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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)
+
##
+## Attaching package: 'dplyr'
+
## The following objects are masked from 'package:stats':
+##
+## filter, lag
+
## The following objects are masked from 'package:base':
+##
+## intersect, setdiff, setequal, union
+
library(ggplot2)
+library(texreg)
+
## Version: 1.37.5
+## Date: 2020-06-17
+## Author: Philip Leifeld (University of Essex)
+##
+## Consider submitting praise using the praise or praise_interactive functions.
+## Please cite the JSS article in your publications -- see citation("texreg").
+
library(readr)
+library(pscl)
+
## Classes and Methods for R developed in the
+## Political Science Computational Laboratory
+## Department of Political Science
+## Stanford University
+## Simon Jackman
+## hurdle and zeroinfl functions by Achim Zeileis
+
library(arm)
+
## Loading required package: MASS
+
##
+## Attaching package: 'MASS'
+
## The following object is masked from 'package:dplyr':
+##
+## select
+
## Loading required package: Matrix
+
## Loading required package: lme4
+
##
+## arm (Version 1.11-2, built: 2020-7-27)
+
## Working directory is /Users/kelseyshoub/Desktop/PinkPolicing/AJPS_ReplicationFiles
+
# Loading the raw data:
+nc_new = read_csv("Data/Officer_Traffic_Stops_Update.csv")
+
##
+## ── Column specification ────────────────────────────────────────────────────────────────────────────────────
+## cols(
+## OBJECTID = col_double(),
+## Month_of_Stop = col_character(),
+## Reason_for_Stop = col_character(),
+## Officer_Race = col_character(),
+## Officer_Gender = col_character(),
+## Officer_Years_of_Service = col_double(),
+## Driver_Race = col_character(),
+## Driver_Ethnicity = col_character(),
+## Driver_Gender = col_character(),
+## Driver_Age = col_double(),
+## Was_a_Search_Conducted = col_character(),
+## Result_of_Stop = col_character(),
+## CMPD_Division = col_character(),
+## GlobalID = col_character()
+## )
+
nc_old = read_csv("Data/Officer_Traffic_Stops_Original.csv")
+
##
+## ── Column specification ────────────────────────────────────────────────────────────────────────────────────
+## cols(
+## Month_of_Stop = col_character(),
+## Reason_for_Stop = col_character(),
+## Officer_Race = col_character(),
+## Officer_Gender = col_character(),
+## Officer_Years_of_Service = col_double(),
+## Driver_Race = col_character(),
+## Driver_Ethnicity = col_character(),
+## Driver_Gender = col_character(),
+## Driver_Age = col_double(),
+## Was_a_Search_Conducted = col_character(),
+## Result_of_Stop = col_character(),
+## CMPD_Division = col_character(),
+## ObjectID = col_double(),
+## CreationDate = col_datetime(format = ""),
+## Creator = col_character(),
+## EditDate = col_datetime(format = ""),
+## Editor = col_character()
+## )
+
nc = bind_rows(nc_new,nc_old)
+fl = read_csv("Data/fl_statewide_2019_08_13.csv")
+
##
+## ── Column specification ────────────────────────────────────────────────────────────────────────────────────
+## cols(
+## .default = col_character(),
+## date = col_date(format = ""),
+## time = col_time(format = ""),
+## subject_age = col_double(),
+## officer_age = col_double(),
+## officer_years_of_service = col_double(),
+## arrest_made = col_logical(),
+## citation_issued = col_logical(),
+## warning_issued = col_logical(),
+## frisk_performed = col_logical(),
+## search_conducted = col_logical()
+## )
+## ℹ Use `spec()` for the full column specifications.
+
###
+### 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)
+
## complete
+## FALSE TRUE
+## 2111746 2731204
+
table(complete2)
+
## complete2
+## FALSE TRUE
+## 692067 4150883
+
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$contra.ttest))
+
##
+## 2-sample test for equality of proportions with continuity correction
+##
+## data: table(fl$of_gender, fl$contra.ttest)
+## X-squared = 16.681, df = 1, p-value = 4.423e-05
+## alternative hypothesis: two.sided
+## 95 percent confidence interval:
+## 0.05554105 0.17724120
+## sample estimates:
+## prop 1 prop 2
+## 0.7009499 0.5845588
+
fl$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")
+
+
+
+
+