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#######
#######
####### 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") |