Datasets:

chuxuan
/

REPRO-Bench

	* FIGURES FOR Carrera, Royer, Stehr, Sydnor, and Taubinsky (2021) *********
	*******************************************************************************

	* Initial set-up *
	clear
	set more off

	* Set directories for data and output *
	cd "$main/Output"
	/* Note: all figures will output to the working directory. Uses a program from
	master_do_file_for_analysis.do */


	* (uniform_range.pdf) *****************************************************

	set obs 500

	* Generate benefit values *
	gen b = _n/500

	* Generate commitment contract demand regions for each beta *
	forvalues beta = 1(1)500 {
	local B = `beta'/500
	gen bindingcommit`beta' = ( ((2b-1)/(2(b^2))) > `B'(1-.5`B') )
	}

	* Reshape data for graphing *
	reshape long bindingcommit, i(b) j(B)
	replace B = B/500

	* Produce Figure 1 *
	twoway contour bindingcommit B b if B >= .75 & B <= .95, levels(2) /*
	/ ccolors(red blue) xlabel(0(.1)1) ylabel(.75(.05).95) /
	/ xtitle("Benefit (share of time beneficial)") ytitle("Perceived short-run discount factor") /
	*/ graphr(color(white) fcolor(white)) bgcolor(white) plotregion(margin(zero)) clegend(off)

	*** Export figure
	graph export uniformrange.pdf, replace
	* with descriptive text superimposed in the paper


	* Load and clean experimental data ****************************************

	* Load dataset *
	use "$main/Data/cleaned_commitment_study_data", clear

	* Variable Creation & Cleaning Steps *
	*** Create an ID variable for use when reshaping long
	gen id = _n

	*** Generate info treatment indicators
	gen first_info = type_of_info=="1-onlygraph"
	gen new_info = type_of_info=="2-graphplus"
	gen control_info = (new_info == 0 & first_info == 0)

	*** Generate wave indicators
	gen wave1 = (wave == "fall")
	gen wave2 = (wave == "winter")
	gen wave3 = (wave == "spring")

	*** Define anticommitment/commitment variables for each threshold
	gen anticommit8 = q170 ==2 if q170<.
	gen commit8 = q169 ==2 if q169<.
	gen anticommit12 = chose_anticommit11
	gen commit12 = chose_commit12
	gen anticommit16 = q296==2 if q296<.
	gen commit16 = q295 ==2 if q295<.

	*** Restrict sample to those exogenously assigned incentives
	keep if flag_low_wtp == 0 & flag_exclude_exog == 0


	* (wtp_ev_tcbench.pdf) ****************************************************

	*** Generate additional variables
	*** Expected value of earnings based on subjective "days" estimate for frequency of visits
	gen ev1 = days_1
	gen ev2 = days_2*2
	gen ev3 = days_3*3
	gen ev5 = days_5*5
	gen ev7 = days_7*7
	gen ev12 = days_12*12

	*** Generate values for the $0 incentive
	gen wtp0 = 0
	gen ev0 = 0

	*** Estimate means with 95% confidence intervals
	mat data = J(7, 7, .) // initialize matrix for storing values
	loc row = 1
	foreach p in 0 1 2 3 5 7 12 {

	* Input incentive in first column
	mat data[`row', 1] = `p'

	* Input means & CI bounds of WTP in next three columns
	ci means wtp`p'
	mat data[`row', 2] = r(mean)
	mat data[`row', 3] = r(lb)
	mat data[`row', 4] = r(ub)

	* Input means & CI bounds of expected earnings in last three columns
	ci mean ev`p'
	mat data[`row', 5] = r(mean)
	mat data[`row', 6] = r(lb)
	mat data[`row', 7] = r(ub)

	loc ++row // increment row
	}

	*** Use data stored in matrix
	preserve
	clear
	svmat data
	ren (data*) (incentive wtp wtplb wtpub ev evlb evub)

	* Store certain statistics *
	* Average expected earnings under $1 incentive
	sum ev if incentive == 1 // EV equals exp. visits under this incentive
	loc avgexp : di %4.2f r(mean)
	latex_write avgexpincone `avgexp' numbers
	* Average WTP for $1 incentive
	sum wtp if incentive == 1
	loc avgwtp : di %4.2f r(mean)
	latex_write avgwtpincone `avgwtp' numbers
	* Difference
	loc diff : di %4.2f `avgwtp'-`avgexp'
	latex_write avgdiffincone `diff' numbers

	* Produce figure *
	graph twoway (connected ev incentive, lc(black) msymbol(none) mc(none) lp(dash)) /*
	/ (rcap evub evlb incentive, lc(black) lw(.5) msize(2.25)) /
	/ (connected wtp incentive, lc(cranberry) mlc(cranberry) mc(none) msymbol(circle) msize(vsmall)) /
	/ (rcap wtpub wtplb incentive, lc(cranberry) lw(.5) msize(2.25)), /
	/ xtitle("Per-visit incentive ($)", height(6)) ylabel(0(25)225, angle(0)) xlabel(1(1)12) /
	/ graphr(color(white) fcolor(white)) bgcolor(white) plotregion(margin(zero)) /
	*/ ytitle("$", height(6)) legend(order(1 3) label(1 "Avg. subjective expected earnings") label(3 "Avg. WTP for that incentive") rows(3))

	*** Export figure
	graph export wtp_ev_tcbench.pdf, replace
	restore


	* (deltas.pdf) ************************************************************

	* Generate additional variables *
	preserve
	*** Better measures of Commitment as per proposition
	/*** These are the amounts, per attendance, by which a person "overpays" for
	the increase in incentive. Second-order approximation */
	gen delta1=(wtp1) - (days_1+days_0)/2
	gen delta2 = (wtp2-wtp1) - (days_2+days_1)/2
	gen delta3=(wtp3-wtp2) - (days_3+days_2)/2
	gen delta5=(wtp5-wtp3)/(5-3) - (days_5+days_3)/2
	gen delta7=(wtp7-wtp5)/(7-5) - (days_7+days_5)/2
	gen delta12=(wtp12-wtp7)/(12-7) - (days_12+days_7)/2

	gen avg_delta = (delta1 + delta2+delta3 + delta5 + delta7 + delta12)/6
	gen avg_delta_ex1 = (delta2+delta3 + delta5 + delta7 + delta12)/5

	// DELTA MEASURES DIFFERENCES FROM TC BENCHMARK
	*** Run the regressions and store results with model names
	quietly reg avg_delta, robust
	est store m1
	loc avgbcp = _b[_cons]
	loc avg : di %4.2f `avgbcp'
	latex_write avgbcp `avg' numbers

	quietly reg avg_delta_ex1, robust
	est store m2
	loc avgbcpex1 = _b[_cons]
	loc avg : di %4.2f `avgbcpex1'
	latex_write avgbcpexone `avg' numbers

	* Produce figure: average delta with CI's for the info-control group *
	coefplot (m1, aseq(Average across incentives))/*
	/ (m2, aseq(Average excluding $1 incentive)) /
	/, xline(0) xlabel(-1(.5)2.5) legend(off) mc(black) offset(0) ciopts(lc(black) /
	/ recast(rcap) lp(dash)) grid(none) aseq swap graphr(fcolor(white) color(white)) /
	*/ xtitle("Behavior change premium ($)", height(6)) plotregion(margin(zero))

	*** Export figure
	graph export deltas.pdf, replace
	restore


	* (overconfidence.pdf) ********************************************

	* Obtain coefficients for plot *
	* Generate placeholder
	forval k = 0/12 {
	gen var`k' = .
	label variable var`k' `k'
	}

	* Generate estimates
	forval k = 0(1)12 {

	replace var`k' = 10
	reg var`k' var`k', nocons
	eststo m_v_`k'

	if `k' != 4 & `k' != 6 & `k' != 8 & `k' != 9 & `k' != 10 & `k' != 11{
	replace var`k' = (flag_exclude_exog==0&flag_low_wtp==0)
	reg days_`k' var`k', robust nocons
	est store m_days`k'_all
	}

	if `k' != 1 & `k' != 3 & `k' != 12 & `k' != 4 & `k' != 6 & `k' != 8 & `k' != 9 & `k' != 10 & `k' != 11{
	replace var`k' = (incentive == `k' &flag_exclude_exog==0 ///
	&flag_low_wtp==0)
	reg visits var`k' if incentive == `k', robust nocons
	est store m_visits`k'_given

	* Store number of observations
	if `k' == 0 loc num "zero"
	else if `k' == 2 loc num "two"
	else if `k' == 5 loc num "five"
	else loc num "seven"
	latex_write incentive`num'obs `e(N)' numbers
	}
	}

	* Produce figure: Overconfidence *
	*** Beliefs vs reality
	coefplot (m_v_, offset(0) m(none) lp(none)) /
	/ (m_days0_all m_days1_all m_days2_all m_days3_all m_days5_all m_days7_all m_days12_all, offset(0) recast(connected) msize(small) mc(black) lp(dash) lc(black) ciopts(lc(black) recast(rcap) lp(solid)) )/
	/ (m_visits0_given m_visits2_given m_visits5_given m_visits7_given, offset(0) recast(connected) msize(small) m(square) mc(red) lc(red) ciopts(lc(red) recast(rcap) lp(dash)) ) /
	/, vertical plotregion(margin(zero)) xlabel(1 "0" 2 "1" 3 "2" 4 "3" 5 "4" 6 "5" 7 "6" 8 "7" 9 "8" 10 "9" 11 "10" 12 "11" 13 "12") /
	/ graphr(color(white) fcolor(white)) bgcolor(white) xtitle("Per-visit incentive ($)", height(6)) ytitle("Visits", height(6))/
	*/ ylabel(0(5)22, angle(0)) legend(order(4 6) label(4 "Average expected visits") label(6 "Average realized visits")) legend(rows(2))

	*** Export figure
	graph export overconfidence.pdf, replace


	* (overconfidence_treatment.pdf) ******************************************

	* Obtain coefficients for plots *
	foreach cdtn in "wave1 == 1" "(wave2 == 1 \| wave3 == 1)"{

	if "`cdtn'" == "wave1 == 1"{
	loc treat "first_info"
	loc name "1"
	loc tlab "basic"
	loc excl "5"
	loc result5_c ""
	loc result5_t ""
	}
	else{
	loc treat "new_info"
	loc name "23"
	loc tlab "enhanced"
	loc excl "100"
	loc result5_c "m_visits5_given_c"
	loc result5_t "m_visits5_given_t"
	}

	* Generate estimates
	forval k = 0(1)12 {

	replace var`k' = 10
	reg var`k' var`k', nocons
	eststo m_v_`k'

	* Information treatment
	if `k' != 4 & `k' != 6 & `k' != 8 & `k' != 9 & `k' != 10 & `k' != 11{
	replace var`k' = (flag_exclude_exog==0&flag_low_wtp==0& ///
	`cdtn'&`treat'==1)
	reg days_`k' var`k' if `cdtn'&`treat'==1, robust nocons
	est store m_days`k'_all_t
	}

	if `k' != 1 & `k' != 3 & `k' != 4 & `k' != 6 & `k' != 8 & `k' != 9 & `k' != 10 & `k' != 11 & `k' != 12 & `k' != `excl'{
	replace var`k' = (incentive == `k' &flag_exclude_exog==0 ///
	&flag_low_wtp==0&`cdtn'&`treat'==1)
	reg visits var`k' if incentive == `k' & `cdtn'&`treat'==1, ///
	robust nocons
	est store m_visits`k'_given_t

	* Store number of observations
	loc treatobs = e(N)

	}

	* Information control
	if `k' != 4 & `k' != 6 & `k' != 8 & `k' != 9 & `k' != 10 & `k' != 11{
	replace var`k' = (flag_exclude_exog==0&flag_low_wtp==0& ///
	`cdtn'&`treat'!=1)
	reg days_`k' var`k' if `cdtn'&`treat'!=1, robust nocons
	est store m_days`k'_all_c
	}

	if `k' != 1 & `k' != 3 & `k' != 4 & `k' != 6 & `k' != 8 & `k' != 9 & `k' != 10 & `k' != 11 & `k' != 12 & `k' != `excl'{
	replace var`k' = (incentive == `k' &flag_exclude_exog==0 ///
	&flag_low_wtp==0&`cdtn'&`treat'!=1)
	reg visits var`k' if incentive == `k' & `cdtn'&`treat'!=1, ///
	robust nocons
	est store m_visits`k'_given_c

	* Store number of observations
	if `k' == 0 loc num "zero"
	else if `k' == 2 loc num "two"
	else if `k' == 5 loc num "five"
	else loc num "seven"
	loc bothobs = e(N) + `treatobs'
	latex_write incentive`num'obs`tlab' `bothobs' numbers

	}

	}

	* Store total observations used
	sum var0 if `cdtn'&`treat'==1
	latex_write obs`tlab'treat `r(N)' numbers
	sum var0 if `cdtn'&`treat'!=1
	latex_write obs`tlab'control `r(N)' numbers

	* Produce figure: Overconfidence *
	local jig = .1
	*** Beliefs vs reality
	coefplot (m_v_, offset(0) m(none) lp(none)) /
	/ (m_days0_all_c m_days1_all_c m_days2_all_c m_days3_all_c m_days5_all_c m_days7_all_c m_days12_all_c, offset(-`jig') recast(connected) msize(small) mc(black) lp(dash) lc(black) ciopts(lc(black) recast(rcap) lp(solid)) )/
	/ (m_visits0_given_c m_visits2_given_c `result5_c' m_visits7_given_c, offset(-`jig') recast(connected) msize(small) m(square) mc(black) lc(black) lp(solid) ciopts(lc(black) recast(rcap) lp(dash)) ) /
	/ (m_days0_all_t m_days1_all_t m_days2_all_t m_days3_all_t m_days5_all_t m_days7_all_t m_days12_all_t, offset(`jig') recast(connected) m(Oh) msize(small) mc(blue) lp(dash) lc(blue) ciopts(lc(blue) recast(rcap) lp(solid)) )/
	/ (m_visits0_given_t m_visits2_given_t `result5_t' m_visits7_given_t, offset(`jig') recast(connected) msize(small) m(Sh) mc(blue) lc(blue) lp(solid) ciopts(lc(blue) recast(rcap) lp(dash)) ) /
	/, vertical plotregion(margin(zero)) xlabel(1 "0" 2 "1" 3 "2" 4 "3" 5 "4" 6 "5" 7 "6" 8 "7" 9 "8" 10 "9" 11 "10" 12 "11" 13 "12") /
	/ graphr(color(white) fcolor(white)) bgcolor(white) xtitle("Per-visit incentive ($)", height(6)) ytitle("Visits", height(6))/
	*/ ylabel(0(5)22, angle(0)) legend(order(4 6 8 10) label(4 "Average expected visits, information control") label(6 "Average realized visits, information control") label(8 "Average expected visits, `tlab' information treatment") label(10 "Average realized visits, `tlab' information treatment")) legend(rows(4))

	*** Export figure
	graph export overconfidence_treatment_wave`name'.pdf, replace
	eststo clear

	}


	* (exp_actual_under_incentive.pdf) ****************************************

	*** Expected attendance w/incentive given vs. actual attendance w/incentive
	* Generate variable for expected attendace w/incentive given
	gen days_exp = .
	foreach i in 0 1 2 3 5 7{
	replace days_exp = days_`i' if incentive == `i'
	}

	* Plot with 45-degree line
	preserve
	binscatter visits days_exp, ///
	ytitle(Actual attendance, height(6)) ///
	xtitle(Expected attendance under assigned incentive, height(6)) ///
	xlab(0(5)30) ylab(0(5)30, angle(0)) plotregion(margin(zero)) ///
	graphregion(color(white)) savedata(binned) replace
	clear
	do binned
	graph addplot function y = x, range(0 30) lpattern(dash) lcolor(gs13) ///
	xlab(0(5)30) ylab(0(5)30, angle(0)) plotregion(margin(zero))
	graph export "exp_actual_under_incentive.pdf", replace
	restore

	* Remove temporary files
	cap erase "binned.csv"
	cap erase "binned.do"


	* (wtp_exp_attendance.pdf) ************************************************

	preserve
	* Reshape to graph each incentive level separately
	reshape long wtp days_, i(id) j(incentive_level)

	* Store average increase in attendance per dollar of incentive
	reg days_ incentive_level, clus(id)
	loc incr = _b[incentive_level]
	loc avg : di %4.2f `incr'
	latex_write incrattinc `avg' numbers
	loc stderr : di %4.3f _se[incentive_level]
	latex_write incrattincse `stderr' numbers

	* Store behavior change premium per dollar of incentive
	loc bcpinc : di %4.2f `avgbcp'/`incr'
	latex_write avgbcpinc `bcpinc' numbers
	loc bcpincex1 : di %4.2f `avgbcpex1'/`incr'
	latex_write avgbcpexoneinc `bcpincex1' numbers

	* Produce plot
	binscatter wtp days_ if incentive_level != 0, by(incentive_level) ///
	ytitle(WTP for incentive ($), height(6)) ///
	xtitle(Expected attendance, height(6)) ///
	xlab(0(5)30) ylab(0(50)300, angle(0)) ///
	legend(order(1 "1" 2 "2" 3 "3" 4 "5" 5 "7" 6 "12") subtitle(Per-visit incentive ($), size(medsmall)) rows(1)) ///
	graphregion(color(white)) plotregion(margin(zero))
	graph export "wtp_exp_attendance.pdf", replace

	restore


	* Load and clean daily experimental data **********************************

	* Load dataset *
	use "$main/Data/cleaned_commitment_study_daily_data", clear

	* Variable Creation & Cleaning Steps *
	*** Generate info treatment indicators
	gen first_info = type_of_info=="1-onlygraph"
	gen new_info = type_of_info=="2-graphplus"
	gen control_info = (new_info == 0 & first_info == 0)

	*** Generate wave indicators
	gen wave1 = (wave == "fall")
	gen wave2 = (wave == "winter")
	gen wave3 = (wave == "spring")

	*** Define anticommitment/commitment variables for each threshold
	gen anticommit8 = q170 ==2 if q170<.
	gen commit8 = q169 ==2 if q169<.
	gen anticommit12 = chose_anticommit11
	gen commit12 = chose_commit12
	gen anticommit16 = q296==2 if q296<.
	gen commit16 = q295 ==2 if q295<.

	*** Restrict sample to those exogenously assigned incentives
	keep if flag_low_wtp == 0 & flag_exclude_exog == 0


	* (daily_att_likelihood.pdf) **********************************************

	preserve

	* Generate variables and restrict data
	keep if commit12 == 1
	gen got_incentive = 1 if treatment == "threshold80_12"
	replace got_incentive = 0 if treatment == "control"
	keep if !missing(got_incentive)
	gen incentive_day = day*got_incentive

	* For trendline of increase in likelihood of attendance
	reg attended day got_incentive incentive_day wave2 wave3, clus(id)
	loc slope = _b[incentive_day]
	loc intercept = _b[got_incentive]

	* Average increase in likelihood of attendance from the contract each day
	reg attended ibn.day ibn.day#c.got_incentive, clus(id) nocons

	* Produce plot
	coefplot, nolabel vertical keep(*#c.got_incentive) yline(0) omitted baselevels ///
	ciopts(recast(rcap) lp(dash) lw(thin)) xlab(0(2)28) xtitle("Day", height(6)) ///
	ylab(-.2(.1).5, angle(0)) ytitle("Change in likelihood of going to the gym", height(4)) ///
	graphregion(color(white)) bgcolor(white) plotregion(margin(zero))
	graph addplot function y = `slope'*x + `intercept', range(1 28) ///
	lw(medthick) lp(longdash) lcolor(ebblue*.7) ///
	xlab(0(2)28) ylab(-.2(.1).5, angle(0)) plotregion(margin(zero))
	graph export "daily_att_likelihood.pdf", replace

	restore