diff --git a/24/paper.pdf b/24/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7ca67187f92025b6293d1e9cd7e6ebddc03b77b5 --- /dev/null +++ b/24/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:93a0f2303479e92741273e98fae9a4a2083f0fc2c3cdd625db813a7b29364d05 +size 986746 diff --git a/24/replication_package/Code/analysis.do b/24/replication_package/Code/analysis.do new file mode 100644 index 0000000000000000000000000000000000000000..d5037b5370849ce83e5a664a7d00e8454aca056d --- /dev/null +++ b/24/replication_package/Code/analysis.do @@ -0,0 +1,2549 @@ + +************************************************************************************************************************************ +*** CLIENTS -MAIN TEXT **** +************************************************************************************************************************************ + +clear +u "${data}Clients_Main.dta" +*** We recruited clients ($N=924$) later (page 21) +**** Overall, 84\% of clients followed the advisor's recommendation (p.21) +tab follow + +*** Footnote 18 (p.22) +clear +u "${data}Clients_MPL.dta" +*** we recruited a total of 866 clients (p. 22) +**** Of these, 80% of clients followed the advisor's recommendation (p. 22) +tab follow + +*** Footnote 19 (p.22) +clear +u "${data}Clients_Blinding.dta" +*** we recruited 188 advisees (p. 22) +**** Of these, 84% followed the advisors' recommendation. (p. 22) +tab follow + +****** TABLE 1 *** SAMPLE SIZES in the table +*NoChoice, SeeIncentiveFirst N=152; Assess Quality First N=147 + u "${data}nochoice.dta", clear + drop if alphavaluefinal==. + tab seeincentivefirst + +*Choice, ChoiceFreeProfessionals=712;ChoiceFree=2574; + *See IncentiveFirst Costly=1562; AssessQualityFirst Costly=1067 + u "${data}choice_experiments.dta", clear + drop if study!=1 & alphavaluefinal==. + tab condition if Highx10==0 & Highx100==0 + * ChoiceFree -HighStakes (10-fold)=275 + tab condition if Highx10==1 + * ChoiceFree -HighStakes (100-fold)=110 + tab condition if Highx100==1 + +*ChoiceDeterministic, Replication=385; Deterministic=369 + u "${data}Choice_Deterministic.dta", clear + drop if alphavaluefinal==. + tab Deterministic + +***ChoiceStakes, Low Incentive=483; IntermediateIncentive=511; HighIncentive=478 + u "${data}stakes.dta", clear + drop if alphavaluefinal==. + tab condition + +***Information Architect: IA-Advisor=245; IA-Client=253 + u "${data}InformationArchitect.dta", clear + drop if alphavaluefinal==. + tab IAAdvisor + +*** Total sample size in Table 1: N=9323 +di 152+147+712+2574+1562+1067+275+110+385+369+483+511+478+245+253 + +************************************************************************************************************************************ +************************************************************************************************************************************ + +* NO CHOICE - MAIN TEXT + +************************************************************************************************************************************ +************************************************************************************************************************************ +************************************************************************************************************************************ +clear +u "${data}nochoice.dta" + + +drop if alphavaluefinal==. + +*PROPORTION TESTS +prtest recommendincentive if conflict==1, by(seeincentivefirst) +prtest recommendincentive if conflict==0, by(seeincentivefirst) + +*** NOCHOICE VS CHOICE COMPARISON for Table C.17 in the Appendix +mean recommendincentive if seeincentivefirst==1 & conflict==1 +mean recommendincentive if seeincentivefirst==0 & conflict==1 + +reg recommendincentive seeincentivefirst##noconflict female age, vce(hc3) + +*ADDITIONAL DESCRIPTIVES - BY COMMISSION +estpost summarize recommendincentive /// + recommendincentive_before recommendincentive_after if conflict==1,detail +eststo d_nochoice + + +forvalues i=0(1)1{ +estpost summarize recommendincentive /// + recommendincentive_before recommendincentive_after if incentiveA==`i' & conflict==1,detail +eststo d_nochoice`i' +} + +** "Throughout, advisors exhibit a preference for product A" +tabstat recommendincentive if incentiveB==1 & noconflict==1, stats(mean n) +** "recommending it 16% of the times even when the quality signal is a $2 ball and the advisor is incentivized to recommend B +gen notrecommendincentive=1-recommendincentive +tabstat notrecommendincentive if incentiveB==1 & noconflict==1, stats(mean n) +/*reg recommendincentive noconflict incentiveB female age stdalpha + if missingalpha==0, vce(hc3) */ + + +*FIGURE NO CHOICE - RECOMMENDATIONS +bys before conflict: egen meanrec=mean(recommendincentive) +bys before conflict: egen sdrec=sd(recommendincentive) +bys before conflict: egen nrec=count(recommendincentive) + +*Binomial SE (formula: sqrt(p(1-p)/n)) +g lorec=meanrec-1.96*(sqrt((meanrec*(1-meanrec))/nrec)) +g hirec=meanrec+1.96*(sqrt((meanrec*(1-meanrec))/nrec)) + +gen xaxis=0 if before==1 & conflict==1 +replace xaxis=1 if before==0 & conflict==1 +replace xaxis=2.5 if before==1 & conflict==0 +replace xaxis=3.5 if before==0 & conflict==0 + + +twoway (bar meanrec xaxis if before==1 & conflict==1, color(black) barw(0.7)) /// +(bar meanrec xaxis if before==0 & conflict==1, fcolor(white) lcolor(black) barw(0.7) ) /// +(bar meanrec xaxis if before==1 & conflict==0, color(black) barw(0.7)) /// +(bar meanrec xaxis if before==0 & conflict==0, color(white) lcolor(black) barw(0.7)) /// +(rcap lorec hirec xaxis , lcolor(black*0.3) lwidth(medthick)) /// +, xtitle(" ") /// +ytitle("{bf: Incentivized Product Recommendation}") /// +graphr(c(white)) plotr(c(white)) /// +ylabel(0(0.1)1, gmax) /// +xlabel(0.5 "{bf: Conflict}" 3 "{bf: No Conflict}" ) /// +xscale(r(-0.5 3.5)) legend(order(1 "See Incentive First" 2 "See Quality First")) +graph export "${main}/nochoice_recommendations.png", replace + + +*REGRESSIONS (Reported in Appendix C.1) +clear +u "${data}nochoice.dta" + +*******APPENDIX********* +** MAIN REGRESSION (APPENDIX, TABLE C.1) +est clear +eststo:reg recommendincentive seeincentivefirst noconflict incentiveB female age stdalpha if missingalpha==0 & conflict==1, vce(hc3) +eststo:reg recommendincentive seeincentivefirst noconflict incentiveB female age stdalpha if missingalpha==0 & conflict==0, vce(hc3) +eststo:reg recommendincentive seeincentivefirst noconflict seeincentivefirst_noconflict incentiveB female age stdalpha if missingalpha==0, vce(hc3) + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{11}\selectfont" "\caption{Recommendations}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "$\ \ \ \ \ \ \ \ \ \ \ \ \ \ $ &\multicolumn{1}{c}{\textbf{Conflict }} &\multicolumn{1}{c}{\textbf{No Conflict}} & \multicolumn{1}{c}{\textbf{Both}} \\\hline & & & \\" + +esttab using "${appendix}NoChoice_Recommendations.tex", se r2 replace nolines cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (seeincentivefirst "See Incentive First" noconflict "No Conflict" seeincentivefirst_noconflict "See Incentive First * No Conflict" /// + incentiveB "Incentive for B" /// +female "Female" age "Age" stdalpha "Selfishness" ) /// +order ( seeincentivefirst noconflict seeincentivefirst_noconflict incentiveB female age stdalpha) collabels(none) /// +drop ( female age) /// +star( * 0.10 ** 0.05 *** 0.01) /// +nomtitle label substitute(" 0.000 " " " " (.) " " ") /// +prehead("`panel'") posthead("`dv'") postfoot("\hline" "\end{tabular}%" "\captionsetup{width=0.7\textwidth}" "\caption*{\footnotesize \textit{Notes:} This table displays the estimated coefficients from linear probability models on the advisors' recommendations. See Incentive first is a binary indicator coded as 1 for participants who were randomly assigned to see the incentive first. Selfishness is a standardized measure of the number of times the advisor chose to recommend the incentivized product in the MPL task aimed at measuring moral costs. The sample includes attentive participants who did not switch multiple times in this elicitation. The regression includes individual controls for the advisor's gender and age. Robust standard errors (HC3) in parentheses}" "\label{tab:nochoicerec}" "\end{table}") + + +** REGRESSION THAT INCLUDES INATTENTIVE (APPENDIX, TABLE C.2) +clear +u "${data}nochoice.dta" + + + +est clear +eststo:reg recommendincentive seeincentivefirst noconflict incentiveB female age if conflict==1, vce(hc3) +eststo:reg recommendincentive seeincentivefirst noconflict incentiveB female age if conflict==0, vce(hc3) +eststo:reg recommendincentive seeincentivefirst noconflict seeincentivefirst_noconflict incentiveB female age, vce(hc3) + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{11}\selectfont" "\caption{Recommendations including Inattentive}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "$\ \ \ \ \ \ \ \ \ \ \ \ \ \ $ &\multicolumn{1}{c}{\textbf{Conflict }} &\multicolumn{1}{c}{\textbf{No Conflict}} & \multicolumn{1}{c}{\textbf{Both}} \\\hline & & & \\" + +esttab using "${appendix}NoChoice_Recommendations_Inattentive.tex", se r2 replace nolines cells(b(star fmt(3)) se(par fmt(3))) collabels(none) /// +coeflabel (seeincentivefirst "See Incentive First" noconflict "No Conflict" seeincentivefirst_noconflict "See Incentive First * No Conflict" /// + incentiveB "Incentive for B" /// +female "Female" age "Age" ) /// +order ( seeincentivefirst noconflict seeincentivefirst_noconflict incentiveB female age) /// +drop ( female age) /// +star( * 0.10 ** 0.05 *** 0.01) /// +nomtitle label substitute(" 0.000 " " " " (.) " " ") /// +prehead("`panel'") posthead("`dv'") postfoot("\hline" "\end{tabular}%" "\captionsetup{width=0.7\textwidth}" "\caption*{\footnotesize \textit{Notes:} This table displays the estimated coefficients from linear probability models on the advisors' recommendations. See Incentive first is a binary indicator coded as 1 for participants who were randomly assigned to see the incentive first. The sample includes all participants, including those who switched multiple times in the MPL task to measure moral costs. The regression includes individual controls for the advisor's gender and age. Robust standard errors (HC3) in parentheses}" "\label{tab:nochoicerec_inattentive}" "\end{table}") + + +/************************************************************************************************************************************ +************************************************************************************************************************************ + + CHOICE - MAIN TEXT + +************************************************************************************************************************************ +************************************************************************************************************************************ +************************************************************************************************************************************/ + + +clear +u "${data}choice_experiments.dta" + +*As pre-registered, drop Mturk participant with inconsistent alpha value +drop if study!=1 & alphavaluefinal==. /*(study1 are profs and there's no MPL')*/ + +************************************ +/*FOOTNOTE 13 +PROFESSIONALS: Comparison Prolific / CloudResearch +************************************ + +We pool these two samples since choices regarding the preferred sequence of information +did not vary significantly across them ($p$=0.308) */ +prtest choicebefore if study==1, by(sample) +*and recommendations did not differ either ($p$=0.820). +reg recommendincentive cloudresearch conflict female age locatedus if study==1 + +************************************ +/*FOOTNOTE 15 +************************************ +*"We found no effect of incentive size, order or their interaction on the preference to see the incentive first ")*/ + reg choicebefore incentiveshigh##incentiveleft if study==4, ro + +*"We also found no effect of incentive size, order or their interaction on recommendations) +reg recommendincentive incentiveshigh##incentiveleft if study==4 & conflict==1, ro +************************************ + + +****************************************************************************************************************************************** +****************************************************************************************************************************************** +** PREFERENCES / DEMAND - MAIN TEXT +****************************************************************************************************************************************** +****************************************************************************************************************************************** + +global covariates "wave2 wave3 professionalscloudresearch incentiveshigh##incentiveleft age female" +global covariates2 "professionalsfree seeincentivecostly seequalitycostly wave2 wave3 professionalscloudresearch incentiveshigh incentiveleft incentiveshigh_incentiveleft age female" + +*FIGURE 4: ADVISOR PREFERENCES + +eststo:reg choicebefore i.treatment $covariates if Highx10==0 & Highx100==0, vce(hc3) +margins i.treatment, atmeans saving("${main}Choice_adjusted_demand", replace) + +*return list +matrix coeffs=r(table) +matrix list coeffs + +g preddemand=coeffs[1,1] if treatment==0 +g sepreddemand=coeffs[2,1] if treatment==0 +forval i=2(1)4{ + local j=`i'-1 +replace preddemand=coeffs[1,`i'] if treatment==`j' +replace sepreddemand=coeffs[2,`i'] if treatment==`j' +} +g ubpreddemand=preddemand+1.96*sepreddemand +g lbpreddemand=preddemand-1.96*sepreddemand + +g xaxis=1 if treatment==1 +replace xaxis=3.10 if treatment==2 +replace xaxis=2.10 if treatment==0 +replace xaxis=4.10 if treatment==3 + +g meancommit=1 + + twoway (bar meancommit xaxis, /// + lcolor(gs10) fcolor(white) barw(0.5) ) /// + (bar preddemand xaxis, color(gs10) barw(0.5) ) /// + (rcap ubpreddemand lbpreddemand xaxis if xaxis!=., color(gs9) lwidth(*0.5)) /// + , xtitle(Study) /// + ytitle("Advisor preference") /// + graphr(c(white)) plotr(c(white)) /// + ylabel(0(0.2)1, gmax) /// + xlabel( 2.10 `" "Choice" "Free" "' /// + 1 `" "Choice Free" "- Professionals" "' /// + 4.10 `" "Quality First" "Costly" "' /// + 3.10 `" "Incentive First" "Costly" "') /// + xscale(r(0.5 3.5)) legend(lab(1 "Prefer to assess quality first") /// + lab(2 "Prefer to see incentive first") order(2 1)) /// + xtitle("") /// + text(0.8 1 "0.55", color(black)) text(0.30 1 "0.45", color(black)) /// + text(0.8 2.10 "0.45") text(0.30 2.10 "0.55") /// + text(0.8 3.10 "0.59")text(0.30 3.10 "0.41") /// + text(0.8 4.10 "0.30") text(0.30 4.10 "0.70") + *graph export "${main}Demand_predicted_errorbars.png", replace + graph export "${main}Choice_Demand_predicted_errorbars.pdf", replace + + +*TABLE 2: PREFERENCES FOR INFORMATION ORDER +est clear +eststo:reg choicebefore $covariates2 if Highx10==0 & Highx100==0, vce(hc3) +eststo:reg choicebefore $covariates2 stdalpha if Highx10==0 & Highx100==0 & professionals==0, vce(hc3) +eststo:reg choicebefore $covariates2 stdalpha selfishseeincentivecostly selfishseequalitycostly if Highx10==0 & Highx100==0, vce(hc3) + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{11}\selectfont" "\caption{Preference for Information Order}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "$\ \ \ \ \ \ \ \ \ \ \ \ \ \ $ &\multicolumn{3}{c}{\textbf{Prefer to See Incentive First}} \\\hline & & & \\" + +esttab using "${main}Choice_Demand.tex", /// + se r2 replace nolines cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (professionalsfree "Choice Free -- Professionals $ \ \ \ \ \ \ \ $" seeincentivecostly "See Incentive First Costly " seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" /// +female "Female" age "Age" stdalpha "Selfishness" professionalscloudresearch "Professionals x Cloudresearch" selfishseeincentivecostly "See Incentive First Costly X Selfishness " selfishseequalitycostly "See Quality First Costly X Selfishness " /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft "Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order") /// +order (seeincentivecostly seequalitycostly professionalsfree stdalpha selfishseeincentivecostly selfishseequalitycostly female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) collabels(none) /// +drop(wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch) /// +nomtitle label substitute(" 0.000 " " " " (.) " " ") /// +prehead("`panel'") posthead("`dv'") postfoot("\hline" "\end{tabular}%" "\captionsetup{width=0.8\textwidth}" "\caption*{\footnotesize \textit{Notes:} This table displays the estimated coefficients from linear probability models on the preference to see the incentive first. See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. Selfishness was elicited at the end of the experiment, using a multiple price list (MPL) with 5 decisions. The variable is a standardized measure of the number of times the advisor chose to recommend the incentivized product in the MPL task. The regression models in columns (2) and (3) include individual controls for the advisor's gender and age, each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:demand}" "\end{table}") + +*************** +* BLINDING ** +*************** + + +tabstat avoid_incentiveinfo if wave3==1 & Highx10==0 & Highx100==0 & age!=. & female!=., by(choiceafter) +**Overall percentages + + +*PROPORTION TESTS +prtest avoid_incentiveinfo if wave3==1 & Highx10==0 & Highx100==0 & age!=. & female!=., by(choiceafter) + + +*STACKED FIGURE +bys choicebefore: egen blind=mean(avoid_incentiveinfo) if Highx10==0 & Highx100==0 & age!=. & female!=. & wave3==1 +bys choicebefore: egen blindsd=sd(avoid_incentiveinfo) if Highx10==0 & Highx100==0 & age!=. & female!=. & wave3==1 +bys choicebefore: egen blindn=count(avoid_incentiveinfo) if Highx10==0 & Highx100==0 & age!=. & female!=. & wave3==1 + +summ avoid_incentiveinfo if Highx10==0 & Highx100==0, d +return list + +g loblind=blind-1.96*(blindsd/sqrt(blindn)) +g hiblind=blind+1.96*(blindsd/sqrt(blindn)) + + +tab blind + +twoway (bar meancommit choiceafter, lcolor(black) fcolor(white) barwidth(0.3)) /// + (bar blind choiceafter, color(gs8) barwidth(0.3)) /// + (rcap loblind hiblind choiceafter, color(gs12)) /// + , graphr(c(white)) xlabel(0 "Prefer to See Incentive First" 1 "Prefer to Assess Quality First") /// + xscale(r(-0.4 1.4)) legend(order(1 "Prefer Not to Blind Incentive Information" /// + 2 "Prefer Blinding Incentive Information") rows(2)) ylabel(0(0.2)1) /// + ytitle("Advisor Preference to Blind" " ") /// + xtitle(" " "Advisor Preference in Choice Experiment") /// + text(0.2 0 "0.32") text(0.2 1 "0.55") /// + text(0.8 0 "0.68") text(0.8 1 "0.45") +graph export "${main}Choice_Blinding.png", replace + + +/*The difference in preference to blind between advisors who prefer to see incentive first +and those who prefer to see quality first remains large (22 percentage points) and significant +in regression analyses that control for treatment, gender, age, advisors facing a conflict of interest in the main experiment, and for being assigned +to their preferred order in the main experiment ($t$-stat$=-7.18$, $p<0.001$, see Online Appendix C.2) */ +reg avoid_incentive choicebefore notgetyourchoice choicebeforenotgetyourchoice $covariates2 noconflict if Highx10==0 & Highx100==0, vce(hc3) + +******************************************************************************** +******************************************************************************** +*CHOICE - CODED EXPLANATIONS OF CHOICE +******************************************************************************** +******************************************************************************** + u "${data}Choice_coding_explanations.dta", clear + + +*Main Text - page 23 - Section 4. +*Appendix C.2.4. + *N=1749 advisors + tab study + + *"classified their responses into four categories, which apply to 91\% of + *the responses. The remaining 9\% consists of empty or unrelated comments according to both raters." + tabstat nocategory if choicebefore!=., s(mean n) + + *"Agreed in over 82% of their classifications..." and Kappa=0.76. + kap mergedcategory1 mergedcategory2 + +*Main Text - Section 6.2 + *rarely report that they are indifferent between seeing the incentive + *first or assessing quality first (on average, 10\% of the comments) + tab nomatter if choicebefore!=. + + *an average of 41\% of AMT participants and 53\% of professionals + * versus 5\% of AMT participants and 7\% of professionals, + *respectively, $\chi^2\textrm{-stat}=405$, $p<$0.001 + tabstat limitbias if nocategory==0 & condition!="ChoiceFree_Professionals", by(choicebefore) + tabstat limitbias if nocategory==0 & condition=="ChoiceFree_Professionals", by(choicebefore) + tab limitbias choicebefore, chi2 + + *an average of 36% of the cases for both AMT and for professionals + tabstat commission_expl if nocategory==0 & condition=="ChoiceFree_Professionals", by(choicebefore) + tabstat commission_expl if nocategory==0 & condition!="ChoiceFree_Professionals", by(choicebefore) + + +******************************************* +* APPENDIX C.2.4 - TABLE C.15 +******************************************* +preserve +collapse limitbias nomatter commission_expl otherreason /// + (count) nlimitbias=limitbias if nocategory==0, by(choicebefore) + egen sumn=total(nlimitbias) + drop nlimitbias +save "${appendix}taba.dta", replace +restore +preserve +collapse limitbias nomatter commission_expl otherreason /// + (count) nlimitbias=limitbias if nocategory==0 & condition!="ChoiceFree_Professionals", by(choicebefore) + egen sumn=total(nlimitbias) + drop nlimitbias +save "${appendix}tabb.dta", replace +restore +preserve +collapse limitbias nomatter commission_expl otherreason /// + (count) nlimitbias=limitbias if nocategory==0 & condition=="ChoiceFree_Professionals", by(choicebefore) + egen sumn=total(nlimitbias) + drop nlimitbias +save "${appendix}tabc.dta", replace +restore + +preserve +clear +u "${appendix}taba.dta" +append using "${appendix}tabb.dta" +append using "${appendix}tabc.dta" +export excel "${appendix}tablec15.xlsx", replace first(var) keepcellfmt +rm "${appendix}taba.dta" +rm "${appendix}tabb.dta" +rm "${appendix}tabc.dta" +restore + + +****************************************************************************************************************************************** +****************************************************************************************************************************************** +** RECOMMENDATIONS - MAIN TEXT +****************************************************************************************************************************************** +****************************************************************************************************************************************** + +u "${data}choice_experiments.dta", clear +*As pre-registered, drop Mturk participant with inconsistent alpha value +drop if study!=1 & alphavaluefinal==. /*(study1 are profs and there's no MPL')*/ +gen incentiveBgetbefore=incentiveB*getbefore + +**** FIGURE ***** MAIN TEXT + +cap drop predrecommend_* ubpredrecommend_* lbpredrecommend_* sepredrecommend_* +g predrecommend_dqf_gqf=. +g sepredrecommend_dqf_gqf=. +g predrecommend_dqf_gif=. +g sepredrecommend_dqf_gif=. +g predrecommend_dif_gqf=. +g sepredrecommend_dif_gqf=. +g predrecommend_dif_gif=. +g sepredrecommend_dif_gif=. + +*demand incentive first +est clear +eststo:reg recommendincentive i.treatment##i.getbefore incentiveB $covariates2 /// +if choicebefore==1 /// +& conflict==1 & Highx10==0 & Highx100==0, vce(hc3) +forval i=0(1)3{ +margins i.getbefore if treatment==`i', atmeans saving("${main}adjusted_recommendations", replace) +matrix coeffs=r(table) +replace predrecommend_dif_gqf=coeffs[1,1] if treatment==`i' & choicebefore==1 & getbefore==0 +replace sepredrecommend_dif_gqf=coeffs[2,1] if treatment==`i' & choicebefore==1 & getbefore==0 +replace predrecommend_dif_gif=coeffs[1,2] if treatment==`i' & choicebefore==1 & getbefore==1 +replace sepredrecommend_dif_gif=coeffs[2,2] if treatment==`i' & choicebefore==1 & getbefore==1 +} +g ubpredrecommend_dif_gif=predrecommend_dif_gif+1.96*sepredrecommend_dif_gif +g lbpredrecommend_dif_gif=predrecommend_dif_gif-1.96*sepredrecommend_dif_gif +g ubpredrecommend_dif_gqf=predrecommend_dif_gqf+1.96*sepredrecommend_dif_gqf +g lbpredrecommend_dif_gqf=predrecommend_dif_gqf-1.96*sepredrecommend_dif_gqf + + +*demand quality first +est clear +eststo:reg recommendincentive i.treatment##i.getbefore incentiveB $covariates2 /// +if choicebefore==0 /// +& conflict==1 & Highx10==0 & Highx100==0, vce(hc3) +forval i=0(1)3{ +margins i.getbefore if treatment==`i', atmeans saving("${main}adjusted_recommendations", replace) +matrix coeffs=r(table) +replace predrecommend_dqf_gqf=coeffs[1,1] if treatment==`i' & choicebefore==0 & getbefore==0 +replace sepredrecommend_dqf_gqf=coeffs[2,1] if treatment==`i' & choicebefore==0 & getbefore==0 +replace predrecommend_dqf_gif=coeffs[1,2] if treatment==`i' & choicebefore==0 & getbefore==1 +replace sepredrecommend_dqf_gif=coeffs[2,2] if treatment==`i' & choicebefore==0 & getbefore==1 +} +g ubpredrecommend_dqf_gif=predrecommend_dqf_gif+1.96*sepredrecommend_dqf_gif +g lbpredrecommend_dqf_gif=predrecommend_dqf_gif-1.96*sepredrecommend_dqf_gif +g ubpredrecommend_dqf_gqf=predrecommend_dqf_gqf+1.96*sepredrecommend_dqf_gqf +g lbpredrecommend_dqf_gqf=predrecommend_dqf_gqf-1.96*sepredrecommend_dqf_gqf + + +cap drop treatnumgraph2 +g treatnumgraph2=2 if choicebefore==1 & getbefore==1 & condition=="ChoiceFree" & professionals==0 +replace treatnumgraph2=2.1 if choicebefore==1 & getbefore==0 & condition=="ChoiceFree" & professionals==0 +replace treatnumgraph2=2.2 if choicebefore==0 & getbefore==1 & condition=="ChoiceFree" & professionals==0 +replace treatnumgraph2=2.3 if choicebefore==0 & getbefore==0 & condition=="ChoiceFree" & professionals==0 + + +replace treatnumgraph2=1 if choicebefore==1 & getbefore==1 & condition=="ChoiceFree_Professionals" +replace treatnumgraph2=1.1 if choicebefore==1 & getbefore==0 & condition=="ChoiceFree_Professionals" +replace treatnumgraph2=1.2 if choicebefore==0 & getbefore==1 & condition=="ChoiceFree_Professionals" +replace treatnumgraph2=1.3 if choicebefore==0 & getbefore==0 & condition=="ChoiceFree_Professionals" + +replace treatnumgraph2=4 if choicebefore==1 & getbefore==1 & condition=="PayAfter" +replace treatnumgraph2=4.1 if choicebefore==1 & getbefore==0 & condition=="PayAfter" +replace treatnumgraph2=4.2 if choicebefore==0 & getbefore==1 & condition=="PayAfter" +replace treatnumgraph2=4.3 if choicebefore==0 & getbefore==0 & condition=="PayAfter" + +replace treatnumgraph2=3 if choicebefore==1 & getbefore==1 & condition=="PayBefore" +replace treatnumgraph2=3.1 if choicebefore==1 & getbefore==0 & condition=="PayBefore" +replace treatnumgraph2=3.2 if choicebefore==0 & getbefore==1 & condition=="PayBefore" +replace treatnumgraph2=3.3 if choicebefore==0 & getbefore==0 & condition=="PayBefore" + + +*Conflict panel for long version of figure +twoway (scatteri 1 0.7 1 1.7, bcolor(gs15) recast(area)) /// + (scatteri 1 2.7 1 3.7, bcolor(gs15) recast(area)) /// + (rcap lbpredrecommend_dif_gif ubpredrecommend_dif_gif treatnumgraph2, lcolor(red) lwidth(thin)) /// + (rcap lbpredrecommend_dqf_gqf ubpredrecommend_dqf_gqf treatnumgraph2, lcolor(black) lwidth(thin)) /// + (rcap lbpredrecommend_dif_gqf ubpredrecommend_dif_gqf treatnumgraph2, lcolor(red*0.3) lwidth(thin)) /// + (rcap lbpredrecommend_dqf_gif ubpredrecommend_dqf_gif treatnumgraph2, lcolor(black*0.3) lwidth(thin)) /// + (scatter predrecommend_dif_gif treatnumgraph2 if /// + condition=="ChoiceFree_Professionals" & professionals==1 , mcolor(red) msize(*0.75) ms(T)) /// + (scatter predrecommend_dif_gqf treatnumgraph2 if /// + condition=="ChoiceFree_Professionals" & professionals==1 & choicebefore==1 & getbefore==0, mfcolor(white) msize(*0.8) mlcolor(red%50) ms(S)) /// + (scatter predrecommend_dqf_gif treatnumgraph2 if /// + condition=="ChoiceFree_Professionals" & professionals==1 & choicebefore==0 & getbefore==1, mfcolor(white) mlcolor(black%50) msize(*0.8) ms(T)) /// + (scatter predrecommend_dqf_gqf treatnumgraph2 if /// + condition=="ChoiceFree_Professionals" & professionals==1 & choicebefore==0 & getbefore==0, mcolor(black) msize(*0.8) ms(S)) /// + (scatter predrecommend_dif_gif treatnumgraph2 if /// + condition=="ChoiceFree" , mcolor(red) msize(*0.75) ms(T)) /// + (scatter predrecommend_dif_gqf treatnumgraph2 if /// + condition=="ChoiceFree" , mfcolor(white) msize(*0.8) mlcolor(red%50) ms(T)) /// + (scatter predrecommend_dqf_gif treatnumgraph2 if /// + condition=="ChoiceFree", mfcolor(white) mlcolor(black*0.3) msize(*0.8) ms(S)) /// + (scatter predrecommend_dqf_gqf treatnumgraph2 if /// + condition=="ChoiceFree", msize(*0.8) mcolor(black) ms(S)) /// + (scatter predrecommend_dif_gif treatnumgraph2 if condition=="PayAfter", mcolor(red) msize(*0.75) ms(T)) /// + (scatter predrecommend_dif_gqf treatnumgraph2 if condition=="PayAfter", mfcolor(white) msize(*0.8) mlcolor(red%50) ms(T)) /// + (scatter predrecommend_dqf_gif treatnumgraph2 if condition=="PayAfter", mfcolor(white) mlcolor(black%50) msize(*0.8) ms(S)) /// + (scatter predrecommend_dqf_gqf treatnumgraph2 if condition=="PayAfter", mcolor(black) msize(*0.8) ms(S)) /// + (scatter predrecommend_dif_gif treatnumgraph2 if condition=="PayBefore", mcolor(red) msize(*0.75) ms(T)) /// + (scatter predrecommend_dif_gqf treatnumgraph2 if condition=="PayBefore", mfcolor(white) msize(*0.8) mlcolor(red%50) ms(T)) /// + (scatter predrecommend_dqf_gif treatnumgraph2 if condition=="PayBefore", mfcolor(white) mlcolor(black%50) msize(*0.8) ms(S)) /// + (scatter predrecommend_dqf_gqf treatnumgraph2 if condition=="PayBefore", msize(*0.8) mcolor(black) ms(S)) /// + , graphr(c(white)) plotr(c(white)) /// + ylabel(0.3(0.1)1) yscale(r(0.3 1)) /// + xtitle(" ") /// + xlabel(none) /// + xscale(r(0.7 4.6)) /// + legend(order( - "{bf:Advisor Prefers to}" "{bf:See Incentive First}" /// + 7 8 - "{bf:Advisor Prefers to}" "{bf:Assess Quality First}" 9 10) /// + lab(7 " Assigned to See Incentive First") /// + lab(8 " Assigned to Assess Quality First") /// + lab(10 " Assigned to Assess Quality First") /// + lab(9 " Assigned to See Incentive First") /// + rows(3) colfirst size(*0.7)) /// + text(0.38 1.15 "{bf: Choice Free}", color(black) size(*0.8)) /// + text(0.35 1.15 "{bf: - Professionals}", color(black) size(*0.8)) /// + text(0.38 2.15 "{bf: Choice}", color(black) size(*0.8)) /// + text(0.35 2.15 "{bf: Free }", color(black) size(*0.8)) /// + text(0.38 4.15 "{bf: Quality First}", color(black) size(*0.8)) /// + text(0.35 4.15 "{bf: Costly}", color(black) size(*0.8)) /// + text(0.38 3.15 "{bf: Incentive First}", color(black) size(*0.8)) /// + text(0.35 3.15 "{bf: Costly}", color(black) size(*0.8)) /// + ytitle("{bf:Incentivized product recommendation}" " " ) + + graph export "${main}Choice_recommendations_conflict_pred.pdf", replace + +/* By contrast, those who prefer and are assigned to see quality first, recommend the incentivized product significantly less often in all cases (t-test, all p<.001) */ +ttest recommendincentive if getyourchoice==1 & Highx10==0 & Highx100==0 & age!=. & treatment==0, by(choicebefore) +ttest recommendincentive if getyourchoice==1 & Highx10==0 & Highx100==0 & age!=. & treatment==1, by(choicebefore) +ttest recommendincentive if getyourchoice==1 & Highx10==0 & Highx100==0 & age!=. & treatment==2, by(choicebefore) +ttest recommendincentive if getyourchoice==1 & Highx10==0 & Highx100==0 & age!=. & treatment==3, by(choicebefore) + + +***Check effect of seeing incentive first vs. quality first among those who prefer to see incentive first in the COSTLY version of Choice, AMT-1 wave (to be used in Prediction study, in Appendix F) +tabstat recommendincentive if choicebefore==1 & conflict==1 & wave=="AMT-1" & condition=="PayBefore" & alphavaluefinal!=., by(getbefore) stats(mean sd) + +******************************************* +* TABLE 3: RECOMMENDATIONS BY ASSIGNMENT & PREFERENCE +******************************************* + + +*get pref +est clear +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==1, vce(hc3) +test choicebefore+choicebeforenoconflict==0 + +*not get +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==0, vce(hc3) + +*combined +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict notgetyourchoice choicebeforenotgetyourchoice notgetyourchoicenoconflict incentiveB $covariates2 /// + if Highx10==0 & Highx100==0, vce(hc3) +test choicebefore+choicebeforenoconflict==0 +test choicebefore+notgetyourchoice+choicebeforenotgetyourchoice==0 +test notgetyourchoice+choicebeforenotgetyourchoice==0 + +lincom notgetyourchoice+choicebeforenotgetyourchoice + + +local panel "\begin{table}[h!]" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{10}\selectfont" "\caption{Advisor Recommendations}" "\hspace{-1cm}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "&\multicolumn{3}{c}{\textbf{Recommend incentivized product}} \\" +local groups "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Both} \\\hline & & & \\ " +local conflict "&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict} \\ \hline & & & & & & \\" + +esttab using "${main}recommendations_pref_assign.tex", se r2 replace cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (noconflict "No Conflict" getbefore "Assigned to See Incentive First" getbeforenoconflict "Assigned to See Incentive First X No Conflict" professionalsfree "Choice Free--Professionals" seeincentivecostly "See Incentive First Costly" seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" notgetyourchoice "Not Assigned Preference" /// +female "Female" age "Age" selfish "Selfish" professionalscloudresearch "Professionals x Cloudresearch" selfishseeincentivecostly "Selfish X See Incentive First Costly" selfishseequalitycostly "Selfish X See Quality First Costly" /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft "Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order" /// +incentiveB "Incentive for B" choicebefore "Prefer to See Incentive First" /// +choicebeforenoconflict "No Conflict X Prefer to See Incentive First" /// +choicebeforenotgetyourchoice "Prefer to See Incentive First X Not Assigned Preference" /// +notgetyourchoicenoconflict "No Conflict X Not Assigned Preference") /// +order (choicebefore notgetyourchoice choicebeforenotgetyourchoice noconflict choicebeforenoconflict notgetyourchoicenoconflict professionalsfree seeincentivecostly seequalitycostly incentiveB selfish female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) title("Advisor Recommendations") /// +drop(selfish wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch) /// +mlabels(none) label collabels(none) substitute(" 0.00 " " " " (.) " " ") /// +posthead("`dv'" "`groups'") prehead("`panel'") nolines postfoot("\hline" "\end{tabular}%" "\captionsetup{width=1.15\textwidth}" "\caption*{\footnotesize Note: This table displays the estimated coefficients from linear probability models on the advisor's decision to recommend the incentivized option. Column (1) focuses on individuals who are assigned their preference, while column (2) focuses on individuals who are not assigned their preference. Both groups are merged in column (3). Prefer to See Incentive First is an indicator of the advisor's preference, and Not Assigned Preference is an indicator for not receiving the preferred order. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. All regression models include individual controls for the advisor's gender and age, each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. The same analysis including a measure of advisor's selfishness are shown in Online Appendix C. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:recommendations}" "\end{table}") + + +/* page 30 On average, advisors who choose to see the incentive first are 9.8 percentage points more likely to recommend the incentivized product if they +are assigned their preferred order ($t-\textrm{stat}=3.66$, $p<0.001$). */ + +reg recommendincentive i.getbefore incentiveB incentiveBgetbefore /// +$covariates2 if choicebefore==1 & conflict==1 & Highx10==0 & Highx100==0, vce(hc3) + +/* page 33: In the Choice experiment, we estimate a 23.5 percentage point gap */ + +reg recommendincentive choicebefore choicebeforenoconflict /// +noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==1 & treatment==0, vce(hc3) +*** 23.5pp [18.67-28.4]: Estimated Gap in recommendations Between Prefer&Get Incentive and Prefer&GetQuality, ChoiceFree + +/* page 33: "which is not significantly different from the gap estimated in the NoChoice experiment ($t-\textrm{stat}=1.26$, $p=0.207$) + but directionally larger by about 7 percentage points. + + +COMPARISON OF GAP IN CHOICE TO GAP IN NOCHOICE (APPENDIX TABLE C.16) */ + + +*clear +*u "${data}choice_experiments.dta" +preserve +keep if treatment==0 +gen seeincentivefirst=getbefore +append using "${data}nochoice.dta" +gen nochoice=0 +replace nochoice=1 if choice=="No Choice" +replace nochoice=1 if seeincentivefirst==. +replace seeincentivefirst=0 if condition=="AfterA" | condition=="AfterB" +replace seeincentivefirst=1 if condition=="BeforeA" | condition=="BeforeB" +replace getbefore=1 if nochoice==1 & seeincentivefirst==1 +replace incentiveshigh=0 if nochoice==1 +replace incentiveleft=0 if nochoice==1 +drop if Highx10==1 +drop if Highx100==1 +replace noconflict=1-conflict if nochoice==1 +replace wave1=0 if nochoice==1 +replace wave2=0 if nochoice==1 +replace wave3=0 if nochoice==1 +gen choice1=1-nochoice +replace seeincentivefirst_noconflict=seeincentivefirst*noconflict +gen seeincentivefirst_nochoice=seeincentivefirst*nochoice +gen seeincentivefirst_nochoiceNocon=seeincentivefirst*nochoice*noconflict +gen nochoice_noconflict=nochoice*noconflict +replace incentiveshigh_incentive=0 if nochoice==1 +drop if alphavaluefinal==. + + +est clear +eststo: reg recommendincentive seeincentivefirst noconflict seeincentivefirst_noconflict incentiveB wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentive age female if choice=="No Choice" +eststo: reg recommendincentive seeincentivefirst noconflict seeincentivefirst_noconflict incentiveB wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentive age female if (getyourchoice==1 ) & choice=="Choice" +eststo: reg recommendincentive seeincentivefirst nochoice seeincentivefirst_nochoice noconflict seeincentivefirst_noconflict nochoice_noconflict seeincentivefirst_nochoiceNocon incentiveB wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentive age female if ((choice=="Choice" & getyourchoice==1) | choice=="No Choice") + + + +local panel "\begin{table}[h!]" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{10}\selectfont" "\caption{Advisor Recommendations}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "&\multicolumn{3}{c}{\textbf{Recommend incentivized product}} \\" +local groups "\multicolumn{1}{r}{\textit{Sample:}}&\multicolumn{1}{c}{NoChoice.}&\multicolumn{1}{c}{Choice}&\multicolumn{1}{c}{Both} \\\hline & & & \\ " +local conflict "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Randomly Assigned}&\multicolumn{1}{c}{Prefer and Assigned}&\multicolumn{1}{c}{Both} \\\hline & & & \\ " + + +esttab using "${appendix}choice_nochoice.tex", se r2 replace cells(b(star fmt(4)) se(par fmt(4))) /// +coeflabel (seeincentivefirst "See Incentive First" noconflict "No Conflict" seeincentivefirst_noconflict "See Incentive First X No Conflict" /// +nochoice "No Choice" seeincentivefirst_nochoice "See IncentiveFirst X NoChoice" nochoice_noconflict "No Choice X No Conflict" seeincentivefirst_nochoiceNocon "See Incentive First X No Choice X No Conflict" /// +wave2 "Wave 2" wave3 "Wave 3" /// +incentiveB "Incentive for B" choicebefore "Prefer to See Incentive First" ) /// +star(* 0.10 ** 0.05 *** 0.01) title("Advisor Recommendations") /// +order(seeincentivefirst nochoice seeincentivefirst_nochoice noconflict nochoice_noconflict seeincentivefirst_noconflict seeincentivefirst_nochoiceNocon incentiveB) /// +drop( wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft female age) /// +mlabels(none) label collabels(none) substitute(" 0.000 " " " " (.) " " ") /// +posthead("`dv'" "`groups'") prehead("`panel'") nolines postfoot("\hline" "\end{tabular}%" "\captionsetup{width=\textwidth}" "\caption*{\footnotesize Note: This table displays the estimated coefficients from linear probability models on the advisor's decision to recommend the incentivized option. Column (1) focuses on the NoChoice Experiment, while column (2) focuses on the Choice Experiment (ChoiceFree Treatment only) and on individuals who are assigned their preference. Both groups are merged in column (3). See Incentive First is an indicator for whether advisors are randomly assigned to see the incentive first in NoChoice, and whether, conditional on preferring to see the incentive first, they are assigned to see the incentive first in Choice. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. All regression models include individual controls for the advisor's gender and age, each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:recommendations}" "\end{table}") + +restore + + +/* PAGE 32: "Relative to advisors who are assigned to receive cognitive flexibility, those who are assigned moral commitment are 9 percentage points less likely +to recommend the incentivized product ($t-\textrm{stat}=3.05$, $p=0.002$) */ +reg recommendincentive i.getbefore incentiveB incentiveBgetbefore /// +$covariates2 if choicebefore==0 & conflict==1 & Highx10==0 & Highx100==0, vce(hc3) + +******************************************* +**** BELIEFS ****** +******************************************* + +*** LOGIT BELIEFS + +*** GENERATE TABLE 4: BELIEF UPDATING + + +cap program drop appendmodels +program appendmodels, eclass + syntax namelist + tempname b V tmp + foreach name of local namelist { + qui est restore `name' + mat `tmp' = e(b) + local eq1: coleq `tmp' + gettoken eq1 : eq1 + mat `tmp' = `tmp'[1,"`eq1':"] + local cons = colnumb(`tmp',"_cons") + if `cons'<. & `cons'>1 { + mat `tmp' = `tmp'[1,1..`cons'-1] + } + mat `b' = nullmat(`b') , `tmp' + mat `tmp' = e(V) + mat `tmp' = `tmp'["`eq1':","`eq1':"] + if `cons'<. & `cons'>1 { + mat `tmp' = `tmp'[1..`cons'-1,1..`cons'-1] + } + capt confirm matrix `V' + if _rc { + mat `V' = `tmp' + } + else { + mat `V' = /// + ( `V' , J(rowsof(`V'),colsof(`tmp'),0) ) \ /// + ( J(rowsof(`tmp'),colsof(`V'),0) , `tmp' ) + } + } + local names: colfullnames `b' + mat coln `V' = `names' + mat rown `V' = `names' + eret post `b' `V' + eret local cmd "whatever" +end + + +est clear +*Column 1-Panel A: Assigned Preferences (All) +eststo belief_all0: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1, vce(hc3) nocons +*mentioned in text: page 34, 5.57, p=0.018 +test good=bad +*Column 1-Panel B: Assigned Preferences (By Choice) +*Quality first: f=q +eststo belief_0: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & choicebefore==0, vce(hc3) nocons + +test good=bad + +*Incentive first: f=i +eststo belief_1: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & choicebefore==1, vce(hc3) nocons + +test good=bad + +eststo bivar: appendmodels belief_all0 belief_1 belief_0 + +*mentioned in text, t-stats from table (2.45 and 2.19, p=0.014 and p=0.029, respectively) +reg logitbelief good bad goodchoicebefore badchoicebefore /// + if Highx10==0 & Highx100==0 & getyourchoice==1, vce(hc3) nocons +test goodchoicebefore==0 +estadd scalar test_good_ifirst=r(p): bivar +test badchoicebefore==0 +estadd scalar test_bad_ifirst=r(p): bivar +summ logitbelief if Highx10==0 & Highx100==0 & getyourchoice==1 +estadd scalar obs=r(N): bivar + +*Column 2-Panel A: Not Assigned Preferences (All) +eststo belief_all1: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0, vce(hc3) nocons + +*Column 2-Panel B: Not Assigned Preferences (By Choice) +*Quality first: f=q +eststo belief_2: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & choicebefore==0, vce(hc3) nocons + +*Incentive first: f=i +eststo belief_3: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & choicebefore==1, vce(hc3) nocons + + +eststo bivar2: appendmodels belief_all1 belief_3 belief_2 + +reg logitbelief good bad goodchoicebefore badchoicebefore /// + if Highx10==0 & Highx100==0 & getyourchoice==0, vce(hc3) nocons + +test goodchoicebefore==0 +estadd scalar test_good_ifirst=r(p): bivar2 +test badchoicebefore==0 +estadd scalar test_bad_ifirst=r(p): bivar2 +summ logitbelief if Highx10==0 & Highx100==0 & getyourchoice==0 +estadd scalar obs=r(N): bivar2 + +*Excluding subjects who update in the wrong direction +*Column 3-Panel A: Assigned Preferences (All) +eststo belief_all2: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0, vce(hc3) nocons +*mentioned in text: page 34, 12.06, p<0.001 +test good=bad + +*Column 3-Panel B: Assigned Preferences (By Choice) +*Quality first: f=q +eststo belief_4: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 & choicebefore==0, vce(hc3) nocons +test good=bad + +*Incentive first: f=i +eststo belief_5: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 & choicebefore==1, vce(hc3) nocons +test good=bad + + +eststo bivar3: appendmodels belief_all2 belief_5 belief_4 +*Mentioned in text: page 35, t-stats from table 1.76 and 0.81 +reg logitbelief good bad goodchoicebefore badchoicebefore /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0, vce(hc3) nocons + +test goodchoicebefore==0 +estadd scalar test_good_ifirst=r(p): bivar3 +test badchoicebefore==0 +estadd scalar test_bad_ifirst=r(p): bivar3 +summ logitbelief if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 +estadd scalar obs=r(N): bivar3 + + +*Column 4-Panel A: Not Assigned Preferences (All) +eststo belief_all3: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0, vce(hc3) nocons +test good=bad + +*Column 4-Panel B: Not Assigned Preferences (BY Choice) +*Quality first: f=q +eststo belief_6: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 & choicebefore==0, vce(hc3) nocons +test good=bad + +*Incentive first: f=i +eststo belief_7: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 & choicebefore==1, vce(hc3) nocons +test good=bad + +eststo bivar4: appendmodels belief_all3 belief_7 belief_6 + +reg logitbelief good bad goodchoicebefore badchoicebefore /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0, robust nocons + +test goodchoicebefore==0 +estadd scalar test_good_ifirst=r(p): bivar4 +test badchoicebefore==0 +estadd scalar test_bad_ifirst=r(p): bivar4 +summ logitbelief if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 +estadd scalar obs=r(N): bivar4 + + +*estadd scalar test_bad_good_news=r(p): belief_0 + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{9}{10}\selectfont" "\begin{tabular}{l*{4}{c}}\hline" +local dv "&\multicolumn{4}{c}{\textbf{Log-odds Belief}} \\" +local pref "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}\\ " +local groups "\multicolumn{1}{r}{\textit{Data:}} &\multicolumn{2}{c}{All}&\multicolumn{2}{c}{Excl. update in wrong direction} \\\hline & & & & \\" + +esttab bivar bivar2 bivar3 bivar4 using "${main}beliefs_pref_assign.tex", se r2 replace nolines cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (bad "$\beta_C$" good "$\beta_{NC}$") /// +order (bad good) /// +star(* 0.10 ** 0.05 *** 0.01) /// +mlabels(none) label collabels(none) /// +stats(obs test_bad_ifirst test_good_ifirst, fmt(%9.0g %9.3f %9.3f %9.3f %9.3f) labels("Observations" "$\beta^{f=q}_C=\beta^{f=i}_{C}$" "$\beta^{f=q}_{NC}=\beta^{f=i}_{NC}$" )) /// +addnotes("Note: * p$<$.10; ** p$<$.05; *** p$<$.01") posthead("`dv'" "`pref'" "`groups'") prehead("`panel'") /// + postfoot("\hline" "\end{tabular}%" "\captionsetup{width=\textwidth}" /// +"\caption*{\footnotesize \textit{Notes:} The outcome in all regressions is the log belief ratio. $\beta^f_C$ and $\beta^f_{NC}$ are the estimated effects of the log likelihood ratio for conflict and no conflict signals, respectively, for advisors who prefer order $f$ ($f=i$ indicates a preference to see the incentive first, and $f=q$ indicates a preference to see quality first). Columns(1) and (2) include all advisors. Columns (3) and (4) exclude advisors who updated in the wrong direction. Columns (1) and (3) include only advisors who were assigned their preference, while columns (2) and (4) include only advisors who were not assigned their preference. Robust standard errors (HC3) in parentheses.\sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\).}" "\label{tab:beliefs}" "\end{table}") + +****************************************** +***** CHOICE EXPERIMENT: APPENDIX ****** +******************************************* +preserve +***** APPENDIX B - TABLE B.1 ****** SAMPLE SIZES in the table +*NoChoice, SeeIncentiveFirst N=152; Assess Quality First N=147 + u "${data}nochoice.dta", clear + drop if alphavaluefinal==. + tab seeincentivefirst + +*Choice, * AMT-1 + *ChoiceFree= 1308; Incentive First Costly= + u "${data}choice_experiments.dta", clear + drop if study!=1 & alphavaluefinal==. + tab condition if Highx10==0 & Highx100==0 & wave1==1 + * AMT-2 + *Choice Free, $0.15 = 511; Choice Free, $15 to 1/100 = 542 + tab condition incentiveshigh if wave2==1 + * AMT-3 + *Choice Free =213 ; QualityFirst Costly=1067, IncentiveFirstCostly=215 + tab condition if Highx10==0 & Highx100==0 & wave3==1 + * ChoiceFree -HighStakes (10-fold)=110 + tab condition if Highx10==1 + * ChoiceFree -HighStakes (100-fold)=110 + tab condition if Highx100==1 + +***ChoiceStakes, Low Incentive=483; IntermediateIncentive=511; HighIncentive=478 + u "${data}stakes.dta", clear + drop if alphavaluefinal==. + tab condition + +***Information Architect: IA-Advisor=245; IA-Client=253 + u "${data}InformationArchitect.dta", clear + drop if alphavaluefinal==. + tab IAAdvisor + +*ChoiceDeterministic, Replication=385; Deterministic=369 + u "${data}Choice_Deterministic.dta", clear + drop if alphavaluefinal==. + tab Deterministic + +***NoChoiceSimultaneous, SeeIncentiveFirst=70; AssessQualityFirst=78; Simultaneous=128 u "${data}NoChoiceSimoultaneous.dta", clear + u "${data}NoChoiceSimoultaneous.dta", clear + drop if alphavaluefinal==. + tab treatment + +***Predictions, N=288 + u "${data}predictionsstudy.dta", clear + tab gap + + +*** Total sample size in Table B.1: N=9599 +di 152+147+1308+1347+511+542+712+213+1067+215+275+110+483+511+478+245+253+385+369+70+78+128 +restore +***** *APPENDIX B.2 - ADDITIONAL ANALYSES ****** +preserve +u "${data}professionals_jobtitles.dta", clear +*Essential: +*Out of 712 professionals, 677 (95\%) provided job descriptions +*that could be used by our independent raters to judge +*whether their position was fiduciary or not. +tab fiduciary_2 fiduciary_1 + +di 677/712 + +*The raters agreed on their classification of fiduciary duty +*in 87\% of the cases (interrater agreement $\kappa$=0.85). +alpha fiduciary_2 fiduciary_1 +tab fiduciary_2 fiduciary_1 +di (247+341)/677 + +*At least one rater classified job titles +*as fiduciary in 62.9% of the cases (50% both, 12.7% one), while they agreed +*that 37% of the job titles conveyed a position without fiduciary duty. +egen meanfiduciary=rowmean(fiduciary_2 fiduciary_1) +tab meanfiduciary +di 100-37.09 +*Focusing on the cases with agreement, 58\% of +*the job titles were considered as fiduciary. +tab fiduciary_2 fiduciary_1 +di 341/(247+341) + + +*JOB TITLE +*Job titles frequently found in the data included the word analyst +*(financial, actuarial, etc., in 9.4\% of the cases), accountant or +*account manager (11.6\% of the cases), and lawyer or paralegal +*(in 7.2\% of the cases). In their job titles, 14\% of participants +*included the word ``manager.'' + +cap drop account +gen account = regexm(jobtitle, "account") | regexm(jobtitle, "Account") +tab account + +gen analyst = regexm(jobtitle, "analyst") | regexm(jobtitle, "Analyst") +tab analyst + +gen lawyer = regexm(jobtitle, "lawyer") | regexm(jobtitle, "Lawyer") | regexm(jobtitle, "Legal") | regexm(jobtitle, "legal") +tab lawyer + +gen manager = regexm(jobtitle, "manager") | regexm(jobtitle, "Manager") +tab manager + + +*Prolific provides this information for some of our participants, but it +* was missing in 156 of 493 cases. +tab industry if cloudresearch==0, m + + +*The agreement between raters regarding +* industry classification was high for CloudResearch ($\kappa=0.80$) and +*somewhat lower for the missing cases on Prolific ($\kappa=0.65$). +alpha industrycode_2 industrycode_1 if cloudresearch==1 +alpha industrycode_2 industrycode_1 if cloudresearch==0 & industry=="" + + +*Overall, for cases in which there is an agreement (636 out of 712), +*we find that 72.5\% of professionals work in the finance and insurance +*industry, 18.9\% in legal service, and for the remaining 8.7\% the industry is unknown. + +g agreedcode=industrycode_2==industrycode_1 +tab industrytype_2 industrytype_1 if agreedcode==1, m cell +restore + + + +****************************************** +***** *APPENDIX C.2 - ADDITIONAL ANALYSES ****** +******************************************* +***APPENDIX TABLE C.3 **** +******************************************* +preserve +collapse year choicebefore (count) countobs=choicebefore, by(wave condition incentivedesign) + +order year wave condition incentivedesign countobs choicebefore +replace condition="Choice Free" if condition=="ChoiceFree" +replace condition="Incentive First Costly" if condition=="PayBefore" +replace condition="Quality First Costly" if condition=="PayAfter" +format choicebefore %9.3fc +format countobs %9.0fc +rename wave Wave +rename condition Treatment +rename incentivedesign Incentives +rename year Year +rename countobs N +rename choicebefore DemandIncentiveFirst +drop Incentives Year +dataout, save("${appendix}AppendixTable_Conditions_Choice.tex") tex head replace +restore + +******************************************* +**** APPENDIX TABLE C.4: BLINDING +******************************************* +******************************************* + +est clear +eststo: reg avoid_incentive choicebefore $covariates2 noconflict if Highx10==0 & Highx100==0 & getyourchoice==1, vce(hc3) +eststo: reg avoid_incentive choicebefore $covariates2 noconflict if Highx10==0 & Highx100==0 & getyourchoice==0, vce(hc3) +eststo: reg avoid_incentive choicebefore notgetyourchoice choicebeforenotgetyourchoice $covariates2 noconflict if Highx10==0 & Highx100==0, vce(hc3) +*reg avoid_incentive choicebefore notgetyourchoice choicebeforenotgetyourchoice noconflict $covariates2 if Highx10==0 & Highx100==0, robust + + + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{11}\selectfont" "\caption{Preferences for Blindness and Preferences for Information Order}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "& \multicolumn{3}{c}{\textbf{Advisor Preference to Blind}} \\" +local pref "&\multicolumn{1}{c}{Assigned Pref.} &\multicolumn{1}{c}{Not Assigned Pref.} &\multicolumn{1}{c}{Both} \\\hline & & & \\" + + +esttab using "${appendix}Choice_Blinding.tex", se r2 replace nolines cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (noconflict "No Conflict" /// + choicebefore "Prefer Incentive First" notgetyourchoice "Not Assigned Preference" /// +choicebeforenotgetyourchoice "Prefer Incentive First X Not Assigned Preference" /// +seequalitycostly "Assess Quality First Costly" seeincentivecostly "See Incentive First Costly") /// +order (choicebefore noconflict notgetyourchoice /// +choicebeforenotgetyourchoice seeincentivecostly seequalitycostly) /// +star(* 0.10 ** 0.05 *** 0.01) /// +mlabels(none) label collabels(none) /// +drop(professionalsfree wave2 wave3 professionalscloudresearch incentiveshigh incentiveleft /// +incentiveshigh_incentiveleft age female) /// +addnotes("Note: * p$<$.10; ** p$<$.05; *** p$<$.01") posthead("`dv'" "`pref'" ) prehead("`panel'") /// + postfoot("\hline" "\end{tabular}%" "\captionsetup{width=1\textwidth}" /// +"\caption*{\footnotesize \textit{Notes:} This table displays the coefficient estimates of OLS regressions on the advisor's preferences to blind themselves to incentives information in the Blinding task. Robust standard errors in parentheses. \sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\).}" "\label{tab:blinding1}" "\end{table}") + + +******************************************* +**** APPENDIX TABLE C.5 - BLINDING ADDING SELFISHNESS +******************************************* + +est clear +eststo: reg avoid_incentive choicebefore $covariates2 noconflict stdalpha if Highx10==0 & Highx100==0 & getyourchoice==1, vce(hc3) +eststo: reg avoid_incentive choicebefore $covariates2 noconflict stdalpha if Highx10==0 & Highx100==0 & getyourchoice==0, vce(hc3) +eststo: reg avoid_incentive choicebefore notgetyourchoice choicebeforenotgetyourchoice $covariates2 noconflict stdalpha if Highx10==0 & Highx100==0, vce(hc3) + + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{11}\selectfont" "\caption{Preferences for Blindness, Information Order \& Selfishness}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "& \multicolumn{3}{c}{\textbf{Advisor Preference to Blind}} \\" +local pref "&\multicolumn{1}{c}{Assigned Pref.} &\multicolumn{1}{c}{Not Assigned Pref.} &\multicolumn{1}{c}{Both} \\\hline & & & \\" + +esttab using "${appendix}Choice_Blinding_selfish.tex", se r2 replace nolines cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (noconflict "No Conflict" /// + choicebefore "Prefer Incentive First" notgetyourchoice "Not Assigned Preference" /// +choicebeforenotgetyourchoice "Prefer Incentive First X Not Assigned Preference" /// +seequalitycostly "Assess Quality First Costly" seeincentivecostly "See Incentive First Costly" /// +stdalpha "Selfishness") /// +order (choicebefore noconflict notgetyourchoice /// +choicebeforenotgetyourchoice seeincentivecostly seequalitycostly stdalpha) /// +star(* 0.10 ** 0.05 *** 0.01) /// +mlabels(none) label collabels(none) /// +drop(professionalsfree wave2 wave3 professionalscloudresearch incentiveshigh incentiveleft /// +incentiveshigh_incentiveleft age female) /// +addnotes("Note: * p$<$.10; ** p$<$.05; *** p$<$.01") posthead("`dv'" "`pref'" ) prehead("`panel'") /// + postfoot("\hline" "\end{tabular}" "\captionsetup{width=1\textwidth}" /// +"\caption*{\footnotesize \textit{Notes:} This table displays the coefficient estimates of OLS regressions on the advisor's preferences to blind themselves to incentives information in the Blinding task, controlling for selfishness. Selfishness was elicited at the end of the experiment, using a multiple price list (MPL) with 5 decisions. The variable is a standardized measure of the number of times the advisor chose to recommend the incentivized product in the MPL task. Robust standard errors in parentheses. \sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\).}" "\label{tab:blinding2}" "\end{table}") + + +******************************************* +*APPENDIX : NO CONFLICT RECOMMENDATIONS - FIGURE C.1 +******************************************* + + +cap drop predrecommend_* ubpredrecommend_* lbpredrecommend_* sepredrecommend_* +g predrecommend_dqf_gqf=. +g sepredrecommend_dqf_gqf=. +g predrecommend_dqf_gif=. +g sepredrecommend_dqf_gif=. +g predrecommend_dif_gqf=. +g sepredrecommend_dif_gqf=. +g predrecommend_dif_gif=. +g sepredrecommend_dif_gif=. + +*demand incentive first +est clear +eststo:reg recommendincentive i.treatment##i.getbefore incentiveB incentiveBgetbefore $covariates2 /// +if choicebefore==1 /// +& conflict==0 & Highx10==0 & Highx100==0, robust +forval i=0(1)3{ +margins i.getbefore if treatment==`i', atmeans saving("${main}adjusted_recommendations", replace) +matrix coeffs=r(table) +replace predrecommend_dif_gqf=coeffs[1,1] if treatment==`i' & choicebefore==1 & getbefore==0 +replace sepredrecommend_dif_gqf=coeffs[2,1] if treatment==`i' & choicebefore==1 & getbefore==0 +replace predrecommend_dif_gif=coeffs[1,2] if treatment==`i' & choicebefore==1 & getbefore==1 +replace sepredrecommend_dif_gif=coeffs[2,2] if treatment==`i' & choicebefore==1 & getbefore==1 +} +g ubpredrecommend_dif_gif=predrecommend_dif_gif+1.96*sepredrecommend_dif_gif +g lbpredrecommend_dif_gif=predrecommend_dif_gif-1.96*sepredrecommend_dif_gif +g ubpredrecommend_dif_gqf=predrecommend_dif_gqf+1.96*sepredrecommend_dif_gqf +g lbpredrecommend_dif_gqf=predrecommend_dif_gqf-1.96*sepredrecommend_dif_gqf + + +*demand quality first +est clear +eststo:reg recommendincentive i.treatment##i.getbefore incentiveB incentiveBgetbefore $covariates2 /// +if choicebefore==0 /// +& conflict==0 & Highx10==0 & Highx100==0, robust +forval i=0(1)3{ +margins i.getbefore if treatment==`i', atmeans saving("${main}adjusted_recommendations", replace) +matrix coeffs=r(table) +replace predrecommend_dqf_gqf=coeffs[1,1] if treatment==`i' & choicebefore==0 & getbefore==0 +replace sepredrecommend_dqf_gqf=coeffs[2,1] if treatment==`i' & choicebefore==0 & getbefore==0 +replace predrecommend_dqf_gif=coeffs[1,2] if treatment==`i' & choicebefore==0 & getbefore==1 +replace sepredrecommend_dqf_gif=coeffs[2,2] if treatment==`i' & choicebefore==0 & getbefore==1 +} +g ubpredrecommend_dqf_gif=predrecommend_dqf_gif+1.96*sepredrecommend_dqf_gif +g lbpredrecommend_dqf_gif=predrecommend_dqf_gif-1.96*sepredrecommend_dqf_gif +g ubpredrecommend_dqf_gqf=predrecommend_dqf_gqf+1.96*sepredrecommend_dqf_gqf +g lbpredrecommend_dqf_gqf=predrecommend_dqf_gqf-1.96*sepredrecommend_dqf_gqf + + + +twoway (scatteri 1 0.7 1 1.7, bcolor(gs15) recast(area)) /// + (scatteri 1 2.7 1 3.7, bcolor(gs15) recast(area)) /// + (rcap lbpredrecommend_dif_gif ubpredrecommend_dif_gif treatnumgraph2, lcolor(red) lwidth(thin)) /// + (rcap lbpredrecommend_dqf_gqf ubpredrecommend_dqf_gqf treatnumgraph2, lcolor(black) lwidth(thin)) /// + (rcap lbpredrecommend_dif_gqf ubpredrecommend_dif_gqf treatnumgraph2, lcolor(red*0.3) lwidth(thin)) /// + (rcap lbpredrecommend_dqf_gif ubpredrecommend_dqf_gif treatnumgraph2, lcolor(black*0.3) lwidth(thin)) /// + (scatter predrecommend_dif_gif treatnumgraph2 if /// + condition=="ChoiceFree_Professionals" & professionals==1 , mcolor(red) msize(*0.75) ms(T)) /// + (scatter predrecommend_dif_gqf treatnumgraph2 if /// + condition=="ChoiceFree_Professionals" & professionals==1 & choicebefore==1 & getbefore==0, mfcolor(white) msize(*0.8) mlcolor(red%50) ms(S)) /// + (scatter predrecommend_dqf_gif treatnumgraph2 if /// + condition=="ChoiceFree_Professionals" & professionals==1 & choicebefore==0 & getbefore==1, mfcolor(white) mlcolor(black%50) msize(*0.8) ms(T)) /// + (scatter predrecommend_dqf_gqf treatnumgraph2 if /// + condition=="ChoiceFree_Professionals" & professionals==1 & choicebefore==0 & getbefore==0, mcolor(black) msize(*0.8) ms(S)) /// + (scatter predrecommend_dif_gif treatnumgraph2 if /// + condition=="ChoiceFree" , mcolor(red) msize(*0.75) ms(T)) /// + (scatter predrecommend_dif_gqf treatnumgraph2 if /// + condition=="ChoiceFree" , mfcolor(white) msize(*0.8) mlcolor(red%50) ms(T)) /// + (scatter predrecommend_dqf_gif treatnumgraph2 if /// + condition=="ChoiceFree", mfcolor(white) mlcolor(black*0.3) msize(*0.8) ms(S)) /// + (scatter predrecommend_dqf_gqf treatnumgraph2 if /// + condition=="ChoiceFree", msize(*0.8) mcolor(black) ms(S)) /// + (scatter predrecommend_dif_gif treatnumgraph2 if condition=="PayAfter", mcolor(red) msize(*0.75) ms(T)) /// + (scatter predrecommend_dif_gqf treatnumgraph2 if condition=="PayAfter", mfcolor(white) msize(*0.8) mlcolor(red%50) ms(T)) /// + (scatter predrecommend_dqf_gif treatnumgraph2 if condition=="PayAfter", mfcolor(white) mlcolor(black%50) msize(*0.8) ms(S)) /// + (scatter predrecommend_dqf_gqf treatnumgraph2 if condition=="PayAfter", mcolor(black) msize(*0.8) ms(S)) /// + (scatter predrecommend_dif_gif treatnumgraph2 if condition=="PayBefore", mcolor(red) msize(*0.75) ms(T)) /// + (scatter predrecommend_dif_gqf treatnumgraph2 if condition=="PayBefore", mfcolor(white) msize(*0.8) mlcolor(red%50) ms(T)) /// + (scatter predrecommend_dqf_gif treatnumgraph2 if condition=="PayBefore", mfcolor(white) mlcolor(black%50) msize(*0.8) ms(S)) /// + (scatter predrecommend_dqf_gqf treatnumgraph2 if condition=="PayBefore", msize(*0.8) mcolor(black) ms(S)) /// + , graphr(c(white)) plotr(c(white)) /// + ylabel(0.3(0.1)1) yscale(r(0.3 1)) /// + xtitle("Treatment") /// + xlabel(none) /// fgfd + xscale(r(0.7 4.6)) /// + legend(order( - "{bf:Advisor Prefers to}" "{bf:See Incentive First}" /// + 7 8 - "{bf:Advisor Prefers to}" "{bf:Assess Quality First}" 9 10) /// + lab(7 " Assigned to See Incentive First") /// + lab(8 " Assigned to Assess Quality First") /// + lab(10 " Assigned to Assess Quality First") /// + lab(9 " Assigned to See Incentive First") /// + rows(3) colfirst size(*0.7)) /// + text(0.38 1.15 "{bf: Choice Free}", color(black) size(*0.8)) /// + text(0.35 1.15 "{bf: - Professionals}", color(black) size(*0.8)) /// + text(0.38 2.15 "{bf: Choice}", color(black) size(*0.8)) /// + text(0.35 2.15 "{bf: Free }", color(black) size(*0.8)) /// + text(0.38 4.15 "{bf: Quality First}", color(black) size(*0.8)) /// + text(0.35 4.15 "{bf: Costly}", color(black) size(*0.8)) /// + text(0.38 3.15 "{bf: Incentive First}", color(black) size(*0.8)) /// + text(0.35 3.15 "{bf: Costly}", color(black) size(*0.8)) /// + ytitle("{bf:Incentivized product recommendation}" " " ) + + graph export "${appendix}Choice_Recommendations_Noconflict.png", replace + + +******************************************* +*APPENDIX TABLE C.6 - RECCOMENDATIONS ADDING SELFISHNESS +******************************************* + +*get pref +est clear +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict incentiveB stdalpha $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==1, vce(hc3) + +*not get +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict incentiveB stdalpha $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==0, vce(hc3) + +*combined +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict notgetyourchoice choicebeforenotgetyourchoice notgetyourchoicenoconflict incentiveB /// + stdalpha $covariates2 /// + if Highx10==0 & Highx100==0, vce(hc3) + + + +local panel "\begin{table}[h!]" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{10}\selectfont" "\caption{Advisor Recommendations}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "&\multicolumn{3}{c}{\textbf{Recommend incentivized product}} \\" +local groups "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Both} \\\hline & & & & \\ " +local conflict "&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict} \\ \hline & & & & & & \\" + +esttab using "${appendix}Choice_Recommendations_Selfishness.tex", se r2 replace cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (noconflict "No Conflict" getbefore "Assigned to See Incentive First" getbeforenoconflict "Assigned to See Incentive First X No Conflict" professionalsfree "Choice Free--Professionals" seeincentivecostly "See Incentive First Costly" seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" notgetyourchoice "Not Assigned Preference" /// +female "Female" age "Age" stdalpha "Selfishness" professionalscloudresearch "Professionals x Cloudresearch" /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft "Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order" /// +incentiveB "Incentive for B" choicebefore "Prefer to See Incentive First" /// +choicebeforenoconflict "No Conflict X Prefer to See Incentive First" /// +choicebeforenotgetyourchoice "Prefer to See Incentive First X Not Assigned Pref." /// +notgetyourchoicenoconflict "No Conflict X Not Assigned Preference") /// +order (choicebefore notgetyourchoice choicebeforenotgetyourchoice noconflict choicebeforenoconflict notgetyourchoicenoconflict seeincentivecostly seequalitycostly professionalsfree incentiveB stdalpha female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) title("Advisor Recommendations") /// +drop(professionalsfree wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch) /// +mlabels(none) label collabels(none) substitute(" 0.000 " " " " (.) " " ") /// +posthead("`dv'" "`groups'") prehead("`panel'") nolines postfoot("\hline" "\end{tabular}%" "\captionsetup{width=\textwidth}" "\caption*{\footnotesize \textit{Notes:} This table displays the estimated coefficients from linear probability models on the advisor's decision to recommend the incentivized option. Column (1) focuses on individuals who are assigned their preference, while column (2) focuses on individuals who are not assigned their preference. Both groups are merged in column (3). Prefer to See Incentive First is an indicator of the advisor's preference, and Not Assigned Preference is an indicator for not receiving the preferred order. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. Choice Free-Professionals, See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. Selfishness was elicited at the end of the experiment, using a multiple price list (MPL) with 5 decisions. The variable is a standardized measure of the number of times the advisor chose to recommend the incentivized product in the MPL task. All regression models include controls for each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:recommendations_selfish}" "\end{table}") + + +************************************************************* + * APPENDIX TABLE C.7: ADVISOR RECOMMENDATION - INCENTIVE A +************************************************************* + +*get pref +est clear +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==1 & incentiveB==0, vce(hc3) + +*not get +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==0 & incentiveB==0, vce(hc3) + +*combined +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict notgetyourchoice choicebeforenotgetyourchoice notgetyourchoicenoconflict $covariates2 /// + if Highx10==0 & Highx100==0 & incentiveB==0, vce(hc3) + + + + +local panel "\begin{table}[h!]" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{10}\selectfont" "\caption{Advisor Recommendations: Incentive for A}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "&\multicolumn{3}{c}{\textbf{Recommend incentivized product}} \\" +local groups "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Both} \\\hline & & & & \\ " +local conflict "&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict} \\ \hline & & & & & & \\" + +esttab using "${appendix}Choice_Recommendations_IncentiveA.tex", se r2 replace cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (noconflict "No Conflict" getbefore "Assigned to See Incentive First" getbeforenoconflict "Assigned to See Incentive First X No Conflict" professionalsfree "Choice Free--Professionals" seeincentivecostly "See Incentive First Costly" seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" notgetyourchoice "Not Assigned Preference" /// +female "Female" age "Age" selfish "Selfish" professionalscloudresearch "Professionals x Cloudresearch" selfishseeincentivecostly "Selfish X See Incentive First Costly" selfishseequalitycostly "Selfish X See Quality First Costly" /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft "Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order" /// +choicebefore "Prefer to See Incentive First" /// +choicebeforenoconflict "No Conflict X Prefer to See Incentive First" /// +choicebeforenotgetyourchoice "Prefer to See Incentive First X Not Assigned Pref." /// +notgetyourchoicenoconflict "No Conflict X Not Assigned Preference" /// +selfishchoicebefore "Prefer to See Incentive First X Selfish" /// +selfishnotgetyourchoice "Not Assigned Preference X Selfish") /// +order (choicebefore notgetyourchoice choicebeforenotgetyourchoice noconflict choicebeforenoconflict notgetyourchoicenoconflict seeincentivecostly seequalitycostly professionalsfree female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) title("Advisor Recommendations") /// +drop(professionalsfree wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch) /// +mlabels(none) label collabels(none) substitute(" 0.000 " " " " (.) " " ") /// +posthead("`dv'" "`groups'") prehead("`panel'") nolines postfoot("\hline" "\end{tabular}%" "\captionsetup{width=\textwidth}" "\caption*{\footnotesize \textit{Notes:} This table displays the estimated coefficients from linear probability models on the advisor's decision to recommend the incentivized option, focusing on the cases in which advisors were incentivized to recommend product A. Column (1) focuses on individuals who are assigned their preference, while column (2) focuses on individuals who are not assigned their preference. Both groups are merged in column (3). Prefer to See Incentive First is an indicator of the advisor's preference, and Not Assigned Preference is an indicator for not receiving the preferred order. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. Choice Free-Professionals, See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. All regression models include controls for each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:recommendations_A}" "\end{table}") + +************************************************************* + * APPENDIX TABLE C.8: ADVISOR RECOMMENDATION - INCENTIVE B +************************************************************* + +*get pref +est clear +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==1 & incentiveB==1, vce(hc3) + +*not get +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==0 & incentiveB==1, vce(hc3) + +*combined +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict notgetyourchoice choicebeforenotgetyourchoice notgetyourchoicenoconflict $covariates2 /// + if Highx10==0 & Highx100==0 & incentiveB==1, vce(hc3) + + + + +local panel "\begin{table}[h!]" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{10}\selectfont" "\caption{Advisor Recommendations: Incentive for B}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "&\multicolumn{3}{c}{\textbf{Recommend incentivized product}} \\" +local groups "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Both} \\\hline & & & & \\ " +local conflict "&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict} \\ \hline & & & & & & \\" + +esttab using "${appendix}Choice_Recommendations_IncentiveB.tex", se r2 replace cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (noconflict "No Conflict" getbefore "Assigned to See Incentive First" getbeforenoconflict "Assigned to See Incentive First X No Conflict" professionalsfree "Choice Free--Professionals" seeincentivecostly "See Incentive First Costly" seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" notgetyourchoice "Not Assigned Preference" /// +female "Female" age "Age" selfish "Selfish" professionalscloudresearch "Professionals x Cloudresearch" selfishseeincentivecostly "Selfish X See Incentive First Costly" selfishseequalitycostly "Selfish X See Quality First Costly" /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft "Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order" /// +choicebefore "Prefer to See Incentive First" /// +choicebeforenoconflict "No Conflict X Prefer to See Incentive First" /// +choicebeforenotgetyourchoice "Prefer to See Incentive First X Not Assigned Pref." /// +notgetyourchoicenoconflict "No Conflict X Not Assigned Preference" /// +selfishchoicebefore "Prefer to See Incentive First X Selfish" /// +selfishnotgetyourchoice "Not Assigned Preference X Selfish") /// +order (choicebefore notgetyourchoice choicebeforenotgetyourchoice noconflict choicebeforenoconflict notgetyourchoicenoconflict seeincentivecostly seequalitycostly professionalsfree female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) title("Advisor Recommendations") /// +drop(professionalsfree wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch) /// +mlabels(none) label collabels(none) substitute(" 0.000 " " " " (.) " " ") /// +posthead("`dv'" "`groups'") prehead("`panel'") nolines postfoot("\hline" "\end{tabular}%" "\captionsetup{width=\textwidth}" "\caption*{\footnotesize \textit{Notes:} This table displays the estimated coefficients from linear probability models on the advisor's decision to recommend the incentivized option, focusing on the cases in which advisors were incentivized to recommend product B. Column (1) focuses on individuals who are assigned their preference, while column (2) focuses on individuals who are not assigned their preference. Both groups are merged in column (3). Prefer to See Incentive First is an indicator of the advisor's preference, and Not Assigned Preference is an indicator for not receiving the preferred order. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. Choice Free-Professionals, See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. All regression models include controls for each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:recommendations_B}" "\end{table}") + + + +************************************************************* + * APPENDIX TABLE C.9: SELECTION VS ASSIGNMENT +************************************************************* +est clear +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getbefore==1, vce(hc3) +test choicebefore+choicebeforenoconflict==0 + +*effect of preference for those assigned to see quality first +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getbefore==0, vce(hc3) + +*effect of assignment for those who prefer to see the incentive first +eststo: reg recommendincentive getbefore getbeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & choicebefore==1, vce(hc3) + +*effect of assignment for those who prefer to see the quality first +eststo: reg recommendincentive getbefore getbeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & choicebefore==0, vce(hc3) + + +**combined + eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict getbefore choicebeforegetbefore getbeforenoconflict incentiveB $covariates2 /// + if Highx10==0 & Highx100==0, vce(hc3) + + test choicebefore=getbefore+choicebeforegetbefore + + +local panel "\begin{table}[h!]" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{10}\selectfont" "\caption{Advisor Recommendations - Role of Selection and Experience}" "\hspace{-1cm}" "\begin{tabular}{l*{5}{c}} \hline" +local dv "&\multicolumn{5}{c}{\textbf{Recommend incentivized product}} \\" +local assigned "\multicolumn{1}{r}{\textit{Sample:}}&\multicolumn{2}{c}{Assigned to See:}&\multicolumn{2}{c}{Prefer to See:}&\multicolumn{1}{c}{Full Sample} \\ " +local groups "\multicolumn{1}{r}{\textit{}}&\multicolumn{1}{c}{Incentive First}&\multicolumn{1}{c}{Quality First}&\multicolumn{1}{c}{Incentive First}&\multicolumn{1}{c}{Quality First} & \\\hline & & & \\ " + +esttab using "${appendix}Choice_recommendations_selection.tex", se r2 replace cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (choicebefore "Prefer See Inc. First" noconflict "No Conflict" getbefore "Assigned See Inc. First" getbeforenoconflict "No Conflict X Assigned See Inc. First" professionalsfree "Choice Free--Professionals" seeincentivecostly "See Inc. First Costly" seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" notgetyourchoice "Not Assigned Preference" /// +choicebeforegetbefore "Prefer X Assigned See Inc. First"female "Female" age "Age" selfish "Selfish" professionalscloudresearch "Professionals x Cloudresearch" selfishseeincentivecostly "Selfish X See Inc. First Costly" selfishseequalitycostly "Selfish X See Quality First Costly" /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft "Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order" /// +incentiveB "Incentive for B" choicebefore "Prefer to See Incentive First" /// +choicebeforenoconflict "No Conflict X Prefer See Inc. First" /// +choicebeforenotgetyourchoice "Prefer See Inc. First X Not Assigned Preference" ) /// +order (choicebefore getbefore choicebeforegetbefore noconflict choicebeforenoconflict getbeforenoconflict professionalsfree seeincentivecostly seequalitycostly incentiveB selfish female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) title("Advisor Recommendations") /// +drop(selfish wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch) /// +mlabels(none) label collabels(none) substitute(" 0.00 " " " " (.) " " ") /// +posthead("`dv'" "`assigned'" "`groups'") prehead("`panel'") nolines postfoot("\hline" "\end{tabular}%" "\captionsetup{width=1.15\textwidth}" "\caption*{\footnotesize Note: This table displays the estimated coefficients from linear probability models on the advisor's decision to recommend the incentivized option. Column (1) and (2) focus on participants assigned to experience a given information order. Column (1) focuses on individuals who are assigned to see the incentive first, column (2) focuses on individuals who are assigned to see quality first. Columns (3) and (4) focus on individuals' who prefer to be assigned to a given order, with Column (3) focusing on those who prefer to see the incentive first, and Column (4) focusing on those who prefer to see quality first. These groups are merged in column (5). Prefer See Inc. First is an indicator of the advisor's preference to see her incentive first, and Assigned See Inc. First is a indicator for whether advisors are assigned to see their incentive first. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. All regression models include individual controls for the advisor's gender and age, each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. The same analysis including a measure of advisor's selfishness are shown in Online Appendix C. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:recommendations}" "\end{table}") + +************************************************************ + * APPENDIX SECTION C.2.3: BELIEFS* + +***************************************************************************** + *FIGURE C.2: CUMULATIVE BELIEFS DISTANCE - ASSIGNED PREFERRED ORDER +***************************************************************************** + + +cumul relbeliefdistance if choicebefore==1 & getbefore==1 & badsignal==1, generate(bel_dif_gif_bad) equal +cumul relbeliefdistance if choicebefore==0 & getbefore==0 & badsignal==1, generate(bel_dqf_gqf_bad) equal + +cumul relbeliefdistance if choicebefore==1 & getbefore==1 & badsignal==0, generate(bel_dif_gif_good) equal +cumul relbeliefdistance if choicebefore==0 & getbefore==0 & badsignal==0, generate(bel_dqf_gqf_good) equal + +cumul relbeliefdistance if choicebefore==1 & getbefore==0 & badsignal==1, generate(bel_dif_gqf_bad) equal +cumul relbeliefdistance if choicebefore==0 & getbefore==1 & badsignal==1, generate(bel_dqf_gif_bad) equal + +cumul relbeliefdistance if choicebefore==1 & getbefore==0 & badsignal==0, generate(bel_dif_gqf_good) equal +cumul relbeliefdistance if choicebefore==0 & getbefore==1 & badsignal==0, generate(bel_dqf_gif_good) equal + +twoway (scatter bel_dif_gif_bad relbeliefdistance, sort c(J) ms(none) color(red) lpattern(longdash)) /// +(scatter bel_dqf_gqf_bad relbeliefdistance, sort c(J) ms(none) color(black) lpattern(longdash)) /// +, ytitle("CDF") xtitle("Relative Distance from Prior" "((Prior - Belief) / (Prior - Bayesian Posterior))") /// +legend( pos(10) ring(0) col(1) /// +label(2 "Prefer to See Quality First") label(1 "Prefer to See Incentive First") size(*0.8)) /// +xlabel(-2(0.5)2) xscale(r(0 1)) /// + graphr(c(white)) /// + xline(1, lcolor(gs12)) xline(0, lcolor(gs12)) /// + text(0.0 0.3 "Prior", color(gs10) size(*0.8)) /// + text(0.0 2 "Bayesian Posterior", color(gs10) size(*0.8)) /// + title("Conflict", color(black) size(*0.8)) +graph export "${appendix}Choice_Beliefs_Getchoice_Bad.png", replace +graph save "${appendix}Choice_Beliefs_Getchoice_bad.gph", replace + +twoway (scatter bel_dif_gif_good relbeliefdistance, sort c(J) ms(none) color(red) lpattern(longdash)) /// +(scatter bel_dqf_gqf_good relbeliefdistance, sort c(J) ms(none) color(black) lpattern(longdash)) /// +, ytitle("CDF") xtitle("Relative Distance from Prior" "((Prior - Belief) / (Prior - Bayesian Posterior))") /// +legend( pos(10) ring(0) col(1) /// +label(2 "Prefer to See Quality First") label(1 "Prefer to See Incentive First") size(*0.8)) /// +xlabel(-2(0.5)2) xscale(r(0 1)) /// + graphr(c(white)) /// + xline(1, lcolor(gs12)) xline(0, lcolor(gs12)) /// + text(0.0 0.3 "Prior", color(gs10) size(*0.8)) /// + text(0.0 2 "Bayesian Posterior", color(gs10) size(*0.8)) /// + title("No Conflict", color(black) size(*0.8)) +graph save "${appendix}Choice_Beliefs_Getchoice_Good.gph", replace + +grc1leg "${appendix}Choice_Beliefs_Getchoice_bad.gph" /// + "${appendix}Choice_Beliefs_Getchoice_Good.gph" /// + , graphr(c(white)) +graph export "${appendix}Choice_Beliefs_Getchoice.pdf", replace + +*********************************************************************************** + *FIGURE C.3: CUMULATIVE BELIEFS DISTANCE* NOT ASSIGNED PREFERRED ORDER +*********************************************************************************** + +twoway (scatter bel_dif_gqf_bad relbeliefdistance, sort c(J) ms(none) color(red) lpattern(longdash)) /// +(scatter bel_dqf_gif_bad relbeliefdistance, sort c(J) ms(none) color(black) lpattern(longdash)) /// +, ytitle("CDF") xtitle("Relative Distance from Prior" "((Prior - Belief) / (Prior - Bayesian Posterior))") /// +legend( pos(10) ring(0) col(1) /// +label(2 "Prefer to See Quality First") label(1 "Prefer to See Incentive First") size(*0.8)) /// +xlabel(-2(0.5)2) xscale(r(0 1)) /// + graphr(c(white)) /// + xline(1, lcolor(gs12)) xline(0, lcolor(gs12)) /// + text(0.0 0.3 "Prior", color(gs10) size(*0.8)) /// + text(0.0 2 "Bayesian Posterior", color(gs10) size(*0.8)) /// + title("Conflict", color(black) size(*0.8)) +graph export "${appendix}Choice_Beliefs_Notgetchoice_Bad.png", replace +graph save "${appendix}Choice_Beliefs_Notgetchoice_Bad.gph", replace + + +twoway (scatter bel_dif_gqf_good relbeliefdistance, sort c(J) ms(none) color(red) lpattern(longdash)) /// +(scatter bel_dqf_gif_good relbeliefdistance, sort c(J) ms(none) color(black) lpattern(longdash)) /// +, ytitle("CDF") xtitle("Relative Distance from Prior" "((Prior - Belief) / (Prior - Bayesian Posterior))") /// +legend( pos(10) ring(0) col(1) /// +label(2 "Prefer to See Quality First") label(1 "Prefer to See Incentive First") size(*0.8)) /// +xlabel(-2(0.5)2) xscale(r(0 1)) /// + graphr(c(white)) /// + xline(1, lcolor(gs12)) xline(0, lcolor(gs12)) /// + text(0.0 0.3 "Prior", color(gs10) size(*0.8)) /// + text(0.0 2 "Bayesian Posterior", color(gs10) size(*0.8)) /// + title("No Conflict", color(black) size(*0.8)) +graph save "${appendix}Choice_Beliefs_Notgetchoice_Good.gph", replace + +grc1leg "${appendix}Choice_Beliefs_Notgetchoice_Bad.gph" /// + "${appendix}Choice_Beliefs_Notgetchoice_Good.gph" /// + , graphr(c(white)) +graph export "${appendix}Choice_Beliefs_Notgetchoice.pdf", replace + + +*********************************************************************************** + *TABLE C.10 0BELIEF UPDATING WHEN SIGNAL IS $0 +*********************************************************************************** +est clear +*get pref +eststo belief_all0: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & signalblue==0, vce(hc3) nocons + +test good=bad +*get pref +eststo belief_0: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & choicebefore==0 & signalblue==0 , vce(hc3) nocons + +test good=bad + +eststo belief_1: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & choicebefore==1 & signalblue==0, vce(hc3) nocons + +test good=bad + +eststo bivar: appendmodels belief_all0 belief_1 belief_0 + +reg logitbelief good bad goodchoicebefore badchoicebefore /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & signalblue==0, vce(hc3) nocons +test goodchoicebefore==0 +estadd scalar test_good_ifirst=r(p): bivar +test badchoicebefore==0 +estadd scalar test_bad_ifirst=r(p): bivar +summ logitbelief if Highx10==0 & Highx100==0 & getyourchoice==1 & signalblue==0 +estadd scalar obs=r(N): bivar + +*not get +eststo belief_all1: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & signalblue==0, vce(hc3) nocons + +eststo belief_2: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & choicebefore==0 & signalblue==0, vce(hc3) nocons + +test good=bad +*estadd scalar test_bad_good_news=r(p): belief_0 + +eststo belief_3: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & choicebefore==1 & signalblue==0, vce(hc3) nocons + +test good=bad + +eststo bivar2: appendmodels belief_all1 belief_3 belief_2 + +reg logitbelief good bad goodchoicebefore badchoicebefore /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & signalblue==0, vce(hc3) nocons + +test goodchoicebefore==0 +estadd scalar test_good_ifirst=r(p): bivar2 +test badchoicebefore==0 +estadd scalar test_bad_ifirst=r(p): bivar2 +summ logitbelief if Highx10==0 & Highx100==0 & getyourchoice==0 & signalblue==0 +estadd scalar obs=r(N): bivar2 + +*Columns 3/4: Focus on participants who update in correct direction only +*get pref +eststo belief_all2: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 & signalblue==0, vce(hc3) nocons +test good=bad + +eststo belief_4: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 & choicebefore==0 & signalblue==0, vce(hc3) nocons +test good=bad + +eststo belief_5: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 & choicebefore==1 & signalblue==0, vce(hc3) nocons +test good=bad + + +eststo bivar3: appendmodels belief_all2 belief_5 belief_4 + +reg logitbelief good bad goodchoicebefore badchoicebefore /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 & signalblue==0, vce(hc3) nocons + +test goodchoicebefore==0 +estadd scalar test_good_ifirst=r(p): bivar3 +test badchoicebefore==0 +estadd scalar test_bad_ifirst=r(p): bivar3 +summ logitbelief if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 & signalblue==0 +estadd scalar obs=r(N): bivar3 + +test goodchoicebefore==badchoicebefore + + +*not get +eststo belief_all3: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 & signalblue==0, vce(hc3) nocons +test good=bad + +eststo belief_6: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 & choicebefore==0 & signalblue==0, vce(hc3) nocons +test good=bad + +eststo belief_7: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 & choicebefore==1 & signalblue==0, vce(hc3) nocons +test good=bad + +eststo bivar4: appendmodels belief_all3 belief_7 belief_6 + +reg logitbelief good bad goodchoicebefore badchoicebefore /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 & signalblue==0, robust nocons + +test goodchoicebefore==0 +estadd scalar test_good_ifirst=r(p): bivar4 +test badchoicebefore==0 +estadd scalar test_bad_ifirst=r(p): bivar4 +summ logitbelief if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 & signalblue==0 +estadd scalar obs=r(N): bivar4 + + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{9}{10}\selectfont" "\begin{tabular}{l*{4}{c}}\hline" +local dv "&\multicolumn{4}{c}{\textbf{Log-odds Belief}} \\" +local pref "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}\\ " +local groups "\multicolumn{1}{r}{\textit{Data:}} &\multicolumn{2}{c}{All}&\multicolumn{2}{c}{Excl. update in wrong direction} \\\hline & & & & \\" + +esttab bivar bivar2 bivar3 bivar4 using "${appendix}beliefs_pref_assign_ball0.tex", se r2 replace nolines cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (bad "$\beta_C$" good "$\beta_{NC}$") /// +order (bad good) /// +star(* 0.10 ** 0.05 *** 0.01) /// +mlabels(none) label collabels(none) /// +stats(obs test_bad_ifirst test_good_ifirst, fmt(%9.0g %9.3f %9.3f %9.3f %9.3f) labels("Observations" "$\beta^{f=q}_C=\beta^{f=i}_{C}$" "$\beta^{f=q}_{NC}=\beta^{f=i}_{NC}$" )) /// +addnotes("Note: * p$<$.10; ** p$<$.05; *** p$<$.01") posthead("`dv'" "`pref'" "`groups'") prehead("`panel'") /// + postfoot("\hline" "\end{tabular}%" "\captionsetup{width=0.8\textwidth}" /// +"\caption*{\footnotesize \textit{Notes:} The outcome in all regressions is the log belief ratio, when the advisors sees a \$0 ball for product $B$. $\beta^f_C$ and $\beta^f_{NC}$ are the estimated effects of the log likelihood ratio for conflict and no conflict signals, respectively, for advisors who prefer order $f$ ($f=i$ indicates a preference to see the incentive first, and $f=q$ indicates a preference to see quality first). Columns(1) and (2) include all advisors. Columns (3) and (4) exclude advisors who updated in the wrong direction. Columns (1) and (3) include only advisors who were assigned their preference, while columns (2) and (4) include only advisors who were not assigned their preference. Robust standard errors (HC3) in parentheses.\sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\).}" "\label{tab:beliefs1}" "\end{table}") + +*********************************************************************************** + *TABLE C.11 0BELIEF UPDATING WHEN SIGNAL IS $2 +*********************************************************************************** +*Columns 1/2: Full Sample +est clear +*get pref +eststo belief_all0: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & signalblue==1, vce(hc3) nocons + +test good=bad +*get pref +eststo belief_0: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & choicebefore==0 & signalblue==1 , vce(hc3) nocons + +test good=bad + +eststo belief_1: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & choicebefore==1 & signalblue==1, vce(hc3) nocons + +test good=bad + +eststo bivar: appendmodels belief_all0 belief_1 belief_0 + +reg logitbelief good bad goodchoicebefore badchoicebefore /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & signalblue==1, vce(hc3) nocons +test goodchoicebefore==0 +estadd scalar test_good_ifirst=r(p): bivar +test badchoicebefore==0 +estadd scalar test_bad_ifirst=r(p): bivar +summ logitbelief if Highx10==0 & Highx100==0 & getyourchoice==1 & signalblue==1 +estadd scalar obs=r(N): bivar + +*not get +eststo belief_all1: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & signalblue==1, vce(hc3) nocons + +eststo belief_2: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & choicebefore==0 & signalblue==1, vce(hc3) nocons + +test good=bad + +eststo belief_3: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & choicebefore==1 & signalblue==1, vce(hc3) nocons + +test good=bad + +eststo bivar2: appendmodels belief_all1 belief_3 belief_2 + +reg logitbelief good bad goodchoicebefore badchoicebefore /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & signalblue==1, vce(hc3) nocons + +test goodchoicebefore==0 +estadd scalar test_good_ifirst=r(p): bivar2 +test badchoicebefore==0 +estadd scalar test_bad_ifirst=r(p): bivar2 +summ logitbelief if Highx10==0 & Highx100==0 & getyourchoice==0 & signalblue==1 +estadd scalar obs=r(N): bivar2 + +*Columns 3/4: Focus on participants who update in correct direction only +*get pref +eststo belief_all2: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 & signalblue==1, vce(hc3) nocons +test good=bad + +eststo belief_4: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 & choicebefore==0 & signalblue==1, vce(hc3) nocons +test good=bad + +eststo belief_5: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 & choicebefore==1 & signalblue==1, vce(hc3) nocons +test good=bad + + +eststo bivar3: appendmodels belief_all2 belief_5 belief_4 + +reg logitbelief good bad goodchoicebefore badchoicebefore /// + if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 & signalblue==1, vce(hc3) nocons + +test goodchoicebefore==0 +estadd scalar test_good_ifirst=r(p): bivar3 +test badchoicebefore==0 +estadd scalar test_bad_ifirst=r(p): bivar3 +summ logitbelief if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0 & signalblue==1 +estadd scalar obs=r(N): bivar3 + + +*not get +eststo belief_all3: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 & signalblue==0, vce(hc3) nocons +test good=bad + +eststo belief_6: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 & choicebefore==0 & signalblue==1, vce(hc3) nocons +test good=bad + +eststo belief_7: reg logitbelief bad good /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 & choicebefore==1 & signalblue==1, vce(hc3) nocons +test good=bad + +eststo bivar4: appendmodels belief_all3 belief_7 belief_6 + +reg logitbelief good bad goodchoicebefore badchoicebefore /// + if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 & signalblue==1, robust nocons + +test goodchoicebefore==0 +estadd scalar test_good_ifirst=r(p): bivar4 +test badchoicebefore==0 +estadd scalar test_bad_ifirst=r(p): bivar4 +summ logitbelief if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0 & signalblue==1 +estadd scalar obs=r(N): bivar4 + + + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{9}{10}\selectfont" "\caption{Belief Updating when Signal is \$2}" "\begin{tabular}{l*{4}{c}}\hline" +local dv "&\multicolumn{4}{c}{\textbf{Log-odds Belief}} \\" +local pref "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}\\ " +local groups "\multicolumn{1}{r}{\textit{Data:}} &\multicolumn{2}{c}{All}&\multicolumn{2}{c}{Excl. update in wrong direction} \\\hline & & & & \\" + +esttab bivar bivar2 bivar3 bivar4 using "${appendix}beliefs_pref_assign_ball2.tex", se r2 replace nolines cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (bad "$\beta_C$" good "$\beta_{NC}$") /// +order (bad good) /// +star(* 0.10 ** 0.05 *** 0.01) /// +mlabels(none) label collabels(none) /// +stats(obs test_bad_ifirst test_good_ifirst, fmt(%9.0g %9.3f %9.3f %9.3f %9.3f) labels("Observations" "$\beta^{f=q}_C=\beta^{f=i}_{C}$" "$\beta^{f=q}_{NC}=\beta^{f=i}_{NC}$" )) /// +addnotes("Note: * p$<$.10; ** p$<$.05; *** p$<$.01") posthead("`dv'" "`pref'" "`groups'") prehead("`panel'") /// + postfoot("\hline" "\end{tabular}%" "\captionsetup{width=0.8\textwidth}" /// +"\caption*{\footnotesize \textit{Notes:} The outcome in all regressions is the log belief ratio, when the advisors sees a \$2 ball for product $B$. $\beta^f_C$ and $\beta^f_{NC}$ are the estimated effects of the log likelihood ratio for conflict and no conflict signals, respectively, for advisors who prefer order $f$ ($f=i$ indicates a preference to see the incentive first, and $f=q$ indicates a preference to see quality first). Columns(1) and (2) include all advisors. Columns (3) and (4) exclude advisors who updated in the wrong direction. Columns (1) and (3) include only advisors who were assigned their preference, while columns (2) and (4) include only advisors who were not assigned their preference. Robust standard errors (HC3) in parentheses.\sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\).}" "\label{tab:beliefs2}" "\end{table}") + +*********************************************************************************** + *TABLE C.12 BELIEF UPDATING - CORRECT CHOICE +*********************************************************************************** + +tab beliefinbin + +tab correctdirbin +est clear + +eststo: reg beliefcorrect choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==1, vce(hc3) + +eststo: reg beliefcorrect choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==0, vce(hc3) + +eststo: reg beliefcorrect choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==1 & correctdirbin==1, vce(hc3) + +eststo: reg beliefcorrect choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==0 & correctdirbin==1, vce(hc3) + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{9}{10}\selectfont" "\caption{Belief Updating: Correct Choice}" "\begin{tabular}{l*{4}{c}}\hline" +local dv "&\multicolumn{4}{c}{\textbf{Belief Correct}} \\" +local pref "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}\\ " +local groups "\multicolumn{1}{r}{\textit{Data:}} &\multicolumn{2}{c}{All}&\multicolumn{2}{c}{Excl. update in wrong direction} \\\hline & & & & \\" + + +esttab using "${appendix}beliefcorrect.tex", se r2 replace cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (noconflict "No Conflict" professionalsfree "Choice Free--Professionals" seeincentivecostly "See Incentive First Costly" seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" notgetyourchoice "Not Assigned Preference" /// +female "Female" age "Age" selfish "Selfish" professionalscloudresearch "Professionals x Cloudresearch" selfishseeincentivecostly "Selfish X See Incentive First Costly" selfishseequalitycostly "Selfish X See Quality First Costly" /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft "Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order" /// +incentiveB "Incentive for B" choicebefore "Prefer to See Incentive First" /// +choicebeforenoconflict "No Conflict X Prefer to See Incentive First" /// +choicebeforenotgetyourchoice "Prefer to See Incentive First X Not Assigned Pref." /// +notgetyourchoicenoconflict "No Conflict X Not Assigned Preference") /// +order (choicebefore noconflict choicebeforenoconflict seeincentivecostly seequalitycostly professionalsfree incentiveB female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) title("Belief Updating: Choice of Correct Belief Bin") /// +drop(professionalsfree wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch) /// +mlabels(none) label collabels(none) substitute(" 0.000 " " " " (.) " " ") /// +posthead("`dv'" "`groups'") prehead("`panel'") nolines postfoot("\hline" "\end{tabular}%" "\captionsetup{width=\textwidth}" "\caption*{\footnotesize \textit{Notes:} This table displays the estimated coefficients from linear probability models on the advisor's beliefs that the quality of product B is low measured via their choice of one out of 10 possible belief bins (ranging from 0 to 100, in steps of 10). Column (1) focuses on individuals who are assigned their preference, while column (2) focuses on individuals who are not assigned their preference. Columns (3) and (4) exclude individuals who chose a bin that is consistent with updating in the incorrect direction. Prefer to See Incentive First is an indicator of the advisor's preference, and Not Assigned Preference is an indicator for not receiving the preferred order. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. Choice Free-Professionals, See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. All regression models include controls for each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:beliefcorrect}" "\end{table}") + + +*************************************************************************************************************** + *TABLE C.13 BELIEF UPDATING - LIKELIHOOD OF STICKING TO PRIOR BELIEF - INCENTIVIZED ELICITATION +*************************************************************************************************************** +est clear + +eststo: reg belief_prior choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==1, vce(hc3) + +eststo: reg belief_prior choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==0, vce(hc3) + +eststo: reg belief_prior choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==1 & correctdirbin==1, vce(hc3) + +eststo: reg belief_prior choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==0 & correctdirbin==1, vce(hc3) + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{9}{10}\selectfont" "\caption{Belief Updating: Likelihood of Sticking to the Prior Belief - Incentivized Elicitation}" "\begin{tabular}{l*{4}{c}}\hline" +local dv "&\multicolumn{4}{c}{\textbf{Belief Bin Containing the Prior of 50\%}} \\" +local pref "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}\\ " +local groups "\multicolumn{1}{r}{\textit{Data:}} &\multicolumn{2}{c}{All}&\multicolumn{2}{c}{Excl. update in wrong direction} \\\hline & & & & \\" + + +esttab using "${appendix}beliefprior.tex", se r2 replace cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (noconflict "No Conflict" professionalsfree "Choice Free--Professionals" seeincentivecostly "See Incentive First Costly" seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" notgetyourchoice "Not Assigned Preference" /// +female "Female" age "Age" selfish "Selfish" professionalscloudresearch "Professionals x Cloudresearch" selfishseeincentivecostly "Selfish X See Incentive First Costly" selfishseequalitycostly "Selfish X See Quality First Costly" /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft "Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order" /// +incentiveB "Incentive for B" choicebefore "Prefer to See Incentive First" /// +choicebeforenoconflict "No Conflict X Prefer to See Incentive First" /// +choicebeforenotgetyourchoice "Prefer to See Incentive First X Not Assigned Pref." /// +notgetyourchoicenoconflict "No Conflict X Not Assigned Preference") /// +order (choicebefore noconflict choicebeforenoconflict seeincentivecostly seequalitycostly professionalsfree incentiveB female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) title("Belief Updating: Likelihood to Stick to the Prior Belief") /// +drop(professionalsfree wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch) /// +mlabels(none) label collabels(none) substitute(" 0.000 " " " " (.) " " ") /// +posthead("`dv'" "`groups'") prehead("`panel'") nolines postfoot("\hline" "\end{tabular}%" "\captionsetup{width=\textwidth}" "\caption*{\footnotesize \textit{Notes:} This table displays the estimated coefficients from linear probability models on the advisor's likelihood to stick to the bin containing the prior belief (50\%) in the incentivized belief elicitation. Column (1) focuses on individuals who are assigned their preference, while column (2) focuses on individuals who are not assigned their preference. Columns (3) and (4) exclude individuals who chose a bin that is consistent with updating in the incorrect direction. Prefer to See Incentive First is an indicator of the advisor's preference, and Not Assigned Preference is an indicator for not receiving the preferred order. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. Choice Free-Professionals, See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. All regression models include controls for each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:beliefcorrect}" "\end{table}") + + +************************************************************************************************************* + *TABLE C.14 BELIEF UPDATING - LIKELIHOOD OF STICKING TO PRIOR BELIEF - CONTINUOUS ELICITATION +************************************************************************************************************* + +est clear + +eststo: reg belief_prior_continuos choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==1, vce(hc3) + +eststo: reg belief_prior_continuos choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==0, vce(hc3) + +eststo: reg belief_prior_continuos choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==1 & updatewrong==0, vce(hc3) + +eststo: reg belief_prior_continuos choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==0 & updatewrong==0, vce(hc3) + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{9}{10}\selectfont" "\caption{Belief Updating: Likelihood of Sticking to the Prior Belief - Continuous Elicitation}" "\begin{tabular}{l*{4}{c}}\hline" +local dv "&\multicolumn{4}{c}{\textbf{Belief at Prior of 50\%}} \\" +local pref "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}\\ " +local groups "\multicolumn{1}{r}{\textit{Data:}} &\multicolumn{2}{c}{All}&\multicolumn{2}{c}{Excl. update in wrong direction} \\\hline & & & & \\" + + +esttab using "${appendix}beliefprior_continuous.tex", se r2 replace cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (noconflict "No Conflict" professionalsfree "Choice Free--Professionals" seeincentivecostly "See Incentive First Costly" seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" notgetyourchoice "Not Assigned Preference" /// +female "Female" age "Age" selfish "Selfish" professionalscloudresearch "Professionals x Cloudresearch" selfishseeincentivecostly "Selfish X See Incentive First Costly" selfishseequalitycostly "Selfish X See Quality First Costly" /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft "Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order" /// +incentiveB "Incentive for B" choicebefore "Prefer to See Incentive First" /// +choicebeforenoconflict "No Conflict X Prefer to See Incentive First" /// +choicebeforenotgetyourchoice "Prefer to See Incentive First X Not Assigned Pref." /// +notgetyourchoicenoconflict "No Conflict X Not Assigned Preference") /// +order (choicebefore noconflict choicebeforenoconflict seeincentivecostly seequalitycostly professionalsfree incentiveB female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) title("Belief Updating: Likelihood to Stick to the Prior Belief") /// +drop(professionalsfree wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch) /// +mlabels(none) label collabels(none) substitute(" 0.000 " " " " (.) " " ") /// +posthead("`dv'" "`groups'") prehead("`panel'") nolines postfoot("\hline" "\end{tabular}%" "\captionsetup{width=\textwidth}" "\caption*{\footnotesize \textit{Notes:} This table displays the estimated coefficients from linear probability models on the advisor's likelihood to stick to the bin containing the prior belief (50\%) in the incentivized belief elicitation. Column (1) focuses on individuals who are assigned their preference, while column (2) focuses on individuals who are not assigned their preference. Columns (3) and (4) exclude individuals who chose a bin that is consistent with updating in the incorrect direction. Prefer to See Incentive First is an indicator of the advisor's preference, and Not Assigned Preference is an indicator for not receiving the preferred order. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. Choice Free-Professionals, See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. All regression models include controls for each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:beliefcorrect}" "\end{table}") + +*************************************************************************** +***** Table C.17 CHOICE panel, numbers reported in table below ******** +*************************************************************************** +*CHOICE PANEL +reg recommendincentive i.treatment##i.getbefore incentiveB $covariates2 /// +if choicebefore==1 & conflict==1 & Highx10==0 & Highx100==0, vce(hc3) +margins i.getbefore if treatment==0, atmeans +***Prefer & assigned to See incentive first: 81% [73%-84%] +***Prefer & not assigned to See incentive first: 71% [66%-77%] + +reg recommendincentive i.treatment##i.getbefore incentiveB $covariates2 /// +if choicebefore==0 & conflict==1 & Highx10==0 & Highx100==0, vce(hc3) +margins i.getbefore if treatment==0, atmeans +***Prefer & assigned to Assess quality first: 68% [61%-74%] +***Prefer & not assigned to Assess quality first: 71% [53%-61%] + +*CHOICE PREDICTED +/* Since on average 55% of participants prefer to see incentive first and 45% quality first, +then the predicted preference to see the incentive first is: */ +di 0.45*.7115466 + 0.55*.8104721 +di 0.45*.5659195 + 0.55*.6759732 + +********************************************************************************** +**** APPENDIX - TABLE C.18: PREFERENCES - INCLUDING HIGHx10 and HIGhx100 TREATMENTS +********************************************************************************** +est clear +eststo:reg choicebefore $covariates2 Highx10 Highx100, vce(hc3) +eststo:reg choicebefore $covariates2 Highx10 Highx100 stdalpha, vce(hc3) +eststo:reg choicebefore $covariates2 Highx10 Highx100 stdalpha selfishseeincentivecostly selfishseequalitycostly, vce(hc3) + + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{11}\selectfont" "\caption{Preference for Information Order: Including Incentives Treatments}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "$\ \ \ \ \ \ \ \ \ \ \ \ \ \ $ &\multicolumn{3}{c}{\textbf{Prefer to See Incentive First}} \\\hline & & & \\" + + +esttab using "${appendix}Choice_Preferences_Including_Stakes.tex", /// + se r2 replace lines cells(b(star fmt(2)) se(par fmt(2))) /// +coeflabel (professionalsfree "Choice Free -- Professionals $ \ \ \ \ \ \ \ $" seeincentivecostly "See Incentive First Costly " seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" /// +female "Female" age "Age" stdalpha "Selfishness" professionalscloudresearch "Professionals x Cloudresearch" selfishseeincentivecostly "See Incentive First Costly X Selfishness " selfishseequalitycostly "See Quality First Costly X Selfishness " /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft /// +"Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order" /// +Highx10 "High Stakes (10-fold incentives)" /// +Highx100 "High Stakes (100-fold incentives)") /// +order (seeincentivecostly seequalitycostly professionalsfree Highx10 Highx100 stdalpha selfishseeincentivecostly selfishseequalitycostly female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) collabels(none) /// +drop(wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch) /// +nomtitle label substitute(" 0.000 " " " " (.) " " ") /// +prehead("`panel'") posthead("`dv'") postfoot("\hline" "\end{tabular}%" "\captionsetup{width=0.75\textwidth}" "\caption*{\footnotesize \textit{Notes:} This table displays the estimated coefficients from linear probability models on the preference to see the incentive first. See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. Selfishness was elicited at the end of the experiment, using a multiple price list (MPL) with 5 decisions. The variable is a standardized measure of the number of times the advisor chose to recommend the incentivized product in the MPL task. The regression models in columns (2) and (3) include individual controls for the advisor's gender and age, each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:demand_highincentives}" "\end{table}") + +********************************************************************************** +**** APPENDIX - TABLE C.19: RECOMMENDATIONS - INCLUDING HIGHx10 and HIGhx100 TREATMENTS +********************************************************************************** + +foreach var in choicebefore choicebeforenoconflict noconflict /// +notgetyourchoice choicebeforenotgetyourchoice notgetyourchoicenoconflict{ + g `var'x10=`var'*Highx10 + g `var'x100=`var'*Highx100 +} + +*get pref +est clear +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict Highx10 Highx100 /// + choicebeforex10 choicebeforenoconflictx10 noconflictx10 /// + choicebeforex100 choicebeforenoconflictx100 noconflictx100 /// + incentiveB $covariates2 if getyourchoice==1, vce(hc3) + +*not get +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict Highx10 Highx100 /// + choicebeforex10 choicebeforenoconflictx10 noconflictx10 /// + choicebeforex100 choicebeforenoconflictx100 noconflictx100 /// +incentiveB $covariates2 if getyourchoice==0, vce(hc3) + +*combined +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict notgetyourchoice choicebeforenotgetyourchoice /// + notgetyourchoicenoconflict Highx10 Highx100 /// +choicebeforex10 choicebeforenoconflictx10 noconflictx10 /// +choicebeforex100 choicebeforenoconflictx100 noconflictx100 /// +notgetyourchoicex10 choicebeforenotgetyourchoicex10 /// + notgetyourchoicenoconflictx10 /// + notgetyourchoicex100 choicebeforenotgetyourchoicex100 /// + notgetyourchoicenoconflictx100 /// +incentiveB $covariates2 /// + , vce(hc3) + +local panel "\begin{table}[h!]" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{10}\selectfont" "\caption{Advisor Recommendations: Including Incentives Treatments}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "&\multicolumn{3}{c}{\textbf{Recommend incentivized product}} \\" +local groups "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Both} \\\hline & & & & \\ " +local conflict "&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict} \\ \hline & & & & & & \\" + + +esttab using "${appendix}Choice_Recommendations_IncludingStakes.tex", se r2 replace cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (noconflict "No Conflict" getbefore "Assigned to See Incentive First" getbeforenoconflict "Assigned to See Incentive First X No Conflict" professionalsfree "Choice Free--Professionals" seeincentivecostly "See Incentive First Costly" seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" notgetyourchoice "Not Assigned Preference" /// +female "Female" age "Age" selfish "Selfish" professionalscloudresearch "Professionals x Cloudresearch" selfishseeincentivecostly "Selfish X See Incentive First Costly" selfishseequalitycostly "Selfish X See Quality First Costly" /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft "Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order" /// +incentiveB "Incentive for B" choicebefore "Prefer to See Incentive First" /// +choicebeforenoconflict "No Conflict X Prefer to See Incentive First" /// +choicebeforenotgetyourchoice "Prefer to See Incentive First X Not Assigned Pref." /// +Highx10 "High Stakes (10-fold incentives)" /// +Highx100 "High Stakes (100-fold incentives)" /// +notgetyourchoicenoconflict "No Conflict X Not Assigned Preference" /// +selfishchoicebefore "Prefer to See Incentive First X Selfish" /// +selfishnotgetyourchoice "Not Assigned Preference X Selfish" /// +choicebeforex10 "Prefer to See Incentive First X High Stakes (10-fold)" /// +choicebeforex100 "Prefer to See Incentive First X High Stakes (100-fold)") /// +order (choicebefore notgetyourchoice choicebeforenotgetyourchoice noconflict choicebeforenoconflict notgetyourchoicenoconflict seeincentivecostly seequalitycostly professionalsfree incentiveB female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) title("Advisor Recommendations") /// +drop(professionalsfree wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch /// +choicebeforenoconflictx10 noconflictx10 /// +choicebeforenoconflictx100 noconflictx100 /// +notgetyourchoicex10 choicebeforenotgetyourchoicex10 /// + notgetyourchoicenoconflictx10 /// + notgetyourchoicex100 choicebeforenotgetyourchoicex100 /// + notgetyourchoicenoconflictx100) /// +mlabels(none) label collabels(none) substitute(" 0.000 " " " " (.) " " ") /// +posthead("`dv'" "`groups'") prehead("`panel'") nolines postfoot("\hline" "\end{tabular}%" "\captionsetup{width=\textwidth}" "\caption*{\footnotesize \textit{Notes:} This table displays the estimated coefficients from linear probability models on the advisor's decision to recommend the incentivized option. Column (1) focuses on individuals who are assigned their preference, while column (2) focuses on individuals who are not assigned their preference. Both groups are merged in column (3). Prefer to See Incentive First is an indicator of the advisor's preference, and Not Assigned Preference is an indicator for not receiving the preferred order. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. Choice Free-Professionals, See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. All regression models include controls for each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:recommendations_highincentives}" "\end{table}") + +********************************************************************************** +**** SECTION C.5: INCLUDING INATTENTIVE PARTICIPANTS +**** APPENDIX - TABLE C.20: PREFERENCES - INCLUDING INATTENTIVE +********************************************************************************** + +clear +u "${data}choice_experiments.dta" +*As pre-registered, drop Mturk participant with inconsistent alpha value +drop if study!=1 & alphavaluefinal==. /*(study1 are profs and there's no MPL')*/ +global covariates "wave2 wave3 professionalscloudresearch incentiveshigh##incentiveleft age female" +global covariates2 "professionalsfree seeincentivecostly seequalitycostly wave2 wave3 professionalscloudresearch incentiveshigh incentiveleft incentiveshigh_incentiveleft age female" + + +tab alphavaluefinal if Highx10==0 | Highx100==0, m + +est clear +eststo:reg choicebefore $covariates2 if Highx10==0 & Highx100==0 & alphavaluefinal!=., vce(hc3) +eststo:reg choicebefore $covariates2 if Highx10==0 & Highx100==0, vce(hc3) + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{11}\selectfont" "\caption{Preference for Information Order---Including Inattentive}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "$\ \ \ \ \ \ \ \ \ \ \ \ \ \ $ &\multicolumn{3}{c}{\textbf{Prefer to See Incentive First}} \\\hline & & & \\" +local pref "\multicolumn{1}{r}{\textit{Sample:}}&\multicolumn{1}{c}{Main Sample}&\multicolumn{1}{c}{Including Inattentive}\\ \hline & & & \\" + +esttab using "${appendix}Choice_Demand_withInattentive.tex", /// + se r2 replace nolines cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (professionalsfree "Choice Free -- Professionals $ \ \ \ \ \ \ \ $" seeincentivecostly "See Incentive First Costly " seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" /// +female "Female" age "Age" stdalpha "Selfishness" professionalscloudresearch "Professionals x Cloudresearch" selfishseeincentivecostly "See Incentive First Costly X Selfish " selfishseequalitycostly "See Quality First Costly X Selfish " /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft "Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order") /// +order (seeincentivecostly seequalitycostly professionalsfree stdalpha selfishseeincentivecostly selfishseequalitycostly female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) collabels(none) /// +drop(wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch stdalpha selfishseeincentivecostly selfishseequalitycostly) /// +nomtitle label substitute(" 0.000 " " " " (.) " " ") /// +prehead("`panel'") posthead("`dv'") postfoot("\hline" "\end{tabular}%" "\captionsetup{width=0.75\textwidth}" "\caption*{\footnotesize Note: This table displays the estimated coefficients from linear probability models on the advisor's preference to see the incentive first. Column (1) focuses on individuals who are assigned their preference, while column (2) focuses on individuals who are not assigned their preference. Both groups are merged in column (3). Prefer to See Incentive First is an indicator of the advisor's preference, and Not Assigned Preference is an indicator for not receiving the preferred order. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. All regression models include individual controls for the advisor's gender and age, each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:demand_inattentive}" "\end{table}") + + + +********************************************************************************** +**** APPENDIX - TABLE C.21: RECOMMENDATIONS - INCLUDING INATTENTIVE +********************************************************************************** + +*get pref +est clear +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==1, vce(hc3) +test choicebefore+choicebeforenoconflict==0 + +*not get +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 if Highx10==0 & Highx100==0 & getyourchoice==0, vce(hc3) + +*combined +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict notgetyourchoice choicebeforenotgetyourchoice notgetyourchoicenoconflict incentiveB $covariates2 /// + if Highx10==0 & Highx100==0, vce(hc3) +test choicebefore+choicebeforenoconflict==0 +test choicebefore+notgetyourchoice+choicebeforenotgetyourchoice==0 +test notgetyourchoice+choicebeforenotgetyourchoice==0 + +lincom notgetyourchoice+choicebeforenotgetyourchoice + + +local panel "\begin{table}[h!]" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{10}\selectfont" "\caption{Advisor Recommendations---Including Inattentive}" "\hspace{-0.5cm}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "&\multicolumn{3}{c}{\textbf{Recommend incentivized product}} \\" +local groups "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Both} \\\hline & & & \\ " +local conflict "&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict} \\ \hline & & & & & & \\" + +esttab using "${appendix}Choice_Recommendations_WithInattentive.tex", se r2 replace cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (noconflict "No Conflict" getbefore "Assigned to See Incentive First" getbeforenoconflict "Assigned to See Incentive First X No Conflict" professionalsfree "Choice Free--Professionals" seeincentivecostly "See Incentive First Costly" seequalitycostly "Assess Quality First Costly" wave2 "Wave 2" wave3 "Wave 3" notgetyourchoice "Not Assigned Preference" /// +female "Female" age "Age" stdalpha "Selfishness" professionalscloudresearch "Professionals x Cloudresearch" selfishseeincentivecostly "Selfish X See Incentive First Costly" selfishseequalitycostly "Selfish X See Quality First Costly" /// +incentiveshigh "Probabilistic Incentive Mturk" incentiveleft "Order" incentiveshigh_incentiveleft "Probabilistic Incentive X Order" /// +incentiveB "Incentive for B" choicebefore "Prefer to See Incentive First" /// +choicebeforenoconflict "No Conflict X Prefer to See Incentive First" /// +choicebeforenotgetyourchoice "Prefer to See Incentive First X Not Assigned Preference" /// +notgetyourchoicenoconflict "No Conflict X Not Assigned Preference") /// +order (choicebefore notgetyourchoice choicebeforenotgetyourchoice noconflict choicebeforenoconflict notgetyourchoicenoconflict professionalsfree seeincentivecostly seequalitycostly incentiveB selfish female age wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch ) /// +star(* 0.10 ** 0.05 *** 0.01) title("Advisor Recommendations") /// +drop(selfish wave2 wave3 incentiveshigh incentiveleft incentiveshigh_incentiveleft professionalscloudresearch) /// +mlabels(none) label collabels(none) substitute(" 0.000 " " " " (.) " " ") /// +posthead("`dv'" "`groups'") prehead("`panel'") nolines postfoot("\hline" "\end{tabular}%" "\captionsetup{width=1.1\textwidth}" "\caption*{\footnotesize Note: This table displays the estimated coefficients from linear probability models on the advisor's decision to recommend the incentivized option. Column (1) focuses on individuals who are assigned their preference, while column (2) focuses on individuals who are not assigned their preference. Both groups are merged in column (3). Prefer to See Incentive First is an indicator of the advisor's preference, and Not Assigned Preference is an indicator for not receiving the preferred order. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. Choice Free-Professionals, See Incentive First Costly and Assess Quality First Costly are indicator variables that take value 1 in the respective treatment, 0 otherwise. All regression models include controls for each wave of the experiment, whether incentives were probabilistic, the position of the products on the screen and the interaction between these two variables. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:recommendations_inattentive}" "\end{table}") + + +******************************************************************************** +******************************************************************************** +*CHOICESTAKES EXPERIMENT +******************************************************************************** +******************************************************************************** +u "${data}stakes.dta", clear + +drop if alphavaluefinal==. + + +global indiva "female age stdalpha" + +****************************************** +*FIGURE 7: DEMAND SEE INCENTIVES BEFORE +****************************************** +bys condition: egen meandemand=mean(choicebefore) +bys condition: egen sddemand=sd(choicebefore) +bys condition: egen ndemand=count(choicebefore) +g lodemand=meandemand-1.96*(sqrt((meandemand*(1-meandemand))/ndemand)) +g hidemand=meandemand+1.96*(sqrt((meandemand*(1-meandemand))/ndemand)) + + +g meancommit =1 + +twoway (bar meancommit conditionnum, fcolor(white) lcolor(gs10) barw(0.65)) /// + (bar meandemand conditionnum if conditionnum==1 , fcolor(gs10) lcolor(gs10) barw(0.65)) /// + (bar meandemand conditionnum if conditionnum==2 , fcolor(gs10) lcolor(gs10) barw(0.65)) /// + (bar meandemand conditionnum if conditionnum==3 , fcolor(gs10) lcolor(gs10) barw(0.65)) /// + (rcap lodemand hidemand conditionnum, lcolor(gs10)) /// + , ytitle("Advisor's preference") xtitle(" " "Treatment") /// + graphr(c(white)) ylabel(0(0.2)1, gmax) /// + xlabel(1 "Low Incentive" 2 "Intermediate Incentive" 3 "High Incentive") /// + xscale(r(0.5 3.5)) /// + legend(order(2 1) lab(1 "Prefer to Assess Quality First") /// + lab(2 "Prefer to See Incentive First") size(*0.9)) /// + text(0.05 1 "0.13") text(0.30 2 "0.41") text(0.3 3 "0.44") /// + text(0.95 1 "0.87") text(0.95 2 "0.59") text(0.95 3 "0.56") +graph export "${main}bar_stakes.pdf", replace + +* Section 8 - page 36: 41%of advisors prefer to see the incentive first, replicating our finding in the Incentives First Costly treatment of Choice Experiment +tabstat choicebefore, by(condition) s(mean n) + +* This fraction decreases significantly in the Low Incentive treatment, to 13% (Z − stat = 9.79, p < 0.001) +prtest choicebefore if conditionnum!=3, by(conditionnum) + +*In the High Incentive treatment, the advisors’ preference to see the incentive first increases by +* only 3 percentage points, to 44% (Z − stat = 0.96, p = 0.337) +prtest choicebefore if conditionnum!=1, by(conditionnum) + +global covariates2 "commissionlow commissionhigh age female" + +/* When advisors are assigned their preferred order, they are + 14 percentage points more likely to recommend the incentivized product + + See Column 1 of Table C.23 in Appendix C.6 (lines 2115-2157 of the analysis do file) + + When doubling the commission of the advisor, however, the preference to see the incentive first + increases by only 3 percentage points, less than 10 percent + + See Column 1 of Table C.22 in Appendix C.6 (lines 2103-2119) + +*/ + +************************************************************************** +* APPPENDIX TABLE C.22: CHOICESTAKES - PREFERENCES FOR INFORMATION ORDER +************************************************************************** + +est clear +eststo:reg choicebefore $covariates2, vce(hc3) +eststo:reg choicebefore $covariates2 stdalpha, vce(hc3) +*eststo:reg choicebefore $covariates2 selfish selfishseeincentivecostly selfishseequalitycostly if Highx10==0 & Highx100==0, vce(hc3) + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{11}\selectfont" "\caption{Preference for Information Order}" "\begin{tabular}{l*{2}{c}} \hline" +local dv "$\ \ \ \ \ \ \ \ \ \ \ \ \ \ $ &\multicolumn{2}{c}{\textbf{Prefer to See Incentive First}} \\\hline & & & \\" + +esttab using "${appendix}Demand_ChoiceStakes.tex", /// + se r2 replace nolines cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (commissionlow "Low Incentive $ \ \ \ \ \ \ \ $" commissionhigh "High Incentive $ \ \ \ \ \ \ \ $" /// +female "Female" age "Age" stdalpha "Selfishness") /// +order (commissionlow commissionhigh stdalpha female age ) /// +star(* 0.10 ** 0.05 *** 0.01) collabels(none) /// +drop() /// +nomtitle label substitute(" 0.000 " " " " (.) " " ") /// +prehead("`panel'") posthead("`dv'") postfoot("\hline" "\end{tabular}%" "\captionsetup{width=0.7\textwidth}" "\caption*{\footnotesize \textit{Note:} This table displays the estimated coefficients from linear probability models on the preference to see the incentive first. Low Incentive and High Incentive are indicator variables that take value 1 in the respective treatment, 0 otherwise. Selfishness was elicited at the end of the experiment, using a multiple price list (MPL) with 5 decisions. The variable is a standardized measure of the number of times the advisor chose to recommend the incentivized product in the MPL task. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:demand_stakes}" "\end{table}") + + +************************************************************************** +* APPPENDIX TABLE C.23: CHOICESTAKES - RECOMMENDATIONS +************************************************************************** + +*get pref +est clear +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 commissionlowchoicebefore commissionhighchoicebefore if getyourchoice==1, vce(hc3) +test choicebefore+choicebeforenoconflict==0 + +*not get +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict incentiveB $covariates2 commissionlowchoicebefore commissionhighchoicebefore if getyourchoice==0, vce(hc3) + +*combined +eststo: reg recommendincentive choicebefore choicebeforenoconflict /// + noconflict notgetyourchoice choicebeforenotgetyourchoice notgetyourchoicenoconflict incentiveB $covariates2 /// + commissionlowchoicebefore commissionhighchoicebefore , vce(hc3) +test choicebefore+choicebeforenoconflict==0 +test choicebefore+notgetyourchoice+choicebeforenotgetyourchoice==0 +test notgetyourchoice+choicebeforenotgetyourchoice==0 + +lincom notgetyourchoice+choicebeforenotgetyourchoice + + +local panel "\begin{table}[h!]" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{10}\selectfont" "\caption{Advisor Recommendations}" "\hspace{-0.5cm}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "&\multicolumn{3}{c}{\textbf{Recommend incentivized product}} \\" +local groups "\multicolumn{1}{r}{\textit{Assignment:}}&\multicolumn{1}{c}{Assigned Pref.}&\multicolumn{1}{c}{Not Assigned Pref.}&\multicolumn{1}{c}{Both} \\\hline & & & \\ " +local conflict "&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict}&\multicolumn{1}{c}{Conflict}&\multicolumn{1}{c}{No Conflict} \\ \hline & & & & & & \\" + +esttab using "${appendix}recommendations_pref_assign_stakes.tex", se r2 replace cells(b(star fmt(4)) se(par fmt(4))) /// +coeflabel (noconflict "No Conflict" choicebefore "Prefer to See Incentive First" getbefore "Assigned to See Incentive First" getbeforenoconflict "Assigned to See Incentive First X No Conflict" notgetyourchoice "Not Assigned Preference" /// +female "Female" age "Age" commissionlow "Low Incentive" commissionhigh "High Incentive" incentiveB "Incentive for B" /// +choicebeforenoconflict "No Conflict X Prefer to See Incentive First" /// +choicebeforenotgetyourchoice "Prefer to See Incentive First X Not Assigned Preference" /// +notgetyourchoicenoconflict "No Conflict X Not Assigned Preference" /// +commissionlowchoicebefore "Low Incentive X Prefer to See Incentive First" /// +commissionhighchoicebefore "High Incentive X Prefer to See Incentive First" stdalpha "Selfishness") /// +order (choicebefore notgetyourchoice choicebeforenotgetyourchoice noconflict choicebeforenoconflict notgetyourchoicenoconflict commissionlow commissionlowchoicebefore commissionhigh commissionhighchoicebefore incentiveB female age) /// +star(* 0.10 ** 0.05 *** 0.01) title("Advisor Recommendations") /// +drop() /// +mlabels(none) label collabels(none) substitute(" 0.000 " " " " (.) " " ") /// +posthead("`dv'" "`groups'") prehead("`panel'") nolines postfoot("\hline" "\end{tabular}%" "\captionsetup{width=1.1\textwidth}" "\caption*{\footnotesize \textit{Notes:} This table displays the estimated coefficients from linear probability models on the advisor's decision to recommend the incentivized option. Column (1) focuses on individuals who are assigned their preference, while column (2) focuses on individuals who are not assigned their preference. Both groups are merged in column (3). Prefer to See Incentive First is an indicator of the advisor's preference, and Not Assigned Preference is an indicator for not receiving the preferred order. No Conflict is an indicator for the cases in which the signal of quality is not in conflict with the advisor's commission. Low Incentive and High Incentive are indicator variables that take value 1 in the respective treatment, 0 otherwise. Robust standard errors (HC3) in parentheses. * p$<$.10; ** p$<$.05; *** p$<$.01}" "\label{tab:recommendations_stakes}" "\end{table}") + + +******************************************************************************** +******************************************************************************** +* INFORMATION ARCHITECT EXPERIMENT +******************************************************************************** +******************************************************************************** + +clear +u "${data}InformationArchitect.dta" +***** MAIN TEXT - Excluding participants inattentive in the MPL +tab IAAdvisor if alphavaluefinal!=. + +*where advisors’ incentives are aligned with the client (58% vs 44%, N = 498, Z − stat = −3.23, p = 0.001), page 37 +prtest choicebefore if alphavaluefinal!=., by(IAAdvisor) + + +************************************************************************** +* APPPENDIX TABLE C.24: INFORMATION ARCHITECT PREFERENCES BY CONDITION +************************************************************************** + +est clear +eststo: reg choicebefore IAAdvisor age female if alphavaluefinal!=., vce(hc3) +eststo: reg choicebefore IAAdvisor age female , vce(hc3) + + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{9}{10}\selectfont" "\begin{tabular}{l*{3}{c}}\hline" +local dv "&\multicolumn{2}{c}{\textbf{DM Choice to See Incentive First}} \\" +local pref "\multicolumn{1}{r}{\textit{Sample:}}&\multicolumn{1}{c}{Main Sample}&\multicolumn{1}{c}{Including Inattentive}\\ \hline & & & \\" + +esttab using "${appendix}IA_Preferences.tex", se r2 replace nolines cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (IAAdvisor "IA-Advisor" /// + IAClient "IA-Client" ) /// +order (DMAdvisor) /// +star(* 0.10 ** 0.05 *** 0.01) /// +mlabels(none) label collabels(none) /// +drop(age female) /// +addnotes("Note: * p$<$.10; ** p$<$.05; *** p$<$.01") posthead("`dv'" "`pref'" ) prehead("`panel'") /// + postfoot("\hline" "\end{tabular}%" "\captionsetup{width=0.55\textwidth}" /// +"\caption*{\footnotesize \textit{Notes:} This table displays the coefficient estimates of OLS regressions on the Information Architect's preferences to have the advisor see the incentive first for the main sample (Column 1) and the sample that includes inattentive participants who switched multiple times in the selifishness measure. DM-Advisor is an indicator for whether advisors have an incentive to receive information about their incentive first. Robust standard errors in parentheses. \sym{*} \(p<0.10\), \sym{**} \(p<0.05\), \sym{***} \(p<0.01\).}" "\label{tab:dmdemand}" "\end{table}") + +**** MAIN TEXT -- Comparing proportion of IA wanting to see info before to main Choice Free experiment +clear +u "${data}InformationArchitect.dta" +append using "${data}choice_experiments.dta" +*As pre-registered, drop Mturk participant with inconsistent alpha value +drop if study!=1 & alphavaluefinal==. /*(study1 are profs and there's no MPL')*/ + +tab study +tab condition +tab professionals + +gen DMAdvisor=0 +replace DMAdvisor=1 if condition=="IA-Advisor" + +drop if alphavaluefinal==. + +** Section 8.2: "..is similar to the average fraction of advisors who prefer to see the incentive first in the Choice Free treatment of the Choice experiment (56%)(Z − stat = 0.497, p = 0.62)", page 37 + +prtest choicebefore if professionals==0 & (condition=="ChoiceFree" & Highx10==0 & Highx100==0 )| condition=="IA-Advisor", by(condition) + + +** Appendix, page 47: CLients +***we recruited $N=50$ clients for the main task +*** of these 86\% followed the recommendations. +preserve +u "${data}Clients_InfoArchitectsMain.dta", clear +tab follow +restore + +***: We also recruited an additional $N=50$ advisees for the MPL task that measured advisors' moral costs and matched them with 1 out of 10 Information Architects +*** and an additional $N=50$ advisees for the same task and matched them with 1 out of 10 advisors. +*** Of these, 86\% and 80\% of advisees followed the recommendation. +preserve +u "${data}Clients_IAInfoArchitects_MPL.dta", clear +tab follow +restore + +preserve +u "${data}Clients_IAAdvisors_MPL.dta", clear +tab follow +restore + + + +******************************************************************************** +******************************************************************************** +* NOCHOICE_SIMOULTANEOUS (APPENDIX) +******************************************************************************** +******************************************************************************** + + + +clear +u "${data}NoChoiceSimoultaneous.dta" + +tab missingalpha +/* 50.63% of participants (N=283) switched multiple times in the MPL task, and cannot be classified as selfish/moral. By contrast, in the original NoChoice experiment, only +N=28 (8.56%) switched multiple times. As for the othe experiments, we pre-registered that we would drop any participant who switches multiple times. However, in all of our other experiments we always had + at the most 15% of participants switching multiple times. At the time we ran he experiment, Cloudresearch changed some of the features it used to filter + participants (https://urldefense.com/v3/__https://www.cloudresearch.com/resources/blog/cloudresearch-is-retiring-the-block-low-quality-participants-option/__;!!Mih3wA!HOIpQ2WhA_qwbAphb6tUVYcbui2LabnX4Z-MXMgq6RR26RYq6gxyZAkrUUeLXM1zVSY8yQ0x0v9WXa5H0Q$ ) In particular, + CloudResearch removed their “Block Low Quality Participants” which is what we have used in all prior experiments. This change resulted in data quality issues + as, at the time we ran the study, we could not filter out inattentive participants/BOTs as well as before. Due to this changes, we decided to analyze this data in + two ways: both excluding all participants who switch multiple times as pre-registered, and by keeping them in our sample. In both cases, we merge this data with the + first wave of the NoChoice experiment, as pre-registered. */ + + +*** MERGE with Original Nochoice *** + +append using "${data}nochoice.dta" +save "${data}nochoice1_2_merged.dta", replace + +replace wave=1 if wave==. +replace seeincentivefirst=1 if condition=="BeforeA" | condition=="BeforeB" +replace seeincentivefirst=0 if condition=="AfterA" | condition=="AfterB" +replace Together=0 if wave==1 +replace noconflict=1-conflict +replace missingalpha=(alphavaluefinal==.) +replace seeincentivefirst_noconflict= seeincentivefirst*noconflict +gen together_noconflict= Together*noconflict +gen wave2=(wave==2) +drop stdalpha +egen stdalpha=std(alphavaluefinal) + + +************************************************************************** +* APPPENDIX TABLE D.1: INFORMATION ARCHITECT PREFERENCES BY CONDITION +************************************************************************** + + +est clear +eststo:reg recommendincentive seeincentivefirst noconflict seeincentivefirst_noconflict Together together_noconflict incentiveB female age wave2 if missingalpha==0, vce(hc3) +eststo:reg recommendincentive seeincentivefirst noconflict seeincentivefirst_noconflict Together together_noconflict incentiveB female age stdalpha wave2 if missingalpha==0, vce(hc3) +eststo:reg recommendincentive seeincentivefirst noconflict seeincentivefirst_noconflict Together together_noconflict incentiveB female age wave2 , vce(hc3) + + + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{11}\selectfont" "\caption{Advisor Recommendations - No Choice (Simultaneous)}" "\begin{tabular}{l*{3}{c}} \hline" +local dv "& & & \textbf{Including} \\" "$\ \ \ \ \ \ \ \ \ \ \ \ \ \ $ &\multicolumn{2}{c}{\textbf{Main Sample}} &\multicolumn{1}{c}{\textbf{Inattentive}} \\\hline & & & \\" + +esttab using "${appendix}Simultaneous.tex", se r2 replace lines cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel (seeincentivefirst "See Incentive First" noconflict "No Conflict" seeincentivefirst_noconflict "See Incentive First * No Conflict" /// +Together "Simultaneous" together_noconflict "Simultaneous X No Conflict" incentiveB "Incentive for B" /// +female "Female" age "Age" stdalpha "Selfishness" ) /// +order ( seeincentivefirst noconflict seeincentivefirst_noconflict Together together_noconflict incentiveB female age stdalpha) /// +drop (wave2 female age) /// +star(* 0.10 ** 0.05 *** 0.01) collabels(none) /// +nomtitle label substitute(" 0.000 " " " " (.) " " ") /// +prehead("`panel'") posthead("`dv'") postfoot("\hline" "\end{tabular}%" "\captionsetup{width=0.75\textwidth}" "\caption*{\footnotesize Note: This table displays the estimated coefficients from linear probability models on the advisors' recommendations. See Incentive first is a binary indicator coded as 1 for participants who were randomly assigned to see the incentive first. Simultaneous is a binary indicator coded as 1 for participants who saw all infornation at the same time. Selfishness was elicited at the end of the experiment, using a multiple price list (MPL) with 5 decisions. The variable is a standardized measure of the number of times the advisor chose to recommend the incentivized product in the MPL task. The regression models in columns (1) and (2) restrict the analyses to participants who did not switch multiple times in the MPL. Column (3) includes all participants. The regression includes individual controls for the advisor's gender and age, and a binary indicator for the wave in which participants took part in the experiment. Robust standard errors (HC3) in parentheses}" "\label{tab:simultaneous}" "\end{table}") + + +***** Appendix, page 49: CLients +** The 276 attentive participants were matched with $N=28$ clients for the main task; +** of these 96\% followed the recommendation. +clear +u "${data}Clients_NoChoiceSimultaneousMain.dta" +tab follow + +*** They were also matched with $N=28$ clients for the MPL task +*** of these 79\% followed the recommendation. +clear +u "${data}Clients_NoChoiceSimultaneousMPL.dta" +tab follow + + +******************************************************************************* +******************************************************************************** +* CHOICE DETERMINISTIC (APPENDIX) +******************************************************************************** +******************************************************************************** + +clear +u "${data}Choice_Deterministic.dta" + + +tab alphavaluefinal, m + *** 20.80% of participants switched multilple times + *** We pre-registered that, if more than 15% of participants switched twice, + *** we would do analysis with and without these participants. +tab condition if alphavaluefinal!=. +* DEMAND * +tab condition choicebefore if alphavaluefinal!=., row +tab condition choicebefore if alphavaluefinal!=., chi2 +/* When we drop the inconsistent MPL participants, we have a marginally significant difference +in the proportion of participants demanding to see the incentive first between the 2 conditions (p<.10) */ + +tab condition choicebefore , row +tab condition choicebefore , chi2 +*Including inattentive participants, demand to see the incentive first is 62.9% and 58.7%, respectively, and the difference is not significant (\chi^2-test= 1.66, p=0.197). + + +************************************************************************** +* APPPENDIX FIGURE E.1: RECOMMENDATIONS IN CHOICE DETERMINISTIC EXPERIMENT +************************************************************************** + +bys condition: egen recbefore_before=mean(recommendincentive) if choicebefore==1 & getbefore==1 & conflict==1 +bys condition: egen recbefore_after=mean(recommendincentive) if choicebefore==1 & getbefore==0 & conflict==1 +bys condition: egen recafter_before=mean(recommendincentive) if choicebefore==0 & getbefore==1 & conflict==1 +bys condition: egen recafter_after=mean(recommendincentive) if choicebefore==0 & getbefore==0 & conflict==1 + + +bys condition: egen sdrecbefore_before=sd(recommendincentive) if choicebefore==1 & getbefore==1 & conflict==1 +bys condition: egen sdrecbefore_after=sd(recommendincentive) if choicebefore==1 & getbefore==0 & conflict==1 +bys condition: egen sdrecafter_after=sd(recommendincentive) if choicebefore==0 & getbefore==0 & conflict==1 +bys condition: egen sdrecafter_before=sd(recommendincentive) if choicebefore==0 & getbefore==1 & conflict==1 +bys condition: egen nrecbefore_before=count(recommendincentive) if choicebefore==1 & getbefore==1 & conflict==1 +bys condition: egen nrecbefore_after=count(recommendincentive) if choicebefore==1 & getbefore==0 & conflict==1 +bys condition: egen nrecafter_after=count(recommendincentive) if choicebefore==0 & getbefore==0 & conflict==1 +bys condition: egen nrecafter_before=count(recommendincentive) if choicebefore==0 & getbefore==1 & conflict==1 + + +g lobefore_before=recbefore_before-1.96*((sdrecbefore_before)/sqrt(nrecbefore_before)) +g hibefore_before=recbefore_before+1.96*((sdrecbefore_before)/sqrt(nrecbefore_before)) +g lobefore_after=recbefore_after-1.96*((sdrecbefore_after)/sqrt(nrecbefore_after)) +g hibefore_after=recbefore_after+1.96*((sdrecbefore_after)/sqrt(nrecbefore_after)) + + +g loafter_after=recafter_after-1.96*((sdrecafter_after)/sqrt(nrecafter_after)) +g loafter_before=recafter_before-1.96*((sdrecafter_before)/sqrt(nrecafter_before)) +g hiafter_after=recafter_after+1.96*((sdrecafter_after)/sqrt(nrecafter_after)) +g hiafter_before=recafter_before+1.96*((sdrecafter_before)/sqrt(nrecafter_before)) + + +g treatnumgraph2=1 if choicebefore==1 & getbefore==1 & condition=="ChoiceFree_Probabilistic" +replace treatnumgraph2=1.1 if choicebefore==1 & getbefore==0 & condition=="ChoiceFree_Probabilistic" +replace treatnumgraph2=1.2 if choicebefore==0 & getbefore==1 & condition=="ChoiceFree_Probabilistic" +replace treatnumgraph2=1.3 if choicebefore==0 & getbefore==0 & condition=="ChoiceFree_Probabilistic" + +replace treatnumgraph2=1.8 if choicebefore==1 & getbefore==1 & condition=="ChoiceFree_Deterministic" +replace treatnumgraph2=2.1 if choicebefore==0 & getbefore==0 & condition=="ChoiceFree_Deterministic" + + + +twoway (scatteri 1 0.8 1 1.6, bcolor(gs15) recast(area)) /// + (rcap lobefore_before hibefore_before treatnumgraph2, lcolor(red ) lwidth(thin)) /// + (rcap loafter_after hiafter_after treatnumgraph2, lcolor( black) lwidth(thin)) /// + (rcap lobefore_after hibefore_after treatnumgraph2, lcolor( red*0.3 ) lwidth(thin)) /// + (rcap loafter_before hiafter_before treatnumgraph2, lcolor( black*0.3) lwidth(thin)) /// + (scatter recbefore_before treatnumgraph2 if /// + condition=="ChoiceFree_Probabilistic", mcolor(red) msize(*0.8) ms(T)) /// + (scatter recbefore_after treatnumgraph2 if /// + condition=="ChoiceFree_Probabilistic", mfcolor(white) msize(*0.8) mlcolor(red%50) ms(T)) /// + (scatter recafter_after treatnumgraph2 if /// + condition=="ChoiceFree_Probabilistic", mcolor(black) mlcolor(black) msize(*0.8) ms(S)) /// + (scatter recafter_before treatnumgraph2 if /// + condition=="ChoiceFree_Probabilistic" , mfcolor(white) mlcolor(black%50) msize(*0.8) ms(S)) /// + (scatter recbefore_before treatnumgraph2 if /// + condition=="ChoiceFree_Deterministic" , mcolor(red) msize(*0.8) ms(T)) /// + (scatter recafter_after treatnumgraph2 if /// + condition=="ChoiceFree_Deterministic", mcolor(black) msize(*0.8) mlcolor(black) ms(S)) /// + , graphr(c(white)) plotr(c(white)) /// + ylabel(0(0.1)1) yscale(r(0.2 1)) /// + xtitle(" " "Treatment") /// + xlabel(none) /// fgfd + xscale(r(0.8 2.3)) /// + legend(order( - "{bf:Advisor Prefers to}" "{bf:See Incentive First}" /// + 6 7 - "{bf:Advisor Prefers to}" "{bf:Assess Quality First}" 9 8) /// + lab(6 " Assigned to See Incentive First") /// + lab(8 " Assigned to Assess Quality First") /// + lab(7 " Assigned to Assess Quality First") /// + lab(9 " Assigned to See Incentive First") /// + rows(3) colfirst size(*0.9)) /// + text(0.15 1.15 "{bf: Choice Free}", color(black) size(*0.9)) /// + text(0.07 1.15 "{bf: Probabilistic}", color(black) size(*0.9)) /// + text(0.15 2 "{bf: Choice Free}", color(black) size(*0.9)) /// + text(0.07 2 "{bf: Deterministic}", color(black) size(*0.9)) /// + ytitle("{bf:Incentivized product recommendation}" " " ) + + graph export "${appendix}Deterministic_Recommendations.png", replace + + +************************************************************************** +* APPPENDIX TABLE E.1: RECOMMENDATIONS +************************************************************************** + +clear +u "${data}Choice_Deterministic.dta" + + + +tab condition recommendincentive if choicebefore==1 & getbefore==1 & conflict==1, row +tab condition recommendincentive if choicebefore==1 & getbefore==0 & conflict==1, row +tab condition recommendincentive if choicebefore==1 & getbefore==1 & conflict==0, row +tab condition recommendincentive if choicebefore==1 & getbefore==0 & conflict==0, row + + +tab condition recommendincentive if choicebefore==0 & getbefore==1 & conflict==1, row +tab condition recommendincentive if choicebefore==0 & getbefore==0 & conflict==1, row +tab condition recommendincentive if choicebefore==0 & getbefore==1 & conflict==0 , row +tab condition recommendincentive if choicebefore==0 & getbefore==0 & conflict==0 , row + + + +*** Recommendation regression (One table with and without participants who switched multiple times in the MPL + + +est clear +eststo:reg recommendincentive choicebefore noconflict choicebefore_noconflict Deterministic Deterministic_noconflict choicebefore_Deterministic Deterministic_choicebeforeNocon incentiveB female age if getyourchoice==1 & alphavaluefinal!=., vce(hc3) +eststo:reg recommendincentive choicebefore noconflict choicebefore_noconflict Deterministic Deterministic_noconflict choicebefore_Deterministic Deterministic_choicebeforeNocon incentiveB female age stdalpha if getyourchoice==1 & alphavaluefinal!=., vce(hc3) +eststo:reg recommendincentive choicebefore noconflict choicebefore_noconflict Deterministic Deterministic_noconflict choicebefore_Deterministic Deterministic_choicebeforeNocon incentiveB female age if getyourchoice==1, vce(hc3) + + +local panel "\begin{table}[h!]" "\centering" "\def\sym#1{\ifmmode^{#1}\else\(^{#1}\)\fi}" "\fontsize{10}{11}\selectfont" "\caption{Recommendations: Assigned Preferences}" "\begin{tabular}{l*{3}{c}} \hline" +local dv " & & & \textbf{Including} \\ $\ \ \ \ \ \ \ \ \ \ \ \ \ \ $ &\multicolumn{2}{c}{\textbf{Main Sample}} &\multicolumn{1}{c}{\textbf{ Inattentve}} \\\hline & & & \\" + +esttab using "${appendix}Deterministic_Recommendations.tex", /// + se r2 replace nolines cells(b(star fmt(3)) se(par fmt(3))) /// +coeflabel(choicebefore "Prefer to See Incentive First" noconflict "No Conflict" /// +choicebefore_noconflict "Prefer to See Incentive First * No Conflict" /// +Deterministic "Deterministic" Deterministic_noconflict "Deterministic X No Conflict" choicebefore_Deterministic "Deterministic X Prefer to See Incentive First" Deterministic_choicebeforeNocon "Deterministic X Prefer to See Incentive First x No Conflict" /// +female "Female" age "Age" stdalpha "Selfishness" /// +incentiveB "Incentive for B") /// +order (choicebefore noconflict choicebefore_noconflict Deterministic Deterministic_noconflict choicebefore_Deterministic Deterministic_choicebeforeNocon incentiveB female age stdalpha) /// +star(* 0.10 ** 0.05 *** 0.01) collabels(none) /// +nomtitle label substitute(" 0.000 " " " " (.) " " ") /// +prehead("`panel'") posthead("`dv'") postfoot("\hline" "\end{tabular}%" "\captionsetup{width=1\textwidth}" "\caption*{\footnotesize Note: This table displays the estimated coefficients from linear probability models on the advisors' recommendations. Deterministic is a binary indicator coded as 1 for participants in the Deterministic treatment. Selfishness was elicited at the end of the experiment, using a multiple price list (MPL) with 5 decisions. The variable is a standardized measure of the number of times the advisor chose to recommend the incentivized product in the MPL task. The regression model in column (3) extends the analyses to included advisors who switched multiple times in the multiple price list eliciting selfishness. The regression includes individual controls for the advisor's gender and age. Robust standard errors (HC3) in parentheses}" "\label{tab:rec_deterministic}" "\end{table}") + + +***** Appendix, page 52: CLients +**we recruited $N=75$ clients and matched them with 1 out of 10 advisors for the main tas +*** of these 87\% followed the recommendation +preserve +u "${data}Clients_ChoiceDeterministic_Main.dta" +tab follow +restore + +*Advisors were also matched with $N=75$ additional advisees for the MPL task that measured moral costs; +*of these 84\% followed the recommendation. +preserve +u "${data}Clients_ChoiceDeterministic_MPL.dta" +tab follow +restore + +******************************************************************************** +******************************************************************************** +* PREDICTIONS (APPENDIX) +******************************************************************************** +******************************************************************************** + + +clear +u "${data}predictionsstudy.dta" +tabstat gap if attentivenum==1, s(mean n) + +preserve +u "${data}choice_experiments.dta" +*As pre-registered, drop Mturk participant with inconsistent alpha value +drop if study!=1 & alphavaluefinal==. /*(study1 are profs and there's no MPL')*/ +***Check effect of seeing incentive first vs. quality first among those who prefer to see incentive first in the COSTLY version of Choice, AMT-1 wave (to be used in Prediction study) +tabstat recommendincentive if choicebefore==1 & conflict==1 & wave=="AMT-1" & condition=="PayBefore" & alphavaluefinal!=., by(getbefore) stats(mean sd) +di 0.0515133*sqrt(393) +di sqrt(0.4702^2+0.4084^2) +di (sqrt(0.4702^2+0.4084^2))/sqrt(393) +restore + +g meanbeh=.7894737-.6759259 +g sebeh=(sqrt(0.4702^2+0.4084^2))/sqrt(393) +g nbeh=393 + + +g red=(gapred!=.) +ttest gap, by(red) + +ttest gap=0 + +cdfplot gap, opt1(lcolor(ebblue)) xlabel(-0.75(.25)0.75) graphr(c(white)) /// + xline(.1135478, lcolor(black)) xline(.0621875, lcolor(red)) /// + xtitle("Forecasted Effect of Seeing Incentive First") /// + text(0.95 -0.1 "Mean forecast" "(0.06)", color(red)) /// + text(0.05 0.31 "Mean actual effect" "(0.11)", color(black)) + graph export "${appendix}/predictionscdf.png", replace + +*mentioned in text +ttesti 393 .1135478 .622799 288 .0621875 .2213746 + +*mentioned in text +tab predictcorrect + + + + diff --git a/24/replication_package/Code/config.do b/24/replication_package/Code/config.do new file mode 100644 index 0000000000000000000000000000000000000000..af1b1ad186d62b5754e233588f7082f298336e6d --- /dev/null +++ b/24/replication_package/Code/config.do @@ -0,0 +1,42 @@ +clear all +set more off + +program main + * *** Add required packages from SSC to this list *** + local ssc_packages "estout grc1leg dataout cdfplot" + * *** Add required packages from SSC to this list *** + + if !missing("`ssc_packages'") { + foreach pkg in "`ssc_packages'" { + * install using ssc, but avoid re-installing if already present + capture which `pkg' + if _rc == 111 { + dis "Installing `pkg'" + quietly ssc install `pkg', replace + } + } + } + + /* Install packages using net, but avoid re-installing if already present + capture which yaml + if _rc == 111 { + quietly net from "https://raw.githubusercontent.com/gslab-econ/stata-misc/master/" + quietly cap ado uninstall yaml + quietly net install yaml + } + * Install complicated packages : moremata (which cannot be tested for with which) + capture confirm file $adobase/plus/m/moremata.hlp + if _rc != 0 { + cap ado uninstall moremata + ssc install moremata + } + * Standard GSLAB stuff + quietly net from "https://raw.githubusercontent.com/gslab-econ/gslab_stata/master/gslab_misc/ado" + quietly cap net uninstall matrix_to_txt + quietly net install matrix_to_txt + quietly cap net uninstall preliminaries + quietly net install preliminaries +*/ +end + +main diff --git a/24/replication_package/Code/datacleaning.do b/24/replication_package/Code/datacleaning.do new file mode 100644 index 0000000000000000000000000000000000000000..0b7c0c8f33389f9bee30f53255b836279e3f085e --- /dev/null +++ b/24/replication_package/Code/datacleaning.do @@ -0,0 +1,2050 @@ + +/*-------------------------------*/ +*NO CHOICE +/*-------------------------------*/ + +clear +import delimited "${raw}NoChoice_deidentified.csv", varnames(1) +drop if status==1 +tab distributionchannel +drop if distributionchannel=="preview" +tab finished +keep if finished==1 + +sort newid +quietly by newid: gen dup = cond(_N==1,0,_n) +tab dup +drop if dup!=0 + +rename q32 attention + +g id=_n +g attentionpassed=attention==3 +keep if attentionpassed==1 + + +*Treatments +g treatment=1 if condition=="BeforeA" +replace treatment=2 if condition=="AfterA" +replace treatment=3 if condition=="BeforeB" +replace treatment=4 if condition=="AfterB" + +label def treat 1 "Before A" 2 "After A" 3 "Before B" 4 "After B" +label val treatment treat + + + +*Beliefs +g beliefbin=q45 +g beliefnum=q43 + +*Recommend b +g recommendb=(recommendationa==2 | recommendationb==2) + +*Signal +g signalblue=(fl_211_do=="Signal_blue2" | fl_221_do=="Signal_blue2" | fl_229_do=="Signal_blue2" | fl_127_do=="Signal_blue2") + +*Conflict +g conflict=1 if treatment<3 & signalblue==1 +replace conflict=1 if treatment>2 & signalblue==0 +replace conflict=0 if conflict==. + +*Commission +g incentiveA=(condition=="AfterA" | condition=="BeforeA") +g incentiveB=1-incentiveA + +*Recommend incentivized +g recommendincentivized=recommendb if treatment>2 +replace recommendincentivized=1-recommendb if treatment<3 + +*Before-After +g before=(treatment==1 | treatment==3) +g after=1-before + +*Prosociality +forval i=2(1)5{ +recode q8`i' (1=0) (2=1) +} + +rename q82 alpha1 +rename q83 alpha2 +rename q84 alpha3 +rename q85 alpha4 +rename q176 alpha5 + +recode alpha5 (1=0) (2=1) + +egen alphamiss=rowmiss(alpha1 alpha2 alpha3 alpha4) +tab alphamiss + +*alpha1-5 equal 1 is recommend the incentivized option + +*RESTRICTING RESPONDENTS TO THOSE WITH COMPLETE ALPHA CHOICES +keep if alphamiss==0 + +*Prosociality switch +reshape long alpha, i(id) j(number) + +sort id number +g previouschoice=alpha[_n-1] if number>1 +g steptob=(alpha-previouschoice) if number>1 +bys id: egen sumsteptob=total(steptob) + +*Alpha value final = number of times you go with the incentive to recommend Y +g alphavalue=0 if sumsteptob==0 & alpha==0 & number==5 /*always choosing X, no incentive*/ +replace alphavalue=5 if sumsteptob==0 & alpha==1 & number==5 /*always choosing Y, incentive*/ +*Alpha value is the question number when the steptob is 1 for those who switch +replace alphavalue=number-1 if sumsteptob==1 & steptob==1 + +g inconsistentchoice=(steptob==-1) +bys id: egen inconsistentid=total(inconsistentchoice) + +bys id: egen alphavaluefinal=total(alphavalue) +replace alphavaluefinal=. if inconsistentid>0 + +tab alphavaluefinal, m +cap drop alphavalue inconsistentchoice previouschoice steptob +reshape wide alpha, i(id) j(number) + +*Average alpha +tab alphavaluefinal if treatment!=., m + +rename recommendincentivized recommendincentive +g female=(gender==2) +replace age="31" if age=="3i" +destring age, replace +recode englishnative (2=0) + +g nodifficulty=(difficulty==2) +replace nodifficulty=. if difficulty==. + +label var female "Female" +label var age "Age" + +gen seeincentivefirst=(condition=="BeforeA" | condition=="BeforeB") + +gen selfish=0 +replace selfish=1 if alphavaluefinal>3 +replace selfish=. if alphavaluefinal==. +gen missingalpha=0 +replace missingalpha=1 if alphavaluefinal==. + +gen noconflict=1-conflict +g seeincentivefirst_noconflict=seeincentivefirst*noconflict + + +g recommendincentive_before=recommendincentive if seeincentivefirst==1 +g recommendincentive_after=recommendincentive if seeincentivefirst==0 + + +egen stdalpha=std(alphavaluefinal) + + +g choice="No Choice" + + +save "${data}nochoice.dta", replace + + + +/*------------------------------*/ +*CHOICE EXPERIMENT +/*------------------------------*/ +*** CLEANING DEIDENTIFIED DATA +clear +import delimited "${raw}Choice1_deidentified.csv", varnames(1) + +drop if status==1 +*drop if workerid=="" +tab distributionchannel +drop if distributionchannel=="preview" +tab finished +keep if finished==1 + + +sort newid +quietly by newid: gen dup = cond(_N==1,0,_n) +tab dup +drop if dup!=0 + + +rename q32 attention + +g id=_n+10000 +g attentionpassed=attention==3 +keep if attentionpassed==1 + +***gen IncentiveB +g incentiveB=. +replace incentiveB=1 if condition=="choiceFreeB" +replace incentiveB=1 if condition=="choicePayB" +replace incentiveB=0 if condition=="choiceFreeA" +replace incentiveB=0 if condition=="choicePayA" + +g incentiveA=. +replace incentiveA=1 if condition=="choiceFreeA" +replace incentiveA=1 if condition=="choicePayA" +replace incentiveA=0 if condition=="choiceFreeB" +replace incentiveA=0 if condition=="choicePayB" + +**Commission +gen commission="commissionB" if incentiveB==1 +replace commission="commissionA" if incentiveB==0 + +*Choice before +g choicebefore=(choicefreeq==1 | choicepay==1) + +*Recommendation +g recommendb=(recommendationa==2 | recommendationb==2) +replace recommendb=. if recommendationa==. & recommendationb==. +g recommenda=1-recommendb + +g recommendincentive=recommendb if commission=="commissionB" +replace recommendincentive=recommenda if commission=="commissionA" + + +*Beliefs +g beliefbin=q45 +g belief=q43 +replace belief=q43*100 if q43<1 + +*Signal +g signalblue=(fl_298_do=="Signal_blue2" /// + | fl_304_do=="Signal_blue2" /// + | fl_211_do=="Signal_blue2" /// + | fl_221_do=="Signal_blue2" /// + | fl_310_do=="Signal_blue2" /// + | fl_229_do=="Signal_blue2" /// + | fl_325_do=="Signal_blue2" /// + | fl_127_do=="Signal_blue2") + + +*Conflict +g conflict=0 +replace conflict=1 if signalblue==1 & commission=="commissionA" +replace conflict=1 if signalblue==0 & commission=="commissionB" + + +*Treatment +g treatment="ChoiceFree" if condition=="choiceFreeA" | condition=="choiceFreeB" +replace treatment="ChoicePay" if condition=="choicePayA" | condition=="choicePayB" + + +*Random assignment to order +g get=fl_264_do if condition=="choiceFreeA" & choicefreeq==2 +replace get=fl_265_do if condition=="choiceFreeA" & choicefreeq==1 + +replace get=fl_289_do if condition=="choiceFreeB" & choicefreeq==1 +replace get=fl_287_do if condition=="choiceFreeB" & choicefreeq==2 + +replace get=fl_342_do if condition=="choicePayA" & choicepayq==2 +replace get=fl_272_do if condition=="choicePayA" & choicepayq==1 + +replace get=fl_320_do if condition=="choicePayB" & choicepayq==1 +replace get=fl_331_do if condition=="choicePayB" & choicepayq==2 + +g getafter=(get=="GetAfter") +g getbefore=1-getafter + +g costlydemand=(condition=="choicePayA" | condition=="choicePayB") +g choicewave=1 + +g study=2 if costlydemand==0 +replace study=3 if costlydemand==1 + +replace age="51" if age=="51`" +replace age="" if age=="re" +destring age, replace + +*Prosociality +destring q82 q83 q84 q85 q176, force replace + +forval i=2(1)5{ +recode q8`i' (1=0) (2=1) +} + +rename q82 alpha1 +rename q83 alpha2 +rename q84 alpha3 +rename q85 alpha4 +rename q176 alpha5 +recode alpha5 (1=0) (2=1) +egen alphamiss=rowmiss(alpha1 alpha2 alpha3 alpha4 alpha5) +keep if alphamiss==0 + +g choice="Choice" + + +drop fl_17_do fl_264_do fl_298_do fl_265_do fl_211_do fl_272_do fl_304_do fl_342_do /// +*fl_289_do fl_221_do fl_310_do fl_287_do fl_229_do fl_320_do fl_320_do fl_325_do fl_331_do fl_127_do + + +save "${data}choice1.dta", replace + +/*-------------------------------*/ +*PROFESSIONALS STUDY +/*-------------------------------*/ + +*** CLEAN DE_IDENTIFIED DATA +**PROLIFIC +clear +import delimited "${raw}ProfessionalsProlific_deidentified.csv", varnames(1) + +drop if status==1 +drop if distributionchannel=="preview" +keep if finished==1 +sort newid +quietly by newid: gen dup = cond(_N==1,0,_n) +drop if dup>0 + + +g sample="Prolific" + + + +save "${data}professionals_prolific", replace + +*CLOUDRESEARCH +clear +import delimited "${raw}ProfessionalsCloudresearch_deidentified.csv", varnames(1) + +g sample="CloudResearch" +drop if status==1 +drop if distributionchannel=="preview" +keep if finished==1 + +sort newid +quietly by newid: gen dup = cond(_N==1,0,_n) +tab dup +drop if dup!=0 + + +drop fl_17_do fl_481_do fl_523_do fl_528_do fl_548_do fl_555_do fl_563_do fl_657_do fl_629_do fl_656_do fl_642_do + +rename industry industry_cloudresearch +append using "${data}professionals_prolific.dta", force + + +g attentionpassed=attention==3 +drop if attentionpassed==0 + + +*Choice +g choicebefore=(choicefree==1) +replace choicebefore=. if choicefree==. +drop if choicebefore==. + +*Beliefs +g beliefbin=q45 +g beliefnum=q43 +g belief=q43 +replace belief=q43*100 if q43<1 + +*Recommendation +g recommendb=(recommendationa==2 | recommendationb==2) +replace recommendb=. if recommendationa==. & recommendationb==. +g recommenda=1-recommendb +g recommendincentive=recommenda if condition=="choicefreeA" +replace recommendincentive=recommendb if condition=="choicefreeB" + +*Get before +g getafter=(getbefore=="GetAfter") +g getbefore2=(getbefore=="GetBefore") +drop getbefore +rename getbefore2 getbefore + +*Signal +g signalblue=signal=="Blue" + +*Conflict +g conflict=0 +replace conflict=1 if signalblue==1 & (condition=="choicefreeA") +replace conflict=1 if signalblue==0 & (condition=="choicefreeB") + +g incentiveA=(commission=="commissionA") +g incentiveB=1-incentiveA +g costlydemand=0 + +g female=(gender==2) + +g choice="Professionals" + +g beliefcorrect=0 +replace beliefcorrect=1 if beliefbin==4 & signalblue==1 /*signal $2, quality low with 25% chance*/ +replace beliefcorrect=1 if beliefbin==8 & signalblue==0 /*signal $0, quality low with 66.7% chance*/ + +g explanationchoice=q211 + +destring age, replace + +g id=_n+20000 + + + +keep recommendincentive getbefore incentiveB incentiveA /// +costlydemand choicebefore belief conflict signalblue /// +female age sample beliefbin choice beliefcorrect /// + newid explanationchoice jobtitle industry shortid currentcountr + +g study=1 + +save "${data}professionals.dta", replace + +/*-------------------------------*/ +*PROFESSIONALS - JOB TITLES +/*-------------------------------*/ + + +clear +import excel "${raw}Professionals_jobtitles.xlsx", first case(l) +save "${data}professionals_jobtitles.dta", replace + +/*-------------------------------*/ +*CHOICE 2 - REVERSED +/*-------------------------------*/ +*** CLEANING DEIDENTIFIED DATA +clear +import delimited "${raw}Choice2_deidentified.csv", varnames(1) + +drop if status==1 + +tab distributionchannel +drop if distributionchannel=="preview" +tab finished +keep if finished==1 + +sort newid +quietly by newid: gen dup = cond(_N==1,0,_n) +tab dup +drop if dup>0 + + + +drop if q_recaptchascore<.50 + + +g id=_n+30000 + +g attention=attention_a +replace attention=attention_b if attention_b!=. +replace attention=att_highro if att_highro!=. +replace attention=att_highflipped if att_highflipped!=. +drop attention_a attention_b att_highro att_highflipped + +g attentionpassed=attention==3 +tab attentionpassed +drop if attentionpassed==0 +rename q211 explanationchoice +replace explanationchoice=q188 if explanationchoice=="" +drop q188 + + + +*Choice before +g choicebefore=. +replace choicebefore=1 if choicefreereghigh==1 +replace choicebefore=0 if choicefreereghigh==2 + +*Beliefs +g beliefbin=q45 +*g beliefnum=q43 +g belief=q43 if incentives=="$0.15 " +replace belief=q551 if incentives=="$15 " +replace beliefbin=q550 if incentives=="$15 " +replace belief=q43*100 if q43<1 + +*Recommendation +g recommendb=. +replace recommendb=1 if recommendationb==2 +replace recommendb=0 if recommendationb==1 +replace recommendb=1 if recommendationb_flip==2 +replace recommendb=0 if recommendationb_flip==1 + +replace recommendb=1 if recommendationa==2 +replace recommendb=0 if recommendationa==1 +replace recommendb=1 if recommendationa_flip==2 +replace recommendb=0 if recommendationa_flip==1 + +g recommenda=1-recommendb + +g recommendincentive=recommendb if commission=="commissionB" +replace recommendincentive=recommenda if commission=="commissionA" + +*Signal +g signalblue=. +*replace signalblue=1 if signal=="Blue" +replace signalblue=1 if fl_272_do=="Signal_blue2" +replace signalblue=1 if fl_459_do=="Signal_blue2HIGHINCENTIVES" +replace signalblue=1 if fl_260_do=="Signal_blue2" +replace signalblue=1 if fl_450_do=="Signal_blue2HIGHINCENTIVES" +replace signalblue=1 if fl_248_do=="Signal_blue2REVERSEPRODUCTORDER" +replace signalblue=1 if fl_508_do=="Signal_blue2REVERSEPRODUCTORDER-HIGHINCENTIVES" +replace signalblue=1 if fl_236_do=="Signal_blue2REVERSEPRODUCTORDER" +replace signalblue=1 if fl_469_do=="Signal_blue2REVERSEPRODUCTORDER-HIGHINCENTIVES" +replace signalblue=1 if fl_163_do=="Signal_blue2" +replace signalblue=1 if fl_476_do=="Signal_blue2HIGHINCENTIVES" +replace signalblue=1 if fl_151_do=="Signal_blue2" +replace signalblue=1 if fl_483_do=="Signal_blue2HIGHINCENTIVES" +replace signalblue=1 if fl_139_do=="Signal_blue2REVERSEPRODUCTORDER" +replace signalblue=1 if fl_491_do=="Signal_blue2REVERSEPRODUCTORDER-HIGHINCENTIVES" +replace signalblue=1 if fl_127_do=="Signal_blue2REVERSEPRODUCTORDER" +replace signalblue=1 if fl_499_do=="Signal_blue2REVERSEPRODUCTORDER-HIGHINCENTIVES" +replace signalblue=1 if signal=="Blue" + + + +replace signalblue=0 if fl_272_do=="Signal_red0" +replace signalblue=0 if fl_459_do=="Signal_red0HIGHINCENTIVES" +replace signalblue=0 if fl_260_do=="Signal_red0" +replace signalblue=0 if fl_450_do=="Signal_red0HIGHINCENTIVES" +replace signalblue=0 if fl_248_do=="Signal_redREVERSEPRODUCTORDER" +replace signalblue=0 if fl_508_do=="Signal_redREVERSEPRODUCTORDER-HIGHINCENTIVES" +replace signalblue=0 if fl_236_do=="Signal_redREVERSEPRODUCTORDER" +replace signalblue=0 if fl_469_do=="Signal_redREVERSEPRODUCTORDER-HIGHINCENTIVES" +replace signalblue=0 if fl_163_do=="Signal_red0" +replace signalblue=0 if fl_476_do=="Signal_red0HIGHINCENTIVES" +replace signalblue=0 if fl_151_do=="Signal_red0" +replace signalblue=0 if fl_483_do=="Signal_red0HIGHINCENTIVES" +replace signalblue=0 if fl_139_do=="Signal_redREVERSEPRODUCTORDER" +replace signalblue=0 if fl_491_do=="Signal_redREVERSEPRODUCTORDER-HIGHINCENTIVES" +replace signalblue=0 if fl_127_do=="Signal_redREVERSEPRODUCTORDER" +replace signalblue=0 if fl_499_do=="Signal_redREVERSEPRODUCTORDER-HIGHINCENTIVES" +replace signalblue=0 if signal=="Red" + + +*Conflict +g conflict=. +replace conflict=1 if signalblue==0 & commission=="commissionB" +replace conflict=0 if signalblue==1 & commission=="commissionB" +replace conflict=1 if signalblue==1 & commission=="commissionA" +replace conflict=0 if signalblue==0 & commission=="commissionA" + +g incentiveshigh=(incentives=="$15 ") +g flippedorder=(uncertainproduct=="Product A") +g costlydemand=0 +g incentiveA=(commission=="commissionA") +g incentiveB=1-incentiveA + +gen incentiveleft=. +replace incentiveleft=1 if incentiveB==1 & flippedorder==1 +replace incentiveleft=0 if incentiveB==1 & flippedorder==0 +replace incentiveleft=1 if incentiveA==1 & flippedorder==0 +replace incentiveleft=0 if incentiveA==1 & flippedorder==1 + +tab condition +replace condition="ChoiceFree" + +g choicewave=2 +g getafter=(getbefore=="GetAfter") +cap drop getbefore +g getbefore=1-getafter +g study=4 + + +*Prosociality + + +forval i=2(1)5{ +recode q8`i' (1=0) (2=1) +} +forval i=4(1)8{ +recode q54`i' (1=0) (2=1) +} + +g alpha1=q82 if incentiveshigh==0 +replace alpha1=q544 if incentiveshigh==1 +g alpha2=q83 if incentiveshigh==0 +replace alpha2=q545 if incentiveshigh==1 +g alpha3=q84 if incentiveshigh==0 +replace alpha3=q546 if incentiveshigh==1 +g alpha4=q85 if incentiveshigh==0 +replace alpha4=q547 if incentiveshigh==1 + +g alpha5=v193 if incentiveshigh==0 +recode alpha5 (1=0) (2=1) +replace alpha5=q548 if incentiveshigh==1 + +egen alphamiss=rowmiss(alpha1 alpha2 alpha3 alpha4 alpha5) +keep if alphamiss==0 +g choice="Choice" + + +drop fl_101_do fl_226_do fl_341_do fl_314_do fl_411_do fl_271_do fl_272_do fl_459_do /// +fl_259_do fl_260_do fl_450_do fl_247_do fl_248_do fl_508_do fl_235_do fl_236_do fl_469_do /// +fl_117_do fl_365_do fl_205_do fl_413_do fl_162_do fl_163_do fl_476_do fl_150_do fl_151_do fl_483_do /// +fl_138_do fl_139_do fl_491_do fl_126_do fl_127_do fl_499_do + +save "${data}choice2.dta", replace + + +/*-------------------------------*/ +*CHOICE 3 - wave3 +/*-------------------------------*/ + + +*** CLEANING DEIDENTIFIED DATA +clear +import delimited "${raw}Choice3_deidentified.csv", varnames(1) + +drop if status==1 +*drop if workerid=="" +tab distributionchannel +drop if distributionchannel=="preview" +tab finished +keep if finished==1 + +tab attention +replace attention=q343 if condition=="Highx10" | condition=="Highx100" +keep if attention!=. +gen attentionpassed=(attention==3) + +tab attentionpassed +**94.86% passed the attention check (N=2095) out of 2202 who completed the attention check question +drop if attentionpassed!=1 + + +sort newid +quietly by newid: gen dup = cond(_N==1,0,_n) +tab dup +drop if dup!=0 + +drop if q_recaptchascore<.50 +tab status + + +sort startdate +g id=_n+40000 + + +**Beliefs +replace beliefbin=beliefbin_high if beliefbin==. +drop beliefbin_high +replace belief=belief_high if belief_high!=. +drop belief_high +replace belief=belief*100 if belief<1 + + +replace age="" if age=="casian" +destring age, replace + +***Gen recommendB and recommenda dummies + +g recommendb=. +replace recommendb=1 if recommendationb==2 +replace recommendb=0 if recommendationb==1 + +replace recommendb=1 if recommendationa==2 +replace recommendb=0 if recommendationa==1 + + +g recommenda=1-recommendb + +***gen IncentiveB +g incentiveB=. +replace incentiveB=1 if commission=="commissionB" +replace incentiveB=0 if commission=="commissionA" + +g incentiveA=. +replace incentiveA=1 if commission=="commissionA" +replace incentiveA=0 if commission=="commissionB" + + +*GET by treatment +g getafter=(getbefore=="GetAfter") +cap drop getbefore +g getbefore=1-getafter + + +**gen bluesignal dummy +g signalblue=. +replace signalblue=1 if signal=="Blue" +replace signalblue=1 if fl_750_do=="SignalBlue_HighIncentives" +replace signalblue=1 if fl_773_do=="SignalBlue_HighIncentives" +replace signalblue=1 if fl_785_do=="SignalBlue_HighIncentives" +replace signalblue=1 if fl_528_do=="Signal_blue2" +replace signalblue=1 if fl_555_do=="Signal_blue2" +replace signalblue=1 if fl_555_do=="Signal_blue2" +replace signalblue=1 if fl_629_do=="Signal_blue2" +replace signalblue=1 if fl_642_do=="Signal_blue2" +replace signalblue=0 if signal=="Red" +replace signalblue=0 if fl_750_do=="SignalRed_highIncentives" +replace signalblue=0 if fl_773_do=="SignalRed_highIncentives" +replace signalblue=0 if fl_785_do=="SignalRed_highIncentives" +replace signalblue=0 if fl_528_do=="Signal_red0" +replace signalblue=0 if fl_555_do=="Signal_red0" +replace signalblue=0 if fl_555_do=="Signal_red0" +replace signalblue=0 if fl_629_do=="Signal_red0" +replace signalblue=0 if fl_642_do=="Signal_red0" + +g conflict=. +replace conflict=1 if signalblue==0 & commission=="commissionB" +replace conflict=0 if signalblue==1 & commission=="commissionB" +replace conflict=1 if signalblue==1 & commission=="commissionA" +replace conflict=0 if signalblue==0 & commission=="commissionA" + + +tab commission +g recommendincentive=recommendb if commission=="commissionB" +replace recommendincentive=recommenda if commission=="commissionA" + + +gen choicebefore=. +replace choicebefore=1 if choicefree==1 +replace choicebefore=0 if choicefree==2 +replace choicebefore=1 if choicepaybefore==1 +replace choicebefore=0 if choicepaybefore==2 +replace choicebefore=0 if choicepayafter==2 +replace choicebefore=1 if choicepayafter==1 + + + +*** DUMMIES FOR STAKE TREATMENTS + +gen HighStakes="" +replace HighStakes=condition if condition=="Highx10" +replace HighStakes=condition if condition=="Highx100" + +gen Highx10=0 +replace Highx10=1 if condition=="Highx10" +gen Highx100=0 +replace Highx100=1 if condition=="Highx100" + +replace condition="ChoiceFree" if Highx10==1 +replace condition="ChoiceFree" if Highx100==1 + + +**Info avoidance +tab ia_choice +gen chooseblindness=ia_choice +replace chooseblindness=0 if ia_choice==2 +tab chooseblindness + +gen ia_recommend1=. +replace ia_recommend1=1 if ia_rec_quality0==1 | ia_recq2==1 | ia_rec_both2==1 | ia_rec_both0==1 +replace ia_recommend1=0 if ia_rec_quality0==2 | ia_recq2==2 | ia_rec_both2==2 | ia_rec_both0==2 + +gen ia_recommendincentive=1 if ia_recommend1==1 & ia_incentive=="Product 1" +replace ia_recommendincentive=0 if ia_recommend1==0 & ia_incentive=="Product 1" +replace ia_recommendincentive=1 if ia_recommend1==0 & ia_incentive=="Product 2" +replace ia_recommendincentive=0 if ia_recommend1==1 & ia_incentive=="Product 2" + + + + +***Prosociality +forval i=1(1)5{ +recode alpha`i' (1=0) (2=1) +} + +egen alphamiss=rowmiss(alpha1 alpha2 alpha3 alpha4) +keep if alphamiss==0 + +gen study=6 +rename q211 explanationchoice +gen costlydemand=0 +replace costlydemand=1 if condition=="PayBefore" + + +g sample="MTurk" + + + +drop fl_728_do fl_816_do fl_737_do fl_759_do fl_750_do fl_761_do fl_773_do fl_778_do /// + fl_785_do fl_793_do fl_800_do fl_825_do fl_837_do fl_903_do fl_843_do fl_870_do fl_850_do /// +fl_880_do fl_855_do fl_889_do fl_17_do fl_481_do fl_523_do fl_528_do fl_548_do fl_555_do /// + fl_563_do fl_657_do fl_629_do fl_656_do fl_642_do fl_933_do fl_916_do fl_935_do + +g choice="Choice" +save "${data}choice3.dta", replace + + + +/*-------------------------------*/ +*POOLING ALL CHOICE WAVES INTO 1 DATASET +/*-------------------------------*/ + + + u "${data}choice1.dta", clear + append using "${data}choice2.dta" + append using "${data}choice3.dta" + + *Prosociality - switch point + reshape long alpha, i(id) j(number) + + sort id number + g previouschoice=alpha[_n-1] if number>1 + g steptob=(alpha-previouschoice) if number>1 + bys id: egen sumsteptob=total(steptob) + + *Alpha value final = number of times you go with the incentive to recommend Y + g alphavalue=0 if sumsteptob==0 & alpha==0 & number==5 /*always choosing X, no incentive*/ + replace alphavalue=5 if sumsteptob==0 & alpha==1 & number==5 /*always choosing Y, incentive*/ + *Alpha value is the question number when the steptob is 1 for those who switch + replace alphavalue=number-1 if sumsteptob==1 & steptob==1 + + g inconsistentchoice=(steptob==-1) + bys id: egen inconsistentid=total(inconsistentchoice) + + bys id: egen alphavaluefinal=total(alphavalue) + replace alphavaluefinal=. if inconsistentid>0 + + cap drop alphavalue inconsistentchoice previouschoice steptob + + reshape wide alpha, i(id) j(number) + + + *ATTENTIVE GUYS are DROPPED FOR INCOMPLETE ANSWERS + *KEEP CONSISTENT GUYS IN ALPHA TASK + tab alphavaluefinal, m + egen stdalpha=std(alphavaluefinal) + + gen selfish=(alphavaluefinal>3) + replace selfish=. if alphavaluefinal==. + + *tab attention0, m + *keep if attention0==1 + + destring gender age englishnative difficulty, force replace + g female=(gender==2) + recode englishnative (2=0) + g nodifficulty=(difficulty==2) + replace nodifficulty=. if difficulty==. + + label var female "Female" + label var age "Age" + + g beliefcorrect=0 + replace beliefcorrect=1 if beliefbin==4 & signalblue==1 + replace beliefcorrect=1 if beliefbin==8 & signalblue==0 + + replace sample="MTurk" + + replace incentiveleft=0 if study!=4 + replace incentiveshigh=0 if study!=4 + + drop if choicebefore==. + tab condition, m + replace condition="ChoiceFree" if condition=="choiceFreeA" + replace condition="ChoiceFree" if condition=="choiceFreeB" + replace condition="PayBefore" if condition=="choicePayA" + replace condition="PayBefore" if condition=="choicePayB" + + +keep id startdate enddate attentionpassed recommendincentive getbefore incentiveB incentiveA commission costlydemand /// +choicebefore belief beliefbin conflict signalblue female age selfish stdalpha /// +alphavaluefinal beliefcorrect newid shortid /// +sample choice incentiveshigh flippedorder /// +incentiveleft condition study Highx10 Highx100 chooseblindness + +append using "${data}professionals.dta" + +rename costlydemand incentivecostly + g qualitycostly=0 + replace qualitycostly=1 if condition=="PayAfter" + +g professionals=(study==1) +g prolific=(sample=="Prolific") +g cloudresearch=(sample=="CloudResearch") +gen professionalscloudresearch=0 if professionals==1 & cloudresearch==1 +replace professionalscloudresearch=1 if professionals==1 & cloudresearch==1 +replace incentiveshigh=0 if incentiveshigh==. +replace incentiveleft=0 if incentiveleft==. +replace Highx10=0 if Highx10==. +replace Highx100=0 if Highx100==. +g mturkwave=. +replace mturkwave=1 if study==2 | study==3 +replace mturkwave=2 if study==4 +replace mturkwave=3 if study==6 + +***drop 1 participant <18 to comply w IRB rules +drop if age<18 +replace age=. if age==545 + + +replace condition="ChoiceFree_Professionals" if study==1 +tab condition + +drop if choicebefore==. + +replace study=7 if study==6 & condition=="PayBefore" +replace study=8 if study==6 & condition=="PayAfter" + +** waves +g wave1=(study==2 | study==3) +g wave2=(study==4) +g wave3=(study==6 | study==7 | study==8) + +**professionals cloudresearch +replace professionalscloudresearch=0 if professionalscloudresearch==. + +**probabilistic incentives +g probabilistic_incentive=1 if incentiveshigh==1 +replace probabilistic_incentive=1 if professionals==1 +replace probabilistic_incentive=0 if professionals==0 & incentiveshigh==0 + +**locatedUS + + g locatedus=1 if study>1 + replace locatedus=1 if study==1 & prolific==0 + replace locatedus=1 if study==1 & prolific==1 & currentcountryofresidence=="United States" + replace locatedus=0 if study==1 & prolific==1 & currentcountryofresidence!="United States" + drop currentcountryofresidence + +**For appendix +g wave="AMT-1" if wave1==1 +replace wave="AMT-2" if wave2==1 +replace wave="AMT-3" if wave3==1 +replace wave="Prolific/CloudResearch" if study==1 + +g incentivedesign="0.15" if wave1==1 +replace incentivedesign="15 (to 1 out of 100)" if incentiveshigh==1 | wave=="Prolific/CloudResearch" +replace incentivedesign="1.50" if Highx10==1 +replace incentivedesign="15 (to everyone)" if Highx100==1 +replace incentivedesign="0.15" if wave2==1 & incentiveshigh==0 +replace incentivedesign="0.15" if wave3==1 & Highx10==0 & Highx100==0 + +g year=2019 if wave1==1 +replace year=2020 if wave2==1 | wave=="Prolific/CloudResearch" +replace year=2021 if wave3==1 + + +g treatment=0 if condition=="ChoiceFree" +replace treatment=1 if professionals==1 +replace treatment=2 if condition=="PayBefore" +replace treatment=3 if condition=="PayAfter" +gen selfishseeincentivecostly=stdalpha*incentivecostly +gen selfishseequalitycostly=stdalpha*qualitycostly + +gen committ=1-choicebefore + +*Separate dummies, instead of factor variables in regression +g professionalsfree=(professionals==1) +g seeincentivecostly=condition=="PayBefore" +g seequalitycostly=condition=="PayAfter" +g incentiveshigh_incentiveleft=incentiveshigh*incentiveleft + +*blinding + +g avoid_incentiveinfo=(chooseblindness==1) +replace avoid_incentiveinfo=. if chooseblindness==. +gen choiceafter=1-choicebefore + +**Recommendations +***Gen indicator for whether participants get their desired choice of info order +gen getyourchoice=0 +replace getyourchoice=1 if choicebefore==1 & getbefore==1 +replace getyourchoice=1 if choicebefore==0 & getbefore==0 +label var getyourchoice "Random Assignment to Preferred Order" + +g choicebefore_getyourchoice=choicebefore*getyourchoice +label var choicebefore_getyourchoice "Prefer to See Incentive First X Random Assignment" + + +g getbeforenoconflict=getbefore*(1-conflict) +g noconflict=1-conflict +g choicebeforenoconflict=choicebefore*(1-conflict) + +g choicebefore_getbefore=choicebefore*getbefore +g choicebefore_getbefore_nocon=choicebefore*getbefore*(1-conflict) + +g notgetyourchoice=(1-getyourchoice) +g notgetyourchoicenoconflict=(1-getyourchoice)*(1-conflict) +g choicebeforenotgetyourchoice=notgetyourchoice*choicebefore + +gen choicebeforegetbefore=choicebefore*getbefore + +***Beliefs +g logsignalhigh=-log(2) /*prob signal H if q=L 0.4 / prob signal H if q=H 0.8 --> log(1/2)=-log(2)*/ +g logsignallow=log(3) /*prob signal L if q=L 0.6 / prob signal L if quality H 0.2 --> log(3) */ + +g logitbelief=log(belief/(100-belief)) /*that B is low quality*/ + + +g good=0 +replace good=logsignallow if incentiveA==1 & signalblue==0 +replace good=logsignalhigh if incentiveB==1 & signalblue==1 +g bad=0 +replace bad=logsignallow if incentiveB==1 & signalblue==0 +replace bad=logsignalhigh if incentiveA==1 & signalblue==1 + +g goodchoicebefore=good*choicebefore +g badchoicebefore=bad*choicebefore + +g goodgetyourchoice=good*getyourchoice +g badgetyourchoice=bad*getyourchoice + +g goodnotgetyourchoice=good*(1-getyourchoice) +g badnotgetyourchoice=bad*(1-getyourchoice) + +g goodchoicebeforenotgetyourchoice=good*(1-getyourchoice)*choicebefore +g badchoicebeforenotgetyourchoice=bad*(1-getyourchoice)*choicebefore + +g bayesianposterior=33 if signalblue==1 +replace bayesianposterior=75 if signalblue==0 + +label var good "Good news (log odds incl.)" +label var bad "Bad news (log odds incl.)" +label var badchoicebefore "Bad news (log) X Pref Inc 1st" +label var goodchoicebefore "Good news (log) X Pref Inc 1st" +label var goodgetyourchoice "Good news (log) X Get Choice" +label var badgetyourchoice "Bad news (log) X Get Choice" +label var goodnotgetyourchoice "Good news (log) X Not Get Choice" +label var badnotgetyourchoice "Bad news (log) X Not Get Choice" +label var goodchoicebeforenotgetyourchoice "Good news (log) X Prefer Inc 1st X Not Get Choice" +label var badchoicebeforenotgetyourchoice "bad news (log) X Prefer Inc 1st X Not Get Choice" +label var bayesianposterior "Bayesian Posterior" + +g badsignal=0 +replace badsignal=1 if incentiveB==1 & signalblue==0 +replace badsignal=1 if incentiveA==1 & signalblue==1 + + +*Gen variables for those who update in correct direction +*belief is likelihood that quality is LOW, so if signal is blue($2), likelihood should be 25 +g updatecorrect=(belief<50) if signalblue==1 +replace updatecorrect=(belief>50) if signalblue==0 + +g updatedirection=1 if signalblue==1 & belief<50 +replace updatedirection=1 if signalblue==0 & belief>50 +replace updatedirection=0 if belief==50 +replace updatedirection=-1 if belief>50 & signalblue==1 +replace updatedirection=-1 if belief<50 & signalblue==0 + +g updatewrong=(updatedirection==-1) + +**cumulative beliefs distance +g beliefdistance=belief-33.3 if signalblue==1 /*positive difference=too little updating*/ +replace beliefdistance=75-belief if signalblue==0 /*positive difference=too little updating*/ + +g relbeliefdistance=(50-belief)/(50-33) if signalblue==1 +replace relbeliefdistance=(50-belief)/(50-75) if signalblue==0 + +**correct direction - incentivized measure +*Prob quality of B is low, bin #5 - 41-50% + +g correctdirbin=0 +replace correctdirbin=1 if signalblue==1 & beliefbin<6 & beliefbin!=. /*if signal is blue, $2, it decreases*/ +replace correctdirbin=1 if signalblue==0 & beliefbin>4 & beliefbin!=. /*if signal is blue, $0, it decreases*/ + +*correct bin - incentivized measure +g beliefinbin=0 +replace beliefinbin=1 if belief<11 & beliefbin==1 +replace beliefinbin=1 if belief>10 & belief<21 & beliefbin==2 +replace beliefinbin=1 if belief>20 & belief<31 & beliefbin==3 +replace beliefinbin=1 if belief>30 & belief<41 & beliefbin==4 +replace beliefinbin=1 if belief>40 & belief<51 & beliefbin==5 +replace beliefinbin=1 if belief>50 & belief<61 & beliefbin==6 +replace beliefinbin=1 if belief>60 & belief<71 & beliefbin==7 +replace beliefinbin=1 if belief>70 & belief<81 & beliefbin==8 +replace beliefinbin=1 if belief>80 & belief<91 & beliefbin==9 +replace beliefinbin=1 if belief>90 & belief!=. & beliefbin==10 +replace beliefinbin=. if belief==. | beliefbin==. + +** belief at prior + +g belief_prior=0 +replace belief_prior=1 if beliefbin==5 +replace belief_prior=. if beliefbin==. + +*** belief at prior - continuous +g belief_prior_continuos=0 +replace belief_prior_continuos=1 if belief==50 +replace belief_prior_continuos=. if belief==. + +drop newid + +save "${data}choice_experiments.dta", replace + + +/*-----CHOICE - CODED EXPLANATIONS OF CHOICE -------*/ +*IMPORT EXPLANATIONS CSV +clear +import excel "${raw}Choice_explanations_deidentified.xlsx", first case(l) +save "${data}coding_explanations.dta", replace + +u "${data}choice_experiments.dta", clear +keep if mturkwave==2 | professionals==1 +rename id participant_id +g id=shortid +bys id: egen countid=count(id) +tab countid study +drop if alphavaluefinal==. & study!=1 + +merge 1:m shortid using "${data}coding_explanations.dta", force +rename _merge _mergedecisions +keep if _mergedecisions==3 +tab countid study if rater==1 + +replace condition="ChoiceFree_Professionals" if study==1 +egen rowsum=rowtotal(limitbias nomatter commission_expl feeling other) + +g mergedcategory=1 if limitbias==1 & rowsum<2 +replace mergedcategory=2 if nomatter==1 & rowsum<2 +replace mergedcategory=3 if commission_expl==1 & rowsum<2 +replace mergedcategory=4 if (feeling==1 | other==1) & (rowsum==1 | rowsum==2) +replace mergedcategory=5 if rowsum==0 +replace mergedcategory=6 if rowsum>1 & limitbias==1 & nomatter==1 +replace mergedcategory=7 if rowsum>1 & limitbias==1 & commission_expl==1 +replace mergedcategory=8 if rowsum>1 & limitbias==1 & (feeling==1 | other==1) +replace mergedcategory=9 if rowsum>1 & commission_expl==1 & nomatter==1 +replace mergedcategory=10 if rowsum>1 & nomatter==1 & (feeling==1 | other==1) +replace mergedcategory=11 if rowsum>1 & commission_expl==1 & (feeling==1 | other==1) + +g otherreason=(other==1 | feeling==1) + +reshape wide limitbias nomatter commission_expl feeling other comments rowsum /// + mergedcategory otherreason, i(shortid) j(rater) + +g nocategory1=(limitbias1==0 & nomatter1==0 & commission_expl1==0 & otherreason1==0) +g nocategory2=(limitbias2==0 & nomatter2==0 & commission_expl2==0 & otherreason2==0) + +foreach v of newlist limitbias nomatter commission_expl feeling other otherreason nocategory { +egen `v'=rowmean(`v'1 `v'2) +} +egen mergedcategory=rowmean(mergedcategory1 mergedcategory2) + +drop shortid + +save "${data}Choice_coding_explanations.dta", replace + + + +/*-------------------------------*/ +*STAKES STUDY +/*-------------------------------*/ + + +*** CLEANING DEIDENTIFIED DATA +clear +import delimited "${raw}Stakes_deidentified.csv", varnames(1) + +drop if status==1 +tab distributionchannel +drop if distributionchannel=="preview" +tab finished +keep if finished==1 + +g attentionpassed=attention==3 +keep if attentionpassed==1 + + +sort newid +quietly by newid: gen dup = cond(_N==1,0,_n) +tab dup +drop if dup!=0 + +drop if q_recaptchascore<.50 + +g id=_n+50000 + + +*Treatment +g condition2=0.30 if condition=="PayBefore_HighCommission" +replace condition2=0.15 if condition=="PayBefore_main" +replace condition2=0.01 if condition=="PayBeforeLowCommission" + +recode choicepaybefore (2=0) +recode choicehighcommission (2=0) +recode choicelowcommission (2=0) + +g choicebefore=choicepaybefore if condition=="PayBefore_main" +replace choicebefore=choicehighcommission if condition=="PayBefore_HighCommission" +replace choicebefore=choicelowcommission if condition=="PayBeforeLowCommission" + +*Signal +g signalblue=(signal=="Blue") + +*Beliefs +g beliefbin=q45 +g belief=q43 + +g beliefcorrect=0 +replace beliefcorrect=1 if beliefbin==4 & signalblue==1 +replace beliefcorrect=1 if beliefbin==8 & signalblue==0 + +*Recommendation +g recommendb=(recommendationa==2 | recommendationb==2) +g recommenda=1-recommendb +g recommendincentive=recommenda if commission=="commissionA" +replace recommendincentive=recommendb if commission=="commissionB" + + + +*Conflict +g conflict=0 +replace conflict=1 if signalblue==1 & commission=="commissionA" +replace conflict=1 if signalblue==0 & commission=="commissionB" + +*Get before +g getbeforenum=(fl_523_do=="GetBeforeIncentiveforA" | /// + fl_548_do=="GetBeforeIncentiveforA" | /// + fl_657_do=="GetBeforeIncentiveforB" | /// + fl_656_do=="GetBeforeIncentiveforB" ) +drop getbefore +rename getbeforenum getbefore + +destring age, replace + +**Gender +gen female=. +replace female=1 if gender==2 +replace female=0 if gender==1 + +*Prosociality +forval i=2(1)5{ +recode q8`i' (1=0) (2=1) +} + +rename q82 alpha1 +rename q83 alpha2 +rename q84 alpha3 +rename q85 alpha4 +rename q176 alpha5 +recode alpha5 (1=0) (2=1) + +egen alphamiss=rowmiss(alpha1 alpha2 alpha3 alpha4) +keep if alphamiss==0 + +*Prosociality - switch point + reshape long alpha, i(id) j(number) + + sort id number + g previouschoice=alpha[_n-1] if number>1 + g steptob=(alpha-previouschoice) if number>1 + bys id: egen sumsteptob=total(steptob) + + *Alpha value final = number of times you go with the incentive to recommend Y + g alphavalue=0 if sumsteptob==0 & alpha==0 & number==5 /*always choosing X, no incentive*/ + replace alphavalue=5 if sumsteptob==0 & alpha==1 & number==5 /*always choosing Y, incentive*/ + *Alpha value is the question number when the steptob is 1 for those who switch + replace alphavalue=number-1 if sumsteptob==1 & steptob==1 + + g inconsistentchoice=(steptob==-1) + bys id: egen inconsistentid=total(inconsistentchoice) + + bys id: egen alphavaluefinal=total(alphavalue) + replace alphavaluefinal=. if inconsistentid>0 + + cap drop alphavalue inconsistentchoice previouschoice steptob + + reshape wide alpha, i(id) j(number) + + + *ATTENTIVE GUYS are DROPPED FOR INCOMPLETE ANSWERS + *KEEP CONSISTENT GUYS IN ALPHA TASK + tab alphavaluefinal, m + gen selfish=(alphavaluefinal>3) + replace selfish=. if alphavaluefinal==. + +g choice="Stakes" + +drop if choicebefore==. + +g conditionnum=1 if condition=="PayBeforeLowCommission" +replace conditionnum=2 if condition=="PayBefore_main" +replace conditionnum=3 if condition=="PayBefore_HighCommission" + +keep if conditionnum!=. + +label var female "Female" +label var age "Age" + +replace age=61 if age==611 + +g commissionlow=conditionnum==1 +g commissionintermediate=conditionnum==2 +g commissionhigh=conditionnum==3 + +g noconflict=1-conflict +g choicebeforenoconflict=choicebefore*noconflict +g incentiveB=commission=="commissionB" + +g getyourchoice=0 +replace getyourchoice=1 if choicebefore==1 & getbefore==1 +replace getyourchoice=1 if choicebefore==0 & getbefore==0 + +g notgetyourchoice=1-getyourchoice +g choicebeforenotgetyourchoice=choicebefore*notgetyourchoice +g notgetyourchoicenoconflict=noconflict*notgetyourchoice + +g commissionlowchoicebefore=commissionlow*choicebefore +g commissionhighchoicebefore=commissionhigh*choicebefore + +egen stdalpha=std(alphavaluefinal) + + +drop responseid newid +drop distributionchannel userlanguage +drop fl_17_do fl_481_do fl_523_do fl_528_do fl_548_do fl_555_do fl_563_do fl_657_do fl_629_do fl_656_do fl_642_do + + +save "${data}stakes.dta", replace + + +/*-------------------------------*/ +* INFORMATION ARCHITECT STUDY +/*-------------------------------*/ + +*** INFORMATION ARCHITECTS***** + +*** CLEANING DEIDENTIFIED DATA +clear +import delimited "${raw}InformationArchitect_deidentified.csv", varnames(1) + + +drop if status==1 +tab distributionchannel +drop if distributionchannel=="preview" +tab finished +keep if finished==1 + + +sort newid +quietly by newid: gen dup = cond(_N==1,0,_n) +tab dup +drop if dup!=0 + +drop if q_recaptchascore<.50 + + +gen attentionpassed=. +replace attentionpassed=1 if attention==3 +replace attentionpassed=0 if attention==1 | attention==2 | attention==4 +tab attentionpassed +**92.11% passed the attention check (N=549) out of 602 who completed the attention check question + + +drop if attentionpassed!=1 +sort startdate +gen id=_n+70000 + +** condition +replace condition="IA-Advisor" if condition=="DM-Advisor" +replace condition="IA-Client" if condition=="DM-Client" + + +**Gender +gen female=. +replace female=1 if gender==2 +replace female=0 if gender==1 + +**Age +destring age, replace + +gen choicebefore=0 +replace choicebefore=1 if condition=="IA-Advisor" & choicedmadvisor==1 +replace choicebefore=1 if condition=="IA-Client" & choicedmclient==1 + +***Prosociality + + +forval i=1(1)5{ + recode alpha`i' (1=0) (2=1) + } + +egen alphamiss=rowmiss(alpha1 alpha2 alpha3 alpha4) +keep if alphamiss==0 + +reshape long alpha, i(id) j(number) + + +sort id number + g previouschoice=alpha[_n-1] if number>1 + g steptob=(alpha-previouschoice) if number>1 + bys id: egen sumsteptob=total(steptob) + + g alphavalue=0 if sumsteptob==0 & alpha==0 & number==5 /*always choosing X, no incentive*/ + replace alphavalue=5 if sumsteptob==0 & alpha==1 & number==5 /*always choosing Y, incentive*/ + replace alphavalue=number-1 if sumsteptob==1 & steptob==1 + + g inconsistentchoice=(steptob==-1) + bys id: egen inconsistentid=total(inconsistentchoice) + + bys id: egen alphavaluefinal=total(alphavalue) + replace alphavaluefinal=. if inconsistentid>0 + +cap drop alphavalue inconsistentchoice previouschoice steptob + + reshape wide alpha, i(id) j(number) + +gen study=9 + +sort choicebefore alphavaluefinal +gen missingalpha=0 +replace missingalpha=1 if alphavaluefinal==. +sort missingalpha choicebefore +gen id_pair=_n + + +gen IAAdvisor=(condition=="IA-Advisor") +gen IAClient=(condition=="IA-Client") + +drop newid + +save "${data}InformationArchitect.dta", replace + +*** ADVISORS***** + +*** CLEANING DEIDENTIFIED DATA +clear +import delimited "${raw}InformationArchitect_Advisor_deidentified.csv", varnames(1) + + +drop if status==1 +tab distributionchannel +drop if distributionchannel=="preview" +tab finished +keep if finished==1 + + +**Gender +gen female=. +replace female=1 if gender==2 +replace female=0 if gender==1 + +**Age +destring age, replace + +**Signal Blue +gen signalblue=. +replace signalblue=1 if fl_211_do=="Signal_blue2" +replace signalblue=0 if fl_211_do=="Signal_red0" +replace signalblue=1 if fl_221_do=="Signal_blue2" +replace signalblue=0 if fl_221_do=="Signal_red0" +replace signalblue=1 if fl_229_do=="Signal_blue2" +replace signalblue=0 if fl_229_do=="Signal_red0" +replace signalblue=1 if fl_127_do=="Signal_blue2" +replace signalblue=0 if fl_127_do=="Signal_red0" + +**Beliefs +g beliefcorrect=0 +replace beliefcorrect=1 if beliefbin==4 & signalblue==1 +replace beliefcorrect=1 if beliefbin==8 & signalblue==0 + +*Recommendation +g recommendb=(recommendationa==2 | recommendationb==2) + +*Conflict +gen conflict=0 +replace conflict=1 if condition=="AfterA" & signalblue==1 +replace conflict=1 if condition=="AfterB" & signalblue==0 +replace conflict=1 if condition=="BeforeA" & signalblue==1 +replace conflict=1 if condition=="BeforeB" & signalblue==0 + +**gen id +gen id_advisor=_n+75000 + + +**Prosociality +forval i=1(1)5{ + recode alpha`i' (1=0) (2=1) + } + + +reshape long alpha, i(id_advisor) j(number) + + sort id_advisor number + g previouschoice=alpha[_n-1] if number>1 + + g steptob=(alpha-previouschoice) if number>1 + bys id_advisor: egen sumsteptob=total(steptob) + + *Alpha value final = number of times you go with the incentive to recommend Y + g alphavalue=0 if sumsteptob==0 & alpha==0 & number==5 /*always choosing X, no incentive*/ + replace alphavalue=5 if sumsteptob==0 & alpha==1 & number==5 /*always choosing Y, incentive*/ + *Alpha value is the question number when the steptob is 1 for those who switch + replace alphavalue=number-1 if sumsteptob==1 & steptob==1 + + g inconsistentchoice=(steptob==-1) + bys id_advisor: egen inconsistentid=total(inconsistentchoice) + + bys id_advisor: egen alphavaluefinal=total(alphavalue) + replace alphavaluefinal=. if inconsistentid>0 + +cap drop alphavalue inconsistentchoice previouschoice steptob + + reshape wide alpha, i(id_advisor) j(number) + +gen study=9 + +tab conflict + +*Recommend incentive +gen recommendincentive=0 +replace recommendincentive=1 if (condition=="AfterA" | condition=="BeforeA" ) & recommendb==0 +replace recommendincentive=1 if (condition=="AfterB" | condition=="BeforeB" ) & recommendb==1 + +gen treatment=0 +replace treatment =1 if condition=="BeforeA" | condition=="BeforeB" +replace treatment =2 if condition=="AfterA" | condition=="AfterB" + +gen incentiveforA=. +replace incentiveforA=1 if (condition=="AfterA" | condition=="BeforeA" ) +replace incentiveforA=0 if (condition=="AfterB" | condition=="BeforeB" ) + +gen incentiveB=1-incentiveforA + +drop newid + +save "${data}InformationArchitect_advisors.dta", replace + + + +******* APPENDIX ********* + +/*-------------------------------*/ +*NO CHOICE - TOGETHER for APPENDIX +/*-------------------------------*/ + +clear +import delimited "${raw}NoChoiceSimoultaneous_deidentified.csv", varnames(1) + + +drop if status==1 +*drop if workerid=="" +tab distributionchannel +drop if distributionchannel=="preview" +tab finished +keep if finished==1 + + +sort newid +quietly by newid: gen dup = cond(_N==1,0,_n) +tab dup +drop if dup!=0 + +rename q32 attention + +g attentionpassed=attention==3 +keep if attentionpassed==1 + + +tab q_recaptchascore +drop if q_recaptchascore<.5 + + +sort startdate +gen id=_n + 80000 + + +*Beliefs +g beliefbin=q45 +g beliefnum=q43 + +**Gender +gen female=. +replace female=1 if gender==2 +replace female=0 if gender==1 + + + +gen treatment=0 +replace treatment =1 if condition=="BeforeA" | condition=="BeforeB" +replace treatment =2 if condition=="AfterA" | condition=="AfterB" +replace treatment =3 if condition=="TogetherA" | condition=="TogetherB" + + +g recommendb=(recommendationa==2 | recommendationb==2) + + +gen signalblue=. +replace signalblue=1 if fl_259_do=="All-Signalblue-incentiveA" +replace signalblue=0 if fl_259_do=="All-Signalred-incentiveA" +replace signalblue=0 if fl_268_do=="All-Signalred-incentiveB" +replace signalblue=1 if fl_268_do=="All-Signalblue-incentiveB" +replace signalblue=1 if fl_211_do=="Signal_blue2" +replace signalblue=0 if fl_211_do=="Signal_red0" +replace signalblue=1 if fl_221_do=="Signal_blue2" +replace signalblue=0 if fl_221_do=="Signal_red0" +replace signalblue=1 if fl_229_do=="Signal_blue2" +replace signalblue=0 if fl_229_do=="Signal_red0" +replace signalblue=1 if fl_127_do=="Signal_blue2" +replace signalblue=0 if fl_127_do=="Signal_red0" + +gen conflict=0 +replace conflict=1 if cond=="AfterA" & signalblue==1 +replace conflict=1 if cond=="AfterB" & signalblue==0 +replace conflict=1 if cond=="BeforeA" & signalblue==1 +replace conflict=1 if cond=="BeforeB" & signalblue==0 +replace conflict=1 if cond=="TogetherA" & signalblue==1 +replace conflict=1 if cond=="TogetherB" & signalblue==0 + + +tab conflict + +gen recommendincentive=0 +replace recommendincentive=1 if (cond=="AfterA" | cond=="BeforeA" | cond=="TogetherA") & recommendb==0 +replace recommendincentive=1 if (cond=="AfterB" | cond=="BeforeB" | cond=="TogetherB") & recommendb==1 + +gen incentiveforA=. +replace incentiveforA=1 if (cond=="AfterA" | cond=="BeforeA" | cond=="TogetherA") +replace incentiveforA=0 if (cond=="AfterB" | cond=="BeforeB" | cond=="TogetherB") + +gen incentiveB=1-incentiveforA + +*Prosociality +forval i=2(1)5{ +recode q8`i' (1=0) (2=1) +} + +rename q82 alpha1 +rename q83 alpha2 +rename q84 alpha3 +rename q85 alpha4 +rename q176 alpha5 +recode alpha5 (1=0) (2=1) + +egen alphamiss=rowmiss(alpha1 alpha2 alpha3 alpha4) +tab alphamiss + +*alpha1-5 equal 1 is recommend the incentivized option + +*RESTRICTING RESPONDENTS TO THOSE WITH COMPLETE ALPHA CHOICES +keep if alphamiss==0 + +*Prosociality switch +reshape long alpha, i(id) j(number) + +sort id number +g previouschoice=alpha[_n-1] if number>1 +g steptob=(alpha-previouschoice) if number>1 +bys id: egen sumsteptob=total(steptob) + +*Alpha value final = number of times you go with the incentive to recommend Y +g alphavalue=0 if sumsteptob==0 & alpha==0 & number==5 /*always choosing X, no incentive*/ +replace alphavalue=5 if sumsteptob==0 & alpha==1 & number==5 /*always choosing Y, incentive*/ +*Alpha value is the question number when the steptob is 1 for those who switch +replace alphavalue=number-1 if sumsteptob==1 & steptob==1 + +g inconsistentchoice=(steptob==-1) +bys id: egen inconsistentid=total(inconsistentchoice) + +bys id: egen alphavaluefinal=total(alphavalue) +replace alphavaluefinal=. if inconsistentid>0 + +tab alphavaluefinal, m +cap drop alphavalue inconsistentchoice previouschoice steptob +reshape wide alpha, i(id) j(number) + +*alpha +tab alphavaluefinal if treatment!=., m + + +gen seeincentivefirst=(treatment==1) + +gen Together=(treatment==3) + +gen noconflict=1-conflict + +gen wave=2 + + +gen missingalpha=(alphavaluefinal==.) + +drop responseid newid shortid + +save "${data}NoChoiceSimoultaneous.dta", replace + + +/*-------------------------------*/ +*CHOICE DETERMINISTIC +/*-------------------------------*/ + +clear +import delimited "${raw}ChoiceDeterministic_deidentified.csv", varnames(1) + + +drop if status==1 +tab distributionchannel +drop if distributionchannel=="preview" +tab finished +keep if finished==1 + + +sort newid +quietly by newid: gen dup = cond(_N==1,0,_n) +tab dup +drop if dup!=0 + +drop if q_recaptchascore<.50 + + +g id=_n + 90000 +tab attention, m +g attentionpassed=attention==3 + +tab attentionpassed +**78.89% passed the attention check (N=953) out of 1200 who completed the attention check question and were not excluded according to the criteria above +drop if attentionpassed!=1 + + +tab belief +replace belief=belief*100 if belief<1 + +***Gen recommendB and recommenda dummies + +g recommendb=. +replace recommendb=1 if recommendationb==2 +replace recommendb=0 if recommendationb==1 + +replace recommendb=1 if recommendationa==2 +replace recommendb=0 if recommendationa==1 + + +g recommenda=1-recommendb + + +***gen IncentiveB +g incentiveB=. +replace incentiveB=1 if commission=="commissionB" +replace incentiveB=0 if commission=="commissionA" + +g incentiveA=. +replace incentiveA=1 if commission=="commissionA" +replace incentiveA=0 if commission=="commissionB" + + +*GET by treatment +g getafter=(getbefore=="GetAfter") +cap drop getbefore +g getbefore=1-getafter + + + +**gen bluesignal dummy +g signalblue=. +replace signalblue=1 if fl_528_do=="Signal_blue2" +replace signalblue=1 if fl_1034_do=="Signal_blue2" +replace signalblue=1 if fl_555_do=="Signal_blue2" +replace signalblue=1 if fl_1047_do=="Signal_blue2" +replace signalblue=1 if fl_629_do=="Signal_blue2" +replace signalblue=1 if fl_642_do=="Signal_blue2" +replace signalblue=1 if fl_1060_do=="Signal_blue2" +replace signalblue=1 if fl_1073_do=="Signal_blue2" + +replace signalblue=0 if fl_528_do=="Signal_red0" +replace signalblue=0 if fl_555_do=="Signal_red0" +replace signalblue=0 if fl_1047_do=="Signal_red0" +replace signalblue=0 if fl_1034_do=="Signal_red0" +replace signalblue=0 if fl_629_do=="Signal_red0" +replace signalblue=0 if fl_642_do=="Signal_red0" +replace signalblue=0 if fl_1060_do=="Signal_red0" +replace signalblue=0 if fl_1073_do=="Signal_red0" +drop if signalblue==. + +g conflict=. +replace conflict=1 if signalblue==0 & commission=="commissionB" +replace conflict=0 if signalblue==1 & commission=="commissionB" +replace conflict=1 if signalblue==1 & commission=="commissionA" +replace conflict=0 if signalblue==0 & commission=="commissionA" + + +tab commission +g recommendincentive=recommendb if commission=="commissionB" +replace recommendincentive=recommenda if commission=="commissionA" + + +gen choicebefore=. +replace choicebefore=1 if choicefree==1 +replace choicebefore=0 if choicefree==2 + + + +**Gender +gen female=. +replace female=1 if gender==2 +replace female=0 if gender==1 + + +***Prosociality +forval i=1(1)5{ +recode alpha`i' (1=0) (2=1) +} + +egen alphamiss=rowmiss(alpha1 alpha2 alpha3 alpha4) +keep if alphamiss==0 + +g sample="MTurk" +rename q211 explanationchoice +gen costlydemand=0 + + +reshape long alpha, i(id) j(number) + +sort id number +g previouschoice=alpha[_n-1] if number>1 +g steptob=(alpha-previouschoice) if number>1 +bys id: egen sumsteptob=total(steptob) + +*Alpha value final = number of times you go with the incentive to recommend Y +g alphavalue=0 if sumsteptob==0 & alpha==0 & number==5 /*always choosing X, no incentive*/ +replace alphavalue=5 if sumsteptob==0 & alpha==1 & number==5 /*always choosing Y, incentive*/ +*Alpha value is the question number when the steptob is 1 for those who switch +replace alphavalue=number-1 if sumsteptob==1 & steptob==1 + +g inconsistentchoice=(steptob==-1) +bys id: egen inconsistentid=total(inconsistentchoice) + +bys id: egen alphavaluefinal=total(alphavalue) +replace alphavaluefinal=. if inconsistentid>0 + +tab alphavaluefinal, m +cap drop alphavalue inconsistentchoice previouschoice steptob +reshape wide alpha, i(id) j(number) + +*Average alpha +tab alphavaluefinal, m + + +gen Deterministic=(condition=="ChoiceFree_Deterministic") + +egen stdalpha=std(alphavaluefinal) + +* prepare data for regressions +g getbefore_Deterministic=getbefore*Deterministic +label var getbefore_Deterministic "Get Before X Deterministic" + +gen Not_getyourchoice=0 +replace Not_getyourchoice=1 if choicebefore==1 & getbefore==0 +replace Not_getyourchoice=1 if choicebefore==0 & getbefore==1 +gen getyourchoice=1-Not_getyourchoice + +gen choicebefore_Notgetyourchoice=choicebefore*Not_getyourchoice +gen noconflict=1-conflict +gen noconflict_choicebefore=noconflict*choicebefore +gen noconflict_Notgetyourchoice=noconflict*Not_getyourchoice + +gen choicebefore_Deterministic=choicebefore*Deterministic +gen choicebefore_getyourchoiceDet=choicebefore*getyourchoice*Deterministic +gen choicebefore_noconflict=choicebefore*noconflict +gen Deterministic_noconflict=Deterministic*noconflict +gen Deterministic_choicebeforeNocon=Deterministic*noconflict*choicebefore + +label var getyourchoice "Random Assignment to Preferred Order" +label var choicebefore_getyourchoice "Prefer to See Incentive First X Random Assignment" + +gen getyourchoice_Det=getyourchoice*Deterministic + +drop responseid newid shortid dup +drop fl_728_do fl_17_do fl_481_do fl_523_do fl_528_do fl_1034_do fl_548_do fl_555_do fl_1047_do fl_563_do fl_657_do fl_629_do fl_1060_do fl_656_do fl_642_do fl_1073_do +save "${data}Choice_Deterministic.dta", replace + +/*-------------------------------*/ +*PREDICTIONS +/*-------------------------------*/ +clear +import excel "${raw}predictions_explanations_coded_deidentified.xlsx", firstrow +save "${data}predictions_explanations_coded.dta", replace + +clear +import excel "${raw}predictions_deidentified.xlsx", firstrow + + +drop if _n<3 +drop if status==1 +drop if q_relevantidduplicate==1 +drop if q_relevantidfraudscore!=0 + +destring predictbeforered_es_1 predictafterred_es_1 predictbeforeblue_es_1 predictafterblue_es_1, force replace + +g predictbeforered1=predictbeforered_es_1 +g predictafterred1=predictafterred_es_1 +g predictbeforeblue1=predictbeforeblue_es_1 +g predictafterblue1=predictafterblue_es_1 +g gapred=predictbeforered1-58 if predictbeforered1!=. +replace gapred=68-predictafterred1 if predictafterred1!=. +g gapblue=predictbeforeblue1-75 if predictbeforeblue1!=. +replace gapblue=86-predictafterblue1 if predictafterblue1!=. + +g gap=gapred if gapred!=. +replace gap=gapblue if gapblue!=. + +g choice="Predictions" + +tab gap +drop if gap==. + +merge 1:1 shortid using "${data}predictions_explanations_coded.dta" + + +g attentivenum=attentive=="YES" +tab attentivenum +keep if attentivenum==1 + +tab gender +destring age, force replace +tabstat age + + +replace gap=gap/100 +replace gapred=gapred/100 +replace gapblue=gapblue/100 + +egen meangap=mean(gap) +egen sdgap=sd(gap) +egen ngap=count(gap) + +g logap = meangap - sdgap/(sqrt(ngap)) +g higap = meangap + sdgap/(sqrt(ngap)) + + +g predicteddirection=predictafterred if predictafterred!=. +replace predicteddirection=predictbeforered if predictbeforered!=. +replace predicteddirection=predictafterblue if predictafterblue!=. +replace predicteddirection=predictbeforeblue if predictbeforeblue!=. + + +g before=1 if predictbeforered!=. | predictbeforeblue!=. +replace before=0 if predictafterred!=. | predictafterblue!=. + +g predictcorrect=1 if (predicteddirection==1 & before==0) | (predicteddirection==3 & before==1) +replace predictcorrect=0 if (predicteddirection==2 & before==0) | (predicteddirection==2 & before==1) +replace predictcorrect=-1 if (predicteddirection==3 & before==0) | (predicteddirection==1 & before==1) + +drop _merge responseid +save "${data}predictionsstudy.dta", replace + +/*-------------------------------*/ +*CLIENTS +/*-------------------------------*/ + +*** NOCHOICE, CHOICE AND STAKES EXPERIMENTS - MAIN +clear +import delimited using "${raw}Clients/Clients_Main_deidentified.csv", varnames(1) + +gen follow=. +replace follow=1 if client_choiceb==1 & advisor_recommendb==1 +replace follow=1 if client_choiceb==0 & advisor_recommendb==0 +replace follow=0 if client_choiceb==0 & advisor_recommendb==1 +replace follow=0 if client_choiceb==1 & advisor_recommendb==0 + +save "${data}Clients_Main.dta", replace + + +*** CLIENTS ** NOCHOICE, CHOICE AND STAKES EXPERIMENTS - MPL + +clear +import delimited using "${raw}Clients/Clients_MPL_deidentified.csv", varnames(1) + +gen follow=. +replace follow=1 if advisor_recommends_producty==1 & client_choicey==1 +replace follow=1 if advisor_recommends_producty==0 & client_choicey==0 +replace follow=0 if advisor_recommends_producty==0 & client_choicey==1 +replace follow=0 if advisor_recommends_producty==1 & client_choicey==0 + +save "${data}Clients_MPL.dta", replace + +*** CLIENTS ** NOCHOICE, CHOICE AND STAKES EXPERIMENTS - BLINDING + +clear +import delimited using "${raw}Clients/Clients_Blinding_deidentified.csv", varnames(1) + + +gen follow=. +replace follow=1 if recommendation_infoavoidance==1 & choice1==1 +replace follow=1 if recommendation_infoavoidance==0 & choice1==0 +replace follow=0 if recommendation_infoavoidance==0 & choice1==1 +replace follow=0 if recommendation_infoavoidance==1 & choice1==0 + +save "${data}Clients_Blinding.dta", replace + +*** CLIENTS ** INFORMATION ARCHITECTS - MAIN +clear +import delimited using "${raw}Clients/Clients_InfoArchitectsMain_deidentified.csv", varnames(1) + +gen follow=. +replace follow=1 if client_choiceb==1 & advisor_recommendb==1 +replace follow=1 if client_choiceb==0 & advisor_recommendb==0 +replace follow=0 if client_choiceb==0 & advisor_recommendb==1 +replace follow=0 if client_choiceb==1 & advisor_recommendb==0 + +save "${data}Clients_InfoArchitectsMain.dta", replace + +*** CLIENTS ** INFORMATION ARCHITECTS - MPL ADVISORS + +clear +import delimited using "${raw}Clients/Clients_IAAdvisors_MPL_deidentified.csv", varnames(1) + + +gen follow=. +replace follow=1 if advisor_recommends_producty==1 & client_choicey==1 +replace follow=1 if advisor_recommends_producty==0 & client_choicey==0 +replace follow=0 if advisor_recommends_producty==0 & client_choicey==1 +replace follow=0 if advisor_recommends_producty==1 & client_choicey==0 + +save "${data}Clients_IAAdvisors_MPL.dta", replace + +*** CLIENTS ** INFORMATION ARCHITECTS - MPL INFO ARCHITECTS +clear +import delimited using "${raw}Clients/Clients_IAInfoArchitects_MPL_deidentified.csv", varnames(1) + + +gen follow=. +replace follow=1 if advisor_recommends_producty==1 & client_choicey==1 +replace follow=1 if advisor_recommends_producty==0 & client_choicey==0 +replace follow=0 if advisor_recommends_producty==0 & client_choicey==1 +replace follow=0 if advisor_recommends_producty==1 & client_choicey==0 + +save "${data}Clients_IAInfoArchitects_MPL.dta", replace + + +*** CLIENTS ** NOCHOICE SIMULTANEOUS - MAIN + +clear +import delimited using "${raw}Clients/Clients_NoChoiceSimultaneousMain_deidentified.csv", varnames(1) + +gen follow=. +replace follow=1 if advisor_recommendb==1 & client_choiceb==1 +replace follow=1 if advisor_recommendb==0 & client_choiceb==0 +replace follow=0 if advisor_recommendb==0 & client_choiceb==1 +replace follow=0 if advisor_recommendb==1 & client_choiceb==0 + +save "${data}Clients_NoChoiceSimultaneousMain.dta", replace + + +*** CLIENTS ** NOCHOICE SIMULTANEOUS - MPL + +clear +import delimited using "${raw}Clients/Clients_NoChoiceSimultaneousMPL_deidentified.csv", varnames(1) + + +gen follow=. +replace follow=1 if advisor_recommends_producty==1 & client_choicey==1 +replace follow=1 if advisor_recommends_producty==0 & client_choicey==0 +replace follow=0 if advisor_recommends_producty==0 & client_choicey==1 +replace follow=0 if advisor_recommends_producty==1 & client_choicey==0 + +save "${data}Clients_NoChoiceSimultaneousMPL.dta", replace + +*** CLIENTS ** CHOICE DETERMINISTIC - MAIN +clear +import delimited using "${raw}Clients/Clients_ChoiceDeterministic_Main_deidentified.csv", varnames(1) +gen follow=. +replace follow=1 if advisor_recommendb==1 & client_choiceb==1 +replace follow=1 if advisor_recommendb==0 & client_choiceb==0 +replace follow=0 if advisor_recommendb==0 & client_choiceb==1 +replace follow=0 if advisor_recommendb==1 & client_choiceb==0 + +save "${data}Clients_ChoiceDeterministic_Main.dta", replace + +*** CLIENTS ** CHOICE DETERMINISTIC - MPL + + +clear +import delimited using "${raw}Clients/Clients_ChoiceDeterministic_MPL_deidentified.csv", varnames(1) + + +gen follow=. +replace follow=1 if advisor_recommends_producty==1 & client_choicey==1 +replace follow=1 if advisor_recommends_producty==0 & client_choicey==0 +replace follow=0 if advisor_recommends_producty==0 & client_choicey==1 +replace follow=0 if advisor_recommends_producty==1 & client_choicey==0 + +save "${data}Clients_ChoiceDeterministic_MPL.dta", replace + +*drop unnecessary files +rm "${data}choice1.dta" +rm "${data}choice2.dta" +rm "${data}choice3.dta" +rm "${data}professionals_prolific.dta" +rm "${data}coding_explanations.dta" diff --git a/24/replication_package/Code/main.do b/24/replication_package/Code/main.do new file mode 100644 index 0000000000000000000000000000000000000000..b4bc3bdb5755478e5f3e5b00ee23eab06302ba55 --- /dev/null +++ b/24/replication_package/Code/main.do @@ -0,0 +1,15 @@ + +clear +*GLOBALS +*set the path to "YOUR PATH" +local path=" " + + global raw "`path'/Data/Raw Data/" + global data "`path'/Data/Clean Data/" + global main "`path'/Results/Main Text/" + global appendix "`path'/Results/Appendix/" + global code "`path'/Code/" + +do "${code}config.do" +do "${code}datacleaning.do" +do "${code}analysis.do" diff --git a/24/replication_package/Data/Clean Data/Choice_Deterministic.dta b/24/replication_package/Data/Clean Data/Choice_Deterministic.dta new file mode 100644 index 0000000000000000000000000000000000000000..d267e04f56be096dfd6856f05e9f45c2ef6277fe --- /dev/null +++ b/24/replication_package/Data/Clean Data/Choice_Deterministic.dta @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:752bc92c0ec66bd937f67db73f599af0acf79649f8cbc5deea0477f6af837b76 +size 756988 diff --git a/24/replication_package/Data/Clean Data/Choice_coding_explanations.dta b/24/replication_package/Data/Clean Data/Choice_coding_explanations.dta new file mode 100644 index 0000000000000000000000000000000000000000..b7689d515c51e32ccdf94728e460116974039c29 --- /dev/null +++ b/24/replication_package/Data/Clean Data/Choice_coding_explanations.dta @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:28857485558b7745b66f2a2bf006cd71dcd6b7c48739233f9e5f2f77f4fbd4ea +size 2434200 diff --git a/24/replication_package/Data/Clean Data/Clients_Blinding.dta b/24/replication_package/Data/Clean Data/Clients_Blinding.dta new file mode 100644 index 0000000000000000000000000000000000000000..a31240af2bbe47ee724ca797f33271e14a3651d0 --- /dev/null +++ b/24/replication_package/Data/Clean Data/Clients_Blinding.dta @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3ae6310137d32e5d50c2cb7b2e9e443fb01da476fb7ff30b4dc377f2c3c8becc +size 18033 diff --git a/24/replication_package/Data/Clean Data/Clients_ChoiceDeterministic_MPL.dta b/24/replication_package/Data/Clean Data/Clients_ChoiceDeterministic_MPL.dta new file mode 100644 index 0000000000000000000000000000000000000000..63dd6b7da40778e8c7453dceb7e4e1a24a4bb5c2 --- /dev/null +++ b/24/replication_package/Data/Clean Data/Clients_ChoiceDeterministic_MPL.dta @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0a1c76da5befecf112c7198280c93c7ebe4db092f8f713b8df30a34ef5fa145d +size 13860 diff --git a/24/replication_package/Data/Clean 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\n\u200b<\/div>\n\n
We drew the following payoff (ball) <\/strong><\/span> from Product Y<\/u>. <\/strong><\/span>\n\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>\n<\/div>","DataExportTag":"alpha2","QuestionType":"MC","Selector":"SAVR","SubSelector":"TX","Configuration":{"QuestionDescriptionOption":"UseText"},"QuestionDescription":"2.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will...","Choices":{"1":{"Display":"Product X"},"2":{"Display":"Product Y"}},"ChoiceOrder":["1","2"],"Validation":{"Settings":{"ForceResponse":"ON","ForceResponseType":"ON","Type":"None"}},"Language":[],"NextChoiceId":4,"NextAnswerId":1,"QuestionID":"QID83","DataVisibility":{"Private":false,"Hidden":false}}},{"SurveyID":"SV_b2QpoTFEltxY6cS","Element":"SQ","PrimaryAttribute":"QID343","SecondaryAttribute":"2.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will..","TertiaryAttribute":null,"Payload":{"QuestionText":"
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\n\u200b<\/div>\n\n
We drew the following payoff (ball) <\/strong><\/span> from Product Y<\/u>. <\/strong><\/span>\n\n
 <\/div>\n\n
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 <\/div>\n\n
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\n\u200b  \n
<\/strong><\/span><\/div>
We drew the following payoff (ball) <\/strong><\/span> from Product Y<\/u>. <\/strong><\/span><\/div>\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>\n<\/div>","DataExportTag":"Q373","QuestionType":"MC","Selector":"SAVR","SubSelector":"TX","DataVisibility":{"Private":false,"Hidden":false},"Configuration":{"QuestionDescriptionOption":"UseText"},"QuestionDescription":"3.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will...","Choices":{"1":{"Display":"Product X"},"2":{"Display":"Product Y"}},"ChoiceOrder":["1","2"],"Validation":{"Settings":{"ForceResponse":"ON","ForceResponseType":"ON","Type":"None"}},"Language":[],"NextChoiceId":4,"NextAnswerId":1,"QuestionID":"QID344"}},{"SurveyID":"SV_b2QpoTFEltxY6cS","Element":"QC","PrimaryAttribute":"Survey Question Count","SecondaryAttribute":"380","TertiaryAttribute":null,"Payload":null},{"SurveyID":"SV_b2QpoTFEltxY6cS","Element":"SQ","PrimaryAttribute":"QID171","SecondaryAttribute":"4. The quality of Product B is high with...","TertiaryAttribute":null,"Payload":{"QuestionText":"4. 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\n\u200b
\n\u200b
\n \n
We drew the following payoff (ball) <\/strong><\/span> from Product Y<\/u>. <\/strong><\/span>\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>\n<\/div>","DataExportTag":"alpha4","QuestionType":"MC","Selector":"SAVR","SubSelector":"TX","Configuration":{"QuestionDescriptionOption":"UseText"},"QuestionDescription":"4.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will...","Choices":{"1":{"Display":"Product X"},"2":{"Display":"Product Y"}},"ChoiceOrder":["1","2"],"Validation":{"Settings":{"ForceResponse":"ON","ForceResponseType":"ON","Type":"None"}},"Language":[],"NextChoiceId":4,"NextAnswerId":1,"QuestionID":"QID85","DataVisibility":{"Private":false,"Hidden":false}}},{"SurveyID":"SV_b2QpoTFEltxY6cS","Element":"SQ","PrimaryAttribute":"QID345","SecondaryAttribute":"4.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will..","TertiaryAttribute":null,"Payload":{"QuestionText":"4. <\/strong>If you recommend product Y for the randomly selected set that is sent to the advisee, you will receive $15.<\/b><\/u>
\n
\n\u200bWe drew the following payoff (ball) <\/strong><\/span> from Product Y<\/u>. <\/strong><\/span>\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>\n<\/div>","DataExportTag":"Q375","QuestionType":"MC","Selector":"SAVR","SubSelector":"TX","DataVisibility":{"Private":false,"Hidden":false},"Configuration":{"QuestionDescriptionOption":"UseText"},"QuestionDescription":"4.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will...","Choices":{"1":{"Display":"Product X"},"2":{"Display":"Product Y"}},"ChoiceOrder":["1","2"],"Validation":{"Settings":{"ForceResponse":"ON","ForceResponseType":"ON","Type":"None"}},"Language":[],"NextChoiceId":4,"NextAnswerId":1,"QuestionID":"QID345","RecodeValues":{"1":"1","2":"2"}}},{"SurveyID":"SV_b2QpoTFEltxY6cS","Element":"SQ","PrimaryAttribute":"QID326","SecondaryAttribute":"5. Which of the following statements is correct? Product B...","TertiaryAttribute":null,"Payload":{"QuestionText":"5. Which of the following statements is correct? 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\n\u200b   
\n\u200b
\n \n
We drew the following payoff (ball) <\/strong><\/span> from Product Y<\/u>. <\/strong><\/span>\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>\n<\/div>","DataExportTag":"alpha5","QuestionType":"MC","Selector":"SAVR","SubSelector":"TX","DataVisibility":{"Private":false,"Hidden":false},"Configuration":{"QuestionDescriptionOption":"UseText"},"QuestionDescription":"5.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will...","Choices":{"1":{"Display":"Product X"},"2":{"Display":"Product Y"}},"ChoiceOrder":["1","2"],"Validation":{"Settings":{"ForceResponse":"ON","ForceResponseType":"ON","Type":"None"}},"Language":[],"NextChoiceId":4,"NextAnswerId":1,"QuestionID":"QID176"}},{"SurveyID":"SV_b2QpoTFEltxY6cS","Element":"SQ","PrimaryAttribute":"QID346","SecondaryAttribute":"5.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will..","TertiaryAttribute":null,"Payload":{"QuestionText":"5. <\/strong>If you recommend product Y for the randomly selected set that is sent to the advisee, you will receive $15.<\/b><\/u>
\n\u200b    
\n\u200b \nWe drew the following payoff (ball) <\/strong><\/span> from Product Y<\/u>. <\/strong><\/span>\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>\n<\/div>","DataExportTag":"Q377","QuestionType":"MC","Selector":"SAVR","SubSelector":"TX","DataVisibility":{"Private":false,"Hidden":false},"Configuration":{"QuestionDescriptionOption":"UseText"},"QuestionDescription":"5.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will...","Choices":{"1":{"Display":"Product X"},"2":{"Display":"Product Y"}},"ChoiceOrder":["1","2"],"Validation":{"Settings":{"ForceResponse":"ON","ForceResponseType":"ON","Type":"None"}},"Language":[],"NextChoiceId":4,"NextAnswerId":1,"QuestionID":"QID346"}},{"SurveyID":"SV_b2QpoTFEltxY6cS","Element":"SQ","PrimaryAttribute":"QID193","SecondaryAttribute":"6. If you recommend Product Y for the randomly selected set that is sent to the advisee, you will...","TertiaryAttribute":null,"Payload":{"QuestionText":"
6. <\/strong>If you recommend Product Y for the randomly selected set that is sent to the advisee, you will receive no additional payment. <\/b><\/u>
\n\u200b
\n\u200b \u200b<\/div>\n\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>","QuestionType":"MC","Selector":"SAVR","SubSelector":"TX","Configuration":{"QuestionDescriptionOption":"UseText"},"QuestionDescription":"6. If you recommend Product Y for the randomly selected set that is sent to the advisee, you will...","Choices":{"1":{"Display":"Product X"},"2":{"Display":"Product Y"}},"ChoiceOrder":["1","2"],"Validation":{"Settings":{"ForceResponse":"ON","ForceResponseType":"ON","Type":"None"}},"Language":[],"NextChoiceId":4,"NextAnswerId":1,"DataVisibility":{"Private":false,"Hidden":false},"QuestionText_Unsafe":"
1. <\/strong>If you recommend Product Y for the randomly selected set that is sent to the advisee, you will receive $0.15.<\/b><\/u>
\n\u200b
\n\u200b \u200b
\n
\nWe drew the following payoff (ball) <\/strong><\/span> from Product Y<\/u>. <\/strong><\/span><\/div>\n\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>","DataExportTag":"Q193","QuestionID":"QID193"}},{"SurveyID":"SV_b2QpoTFEltxY6cS","Element":"SQ","PrimaryAttribute":"QID194","SecondaryAttribute":"7.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will..","TertiaryAttribute":null,"Payload":{"QuestionText":"
7. <\/strong>If you recommend product Y for the randomly selected set that is sent to the advisee, you will receive no additional payment<\/b><\/u>
\n\u200b
\n <\/div>\n\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>\n<\/div>","QuestionType":"MC","Selector":"SAVR","SubSelector":"TX","Configuration":{"QuestionDescriptionOption":"UseText"},"QuestionDescription":"7.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will...","Choices":{"1":{"Display":"Product X"},"2":{"Display":"Product Y"}},"ChoiceOrder":["1","2"],"Validation":{"Settings":{"ForceResponse":"ON","ForceResponseType":"ON","Type":"None"}},"Language":[],"NextChoiceId":4,"NextAnswerId":1,"DataVisibility":{"Private":false,"Hidden":false},"QuestionText_Unsafe":"
2. <\/strong>If you recommend product Y for the randomly selected set that is sent to the advisee, you will receive $0.15.<\/b><\/u>
\n\u200b<\/div>\n\n
We drew the following payoff (ball) <\/strong><\/span> from Product Y<\/u>. <\/strong><\/span>\n\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>\n<\/div>","DataExportTag":"Q194","QuestionID":"QID194"}},{"SurveyID":"SV_b2QpoTFEltxY6cS","Element":"SQ","PrimaryAttribute":"QID195","SecondaryAttribute":"8.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will..","TertiaryAttribute":null,"Payload":{"QuestionText":"8. <\/strong>If you recommend product Y for the randomly selected set that is sent to the advisee, you will receive no additional payment<\/b><\/u>
\n\u200b <\/strong>\n
\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>\n<\/div>","QuestionType":"MC","Selector":"SAVR","SubSelector":"TX","Configuration":{"QuestionDescriptionOption":"UseText"},"QuestionDescription":"8.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will...","Choices":{"1":{"Display":"Product X"},"2":{"Display":"Product Y"}},"ChoiceOrder":["1","2"],"Validation":{"Settings":{"ForceResponse":"ON","ForceResponseType":"ON","Type":"None"}},"Language":[],"NextChoiceId":4,"NextAnswerId":1,"DataVisibility":{"Private":false,"Hidden":false},"QuestionText_Unsafe":"3. <\/strong>If you recommend product Y for the randomly selected set that is sent to the advisee, you will receive $0.15.<\/b><\/u>
\n\u200b <\/strong>\n
We drew the following payoff (ball) <\/strong><\/span> from Product Y<\/u>. <\/strong><\/span>\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>\n<\/div>","DataExportTag":"Q195","QuestionID":"QID195"}},{"SurveyID":"SV_b2QpoTFEltxY6cS","Element":"SQ","PrimaryAttribute":"QID196","SecondaryAttribute":"9.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will..","TertiaryAttribute":null,"Payload":{"QuestionText":"9. <\/strong>If you recommend product Y for the randomly selected set that is sent to the advisee, you will receive no additional payment<\/b><\/u>
\n\u200b
\n\u200b
\n \n
We drew the following payoff (ball) <\/strong><\/span> from Product Y<\/u>. <\/strong><\/span>\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>\n<\/div>","QuestionType":"MC","Selector":"SAVR","SubSelector":"TX","Configuration":{"QuestionDescriptionOption":"UseText"},"QuestionDescription":"9.\u00a0If you recommend product Y for the randomly selected set that is sent to the advisee, you will...","Choices":{"1":{"Display":"Product X"},"2":{"Display":"Product Y"}},"ChoiceOrder":["1","2"],"Validation":{"Settings":{"ForceResponse":"ON","ForceResponseType":"ON","Type":"None"}},"Language":[],"NextChoiceId":4,"NextAnswerId":1,"DataVisibility":{"Private":false,"Hidden":false},"QuestionText_Unsafe":"4. <\/strong>If you recommend product Y for the randomly selected set that is sent to the advisee, you will receive $0.15.<\/b><\/u>
\n\u200b
\n\u200b
\n \n
We drew the following payoff (ball) <\/strong><\/span> from Product Y<\/u>. <\/strong><\/span>\n
 <\/div>\n\n
Which product do you recommend to the advisee?<\/em><\/div>\n<\/div>","DataExportTag":"Q196","QuestionID":"QID196"}},{"SurveyID":"SV_b2QpoTFEltxY6cS","Element":"SQ","PrimaryAttribute":"QID192","SecondaryAttribute":"A different recommendationNext, we will show you 5 new sets of Products. For each\u00a0set, we will as..","TertiaryAttribute":null,"Payload":{"QuestionText":"A different recommendation<\/strong>\n