107/replication_package/Code/Stata/Figures.do

*** 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' = ( ((2*b-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