Datasets:

chuxuan
/

REPRO-Bench

+##############################################################
+##############################################################
+####### Replication code for "Do museums promote reconciliation? Evidence from a field experiment," Journal of Politics
+####### This file includes code for all analyses and figures in the online appendix
+##############################################################
+##############################################################
+require("sandwich")
+require("plyr")
+require("lmtest")
+require(dplyr)
+require(gridExtra)
+require("RColorBrewer")
+require(ggplot2)
+##############################################################
+###### Read in data and establish main functions
+##############################################################
+load(file = "all.Rdata")
+### ATE FUNCTIONS ##
+# This estimates ATE when we have a pre-treatment measurement
+est.ate<-function(dv, predv, df){
+  predv <- f(predv)
+  dv <- f(dv)
+  summary(fit.1 <- lm(dv~df$treat + df$pre_ideology_1 +
+                        + predv*df$date_diff + df$base_gender +df$age + df$v))
+  vcv <- vcovHC(fit.1)
+  n <- nobs(fit.1)
+  result <- coeftest(fit.1, vcv)[2, 1:4] / itt.d
+  result <- list("point.estimate" = result[1],"se"=result[2],"pvalue"=result[4], "obs" = n)
+  return(result)
+}
+# This estimates ATE when we don't have a pre-treatment measurement
+est.ate.np<-function(dv, df){
+  dv <- f(dv)
+  summary(fit.1 <- lm(dv~df$treat + df$pre_ideology_1 + df$base_gender +df$age+df$v))
+  vcv <- vcovHC(fit.1)
+  n <- nobs(fit.1)
+  result <- coeftest(fit.1, vcv)[2, 1:4] / itt.d
+  result <- list("point.estimate" = result[1],"se"=result[2],"pvalue"=result[4], "obs" = n)
+  return(result)
+}
+# this function recodes NAs to the mean, per our PAP
+f <- function(x){
+  m <- mean(x, na.rm = TRUE)
+  x[is.na(x)] <- m
+  x
+}
+## recode covariates to means
+all$age <- f(all$age)
+all$pre_ideology_1 <- f(all$pre_ideology_1)
+all$base_gender <- f(all$base_gender)
+all$date_diff <- f(all$date_diff)
+# split dataset into left, right, and related to victim for heterogeneous analyses
+left <- all[all$right == 0,]
+right <- all[all$right == 1,]
+itt.d <- all$itt.d
+##############################################################
+###### Table A1: Number of respondents by condition
+##############################################################
+# Note: we do not have data on those who did not show up or opted not to comply with their random assignment
+# completed by condition
+sum(all$treat==1)
+sum(all$treat==0)
+# first follow up by condition
+sum(all$f1[all$treat==1])
+sum(all$f1[all$treat==0])
+# second follow up by condition
+sum(all$f2[all$treat==1])
+sum(all$f2[all$treat==0])
+# third follow up by condition
+sum(all$f3[all$treat==1])
+sum(all$f3[all$treat==0])
+##############################################################
+###### Table A2: Covariate balance
+##############################################################
+t.test(all$age~all$treat)
+t.test(all$base_gender~all$treat)
+t.test(all$pre_ideology_1~all$treat)
+t.test(all$v~all$treat)
+t.test(all$pre_political_interest~all$treat)
+t.test(all$pre_party_id~all$treat)
+t.test(all$pre_positive~all$treat)
+t.test(all$pre_negative~all$treat)
+t.test(all$pre_conf_gov~all$treat)
+##############################################################
+###### Table A3: Perceptions of museum by ideology
+##############################################################
+t.test(all$mm_obj~all$right)
+t.test(all$mm_views_like~all$right)
+t.test(all$mm_views_content~all$right)
+t.test(all$mm_views_inhibit~all$right)
+t.test(all$mm_views_important~all$right)
+t.test(all$mm_new~all$right)
+##############################################################
+###### Table A5: Perceptions of inequality after visiting the MMDH
+##############################################################
+est.ate(all$current_ineq, all$pre_current_ineq, all)
+##############################################################
+###### Table A6: Full regression results, Political institutions
+##############################################################
+# See lines 64-72 in "BPV_museums_maintext.R"
+##############################################################
+###### Table A7: Full regression results, Political institutions by ideology
+##############################################################
+# See lines 75-93 in "BPV_museums_maintext.R"
+# Interactions (final three columns of table) reproduced here
+est.ate.int<-function(dv, predv, df){
+  predv <- f(predv)
+  dv <- f(dv)
+  summary(fit.1 <- lm(dv~df$treat*df$pre_ideology_1+
+                        + predv*df$date_diff + df$base_gender +df$age + df$v))
+  vcv <- vcovHC(fit.1)
+  n <- nobs(fit.1)
+  result <- coeftest(fit.1, vcv)[9, 1:4] / itt.d
+  result <- list("point.estimate" = result[1],"se"=result[2],"pvalue"=result[4], "obs" = n)
+  return(result)
+}
+est.ate.int(all$democracy, all$pre_democracy, all)
+est.ate.int(all$military_gov, all$pre_military_gov, all)
+est.ate.int(all$inst_gov, all$pre_inst_gov, all)
+est.ate.int(all$inst_mil, all$pre_inst_mil, all)
+est.ate.int(all$inst_police, all$pre_inst_police, all)
+est.ate.int(all$conf_gov, all$pre_conf_gov, all)
+est.ate.int(all$conf_mil, all$pre_conf_mil, all)
+est.ate.int(all$conf_police, all$pre_conf_police, all)
+est.ate.int(all$conf_church, all$pre_conf_church, all)
+##############################################################
+###### Table A8: Full regression results, Transitional justice
+##############################################################
+# See lines 164-171 in "BPV_museums_maintext.R"
+##############################################################
+###### Table A9: Full regression results, Transitional justice by ideology
+##############################################################
+# See lines 75-93 in "BPV_museums_maintext.R"
+# Interactions (final three columns of table) reproduced below
+est.ate.int.np<-function(dv, df){
+  dv <- f(dv)
+  summary(fit.1 <- lm(dv~df$treat*df$pre_ideology_1+
+                        + df$base_gender +df$age + df$v))
+  vcv <- vcovHC(fit.1)
+  n <- nobs(fit.1)
+  result <- coeftest(fit.1, vcv)[7, 1:4] / itt.d
+  result <- list("point.estimate" = result[1],"se"=result[2],"pvalue"=result[4], "obs" = n)
+  return(result)
+}
+est.ate.int.np(all$justice_advance, all)
+est.ate.int.np(all$justice_account, all)
+est.ate.int(all$current_recomp, all$pre_current_recomp, all)
+est.ate.int.np(all$tj_judicial, all)
+est.ate.int.np(all$tj_apology, all)
+est.ate.int.np(all$policies_apologize, all)
+est.ate.int.np(all$policies_compensate, all)
+est.ate.int.np(all$policies_pardon, all)
+##############################################################
+###### Table A10: Full regression results, emotions
+##############################################################
+# See lines 261-297 in "BPV_museums_maintext.R"
+##############################################################
+###### Table A11: Number of respondents by condition
+##############################################################
+# See code for A1, above
+##############################################################
+###### Table A12: Test for differential attrition
+##############################################################
+## F-test
+diff.att.full <- lm(all$observerd~(all$treat*all$right)
+                    +(all$treat*all$base_gender) + (all$treat*all$age) +
+                      (all$treat*all$v))
+diff.att.1.full <- lm(all$f1~(all$treat*all$right)
+                      +(all$treat*all$base_gender) + (all$treat*all$age) +
+                        (all$treat*all$v))
+diff.att.2.full <- lm(all$f2~(all$treat*all$right)
+                      +(all$treat*all$base_gender) + (all$treat*all$age) +
+                        (all$treat*all$v))
+diff.att.3.full <- lm(all$f3~(all$treat*all$right)
+                      +(all$treat*all$base_gender) + (all$treat*all$age) +
+                        (all$treat*all$v))
+diff.att.red <- lm(all$observerd~all$treat+all$right
+                   +all$base_gender+all$age + all$v)
+diff.att.1.red <- lm(all$f1~all$treat+all$right
+                     +all$base_gender+all$age + all$v)
+diff.att.2.red <- lm(all$f2~all$treat+all$right
+                     +all$base_gender+all$age + all$v)
+diff.att.3.red <- lm(all$f3~all$treat+all$right
+                     +all$base_gender+all$age + all$v)
+anova(diff.att.full, diff.att.red)
+anova(diff.att.1.full, diff.att.1.red)
+anova(diff.att.2.full, diff.att.2.red)
+anova(diff.att.3.full, diff.att.3.red)
+##############################################################
+###### Table A13: Differential attrition by pre-treatment covariates
+##############################################################
+#age
+summary(lm(f(all$f1)~f(all$age)))
+summary(lm(f(all$f2)~f(all$age)))
+summary(lm(f(all$f3)~f(all$age)))
+# gender
+summary(lm(f(all$f1)~f(all$base_gender)))
+summary(lm(f(all$f2)~f(all$base_gender)))
+summary(lm(f(all$f3)~f(all$base_gender)))
+# ideology
+summary(lm(f(all$f1)~f(all$pre_ideology_1)))
+summary(lm(f(all$f2)~f(all$pre_ideology_1)))
+summary(lm(f(all$f3)~f(all$pre_ideology_1)))
+# economic situation
+summary(lm(f(all$f1)~f(all$pre_economic_situation)))
+summary(lm(f(all$f2)~f(all$pre_economic_situation)))
+summary(lm(f(all$f3)~f(all$pre_economic_situation)))
+# political interest
+summary(lm(f(all$f1)~f(all$pre_political_interest)))
+summary(lm(f(all$f2)~f(all$pre_political_interest)))
+summary(lm(f(all$f3)~f(all$pre_political_interest)))
+# religiosity
+summary(lm(f(all$f1)~f(all$pre_religion_importance)))
+summary(lm(f(all$f2)~f(all$pre_religion_importance)))
+summary(lm(f(all$f3)~f(all$pre_religion_importance)))
+# museum visits
+summary(lm(f(all$f1)~f(all$totalmuseums)))
+summary(lm(f(all$f2)~f(all$totalmuseums)))
+summary(lm(f(all$f3)~f(all$totalmuseums)))
+# trust in the government
+summary(lm(f(all$f1)~f(all$pre_conf_gov)))
+summary(lm(f(all$f2)~f(all$pre_conf_gov)))
+summary(lm(f(all$f3)~f(all$pre_conf_gov)))
+# satisfaction with the government
+summary(lm(f(all$f1)~f(all$pre_inst_gov)))
+summary(lm(f(all$f2)~f(all$pre_inst_gov)))
+summary(lm(f(all$f3)~f(all$pre_inst_gov)))
+# inequality is a problem
+summary(lm(f(all$f1)~f(all$pre_current_ineq)))
+summary(lm(f(all$f2)~f(all$pre_current_ineq)))
+summary(lm(f(all$f3)~f(all$pre_current_ineq)))
+# positive emotions
+summary(lm(f(all$f1)~f(all$pre_positive)))
+summary(lm(f(all$f2)~f(all$pre_positive)))
+summary(lm(f(all$f3)~f(all$pre_positive)))
+#negative emotions
+summary(lm(f(all$f1)~f(all$pre_negative)))
+summary(lm(f(all$f2)~f(all$pre_negative)))
+summary(lm(f(all$f3)~f(all$pre_negative)))
+##############################################################
+###### Table A14: Differential attrition by round 1 responses
+##############################################################
+# pol institutions index
+summary(lm(f(all$f1)~f(all$pol.inst.index)))
+summary(lm(f(all$f2)~f(all$pol.inst.index)))
+summary(lm(f(all$f3)~f(all$pol.inst.index)))
+# military gov
+summary(lm(f(all$f1)~f(all$military_gov)))
+summary(lm(f(all$f2)~f(all$military_gov)))
+summary(lm(f(all$f3)~f(all$military_gov)))
+# tj index
+summary(lm(f(all$f1)~f(all$tj.index)))
+summary(lm(f(all$f2)~f(all$tj.index)))
+summary(lm(f(all$f3)~f(all$tj.index)))
+# compensation
+summary(lm(f(all$f1)~f(all$current_recomp)))
+summary(lm(f(all$f2)~f(all$current_recomp)))
+summary(lm(f(all$f3)~f(all$current_recomp)))
+# pardon
+summary(lm(f(all$f1)~f(all$policies_pardon)))
+summary(lm(f(all$f2)~f(all$policies_pardon)))
+summary(lm(f(all$f3)~f(all$policies_pardon)))
+# negative emotions
+summary(lm(f(all$f1)~f(all$negative)))
+summary(lm(f(all$f2)~f(all$negative)))
+summary(lm(f(all$f3)~f(all$negative)))
+# positive emotions
+summary(lm(f(all$f1)~f(all$positive)))
+summary(lm(f(all$f2)~f(all$positive)))
+summary(lm(f(all$f3)~f(all$positive)))
+##############################################################
+###### Table A15: Political institutions adjusted for multiple comparisons
+##############################################################
+# conducted using results from "BPV_museums_maintext.R" and EGAP calculator - https://egap.shinyapps.io/multiple-comparisons-app/
+# adjusted significance with Bejamini and Hochberg correction
+##############################################################
+###### Table A16: Transitional justice adjusted for multiple comparisons
+##############################################################
+# conducted using results from "BPV_museums_maintext.R" and EGAP calculator - https://egap.shinyapps.io/multiple-comparisons-app/
+# adjusted significance with Bejamini and Hochberg correction
+##############################################################
+######Table A17. General museum impressions by recoded ideology.
+##############################################################
+# Split up by ideology
+## RECODE FOR ROBUSTNESS HERE ##
+all$pre_ideology_1 <- f(all$pre_ideology_1)
+all$right <- ifelse(all$pre_ideology_1 > 5, 1,0)
+left <- all[all$right == 0,]
+right <- all[all$right == 1,]
+# mean values on dvs
+t.test(all$mm_obj~all$right)
+t.test(all$mm_views_like~all$right)
+t.test(all$mm_views_content~all$right)
+t.test(all$mm_views_inhibit~all$right)
+t.test(all$mm_views_important~all$right)
+t.test(all$mm_new~all$right)
+##############################################################
+######Table A18. Political institutions by recoded ideology
+##############################################################
+dem.right <- est.ate(right$democracy, right$pre_democracy, right)
+mil.right <- est.ate(right$military_gov, right$pre_military_gov, right)
+gov_sat.right <- est.ate(right$inst_gov, right$pre_inst_gov, right)
+mil_sat.right <- est.ate(right$inst_mil, right$pre_inst_mil, right)
+pol_sat.right <- est.ate(right$inst_police, right$pre_inst_police, right)
+gov_trust.right <- est.ate(right$conf_gov, right$pre_conf_gov, right)
+mil_trust.right <- est.ate(right$conf_mil, right$pre_conf_mil, right)
+pol_trust.right <- est.ate(right$conf_police, right$pre_conf_police, right)
+church_trust.right <- est.ate(right$conf_church, right$pre_conf_church, right)
+dem.left <- est.ate(left$democracy, left$pre_democracy, left)
+mil.left <- est.ate(left$military_gov, left$pre_military_gov, left)
+gov_sat.left <- est.ate(left$inst_gov, left$pre_inst_gov, left)
+mil_sat.left <- est.ate(left$inst_mil, left$pre_inst_mil, left)
+pol_sat.left <- est.ate(left$inst_police, left$pre_inst_police, left)
+gov_trust.left <- est.ate(left$conf_gov, left$pre_conf_gov, left)
+mil_trust.left <- est.ate(left$conf_mil, left$pre_conf_mil, left)
+pol_trust.left <- est.ate(left$conf_police, left$pre_conf_police, left)
+church_trust.left <- est.ate(left$conf_church, left$pre_conf_church, left)
+## interactions (for appendix)
+dem_int <- est.ate.int(all$democracy, all$pre_democracy, all)
+mil.int <- est.ate.int(all$military_gov, all$pre_military_gov, all)
+gov_sat.int <- est.ate.int(all$inst_gov, all$pre_inst_gov, all)
+mil_sat.int <- est.ate.int(all$inst_mil, all$pre_inst_mil, all)
+pol_sat.int <- est.ate.int(all$inst_police, all$pre_inst_police, all)
+gov_trust.int <- est.ate.int(all$conf_gov, all$pre_conf_gov, all)
+mil_trust.int <- est.ate.int(all$conf_mil, all$pre_conf_mil, all)
+pol_trust.int <- est.ate.int(all$conf_police, all$pre_conf_police, all)
+church_trust.int <- est.ate.int(all$conf_church, all$pre_conf_church, all)
+##############################################################
+######Table A19. Transitional justice by recoded ideology
+##############################################################
+advance.right <- est.ate.np(right$justice_advance, right)
+justice_account.right <- est.ate.np(right$justice_account, right)
+compensation.right <- est.ate(right$current_recomp, right$pre_current_recomp, right)
+judicial.right <- est.ate.np(right$tj_judicial, right)
+inst_apology.right <- est.ate.np(right$tj_apology, right)
+apologize.right <- est.ate.np(right$policies_apologize, right)
+compensate.right <- est.ate.np(right$policies_compensate, right)
+pardoned.right <- est.ate.np(right$policies_pardon, right)
+advance.left <- est.ate.np(left$justice_advance, left)
+justice_account.left <- est.ate.np(left$justice_account, left)
+compensation.left <- est.ate(left$current_recomp, left$pre_current_recomp, left)
+judicial.left <- est.ate.np(left$tj_judicial, left)
+inst_apology.left <- est.ate.np(left$tj_apology, left)
+apologize.left <- est.ate.np(left$policies_apologize, left)
+compensate.left <- est.ate.np(left$policies_compensate, left)
+pardoned.left <- est.ate.np(left$policies_pardon, left)
+##############################################################
+###### Figure A3: Persistence of responses across treatment groups and ideologies
+##############################################################
+# unrecode ideology
+# Split up by ideology
+## RECODE FOR ROBUSTNESS HERE ##
+all$pre_ideology_1 <- f(all$pre_ideology_1)
+all$right <- ifelse(all$pre_ideology_1 > 4, 1,0)
+left <- all[all$right == 0,]
+right <- all[all$right == 1,]
+est.ate.np.f<-function(dv){
+  summary(fit.1 <- lm(dv~all$treat + all$pre_ideology_1 + all$base_gender +all$age+all$v))
+  vcv <- vcovHC(fit.1)
+  n <- nobs(fit.1)
+  result <- coeftest(fit.1, vcv)[2, 1:4] / itt.d
+  result <- list("point.estimate" = result[1],"se"=result[2],"pvalue"=result[4], "obs" = n)
+  return(result)
+}
+## PARDON
+pardoned <- est.ate.np(all$policies_pardon, all)
+pardoned_f1<- est.ate.np.f(all$policies_pardon_f1)
+pardoned_f2 <- est.ate.np.f(all$policies_pardon_f2)
+pardoned_f3 <- est.ate.np.f(all$policies_pardon_f3)
+prop.table(table(all$policies_pardon[all$treat==1&all$right==1]))
+prop.table(table(all$policies_pardon_f1[all$treat==1&all$right==1]))
+prop.table(table(all$policies_pardon_f2[all$treat==1&all$right==1]))
+prop.table(table(all$policies_pardon_f3[all$treat==1&all$right==1]))
+all$pardon <- ifelse(all$policies_pardon==1|all$policies_pardon==0,0,1)
+all$pardon_f1 <- ifelse(all$policies_pardon_f1==1|all$policies_pardon_f1==0,0,1)
+all$pardon_f2 <- ifelse(all$policies_pardon_f2==1|all$policies_pardon_f2==0,0,1)
+all$pardon_f3 <- ifelse(all$policies_pardon_f3==1|all$policies_pardon_f3==0,0,1)
+pardon.df <- data.frame(all$pardon, all$pardon_f1,all$pardon_f2,all$pardon_f3,all$treat,all$right)
+pardon.df.treat.right <- pardon.df[pardon.df$all.treat==1&pardon.df$all.right==1,]
+pardon.df.treat.right$num <- rowSums(pardon.df.treat.right==1)-2
+prop.table(table(pardon.df.treat.right$num))
+pardon.df.treat.left <- pardon.df[pardon.df$all.treat==1&pardon.df$all.right==0,]
+pardon.df.treat.left$num <- rowSums(pardon.df.treat.left==1)-1
+prop.table(table(pardon.df.treat.left$num))
+pardon.df.control.right <- pardon.df[pardon.df$all.treat==0&pardon.df$all.right==1,]
+pardon.df.control.right$num <- rowSums(pardon.df.control.right==1)-1
+prop.table(table(pardon.df.control.right$num))
+pardon.df.control.left <- pardon.df[pardon.df$all.treat==0&pardon.df$all.right==0,]
+pardon.df.control.left$num <- rowSums(pardon.df.control.left==1)
+pardon.df.control.left$num <- factor(pardon.df.control.left$num, levels = c(0:4))
+prop.table(table(pardon.df.control.left$num))
+df <- data.frame(waveschosen=c(0:4),percent=NA)
+df$percent <- prop.table(table(pardon.df.control.left$num))
+df$group <- "control left"
+df2 <- data.frame(waveschosen=c(0:4),percent=NA)
+pardon.df.control.right$num <- factor(pardon.df.control.right$num, levels = c(0:4))
+df2$percent <- prop.table(table(pardon.df.control.right$num))
+df2$group <- "control right"
+df3 <- data.frame(waveschosen=c(0:4),percent=NA)
+pardon.df.treat.right$num <- factor(pardon.df.treat.right$num, levels = c(0:4))
+df3$percent <- prop.table(table(pardon.df.treat.right$num))
+df3$group <- "treat right"
+df4 <- data.frame(waveschosen=c(0:4),percent=NA)
+pardon.df.treat.left$num <- factor(pardon.df.treat.left$num, levels = c(0:4))
+df4$percent <- prop.table(table(pardon.df.treat.left$num))
+df4$group <- "treat left"
+pardon.df <- rbind (df,df2,df3,df4)
+## trust church
+all$pre_conf_church <- ifelse(all$pre_conf_church==1|all$pre_conf_church==0,0,1)
+all$conf_church <- ifelse(all$conf_church==1|all$conf_church==0,0,1)
+all$conf_church_f1 <- ifelse(all$conf_church_f1==1|all$conf_church_f1==0,0,1)
+all$conf_church_f2 <- ifelse(all$conf_church_f2==1|all$conf_church_f2==0,0,1)
+all$conf_church_f3 <- ifelse(all$conf_church_f3==1|all$conf_church_f3==0,0,1)
+trust_church.df <- data.frame(all$conf_church, all$conf_church_f1,all$conf_church_f2,all$conf_church_f3,all$treat,all$right)
+trust_church.treat.right <- trust_church.df[trust_church.df$all.treat==1&trust_church.df$all.right==1,]
+trust_church.treat.right$num <- rowSums(trust_church.treat.right==1)-2
+prop.table(table(trust_church.treat.right$num))
+trust_church.treat.left <- trust_church.df[trust_church.df$all.treat==1&trust_church.df$all.right==0,]
+trust_church.treat.left$num <- rowSums(trust_church.treat.left==1)-1
+prop.table(table(trust_church.treat.left$num))
+trust_church.control.right <- trust_church.df[trust_church.df$all.treat==0&trust_church.df$all.right==1,]
+trust_church.control.right$num <- rowSums(trust_church.control.right==1)-1
+prop.table(table(trust_church.control.right$num))
+trust_church.control.left <- trust_church.df[trust_church.df$all.treat==0&trust_church.df$all.right==0,]
+trust_church.control.left$num <- rowSums(trust_church.control.left==1)
+trust_church.control.left$num <- factor(trust_church.control.left$num, levels = c(0:4))
+prop.table(table(trust_church.control.left$num))
+df <- data.frame(waveschosen=c(0:4),percent=NA)
+df$percent <- prop.table(table(trust_church.control.left$num))
+df$group <- "control left"
+df2 <- data.frame(waveschosen=c(0:4),percent=NA)
+trust_church.control.right$num <- factor(trust_church.control.right$num, levels = c(0:4))
+df2$percent <- prop.table(table(trust_church.control.right$num))
+df2$group <- "control right"
+df3 <- data.frame(waveschosen=c(0:4),percent=NA)
+trust_church.treat.right$num <- factor(trust_church.treat.right$num, levels = c(0:4))
+df3$percent <- prop.table(table(trust_church.treat.right$num))
+df3$group <- "treat right"
+df4 <- data.frame(waveschosen=c(0:4),percent=NA)
+trust_church.treat.left$num <- factor(trust_church.treat.left$num, levels = c(0:4))
+df4$percent <- prop.table(table(trust_church.treat.left$num))
+df4$group <- "treat left"
+churchtrust.df <- rbind (df,df2,df3,df4)
+## satisfaction with government
+all$pre_inst_gov <- ifelse(all$pre_inst_gov==1|all$pre_inst_gov==0,0,1)
+all$inst_gov <- ifelse(all$inst_gov==1|all$inst_gov==0,0,1)
+all$inst_gov_f1 <- ifelse(all$inst_gov_f1==1|all$inst_gov_f1==0,0,1)
+all$inst_gov_f2 <- ifelse(all$inst_gov_f2==1|all$inst_gov_f2==0,0,1)
+all$inst_gov_f3 <- ifelse(all$inst_gov_f3==1|all$inst_gov_f3==0,0,1)
+inst_gov.df <- data.frame(all$inst_gov, all$inst_gov_f1,all$inst_gov_f2,all$inst_gov_f3,all$treat,all$right)
+inst_gov.treat.right <- inst_gov.df[inst_gov.df$all.treat==1&inst_gov.df$all.right==1,]
+inst_gov.treat.right$num <- rowSums(inst_gov.treat.right==1)-2
+prop.table(table(inst_gov.treat.right$num))
+inst_gov.treat.left <- inst_gov.df[inst_gov.df$all.treat==1&inst_gov.df$all.right==0,]
+inst_gov.treat.left$num <- rowSums(inst_gov.treat.left==1)-1
+prop.table(table(inst_gov.treat.left$num))
+inst_gov.control.right <- inst_gov.df[inst_gov.df$all.treat==0&inst_gov.df$all.right==1,]
+inst_gov.control.right$num <- rowSums(inst_gov.control.right==1)-1
+prop.table(table(inst_gov.control.right$num))
+inst_gov.control.left <- inst_gov.df[inst_gov.df$all.treat==0&inst_gov.df$all.right==0,]
+inst_gov.control.left$num <- rowSums(inst_gov.control.left==1)
+inst_gov.control.left$num <- factor(inst_gov.control.left$num, levels = c(0:4))
+prop.table(table(inst_gov.control.left$num))
+df <- data.frame(waveschosen=c(0:4),percent=NA)
+df$percent <- prop.table(table(inst_gov.control.left$num))
+df$group <- "control left"
+df2 <- data.frame(waveschosen=c(0:4),percent=NA)
+inst_gov.control.right$num <- factor(inst_gov.control.right$num, levels = c(0:4))
+df2$percent <- prop.table(table(inst_gov.control.right$num))
+df2$group <- "control right"
+df3 <- data.frame(waveschosen=c(0:4),percent=NA)
+inst_gov.treat.right$num <- factor(inst_gov.treat.right$num, levels = c(0:4))
+df3$percent <- prop.table(table(inst_gov.treat.right$num))
+df3$group <- "treat right"
+df4 <- data.frame(waveschosen=c(0:4),percent=NA)
+inst_gov.treat.left$num <- factor(inst_gov.treat.left$num, levels = c(0:4))
+df4$percent <- prop.table(table(inst_gov.treat.left$num))
+df4$group <- "treat left"
+govsat.df <- rbind (df,df2,df3,df4)
+## satisfaction with democracy
+all$pre_democracy <- ifelse(all$pre_democracy==1|all$pre_democracy==0,0,1)
+all$democracy <- ifelse(all$democracy==1|all$democracy==0,0,1)
+all$democracy_f1 <- ifelse(all$democracy_f1==1|all$democracy_f1==0,0,1)
+all$democracy_f2 <- ifelse(all$democracy_f2==1|all$democracy_f2==0,0,1)
+all$democracy_f3 <- ifelse(all$democracy_f3==1|all$democracy_f3==0,0,1)
+democracy.df <- data.frame(all$democracy, all$democracy_f1,all$democracy_f2,all$democracy_f3,all$treat,all$right)
+democracy.treat.right <- democracy.df[democracy.df$all.treat==1&democracy.df$all.right==1,]
+democracy.treat.right$num <- rowSums(democracy.treat.right==1)-2
+prop.table(table(democracy.treat.right$num))
+democracy.treat.left <- democracy.df[democracy.df$all.treat==1&democracy.df$all.right==0,]
+democracy.treat.left$num <- rowSums(democracy.treat.left==1)-1
+prop.table(table(democracy.treat.left$num))
+democracy.control.right <- democracy.df[democracy.df$all.treat==0&democracy.df$all.right==1,]
+democracy.control.right$num <- rowSums(democracy.control.right==1)-1
+prop.table(table(democracy.control.right$num))
+democracy.control.left <- democracy.df[democracy.df$all.treat==0&democracy.df$all.right==0,]
+democracy.control.left$num <- rowSums(democracy.control.left==1)
+democracy.control.left$num <- factor(democracy.control.left$num, levels = c(0:4))
+prop.table(table(democracy.control.left$num))
+df <- data.frame(waveschosen=c(0:4),percent=NA)
+df$percent <- prop.table(table(democracy.control.left$num))
+df$group <- "control left"
+df2 <- data.frame(waveschosen=c(0:4),percent=NA)
+democracy.control.right$num <- factor(democracy.control.right$num, levels = c(0:4))
+df2$percent <- prop.table(table(democracy.control.right$num))
+df2$group <- "control right"
+df3 <- data.frame(waveschosen=c(0:4),percent=NA)
+democracy.treat.right$num <- factor(democracy.treat.right$num, levels = c(0:4))
+df3$percent <- prop.table(table(democracy.treat.right$num))
+df3$group <- "treat right"
+df4 <- data.frame(waveschosen=c(0:4),percent=NA)
+democracy.treat.left$num <- factor(democracy.treat.left$num, levels = c(0:4))
+df4$percent <- prop.table(table(democracy.treat.left$num))
+df4$group <- "treat left"
+democracy.df <- rbind (df,df2,df3,df4)
+## military government
+military_gov.df <- data.frame(all$military_gov, all$military_gov_f1,all$military_gov_f2,all$military_gov_f3,all$treat,all$right)
+military_gov.treat.right <- military_gov.df[military_gov.df$all.treat==1&military_gov.df$all.right==1,]
+military_gov.treat.right$num <- rowSums(military_gov.treat.right==1)-2
+prop.table(table(military_gov.treat.right$num))
+military_gov.treat.left <- military_gov.df[military_gov.df$all.treat==1&military_gov.df$all.right==0,]
+military_gov.treat.left$num <- rowSums(military_gov.treat.left==1)-1
+prop.table(table(military_gov.treat.left$num))
+military_gov.control.right <- military_gov.df[military_gov.df$all.treat==0&military_gov.df$all.right==1,]
+military_gov.control.right$num <- rowSums(military_gov.control.right==1)-1
+prop.table(table(military_gov.control.right$num))
+military_gov.control.left <- military_gov.df[military_gov.df$all.treat==0&military_gov.df$all.right==0,]
+military_gov.control.left$num <- rowSums(military_gov.control.left==1)
+military_gov.control.left$num <- factor(military_gov.control.left$num, levels = c(0:4))
+prop.table(table(military_gov.control.left$num))
+df <- data.frame(waveschosen=c(0:4),percent=NA)
+df$percent <- prop.table(table(military_gov.control.left$num))
+df$group <- "control left"
+df2 <- data.frame(waveschosen=c(0:4),percent=NA)
+military_gov.control.right$num <- factor(military_gov.control.right$num, levels = c(0:4))
+df2$percent <- prop.table(table(military_gov.control.right$num))
+df2$group <- "control right"
+df3 <- data.frame(waveschosen=c(0:4),percent=NA)
+military_gov.treat.right$num <- factor(military_gov.treat.right$num, levels = c(0:4))
+df3$percent <- prop.table(table(military_gov.treat.right$num))
+df3$group <- "treat right"
+df4 <- data.frame(waveschosen=c(0:4),percent=NA)
+military_gov.treat.left$num <- factor(military_gov.treat.left$num, levels = c(0:4))
+df4$percent <- prop.table(table(military_gov.treat.left$num))
+df4$group <- "treat left"
+milgov.df <- rbind (df,df2,df3,df4)
+## satisfaction with police
+all$pre_inst_police <- ifelse(all$pre_inst_police==1|all$pre_inst_police==0,0,1)
+all$inst_police <- ifelse(all$inst_police==1|all$inst_police==0,0,1)
+all$inst_police_f1 <- ifelse(all$inst_police_f1==1|all$inst_police_f1==0,0,1)
+all$inst_police_f2 <- ifelse(all$inst_police_f2==1|all$inst_police_f2==0,0,1)
+all$inst_police_f3 <- ifelse(all$inst_police_f3==1|all$inst_police_f3==0,0,1)
+inst_police.df <- data.frame(all$inst_police, all$inst_police_f1,all$inst_police_f2,all$inst_police_f3,all$treat,all$right)
+inst_police.treat.right <- inst_police.df[inst_police.df$all.treat==1&inst_police.df$all.right==1,]
+inst_police.treat.right$num <- rowSums(inst_police.treat.right==1)-2
+prop.table(table(inst_police.treat.right$num))
+inst_police.treat.left <- inst_police.df[inst_police.df$all.treat==1&inst_police.df$all.right==0,]
+inst_police.treat.left$num <- rowSums(inst_police.treat.left==1)-1
+prop.table(table(inst_police.treat.left$num))
+inst_police.control.right <- inst_police.df[inst_police.df$all.treat==0&inst_police.df$all.right==1,]
+inst_police.control.right$num <- rowSums(inst_police.control.right==1)-1
+prop.table(table(inst_police.control.right$num))
+inst_police.control.left <- inst_police.df[inst_police.df$all.treat==0&inst_police.df$all.right==0,]
+inst_police.control.left$num <- rowSums(inst_police.control.left==1)
+inst_police.control.left$num <- factor(inst_police.control.left$num, levels = c(0:4))
+prop.table(table(inst_police.control.left$num))
+df <- data.frame(waveschosen=c(0:4),percent=NA)
+df$percent <- prop.table(table(inst_police.control.left$num))
+df$group <- "control left"
+df2 <- data.frame(waveschosen=c(0:4),percent=NA)
+inst_police.control.right$num <- factor(inst_police.control.right$num, levels = c(0:4))
+df2$percent <- prop.table(table(inst_police.control.right$num))
+df2$group <- "control right"
+df3 <- data.frame(waveschosen=c(0:4),percent=NA)
+inst_police.treat.right$num <- factor(inst_police.treat.right$num, levels = c(0:4))
+df3$percent <- prop.table(table(inst_police.treat.right$num))
+df3$group <- "treat right"
+df4 <- data.frame(waveschosen=c(0:4),percent=NA)
+inst_police.treat.left$num <- factor(inst_police.treat.left$num, levels = c(0:4))
+df4$percent <- prop.table(table(inst_police.treat.left$num))
+df4$group <- "treat left"
+police.df <- rbind (df,df2,df3,df4)
+p1 <- ggplot(data=pardon.df, aes(x=waveschosen, y=percent)) +
+  geom_bar(stat="identity", width=0.5,colour="black") +
+  facet_grid(. ~ group) +
+  geom_text(data=pardon.df, aes(x = waveschosen, y = (percent + .05), label = paste0(round(percent*100,1),"%")), colour="black", size = 2.5) +
+  theme_bw() + theme(axis.text.y=element_blank(),
+                     axis.ticks.y=element_blank(),strip.background =element_rect(fill="white")) +
+  ggtitle("Support pardoning perpetrators") +
+  xlab("Waves chosen") + ylab(NULL)
+p2<-ggplot(data=churchtrust.df, aes(x=waveschosen, y=percent)) +
+  geom_bar(stat="identity", width=0.5,colour="black") +
+  facet_grid(. ~ group) +
+  geom_text(data=churchtrust.df, aes(x = waveschosen, y = (percent + .05), label = paste0(round(percent*100,1),"%")), colour="black", size = 2.5) +
+  theme_bw() + theme(axis.text.y=element_blank(),
+                     axis.ticks.y=element_blank(),strip.background =element_rect(fill="white")) +
+  ggtitle("Trust in church") +
+  xlab("Waves chosen") + ylab(NULL)
+p3<-ggplot(data=govsat.df, aes(x=waveschosen, y=percent)) +
+  geom_bar(stat="identity", width=0.5,colour="black") +
+  facet_grid(. ~ group) +
+  geom_text(data=govsat.df, aes(x = waveschosen, y = (percent + .05), label = paste0(round(percent*100,1),"%")), colour="black", size = 2.5) +
+  theme_bw() + theme(axis.text.y=element_blank(),
+                     axis.ticks.y=element_blank(),strip.background =element_rect(fill="white")) +
+  ggtitle("Satisfaction with government") +
+  xlab("Waves chosen") + ylab(NULL)
+p4<-ggplot(data=democracy.df, aes(x=waveschosen, y=percent)) +
+  geom_bar(stat="identity", width=0.5,colour="black") +
+  facet_grid(. ~ group) +
+  geom_text(data=democracy.df, aes(x = waveschosen, y = (percent + .05), label = paste0(round(percent*100,1),"%")), colour="black", size = 2.5) +
+  theme_bw() + theme(axis.text.y=element_blank(),
+                     axis.ticks.y=element_blank(),strip.background =element_rect(fill="white")) +
+  ggtitle("Satisfaction with Democracy") +
+  xlab("Waves chosen") + ylab(NULL)
+p5<-ggplot(data=milgov.df, aes(x=waveschosen, y=percent)) +
+  geom_bar(stat="identity", width=0.5,colour="black") +
+  facet_grid(. ~ group) +
+  geom_text(data=milgov.df, aes(x = waveschosen, y = (percent + .05), label = paste0(round(percent*100,1),"%")), colour="black", size = 2.5) +
+  theme_bw() + theme(axis.text.y=element_blank(),
+                     axis.ticks.y=element_blank(),strip.background =element_rect(fill="white")) +
+  ggtitle("Support for military government") +
+  xlab("Waves chosen") + ylab(NULL)
+p6<-ggplot(data=police.df, aes(x=waveschosen, y=percent)) +
+  geom_bar(stat="identity", width=0.5,colour="black") +
+  facet_grid(. ~ group) +
+  geom_text(data=police.df, aes(x = waveschosen, y = (percent + .05), label = paste0(round(percent*100,1),"%")), colour="black", size = 2.5) +
+  theme_bw() + theme(axis.text.y=element_blank(),
+                     axis.ticks.y=element_blank(),strip.background =element_rect(fill="white")) +
+  ggtitle("Satisfaction with Police") +
+  xlab("Waves chosen") + ylab(NULL)
+grid.arrange(p1, p2,p3,p4,p5,p6, nrow = 3)
+#g <- arrangeGrob(p1, p2, p3,p4,p5,p6, nrow=3) #generates g
+##############################################################
+######Table A20. Political insitutions - dropping missing observations
+##############################################################
+load(file = "all.Rdata")
+est.ate<-function(dv, predv, df){
+  summary(fit.1 <- lm(dv~df$treat + df$pre_ideology_1 +
+                        + predv*df$date_diff + df$base_gender +df$age + df$v))
+  vcv <- vcovHC(fit.1)
+  n <- nobs(fit.1)
+  result <- coeftest(fit.1, vcv)[2, 1:4] / itt.d
+  result <- list("point.estimate" = result[1],"se"=result[2],"pvalue"=result[4], "obs" = n)
+  return(result)
+}
+# This estimates ATE when we don't have a pre-treatment measurement
+est.ate.np<-function(dv, df){
+  summary(fit.1 <- lm(dv~df$treat + df$pre_ideology_1 + df$base_gender +df$age+df$v))
+  vcv <- vcovHC(fit.1)
+  n <- nobs(fit.1)
+  result <- coeftest(fit.1, vcv)[2, 1:4] / itt.d
+  result <- list("point.estimate" = result[1],"se"=result[2],"pvalue"=result[4], "obs" = n)
+  return(result)
+}
+# pol inst DVs
+dem <- est.ate(all$democracy, all$pre_democracy, all)
+mil <- est.ate(all$military_gov, all$pre_military_gov, all)
+gov_sat <- est.ate(all$inst_gov, all$pre_inst_gov, all)
+mil_sat <- est.ate(all$inst_mil, all$pre_inst_mil, all)
+pol_sat <- est.ate(all$inst_police, all$pre_inst_police, all)
+gov_trust <- est.ate(all$conf_gov, all$pre_conf_gov, all)
+mil_trust <- est.ate(all$conf_mil, all$pre_conf_mil, all)
+pol_trust <- est.ate(all$conf_police, all$pre_conf_police, all)
+church_trust <- est.ate(all$conf_church, all$pre_conf_church, all)
+##############################################################
+######Table A21. Transitional justice - dropping missing observations
+##############################################################
+# TJ DVs
+advance <- est.ate.np(all$justice_advance, all)
+justice_account <- est.ate.np(all$justice_account, all)
+compensation <- est.ate(all$current_recomp, all$pre_current_recomp, all)
+judicial <- est.ate.np(all$tj_judicial, all)
+inst_apology <- est.ate.np(all$tj_apology, all)
+apologize <- est.ate.np(all$policies_apologize, all)
+compensate <- est.ate.np(all$policies_compensate, all)
+pardoned <- est.ate.np(all$policies_pardon, all)
+##############################################################
+######Table A22. Balance on measurements collected at baseline among
+# nonparticipants and participants
+##############################################################
+# Note that not all subjects who eventually participated in our experiment completed the baseline - nonetheless, many did
+load(file = "baseline.Rdata")
+nonparticipants <- baseline[!(baseline$ID %in% all$ID),]
+participants <- baseline[(baseline$ID %in% all$ID),]
+t.test(participants$female, nonparticipants$female)
+t.test(participants$ideology, nonparticipants$ideology)
+t.test(participants$pinochet, nonparticipants$pinochet)
+t.test(participants$pinochet_london, nonparticipants$pinochet_london)
+t.test(participants$prosecution, nonparticipants$prosecution)

19/replication_package/BPV_museums_maintext.R ADDED Viewed

	@@ -0,0 +1,829 @@

+##############################################################
+##############################################################
+####### Replication code for "Do museums promote reconciliation? Evidence from a field experiment," Journal of Politics
+####### This file includes code for all analyses and figures in the main text
+####### Plots based on those used by Broockman and Kalla (2016)
+##############################################################
+##############################################################
+require("sandwich")
+require("plyr")
+require("lmtest")
+require(dplyr)
+require(gridExtra)
+require("RColorBrewer")
+require(ggplot2)
+##############################################################
+###### Read in data
+##############################################################
+load(file = "all.Rdata")
+##############################################################
+###### Establish main functions
+##############################################################
+### ATE FUNCTIONS ##
+# This estimates ATE when we have a pre-treatment measurement
+est.ate<-function(dv, predv, df){
+  predv <- f(predv)
+  dv <- f(dv)
+  summary(fit.1 <- lm(dv~df$treat + df$pre_ideology_1 +
+                        + predv*df$date_diff + df$base_gender +df$age + df$v))
+  vcv <- vcovHC(fit.1)
+  n <- nobs(fit.1)
+  result <- coeftest(fit.1, vcv)[2, 1:4] / itt.d
+  result <- list("point.estimate" = result[1],"se"=result[2],"pvalue"=result[4], "obs" = n)
+  return(result)
+}
+# This estimates ATE when we don't have a pre-treatment measurement
+est.ate.np<-function(dv, df){
+  dv <- f(dv)
+  summary(fit.1 <- lm(dv~df$treat + df$pre_ideology_1 + df$base_gender +df$age+df$v))
+  vcv <- vcovHC(fit.1)
+  n <- nobs(fit.1)
+  result <- coeftest(fit.1, vcv)[2, 1:4] / itt.d
+  result <- list("point.estimate" = result[1],"se"=result[2],"pvalue"=result[4], "obs" = n)
+  return(result)
+}
+# this function recodes NAs to the mean, per our PAP
+f <- function(x){
+  m <- mean(x, na.rm = TRUE)
+  x[is.na(x)] <- m
+  x
+}
+## recode covariates to means
+all$age <- f(all$age)
+all$pre_ideology_1 <- f(all$pre_ideology_1)
+all$base_gender <- f(all$base_gender)
+all$date_diff <- f(all$date_diff)
+# split dataset into left, right, and related to victim for heterogeneous analyses
+left <- all[all$right == 0,]
+right <- all[all$right == 1,]
+itt.d <- all$itt.d
+##############################################################
+###### Figure 1: Political institutions treatment effects
+##############################################################
+# Overall
+dem <- est.ate(all$democracy, all$pre_democracy, all)
+mil <- est.ate(all$military_gov, all$pre_military_gov, all)
+gov_sat <- est.ate(all$inst_gov, all$pre_inst_gov, all)
+mil_sat <- est.ate(all$inst_mil, all$pre_inst_mil, all)
+pol_sat <- est.ate(all$inst_police, all$pre_inst_police, all)
+gov_trust <- est.ate(all$conf_gov, all$pre_conf_gov, all)
+mil_trust <- est.ate(all$conf_mil, all$pre_conf_mil, all)
+pol_trust <- est.ate(all$conf_police, all$pre_conf_police, all)
+church_trust <- est.ate(all$conf_church, all$pre_conf_church, all)
+# right and left
+dem.right <- est.ate(right$democracy, right$pre_democracy, right)
+mil.right <- est.ate(right$military_gov, right$pre_military_gov, right)
+gov_sat.right <- est.ate(right$inst_gov, right$pre_inst_gov, right)
+mil_sat.right <- est.ate(right$inst_mil, right$pre_inst_mil, right)
+pol_sat.right <- est.ate(right$inst_police, right$pre_inst_police, right)
+gov_trust.right <- est.ate(right$conf_gov, right$pre_conf_gov, right)
+mil_trust.right <- est.ate(right$conf_mil, right$pre_conf_mil, right)
+pol_trust.right <- est.ate(right$conf_police, right$pre_conf_police, right)
+church_trust.right <- est.ate(right$conf_church, right$pre_conf_church, right)
+dem.left <- est.ate(left$democracy, left$pre_democracy, left)
+mil.left <- est.ate(left$military_gov, left$pre_military_gov, left)
+gov_sat.left <- est.ate(left$inst_gov, left$pre_inst_gov, left)
+mil_sat.left <- est.ate(left$inst_mil, left$pre_inst_mil, left)
+pol_sat.left <- est.ate(left$inst_police, left$pre_inst_police, left)
+gov_trust.left <- est.ate(left$conf_gov, left$pre_conf_gov, left)
+mil_trust.left <- est.ate(left$conf_mil, left$pre_conf_mil, left)
+pol_trust.left <- est.ate(left$conf_police, left$pre_conf_police, left)
+church_trust.left <- est.ate(left$conf_church, left$pre_conf_church, left)
+# make figure
+results.df <- as.data.frame(rbind(dem[1], dem.left[1], dem.right[1],
+                                        mil[1],  mil.left[1], mil.right[1],
+                                        gov_sat[1], gov_sat.left[1], gov_sat.right[1],
+                                        mil_sat[1],  mil_sat.left[1], mil_sat.right[1],
+                                        pol_sat[1],  pol_sat.left[1], pol_sat.right[1],
+                                        gov_trust[1],  gov_trust.left[1],gov_trust.right[1],
+                                        mil_trust[1],  mil_trust.left[1], mil_trust.right[1],
+                                        pol_trust[1],  pol_trust.left[1], pol_trust.right[1],
+                                        church_trust[1],  church_trust.left[1], church_trust.right[1]
+                                        ))
+results.df$se <- c(dem[2], dem.left[2], dem.right[2],
+                   mil[2],  mil.left[2], mil.right[2],
+                   gov_sat[2], gov_sat.left[2], gov_sat.right[2],
+                   mil_sat[2],  mil_sat.left[2], mil_sat.right[2],
+                   pol_sat[2],  pol_sat.left[2], pol_sat.right[2],
+                   gov_trust[2],  gov_trust.left[2],gov_trust.right[2],
+                   mil_trust[2],  mil_trust.left[2], mil_trust.right[2],
+                   pol_trust[2],  pol_trust.left[2], pol_trust.right[2],
+                   church_trust[2],  church_trust.left[2], church_trust.right[2])
+results.df$se <- unlist(results.df$se)
+results.df$point.estimate <- unlist(results.df$point.estimate)
+results.df$Variable <- NA
+results.df$xpos <- NA
+for (i in 1:3){results.df$Variable[i] <- "democracy"}
+for (i in 4:6){results.df$Variable[i] <- "military govt"}
+for (i in 7:9){results.df$Variable[i] <- "govt satisfaction"}
+for (i in 10:12){results.df$Variable[i] <- "military satisfaction"}
+for (i in 13:25){results.df$Variable[i] <- "police satisfaction"}
+for (i in 16:18){results.df$Variable[i] <- "govt trust"}
+for (i in 19:21){results.df$Variable[i] <- "military trust"}
+for (i in 22:24){results.df$Variable[i] <- "police trust"}
+for (i in 25:27){results.df$Variable[i] <- "church trust"}
+results.df$varnum<- with(results.df, paste0(as.numeric(factor(Variable))))
+results.df$varnum <- as.numeric(results.df$varnum)
+results.df$sample <- rep(1:3, 9)
+results.df$Population <- mapvalues(results.df$sample, c(1,2,3), c("All", "Left", "Right"), warn_missing = TRUE)
+results.df$xpos<- as.numeric(paste(results.df$varnum, results.df$sample, sep = "."))
+results.df$ci.hi <- unlist(results.df$point.estimate) + unlist(results.df$se) * 1.96
+results.df$ci.low <- unlist(results.df$point.estimate) - unlist(results.df$se) * 1.96
+results.df$se.hi <- unlist(results.df$point.estimate) + unlist(results.df$se)
+results.df$se.low <- unlist(results.df$point.estimate) - unlist(results.df$se)
+labels <- unique(results.df$Variable)
+g <- ggplot(results.df,
+            aes(x=xpos, y=point.estimate,
+                group=Variable, color=Population)) +
+  theme(axis.text.x=element_blank(),
+        axis.ticks.x=element_blank()) +
+  geom_linerange(aes(ymin=ci.low, ymax=ci.hi)) +
+  geom_linerange(aes(ymin=se.low, ymax=se.hi),lwd=1) +
+  geom_point(color="black") +
+  geom_hline(yintercept = 0, linetype = "dashed") +
+  ylab("Coefficient") +
+  xlab("Variable") +
+  ggtitle("Political Institutions")
+g +  scale_color_grey() +
+  ggplot2::annotate("text",
+           label = c("church trust \n ctrl mean=1.15", "dem satisfaction \n ctrl mean=1.16", "govt satisfaction \n ctrl mean=0.80",
+                     "govt trust \n ctrl mean=0.98", "mil govt (0-1) \n ctrl mean=0.28", "mil satisfaction \n ctrl mean=1.18",
+                     "mil trust \n ctrl mean=1.17", "pol satisfaction \n ctrl mean=1.56", "pol trust \n ctrl mean=1.67"),
+           x = c(1:9)+.2, y = -.5,
+           colour = "black", size = 2.8) + theme_bw()
+ggsave("polinst.pdf", width = 10, height = 6)
+##############################################################
+###### Figure 2: Transitional justice treatment effects
+##############################################################
+advance <- est.ate.np(all$justice_advance, all)
+justice_account <- est.ate.np(all$justice_account, all)
+compensation <- est.ate(all$current_recomp, all$pre_current_recomp, all)
+judicial <- est.ate.np(all$tj_judicial, all)
+inst_apology <- est.ate.np(all$tj_apology, all)
+apologize <- est.ate.np(all$policies_apologize, all)
+compensate <- est.ate.np(all$policies_compensate, all)
+pardoned <- est.ate.np(all$policies_pardon, all)
+advance.right <- est.ate.np(right$justice_advance, right)
+justice_account.right <- est.ate.np(right$justice_account, right)
+compensation.right <- est.ate(right$current_recomp, right$pre_current_recomp, right)
+judicial.right <- est.ate.np(right$tj_judicial, right)
+inst_apology.right <- est.ate.np(right$tj_apology, right)
+apologize.right <- est.ate.np(right$policies_apologize, right)
+compensate.right <- est.ate.np(right$policies_compensate, right)
+pardoned.right <- est.ate.np(right$policies_pardon, right)
+advance.left <- est.ate.np(left$justice_advance, left)
+justice_account.left <- est.ate.np(left$justice_account, left)
+compensation.left <- est.ate(left$current_recomp, left$pre_current_recomp, left)
+judicial.left <- est.ate.np(left$tj_judicial, left)
+inst_apology.left <- est.ate.np(left$tj_apology, left)
+apologize.left <- est.ate.np(left$policies_apologize, left)
+compensate.left <- est.ate.np(left$policies_compensate, left)
+pardoned.left <- est.ate.np(left$policies_pardon, left)
+results.df.tj <- as.data.frame(rbind(advance[1], advance.left[1],advance.right[1],
+                                           justice_account[1], justice_account.left[1], justice_account.right[1],
+                                           compensation[1],  compensation.left[1], compensation.right[1],
+                                           judicial[1],judicial.left[1], judicial.right[1],
+                                           inst_apology[1],  inst_apology.left[1], inst_apology.right[1],
+                                          apologize[1],  apologize.left[1], apologize.right[1],
+                                           compensate[1],  compensate.left[1], compensate.right[1],
+                                           pardoned[1], pardoned.left[1],pardoned.right[1]))
+results.df.tj$se <- as.data.frame(rbind(advance[2], advance.left[2],advance.right[2],
+                                     justice_account[2], justice_account.left[2], justice_account.right[2],
+                                     compensation[2],  compensation.left[2], compensation.right[2],
+                                     judicial[2],judicial.left[2], judicial.right[2],
+                                     inst_apology[2],  inst_apology.left[2], inst_apology.right[2],
+                                     apologize[2],  apologize.left[2], apologize.right[2],
+                                     compensate[2],  compensate.left[2], compensate.right[2],
+                                     pardoned[2], pardoned.left[2],pardoned.right[2]))
+results.df.tj$se <- unlist(results.df.tj$se)
+results.df.tj$point.estimate <- unlist(results.df.tj$point.estimate)
+results.df.tj$Variable <- NA
+results.df.tj$xpos <- NA
+for (i in 1:3){results.df.tj$Variable[i] <- "advance"}
+for (i in 4:6){results.df.tj$Variable[i] <- "accountable"}
+for (i in 7:9){results.df.tj$Variable[i] <- "compensation"}
+for (i in 10:12){results.df.tj$Variable[i] <- "punish"}
+for (i in 13:15){results.df.tj$Variable[i] <- "public apology"}
+for (i in 16:18){results.df.tj$Variable[i] <- "forced apology"}
+for (i in 19:21){results.df.tj$Variable[i] <- "forced compensation"}
+for (i in 22:34){results.df.tj$Variable[i] <- "pardoned"}
+# X position of different canvasser groups
+results.df.tj$varnum<- with(results.df.tj, paste0(as.numeric(factor(Variable))))
+results.df.tj$varnum <- as.numeric(results.df.tj$varnum)
+results.df.tj$sample <- rep(1:3, 8)
+results.df.tj$Population <- mapvalues(results.df.tj$sample, c(1,2,3), c("All", "Left", "Right"), warn_missing = TRUE)
+results.df.tj$xpos<- as.numeric(paste(results.df.tj$varnum, results.df.tj$sample, sep = "."))
+results.df.tj$se.high <- results.df.tj$point.estimate + results.df.tj$se
+results.df.tj$se.low <- results.df.tj$point.estimate - results.df.tj$se
+results.df.tj$ci.high <- results.df.tj$point.estimate + results.df.tj$se * 1.96
+results.df.tj$ci.low <- results.df.tj$point.estimate - results.df.tj$se * 1.96
+labels <- unique(results.df.tj$Variable)
+q <- ggplot(results.df.tj,
+            aes(x=xpos, y=point.estimate,
+                group=Variable, color=Population)) +
+  theme(axis.text.x=element_blank(),
+        axis.ticks.x=element_blank()) +
+  # CIs
+  geom_linerange(aes(ymin=se.low, ymax=se.high), lwd=1) +
+  geom_linerange(aes(ymin=ci.low, ymax=ci.high)) +
+  # Point estimate points
+  geom_point(color="black") +
+  geom_hline(yintercept = 0, linetype = "dashed") +
+  ggplot2::annotate("text",
+           label = c("accountable (0-4) \n ctrl mean=2.79", "advance (0-4) \n ctrl mean=1.94", "compensation (0-3) \n ctrl mean=1.93 ",
+                     "force apology (0-3) \n ctrl mean=1.83", "force compensation (0-3) \n ctrl mean = 1.51", "pardoned (0-3) \n ctrl mean=0.56",
+                     "public apology (0-3) \n ctrl mean=1.99", "punish (0-3) \n ctrl mean=2.19"),
+           x = c(1:8)+.28, y = -1.5,
+           colour = "black", size = 2.6) +
+  xlab("Variable") + ylab("Coefficient") +
+  ggtitle("Transitional Justice")
+q +  scale_color_grey()+theme_bw()
+ggsave("tj.pdf", width = 10, height = 6)
+##############################################################
+###### Figure 3: Positive (a) and negative (b) emotions treatment effects
+##############################################################
+##### Part (a) - Positive emotions
+## all positive
+positive <- est.ate(all$positive, all$pre_positive, all)
+interested <- est.ate(all$interested, all$pre_interested, all)
+stimulated <- est.ate(all$stimulated, all$pre_stimulated, all)
+enthusiastic <- est.ate(all$enthusiastic, all$pre_enthusiastic, all)
+energetic <- est.ate(all$energetic, all$pre_energetic, all)
+proud <- est.ate(all$proud, all$pre_proud, all)
+alert <- est.ate(all$alert, all$pre_alert, all)
+inspired <- est.ate(all$inspired, all$pre_inspired, all)
+decided <- est.ate(all$decided, all$pre_decided, all)
+attentive <- est.ate(all$attentive, all$pre_attentive, all)
+active <- est.ate(all$active, all$pre_active, all)
+## right positive
+positive.right <- est.ate(right$positive, right$pre_positive, right)
+interested.right <- est.ate(right$interested, right$pre_interested, right)
+stimulated.right <- est.ate(right$stimulated, right$pre_stimulated, right)
+enthusiastic.right <- est.ate(right$enthusiastic, right$pre_enthusiastic, right)
+energetic.right <- est.ate(right$energetic, right$pre_energetic, right)
+proud.right <- est.ate(right$proud, right$pre_proud, right)
+alert.right <- est.ate(right$alert, right$pre_alert, right)
+inspired.right <- est.ate(right$inspired, right$pre_inspired, right)
+decided.right <- est.ate(right$decided, right$pre_decided, right)
+attentive.right <- est.ate(right$attentive, right$pre_attentive, right)
+active.right <- est.ate(right$active, right$pre_active, right)
+## left positive
+positive.left <- est.ate(left$positive, left$pre_positive, left)
+interested.left <- est.ate(left$interested, left$pre_interested, left)
+stimulated.left <- est.ate(left$stimulated, left$pre_stimulated, left)
+enthusiastic.left <- est.ate(left$enthusiastic, left$pre_enthusiastic, left)
+energetic.left <- est.ate(left$energetic, left$pre_energetic, left)
+proud.left <- est.ate(left$proud, left$pre_proud, left)
+alert.left <- est.ate(left$alert, left$pre_alert, left)
+inspired.left <- est.ate(left$inspired, left$pre_inspired, left)
+decided.left <- est.ate(left$decided, left$pre_decided, left)
+attentive.left <- est.ate(left$attentive, left$pre_attentive, left)
+active.left <- est.ate(left$active, left$pre_active, left)
+results.df.pos <- as.data.frame(rbind(positive[1], positive.left[1], positive.right[1],
+                                            interested[1],interested.left[1], interested.right[1],
+                                            stimulated[1], stimulated.left[1], stimulated.right[1],
+                                            enthusiastic[1],  enthusiastic.left[1], enthusiastic.right[1],
+                                            energetic[1], energetic.left[1],  energetic.right[1],
+                                            proud[1], proud.left[1],  proud.right[1],
+                                            alert[1], alert.left[1], alert.right[1],
+                                            inspired[1], inspired.left[1], inspired.right[1],
+                                            decided[1],  decided.left[1],decided.right[1],
+                                            attentive[1], attentive.left[1],attentive.right[1],
+                                            active[1], active.left[1],active.right[1]))
+results.df.pos$se <- c(positive[2], positive.left[2], positive.right[2],
+                      interested[2],interested.left[2], interested.right[2],
+                      stimulated[2], stimulated.left[2], stimulated.right[2],
+                      enthusiastic[2],  enthusiastic.left[2], enthusiastic.right[2],
+                      energetic[2], energetic.left[2],  energetic.right[2],
+                      proud[2], proud.left[2],  proud.right[2],
+                      alert[2], alert.left[2], alert.right[2],
+                      inspired[2], inspired.left[2], inspired.right[2],
+                      decided[2],  decided.left[2],decided.right[2],
+                      attentive[2], attentive.left[2],attentive.right[2],
+                      active[2], active.left[2],active.right[2])
+results.df.pos$se <- unlist(results.df.pos$se)
+results.df.pos$point.estimate <- unlist(results.df.pos$point.estimate)
+results.df.pos$Variable <- NA
+results.df.pos$xpos <- NA
+for (i in 1:3){results.df.pos$Variable[i] <- "positive"}
+for (i in 4:6){results.df.pos$Variable[i] <- "interested"}
+for (i in 7:9){results.df.pos$Variable[i] <- "stimulated"}
+for (i in 10:12){results.df.pos$Variable[i] <- "enthusiastic"}
+for (i in 13:15){results.df.pos$Variable[i] <- "energetic"}
+for (i in 16:18){results.df.pos$Variable[i] <- "proud"}
+for (i in 19:21){results.df.pos$Variable[i] <- "alert"}
+for (i in 22:24){results.df.pos$Variable[i] <- "inspired"}
+for (i in 25:27){results.df.pos$Variable[i] <- "decided"}
+for (i in 28:30){results.df.pos$Variable[i] <- "attentive"}
+for (i in 31:33){results.df.pos$Variable[i] <- "active"}
+# X position of different canvasser groups
+results.df.pos$varnum<- with(results.df.pos, paste0(as.numeric(factor(Variable))))
+results.df.pos$varnum <- as.numeric(results.df.pos$varnum)
+results.df.pos$sample <- rep(1:3, 11)
+results.df.pos$Population <- mapvalues(results.df.pos$sample, c(1,2,3), c("All", "Left", "Right"), warn_missing = TRUE)
+results.df.pos$xpos<- as.numeric(paste(results.df.pos$varnum, results.df.pos$sample, sep = "."))
+results.df.pos$se.high <- results.df.pos$point.estimate + results.df.pos$se
+results.df.pos$se.low <- results.df.pos$point.estimate - results.df.pos$se
+results.df.pos$ci.high <- results.df.pos$point.estimate + results.df.pos$se * 1.96
+results.df.pos$ci.low <- results.df.pos$point.estimate - results.df.pos$se * 1.96
+labels <- unique(results.df.pos$Variable)
+results.df.pos[1:3, 4] = results.df.pos$xpos[1:3] - 8
+results.df.pos[31:33, 4] = results.df.pos$xpos[31:33] + 8
+pos <- ggplot(results.df.pos,
+              aes(x=xpos, y=point.estimate,
+                  group=Variable, color=Population)) +
+  theme(axis.text.x=element_blank(),
+        axis.ticks.x=element_blank(),
+        plot.title=element_blank()) +
+  # CIs
+  geom_linerange(aes(ymin=se.low, ymax=se.high), lwd=1) +
+  geom_linerange(aes(ymin=ci.low, ymax=ci.high)) +
+  # Point estimate points
+  geom_point(color="black") +
+  geom_hline(yintercept = 0, linetype = "dashed") +
+  ggplot2::annotate("text",
+           label = c("positive (aggregated) \n ctrl mean=18.76", "alert \n ctrl mean=1.26", "attentive \n ctrl mean=2.41",
+                     "decided \n ctrl mean=2.14", "energetic \n ctrl mean=1.81","enthusiastic \n ctrl mean=2.01",
+                     "inspired \n ctrl mean=1.52", "interested \n ctrl mean=2.53",
+                     "active \n ctrl mean=2.07","proud \n ctrl mean=1.24", "stimulated \n ctrl mean=1.70"),
+           x = c(1:11)+.28, y = -5.8,
+           colour = "black", size = 2.6) +
+  xlab("Variable") + ylab("Coefficient") +ggtitle("Positive Emotions")+scale_color_grey()+theme_bw()
+pos <- pos + labs(title="Positive Emotions")
+ggsave("pos.pdf", width = 10, height = 6)
+##### Part (b) - Negative emotions
+negative <- est.ate(all$negative, all$pre_negative, all)
+tense <- est.ate(all$tense, all$pre_tense, all)
+scared <- est.ate(all$scared, all$pre_scared, all)
+guilty <- est.ate(all$guilty, all$pre_guilty, all)
+hostile <- est.ate(all$hostile, all$pre_hostile, all)
+irritable <- est.ate(all$irritable, all$pre_irritable, all)
+nervous <- est.ate(all$nervous, all$pre_nervous, all)
+fearful <- est.ate(all$fearful, all$pre_fearful, all)
+disgusted <- est.ate(all$disgusted, all$pre_disgusted, all)
+afraid <- est.ate(all$afraid, all$pre_afraid, all)
+embarrassed <- est.ate(all$embarrassed, all$pre_embarrassed, all)
+## right negative
+negative.right <- est.ate(right$negative, right$pre_negative, right)
+tense.right <- est.ate(right$tense, right$pre_tense, right)
+scared.right <- est.ate(right$scared, right$pre_scared, right)
+guilty.right <- est.ate(right$guilty, right$pre_guilty, right)
+hostile.right <- est.ate(right$hostile, right$pre_hostile, right)
+irritable.right <- est.ate(right$irritable, right$pre_irritable, right)
+nervous.right <- est.ate(right$nervous, right$pre_nervous, right)
+fearful.right <- est.ate(right$fearful, right$pre_fearful, right)
+disgusted.right <- est.ate(right$disgusted, right$pre_disgusted, right)
+afraid.right <- est.ate(right$afraid, right$pre_afraid, right)
+embarrassed.right <- est.ate(right$embarrassed, right$pre_embarrassed, right)
+## left negative
+negative.left <- est.ate(left$negative, left$pre_negative, left)
+tense.left <- est.ate(left$tense, left$pre_tense, left)
+scared.left <- est.ate(left$scared, left$pre_scared, left)
+guilty.left <- est.ate(left$guilty, left$pre_guilty, left)
+hostile.left <- est.ate(left$hostile, left$pre_hostile, left)
+irritable.left <- est.ate(left$irritable, left$pre_irritable, left)
+nervous.left <- est.ate(left$nervous, left$pre_nervous, left)
+fearful.left <- est.ate(left$fearful, left$pre_fearful, left)
+disgusted.left <- est.ate(left$disgusted, left$pre_disgusted, left)
+afraid.left <- est.ate(left$afraid, left$pre_afraid, left)
+embarrassed.left <- est.ate(left$embarrassed, left$pre_embarrassed, left)
+# Make DF of summary stats
+results.df.neg <- as.data.frame(rbind(negative[1], negative.left[1], negative.right[1],
+                                            tense[1], tense.left[1], tense.right[1],
+                                            scared[1],scared.left[1], scared.right[1],
+                                            guilty[1], guilty.left[1], guilty.right[1],
+                                            hostile[1], hostile.left[1], hostile.right[1],
+                                            irritable[1], irritable.left[1], irritable.right[1],
+                                            nervous[1], nervous.left[1], nervous.right[1],
+                                            fearful[1],  fearful.left[1], fearful.right[1],
+                                            disgusted[1], disgusted.left[1],disgusted.right[1],
+                                            afraid[1], afraid.left[1],afraid.right[1],
+                                            embarrassed[1], embarrassed.left[1], embarrassed.right[1]))
+results.df.neg$se <- c(negative[2], negative.left[2], negative.right[2],
+                                      tense[2], tense.left[2], tense.right[2],
+                                      scared[2],scared.left[2], scared.right[2],
+                                      guilty[2], guilty.left[2], guilty.right[2],
+                                      hostile[2], hostile.left[2], hostile.right[2],
+                                      irritable[2], irritable.left[2], irritable.right[2],
+                                      nervous[2], nervous.left[2], nervous.right[2],
+                                      fearful[2],  fearful.left[2], fearful.right[2],
+                                      disgusted[2], disgusted.left[2],disgusted.right[2],
+                                      afraid[2], afraid.left[2],afraid.right[2],
+                                      embarrassed[2], embarrassed.left[2], embarrassed.right[2])
+results.df.neg$point.estimate <- unlist(results.df.neg$point.estimate)
+results.df.neg$se <- unlist(results.df.neg$se)
+results.df.neg$Variable <- NA
+results.df.neg$xneg <- NA
+for (i in 1:3){results.df.neg$Variable[i] <- "negative"}
+for (i in 4:6){results.df.neg$Variable[i] <- "tense"}
+for (i in 7:9){results.df.neg$Variable[i] <- "scared"}
+for (i in 10:12){results.df.neg$Variable[i] <- "guilty"}
+for (i in 13:15){results.df.neg$Variable[i] <- "hostile"}
+for (i in 16:18){results.df.neg$Variable[i] <- "irritable"}
+for (i in 19:21){results.df.neg$Variable[i] <- "nervous"}
+for (i in 22:24){results.df.neg$Variable[i] <- "fearful"}
+for (i in 25:27){results.df.neg$Variable[i] <- "disgusted"}
+for (i in 28:30){results.df.neg$Variable[i] <- "afraid"}
+for (i in 31:33){results.df.neg$Variable[i] <- "embarrassed"}
+results.df.neg$varnum<- with(results.df.neg, paste0(as.numeric(factor(Variable))))
+results.df.neg$varnum <- as.numeric(results.df.neg$varnum)
+results.df.neg$sample <- rep(1:3, 11)
+results.df.neg$Population <- mapvalues(results.df.neg$sample, c(1,2,3), c("All", "Left", "Right"), warn_missing = TRUE)
+results.df.neg$xneg<- as.numeric(paste(results.df.neg$varnum, results.df.neg$sample, sep = "."))
+results.df.neg$se.high <- results.df.neg$point.estimate + results.df.neg$se
+results.df.neg$se.low <- results.df.neg$point.estimate - results.df.neg$se
+results.df.neg$ci.high <- results.df.neg$point.estimate + results.df.neg$se * 1.96
+results.df.neg$ci.low <- results.df.neg$point.estimate - results.df.neg$se * 1.96
+labels <- unique(results.df.neg$Variable)
+results.df.neg[1:3, 4] = results.df.neg$xneg[1:3] - 7
+results.df.neg[28:30, 4] = results.df.neg$xneg[28:30] + 7
+neg <- ggplot(results.df.neg,
+              aes(x=xneg, y=point.estimate,
+                  group=Variable, color=Population)) +
+  theme(axis.text.x=element_blank(),
+        axis.ticks.x=element_blank()) +
+  # CIs
+  geom_linerange(aes(ymin=se.low, ymax=se.high), lwd=1) +
+  geom_linerange(aes(ymin=ci.low, ymax=ci.high)) +
+  # Point estimate points
+  geom_point(color="black") +
+  geom_hline(yintercept = 0, linetype = "dashed") +
+  ggplot2::annotate("text",
+           label = c("negative \n ctrl mean=3.44","disgusted \n ctrl mean=0.29", "embarrassed \n ctrl mean=0.26",
+                     "fearful \n ctrl mean=0.22","guilty \n ctrl mean=0.24","hostile \n ctrl mean=0.33",
+                     "irritable \n ctrl mean=0.54","afraid \n ctrl mean=0.16", "nervous \n ctrl mean=0.56",
+                     "scared \n ctrl mean = 0.16","tense \n ctrl mean = 0.68"),
+           x = c(1:11)+.28, y = -1.5,
+           colour = "black", size = 2.6) +
+  xlab("Variable") + ylab("Coefficient")
+neg +  scale_color_grey()+theme_bw()
+ggsave("neg.pdf", width = 10, height = 6)
+##############################################################
+###### Figure 4: Durability of treatment effects
+##############################################################
+### this estimates ATE for follow up variables
+est.ate.f <-function(dv, predv){
+  predv <- f(predv)
+  summary(fit.1 <- lm(dv~all$treat + all$pre_ideology_1 +
+                        + predv*all$date_diff + all$base_gender +all$age + all$v))
+  vcv <- vcovHC(fit.1)
+  n <- nobs(fit.1)
+  result <- coeftest(fit.1, vcv)[2, 1:4] / itt.d
+  result <- list("point.estimate" = result[1],"se"=result[2],"pvalue"=result[4], "obs" = n)
+  return(result)
+}
+# estimates ATE for follow up when we don't have a pre-treatment measurement
+est.ate.np.f<-function(dv){
+  summary(fit.1 <- lm(dv~all$treat + all$pre_ideology_1 + all$base_gender +all$age+all$v))
+  vcv <- vcovHC(fit.1)
+  n <- nobs(fit.1)
+  result <- coeftest(fit.1, vcv)[2, 1:4] / itt.d
+  result <- list("point.estimate" = result[1],"se"=result[2],"pvalue"=result[4], "obs" = n)
+  return(result)
+}
+par(mfrow=c(4, 2))
+par(oma = c(2.9, 3, 1, 0)) # make room (i.e. the 4's) for the overall x and y axis titles
+par(mar = c(2.5, 2, 1.5, 1)) # make the plots be closer together
+par(cex.main = 0.8)
+##### GRAPH 1 - CHURCH TRUST ######
+church_trust_pre <- est.ate.np(all$pre_conf_church, all)
+church_trust <- est.ate(all$conf_church, all$pre_conf_church,all)
+church_trust_f1 <- est.ate.f(all$conf_church_f1, all$pre_conf_church)
+church_trust_f2 <- est.ate.f(all$conf_church_f2, all$pre_conf_church)
+church_trust_f3 <- est.ate.f(all$conf_church_f3, all$pre_conf_church)
+coefs <- unlist(c(church_trust_pre[1], church_trust[1], church_trust_f1[1],
+           church_trust_f2[1], church_trust_f3[1]))
+ses <- unlist(c(church_trust_pre[2], church_trust[2], church_trust_f1[2],
+         church_trust_f2[2], church_trust_f3[2]))
+plot(NA, xlim = c(-2, 25), ylim = c(-.5, .5),xlab = '', ylab = '')
+title("Trust in church (0-3)")
+abline(v = -1, col = "gray")
+abline(v = 0, col = "gray")
+abline(v = 1, col = "gray")
+abline(v = 8, col = "gray")
+abline(v = 24, col = "gray")
+abline(h = 0, col = "red")
+points(-1, coefs[1], pch = 23, col = "black", bg = "black")
+points(0, coefs[2], pch = 23, col = "black", bg = "black")
+points(1, coefs[3], pch = 23, col = "black", bg = "black")
+points(8, coefs[4], pch = 23, col = "black", bg = "black")
+points(24, coefs[5], pch = 23, col = "black", bg = "black")
+segments(-1, (coefs - ses)[1], -1, (coefs + ses)[1], col = "black", lwd = 2)
+segments(0, (coefs - ses)[2], 0, (coefs + ses)[2], col = "black", lwd = 2)
+segments(1, (coefs - ses)[3], 1, (coefs + ses)[3], col = "black", lwd = 2)
+segments(8.0, (coefs - ses)[4], 8, (coefs + ses)[4], col = "black", lwd = 2)
+segments(24.0, (coefs - ses)[5], 24, (coefs + ses)[5], col = "black", lwd = 2)
+segments(-1, (coefs - 1.96*ses)[1], -1, (coefs + 1.96*ses)[1], col = "black", lwd = 1)
+segments(0, (coefs - 1.96*ses)[2], 0, (coefs + 1.96*ses)[2], col = "black", lwd = 1)
+segments(1, (coefs - 1.96*ses)[3], 1, (coefs + 1.96*ses)[3], col = "black", lwd = 1)
+segments(8.0, (coefs - 1.96*ses)[4], 8, (coefs + 1.96*ses)[4], col = "black", lwd = 1)
+segments(24.0, (coefs - 1.96*ses)[5], 24, (coefs + 1.96*ses)[5], col = "black", lwd = 1)
+text(-2, -.2, "pre", cex = .6)
+text(-1, -.25, "treatment", cex = .6)
+text(.2, .3, "treatment", cex = .6)
+text(3, .07, "follow up", cex = .6)
+text(10, .07, "follow up", cex = .6)
+text(22, .13, "follow up", cex = .6)
+##### GRAPH 2 - PARDONING ######
+pardoned <- est.ate.np(all$policies_pardon,all)
+pardoned_f1<- est.ate.np.f(all$policies_pardon_f1)
+pardoned_f2 <- est.ate.np.f(all$policies_pardon_f2)
+pardoned_f3 <- est.ate.np.f(all$policies_pardon_f3)
+coefs <- unlist(c(pardoned[1], pardoned_f1[1],
+           pardoned_f2[1], pardoned_f3[1]))
+ses <- unlist(c(pardoned[2], pardoned_f1[2],
+         pardoned_f2[2], pardoned_f3[2]))
+plot(NA, xlim = c(-.5, 25), ylim = c(-.2, .5), xlab = '', ylab = '')
+title("Support for pardoning perpetrators (0-4)")
+abline(v = 0, col = "gray")
+abline(v = 1, col = "gray")
+abline(v = 8, col = "gray")
+abline(v = 24, col = "gray")
+abline(h = 0, col = "red")
+points(0, coefs[1], pch = 23, col = "black", bg = "black")
+points(1, coefs[2], pch = 23, col = "black", bg = "black")
+points(8, coefs[3], pch = 23, col = "black", bg = "black")
+points(24, coefs[4], pch = 23, col = "black", bg = "black")
+segments(0, (coefs - ses)[1], 0, (coefs + ses)[1], col = "black", lwd = 2)
+segments(1, (coefs - ses)[2], 1, (coefs + ses)[2], col = "black", lwd = 2)
+segments(8.0, (coefs - ses)[3], 8, (coefs + ses)[3], col = "black", lwd = 2)
+segments(24.0, (coefs - ses)[4], 24, (coefs + ses)[4], col = "black", lwd = 2)
+segments(0, (coefs - 1.96*ses)[1], 0, (coefs + 1.96*ses)[1], col = "black", lwd = 1)
+segments(1, (coefs - 1.96*ses)[2], 1, (coefs + 1.96*ses)[2], col = "black", lwd = 1)
+segments(8.0, (coefs - 1.96*ses)[3], 8, (coefs + 1.96*ses)[3], col = "black", lwd = 1)
+segments(24.0, (coefs - 1.96*ses)[4], 24, (coefs + 1.96*ses)[4], col = "black", lwd = 1)
+##### GRAPH 3 - GOVT SATISFACTION ######
+gov_sat_pre <- est.ate.np(all$pre_inst_gov,all)
+gov_sat <- est.ate(all$inst_gov, all$pre_inst_gov,all)
+gov_sat_f1 <- est.ate.f(all$inst_gov_f1, all$pre_inst_gov)
+gov_sat_f2 <- est.ate.f(all$inst_gov_f2, all$pre_inst_gov)
+gov_sat_f3 <- est.ate.f(all$inst_gov_f3, all$pre_inst_gov)
+coefs <- unlist(c(gov_sat_pre[1], gov_sat[1], gov_sat_f1[1],
+           gov_sat_f2[1], gov_sat_f3[1]))
+ses <- unlist(c(gov_sat_pre[2], gov_sat[2], gov_sat_f1[2],
+         gov_sat_f2[2], gov_sat_f3[2]))
+plot(NA, xlim = c(-2, 25), ylim = c(-.4, .5), xlab = '', ylab = '')
+title("Satisfaction with government (0-3)")
+abline(v = -1, col = "gray")
+abline(v = 0, col = "gray")
+abline(v = 1, col = "gray")
+abline(v = 8, col = "gray")
+abline(v = 24, col = "gray")
+abline(h = 0, col = "red")
+points(-1, coefs[1], pch = 23, col = "black", bg = "black")
+points(0, coefs[2], pch = 23, col = "black", bg = "black")
+points(1, coefs[3], pch = 23, col = "black", bg = "black")
+points(8, coefs[4], pch = 23, col = "black", bg = "black")
+points(24, coefs[5], pch = 23, col = "black", bg = "black")
+segments(-1, (coefs - ses)[1], -1, (coefs + ses)[1], col = "black", lwd = 2)
+segments(0, (coefs - ses)[2], 0, (coefs + ses)[2], col = "black", lwd = 2)
+segments(1, (coefs - ses)[3], 1, (coefs + ses)[3], col = "black", lwd = 2)
+segments(8.0, (coefs - ses)[4], 8, (coefs + ses)[4], col = "black", lwd = 2)
+segments(24.0, (coefs - ses)[5], 24, (coefs + ses)[5], col = "black", lwd = 2)
+segments(-1, (coefs - 1.96*ses)[1], -1, (coefs + 1.96*ses)[1], col = "black", lwd = 1)
+segments(0, (coefs - 1.96*ses)[2], 0, (coefs + 1.96*ses)[2], col = "black", lwd = 1)
+segments(1, (coefs - 1.96*ses)[3], 1, (coefs + 1.96*ses)[3], col = "black", lwd = 1)
+segments(8.0, (coefs - 1.96*ses)[4], 8, (coefs + 1.96*ses)[4], col = "black", lwd = 1)
+segments(24.0, (coefs - 1.96*ses)[5], 24, (coefs + 1.96*ses)[5], col = "black", lwd = 1)
+##### GRAPH 4 - DEMOCRACY ######
+dem_pre <- est.ate.np(all$pre_democracy,all)
+dem <- est.ate(all$democracy, all$pre_democracy,all)
+dem_f1 <- est.ate.f(all$democracy_f1, all$pre_democracy)
+dem_f2 <- est.ate.f(all$democracy_f2, all$pre_democracy)
+dem_f3 <- est.ate.f(all$democracy_f3, all$pre_democracy)
+coefs <- unlist(c(dem_pre[1], dem[1], dem_f1[1],dem_f2[1], dem_f3[1]))
+ses <- unlist(c(dem_pre[2], dem[2], dem_f1[2],dem_f2[2], dem_f3[2]))
+plot(NA, xlim = c(-2, 25), ylim = c(-.4, .3), xlab = '', ylab = '')
+title("Satisfaction with democracy (0-3)")
+abline(v = -1, col = "gray")
+abline(v = 0, col = "gray")
+abline(v = 1, col = "gray")
+abline(v = 8, col = "gray")
+abline(v = 24, col = "gray")
+abline(h = 0, col = "red")
+points(-1, coefs[1], pch = 23, col = "black", bg = "black")
+points(0, coefs[2], pch = 23, col = "black", bg = "black")
+points(1, coefs[3], pch = 23, col = "black", bg = "black")
+points(8, coefs[4], pch = 23, col = "black", bg = "black")
+points(24, coefs[5], pch = 23, col = "black", bg = "black")
+segments(-1, (coefs - ses)[1], -1, (coefs + ses)[1], col = "black", lwd = 2)
+segments(0, (coefs - ses)[2], 0, (coefs + ses)[2], col = "black", lwd = 2)
+segments(1, (coefs - ses)[3], 1, (coefs + ses)[3], col = "black", lwd = 2)
+segments(8.0, (coefs - ses)[4], 8, (coefs + ses)[4], col = "black", lwd = 2)
+segments(24.0, (coefs - ses)[5], 24, (coefs + ses)[5], col = "black", lwd = 2)
+segments(-1, (coefs - 1.96*ses)[1], -1, (coefs + 1.96*ses)[1], col = "black", lwd = 1)
+segments(0, (coefs - 1.96*ses)[2], 0, (coefs + 1.96*ses)[2], col = "black", lwd = 1)
+segments(1, (coefs - 1.96*ses)[3], 1, (coefs + 1.96*ses)[3], col = "black", lwd = 1)
+segments(8.0, (coefs - 1.96*ses)[4], 8, (coefs + 1.96*ses)[4], col = "black", lwd = 1)
+segments(24.0, (coefs - 1.96*ses)[5], 24, (coefs + 1.96*ses)[5], col = "black", lwd = 1)
+##### GRAPH 5 - MIL GOV ######
+mil_pre <- est.ate.np(all$pre_military_gov,all)
+mil <- est.ate(all$military_gov, all$pre_military_gov,all)
+mil_f1 <- est.ate.f(all$military_gov_f1, all$pre_military_gov)
+mil_f2 <- est.ate.f(all$military_gov_f2, all$pre_military_gov)
+mil_f3 <- est.ate.f(all$military_gov_f3, all$pre_military_gov)
+coefs <- unlist(c(mil_pre[1], mil[1], mil_f1[1],mil_f2[1], mil_f3[1]))
+ses <- unlist(c(mil_pre[2], mil[2], mil_f1[2],mil_f2[2], mil_f3[2]))
+plot(NA, xlim = c(-2, 25), ylim = c(-.2, .3), xlab = '', ylab = '')
+title("Support for military gov (0-1)")
+abline(v = -1, col = "gray")
+abline(v = 0, col = "gray")
+abline(v = 1, col = "gray")
+abline(v = 8, col = "gray")
+abline(v = 24, col = "gray")
+abline(h = 0, col = "red")
+points(-1, coefs[1], pch = 23, col = "black", bg = "black")
+points(0, coefs[2], pch = 23, col = "black", bg = "black")
+points(1, coefs[3], pch = 23, col = "black", bg = "black")
+points(8, coefs[4], pch = 23, col = "black", bg = "black")
+points(24, coefs[5], pch = 23, col = "black", bg = "black")
+segments(-1, (coefs - ses)[1], -1, (coefs + ses)[1], col = "black", lwd = 2)
+segments(0, (coefs - ses)[2], 0, (coefs + ses)[2], col = "black", lwd = 2)
+segments(1, (coefs - ses)[3], 1, (coefs + ses)[3], col = "black", lwd = 2)
+segments(8.0, (coefs - ses)[4], 8, (coefs + ses)[4], col = "black", lwd = 2)
+segments(24.0, (coefs - ses)[5], 24, (coefs + ses)[5], col = "black", lwd = 2)
+segments(-1, (coefs - 1.96*ses)[1], -1, (coefs + 1.96*ses)[1], col = "black", lwd = 1)
+segments(0, (coefs - 1.96*ses)[2], 0, (coefs + 1.96*ses)[2], col = "black", lwd = 1)
+segments(1, (coefs - 1.96*ses)[3], 1, (coefs + 1.96*ses)[3], col = "black", lwd = 1)
+segments(8.0, (coefs - 1.96*ses)[4], 8, (coefs + 1.96*ses)[4], col = "black", lwd = 1)
+segments(24.0, (coefs - 1.96*ses)[5], 24, (coefs + 1.96*ses)[5], col = "black", lwd = 1)
+##### GRAPH 6 - ADVANCE ######
+advance <- est.ate.np(all$justice_advance,all)
+advance_f1 <- est.ate.np.f(all$justice_advance_f1)
+advance_f2 <- est.ate.np.f(all$justice_advance_f2)
+advance_f3 <- est.ate.np.f(all$justice_advance_f3)
+coefs <- unlist(c(advance[1], advance_f1[1], advance_f2[1], advance_f3[1]))
+ses <- unlist(c(advance[2], advance_f1[2], advance_f2[2], advance_f3[2]))
+plot(NA, xlim = c(-.5, 25), ylim = c(-.8, .2), xlab = '', ylab = '')
+title("Obsession with the past \n makes it hard to advance (0-4)")
+abline(v = 0, col = "gray")
+abline(v = 1, col = "gray")
+abline(v = 8, col = "gray")
+abline(v = 24, col = "gray")
+abline(h = 0, col = "red")
+points(0, coefs[1], pch = 23, col = "black", bg = "black")
+points(1, coefs[2], pch = 23, col = "black", bg = "black")
+points(8, coefs[3], pch = 23, col = "black", bg = "black")
+points(24, coefs[4], pch = 23, col = "black", bg = "black")
+segments(0, (coefs - ses)[1], 0, (coefs + ses)[1], col = "black", lwd = 2)
+segments(1, (coefs - ses)[2], 1, (coefs + ses)[2], col = "black", lwd = 2)
+segments(8.0, (coefs - ses)[3], 8, (coefs + ses)[3], col = "black", lwd = 2)
+segments(24, (coefs - ses)[4], 24, (coefs + ses)[4], col = "black", lwd = 2)
+segments(0, (coefs - 1.96*ses)[1], 0, (coefs + 1.96*ses)[1], col = "black", lwd = 1)
+segments(1, (coefs - 1.96*ses)[2], 1, (coefs + 1.96*ses)[2], col = "black", lwd = 1)
+segments(8.0, (coefs - 1.96*ses)[3], 8, (coefs + 1.96*ses)[3], col = "black", lwd = 1)
+segments(24.0, (coefs - 1.96*ses)[4], 24, (coefs + 1.96*ses)[4], col = "black", lwd = 1)
+## GRAPH 7 - COMPENSATION ##
+comp_pre <- est.ate.np(all$pre_current_recomp,all)
+comp <- est.ate(all$current_recomp, all$pre_current_recomp,all)
+comp_f1 <- est.ate.f(all$current_recomp_f1, all$pre_current_recomp)
+comp_f2 <- est.ate.f(all$current_recomp_f2, all$pre_current_recomp)
+comp_f3 <- est.ate.f(all$current_recomp_f3, all$pre_current_recomp)
+coefs <- unlist(c(comp_pre[1], comp[1], comp_f1[1],comp_f2[1], comp_f3[1]))
+ses <- unlist(c(comp_pre[2], comp[2], comp_f1[2], comp_f2[2], comp_f3[2]))
+plot(NA, xlim = c(-2, 25), ylim = c(-.4, .4), xlab = '', ylab = '')
+title("Support for victim compensation (0-3)")
+abline(v = -1, col = "gray")
+abline(v = 0, col = "gray")
+abline(v = 1, col = "gray")
+abline(v = 8, col = "gray")
+abline(v = 24, col = "gray")
+abline(h = 0, col = "red")
+points(-1, coefs[1], pch = 23, col = "black", bg = "black")
+points(0, coefs[2], pch = 23, col = "black", bg = "black")
+points(1, coefs[3], pch = 23, col = "black", bg = "black")
+points(8, coefs[4], pch = 23, col = "black", bg = "black")
+points(24, coefs[5], pch = 23, col = "black", bg = "black")
+segments(-1, (coefs - ses)[1], -1, (coefs + ses)[1], col = "black", lwd = 2)
+segments(0, (coefs - ses)[2], 0, (coefs + ses)[2], col = "black", lwd = 1)
+segments(1, (coefs - ses)[3], 1, (coefs + ses)[3], col = "black", lwd = 2)
+segments(8.0, (coefs - ses)[4], 8, (coefs + ses)[4], col = "black", lwd = 2)
+segments(24.0, (coefs - ses)[5], 24, (coefs + ses)[5], col = "black", lwd = 2)
+segments(-1, (coefs - 1.96*ses)[1], -1, (coefs + 1.96*ses)[1], col = "black", lwd = 1)
+segments(0, (coefs - 1.96*ses)[2], 0, (coefs + 1.96*ses)[2], col = "black", lwd = 1)
+segments(1, (coefs - 1.96*ses)[3], 1, (coefs + 1.96*ses)[3], col = "black", lwd = 1)
+segments(8.0, (coefs - 1.96*ses)[4], 8, (coefs + 1.96*ses)[4], col = "black", lwd = 1)
+segments(24.0, (coefs - 1.96*ses)[5], 24, (coefs + 1.96*ses)[5], col = "black", lwd = 1)
+## GRAPH 8 - PUBLIC APOLOGY ##
+tj_apology <- est.ate.np(all$tj_apology,all)
+tj_apology_f1 <- est.ate.np.f(all$tj_apology_f1)
+tj_apology_f2 <- est.ate.np.f(all$tj_apology_f2)
+tj_apology_f3 <- est.ate.np.f(all$tj_apology_f3)
+coefs <- unlist(c(tj_apology[1], tj_apology_f1[1], tj_apology_f2[1], tj_apology_f3[1]))
+ses <- unlist(c(tj_apology[2], tj_apology_f1[2], tj_apology_f2[2], tj_apology_f3[2]))
+plot(NA, xlim = c(-.5, 25), ylim = c(-.2, .5), xlab = '', ylab = '')
+title("Military should apologize (0-4)")
+abline(v = 0, col = "gray")
+abline(v = 1, col = "gray")
+abline(v = 8, col = "gray")
+abline(v = 24, col = "gray")
+abline(h = 0, col = "red")
+points(0, coefs[1], pch = 23, col = "black", bg = "black")
+points(1, coefs[2], pch = 23, col = "black", bg = "black")
+points(8, coefs[3], pch = 23, col = "black", bg = "black")
+points(24, coefs[4], pch = 23, col = "black", bg = "black")
+segments(0, (coefs - ses)[1], 0, (coefs + ses)[1], col = "black", lwd = 2)
+segments(1, (coefs - ses)[2], 1, (coefs + ses)[2], col = "black", lwd = 2)
+segments(8.0, (coefs - ses)[3], 8, (coefs + ses)[3], col = "black", lwd = 2)
+segments(24.0, (coefs - ses)[4], 24, (coefs + ses)[4], col = "black", lwd = 2)
+segments(0, (coefs - 1.96*ses)[1], 0, (coefs + 1.96*ses)[1], col = "black", lwd = 1)
+segments(1, (coefs - 1.96*ses)[2], 1, (coefs + 1.96*ses)[2], col = "black", lwd = 1)
+segments(8.0, (coefs - 1.96*ses)[3], 8, (coefs + 1.96*ses)[3], col = "black", lwd = 1)
+segments(24.0, (coefs - 1.96*ses)[4], 24, (coefs + 1.96*ses)[4], col = "black", lwd = 1)
+mtext('Weeks from Treatment', side = 1, outer = TRUE, line = 2)
+mtext('Coefficient', side = 2, outer = TRUE, line = 2)

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