####### ####### ####### 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))