***************************************************************************************** ** LJZ analysis for Peyton and Huber 2021 JOP ** https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/1Y2DLT ***************************************************************************************** ***************************************************************************************** ** Get sample sizes for non-White respondents: ***************************************************************************************** // Run Peyton and Huber Stata code lines 14 through 60. Then... sum responder_id if dem_racewhite==1 di 18450/738 sum responder_id if dem_racewhite==0 di 4325/25 sum responder_id if dem_racewhite==. di 500/25 clear all ***************************************************************************************** ** Analysis using only White respondents: ***************************************************************************************** // Run Peyton and Huber Stata code lines 11 through 60. Then... ***************************************************************************************** ** Test whether racial resentment or explicit prejudice better captured discrimination ***************************************************************************************** reg accept_offer i.offer_amount i.round black_proposer if dem_racewhite==1 & symbolic_binary==1 estimates store RR reg accept_offer i.offer_amount i.round black_proposer if dem_racewhite==1 & overt_binary==1 estimates store EP suest RR EP, robust cluster(responder_id) test [EP_mean]black_proposer = [RR_mean]black_proposer lincom [EP_mean]black_proposer - [RR_mean]black_proposer reg accept_offer i.offer_amount i.round black_proposer if dem_racewhite==1 & symbolic_binary==1, robust cluster(responder_id) // to check the suest estimates reg accept_offer i.offer_amount i.round black_proposer if dem_racewhite==1 & overt_binary==1 , robust cluster(responder_id) // to check the suest estimates ***************************************************************************************** ** Separate category for respondents who rated Whites lower than Blacks on net ***************************************************************************************** gen WB = (dem_lazy_white - dem_lazy_black) + (dem_unint_white - dem_unint_black) + (dem_untrst_white - dem_untrst_black) + (dem_viol_white - dem_viol_black) tab WB tab WB if overt_binary==1 tab WB if overt_binary==0 recode WB (-100/-1=1) (0=2) (1/100=3), gen(WB3) label define WB3 1 "[1] Rated Whites > Blacks" 2 "[2] Rated Whites = Blacks" 3 "[3] Rated Whites < Blacks" label values WB3 WB3 tab WB3 ***************************************************************************************** ** Compare to Peyton and Huber 2021 measure ***************************************************************************************** tab WB3 overt_binary ***************************************************************************************** ** Analysis for each level of WB3 ***************************************************************************************** reg accept_offer i.offer_amount i.round black_proposer if WB3==1, robust cluster(responder_id) reg accept_offer i.offer_amount i.round black_proposer if WB3==2, robust cluster(responder_id) reg accept_offer i.offer_amount i.round black_proposer if WB3==3, robust cluster(responder_id) ***************************************************************************************** ** Check for differences ***************************************************************************************** reg accept_offer i.offer_amount i.round black_proposer##ib2.WB3, robust cluster(responder_id)