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10/replication_package/readme.rtf

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+{\rtf1\ansi\ansicpg1252\cocoartf2513
+\cocoatextscaling0\cocoaplatform0{\fonttbl\f0\fswiss\fcharset0 ArialMT;\f1\fswiss\fcharset0 Helvetica;}
+{\colortbl;\red255\green255\blue255;\red26\green26\blue26;\red255\green255\blue255;\red26\green26\blue26;
+}
+{\*\expandedcolortbl;;\cssrgb\c13348\c13348\c13331;\cssrgb\c100000\c100000\c100000\c0;\cssrgb\c13348\c13348\c13331;
+}
+\paperw11900\paperh16840\margl1440\margr1440\vieww10800\viewh8400\viewkind0
+\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0
+
+\f0\fs24 \cf0 Replication Data ReadMe for \cf2 \cb3 \expnd0\expndtw0\kerning0
+Can\'92t We All Just Get Along? How Women MPs Can Ameliorate Affective Polarization in Western Publics\
+\
+Code files: \
+\
+1. replication_code.r - Contains code to replicate all figures and tables in both article and supplementary information memo\
+\
+Datasets:\
+1. dyadic_data_1-4-22.Rdata - Dataset of directed party dyads, associated with codebook \'93Codebook for Dyadic Party Dataset\'94. Dataset required to replicate table 1 and Figure 1 in article, as well as tables S2, S3A, S3B, S4, S5, S6, S7, S8, S9, S10, and Figure S1 and S2 in supplementary information memo.\
+\
+2. gender_disagregated_8-8-21.rds - Dataset of directed party dyads, disaggregated by gender of partisans, associated with codebook \'93Codebook for Gender Disaggregated Dyadic Party Dataset\'94. Dataset required to replicate Table 1 in main article, as well as Table S2 in the supplementary \cf4  information memo\cf2 .\
+\
+3. multilevel_1-5-22.Rdata - Dataset of individual evaluations of out-parties, with contextual variables, associated with Codebook \'93Codebook for Multilevel Dataset\'94. Required to replicate Tables S11 and S12 in the supplementary information memo.\
+\
+\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0
+
+\f1 \cf0 \cb1 \kerning1\expnd0\expndtw0 *** NOTE: TO RUN THESE FILES AS THEY ARE SET UP, CREATE A DIRECTORY INCLUDING ALL THREE DATASETS***\
+\
+}

10/replication_package/replication_code.R

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+#install.packages("tidyverse")
+#install.packages("stargazer")
+library(tidyverse) ##data cleaning
+library(stargazer) ##tex output
+library(haven)
+library(estimatr)
+library(dplyr)
+library(fixest)
+library(modelsummary)
+
+
+############################################
+############## CREATING FIGURE 1 ###########
+############################################
+
+#Load in data
+load("dyadic_data_1-4-22.Rdata")
+dta <- updated_data 
+
+#### Remove unneeded variables
+vars <- c("to_mp_number", "to_rile", "to_economy", "to_society", "year", "country", 
+          "to_pfeml", "to_femaleleader")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+### Identiy unique parties being evaluated
+dta_unique <- unique(dta)
+
+
+fig1 <- ggplot(dta_unique, aes(x = to_pfeml)) +
+  geom_histogram(color="black", fill="grey40", binwidth =0.1, center=0.25) +
+  scale_x_continuous(breaks = seq(0,1,0.1)) +
+  theme_minimal() +
+  theme(plot.title = element_text(size=12)) +
+  ylab("Frequency")+
+  xlab("Proportion of Women MPs");fig1
+
+
+############################################
+###### CREATING TABLE 1 COLUMNS 1 & 2 ######
+############################################
+
+#Out party % women, non-clustered SEs
+load("dyadic_data_1-4-22.Rdata")
+
+dta <-updated_data
+
+#creating the country-year fixed effects
+dta$cntryyr <-paste(dta$country, dta$year, sep = "")
+
+## Removing smaller parties
+dta <- subset(dta, dta$to_prior_seats >=4)
+
+
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "party_dislike", "party_like", "cntryyr", "to_pfeml", "to_prior_seats", "to_mp_number")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+
+table1.1 <-lm(party_like ~ to_pfeml + as.factor(cntryyr), data = dta)
+table1.2 <-lm(party_like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(cntryyr), data = dta)
+
+### With clustered SEs
+stargazer(table1.1, table1.2, 
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table1.1, table1.2,
+                        clusters = dta$country),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition", 
+                   "econ_distance_s", "society_distance_s"))
+
+############################################
+###### CREATING TABLE 1 COLUMNS 3 & 4 ######
+############################################
+
+## Note in gendered data, the party_like and party_dislike variable indicate mean levels of
+## like/dislike for party by ALL partisans
+## the "dislike" variable indicates level of dislike towards out-party by partisans of specified gender
+dta <- readRDS("gender_disagregated_8-8-21.rds")
+
+#creating the country-year fixed effects
+dta$countryyear <-paste(dta$country, dta$year, sep = "")
+
+## Removing smaller parties
+dta <- subset(dta, dta$to_prior_seats >=4)
+
+### Create Like variable for gendered data from dislike
+dta$like <- 10- dta$dislike
+
+## Remove unneeded variables and NAs
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "to_pfeml",
+          "countryyear", "gender", "like", "dislike", "to_prior_seats")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+## Only men subset
+dta_male <- subset(dta, gender==1)
+dta_female <- subset(dta, gender==2)
+
+table1.3 <-lm(like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(countryyear), data = dta_female)
+table1.4 <-lm(like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(countryyear), data = dta_male)
+
+### With clustered SEs - women
+stargazer(table1.3,
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Out-Party Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table1.3,
+                        clusters = dta_female$country),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition", "to_pfeml2"))
+
+### With clustered SEs - men
+stargazer(table1.4, 
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Out-Party Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table1.4, 
+                        clusters = dta_male$country),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition", "to_pfeml2"))
+
+
+############################################
+###### CREATING TABLE S2 COLUMNS 1 & 2 ######
+############################################
+
+#Out party % women, non-clustered SEs
+load("dyadic_data_1-4-22.Rdata")
+
+dta <-updated_data
+
+#creating the country-year fixed effects
+dta$cntryyr <-paste(dta$country, dta$year, sep = "")
+
+
+
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "party_dislike", "party_like", "cntryyr", "to_pfeml", "to_prior_seats", "to_mp_number")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+
+tableS2.1 <-lm(party_like ~ to_pfeml + as.factor(cntryyr), data = dta)
+tableS2.2 <-lm(party_like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(cntryyr), data = dta)
+
+summary(tableS2.1)
+summary(tableS2.2)
+
+### With clustered SEs
+stargazer(tableS2.1, tableS2.2, 
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(tableS2.1, tableS2.2,
+                        clusters = dta$country),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition", 
+                   "econ_distance_s", "society_distance_s"))
+
+############################################
+###### CREATING TABLE S2 COLUMNS 3 & 4 ######
+############################################
+
+dta <- readRDS("gender_disagregated_8-8-21.rds")
+
+#creating the country-year fixed effects
+dta$countryyear <-paste(dta$country, dta$year, sep = "")
+
+
+### Create Like variable for gendered data from dislike
+dta$like <- 10- dta$dislike
+
+## Remove unneeded variables and NAs
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "to_pfeml",
+          "countryyear", "gender", "like", "dislike", "to_prior_seats")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+## Only men subset
+dta_male <- subset(dta, gender==1)
+dta_female <- subset(dta, gender==2)
+
+tableS2.3 <-lm(like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(countryyear), data = dta_female)
+tableS2.4 <-lm(like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(countryyear), data = dta_male)
+
+summary(tableS2.3)
+summary(tableS2.4)
+
+### With clustered SEs - women
+stargazer(tableS2.3,
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Out-Party Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(tableS2.3,
+                        clusters = dta_female$country),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition", "to_pfeml2"))
+
+### With clustered SEs - men
+stargazer(tableS2.4, 
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Out-Party Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(tableS2.4, 
+                        clusters = dta_male$country),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition", "to_pfeml2"))
+
+
+############################################
+############ CREATING TABLE S3 #############
+############################################
+
+## Read in data
+load("dyadic_data_1-4-22.Rdata")
+
+dta <-updated_data
+colnames(dta)
+
+#creating the country-year fixed effects
+dta$cntryyr <-paste(dta$country, dta$year, sep = "")
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "party_dislike","party_like", "cntryyr", "to_pfeml", "from_rile", "to_rile",
+          "from_left_bloc", "from_right_bloc", "to_left_bloc", "to_right_bloc", "from_parfam", "to_parfam",
+          "to_prior_seats")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+dta_nrr <- subset(dta, dta$to_parfam!=70)
+dta_nrr <- subset(dta_nrr, dta_nrr$from_parfam!=70)
+
+## Remove small parties, with fewer than 4 seats
+dta_small_nrr <- subset(dta_nrr, dta_nrr$to_prior_seats >=4)
+
+table.S3 <-lm(party_like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(country), data = dta_small_nrr)
+summary(table.S3)
+
+stargazer(table.S3, 
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table.S3,
+                        clusters = dta_small_nrr$country),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition", 
+                   "econ_distance_s", "society_distance_s"))
+
+
+############################################
+############ CREATING TABLE S3B ############
+############################################
+
+## Load
+load("dyadic_data_1-4-22.Rdata")
+
+dta <-updated_data
+
+#creating the country-year fixed effects
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "party_dislike", "party_like", "to_parfam", "to_left_bloc", "to_right_bloc", "cntryyr", "to_pfeml",
+          "to_prior_seats")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+## Remove small parties, with fewer than 4 seats
+dta_small <- subset(dta, dta$to_prior_seats >=4)
+
+table.3B.1 <-lm(party_like ~ to_pfeml + as.factor(cntryyr), data = dta_small)
+table.3B.2 <-lm(party_like ~ to_pfeml + rile_distance_s + to_left_bloc + prior_coalition + prior_opposition + as.factor(cntryyr), data = dta_small)
+
+summary(table.3B.2)
+
+### With clustered SEs
+stargazer(table.3B.1, table.3B.2,
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table.3B.1, table.3B.2,
+                        clusters = dta_small$country),
+          keep = c("to_pfeml", "rile_distance_s", "to_left_bloc", "prior_coalition", "prior_opposition", 
+                   "econ_distance_s", "society_distance_s"))
+
+
+############################################
+############ CREATING TABLE S4 ############
+############################################
+
+## Read in data
+load("dyadic_data_1-4-22.Rdata")
+
+dta <-updated_data
+
+#creating the country-year fixed effects
+dta$countryyear <-paste(dta$country, dta$year, sep = "")
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "party_dislike","party_like", "countryyear", "to_pfeml", "from_rile", "to_rile",
+          "logDM", "to_left_bloc", "to_prior_seats")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+### Split by year, 1996-2006 and 2007-2017
+dta_early <- subset(dta, dta$year<=2006)
+dta_late <- subset(dta, dta$year>=2007)
+
+
+table.early <-lm(party_like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(countryyear), data = dta_early)
+table.late <-lm(party_like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition  + as.factor(countryyear), data = dta_late)
+
+### Without small parties
+dta_early_small <- subset(dta_early, dta_early$to_prior_seats >=4)
+dta_late_small <- subset(dta_late, dta_late$to_prior_seats >=4)
+
+table.4.1 <-lm(party_like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(countryyear), data = dta_early_small)
+table.4.2 <-lm(party_like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition  + as.factor(countryyear), data = dta_late_small)
+
+summary(table.4.1)
+summary(table.4.2)
+
+### With clustered SEs
+stargazer(table.4.1,
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Out-Party Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table.4.1,
+                        clusters = dta_early_small$country),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition"
+          ))
+
+### With clustered SEs
+stargazer(table.4.2,
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Out-Party Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table.4.2,
+                        clusters = dta_late_small$country),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition"
+          ))
+
+
+
+############################################
+############ CREATING TABLE S5 #############
+############################################
+
+load("dyadic_data_1-4-22.Rdata")
+
+dta <-updated_data
+
+#creating the country-year fixed effects
+dta$countryyear <-paste(dta$country, dta$year, sep = "")
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "party_dislike", "party_like", "countryyear", 
+          "to_pfeml", "from_pfeml", "diff_pfeml", "to_prior_seats")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+## Remove small parties, with fewer than 4 seats
+dta_small <- subset(dta, dta$to_prior_seats >=4)
+
+table.S5.1 <-lm(party_like ~ to_pfeml + from_pfeml + diff_pfeml + as.factor(countryyear), data = dta_small)
+table.S5.2 <-lm(party_like ~ to_pfeml + from_pfeml + diff_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(countryyear), data = dta_small)
+
+summary(table.S5.1)
+summary(table.S5.2)
+
+### With clustered SEs
+stargazer(table.S5.1, table.S5.2,
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table.S5.1,  table.S5.2,
+                        clusters = dta_small$country),
+          keep = c("to_pfeml", "from_pfeml", "diff_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition", 
+                   "econ_distance_s", "society_distance_s"))
+
+############################################
+############ CREATING FIG. S1 #############
+############################################
+
+
+### Create Plot Data
+
+## All values of Out-Party % women
+plot_1 <- as.data.frame((unique(dta$to_pfeml)))
+colnames(plot_1) <- c("to_pfeml")
+
+## All 1 Sd above mean of In-party % women
+plot_1$from_pfeml <- mean(dta$from_pfeml, na.rm=T) + sd(dta$from_pfeml, na.rm=T)
+
+## Create difference between in-and out-party women
+plot_1$diff_pfeml <- abs(plot_1$to_pfeml - plot_1$from_pfeml)
+
+## Select other values (mean RILE distance, opposition together, France 2012 country year)
+plot_1$rile_distance_s <- mean(dta$rile_distance_s, na.rm=T)
+plot_1$prior_coalition <- 0
+plot_1$prior_opposition <- 1
+plot_1$countryyear <- "France2012"
+plot_1$to_mp_number <- "31320"
+plot_1$group <- "above_mean"
+
+## All values of Out-Party % women
+plot_2 <- as.data.frame((unique(dta$to_pfeml)))
+colnames(plot_2) <- c("to_pfeml")
+
+## All 1 Sd below mean of In-party % women
+plot_2$from_pfeml <- mean(dta$from_pfeml, na.rm=T) - sd(dta$from_pfeml, na.rm=T)
+
+## Create difference between in-and out-party women
+plot_2$diff_pfeml <- abs(plot_2$to_pfeml - plot_2$from_pfeml)
+
+## Select other values (opposition together, France 2012 country year)
+plot_2$rile_distance_s <- mean(dta$rile_distance_s, na.rm=T)
+plot_2$prior_coalition <- 0
+plot_2$prior_opposition <- 1
+plot_2$countryyear <- "France2012"
+plot_2$to_mp_number <- "31320"
+plot_2$group <- "below_mean"
+
+plot_dta <- rbind(plot_1, plot_2)
+
+
+###### Plot based on table.S5.2
+figureS1.data <- as.data.frame(predict(table.S5.2, newdata = plot_dta, interval = "confidence"))
+
+plot_dta$fit <- figureS1.data$fit
+plot_dta$lwr <- figureS1.data$lwr
+plot_dta$upr <- figureS1.data$upr
+
+figS1 <- ggplot(plot_dta, aes(x=to_pfeml, y=fit, lty=group))
+figS1 <- figS1 + geom_line() +
+  geom_ribbon(aes(x = to_pfeml, y = fit, ymin = lwr,
+                  ymax = upr),
+              lwd = 1/2, alpha=0.1) +
+  theme_minimal() + 
+  theme(plot.title = element_text(size=12)) +
+  ylab("Predicted Out-Party Thermometer Rating")+
+  xlab("Proportion of Out-Party Women MPs") + 
+  theme(legend.position = "none") + 
+  geom_text(x=0.70, y=5.3, label="in-party % of women is \n1 SD above the mean") +
+  geom_text(x=0.70, y=4.0, label="in-party % of women is \n1 SD below the mean", color="grey37") +
+  ylim(c(2.5,6.5));figS1
+
+pdf("figS1.pdf")
+figS1
+dev.off()
+
+
+############################################
+############ CREATING TABLE S6 #############
+############################################
+
+## Read in data
+load("dyadic_data_1-4-22.Rdata")
+
+dta <-updated_data
+
+#creating the country-year fixed effects
+dta$countryyear <-paste(dta$country, dta$year, sep = "")
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "party_dislike","party_like", "countryyear", "to_pfeml", "from_rile", "to_rile",
+          "logDM", "to_left_bloc", "to_prior_seats")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+dta_small <- subset(dta, dta$to_prior_seats >=4)
+
+table.S6.1 <-lm(party_like ~ to_pfeml + rile_distance_s + logDM + prior_coalition + prior_opposition  + as.factor(year), data = dta_small)
+table.S6.2 <-lm(party_like ~ to_pfeml*logDM + rile_distance_s + prior_coalition + prior_opposition  + as.factor(year), data = dta_small)
+table.S6.3 <-lm(party_like ~ to_pfeml*logDM + rile_distance_s*logDM + prior_coalition*logDM + prior_opposition*logDM  + as.factor(year), data = dta_small)
+
+summary(table.S6.1)
+summary(table.S6.2)
+summary(table.S6.3)
+
+stargazer(table.S6.1, table.S6.2, table.S6.3, 
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table.S6.1, table.S6.2, table.S6.3,
+                        clusters = dta_small$country),
+          keep = c("to_pfeml", "rile_distance_s", "logDM", "prior_coalition", "prior_opposition", 
+                   "econ_distance_s", "society_distance_s"))
+
+
+############################################
+############ CREATING TABLE S7 #############
+############################################
+
+#Out party % women, non-clustered SEs
+load("dyadic_data_1-4-22.Rdata")
+
+dta <-updated_data
+
+#creating the country-year fixed effects
+dta$cntryyr <-paste(dta$country, dta$year, sep = "")
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "party_dislike", "party_like", "cntryyr", "to_pfeml", "to_prior_seats")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+## Creating squared term for out-party % women
+dta$to_pfeml2 <- dta$to_pfeml^2
+
+## Remove small parties, with fewer than 4 seats
+dta <- subset(dta, dta$to_prior_seats >=4)
+
+table.S7.1 <-lm(party_like ~ to_pfeml + to_pfeml2 + as.factor(cntryyr), data = dta)
+table.S7.2 <-lm(party_like ~ to_pfeml + to_pfeml2 + rile_distance_s + prior_coalition + prior_opposition + as.factor(cntryyr), data = dta)
+
+summary(table.S7.1)
+summary(table.S7.2)
+
+### With clustered SEs
+stargazer(table.S7.1, table.S7.2, 
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table.S7.1, table.S7.2,
+                        clusters = dta$country),
+          keep = c("to_pfeml", "to_pfeml2", "rile_distance_s", "prior_coalition", "prior_opposition", 
+                   "econ_distance_s", "society_distance_s"))
+
+############################################
+############ CREATING FIG. S2 #############
+############################################
+
+### Create Plot Data
+
+## All values of Out-Party % women
+plot_S2 <- as.data.frame((unique(dta$to_pfeml)))
+colnames(plot_S2) <- c("to_pfeml")
+
+## Create difference between in-and out-party women
+plot_S2$to_pfeml2 <- plot_S2$to_pfeml^2
+
+## Select other values (mean RILE distance, opposition together, France 2012 country year)
+plot_S2$rile_distance_s <- mean(dta$rile_distance_s, na.rm=T)
+plot_S2$prior_coalition <- 0
+plot_S2$prior_opposition <- 1
+plot_S2$cntryyr <- "France2012"
+plot_S2$to_mp_number <- "31320"
+
+figureS2.data <- as.data.frame(predict(table.S7.2, newdata = plot_S2, interval = "confidence"))
+
+plot_S2$fit <- figureS2.data$fit
+plot_S2$lwr <- figureS2.data$lwr
+plot_S2$upr <- figureS2.data$upr
+
+figS2 <- ggplot(plot_S2, aes(x=to_pfeml, y=fit))
+figS2 <- figS2 + geom_line() +
+  geom_ribbon(aes(x = to_pfeml, y = fit, ymin = lwr,
+                  ymax = upr),
+              lwd = 1/2, alpha=0.1) +
+  theme_minimal() + 
+  theme(plot.title = element_text(size=12)) +
+  ylab("Predicted Out-Party Thermometer Rating")+
+  xlab("Proportion of Out-Party Women MPs") + 
+  ylim(c(2,5));figS2
+
+pdf("figS2.pdf")
+figS2
+dev.off()
+
+############################################
+############ CREATING TABLE S8 #############
+############################################
+
+#Women-led parties
+load("dyadic_data_1-4-22.Rdata")
+
+dta <-updated_data
+
+dta_womenlead <- subset(dta, dta$to_femaleleader==1)
+
+#creating the country-year fixed effects
+dta_womenlead$countryyear <-paste(dta_womenlead$country, dta_womenlead$year, sep = "")
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "party_dislike","party_like", "countryyear", "to_pfeml", "to_prior_seats")
+dta_womenlead <- dta_womenlead[vars]
+dta_womenlead <- na.omit(dta_womenlead)
+
+## Exclude small parties
+dta_womenlead <- subset(dta_womenlead, dta_womenlead$to_prior_seats >=4)
+
+table.S8A1 <-lm(party_like ~ to_pfeml + as.factor(countryyear), data = dta_womenlead)
+table.S8A2 <-lm(party_like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(countryyear), data = dta_womenlead)
+
+summary(table.S8A1)
+summary(table.S8A2)
+
+### With clustered SEs
+stargazer(table.S8A1, table.S8A2, 
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table.S8A1, table.S8A2,
+                        clusters = dta_womenlead$country),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition", 
+                   "econ_distance_s", "society_distance_s"))
+
+#Male-led parties
+load("dyadic_data_1-4-22.Rdata")
+dta <-updated_data
+
+dta_malelead <- subset(dta, dta$to_femaleleader==0)
+
+#creating the country-year fixed effects
+dta_malelead$countryyear <-paste(dta_malelead$country, dta_malelead$year, sep = "")
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "party_dislike","party_like", "countryyear", "to_pfeml", "to_prior_seats")
+dta_malelead <- dta_malelead[vars]
+dta_malelead <- na.omit(dta_malelead)
+
+## Exclude small parties
+dta_malelead <- subset(dta_malelead, dta_malelead$to_prior_seats >=4)
+
+table.S8B1 <-lm(party_like ~ to_pfeml + as.factor(countryyear), data = dta_malelead)
+table.S8B2 <-lm(party_like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(countryyear), data = dta_malelead)
+
+summary(table.S8B1)
+summary(table.S8B2)
+
+### With clustered SEs
+stargazer(table.S8B1, table.S8B2, 
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table.S8B1, table.S8B2, 
+                        clusters = dta_malelead$country),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition", 
+                   "econ_distance_s", "society_distance_s"))
+
+############################################
+############ CREATING TABLE S9 #############
+############################################
+
+## Read in data
+load("dyadic_data_1-4-22.Rdata")
+
+dta <- updated_data
+
+#creating the country-year fixed effects
+dta$countryyear <-paste(dta$country, dta$year, sep = "")
+
+#creating the party fixed effects / cluster
+dta$partydyad <-paste(dta$from_mp_number, dta$to_mp_number, sep = "")
+
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "party_dislike","party_like", "countryyear", "to_pfeml", 
+          "from_rile", "to_rile", "to_mp_number", "partydyad", "to_prior_seats")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+## Exclude small parties
+dta <- subset(dta, dta$to_prior_seats >=4)
+
+table.S9 <-lm(party_like ~ to_pfeml  + rile_distance_s + prior_coalition + prior_opposition + as.factor(countryyear), data = dta)
+summary(table.S9)
+
+### With clustered SEs
+stargazer(table.S9,
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Out-Party Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table.S9,
+                        clusters = dta$partydyad),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition", 
+                   "econ_distance_s", "society_distance_s"))
+
+
+############################################
+############ CREATING TABLE 10 #############
+############################################
+
+load("dyadic_data_1-4-22.Rdata")
+
+dta <-updated_data
+
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country", "party_dislike", "to_pfeml", "party_like", "to_prior_seats")
+dta <- dta[vars]
+dta <- na.omit(dta)
+
+## Remove small parties, with fewer than 4 seats
+dta_small <- subset(dta, dta$to_prior_seats >=4)
+
+table.S10.1 <-lm(party_like ~ to_pfeml + as.factor(country), data = dta_small)
+table.S10.2 <-lm(party_like ~ to_pfeml + rile_distance_s + prior_coalition + prior_opposition + as.factor(country), data = dta_small)
+
+summary(table.S10.2)
+
+### With clustered SEs
+stargazer(table.S10.1, table.S10.2,
+          add.lines = list(c("Country-Year Fixed Effects?", "Yes"), c("Country-Level Clustered SEs?", "Yes")),
+          se = starprep(table.S10.1, table.S10.2,
+                        clusters = dta_small$country),
+          keep = c("to_pfeml", "rile_distance_s", "prior_coalition", "prior_opposition", 
+                   "econ_distance_s", "society_distance_s"))
+
+##################################################
+############ CREATING TABLES 11 & 12 #############
+##################################################
+
+load("Data/multilevel_1-5-22.Rdata") 
+
+indiv_data <-multilevel_data
+
+
+vars <- c("rile_distance_s", "prior_coalition", "prior_opposition", "econ_distance_s", "society_distance_s",
+          "year", "country",  "cntryyr", "to_pfeml", "from_pfeml", "thermometer_score", "ID", "party_to", "party_from",
+          "from_partyname", "to_partyname", "to_left_bloc", "from_left_bloc",
+          "to_right_bloc", "from_right_bloc", "gender", "to_parfam", "from_parfam", "to_prior_seats",
+          "from_mp_number", "to_mp_number")
+
+
+indiv_data <- indiv_data[vars]
+
+## Create gender variable
+indiv_data <-mutate(indiv_data, gender = ifelse(gender == "1", "male",
+                                                ifelse(gender == "2", "female", NA)))
+
+indiv_data$gender <-as.factor(indiv_data$gender)
+
+##filter out parties with no data, mainly parties who were not in parliament plus a few cases from early 1990s
+indiv_data <-filter(indiv_data, is.na(to_pfeml) == F)
+
+### Create dyads for FEs/Clustered SEs
+indiv_data$dyad <-paste(indiv_data$from_mp_number, indiv_data$to_mp_number, sep ="_to_")
+
+### Create Table 11, column 1, with Standard errors clustered at country-year, party-dyad, and individual levels
+table11A.1.1 <-feols(thermometer_score ~ to_pfeml | ID, data = indiv_data, cluster = ~cntryyr)
+table11A.1.2 <-feols(thermometer_score ~ to_pfeml | ID, data = indiv_data, cluster = ~dyad)
+table11A.1.3 <-feols(thermometer_score ~ to_pfeml | ID, data = indiv_data, cluster = ~ID)
+
+### Create Table 11, column 2, with Standard errors clustered at country-year, party-dyad, and individual levels
+table11A.2.1 <-feols(thermometer_score ~ to_pfeml + + rile_distance_s + prior_coalition + prior_opposition | ID, data = indiv_data, cluster = ~cntryyr)
+table11A.2.2 <-feols(thermometer_score ~ to_pfeml + + rile_distance_s + prior_coalition + prior_opposition | ID, data = indiv_data, cluster = ~dyad)
+table11A.2.3 <-feols(thermometer_score ~ to_pfeml + + rile_distance_s + prior_coalition + prior_opposition | ID, data = indiv_data, cluster = ~ID)
+
+### Create Table 11B, column 1, with Standard errors clustered at country-year, party-dyad, and individual levels
+table11B.1.1 <-feols(thermometer_score ~ to_pfeml | cntryyr, data = indiv_data, cluster = ~cntryyr)
+table11B.1.2 <-feols(thermometer_score ~ to_pfeml | cntryyr, data = indiv_data, cluster = ~dyad)
+table11B.1.3 <-feols(thermometer_score ~ to_pfeml | cntryyr, data = indiv_data, cluster = ~ID)
+
+### Create Table 11B, column 2, with Standard errors clustered at country-year, party-dyad, and individual levels
+table11B.2.1 <-feols(thermometer_score ~ to_pfeml + + rile_distance_s + prior_coalition + prior_opposition | cntryyr, data = indiv_data, cluster = ~cntryyr)
+table11B.2.2 <-feols(thermometer_score ~ to_pfeml + + rile_distance_s + prior_coalition + prior_opposition | cntryyr, data = indiv_data, cluster = ~dyad)
+table11B.2.3 <-feols(thermometer_score ~ to_pfeml + + rile_distance_s + prior_coalition + prior_opposition | cntryyr, data = indiv_data, cluster = ~ID)
+

10/should_reproduce.txt

@@ -0,0 +1,3 @@
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