\shortstack{Introduce\\gender\\quotas}" "\shortstack{Statutory\\affirmative\\acti > on}" /// > "\shortstack{Stricter\\equal pay\\legislation}" "\shortstack{Wage transp.\\within\\ companies}" > "\shortstack{Introduce\\reporting\\website}" "\shortstack{Increase\\subsidies\\to child care}" /// > "\shortstack{Policy\\demand\\index}", pattern(1 1 1 1 1 1 1 ) prefix(\multicolumn{@span}{c}{) s > uffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\policyaltspec_interact.tex) . . eststo clear . . . . *********************************************************************************** . // Table D.9: Treatment effect on beliefs about the wage gap and related perceptions . *********************************************************************************** . . use "$path\data\SurveyStageI_AB_final.dta", clear . . gen bias74=74-prior //degree of underestimation of females' wages compared to > T1 . gen bias94=94-prior //degree of underestimation of females' wages compared to > T2 . . * Generate a measure of the update each respondent receives . gen signal=0 if rand==0 (3,031 missing values generated) . replace signal=bias74 if T1==1 (1,531 real changes made) . replace signal=bias94 if T2==1 (1,500 real changes made) . . * Dummy for "any treatment group" . gen treatment=T1+T2 . . * Dummies for whether information update compared to prior belief is positive/negative . gen posshock = 0 . replace posshock= 1 if bias74>0&T1==1 (327 real changes made) . replace posshock = 1 if bias94>0&T2==1 (1,160 real changes made) . gen negshock=0 . replace negshock=1 if bias74<0&T1==1 (1,181 real changes made) . replace negshock=1 if bias94<0&T2==1 (331 real changes made) . . // Interaction terms: . gen signalpos=signal*posshock . gen signalneg=signal*negshock . . gen signalT1=signal*T1 . gen signalT2=signal*T2 . . . loc experiments "posterior large problem govmore z_mani_index" . . *Standardize prior based on mean and st. dev. in the pure control group . foreach var of varlist prior{ 2. egen mean_`var'=mean(`var') if rand==0 3. egen max_mean_`var'=min(mean_`var') 4. replace mean_`var'=max_mean_`var' 5. drop max_mean_`var' 6. egen sd_`var'=sd(`var') if rand==0 7. egen min_sd_`var'=min(sd_`var') 8. replace sd_`var'=min_sd_`var' 9. drop min_sd_`var' 10. replace `var'=(`var'-mean_`var')/sd_`var' 11. drop mean_`var' sd_`var' 12. } (3031 missing values generated) (3,031 real changes made) (3031 missing values generated) (3,031 real changes made) (4,065 real changes made) . . . *Keep only those with prior beliefs above the 5th and below the 95th percentile of the control gro > up distribution . sum prior if rand==0,d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% -2.432051 -3.873509 5% -1.548576 -3.780512 10% -.9905922 -3.780512 Obs 1,034 25% -.3861097 -3.362024 Sum of Wgt. 1,034 50% -.130367 Mean 1.04e-07 Largest Std. Dev. 1 75% .3113702 5.193729 90% .7763568 5.426223 Variance 1 95% 1.380839 5.426223 Skewness 1.506369 99% 4.263756 5.426223 Kurtosis 10.77207 . keep if prior > r(p5) & prior < r(p95) (458 observations deleted) . . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . . . foreach choice in `experiments' { 2. . . ***Panel A: Role of information update . qui reg `choice' signal $controls [pweight=pweight], vce(r) 3. local n = round(e(N)) 4. . sigstar signal, prec(3) 5. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 7. sigstar prior, prec(3) 8. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat7 = "`r(sestar)'": col`colnum' 10. . estadd loc thisstat9 = "`n'": col`colnum' 11. . . *** Panel B: Interaction of update with T74 and T94 . reg `choice' signalT1 signalT2 $controls [pweight=pweight], vce(r) 12. local n = round(e(N)) 13. . sigstar signalT1, prec(3) 14. estadd loc thisstat12 = "`r(bstar)'": col`colnum' 15. estadd loc thisstat13 = "`r(sestar)'": col`colnum' 16. sigstar signalT2, prec(3) 17. estadd loc thisstat15 = "`r(bstar)'": col`colnum' 18. estadd loc thisstat16 = "`r(sestar)'": col`colnum' 19. . test signalT1 - signalT2 = 0 20. estadd loc thisstat17 = string(r(p), "%9.3f"): col`colnum' 21. . sigstar prior, prec(3) 22. estadd loc thisstat19 = "`r(bstar)'": col`colnum' 23. estadd loc thisstat20 = "`r(sestar)'": col`colnum' 24. . estadd loc thisstat22 = "`n'": col`colnum' 25. . *** Panel C: Interaction of update with pos. update and neg. update . reg `choice' signalpos signalneg $controls [pweight=pweight], vce(r) 26. local n = round(e(N)) 27. . sigstar signalpos, prec(3) 28. estadd loc thisstat25 = "`r(bstar)'": col`colnum' 29. estadd loc thisstat26 = "`r(sestar)'": col`colnum' 30. sigstar signalneg, prec(3) 31. estadd loc thisstat28 = "`r(bstar)'": col`colnum' 32. estadd loc thisstat29 = "`r(sestar)'": col`colnum' 33. . test signalpos - signalneg = 0 34. estadd loc thisstat30 = string(r(p), "%9.3f"): col`colnum' 35. . sigstar prior, prec(3) 36. estadd loc thisstat32 = "`r(bstar)'": col`colnum' 37. estadd loc thisstat33 = "`r(sestar)'": col`colnum' 38. . estadd loc thisstat35 = "`n'": col`colnum' 39. . loc ++colnum 40. loc colnames "`colnames' `"`: var la `choice''"'" 41. . } (sum of wgt is 3.5488e+03) Linear regression Number of obs = 3,596 F(23, 3572) = 53.54 Prob > F = 0.0000 R-squared = 0.2525 Root MSE = 13.12 ------------------------------------------------------------------------------- | Robust posterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalT1 | .5967382 .04021 14.84 0.000 .5179014 .6755749 signalT2 | .5938486 .0339011 17.52 0.000 .527381 .6603161 wave | 2.128223 .4522971 4.71 0.000 1.241436 3.015009 gender | -1.011189 .4707799 -2.15 0.032 -1.934213 -.0881643 prior | 17.99682 .6812507 26.42 0.000 16.66114 19.3325 democrat | -.6960023 .5348213 -1.30 0.193 -1.744588 .3525835 indep | -.0851039 .6110048 -0.14 0.889 -1.283057 1.112849 otherpol | 3.140555 3.001982 1.05 0.296 -2.745216 9.026326 midwest | .0471114 .7211699 0.07 0.948 -1.366835 1.461057 south | .1952439 .6494509 0.30 0.764 -1.078088 1.468576 west | -.3525282 .6809503 -0.52 0.605 -1.687619 .9825622 age1 | 1.564708 1.076908 1.45 0.146 -.5467093 3.676124 age2 | 1.787317 .7402449 2.41 0.016 .3359724 3.238663 age3 | .0978361 .6308753 0.16 0.877 -1.139076 1.334748 age4 | -.2261918 .6040836 -0.37 0.708 -1.410575 .9581917 anychildren | 1.398226 .4836645 2.89 0.004 .4499394 2.346512 loghhinc | -.2609381 .3120587 -0.84 0.403 -.8727692 .3508929 associatemore | .2112681 .5011958 0.42 0.673 -.7713906 1.193927 fulltime | .2595149 .6017549 0.43 0.666 -.9203027 1.439333 parttime | .5244355 .8706698 0.60 0.547 -1.182624 2.231495 selfemp | -.612209 .8573847 -0.71 0.475 -2.293222 1.068804 unemployed | .0086271 .9676396 0.01 0.993 -1.888555 1.905809 student | 1.403748 1.751794 0.80 0.423 -2.030868 4.838364 _cons | 82.51923 3.608444 22.87 0.000 75.44442 89.59405 ------------------------------------------------------------------------------- ( 1) signalT1 - signalT2 = 0 F( 1, 3572) = 0.00 Prob > F = 0.9567 (sum of wgt is 3.5488e+03) Linear regression Number of obs = 3,596 F(23, 3572) = 53.58 Prob > F = 0.0000 R-squared = 0.2531 Root MSE = 13.115 ------------------------------------------------------------------------------- | Robust posterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalpos | .6287785 .0373552 16.83 0.000 .5555389 .7020181 signalneg | .5502776 .0447288 12.30 0.000 .462581 .6379743 wave | 2.186722 .454107 4.82 0.000 1.296387 3.077057 gender | -.9811456 .4713629 -2.08 0.037 -1.905313 -.0569781 prior | 17.97677 .6863849 26.19 0.000 16.63103 19.32252 democrat | -.6771873 .5368978 -1.26 0.207 -1.729844 .3754697 indep | -.0529837 .6115736 -0.09 0.931 -1.252052 1.146085 otherpol | 3.105881 3.002734 1.03 0.301 -2.781363 8.993126 midwest | .0675772 .7211919 0.09 0.925 -1.346412 1.481566 south | .2057514 .6491698 0.32 0.751 -1.067029 1.478532 west | -.3476321 .6806339 -0.51 0.610 -1.682102 .9868381 age1 | 1.5534 1.077061 1.44 0.149 -.5583156 3.665116 age2 | 1.745574 .739668 2.36 0.018 .2953601 3.195788 age3 | .0814797 .6317452 0.13 0.897 -1.157138 1.320097 age4 | -.2285174 .6042293 -0.38 0.705 -1.413187 .9561517 anychildren | 1.360375 .4820545 2.82 0.005 .4152449 2.305504 loghhinc | -.2611623 .3115155 -0.84 0.402 -.8719285 .3496039 associatemore | .2298317 .5031229 0.46 0.648 -.7566053 1.216269 fulltime | .2783031 .6026851 0.46 0.644 -.9033384 1.459945 parttime | .5241621 .8734014 0.60 0.548 -1.188253 2.236578 selfemp | -.5832633 .8581923 -0.68 0.497 -2.265859 1.099333 unemployed | -.0006696 .969316 -0.00 0.999 -1.901138 1.899799 student | 1.380124 1.753339 0.79 0.431 -2.057522 4.817769 _cons | 82.04925 3.614483 22.70 0.000 74.96259 89.13591 ------------------------------------------------------------------------------- ( 1) signalpos - signalneg = 0 F( 1, 3572) = 1.51 Prob > F = 0.2192 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 42.74 Prob > F = 0.0000 R-squared = 0.2310 Root MSE = .92393 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalT1 | -.0303191 .0027555 -11.00 0.000 -.0357216 -.0249166 signalT2 | -.0256864 .0019137 -13.42 0.000 -.0294383 -.0219344 wave | -.0248532 .0324714 -0.77 0.444 -.0885174 .038811 gender | .1769724 .0317618 5.57 0.000 .1146995 .2392454 prior | -.995141 .0451066 -22.06 0.000 -1.083578 -.9067038 democrat | .5106286 .0359749 14.19 0.000 .4400953 .5811618 indep | .1876744 .0493201 3.81 0.000 .0909761 .2843728 otherpol | .0439946 .1310852 0.34 0.737 -.2130145 .3010037 midwest | -.006996 .0490904 -0.14 0.887 -.1032439 .0892518 south | .0742691 .045138 1.65 0.100 -.0142296 .1627678 west | -.0084949 .0489379 -0.17 0.862 -.1044438 .087454 age1 | -.0106028 .0723688 -0.15 0.884 -.152491 .1312854 age2 | .0113494 .0502292 0.23 0.821 -.0871312 .10983 age3 | -.0179223 .0485217 -0.37 0.712 -.1130552 .0772107 age4 | -.104715 .0461275 -2.27 0.023 -.1951538 -.0142761 anychildren | .0954614 .0350008 2.73 0.006 .0268379 .164085 loghhinc | .0242656 .0212884 1.14 0.254 -.017473 .0660042 associatemore | -.0118751 .0353001 -0.34 0.737 -.0810855 .0573352 fulltime | .0297537 .0481172 0.62 0.536 -.0645861 .1240935 parttime | -.1774961 .0625635 -2.84 0.005 -.3001598 -.0548325 selfemp | .0573851 .0660344 0.87 0.385 -.0720836 .1868538 unemployed | -.0063277 .0766264 -0.08 0.934 -.1565635 .1439081 student | .0210801 .0977455 0.22 0.829 -.1705624 .2127226 _cons | -.7323891 .2350704 -3.12 0.002 -1.193274 -.2715038 ------------------------------------------------------------------------------- ( 1) signalT1 - signalT2 = 0 F( 1, 3583) = 2.03 Prob > F = 0.1538 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 42.70 Prob > F = 0.0000 R-squared = 0.2307 Root MSE = .92411 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalpos | -.0292579 .0020189 -14.49 0.000 -.0332161 -.0252997 signalneg | -.0255491 .0030082 -8.49 0.000 -.031447 -.0196512 wave | -.0310829 .0325771 -0.95 0.340 -.0949544 .0327886 gender | .1743365 .0317715 5.49 0.000 .1120445 .2366285 prior | -.9881241 .0446305 -22.14 0.000 -1.075628 -.9006204 democrat | .5110459 .0360192 14.19 0.000 .4404257 .5816661 indep | .184864 .04933 3.75 0.000 .0881462 .2815817 otherpol | .0511758 .1300236 0.39 0.694 -.2037518 .3061034 midwest | -.0093388 .0492116 -0.19 0.850 -.1058244 .0871468 south | .0724649 .0452251 1.60 0.109 -.0162046 .1611344 west | -.0091207 .0489805 -0.19 0.852 -.1051531 .0869117 age1 | -.0117629 .0726594 -0.16 0.871 -.1542208 .130695 age2 | .01386 .0502653 0.28 0.783 -.0846914 .1124114 age3 | -.0170664 .0484709 -0.35 0.725 -.1120997 .0779668 age4 | -.1045534 .046069 -2.27 0.023 -.1948775 -.0142294 anychildren | .0981012 .0349741 2.80 0.005 .02953 .1666724 loghhinc | .024114 .0213005 1.13 0.258 -.0176483 .0658763 associatemore | -.012057 .0353048 -0.34 0.733 -.0812765 .0571624 fulltime | .027054 .0479729 0.56 0.573 -.0670029 .1211109 parttime | -.178525 .0625611 -2.85 0.004 -.3011839 -.0558661 selfemp | .0538393 .0660505 0.82 0.415 -.075661 .1833396 unemployed | -.0051392 .0766968 -0.07 0.947 -.155513 .1452346 student | .0236036 .0981013 0.24 0.810 -.1687364 .2159436 _cons | -.6860109 .235747 -2.91 0.004 -1.148223 -.223799 ------------------------------------------------------------------------------- ( 1) signalpos - signalneg = 0 F( 1, 3583) = 0.98 Prob > F = 0.3231 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 43.44 Prob > F = 0.0000 R-squared = 0.2331 Root MSE = .91649 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalT1 | -.0226759 .002658 -8.53 0.000 -.0278873 -.0174645 signalT2 | -.0198071 .0018466 -10.73 0.000 -.0234276 -.0161866 wave | -.0311161 .0318665 -0.98 0.329 -.0935945 .0313622 gender | .2544104 .0314712 8.08 0.000 .1927071 .3161137 prior | -.8549835 .0433796 -19.71 0.000 -.9400347 -.7699323 democrat | .6308858 .0359852 17.53 0.000 .5603323 .7014394 indep | .239376 .0495378 4.83 0.000 .1422508 .3365011 otherpol | .1829759 .1228529 1.49 0.136 -.0578927 .4238446 midwest | -.0837561 .0495283 -1.69 0.091 -.1808626 .0133504 south | -.0259976 .0449252 -0.58 0.563 -.1140791 .0620839 west | -.0651079 .0483595 -1.35 0.178 -.1599228 .029707 age1 | .0433081 .0699147 0.62 0.536 -.0937685 .1803846 age2 | .0000244 .0497179 0.00 1.000 -.0974537 .0975025 age3 | -.0056615 .0484629 -0.12 0.907 -.1006792 .0893562 age4 | -.0704199 .0456318 -1.54 0.123 -.1598869 .0190471 anychildren | .0640419 .0346618 1.85 0.065 -.003917 .1320008 loghhinc | .0057164 .0213006 0.27 0.788 -.036046 .0474789 associatemore | .0181539 .0353662 0.51 0.608 -.051186 .0874938 fulltime | .0012592 .0470354 0.03 0.979 -.0909596 .0934781 parttime | -.1562375 .0611406 -2.56 0.011 -.2761114 -.0363636 selfemp | -.0170647 .0659744 -0.26 0.796 -.1464158 .1122864 unemployed | -.0222386 .0776581 -0.29 0.775 -.1744971 .1300199 student | .0035966 .099757 0.04 0.971 -.1919896 .1991827 _cons | -.5180718 .2373068 -2.18 0.029 -.9833417 -.0528019 ------------------------------------------------------------------------------- ( 1) signalT1 - signalT2 = 0 F( 1, 3583) = 0.86 Prob > F = 0.3550 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 43.40 Prob > F = 0.0000 R-squared = 0.2335 Root MSE = .91623 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalpos | -.0235477 .0019458 -12.10 0.000 -.0273626 -.0197328 signalneg | -.0176879 .0028811 -6.14 0.000 -.0233366 -.0120392 wave | -.0375137 .031932 -1.17 0.240 -.1001205 .0250931 gender | .2514142 .0314771 7.99 0.000 .1896994 .3131291 prior | -.8499358 .0425987 -19.95 0.000 -.9334559 -.7664157 democrat | .6302293 .0358922 17.56 0.000 .5598581 .7006005 indep | .2361459 .0495312 4.77 0.000 .1390338 .333258 otherpol | .1888058 .1225709 1.54 0.124 -.05151 .4291216 midwest | -.0861847 .0496001 -1.74 0.082 -.1834319 .0110625 south | -.0275591 .0450084 -0.61 0.540 -.1158038 .0606855 west | -.0657347 .0483798 -1.36 0.174 -.1605895 .02912 age1 | .0431108 .0701495 0.61 0.539 -.0944262 .1806479 age2 | .0034385 .0497218 0.07 0.945 -.0940473 .1009243 age3 | -.0043954 .0484313 -0.09 0.928 -.099351 .0905603 age4 | -.0702117 .0455255 -1.54 0.123 -.1594702 .0190468 anychildren | .0673275 .0346326 1.94 0.052 -.0005742 .1352292 loghhinc | .0056325 .0213252 0.26 0.792 -.0361781 .0474432 associatemore | .0171826 .0353844 0.49 0.627 -.0521929 .0865582 fulltime | -.0012299 .0469446 -0.03 0.979 -.0932707 .0908109 parttime | -.1568321 .0611623 -2.56 0.010 -.2767485 -.0369157 selfemp | -.0205235 .0658994 -0.31 0.755 -.1497276 .1086806 unemployed | -.021162 .0778653 -0.27 0.786 -.1738268 .1315029 student | .0061918 .1000546 0.06 0.951 -.1899779 .2023615 _cons | -.4685895 .237181 -1.98 0.048 -.9336129 -.0035661 ------------------------------------------------------------------------------- ( 1) signalpos - signalneg = 0 F( 1, 3583) = 2.71 Prob > F = 0.0999 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 40.88 Prob > F = 0.0000 R-squared = 0.2164 Root MSE = .93845 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalT1 | -.0125382 .0026967 -4.65 0.000 -.0178255 -.007251 signalT2 | -.0110406 .0018194 -6.07 0.000 -.0146078 -.0074734 wave | -.0144993 .0326948 -0.44 0.657 -.0786016 .049603 gender | .2352891 .0320272 7.35 0.000 .1724958 .2980824 prior | -.6052979 .0429438 -14.10 0.000 -.6894945 -.5211012 democrat | .7680634 .0368733 20.83 0.000 .6957686 .8403582 indep | .2966001 .0518374 5.72 0.000 .1949662 .3982339 otherpol | .1859415 .1291682 1.44 0.150 -.0673091 .4391922 midwest | -.1348447 .051418 -2.62 0.009 -.2356562 -.0340331 south | -.0389776 .0454752 -0.86 0.391 -.1281375 .0501823 west | -.0889008 .0492363 -1.81 0.071 -.1854347 .0076332 age1 | .2130949 .0698356 3.05 0.002 .0761733 .3500164 age2 | .1897275 .051162 3.71 0.000 .089418 .290037 age3 | .1156704 .0520615 2.22 0.026 .0135972 .2177436 age4 | .0441426 .050029 0.88 0.378 -.0539455 .1422307 anychildren | .1079684 .0345189 3.13 0.002 .0402898 .175647 loghhinc | -.0314548 .0218863 -1.44 0.151 -.0743656 .0114561 associatemore | -.0501919 .0361566 -1.39 0.165 -.1210814 .0206976 fulltime | -.0238726 .0508743 -0.47 0.639 -.1236181 .0758728 parttime | -.1717608 .0626732 -2.74 0.006 -.2946395 -.0488821 selfemp | -.0818414 .0705544 -1.16 0.246 -.2201721 .0564893 unemployed | -.0324428 .0773665 -0.42 0.675 -.1841295 .119244 student | -.0631796 .0976302 -0.65 0.518 -.254596 .1282368 _cons | -.2630706 .2440041 -1.08 0.281 -.7414714 .2153302 ------------------------------------------------------------------------------- ( 1) signalT1 - signalT2 = 0 F( 1, 3583) = 0.24 Prob > F = 0.6257 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 40.87 Prob > F = 0.0000 R-squared = 0.2166 Root MSE = .93833 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalpos | -.0132821 .0019441 -6.83 0.000 -.0170937 -.0094705 signalneg | -.0095501 .0029705 -3.22 0.001 -.015374 -.0037261 wave | -.0183188 .0327013 -0.56 0.575 -.0824339 .0457963 gender | .2334673 .0320768 7.28 0.000 .1705768 .2963579 prior | -.6025301 .0421273 -14.30 0.000 -.685126 -.5199342 democrat | .7675478 .0368813 20.81 0.000 .6952373 .8398583 indep | .2946325 .0518537 5.68 0.000 .1929667 .3962982 otherpol | .1892461 .128605 1.47 0.141 -.0629002 .4413924 midwest | -.1362972 .0514547 -2.65 0.008 -.2371806 -.0354138 south | -.0398767 .0454871 -0.88 0.381 -.1290598 .0493065 west | -.0892732 .0492178 -1.81 0.070 -.1857708 .0072245 age1 | .2130903 .0697943 3.05 0.002 .0762499 .3499308 age2 | .191861 .0511311 3.75 0.000 .0916122 .2921099 age3 | .1164704 .0520878 2.24 0.025 .0143457 .2185952 age4 | .0442717 .049975 0.89 0.376 -.0537105 .142254 anychildren | .1099955 .034497 3.19 0.001 .0423597 .1776312 loghhinc | -.0314967 .0218945 -1.44 0.150 -.0744236 .0114303 associatemore | -.0508611 .0361959 -1.41 0.160 -.1218278 .0201056 fulltime | -.0253265 .0508369 -0.50 0.618 -.1249986 .0743457 parttime | -.1720632 .0627317 -2.74 0.006 -.2950567 -.0490697 selfemp | -.0838856 .0704903 -1.19 0.234 -.2220907 .0543194 unemployed | -.0318164 .0773708 -0.41 0.681 -.1835115 .1198788 student | -.0616298 .0976757 -0.63 0.528 -.2531353 .1298757 _cons | -.233317 .2439975 -0.96 0.339 -.7117051 .245071 ------------------------------------------------------------------------------- ( 1) signalpos - signalneg = 0 F( 1, 3583) = 1.05 Prob > F = 0.3054 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 48.18 Prob > F = 0.0000 R-squared = 0.2544 Root MSE = .8369 ------------------------------------------------------------------------------- | Robust z_mani_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalT1 | -.0214821 .0024857 -8.64 0.000 -.0263557 -.0166085 signalT2 | -.0184839 .0016642 -11.11 0.000 -.0217469 -.015221 wave | -.0216739 .0291684 -0.74 0.457 -.0788623 .0355144 gender | .2155684 .028634 7.53 0.000 .1594279 .2717089 prior | -.8064241 .0396167 -20.36 0.000 -.8840977 -.7287504 democrat | .6403339 .0326486 19.61 0.000 .5763221 .7043457 indep | .2427055 .0460271 5.27 0.000 .1524635 .3329475 otherpol | .1288524 .1165821 1.11 0.269 -.0997215 .3574262 midwest | -.0745383 .0450922 -1.65 0.098 -.1629472 .0138707 south | .008517 .0407408 0.21 0.834 -.0713606 .0883945 west | -.0525036 .0440945 -1.19 0.234 -.1389564 .0339492 age1 | .092819 .0623569 1.49 0.137 -.0294396 .2150776 age2 | .083849 .0452983 1.85 0.064 -.004964 .172662 age3 | .0401822 .0449446 0.89 0.371 -.0479373 .1283018 age4 | -.0361838 .0427143 -0.85 0.397 -.1199306 .0475631 anychildren | .0949232 .0312361 3.04 0.002 .0336809 .1561656 loghhinc | -.0024354 .0194095 -0.13 0.900 -.0404902 .0356194 associatemore | -.022383 .0321235 -0.70 0.486 -.0853651 .0405991 fulltime | .0021026 .044102 0.05 0.962 -.084365 .0885702 parttime | -.1711961 .0558264 -3.07 0.002 -.2806507 -.0617414 selfemp | -.0144901 .0609878 -0.24 0.812 -.1340644 .1050842 unemployed | -.0201668 .0688818 -0.29 0.770 -.1552182 .1148847 student | -.0173574 .0904435 -0.19 0.848 -.1946833 .1599686 _cons | -.4968317 .215708 -2.30 0.021 -.9197545 -.0739088 ------------------------------------------------------------------------------- ( 1) signalT1 - signalT2 = 0 F( 1, 3583) = 1.10 Prob > F = 0.2944 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 47.96 Prob > F = 0.0000 R-squared = 0.2545 Root MSE = .83684 ------------------------------------------------------------------------------- | Robust z_mani_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalpos | -.0215304 .0017642 -12.20 0.000 -.0249894 -.0180715 signalneg | -.0174171 .0027182 -6.41 0.000 -.0227464 -.0120877 wave | -.0269268 .0292089 -0.92 0.357 -.0841945 .0303409 gender | .2132067 .028661 7.44 0.000 .1570131 .2694003 prior | -.8015451 .0389336 -20.59 0.000 -.8778793 -.725211 democrat | .6401641 .0326225 19.62 0.000 .5762036 .7041246 indep | .2401704 .046032 5.22 0.000 .1499189 .3304219 otherpol | .1341647 .1157832 1.16 0.247 -.0928428 .3611723 midwest | -.0765246 .0451667 -1.69 0.090 -.1650797 .0120305 south | .0071358 .0407996 0.17 0.861 -.072857 .0871286 west | -.0530237 .0440979 -1.20 0.229 -.1394831 .0334358 age1 | .0923188 .0624519 1.48 0.139 -.0301261 .2147636 age2 | .0863678 .0452751 1.91 0.057 -.0023998 .1751354 age3 | .0410901 .0449374 0.91 0.361 -.0470154 .1291955 age4 | -.0360272 .0426397 -0.84 0.398 -.1196277 .0475732 anychildren | .0974254 .0312108 3.12 0.002 .0362327 .1586181 loghhinc | -.0025288 .0194248 -0.13 0.896 -.0406135 .035556 associatemore | -.0229135 .0321447 -0.71 0.476 -.0859374 .0401103 fulltime | -.0000375 .0440093 -0.00 0.999 -.0863234 .0862483 parttime | -.1718415 .0558763 -3.08 0.002 -.2813941 -.0622889 selfemp | -.0173921 .0609428 -0.29 0.775 -.1368781 .1020938 unemployed | -.0192338 .0689451 -0.28 0.780 -.1544094 .1159418 student | -.0152278 .0906615 -0.17 0.867 -.192981 .1625254 _cons | -.4568343 .2158814 -2.12 0.034 -.8800971 -.0335715 ------------------------------------------------------------------------------- ( 1) signalpos - signalneg = 0 F( 1, 3583) = 1.52 Prob > F = 0.2173 . . loc rowlabels " "{\bf Panel A: Baseline Specification}" " " "(Signal -- Prior)" " " " " "Prior" " > " " " "Observations" "\hline {\bf Panel B: Interaction with T$^{74}$ and T$^{94}$}" " " "(Signal - > - Prior) x T$^{74}$ (a)" " " " " "(Signal -- Prior) x T$^{94}$ (b)" " " "p-value [(a) -- (b) = 0]" > " " "Prior" " " " " "Observations" "\hline {\bf Panel C: Interact. with pos./neg. Signal}" " " "( > Signal -- Prior) x $\textbf{1}$(Signal -- Prior > 0) (a)" " " " " "(Signal -- Prior) x $\textbf{1} > $(Signal -- Prior < 0) (b)" " " "p-value [(a) -- (b) = 0]" " " "Prior" " " " " "Observations" " " > " . loc rowstats "" . . forval i = 1/35 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\manicheckalt_infoupdate.tex", replace cells(none) booktabs nonotes nomtitl > es compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mgroups("\shortstack{Posterior\\belief about\\fem. rel. wage\\ (percent)}" "\shortstack{Gender\\ > differences\\ in wages\\are large}" "\shortstack{Gender\\ differences\\ in wages\\are a problem}" > /// > "\shortstack{Gov. should\\promote\\gender wage\\equality}" "\shortstack{Index\\(2)-(4)}", patter > n(1 1 1 1 1) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\manicheckalt_infoupdate.tex) . . . eststo clear . . . . . *********************************************************************************** . // Table D.10: Treatment effect on the demand for specific policies . *********************************************************************************** . . loc experiments "quotaanchor AAanchor legislationanchor transparencyanchor UKtool childcare z_lmpo > licy_index" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . . . foreach choice in `experiments' { 2. . . ***Panel A: Role of information update . qui reg `choice' signal $controls [pweight=pweight], vce(r) 3. local n = round(e(N)) 4. . sigstar signal, prec(3) 5. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 7. sigstar prior, prec(3) 8. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat7 = "`r(sestar)'": col`colnum' 10. . estadd loc thisstat9 = "`n'": col`colnum' 11. . . *** Panel B: Interaction of update with T74 and T94 . reg `choice' signalT1 signalT2 $controls [pweight=pweight], vce(r) 12. local n = round(e(N)) 13. . sigstar signalT1, prec(3) 14. estadd loc thisstat12 = "`r(bstar)'": col`colnum' 15. estadd loc thisstat13 = "`r(sestar)'": col`colnum' 16. sigstar signalT2, prec(3) 17. estadd loc thisstat15 = "`r(bstar)'": col`colnum' 18. estadd loc thisstat16 = "`r(sestar)'": col`colnum' 19. . test signalT1 - signalT2 = 0 20. estadd loc thisstat17 = string(r(p), "%9.3f"): col`colnum' 21. . sigstar prior, prec(3) 22. estadd loc thisstat19 = "`r(bstar)'": col`colnum' 23. estadd loc thisstat20 = "`r(sestar)'": col`colnum' 24. . estadd loc thisstat22 = "`n'": col`colnum' 25. . *** Panel C: Interaction of update with pos. update and neg. update . reg `choice' signalpos signalneg $controls [pweight=pweight], vce(r) 26. local n = round(e(N)) 27. . sigstar signalpos, prec(3) 28. estadd loc thisstat25 = "`r(bstar)'": col`colnum' 29. estadd loc thisstat26 = "`r(sestar)'": col`colnum' 30. sigstar signalneg, prec(3) 31. estadd loc thisstat28 = "`r(bstar)'": col`colnum' 32. estadd loc thisstat29 = "`r(sestar)'": col`colnum' 33. . test signalpos - signalneg = 0 34. estadd loc thisstat30 = string(r(p), "%9.3f"): col`colnum' 35. . sigstar prior, prec(3) 36. estadd loc thisstat32 = "`r(bstar)'": col`colnum' 37. estadd loc thisstat33 = "`r(sestar)'": col`colnum' 38. . estadd loc thisstat35 = "`n'": col`colnum' 39. . loc ++colnum 40. loc colnames "`colnames' `"`: var la `choice''"'" 41. . } (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 24.82 Prob > F = 0.0000 R-squared = 0.1375 Root MSE = .95042 ------------------------------------------------------------------------------- | Robust quotaanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalT1 | -.0018579 .0024994 -0.74 0.457 -.0067582 .0030424 signalT2 | .0000431 .0019863 0.02 0.983 -.0038512 .0039374 wave | .1034422 .033599 3.08 0.002 .0375672 .1693172 gender | .2472416 .0327614 7.55 0.000 .1830087 .3114746 prior | -.2507218 .0423209 -5.92 0.000 -.3336972 -.1677463 democrat | .5848254 .037325 15.67 0.000 .511645 .6580058 indep | .1421748 .048505 2.93 0.003 .0470746 .2372751 otherpol | .0597106 .1136475 0.53 0.599 -.1631098 .2825309 midwest | -.0918006 .0517943 -1.77 0.076 -.1933499 .0097487 south | .0093798 .0469107 0.20 0.842 -.0825945 .1013541 west | -.0307008 .0508083 -0.60 0.546 -.1303169 .0689152 age1 | .2522027 .0703531 3.58 0.000 .1142666 .3901389 age2 | .2525142 .0526697 4.79 0.000 .1492485 .3557798 age3 | .2015909 .0508849 3.96 0.000 .1018246 .3013572 age4 | .0920375 .0489154 1.88 0.060 -.0038674 .1879425 anychildren | .0950953 .0355539 2.67 0.008 .0253874 .1648033 loghhinc | -.064302 .0218345 -2.94 0.003 -.1071113 -.0214928 associatemore | -.0910609 .0366029 -2.49 0.013 -.1628256 -.0192963 fulltime | .0648472 .0496655 1.31 0.192 -.0325283 .1622228 parttime | .0493232 .0638357 0.77 0.440 -.0758347 .1744811 selfemp | .1062754 .070048 1.52 0.129 -.0310625 .2436134 unemployed | .0677736 .0790731 0.86 0.391 -.0872593 .2228065 student | -.0327095 .0989332 -0.33 0.741 -.2266804 .1612614 _cons | -.0706278 .2453897 -0.29 0.774 -.5517452 .4104897 ------------------------------------------------------------------------------- ( 1) signalT1 - signalT2 = 0 F( 1, 3583) = 0.39 Prob > F = 0.5310 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 24.90 Prob > F = 0.0000 R-squared = 0.1377 Root MSE = .95033 ------------------------------------------------------------------------------- | Robust quotaanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalpos | .0008038 .002111 0.38 0.703 -.0033351 .0049428 signalneg | -.0028626 .0027391 -1.05 0.296 -.008233 .0025077 wave | .1045842 .0335723 3.12 0.002 .0387616 .1704069 gender | .248146 .0327739 7.57 0.000 .1838887 .3124033 prior | -.2488655 .0419589 -5.93 0.000 -.3311312 -.1665998 democrat | .5863289 .0373162 15.71 0.000 .5131657 .6594921 indep | .1431908 .0484959 2.95 0.003 .0481085 .2382731 otherpol | .0606435 .1137039 0.53 0.594 -.1622873 .2835743 midwest | -.0913379 .0517852 -1.76 0.078 -.1928694 .0101936 south | .0092864 .0468822 0.20 0.843 -.0826322 .1012049 west | -.0306092 .050799 -0.60 0.547 -.1302071 .0689886 age1 | .2509678 .0703378 3.57 0.000 .1130616 .388874 age2 | .2508368 .0526495 4.76 0.000 .1476108 .3540628 age3 | .2008702 .0508473 3.95 0.000 .1011777 .3005626 age4 | .0919463 .048923 1.88 0.060 -.0039735 .1878661 anychildren | .0937746 .035572 2.64 0.008 .0240312 .1635179 loghhinc | -.0643789 .0218299 -2.95 0.003 -.1071791 -.0215787 associatemore | -.0898853 .0366172 -2.45 0.014 -.161678 -.0180927 fulltime | .0649297 .0496288 1.31 0.191 -.0323738 .1622331 parttime | .0488392 .0638319 0.77 0.444 -.0763113 .1739896 selfemp | .1066597 .0700082 1.52 0.128 -.0306002 .2439196 unemployed | .0677652 .0789987 0.86 0.391 -.0871217 .2226521 student | -.0331775 .0989723 -0.34 0.737 -.2272252 .1608702 _cons | -.0818304 .2461695 -0.33 0.740 -.5644769 .400816 ------------------------------------------------------------------------------- ( 1) signalpos - signalneg = 0 F( 1, 3583) = 1.07 Prob > F = 0.3019 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 27.42 Prob > F = 0.0000 R-squared = 0.1522 Root MSE = .91894 ------------------------------------------------------------------------------- | Robust AAanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalT1 | -.0049638 .0024358 -2.04 0.042 -.0097394 -.0001882 signalT2 | -.0044486 .0019383 -2.30 0.022 -.0082489 -.0006483 wave | .0158523 .0325302 0.49 0.626 -.0479273 .0796318 gender | .161314 .0319011 5.06 0.000 .0987679 .22386 prior | -.3196748 .0424633 -7.53 0.000 -.4029295 -.2364201 democrat | .6795299 .0362385 18.75 0.000 .6084798 .75058 indep | .2213891 .0477046 4.64 0.000 .1278582 .31492 otherpol | .0219331 .1145382 0.19 0.848 -.2026336 .2464997 midwest | -.0712154 .0499275 -1.43 0.154 -.1691046 .0266738 south | .0511184 .0455514 1.12 0.262 -.0381908 .1404276 west | -.0527622 .0487455 -1.08 0.279 -.1483339 .0428096 age1 | .1577307 .0665355 2.37 0.018 .0272796 .2881819 age2 | .1197462 .0500005 2.39 0.017 .0217139 .2177786 age3 | .0287708 .0493843 0.58 0.560 -.0680534 .1255949 age4 | .0095305 .0463319 0.21 0.837 -.0813091 .10037 anychildren | .0719982 .0346529 2.08 0.038 .0040569 .1399395 loghhinc | -.0415232 .021179 -1.96 0.050 -.0830473 8.08e-07 associatemore | .0248484 .0349927 0.71 0.478 -.0437592 .093456 fulltime | -.0125044 .0466203 -0.27 0.789 -.1039094 .0789006 parttime | -.0138489 .0589077 -0.24 0.814 -.1293449 .101647 selfemp | -.0139087 .0688109 -0.20 0.840 -.148821 .1210037 unemployed | -.0245345 .0752929 -0.33 0.745 -.1721557 .1230866 student | .0639213 .0933017 0.69 0.493 -.1190085 .2468511 _cons | -.1327244 .2395827 -0.55 0.580 -.6024567 .3370078 ------------------------------------------------------------------------------- ( 1) signalT1 - signalT2 = 0 F( 1, 3583) = 0.03 Prob > F = 0.8624 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 27.39 Prob > F = 0.0000 R-squared = 0.1523 Root MSE = .9189 ------------------------------------------------------------------------------- | Robust AAanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalpos | -.0037749 .0020859 -1.81 0.070 -.0078646 .0003149 signalneg | -.0058582 .0026502 -2.21 0.027 -.0110542 -.0006621 wave | .0169385 .0325413 0.52 0.603 -.0468629 .0807398 gender | .1619762 .0319356 5.07 0.000 .0993625 .2245899 prior | -.3193866 .041898 -7.62 0.000 -.4015328 -.2372403 democrat | .6802172 .0362423 18.77 0.000 .6091596 .7512748 indep | .22212 .0477144 4.66 0.000 .1285699 .3156701 otherpol | .021763 .1146 0.19 0.849 -.2029249 .2464509 midwest | -.0707909 .0499305 -1.42 0.156 -.1686861 .0271042 south | .0512289 .0455446 1.12 0.261 -.0380671 .1405249 west | -.0526642 .0487433 -1.08 0.280 -.1482316 .0429033 age1 | .1572366 .0664573 2.37 0.018 .0269387 .2875345 age2 | .118723 .0499854 2.38 0.018 .0207203 .2167256 age3 | .0283503 .0493649 0.57 0.566 -.0684358 .1251364 age4 | .0094726 .046343 0.20 0.838 -.0813887 .100334 anychildren | .0711353 .0346983 2.05 0.040 .0031049 .1391657 loghhinc | -.0415471 .0211786 -1.96 0.050 -.0830705 -.0000237 associatemore | .0254296 .0350245 0.73 0.468 -.0432404 .0940996 fulltime | -.0122321 .046632 -0.26 0.793 -.1036599 .0791957 parttime | -.0139931 .0589338 -0.24 0.812 -.1295403 .1015541 selfemp | -.0134183 .0688109 -0.20 0.845 -.1483307 .1214941 unemployed | -.024641 .0752579 -0.33 0.743 -.1721936 .1229116 student | .0634787 .0932874 0.68 0.496 -.119423 .2463804 _cons | -.1421101 .2402552 -0.59 0.554 -.6131607 .3289405 ------------------------------------------------------------------------------- ( 1) signalpos - signalneg = 0 F( 1, 3583) = 0.36 Prob > F = 0.5499 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 25.23 Prob > F = 0.0000 R-squared = 0.1378 Root MSE = .91599 ------------------------------------------------------------------------------- | Robust legislation~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalT1 | -.005098 .0024741 -2.06 0.039 -.0099487 -.0002473 signalT2 | -.0051831 .0019815 -2.62 0.009 -.0090682 -.0012981 wave | -.0136296 .0322897 -0.42 0.673 -.0769377 .0496785 gender | .2548275 .0320928 7.94 0.000 .1919055 .3177495 prior | -.2692698 .0428505 -6.28 0.000 -.3532836 -.185256 democrat | .6459551 .0362247 17.83 0.000 .5749319 .7169782 indep | .2988312 .0455941 6.55 0.000 .2094383 .3882242 otherpol | .3582176 .1203289 2.98 0.003 .1222975 .5941377 midwest | -.0044452 .0497045 -0.09 0.929 -.1018972 .0930068 south | .0067965 .045181 0.15 0.880 -.0817866 .0953796 west | -.0153092 .0476703 -0.32 0.748 -.1087728 .0781545 age1 | -.0953901 .0666889 -1.43 0.153 -.2261422 .0353619 age2 | -.055029 .0501884 -1.10 0.273 -.1534298 .0433717 age3 | -.0804405 .0508607 -1.58 0.114 -.1801594 .0192784 age4 | -.0311585 .0475893 -0.65 0.513 -.1244634 .0621465 anychildren | .0088231 .0344575 0.26 0.798 -.0587353 .0763814 loghhinc | -.0033422 .0211328 -0.16 0.874 -.0447756 .0380913 associatemore | -.0040623 .0340931 -0.12 0.905 -.0709062 .0627816 fulltime | -.0218091 .0488205 -0.45 0.655 -.1175279 .0739097 parttime | -.0653467 .0630615 -1.04 0.300 -.1889867 .0582933 selfemp | .1414385 .0666332 2.12 0.034 .0107956 .2720814 unemployed | .0826858 .0785815 1.05 0.293 -.0713832 .2367548 student | .1463762 .0929858 1.57 0.116 -.0359342 .3286865 _cons | -.3705517 .2352407 -1.58 0.115 -.8317708 .0906675 ------------------------------------------------------------------------------- ( 1) signalT1 - signalT2 = 0 F( 1, 3583) = 0.00 Prob > F = 0.9778 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 25.28 Prob > F = 0.0000 R-squared = 0.1381 Root MSE = .91586 ------------------------------------------------------------------------------- | Robust legislation~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalpos | -.0066276 .0021332 -3.11 0.002 -.01081 -.0024451 signalneg | -.0031765 .0027055 -1.17 0.240 -.0084809 .0021279 wave | -.0160236 .0323695 -0.50 0.621 -.0794882 .0474409 gender | .2535289 .0321301 7.89 0.000 .1905337 .316524 prior | -.2687048 .0426437 -6.30 0.000 -.3523132 -.1850964 democrat | .6450437 .0362667 17.79 0.000 .5739382 .7161493 indep | .2974115 .0455882 6.52 0.000 .2080301 .3867929 otherpol | .3594516 .1201243 2.99 0.003 .1239327 .5949704 midwest | -.005368 .0497048 -0.11 0.914 -.1028204 .0920845 south | .0063909 .0451834 0.14 0.888 -.0821968 .0949786 west | -.015534 .0476735 -0.33 0.745 -.1090038 .0779359 age1 | -.0948541 .0667309 -1.42 0.155 -.2256885 .0359803 age2 | -.0532386 .0501469 -1.06 0.288 -.151558 .0450807 age3 | -.0797292 .0508593 -1.57 0.117 -.1794453 .0199869 age4 | -.0310546 .0475727 -0.65 0.514 -.1243268 .0622177 anychildren | .0104052 .0345087 0.30 0.763 -.0572534 .0780637 loghhinc | -.0033295 .0211152 -0.16 0.875 -.0447284 .0380695 associatemore | -.004907 .0341483 -0.14 0.886 -.0718591 .0620451 fulltime | -.0225668 .0487973 -0.46 0.644 -.11824 .0731064 parttime | -.0652858 .0630776 -1.04 0.301 -.1889574 .0583858 selfemp | .1402561 .0666115 2.11 0.035 .0096559 .2708564 unemployed | .0830004 .0785901 1.06 0.291 -.0710854 .2370862 student | .1473496 .0930682 1.58 0.113 -.0351223 .3298214 _cons | -.3508971 .23651 -1.48 0.138 -.8146048 .1128106 ------------------------------------------------------------------------------- ( 1) signalpos - signalneg = 0 F( 1, 3583) = 0.92 Prob > F = 0.3378 (sum of wgt is 2.2230e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,223 F(22, 2200) = 14.47 Prob > F = 0.0000 R-squared = 0.1222 Root MSE = .92415 ------------------------------------------------------------------------------- | Robust transparenc~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalT1 | .0000487 .0031581 0.02 0.988 -.0061444 .0062418 signalT2 | -.0021257 .002461 -0.86 0.388 -.0069518 .0027004 wave | 0 (omitted) gender | .2303655 .0403997 5.70 0.000 .15114 .309591 prior | -.2209113 .0555737 -3.98 0.000 -.3298937 -.1119288 democrat | .5771787 .0458639 12.58 0.000 .4872376 .6671199 indep | .2410863 .0613782 3.93 0.000 .120721 .3614516 otherpol | .1105551 .1671836 0.66 0.509 -.2172992 .4384094 midwest | -.0497077 .0631942 -0.79 0.432 -.1736342 .0742188 south | -.0210231 .0572459 -0.37 0.713 -.1332848 .0912387 west | .001592 .0614354 0.03 0.979 -.1188856 .1220695 age1 | .144876 .0931484 1.56 0.120 -.0377921 .3275441 age2 | .1009544 .0635873 1.59 0.113 -.023743 .2256518 age3 | .0446422 .0639054 0.70 0.485 -.080679 .1699635 age4 | .0525287 .0620045 0.85 0.397 -.0690647 .174122 anychildren | .0589183 .0449031 1.31 0.190 -.0291385 .1469751 loghhinc | -.02058 .0266254 -0.77 0.440 -.0727935 .0316336 associatemore | .0994758 .0442345 2.25 0.025 .01273 .1862217 fulltime | -.0692036 .0618633 -1.12 0.263 -.1905202 .052113 parttime | -.1299923 .077116 -1.69 0.092 -.2812201 .0212354 selfemp | .0172828 .0863812 0.20 0.841 -.1521144 .1866801 unemployed | .0123152 .0934188 0.13 0.895 -.170883 .1955134 student | .1956398 .1208888 1.62 0.106 -.0414283 .4327079 _cons | -.3075519 .2871003 -1.07 0.284 -.8705679 .2554641 ------------------------------------------------------------------------------- ( 1) signalT1 - signalT2 = 0 F( 1, 2200) = 0.31 Prob > F = 0.5784 (sum of wgt is 2.2230e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,223 F(22, 2200) = 14.61 Prob > F = 0.0000 R-squared = 0.1231 Root MSE = .92365 ------------------------------------------------------------------------------- | Robust transparenc~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalpos | -.0042731 .0026449 -1.62 0.106 -.00946 .0009137 signalneg | .0029939 .0034699 0.86 0.388 -.0038106 .0097985 wave | 0 (omitted) gender | .2283391 .0403384 5.66 0.000 .1492337 .3074444 prior | -.2220872 .0550148 -4.04 0.000 -.3299737 -.1142008 democrat | .5741379 .0459302 12.50 0.000 .4840668 .6642089 indep | .2387332 .0614001 3.89 0.000 .118325 .3591415 otherpol | .1117899 .1674327 0.67 0.504 -.2165528 .4401325 midwest | -.0509946 .0632103 -0.81 0.420 -.1749526 .0729634 south | -.020995 .057171 -0.37 0.713 -.1331098 .0911199 west | .0020118 .0614184 0.03 0.974 -.1184323 .1224559 age1 | .1439708 .0930749 1.55 0.122 -.0385531 .3264947 age2 | .1030993 .0634887 1.62 0.105 -.0214048 .2276033 age3 | .0450452 .0639242 0.70 0.481 -.0803128 .1704032 age4 | .0516381 .0618997 0.83 0.404 -.0697499 .1730261 anychildren | .0625339 .0448768 1.39 0.164 -.0254715 .1505394 loghhinc | -.020915 .0266253 -0.79 0.432 -.0731284 .0312984 associatemore | .0969907 .0442615 2.19 0.029 .010192 .1837894 fulltime | -.0678099 .0617733 -1.10 0.272 -.1889499 .0533301 parttime | -.1280621 .0771073 -1.66 0.097 -.2792728 .0231487 selfemp | .0165711 .0862637 0.19 0.848 -.1525956 .1857379 unemployed | .0164735 .093444 0.18 0.860 -.1667742 .1997213 student | .1960006 .1213896 1.61 0.107 -.0420496 .4340509 _cons | -.2764752 .2883894 -0.96 0.338 -.8420193 .2890688 ------------------------------------------------------------------------------- ( 1) signalpos - signalneg = 0 F( 1, 2200) = 2.49 Prob > F = 0.1144 (sum of wgt is 1.3368e+03) note: wave omitted because of collinearity Linear regression Number of obs = 1,384 F(22, 1361) = 11.37 Prob > F = 0.0000 R-squared = 0.1491 Root MSE = .9307 ------------------------------------------------------------------------------- | Robust UKtool | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalT1 | -.0037213 .0042994 -0.87 0.387 -.0121554 .0047128 signalT2 | -.0040502 .0034092 -1.19 0.235 -.010738 .0026376 wave | 0 (omitted) gender | .3759826 .0535527 7.02 0.000 .2709278 .4810374 prior | -.2236146 .0659252 -3.39 0.001 -.3529407 -.0942885 democrat | .5970233 .0616584 9.68 0.000 .4760674 .7179792 indep | .26 .0772627 3.37 0.001 .108433 .4115669 otherpol | .0746162 .2401173 0.31 0.756 -.3964239 .5456564 midwest | -.0704371 .0821928 -0.86 0.392 -.2316755 .0908012 south | -.0975971 .0742357 -1.31 0.189 -.2432259 .0480317 west | -.0716688 .0792894 -0.90 0.366 -.2272115 .0838739 age1 | .0599835 .1122864 0.53 0.593 -.1602897 .2802566 age2 | -.0120106 .0877472 -0.14 0.891 -.1841451 .1601239 age3 | .0294054 .084225 0.35 0.727 -.1358196 .1946304 age4 | .0702789 .078919 0.89 0.373 -.0845371 .2250949 anychildren | -.0740893 .0581151 -1.27 0.203 -.1880942 .0399156 loghhinc | .0389024 .0363823 1.07 0.285 -.0324692 .1102739 associatemore | .0300973 .0593473 0.51 0.612 -.0863248 .1465193 fulltime | -.050561 .079733 -0.63 0.526 -.2069739 .1058519 parttime | -.045433 .1009288 -0.45 0.653 -.2434258 .1525599 selfemp | .1846725 .1155446 1.60 0.110 -.0419923 .4113373 unemployed | -.0941685 .1441764 -0.65 0.514 -.3770005 .1886635 student | .2033988 .1398744 1.45 0.146 -.0709939 .4777915 _cons | -.8412531 .4137721 -2.03 0.042 -1.652953 -.0295529 ------------------------------------------------------------------------------- ( 1) signalT1 - signalT2 = 0 F( 1, 1361) = 0.00 Prob > F = 0.9505 (sum of wgt is 1.3368e+03) note: wave omitted because of collinearity Linear regression Number of obs = 1,384 F(22, 1361) = 11.47 Prob > F = 0.0000 R-squared = 0.1510 Root MSE = .92967 ------------------------------------------------------------------------------- | Robust UKtool | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalpos | -.0081041 .00351 -2.31 0.021 -.0149896 -.0012185 signalneg | .0012759 .0046365 0.28 0.783 -.0078195 .0103714 wave | 0 (omitted) gender | .372508 .0534439 6.97 0.000 .2676665 .4773494 prior | -.2240818 .0655912 -3.42 0.001 -.3527526 -.0954111 democrat | .5959181 .0613164 9.72 0.000 .4756332 .7162029 indep | .2554204 .077244 3.31 0.001 .1038902 .4069505 otherpol | .0756373 .2361999 0.32 0.749 -.3877182 .5389927 midwest | -.0725838 .0821721 -0.88 0.377 -.2337815 .088614 south | -.0988201 .0741796 -1.33 0.183 -.2443389 .0466986 west | -.0732598 .0792719 -0.92 0.356 -.2287681 .0822486 age1 | .0668376 .1116059 0.60 0.549 -.1521008 .2857759 age2 | -.003003 .0877212 -0.03 0.973 -.1750864 .1690804 age3 | .0343804 .084132 0.41 0.683 -.1306621 .199423 age4 | .0740958 .0788924 0.94 0.348 -.0806681 .2288596 anychildren | -.0721899 .0580239 -1.24 0.214 -.1860159 .0416361 loghhinc | .0399876 .0362078 1.10 0.270 -.0310415 .1110167 associatemore | .0284373 .0593317 0.48 0.632 -.0879542 .1448288 fulltime | -.0572223 .0791494 -0.72 0.470 -.2124904 .0980458 parttime | -.0491037 .100622 -0.49 0.626 -.2464948 .1482873 selfemp | .1808331 .1150676 1.57 0.116 -.044896 .4065622 unemployed | -.1043926 .1443241 -0.72 0.470 -.3875144 .1787292 student | .2048795 .1387542 1.48 0.140 -.0673158 .4770748 _cons | -.8115085 .412058 -1.97 0.049 -1.619846 -.0031709 ------------------------------------------------------------------------------- ( 1) signalpos - signalneg = 0 F( 1, 1361) = 2.51 Prob > F = 0.1134 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 22.20 Prob > F = 0.0000 R-squared = 0.1298 Root MSE = .92784 ------------------------------------------------------------------------------- | Robust childcare | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalT1 | -.000724 .0024578 -0.29 0.768 -.0055429 .0040949 signalT2 | .0001095 .0019719 0.06 0.956 -.0037566 .0039757 wave | -.0836053 .0326563 -2.56 0.011 -.1476321 -.0195784 gender | .1339316 .032599 4.11 0.000 .0700171 .197846 prior | -.1989022 .0426267 -4.67 0.000 -.2824772 -.1153272 democrat | .5939691 .0365151 16.27 0.000 .5223766 .6655616 indep | .128187 .0471904 2.72 0.007 .0356642 .2207097 otherpol | .1189082 .1311003 0.91 0.364 -.1381304 .3759468 midwest | -.0874616 .0495595 -1.76 0.078 -.1846293 .0097061 south | -.0494448 .0448975 -1.10 0.271 -.137472 .0385824 west | -.1228952 .0498239 -2.47 0.014 -.2205812 -.0252092 age1 | .324416 .0672492 4.82 0.000 .1925655 .4562665 age2 | .3110043 .0516148 6.03 0.000 .2098069 .4122017 age3 | .197031 .0517504 3.81 0.000 .0955678 .2984941 age4 | .0711031 .0481784 1.48 0.140 -.0233568 .165563 anychildren | .1566463 .0352839 4.44 0.000 .0874677 .2258249 loghhinc | -.0424397 .0218143 -1.95 0.052 -.0852093 .0003299 associatemore | -.0111174 .0350475 -0.32 0.751 -.0798325 .0575976 fulltime | -.0421135 .049408 -0.85 0.394 -.1389842 .0547572 parttime | -.0139756 .0629164 -0.22 0.824 -.1373313 .10938 selfemp | .0508156 .0697241 0.73 0.466 -.0858873 .1875184 unemployed | -.0344559 .079205 -0.44 0.664 -.1897474 .1208356 student | -.0046027 .0853415 -0.05 0.957 -.1719254 .1627201 _cons | .0315003 .2432027 0.13 0.897 -.4453293 .5083299 ------------------------------------------------------------------------------- ( 1) signalT1 - signalT2 = 0 F( 1, 3583) = 0.08 Prob > F = 0.7815 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 22.21 Prob > F = 0.0000 R-squared = 0.1299 Root MSE = .92782 ------------------------------------------------------------------------------- | Robust childcare | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalpos | -.0009104 .0021237 -0.43 0.668 -.0050742 .0032533 signalneg | .0006363 .002714 0.23 0.815 -.0046848 .0059575 wave | -.085353 .0327326 -2.61 0.009 -.1495294 -.0211767 gender | .1331206 .0326659 4.08 0.000 .069075 .1971663 prior | -.1974663 .0422141 -4.68 0.000 -.2802324 -.1147001 democrat | .5938184 .0364925 16.27 0.000 .5222701 .6653666 indep | .1273136 .047216 2.70 0.007 .0347406 .2198866 otherpol | .1205416 .1309695 0.92 0.357 -.1362406 .3773238 midwest | -.0881245 .0495554 -1.78 0.075 -.1852841 .0090351 south | -.0498791 .0448823 -1.11 0.267 -.1378766 .0381184 west | -.1230668 .0497914 -2.47 0.013 -.2206891 -.0254446 age1 | .3243359 .0671735 4.83 0.000 .1926338 .456038 age2 | .311915 .0516562 6.04 0.000 .2106366 .4131934 age3 | .1973666 .0517643 3.81 0.000 .0958762 .298857 age4 | .0711589 .0481625 1.48 0.140 -.0232699 .1655876 anychildren | .1575287 .0353783 4.45 0.000 .0881652 .2268923 loghhinc | -.0424645 .0218101 -1.95 0.052 -.0852259 .0002969 associatemore | -.0113621 .0350394 -0.32 0.746 -.0800613 .0573371 fulltime | -.042801 .0493622 -0.87 0.386 -.1395819 .0539799 parttime | -.0141502 .0628955 -0.22 0.822 -.1374647 .1091642 selfemp | .0498658 .069622 0.72 0.474 -.086637 .1863686 unemployed | -.0341579 .0792017 -0.43 0.666 -.1894429 .1211271 student | -.0038938 .0853284 -0.05 0.964 -.1711908 .1634033 _cons | .0449697 .2436999 0.18 0.854 -.4328348 .5227742 ------------------------------------------------------------------------------- ( 1) signalpos - signalneg = 0 F( 1, 3583) = 0.19 Prob > F = 0.6657 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 43.64 Prob > F = 0.0000 R-squared = 0.2257 Root MSE = .65956 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalT1 | -.0025981 .0017793 -1.46 0.144 -.0060867 .0008905 signalT2 | -.002191 .0014255 -1.54 0.124 -.0049859 .0006039 wave | .010108 .0234806 0.43 0.667 -.0359286 .0561447 gender | .2189948 .0228596 9.58 0.000 .1741757 .2638138 prior | -.2457722 .030727 -8.00 0.000 -.3060163 -.1855282 democrat | .6118825 .0261752 23.38 0.000 .5605628 .6632022 indep | .2035394 .033787 6.02 0.000 .1372958 .2697831 otherpol | .1360202 .0937214 1.45 0.147 -.0477325 .3197729 midwest | -.0639602 .0356104 -1.80 0.073 -.1337788 .0058585 south | -.0125262 .0317379 -0.39 0.693 -.0747524 .0497 west | -.0501145 .0345011 -1.45 0.146 -.1177584 .0175293 age1 | .1540081 .0461172 3.34 0.001 .0635895 .2444268 age2 | .1437085 .0366935 3.92 0.000 .0717663 .2156508 age3 | .083529 .0373461 2.24 0.025 .0103073 .1567506 age4 | .0429545 .0345906 1.24 0.214 -.0248648 .1107738 anychildren | .0701629 .0246677 2.84 0.004 .0217987 .118527 loghhinc | -.0293278 .0153005 -1.92 0.055 -.0593264 .0006707 associatemore | -.0042396 .0251792 -0.17 0.866 -.0536065 .0451274 fulltime | -.0148995 .0353963 -0.42 0.674 -.0842984 .0544994 parttime | -.0275468 .0437646 -0.63 0.529 -.1133529 .0582592 selfemp | .0767725 .0504098 1.52 0.128 -.0220622 .1756072 unemployed | .0160356 .0541909 0.30 0.767 -.0902124 .1222837 student | .0754352 .0624555 1.21 0.227 -.0470168 .1978872 _cons | -.2225891 .1713725 -1.30 0.194 -.5585865 .1134083 ------------------------------------------------------------------------------- ( 1) signalT1 - signalT2 = 0 F( 1, 3583) = 0.03 Prob > F = 0.8539 (sum of wgt is 3.5598e+03) Linear regression Number of obs = 3,607 F(23, 3583) = 43.63 Prob > F = 0.0000 R-squared = 0.2258 Root MSE = .65952 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- signalpos | -.003064 .0015132 -2.02 0.043 -.0060308 -.0000972 signalneg | -.001435 .0019297 -0.74 0.457 -.0052184 .0023484 wave | .0086318 .0235191 0.37 0.714 -.0374803 .0547438 gender | .2182643 .0228962 9.53 0.000 .1733735 .2631552 prior | -.2448987 .0304658 -8.04 0.000 -.3046308 -.1851667 democrat | .6115845 .0261648 23.37 0.000 .5602852 .6628839 indep | .2027477 .0338059 6.00 0.000 .136467 .2690283 otherpol | .137157 .0935317 1.47 0.143 -.0462236 .3205376 midwest | -.0645237 .0356091 -1.81 0.070 -.1343397 .0052924 south | -.0128472 .0317371 -0.40 0.686 -.0750718 .0493774 west | -.050257 .0344948 -1.46 0.145 -.1178884 .0173743 age1 | .1540968 .0460641 3.35 0.001 .0637824 .2444112 age2 | .1446091 .0366645 3.94 0.000 .0727238 .2164945 age3 | .0838734 .0373582 2.25 0.025 .010628 .1571188 age4 | .0430083 .0345771 1.24 0.214 -.0247846 .1108011 anychildren | .0709986 .0246882 2.88 0.004 .0225942 .1194029 loghhinc | -.0293375 .0152961 -1.92 0.055 -.0593274 .0006524 associatemore | -.0045695 .0251914 -0.18 0.856 -.0539605 .0448215 fulltime | -.0154356 .0353819 -0.44 0.663 -.0848064 .0539351 parttime | -.0276217 .0437901 -0.63 0.528 -.1134776 .0582342 selfemp | .075999 .0503878 1.51 0.132 -.0227926 .1747906 unemployed | .0162647 .0542264 0.30 0.764 -.090053 .1225823 student | .0760346 .0624719 1.22 0.224 -.0464496 .1985187 _cons | -.2109206 .1718824 -1.23 0.220 -.5479177 .1260765 ------------------------------------------------------------------------------- ( 1) signalpos - signalneg = 0 F( 1, 3583) = 0.41 Prob > F = 0.5219 . . loc rowlabels " "{\bf Panel A: Baseline Specification}" " " "(Signal -- Prior)" " " " " "Prior" " > " " " "Observations" "\hline {\bf Panel B: Interaction with T$^{74}$ and T$^{94}$}" " " "(Signal - > - Prior) x T$^{74}$ (a)" " " " " "(Signal -- Prior) x T$^{94}$ (b)" " " "p-value [(a) -- (b) = 0]" > " " "Prior" " " " " "Observations" "\hline {\bf Panel C: Interact. with pos./neg. Signal}" " " "( > Signal -- Prior) x $\textbf{1}$(Signal -- Prior > 0) (a)" " " " " "(Signal -- Prior) x $\textbf{1} > $(Signal -- Prior < 0) (b)" " " "p-value [(a) -- (b) = 0]" " " "Prior" " " " " "Observations" " " > " . loc rowstats "" . . forval i = 1/35 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\policyalt_infoupdate.tex", replace cells(none) booktabs nonotes nomtitles > compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mgroups("\shortstack{Introduce\\gender\\quotas}" "\shortstack{Statutory\\affirmative\\action}" / > // > "\shortstack{Stricter\\equal pay\\legislation}" "\shortstack{Wage transp.\\within\\ companies}" > "\shortstack{Introduce\\reporting\\website}" "\shortstack{Increase\\subsidies\\to child care}" /// > "\shortstack{Policy\\demand\\index}", pattern(1 1 1 1 1 1 1 ) prefix(\multicolumn{@span}{c}{) s > uffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\policyalt_infoupdate.tex) . . . eststo clear . . . *********************************************************************************** . // Table D.11: Robustness of main treatment effect to FWER control . *********************************************************************************** . . use "$path\data\SurveyStageI_AB_final.dta", clear . . drop if rand==0 (1,034 observations deleted) . . loc experiments "z_mani_index z_lmpolicy_index z_extreasons_index" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experiments' { 2. . . reg `choice' T1 $controls [pweight=pweight], vce(r) 3. sigstar T1, prec(3) 4. estadd loc thisstat2 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat3 = "(`r(pstar)')": col`colnum' 6. sigstar gender, prec(3) 7. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat7 = "(`r(pstar)')": col`colnum' 9. sigstar democrat, prec(3) 10. estadd loc thisstat9 = "`r(bstar)'": col`colnum' 11. estadd loc thisstat10 = "(`r(pstar)')": col`colnum' 12. . qui sum `choice' 13. estadd loc thisstat12 = r(N): col`colnum' 14. . . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. . } (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 32.19 Prob > F = 0.0000 R-squared = 0.1988 Root MSE = .87434 ------------------------------------------------------------------------------- | Robust z_mani_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4170495 .0322181 12.94 0.000 .3538779 .4802212 wave | -.0177316 .0343865 -0.52 0.606 -.0851551 .0496919 gender | .2773129 .0327356 8.47 0.000 .2131265 .3414992 prior | -.005303 .0008692 -6.10 0.000 -.0070072 -.0035988 democrat | .6653624 .0368133 18.07 0.000 .5931805 .7375442 indep | .251875 .0513566 4.90 0.000 .1511774 .3525726 otherpol | .2426557 .1382573 1.76 0.079 -.0284328 .5137441 midwest | -.1208645 .0519175 -2.33 0.020 -.2226618 -.0190672 south | -.007256 .0462316 -0.16 0.875 -.0979048 .0833928 west | -.072072 .0499037 -1.44 0.149 -.1699209 .0257769 age1 | .0996826 .0719346 1.39 0.166 -.0413634 .2407286 age2 | .1077443 .0510046 2.11 0.035 .0077368 .2077517 age3 | .059659 .0507031 1.18 0.239 -.0397572 .1590752 age4 | -.046534 .0491284 -0.95 0.344 -.1428627 .0497946 anychildren | .1307584 .0354388 3.69 0.000 .0612716 .2002452 loghhinc | .009372 .0224739 0.42 0.677 -.0346938 .0534378 associatemore | -.0070954 .0365091 -0.19 0.846 -.0786808 .0644899 fulltime | .0542119 .0497542 1.09 0.276 -.0433438 .1517675 parttime | -.0992796 .0651583 -1.52 0.128 -.2270389 .0284797 selfemp | -.0244499 .0692565 -0.35 0.724 -.1602447 .111345 unemployed | .0365494 .0802278 0.46 0.649 -.1207575 .1938563 student | .02181 .1024313 0.21 0.831 -.1790325 .2226525 _cons | -.4614966 .2596682 -1.78 0.076 -.9706419 .0476486 ------------------------------------------------------------------------------- (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 30.19 Prob > F = 0.0000 R-squared = 0.1867 Root MSE = .68334 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0562661 .0251019 2.24 0.025 .0070476 .1054847 wave | .0197672 .0276836 0.71 0.475 -.0345136 .0740479 gender | .2029651 .0255524 7.94 0.000 .1528633 .253067 prior | -.0037725 .0007382 -5.11 0.000 -.0052199 -.0023251 democrat | .5935843 .0289701 20.49 0.000 .5367811 .6503875 indep | .1922975 .0382281 5.03 0.000 .1173417 .2672533 otherpol | .2365913 .0989824 2.39 0.017 .0425112 .4306714 midwest | -.1029934 .0414268 -2.49 0.013 -.184221 -.0217657 south | -.0001163 .0365547 -0.00 0.997 -.0717909 .0715584 west | -.0744621 .040303 -1.85 0.065 -.1534864 .0045622 age1 | .1008579 .0547726 1.84 0.066 -.0065376 .2082534 age2 | .1402544 .040765 3.44 0.001 .0603244 .2201845 age3 | .0901361 .0408989 2.20 0.028 .0099433 .1703288 age4 | .0323242 .0389596 0.83 0.407 -.0440659 .1087144 anychildren | .1131292 .0277134 4.08 0.000 .0587901 .1674683 loghhinc | -.0172534 .017781 -0.97 0.332 -.0521175 .0176107 associatemore | .0023243 .0280954 0.08 0.934 -.0527639 .0574125 fulltime | .0236588 .0396963 0.60 0.551 -.0541758 .1014934 parttime | -.0092615 .0510459 -0.18 0.856 -.1093498 .0908268 selfemp | .0897213 .0563452 1.59 0.111 -.0207577 .2002003 unemployed | .1115593 .0592556 1.88 0.060 -.0046262 .2277449 student | .1018654 .0751883 1.35 0.176 -.0455603 .2492912 _cons | -.0983964 .2090348 -0.47 0.638 -.508262 .3114692 ------------------------------------------------------------------------------- (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 14.14 Prob > F = 0.0000 R-squared = 0.1326 Root MSE = .71857 ------------------------------------------------------------------------------- | Robust z_extreason~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1106092 .032187 3.44 0.001 .0474854 .173733 wave | 0 (omitted) gender | .2507224 .0330813 7.58 0.000 .1858449 .3156 prior | -.0035808 .0008879 -4.03 0.000 -.0053222 -.0018395 democrat | .4419213 .0375026 11.78 0.000 .3683728 .5154699 indep | .1893519 .0486072 3.90 0.000 .0940256 .2846782 otherpol | .004112 .1413676 0.03 0.977 -.2731321 .281356 midwest | .0558993 .0536599 1.04 0.298 -.0493361 .1611347 south | .0517613 .0467073 1.11 0.268 -.0398391 .1433618 west | .0716621 .0510618 1.40 0.161 -.028478 .1718022 age1 | -.0438919 .0786376 -0.56 0.577 -.1981126 .1103289 age2 | -.0581802 .0503488 -1.16 0.248 -.1569221 .0405618 age3 | -.0820933 .0493262 -1.66 0.096 -.1788298 .0146432 age4 | -.1287332 .0485146 -2.65 0.008 -.2238779 -.0335885 anychildren | -.0043703 .0358468 -0.12 0.903 -.0746715 .0659309 loghhinc | .0314941 .022441 1.40 0.161 -.0125163 .0755044 associatemore | .0911816 .0362447 2.52 0.012 .0201 .1622632 fulltime | .0406524 .0495255 0.82 0.412 -.0564748 .1377796 parttime | -.0038261 .065662 -0.06 0.954 -.1325997 .1249474 selfemp | .0429516 .0671067 0.64 0.522 -.0886552 .1745584 unemployed | .0310763 .0744142 0.42 0.676 -.1148615 .1770142 student | .1011267 .1049782 0.96 0.336 -.1047521 .3070054 _cons | -.5431141 .2566681 -2.12 0.034 -1.046481 -.0397476 ------------------------------------------------------------------------------- . . //FWER control for multiple hypothesis testing: . *** Calculate Westfall-Young adjusted p-vals using free step-down resampling method (Westfall and > Young, 1993) with 100,000 replications . mat def P = J(3, 1, .) . wyoung z_mani_index z_lmpolicy_index z_extreasons_index, cmd(regress OUTCOMEVAR T1 $controls) fam > ilyp(T1) bootstraps(100000) seed(1) Estimating the family-wise p-values for T1 in the following set of regressions: regress z_mani_index T1 wave gender prior democrat indep otherpol midwest south west age1 age2 age3 > age4 anychildren loghhinc associatemore fulltime parttime selfemp unemp student regress z_lmpolicy_index T1 wave gender prior democrat indep otherpol midwest south west age1 age2 a > ge3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemp student regress z_extreasons_index T1 wave gender prior democrat indep otherpol midwest south west age1 age2 > age3 age4 anychildren loghhinc associatemore fulltime parttime selfemp unemp student +---------------------------------------------------------------------------------------------+ 1. | k | | 1 | |---------------------------------------------------------------------------------------------| | model | | regress z_mani_index T1 wave gender prior democrat indep otherpol midwest south west age1.. | |---------------------------------------------------------------------------------------------| | outcome | regres~r | coef | stderr | p | pwyoung | pbonf | | z_mani_index | T1 | .41723777 | .03179729 | 2.727e-38 | 0 | 8.180e-38 | |---------------------------------------------------------------------------------------------| | psidak | | 0 | +---------------------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------------------+ 2. | k | | 2 | |---------------------------------------------------------------------------------------------| | model | | regress z_lmpolicy_index T1 wave gender prior democrat indep otherpol midwest south west .. | |---------------------------------------------------------------------------------------------| | outcome | regres~r | coef | stderr | p | pwyoung | pbonf | | z_lmpolicy_index | T1 | .06164929 | .02488954 | .01330689 | .01347 | .01330689 | |---------------------------------------------------------------------------------------------| | psidak | | .01330689 | +---------------------------------------------------------------------------------------------+ +---------------------------------------------------------------------------------------------+ 3. | k | | 3 | |---------------------------------------------------------------------------------------------| | model | | regress z_extreasons_index T1 wave gender prior democrat indep otherpol midwest south wes.. | |---------------------------------------------------------------------------------------------| | outcome | regres~r | coef | stderr | p | pwyoung | pbonf | | z_extreasons_index | T1 | .11060917 | .03218702 | .00060149 | .00151 | .00120299 | |---------------------------------------------------------------------------------------------| | psidak | | .00120262 | +---------------------------------------------------------------------------------------------+ . . matrix mean1=r(table) . . matrix list mean1 mean1[3,6] coef stderr p pwyoung pbonf psidak r1 .41723777 .03179729 2.727e-38 0 8.180e-38 0 r2 .06164929 .02488954 .01330689 .01347 .01330689 .01330689 r3 .11060917 .03218702 .00060149 .00151 .00120299 .00120262 . . mat def P[1, 1] = mean1[1,4] . mat def P[2, 1] = mean1[2,4] . mat def P[3, 1] = mean1[3,4] . . . estadd loc thisstat4 = "[" + string(P[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat4 = "[" +string(P[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat4 = "[" + string(P[3, 1], "%9.3f")+"]": col3 . . . . loc rowlabels " " " "T$^{74}$" "Standard p-value" "FWER-adjusted p-value" " " "Female" " " " " "De > mocrat" " " " " "Observations" " " " . loc rowstats "" . . forval i = 1/12 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . . esttab * using "$output\FWER_AB.tex", replace cells(none) booktabs nonotes compress alignment(c) n > ogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mtitle("General Perceptions" "Spec. Policy Demand" "Perceived Imp. Reasons") /// > mgroups("Index", pattern(1 0 0) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidru > le(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\FWER_AB.tex) . . . eststo clear . . . *********************************************************************************** . // Table E.1: Importance of other beliefs and preferences . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . loc experiments "1 2 3 4 5 6 7" . . * Keep Wave B control group . keep if wave==2&rand==0 (3,529 observations deleted) . . global worldviews tallshort reversedisc GRA zerosum meritocracy . > . // generate perceived cost index: . gen cost_index=(monetary+distortion+bureaucracy)/3 . // generate perceived adverse effects index . gen adverse=(zerosum + reversedisc)/2 . . **z-score independent vars: . foreach var of varlist prior cost_index GRA tallshort adverse{ 2. egen z_`var'=std(`var') 3. replace `var'=z_`var' 4. drop z_`var' 5. } (536 real changes made) (536 real changes made) (536 real changes made) (536 real changes made) (536 real changes made) . . // Interaction terms . foreach var of varlist cost_index GRA tallshort adverse { 2. gen `var'_prior = `var' * prior 3. } . . sum prior, d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% -2.078728 -3.981144 5% -1.603123 -2.506771 10% -1.032398 -2.31653 Obs 536 25% -.4141129 -2.31653 Sum of Wgt. 536 50% -.1287503 Mean -1.58e-09 Largest Std. Dev. 1 75% .3944143 5.293138 90% .7748977 5.293138 Variance 1 95% 1.393183 5.53094 Skewness 1.717166 99% 4.341929 5.53094 Kurtosis 11.61069 . keep if prior > r(p5) & prior < r(p95) (58 observations deleted) . . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . ***Correlates with dem, rep and gender . reg z_lmpolicy_index democrat indep otherpol gender [pweight=pweight], vce(r) (sum of wgt is 4.6187e+02) Linear regression Number of obs = 478 F(4, 473) = 27.03 Prob > F = 0.0000 R-squared = 0.2108 Root MSE = .66197 ------------------------------------------------------------------------------ | Robust z_lmpolicy~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- democrat | .6141158 .0711095 8.64 0.000 .4743862 .7538455 indep | .1658696 .0839279 1.98 0.049 .000952 .3307872 otherpol | -.2934057 .3421586 -0.86 0.392 -.9657446 .3789332 gender | .3112349 .0611811 5.09 0.000 .1910145 .4314554 _cons | -.4796929 .0700922 -6.84 0.000 -.6174235 -.3419623 ------------------------------------------------------------------------------ . . local n = round(e(N)) . local r2 = e(r2) . . sigstar democrat, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar gender, prec(3) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . . estadd loc thisstat31 = string(`r2', "%9.2f"): col`colnum' . estadd loc thisstat32 = "`n'": col`colnum' . . . loc ++colnum . . . ***Correlates including gender, political orientation and prior . reg z_lmpolicy_index prior gender democrat indep otherpol [pweight=pweight], vce(r) (sum of wgt is 4.6187e+02) Linear regression Number of obs = 478 F(5, 472) = 23.71 Prob > F = 0.0000 R-squared = 0.2224 Root MSE = .6578 ------------------------------------------------------------------------------ | Robust z_lmpolicy~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.1522285 .0678604 -2.24 0.025 -.2855744 -.0188827 gender | .2903533 .0625406 4.64 0.000 .1674608 .4132458 democrat | .5985682 .0714364 8.38 0.000 .4581955 .7389409 indep | .1721127 .0836789 2.06 0.040 .0076835 .3365418 otherpol | -.2592563 .337858 -0.77 0.443 -.9231483 .4046356 _cons | -.470522 .070265 -6.70 0.000 -.6085929 -.3324511 ------------------------------------------------------------------------------ . . local n = round(e(N)) . local r2 = e(r2) . . sigstar democrat, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar gender, prec(3) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar prior, prec(3) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . . estadd loc thisstat31 = string(`r2', "%9.2f"): col`colnum' . estadd loc thisstat32 = "`n'": col`colnum' . . loc ++colnum . . reg z_lmpolicy_index prior gender democrat indep otherpol cost_index cost_index_prior [pwe > ight=pweight], vce(r) (sum of wgt is 4.6187e+02) Linear regression Number of obs = 478 F(7, 470) = 27.28 Prob > F = 0.0000 R-squared = 0.3310 Root MSE = .61143 ---------------------------------------------------------------------------------- | Robust z_lmpolicy_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- prior | -.1544388 .0637229 -2.42 0.016 -.2796558 -.0292217 gender | .2331775 .0579837 4.02 0.000 .1192381 .3471169 democrat | .4143393 .0710433 5.83 0.000 .2747374 .5539412 indep | .1056796 .0799983 1.32 0.187 -.0515189 .2628781 otherpol | -.2273294 .2990966 -0.76 0.448 -.8150615 .3604026 cost_index | -.249381 .0383577 -6.50 0.000 -.3247548 -.1740072 cost_index_prior | .094987 .0691473 1.37 0.170 -.0408891 .2308631 _cons | -.3571165 .0653562 -5.46 0.000 -.4855429 -.22869 ---------------------------------------------------------------------------------- . . local n = round(e(N)) . local r2 = e(r2) . . sigstar democrat, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar gender, prec(3) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar prior, prec(3) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . sigstar cost_index, prec(3) . estadd loc thisstat11 = "`r(bstar)'": col`colnum' . estadd loc thisstat12 = "`r(sestar)'": col`colnum' . sigstar cost_index_prior, prec(3) . estadd loc thisstat13 = "`r(bstar)'": col`colnum' . estadd loc thisstat14 = "`r(sestar)'": col`colnum' . . estadd loc thisstat31 = string(`r2', "%9.2f"): col`colnum' . estadd loc thisstat32 = "`n'": col`colnum' . . loc ++colnum . . reg z_lmpolicy_index prior gender democrat indep otherpol adverse adverse_prior [pweight=p > weight], vce(r) (sum of wgt is 4.6187e+02) Linear regression Number of obs = 478 F(7, 470) = 30.48 Prob > F = 0.0000 R-squared = 0.3308 Root MSE = .61155 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- prior | -.155654 .0625839 -2.49 0.013 -.2786329 -.032675 gender | .1854922 .0583601 3.18 0.002 .0708132 .3001712 democrat | .3934155 .0728967 5.40 0.000 .2501718 .5366593 indep | .1260053 .078776 1.60 0.110 -.0287916 .2808021 otherpol | -.1737772 .3263015 -0.53 0.595 -.8149675 .467413 adverse | -.2713965 .0379256 -7.16 0.000 -.3459213 -.1968718 adverse_prior | .0752086 .0707415 1.06 0.288 -.0638002 .2142175 _cons | -.3285025 .0698082 -4.71 0.000 -.4656772 -.1913277 ------------------------------------------------------------------------------- . . local n = round(e(N)) . local r2 = e(r2) . . sigstar democrat, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar gender, prec(3) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar prior, prec(3) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . . sigstar adverse, prec(3) . estadd loc thisstat16 = "`r(bstar)'": col`colnum' . estadd loc thisstat17 = "`r(sestar)'": col`colnum' . sigstar adverse_prior, prec(3) . estadd loc thisstat18 = "`r(bstar)'": col`colnum' . estadd loc thisstat19 = "`r(sestar)'": col`colnum' . . estadd loc thisstat31 = string(`r2', "%9.2f"): col`colnum' . estadd loc thisstat32 = "`n'": col`colnum' . . loc ++colnum . . reg z_lmpolicy_index prior gender democrat indep otherpol GRA GRA_prior [pweight=pweight] > , vce(r) (sum of wgt is 4.6187e+02) Linear regression Number of obs = 478 F(7, 470) = 21.55 Prob > F = 0.0000 R-squared = 0.2657 Root MSE = .64056 ------------------------------------------------------------------------------ | Robust z_lmpolicy~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.1504177 .0677227 -2.22 0.027 -.2834944 -.017341 gender | .2436246 .0608683 4.00 0.000 .1240169 .3632323 democrat | .5217422 .0721003 7.24 0.000 .3800633 .663421 indep | .1229322 .0823531 1.49 0.136 -.0388936 .284758 otherpol | -.2999342 .3450284 -0.87 0.385 -.9779234 .378055 GRA | -.1470395 .0360679 -4.08 0.000 -.2179138 -.0761652 GRA_prior | .1256944 .0644902 1.95 0.052 -.0010305 .2524193 _cons | -.4168924 .0700867 -5.95 0.000 -.5546144 -.2791704 ------------------------------------------------------------------------------ . . local n = round(e(N)) . local r2 = e(r2) . . sigstar democrat, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar gender, prec(3) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar prior, prec(3) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . sigstar GRA, prec(3) . estadd loc thisstat21 = "`r(bstar)'": col`colnum' . estadd loc thisstat22 = "`r(sestar)'": col`colnum' . sigstar GRA_prior, prec(3) . estadd loc thisstat23 = "`r(bstar)'": col`colnum' . estadd loc thisstat24 = "`r(sestar)'": col`colnum' . . estadd loc thisstat31 = string(`r2', "%9.2f"): col`colnum' . estadd loc thisstat32 = "`n'": col`colnum' . . loc ++colnum . . . reg z_lmpolicy_index prior gender democrat indep otherpol tallshort tallshort_prior [pweig > ht=pweight], vce(r) (sum of wgt is 4.6187e+02) Linear regression Number of obs = 478 F(7, 470) = 30.43 Prob > F = 0.0000 R-squared = 0.3441 Root MSE = .60542 --------------------------------------------------------------------------------- | Robust z_lmpolicy_in~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] ----------------+---------------------------------------------------------------- prior | -.079888 .0628286 -1.27 0.204 -.2033476 .0435717 gender | .2073185 .0595739 3.48 0.001 .0902543 .3243826 democrat | .4012452 .0677764 5.92 0.000 .268063 .5344274 indep | .1452565 .0781288 1.86 0.064 -.0082684 .2987814 otherpol | -.2557377 .3086808 -0.83 0.408 -.8623029 .3508275 tallshort | -.2873288 .0327977 -8.76 0.000 -.3517771 -.2228806 tallshort_prior | .0183916 .0625578 0.29 0.769 -.104536 .1413192 _cons | -.3297668 .0622471 -5.30 0.000 -.4520838 -.2074497 --------------------------------------------------------------------------------- . . local n = round(e(N)) . local r2 = e(r2) . . sigstar democrat, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar gender, prec(3) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar prior, prec(3) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . . sigstar tallshort, prec(3) . estadd loc thisstat26 = "`r(bstar)'": col`colnum' . estadd loc thisstat27 = "`r(sestar)'": col`colnum' . sigstar tallshort_prior, prec(3) . estadd loc thisstat28 = "`r(bstar)'": col`colnum' . estadd loc thisstat29 = "`r(sestar)'": col`colnum' . . estadd loc thisstat31 = string(`r2', "%9.2f"): col`colnum' . estadd loc thisstat32 = "`n'": col`colnum' . . loc ++colnum . . . reg z_lmpolicy_index prior gender democrat indep otherpol cost_index cost_index_prior GRA > GRA_prior adverse adverse_prior tallshort tallshort_prior [pweight=pweight], vce(r) (sum of wgt is 4.6187e+02) Linear regression Number of obs = 478 F(13, 464) = 20.46 Prob > F = 0.0000 R-squared = 0.3991 Root MSE = .58321 ---------------------------------------------------------------------------------- | Robust z_lmpolicy_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- prior | -.1139001 .0588709 -1.93 0.054 -.2295868 .0017865 gender | .1741195 .0560123 3.11 0.002 .0640503 .2841888 democrat | .3045015 .0699874 4.35 0.000 .1669699 .4420331 indep | .1074863 .077515 1.39 0.166 -.0448376 .2598102 otherpol | -.1950569 .2811288 -0.69 0.488 -.7475003 .3573865 cost_index | -.1178364 .0452029 -2.61 0.009 -.2066641 -.0290088 cost_index_prior | .0851396 .1021781 0.83 0.405 -.1156495 .2859287 GRA | .0016917 .0381739 0.04 0.965 -.0733235 .0767069 GRA_prior | .0968907 .0739014 1.31 0.190 -.0483321 .2421135 adverse | -.1238622 .0490571 -2.52 0.012 -.2202637 -.0274607 adverse_prior | -.0436749 .1076205 -0.41 0.685 -.2551589 .1678091 tallshort | -.1749322 .0356136 -4.91 0.000 -.2449161 -.1049483 tallshort_prior | -.0746083 .0788936 -0.95 0.345 -.2296413 .0804247 _cons | -.2713388 .0613006 -4.43 0.000 -.3917999 -.1508777 ---------------------------------------------------------------------------------- . . local n = round(e(N)) . local r2 = e(r2) . . sigstar democrat, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar gender, prec(3) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar prior, prec(3) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . sigstar cost_index, prec(3) . estadd loc thisstat11 = "`r(bstar)'": col`colnum' . estadd loc thisstat12 = "`r(sestar)'": col`colnum' . sigstar cost_index_prior, prec(3) . estadd loc thisstat13 = "`r(bstar)'": col`colnum' . estadd loc thisstat14 = "`r(sestar)'": col`colnum' . sigstar adverse, prec(3) . estadd loc thisstat16 = "`r(bstar)'": col`colnum' . estadd loc thisstat17 = "`r(sestar)'": col`colnum' . sigstar adverse_prior, prec(3) . estadd loc thisstat18 = "`r(bstar)'": col`colnum' . estadd loc thisstat19 = "`r(sestar)'": col`colnum' . sigstar GRA, prec(3) . estadd loc thisstat21 = "`r(bstar)'": col`colnum' . estadd loc thisstat22 = "`r(sestar)'": col`colnum' . sigstar GRA_prior, prec(3) . estadd loc thisstat23 = "`r(bstar)'": col`colnum' . estadd loc thisstat24 = "`r(sestar)'": col`colnum' . sigstar tallshort, prec(3) . estadd loc thisstat26 = "`r(bstar)'": col`colnum' . estadd loc thisstat27 = "`r(sestar)'": col`colnum' . sigstar tallshort_prior, prec(3) . estadd loc thisstat28 = "`r(bstar)'": col`colnum' . estadd loc thisstat29 = "`r(sestar)'": col`colnum' . . estadd loc thisstat31 = string(`r2', "%9.2f"): col`colnum' . estadd loc thisstat32 = "`n'": col`colnum' . . loc ++colnum . . . loc rowlabels " " " "Democrat" " " " " "Female" " " " " "Prior belief" "(z-scored)" " " "High cost > s" " " "High costs x prior" " " " " "Adverse effects men" " " "Adv. effects x prior" " " " " "Trad > itional gender role attitudes" " " "Traditional GRA x prior" " " " " "No role for government" " " > "No role for gov. x prior" " " " " "R$^{2}$" "Observations" " . loc rowstats "" . . . loc rowstats "" . . forval i = 1/32 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\tab_allcorrelates_policypref_5_95.tex", replace cells(none) booktabs nonot > es nonum compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels' > )) /// > mtitle("(1)" "(2)" "(3)" "(4)" "(5)" "(6)" "(7)") /// > mgroups("Policy Demand (Index)", pattern(1 0 0 0) prefix(\multicolumn{@span}{c}{) suffix(}) span e > repeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\tab_allcorrelates_policypref_5_95.tex) . . . eststo clear . . . *********************************************************************************** . // Table F.1: Incentivized vs. unincentivized beliefs about the wage gap . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . . loc experiments "1 2 3 4 5 6" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . * Define controls similar to baseline controls but with Democrats and female respondents as the om > itted category . global controls1 wave male republican indep otherpol midwest south west age1 age2 age3 age4 anychi > ldren loghhinc associatemore fulltime parttime selfemp unemp student . . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . //COl 1: . reg prior prior1 $controls1 [pweight=pweight], vce(r) (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(21, 4043) = 7.92 Prob > F = 0.0000 R-squared = 0.0468 Root MSE = 21.262 ------------------------------------------------------------------------------- | Robust prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- prior1 | -.3838474 .6889641 -0.56 0.577 -1.734597 .9669019 wave | -.1258709 .6946375 -0.18 0.856 -1.487743 1.236001 male | 6.023255 .6746892 8.93 0.000 4.700493 7.346017 republican | 4.48389 .791708 5.66 0.000 2.931707 6.036074 indep | 2.934608 .9113103 3.22 0.001 1.147938 4.721278 otherpol | 1.876594 2.751052 0.68 0.495 -3.516985 7.270172 midwest | -1.209463 1.072689 -1.13 0.260 -3.312525 .8935977 south | -1.062942 .9982247 -1.06 0.287 -3.020012 .8941282 west | .2696958 1.058763 0.25 0.799 -1.806062 2.345454 age1 | 6.616072 1.474211 4.49 0.000 3.725806 9.506339 age2 | 5.727601 1.065584 5.38 0.000 3.638469 7.816732 age3 | 3.931617 1.016479 3.87 0.000 1.938759 5.924475 age4 | .3708233 .8963986 0.41 0.679 -1.386612 2.128258 anychildren | 2.488038 .7602117 3.27 0.001 .9976038 3.978471 loghhinc | .7459281 .4795185 1.56 0.120 -.1941923 1.686049 associatemore | .735053 .724599 1.01 0.310 -.6855602 2.155666 fulltime | -.4538354 .9602295 -0.47 0.637 -2.336414 1.428743 parttime | -.1287528 1.245339 -0.10 0.918 -2.570303 2.312797 selfemp | -1.862187 1.377751 -1.35 0.177 -4.563339 .8389637 unemployed | .3863109 1.635373 0.24 0.813 -2.819921 3.592543 student | -1.435202 1.879058 -0.76 0.445 -5.119191 2.248788 _cons | 66.77521 5.383151 12.40 0.000 56.22127 77.32915 ------------------------------------------------------------------------------- . local n = round(e(N)) . . sigstar prior1, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar male, prec(3) . estadd loc thisstat14 = "`r(bstar)'": col`colnum' . estadd loc thisstat15 = "`r(sestar)'": col`colnum' . sigstar republican, prec(3) . estadd loc thisstat17 = "`r(bstar)'": col`colnum' . estadd loc thisstat18 = "`r(sestar)'": col`colnum' . . sigstar _cons, prec(3) . estadd loc thisstat23 = "`r(bstar)'": col`colnum' . estadd loc thisstat24 = "`r(sestar)'": col`colnum' . . estadd loc thisstat27= "Yes": col`colnum' . estadd loc thisstat28= "No": col`colnum' . estadd loc thisstat29= "No": col`colnum' . . estadd loc thisstat31 = "`n'": col`colnum' . . . loc ++colnum . . //Col 2: . reg prior prior1 incmale $controls1 [pweight=pweight], vce(r) (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(22, 4042) = 7.87 Prob > F = 0.0000 R-squared = 0.0488 Root MSE = 21.242 ------------------------------------------------------------------------------- | Robust prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- prior1 | 1.596729 .9081922 1.76 0.079 -.183828 3.377286 incmale | -3.974543 1.357416 -2.93 0.003 -6.635826 -1.313259 wave | -.0451533 .6940758 -0.07 0.948 -1.405924 1.315618 male | 8.255372 1.026339 8.04 0.000 6.243182 10.26756 republican | 4.478633 .7913023 5.66 0.000 2.927244 6.030021 indep | 2.950583 .9091702 3.25 0.001 1.168109 4.733058 otherpol | 1.903407 2.742743 0.69 0.488 -3.473881 7.280696 midwest | -1.261131 1.071661 -1.18 0.239 -3.362178 .8399158 south | -1.074871 .9970419 -1.08 0.281 -3.029623 .8798806 west | .2517773 1.058294 0.24 0.812 -1.823063 2.326618 age1 | 6.563064 1.469347 4.47 0.000 3.682335 9.443793 age2 | 5.628822 1.059848 5.31 0.000 3.550937 7.706707 age3 | 3.886698 1.016139 3.82 0.000 1.894505 5.878891 age4 | .3367646 .8941659 0.38 0.706 -1.416293 2.089823 anychildren | 2.469085 .7590423 3.25 0.001 .9809442 3.957226 loghhinc | .7415627 .4787077 1.55 0.121 -.1969681 1.680094 associatemore | .7878871 .7220616 1.09 0.275 -.6277516 2.203526 fulltime | -.3817395 .9585928 -0.40 0.690 -2.26111 1.497631 parttime | -.1086064 1.240065 -0.09 0.930 -2.539817 2.322605 selfemp | -1.830056 1.376213 -1.33 0.184 -4.528192 .8680801 unemployed | .3757959 1.632246 0.23 0.818 -2.824305 3.575897 student | -1.424153 1.879258 -0.76 0.449 -5.108534 2.260229 _cons | 65.61071 5.39704 12.16 0.000 55.02954 76.19188 ------------------------------------------------------------------------------- . local n = round(e(N)) . . sigstar prior1, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar incmale, prec(3) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar male, prec(3) . estadd loc thisstat14 = "`r(bstar)'": col`colnum' . estadd loc thisstat15 = "`r(sestar)'": col`colnum' . sigstar republican, prec(3) . estadd loc thisstat17 = "`r(bstar)'": col`colnum' . estadd loc thisstat18 = "`r(sestar)'": col`colnum' . . sigstar _cons, prec(3) . estadd loc thisstat23 = "`r(bstar)'": col`colnum' . estadd loc thisstat24 = "`r(sestar)'": col`colnum' . . estadd loc thisstat27= "Yes": col`colnum' . estadd loc thisstat28= "No": col`colnum' . estadd loc thisstat29= "No": col`colnum' . . estadd loc thisstat31 = "`n'": col`colnum' . . loc ++colnum . . // Col 3: . reg prior prior1 increpub $controls1 incindep incotherpol [pweight=pweight], vce(r) (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(24, 4040) = 6.95 Prob > F = 0.0000 R-squared = 0.0471 Root MSE = 21.266 ------------------------------------------------------------------------------- | Robust prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- prior1 | -.4684151 1.005748 -0.47 0.641 -2.440235 1.503405 increpub | .4371915 1.547424 0.28 0.778 -2.596612 3.470995 wave | -.1230286 .6965441 -0.18 0.860 -1.488639 1.242582 male | 6.022985 .674582 8.93 0.000 4.700432 7.345538 republican | 4.232699 1.177895 3.59 0.000 1.923376 6.542022 indep | 2.831437 1.512609 1.87 0.061 -.1341111 5.796986 otherpol | 4.458304 3.687604 1.21 0.227 -2.771433 11.68804 midwest | -1.255386 1.070794 -1.17 0.241 -3.354733 .8439605 south | -1.086906 .9995173 -1.09 0.277 -3.046511 .8726991 west | .2328948 1.0613 0.22 0.826 -1.847838 2.313628 age1 | 6.575347 1.475043 4.46 0.000 3.683451 9.467244 age2 | 5.720796 1.066229 5.37 0.000 3.630399 7.811194 age3 | 3.934422 1.016854 3.87 0.000 1.940828 5.928017 age4 | .3615228 .8978275 0.40 0.687 -1.398714 2.12176 anychildren | 2.512005 .7611711 3.30 0.001 1.01969 4.00432 loghhinc | .7413972 .4782797 1.55 0.121 -.1962946 1.679089 associatemore | .7455462 .7251572 1.03 0.304 -.6761618 2.167254 fulltime | -.4436626 .9601289 -0.46 0.644 -2.326045 1.438719 parttime | -.1126968 1.246057 -0.09 0.928 -2.555655 2.330262 selfemp | -1.828753 1.376979 -1.33 0.184 -4.52839 .8708846 unemployed | .3904837 1.633733 0.24 0.811 -2.812534 3.593501 student | -1.36193 1.87915 -0.72 0.469 -5.0461 2.32224 incindep | .1835605 1.853875 0.10 0.921 -3.451056 3.818177 incotherpol | -5.413405 5.473226 -0.99 0.323 -16.14395 5.317136 _cons | 66.87278 5.420195 12.34 0.000 56.24621 77.49935 ------------------------------------------------------------------------------- . local n = round(e(N)) . . sigstar prior1, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar increpub, prec(3) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . sigstar male, prec(3) . estadd loc thisstat14 = "`r(bstar)'": col`colnum' . estadd loc thisstat15 = "`r(sestar)'": col`colnum' . sigstar republican, prec(3) . estadd loc thisstat17 = "`r(bstar)'": col`colnum' . estadd loc thisstat18 = "`r(sestar)'": col`colnum' . . sigstar _cons, prec(3) . estadd loc thisstat23 = "`r(bstar)'": col`colnum' . estadd loc thisstat24 = "`r(sestar)'": col`colnum' . . estadd loc thisstat27= "Yes": col`colnum' . estadd loc thisstat28= "No": col`colnum' . estadd loc thisstat29= "No": col`colnum' . . estadd loc thisstat31 = "`n'": col`colnum' . . loc ++colnum . . // Col 4: . reg prior prior1 incmale increpub incmalerepub incindep incotherpol incmaleindep incmaleot > herpol malerepub maleindep maleotherpol $controls1 [pweight=pweight], vce(r) (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(31, 4033) = 6.14 Prob > F = 0.0000 R-squared = 0.0517 Root MSE = 21.233 --------------------------------------------------------------------------------- | Robust prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] ----------------+---------------------------------------------------------------- prior1 | 1.799197 1.302133 1.38 0.167 -.7537032 4.352098 incmale | -4.739236 2.001814 -2.37 0.018 -8.663896 -.8145751 increpub | -.7057641 2.016342 -0.35 0.726 -4.658907 3.247379 incmalerepub | 2.66461 3.082759 0.86 0.387 -3.379301 8.708521 incindep | .3899275 2.546843 0.15 0.878 -4.603292 5.383147 incotherpol | -4.589625 7.371924 -0.62 0.534 -19.04267 9.863418 incmaleindep | -.4972458 3.666802 -0.14 0.892 -7.686203 6.691712 incmaleotherpol | -2.140845 10.58059 -0.20 0.840 -22.88465 18.60296 malerepub | -2.10568 2.304976 -0.91 0.361 -6.624706 2.413346 maleindep | 2.700986 2.969767 0.91 0.363 -3.121398 8.52337 maleotherpol | 14.12068 7.273731 1.94 0.052 -.1398517 28.38121 wave | -.0651427 .6961109 -0.09 0.925 -1.429905 1.299619 male | 8.215818 1.511392 5.44 0.000 5.252654 11.17898 republican | 5.203999 1.449858 3.59 0.000 2.361476 8.046521 indep | 1.564543 2.07212 0.76 0.450 -2.497957 5.627044 otherpol | -1.934457 3.086948 -0.63 0.531 -7.986579 4.117666 midwest | -1.347136 1.067714 -1.26 0.207 -3.440445 .7461725 south | -1.109128 .9980342 -1.11 0.267 -3.065826 .8475705 west | .2026364 1.062635 0.19 0.849 -1.880714 2.285987 age1 | 6.673009 1.471851 4.53 0.000 3.787369 9.55865 age2 | 5.625128 1.059534 5.31 0.000 3.547856 7.7024 age3 | 4.026564 1.014716 3.97 0.000 2.03716 6.015967 age4 | .3249097 .8947339 0.36 0.717 -1.429263 2.079082 anychildren | 2.582085 .7648153 3.38 0.001 1.082625 4.081546 loghhinc | .7079571 .4775709 1.48 0.138 -.2283456 1.64426 associatemore | .8000547 .7240736 1.10 0.269 -.6195295 2.219639 fulltime | -.3057107 .958981 -0.32 0.750 -2.185843 1.574422 parttime | -.0900658 1.241179 -0.07 0.942 -2.523463 2.343331 selfemp | -1.771426 1.372195 -1.29 0.197 -4.461687 .9188348 unemployed | .4072419 1.628955 0.25 0.803 -2.78641 3.600894 student | -1.38999 1.876568 -0.74 0.459 -5.0691 2.289119 _cons | 66.00919 5.488201 12.03 0.000 55.24928 76.76909 --------------------------------------------------------------------------------- . local n = round(e(N)) . . sigstar prior1, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar incmale, prec(3) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar increpub, prec(3) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . sigstar incmalerepub, prec(3) . estadd loc thisstat11 = "`r(bstar)'": col`colnum' . estadd loc thisstat12 = "`r(sestar)'": col`colnum' . sigstar male, prec(3) . estadd loc thisstat14 = "`r(bstar)'": col`colnum' . estadd loc thisstat15 = "`r(sestar)'": col`colnum' . sigstar republican, prec(3) . estadd loc thisstat17 = "`r(bstar)'": col`colnum' . estadd loc thisstat18 = "`r(sestar)'": col`colnum' . sigstar malerepub, prec(3) . estadd loc thisstat20 = "`r(bstar)'": col`colnum' . estadd loc thisstat21 = "`r(sestar)'": col`colnum' . sigstar _cons, prec(3) . estadd loc thisstat23 = "`r(bstar)'": col`colnum' . estadd loc thisstat24 = "`r(sestar)'": col`colnum' . . estadd loc thisstat27= "Yes": col`colnum' . estadd loc thisstat28= "No": col`colnum' . estadd loc thisstat29= "No": col`colnum' . . estadd loc thisstat31 = "`n'": col`colnum' . . loc ++colnum . . // Col 5: . reg prior prior1 incmale timeprior maletime $controls1 [pweight=pweight], vce(r) (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(24, 4040) = 7.25 Prob > F = 0.0000 R-squared = 0.0495 Root MSE = 21.239 ------------------------------------------------------------------------------- | Robust prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- prior1 | 1.613923 .9119445 1.77 0.077 -.1739907 3.401837 incmale | -3.868189 1.36522 -2.83 0.005 -6.544772 -1.191605 timeprior | -.0011156 .0026434 -0.42 0.673 -.0062981 .0040668 maletime | -.0065972 .0050298 -1.31 0.190 -.0164584 .003264 wave | -.0567552 .6941289 -0.08 0.935 -1.417631 1.30412 male | 8.679065 1.085525 8.00 0.000 6.550836 10.80729 republican | 4.470533 .7913615 5.65 0.000 2.919028 6.022038 indep | 2.988227 .908614 3.29 0.001 1.206843 4.769611 otherpol | 1.874727 2.743769 0.68 0.494 -3.504573 7.254027 midwest | -1.251758 1.071836 -1.17 0.243 -3.353147 .8496312 south | -1.039837 .9979097 -1.04 0.297 -2.996291 .9166158 west | .2974762 1.058509 0.28 0.779 -1.777785 2.372738 age1 | 6.502299 1.464169 4.44 0.000 3.63172 9.372877 age2 | 5.560141 1.057596 5.26 0.000 3.48667 7.633612 age3 | 3.8682 1.016173 3.81 0.000 1.875941 5.860458 age4 | .3365396 .8948996 0.38 0.707 -1.417957 2.091036 anychildren | 2.487451 .7599652 3.27 0.001 .9974999 3.977402 loghhinc | .7189897 .4786978 1.50 0.133 -.2195219 1.657501 associatemore | .7627153 .7221688 1.06 0.291 -.6531337 2.178564 fulltime | -.374232 .9587219 -0.39 0.696 -2.253855 1.505391 parttime | -.1082106 1.239882 -0.09 0.930 -2.539062 2.322641 selfemp | -1.833377 1.37672 -1.33 0.183 -4.532508 .8657546 unemployed | .3754666 1.632311 0.23 0.818 -2.824763 3.575697 student | -1.40109 1.874865 -0.75 0.455 -5.076858 2.274678 _cons | 65.94689 5.413972 12.18 0.000 55.33252 76.56126 ------------------------------------------------------------------------------- . local n = round(e(N)) . . sigstar prior1, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar incmale, prec(3) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar male, prec(3) . estadd loc thisstat14 = "`r(bstar)'": col`colnum' . estadd loc thisstat15 = "`r(sestar)'": col`colnum' . sigstar republican, prec(3) . estadd loc thisstat17 = "`r(bstar)'": col`colnum' . estadd loc thisstat18 = "`r(sestar)'": col`colnum' . . sigstar _cons, prec(3) . estadd loc thisstat23 = "`r(bstar)'": col`colnum' . estadd loc thisstat24 = "`r(sestar)'": col`colnum' . . estadd loc thisstat27= "Yes": col`colnum' . estadd loc thisstat28= "Yes": col`colnum' . estadd loc thisstat29= "No": col`colnum' . . estadd loc thisstat31 = "`n'": col`colnum' . . loc ++colnum . . // Col 6: . reg prior prior1 incmale increpub incindep incotherpol incmalerepub incmaleindep incmaleot > herpol malerepub maleindep maleotherpol timeprior maletime repubtime indeptime otherpoltime malere > pubtime maleindeptime maleotherpoltime $controls1 [pweight=pweight], vce(r) (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(39, 4025) = 5.75 Prob > F = 0.0000 R-squared = 0.0535 Root MSE = 21.234 ---------------------------------------------------------------------------------- | Robust prior | Coef. Std. Err. t P>|t| [95% Conf. Interval] -----------------+---------------------------------------------------------------- prior1 | 1.812608 1.304501 1.39 0.165 -.744936 4.370152 incmale | -4.492902 2.000391 -2.25 0.025 -8.414776 -.5710276 increpub | -.8240061 2.028586 -0.41 0.685 -4.801157 3.153145 incindep | .4782413 2.563662 0.19 0.852 -4.547956 5.504438 incotherpol | -4.628599 7.376455 -0.63 0.530 -19.09053 9.833336 incmalerepub | 2.570828 3.100119 0.83 0.407 -3.507121 8.648777 incmaleindep | -.7526482 3.679308 -0.20 0.838 -7.966129 6.460832 incmaleotherpol | .3521878 9.87 0.04 0.972 -18.99848 19.70285 malerepub | -2.283816 2.520785 -0.91 0.365 -7.225949 2.658318 maleindep | 1.843982 3.061967 0.60 0.547 -4.159169 7.847133 maleotherpol | 26.15989 14.85712 1.76 0.078 -2.968279 55.28806 timeprior | -.000868 .0027005 -0.32 0.748 -.0061624 .0044265 maletime | -.0138563 .0069628 -1.99 0.047 -.0275071 -.0002054 repubtime | .0072467 .0087462 0.83 0.407 -.0099008 .0243942 indeptime | -.0033982 .0058384 -0.58 0.561 -.0148446 .0080482 otherpoltime | -.0003443 .0034178 -0.10 0.920 -.0070451 .0063566 malerepubtime | .0036759 .0146646 0.25 0.802 -.0250749 .0324267 maleindeptime | .0137351 .0098229 1.40 0.162 -.0055233 .0329934 maleotherpoltime | -.2363113 .1907719 -1.24 0.216 -.6103299 .1377072 wave | -.0966674 .6967777 -0.14 0.890 -1.462737 1.269403 male | 9.072395 1.624031 5.59 0.000 5.888395 12.25639 republican | 4.703139 1.557062 3.02 0.003 1.650437 7.755842 indep | 1.822527 2.069551 0.88 0.379 -2.234938 5.879992 otherpol | -1.83019 3.289672 -0.56 0.578 -8.279768 4.619389 midwest | -1.330447 1.07063 -1.24 0.214 -3.429475 .7685807 south | -1.079327 .9997689 -1.08 0.280 -3.039428 .8807735 west | .2453865 1.064523 0.23 0.818 -1.841668 2.332441 age1 | 6.583791 1.468167 4.48 0.000 3.705371 9.46221 age2 | 5.449508 1.056726 5.16 0.000 3.377741 7.521275 age3 | 3.971789 1.016665 3.91 0.000 1.978563 5.965015 age4 | .2962803 .8974404 0.33 0.741 -1.4632 2.05576 anychildren | 2.602548 .7660775 3.40 0.001 1.100612 4.104484 loghhinc | .6961016 .4784796 1.45 0.146 -.2419834 1.634186 associatemore | .8149315 .7243038 1.13 0.261 -.6051048 2.234968 fulltime | -.3063823 .9618667 -0.32 0.750 -2.192173 1.579409 parttime | -.095955 1.246432 -0.08 0.939 -2.539652 2.347742 selfemp | -1.873264 1.357857 -1.38 0.168 -4.535416 .7888885 unemployed | .4103269 1.633775 0.25 0.802 -2.792777 3.613431 student | -1.380041 1.87761 -0.73 0.462 -5.061197 2.301115 _cons | 66.2765 5.520222 12.01 0.000 55.45381 77.09919 ---------------------------------------------------------------------------------- . local n = round(e(N)) . . sigstar prior1, prec(3) . estadd loc thisstat2 = "`r(bstar)'": col`colnum' . estadd loc thisstat3 = "`r(sestar)'": col`colnum' . sigstar incmale, prec(3) . estadd loc thisstat5 = "`r(bstar)'": col`colnum' . estadd loc thisstat6 = "`r(sestar)'": col`colnum' . sigstar increpub, prec(3) . estadd loc thisstat8 = "`r(bstar)'": col`colnum' . estadd loc thisstat9 = "`r(sestar)'": col`colnum' . sigstar incmalerepub, prec(3) . estadd loc thisstat11 = "`r(bstar)'": col`colnum' . estadd loc thisstat12 = "`r(sestar)'": col`colnum' . sigstar male, prec(3) . estadd loc thisstat14 = "`r(bstar)'": col`colnum' . estadd loc thisstat15 = "`r(sestar)'": col`colnum' . sigstar republican, prec(3) . estadd loc thisstat17 = "`r(bstar)'": col`colnum' . estadd loc thisstat18 = "`r(sestar)'": col`colnum' . sigstar malerepub, prec(3) . estadd loc thisstat20 = "`r(bstar)'": col`colnum' . estadd loc thisstat21 = "`r(sestar)'": col`colnum' . sigstar _cons, prec(3) . estadd loc thisstat23 = "`r(bstar)'": col`colnum' . estadd loc thisstat24 = "`r(sestar)'": col`colnum' . . estadd loc thisstat27= "Yes": col`colnum' . estadd loc thisstat28= "Yes": col`colnum' . estadd loc thisstat29= "Yes": col`colnum' . . estadd loc thisstat31 = "`n'": col`colnum' . . . loc rowlabels " " " "Incentive" " " " " "Incentive x male" " " " " "Incentive x Republican" " " " > " "Inc. x male x Republican" " " " " "Male" " " " " "Republican" " " " " "Male x Republican" " " " > " "Constant" " " " " "\hline" " Baseline controls" "Control for response time x gender" "Control > for resp. time x (Repub. and gender x Repub.)" " " "Observations" " . loc rowstats "" . . forval i = 1/31 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output/motbeliefsAB_6col.tex", replace cells(none) booktabs nonotes nonum compres > s alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mtitle("(1)" "(2)" "(3)" "(4)" "(5)" "(6)") /// > mgroups("Outcome variable: Prior belief about gender wage gap", pattern(1 0 0 0 0 0) prefix(\mult > icolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output/motbeliefsAB_6col.tex) . . . eststo clear . . // Table notes: . . * Median prior . sum prior,d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% 31 0 5% 50 1 10% 61 2 Obs 4,065 25% 75 2 Sum of Wgt. 4,065 50% 81 Mean 83.36531 Largest Std. Dev. 21.67554 75% 90 200 90% 100 200 Variance 469.8289 95% 116 200 Skewness 1.382362 99% 173 200 Kurtosis 10.03941 . * Mean prior . mean prior [pweight=pweight] Mean estimation Number of obs = 4,065 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ prior | 83.49412 .3432603 82.82114 84.1671 -------------------------------------------------------------- . * 5th and 95th percentile of response time . sum timeprior,d timeprior ------------------------------------------------------------- Percentiles Smallest 1% 8.01 3.73 5% 17.65 4.06 10% 24.35 4.1 Obs 4,065 25% 35.96 4.25 Sum of Wgt. 4,065 50% 52.63 Mean 76.15218 Largest Std. Dev. 121.089 75% 80.21 2018.74 90% 126.36 2094.25 Variance 14662.54 95% 176.73 2153.28 Skewness 10.48757 99% 468.26 2777.44 Kurtosis 158.9598 . * Max response time . sum timeprior Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- timeprior | 4,065 76.15218 121.089 3.73 2777.44 . . . . *********************************************************************************** . // Table G.1: Treatment effect on general views without probability weights . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . * Drop pure control group . drop if rand==0 (1,034 observations deleted) . . zscore posterior, stub(z) zposterior created with 9 missing values . . loc experiments "posterior zposterior large problem govmore z_mani_index" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . . foreach choice in `experiments' { 2. . reg `choice' T1 $controls, vce(r) 3. . local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat2 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat3 = "`r(sestar)'": col`colnum' 7. . sigstar gender, prec(3) 8. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat7 = "`r(sestar)'": col`colnum' 10. . sigstar democrat, prec(3) 11. estadd loc thisstat9 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat10 = "`r(sestar)'": col`colnum' 13. . estadd loc thisstat12 = "`n'": col`colnum' 14. . loc ++colnum 15. } Linear regression Number of obs = 3,022 F(22, 2999) = 41.39 Prob > F = 0.0000 R-squared = 0.3156 Root MSE = 16.305 ------------------------------------------------------------------------------- | Robust posterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -13.00296 .590295 -22.03 0.000 -14.16039 -11.84554 wave | 2.506823 .6425652 3.90 0.000 1.246909 3.766736 gender | -1.611715 .619087 -2.60 0.009 -2.825593 -.3978365 prior | .3811469 .0294883 12.93 0.000 .3233276 .4389661 democrat | -.0100648 .7004761 -0.01 0.989 -1.383527 1.363397 indep | .5615665 .8477622 0.66 0.508 -1.100688 2.223821 otherpol | 1.004633 2.983946 0.34 0.736 -4.846155 6.855421 midwest | -.2186811 .9503664 -0.23 0.818 -2.082117 1.644755 south | .0380881 .8644568 0.04 0.965 -1.6569 1.733076 west | -.531275 .9369798 -0.57 0.571 -2.368463 1.305913 age1 | 3.090245 1.310178 2.36 0.018 .5213067 5.659183 age2 | 2.919546 .9491287 3.08 0.002 1.058537 4.780556 age3 | .6664081 .8828065 0.75 0.450 -1.064559 2.397376 age4 | -.3237828 .7925193 -0.41 0.683 -1.877719 1.230154 anychildren | 1.379476 .670914 2.06 0.040 .0639777 2.694974 loghhinc | -.3422727 .4082244 -0.84 0.402 -1.142701 .4581554 associatemore | 1.035565 .654657 1.58 0.114 -.2480568 2.319188 fulltime | .7076891 .8123483 0.87 0.384 -.8851272 2.300505 parttime | -.7486296 1.128155 -0.66 0.507 -2.960666 1.463407 selfemp | -.1465352 1.23497 -0.12 0.906 -2.568009 2.274939 unemployed | -2.78296 1.197272 -2.32 0.020 -5.130517 -.4354022 student | -1.295695 1.540308 -0.84 0.400 -4.315863 1.724473 _cons | 57.80108 5.231803 11.05 0.000 47.5428 68.05937 ------------------------------------------------------------------------------- Linear regression Number of obs = 3,022 F(22, 2999) = 41.39 Prob > F = 0.0000 R-squared = 0.3156 Root MSE = .8303 ------------------------------------------------------------------------------- | Robust zposterior | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.6621502 .0300596 -22.03 0.000 -.7210898 -.6032107 wave | .127655 .0327214 3.90 0.000 .0634964 .1918136 gender | -.0820734 .0315258 -2.60 0.009 -.1438877 -.020259 prior | .0194092 .0015016 12.93 0.000 .0164648 .0223535 democrat | -.0005125 .0356704 -0.01 0.989 -.0704534 .0694283 indep | .0285967 .0431706 0.66 0.508 -.0560503 .1132437 otherpol | .051159 .1519516 0.34 0.736 -.2467809 .3490988 midwest | -.0111359 .0483955 -0.23 0.818 -.1060277 .0837559 south | .0019396 .0440208 0.04 0.965 -.0843744 .0882535 west | -.0270541 .0477138 -0.57 0.571 -.1206093 .066501 age1 | .1573646 .0667182 2.36 0.018 .0265465 .2881827 age2 | .1486721 .0483325 3.08 0.002 .0539039 .2434403 age3 | .0339355 .0449552 0.75 0.450 -.0542106 .1220816 age4 | -.016488 .0403575 -0.41 0.683 -.0956191 .0626431 anychildren | .0702471 .034165 2.06 0.040 .0032579 .1372362 loghhinc | -.0174296 .020788 -0.84 0.402 -.0581898 .0233307 associatemore | .0527341 .0333371 1.58 0.114 -.0126318 .1181001 fulltime | .0360377 .0413672 0.87 0.384 -.0450734 .1171487 parttime | -.0381225 .0574491 -0.66 0.507 -.1507661 .0745211 selfemp | -.007462 .0628884 -0.12 0.906 -.1307708 .1158468 unemployed | -.1417167 .0609687 -2.32 0.020 -.2612615 -.022172 student | -.0659807 .0784372 -0.84 0.400 -.2197768 .0878154 _cons | -1.349395 .2664192 -5.06 0.000 -1.871778 -.827012 ------------------------------------------------------------------------------- Linear regression Number of obs = 3,031 F(22, 3008) = 29.32 Prob > F = 0.0000 R-squared = 0.1744 Root MSE = .96523 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .5978727 .0353286 16.92 0.000 .5286021 .6671434 wave | -.0229703 .0378392 -0.61 0.544 -.0971636 .051223 gender | .2316584 .0360556 6.43 0.000 .1609623 .3023546 prior | -.0057883 .0009804 -5.90 0.000 -.0077105 -.0038661 democrat | .5228397 .0402484 12.99 0.000 .4439224 .6017569 indep | .2059464 .0544515 3.78 0.000 .0991804 .3127124 otherpol | .1650488 .1483963 1.11 0.266 -.1259197 .4560174 midwest | -.0517151 .056884 -0.91 0.363 -.1632505 .0598203 south | .0557149 .0515091 1.08 0.279 -.0452817 .1567116 west | -.0373775 .0556354 -0.67 0.502 -.1464648 .0717098 age1 | .0450662 .0781854 0.58 0.564 -.108236 .1983684 age2 | .0415001 .0556713 0.75 0.456 -.0676576 .1506578 age3 | -.0072657 .0550522 -0.13 0.895 -.1152095 .100678 age4 | -.132192 .0531549 -2.49 0.013 -.2364157 -.0279683 anychildren | .1261606 .0392678 3.21 0.001 .0491663 .203155 loghhinc | .0389186 .0243613 1.60 0.110 -.008848 .0866852 associatemore | .0091502 .039552 0.23 0.817 -.0684015 .0867019 fulltime | .0918118 .0535014 1.72 0.086 -.0130912 .1967149 parttime | -.1075997 .0716646 -1.50 0.133 -.2481161 .0329168 selfemp | .0625884 .0750307 0.83 0.404 -.0845283 .2097051 unemployed | .0459053 .0872189 0.53 0.599 -.1251095 .2169201 student | .0483713 .1066581 0.45 0.650 -.160759 .2575015 _cons | -.761037 .2825089 -2.69 0.007 -1.314967 -.2071067 ------------------------------------------------------------------------------- Linear regression Number of obs = 3,031 F(22, 3008) = 29.17 Prob > F = 0.0000 R-squared = 0.1780 Root MSE = .94963 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4207466 .0347707 12.10 0.000 .3525698 .4889234 wave | -.0149476 .0369325 -0.40 0.686 -.0873632 .057468 gender | .2955527 .0355682 8.31 0.000 .2258123 .3652931 prior | -.0061569 .0009123 -6.75 0.000 -.0079456 -.0043681 democrat | .6532991 .0400058 16.33 0.000 .5748576 .7317406 indep | .2546046 .054712 4.65 0.000 .1473279 .3618814 otherpol | .2983026 .13982 2.13 0.033 .0241501 .572455 midwest | -.115888 .0564326 -2.05 0.040 -.2265385 -.0052376 south | -.0283715 .0498796 -0.57 0.570 -.1261731 .0694301 west | -.070311 .0540873 -1.30 0.194 -.1763627 .0357408 age1 | .0714673 .0764137 0.94 0.350 -.0783611 .2212957 age2 | .0227336 .0560379 0.41 0.685 -.0871429 .1326102 age3 | .0082397 .0543265 0.15 0.879 -.0982812 .1147606 age4 | -.1095931 .0531071 -2.06 0.039 -.2137231 -.0054632 anychildren | .0842583 .0390876 2.16 0.031 .0076172 .1608994 loghhinc | .016944 .0240465 0.70 0.481 -.0302053 .0640933 associatemore | .0244311 .0396191 0.62 0.538 -.0532521 .1021143 fulltime | .0597567 .0528455 1.13 0.258 -.0438603 .1633737 parttime | -.1043584 .0706722 -1.48 0.140 -.2429292 .0342124 selfemp | .0047282 .0746789 0.06 0.950 -.1416987 .1511552 unemployed | .0537398 .0888813 0.60 0.545 -.1205345 .2280142 student | .0621807 .1064676 0.58 0.559 -.1465759 .2709372 _cons | -.4158604 .2765891 -1.50 0.133 -.9581833 .1264625 ------------------------------------------------------------------------------- Linear regression Number of obs = 3,031 F(22, 3008) = 30.51 Prob > F = 0.0000 R-squared = 0.1858 Root MSE = .95681 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2435908 .034936 6.97 0.000 .1750899 .3120917 wave | -.0038447 .037254 -0.10 0.918 -.0768905 .0692011 gender | .3062497 .0358705 8.54 0.000 .2359165 .376583 prior | -.0045888 .0008689 -5.28 0.000 -.0062925 -.0028851 democrat | .7952319 .0402764 19.74 0.000 .7162597 .874204 indep | .2991666 .0568931 5.26 0.000 .1876132 .4107199 otherpol | .3425585 .1370068 2.50 0.012 .0739221 .611195 midwest | -.1904436 .0575497 -3.31 0.001 -.3032843 -.0776029 south | -.0537984 .0495112 -1.09 0.277 -.1508778 .0432809 west | -.1060247 .0540652 -1.96 0.050 -.2120332 -.0000161 age1 | .22105 .0753101 2.94 0.003 .0733856 .3687144 age2 | .2117057 .0567397 3.73 0.000 .1004532 .3229583 age3 | .1495938 .0573044 2.61 0.009 .037234 .2619537 age4 | .0659208 .0560191 1.18 0.239 -.0439188 .1757604 anychildren | .1453572 .0384944 3.78 0.000 .0698792 .2208352 loghhinc | -.02127 .0245141 -0.87 0.386 -.0693361 .0267962 associatemore | -.0333515 .0401205 -0.83 0.406 -.1120179 .045315 fulltime | .0129509 .0560234 0.23 0.817 -.0968971 .122799 parttime | -.0842747 .0699974 -1.20 0.229 -.2215224 .0529729 selfemp | -.1157171 .0774608 -1.49 0.135 -.2675986 .0361643 unemployed | .0103219 .0869966 0.12 0.906 -.1602569 .1809007 student | -.0287907 .1030508 -0.28 0.780 -.2308479 .1732665 _cons | -.2012222 .2795049 -0.72 0.472 -.7492622 .3468178 ------------------------------------------------------------------------------- Linear regression Number of obs = 3,031 F(22, 3008) = 32.93 Prob > F = 0.0000 R-squared = 0.1986 Root MSE = .87207 ------------------------------------------------------------------------------- | Robust z_mani_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4172378 .0319059 13.08 0.000 .3546783 .4797973 wave | -.0135014 .0339599 -0.40 0.691 -.0800884 .0530855 gender | .2745727 .0326283 8.42 0.000 .2105966 .3385487 prior | -.0053545 .0008631 -6.20 0.000 -.0070469 -.0036621 democrat | .6606712 .0365557 18.07 0.000 .5889945 .7323479 indep | .2538525 .0507806 5.00 0.000 .1542842 .3534208 otherpol | .2637237 .1325577 1.99 0.047 .0038108 .5236367 midwest | -.1214957 .0517291 -2.35 0.019 -.2229236 -.0200678 south | -.0055063 .0458435 -0.12 0.904 -.0953941 .0843815 west | -.0721234 .0494629 -1.46 0.145 -.169108 .0248611 age1 | .1234896 .0688209 1.79 0.073 -.0114512 .2584305 age2 | .1092169 .0508848 2.15 0.032 .0094444 .2089894 age3 | .061162 .0506173 1.21 0.227 -.0380859 .16041 age4 | -.0452146 .0490469 -0.92 0.357 -.1413834 .0509542 anychildren | .1264951 .0353262 3.58 0.000 .0572291 .1957611 loghhinc | .0097207 .0220847 0.44 0.660 -.033582 .0530234 associatemore | -.0058145 .0361006 -0.16 0.872 -.0765989 .06497 fulltime | .0529568 .0493172 1.07 0.283 -.043742 .1496557 parttime | -.0972528 .0640215 -1.52 0.129 -.2227831 .0282776 selfemp | -.0225802 .0688867 -0.33 0.743 -.15765 .1124896 unemployed | .0324663 .0791906 0.41 0.682 -.1228069 .1877395 student | .0186453 .0981275 0.19 0.849 -.1737585 .2110492 _cons | -.4636237 .2559426 -1.81 0.070 -.965464 .0382166 ------------------------------------------------------------------------------- . . . * Calculate sharpened q-values for columns 3-5 . mat def P = J(5, 1, .) . reg large T1 $controls, vce(r) Linear regression Number of obs = 3,031 F(22, 3008) = 29.32 Prob > F = 0.0000 R-squared = 0.1744 Root MSE = .96523 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .5978727 .0353286 16.92 0.000 .5286021 .6671434 wave | -.0229703 .0378392 -0.61 0.544 -.0971636 .051223 gender | .2316584 .0360556 6.43 0.000 .1609623 .3023546 prior | -.0057883 .0009804 -5.90 0.000 -.0077105 -.0038661 democrat | .5228397 .0402484 12.99 0.000 .4439224 .6017569 indep | .2059464 .0544515 3.78 0.000 .0991804 .3127124 otherpol | .1650488 .1483963 1.11 0.266 -.1259197 .4560174 midwest | -.0517151 .056884 -0.91 0.363 -.1632505 .0598203 south | .0557149 .0515091 1.08 0.279 -.0452817 .1567116 west | -.0373775 .0556354 -0.67 0.502 -.1464648 .0717098 age1 | .0450662 .0781854 0.58 0.564 -.108236 .1983684 age2 | .0415001 .0556713 0.75 0.456 -.0676576 .1506578 age3 | -.0072657 .0550522 -0.13 0.895 -.1152095 .100678 age4 | -.132192 .0531549 -2.49 0.013 -.2364157 -.0279683 anychildren | .1261606 .0392678 3.21 0.001 .0491663 .203155 loghhinc | .0389186 .0243613 1.60 0.110 -.008848 .0866852 associatemore | .0091502 .039552 0.23 0.817 -.0684015 .0867019 fulltime | .0918118 .0535014 1.72 0.086 -.0130912 .1967149 parttime | -.1075997 .0716646 -1.50 0.133 -.2481161 .0329168 selfemp | .0625884 .0750307 0.83 0.404 -.0845283 .2097051 unemployed | .0459053 .0872189 0.53 0.599 -.1251095 .2169201 student | .0483713 .1066581 0.45 0.650 -.160759 .2575015 _cons | -.761037 .2825089 -2.69 0.007 -1.314967 -.2071067 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg problem T1 $controls, vce(r) Linear regression Number of obs = 3,031 F(22, 3008) = 29.17 Prob > F = 0.0000 R-squared = 0.1780 Root MSE = .94963 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4207466 .0347707 12.10 0.000 .3525698 .4889234 wave | -.0149476 .0369325 -0.40 0.686 -.0873632 .057468 gender | .2955527 .0355682 8.31 0.000 .2258123 .3652931 prior | -.0061569 .0009123 -6.75 0.000 -.0079456 -.0043681 democrat | .6532991 .0400058 16.33 0.000 .5748576 .7317406 indep | .2546046 .054712 4.65 0.000 .1473279 .3618814 otherpol | .2983026 .13982 2.13 0.033 .0241501 .572455 midwest | -.115888 .0564326 -2.05 0.040 -.2265385 -.0052376 south | -.0283715 .0498796 -0.57 0.570 -.1261731 .0694301 west | -.070311 .0540873 -1.30 0.194 -.1763627 .0357408 age1 | .0714673 .0764137 0.94 0.350 -.0783611 .2212957 age2 | .0227336 .0560379 0.41 0.685 -.0871429 .1326102 age3 | .0082397 .0543265 0.15 0.879 -.0982812 .1147606 age4 | -.1095931 .0531071 -2.06 0.039 -.2137231 -.0054632 anychildren | .0842583 .0390876 2.16 0.031 .0076172 .1608994 loghhinc | .016944 .0240465 0.70 0.481 -.0302053 .0640933 associatemore | .0244311 .0396191 0.62 0.538 -.0532521 .1021143 fulltime | .0597567 .0528455 1.13 0.258 -.0438603 .1633737 parttime | -.1043584 .0706722 -1.48 0.140 -.2429292 .0342124 selfemp | .0047282 .0746789 0.06 0.950 -.1416987 .1511552 unemployed | .0537398 .0888813 0.60 0.545 -.1205345 .2280142 student | .0621807 .1064676 0.58 0.559 -.1465759 .2709372 _cons | -.4158604 .2765891 -1.50 0.133 -.9581833 .1264625 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg govmore T1 $controls, vce(r) Linear regression Number of obs = 3,031 F(22, 3008) = 30.51 Prob > F = 0.0000 R-squared = 0.1858 Root MSE = .95681 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2435908 .034936 6.97 0.000 .1750899 .3120917 wave | -.0038447 .037254 -0.10 0.918 -.0768905 .0692011 gender | .3062497 .0358705 8.54 0.000 .2359165 .376583 prior | -.0045888 .0008689 -5.28 0.000 -.0062925 -.0028851 democrat | .7952319 .0402764 19.74 0.000 .7162597 .874204 indep | .2991666 .0568931 5.26 0.000 .1876132 .4107199 otherpol | .3425585 .1370068 2.50 0.012 .0739221 .611195 midwest | -.1904436 .0575497 -3.31 0.001 -.3032843 -.0776029 south | -.0537984 .0495112 -1.09 0.277 -.1508778 .0432809 west | -.1060247 .0540652 -1.96 0.050 -.2120332 -.0000161 age1 | .22105 .0753101 2.94 0.003 .0733856 .3687144 age2 | .2117057 .0567397 3.73 0.000 .1004532 .3229583 age3 | .1495938 .0573044 2.61 0.009 .037234 .2619537 age4 | .0659208 .0560191 1.18 0.239 -.0439188 .1757604 anychildren | .1453572 .0384944 3.78 0.000 .0698792 .2208352 loghhinc | -.02127 .0245141 -0.87 0.386 -.0693361 .0267962 associatemore | -.0333515 .0401205 -0.83 0.406 -.1120179 .045315 fulltime | .0129509 .0560234 0.23 0.817 -.0968971 .122799 parttime | -.0842747 .0699974 -1.20 0.229 -.2215224 .0529729 selfemp | -.1157171 .0774608 -1.49 0.135 -.2675986 .0361643 unemployed | .0103219 .0869966 0.12 0.906 -.1602569 .1809007 student | -.0287907 .1030508 -0.28 0.780 -.2308479 .1732665 _cons | -.2012222 .2795049 -0.72 0.472 -.7492622 .3468178 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . . . minq P, q("Q") step(0.001) number of observations will be reset to 5 Press any key to continue, or Break to abort number of observations (_N) was 0, now 5 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = .7109999999999997 Correction with q = .7099999999999997 Correction with q = .7089999999999997 Correction with q = .7079999999999997 Correction with q = .7069999999999997 Correction with q = .7059999999999997 Correction with q = .7049999999999997 Correction with q = .7039999999999997 Correction with q = .7029999999999997 Correction with q = .7019999999999997 Correction with q = .7009999999999997 Correction with q = .6999999999999997 Correction with q = .6989999999999997 Correction with q = .6979999999999997 Correction with q = .6969999999999997 Correction with q = .6959999999999997 Correction with q = .6949999999999997 Correction with q = .6939999999999997 Correction with q = .6929999999999997 Correction with q = .6919999999999997 Correction with q = .6909999999999997 Correction with q = .6899999999999997 Correction with q = .6889999999999997 Correction with q = .6879999999999997 Correction with q = .6869999999999997 Correction with q = .6859999999999997 Correction with q = 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.3209999999999994 Correction with q = .3199999999999994 Correction with q = .3189999999999994 Correction with q = .3179999999999994 Correction with q = .3169999999999994 Correction with q = .3159999999999994 Correction with q = .3149999999999994 Correction with q = .3139999999999994 Correction with q = .3129999999999994 Correction with q = .3119999999999994 Correction with q = .3109999999999994 Correction with q = .3099999999999994 Correction with q = .3089999999999994 Correction with q = .3079999999999994 Correction with q = .3069999999999994 Correction with q = .3059999999999994 Correction with q = .3049999999999994 Correction with q = .3039999999999994 Correction with q = .3029999999999994 Correction with q = .3019999999999994 Correction with q = .3009999999999994 Correction with q = .2999999999999994 Correction with q = .2989999999999994 Correction with q = .2979999999999994 Correction with q = .2969999999999994 Correction with q = .2959999999999994 Correction with q = 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.2689999999999994 Correction with q = .2679999999999994 Correction with q = .2669999999999994 Correction with q = .2659999999999994 Correction with q = .2649999999999994 Correction with q = .2639999999999994 Correction with q = .2629999999999994 Correction with q = .2619999999999993 Correction with q = .2609999999999993 Correction with q = .2599999999999993 Correction with q = .2589999999999993 Correction with q = .2579999999999993 Correction with q = .2569999999999993 Correction with q = .2559999999999993 Correction with q = .2549999999999993 Correction with q = .2539999999999993 Correction with q = .2529999999999993 Correction with q = .2519999999999993 Correction with q = .2509999999999993 Correction with q = .2499999999999993 Correction with q = .2489999999999993 Correction with q = .2479999999999993 Correction with q = .2469999999999993 Correction with q = .2459999999999993 Correction with q = .2449999999999993 Correction with q = .2439999999999993 Correction with q = .2429999999999993 Correction with q = .2419999999999993 Correction with q = .2409999999999993 Correction with q = .2399999999999993 Correction with q = .2389999999999993 Correction with q = .2379999999999993 Correction with q = .2369999999999993 Correction with q = .2359999999999993 Correction with q = .2349999999999993 Correction with q = .2339999999999993 Correction with q = .2329999999999993 Correction with q = .2319999999999993 Correction with q = .2309999999999993 Correction with q = .2299999999999993 Correction with q = .2289999999999993 Correction with q = .2279999999999993 Correction with q = .2269999999999993 Correction with q = .2259999999999993 Correction with q = .2249999999999993 Correction with q = .2239999999999993 Correction with q = .2229999999999993 Correction with q = .2219999999999993 Correction with q = .2209999999999993 Correction with q = .2199999999999993 Correction with q = .2189999999999993 Correction with q = .2179999999999993 Correction with q = .2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = .1909999999999993 Correction with q = .1899999999999993 Correction with q = .1889999999999993 Correction with q = .1879999999999993 Correction with q = .1869999999999993 Correction with q = .1859999999999993 Correction with q = .1849999999999993 Correction with q = .1839999999999993 Correction with q = .1829999999999993 Correction with q = .1819999999999993 Correction with q = .1809999999999993 Correction with q = .1799999999999993 Correction with q = .1789999999999993 Correction with q = .1779999999999993 Correction with q = .1769999999999993 Correction with q = .1759999999999993 Correction with q = .1749999999999993 Correction with q = .1739999999999993 Correction with q = .1729999999999993 Correction with q = .1719999999999993 Correction with q = .1709999999999993 Correction with q = .1699999999999993 Correction with q = .1689999999999993 Correction with q = .1679999999999993 Correction with q = .1669999999999993 Correction with q = .1659999999999993 Correction with q = .1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 Correction with q = .0069999999999991 Correction with q = .0059999999999991 Correction with q = .0049999999999991 Correction with q = .0039999999999991 Correction with q = .0029999999999991 Correction with q = .0019999999999991 (5 real changes made) (2 real changes made, 2 to missing) . . estadd loc thisstat4 = "[" + string(Q[1, 1], "%9.3f") +"]" : col3 . estadd loc thisstat4 = "[" +string(Q[2, 1], "%9.3f")+"]": col4 . estadd loc thisstat4 = "[" + string(Q[3, 1], "%9.3f")+"]": col5 . . . loc rowlabels " " " "T$^{74}$" " " "Sharpened q-value" " " "Female" " " " " "Democrat" " " " " "Ob > servations" " . . loc rowstats "" . . forval i = 1/12 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\manicheckmainAB_short_noweights.tex", replace cells(none) booktabs nonotes > nomtitles compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabel > s')) /// > mgroups("\shortstack{Post. belief about\\fem. rel. wage\\(0-200)}" "\shortstack{Post. beli > ef about\\fem. rel. wage\\(z-scored)}" "\shortstack{Gender diff.\\ in wages\\are large}" "\shortst > ack{Gender diff.\\ in wages\\are a problem}" /// > "\shortstack{Government\\should mitigate\\gender wage gap}" "\shortstack{Perception\\Index\\((2) > -(4))}", pattern(1 1 1 1 1 1) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr) > {@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\manicheckmainAB_short_noweights.tex) . . . eststo clear . . . *********************************************************************************** . // Table G.2: Treatment effect on policy demand without probability weights . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . * Drop pure control group . drop if rand==0 (1,034 observations deleted) . . loc experiments "quotaanchor AAanchor legislationanchor transparencyanchor UKtool childcare z_lmpo > licy_index" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . . foreach choice in `experiments' { 2. . reg `choice' T1 $controls, vce(r) 3. . local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat2 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat3 = "`r(sestar)'": col`colnum' 7. . sigstar gender, prec(3) 8. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat7 = "`r(sestar)'": col`colnum' 10. . sigstar democrat, prec(3) 11. estadd loc thisstat9 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat10 = "`r(sestar)'": col`colnum' 13. . estadd loc thisstat12 = "`n'": col`colnum' 14. . loc ++colnum 15. } Linear regression Number of obs = 3,031 F(22, 3008) = 18.07 Prob > F = 0.0000 R-squared = 0.1172 Root MSE = .9688 ------------------------------------------------------------------------------- | Robust quotaanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0553954 .0352988 1.57 0.117 -.0138168 .1246075 wave | .1413985 .0381101 3.71 0.000 .066674 .2161229 gender | .2547495 .0364593 6.99 0.000 .1832619 .326237 prior | -.0039144 .0009352 -4.19 0.000 -.0057481 -.0020807 democrat | .5567124 .0406182 13.71 0.000 .4770701 .6363547 indep | .1586265 .0535626 2.96 0.003 .0536035 .2636495 otherpol | .1825468 .1270349 1.44 0.151 -.0665372 .4316308 midwest | -.1164282 .0585453 -1.99 0.047 -.231221 -.0016354 south | .0226438 .0518849 0.44 0.663 -.0790897 .1243772 west | -.0252372 .0559186 -0.45 0.652 -.1348797 .0844053 age1 | .2685733 .0767694 3.50 0.000 .1180475 .4190991 age2 | .2940541 .0576745 5.10 0.000 .1809686 .4071396 age3 | .2042946 .0561753 3.64 0.000 .0941487 .3144406 age4 | .0509717 .0549522 0.93 0.354 -.0567759 .1587193 anychildren | .1263693 .0391135 3.23 0.001 .0496774 .2030612 loghhinc | -.0361902 .0244033 -1.48 0.138 -.084039 .0116586 associatemore | -.0645246 .039995 -1.61 0.107 -.1429449 .0138956 fulltime | .0607097 .0541962 1.12 0.263 -.0455556 .166975 parttime | .0345804 .07167 0.48 0.629 -.1059468 .1751075 selfemp | .091988 .0774766 1.19 0.235 -.0599244 .2439005 unemployed | .1201816 .083948 1.43 0.152 -.0444196 .2847828 student | -.0402219 .1079008 -0.37 0.709 -.2517887 .1713449 _cons | -.1107841 .2859781 -0.39 0.698 -.6715164 .4499483 ------------------------------------------------------------------------------- Linear regression Number of obs = 3,031 F(22, 3008) = 21.34 Prob > F = 0.0000 R-squared = 0.1352 Root MSE = .93504 ------------------------------------------------------------------------------- | Robust AAanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1156892 .0340574 3.40 0.001 .0489112 .1824673 wave | .028759 .0372113 0.77 0.440 -.0442033 .1017212 gender | .1797219 .0350154 5.13 0.000 .1110654 .2483785 prior | -.004534 .0009668 -4.69 0.000 -.0064296 -.0026384 democrat | .662378 .0394629 16.78 0.000 .585001 .7397549 indep | .2493835 .051931 4.80 0.000 .1475597 .3512073 otherpol | .1200816 .1254735 0.96 0.339 -.1259409 .3661042 midwest | -.0907982 .055968 -1.62 0.105 -.2005376 .0189413 south | .0714102 .0503452 1.42 0.156 -.0273042 .1701246 west | -.0485374 .0543569 -0.89 0.372 -.1551178 .0580431 age1 | .1716369 .0723658 2.37 0.018 .0297455 .3135283 age2 | .1613597 .0554117 2.91 0.004 .052711 .2700084 age3 | .0608715 .0546676 1.11 0.266 -.0463183 .1680612 age4 | .0146144 .0520722 0.28 0.779 -.0874864 .1167151 anychildren | .1345906 .0378995 3.55 0.000 .0602791 .2089022 loghhinc | -.0116933 .0233566 -0.50 0.617 -.0574898 .0341032 associatemore | .0587787 .0376924 1.56 0.119 -.0151269 .1326842 fulltime | -.0210369 .0513614 -0.41 0.682 -.1217439 .0796701 parttime | -.0499881 .0677276 -0.74 0.461 -.1827852 .082809 selfemp | -.0072996 .0759103 -0.10 0.923 -.156141 .1415418 unemployed | -.0056021 .0828659 -0.07 0.946 -.1680816 .1568773 student | .1388002 .0999462 1.39 0.165 -.0571695 .3347699 _cons | -.2235044 .2811675 -0.79 0.427 -.7748045 .3277957 ------------------------------------------------------------------------------- Linear regression Number of obs = 3,031 F(22, 3008) = 18.78 Prob > F = 0.0000 R-squared = 0.1204 Root MSE = .94779 ------------------------------------------------------------------------------- | Robust legislation~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1219403 .0346884 3.52 0.000 .0539248 .1899557 wave | .014065 .0377905 0.37 0.710 -.0600328 .0881628 gender | .2359678 .035655 6.62 0.000 .1660571 .3058785 prior | -.0040109 .0010566 -3.80 0.000 -.0060826 -.0019392 democrat | .6192279 .0401152 15.44 0.000 .5405719 .697884 indep | .2328022 .0505516 4.61 0.000 .133683 .3319214 otherpol | .4786527 .1291036 3.71 0.000 .2255123 .731793 midwest | -.0702965 .0569502 -1.23 0.217 -.1819617 .0413687 south | -.0283776 .0506259 -0.56 0.575 -.1276425 .0708874 west | -.0623346 .0546041 -1.14 0.254 -.1693998 .0447306 age1 | -.1738019 .0744882 -2.33 0.020 -.3198547 -.027749 age2 | -.1145397 .056043 -2.04 0.041 -.2244261 -.0046533 age3 | -.1046654 .055562 -1.88 0.060 -.2136088 .0042779 age4 | -.0528755 .0533103 -0.99 0.321 -.1574037 .0516528 anychildren | .0313895 .0386702 0.81 0.417 -.0444333 .1072123 loghhinc | .0045808 .0234662 0.20 0.845 -.0414307 .0505922 associatemore | .0060241 .0378113 0.16 0.873 -.0681145 .0801627 fulltime | .0456327 .0538265 0.85 0.397 -.0599077 .1511731 parttime | -.0073176 .0722372 -0.10 0.919 -.1489569 .1343217 selfemp | .1757384 .0743834 2.36 0.018 .0298908 .321586 unemployed | .2840083 .0835794 3.40 0.001 .1201298 .4478869 student | .1795845 .1035118 1.73 0.083 -.0233765 .3825454 _cons | -.209031 .2804245 -0.75 0.456 -.7588741 .3408121 ------------------------------------------------------------------------------- note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 10.39 Prob > F = 0.0000 R-squared = 0.0957 Root MSE = .94476 ------------------------------------------------------------------------------- | Robust transparenc~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0145207 .0421926 -0.34 0.731 -.097267 .0682256 wave | 0 (omitted) gender | .1968569 .0436751 4.51 0.000 .1112032 .2825107 prior | -.0026412 .0011873 -2.22 0.026 -.0049697 -.0003127 democrat | .5648746 .048392 11.67 0.000 .4699703 .6597789 indep | .2426681 .0645124 3.76 0.000 .1161491 .3691871 otherpol | .1372274 .1916125 0.72 0.474 -.2385547 .5130095 midwest | -.1229332 .0686787 -1.79 0.074 -.257623 .0117565 south | -.0270683 .0603186 -0.45 0.654 -.1453626 .0912259 west | -.048422 .0656441 -0.74 0.461 -.1771604 .0803163 age1 | .0478043 .1037241 0.46 0.645 -.1556149 .2512235 age2 | .0581052 .0678145 0.86 0.392 -.0748897 .1911001 age3 | .0554398 .0677336 0.82 0.413 -.0773964 .188276 age4 | .0267632 .0655902 0.41 0.683 -.1018694 .1553958 anychildren | .0940054 .0484718 1.94 0.053 -.0010554 .1890661 loghhinc | -.0266598 .0288938 -0.92 0.356 -.083325 .0300055 associatemore | .1015891 .0476979 2.13 0.033 .0080459 .1951322 fulltime | .0090151 .0654756 0.14 0.891 -.1193927 .137423 parttime | -.1014771 .0838587 -1.21 0.226 -.2659372 .062983 selfemp | .0372718 .0988473 0.38 0.706 -.1565833 .2311269 unemployed | .0970394 .0974852 1.00 0.320 -.0941443 .2882232 student | .3350384 .129311 2.59 0.010 .0814392 .5886376 _cons | -.0332594 .3314474 -0.10 0.920 -.6832798 .6167609 ------------------------------------------------------------------------------- note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 7.93 Prob > F = 0.0000 R-squared = 0.1289 Root MSE = .95388 ------------------------------------------------------------------------------- | Robust UKtool | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .11442 .0605462 1.89 0.059 -.0043926 .2332326 wave | 0 (omitted) gender | .3067953 .0623663 4.92 0.000 .1844109 .4291796 prior | -.0036271 .0016965 -2.14 0.033 -.0069562 -.000298 democrat | .5844135 .0716605 8.16 0.000 .4437908 .7250362 indep | .2297374 .092561 2.48 0.013 .0481006 .4113741 otherpol | .3192605 .2436944 1.31 0.190 -.1589523 .7974733 midwest | -.1463091 .0971106 -1.51 0.132 -.3368738 .0442556 south | -.0616318 .0826656 -0.75 0.456 -.2238504 .1005868 west | -.0918256 .0934022 -0.98 0.326 -.275113 .0914618 age1 | -.0022926 .1200111 -0.02 0.985 -.2377959 .2332106 age2 | -.0478419 .1032964 -0.46 0.643 -.2505453 .1548615 age3 | -.0179013 .0975762 -0.18 0.854 -.2093795 .1735769 age4 | .0970219 .095069 1.02 0.308 -.0895365 .2835803 anychildren | -.0397498 .0694486 -0.57 0.567 -.176032 .0965325 loghhinc | .0602127 .0418132 1.44 0.150 -.0218393 .1422646 associatemore | .0036374 .0679316 0.05 0.957 -.129668 .1369428 fulltime | .0455391 .0917915 0.50 0.620 -.1345876 .2256657 parttime | .0376446 .1234382 0.30 0.760 -.2045839 .2798732 selfemp | .2789413 .126928 2.20 0.028 .0298646 .5280179 unemployed | .1738671 .1612417 1.08 0.281 -.142545 .4902792 student | .2392582 .1446906 1.65 0.099 -.0446748 .5231913 _cons | -.854755 .4950215 -1.73 0.085 -1.826159 .1166486 ------------------------------------------------------------------------------- Linear regression Number of obs = 3,031 F(22, 3008) = 17.39 Prob > F = 0.0000 R-squared = 0.1155 Root MSE = .94563 ------------------------------------------------------------------------------- | Robust childcare | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0124395 .0345397 0.36 0.719 -.0552844 .0801633 wave | -.0939769 .0373301 -2.52 0.012 -.167172 -.0207819 gender | .1098226 .036238 3.03 0.002 .0387688 .1808764 prior | -.0039474 .0009789 -4.03 0.000 -.0058667 -.0020281 democrat | .5828621 .0401554 14.52 0.000 .5041274 .6615969 indep | .1022125 .0524848 1.95 0.052 -.0006972 .2051223 otherpol | .2473362 .1345061 1.84 0.066 -.0163971 .5110694 midwest | -.0964789 .0560455 -1.72 0.085 -.2063703 .0134126 south | .0008591 .0500833 0.02 0.986 -.0973419 .0990602 west | -.1467767 .0561199 -2.62 0.009 -.2568141 -.0367394 age1 | .2856099 .079989 3.57 0.000 .1287711 .4424486 age2 | .3223321 .0565029 5.70 0.000 .2115438 .4331204 age3 | .2392862 .0563747 4.24 0.000 .1287494 .3498231 age4 | .0993498 .0540695 1.84 0.066 -.006667 .2053666 anychildren | .2003478 .0389217 5.15 0.000 .1240319 .2766636 loghhinc | -.0278649 .0242215 -1.15 0.250 -.0753572 .0196274 associatemore | -.0257215 .0381731 -0.67 0.500 -.1005694 .0491264 fulltime | -.0127833 .0547656 -0.23 0.815 -.1201652 .0945986 parttime | .0168949 .07131 0.24 0.813 -.1229263 .1567162 selfemp | .0672383 .0778018 0.86 0.388 -.0853118 .2197884 unemployed | .033089 .0860585 0.38 0.701 -.1356505 .2018284 student | .0525343 .0986387 0.53 0.594 -.1408718 .2459405 _cons | .1680414 .2811804 0.60 0.550 -.3832839 .7193668 ------------------------------------------------------------------------------- Linear regression Number of obs = 3,031 F(22, 3008) = 31.15 Prob > F = 0.0000 R-squared = 0.1891 Root MSE = .68262 ------------------------------------------------------------------------------- | Robust z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0616493 .024952 2.47 0.014 .0127246 .110574 wave | .0240571 .0275243 0.87 0.382 -.0299113 .0780256 gender | .2025261 .0255503 7.93 0.000 .1524283 .2526239 prior | -.0038348 .0007373 -5.20 0.000 -.0052806 -.0023891 democrat | .5924584 .0288843 20.51 0.000 .5358234 .6490934 indep | .1888987 .0380083 4.97 0.000 .1143738 .2634235 otherpol | .2522296 .0991213 2.54 0.011 .0578772 .446582 midwest | -.1024879 .0411317 -2.49 0.013 -.1831369 -.0218388 south | -.0001231 .036337 -0.00 0.997 -.071371 .0711248 west | -.0715493 .0400097 -1.79 0.074 -.1499984 .0068997 age1 | .1193497 .054081 2.21 0.027 .0133102 .2253893 age2 | .1409319 .0406557 3.47 0.001 .061216 .2206478 age3 | .0917541 .0408421 2.25 0.025 .0116729 .1718353 age4 | .0339092 .0388988 0.87 0.383 -.0423617 .11018 anychildren | .108789 .0276402 3.94 0.000 .0545934 .1629846 loghhinc | -.0149127 .0175152 -0.85 0.395 -.0492557 .0194303 associatemore | .0036124 .0278105 0.13 0.897 -.0509171 .058142 fulltime | .0218228 .0394642 0.55 0.580 -.0555568 .0992024 parttime | -.007551 .0507248 -0.15 0.882 -.1070097 .0919077 selfemp | .0930349 .0562402 1.65 0.098 -.0172384 .2033081 unemployed | .1165967 .0588484 1.98 0.048 .0012095 .2319838 student | .1199121 .0723097 1.66 0.097 -.0218694 .2616937 _cons | -.126449 .2067102 -0.61 0.541 -.5317566 .2788585 ------------------------------------------------------------------------------- . . . * Calculate sharpened q-values for Panel B, columns 1-6 . mat def P = J(6, 1, .) . reg quotaanchor T1 $controls, vce(r) Linear regression Number of obs = 3,031 F(22, 3008) = 18.07 Prob > F = 0.0000 R-squared = 0.1172 Root MSE = .9688 ------------------------------------------------------------------------------- | Robust quotaanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0553954 .0352988 1.57 0.117 -.0138168 .1246075 wave | .1413985 .0381101 3.71 0.000 .066674 .2161229 gender | .2547495 .0364593 6.99 0.000 .1832619 .326237 prior | -.0039144 .0009352 -4.19 0.000 -.0057481 -.0020807 democrat | .5567124 .0406182 13.71 0.000 .4770701 .6363547 indep | .1586265 .0535626 2.96 0.003 .0536035 .2636495 otherpol | .1825468 .1270349 1.44 0.151 -.0665372 .4316308 midwest | -.1164282 .0585453 -1.99 0.047 -.231221 -.0016354 south | .0226438 .0518849 0.44 0.663 -.0790897 .1243772 west | -.0252372 .0559186 -0.45 0.652 -.1348797 .0844053 age1 | .2685733 .0767694 3.50 0.000 .1180475 .4190991 age2 | .2940541 .0576745 5.10 0.000 .1809686 .4071396 age3 | .2042946 .0561753 3.64 0.000 .0941487 .3144406 age4 | .0509717 .0549522 0.93 0.354 -.0567759 .1587193 anychildren | .1263693 .0391135 3.23 0.001 .0496774 .2030612 loghhinc | -.0361902 .0244033 -1.48 0.138 -.084039 .0116586 associatemore | -.0645246 .039995 -1.61 0.107 -.1429449 .0138956 fulltime | .0607097 .0541962 1.12 0.263 -.0455556 .166975 parttime | .0345804 .07167 0.48 0.629 -.1059468 .1751075 selfemp | .091988 .0774766 1.19 0.235 -.0599244 .2439005 unemployed | .1201816 .083948 1.43 0.152 -.0444196 .2847828 student | -.0402219 .1079008 -0.37 0.709 -.2517887 .1713449 _cons | -.1107841 .2859781 -0.39 0.698 -.6715164 .4499483 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg AAanchor T1 $controls, vce(r) Linear regression Number of obs = 3,031 F(22, 3008) = 21.34 Prob > F = 0.0000 R-squared = 0.1352 Root MSE = .93504 ------------------------------------------------------------------------------- | Robust AAanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1156892 .0340574 3.40 0.001 .0489112 .1824673 wave | .028759 .0372113 0.77 0.440 -.0442033 .1017212 gender | .1797219 .0350154 5.13 0.000 .1110654 .2483785 prior | -.004534 .0009668 -4.69 0.000 -.0064296 -.0026384 democrat | .662378 .0394629 16.78 0.000 .585001 .7397549 indep | .2493835 .051931 4.80 0.000 .1475597 .3512073 otherpol | .1200816 .1254735 0.96 0.339 -.1259409 .3661042 midwest | -.0907982 .055968 -1.62 0.105 -.2005376 .0189413 south | .0714102 .0503452 1.42 0.156 -.0273042 .1701246 west | -.0485374 .0543569 -0.89 0.372 -.1551178 .0580431 age1 | .1716369 .0723658 2.37 0.018 .0297455 .3135283 age2 | .1613597 .0554117 2.91 0.004 .052711 .2700084 age3 | .0608715 .0546676 1.11 0.266 -.0463183 .1680612 age4 | .0146144 .0520722 0.28 0.779 -.0874864 .1167151 anychildren | .1345906 .0378995 3.55 0.000 .0602791 .2089022 loghhinc | -.0116933 .0233566 -0.50 0.617 -.0574898 .0341032 associatemore | .0587787 .0376924 1.56 0.119 -.0151269 .1326842 fulltime | -.0210369 .0513614 -0.41 0.682 -.1217439 .0796701 parttime | -.0499881 .0677276 -0.74 0.461 -.1827852 .082809 selfemp | -.0072996 .0759103 -0.10 0.923 -.156141 .1415418 unemployed | -.0056021 .0828659 -0.07 0.946 -.1680816 .1568773 student | .1388002 .0999462 1.39 0.165 -.0571695 .3347699 _cons | -.2235044 .2811675 -0.79 0.427 -.7748045 .3277957 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg legislationanchor T1 $controls, vce(r) Linear regression Number of obs = 3,031 F(22, 3008) = 18.78 Prob > F = 0.0000 R-squared = 0.1204 Root MSE = .94779 ------------------------------------------------------------------------------- | Robust legislation~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1219403 .0346884 3.52 0.000 .0539248 .1899557 wave | .014065 .0377905 0.37 0.710 -.0600328 .0881628 gender | .2359678 .035655 6.62 0.000 .1660571 .3058785 prior | -.0040109 .0010566 -3.80 0.000 -.0060826 -.0019392 democrat | .6192279 .0401152 15.44 0.000 .5405719 .697884 indep | .2328022 .0505516 4.61 0.000 .133683 .3319214 otherpol | .4786527 .1291036 3.71 0.000 .2255123 .731793 midwest | -.0702965 .0569502 -1.23 0.217 -.1819617 .0413687 south | -.0283776 .0506259 -0.56 0.575 -.1276425 .0708874 west | -.0623346 .0546041 -1.14 0.254 -.1693998 .0447306 age1 | -.1738019 .0744882 -2.33 0.020 -.3198547 -.027749 age2 | -.1145397 .056043 -2.04 0.041 -.2244261 -.0046533 age3 | -.1046654 .055562 -1.88 0.060 -.2136088 .0042779 age4 | -.0528755 .0533103 -0.99 0.321 -.1574037 .0516528 anychildren | .0313895 .0386702 0.81 0.417 -.0444333 .1072123 loghhinc | .0045808 .0234662 0.20 0.845 -.0414307 .0505922 associatemore | .0060241 .0378113 0.16 0.873 -.0681145 .0801627 fulltime | .0456327 .0538265 0.85 0.397 -.0599077 .1511731 parttime | -.0073176 .0722372 -0.10 0.919 -.1489569 .1343217 selfemp | .1757384 .0743834 2.36 0.018 .0298908 .321586 unemployed | .2840083 .0835794 3.40 0.001 .1201298 .4478869 student | .1795845 .1035118 1.73 0.083 -.0233765 .3825454 _cons | -.209031 .2804245 -0.75 0.456 -.7588741 .3408121 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . reg transparencyanchor T1 $controls, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 10.39 Prob > F = 0.0000 R-squared = 0.0957 Root MSE = .94476 ------------------------------------------------------------------------------- | Robust transparenc~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0145207 .0421926 -0.34 0.731 -.097267 .0682256 wave | 0 (omitted) gender | .1968569 .0436751 4.51 0.000 .1112032 .2825107 prior | -.0026412 .0011873 -2.22 0.026 -.0049697 -.0003127 democrat | .5648746 .048392 11.67 0.000 .4699703 .6597789 indep | .2426681 .0645124 3.76 0.000 .1161491 .3691871 otherpol | .1372274 .1916125 0.72 0.474 -.2385547 .5130095 midwest | -.1229332 .0686787 -1.79 0.074 -.257623 .0117565 south | -.0270683 .0603186 -0.45 0.654 -.1453626 .0912259 west | -.048422 .0656441 -0.74 0.461 -.1771604 .0803163 age1 | .0478043 .1037241 0.46 0.645 -.1556149 .2512235 age2 | .0581052 .0678145 0.86 0.392 -.0748897 .1911001 age3 | .0554398 .0677336 0.82 0.413 -.0773964 .188276 age4 | .0267632 .0655902 0.41 0.683 -.1018694 .1553958 anychildren | .0940054 .0484718 1.94 0.053 -.0010554 .1890661 loghhinc | -.0266598 .0288938 -0.92 0.356 -.083325 .0300055 associatemore | .1015891 .0476979 2.13 0.033 .0080459 .1951322 fulltime | .0090151 .0654756 0.14 0.891 -.1193927 .137423 parttime | -.1014771 .0838587 -1.21 0.226 -.2659372 .062983 selfemp | .0372718 .0988473 0.38 0.706 -.1565833 .2311269 unemployed | .0970394 .0974852 1.00 0.320 -.0941443 .2882232 student | .3350384 .129311 2.59 0.010 .0814392 .5886376 _cons | -.0332594 .3314474 -0.10 0.920 -.6832798 .6167609 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[4, 1] = r(p) . reg UKtool T1 $controls, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 7.93 Prob > F = 0.0000 R-squared = 0.1289 Root MSE = .95388 ------------------------------------------------------------------------------- | Robust UKtool | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .11442 .0605462 1.89 0.059 -.0043926 .2332326 wave | 0 (omitted) gender | .3067953 .0623663 4.92 0.000 .1844109 .4291796 prior | -.0036271 .0016965 -2.14 0.033 -.0069562 -.000298 democrat | .5844135 .0716605 8.16 0.000 .4437908 .7250362 indep | .2297374 .092561 2.48 0.013 .0481006 .4113741 otherpol | .3192605 .2436944 1.31 0.190 -.1589523 .7974733 midwest | -.1463091 .0971106 -1.51 0.132 -.3368738 .0442556 south | -.0616318 .0826656 -0.75 0.456 -.2238504 .1005868 west | -.0918256 .0934022 -0.98 0.326 -.275113 .0914618 age1 | -.0022926 .1200111 -0.02 0.985 -.2377959 .2332106 age2 | -.0478419 .1032964 -0.46 0.643 -.2505453 .1548615 age3 | -.0179013 .0975762 -0.18 0.854 -.2093795 .1735769 age4 | .0970219 .095069 1.02 0.308 -.0895365 .2835803 anychildren | -.0397498 .0694486 -0.57 0.567 -.176032 .0965325 loghhinc | .0602127 .0418132 1.44 0.150 -.0218393 .1422646 associatemore | .0036374 .0679316 0.05 0.957 -.129668 .1369428 fulltime | .0455391 .0917915 0.50 0.620 -.1345876 .2256657 parttime | .0376446 .1234382 0.30 0.760 -.2045839 .2798732 selfemp | .2789413 .126928 2.20 0.028 .0298646 .5280179 unemployed | .1738671 .1612417 1.08 0.281 -.142545 .4902792 student | .2392582 .1446906 1.65 0.099 -.0446748 .5231913 _cons | -.854755 .4950215 -1.73 0.085 -1.826159 .1166486 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[5, 1] = r(p) . reg childcare T1 $controls, vce(r) Linear regression Number of obs = 3,031 F(22, 3008) = 17.39 Prob > F = 0.0000 R-squared = 0.1155 Root MSE = .94563 ------------------------------------------------------------------------------- | Robust childcare | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0124395 .0345397 0.36 0.719 -.0552844 .0801633 wave | -.0939769 .0373301 -2.52 0.012 -.167172 -.0207819 gender | .1098226 .036238 3.03 0.002 .0387688 .1808764 prior | -.0039474 .0009789 -4.03 0.000 -.0058667 -.0020281 democrat | .5828621 .0401554 14.52 0.000 .5041274 .6615969 indep | .1022125 .0524848 1.95 0.052 -.0006972 .2051223 otherpol | .2473362 .1345061 1.84 0.066 -.0163971 .5110694 midwest | -.0964789 .0560455 -1.72 0.085 -.2063703 .0134126 south | .0008591 .0500833 0.02 0.986 -.0973419 .0990602 west | -.1467767 .0561199 -2.62 0.009 -.2568141 -.0367394 age1 | .2856099 .079989 3.57 0.000 .1287711 .4424486 age2 | .3223321 .0565029 5.70 0.000 .2115438 .4331204 age3 | .2392862 .0563747 4.24 0.000 .1287494 .3498231 age4 | .0993498 .0540695 1.84 0.066 -.006667 .2053666 anychildren | .2003478 .0389217 5.15 0.000 .1240319 .2766636 loghhinc | -.0278649 .0242215 -1.15 0.250 -.0753572 .0196274 associatemore | -.0257215 .0381731 -0.67 0.500 -.1005694 .0491264 fulltime | -.0127833 .0547656 -0.23 0.815 -.1201652 .0945986 parttime | .0168949 .07131 0.24 0.813 -.1229263 .1567162 selfemp | .0672383 .0778018 0.86 0.388 -.0853118 .2197884 unemployed | .033089 .0860585 0.38 0.701 -.1356505 .2018284 student | .0525343 .0986387 0.53 0.594 -.1408718 .2459405 _cons | .1680414 .2811804 0.60 0.550 -.3832839 .7193668 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[6, 1] = r(p) . . . minq P, q("Q") step(0.001) number of observations will be reset to 6 Press any key to continue, or Break to abort number of observations (_N) was 0, now 6 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = .7109999999999997 Correction with q = .7099999999999997 Correction with q = .7089999999999997 Correction with q = .7079999999999997 Correction with q = .7069999999999997 Correction with q = .7059999999999997 Correction with q = .7049999999999997 Correction with q = .7039999999999997 Correction with q = .7029999999999997 Correction with q = .7019999999999997 Correction with q = .7009999999999997 Correction with q = .6999999999999997 Correction with q = .6989999999999997 Correction with q = .6979999999999997 Correction with q = .6969999999999997 Correction with q = .6959999999999997 Correction with q = .6949999999999997 Correction with q = .6939999999999997 Correction with q = .6929999999999997 Correction with q = .6919999999999997 Correction with q = .6909999999999997 Correction with q = .6899999999999997 Correction with q = .6889999999999997 Correction with q = .6879999999999997 Correction with q = .6869999999999997 Correction with q = .6859999999999997 Correction with q = .6849999999999997 Correction with q = .6839999999999997 Correction with q = .6829999999999997 Correction with q = .6819999999999997 Correction with q = .6809999999999997 Correction with q = .6799999999999997 Correction with q = .6789999999999997 Correction with q = .6779999999999997 Correction with q = .6769999999999997 Correction with q = .6759999999999997 Correction with q = .6749999999999997 Correction with q = .6739999999999997 Correction with q = .6729999999999997 Correction with q = .6719999999999997 Correction with q = .6709999999999997 Correction with q = .6699999999999997 Correction with q = .6689999999999997 Correction with q = .6679999999999997 Correction with q = .6669999999999997 Correction with q = .6659999999999997 Correction with q = .6649999999999997 Correction with q = .6639999999999997 Correction with q = .6629999999999997 Correction with q = .6619999999999997 Correction with q = .6609999999999997 Correction with q = .6599999999999997 Correction with q = .6589999999999997 Correction with q = .6579999999999997 Correction with q = .6569999999999997 Correction with q = .6559999999999997 Correction with q = .6549999999999997 Correction with q = .6539999999999997 Correction with q = .6529999999999997 Correction with q = .6519999999999997 Correction with q = .6509999999999997 Correction with q = .6499999999999997 Correction with q = .6489999999999997 Correction with q = .6479999999999997 Correction with q = .6469999999999997 Correction with q = .6459999999999997 Correction with q = .6449999999999997 Correction with q = .6439999999999997 Correction with q = .6429999999999997 Correction with q = .6419999999999997 Correction with q = .6409999999999997 Correction with q = .6399999999999997 Correction with q = .6389999999999997 Correction with q = .6379999999999997 Correction with q = .6369999999999997 Correction with q = .6359999999999997 Correction with q = .6349999999999997 Correction with q = .6339999999999997 Correction with q = .6329999999999997 Correction with q = .6319999999999997 Correction with q = .6309999999999997 Correction with q = .6299999999999997 Correction with q = .6289999999999997 Correction with q = .6279999999999997 Correction with q = .6269999999999997 Correction with q = .6259999999999997 Correction with q = .6249999999999997 Correction with q = .6239999999999997 Correction with q = .6229999999999997 Correction with q = .6219999999999997 Correction with q = .6209999999999997 Correction with q = .6199999999999997 Correction with q = .6189999999999997 Correction with q = .6179999999999997 Correction with q = .6169999999999997 Correction with q = .6159999999999997 Correction with q = .6149999999999997 Correction with q = .6139999999999997 Correction with q = .6129999999999997 Correction with q = .6119999999999997 Correction with q = .6109999999999997 Correction with q = .6099999999999997 Correction with q = .6089999999999997 Correction with q = .6079999999999997 Correction with q = .6069999999999997 Correction with q = .6059999999999997 Correction with q = .6049999999999997 Correction with q = .6039999999999997 Correction with q = .6029999999999997 Correction with q = .6019999999999997 Correction with q = .6009999999999997 Correction with q = .5999999999999996 Correction with q = .5989999999999996 Correction with q = .5979999999999996 Correction with q = .5969999999999996 Correction with q = .5959999999999996 Correction with q = .5949999999999996 Correction with q = .5939999999999996 Correction with q = .5929999999999996 Correction with q = .5919999999999996 Correction with q = .5909999999999996 Correction with q = .5899999999999996 Correction with q = .5889999999999996 Correction with q = .5879999999999996 Correction with q = .5869999999999996 Correction with q = .5859999999999996 Correction with q = .5849999999999996 Correction with q = .5839999999999996 Correction with q = .5829999999999996 Correction with q = .5819999999999996 Correction with q = .5809999999999996 Correction with q = .5799999999999996 Correction with q = .5789999999999996 Correction with q = .5779999999999996 Correction with q = .5769999999999996 Correction with q = .5759999999999996 Correction with q = .5749999999999996 Correction with q = .5739999999999996 Correction with q = .5729999999999996 Correction with q = .5719999999999996 Correction with q = .5709999999999996 Correction with q = .5699999999999996 Correction with q = .5689999999999996 Correction with q = .5679999999999996 Correction with q = .5669999999999996 Correction with q = .5659999999999996 Correction with q = .5649999999999996 Correction with q = .5639999999999996 Correction with q = .5629999999999996 Correction with q = .5619999999999996 Correction with q = .5609999999999996 Correction with q = .5599999999999996 Correction with q = .5589999999999996 Correction with q = .5579999999999996 Correction with q = .5569999999999996 Correction with q = .5559999999999996 Correction with q = .5549999999999996 Correction with q = .5539999999999996 Correction with q = .5529999999999996 Correction with q = .5519999999999996 Correction with q = .5509999999999996 Correction with q = .5499999999999996 Correction with q = .5489999999999996 Correction with q = .5479999999999996 Correction with q = .5469999999999996 Correction with q = .5459999999999996 Correction with q = .5449999999999996 Correction with q = .5439999999999996 Correction with q = .5429999999999996 Correction with q = .5419999999999996 Correction with q = .5409999999999996 Correction with q = .5399999999999996 Correction with q = .5389999999999996 Correction with q = .5379999999999996 Correction with q = .5369999999999996 Correction with q = .5359999999999996 Correction with q = .5349999999999996 Correction with q = .5339999999999996 Correction with q = .5329999999999996 Correction with q = .5319999999999996 Correction with q = .5309999999999996 Correction with q = .5299999999999996 Correction with q = .5289999999999996 Correction with q = .5279999999999996 Correction with q = .5269999999999996 Correction with q = .5259999999999996 Correction with q = .5249999999999996 Correction with q = .5239999999999996 Correction with q = .5229999999999996 Correction with q = .5219999999999996 Correction with q = .5209999999999996 Correction with q = .5199999999999996 Correction with q = .5189999999999996 Correction with q = .5179999999999996 Correction with q = .5169999999999996 Correction with q = .5159999999999996 Correction with q = .5149999999999996 Correction with q = .5139999999999996 Correction with q = .5129999999999996 Correction with q = .5119999999999996 Correction with q = .5109999999999996 Correction with q = .5099999999999996 Correction with q = .5089999999999996 Correction with q = .5079999999999996 Correction with q = .5069999999999996 Correction with q = .5059999999999996 Correction with q = .5049999999999996 Correction with q = .5039999999999996 Correction with q = .5029999999999996 Correction with q = .5019999999999996 Correction with q = .5009999999999996 Correction with q = .4999999999999996 Correction with q = .4989999999999996 Correction with q = .4979999999999996 Correction with q = .4969999999999996 Correction with q = .4959999999999996 Correction with q = .4949999999999996 Correction with q = .4939999999999996 Correction with q = .4929999999999996 Correction with q = .4919999999999996 Correction with q = .4909999999999996 Correction with q = .4899999999999996 Correction with q = .4889999999999996 Correction with q = .4879999999999996 Correction with q = .4869999999999995 Correction with q = .4859999999999995 Correction with q = .4849999999999995 Correction with q = .4839999999999995 Correction with q = .4829999999999995 Correction with q = .4819999999999995 Correction with q = .4809999999999995 Correction with q = .4799999999999995 Correction with q = .4789999999999995 Correction with q = .4779999999999995 Correction with q = .4769999999999995 Correction with q = .4759999999999995 Correction with q = .4749999999999995 Correction with q = .4739999999999995 Correction with q = .4729999999999995 Correction with q = .4719999999999995 Correction with q = .4709999999999995 Correction with q = .4699999999999995 Correction with q = .4689999999999995 Correction with q = .4679999999999995 Correction with q = .4669999999999995 Correction with q = .4659999999999995 Correction with q = .4649999999999995 Correction with q = .4639999999999995 Correction with q = .4629999999999995 Correction with q = .4619999999999995 Correction with q = .4609999999999995 Correction with q = .4599999999999995 Correction with q = .4589999999999995 Correction with q = .4579999999999995 Correction with q = .4569999999999995 Correction with q = .4559999999999995 Correction with q = .4549999999999995 Correction with q = .4539999999999995 Correction with q = .4529999999999995 Correction with q = .4519999999999995 Correction with q = .4509999999999995 Correction with q = .4499999999999995 Correction with q = .4489999999999995 Correction with q = .4479999999999995 Correction with q = .4469999999999995 Correction with q = .4459999999999995 Correction with q = .4449999999999995 Correction with q = .4439999999999995 Correction with q = .4429999999999995 Correction with q = .4419999999999995 Correction with q = .4409999999999995 Correction with q = .4399999999999995 Correction with q = .4389999999999995 Correction with q = .4379999999999995 Correction with q = .4369999999999995 Correction with q = .4359999999999995 Correction with q = .4349999999999995 Correction with q = .4339999999999995 Correction with q = .4329999999999995 Correction with q = .4319999999999995 Correction with q = .4309999999999995 Correction with q = .4299999999999995 Correction with q = .4289999999999995 Correction with q = .4279999999999995 Correction with q = .4269999999999995 Correction with q = .4259999999999995 Correction with q = .4249999999999995 Correction with q = .4239999999999995 Correction with q = .4229999999999995 Correction with q = .4219999999999995 Correction with q = .4209999999999995 Correction with q = .4199999999999995 Correction with q = .4189999999999995 Correction with q = .4179999999999995 Correction with q = .4169999999999995 Correction with q = .4159999999999995 Correction with q = .4149999999999995 Correction with q = .4139999999999995 Correction with q = .4129999999999995 Correction with q = .4119999999999995 Correction with q = .4109999999999995 Correction with q = .4099999999999995 Correction with q = .4089999999999995 Correction with q = .4079999999999995 Correction with q = .4069999999999995 Correction with q = .4059999999999995 Correction with q = .4049999999999995 Correction with q = .4039999999999995 Correction with q = .4029999999999995 Correction with q = .4019999999999995 Correction with q = .4009999999999995 Correction with q = .3999999999999995 Correction with q = .3989999999999995 Correction with q = .3979999999999995 Correction with q = .3969999999999995 Correction with q = .3959999999999995 Correction with q = .3949999999999995 Correction with q = .3939999999999995 Correction with q = .3929999999999995 Correction with q = .3919999999999995 Correction with q = .3909999999999995 Correction with q = .3899999999999995 Correction with q = .3889999999999995 Correction with q = .3879999999999995 Correction with q = .3869999999999995 Correction with q = .3859999999999995 Correction with q = .3849999999999995 Correction with q = .3839999999999995 Correction with q = .3829999999999995 Correction with q = .3819999999999995 Correction with q = .3809999999999995 Correction with q = .3799999999999995 Correction with q = .3789999999999995 Correction with q = .3779999999999995 Correction with q = .3769999999999995 Correction with q = .3759999999999995 Correction with q = .3749999999999994 Correction with q = .3739999999999994 Correction with q = .3729999999999994 Correction with q = .3719999999999994 Correction with q = .3709999999999994 Correction with q = .3699999999999994 Correction with q = .3689999999999994 Correction with q = .3679999999999994 Correction with q = .3669999999999994 Correction with q = .3659999999999994 Correction with q = .3649999999999994 Correction with q = .3639999999999994 Correction with q = .3629999999999994 Correction with q = .3619999999999994 Correction with q = .3609999999999994 Correction with q = .3599999999999994 Correction with q = .3589999999999994 Correction with q = .3579999999999994 Correction with q = .3569999999999994 Correction with q = .3559999999999994 Correction with q = .3549999999999994 Correction with q = .3539999999999994 Correction with q = .3529999999999994 Correction with q = .3519999999999994 Correction with q = .3509999999999994 Correction with q = .3499999999999994 Correction with q = .3489999999999994 Correction with q = .3479999999999994 Correction with q = .3469999999999994 Correction with q = .3459999999999994 Correction with q = .3449999999999994 Correction with q = .3439999999999994 Correction with q = .3429999999999994 Correction with q = .3419999999999994 Correction with q = .3409999999999994 Correction with q = .3399999999999994 Correction with q = .3389999999999994 Correction with q = .3379999999999994 Correction with q = .3369999999999994 Correction with q = .3359999999999994 Correction with q = .3349999999999994 Correction with q = .3339999999999994 Correction with q = .3329999999999994 Correction with q = .3319999999999994 Correction with q = .3309999999999994 Correction with q = .3299999999999994 Correction with q = .3289999999999994 Correction with q = .3279999999999994 Correction with q = .3269999999999994 Correction with q = .3259999999999994 Correction with q = .3249999999999994 Correction with q = .3239999999999994 Correction with q = .3229999999999994 Correction with q = .3219999999999994 Correction with q = .3209999999999994 Correction with q = .3199999999999994 Correction with q = .3189999999999994 Correction with q = .3179999999999994 Correction with q = .3169999999999994 Correction with q = .3159999999999994 Correction with q = .3149999999999994 Correction with q = .3139999999999994 Correction with q = .3129999999999994 Correction with q = .3119999999999994 Correction with q = .3109999999999994 Correction with q = .3099999999999994 Correction with q = .3089999999999994 Correction with q = .3079999999999994 Correction with q = .3069999999999994 Correction with q = .3059999999999994 Correction with q = .3049999999999994 Correction with q = .3039999999999994 Correction with q = .3029999999999994 Correction with q = .3019999999999994 Correction with q = .3009999999999994 Correction with q = .2999999999999994 Correction with q = .2989999999999994 Correction with q = .2979999999999994 Correction with q = .2969999999999994 Correction with q = .2959999999999994 Correction with q = .2949999999999994 Correction with q = .2939999999999994 Correction with q = .2929999999999994 Correction with q = .2919999999999994 Correction with q = .2909999999999994 Correction with q = .2899999999999994 Correction with q = .2889999999999994 Correction with q = .2879999999999994 Correction with q = .2869999999999994 Correction with q = .2859999999999994 Correction with q = .2849999999999994 Correction with q = .2839999999999994 Correction with q = .2829999999999994 Correction with q = .2819999999999994 Correction with q = .2809999999999994 Correction with q = .2799999999999994 Correction with q = .2789999999999994 Correction with q = .2779999999999994 Correction with q = .2769999999999994 Correction with q = .2759999999999994 Correction with q = .2749999999999994 Correction with q = .2739999999999994 Correction with q = .2729999999999994 Correction with q = .2719999999999994 Correction with q = .2709999999999994 Correction with q = .2699999999999994 Correction with q = .2689999999999994 Correction with q = .2679999999999994 Correction with q = .2669999999999994 Correction with q = .2659999999999994 Correction with q = .2649999999999994 Correction with q = .2639999999999994 Correction with q = .2629999999999994 Correction with q = .2619999999999993 Correction with q = .2609999999999993 Correction with q = .2599999999999993 Correction with q = .2589999999999993 Correction with q = .2579999999999993 Correction with q = .2569999999999993 Correction with q = .2559999999999993 Correction with q = .2549999999999993 Correction with q = .2539999999999993 Correction with q = .2529999999999993 Correction with q = .2519999999999993 Correction with q = .2509999999999993 Correction with q = .2499999999999993 Correction with q = .2489999999999993 Correction with q = .2479999999999993 Correction with q = .2469999999999993 Correction with q = .2459999999999993 Correction with q = .2449999999999993 Correction with q = .2439999999999993 Correction with q = .2429999999999993 Correction with q = .2419999999999993 Correction with q = .2409999999999993 Correction with q = .2399999999999993 Correction with q = .2389999999999993 Correction with q = .2379999999999993 Correction with q = .2369999999999993 Correction with q = .2359999999999993 Correction with q = .2349999999999993 Correction with q = .2339999999999993 Correction with q = .2329999999999993 Correction with q = .2319999999999993 Correction with q = .2309999999999993 Correction with q = .2299999999999993 Correction with q = .2289999999999993 Correction with q = .2279999999999993 Correction with q = .2269999999999993 Correction with q = .2259999999999993 Correction with q = .2249999999999993 Correction with q = .2239999999999993 Correction with q = .2229999999999993 Correction with q = .2219999999999993 Correction with q = .2209999999999993 Correction with q = .2199999999999993 Correction with q = .2189999999999993 Correction with q = .2179999999999993 Correction with q = .2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = .1909999999999993 Correction with q = .1899999999999993 Correction with q = .1889999999999993 Correction with q = .1879999999999993 Correction with q = .1869999999999993 Correction with q = .1859999999999993 Correction with q = .1849999999999993 Correction with q = .1839999999999993 Correction with q = .1829999999999993 Correction with q = .1819999999999993 Correction with q = .1809999999999993 Correction with q = .1799999999999993 Correction with q = .1789999999999993 Correction with q = .1779999999999993 Correction with q = .1769999999999993 Correction with q = .1759999999999993 Correction with q = .1749999999999993 Correction with q = .1739999999999993 Correction with q = .1729999999999993 Correction with q = .1719999999999993 Correction with q = .1709999999999993 Correction with q = .1699999999999993 Correction with q = .1689999999999993 Correction with q = .1679999999999993 Correction with q = .1669999999999993 Correction with q = .1659999999999993 Correction with q = .1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 Correction with q = .0069999999999991 Correction with q = .0059999999999991 Correction with q = .0049999999999991 Correction with q = .0039999999999991 Correction with q = .0029999999999991 Correction with q = .0019999999999991 (6 real changes made) (0 real changes made) . . estadd loc thisstat4 = "[" + string(Q[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat4 = "[" +string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat4 = "[" + string(Q[3, 1], "%9.3f")+"]": col3 . estadd loc thisstat4 = "["+ string(Q[4, 1], "%9.3f")+"]": col4 . estadd loc thisstat4 = "["+ string(Q[5, 1], "%9.3f")+"]": col5 . estadd loc thisstat4 = "["+ string(Q[6, 1], "%9.3f")+"]": col6 . . . loc rowlabels " " " "T$^{74}$" " " "Sharpened q-value" " " "Female" " " " " "Democrat" " " " " "Ob > servations" " . . loc rowstats "" . . forval i = 1/12 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\policydemandmainAB_noweights.tex", replace cells(none) booktabs nonotes no > mtitles compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels') > ) /// > mgroups("\shortstack{Introduce\\gender\\quotas}" "\shortstack{Statutory\\affirmative\\acti > on}" /// > "\shortstack{Stricter\\equal pay\\legislation}" "\shortstack{Wage transp.\\within\\ companies}" > "\shortstack{Introduce\\reporting\\website}" "\shortstack{Increase\\subsidies\\to child care}" /// > "\shortstack{Policy\\demand\\index}", pattern(1 1 1 1 1 1 1 ) prefix(\multicolumn{@span}{c}{) s > uffix(}) span erepeat(\cmidrule(lr){@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\policydemandmainAB_noweights.tex) . . eststo clear . . . *********************************************************************************** . // Table G.3: Treatment effect on views related to the gender wage gap (by wave) . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . drop if rand==0 (1,034 observations deleted) . . loc experiments "large problem govmore z_mani_index" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experiments' { 2. . ***Panel A: Main specification . . reg `choice' T1 $controls [pweight=pweight], r 3. . sigstar T1, prec(3) 4. estadd loc thisstat4 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat5 = "`r(sestar)'": col`colnum' 6. . sigstar democrat, prec(3) 7. estadd loc thisstat8 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat9 = "`r(sestar)'": col`colnum' 9. . sigstar gender, prec(3) 10. estadd loc thisstat11 = "`r(bstar)'": col`colnum' 11. estadd loc thisstat12 = "`r(sestar)'": col`colnum' 12. . qui sum `choice' 13. estadd loc thisstat14 = r(N): col`colnum' 14. . . ***Panel B: Wave A only . . reg `choice' T1 $controls [pweight=pweight] if wave==1, r 15. . sigstar T1, prec(3) 16. estadd loc thisstat19 = "`r(bstar)'": col`colnum' 17. estadd loc thisstat20 = "`r(sestar)'": col`colnum' 18. . sigstar democrat, prec(3) 19. estadd loc thisstat23 = "`r(bstar)'": col`colnum' 20. estadd loc thisstat24 = "`r(sestar)'": col`colnum' 21. . sigstar gender, prec(3) 22. estadd loc thisstat26 = "`r(bstar)'": col`colnum' 23. estadd loc thisstat27 = "`r(sestar)'": col`colnum' 24. . qui sum `choice' if wave==1 25. estadd loc thisstat29 = r(N): col`colnum' 26. . . ***Panel C: Wave B only . . reg `choice' T1 $controls [pweight=pweight] if wave==2, r 27. . sigstar T1, prec(3) 28. estadd loc thisstat34 = "`r(bstar)'": col`colnum' 29. estadd loc thisstat35 = "`r(sestar)'": col`colnum' 30. . sigstar democrat, prec(3) 31. estadd loc thisstat38 = "`r(bstar)'": col`colnum' 32. estadd loc thisstat39 = "`r(sestar)'": col`colnum' 33. . sigstar gender, prec(3) 34. estadd loc thisstat41 = "`r(bstar)'": col`colnum' 35. estadd loc thisstat42 = "`r(sestar)'": col`colnum' 36. . qui sum `choice' if wave==2 37. estadd loc thisstat44 = r(N): col`colnum' 38. . loc ++colnum 39. loc colnames "`colnames' `"`: var la `choice''"'" 40. . } (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 28.60 Prob > F = 0.0000 R-squared = 0.1745 Root MSE = .96735 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .5972595 .0356733 16.74 0.000 .5273129 .6672061 wave | -.0269099 .0382786 -0.70 0.482 -.1019648 .048145 gender | .2354511 .036212 6.50 0.000 .1644483 .3064539 prior | -.0057459 .0009906 -5.80 0.000 -.0076882 -.0038036 democrat | .5251511 .0405832 12.94 0.000 .4455776 .6047247 indep | .2014438 .0549588 3.67 0.000 .0936831 .3092045 otherpol | .1360499 .1555581 0.87 0.382 -.1689611 .441061 midwest | -.0463282 .0571594 -0.81 0.418 -.1584037 .0657473 south | .0559538 .0518565 1.08 0.281 -.0457239 .1576315 west | -.0351877 .0561643 -0.63 0.531 -.145312 .0749366 age1 | .0210497 .0824246 0.26 0.798 -.1405645 .182664 age2 | .0410061 .0557812 0.74 0.462 -.068367 .1503793 age3 | -.007434 .0551204 -0.13 0.893 -.1155115 .1006435 age4 | -.1330057 .0532157 -2.50 0.012 -.2373486 -.0286629 anychildren | .1281935 .0394718 3.25 0.001 .050799 .205588 loghhinc | .0382895 .0247828 1.55 0.122 -.0103035 .0868826 associatemore | .0061067 .0400086 0.15 0.879 -.0723404 .0845538 fulltime | .0912619 .0539965 1.69 0.091 -.0146119 .1971356 parttime | -.1105413 .07271 -1.52 0.129 -.2531076 .032025 selfemp | .0609548 .0753707 0.81 0.419 -.0868285 .2087382 unemployed | .0541613 .087939 0.62 0.538 -.1182653 .2265878 student | .0485873 .1105169 0.44 0.660 -.1681092 .2652837 _cons | -.7538024 .2863028 -2.63 0.009 -1.315171 -.1924334 ------------------------------------------------------------------------------- (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 18.53 Prob > F = 0.0000 R-squared = 0.1662 Root MSE = .96474 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .5852879 .0431661 13.56 0.000 .5006324 .6699433 wave | 0 (omitted) gender | .2134097 .044375 4.81 0.000 .1263833 .3004361 prior | -.0060249 .0011754 -5.13 0.000 -.0083301 -.0037197 democrat | .5061398 .0490367 10.32 0.000 .4099711 .6023085 indep | .2405437 .0676703 3.55 0.000 .1078316 .3732558 otherpol | .0566693 .1899884 0.30 0.766 -.3159278 .4292664 midwest | -.0304249 .0722535 -0.42 0.674 -.1721254 .1112755 south | .0133472 .0644622 0.21 0.836 -.1130732 .1397676 west | .0080856 .0693246 0.12 0.907 -.1278708 .144042 age1 | .0983591 .1032273 0.95 0.341 -.1040859 .300804 age2 | -.0190672 .0674947 -0.28 0.778 -.1514349 .1133005 age3 | -.0731723 .0671324 -1.09 0.276 -.2048294 .0584848 age4 | -.1683095 .0643593 -2.62 0.009 -.2945282 -.0420907 anychildren | .1531133 .0473704 3.23 0.001 .0602126 .2460141 loghhinc | .0270903 .0295885 0.92 0.360 -.0309375 .0851181 associatemore | .0114634 .0490779 0.23 0.815 -.084786 .1077127 fulltime | .0656225 .0650922 1.01 0.314 -.0620336 .1932786 parttime | -.1006799 .086536 -1.16 0.245 -.2703906 .0690308 selfemp | .0398741 .0894698 0.45 0.656 -.1355902 .2153383 unemployed | .0275389 .1018332 0.27 0.787 -.172172 .2272498 student | -.032536 .1465261 -0.22 0.824 -.3198967 .2548246 _cons | -.5784637 .3315633 -1.74 0.081 -1.228711 .071784 ------------------------------------------------------------------------------- (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 13.26 Prob > F = 0.0000 R-squared = 0.2086 Root MSE = .97164 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .628054 .0635287 9.89 0.000 .5033887 .7527192 wave | 0 (omitted) gender | .2804918 .0635015 4.42 0.000 .1558798 .4051038 prior | -.0050007 .0017884 -2.80 0.005 -.0085102 -.0014911 democrat | .5398777 .0734168 7.35 0.000 .3958085 .6839469 indep | .1123585 .0952876 1.18 0.239 -.0746288 .2993457 otherpol | .3184052 .2525799 1.26 0.208 -.1772441 .8140545 midwest | -.0649516 .0937909 -0.69 0.489 -.2490019 .1190987 south | .1377281 .0877143 1.57 0.117 -.0343977 .3098539 west | -.1073857 .0988231 -1.09 0.277 -.3013109 .0865394 age1 | -.0531808 .1363709 -0.39 0.697 -.3207878 .2144261 age2 | .1761198 .1017402 1.73 0.084 -.0235298 .3757693 age3 | .1288002 .098086 1.31 0.189 -.0636785 .3212789 age4 | -.0650915 .0960533 -0.68 0.498 -.2535814 .1233983 anychildren | .0496479 .0731671 0.68 0.498 -.0939313 .193227 loghhinc | .0698563 .0471506 1.48 0.139 -.0226695 .1623821 associatemore | -.0035129 .0690951 -0.05 0.959 -.1391013 .1320756 fulltime | .1293563 .0965719 1.34 0.181 -.0601512 .3188637 parttime | -.1248257 .1322499 -0.94 0.345 -.3843459 .1346944 selfemp | .1279262 .1382231 0.93 0.355 -.1433154 .3991677 unemployed | .1200966 .1808678 0.66 0.507 -.2348287 .4750218 student | .1640887 .1727228 0.95 0.342 -.1748532 .5030306 _cons | -1.299399 .5460354 -2.38 0.018 -2.370909 -.2278885 ------------------------------------------------------------------------------- (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 28.42 Prob > F = 0.0000 R-squared = 0.1775 Root MSE = .95226 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4215353 .0351138 12.00 0.000 .3526858 .4903849 wave | -.0162456 .0374007 -0.43 0.664 -.0895791 .0570878 gender | .2970653 .0356542 8.33 0.000 .2271563 .3669743 prior | -.0061382 .0009184 -6.68 0.000 -.007939 -.0043374 democrat | .6560668 .0403051 16.28 0.000 .5770385 .7350951 indep | .2531112 .0553512 4.57 0.000 .1445812 .3616413 otherpol | .2965088 .1414328 2.10 0.036 .0191939 .5738237 midwest | -.1166761 .0567613 -2.06 0.040 -.227971 -.0053813 south | -.0312535 .0503927 -0.62 0.535 -.1300611 .0675541 west | -.0710708 .0545453 -1.30 0.193 -.1780206 .035879 age1 | .0597321 .079137 0.75 0.450 -.0954361 .2149002 age2 | .0223707 .0561827 0.40 0.691 -.0877896 .132531 age3 | .0067231 .054451 0.12 0.902 -.100042 .1134881 age4 | -.1110718 .0532144 -2.09 0.037 -.2154121 -.0067315 anychildren | .0898165 .0392609 2.29 0.022 .0128357 .1667974 loghhinc | .0179323 .0243871 0.74 0.462 -.0298847 .0657493 associatemore | .0221103 .0400605 0.55 0.581 -.0564384 .1006589 fulltime | .0578635 .0531468 1.09 0.276 -.0463443 .1620712 parttime | -.1076561 .0717129 -1.50 0.133 -.2482674 .0329552 selfemp | -.0018603 .0750208 -0.02 0.980 -.1489577 .145237 unemployed | .0524455 .0901715 0.58 0.561 -.1243585 .2292496 student | .0620267 .1116342 0.56 0.579 -.1568603 .2809137 _cons | -.4256575 .2795707 -1.52 0.128 -.9738266 .1225116 ------------------------------------------------------------------------------- (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 19.49 Prob > F = 0.0000 R-squared = 0.1733 Root MSE = .95795 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .3829685 .0430073 8.90 0.000 .2986245 .4673125 wave | 0 (omitted) gender | .3162822 .043932 7.20 0.000 .2301246 .4024398 prior | -.006366 .0011373 -5.60 0.000 -.0085963 -.0041356 democrat | .6595885 .0494341 13.34 0.000 .5626405 .7565365 indep | .2834168 .0679424 4.17 0.000 .1501711 .4166625 otherpol | .238136 .1930606 1.23 0.218 -.1404861 .6167581 midwest | -.1071298 .0710276 -1.51 0.132 -.2464261 .0321665 south | -.0365529 .0630617 -0.58 0.562 -.1602268 .087121 west | -.0277818 .0687108 -0.40 0.686 -.1625344 .1069708 age1 | .0437535 .1048982 0.42 0.677 -.1619683 .2494754 age2 | -.0345082 .0679376 -0.51 0.612 -.1677444 .098728 age3 | -.0315483 .0668427 -0.47 0.637 -.1626373 .0995407 age4 | -.1551328 .0646393 -2.40 0.016 -.2819005 -.0283651 anychildren | .0849317 .0472267 1.80 0.072 -.0076874 .1775507 loghhinc | .0152134 .0296425 0.51 0.608 -.0429203 .0733471 associatemore | -.0110761 .0497302 -0.22 0.824 -.1086047 .0864525 fulltime | .0301131 .0644917 0.47 0.641 -.0963652 .1565913 parttime | -.1123661 .0865676 -1.30 0.194 -.2821387 .0574066 selfemp | -.0490304 .091584 -0.54 0.592 -.228641 .1305801 unemployed | .0450447 .1035412 0.44 0.664 -.158016 .2481053 student | .0428454 .1536976 0.28 0.780 -.2585797 .3442704 _cons | -.3223758 .3320066 -0.97 0.332 -.9734929 .3287413 ------------------------------------------------------------------------------- (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 11.17 Prob > F = 0.0000 R-squared = 0.1995 Root MSE = .94272 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .5040322 .061319 8.22 0.000 .3837032 .6243612 wave | 0 (omitted) gender | .2659578 .0618926 4.30 0.000 .1445031 .3874126 prior | -.0055384 .0015219 -3.64 0.000 -.0085249 -.0025519 democrat | .6298038 .0701389 8.98 0.000 .492167 .7674407 indep | .1827792 .0966333 1.89 0.059 -.0068488 .3724073 otherpol | .4504328 .165796 2.72 0.007 .1250836 .7757819 midwest | -.1396837 .0955752 -1.46 0.144 -.3272353 .0478679 south | -.022356 .0848793 -0.26 0.792 -.1889186 .1442066 west | -.1311443 .0917483 -1.43 0.153 -.3111863 .0488978 age1 | .1400926 .1209249 1.16 0.247 -.0972039 .3773892 age2 | .1636361 .101648 1.61 0.108 -.0358325 .3631048 age3 | .1010286 .0958169 1.05 0.292 -.0869973 .2890546 age4 | -.0071856 .0959342 -0.07 0.940 -.1954417 .1810705 anychildren | .0777415 .0722178 1.08 0.282 -.0639749 .2194578 loghhinc | .0299937 .0447145 0.67 0.503 -.0577516 .1177389 associatemore | .0934574 .067536 1.38 0.167 -.0390717 .2259865 fulltime | .10869 .095133 1.14 0.254 -.0779939 .2953739 parttime | -.0974369 .1290446 -0.76 0.450 -.3506671 .1557932 selfemp | .0950274 .1299059 0.73 0.465 -.1598929 .3499478 unemployed | .0547977 .1856951 0.30 0.768 -.3096004 .4191958 student | .094424 .1667344 0.57 0.571 -.2327666 .4216147 _cons | -.7865916 .5099089 -1.54 0.123 -1.787209 .2140261 ------------------------------------------------------------------------------- (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 30.21 Prob > F = 0.0000 R-squared = 0.1861 Root MSE = .95956 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2433785 .0352867 6.90 0.000 .17419 .312567 wave | -.0096391 .0377105 -0.26 0.798 -.08358 .0643019 gender | .3085263 .0359816 8.57 0.000 .2379752 .3790773 prior | -.0045144 .0008754 -5.16 0.000 -.0062308 -.002798 democrat | .8030348 .0405338 19.81 0.000 .723558 .8825116 indep | .2993824 .0575167 5.21 0.000 .1866064 .4121584 otherpol | .3205847 .1439486 2.23 0.026 .038337 .6028325 midwest | -.1937205 .0578454 -3.35 0.001 -.307141 -.0802999 south | -.0569456 .0500487 -1.14 0.255 -.1550787 .0411875 west | -.1076544 .0546978 -1.97 0.049 -.2149032 -.0004056 age1 | .1921427 .0786191 2.44 0.015 .0379901 .3462953 age2 | .2088134 .0569403 3.67 0.000 .0971676 .3204593 age3 | .146825 .0574529 2.56 0.011 .0341742 .2594759 age4 | .0641893 .0561595 1.14 0.253 -.0459256 .1743042 anychildren | .1511771 .0385274 3.92 0.000 .0756343 .2267199 loghhinc | -.0219385 .0248728 -0.88 0.378 -.070708 .0268309 associatemore | -.0324966 .0405884 -0.80 0.423 -.1120804 .0470873 fulltime | .0173079 .0565705 0.31 0.760 -.0936129 .1282288 parttime | -.0848721 .0713493 -1.19 0.234 -.2247704 .0550262 selfemp | -.1157398 .0778424 -1.49 0.137 -.2683696 .0368899 unemployed | .0127901 .0882162 0.14 0.885 -.1601801 .1857602 student | -.0213595 .1083587 -0.20 0.844 -.2338242 .1911052 _cons | -.1987263 .2832389 -0.70 0.483 -.7540879 .3566352 ------------------------------------------------------------------------------- (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 21.57 Prob > F = 0.0000 R-squared = 0.1891 Root MSE = .96023 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2284106 .0429523 5.32 0.000 .1441745 .3126468 wave | 0 (omitted) gender | .318922 .0443202 7.20 0.000 .2320032 .4058409 prior | -.0049918 .0011012 -4.53 0.000 -.0071514 -.0028322 democrat | .8143638 .0496691 16.40 0.000 .7169549 .9117727 indep | .3605876 .0708067 5.09 0.000 .2217246 .4994505 otherpol | .3170233 .1742351 1.82 0.069 -.0246791 .6587257 midwest | -.1524362 .0716047 -2.13 0.033 -.2928642 -.0120081 south | -.0772872 .0615744 -1.26 0.210 -.1980442 .0434699 west | -.049093 .0667924 -0.74 0.462 -.1800834 .0818975 age1 | .2260736 .1005247 2.25 0.025 .028929 .4232182 age2 | .1569779 .0687745 2.28 0.023 .0221003 .2918555 age3 | .1033495 .0704572 1.47 0.143 -.0348281 .2415271 age4 | .0182826 .0676724 0.27 0.787 -.1144336 .1509988 anychildren | .1593225 .0464993 3.43 0.001 .06813 .250515 loghhinc | -.032888 .0306466 -1.07 0.283 -.0929907 .0272147 associatemore | -.0521586 .0499884 -1.04 0.297 -.1501936 .0458765 fulltime | .0195403 .0680797 0.29 0.774 -.1139747 .1530552 parttime | -.1240726 .0860166 -1.44 0.149 -.2927646 .0446194 selfemp | -.1966613 .0946615 -2.08 0.038 -.3823073 -.0110153 unemployed | .0086735 .1033726 0.08 0.933 -.1940563 .2114033 student | -.11355 .144531 -0.79 0.432 -.3969979 .1698978 _cons | -.0269226 .3369615 -0.08 0.936 -.6877569 .6339117 ------------------------------------------------------------------------------- (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 11.36 Prob > F = 0.0000 R-squared = 0.1973 Root MSE = .95792 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2803166 .0625821 4.48 0.000 .1575088 .4031244 wave | 0 (omitted) gender | .2991447 .062588 4.78 0.000 .1763254 .421964 prior | -.0034895 .0013957 -2.50 0.013 -.0062284 -.0007505 democrat | .7538227 .0701134 10.75 0.000 .6162358 .8914096 indep | .167489 .099655 1.68 0.093 -.0280686 .3630466 otherpol | .3743769 .2523974 1.48 0.138 -.1209143 .869668 midwest | -.2686761 .0992248 -2.71 0.007 -.4633896 -.0739626 south | -.0088886 .0863393 -0.10 0.918 -.1783163 .160539 west | -.2069527 .0968676 -2.14 0.033 -.3970406 -.0168649 age1 | .174519 .1287495 1.36 0.176 -.0781321 .4271701 age2 | .333405 .1035967 3.22 0.001 .1301123 .5366977 age3 | .2466502 .1004027 2.46 0.014 .0496254 .443675 age4 | .1690269 .1021401 1.65 0.098 -.0314073 .3694612 anychildren | .1033225 .0700472 1.48 0.141 -.0341344 .2407794 loghhinc | -.0022594 .0436496 -0.05 0.959 -.0879151 .0833963 associatemore | .0076636 .0694868 0.11 0.912 -.1286935 .1440208 fulltime | .0123732 .101145 0.12 0.903 -.1861084 .2108548 parttime | -.0087134 .1276043 -0.07 0.946 -.2591172 .2416903 selfemp | .0701519 .1321905 0.53 0.596 -.1892516 .3295554 unemployed | .0130137 .1714887 0.08 0.940 -.3235065 .3495339 student | .0975121 .1689955 0.58 0.564 -.2341157 .4291398 _cons | -.557904 .5087346 -1.10 0.273 -1.556217 .4404094 ------------------------------------------------------------------------------- (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 32.19 Prob > F = 0.0000 R-squared = 0.1988 Root MSE = .87434 ------------------------------------------------------------------------------- | Robust z_mani_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4170495 .0322181 12.94 0.000 .3538779 .4802212 wave | -.0177316 .0343865 -0.52 0.606 -.0851551 .0496919 gender | .2773129 .0327356 8.47 0.000 .2131265 .3414992 prior | -.005303 .0008692 -6.10 0.000 -.0070072 -.0035988 democrat | .6653624 .0368133 18.07 0.000 .5931805 .7375442 indep | .251875 .0513566 4.90 0.000 .1511774 .3525726 otherpol | .2426557 .1382573 1.76 0.079 -.0284328 .5137441 midwest | -.1208645 .0519175 -2.33 0.020 -.2226618 -.0190672 south | -.007256 .0462316 -0.16 0.875 -.0979048 .0833928 west | -.072072 .0499037 -1.44 0.149 -.1699209 .0257769 age1 | .0996826 .0719346 1.39 0.166 -.0413634 .2407286 age2 | .1077443 .0510046 2.11 0.035 .0077368 .2077517 age3 | .059659 .0507031 1.18 0.239 -.0397572 .1590752 age4 | -.046534 .0491284 -0.95 0.344 -.1428627 .0497946 anychildren | .1307584 .0354388 3.69 0.000 .0612716 .2002452 loghhinc | .009372 .0224739 0.42 0.677 -.0346938 .0534378 associatemore | -.0070954 .0365091 -0.19 0.846 -.0786808 .0644899 fulltime | .0542119 .0497542 1.09 0.276 -.0433438 .1517675 parttime | -.0992796 .0651583 -1.52 0.128 -.2270389 .0284797 selfemp | -.0244499 .0692565 -0.35 0.724 -.1602447 .111345 unemployed | .0365494 .0802278 0.46 0.649 -.1207575 .1938563 student | .02181 .1024313 0.21 0.831 -.1790325 .2226525 _cons | -.4614966 .2596682 -1.78 0.076 -.9706419 .0476486 ------------------------------------------------------------------------------- (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 21.78 Prob > F = 0.0000 R-squared = 0.1942 Root MSE = .87573 ------------------------------------------------------------------------------- | Robust z_mani_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .3989435 .0392146 10.17 0.000 .3220375 .4758494 wave | 0 (omitted) gender | .2764064 .0402838 6.86 0.000 .1974037 .3554092 prior | -.0056556 .001066 -5.31 0.000 -.0077462 -.0035649 democrat | .6631722 .0449991 14.74 0.000 .5749219 .7514225 indep | .2986027 .0628485 4.75 0.000 .175347 .4218584 otherpol | .1988305 .1729207 1.15 0.250 -.1402941 .5379552 midwest | -.0955165 .065323 -1.46 0.144 -.2236252 .0325922 south | -.0337057 .0575293 -0.59 0.558 -.1465298 .0791183 west | -.022404 .0620414 -0.36 0.718 -.1440768 .0992688 age1 | .1417322 .0915365 1.55 0.122 -.0377852 .3212496 age2 | .0517014 .0615856 0.84 0.401 -.0690775 .1724804 age3 | .0082703 .0619208 0.13 0.894 -.1131662 .1297068 age4 | -.0878783 .059292 -1.48 0.138 -.2041591 .0284026 anychildren | .1431941 .0427748 3.35 0.001 .059306 .2270822 loghhinc | -.0001662 .0272248 -0.01 0.995 -.0535583 .0532259 associatemore | -.0192737 .0451175 -0.43 0.669 -.1077562 .0692088 fulltime | .0398379 .0598605 0.67 0.506 -.0775579 .1572337 parttime | -.1126053 .0783088 -1.44 0.151 -.2661811 .0409705 selfemp | -.0753384 .0832663 -0.90 0.366 -.2386367 .0879599 unemployed | .0228647 .0923055 0.25 0.804 -.1581609 .2038904 student | -.0525705 .1386377 -0.38 0.705 -.3244607 .2193197 _cons | -.3008624 .304364 -0.99 0.323 -.897768 .2960432 ------------------------------------------------------------------------------- (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 13.28 Prob > F = 0.0000 R-squared = 0.2245 Root MSE = .87135 ------------------------------------------------------------------------------- | Robust z_mani_index | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4599029 .0570177 8.07 0.000 .3480144 .5717914 wave | 0 (omitted) gender | .2856226 .0570556 5.01 0.000 .1736597 .3975855 prior | -.0044675 .0014584 -3.06 0.002 -.0073294 -.0016057 democrat | .6458328 .0644293 10.02 0.000 .5194003 .7722654 indep | .1483343 .0898737 1.65 0.099 -.028029 .3246976 otherpol | .3660411 .2195278 1.67 0.096 -.0647484 .7968306 midwest | -.1638447 .0860778 -1.90 0.057 -.3327591 .0050698 south | .0470444 .0785828 0.60 0.550 -.1071622 .201251 west | -.1533749 .0867095 -1.77 0.077 -.323529 .0167792 age1 | .0774952 .1168621 0.66 0.507 -.1518288 .3068191 age2 | .2395879 .0929185 2.58 0.010 .0572497 .4219262 age3 | .1729746 .0893094 1.94 0.053 -.0022814 .3482306 age4 | .0434204 .0891373 0.49 0.626 -.1314978 .2183386 anychildren | .0772455 .0647512 1.19 0.233 -.0498187 .2043097 loghhinc | .0323883 .0414144 0.78 0.434 -.0488811 .1136577 associatemore | .0189595 .0619981 0.31 0.760 -.1027023 .1406212 fulltime | .0766532 .0891425 0.86 0.390 -.0982753 .2515817 parttime | -.0712528 .1172415 -0.61 0.543 -.3013213 .1588157 selfemp | .0977325 .1218415 0.80 0.423 -.1413627 .3368276 unemployed | .0633401 .1668787 0.38 0.704 -.2641337 .3908139 student | .1234662 .1570325 0.79 0.432 -.184686 .4316183 _cons | -.895257 .4819133 -1.86 0.064 -1.840938 .0504237 ------------------------------------------------------------------------------- . . mat def P = J(3, 1, .) . reg large T1 $controls [pweight=pweight], r (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 28.60 Prob > F = 0.0000 R-squared = 0.1745 Root MSE = .96735 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .5972595 .0356733 16.74 0.000 .5273129 .6672061 wave | -.0269099 .0382786 -0.70 0.482 -.1019648 .048145 gender | .2354511 .036212 6.50 0.000 .1644483 .3064539 prior | -.0057459 .0009906 -5.80 0.000 -.0076882 -.0038036 democrat | .5251511 .0405832 12.94 0.000 .4455776 .6047247 indep | .2014438 .0549588 3.67 0.000 .0936831 .3092045 otherpol | .1360499 .1555581 0.87 0.382 -.1689611 .441061 midwest | -.0463282 .0571594 -0.81 0.418 -.1584037 .0657473 south | .0559538 .0518565 1.08 0.281 -.0457239 .1576315 west | -.0351877 .0561643 -0.63 0.531 -.145312 .0749366 age1 | .0210497 .0824246 0.26 0.798 -.1405645 .182664 age2 | .0410061 .0557812 0.74 0.462 -.068367 .1503793 age3 | -.007434 .0551204 -0.13 0.893 -.1155115 .1006435 age4 | -.1330057 .0532157 -2.50 0.012 -.2373486 -.0286629 anychildren | .1281935 .0394718 3.25 0.001 .050799 .205588 loghhinc | .0382895 .0247828 1.55 0.122 -.0103035 .0868826 associatemore | .0061067 .0400086 0.15 0.879 -.0723404 .0845538 fulltime | .0912619 .0539965 1.69 0.091 -.0146119 .1971356 parttime | -.1105413 .07271 -1.52 0.129 -.2531076 .032025 selfemp | .0609548 .0753707 0.81 0.419 -.0868285 .2087382 unemployed | .0541613 .087939 0.62 0.538 -.1182653 .2265878 student | .0485873 .1105169 0.44 0.660 -.1681092 .2652837 _cons | -.7538024 .2863028 -2.63 0.009 -1.315171 -.1924334 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg problem T1 $controls [pweight=pweight], r (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 28.42 Prob > F = 0.0000 R-squared = 0.1775 Root MSE = .95226 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .4215353 .0351138 12.00 0.000 .3526858 .4903849 wave | -.0162456 .0374007 -0.43 0.664 -.0895791 .0570878 gender | .2970653 .0356542 8.33 0.000 .2271563 .3669743 prior | -.0061382 .0009184 -6.68 0.000 -.007939 -.0043374 democrat | .6560668 .0403051 16.28 0.000 .5770385 .7350951 indep | .2531112 .0553512 4.57 0.000 .1445812 .3616413 otherpol | .2965088 .1414328 2.10 0.036 .0191939 .5738237 midwest | -.1166761 .0567613 -2.06 0.040 -.227971 -.0053813 south | -.0312535 .0503927 -0.62 0.535 -.1300611 .0675541 west | -.0710708 .0545453 -1.30 0.193 -.1780206 .035879 age1 | .0597321 .079137 0.75 0.450 -.0954361 .2149002 age2 | .0223707 .0561827 0.40 0.691 -.0877896 .132531 age3 | .0067231 .054451 0.12 0.902 -.100042 .1134881 age4 | -.1110718 .0532144 -2.09 0.037 -.2154121 -.0067315 anychildren | .0898165 .0392609 2.29 0.022 .0128357 .1667974 loghhinc | .0179323 .0243871 0.74 0.462 -.0298847 .0657493 associatemore | .0221103 .0400605 0.55 0.581 -.0564384 .1006589 fulltime | .0578635 .0531468 1.09 0.276 -.0463443 .1620712 parttime | -.1076561 .0717129 -1.50 0.133 -.2482674 .0329552 selfemp | -.0018603 .0750208 -0.02 0.980 -.1489577 .145237 unemployed | .0524455 .0901715 0.58 0.561 -.1243585 .2292496 student | .0620267 .1116342 0.56 0.579 -.1568603 .2809137 _cons | -.4256575 .2795707 -1.52 0.128 -.9738266 .1225116 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg govmore T1 $controls [pweight=pweight], r (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 30.21 Prob > F = 0.0000 R-squared = 0.1861 Root MSE = .95956 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2433785 .0352867 6.90 0.000 .17419 .312567 wave | -.0096391 .0377105 -0.26 0.798 -.08358 .0643019 gender | .3085263 .0359816 8.57 0.000 .2379752 .3790773 prior | -.0045144 .0008754 -5.16 0.000 -.0062308 -.002798 democrat | .8030348 .0405338 19.81 0.000 .723558 .8825116 indep | .2993824 .0575167 5.21 0.000 .1866064 .4121584 otherpol | .3205847 .1439486 2.23 0.026 .038337 .6028325 midwest | -.1937205 .0578454 -3.35 0.001 -.307141 -.0802999 south | -.0569456 .0500487 -1.14 0.255 -.1550787 .0411875 west | -.1076544 .0546978 -1.97 0.049 -.2149032 -.0004056 age1 | .1921427 .0786191 2.44 0.015 .0379901 .3462953 age2 | .2088134 .0569403 3.67 0.000 .0971676 .3204593 age3 | .146825 .0574529 2.56 0.011 .0341742 .2594759 age4 | .0641893 .0561595 1.14 0.253 -.0459256 .1743042 anychildren | .1511771 .0385274 3.92 0.000 .0756343 .2267199 loghhinc | -.0219385 .0248728 -0.88 0.378 -.070708 .0268309 associatemore | -.0324966 .0405884 -0.80 0.423 -.1120804 .0470873 fulltime | .0173079 .0565705 0.31 0.760 -.0936129 .1282288 parttime | -.0848721 .0713493 -1.19 0.234 -.2247704 .0550262 selfemp | -.1157398 .0778424 -1.49 0.137 -.2683696 .0368899 unemployed | .0127901 .0882162 0.14 0.885 -.1601801 .1857602 student | -.0213595 .1083587 -0.20 0.844 -.2338242 .1911052 _cons | -.1987263 .2832389 -0.70 0.483 -.7540879 .3566352 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . . minq P, q("Q") step(0.001) number of observations will be reset to 3 Press any key to continue, or Break to abort number of observations (_N) was 0, now 3 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = .7109999999999997 Correction with q = .7099999999999997 Correction with q = .7089999999999997 Correction with q = .7079999999999997 Correction with q = .7069999999999997 Correction with q = .7059999999999997 Correction with q = .7049999999999997 Correction with q = .7039999999999997 Correction with q = .7029999999999997 Correction with q = .7019999999999997 Correction with q = .7009999999999997 Correction with q = .6999999999999997 Correction with q = .6989999999999997 Correction with q = .6979999999999997 Correction with q = .6969999999999997 Correction with q = .6959999999999997 Correction with q = .6949999999999997 Correction with q = .6939999999999997 Correction with q = .6929999999999997 Correction with q = .6919999999999997 Correction with q = .6909999999999997 Correction with q = .6899999999999997 Correction with q = .6889999999999997 Correction with q = .6879999999999997 Correction with q = .6869999999999997 Correction with q = .6859999999999997 Correction with q = .6849999999999997 Correction with q = .6839999999999997 Correction with q = .6829999999999997 Correction with q = .6819999999999997 Correction with q = .6809999999999997 Correction with q = .6799999999999997 Correction with q = .6789999999999997 Correction with q = .6779999999999997 Correction with q = .6769999999999997 Correction with q = .6759999999999997 Correction with q = .6749999999999997 Correction with q = .6739999999999997 Correction with q = .6729999999999997 Correction with q = .6719999999999997 Correction with q = .6709999999999997 Correction with q = .6699999999999997 Correction with q = .6689999999999997 Correction with q = .6679999999999997 Correction with q = .6669999999999997 Correction with q = .6659999999999997 Correction with q = .6649999999999997 Correction with q = .6639999999999997 Correction with q = .6629999999999997 Correction with q = .6619999999999997 Correction with q = .6609999999999997 Correction with q = .6599999999999997 Correction with q = .6589999999999997 Correction with q = .6579999999999997 Correction with q = .6569999999999997 Correction with q = .6559999999999997 Correction with q = .6549999999999997 Correction with q = .6539999999999997 Correction with q = .6529999999999997 Correction with q = .6519999999999997 Correction with q = .6509999999999997 Correction with q = .6499999999999997 Correction with q = .6489999999999997 Correction with q = .6479999999999997 Correction with q = .6469999999999997 Correction with q = .6459999999999997 Correction with q = .6449999999999997 Correction with q = .6439999999999997 Correction with q = .6429999999999997 Correction with q = .6419999999999997 Correction with q = .6409999999999997 Correction with q = .6399999999999997 Correction with q = .6389999999999997 Correction with q = .6379999999999997 Correction with q = .6369999999999997 Correction with q = .6359999999999997 Correction with q = .6349999999999997 Correction with q = .6339999999999997 Correction with q = .6329999999999997 Correction with q = .6319999999999997 Correction with q = .6309999999999997 Correction with q = .6299999999999997 Correction with q = .6289999999999997 Correction with q = .6279999999999997 Correction with q = .6269999999999997 Correction with q = .6259999999999997 Correction with q = .6249999999999997 Correction with q = .6239999999999997 Correction with q = .6229999999999997 Correction with q = .6219999999999997 Correction with q = .6209999999999997 Correction with q = .6199999999999997 Correction with q = .6189999999999997 Correction with q = .6179999999999997 Correction with q = .6169999999999997 Correction with q = .6159999999999997 Correction with q = .6149999999999997 Correction with q = .6139999999999997 Correction with q = .6129999999999997 Correction with q = .6119999999999997 Correction with q = .6109999999999997 Correction with q = .6099999999999997 Correction with q = .6089999999999997 Correction with q = .6079999999999997 Correction with q = .6069999999999997 Correction with q = .6059999999999997 Correction with q = .6049999999999997 Correction with q = .6039999999999997 Correction with q = .6029999999999997 Correction with q = .6019999999999997 Correction with q = .6009999999999997 Correction with q = .5999999999999996 Correction with q = .5989999999999996 Correction with q = .5979999999999996 Correction with q = .5969999999999996 Correction with q = .5959999999999996 Correction with q = .5949999999999996 Correction with q = .5939999999999996 Correction with q = .5929999999999996 Correction with q = .5919999999999996 Correction with q = .5909999999999996 Correction with q = .5899999999999996 Correction with q = .5889999999999996 Correction with q = .5879999999999996 Correction with q = .5869999999999996 Correction with q = .5859999999999996 Correction with q = .5849999999999996 Correction with q = .5839999999999996 Correction with q = .5829999999999996 Correction with q = .5819999999999996 Correction with q = .5809999999999996 Correction with q = .5799999999999996 Correction with q = .5789999999999996 Correction with q = .5779999999999996 Correction with q = .5769999999999996 Correction with q = .5759999999999996 Correction with q = .5749999999999996 Correction with q = .5739999999999996 Correction with q = .5729999999999996 Correction with q = .5719999999999996 Correction with q = .5709999999999996 Correction with q = .5699999999999996 Correction with q = .5689999999999996 Correction with q = .5679999999999996 Correction with q = .5669999999999996 Correction with q = .5659999999999996 Correction with q = .5649999999999996 Correction with q = .5639999999999996 Correction with q = .5629999999999996 Correction with q = .5619999999999996 Correction with q = .5609999999999996 Correction with q = .5599999999999996 Correction with q = .5589999999999996 Correction with q = .5579999999999996 Correction with q = .5569999999999996 Correction with q = .5559999999999996 Correction with q = .5549999999999996 Correction with q = .5539999999999996 Correction with q = .5529999999999996 Correction with q = .5519999999999996 Correction with q = .5509999999999996 Correction with q = .5499999999999996 Correction with q = .5489999999999996 Correction with q = .5479999999999996 Correction with q = .5469999999999996 Correction with q = .5459999999999996 Correction with q = .5449999999999996 Correction with q = .5439999999999996 Correction with q = .5429999999999996 Correction with q = .5419999999999996 Correction with q = .5409999999999996 Correction with q = .5399999999999996 Correction with q = .5389999999999996 Correction with q = .5379999999999996 Correction with q = .5369999999999996 Correction with q = .5359999999999996 Correction with q = .5349999999999996 Correction with q = .5339999999999996 Correction with q = .5329999999999996 Correction with q = .5319999999999996 Correction with q = .5309999999999996 Correction with q = .5299999999999996 Correction with q = .5289999999999996 Correction with q = .5279999999999996 Correction with q = .5269999999999996 Correction with q = .5259999999999996 Correction with q = .5249999999999996 Correction with q = .5239999999999996 Correction with q = .5229999999999996 Correction with q = .5219999999999996 Correction with q = .5209999999999996 Correction with q = .5199999999999996 Correction with q = .5189999999999996 Correction with q = .5179999999999996 Correction with q = .5169999999999996 Correction with q = .5159999999999996 Correction with q = .5149999999999996 Correction with q = .5139999999999996 Correction with q = .5129999999999996 Correction with q = .5119999999999996 Correction with q = .5109999999999996 Correction with q = .5099999999999996 Correction with q = .5089999999999996 Correction with q = .5079999999999996 Correction with q = .5069999999999996 Correction with q = .5059999999999996 Correction with q = .5049999999999996 Correction with q = .5039999999999996 Correction with q = .5029999999999996 Correction with q = .5019999999999996 Correction with q = .5009999999999996 Correction with q = .4999999999999996 Correction with q = .4989999999999996 Correction with q = .4979999999999996 Correction with q = .4969999999999996 Correction with q = .4959999999999996 Correction with q = .4949999999999996 Correction with q = .4939999999999996 Correction with q = .4929999999999996 Correction with q = .4919999999999996 Correction with q = .4909999999999996 Correction with q = .4899999999999996 Correction with q = .4889999999999996 Correction with q = .4879999999999996 Correction with q = .4869999999999995 Correction with q = .4859999999999995 Correction with q = .4849999999999995 Correction with q = .4839999999999995 Correction with q = .4829999999999995 Correction with q = .4819999999999995 Correction with q = .4809999999999995 Correction with q = .4799999999999995 Correction with q = .4789999999999995 Correction with q = .4779999999999995 Correction with q = .4769999999999995 Correction with q = .4759999999999995 Correction with q = .4749999999999995 Correction with q = .4739999999999995 Correction with q = .4729999999999995 Correction with q = .4719999999999995 Correction with q = .4709999999999995 Correction with q = .4699999999999995 Correction with q = .4689999999999995 Correction with q = .4679999999999995 Correction with q = .4669999999999995 Correction with q = .4659999999999995 Correction with q = .4649999999999995 Correction with q = .4639999999999995 Correction with q = .4629999999999995 Correction with q = .4619999999999995 Correction with q = .4609999999999995 Correction with q = .4599999999999995 Correction with q = .4589999999999995 Correction with q = .4579999999999995 Correction with q = .4569999999999995 Correction with q = .4559999999999995 Correction with q = .4549999999999995 Correction with q = .4539999999999995 Correction with q = .4529999999999995 Correction with q = .4519999999999995 Correction with q = .4509999999999995 Correction with q = .4499999999999995 Correction with q = .4489999999999995 Correction with q = .4479999999999995 Correction with q = .4469999999999995 Correction with q = .4459999999999995 Correction with q = .4449999999999995 Correction with q = .4439999999999995 Correction with q = .4429999999999995 Correction with q = .4419999999999995 Correction with q = .4409999999999995 Correction with q = .4399999999999995 Correction with q = .4389999999999995 Correction with q = .4379999999999995 Correction with q = .4369999999999995 Correction with q = .4359999999999995 Correction with q = .4349999999999995 Correction with q = .4339999999999995 Correction with q = .4329999999999995 Correction with q = .4319999999999995 Correction with q = .4309999999999995 Correction with q = .4299999999999995 Correction with q = .4289999999999995 Correction with q = .4279999999999995 Correction with q = .4269999999999995 Correction with q = .4259999999999995 Correction with q = .4249999999999995 Correction with q = .4239999999999995 Correction with q = .4229999999999995 Correction with q = .4219999999999995 Correction with q = .4209999999999995 Correction with q = .4199999999999995 Correction with q = .4189999999999995 Correction with q = .4179999999999995 Correction with q = .4169999999999995 Correction with q = .4159999999999995 Correction with q = .4149999999999995 Correction with q = .4139999999999995 Correction with q = .4129999999999995 Correction with q = .4119999999999995 Correction with q = .4109999999999995 Correction with q = .4099999999999995 Correction with q = .4089999999999995 Correction with q = .4079999999999995 Correction with q = .4069999999999995 Correction with q = .4059999999999995 Correction with q = .4049999999999995 Correction with q = .4039999999999995 Correction with q = .4029999999999995 Correction with q = .4019999999999995 Correction with q = .4009999999999995 Correction with q = .3999999999999995 Correction with q = .3989999999999995 Correction with q = .3979999999999995 Correction with q = .3969999999999995 Correction with q = .3959999999999995 Correction with q = .3949999999999995 Correction with q = .3939999999999995 Correction with q = .3929999999999995 Correction with q = .3919999999999995 Correction with q = .3909999999999995 Correction with q = .3899999999999995 Correction with q = .3889999999999995 Correction with q = .3879999999999995 Correction with q = .3869999999999995 Correction with q = .3859999999999995 Correction with q = .3849999999999995 Correction with q = .3839999999999995 Correction with q = .3829999999999995 Correction with q = .3819999999999995 Correction with q = .3809999999999995 Correction with q = .3799999999999995 Correction with q = .3789999999999995 Correction with q = .3779999999999995 Correction with q = .3769999999999995 Correction with q = .3759999999999995 Correction with q = .3749999999999994 Correction with q = .3739999999999994 Correction with q = .3729999999999994 Correction with q = .3719999999999994 Correction with q = .3709999999999994 Correction with q = .3699999999999994 Correction with q = .3689999999999994 Correction with q = .3679999999999994 Correction with q = .3669999999999994 Correction with q = .3659999999999994 Correction with q = .3649999999999994 Correction with q = .3639999999999994 Correction with q = .3629999999999994 Correction with q = .3619999999999994 Correction with q = .3609999999999994 Correction with q = .3599999999999994 Correction with q = .3589999999999994 Correction with q = .3579999999999994 Correction with q = .3569999999999994 Correction with q = .3559999999999994 Correction with q = .3549999999999994 Correction with q = .3539999999999994 Correction with q = .3529999999999994 Correction with q = .3519999999999994 Correction with q = .3509999999999994 Correction with q = .3499999999999994 Correction with q = .3489999999999994 Correction with q = .3479999999999994 Correction with q = .3469999999999994 Correction with q = .3459999999999994 Correction with q = .3449999999999994 Correction with q = .3439999999999994 Correction with q = .3429999999999994 Correction with q = .3419999999999994 Correction with q = .3409999999999994 Correction with q = .3399999999999994 Correction with q = .3389999999999994 Correction with q = .3379999999999994 Correction with q = .3369999999999994 Correction with q = .3359999999999994 Correction with q = .3349999999999994 Correction with q = .3339999999999994 Correction with q = .3329999999999994 Correction with q = .3319999999999994 Correction with q = .3309999999999994 Correction with q = .3299999999999994 Correction with q = .3289999999999994 Correction with q = .3279999999999994 Correction with q = .3269999999999994 Correction with q = .3259999999999994 Correction with q = .3249999999999994 Correction with q = .3239999999999994 Correction with q = .3229999999999994 Correction with q = .3219999999999994 Correction with q = .3209999999999994 Correction with q = .3199999999999994 Correction with q = .3189999999999994 Correction with q = .3179999999999994 Correction with q = .3169999999999994 Correction with q = .3159999999999994 Correction with q = .3149999999999994 Correction with q = .3139999999999994 Correction with q = .3129999999999994 Correction with q = .3119999999999994 Correction with q = .3109999999999994 Correction with q = .3099999999999994 Correction with q = .3089999999999994 Correction with q = .3079999999999994 Correction with q = .3069999999999994 Correction with q = .3059999999999994 Correction with q = .3049999999999994 Correction with q = .3039999999999994 Correction with q = .3029999999999994 Correction with q = .3019999999999994 Correction with q = .3009999999999994 Correction with q = .2999999999999994 Correction with q = .2989999999999994 Correction with q = .2979999999999994 Correction with q = .2969999999999994 Correction with q = .2959999999999994 Correction with q = .2949999999999994 Correction with q = .2939999999999994 Correction with q = .2929999999999994 Correction with q = .2919999999999994 Correction with q = .2909999999999994 Correction with q = .2899999999999994 Correction with q = .2889999999999994 Correction with q = .2879999999999994 Correction with q = .2869999999999994 Correction with q = .2859999999999994 Correction with q = .2849999999999994 Correction with q = .2839999999999994 Correction with q = .2829999999999994 Correction with q = .2819999999999994 Correction with q = .2809999999999994 Correction with q = .2799999999999994 Correction with q = .2789999999999994 Correction with q = .2779999999999994 Correction with q = .2769999999999994 Correction with q = .2759999999999994 Correction with q = .2749999999999994 Correction with q = .2739999999999994 Correction with q = .2729999999999994 Correction with q = .2719999999999994 Correction with q = .2709999999999994 Correction with q = .2699999999999994 Correction with q = .2689999999999994 Correction with q = .2679999999999994 Correction with q = .2669999999999994 Correction with q = .2659999999999994 Correction with q = .2649999999999994 Correction with q = .2639999999999994 Correction with q = .2629999999999994 Correction with q = .2619999999999993 Correction with q = .2609999999999993 Correction with q = .2599999999999993 Correction with q = .2589999999999993 Correction with q = .2579999999999993 Correction with q = .2569999999999993 Correction with q = .2559999999999993 Correction with q = .2549999999999993 Correction with q = .2539999999999993 Correction with q = .2529999999999993 Correction with q = .2519999999999993 Correction with q = .2509999999999993 Correction with q = .2499999999999993 Correction with q = .2489999999999993 Correction with q = .2479999999999993 Correction with q = .2469999999999993 Correction with q = .2459999999999993 Correction with q = .2449999999999993 Correction with q = .2439999999999993 Correction with q = .2429999999999993 Correction with q = .2419999999999993 Correction with q = .2409999999999993 Correction with q = .2399999999999993 Correction with q = .2389999999999993 Correction with q = .2379999999999993 Correction with q = .2369999999999993 Correction with q = .2359999999999993 Correction with q = .2349999999999993 Correction with q = .2339999999999993 Correction with q = .2329999999999993 Correction with q = .2319999999999993 Correction with q = .2309999999999993 Correction with q = .2299999999999993 Correction with q = .2289999999999993 Correction with q = .2279999999999993 Correction with q = .2269999999999993 Correction with q = .2259999999999993 Correction with q = .2249999999999993 Correction with q = .2239999999999993 Correction with q = .2229999999999993 Correction with q = .2219999999999993 Correction with q = .2209999999999993 Correction with q = .2199999999999993 Correction with q = .2189999999999993 Correction with q = .2179999999999993 Correction with q = .2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = .1909999999999993 Correction with q = .1899999999999993 Correction with q = .1889999999999993 Correction with q = .1879999999999993 Correction with q = .1869999999999993 Correction with q = .1859999999999993 Correction with q = .1849999999999993 Correction with q = .1839999999999993 Correction with q = .1829999999999993 Correction with q = .1819999999999993 Correction with q = .1809999999999993 Correction with q = .1799999999999993 Correction with q = .1789999999999993 Correction with q = .1779999999999993 Correction with q = .1769999999999993 Correction with q = .1759999999999993 Correction with q = .1749999999999993 Correction with q = .1739999999999993 Correction with q = .1729999999999993 Correction with q = .1719999999999993 Correction with q = .1709999999999993 Correction with q = .1699999999999993 Correction with q = .1689999999999993 Correction with q = .1679999999999993 Correction with q = .1669999999999993 Correction with q = .1659999999999993 Correction with q = .1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 Correction with q = .0069999999999991 Correction with q = .0059999999999991 Correction with q = .0049999999999991 Correction with q = .0039999999999991 Correction with q = .0029999999999991 Correction with q = .0019999999999991 (3 real changes made) (0 real changes made) . . estadd loc thisstat6 = "[" + string(Q[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat6 = "[" +string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat6 = "[" + string(Q[3, 1], "%9.3f")+"]": col3 . . mat def P = J(3, 1, .) . reg large T1 $controls [pweight=pweight] if wave==1, r (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 18.53 Prob > F = 0.0000 R-squared = 0.1662 Root MSE = .96474 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .5852879 .0431661 13.56 0.000 .5006324 .6699433 wave | 0 (omitted) gender | .2134097 .044375 4.81 0.000 .1263833 .3004361 prior | -.0060249 .0011754 -5.13 0.000 -.0083301 -.0037197 democrat | .5061398 .0490367 10.32 0.000 .4099711 .6023085 indep | .2405437 .0676703 3.55 0.000 .1078316 .3732558 otherpol | .0566693 .1899884 0.30 0.766 -.3159278 .4292664 midwest | -.0304249 .0722535 -0.42 0.674 -.1721254 .1112755 south | .0133472 .0644622 0.21 0.836 -.1130732 .1397676 west | .0080856 .0693246 0.12 0.907 -.1278708 .144042 age1 | .0983591 .1032273 0.95 0.341 -.1040859 .300804 age2 | -.0190672 .0674947 -0.28 0.778 -.1514349 .1133005 age3 | -.0731723 .0671324 -1.09 0.276 -.2048294 .0584848 age4 | -.1683095 .0643593 -2.62 0.009 -.2945282 -.0420907 anychildren | .1531133 .0473704 3.23 0.001 .0602126 .2460141 loghhinc | .0270903 .0295885 0.92 0.360 -.0309375 .0851181 associatemore | .0114634 .0490779 0.23 0.815 -.084786 .1077127 fulltime | .0656225 .0650922 1.01 0.314 -.0620336 .1932786 parttime | -.1006799 .086536 -1.16 0.245 -.2703906 .0690308 selfemp | .0398741 .0894698 0.45 0.656 -.1355902 .2153383 unemployed | .0275389 .1018332 0.27 0.787 -.172172 .2272498 student | -.032536 .1465261 -0.22 0.824 -.3198967 .2548246 _cons | -.5784637 .3315633 -1.74 0.081 -1.228711 .071784 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg problem T1 $controls [pweight=pweight] if wave==1, r (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 19.49 Prob > F = 0.0000 R-squared = 0.1733 Root MSE = .95795 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .3829685 .0430073 8.90 0.000 .2986245 .4673125 wave | 0 (omitted) gender | .3162822 .043932 7.20 0.000 .2301246 .4024398 prior | -.006366 .0011373 -5.60 0.000 -.0085963 -.0041356 democrat | .6595885 .0494341 13.34 0.000 .5626405 .7565365 indep | .2834168 .0679424 4.17 0.000 .1501711 .4166625 otherpol | .238136 .1930606 1.23 0.218 -.1404861 .6167581 midwest | -.1071298 .0710276 -1.51 0.132 -.2464261 .0321665 south | -.0365529 .0630617 -0.58 0.562 -.1602268 .087121 west | -.0277818 .0687108 -0.40 0.686 -.1625344 .1069708 age1 | .0437535 .1048982 0.42 0.677 -.1619683 .2494754 age2 | -.0345082 .0679376 -0.51 0.612 -.1677444 .098728 age3 | -.0315483 .0668427 -0.47 0.637 -.1626373 .0995407 age4 | -.1551328 .0646393 -2.40 0.016 -.2819005 -.0283651 anychildren | .0849317 .0472267 1.80 0.072 -.0076874 .1775507 loghhinc | .0152134 .0296425 0.51 0.608 -.0429203 .0733471 associatemore | -.0110761 .0497302 -0.22 0.824 -.1086047 .0864525 fulltime | .0301131 .0644917 0.47 0.641 -.0963652 .1565913 parttime | -.1123661 .0865676 -1.30 0.194 -.2821387 .0574066 selfemp | -.0490304 .091584 -0.54 0.592 -.228641 .1305801 unemployed | .0450447 .1035412 0.44 0.664 -.158016 .2481053 student | .0428454 .1536976 0.28 0.780 -.2585797 .3442704 _cons | -.3223758 .3320066 -0.97 0.332 -.9734929 .3287413 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg govmore T1 $controls [pweight=pweight] if wave==1, r (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 21.57 Prob > F = 0.0000 R-squared = 0.1891 Root MSE = .96023 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2284106 .0429523 5.32 0.000 .1441745 .3126468 wave | 0 (omitted) gender | .318922 .0443202 7.20 0.000 .2320032 .4058409 prior | -.0049918 .0011012 -4.53 0.000 -.0071514 -.0028322 democrat | .8143638 .0496691 16.40 0.000 .7169549 .9117727 indep | .3605876 .0708067 5.09 0.000 .2217246 .4994505 otherpol | .3170233 .1742351 1.82 0.069 -.0246791 .6587257 midwest | -.1524362 .0716047 -2.13 0.033 -.2928642 -.0120081 south | -.0772872 .0615744 -1.26 0.210 -.1980442 .0434699 west | -.049093 .0667924 -0.74 0.462 -.1800834 .0818975 age1 | .2260736 .1005247 2.25 0.025 .028929 .4232182 age2 | .1569779 .0687745 2.28 0.023 .0221003 .2918555 age3 | .1033495 .0704572 1.47 0.143 -.0348281 .2415271 age4 | .0182826 .0676724 0.27 0.787 -.1144336 .1509988 anychildren | .1593225 .0464993 3.43 0.001 .06813 .250515 loghhinc | -.032888 .0306466 -1.07 0.283 -.0929907 .0272147 associatemore | -.0521586 .0499884 -1.04 0.297 -.1501936 .0458765 fulltime | .0195403 .0680797 0.29 0.774 -.1139747 .1530552 parttime | -.1240726 .0860166 -1.44 0.149 -.2927646 .0446194 selfemp | -.1966613 .0946615 -2.08 0.038 -.3823073 -.0110153 unemployed | .0086735 .1033726 0.08 0.933 -.1940563 .2114033 student | -.11355 .144531 -0.79 0.432 -.3969979 .1698978 _cons | -.0269226 .3369615 -0.08 0.936 -.6877569 .6339117 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . . minq P, q("Q") step(0.001) number of observations will be reset to 3 Press any key to continue, or Break to abort number of observations (_N) was 0, now 3 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = .7109999999999997 Correction with q = .7099999999999997 Correction with q = .7089999999999997 Correction with q = .7079999999999997 Correction with q = .7069999999999997 Correction with q = .7059999999999997 Correction with q = .7049999999999997 Correction with q = .7039999999999997 Correction with q = .7029999999999997 Correction with q = .7019999999999997 Correction with q = .7009999999999997 Correction with q = .6999999999999997 Correction with q = .6989999999999997 Correction with q = .6979999999999997 Correction with q = .6969999999999997 Correction with q = .6959999999999997 Correction with q = .6949999999999997 Correction with q = .6939999999999997 Correction with q = .6929999999999997 Correction with q = .6919999999999997 Correction with q = .6909999999999997 Correction with q = .6899999999999997 Correction with q = .6889999999999997 Correction with q = .6879999999999997 Correction with q = .6869999999999997 Correction with q = .6859999999999997 Correction with q = .6849999999999997 Correction with q = .6839999999999997 Correction with q = .6829999999999997 Correction with q = .6819999999999997 Correction with q = .6809999999999997 Correction with q = .6799999999999997 Correction with q = .6789999999999997 Correction with q = .6779999999999997 Correction with q = .6769999999999997 Correction with q = .6759999999999997 Correction with q = .6749999999999997 Correction with q = .6739999999999997 Correction with q = .6729999999999997 Correction with q = .6719999999999997 Correction with q = .6709999999999997 Correction with q = .6699999999999997 Correction with q = .6689999999999997 Correction with q = .6679999999999997 Correction with q = .6669999999999997 Correction with q = .6659999999999997 Correction with q = .6649999999999997 Correction with q = .6639999999999997 Correction with q = .6629999999999997 Correction with q = .6619999999999997 Correction with q = .6609999999999997 Correction with q = .6599999999999997 Correction with q = .6589999999999997 Correction with q = .6579999999999997 Correction with q = .6569999999999997 Correction with q = .6559999999999997 Correction with q = .6549999999999997 Correction with q = .6539999999999997 Correction with q = .6529999999999997 Correction with q = .6519999999999997 Correction with q = .6509999999999997 Correction with q = .6499999999999997 Correction with q = .6489999999999997 Correction with q = .6479999999999997 Correction with q = .6469999999999997 Correction with q = .6459999999999997 Correction with q = .6449999999999997 Correction with q = .6439999999999997 Correction with q = .6429999999999997 Correction with q = .6419999999999997 Correction with q = .6409999999999997 Correction with q = .6399999999999997 Correction with q = .6389999999999997 Correction with q = .6379999999999997 Correction with q = .6369999999999997 Correction with q = .6359999999999997 Correction with q = .6349999999999997 Correction with q = .6339999999999997 Correction with q = .6329999999999997 Correction with q = .6319999999999997 Correction with q = .6309999999999997 Correction with q = .6299999999999997 Correction with q = .6289999999999997 Correction with q = .6279999999999997 Correction with q = .6269999999999997 Correction with q = .6259999999999997 Correction with q = .6249999999999997 Correction with q = .6239999999999997 Correction with q = .6229999999999997 Correction with q = .6219999999999997 Correction with q = .6209999999999997 Correction with q = .6199999999999997 Correction with q = .6189999999999997 Correction with q = .6179999999999997 Correction with q = .6169999999999997 Correction with q = .6159999999999997 Correction with q = .6149999999999997 Correction with q = .6139999999999997 Correction with q = .6129999999999997 Correction with q = .6119999999999997 Correction with q = .6109999999999997 Correction with q = .6099999999999997 Correction with q = .6089999999999997 Correction with q = .6079999999999997 Correction with q = .6069999999999997 Correction with q = .6059999999999997 Correction with q = .6049999999999997 Correction with q = .6039999999999997 Correction with q = .6029999999999997 Correction with q = .6019999999999997 Correction with q = .6009999999999997 Correction with q = .5999999999999996 Correction with q = .5989999999999996 Correction with q = .5979999999999996 Correction with q = .5969999999999996 Correction with q = .5959999999999996 Correction with q = .5949999999999996 Correction with q = .5939999999999996 Correction with q = .5929999999999996 Correction with q = .5919999999999996 Correction with q = .5909999999999996 Correction with q = .5899999999999996 Correction with q = .5889999999999996 Correction with q = .5879999999999996 Correction with q = .5869999999999996 Correction with q = .5859999999999996 Correction with q = .5849999999999996 Correction with q = .5839999999999996 Correction with q = .5829999999999996 Correction with q = .5819999999999996 Correction with q = .5809999999999996 Correction with q = .5799999999999996 Correction with q = .5789999999999996 Correction with q = .5779999999999996 Correction with q = .5769999999999996 Correction with q = .5759999999999996 Correction with q = .5749999999999996 Correction with q = .5739999999999996 Correction with q = .5729999999999996 Correction with q = .5719999999999996 Correction with q = .5709999999999996 Correction with q = .5699999999999996 Correction with q = .5689999999999996 Correction with q = .5679999999999996 Correction with q = .5669999999999996 Correction with q = .5659999999999996 Correction with q = .5649999999999996 Correction with q = .5639999999999996 Correction with q = .5629999999999996 Correction with q = .5619999999999996 Correction with q = .5609999999999996 Correction with q = .5599999999999996 Correction with q = .5589999999999996 Correction with q = .5579999999999996 Correction with q = .5569999999999996 Correction with q = .5559999999999996 Correction with q = .5549999999999996 Correction with q = .5539999999999996 Correction with q = .5529999999999996 Correction with q = .5519999999999996 Correction with q = .5509999999999996 Correction with q = .5499999999999996 Correction with q = .5489999999999996 Correction with q = .5479999999999996 Correction with q = .5469999999999996 Correction with q = .5459999999999996 Correction with q = .5449999999999996 Correction with q = .5439999999999996 Correction with q = .5429999999999996 Correction with q = .5419999999999996 Correction with q = .5409999999999996 Correction with q = .5399999999999996 Correction with q = .5389999999999996 Correction with q = .5379999999999996 Correction with q = .5369999999999996 Correction with q = .5359999999999996 Correction with q = .5349999999999996 Correction with q = .5339999999999996 Correction with q = .5329999999999996 Correction with q = .5319999999999996 Correction with q = .5309999999999996 Correction with q = .5299999999999996 Correction with q = .5289999999999996 Correction with q = .5279999999999996 Correction with q = .5269999999999996 Correction with q = .5259999999999996 Correction with q = .5249999999999996 Correction with q = .5239999999999996 Correction with q = .5229999999999996 Correction with q = .5219999999999996 Correction with q = .5209999999999996 Correction with q = .5199999999999996 Correction with q = .5189999999999996 Correction with q = .5179999999999996 Correction with q = .5169999999999996 Correction with q = .5159999999999996 Correction with q = .5149999999999996 Correction with q = .5139999999999996 Correction with q = .5129999999999996 Correction with q = .5119999999999996 Correction with q = .5109999999999996 Correction with q = .5099999999999996 Correction with q = .5089999999999996 Correction with q = .5079999999999996 Correction with q = .5069999999999996 Correction with q = .5059999999999996 Correction with q = .5049999999999996 Correction with q = .5039999999999996 Correction with q = .5029999999999996 Correction with q = .5019999999999996 Correction with q = .5009999999999996 Correction with q = .4999999999999996 Correction with q = .4989999999999996 Correction with q = .4979999999999996 Correction with q = .4969999999999996 Correction with q = .4959999999999996 Correction with q = .4949999999999996 Correction with q = .4939999999999996 Correction with q = .4929999999999996 Correction with q = .4919999999999996 Correction with q = .4909999999999996 Correction with q = .4899999999999996 Correction with q = .4889999999999996 Correction with q = .4879999999999996 Correction with q = .4869999999999995 Correction with q = .4859999999999995 Correction with q = .4849999999999995 Correction with q = .4839999999999995 Correction with q = .4829999999999995 Correction with q = .4819999999999995 Correction with q = .4809999999999995 Correction with q = .4799999999999995 Correction with q = .4789999999999995 Correction with q = .4779999999999995 Correction with q = .4769999999999995 Correction with q = .4759999999999995 Correction with q = .4749999999999995 Correction with q = .4739999999999995 Correction with q = .4729999999999995 Correction with q = .4719999999999995 Correction with q = .4709999999999995 Correction with q = .4699999999999995 Correction with q = .4689999999999995 Correction with q = .4679999999999995 Correction with q = .4669999999999995 Correction with q = .4659999999999995 Correction with q = .4649999999999995 Correction with q = .4639999999999995 Correction with q = .4629999999999995 Correction with q = .4619999999999995 Correction with q = .4609999999999995 Correction with q = .4599999999999995 Correction with q = .4589999999999995 Correction with q = .4579999999999995 Correction with q = .4569999999999995 Correction with q = .4559999999999995 Correction with q = .4549999999999995 Correction with q = .4539999999999995 Correction with q = .4529999999999995 Correction with q = .4519999999999995 Correction with q = .4509999999999995 Correction with q = .4499999999999995 Correction with q = .4489999999999995 Correction with q = .4479999999999995 Correction with q = .4469999999999995 Correction with q = .4459999999999995 Correction with q = .4449999999999995 Correction with q = .4439999999999995 Correction with q = .4429999999999995 Correction with q = .4419999999999995 Correction with q = .4409999999999995 Correction with q = .4399999999999995 Correction with q = .4389999999999995 Correction with q = .4379999999999995 Correction with q = .4369999999999995 Correction with q = .4359999999999995 Correction with q = .4349999999999995 Correction with q = .4339999999999995 Correction with q = .4329999999999995 Correction with q = .4319999999999995 Correction with q = .4309999999999995 Correction with q = .4299999999999995 Correction with q = .4289999999999995 Correction with q = .4279999999999995 Correction with q = .4269999999999995 Correction with q = .4259999999999995 Correction with q = .4249999999999995 Correction with q = .4239999999999995 Correction with q = .4229999999999995 Correction with q = .4219999999999995 Correction with q = .4209999999999995 Correction with q = .4199999999999995 Correction with q = .4189999999999995 Correction with q = .4179999999999995 Correction with q = .4169999999999995 Correction with q = .4159999999999995 Correction with q = .4149999999999995 Correction with q = .4139999999999995 Correction with q = .4129999999999995 Correction with q = .4119999999999995 Correction with q = .4109999999999995 Correction with q = .4099999999999995 Correction with q = .4089999999999995 Correction with q = .4079999999999995 Correction with q = .4069999999999995 Correction with q = .4059999999999995 Correction with q = .4049999999999995 Correction with q = .4039999999999995 Correction with q = .4029999999999995 Correction with q = .4019999999999995 Correction with q = .4009999999999995 Correction with q = .3999999999999995 Correction with q = .3989999999999995 Correction with q = .3979999999999995 Correction with q = .3969999999999995 Correction with q = .3959999999999995 Correction with q = .3949999999999995 Correction with q = .3939999999999995 Correction with q = .3929999999999995 Correction with q = .3919999999999995 Correction with q = .3909999999999995 Correction with q = .3899999999999995 Correction with q = .3889999999999995 Correction with q = .3879999999999995 Correction with q = .3869999999999995 Correction with q = .3859999999999995 Correction with q = .3849999999999995 Correction with q = .3839999999999995 Correction with q = .3829999999999995 Correction with q = .3819999999999995 Correction with q = .3809999999999995 Correction with q = .3799999999999995 Correction with q = .3789999999999995 Correction with q = .3779999999999995 Correction with q = .3769999999999995 Correction with q = .3759999999999995 Correction with q = .3749999999999994 Correction with q = .3739999999999994 Correction with q = .3729999999999994 Correction with q = .3719999999999994 Correction with q = .3709999999999994 Correction with q = .3699999999999994 Correction with q = .3689999999999994 Correction with q = .3679999999999994 Correction with q = .3669999999999994 Correction with q = .3659999999999994 Correction with q = .3649999999999994 Correction with q = .3639999999999994 Correction with q = .3629999999999994 Correction with q = .3619999999999994 Correction with q = .3609999999999994 Correction with q = .3599999999999994 Correction with q = .3589999999999994 Correction with q = .3579999999999994 Correction with q = .3569999999999994 Correction with q = .3559999999999994 Correction with q = .3549999999999994 Correction with q = .3539999999999994 Correction with q = .3529999999999994 Correction with q = .3519999999999994 Correction with q = .3509999999999994 Correction with q = .3499999999999994 Correction with q = .3489999999999994 Correction with q = .3479999999999994 Correction with q = .3469999999999994 Correction with q = .3459999999999994 Correction with q = .3449999999999994 Correction with q = .3439999999999994 Correction with q = .3429999999999994 Correction with q = .3419999999999994 Correction with q = .3409999999999994 Correction with q = .3399999999999994 Correction with q = .3389999999999994 Correction with q = .3379999999999994 Correction with q = .3369999999999994 Correction with q = .3359999999999994 Correction with q = .3349999999999994 Correction with q = .3339999999999994 Correction with q = .3329999999999994 Correction with q = .3319999999999994 Correction with q = .3309999999999994 Correction with q = .3299999999999994 Correction with q = .3289999999999994 Correction with q = .3279999999999994 Correction with q = .3269999999999994 Correction with q = .3259999999999994 Correction with q = .3249999999999994 Correction with q = .3239999999999994 Correction with q = .3229999999999994 Correction with q = .3219999999999994 Correction with q = .3209999999999994 Correction with q = .3199999999999994 Correction with q = .3189999999999994 Correction with q = .3179999999999994 Correction with q = .3169999999999994 Correction with q = .3159999999999994 Correction with q = .3149999999999994 Correction with q = .3139999999999994 Correction with q = .3129999999999994 Correction with q = .3119999999999994 Correction with q = .3109999999999994 Correction with q = .3099999999999994 Correction with q = .3089999999999994 Correction with q = .3079999999999994 Correction with q = .3069999999999994 Correction with q = .3059999999999994 Correction with q = .3049999999999994 Correction with q = .3039999999999994 Correction with q = .3029999999999994 Correction with q = .3019999999999994 Correction with q = .3009999999999994 Correction with q = .2999999999999994 Correction with q = .2989999999999994 Correction with q = .2979999999999994 Correction with q = .2969999999999994 Correction with q = .2959999999999994 Correction with q = .2949999999999994 Correction with q = .2939999999999994 Correction with q = .2929999999999994 Correction with q = .2919999999999994 Correction with q = .2909999999999994 Correction with q = .2899999999999994 Correction with q = .2889999999999994 Correction with q = .2879999999999994 Correction with q = .2869999999999994 Correction with q = .2859999999999994 Correction with q = .2849999999999994 Correction with q = .2839999999999994 Correction with q = .2829999999999994 Correction with q = .2819999999999994 Correction with q = .2809999999999994 Correction with q = .2799999999999994 Correction with q = .2789999999999994 Correction with q = .2779999999999994 Correction with q = .2769999999999994 Correction with q = .2759999999999994 Correction with q = .2749999999999994 Correction with q = .2739999999999994 Correction with q = .2729999999999994 Correction with q = .2719999999999994 Correction with q = .2709999999999994 Correction with q = .2699999999999994 Correction with q = .2689999999999994 Correction with q = .2679999999999994 Correction with q = .2669999999999994 Correction with q = .2659999999999994 Correction with q = .2649999999999994 Correction with q = .2639999999999994 Correction with q = .2629999999999994 Correction with q = .2619999999999993 Correction with q = .2609999999999993 Correction with q = .2599999999999993 Correction with q = .2589999999999993 Correction with q = .2579999999999993 Correction with q = .2569999999999993 Correction with q = .2559999999999993 Correction with q = .2549999999999993 Correction with q = .2539999999999993 Correction with q = .2529999999999993 Correction with q = .2519999999999993 Correction with q = .2509999999999993 Correction with q = .2499999999999993 Correction with q = .2489999999999993 Correction with q = .2479999999999993 Correction with q = .2469999999999993 Correction with q = .2459999999999993 Correction with q = .2449999999999993 Correction with q = .2439999999999993 Correction with q = .2429999999999993 Correction with q = .2419999999999993 Correction with q = .2409999999999993 Correction with q = .2399999999999993 Correction with q = .2389999999999993 Correction with q = .2379999999999993 Correction with q = .2369999999999993 Correction with q = .2359999999999993 Correction with q = .2349999999999993 Correction with q = .2339999999999993 Correction with q = .2329999999999993 Correction with q = .2319999999999993 Correction with q = .2309999999999993 Correction with q = .2299999999999993 Correction with q = .2289999999999993 Correction with q = .2279999999999993 Correction with q = .2269999999999993 Correction with q = .2259999999999993 Correction with q = .2249999999999993 Correction with q = .2239999999999993 Correction with q = .2229999999999993 Correction with q = .2219999999999993 Correction with q = .2209999999999993 Correction with q = .2199999999999993 Correction with q = .2189999999999993 Correction with q = .2179999999999993 Correction with q = .2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = .1909999999999993 Correction with q = .1899999999999993 Correction with q = .1889999999999993 Correction with q = .1879999999999993 Correction with q = .1869999999999993 Correction with q = .1859999999999993 Correction with q = .1849999999999993 Correction with q = .1839999999999993 Correction with q = .1829999999999993 Correction with q = .1819999999999993 Correction with q = .1809999999999993 Correction with q = .1799999999999993 Correction with q = .1789999999999993 Correction with q = .1779999999999993 Correction with q = .1769999999999993 Correction with q = .1759999999999993 Correction with q = .1749999999999993 Correction with q = .1739999999999993 Correction with q = .1729999999999993 Correction with q = .1719999999999993 Correction with q = .1709999999999993 Correction with q = .1699999999999993 Correction with q = .1689999999999993 Correction with q = .1679999999999993 Correction with q = .1669999999999993 Correction with q = .1659999999999993 Correction with q = .1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 Correction with q = .0069999999999991 Correction with q = .0059999999999991 Correction with q = .0049999999999991 Correction with q = .0039999999999991 Correction with q = .0029999999999991 Correction with q = .0019999999999991 (3 real changes made) (0 real changes made) . . estadd loc thisstat21 = "[" + string(Q[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat21 = "[" +string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat21 = "[" + string(Q[3, 1], "%9.3f")+"]": col3 . . mat def P = J(3, 1, .) . reg large T1 $controls [pweight=pweight] if wave==2, r (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 13.26 Prob > F = 0.0000 R-squared = 0.2086 Root MSE = .97164 ------------------------------------------------------------------------------- | Robust large | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .628054 .0635287 9.89 0.000 .5033887 .7527192 wave | 0 (omitted) gender | .2804918 .0635015 4.42 0.000 .1558798 .4051038 prior | -.0050007 .0017884 -2.80 0.005 -.0085102 -.0014911 democrat | .5398777 .0734168 7.35 0.000 .3958085 .6839469 indep | .1123585 .0952876 1.18 0.239 -.0746288 .2993457 otherpol | .3184052 .2525799 1.26 0.208 -.1772441 .8140545 midwest | -.0649516 .0937909 -0.69 0.489 -.2490019 .1190987 south | .1377281 .0877143 1.57 0.117 -.0343977 .3098539 west | -.1073857 .0988231 -1.09 0.277 -.3013109 .0865394 age1 | -.0531808 .1363709 -0.39 0.697 -.3207878 .2144261 age2 | .1761198 .1017402 1.73 0.084 -.0235298 .3757693 age3 | .1288002 .098086 1.31 0.189 -.0636785 .3212789 age4 | -.0650915 .0960533 -0.68 0.498 -.2535814 .1233983 anychildren | .0496479 .0731671 0.68 0.498 -.0939313 .193227 loghhinc | .0698563 .0471506 1.48 0.139 -.0226695 .1623821 associatemore | -.0035129 .0690951 -0.05 0.959 -.1391013 .1320756 fulltime | .1293563 .0965719 1.34 0.181 -.0601512 .3188637 parttime | -.1248257 .1322499 -0.94 0.345 -.3843459 .1346944 selfemp | .1279262 .1382231 0.93 0.355 -.1433154 .3991677 unemployed | .1200966 .1808678 0.66 0.507 -.2348287 .4750218 student | .1640887 .1727228 0.95 0.342 -.1748532 .5030306 _cons | -1.299399 .5460354 -2.38 0.018 -2.370909 -.2278885 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg problem T1 $controls [pweight=pweight] if wave==2, r (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 11.17 Prob > F = 0.0000 R-squared = 0.1995 Root MSE = .94272 ------------------------------------------------------------------------------- | Robust problem | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .5040322 .061319 8.22 0.000 .3837032 .6243612 wave | 0 (omitted) gender | .2659578 .0618926 4.30 0.000 .1445031 .3874126 prior | -.0055384 .0015219 -3.64 0.000 -.0085249 -.0025519 democrat | .6298038 .0701389 8.98 0.000 .492167 .7674407 indep | .1827792 .0966333 1.89 0.059 -.0068488 .3724073 otherpol | .4504328 .165796 2.72 0.007 .1250836 .7757819 midwest | -.1396837 .0955752 -1.46 0.144 -.3272353 .0478679 south | -.022356 .0848793 -0.26 0.792 -.1889186 .1442066 west | -.1311443 .0917483 -1.43 0.153 -.3111863 .0488978 age1 | .1400926 .1209249 1.16 0.247 -.0972039 .3773892 age2 | .1636361 .101648 1.61 0.108 -.0358325 .3631048 age3 | .1010286 .0958169 1.05 0.292 -.0869973 .2890546 age4 | -.0071856 .0959342 -0.07 0.940 -.1954417 .1810705 anychildren | .0777415 .0722178 1.08 0.282 -.0639749 .2194578 loghhinc | .0299937 .0447145 0.67 0.503 -.0577516 .1177389 associatemore | .0934574 .067536 1.38 0.167 -.0390717 .2259865 fulltime | .10869 .095133 1.14 0.254 -.0779939 .2953739 parttime | -.0974369 .1290446 -0.76 0.450 -.3506671 .1557932 selfemp | .0950274 .1299059 0.73 0.465 -.1598929 .3499478 unemployed | .0547977 .1856951 0.30 0.768 -.3096004 .4191958 student | .094424 .1667344 0.57 0.571 -.2327666 .4216147 _cons | -.7865916 .5099089 -1.54 0.123 -1.787209 .2140261 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg govmore T1 $controls [pweight=pweight] if wave==2, r (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 11.36 Prob > F = 0.0000 R-squared = 0.1973 Root MSE = .95792 ------------------------------------------------------------------------------- | Robust govmore | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2803166 .0625821 4.48 0.000 .1575088 .4031244 wave | 0 (omitted) gender | .2991447 .062588 4.78 0.000 .1763254 .421964 prior | -.0034895 .0013957 -2.50 0.013 -.0062284 -.0007505 democrat | .7538227 .0701134 10.75 0.000 .6162358 .8914096 indep | .167489 .099655 1.68 0.093 -.0280686 .3630466 otherpol | .3743769 .2523974 1.48 0.138 -.1209143 .869668 midwest | -.2686761 .0992248 -2.71 0.007 -.4633896 -.0739626 south | -.0088886 .0863393 -0.10 0.918 -.1783163 .160539 west | -.2069527 .0968676 -2.14 0.033 -.3970406 -.0168649 age1 | .174519 .1287495 1.36 0.176 -.0781321 .4271701 age2 | .333405 .1035967 3.22 0.001 .1301123 .5366977 age3 | .2466502 .1004027 2.46 0.014 .0496254 .443675 age4 | .1690269 .1021401 1.65 0.098 -.0314073 .3694612 anychildren | .1033225 .0700472 1.48 0.141 -.0341344 .2407794 loghhinc | -.0022594 .0436496 -0.05 0.959 -.0879151 .0833963 associatemore | .0076636 .0694868 0.11 0.912 -.1286935 .1440208 fulltime | .0123732 .101145 0.12 0.903 -.1861084 .2108548 parttime | -.0087134 .1276043 -0.07 0.946 -.2591172 .2416903 selfemp | .0701519 .1321905 0.53 0.596 -.1892516 .3295554 unemployed | .0130137 .1714887 0.08 0.940 -.3235065 .3495339 student | .0975121 .1689955 0.58 0.564 -.2341157 .4291398 _cons | -.557904 .5087346 -1.10 0.273 -1.556217 .4404094 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . . minq P, q("Q") step(0.001) number of observations will be reset to 3 Press any key to continue, or Break to abort number of observations (_N) was 0, now 3 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = 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.4489999999999995 Correction with q = .4479999999999995 Correction with q = .4469999999999995 Correction with q = .4459999999999995 Correction with q = .4449999999999995 Correction with q = .4439999999999995 Correction with q = .4429999999999995 Correction with q = .4419999999999995 Correction with q = .4409999999999995 Correction with q = .4399999999999995 Correction with q = .4389999999999995 Correction with q = .4379999999999995 Correction with q = .4369999999999995 Correction with q = .4359999999999995 Correction with q = .4349999999999995 Correction with q = .4339999999999995 Correction with q = .4329999999999995 Correction with q = .4319999999999995 Correction with q = .4309999999999995 Correction with q = .4299999999999995 Correction with q = .4289999999999995 Correction with q = .4279999999999995 Correction with q = .4269999999999995 Correction with q = .4259999999999995 Correction with q = .4249999999999995 Correction with q = .4239999999999995 Correction with q = 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.3449999999999994 Correction with q = .3439999999999994 Correction with q = .3429999999999994 Correction with q = .3419999999999994 Correction with q = .3409999999999994 Correction with q = .3399999999999994 Correction with q = .3389999999999994 Correction with q = .3379999999999994 Correction with q = .3369999999999994 Correction with q = .3359999999999994 Correction with q = .3349999999999994 Correction with q = .3339999999999994 Correction with q = .3329999999999994 Correction with q = .3319999999999994 Correction with q = .3309999999999994 Correction with q = .3299999999999994 Correction with q = .3289999999999994 Correction with q = .3279999999999994 Correction with q = .3269999999999994 Correction with q = .3259999999999994 Correction with q = .3249999999999994 Correction with q = .3239999999999994 Correction with q = .3229999999999994 Correction with q = .3219999999999994 Correction with q = .3209999999999994 Correction with q = .3199999999999994 Correction with q = .3189999999999994 Correction with q = .3179999999999994 Correction with q = .3169999999999994 Correction with q = .3159999999999994 Correction with q = .3149999999999994 Correction with q = .3139999999999994 Correction with q = .3129999999999994 Correction with q = .3119999999999994 Correction with q = .3109999999999994 Correction with q = .3099999999999994 Correction with q = .3089999999999994 Correction with q = .3079999999999994 Correction with q = .3069999999999994 Correction with q = .3059999999999994 Correction with q = .3049999999999994 Correction with q = .3039999999999994 Correction with q = .3029999999999994 Correction with q = .3019999999999994 Correction with q = .3009999999999994 Correction with q = .2999999999999994 Correction with q = .2989999999999994 Correction with q = .2979999999999994 Correction with q = .2969999999999994 Correction with q = .2959999999999994 Correction with q = .2949999999999994 Correction with q = .2939999999999994 Correction with q = .2929999999999994 Correction with q = .2919999999999994 Correction with q = .2909999999999994 Correction with q = .2899999999999994 Correction with q = .2889999999999994 Correction with q = .2879999999999994 Correction with q = .2869999999999994 Correction with q = .2859999999999994 Correction with q = .2849999999999994 Correction with q = .2839999999999994 Correction with q = .2829999999999994 Correction with q = .2819999999999994 Correction with q = .2809999999999994 Correction with q = .2799999999999994 Correction with q = .2789999999999994 Correction with q = .2779999999999994 Correction with q = .2769999999999994 Correction with q = .2759999999999994 Correction with q = .2749999999999994 Correction with q = .2739999999999994 Correction with q = .2729999999999994 Correction with q = .2719999999999994 Correction with q = .2709999999999994 Correction with q = .2699999999999994 Correction with q = .2689999999999994 Correction with q = .2679999999999994 Correction with q = .2669999999999994 Correction with q = .2659999999999994 Correction with q = .2649999999999994 Correction with q = .2639999999999994 Correction with q = .2629999999999994 Correction with q = .2619999999999993 Correction with q = .2609999999999993 Correction with q = .2599999999999993 Correction with q = .2589999999999993 Correction with q = .2579999999999993 Correction with q = .2569999999999993 Correction with q = .2559999999999993 Correction with q = .2549999999999993 Correction with q = .2539999999999993 Correction with q = .2529999999999993 Correction with q = .2519999999999993 Correction with q = .2509999999999993 Correction with q = .2499999999999993 Correction with q = .2489999999999993 Correction with q = .2479999999999993 Correction with q = .2469999999999993 Correction with q = .2459999999999993 Correction with q = .2449999999999993 Correction with q = .2439999999999993 Correction with q = .2429999999999993 Correction with q = .2419999999999993 Correction with q = .2409999999999993 Correction with q = .2399999999999993 Correction with q = .2389999999999993 Correction with q = .2379999999999993 Correction with q = .2369999999999993 Correction with q = .2359999999999993 Correction with q = .2349999999999993 Correction with q = .2339999999999993 Correction with q = .2329999999999993 Correction with q = .2319999999999993 Correction with q = .2309999999999993 Correction with q = .2299999999999993 Correction with q = .2289999999999993 Correction with q = .2279999999999993 Correction with q = .2269999999999993 Correction with q = .2259999999999993 Correction with q = .2249999999999993 Correction with q = .2239999999999993 Correction with q = .2229999999999993 Correction with q = .2219999999999993 Correction with q = .2209999999999993 Correction with q = .2199999999999993 Correction with q = .2189999999999993 Correction with q = .2179999999999993 Correction with q = .2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = .1909999999999993 Correction with q = .1899999999999993 Correction with q = .1889999999999993 Correction with q = .1879999999999993 Correction with q = .1869999999999993 Correction with q = .1859999999999993 Correction with q = .1849999999999993 Correction with q = .1839999999999993 Correction with q = .1829999999999993 Correction with q = .1819999999999993 Correction with q = .1809999999999993 Correction with q = .1799999999999993 Correction with q = .1789999999999993 Correction with q = .1779999999999993 Correction with q = .1769999999999993 Correction with q = .1759999999999993 Correction with q = .1749999999999993 Correction with q = .1739999999999993 Correction with q = .1729999999999993 Correction with q = .1719999999999993 Correction with q = .1709999999999993 Correction with q = .1699999999999993 Correction with q = .1689999999999993 Correction with q = .1679999999999993 Correction with q = .1669999999999993 Correction with q = .1659999999999993 Correction with q = .1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 Correction with q = .0069999999999991 Correction with q = .0059999999999991 Correction with q = .0049999999999991 Correction with q = .0039999999999991 Correction with q = .0029999999999991 Correction with q = .0019999999999991 (3 real changes made) (0 real changes made) . . estadd loc thisstat36 = "[" + string(Q[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat36 = "[" +string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat36 = "[" + string(Q[3, 1], "%9.3f")+"]": col3 . . loc rowlabels " "\hline" "{\bf Panel A: Both waves}" " " "T$^{74}$" " " "Sharpened q-value" " " "D > emocrat" " " " " "Female" " " " " "Observations" " " "\hline" "{\bf Panel B: Wave A}" " " "T$^{7 > 4}$" " " "Sharpened q-value" " " "Democrat" " " " " "Female" " " " " "Observations" " " "\hline" > "{\bf Panel C: Wave B}" " " "T$^{74}$" " " "Sharpened q-value" " " "Democrat" " " " " "Female" " " > " " "Observations" " " "\hline" "\hline" " " " . loc rowstats "" . . . loc rowstats "" . . forval i = 1/45 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\tab_treatment_maniABsep.tex", replace cells(none) booktabs nonotes nomtitl > es compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mgroups("\shortstack{Gender diff.\\ in wages\\are large}" "\shortstack{Gender diff.\\ in w > ages\\are a problem}" /// > "\shortstack{Government\\should mitigate\\gender wage gap}" "\shortstack{Perception\\Index\\((1) > -(3))}", pattern(1 1 1 1 1 1) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr) > {@span})) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\tab_treatment_maniABsep.tex) . . . *********************************************************************************** . // Table G.4: Treatment effect on demand for specific policies (by wave) . *********************************************************************************** . . . use "$path\data\SurveyStageI_AB_final.dta", clear . . drop if rand==0 (1,034 observations deleted) . . loc experiments "quotaanchor AAanchor legislationanchor transparencyanchor UKtool childcare z_lmpo > licy_index" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experiments' { 2. . ***Panel A: All data . . qui reg `choice' T1 $controls [pweight=pweight], vce(r) 3. sigstar T1, prec(3) 4. estadd loc thisstat4 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat5 = "`r(sestar)'": col`colnum' 6. sigstar gender, prec(3) 7. estadd loc thisstat8 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat9 = "`r(sestar)'": col`colnum' 9. sigstar democrat, prec(3) 10. estadd loc thisstat11 = "`r(bstar)'": col`colnum' 11. estadd loc thisstat12 = "`r(sestar)'": col`colnum' 12. qui sum `choice' 13. estadd loc thisstat14 = r(N): col`colnum' 14. . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. . } . . . ***Panel B : Wave A only . . loc experiments "quotaanchor AAanchor legislationanchor transparencyanchor" . loc colnum = 1 . foreach choice in `experiments' { 2. qui reg `choice' T1 $controls [pweight=pweight] if wave==1, vce(r) 3. sigstar T1, prec(3) 4. estadd loc thisstat20 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat21 = "`r(sestar)'": col`colnum' 6. sigstar gender, prec(3) 7. estadd loc thisstat24 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat25 = "`r(sestar)'": col`colnum' 9. sigstar democrat, prec(3) 10. estadd loc thisstat27 = "`r(bstar)'": col`colnum' 11. estadd loc thisstat28 = "`r(sestar)'": col`colnum' 12. qui sum `choice' if wave==1 13. estadd loc thisstat30 = r(N): col`colnum' 14. . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. } . . loc ++colnum . . loc experiments "childcare z_lmpolicy_index" . foreach choice in `experiments' { 2. qui reg `choice' T1 $controls [pweight=pweight] if wave==1, vce(r) 3. sigstar T1, prec(3) 4. estadd loc thisstat20 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat21 = "`r(sestar)'": col`colnum' 6. sigstar gender, prec(3) 7. estadd loc thisstat24 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat25 = "`r(sestar)'": col`colnum' 9. sigstar democrat, prec(3) 10. estadd loc thisstat27 = "`r(bstar)'": col`colnum' 11. estadd loc thisstat28 = "`r(sestar)'": col`colnum' 12. qui sum `choice' if wave==1 13. estadd loc thisstat30 = r(N): col`colnum' 14. . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. } . . . ***Panel C : Wave B only . . loc experiments "quotaanchor AAanchor legislationanchor" . loc colnum = 1 . foreach choice in `experiments' { 2. qui reg `choice' T1 $controls [pweight=pweight] if wave==2, vce(r) 3. sigstar T1, prec(3) 4. estadd loc thisstat36 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat37 = "`r(sestar)'": col`colnum' 6. sigstar gender, prec(3) 7. estadd loc thisstat40 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat41 = "`r(sestar)'": col`colnum' 9. sigstar democrat, prec(3) 10. estadd loc thisstat43 = "`r(bstar)'": col`colnum' 11. estadd loc thisstat44 = "`r(sestar)'": col`colnum' 12. qui sum `choice' if wave==2 13. estadd loc thisstat46 = r(N): col`colnum' 14. . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. } . . loc ++colnum . . loc experiments "UKtool childcare z_lmpolicy_index" . foreach choice in `experiments' { 2. qui reg `choice' T1 $controls [pweight=pweight] if wave==2, vce(r) 3. sigstar T1, prec(3) 4. estadd loc thisstat36 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat37 = "`r(sestar)'": col`colnum' 6. sigstar gender, prec(3) 7. estadd loc thisstat40 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat41 = "`r(sestar)'": col`colnum' 9. sigstar democrat, prec(3) 10. estadd loc thisstat43 = "`r(bstar)'": col`colnum' 11. estadd loc thisstat44 = "`r(sestar)'": col`colnum' 12. qui sum `choice' if wave==2 13. estadd loc thisstat46 = r(N): col`colnum' 14. . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. } . . . ** For Panel A . mat def P = J(6, 1, .) . reg quotaanchor T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 17.54 Prob > F = 0.0000 R-squared = 0.1149 Root MSE = .97022 ------------------------------------------------------------------------------- | Robust quotaanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0555262 .0355592 1.56 0.119 -.0141966 .1252491 wave | .135324 .0384106 3.52 0.000 .0600104 .2106376 gender | .2540003 .0365454 6.95 0.000 .1823439 .3256567 prior | -.0037742 .0009395 -4.02 0.000 -.0056164 -.001932 democrat | .5591765 .0409111 13.67 0.000 .4789598 .6393931 indep | .1584659 .0539054 2.94 0.003 .0527707 .2641611 otherpol | .1682778 .1265152 1.33 0.184 -.0797874 .4163429 midwest | -.1216167 .0589867 -2.06 0.039 -.2372751 -.0059583 south | .0183978 .0525728 0.35 0.726 -.0846844 .1214801 west | -.0313563 .0564172 -0.56 0.578 -.1419765 .079264 age1 | .2485652 .0795284 3.13 0.002 .0926296 .4045007 age2 | .2953435 .0578768 5.10 0.000 .1818614 .4088256 age3 | .2047762 .0563071 3.64 0.000 .0943719 .3151806 age4 | .0512901 .0550873 0.93 0.352 -.0567225 .1593027 anychildren | .1317022 .0392475 3.36 0.001 .0547476 .2086568 loghhinc | -.0397111 .0247723 -1.60 0.109 -.0882835 .0088612 associatemore | -.0639229 .0404276 -1.58 0.114 -.1431914 .0153456 fulltime | .058038 .054327 1.07 0.285 -.0484839 .1645599 parttime | .0364979 .0720797 0.51 0.613 -.1048326 .1778284 selfemp | .0849985 .0780653 1.09 0.276 -.0680683 .2380652 unemployed | .1133252 .0851681 1.33 0.183 -.0536684 .2803189 student | -.0747328 .111563 -0.67 0.503 -.2934803 .1440148 _cons | -.0734587 .2908389 -0.25 0.801 -.643722 .4968046 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg AAanchor T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 20.80 Prob > F = 0.0000 R-squared = 0.1332 Root MSE = .93684 ------------------------------------------------------------------------------- | Robust AAanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1124561 .0342902 3.28 0.001 .0452216 .1796907 wave | .0223997 .0374271 0.60 0.550 -.0509856 .095785 gender | .1792126 .0350613 5.11 0.000 .1104659 .2479592 prior | -.0043927 .0009723 -4.52 0.000 -.006299 -.0024863 democrat | .6690384 .0397297 16.84 0.000 .5911382 .7469385 indep | .2537192 .0523123 4.85 0.000 .1511478 .3562907 otherpol | .1193334 .1249381 0.96 0.340 -.1256392 .3643061 midwest | -.0915092 .0564249 -1.62 0.105 -.2021444 .0191261 south | .0705323 .0507837 1.39 0.165 -.029042 .1701067 west | -.0527099 .0547876 -0.96 0.336 -.1601348 .054715 age1 | .145654 .0740603 1.97 0.049 .0004401 .290868 age2 | .1579825 .0555227 2.85 0.004 .0491161 .2668489 age3 | .0576528 .0546985 1.05 0.292 -.0495975 .1649031 age4 | .0124483 .0521028 0.24 0.811 -.0897124 .114609 anychildren | .1346613 .0380059 3.54 0.000 .0601411 .2091815 loghhinc | -.0112497 .023842 -0.47 0.637 -.0579981 .0354986 associatemore | .0594325 .0381496 1.56 0.119 -.0153694 .1342343 fulltime | -.0205963 .0515 -0.40 0.689 -.1215751 .0803825 parttime | -.0489328 .0684958 -0.71 0.475 -.1832362 .0853706 selfemp | -.0188682 .0759922 -0.25 0.804 -.1678701 .1301336 unemployed | -.013886 .083436 -0.17 0.868 -.1774833 .1497113 student | .1036868 .1046431 0.99 0.322 -.1014925 .3088661 _cons | -.228448 .2858848 -0.80 0.424 -.7889976 .3321015 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg legislationanchor T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 18.37 Prob > F = 0.0000 R-squared = 0.1192 Root MSE = .94848 ------------------------------------------------------------------------------- | Robust legislation~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1152908 .0348747 3.31 0.001 .0469102 .1836714 wave | .0169243 .0380401 0.44 0.656 -.057663 .0915116 gender | .237316 .0357167 6.64 0.000 .1672843 .3073477 prior | -.004017 .0010618 -3.78 0.000 -.006099 -.0019351 democrat | .6182478 .0402208 15.37 0.000 .5393847 .6971108 indep | .2353013 .0509727 4.62 0.000 .1353564 .3352462 otherpol | .4703131 .1283571 3.66 0.000 .2186365 .7219897 midwest | -.069528 .0573761 -1.21 0.226 -.1820284 .0429724 south | -.0282264 .0513194 -0.55 0.582 -.128851 .0723982 west | -.059146 .0551212 -1.07 0.283 -.1672251 .048933 age1 | -.1681727 .0752971 -2.23 0.026 -.3158117 -.0205337 age2 | -.1143301 .0561517 -2.04 0.042 -.2244298 -.0042304 age3 | -.1067755 .0555956 -1.92 0.055 -.2157846 .0022337 age4 | -.0552778 .0533647 -1.04 0.300 -.1599128 .0493572 anychildren | .0378855 .0387236 0.98 0.328 -.0380419 .1138128 loghhinc | .0033672 .023876 0.14 0.888 -.0434478 .0501822 associatemore | .0011689 .0380079 0.03 0.975 -.0733553 .075693 fulltime | .0452467 .0542015 0.83 0.404 -.0610291 .1515225 parttime | -.0198583 .073063 -0.27 0.786 -.1631169 .1234002 selfemp | .1711308 .0744418 2.30 0.022 .0251689 .3170928 unemployed | .2739314 .0840264 3.26 0.001 .1091764 .4386864 student | .1661792 .1074768 1.55 0.122 -.0445562 .3769147 _cons | -.1935231 .285267 -0.68 0.498 -.7528613 .365815 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . reg transparencyanchor T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 10.39 Prob > F = 0.0000 R-squared = 0.0957 Root MSE = .94476 ------------------------------------------------------------------------------- | Robust transparenc~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0145207 .0421926 -0.34 0.731 -.097267 .0682256 wave | 0 (omitted) gender | .1968569 .0436751 4.51 0.000 .1112032 .2825107 prior | -.0026412 .0011873 -2.22 0.026 -.0049697 -.0003127 democrat | .5648746 .048392 11.67 0.000 .4699703 .6597789 indep | .2426681 .0645124 3.76 0.000 .1161491 .3691871 otherpol | .1372274 .1916125 0.72 0.474 -.2385547 .5130095 midwest | -.1229332 .0686787 -1.79 0.074 -.257623 .0117565 south | -.0270683 .0603186 -0.45 0.654 -.1453626 .0912259 west | -.048422 .0656441 -0.74 0.461 -.1771604 .0803163 age1 | .0478043 .1037241 0.46 0.645 -.1556149 .2512235 age2 | .0581052 .0678145 0.86 0.392 -.0748897 .1911001 age3 | .0554398 .0677336 0.82 0.413 -.0773964 .188276 age4 | .0267632 .0655902 0.41 0.683 -.1018694 .1553958 anychildren | .0940054 .0484718 1.94 0.053 -.0010554 .1890661 loghhinc | -.0266598 .0288938 -0.92 0.356 -.083325 .0300055 associatemore | .1015891 .0476979 2.13 0.033 .0080459 .1951322 fulltime | .0090151 .0654756 0.14 0.891 -.1193927 .137423 parttime | -.1014771 .0838587 -1.21 0.226 -.2659372 .062983 selfemp | .0372718 .0988473 0.38 0.706 -.1565833 .2311269 unemployed | .0970394 .0974852 1.00 0.320 -.0941443 .2882232 student | .3350384 .129311 2.59 0.010 .0814392 .5886376 _cons | -.0332594 .3314474 -0.10 0.920 -.6832798 .6167609 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[4, 1] = r(p) . reg UKtool T1 $controls, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 7.93 Prob > F = 0.0000 R-squared = 0.1289 Root MSE = .95388 ------------------------------------------------------------------------------- | Robust UKtool | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .11442 .0605462 1.89 0.059 -.0043926 .2332326 wave | 0 (omitted) gender | .3067953 .0623663 4.92 0.000 .1844109 .4291796 prior | -.0036271 .0016965 -2.14 0.033 -.0069562 -.000298 democrat | .5844135 .0716605 8.16 0.000 .4437908 .7250362 indep | .2297374 .092561 2.48 0.013 .0481006 .4113741 otherpol | .3192605 .2436944 1.31 0.190 -.1589523 .7974733 midwest | -.1463091 .0971106 -1.51 0.132 -.3368738 .0442556 south | -.0616318 .0826656 -0.75 0.456 -.2238504 .1005868 west | -.0918256 .0934022 -0.98 0.326 -.275113 .0914618 age1 | -.0022926 .1200111 -0.02 0.985 -.2377959 .2332106 age2 | -.0478419 .1032964 -0.46 0.643 -.2505453 .1548615 age3 | -.0179013 .0975762 -0.18 0.854 -.2093795 .1735769 age4 | .0970219 .095069 1.02 0.308 -.0895365 .2835803 anychildren | -.0397498 .0694486 -0.57 0.567 -.176032 .0965325 loghhinc | .0602127 .0418132 1.44 0.150 -.0218393 .1422646 associatemore | .0036374 .0679316 0.05 0.957 -.129668 .1369428 fulltime | .0455391 .0917915 0.50 0.620 -.1345876 .2256657 parttime | .0376446 .1234382 0.30 0.760 -.2045839 .2798732 selfemp | .2789413 .126928 2.20 0.028 .0298646 .5280179 unemployed | .1738671 .1612417 1.08 0.281 -.142545 .4902792 student | .2392582 .1446906 1.65 0.099 -.0446748 .5231913 _cons | -.854755 .4950215 -1.73 0.085 -1.826159 .1166486 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[5, 1] = r(p) . reg childcare T1 $controls [pweight=pweight], vce(r) (sum of wgt is 2.9952e+03) Linear regression Number of obs = 3,031 F(22, 3008) = 16.85 Prob > F = 0.0000 R-squared = 0.1142 Root MSE = .94481 ------------------------------------------------------------------------------- | Robust childcare | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0026486 .0346768 0.08 0.939 -.0653441 .0706412 wave | -.099568 .0374546 -2.66 0.008 -.1730073 -.0261287 gender | .1118256 .0361868 3.09 0.002 .0408722 .1827789 prior | -.0038869 .0009617 -4.04 0.000 -.0057725 -.0020014 democrat | .5781269 .0401769 14.39 0.000 .4993498 .6569039 indep | .1065453 .0524179 2.03 0.042 .0037668 .2093238 otherpol | .2258622 .1341441 1.68 0.092 -.0371613 .4888857 midwest | -.0919717 .0562669 -1.63 0.102 -.2022972 .0183539 south | .0059153 .0502006 0.12 0.906 -.0925156 .1043462 west | -.146718 .0564412 -2.60 0.009 -.2573853 -.0360507 age1 | .2577494 .0806588 3.20 0.001 .0995974 .4159014 age2 | .3202499 .0565913 5.66 0.000 .2092884 .4312114 age3 | .2364555 .0564334 4.19 0.000 .1258036 .3471074 age4 | .0964648 .0541442 1.78 0.075 -.0096985 .2026282 anychildren | .2043124 .0388232 5.26 0.000 .1281896 .2804352 loghhinc | -.0321861 .0244382 -1.32 0.188 -.0801032 .0157311 associatemore | -.0264179 .0383545 -0.69 0.491 -.1016215 .0487858 fulltime | -.0064537 .055003 -0.12 0.907 -.1143011 .1013937 parttime | .0186614 .0715453 0.26 0.794 -.1216214 .1589441 selfemp | .0714849 .0774456 0.92 0.356 -.0803668 .2233366 unemployed | .0308187 .0857939 0.36 0.719 -.137402 .1990394 student | .0482532 .1013599 0.48 0.634 -.1504884 .2469948 _cons | .217017 .2832473 0.77 0.444 -.338361 .772395 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[6, 1] = r(p) . . . . minq P, q("Q") step(0.001) number of observations will be reset to 6 Press any key to continue, or Break to abort number of observations (_N) was 0, now 6 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = .7109999999999997 Correction with q = .7099999999999997 Correction with q = .7089999999999997 Correction with q = .7079999999999997 Correction with q = .7069999999999997 Correction with q = .7059999999999997 Correction with q = .7049999999999997 Correction with q = .7039999999999997 Correction with q = .7029999999999997 Correction with q = .7019999999999997 Correction with q = .7009999999999997 Correction with q = .6999999999999997 Correction with q = .6989999999999997 Correction with q = .6979999999999997 Correction with q = .6969999999999997 Correction with q = .6959999999999997 Correction with q = .6949999999999997 Correction with q = .6939999999999997 Correction with q = .6929999999999997 Correction with q = .6919999999999997 Correction with q = .6909999999999997 Correction with q = .6899999999999997 Correction with q = .6889999999999997 Correction with q = .6879999999999997 Correction with q = .6869999999999997 Correction with q = .6859999999999997 Correction with q = .6849999999999997 Correction with q = .6839999999999997 Correction with q = .6829999999999997 Correction with q = .6819999999999997 Correction with q = .6809999999999997 Correction with q = .6799999999999997 Correction with q = .6789999999999997 Correction with q = .6779999999999997 Correction with q = .6769999999999997 Correction with q = .6759999999999997 Correction with q = .6749999999999997 Correction with q = .6739999999999997 Correction with q = .6729999999999997 Correction with q = .6719999999999997 Correction with q = .6709999999999997 Correction with q = .6699999999999997 Correction with q = .6689999999999997 Correction with q = .6679999999999997 Correction with q = .6669999999999997 Correction with q = .6659999999999997 Correction with q = .6649999999999997 Correction with q = .6639999999999997 Correction with q = .6629999999999997 Correction with q = .6619999999999997 Correction with q = .6609999999999997 Correction with q = .6599999999999997 Correction with q = .6589999999999997 Correction with q = .6579999999999997 Correction with q = .6569999999999997 Correction with q = .6559999999999997 Correction with q = .6549999999999997 Correction with q = .6539999999999997 Correction with q = .6529999999999997 Correction with q = .6519999999999997 Correction with q = .6509999999999997 Correction with q = .6499999999999997 Correction with q = .6489999999999997 Correction with q = .6479999999999997 Correction with q = .6469999999999997 Correction with q = .6459999999999997 Correction with q = .6449999999999997 Correction with q = .6439999999999997 Correction with q = .6429999999999997 Correction with q = .6419999999999997 Correction with q = .6409999999999997 Correction with q = .6399999999999997 Correction with q = .6389999999999997 Correction with q = .6379999999999997 Correction with q = .6369999999999997 Correction with q = .6359999999999997 Correction with q = .6349999999999997 Correction with q = .6339999999999997 Correction with q = .6329999999999997 Correction with q = .6319999999999997 Correction with q = .6309999999999997 Correction with q = .6299999999999997 Correction with q = .6289999999999997 Correction with q = .6279999999999997 Correction with q = .6269999999999997 Correction with q = .6259999999999997 Correction with q = .6249999999999997 Correction with q = .6239999999999997 Correction with q = .6229999999999997 Correction with q = .6219999999999997 Correction with q = .6209999999999997 Correction with q = .6199999999999997 Correction with q = .6189999999999997 Correction with q = .6179999999999997 Correction with q = .6169999999999997 Correction with q = .6159999999999997 Correction with q = .6149999999999997 Correction with q = .6139999999999997 Correction with q = .6129999999999997 Correction with q = .6119999999999997 Correction with q = .6109999999999997 Correction with q = .6099999999999997 Correction with q = .6089999999999997 Correction with q = .6079999999999997 Correction with q = .6069999999999997 Correction with q = .6059999999999997 Correction with q = .6049999999999997 Correction with q = .6039999999999997 Correction with q = .6029999999999997 Correction with q = .6019999999999997 Correction with q = .6009999999999997 Correction with q = .5999999999999996 Correction with q = .5989999999999996 Correction with q = .5979999999999996 Correction with q = .5969999999999996 Correction with q = .5959999999999996 Correction with q = .5949999999999996 Correction with q = .5939999999999996 Correction with q = .5929999999999996 Correction with q = .5919999999999996 Correction with q = .5909999999999996 Correction with q = .5899999999999996 Correction with q = .5889999999999996 Correction with q = .5879999999999996 Correction with q = .5869999999999996 Correction with q = .5859999999999996 Correction with q = .5849999999999996 Correction with q = .5839999999999996 Correction with q = .5829999999999996 Correction with q = .5819999999999996 Correction with q = .5809999999999996 Correction with q = .5799999999999996 Correction with q = .5789999999999996 Correction with q = .5779999999999996 Correction with q = .5769999999999996 Correction with q = .5759999999999996 Correction with q = .5749999999999996 Correction with q = .5739999999999996 Correction with q = .5729999999999996 Correction with q = .5719999999999996 Correction with q = .5709999999999996 Correction with q = .5699999999999996 Correction with q = .5689999999999996 Correction with q = .5679999999999996 Correction with q = .5669999999999996 Correction with q = .5659999999999996 Correction with q = .5649999999999996 Correction with q = .5639999999999996 Correction with q = .5629999999999996 Correction with q = .5619999999999996 Correction with q = .5609999999999996 Correction with q = .5599999999999996 Correction with q = .5589999999999996 Correction with q = .5579999999999996 Correction with q = .5569999999999996 Correction with q = .5559999999999996 Correction with q = .5549999999999996 Correction with q = .5539999999999996 Correction with q = .5529999999999996 Correction with q = .5519999999999996 Correction with q = .5509999999999996 Correction with q = .5499999999999996 Correction with q = .5489999999999996 Correction with q = .5479999999999996 Correction with q = .5469999999999996 Correction with q = .5459999999999996 Correction with q = .5449999999999996 Correction with q = .5439999999999996 Correction with q = .5429999999999996 Correction with q = .5419999999999996 Correction with q = .5409999999999996 Correction with q = .5399999999999996 Correction with q = .5389999999999996 Correction with q = .5379999999999996 Correction with q = .5369999999999996 Correction with q = .5359999999999996 Correction with q = .5349999999999996 Correction with q = .5339999999999996 Correction with q = .5329999999999996 Correction with q = .5319999999999996 Correction with q = .5309999999999996 Correction with q = .5299999999999996 Correction with q = .5289999999999996 Correction with q = .5279999999999996 Correction with q = .5269999999999996 Correction with q = .5259999999999996 Correction with q = .5249999999999996 Correction with q = .5239999999999996 Correction with q = .5229999999999996 Correction with q = .5219999999999996 Correction with q = .5209999999999996 Correction with q = .5199999999999996 Correction with q = .5189999999999996 Correction with q = .5179999999999996 Correction with q = .5169999999999996 Correction with q = .5159999999999996 Correction with q = .5149999999999996 Correction with q = .5139999999999996 Correction with q = .5129999999999996 Correction with q = .5119999999999996 Correction with q = .5109999999999996 Correction with q = .5099999999999996 Correction with q = .5089999999999996 Correction with q = .5079999999999996 Correction with q = .5069999999999996 Correction with q = .5059999999999996 Correction with q = .5049999999999996 Correction with q = .5039999999999996 Correction with q = .5029999999999996 Correction with q = .5019999999999996 Correction with q = .5009999999999996 Correction with q = .4999999999999996 Correction with q = .4989999999999996 Correction with q = .4979999999999996 Correction with q = .4969999999999996 Correction with q = .4959999999999996 Correction with q = .4949999999999996 Correction with q = .4939999999999996 Correction with q = .4929999999999996 Correction with q = .4919999999999996 Correction with q = .4909999999999996 Correction with q = .4899999999999996 Correction with q = .4889999999999996 Correction with q = .4879999999999996 Correction with q = .4869999999999995 Correction with q = .4859999999999995 Correction with q = .4849999999999995 Correction with q = .4839999999999995 Correction with q = .4829999999999995 Correction with q = .4819999999999995 Correction with q = .4809999999999995 Correction with q = .4799999999999995 Correction with q = .4789999999999995 Correction with q = .4779999999999995 Correction with q = .4769999999999995 Correction with q = .4759999999999995 Correction with q = .4749999999999995 Correction with q = .4739999999999995 Correction with q = .4729999999999995 Correction with q = .4719999999999995 Correction with q = .4709999999999995 Correction with q = .4699999999999995 Correction with q = .4689999999999995 Correction with q = .4679999999999995 Correction with q = .4669999999999995 Correction with q = .4659999999999995 Correction with q = .4649999999999995 Correction with q = .4639999999999995 Correction with q = .4629999999999995 Correction with q = .4619999999999995 Correction with q = .4609999999999995 Correction with q = .4599999999999995 Correction with q = .4589999999999995 Correction with q = .4579999999999995 Correction with q = .4569999999999995 Correction with q = .4559999999999995 Correction with q = .4549999999999995 Correction with q = .4539999999999995 Correction with q = .4529999999999995 Correction with q = .4519999999999995 Correction with q = .4509999999999995 Correction with q = .4499999999999995 Correction with q = .4489999999999995 Correction with q = .4479999999999995 Correction with q = .4469999999999995 Correction with q = .4459999999999995 Correction with q = .4449999999999995 Correction with q = .4439999999999995 Correction with q = .4429999999999995 Correction with q = .4419999999999995 Correction with q = .4409999999999995 Correction with q = .4399999999999995 Correction with q = .4389999999999995 Correction with q = .4379999999999995 Correction with q = .4369999999999995 Correction with q = .4359999999999995 Correction with q = .4349999999999995 Correction with q = .4339999999999995 Correction with q = .4329999999999995 Correction with q = .4319999999999995 Correction with q = .4309999999999995 Correction with q = .4299999999999995 Correction with q = .4289999999999995 Correction with q = .4279999999999995 Correction with q = .4269999999999995 Correction with q = .4259999999999995 Correction with q = .4249999999999995 Correction with q = .4239999999999995 Correction with q = .4229999999999995 Correction with q = .4219999999999995 Correction with q = .4209999999999995 Correction with q = .4199999999999995 Correction with q = .4189999999999995 Correction with q = .4179999999999995 Correction with q = .4169999999999995 Correction with q = .4159999999999995 Correction with q = .4149999999999995 Correction with q = .4139999999999995 Correction with q = .4129999999999995 Correction with q = .4119999999999995 Correction with q = .4109999999999995 Correction with q = .4099999999999995 Correction with q = .4089999999999995 Correction with q = .4079999999999995 Correction with q = .4069999999999995 Correction with q = .4059999999999995 Correction with q = .4049999999999995 Correction with q = .4039999999999995 Correction with q = .4029999999999995 Correction with q = .4019999999999995 Correction with q = .4009999999999995 Correction with q = .3999999999999995 Correction with q = .3989999999999995 Correction with q = .3979999999999995 Correction with q = .3969999999999995 Correction with q = .3959999999999995 Correction with q = .3949999999999995 Correction with q = .3939999999999995 Correction with q = .3929999999999995 Correction with q = .3919999999999995 Correction with q = .3909999999999995 Correction with q = .3899999999999995 Correction with q = .3889999999999995 Correction with q = .3879999999999995 Correction with q = .3869999999999995 Correction with q = .3859999999999995 Correction with q = .3849999999999995 Correction with q = .3839999999999995 Correction with q = .3829999999999995 Correction with q = .3819999999999995 Correction with q = .3809999999999995 Correction with q = .3799999999999995 Correction with q = .3789999999999995 Correction with q = .3779999999999995 Correction with q = .3769999999999995 Correction with q = .3759999999999995 Correction with q = .3749999999999994 Correction with q = .3739999999999994 Correction with q = .3729999999999994 Correction with q = .3719999999999994 Correction with q = .3709999999999994 Correction with q = .3699999999999994 Correction with q = .3689999999999994 Correction with q = .3679999999999994 Correction with q = .3669999999999994 Correction with q = .3659999999999994 Correction with q = .3649999999999994 Correction with q = .3639999999999994 Correction with q = .3629999999999994 Correction with q = .3619999999999994 Correction with q = .3609999999999994 Correction with q = .3599999999999994 Correction with q = .3589999999999994 Correction with q = .3579999999999994 Correction with q = .3569999999999994 Correction with q = .3559999999999994 Correction with q = .3549999999999994 Correction with q = .3539999999999994 Correction with q = .3529999999999994 Correction with q = .3519999999999994 Correction with q = .3509999999999994 Correction with q = .3499999999999994 Correction with q = .3489999999999994 Correction with q = .3479999999999994 Correction with q = .3469999999999994 Correction with q = .3459999999999994 Correction with q = .3449999999999994 Correction with q = .3439999999999994 Correction with q = .3429999999999994 Correction with q = .3419999999999994 Correction with q = .3409999999999994 Correction with q = .3399999999999994 Correction with q = .3389999999999994 Correction with q = .3379999999999994 Correction with q = .3369999999999994 Correction with q = .3359999999999994 Correction with q = .3349999999999994 Correction with q = .3339999999999994 Correction with q = .3329999999999994 Correction with q = .3319999999999994 Correction with q = .3309999999999994 Correction with q = .3299999999999994 Correction with q = .3289999999999994 Correction with q = .3279999999999994 Correction with q = .3269999999999994 Correction with q = .3259999999999994 Correction with q = .3249999999999994 Correction with q = .3239999999999994 Correction with q = .3229999999999994 Correction with q = .3219999999999994 Correction with q = .3209999999999994 Correction with q = .3199999999999994 Correction with q = .3189999999999994 Correction with q = .3179999999999994 Correction with q = .3169999999999994 Correction with q = .3159999999999994 Correction with q = .3149999999999994 Correction with q = .3139999999999994 Correction with q = .3129999999999994 Correction with q = .3119999999999994 Correction with q = .3109999999999994 Correction with q = .3099999999999994 Correction with q = .3089999999999994 Correction with q = .3079999999999994 Correction with q = .3069999999999994 Correction with q = .3059999999999994 Correction with q = .3049999999999994 Correction with q = .3039999999999994 Correction with q = .3029999999999994 Correction with q = .3019999999999994 Correction with q = .3009999999999994 Correction with q = .2999999999999994 Correction with q = .2989999999999994 Correction with q = .2979999999999994 Correction with q = .2969999999999994 Correction with q = .2959999999999994 Correction with q = .2949999999999994 Correction with q = .2939999999999994 Correction with q = .2929999999999994 Correction with q = .2919999999999994 Correction with q = .2909999999999994 Correction with q = .2899999999999994 Correction with q = .2889999999999994 Correction with q = .2879999999999994 Correction with q = .2869999999999994 Correction with q = .2859999999999994 Correction with q = .2849999999999994 Correction with q = .2839999999999994 Correction with q = .2829999999999994 Correction with q = .2819999999999994 Correction with q = .2809999999999994 Correction with q = .2799999999999994 Correction with q = .2789999999999994 Correction with q = .2779999999999994 Correction with q = .2769999999999994 Correction with q = .2759999999999994 Correction with q = .2749999999999994 Correction with q = .2739999999999994 Correction with q = .2729999999999994 Correction with q = .2719999999999994 Correction with q = .2709999999999994 Correction with q = .2699999999999994 Correction with q = .2689999999999994 Correction with q = .2679999999999994 Correction with q = .2669999999999994 Correction with q = .2659999999999994 Correction with q = .2649999999999994 Correction with q = .2639999999999994 Correction with q = .2629999999999994 Correction with q = .2619999999999993 Correction with q = .2609999999999993 Correction with q = .2599999999999993 Correction with q = .2589999999999993 Correction with q = .2579999999999993 Correction with q = .2569999999999993 Correction with q = .2559999999999993 Correction with q = .2549999999999993 Correction with q = .2539999999999993 Correction with q = .2529999999999993 Correction with q = .2519999999999993 Correction with q = .2509999999999993 Correction with q = .2499999999999993 Correction with q = .2489999999999993 Correction with q = .2479999999999993 Correction with q = .2469999999999993 Correction with q = .2459999999999993 Correction with q = .2449999999999993 Correction with q = .2439999999999993 Correction with q = .2429999999999993 Correction with q = .2419999999999993 Correction with q = .2409999999999993 Correction with q = .2399999999999993 Correction with q = .2389999999999993 Correction with q = .2379999999999993 Correction with q = .2369999999999993 Correction with q = .2359999999999993 Correction with q = .2349999999999993 Correction with q = .2339999999999993 Correction with q = .2329999999999993 Correction with q = .2319999999999993 Correction with q = .2309999999999993 Correction with q = .2299999999999993 Correction with q = .2289999999999993 Correction with q = .2279999999999993 Correction with q = .2269999999999993 Correction with q = .2259999999999993 Correction with q = .2249999999999993 Correction with q = .2239999999999993 Correction with q = .2229999999999993 Correction with q = .2219999999999993 Correction with q = .2209999999999993 Correction with q = .2199999999999993 Correction with q = .2189999999999993 Correction with q = .2179999999999993 Correction with q = .2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = .1909999999999993 Correction with q = .1899999999999993 Correction with q = .1889999999999993 Correction with q = .1879999999999993 Correction with q = .1869999999999993 Correction with q = .1859999999999993 Correction with q = .1849999999999993 Correction with q = .1839999999999993 Correction with q = .1829999999999993 Correction with q = .1819999999999993 Correction with q = .1809999999999993 Correction with q = .1799999999999993 Correction with q = .1789999999999993 Correction with q = .1779999999999993 Correction with q = .1769999999999993 Correction with q = .1759999999999993 Correction with q = .1749999999999993 Correction with q = .1739999999999993 Correction with q = .1729999999999993 Correction with q = .1719999999999993 Correction with q = .1709999999999993 Correction with q = .1699999999999993 Correction with q = .1689999999999993 Correction with q = .1679999999999993 Correction with q = .1669999999999993 Correction with q = .1659999999999993 Correction with q = .1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 Correction with q = .0069999999999991 Correction with q = .0059999999999991 Correction with q = .0049999999999991 Correction with q = .0039999999999991 Correction with q = .0029999999999991 (6 real changes made) (0 real changes made) . . estadd loc thisstat6 = "[" + string(Q[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat6 = "[" +string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat6 = "[" + string(Q[3, 1], "%9.3f")+"]": col3 . estadd loc thisstat6 = "["+ string(Q[4, 1], "%9.3f")+"]": col4 . estadd loc thisstat6 = "["+ string(Q[5, 1], "%9.3f")+"]": col5 . estadd loc thisstat6 = "["+ string(Q[6, 1], "%9.3f")+"]": col6 . . . ** For Panel B . mat def P = J(5, 1, .) . reg quotaanchor T1 $controls [pweight=pweight] if wave==1, vce(r) (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 11.55 Prob > F = 0.0000 R-squared = 0.1101 Root MSE = .96312 ------------------------------------------------------------------------------- | Robust quotaanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0442574 .043224 1.02 0.306 -.0405117 .1290265 wave | 0 (omitted) gender | .2510971 .0447273 5.61 0.000 .1633798 .3388144 prior | -.0034199 .0010886 -3.14 0.002 -.0055548 -.0012849 democrat | .5559845 .0494151 11.25 0.000 .4590737 .6528954 indep | .1822735 .066153 2.76 0.006 .0525371 .3120099 otherpol | .1626051 .1544004 1.05 0.292 -.1401983 .4654085 midwest | -.057274 .0720587 -0.79 0.427 -.1985924 .0840443 south | .0455968 .0637698 0.72 0.475 -.0794658 .1706594 west | .0240026 .0683839 0.35 0.726 -.1101091 .1581142 age1 | .2324831 .1018737 2.28 0.023 .0326927 .4322735 age2 | .2469436 .0711166 3.47 0.001 .1074728 .3864145 age3 | .1933515 .0685902 2.82 0.005 .0588354 .3278675 age4 | .0096949 .0663998 0.15 0.884 -.1205255 .1399153 anychildren | .1768622 .0474878 3.72 0.000 .0837312 .2699932 loghhinc | -.067961 .0297597 -2.28 0.022 -.1263245 -.0095975 associatemore | -.0677731 .0487695 -1.39 0.165 -.1634177 .0278714 fulltime | .1082446 .0671014 1.61 0.107 -.0233519 .239841 parttime | -.0046427 .0884519 -0.05 0.958 -.1781107 .1688253 selfemp | .1611405 .0923577 1.74 0.081 -.0199875 .3422685 unemployed | .1522813 .0975583 1.56 0.119 -.0390458 .3436084 student | -.063505 .1512924 -0.42 0.675 -.3602132 .2332031 _cons | .2794942 .337708 0.83 0.408 -.3828041 .9417926 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg AAanchor T1 $controls [pweight=pweight] if wave==1, vce(r) (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 15.07 Prob > F = 0.0000 R-squared = 0.1382 Root MSE = .92427 ------------------------------------------------------------------------------- | Robust AAanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1293991 .0412994 3.13 0.002 .0484046 .2103936 wave | 0 (omitted) gender | .1691479 .042633 3.97 0.000 .085538 .2527578 prior | -.0047639 .0011785 -4.04 0.000 -.007075 -.0024527 democrat | .6783654 .0473593 14.32 0.000 .5854864 .7712444 indep | .312786 .0633932 4.93 0.000 .1884619 .4371101 otherpol | .1595678 .1686344 0.95 0.344 -.1711507 .4902862 midwest | -.0930201 .0689559 -1.35 0.177 -.2282534 .0422132 south | .0654471 .061654 1.06 0.289 -.0554661 .1863603 west | .0021247 .0673585 0.03 0.975 -.1299758 .1342252 age1 | .0768058 .0988622 0.78 0.437 -.1170785 .27069 age2 | .0791668 .0665844 1.19 0.235 -.0514157 .2097493 age3 | .039416 .0648016 0.61 0.543 -.0876702 .1665021 age4 | -.0297298 .0616828 -0.48 0.630 -.1506994 .0912398 anychildren | .1574382 .0456286 3.45 0.001 .0679533 .2469232 loghhinc | -.0287244 .0289264 -0.99 0.321 -.0854536 .0280047 associatemore | .0842586 .0459947 1.83 0.067 -.0059442 .1744614 fulltime | .0080291 .0639795 0.13 0.900 -.1174448 .1335031 parttime | -.1199154 .0822845 -1.46 0.145 -.2812882 .0414575 selfemp | -.0210642 .0882342 -0.24 0.811 -.1941054 .151977 unemployed | .0528622 .0981415 0.54 0.590 -.1396086 .2453331 student | .1829537 .1437746 1.27 0.203 -.0990108 .4649182 _cons | -.0170777 .3388746 -0.05 0.960 -.6816639 .6475084 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg legislationanchor T1 $controls [pweight=pweight] if wave==1, vce(r) (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 14.37 Prob > F = 0.0000 R-squared = 0.1322 Root MSE = .92881 ------------------------------------------------------------------------------- | Robust legislation~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0980796 .0416756 2.35 0.019 .0163473 .1798119 wave | 0 (omitted) gender | .223515 .0430712 5.19 0.000 .1390457 .3079844 prior | -.0051193 .0012161 -4.21 0.000 -.0075042 -.0027344 democrat | .6438062 .0482645 13.34 0.000 .549152 .7384605 indep | .2807823 .0603016 4.66 0.000 .1625213 .3990432 otherpol | .4855286 .1655902 2.93 0.003 .1607801 .810277 midwest | -.0721143 .0681278 -1.06 0.290 -.2057236 .061495 south | -.0158176 .060361 -0.26 0.793 -.1341949 .1025598 west | -.027478 .0656356 -0.42 0.676 -.1561998 .1012437 age1 | -.1465709 .0968788 -1.51 0.130 -.3365654 .0434235 age2 | -.1295292 .0673531 -1.92 0.055 -.2616192 .0025609 age3 | -.0726482 .0653231 -1.11 0.266 -.2007569 .0554606 age4 | -.0696701 .0637704 -1.09 0.275 -.1947338 .0553936 anychildren | .0781225 .0463616 1.69 0.092 -.0127999 .1690448 loghhinc | .001977 .0285071 0.07 0.945 -.0539299 .0578838 associatemore | -.0270434 .045977 -0.59 0.556 -.1172116 .0631248 fulltime | .0730988 .0658872 1.11 0.267 -.0561163 .2023139 parttime | -.0223153 .0876111 -0.25 0.799 -.1941344 .1495038 selfemp | .1044728 .0905753 1.15 0.249 -.0731595 .2821052 unemployed | .2912825 .0996376 2.92 0.004 .0958774 .4866875 student | .1996098 .1459836 1.37 0.172 -.086687 .4859066 _cons | -.1037783 .327537 -0.32 0.751 -.7461296 .5385731 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . reg transparencyanchor T1 $controls [pweight=pweight] if wave==1, vce(r) (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 10.39 Prob > F = 0.0000 R-squared = 0.0957 Root MSE = .94476 ------------------------------------------------------------------------------- | Robust transparenc~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0145207 .0421926 -0.34 0.731 -.097267 .0682256 wave | 0 (omitted) gender | .1968569 .0436751 4.51 0.000 .1112032 .2825107 prior | -.0026412 .0011873 -2.22 0.026 -.0049697 -.0003127 democrat | .5648746 .048392 11.67 0.000 .4699703 .6597789 indep | .2426681 .0645124 3.76 0.000 .1161491 .3691871 otherpol | .1372274 .1916125 0.72 0.474 -.2385547 .5130095 midwest | -.1229332 .0686787 -1.79 0.074 -.257623 .0117565 south | -.0270683 .0603186 -0.45 0.654 -.1453626 .0912259 west | -.048422 .0656441 -0.74 0.461 -.1771604 .0803163 age1 | .0478043 .1037241 0.46 0.645 -.1556149 .2512235 age2 | .0581052 .0678145 0.86 0.392 -.0748897 .1911001 age3 | .0554398 .0677336 0.82 0.413 -.0773964 .188276 age4 | .0267632 .0655902 0.41 0.683 -.1018694 .1553958 anychildren | .0940054 .0484718 1.94 0.053 -.0010554 .1890661 loghhinc | -.0266598 .0288938 -0.92 0.356 -.083325 .0300055 associatemore | .1015891 .0476979 2.13 0.033 .0080459 .1951322 fulltime | .0090151 .0654756 0.14 0.891 -.1193927 .137423 parttime | -.1014771 .0838587 -1.21 0.226 -.2659372 .062983 selfemp | .0372718 .0988473 0.38 0.706 -.1565833 .2311269 unemployed | .0970394 .0974852 1.00 0.320 -.0941443 .2882232 student | .3350384 .129311 2.59 0.010 .0814392 .5886376 _cons | -.0332594 .3314474 -0.10 0.920 -.6832798 .6167609 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[4, 1] = r(p) . . reg childcare T1 $controls [pweight=pweight] if wave==1, vce(r) (sum of wgt is 2.0120e+03) note: wave omitted because of collinearity Linear regression Number of obs = 2,012 F(21, 1990) = 11.68 Prob > F = 0.0000 R-squared = 0.1085 Root MSE = .93669 ------------------------------------------------------------------------------- | Robust childcare | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0105263 .0419092 0.25 0.802 -.0716643 .0927169 wave | 0 (omitted) gender | .120495 .044533 2.71 0.007 .0331588 .2078311 prior | -.0034371 .0011343 -3.03 0.002 -.0056617 -.0012125 democrat | .5796113 .048294 12.00 0.000 .4848992 .6743234 indep | .157156 .0636526 2.47 0.014 .0323232 .2819888 otherpol | .1692525 .1706141 0.99 0.321 -.1653485 .5038536 midwest | -.1390803 .0678983 -2.05 0.041 -.2722396 -.0059211 south | -.0494183 .0608583 -0.81 0.417 -.168771 .0699344 west | -.1888414 .0685886 -2.75 0.006 -.3233545 -.0543283 age1 | .2303243 .1061125 2.17 0.030 .022221 .4384276 age2 | .2711322 .0683959 3.96 0.000 .136997 .4052674 age3 | .1987612 .0681305 2.92 0.004 .0651465 .3323759 age4 | .0591264 .066119 0.89 0.371 -.0705433 .1887961 anychildren | .2076927 .0471689 4.40 0.000 .115187 .3001984 loghhinc | -.0402938 .0297665 -1.35 0.176 -.0986706 .0180829 associatemore | -.0201251 .0470783 -0.43 0.669 -.1124531 .0722029 fulltime | .0077474 .0688641 0.11 0.910 -.127306 .1428008 parttime | -.0165261 .0889066 -0.19 0.853 -.1908859 .1578337 selfemp | .0455916 .0942769 0.48 0.629 -.1393001 .2304834 unemployed | .0624466 .101007 0.62 0.536 -.1356438 .2605371 student | .2133131 .126362 1.69 0.092 -.0345027 .4611289 _cons | .2090974 .3372655 0.62 0.535 -.452333 .8705279 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[5, 1] = r(p) . . . minq P, q("Q") step(0.001) number of observations will be reset to 5 Press any key to continue, or Break to abort number of observations (_N) was 0, now 5 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = .7109999999999997 Correction with q = .7099999999999997 Correction with q = .7089999999999997 Correction with q = .7079999999999997 Correction with q = .7069999999999997 Correction with q = .7059999999999997 Correction with q = .7049999999999997 Correction with q = .7039999999999997 Correction with q = .7029999999999997 Correction with q = .7019999999999997 Correction with q = .7009999999999997 Correction with q = .6999999999999997 Correction with q = .6989999999999997 Correction with q = .6979999999999997 Correction with q = .6969999999999997 Correction with q = .6959999999999997 Correction with q = .6949999999999997 Correction with q = .6939999999999997 Correction with q = .6929999999999997 Correction with q = .6919999999999997 Correction with q = .6909999999999997 Correction with q = .6899999999999997 Correction with q = .6889999999999997 Correction with q = .6879999999999997 Correction with q = .6869999999999997 Correction with q = .6859999999999997 Correction with q = .6849999999999997 Correction with q = .6839999999999997 Correction with q = .6829999999999997 Correction with q = .6819999999999997 Correction with q = .6809999999999997 Correction with q = .6799999999999997 Correction with q = .6789999999999997 Correction with q = .6779999999999997 Correction with q = .6769999999999997 Correction with q = .6759999999999997 Correction with q = .6749999999999997 Correction with q = .6739999999999997 Correction with q = .6729999999999997 Correction with q = .6719999999999997 Correction with q = .6709999999999997 Correction with q = .6699999999999997 Correction with q = .6689999999999997 Correction with q = .6679999999999997 Correction with q = .6669999999999997 Correction with q = .6659999999999997 Correction with q = .6649999999999997 Correction with q = .6639999999999997 Correction with q = .6629999999999997 Correction with q = .6619999999999997 Correction with q = .6609999999999997 Correction with q = .6599999999999997 Correction with q = .6589999999999997 Correction with q = .6579999999999997 Correction with q = .6569999999999997 Correction with q = .6559999999999997 Correction with q = .6549999999999997 Correction with q = .6539999999999997 Correction with q = .6529999999999997 Correction with q = .6519999999999997 Correction with q = .6509999999999997 Correction with q = .6499999999999997 Correction with q = .6489999999999997 Correction with q = .6479999999999997 Correction with q = .6469999999999997 Correction with q = .6459999999999997 Correction with q = .6449999999999997 Correction with q = .6439999999999997 Correction with q = .6429999999999997 Correction with q = .6419999999999997 Correction with q = .6409999999999997 Correction with q = .6399999999999997 Correction with q = .6389999999999997 Correction with q = .6379999999999997 Correction with q = .6369999999999997 Correction with q = .6359999999999997 Correction with q = .6349999999999997 Correction with q = .6339999999999997 Correction with q = .6329999999999997 Correction with q = .6319999999999997 Correction with q = .6309999999999997 Correction with q = .6299999999999997 Correction with q = .6289999999999997 Correction with q = .6279999999999997 Correction with q = .6269999999999997 Correction with q = .6259999999999997 Correction with q = .6249999999999997 Correction with q = .6239999999999997 Correction with q = .6229999999999997 Correction with q = .6219999999999997 Correction with q = .6209999999999997 Correction with q = .6199999999999997 Correction with q = .6189999999999997 Correction with q = .6179999999999997 Correction with q = .6169999999999997 Correction with q = .6159999999999997 Correction with q = .6149999999999997 Correction with q = .6139999999999997 Correction with q = .6129999999999997 Correction with q = .6119999999999997 Correction with q = .6109999999999997 Correction with q = .6099999999999997 Correction with q = .6089999999999997 Correction with q = .6079999999999997 Correction with q = .6069999999999997 Correction with q = .6059999999999997 Correction with q = .6049999999999997 Correction with q = .6039999999999997 Correction with q = .6029999999999997 Correction with q = .6019999999999997 Correction with q = .6009999999999997 Correction with q = .5999999999999996 Correction with q = .5989999999999996 Correction with q = .5979999999999996 Correction with q = .5969999999999996 Correction with q = .5959999999999996 Correction with q = .5949999999999996 Correction with q = .5939999999999996 Correction with q = .5929999999999996 Correction with q = .5919999999999996 Correction with q = .5909999999999996 Correction with q = .5899999999999996 Correction with q = .5889999999999996 Correction with q = .5879999999999996 Correction with q = .5869999999999996 Correction with q = .5859999999999996 Correction with q = .5849999999999996 Correction with q = .5839999999999996 Correction with q = .5829999999999996 Correction with q = .5819999999999996 Correction with q = .5809999999999996 Correction with q = .5799999999999996 Correction with q = .5789999999999996 Correction with q = .5779999999999996 Correction with q = .5769999999999996 Correction with q = .5759999999999996 Correction with q = .5749999999999996 Correction with q = .5739999999999996 Correction with q = .5729999999999996 Correction with q = .5719999999999996 Correction with q = .5709999999999996 Correction with q = .5699999999999996 Correction with q = .5689999999999996 Correction with q = .5679999999999996 Correction with q = .5669999999999996 Correction with q = .5659999999999996 Correction with q = .5649999999999996 Correction with q = .5639999999999996 Correction with q = .5629999999999996 Correction with q = .5619999999999996 Correction with q = .5609999999999996 Correction with q = .5599999999999996 Correction with q = .5589999999999996 Correction with q = .5579999999999996 Correction with q = .5569999999999996 Correction with q = .5559999999999996 Correction with q = .5549999999999996 Correction with q = .5539999999999996 Correction with q = .5529999999999996 Correction with q = .5519999999999996 Correction with q = .5509999999999996 Correction with q = .5499999999999996 Correction with q = .5489999999999996 Correction with q = .5479999999999996 Correction with q = .5469999999999996 Correction with q = .5459999999999996 Correction with q = .5449999999999996 Correction with q = .5439999999999996 Correction with q = .5429999999999996 Correction with q = .5419999999999996 Correction with q = .5409999999999996 Correction with q = .5399999999999996 Correction with q = .5389999999999996 Correction with q = .5379999999999996 Correction with q = .5369999999999996 Correction with q = .5359999999999996 Correction with q = .5349999999999996 Correction with q = .5339999999999996 Correction with q = .5329999999999996 Correction with q = .5319999999999996 Correction with q = .5309999999999996 Correction with q = .5299999999999996 Correction with q = .5289999999999996 Correction with q = .5279999999999996 Correction with q = .5269999999999996 Correction with q = .5259999999999996 Correction with q = .5249999999999996 Correction with q = .5239999999999996 Correction with q = .5229999999999996 Correction with q = .5219999999999996 Correction with q = .5209999999999996 Correction with q = .5199999999999996 Correction with q = .5189999999999996 Correction with q = .5179999999999996 Correction with q = .5169999999999996 Correction with q = .5159999999999996 Correction with q = .5149999999999996 Correction with q = .5139999999999996 Correction with q = .5129999999999996 Correction with q = .5119999999999996 Correction with q = .5109999999999996 Correction with q = .5099999999999996 Correction with q = .5089999999999996 Correction with q = .5079999999999996 Correction with q = .5069999999999996 Correction with q = .5059999999999996 Correction with q = .5049999999999996 Correction with q = .5039999999999996 Correction with q = .5029999999999996 Correction with q = .5019999999999996 Correction with q = .5009999999999996 Correction with q = .4999999999999996 Correction with q = .4989999999999996 Correction with q = .4979999999999996 Correction with q = .4969999999999996 Correction with q = .4959999999999996 Correction with q = .4949999999999996 Correction with q = .4939999999999996 Correction with q = .4929999999999996 Correction with q = .4919999999999996 Correction with q = .4909999999999996 Correction with q = .4899999999999996 Correction with q = .4889999999999996 Correction with q = .4879999999999996 Correction with q = .4869999999999995 Correction with q = .4859999999999995 Correction with q = .4849999999999995 Correction with q = .4839999999999995 Correction with q = .4829999999999995 Correction with q = .4819999999999995 Correction with q = .4809999999999995 Correction with q = .4799999999999995 Correction with q = .4789999999999995 Correction with q = .4779999999999995 Correction with q = .4769999999999995 Correction with q = .4759999999999995 Correction with q = .4749999999999995 Correction with q = .4739999999999995 Correction with q = .4729999999999995 Correction with q = .4719999999999995 Correction with q = .4709999999999995 Correction with q = .4699999999999995 Correction with q = .4689999999999995 Correction with q = .4679999999999995 Correction with q = .4669999999999995 Correction with q = .4659999999999995 Correction with q = .4649999999999995 Correction with q = .4639999999999995 Correction with q = .4629999999999995 Correction with q = .4619999999999995 Correction with q = .4609999999999995 Correction with q = .4599999999999995 Correction with q = .4589999999999995 Correction with q = .4579999999999995 Correction with q = .4569999999999995 Correction with q = .4559999999999995 Correction with q = .4549999999999995 Correction with q = .4539999999999995 Correction with q = .4529999999999995 Correction with q = .4519999999999995 Correction with q = .4509999999999995 Correction with q = .4499999999999995 Correction with q = .4489999999999995 Correction with q = .4479999999999995 Correction with q = .4469999999999995 Correction with q = .4459999999999995 Correction with q = .4449999999999995 Correction with q = .4439999999999995 Correction with q = .4429999999999995 Correction with q = .4419999999999995 Correction with q = .4409999999999995 Correction with q = .4399999999999995 Correction with q = .4389999999999995 Correction with q = .4379999999999995 Correction with q = .4369999999999995 Correction with q = .4359999999999995 Correction with q = .4349999999999995 Correction with q = .4339999999999995 Correction with q = .4329999999999995 Correction with q = .4319999999999995 Correction with q = .4309999999999995 Correction with q = .4299999999999995 Correction with q = .4289999999999995 Correction with q = .4279999999999995 Correction with q = .4269999999999995 Correction with q = .4259999999999995 Correction with q = .4249999999999995 Correction with q = .4239999999999995 Correction with q = .4229999999999995 Correction with q = .4219999999999995 Correction with q = .4209999999999995 Correction with q = .4199999999999995 Correction with q = .4189999999999995 Correction with q = .4179999999999995 Correction with q = .4169999999999995 Correction with q = .4159999999999995 Correction with q = .4149999999999995 Correction with q = .4139999999999995 Correction with q = .4129999999999995 Correction with q = .4119999999999995 Correction with q = .4109999999999995 Correction with q = .4099999999999995 Correction with q = .4089999999999995 Correction with q = .4079999999999995 Correction with q = .4069999999999995 Correction with q = .4059999999999995 Correction with q = .4049999999999995 Correction with q = .4039999999999995 Correction with q = .4029999999999995 Correction with q = .4019999999999995 Correction with q = .4009999999999995 Correction with q = .3999999999999995 Correction with q = .3989999999999995 Correction with q = .3979999999999995 Correction with q = .3969999999999995 Correction with q = .3959999999999995 Correction with q = .3949999999999995 Correction with q = .3939999999999995 Correction with q = .3929999999999995 Correction with q = .3919999999999995 Correction with q = .3909999999999995 Correction with q = .3899999999999995 Correction with q = .3889999999999995 Correction with q = .3879999999999995 Correction with q = .3869999999999995 Correction with q = .3859999999999995 Correction with q = .3849999999999995 Correction with q = .3839999999999995 Correction with q = .3829999999999995 Correction with q = .3819999999999995 Correction with q = .3809999999999995 Correction with q = .3799999999999995 Correction with q = .3789999999999995 Correction with q = .3779999999999995 Correction with q = .3769999999999995 Correction with q = .3759999999999995 Correction with q = .3749999999999994 Correction with q = .3739999999999994 Correction with q = .3729999999999994 Correction with q = .3719999999999994 Correction with q = .3709999999999994 Correction with q = .3699999999999994 Correction with q = .3689999999999994 Correction with q = .3679999999999994 Correction with q = .3669999999999994 Correction with q = .3659999999999994 Correction with q = .3649999999999994 Correction with q = .3639999999999994 Correction with q = .3629999999999994 Correction with q = .3619999999999994 Correction with q = .3609999999999994 Correction with q = .3599999999999994 Correction with q = .3589999999999994 Correction with q = .3579999999999994 Correction with q = .3569999999999994 Correction with q = .3559999999999994 Correction with q = .3549999999999994 Correction with q = .3539999999999994 Correction with q = .3529999999999994 Correction with q = .3519999999999994 Correction with q = .3509999999999994 Correction with q = .3499999999999994 Correction with q = .3489999999999994 Correction with q = .3479999999999994 Correction with q = .3469999999999994 Correction with q = .3459999999999994 Correction with q = .3449999999999994 Correction with q = .3439999999999994 Correction with q = .3429999999999994 Correction with q = .3419999999999994 Correction with q = .3409999999999994 Correction with q = .3399999999999994 Correction with q = .3389999999999994 Correction with q = .3379999999999994 Correction with q = .3369999999999994 Correction with q = .3359999999999994 Correction with q = .3349999999999994 Correction with q = .3339999999999994 Correction with q = .3329999999999994 Correction with q = .3319999999999994 Correction with q = .3309999999999994 Correction with q = .3299999999999994 Correction with q = .3289999999999994 Correction with q = .3279999999999994 Correction with q = .3269999999999994 Correction with q = .3259999999999994 Correction with q = .3249999999999994 Correction with q = .3239999999999994 Correction with q = .3229999999999994 Correction with q = .3219999999999994 Correction with q = .3209999999999994 Correction with q = .3199999999999994 Correction with q = .3189999999999994 Correction with q = .3179999999999994 Correction with q = .3169999999999994 Correction with q = .3159999999999994 Correction with q = .3149999999999994 Correction with q = .3139999999999994 Correction with q = .3129999999999994 Correction with q = .3119999999999994 Correction with q = .3109999999999994 Correction with q = .3099999999999994 Correction with q = .3089999999999994 Correction with q = .3079999999999994 Correction with q = .3069999999999994 Correction with q = .3059999999999994 Correction with q = .3049999999999994 Correction with q = .3039999999999994 Correction with q = .3029999999999994 Correction with q = .3019999999999994 Correction with q = .3009999999999994 Correction with q = .2999999999999994 Correction with q = .2989999999999994 Correction with q = .2979999999999994 Correction with q = .2969999999999994 Correction with q = .2959999999999994 Correction with q = .2949999999999994 Correction with q = .2939999999999994 Correction with q = .2929999999999994 Correction with q = .2919999999999994 Correction with q = .2909999999999994 Correction with q = .2899999999999994 Correction with q = .2889999999999994 Correction with q = .2879999999999994 Correction with q = .2869999999999994 Correction with q = .2859999999999994 Correction with q = .2849999999999994 Correction with q = .2839999999999994 Correction with q = .2829999999999994 Correction with q = .2819999999999994 Correction with q = .2809999999999994 Correction with q = .2799999999999994 Correction with q = .2789999999999994 Correction with q = .2779999999999994 Correction with q = .2769999999999994 Correction with q = .2759999999999994 Correction with q = .2749999999999994 Correction with q = .2739999999999994 Correction with q = .2729999999999994 Correction with q = .2719999999999994 Correction with q = .2709999999999994 Correction with q = .2699999999999994 Correction with q = .2689999999999994 Correction with q = .2679999999999994 Correction with q = .2669999999999994 Correction with q = .2659999999999994 Correction with q = .2649999999999994 Correction with q = .2639999999999994 Correction with q = .2629999999999994 Correction with q = .2619999999999993 Correction with q = .2609999999999993 Correction with q = .2599999999999993 Correction with q = .2589999999999993 Correction with q = .2579999999999993 Correction with q = .2569999999999993 Correction with q = .2559999999999993 Correction with q = .2549999999999993 Correction with q = .2539999999999993 Correction with q = .2529999999999993 Correction with q = .2519999999999993 Correction with q = .2509999999999993 Correction with q = .2499999999999993 Correction with q = .2489999999999993 Correction with q = .2479999999999993 Correction with q = .2469999999999993 Correction with q = .2459999999999993 Correction with q = .2449999999999993 Correction with q = .2439999999999993 Correction with q = .2429999999999993 Correction with q = .2419999999999993 Correction with q = .2409999999999993 Correction with q = .2399999999999993 Correction with q = .2389999999999993 Correction with q = .2379999999999993 Correction with q = .2369999999999993 Correction with q = .2359999999999993 Correction with q = .2349999999999993 Correction with q = .2339999999999993 Correction with q = .2329999999999993 Correction with q = .2319999999999993 Correction with q = .2309999999999993 Correction with q = .2299999999999993 Correction with q = .2289999999999993 Correction with q = .2279999999999993 Correction with q = .2269999999999993 Correction with q = .2259999999999993 Correction with q = .2249999999999993 Correction with q = .2239999999999993 Correction with q = .2229999999999993 Correction with q = .2219999999999993 Correction with q = .2209999999999993 Correction with q = .2199999999999993 Correction with q = .2189999999999993 Correction with q = .2179999999999993 Correction with q = .2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = .1909999999999993 Correction with q = .1899999999999993 Correction with q = .1889999999999993 Correction with q = .1879999999999993 Correction with q = .1869999999999993 Correction with q = .1859999999999993 Correction with q = .1849999999999993 Correction with q = .1839999999999993 Correction with q = .1829999999999993 Correction with q = .1819999999999993 Correction with q = .1809999999999993 Correction with q = .1799999999999993 Correction with q = .1789999999999993 Correction with q = .1779999999999993 Correction with q = .1769999999999993 Correction with q = .1759999999999993 Correction with q = .1749999999999993 Correction with q = .1739999999999993 Correction with q = .1729999999999993 Correction with q = .1719999999999993 Correction with q = .1709999999999993 Correction with q = .1699999999999993 Correction with q = .1689999999999993 Correction with q = .1679999999999993 Correction with q = .1669999999999993 Correction with q = .1659999999999993 Correction with q = .1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 (5 real changes made) (0 real changes made) . . estadd loc thisstat22 = "[" + string(Q[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat22 = "[" +string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat22 = "[" + string(Q[3, 1], "%9.3f")+"]": col3 . estadd loc thisstat22 = "["+ string(Q[4, 1], "%9.3f")+"]": col4 . estadd loc thisstat22 = "["+ string(Q[5, 1], "%9.3f")+"]": col6 . . . ** For Panel C . mat def P = J(5, 1, .) . reg quotaanchor T1 $controls [pweight=pweight] if wave==2, vce(r) (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 7.02 Prob > F = 0.0000 R-squared = 0.1324 Root MSE = .98593 ------------------------------------------------------------------------------- | Robust quotaanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0758928 .0635068 1.20 0.232 -.0487295 .2005151 wave | 0 (omitted) gender | .2659898 .0647979 4.10 0.000 .1388338 .3931458 prior | -.0046226 .0018347 -2.52 0.012 -.0082229 -.0010224 democrat | .5540515 .0735804 7.53 0.000 .4096612 .6984418 indep | .0997563 .0940127 1.06 0.289 -.0847292 .2842418 otherpol | .1822015 .2210103 0.82 0.410 -.2514972 .6159002 midwest | -.2514075 .1041734 -2.41 0.016 -.4558318 -.0469832 south | -.0347882 .0936312 -0.37 0.710 -.218525 .1489486 west | -.1309343 .1025547 -1.28 0.202 -.3321821 .0703136 age1 | .2745936 .1290556 2.13 0.034 .0213418 .5278453 age2 | .4137631 .102348 4.04 0.000 .2129209 .6146052 age3 | .2161004 .100948 2.14 0.033 .0180054 .4141954 age4 | .1374384 .1000966 1.37 0.170 -.0589858 .3338627 anychildren | .0304076 .0710867 0.43 0.669 -.1090892 .1699044 loghhinc | .0137428 .0451734 0.30 0.761 -.074903 .1023886 associatemore | -.0344107 .0719628 -0.48 0.633 -.1756266 .1068051 fulltime | -.0315151 .0926355 -0.34 0.734 -.213298 .1502679 parttime | .1059073 .1245826 0.85 0.395 -.138567 .3503816 selfemp | -.0626523 .1433976 -0.44 0.662 -.344048 .2187435 unemployed | .0288343 .1794095 0.16 0.872 -.3232293 .380898 student | -.1235715 .1685362 -0.73 0.464 -.4542979 .2071549 _cons | -.2077987 .540975 -0.38 0.701 -1.269379 .8537815 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg AAanchor T1 $controls [pweight=pweight] if wave==2, vce(r) (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 8.49 Prob > F = 0.0000 R-squared = 0.1431 Root MSE = .961 ------------------------------------------------------------------------------- | Robust AAanchor | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0713351 .0615488 1.16 0.247 -.049445 .1921151 wave | 0 (omitted) gender | .2205832 .0624133 3.53 0.000 .0981068 .3430597 prior | -.0034848 .0017206 -2.03 0.043 -.0068612 -.0001084 democrat | .640201 .0741557 8.63 0.000 .4946819 .7857202 indep | .1241986 .0924584 1.34 0.179 -.0572368 .3056341 otherpol | .055369 .1724086 0.32 0.748 -.2829564 .3936944 midwest | -.0856806 .0982772 -0.87 0.384 -.2785345 .1071732 south | .0906875 .0901799 1.01 0.315 -.0862767 .2676517 west | -.1420713 .0964401 -1.47 0.141 -.3313202 .0471775 age1 | .2387284 .1162564 2.05 0.040 .010593 .4668638 age2 | .3146897 .1028998 3.06 0.002 .1127647 .5166148 age3 | .0778003 .1040145 0.75 0.455 -.1263122 .2819128 age4 | .0952221 .0971705 0.98 0.327 -.09546 .2859042 anychildren | .0718937 .0698983 1.03 0.304 -.0652708 .2090583 loghhinc | .0237145 .0427649 0.55 0.579 -.0602051 .107634 associatemore | .0246026 .0675764 0.36 0.716 -.1080056 .1572108 fulltime | -.066438 .0880022 -0.75 0.450 -.2391288 .1062527 parttime | .077444 .1217688 0.64 0.525 -.1615085 .3163965 selfemp | -.0000969 .145028 -0.00 0.999 -.2846922 .2844983 unemployed | -.2218446 .1614906 -1.37 0.170 -.5387451 .0950559 student | .0026771 .1554913 0.02 0.986 -.3024507 .3078049 _cons | -.5803994 .5068628 -1.15 0.252 -1.57504 .4142409 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg legislationanchor T1 $controls [pweight=pweight] if wave==2, vce(r) (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 6.04 Prob > F = 0.0000 R-squared = 0.1101 Root MSE = .9892 ------------------------------------------------------------------------------- | Robust legislation~r | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1559319 .0638879 2.44 0.015 .0305617 .2813021 wave | 0 (omitted) gender | .284577 .0648322 4.39 0.000 .1573537 .4118003 prior | -.0016333 .0020524 -0.80 0.426 -.0056608 .0023943 democrat | .5573477 .0729452 7.64 0.000 .414204 .7004915 indep | .1402841 .094315 1.49 0.137 -.0447946 .3253628 otherpol | .4699653 .19647 2.39 0.017 .0844232 .8555073 midwest | -.081961 .1062182 -0.77 0.441 -.2903979 .126476 south | -.0594719 .0971712 -0.61 0.541 -.2501554 .1312117 west | -.1498914 .102719 -1.46 0.145 -.3514617 .0516788 age1 | -.1974038 .1237175 -1.60 0.111 -.4401804 .0453727 age2 | -.0546562 .1036631 -0.53 0.598 -.2580792 .1487668 age3 | -.1485705 .1067226 -1.39 0.164 -.3579971 .0608562 age4 | -.0009889 .0981258 -0.01 0.992 -.1935457 .1915679 anychildren | -.0548772 .071088 -0.77 0.440 -.1943765 .0846221 loghhinc | .0004893 .0455283 0.01 0.991 -.088853 .0898316 associatemore | .0564593 .0686017 0.82 0.411 -.078161 .1910796 fulltime | -.0150203 .0972381 -0.15 0.877 -.2058351 .1757945 parttime | -.0184665 .1336412 -0.14 0.890 -.2807169 .2437839 selfemp | .3196958 .1297632 2.46 0.014 .0650554 .5743362 unemployed | .2371919 .1535534 1.54 0.123 -.0641331 .5385169 student | .1171032 .170393 0.69 0.492 -.2172669 .4514733 _cons | -.2615686 .5420366 -0.48 0.630 -1.325232 .802095 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . . reg UKtool T1 $controls [pweight=pweight] if wave==2, vce(r) (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 7.10 Prob > F = 0.0000 R-squared = 0.1242 Root MSE = .96536 ------------------------------------------------------------------------------- | Robust UKtool | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0976392 .0627325 1.56 0.120 -.0254636 .2207421 wave | 0 (omitted) gender | .3101848 .0625144 4.96 0.000 .18751 .4328596 prior | -.0036998 .0017424 -2.12 0.034 -.007119 -.0002807 democrat | .5962684 .0739746 8.06 0.000 .4511045 .7414322 indep | .2463499 .0943696 2.61 0.009 .061164 .4315357 otherpol | .2376436 .2738113 0.87 0.386 -.299669 .7749563 midwest | -.1507862 .101069 -1.49 0.136 -.3491186 .0475462 south | -.0618433 .086354 -0.72 0.474 -.2312998 .1076132 west | -.1117538 .097955 -1.14 0.254 -.3039755 .0804679 age1 | -.0525978 .1305981 -0.40 0.687 -.3088765 .2036809 age2 | -.0586592 .1048296 -0.56 0.576 -.2643711 .1470527 age3 | -.0298531 .0985224 -0.30 0.762 -.2231882 .163482 age4 | .086665 .0958668 0.90 0.366 -.1014588 .2747888 anychildren | -.0297168 .070903 -0.42 0.675 -.1688532 .1094195 loghhinc | .057605 .0460921 1.25 0.212 -.0328436 .1480537 associatemore | -.0051252 .0710553 -0.07 0.943 -.1445604 .13431 fulltime | .0581511 .0943182 0.62 0.538 -.126934 .2432361 parttime | .0347192 .1274041 0.27 0.785 -.2152917 .2847301 selfemp | .2747962 .1322147 2.08 0.038 .0153452 .5342472 unemployed | .1769462 .1682875 1.05 0.293 -.1532921 .5071845 student | .2265534 .166965 1.36 0.175 -.1010897 .5541965 _cons | -.811541 .5433414 -1.49 0.136 -1.877765 .254683 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[4, 1] = r(p) . reg childcare T1 $controls [pweight=pweight] if wave==2, vce(r) (sum of wgt is 9.8324e+02) note: wave omitted because of collinearity Linear regression Number of obs = 1,019 F(21, 997) = 6.93 Prob > F = 0.0000 R-squared = 0.1300 Root MSE = .96421 ------------------------------------------------------------------------------- | Robust childcare | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.017581 .0613026 -0.29 0.774 -.1378779 .1027159 wave | 0 (omitted) gender | .0978807 .0637296 1.54 0.125 -.0271789 .2229403 prior | -.0046373 .0018147 -2.56 0.011 -.0081984 -.0010762 democrat | .5799662 .0733305 7.91 0.000 .4360664 .7238659 indep | .0092891 .0924078 0.10 0.920 -.1720471 .1906253 otherpol | .3347764 .2174691 1.54 0.124 -.0919732 .761526 midwest | .0040394 .0997212 0.04 0.968 -.1916481 .1997269 south | .1242961 .0891765 1.39 0.164 -.050699 .2992912 west | -.0336056 .1030828 -0.33 0.744 -.2358896 .1686785 age1 | .3375028 .127759 2.64 0.008 .0867954 .5882102 age2 | .4277647 .1026366 4.17 0.000 .2263561 .6291733 age3 | .3090023 .1006445 3.07 0.002 .1115031 .5065016 age4 | .179071 .0943006 1.90 0.058 -.0059795 .3641214 anychildren | .1918359 .0698665 2.75 0.006 .0547337 .3289381 loghhinc | .0030775 .0444964 0.07 0.945 -.0842398 .0903948 associatemore | -.0402462 .0661424 -0.61 0.543 -.1700406 .0895482 fulltime | -.0224671 .0924315 -0.24 0.808 -.2038498 .1589156 parttime | .0959306 .1216321 0.79 0.430 -.1427537 .334615 selfemp | .1518345 .1366172 1.11 0.267 -.1162558 .4199247 unemployed | -.0711258 .1611638 -0.44 0.659 -.387385 .2451335 student | -.1009351 .1619045 -0.62 0.533 -.4186477 .2167775 _cons | -.4050091 .5153668 -0.79 0.432 -1.416337 .606319 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[5, 1] = r(p) . . . minq P, q("Q") step(0.001) number of observations will be reset to 5 Press any key to continue, or Break to abort number of observations (_N) was 0, now 5 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = .7109999999999997 Correction with q = .7099999999999997 Correction with q = .7089999999999997 Correction with q = .7079999999999997 Correction with q = .7069999999999997 Correction with q = .7059999999999997 Correction with q = .7049999999999997 Correction with q = .7039999999999997 Correction with q = .7029999999999997 Correction with q = .7019999999999997 Correction with q = .7009999999999997 Correction with q = .6999999999999997 Correction with q = .6989999999999997 Correction with q = .6979999999999997 Correction with q = .6969999999999997 Correction with q = .6959999999999997 Correction with q = .6949999999999997 Correction with q = .6939999999999997 Correction with q = .6929999999999997 Correction with q = .6919999999999997 Correction with q = .6909999999999997 Correction with q = .6899999999999997 Correction with q = .6889999999999997 Correction with q = .6879999999999997 Correction with q = .6869999999999997 Correction with q = .6859999999999997 Correction with q = .6849999999999997 Correction with q = .6839999999999997 Correction with q = .6829999999999997 Correction with q = .6819999999999997 Correction with q = .6809999999999997 Correction with q = .6799999999999997 Correction with q = .6789999999999997 Correction with q = .6779999999999997 Correction with q = .6769999999999997 Correction with q = .6759999999999997 Correction with q = .6749999999999997 Correction with q = .6739999999999997 Correction with q = .6729999999999997 Correction with q = .6719999999999997 Correction with q = .6709999999999997 Correction with q = .6699999999999997 Correction with q = .6689999999999997 Correction with q = .6679999999999997 Correction with q = .6669999999999997 Correction with q = .6659999999999997 Correction with q = .6649999999999997 Correction with q = .6639999999999997 Correction with q = .6629999999999997 Correction with q = .6619999999999997 Correction with q = .6609999999999997 Correction with q = .6599999999999997 Correction with q = .6589999999999997 Correction with q = .6579999999999997 Correction with q = .6569999999999997 Correction with q = .6559999999999997 Correction with q = .6549999999999997 Correction with q = .6539999999999997 Correction with q = .6529999999999997 Correction with q = .6519999999999997 Correction with q = .6509999999999997 Correction with q = .6499999999999997 Correction with q = .6489999999999997 Correction with q = .6479999999999997 Correction with q = .6469999999999997 Correction with q = .6459999999999997 Correction with q = .6449999999999997 Correction with q = .6439999999999997 Correction with q = .6429999999999997 Correction with q = .6419999999999997 Correction with q = .6409999999999997 Correction with q = .6399999999999997 Correction with q = .6389999999999997 Correction with q = .6379999999999997 Correction with q = .6369999999999997 Correction with q = .6359999999999997 Correction with q = .6349999999999997 Correction with q = .6339999999999997 Correction with q = .6329999999999997 Correction with q = .6319999999999997 Correction with q = .6309999999999997 Correction with q = .6299999999999997 Correction with q = .6289999999999997 Correction with q = .6279999999999997 Correction with q = .6269999999999997 Correction with q = .6259999999999997 Correction with q = .6249999999999997 Correction with q = .6239999999999997 Correction with q = .6229999999999997 Correction with q = .6219999999999997 Correction with q = .6209999999999997 Correction with q = .6199999999999997 Correction with q = .6189999999999997 Correction with q = .6179999999999997 Correction with q = .6169999999999997 Correction with q = .6159999999999997 Correction with q = .6149999999999997 Correction with q = .6139999999999997 Correction with q = .6129999999999997 Correction with q = .6119999999999997 Correction with q = .6109999999999997 Correction with q = .6099999999999997 Correction with q = .6089999999999997 Correction with q = .6079999999999997 Correction with q = .6069999999999997 Correction with q = .6059999999999997 Correction with q = .6049999999999997 Correction with q = .6039999999999997 Correction with q = .6029999999999997 Correction with q = .6019999999999997 Correction with q = .6009999999999997 Correction with q = .5999999999999996 Correction with q = .5989999999999996 Correction with q = .5979999999999996 Correction with q = .5969999999999996 Correction with q = .5959999999999996 Correction with q = .5949999999999996 Correction with q = .5939999999999996 Correction with q = .5929999999999996 Correction with q = .5919999999999996 Correction with q = .5909999999999996 Correction with q = .5899999999999996 Correction with q = .5889999999999996 Correction with q = .5879999999999996 Correction with q = .5869999999999996 Correction with q = .5859999999999996 Correction with q = .5849999999999996 Correction with q = .5839999999999996 Correction with q = .5829999999999996 Correction with q = .5819999999999996 Correction with q = .5809999999999996 Correction with q = .5799999999999996 Correction with q = .5789999999999996 Correction with q = .5779999999999996 Correction with q = .5769999999999996 Correction with q = .5759999999999996 Correction with q = .5749999999999996 Correction with q = .5739999999999996 Correction with q = .5729999999999996 Correction with q = .5719999999999996 Correction with q = .5709999999999996 Correction with q = .5699999999999996 Correction with q = .5689999999999996 Correction with q = .5679999999999996 Correction with q = .5669999999999996 Correction with q = .5659999999999996 Correction with q = .5649999999999996 Correction with q = .5639999999999996 Correction with q = .5629999999999996 Correction with q = .5619999999999996 Correction with q = .5609999999999996 Correction with q = .5599999999999996 Correction with q = .5589999999999996 Correction with q = .5579999999999996 Correction with q = .5569999999999996 Correction with q = .5559999999999996 Correction with q = .5549999999999996 Correction with q = .5539999999999996 Correction with q = .5529999999999996 Correction with q = .5519999999999996 Correction with q = .5509999999999996 Correction with q = .5499999999999996 Correction with q = .5489999999999996 Correction with q = .5479999999999996 Correction with q = .5469999999999996 Correction with q = .5459999999999996 Correction with q = .5449999999999996 Correction with q = .5439999999999996 Correction with q = .5429999999999996 Correction with q = .5419999999999996 Correction with q = .5409999999999996 Correction with q = .5399999999999996 Correction with q = .5389999999999996 Correction with q = .5379999999999996 Correction with q = .5369999999999996 Correction with q = .5359999999999996 Correction with q = .5349999999999996 Correction with q = .5339999999999996 Correction with q = .5329999999999996 Correction with q = .5319999999999996 Correction with q = .5309999999999996 Correction with q = .5299999999999996 Correction with q = .5289999999999996 Correction with q = .5279999999999996 Correction with q = .5269999999999996 Correction with q = .5259999999999996 Correction with q = .5249999999999996 Correction with q = .5239999999999996 Correction with q = .5229999999999996 Correction with q = .5219999999999996 Correction with q = .5209999999999996 Correction with q = .5199999999999996 Correction with q = .5189999999999996 Correction with q = .5179999999999996 Correction with q = .5169999999999996 Correction with q = .5159999999999996 Correction with q = .5149999999999996 Correction with q = .5139999999999996 Correction with q = .5129999999999996 Correction with q = .5119999999999996 Correction with q = .5109999999999996 Correction with q = .5099999999999996 Correction with q = .5089999999999996 Correction with q = .5079999999999996 Correction with q = .5069999999999996 Correction with q = .5059999999999996 Correction with q = .5049999999999996 Correction with q = .5039999999999996 Correction with q = .5029999999999996 Correction with q = .5019999999999996 Correction with q = .5009999999999996 Correction with q = .4999999999999996 Correction with q = .4989999999999996 Correction with q = .4979999999999996 Correction with q = .4969999999999996 Correction with q = .4959999999999996 Correction with q = .4949999999999996 Correction with q = .4939999999999996 Correction with q = .4929999999999996 Correction with q = .4919999999999996 Correction with q = .4909999999999996 Correction with q = .4899999999999996 Correction with q = .4889999999999996 Correction with q = .4879999999999996 Correction with q = .4869999999999995 Correction with q = .4859999999999995 Correction with q = .4849999999999995 Correction with q = .4839999999999995 Correction with q = .4829999999999995 Correction with q = .4819999999999995 Correction with q = .4809999999999995 Correction with q = .4799999999999995 Correction with q = .4789999999999995 Correction with q = .4779999999999995 Correction with q = .4769999999999995 Correction with q = .4759999999999995 Correction with q = .4749999999999995 Correction with q = .4739999999999995 Correction with q = .4729999999999995 Correction with q = .4719999999999995 Correction with q = .4709999999999995 Correction with q = .4699999999999995 Correction with q = .4689999999999995 Correction with q = .4679999999999995 Correction with q = .4669999999999995 Correction with q = .4659999999999995 Correction with q = .4649999999999995 Correction with q = .4639999999999995 Correction with q = .4629999999999995 Correction with q = .4619999999999995 Correction with q = .4609999999999995 Correction with q = .4599999999999995 Correction with q = .4589999999999995 Correction with q = .4579999999999995 Correction with q = .4569999999999995 Correction with q = .4559999999999995 Correction with q = .4549999999999995 Correction with q = .4539999999999995 Correction with q = .4529999999999995 Correction with q = .4519999999999995 Correction with q = .4509999999999995 Correction with q = .4499999999999995 Correction with q = .4489999999999995 Correction with q = .4479999999999995 Correction with q = .4469999999999995 Correction with q = .4459999999999995 Correction with q = .4449999999999995 Correction with q = .4439999999999995 Correction with q = .4429999999999995 Correction with q = .4419999999999995 Correction with q = .4409999999999995 Correction with q = .4399999999999995 Correction with q = .4389999999999995 Correction with q = .4379999999999995 Correction with q = .4369999999999995 Correction with q = .4359999999999995 Correction with q = .4349999999999995 Correction with q = .4339999999999995 Correction with q = .4329999999999995 Correction with q = .4319999999999995 Correction with q = .4309999999999995 Correction with q = .4299999999999995 Correction with q = .4289999999999995 Correction with q = .4279999999999995 Correction with q = .4269999999999995 Correction with q = .4259999999999995 Correction with q = .4249999999999995 Correction with q = .4239999999999995 Correction with q = .4229999999999995 Correction with q = .4219999999999995 Correction with q = .4209999999999995 Correction with q = .4199999999999995 Correction with q = .4189999999999995 Correction with q = .4179999999999995 Correction with q = .4169999999999995 Correction with q = .4159999999999995 Correction with q = .4149999999999995 Correction with q = .4139999999999995 Correction with q = .4129999999999995 Correction with q = .4119999999999995 Correction with q = .4109999999999995 Correction with q = .4099999999999995 Correction with q = .4089999999999995 Correction with q = .4079999999999995 Correction with q = .4069999999999995 Correction with q = .4059999999999995 Correction with q = .4049999999999995 Correction with q = .4039999999999995 Correction with q = .4029999999999995 Correction with q = .4019999999999995 Correction with q = .4009999999999995 Correction with q = .3999999999999995 Correction with q = .3989999999999995 Correction with q = .3979999999999995 Correction with q = .3969999999999995 Correction with q = .3959999999999995 Correction with q = .3949999999999995 Correction with q = .3939999999999995 Correction with q = .3929999999999995 Correction with q = .3919999999999995 Correction with q = .3909999999999995 Correction with q = .3899999999999995 Correction with q = .3889999999999995 Correction with q = .3879999999999995 Correction with q = .3869999999999995 Correction with q = .3859999999999995 Correction with q = .3849999999999995 Correction with q = .3839999999999995 Correction with q = .3829999999999995 Correction with q = .3819999999999995 Correction with q = .3809999999999995 Correction with q = .3799999999999995 Correction with q = .3789999999999995 Correction with q = .3779999999999995 Correction with q = .3769999999999995 Correction with q = .3759999999999995 Correction with q = .3749999999999994 Correction with q = .3739999999999994 Correction with q = .3729999999999994 Correction with q = .3719999999999994 Correction with q = .3709999999999994 Correction with q = .3699999999999994 Correction with q = .3689999999999994 Correction with q = .3679999999999994 Correction with q = .3669999999999994 Correction with q = .3659999999999994 Correction with q = .3649999999999994 Correction with q = .3639999999999994 Correction with q = .3629999999999994 Correction with q = .3619999999999994 Correction with q = .3609999999999994 Correction with q = .3599999999999994 Correction with q = .3589999999999994 Correction with q = .3579999999999994 Correction with q = .3569999999999994 Correction with q = .3559999999999994 Correction with q = .3549999999999994 Correction with q = .3539999999999994 Correction with q = .3529999999999994 Correction with q = .3519999999999994 Correction with q = .3509999999999994 Correction with q = .3499999999999994 Correction with q = .3489999999999994 Correction with q = .3479999999999994 Correction with q = .3469999999999994 Correction with q = .3459999999999994 Correction with q = .3449999999999994 Correction with q = .3439999999999994 Correction with q = .3429999999999994 Correction with q = .3419999999999994 Correction with q = .3409999999999994 Correction with q = .3399999999999994 Correction with q = .3389999999999994 Correction with q = .3379999999999994 Correction with q = .3369999999999994 Correction with q = .3359999999999994 Correction with q = .3349999999999994 Correction with q = .3339999999999994 Correction with q = .3329999999999994 Correction with q = .3319999999999994 Correction with q = .3309999999999994 Correction with q = .3299999999999994 Correction with q = .3289999999999994 Correction with q = .3279999999999994 Correction with q = .3269999999999994 Correction with q = .3259999999999994 Correction with q = .3249999999999994 Correction with q = .3239999999999994 Correction with q = .3229999999999994 Correction with q = .3219999999999994 Correction with q = .3209999999999994 Correction with q = .3199999999999994 Correction with q = .3189999999999994 Correction with q = .3179999999999994 Correction with q = .3169999999999994 Correction with q = .3159999999999994 Correction with q = .3149999999999994 Correction with q = .3139999999999994 Correction with q = .3129999999999994 Correction with q = .3119999999999994 Correction with q = .3109999999999994 Correction with q = .3099999999999994 Correction with q = .3089999999999994 Correction with q = .3079999999999994 Correction with q = .3069999999999994 Correction with q = .3059999999999994 Correction with q = .3049999999999994 Correction with q = .3039999999999994 Correction with q = .3029999999999994 Correction with q = .3019999999999994 Correction with q = .3009999999999994 Correction with q = .2999999999999994 Correction with q = .2989999999999994 Correction with q = .2979999999999994 Correction with q = .2969999999999994 Correction with q = .2959999999999994 Correction with q = .2949999999999994 Correction with q = .2939999999999994 Correction with q = .2929999999999994 Correction with q = .2919999999999994 Correction with q = .2909999999999994 Correction with q = .2899999999999994 Correction with q = .2889999999999994 Correction with q = .2879999999999994 Correction with q = .2869999999999994 Correction with q = .2859999999999994 Correction with q = .2849999999999994 Correction with q = .2839999999999994 Correction with q = .2829999999999994 Correction with q = .2819999999999994 Correction with q = .2809999999999994 Correction with q = .2799999999999994 Correction with q = .2789999999999994 Correction with q = .2779999999999994 Correction with q = .2769999999999994 Correction with q = .2759999999999994 Correction with q = .2749999999999994 Correction with q = .2739999999999994 Correction with q = .2729999999999994 Correction with q = .2719999999999994 Correction with q = .2709999999999994 Correction with q = .2699999999999994 Correction with q = .2689999999999994 Correction with q = .2679999999999994 Correction with q = .2669999999999994 Correction with q = .2659999999999994 Correction with q = .2649999999999994 Correction with q = .2639999999999994 Correction with q = .2629999999999994 Correction with q = .2619999999999993 Correction with q = .2609999999999993 Correction with q = .2599999999999993 Correction with q = .2589999999999993 Correction with q = .2579999999999993 Correction with q = .2569999999999993 Correction with q = .2559999999999993 Correction with q = .2549999999999993 Correction with q = .2539999999999993 Correction with q = .2529999999999993 Correction with q = .2519999999999993 Correction with q = .2509999999999993 Correction with q = .2499999999999993 Correction with q = .2489999999999993 Correction with q = .2479999999999993 Correction with q = .2469999999999993 Correction with q = .2459999999999993 Correction with q = .2449999999999993 Correction with q = .2439999999999993 Correction with q = .2429999999999993 Correction with q = .2419999999999993 Correction with q = .2409999999999993 Correction with q = .2399999999999993 Correction with q = .2389999999999993 Correction with q = .2379999999999993 Correction with q = .2369999999999993 Correction with q = .2359999999999993 Correction with q = .2349999999999993 Correction with q = .2339999999999993 Correction with q = .2329999999999993 Correction with q = .2319999999999993 Correction with q = .2309999999999993 Correction with q = .2299999999999993 Correction with q = .2289999999999993 Correction with q = .2279999999999993 Correction with q = .2269999999999993 Correction with q = .2259999999999993 Correction with q = .2249999999999993 Correction with q = .2239999999999993 Correction with q = .2229999999999993 Correction with q = .2219999999999993 Correction with q = .2209999999999993 Correction with q = .2199999999999993 Correction with q = .2189999999999993 Correction with q = .2179999999999993 Correction with q = .2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = .1909999999999993 Correction with q = .1899999999999993 Correction with q = .1889999999999993 Correction with q = .1879999999999993 Correction with q = .1869999999999993 Correction with q = .1859999999999993 Correction with q = .1849999999999993 Correction with q = .1839999999999993 Correction with q = .1829999999999993 Correction with q = .1819999999999993 Correction with q = .1809999999999993 Correction with q = .1799999999999993 Correction with q = .1789999999999993 Correction with q = .1779999999999993 Correction with q = .1769999999999993 Correction with q = .1759999999999993 Correction with q = .1749999999999993 Correction with q = .1739999999999993 Correction with q = .1729999999999993 Correction with q = .1719999999999993 Correction with q = .1709999999999993 Correction with q = .1699999999999993 Correction with q = .1689999999999993 Correction with q = .1679999999999993 Correction with q = .1669999999999993 Correction with q = .1659999999999993 Correction with q = .1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 (5 real changes made) (0 real changes made) . . estadd loc thisstat38 = "[" + string(Q[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat38 = "[" +string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat38 = "[" + string(Q[3, 1], "%9.3f")+"]": col3 . estadd loc thisstat38 = "["+ string(Q[4, 1], "%9.3f")+"]": col5 . estadd loc thisstat38 = "["+ string(Q[5, 1], "%9.3f")+"]": col6 . . . loc rowlabels " " " "{\bf Panel A: Both waves}" " " "T$^{74}$" " " "Sharpened q-value" " " "Female > " " " " " "Democrat" " " " " "Observations" " " "\hline" " " "{\bf Panel B: Wave A}" " " "T$^{74 > }$" " " "Sharpened q-value" " " "Female" " " " " "Democrat" " " " " "Observations" " " "\hline" > " " "{\bf Panel C: Wave B}" " " "T$^{74}$" " " "Sharpened q-value" " " "Female" " " " " "Democrat" > " " " " "Observations" " " " . loc rowstats "" . . forval i = 1/47 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\tab_treatment_policyABsep.tex", replace cells(none) booktabs nonotes nomti > tles compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) / > // > mgroups("\shortstack{Introduce\\gender\\quotas}" "\shortstack{Statutory\\affirmative\\acti > on}" /// > "\shortstack{Stricter\\equal pay\\legislation}" "\shortstack{Wage transp.\\within\\ companies}" > "\shortstack{Introduce\\reporting\\website}" "\shortstack{Increase\\subsidies\\to child care}" /// > "\shortstack{\\Index}", pattern(1 1 1 1 1 1 1 ) prefix(\multicolumn{@span}{c}{) suffix(}) span > erepeat(\cmidrule(lr){@span})) (note: file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFi > les\output\tab_treatment_policyABsep.tex not found) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\tab_treatment_policyABsep.tex) . . . *********************************************************************************** . // Table G.5: Persistence of first stage treatment effect after 2-4 weeks (by wave) . *********************************************************************************** . . clear all . . use "$path\data\SurveyStageIIAB_final.dta" . . loc experimentsII "posteriorII problemII problemIIHS problemIILS fairII govmoreII antidiscII fampo > lII AAanchorII legislationanchorII" . . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experimentsII' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . loc experimentsII "posteriorII problemII problemIIHS problemIILS fairII govmoreII antidiscII fampo > lII" . . . foreach choice in `experimentsII' { 2. . ***Panel A: Wave A . . qui reg `choice' T1 $controls if wave==1 [pweight=pweight], vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat4 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat5 = "`r(sestar)'": col`colnum' 7. sigstar gender, prec(3) 8. estadd loc thisstat8 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat9 = "`r(sestar)'": col`colnum' 10. sigstar democrat, prec(3) 11. estadd loc thisstat11 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat12 = "`r(sestar)'": col`colnum' 13. estadd loc thisstat14 = "`n'": col`colnum' 14. . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. . } . . . mat def P = J(8, 1, .) . reg posteriorII T1 $controls if wave==1, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 494 F(21, 472) = 3.12 Prob > F = 0.0000 R-squared = 0.1468 Root MSE = 20.359 ------------------------------------------------------------------------------- | Robust posteriorII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -10.12066 1.850766 -5.47 0.000 -13.75742 -6.483898 wave | 0 (omitted) gender | -.9560814 1.972214 -0.48 0.628 -4.831488 2.919325 prior | .2119664 .0573653 3.70 0.000 .0992434 .3246895 democrat | 2.124501 2.101822 1.01 0.313 -2.005585 6.254588 indep | 5.011788 2.788009 1.80 0.073 -.4666568 10.49023 otherpol | 1.297768 4.469721 0.29 0.772 -7.485246 10.08078 midwest | -.7176763 2.988154 -0.24 0.810 -6.589407 5.154054 south | -2.424726 2.213131 -1.10 0.274 -6.773534 1.924081 west | -.0553764 2.777423 -0.02 0.984 -5.51302 5.402268 age1 | .2745808 2.998965 0.09 0.927 -5.618393 6.167554 age2 | 2.709805 3.002739 0.90 0.367 -3.190586 8.610195 age3 | .810852 3.227442 0.25 0.802 -5.53108 7.152784 age4 | .4056129 2.151006 0.19 0.851 -3.821119 4.632345 anychildren | .3877981 2.069943 0.19 0.851 -3.679645 4.455241 loghhinc | 2.391013 1.197616 2.00 0.046 .0376948 4.744331 associatemore | -2.602643 1.997966 -1.30 0.193 -6.528652 1.323366 fulltime | 2.853812 2.743496 1.04 0.299 -2.537165 8.244789 parttime | 4.347299 3.3 1.32 0.188 -2.137211 10.83181 selfemp | 5.7737 3.982165 1.45 0.148 -2.051266 13.59867 unemployed | 1.945437 3.116747 0.62 0.533 -4.178979 8.069854 student | -1.46084 6.909989 -0.21 0.833 -15.03899 12.11731 _cons | 42.59675 13.78451 3.09 0.002 15.51015 69.68335 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg problemII T1 $controls if wave==1, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 499 F(21, 477) = 4.87 Prob > F = 0.0000 R-squared = 0.1358 Root MSE = .94412 ------------------------------------------------------------------------------- | Robust problemII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1963463 .0851661 2.31 0.022 .0289992 .3636933 wave | 0 (omitted) gender | .18012 .0907143 1.99 0.048 .0018709 .3583691 prior | -.0043045 .0018285 -2.35 0.019 -.0078975 -.0007115 democrat | .5512496 .0908199 6.07 0.000 .3727931 .7297061 indep | .3060801 .1390087 2.20 0.028 .0329351 .5792252 otherpol | .1722351 .3118862 0.55 0.581 -.4406057 .7850759 midwest | -.0050035 .1417342 -0.04 0.972 -.283504 .2734971 south | .0422215 .125547 0.34 0.737 -.2044722 .2889151 west | -.0597153 .1446558 -0.41 0.680 -.3439568 .2245261 age1 | -.1573852 .2183183 -0.72 0.471 -.5863696 .2715992 age2 | -.0401493 .130711 -0.31 0.759 -.2969899 .2166912 age3 | -.0441172 .148369 -0.30 0.766 -.3356549 .2474205 age4 | -.2352427 .1248264 -1.88 0.060 -.4805203 .0100348 anychildren | .0088516 .0954108 0.09 0.926 -.1786258 .1963289 loghhinc | -.0782664 .0633527 -1.24 0.217 -.2027513 .0462185 associatemore | .1298859 .0892896 1.45 0.146 -.0455637 .3053354 fulltime | -.0807339 .1348446 -0.60 0.550 -.3456967 .1842289 parttime | -.4648272 .1649683 -2.82 0.005 -.7889815 -.1406728 selfemp | -.1372481 .1917944 -0.72 0.475 -.5141144 .2396183 unemployed | -.1394616 .1789821 -0.78 0.436 -.4911524 .2122291 student | .7760965 .2994262 2.59 0.010 .187739 1.364454 _cons | .8793638 .6947548 1.27 0.206 -.4857944 2.244522 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg problemIIHS T1 $controls if wave==1, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 499 F(21, 477) = 2.90 Prob > F = 0.0000 R-squared = 0.0989 Root MSE = .95526 ------------------------------------------------------------------------------- | Robust problemIIHS | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1439892 .0862054 1.67 0.096 -.0254 .3133784 wave | 0 (omitted) gender | .0752556 .0904043 0.83 0.406 -.1023843 .2528954 prior | -.0038792 .0018648 -2.08 0.038 -.0075434 -.000215 democrat | .482406 .0961627 5.02 0.000 .2934511 .6713608 indep | .1713093 .1351742 1.27 0.206 -.0943011 .4369198 otherpol | .0824446 .3058735 0.27 0.788 -.5185814 .6834706 midwest | -.0680937 .1429888 -0.48 0.634 -.3490595 .2128721 south | .0033862 .1276758 0.03 0.979 -.2474904 .2542627 west | .0423253 .1432639 0.30 0.768 -.239181 .3238316 age1 | -.0501632 .2110271 -0.24 0.812 -.4648209 .3644945 age2 | -.0034804 .1312664 -0.03 0.979 -.2614123 .2544516 age3 | -.070347 .1493186 -0.47 0.638 -.3637505 .2230566 age4 | -.2485495 .1273037 -1.95 0.051 -.4986948 .0015958 anychildren | .065704 .0948214 0.69 0.489 -.1206153 .2520234 loghhinc | -.0412131 .0655664 -0.63 0.530 -.1700478 .0876216 associatemore | .0824074 .0904688 0.91 0.363 -.0953592 .2601741 fulltime | -.1595758 .1384555 -1.15 0.250 -.4316338 .1124823 parttime | -.2936658 .172531 -1.70 0.089 -.6326805 .045349 selfemp | -.1217962 .1848421 -0.66 0.510 -.4850015 .2414092 unemployed | -.0635502 .1788865 -0.36 0.723 -.4150531 .2879528 student | .5475113 .335368 1.63 0.103 -.11147 1.206493 _cons | .5832997 .7352096 0.79 0.428 -.8613502 2.02795 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . reg problemIILS T1 $controls if wave==1, vce(r) note: wave omitted because of collinearity Linear regression Number of obs = 499 F(21, 477) = 2.79 Prob > F = 0.0000 R-squared = 0.0885 Root MSE = .99479 ------------------------------------------------------------------------------- | Robust problemIILS | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .209746 .0888475 2.36 0.019 .035165 .3843269 wave | 0 (omitted) gender | .0634409 .0960412 0.66 0.509 -.1252752 .252157 prior | -.0022718 .0019183 -1.18 0.237 -.0060412 .0014977 democrat | .4564685 .0973148 4.69 0.000 .2652498 .6476872 indep | .0532946 .1455129 0.37 0.714 -.232631 .3392202 otherpol | .2319254 .3328163 0.70 0.486 -.4220419 .8858928 midwest | -.0692046 .1531786 -0.45 0.652 -.3701928 .2317836 south | .0312796 .1398485 0.22 0.823 -.2435157 .3060749 west | -.0425871 .1551731 -0.27 0.784 -.3474943 .2623202 age1 | -.0910279 .2352257 -0.39 0.699 -.5532345 .3711787 age2 | -.1246655 .1349669 -0.92 0.356 -.3898687 .1405377 age3 | -.003285 .1494192 -0.02 0.982 -.2968863 .2903162 age4 | -.2740926 .1298624 -2.11 0.035 -.5292658 -.0189194 anychildren | .093915 .0992765 0.95 0.345 -.1011584 .2889884 loghhinc | -.0548448 .0639502 -0.86 0.392 -.1805037 .0708142 associatemore | .0713616 .098329 0.73 0.468 -.12185 .2645731 fulltime | -.0349754 .1428541 -0.24 0.807 -.3156764 .2457257 parttime | -.120901 .1725685 -0.70 0.484 -.4599894 .2181874 selfemp | -.1561282 .1886763 -0.83 0.408 -.5268676 .2146112 unemployed | .1294914 .1899895 0.68 0.496 -.2438284 .5028112 student | .8501564 .3654689 2.33 0.020 .1320285 1.568284 _cons | .4971173 .7044882 0.71 0.481 -.8871667 1.881401 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[4, 1] = r(p) . reg fairII T1 T1 $controls if wave==1, vce(r) note: T1 omitted because of collinearity note: wave omitted because of collinearity Linear regression Number of obs = 499 F(21, 477) = 5.04 Prob > F = 0.0000 R-squared = 0.1829 Root MSE = .90019 ------------------------------------------------------------------------------- | Robust fairII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.1321235 .0820829 -1.61 0.108 -.2934123 .0291654 T1 | 0 (omitted) wave | 0 (omitted) gender | .1093157 .0864398 1.26 0.207 -.0605341 .2791656 prior | .0084133 .0018488 4.55 0.000 .0047806 .012046 democrat | -.3915217 .0918032 -4.26 0.000 -.5719105 -.211133 indep | -.3852041 .1281632 -3.01 0.003 -.6370384 -.1333698 otherpol | -.3747987 .2443295 -1.53 0.126 -.854894 .1052965 midwest | -.1556723 .1263681 -1.23 0.219 -.4039793 .0926346 south | -.1874164 .1144088 -1.64 0.102 -.4122239 .0373911 west | -.2272765 .133701 -1.70 0.090 -.4899922 .0354393 age1 | .2791171 .1894398 1.47 0.141 -.0931226 .6513568 age2 | .4119572 .1309185 3.15 0.002 .1547089 .6692055 age3 | .3337538 .1462062 2.28 0.023 .046466 .6210416 age4 | .0594868 .0994869 0.60 0.550 -.1359999 .2549735 anychildren | .0068593 .0903188 0.08 0.939 -.1706127 .1843313 loghhinc | .1275833 .059421 2.15 0.032 .0108241 .2443425 associatemore | -.0539484 .0854182 -0.63 0.528 -.2217909 .1138942 fulltime | .1399205 .1276927 1.10 0.274 -.1109892 .3908301 parttime | .1993476 .1527467 1.31 0.192 -.100792 .4994872 selfemp | .3460337 .1664588 2.08 0.038 .0189506 .6731167 unemployed | .0897615 .1490905 0.60 0.547 -.2031937 .3827168 student | -.9110769 .3381323 -2.69 0.007 -1.57549 -.2466639 _cons | -2.030389 .669589 -3.03 0.003 -3.346098 -.7146802 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[5, 1] = r(p) . reg govmoreII T1 T1 $controls if wave==1, vce(r) note: T1 omitted because of collinearity note: wave omitted because of collinearity Linear regression Number of obs = 499 F(21, 477) = 5.36 Prob > F = 0.0000 R-squared = 0.1772 Root MSE = .90898 ------------------------------------------------------------------------------- | Robust govmoreII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2815178 .0819794 3.43 0.001 .1204325 .4426031 T1 | 0 (omitted) wave | 0 (omitted) gender | .1905215 .0851867 2.24 0.026 .0231339 .3579091 prior | -.0040949 .0019116 -2.14 0.033 -.0078511 -.0003388 democrat | .6928261 .0923436 7.50 0.000 .5113756 .8742766 indep | .2129009 .1311471 1.62 0.105 -.0447967 .4705984 otherpol | .0657916 .2565199 0.26 0.798 -.4382572 .5698404 midwest | -.1191404 .1365319 -0.87 0.383 -.3874187 .1491379 south | .0401545 .1170657 0.34 0.732 -.1898737 .2701827 west | -.0081641 .1435221 -0.06 0.955 -.2901778 .2738496 age1 | .3348368 .1714515 1.95 0.051 -.0020568 .6717305 age2 | .2421122 .1264676 1.91 0.056 -.0063902 .4906147 age3 | .4177039 .1482235 2.82 0.005 .1264522 .7089556 age4 | .0429482 .1130536 0.38 0.704 -.1791964 .2650929 anychildren | .0321244 .0893072 0.36 0.719 -.1433597 .2076084 loghhinc | .0645399 .0537469 1.20 0.230 -.04107 .1701498 associatemore | -.0618518 .0852254 -0.73 0.468 -.2293154 .1056118 fulltime | -.1831333 .1224951 -1.50 0.136 -.4238301 .0575634 parttime | -.2412065 .1505488 -1.60 0.110 -.5370273 .0546144 selfemp | -.3108292 .1562308 -1.99 0.047 -.6178148 -.0038435 unemployed | -.0179133 .1632624 -0.11 0.913 -.3387157 .3028891 student | .3111616 .332345 0.94 0.350 -.3418797 .9642029 _cons | -.7983357 .6093818 -1.31 0.191 -1.99574 .3990689 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[6, 1] = r(p) . reg antidiscII T1 T1 $controls if wave==1, vce(r) note: T1 omitted because of collinearity note: wave omitted because of collinearity Linear regression Number of obs = 499 F(21, 477) = 3.43 Prob > F = 0.0000 R-squared = 0.1306 Root MSE = .97478 ------------------------------------------------------------------------------- | Robust antidiscII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1144538 .0894577 1.28 0.201 -.0613262 .2902337 T1 | 0 (omitted) wave | 0 (omitted) gender | .2402756 .0899044 2.67 0.008 .0636179 .4169333 prior | -.0030811 .0018839 -1.64 0.103 -.0067829 .0006207 democrat | .6401644 .0955996 6.70 0.000 .452316 .8280127 indep | .1728387 .1380736 1.25 0.211 -.0984691 .4441465 otherpol | .2827294 .3069771 0.92 0.358 -.3204651 .8859239 midwest | -.1188168 .1450795 -0.82 0.413 -.4038908 .1662571 south | -.0023309 .1298565 -0.02 0.986 -.2574923 .2528305 west | -.025858 .144656 -0.18 0.858 -.3100998 .2583839 age1 | .2006485 .2011965 1.00 0.319 -.1946925 .5959895 age2 | .1814058 .1360853 1.33 0.183 -.085995 .4488067 age3 | .2509814 .1535852 1.63 0.103 -.0508058 .5527686 age4 | .0092217 .1199126 0.08 0.939 -.2264006 .244844 anychildren | -.0552497 .0931808 -0.59 0.554 -.2383453 .1278459 loghhinc | .107216 .0645811 1.66 0.098 -.0196828 .2341147 associatemore | -.0448664 .0897701 -0.50 0.617 -.22126 .1315273 fulltime | -.0427706 .1343445 -0.32 0.750 -.3067507 .2212095 parttime | -.2513474 .1730865 -1.45 0.147 -.5914536 .0887588 selfemp | -.0709844 .174382 -0.41 0.684 -.4136362 .2716675 unemployed | .2524551 .1670301 1.51 0.131 -.0757506 .5806608 student | .4101908 .3466681 1.18 0.237 -.2709945 1.091376 _cons | -1.329888 .7242966 -1.84 0.067 -2.753094 .0933184 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[7, 1] = r(p) . reg fampolII T1 T1 $controls if wave==1, vce(r) note: T1 omitted because of collinearity note: wave omitted because of collinearity Linear regression Number of obs = 499 F(21, 477) = 5.25 Prob > F = 0.0000 R-squared = 0.1729 Root MSE = .94459 ------------------------------------------------------------------------------- | Robust fampolII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2410417 .0848885 2.84 0.005 .0742401 .4078433 T1 | 0 (omitted) wave | 0 (omitted) gender | .1779489 .0882336 2.02 0.044 .0045744 .3513235 prior | -.0017123 .0017611 -0.97 0.331 -.0051728 .0017482 democrat | .7533441 .0947295 7.95 0.000 .5672054 .9394829 indep | .2557119 .1343174 1.90 0.058 -.008215 .5196387 otherpol | .175733 .3314565 0.53 0.596 -.4755624 .8270285 midwest | -.2032081 .1292898 -1.57 0.117 -.4572561 .0508399 south | -.0912868 .1186314 -0.77 0.442 -.3243915 .1418179 west | -.1454485 .1476266 -0.99 0.325 -.4355274 .1446303 age1 | .105061 .2039608 0.52 0.607 -.2957117 .5058338 age2 | .1369499 .1315615 1.04 0.298 -.1215618 .3954615 age3 | .2717204 .1447054 1.88 0.061 -.0126184 .5560593 age4 | -.0442108 .1220897 -0.36 0.717 -.284111 .1956894 anychildren | .072181 .0968314 0.75 0.456 -.1180877 .2624497 loghhinc | .0448582 .0597034 0.75 0.453 -.072456 .1621725 associatemore | -.0652695 .0922122 -0.71 0.479 -.2464618 .1159229 fulltime | -.2214883 .1289031 -1.72 0.086 -.4747763 .0317998 parttime | -.3926782 .1758001 -2.23 0.026 -.7381166 -.0472398 selfemp | -.3050394 .1533552 -1.99 0.047 -.6063746 -.0037041 unemployed | -.0016513 .1578447 -0.01 0.992 -.3118081 .3085055 student | .5781188 .3762102 1.54 0.125 -.1611153 1.317353 _cons | -.6326805 .6564971 -0.96 0.336 -1.922664 .6573033 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[8, 1] = r(p) . . . minq P, q("Q") step(0.001) number of observations will be reset to 8 Press any key to continue, or Break to abort number of observations (_N) was 0, now 8 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = .7109999999999997 Correction with q = .7099999999999997 Correction with q = .7089999999999997 Correction with q = .7079999999999997 Correction with q = .7069999999999997 Correction with q = .7059999999999997 Correction with q = .7049999999999997 Correction with q = .7039999999999997 Correction with q = .7029999999999997 Correction with q = .7019999999999997 Correction with q = .7009999999999997 Correction with q = .6999999999999997 Correction with q = .6989999999999997 Correction with q = .6979999999999997 Correction with q = .6969999999999997 Correction with q = .6959999999999997 Correction with q = .6949999999999997 Correction with q = .6939999999999997 Correction with q = .6929999999999997 Correction with q = .6919999999999997 Correction with q = .6909999999999997 Correction with q = .6899999999999997 Correction with q = .6889999999999997 Correction with q = .6879999999999997 Correction with q = .6869999999999997 Correction with q = .6859999999999997 Correction with q = .6849999999999997 Correction with q = .6839999999999997 Correction with q = .6829999999999997 Correction with q = .6819999999999997 Correction with q = .6809999999999997 Correction with q = .6799999999999997 Correction with q = .6789999999999997 Correction with q = .6779999999999997 Correction with q = .6769999999999997 Correction with q = .6759999999999997 Correction with q = .6749999999999997 Correction with q = .6739999999999997 Correction with q = .6729999999999997 Correction with q = .6719999999999997 Correction with q = .6709999999999997 Correction with q = .6699999999999997 Correction with q = .6689999999999997 Correction with q = .6679999999999997 Correction with q = .6669999999999997 Correction with q = .6659999999999997 Correction with q = .6649999999999997 Correction with q = .6639999999999997 Correction with q = .6629999999999997 Correction with q = .6619999999999997 Correction with q = .6609999999999997 Correction with q = .6599999999999997 Correction with q = .6589999999999997 Correction with q = .6579999999999997 Correction with q = .6569999999999997 Correction with q = .6559999999999997 Correction with q = .6549999999999997 Correction with q = .6539999999999997 Correction with q = .6529999999999997 Correction with q = .6519999999999997 Correction with q = .6509999999999997 Correction with q = .6499999999999997 Correction with q = .6489999999999997 Correction with q = .6479999999999997 Correction with q = .6469999999999997 Correction with q = .6459999999999997 Correction with q = .6449999999999997 Correction with q = .6439999999999997 Correction with q = .6429999999999997 Correction with q = .6419999999999997 Correction with q = .6409999999999997 Correction with q = .6399999999999997 Correction with q = .6389999999999997 Correction with q = .6379999999999997 Correction with q = .6369999999999997 Correction with q = .6359999999999997 Correction with q = .6349999999999997 Correction with q = .6339999999999997 Correction with q = .6329999999999997 Correction with q = .6319999999999997 Correction with q = .6309999999999997 Correction with q = .6299999999999997 Correction with q = .6289999999999997 Correction with q = .6279999999999997 Correction with q = .6269999999999997 Correction with q = .6259999999999997 Correction with q = .6249999999999997 Correction with q = .6239999999999997 Correction with q = .6229999999999997 Correction with q = .6219999999999997 Correction with q = .6209999999999997 Correction with q = .6199999999999997 Correction with q = .6189999999999997 Correction with q = .6179999999999997 Correction with q = .6169999999999997 Correction with q = .6159999999999997 Correction with q = .6149999999999997 Correction with q = .6139999999999997 Correction with q = .6129999999999997 Correction with q = .6119999999999997 Correction with q = .6109999999999997 Correction with q = .6099999999999997 Correction with q = .6089999999999997 Correction with q = .6079999999999997 Correction with q = .6069999999999997 Correction with q = .6059999999999997 Correction with q = .6049999999999997 Correction with q = .6039999999999997 Correction with q = .6029999999999997 Correction with q = .6019999999999997 Correction with q = .6009999999999997 Correction with q = .5999999999999996 Correction with q = .5989999999999996 Correction with q = .5979999999999996 Correction with q = .5969999999999996 Correction with q = .5959999999999996 Correction with q = .5949999999999996 Correction with q = .5939999999999996 Correction with q = .5929999999999996 Correction with q = .5919999999999996 Correction with q = .5909999999999996 Correction with q = .5899999999999996 Correction with q = .5889999999999996 Correction with q = .5879999999999996 Correction with q = .5869999999999996 Correction with q = .5859999999999996 Correction with q = .5849999999999996 Correction with q = .5839999999999996 Correction with q = .5829999999999996 Correction with q = .5819999999999996 Correction with q = .5809999999999996 Correction with q = .5799999999999996 Correction with q = .5789999999999996 Correction with q = .5779999999999996 Correction with q = .5769999999999996 Correction with q = .5759999999999996 Correction with q = .5749999999999996 Correction with q = .5739999999999996 Correction with q = .5729999999999996 Correction with q = .5719999999999996 Correction with q = .5709999999999996 Correction with q = .5699999999999996 Correction with q = .5689999999999996 Correction with q = .5679999999999996 Correction with q = .5669999999999996 Correction with q = .5659999999999996 Correction with q = .5649999999999996 Correction with q = .5639999999999996 Correction with q = .5629999999999996 Correction with q = .5619999999999996 Correction with q = .5609999999999996 Correction with q = .5599999999999996 Correction with q = .5589999999999996 Correction with q = .5579999999999996 Correction with q = .5569999999999996 Correction with q = .5559999999999996 Correction with q = .5549999999999996 Correction with q = .5539999999999996 Correction with q = .5529999999999996 Correction with q = .5519999999999996 Correction with q = .5509999999999996 Correction with q = .5499999999999996 Correction with q = .5489999999999996 Correction with q = .5479999999999996 Correction with q = .5469999999999996 Correction with q = .5459999999999996 Correction with q = .5449999999999996 Correction with q = .5439999999999996 Correction with q = .5429999999999996 Correction with q = .5419999999999996 Correction with q = .5409999999999996 Correction with q = .5399999999999996 Correction with q = .5389999999999996 Correction with q = .5379999999999996 Correction with q = .5369999999999996 Correction with q = .5359999999999996 Correction with q = .5349999999999996 Correction with q = .5339999999999996 Correction with q = .5329999999999996 Correction with q = .5319999999999996 Correction with q = .5309999999999996 Correction with q = .5299999999999996 Correction with q = .5289999999999996 Correction with q = .5279999999999996 Correction with q = .5269999999999996 Correction with q = .5259999999999996 Correction with q = .5249999999999996 Correction with q = .5239999999999996 Correction with q = .5229999999999996 Correction with q = .5219999999999996 Correction with q = .5209999999999996 Correction with q = .5199999999999996 Correction with q = .5189999999999996 Correction with q = .5179999999999996 Correction with q = .5169999999999996 Correction with q = .5159999999999996 Correction with q = .5149999999999996 Correction with q = .5139999999999996 Correction with q = .5129999999999996 Correction with q = .5119999999999996 Correction with q = .5109999999999996 Correction with q = .5099999999999996 Correction with q = .5089999999999996 Correction with q = .5079999999999996 Correction with q = .5069999999999996 Correction with q = .5059999999999996 Correction with q = .5049999999999996 Correction with q = .5039999999999996 Correction with q = .5029999999999996 Correction with q = .5019999999999996 Correction with q = .5009999999999996 Correction with q = .4999999999999996 Correction with q = .4989999999999996 Correction with q = .4979999999999996 Correction with q = .4969999999999996 Correction with q = .4959999999999996 Correction with q = .4949999999999996 Correction with q = .4939999999999996 Correction with q = .4929999999999996 Correction with q = .4919999999999996 Correction with q = .4909999999999996 Correction with q = .4899999999999996 Correction with q = .4889999999999996 Correction with q = .4879999999999996 Correction with q = .4869999999999995 Correction with q = .4859999999999995 Correction with q = .4849999999999995 Correction with q = .4839999999999995 Correction with q = .4829999999999995 Correction with q = .4819999999999995 Correction with q = .4809999999999995 Correction with q = .4799999999999995 Correction with q = .4789999999999995 Correction with q = .4779999999999995 Correction with q = .4769999999999995 Correction with q = .4759999999999995 Correction with q = .4749999999999995 Correction with q = .4739999999999995 Correction with q = .4729999999999995 Correction with q = .4719999999999995 Correction with q = .4709999999999995 Correction with q = .4699999999999995 Correction with q = .4689999999999995 Correction with q = .4679999999999995 Correction with q = .4669999999999995 Correction with q = .4659999999999995 Correction with q = .4649999999999995 Correction with q = .4639999999999995 Correction with q = .4629999999999995 Correction with q = .4619999999999995 Correction with q = .4609999999999995 Correction with q = .4599999999999995 Correction with q = .4589999999999995 Correction with q = .4579999999999995 Correction with q = .4569999999999995 Correction with q = .4559999999999995 Correction with q = .4549999999999995 Correction with q = .4539999999999995 Correction with q = .4529999999999995 Correction with q = .4519999999999995 Correction with q = .4509999999999995 Correction with q = .4499999999999995 Correction with q = .4489999999999995 Correction with q = .4479999999999995 Correction with q = .4469999999999995 Correction with q = .4459999999999995 Correction with q = .4449999999999995 Correction with q = .4439999999999995 Correction with q = .4429999999999995 Correction with q = .4419999999999995 Correction with q = .4409999999999995 Correction with q = .4399999999999995 Correction with q = .4389999999999995 Correction with q = .4379999999999995 Correction with q = .4369999999999995 Correction with q = .4359999999999995 Correction with q = .4349999999999995 Correction with q = .4339999999999995 Correction with q = .4329999999999995 Correction with q = .4319999999999995 Correction with q = .4309999999999995 Correction with q = .4299999999999995 Correction with q = .4289999999999995 Correction with q = .4279999999999995 Correction with q = .4269999999999995 Correction with q = .4259999999999995 Correction with q = .4249999999999995 Correction with q = .4239999999999995 Correction with q = .4229999999999995 Correction with q = .4219999999999995 Correction with q = .4209999999999995 Correction with q = .4199999999999995 Correction with q = .4189999999999995 Correction with q = .4179999999999995 Correction with q = .4169999999999995 Correction with q = .4159999999999995 Correction with q = .4149999999999995 Correction with q = .4139999999999995 Correction with q = .4129999999999995 Correction with q = .4119999999999995 Correction with q = .4109999999999995 Correction with q = .4099999999999995 Correction with q = .4089999999999995 Correction with q = .4079999999999995 Correction with q = .4069999999999995 Correction with q = .4059999999999995 Correction with q = .4049999999999995 Correction with q = .4039999999999995 Correction with q = .4029999999999995 Correction with q = .4019999999999995 Correction with q = .4009999999999995 Correction with q = .3999999999999995 Correction with q = .3989999999999995 Correction with q = .3979999999999995 Correction with q = .3969999999999995 Correction with q = .3959999999999995 Correction with q = .3949999999999995 Correction with q = .3939999999999995 Correction with q = .3929999999999995 Correction with q = .3919999999999995 Correction with q = .3909999999999995 Correction with q = .3899999999999995 Correction with q = .3889999999999995 Correction with q = .3879999999999995 Correction with q = .3869999999999995 Correction with q = .3859999999999995 Correction with q = .3849999999999995 Correction with q = .3839999999999995 Correction with q = .3829999999999995 Correction with q = .3819999999999995 Correction with q = .3809999999999995 Correction with q = .3799999999999995 Correction with q = .3789999999999995 Correction with q = .3779999999999995 Correction with q = .3769999999999995 Correction with q = .3759999999999995 Correction with q = .3749999999999994 Correction with q = .3739999999999994 Correction with q = .3729999999999994 Correction with q = .3719999999999994 Correction with q = .3709999999999994 Correction with q = .3699999999999994 Correction with q = .3689999999999994 Correction with q = .3679999999999994 Correction with q = .3669999999999994 Correction with q = .3659999999999994 Correction with q = .3649999999999994 Correction with q = .3639999999999994 Correction with q = .3629999999999994 Correction with q = .3619999999999994 Correction with q = .3609999999999994 Correction with q = .3599999999999994 Correction with q = .3589999999999994 Correction with q = .3579999999999994 Correction with q = .3569999999999994 Correction with q = .3559999999999994 Correction with q = .3549999999999994 Correction with q = .3539999999999994 Correction with q = .3529999999999994 Correction with q = .3519999999999994 Correction with q = .3509999999999994 Correction with q = .3499999999999994 Correction with q = .3489999999999994 Correction with q = .3479999999999994 Correction with q = .3469999999999994 Correction with q = .3459999999999994 Correction with q = .3449999999999994 Correction with q = .3439999999999994 Correction with q = .3429999999999994 Correction with q = .3419999999999994 Correction with q = .3409999999999994 Correction with q = .3399999999999994 Correction with q = .3389999999999994 Correction with q = .3379999999999994 Correction with q = .3369999999999994 Correction with q = .3359999999999994 Correction with q = .3349999999999994 Correction with q = .3339999999999994 Correction with q = .3329999999999994 Correction with q = .3319999999999994 Correction with q = .3309999999999994 Correction with q = .3299999999999994 Correction with q = .3289999999999994 Correction with q = .3279999999999994 Correction with q = .3269999999999994 Correction with q = .3259999999999994 Correction with q = .3249999999999994 Correction with q = .3239999999999994 Correction with q = .3229999999999994 Correction with q = .3219999999999994 Correction with q = 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.2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = 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.1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 Correction with q = .0069999999999991 Correction with q = .0059999999999991 Correction with q = .0049999999999991 Correction with q = .0039999999999991 Correction with q = .0029999999999991 Correction with q = .0019999999999991 (8 real changes made) (0 real changes made) . . estadd loc thisstat6 = "[" + string(Q[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat6 = "[" +string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat6 = "[" + string(Q[3, 1], "%9.3f")+"]": col3 . estadd loc thisstat6 = "["+ string(Q[4, 1], "%9.3f")+"]": col4 . estadd loc thisstat6 = "["+ string(Q[5, 1], "%9.3f")+"]": col5 . estadd loc thisstat6 = "["+ string(Q[6, 1], "%9.3f")+"]": col6 . estadd loc thisstat6 = "["+ string(Q[7, 1], "%9.3f")+"]": col7 . estadd loc thisstat6 = "["+ string(Q[8, 1], "%9.3f")+"]": col8 . . . loc experimentsII "posteriorII problemII problemIIHS problemIILS fairII govmoreII antidiscII fampo > lII AAanchorII legislationanchorII" . . . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experimentsII' { 2. . . ***Panel B: Wave B . . . qui reg `choice' T1 $controls if wave==2 [pweight=pweight], vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat20 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat21 = "`r(sestar)'": col`colnum' 7. sigstar gender, prec(3) 8. estadd loc thisstat24 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat25 = "`r(sestar)'": col`colnum' 10. sigstar democrat, prec(3) 11. estadd loc thisstat27 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat28 = "`r(sestar)'": col`colnum' 13. estadd loc thisstat30 = "`n'": col`colnum' 14. . . loc ++colnum 15. loc colnames "`colnames' `"`: var la `choice''"'" 16. . } . . . mat def P = J(10, 1, .) . reg posterior T1 $controls if wave==2 [pweight=pweight], vce(r) (sum of wgt is 5.8173e+02) note: wave omitted because of collinearity Linear regression Number of obs = 595 F(21, 573) = 7.25 Prob > F = 0.0000 R-squared = 0.2514 Root MSE = 18.245 ------------------------------------------------------------------------------- | Robust posteriorII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -11.19467 1.51957 -7.37 0.000 -14.17928 -8.210066 wave | 0 (omitted) gender | -3.293559 1.576255 -2.09 0.037 -6.389502 -.1976165 prior | .317477 .0739661 4.29 0.000 .1721993 .4627548 democrat | -.511369 1.695482 -0.30 0.763 -3.841486 2.818748 indep | -1.177065 2.296926 -0.51 0.609 -5.688486 3.334356 otherpol | -13.04041 7.376844 -1.77 0.078 -27.52936 1.44854 midwest | .9109612 2.392464 0.38 0.704 -3.788108 5.61003 south | 3.342076 2.153824 1.55 0.121 -.8882764 7.572429 west | 2.814374 1.878945 1.50 0.135 -.8760866 6.504834 age1 | -1.412002 5.154028 -0.27 0.784 -11.53509 8.711089 age2 | 4.468551 2.516472 1.78 0.076 -.4740837 9.411186 age3 | 1.722814 2.246847 0.77 0.444 -2.690246 6.135874 age4 | 1.972797 1.940533 1.02 0.310 -1.838628 5.784222 anychildren | 3.168496 1.737195 1.82 0.069 -.2435507 6.580542 loghhinc | .4126805 1.114411 0.37 0.711 -1.776149 2.60151 associatemore | 2.352855 1.497536 1.57 0.117 -.5884739 5.294184 fulltime | 2.767877 1.762452 1.57 0.117 -.6937761 6.229531 parttime | .2595066 2.675031 0.10 0.923 -4.994555 5.513568 selfemp | 2.572564 2.479744 1.04 0.300 -2.297933 7.44306 unemployed | 1.113798 4.042718 0.28 0.783 -6.826556 9.054152 student | 1.038982 4.76569 0.22 0.827 -8.321369 10.39933 _cons | 54.3869 15.53025 3.50 0.000 23.88373 84.89007 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[1, 1] = r(p) . reg problemII T1 $controls if wave==2 [pweight=pweight], vce(r) (sum of wgt is 5.9260e+02) note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 4.59 Prob > F = 0.0000 R-squared = 0.1335 Root MSE = .95234 ------------------------------------------------------------------------------- | Robust problemII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1835242 .0786091 2.33 0.020 .0291333 .337915 wave | 0 (omitted) gender | .3373058 .0810908 4.16 0.000 .1780406 .496571 prior | -.002321 .0024977 -0.93 0.353 -.0072265 .0025845 democrat | .5502075 .0932599 5.90 0.000 .3670419 .7333731 indep | .0844744 .1045546 0.81 0.419 -.1208744 .2898233 otherpol | .8375833 .2970463 2.82 0.005 .2541743 1.420992 midwest | -.100132 .1207913 -0.83 0.407 -.3373703 .1371063 south | -.0859829 .1151309 -0.75 0.455 -.312104 .1401382 west | -.0083855 .118625 -0.07 0.944 -.2413691 .2245981 age1 | -.1959994 .2416915 -0.81 0.418 -.6706899 .2786911 age2 | .0088767 .1281755 0.07 0.945 -.2428644 .2606178 age3 | .0737691 .1109395 0.66 0.506 -.1441199 .2916581 age4 | -.0631219 .1025526 -0.62 0.538 -.2645388 .138295 anychildren | .0056023 .0820579 0.07 0.946 -.1555623 .1667668 loghhinc | .0120571 .0642639 0.19 0.851 -.1141594 .1382737 associatemore | .1353426 .0891682 1.52 0.130 -.0397869 .310472 fulltime | -.0362092 .1040491 -0.35 0.728 -.2405652 .1681467 parttime | -.1102991 .1539867 -0.72 0.474 -.4127342 .1921361 selfemp | -.0240639 .158956 -0.15 0.880 -.3362589 .2881311 unemployed | .4184681 .2104445 1.99 0.047 .0051478 .8317884 student | .2928215 .3329341 0.88 0.379 -.3610726 .9467156 _cons | -.5373314 .7718585 -0.70 0.487 -2.053288 .9786251 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[2, 1] = r(p) . reg problemIIHS T1 $controls if wave==2 [pweight=pweight], vce(r) (sum of wgt is 5.9260e+02) note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 3.31 Prob > F = 0.0000 R-squared = 0.1048 Root MSE = .97146 ------------------------------------------------------------------------------- | Robust problemIIHS | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1157115 .0797367 1.45 0.147 -.0408941 .2723172 wave | 0 (omitted) gender | .2841635 .08268 3.44 0.001 .1217772 .4465498 prior | -.0022529 .0023156 -0.97 0.331 -.0068009 .0022951 democrat | .5262482 .093417 5.63 0.000 .3427741 .7097224 indep | .2011341 .1077432 1.87 0.062 -.0104773 .4127455 otherpol | .9086033 .2904302 3.13 0.002 .3381884 1.479018 midwest | -.0034608 .1279635 -0.03 0.978 -.2547854 .2478639 south | .0064725 .1143193 0.06 0.955 -.2180546 .2309996 west | .0375659 .1220651 0.31 0.758 -.2021743 .277306 age1 | .1106629 .2537731 0.44 0.663 -.3877561 .609082 age2 | .0936354 .1335405 0.70 0.483 -.1686428 .3559136 age3 | .0978357 .1140477 0.86 0.391 -.126158 .3218293 age4 | .0118015 .1047988 0.11 0.910 -.1940269 .21763 anychildren | .0364952 .084571 0.43 0.666 -.1296051 .2025955 loghhinc | .0203147 .0623245 0.33 0.745 -.1020927 .1427222 associatemore | .1644283 .0917592 1.79 0.074 -.0157898 .3446465 fulltime | -.0480407 .1109431 -0.43 0.665 -.2659369 .1698555 parttime | -.2531054 .1565767 -1.62 0.107 -.5606274 .0544166 selfemp | .114675 .16397 0.70 0.485 -.2073677 .4367177 unemployed | .0817987 .2208516 0.37 0.711 -.3519615 .5155589 student | .1766507 .3377185 0.52 0.601 -.4866401 .8399415 _cons | -.7137186 .7240181 -0.99 0.325 -2.135715 .7082778 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[3, 1] = r(p) . reg problemIILS T1 $controls if wave==2 [pweight=pweight], vce(r) (sum of wgt is 5.9260e+02) note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 3.39 Prob > F = 0.0000 R-squared = 0.0976 Root MSE = .9503 ------------------------------------------------------------------------------- | Robust problemIILS | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .1074452 .0781228 1.38 0.170 -.0459907 .2608811 wave | 0 (omitted) gender | .3110062 .0806514 3.86 0.000 .1526041 .4694083 prior | .0029908 .0021134 1.42 0.158 -.0011601 .0071416 democrat | .3607569 .0934838 3.86 0.000 .1771514 .5443623 indep | -.058156 .1057412 -0.55 0.583 -.2658354 .1495233 otherpol | .8369538 .2816721 2.97 0.003 .28374 1.390167 midwest | -.1298745 .1200065 -1.08 0.280 -.3655713 .1058223 south | -.1664009 .1082894 -1.54 0.125 -.3790851 .0462832 west | -.2155554 .1161786 -1.86 0.064 -.4437341 .0126234 age1 | -.2863421 .1926952 -1.49 0.138 -.6648022 .092118 age2 | .0690119 .1298066 0.53 0.595 -.1859328 .3239566 age3 | .2570935 .1113246 2.31 0.021 .0384481 .4757389 age4 | -.0532869 .1088742 -0.49 0.625 -.2671196 .1605458 anychildren | .0590314 .0844247 0.70 0.485 -.1067817 .2248445 loghhinc | -.0344736 .0627312 -0.55 0.583 -.1576798 .0887325 associatemore | .0824162 .088859 0.93 0.354 -.0921059 .2569382 fulltime | -.0491525 .1106827 -0.44 0.657 -.2665371 .1682321 parttime | -.1328745 .1461304 -0.91 0.364 -.4198796 .1541305 selfemp | .1023409 .1678401 0.61 0.542 -.2273029 .4319847 unemployed | .2061358 .2042743 1.01 0.313 -.195066 .6073376 student | .0210588 .2643375 0.08 0.937 -.4981091 .5402266 _cons | -.2407715 .7430633 -0.32 0.746 -1.700173 1.21863 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[4, 1] = r(p) . reg fairII T1 T1 $controls if wave==2 [pweight=pweight], vce(r) (sum of wgt is 5.9260e+02) note: T1 omitted because of collinearity note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 10.24 Prob > F = 0.0000 R-squared = 0.2412 Root MSE = .90145 ------------------------------------------------------------------------------- | Robust fairII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.0909848 .0743476 -1.22 0.222 -.237006 .0550363 T1 | 0 (omitted) wave | 0 (omitted) gender | -.2943619 .0768365 -3.83 0.000 -.4452713 -.1434525 prior | .0101472 .0020848 4.87 0.000 .0060525 .0142418 democrat | -.4656604 .0911086 -5.11 0.000 -.6446008 -.2867201 indep | -.321097 .0955064 -3.36 0.001 -.5086748 -.1335193 otherpol | -.5612685 .1733753 -3.24 0.001 -.9017836 -.2207534 midwest | -.0887937 .1224951 -0.72 0.469 -.3293782 .1517909 south | .1738469 .1120165 1.55 0.121 -.0461574 .3938511 west | -.0513301 .1099523 -0.47 0.641 -.2672801 .1646199 age1 | .2114235 .183466 1.15 0.250 -.14891 .571757 age2 | .5820189 .1246047 4.67 0.000 .337291 .8267468 age3 | .1851556 .1084586 1.71 0.088 -.0278609 .398172 age4 | .0803081 .0881728 0.91 0.363 -.0928664 .2534826 anychildren | .1876204 .0795589 2.36 0.019 .0313641 .3438767 loghhinc | -.0416905 .0586116 -0.71 0.477 -.1568057 .0734246 associatemore | .0709251 .0837456 0.85 0.397 -.0935542 .2354044 fulltime | .1663587 .0967382 1.72 0.086 -.0236384 .3563557 parttime | -.0347838 .1388721 -0.25 0.802 -.3075333 .2379658 selfemp | -.14672 .1550267 -0.95 0.344 -.4511979 .1577578 unemployed | -.2598764 .1541895 -1.69 0.092 -.5627099 .042957 student | -.5887569 .2370135 -2.48 0.013 -1.054259 -.1232543 _cons | -.2463875 .692915 -0.36 0.722 -1.607296 1.114521 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[5, 1] = r(p) . reg govmoreII T1 T1 $controls if wave==2 [pweight=pweight], vce(r) (sum of wgt is 5.9260e+02) note: T1 omitted because of collinearity note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 4.48 Prob > F = 0.0000 R-squared = 0.1435 Root MSE = .95008 ------------------------------------------------------------------------------- | Robust govmoreII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0956871 .0782354 1.22 0.222 -.0579699 .2493441 T1 | 0 (omitted) wave | 0 (omitted) gender | .1836405 .0800784 2.29 0.022 .0263637 .3409173 prior | -.0027345 .0023135 -1.18 0.238 -.0072782 .0018092 democrat | .6975155 .088531 7.88 0.000 .5236375 .8713934 indep | .20079 .1165324 1.72 0.085 -.0280836 .4296636 otherpol | .8280073 .2987982 2.77 0.006 .2411574 1.414857 midwest | -.1429614 .126491 -1.13 0.259 -.391394 .1054712 south | -.0409876 .1127524 -0.36 0.716 -.2624371 .1804619 west | -.1035246 .1113675 -0.93 0.353 -.3222541 .1152049 age1 | -.0709952 .2457933 -0.29 0.773 -.5537418 .4117514 age2 | .2310968 .1302431 1.77 0.077 -.0247051 .4868987 age3 | .1451714 .109228 1.33 0.184 -.0693562 .359699 age4 | -.0246843 .100156 -0.25 0.805 -.2213942 .1720256 anychildren | .0787345 .0803525 0.98 0.328 -.0790805 .2365495 loghhinc | .0072123 .05735 0.13 0.900 -.105425 .1198496 associatemore | -.0391288 .082931 -0.47 0.637 -.202008 .1237505 fulltime | -.0287705 .0987184 -0.29 0.771 -.2226569 .1651159 parttime | .237196 .1382126 1.72 0.087 -.0342583 .5086504 selfemp | .059291 .1522325 0.39 0.697 -.2396989 .3582809 unemployed | .0516505 .2223055 0.23 0.816 -.3849652 .4882661 student | .0476274 .2856982 0.17 0.868 -.5134937 .6087486 _cons | -.4476124 .6554836 -0.68 0.495 -1.735005 .8397799 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[6, 1] = r(p) . reg antidiscII T1 T1 $controls if wave==2 [pweight=pweight], vce(r) (sum of wgt is 5.9260e+02) note: T1 omitted because of collinearity note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 5.51 Prob > F = 0.0000 R-squared = 0.1687 Root MSE = .91046 ------------------------------------------------------------------------------- | Robust antidiscII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0727422 .0758925 0.96 0.338 -.0763133 .2217977 T1 | 0 (omitted) wave | 0 (omitted) gender | .2293393 .077542 2.96 0.003 .0770442 .3816344 prior | -.0017783 .0021385 -0.83 0.406 -.0059784 .0024218 democrat | .7282344 .0847187 8.60 0.000 .561844 .8946249 indep | .1947933 .115382 1.69 0.092 -.0318209 .4214076 otherpol | .9499567 .2960057 3.21 0.001 .3685914 1.531322 midwest | -.2767244 .1242311 -2.23 0.026 -.5207186 -.0327302 south | -.1300975 .1050158 -1.24 0.216 -.3363523 .0761572 west | -.2006851 .1090031 -1.84 0.066 -.4147709 .0134007 age1 | -.1338985 .2845207 -0.47 0.638 -.692707 .42491 age2 | .1058031 .1172824 0.90 0.367 -.1245436 .3361499 age3 | .1017119 .1069796 0.95 0.342 -.1083997 .3118235 age4 | -.0337143 .1019645 -0.33 0.741 -.233976 .1665475 anychildren | .0104065 .0773254 0.13 0.893 -.1414633 .1622762 loghhinc | .0575616 .0571403 1.01 0.314 -.0546639 .1697871 associatemore | .0238005 .0839678 0.28 0.777 -.1411151 .1887161 fulltime | -.0621024 .1015047 -0.61 0.541 -.2614612 .1372563 parttime | .2367499 .1443368 1.64 0.101 -.0467325 .5202323 selfemp | .1777073 .1534528 1.16 0.247 -.1236792 .4790938 unemployed | .0648093 .2150692 0.30 0.763 -.357594 .4872127 student | .1944274 .295188 0.66 0.510 -.3853321 .7741868 _cons | -.9292395 .664819 -1.40 0.163 -2.234967 .3764878 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[7, 1] = r(p) . reg fampolII T1 T1 $controls if wave==2 [pweight=pweight], vce(r) (sum of wgt is 5.9260e+02) note: T1 omitted because of collinearity note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 3.80 Prob > F = 0.0000 R-squared = 0.1286 Root MSE = .93239 ------------------------------------------------------------------------------- | Robust fampolII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0747551 .0773745 0.97 0.334 -.0772112 .2267213 T1 | 0 (omitted) wave | 0 (omitted) gender | .2054342 .0794755 2.58 0.010 .0493415 .3615268 prior | -.0014072 .002312 -0.61 0.543 -.005948 .0031336 democrat | .6180418 .0874878 7.06 0.000 .4462128 .7898708 indep | .1137718 .1134275 1.00 0.316 -.1090038 .3365474 otherpol | .5313052 .2668732 1.99 0.047 .0071572 1.055453 midwest | -.2197269 .1293979 -1.70 0.090 -.4738689 .0344151 south | -.1696417 .1140475 -1.49 0.137 -.3936349 .0543515 west | -.1905709 .1128329 -1.69 0.092 -.4121785 .0310367 age1 | -.05189 .287492 -0.18 0.857 -.6165342 .5127543 age2 | .207698 .1298946 1.60 0.110 -.0474195 .4628155 age3 | .1188637 .1082932 1.10 0.273 -.0938278 .3315553 age4 | .0117304 .1015811 0.12 0.908 -.1877784 .2112391 anychildren | .1455675 .0808704 1.80 0.072 -.0132648 .3043999 loghhinc | .0045858 .0545407 0.08 0.933 -.1025339 .1117056 associatemore | -.0123307 .0871596 -0.14 0.888 -.1835152 .1588539 fulltime | -.090082 .1030379 -0.87 0.382 -.292452 .1122879 parttime | .1607015 .1592063 1.01 0.313 -.1519851 .4733881 selfemp | .0205339 .1548903 0.13 0.895 -.283676 .3247438 unemployed | .0891664 .2472743 0.36 0.719 -.3964888 .5748216 student | .2153326 .3010389 0.72 0.475 -.3759182 .8065834 _cons | -.3992667 .6321685 -0.63 0.528 -1.640867 .8423339 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[8, 1] = r(p) . reg AAanchorII T1 $controls if wave==2 [pweight=pweight], vce(r) (sum of wgt is 5.9260e+02) note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 3.88 Prob > F = 0.0000 R-squared = 0.1250 Root MSE = .95179 ------------------------------------------------------------------------------- | Robust AAanchorII | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0089001 .0781448 0.11 0.909 -.144579 .1623792 wave | 0 (omitted) gender | .1498878 .0800418 1.87 0.062 -.0073171 .3070927 prior | -.0013343 .002229 -0.60 0.550 -.0057121 .0030434 democrat | .5830079 .0914901 6.37 0.000 .4033182 .7626976 indep | .1785287 .1089496 1.64 0.102 -.035452 .3925094 otherpol | .2292134 .2976949 0.77 0.442 -.3554697 .8138965 midwest | -.1344534 .1239589 -1.08 0.279 -.377913 .1090062 south | -.1062846 .1128784 -0.94 0.347 -.3279818 .1154125 west | -.1333294 .1118277 -1.19 0.234 -.3529629 .086304 age1 | .4890332 .2283838 2.14 0.033 .0404797 .9375868 age2 | .3803372 .1289624 2.95 0.003 .1270506 .6336238 age3 | .3637203 .1149816 3.16 0.002 .1378924 .5895481 age4 | .2137002 .1026468 2.08 0.038 .0120984 .4153021 anychildren | .1036842 .0833331 1.24 0.214 -.0599849 .2673533 loghhinc | -.0174578 .0610393 -0.29 0.775 -.1373411 .1024255 associatemore | -.0634021 .0866087 -0.73 0.464 -.2335045 .1067003 fulltime | -.0269664 .1064979 -0.25 0.800 -.236132 .1821992 parttime | .182043 .1461773 1.25 0.213 -.1050541 .4691402 selfemp | .066374 .1739631 0.38 0.703 -.2752955 .4080434 unemployed | -.2786128 .2253217 -1.24 0.217 -.7211523 .1639268 student | .0381993 .2561315 0.15 0.881 -.4648518 .5412504 _cons | -.1781802 .6976993 -0.26 0.799 -1.548486 1.192125 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[9, 1] = r(p) . reg legislationanchorII T1 $controls if wave==2 [pweight=pweight], vce(r) (sum of wgt is 5.9260e+02) note: wave omitted because of collinearity Linear regression Number of obs = 606 F(21, 584) = 3.91 Prob > F = 0.0000 R-squared = 0.1212 Root MSE = .95714 ------------------------------------------------------------------------------- | Robust legislation~I | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .0960297 .0790875 1.21 0.225 -.0593009 .2513603 wave | 0 (omitted) gender | .1974441 .0832618 2.37 0.018 .0339151 .3609731 prior | -.0000512 .0025113 -0.02 0.984 -.0049835 .0048811 democrat | .6415791 .0905637 7.08 0.000 .4637088 .8194493 indep | .0635866 .112058 0.57 0.571 -.1564991 .2836724 otherpol | .6588273 .363019 1.81 0.070 -.0541546 1.371809 midwest | -.1437006 .1226844 -1.17 0.242 -.384657 .0972559 south | -.137414 .1145276 -1.20 0.231 -.3623502 .0875222 west | -.1325884 .1141376 -1.16 0.246 -.3567586 .0915819 age1 | -.1541154 .254164 -0.61 0.545 -.6533021 .3450714 age2 | .1223774 .1269558 0.96 0.335 -.1269682 .3717229 age3 | .1643467 .1100414 1.49 0.136 -.0517784 .3804718 age4 | .0301334 .1062233 0.28 0.777 -.1784929 .2387597 anychildren | .0160008 .082434 0.19 0.846 -.1459025 .1779041 loghhinc | -.012623 .0594664 -0.21 0.832 -.1294171 .1041711 associatemore | .0322715 .0885371 0.36 0.716 -.1416185 .2061615 fulltime | -.0889138 .1071599 -0.83 0.407 -.2993794 .1215519 parttime | -.0564062 .136518 -0.41 0.680 -.3245323 .2117199 selfemp | .0515579 .1710974 0.30 0.763 -.2844832 .387599 unemployed | .1532315 .257608 0.59 0.552 -.3527195 .6591825 student | .3035541 .30002 1.01 0.312 -.2856956 .8928038 _cons | -.2258347 .6909103 -0.33 0.744 -1.582806 1.131137 ------------------------------------------------------------------------------- . qui test T1 = 0 . mat def P[10, 1] = r(p) . . . minq P, q("Q") step(0.001) number of observations will be reset to 10 Press any key to continue, or Break to abort number of observations (_N) was 0, now 10 Correction with q = 1 Correction with q = .999 Correction with q = .998 Correction with q = .997 Correction with q = .996 Correction with q = .995 Correction with q = .994 Correction with q = .993 Correction with q = .992 Correction with q = .991 Correction with q = .99 Correction with q = .989 Correction with q = .988 Correction with q = .987 Correction with q = .986 Correction with q = .985 Correction with q = .984 Correction with q = .983 Correction with q = .982 Correction with q = .981 Correction with q = .98 Correction with q = .979 Correction with q = .978 Correction with q = .977 Correction with q = .976 Correction with q = .975 Correction with q = .974 Correction with q = .973 Correction with q = .972 Correction with q = .971 Correction with q = .97 Correction with q = .969 Correction with q = .968 Correction with q = .967 Correction with q = .966 Correction with q = .965 Correction with q = .964 Correction with q = .963 Correction with q = .962 Correction with q = .961 Correction with q = .96 Correction with q = .959 Correction with q = .958 Correction with q = .957 Correction with q = .956 Correction with q = .955 Correction with q = .954 Correction with q = .953 Correction with q = .952 Correction with q = .951 Correction with q = .95 Correction with q = .949 Correction with q = .948 Correction with q = .947 Correction with q = .946 Correction with q = .945 Correction with q = .944 Correction with q = .943 Correction with q = .942 Correction with q = .941 Correction with q = .94 Correction with q = .939 Correction with q = .9379999999999999 Correction with q = .9369999999999999 Correction with q = .9359999999999999 Correction with q = .9349999999999999 Correction with q = .9339999999999999 Correction with q = .9329999999999999 Correction with q = .9319999999999999 Correction with q = .9309999999999999 Correction with q = .9299999999999999 Correction with q = .9289999999999999 Correction with q = .9279999999999999 Correction with q = .9269999999999999 Correction with q = .9259999999999999 Correction with q = .9249999999999999 Correction with q = .9239999999999999 Correction with q = .9229999999999999 Correction with q = .9219999999999999 Correction with q = .9209999999999999 Correction with q = .9199999999999999 Correction with q = .9189999999999999 Correction with q = .9179999999999999 Correction with q = .9169999999999999 Correction with q = .9159999999999999 Correction with q = .9149999999999999 Correction with q = .9139999999999999 Correction with q = .9129999999999999 Correction with q = .9119999999999999 Correction with q = .9109999999999999 Correction with q = .9099999999999999 Correction with q = .9089999999999999 Correction with q = .9079999999999999 Correction with q = .9069999999999999 Correction with q = .9059999999999999 Correction with q = .9049999999999999 Correction with q = .9039999999999999 Correction with q = .9029999999999999 Correction with q = .9019999999999999 Correction with q = .9009999999999999 Correction with q = .8999999999999999 Correction with q = .8989999999999999 Correction with q = .8979999999999999 Correction with q = .8969999999999999 Correction with q = .8959999999999999 Correction with q = .8949999999999999 Correction with q = .8939999999999999 Correction with q = .8929999999999999 Correction with q = .8919999999999999 Correction with q = .8909999999999999 Correction with q = .8899999999999999 Correction with q = .8889999999999999 Correction with q = .8879999999999999 Correction with q = .8869999999999999 Correction with q = .8859999999999999 Correction with q = .8849999999999999 Correction with q = .8839999999999999 Correction with q = .8829999999999999 Correction with q = .8819999999999999 Correction with q = .8809999999999999 Correction with q = .8799999999999999 Correction with q = .8789999999999999 Correction with q = .8779999999999999 Correction with q = .8769999999999999 Correction with q = .8759999999999999 Correction with q = .8749999999999999 Correction with q = .8739999999999999 Correction with q = .8729999999999999 Correction with q = .8719999999999999 Correction with q = .8709999999999999 Correction with q = .8699999999999999 Correction with q = .8689999999999999 Correction with q = .8679999999999999 Correction with q = .8669999999999999 Correction with q = .8659999999999999 Correction with q = .8649999999999999 Correction with q = .8639999999999999 Correction with q = .8629999999999999 Correction with q = .8619999999999999 Correction with q = .8609999999999999 Correction with q = .8599999999999999 Correction with q = .8589999999999999 Correction with q = .8579999999999999 Correction with q = .8569999999999999 Correction with q = .8559999999999999 Correction with q = .8549999999999999 Correction with q = .8539999999999999 Correction with q = .8529999999999999 Correction with q = .8519999999999999 Correction with q = .8509999999999999 Correction with q = .8499999999999999 Correction with q = .8489999999999999 Correction with q = .8479999999999999 Correction with q = .8469999999999999 Correction with q = .8459999999999999 Correction with q = .8449999999999999 Correction with q = .8439999999999999 Correction with q = .8429999999999999 Correction with q = .8419999999999999 Correction with q = .8409999999999999 Correction with q = .8399999999999999 Correction with q = .8389999999999999 Correction with q = .8379999999999999 Correction with q = .8369999999999999 Correction with q = .8359999999999999 Correction with q = .8349999999999999 Correction with q = .8339999999999999 Correction with q = .8329999999999999 Correction with q = .8319999999999999 Correction with q = .8309999999999999 Correction with q = .8299999999999999 Correction with q = .8289999999999999 Correction with q = .8279999999999999 Correction with q = .8269999999999999 Correction with q = .8259999999999999 Correction with q = .8249999999999998 Correction with q = .8239999999999998 Correction with q = .8229999999999998 Correction with q = .8219999999999998 Correction with q = .8209999999999998 Correction with q = .8199999999999998 Correction with q = .8189999999999998 Correction with q = .8179999999999998 Correction with q = .8169999999999998 Correction with q = .8159999999999998 Correction with q = .8149999999999998 Correction with q = .8139999999999998 Correction with q = .8129999999999998 Correction with q = .8119999999999998 Correction with q = .8109999999999998 Correction with q = .8099999999999998 Correction with q = .8089999999999998 Correction with q = .8079999999999998 Correction with q = .8069999999999998 Correction with q = .8059999999999998 Correction with q = .8049999999999998 Correction with q = .8039999999999998 Correction with q = .8029999999999998 Correction with q = .8019999999999998 Correction with q = .8009999999999998 Correction with q = .7999999999999998 Correction with q = .7989999999999998 Correction with q = .7979999999999998 Correction with q = .7969999999999998 Correction with q = .7959999999999998 Correction with q = .7949999999999998 Correction with q = .7939999999999998 Correction with q = .7929999999999998 Correction with q = .7919999999999998 Correction with q = .7909999999999998 Correction with q = .7899999999999998 Correction with q = .7889999999999998 Correction with q = .7879999999999998 Correction with q = .7869999999999998 Correction with q = .7859999999999998 Correction with q = .7849999999999998 Correction with q = .7839999999999998 Correction with q = .7829999999999998 Correction with q = .7819999999999998 Correction with q = .7809999999999998 Correction with q = .7799999999999998 Correction with q = .7789999999999998 Correction with q = .7779999999999998 Correction with q = .7769999999999998 Correction with q = .7759999999999998 Correction with q = .7749999999999998 Correction with q = .7739999999999998 Correction with q = .7729999999999998 Correction with q = .7719999999999998 Correction with q = .7709999999999998 Correction with q = .7699999999999998 Correction with q = .7689999999999998 Correction with q = .7679999999999998 Correction with q = .7669999999999998 Correction with q = .7659999999999998 Correction with q = .7649999999999998 Correction with q = .7639999999999998 Correction with q = .7629999999999998 Correction with q = .7619999999999998 Correction with q = .7609999999999998 Correction with q = .7599999999999998 Correction with q = .7589999999999998 Correction with q = .7579999999999998 Correction with q = .7569999999999998 Correction with q = .7559999999999998 Correction with q = .7549999999999998 Correction with q = .7539999999999998 Correction with q = .7529999999999998 Correction with q = .7519999999999998 Correction with q = .7509999999999998 Correction with q = .7499999999999998 Correction with q = .7489999999999998 Correction with q = .7479999999999998 Correction with q = .7469999999999998 Correction with q = .7459999999999998 Correction with q = .7449999999999998 Correction with q = .7439999999999998 Correction with q = .7429999999999998 Correction with q = .7419999999999998 Correction with q = .7409999999999998 Correction with q = .7399999999999998 Correction with q = .7389999999999998 Correction with q = .7379999999999998 Correction with q = .7369999999999998 Correction with q = .7359999999999998 Correction with q = .7349999999999998 Correction with q = .7339999999999998 Correction with q = .7329999999999998 Correction with q = .7319999999999998 Correction with q = .7309999999999998 Correction with q = .7299999999999998 Correction with q = .7289999999999998 Correction with q = .7279999999999998 Correction with q = .7269999999999998 Correction with q = .7259999999999998 Correction with q = .7249999999999998 Correction with q = .7239999999999998 Correction with q = .7229999999999998 Correction with q = .7219999999999998 Correction with q = .7209999999999998 Correction with q = .7199999999999998 Correction with q = .7189999999999998 Correction with q = .7179999999999998 Correction with q = .7169999999999998 Correction with q = .7159999999999998 Correction with q = .7149999999999998 Correction with q = .7139999999999998 Correction with q = .7129999999999998 Correction with q = .7119999999999997 Correction with q = .7109999999999997 Correction with q = .7099999999999997 Correction with q = .7089999999999997 Correction with q = .7079999999999997 Correction with q = .7069999999999997 Correction with q = .7059999999999997 Correction with q = .7049999999999997 Correction with q = .7039999999999997 Correction with q = .7029999999999997 Correction with q = .7019999999999997 Correction with q = .7009999999999997 Correction with q = .6999999999999997 Correction with q = .6989999999999997 Correction with q = .6979999999999997 Correction with q = .6969999999999997 Correction with q = .6959999999999997 Correction with q = .6949999999999997 Correction with q = .6939999999999997 Correction with q = .6929999999999997 Correction with q = .6919999999999997 Correction with q = .6909999999999997 Correction with q = .6899999999999997 Correction with q = .6889999999999997 Correction with q = .6879999999999997 Correction with q = .6869999999999997 Correction with q = .6859999999999997 Correction with q = .6849999999999997 Correction with q = .6839999999999997 Correction with q = .6829999999999997 Correction with q = .6819999999999997 Correction with q = .6809999999999997 Correction with q = .6799999999999997 Correction with q = .6789999999999997 Correction with q = .6779999999999997 Correction with q = .6769999999999997 Correction with q = .6759999999999997 Correction with q = .6749999999999997 Correction with q = .6739999999999997 Correction with q = .6729999999999997 Correction with q = .6719999999999997 Correction with q = .6709999999999997 Correction with q = .6699999999999997 Correction with q = .6689999999999997 Correction with q = .6679999999999997 Correction with q = .6669999999999997 Correction with q = .6659999999999997 Correction with q = .6649999999999997 Correction with q = .6639999999999997 Correction with q = .6629999999999997 Correction with q = .6619999999999997 Correction with q = .6609999999999997 Correction with q = .6599999999999997 Correction with q = .6589999999999997 Correction with q = .6579999999999997 Correction with q = .6569999999999997 Correction with q = .6559999999999997 Correction with q = .6549999999999997 Correction with q = .6539999999999997 Correction with q = .6529999999999997 Correction with q = .6519999999999997 Correction with q = .6509999999999997 Correction with q = .6499999999999997 Correction with q = .6489999999999997 Correction with q = .6479999999999997 Correction with q = .6469999999999997 Correction with q = .6459999999999997 Correction with q = .6449999999999997 Correction with q = .6439999999999997 Correction with q = .6429999999999997 Correction with q = .6419999999999997 Correction with q = .6409999999999997 Correction with q = .6399999999999997 Correction with q = .6389999999999997 Correction with q = .6379999999999997 Correction with q = .6369999999999997 Correction with q = .6359999999999997 Correction with q = .6349999999999997 Correction with q = .6339999999999997 Correction with q = .6329999999999997 Correction with q = .6319999999999997 Correction with q = .6309999999999997 Correction with q = .6299999999999997 Correction with q = .6289999999999997 Correction with q = .6279999999999997 Correction with q = .6269999999999997 Correction with q = .6259999999999997 Correction with q = .6249999999999997 Correction with q = .6239999999999997 Correction with q = .6229999999999997 Correction with q = .6219999999999997 Correction with q = .6209999999999997 Correction with q = .6199999999999997 Correction with q = .6189999999999997 Correction with q = .6179999999999997 Correction with q = .6169999999999997 Correction with q = .6159999999999997 Correction with q = .6149999999999997 Correction with q = .6139999999999997 Correction with q = .6129999999999997 Correction with q = .6119999999999997 Correction with q = .6109999999999997 Correction with q = .6099999999999997 Correction with q = .6089999999999997 Correction with q = .6079999999999997 Correction with q = .6069999999999997 Correction with q = .6059999999999997 Correction with q = .6049999999999997 Correction with q = .6039999999999997 Correction with q = .6029999999999997 Correction with q = .6019999999999997 Correction with q = .6009999999999997 Correction with q = .5999999999999996 Correction with q = .5989999999999996 Correction with q = .5979999999999996 Correction with q = .5969999999999996 Correction with q = .5959999999999996 Correction with q = .5949999999999996 Correction with q = .5939999999999996 Correction with q = .5929999999999996 Correction with q = .5919999999999996 Correction with q = .5909999999999996 Correction with q = .5899999999999996 Correction with q = .5889999999999996 Correction with q = .5879999999999996 Correction with q = .5869999999999996 Correction with q = .5859999999999996 Correction with q = .5849999999999996 Correction with q = .5839999999999996 Correction with q = .5829999999999996 Correction with q = .5819999999999996 Correction with q = .5809999999999996 Correction with q = .5799999999999996 Correction with q = .5789999999999996 Correction with q = .5779999999999996 Correction with q = .5769999999999996 Correction with q = .5759999999999996 Correction with q = .5749999999999996 Correction with q = .5739999999999996 Correction with q = .5729999999999996 Correction with q = .5719999999999996 Correction with q = .5709999999999996 Correction with q = .5699999999999996 Correction with q = .5689999999999996 Correction with q = .5679999999999996 Correction with q = .5669999999999996 Correction with q = .5659999999999996 Correction with q = .5649999999999996 Correction with q = .5639999999999996 Correction with q = .5629999999999996 Correction with q = .5619999999999996 Correction with q = .5609999999999996 Correction with q = .5599999999999996 Correction with q = .5589999999999996 Correction with q = .5579999999999996 Correction with q = .5569999999999996 Correction with q = .5559999999999996 Correction with q = .5549999999999996 Correction with q = .5539999999999996 Correction with q = .5529999999999996 Correction with q = .5519999999999996 Correction with q = .5509999999999996 Correction with q = .5499999999999996 Correction with q = .5489999999999996 Correction with q = .5479999999999996 Correction with q = .5469999999999996 Correction with q = .5459999999999996 Correction with q = .5449999999999996 Correction with q = .5439999999999996 Correction with q = .5429999999999996 Correction with q = .5419999999999996 Correction with q = .5409999999999996 Correction with q = .5399999999999996 Correction with q = .5389999999999996 Correction with q = .5379999999999996 Correction with q = .5369999999999996 Correction with q = .5359999999999996 Correction with q = .5349999999999996 Correction with q = .5339999999999996 Correction with q = .5329999999999996 Correction with q = .5319999999999996 Correction with q = .5309999999999996 Correction with q = .5299999999999996 Correction with q = .5289999999999996 Correction with q = .5279999999999996 Correction with q = .5269999999999996 Correction with q = .5259999999999996 Correction with q = .5249999999999996 Correction with q = .5239999999999996 Correction with q = .5229999999999996 Correction with q = .5219999999999996 Correction with q = .5209999999999996 Correction with q = .5199999999999996 Correction with q = .5189999999999996 Correction with q = .5179999999999996 Correction with q = .5169999999999996 Correction with q = .5159999999999996 Correction with q = .5149999999999996 Correction with q = .5139999999999996 Correction with q = .5129999999999996 Correction with q = .5119999999999996 Correction with q = .5109999999999996 Correction with q = .5099999999999996 Correction with q = .5089999999999996 Correction with q = .5079999999999996 Correction with q = .5069999999999996 Correction with q = .5059999999999996 Correction with q = .5049999999999996 Correction with q = .5039999999999996 Correction with q = .5029999999999996 Correction with q = .5019999999999996 Correction with q = .5009999999999996 Correction with q = .4999999999999996 Correction with q = .4989999999999996 Correction with q = .4979999999999996 Correction with q = .4969999999999996 Correction with q = .4959999999999996 Correction with q = .4949999999999996 Correction with q = .4939999999999996 Correction with q = .4929999999999996 Correction with q = .4919999999999996 Correction with q = .4909999999999996 Correction with q = .4899999999999996 Correction with q = .4889999999999996 Correction with q = .4879999999999996 Correction with q = .4869999999999995 Correction with q = .4859999999999995 Correction with q = .4849999999999995 Correction with q = .4839999999999995 Correction with q = .4829999999999995 Correction with q = .4819999999999995 Correction with q = .4809999999999995 Correction with q = .4799999999999995 Correction with q = .4789999999999995 Correction with q = .4779999999999995 Correction with q = .4769999999999995 Correction with q = 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.4499999999999995 Correction with q = .4489999999999995 Correction with q = .4479999999999995 Correction with q = .4469999999999995 Correction with q = .4459999999999995 Correction with q = .4449999999999995 Correction with q = .4439999999999995 Correction with q = .4429999999999995 Correction with q = .4419999999999995 Correction with q = .4409999999999995 Correction with q = .4399999999999995 Correction with q = .4389999999999995 Correction with q = .4379999999999995 Correction with q = .4369999999999995 Correction with q = .4359999999999995 Correction with q = .4349999999999995 Correction with q = .4339999999999995 Correction with q = .4329999999999995 Correction with q = .4319999999999995 Correction with q = .4309999999999995 Correction with q = .4299999999999995 Correction with q = .4289999999999995 Correction with q = .4279999999999995 Correction with q = .4269999999999995 Correction with q = .4259999999999995 Correction with q = .4249999999999995 Correction with q = .4239999999999995 Correction with q = .4229999999999995 Correction with q = .4219999999999995 Correction with q = .4209999999999995 Correction with q = .4199999999999995 Correction with q = .4189999999999995 Correction with q = .4179999999999995 Correction with q = .4169999999999995 Correction with q = .4159999999999995 Correction with q = .4149999999999995 Correction with q = .4139999999999995 Correction with q = .4129999999999995 Correction with q = .4119999999999995 Correction with q = .4109999999999995 Correction with q = .4099999999999995 Correction with q = .4089999999999995 Correction with q = .4079999999999995 Correction with q = .4069999999999995 Correction with q = .4059999999999995 Correction with q = .4049999999999995 Correction with q = .4039999999999995 Correction with q = .4029999999999995 Correction with q = .4019999999999995 Correction with q = .4009999999999995 Correction with q = .3999999999999995 Correction with q = .3989999999999995 Correction with q = .3979999999999995 Correction with q = .3969999999999995 Correction with q = .3959999999999995 Correction with q = .3949999999999995 Correction with q = .3939999999999995 Correction with q = .3929999999999995 Correction with q = .3919999999999995 Correction with q = .3909999999999995 Correction with q = .3899999999999995 Correction with q = .3889999999999995 Correction with q = .3879999999999995 Correction with q = .3869999999999995 Correction with q = .3859999999999995 Correction with q = .3849999999999995 Correction with q = .3839999999999995 Correction with q = .3829999999999995 Correction with q = .3819999999999995 Correction with q = .3809999999999995 Correction with q = .3799999999999995 Correction with q = .3789999999999995 Correction with q = .3779999999999995 Correction with q = .3769999999999995 Correction with q = .3759999999999995 Correction with q = .3749999999999994 Correction with q = .3739999999999994 Correction with q = .3729999999999994 Correction with q = .3719999999999994 Correction with q = .3709999999999994 Correction with q = .3699999999999994 Correction with q = .3689999999999994 Correction with q = .3679999999999994 Correction with q = .3669999999999994 Correction with q = .3659999999999994 Correction with q = .3649999999999994 Correction with q = .3639999999999994 Correction with q = .3629999999999994 Correction with q = .3619999999999994 Correction with q = .3609999999999994 Correction with q = .3599999999999994 Correction with q = .3589999999999994 Correction with q = .3579999999999994 Correction with q = .3569999999999994 Correction with q = .3559999999999994 Correction with q = .3549999999999994 Correction with q = .3539999999999994 Correction with q = .3529999999999994 Correction with q = .3519999999999994 Correction with q = .3509999999999994 Correction with q = .3499999999999994 Correction with q = .3489999999999994 Correction with q = .3479999999999994 Correction with q = .3469999999999994 Correction with q = 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.3199999999999994 Correction with q = .3189999999999994 Correction with q = .3179999999999994 Correction with q = .3169999999999994 Correction with q = .3159999999999994 Correction with q = .3149999999999994 Correction with q = .3139999999999994 Correction with q = .3129999999999994 Correction with q = .3119999999999994 Correction with q = .3109999999999994 Correction with q = .3099999999999994 Correction with q = .3089999999999994 Correction with q = .3079999999999994 Correction with q = .3069999999999994 Correction with q = .3059999999999994 Correction with q = .3049999999999994 Correction with q = .3039999999999994 Correction with q = .3029999999999994 Correction with q = .3019999999999994 Correction with q = .3009999999999994 Correction with q = .2999999999999994 Correction with q = .2989999999999994 Correction with q = .2979999999999994 Correction with q = .2969999999999994 Correction with q = .2959999999999994 Correction with q = .2949999999999994 Correction with q = .2939999999999994 Correction with q = .2929999999999994 Correction with q = .2919999999999994 Correction with q = .2909999999999994 Correction with q = .2899999999999994 Correction with q = .2889999999999994 Correction with q = .2879999999999994 Correction with q = .2869999999999994 Correction with q = .2859999999999994 Correction with q = .2849999999999994 Correction with q = .2839999999999994 Correction with q = .2829999999999994 Correction with q = .2819999999999994 Correction with q = .2809999999999994 Correction with q = .2799999999999994 Correction with q = .2789999999999994 Correction with q = .2779999999999994 Correction with q = .2769999999999994 Correction with q = .2759999999999994 Correction with q = .2749999999999994 Correction with q = .2739999999999994 Correction with q = .2729999999999994 Correction with q = .2719999999999994 Correction with q = .2709999999999994 Correction with q = .2699999999999994 Correction with q = .2689999999999994 Correction with q = 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.2419999999999993 Correction with q = .2409999999999993 Correction with q = .2399999999999993 Correction with q = .2389999999999993 Correction with q = .2379999999999993 Correction with q = .2369999999999993 Correction with q = .2359999999999993 Correction with q = .2349999999999993 Correction with q = .2339999999999993 Correction with q = .2329999999999993 Correction with q = .2319999999999993 Correction with q = .2309999999999993 Correction with q = .2299999999999993 Correction with q = .2289999999999993 Correction with q = .2279999999999993 Correction with q = .2269999999999993 Correction with q = .2259999999999993 Correction with q = .2249999999999993 Correction with q = .2239999999999993 Correction with q = .2229999999999993 Correction with q = .2219999999999993 Correction with q = .2209999999999993 Correction with q = .2199999999999993 Correction with q = .2189999999999993 Correction with q = .2179999999999993 Correction with q = .2169999999999993 Correction with q = .2159999999999993 Correction with q = .2149999999999993 Correction with q = .2139999999999993 Correction with q = .2129999999999993 Correction with q = .2119999999999993 Correction with q = .2109999999999993 Correction with q = .2099999999999993 Correction with q = .2089999999999993 Correction with q = .2079999999999993 Correction with q = .2069999999999993 Correction with q = .2059999999999993 Correction with q = .2049999999999993 Correction with q = .2039999999999993 Correction with q = .2029999999999993 Correction with q = .2019999999999993 Correction with q = .2009999999999993 Correction with q = .1999999999999993 Correction with q = .1989999999999993 Correction with q = .1979999999999993 Correction with q = .1969999999999993 Correction with q = .1959999999999993 Correction with q = .1949999999999993 Correction with q = .1939999999999993 Correction with q = .1929999999999993 Correction with q = .1919999999999993 Correction with q = .1909999999999993 Correction with q = .1899999999999993 Correction with q = .1889999999999993 Correction with q = .1879999999999993 Correction with q = .1869999999999993 Correction with q = .1859999999999993 Correction with q = .1849999999999993 Correction with q = .1839999999999993 Correction with q = .1829999999999993 Correction with q = .1819999999999993 Correction with q = .1809999999999993 Correction with q = .1799999999999993 Correction with q = .1789999999999993 Correction with q = .1779999999999993 Correction with q = .1769999999999993 Correction with q = .1759999999999993 Correction with q = .1749999999999993 Correction with q = .1739999999999993 Correction with q = .1729999999999993 Correction with q = .1719999999999993 Correction with q = .1709999999999993 Correction with q = .1699999999999993 Correction with q = .1689999999999993 Correction with q = .1679999999999993 Correction with q = .1669999999999993 Correction with q = .1659999999999993 Correction with q = .1649999999999993 Correction with q = .1639999999999993 Correction with q = .1629999999999993 Correction with q = .1619999999999993 Correction with q = .1609999999999993 Correction with q = .1599999999999993 Correction with q = .1589999999999993 Correction with q = .1579999999999993 Correction with q = .1569999999999993 Correction with q = .1559999999999993 Correction with q = .1549999999999993 Correction with q = .1539999999999993 Correction with q = .1529999999999993 Correction with q = .1519999999999993 Correction with q = .1509999999999993 Correction with q = .1499999999999993 Correction with q = .1489999999999992 Correction with q = .1479999999999992 Correction with q = .1469999999999992 Correction with q = .1459999999999992 Correction with q = .1449999999999992 Correction with q = .1439999999999992 Correction with q = .1429999999999992 Correction with q = .1419999999999992 Correction with q = .1409999999999992 Correction with q = .1399999999999992 Correction with q = .1389999999999992 Correction with q = .1379999999999992 Correction with q = .1369999999999992 Correction with q = .1359999999999992 Correction with q = .1349999999999992 Correction with q = .1339999999999992 Correction with q = .1329999999999992 Correction with q = .1319999999999992 Correction with q = .1309999999999992 Correction with q = .1299999999999992 Correction with q = .1289999999999992 Correction with q = .1279999999999992 Correction with q = .1269999999999992 Correction with q = .1259999999999992 Correction with q = .1249999999999992 Correction with q = .1239999999999992 Correction with q = .1229999999999992 Correction with q = .1219999999999992 Correction with q = .1209999999999992 Correction with q = .1199999999999992 Correction with q = .1189999999999992 Correction with q = .1179999999999992 Correction with q = .1169999999999992 Correction with q = .1159999999999992 Correction with q = .1149999999999992 Correction with q = .1139999999999992 Correction with q = .1129999999999992 Correction with q = .1119999999999992 Correction with q = .1109999999999992 Correction with q = .1099999999999992 Correction with q = .1089999999999992 Correction with q = .1079999999999992 Correction with q = .1069999999999992 Correction with q = .1059999999999992 Correction with q = .1049999999999992 Correction with q = .1039999999999992 Correction with q = .1029999999999992 Correction with q = .1019999999999992 Correction with q = .1009999999999992 Correction with q = .0999999999999992 Correction with q = .0989999999999992 Correction with q = .0979999999999992 Correction with q = .0969999999999992 Correction with q = .0959999999999992 Correction with q = .0949999999999992 Correction with q = .0939999999999992 Correction with q = .0929999999999992 Correction with q = .0919999999999992 Correction with q = .0909999999999992 Correction with q = .0899999999999992 Correction with q = .0889999999999992 Correction with q = .0879999999999992 Correction with q = .0869999999999992 Correction with q = .0859999999999992 Correction with q = .0849999999999992 Correction with q = .0839999999999992 Correction with q = .0829999999999992 Correction with q = .0819999999999992 Correction with q = .0809999999999992 Correction with q = .0799999999999992 Correction with q = .0789999999999992 Correction with q = .0779999999999992 Correction with q = .0769999999999992 Correction with q = .0759999999999992 Correction with q = .0749999999999992 Correction with q = .0739999999999992 Correction with q = .0729999999999992 Correction with q = .0719999999999992 Correction with q = .0709999999999992 Correction with q = .0699999999999992 Correction with q = .0689999999999992 Correction with q = .0679999999999992 Correction with q = .0669999999999992 Correction with q = .0659999999999992 Correction with q = .0649999999999992 Correction with q = .0639999999999992 Correction with q = .0629999999999992 Correction with q = .0619999999999992 Correction with q = .0609999999999992 Correction with q = .0599999999999992 Correction with q = .0589999999999992 Correction with q = .0579999999999992 Correction with q = .0569999999999992 Correction with q = .0559999999999992 Correction with q = .0549999999999992 Correction with q = .0539999999999992 Correction with q = .0529999999999992 Correction with q = .0519999999999992 Correction with q = .0509999999999992 Correction with q = .0499999999999992 Correction with q = .0489999999999992 Correction with q = .0479999999999992 Correction with q = .0469999999999992 Correction with q = .0459999999999992 Correction with q = .0449999999999992 Correction with q = .0439999999999992 Correction with q = .0429999999999992 Correction with q = .0419999999999991 Correction with q = .0409999999999991 Correction with q = .0399999999999991 Correction with q = .0389999999999991 Correction with q = .0379999999999991 Correction with q = .0369999999999991 Correction with q = .0359999999999991 Correction with q = .0349999999999991 Correction with q = .0339999999999991 Correction with q = .0329999999999991 Correction with q = .0319999999999991 Correction with q = .0309999999999991 Correction with q = .0299999999999991 Correction with q = .0289999999999991 Correction with q = .0279999999999991 Correction with q = .0269999999999991 Correction with q = .0259999999999991 Correction with q = .0249999999999991 Correction with q = .0239999999999991 Correction with q = .0229999999999991 Correction with q = .0219999999999991 Correction with q = .0209999999999991 Correction with q = .0199999999999991 Correction with q = .0189999999999991 Correction with q = .0179999999999991 Correction with q = .0169999999999991 Correction with q = .0159999999999991 Correction with q = .0149999999999991 Correction with q = .0139999999999991 Correction with q = .0129999999999991 Correction with q = .0119999999999991 Correction with q = .0109999999999991 Correction with q = .0099999999999991 Correction with q = .0089999999999991 Correction with q = .0079999999999991 Correction with q = .0069999999999991 Correction with q = .0059999999999991 Correction with q = .0049999999999991 Correction with q = .0039999999999991 Correction with q = .0029999999999991 Correction with q = .0019999999999991 (10 real changes made) (0 real changes made) . . estadd loc thisstat22 = "[" + string(Q[1, 1], "%9.3f") +"]" : col1 . estadd loc thisstat22 = "[" +string(Q[2, 1], "%9.3f")+"]": col2 . estadd loc thisstat22 = "[" + string(Q[3, 1], "%9.3f")+"]": col3 . estadd loc thisstat22 = "["+ string(Q[4, 1], "%9.3f")+"]": col4 . estadd loc thisstat22 = "["+ string(Q[5, 1], "%9.3f")+"]": col5 . estadd loc thisstat22 = "["+ string(Q[6, 1], "%9.3f")+"]": col6 . estadd loc thisstat22 = "["+ string(Q[7, 1], "%9.3f")+"]": col7 . estadd loc thisstat22 = "["+ string(Q[8, 1], "%9.3f")+"]": col8 . estadd loc thisstat22 = "["+ string(Q[9, 1], "%9.3f")+"]": col9 . estadd loc thisstat22 = "["+ string(Q[10, 1], "%9.3f")+"]": col10 . . . loc rowlabels " " " "{\bf Panel A: Wave A}" " " "T$^{74}$" " " "Sharpened q-value" " " "Female" " > " " " "Democrat" " " " " "Observations" " " "\hline" " " "{\bf Panel B: Wave B}" " " "T$^{74}$" " > " "Sharpened q-value" " " "Female" " " " " "Democrat" " " " " "Observations" " " " . loc rowstats "" . . . . forval i = 1/30 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\StageII_ABsep.tex", replace cells(none) booktabs nonotes nomtitles compres > s alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mgroups("\shortstack{Posterior\\belief about\\fem.rel.wage\\(percent)}" "\shortstack{Gende > r\\differences\\in wages\\are a problem}" /// > "\shortstack{Gender diff. in\\wages are a\\problem among\\high-skilled}" "\shortstack{Gender dif > f. in\\wages are a\\problem among\\low-skilled}" /// > "\shortstack{Women's\\wages\\are\\fair}" "\shortstack{Government\\should mitigate\\gender\\wage > gap}" /// > "\shortstack{Anti-\\discrimination\\policy}" "\shortstack{Supportive\\policy}" /// > "\shortstack{Statutory\\affirmative\\action}" "\shortstack{Stricter\\equal pay\\legislation}", p > attern(1 1 1 1 1 1 1 1 1 1) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@ > span})) (note: file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFi > les\output\StageII_ABsep.tex not found) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\StageII_ABsep.tex) . . eststo clear . . . *********************************************************************************** . // Table G.6: Signatures on petitions by survey wave, control group . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . * Keep only pure control group . keep if rand==0 (3,031 observations deleted) . . // (a) Wave a . // Numbers of respondents per group: . tab gender if wave==1 gender | Freq. Percent Cum. ------------+----------------------------------- male | 248 49.80 49.80 female | 250 50.20 100.00 ------------+----------------------------------- Total | 498 100.00 . tab democrat if wave==1 Democrat | (incl. | indep. | leaning | dem.) | Freq. Percent Cum. ------------+----------------------------------- 0 | 268 53.82 53.82 1 | 230 46.18 100.00 ------------+----------------------------------- Total | 498 100.00 . . // Numbers of signatures -> Retrieved from White House Petition Website and manually entered . . // p-value (diff) from 2-sided proportion tests . prtesti 248 23 250 51, count Two-sample test of proportions x: Number of obs = 248 y: Number of obs = 250 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0927419 .0184195 .0566404 .1288435 y | .204 .025486 .1540484 .2539516 -------------+---------------------------------------------------------------- diff | -.1112581 .0314454 -.1728899 -.0496262 | under Ho: .0318778 -3.49 0.000 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -3.4901 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.0002 Pr(|Z| < |z|) = 0.0005 Pr(Z > z) = 0.9998 . prtesti 248 7 250 3, count Two-sample test of proportions x: Number of obs = 248 y: Number of obs = 250 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0282258 .0105167 .0076134 .0488382 y | .012 .0068865 -.0014973 .0254973 -------------+---------------------------------------------------------------- diff | .0162258 .0125708 -.0084125 .0408641 | under Ho: .0125719 1.29 0.197 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 1.2906 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.9016 Pr(|Z| < |z|) = 0.1968 Pr(Z > z) = 0.0984 . prtesti 268 24 230 50, count Two-sample test of proportions x: Number of obs = 268 y: Number of obs = 230 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0895522 .0174421 .0553664 .1237381 y | .2173913 .0271975 .1640851 .2706975 -------------+---------------------------------------------------------------- diff | -.1278391 .0323099 -.1911654 -.0645128 | under Ho: .0319707 -4.00 0.000 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -3.9986 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.0000 Pr(|Z| < |z|) = 0.0001 Pr(Z > z) = 1.0000 . prtesti 268 8 230 2, count Two-sample test of proportions x: Number of obs = 268 y: Number of obs = 230 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0298507 .0103951 .0094767 .0502248 y | .0086957 .006122 -.0033032 .0206945 -------------+---------------------------------------------------------------- diff | .0211551 .0120639 -.0024897 .0447998 | under Ho: .0126085 1.68 0.093 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 1.6778 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.9533 Pr(|Z| < |z|) = 0.0934 Pr(Z > z) = 0.0467 . . // (b) Wave b . // Numbers of respondents per group: . tab gender if wave==2 gender | Freq. Percent Cum. ------------+----------------------------------- male | 242 45.15 45.15 female | 294 54.85 100.00 ------------+----------------------------------- Total | 536 100.00 . . // Numbers of signatures -> Retrieved from White House Petition Website and manually entered . . // p-value (diff) from 2-sided proportion tests . prtesti 242 35 294 50, count Two-sample test of proportions x: Number of obs = 242 y: Number of obs = 294 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .1446281 .0226098 .1003138 .1889424 y | .170068 .0219108 .1271236 .2130125 -------------+---------------------------------------------------------------- diff | -.0254399 .0314847 -.0871488 .036269 | under Ho: .0317055 -0.80 0.422 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -0.8024 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.2112 Pr(|Z| < |z|) = 0.4223 Pr(Z > z) = 0.7888 . prtesti 242 10 294 0, count Two-sample test of proportions x: Number of obs = 242 y: Number of obs = 294 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0413223 .0127944 .0162457 .0663989 y | 0 0 0 0 -------------+---------------------------------------------------------------- diff | .0413223 .0127944 .0162457 .0663989 | under Ho: .0117444 3.52 0.000 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 3.5185 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.9998 Pr(|Z| < |z|) = 0.0004 Pr(Z > z) = 0.0002 . . . *********************************************************************************** . // Table G.7: Signatures on petitions by survey wave, treatment effects . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . * Drop pure control group . drop if rand==0 (1,034 observations deleted) . . // (a) Wave a . // Numbers of respondents per group: . * Overall: . tab T1 if wave==1 T1 | Freq. Percent Cum. ------------+----------------------------------- 0 | 1,007 50.05 50.05 1 | 1,005 49.95 100.00 ------------+----------------------------------- Total | 2,012 100.00 . * By gender: . tab gender T1 if wave==1 | T1 gender | 0 1 | Total -----------+----------------------+---------- male | 503 499 | 1,002 female | 504 506 | 1,010 -----------+----------------------+---------- Total | 1,007 1,005 | 2,012 . * By pol. orientation: . tab democrat T1 if wave==1 Democrat | (incl. | indep. | leaning | T1 dem.) | 0 1 | Total -----------+----------------------+---------- 0 | 557 558 | 1,115 1 | 450 447 | 897 -----------+----------------------+---------- Total | 1,007 1,005 | 2,012 . . // Numbers of signatures -> Retrieved from White House Petition Website and manually entered . . // p-value (diff) from 2-sided proportion tests (3rd column) and from 1-sided proportion tests (4t > h columns) . prtesti 1005 169 1007 159, count Two-sample test of proportions x: Number of obs = 1005 y: Number of obs = 1007 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .1681592 .0117977 .1450361 .1912823 y | .1578947 .0114909 .1353731 .1804164 -------------+---------------------------------------------------------------- diff | .0102645 .0164689 -.022014 .042543 | under Ho: .0164701 0.62 0.533 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 0.6232 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.7334 Pr(|Z| < |z|) = 0.5331 Pr(Z > z) = 0.2666 . prtesti 1005 13 1007 20, count Two-sample test of proportions x: Number of obs = 1005 y: Number of obs = 1007 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0129353 .0035643 .0059494 .0199213 y | .019861 .0043967 .0112435 .0284784 -------------+---------------------------------------------------------------- diff | -.0069256 .00566 -.0180191 .0041678 | under Ho: .0056633 -1.22 0.221 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -1.2229 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.1107 Pr(|Z| < |z|) = 0.2214 Pr(Z > z) = 0.8893 . prtesti 499 63 503 58, count Two-sample test of proportions x: Number of obs = 499 y: Number of obs = 503 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .1262525 .0148683 .0971111 .1553939 y | .1153082 .0142411 .0873962 .1432201 -------------+---------------------------------------------------------------- diff | .0109444 .0205882 -.0294078 .0512965 | under Ho: .0205879 0.53 0.595 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 0.5316 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.7025 Pr(|Z| < |z|) = 0.5950 Pr(Z > z) = 0.2975 . prtesti 499 8 503 11, count Two-sample test of proportions x: Number of obs = 499 y: Number of obs = 503 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0160321 .0056226 .005012 .0270521 y | .0218688 .0065212 .0090875 .0346501 -------------+---------------------------------------------------------------- diff | -.0058367 .0086104 -.0227128 .0110394 | under Ho: .0086176 -0.68 0.498 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -0.6773 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.2491 Pr(|Z| < |z|) = 0.4982 Pr(Z > z) = 0.7509 . prtesti 506 106 504 101, count Two-sample test of proportions x: Number of obs = 506 y: Number of obs = 504 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .2094862 .0180908 .1740289 .2449434 y | .2003968 .0178307 .1654494 .2353443 -------------+---------------------------------------------------------------- diff | .0090893 .025401 -.0406956 .0588743 | under Ho: .0254034 0.36 0.720 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 0.3578 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.6398 Pr(|Z| < |z|) = 0.7205 Pr(Z > z) = 0.3602 . prtesti 506 5 504 9, count Two-sample test of proportions x: Number of obs = 506 y: Number of obs = 504 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0098814 .0043972 .001263 .0184998 y | .0178571 .005899 .0062953 .029419 -------------+---------------------------------------------------------------- diff | -.0079757 .0073576 -.0223963 .0064448 | under Ho: .0073577 -1.08 0.278 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -1.0840 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.1392 Pr(|Z| < |z|) = 0.2784 Pr(Z > z) = 0.8608 . prtesti 447 106 450 99, count Two-sample test of proportions x: Number of obs = 447 y: Number of obs = 450 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .2371365 .0201173 .1977074 .2765656 y | .22 .0195278 .1817263 .2582737 -------------+---------------------------------------------------------------- diff | .0171365 .0280364 -.0378138 .0720867 | under Ho: .0280397 0.61 0.541 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 0.6111 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.7294 Pr(|Z| < |z|) = 0.5411 Pr(Z > z) = 0.2706 . prtesti 447 3 450 2, count Two-sample test of proportions x: Number of obs = 447 y: Number of obs = 450 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0067114 .0038618 -.0008576 .0142804 y | .0044444 .0031357 -.0017014 .0105903 -------------+---------------------------------------------------------------- diff | .002267 .0049746 -.007483 .0120169 | under Ho: .0049718 0.46 0.648 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 0.4560 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.6758 Pr(|Z| < |z|) = 0.6484 Pr(Z > z) = 0.3242 . prtesti 558 63 557 60, count Two-sample test of proportions x: Number of obs = 558 y: Number of obs = 557 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .1129032 .0133974 .0866447 .1391617 y | .1077199 .0131362 .0819734 .1334665 -------------+---------------------------------------------------------------- diff | .0051833 .018763 -.0315916 .0419582 | under Ho: .018764 0.28 0.782 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 0.2762 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.6088 Pr(|Z| < |z|) = 0.7824 Pr(Z > z) = 0.3912 . prtesti 558 10 557 18, count Two-sample test of proportions x: Number of obs = 558 y: Number of obs = 557 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0179211 .0056162 .0069137 .0289286 y | .032316 .0074929 .0176302 .0470017 -------------+---------------------------------------------------------------- diff | -.0143948 .009364 -.0327479 .0039582 | under Ho: .0093716 -1.54 0.125 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -1.5360 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.0623 Pr(|Z| < |z|) = 0.1245 Pr(Z > z) = 0.9377 . . . // (a) Wave a . // Numbers of respondents per group: . * Overall: . tab T1 if wave==2 T1 | Freq. Percent Cum. ------------+----------------------------------- 0 | 493 48.38 48.38 1 | 526 51.62 100.00 ------------+----------------------------------- Total | 1,019 100.00 . * By gender: . tab gender T1 if wave==2 | T1 gender | 0 1 | Total -----------+----------------------+---------- male | 231 234 | 465 female | 262 292 | 554 -----------+----------------------+---------- Total | 493 526 | 1,019 . . // Numbers of signatures -> Retrieved from White House Petition Website and manually entered . . // p-value (diff) from 2-sided proportion tests (3rd column) and from 1-sided proportion tests (4t > h columns) . prtesti 526 90 493 61, count Two-sample test of proportions x: Number of obs = 526 y: Number of obs = 493 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .1711027 .0164205 .1389191 .2032862 y | .1237323 .0148298 .0946663 .1527982 -------------+---------------------------------------------------------------- diff | .0473704 .0221259 .0040044 .0907364 | under Ho: .0222713 2.13 0.033 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 2.1270 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.9833 Pr(|Z| < |z|) = 0.0334 Pr(Z > z) = 0.0167 . prtesti 526 6 493 15, count Two-sample test of proportions x: Number of obs = 526 y: Number of obs = 493 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0114068 .0046302 .0023318 .0204818 y | .030426 .0077355 .0152646 .0455873 -------------+---------------------------------------------------------------- diff | -.0190191 .0090154 -.0366889 -.0013493 | under Ho: .0089058 -2.14 0.033 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -2.1356 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.0164 Pr(|Z| < |z|) = 0.0327 Pr(Z > z) = 0.9836 . prtesti 234 35 231 28, count Two-sample test of proportions x: Number of obs = 234 y: Number of obs = 231 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .1495726 .0233151 .1038759 .1952694 y | .1212121 .0214738 .0791242 .1633 -------------+---------------------------------------------------------------- diff | .0283605 .0316973 -.033765 .090486 | under Ho: .0317426 0.89 0.372 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 0.8935 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.8142 Pr(|Z| < |z|) = 0.3716 Pr(Z > z) = 0.1858 . prtesti 234 5 231 8, count Two-sample test of proportions x: Number of obs = 234 y: Number of obs = 231 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0213675 .0094532 .0028396 .0398955 y | .034632 .0120304 .0110529 .0582112 -------------+---------------------------------------------------------------- diff | -.0132645 .0153001 -.0432522 .0167231 | under Ho: .0152898 -0.87 0.386 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -0.8675 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.1928 Pr(|Z| < |z|) = 0.3856 Pr(Z > z) = 0.8072 . prtesti 292 55 262 33, count Two-sample test of proportions x: Number of obs = 292 y: Number of obs = 262 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .1883562 .0228813 .1435096 .2332027 y | .1259542 .0204985 .0857778 .1661306 -------------+---------------------------------------------------------------- diff | .062402 .0307204 .002191 .1226129 | under Ho: .0311055 2.01 0.045 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = 2.0061 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.9776 Pr(|Z| < |z|) = 0.0448 Pr(Z > z) = 0.0224 . prtesti 292 1 262 7, count Two-sample test of proportions x: Number of obs = 292 y: Number of obs = 262 ------------------------------------------------------------------------------ Variable | Mean Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- x | .0034247 .0034188 -.003276 .0101254 y | .0267176 .0099625 .0071915 .0462436 -------------+---------------------------------------------------------------- diff | -.0232929 .0105328 -.0439367 -.0026491 | under Ho: .0101518 -2.29 0.022 ------------------------------------------------------------------------------ diff = prop(x) - prop(y) z = -2.2945 Ho: diff = 0 Ha: diff < 0 Ha: diff != 0 Ha: diff > 0 Pr(Z < z) = 0.0109 Pr(|Z| < |z|) = 0.0218 Pr(Z > z) = 0.9891 . . . *********************************************************************************** . // Table G.8: Correlates of views related to the wage gap (including outliers) . *********************************************************************************** . . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . loc experiments "large problem govmore z_mani_index" . . *Keep only control group respondents . keep if rand==0 (3,031 observations deleted) . . **z-score prior beliefs: . foreach var of varlist prior{ 2. egen mean_`var'=mean(`var') 3. egen sd_`var'=sd(`var') 4. replace `var'=(`var'-mean_`var')/sd_`var' 5. drop mean_`var' sd_`var' 6. } (1,034 real changes made) . . . // Build Table . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experiments' { 2. . . ***Panel A: Main correlates: Dem, Female . . qui reg `choice' i.wave democrat indep otherpol gender [pweight=pweight],r 3. . sigstar democrat, prec(3) 4. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 6. . sigstar gender, prec(3) 7. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat7 = "`r(sestar)'": col`colnum' 9. . . ***Panel B: Raw correlation: z-scored prior . . qui reg `choice' i.wave prior [pweight=pweight],r 10. . sigstar prior, prec(3) 11. estadd loc thisstat10 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat11 = "`r(sestar)'": col`colnum' 13. . . ***Panel C: Prior and main correlates: Dem, Rep, Female . . qui reg `choice' prior i.wave democrat indep otherpol gender [pweight=pweight],r 14. . sigstar prior, prec(3) 15. estadd loc thisstat14 = "`r(bstar)'": col`colnum' 16. estadd loc thisstat15 = "`r(sestar)'": col`colnum' 17. . sigstar democrat, prec(3) 18. estadd loc thisstat17 = "`r(bstar)'": col`colnum' 19. estadd loc thisstat18= "`r(sestar)'": col`colnum' 20. . sigstar gender, prec(3) 21. estadd loc thisstat20 = "`r(bstar)'": col`colnum' 22. estadd loc thisstat21 = "`r(sestar)'": col`colnum' 23. . qui sum `choice' 24. estadd loc thisstat22 = r(N): col`colnum' 25. . loc ++colnum 26. loc colnames "`colnames' `"`: var la `choice''"'" 27. . } . . . . loc rowlabels " "{\bf Panel A}" " " "Democrat" " " " " "Female" " " "\hline {\bf Panel B}" " " "P > rior (z-scored)" " " "\hline {\bf Panel C}" " " "Prior (z-scored)" " " " " "Democrat" " " " " "Fem > ale" " " "\hline Observations" "\hline" " . loc rowstats "" . . . loc rowstats "" . . forval i = 1/22 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . esttab * using "$output\tab_correlates_maniAB.tex", replace cells(none) booktabs nonotes nomtitles > compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mgroups("\shortstack{Gender diff.\\ in wages\\are large}" "\shortstack{Gender diff.\\ in w > ages\\are a problem}" /// > "\shortstack{Government\\should mitigate\\gender wage gap}" "\shortstack{Perception\\Index}", pa > ttern(1 1 1 1) prefix(\multicolumn{@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (note: file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFi > les\output\tab_correlates_maniAB.tex not found) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\tab_correlates_maniAB.tex) . . eststo clear . . . *********************************************************************************** . // Table G.9: Correlates of specific policy demand (including outliers) . *********************************************************************************** . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . loc experiments "quotaanchor AAanchor legislationanchor transparencyanchor UKtool childcare z_lmpo > licy_index" . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experiments' { 2. . . ***Panel A: Main correlates: Dem, Female . . qui reg `choice' i.wave democrat indep otherpol gender [pweight=pweight],r 3. . sigstar democrat, prec(3) 4. estadd loc thisstat3 = "`r(bstar)'": col`colnum' 5. estadd loc thisstat4 = "`r(sestar)'": col`colnum' 6. . sigstar gender, prec(3) 7. estadd loc thisstat6 = "`r(bstar)'": col`colnum' 8. estadd loc thisstat7 = "`r(sestar)'": col`colnum' 9. . . ***Panel B: Raw correlation: z-scored prior . . qui reg `choice' i.wave prior [pweight=pweight],r 10. . sigstar prior, prec(3) 11. estadd loc thisstat10 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat11 = "`r(sestar)'": col`colnum' 13. . . ***Panel C: Prior and main correlates: Dem, Rep, Female . . qui reg `choice' prior i.wave democrat indep otherpol gender [pweight=pweight],r 14. . sigstar prior, prec(3) 15. estadd loc thisstat14 = "`r(bstar)'": col`colnum' 16. estadd loc thisstat15 = "`r(sestar)'": col`colnum' 17. . sigstar democrat, prec(3) 18. estadd loc thisstat17 = "`r(bstar)'": col`colnum' 19. estadd loc thisstat18= "`r(sestar)'": col`colnum' 20. . sigstar gender, prec(3) 21. estadd loc thisstat20 = "`r(bstar)'": col`colnum' 22. estadd loc thisstat21 = "`r(sestar)'": col`colnum' 23. . qui sum `choice' 24. estadd loc thisstat22 = r(N): col`colnum' 25. . loc ++colnum 26. loc colnames "`colnames' `"`: var la `choice''"'" 27. . } . . . . loc rowlabels " "{\bf Panel A}" " " "Democrat" " " " " "Female" " " "\hline {\bf Panel B}" " " "P > rior (z-scored)" " " "\hline {\bf Panel C}" " " "Prior (z-scored)" " " " " "Democrat" " " " " "Fem > ale" " " "\hline Observations" "\hline" " . loc rowstats "" . . . loc rowstats "" . . forval i = 1/22 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\tab_correlates_policypref_AB.tex", replace cells(none) booktabs nonotes no > mtitles compress alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels') > ) /// > mgroups("\shortstack{Introduce\\gender\\quotas}" "\shortstack{Statutory\\affirmative\\acti > on}" /// > "\shortstack{Stricter\\equal pay\\legislation}" "\shortstack{Wage transp.\\within\\ companies}" > "\shortstack{Introduce\\reporting\\website}" "\shortstack{Increase\\subsidies\\to child care}" /// > "\shortstack{Policy\\demand\\index}", pattern(1 1 1 1 1 1 1 ) prefix(\multicolumn{@span}{c}{) s > uffix(}) span erepeat(\cmidrule(lr){@span})) (note: file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFi > les\output\tab_correlates_policypref_AB.tex not found) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\tab_correlates_policypref_AB.tex) . . . *********************************************************************************** . // Table G.10: Correlates of beliefs about underlying reasons (including outliers) . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . loc experiments "discrimination boys society z_extreasons_index ambitious talented interested z_pe > rsonalreasons_index" . . keep if rand==0 (3,031 observations deleted) . . **z-score prior: . egen z_prior=std(prior) . replace prior=z_prior (1,034 real changes made) . . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . . foreach choice in `experiments' { 2. . ***Correlates with prior . reg `choice' prior, vce(r) 3. local n = round(e(N)) 4. . sigstar prior, prec(3) 5. estadd loc thisstat4 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat5 = "`r(sestar)'": col`colnum' 7. estadd loc thisstat7 = "`n'": col`colnum' 8. . . ***Correlates including gender and political orientation . reg `choice' prior gender democrat indep otherpol, vce(r) 9. local n = round(e(N)) 10. . sigstar prior, prec(3) 11. estadd loc thisstat12 = "`r(bstar)'": col`colnum' 12. estadd loc thisstat13 = "`r(sestar)'": col`colnum' 13. sigstar gender, prec(3) 14. estadd loc thisstat15 = "`r(bstar)'": col`colnum' 15. estadd loc thisstat16 = "`r(sestar)'": col`colnum' 16. sigstar democrat, prec(3) 17. estadd loc thisstat18 = "`r(bstar)'": col`colnum' 18. estadd loc thisstat19 = "`r(sestar)'": col`colnum' 19. . estadd loc thisstat21 = "`n'": col`colnum' 20. . loc ++colnum 21. loc colnames "`colnames' `"`: var la `choice''"'" 22. . } Linear regression Number of obs = 498 F(1, 496) = 12.66 Prob > F = 0.0004 R-squared = 0.0346 Root MSE = .98356 ------------------------------------------------------------------------------ | Robust discrimina~n | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.1815337 .0510171 -3.56 0.000 -.28177 -.0812974 _cons | -.0036651 .0442137 -0.08 0.934 -.0905342 .0832041 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(5, 492) = 18.95 Prob > F = 0.0000 R-squared = 0.1571 Root MSE = .92277 ------------------------------------------------------------------------------ | Robust discrimina~n | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.1291811 .0479967 -2.69 0.007 -.2234849 -.0348774 gender | .1953004 .0829159 2.36 0.019 .0323876 .3582133 democrat | .7090386 .0970638 7.30 0.000 .518328 .8997492 indep | .1432182 .1423898 1.01 0.315 -.1365489 .4229854 otherpol | .6905886 .2330426 2.96 0.003 .2327071 1.14847 _cons | -.466379 .0847608 -5.50 0.000 -.6329169 -.2998411 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(1, 496) = 3.34 Prob > F = 0.0682 R-squared = 0.0080 Root MSE = .99699 ------------------------------------------------------------------------------ | Robust boys | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.0874517 .0478495 -1.83 0.068 -.1814644 .0065609 _cons | -.0017655 .0447441 -0.04 0.969 -.0896769 .0861458 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(5, 492) = 7.32 Prob > F = 0.0000 R-squared = 0.0663 Root MSE = .9712 ------------------------------------------------------------------------------ | Robust boys | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.0499657 .0451369 -1.11 0.269 -.1386506 .0387192 gender | .2752058 .0873036 3.15 0.002 .1036719 .4467396 democrat | .4125952 .0981023 4.21 0.000 .2198441 .6053463 indep | .0822724 .1378752 0.60 0.551 -.1886246 .3531693 otherpol | .3881 .2593996 1.50 0.135 -.1215676 .8977675 _cons | -.3515092 .0828639 -4.24 0.000 -.51432 -.1886983 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(1, 496) = 0.04 Prob > F = 0.8467 R-squared = 0.0001 Root MSE = 1.001 ------------------------------------------------------------------------------ | Robust society | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.0095618 .0494175 -0.19 0.847 -.1066553 .0875317 _cons | -.0001931 .0448606 -0.00 0.997 -.0883333 .0879471 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(5, 492) = 1.14 Prob > F = 0.3366 R-squared = 0.0119 Root MSE = .99907 ------------------------------------------------------------------------------ | Robust society | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | .0025153 .0489068 0.05 0.959 -.0935766 .0986072 gender | .0986576 .089488 1.10 0.271 -.0771683 .2744834 democrat | .14913 .1012649 1.47 0.141 -.049835 .3480949 indep | -.1074443 .1294104 -0.83 0.407 -.3617095 .1468209 otherpol | .0793115 .3078561 0.26 0.797 -.5255633 .6841862 _cons | -.1028432 .0827184 -1.24 0.214 -.2653681 .0596817 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(1, 496) = 5.27 Prob > F = 0.0221 R-squared = 0.0178 Root MSE = .70581 ------------------------------------------------------------------------------ | Robust z_extreaso~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.0927095 .0403718 -2.30 0.022 -.1720302 -.0133888 _cons | -.0018718 .0317745 -0.06 0.953 -.064301 .0605575 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(5, 492) = 13.44 Prob > F = 0.0000 R-squared = 0.1190 Root MSE = .67117 ------------------------------------------------------------------------------ | Robust z_extreaso~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | -.0590453 .0371964 -1.59 0.113 -.1321287 .0140382 gender | .1845485 .0597909 3.09 0.002 .0670716 .3020254 democrat | .4227548 .0698016 6.06 0.000 .2856087 .5599008 indep | .0362222 .0968251 0.37 0.708 -.1540195 .2264639 otherpol | .3842789 .1792726 2.14 0.033 .0320447 .7365131 _cons | -.303392 .060925 -4.98 0.000 -.4230973 -.1836867 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(1, 496) = 7.41 Prob > F = 0.0067 R-squared = 0.0217 Root MSE = .99011 ------------------------------------------------------------------------------ | Robust ambitious | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | .1437254 .0528073 2.72 0.007 .0399718 .247479 _cons | .0029018 .0445002 0.07 0.948 -.0845303 .0903339 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(5, 492) = 9.51 Prob > F = 0.0000 R-squared = 0.0944 Root MSE = .95646 ------------------------------------------------------------------------------ | Robust ambitious | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | .1078498 .0530561 2.03 0.043 .0036052 .2120943 gender | -.4820394 .0862878 -5.59 0.000 -.6515775 -.3125013 democrat | -.2324836 .0985423 -2.36 0.019 -.4260992 -.038868 indep | -.2040127 .1210817 -1.68 0.093 -.4419137 .0338883 otherpol | -.0252589 .2863272 -0.09 0.930 -.5878338 .5373161 _cons | .3848681 .0830758 4.63 0.000 .221641 .5480952 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(1, 496) = 7.56 Prob > F = 0.0062 R-squared = 0.0218 Root MSE = .99002 ------------------------------------------------------------------------------ | Robust talented | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | .1443133 .0524758 2.75 0.006 .041211 .2474157 _cons | .0029136 .0443888 0.07 0.948 -.0842996 .0901268 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(5, 492) = 11.11 Prob > F = 0.0000 R-squared = 0.1044 Root MSE = .95117 ------------------------------------------------------------------------------ | Robust talented | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | .1018335 .0546312 1.86 0.063 -.0055056 .2091727 gender | -.4713728 .0864653 -5.45 0.000 -.6412597 -.301486 democrat | -.3391947 .1015367 -3.34 0.001 -.5386937 -.1396958 indep | -.2397619 .1261979 -1.90 0.058 -.4877153 .0081914 otherpol | -.205827 .249197 -0.83 0.409 -.6954487 .2837946 _cons | .4384075 .0871466 5.03 0.000 .267182 .6096329 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(1, 496) = 8.90 Prob > F = 0.0030 R-squared = 0.0230 Root MSE = .98941 ------------------------------------------------------------------------------ | Robust interested | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | .1481833 .0496789 2.98 0.003 .0505763 .2457903 _cons | .0029917 .0443613 0.07 0.946 -.0841675 .090151 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(5, 492) = 9.15 Prob > F = 0.0000 R-squared = 0.0955 Root MSE = .95586 ------------------------------------------------------------------------------ | Robust interested | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | .108349 .0522398 2.07 0.039 .0057084 .2109897 gender | -.403168 .0851916 -4.73 0.000 -.5705522 -.2357839 democrat | -.3749544 .0990794 -3.78 0.000 -.5696253 -.1802835 indep | -.136156 .1206621 -1.13 0.260 -.3732325 .1009205 otherpol | -.0455239 .303849 -0.15 0.881 -.6425255 .5514778 _cons | .4006308 .0805366 4.97 0.000 .2423926 .558869 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(1, 496) = 10.01 Prob > F = 0.0017 R-squared = 0.0310 Root MSE = .83483 ------------------------------------------------------------------------------ | Robust z_personal~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | .1456933 .0460433 3.16 0.002 .0552293 .2361573 _cons | .0029415 .0374705 0.08 0.937 -.0706789 .0765619 ------------------------------------------------------------------------------ Linear regression Number of obs = 498 F(5, 492) = 13.30 Prob > F = 0.0000 R-squared = 0.1334 Root MSE = .79268 ------------------------------------------------------------------------------ | Robust z_personal~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior | .1064118 .0479257 2.22 0.027 .0122476 .200576 gender | -.4471504 .071 -6.30 0.000 -.586651 -.3076498 democrat | -.3193489 .0837853 -3.81 0.000 -.48397 -.1547278 indep | -.186246 .0990203 -1.88 0.061 -.3808008 .0083089 otherpol | -.0827109 .2226811 -0.37 0.710 -.520234 .3548123 _cons | .4058579 .0696291 5.83 0.000 .2690509 .542665 ------------------------------------------------------------------------------ . . . loc rowlabels " "\hline" "{\bf Panel A: Priors only}" " " "Prior (z-scored)" " " " " "Observations > " " " "\hline" "{\bf Panel B: Prior, gender, pol. orient.}" " " "Prior (z-scored)" " " " " "Fema > le" " " " " "Democrat" " " " " "Observations" " " "\hline" "\hline" " " " . loc rowstats "" . . . loc rowstats "" . . forval i = 1/21 { 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . esttab * using "$output\tab_correlates_reasons_A.tex", replace cells(none) booktabs nonotes compre > ss alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mtitle("Discrimination" "Socialization" "Work-Family" "Index" "Ambitions" "Talents" "Pre > ferences" "Index") /// > mgroups("Impersonal Factors" "Personal Factors", pattern(1 0 0 0 1 0 0 0) prefix(\multicolumn > {@span}{c}{) suffix(}) span erepeat(\cmidrule(lr){@span})) (note: file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFi > les\output\tab_correlates_reasons_A.tex not found) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\tab_correlates_reasons_A.tex) . . . . *********************************************************************************** . // Table G.11: Heterogeneity in the treatment effect by Democrat vs. Non-Democrat . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . drop if rand==0 (1,034 observations deleted) . . loc experiments "posterior z_mani_index quotaanchor AAanchor legislationanchor transparencyanchor > UKtool childcare z_lmpolicy_index" . . preserve . . clear all . eststo clear . estimates drop _all . . set obs 10 number of observations (_N) was 0, now 10 . qui gen x = 1 . qui gen y = 1 . . loc columns = 0 . . foreach choice in `experiments' { 2. . loc ++columns 3. qui eststo col`columns': reg x y 4. . } . . restore . . /* Statistics */ . . loc colnum = 1 . loc colnames "" . . . foreach choice in `experiments' { 2. . qui reg `choice' T1 T1democrat $controls [pweight=pweight], vce(r) 3. local n = round(e(N)) 4. . sigstar T1, prec(3) 5. estadd loc thisstat2 = "`r(bstar)'": col`colnum' 6. estadd loc thisstat3 = "`r(sestar)'": col`colnum' 7. sigstar T1democrat, prec(3) 8. estadd loc thisstat5 = "`r(bstar)'": col`colnum' 9. estadd loc thisstat6 = "`r(sestar)'": col`colnum' 10. test T1 + T1democrat = 0 11. estadd loc thisstat7 = string(r(p), "%9.3f"): col`colnum' 12. sigstar democrat, prec(3) 13. estadd loc thisstat9 = "`r(bstar)'": col`colnum' 14. estadd loc thisstat10 = "`r(sestar)'": col`colnum' 15. . estadd loc thisstat12 = "`n'": col`colnum' 16. . loc ++colnum 17. loc colnames "`colnames' `"`: var la `choice''"'" 18. . } ( 1) T1 + T1democrat = 0 F( 1, 2998) = 183.88 Prob > F = 0.0000 ( 1) T1 + T1democrat = 0 F( 1, 3007) = 106.97 Prob > F = 0.0000 ( 1) T1 + T1democrat = 0 F( 1, 3007) = 1.06 Prob > F = 0.3031 ( 1) T1 + T1democrat = 0 F( 1, 3007) = 6.68 Prob > F = 0.0098 ( 1) T1 + T1democrat = 0 F( 1, 3007) = 18.55 Prob > F = 0.0000 ( 1) T1 + T1democrat = 0 F( 1, 1989) = 0.42 Prob > F = 0.5188 ( 1) T1 + T1democrat = 0 F( 1, 996) = 1.55 Prob > F = 0.2128 ( 1) T1 + T1democrat = 0 F( 1, 3007) = 0.42 Prob > F = 0.5159 ( 1) T1 + T1democrat = 0 F( 1, 3007) = 5.69 Prob > F = 0.0172 . . . loc rowlabels " " " "T$^{74}$" " " " " "T$^{74}$ x Democrat" " " "p-value [T$^{74}$ + T$^{74}$ x D > emocrat] " " " "Democrat" " " " " "Observations" " " "\hline" " " " . loc rowstats "" . . forval i = 1/13{ 2. loc rowstats "`rowstats' thisstat`i'" 3. } . . . . . esttab * using "$output\policyhetAB_DemNonDem_short.tex", replace cells(none) booktabs nonotes com > press alignment(c) nogap noobs nobaselevels label stats(`rowstats', labels(`rowlabels')) /// > mtitle("\shortstack{Posterior\\belief about\\fem. rel. wage}" "\shortstack{Perception\\ind > ex}" /// > "\shortstack{Introduce\\gender\\quotas}" "\shortstack{Statutory\\affirmative\\action}" // > / > "\shortstack{Stricter\\equal pay\\legislation}" "\shortstack{Wage transp.\\within\\ companies}" > "\shortstack{Introduce\\reporting\\website}" "\shortstack{Increase\\subsidies\\to child care}" /// > "\shortstack{Policy\\demand\\index}" ) /// > mgroups("First Stage" "Policy Demand", pattern(1 0 1 0 0 0 0 0 0) prefix(\multicolumn{@span}{c} > {) suffix(}) span erepeat(\cmidrule(lr){@span})) (note: file C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationFi > les\output\policyhetAB_DemNonDem_short.tex not found) (output written to C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\Replic > ationFiles\output\policyhetAB_DemNonDem_short.tex) . . . eststo clear . . . end of do-file . . // In-text Results . do "11_InTextResults.do" . . *********************************************************************************** . // Replication Files . ********************************************************************************** . /* > HOW DO BELIEFS ABOUT THE GENDER WAGE GAP AFFECT THE DEMAND FOR PUBLIC POLICY? > Sonja Settele > AEJ:pol > */ . ********************************************************************************** . . *********************************************************************************** . **** Replication of in-text results: . *********************************************************************************** . . *********************************************************************************** . /* Page 11: "Roughly 20 percent of the respondents hold a prior belief below the ACS- > based value of $74, while another 20 percent hold a belief above the CPS-based value > of $94. The median belief is $81." */ . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . gen prior_below_74=prior<74 . gen prior_above_94=prior>94 . . tab prior_below_74 prior_below | _74 | Freq. Percent Cum. ------------+----------------------------------- 0 | 3,181 78.25 78.25 1 | 884 21.75 100.00 ------------+----------------------------------- Total | 4,065 100.00 . tab prior_above_94 prior_above | _94 | Freq. Percent Cum. ------------+----------------------------------- 0 | 3,236 79.61 79.61 1 | 829 20.39 100.00 ------------+----------------------------------- Total | 4,065 100.00 . . sum prior,d Prior belief ------------------------------------------------------------- Percentiles Smallest 1% 31 0 5% 50 1 10% 61 2 Obs 4,065 25% 75 2 Sum of Wgt. 4,065 50% 81 Mean 83.36531 Largest Std. Dev. 21.67554 75% 90 200 90% 100 200 Variance 469.8289 95% 116 200 Skewness 1.382362 99% 173 200 Kurtosis 10.03941 . . *********************************************************************************** . /* Page 11: "When beliefs are incentivized, (...) respondents spend on average 16 seconds > more on their prior estimate." */ . *********************************************************************************** . . reg timeprior prior1 [pweight=pweight],robust (sum of wgt is 4.0120e+03) Linear regression Number of obs = 4,065 F(1, 4063) = 15.06 Prob > F = 0.0001 R-squared = 0.0040 Root MSE = 121.9 ------------------------------------------------------------------------------ | Robust timeprior | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- prior1 | 15.63951 4.029458 3.88 0.000 7.739561 23.53945 _cons | 67.52833 3.329894 20.28 0.000 60.99992 74.05675 ------------------------------------------------------------------------------ . . *********************************************************************************** . /* Footnote 15: "Only about one third of individuals in the ACS with a > Bachelor's degree also hold a higher degree, and females' relative wages remain almost > the same when those indiviudals are included ($75 vs. $74)." */ . *********************************************************************************** . . clear all . set more off . . use "$path\data\usa_00025.dta", clear . . keep if year==2016 (60,038,334 observations deleted) . . keep if age>17&age<66 (1,211,541 observations deleted) . . // Set missing wages to missing if applicable . replace incwage=. if incwage==9999999 (0 real changes made) . replace incwage=. if incwage==999999 (0 real changes made) . . gen female=0 if sex!=. . replace female=1 if sex==2 (983,904 real changes made) . . gen GWG_ACS_45_Bachelormore=. (1,944,946 missing values generated) . . gen e_sample=. (1,944,946 missing values generated) . . * uhrswork = weekly hours worked . * educd==101 -> Bachelor's degree . * classwkr=2 -> Employee . . /* Calculate women's wages as a share of male wages in the group of 45-year-old employees with > AT LEAST a Bachelors degree who work 40 hours per week */ . . reg incwage female if age==45&empstat==1&uhrswork==40&educd>=101&classwkr==2 [pweight=perwt], robu > st (sum of wgt is 5.5242e+05) Linear regression Number of obs = 5,234 F(1, 5232) = 125.75 Prob > F = 0.0000 R-squared = 0.0353 Root MSE = 62389 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -23903.17 2131.598 -11.21 0.000 -28081.99 -19724.35 _cons | 94184.16 1837.698 51.25 0.000 90581.51 97786.82 ------------------------------------------------------------------------------ . mat beta = e(b) . sca feml = beta[1,1] . disp feml -23903.169 . replace e_sample=0 (1,944,946 real changes made) . replace e_sample=1 if e(sample)==1 (5,234 real changes made) . mean incwage if female==0&age==45&empstat==1&uhrswork==40&educd>=101&classwkr==2 [pweight=perwt] Mean estimation Number of obs = 2,417 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 94184.16 1837.727 90580.48 97787.84 -------------------------------------------------------------- . matrix mean=e(b) . mat list mean symmetric mean[1,1] incwage y1 94184.161 . sca const=mean[1,1] . replace GWG_ACS_45_Bachelormore = 1+(feml/const) if educd>=101&age==45&empstat==1&uhrswork==40&cla > sswkr==2 (5,234 real changes made) . . tab GWG_ACS_45_Bachelormore GWG_ACS_45_ | Bachelormor | e | Freq. Percent Cum. ------------+----------------------------------- .7462082 | 5,234 100.00 100.00 ------------+----------------------------------- Total | 5,234 100.00 . . . // Share of those with a Bachelor's who hold more than a Bachelor's degree: . tab educd if educd>=101 educational attainment [detailed | version] | Freq. Percent Cum. ----------------------------------------+----------------------------------- bachelor's degree | 378,380 63.61 63.61 master's degree | 155,860 26.20 89.81 professional degree beyond a bachelor's | 37,235 6.26 96.07 doctoral degree | 23,388 3.93 100.00 ----------------------------------------+----------------------------------- Total | 594,863 100.00 . . . *********************************************************************************** . /* Page 12: "The difference between the 10th and the 90th percentile of the distribution of > females' actual relative wages across occupation groups amounts to 18 percentage points." */ . *********************************************************************************** . . clear all . set more off . . use "$path\data\usa_00025.dta", clear . . keep if year==2016 (60,038,334 observations deleted) . . keep if classwkr==2 //keep only employees (1,505,791 observations deleted) . . //wage and salary income, set missings to missing . replace incwage=. if incwage==9999999 (0 real changes made) . replace incwage=. if incwage==999999 (0 real changes made) . . gen female=0 if sex!=. . replace female=1 if sex==2 (824,468 real changes made) . . // 26 occupation groups based on ACS //Name of group > BLS category . gen occ=1 if occ2010>=10&occ2010<=430 //Management, Business, Science an > d arts 1 (1,496,144 missing values generated) . replace occ=2 if occ2010>=500&occ2010<=730 //Business Operations Specialists > 2 (41,343 real changes made) . replace occ=3 if occ2010>=800&occ2010<=950 //Financial Specialists > 2 (35,162 real changes made) . replace occ=4 if occ2010>=1000&occ2010<=1240 //Computer and Mathematical > 3 (47,034 real changes made) . replace occ=5 if occ2010>=1300&occ2010<=1540 //Architecture and Engineering > 4 (25,435 real changes made) . replace occ=6 if occ2010>=1550&occ2010<=1560 //Technicians > 4 (5,258 real changes made) . replace occ=7 if occ2010>=1600&occ2010<=1980 //Life, physical and social science > 5 (14,792 real changes made) . replace occ=8 if occ2010>=2000&occ2010<=2060 //Community and Social services > 6 (30,128 real changes made) . replace occ=9 if occ2010>=2100&occ2010<=2150 //Legal > 7 (15,474 real changes made) . replace occ=10 if occ2010>=2200&occ2010<=2550 //Education, Training and library > 8 (116,651 real changes made) . replace occ=11 if occ2010>=2600&occ2010<=2920 //Arts, design, entertainment, sports and > media 9 (27,133 real changes made) . replace occ=12 if occ2010>=3000&occ2010<=3540 //Healthcare practitioners and technical > 10 (96,762 real changes made) . replace occ=13 if occ2010>=3600&occ2010<=3650 //Healthcare support > 11 (37,061 real changes made) . replace occ=14 if occ2010>=3700&occ2010<=3950 //Protective Service > 12 (38,307 real changes made) . replace occ=15 if occ2010>=4000&occ2010<=4150 //Food preparation and serving > 13 (101,680 real changes made) . replace occ=16 if occ2010>=4200&occ2010<=4250 //Building and grounds cleaning and mainte > ntance 14 (59,517 real changes made) . replace occ=17 if occ2010>=4300&occ2010<=4650 //Personal care and service > 15 (54,160 real changes made) . replace occ=18 if occ2010>=4700&occ2010<=4965 //Sales and related > 16 (165,634 real changes made) . replace occ=19 if occ2010>=5000&occ2010<=5940 //Office and administrative support > 17 (237,417 real changes made) . replace occ=20 if occ2010>=6005&occ2010<=6130 //Farming, fishing and forestry > 18 (14,176 real changes made) . replace occ=21 if occ2010>=6200&occ2010<=6765 //Construction > 19 (66,875 real changes made) . replace occ=22 if occ2010>=6800&occ2010<=6940 //Extraction > 19 (2,317 real changes made) . replace occ=23 if occ2010>=7000&occ2010<=7630 //Installation, maintenance and repair > 20 (49,777 real changes made) . replace occ=24 if occ2010>=7799&occ2010<=8965 //Production > 21 (76,002 real changes made) . replace occ=25 if occ2010>=9000&occ2010<=9750 //Transportation and material moving > 22 (107,597 real changes made) . replace occ=26 if occ2010>=9800&occ2010<=9830 //Military specific (6,191 real changes made) . . . keep if age>24&age<66 (380,349 observations deleted) . . // Drop following industries: 0=N/A, 992=last worked 1984 or earlier, 999=did not respond . drop if ind1990==0|ind1990==992|ind1990==999 (0 observations deleted) . . //Summarize some occupation groups -> map to Bureau of Labor Statistics categories: . replace occ=21 if occ==22 //Extraction: Add to Construction (1,973 real changes made) . replace occ=2 if occ==3 //Financial Specialists: Add to business specialis > ts (30,395 real changes made) . replace occ=5 if occ==6 //Technicians: Add to architecture and engineering (4,228 real changes made) . replace occ=. if occ==26 //Drop military (3,613 real changes made, 3,613 to missing) . . egen occnew= group (occ) (24063 missing values generated) . . rename occ occold . rename occnew occ . . . //BLS categories . *occ=1 //Management, Business, Science and arts . *occ=2 //Business Operations Specialists and Financial Specialists > . *occ=3 //Computer and Mathematical > . *occ=4 //Architecture and Engineering and Technicians > . *occ=5 //Life, physical and social science . *occ=6 //Community and Social services . *occ=7 //Legal > . *occ=8 //Education, Training and library . *occ=9 //Arts, design, entertainment, sports and media . *occ=10 //Healthcare practitioners and technical . *occ=11 //Healthcare support > . *occ=12 //Protective Service > . *occ=13 //Food preparation and serving . *occ=14 //Building and grounds cleaning and maintentance . *occ=15 //Personal care and service > . *occ=16 //Sales and related > . *occ=17 //Office and administrative support . *occ=18 //Farming, fishing and forestry . *occ=19 //Construction and Extraction > . *occ=20 //Installation, maintenance and repair . *occ=21 //Production > . *occ=22 //Transportation and material moving . . . // Generate occupation-level measure of females' relative wages . gen GWG_ACS_2565_occ=. (1,270,347 missing values generated) . gen e_sample=. (1,270,347 missing values generated) . . forvalues i= 1/22 { 2. disp `i' 3. reg incwage female if empstat==1&uhrswork==40&classwkr==2&occ==`i' [pweight=perwt], robust 4. mat beta = e(b) 5. sca feml = beta[1,1] 6. disp feml 7. replace e_sample=0 8. replace e_sample=1 if e(sample)==1 9. mean incwage if female==0&empstat==1&uhrswork==40&classwkr==2&occ==`i' [pweight=perwt] 10. matrix mean=e(b) 11. mat list mean 12. sca const=mean[1,1] 13. replace GWG_ACS_2565_occ = 1+(feml/const) if empstat==1&uhrswork==40&classwkr==2&occ==`i' 14. replace e_sample=. 15. } 1 (sum of wgt is 5.1756e+06) Linear regression Number of obs = 49,797 F(1, 49795) = 1318.51 Prob > F = 0.0000 R-squared = 0.0363 Root MSE = 62628 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -24313.28 669.5783 -36.31 0.000 -25625.66 -23000.9 _cons | 90524.84 568.7646 159.16 0.000 89410.05 91639.62 ------------------------------------------------------------------------------ -24313.277 (1,270,347 real changes made) (49,797 real changes made) Mean estimation Number of obs = 25,652 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 90524.84 568.7643 89410.03 91639.65 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 90524.838 (49,797 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 2 (sum of wgt is 3.3051e+06) Linear regression Number of obs = 31,396 F(1, 31394) = 533.21 Prob > F = 0.0000 R-squared = 0.0275 Root MSE = 49031 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -16894.48 731.6356 -23.09 0.000 -18328.51 -15460.45 _cons | 76432.37 655.7739 116.55 0.000 75147.02 77717.71 ------------------------------------------------------------------------------ -16894.48 (1,270,347 real changes made) (31,396 real changes made) Mean estimation Number of obs = 12,100 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 76432.37 655.7801 75146.93 77717.8 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 76432.365 (31,396 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 3 (sum of wgt is 2.5729e+06) Linear regression Number of obs = 24,330 F(1, 24328) = 341.35 Prob > F = 0.0000 R-squared = 0.0149 Root MSE = 48246 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -13424.31 726.5976 -18.48 0.000 -14848.49 -12000.13 _cons | 86671.09 443.5112 195.42 0.000 85801.79 87540.4 ------------------------------------------------------------------------------ -13424.31 (1,270,347 real changes made) (24,330 real changes made) Mean estimation Number of obs = 17,775 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 86671.09 443.5054 85801.78 87540.41 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 86671.095 (24,330 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 4 (sum of wgt is 1.3362e+06) Linear regression Number of obs = 13,133 F(1, 13131) = 132.84 Prob > F = 0.0000 R-squared = 0.0098 Root MSE = 47943 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -12861.29 1115.867 -11.53 0.000 -15048.55 -10674.03 _cons | 84771.8 563.4733 150.45 0.000 83667.31 85876.29 ------------------------------------------------------------------------------ -12861.289 (1,270,347 real changes made) (13,133 real changes made) Mean estimation Number of obs = 10,976 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 84771.8 563.456 83667.32 85876.28 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 84771.8 (13,133 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 5 (sum of wgt is 5.9982e+05) Linear regression Number of obs = 6,030 F(1, 6028) = 39.61 Prob > F = 0.0000 R-squared = 0.0090 Root MSE = 49197 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -9399.285 1493.531 -6.29 0.000 -12327.14 -6471.43 _cons | 75101.42 1139.679 65.90 0.000 72867.25 77335.6 ------------------------------------------------------------------------------ -9399.2851 (1,270,347 real changes made) (6,030 real changes made) Mean estimation Number of obs = 3,215 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 75101.42 1139.667 72866.88 77335.97 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 75101.425 (6,030 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 6 (sum of wgt is 1.2267e+06) Linear regression Number of obs = 11,636 F(1, 11634) = 5.34 Prob > F = 0.0209 R-squared = 0.0007 Root MSE = 22832 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -1342.259 580.9757 -2.31 0.021 -2481.069 -203.4487 _cons | 46212.57 498.9845 92.61 0.000 45234.47 47190.66 ------------------------------------------------------------------------------ -1342.2586 (1,270,347 real changes made) (11,636 real changes made) Mean estimation Number of obs = 3,593 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 46212.57 499.011 45234.19 47190.94 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 46212.568 (11,636 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 7 (sum of wgt is 5.3694e+05) Linear regression Number of obs = 5,219 F(1, 5217) = 169.26 Prob > F = 0.0000 R-squared = 0.0589 Root MSE = 73578 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -39392.21 3027.814 -13.01 0.000 -45328 -33456.43 _cons | 110443.8 2786.314 39.64 0.000 104981.5 115906.2 ------------------------------------------------------------------------------ -39392.211 (1,270,347 real changes made) (5,219 real changes made) Mean estimation Number of obs = 1,685 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 110443.8 2786.607 104978.3 115909.4 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 110443.85 (5,219 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 8 (sum of wgt is 3.3125e+06) Linear regression Number of obs = 33,468 F(1, 33466) = 625.37 Prob > F = 0.0000 R-squared = 0.0314 Root MSE = 30412 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -12484.67 499.2378 -25.01 0.000 -13463.19 -11506.15 _cons | 59585.95 451.3607 132.01 0.000 58701.27 60470.64 ------------------------------------------------------------------------------ -12484.67 (1,270,347 real changes made) (33,468 real changes made) Mean estimation Number of obs = 8,713 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 59585.95 451.3731 58701.15 60470.75 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 59585.953 (33,468 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 9 (sum of wgt is 8.4473e+05) Linear regression Number of obs = 7,857 F(1, 7855) = 74.24 Prob > F = 0.0000 R-squared = 0.0121 Root MSE = 46063 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -10225.62 1186.765 -8.62 0.000 -12552 -7899.247 _cons | 65474.06 946.0176 69.21 0.000 63619.62 67328.51 ------------------------------------------------------------------------------ -10225.622 (1,270,347 real changes made) (7,857 real changes made) Mean estimation Number of obs = 4,197 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 65474.06 946.0099 63619.38 67328.74 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 65474.063 (7,857 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 10 (sum of wgt is 3.4313e+06) Linear regression Number of obs = 32,120 F(1, 32118) = 434.18 Prob > F = 0.0000 R-squared = 0.0387 Root MSE = 56346 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -27519.82 1320.726 -20.84 0.000 -30108.49 -24931.14 _cons | 89545.27 1277.011 70.12 0.000 87042.28 92048.26 ------------------------------------------------------------------------------ -27519.817 (1,270,347 real changes made) (32,120 real changes made) Mean estimation Number of obs = 6,795 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 89545.27 1277.065 87041.83 92048.72 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 89545.273 (32,120 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 11 (sum of wgt is 1.3203e+06) Linear regression Number of obs = 10,851 F(1, 10849) = 40.02 Prob > F = 0.0000 R-squared = 0.0121 Root MSE = 24470 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -7847.728 1240.505 -6.33 0.000 -10279.34 -5416.112 _cons | 38338.82 1209.893 31.69 0.000 35967.21 40710.43 ------------------------------------------------------------------------------ -7847.7282 (1,270,347 real changes made) (10,851 real changes made) Mean estimation Number of obs = 1,468 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 38338.82 1210.193 35964.93 40712.72 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 38338.824 (10,851 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 12 (sum of wgt is 1.4339e+06) Linear regression Number of obs = 13,333 F(1, 13331) = 98.93 Prob > F = 0.0000 R-squared = 0.0107 Root MSE = 32484 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -8099.826 814.36 -9.95 0.000 -9696.087 -6503.565 _cons | 55199.69 401.1959 137.59 0.000 54413.29 55986.09 ------------------------------------------------------------------------------ -8099.826 (1,270,347 real changes made) (13,333 real changes made) Mean estimation Number of obs = 10,331 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 55199.69 401.1852 54413.29 55986.09 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 55199.692 (13,333 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 13 (sum of wgt is 1.9037e+06) Linear regression Number of obs = 14,091 F(1, 14089) = 146.66 Prob > F = 0.0000 R-squared = 0.0130 Root MSE = 17257 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -3961.771 327.1444 -12.11 0.000 -4603.018 -3320.525 _cons | 27546.97 245.8215 112.06 0.000 27065.13 28028.81 ------------------------------------------------------------------------------ -3961.7712 (1,270,347 real changes made) (14,091 real changes made) Mean estimation Number of obs = 6,881 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 27546.97 245.8219 27065.08 28028.86 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 27546.97 (14,091 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 14 (sum of wgt is 2.2914e+06) Linear regression Number of obs = 19,173 F(1, 19171) = 406.67 Prob > F = 0.0000 R-squared = 0.0231 Root MSE = 23357 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -7499.417 371.8846 -20.17 0.000 -8228.343 -6770.49 _cons | 31825.54 271.644 117.16 0.000 31293.1 32357.99 ------------------------------------------------------------------------------ -7499.4166 (1,270,347 real changes made) (19,173 real changes made) Mean estimation Number of obs = 12,650 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 31825.54 271.6406 31293.09 32358 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 31825.543 (19,173 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 15 (sum of wgt is 1.1258e+06) Linear regression Number of obs = 9,339 F(1, 9337) = 195.98 Prob > F = 0.0000 R-squared = 0.0309 Root MSE = 19310 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -7746.618 553.3587 -14.00 0.000 -8831.322 -6661.914 _cons | 34136.58 491.1686 69.50 0.000 33173.78 35099.37 ------------------------------------------------------------------------------ -7746.6179 (1,270,347 real changes made) (9,339 real changes made) Mean estimation Number of obs = 2,458 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 34136.58 491.2159 33173.34 35099.82 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 34136.577 (9,339 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 16 (sum of wgt is 4.2802e+06) Linear regression Number of obs = 37,037 F(1, 37035) = 734.55 Prob > F = 0.0000 R-squared = 0.0282 Root MSE = 53235 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -18145.71 669.5185 -27.10 0.000 -19457.99 -16833.44 _cons | 61327.26 563.1754 108.90 0.000 60223.42 62431.1 ------------------------------------------------------------------------------ -18145.712 (1,270,347 real changes made) (37,037 real changes made) Mean estimation Number of obs = 19,303 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 61327.26 563.1748 60223.39 62431.13 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 61327.261 (37,037 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 17 (sum of wgt is 9.0466e+06) Linear regression Number of obs = 85,531 F(1, 85529) = 473.90 Prob > F = 0.0000 R-squared = 0.0111 Root MSE = 26927 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -6487.245 298.0004 -21.77 0.000 -7071.324 -5903.167 _cons | 44012.02 277.7338 158.47 0.000 43467.66 44556.37 ------------------------------------------------------------------------------ -6487.2453 (1,270,347 real changes made) (85,531 real changes made) Mean estimation Number of obs = 20,960 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 44012.02 277.7372 43467.63 44556.4 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 44012.017 (85,531 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 18 (sum of wgt is 3.4282e+05) Linear regression Number of obs = 3,075 F(1, 3073) = 37.90 Prob > F = 0.0000 R-squared = 0.0182 Root MSE = 19296 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -5886.946 956.2293 -6.16 0.000 -7761.86 -4012.033 _cons | 26884.99 473.6615 56.76 0.000 25956.26 27813.71 ------------------------------------------------------------------------------ -5886.9464 (1,270,347 real changes made) (3,075 real changes made) Mean estimation Number of obs = 2,252 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 26884.99 473.6126 25956.22 27813.75 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 26884.986 (3,075 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 19 (sum of wgt is 3.3053e+06) Linear regression Number of obs = 27,724 F(1, 27722) = 18.28 Prob > F = 0.0000 R-squared = 0.0009 Root MSE = 29227 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -5295.832 1238.583 -4.28 0.000 -7723.516 -2868.148 _cons | 44011.43 214.1374 205.53 0.000 43591.71 44431.15 ------------------------------------------------------------------------------ -5295.832 (1,270,347 real changes made) (27,724 real changes made) Mean estimation Number of obs = 26,973 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 44011.43 214.1337 43591.72 44431.14 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 44011.428 (27,724 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 20 (sum of wgt is 2.2351e+06) Linear regression Number of obs = 20,842 F(1, 20840) = 14.05 Prob > F = 0.0002 R-squared = 0.0008 Root MSE = 26183 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -3851.394 1027.519 -3.75 0.000 -5865.412 -1837.377 _cons | 47663.58 225.184 211.67 0.000 47222.2 48104.96 ------------------------------------------------------------------------------ -3851.3945 (1,270,347 real changes made) (20,842 real changes made) Mean estimation Number of obs = 20,053 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 47663.58 225.1788 47222.21 48104.95 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 47663.582 (20,842 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 21 (sum of wgt is 3.3490e+06) Linear regression Number of obs = 30,463 F(1, 30461) = 932.24 Prob > F = 0.0000 R-squared = 0.0402 Root MSE = 26185 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -11812.9 386.8947 -30.53 0.000 -12571.23 -11054.57 _cons | 40979.1 245.2016 167.12 0.000 40498.5 41459.71 ------------------------------------------------------------------------------ -11812.9 (1,270,347 real changes made) (30,463 real changes made) Mean estimation Number of obs = 21,852 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 40979.1 245.1992 40498.49 41459.71 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 40979.103 (30,463 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 22 (sum of wgt is 3.4333e+06) Linear regression Number of obs = 29,048 F(1, 29046) = 360.06 Prob > F = 0.0000 R-squared = 0.0129 Root MSE = 32384 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -9632.637 507.6428 -18.98 0.000 -10627.64 -8637.634 _cons | 40087.49 256.88 156.06 0.000 39583.99 40590.98 ------------------------------------------------------------------------------ -9632.6374 (1,270,347 real changes made) (29,048 real changes made) Mean estimation Number of obs = 23,898 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 40087.49 256.8765 39583.99 40590.98 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 40087.486 (29,048 real changes made) (1,270,347 real changes made, 1,270,347 to missing) . . _pctile GWG_ACS_2565_occ [pw=perwt], p(10) . return list scalars: r(r1) = .704116702079773 . *-> .704116702079773 . . _pctile GWG_ACS_2565_occ [pw=perwt], p(90) . return list scalars: r(r1) = .8796714544296265 . *-> .8796714544296265 . . // Diff: 0.1755 . . *********************************************************************************** . /* Page 12: "The difference between the 10th and the 90th percentile of females' actual > relative wages across states amounts to 8 percentage points." */ . *********************************************************************************** . . gen state="" (1,270,347 missing values generated) . replace state="Alabama" if statefip==1 variable state was str1 now str7 (18,000 real changes made) . replace state="Alaska" if statefip==2 (2,827 real changes made) . replace state="Arizona" if statefip==4 (25,347 real changes made) . replace state="Arkansas" if statefip==5 variable state was str7 now str8 (10,961 real changes made) . replace state="California" if statefip==6 variable state was str8 now str10 (149,361 real changes made) . replace state="Colorado" if statefip==8 (23,167 real changes made) . replace state="Connecticut" if statefip==9 variable state was str10 now str11 (14,903 real changes made) . replace state="Delaware" if statefip==10 (3,723 real changes made) . replace state="District of Columbia" if statefip==11 variable state was str11 now str20 (3,156 real changes made) . replace state="Florida" if statefip==12 (74,116 real changes made) . replace state="Georgia" if statefip==13 (39,119 real changes made) . replace state="Hawaii" if statefip==15 (5,837 real changes made) . replace state="Idaho" if statefip==16 (6,028 real changes made) . replace state="Illinois" if statefip==17 (53,248 real changes made) . replace state="Indiana" if statefip==18 (27,246 real changes made) . replace state="Iowa" if statefip==19 (12,884 real changes made) . replace state="Kansas" if statefip==20 (11,678 real changes made) . replace state="Kentucky" if statefip==21 (17,485 real changes made) . replace state="Louisiana" if statefip==22 (16,684 real changes made) . replace state="Maine" if statefip==23 (4,858 real changes made) . replace state="Maryland" if statefip==24 (25,932 real changes made) . replace state="Massachusetts" if statefip==25 (29,336 real changes made) . replace state="Michigan" if statefip==26 (39,575 real changes made) . replace state="Minnesota" if statefip==27 (22,908 real changes made) . replace state="Mississippi" if statefip==28 (10,609 real changes made) . replace state="Missouri" if statefip==29 (24,540 real changes made) . replace state="Montana" if statefip==30 (3,778 real changes made) . replace state="Nebraska" if statefip==31 (7,676 real changes made) . replace state="Nevada" if statefip==32 (11,226 real changes made) . replace state="New Hampshire" if statefip==33 (5,848 real changes made) . replace state="New Jersey" if statefip==34 (37,348 real changes made) . replace state="New Mexico" if statefip==35 (7,118 real changes made) . replace state="New York" if statefip==36 (79,938 real changes made) . replace state="North Carolina" if statefip==37 (39,460 real changes made) . replace state="North Dakota" if statefip==38 (3,133 real changes made) . replace state="Ohio" if statefip==39 (48,648 real changes made) . replace state="Oklahoma" if statefip==40 (13,774 real changes made) . replace state="Oregon" if statefip==41 (16,419 real changes made) . replace state="Pennsylvania" if statefip==42 (52,388 real changes made) . replace state="Rhode Island" if statefip==44 (4,265 real changes made) . replace state="South Carolina" if statefip==45 (18,756 real changes made) . replace state="South Dakota" if statefip==46 (3,321 real changes made) . replace state="Tennessee" if statefip==47 (25,684 real changes made) . replace state="Texas" if statefip==48 (103,909 real changes made) . replace state="Utah" if statefip==49 (11,153 real changes made) . replace state="Vermont" if statefip==50 (2,590 real changes made) . replace state="Virginia" if statefip==51 (36,005 real changes made) . replace state="Washington" if statefip==53 (30,451 real changes made) . replace state="West Virginia" if statefip==54 (6,770 real changes made) . replace state="Wisconsin" if statefip==55 (24,763 real changes made) . replace state="Wyoming" if statefip==56 (2,398 real changes made) . . . // Generate state-level measure of females' relative wages . replace e_sample=. (0 real changes made) . gen GWG_ACS_2565_state=. (1,270,347 missing values generated) . . egen group= group (statefip) . sum group, meanonly . . forvalues i= 1/`r(max)' { 2. disp `i' 3. reg incwage female if empstat==1&uhrswork==40&classwkr==2&group==`i' [pweight=perwt], robust 4. mat beta = e(b) 5. sca feml = beta[1,1] 6. disp feml 7. replace e_sample=0 8. replace e_sample=1 if e(sample)==1 9. mean incwage if female==0&empstat==1&uhrswork==40&classwkr==2&group==`i' [pweight=perwt] 10. matrix mean=e(b) 11. mat list mean 12. sca const=mean[1,1] 13. replace GWG_ACS_2565_state = 1+(feml/const) if empstat==1&uhrswork==40&classwkr==2&group==`i' 14. replace e_sample=. 15. } 1 (sum of wgt is 8.5115e+05) Linear regression Number of obs = 7,765 F(1, 7763) = 125.76 Prob > F = 0.0000 R-squared = 0.0250 Root MSE = 33750 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -10814.59 964.363 -11.21 0.000 -12705 -8924.18 _cons | 50236.11 786.7241 63.85 0.000 48693.92 51778.3 ------------------------------------------------------------------------------ -10814.591 (1,270,347 real changes made) (7,765 real changes made) Mean estimation Number of obs = 3,846 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 50236.11 786.7251 48693.67 51778.55 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 50236.113 (7,765 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 2 (sum of wgt is 1.2551e+05) Linear regression Number of obs = 897 F(1, 895) = 12.18 Prob > F = 0.0005 R-squared = 0.0228 Root MSE = 37619 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -11597.96 3323.461 -3.49 0.001 -18120.65 -5075.279 _cons | 61367 2744.172 22.36 0.000 55981.24 66752.76 ------------------------------------------------------------------------------ -11597.963 (1,270,347 real changes made) (897 real changes made) Mean estimation Number of obs = 461 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 61367 2744.088 55974.5 66759.51 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 61367.004 (897 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 3 (sum of wgt is 1.1630e+06) Linear regression Number of obs = 10,751 F(1, 10749) = 104.57 Prob > F = 0.0000 R-squared = 0.0122 Root MSE = 41504 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -9230.402 902.6328 -10.23 0.000 -10999.73 -7461.075 _cons | 52134.01 745.5093 69.93 0.000 50672.67 53595.34 ------------------------------------------------------------------------------ -9230.4021 (1,270,347 real changes made) (10,751 real changes made) Mean estimation Number of obs = 5,572 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 52134.01 745.5069 50672.52 53595.49 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 52134.007 (10,751 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 4 (sum of wgt is 5.0122e+05) Linear regression Number of obs = 4,618 F(1, 4616) = 30.75 Prob > F = 0.0000 R-squared = 0.0104 Root MSE = 35891 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -7368.519 1328.773 -5.55 0.000 -9973.549 -4763.488 _cons | 44789.99 1059.503 42.27 0.000 42712.86 46867.12 ------------------------------------------------------------------------------ -7368.5186 (1,270,347 real changes made) (4,618 real changes made) Mean estimation Number of obs = 2,212 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 44789.99 1059.513 42712.25 46867.74 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 44789.991 (4,618 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 5 (sum of wgt is 7.5362e+06) Linear regression Number of obs = 67,884 F(1, 67882) = 405.47 Prob > F = 0.0000 R-squared = 0.0072 Root MSE = 51444 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -8827.175 438.3694 -20.14 0.000 -9686.379 -7967.972 _cons | 60972.5 337.4819 180.67 0.000 60311.03 61633.96 ------------------------------------------------------------------------------ -8827.1751 (1,270,347 real changes made) (67,884 real changes made) Mean estimation Number of obs = 37,025 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 60972.5 337.4815 60311.03 61633.97 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 60972.499 (67,884 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 6 (sum of wgt is 9.7466e+05) Linear regression Number of obs = 9,173 F(1, 9171) = 113.82 Prob > F = 0.0000 R-squared = 0.0150 Root MSE = 42569 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -10536.32 987.576 -10.67 0.000 -12472.19 -8600.452 _cons | 57999.73 814.2585 71.23 0.000 56403.6 59595.86 ------------------------------------------------------------------------------ -10536.321 (1,270,347 real changes made) (9,173 real changes made) Mean estimation Number of obs = 4,859 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 57999.73 814.2535 56403.43 59596.04 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 57999.732 (9,173 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 7 (sum of wgt is 5.8632e+05) Linear regression Number of obs = 5,481 F(1, 5479) = 55.99 Prob > F = 0.0000 R-squared = 0.0117 Root MSE = 52566 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -11464.6 1532.153 -7.48 0.000 -14468.23 -8460.974 _cons | 66766.84 1229.457 54.31 0.000 64356.61 69177.06 ------------------------------------------------------------------------------ -11464.603 (1,270,347 real changes made) (5,481 real changes made) Mean estimation Number of obs = 2,768 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 66766.84 1229.455 64356.1 69177.58 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 66766.839 (5,481 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 8 (sum of wgt is 1.8257e+05) Linear regression Number of obs = 1,565 F(1, 1563) = 13.06 Prob > F = 0.0003 R-squared = 0.0109 Root MSE = 34035 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -7158.615 1980.986 -3.61 0.000 -11044.28 -3272.946 _cons | 54883.34 1458.7 37.62 0.000 52022.12 57744.55 ------------------------------------------------------------------------------ -7158.6153 (1,270,347 real changes made) (1,565 real changes made) Mean estimation Number of obs = 790 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 54883.34 1458.691 52019.96 57746.71 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 54883.339 (1,565 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 9 (sum of wgt is 1.5163e+05) Linear regression Number of obs = 1,359 F(1, 1357) = 4.45 Prob > F = 0.0352 R-squared = 0.0034 Root MSE = 59832 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -6946.065 3294.027 -2.11 0.035 -13408 -484.1276 _cons | 72743.94 2612.749 27.84 0.000 67618.48 77869.41 ------------------------------------------------------------------------------ -6946.0647 (1,270,347 real changes made) (1,359 real changes made) Mean estimation Number of obs = 622 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 72743.94 2612.927 67612.7 77875.19 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 72743.943 (1,359 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 10 (sum of wgt is 3.6308e+06) Linear regression Number of obs = 31,506 F(1, 31504) = 242.30 Prob > F = 0.0000 R-squared = 0.0104 Root MSE = 40254 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -8253.139 530.2 -15.57 0.000 -9292.352 -7213.926 _cons | 48661.43 439.6415 110.68 0.000 47799.71 49523.14 ------------------------------------------------------------------------------ -8253.1393 (1,270,347 real changes made) (31,506 real changes made) Mean estimation Number of obs = 15,686 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 48661.43 439.6415 47799.68 49523.17 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 48661.425 (31,506 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 11 (sum of wgt is 1.8321e+06) Linear regression Number of obs = 16,152 F(1, 16150) = 148.78 Prob > F = 0.0000 R-squared = 0.0160 Root MSE = 40903 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -10427.69 854.909 -12.20 0.000 -12103.4 -8751.969 _cons | 52620.32 755.0096 69.69 0.000 51140.42 54100.22 ------------------------------------------------------------------------------ -10427.686 (1,270,347 real changes made) (16,152 real changes made) Mean estimation Number of obs = 7,921 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 52620.32 755.0105 51140.3 54100.34 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 52620.321 (16,152 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 12 (sum of wgt is 3.1891e+05) Linear regression Number of obs = 3,030 F(1, 3028) = 33.17 Prob > F = 0.0000 R-squared = 0.0154 Root MSE = 35688 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -8941.18 1552.55 -5.76 0.000 -11985.34 -5897.022 _cons | 54374 1252.907 43.40 0.000 51917.36 56830.63 ------------------------------------------------------------------------------ -8941.1804 (1,270,347 real changes made) (3,030 real changes made) Mean estimation Number of obs = 1,565 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 54374 1252.894 51916.47 56831.53 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 54373.998 (3,030 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 13 (sum of wgt is 2.5536e+05) Linear regression Number of obs = 2,284 F(1, 2282) = 50.86 Prob > F = 0.0000 R-squared = 0.0315 Root MSE = 33877 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -12257.13 1718.631 -7.13 0.000 -15627.38 -8886.892 _cons | 50273.03 1464.73 34.32 0.000 47400.68 53145.37 ------------------------------------------------------------------------------ -12257.135 (1,270,347 real changes made) (2,284 real changes made) Mean estimation Number of obs = 1,245 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 50273.03 1464.677 47399.52 53146.53 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 50273.025 (2,284 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 14 (sum of wgt is 2.3344e+06) Linear regression Number of obs = 21,679 F(1, 21677) = 192.67 Prob > F = 0.0000 R-squared = 0.0151 Root MSE = 47671 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -11838.32 852.8735 -13.88 0.000 -13510.01 -10166.62 _cons | 59810.96 705.4398 84.79 0.000 58428.24 61193.67 ------------------------------------------------------------------------------ -11838.316 (1,270,347 real changes made) (21,679 real changes made) Mean estimation Number of obs = 11,184 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 59810.96 705.4388 58428.17 61193.74 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 59810.956 (21,679 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 15 (sum of wgt is 1.0901e+06) Linear regression Number of obs = 10,352 F(1, 10350) = 123.95 Prob > F = 0.0000 R-squared = 0.0189 Root MSE = 39639 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -11013.38 989.2421 -11.13 0.000 -12952.48 -9074.27 _cons | 51400.23 809.8465 63.47 0.000 49812.78 52987.69 ------------------------------------------------------------------------------ -11013.376 (1,270,347 real changes made) (10,352 real changes made) Mean estimation Number of obs = 5,398 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 51400.23 809.8433 49812.62 52987.85 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 51400.235 (10,352 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 16 (sum of wgt is 5.2839e+05) Linear regression Number of obs = 5,163 F(1, 5161) = 26.35 Prob > F = 0.0000 R-squared = 0.0096 Root MSE = 33363 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -6552.609 1276.466 -5.13 0.000 -9055.024 -4050.194 _cons | 49787.08 962.7443 51.71 0.000 47899.69 51674.47 ------------------------------------------------------------------------------ -6552.609 (1,270,347 real changes made) (5,163 real changes made) Mean estimation Number of obs = 2,551 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 49787.08 962.7465 47899.24 51674.93 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 49787.081 (5,163 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 17 (sum of wgt is 4.8996e+05) Linear regression Number of obs = 4,634 F(1, 4632) = 89.36 Prob > F = 0.0000 R-squared = 0.0263 Root MSE = 34385 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -11316.76 1197.139 -9.45 0.000 -13663.72 -8969.793 _cons | 51733.55 953.0432 54.28 0.000 49865.13 53601.97 ------------------------------------------------------------------------------ -11316.756 (1,270,347 real changes made) (4,634 real changes made) Mean estimation Number of obs = 2,385 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 51733.55 953.0373 49864.68 53602.42 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 51733.549 (4,634 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 18 (sum of wgt is 6.9479e+05) Linear regression Number of obs = 6,657 F(1, 6655) = 71.31 Prob > F = 0.0000 R-squared = 0.0153 Root MSE = 34831 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -8691.238 1029.242 -8.44 0.000 -10708.88 -6673.595 _cons | 48481.72 851.8276 56.91 0.000 46811.86 50151.57 ------------------------------------------------------------------------------ -8691.2383 (1,270,347 real changes made) (6,657 real changes made) Mean estimation Number of obs = 3,324 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 48481.72 851.8278 46811.56 50151.88 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 48481.716 (6,657 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 19 (sum of wgt is 8.3345e+05) Linear regression Number of obs = 7,215 F(1, 7213) = 168.00 Prob > F = 0.0000 R-squared = 0.0378 Root MSE = 39465 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -15650 1207.419 -12.96 0.000 -18016.89 -13283.1 _cons | 54127.36 1066.359 50.76 0.000 52036.98 56217.74 ------------------------------------------------------------------------------ -15649.998 (1,270,347 real changes made) (7,215 real changes made) Mean estimation Number of obs = 3,436 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 54127.36 1066.366 52036.58 56218.14 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 54127.36 (7,215 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 20 (sum of wgt is 2.1141e+05) Linear regression Number of obs = 1,804 F(1, 1802) = 17.60 Prob > F = 0.0000 R-squared = 0.0131 Root MSE = 33246 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -7653.567 1824.369 -4.20 0.000 -11231.67 -4075.466 _cons | 49235.13 1451.944 33.91 0.000 46387.46 52082.8 ------------------------------------------------------------------------------ -7653.5671 (1,270,347 real changes made) (1,804 real changes made) Mean estimation Number of obs = 882 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 49235.13 1451.962 46385.42 52084.83 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 49235.125 (1,804 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 21 (sum of wgt is 1.3276e+06) Linear regression Number of obs = 12,135 F(1, 12133) = 134.78 Prob > F = 0.0000 R-squared = 0.0143 Root MSE = 46910 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -11308.68 974.0889 -11.61 0.000 -13218.05 -9399.315 _cons | 68973.95 793.4005 86.93 0.000 67418.76 70529.14 ------------------------------------------------------------------------------ -11308.685 (1,270,347 real changes made) (12,135 real changes made) Mean estimation Number of obs = 6,069 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 68973.95 793.4004 67418.6 70529.29 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 68973.948 (12,135 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 22 (sum of wgt is 1.2292e+06) Linear regression Number of obs = 11,392 F(1, 11390) = 109.62 Prob > F = 0.0000 R-squared = 0.0113 Root MSE = 50206 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -10767.75 1028.432 -10.47 0.000 -12783.66 -8751.849 _cons | 68164.76 829.3224 82.19 0.000 66539.15 69790.38 ------------------------------------------------------------------------------ -10767.753 (1,270,347 real changes made) (11,392 real changes made) Mean estimation Number of obs = 5,956 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 68164.76 829.3193 66538.99 69790.53 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 68164.761 (11,392 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 23 (sum of wgt is 1.5433e+06) Linear regression Number of obs = 14,483 F(1, 14481) = 137.23 Prob > F = 0.0000 R-squared = 0.0133 Root MSE = 38042 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -8850.026 755.4653 -11.71 0.000 -10330.83 -7369.218 _cons | 52759.35 582.8394 90.52 0.000 51616.91 53901.79 ------------------------------------------------------------------------------ -8850.0263 (1,270,347 real changes made) (14,483 real changes made) Mean estimation Number of obs = 7,465 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 52759.35 582.8382 51616.83 53901.88 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 52759.353 (14,483 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 24 (sum of wgt is 1.0528e+06) Linear regression Number of obs = 9,195 F(1, 9193) = 57.74 Prob > F = 0.0000 R-squared = 0.0116 Root MSE = 40469 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -8796.379 1157.622 -7.60 0.000 -11065.58 -6527.183 _cons | 57909.07 908.2375 63.76 0.000 56128.72 59689.41 ------------------------------------------------------------------------------ -8796.3789 (1,270,347 real changes made) (9,195 real changes made) Mean estimation Number of obs = 4,759 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 57909.07 908.2341 56128.51 59689.63 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 57909.066 (9,195 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 25 (sum of wgt is 5.3125e+05) Linear regression Number of obs = 4,830 F(1, 4828) = 95.11 Prob > F = 0.0000 R-squared = 0.0294 Root MSE = 34370 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -11989.46 1229.378 -9.75 0.000 -14399.61 -9579.322 _cons | 48154.23 1025.718 46.95 0.000 46143.36 50165.11 ------------------------------------------------------------------------------ -11989.464 (1,270,347 real changes made) (4,830 real changes made) Mean estimation Number of obs = 2,200 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 48154.23 1025.739 46142.71 50165.75 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 48154.233 (4,830 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 26 (sum of wgt is 1.0768e+06) Linear regression Number of obs = 10,285 F(1, 10283) = 122.11 Prob > F = 0.0000 R-squared = 0.0199 Root MSE = 39078 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -11142.31 1008.321 -11.05 0.000 -13118.82 -9165.807 _cons | 52275.46 824.1601 63.43 0.000 50659.95 53890.97 ------------------------------------------------------------------------------ -11142.312 (1,270,347 real changes made) (10,285 real changes made) Mean estimation Number of obs = 5,130 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 52275.46 824.1603 50659.75 53891.17 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 52275.459 (10,285 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 27 (sum of wgt is 1.7294e+05) Linear regression Number of obs = 1,519 F(1, 1517) = 44.57 Prob > F = 0.0000 R-squared = 0.0437 Root MSE = 31643 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -13530.73 2026.808 -6.68 0.000 -17506.37 -9555.088 _cons | 50288.51 1771.754 28.38 0.000 46813.16 53763.86 ------------------------------------------------------------------------------ -13530.73 (1,270,347 real changes made) (1,519 real changes made) Mean estimation Number of obs = 776 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 50288.51 1771.729 46810.55 53766.47 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 50288.512 (1,519 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 28 (sum of wgt is 3.2465e+05) Linear regression Number of obs = 3,023 F(1, 3021) = 48.92 Prob > F = 0.0000 R-squared = 0.0239 Root MSE = 31775 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -9940.606 1421.234 -6.99 0.000 -12727.29 -7153.922 _cons | 49693.51 1184.533 41.95 0.000 47370.94 52016.08 ------------------------------------------------------------------------------ -9940.6063 (1,270,347 real changes made) (3,023 real changes made) Mean estimation Number of obs = 1,449 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 49693.51 1184.55 47369.89 52017.13 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 49693.512 (3,023 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 29 (sum of wgt is 6.0858e+05) Linear regression Number of obs = 5,218 F(1, 5216) = 54.98 Prob > F = 0.0000 R-squared = 0.0128 Root MSE = 38795 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -8886.493 1198.472 -7.41 0.000 -11236 -6536.986 _cons | 50485.99 965.4002 52.30 0.000 48593.4 52378.58 ------------------------------------------------------------------------------ -8886.4928 (1,270,347 real changes made) (5,218 real changes made) Mean estimation Number of obs = 2,812 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 50485.99 965.3868 48593.05 52378.93 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 50485.991 (5,218 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 30 (sum of wgt is 2.3028e+05) Linear regression Number of obs = 2,226 F(1, 2224) = 39.33 Prob > F = 0.0000 R-squared = 0.0233 Root MSE = 43592 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -13483.31 2150.023 -6.27 0.000 -17699.58 -9267.052 _cons | 62516.53 1736.97 35.99 0.000 59110.28 65922.78 ------------------------------------------------------------------------------ -13483.314 (1,270,347 real changes made) (2,226 real changes made) Mean estimation Number of obs = 1,181 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 62516.53 1736.925 59108.72 65924.33 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 62516.529 (2,226 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 31 (sum of wgt is 1.7300e+06) Linear regression Number of obs = 15,659 F(1, 15657) = 155.78 Prob > F = 0.0000 R-squared = 0.0127 Root MSE = 53541 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -12192.81 976.8934 -12.48 0.000 -14107.64 -10277.99 _cons | 68948.18 740.286 93.14 0.000 67497.13 70399.22 ------------------------------------------------------------------------------ -12192.815 (1,270,347 real changes made) (15,659 real changes made) Mean estimation Number of obs = 8,167 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 68948.18 740.284 67497.03 70399.32 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 68948.177 (15,659 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 32 (sum of wgt is 3.6197e+05) Linear regression Number of obs = 3,305 F(1, 3303) = 17.94 Prob > F = 0.0000 R-squared = 0.0100 Root MSE = 39903 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -8013.309 1891.675 -4.24 0.000 -11722.28 -4304.336 _cons | 50551.97 1397.646 36.17 0.000 47811.63 53292.31 ------------------------------------------------------------------------------ -8013.3089 (1,270,347 real changes made) (3,305 real changes made) Mean estimation Number of obs = 1,692 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 50551.97 1397.636 47810.69 53293.25 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 50551.969 (3,305 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 33 (sum of wgt is 3.4197e+06) Linear regression Number of obs = 31,485 F(1, 31483) = 103.43 Prob > F = 0.0000 R-squared = 0.0046 Root MSE = 49410 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -6721.514 660.9273 -10.17 0.000 -8016.957 -5426.07 _cons | 59390.69 522.328 113.70 0.000 58366.91 60414.48 ------------------------------------------------------------------------------ -6721.5136 (1,270,347 real changes made) (31,485 real changes made) Mean estimation Number of obs = 16,458 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 59390.69 522.3273 58366.87 60414.51 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 59390.692 (31,485 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 34 (sum of wgt is 1.7225e+06) Linear regression Number of obs = 15,851 F(1, 15849) = 152.00 Prob > F = 0.0000 R-squared = 0.0122 Root MSE = 40323 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -8955.71 726.3937 -12.33 0.000 -10379.52 -7531.896 _cons | 51137.97 609.8604 83.85 0.000 49942.57 52333.36 ------------------------------------------------------------------------------ -8955.7101 (1,270,347 real changes made) (15,851 real changes made) Mean estimation Number of obs = 7,838 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 51137.97 609.8608 49942.48 52333.46 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 51137.969 (15,851 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 35 (sum of wgt is 1.3242e+05) Linear regression Number of obs = 1,256 F(1, 1254) = 27.80 Prob > F = 0.0000 R-squared = 0.0456 Root MSE = 25962 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -11372.74 2157.01 -5.27 0.000 -15604.49 -7140.998 _cons | 53031.6 1752.26 30.26 0.000 49593.92 56469.29 ------------------------------------------------------------------------------ -11372.744 (1,270,347 real changes made) (1,256 real changes made) Mean estimation Number of obs = 589 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 53031.6 1752.352 49589.97 56473.24 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 53031.604 (1,256 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 36 (sum of wgt is 1.9442e+06) Linear regression Number of obs = 18,911 F(1, 18909) = 263.48 Prob > F = 0.0000 R-squared = 0.0195 Root MSE = 34287 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -9667.169 595.5555 -16.23 0.000 -10834.51 -8499.827 _cons | 51883.45 488.5325 106.20 0.000 50925.88 52841.02 ------------------------------------------------------------------------------ -9667.1687 (1,270,347 real changes made) (18,911 real changes made) Mean estimation Number of obs = 9,675 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 51883.45 488.5319 50925.82 52841.07 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 51883.449 (18,911 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 37 (sum of wgt is 6.7153e+05) Linear regression Number of obs = 6,096 F(1, 6094) = 127.25 Prob > F = 0.0000 R-squared = 0.0286 Root MSE = 36030 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -12359.39 1095.63 -11.28 0.000 -14507.21 -10211.57 _cons | 50661.8 879.0973 57.63 0.000 48938.46 52385.14 ------------------------------------------------------------------------------ -12359.39 (1,270,347 real changes made) (6,096 real changes made) Mean estimation Number of obs = 3,087 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 50661.8 879.0954 48938.13 52385.47 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 50661.797 (6,096 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 38 (sum of wgt is 6.6030e+05) Linear regression Number of obs = 6,184 F(1, 6182) = 78.61 Prob > F = 0.0000 R-squared = 0.0150 Root MSE = 40199 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -9954.141 1122.67 -8.87 0.000 -12154.96 -7753.317 _cons | 54651.77 883.0319 61.89 0.000 52920.72 56382.82 ------------------------------------------------------------------------------ -9954.141 (1,270,347 real changes made) (6,184 real changes made) Mean estimation Number of obs = 3,359 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 54651.77 883.0205 52920.46 56383.09 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 54651.774 (6,184 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 39 (sum of wgt is 2.2280e+06) Linear regression Number of obs = 21,121 F(1, 21119) = 224.64 Prob > F = 0.0000 R-squared = 0.0156 Root MSE = 39897 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -10052.77 670.7237 -14.99 0.000 -11367.44 -8738.097 _cons | 56233.46 527.5907 106.59 0.000 55199.34 57267.58 ------------------------------------------------------------------------------ -10052.766 (1,270,347 real changes made) (21,121 real changes made) Mean estimation Number of obs = 10,859 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 56233.46 527.5901 55199.29 57267.63 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 56233.461 (21,121 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 40 (sum of wgt is 1.8268e+05) Linear regression Number of obs = 1,683 F(1, 1681) = 34.26 Prob > F = 0.0000 R-squared = 0.0241 Root MSE = 38371 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -12057.19 2060.071 -5.85 0.000 -16097.76 -8016.614 _cons | 59021.61 1770.535 33.34 0.000 55548.93 62494.3 ------------------------------------------------------------------------------ -12057.188 (1,270,347 real changes made) (1,683 real changes made) Mean estimation Number of obs = 858 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 59021.61 1770.515 55546.56 62496.67 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 59021.613 (1,683 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 41 (sum of wgt is 8.5218e+05) Linear regression Number of obs = 7,642 F(1, 7640) = 99.77 Prob > F = 0.0000 R-squared = 0.0192 Root MSE = 35266 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -9867.554 987.8953 -9.99 0.000 -11804.1 -7931.008 _cons | 48360.2 798.6857 60.55 0.000 46794.56 49925.84 ------------------------------------------------------------------------------ -9867.554 (1,270,347 real changes made) (7,642 real changes made) Mean estimation Number of obs = 3,711 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 48360.2 798.6888 46794.29 49926.11 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 48360.2 (7,642 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 42 (sum of wgt is 1.4306e+05) Linear regression Number of obs = 1,382 F(1, 1380) = 4.46 Prob > F = 0.0349 R-squared = 0.0055 Root MSE = 32772 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -4889.695 2315.968 -2.11 0.035 -9432.894 -346.4964 _cons | 43996.92 1650.173 26.66 0.000 40759.8 47234.04 ------------------------------------------------------------------------------ -4889.6953 (1,270,347 real changes made) (1,382 real changes made) Mean estimation Number of obs = 643 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 43996.92 1650.262 40756.36 47237.48 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 43996.919 (1,382 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 43 (sum of wgt is 1.1271e+06) Linear regression Number of obs = 10,509 F(1, 10507) = 114.37 Prob > F = 0.0000 R-squared = 0.0143 Root MSE = 38962 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -9401.956 879.138 -10.69 0.000 -11125.23 -7678.679 _cons | 49242.3 725.4201 67.88 0.000 47820.34 50664.26 ------------------------------------------------------------------------------ -9401.9562 (1,270,347 real changes made) (10,509 real changes made) Mean estimation Number of obs = 5,313 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 49242.3 725.4194 47820.18 50664.42 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 49242.3 (10,509 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 44 (sum of wgt is 5.0363e+06) Linear regression Number of obs = 44,184 F(1, 44182) = 613.71 Prob > F = 0.0000 R-squared = 0.0180 Root MSE = 43361 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -11738.5 473.8401 -24.77 0.000 -12667.23 -10809.76 _cons | 54129.89 388.0902 139.48 0.000 53369.23 54890.56 ------------------------------------------------------------------------------ -11738.497 (1,270,347 real changes made) (44,184 real changes made) Mean estimation Number of obs = 22,470 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 54129.89 388.09 53369.21 54890.58 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 54129.892 (44,184 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 45 (sum of wgt is 4.9132e+05) Linear regression Number of obs = 4,440 F(1, 4438) = 193.09 Prob > F = 0.0000 R-squared = 0.0484 Root MSE = 35926 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -16471.64 1185.368 -13.90 0.000 -18795.55 -14147.72 _cons | 55405.03 1008.445 54.94 0.000 53427.98 57382.09 ------------------------------------------------------------------------------ -16471.636 (1,270,347 real changes made) (4,440 real changes made) Mean estimation Number of obs = 2,621 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 55405.03 1008.41 53427.67 57382.39 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 55405.032 (4,440 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 46 (sum of wgt is 1.0040e+05) Linear regression Number of obs = 988 F(1, 986) = 4.55 Prob > F = 0.0331 R-squared = 0.0084 Root MSE = 35446 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -6520.671 3056.039 -2.13 0.033 -12517.76 -523.5835 _cons | 50534.92 2257.309 22.39 0.000 46105.24 54964.6 ------------------------------------------------------------------------------ -6520.6714 (1,270,347 real changes made) (988 real changes made) Mean estimation Number of obs = 478 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 50534.92 2257.386 46099.27 54970.57 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 50534.917 (988 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 47 (sum of wgt is 1.6441e+06) Linear regression Number of obs = 15,422 F(1, 15420) = 216.59 Prob > F = 0.0000 R-squared = 0.0178 Root MSE = 48090 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -12944.2 879.5325 -14.72 0.000 -14668.19 -11220.22 _cons | 64987.07 678.435 95.79 0.000 63657.26 66316.88 ------------------------------------------------------------------------------ -12944.203 (1,270,347 real changes made) (15,422 real changes made) Mean estimation Number of obs = 7,900 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 64987.07 678.4339 63657.16 66316.98 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 64987.068 (15,422 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 48 (sum of wgt is 1.3373e+06) Linear regression Number of obs = 12,646 F(1, 12644) = 233.91 Prob > F = 0.0000 R-squared = 0.0228 Root MSE = 45935 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -14179.07 927.0829 -15.29 0.000 -15996.29 -12361.84 _cons | 64408.09 694.4752 92.74 0.000 63046.82 65769.37 ------------------------------------------------------------------------------ -14179.065 (1,270,347 real changes made) (12,646 real changes made) Mean estimation Number of obs = 7,069 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 64408.09 694.4694 63046.72 65769.46 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 64408.092 (12,646 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 49 (sum of wgt is 3.0142e+05) Linear regression Number of obs = 2,813 F(1, 2811) = 68.06 Prob > F = 0.0000 R-squared = 0.0325 Root MSE = 32856 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -12041.7 1459.64 -8.25 0.000 -14903.78 -9179.63 _cons | 50312.08 1253.366 40.14 0.000 47854.47 52769.69 ------------------------------------------------------------------------------ -12041.703 (1,270,347 real changes made) (2,813 real changes made) Mean estimation Number of obs = 1,413 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 50312.08 1253.364 47853.43 52770.74 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 50312.083 (2,813 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 50 (sum of wgt is 9.7794e+05) Linear regression Number of obs = 9,250 F(1, 9248) = 72.08 Prob > F = 0.0000 R-squared = 0.0138 Root MSE = 34948 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -8271.715 974.2843 -8.49 0.000 -10181.53 -6361.903 _cons | 50505.67 775.8649 65.10 0.000 48984.81 52026.54 ------------------------------------------------------------------------------ -8271.7153 (1,270,347 real changes made) (9,250 real changes made) Mean estimation Number of obs = 4,753 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 50505.67 775.8627 48984.62 52026.72 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 50505.672 (9,250 real changes made) (1,270,347 real changes made, 1,270,347 to missing) 51 (sum of wgt is 1.0179e+05) Linear regression Number of obs = 950 F(1, 948) = 35.65 Prob > F = 0.0000 R-squared = 0.0515 Root MSE = 35669 ------------------------------------------------------------------------------ | Robust incwage | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- female | -16622.96 2784.087 -5.97 0.000 -22086.65 -11159.28 _cons | 57248.17 2267.188 25.25 0.000 52798.88 61697.46 ------------------------------------------------------------------------------ -16622.964 (1,270,347 real changes made) (950 real changes made) Mean estimation Number of obs = 497 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ incwage | 57248.17 2267.082 52793.9 61702.44 -------------------------------------------------------------- symmetric mean[1,1] incwage y1 57248.171 (950 real changes made) (1,270,347 real changes made, 1,270,347 to missing) . . _pctile GWG_ACS_2565_state [pw=perwt], p(10) . return list scalars: r(r1) = .78314208984375 . . _pctile GWG_ACS_2565_state [pw=perwt], p(90) . return list scalars: r(r1) = .8552269339561462 . . // Diff: 0.07 . . . *********************************************************************************** . /* Footnote 30: "Young individuals are an exception: The treatment effect > on specific policy demand in the group of 18 to 24-year-olds corresponds to a > substantial 0.25 standard deviations for female and to zero for male respondents > (p-value of the difference <0.05). */ . *********************************************************************************** . . clear all . . use "$path\data\SurveyStageI_AB_final.dta", clear . . global controls wave democrat indep otherpol prior midwest south west anychildren loghhinc associa > temore fulltime parttime selfemp unemp student . . // Drop control group, keep only individuals aged 18-24 . drop if rand==0 (1,034 observations deleted) . keep if age==1 (2,700 observations deleted) . . * Split samples by gender: Test for equality of treatment coefficient . eststo: reg z_lmpolicy_index T1 $controls [aweight=pweight] if female==0 (sum of wgt is 1.5792e+02) Source | SS df MS Number of obs = 133 -------------+---------------------------------- F(17, 115) = 2.16 Model | 14.4234447 17 .848437923 Prob > F = 0.0087 Residual | 45.1534058 115 .392638311 R-squared = 0.2421 -------------+---------------------------------- Adj R-squared = 0.1301 Total | 59.5768505 132 .451339776 Root MSE = .62661 ------------------------------------------------------------------------------- z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | -.1168433 .116146 -1.01 0.317 -.3469061 .1132195 wave | -.0435838 .1171095 -0.37 0.710 -.2755552 .1883876 democrat | .6151328 .132098 4.66 0.000 .3534721 .8767934 indep | .1262494 .1684312 0.75 0.455 -.2073803 .4598792 otherpol | .5162042 .7215293 0.72 0.476 -.9130066 1.945415 prior | -.0039936 .0026143 -1.53 0.129 -.0091721 .0011848 midwest | -.2332486 .1904752 -1.22 0.223 -.6105433 .144046 south | .0218046 .1405596 0.16 0.877 -.256617 .3002262 west | -.1053799 .1694686 -0.62 0.535 -.4410646 .2303049 anychildren | -.0522155 .1934328 -0.27 0.788 -.4353687 .3309377 loghhinc | -.04532 .0660504 -0.69 0.494 -.1761531 .0855132 associatemore | .0645494 .1303712 0.50 0.621 -.1936908 .3227897 fulltime | .5758782 .4333027 1.33 0.186 -.282411 1.434167 parttime | .5952461 .4252181 1.40 0.164 -.2470291 1.437521 selfemp | .5844601 .4831557 1.21 0.229 -.3725785 1.541499 unemployed | .6161809 .4449628 1.38 0.169 -.2652048 1.497567 student | .7384743 .4208762 1.75 0.082 -.0952005 1.572149 _cons | -.1295352 .8563973 -0.15 0.880 -1.825893 1.566823 ------------------------------------------------------------------------------- (est1 stored) . eststo: reg z_lmpolicy_index T1 $controls [aweight=pweight] if female==1 (sum of wgt is 1.5061e+02) Source | SS df MS Number of obs = 198 -------------+---------------------------------- F(17, 180) = 4.77 Model | 26.2857626 17 1.54622133 Prob > F = 0.0000 Residual | 58.3315037 180 .324063909 R-squared = 0.3106 -------------+---------------------------------- Adj R-squared = 0.2455 Total | 84.6172663 197 .429529271 Root MSE = .56927 ------------------------------------------------------------------------------- z_lmpolicy_~x | Coef. Std. Err. t P>|t| [95% Conf. Interval] --------------+---------------------------------------------------------------- T1 | .2544284 .0859986 2.96 0.004 .0847334 .4241235 wave | .0978686 .0860385 1.14 0.257 -.0719052 .2676425 democrat | .4609343 .111814 4.12 0.000 .2402996 .6815691 indep | .1338473 .1309337 1.02 0.308 -.124515 .3922097 otherpol | .4528333 .2511702 1.80 0.073 -.0427835 .9484501 prior | -.0064348 .0022707 -2.83 0.005 -.0109154 -.0019542 midwest | -.100241 .1332746 -0.75 0.453 -.3632226 .1627406 south | .0320049 .1120787 0.29 0.776 -.1891523 .2531621 west | .0404729 .1377482 0.29 0.769 -.2313361 .3122818 anychildren | -.1474133 .1220869 -1.21 0.229 -.388319 .0934923 loghhinc | .0573497 .0462216 1.24 0.216 -.0338562 .1485556 associatemore | .1921854 .0919667 2.09 0.038 .010714 .3736569 fulltime | .19816 .2212226 0.90 0.372 -.2383631 .6346832 parttime | .3860633 .2250075 1.72 0.088 -.0579284 .8300551 selfemp | .5150216 .2734133 1.88 0.061 -.024486 1.054529 unemployed | .6156393 .241156 2.55 0.012 .1397829 1.091496 student | .4491638 .2191533 2.05 0.042 .0167238 .8816038 _cons | -.8085569 .5965078 -1.36 0.177 -1.985604 .3684906 ------------------------------------------------------------------------------- (est2 stored) . . suest est1 est2, vce(robust) Simultaneous results for est1, est2 Number of obs = 331 ------------------------------------------------------------------------------- | Robust | Coef. Std. Err. z P>|z| [95% Conf. Interval] --------------+---------------------------------------------------------------- est1_mean | T1 | -.1168433 .1366758 -0.85 0.393 -.3847229 .1510363 wave | -.0435838 .1347294 -0.32 0.746 -.3076486 .2204809 democrat | .6151328 .1460867 4.21 0.000 .3288081 .9014574 indep | .1262494 .2126404 0.59 0.553 -.2905181 .543017 otherpol | .5162042 .316432 1.63 0.103 -.1039911 1.136399 prior | -.0039936 .0034646 -1.15 0.249 -.0107841 .0027969 midwest | -.2332486 .1975773 -1.18 0.238 -.6204931 .1539959 south | .0218046 .1729281 0.13 0.900 -.3171283 .3607375 west | -.1053799 .2161339 -0.49 0.626 -.5289946 .3182349 anychildren | -.0522155 .259895 -0.20 0.841 -.5616003 .4571693 loghhinc | -.04532 .0773141 -0.59 0.558 -.1968528 .1062128 associatemore | .0645494 .155589 0.41 0.678 -.2403994 .3694982 fulltime | .5758782 .2499989 2.30 0.021 .0858894 1.065867 parttime | .5952461 .2627139 2.27 0.023 .0803363 1.110156 selfemp | .5844601 .3630352 1.61 0.107 -.1270758 1.295996 unemployed | .6161809 .2420277 2.55 0.011 .1418152 1.090546 student | .7384743 .226052 3.27 0.001 .2954205 1.181528 _cons | -.1295352 .9059127 -0.14 0.886 -1.905091 1.646021 --------------+---------------------------------------------------------------- est1_lnvar | _cons | -.9348664 .1521854 -6.14 0.000 -1.233144 -.6365884 --------------+---------------------------------------------------------------- est2_mean | T1 | .2544284 .068603 3.71 0.000 .1199689 .3888879 wave | .0978686 .0702761 1.39 0.164 -.0398699 .2356072 democrat | .4609343 .0832442 5.54 0.000 .2977787 .62409 indep | .1338473 .1113015 1.20 0.229 -.0842996 .3519942 otherpol | .4528333 .1970602 2.30 0.022 .0666025 .8390641 prior | -.0064348 .0023485 -2.74 0.006 -.0110378 -.0018318 midwest | -.100241 .1013479 -0.99 0.323 -.2988793 .0983973 south | .0320049 .0910518 0.35 0.725 -.1464533 .2104632 west | .0404729 .1069061 0.38 0.705 -.1690592 .250005 anychildren | -.1474133 .1006112 -1.47 0.143 -.3446077 .049781 loghhinc | .0573497 .0405502 1.41 0.157 -.0221272 .1368267 associatemore | .1921854 .0673818 2.85 0.004 .0601196 .3242513 fulltime | .19816 .1464106 1.35 0.176 -.0887995 .4851196 parttime | .3860633 .1585628 2.43 0.015 .075286 .6968406 selfemp | .5150216 .2151961 2.39 0.017 .0932451 .9367981 unemployed | .6156393 .1537303 4.00 0.000 .3143334 .9169452 student | .4491638 .1368576 3.28 0.001 .1809278 .7173998 _cons | -.8085569 .6411684 -1.26 0.207 -2.065224 .44811 --------------+---------------------------------------------------------------- est2_lnvar | _cons | -1.126815 .0908745 -12.40 0.000 -1.304925 -.9487038 ------------------------------------------------------------------------------- . test [est1_mean]T1 = [est2_mean]T1 ( 1) [est1_mean]T1 - [est2_mean]T1 = 0 chi2( 1) = 5.89 Prob > chi2 = 0.0152 . . . *********************************************************************************** . /* Footnote 31: "15, 14 and 36 percent of respondents in the pure control group agree > that men are inherently more i) ambitious, ii) talented for demanding tasks and iii) interested > in ``technical'' jobs, respectively. Conversely, 71, 70 and 62 percent believe that i) women > face discrimination in the labor market, ii) men have been encouraged more to pursue ambitious > careers and iii) women face larger difficulties in combining work and family in today's society." > */ . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . * keep only pure control group . keep if rand==0 (3,031 observations deleted) . . // Answer scale: 1=completely disagree, 2=disagree, 3=neither agree nor disagree, 4=agree, 5=compl > etely agree . . gen ambitiousagree=ambitiousraw>3 if ambitiousraw!=. (536 missing values generated) . gen talentedagree=talentedraw>3 if talentedraw!=. (536 missing values generated) . gen interestedagree=interestedraw>3 if interestedraw!=. (536 missing values generated) . . tab ambitiousagree ambitiousag | ree | Freq. Percent Cum. ------------+----------------------------------- 0 | 425 85.34 85.34 1 | 73 14.66 100.00 ------------+----------------------------------- Total | 498 100.00 . tab talentedagree talentedagr | ee | Freq. Percent Cum. ------------+----------------------------------- 0 | 427 85.74 85.74 1 | 71 14.26 100.00 ------------+----------------------------------- Total | 498 100.00 . tab interestedagree interesteda | gree | Freq. Percent Cum. ------------+----------------------------------- 0 | 319 64.06 64.06 1 | 179 35.94 100.00 ------------+----------------------------------- Total | 498 100.00 . . gen discriminationagree=discriminationraw>3 if discriminationraw!=. (536 missing values generated) . gen encouragedagree=boysraw>3 if boysraw!=. (536 missing values generated) . gen difficultiesagree=societyraw>3 if societyraw!=. (536 missing values generated) . . tab discriminationagree discriminat | ionagree | Freq. Percent Cum. ------------+----------------------------------- 0 | 143 28.71 28.71 1 | 355 71.29 100.00 ------------+----------------------------------- Total | 498 100.00 . tab encouragedagree encourageda | gree | Freq. Percent Cum. ------------+----------------------------------- 0 | 147 29.52 29.52 1 | 351 70.48 100.00 ------------+----------------------------------- Total | 498 100.00 . tab difficultiesagree difficultie | sagree | Freq. Percent Cum. ------------+----------------------------------- 0 | 189 37.95 37.95 1 | 309 62.05 100.00 ------------+----------------------------------- Total | 498 100.00 . . *********************************************************************************** . /* Footnote 34: "34, 30 and 36 percent of the respondents believe that > anti-discrimination, affirmative action and family policies, respectively, are > `` somewhat effective'' or ``highly effective'' rather than ``strongly/somewhat > counterproductive'' or ``neither effective nor counterproductive''." */ . *********************************************************************************** . . clear all . set more off . . use "$path\data\SurveyStageI_AB_final.dta", clear . . /* Answer scale: 1=strongly counterproductive, 2=somewhat counterproductive, 3=Neither > effective nor counterproductive, 4=somewhat effective, 5=highly effective */ . . gen effdisagree=effdisraw>3 if effdisraw!=. (2,510 missing values generated) . gen effAAagree=effAAraw>3 if effAAraw!=. (2,510 missing values generated) . gen effworkfamagree=effworkfamraw>3 if effworkfamraw!=. (2,510 missing values generated) . . tab effdisagree effdisagree | Freq. Percent Cum. ------------+----------------------------------- 0 | 1,027 66.05 66.05 1 | 528 33.95 100.00 ------------+----------------------------------- Total | 1,555 100.00 . tab effAAagree effAAagree | Freq. Percent Cum. ------------+----------------------------------- 0 | 1,092 70.23 70.23 1 | 463 29.77 100.00 ------------+----------------------------------- Total | 1,555 100.00 . tab effworkfamagree effworkfama | gree | Freq. Percent Cum. ------------+----------------------------------- 0 | 1,003 64.50 64.50 1 | 552 35.50 100.00 ------------+----------------------------------- Total | 1,555 100.00 . . . . . . . . end of do-file . . . . ******************************************************************************************** . // END . ******************************************************************************************* . . log off name: log: C:\Users\gxf271\Dropbox\Forschung\Gender\redistribution\AEJPolicy_Revision\ReplicationF > iles\All.log log type: text paused on: 2 Jul 2021, 18:23:20 ----------------------------------------------------------------------------------------------------