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# 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() |