1

1/replication_package/README.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:591b8bd1ed84ebf13e4c3052d3d98fcf4a7e33ab4a2be787061ad97eb5dea5c1
+size 6048

1/replication_package/data/tpnw_aware_raw.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f15f6cf1386eb15f6412a917e06777cbe9f628415eb5e0e5389fad8d1fdd2944
+size 222617

1/replication_package/data/tpnw_orig_income.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39cfbdce237bdd88638c0e559db179ea2bb3d34847e8482ef764b44d01991401
+size 7133

1/replication_package/data/tpnw_raw.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dbad02b01d1e0a31d6dbdf8c53460278c016864b72f8e7388a2b18e8f8c6ce64
+size 353061

1/replication_package/meta/hbg_codebook.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5edd9b84c0e52d121e8996dd0e176850d103dc94a29c61795ab806d30bdd4f8b
+size 29133

1/replication_package/scripts/hbg_analysis.R

@@ -0,0 +1,1033 @@
+### Initialize workspace.
+## Clear workspace.
+rm(list = ls(all = TRUE))
+
+## Confirm working directory.
+setwd("~/Downloads/hbg_replication")
+
+## Set seed.
+set.seed(123)
+
+## Set number of iterations for bootstrap replication.
+n_iter <- 10000
+
+## Load relevant packages.
+library(sandwich)
+library(car)
+
+## Load relevant helper functions.
+source("scripts/helper_functions.R")
+
+## Load data.
+# Load experimental data.
+tpnw <- read.csv("data/tpnw_data.csv", row.names = 1, 
+				 stringsAsFactors = FALSE)
+
+# Load YouGov data.
+aware <- read.csv("data/tpnw_aware.csv", row.names = 1,
+				  stringsAsFactors = FALSE)
+
+### Define relevant objects.
+## Define objects specifying outcomes.
+# Specify join_tpnw object, representing main outcome.
+join_tpnw <- "join_tpnw"
+
+# Specify tpnw_atts object, representing attitudinal outcomes.
+tpnw_atts <- names(tpnw)[startsWith(names(tpnw), "tpnw_atts")]
+
+# Specify all_outs object, concatenating main and attitudinal outcomes.
+all_outs <- c(join_tpnw, tpnw_atts)
+
+## Define objects specifying predictors.
+# Define object specifying main treatments.
+treats <- c("group_cue", "security_cue", "norms_cue", "institutions_cue")
+
+# Define object specifying general demographics.
+demos <- c("age", "female", "midwest", "west", "south", "income", "educ")
+
+# Define object specifying politically relevant demographics.
+pol_demos <- c("ideo", "pid3")
+
+# Define list of conditioning sets (NULL corresponds to Model 1, whereas the use
+# of demographic and political covariates corresponds to Model 2).
+covars <- list(NULL, c(demos, pol_demos))
+
+### Produce analysis.
+## Produce balance table.
+# Specify covariates to be used for balance table.
+bal_covars <- c("age", "female", "northeast", "midwest", "west", 
+				"south", "income", "educ", "ideo", "pid3")
+
+# Produce balance table matrix output, looping over treatment group.
+bal_mat <- lapply(0:4, function (i) {
+				# For each treatment value ...
+		   		apply(tpnw[bal_covars][tpnw$treatment == i,], 2, function (x) {
+		   			
+		   			# Calculate the mean of each covariate.
+		   			mean_x <- mean(x)
+
+		   			# Calculate SE estimates using 10,000 bootstrap replicates.
+		   			sd_x <- sd(replicate(10000, {
+		   						samp <- x[sample(length(x), replace = TRUE)]
+		   						return(mean(samp))
+		   				}))
+
+		   			# Return a list containing both point estimates.
+		   			return(list(mean = mean_x, sd = sd_x))
+		   			})
+		   		})
+
+# Bind point estimates for each treatment group.
+bal_mat <- lapply(bal_mat, function (treat) {
+				  do.call("rbind", unlist(treat, recursive = FALSE))
+		   })
+
+# Convert list into a matrix, with columns representing treatment group.
+bal_mat <- do.call("cbind", bal_mat)
+
+# Round all estimates to within three decimal points and convert to character
+# for the purposes of producing tabular output.
+bal_tab <- apply(bal_mat, 2, function (x) format(round(x, 3), digits = 3))
+
+# Specify rows containing mean point estimates.
+mean_rows <- endsWith(rownames(bal_tab), ".mean")
+
+# Specify rows containing SE point estimates.
+se_rows <- endsWith(rownames(bal_tab), ".sd")
+
+# Reformat SE estimates to be within parentheses.
+bal_tab[se_rows,] <- apply(bal_tab[se_rows,], 2, function (x) {
+						   paste0("(", x, ")")
+					 })
+
+# Remove row names for rows with SE estimates.
+rownames(bal_tab)[se_rows] <- ""
+
+# Remove ".mean" string in row names for rows with mean estimates.
+rownames(bal_tab)[mean_rows] <- gsub(".mean", "", rownames(bal_tab)[mean_rows])
+
+# Concatenate data to comport with LaTeX tabular markup.
+bal_tab <- paste(paste(paste(
+		capwords(rownames(bal_tab)), apply(bal_tab, 1, function (x) {
+										   paste(x, collapse = " & ")
+									 }), 
+	   sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+bal_tab <- gsub("\\( ", "\\(", bal_tab)
+
+# Produce tabular output.
+sink("output/balance_tab.tex")
+cat("\\begin{table}\n",
+	  "\\caption{Covariate Balance Across Treatment Arms}\n",
+	  "\\centering\\small\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{2.75cm}*{5}",
+	  		 "{S[table-number-alignment = center, table-column-width = 1.75cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{5}{c}{Arm}\\\\\\cmidrule{2-6}\n",
+	  "& {Control} & {Group} & {Security} & {Norms} & {Institutions} \\\\\\midrule\n",
+	  bal_tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{table}\n")
+sink()
+
+## Produce main results.
+# Compute main results, looping over conditioning sets.
+main_results <- lapply(covars, function (covar) {
+					# For each conditioning set ...
+					# Specify the relevant regression formula.
+					form <- as.formula(paste(join_tpnw, paste(c(treats, covar), 
+									   collapse = " + "), sep = " ~ "))
+
+					# Fit the OLS model per the specification.
+					fit <- lm(form, data = tpnw)
+
+					# Compute HC2 robust standard errors.
+					ses <- sqrt(diag(vcovHC(fit, type = "HC2")))
+
+					# Bind coefficient and SE output.
+					reg_out <- cbind(fit$coef[2:5], ses[2:5])
+
+					# Name output matrix columns and rows.
+					colnames(reg_out) <- c("coef", "se")
+					rownames(reg_out) <- treats
+
+					# Return output
+					return(as.data.frame(reg_out))
+				})
+
+# Name results to distinguish between Model 1 and Model 2 estimates.
+names(main_results) <- c("model_1", "model_2")
+
+## Assess significance of effect estimates and differences.
+# Estimate Bonferroni-Holm-adjusted p-values.
+bf_ps <- lapply(main_results, function (x) {
+				round(p.adjust(pnorm(x[, 1] / x[, 2], lower.tail = TRUE), 
+							   method = "holm"), 3)
+		 })
+
+# Estimate FDR-adjusted p-values, as an added robustness check.
+fdr_ps <- lapply(main_results, function (x) {
+				 round(p.adjust(pnorm(x[, 1] / x[, 2], lower.tail = TRUE), 
+				 	   method = "fdr"), 3)
+		  })
+
+# Redefine the main model (Model 2), and store full VCOV matrix.
+main_model <- lm(join_tpnw ~ group_cue + security_cue + norms_cue + 
+				 institutions_cue + age + female + midwest + 
+				 west + south + income + educ + ideo + pid3, tpnw)
+main_vcov <- vcovHC(main_model, "HC2")
+
+# Specify diff_sig function for assessing significance between two effect
+# estimates (defined here for the sake of clarity).
+diff_sig <- function (eff_1, eff_2) {
+				diff <- main_model$coef[eff_1] - main_model$coef[eff_2]
+				se <- sqrt(main_vcov[eff_1, eff_1] + main_vcov[eff_2, eff_2] -
+						   2 * main_vcov[eff_1, eff_2])
+				p <- 2 * (1 - pnorm(abs(diff) / se))
+				return (p)
+}
+
+# Assess the significance of the difference between institution and security cue
+# effect estimates .
+inst_sec_diff_p <- diff_sig("institutions_cue", "security_cue")
+
+# Assess the significance of the difference between institution and group cue
+# effect estimates
+inst_grp_diff_p <- diff_sig("institutions_cue", "group_cue")
+
+# Assess the significance of the difference between security and group cue
+# effect estimates
+sec_grp_diff_p <- diff_sig("security_cue", "group_cue")
+
+# Assess the significance of the difference between security and norms cue
+# effect estimates
+sec_norms_diff_p <- diff_sig("security_cue", "norms_cue")
+
+# Assess the significance of the difference between institution and group cue
+# effect estimates
+inst_norms_diff_p <- diff_sig("institutions_cue", "norms_cue")
+
+# Assess the significance of the difference between institution and group cue
+# effect estimates
+grp_norms_diff_p <- diff_sig("group_cue", "norms_cue")
+
+# The significance of differences between effect estimates was also assessed
+# using 10,000 bootstrap replicates and two-tailed p-values; relevant code is 
+# included below with the institutions and security cues, for posterity, but is 
+# not run.
+
+# Compute SE estimates.
+# diffs <- replicate(10000, {
+# 	samp <- tpnw[sample(nrow(tpnw), replace = TRUE),]
+# 	model <- lm(join_tpnw ~ group_cue + security_cue + norms_cue + 
+# 				institutions_cue + age + female + midwest + 
+# 				west + south + income + educ + ideo + pid3, samp)
+# 	model$coef[5] - model$coef[3]
+# })
+# diffs_se <- sd(diffs)
+# 
+# # Fit model.
+# model <- lm(join_tpnw ~ group_cue + security_cue + norms_cue + 
+# 				institutions_cue + age + female + midwest + 
+# 				west + south + income + educ + ideo + pid3, tpnw)
+# 
+# # Compute two-tailed p-value.
+# 2 * (1 - pnorm(abs((model$coef[5] - model$coef[3])/diffs_se)))
+
+## Assess YouGov results.
+# Tabulate responses.
+aware_table <- table(aware$awareness, useNA = "ifany")
+names(aware_table) <-  c("Yes, support", "Yes, oppose", 
+						 "No, support", "No, oppose", "Skipped")
+
+# Compute both weighted and unweighted means.
+aware_results <- lapply(1:4, function (resp) {
+				 	# Calculate weighted mean.
+				 	wt_mean <- with(aware, weighted.mean(awareness == resp, 
+				 										 w = weight, na.rm = TRUE))
+				 	
+				 	# Calculate raw mean.
+				 	rw_mean <- with(aware, mean(awareness == resp, na.rm = TRUE))
+				 	
+				 	# Concatenate means and rename vector.
+				 	means <- c(wt_mean, rw_mean)
+				 	names(means) <- c("weighted_mean", "raw_mean")
+
+				 	# Calculate SE estimates with 10,000 bootstrap replicates.
+				 	ses <- replicate(10000, {
+		   					      samp <- aware[sample(nrow(aware), 
+		   					      				replace = TRUE),]
+		   					      wt_mean <- with(samp, weighted.mean(awareness == resp, 
+		   					      									  w = weight, na.rm = TRUE))
+		   					      rw_mean <- with(samp, mean(awareness == resp, 
+		   					      							 na.rm = TRUE))
+		   					      return(c(wt_mean, rw_mean))
+		   				   })
+					ses <- apply(ses, 1, sd)
+					names(ses) <- c("weighted_mean", "raw_mean")
+		   			
+		   			# Bind mean and SE estimates.
+		   			outs <- rbind(means, ses)
+		   			rownames(outs) <- paste(names(aware_table)[resp], 
+		   									c("mean", "se"), sep = "_")
+		   			return(outs)
+				 })
+
+# Name results to distinguish between responses.
+names(aware_results) <- c("Yes, support", "Yes, oppose", 
+						  "No, support", "No, oppose")
+
+## Assess covariate means for experimental and YouGov data (used in Table A1).
+# Indicate the list of covariates to be assessed.
+demo_tab_vars <- c("age", "female", "northeast", "midwest", "west", "south")
+
+# Compute covariate averages for experimental data.
+tpnw_means <- apply(tpnw[demo_tab_vars], 2, mean, na.rm = TRUE)
+
+# Compute covariate averages for YouGov data.
+aware_means <- apply(aware[demo_tab_vars], 2, function (x) {
+					 	weighted.mean(x, na.rm = TRUE, w = aware$weight)
+					})
+
+# Compute bootstrap standard errors for demographic means.
+demo_ses <- replicate(10000, {
+	# Sample the experimental data.
+	samp_tpnw <- tpnw[sample(nrow(tpnw), replace = TRUE), demo_tab_vars]
+	
+	# Sample the YouGov data.
+	samp_aware <- aware[sample(nrow(aware), replace = TRUE), 
+						c(demo_tab_vars, "weight")]
+	
+	# Compute bootstrap means for experimental data.
+	tpnw_means <- apply(samp_tpnw[demo_tab_vars], 2, mean, na.rm = TRUE)
+
+	# Compute bootstrap means for YouGov data.
+	aware_means <- apply(samp_aware[demo_tab_vars], 2, function (x) {
+					 	weighted.mean(x, na.rm = TRUE, w = samp_aware$weight)
+					})
+
+	# Return the results as a list, and ensure that replicate() also returns a 
+	# list.
+	return(list(tpnw = tpnw_means, aware = aware_means))
+	}, simplify = FALSE)
+
+# Compute SE estimates for each set of demographics.
+demo_ses <- lapply(c("tpnw", "aware"), function (dataset) {
+	# Group all estimates from each dataset.
+	sep_res <- lapply(demo_ses, function (iteration) {
+					  	return(iteration[[dataset]])
+			   })
+
+	# Bind estimates.
+	sep_res <- do.call("rbind", sep_res)
+
+	# Compute SE estimates.
+	sep_ses <- apply(sep_res, 2, sd)
+
+	# Return SE estimates.
+	return(sep_ses)
+	})
+
+## Assess responses to the attitudinal battery.
+# Assess responses to the attitudinal battery, looping over treatment group. For
+# each treatment value ...
+att_results <- lapply(0:4, function (i) {
+	# Calculate the average response to each attitudinal battery question.
+	atts_mean <- apply(tpnw[tpnw$treatment == i, tpnw_atts], 2, function (x) {
+					   mean(x, na.rm = TRUE)
+				 })
+
+	# Calculate SE estimates using 10,000 bootstrap replicates.
+	bl_atts_boot <- replicate(10000, {
+		dat <- tpnw[tpnw$treatment == i, tpnw_atts]
+		samp <- dat[sample(nrow(dat), replace = TRUE),]
+		apply(samp, 2, function (x) mean(x, na.rm = TRUE))
+	})
+	bl_atts_ses <- apply(bl_atts_boot, 1, sd)
+	
+	# Combine mean and SE estimates and return results.
+	return(cbind(atts_mean, bl_atts_ses))
+})
+
+# Compute treatment effects on responses to the attitudinal battery, looping 
+# over conditioning sets.
+att_effs <- lapply(covars, function (covar) {
+			# For each conditioning set ...
+			model_res <- lapply(tpnw_atts, function (out) {
+								# Specify the relevant regression formula.
+								form <- as.formula(paste(out, 
+												   paste(c(treats, covar), 
+												   collapse = " + "), 
+												   sep = " ~ "))
+								
+								# Fit the OLS model per the specification.
+								fit <- lm(form, data = tpnw)
+
+								# Compute HC2 robust standard errors.
+								ses <- sqrt(diag(vcovHC(fit, type = "HC2")))
+								
+								# Bind coefficient and SE output.
+								reg_out <- cbind(fit$coef[2:5], ses[2:5])
+								
+								# Name output matrix columns and rows.
+								colnames(reg_out) <- c("coef", "se")
+								rownames(reg_out) <- treats
+
+								# Return output.
+								return(as.data.frame(reg_out))
+						 })
+			# Name results to distinguish between each attitudinal battery 
+			# outcome and return results.
+			names(model_res) <- tpnw_atts
+			return(model_res)
+	})
+
+# Name results to distinguish between Model 1 and Model 2 estimates.
+names(att_effs) <- c("model_1", "model_2")
+
+## Perform subgroup analysis.
+# Compute mean support by political party, looping over treatment group.
+pid_results <- lapply(0:4, function (treat) {
+					  # For each partisan group ...
+					  out <- lapply(-1:1, function (i) {
+					      			# Calculate average support.
+					      			pid_mean <- with(tpnw, 
+					      							 mean(join_tpnw[pid3 == i & 
+					      							 	  treatment == treat], 
+					      							 	  na.rm = TRUE))
+
+					      			# Calculate SE estimates with 10,000 
+					      			# bootstrap replicates.
+					      			pid_boot <- replicate(10000, {
+					      								 dat <- tpnw$join_tpnw[tpnw$pid3 == i & 
+					      							   			tpnw$treatment == treat]
+					      								 samp <- dat[sample(length(dat), 
+					      											 		replace = TRUE)]
+					      								 mean(samp, na.rm = TRUE)
+					  		 					})
+
+					      			# Concatenate and return mean and SE 
+					      			# estimates.
+					  	  	 		return(c(mean = pid_mean, se = sd(pid_boot)))
+			   		 })
+
+					 # Name results to distinguish estimates by political party,
+					 # and return output.
+					 names(out) <- c("dem", "ind", "rep")
+					 return(as.data.frame(out))
+			   })
+
+# Name results to distinguish between treatment groups.
+names(pid_results) <- c("Control", paste(c("Group", "Security", "Norms", 
+										   "Institutions"), "Cue"))
+
+# Assess significance between control-group means; for 10,000 bootstrap
+# replicates ...
+pid_diff_ses <- replicate(10000, {
+						  # Sample with replacement.
+		  				  samp <- tpnw[sample(nrow(tpnw), replace = TRUE),]
+		  				  
+		  				  # Compute the difference between Democrats' and
+		  				  # Independents' support.
+		  				  dem_ind_diff <- with(samp[samp$treatment == 0,], 
+						  			 		   mean(join_tpnw[pid3 == -1], 
+						  			 		   		na.rm = TRUE) - 
+						  			 		   mean(join_tpnw[pid3 == 0], 
+						  			 		   		na.rm = TRUE))
+						  # Compute the difference between Democrats' and
+		  				  # Republicans' support.
+		  				  dem_rep_diff <- with(samp[samp$treatment == 0,], 
+						  			 		   mean(join_tpnw[pid3 == -1], 
+						  			 		   		na.rm = TRUE) - 
+						  			 		   mean(join_tpnw[pid3 == 1], 
+						  			 		   		na.rm = TRUE))
+						  # Compute the difference between Independents' and
+		  				  # Republicans' support.
+		  				  ind_rep_diff <- with(samp[samp$treatment == 0,], 
+						  			 		   mean(join_tpnw[pid3 == 1], 
+						  			 		   		na.rm = TRUE) - 
+						  			 		   mean(join_tpnw[pid3 == 0], 
+						  			 		   		na.rm = TRUE))
+
+		  				  # Concatenate and name results.
+		  				  out <- c(dem_ind_diff, dem_rep_diff, ind_rep_diff)
+		  				  names(out) <- c("dem_ind", "dem_rep", "ind_rep")
+		  				  return(out)
+				})
+
+# Compute SE estimates for each difference.
+pid_diff_ses <- apply(pid_diff_ses, 1, sd)
+
+# Assess significance for each difference.
+dem_ind_p <- 2 * (1 - pnorm(abs(pid_results$Control["mean", "dem"] - 
+				pid_results$Control["mean", "ind"]) / pid_diff_ses["dem_ind"]))
+dem_rep_p <- 2 * (1 - pnorm(abs(pid_results$Control["mean", "dem"] - 
+				pid_results$Control["mean", "rep"]) / pid_diff_ses["dem_rep"]))
+ind_rep_p <- 2 * (1 - pnorm(abs(pid_results$Control["mean", "ind"] - 
+				pid_results$Control["mean", "rep"]) / pid_diff_ses["ind_rep"]))
+
+# Compute mean support by political ideology, looping over treatment group.
+tpnw$ideo <- recode(tpnw$ideo, "c(-2, -1) = 'liberal'; 
+								0 = 'moderate'; 
+								c(1, 2) = 'conservative'")
+ideo_results <- lapply(0:4, function (treat) {
+					  # For each ideological group ...
+					  out <- lapply(c("liberal", "moderate", "conservative"), function (i) {
+					      			# Calculate average support.
+					      			pid_mean <- with(tpnw, 
+					      							 mean(join_tpnw[ideo == i & 
+					      							 	  treatment == treat], 
+					      							 	  na.rm = TRUE))
+
+					      			# Calculate SE estimates with 10,000 
+					      			# bootstrap replicates.
+					      			pid_boot <- replicate(10000, {
+					      								 dat <- tpnw$join_tpnw[tpnw$ideo == i & 
+					      							   			tpnw$treatment == treat]
+					      								 samp <- dat[sample(length(dat), 
+					      											 		replace = TRUE)]
+					      								 mean(samp, na.rm = TRUE)
+					  		 					})
+
+					      			# Concatenate and return mean and SE 
+					      			# estimates.
+					  	  	 		return(c(mean = pid_mean, se = sd(pid_boot)))
+			   		 })
+
+					 # Name results to distinguish estimates by political ideology,
+					 # and return output.
+					 names(out) <- c("liberal", "moderate", "conservative")
+					 return(as.data.frame(out))
+			   })
+
+# Name results to distinguish between treatment groups.
+names(ideo_results) <- c("Control", paste(c("Group", "Security", "Norms", 
+										   "Institutions"), "Cue"))
+
+## Produce weighted main results.
+# Compute weighted main results, looping over conditioning sets.
+w_main_results <- lapply(covars, function (covar) {
+				  	# For each conditioning set ...
+				  	# Specify the relevant regression formula.
+				  	form <- as.formula(paste(join_tpnw, paste(c(treats, covar), 
+				  					   collapse = " + "), sep = " ~ "))
+
+					# Fit the OLS model per the specification.
+					fit <- lm(form, data = tpnw, weights = anesrake_weight)
+
+					# Compute HC2 robust standard errors.
+					ses <- sqrt(diag(vcovHC(fit, type = "HC2")))
+
+					# Bind coefficient and SE output.
+					reg_out <- cbind(fit$coef[2:5], ses[2:5])
+
+					# Name output matrix columns and rows.
+					colnames(reg_out) <- c("coef", "se")
+					rownames(reg_out) <- treats
+
+					# Return output
+					return(as.data.frame(reg_out))
+				})
+
+# Name results to distinguish between Model 1 and Model 2 estimates.
+names(w_main_results) <- c("model_1", "model_2")
+
+### Produce plots and tables.
+## Produce main results plot.
+# Produce main results matrix for plotting.
+main_mat <- do.call("rbind", lapply(1:2, function (model) {
+									cbind(main_results[[model]], model)
+							 }))
+
+# Store values for constructing 90- and 95-percent CIs.
+z_90 <- qnorm(.95)
+z_95 <- qnorm(.975)
+
+# Open new pdf device.
+setEPS()
+postscript("output/fg1.eps", width = 8, height = 5.5)
+
+# Define custom graphical parameters.
+par(mar = c(8, 7, 2, 2))
+
+# Open new, empty plot.
+plot(0, type = "n", axes = FALSE, ann = FALSE, 
+	 xlim = c(-.3, .05), ylim = c(.8, 4))
+
+# Produce guidelines to go behind point estimates and error bars.
+abline(v = seq(-.3, .05, .05)[-7], col = "lightgrey", lty = 3)
+
+# Add Model 1 point estimates.
+par(new = TRUE)
+plot(x = main_mat$coef[main_mat$model == 1], y = 1:4 + .05, 
+	 xlim = c(-.3, .05), ylim = c(.8, 4), pch = 16, col = "steelblue2", 
+	 xlab = "", ylab = "", axes = FALSE)
+
+# Add Model 2 point estimates.
+par(new = TRUE)
+plot(x = main_mat$coef[main_mat$model == 2], y = 1:4 - .05, 
+	 xlim = c(-.3, .05), ylim = c(.8, 4), pch = 16, col = "#FF8F37", main = "", 
+	 xlab = "", ylab = "", axes = FALSE)
+
+# Add horizontal axis indicating effect estimate size.
+axis(side = 1, at = round(seq(-.3, 0, .05), 2), labels = FALSE)
+mtext(side = 1, at = seq(-.3, .1, .1), text = c("-30", "-20", "-10", "0"), 
+	  cex = .9, line = .75)
+axis(side = 1, at = round(seq(-.25, .05, .05), 2), tck = -.01, labels = FALSE)
+
+# Add vertical axis specifying treatment names corresponding to point estimates.
+axis(side = 2, at = 1:4, labels = FALSE)
+mtext(side = 2, line = .75, at = 1:4, 
+	  text = paste(c("Group", "Security", "Norms", "Institutions"), "Cue"), 
+	  las = 1, padj = .35, cex = .9)
+
+# Add axis labels.
+mtext(side = 2, line = 2.3, at = 4.2, text = "Treatment", 
+	  font = 2, las = 1, xpd = TRUE)
+mtext(side = 1, text = "Estimated Effect Size", line = 2.5, at = -.15, font = 2)
+
+# Add a dashed line at zero.
+abline(v = 0.00, lty = 2)
+
+# Add two-sided, 90-percent CIs.
+with(main_mat[main_mat$model == 1,], 
+	 segments(x0 = coef - z_90 * se, y0 = 1:4 + .05, x1 = coef + z_90 * se, 
+	 	      y1 = 1:4 + .05, col = "steelblue2", lwd = 3))
+with(main_mat[main_mat$model == 2,], 
+	 segments(x0 = coef - z_90 * se, y0 = 1:4 - .05, x1 = coef + z_90 * se, 
+	 		  y1 = 1:4 - .05, col = "#FF8F37", lwd = 3))
+
+# Add two-sided 95-percent CIs.
+with(main_mat[main_mat$model == 1,], 
+	 segments(x0 = coef - z_95 *se, y0 = 1:4 + .05, x1 = coef + z_95 *se, 
+	 		  y1 = 1:4 + .05, col = "steelblue2", lwd = 1))
+with(main_mat[main_mat$model == 2,], 
+	 segments(x0 = coef - z_95 *se, y0 = 1:4 - .05, x1 = coef + z_95 *se, 
+	 		  y1 = 1:4 - .05, col = "#FF8F37", lwd = 1))
+
+# Add legend.
+legend(legend = paste("Model", 1:2), x = -.15, y = -.275, horiz = TRUE, 
+	   pch = 16, col = c("steelblue2", "#FF8F37"), xjust = .5, xpd = TRUE, 
+	   text.width = .05, cex = .9)
+
+# Draw a box around the plot.
+box()
+
+# Close the grpahical device.
+dev.off()
+
+## Create tabular output for main results.
+# Define matrix object of main results.
+tab_dat <- do.call("cbind", main_results)
+
+# Compute control-group means, with SE estimates; define OLS formula.
+ctrl_form <- as.formula(paste(join_tpnw, paste(treats, 
+							  collapse = " + "), sep = " ~ "))
+
+# Fit the OLS model per the specification and recover the control mean.
+ctrl_fit <- lm(ctrl_form, data = tpnw)
+
+# Recover the control-group mean.
+ctrl_mean <- ctrl_fit$coef["(Intercept)"]
+
+# Compute control SE.
+ctrl_se <- sqrt(diag(vcovHC(ctrl_fit, "HC2")))["(Intercept)"]
+
+# Concatenate mean and SE output with blank values for Model 2.
+ctrl_results <- c(format(round(c(ctrl_mean, ctrl_se), 3) * 100, digits = 2), 
+			  	  "|", "|")
+
+# Reformat data to include a decimal point.
+tab_dat <- apply(tab_dat, 2, function (y) format(round(y, 3) * 100, digits = 2))
+
+# Bind control-group means with main results data.
+tab <- rbind(ctrl_results, tab_dat)
+
+# Rename row containing control-group means.
+rownames(tab)[which(rownames(tab) == "1")] <- "control_mean"
+
+# Relabel coefficient columns.
+coef_cols <- grep("coef$", colnames(tab))
+
+# Relabel SE columns.
+se_cols <- grep("se$", colnames(tab))
+
+# Reformat SE estimates to be within parentheses.
+tab[,se_cols] <- apply(tab[, se_cols], 2, function (y) paste0("(", y, ")"))
+
+# Concatenate data to comport with LaTeX tabular markup.
+tab <- paste(paste(paste(capwords(gsub("_", " ", rownames(tab))), 
+			 			 apply(tab, 1, function (x) {
+			 			 	   paste(x, collapse = " & ")
+				 		 }), sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+
+# Produce tabular output.
+sink("output/main_results_tab.tex")
+cat("\\begin{table}\n",
+	  "\\caption{Estimated Treatment Effects on Support for TPNW}\n",
+	  "\\begin{adjustbox}{width = \\textwidth, center}\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",
+      "\ttable-space-text-post = *,\n",
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{3.5cm}*{4}",
+	  		 "{S[table-number-alignment = right, table-column-width=1.25cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{4}{c}{Model}\\\\\\cmidrule{2-5}\n",
+	  "& \\multicolumn{2}{c}{{(1)}} & \\multicolumn{2}{c}{{(2)}} \\\\\\midrule\n",
+	  tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{adjustbox}\n",
+	  "\\end{table}\n")
+sink()
+
+## Create tabular output for YouGov results.
+# Restructure data as a matrix.
+aware_tab <- rbind(do.call("rbind", aware_results))
+
+# Reformat data to include three decimal points.
+aware_tab <- apply(aware_tab, 2, function (y) format(round(y, 3) * 100, 
+				   digits = 3))
+
+# Relabel mean rows.
+mean_rows <- endsWith(rownames(aware_tab), "mean")
+
+# Relabel SE rows.
+se_rows <- endsWith(rownames(aware_tab), "se")
+
+# Reformat SE estimates to be within parentheses.
+aware_tab[se_rows,] <- paste0("(", aware_tab[se_rows,], ")")
+
+# Remove row names for rows with SE estimates.
+rownames(aware_tab)[se_rows] <- ""
+
+# Remove "_mean" indication in mean_rows.
+rownames(aware_tab)[mean_rows] <- gsub("_mean", "", 
+										rownames(aware_tab)[mean_rows])
+
+# Add an empty row, where excluded calculations of responses among skips are
+# noted in the table, and rename the relevant row.
+aware_tab <- rbind(aware_tab, c("|", "|"))
+rownames(aware_tab)[nrow(aware_tab)] <- "Skipped"
+
+# Add an empty column to the table, and insert the count column at the relevant
+# indices.
+aware_tab[which(rownames(aware_tab) %in% names(aware_table)),]
+aware_tab <- cbind(aware_tab, "")
+colnames(aware_tab)[ncol(aware_tab)] <- "N"
+aware_tab[which(rownames(aware_tab) %in% names(aware_table)), "N"] <- aware_table
+
+# Concatenate data to comport with LaTeX tabular markup.
+aware_tab <- paste(paste(paste(capwords(gsub("_", " ", rownames(aware_tab))), 
+				 apply(aware_tab, 1, function (x) {
+				 	   paste(x, collapse = " & ")
+				 }), 
+	   			 sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+
+# Produce tabular output.
+sink("output/yougov_tab.tex")
+cat("\\begin{table}\n",
+	  "\\caption{YouGov Survey Responses}\n",
+	  "\\centering\\small\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",      
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{3.5cm}*{5}", 
+	  		 "{S[table-number-alignment = right, table-column-width=1.25cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{5}{c}{Arm}\\\\\\cmidrule{2-6}\n",
+	  "& {Control} & {Group} & {Security} & {Norms} & {Institutions} \\\\\\midrule\n",
+	  aware_tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{table}\n")
+sink()
+
+## Create tabular output for attitudinal results.
+# Define matrix object of main results.
+tab_dat <- do.call("cbind", att_results)
+
+# Reformat matrix to alternate mean and SE estimates.
+tab <- sapply(seq(0, 8, 2), function (i) {
+	matrix(c(t(tab_dat[,1:2 + i])), 14, 1)
+})
+
+# Reformat data to include three decimal points.
+tab <- apply(tab, 2, function (y) format(round(y, 3), digits = 3))
+
+# Rename rows to indicate mean and SE estimates.
+rownames(tab) <- paste(rep(rownames(tab_dat), each = 2), 
+					   c("mean", "se"), sep = "_")
+
+# Relabel mean rows.
+mean_rows <- grep("_mean", rownames(tab))
+
+# Relabel SE rows
+se_rows <- grep("_se", rownames(tab))
+
+# Reformat SE estimates to be within parentheses.
+tab[se_rows,] <- apply(tab[se_rows,], 1, function (y) {
+					   paste0("(", gsub(" ", "", y), ")")
+				 })
+
+# Rename rows to improve tabular labels; remove "tpnw_atts, "mean," and "se" row
+# name strings.
+rownames(tab) <- gsub("tpnw_atts|mean$|se$", "", rownames(tab))
+
+# Remove leading and tailing underscores.
+rownames(tab) <- gsub("^_|_$", "", rownames(tab))
+
+# Remove row names for rows with SE estimates.
+rownames(tab)[se_rows] <- ""
+
+# Concatenate data to comport with LaTeX tabular markup.
+tab <- paste(paste(paste(capwords(gsub("_", " ", rownames(tab))), 
+						 apply(tab, 1, function (x) {
+						 	   paste(x, collapse = " & ")
+						 }), 
+	   sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+
+# Produce tabular output.
+sink("output/atts_tab.tex")
+cat("\\begin{table}\n",
+	  "\\caption{Attitudes Toward Nuclear Weapons by Arm}\n",
+	  "\\centering\\small\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",
+      "\ttable-space-text-post = *,\n",
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{3.5cm}*{5}", 
+	  		 "{S[table-number-alignment = center, table-column-width=1.25cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{5}{c}{Arm}\\\\\\cmidrule{2-6}\n",
+	  "& {Control} & {Group} & {Security} & {Norms} & {Institutions} \\\\\\midrule\n",
+	  tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{table}\n")
+sink()
+
+## Create tabular output for results by political party.
+# Restructure data such that mean and SE estimates are alternating rows in a
+# 1 x 6 matrix, in each of five list elements, corresponding to each treatment
+# group; and bind the results for each treatment group.
+pid_tab <- lapply(pid_results, function (x) {
+				  matrix(unlist(x), nrow = 6, ncol = 1)
+		   })
+pid_tab <- do.call("cbind", pid_tab)
+
+# Assign row names to distinguish results for each partisan group, and mean and
+# SE estimates.
+rownames(pid_tab) <- paste(rep(c("democrat", "independent", "republican"), 
+							   each = 2), c("mean", "se"))
+
+# Relabel mean rows.
+mean_rows <- endsWith(rownames(pid_tab), "mean")
+
+# Relabel SE rows.
+se_rows <- endsWith(rownames(pid_tab), "se")
+
+# Label columns per treatment, for the computation of ATEs.
+colnames(pid_tab) <- c("control", treats)
+
+# Compute ATEs, with control as baseline, and update tabular data.
+pid_tab[mean_rows, treats] <- pid_tab[mean_rows, treats] - 
+							  pid_tab[mean_rows, "control"]
+
+# Reformat data to include three decimal points.
+pid_tab <- apply(pid_tab, 2, function (y) format(round(y, 3) * 100, digits = 3))
+
+# Remove extraneous spacing.
+pid_tab <- gsub(" ", "", pid_tab)
+
+# Reformat SE estimates to be within parentheses.
+pid_tab[se_rows,] <- paste0("(", pid_tab[se_rows,], ")")
+
+# Remove row names for rows with SE estimates.
+rownames(pid_tab)[se_rows] <- ""
+
+# Concatenate data to comport with LaTeX tabular markup.
+pid_tab <- paste(paste(paste(capwords(gsub("_", " ", rownames(pid_tab))), 
+				 apply(pid_tab, 1, function (x) {
+				 	   paste(x, collapse = " & ")
+				 }), 
+	   			 sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+
+# Produce tabular output.
+sink("output/pid_support.tex")
+cat("\\begin{table}\n",
+	  "\\caption{Support for Joining TPNW by Party ID}\n",
+	  "\\centering\\small\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",      
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{3.5cm}*{5}", 
+	  		 "{S[table-number-alignment = right, table-column-width=1.25cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{5}{c}{Arm}\\\\\\cmidrule{2-6}\n",
+	  "& {Control} & {Group} & {Security} & {Norms} & {Institutions} \\\\\\midrule\n",
+	  pid_tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{table}\n")
+sink()
+
+## Create tabular output for results by political ideology.
+# Restructure data such that mean and SE estimates are alternating rows in a
+# 1 x 6 matrix, in each of five list elements, corresponding to each treatment
+# group; and bind the results for each treatment group.
+ideo_tab <- lapply(ideo_results, function (x) {
+				   matrix(unlist(x), nrow = 6, ncol = 1)
+			})
+ideo_tab <- do.call("cbind", ideo_tab)
+
+# Assign row names to distinguish results for each idelogical group, and mean 
+# and SE estimates.
+rownames(ideo_tab) <- paste(rep(c("liberal", "moderate", "conservative"), 
+							   each = 2), c("mean", "se"))
+
+# Reformat data to include three decimal points.
+ideo_tab <- apply(ideo_tab, 2, function (y) format(round(y, 3) * 100,
+				  digits = 3))
+
+# Relabel mean rows.
+mean_rows <- endsWith(rownames(ideo_tab), "mean")
+
+# Relabel SE rows.
+se_rows <- endsWith(rownames(ideo_tab), "se")
+
+# Reformat SE estimates to be within parentheses.
+ideo_tab[se_rows,] <- paste0("(", ideo_tab[se_rows,], ")")
+
+# Remove row names for rows with SE estimates.
+rownames(ideo_tab)[se_rows] <- ""
+
+# Concatenate data to comport with LaTeX tabular markup.
+ideo_tab <- paste(paste(paste(capwords(gsub("_", " ", rownames(ideo_tab))), 
+				 apply(ideo_tab, 1, function (x) {
+				 	   paste(x, collapse = " & ")
+				 }), 
+	   			 sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+
+# Produce tabular output.
+sink("output/ideo_support_tab.tex")
+cat("\\begin{table}\n",
+	  "\\caption{Support for Joining TPNW by Ideology}\n",
+	  "\\centering\\small\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",      
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{3.5cm}*{5}",
+	  		 "{S[table-number-alignment = right, table-column-width=1.25cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{5}{c}{Arm}\\\\\\cmidrule{2-6}\n",
+	  "& {Control} & {Group} & {Security} & {Norms} & {Institutions} \\\\\\midrule\n",
+	  ideo_tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{table}\n")
+sink()
+
+## Create tabular output for weighted main results.
+# Define matrix object of weighted main results.
+w_tab_dat <- do.call("cbind", w_main_results)
+
+# Compute weighted control-group means, with SE estimates; define OLS formula.
+w_ctrl_form <- as.formula(paste(join_tpnw, paste(treats, 
+							  collapse = " + "), sep = " ~ "))
+
+# Fit the OLS model per the specification and recover the control mean.
+w_ctrl_fit <- lm(w_ctrl_form, data = tpnw, 
+				  weights = anesrake_weight)
+
+# Recover the control-group mean.
+w_ctrl_mean <- w_ctrl_fit$coef["(Intercept)"]
+
+# Compute control SE.
+w_ctrl_se <- sqrt(diag(vcovHC(w_ctrl_fit, "HC2")))["(Intercept)"]
+
+
+# Concatenate mean and SE output with blank values for Model 2.
+w_ctrl_results <- c(format(round(c(w_ctrl_mean, w_ctrl_se), 3) * 100, 
+						   digits = 2), "|", "|")
+
+# Reformat data to include a decimal point.
+w_tab_dat <- apply(w_tab_dat, 2, function (y) format(round(y, 3) * 100, 
+				   digits = 2))
+
+# Bind control-group means with main results data.
+w_tab <- rbind(w_ctrl_results, w_tab_dat)
+
+# Rename row containing control-group means.
+rownames(w_tab)[which(rownames(w_tab) == "1")] <- "control_mean"
+
+# Relabel coefficient columns.
+coef_cols <- grep("coef$", colnames(w_tab))
+
+# Relabel SE columns.
+se_cols <- grep("se$", colnames(w_tab))
+
+# Reformat SE estimates to be within parentheses.
+w_tab[,se_cols] <- apply(w_tab[, se_cols], 2, function (y) paste0("(", y, ")"))
+
+# Concatenate data to comport with LaTeX tabular markup.
+w_tab <- paste(paste(paste(capwords(gsub("_", " ", rownames(w_tab))), 
+			 			 apply(w_tab, 1, function (x) {
+			 			 	   paste(x, collapse = " & ")
+				 		 }), sep = " & "), collapse = " \\\\\n"), "\\\\\n")
+
+# Produce tabular output.
+sink("output/weighted_main_results_tab.tex")
+cat("\\begin{table}\n",
+	  "\\caption{Estimated Treatment Effects on Support for TPNW (Weighted)}\n",
+	  "\\begin{adjustbox}{width = \\textwidth, center}\n",
+	  "\\sisetup{\n",
+      "\tdetect-all,\n",
+      "\ttable-number-alignment = center,\n",
+      "\ttable-figures-integer = 1,\n",
+      "\ttable-figures-decimal = 3,\n",
+      "\ttable-space-text-post = *,\n",
+      "\tinput-symbols = {()}\n",
+  	  "}\n",
+	  paste0("\\begin{tabular}{@{\\extracolsep{5pt}}L{3.5cm}*{4}", 
+	  		 "{S[table-number-alignment = right, table-column-width=1.25cm]}}\n"),
+	  "\\toprule\n",
+	  "& \\multicolumn{4}{c}{Model}\\\\\\cmidrule{2-5}\n",
+	  "& \\multicolumn{2}{c}{{(1)}} & \\multicolumn{2}{c}{{(2)}} \\\\\\midrule\n",
+	  w_tab,
+	  "\\bottomrule\n",
+	  "\\end{tabular}\n",
+	  "\\end{adjustbox}\n",
+	  "\\end{table}\n")
+sink()
+
+### Save image containing all objects.
+save.image(file = "output/hbg_replication_out.RData")

1/replication_package/scripts/hbg_cleaning.R

@@ -0,0 +1,406 @@
+### Initialize workspace.
+rm(list = ls(all = TRUE))
+setwd("~/Downloads/hbg_replication")
+
+# Load required packages
+library(plyr)
+library(car)
+library(anesrake)
+
+# Load relevant functions.
+source("scripts/helper_functions.R")
+
+## Load data.
+# Load TPNW experimental data.
+tpnw <- read.csv("data/tpnw_raw.csv", stringsAsFactors = FALSE, row.names = 1)
+
+# Load original income question data.
+orig_inc <- read.csv("data/tpnw_orig_income.csv", stringsAsFactors = FALSE, 
+                     row.names = 1)
+
+# Load YouGov data (including covariates and awareness question).
+aware <- read.csv("data/tpnw_aware_raw.csv", stringsAsFactors = FALSE, 
+                  row.names = 1)
+
+### Clean TPNW data.
+## Clean data.
+# Remove first two (extraneous) rows.
+tpnw <- tpnw[-c(1, 2),]
+orig_inc <- orig_inc[-c(1, 2),]
+
+# Remove respondents who did not consent.
+tpnw <- tpnw[tpnw$consent == "1",]
+orig_inc <- orig_inc[orig_inc$consent == "1",]
+
+# Coalesce income variables.
+orig_inc <- within(orig_inc, {
+	income <- as.numeric(income)
+	income <- ifelse(income < 1000, NA, income)
+	income <- ifelse(income < 15000, 1, income)
+	income <- ifelse(income >= 15000 & income < 25000, 2, income)
+	income <- ifelse(income >= 25000 & income < 50000, 3, income)
+	income <- ifelse(income >= 50000 & income < 75000, 4, income)
+	income <- ifelse(income >= 75000 & income < 100000, 5, income)
+	income <- ifelse(income >= 100000 & income < 150000, 6, income)
+	income <- ifelse(income >= 150000 & income < 200000, 7, income)
+	income <- ifelse(income >= 200000 & income < 250000, 8, income)
+	income <- ifelse(income >= 250000 & income < 500000, 9, income)
+	income <- ifelse(income >= 500000 & income < 1000000, 10, income)
+	income <- ifelse(income >= 1000000, 11, income)
+})
+orig_inc <- data.frame(pid = orig_inc$pid, income_old = orig_inc$income)
+tpnw <- plyr::join(tpnw, orig_inc, by = "pid", type = "left")
+tpnw <- within(tpnw, {
+			income <- coalesce(as.numeric(income), as.numeric(income_old))
+		})
+
+# Note meta variables.
+meta <- c("consent", "confirmation_code", "new_income_q")
+
+# Note Qualtrics variables.
+qualtrics_vars <- c("StartDate", "EndDate", "Status", "Progress", 
+                    "Duration..in.seconds.", "Finished", "RecordedDate", 
+                    "DistributionChannel", "UserLanguage")
+
+# Note Dynata variables.
+dynata_vars <- c("pid", "psid")
+
+# Note non-numeric variables.
+char_vars <- c(qualtrics_vars, dynata_vars,
+			   c("ResponseId"), names(tpnw)[grep("text", tolower(names(tpnw)))])
+char_cols <- which(names(tpnw) %in% char_vars)
+
+# Numericize other variables
+tpnw <- data.frame(apply(tpnw[, -char_cols], 2, as.numeric), tpnw[char_cols])
+
+tpnw_atts <- which(names(tpnw) %in% c("danger", "peace", "safe", "use_unaccept", 
+									  "always_cheat", "cannot_elim", "slow_reduc"))
+names(tpnw)[tpnw_atts] <- paste("tpnw_atts", names(tpnw)[tpnw_atts], sep = "_")
+
+# Coalesce relevant variables.
+tpnw <- within(tpnw, {
+	# Clean gender variable.
+	female <- ifelse(gender == 95, NA, gender)
+
+	# Transform birthyr variable to age.
+	age <- 2019 - birthyr
+
+	# Transform income variable.
+	income <- car::recode(income, "95 = NA")
+	
+	# Combine pid and pid_forc variables.
+	pid3 <- ifelse(pid3 == 0, pid_forc, pid3)
+	
+	# Recode ideology variable.
+	ideo <- car::recode(ideo, "3 = NA")
+
+	# Recode education variable.
+	educ <- car::recode(educ, "95 = NA")
+
+	# Recode state variable.
+	state <- recode(state, "1 = 'Alabama';
+                          2 = 'Alaska';
+                          4 = 'Arizona';
+                          5 = 'Arkansas';
+                          6 = 'California';
+                          8 = 'Colorado';
+                          9 = 'Connecticut';
+                          10 = 'Delaware';
+                          11 = 'Washington DC';
+                          12 = 'Florida';
+                          13 = 'Georgia';
+                          15 = 'Hawaii';
+                          16 = 'Idaho';
+                          17 = 'Illinois';
+                          18 = 'Indiana';
+                          19 = 'Iowa';
+                          20 = 'Kansas';
+                          21 = 'Kentucky';
+                          22 = 'Louisiana';
+                          23 = 'Maine';
+                          24 = 'Maryland';
+                          25 = 'Massachusetts';
+                          26 = 'Michigan';
+                          27 = 'Minnesota';
+                          28 = 'Mississippi';
+                          29 = 'Missouri';
+                          30 = 'Montana';
+                          31 = 'Nebraska';
+                          32 = 'Nevada';
+                          33 = 'New Hampshire';
+                          34 = 'New Jersey';
+                          35 = 'New Mexico';
+                          36 = 'New York';
+                          37 = 'North Carolina';
+                          38 = 'North Dakota';
+                          39 = 'Ohio';
+                          40 = 'Oklahoma';
+                          41 = 'Oregon';
+                          42 = 'Pennsylvania';
+                          44 = 'Rhode Island';
+                          45 = 'South Carolina';
+                          46 = 'South Dakota';
+                          47 = 'Tennessee';
+                          48 = 'Texas';
+                          49 = 'Utah';
+                          50 = 'Vermont';
+                          51 = 'Virginia';
+                          53 = 'Washington';
+                          54 = 'West Virginia';
+                          55 = 'Wisconsin';
+                          56 = 'Wyoming'")
+
+  # Create regional indicators.
+  northeast <- state %in% c("Connecticut", "Maine", "Massachusetts", 
+                            "New Hampshire", "Rhode Island", "Vermont", 
+                            "New Jersey", "New York", "Pennsylvania")
+  midwest <- state %in% c("Illinois", "Indiana", "Michigan", "Ohio", 
+                          "Wisconsin", "Iowa", "Kansas", "Minnesota", 
+                          "Missouri", "Nebraska", "North Dakota", 
+                          "South Dakota")
+  south <- state %in% c("Delaware", "Florida", "Georgia", "Maryland", 
+                        "North Carolina", "South Carolina", "Virginia", 
+                        "Washington DC", "West Virginia", "Alabama", 
+                        "Kentucky", "Mississippi", "Tennessee", "Arkansas", 
+                        "Louisiana", "Oklahoma", "Texas")
+  west <- state %in% c("Arizona", "Colorado", "Idaho", "Montana", "Nevada", 
+                        "New Mexico", "Utah", "Wyoming", "Alaska", 
+                        "California", "Hawaii", "Oregon", "Washington")
+
+  # Recode join_tpnw outcome.
+  join_tpnw <- car::recode(join_tpnw, "2 = 0")
+
+  # Create indicator variables for each treatment arm.
+  control <- treatment == 0
+  group_cue <- treatment == 1
+  security_cue <- treatment == 2
+  norms_cue <- treatment == 3
+  institutions_cue <- treatment == 4
+
+  # Recode attitudinal outcomes.
+  tpnw_atts_danger <- recode(tpnw_atts_danger, "-2 = 2; -1 = 1; 1 = -1; 2 = -2")
+  tpnw_atts_use_unaccept <- recode(tpnw_atts_use_unaccept, "-2 = 2; -1 = 1; 
+                                                            1 = -1; 2 = -2")
+  tpnw_atts_always_cheat <- recode(tpnw_atts_always_cheat, "-2 = 2; -1 = 1; 
+                                                            1 = -1; 2 = -2")
+  tpnw_atts_cannot_elim <- recode(tpnw_atts_cannot_elim, "-2 = 2; -1 = 1; 
+                                                            1 = -1; 2 = -2")
+})
+
+# Use mean imputation for missingness.
+# Redefine char_cols object.
+char_cols <- which(names(tpnw) %in% c(char_vars, meta, "state", "pid_forc", 
+									  "income_old", "gender"))
+
+# Define out_vars object.
+out_vars <- which(names(tpnw) %in% c("join_tpnw", "n_nukes", "n_tests") |
+				  startsWith(names(tpnw), "tpnw_atts") | 
+				  startsWith(names(tpnw), "physical_eff") |
+				  startsWith(names(tpnw), "testing_matrix"))
+
+# Mean impute.
+tpnw[,-c(char_cols, out_vars)] <- 
+      data.frame(apply(tpnw[, -c(char_cols, out_vars)], 2, function (x) {
+ 								     replace(x, is.na(x), mean(x, na.rm = TRUE))
+							 	 }))
+
+### Clean YouGov data.
+## Indicate all non-numeric variables.
+# Indicate YouGov metadata variables (e.g., start/end time, respondent ID) that
+# may contain characters.
+yougov_vars <- c("starttime", "endtime")
+
+# Numericize all numeric variables
+aware <- data.frame(apply(aware[, -which(names(aware) %in% yougov_vars)], 2, 
+             as.numeric), aware[which(names(aware) %in% yougov_vars)])
+
+# Coalesce relevant variables.
+aware <- within(aware, {
+  # Clean gender variable to an indicator of female gender (renamed below).
+  gender <- recode(gender, "8 = NA") - 1
+
+  # Transform birthyr variable to age (renamed below).
+  birthyr <- 2020 - birthyr
+
+  # Recode pid3 variable.
+  pid3 <- recode(pid3, "1 = -1; 2 = 1; 3 = 0; c(5, 8, 9) = NA")
+
+  # Recode pid7
+  pid7 <- recode(pid7, "1 = -3; 2 = -2; 3 = -1; 4 = 0; 5 = 1; 6 = 2; 7 = 3;
+              c(8, 98) = NA")
+
+  # Code pid variable from pid7.
+  party <- recode(pid7, "c(-3, -2, -1) = -1; c(1, 2, 3) = 1")
+
+  # Recode ideology variable.
+  ideo5 <- recode(ideo5, "c(6, 8, 9) = NA") - 3
+
+  # Recode education variable.
+  educ <- recode(educ, "c(8, 9) = NA")
+
+  # Recode state variable.
+  state <- recode(inputstate, "1 = 'Alabama';
+                               2 = 'Alaska';
+                               4 = 'Arizona';
+                               5 = 'Arkansas';
+                               6 = 'California';
+                               8 = 'Colorado';
+                               9 = 'Connecticut';
+                               10 = 'Delaware';
+                               11 = 'Washington DC';
+                               12 = 'Florida';
+                               13 = 'Georgia';
+                               15 = 'Hawaii';
+                               16 = 'Idaho';
+                               17 = 'Illinois';
+                               18 = 'Indiana';
+                               19 = 'Iowa';
+                               20 = 'Kansas';
+                               21 = 'Kentucky';
+                               22 = 'Louisiana';
+                               23 = 'Maine';
+                               24 = 'Maryland';
+                               25 = 'Massachusetts';
+                               26 = 'Michigan';
+                               27 = 'Minnesota';
+                               28 = 'Mississippi';
+                               29 = 'Missouri';
+                               30 = 'Montana';
+                               31 = 'Nebraska';
+                               32 = 'Nevada';
+                               33 = 'New Hampshire';
+                               34 = 'New Jersey';
+                               35 = 'New Mexico';
+                               36 = 'New York';
+                               37 = 'North Carolina';
+                               38 = 'North Dakota';
+                               39 = 'Ohio';
+                               40 = 'Oklahoma';
+                               41 = 'Oregon';
+                               42 = 'Pennsylvania';
+                               44 = 'Rhode Island';
+                               45 = 'South Carolina';
+                               46 = 'South Dakota';
+                               47 = 'Tennessee';
+                               48 = 'Texas';
+                               49 = 'Utah';
+                               50 = 'Vermont';
+                               51 = 'Virginia';
+                               53 = 'Washington';
+                               54 = 'West Virginia';
+                               55 = 'Wisconsin';
+                               56 = 'Wyoming'")
+
+  # Define US Census geographic regions.
+  northeast <- inputstate %in% c(9, 23, 25, 33, 44, 50, 34, 36, 42)
+  midwest <- inputstate %in% c(18, 17, 26, 39, 55, 19, 20, 27, 29, 31, 38, 46)
+  south <- inputstate %in% c(10, 11, 12, 13, 24, 37, 45, 51, 
+              54, 1, 21, 28, 47, 5, 22, 40, 48)
+  west <- inputstate %in% c(4, 8, 16, 35, 30, 49, 32, 56, 2, 6, 15, 41, 53)
+
+  # Recode employment.
+  employ <- recode(employ, "c(9, 98, 99) = NA")
+
+  # Recode outcome.
+  awareness <- recode(awareness, "8 = NA")
+
+  # Normalize weights.
+  weight <- weight / sum(weight)
+})
+
+# Rename demographic questions.
+aware <- rename(aware, c("gender" = "female", "birthyr" = "age", 
+             "faminc_new" = "income", "ideo5" = "ideo"))
+
+## Impute missing values.
+# Specify non-covariate numerical variables (other is exempted since over 10% of 
+# responses are missing; state is exempted since the variable is categorical).
+non_covars <- names(aware)[names(aware) %in% c("caseid", "starttime", "endtime", 
+                                               "awareness", "state", "weight")]
+
+# Use mean imputation for missingness in covariates.
+aware[, -which(names(aware) %in% non_covars)] <- 
+  data.frame(apply(aware[, -which(names(aware) %in% 
+               non_covars)], 2, function (x) {
+                     replace(x, is.na(x), mean(x, na.rm = TRUE))
+                 }))
+
+### Produce weights for TPNW experimental data using anesrake.
+## Create unique identifier variable for assigning weights.
+tpnw$caseid <- 1:nrow(tpnw)
+
+## Recode relevant covariates for reweighting: coarsen age; recode female; and 
+## recode geographic covariates.
+# Coarsen age into a categorical variable for age groups.
+tpnw$age_wtng <- cut(tpnw$age, c(0, 25, 35, 45, 55, 65, 99))
+levels(tpnw$age_wtng) <- c("age1824", "age2534", "age3544",
+                      "age4554", "age5564", "age6599")
+
+# Recode female as a factor to account for NA values.
+tpnw$female_wtng <- as.factor(tpnw$female)
+levels(tpnw$female_wtng) <- c("male", "na", "female")
+
+# Recode northeast as a factor.
+tpnw$northeast_wtng <- as.factor(tpnw$northeast)
+levels(tpnw$northeast_wtng) <- c("other", "northeast")
+
+# Recode midwest as a factor.
+tpnw$midwest_wtng <- as.factor(tpnw$midwest)
+levels(tpnw$midwest_wtng) <- c("other", "midwest")
+
+# Recode south as a factor.
+tpnw$south_wtng <- as.factor(tpnw$south)
+levels(tpnw$south_wtng) <- c("other", "south")
+
+# Recode west as a factor.
+tpnw$west_wtng <- as.factor(tpnw$west)
+levels(tpnw$west_wtng) <- c("other", "west")
+
+## Specify population targets for balancing (from US Census 2018 data).
+# Specify gender proportion targets and assign names to comport with factors.
+femaletarg <- c(.508, 0, .492)
+names(femaletarg) <- c("female", "na", "male")
+
+# Specify age-group proportion targets and assign names to comport with factors.
+agetarg <- c(29363, 44854, 40659, 41537, 41700, 51080)/249193
+names(agetarg) <- c("age1824", "age2534", "age3544",
+               "age4554", "age5564", "age6599")
+
+# Specify northeast proportion targets and assign names to comport with factors.
+northeasttarg <- c(1 - .173, .173)
+names(northeasttarg) <- c("other", "northeast")
+
+# Specify midwest proportion targets and assign names to comport with factors.
+midwesttarg <- c(1 - .209, .209)
+names(midwesttarg) <- c("other", "midwest")
+
+# Specify south proportion targets and assign names to comport with factors.
+southtarg <- c(1 - .380, .380)
+names(southtarg) <- c("other", "south")
+
+# Specify west proportion targets and assign names to comport with factors.
+westtarg <- c(1 - .238, .238)
+names(westtarg) <- c("other", "west")
+
+# Create a list of all targets, with names to comport with relevant variables.
+targets <- list(femaletarg, agetarg, northeasttarg, 
+                midwesttarg, southtarg, westtarg)
+names(targets) <- c("female_wtng", "age_wtng", "northeast_wtng", 
+                    "midwest_wtng", "south_wtng", "west_wtng")
+
+# Produce anesrake weights.
+anesrake_out <- anesrake(targets, tpnw, caseid = tpnw$caseid,
+                             verbose = TRUE)
+
+# Append anesrake weights to TPNW experimental data.
+tpnw$anesrake_weight <- anesrake_out$weightvec
+
+# Remove variables used for weighting.
+tpnw <- tpnw[-grep("wtng$", names(tpnw))]
+
+## Write data.
+# Write full experimental dataset.
+write.csv(tpnw, "data/tpnw_data.csv")
+
+# write full YouGov dataset.
+write.csv(aware, "data/tpnw_aware.csv")

1/replication_package/scripts/hbg_group_cue.R

@@ -0,0 +1,53 @@
+## Initialize workspace.
+# Remove objects.
+rm(list = ls(all = TRUE))
+
+## Generate data.
+# Create count object storing count data.
+count <- as.matrix(c(1547, 54, 2346))
+
+# Convert count object to an object storing percentages.
+perc <- sapply(count, function (x) x/sum(count))
+
+# Create a cumulative percentage object.
+cum_perc <- cumsum(perc)
+
+# Create separate objects for the plotting of each proportion.
+power_x <- c(0, rep(.74, 2), 0)
+both_x <- c(.74, rep(.96, 2), .74)
+weap_x <- c(.96, rep(1, 2), .96)
+
+# Create an object representing the y-axis plotting points for each polygon.
+plot_y <- c(2.25, 2.25, 3, 3)
+
+# Open new .pdf file.
+setEPS()
+postscript("fgc1.eps", width = 10, height = 3)
+
+# Modify graphical parameters (margins).
+par(mar = c(0, 6, 6, 1))
+
+# Create an empty plot.
+plot(1, type = "n", xlab = "", ylab = "", xlim = c(0, 1), ylim = c(1.5, 3), axes = FALSE)
+
+# Create polygons representing each proportion.
+polygon(power_x, plot_y, col = "#FF8F37", border = "white")
+polygon(both_x, plot_y, col = "steelblue3", border = "white")
+polygon(weap_x, plot_y, col = "gray", border = "white")
+
+# Create an axis and tick and axis labels.
+axis(side = 3, at = seq(0, 1, .1), labels = FALSE)
+text(x = seq(0, 1, .2), y = par("usr")[4] + .2, labels = c("0%", "20%", "40%", "60%", "80%", "100%"), xpd = TRUE)
+mtext(text = "Proportion of Responses", side = 3, line = 2.5, cex = 1.25, font = 2)
+
+# Add text denoting the percentage number associated of each proportion.
+text(x = .74/2, y = 2.2, pos = 1, cex = 2, labels = "74%", col = "#FF8F37", font = 2)
+text(x = .85, y = 2.2, pos = 1, cex = 2, labels = "22%", col = "steelblue3", font = 2)
+text(x = .98, y = 2.2, labels = "4%", pos = 1, cex = 2, col = "grey", font = 2, xpd = TRUE)
+
+# Add a legend.
+leg = legend(x = -.16,, y = 2.625, legend = c("Oppose", "Support", "Prefer not\nto answer"), xpd = TRUE, 
+	pch = 16, col = c("#FF8F37", "steelblue3", "grey"), box.lty = 0, cex = .9, y.intersp = 1.5, yjust = .5)
+
+# Close the device.
+dev.off()

1/replication_package/scripts/helper_functions.R

@@ -0,0 +1,16 @@
+# Define coalesce function for recoding of post-election thermometers.
+coalesce <- function (...) {
+  Reduce(function(x, y) {
+    i <- which(is.na(x))
+    x[i] <- y[i]
+    x},
+    list(...))
+}
+
+# Define capwords() function from the toupper() documentation.
+capwords <- function(s, strict = FALSE) {
+         cap <- function(s) paste(toupper(substring(s, 1, 1)),
+                       {s <- substring(s, 2); if(strict) tolower(s) else s},
+                                  sep = "", collapse = " " )
+         sapply(strsplit(s, split = " "), cap, USE.NAMES = !is.null(names(s)))
+     }

1/replication_package/scripts/run_hbg_replication.R

@@ -0,0 +1,36 @@
+## Initialize workspace.
+# Clear workspace.
+rm(list = ls(all = TRUE))
+
+# Set working directory to abp_replication directory.
+setwd("~/Downloads/hbg_replication")
+
+## Prepare output directory and main output files.
+# If an output directory does not exist, create the directory.
+if (!file.exists("output")) {
+	dir.create("output")
+}
+
+# Create a log file for console output.
+hbg_log <- file("output/hbg_log.txt", open = "wt")
+
+# Echo and sink console log to psv_log file.
+sink(hbg_log, append = TRUE)
+sink(hbg_log, append = TRUE, type = "message")
+
+## Replicate files and produce main output.
+# Run abp_replication_code.R script, storing run-time statistics.
+run_time <- system.time({source("scripts/hbg_cleaning.R", echo = TRUE, 
+								max.deparse.length = 10000)
+						 source("scripts/hbg_analysis.R", echo = TRUE,
+						 	    max.deparse.length = 10000)})
+
+# Close main output sink.
+sink()
+sink(type = "message")
+
+## Sink run-time statistics to a run_time output file.
+run_time_file <- file("output/run_time", open = "wt")
+sink(run_time_file, append = TRUE)
+print(run_time)
+sink()

1/should_reproduce.txt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42700f9f34ac54e4543b10597e32ddc588738ad06fc1950909d4ca4bbf092d23
+size 214