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+oid sha256:15d29b2b15d48f28c8e6962fa1ba757e5175e6d2cf7ff1a146329c8c95a7aa2f
+size 2599693

29/replication_package/Code/.Rhistory

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+}
+# Determine intervals between values of the moderator
+if (incr == "default"){
+increment = (max_val - min_val)/(num_points - 1)
+}else{
+increment = incr
+}
+# Create list of moderator values at which marginal effect is evaluated
+x_2 <- seq(from=min_val, to=max_val, by=increment)
+# Compute marginal effects
+delta_1 = beta_1 + beta_3*x_2
+# Compute variances
+var_1 = covMat[effect,effect] + (x_2^2)*covMat[interaction, interaction] + 2*x_2*covMat[effect, interaction]
+# Standard errors
+se_1 = sqrt(var_1)
+# Upper and lower confidence bounds
+z_score = qnorm(1 - ((1 - conf)/2))
+upper_bound = sapply(1:length(z_score), function(x) delta_1 + z_score[x]*se_1)
+lower_bound = sapply(1:length(z_score), function(x) delta_1 - z_score[x]*se_1)
+# Determine the bounds of the graphing area
+max_y = max(upper_bound)
+min_y = min(lower_bound)
+# Make the histogram color
+hist_col = colr
+stars <- ifelse(abs(summary(model)$coefficients[interaction,3]) >2.6,"***",
+ifelse(abs(summary(model)$coefficients[interaction,3]) > 1.96,"**",
+ifelse(abs(summary(model)$coefficients[interaction,3]) > 1.65,"*","")))
+est <- paste("Interaction: ",round(summary(model)$coefficients[interaction,1],3),stars," (",
+round(summary(model)$coefficients[interaction,2],3),")",sep="")
+# Initialize plotting window
+if(plot) {
+plot(x=c(), y=c(), ylim=c(min_y, max_y), xlim=c(min_val, max_val),
+xlab=xlabel, ylab=ylabel, main=title)
+# Plot estimated effects
+if(!pointsplot) {
+lines(y=delta_1, x=x_2,col = colr)
+for(i in ncol(upper_bound):1) {
+polygon(c(x_2,rev(x_2)),c(upper_bound[,i],rev(lower_bound[,i])),border = NA,
+col = colr)
+}
+}else{
+points(y = delta_1,x = x_2,col = colr,pch = 19)
+for(i in ncol(upper_bound):1) {
+segments(x_2,upper_bound[,i],x_2,lower_bound[,i],col = colr,lwd = i)
+}
+}
+# Add a dashed horizontal line for zero
+abline(h=0, lty=3)
+# Add a vertical line at the mean
+if (mean){
+abline(v = mean(mod_frame[[moderator]]), lty=2, col="red")
+}
+# Add a vertical line at the median
+if (median){
+abline(v = median(mod_frame[[moderator]]), lty=3, col="blue")
+}
+# Add Rug plot
+if (rugplot){
+rug(mod_frame[[moderator]])
+}
+if (show_est) {
+text(par('usr')[ 2 ], par('usr')[ 4 ],adj=c(1.05,1.2),
+labels = est)
+}
+#Add Histogram (Histogram only plots when minimum and maximum are the min/max of the moderator)
+if (histogram & minimum=="min" & maximum=="max"){
+par(new=T)
+hist(mod_frame[[moderator]], axes=F, xlab="", ylab="",main="", border=hist_col, col=hist_col)
+}
+}
+return(list(delta_1 = delta_1,x_2 = x_2,ub = upper_bound,lb = lower_bound,inc = increment,est = est))
+}
+####################################################################################################################### Preparing variables
+Y <- paste0(c("pct_","VAP_","pcttw_"),"sanders")
+D <- unlist(lapply(c("sc_","ln_"),function(x) paste0(x,paste0(c("county_","DMA_","state_"),"cases"))))
+FE <- c("0","DMA_CODE","date")
+covariates <- c("sc_CTY_LTHS","sc_CTY_CollUp",'caucus_switch','caucus',
+"sc_CTY_LT30yo","sc_CTY_60Up",#"sc_CTY_Old_age_dep_ratio",
+"sc_CTY_Below_poverty_level_AGE_18_64","sc_CTY_Female_hher_no_husbandhh",
+"sc_CTY_Unem_rate_pop_16_over","sc_CTY_Labor_Force_Part_Rate_pop_16_over",
+"sc_CTY_Manufactur","sc_CTY_Md_inc_hhs",
+"sc_CTY_POPPCT_RURAL","sc_CTY_Speak_only_English","sc_CTY_White","sc_CTY_Black_or_African_American",
+"ln_CTY_tot_pop","sc_turnout_pct_20")
+####################################################################################################################### Looping across specifications
+finalDat$sc_turnout_pct_chg <- scale(finalDat$turnout_pct_chg)
+finalDat$sc_VAP_turnout_20 <- scale(finalDat$VAP_turnout_20)
+####################################################################################################################### Looping across specifications
+resRegs <- resBal <- resTabs <- list()
+y
+Y <- c('pct_sanders16')
+D <- unlist(lapply(c("sc_","ln_"),function(x) paste0(x,paste0(c("county_","DMA_","state_"),"cases"))))
+FE <- c("0","DMA_CODE","stab")
+covariates <- c("sc_CTY_LTHS","sc_CTY_CollUp",'caucus_switch','caucus',
+"sc_CTY_LT30yo","sc_CTY_60Up",#"sc_CTY_Old_age_dep_ratio",
+"sc_CTY_Below_poverty_level_AGE_18_64","sc_CTY_Female_hher_no_husbandhh",
+"sc_CTY_Unem_rate_pop_16_over","sc_CTY_Labor_Force_Part_Rate_pop_16_over",
+"sc_CTY_Manufactur","sc_CTY_Md_inc_hhs",
+"sc_CTY_POPPCT_RURAL","sc_CTY_Speak_only_English",
+"sc_CTY_White","sc_CTY_Black_or_African_American",
+"ln_CTY_tot_pop","sc_turnout_pct_20")
+####################################################################################################################### Looping across specifications
+resRegs <- resBal <- resTabs <- list()
+for(y in Y) {
+for(d in D[c(1:3)]) {
+for(fe in FE) {
+stars <- list()
+for(dateThresh in c("2020-01-01","2020-03-01")) {
+if(dateThresh == '2020-03-01') {
+covs <- covariates[-which(covariates == 'caucus')]
+} else {
+covs <- covariates
+}
+if(!is.null(resRegs[[y]][[d]][[fe]][[dateThresh]]$basic$felm$cont)) { next }
+finalDat$treatBin <- ifelse(finalDat[[gsub("sc_|ln_","",d)]] > 1,1,0)
+foranal.weight <- finalDat %>% select(y,d,treatBin,gsub("sc_|ln_","",d),DMA_CODE,date,stab,covs) %>%
+filter(complete.cases(.),date >= as.Date(dateThresh))
+m.out <- matchit(formula = as.formula(paste("treatBin ~ ",paste(covs,collapse = " + "))),
+data = foranal.weight,
+method = "nearest",
+distance = "mahalanobis")
+m.data <- match.data(m.out)
+W.out <- weightit(formula(paste0("treatBin ~ ",paste(covs,collapse = " + "))),
+data = foranal.weight, estimand = "ATT", method = "cbps")
+# Balance tables
+balt.pre <- bal.tab(formula(paste0("treatBin ~ ",paste(covs,collapse = " + "))),
+data = foranal.weight, estimand = "ATT",m.threshold = .05)
+balt.post <- bal.tab(W.out, m.threshold = .05, disp.v.ratio = TRUE)
+unm <- data.frame(Covs = rownames(balt.pre$Balance),
+balt.pre$Balance %>% mutate(Diff_Unm = round(Diff.Un,2),
+Bal_Test_Unm = M.Threshold.Un)) %>%
+select(Covs,Diff_Unm,Bal_Test_Unm)
+match <- data.frame(Covs = rownames(balt.post$Balance),
+balt.post$Balance %>% mutate(Diff_Match = round(Diff.Adj,2),
+Bal_Test_Match = M.Threshold)) %>%
+select(Covs,Diff_Match,Bal_Test_Match)
+# balTab <- match %>% left_join(unm) %>% select(Covs,Diff_Unm,Bal_Test_Unm,Diff_Match,Bal_Test_Match)
+# balTab$Covs <- Hmisc::capitalize(trimws(gsub("manufactur","Manufacturing",gsub("labor force part rate","LFPR",
+#                                                                                gsub("hher|hhs","HH",gsub("bachelor s","Bachelor's",gsub("_"," ",gsub("sc_|cty_|age_18_64|pop_16_over|hh$|poppct","",tolower(balTab$Covs)))))))))
+# resBal[[y]][[d]][[fe]][[dateThresh]]$balTab <- balTab
+# resBal[[y]][[d]][[fe]][[dateThresh]]$balPlot <- qqprep(m.out)
+# Basic
+resRegs[[y]][[d]][[fe]][[dateThresh]]$basic$felm$cont <- summary(stars$cont[[paste0(dateThresh,"_1")]] <- felm(as.formula(paste0("scale(",y,") ~ ",d," + ",paste(c(covs),collapse = " + ")," | ",fe," | 0 | ",fe)),finalDat %>% filter(date >= as.Date(dateThresh))))$coefficients
+resRegs[[y]][[d]][[fe]][[dateThresh]]$basic$felm$bin <- summary(stars$bin[[paste0(dateThresh,"_1")]] <- felm(as.formula(paste0("scale(",y,") ~ treatBin + ",paste(c(covs),collapse = " + ")," | ",fe," | 0 | ",fe)),finalDat %>% filter(date >= as.Date(dateThresh))))$coefficients
+# Matching
+resRegs[[y]][[d]][[fe]][[dateThresh]]$matching$felm$cont <- summary(stars$cont[[paste0(dateThresh,"_2")]] <- felm(as.formula(paste0("scale(",y,") ~ ",d," + ",paste(c(covs),collapse = " + ")," | ",fe," | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+resRegs[[y]][[d]][[fe]][[dateThresh]]$matching$felm$bin <- summary(stars$bin[[paste0(dateThresh,"_2")]] <- felm(as.formula(paste0("scale(",y,") ~ treatBin + ",paste(c(covs),collapse = " + ")," | ",fe," | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+# Weighting
+resRegs[[y]][[d]][[fe]][[dateThresh]]$weighting$felm$cont <- summary(stars$cont[[paste0(dateThresh,"_3")]] <- felm(as.formula(paste0("scale(",y,") ~ ",d," + ",paste(c(covs),collapse = " + ")," | ",fe," | 0 | ",fe)),foranal.weight,weights = W.out$weights))$coefficients
+resRegs[[y]][[d]][[fe]][[dateThresh]]$weighting$felm$bin <- summary(stars$bin[[paste0(dateThresh,"_3")]] <- felm(as.formula(paste0("scale(",y,") ~ treatBin + ",paste(c(covs),collapse = " + ")," | ",fe," | 0 | ",fe)),foranal.weight,weights = W.out$weights))$coefficients
+if(fe != "0") {
+resRegs[[y]][[d]][[fe]][[dateThresh]]$basic$lmer <- summary(lmer(as.formula(paste0("scale(",y,") ~ ",d," + ",paste(c(covs),collapse = " + ")," + (1| ",fe,")")),finalDat %>% filter(date >= as.Date(dateThresh))))$coefficients
+resRegs[[y]][[d]][[fe]][[dateThresh]]$matching$lmer <- summary(lmer(as.formula(paste0("scale(",y,") ~ ",d," + ",paste(c(covs),collapse = " + ")," + (1| ",fe,")")),m.data,weights = m.data$weights))$coefficients
+resRegs[[y]][[d]][[fe]][[dateThresh]]$weighting$lmer <- summary(lmer(as.formula(paste0("scale(",y,") ~ ",d," + ",paste(c(covs),collapse = " + ")," + (1| ",fe,")")),foranal.weight,weights = W.out$weights))$coefficients
+}
+# covars <- rownames(stars$cont[[1]]$coefficients)
+# labs <- gsub("Lths","LTHS",gsub("Collup","Coll. Up",gsub("Md inc HH","Med HH Inc",gsub("Sc ","",gsub(" or african american|band$","",Hmisc::capitalize(trimws(gsub("manufactur","manufacturing",gsub("hher|hhs","HH",gsub("labor force part rate","LFPR",gsub("bachelor s","bachelor's",gsub("age 18 64|pop 16 over|hh$|poppct","",gsub("\\_"," ",gsub("^sc_|^.*cty\\_","",tolower(covars)))))))))))))))
+# resTabs[[y]][[d]][[fe]]$cont$html <- stargazer(stars$cont,type = "html",keep.stat = c("n","rsq"),covariate.labels = labs)
+# resTabs[[y]][[d]][[fe]]$cont$tex <- stargazer(stars$cont,keep.stat = c("n","rsq"),covariate.labels = labs)
+# covars <- rownames(stars$bin[[1]]$coefficients)
+# labs <- gsub("Lths","LTHS",gsub("Collup","Coll. Up",gsub("Md inc HH","Med HH Inc",gsub("Sc ","",gsub(" or african american|band$","",Hmisc::capitalize(trimws(gsub("manufactur","manufacturing",gsub("hher|hhs","HH",gsub("labor force part rate","LFPR",gsub("bachelor s","bachelor's",gsub("age 18 64|pop 16 over|hh$|poppct","",gsub("\\_"," ",gsub("^sc_|^.*cty\\_","",tolower(covars)))))))))))))))
+# resTabs[[y]][[d]][[fe]]$bin$html <- stargazer(stars$bin,type = "html",keep.stat = c("n","rsq"),covariate.labels = labs)
+# resTabs[[y]][[d]][[fe]]$bin$tex <- stargazer(stars$bin,keep.stat = c("n","rsq"),covariate.labels = labs)
+}
+}
+cat(y,"\n")
+}
+}
+toplot <- NULL
+for(fe in names(resRegs$pct_sanders16$sc_DMA_cases)) {
+for(mod in c('basic','matching','weighting')) {
+for(meas in c('bin','cont')) {
+tmp <- resRegs$pct_sanders16$sc_DMA_cases[[fe]]$`2020-01-01`[[mod]]$felm[[meas]][2,]
+if(is.null(tmp)) { next }
+tmp <- data.frame(t(tmp))
+colnames(tmp) <- c('est','se','tstat','pval')
+tmp$fe <- fe
+tmp$mod <- mod
+tmp$meas <- meas
+toplot <- bind_rows(toplot,tmp)
+}
+}
+}
+toplot %>%
+filter(meas != 'cont') %>%
+mutate(fe = factor(ifelse(fe == 0,'None',
+ifelse(fe == 'DMA_CODE','DMA','State')),levels = c('None','DMA','State'))) %>%
+ggplot(aes(x = fe,y = est,color = mod)) +
+geom_point(position = position_dodge(width = .2)) +
+geom_errorbar(aes(ymin = est - 2*se,ymax = est+2*se),width = .1,
+position = position_dodge(width = .2)) +
+geom_hline(yintercept = 0,linetype = 'dashed') +
+scale_color_manual(name = 'Model',values = c('basic' = 'grey70',
+'matching' = 'grey40',
+'weighting' = 'black')) +
+xlab('Fixed Effect') + ylab('Effect of Covid Exposure on Bernie 2016') +
+theme_ridges()
+list.files('./')
+# Master script
+for(f in list.files('./')) {
+if(grepl('helper_functions|MASTER|LOG',f)) { next }
+# stop()
+con <- file(paste0('./LOG/log_',gsub('.R','.txt',f)))
+cat(f,'\n')
+sink(con,append = TRUE)
+sink(con,append = TRUE,type = 'message')
+source(f,echo = T,max.deparse.length = 10000)
+sink()
+sink(type = 'message')
+}
+list.files('./')
+file.exits(paste0('./LOG/log_',gsub('.R','.txt',f)))
+file.exists(paste0('./LOG/log_',gsub('.R','.txt',f)))
+# Master script
+for(f in list.files('./')) {
+if(grepl('helper_functions|MASTER|LOG',f)) { next }
+if(file.exists(paste0('./LOG/log_',gsub('.R','.txt',f)))) { next }
+# stop()
+con <- file(paste0('./LOG/log_',gsub('.R','.txt',f)))
+cat(f,'\n')
+sink(con,append = TRUE)
+sink(con,append = TRUE,type = 'message')
+source(f,echo = T,max.deparse.length = 10000)
+sink()
+sink(type = 'message')
+}
+require(lme4)
+require(lfe)
+require(MatchIt)
+require(WeightIt)
+require(tjbal)
+require(optmatch)
+require(stargazer)
+require(cobalt)
+require(gridExtra)
+require(ggridges)
+require(ggrepel)
+require(CBPS)
+require(tidyverse)
+rm(list = ls())
+gc()
+####################################################################################################################### Loading data
+load('../Data/replication_data.RData')
+####################################################################################################################### Loading functions
+source('./helper_functions.R')
+# ####################################################################################################################### Command line arguments
+# args <- commandArgs(trailingOnly = T)
+# # args <- c(3,2,2,2)
+# # Y: 1-3
+# # D: 1-2
+# # FE: 1-4
+# # GEO: 1-2
+# yInd <- as.numeric(args[1])
+# dInd <- as.numeric(args[2])
+# feInd <- as.numeric(args[3])
+# geoInd <- as.numeric(args[4])
+####################################################################################################################### Preparing variables
+Y <- paste0(c("pct_","VAP_","pcttw_"),"sanders")
+D <- unlist(lapply(c("sc_","ln_"),function(x) paste0(x,paste0(c("county_","DMA_","state_"),"cases"))))
+FE <- c("0","DMA_CODE","date")
+covariates <- c("sc_CTY_LTHS","sc_CTY_CollUp",
+"sc_CTY_LT30yo","sc_CTY_60Up",
+"sc_CTY_Below_poverty_level_AGE_18_64","sc_CTY_Female_hher_no_husbandhh",
+"sc_CTY_Unem_rate_pop_16_over","sc_CTY_Labor_Force_Part_Rate_pop_16_over",
+"sc_CTY_Manufactur","sc_CTY_Md_inc_hhs",
+"sc_CTY_POPPCT_RURAL","sc_CTY_Speak_only_English","sc_CTY_White","sc_CTY_Black_or_African_American",
+"ln_CTY_tot_pop","sc_turnout_pct_20")
+set.seed(123)
+resSI <- list()
+for(y in 'pcttw_sanders') {
+for(fe in c('DMA_CODE','0')) {
+for(geo in 'DMA_') {
+for(d in c('March10Cases','March17Cases')) {
+for(pre in c("2020-03-01","2020-03-03","2020-03-10","2020-03-17")) {
+for(post in c("2020-03-01","2020-03-03","2020-03-10","2020-03-17")) {
+if(pre == post) { next }
+if(pre == "2020-03-01") {
+finalDat$post <- ifelse(finalDat$date == as.Date(post),1,
+ifelse(finalDat$date <= as.Date(pre),0,NA))
+} else if(post == "2020-03-01") {
+finalDat$post <- ifelse(finalDat$date <= as.Date(post),1,
+ifelse(finalDat$date == as.Date(pre),0,NA))
+} else {
+finalDat$post <- ifelse(finalDat$date == as.Date(post),1,
+ifelse(finalDat$date == as.Date(pre),0,NA))
+}
+finalDat$treatBin <- ifelse(finalDat[[paste0(geo,d)]] > 1,1,0)
+finalDat$treat <- ifelse(finalDat$post == 1 & finalDat$treatBin == 1,1,0)
+tmpAnal <- finalDat %>% select(Y,treat,treatBin,c(covariates,paste0(y,"16")),pcttw_sanders16,VAP_sanders16,DMA_CODE,date,stab,matches("cases"),post) %>% filter(complete.cases(.))
+m.out <- matchit(formula = as.formula(paste("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+data = tmpAnal,
+method = "nearest",
+distance = "mahalanobis")
+m.data <- match.data(m.out)
+W.out <- weightit(formula(paste0("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+data = tmpAnal, estimand = "ATT", method = "cbps")
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$bin <- summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$cont <- summary(felm(as.formula(paste0('scale(',y,") ~ ",paste0("scale(",geo,d,")")," + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$did$coefs <- summary(tmp <- felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$did$mfx <- interaction_plot_continuous(tmp,num_points = 2,pointsplot = T)
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$bin <- summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$cont <- summary(felm(as.formula(paste0('scale(',y,") ~ ",paste0("scale(",geo,d,")")," + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$did$coefs <- summary(tmp <- felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$did$mfx <- interaction_plot_continuous(tmp,num_points = 2,pointsplot = T)
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$bin <- summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$cont <- summary(felm(as.formula(paste0('scale(',y,") ~ ",paste0("scale(",geo,d,")")," + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$did$coefs <- summary(tmp <- felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$did$mfx <- interaction_plot_continuous(tmp,num_points = 2,pointsplot = T)
+# Permutation SHEEYAHT
+basic.bin.plac <- basic.cont.plac <- basic.did.plac <- matching.bin.plac <- matching.cont.plac <- matching.did.plac <- weighting.bin.plac <- weighting.cont.plac <- weighting.did.plac <- NULL
+for(bs in 1:100) {
+tmpAnal$perm <- sample(tmpAnal[[paste0(geo,d)]],size = nrow(tmpAnal))
+tmpAnal$treatBin <- ifelse(tmpAnal$perm > 1,1,0)
+tmpAnal$treat <- ifelse(tmpAnal$post == 1 & tmpAnal$treatBin == 1,1,0)
+m.out <- matchit(formula = as.formula(paste("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+data = tmpAnal,
+method = "full",
+distance = "mahalanobis")
+m.data <- match.data(m.out)
+W.out <- weightit(formula(paste0("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+data = tmpAnal, estimand = "ATT", method = "cbps")
+test <- tryCatch(felm(as.formula(paste0('scale(',y,") ~ ",paste0("scale(",geo,d,")")," + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights),error = function(e) e)
+if(inherits(test,"error")) { next }
+basic.bin.plac <- c(basic.bin.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients['treat',1])
+basic.cont.plac <- c(basic.cont.plac,summary(felm(as.formula(paste0('scale(',y,") ~ scale(perm) + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients["scale(perm)",1])
+basic.did.plac <- c(basic.did.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients["treatBin:post",1])
+matching.bin.plac <- c(matching.bin.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients['treat',1])
+matching.cont.plac <- c(matching.cont.plac,summary(felm(as.formula(paste0('scale(',y,") ~ scale(perm) + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients["scale(perm)",1])
+matching.did.plac <- c(matching.did.plac,
+summary(felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients["treatBin:post",1])
+weighting.bin.plac <- c(weighting.bin.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients['treat',1])
+weighting.cont.plac <- c(weighting.cont.plac,summary(felm(as.formula(paste0('scale(',y,") ~ scale(perm) + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients["scale(perm)",1])
+weighting.did.plac <- c(weighting.did.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients["treatBin:post",1])
+cat(".")
+}
+cat('\n')
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$plac$bin <- basic.bin.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$plac$cont <- basic.cont.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$plac$did <- basic.did.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$plac$bin <- matching.bin.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$plac$cont <- matching.cont.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$plac$did <- matching.did.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$plac$bin <- weighting.bin.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$plac$cont <- weighting.cont.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$plac$did <- weighting.did.plac
+}
+}
+}
+}
+}
+cat(y," done\n")
+}
+require(lme4)
+require(lfe)
+require(MatchIt)
+require(WeightIt)
+require(tjbal)
+require(optmatch)
+require(stargazer)
+require(cobalt)
+require(gridExtra)
+require(ggridges)
+require(ggrepel)
+require(CBPS)
+require(tidyverse)
+rm(list = ls())
+gc()
+####################################################################################################################### Loading data
+load('../Data/replication_data.RData')
+####################################################################################################################### Loading functions
+source('./helper_functions.R')
+# ####################################################################################################################### Command line arguments
+# args <- commandArgs(trailingOnly = T)
+# # args <- c(3,2,2,2)
+# # Y: 1-3
+# # D: 1-2
+# # FE: 1-4
+# # GEO: 1-2
+# yInd <- as.numeric(args[1])
+# dInd <- as.numeric(args[2])
+# feInd <- as.numeric(args[3])
+# geoInd <- as.numeric(args[4])
+####################################################################################################################### Preparing variables
+Y <- paste0(c("pct_","VAP_","pcttw_"),"sanders")
+D <- unlist(lapply(c("sc_","ln_"),function(x) paste0(x,paste0(c("county_","DMA_","state_"),"cases"))))
+FE <- c("0","DMA_CODE","date")
+covariates <- c("sc_CTY_LTHS","sc_CTY_CollUp",
+"sc_CTY_LT30yo","sc_CTY_60Up",
+"sc_CTY_Below_poverty_level_AGE_18_64","sc_CTY_Female_hher_no_husbandhh",
+"sc_CTY_Unem_rate_pop_16_over","sc_CTY_Labor_Force_Part_Rate_pop_16_over",
+"sc_CTY_Manufactur","sc_CTY_Md_inc_hhs",
+"sc_CTY_POPPCT_RURAL","sc_CTY_Speak_only_English","sc_CTY_White","sc_CTY_Black_or_African_American",
+"ln_CTY_tot_pop","sc_turnout_pct_20")
+set.seed(123)
+resSI <- list()
+for(y in 'pcttw_sanders') {
+for(fe in c('DMA_CODE','0')) {
+for(geo in 'DMA_') {
+for(d in c('March10Cases','March17Cases')) {
+for(pre in c("2020-03-01","2020-03-03","2020-03-10","2020-03-17")) {
+for(post in c("2020-03-01","2020-03-03","2020-03-10","2020-03-17")) {
+if(pre == post) { next }
+if(pre == "2020-03-01") {
+finalDat$post <- ifelse(finalDat$date == as.Date(post),1,
+ifelse(finalDat$date <= as.Date(pre),0,NA))
+} else if(post == "2020-03-01") {
+finalDat$post <- ifelse(finalDat$date <= as.Date(post),1,
+ifelse(finalDat$date == as.Date(pre),0,NA))
+} else {
+finalDat$post <- ifelse(finalDat$date == as.Date(post),1,
+ifelse(finalDat$date == as.Date(pre),0,NA))
+}
+finalDat$treatBin <- ifelse(finalDat[[paste0(geo,d)]] > 1,1,0)
+finalDat$treat <- ifelse(finalDat$post == 1 & finalDat$treatBin == 1,1,0)
+tmpAnal <- finalDat %>% select(Y,treat,treatBin,c(covariates,paste0(y,"16")),pcttw_sanders16,VAP_sanders16,DMA_CODE,date,stab,matches("cases"),post) %>% filter(complete.cases(.))
+m.out <- matchit(formula = as.formula(paste("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+data = tmpAnal,
+method = "nearest",
+distance = "mahalanobis")
+m.data <- match.data(m.out)
+W.out <- weightit(formula(paste0("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+data = tmpAnal, estimand = "ATT", method = "cbps")
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$bin <- summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$cont <- summary(felm(as.formula(paste0('scale(',y,") ~ ",paste0("scale(",geo,d,")")," + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$did$coefs <- summary(tmp <- felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$did$mfx <- interaction_plot_continuous(tmp,num_points = 2,pointsplot = T)
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$bin <- summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$cont <- summary(felm(as.formula(paste0('scale(',y,") ~ ",paste0("scale(",geo,d,")")," + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$did$coefs <- summary(tmp <- felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$did$mfx <- interaction_plot_continuous(tmp,num_points = 2,pointsplot = T)
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$bin <- summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$cont <- summary(felm(as.formula(paste0('scale(',y,") ~ ",paste0("scale(",geo,d,")")," + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$did$coefs <- summary(tmp <- felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$did$mfx <- interaction_plot_continuous(tmp,num_points = 2,pointsplot = T)
+# Permutation SHEEYAHT
+basic.bin.plac <- basic.cont.plac <- basic.did.plac <- matching.bin.plac <- matching.cont.plac <- matching.did.plac <- weighting.bin.plac <- weighting.cont.plac <- weighting.did.plac <- NULL
+for(bs in 1:100) {
+tmpAnal$perm <- sample(tmpAnal[[paste0(geo,d)]],size = nrow(tmpAnal))
+tmpAnal$treatBin <- ifelse(tmpAnal$perm > 1,1,0)
+tmpAnal$treat <- ifelse(tmpAnal$post == 1 & tmpAnal$treatBin == 1,1,0)
+m.out <- matchit(formula = as.formula(paste("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+data = tmpAnal,
+method = "full",
+distance = "mahalanobis")
+m.data <- match.data(m.out)
+W.out <- weightit(formula(paste0("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+data = tmpAnal, estimand = "ATT", method = "cbps")
+test <- tryCatch(felm(as.formula(paste0('scale(',y,") ~ ",paste0("scale(",geo,d,")")," + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights),error = function(e) e)
+if(inherits(test,"error")) { next }
+basic.bin.plac <- c(basic.bin.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients['treat',1])
+basic.cont.plac <- c(basic.cont.plac,summary(felm(as.formula(paste0('scale(',y,") ~ scale(perm) + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients["scale(perm)",1])
+basic.did.plac <- c(basic.did.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients["treatBin:post",1])
+matching.bin.plac <- c(matching.bin.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients['treat',1])
+matching.cont.plac <- c(matching.cont.plac,summary(felm(as.formula(paste0('scale(',y,") ~ scale(perm) + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients["scale(perm)",1])
+matching.did.plac <- c(matching.did.plac,
+summary(felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients["treatBin:post",1])
+weighting.bin.plac <- c(weighting.bin.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients['treat',1])
+weighting.cont.plac <- c(weighting.cont.plac,summary(felm(as.formula(paste0('scale(',y,") ~ scale(perm) + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients["scale(perm)",1])
+weighting.did.plac <- c(weighting.did.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients["treatBin:post",1])
+cat(".")
+}
+cat('\n')
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$plac$bin <- basic.bin.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$plac$cont <- basic.cont.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$plac$did <- basic.did.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$plac$bin <- matching.bin.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$plac$cont <- matching.cont.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$plac$did <- matching.did.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$plac$bin <- weighting.bin.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$plac$cont <- weighting.cont.plac
+resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$plac$did <- weighting.did.plac
+}
+}
+}
+}
+}
+cat(y," done\n")
+}
+# Master script
+for(f in list.files('./')) {
+if(grepl('helper_functions|MASTER|LOG',f)) { next }
+if(file.exists(paste0('./LOG/log_',gsub('.R','.txt',f)))) { next }
+stop()
+con <- file(paste0('./LOG/log_',gsub('.R','.txt',f)))
+cat(f,'\n')
+sink(con,append = TRUE)
+sink(con,append = TRUE,type = 'message')
+source(f,echo = T,max.deparse.length = 10000)
+sink()
+sink(type = 'message')
+}
+f
+# stop()
+con <- file(paste0('./LOG/log_',gsub('.R','.txt',f)))
+cat(f,'\n')
+sink(con,append = TRUE)
+sink(con,append = TRUE,type = 'message')
+source(f,echo = T,max.deparse.length = 10000)
+sink()
+sink(type = 'message')
+# Master script
+for(f in list.files('./')) {
+if(grepl('helper_functions|MASTER|LOG',f)) { next }
+if(file.exists(paste0('./LOG/log_',gsub('.R','.txt',f)))) { next }
+# stop()
+con <- file(paste0('./LOG/log_',gsub('.R','.txt',f)))
+cat(f,'\n')
+sink(con,append = TRUE)
+sink(con,append = TRUE,type = 'message')
+source(f,echo = T,max.deparse.length = 10000)
+sink()
+sink(type = 'message')
+}

29/replication_package/Code/Figure 2, 3, Table SI1.R

@@ -0,0 +1,183 @@
+# File name: figure2_figure3_SItable1.R
+# In: 
+#   - replication_data.RData
+# Out: 
+#   - /Figures/figure2.pdf
+#   - /Figures/figure3.pdf
+#   - /Tables/SI-table1.tex
+
+require(lme4)
+require(lfe)
+require(MatchIt)
+require(WeightIt)
+require(tjbal)
+require(optmatch)
+require(stargazer)
+require(cobalt)
+require(tidyverse)
+require(sf)
+require(leaflet)
+require(ggridges)
+require(ggthemes)
+require(gridExtra)  
+
+rm(list = ls())
+gc()
+########################################################################################## Loading data
+load('../Data/replication_data.RData')
+
+
+# Figure 2: Map (compressed to PNG in manuscript)
+toplot <- maps$county %>% filter(!state %in% c("Alaska","Hawaii"))
+toplot$DMA.CODE[which(toplot$state == 'Texas' & toplot$name %in% c('Throckmorton','Knox'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Texas' & toplot$name %in% c('Baylor'))])
+toplot$DMA.CODE[which(toplot$state == 'Texas' & toplot$name %in% c('Montague'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Texas' & toplot$name %in% c('Wise'))])
+toplot$DMA.CODE[which(toplot$state == 'Colorado' & toplot$name %in% c('Cheyenne'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Colorado' & toplot$name %in% c('Kiowa'))])
+toplot$DMA.CODE[which(toplot$state == 'Nebraska' & toplot$name %in% c('Hooker','Arthur','Blaine'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Nebraska' & toplot$name %in% c('Thomas'))])
+toplot$DMA.CODE[which(toplot$state == 'Wyoming' & toplot$name %in% c('Johnson'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Wyoming' & toplot$name %in% c('Natrona'))])
+toplot$DMA.CODE[which(toplot$state == 'Iowa' & toplot$name %in% c('Emmet'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Minnesota' & toplot$name %in% c('Martin'))])
+toplot$DMA.CODE[which(toplot$state == 'Minnesota' & toplot$name %in% c('Nicollet'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Minnesota' & toplot$name %in% c('Sibley'))])
+toplot$DMA.CODE[which(toplot$state == 'Illinois' & toplot$name %in% c('Edgar'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Illinois' & toplot$name %in% c('Clark'))])
+toplot$DMA.CODE[which(toplot$state == 'Kentucky' & toplot$name %in% c('Adair','Cumberland'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Kentucky' & toplot$name %in% c('Metcalfe'))])
+toplot$DMA.CODE[which(toplot$state == 'Ohio' & toplot$name %in% c('Athens'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Ohio' & toplot$name %in% c('Meigs'))])
+toplot$DMA.CODE[which(toplot$state == 'Ohio' & toplot$name %in% c('Guernsey','Noble'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Ohio' & toplot$name %in% c('Belmont'))])
+toplot$DMA.CODE[which(toplot$state == 'Arkansas' & toplot$name %in% c('Nevada'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Arkansas' & toplot$name %in% c('Columbia'))])
+toplot$DMA.CODE[which(toplot$state == 'Tennessee' & toplot$name %in% c('Gibson','Chester'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Tennessee' & toplot$name %in% c('McNairy'))])
+toplot$DMA.CODE[which(toplot$state == 'South Carolina' & toplot$name %in% c('Saluda'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'South Carolina' & toplot$name %in% c('Edgefield'))])
+toplot$DMA.CODE[which(toplot$state == 'California' & toplot$name %in% c('Modoc'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'California' & toplot$name %in% c('Siskiyou'))])
+toplot$DMA.CODE[which(toplot$state == 'Arkansas' & toplot$name %in% c('Johnson'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Arkansas' & toplot$name %in% c('Pope'))])
+toplot$DMA.CODE[which(toplot$state == 'Colorado' & toplot$name %in% c('Custer'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Colorado' & toplot$name %in% c('Saguache'))])
+toplot$DMA.CODE[which(toplot$state == 'Wyoming' & toplot$name %in% c('Lincoln'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Wyoming' & toplot$name %in% c('Teton'))])
+toplot$DMA.CODE[which(toplot$state == 'Nebraska' & toplot$name %in% c('Banner'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Nebraska' & toplot$name %in% c('Morrill'))])
+toplot$DMA.CODE[which(toplot$state == 'South Dakota' & toplot$name %in% c('Jones'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'South Dakota' & toplot$name %in% c('Lyman'))])
+toplot$DMA.CODE[which(toplot$state == 'South Dakota' & toplot$name %in% c('Dewey','Campbell'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'South Dakota' & toplot$name %in% c('Corson'))])
+toplot$DMA.CODE[which(toplot$state == 'Iowa' & toplot$name %in% c('Adams'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Iowa' & toplot$name %in% c('Montgomery'))])
+toplot$DMA.CODE[which(toplot$state == 'Missouri' & toplot$name %in% c('Mercer'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Iowa' & toplot$name %in% c('Decatur'))])
+toplot$DMA.CODE[which(toplot$state == 'Missouri' & toplot$name %in% c('Phelps'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Missouri' & toplot$name %in% c('Pulaski'))])
+toplot$DMA.CODE[which(toplot$state == 'Missouri' & toplot$name %in% c('Reynolds'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Missouri' & toplot$name %in% c('Wayne'))])
+toplot$DMA.CODE[which(toplot$state == 'Oklahoma' & toplot$name %in% c('Ottawa'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Kansas' & toplot$name %in% c('Cherokee'))])
+toplot$DMA.CODE[which(toplot$state == 'Kentucky' & toplot$name %in% c('Magoffin'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Kentucky' & toplot$name %in% c('Johnson'))])
+toplot$DMA.CODE[which(toplot$state == 'Pennsylvania' & toplot$name %in% c('Franklin'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Pennsylvania' & toplot$name %in% c('Fulton'))])
+toplot$DMA.CODE[which(toplot$state == 'Ohio' & toplot$name %in% c('Auglaize'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Ohio' & toplot$name %in% c('Shelby'))])
+toplot$DMA.CODE[which(toplot$state == 'Georgia' & toplot$name %in% c('Taylor'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Georgia' & toplot$name %in% c('Marion'))])
+toplot$DMA.CODE[which(toplot$state == 'Illinois' & toplot$name %in% c('Cass'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Illinois' & toplot$name %in% c('Brown'))])
+toplot$DMA.CODE[which(toplot$state == 'Tennessee' & toplot$name %in% c('Wayne'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Alabama' & toplot$name %in% c('Lauderdale'))])
+toplot$DMA.CODE[which(toplot$state == 'Texas' & toplot$name %in% c('Red River','Camp'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Texas' & toplot$name %in% c('Titus'))])
+toplot$DMA.CODE[which(toplot$state == 'Nebraska' & toplot$name %in% c('Butler','Pawnee'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Nebraska' & toplot$name %in% c('Johnson'))])
+toplot$DMA.CODE[which(toplot$state == 'New Mexico' & toplot$name %in% c('Lea'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Texas' & toplot$name %in% c('Winkler'))])
+toplot$DMA.CODE[which(toplot$state == 'Kansas' & toplot$name %in% c('Morton'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Oklahoma' & toplot$name %in% c('Texas'))])
+toplot$DMA.CODE[which(toplot$state == 'Georgia' & toplot$name %in% c('Seminole'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'Georgia' & toplot$name %in% c('Early'))])
+toplot$DMA.CODE[which(toplot$state == 'Virginia' & toplot$name %in% c('Grayson'))] <- unique(toplot$DMA.CODE[which(toplot$state == 'North Carolina' & toplot$name %in% c('Alleghany'))])
+
+as.data.frame(toplot) %>% ungroup() %>% select(state,DMA.CODE) %>% distinct() %>%
+  group_by(DMA.CODE) %>% summarise(n=n()) -> multiStateDMA
+
+toplot %>% left_join(multiStateDMA %>% mutate(DMA.CODE,multiState = (n > 1)+0) %>% select(-n)) -> toplot
+
+
+
+toplot$treated <- ifelse(toplot$state %in% c('Iowa','New Hampshire','South Carolina','Nevada',
+                                             'Alabama','Arkansas','California','Colorado','Maine',
+                                             'Massachusetts','Minnesota','North Carolina','Oklahoma',
+                                             'Tennessee','Texas','Utah','Vermont','Virginia'),0,1)
+comparisons <- as.data.frame(toplot) %>% select(DMA.CODE,multiState,stab,treated) %>%
+  filter(multiState == 1) %>%
+  group_by(DMA.CODE) %>%
+  summarise(IDvar = mean(treated)) %>%
+  filter(IDvar > 0 & IDvar < 1)
+
+
+pdf('../Figures/figure2.pdf',width = 16,height = 12)
+toplot %>%
+  left_join(comparisons) %>%
+  mutate(IDvar = ifelse(is.na(IDvar),0,IDvar)) %>%
+  mutate(treated2 = ifelse(treated == 1 & IDvar > 0,4,
+                           ifelse(treated == 0 & IDvar > 0,3,
+                                  ifelse(treated == 1 & IDvar == 0,2,1)))) %>%
+  mutate(treated2 = ifelse(IDvar > 0,treated2,'drop')) %>%
+  ggplot() + 
+  geom_sf(aes(fill = factor(treated2),color = treated2),size = .1) + 
+  scale_fill_manual(name = 'Primary Date',
+                    values = c('4' = '#1e1e1e','3' = '#878787',
+                               '2' = 'white','1' = 'white'),
+                    breaks = c('4','3'),
+                    labels = c('4' = 'Post-Outbreak','3' = 'Pre-Outbreak')) + 
+  scale_color_manual(guide = F,values = c('4' = 'white','3' = 'white','drop' = 'grey80'),na.translate = F) +
+  geom_sf(data = maps$DMA,color = 'black',fill = NA) +
+  theme_map()
+dev.off()
+
+
+
+# Figure 3
+p1 <- finalDat %>%
+  mutate(treat = ifelse(county_March17Cases > 0,1,0),
+         post = ifelse(date > as.Date('2020-03-10'),'Post March 10','Pre March 17')) %>%
+  group_by(treat,post) %>%
+  summarise(outcome = mean(pcttw_sanders,na.rm=T),
+            sd = sd(pcttw_sanders,na.rm=T)) %>%
+  ggplot(aes(x = factor(post,levels = c("Pre March 17","Post March 10")),y = outcome,
+             fill = factor(treat))) + 
+  stat_summary(fun.y = mean,geom = "bar",position = position_dodge(width = .9),
+               size = 3,alpha = .7) + 
+  scale_fill_manual(name = '',values = c('0' = 'grey80','1' = 'grey30'),
+                    labels = c('0' = 'Insulated on March 17th','1' = 'Exposed on March 17th')) + 
+  theme_ridges() + xlab('Period') + ylab('Sanders Two-Way Vote Share')
+
+p2 <- finalDat %>%
+  mutate(post = ifelse(date > as.Date('2020-03-10'),'Post','Pre'),
+         logcases = log(county_March17Cases+1)) %>%
+  ggplot(aes(x = logcases,y = pcttw_sanders,color = factor(post,levels = c("Pre","Post")),size = turnout_20,weight = log(turnout_20+1))) + 
+  geom_point(alpha = .6) + 
+  geom_smooth(method = 'lm') +
+  scale_color_manual(name = "Period",values = c('Post' = 'grey30',"Pre" = "grey70")) + 
+  scale_size_continuous(guide = F) + 
+  theme_ridges() + xlab("March 17 Cases (logged)") + ylab('Sanders Two-Way Vote Share')
+
+
+pdf('../Figures/figure3.pdf',width = 7,height = 5)
+grid.arrange(p1 + theme(legend.position = 'bottom'),
+             p2 + theme(legend.position = 'bottom'),ncol = 2)
+dev.off()
+
+
+
+# SI Table 1
+covariates <- c("sc_CTY_LTHS","sc_CTY_CollUp",'caucus_switch','caucus',
+                "sc_CTY_LT30yo","sc_CTY_60Up",
+                "sc_CTY_Below_poverty_level_AGE_18_64","sc_CTY_Female_hher_no_husbandhh",
+                "sc_CTY_Unem_rate_pop_16_over","sc_CTY_Labor_Force_Part_Rate_pop_16_over",
+                "sc_CTY_Manufactur","sc_CTY_Md_inc_hhs",
+                "sc_CTY_POPPCT_RURAL","sc_CTY_Speak_only_English","sc_CTY_White","sc_CTY_Black_or_African_American",
+                "ln_CTY_tot_pop","sc_turnout_pct_20")
+
+maps$county %>% left_join(multiStateDMA %>% mutate(DMA.CODE,multiState = (n > 1)+0) %>% select(-n)) -> maps$county
+as.data.frame(maps$county) %>% ungroup() %>% select(FIPS,multiState) %>% 
+  distinct() %>%
+  left_join(finalDat,by = c("FIPS" = "stcou")) %>%
+  select(multiState,gsub("sc_|ln_","",covariates),pct_sanders16) -> tocompare
+
+zeroOne <- function(x) {
+  return(x > 0 & x < 1)
+}
+
+div100 <- function(x) {
+  return(x/100)
+}
+tocompare %>% 
+  mutate(turnout_pct_20 = turnout_pct_20*100) %>%
+  gather(variable,value,-multiState) %>%
+  filter(complete.cases(.)) %>%
+  group_by(multiState,variable) %>%
+  summarise(value = list(value)) %>%
+  spread(multiState,value,sep = "_") %>% 
+  group_by(variable) %>%
+  mutate(p_value = round(t.test(unlist(multiState_0), unlist(multiState_1))$p.value,2),
+         t_value = round(t.test(unlist(multiState_0), unlist(multiState_1))$statistic,1),
+         avg_0 = round(mean(unlist(multiState_0),na.rm=T),1),
+         avg_1 = round(mean(unlist(multiState_1),na.rm=T),1),
+         sd_0 = round(sd(unlist(multiState_0),na.rm=T),1),
+         sd_1 = round(sd(unlist(multiState_1),na.rm=T),1)) %>%
+  select(variable,matches("avg|sd"),p_value,t_value) %>%
+  arrange(t_value) -> dma_fe_balance
+dma_fe_balance$variable <- gsub("_"," ",gsub("CTY_","",dma_fe_balance$variable))
+stargazer(data.frame(dma_fe_balance %>% select(-matches("sd_"))),summary = F,digits = 1,out = '../Tables/SI-table1.tex')

29/replication_package/Code/Figure 4.R

@@ -0,0 +1,53 @@
+# File name: figure4.R
+# In: 
+#   - replication_data.RData
+# Out: 
+#   - /Figures/figure4.pdf
+
+require(lme4)
+require(lfe)
+require(MatchIt)
+require(WeightIt)
+require(tjbal)
+require(optmatch)
+require(stargazer)
+require(cobalt)
+require(tidyverse)
+require(ggridges)
+rm(list = ls())
+gc()
+####################################################################################################################### Loading data
+load('../Data/replication_data.RData')
+
+
+
+
+
+
+
+
+
+####################################################################################################################### Loading functions
+source('./helper_functions.R')
+
+
+
+
+
+# Figure 4
+finalDat$post <- ifelse(finalDat$date > as.Date('2020-03-10'),1,0)
+finalDat$treat <- ifelse(finalDat$DMA_March17Cases > 1,1,0)
+summary(tmp <- lmer(as.formula(paste0("pcttw_sanders ~ treat*post + (1|DMA_CODE)")),finalDat))$coefficients
+toplot <- data.frame(interaction_plot_continuous(tmp,num_points = 2,pointsplot = T))
+
+
+pdf('../Figures/figure4.pdf',width = 7,height = 5)
+toplot %>%
+  mutate(x = factor(ifelse(x_2 == 0,'Pre','Post'),levels = c('Pre','Post'))) %>%
+  ggplot(aes(x = x,y = delta_1))+ 
+  geom_point() + 
+  geom_errorbar(aes(ymin = lb,ymax = ub),width= .1) + 
+  geom_hline(yintercept = 0,linetype = 'dashed') + 
+  xlab("Period") + ylab("Marginal Effect of Exposure") + 
+  theme_ridges()
+dev.off()

29/replication_package/Code/Figure 5.R

@@ -0,0 +1,145 @@
+# File name: figure5.R
+# In: 
+#   - replication_data.RData
+# Out: 
+#   - /Figures/figure5.pdf
+
+
+require(lme4)
+require(lfe)
+require(MatchIt)
+require(WeightIt)
+require(tjbal)
+require(optmatch)
+require(stargazer)
+require(cobalt)
+require(tidyverse)
+require(ggridges)
+
+rm(list = ls())
+gc()
+####################################################################################################################### Loading data
+load('../Data/replication_data.RData')
+
+
+####################################################################################################################### Loading functions
+source('./helper_functions.R')
+
+
+
+
+
+
+
+D <- unlist(lapply(c("sc_","ln_"),function(x) paste0(x,paste0(c("county_","DMA_","state_"),"cases"))))
+FE <- c("0","DMA_CODE","date")
+covariates <- c("sc_CTY_LTHS","sc_CTY_CollUp",
+                "sc_CTY_LT30yo","sc_CTY_60Up",
+                "sc_CTY_Below_poverty_level_AGE_18_64","sc_CTY_Female_hher_no_husbandhh",
+                "sc_CTY_Unem_rate_pop_16_over","sc_CTY_Labor_Force_Part_Rate_pop_16_over",
+                "sc_CTY_Manufactur","sc_CTY_Md_inc_hhs",
+                "sc_CTY_POPPCT_RURAL","sc_CTY_Speak_only_English","sc_CTY_White","sc_CTY_Black_or_African_American",
+                "ln_CTY_tot_pop","sc_turnout_pct_20")
+
+
+
+
+
+
+
+# Week-by-week
+weeks <- c("2020-02-22","2020-02-29",
+           "2020-03-03","2020-03-10","2020-03-17","2020-04-07")
+
+resWkly <- list()
+for(y in c("pcttw_sanders")) {
+  for(d in c("DMA_March10Cases",
+             "DMA_March17Cases")) {
+    for(fe in "0") {
+      
+      wkly <- wklyBin <- wklyMatch <- wklyWeight <- wklyMatchBin <- wklyWeightBin <- NULL
+      if(grepl("March",d)) {
+        wks <- weeks
+      } else {
+        wks <- weeks[4:6]
+      }
+      for(week in wks) {
+        if(fe == 'stab' & week == '2020-04-07') { next }
+        cat(paste0(week,'/',y,'/',d,'/',fe,'\n'))
+        if(grepl("county",d) & week == "2020-03-01") { next }
+        if(week < as.Date("2020-03-01")) {
+          tmp <- finalDat %>% select(y,matches(gsub("sc_|ln_","",d)),DMA_CODE,stab,date,c(covariates,paste0(y,"16"))) %>% filter(date < as.Date(week)) %>% filter(complete.cases(.))
+        } else {
+          tmp <- finalDat %>% select(y,matches(gsub("sc_|ln_","",d)),DMA_CODE,stab,date,c(covariates,paste0(y,"16"))) %>% filter(date == as.Date(week)) %>% filter(complete.cases(.))
+        }
+        tmp$treatBin <- ifelse(tmp[[gsub("sc_|ln_","",d)]] > 0,1,0)
+        
+        wkly <- bind_rows(wkly,data.frame(t(summary(felm(as.formula(paste0("scale(",y,") ~ scale(",gsub("sc_","",d),") + ",
+                                                                           paste(unique(c(covariates,paste0(y,"16"))),collapse = "+"),
+                                                                           "| ",fe," | 0 | 0")),tmp))$coefficients[paste0("scale(",gsub("sc_","",d),")"),]),date = week))
+        
+        wklyBin <- bind_rows(wklyBin,data.frame(t(summary(felm(as.formula(paste0("scale(",y,") ~ treatBin + ",
+                                                                                 paste(unique(c(covariates,paste0(y,"16"))),collapse = "+"),
+                                                                                 "| ",fe," | 0 | 0")),tmp))$coefficients["treatBin",]),date = week))
+        
+        if(length(unique(tmp$treatBin)) == 2) { 
+          m.out <- try(matchit(formula = as.formula(paste("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+                               data = tmp,
+                               method = "nearest",
+                               distance = "mahalanobis"))
+          if(class(m.out) != "try-error") {
+            m.data <- match.data(m.out)
+            wklyMatch <- bind_rows(wklyMatch,data.frame(t(summary(felm(as.formula(paste0("scale(",y,") ~ scale(",gsub("sc_","",d),") + ",
+                                                                                         paste(c(covariates,paste0(y,"16")),collapse = " + ")," | ",fe," | 0 | ",fe)),
+                                                                       m.data,weights = m.data$weights))$coefficients[paste0("scale(",gsub("sc_","",d),")"),]),date = week))
+            wklyMatchBin <- bind_rows(wklyMatchBin,data.frame(t(summary(felm(as.formula(paste0("scale(",y,") ~ treatBin + ",
+                                                                                               paste(c(covariates,paste0(y,"16")),collapse = " + ")," | ",fe," | 0 | ",fe)),
+                                                                             m.data,weights = m.data$weights))$coefficients["treatBin",]),date = week))
+          }
+          
+          W.out <- try(weightit(formula(paste0("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+                                data = tmp, estimand = "ATT", method = "cbps"))
+          
+          
+          if(class(W.out) != "try_error") {
+            wklyWeight <- bind_rows(wklyWeight,data.frame(t(summary(felm(as.formula(paste0("scale(",y,") ~ scale(",gsub("sc_","",d),") + ",
+                                                                                           paste(c(covariates,paste0(y,"16")),collapse = " + ")," | ",fe," | 0 | ",fe)),
+                                                                         tmp,weights = W.out$weights))$coefficients[paste0("scale(",gsub("sc_","",d),")"),]),date = week))
+            
+            wklyWeightBin <- bind_rows(wklyWeightBin,data.frame(t(summary(felm(as.formula(paste0("scale(",y,") ~ treatBin + ",
+                                                                                                 paste(c(covariates,paste0(y,"16")),collapse = " + ")," | ",fe," | 0 | ",fe)),
+                                                                               tmp,weights = W.out$weights))$coefficients["treatBin",]),date = week))
+          }
+        }
+      }
+      resWkly[[y]][[d]][[fe]]$basic$cont <- wkly
+      resWkly[[y]][[d]][[fe]]$basic$bin <- wklyBin
+      resWkly[[y]][[d]][[fe]]$matching$cont <- wklyMatch
+      resWkly[[y]][[d]][[fe]]$matching$bin <- wklyMatchBin
+      resWkly[[y]][[d]][[fe]]$weighting$cont <- wklyWeight
+      resWkly[[y]][[d]][[fe]]$weighting$bin <- wklyWeightBin
+    }
+  }
+}
+
+
+pdf('../Figures/figure5.pdf',width = 7,height = 5)
+bind_rows(resWkly$pcttw_sanders$DMA_March17Cases$`0`$basic$cont %>%
+                      mutate(type = 'basic',
+                             date = as.Date(date) - .25),
+                    resWkly$pcttw_sanders$DMA_March17Cases$`0`$weighting$cont %>%
+                      mutate(type = 'weighting',
+                             date = as.Date(date) + .25)) %>%
+  ggplot(aes(x = date,y = Estimate,color = type,fill = type)) + 
+  geom_hline(yintercept = 0,linetype = 'dashed') + 
+  geom_point(size = 1.5) + 
+  geom_rect(aes(xmin = date-.25,ymin = Estimate - 1.96*`Std..Error`,
+                    xmax = date+.25,ymax = Estimate + 1.96*`Std..Error`),alpha = .5,color = NA) + 
+  theme_ridges() + 
+  scale_color_manual(values = c('basic' = 'grey50','weighting' = 'grey40'),
+                     guide = F) + 
+  scale_fill_manual(name = 'Method',values = c('basic' = 'grey70','weighting' = 'grey40'),
+                    labels = c('basic' = 'Unweighted OLS','weighting' = 'CBPS Weights')) + 
+  xlab('Date') + ylab('Coefficient on March 17 Cases') + 
+  theme(legend.position = 'bottom')
+dev.off()

29/replication_package/Code/Figure 6, SI1.R

@@ -0,0 +1,221 @@
+# File name: figure6_SIfigure1.R
+# In: 
+#   - replication_data.RData
+# Out: 
+#   - /Data/Results/tjbalWgtsNEW.RData (saving time re-estimating the weights)
+#   - /Figures/figure6.pdf
+#   - /Figures/SI_figure1.pdf
+
+require(lme4)
+require(lfe)
+require(MatchIt)
+require(WeightIt)
+require(tjbal)
+require(optmatch)
+require(stargazer)
+require(cobalt)
+require(tidyverse)
+require(ggridges)
+
+
+rm(list = ls())
+gc()
+####################################################################################################################### Loading data
+load('../Data/replication_data.RData')
+
+
+####################################################################################################################### Loading functions
+source('./helper_functions.R')
+
+
+
+
+
+
+####################################################################################################################### Preparing variables
+Y <- paste0(c("pct_","VAP_","pcttw_"),"sanders")
+D <- unlist(lapply(c("sc_","ln_"),function(x) paste0(x,paste0(c("county_","DMA_","state_"),"cases"))))
+FE <- c("0","DMA_CODE","date")
+covariates <- c("sc_CTY_LTHS","sc_CTY_CollUp",
+                "sc_CTY_LT30yo","sc_CTY_60Up",
+                "sc_CTY_Below_poverty_level_AGE_18_64","sc_CTY_Female_hher_no_husbandhh",
+                "sc_CTY_Unem_rate_pop_16_over","sc_CTY_Labor_Force_Part_Rate_pop_16_over",
+                "sc_CTY_Manufactur","sc_CTY_Md_inc_hhs",
+                "sc_CTY_POPPCT_RURAL","sc_CTY_Speak_only_English","sc_CTY_White","sc_CTY_Black_or_African_American",
+                "ln_CTY_tot_pop","sc_turnout_pct_20")
+
+
+
+
+# SI Figure 1
+set.seed(123)
+treatInd <- "March17Cases"
+vd <- '2020-03-17'
+d <- 'ln_DMA_cases'
+est <- 'meanfirst'
+treatInd <- paste0("DMA_",treatInd)
+forTjbalAnal$treatGroup <- ifelse(forTjbalAnal$voteDate == vd & forTjbalAnal[[treatInd]] > 0,1,0)
+forTjbalAnal$treat <- ifelse(forTjbalAnal$treatGroup == 1 & forTjbalAnal$date > as.Date("2020-04-17"),1,0)
+
+inds <- which(forTjbalAnal$treatGroup == 1)
+inds <- unique(forTjbalAnal$stcou[inds])
+ctrlInds <- unique(forTjbalAnal$stcou[-c(which(forTjbalAnal$stcou %in% inds),which(forTjbalAnal$voteDate >= as.Date(vd)))])
+
+tmpTjbalAnal <- forTjbalAnal %>% filter(stcou %in% c(inds,ctrlInds))
+tjout <- try(tjbal(data = as.data.frame(tmpTjbalAnal),Y = d,D = "treat",X = unique(c(covariates,"pct_sanders16")),
+                   index = c("stcou","dateNum"),estimator = est,demean = T,vce = "fixed"))
+
+
+toplot <- as.data.frame(tjout$Y.bar)
+toplot$date <- seq.Date(from = as.Date("2020-01-23"),to = as.Date("2020-05-01"),length.out = nrow(tjout$Y.bar))
+getwd()
+pdf("../Figures/SI_figure1.pdf")
+toplot %>%
+  gather(type,value,-date) %>%
+  ggplot(aes(x = date,y = value,color = type,size = type,linetype = type,alpha = type)) + 
+  geom_line() + 
+  geom_vline(xintercept = as.Date(unique(finalDat$Date)[which(as.Date(unique(finalDat$Date)) < as.Date("2020-03-17"))]),linetype = "dashed",alpha = .6) + 
+  geom_vline(xintercept = as.Date("2020-03-17"),size = 1.2) + 
+  xlab("Date") + ylab("Number of Cases (logged)") + 
+  annotate(geom = "text",label = c("Iowa Caucuses","New Hampshire","Nevada","South Carolina","Super Tuesday","March 10","March 17"),
+           x = as.Date(c("2020-02-03","2020-02-11","2020-02-22","2020-02-29",
+                         "2020-03-03","2020-03-10","2020-03-17")),y = Inf,angle = 90,vjust = 1,hjust = 1,size = 3.5,color = "grey30") + 
+  annotate(geom = "text",label = c("Treated","Weighted","Control"),
+           y = c(7.8,7.5,6.6),x = as.Date("2020-05-02"),angle = 0,
+           parse = F,hjust = 0,vjust = .3,size = 3.5,color = "grey30") +
+  scale_linetype_manual(values = c("solid","dotted","solid")) + 
+  scale_size_manual(values = c(1.2,1.2,2.5)) +
+  scale_color_manual(values = c("red","black","black")) + 
+  scale_alpha_manual(values = c(1,1,.3)) + 
+  xlim(as.Date("2020-01-23"),as.Date("2020-05-10")) + theme_ridges() + 
+  theme(legend.position = "none") + 
+  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
+        panel.background = element_blank(), axis.line = element_line(colour = "black"))
+dev.off()
+
+
+
+# Figure 6 
+set.seed(123)
+tjBalWgts <- list()
+for(vd in c("2020-03-03","2020-03-10","2020-03-17")) {
+  for(d in c("county_cases")) {
+    for(est in c("kernel")) {
+      
+      if(vd == "2020-03-03") {
+        treatInd <- "March3Cases"
+      } else if(vd == "2020-03-10") {
+        treatInd <- "March10Cases"
+      } else {
+        treatInd <- "March17Cases"
+      }
+      if(grepl("county",d)) {
+        treatInd <- paste0("county_",treatInd)
+      } else {
+        treatInd <- paste0("DMA_",treatInd)
+      }
+      
+      forTjbalAnal$treatGroup <- ifelse(forTjbalAnal$voteDate == vd & forTjbalAnal[[treatInd]] > 0,1,0)
+      forTjbalAnal$treat <- ifelse(forTjbalAnal$treatGroup == 1 & forTjbalAnal$date > as.Date("2020-04-17"),1,0)
+      
+      inds <- which(forTjbalAnal$treatGroup == 1)
+      inds <- unique(forTjbalAnal$stcou[inds])
+      ctrlInds <- unique(forTjbalAnal$stcou[-c(which(forTjbalAnal$stcou %in% inds),which(forTjbalAnal$voteDate >= as.Date(vd)))])
+      
+      ests <- finalDat %>% select(stcou)
+      for(i in 1:length(inds)) {
+        tmpTjbalAnal <- forTjbalAnal %>% filter(stcou %in% c(inds[-i],ctrlInds))
+        sink <- capture.output(tjout <- try(tjbal(data = as.data.frame(tmpTjbalAnal),Y = d,D = "treat",
+                           X = unique(c(covariates,"pct_sanders16")),seed = 123,
+                           index = c("stcou","dateNum"),estimator = est,demean = T,vce = "fixed")))
+        
+        if(class(tjout) == "try-error") { next }
+        tjout$data.wide$w <- tjout$w
+        ests <- ests %>% left_join(tjout$data.wide %>% select(stcou = unit,w),by = 'stcou')
+        colnames(ests)[which(colnames(ests) == "w")] <- paste0("w",i)
+        cat('.')
+      }
+      cat('\n')
+      
+      tjBalWgts[[vd]][[d]][[est]]$jacknife <- ests
+      tmpTjbalAnal <- forTjbalAnal %>% filter(stcou %in% c(inds,ctrlInds))
+      sink <- capture.output(tjout <- try(tjbal(data = as.data.frame(tmpTjbalAnal),Y = d,D = "treat",X = unique(c(covariates,"pct_sanders16")),
+                         index = c("stcou","dateNum"),estimator = est,demean = T,seed = 123,
+                         vce = "fixed")))
+      
+      if(class(tjout) == "try-error") { next }
+      tjout$data.wide$w <- tjout$w
+      tjBalWgts[[vd]][[d]][[est]]$full <- tjout$data.wide %>% select(stcou = unit,w)
+    }
+  }
+}
+
+save(tjBalWgts,file = "../Data/Results/tjbalWgtsNEW.RData")
+load("../Data/Results/tjbalWgtsNEW.RData")
+
+
+
+
+
+
+
+
+
+resTjbal <- list()
+for(y in Y) {
+  for(geo in c("county_","DMA_")) {
+    for(d in c("March3Cases","March10Cases","March17Cases")) {
+      if(d == "March3Cases") {
+        int <- "2020-03-03"
+      } else if(d == "March10Cases") {
+        int <- "2020-03-10"
+      } else {
+        int <- "2020-03-17"
+      }
+      for(balCases in names(tjBalWgts[[int]])) {
+        for(est in names(tjBalWgts[[int]][[balCases]])) {
+          
+          if(is.null(tjBalWgts[[int]][[balCases]][[est]]$full)) { next }
+          
+          finalDat %>% left_join(tjBalWgts[[int]][[balCases]][[est]]$full) %>% 
+            left_join(tjBalWgts[[int]][[balCases]][[est]]$jacknife) %>% filter(!is.na(w)) -> tmpAnal
+          tmpAnal$post <- ifelse(tmpAnal$date == as.Date(int),1,0)
+          tmpAnal$treatGroup <- ifelse(tmpAnal[[paste0(geo,d)]] > 0,1,0)
+          tmpAnal$treat <- ifelse(tmpAnal$post == 1 & tmpAnal$treatGroup == 1,1,0)
+          tmpAnal %>% select(post,treatGroup,treat,date)
+          
+          ests <- NULL
+          for(i in colnames(tmpAnal %>% select(matches("^w\\d")))) {
+            ests <- c(ests,sum(tmpAnal[[y]]*tmpAnal[[i]],na.rm=T))
+          }
+          resTjbal[[y]][[geo]][[d]][[balCases]][[est]]$bootstrapped <- ests
+          resTjbal[[y]][[geo]][[d]][[balCases]][[est]]$lmCont <- summary(lm(as.formula(paste0(y," ~ ",balCases)),tmpAnal,weights = abs(tmpAnal$w)))$coefficients
+          resTjbal[[y]][[geo]][[d]][[balCases]][[est]]$lmBin <- summary(lm(as.formula(paste0(y," ~ treat")),tmpAnal,weights = abs(tmpAnal$w)))$coefficients
+        }
+      }
+    }
+  }
+}
+
+
+toplot <- NULL
+for(period in names(resTjbal$pcttw_sanders$DMA_)) {
+  toplot <- bind_rows(toplot,
+                      data.frame(bs = resTjbal$pcttw_sanders$county_[[period]]$county_cases$kernel$bootstrapped,
+             period = period))
+}
+
+
+
+
+# Figure 6: 
+pdf('../Figures/figure6.pdf',width = 7,height = 5)
+toplot %>%
+  mutate(period = factor(gsub('Cases','',
+                       gsub('March','March ',period)),levels = rev(c('March 3','March 10','March 17')))) %>%
+  ggplot(aes(x = bs,y = period)) + 
+  geom_density_ridges(alpha = .7,color = 'black') + 
+  theme_ridges() + 
+  geom_vline(xintercept = 0,linetype = 'dashed') + 
+  xlab('ATT of Exposure on Sanders Support') +  ylab('Outbreak Date')
+dev.off()

29/replication_package/Code/Figure 7, SI2, SI17.R

@@ -0,0 +1,125 @@
+# File name: figure7_SIfigure2_SIfigure17.R
+# In: 
+#   - /Data/gtrends_data.RData: DMA-level data on google searches for "coronavirus" linked with DMA-level cases and deaths
+#   - /Data/mobility_data.RData: county-level data on physical mobility linked with county-level cases and deaths
+#   - /Data/pew/biden_sanders_ideo_feb_march_PEW.csv: Pew survey data on public's placement of Sanders and Biden on an ideological scale
+# Out: 
+#   - /Figures/figure7.pdf
+#   - /Figures/SI_figure2.pdf
+#   - /Figures/SI_figure17.pdf
+
+require(lme4)
+require(lfe)
+require(MatchIt)
+require(WeightIt)
+require(tjbal)
+require(optmatch)
+require(stargazer)
+require(cobalt)
+require(tidyverse)
+require(ggridges)
+
+rm(list = ls())
+gc()
+
+# Figure 7
+load('../Data/gtrends_data.RData')
+coefs <- NULL
+for(day in as.character(unique(gtrends_cases$date))[5:95]) {
+  tmp <- summary(lmer(hits ~ log(cases+1) + (1|stab),gtrends_cases %>% filter(date == as.Date(day))))$coefficients
+  if(nrow(tmp) == 1) { next }
+  txttmp <- unique(trimws(gsub("coronavirus|the","",queries[[day]]$value)))
+  if(any(txttmp == "")) {
+    txttmp <- txttmp[-which(txttmp == "")]
+  }
+  txttmp <- txttmp[1:3]
+  if(any(txttmp == "DGSGsymptoms")) {
+    txttmp <- ifelse(txttmp == "symptoms",'"symptoms"',paste0('phantom("',txttmp,'")'))
+    txttmp <- paste(txttmp,collapse = " ")
+    txttmp <- gsub(' "symptoms"',' * "symptoms"',txttmp)
+    txttmp <- gsub('"symptoms" ','"symptoms" * ',txttmp)
+    txttmp <- gsub('"\\) phantom\\("', ' ',txttmp)
+  } else {
+    txttmp <- paste(txttmp,collapse = " ")
+  }
+  tq <- txttmp
+  
+  coefs <- bind_rows(coefs,data.frame(t(tmp[2,]),date = as.Date(day),topQ = tq,stringsAsFactors = F))
+}
+
+pdf("../figures/figure7.pdf",width = 7,height = 5)
+coefs %>%
+  ggplot(aes(x = date,y = Estimate)) + 
+  geom_point() + 
+  geom_errorbar(aes(ymin = Estimate - 2*Std..Error,ymax = Estimate + 2*Std..Error)) + 
+  theme_bw() + 
+  geom_hline(yintercept = 0) + 
+  theme_bw() +
+  geom_vline(xintercept = as.Date(c("2020-02-03","2020-02-11","2020-02-22","2020-02-29",
+                                    "2020-03-03","2020-03-10","2020-03-17")),linetype = "dashed",alpha = .3) + 
+  annotate(geom = "text",label = c("IA","NH","NV","SC","SupTues","Mar 10th","AZ, FL, IL"),
+           x = as.Date(c("2020-02-03","2020-02-11","2020-02-22","2020-02-29",
+                         "2020-03-03","2020-03-10","2020-03-17")),y = -Inf,angle = 90,vjust = 1,hjust = 0) + 
+  theme_ridges() + 
+  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
+        panel.background = element_blank(), axis.line = element_line(colour = "black")) + xlab("Date") + ylab("Coefficient on Exposure") + 
+  annotate(geom = "text",label = coefs$topQ,y = coefs$Estimate + 2*coefs$Std..Error + 1,x = coefs$date,angle = 90,
+           parse = F,hjust = 0,vjust = .3,size = 2,color = "grey30") + 
+  ylim(c(-10,45))
+dev.off()
+
+
+
+# SI Figure 2
+load('../Data/mobility_data.RData')
+
+dma_coefs <- cty_coefs <- NULL
+for(week in as.character(unique(mobility_cases$weeks))[-13]) {
+  tmp <- summary(felm(raw ~ log(cases+1) | stab | 0 | stab,mobility_cases %>% filter(weeks == as.Date(week))))$coefficients[1,1:3]
+  names(tmp) <- c("Estimate","Std..Error","tstat")
+  cty_coefs <- bind_rows(cty_coefs,
+                         data.frame(t(tmp),date = as.Date(week),stringsAsFactors = F))
+}
+
+pdf("../figures/SI_figure2.pdf",width= 7,height = 5)
+cty_coefs %>%
+  ggplot(aes(x = date,y = Estimate)) + 
+  geom_point(position = position_dodge(width = 4)) + 
+  geom_errorbar(aes(ymin = Estimate - 2*Std..Error,
+                    ymax = Estimate + 2*Std..Error),width = 1) + 
+  geom_hline(yintercept = 0) + 
+  theme_bw()  +
+  geom_vline(xintercept = as.Date(c("2020-02-03","2020-02-11","2020-02-22","2020-02-29",
+                                    "2020-03-03","2020-03-10","2020-03-17")),linetype = "dashed",alpha = .5) + 
+  annotate(geom = "text",label = c("IA","NH","NV","SC","SupTues","Mar 10th","AZ, FL, IL"),
+           x = as.Date(c("2020-02-03","2020-02-11","2020-02-22","2020-02-29",
+                         "2020-03-03","2020-03-10","2020-03-17")),y = Inf,angle = 90,vjust = 1,hjust = 1) + 
+  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
+        panel.background = element_blank(), axis.line = element_line(colour = "black")) + xlab("Date") + ylab("Coefficient on Exposure") + 
+  theme_ridges()
+dev.off()
+
+
+
+
+
+
+
+# SI Figure 17
+toplot <- read.csv('../Data/pew/biden_sanders_ideo_feb_march_PEW.csv',stringsAsFactors = F)
+
+pdf('../figures/SI_figure17.pdf',width = 8,height = 4)
+toplot %>%
+  mutate_at(vars(matches('\\.|Refused|Moderate')),function(x) as.numeric(gsub('%','',x))) %>%
+  gather(key,value,-Group,-Candidate) %>%
+  filter(!grepl('Refused|Not\\.sure',key)) %>% 
+  mutate(key = factor(gsub('\\.','\n',key),levels = c('Very\nliberal','Mostly\nliberal','Slightly\nliberal',
+                                                     'Moderate','Slightly\nconservative','Mostly\nconservative','Very\nconservative'))) %>%
+  filter(grepl('liberal dem',tolower(Group))) %>%
+  ggplot(aes(x = key,y = value,group = Candidate,fill = Candidate)) + 
+  geom_bar(stat = 'identity',position= position_dodge(.8),alpha = .6) + 
+  ylab('% Agreeing') + xlab('Candidate Ideology') + 
+  scale_fill_manual(name = '',values = c('Biden' = 'grey40','Sanders' = 'grey70')) + 
+  theme_ridges() + 
+  theme(legend.position = 'bottom')
+dev.off()

29/replication_package/Code/Figure 8, SI19.R

@@ -0,0 +1,95 @@
+# File name: figure8_SIfigure19.R
+# In: 
+#   - /Data/france_data.RData: Data on 2020 municipal elections in France, linked with local-level cases and deaths
+# Out: 
+#   - /Figures/figure8.pdf
+#   - /Figures/SI_figure19.pdf
+
+
+require(lfe)
+require(ggridges)
+require(tidyverse)
+require(gridExtra)
+rm(list =ls())
+gc()
+####################################################################################################################### Loading functions
+source('./helper_functions.R')
+
+
+load('../Data/france_data.RData')
+
+# Figure 8
+p1 <- toanal %>%
+  mutate(mainstream = ifelse(mainstream == 0,'Non-Mainstream','Mainstream'),
+         period = factor(ifelse(date == as.Date('2020-03-15'),'March Elections','June Elections'),
+                         levels = c('March Elections','June Elections'))) %>%
+  group_by(mainstream,period) %>%
+  summarise(votes = mean(votes)) %>%
+  ungroup() %>%
+  filter(complete.cases(.)) %>%
+  ggplot(aes(x = period,y = votes,fill = factor(mainstream))) + 
+  stat_summary(fun.y = mean,geom = "bar",position = position_dodge(width = .9),
+               size = 3,alpha = .7) + 
+  scale_fill_manual(name = '',values = c('Mainstream' = 'grey80','Non-Mainstream' = 'grey30')) + 
+  theme_ridges() + xlab('Period') + ylab('Aggregate Vote Share')
+
+p2 <- toanal %>%
+  mutate(mainstream = ifelse(mainstream == 0,'Non-Mainstream','Mainstream'),
+         period = factor(ifelse(date == as.Date('2020-03-15'),'March Elections','June Elections'),
+                         levels = c('March Elections','June Elections')),
+         logcases = log(deaths+1)) %>%
+  filter(!is.na(period)) %>%
+  ggplot(aes(x = logcases,y = votes,color = mainstream)) + 
+  geom_point(alpha = .7,size = 2) + 
+  geom_smooth(method = 'lm') +
+  scale_color_manual(name = "",values = c('Mainstream' = 'grey80',"Non-Mainstream" = "grey30")) + 
+  theme_ridges() + xlab("Deaths (logged)") + ylab('Aggregate Vote Share')
+
+pdf('../figures/figure8.pdf',width = 7,height = 4)
+grid.arrange(p1 + theme(legend.position = 'bottom'),
+             p2 + theme(legend.position = 'bottom'),ncol = 2)
+dev.off()
+
+
+
+# SI Figure 19
+toanal$post <- ifelse(toanal$date == '2020-03-15',0,1)
+
+toanal$antiEst <- (toanal$mainstream - 1)^2
+summary(mod1 <- felm(votes ~ antiEst*post | dpt_code | 0 | dpt_code,toanal))
+summary(mod2 <- felm(votes ~ antiEst*log(deaths+1) | post + dpt_code | 0 | dpt_code,toanal))
+
+toplot <- data.frame(interaction_plot_continuous(mod1,plot = T,pointsplot = T,colr = 'black',alph = 200,num_points = 2,
+                            xlabel = "Election Wave",ylabel = 'Marginal Coefficient of Mainstream')) %>%
+  mutate(type = 'Temporal')
+
+toplot <- bind_rows(toplot,data.frame(interaction_plot_continuous(mod2,plot = T,pointsplot = T,
+                                                                  colr = 'black',alph = 200,num_points = 20,
+                                                 xlabel = "Election Wave",ylabel = 'Marginal Coefficient of Mainstream')) %>%
+  mutate(type = 'Geographic'))
+
+
+
+p1 <- toplot %>%
+  filter(type == 'Temporal') %>%
+  mutate(x = factor(ifelse(x_2 == 0,'March Elections','June Elections'),levels = c('March Elections','June Elections'))) %>%
+  ggplot(aes(x = x,y = delta_1)) + 
+  geom_point() + 
+  geom_errorbar(aes(ymin = lb,ymax = ub),width = .1) + 
+  geom_hline(yintercept = 0,linetype = 'dashed') +
+  ylab('Margnial Coefficient on Non-Mainstream') + xlab('') + 
+  theme_ridges() + 
+  ggtitle('Temporal Variation')
+p2 <- toplot %>%
+  filter(type == 'Geographic') %>%
+  ggplot(aes(x = x_2,y = delta_1)) + 
+  geom_point() + 
+  geom_errorbar(aes(ymin = lb,ymax = ub),width = .1) + 
+  geom_hline(yintercept = 0,linetype = 'dashed') +
+  ylab('Margnial Coefficient on Non-Mainstream') + xlab('Logged Deaths') + 
+  theme_ridges() + 
+  ggtitle('Geographic Variation')
+
+pdf('../Figures/SI_figure19.pdf',width = 8,height = 4)
+grid.arrange(p1,p2,ncol = 2)
+dev.off()

29/replication_package/Code/Figure SI11, SI12, SI15, SI16.R

@@ -0,0 +1,115 @@
+# File name: SIfigure11_SIfigure12_SIfigure15_SIfigure16.R
+# In: 
+#   - nationscape_prepped.RData: Nationscape survey data merged with NYT covid data, aggregated to the congressional district level
+# Out: 
+#   - /Figures/SI_figure11.pdf
+#   - /Figures/SI_figure12.pdf
+#   - /Figures/SI_figure15.pdf
+#   - /Figures/SI_figure16.pdf
+
+
+
+rm(list = ls())
+gc()
+require(tidyverse)
+require(stargazer)
+require(lubridate)
+require(ggridges)
+require(lfe)
+
+
+
+####################################################################################################################### Loading data
+load('../Data/nationscape_data.RData')
+
+
+
+
+####################################################################################################################### Loading functions
+source('./helper_functions.R')
+
+
+
+# SI Figure 11
+pdf('../Figures/SI_figure11.pdf',width = 7,height = 5)
+forAnal %>%
+  filter(dateMo < as.Date('2020-07-01') & dateMo > as.Date('2019-07-01')) %>%
+  group_by(stcd,dateMo,exposedMean) %>%
+  summarise(ideoM = mean(economy_better,na.rm=T)) %>%
+  ggplot(aes(x = ideoM,y = fct_rev(factor(dateMo)),group = paste(dateMo,exposedMean),
+             fill = paste(dateMo,exposedMean))) +
+  geom_density_ridges(alpha = .6,color = 'white',quantile_lines = T,quantiles = 2) + 
+  scale_fill_cyclical(
+    breaks = c("2019-08-01 0", "2019-08-01 1"),
+    labels = c(`2019-08-01 0` = "Insulated", `2019-08-01 1` = "Exposed"),
+    values = rev(c("#000000", "#b0b0b0", "#000000", "#b0b0b0")),
+    name = "", guide = "legend"
+  ) + 
+  theme_ridges() + 
+  xlab("Economic Evaluation") + 
+  ylab("") + 
+  scale_x_continuous(limits = c(1,3),breaks = 1:3,labels = c('Improved','The Same','Worse'))
+dev.off()
+
+# SI Figure 12
+forAnal %>%
+  filter(dateMo < as.Date('2020-07-01') & dateMo > as.Date('2019-07-01')) %>%
+  group_by(stcd,dateWk,dateMo,exposedMean) %>%
+  summarise(econ = mean(economy_better,na.rm=T)) %>%
+  mutate(post = ifelse(dateMo > as.Date('2020-02-01'),1,0)) -> forDID
+
+summary(mEcon <- felm(scale(econ) ~ exposedMean*post | 0 | 0 | stcd,forDID %>% filter(dateMo < as.Date('2020-04-01'))))
+toplot <- data.frame(interaction_plot_continuous(mEcon,pointsplot = T,num_points = 2,plot = T))
+
+pdf('../Figures/SI_figure12.pdf',width = 7,height = 4)
+toplot %>%
+  mutate(x_2 = factor(ifelse(x_2 == 0,'Pre-March','March'),levels = c('Pre-March','March'))) %>%
+  ggplot(aes(x = x_2,y = delta_1)) + 
+  geom_point() + 
+  geom_errorbar(aes(ymin = lb,ymax = ub),width = .1) +
+  geom_hline(yintercept = 0,linetype = 'dashed') + 
+  theme_ridges() +
+  xlab('Period') + ylab('Effect of Covid Exposure')
+dev.off()
+
+
+
+# SI Figure 15
+(pSanders <- forAnal %>%
+  mutate(sanders = (cand_favorability_sanders - 5)*-1) %>% #ifelse(cand_favorability_sanders < 3,1,0)) %>%
+  filter(dateMo < as.Date('2020-05-01') & dateMo > as.Date('2019-07-01')) %>%
+  group_by(stcd,dateMo,exposedMean) %>%
+  summarise(ideoM = mean(sanders,na.rm=T)) %>%
+  ggplot(aes(x = ideoM,y = fct_rev(factor(dateMo)),group = paste(dateMo,exposedMean),
+             fill = paste(dateMo,exposedMean))) +
+  geom_density_ridges(alpha = .6,color = 'white',quantile_lines = T,quantiles = 2) + 
+  scale_fill_cyclical(
+    breaks = c("2019-08-01 0", "2019-08-01 1"),
+    labels = c(`2019-08-01 0` = "Insulated", `2019-08-01 1` = "Exposed"),
+    values = rev(c("#000000", "#b0b0b0", "#000000", "#b0b0b0")),
+    name = "", guide = "legend"
+  ) + 
+  theme_ridges() + 
+  xlab("Sanders Approval") + 
+  ylab("") + 
+  scale_x_continuous(breaks = 1:4,labels = c('Very\nunfavorable','Somewhat\nunfavorable','Somewhat\nfavorable','Very\nfavorable')))
+
+pdf('../Figures/SI_figure15.pdf',width = 7,height = 5)
+print(pSanders)
+dev.off()
+
+# SI Figure 16
+pdf('../Figures/SI_figure16.pdf',width = 8,height = 5)
+forAnal %>%
+  mutate(sanders = (cand_favorability_sanders - 5)*-1) %>% #ifelse(cand_favorability_sanders < 3,1,0)) %>%
+  filter(dateWk %in% as.Date(c('2020-02-02','2020-02-09','2020-02-16','2020-02-23','2020-03-01','2020-03-08','2020-03-15','2020-03-22','2020-03-29'))) %>%
+  group_by(stcd,dateWk) %>%
+  summarise(sanders = mean(sanders,na.rm=T),
+            cases = log(mean(casesSum,na.rm=T)+1)) %>%
+  ggplot(aes(x = cases,y = sanders)) + 
+  geom_point() + 
+  geom_smooth(method = 'lm') + 
+  facet_wrap(~dateWk) + 
+  theme_ridges() + 
+  xlab('Logged Cases') + ylab('Sanders Approval')
+dev.off()

29/replication_package/Code/Figure SI3, SI4, SI5.R

@@ -0,0 +1,167 @@
+# File name: SIfigure3_SIfigure4_SIfigure_5.R
+# In: 
+#   - replication_data.RData
+#   - 
+# Out: 
+#   - /Figures/SI_figure3.pdf
+#   - /Figures/SI_figure4.pdf
+#   - /Figures/SI_figure5.pdf
+
+require(lme4)
+require(lfe)
+require(MatchIt)
+require(WeightIt)
+require(tjbal)
+require(optmatch)
+require(stargazer)
+require(cobalt)
+require(tidyverse)
+require(gridExtra)
+require(ggridges)
+require(ggrepel)
+
+rm(list = ls())
+gc()
+
+
+
+
+####################################################################################################################### Loading data
+load(paste0('../Data/Results/SI-data.RData')) ## SI_robust_prep.R script which is designed to run on a computing cluster for speed.
+
+
+
+
+
+####################################################################################################################### Loading functions
+source('./helper_functions.R')
+
+
+
+
+# SI Figure 3
+toplot <- NULL
+for(post in c('2020-03-03','2020-03-10','2020-03-17')) {
+  for(mod in c('basic','matching','weighting')) {
+    toplot <- bind_rows(toplot,data.frame(est = resSI$pcttw_sanders$DMA_$March17Cases$DMA_CODE$`2020-03-01`[[post]]$felm[[mod]]$plac$did,
+                                          post = factor(ifelse(post == '2020-03-03','Control: February\nTreated: March 3rd',
+                                                               ifelse(post == '2020-03-10','Control: February\nTreated: March 10th',
+                                                                      'Control: February\nTreated: March 17th')),
+                                                        levels = c('Control: February\nTreated: March 3rd',
+                                                                   'Control: February\nTreated: March 10th',
+                                                                   'Control: February\nTreated: March 17th')),
+                                          model = Hmisc::capitalize(mod),stringsAsFactors = F))
+  }
+}
+
+
+pdf('../Figures/SI_figure3.pdf',width = 8,height = 5)
+toplot %>%
+  ggplot(aes(x = est,y = factor(model,levels = rev(c('Basic','Matching','Weighting'))))) + 
+  geom_vline(xintercept = 0,linetype = 'dashed') + 
+  geom_density_ridges(alpha = .4,color = 'white') + 
+  facet_wrap(~post) + ylab('Method') + xlab("Bootstrapped Estimates") + 
+  theme_ridges() + 
+  theme(
+    strip.text.x = element_text(
+      size = 10, color = "black", face = "bold",hjust = 0,vjust = .5
+    ),
+    strip.background = element_rect(
+      color="white", fill="white", size=0, linetype="solid"
+    )
+  )
+dev.off()
+
+
+
+
+# SI Figure 4
+toplot <- NULL
+for(pre in c('2020-03-01','2020-03-03','2020-03-10','2020-03-17')) {
+  for(post in c('2020-03-01','2020-03-03','2020-03-10','2020-03-17')) {
+    for(mod in c('basic','weighting','matching')) {
+      tmp <- resSI$pcttw_sanders$DMA_$March10Cases$DMA_CODE[[pre]][[post]]$felm[[mod]]$bin['treat',]
+      if(is.null(tmp)) { next }
+      tmp <- data.frame(t(tmp))
+      colnames(tmp) <- c('est','se','tstat','pval')
+      tmp$pre <- pre
+      tmp$post <- post
+      tmp$mod <- mod
+      toplot <- bind_rows(toplot,tmp)
+    }
+  }
+}
+
+
+pdf('../Figures/SI_figure4.pdf',width = 8,height = 5)
+toplot %>%
+  mutate(post = factor(gsub('03-03','ST',
+                     gsub('03-01','Feb',
+                          gsub('2020-','',post))),levels = c('Feb','ST','03-10','03-17')),
+         pre = factor(paste0('Pre Election: ',gsub('03-03','Super Tuesday',
+                                   gsub('03-01','February',
+                                        gsub('2020-','',pre)))),
+                      levels = paste0('Pre Election: ',c('February','Super Tuesday','03-10','03-17')))) %>%
+  ggplot(aes(x = post,y = est,shape = mod)) + 
+  geom_point(position = position_dodge(width = .2),size = 2,alpha = .7) +
+  geom_errorbar(aes(ymin = est-2*se,ymax = est+2*se),width = .2,
+                position = position_dodge(width = .2),alpha = .4) + 
+  geom_hline(yintercept = 0,linetype = 'dashed') +
+  facet_wrap(~pre) + 
+  scale_shape_manual(name = 'Model',
+                     values = c('basic' = 19,'matching' = 17,'weighting' = 15),
+                     labels = c('Basic','Matching','Weighting')) + 
+  theme_ridges() + 
+  theme(legend.position = 'bottom') + 
+  xlab('Post Election') + ylab('Effect of DMA Exposure')
+dev.off()
+
+
+
+
+# SI Figure 5
+toplot <- NULL
+for(pre in c('2020-03-01','2020-03-03','2020-03-10','2020-03-17')) {
+  for(post in c('2020-03-01','2020-03-03','2020-03-10','2020-03-17')) {
+    for(mod in c('basic','weighting','matching')) {
+      tmp <- resSI$pcttw_sanders$DMA_$March17Cases$`0`[[pre]][[post]]$felm[[mod]]$did$mfx
+      if(is.null(tmp)) { next }
+      tmp <- data.frame(tmp)
+      tmp$pre <- pre
+      tmp$post <- post
+      tmp$mod <- mod
+      toplot <- bind_rows(toplot,tmp)
+    }
+  }
+}
+
+
+pdf('../Figures/SI_figure5.pdf',width = 8,height = 5)
+toplot %>%
+  filter(pre < post,
+         mod == 'weighting') %>% 
+  mutate(x_2 = ifelse(x_2 == 0,gsub('2020-','',pre),gsub('2020-','',post)),
+         sig = ifelse(grepl('\\*',est),'Sig','Insig')) %>%
+  mutate(x_2 = factor(ifelse(x_2 == '03-01','Feb',
+                             ifelse(x_2 == '03-03','ST',x_2)),
+                      levels = c('Feb','ST','03-10','03-17'))) %>%
+  mutate(pre = factor(paste0('Pre: ',ifelse(pre == '2020-03-01','February',
+                                     ifelse(pre == '2020-03-03','Super Tuesday',pre))),
+                      levels = paste0('Pre: ',c('February','Super Tuesday','2020-03-10','2020-03-17'))),
+         post = factor(paste0('Post: ',ifelse(post == '2020-03-01','February',
+                                     ifelse(post == '2020-03-03','Super Tuesday',post))),
+                       levels = paste0('Post: ',c('February','Super Tuesday','2020-03-10','2020-03-17')))) %>%
+  ggplot(aes(x = x_2,y = delta_1,color = sig)) + 
+  geom_point(position = position_dodge(width = .2),size = 2) +
+  geom_errorbar(aes(ymin = lb,ymax = ub),width = .1,size = 1,
+                position = position_dodge(width = .2)) + 
+  geom_hline(yintercept = 0,linetype = 'dashed') +
+  facet_wrap(~pre+post,scales = 'free',ncol = 3) + 
+  scale_color_manual(name = 'Sig.',values = c('Sig' = 'black','Insig' = 'grey50')) + 
+  scale_linetype_manual(name = 'Model',
+                        values = c('basic' = 'dotdash','matching' = 'dashed','weighting' = 'solid')) +
+  scale_shape_manual(name = 'Model',
+                        values = c('basic' = 19,'matching' = 17,'weighting' = 15)) +
+  theme_ridges() + xlab('Comparison Periods') + ylab('Marginal Effect of Exposure') + 
+  theme(legend.position = 'bottom')
+dev.off()

29/replication_package/Code/Figure SI6, SI7, SI8, SI9, SI10.R

@@ -0,0 +1,348 @@
+# File name: SIfigure6_SIfigure7_SIfigure8_SIfigure9_SIfigure10.R
+# In: 
+#   - replication_data.RData
+# Out:
+#   - /Figures/SI_figure6.pdf
+#   - /Figures/SI_figure7.pdf
+#   - /Figures/SI_figure8.pdf
+#   - /Figures/SI_figure9.pdf
+#   - /Figures/SI_figure10.pdf
+
+require(lme4)
+require(lfe)
+require(MatchIt)
+require(WeightIt)
+require(tjbal)
+require(optmatch)
+require(stargazer)
+require(cobalt)
+require(tidyverse)
+require(gridExtra)
+require(ggridges)
+require(ggrepel)
+
+rm(list = ls())
+gc()
+
+
+
+
+####################################################################################################################### Loading data
+load('../Data/replication_data.RData')
+
+
+
+
+####################################################################################################################### Loading functions
+source('./helper_functions.R')
+
+
+
+
+
+
+
+####################################################################################################################### Preparing variables
+Y <- paste0(c("pct_","VAP_","pcttw_"),"sanders")
+D <- unlist(lapply(c("sc_","ln_"),function(x) paste0(x,paste0(c("county_","DMA_","state_"),"cases"))))
+FE <- c("0","DMA_CODE","date")
+covariates <- c("sc_CTY_LTHS","sc_CTY_CollUp",'caucus_switch','caucus',
+                "sc_CTY_LT30yo","sc_CTY_60Up",
+                "sc_CTY_Below_poverty_level_AGE_18_64","sc_CTY_Female_hher_no_husbandhh",
+                "sc_CTY_Unem_rate_pop_16_over","sc_CTY_Labor_Force_Part_Rate_pop_16_over",
+                "sc_CTY_Manufactur","sc_CTY_Md_inc_hhs",
+                "sc_CTY_POPPCT_RURAL","sc_CTY_Speak_only_English","sc_CTY_White","sc_CTY_Black_or_African_American",
+                "ln_CTY_tot_pop","sc_turnout_pct_20")
+
+
+
+
+
+
+####################################################################################################################### Looping across specifications
+finalDat$sc_turnout_pct_chg <- scale(finalDat$turnout_pct_chg)
+finalDat$sc_VAP_turnout_20 <- scale(finalDat$VAP_turnout_20)
+
+Y <- c("sc_turnout_pct_20","sc_turnout_pct_chg","sc_VAP_turnout_20")
+resTurnout <- list()
+for(y in Y) {
+  for(d in D) {
+    for(fe in FE) {
+      for(m in c("sc_CTY_LT30yo","sc_CTY_60Up","sc_CTY_Unem_rate_pop_16_over","sc_CTY_Old_age_dep_ratio","sc_CTY_POPPCT_RURAL","sc_CTY_Manufactur","sc_CTY_LTHS","sc_CTY_CollUp")) {
+        resTurnout[[y]][[d]][[fe]][[m]]$felm$coefs <- summary(tmp <- felm(as.formula(paste0(y," ~ ",d,"*",m," + ",
+                                                                                            paste(c(covariates)[-which(c(covariates) %in% c("sc_CTY_LT30yo","sc_CTY_60Up","sc_turnout_pct_20"))],collapse = "+")," | ",fe," | 0 | ",fe)),finalDat))$coefficients
+        resTurnout[[y]][[d]][[fe]][[m]]$felm$mfx <- interaction_plot_continuous(tmp)
+        if(fe != "0") {
+          resTurnout[[y]][[d]][[fe]][[m]]$lmer$coefs <- summary(tmp <- lmer(as.formula(paste0(y," ~ ",d,"*",m," + ",
+                                                                                              paste(c(covariates)[-which(c(covariates) %in% c("sc_CTY_LT30yo","sc_CTY_60Up","sc_turnout_pct_20"))],collapse = "+")," + (1| ",fe,")")),finalDat))$coefficients
+          resTurnout[[y]][[d]][[fe]][[m]]$lmer$mfx <- interaction_plot_continuous(tmp)
+          
+        }
+        
+      }
+    }
+  }
+}
+
+
+p1 <- data.frame(resTurnout$sc_turnout_pct_20$sc_DMA_cases$DMA_CODE$sc_CTY_60Up$felm$mfx) %>%
+  ggplot(aes(x = x_2,y = delta_1)) + 
+  geom_point() + 
+  geom_errorbar(aes(ymin = lb,ymax = ub),width = .1) + 
+  geom_hline(yintercept = 0,linetype = 'dashed') + 
+  xlab(expression(Younger %<->% Older)) +
+  ylab('MFX of Exposure on Turnout') + 
+  theme_ridges() + 
+  ggtitle(label = '',subtitle = '% of county 60 years or older')
+
+p2 <- data.frame(resTurnout$sc_turnout_pct_20$sc_DMA_cases$DMA_CODE$sc_CTY_LT30yo$felm$mfx) %>%
+  ggplot(aes(x = x_2,y = delta_1)) + 
+  geom_point() + 
+  geom_errorbar(aes(ymin = lb,ymax = ub),width = .1) + 
+  geom_hline(yintercept = 0,linetype = 'dashed') + 
+  xlab(expression(Older %<->% Younger)) +
+  ylab('MFX of Exposure on Turnout') + 
+  theme_ridges() + 
+  ggtitle(label = '',subtitle = '% of county 30 years or younger')
+
+
+# SI Figure 6: Turnout and age
+pdf('../Figures/SI_figure6.pdf',width = 8,height = 5)
+grid.arrange(p1,p2,ncol = 2)
+dev.off()
+
+
+
+# SI Figure 7
+pdf('../Figures/SI_figure7.pdf',width = 8,height = 5)
+finalDat %>%
+  group_by(date,stab) %>%
+  summarise(pct_sanders16 = mean(pct_sanders16,na.rm=T),
+            turnout_pct_20 = mean(turnout_pct_20*100,na.rm=T)) %>%
+  ggplot(aes(x = date,y = turnout_pct_20,size = pct_sanders16,label = stab)) + 
+  geom_point(alpha = .3) +
+  geom_text_repel(size = 4) + 
+  theme_ridges() + 
+  xlab('Date') + ylab('Turnout 2020 (%)') +
+  scale_size(name = 'Sanders 2016 Vote Share (%)') + 
+  theme(legend.position = 'bottom')
+dev.off()
+
+
+# SI Figure 8
+p1 <- finalDat %>%
+  filter(date %in% as.Date(c('2020-03-03',
+                             '2020-03-10',
+                             '2020-03-17'))) %>%
+  mutate(turnout_pct_20 = turnout_pct_20*100) %>%
+  ggplot(aes(x = pct_sanders16,y = turnout_pct_20)) + 
+  geom_point() + 
+  geom_smooth() + 
+  theme_ridges() + 
+  xlab('Sanders 2016 Vote Share (%)') + 
+  ylab('Turnout 2020 (%)') + ylim(c(0,50)) + 
+  ggtitle(label = '',subtitle = 'March Data')
+  
+p2 <- finalDat %>%
+  filter(date < as.Date('2020-03-03')) %>%
+  mutate(turnout_pct_20 = turnout_pct_20*100) %>%
+  ggplot(aes(x = pct_sanders16,y = turnout_pct_20)) + 
+  geom_point() + 
+  geom_smooth() + 
+  theme_ridges() + 
+  xlab('Sanders 2016 Vote Share (%)') + 
+  ylab('Turnout 2020 (%)') + ylim(c(0,50)) + 
+  ggtitle(label = '',subtitle = 'Pre-March Data')
+
+pdf('../Figures/SI_figure8.pdf',width = 9,height = 5)
+grid.arrange(p2,p1,ncol = 2)
+dev.off()
+
+
+
+
+# SI Figure 9
+finalDat$post <- ifelse(finalDat$date > as.Date('2020-02-29'),1,0)
+toplot <- data.frame(summary(felm(as.formula(paste0("turnout_pct_20 ~ scale(pct_sanders16)*factor(date) + ",
+                                                    paste(covariates[c(1:2,5:17)],collapse = '+'),'| 0 | 0 | 0')),finalDat))$coefficients)
+colnames(toplot) <- c('est','se','tstat','pval')
+toplot$vars <- rownames(toplot)
+rownames(toplot) <- NULL
+
+
+toplot %>%
+  filter(grepl(':factor',vars)) %>%
+  mutate(date = as.Date(gsub('scale.*?date\\)','',vars))) %>%
+  ggplot(aes(x = date,y = est)) + 
+  geom_point() + 
+  geom_errorbar(aes(ymin = est - 2*se,ymax = est+2*se)) + 
+  geom_hline(yintercept = 0,linetype = 'dashed')
+
+
+finalDat$dateTmp <- as.Date(ifelse(finalDat$Date %in% as.Date(c('2020-02-11','2020-02-22')),as.Date('2020-02-22'),finalDat$Date),origin = '1970-01-01')
+toplot <- NULL
+for(d in unique(finalDat$Date)) {
+  if(nrow(finalDat %>% filter(Date == d)) < 50) {
+    covs <- covariates[c(2,6,9,15)]
+  } else {
+    covs <- covariates[c(2,6,9,15)]
+  }
+  tmp <- data.frame(t(summary(felm(as.formula(paste0("scale(turnout_pct_20) ~ scale(pct_sanders16) + ",
+                                                     paste(covs,collapse = '+'),'| 0 | 0 | 0')),finalDat %>%
+                                     filter(Date == d)))$coefficients[2,]))
+  colnames(tmp) <- c('est','se','tstat','pval')
+  tmp$date <- as.Date(d,origin = '1970-01-01')
+  toplot <- bind_rows(toplot,tmp)
+}
+
+
+labs <- finalDat %>%
+  select(stab,Date) %>% distinct()
+toplot$lab <- NA
+for(d in unique(toplot$date)) {
+  toplot$lab[which(toplot$date == d)] <- paste(unique(labs$stab[which(labs$Date == d)])[order(unique(labs$stab[which(labs$Date == d)]))],collapse = '\n')
+}
+
+
+pdf('../Figures/SI_figure9.pdf',width = 8,height = 5)
+toplot %>%
+  filter(date < as.Date('2020-04-15')) %>%
+  ggplot(aes(x = date,y = est,label = lab,color = ifelse(lab == 'SC','SC','black'))) + 
+  geom_point() + 
+  geom_errorbar(aes(ymin = est - 2*se,ymax = est+2*se),width = .5) + 
+  geom_hline(yintercept = 0,linetype = 'dashed') + 
+  theme_ridges() + 
+  xlab('Election Date') + ylab('Relationship between Sanders 2016 and Turnout') + 
+  scale_color_manual(values = c('SC' = 'red','black' = 'black')) +
+  geom_text_repel(size = 3,hjust = 0,vjust = .5,alpha = .8) + 
+  theme(legend.position = 'none')
+dev.off()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# SI Figure 10
+set.seed(123)
+Y <- c('pct_sanders16')
+
+D <- unlist(lapply(c("sc_","ln_"),function(x) paste0(x,paste0(c("county_","DMA_","state_"),"cases"))))
+FE <- c("0","DMA_CODE","stab")
+covariates <- c("sc_CTY_LTHS","sc_CTY_CollUp",'caucus_switch','caucus',
+                "sc_CTY_LT30yo","sc_CTY_60Up",
+                "sc_CTY_Below_poverty_level_AGE_18_64","sc_CTY_Female_hher_no_husbandhh",
+                "sc_CTY_Unem_rate_pop_16_over","sc_CTY_Labor_Force_Part_Rate_pop_16_over",
+                "sc_CTY_Manufactur","sc_CTY_Md_inc_hhs",
+                "sc_CTY_POPPCT_RURAL","sc_CTY_Speak_only_English",
+                "sc_CTY_White","sc_CTY_Black_or_African_American",
+                "ln_CTY_tot_pop","sc_turnout_pct_20")
+
+
+resRegs <- resBal <- resTabs <- list()
+for(y in Y) {
+  for(d in D[c(1:3)]) {
+    
+    for(fe in FE) {
+      stars <- list()
+      for(dateThresh in c("2020-01-01","2020-03-01")) {
+        if(dateThresh == '2020-03-01') {
+          covs <- covariates[-which(covariates == 'caucus')]
+        } else {
+          covs <- covariates
+        }
+        if(!is.null(resRegs[[y]][[d]][[fe]][[dateThresh]]$basic$felm$cont)) { next }
+        finalDat$treatBin <- ifelse(finalDat[[gsub("sc_|ln_","",d)]] > 1,1,0)
+        foranal.weight <- finalDat %>% select(y,d,treatBin,gsub("sc_|ln_","",d),DMA_CODE,date,stab,covs) %>% 
+          filter(complete.cases(.),date >= as.Date(dateThresh))
+        
+        m.out <- matchit(formula = as.formula(paste("treatBin ~ ",paste(covs,collapse = " + "))),
+                         data = foranal.weight,
+                         method = "nearest",
+                         distance = "mahalanobis")
+        
+        m.data <- match.data(m.out)
+        W.out <- weightit(formula(paste0("treatBin ~ ",paste(covs,collapse = " + "))),
+                          data = foranal.weight, estimand = "ATT", method = "cbps")
+        
+        # Balance tables
+        balt.pre <- bal.tab(formula(paste0("treatBin ~ ",paste(covs,collapse = " + "))),
+                            data = foranal.weight, estimand = "ATT",m.threshold = .05)
+        
+        balt.post <- bal.tab(W.out, m.threshold = .05, disp.v.ratio = TRUE)
+        unm <- data.frame(Covs = rownames(balt.pre$Balance),
+                          balt.pre$Balance %>% mutate(Diff_Unm = round(Diff.Un,2),
+                                                      Bal_Test_Unm = M.Threshold.Un)) %>%
+          select(Covs,Diff_Unm,Bal_Test_Unm)
+        
+        match <- data.frame(Covs = rownames(balt.post$Balance),
+                            balt.post$Balance %>% mutate(Diff_Match = round(Diff.Adj,2),
+                                                         Bal_Test_Match = M.Threshold)) %>%
+          select(Covs,Diff_Match,Bal_Test_Match)
+        
+        
+        # Basic
+        resRegs[[y]][[d]][[fe]][[dateThresh]]$basic$felm$cont <- summary(stars$cont[[paste0(dateThresh,"_1")]] <- felm(as.formula(paste0("scale(",y,") ~ ",d," + ",paste(c(covs),collapse = " + ")," | ",fe," | 0 | ",fe)),finalDat %>% filter(date >= as.Date(dateThresh))))$coefficients
+        resRegs[[y]][[d]][[fe]][[dateThresh]]$basic$felm$bin <- summary(stars$bin[[paste0(dateThresh,"_1")]] <- felm(as.formula(paste0("scale(",y,") ~ treatBin + ",paste(c(covs),collapse = " + ")," | ",fe," | 0 | ",fe)),finalDat %>% filter(date >= as.Date(dateThresh))))$coefficients
+        
+        # Matching
+        resRegs[[y]][[d]][[fe]][[dateThresh]]$matching$felm$cont <- summary(stars$cont[[paste0(dateThresh,"_2")]] <- felm(as.formula(paste0("scale(",y,") ~ ",d," + ",paste(c(covs),collapse = " + ")," | ",fe," | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+        resRegs[[y]][[d]][[fe]][[dateThresh]]$matching$felm$bin <- summary(stars$bin[[paste0(dateThresh,"_2")]] <- felm(as.formula(paste0("scale(",y,") ~ treatBin + ",paste(c(covs),collapse = " + ")," | ",fe," | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+        
+        # Weighting
+        resRegs[[y]][[d]][[fe]][[dateThresh]]$weighting$felm$cont <- summary(stars$cont[[paste0(dateThresh,"_3")]] <- felm(as.formula(paste0("scale(",y,") ~ ",d," + ",paste(c(covs),collapse = " + ")," | ",fe," | 0 | ",fe)),foranal.weight,weights = W.out$weights))$coefficients
+        resRegs[[y]][[d]][[fe]][[dateThresh]]$weighting$felm$bin <- summary(stars$bin[[paste0(dateThresh,"_3")]] <- felm(as.formula(paste0("scale(",y,") ~ treatBin + ",paste(c(covs),collapse = " + ")," | ",fe," | 0 | ",fe)),foranal.weight,weights = W.out$weights))$coefficients
+        
+        if(fe != "0") {
+          resRegs[[y]][[d]][[fe]][[dateThresh]]$basic$lmer <- summary(lmer(as.formula(paste0("scale(",y,") ~ ",d," + ",paste(c(covs),collapse = " + ")," + (1| ",fe,")")),finalDat %>% filter(date >= as.Date(dateThresh))))$coefficients
+          resRegs[[y]][[d]][[fe]][[dateThresh]]$matching$lmer <- summary(lmer(as.formula(paste0("scale(",y,") ~ ",d," + ",paste(c(covs),collapse = " + ")," + (1| ",fe,")")),m.data,weights = m.data$weights))$coefficients
+          resRegs[[y]][[d]][[fe]][[dateThresh]]$weighting$lmer <- summary(lmer(as.formula(paste0("scale(",y,") ~ ",d," + ",paste(c(covs),collapse = " + ")," + (1| ",fe,")")),foranal.weight,weights = W.out$weights))$coefficients
+        }
+      }
+    }
+    cat(y,"\n")
+  }
+}
+
+
+toplot <- NULL
+for(fe in names(resRegs$pct_sanders16$sc_DMA_cases)) {
+  for(mod in c('basic','matching','weighting')) {
+    for(meas in c('bin','cont')) {
+      tmp <- resRegs$pct_sanders16$sc_DMA_cases[[fe]]$`2020-01-01`[[mod]]$felm[[meas]][2,]
+      if(is.null(tmp)) { next }
+      tmp <- data.frame(t(tmp))
+      colnames(tmp) <- c('est','se','tstat','pval')
+      tmp$fe <- fe
+      tmp$mod <- mod
+      tmp$meas <- meas
+      toplot <- bind_rows(toplot,tmp)
+    }
+  }
+}
+
+
+pdf('../Figures/SI_figure10.pdf',width = 8,height = 5)
+toplot %>%
+  filter(meas != 'cont') %>%
+  mutate(fe = factor(ifelse(fe == 0,'None',
+                     ifelse(fe == 'DMA_CODE','DMA','State')),levels = c('None','DMA','State'))) %>%
+  ggplot(aes(x = fe,y = est,color = mod)) + 
+  geom_point(position = position_dodge(width = .2)) + 
+  geom_errorbar(aes(ymin = est - 2*se,ymax = est+2*se),width = .1,
+                position = position_dodge(width = .2)) + 
+  geom_hline(yintercept = 0,linetype = 'dashed') + 
+  scale_color_manual(name = 'Model',values = c('basic' = 'grey70',
+                                               'matching' = 'grey40',
+                                               'weighting' = 'black')) +
+  xlab('Fixed Effect') + ylab('Effect of Covid Exposure on Bernie 2016') + 
+  theme_ridges()
+dev.off()

29/replication_package/Code/LOG/log_Figure 2, 3, Table SI1.txt

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+size 7395

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+size 16586

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+size 13355

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+size 63166

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29/replication_package/Code/LOG/log_zzSI_robust_prep.txt

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+size 18906

29/replication_package/Code/MASTER.R

@@ -0,0 +1,13 @@
+# Master script
+for(f in list.files('./')) {
+  if(grepl('helper_functions|MASTER|LOG',f)) { next }
+  if(file.exists(paste0('./LOG/log_',gsub('.R','.txt',f)))) { next }
+  # stop()
+  con <- file(paste0('./LOG/log_',gsub('.R','.txt',f)))
+  cat(f,'\n')
+  sink(con,append = TRUE)
+  sink(con,append = TRUE,type = 'message')
+  source(f,echo = T,max.deparse.length = 10000)
+  sink()
+  sink(type = 'message')
+}

29/replication_package/Code/Table 1, SI3, SI4, SI5, Figure SI13, SI14.R

@@ -0,0 +1,349 @@
+# File name: table1_SItable3_SItable5_SItable4_SIfigure13_SIfigure14.R
+# In: 
+#   - replication_data.RData
+# Out: 
+#   - /Tables/table1.tex
+#   - /Tables/SI-table3.tex
+#   - /Figures/SI_figure13.pdf
+#   - /Tables/SI-table4.tex
+#   - /Tables/SI-table5.tex
+#   - /Figures/SI_figure14.pdf
+
+require(lme4)
+require(lfe)
+require(MatchIt)
+require(WeightIt)
+require(tjbal)
+require(optmatch)
+require(stargazer)
+require(cobalt)
+require(tidyverse)
+
+rm(list = ls())
+gc()
+####################################################################################################################### Loading data
+load('../Data/replication_data.RData')
+
+
+####################################################################################################################### Loading functions
+source('./helper_functions.R')
+
+
+
+
+
+####################################################################################################################### Preparing variables
+Y <- c('pct_sanders16',paste0(c("pct_","VAP_","pcttw_"),"sanders"))
+D <- unlist(lapply(c("sc_","ln_"),function(x) paste0(x,paste0(c("county_","DMA_","state_"),"cases"))))
+FE <- c("0","DMA_CODE","date")
+covariates <- c("sc_CTY_LTHS","sc_CTY_CollUp",
+                "sc_CTY_LT30yo","sc_CTY_60Up",
+                "sc_CTY_Below_poverty_level_AGE_18_64","sc_CTY_Female_hher_no_husbandhh",
+                "sc_CTY_Unem_rate_pop_16_over","sc_CTY_Labor_Force_Part_Rate_pop_16_over",
+                "sc_CTY_Manufactur","sc_CTY_Md_inc_hhs",
+                "sc_CTY_POPPCT_RURAL","sc_CTY_Speak_only_English","sc_CTY_White","sc_CTY_Black_or_African_American",
+                "ln_CTY_tot_pop","sc_turnout_pct_20",'caucus_switch','caucus')
+
+
+
+# Function for generating matched data + weighted data
+wgtMatchFun <- function(dat,formMatch,formWgt,matchMethod = 'nearest',wgtMethod = 'cbps') {
+  m.out <- matchit(formula = formula(formMatch),
+                   data = dat,
+                   method = matchMethod,
+                   distance = "mahalanobis")
+  
+  m.data <- match.data(m.out)
+  W.out <- weightit(formula = formula(formWgt),
+                    data = dat, 
+                    estimand = "ATT", 
+                    method = wgtMethod)
+  
+  # Balance tables
+  balt.pre <- bal.tab(formula(formWgt),
+                      data = dat, 
+                      estimand = "ATT",
+                      m.threshold = .05)
+  
+  balt.post <- bal.tab(W.out, m.threshold = .05, disp.v.ratio = TRUE)
+  unm <- data.frame(Covs = gsub('_$','',rownames(balt.pre$Balance)),
+                    balt.pre$Balance %>% 
+                      mutate(Diff_Unm = round(Diff.Un,2),
+                             Bal_Test_Unm = M.Threshold.Un)) %>%
+    select(Covs,Diff_Unm,Bal_Test_Unm)
+  
+  match <- data.frame(Covs = rownames(balt.post$Balance),
+                      balt.post$Balance %>% mutate(Diff_Match = round(Diff.Adj,2),
+                                                   Bal_Test_Match = M.Threshold)) %>%
+    select(Covs,Diff_Match,Bal_Test_Match)
+  
+  
+  balTab <- match %>% left_join(unm) %>% select(Covs,Diff_Unm,Bal_Test_Unm,Diff_Match,Bal_Test_Match)
+  balPlot <- qqprep(x = m.out)
+  
+ return(list(balTab = balTab,
+             m.data = m.data,
+             W.out = W.out,
+             dat = dat,
+             balPlot = balPlot))
+}
+
+
+set.seed(123)
+finalDat$treatBin <- ifelse(finalDat$DMA_cases > 1,1,0)
+
+# Full Sample Regressions: Nearest neighbor + CBPS
+summary(mFullRaw <- felm(as.formula(paste0("scale(pcttw_sanders) ~ treatBin + ",
+                         paste(c(covariates,"pcttw_sanders16"),collapse = " + "),
+                         " | DMA_CODE | 0 | DMA_CODE")),
+                         finalDat))
+
+
+foranal.weight <- finalDat %>% 
+  select(pcttw_sanders,DMA_cases,treatBin,DMA_CODE,date,stab,covariates,pcttw_sanders16) %>% 
+  filter(complete.cases(.))
+
+fullPrepped <- wgtMatchFun(dat = foranal.weight,
+                           formMatch = paste0('treatBin ~ ',paste(covariates,collapse = " + ")),
+                           formWgt = paste0('treatBin ~ ',paste(covariates,collapse = " + ")),
+                           matchMethod = 'nearest',
+                           wgtMethod = 'cbps')
+
+summary(mFullMatch <- felm(as.formula(paste0("scale(pcttw_sanders) ~ treatBin + ",
+                                        paste(c(covariates,"pcttw_sanders16"),collapse = " + "),
+                                        " | DMA_CODE | 0 | DMA_CODE")),
+                           fullPrepped$m.data,weights = fullPrepped$m.data$weights))
+
+
+summary(mFullWgt <- felm(as.formula(paste0("scale(pcttw_sanders) ~ treatBin + ",
+                                             paste(c(covariates,"pcttw_sanders16"),collapse = " + "),
+                                           " | DMA_CODE | 0 | DMA_CODE")),
+                         fullPrepped$dat,weights = fullPrepped$W.out$weights))
+
+
+# March + April Regressions
+foranal.weight <- finalDat %>% 
+  select(pcttw_sanders,DMA_cases,treatBin,
+         DMA_CODE,date,stab,covariates,
+         pcttw_sanders16) %>% 
+  filter(complete.cases(.),
+         date >= as.Date('2020-03-01'))
+
+covariates <- covariates[-which(covariates == 'caucus')]
+
+marPrepped <- wgtMatchFun(dat = foranal.weight,
+                          formMatch = paste0('treatBin ~ ',paste(covariates,collapse = " + ")),
+                          formWgt = paste0('treatBin ~ ',paste(covariates,collapse = " + ")),
+                          matchMethod = 'nearest',
+                          wgtMethod = 'cbps')
+
+summary(mMarRaw <- felm(as.formula(paste0("scale(pcttw_sanders) ~ treatBin + ",
+                                           paste(c(covariates,"pcttw_sanders16"),collapse = " + ")," | DMA_CODE | 0 | DMA_CODE")),
+                         finalDat %>% filter(date >= as.Date("2020-03-01"))))
+
+summary(mMarMatch <- felm(as.formula(paste0("scale(pcttw_sanders) ~ treatBin + ",
+                                             paste(c(covariates,"pcttw_sanders16"),collapse = " + ")," | DMA_CODE | 0 | DMA_CODE")),
+                          marPrepped$m.data,weights = marPrepped$m.data$weights))
+
+
+summary(mMarWgt <- felm(as.formula(paste0("scale(pcttw_sanders) ~ treatBin + ",
+                                           paste(c(covariates,"pcttw_sanders16"),collapse = " + ")," | DMA_CODE | 0 | DMA_CODE")),
+                        marPrepped$dat,weights = marPrepped$W.out$weights))
+
+
+# Table 1
+covars <- rownames(mFullRaw$coefficients)
+labs <- gsub('Treatbin','Exposure Dummy',gsub('Turnout pct 20','Turnout 2020',gsub('Pcttw sanders16','Sanders 2016',gsub('Caucus$','Caucus dummy',gsub("Lths","LTHS",gsub("Collup","Coll. Up",gsub("Md inc HH","Med HH Inc",gsub("Sc ","",gsub(" or african american|band$","",Hmisc::capitalize(trimws(gsub("manufactur","manufacturing",gsub("hher|hhs","HH",gsub("labor force part rate","LFPR",gsub("bachelor s","bachelor's",gsub("age 18 64|pop 16 over|hh$|poppct","",gsub("\\_"," ",gsub("^sc_|^.*cty\\_","",tolower(covars)))))))))))))))))))
+
+prepTable <- function(tab,keeps = c('Exposure Dummy','Turnout 2020','Sanders 2016',
+                                    'Caucus dummy','Caucus switch'),tops = 1:14,bottoms = (length(tab) - 8):length(tab)) {
+  inds <- NULL
+  for(keep in keeps) {
+    tmpInds <- which(grepl(keep,tab))
+    for(i in tmpInds) {
+      inds <- c(inds,i:(i+2))
+    }
+  }
+  inds <- unique(c(tops,inds,bottoms))
+  
+  return(tab[inds])
+}
+
+tex <- stargazer(mFullRaw,mFullMatch,mFullWgt,
+          mMarRaw,mMarMatch,mMarWgt,
+          keep.stat = c("n","rsq"),
+          star.cutoffs = c(.1,.05,.01,.001),
+          star.char = c('\\dag','*','**','***'),
+          covariate.labels = labs)
+
+cat(paste(prepTable(tex,keeps = c('Exposure Dummy','Turnout 2020','Sanders 2016',
+                            'Caucus switch','Caucus dummy')),
+          collapse = '\n'),file = '../Tables/table1.tex')
+
+
+
+# SI Table 3
+stargazer(marPrepped$balTab,summary = F,out = '../Tables/SI-table3.tex')
+
+
+
+# SI Figure 13
+toplot <- fullPrepped$balPlot$toplot
+# rr <- marPrepped$balPlot$rr
+
+dist2d <- function(x,y) {
+  m <- cbind(c(-1,-1),c(x,y))
+  abs(det(m))/sqrt(sum(c(-1,-1)^2))
+}
+vdist2d <- Vectorize(dist2d,vectorize.args = c("x","y"))
+toplot$type <- as.character(toplot$type)
+toplot$cov <- as.character(toplot$cov)
+toplot <- toplot %>% bind_rows(toplot %>% group_by(cov) %>% summarise(x = min(x,y),y = min(x,y)),
+                               toplot %>% group_by(cov) %>% summarise(x = max(x,y),y = max(x,y)))
+
+
+toplot$cov <- Hmisc::capitalize(tolower(trimws(gsub("Manufactur","Manufacturing",gsub("Labor Force Part Rate","LFPR",gsub("hher|hhs","HH",gsub("Bachelor s","Bachelor's",gsub("_"," ",gsub("CTY_|AGE_18_64|pop_16_over|hh$|POPPCT","",toplot$cov)))))))))
+toplot %>% group_by(type,cov) %>%
+  summarise(eucdist = mean(vdist2d(x = x,y= y))) %>%
+  filter(!is.na(type)) -> sum.euc
+
+
+
+pdf('../Figures/SI_figure13.pdf',width = 7,height = 7)
+toplot %>%
+  filter(!grepl('caucus',tolower(cov))) %>%
+  ggplot(aes(x = x,y = y,color = factor(type,levels = c("Raw","Matched")),
+             text = paste0(type,"\nEuc Dist = ",round(vdist2d(x = x,y = y),2)))) +
+  geom_point(alpha = .3,size = .5) +
+  geom_abline() +
+  geom_abline(aes(intercept = (rrlb - rrub) * 0.1,slope = 1),linetype = "dashed") +
+  geom_abline(aes(intercept = -(rrlb - rrub) * 0.1,slope = 1),linetype = "dashed") +
+  theme_bw() +
+  xlab("Control") + ylab("Treated") +
+  theme(legend.position = "bottom") +
+  facet_wrap(~cov,scales = "free") +
+  scale_color_discrete("",na.translate = F) +
+  guides(color = guide_legend(override.aes = list(size = 5,alpha = 1)))
+dev.off()
+
+
+
+
+
+
+
+
+# SI Tables 4-5 and Figure 14
+# Full Sample Regressions: CEM + optweights
+summary(mFullRaw <- felm(as.formula(paste0("scale(pcttw_sanders) ~ treatBin + ",
+                                           paste(c(covariates,"pcttw_sanders16"),collapse = " + "),
+                                           " | DMA_CODE | 0 | DMA_CODE")),
+                         finalDat))
+
+
+foranal.weight <- finalDat %>% select(pcttw_sanders,DMA_cases,treatBin,DMA_CODE,date,stab,covariates,pcttw_sanders16) %>% filter(complete.cases(.))
+fullPrepped <- wgtMatchFun(dat = foranal.weight,
+                           formMatch = paste0('treatBin ~ ',paste(covariates[c(4,2,10,12,7,13)],collapse = " + ")),
+                           formWgt = paste0('treatBin ~ ',paste(covariates,collapse = " + ")),
+                           matchMethod = 'cem',
+                           wgtMethod = 'optweight')
+
+summary(mFullMatch <- felm(as.formula(paste0("scale(pcttw_sanders) ~ treatBin + ",
+                                             paste(c(covariates,"pcttw_sanders16"),collapse = " + "),
+                                             " | DMA_CODE | 0 | DMA_CODE")),
+                           fullPrepped$m.data,weights = fullPrepped$m.data$weights))
+
+
+summary(mFullWgt <- felm(as.formula(paste0("scale(pcttw_sanders) ~ treatBin + ",
+                                           paste(c(covariates,"pcttw_sanders16"),collapse = " + "),
+                                           " | DMA_CODE | 0 | DMA_CODE")),
+                         fullPrepped$dat,weights = fullPrepped$W.out$weights))
+
+
+# March + April Regressions
+foranal.weight <- finalDat %>% 
+  select(pcttw_sanders,DMA_cases,treatBin,
+         DMA_CODE,date,stab,covariates,
+         pcttw_sanders16) %>% 
+  filter(complete.cases(.),
+         date >= as.Date('2020-03-01'))
+
+covs <- covariates[-which(grepl('caucus',covariates))]
+
+marPrepped <- wgtMatchFun(dat = foranal.weight,
+                          formMatch = paste0('treatBin ~ ',paste(covs[c(4,2,10,12,7,13)],collapse = " + ")), # CEM fails with the full set of covariates. See SI page 13-15 for discussion.
+                          formWgt = paste0('treatBin ~ ',paste(covs,collapse = " + ")),
+                          matchMethod = 'cem',
+                          wgtMethod = 'optweight')
+
+summary(mMarRaw <- felm(as.formula(paste0("scale(pcttw_sanders) ~ treatBin + ",
+                                          paste(c(covs,"pcttw_sanders16"),collapse = " + "),
+                                          " | DMA_CODE | 0 | DMA_CODE")),
+                        finalDat %>% filter(date >= as.Date("2020-03-01"))))
+
+summary(mMarMatch <- felm(as.formula(paste0("scale(pcttw_sanders) ~ treatBin + ",
+                                            paste(c(covs,"pcttw_sanders16"),collapse = " + "),
+                                            " | DMA_CODE | 0 | DMA_CODE")),
+                          marPrepped$m.data,weights = marPrepped$m.data$weights))
+
+
+summary(mMarWgt <- felm(as.formula(paste0("scale(pcttw_sanders) ~ treatBin + ",
+                                          paste(c(covs,"pcttw_sanders16"),collapse = " + "),
+                                          " | DMA_CODE | 0 | DMA_CODE")),
+                        marPrepped$dat,weights = marPrepped$W.out$weights))
+
+
+# SI Table 4
+stargazer(fullPrepped$balTab,summary = F,out = '../Tables/SI-table4.tex')
+
+
+# SI Table 5
+covars <- rownames(mFullRaw$coefficients)
+labs <- gsub('Treatbin','Exposure Dummy',gsub('Turnout pct 20','Turnout 2020',gsub('Pcttw sander16','Sanders 2016',gsub('Caucus$','Caucus dummy',gsub("Lths","LTHS",gsub("Collup","Coll. Up",gsub("Md inc HH","Med HH Inc",gsub("Sc ","",gsub(" or african american|band$","",Hmisc::capitalize(trimws(gsub("manufactur","manufacturing",gsub("hher|hhs","HH",gsub("labor force part rate","LFPR",gsub("bachelor s","bachelor's",gsub("age 18 64|pop 16 over|hh$|poppct","",gsub("\\_"," ",gsub("^sc_|^.*cty\\_","",tolower(covars)))))))))))))))))))
+
+stargazer(mFullRaw,mFullMatch,mFullWgt,
+                 mMarRaw,mMarMatch,mMarWgt,
+                 keep.stat = c("n","rsq"),
+                 star.cutoffs = c(.1,.05,.01,.001),
+                 star.char = c('\\dag','*','**','***'),
+                 covariate.labels = labs,out = '../Tables/SI-table5.tex')
+
+
+
+
+# SI Figure 14
+toplot <- fullPrepped$balPlot$toplot
+
+dist2d <- function(x,y) {
+  m <- cbind(c(-1,-1),c(x,y))
+  abs(det(m))/sqrt(sum(c(-1,-1)^2))
+}
+vdist2d <- Vectorize(dist2d,vectorize.args = c("x","y"))
+toplot$type <- as.character(toplot$type)
+toplot$cov <- as.character(toplot$cov)
+toplot <- toplot %>% bind_rows(toplot %>% group_by(cov) %>% summarise(x = min(x,y),y = min(x,y)),
+                               toplot %>% group_by(cov) %>% summarise(x = max(x,y),y = max(x,y)))
+
+
+toplot$cov <- Hmisc::capitalize(tolower(trimws(gsub("Manufactur","Manufacturing",gsub("Labor Force Part Rate","LFPR",gsub("hher|hhs","HH",gsub("Bachelor s","Bachelor's",gsub("_"," ",gsub("CTY_|AGE_18_64|pop_16_over|hh$|POPPCT","",toplot$cov)))))))))
+toplot %>% group_by(type,cov) %>%
+  summarise(eucdist = mean(vdist2d(x = x,y= y))) %>%
+  filter(!is.na(type)) -> sum.euc
+
+
+
+pdf('../Figures/SI_figure14.pdf',width = 7,height = 5)
+toplot %>%
+  filter(!grepl('caucus',tolower(cov))) %>%
+  ggplot(aes(x = x,y = y,color = factor(type,levels = c("Raw","Matched")),text = paste0(type,"\nEuc Dist = ",round(vdist2d(x = x,y = y),2)))) +
+  geom_point(alpha = .3,size = .5) +
+  geom_abline() +
+  geom_abline(aes(intercept = (rrlb - rrub) * 0.1,slope = 1),linetype = "dashed") +
+  geom_abline(aes(intercept = -(rrlb - rrub) * 0.1,slope = 1),linetype = "dashed") +
+  theme_bw() +
+  xlab("Control") + ylab("Treated") +
+  theme(legend.position = "bottom") +
+  facet_wrap(~cov,scales = "free") +
+  scale_color_discrete("",na.translate = F) +
+  guides(color = guide_legend(override.aes = list(size = 5,alpha = 1)))
+dev.off()

29/replication_package/Code/Table 2.R

@@ -0,0 +1,82 @@
+# File name: table2.R
+# In: 
+#   - /Data/survey_experiment_data.RData
+# Out: 
+#   - /Tables/table2.tex
+
+rm(list = ls())
+gc()
+require(tidyverse)
+
+load('../Data/survey_experiment_data.RData')
+# Basic test: Does pessimistic treatment increase support for mainstream candidate?
+summary(main <- lm(scale(y) ~ z + factor(polAge) + factor(polOcc) + factor(polPlat) + 
+             factor(age) + factor(sex) + factor(hisp) + white + black + asian + factor(pid),
+             foranal %>%
+               filter(duration > 180) %>%
+               mutate(sex = factor(sex,levels = c("Neither male nor female describes me accurately OR prefer not to say","Female","Male")),
+                      polPlat = factor(polPlat,levels = c("none","hlth","educ")),
+                      pid = factor(pid,levels = c("Strong Democrat","Democrat","Lean Democrat","Independent","Lean Republican","Republican","Strong Republican")),
+                      covid_self = factor(covid_self,levels = c("No","Maybe","Yes")),
+                      covid_pers = factor(covid_pers,levels = c("No","Maybe","Yes")))))
+
+summary(biv <- lm(yBin ~ z,
+                   foranal %>%
+                     filter(duration > 0) %>%
+                     mutate(sex = factor(sex,levels = c("Neither male nor female describes me accurately OR prefer not to say","Female","Male")),
+                            polPlat = factor(polPlat,levels = c("none","hlth","educ")),
+                            pid = factor(pid,levels = c("Strong Democrat","Democrat","Lean Democrat","Independent","Lean Republican","Republican","Strong Republican")),
+                            covid_self = factor(covid_self,levels = c("No","Maybe","Yes")),
+                            covid_pers = factor(covid_pers,levels = c("No","Maybe","Yes")))))
+
+summary(ind <- lm(yBin ~ z + 
+                    factor(age) + factor(sex) + factor(hisp) + white + black + asian + factor(pid),
+                  foranal %>%
+                    filter(duration > 0) %>%
+                    mutate(sex = factor(sex,levels = c("Neither male nor female describes me accurately OR prefer not to say","Female","Male")),
+                           polPlat = factor(polPlat,levels = c("none","hlth","educ")),
+                           pid = factor(pid,levels = c("Strong Democrat","Democrat","Lean Democrat","Independent","Lean Republican","Republican","Strong Republican")),
+                           covid_self = factor(covid_self,levels = c("No","Maybe","Yes")),
+                           covid_pers = factor(covid_pers,levels = c("No","Maybe","Yes")))))
+
+summary(indcov <- lm(yBin ~ z + 
+                    factor(age) + factor(sex) + factor(hisp) + white + black + asian + factor(pid),
+                  foranal %>%
+                    filter(duration > 0) %>%
+                    mutate(sex = factor(sex,levels = c("Neither male nor female describes me accurately OR prefer not to say","Female","Male")),
+                           polPlat = factor(polPlat,levels = c("none","hlth","educ")),
+                           pid = factor(pid,levels = c("Strong Democrat","Democrat","Lean Democrat","Independent","Lean Republican","Republican","Strong Republican")),
+                           covid_self = factor(covid_self,levels = c("No","Maybe","Yes")),
+                           covid_pers = factor(covid_pers,levels = c("No","Maybe","Yes")))))
+
+summary(full <- lm(yBin ~ z + 
+                       factor(age) + factor(sex) + factor(hisp) + white + black + asian + factor(pid) + 
+                     factor(polAge) + factor(polOcc) + factor(polPlat),
+                     foranal %>%
+                       filter(duration > 0) %>%
+                       mutate(sex = factor(sex,levels = c("Neither male nor female describes me accurately OR prefer not to say","Female","Male")),
+                              polPlat = factor(polPlat,levels = c("none","hlth","educ")),
+                              pid = factor(pid,levels = c("Strong Democrat","Democrat","Lean Democrat","Independent","Lean Republican","Republican","Strong Republican")),
+                              covid_self = factor(covid_self,levels = c("No","Maybe","Yes")),
+                              covid_pers = factor(covid_pers,levels = c("No","Maybe","Yes")))))
+
+
+
+summary(attentive <- lm(yBin ~ z + 
+                     factor(age) + factor(sex) + factor(hisp) + white + black + asian + factor(pid) + 
+                     factor(polAge) + factor(polOcc) + factor(polPlat),
+                   foranal %>%
+                     filter(duration > 150) %>%
+                     mutate(sex = factor(sex,levels = c("Neither male nor female describes me accurately OR prefer not to say","Female","Male")),
+                            polPlat = factor(polPlat,levels = c("none","hlth","educ")),
+                            pid = factor(pid,levels = c("Strong Democrat","Democrat","Lean Democrat","Independent","Lean Republican","Republican","Strong Republican")),
+                            covid_self = factor(covid_self,levels = c("No","Maybe","Yes")),
+                            covid_pers = factor(covid_pers,levels = c("No","Maybe","Yes")))))
+require(stargazer)
+stargazer(biv,ind,full,attentive,keep.stat = c("n","rsq"),keep = "pid|covid|z",
+          covariate.labels = c("Anxiety Prime","Democrat","Lean Dem","Independent","Lean GOP","Republican","Strong GOP"),
+          star.char = c('\\dag','*','**','***'),
+          star.cutoffs = c(.1,.05,.01,.001),
+          out = "../Tables/table2.tex")
+
+

29/replication_package/Code/Table 3, Figure SI18.R

@@ -0,0 +1,109 @@
+# File name: table3_SIfigure18.R
+# In: 
+#   - /Data/primary_data.RData: Primary election results for the house of representatives, linked with district-level cases and deaths
+# Out: 
+#   - /Tables/table3.tex
+#   - /Figures/SI_figure18.pdf
+
+require(lme4)
+require(lfe)
+require(MatchIt)
+require(WeightIt)
+require(tjbal)
+require(optmatch)
+require(stargazer)
+require(cobalt)
+require(tidyverse)
+require(stargazer)
+require(gridExtra)
+require(ggridges)
+require(ggrepel)
+
+rm(list = ls())
+gc()
+
+
+load('../Data/primary_data.RData')
+
+# Table 3
+summary(mVScaseEXT <- felm(voteSh ~ extreme*log(casesSum+1) | stcd | 0 | stcd,toAnal))
+summary(mVScaseJST <- felm(voteSh ~ justice*log(casesSum+1) | stcd | 0 | stcd,toAnal))
+summary(mVScaseTEA <- felm(voteSh ~ tea*log(casesSum+1) | stcd | 0 | stcd,toAnal))
+
+summary(mVSdthEXT <- felm(voteSh ~ extreme*log(deathsSum+1) | stcd | 0 | stcd,toAnal))
+summary(mVSdthJST <- felm(voteSh ~ justice*log(deathsSum+1) | stcd | 0 | stcd,toAnal))
+summary(mVSdthTEA <- felm(voteSh ~ tea*log(deathsSum+1) | stcd | 0 | stcd,toAnal))
+
+summary(mVTcaseEXT <- felm(log(votes+1) ~ extreme*log(casesSum+1) + log(totVotes+1) | stcd | 0 | stcd,toAnal %>% filter(votes != 0)))
+summary(mVTcaseJST <- felm(log(votes+1) ~ justice*log(casesSum+1) + log(totVotes+1) | stcd | 0 | stcd,toAnal %>% filter(votes != 0)))
+summary(mVTcaseTEA <- felm(log(votes+1) ~ tea*log(casesSum+1) + log(totVotes+1) | stcd | 0 | stcd,toAnal %>% filter(votes != 0)))
+
+summary(mVTdthEXT <- felm(log(votes+1) ~ extreme*log(deathsSum+1) + log(totVotes+1) | stcd | 0 | stcd,toAnal %>% filter(votes != 0)))
+summary(mVTdthJST <- felm(log(votes+1) ~ justice*log(deathsSum+1) + log(totVotes+1) | stcd | 0 | stcd,toAnal %>% filter(votes != 0)))
+summary(mVTdthTEA <- felm(log(votes+1) ~ tea*log(deathsSum+1) + log(totVotes+1) | stcd | 0 | stcd,toAnal %>% filter(votes != 0)))
+
+regs <- list()
+for(m in objects(pattern = '^mVScase')) {
+  regs[[m]] <- get(m)
+  rownames(regs[[m]]$coefficients) <- rownames(regs[[m]]$beta) <- gsub(':',' X ',
+                                                                       gsub('tea|justice|extreme','Anti-Est.',
+                                        gsub('log\\(deathsSum \\+ 1\\)','Deaths (ln)',
+                                             gsub('log\\(casesSum \\+ 1\\)','Cases (ln)',
+                                                  gsub('log\\(totVotes \\+ 1\\)','Total Votes (ln)',rownames(regs[[m]]$coefficients))))))
+}
+
+stargazer(regs,keep.stat = c('n','rsq'),
+          star.char = c('\\dag','*','**','***'),
+          star.cutoffs = c(.1,.05,.01,.001),
+          add.lines = list(c('District FE','Y','Y','Y','Y')), out = '../Tables/table3.tex')
+
+# SI Figure 18
+toplot <- toAnal %>%
+  filter(date < as.Date('2020-11-01'),
+         votes > 0)
+
+toplot$lab <- NA
+for(d in unique(toplot$date)) {
+  toplot$lab[which(toplot$date == d)] <- paste(unique(toplot$stab[which(toplot$date == d)]),collapse = '\n')
+}
+
+
+
+p1 <- toplot %>%
+  ggplot(aes(x = date,y = voteSh,color = factor(extreme),size = casesSum,weight = log(casesSum+1),
+             alpha = factor(extreme),
+             label = lab)) + 
+  geom_jitter(width = 0) + 
+  geom_smooth(method = 'lm') + 
+  geom_text(data = toplot %>%
+              group_by(date) %>%
+              slice(1) %>%
+              filter(!stab %in% c('HI','TN')) %>%
+              ungroup() %>%
+              mutate(voteSh = 1.01,
+                     date = date - 3),
+            size = 2.5,hjust = 0,vjust = 0,color = 'black',alpha = .8) + 
+  theme_ridges() + 
+  scale_color_manual(name = 'Type',values = c(`0` = 'grey60',`1` = 'black'),label = c('Mainstream','Extreme')) + 
+  scale_alpha_manual(guide = F,values = c(`0` = .15,`1` = .6)) + 
+  scale_size_continuous(guide = F) + 
+  xlab('Date') + ylab('Vote Share') + 
+  scale_y_continuous(breaks = seq(0,1,by = .25),limits = c(0,1.25)) +
+  theme(legend.position = 'bottom')
+
+
+p2 <- toplot %>%
+  ggplot(aes(x = log(casesSum+1),y = voteSh,color = factor(extreme))) + 
+  geom_jitter(alpha = .2,width = .2) + 
+  geom_smooth(method = 'lm') + 
+  theme_ridges() + 
+  scale_color_manual(name = 'Type',values = c(`0` = 'grey60',`1` = 'black'),label = c('Mainstream','Extreme')) + 
+  scale_alpha_manual(guide = F,values = c(`0` = .15,`1` = .6)) + 
+  scale_size_continuous(guide = F) + 
+  xlab('Logged Cases') + ylab('Vote Share') + 
+  scale_y_continuous(breaks = seq(0,1,by = .25),limits = c(0,1.25)) +
+  theme(legend.position = 'bottom')
+
+pdf('../Figures/SI_figure18.pdf',width = 12,height = 8)
+grid.arrange(p1,p2,ncol = 2)
+dev.off()

29/replication_package/Code/helper_functions.R

@@ -0,0 +1,206 @@
+# QQ plot to evaluate balance
+qqprep <- function (x, discrete.cutoff, which.subclass = NULL, numdraws = 5000, 
+                    interactive = T, which.xs = NULL, ...) {
+  X <- x$X
+  varnames <- colnames(X)
+  for (var in varnames) {
+    if (is.factor(X[, var])) {
+      tempX <- X[, !colnames(X) %in% c(var)]
+      form <- formula(substitute(~dummy - 1, list(dummy = as.name(var))))
+      X <- cbind(tempX, model.matrix(form, X))
+    }
+  }
+  covariates <- X
+  if (!is.null(which.xs)) {
+    if (sum(which.xs %in% dimnames(covariates)[[2]]) != length(which.xs)) {
+      stop("which.xs is incorrectly specified")
+    }
+    covariates <- covariates[, which.xs, drop = F]
+  }
+  treat <- x$treat
+  matched <- x$weights != 0
+  ratio <- x$call$ratio
+  if (is.null(ratio)) {
+    ratio <- 1
+  }
+  if (identical(x$call$method, "full") | (ratio != 1)) {
+    t.plot <- sample(names(treat)[treat == 1], numdraws/2, 
+                     replace = TRUE, prob = x$weights[treat == 1])
+    c.plot <- sample(names(treat)[treat == 0], numdraws/2, 
+                     replace = TRUE, prob = x$weights[treat == 0])
+    m.covariates <- x$X[c(t.plot, c.plot), ]
+    m.treat <- x$treat[c(t.plot, c.plot)]
+  }
+  else {
+    m.covariates <- covariates[matched, , drop = F]
+    m.treat <- treat[matched]
+  }
+  if (!is.null(which.subclass)) {
+    subclass <- x$subclass
+    sub.index <- subclass == which.subclass & !is.na(subclass)
+    sub.covariates <- covariates[sub.index, , drop = F]
+    sub.treat <- treat[sub.index]
+    sub.matched <- matched[sub.index]
+    m.covariates <- sub.covariates[sub.matched, , drop = F]
+    m.treat <- sub.treat[sub.matched]
+  }
+  nn <- dimnames(covariates)[[2]]
+  nc <- length(nn)
+  covariates <- data.matrix(covariates)
+  toplot <- NULL
+  for (i in 1:nc) {
+    xi <- covariates[, i]
+    m.xi <- m.covariates[, i]
+    rr <- range(xi)
+    
+    
+    eqqplot <- function(x,y) {
+      sx <- sort(x)
+      sy <- sort(y)
+      lenx <- length(sx)
+      leny <- length(sy)
+      if (leny < lenx) 
+        sx <- approx(1:lenx, sx, n = leny, method = "constant")$y
+      if (leny > lenx) 
+        sy <- approx(1:leny, sy, n = lenx, method = "constant")$y
+      return(list(x = sx,y = sy))
+    }
+    toplot <- bind_rows(toplot,
+                        bind_rows(data.frame(eqqplot(x = xi[treat == 0],y = xi[treat == 1]),type = "Raw",cov = nn[i],rrlb = rr[1],rrub = rr[2]),
+                                  data.frame(eqqplot(x = m.xi[m.treat == 0],y = m.xi[m.treat == 1]),type = "Matched",cov = nn[i],rrlb = rr[1],rrub = rr[2])))
+  }
+  return(list(toplot = toplot))
+}
+
+
+# Ineration plot
+interaction_plot_continuous <- function(model, effect = "", moderator = "", varcov="default", minimum="min", maximum="max",colr = "grey",
+                                        incr="default", num_points = 10, conf=.95, mean=FALSE, median=FALSE, alph=80, rugplot=T, 
+                                        histogram=F, title="Marginal effects plot", xlabel="Value of moderator", 
+                                        ylabel="Estimated marginal coefficient",pointsplot = F,plot = F,show_est = F) {
+  # Extract Variance Covariance matrix
+  if (varcov == "default"){
+    covMat = vcov(model)
+  }else{
+    covMat = varcov
+  }
+  
+  # Extract the data frame of the model
+  mod_frame = model.frame(model)
+  
+  # Get coefficients of variables
+  if(effect == "") {
+    int.string <- rownames(summary(model)$coefficients)[grepl(":",rownames(summary(model)$coefficients))]
+    effect <- substr(int.string,1,regexpr(":",int.string)[1]-1)
+  }
+  if(moderator == "") {
+    int.string <- rownames(summary(model)$coefficients)[grepl(":",rownames(summary(model)$coefficients))]
+    moderator <- substr(int.string,regexpr(":",int.string)[1]+1,nchar(int.string))
+  }
+  interaction <- paste(effect,":",moderator,sep="")
+  beta_1 = summary(model)$coefficients[effect,1]
+  beta_3 = summary(model)$coefficients[interaction,1]
+  
+  # Set range of the moderator variable
+  # Minimum
+  if (minimum == "min"){
+    min_val = min(mod_frame[[moderator]])
+  }else{
+    min_val = minimum
+  }
+  # Maximum
+  if (maximum == "max"){
+    max_val = max(mod_frame[[moderator]])
+  }else{
+    max_val = maximum
+  }
+  
+  # Check if minimum smaller than maximum
+  if (min_val > max_val){
+    stop("Error: Minimum moderator value greater than maximum value.")
+  }
+  
+  # Determine intervals between values of the moderator
+  if (incr == "default"){
+    increment = (max_val - min_val)/(num_points - 1)
+  }else{
+    increment = incr
+  }
+  
+  # Create list of moderator values at which marginal effect is evaluated
+  x_2 <- seq(from=min_val, to=max_val, by=increment)
+  
+  # Compute marginal effects
+  delta_1 = beta_1 + beta_3*x_2
+  
+  # Compute variances
+  var_1 = covMat[effect,effect] + (x_2^2)*covMat[interaction, interaction] + 2*x_2*covMat[effect, interaction]
+  
+  # Standard errors
+  se_1 = sqrt(var_1)
+  
+  # Upper and lower confidence bounds
+  z_score = qnorm(1 - ((1 - conf)/2))
+  upper_bound = sapply(1:length(z_score), function(x) delta_1 + z_score[x]*se_1)
+  lower_bound = sapply(1:length(z_score), function(x) delta_1 - z_score[x]*se_1)
+  
+  # Determine the bounds of the graphing area
+  max_y = max(upper_bound)
+  min_y = min(lower_bound)
+  
+  # Make the histogram color
+  hist_col = colr
+  
+  stars <- ifelse(abs(summary(model)$coefficients[interaction,3]) >2.6,"***",
+                  ifelse(abs(summary(model)$coefficients[interaction,3]) > 1.96,"**",
+                         ifelse(abs(summary(model)$coefficients[interaction,3]) > 1.65,"*","")))
+  est <- paste("Interaction: ",round(summary(model)$coefficients[interaction,1],3),stars," (",
+               round(summary(model)$coefficients[interaction,2],3),")",sep="")
+  # Initialize plotting window
+  if(plot) {
+    plot(x=c(), y=c(), ylim=c(min_y, max_y), xlim=c(min_val, max_val), 
+         xlab=xlabel, ylab=ylabel, main=title)
+    
+    # Plot estimated effects
+    if(!pointsplot) {
+      lines(y=delta_1, x=x_2,col = colr)
+      for(i in ncol(upper_bound):1) {
+        polygon(c(x_2,rev(x_2)),c(upper_bound[,i],rev(lower_bound[,i])),border = NA,col = colr)
+      }
+    }else{
+      points(y = delta_1,x = x_2,col = colr,pch = 19)
+      for(i in ncol(upper_bound):1) {
+        segments(x_2,upper_bound[,i],x_2,lower_bound[,i],col = colr,lwd = i)
+      }
+    }
+    # Add a dashed horizontal line for zero
+    abline(h=0, lty=3)
+    
+    # Add a vertical line at the mean
+    if (mean){
+      abline(v = mean(mod_frame[[moderator]]), lty=2, col="red")
+    }
+    
+    # Add a vertical line at the median
+    if (median){
+      abline(v = median(mod_frame[[moderator]]), lty=3, col="blue")
+    }
+    
+    # Add Rug plot
+    if (rugplot){
+      rug(mod_frame[[moderator]])
+    }
+    
+    if (show_est) {
+      text(par('usr')[ 2 ], par('usr')[ 4 ],adj=c(1.05,1.2),
+           labels = est)
+      
+    }
+    #Add Histogram (Histogram only plots when minimum and maximum are the min/max of the moderator)
+    if (histogram & minimum=="min" & maximum=="max"){
+      par(new=T)
+      hist(mod_frame[[moderator]], axes=F, xlab="", ylab="",main="", border=hist_col, col=hist_col)
+    } 
+  }
+  return(list(delta_1 = delta_1,x_2 = x_2,ub = upper_bound,lb = lower_bound,inc = increment,est = est))
+}

29/replication_package/Code/zzSI_robust_prep.R

@@ -0,0 +1,161 @@
+# File name: SI_robust_prep.R
+# In: 
+#   - replication_data.RData
+# Out: 
+#   - /Data/Results/SI-data.RData
+
+require(lme4)
+require(lfe)
+require(MatchIt)
+require(WeightIt)
+require(tjbal)
+require(optmatch)
+require(stargazer)
+require(cobalt)
+require(gridExtra)
+require(ggridges)
+require(ggrepel)
+require(CBPS)
+require(tidyverse)
+rm(list = ls())
+gc()
+
+####################################################################################################################### Loading data
+load('../Data/replication_data.RData')
+
+
+
+
+####################################################################################################################### Loading functions
+source('./helper_functions.R')
+
+
+
+# ####################################################################################################################### Command line arguments
+# args <- commandArgs(trailingOnly = T)
+# # args <- c(3,2,2,2)
+# # Y: 1-3
+# # D: 1-2
+# # FE: 1-4
+# # GEO: 1-2
+# yInd <- as.numeric(args[1])
+# dInd <- as.numeric(args[2])
+# feInd <- as.numeric(args[3])
+# geoInd <- as.numeric(args[4])
+
+
+
+####################################################################################################################### Preparing variables
+Y <- paste0(c("pct_","VAP_","pcttw_"),"sanders")
+D <- unlist(lapply(c("sc_","ln_"),function(x) paste0(x,paste0(c("county_","DMA_","state_"),"cases"))))
+FE <- c("0","DMA_CODE","date")
+covariates <- c("sc_CTY_LTHS","sc_CTY_CollUp",
+                "sc_CTY_LT30yo","sc_CTY_60Up",
+                "sc_CTY_Below_poverty_level_AGE_18_64","sc_CTY_Female_hher_no_husbandhh",
+                "sc_CTY_Unem_rate_pop_16_over","sc_CTY_Labor_Force_Part_Rate_pop_16_over",
+                "sc_CTY_Manufactur","sc_CTY_Md_inc_hhs",
+                "sc_CTY_POPPCT_RURAL","sc_CTY_Speak_only_English","sc_CTY_White","sc_CTY_Black_or_African_American",
+                "ln_CTY_tot_pop","sc_turnout_pct_20")
+
+
+set.seed(123)
+resSI <- list()
+for(y in 'pcttw_sanders') {
+  for(fe in c('DMA_CODE','0')) {
+    for(geo in 'DMA_') {
+      for(d in c('March10Cases','March17Cases')) {
+        for(pre in c("2020-03-01","2020-03-03","2020-03-10","2020-03-17")) {
+          for(post in c("2020-03-01","2020-03-03","2020-03-10","2020-03-17")) {
+            if(pre == post) { next }
+            
+            if(pre == "2020-03-01") {
+              finalDat$post <- ifelse(finalDat$date == as.Date(post),1,
+                                      ifelse(finalDat$date <= as.Date(pre),0,NA))
+            } else if(post == "2020-03-01") {
+              finalDat$post <- ifelse(finalDat$date <= as.Date(post),1,
+                                      ifelse(finalDat$date == as.Date(pre),0,NA))
+            } else {
+              finalDat$post <- ifelse(finalDat$date == as.Date(post),1,
+                                      ifelse(finalDat$date == as.Date(pre),0,NA))
+            }
+            finalDat$treatBin <- ifelse(finalDat[[paste0(geo,d)]] > 1,1,0)
+            finalDat$treat <- ifelse(finalDat$post == 1 & finalDat$treatBin == 1,1,0)
+            tmpAnal <- finalDat %>% select(Y,treat,treatBin,c(covariates,paste0(y,"16")),pcttw_sanders16,VAP_sanders16,DMA_CODE,date,stab,matches("cases"),post) %>% filter(complete.cases(.))
+            m.out <- matchit(formula = as.formula(paste("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+                             data = tmpAnal,
+                             method = "nearest",
+                             distance = "mahalanobis")
+            
+            m.data <- match.data(m.out)
+            W.out <- weightit(formula(paste0("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+                              data = tmpAnal, estimand = "ATT", method = "cbps")
+            
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$bin <- summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$cont <- summary(felm(as.formula(paste0('scale(',y,") ~ ",paste0("scale(",geo,d,")")," + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$did$coefs <- summary(tmp <- felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$did$mfx <- interaction_plot_continuous(tmp,num_points = 2,pointsplot = T)
+            
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$bin <- summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$cont <- summary(felm(as.formula(paste0('scale(',y,") ~ ",paste0("scale(",geo,d,")")," + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$did$coefs <- summary(tmp <- felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$did$mfx <- interaction_plot_continuous(tmp,num_points = 2,pointsplot = T)
+            
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$bin <- summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$cont <- summary(felm(as.formula(paste0('scale(',y,") ~ ",paste0("scale(",geo,d,")")," + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$did$coefs <- summary(tmp <- felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$did$mfx <- interaction_plot_continuous(tmp,num_points = 2,pointsplot = T)
+            
+            # Permutation SHEEYAHT
+            basic.bin.plac <- basic.cont.plac <- basic.did.plac <- matching.bin.plac <- matching.cont.plac <- matching.did.plac <- weighting.bin.plac <- weighting.cont.plac <- weighting.did.plac <- NULL
+            for(bs in 1:100) {
+              tmpAnal$perm <- sample(tmpAnal[[paste0(geo,d)]],size = nrow(tmpAnal))
+              tmpAnal$treatBin <- ifelse(tmpAnal$perm > 1,1,0)
+              tmpAnal$treat <- ifelse(tmpAnal$post == 1 & tmpAnal$treatBin == 1,1,0)
+              m.out <- matchit(formula = as.formula(paste("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+                               data = tmpAnal,
+                               method = "full",
+                               distance = "mahalanobis")
+              
+              m.data <- match.data(m.out)
+              W.out <- weightit(formula(paste0("treatBin ~ ",paste(c(covariates,paste0(y,"16")),collapse = " + "))),
+                                data = tmpAnal, estimand = "ATT", method = "cbps")
+              
+              test <- tryCatch(felm(as.formula(paste0('scale(',y,") ~ ",paste0("scale(",geo,d,")")," + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights),error = function(e) e)
+              if(inherits(test,"error")) { next }
+              basic.bin.plac <- c(basic.bin.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients['treat',1])
+              basic.cont.plac <- c(basic.cont.plac,summary(felm(as.formula(paste0('scale(',y,") ~ scale(perm) + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients["scale(perm)",1])
+              basic.did.plac <- c(basic.did.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal))$coefficients["treatBin:post",1])
+              
+              matching.bin.plac <- c(matching.bin.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients['treat',1])
+              matching.cont.plac <- c(matching.cont.plac,summary(felm(as.formula(paste0('scale(',y,") ~ scale(perm) + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients["scale(perm)",1])
+              matching.did.plac <- c(matching.did.plac,
+                                     summary(felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),m.data,weights = m.data$weights))$coefficients["treatBin:post",1])
+              
+              weighting.bin.plac <- c(weighting.bin.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treat + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients['treat',1])
+              weighting.cont.plac <- c(weighting.cont.plac,summary(felm(as.formula(paste0('scale(',y,") ~ scale(perm) + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients["scale(perm)",1])
+              weighting.did.plac <- c(weighting.did.plac,summary(felm(as.formula(paste0('scale(',y,") ~ treatBin*post + ",paste(c(covariates,paste0(y,"16")),collapse = "+"),"| 0 | 0 | ",fe)),tmpAnal,weights = W.out$weights))$coefficients["treatBin:post",1])
+              
+              cat(".")
+            }
+            cat('\n')
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$plac$bin <- basic.bin.plac
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$plac$cont <- basic.cont.plac
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$basic$plac$did <- basic.did.plac
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$plac$bin <- matching.bin.plac
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$plac$cont <- matching.cont.plac
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$matching$plac$did <- matching.did.plac
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$plac$bin <- weighting.bin.plac
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$plac$cont <- weighting.cont.plac
+            resSI[[y]][[geo]][[d]][[fe]][[pre]][[post]]$felm$weighting$plac$did <- weighting.did.plac
+          }
+        }
+      }
+    }
+  }
+  cat(y," done\n")
+}
+
+
+save(resSI,file = paste0("../Data/Results/SI-data.RData"))
+
+

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