library(haven) library(foreign) library(readr) load("~/Documents/Active Projects/Primaries and Polarization/data/boatright.RData") load("~/Documents/Active Projects/Primaries and Polarization/data/Bill Sponsorship/sponsor_house.Rdata") Bonicaprimary <- read_csv("~/Documents/Active Projects/Primaries and Polarization/data/bonica_house.csv") DV.data <- read.csv("~/Documents/Active Projects/Primaries and Polarization/data/Nominate/HSall_members-3.csv") DV.data <- subset(DV.data, subset = (chamber=="House" & congress>90), select=c(icpsr, congress, state_abbrev, party_code, bioname, district_code, nokken_poole_dim1)) #NokkenPoole <- read_csv("https://voteview.com/static/data/out/members/HSall_members.csv") DV.data["nokken_poole"] <- DV.data$nokken_poole_dim1 #NokkenPoole["extremism_Nokken"] <- NokkenPoole$nokken_poole #NokkenPoole$extremism_Nokken[NokkenPoole$party_code==100] <- -1*NokkenPoole$extremism_Nokken[NokkenPoole$party_code==100] cosponsor.hou$congress <- cosponsor.hou$Congress DV.data <- merge(DV.data, cosponsor.hou, by=c("icpsr", "congress"), all=TRUE) ###Part 2: Bonica data### #I use these data to 1) create dichotomous measures of ideological challenges, and measures of ideological #2) and centrist challenger receipts by total campaign receipts from all candidates who run in district bonica_house <- subset(Bonicaprimary, subset = (seat=="federal:house")) bonica_house$party[bonica_house$party=="R"] <- 2 bonica_house$party[bonica_house$party=="D"] <- 1 bonica_house <- bonica_house[!(bonica_house$party=="I"),] bonica_house <- bonica_house[!(bonica_house$cycle==2018),] bonica_house <- bonica_house[!(bonica_house$cycle==2016),] hs_scores_for_rich_6_<- read_dta("~/Documents/Active Projects/Primaries and Polarization/data/hs scores for rich (6).dta") hs_scores_for_rich_6_ <- transform(hs_scores_for_rich_6_, district=paste(state, dist, sep="")) hs_scores_for_rich_6_$party <- hs_scores_for_rich_6_$dem hs_scores_for_rich_6_$party[hs_scores_for_rich_6_$party=="0"] <- 2 hs_scores_for_rich_6_$cycle <- hs_scores_for_rich_6_$year hs_scores_for_rich_6_ <- subset(hs_scores_for_rich_6_, select = c(cycle, party, district, hs_score)) bonica_house <- merge(bonica_house, hs_scores_for_rich_6_, c("cycle", "party" , "district"), all.x=TRUE) #Now I create separate DFs for incumbents and competitive primary challengers challenger <- subset(bonica_house, Incum_Chall=="C") incumbent <- subset(bonica_house, Incum_Chall=="I") incumbent$incum_cfscore <- incumbent$recipient_cfscore incumbent$incum_hs_score <- incumbent$hs_score challenger$voteshare <- challenger$ppct challenger$chal_cfscore <- challenger$recipient_cfscore challenger$chal_hs_score <- challenger$hs_score challenger <- challenger[!is.na(challenger$chal_cfscore),] challenger <- challenger[!is.na(challenger$voteshare),] challenger$voteshare <- as.numeric(challenger$voteshare) challenger <- subset(challenger, select=c(cycle, party, district, state, chal_cfscore, voteshare, chal_hs_score)) incumbent2 <- subset(incumbent, select=c(cycle, party, district, state, incum_cfscore, incum_hs_score )) challenger2 <- merge(challenger, incumbent2, c("cycle", "party" , "district"), all.x=TRUE) #challenger2["ideo_diff"] <- (challenger2$chal_hs_score - challenger2$incum_hs_score) challenger2["ideo_diff"] <- (challenger2$chal_cfscore - challenger2$incum_cfscore) challenger2$ideo_diff[challenger2$party==1] <- (-1*(challenger2$ideo_diff))[challenger2$party==1] sd(challenger2$ideo_diff, na.rm = TRUE) mean(challenger2$ideo_diff, na.rm = TRUE) ideochal <- challenger2[!(challenger2$ideo_diff<0),] ideochal <- aggregate(voteshare ~ cycle + party + district , data=ideochal, sum) IV.data <- merge(incumbent, ideochal, by=c("cycle", "party", "district"), all.x=TRUE) #IV.data$donors[is.na(IV.data$donors)] <- 0 IV.data$voteshare[is.na(IV.data$voteshare)] <- 0 IV.data$voteshare[IV.data$voteshare>1] <- 0.5 boatright.data["party"] <- boatright.data$primary_party boatright.data["congress"] <- (boatright.data$cong - 1) boatright.data <- subset(boatright.data, select = c(elect_year, congress, party, icpsr, region_icpsr, ideochal, Gmargin_pct, district_dpres, district_black, district_blue_collar, district_college_educ, district_foreign_born, district_land_area, district_median_income, district_pop_density, district_unemployment, district_poverty, district_white_collar, eastnorthcent, midatlantic, mountain, deepsouth, pacific, westnorthcent, dummy_1980s, dummy_1990s, dummy_2000s, dummy_2010s)) IV.data["icpsr"] <- IV.data$ICPSR2 IV.data["elect_year"] <- IV.data$cycle IV.data <- merge(IV.data, boatright.data, by=c("icpsr","elect_year", "party"), all.x=TRUE) IV.data["ideochal_Bonica"] <- IV.data$ideo_chal IV.data["centchal_Bonica"] <- IV.data$cent_chal IV.data["ideochal_Boatright"] <- IV.data$ideochal IV.data["centchal_Boatright"] <- IV.data$centchal primary.full.house <- merge(DV.data, IV.data, c("icpsr", "congress"), all=FALSE) primary.full.house$party <- primary.full.house$party.x primary.full.house <- primary.full.house[!is.na(primary.full.house$nokken_poole),] primary.full.house$ideochal_Boatright[is.na(primary.full.house$ideochal_Boatright)] <- 0 #primary.full.house$ideochal_Bonica[is.na(primary.full.house$ideochal_Bonica)] <- 0 primary.full.house$cycle[primary.full.house$congress==113] <- 2014 primary.full.house$cycle[primary.full.house$congress==112] <- 2012 primary.full.house$cycle[primary.full.house$congress==111] <- 2010 primary.full.house$cycle[primary.full.house$congress==110] <- 2008 primary.full.house$cycle[primary.full.house$congress==109] <- 2006 primary.full.house$cycle[primary.full.house$congress==108] <- 2004 primary.full.house$cycle[primary.full.house$congress==107] <- 2002 primary.full.house$cycle[primary.full.house$congress==106] <- 2000 primary.full.house$cycle[primary.full.house$congress==105] <- 1998 primary.full.house$cycle[primary.full.house$congress==104] <- 1996 primary.full.house$cycle[primary.full.house$congress==103] <- 1994 primary.full.house$cycle[primary.full.house$congress==102] <- 1992 primary.full.house$cycle[primary.full.house$congress==101] <- 1990 primary.full.house$cycle[primary.full.house$congress==100] <- 1988 primary.full.house$cycle[primary.full.house$congress==99] <- 1986 primary.full.house$cycle[primary.full.house$congress==98] <- 1984 primary.full.house$cycle[primary.full.house$congress==97] <- 1982 primary.full.house$cycle[primary.full.house$congress==96] <- 1980 primary.full.house$cycle[primary.full.house$congress==95] <- 1978 primary.full.house$cycle[primary.full.house$congress==94] <- 1976 primary.full.house$cycle[primary.full.house$congress==93] <- 1974 primary.full.house$cycle[primary.full.house$congress==92] <- 1972 primary.full.house$cycle[primary.full.house$congress==91] <- 1970 primary.full.house <- primary.full.house[!(primary.full.house$cycle<1970),] primary.full.house$state <- primary.full.house$state_abbrev primary.full.house$party[primary.full.house$party_code==100] <- 1 primary.full.house$party[primary.full.house$party_code==200] <- 2 primary.full.house$party[primary.full.house$party_code==328] <- 1 primary.full.house["majority"] <- primary.full.house$party primary.full.house$majority[primary.full.house$cong==92&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==92&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==93&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==93&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==94&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==94&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==95&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==95&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==96&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==96&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==97&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==97&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==98&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==98&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==99&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==99&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==100&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==100&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==101&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==101&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==102&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==102&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==103&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==103&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==104&primary.full.house$party==1] <- 0 primary.full.house$majority[primary.full.house$cong==104&primary.full.house$party==2] <- 1 primary.full.house$majority[primary.full.house$cong==105&primary.full.house$party==1] <- 0 primary.full.house$majority[primary.full.house$cong==105&primary.full.house$party==2] <- 1 primary.full.house$majority[primary.full.house$cong==106&primary.full.house$party==1] <- 0 primary.full.house$majority[primary.full.house$cong==106&primary.full.house$party==2] <- 1 primary.full.house$majority[primary.full.house$cong==107&primary.full.house$party==1] <- 0 primary.full.house$majority[primary.full.house$cong==107&primary.full.house$party==2] <- 1 primary.full.house$majority[primary.full.house$cong==108&primary.full.house$party==1] <- 0 primary.full.house$majority[primary.full.house$cong==108&primary.full.house$party==2] <- 1 primary.full.house$majority[primary.full.house$cong==109&primary.full.house$party==1] <- 0 primary.full.house$majority[primary.full.house$cong==109&primary.full.house$party==2] <- 1 primary.full.house$majority[primary.full.house$cong==110&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==110&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==111&primary.full.house$party==1] <- 1 primary.full.house$majority[primary.full.house$cong==111&primary.full.house$party==2] <- 0 primary.full.house$majority[primary.full.house$cong==112&primary.full.house$party==1] <- 0 primary.full.house$majority[primary.full.house$cong==112&primary.full.house$party==2] <- 1 primary.full.house$majority[primary.full.house$cong==113&primary.full.house$party==1] <- 0 primary.full.house$majority[primary.full.house$cong==113&primary.full.house$party==2] <- 1 primary.full.house$majority[primary.full.house$cong==114&primary.full.house$party==1] <- 0 primary.full.house$majority[primary.full.house$cong==114&primary.full.house$party==2] <- 1 primary.full.house["copartisans"] <- 0 primary.full.house$copartisans[primary.full.house$congress==92&primary.full.house$party==1] <- 255 primary.full.house$copartisans[primary.full.house$congress==92&primary.full.house$party==2] <- 180 primary.full.house$copartisans[primary.full.house$congress==93&primary.full.house$party==1] <- 242 primary.full.house$copartisans[primary.full.house$congress==93&primary.full.house$party==2] <- 192 primary.full.house$copartisans[primary.full.house$congress==94&primary.full.house$party==1] <- 291 primary.full.house$copartisans[primary.full.house$congress==94&primary.full.house$party==2] <- 144 primary.full.house$copartisans[primary.full.house$congress==95&primary.full.house$party==1] <- 292 primary.full.house$copartisans[primary.full.house$congress==95&primary.full.house$party==2] <- 143 primary.full.house$copartisans[primary.full.house$congress==96&primary.full.house$party==1] <- 277 primary.full.house$copartisans[primary.full.house$congress==96&primary.full.house$party==2] <- 158 primary.full.house$copartisans[primary.full.house$congress==97&primary.full.house$party==1] <- 242 primary.full.house$copartisans[primary.full.house$congress==97&primary.full.house$party==2] <- 192 primary.full.house$copartisans[primary.full.house$congress==98&primary.full.house$party==1] <- 269 primary.full.house$copartisans[primary.full.house$congress==98&primary.full.house$party==2] <- 166 primary.full.house$copartisans[primary.full.house$congress==99&primary.full.house$party==1] <- 253 primary.full.house$copartisans[primary.full.house$congress==99&primary.full.house$party==2] <- 182 primary.full.house$copartisans[primary.full.house$congress==100&primary.full.house$party==1] <- 258 primary.full.house$copartisans[primary.full.house$congress==100&primary.full.house$party==2] <- 177 primary.full.house$copartisans[primary.full.house$congress==101&primary.full.house$party==1] <- 260 primary.full.house$copartisans[primary.full.house$congress==101&primary.full.house$party==2] <- 175 primary.full.house$copartisans[primary.full.house$congress==102&primary.full.house$party==1] <- 267 primary.full.house$copartisans[primary.full.house$congress==102&primary.full.house$party==2] <- 167 primary.full.house$copartisans[primary.full.house$congress==103&primary.full.house$party==1] <- 258 primary.full.house$copartisans[primary.full.house$congress==103&primary.full.house$party==2] <- 176 primary.full.house$copartisans[primary.full.house$congress==104&primary.full.house$party==1] <- 204 primary.full.house$copartisans[primary.full.house$congress==104&primary.full.house$party==2] <- 230 primary.full.house$copartisans[primary.full.house$congress==105&primary.full.house$party==1] <- 206 primary.full.house$copartisans[primary.full.house$congress==105&primary.full.house$party==2] <- 227 primary.full.house$copartisans[primary.full.house$congress==106&primary.full.house$party==1] <- 211 primary.full.house$copartisans[primary.full.house$congress==106&primary.full.house$party==2] <- 223 primary.full.house$copartisans[primary.full.house$congress==107&primary.full.house$party==1] <- 212 primary.full.house$copartisans[primary.full.house$congress==107&primary.full.house$party==2] <- 221 primary.full.house$copartisans[primary.full.house$congress==108&primary.full.house$party==1] <- 205 primary.full.house$copartisans[primary.full.house$congress==108&primary.full.house$party==2] <- 229 primary.full.house$copartisans[primary.full.house$congress==109&primary.full.house$party==1] <- 202 primary.full.house$copartisans[primary.full.house$congress==109&primary.full.house$party==2] <- 232 primary.full.house$copartisans[primary.full.house$congress==110&primary.full.house$party==1] <- 233 primary.full.house$copartisans[primary.full.house$congress==110&primary.full.house$party==2] <- 202 primary.full.house$copartisans[primary.full.house$congress==111&primary.full.house$party==1] <- 257 primary.full.house$copartisans[primary.full.house$congress==111&primary.full.house$party==2] <- 178 primary.full.house$copartisans[primary.full.house$congress==112&primary.full.house$party==1] <- 193 primary.full.house$copartisans[primary.full.house$congress==112&primary.full.house$party==2] <- 242 primary.full.house$copartisans[primary.full.house$congress==113&primary.full.house$party==1] <- 201 primary.full.house$copartisans[primary.full.house$congress==113&primary.full.house$party==2] <- 234 primary.full.house$copartisans[primary.full.house$congress==114&primary.full.house$party==1] <- 188 primary.full.house$copartisans[primary.full.house$congress==114&primary.full.house$party==2] <- 247 primary.full.house["otherparty_partisanship"] <- 0 primary.full.house$otherparty_partisanship[primary.full.house$congress==96&primary.full.house$party==2] <- 29.86 primary.full.house$otherparty_partisanship[primary.full.house$congress==96&primary.full.house$party==1] <- 47.15 primary.full.house$otherparty_partisanship[primary.full.house$congress==97&primary.full.house$party==2] <- 34.67 primary.full.house$otherparty_partisanship[primary.full.house$congress==97&primary.full.house$party==1] <- 44.61 primary.full.house$otherparty_partisanship[primary.full.house$congress==98&primary.full.house$party==2] <- 27.27 primary.full.house$otherparty_partisanship[primary.full.house$congress==98&primary.full.house$party==1] <- 54.04 primary.full.house$otherparty_partisanship[primary.full.house$congress==99&primary.full.house$party==2] <- 28.81 primary.full.house$otherparty_partisanship[primary.full.house$congress==99&primary.full.house$party==1] <- 50.33 primary.full.house$otherparty_partisanship[primary.full.house$congress==100&primary.full.house$party==2] <- 28.43 primary.full.house$otherparty_partisanship[primary.full.house$congress==100&primary.full.house$party==1] <- 50.13 primary.full.house$otherparty_partisanship[primary.full.house$congress==101&primary.full.house$party==2] <- 28.55 primary.full.house$otherparty_partisanship[primary.full.house$congress==101&primary.full.house$party==1] <- 49.53 primary.full.house$otherparty_partisanship[primary.full.house$congress==102&primary.full.house$party==2] <- 27.2 primary.full.house$otherparty_partisanship[primary.full.house$congress==102&primary.full.house$party==1] <- 46.93 primary.full.house$otherparty_partisanship[primary.full.house$congress==103&primary.full.house$party==2] <- 27.05 primary.full.house$otherparty_partisanship[primary.full.house$congress==103&primary.full.house$party==1] <- 37.59 primary.full.house$otherparty_partisanship[primary.full.house$congress==104&primary.full.house$party==2] <- 30.24 primary.full.house$otherparty_partisanship[primary.full.house$congress==104&primary.full.house$party==1] <- 28.16 primary.full.house$otherparty_partisanship[primary.full.house$congress==105&primary.full.house$party==2] <- 30.24 primary.full.house$otherparty_partisanship[primary.full.house$congress==105&primary.full.house$party==1] <- 31.59 primary.full.house$otherparty_partisanship[primary.full.house$congress==106&primary.full.house$party==2] <- 28.3 primary.full.house$otherparty_partisanship[primary.full.house$congress==106&primary.full.house$party==1] <- 36.3 primary.full.house$otherparty_partisanship[primary.full.house$congress==107&primary.full.house$party==2] <- 24.09 primary.full.house$otherparty_partisanship[primary.full.house$congress==107&primary.full.house$party==1] <- 37.34 primary.full.house$otherparty_partisanship[primary.full.house$congress==108&primary.full.house$party==2] <- 23.73 primary.full.house$otherparty_partisanship[primary.full.house$congress==108&primary.full.house$party==1] <- 34.97 primary.full.house$otherparty_partisanship[primary.full.house$congress==109&primary.full.house$party==2] <- 23.43 primary.full.house$otherparty_partisanship[primary.full.house$congress==109&primary.full.house$party==1] <- 33.0 primary.full.house$otherparty_partisanship[primary.full.house$congress==110&primary.full.house$party==2] <- 20.92 primary.full.house$otherparty_partisanship[primary.full.house$congress==110&primary.full.house$party==1] <- 32.25 primary.full.house$otherparty_partisanship[primary.full.house$congress==111&primary.full.house$party==2] <- 18.87 primary.full.house$otherparty_partisanship[primary.full.house$congress==111&primary.full.house$party==1] <- 33.36 primary.full.house$otherparty_partisanship[primary.full.house$congress==112&primary.full.house$party==2] <- 22.47 primary.full.house$otherparty_partisanship[primary.full.house$congress==112&primary.full.house$party==1] <- 22.42 primary.full.house$otherparty_partisanship[primary.full.house$congress==113&primary.full.house$party==2] <- 20.8 primary.full.house$otherparty_partisanship[primary.full.house$congress==113&primary.full.house$party==1] <- 26.6 primary.full.house$otherparty_partisanship <- 100 - primary.full.house$otherparty_partisanship #these legislators had 2 demshares in a particular Congress, so I dropped the more extreme score (they were all similar) primary.full.house <- primary.full.house[!(primary.full.house$icpsr==29905 & primary.full.house$cong==107 & primary.full.house$demshare<0.30),] #primary.full.data <- primary.full.data[!(primary.full.data$icpsr==12035 & primary.full.data$cong==99 & primary.full.data$demshare>),] primary.full.house <- primary.full.house[!(primary.full.house$icpsr==20349 & primary.full.house$cong==110 & primary.full.house$demshare>0.73),] primary.full.house <- primary.full.house[!(primary.full.house$icpsr==29573 & primary.full.house$cong==111 & primary.full.house$demshare>0.86),] primary.full.house <- unique(primary.full.house) primary.full.house <- primary.full.house[!duplicated(primary.full.house[c("icpsr", "congress")]),] primary.full.house$demshare <- primary.full.house$demshare*100 primary.full.house["repshare"] <- 100 - primary.full.house$demshare primary.full.house$ideochal_Bonica[primary.full.house$voteshare>=.25] <- 1 primary.full.house$ideochal_Bonica[primary.full.house$voteshare<.25] <- 0 Therms_MOSpaper <- read_dta("~/Documents/Active Projects/Primaries and Polarization/data/other/Therms_MOSpaper.dta") ANES <- subset(Therms_MOSpaper, select=c(partyID_nolean, year, LiberalTherm, ConservativeTherm)) ANES$cycle <- ANES$year ANES$party <- ANES$partyID_nolean primary.full.house <- merge(primary.full.house, ANES, by=c("cycle", "party"), all=FALSE) primary.full.house <- subset(primary.full.house, congress>95) primary.full.house <- subset(primary.full.house, congress<114) primary.full.house <- primary.full.house[!duplicated(primary.full.house[c("icpsr", "congress")]),] primary.full.house <- primary.full.house[!is.na(primary.full.house$congress),] #primary.full.house <- primary.full.house[!is.na(primary.full.house$Gmargin_pct),] primary.full.house <- primary.full.house[!is.na(primary.full.house$voteshare),] GOP.full.data <- subset(primary.full.house, party==2 ) DP.full.data <- subset(primary.full.house, party==1 ) library(foreign) save(primary.full.house, file="~/Documents/Active Projects/Primaries and Polarization/data/primary_house_data.RData") install.packages("plm") library(plm) library(clubSandwich) GOP.threat.model <- plm(repshare ~ ideochal_Bonica + Gmargin_pct + copartisans + majority + otherparty_partisanship , data=GOP.full.data, index=c("icpsr", "congress"), model="within") summary(GOP.threat.model) coef_test(GOP.threat.model, vcov = "CR2", test = "Satterthwaite")[1:7,] DP.threat.model <- plm(demshare ~ ideochal_Bonica + Gmargin_pct + copartisans + majority + otherparty_partisanship , data=DP.full.data, index=c("icpsr", "congress"), model="within") summary(DP.threat.model) coef_test(DP.threat.model, vcov = "CR2", test = "Satterthwaite")[1:7,] GOP.threat.model <- plm(repshare ~ ideochal_Boatright + Gmargin_pct + copartisans + majority + otherparty_partisanship , data=GOP.full.data, index=c("icpsr", "congress"), model="within") summary(GOP.threat.model) coef_test(GOP.threat.model, vcov = "CR2", test = "Satterthwaite")[1:7,] DP.threat.model <- plm(demshare ~ ideochal_Boatright + Gmargin_pct + copartisans + majority + otherparty_partisanship , data=DP.full.data, index=c("icpsr", "congress"), model="within") summary(DP.threat.model) coef_test(DP.threat.model, vcov = "CR2", test = "Satterthwaite")[1:7,] GOP.full.data["neg_partisan"] <- (100 - GOP.full.data$LiberalTherm) GOP.full.data["neg_dpres"] <- (1 - GOP.full.data$district_dpres)*100 GOP_ideothreat <- lm(formula = voteshare ~ neg_dpres*neg_partisan + district_black + district_blue_collar + district_foreign_born + district_unemployment + district_pop_density + district_poverty + eastnorthcent + midatlantic + mountain + deepsouth + pacific + westnorthcent, data = GOP.full.data) summary(GOP_ideothreat) GOP.full.data["ideothreat"] <- predict(GOP_ideothreat) GOP.threat.model <- plm(repshare ~ ideothreat + Gmargin_pct +copartisans + majority + otherparty_partisanship , data=GOP.full.data, index=c("icpsr", "congress"), model="within") summary(GOP.threat.model) install.packages("clubSandwich") library(clubSandwich) coef_test(GOP.threat.model, vcov = "CR2", test = "Satterthwaite")[1:7,] DP.full.data["neg_partisan"] <- (100 - DP.full.data$ConservativeTherm) DP_ideothreat <- lm(formula = voteshare ~ district_dpres*neg_partisan + district_black + district_blue_collar + district_foreign_born + district_unemployment + district_pop_density + district_poverty + eastnorthcent + midatlantic + mountain + deepsouth + pacific + westnorthcent, data = DP.full.data) summary(DP_ideothreat) DP.full.data["ideothreat"] <- predict(DP_ideothreat ) DP.threat.model <- plm(demshare ~ ideothreat + Gmargin_pct + majority + copartisans + otherparty_partisanship , data=DP.full.data, index=c("icpsr", "congress"), model="within") summary(DP.threat.model) DP.threat.model <- plm(demshare ~ ideothreat + Gmargin_pct + majority + copartisans + otherparty_partisanship + district_dpres + + nokken_poole, data=DP.full.data, index=c("icpsr", "congress"), model="within") summary(DP.threat.model) coef_test(DP.threat.model, vcov = "CR2", test = "Satterthwaite")[1:7,] GOPavg <- aggregate(cbind(demshare,repshare,ideothreat, voteshare, copartisans, majority, otherparty_partisanship)~congress, data=GOP.full.data, mean) GOPavg80s <- subset(GOPavg, subset=(congress<101)) GOPavg10s <- subset(GOPavg, subset=(congress>111)) #why is the voteshare of GOPs so much lower here in these data? what's going wrong? DPavg <- aggregate(cbind(demshare,repshare,ideothreat, voteshare, copartisans, majority, otherparty_partisanship)~congress, data=DP.full.data, mean) DPavg80s <- subset(DPavg, subset=(congress<101)) DPavg10s <- subset(DPavg, subset=(congress>111)) mean(GOPavg80s$repshare) mean(GOPavg10s$repshare) (mean(GOPavg10s$repshare) - mean(GOPavg80s$repshare)) - (0.12*(mean(GOPavg10s$copartisans) - mean(GOPavg80s$copartisans))) (96.16*(mean(GOPavg10s$ideothreat) - mean(GOPavg80s$ideothreat)))/ ((mean(GOPavg10s$repshare) - mean(GOPavg80s$repshare)) - (0.12*(mean(GOPavg10s$copartisans) - mean(GOPavg80s$copartisans)))) (mean(DPavg10s$demshare) - mean(DPavg80s$demshare)) - (0.10*(mean(DPavg10s$copartisans) - mean(DPavg80s$copartisans))) (287.7*(mean(DPavg10s$ideothreat) - mean(DPavg80s$ideothreat)))/ ((mean(DPavg10s$demshare) - mean(DPavg80s$demshare)) - (0.10*(mean(DPavg10s$copartisans) - mean(DPavg80s$copartisans)))) mean(DPavg80s$demshare) mean(DPavg10s$demshare) mean(DPavg80s$copartisans) mean(DPavg10s$copartisans) aggregate(ideothreat~congress, data=DP.full.data, mean) sd(GOP.full.data$ideothreat, na.rm = TRUE) sd(GOP.full.data$repshare, na.rm = TRUE) sd(DP.full.data$ideothreat, na.rm = TRUE) sd(DP.full.data$demshare, na.rm = TRUE) DPrelativethreat <- aggregate( ideothreat ~ cycle + congress + party, data=DP.full.data, mean) DPrelativethreat["ideothreat"] <- DPrelativethreat$ideothreat GOPrelativethreat <- aggregate( ideothreat ~ cycle + congress + party, data=GOP.full.data, mean) GOPrelativethreat["ideothreat"] <- GOPrelativethreat$ideothreat DPrelativevote <- aggregate( voteshare ~ cycle + congress + party, data=DP.full.data, mean) DPrelativevote["voteshare"] <- DPrelativevote$voteshare GOPrelativevote <- aggregate( voteshare ~ cycle + congress + party, data=GOP.full.data, mean) GOPrelativevote["voteshare"] <- GOPrelativevote$voteshare relative <- merge(DPrelativethreat, GOPrelativethreat, by=c("congress", "cycle", "party"), all=TRUE) relative <- merge(relative, DPrelativevote, by=c("congress", "cycle", "party"), all=TRUE) relative <- merge(relative, GOPrelativevote, by=c("congress", "cycle", "party"), all=TRUE) relative$voteshare_r <- c(relative$voteshare.x, relative$voteshare.y) relative$voteshare_r <- relative$voteshare.y library(foreign) write.dta(votesharegraph, "~/Documents/Active Projects/Primaries and Polarization/data/voteshare.dta") library(foreign) write.dta(primary.full.house, "~/Documents/Active Projects/Primaries and Polarization/data/primary_house_data.dta")