####### ####### ####### Replication files for Do Women Officers Police Differently? Evidence from Traffic Stops ####### This file runs most of the supplemental regressions shown in the appendix. ####### Last Updated: Jan. 2021 ####### ####### # Opening up those libraries: library(dplyr) library(ggplot2) library(texreg) library(readr) library(pscl) library(arm) # Setting the working directory: setwd("~/Desktop/PinkPolicing/AJPS_ReplicationFiles") # # Appendix: Alternative Specifications # # Clearing the workspace. rm(list = ls()) # Loading in the Data load("Data/FloridaSmall.RData") load("Data/FL_Aggregated.RData") # FE for Officer fl.search = lmer(search_occur~factor(race_gender)+ subject_age+out_of_state+ investigatory+ factor(of_gender)+factor(of_race)+ officer_years_of_service+officer_age+ factor(hour_of_day)+factor(month)+factor(year)+ factor(county_name)+(1|officer_id_hash), data=fl.sm, subset=fl.sm$county_include==1&fl.sm$officer_exclude==0) save(fl.search,file="Data/FLSearch_OLS_FE.RData") fl.contra = lmer(contra~factor(race_gender)+ subject_age+out_of_state+ investigatory+ factor(of_gender)+factor(of_race)+ officer_years_of_service+officer_age+ factor(hour_of_day)+factor(month)+factor(year)+factor(county_name)+ (1|officer_id_hash), data=fl.sm, subset=fl.sm$county_include==1& fl.sm$search_occur==1& fl.sm$officer_exclude==0) save(fl.contra,file="Data/FlContra_OLS_FE.RData") contra.search.rate.reg = lmer(contra.search.rate ~ factor(of_gender) + factor(of_exper) + factor(of_age) +factor(of_race) + factor(race_gender) + factor(driver_age)+ investigatory + out_of_state + factor(year)+factor(tod)+ (1|officer_id), data=fl.ag.officers, subset=fl.ag.officers$search_occur>0) save(contra.search.rate.reg,file="Data/FlSearchRate_OLS_FE.RData") contra.stop.rate.reg = lmer(contra.stop.rate ~ factor(of_gender) + factor(of_exper) + factor(of_age) + factor(of_race) + factor(race_gender) + factor(driver_age)+ investigatory + out_of_state + factor(year)+factor(tod)+(1|officer_id), data=fl.ag.officers) save(contra.stop.rate.reg,file="Data/FlStopRate_OLS_FE.RData") # Logistc Regressions rm(list = ls()) load("Data/NorthCarolina.RData") load("Data/FloridaSmall.RData") fl.search = glm(search_occur~factor(race_gender)+ subject_age+out_of_state+ investigatory+ factor(of_gender)+factor(of_race)+ officer_years_of_service+officer_age+ factor(hour_of_day)+factor(month)+factor(year)+ factor(county_name), data=fl.sm,family="binomial", subset=fl.sm$county_include==1&fl.sm$officer_exclude==0) save(fl.search,file="Data/FLSearch_Logit.RData") nc.search = glm(search~factor(race_gender)+subject_age+ investigatory+ factor(of_race)+ factor(of_gender)+Officer_Years_of_Service+ factor(month)+factor(year)+ factor(CMPD_Division), family="binomial", data=nc) save(nc.search,file="Data/NCSearch_Logit.RData") fl.contra = glm(contra~factor(race_gender)+ subject_age+out_of_state+ investigatory+ factor(of_gender)+factor(of_race)+ officer_years_of_service+officer_age+ factor(hour_of_day)+factor(month)+factor(year)+ factor(county_name), data=fl.sm, family = "binomial", subset=fl.sm$county_include==1& fl.sm$search_occur==1& fl.sm$officer_exclude==0) save(fl.contra,file="Data/FlContra_Logit.RData") # # Appendix: Interaction Models # rm(list = ls()) load("Data/NorthCarolina.RData") load("Data/FloridaSmall.RData") load("Data/FloridaLarge.RData") load("Data/FL_Aggregated.RData") # Experience fl.search.exper = lm(search_occur~factor(race_gender)+ subject_age+out_of_state+ investigatory+factor(of_race)+ factor(of_gender)*officer_years_of_service+officer_age+ factor(hour_of_day)+factor(month)+factor(year)+ factor(county_name), data=fl.sm, subset=fl.sm$county_include==1&fl.sm$officer_exclude==0) save(fl.search.exper,file="Data/FLSearch_Exper_OLS.RData") nc.search.exper = lm(search~factor(race_gender)+subject_age+ investigatory+factor(of_race)+ factor(of_gender)*Officer_Years_of_Service+ factor(month)+factor(year)+ factor(CMPD_Division), data=nc) save(nc.search.exper,file="Data/NCSearch_Exper_OLS.RData") fl.contra.exper = lm(contra~factor(race_gender)+ subject_age+out_of_state+ investigatory+factor(of_gender)*officer_years_of_service+ factor(of_race)+officer_age+ factor(hour_of_day)+factor(month)+factor(year)+ factor(county_name), data=fl.sm, subset=fl.sm$county_include==1& fl.sm$search_occur==1& fl.sm$officer_exclude==0) save(fl.contra.exper,file="Data/FlContra_Exper_OLS.RData") contra.search.rate.exper = lm(contra.search.rate ~ factor(of_gender)*factor(of_exper) + investigatory+factor(of_age) +factor(of_race) + factor(race_gender) + factor(driver_age)+ out_of_state + factor(year), data=fl.ag.officers, subset=fl.ag.officers$search_occur>0) save(contra.search.rate.exper,file="Data/FlSearchRate_Exper_OLS.RData") contra.stop.rate.exper = lm(contra.stop.rate ~ factor(of_gender)*factor(of_exper) + investigatory+ factor(of_age) +factor(of_race) + factor(race_gender) + factor(driver_age)+ out_of_state + factor(year), data=fl.ag.officers) save(contra.stop.rate.exper,file="Data/FlStopRate_Exper_OLS.RData") # Prop Female fl$male.officer = ifelse(fl$of_gender==1,0,1) fl.ag = aggregate(fl$officer_id_hash, by=list(fl$of_gender,fl$county_name,fl$year), function(x){length(unique(x))}) fl.ag.m = fl.ag[fl.ag$Group.1==0,] fl.ag.f = fl.ag[fl.ag$Group.1==1,] colnames(fl.ag.m)=c("male","county_name","year","male.count") colnames(fl.ag.f)=c("female","county_name","year","female.count") fl.ag = merge(fl.ag.m,fl.ag.f,all=T) fl.ag$male.count[is.na(fl.ag$male.count)] = 0 fl.ag$female.count[is.na(fl.ag$female.count)] = 0 fl.ag$female.prop = fl.ag$female.count/(fl.ag$female.count+fl.ag$male.count) summary(fl.ag$female.prop) fl.sm = merge(fl.sm,fl.ag) fl.search.prop = lm(search_occur~factor(race_gender)+ subject_age+out_of_state+ investigatory+factor(of_race)+ factor(of_gender)*female.prop+officer_years_of_service+officer_age+ factor(hour_of_day)+factor(month)+factor(year)+ factor(county_name), data=fl.sm, subset=fl.sm$county_include==1&fl.sm$officer_exclude==0) save(fl.search.prop,file="Data/FLSearch_Prop_OLS.RData") fl.contra.prop = lm(contra~factor(race_gender)+ subject_age+out_of_state+ investigatory+factor(of_gender)*female.prop+ officer_years_of_service+ factor(of_race)+officer_age+ factor(hour_of_day)+factor(month)+factor(year)+ factor(county_name), data=fl.sm, subset=fl.sm$county_include==1& fl.sm$search_occur==1& fl.sm$officer_exclude==0) save(fl.contra.prop,file="Data/FlContra_Prop_OLS.RData") # Stop Type fl.search.st = lm(search_occur~factor(race_gender)+ subject_age+out_of_state+ factor(of_gender)+factor(of_race)+ officer_years_of_service+officer_age+ factor(hour_of_day)+factor(month)+factor(year)+ factor(county_name), data=fl.sm, subset=fl.sm$county_include==1&fl.sm$officer_exclude==0& fl.sm$investigatory==1) save(fl.search.st,file="Data/FLSearch_StopType_OLS.RData") nc.search.st = lm(search~factor(race_gender)+subject_age+ factor(of_gender)+ factor(of_race)+Officer_Years_of_Service+ factor(month)+factor(year)+ factor(CMPD_Division), data=nc, subset = nc$investigatory==1) save(nc.search.st,file="Data/NCSearch_StopType_OLS.RData") fl.contra.st = lm(contra~factor(race_gender)+ subject_age+out_of_state+ factor(of_gender)+ factor(of_race)+ officer_years_of_service+officer_age+ factor(hour_of_day)+factor(month)+factor(year)+ factor(county_name), data=fl.sm, subset=fl.sm$county_include==1& fl.sm$search_occur==1& fl.sm$officer_exclude==0& fl.sm$investigatory==1) save(fl.contra.st,file="Data/FlContra_StopType_OLS.RData") contra.search.rate.st = lm(contra.search.rate ~ factor(of_gender)+ factor(of_exper) + factor(of_age) +factor(of_race) + factor(race_gender) + factor(driver_age)+ out_of_state + factor(year), data=fl.ag.officers, subset=fl.ag.officers$search_occur>0& fl.ag.officers$investigatory==1) save(contra.search.rate.st,file="Data/FlSearchRate_StopType_OLS.RData") contra.stop.rate.st = lm(contra.stop.rate ~ factor(of_gender)+ factor(of_exper) + factor(of_age) +factor(of_race) + factor(race_gender) + factor(driver_age)+ out_of_state + factor(year), data=fl.ag.officers, subset=fl.ag.officers$investigatory==1) save(contra.stop.rate.st,file="Data/FlStopRate_StopType_OLS.RData") # Driver Characteristics fl.sm$subject_female = ifelse(fl.sm$subject_sex=="female",1,0) fl.sm$subject_race2 = ifelse(fl.sm$subject_race=="white",0, ifelse(fl.sm$subject_race=="black",1,2)) fl.search.inter = lm(search_occur~factor(of_gender)*factor(subject_female)+ factor(of_race)*factor(subject_race2)+ subject_age+out_of_state+investigatory+ officer_years_of_service+officer_age+ factor(hour_of_day)+factor(month)+factor(year)+ factor(county_name), data=fl.sm, subset=fl.sm$county_include==1& fl.sm$officer_exclude==0& as.numeric(fl.sm$of_race)<3) save(fl.search.inter,file="Data/FLInter_Search.RData") fl.contra.inter = lm(contra~factor(of_gender)*factor(subject_female)+ factor(of_race)*factor(subject_race2)+ subject_age+out_of_state+investigatory+ officer_years_of_service+officer_age+ factor(hour_of_day)+factor(month)+factor(year)+ factor(county_name), data=fl.sm, subset=fl.sm$search_occur==1& fl.sm$county_include==1& fl.sm$officer_exclude==0& as.numeric(fl.sm$of_race)<3) save(fl.contra.inter,file="Data/FLInter_Contra.RData") fl.ag.officers$subject_female = ifelse(fl.ag.officers$race_gender%in%c(1,3,5),1,0) fl.ag.officers$subject_race2 = ifelse(fl.ag.officers$race_gender%in%c(0,1),0, ifelse(fl.ag.officers$race_gender%in%c(2,3),1,2)) contra.search.rate.inter = lm(contra.search.rate ~ factor(of_gender)*factor(subject_female) + factor(of_race) * factor(subject_race2)+ factor(of_exper) + factor(of_age) + factor(race_gender) + factor(driver_age)+ investigatory + out_of_state + factor(year), data=fl.ag.officers, subset=fl.ag.officers$search_occur>0) save(contra.search.rate.inter,file="Data/FlSearchRate_Inter_OLS.RData") contra.stop.rate.inter = lm(contra.stop.rate ~ factor(of_gender)*factor(subject_female) + factor(of_race) * factor(subject_race2)+ factor(of_exper) + factor(of_age) + factor(race_gender) + factor(driver_age)+ investigatory + out_of_state + factor(year), data=fl.ag.officers) save(contra.stop.rate.inter,file="Data/FlStopRate_Inter_OLS.RData") nc$of_race = ifelse(nc$Officer_Race=="White",0, ifelse(nc$Officer_Race=="Black/African American",1, ifelse(nc$Officer_Race=="Hispanic/Latino",2,NA))) nc$subject_female = ifelse(nc$Driver_Gender=="Female",1,0) nc$subject_race2 = ifelse(nc$Driver_Race=="White"& nc$Driver_Ethnicity=="Non-Hispanic",0, ifelse(nc$Driver_Race=="Black"& nc$Driver_Ethnicity=="Non-Hispanic",1, ifelse(nc$Driver_Ethnicity=="Hispanic",2,NA))) nc.search.inter = lm(search~factor(of_gender)*factor(subject_female)+ factor(of_race)*factor(subject_race2)+ subject_age+investigatory+ Officer_Years_of_Service+ factor(month)+factor(year)+ factor(CMPD_Division), data=nc) save(nc.search.inter,file = "Data/NCInter_Search.RData")