add 108

108/paper.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1b66be3b035bbc53e722346d2b7c0ee93865e87f98eb560a4303f2fca449eb93
+size 1047731

108/replication_package/Code/Step1_MainAnalysisAndData.R

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+#######
+#######
+####### Replication files for Do Women Officers Police Differently? Evidence from Traffic Stops
+####### This file cleans the raw data and runs the analysis for the body of the paper. 
+####### Last Updated: Jan. 2021
+#######
+#######
+
+
+###
+### 1. Setting up the space. 
+###
+
+# Setting the working directory:
+setwd("~/Desktop/PinkPolicing/AJPS_ReplicationFiles")
+
+# Installing the needed libraries:
+#install.packages("pscl",dependencies = T)
+#install.packages("ggplot2",dependencies = T)
+#install.packages("texreg",dependencies = T)
+#install.packages("readr",dependencies = T)
+#install.packages("arm",dependencies = T)
+#install.packages("dplyr",dependencies = T)
+
+# Opening up those libraries:
+library(dplyr)
+library(ggplot2)
+library(texreg)
+library(readr)
+library(pscl)
+library(arm)
+
+# Loading the raw data:
+nc_new = read_csv("Data/Officer_Traffic_Stops_Update.csv")
+nc_old = read_csv("Data/Officer_Traffic_Stops_Original.csv")
+nc = bind_rows(nc_new,nc_old)
+fl = read_csv("Data/fl_statewide_2019_08_13.csv")
+
+
+###
+### 2. Producing the data sets for each table. 
+###
+
+# Cleaning the NC Data
+nc$driver_re = as.numeric(ifelse(nc$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$of_rg = ifelse(nc$Officer_Race=="White",
+                  ifelse(nc$Officer_Gender=="Male","0","1"),
+                  ifelse(nc$Officer_Race=="Black/African American",
+                         ifelse(nc$Officer_Gender=="Male","2","3"),NA))
+nc$of_race = ifelse(nc$Officer_Race=="White",0,
+                    ifelse(nc$Officer_Race=="Black/African American",1,NA))
+nc$of_gender = ifelse(nc$Officer_Gender=="Male","0","1")
+nc$investigatory = ifelse(grepl("Impaired|Speeding|Light|Movement",
+                                as.character(nc$Reason_for_Stop)),0,1)
+nc$investigatory = ifelse(grepl("Check",as.character(nc$Reason_for_Stop)),
+                          NA,nc$investigatory)
+nc$race_gender = ifelse(nc$driver_re=="0",
+                        ifelse(nc$Driver_Gender=="Male","0","1"),
+                        ifelse(nc$driver_re=="1",
+                               ifelse(nc$Driver_Gender=="Male","2","3"),NA))
+nc$search = ifelse(nc$Was_a_Search_Conducted=="Yes",1,0)
+
+nc$subject_sex = tolower(nc$Driver_Gender)
+nc$subject_age = nc$Driver_Age
+nc$officer_sex = tolower(nc$Officer_Gender)
+nc$month = apply(as.matrix(as.character(nc$Month_of_Stop)),1,
+                 function(x){strsplit(x,"/",fixed=T)[[1]][2]})
+nc$year = apply(as.matrix(as.character(nc$Month_of_Stop)),1,
+                function(x){strsplit(x,"/",fixed=T)[[1]][1]})
+
+nc$arrest = ifelse(nc$Result_of_Stop=="Arrest",1,0)
+save(nc,file="Data/NorthCarolina.RData")
+
+# Cleaning the FL data.
+violations_list = strsplit(paste(fl$reason_for_stop,collapse = "|"),"|",fixed = T)
+violations_list_small = unique(violations_list[[1]])[2:71]
+violations_indicator = violations_list_small[c(1,2,5,6,7,9,10,14,19,
+                                               20,23,40,45)]
+fl$investigatory = ifelse(is.na(fl$violation),NA,
+                          ifelse(fl$violation %in% violations_indicator, 0, 1))
+fl$contraband_found = ifelse(grepl("contraband",
+                                   tolower(fl$violation)),1,0)
+fl$race_gender = ifelse(fl$subject_race=="white",
+                        ifelse(fl$subject_sex=="male",0,1),
+                        ifelse(fl$subject_race=="black",
+                               ifelse(fl$subject_sex=="male",2,3),
+                               ifelse(fl$subject_race=="hispanic",
+                                      ifelse(fl$subject_sex=="male",4,5),NA)))
+fl$of_rg = ifelse(fl$officer_race=="white",
+                  ifelse(fl$officer_sex=="male",0,1),
+                  ifelse(fl$officer_race=="black",
+                         ifelse(fl$officer_sex=="male",2,3),
+                         ifelse(fl$officer_race=="hispanic",
+                                ifelse(fl$officer_sex=="male",4,5),NA)))
+fl$of_race = ifelse(fl$officer_race=="white",0,
+                    ifelse(fl$officer_race=="black",1,
+                           ifelse(fl$officer_race=="hispanic",2,
+                                  ifelse(fl$officer_race=="asian/pacific islander",3,
+                                         ifelse(fl$officer_race=="other",4,NA)))))
+fl$of_gender = ifelse(fl$officer_sex=="male",0,1)
+fl$out_of_state = ifelse(fl$vehicle_registration_state=="FL",0,1)
+fl$hour_of_day = apply(as.matrix(as.character(fl$time)),1,
+                       function(x)(strsplit(x,":",fixed = T)[[1]][1]))
+fl$month = apply(as.matrix(as.character(fl$date)),1,
+                 function(x)(paste(strsplit(x,"-",fixed = T)[[1]][2],
+                                   collapse = "_")))
+fl$year = apply(as.matrix(as.character(fl$date)),1,
+                function(x)(paste(strsplit(x,"-",fixed = T)[[1]][1],
+                                  collapse = "_")))
+fl = subset(fl,fl$year!="2016"&fl$year!="2017"&fl$year!="2018") #Narrows down to complete years that don't report extreme misingness on key outcome. 
+fl.officers = names(table(fl$officer_id_hash))[table(fl$officer_id_hash)>1000]
+fl$officers_include = ifelse(fl$officer_id_hash%in%fl.officers,1,0)
+fl.counties = names(table(fl$county_name))[table(fl$county_name)>1000]
+fl$county_include = ifelse(fl$county_name%in%fl.counties,1,0)
+fl.ag.id = aggregate(fl$of_gender,
+                     list(fl$officer_id_hash,fl$year,fl$county_name),
+                     mean)
+fl.ag.id$officer = ifelse(!is.na(fl.ag.id$x),1,0)
+fl.ag.gender = aggregate(fl.ag.id[,c("x","officer")],
+                         list(fl.ag.id$Group.2,fl.ag.id$Group.3),
+                         sum,na.rm=T)
+fl.ag.gender$prop.female = fl.ag.gender$x/fl.ag.gender$officer
+colnames(fl.ag.gender) = c("year","county_name","count.female","tot.officer","prop.female")
+fl = merge(fl,fl.ag.gender,by=c("year","county_name"),all.x=T)
+fl$officer_exclude = ifelse(fl$officer_years_of_service<0|fl$officer_years_of_service>40,1,0)
+fl.ag.id2 = aggregate(fl$of_gender,
+                      list(fl$officer_id_hash),
+                      mean)
+fl$search_occur = ifelse(fl$search_conducted == 0, 0, 
+                         ifelse(fl$search_basis != "other",1,NA))
+fl$contra = ifelse(is.na(fl$search_occur),0,
+                   ifelse(fl$search_occur==1,fl$contraband_found,0))
+
+complete = complete.cases(fl[,c("search_occur","race_gender","subject_age",
+                                "out_of_state","investigatory","of_gender",
+                                "of_race","officer_years_of_service","officer_age",
+                                "hour_of_day","month","year","county_name")])
+fl.sm = fl[complete,]
+complete2 = complete.cases(fl[,c("search_occur","of_gender")])
+table(complete)
+table(complete2)
+
+fl.missingness = apply(fl[,c("search_occur","race_gender","subject_age",
+                             "out_of_state","investigatory","of_gender",
+                             "of_race","officer_years_of_service","officer_age",
+                             "county_name")],
+                       2,
+                       FUN = function(x){table(is.na(x))})
+save(fl,file="Data/FloridaLarge.RData")
+save(fl.sm,file="Data/FloridaSmall.RData")
+
+fl$stops = ifelse(!is.na(fl$search_occur),1,0)
+fl$contra.ttest = ifelse(fl$search_occur==1,fl$contra,NA)
+prop.test(table(fl$of_gender,fl$contra.ttest))
+fl$of_exper = ifelse(fl$officer_years_of_service>=
+                       mean(fl$officer_years_of_service,na.rm=T),1,0)
+fl$of_age = ifelse(fl$officer_age<30,1,
+                   ifelse(fl$officer_age>64,3,2))
+fl$driver_age = ifelse(fl$subject_age<30,1,
+                       ifelse(fl$subject_age>64,3,2))
+fl$hour_of_day2 = as.numeric(fl$hour_of_day)
+fl$tod = ifelse(fl$hour_of_day2<3,1,
+                ifelse(fl$hour_of_day2<6,2,
+                       ifelse(fl$hour_of_day2<9,3,
+                              ifelse(fl$hour_of_day2<12,4,
+                                     ifelse(fl$hour_of_day2<15,5,
+                                            ifelse(fl$hour_of_day2<18,6,
+                                                   ifelse(fl$hour_of_day2<21,7,8)))))))
+
+fl.ag.officers = aggregate(fl[,c("stops","search_occur","contra")],
+                           by=list(fl$officer_id_hash,
+                                   fl$of_race,fl$of_gender,
+                                   fl$of_exper,fl$of_age,
+                                   fl$race_gender,fl$driver_age,
+                                   fl$out_of_state,fl$investigatory,
+                                   fl$year,fl$tod),
+                           sum,na.rm=T)
+colnames(fl.ag.officers) = c("officer_id","of_race","of_gender","of_exper",
+                             "of_age","race_gender","driver_age",
+                             "out_of_state","investigatory","year",
+                             "tod","stops","search_occur","contra")
+fl.ag.officers$contra.search.rate = (fl.ag.officers$contra/fl.ag.officers$search_occur)*10
+fl.ag.officers$contra.stop.rate = (fl.ag.officers$contra/fl.ag.officers$stops)*100
+save(fl.ag.officers,file="Data/FL_Aggregated.RData")
+
+# Data for Figure 1
+search.df = data.frame("Department" = c("CPD","CPD","FHP","FHP"),
+                       "Gender" = c("Male","Female","Male","Female"),
+                       "Rate" = c(prop.table(table(nc$of_gender,nc$search),1)[,2],
+                                  prop.table(table(fl$of_gender[fl.sm$county_include==1&
+                                                                  fl.sm$officer_exclude==0],
+                                                   fl$search_occur[fl.sm$county_include==1&
+                                                                     fl.sm$officer_exclude==0]),1)[,2]))
+save(search.df,file="Data/Fig1_Data.RData")
+
+###
+### 3. Regressions
+###
+
+#
+# For the Main Text:
+#
+
+# Search Regressions
+fl.search.sm = lm(search_occur~factor(of_gender),data=fl)
+save(fl.search.sm, file="Data/FLSearch_Sm_OLS.RData")
+fl.search = lm(search_occur~factor(race_gender)+
+                 subject_age+out_of_state+
+                 investigatory+
+                 factor(of_gender)+factor(of_race)+
+                 officer_years_of_service+officer_age+
+                 factor(hour_of_day)+factor(month)+factor(year)+
+                 factor(county_name),
+               data=fl.sm,  
+               subset=fl.sm$county_include==1&fl.sm$officer_exclude==0)
+save(fl.search,file="Data/FLSearch_OLS.RData")
+nc.search.sm = lm(search~factor(of_gender),data = nc)
+save(nc.search.sm,file="Data/NCSearch_Sm_OLS.RData")
+nc.search = lm(search~factor(race_gender)+subject_age+
+                 investigatory+
+                 factor(of_race)+
+                 factor(of_gender)+Officer_Years_of_Service+
+                 factor(month)+factor(year)+
+                 factor(CMPD_Division),
+               data=nc)
+save(nc.search,file="Data/NCSearch_OLS.RData")
+
+# Contraband Regressions
+fl.contra = lm(contra~factor(race_gender)+
+                 subject_age+out_of_state+
+                 investigatory+
+                 factor(of_gender)+factor(of_race)+
+                 officer_years_of_service+officer_age+
+                 factor(hour_of_day)+factor(month)+factor(year)+
+                 factor(county_name),
+               data=fl.sm,  
+               subset=fl.sm$county_include==1&
+                 fl.sm$search_occur==1&
+                 fl.sm$officer_exclude==0)
+save(fl.contra,file="Data/FlContra_OLS.RData")
+contra.search.rate.reg = lm(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),
+                            data=fl.ag.officers,
+                            subset=fl.ag.officers$search_occur>0)
+save(contra.search.rate.reg,file="Data/FlSearchRate_OLS.RData")
+contra.stop.rate.reg = lm(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),
+                          data=fl.ag.officers)
+save(contra.stop.rate.reg,file="Data/FlStopRate_OLS.RData")

108/replication_package/Code/Step2_AppendixAnalysis.R

@@ -0,0 +1,320 @@
+#######
+#######
+####### 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")

108/replication_package/Code/Step3_TablesAndFigures.R

@@ -0,0 +1,630 @@
+#######
+#######
+####### Replication files for Do Women Officers Police Differently? Evidence from Traffic Stops
+####### This file produces the tables and figures seen in the paper and appendix.
+####### Last Updated: Jan. 2021
+#######
+#######
+
+###
+### 1. Setting up the space. 
+###
+
+# Setting the working directory:
+setwd("~/Desktop/PinkPolicing/AJPS_ReplicationFiles")
+
+# Installing the needed libraries:
+#install.packages("pscl",dependencies = T)
+#install.packages("ggplot2",dependencies = T)
+#install.packages("texreg",dependencies = T)
+#install.packages("readr",dependencies = T)
+#install.packages("arm",dependencies = T)
+
+# Opening up those libraries:
+library(ggplot2)
+library(texreg)
+library(readr)
+library(pscl)
+library(arm)
+
+###
+### 2. Body of the Paper
+###
+
+# Clearing the workspace + reading in data bit by bit to produce each table and figure. 
+rm(list = ls())
+
+# Loading in the Data
+load("Data/NorthCarolina.RData")
+load("Data/FloridaLarge.RData")
+load("Data/FloridaSmall.RData")
+cmpd.employee = read_csv("Data/CMPD_Employee_Demographics.csv")
+
+# Number of stops and searches by sex:
+dim(fl)
+dim(nc)
+
+table(fl$search_occur)
+table(nc$search)
+
+prop.table(table(fl$search_occur))
+prop.table(table(nc$search))
+
+table(fl$of_gender)
+table(nc$of_gender)
+
+table(fl$of_gender,fl$search_occur)
+table(nc$of_gender,nc$search)
+
+prop.table(table(fl$of_gender,fl$search_occur),1)
+prop.table(table(nc$of_gender,nc$search),1)
+
+table(fl$of_gender,fl$contra)
+
+# Number of officers by sex in FL
+length(unique(fl$officer_id_hash))
+length(unique(fl$officer_id_hash[fl$of_gender==0]))
+length(unique(fl$officer_id_hash[fl$of_gender==1]))
+
+length(unique(fl$officer_id_hash[fl$officer_exclude==0]))
+length(unique(fl$officer_id_hash[fl$of_gender==0&fl$officer_exclude==0]))
+length(unique(fl$officer_id_hash[fl$of_gender==1&fl$officer_exclude==0]))
+
+table(cmpd.employee$JOB_TITLE[cmpd.employee$JOB_TITLE=="Police Officer"])
+sum(table(cmpd.employee$Gender[cmpd.employee$JOB_TITLE=="Police Officer"]))
+
+table(fl$year)
+(table(fl$of_gender)/c(length(unique(fl$officer_id_hash[fl$of_gender==0&fl$officer_exclude==0])),length(unique(fl$officer_id_hash[fl$of_gender==1&fl$officer_exclude==0]))))/6
+
+avg.stops = aggregate(fl$year,by=list(fl$officer_id_hash,fl$year,fl$of_gender),length)
+summary(avg.stops)
+mean(avg.stops$x)
+median(avg.stops$x[avg.stops$Group.3==0])
+median(avg.stops$x[avg.stops$Group.3==1])
+
+prop.table(table(fl$investigatory[fl$of_gender==0]))
+prop.table(table(fl$investigatory[fl$of_gender==1]))
+
+table(nc$of_gender[nc$year==2019])[2:1]/table(cmpd.employee$Gender[cmpd.employee$JOB_TITLE=="Police Officer"])
+
+# Excluding Cases:
+dim(nc)
+dim(nc)-dim(nc[!is.na(nc$search),])
+dim(fl)
+dim(fl)-dim(fl[!is.na(fl$search_occur),])
+(dim(fl[!is.na(fl$search_occur),])-dim(fl.sm))+table(fl.sm$officer_exclude)[2]
+table(fl.sm$county_include)
+
+# Table 1
+tab1 = data.frame("Department"=c("Charlotte PD (NC)",
+                                 "Male Officers","Female Officers",
+                                 "Florida Highwar Patrol",
+                                 "Male Officers","Female Officers"),
+                  "Type"=c("Municipal","","","Statewide","",""),
+                  "Years"=c("2016-2017","","",
+                            "2010-2015","",""),
+                  "Stops"=c(dim(nc)[1],table(nc$of_gender),
+                            dim(fl[!is.na(fl$search_occur),])[1],
+                            table(fl$of_gender[!is.na(fl$search_occur)])),
+                  "Searches"=c(table(nc$search)[2],table(nc$of_gender,nc$search)[,2],
+                               table(fl$search_occur)[2],
+                               table(fl$of_gender,fl$search_occur)[,2]),
+                  "Search Rate"=c(table(nc$search)[2]/dim(nc)[1],
+                                  table(nc$of_gender,nc$search)[,2]/table(nc$of_gender),
+                                  table(fl$search_occur)[2]/dim(fl[!is.na(fl$search_occur),])[1],
+                                  table(fl$of_gender,fl$search_occur)[,2]/
+                                    table(fl$of_gender[!is.na(fl$search_occur)])))
+tab1 = rbind(tab1,
+             c("Total","","",
+               sum(tab1[c(1,4),4]),sum(tab1[c(1,4),5]),
+               sum(tab1[c(1,4),5])/sum(tab1[c(1,4),4])))
+tab1
+
+# Figure 1
+load("Data/Fig1_Data.RData")
+png("Figures/Fig1_PredProb.png",
+    750,519)
+ggplot(data = search.df, aes(x=Department,y=Rate,fill=Gender)) + 
+  geom_bar(stat="identity", position=position_dodge()) + 
+  ylab("Search Rate") +
+  theme_bw(base_size=15)+
+  theme(legend.position = "bottom") +
+  labs(fill="Officer Sex")+
+  scale_fill_grey(start = 0.25, end = .75) 
+dev.off()
+
+prop.test(table(fl$of_gender,fl$search_occur))
+prop.test(table(nc$of_gender,nc$search))
+
+# Table 2
+load("Data/FLSearch_Sm_OLS.RData")
+load("Data/FLSearch_OLS.RData")
+load("Data/NCSearch_Sm_OLS.RData")
+load("Data/NCSearch_OLS.RData")
+screenreg(list(nc.search,fl.search),
+          stars=c(0.01,0.05),
+          custom.coef.map = list("(Intercept)"="(Intercept)",
+                                 "factor(of_gender)1"="Female Officer",
+                                 "factor(of_race)1"="Black Officer",
+                                 "factor(race_gender)1"="White Female",
+                                 "factor(race_gender)2"="Black Male",
+                                 "factor(race_gender)3"="Black Female",
+                                 "factor(race_gender)4"="Latino Male",
+                                 "factor(race_gender)5"="Latina Female",
+                                 "investigatory" = "Investigatory Stop Purpose"),
+          custom.model.names = c("(1) NC Search",
+                                 "(2) FL Search"),
+          digits=4)
+
+# Figure 2
+fl.of.pred = predict(fl.search,
+                     newdata = data.frame("of_gender"=c(0,1),"race_gender"=0,
+                                            "subject_age"=35,"out_of_state"=0,
+                                            "investigatory"=1,
+                                          "officer_years_of_service"=6,
+                                          "of_race"=0,"officer_age"=39,
+                                          "hour_of_day"=15,
+                                          "month"="05","year"=2013,
+                                          "county_name"="Orange County"),
+                     type="response",se.fit=T)
+nc.of.pred = predict(nc.search,
+                     newdata = data.frame("of_gender"=c(0,1),
+                                          "race_gender"=0,
+                                          "subject_age"=36,
+                                          "investigatory"=1,
+                                          "Officer_Years_of_Service"=10.25,
+                                          "of_race"=0,"month"="01",
+                                          "year"=2019,"CMPD_Division"="South Division"),
+                     type="response",se.fit=T)
+
+
+
+pred.df = data.frame("Department" = c("Charlotte Police Department",
+                                      "Charlotte Police Department",
+                                      "Florida Highway Patrol",
+                                      "Florida Highway Patrol"),
+                     "Gender" = c("Male","Female","Male","Female"),
+                     "Predict" = c(nc.of.pred$fit,
+                                   fl.of.pred$fit),
+                     "Lower"=c(nc.of.pred$fit-1.96*nc.of.pred$se.fit,
+                               fl.of.pred$fit-1.96*fl.of.pred$se.fit),
+                     "Upper"=c(nc.of.pred$fit+1.96*nc.of.pred$se.fit,
+                               fl.of.pred$fit+1.96*fl.of.pred$se.fit))
+
+png("Figures/Fig2_PredProb.png",
+    900,514)
+ggplot(data = pred.df, aes(x=Gender,y=Predict)) + 
+  geom_point(size=4) +  
+  geom_errorbar(aes(ymin = Lower, ymax = Upper),
+                width=.2,size = 0.75,                   
+                position=position_dodge(.9)) + 
+  ylab("Expected Probbility of a Search") +
+  xlab("Officer Sex") +
+  theme_bw(base_size=15) +facet_wrap(~Department) 
+dev.off()
+
+pred.df$Predict[1]/pred.df$Predict[2]
+pred.df$Predict[3]/pred.df$Predict[4]
+
+# Table 3
+tab3 = data.frame("Officer Gender"=c("Male","Female"),
+                  "Searches"=table(fl$of_gender[!is.na(fl$search_occur)],
+                                   fl$search_occur[!is.na(fl$search_occur)])[,2],
+                  "Contraband"=table(fl$of_gender[!is.na(fl$search_occur)],
+                                     fl$contra[!is.na(fl$search_occur)])[,2],
+                  "Contraband Hit Rate"=table(fl$of_gender[!is.na(fl$search_occur)],
+                                              fl$contra[!is.na(fl$search_occur)])[,2]/
+                    table(fl$of_gender[!is.na(fl$search_occur)],
+                          fl$search_occur[!is.na(fl$search_occur)])[,2],
+                  "Difference"=c((table(fl$of_gender[!is.na(fl$search_occur)],
+                                        fl$contra[!is.na(fl$search_occur)])[,2]/
+                                    table(fl$of_gender[!is.na(fl$search_occur)],
+                                          fl$search_occur[!is.na(fl$search_occur)])[,2])[1]-
+                                   (table(fl$of_gender[!is.na(fl$search_occur)],
+                                          fl$contra[!is.na(fl$search_occur)])[,2]/
+                                      table(fl$of_gender[!is.na(fl$search_occur)],
+                                            fl$search_occur[!is.na(fl$search_occur)])[,2])[2],NA))
+tab3
+prop.test(table(fl$of_gender[fl$search_occur==1],
+                fl$contra[fl$search_occur==1]))
+
+# Table 4
+load("Data/FlContra_OLS.RData")
+load("Data/FlSearchRate_OLS.RData")
+load("Data/FlStopRate_OLS.RData")
+screenreg(list(fl.contra,contra.search.rate.reg,contra.stop.rate.reg),
+          stars=c(0.01,0.05),
+          custom.coef.map = list("(Intercept)"="(Intercept)",
+                                 "factor(of_gender)1"="Female Officer",
+                                 "factor(of_race)1"="Black Officer",
+                                 "factor(race_gender)1"="White Female",
+                                 "factor(race_gender)2"="Black Male",
+                                 "factor(race_gender)3"="Black Female",
+                                 "factor(race_gender)4"="Latino Male",
+                                 "factor(race_gender)5"="Latina Female",
+                                 "investigatory" = "Investigatory Stop Purpose"),
+          custom.model.names = c("(1) Contra|Search",
+                                 "(2) Hit Rate, per 10 Searches",
+                                 "(3) Hit Rate, per 100 Stops"),
+          digits=4)
+
+###
+### 3. Appendix A: Full Regression Results
+###
+
+screenreg(list(nc.search,fl.search,
+               fl.contra,contra.search.rate.reg,contra.stop.rate.reg),
+          stars=c(0.01,0.05),
+          custom.coef.map = list("(Intercept)"="(Intercept)",
+                                 "factor(of_gender)1"="Female Officer",
+                                 "factor(of_race)1"="Black Officer",
+                                 "officer_age"="Officer Age",
+                                 "factor(of_age)2"="Officer Age: 30-64",
+                                 "factor(of_age)3"="Officer Age: 65+",
+                                 "officer_years_of_service"="Officer Years of Service",
+                                 "Officer_Years_of_Service"="Officer Years of Service",
+                                 "factor(of_exper)1"="Experienced Officer",
+                                 "factor(race_gender)1"="White Female",
+                                 "factor(race_gender)2"="Black Male",
+                                 "factor(race_gender)3"="Black Female",
+                                 "factor(race_gender)4"="Latino Male",
+                                 "factor(race_gender)5"="Latina Female",
+                                 "subject_age"="Driver Age",
+                                 "factor(driver_age)2"="Driver Age: 30-64",
+                                 "factor(driver_age)3"="Driver Age: 65+",
+                                 "investigatory" = "Investigatory Stop Purpose",
+                                 "out_of_state"="Out of State"),
+          custom.model.names = c("(1)","(2)",
+                                 "(3)","(4)","(5)"),
+          digits=3)
+
+###
+### 4. Appendix B: Alternative Test of Differences in Search and Contraband Hit Rates
+###
+
+# Florida
+fl$stop = 1
+fl$of_exper = ifelse(fl$officer_years_of_service>=
+                       mean(fl$officer_years_of_service,na.rm=T),1,0)
+fl$of_age = ifelse(fl$officer_age<30,1,
+                   ifelse(fl$officer_age>64,3,2))
+fl$driver_age = ifelse(fl$subject_age<30,1,
+                       ifelse(fl$subject_age>64,3,2))
+fl$hour_of_day=as.numeric(fl$hour_of_day)
+fl$tod = ifelse(fl$hour_of_day<3,1,
+                ifelse(fl$hour_of_day<6,2,
+                       ifelse(fl$hour_of_day<9,3,
+                              ifelse(fl$hour_of_day<12,4,
+                                     ifelse(fl$hour_of_day<15,5,
+                                            ifelse(fl$hour_of_day<18,6,
+                                                   ifelse(fl$hour_of_day<21,7,8)))))))
+
+fl.ag = aggregate(fl[!is.na(fl$search_occur),c("stop","search_occur","contra")], 
+                  by = list(fl$tod[!is.na(fl$search_occur)],
+                            fl$officer_race[!is.na(fl$search_occur)],
+                            fl$officer_sex[!is.na(fl$search_occur)],
+                            fl$of_exper[!is.na(fl$search_occur)],
+                            fl$race_gender[!is.na(fl$search_occur)],
+                            fl$driver_age[!is.na(fl$search_occur)],
+                            fl$out_of_state[!is.na(fl$search_occur)],
+                            fl$investigatory[!is.na(fl$search_occur)]),
+                  sum,na.rm=T)
+colnames(fl.ag) = c("tod",
+                    "of_race","of_sex","of_exper","driver_rg",
+                    "driver_age","out_of_state","invest",
+                    "stop","search","contraband")
+fl.ag.female = fl.ag[fl.ag$of_sex=="female",]
+colnames(fl.ag.female)[c(3,9:11)] = c("female","stop.f",
+                                       "search.f","contra.f")
+fl.ag.male = fl.ag[fl.ag$of_sex=="male",]
+colnames(fl.ag.male)[c(3,9:11)] = c("male","stop.m",
+                                     "search.m","contra.m")
+
+fl.matches = merge(fl.ag.female,fl.ag.male)
+min.stops = 9
+table(fl.matches$stop.f>min.stops&
+        fl.matches$stop.m>min.stops)
+min.searches = 0
+table(fl.matches$search.f>min.searches&
+        fl.matches$search.m>min.searches)
+table(fl.matches$search.f>min.searches&
+        fl.matches$search.m>min.searches&
+        fl.matches$stop.f>min.stops&
+        fl.matches$stop.m>min.stops)
+
+# North Carolina
+nc$stop = 1
+nc$search = ifelse(nc$Was_a_Search_Conducted=="Yes",1,0)
+nc$driver_age = ifelse(nc$Driver_Age<30,1,
+                       ifelse(nc$Driver_Age>65,3,2))
+nc$of_exper = ifelse(nc$Officer_Years_of_Service>=mean(nc$Officer_Years_of_Service),
+                     1,0)
+nc.ag = aggregate(nc[,c("search","stop")], 
+                  by = list(nc$CMPD_Division,
+                            nc$Officer_Gender,nc$Officer_Race,
+                            nc$of_exper,
+                            nc$race_gender,nc$driver_age,
+                            nc$investigatory,
+                            nc$year),
+                  sum)
+nc.ag.female = nc.ag[nc.ag$Group.2=="Female",]
+colnames(nc.ag.female) = c("division","female","race","of_exper",
+                           "driver.rg","driver_age","investigatory",
+                           "year",
+                           "searches.f","stops.f")
+nc.ag.male = nc.ag[nc.ag$Group.2=="Male",]
+colnames(nc.ag.male) = c("division","male","race","of_exper",
+                         "driver.rg","driver_age","investigatory",
+                         "year",
+                         "searches.m","stops.m")
+
+
+# Searches
+fl.matches$sr.f = fl.matches$search.f/fl.matches$stop.f
+fl.matches$sr.m = fl.matches$search.m/fl.matches$stop.m
+fl.matches$cr.f = fl.matches$contra.f/fl.matches$search.f
+fl.matches$cr.m = fl.matches$contra.m/fl.matches$search.m
+t.test(fl.matches$sr.f[fl.matches$stop.f>min.stops&
+                         fl.matches$stop.m>min.stops],
+       fl.matches$sr.m[fl.matches$stop.f>min.stops&
+                         fl.matches$stop.m>min.stops],
+       paired = T)
+length(fl.matches$sr.f[fl.matches$stop.f>min.stops&
+                         fl.matches$stop.m>min.stops])
+mean(fl.matches$sr.f[fl.matches$stop.f>min.stops&
+                       fl.matches$stop.m>min.stops])
+mean(fl.matches$sr.m[fl.matches$stop.f>min.stops&
+                       fl.matches$stop.m>min.stops])
+
+nc.matches = merge(nc.ag.female,nc.ag.male)
+min.stops = 9
+nc.matches$sr.f = nc.matches$searches.f/nc.matches$stops.f
+nc.matches$sr.m = nc.matches$searches.m/nc.matches$stops.m
+t.test(nc.matches$sr.f[nc.matches$stops.f>min.stops&
+                         nc.matches$stops.m>min.stops],
+       nc.matches$sr.m[nc.matches$stops.f>min.stops&
+                         nc.matches$stops.m>min.stops],
+       paired = T)
+
+length(nc.matches$sr.f[nc.matches$stops.f>min.stops&
+                         nc.matches$stops.m>min.stops])
+mean(nc.matches$sr.f[nc.matches$stops.f>min.stops&
+                       nc.matches$stops.m>min.stops])
+mean(nc.matches$sr.m[nc.matches$stops.f>min.stops&
+                       nc.matches$stops.m>min.stops],)
+
+# Contraband
+t.test(fl.matches$cr.f[fl.matches$search.f>min.searches&
+                         fl.matches$search.m>min.searches&
+                         fl.matches$stop.f>min.stops&
+                         fl.matches$stop.m>min.stops],
+       fl.matches$cr.m[fl.matches$search.f>min.searches&
+                         fl.matches$search.m>min.searches&
+                         fl.matches$stop.f>min.stops&
+                         fl.matches$stop.m>min.stops],
+       paired = T)
+length(fl.matches$cr.f[fl.matches$search.f>min.searches&
+                         fl.matches$search.m>min.searches&
+                         fl.matches$stop.f>min.stops&
+                         fl.matches$stop.m>min.stops])
+mean(fl.matches$cr.f[fl.matches$search.f>min.searches&
+                       fl.matches$search.m>min.searches&
+                       fl.matches$stop.f>min.stops&
+                       fl.matches$stop.m>min.stops])
+mean(fl.matches$cr.m[fl.matches$search.f>min.searches&
+                       fl.matches$search.m>min.searches&
+                       fl.matches$stop.f>min.stops&
+                       fl.matches$stop.m>min.stops])
+
+###
+### 5. Appendix C: Logistic Regrssion Models
+###
+
+rm(list = ls())
+
+load("Data/FlContra_Logit.RData")
+load("Data/FLSearch_Logit.RData")
+load("Data/NCSearch_Logit.RData")
+
+texreg(list(nc.search,fl.search,fl.contra),
+          stars=c(0.01,0.05),
+          custom.coef.map = list("(Intercept)"="(Intercept)",
+                                 "factor(of_gender)1"="Female Officer",
+                                 "factor(of_race)1"="Black Officer",
+                                 "factor(of_race)2"="Latinx Officer",
+                                 "factor(of_race)3"="Asain/Pacific Islander Officer",
+                                 "factor(of_race)4"="Other Race Officer",
+                                 "officer_age"="Officer Age",
+                                 "officer_years_of_service"="Officer Years of Service",
+                                 "Officer_Years_of_Service"="Officer Years of Service",
+                                 "factor(race_gender)1"="White Female",
+                                 "factor(race_gender)2"="Black Male",
+                                 "factor(race_gender)3"="Black Female",
+                                 "factor(race_gender)4"="Latino Male",
+                                 "factor(race_gender)5"="Latina Female",
+                                 "subject_age"="Driver Age",
+                                 "investigatory" = "Investigatory Stop Purpose",
+                                 "out_of_state"="Out of State"),
+          custom.model.names = c("(1) NC Search",
+                                 "(2) FL Search",
+                                 "(3) FL Contra|Search"),
+          digits=4)
+
+###
+### 6. Appendix C: Fixed Effects 
+###
+
+rm(list = ls())
+
+load("Data/FLSearch_OLS_FE.RData")
+load("Data/FlContra_OLS_FE.RData")
+load("Data/FlSearchRate_OLS_FE.RData")
+load("Data/FlStopRate_OLS_FE.RData")
+
+texreg(list(fl.search,
+               fl.contra,
+               contra.search.rate.reg,
+               contra.stop.rate.reg),
+          stars=c(0.01,0.05),
+          custom.coef.map = list("(Intercept)"="(Intercept)",
+                                 "factor(of_gender)1"="Female Officer",
+                                 "factor(of_race)1"="Black Officer",
+                                 "officer_age"="Officer Age",
+                                 "factor(of_age)2"="Officer Age: 30-64",
+                                 "factor(of_age)3"="Officer Age: 65+",
+                                 "officer_years_of_service"="Officer Years of Service",
+                                 "Officer_Years_of_Service"="Officer Years of Service",
+                                 "factor(of_exper)1"="Experienced Officer",
+                                 "factor(race_gender)1"="White Female",
+                                 "factor(race_gender)2"="Black Male",
+                                 "factor(race_gender)3"="Black Female",
+                                 "factor(race_gender)4"="Latino Male",
+                                 "factor(race_gender)5"="Latina Female",
+                                 "subject_age"="Driver Age",
+                                 "factor(driver_age)2"="Driver Age: 30-64",
+                                 "factor(driver_age)3"="Driver Age: 65+",
+                                 "investigatory" = "Investigatory Stop Purpose",
+                                 "out_of_state"="Out of State"),
+          custom.model.names = c("(1) Search",
+                                 "(2) Contra|Search",
+                                 "(3) Hit Rate, per 10 Searches",
+                                 "(4) Hit Rate, per 100 Stops"),
+          digits=4)
+
+###
+### 7. Appendix D: Interaction Models
+###
+
+rm(list = ls())
+
+# Table 1. Officer Experience
+load("Data/FLSearch_Exper_OLS.RData")
+load("Data/NCSearch_Exper_OLS.RData")
+load("Data/FlContra_Exper_OLS.RData")
+load("Data/FlSearchRate_Exper_OLS.RData")
+load("Data/FlStopRate_Exper_OLS.RData")
+
+texreg(list(nc.search.exper,fl.search.exper,fl.contra.exper,
+            contra.search.rate.exper,contra.stop.rate.exper),
+       stars=c(0.05,0.01),
+       custom.coef.map = list("factor(of_gender)1"="Female Officer",
+                              "officer_years_of_service"="Officer Years of Service",
+                              "Officer_Years_of_Service"="Officer Years of Service",
+                              "factor(of_exper)1"="Experienced Officer",
+                              "factor(of_gender)1:officer_years_of_service"="Female Officer * Exper.",
+                              "factor(of_gender)1:Officer_Years_of_Service"="Female Officer * Exper.",
+                              "factor(of_gender)1:factor(of_exper)1"="Female Officer * Exper."),
+       digits = 3)
+
+# Table 2. Prop Female
+load("Data/FLSearch_Prop_OLS.RData")
+load("Data/FlContra_Prop_OLS.RData")
+
+texreg(list(fl.search.prop,fl.contra.prop),
+       stars=c(0.05,0.01),
+       custom.coef.map = list("factor(of_gender)1"="Female Officer",
+                              "female.prop"="Female Proportion of Proximate Force",
+                              "factor(of_gender)1:female.prop"="Female Officer * Female Prop."),
+       digits = 3)
+
+# Table 3. Stop Type
+load("Data/FLSearch_StopType_OLS.RData")
+load("Data/NCSearch_StopType_OLS.RData")
+load("Data/FlContra_StopType_OLS.RData")
+load("Data/FlSearchRate_StopType_OLS.RData")
+load("Data/FlStopRate_StopType_OLS.RData")
+
+texreg(list(nc.search.st,fl.search.st,fl.contra.st,
+            contra.search.rate.st,contra.stop.rate.st),
+       stars=c(0.05,0.01),
+       custom.coef.map = list("(Intercept)"="(Intercept)",
+                              "factor(of_gender)1"="Female Officer",
+                              "factor(of_race)1"="Black Officer",
+                              "officer_age"="Officer Age",
+                              "factor(of_age)2"="Officer Age: 30-64",
+                              "factor(of_age)3"="Officer Age: 65+",
+                              "officer_years_of_service"="Officer Years of Service",
+                              "Officer_Years_of_Service"="Officer Years of Service",
+                              "factor(of_exper)1"="Experienced Officer",
+                              "factor(race_gender)1"="White Female",
+                              "factor(race_gender)2"="Black Male",
+                              "factor(race_gender)3"="Black Female",
+                              "factor(race_gender)4"="Latino Male",
+                              "factor(race_gender)5"="Latina Female",
+                              "subject_age"="Driver Age",
+                              "factor(driver_age)2"="Driver Age: 30-64",
+                              "factor(driver_age)3"="Driver Age: 65+",
+                              "investigatory" = "Investigatory Stop Purpose",
+                              "out_of_state"="Out of State"),
+       digits = 3)
+
+# Table 4. Driver Characteristics
+load("Data/FLInter_Search.RData")
+load("Data/FLInter_Contra.RData")
+load("Data/FLStopRate_Inter_OLS.RData")
+load("Data/FLSearchRate_Inter_OLS.RData")
+load("Data/NCInter_Search.RData")
+
+texreg(list(nc.search.inter,fl.search.inter,fl.contra.inter,
+            contra.search.rate.inter,contra.stop.rate.inter),
+          stars=c(0.01,0.05),
+          custom.coef.map = list("factor(of_gender)1"="Female Officer",
+                                 "factor(subject_female)1"="Female Driver",
+                                 "factor(of_race)1"="Black Officer",
+                                 "factor(of_race)2"="Latinx Officer",
+                                 "factor(subject_race2)1"="Black Driver",
+                                 "factor(subject_race2)2"="Latinx Driver",
+                                 "factor(of_gender)1:factor(subject_female)1"="Female Officer*Driver",
+                                 "factor(of_race)1:factor(subject_race2)1"="Black Officer*Driver",
+                                 "factor(of_race)2:factor(subject_race2)1"="Latinx Officer*Black Driver",
+                                 "factor(of_race)1:factor(subject_race2)2"="Black Officer*Latinx Driver",
+                                 "factor(of_race)2:factor(subject_race2)2"="Latinx Officer* Driver"),digits=3)
+
+###
+### 8. Appendix E: A Conservative Test with the Charlotte Police Department
+###
+
+load("Data/NorthCarolina.RData")
+
+table(nc$year)
+
+nc.search16 = lm(search~factor(race_gender)+subject_age+
+                   investigatory+
+                   factor(of_race)+
+                   factor(of_gender)+Officer_Years_of_Service+
+                   factor(month)+
+                   factor(CMPD_Division),
+                 data=nc,subset=nc$year==2016)
+nc.search17 = lm(search~factor(race_gender)+subject_age+
+                   investigatory+
+                   factor(of_race)+
+                   factor(of_gender)+Officer_Years_of_Service+
+                   factor(month)+
+                   factor(CMPD_Division),
+                 data=nc,subset=nc$year==2017)
+nc.search19 = lm(search~factor(race_gender)+subject_age+
+                   investigatory+
+                   factor(of_race)+
+                   factor(of_gender)+Officer_Years_of_Service+
+                   factor(month)+
+                   factor(CMPD_Division),
+                 data=nc,subset=nc$year==2019)
+nc.search20 = lm(search~factor(race_gender)+subject_age+
+                   investigatory+
+                   factor(of_race)+
+                   factor(of_gender)+Officer_Years_of_Service+
+                   factor(month)+
+                   factor(CMPD_Division),
+                 data=nc,subset=nc$year==2020)
+texreg(list(nc.search16,nc.search17,nc.search19,nc.search20),
+       omit.coef = "Division*|month*",
+       custom.coef.map = list("(Intercept)"="(Intercept)",
+                              "factor(of_gender)1"="Female Officer",
+                              "factor(of_race)1"="Black Officer",
+                              "Officer_Years_of_Service"="Officer Years of Service",
+                              "investigatory"="Investigatory Stop",
+                              "factor(race_gender)1"="White Female",
+                              "factor(race_gender)2"="Black Male",
+                              "factor(race_gender)3"="Black Female",
+                              "subject_age"="Driver Age"),
+       stars=c(0.01,0.05))

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