diff --git a/23/paper.pdf b/23/paper.pdf new file mode 100644 index 0000000000000000000000000000000000000000..13c0710268986327515e04ccfaf930051ef0585b --- /dev/null +++ b/23/paper.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:af1909814ddf0ce5ad9c91bd3cdfae0676eb304e8c92c438d56fd9e8d77a407f +size 2076097 diff --git a/23/replication_package/Code/Manekin_Mitts_Effective_for_Whom_Replication.R b/23/replication_package/Code/Manekin_Mitts_Effective_for_Whom_Replication.R new file mode 100644 index 0000000000000000000000000000000000000000..3277e5e5d9e11bddc6ef39c65d5302dab51da8ca --- /dev/null +++ b/23/replication_package/Code/Manekin_Mitts_Effective_for_Whom_Replication.R @@ -0,0 +1,128 @@ +# ---------------------------------------------------------------------- +# Replication for "Effective for Whom? Ethnic Identity and Nonviolent Resistance" +# Devorah Manekin and Tamar Mitts +# American Political Science Review (2021) +# ---------------------------------------------------------------------- +# This code replicates the tables figures in the article and the appendix. +# It requires the packages listed below. If not already installed, +# use the function install.packages() to install them. +# ---------------------------------------------------------------------- + +rm(list=ls()) + +library(lemon) +library(stargazer) +library(xtable) +library(fastDummies) +library(tidyr) +library(gridExtra) +library(stm) +library(quanteda) +library(ggplot2) +library(dotwhisker) +library(ggeffects) +library(estimatr) +library(corrplot) + +# SET WORKING DIRECTORY +setwd("/Users/tamarmitts/Dropbox (Mitts)/Projects/Non-violence/Replication/") + +# LOAD DATASETS +load("Data/EBCR_EPR_NAVCO2.rdata") +load("Data/NAVCO2_EPR.rdata") +load("Data/NAVCO2.rdata") +load("Data/us_survey_wave1.rdata") +load("Data/isr_survey_wave1.rdata") +load("Data/us_survey_wave2.rdata") +load("Data/isr_survey_wave2.rdata") +load("Data/us_survey_text_analysis.rdata") +load("Data/isr_survey_text_analysis_eth.rdata") +load("Data/isr_survey_text_analysis_arab.rdata") + +##--##--##--## +## Manuscript +##--##--##--## + +# Figure 1 +source("Code/replicate_fig_1.R") +# Figure 3 +source("Code/replicate_fig_3.R") +# Figure 4 +source("Code/replicate_fig_4.R") +# Figure 6 +source("Code/replicate_fig_6.R") +# Figure 7 +source("Code/replicate_fig_7.R") +# Figure 8 +source("Code/replicate_fig_8.R") +# Figure 9 +source("Code/replicate_fig_9.R") +# Figure 10 +source("Code/replicate_fig_10.R") + +# Table 1 +source("Code/replicate_table_1.R") +# Table 4 +source("Code/replicate_table_4.R") + +##--##--##--## +## Appendix +##--##--##--## + +# Figure A1 +source("Code/replicate_fig_A1.R") +# Figure A2 +source("Code/replicate_fig_A2.R") +# Figure A3 +source("Code/replicate_fig_A3.R") +# Figure A6 +source("Code/replicate_fig_A6.R") +# Figure A7 +source("Code/replicate_fig_A7.R") +# Figure A8 +source("Code/replicate_fig_A8.R") +# Figure A9 +source("Code/replicate_fig_A9.R") +# Figure A10 +source("Code/replicate_fig_A10.R") +# Figure A11 +source("Code/replicate_fig_A11.R") +# Figure A11 +source("Code/replicate_fig_A12.R") + +# Table A1 +source("Code/replicate_table_A1.R") +# Table A2 +source("Code/replicate_table_A2.R") +# Table A3 +source("Code/replicate_table_A3.R") +# Table A4 +source("Code/replicate_table_A4.R") +# Table A5 +source("Code/replicate_table_A5.R") +# Table A6 +source("Code/replicate_table_A6.R") +# Table A7 +source("Code/replicate_table_A7.R") +# Table A8 +source("Code/replicate_table_A8.R") +# Table A9 +source("Code/replicate_table_A9.R") +# Table A10 +source("Code/replicate_table_A10.R") +# Table A10 +source("Code/replicate_table_A11.R") +# Table A13 +source("Code/replicate_table_A13.R") +# Table A14 +source("Code/replicate_table_A14.R") +# Table A15 +source("Code/replicate_table_A15.R") +# Table A16 +source("Code/replicate_table_A16.R") +# Table A17 +source("Code/replicate_table_A17.R") +# Table A18 +source("Code/replicate_table_A18.R") + +## END \ No newline at end of file diff --git a/23/replication_package/Code/replicate_fig_1.R b/23/replication_package/Code/replicate_fig_1.R new file mode 100644 index 0000000000000000000000000000000000000000..aad3470a47248a72ed31c570cfa2245cac4d1ae8 --- /dev/null +++ b/23/replication_package/Code/replicate_fig_1.R @@ -0,0 +1,62 @@ +##--##--##--## +## Figure 1 +##--##--##--## + +# Status (excluded, not excluded) +nv_status = lm(success ~ EPR_STATUS_EXCL + EPR_GROUPSIZE + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + EPR_TEK_EGIP + EPR_DOWNGRADED5 + HORIZ_INEQ + NVYEARS + I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3) , data= EBCR_EPR_NAVCO2[EBCR_EPR_NAVCO2$INIT_NV_ONSET==1,]) +v_status = lm(success ~ EPR_STATUS_EXCL + EPR_GROUPSIZE + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + EPR_TEK_EGIP + EPR_DOWNGRADED5 + HORIZ_INEQ + NVYEARS + I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3) , data= EBCR_EPR_NAVCO2[EBCR_EPR_NAVCO2$INIT_V_ONSET==1,]) + +preds_nv = ggpredict(nv_status, terms = c("EPR_STATUS_EXCL")) +preds_nv$tactic = "Non-violent" +preds_v = ggpredict(v_status, terms = c("EPR_STATUS_EXCL")) +preds_v$tactic = "Violent" + +preds = rbind(preds_nv, preds_v) +preds$tactic <- factor(preds$tactic, levels = c("Violent", "Non-violent")) +preds$Status = "Minority/disadvantaged" +preds$Status[preds$x==0] = "Majority/dominant" + +xtable(preds[,-1], digits=2) +preds = as.data.frame(preds) +preds$Status = as.factor(preds$Status) + +status = ggplot(preds, aes(x = tactic, y = predicted, group=Status)) + + geom_line(aes(color=Status))+ geom_errorbar(aes(color = Status), width = 0, ymin=preds$conf.low, ymax=preds$conf.high)+ + geom_point(aes(color=Status, shape=Status), size=3) + ylim(-0.1,0.5) + theme_bw() + + scale_colour_grey(start = 0.1, end = 0.65) + geom_hline(yintercept=0, linetype="dashed")+ + ylab("Pr(Campaign Success)") + xlab("Tactic") +ggtitle("(A) Group Status") + + theme(legend.position="none") + +## Size (above and below the mean of the distribution) +EBCR_EPR_NAVCO2$small_size = NA +EBCR_EPR_NAVCO2$small_size[EBCR_EPR_NAVCO2$EPR_GROUPSIZE < mean(EBCR_EPR_NAVCO2$EPR_GROUPSIZE, na.rm=T)] = 1 +EBCR_EPR_NAVCO2$small_size[EBCR_EPR_NAVCO2$EPR_GROUPSIZE >= mean(EBCR_EPR_NAVCO2$EPR_GROUPSIZE, na.rm=T)] = 0 + +nv_size = lm(success ~ small_size + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + EPR_TEK_EGIP + EPR_DOWNGRADED5 + HORIZ_INEQ + NVYEARS + I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3) , data= EBCR_EPR_NAVCO2[EBCR_EPR_NAVCO2$INIT_NV_ONSET==1,]) +v_size = lm(success ~ small_size + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + EPR_TEK_EGIP + EPR_DOWNGRADED5 + HORIZ_INEQ + NVYEARS + I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3) , data= EBCR_EPR_NAVCO2[EBCR_EPR_NAVCO2$INIT_V_ONSET==1,]) + +preds_nv = ggpredict(nv_size, terms = c("small_size")) +preds_nv$tactic = "Non-violent" +preds_v = ggpredict(v_size, terms = c("small_size")) +preds_v$tactic = "Violent" + +preds = rbind(preds_nv, preds_v) +preds$tactic <- factor(preds$tactic, levels = c("Violent", "Non-violent")) +preds$Status = "Minority/disadvantaged" +preds$Status[preds$x==0] = "Majority/dominant" + +preds = as.data.frame(preds) +preds$Status = as.factor(preds$Status) + +xtable(preds[,-1], digits=2) + +size = ggplot(preds, aes(x = tactic, y = predicted, group=Status)) + + geom_line(aes(color=Status))+ geom_errorbar(aes(color = Status), width = 0, ymin=preds$conf.low, ymax=preds$conf.high)+ + geom_point(aes(color=Status, shape=Status), size=3) + ylim(-0.1,0.5) + theme_bw() + + #scale_color_manual(values=c("#E69F00", "#56B4E9")) + geom_hline(yintercept=0, linetype="dashed")+ + scale_colour_grey(start = 0.1, end = 0.65) + geom_hline(yintercept=0, linetype="dashed")+ + ylab("Pr(Campaign Success)") + xlab("Tactic") +ggtitle("(B) Group Size") + + theme(legend.position="none") + +fig1 = grid_arrange_shared_legend(status, size, ncol = 2, nrow = 1) +ggsave(file="Figures/fig_1.pdf", fig1, width=8, height=4) \ No newline at end of file diff --git a/23/replication_package/Code/replicate_fig_10.R b/23/replication_package/Code/replicate_fig_10.R new file mode 100644 index 0000000000000000000000000000000000000000..1f8d449831a76e9efd79cd147f400eae0c3a8d5e --- /dev/null +++ b/23/replication_package/Code/replicate_fig_10.R @@ -0,0 +1,36 @@ +##--##--##--## +## Figure 10 +##--##--##--## + +isr_survey_eth_content_covars <- estimateEffect(1:10 ~ identity_protesters_eth, isr_survey_text_analysis_eth$stm_topics, meta = docvars(isr_survey_text_analysis_eth$nv_dfm_eth), uncertainty = "Global") +isr_survey_eth_sum_content_covars = summary(isr_survey_eth_content_covars) + +term_translated = c("protest, right, democracy, ethiopians", + "discrimination, justice, understand, frustration", + "equal, rights, treatment, deserve", + "racism, violence, protest", + "judge, physical, right, treatment", + "economic, crisis, unemployment, justified", + "support, people, understand", + "voice, protest, express", + "ethiopian, community, racism", + "violence, express, furious") + + +estimate = rep(NA, 10) +std.error = rep(NA, 10) +for(i in 1:length(isr_survey_eth_sum_content_covars$tables)){ + estimate[i] = isr_survey_eth_sum_content_covars$tables[[i]][2,1] + std.error[i] = isr_survey_eth_sum_content_covars$tables[[i]][2,2] +} + +isr_survey_eth_stm_results = data.frame("term" = term_translated, "estimate" = estimate, "std.error"= std.error) + +isr_survey_eth_stm_results = isr_survey_eth_stm_results[order(isr_survey_eth_stm_results$estimate, decreasing = T),] + +dwplot(isr_survey_eth_stm_results, + vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2), dot_args = list(size = 2.5)) + + theme_bw() + xlab("\nIsraeli white protesters . . . . . . . . . . . . . Israeli Ethiopian protesters") + + theme(text = element_text(size=12), axis.text.x = element_text(angle = 0, hjust = 1), legend.position = "none") + + scale_colour_grey(start = 0, end = 0) +ggsave(file="Figures/fig_10.pdf", width=8, height=6) diff --git a/23/replication_package/Code/replicate_fig_3.R b/23/replication_package/Code/replicate_fig_3.R new file mode 100644 index 0000000000000000000000000000000000000000..ed99959ed0dbf8c0f2d6544f8b280c3481cd15b1 --- /dev/null +++ b/23/replication_package/Code/replicate_fig_3.R @@ -0,0 +1,204 @@ +##--##--##--## +## Figure 3 +##--##--##--## + +us_survey_wave1$identity_protesters_fac = as.factor(us_survey_wave1$identity_protesters) +levels(us_survey_wave1$identity_protesters_fac) = c("White", "Black") +us_survey_wave1$tactic_fac = as.factor(us_survey_wave1$tactic) +levels(us_survey_wave1$tactic_fac) = c("March in streets", "Shut down traffic", "Destroy police cars") + +isr_survey_wave1$identity_protesters_fac = as.factor(isr_survey_wave1$identity_protesters) +levels(isr_survey_wave1$identity_protesters_fac) = c("White", "Ethiopian", "Arab") +isr_survey_wave1$tactic_fac = as.factor(isr_survey_wave1$tactic) +levels(isr_survey_wave1$tactic_fac) = c("March in streets", "Shut down traffic", "Destroy garbage cans") + + +## US sample +us_survey_wave1$degree_violence_std = scale(us_survey_wave1$degree_violence) +us_survey_wave1$police_action_required_std = scale(us_survey_wave1$police_action_required) +us_survey_wave1$recall_violence2_std = scale(us_survey_wave1$recall_violence2) + +march1 = lm_robust(degree_violence_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==0,], weights=weight) +shut1 = lm_robust(degree_violence_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==1,], weights=weight) +destroy1 = lm_robust(degree_violence_std ~ identity_protesters_fac , data=us_survey_wave1[us_survey_wave1$tactic==2,], weights=weight) + +march2 = lm_robust(police_action_required_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==0,], weights=weight) +shut2 = lm_robust(police_action_required_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==1,], weights=weight) +destroy2 = lm_robust(police_action_required_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==2,], weights=weight) + +march3 = lm_robust(recall_violence2_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==0,], weights=weight) +shut3 = lm_robust(recall_violence2_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==1,], weights=weight) +destroy3 = lm_robust(recall_violence2_std ~ identity_protesters_fac , data=us_survey_wave1[us_survey_wave1$tactic==2,], weights=weight) + +# Plot differences + +term_degree_violence = c(march1$coefficients[2], shut1$coefficients[2], destroy1$coefficients[2]) +se_degree_violence = c(march1$std.error[2], shut1$std.error[2], destroy1$std.error[2]) +statistic_degree_violence = c(march1$statistic[2], shut1$statistic[2], destroy1$statistic[2]) +pval_degree_violence = c(march1$p.value[2], shut1$p.value[2], destroy1$p.value[2]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +degree_violence = as.data.frame(cbind(term_degree_violence, se_degree_violence, statistic_degree_violence, pval_degree_violence)) +degree_violence = cbind(degree_violence, term) +colnames(degree_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(degree_violence) = term +degree_violence$model = "Perceived degree of violence" + +term_police_action_required = c(march2$coefficients[2], shut2$coefficients[2], destroy2$coefficients[2]) +se_police_action_required = c(march2$std.error[2], shut2$std.error[2], destroy2$std.error[2]) +statistic_police_action_required = c(march2$statistic[2], shut2$statistic[2], destroy2$statistic[2]) +pval_police_action_required = c(march2$p.value[2], shut2$p.value[2], destroy2$p.value[2]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +police_action_required = as.data.frame(cbind(term_police_action_required, se_police_action_required, statistic_police_action_required, pval_police_action_required)) +police_action_required = cbind(police_action_required, term) +colnames(police_action_required) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(police_action_required) = term +police_action_required$model = "Police action required" + +term_recall_violence = c(march3$coefficients[2], shut3$coefficients[2], destroy3$coefficients[2]) +se_recall_violence = c(march3$std.error[2], shut3$std.error[2], destroy3$std.error[2]) +statistic_recall_violence = c(march3$statistic[2], shut3$statistic[2], destroy3$statistic[2]) +pval_recall_violence = c(march3$p.value[2], shut3$p.value[2], destroy3$p.value[2]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +recall_violence = as.data.frame(cbind(term_recall_violence, se_recall_violence, statistic_recall_violence, pval_recall_violence)) +recall_violence = cbind(recall_violence, term) +colnames(recall_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(recall_violence) = term +recall_violence$model = "Recall violence" + +differences_us = rbind(degree_violence, police_action_required, recall_violence) + + +## Israel respondents +isr_survey_wave1$degree_violence_std = scale(isr_survey_wave1$degree_violence) +isr_survey_wave1$police_action_required_std = scale(isr_survey_wave1$police_action_required) +isr_survey_wave1$recall_violence2_std = scale(isr_survey_wave1$recall_violence2) + +march1 = lm_robust(degree_violence_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==0,], weights=weight) +shut1 = lm_robust(degree_violence_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==1,], weights=weight) +destroy1 = lm_robust(degree_violence_std ~ identity_protesters_fac , data=isr_survey_wave1[isr_survey_wave1$tactic==2,], weights=weight) + +march2 = lm_robust(police_action_required_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==0,], weights=weight) +shut2 = lm_robust(police_action_required_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==1,], weights=weight) +destroy2 = lm_robust(police_action_required_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==2,], weights=weight) + +march3 = lm_robust(recall_violence2_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==0,], weights=weight) +shut3 = lm_robust(recall_violence2_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==1,], weights=weight) +destroy3 = lm_robust(recall_violence2_std ~ identity_protesters_fac , data=isr_survey_wave1[isr_survey_wave1$tactic==2,], weights=weight) + + +# Ethiopian protesters +term_degree_violence = c(march1$coefficients[2], shut1$coefficients[2], destroy1$coefficients[2]) +se_degree_violence = c(march1$std.error[2], shut1$std.error[2], destroy1$std.error[2]) +statistic_degree_violence = c(march1$statistic[2], shut1$statistic[2], destroy1$statistic[2]) +pval_degree_violence = c(march1$p.value[2], shut1$p.value[2], destroy1$p.value[2]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +degree_violence = as.data.frame(cbind(term_degree_violence, se_degree_violence, statistic_degree_violence, pval_degree_violence)) +degree_violence = cbind(degree_violence, term) +colnames(degree_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(degree_violence) = term +degree_violence$model = "Perceived degree of violence" + +term_police_action_required = c(march2$coefficients[2], shut2$coefficients[2], destroy2$coefficients[2]) +se_police_action_required = c(march2$std.error[2], shut2$std.error[2], destroy2$std.error[2]) +statistic_police_action_required = c(march2$statistic[2], shut2$statistic[2], destroy2$statistic[2]) +pval_police_action_required = c(march2$p.value[2], shut2$p.value[2], destroy2$p.value[2]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +police_action_required = as.data.frame(cbind(term_police_action_required, se_police_action_required, statistic_police_action_required, pval_police_action_required)) +police_action_required = cbind(police_action_required, term) +colnames(police_action_required) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(police_action_required) = term +police_action_required$model = "Police action required" + +term_recall_violence = c(march3$coefficients[2], shut3$coefficients[2], destroy3$coefficients[2]) +se_recall_violence = c(march3$std.error[2], shut3$std.error[2], destroy3$std.error[2]) +statistic_recall_violence = c(march3$statistic[2], shut3$statistic[2], destroy3$statistic[2]) +pval_recall_violence = c(march3$p.value[2], shut3$p.value[2], destroy3$p.value[2]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +recall_violence = as.data.frame(cbind(term_recall_violence, se_recall_violence, statistic_recall_violence, pval_recall_violence)) +recall_violence = cbind(recall_violence, term) +colnames(recall_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(recall_violence) = term +recall_violence$model = "Recall violence" + +differences_isr_black = rbind(degree_violence, police_action_required, recall_violence) + +term_degree_violence = c(march1$coefficients[3], shut1$coefficients[3], destroy1$coefficients[3]) +se_degree_violence = c(march1$std.error[3], shut1$std.error[3], destroy1$std.error[3]) +statistic_degree_violence = c(march1$statistic[3], shut1$statistic[3], destroy1$statistic[3]) +pval_degree_violence = c(march1$p.value[3], shut1$p.value[3], destroy1$p.value[3]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +degree_violence = as.data.frame(cbind(term_degree_violence, se_degree_violence, statistic_degree_violence, pval_degree_violence)) +degree_violence = cbind(degree_violence, term) +colnames(degree_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(degree_violence) = term +degree_violence$model = "Perceived degree of violence" + +term_police_action_required = c(march2$coefficients[3], shut2$coefficients[3], destroy2$coefficients[3]) +se_police_action_required = c(march2$std.error[3], shut2$std.error[3], destroy2$std.error[3]) +statistic_police_action_required = c(march2$statistic[3], shut2$statistic[3], destroy2$statistic[3]) +pval_police_action_required = c(march2$p.value[3], shut2$p.value[3], destroy2$p.value[3]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +police_action_required = as.data.frame(cbind(term_police_action_required, se_police_action_required, statistic_police_action_required, pval_police_action_required)) +police_action_required = cbind(police_action_required, term) +colnames(police_action_required) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(police_action_required) = term +police_action_required$model = "Police action required" + +term_recall_violence = c(march3$coefficients[3], shut3$coefficients[3], destroy3$coefficients[3]) +se_recall_violence = c(march3$std.error[3], shut3$std.error[3], destroy3$std.error[3]) +statistic_recall_violence = c(march3$statistic[3], shut3$statistic[3], destroy3$statistic[3]) +pval_recall_violence = c(march3$p.value[3], shut3$p.value[3], destroy3$p.value[3]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +recall_violence = as.data.frame(cbind(term_recall_violence, se_recall_violence, statistic_recall_violence, pval_recall_violence)) +recall_violence = cbind(recall_violence, term) +colnames(recall_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(recall_violence) = term +recall_violence$model = "Recall violence" + +differences_isr_arab = rbind(degree_violence, police_action_required, recall_violence) + +## Result tables for plot +differences_us2 = differences_us +differences_us2$submodel = differences_us2$term +differences_us2$term = "Perception of Blacks \n(United States)" + +differences_isr_black2 = differences_isr_black +differences_isr_black2$submodel = differences_isr_black2$term +differences_isr_black2$term = "Perception of Ethiopians \n(Israel)" + +differences_isr_arab2 = differences_isr_arab +differences_isr_arab2$submodel = differences_isr_arab2$term +differences_isr_arab2$term = "Perception of Arabs \n(Israel)" + +diffs_sm = rbind(differences_us2, differences_isr_arab2, differences_isr_black2) +rownames(diffs_sm) = 1:nrow(diffs_sm) + +results_df <- data.frame(term = diffs_sm$term, + estimate = diffs_sm$estimate, + std.error = diffs_sm$std.error, + model = diffs_sm$model, + submodel = as.character(diffs_sm$submodel), + stringsAsFactors = FALSE) + +results_df$submodel[results_df$submodel=="1) Minority: March in streets"] = "March in streets" +results_df$submodel[results_df$submodel=="2) Minority: Shut down traffic"] = "Shut down traffic" +results_df$submodel[results_df$submodel=="3) Minority: Destroy police cars / garbage cans"] = "Destroy property" + +results_df$model[results_df$model=="Perceived degree of violence"] = "1. Perceived degree \nof violence" +results_df$model[results_df$model=="Police action required"] = "3. Police action \nrequired" +results_df$model[results_df$model=="Recall violence"] = "2. Recall \nviolence" + +small_multiple(results_df, dot_args = list(aes(shape = submodel))) + + ylab("Coefficient Estimate (Std. Dev. Units)") + theme_bw()+ + geom_hline(yintercept = 0, colour = "grey60", linetype = 2) + + #theme(plot.margin=unit(c(0.4,0.1,2.6,1),"cm"))+ + theme(legend.position = "bottom", + legend.justification=c(0, 0), + # legend.background = element_rect(color="white"), + legend.spacing = unit(-4, "pt"), + legend.key.size = unit(10, "pt"))+ + scale_shape_discrete(name = "Protester Tactic") + + scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"), name = "Protester Tactic") +ggsave(filename="Figures/fig_3.pdf", width=6, height=6) + +table_a14 = results_df diff --git a/23/replication_package/Code/replicate_fig_4.R b/23/replication_package/Code/replicate_fig_4.R new file mode 100644 index 0000000000000000000000000000000000000000..dec55d619d1d8301f9cff73846c4a0ee5961eeed --- /dev/null +++ b/23/replication_package/Code/replicate_fig_4.R @@ -0,0 +1,94 @@ +##--##--##--## +## Figure 4 +##--##--##--## + +march1 = lm(degree_violence ~ identity_protesters, data=us_survey_wave1[us_survey_wave1$tactic==0,], weights=weight) +march1_maj = lm(degree_violence ~ identity_protesters, data=us_survey_wave1[us_survey_wave1$tactic==0 & us_survey_wave1$race == "White",], weights=weight) +march1_min = lm(degree_violence ~ identity_protesters, data=us_survey_wave1[us_survey_wave1$tactic==0 & us_survey_wave1$race == "Black",], weights=weight) + +march2 = lm(police_action_required ~ identity_protesters, data=us_survey_wave1[us_survey_wave1$tactic==0,], weights=weight) +march2_maj = lm(police_action_required ~ identity_protesters, data=us_survey_wave1[us_survey_wave1$tactic==0 & us_survey_wave1$race == "White",], weights=weight) +march2_min = lm(police_action_required ~ identity_protesters, data=us_survey_wave1[us_survey_wave1$tactic==0 & us_survey_wave1$race == "Black",], weights=weight) + +march1_isr = lm(degree_violence ~ identity_protesters, data=isr_survey_wave1[isr_survey_wave1$tactic==0,], weights=weight) +march1_isr_maj = lm(degree_violence ~ identity_protesters, data=isr_survey_wave1[isr_survey_wave1$tactic==0 & isr_survey_wave1$ethnicity %in% c("Ashkenazi", "Mizrachi", "Mixed", "Soviet Union"),], weights=weight) +march1_isr_min = lm(degree_violence ~ identity_protesters, data=isr_survey_wave1[isr_survey_wave1$tactic==0 & isr_survey_wave1$ethnicity == "Arab",], weights=weight) + +march2_isr = lm(police_action_required ~ identity_protesters, data=isr_survey_wave1[isr_survey_wave1$tactic==0,], weights=weight) +march2_isr_maj = lm(police_action_required ~ identity_protesters, data=isr_survey_wave1[isr_survey_wave1$tactic==0 & isr_survey_wave1$ethnicity %in% c("Ashkenazi", "Mizrachi", "Mixed", "Soviet Union"),], weights=weight) +march2_isr_min = lm(police_action_required ~ identity_protesters, data=isr_survey_wave1[isr_survey_wave1$tactic==0 & isr_survey_wave1$ethnicity == "Arab",], weights=weight) + +# Predicted values +perceptions_of_majority_us = c(ggpredict(march1_maj)$identity_protesters[,2], + ggpredict(march2_maj)$identity_protesters[,2]) + +perceptions_of_minority_us = c(ggpredict(march1_min)$identity_protesters[,2], + ggpredict(march2_min)$identity_protesters[,2]) + +perceptions_of_majority_isr = c(ggpredict(march1_isr_maj)$identity_protesters[c(1,3),2], + ggpredict(march2_isr_maj)$identity_protesters[c(1,3),2]) + +perceptions_of_minority_isr = c(ggpredict(march1_isr_min)$identity_protesters[c(1,3),2], + ggpredict(march2_isr_min)$identity_protesters[c(1,3),2]) + +# Std. errors + +se_of_majority_us = c(ggpredict(march1_maj)$identity_protesters[,3], + ggpredict(march2_maj)$identity_protesters[,3]) + +se_of_minority_us = c(ggpredict(march1_min)$identity_protesters[,3], + ggpredict(march2_min)$identity_protesters[,3]) + +se_of_majority_isr = c(ggpredict(march1_isr_maj)$identity_protesters[c(1,3),3], + ggpredict(march2_isr_maj)$identity_protesters[c(1,3),3]) + +se_of_minority_isr = c(ggpredict(march1_isr_min)$identity_protesters[c(1,3),3], + ggpredict(march2_isr_min)$identity_protesters[c(1,3),3]) + + +majority_us = data.frame("value" = perceptions_of_majority_us, + "se" = se_of_majority_us, + "Protesters" = rep(c("Majority", "Minority"),2), + "outcome" = c(rep("Perceived degree of violence", 2), + rep("Police action required", 2))) +majority_us$respondents = "White \nrespondents" +majority_us$survey = "U.S.A" + +minority_us = data.frame("value" = perceptions_of_minority_us, + "se" = se_of_minority_us, + "Protesters" = rep(c("Majority", "Minority"),2), + "outcome" = c(rep("Perceived degree of violence", 2), + rep("Police action required", 2))) +minority_us$respondents = "Black \nrespondents" +minority_us$survey = "U.S.A" + +majority_isr = data.frame("value" = perceptions_of_majority_isr, + "se" = se_of_majority_isr, + "Protesters" = rep(c("Majority", "Minority"),2), + "outcome" = c(rep("Perceived degree of violence", 2), + rep("Police action required", 2))) +majority_isr$respondents = "White Jewish \nrespondents" +majority_isr$survey = "Israel" + +minority_isr = data.frame("value" = perceptions_of_minority_isr, + "se" = se_of_minority_isr, + "Protesters" = rep(c("Majority", "Minority"),2), + "outcome" = c(rep("Perceived degree of violence", 2), + rep("Police action required", 2))) +minority_isr$respondents = "Arab \nrespondents" +minority_isr$survey = "Israel" + + +df_all = rbind(majority_us, minority_us, majority_isr, minority_isr) +df_all = df_all[df_all$outcome != "Recall violence",] +df_all$respondents = factor(df_all$respondents, levels=c("White \nrespondents", "White Jewish \nrespondents", "Black \nrespondents", "Arab \nrespondents")) + +ggplot(df_all, aes(x = respondents, y = value, group=Protesters)) + + geom_point(aes(color=Protesters, shape=Protesters), position = position_dodge(0.3))+ + geom_errorbar(aes(ymin=value-se, ymax=value+se, color=Protesters), width=0, + position=position_dodge(0.3))+ + facet_wrap(~outcome + survey, nrow=2, scales = "free") + theme_bw() + + scale_shape_discrete(name = "Protesters") + + theme(legend.position = "bottom") + xlab("") + ylab("") + + scale_color_manual(values=c("#E69F00", "#56B4E9")) +ggsave("Figures/fig_4.pdf", width=6, height=6) \ No newline at end of file diff --git a/23/replication_package/Code/replicate_fig_6.R b/23/replication_package/Code/replicate_fig_6.R new file mode 100644 index 0000000000000000000000000000000000000000..d4f98bd2b67ce4d54fd97a7132374056d4b54f7a --- /dev/null +++ b/23/replication_package/Code/replicate_fig_6.R @@ -0,0 +1,70 @@ +##--##--##--## +## Figure 6 +##--##--##--## + +goal_white_usa_pdv = lm_robust(degree_violence_std ~ group_goal, data=us_survey_wave2[us_survey_wave2$identity_protesters_fac=="White",]) +goal_black_usa_pdv = lm_robust(degree_violence_std ~ group_goal, data=us_survey_wave2[us_survey_wave2$identity_protesters_fac=="Black",]) +goal_white_usa_rv = lm_robust(recall_violence_std ~ group_goal, data=us_survey_wave2[us_survey_wave2$identity_protesters_fac=="White",]) +goal_black_usa_rv = lm_robust(recall_violence_std ~ group_goal, data=us_survey_wave2[us_survey_wave2$identity_protesters_fac=="Black",]) +goal_white_usa_paq = lm_robust(police_action_required_std ~ group_goal, data=us_survey_wave2[us_survey_wave2$identity_protesters_fac=="White",]) +goal_black_usa_paq = lm_robust(police_action_required_std ~ group_goal, data=us_survey_wave2[us_survey_wave2$identity_protesters_fac=="Black",]) + +## Plot interaction of goal and identity +term_degree_violence = c(goal_white_usa_pdv$coefficients[2], goal_black_usa_pdv$coefficients[2]) +se_degree_violence = c(goal_white_usa_pdv$std.error[2], goal_black_usa_pdv$std.error[2]) +statistic_degree_violence = c(goal_white_usa_pdv$statistic[2], goal_black_usa_pdv$statistic[2]) +pval_degree_violence = c(goal_white_usa_pdv$p.value[2], goal_black_usa_pdv$p.value[2]) +term = c("Majority group protesters", "Minority group protesters") +degree_violence = as.data.frame(cbind(term_degree_violence, se_degree_violence, statistic_degree_violence, pval_degree_violence)) +degree_violence = cbind(degree_violence, term) +colnames(degree_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(degree_violence) = term +degree_violence$model = "Perceived degree \nof violence" + +term_recall_violence = c(goal_white_usa_rv$coefficients[2], goal_black_usa_rv$coefficients[2]) +se_recall_violence = c(goal_white_usa_rv$std.error[2], goal_black_usa_rv$std.error[2]) +statistic_recall_violence = c(goal_white_usa_rv$statistic[2], goal_black_usa_rv$statistic[2]) +pval_recall_violence = c(goal_white_usa_rv$p.value[2], goal_black_usa_rv$p.value[2]) +term = c("Majority group protesters", "Minority group protesters") +recall_violence = as.data.frame(cbind(term_recall_violence, se_recall_violence, statistic_recall_violence, pval_recall_violence)) +recall_violence = cbind(recall_violence, term) +colnames(recall_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(recall_violence) = term +recall_violence$model = "Recall \nviolence" + +term_police_action_required = c(goal_white_usa_paq$coefficients[2], goal_black_usa_paq$coefficients[2]) +se_police_action_required = c(goal_white_usa_paq$std.error[2], goal_black_usa_paq$std.error[2]) +statistic_police_action_required = c(goal_white_usa_paq$statistic[2], goal_black_usa_paq$statistic[2]) +pval_police_action_required = c(goal_white_usa_paq$p.value[2], goal_black_usa_paq$p.value[2]) +term = c("Majority group protesters", "Minority group protesters") +police_action_required = as.data.frame(cbind(term_police_action_required, se_police_action_required, statistic_police_action_required, pval_police_action_required)) +police_action_required = cbind(police_action_required, term) +colnames(police_action_required) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(police_action_required) = term +police_action_required$model = "Police action \nrequired" + +differences_us = rbind(degree_violence, recall_violence, police_action_required) + +## Result tables for plot + +differences_us2 = differences_us +differences_us2$submodel = differences_us2$term +differences_us2$term = "Protesting against police brutality" +diffs_sm = differences_us2 +results_df = data.frame(term = diffs_sm$term, + estimate = diffs_sm$estimate, + std.error = diffs_sm$std.error, + model = diffs_sm$model, + submodel = as.character(diffs_sm$submodel), + stringsAsFactors = FALSE) +results_df$term = factor(results_df$model, levels=c("Perceived degree \nof violence", + "Recall \nviolence", + "Police action \nrequired", + "Does not support \nprotest")) +results_df$model[results_df$submodel=="Majority group protesters"] = "White protesters" +results_df$model[results_df$submodel=="Minority group protesters"] = "Black protesters" + +dwplot(results_df, vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2), dot_args = list(size = 2.5)) + + theme_bw() + xlab("Coefficient Estimate (Std. Dev. Units)") + theme(legend.position = "bottom", axis.text=element_text(size=9), legend.title = element_blank()) + + scale_colour_grey(start = .1, end = .7) +ggsave(filename="Figures/fig_6.pdf", width=5, height=4) diff --git a/23/replication_package/Code/replicate_fig_7.R b/23/replication_package/Code/replicate_fig_7.R new file mode 100644 index 0000000000000000000000000000000000000000..45b9aeaba9c777ea5a849492410869519df73cef --- /dev/null +++ b/23/replication_package/Code/replicate_fig_7.R @@ -0,0 +1,76 @@ +##--##--##--## +## Figure 7 +##--##--##--## + +commitment_white_usa_pdv = lm_robust(degree_violence_std ~ commitment, data=us_survey_wave2[us_survey_wave2$identity_protesters_fac=="White",]) +commitment_black_usa_pdv = lm_robust(degree_violence_std ~ commitment, data=us_survey_wave2[us_survey_wave2$identity_protesters_fac=="Black",]) + +commitment_white_israel_pdv = lm_robust(degree_violence_std ~ commitment, data=isr_survey_wave2[isr_survey_wave2$identity_protesters==0,]) +commitment_ethiopian_israel_pdv = lm_robust(degree_violence_std ~ commitment, data=isr_survey_wave2[isr_survey_wave2$identity_protesters==1,]) +commitment_arab_israel_pdv = lm_robust(degree_violence_std ~ commitment, data=isr_survey_wave2[isr_survey_wave2$identity_protesters==2,]) + +term_degree_violence = c(commitment_white_usa_pdv$coefficients[2], commitment_black_usa_pdv$coefficients[2]) +se_degree_violence = c(commitment_white_usa_pdv$std.error[2], commitment_black_usa_pdv$std.error[2]) +statistic_degree_violence = c(commitment_white_usa_pdv$statistic[2], commitment_black_usa_pdv$statistic[2]) +pval_degree_violence = c(commitment_white_usa_pdv$p.value[2], commitment_black_usa_pdv$p.value[2]) +term = c("Majority group", "Minority group") +degree_violence = as.data.frame(cbind(term_degree_violence, se_degree_violence, statistic_degree_violence, pval_degree_violence)) +degree_violence = cbind(degree_violence, term) +colnames(degree_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(degree_violence) = term +degree_violence$model = "Perceived degree of violence" + +differences_us = rbind(degree_violence) + +term_degree_violence = c(commitment_white_israel_pdv$coefficients[2], commitment_arab_israel_pdv$coefficients[2]) +se_degree_violence = c(commitment_white_israel_pdv$std.error[2], commitment_arab_israel_pdv$std.error[2]) +statistic_degree_violence = c(commitment_white_israel_pdv$statistic[2], commitment_arab_israel_pdv$statistic[2]) +pval_degree_violence = c(commitment_white_israel_pdv$p.value[2], commitment_arab_israel_pdv$p.value[2]) +term = c("Majority group", "Minority group") +degree_violence = as.data.frame(cbind(term_degree_violence, se_degree_violence, statistic_degree_violence, pval_degree_violence)) +degree_violence = cbind(degree_violence, term) +colnames(degree_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(degree_violence) = term +degree_violence$model = "Perceived degree of violence" + +differences_isr_arab = degree_violence + +term_degree_violence = c(commitment_white_israel_pdv$coefficients[2], commitment_ethiopian_israel_pdv$coefficients[2]) +se_degree_violence = c(commitment_white_israel_pdv$std.error[2], commitment_ethiopian_israel_pdv$std.error[2]) +statistic_degree_violence = c(commitment_white_israel_pdv$statistic[2], commitment_ethiopian_israel_pdv$statistic[2]) +pval_degree_violence = c(commitment_white_israel_pdv$p.value[2], commitment_ethiopian_israel_pdv$p.value[2]) +term = c("Majority group", "Minority group") +degree_violence = as.data.frame(cbind(term_degree_violence, se_degree_violence, statistic_degree_violence, pval_degree_violence)) +degree_violence = cbind(degree_violence, term) +colnames(degree_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(degree_violence) = term +degree_violence$model = "Perceived degree of violence" + +differences_isr_ethiopian = degree_violence + +## Result tables for plot +differences_us2 = differences_us +differences_us2$submodel = differences_us2$term +differences_us2$term = "Perception of Blacks \n(United States)" + +differences_isr_black2 = differences_isr_ethiopian +differences_isr_black2$submodel = differences_isr_black2$term +differences_isr_black2$term = "Perception of Ethiopians \n(Israel)" + +differences_isr_arab2 = differences_isr_arab +differences_isr_arab2$submodel = differences_isr_arab2$term +differences_isr_arab2$term = "Perception of Arabs \n(Israel)" + +diffs_sm = rbind(differences_us2, differences_isr_black2, differences_isr_arab2) +rownames(diffs_sm) = 1:nrow(diffs_sm) + +results_df <- data.frame(term = diffs_sm$term, + estimate = diffs_sm$estimate, + std.error = diffs_sm$std.error, + model = as.character(diffs_sm$submodel), + stringsAsFactors = FALSE) + +dwplot(results_df, vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2), dot_args = list(size = 2.5)) + + theme_bw() + xlab("Coefficient Estimate (Std. Dev. Units)") + theme(legend.position = "bottom", axis.text=element_text(size=9), legend.title = element_blank()) + + scale_colour_grey(start = .1, end = .7) +xlim(-0.35,0.1) +ggsave(filename="Figures/fig_7.pdf", width=5.5, height=4) diff --git a/23/replication_package/Code/replicate_fig_8.R b/23/replication_package/Code/replicate_fig_8.R new file mode 100644 index 0000000000000000000000000000000000000000..ed2eb34f077704e3062f19174f57ebd5992b95dc --- /dev/null +++ b/23/replication_package/Code/replicate_fig_8.R @@ -0,0 +1,36 @@ +##--##--##--## +## Figure 8 +##--##--##--## + +us_survey_content_covars <- estimateEffect(1:10 ~ identity_protesters, us_survey_text_analysis$stm_topics, meta = docvars(us_survey_text_analysis$nv_dfm), uncertainty = "Global") +us_survey_sum_content_covars = summary(us_survey_content_covars) + +topics = labelTopics(us_survey_text_analysis$stm_topics, n=20) + +term = c(paste(topics$prob[1,1:5], collapse = ", "), + paste(topics$prob[2,c(1,2,7,8,15)], collapse = ", "), + paste(topics$prob[3,1:5], collapse = ", "), + paste(topics$prob[4,4:9], collapse = ", "), + paste(topics$prob[5,c(1,2,3,5,15)], collapse = ", "), + paste(topics$prob[6,1:5], collapse = ", "), + paste(topics$prob[7,1:5], collapse = ", "), + paste(topics$prob[8,1:5], collapse = ", "), + paste(topics$prob[9,c(3,4,9,15,17)], collapse = ", "), + paste(topics$prob[10,c(8,12,13,15,16)], collapse = ", ")) + +estimate = rep(NA, 10) +std.error = rep(NA, 10) +for(i in 1:length(us_survey_sum_content_covars$tables)){ + estimate[i] = us_survey_sum_content_covars$tables[[i]][2,1] + std.error[i] = us_survey_sum_content_covars$tables[[i]][2,2] +} + +us_survey_stm_results = data.frame("term" = term, "estimate" = estimate, "std.error"= std.error) +us_survey_stm_results = us_survey_stm_results[order(us_survey_stm_results$estimate, decreasing = T),] + +dwplot(us_survey_stm_results, + vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2), dot_args = list(size = 2.5)) + + theme_bw() + xlab("\nWhite protesters . . . . . . . . . . . . . . . . . . Black protesters") + + theme(text = element_text(size=12), axis.text.x = element_text(angle = 0, hjust = 1), legend.position = "none") + + scale_colour_grey(start = 0, end = 0) +ggsave(file="Figures/fig_8.pdf", width=8, height=6) diff --git a/23/replication_package/Code/replicate_fig_9.R b/23/replication_package/Code/replicate_fig_9.R new file mode 100644 index 0000000000000000000000000000000000000000..d2ef15fde900d1c30a67ffdab3e2c375f9e13511 --- /dev/null +++ b/23/replication_package/Code/replicate_fig_9.R @@ -0,0 +1,35 @@ +##--##--##--## +## Figure 9 +##--##--##--## + +isr_survey_arab_content_covars <- estimateEffect(1:10 ~ identity_protesters_arab, isr_survey_text_analysis_arab$stm_topics, meta = docvars(isr_survey_text_analysis_arab$nv_dfm_arab), uncertainty = "Global") +isr_survey_arab_sum_content_covars = summary(isr_survey_arab_content_covars) + +term_translated_arab = c("violent, protest, israel", + "justified, right, protest", + "violent, express, equal, demands", + "democracy, protest, rights", + "legitimate, support, sympathize, voice", + "difficult, situation, understand, pain", + "violent, protest, against", + "social, justice, protest", + "information, details, reason", + "violence, arab, community, minority") + +estimate = rep(NA, 10) +std.error = rep(NA, 10) + +for(i in 1:length(isr_survey_arab_sum_content_covars$tables)){ + estimate[i] = isr_survey_arab_sum_content_covars$tables[[i]][2,1] + std.error[i] = isr_survey_arab_sum_content_covars$tables[[i]][2,2] +} + +isr_survey_arab_stm_results = data.frame("term" = term_translated_arab, "estimate" = estimate, "std.error"= std.error) +isr_survey_arab_stm_results = isr_survey_arab_stm_results[order(isr_survey_arab_stm_results$estimate, decreasing = T),] + +dwplot(isr_survey_arab_stm_results, + vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2), dot_args = list(size = 2.5)) + + theme_bw() + xlab("\nIsraeli white protesters . . . . . . . . . . . . . . . . . . Israeli Arab protesters") + + theme(text = element_text(size=12), axis.text.x = element_text(angle = 0, hjust = 1), legend.position = "none") + + scale_colour_grey(start = 0, end = 0) +ggsave(file="Figures/fig_9.pdf", width=8, height=6) diff --git a/23/replication_package/Code/replicate_fig_A1.R b/23/replication_package/Code/replicate_fig_A1.R new file mode 100644 index 0000000000000000000000000000000000000000..0ed4486688bb5ad02a2bdbc53f4916096b69e0c3 --- /dev/null +++ b/23/replication_package/Code/replicate_fig_A1.R @@ -0,0 +1,25 @@ +##--##--##--##--##--##--## +## Appendix: Figure A1 +##--##--##--##--##--##--## + +EBCR_EPR_NAVCO2$camp_goals_text = NA +EBCR_EPR_NAVCO2$camp_goals_text[EBCR_EPR_NAVCO2$camp_goals==0] = "Regime change" +EBCR_EPR_NAVCO2$camp_goals_text[EBCR_EPR_NAVCO2$camp_goals==1] = "Significant institutional reform" +EBCR_EPR_NAVCO2$camp_goals_text[EBCR_EPR_NAVCO2$camp_goals==2] = "Policy change" +EBCR_EPR_NAVCO2$camp_goals_text[EBCR_EPR_NAVCO2$camp_goals==3] = "Territorial secession" +EBCR_EPR_NAVCO2$camp_goals_text[EBCR_EPR_NAVCO2$camp_goals==4] = "Greater autonomy" +EBCR_EPR_NAVCO2$camp_goals_text[EBCR_EPR_NAVCO2$camp_goals==5] = "Anti occupation" + +EBCR_EPR_NAVCO2$EPR_STATUS = factor(EBCR_EPR_NAVCO2$EPR_STATUS, + levels=c("DISCRIMINATED", "POWERLESS", "SELF-EXCLUSION", + "JUNIOR PARTNER", "SENIOR PARTNER", "DOMINANT", + "MONOPOLY", "IRRELEVANT", "STATE COLLAPSE")) + +goals_status = prop.table(table(EBCR_EPR_NAVCO2$camp_goals_text[EBCR_EPR_NAVCO2$navco1designation==1], EBCR_EPR_NAVCO2$EPR_STATUS[EBCR_EPR_NAVCO2$navco1designation==1])) + +col6 = c("#9ECAE1", "#6BAED6", "#4292C6", "#2171B5", "#08519C", "#08306B") + +pdf(file="Figures/fig_A1.pdf", width=8, height=6) +corrplot(goals_status[,c("DISCRIMINATED", "POWERLESS", "SELF-EXCLUSION", + "JUNIOR PARTNER", "SENIOR PARTNER", "DOMINANT", "MONOPOLY")], method = "circle", cl.lim = c(0,0.15), is.corr = F, col=col6, tl.col="black") +dev.off() diff --git a/23/replication_package/Code/replicate_fig_A10.R b/23/replication_package/Code/replicate_fig_A10.R new file mode 100644 index 0000000000000000000000000000000000000000..7d44364fcdfd8f177f0f58351d6592abaa390c2d --- /dev/null +++ b/23/replication_package/Code/replicate_fig_A10.R @@ -0,0 +1,68 @@ +##--##--##--##--##--##--## +## Appendix: Figure A10 +##--##--##--##--##--##--## + +commitment_white_usa_rv = lm_robust(recall_violence_std ~ commitment, data=us_survey_wave2[us_survey_wave2$identity_protesters_fac=="White",]) +commitment_black_usa_rv = lm_robust(recall_violence_std ~ commitment, data=us_survey_wave2[us_survey_wave2$identity_protesters_fac=="Black",]) +commitment_white_israel_rv = lm_robust(recall_violence_std ~ commitment, data=isr_survey_wave2[isr_survey_wave2$identity_protesters==0,]) +commitment_ethiopian_israel_rv = lm_robust(recall_violence_std ~ commitment, data=isr_survey_wave2[isr_survey_wave2$identity_protesters==1,]) +commitment_arab_israel_rv = lm_robust(recall_violence_std ~ commitment, data=isr_survey_wave2[isr_survey_wave2$identity_protesters==2,]) + +## U.S. +term_recall_violence = c(commitment_white_usa_rv$coefficients[2], commitment_black_usa_rv$coefficients[2]) +se_recall_violence = c(commitment_white_usa_rv$std.error[2], commitment_black_usa_rv$std.error[2]) +statistic_recall_violence = c(commitment_white_usa_rv$statistic[2], commitment_black_usa_rv$statistic[2]) +pval_recall_violence = c(commitment_white_usa_rv$p.value[2], commitment_black_usa_rv$p.value[2]) +term = c("Majority group", "Minority group") +recall_violence = as.data.frame(cbind(term_recall_violence, se_recall_violence, statistic_recall_violence, pval_recall_violence)) +recall_violence = cbind(recall_violence, term) +colnames(recall_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(recall_violence) = term +recall_violence$model = "Recall violence" +differences_us = recall_violence + +## Israel (Arabs) +term_recall_violence = c(commitment_white_israel_rv$coefficients[2], commitment_arab_israel_rv$coefficients[2]) +se_recall_violence = c(commitment_white_israel_rv$std.error[2], commitment_arab_israel_rv$std.error[2]) +statistic_recall_violence = c(commitment_white_israel_rv$statistic[2], commitment_arab_israel_rv$statistic[2]) +pval_recall_violence = c(commitment_white_israel_rv$p.value[2], commitment_arab_israel_rv$p.value[2]) +term = c("Majority group", "Minority group") +recall_violence = as.data.frame(cbind(term_recall_violence, se_recall_violence, statistic_recall_violence, pval_recall_violence)) +recall_violence = cbind(recall_violence, term) +colnames(recall_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(recall_violence) = term +recall_violence$model = "Recall violence" +differences_isr_arab = recall_violence + +## Israel (Ethiopian) +term_recall_violence = c(commitment_white_israel_rv$coefficients[2], commitment_ethiopian_israel_rv$coefficients[2]) +se_recall_violence = c(commitment_white_israel_rv$std.error[2], commitment_ethiopian_israel_rv$std.error[2]) +statistic_recall_violence = c(commitment_white_israel_rv$statistic[2], commitment_ethiopian_israel_rv$statistic[2]) +pval_recall_violence = c(commitment_white_israel_rv$p.value[2], commitment_ethiopian_israel_rv$p.value[2]) +term = c("Majority group", "Minority group") +recall_violence = as.data.frame(cbind(term_recall_violence, se_recall_violence, statistic_recall_violence, pval_recall_violence)) +recall_violence = cbind(recall_violence, term) +colnames(recall_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(recall_violence) = term +recall_violence$model = "Recall violence" +differences_isr_ethiopian = recall_violence + +## Result tables for plot +differences_us$term = "Perception of Blacks \n(United States)" +differences_isr_ethiopian$term = "Perception of Ethiopians \n(Israel)" +differences_isr_arab$term = "Perception of Arabs \n(Israel)" + +diffs_sm = rbind(differences_us, differences_isr_ethiopian, differences_isr_arab) +diffs_sm$model = rep(c("Majority group", "Minority group"),3) +rownames(diffs_sm) = 1:nrow(diffs_sm) + +results_df <- data.frame(term = diffs_sm$term, + estimate = diffs_sm$estimate, + std.error = diffs_sm$std.error, + model = diffs_sm$model, + stringsAsFactors = FALSE) + +dwplot(results_df,vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2), dot_args = list(size = 3)) + + theme_bw() + xlab("Coefficient Estimate (Std. Dev. Units)") + theme(legend.position = "bottom", axis.text=element_text(size=9), legend.title = element_blank()) + + scale_colour_grey(start = .1, end = .7) +ggsave(filename="Figures/fig_A10.pdf", width=6, height=5) diff --git a/23/replication_package/Code/replicate_fig_A11.R b/23/replication_package/Code/replicate_fig_A11.R new file mode 100644 index 0000000000000000000000000000000000000000..5d35c03b1ffa0fff117c6cf855be351e30e84895 --- /dev/null +++ b/23/replication_package/Code/replicate_fig_A11.R @@ -0,0 +1,68 @@ +##--##--##--##--##--##--## +## Appendix: Figure A11 +##--##--##--##--##--##--## + +commitment_white_usa_paq = lm_robust(police_action_required_std ~ commitment, data=us_survey_wave2[us_survey_wave2$identity_protesters_fac=="White",]) +commitment_black_usa_paq = lm_robust(police_action_required_std ~ commitment, data=us_survey_wave2[us_survey_wave2$identity_protesters_fac=="Black",]) +commitment_white_israel_paq = lm_robust(police_action_required_std ~ commitment, data=isr_survey_wave2[isr_survey_wave2$identity_protesters==0,]) +commitment_ethiopian_israel_paq = lm_robust(police_action_required_std ~ commitment, data=isr_survey_wave2[isr_survey_wave2$identity_protesters==1,]) +commitment_arab_israel_paq = lm_robust(police_action_required_std ~ commitment, data=isr_survey_wave2[isr_survey_wave2$identity_protesters==2,]) + +## U.S. +term_police_action_required = c(commitment_white_usa_paq$coefficients[2], commitment_black_usa_paq$coefficients[2]) +se_police_action_required = c(commitment_white_usa_paq$std.error[2], commitment_black_usa_paq$std.error[2]) +statistic_police_action_required = c(commitment_white_usa_paq$statistic[2], commitment_black_usa_paq$statistic[2]) +pval_police_action_required = c(commitment_white_usa_paq$p.value[2], commitment_black_usa_paq$p.value[2]) +term = c("Majority group", "Minority group") +police_action_required = as.data.frame(cbind(term_police_action_required, se_police_action_required, statistic_police_action_required, pval_police_action_required)) +police_action_required = cbind(police_action_required, term) +colnames(police_action_required) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(police_action_required) = term +police_action_required$model = "Police action required" +differences_us = police_action_required + +## Israel (Arabs) +term_police_action_required = c(commitment_white_israel_paq$coefficients[2], commitment_arab_israel_paq$coefficients[2]) +se_police_action_required = c(commitment_white_israel_paq$std.error[2], commitment_arab_israel_paq$std.error[2]) +statistic_police_action_required = c(commitment_white_israel_paq$statistic[2], commitment_arab_israel_paq$statistic[2]) +pval_police_action_required = c(commitment_white_israel_paq$p.value[2], commitment_arab_israel_paq$p.value[2]) +term = c("Majority group", "Minority group") +police_action_required = as.data.frame(cbind(term_police_action_required, se_police_action_required, statistic_police_action_required, pval_police_action_required)) +police_action_required = cbind(police_action_required, term) +colnames(police_action_required) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(police_action_required) = term +police_action_required$model = "Police action required" +differences_isr_arab = police_action_required + +## Israel (Ethiopian) +term_police_action_required = c(commitment_white_israel_paq$coefficients[2], commitment_ethiopian_israel_paq$coefficients[2]) +se_police_action_required = c(commitment_white_israel_paq$std.error[2], commitment_ethiopian_israel_paq$std.error[2]) +statistic_police_action_required = c(commitment_white_israel_paq$statistic[2], commitment_ethiopian_israel_paq$statistic[2]) +pval_police_action_required = c(commitment_white_israel_paq$p.value[2], commitment_ethiopian_israel_paq$p.value[2]) +term = c("Majority group", "Minority group") +police_action_required = as.data.frame(cbind(term_police_action_required, se_police_action_required, statistic_police_action_required, pval_police_action_required)) +police_action_required = cbind(police_action_required, term) +colnames(police_action_required) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(police_action_required) = term +police_action_required$model = "Police action required" +differences_isr_ethiopian = police_action_required + +## Result tables for plot +differences_us$term = "Perception of Blacks \n(United States)" +differences_isr_ethiopian$term = "Perception of Ethiopians \n(Israel)" +differences_isr_arab$term = "Perception of Arabs \n(Israel)" + +diffs_sm = rbind(differences_us, differences_isr_ethiopian, differences_isr_arab) +diffs_sm$model = rep(c("Majority group", "Minority group"),3) +rownames(diffs_sm) = 1:nrow(diffs_sm) + +results_df <- data.frame(term = diffs_sm$term, + estimate = diffs_sm$estimate, + std.error = diffs_sm$std.error, + model = diffs_sm$model, + stringsAsFactors = FALSE) + +dwplot(results_df,vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2), dot_args = list(size = 3)) + + theme_bw() + xlab("Coefficient Estimate (Std. Dev. Units)") + theme(legend.position = "bottom", axis.text=element_text(size=9), legend.title = element_blank()) + + scale_colour_grey(start = .1, end = .7) +ggsave(filename="Figures/fig_A11.pdf", width=6, height=5) diff --git a/23/replication_package/Code/replicate_fig_A12.R b/23/replication_package/Code/replicate_fig_A12.R new file mode 100644 index 0000000000000000000000000000000000000000..d04e6f0dc1d78c0d3b16f4312b1eacb8c533e542 --- /dev/null +++ b/23/replication_package/Code/replicate_fig_A12.R @@ -0,0 +1,50 @@ +##--##--##--##--##--##--## +## Appendix: Figure A12 +##--##--##--##--##--##--## + +## U.S. +us_survey_wave1_dv = lm(degree_violence ~ identity_protesters*interest_news +age +female + education + income + ideology + race, data=us_survey_wave1[us_survey_wave1$tactic==0,], weights=weight) +us_survey_wave1_dv_all = ggpredict(us_survey_wave1_dv, terms = c("interest_news")) +us_survey_wave1_dv_all$interest_news = c("Hardly at all", "Only now \nand then", "Some of \nthe time", "Most of \nthe time") +us_survey_wave1_dv_all$interest_news = factor(us_survey_wave1_dv_all$interest_news, levels=c("Hardly at all", "Only now \nand then", "Some of \nthe time", "Most of \nthe time")) +us_survey_wave1_dv_all = as.data.frame(us_survey_wave1_dv_all) + +news_usa_study1 = ggplot(us_survey_wave1_dv_all, aes(x = interest_news, y = predicted, group="all")) + + geom_line()+ geom_errorbar(width = 0, ymin=us_survey_wave1_dv_all$conf.low, ymax=us_survey_wave1_dv_all$conf.high)+ + geom_point(size=3) + ylim(-0.05,10) + theme_bw() + + scale_colour_grey(start = 0.1, end = 0.65) + geom_hline(yintercept=0, linetype="dashed")+ + ylab("Perceived degree violence") + xlab("\nHow often do you follow the news?") + ggtitle("Perceptions of Black Protesters in the U.S.") + +## Israel +isr_survey_wave1$interest_news2 = isr_survey_wave1$interest_news-1 + +isr_survey_dv_eth = lm(degree_violence ~ identity_protesters*interest_news2 + age +female + education + income + ideology + ethnicity, data=isr_survey_wave1[isr_survey_wave1$identity_protesters %in% c(0,1) & isr_survey_wave1$tactic==0,], weights=weight) +isr_survey_dv_all_eth = ggpredict(isr_survey_dv_eth, terms = c("interest_news2")) +isr_survey_dv_all_eth$interest_news = c("Hardly at all", "Only now \nand then", "Some of \nthe time", "Most of \nthe time") +isr_survey_dv_all_eth$interest_news = factor(isr_survey_dv_all_eth$interest_news, levels=c("Hardly at all", "Only now \nand then", "Some of \nthe time", "Most of \nthe time")) +isr_survey_dv_all_eth = as.data.frame(isr_survey_dv_all_eth) +isr_survey_dv_all_eth$Minority = "Ethiopians" + +isr_survey_dv_arab = lm(degree_violence ~ identity_protesters*interest_news2 + age +female + education + income + ideology + ethnicity, data=isr_survey_wave1[isr_survey_wave1$identity_protesters %in% c(0,2) & isr_survey_wave1$tactic==0,], weights=weight) +isr_survey_dv_all_arab = ggpredict(isr_survey_dv_arab, terms = c("interest_news2")) +isr_survey_dv_all_arab$interest_news = c("Hardly at all", "Only now \nand then", "Some of \nthe time", "Most of \nthe time") +isr_survey_dv_all_arab$interest_news = factor(isr_survey_dv_all_arab$interest_news, levels=c("Hardly at all", "Only now \nand then", "Some of \nthe time", "Most of \nthe time")) +isr_survey_dv_all_arab = as.data.frame(isr_survey_dv_all_arab) +isr_survey_dv_all_arab$Minority = "Arabs" + +news_israel_study1_eth = ggplot(isr_survey_dv_all_eth, aes(x = interest_news, y = predicted, group="all")) + + geom_line()+ geom_errorbar(width = 0, ymin=isr_survey_dv_all_eth$conf.low, ymax=isr_survey_dv_all_eth$conf.high)+ + geom_point(size=3) + ylim(-0.05,10) + theme_bw() + + scale_colour_grey(start = 0.1, end = 0.65) + geom_hline(yintercept=0, linetype="dashed")+ + ylab("Perceived degree violence") + xlab("\nHow often do you follow the news?") + ggtitle("Perception of Ethiopian Protesters in Israel") + +news_israel_study1_arab = ggplot(isr_survey_dv_all_arab, aes(x = interest_news, y = predicted, group="all")) + + geom_line()+ geom_errorbar(width = 0, ymin=isr_survey_dv_all_arab$conf.low, ymax=isr_survey_dv_all_arab$conf.high)+ + geom_point(size=3) + ylim(-0.05,10) + theme_bw() + + scale_colour_grey(start = 0.1, end = 0.65) + geom_hline(yintercept=0, linetype="dashed")+ + ylab("Perceived degree violence") + xlab("\nHow often do you follow the news?") + ggtitle("Perception of Arab Protesters in Israel") + + +grid_study1 = grid.arrange(news_usa_study1, news_israel_study1_eth, news_israel_study1_arab, nrow = 1) +ggsave(filename= "Figures/fig_A12.pdf", plot=grid_study1, width=13, height=4, units="in") + diff --git a/23/replication_package/Code/replicate_fig_A2.R b/23/replication_package/Code/replicate_fig_A2.R new file mode 100644 index 0000000000000000000000000000000000000000..8fa7ef3d9b44b7bcf86ed32e1044148902295452 --- /dev/null +++ b/23/replication_package/Code/replicate_fig_A2.R @@ -0,0 +1,36 @@ +##--##--##--##--##--##--## +## Appendix: Figure A2 +##--##--##--##--##--##--## + +mean_sucess_anti_occupation_minority = mean(EBCR_EPR_NAVCO2$success[EBCR_EPR_NAVCO2$camp_goals_text %in% c("Anti occupation") & EBCR_EPR_NAVCO2$EPR_STATUS_EXCL==1 & EBCR_EPR_NAVCO2$navco1designation==1], na.rm=T) +mean_sucess_anti_occupation_majority = mean(EBCR_EPR_NAVCO2$success[EBCR_EPR_NAVCO2$camp_goals_text %in% c("Anti occupation") & EBCR_EPR_NAVCO2$EPR_STATUS_EXCL==0 & EBCR_EPR_NAVCO2$navco1designation==1], na.rm=T) + +mean_sucess_greater_autonomy_minority = mean(EBCR_EPR_NAVCO2$success[EBCR_EPR_NAVCO2$camp_goals_text %in% c("Greater autonomy") & EBCR_EPR_NAVCO2$EPR_STATUS_EXCL==1 & EBCR_EPR_NAVCO2$navco1designation==1], na.rm=T) +mean_sucess_greater_autonomy_majority = mean(EBCR_EPR_NAVCO2$success[EBCR_EPR_NAVCO2$camp_goals_text %in% c("Greater autonomy") & EBCR_EPR_NAVCO2$EPR_STATUS_EXCL==0 & EBCR_EPR_NAVCO2$navco1designation==1], na.rm=T) + +mean_sucess_policy_change_minority = mean(EBCR_EPR_NAVCO2$success[EBCR_EPR_NAVCO2$camp_goals_text %in% c("Policy change") & EBCR_EPR_NAVCO2$EPR_STATUS_EXCL==1 & EBCR_EPR_NAVCO2$navco1designation==1], na.rm=T) +mean_sucess_policy_change_majority = mean(EBCR_EPR_NAVCO2$success[EBCR_EPR_NAVCO2$camp_goals_text %in% c("Policy change") & EBCR_EPR_NAVCO2$EPR_STATUS_EXCL==0 & EBCR_EPR_NAVCO2$navco1designation==1], na.rm=T) + +mean_sucess_regime_change_minority = mean(EBCR_EPR_NAVCO2$success[EBCR_EPR_NAVCO2$camp_goals_text %in% c("Regime change") & EBCR_EPR_NAVCO2$EPR_STATUS_EXCL==1 & EBCR_EPR_NAVCO2$navco1designation==1], na.rm=T) +mean_sucess_regime_change_majority = mean(EBCR_EPR_NAVCO2$success[EBCR_EPR_NAVCO2$camp_goals_text %in% c("Regime change") & EBCR_EPR_NAVCO2$EPR_STATUS_EXCL==0 & EBCR_EPR_NAVCO2$navco1designation==1], na.rm=T) + +mean_sucess_inst_reform_minority = mean(EBCR_EPR_NAVCO2$success[EBCR_EPR_NAVCO2$camp_goals_text %in% c("Significant institutional reform") & EBCR_EPR_NAVCO2$EPR_STATUS_EXCL==1 & EBCR_EPR_NAVCO2$navco1designation==1], na.rm=T) +mean_sucess_inst_reform_majority = mean(EBCR_EPR_NAVCO2$success[EBCR_EPR_NAVCO2$camp_goals_text %in% c("Significant institutional reform") & EBCR_EPR_NAVCO2$EPR_STATUS_EXCL==0 & EBCR_EPR_NAVCO2$navco1designation==1], na.rm=T) + +mean_sucess_territorial_secession_minority = mean(EBCR_EPR_NAVCO2$success[EBCR_EPR_NAVCO2$camp_goals_text %in% c("Territorial secession") & EBCR_EPR_NAVCO2$EPR_STATUS_EXCL==1 & EBCR_EPR_NAVCO2$navco1designation==1], na.rm=T) +mean_sucess_territorial_secession_majority = mean(EBCR_EPR_NAVCO2$success[EBCR_EPR_NAVCO2$camp_goals_text %in% c("Territorial secession") & EBCR_EPR_NAVCO2$EPR_STATUS_EXCL==0 & EBCR_EPR_NAVCO2$navco1designation==1], na.rm=T) + +success_props = data.frame("prop_success" = c(mean_sucess_anti_occupation_minority, mean_sucess_anti_occupation_majority, + mean_sucess_greater_autonomy_minority, mean_sucess_greater_autonomy_majority, + mean_sucess_policy_change_minority, mean_sucess_policy_change_majority, + mean_sucess_regime_change_minority, mean_sucess_regime_change_majority, + mean_sucess_inst_reform_minority, mean_sucess_inst_reform_majority, + mean_sucess_territorial_secession_minority, mean_sucess_territorial_secession_majority), + "Status" =rep(c("Excluded", "Included"), 6), "goal" = c(rep("Anti \noccupation", 2), rep("Greater \nautonomy", 2), rep("Policy \nchange", 2), + rep("Regime \nchange", 2), rep("Significant \ninstitutional \nreform", 2), rep("Territorial \nsecession", 2))) + + +ggplot(data=success_props, aes(x=goal, y=prop_success, fill=Status)) + + geom_bar(stat="identity", position=position_dodge()) + theme_bw() + + ylab("Proportion of successful campaigns") + xlab("Campaign goal") + scale_fill_brewer(palette="Paired") +ggsave(file="Figures/fig_A2.pdf", width=6.5, height=4) diff --git a/23/replication_package/Code/replicate_fig_A3.R b/23/replication_package/Code/replicate_fig_A3.R new file mode 100644 index 0000000000000000000000000000000000000000..31418cfc60d4ebfcaa6ba85a1330384cbea05319 --- /dev/null +++ b/23/replication_package/Code/replicate_fig_A3.R @@ -0,0 +1,11 @@ +##--##--##--##--##--##--## +## Appendix: Figure A3 +##--##--##--##--##--##--## + +status_names = c("DISCRIMINATED", "POWERLESS", "SELF-EXCLUSION", "JUNIOR PARTNER", "SENIOR PARTNER", "DOMINANT", "MONOPOLY") + +ggplot(EBCR_EPR_NAVCO2, aes(x=EPR_GROUPSIZE, y=EPR_STATUS_ORD)) + + geom_point(alpha=0.2, color="gray50") + geom_smooth(color="black") + + theme_bw() + xlab("Group size") + ylab("Group status") + + scale_y_continuous(breaks = c(1,2,3,4,5,6,7), labels = status_names) +ggsave("Figures/fig_A3.pdf", width=6, height=5) diff --git a/23/replication_package/Code/replicate_fig_A6.R b/23/replication_package/Code/replicate_fig_A6.R new file mode 100644 index 0000000000000000000000000000000000000000..c039d6c0335881f5e5fb985cc4109ac5291c4924 --- /dev/null +++ b/23/replication_package/Code/replicate_fig_A6.R @@ -0,0 +1,110 @@ +##--##--##--##--##--##--## +## Appendix: Figure A6 +##--##--##--##--##--##--## + +us_survey_wave1$education_numeric = as.numeric(us_survey_wave1$education) +us_survey_wave1$income_numeric = as.numeric(us_survey_wave1$income) +us_survey_wave1$ideology_numeric = as.numeric(us_survey_wave1$ideology) + +us_survey_wave1 = dummy_cols(us_survey_wave1, select_columns = c("partyID", "race")) +vars_us_survey_wave1 = c("age", "female", "education_numeric", "income_numeric", "partyID_D", "partyID_I", "partyID_R", "ideology_numeric", "race_White", "race_Black") + +means_black_march = means_white_march = means_black_shut = means_white_shut = means_black_destroy = means_white_destroy = rep(NA, length(vars_us_survey_wave1)) +se_black_march = se_white_march = se_black_shut = se_white_shut = se_black_destroy = se_white_destroy = rep(NA, length(vars_us_survey_wave1)) + +for(i in 1:length(vars_us_survey_wave1)){ + means_black_march[i] = mean(us_survey_wave1[us_survey_wave1$identity_protesters==1 & us_survey_wave1$tactic==0, vars_us_survey_wave1[i]], na.rm=T) + means_white_march[i] = mean(us_survey_wave1[us_survey_wave1$identity_protesters==0 & us_survey_wave1$tactic==0, vars_us_survey_wave1[i]], na.rm=T) + means_black_shut[i] = mean(us_survey_wave1[us_survey_wave1$identity_protesters==1 & us_survey_wave1$tactic==1, vars_us_survey_wave1[i]], na.rm=T) + means_white_shut[i] = mean(us_survey_wave1[us_survey_wave1$identity_protesters==0 & us_survey_wave1$tactic==1, vars_us_survey_wave1[i]], na.rm=T) + means_black_destroy[i] = mean(us_survey_wave1[us_survey_wave1$identity_protesters==1 & us_survey_wave1$tactic==2, vars_us_survey_wave1[i]], na.rm=T) + means_white_destroy[i] = mean(us_survey_wave1[us_survey_wave1$identity_protesters==0 & us_survey_wave1$tactic==2, vars_us_survey_wave1[i]], na.rm=T) + + se_black_march[i] = sd(us_survey_wave1[us_survey_wave1$identity_protesters==1 & us_survey_wave1$tactic==0, vars_us_survey_wave1[i]], na.rm=T) + se_white_march[i] = sd(us_survey_wave1[us_survey_wave1$identity_protesters==0 & us_survey_wave1$tactic==0, vars_us_survey_wave1[i]], na.rm=T) + se_black_shut[i] = sd(us_survey_wave1[us_survey_wave1$identity_protesters==1 & us_survey_wave1$tactic==1, vars_us_survey_wave1[i]], na.rm=T) + se_white_shut[i] = sd(us_survey_wave1[us_survey_wave1$identity_protesters==0 & us_survey_wave1$tactic==1, vars_us_survey_wave1[i]], na.rm=T) + se_black_destroy[i] = sd(us_survey_wave1[us_survey_wave1$identity_protesters==1 & us_survey_wave1$tactic==2, vars_us_survey_wave1[i]], na.rm=T) + se_white_destroy[i] = sd(us_survey_wave1[us_survey_wave1$identity_protesters==0 & us_survey_wave1$tactic==2, vars_us_survey_wave1[i]], na.rm=T) + +} + +us_survey_wave1_balance = cbind(means_black_march, se_black_march, + means_white_march, se_white_march, + means_black_shut, se_black_shut, + means_white_shut, se_white_shut, + means_black_destroy, se_black_destroy, + means_white_destroy, se_white_destroy) + +us_survey_wave1_balance = as.data.frame(us_survey_wave1_balance) +colnames(us_survey_wave1_balance) = c("Blacks, march in streets (mean)", "Blacks, march in streets (sd)", + "Whites, march in streets (mean)", "Whites, march in streets (sd)", + "Blacks, shut down traffic (mean)", "Blacks, shut down traffic (sd)", + "Whites, shut down traffic (mean)", "Whites, shut down traffic (sd)", + "Blacks, destroy police cars (mean)", "Blacks, destroy police cars (sd)", + "Whites, destroy police cars (mean)", "Whites, destroy police cars (sd)") +rownames(us_survey_wave1_balance) = vars_us_survey_wave1 + + +us_survey_wave1_balance_means = as.data.frame(cbind(means_black_march, + means_white_march, + means_black_shut, + means_white_shut, + means_black_destroy, + means_white_destroy)) + +rownames(us_survey_wave1_balance_means) = vars_us_survey_wave1 +us_survey_wave1_balance_means$var = vars_us_survey_wave1 + +keycol <- "condition" +valuecol <- "mean" +gathercols <- c("means_black_march", "means_white_march", "means_black_shut", "means_white_shut", + "means_black_destroy", "means_white_destroy") + +us_survey_wave1_balance_means_long = gather_(us_survey_wave1_balance_means, keycol, valuecol, gathercols) + + +us_survey_wave1_balance_se = as.data.frame(cbind(se_black_march, + se_white_march, + se_black_shut, + se_white_shut, + se_black_destroy, + se_white_destroy)) + +rownames(us_survey_wave1_balance_se) = vars_us_survey_wave1 +us_survey_wave1_balance_se$var = vars_us_survey_wave1 + +keycol <- "condition" +valuecol <- "se" +gathercols <- c("se_black_march", "se_white_march", "se_black_shut", "se_white_shut", + "se_black_destroy", "se_white_destroy") + +us_survey_wave1_balance_se_long = gather_(us_survey_wave1_balance_se, keycol, valuecol, gathercols) + +us_survey_wave1_balance_long = cbind(us_survey_wave1_balance_means_long, us_survey_wave1_balance_se_long[,"se"]) + +colnames(us_survey_wave1_balance_long) = c("term", "model", "estimate", "std.error") + +us_survey_wave1_balance_long$model[us_survey_wave1_balance_long$model == "means_black_destroy"] = "Black, destroy property" +us_survey_wave1_balance_long$model[us_survey_wave1_balance_long$model == "means_black_march"] = "Black, march in streets" +us_survey_wave1_balance_long$model[us_survey_wave1_balance_long$model == "means_black_shut"] = "Black, shut down traffic" +us_survey_wave1_balance_long$model[us_survey_wave1_balance_long$model == "means_white_destroy"] = "White, destroy property" +us_survey_wave1_balance_long$model[us_survey_wave1_balance_long$model == "means_white_march"] = "White, march in streets" +us_survey_wave1_balance_long$model[us_survey_wave1_balance_long$model == "means_white_shut"] = "White, shut down traffic" + +us_survey_wave1_balance_long$term[us_survey_wave1_balance_long$term == "age"] = "Age" +us_survey_wave1_balance_long$term[us_survey_wave1_balance_long$term == "female"] = "Female" +us_survey_wave1_balance_long$term[us_survey_wave1_balance_long$term == "education_numeric"] = "Education (1-6 scale)" +us_survey_wave1_balance_long$term[us_survey_wave1_balance_long$term == "income_numeric"] = "Income (1-17 scale)" +us_survey_wave1_balance_long$term[us_survey_wave1_balance_long$term == "partyID_D"] = "Party ID: Democrat" +us_survey_wave1_balance_long$term[us_survey_wave1_balance_long$term == "partyID_I"] = "Party ID: Independent" +us_survey_wave1_balance_long$term[us_survey_wave1_balance_long$term == "partyID_R"] = "Party ID: Republican" +us_survey_wave1_balance_long$term[us_survey_wave1_balance_long$term == "ideology_numeric"] = "Ideology (1-6 scale)" +us_survey_wave1_balance_long$term[us_survey_wave1_balance_long$term == "race_White"] = "Race: White" +us_survey_wave1_balance_long$term[us_survey_wave1_balance_long$term == "race_Black"] = "Race: Black" + +us_survey_wave1_balance_long = us_survey_wave1_balance_long[us_survey_wave1_balance_long$term != "Age",] + +dwplot(us_survey_wave1_balance_long) + theme_minimal() + theme(legend.title=element_blank()) + + geom_vline(xintercept = 0, colour = "grey60", linetype = 2) +ggsave("Figures/fig_A6.pdf", height=5, width=7) \ No newline at end of file diff --git a/23/replication_package/Code/replicate_fig_A7.R b/23/replication_package/Code/replicate_fig_A7.R new file mode 100644 index 0000000000000000000000000000000000000000..cf173b2ef73a7a801f2c79f783bdb0ac54782ba1 --- /dev/null +++ b/23/replication_package/Code/replicate_fig_A7.R @@ -0,0 +1,133 @@ +##--##--##--##--##--##--## +## Appendix: Figure A7 +##--##--##--##--##--##--## + +isr_survey_wave1$age_numeric = as.numeric(isr_survey_wave1$age) +age1 = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==1 & isr_survey_wave1$tactic==0, "age_numeric"], na.rm=T) +age2 = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==1 & isr_survey_wave1$tactic==1, "age_numeric"], na.rm=T) +age3 = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==1 & isr_survey_wave1$tactic==2, "age_numeric"], na.rm=T) +age4 = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==2 & isr_survey_wave1$tactic==0, "age_numeric"], na.rm=T) +age5 = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==2 & isr_survey_wave1$tactic==1, "age_numeric"], na.rm=T) +age6 = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==2 & isr_survey_wave1$tactic==2, "age_numeric"], na.rm=T) +age7 = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==0 & isr_survey_wave1$tactic==0, "age_numeric"], na.rm=T) +age8 = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==0 & isr_survey_wave1$tactic==1, "age_numeric"], na.rm=T) +age9 = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==0 & isr_survey_wave1$tactic==2, "age_numeric"], na.rm=T) + +ages_isr_survey_wave1 = c(age1, age2, age3, age4, age5, age6, age7, age8, age9) + + + +isr_survey_wave1 = dummy_cols(isr_survey_wave1, select_columns = c("partyID", "ethnicity")) + +vars_isr_survey_wave1 = c("female", "education", "income", "partyID_C", "partyID_L", "partyID_R", "ideology", + "ethnicity_Ethiopia", "ethnicity_Arab", "ethnicity_Ashkenazi", "ethnicity_Mizrachi", "ethnicity_Soviet Union") + +means_black_march = means_arab_march = means_white_march = rep(NA, length(vars_isr_survey_wave1)) +means_black_shut = means_arab_shut = means_white_shut = rep(NA, length(vars_isr_survey_wave1)) +means_black_destroy = means_arab_destroy = means_white_destroy = rep(NA, length(vars_isr_survey_wave1)) + +se_black_march = se_arab_march = se_white_march = rep(NA, length(vars_isr_survey_wave1)) +se_black_shut = se_arab_shut = se_white_shut = rep(NA, length(vars_isr_survey_wave1)) +se_black_destroy = se_arab_destroy = se_white_destroy = rep(NA, length(vars_isr_survey_wave1)) + +for(i in 1:length(vars_isr_survey_wave1)){ + + means_black_march[i] = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==1 & isr_survey_wave1$tactic==0, vars_isr_survey_wave1[i]], na.rm=T) + se_black_march[i] = sd(isr_survey_wave1[isr_survey_wave1$identity_protesters==1 & isr_survey_wave1$tactic==0, vars_isr_survey_wave1[i]], na.rm=T) + means_arab_march[i] = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==2 & isr_survey_wave1$tactic==0, vars_isr_survey_wave1[i]], na.rm=T) + se_arab_march[i] = sd(isr_survey_wave1[isr_survey_wave1$identity_protesters==2 & isr_survey_wave1$tactic==0, vars_isr_survey_wave1[i]], na.rm=T) + means_white_march[i] = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==0 & isr_survey_wave1$tactic==0, vars_isr_survey_wave1[i]], na.rm=T) + se_white_march[i] = sd(isr_survey_wave1[isr_survey_wave1$identity_protesters==0 & isr_survey_wave1$tactic==0, vars_isr_survey_wave1[i]], na.rm=T) + + means_black_shut[i] = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==1 & isr_survey_wave1$tactic==1, vars_isr_survey_wave1[i]], na.rm=T) + se_black_shut[i] = sd(isr_survey_wave1[isr_survey_wave1$identity_protesters==1 & isr_survey_wave1$tactic==1, vars_isr_survey_wave1[i]], na.rm=T) + means_arab_shut[i] = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==2 & isr_survey_wave1$tactic==1, vars_isr_survey_wave1[i]], na.rm=T) + se_arab_shut[i] = sd(isr_survey_wave1[isr_survey_wave1$identity_protesters==2 & isr_survey_wave1$tactic==1, vars_isr_survey_wave1[i]], na.rm=T) + means_white_shut[i] = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==0 & isr_survey_wave1$tactic==1, vars_isr_survey_wave1[i]], na.rm=T) + se_white_shut[i] = sd(isr_survey_wave1[isr_survey_wave1$identity_protesters==0 & isr_survey_wave1$tactic==1, vars_isr_survey_wave1[i]], na.rm=T) + + means_black_destroy[i] = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==1 & isr_survey_wave1$tactic==2, vars_isr_survey_wave1[i]], na.rm=T) + se_black_destroy[i] = sd(isr_survey_wave1[isr_survey_wave1$identity_protesters==1 & isr_survey_wave1$tactic==2, vars_isr_survey_wave1[i]], na.rm=T) + means_arab_destroy[i] = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==2 & isr_survey_wave1$tactic==2, vars_isr_survey_wave1[i]], na.rm=T) + se_arab_destroy[i] = sd(isr_survey_wave1[isr_survey_wave1$identity_protesters==2 & isr_survey_wave1$tactic==2, vars_isr_survey_wave1[i]], na.rm=T) + means_white_destroy[i] = mean(isr_survey_wave1[isr_survey_wave1$identity_protesters==0 & isr_survey_wave1$tactic==2, vars_isr_survey_wave1[i]], na.rm=T) + se_white_destroy[i] = sd(isr_survey_wave1[isr_survey_wave1$identity_protesters==0 & isr_survey_wave1$tactic==2, vars_isr_survey_wave1[i]], na.rm=T) + +} + +israel_balance_means = as.data.frame(cbind(means_black_march, + means_arab_march, + means_white_march, + means_black_shut, + means_arab_shut, + means_white_shut, + means_black_destroy, + means_arab_destroy, + means_white_destroy)) + +rownames(israel_balance_means) = vars_isr_survey_wave1 +israel_balance_means$var = vars_isr_survey_wave1 + +keycol <- "condition" +valuecol <- "mean" +gathercols <- c("means_black_march", "means_arab_march", "means_white_march", "means_black_shut", "means_arab_shut", "means_white_shut", + "means_black_destroy", "means_white_destroy", "means_black_destroy", "means_arab_destroy", + "means_white_destroy") + +israel_balance_means_long = gather_(israel_balance_means, keycol, valuecol, gathercols) + + +israel_balance_se = as.data.frame(cbind(se_black_march, + se_arab_march, + se_white_march, + se_black_shut, + se_arab_shut, + se_white_shut, + se_black_destroy, + se_arab_destroy, + se_white_destroy)) + +rownames(israel_balance_se) = vars_isr_survey_wave1 +israel_balance_se$var = vars_isr_survey_wave1 + +keycol <- "condition" +valuecol <- "se" +gathercols <- c("se_black_march", "se_arab_march", "se_white_march", "se_black_shut", "se_arab_shut", "se_white_shut", + "se_black_destroy", "se_white_destroy", "se_black_destroy", "se_arab_destroy", + "se_white_destroy") + +israel_balance_se_long = gather_(israel_balance_se, keycol, valuecol, gathercols) + +israel_balance_long = cbind(israel_balance_means_long, israel_balance_se_long[,"se"]) + +colnames(israel_balance_long) = c("term", "model", "estimate", "std.error") + +israel_balance_long$model[israel_balance_long$model == "means_black_destroy"] = "Black, destroy property" +israel_balance_long$model[israel_balance_long$model == "means_black_march"] = "Black, march in streets" +israel_balance_long$model[israel_balance_long$model == "means_black_shut"] = "Black, shut down traffic" +israel_balance_long$model[israel_balance_long$model == "means_white_destroy"] = "White, destroy property" +israel_balance_long$model[israel_balance_long$model == "means_white_march"] = "White, march in streets" +israel_balance_long$model[israel_balance_long$model == "means_white_shut"] = "White, shut down traffic" +israel_balance_long$model[israel_balance_long$model == "means_arab_destroy"] = "Arab, destroy property" +israel_balance_long$model[israel_balance_long$model == "means_arab_march"] = "Arab, march in streets" +israel_balance_long$model[israel_balance_long$model == "means_arab_shut"] = "Arab, shut down traffic" + + +israel_balance_long$term[israel_balance_long$term == "age_numeric"] = "Age (1-6 scale)" +israel_balance_long$term[israel_balance_long$term == "education"] = "Education (0-6 scale)" +israel_balance_long$term[israel_balance_long$term == "ethnicity2_Ethiopia"] = "Ethnicity: Jewish Ethiopian" +israel_balance_long$term[israel_balance_long$term == "female"] = "Female" +israel_balance_long$term[israel_balance_long$term == "ideology"] = "Ideology (1-7 scale)" +israel_balance_long$term[israel_balance_long$term == "income"] = "Income (0-8 scale)" +israel_balance_long$term[israel_balance_long$term == "partyID_C"] = "Party ID: Center" +israel_balance_long$term[israel_balance_long$term == "partyID_L"] = "Party ID: Left" +israel_balance_long$term[israel_balance_long$term == "partyID_R"] = "Party ID: Right" +israel_balance_long$term[israel_balance_long$term == "ethnicity2_Arab"] = "Ethnicity: Arab" +israel_balance_long$term[israel_balance_long$term == "ethnicity2_Ashkenazi"] = "Ethnicity: Jewish Ashkenazi" +israel_balance_long$term[israel_balance_long$term == "ethnicity2_Mizrachi"] = "Ethnicity: Jewish Mizrachi" +israel_balance_long$term[israel_balance_long$term == "ethnicity2_Soviet Union"] = "Ethnicity: Jewish from Soviet Union" + + +dwplot(israel_balance_long) + theme_minimal() + theme(legend.title=element_blank()) + + geom_vline(xintercept = 0, colour = "grey60", linetype = 2) +ggsave("Figures/fig_A7.pdf", height=5, width=7) diff --git a/23/replication_package/Code/replicate_fig_A8.R b/23/replication_package/Code/replicate_fig_A8.R new file mode 100644 index 0000000000000000000000000000000000000000..27c4bb4aac047911fa6b20556019ef7ed0e0a1ca --- /dev/null +++ b/23/replication_package/Code/replicate_fig_A8.R @@ -0,0 +1,150 @@ +##--##--##--##--##--##--## +## Appendix: Figure A8 +##--##--##--##--##--##--## + +us_survey_wave2$age_numeric = as.numeric(us_survey_wave2$age) +age1 = mean(us_survey_wave2[us_survey_wave2$black==1 & us_survey_wave2$group_goal==0 & us_survey_wave2$commitment==0, "age_numeric"], na.rm=T) +age2 = mean(us_survey_wave2[us_survey_wave2$black==1 & us_survey_wave2$group_goal==0 & us_survey_wave2$commitment==1, "age_numeric"], na.rm=T) +age3 = mean(us_survey_wave2[us_survey_wave2$black==1 & us_survey_wave2$group_goal==1 & us_survey_wave2$commitment==0, "age_numeric"], na.rm=T) +age4 = mean(us_survey_wave2[us_survey_wave2$black==1 & us_survey_wave2$group_goal==1 & us_survey_wave2$commitment==1, "age_numeric"], na.rm=T) +age5 = mean(us_survey_wave2[us_survey_wave2$black==0 & us_survey_wave2$group_goal==0 & us_survey_wave2$commitment==0, "age_numeric"], na.rm=T) +age6 = mean(us_survey_wave2[us_survey_wave2$black==0 & us_survey_wave2$group_goal==0 & us_survey_wave2$commitment==1, "age_numeric"], na.rm=T) +age7 = mean(us_survey_wave2[us_survey_wave2$black==0 & us_survey_wave2$group_goal==1 & us_survey_wave2$commitment==0, "age_numeric"], na.rm=T) +age8 = mean(us_survey_wave2[us_survey_wave2$black==0 & us_survey_wave2$group_goal==1 & us_survey_wave2$commitment==1, "age_numeric"], na.rm=T) + +ages_us_survey_wave2 = c(age1, age2, age3, age4, age5, age6, age7, age8) + + +us_survey_wave2$education_numeric = as.numeric(us_survey_wave2$education) +us_survey_wave2$income_numeric = as.numeric(us_survey_wave2$income) +us_survey_wave2$ideology_numeric = as.numeric(us_survey_wave2$pol_views) +1 + +us_survey_wave2 = dummy_cols(us_survey_wave2, select_columns = c("partyID", "race")) +vars_us_survey_wave2 = c("female", "education_numeric", "income_numeric", "partyID_D", "partyID_I", "partyID_R", "ideology_numeric", "race_White / Caucasian","race_Black / African American") + +means_black_generic_nocomm = means_black_generic_comm = means_black_group_nocomm = means_black_group_comm = + means_white_generic_nocomm = means_white_generic_comm = means_white_group_nocomm = means_white_group_comm = rep(NA, length(vars_us_survey_wave2)) + +se_black_generic_nocomm = se_black_generic_comm = se_black_group_nocomm = se_black_group_comm = + se_white_generic_nocomm = se_white_generic_comm = se_white_group_nocomm = se_white_group_comm = rep(NA, length(vars_us_survey_wave2)) + + +for(i in 1:length(vars_us_survey_wave2)){ + means_black_generic_nocomm[i] = mean(us_survey_wave2[us_survey_wave2$black==1 & us_survey_wave2$group_goal==0 & us_survey_wave2$commitment==0, vars_us_survey_wave2[i]], na.rm=T) + means_black_generic_comm[i] = mean(us_survey_wave2[us_survey_wave2$black==1 & us_survey_wave2$group_goal==0 & us_survey_wave2$commitment==1, vars_us_survey_wave2[i]], na.rm=T) + means_black_group_nocomm[i] = mean(us_survey_wave2[us_survey_wave2$black==1 & us_survey_wave2$group_goal==1 & us_survey_wave2$commitment==0, vars_us_survey_wave2[i]], na.rm=T) + means_black_group_comm[i] = mean(us_survey_wave2[us_survey_wave2$black==1 & us_survey_wave2$group_goal==1 & us_survey_wave2$commitment==1, vars_us_survey_wave2[i]], na.rm=T) + means_white_generic_nocomm[i] = mean(us_survey_wave2[us_survey_wave2$black==0 & us_survey_wave2$group_goal==0 & us_survey_wave2$commitment==0, vars_us_survey_wave2[i]], na.rm=T) + means_white_generic_comm[i] = mean(us_survey_wave2[us_survey_wave2$black==0 & us_survey_wave2$group_goal==0 & us_survey_wave2$commitment==1, vars_us_survey_wave2[i]], na.rm=T) + means_white_group_nocomm[i] = mean(us_survey_wave2[us_survey_wave2$black==0 & us_survey_wave2$group_goal==1 & us_survey_wave2$commitment==0, vars_us_survey_wave2[i]], na.rm=T) + means_white_group_comm[i] = mean(us_survey_wave2[us_survey_wave2$black==0 & us_survey_wave2$group_goal==1 & us_survey_wave2$commitment==1, vars_us_survey_wave2[i]], na.rm=T) + + se_black_generic_nocomm[i] = sd(us_survey_wave2[us_survey_wave2$black==1 & us_survey_wave2$group_goal==0 & us_survey_wave2$commitment==0, vars_us_survey_wave2[i]], na.rm=T) + se_black_generic_comm[i] = sd(us_survey_wave2[us_survey_wave2$black==1 & us_survey_wave2$group_goal==0 & us_survey_wave2$commitment==1, vars_us_survey_wave2[i]], na.rm=T) + se_black_group_nocomm[i] = sd(us_survey_wave2[us_survey_wave2$black==1 & us_survey_wave2$group_goal==1 & us_survey_wave2$commitment==0, vars_us_survey_wave2[i]], na.rm=T) + se_black_group_comm[i] = sd(us_survey_wave2[us_survey_wave2$black==1 & us_survey_wave2$group_goal==1 & us_survey_wave2$commitment==1, vars_us_survey_wave2[i]], na.rm=T) + se_white_generic_nocomm[i] = sd(us_survey_wave2[us_survey_wave2$black==0 & us_survey_wave2$group_goal==0 & us_survey_wave2$commitment==0, vars_us_survey_wave2[i]], na.rm=T) + se_white_generic_comm[i] = sd(us_survey_wave2[us_survey_wave2$black==0 & us_survey_wave2$group_goal==0 & us_survey_wave2$commitment==1, vars_us_survey_wave2[i]], na.rm=T) + se_white_group_nocomm[i] = sd(us_survey_wave2[us_survey_wave2$black==0 & us_survey_wave2$group_goal==1 & us_survey_wave2$commitment==0, vars_us_survey_wave2[i]], na.rm=T) + se_white_group_comm[i] = sd(us_survey_wave2[us_survey_wave2$black==0 & us_survey_wave2$group_goal==1 & us_survey_wave2$commitment==1, vars_us_survey_wave2[i]], na.rm=T) + +} + +us_survey_wave2_balance = cbind(means_black_generic_nocomm, se_black_generic_nocomm, + means_black_generic_comm, se_black_generic_comm, + means_black_group_nocomm, se_black_group_nocomm, + means_black_group_comm, se_black_group_comm, + means_white_generic_nocomm, se_white_generic_nocomm, + means_white_generic_comm, se_white_generic_comm, + means_white_group_nocomm, se_white_group_nocomm, + means_white_group_comm, se_white_group_comm) + + + + +us_survey_wave2_balance = as.data.frame(us_survey_wave2_balance) +rownames(us_survey_wave2_balance) = vars_us_survey_wave2 + + +us_survey_wave2_balance_means = as.data.frame(cbind(means_black_generic_nocomm, + means_black_generic_comm, + means_black_group_nocomm, + means_black_group_comm, + means_white_generic_nocomm, + means_white_generic_comm, + means_white_group_nocomm, + means_white_group_comm)) + +rownames(us_survey_wave2_balance_means) = vars_us_survey_wave2 +us_survey_wave2_balance_means$var = vars_us_survey_wave2 + +keycol <- "condition" +valuecol <- "mean" +gathercols <- c("means_black_generic_nocomm", + "means_black_generic_comm", + "means_black_group_nocomm", + "means_black_group_comm", + "means_white_generic_nocomm", + "means_white_generic_comm", + "means_white_group_nocomm", + "means_white_group_comm") + +us_survey_wave2_balance_means_long = gather_(us_survey_wave2_balance_means, keycol, valuecol, gathercols) + + +us_survey_wave2_balance_se = as.data.frame(cbind(se_black_generic_nocomm, + se_black_generic_comm, + se_black_group_nocomm, + se_black_group_comm, + se_white_generic_nocomm, + se_white_generic_comm, + se_white_group_nocomm, + se_white_group_comm)) + +rownames(us_survey_wave2_balance_se) = vars_us_survey_wave2 +us_survey_wave2_balance_se$var = vars_us_survey_wave2 + +keycol <- "condition" +valuecol <- "se" +gathercols <- c("se_black_generic_nocomm", + "se_black_generic_comm", + "se_black_group_nocomm", + "se_black_group_comm", + "se_white_generic_nocomm", + "se_white_generic_comm", + "se_white_group_nocomm", + "se_white_group_comm") + +us_survey_wave2_balance_se_long = gather_(us_survey_wave2_balance_se, keycol, valuecol, gathercols) + +us_survey_wave2_balance_long = cbind(us_survey_wave2_balance_means_long, us_survey_wave2_balance_se_long[,"se"]) + +colnames(us_survey_wave2_balance_long) = c("term", "model", "estimate", "std.error") + +us_survey_wave2_balance_long$model[us_survey_wave2_balance_long$model == "means_black_generic_nocomm"] = "Black, generic goal, no commitment" +us_survey_wave2_balance_long$model[us_survey_wave2_balance_long$model == "means_black_generic_comm"] = "Black, generic goal, commitment" +us_survey_wave2_balance_long$model[us_survey_wave2_balance_long$model == "means_black_group_nocomm"] = "Black, group goal, no commitment" +us_survey_wave2_balance_long$model[us_survey_wave2_balance_long$model == "means_black_group_comm"] = "Black, group goal, no commitment" +us_survey_wave2_balance_long$model[us_survey_wave2_balance_long$model == "means_white_generic_nocomm"] = "White, generic goal, no commitment" +us_survey_wave2_balance_long$model[us_survey_wave2_balance_long$model == "means_white_generic_comm"] = "White, generic goal, commitment" +us_survey_wave2_balance_long$model[us_survey_wave2_balance_long$model == "means_white_group_nocomm"] = "White, group goal, no commitment" +us_survey_wave2_balance_long$model[us_survey_wave2_balance_long$model == "means_white_group_comm"] = "White, group goal, no commitment" + + +us_survey_wave2_balance_long$term[us_survey_wave2_balance_long$term == "age"] = "Age" +us_survey_wave2_balance_long$term[us_survey_wave2_balance_long$term == "female"] = "Female" +us_survey_wave2_balance_long$term[us_survey_wave2_balance_long$term == "education_numeric"] = "Education (1-7 scale)" +us_survey_wave2_balance_long$term[us_survey_wave2_balance_long$term == "income_numeric"] = "Income (1-6 scale)" +us_survey_wave2_balance_long$term[us_survey_wave2_balance_long$term == "partyID_D"] = "Party ID: Democrat" +us_survey_wave2_balance_long$term[us_survey_wave2_balance_long$term == "partyID_I"] = "Party ID: Independent" +us_survey_wave2_balance_long$term[us_survey_wave2_balance_long$term == "partyID_R"] = "Party ID: Republican" +us_survey_wave2_balance_long$term[us_survey_wave2_balance_long$term == "ideology_numeric"] = "Ideology (1-7 scale)" +us_survey_wave2_balance_long$term[us_survey_wave2_balance_long$term == "race_White / Caucasian"] = "Race: White" +us_survey_wave2_balance_long$term[us_survey_wave2_balance_long$term == "race_Black / African American"] = "Race: Black" + + +us_survey_wave2_balance_long = us_survey_wave2_balance_long[us_survey_wave2_balance_long$term != "Age",] + +dwplot(us_survey_wave2_balance_long) + theme_minimal() + theme(legend.title=element_blank()) + + geom_vline(xintercept = 0, colour = "grey60", linetype = 2) +ggsave("Figures/fig_A8.pdf", height=5, width=7) diff --git a/23/replication_package/Code/replicate_fig_A9.R b/23/replication_package/Code/replicate_fig_A9.R new file mode 100644 index 0000000000000000000000000000000000000000..7da894d44873cd893491e02bc605e3c91b7d1f00 --- /dev/null +++ b/23/replication_package/Code/replicate_fig_A9.R @@ -0,0 +1,149 @@ +##--##--##--##--##--##--## +## Appendix: Figure A9 +##--##--##--##--##--##--## + +isr_survey_wave2 = dummy_cols(isr_survey_wave2, select_columns = c("partyID", "ethnicity")) + +vars_isr_survey_wave2 = c("female", "education", "income", "partyID_L", "partyID_R", "ideology", + "ethnicity_Ethiopia", "ethnicity_Ashkenazi", "ethnicity_Mizrachi", "ethnicity_Soviet Union") + +isr_survey_wave2$age_numeric = as.numeric(isr_survey_wave2$age) +age1 = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==1 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==0, "age_numeric"], na.rm=T) +age2 = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==1 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==1, "age_numeric"], na.rm=T) +age3 = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==1 & isr_survey_wave2$group_goal==1 & isr_survey_wave2$commitment==0, "age_numeric"], na.rm=T) +age4 = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==1 & isr_survey_wave2$group_goal==1 & isr_survey_wave2$commitment==1, "age_numeric"], na.rm=T) +age5 = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==2 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==0, "age_numeric"], na.rm=T) +age6 = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==2 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==1, "age_numeric"], na.rm=T) +age7 = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==2 & isr_survey_wave2$group_goal==1 & isr_survey_wave2$commitment==0, "age_numeric"], na.rm=T) +age8 = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==2 & isr_survey_wave2$group_goal==1 & isr_survey_wave2$commitment==1, "age_numeric"], na.rm=T) +age9 = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==0 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==0, "age_numeric"], na.rm=T) +age10 = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==0 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==1, "age_numeric"], na.rm=T) + +ages_isr_survey_wave2 = c(age1, age2, age3, age4, age5, age6, age7, age8, age9, age10) + +means_eth_generic_nocomm = means_eth_generic_comm = means_eth_group_nocomm = means_eth_group_comm = + means_arab_generic_nocomm = means_arab_generic_comm = means_arab_group_nocomm = means_arab_group_comm = + means_white_generic_nocomm = means_white_generic_comm = rep(NA, length(vars_isr_survey_wave2)) + +se_eth_generic_nocomm = se_eth_generic_comm = se_eth_group_nocomm = se_eth_group_comm = + se_arab_generic_nocomm = se_arab_generic_comm = se_arab_group_nocomm = se_arab_group_comm = + se_white_generic_nocomm = se_white_generic_comm = rep(NA, length(vars_isr_survey_wave2)) + + +for(i in 1:length(vars_isr_survey_wave2)){ + means_eth_generic_nocomm[i] = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==1 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==0, vars_isr_survey_wave2[i]], na.rm=T) + means_eth_generic_comm[i] = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==1 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==1, vars_isr_survey_wave2[i]], na.rm=T) + means_eth_group_nocomm[i] = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==1 & isr_survey_wave2$group_goal==1 & isr_survey_wave2$commitment==0, vars_isr_survey_wave2[i]], na.rm=T) + means_eth_group_comm[i] = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==1 & isr_survey_wave2$group_goal==1 & isr_survey_wave2$commitment==1, vars_isr_survey_wave2[i]], na.rm=T) + means_arab_generic_nocomm[i] = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==2 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==0, vars_isr_survey_wave2[i]], na.rm=T) + means_arab_generic_comm[i] = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==2 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==1, vars_isr_survey_wave2[i]], na.rm=T) + means_arab_group_nocomm[i] = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==2 & isr_survey_wave2$group_goal==1 & isr_survey_wave2$commitment==0, vars_isr_survey_wave2[i]], na.rm=T) + means_arab_group_comm[i] = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==2 & isr_survey_wave2$group_goal==1 & isr_survey_wave2$commitment==1, vars_isr_survey_wave2[i]], na.rm=T) + means_white_generic_nocomm[i] = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==0 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==0, vars_isr_survey_wave2[i]], na.rm=T) + means_white_generic_comm[i] = mean(isr_survey_wave2[isr_survey_wave2$identity_protesters==0 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==1, vars_isr_survey_wave2[i]], na.rm=T) + + se_eth_generic_nocomm[i] = sd(isr_survey_wave2[isr_survey_wave2$identity_protesters==1 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==0, vars_isr_survey_wave2[i]], na.rm=T) + se_eth_generic_comm[i] = sd(isr_survey_wave2[isr_survey_wave2$identity_protesters==1 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==1, vars_isr_survey_wave2[i]], na.rm=T) + se_eth_group_nocomm[i] = sd(isr_survey_wave2[isr_survey_wave2$identity_protesters==1 & isr_survey_wave2$group_goal==1 & isr_survey_wave2$commitment==0, vars_isr_survey_wave2[i]], na.rm=T) + se_eth_group_comm[i] = sd(isr_survey_wave2[isr_survey_wave2$identity_protesters==1 & isr_survey_wave2$group_goal==1 & isr_survey_wave2$commitment==1, vars_isr_survey_wave2[i]], na.rm=T) + se_arab_generic_nocomm[i] = sd(isr_survey_wave2[isr_survey_wave2$identity_protesters==2 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==0, vars_isr_survey_wave2[i]], na.rm=T) + se_arab_generic_comm[i] = sd(isr_survey_wave2[isr_survey_wave2$identity_protesters==2 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==1, vars_isr_survey_wave2[i]], na.rm=T) + se_arab_group_nocomm[i] = sd(isr_survey_wave2[isr_survey_wave2$identity_protesters==2 & isr_survey_wave2$group_goal==1 & isr_survey_wave2$commitment==0, vars_isr_survey_wave2[i]], na.rm=T) + se_arab_group_comm[i] = sd(isr_survey_wave2[isr_survey_wave2$identity_protesters==2 & isr_survey_wave2$group_goal==1 & isr_survey_wave2$commitment==1, vars_isr_survey_wave2[i]], na.rm=T) + se_white_generic_nocomm[i] = sd(isr_survey_wave2[isr_survey_wave2$identity_protesters==0 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==0, vars_isr_survey_wave2[i]], na.rm=T) + se_white_generic_comm[i] = sd(isr_survey_wave2[isr_survey_wave2$identity_protesters==0 & isr_survey_wave2$group_goal==0 & isr_survey_wave2$commitment==1, vars_isr_survey_wave2[i]], na.rm=T) + +} + + +isr_survey_wave2_balance_means = as.data.frame(cbind(means_eth_generic_nocomm, + means_eth_generic_comm, + means_eth_group_nocomm, + means_eth_group_comm, + means_arab_generic_nocomm, + means_arab_generic_comm, + means_arab_group_nocomm, + means_arab_group_comm, + means_white_generic_nocomm, + means_white_generic_comm)) + +rownames(isr_survey_wave2_balance_means) = vars_isr_survey_wave2 +isr_survey_wave2_balance_means$var = vars_isr_survey_wave2 + +keycol <- "condition" +valuecol <- "mean" +gathercols <- c("means_eth_generic_nocomm", + "means_eth_generic_comm", + "means_eth_group_nocomm", + "means_eth_group_comm", + "means_arab_generic_nocomm", + "means_arab_generic_comm", + "means_arab_group_nocomm", + "means_arab_group_comm", + "means_white_generic_nocomm", + "means_white_generic_comm") + +isr_survey_wave2_balance_means_long = gather_(isr_survey_wave2_balance_means, keycol, valuecol, gathercols) + +isr_survey_wave2_balance_se = as.data.frame(cbind(se_eth_generic_nocomm, + se_eth_generic_comm, + se_eth_group_nocomm, + se_eth_group_comm, + se_arab_generic_nocomm, + se_arab_generic_comm, + se_arab_group_nocomm, + se_arab_group_comm, + se_white_generic_nocomm, + se_white_generic_comm)) + +rownames(isr_survey_wave2_balance_se) = vars_isr_survey_wave2 +isr_survey_wave2_balance_se$var = vars_isr_survey_wave2 + +keycol <- "condition" +valuecol <- "se" +gathercols <- c("se_eth_generic_nocomm", + "se_eth_generic_comm", + "se_eth_group_nocomm", + "se_eth_group_comm", + "se_arab_generic_nocomm", + "se_arab_generic_comm", + "se_arab_group_nocomm", + "se_arab_group_comm", + "se_white_generic_nocomm", + "se_white_generic_comm") + +isr_survey_wave2_balance_se_long = gather_(isr_survey_wave2_balance_se, keycol, valuecol, gathercols) + +isr_survey_wave2_balance_long = cbind(isr_survey_wave2_balance_means_long, isr_survey_wave2_balance_se_long[,"se"]) + +colnames(isr_survey_wave2_balance_long) = c("term", "model", "estimate", "std.error") + + +isr_survey_wave2_balance_long$model[isr_survey_wave2_balance_long$model == "means_eth_generic_nocomm"] = "Ethiopian, generic goal, no commitment" +isr_survey_wave2_balance_long$model[isr_survey_wave2_balance_long$model == "means_eth_generic_comm"] = "Ethiopian, generic goal, commitment" +isr_survey_wave2_balance_long$model[isr_survey_wave2_balance_long$model == "means_eth_group_nocomm"] = "Ethiopian, group goal, no commitment" +isr_survey_wave2_balance_long$model[isr_survey_wave2_balance_long$model == "means_eth_group_comm"] = "Ethiopian, group goal, no commitment" +isr_survey_wave2_balance_long$model[isr_survey_wave2_balance_long$model == "means_arab_generic_nocomm"] = "Arab, generic goal, no commitment" +isr_survey_wave2_balance_long$model[isr_survey_wave2_balance_long$model == "means_arab_generic_comm"] = "Arab, generic goal, commitment" +isr_survey_wave2_balance_long$model[isr_survey_wave2_balance_long$model == "means_arab_group_nocomm"] = "Arab, group goal, no commitment" +isr_survey_wave2_balance_long$model[isr_survey_wave2_balance_long$model == "means_arab_group_comm"] = "Arab, group goal, no commitment" +isr_survey_wave2_balance_long$model[isr_survey_wave2_balance_long$model == "means_white_generic_nocomm"] = "White, generic goal, no commitment" +isr_survey_wave2_balance_long$model[isr_survey_wave2_balance_long$model == "means_white_generic_comm"] = "White, generic goal, commitment" + + + +isr_survey_wave2_balance_long$term[isr_survey_wave2_balance_long$term == "education"] = "Education (0-6 scale)" +isr_survey_wave2_balance_long$term[isr_survey_wave2_balance_long$term == "ethnicity_Ethiopia"] = "Ethnicity: Ethiopian" +isr_survey_wave2_balance_long$term[isr_survey_wave2_balance_long$term == "ethnicity_Ashkenazi"] = "Ethnicity: Ashkenazi" +isr_survey_wave2_balance_long$term[isr_survey_wave2_balance_long$term == "ethnicity_Mizrachi"] = "Ethnicity: Mizrachi" +isr_survey_wave2_balance_long$term[isr_survey_wave2_balance_long$term == "ethnicity_Soviet Union"] = "Ethnicity: Soviet Union" +isr_survey_wave2_balance_long$term[isr_survey_wave2_balance_long$term == "female"] = "Female" +isr_survey_wave2_balance_long$term[isr_survey_wave2_balance_long$term == "ideology"] = "Ideology (1-7 scale)" +isr_survey_wave2_balance_long$term[isr_survey_wave2_balance_long$term == "income"] = "Income (0-8 scale)" +isr_survey_wave2_balance_long$term[isr_survey_wave2_balance_long$term == "partyID_L"] = "Party ID: Left" +isr_survey_wave2_balance_long$term[isr_survey_wave2_balance_long$term == "partyID_R"] = "Party ID: Right" + + +dwplot(isr_survey_wave2_balance_long) + theme_minimal() + theme(legend.title=element_blank()) + + geom_vline(xintercept = 0, colour = "grey60", linetype = 2) +ggsave("Figures/fig_A9.pdf", height=5, width=7) diff --git a/23/replication_package/Code/replicate_table_1.R b/23/replication_package/Code/replicate_table_1.R new file mode 100644 index 0000000000000000000000000000000000000000..3159856aa56f431ee39f71da079243670c3dd2aa --- /dev/null +++ b/23/replication_package/Code/replicate_table_1.R @@ -0,0 +1,24 @@ +##--##--##--## +## Table 1 +##--##--##--## + +## A. U.S +degv_yg = lm(degree_violence ~ identity_protesters + tactic , data=us_survey_wave1, weights=weight) +police_yg = lm(police_action_required ~ identity_protesters + tactic , data=us_survey_wave1, weights=weight) +recall_violence_yg = lm(recall_violence2 ~ identity_protesters + tactic , data=us_survey_wave1, weights=weight) +approve_protest_yg = lm(approve_protest_scale ~ identity_protesters + tactic , data=us_survey_wave1, weights=weight) +stargazer(degv_yg, recall_violence_yg, police_yg, + covariate.labels = c("Black protesters", "Shut down traffic", + "Destroy police cars", "Intercept: White protesters, March in streets"), + out = "Tables/table_1_panel_A.tex") + +## B. Israel +degv_isr = lm(degree_violence ~ identity_protesters + tactic , data=isr_survey_wave1, weights=weight) +police_isr = lm(police_action_required ~ identity_protesters + tactic , data=isr_survey_wave1, weights=weight) +recall_violence_isr = lm(recall_violence2 ~ identity_protesters + tactic , data=isr_survey_wave1, weights=weight) +approve_protest_isr = lm(approve_protest_scale ~ identity_protesters + tactic , data=isr_survey_wave1, weights=weight) +stargazer(degv_isr, recall_violence_isr, police_isr, + covariate.labels = c("Ethiopian protesters", "Arab protesters", + "Shut down traffic", "Destroy garbage cans", + "Intercept: White protesters, March in streets"), + out = "Tables/table_1_panel_B.tex") diff --git a/23/replication_package/Code/replicate_table_4.R b/23/replication_package/Code/replicate_table_4.R new file mode 100644 index 0000000000000000000000000000000000000000..16e012ccc605724294d008be52e3da5e1e590195 --- /dev/null +++ b/23/replication_package/Code/replicate_table_4.R @@ -0,0 +1,22 @@ +##--##--##--## +## Table 4 +##--##--##--## + +## A. U.S. sample +mod1 = lm(degree_violence ~ black + group_goal + commitment , data=us_survey_wave2) +mod2 = lm(recall_violence2 ~ black + group_goal + commitment, data=us_survey_wave2) +mod3 = lm(police_action_required ~ black + group_goal + commitment, data=us_survey_wave2) +stargazer(mod1, mod2, mod3, + covariate.labels = c("Black protesters", "Minority group goal", + "Commitment to nonviolence", "Intercept: White protesters, generic goal, no commitment"), + out = "Tables/table_4_panel_A.tex") + +## B. Israel sample +mod1_israel = lm(degree_violence ~ identity_protesters + group_goal + commitment, data=isr_survey_wave2) +mod2_israel = lm(recall_violence2 ~ identity_protesters + group_goal + commitment, data=isr_survey_wave2) +mod3_israel = lm(police_action_required ~ identity_protesters + group_goal + commitment, data=isr_survey_wave2) +stargazer(mod1_israel, mod2_israel, mod3_israel, + covariate.labels = c("Ethiopian protesters", "Arab protesters", + "Minority group goal", "Commitment to nonviolence", + "Intercept: White protesters, generic goal, no commitment"), + out = "Tables/table_4_panel_B.tex") diff --git a/23/replication_package/Code/replicate_table_A1.R b/23/replication_package/Code/replicate_table_A1.R new file mode 100644 index 0000000000000000000000000000000000000000..5b3ddeeb10a18f7452577d700f54088192ace21c --- /dev/null +++ b/23/replication_package/Code/replicate_table_A1.R @@ -0,0 +1,15 @@ +##--##--##--##--##--##--## +## Appendix: Table A1 +##--##--##--##--##--##--## + +stargazer(EBCR_EPR_NAVCO2[,c("YEAR", "success", "INIT_NV_ONSET", "EPR_GROUPSIZE", "EPR_STATUS_ORD", + "EPR_STATUS_EXCL", "POP_LOG_LAG_EXT", "GDPPC_LOG_LAG_EXT", + "PASTNV", "PASTV", "VDEM_POLYARCHY_LAG", "VDEM_PHYSINT_LAG", + "EPR_TEK_EGIP", "EPR_DOWNGRADED5", "HORIZ_INEQ", "NVYEARS", + "VYEARS")], + covariate.labels = c("Year", "Campaign success", "NV campaign", "EPR group size", "EPR status", + "EPR Status: excluded", "Country population (logged)", "Country GDP per capita (logged)", "Prior participation in nonviolence", "Prior participation in violence", + "Level of democracy", "Physical integrity index", "Neighboring kin in power", + "Downgraded", "Horizontal inequality", "Nonviolent years", "Violent years"), + out="Tables/table_A1.tex") + diff --git a/23/replication_package/Code/replicate_table_A10.R b/23/replication_package/Code/replicate_table_A10.R new file mode 100644 index 0000000000000000000000000000000000000000..f7852a093b50ea40cc7c43bb2ef2aa2d063416db --- /dev/null +++ b/23/replication_package/Code/replicate_table_A10.R @@ -0,0 +1,9 @@ +##--##--##--##--##--##--## +## Appendix: Table A10 +##--##--##--##--##--##--## + +us_wave2_sumstats = us_survey_wave2[,c("age", "female", "education", "income", "partyID", "pol_views", "interest_politics", + "degree_violence", "police_action_required", "recall_violence2", "race")] +us_wave2_sumstats = dummy_cols(us_wave2_sumstats, select_columns = c("age", "education", "income", "partyID", "pol_views", "race")) +table_a10 = stargazer(us_wave2_sumstats, omit.summary.stat=c("p25", "p75")) +cat(table_a10, sep = '\n', file = "Tables/table_A10.tex") diff --git a/23/replication_package/Code/replicate_table_A11.R b/23/replication_package/Code/replicate_table_A11.R new file mode 100644 index 0000000000000000000000000000000000000000..33db5f5be4900d0a52a39ded69d12910ebb8ca05 --- /dev/null +++ b/23/replication_package/Code/replicate_table_A11.R @@ -0,0 +1,9 @@ +##--##--##--##--##--##--## +## Appendix: Table A11 +##--##--##--##--##--##--## + +isr_wave2_sumstats = isr_survey_wave2[,c("age", "female", "religiosity", "ethnicity", "education", "income", "partyID", "ideology", + "degree_violence", "police_action_required", "recall_violence2")] +isr_wave2_sumstats = dummy_cols(isr_wave2_sumstats, select_columns = c("age", "religiosity", "ethnicity", "education", "income", "partyID")) +table_a11 = stargazer(isr_wave2_sumstats, omit.summary.stat=c("p25", "p75")) +cat(table_a11, sep = '\n', file = "Tables/table_A11.tex") diff --git a/23/replication_package/Code/replicate_table_A13.R b/23/replication_package/Code/replicate_table_A13.R new file mode 100644 index 0000000000000000000000000000000000000000..05cab90defac3a7ac68416fb474f94649ae1a6d9 --- /dev/null +++ b/23/replication_package/Code/replicate_table_A13.R @@ -0,0 +1,15 @@ +##--##--##--##--##--##--## +## Appendix: Table A13 +##--##--##--##--##--##--## + +## A. U.S. sample +degv_yg = lm(degree_violence ~ identity_protesters + tactic + age +female + education + income + ideology + race, data=us_survey_wave1, weights=weight) +police_yg = lm(police_action_required ~ identity_protesters + tactic + age +female + education + income + ideology + race, data=us_survey_wave1, weights=weight) +recall_violence_yg = lm(recall_violence2 ~ identity_protesters + tactic + age +female + education + income + ideology + race, data=us_survey_wave1, weights=weight) +stargazer(degv_yg, recall_violence_yg, police_yg, out = "Tables/table_A13_panel_A.tex") + +## B. Israel sample +degv_ip = lm(degree_violence ~ identity_protesters + tactic + age +female + education + income + ideology + ethnicity, data=isr_survey_wave1, weights=weight) +police_ip = lm(police_action_required ~ identity_protesters + tactic + age +female + education + income + ideology + ethnicity , data=isr_survey_wave1, weights=weight) +recall_violence_ip = lm(recall_violence2 ~ identity_protesters + tactic + age +female + education + income + ideology + ethnicity , data=isr_survey_wave1, weights=weight) +stargazer(degv_ip, recall_violence_ip, police_ip, out = "Tables/table_A13_panel_B.tex") diff --git a/23/replication_package/Code/replicate_table_A14.R b/23/replication_package/Code/replicate_table_A14.R new file mode 100644 index 0000000000000000000000000000000000000000..0f9496de1cf36425f42b3bb28eea6034f903b8d6 --- /dev/null +++ b/23/replication_package/Code/replicate_table_A14.R @@ -0,0 +1,194 @@ +##--##--##--## +## Table A14 +##--##--##--## + +us_survey_wave1$identity_protesters_fac = as.factor(us_survey_wave1$identity_protesters) +levels(us_survey_wave1$identity_protesters_fac) = c("White", "Black") +us_survey_wave1$tactic_fac = as.factor(us_survey_wave1$tactic) +levels(us_survey_wave1$tactic_fac) = c("March in streets", "Shut down traffic", "Destroy police cars") + +isr_survey_wave1$identity_protesters_fac = as.factor(isr_survey_wave1$identity_protesters) +levels(isr_survey_wave1$identity_protesters_fac) = c("White", "Ethiopian", "Arab") +isr_survey_wave1$tactic_fac = as.factor(isr_survey_wave1$tactic) +levels(isr_survey_wave1$tactic_fac) = c("March in streets", "Shut down traffic", "Destroy garbage cans") + + +## US sample +us_survey_wave1$degree_violence_std = scale(us_survey_wave1$degree_violence) +us_survey_wave1$police_action_required_std = scale(us_survey_wave1$police_action_required) +us_survey_wave1$recall_violence2_std = scale(us_survey_wave1$recall_violence2) + +march1 = lm_robust(degree_violence_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==0,], weights=weight) +shut1 = lm_robust(degree_violence_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==1,], weights=weight) +destroy1 = lm_robust(degree_violence_std ~ identity_protesters_fac , data=us_survey_wave1[us_survey_wave1$tactic==2,], weights=weight) + +march2 = lm_robust(police_action_required_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==0,], weights=weight) +shut2 = lm_robust(police_action_required_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==1,], weights=weight) +destroy2 = lm_robust(police_action_required_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==2,], weights=weight) + +march3 = lm_robust(recall_violence2_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==0,], weights=weight) +shut3 = lm_robust(recall_violence2_std ~ identity_protesters_fac, data=us_survey_wave1[us_survey_wave1$tactic==1,], weights=weight) +destroy3 = lm_robust(recall_violence2_std ~ identity_protesters_fac , data=us_survey_wave1[us_survey_wave1$tactic==2,], weights=weight) + +# Plot differences + +term_degree_violence = c(march1$coefficients[2], shut1$coefficients[2], destroy1$coefficients[2]) +se_degree_violence = c(march1$std.error[2], shut1$std.error[2], destroy1$std.error[2]) +statistic_degree_violence = c(march1$statistic[2], shut1$statistic[2], destroy1$statistic[2]) +pval_degree_violence = c(march1$p.value[2], shut1$p.value[2], destroy1$p.value[2]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +degree_violence = as.data.frame(cbind(term_degree_violence, se_degree_violence, statistic_degree_violence, pval_degree_violence)) +degree_violence = cbind(degree_violence, term) +colnames(degree_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(degree_violence) = term +degree_violence$model = "Perceived degree of violence" + +term_police_action_required = c(march2$coefficients[2], shut2$coefficients[2], destroy2$coefficients[2]) +se_police_action_required = c(march2$std.error[2], shut2$std.error[2], destroy2$std.error[2]) +statistic_police_action_required = c(march2$statistic[2], shut2$statistic[2], destroy2$statistic[2]) +pval_police_action_required = c(march2$p.value[2], shut2$p.value[2], destroy2$p.value[2]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +police_action_required = as.data.frame(cbind(term_police_action_required, se_police_action_required, statistic_police_action_required, pval_police_action_required)) +police_action_required = cbind(police_action_required, term) +colnames(police_action_required) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(police_action_required) = term +police_action_required$model = "Police action required" + +term_recall_violence = c(march3$coefficients[2], shut3$coefficients[2], destroy3$coefficients[2]) +se_recall_violence = c(march3$std.error[2], shut3$std.error[2], destroy3$std.error[2]) +statistic_recall_violence = c(march3$statistic[2], shut3$statistic[2], destroy3$statistic[2]) +pval_recall_violence = c(march3$p.value[2], shut3$p.value[2], destroy3$p.value[2]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +recall_violence = as.data.frame(cbind(term_recall_violence, se_recall_violence, statistic_recall_violence, pval_recall_violence)) +recall_violence = cbind(recall_violence, term) +colnames(recall_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(recall_violence) = term +recall_violence$model = "Recall violence" + +differences_us = rbind(degree_violence, police_action_required, recall_violence) + + +## Israel respondents +isr_survey_wave1$degree_violence_std = scale(isr_survey_wave1$degree_violence) +isr_survey_wave1$police_action_required_std = scale(isr_survey_wave1$police_action_required) +isr_survey_wave1$recall_violence2_std = scale(isr_survey_wave1$recall_violence2) + +march1 = lm_robust(degree_violence_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==0,], weights=weight) +shut1 = lm_robust(degree_violence_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==1,], weights=weight) +destroy1 = lm_robust(degree_violence_std ~ identity_protesters_fac , data=isr_survey_wave1[isr_survey_wave1$tactic==2,], weights=weight) + +march2 = lm_robust(police_action_required_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==0,], weights=weight) +shut2 = lm_robust(police_action_required_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==1,], weights=weight) +destroy2 = lm_robust(police_action_required_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==2,], weights=weight) + +march3 = lm_robust(recall_violence2_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==0,], weights=weight) +shut3 = lm_robust(recall_violence2_std ~ identity_protesters_fac, data=isr_survey_wave1[isr_survey_wave1$tactic==1,], weights=weight) +destroy3 = lm_robust(recall_violence2_std ~ identity_protesters_fac , data=isr_survey_wave1[isr_survey_wave1$tactic==2,], weights=weight) + + +# Ethiopian protesters +term_degree_violence = c(march1$coefficients[2], shut1$coefficients[2], destroy1$coefficients[2]) +se_degree_violence = c(march1$std.error[2], shut1$std.error[2], destroy1$std.error[2]) +statistic_degree_violence = c(march1$statistic[2], shut1$statistic[2], destroy1$statistic[2]) +pval_degree_violence = c(march1$p.value[2], shut1$p.value[2], destroy1$p.value[2]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +degree_violence = as.data.frame(cbind(term_degree_violence, se_degree_violence, statistic_degree_violence, pval_degree_violence)) +degree_violence = cbind(degree_violence, term) +colnames(degree_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(degree_violence) = term +degree_violence$model = "Perceived degree of violence" + +term_police_action_required = c(march2$coefficients[2], shut2$coefficients[2], destroy2$coefficients[2]) +se_police_action_required = c(march2$std.error[2], shut2$std.error[2], destroy2$std.error[2]) +statistic_police_action_required = c(march2$statistic[2], shut2$statistic[2], destroy2$statistic[2]) +pval_police_action_required = c(march2$p.value[2], shut2$p.value[2], destroy2$p.value[2]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +police_action_required = as.data.frame(cbind(term_police_action_required, se_police_action_required, statistic_police_action_required, pval_police_action_required)) +police_action_required = cbind(police_action_required, term) +colnames(police_action_required) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(police_action_required) = term +police_action_required$model = "Police action required" + +term_recall_violence = c(march3$coefficients[2], shut3$coefficients[2], destroy3$coefficients[2]) +se_recall_violence = c(march3$std.error[2], shut3$std.error[2], destroy3$std.error[2]) +statistic_recall_violence = c(march3$statistic[2], shut3$statistic[2], destroy3$statistic[2]) +pval_recall_violence = c(march3$p.value[2], shut3$p.value[2], destroy3$p.value[2]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +recall_violence = as.data.frame(cbind(term_recall_violence, se_recall_violence, statistic_recall_violence, pval_recall_violence)) +recall_violence = cbind(recall_violence, term) +colnames(recall_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(recall_violence) = term +recall_violence$model = "Recall violence" + +differences_isr_black = rbind(degree_violence, police_action_required, recall_violence) + +term_degree_violence = c(march1$coefficients[3], shut1$coefficients[3], destroy1$coefficients[3]) +se_degree_violence = c(march1$std.error[3], shut1$std.error[3], destroy1$std.error[3]) +statistic_degree_violence = c(march1$statistic[3], shut1$statistic[3], destroy1$statistic[3]) +pval_degree_violence = c(march1$p.value[3], shut1$p.value[3], destroy1$p.value[3]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +degree_violence = as.data.frame(cbind(term_degree_violence, se_degree_violence, statistic_degree_violence, pval_degree_violence)) +degree_violence = cbind(degree_violence, term) +colnames(degree_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(degree_violence) = term +degree_violence$model = "Perceived degree of violence" + +term_police_action_required = c(march2$coefficients[3], shut2$coefficients[3], destroy2$coefficients[3]) +se_police_action_required = c(march2$std.error[3], shut2$std.error[3], destroy2$std.error[3]) +statistic_police_action_required = c(march2$statistic[3], shut2$statistic[3], destroy2$statistic[3]) +pval_police_action_required = c(march2$p.value[3], shut2$p.value[3], destroy2$p.value[3]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +police_action_required = as.data.frame(cbind(term_police_action_required, se_police_action_required, statistic_police_action_required, pval_police_action_required)) +police_action_required = cbind(police_action_required, term) +colnames(police_action_required) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(police_action_required) = term +police_action_required$model = "Police action required" + +term_recall_violence = c(march3$coefficients[3], shut3$coefficients[3], destroy3$coefficients[3]) +se_recall_violence = c(march3$std.error[3], shut3$std.error[3], destroy3$std.error[3]) +statistic_recall_violence = c(march3$statistic[3], shut3$statistic[3], destroy3$statistic[3]) +pval_recall_violence = c(march3$p.value[3], shut3$p.value[3], destroy3$p.value[3]) +term = c("1) Minority: March in streets", "2) Minority: Shut down traffic", "3) Minority: Destroy police cars / garbage cans") +recall_violence = as.data.frame(cbind(term_recall_violence, se_recall_violence, statistic_recall_violence, pval_recall_violence)) +recall_violence = cbind(recall_violence, term) +colnames(recall_violence) = c("estimate", "std.error", "statistic", "p.value", "term") +rownames(recall_violence) = term +recall_violence$model = "Recall violence" + +differences_isr_arab = rbind(degree_violence, police_action_required, recall_violence) + +## Result tables for plot +differences_us2 = differences_us +differences_us2$submodel = differences_us2$term +differences_us2$term = "Perception of Blacks \n(United States)" + +differences_isr_black2 = differences_isr_black +differences_isr_black2$submodel = differences_isr_black2$term +differences_isr_black2$term = "Perception of Ethiopians \n(Israel)" + +differences_isr_arab2 = differences_isr_arab +differences_isr_arab2$submodel = differences_isr_arab2$term +differences_isr_arab2$term = "Perception of Arabs \n(Israel)" + +diffs_sm = rbind(differences_us2, differences_isr_arab2, differences_isr_black2) +rownames(diffs_sm) = 1:nrow(diffs_sm) + +results_df <- data.frame(term = diffs_sm$term, + estimate = diffs_sm$estimate, + std.error = diffs_sm$std.error, + model = diffs_sm$model, + submodel = as.character(diffs_sm$submodel), + stringsAsFactors = FALSE) + +results_df$submodel[results_df$submodel=="1) Minority: March in streets"] = "March in streets" +results_df$submodel[results_df$submodel=="2) Minority: Shut down traffic"] = "Shut down traffic" +results_df$submodel[results_df$submodel=="3) Minority: Destroy police cars / garbage cans"] = "Destroy property" + +results_df$model[results_df$model=="Perceived degree of violence"] = "1. Perceived degree \nof violence" +results_df$model[results_df$model=="Police action required"] = "3. Police action \nrequired" +results_df$model[results_df$model=="Recall violence"] = "2. Recall \nviolence" + + +table_a14 = xtable(results_df[,-1], digits=2) +print(table_a14, file="Tables/table_A14.tex") + diff --git a/23/replication_package/Code/replicate_table_A15.R b/23/replication_package/Code/replicate_table_A15.R new file mode 100644 index 0000000000000000000000000000000000000000..9fd0559e2f386ea7bbd44e6651a79c4032b448a1 --- /dev/null +++ b/23/replication_package/Code/replicate_table_A15.R @@ -0,0 +1,25 @@ +##--##--##--##--##--##--## +## Appendix: Table A15 +##--##--##--##--##--##--## + +## A. U.S. sample +degv_yg = lm(degree_violence ~ identity_protesters * tactic , data=us_survey_wave1, weights=weight) +police_yg = lm(police_action_required ~ identity_protesters * tactic , data=us_survey_wave1, weights=weight) +recall_violence_yg = lm(recall_violence2 ~ identity_protesters * tactic , data=us_survey_wave1, weights=weight) +stargazer(degv_yg, recall_violence_yg, police_yg, + covariate.labels = c("Black protesters", "Shut down traffic", "Destroy police cars", + "Black protesters x Shut down traffic", + "Black protesters x Destroy police cars", "Intercept"), + out = "Tables/table_A15_panel_A.tex") + +## B. Israel sample +degv_isr = lm(degree_violence ~ identity_protesters * tactic , data=isr_survey_wave1, weights=weight) +police_isr = lm(police_action_required ~ identity_protesters * tactic , data=isr_survey_wave1, weights=weight) +recall_violence_isr = lm(recall_violence2 ~ identity_protesters * tactic , data=isr_survey_wave1, weights=weight) +stargazer(degv_isr, recall_violence_isr, police_isr, + covariate.labels = c("Ethiopian protesters", "Arab protesters", "Shut down traffic", + "Destroy garbage cans", "Ethiopian protesters x Shut down traffic", + "Arab protesters x Shut down traffic", "Ethiopian protesters x Destroy garbage cans", + "Arab protesters x Destroy garbage cans", "Intercept"), + out = "Tables/table_A15_panel_B.tex") + diff --git a/23/replication_package/Code/replicate_table_A16.R b/23/replication_package/Code/replicate_table_A16.R new file mode 100644 index 0000000000000000000000000000000000000000..dc540ff9093ee5fc7025e1a2dfb7356065060878 --- /dev/null +++ b/23/replication_package/Code/replicate_table_A16.R @@ -0,0 +1,102 @@ +##--##--##--##--##--##--## +## Appendix: Table A16 +##--##--##--##--##--##--## + +## A. U.S. +degv_yg_nv_maj = lm_robust(degree_violence ~ identity_protesters , data=us_survey_wave1[us_survey_wave1$tactic==0 & us_survey_wave1$race=="White",], weights=weight) +police_yg_nv_maj = lm_robust(police_action_required ~ identity_protesters , data=us_survey_wave1[us_survey_wave1$tactic==0 & us_survey_wave1$race == "White",], weights=weight) +degv_yg_nv_min = lm_robust(degree_violence ~ identity_protesters , data=us_survey_wave1[us_survey_wave1$tactic==0 & us_survey_wave1$race=="Black",], weights=weight) +police_yg_nv_min = lm_robust(police_action_required ~ identity_protesters , data=us_survey_wave1[us_survey_wave1$tactic==0 & us_survey_wave1$race == "Black",], weights=weight) + +# Majority perceptions +us_mean_degv_maj_white = degv_yg_nv_maj$coefficients[1] +us_mean_degv_maj_black = degv_yg_nv_maj$coefficients[1] + degv_yg_nv_maj$coefficients[2] +us_diff_degv_maj = degv_yg_nv_maj$coefficients[2] +us_pval_diff_degv_maj = degv_yg_nv_maj$p.value[2] +us_pct_change_degv_maj = (degv_yg_nv_maj$coefficients[2] /us_mean_degv_maj_white)*100 +us_mean_police_maj_white = police_yg_nv_maj$coefficients[1] +us_mean_police_maj_black = police_yg_nv_maj$coefficients[1] + police_yg_nv_maj$coefficients[2] +us_diff_police_maj = police_yg_nv_maj$coefficients[2] +us_pval_diff_police_maj = police_yg_nv_maj$p.value[2] +us_pct_change_police_maj = (police_yg_nv_maj$coefficients[2] /us_mean_police_maj_white)*100 + +# Minority perceptions +us_mean_degv_min_white = degv_yg_nv_min$coefficients[1] +us_mean_degv_min_black = degv_yg_nv_min$coefficients[1] + degv_yg_nv_min$coefficients[2] +us_diff_degv_min = degv_yg_nv_min$coefficients[2] +us_pval_diff_degv_min = degv_yg_nv_min$p.value[2] +us_pct_change_degv_min = (degv_yg_nv_min$coefficients[2] /us_mean_degv_min_white)*100 +us_mean_police_min_white = police_yg_nv_min$coefficients[1] +us_mean_police_min_black = police_yg_nv_min$coefficients[1] + police_yg_nv_min$coefficients[2] +us_diff_police_min = police_yg_nv_min$coefficients[2] +us_pval_diff_police_min = police_yg_nv_min$p.value[2] +us_pct_change_police_min = (police_yg_nv_min$coefficients[2] /us_mean_police_min_white)*100 + + +## B. ISRAEL +degv_isr_nv_maj = lm_robust(degree_violence ~ identity_protesters , data=isr_survey_wave1[isr_survey_wave1$survey=="isr" & isr_survey_wave1$tactic==0 & isr_survey_wave1$ethnicity!= "Ethiopia",]) +police_isr_nv_maj = lm_robust(police_action_required ~ identity_protesters , data=isr_survey_wave1[isr_survey_wave1$survey=="isr" & isr_survey_wave1$tactic==0 & isr_survey_wave1$ethnicity!= "Ethiopia",]) +degv_isr_nv_min = lm_robust(degree_violence ~ identity_protesters , data=isr_survey_wave1[isr_survey_wave1$survey=="isr_ar" & isr_survey_wave1$tactic==0,]) +police_isr_nv_min = lm_robust(police_action_required ~ identity_protesters , data=isr_survey_wave1[isr_survey_wave1$survey=="isr_ar" & isr_survey_wave1$tactic==0,]) + +# Majority perceptions +israel_mean_degv_maj_whitejew = degv_isr_nv_maj$coefficients[1] +isr_mean_degv_maj_eth = degv_isr_nv_maj$coefficients[1] + degv_isr_nv_maj$coefficients[2] +isr_mean_degv_maj_arab = degv_isr_nv_maj$coefficients[1] + degv_isr_nv_maj$coefficients[3] +isr_diff_degv_maj_eth = degv_isr_nv_maj$coefficients[2] +isr_diff_degv_maj_arab = degv_isr_nv_maj$coefficients[3] +isr_pval_diff_degv_maj_eth = degv_isr_nv_maj$p.value[2] +isr_pval_diff_degv_maj_arab = degv_isr_nv_maj$p.value[3] +isr_pct_change_degv_maj_eth = (degv_isr_nv_maj$coefficients[2] /israel_mean_degv_maj_whitejew)*100 +isr_pct_change_degv_maj_arab = (degv_isr_nv_maj$coefficients[3] /israel_mean_degv_maj_whitejew)*100 +israel_mean_police_maj_whitejew = police_isr_nv_maj$coefficients[1] +isr_mean_police_maj_eth = police_isr_nv_maj$coefficients[1] + police_isr_nv_maj$coefficients[2] +isr_mean_police_maj_arab = police_isr_nv_maj$coefficients[1] + police_isr_nv_maj$coefficients[3] +isr_diff_police_maj_eth = police_isr_nv_maj$coefficients[2] +isr_diff_police_maj_arab = police_isr_nv_maj$coefficients[3] +isr_pval_diff_police_maj_eth = police_isr_nv_maj$p.value[2] +isr_pval_diff_police_maj_arab = police_isr_nv_maj$p.value[3] +isr_pct_change_police_maj_eth = (police_isr_nv_maj$coefficients[2] /israel_mean_police_maj_whitejew)*100 +isr_pct_change_police_maj_arab = (police_isr_nv_maj$coefficients[3] /israel_mean_police_maj_whitejew)*100 + +## Minority perceptions +israel_mean_degv_min_whitejew = degv_isr_nv_min$coefficients[1] +isr_mean_degv_min_eth = degv_isr_nv_min$coefficients[1] + degv_isr_nv_min$coefficients[2] +isr_mean_degv_min_arab = degv_isr_nv_min$coefficients[1] + degv_isr_nv_min$coefficients[3] +isr_diff_degv_min_eth = degv_isr_nv_min$coefficients[2] +isr_diff_degv_min_arab = degv_isr_nv_min$coefficients[3] +isr_pval_diff_degv_min_eth = degv_isr_nv_min$p.value[2] +isr_pval_diff_degv_min_arab = degv_isr_nv_min$p.value[3] +isr_pct_change_degv_min_eth = (degv_isr_nv_min$coefficients[2] /israel_mean_degv_min_whitejew)*100 +isr_pct_change_degv_min_arab = (degv_isr_nv_min$coefficients[3] /israel_mean_degv_min_whitejew)*100 +israel_mean_police_min_whitejew = police_isr_nv_min$coefficients[1] +isr_mean_police_min_eth = police_isr_nv_min$coefficients[1] + police_isr_nv_min$coefficients[2] +isr_mean_police_min_arab = police_isr_nv_min$coefficients[1] + police_isr_nv_min$coefficients[3] +isr_diff_police_min_eth = police_isr_nv_min$coefficients[2] +isr_diff_police_min_arab = police_isr_nv_min$coefficients[3] +isr_pval_diff_police_min_eth = police_isr_nv_min$p.value[2] +isr_pval_diff_police_min_arab = police_isr_nv_min$p.value[3] +isr_pct_change_police_min_eth = (police_isr_nv_min$coefficients[2] /israel_mean_police_min_whitejew)*100 +isr_pct_change_police_min_arab = (police_isr_nv_min$coefficients[3] /israel_mean_police_min_whitejew)*100 + +us_degv_maj = c(us_mean_degv_maj_white, us_mean_degv_maj_black, us_diff_degv_maj, us_pval_diff_degv_maj, us_pct_change_degv_maj) +us_degv_min = c(us_mean_degv_min_white, us_mean_degv_min_black, us_diff_degv_min, us_pval_diff_degv_min, us_pct_change_degv_min) +us_police_maj = c(us_mean_police_maj_white, us_mean_police_maj_black, us_diff_police_maj, us_pval_diff_police_maj, us_pct_change_police_maj) +us_police_min = c(us_mean_police_min_white, us_mean_police_min_black, us_diff_police_min, us_pval_diff_police_min, us_pct_change_police_min) + +isr_degv_maj_eth = c(israel_mean_degv_maj_whitejew, isr_mean_degv_maj_eth, isr_diff_degv_maj_eth, isr_pval_diff_degv_maj_eth, isr_pct_change_degv_maj_eth) +isr_degv_maj_arab = c(israel_mean_degv_maj_whitejew, isr_mean_degv_maj_arab, isr_diff_degv_maj_arab, isr_pval_diff_degv_maj_arab, isr_pct_change_degv_maj_arab) +isr_degv_min_eth = c(israel_mean_degv_min_whitejew, isr_mean_degv_min_eth, isr_diff_degv_min_eth, isr_pval_diff_degv_min_eth, isr_pct_change_degv_min_eth) +isr_degv_min_arab = c(israel_mean_degv_min_whitejew, isr_mean_degv_min_arab, isr_diff_degv_min_arab, isr_pval_diff_degv_min_arab, isr_pct_change_degv_min_arab) +isr_police_maj_eth = c(israel_mean_police_maj_whitejew, isr_mean_police_maj_eth, isr_diff_police_maj_eth, isr_pval_diff_police_maj_eth, isr_pct_change_police_maj_eth) +isr_police_maj_arab = c(israel_mean_police_maj_whitejew, isr_mean_police_maj_arab, isr_diff_police_maj_arab, isr_pval_diff_police_maj_arab, isr_pct_change_police_maj_arab) +isr_police_min_eth = c(israel_mean_police_min_whitejew, isr_mean_police_min_eth, isr_diff_police_min_eth, isr_pval_diff_police_min_eth, isr_pct_change_police_min_eth) +isr_police_min_arab = c(israel_mean_police_min_whitejew, isr_mean_police_min_arab, isr_diff_police_min_arab, isr_pval_diff_police_min_arab, isr_pct_change_police_min_arab) + +pct_change_tab = rbind(us_degv_maj, us_degv_min, us_police_maj, us_police_min, + isr_degv_maj_arab, isr_degv_min_arab, isr_police_maj_arab, isr_police_min_arab, + isr_degv_maj_eth, isr_degv_min_eth, isr_police_maj_eth, isr_police_min_eth) +colnames(pct_change_tab) = c("Mean (majority)", "Mean (minority)", "Difference", "P-value", "Percent change") + +table_a15 = xtable(pct_change_tab, digits=2) +print(table_a15, file="Tables/table_A16.tex") diff --git a/23/replication_package/Code/replicate_table_A17.R b/23/replication_package/Code/replicate_table_A17.R new file mode 100644 index 0000000000000000000000000000000000000000..50e2ca22ca1ca5d3d82c630ed20cd2403625560e --- /dev/null +++ b/23/replication_package/Code/replicate_table_A17.R @@ -0,0 +1,15 @@ +##--##--##--##--##--##--## +## Appendix: Table A17 +##--##--##--##--##--##--## + +## A. U.S. sample +mod1 = lm(degree_violence ~ black + group_goal + commitment + age + female +education + income + pol_views + race, data=us_survey_wave2) +mod2 = lm(recall_violence2 ~ black + group_goal + commitment + age + female +education + income + pol_views + race, data=us_survey_wave2) +mod3 = lm(police_action_required ~ black + group_goal + commitment + age + female +education + income + pol_views + race, data=us_survey_wave2) +stargazer(mod1, mod2, mod3, out="Tables/table_A17_panel_A.tex") + +## B. Israel sample +mod1_israel = lm(degree_violence ~ identity_protesters + group_goal + commitment + age + female +education + income + ideology + ethnicity, data=isr_survey_wave2) +mod2_israel = lm(recall_violence2 ~ identity_protesters + group_goal + commitment + age + female +education + income + ideology + ethnicity, data=isr_survey_wave2) +mod3_israel = lm(police_action_required ~ identity_protesters + group_goal + commitment + age + female +education + income + ideology + ethnicity, data=isr_survey_wave2) +stargazer(mod1_israel, mod2_israel, mod3_israel, out="Tables/table_A17_panel_B.tex") diff --git a/23/replication_package/Code/replicate_table_A18.R b/23/replication_package/Code/replicate_table_A18.R new file mode 100644 index 0000000000000000000000000000000000000000..36ae63bcf48e351dbff25dce5f27635c646e3db4 --- /dev/null +++ b/23/replication_package/Code/replicate_table_A18.R @@ -0,0 +1,11 @@ +##--##--##--##--##--##--## +## Appendix: Table A18 +##--##--##--##--##--##--## + +generic_goal_dv = lm(degree_violence ~ identity_protesters + commitment , data=isr_survey_wave2[isr_survey_wave2$group_goal==0,]) +generic_goal_rv = lm(recall_violence2 ~ identity_protesters + commitment , data=isr_survey_wave2[isr_survey_wave2$group_goal==0,]) +generic_goal_paq = lm(police_action_required ~ identity_protesters + commitment , data=isr_survey_wave2[isr_survey_wave2$group_goal==0,]) +stargazer(generic_goal_dv, generic_goal_rv, generic_goal_paq, + covariate.labels = c("Ethiopian protesters", "Arab protesters", + "Commitment to nonviolence", "Intercept: White protesters, generic goal, no commitment"), + out = "Tables/table_A18.tex") diff --git a/23/replication_package/Code/replicate_table_A2.R b/23/replication_package/Code/replicate_table_A2.R new file mode 100644 index 0000000000000000000000000000000000000000..74004ffa49d0f74be8c81aefae704dfb6b729820 --- /dev/null +++ b/23/replication_package/Code/replicate_table_A2.R @@ -0,0 +1,14 @@ +##--##--##--##--##--##--## +## Appendix: Table A2 +##--##--##--##--##--##--## + +nv_majority = prop.table(table(NAVCO2_EPR$success[NAVCO2_EPR$navco1designation==1 & NAVCO2_EPR$EPR_STATUS_EXCL==0])) +v_majority = prop.table(table(NAVCO2_EPR$success[NAVCO2_EPR$navco1designation==0 & NAVCO2_EPR$EPR_STATUS_EXCL==0])) +nv_minority = prop.table(table(NAVCO2_EPR$success[NAVCO2_EPR$navco1designation==1 & NAVCO2_EPR$EPR_STATUS_EXCL==1])) +v_minority = prop.table(table(NAVCO2_EPR$success[NAVCO2_EPR$navco1designation==0 & NAVCO2_EPR$EPR_STATUS_EXCL==1])) + +navco_table = cbind(nv_majority, v_majority, nv_minority, v_minority) +rownames(navco_table) = c("Campaign failure", "Campaign success") + +table_a2 = xtable(navco_table, digits=2) +print(table_a2, file="Tables/table_A2.tex") \ No newline at end of file diff --git a/23/replication_package/Code/replicate_table_A3.R b/23/replication_package/Code/replicate_table_A3.R new file mode 100644 index 0000000000000000000000000000000000000000..e6759db485fd0201b8f9e7992dae81cbab8e1b84 --- /dev/null +++ b/23/replication_package/Code/replicate_table_A3.R @@ -0,0 +1,20 @@ +##--##--##--##--##--##--## +## Appendix: Table A3 +##--##--##--##--##--##--## + +tab1 = lm(success ~ EPR_STATUS_EXCL* INIT_NV_ONSET , data= EBCR_EPR_NAVCO2) +tab2 = lm(success ~ EPR_STATUS_EXCL * INIT_NV_ONSET + EPR_GROUPSIZE + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + PASTNV + PASTV + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + NVYEARS +I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3), data= EBCR_EPR_NAVCO2) +tab3 = lm(success ~ EPR_STATUS_EXCL * INIT_NV_ONSET + EPR_GROUPSIZE + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + PASTNV + PASTV + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + EPR_TEK_EGIP + EPR_DOWNGRADED5 + HORIZ_INEQ + NVYEARS + I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3) , data= EBCR_EPR_NAVCO2) +tab4 = lm(success ~ EPR_STATUS_ORD* INIT_NV_ONSET , data= EBCR_EPR_NAVCO2) +tab5 = lm(success ~ EPR_STATUS_ORD * INIT_NV_ONSET + EPR_GROUPSIZE + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + PASTNV + PASTV + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + NVYEARS +I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3), data= EBCR_EPR_NAVCO2) +tab6 = lm(success ~ EPR_STATUS_ORD * INIT_NV_ONSET + EPR_GROUPSIZE + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + PASTNV + PASTV + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + EPR_TEK_EGIP + EPR_DOWNGRADED5 + HORIZ_INEQ + NVYEARS + I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3) , data= EBCR_EPR_NAVCO2) + +stargazer(tab4, tab5, tab6, tab1, tab2, tab3, table.placement="H", order = c(3,1,20, 2, 21), + covariate.labels = c("NV campaign", "EPR Status", "EPR Status × NV Campaign", + "EPR Status: Excluded", "EPR Status: Excluded × NV Campaign", + "EPR group size", "Country population", "Country GDP per capita", + "Prior participation in NV", "Prior participation in V", + "Level of democracy", "Physical integrity index", "Neighboring kin in power", + "Group status downgraded", "Horizontal inequality"), + omit = c("VYEARS", "NVYEARS"), + out="Tables/table_A3.tex") diff --git a/23/replication_package/Code/replicate_table_A4.R b/23/replication_package/Code/replicate_table_A4.R new file mode 100644 index 0000000000000000000000000000000000000000..2185232e3a24c2a7c25b7831f9af70460e641ef8 --- /dev/null +++ b/23/replication_package/Code/replicate_table_A4.R @@ -0,0 +1,17 @@ +##--##--##--##--##--##--## +## Appendix: Table A4 +##--##--##--##--##--##--## + +tab7 = lm(success ~ EPR_GROUPSIZE* INIT_NV_ONSET , data= EBCR_EPR_NAVCO2) +tab8 = lm(success ~ EPR_GROUPSIZE * INIT_NV_ONSET + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + PASTNV + PASTV + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + NVYEARS +I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3), data= EBCR_EPR_NAVCO2) +tab9 = lm(success ~ EPR_GROUPSIZE * INIT_NV_ONSET + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + PASTNV + PASTV + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + EPR_TEK_EGIP + EPR_DOWNGRADED5 + HORIZ_INEQ + NVYEARS + I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3) , data= EBCR_EPR_NAVCO2) + +stargazer(tab7, tab8, tab9, table.placement="H", order = c(2,1,18), + covariate.labels = c("NV campaign", "EPR group size", "EPR group size × NV campaign", + "Country population", "Country GDP per capita", + "Prior participation in NV", "Prior participation in V", + "Level of democracy", "Physical integrity index", + "Neighboring kin in power", "Group status downgraded", + "Horizontal inequality"), + omit = c("VYEARS", "NVYEARS"), + out="Tables/table_A4.tex") \ No newline at end of file diff --git a/23/replication_package/Code/replicate_table_A5.R b/23/replication_package/Code/replicate_table_A5.R new file mode 100644 index 0000000000000000000000000000000000000000..0fab076e7c2ae355823d4081f0e49b7bbbf59b3a --- /dev/null +++ b/23/replication_package/Code/replicate_table_A5.R @@ -0,0 +1,45 @@ +##--##--##--##--##--##--## +## Appendix: Table A5 +##--##--##--##--##--##--## + +# Status (excluded, not excluded) + +nv_status = lm(success ~ EPR_STATUS_EXCL + EPR_GROUPSIZE + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + PASTNV + PASTV + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + EPR_TEK_EGIP + EPR_DOWNGRADED5 + HORIZ_INEQ + NVYEARS + I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3) , data= EBCR_EPR_NAVCO2[EBCR_EPR_NAVCO2$INIT_NV_ONSET==1,]) +v_status = lm(success ~ EPR_STATUS_EXCL + EPR_GROUPSIZE + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + PASTNV + PASTV + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + EPR_TEK_EGIP + EPR_DOWNGRADED5 + HORIZ_INEQ + NVYEARS + I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3) , data= EBCR_EPR_NAVCO2[EBCR_EPR_NAVCO2$INIT_V_ONSET==1,]) + +preds_nv = ggpredict(nv_status, terms = c("EPR_STATUS_EXCL")) +preds_nv$tactic = "Non-violent" +preds_v = ggpredict(v_status, terms = c("EPR_STATUS_EXCL")) +preds_v$tactic = "Violent" + +preds_status = rbind(preds_nv, preds_v) +preds_status$tactic <- factor(preds_status$tactic, levels = c("Violent", "Non-violent")) +preds_status$Status = "Excluded" +preds_status$Status[preds$x==0] = "Not excluded" +preds_status$type = "status" + +## Size (above and below the mean of the distribution) + +EBCR_EPR_NAVCO2$small_size = NA +EBCR_EPR_NAVCO2$small_size[EBCR_EPR_NAVCO2$EPR_GROUPSIZE < mean(EBCR_EPR_NAVCO2$EPR_GROUPSIZE, na.rm=T)] = 1 +EBCR_EPR_NAVCO2$small_size[EBCR_EPR_NAVCO2$EPR_GROUPSIZE >= mean(EBCR_EPR_NAVCO2$EPR_GROUPSIZE, na.rm=T)] = 0 + +nv_size = lm(success ~ small_size + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + PASTNV + PASTV + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + EPR_TEK_EGIP + EPR_DOWNGRADED5 + HORIZ_INEQ + NVYEARS + I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3) , data= EBCR_EPR_NAVCO2[EBCR_EPR_NAVCO2$INIT_NV_ONSET==1,]) +v_size = lm(success ~ small_size + POP_LOG_LAG_EXT + GDPPC_LOG_LAG_EXT + PASTNV + PASTV + VDEM_POLYARCHY_LAG + VDEM_PHYSINT_LAG + EPR_TEK_EGIP + EPR_DOWNGRADED5 + HORIZ_INEQ + NVYEARS + I(NVYEARS^2) + I(NVYEARS^3) + VYEARS + I(VYEARS^2) + I(VYEARS^3) , data= EBCR_EPR_NAVCO2[EBCR_EPR_NAVCO2$INIT_V_ONSET==1,]) + +preds_nv = ggpredict(nv_size, terms = c("small_size")) +preds_nv$tactic = "Non-violent" +preds_v = ggpredict(v_size, terms = c("small_size")) +preds_v$tactic = "Violent" + +preds_size = rbind(preds_nv, preds_v) +preds_size$tactic <- factor(preds_size$tactic, levels = c("Violent", "Non-violent")) +preds_size$Status = "Group size < mean" +preds_size$Status[preds_size$x==0] = "Group size >= mean" +preds_size$type = "size" + +preds_all = as.data.frame(rbind(preds_status, preds_size)) +preds_all = preds_all[,c("predicted", "std.error", "conf.low", "conf.high", + "tactic", "Status")] +table_a5 = xtable(preds_all, digits=2) +print(table_a5, file="Tables/table_A5.tex") diff --git a/23/replication_package/Code/replicate_table_A6.R b/23/replication_package/Code/replicate_table_A6.R new file mode 100644 index 0000000000000000000000000000000000000000..b503ae2cc22ebd1aed3659153f17abd7a8f11990 --- /dev/null +++ b/23/replication_package/Code/replicate_table_A6.R @@ -0,0 +1,11 @@ +##--##--##--##--##--##--## +## Appendix: Table A6 +##--##--##--##--##--##--## + +pol_irr1 = lm(success ~ navco1designation , + data= NAVCO2[!(NAVCO2$id %in% NAVCO2_EPR$id),]) +pol_irr2 = lm(success ~ navco1designation , + data= NAVCO2[NAVCO2$id %in% NAVCO2_EPR$id[NAVCO2_EPR$EPR_STATUS == "IRRELEVANT"],]) + +stargazer(pol_irr1, pol_irr2, covariate.labels = c("Nonviolent campaign", "Constant"), + out="Tables/table_A6.tex") diff --git a/23/replication_package/Code/replicate_table_A7.R b/23/replication_package/Code/replicate_table_A7.R new file mode 100644 index 0000000000000000000000000000000000000000..e9242189cea1d7936ba79bd5b613e6d5ca1b430e --- /dev/null +++ b/23/replication_package/Code/replicate_table_A7.R @@ -0,0 +1,8 @@ +##--##--##--##--##--##--## +## Appendix: Table A7 +##--##--##--##--##--##--## + +us_wave1_sumstats = us_survey_wave1[,c("age", "female", "education", "income", "partyID", "ideology", "interest_politics", + "degree_violence", "police_action_required", "recall_violence2", "race")] +us_wave1_sumstats = dummy_cols(us_wave1_sumstats, select_columns = c("education", "income", "partyID", "ideology", "race")) +stargazer(us_wave1_sumstats, omit.summary.stat=c("p25", "p75"), out="Tables/table_A7.tex") \ No newline at end of file diff --git a/23/replication_package/Code/replicate_table_A8.R b/23/replication_package/Code/replicate_table_A8.R new file mode 100644 index 0000000000000000000000000000000000000000..847271c64117660464f040f0b2ce097d64ff629f --- /dev/null +++ b/23/replication_package/Code/replicate_table_A8.R @@ -0,0 +1,10 @@ +##--##--##--##--##--##--## +## Appendix: Table A8 +##--##--##--##--##--##--## + +isr_wave1_jewish = isr_survey_wave1[isr_survey_wave1$survey=="isr",] +isr_wave1_jewish_sumstats = isr_wave1_jewish[,c("age", "female", "religiosity", "ethnicity", "education", "income", "partyID", "ideology", "interest_news", + "degree_violence", "police_action_required", "recall_violence2")] +isr_wave1_jewish_sumstats = dummy_cols(isr_wave1_jewish_sumstats, select_columns = c("age", "religiosity", "ethnicity", "education", "income", "partyID")) +table_a8 = stargazer(isr_wave1_jewish_sumstats, omit.summary.stat=c("p25", "p75")) +cat(table_a8, sep = '\n', file = "Tables/table_A8.tex") diff --git a/23/replication_package/Code/replicate_table_A9.R b/23/replication_package/Code/replicate_table_A9.R new file mode 100644 index 0000000000000000000000000000000000000000..29bc04d92def928b993f586ef98db08d316f7eec --- /dev/null +++ b/23/replication_package/Code/replicate_table_A9.R @@ -0,0 +1,11 @@ +##--##--##--##--##--##--## +## Appendix: Table A9 +##--##--##--##--##--##--## + +isr_wave1_arab = isr_survey_wave1[isr_survey_wave1$survey=="isr_ar",] +isr_wave1_arab_sumstats = isr_wave1_arab[,c("age", "female", "religiosity", "ethnicity", "education", "income", "partyID", "ideology", "interest_news", + "degree_violence", "police_action_required", "recall_violence2")] +isr_wave1_arab_sumstats = dummy_cols(isr_wave1_arab_sumstats, select_columns = c("age", "religiosity", "ethnicity", "education", "income", "partyID")) +table_a9 = stargazer(isr_wave1_arab_sumstats, omit.summary.stat=c("p25", "p75")) +cat(table_a9, sep = '\n', file = "Tables/table_A9.tex") + diff --git a/23/replication_package/Data/EBCR_EPR_NAVCO2.rdata b/23/replication_package/Data/EBCR_EPR_NAVCO2.rdata new file mode 100644 index 0000000000000000000000000000000000000000..03c5761a78dddedeedd7e20a1a7412171ed1fe21 --- /dev/null +++ b/23/replication_package/Data/EBCR_EPR_NAVCO2.rdata @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75451734eeb300dd765549b181cb21c1d4c91c9b0702562c19e1094898fa9764 +size 49172 diff --git a/23/replication_package/Data/NAVCO2.rdata b/23/replication_package/Data/NAVCO2.rdata new file mode 100644 index 0000000000000000000000000000000000000000..f115878528eb0f17dee0f13b785e7fc514f0f97a --- 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E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:06 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ +\cline{2-4} +\\[-1.8ex] & degree\_violence & recall\_violence2 & police\_action\_required \\ +\\[-1.8ex] & (1) & (2) & (3)\\ +\hline \\[-1.8ex] + Black protesters & 0.235$^{**}$ & 0.039$^{**}$ & 0.268$^{**}$ \\ + & (0.117) & (0.018) & (0.127) \\ + & & & \\ + Shut down traffic & 0.271$^{*}$ & 0.017 & 0.429$^{***}$ \\ + & (0.143) & (0.022) & (0.155) \\ + & & & \\ + Destroy police cars & 3.382$^{***}$ & 0.644$^{***}$ & 2.971$^{***}$ \\ + & (0.143) & (0.022) & (0.155) \\ + & & & \\ + Intercept: White protesters, March in streets & 2.774$^{***}$ & 0.052$^{***}$ & 3.837$^{***}$ \\ + & (0.117) & (0.018) & (0.127) \\ + & & & \\ +\hline \\[-1.8ex] +Observations & 2,269 & 2,269 & 2,269 \\ +R$^{2}$ & 0.235 & 0.322 & 0.160 \\ +Adjusted R$^{2}$ & 0.234 & 0.321 & 0.159 \\ +Residual Std. Error (df = 2265) & 2.813 & 0.441 & 3.055 \\ +F Statistic (df = 3; 2265) & 231.324$^{***}$ & 358.155$^{***}$ & 143.835$^{***}$ \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_1_panel_B.tex b/23/replication_package/Tables/table_1_panel_B.tex new file mode 100644 index 0000000000000000000000000000000000000000..bf72f5235d553983fa85b0be8318ad14b6ede7c8 --- /dev/null +++ b/23/replication_package/Tables/table_1_panel_B.tex @@ -0,0 +1,40 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:07 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ +\cline{2-4} +\\[-1.8ex] & degree\_violence & recall\_violence2 & police\_action\_required \\ +\\[-1.8ex] & (1) & (2) & (3)\\ +\hline \\[-1.8ex] + Ethiopian protesters & 0.425$^{***}$ & 0.085$^{***}$ & 0.181 \\ + & (0.107) & (0.023) & (0.118) \\ + & & & \\ + Arab protesters & 0.623$^{***}$ & 0.047$^{**}$ & 0.964$^{***}$ \\ + & (0.108) & (0.023) & (0.119) \\ + & & & \\ + Shut down traffic & 0.254$^{**}$ & $-$0.032 & 0.394$^{***}$ \\ + & (0.108) & (0.023) & (0.120) \\ + & & & \\ + Destroy garbage cans & 2.538$^{***}$ & 0.414$^{***}$ & 2.150$^{***}$ \\ + & (0.107) & (0.023) & (0.118) \\ + & & & \\ + Intercept: White protesters, March in streets & 3.915$^{***}$ & 0.091$^{***}$ & 4.679$^{***}$ \\ + & (0.099) & (0.021) & (0.110) \\ + & & & \\ +\hline \\[-1.8ex] +Observations & 3,063 & 3,063 & 3,063 \\ +R$^{2}$ & 0.192 & 0.137 & 0.130 \\ +Adjusted R$^{2}$ & 0.191 & 0.136 & 0.129 \\ +Residual Std. Error (df = 3058) & 2.412 & 0.519 & 2.669 \\ +F Statistic (df = 4; 3058) & 181.473$^{***}$ & 121.290$^{***}$ & 114.070$^{***}$ \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_4_panel_A.tex b/23/replication_package/Tables/table_4_panel_A.tex new file mode 100644 index 0000000000000000000000000000000000000000..a5afe20582ad0ea7300f036358abfa2392e16255 --- /dev/null +++ b/23/replication_package/Tables/table_4_panel_A.tex @@ -0,0 +1,37 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:07 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ +\cline{2-4} +\\[-1.8ex] & degree\_violence & recall\_violence2 & police\_action\_required \\ +\\[-1.8ex] & (1) & (2) & (3)\\ +\hline \\[-1.8ex] + Black protesters & 0.347$^{***}$ & 0.044 & 0.429$^{***}$ \\ + & (0.115) & (0.028) & (0.114) \\ + & & & \\ + Minority group goal & 0.628$^{***}$ & 0.168$^{***}$ & 0.547$^{***}$ \\ + & (0.115) & (0.028) & (0.114) \\ + & & & \\ + Commitment to nonviolence & $-$0.243$^{**}$ & $-$0.021 & $-$0.212$^{*}$ \\ + & (0.115) & (0.027) & (0.114) \\ + & & & \\ + Intercept: White protesters, generic goal, no commitment & 3.326$^{***}$ & 0.310$^{***}$ & 4.130$^{***}$ \\ + & (0.114) & (0.027) & (0.113) \\ + & & & \\ +\hline \\[-1.8ex] +Observations & 3,013 & 3,013 & 3,008 \\ +R$^{2}$ & 0.015 & 0.013 & 0.014 \\ +Adjusted R$^{2}$ & 0.014 & 0.013 & 0.013 \\ +Residual Std. Error & 3.155 (df = 3009) & 0.754 (df = 3009) & 3.130 (df = 3004) \\ +F Statistic & 14.876$^{***}$ (df = 3; 3009) & 13.712$^{***}$ (df = 3; 3009) & 13.937$^{***}$ (df = 3; 3004) \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_4_panel_B.tex b/23/replication_package/Tables/table_4_panel_B.tex new file mode 100644 index 0000000000000000000000000000000000000000..d1120ca0d413e23c8c76f3806cc633888c799da8 --- /dev/null +++ b/23/replication_package/Tables/table_4_panel_B.tex @@ -0,0 +1,40 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:08 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ +\cline{2-4} +\\[-1.8ex] & degree\_violence & recall\_violence2 & police\_action\_required \\ +\\[-1.8ex] & (1) & (2) & (3)\\ +\hline \\[-1.8ex] + Ethiopian protesters & 0.950$^{***}$ & 0.285$^{***}$ & 0.732$^{***}$ \\ + & (0.139) & (0.028) & (0.144) \\ + & & & \\ + Arab protesters & 0.587$^{***}$ & 0.062$^{**}$ & 0.802$^{***}$ \\ + & (0.139) & (0.028) & (0.144) \\ + & & & \\ + Minority group goal & 0.628$^{***}$ & 0.119$^{***}$ & 0.574$^{***}$ \\ + & (0.109) & (0.022) & (0.112) \\ + & & & \\ + Commitment to nonviolence & $-$0.309$^{***}$ & 0.004 & $-$0.151 \\ + & (0.096) & (0.020) & (0.099) \\ + & & & \\ + Intercept: White protesters, generic goal, no commitment & 3.422$^{***}$ & 0.053$^{**}$ & 3.914$^{***}$ \\ + & (0.113) & (0.023) & (0.117) \\ + & & & \\ +\hline \\[-1.8ex] +Observations & 3,465 & 3,465 & 3,465 \\ +R$^{2}$ & 0.040 & 0.064 & 0.030 \\ +Adjusted R$^{2}$ & 0.039 & 0.063 & 0.029 \\ +Residual Std. Error (df = 3460) & 2.820 & 0.574 & 2.918 \\ +F Statistic (df = 4; 3460) & 36.426$^{***}$ & 59.344$^{***}$ & 26.642$^{***}$ \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A1.tex b/23/replication_package/Tables/table_A1.tex new file mode 100644 index 0000000000000000000000000000000000000000..715dd077883f379d9b45256923291f2337789c2b --- /dev/null +++ b/23/replication_package/Tables/table_A1.tex @@ -0,0 +1,31 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:14 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccccccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] +Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\ +\hline \\[-1.8ex] +Year & 2,628 & 1,974.008 & 14.179 & 1,946 & 1,962 & 1,984 & 2,006 \\ +Campaign success & 2,591 & 0.075 & 0.264 & 0.000 & 0.000 & 0.000 & 1.000 \\ +NV campaign & 2,135 & 0.190 & 0.392 & 0.000 & 0.000 & 0.000 & 1.000 \\ +EPR group size & 2,628 & 0.244 & 0.301 & 0.000 & 0.027 & 0.380 & 1.000 \\ +EPR status & 2,377 & 2.920 & 2.005 & 1.000 & 1.000 & 4.000 & 7.000 \\ +EPR Status: excluded & 2,628 & 0.619 & 0.486 & 0 & 0 & 1 & 1 \\ +Country population (logged) & 2,628 & 10.003 & 1.355 & 6.332 & 9.057 & 11.104 & 13.902 \\ +Country GDP per capita (logged) & 2,628 & 7.601 & 0.893 & 4.965 & 6.993 & 8.178 & 10.536 \\ +Prior participation in nonviolence & 2,444 & 0.137 & 0.481 & 0.000 & 0.000 & 0.000 & 4.000 \\ +Prior participation in violence & 2,444 & 0.187 & 0.461 & 0.000 & 0.000 & 0.000 & 3.000 \\ +Level of democracy & 2,605 & 0.267 & 0.178 & 0.025 & 0.153 & 0.351 & 0.838 \\ +Physical integrity index & 2,609 & 0.374 & 0.235 & 0.022 & 0.140 & 0.603 & 0.961 \\ +Neighboring kin in power & 2,628 & 0.328 & 0.470 & 0 & 0 & 1 & 1 \\ +Downgraded & 2,377 & 0.067 & 0.250 & 0.000 & 0.000 & 0.000 & 1.000 \\ +Horizontal inequality & 2,128 & 0.106 & 0.317 & 0.000 & 0.0005 & 0.043 & 3.238 \\ +Nonviolent years & 2,628 & 1.977 & 1.405 & 0.000 & 0.700 & 3.000 & 6.000 \\ +Violent years & 2,628 & 1.852 & 1.451 & 0.000 & 0.700 & 2.925 & 6.000 \\ +\hline \\[-1.8ex] +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A10.tex b/23/replication_package/Tables/table_A10.tex new file mode 100644 index 0000000000000000000000000000000000000000..8ac9d1601bed0366bcd97d181e6fadfa2c8d47d9 --- /dev/null +++ b/23/replication_package/Tables/table_A10.tex @@ -0,0 +1,59 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:21 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] +Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Max} \\ +\hline \\[-1.8ex] +female & 3,013 & 0.514 & 0.500 & 0 & 1 \\ +pol\_views & 3,013 & 2.916 & 1.696 & 0 & 6 \\ +degree\_violence & 3,013 & 3.699 & 3.177 & 0 & 10 \\ +police\_action\_required & 3,008 & 4.517 & 3.151 & 0.000 & 10.000 \\ +recall\_violence2 & 3,013 & 0.408 & 0.759 & 0 & 2 \\ +age\_18-24 & 3,013 & 0.110 & 0.313 & 0 & 1 \\ +age\_25-34 & 3,013 & 0.195 & 0.396 & 0 & 1 \\ +age\_35-44 & 3,013 & 0.202 & 0.401 & 0 & 1 \\ +age\_45-54 & 3,013 & 0.153 & 0.360 & 0 & 1 \\ +age\_55-64 & 3,013 & 0.163 & 0.369 & 0 & 1 \\ +age\_65+ & 3,013 & 0.178 & 0.382 & 0 & 1 \\ +education\_Associate's degree & 3,013 & 0.126 & 0.332 & 0 & 1 \\ +education\_Bachelor's degree & 3,013 & 0.234 & 0.424 & 0 & 1 \\ +education\_Did not complete high school & 3,013 & 0.025 & 0.157 & 0 & 1 \\ +education\_Graduate degree & 3,013 & 0.145 & 0.352 & 0 & 1 \\ +education\_High school / GED & 3,013 & 0.232 & 0.422 & 0 & 1 \\ +education\_Some college & 3,013 & 0.208 & 0.406 & 0 & 1 \\ +education\_Some graduate school & 3,013 & 0.029 & 0.167 & 0 & 1 \\ +education\_ & 3,013 & 0.000 & 0.000 & 0 & 0 \\ +income\_\$100,000-\$124,999 & 3,013 & 0.080 & 0.271 & 0 & 1 \\ +income\_\$125,000 and above & 3,013 & 0.133 & 0.339 & 0 & 1 \\ +income\_\$25,000-\$49,999 & 3,013 & 0.257 & 0.437 & 0 & 1 \\ +income\_\$50,000-\$74,999 & 3,013 & 0.193 & 0.395 & 0 & 1 \\ +income\_\$75,000-\$99,999 & 3,013 & 0.119 & 0.324 & 0 & 1 \\ +income\_Less than \$25,000 & 3,013 & 0.218 & 0.413 & 0 & 1 \\ +income\_ & 3,013 & 0.000 & 0.000 & 0 & 0 \\ +partyID\_D & 2,789 & 0.411 & 0.492 & 0.000 & 1.000 \\ +partyID\_I & 2,789 & 0.250 & 0.433 & 0.000 & 1.000 \\ +partyID\_R & 2,789 & 0.339 & 0.474 & 0.000 & 1.000 \\ +partyID\_NA & 3,013 & 0.074 & 0.262 & 0 & 1 \\ +pol\_views\_0 & 3,013 & 0.085 & 0.279 & 0 & 1 \\ +pol\_views\_1 & 3,013 & 0.174 & 0.379 & 0 & 1 \\ +pol\_views\_2 & 3,013 & 0.088 & 0.283 & 0 & 1 \\ +pol\_views\_3 & 3,013 & 0.339 & 0.473 & 0 & 1 \\ +pol\_views\_4 & 3,013 & 0.094 & 0.292 & 0 & 1 \\ +pol\_views\_5 & 3,013 & 0.149 & 0.356 & 0 & 1 \\ +pol\_views\_6 & 3,013 & 0.072 & 0.258 & 0 & 1 \\ +race\_Asian / Asian American & 3,013 & 0.042 & 0.202 & 0 & 1 \\ +race\_Black / African American & 3,013 & 0.111 & 0.314 & 0 & 1 \\ +race\_Latino or Hispanic American & 3,013 & 0.126 & 0.332 & 0 & 1 \\ +race\_Multiracial & 3,013 & 0.016 & 0.125 & 0 & 1 \\ +race\_Native American & 3,013 & 0.005 & 0.073 & 0 & 1 \\ +race\_Other & 3,013 & 0.008 & 0.089 & 0 & 1 \\ +race\_White / Caucasian & 3,013 & 0.691 & 0.462 & 0 & 1 \\ +race\_ & 3,013 & 0.000 & 0.000 & 0 & 0 \\ +\hline \\[-1.8ex] +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A11.tex b/23/replication_package/Tables/table_A11.tex new file mode 100644 index 0000000000000000000000000000000000000000..515572c217e54be6958e6797dbfb6cf1dbe334e6 --- /dev/null +++ b/23/replication_package/Tables/table_A11.tex @@ -0,0 +1,59 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:21 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] +Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Max} \\ +\hline \\[-1.8ex] +female & 3,465 & 0.510 & 0.500 & 0 & 1 \\ +education & 3,434 & 3.102 & 1.697 & 0.000 & 6.000 \\ +income & 2,956 & 3.053 & 1.775 & 0.000 & 8.000 \\ +ideology & 3,465 & 2.989 & 1.517 & 1 & 7 \\ +degree\_violence & 3,465 & 4.117 & 2.877 & 0 & 10 \\ +police\_action\_required & 3,465 & 4.661 & 2.961 & 0 & 10 \\ +recall\_violence2 & 3,465 & 0.242 & 0.593 & 0 & 2 \\ +age\_18-22 & 3,465 & 0.122 & 0.328 & 0 & 1 \\ +age\_23-29 & 3,465 & 0.177 & 0.382 & 0 & 1 \\ +age\_30-39 & 3,465 & 0.229 & 0.421 & 0 & 1 \\ +age\_40-49 & 3,465 & 0.186 & 0.389 & 0 & 1 \\ +age\_50-70 & 3,465 & 0.285 & 0.452 & 0 & 1 \\ +religiosity\_Haredi & 3,415 & 0.081 & 0.273 & 0.000 & 1.000 \\ +religiosity\_Religious & 3,415 & 0.116 & 0.321 & 0.000 & 1.000 \\ +religiosity\_Secular & 3,415 & 0.447 & 0.497 & 0.000 & 1.000 \\ +religiosity\_Traditional & 3,415 & 0.356 & 0.479 & 0.000 & 1.000 \\ +religiosity\_NA & 3,465 & 0.014 & 0.119 & 0 & 1 \\ +ethnicity\_Ashkenazi & 3,415 & 0.355 & 0.479 & 0.000 & 1.000 \\ +ethnicity\_Ethiopia & 3,415 & 0.005 & 0.068 & 0.000 & 1.000 \\ +ethnicity\_Mixed & 3,415 & 0.139 & 0.345 & 0.000 & 1.000 \\ +ethnicity\_Mizrachi & 3,415 & 0.411 & 0.492 & 0.000 & 1.000 \\ +ethnicity\_Other & 3,415 & 0.024 & 0.152 & 0.000 & 1.000 \\ +ethnicity\_Soviet Union & 3,415 & 0.066 & 0.249 & 0.000 & 1.000 \\ +ethnicity\_NA & 3,465 & 0.014 & 0.119 & 0 & 1 \\ +education\_0 & 3,434 & 0.009 & 0.093 & 0.000 & 1.000 \\ +education\_1 & 3,434 & 0.216 & 0.412 & 0.000 & 1.000 \\ +education\_2 & 3,434 & 0.228 & 0.420 & 0.000 & 1.000 \\ +education\_3 & 3,434 & 0.074 & 0.261 & 0.000 & 1.000 \\ +education\_4 & 3,434 & 0.304 & 0.460 & 0.000 & 1.000 \\ +education\_5 & 3,434 & 0.022 & 0.145 & 0.000 & 1.000 \\ +education\_6 & 3,434 & 0.147 & 0.355 & 0.000 & 1.000 \\ +education\_NA & 3,465 & 0.009 & 0.094 & 0 & 1 \\ +income\_0 & 2,956 & 0.091 & 0.287 & 0.000 & 1.000 \\ +income\_1 & 2,956 & 0.121 & 0.326 & 0.000 & 1.000 \\ +income\_2 & 2,956 & 0.168 & 0.374 & 0.000 & 1.000 \\ +income\_3 & 2,956 & 0.192 & 0.394 & 0.000 & 1.000 \\ +income\_4 & 2,956 & 0.235 & 0.424 & 0.000 & 1.000 \\ +income\_5 & 2,956 & 0.119 & 0.324 & 0.000 & 1.000 \\ +income\_6 & 2,956 & 0.044 & 0.206 & 0.000 & 1.000 \\ +income\_7 & 2,956 & 0.018 & 0.133 & 0.000 & 1.000 \\ +income\_8 & 2,956 & 0.012 & 0.107 & 0.000 & 1.000 \\ +income\_NA & 3,465 & 0.147 & 0.354 & 0 & 1 \\ +partyID\_L & 3,227 & 0.434 & 0.496 & 0.000 & 1.000 \\ +partyID\_R & 3,227 & 0.566 & 0.496 & 0.000 & 1.000 \\ +partyID\_NA & 3,465 & 0.069 & 0.253 & 0 & 1 \\ +\hline \\[-1.8ex] +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A13_panel_A.tex b/23/replication_package/Tables/table_A13_panel_A.tex new file mode 100644 index 0000000000000000000000000000000000000000..ec047f9e2548470ad8082910ce05c6cd7fac41af --- /dev/null +++ b/23/replication_package/Tables/table_A13_panel_A.tex @@ -0,0 +1,142 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:22 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ +\cline{2-4} +\\[-1.8ex] & degree\_violence & recall\_violence2 & police\_action\_required \\ +\\[-1.8ex] & (1) & (2) & (3)\\ +\hline \\[-1.8ex] + identity\_protesters1 & 0.256$^{**}$ & 0.036$^{**}$ & 0.313$^{***}$ \\ + & (0.113) & (0.018) & (0.120) \\ + & & & \\ + tactic1 & 0.318$^{**}$ & 0.017 & 0.497$^{***}$ \\ + & (0.139) & (0.022) & (0.147) \\ + & & & \\ + tactic2 & 3.399$^{***}$ & 0.650$^{***}$ & 3.002$^{***}$ \\ + & (0.138) & (0.022) & (0.147) \\ + & & & \\ + age & 0.0002 & 0.0004 & 0.009$^{**}$ \\ + & (0.003) & (0.001) & (0.004) \\ + & & & \\ + female & $-$0.002 & $-$0.069$^{***}$ & 0.179 \\ + & (0.115) & (0.018) & (0.122) \\ + & & & \\ + educationHigh school graduate & 0.150 & 0.054 & 0.340 \\ + & (0.233) & (0.037) & (0.247) \\ + & & & \\ + educationSome college & $-$0.132 & 0.036 & $-$0.236 \\ + & (0.250) & (0.040) & (0.265) \\ + & & & \\ + education2-year & $-$0.312 & 0.039 & $-$0.345 \\ + & (0.277) & (0.044) & (0.294) \\ + & & & \\ + education4-year & $-$0.142 & 0.045 & $-$0.434 \\ + & (0.264) & (0.042) & (0.280) \\ + & & & \\ + educationPost-grad & $-$0.410 & 0.051 & $-$0.844$^{***}$ \\ + & (0.295) & (0.047) & (0.312) \\ + & & & \\ + 19,999 & $-$0.124 & 0.026 & 0.307 \\ + & (0.274) & (0.044) & (0.291) \\ + & & & \\ + 29,999 & $-$0.227 & 0.017 & 0.370 \\ + & (0.273) & (0.044) & (0.290) \\ + & & & \\ + 39,999 & $-$0.291 & 0.033 & 0.023 \\ + & (0.281) & (0.045) & (0.298) \\ + & & & \\ + 49,999 & $-$0.557$^{*}$ & 0.032 & $-$0.152 \\ + & (0.295) & (0.047) & (0.313) \\ + & & & \\ + 59,999 & $-$0.424 & $-$0.016 & $-$0.106 \\ + & (0.303) & (0.049) & (0.321) \\ + & & & \\ + 69,999 & $-$0.862$^{***}$ & $-$0.010 & $-$0.011 \\ + & (0.316) & (0.051) & (0.335) \\ + & & & \\ + 79,999 & $-$0.219 & 0.013 & 0.300 \\ + & (0.327) & (0.052) & (0.346) \\ + & & & \\ + 99,999 & $-$0.590$^{*}$ & 0.040 & 0.210 \\ + & (0.325) & (0.052) & (0.344) \\ + & & & \\ + 119,999 & $-$0.789$^{**}$ & 0.013 & 0.316 \\ + & (0.345) & (0.055) & (0.366) \\ + & & & \\ + 149,999 & $-$0.437 & $-$0.041 & 0.810$^{**}$ \\ + & (0.378) & (0.061) & (0.401) \\ + & & & \\ + 199,999 & $-$0.586 & $-$0.071 & 0.259 \\ + & (0.408) & (0.065) & (0.433) \\ + & & & \\ + 249,999 & $-$0.683 & $-$0.099 & $-$0.054 \\ + & (0.546) & (0.087) & (0.579) \\ + & & & \\ + 349,999 & 0.809 & 0.211 & $-$0.004 \\ + & (1.026) & (0.164) & (1.088) \\ + & & & \\ + 499,999 & 0.050 & $-$0.181 & 0.525 \\ + & (0.948) & (0.152) & (1.005) \\ + & & & \\ + 500,000 or more & 2.080$^{**}$ & $-$0.288$^{*}$ & 1.711 \\ + & (1.059) & (0.170) & (1.123) \\ + & & & \\ + incomePrefer not to say & $-$0.592$^{**}$ & 0.005 & $-$0.175 \\ + & (0.266) & (0.043) & (0.282) \\ + & & & \\ + ideologyLiberal & 0.528$^{**}$ & $-$0.044 & 0.890$^{***}$ \\ + & (0.216) & (0.035) & (0.229) \\ + & & & \\ + ideologyModerate & 1.272$^{***}$ & $-$0.032 & 1.739$^{***}$ \\ + & (0.201) & (0.032) & (0.213) \\ + & & & \\ + ideologyConservative & 1.841$^{***}$ & 0.018 & 2.729$^{***}$ \\ + & (0.213) & (0.034) & (0.226) \\ + & & & \\ + ideologyVery conservative & 2.445$^{***}$ & 0.090$^{**}$ & 2.949$^{***}$ \\ + & (0.234) & (0.037) & (0.248) \\ + & & & \\ + ideologyNot sure & 1.342$^{***}$ & $-$0.136$^{***}$ & 1.627$^{***}$ \\ + & (0.259) & (0.041) & (0.274) \\ + & & & \\ + raceBlack & 0.177 & 0.003 & 0.381$^{*}$ \\ + & (0.194) & (0.031) & (0.205) \\ + & & & \\ + raceHispanic & 0.096 & $-$0.064$^{**}$ & 0.267 \\ + & (0.166) & (0.027) & (0.176) \\ + & & & \\ + raceAsian & 0.506 & $-$0.008 & 0.297 \\ + & (0.332) & (0.053) & (0.352) \\ + & & & \\ + raceNative American & 0.307 & 0.111 & $-$0.021 \\ + & (0.543) & (0.087) & (0.576) \\ + & & & \\ + raceMixed & 1.267$^{***}$ & 0.222$^{***}$ & 1.327$^{***}$ \\ + & (0.359) & (0.057) & (0.380) \\ + & & & \\ + raceOther & 0.126 & 0.068 & 0.036 \\ + & (0.436) & (0.070) & (0.463) \\ + & & & \\ + raceMiddle Eastern & 0.125 & $-$0.159 & 0.294 \\ + & (1.392) & (0.223) & (1.476) \\ + & & & \\ + Constant & 1.844$^{***}$ & 0.034 & 1.417$^{***}$ \\ + & (0.376) & (0.060) & (0.398) \\ + & & & \\ +\hline \\[-1.8ex] +Observations & 2,269 & 2,269 & 2,269 \\ +R$^{2}$ & 0.303 & 0.355 & 0.271 \\ +Adjusted R$^{2}$ & 0.291 & 0.344 & 0.259 \\ +Residual Std. Error (df = 2230) & 2.704 & 0.433 & 2.867 \\ +F Statistic (df = 38; 2230) & 25.552$^{***}$ & 32.309$^{***}$ & 21.870$^{***}$ \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A13_panel_B.tex b/23/replication_package/Tables/table_A13_panel_B.tex new file mode 100644 index 0000000000000000000000000000000000000000..b5403c36fdf19fe915b63e4e70d0884ace6c4a15 --- /dev/null +++ b/23/replication_package/Tables/table_A13_panel_B.tex @@ -0,0 +1,94 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:24 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ +\cline{2-4} +\\[-1.8ex] & degree\_violence & recall\_violence2 & police\_action\_required \\ +\\[-1.8ex] & (1) & (2) & (3)\\ +\hline \\[-1.8ex] + identity\_protesters1 & 0.498$^{***}$ & 0.092$^{***}$ & 0.266$^{**}$ \\ + & (0.115) & (0.026) & (0.127) \\ + & & & \\ + identity\_protesters2 & 0.637$^{***}$ & 0.047$^{*}$ & 1.008$^{***}$ \\ + & (0.115) & (0.026) & (0.127) \\ + & & & \\ + tactic1 & 0.269$^{**}$ & $-$0.037 & 0.430$^{***}$ \\ + & (0.115) & (0.026) & (0.127) \\ + & & & \\ + tactic2 & 2.536$^{***}$ & 0.403$^{***}$ & 2.180$^{***}$ \\ + & (0.114) & (0.026) & (0.126) \\ + & & & \\ + age23-29 & $-$0.076 & $-$0.069 & 0.100 \\ + & (0.196) & (0.044) & (0.216) \\ + & & & \\ + age30-39 & $-$0.397$^{**}$ & $-$0.058 & $-$0.414$^{*}$ \\ + & (0.197) & (0.044) & (0.217) \\ + & & & \\ + age40-49 & $-$0.499$^{**}$ & $-$0.107$^{**}$ & $-$0.572$^{**}$ \\ + & (0.208) & (0.047) & (0.230) \\ + & & & \\ + age50-70 & $-$0.486$^{**}$ & $-$0.043 & $-$0.724$^{***}$ \\ + & (0.193) & (0.043) & (0.213) \\ + & & & \\ + age18-24 & $-$1.244$^{***}$ & $-$0.128 & $-$0.011 \\ + & (0.347) & (0.078) & (0.383) \\ + & & & \\ + age25-34 & $-$1.200$^{***}$ & $-$0.136$^{*}$ & 0.819$^{**}$ \\ + & (0.309) & (0.070) & (0.341) \\ + & & & \\ + age35-40 & $-$0.497 & $-$0.053 & 0.881$^{*}$ \\ + & (0.407) & (0.092) & (0.449) \\ + & & & \\ + age41-65 & $-$1.361$^{**}$ & $-$0.221 & 1.398$^{*}$ \\ + & (0.690) & (0.156) & (0.762) \\ + & & & \\ + female & 0.142 & $-$0.035 & $-$0.097 \\ + & (0.098) & (0.022) & (0.108) \\ + & & & \\ + education & $-$0.0003 & $-$0.003 & $-$0.020 \\ + & (0.032) & (0.007) & (0.035) \\ + & & & \\ + income & 0.044 & 0.0004 & $-$0.003 \\ + & (0.034) & (0.008) & (0.038) \\ + & & & \\ + ideology & $-$0.199$^{***}$ & $-$0.014$^{*}$ & $-$0.238$^{***}$ \\ + & (0.032) & (0.007) & (0.035) \\ + & & & \\ + ethnicityAshkenazi & $-$0.189 & 0.026 & $-$0.474$^{**}$ \\ + & (0.203) & (0.046) & (0.224) \\ + & & & \\ + ethnicityEthiopia & $-$2.900$^{***}$ & $-$0.239 & $-$3.236$^{***}$ \\ + & (0.830) & (0.187) & (0.917) \\ + & & & \\ + ethnicityMixed & $-$0.297 & $-$0.0002 & $-$0.422$^{*}$ \\ + & (0.228) & (0.052) & (0.252) \\ + & & & \\ + ethnicityMizrachi & $-$0.159 & 0.070 & $-$0.182 \\ + & (0.198) & (0.045) & (0.219) \\ + & & & \\ + ethnicityOther & 0.228 & $-$0.024 & 0.055 \\ + & (0.376) & (0.085) & (0.416) \\ + & & & \\ + ethnicitySoviet Union & & & \\ + & & & \\ + & & & \\ + Constant & 4.924$^{***}$ & 0.207$^{***}$ & 6.084$^{***}$ \\ + & (0.274) & (0.062) & (0.303) \\ + & & & \\ +\hline \\[-1.8ex] +Observations & 2,534 & 2,534 & 2,534 \\ +R$^{2}$ & 0.229 & 0.143 & 0.182 \\ +Adjusted R$^{2}$ & 0.222 & 0.136 & 0.175 \\ +Residual Std. Error (df = 2512) & 2.341 & 0.528 & 2.587 \\ +F Statistic (df = 21; 2512) & 35.510$^{***}$ & 19.950$^{***}$ & 26.545$^{***}$ \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A14.tex b/23/replication_package/Tables/table_A14.tex new file mode 100644 index 0000000000000000000000000000000000000000..6bb5edc1b3b1293cb3fc5844c3fcb1298a0aae73 --- /dev/null +++ b/23/replication_package/Tables/table_A14.tex @@ -0,0 +1,65 @@ +% latex table generated in R 4.1.0 by xtable 1.8-4 package +% Thu Jul 22 18:44:25 2021 +\begin{table}[ht] +\centering +\begin{tabular}{rrrll} + \hline + & estimate & std.error & model & submodel \\ + \hline +1 & 0.14 & 0.07 & 1. Perceived degree +of violence & March in streets \\ + 2 & 0.08 & 0.07 & 1. Perceived degree +of violence & Shut down traffic \\ + 3 & -0.01 & 0.07 & 1. Perceived degree +of violence & Destroy property \\ + 4 & 0.14 & 0.07 & 3. Police action +required & March in streets \\ + 5 & 0.09 & 0.07 & 3. Police action +required & Shut down traffic \\ + 6 & 0.01 & 0.07 & 3. Police action +required & Destroy property \\ + 7 & 0.03 & 0.05 & 2. Recall +violence & March in streets \\ + 8 & 0.09 & 0.06 & 2. Recall +violence & Shut down traffic \\ + 9 & 0.10 & 0.09 & 2. Recall +violence & Destroy property \\ + 10 & 0.39 & 0.08 & 1. Perceived degree +of violence & March in streets \\ + 11 & 0.36 & 0.07 & 1. Perceived degree +of violence & Shut down traffic \\ + 12 & -0.04 & 0.06 & 1. Perceived degree +of violence & Destroy property \\ + 13 & 0.53 & 0.08 & 3. Police action +required & March in streets \\ + 14 & 0.46 & 0.08 & 3. Police action +required & Shut down traffic \\ + 15 & 0.04 & 0.06 & 3. Police action +required & Destroy property \\ + 16 & 0.18 & 0.06 & 2. Recall +violence & March in streets \\ + 17 & 0.10 & 0.04 & 2. Recall +violence & Shut down traffic \\ + 18 & -0.02 & 0.09 & 2. Recall +violence & Destroy property \\ + 19 & 0.36 & 0.07 & 1. Perceived degree +of violence & March in streets \\ + 20 & 0.32 & 0.07 & 1. Perceived degree +of violence & Shut down traffic \\ + 21 & -0.19 & 0.06 & 1. Perceived degree +of violence & Destroy property \\ + 22 & 0.32 & 0.07 & 3. Police action +required & March in streets \\ + 23 & 0.16 & 0.07 & 3. Police action +required & Shut down traffic \\ + 24 & -0.28 & 0.06 & 3. Police action +required & Destroy property \\ + 25 & 0.25 & 0.06 & 2. Recall +violence & March in streets \\ + 26 & 0.26 & 0.05 & 2. Recall +violence & Shut down traffic \\ + 27 & -0.04 & 0.10 & 2. Recall +violence & Destroy property \\ + \hline +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A15_panel_A.tex b/23/replication_package/Tables/table_A15_panel_A.tex new file mode 100644 index 0000000000000000000000000000000000000000..cc1f93edf5daf19964aad68a7fd822a56c80939b --- /dev/null +++ b/23/replication_package/Tables/table_A15_panel_A.tex @@ -0,0 +1,43 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:25 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ +\cline{2-4} +\\[-1.8ex] & degree\_violence & recall\_violence2 & police\_action\_required \\ +\\[-1.8ex] & (1) & (2) & (3)\\ +\hline \\[-1.8ex] + Black protesters & 0.454$^{**}$ & 0.016 & 0.467$^{**}$ \\ + & (0.202) & (0.032) & (0.220) \\ + & & & \\ + Shut down traffic & 0.364$^{*}$ & 0.0005 & 0.510$^{**}$ \\ + & (0.202) & (0.032) & (0.220) \\ + & & & \\ + Destroy police cars & 3.617$^{***}$ & 0.626$^{***}$ & 3.188$^{***}$ \\ + & (0.202) & (0.032) & (0.220) \\ + & & & \\ + Black protesters x Shut down traffic & $-$0.187 & 0.034 & $-$0.161 \\ + & (0.286) & (0.045) & (0.310) \\ + & & & \\ + Black protesters x Destroy police cars & $-$0.471 & 0.037 & $-$0.435 \\ + & (0.286) & (0.045) & (0.311) \\ + & & & \\ + Intercept & 2.664$^{***}$ & 0.064$^{***}$ & 3.737$^{***}$ \\ + & (0.143) & (0.022) & (0.155) \\ + & & & \\ +\hline \\[-1.8ex] +Observations & 2,269 & 2,269 & 2,269 \\ +R$^{2}$ & 0.235 & 0.322 & 0.161 \\ +Adjusted R$^{2}$ & 0.234 & 0.320 & 0.159 \\ +Residual Std. Error (df = 2263) & 2.812 & 0.441 & 3.055 \\ +F Statistic (df = 5; 2263) & 139.389$^{***}$ & 214.945$^{***}$ & 86.702$^{***}$ \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A15_panel_B.tex b/23/replication_package/Tables/table_A15_panel_B.tex new file mode 100644 index 0000000000000000000000000000000000000000..2e7bea538db614f48d8520b74bd7c55e146839b6 --- /dev/null +++ b/23/replication_package/Tables/table_A15_panel_B.tex @@ -0,0 +1,52 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:26 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ +\cline{2-4} +\\[-1.8ex] & degree\_violence & recall\_violence2 & police\_action\_required \\ +\\[-1.8ex] & (1) & (2) & (3)\\ +\hline \\[-1.8ex] + Ethiopian protesters & 0.966$^{***}$ & 0.138$^{***}$ & 0.931$^{***}$ \\ + & (0.184) & (0.040) & (0.203) \\ + & & & \\ + Arab protesters & 1.061$^{***}$ & 0.100$^{**}$ & 1.514$^{***}$ \\ + & (0.188) & (0.041) & (0.209) \\ + & & & \\ + Shut down traffic & 0.331$^{*}$ & $-$0.018 & 0.637$^{***}$ \\ + & (0.187) & (0.040) & (0.207) \\ + & & & \\ + Destroy garbage cans & 3.435$^{***}$ & 0.506$^{***}$ & 3.209$^{***}$ \\ + & (0.186) & (0.040) & (0.206) \\ + & & & \\ + Ethiopian protesters x Shut down traffic & $-$0.111 & 0.006 & $-$0.480$^{*}$ \\ + & (0.262) & (0.057) & (0.289) \\ + & & & \\ + Arab protesters x Shut down traffic & $-$0.085 & $-$0.045 & $-$0.200 \\ + & (0.265) & (0.057) & (0.293) \\ + & & & \\ + Ethiopian protesters x Destroy garbage cans & $-$1.484$^{***}$ & $-$0.161$^{***}$ & $-$1.745$^{***}$ \\ + & (0.259) & (0.056) & (0.287) \\ + & & & \\ + Arab protesters x Destroy garbage cans & $-$1.181$^{***}$ & $-$0.112$^{**}$ & $-$1.389$^{***}$ \\ + & (0.261) & (0.057) & (0.289) \\ + & & & \\ + Intercept & 3.580$^{***}$ & 0.055$^{*}$ & 4.232$^{***}$ \\ + & (0.134) & (0.029) & (0.148) \\ + & & & \\ +\hline \\[-1.8ex] +Observations & 3,063 & 3,063 & 3,063 \\ +R$^{2}$ & 0.204 & 0.140 & 0.143 \\ +Adjusted R$^{2}$ & 0.202 & 0.138 & 0.140 \\ +Residual Std. Error (df = 3054) & 2.395 & 0.518 & 2.651 \\ +F Statistic (df = 8; 3054) & 97.637$^{***}$ & 62.346$^{***}$ & 63.520$^{***}$ \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A16.tex b/23/replication_package/Tables/table_A16.tex new file mode 100644 index 0000000000000000000000000000000000000000..09819c8406283f7ab2c579f137115ff4f6adcefc --- /dev/null +++ b/23/replication_package/Tables/table_A16.tex @@ -0,0 +1,23 @@ +% latex table generated in R 4.1.0 by xtable 1.8-4 package +% Thu Jul 22 18:44:26 2021 +\begin{table}[ht] +\centering +\begin{tabular}{rrrrrr} + \hline + & Mean (majority) & Mean (minority) & Difference & P-value & Percent change \\ + \hline +us\_degv\_maj & 2.35 & 3.02 & 0.66 & 0.01 & 28.16 \\ + us\_degv\_min & 3.50 & 3.32 & -0.18 & 0.72 & -5.21 \\ + us\_police\_maj & 3.38 & 3.90 & 0.52 & 0.07 & 15.45 \\ + us\_police\_min & 4.56 & 4.83 & 0.27 & 0.59 & 5.97 \\ + isr\_degv\_maj\_arab & 3.67 & 4.79 & 1.12 & 0.00 & 30.42 \\ + isr\_degv\_min\_arab & 3.09 & 3.45 & 0.36 & 0.48 & 11.75 \\ + isr\_police\_maj\_arab & 3.86 & 5.66 & 1.80 & 0.00 & 46.76 \\ + isr\_police\_min\_arab & 7.00 & 6.51 & -0.49 & 0.42 & -7.00 \\ + isr\_degv\_maj\_eth & 3.67 & 4.61 & 0.94 & 0.00 & 25.61 \\ + isr\_degv\_min\_eth & 3.09 & 4.26 & 1.17 & 0.01 & 38.06 \\ + isr\_police\_maj\_eth & 3.86 & 4.95 & 1.09 & 0.00 & 28.36 \\ + isr\_police\_min\_eth & 7.00 & 6.62 & -0.38 & 0.50 & -5.38 \\ + \hline +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A17_panel_A.tex b/23/replication_package/Tables/table_A17_panel_A.tex new file mode 100644 index 0000000000000000000000000000000000000000..05f5f39eed8b6bccfc1fa03551a729098e1ab59b --- /dev/null +++ b/23/replication_package/Tables/table_A17_panel_A.tex @@ -0,0 +1,109 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:27 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ +\cline{2-4} +\\[-1.8ex] & degree\_violence & recall\_violence2 & police\_action\_required \\ +\\[-1.8ex] & (1) & (2) & (3)\\ +\hline \\[-1.8ex] + black & 0.364$^{***}$ & 0.048$^{*}$ & 0.406$^{***}$ \\ + & (0.112) & (0.027) & (0.112) \\ + & & & \\ + group\_goal & 0.581$^{***}$ & 0.165$^{***}$ & 0.513$^{***}$ \\ + & (0.112) & (0.027) & (0.112) \\ + & & & \\ + commitment & $-$0.244$^{**}$ & $-$0.017 & $-$0.222$^{**}$ \\ + & (0.112) & (0.027) & (0.112) \\ + & & & \\ + age25-34 & 0.124 & $-$0.095$^{*}$ & 0.253 \\ + & (0.214) & (0.052) & (0.214) \\ + & & & \\ + age35-44 & $-$0.145 & $-$0.208$^{***}$ & 0.260 \\ + & (0.217) & (0.052) & (0.216) \\ + & & & \\ + age45-54 & $-$0.501$^{**}$ & $-$0.198$^{***}$ & 0.093 \\ + & (0.226) & (0.055) & (0.226) \\ + & & & \\ + age55-64 & $-$1.221$^{***}$ & $-$0.226$^{***}$ & $-$0.277 \\ + & (0.229) & (0.055) & (0.229) \\ + & & & \\ + age65+ & $-$1.281$^{***}$ & $-$0.268$^{***}$ & $-$0.601$^{***}$ \\ + & (0.226) & (0.055) & (0.226) \\ + & & & \\ + female & $-$0.262$^{**}$ & $-$0.095$^{***}$ & $-$0.395$^{***}$ \\ + & (0.118) & (0.028) & (0.118) \\ + & & & \\ + educationBachelor's degree & $-$0.225 & $-$0.058 & $-$0.196 \\ + & (0.199) & (0.048) & (0.199) \\ + & & & \\ + educationDid not complete high school & 0.777$^{**}$ & 0.220$^{**}$ & 0.865$^{**}$ \\ + & (0.394) & (0.095) & (0.397) \\ + & & & \\ + educationGraduate degree & 0.266 & $-$0.040 & 0.345 \\ + & (0.228) & (0.055) & (0.228) \\ + & & & \\ + educationHigh school / GED & 0.333$^{*}$ & 0.114$^{**}$ & 0.271 \\ + & (0.199) & (0.048) & (0.199) \\ + & & & \\ + educationSome college & $-$0.166 & 0.0003 & $-$0.258 \\ + & (0.201) & (0.048) & (0.201) \\ + & & & \\ + educationSome graduate school & $-$0.446 & $-$0.138 & $-$1.167$^{***}$ \\ + & (0.367) & (0.089) & (0.367) \\ + & & & \\ + 125,000 and above & 0.533$^{**}$ & 0.107$^{*}$ & 0.352 \\ + & (0.252) & (0.061) & (0.251) \\ + & & & \\ + 49,999 & $-$0.107 & 0.002 & $-$0.134 \\ + & (0.235) & (0.057) & (0.234) \\ + & & & \\ + 74,999 & 0.219 & 0.077 & 0.069 \\ + & (0.239) & (0.058) & (0.239) \\ + & & & \\ + 99,999 & $-$0.192 & $-$0.050 & $-$0.199 \\ + & (0.257) & (0.062) & (0.256) \\ + & & & \\ + 25,000 & 0.159 & 0.040 & $-$0.073 \\ + & (0.245) & (0.059) & (0.245) \\ + & & & \\ + pol\_views & 0.133$^{***}$ & 0.032$^{***}$ & 0.272$^{***}$ \\ + & (0.034) & (0.008) & (0.034) \\ + & & & \\ + raceBlack / African American & 0.611$^{*}$ & 0.090 & 0.198 \\ + & (0.321) & (0.077) & (0.321) \\ + & & & \\ + raceLatino or Hispanic American & 0.380 & 0.025 & 0.304 \\ + & (0.316) & (0.076) & (0.316) \\ + & & & \\ + raceMultiracial & $-$0.392 & 0.101 & $-$0.330 \\ + & (0.520) & (0.126) & (0.520) \\ + & & & \\ + raceNative American & 0.493 & $-$0.071 & 0.609 \\ + & (0.816) & (0.197) & (0.815) \\ + & & & \\ + raceOther & 0.197 & 0.177 & $-$0.189 \\ + & (0.685) & (0.165) & (0.684) \\ + & & & \\ + raceWhite / Caucasian & $-$0.081 & $-$0.081 & $-$0.103 \\ + & (0.287) & (0.069) & (0.287) \\ + & & & \\ + Constant & 3.408$^{***}$ & 0.437$^{***}$ & 3.638$^{***}$ \\ + & (0.421) & (0.102) & (0.421) \\ + & & & \\ +\hline \\[-1.8ex] +Observations & 3,013 & 3,013 & 3,008 \\ +R$^{2}$ & 0.079 & 0.060 & 0.068 \\ +Adjusted R$^{2}$ & 0.071 & 0.051 & 0.059 \\ +Residual Std. Error & 3.062 (df = 2985) & 0.739 (df = 2985) & 3.056 (df = 2980) \\ +F Statistic & 9.518$^{***}$ (df = 27; 2985) & 7.018$^{***}$ (df = 27; 2985) & 8.021$^{***}$ (df = 27; 2980) \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A17_panel_B.tex b/23/replication_package/Tables/table_A17_panel_B.tex new file mode 100644 index 0000000000000000000000000000000000000000..1af1ccacda0ecb66354f7c9bbf4d0e49c422bf54 --- /dev/null +++ b/23/replication_package/Tables/table_A17_panel_B.tex @@ -0,0 +1,79 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:28 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ +\cline{2-4} +\\[-1.8ex] & degree\_violence & recall\_violence2 & police\_action\_required \\ +\\[-1.8ex] & (1) & (2) & (3)\\ +\hline \\[-1.8ex] + identity\_protesters1 & 1.079$^{***}$ & 0.310$^{***}$ & 0.920$^{***}$ \\ + & (0.148) & (0.031) & (0.149) \\ + & & & \\ + identity\_protesters2 & 0.623$^{***}$ & 0.077$^{**}$ & 0.855$^{***}$ \\ + & (0.147) & (0.031) & (0.149) \\ + & & & \\ + group\_goal & 0.513$^{***}$ & 0.112$^{***}$ & 0.512$^{***}$ \\ + & (0.116) & (0.024) & (0.117) \\ + & & & \\ + commitment & $-$0.314$^{***}$ & 0.008 & $-$0.160 \\ + & (0.102) & (0.021) & (0.103) \\ + & & & \\ + age23-29 & 0.009 & 0.032 & 0.397$^{**}$ \\ + & (0.198) & (0.041) & (0.199) \\ + & & & \\ + age30-39 & $-$0.348$^{*}$ & $-$0.042 & $-$0.004 \\ + & (0.206) & (0.043) & (0.207) \\ + & & & \\ + age40-49 & $-$0.393$^{*}$ & $-$0.059 & 0.001 \\ + & (0.214) & (0.045) & (0.215) \\ + & & & \\ + age50-70 & $-$0.478$^{**}$ & $-$0.053 & $-$0.539$^{***}$ \\ + & (0.199) & (0.042) & (0.201) \\ + & & & \\ + female & 0.155 & 0.018 & 0.356$^{***}$ \\ + & (0.107) & (0.022) & (0.107) \\ + & & & \\ + education & $-$0.077$^{**}$ & $-$0.016$^{**}$ & $-$0.156$^{***}$ \\ + & (0.035) & (0.007) & (0.035) \\ + & & & \\ + income & 0.080$^{**}$ & 0.006 & 0.113$^{***}$ \\ + & (0.036) & (0.007) & (0.036) \\ + & & & \\ + ideology & $-$0.427$^{***}$ & $-$0.044$^{***}$ & $-$0.513$^{***}$ \\ + & (0.036) & (0.008) & (0.036) \\ + & & & \\ + ethnicityEthiopia & $-$0.961 & $-$0.133 & $-$1.211 \\ + & (0.738) & (0.155) & (0.743) \\ + & & & \\ + ethnicityMixed & $-$0.126 & 0.021 & $-$0.008 \\ + & (0.163) & (0.034) & (0.164) \\ + & & & \\ + ethnicityMizrachi & 0.255$^{**}$ & 0.060$^{**}$ & 0.498$^{***}$ \\ + & (0.123) & (0.026) & (0.124) \\ + & & & \\ + ethnicityOther & $-$0.168 & 0.034 & 0.043 \\ + & (0.356) & (0.075) & (0.359) \\ + & & & \\ + ethnicitySoviet Union & $-$0.046 & $-$0.013 & 0.021 \\ + & (0.213) & (0.045) & (0.215) \\ + & & & \\ + Constant & 4.774$^{***}$ & 0.199$^{***}$ & 5.187$^{***}$ \\ + & (0.238) & (0.050) & (0.240) \\ + & & & \\ +\hline \\[-1.8ex] +Observations & 2,888 & 2,888 & 2,888 \\ +R$^{2}$ & 0.108 & 0.092 & 0.145 \\ +Adjusted R$^{2}$ & 0.103 & 0.087 & 0.140 \\ +Residual Std. Error (df = 2870) & 2.730 & 0.572 & 2.751 \\ +F Statistic (df = 17; 2870) & 20.403$^{***}$ & 17.135$^{***}$ & 28.564$^{***}$ \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A18.tex b/23/replication_package/Tables/table_A18.tex new file mode 100644 index 0000000000000000000000000000000000000000..4e4c19663fa9ed223700689d1a2cbad07cb15253 --- /dev/null +++ b/23/replication_package/Tables/table_A18.tex @@ -0,0 +1,37 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:29 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ +\cline{2-4} +\\[-1.8ex] & degree\_violence & recall\_violence2 & police\_action\_required \\ +\\[-1.8ex] & (1) & (2) & (3)\\ +\hline \\[-1.8ex] + Ethiopian protesters & 1.063$^{***}$ & 0.280$^{***}$ & 0.850$^{***}$ \\ + & (0.145) & (0.026) & (0.152) \\ + & & & \\ + Arab protesters & 0.479$^{***}$ & 0.068$^{***}$ & 0.687$^{***}$ \\ + & (0.146) & (0.026) & (0.153) \\ + & & & \\ + Commitment to nonviolence & $-$0.451$^{***}$ & $-$0.015 & $-$0.252$^{**}$ \\ + & (0.120) & (0.021) & (0.126) \\ + & & & \\ + Intercept: White protesters, generic goal, no commitment & 3.493$^{***}$ & 0.063$^{***}$ & 3.965$^{***}$ \\ + & (0.116) & (0.020) & (0.122) \\ + & & & \\ +\hline \\[-1.8ex] +Observations & 2,098 & 2,098 & 2,098 \\ +R$^{2}$ & 0.031 & 0.057 & 0.018 \\ +Adjusted R$^{2}$ & 0.029 & 0.056 & 0.017 \\ +Residual Std. Error (df = 2094) & 2.743 & 0.483 & 2.880 \\ +F Statistic (df = 3; 2094) & 22.019$^{***}$ & 42.419$^{***}$ & 13.002$^{***}$ \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A2.tex b/23/replication_package/Tables/table_A2.tex new file mode 100644 index 0000000000000000000000000000000000000000..008d56200261db98712306d15554adfeae1526a3 --- /dev/null +++ b/23/replication_package/Tables/table_A2.tex @@ -0,0 +1,13 @@ +% latex table generated in R 4.1.0 by xtable 1.8-4 package +% Thu Jul 22 18:44:15 2021 +\begin{table}[ht] +\centering +\begin{tabular}{rrrrr} + \hline + & nv\_majority & v\_majority & nv\_minority & v\_minority \\ + \hline +Campaign failure & 0.50 & 0.85 & 0.79 & 0.93 \\ + Campaign success & 0.50 & 0.15 & 0.21 & 0.07 \\ + \hline +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A3.tex b/23/replication_package/Tables/table_A3.tex new file mode 100644 index 0000000000000000000000000000000000000000..367893ec7f3b155c2eff02720a705195edb037a4 --- /dev/null +++ b/23/replication_package/Tables/table_A3.tex @@ -0,0 +1,67 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Fri, Jul 23, 2021 - 10:05:15 +\begin{table}[H] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lcccccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{6}{c}{\textit{Dependent variable:}} \\ +\cline{2-7} +\\[-1.8ex] & \multicolumn{6}{c}{success} \\ +\\[-1.8ex] & (1) & (2) & (3) & (4) & (5) & (6)\\ +\hline \\[-1.8ex] + NV campaign & 0.001 & 0.005 & $-$0.013 & 0.226$^{***}$ & 0.220$^{***}$ & 0.255$^{***}$ \\ + & (0.024) & (0.026) & (0.027) & (0.017) & (0.019) & (0.022) \\ + & & & & & & \\ + EPR Status & 0.003 & 0.005 & 0.004 & & & \\ + & (0.003) & (0.004) & (0.004) & & & \\ + & & & & & & \\ + EPR Status × NV Campaign & 0.022$^{**}$ & 0.265$^{***}$ & 0.342$^{***}$ & 0.035$^{***}$ & 0.276$^{***}$ & 0.334$^{***}$ \\ + & (0.010) & (0.063) & (0.071) & (0.009) & (0.063) & (0.073) \\ + & & & & & & \\ + EPR Status: Excluded & & & & $-$0.008 & $-$0.001 & $-$0.010 \\ + & & & & (0.011) & (0.015) & (0.018) \\ + & & & & & & \\ + EPR Status: Excluded × NV Campaign & & $-$0.030 & $-$0.022 & & $-$0.003 & $-$0.017 \\ + & & (0.028) & (0.032) & & (0.024) & (0.030) \\ + & & & & & & \\ + EPR group size & & $-$0.018$^{***}$ & $-$0.019$^{***}$ & & $-$0.016$^{***}$ & $-$0.017$^{***}$ \\ + & & (0.004) & (0.005) & & (0.004) & (0.005) \\ + & & & & & & \\ + Country population & & $-$0.010 & $-$0.020$^{***}$ & & $-$0.013$^{**}$ & $-$0.019$^{**}$ \\ + & & (0.006) & (0.007) & & (0.006) & (0.007) \\ + & & & & & & \\ + Country GDP per capita & & 0.011 & 0.050 & & 0.011 & 0.030 \\ + & & (0.041) & (0.047) & & (0.040) & (0.047) \\ + & & & & & & \\ + Level of democracy & & 0.007 & $-$0.011 & & $-$0.001 & $-$0.005 \\ + & & (0.035) & (0.039) & & (0.033) & (0.038) \\ + & & & & & & \\ + Physical integrity index & & & $-$0.003 & & & $-$0.005 \\ + & & & (0.013) & & & (0.013) \\ + & & & & & & \\ + Neighboring kin in power & & & $-$0.015 & & & $-$0.019 \\ + & & & (0.020) & & & (0.019) \\ + & & & & & & \\ + Group status downgraded & & & 0.015 & & & 0.014 \\ + & & & (0.016) & & & (0.016) \\ + & & & & & & \\ + Horizontal inequality & 0.031$^{***}$ & 0.030$^{***}$ & 0.042$^{***}$ & & & \\ + & (0.006) & (0.006) & (0.006) & & & \\ + & & & & & & \\ + EPR\_STATUS\_EXCL:INIT\_NV\_ONSET & & & & $-$0.202$^{***}$ & $-$0.182$^{***}$ & $-$0.217$^{***}$ \\ + & & & & (0.025) & (0.026) & (0.029) \\ + & & & & & & \\ +\hline \\[-1.8ex] +Observations & 1,944 & 1,922 & 1,698 & 2,099 & 2,077 & 1,698 \\ +R$^{2}$ & 0.072 & 0.085 & 0.117 & 0.096 & 0.102 & 0.123 \\ +Adjusted R$^{2}$ & 0.071 & 0.078 & 0.108 & 0.095 & 0.096 & 0.114 \\ +Residual Std. Error & 0.220 (df = 1940) & 0.214 (df = 1907) & 0.213 (df = 1680) & 0.218 (df = 2095) & 0.214 (df = 2062) & 0.212 (df = 1680) \\ +F Statistic & 50.548$^{***}$ (df = 3; 1940) & 12.585$^{***}$ (df = 14; 1907) & 13.103$^{***}$ (df = 17; 1680) & 73.993$^{***}$ (df = 3; 2095) & 16.708$^{***}$ (df = 14; 2062) & 13.804$^{***}$ (df = 17; 1680) \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{6}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A4.tex b/23/replication_package/Tables/table_A4.tex new file mode 100644 index 0000000000000000000000000000000000000000..cabb7b52b7fb2be2a3756d6dec06c40bbc3b9faa --- /dev/null +++ b/23/replication_package/Tables/table_A4.tex @@ -0,0 +1,64 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:16 +\begin{table}[H] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ +\cline{2-4} +\\[-1.8ex] & \multicolumn{3}{c}{success} \\ +\\[-1.8ex] & (1) & (2) & (3)\\ +\hline \\[-1.8ex] + NV campaign & 0.070$^{***}$ & 0.082$^{***}$ & 0.086$^{***}$ \\ + & (0.019) & (0.021) & (0.023) \\ + & & & \\ + EPR group size & 0.026 & 0.013 & 0.016 \\ + & (0.019) & (0.023) & (0.029) \\ + & & & \\ + EPR group size × NV campaign & 0.134$^{***}$ & 0.116$^{***}$ & 0.148$^{***}$ \\ + & (0.035) & (0.038) & (0.043) \\ + & & & \\ + Country population & & $-$0.018$^{***}$ & $-$0.021$^{***}$ \\ + & & (0.004) & (0.005) \\ + & & & \\ + Country GDP per capita & & $-$0.016$^{**}$ & $-$0.026$^{***}$ \\ + & & (0.007) & (0.008) \\ + & & & \\ + Prior participation in NV & & 0.023 & 0.017 \\ + & & (0.016) & (0.017) \\ + & & & \\ + Prior participation in V & & $-$0.003 & 0.003 \\ + & & (0.018) & (0.020) \\ + & & & \\ + Level of democracy & & $-$0.0002 & 0.027 \\ + & & (0.044) & (0.051) \\ + & & & \\ + Physical integrity index & & 0.001 & 0.002 \\ + & & (0.037) & (0.043) \\ + & & & \\ + Neighboring kin in power & & & $-$0.011 \\ + & & & (0.014) \\ + & & & \\ + Group status downgraded & & & $-$0.026 \\ + & & & (0.021) \\ + & & & \\ + Horizontal inequality & & & 0.008 \\ + & & & (0.017) \\ + & & & \\ + Constant & 0.023$^{***}$ & 0.362$^{***}$ & 0.438$^{***}$ \\ + & (0.007) & (0.069) & (0.080) \\ + & & & \\ +\hline \\[-1.8ex] +Observations & 2,099 & 1,901 & 1,569 \\ +R$^{2}$ & 0.070 & 0.086 & 0.098 \\ +Adjusted R$^{2}$ & 0.069 & 0.079 & 0.088 \\ +Residual Std. Error & 0.221 (df = 2095) & 0.225 (df = 1885) & 0.223 (df = 1550) \\ +F Statistic & 52.848$^{***}$ (df = 3; 2095) & 11.870$^{***}$ (df = 15; 1885) & 9.385$^{***}$ (df = 18; 1550) \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{3}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A5.tex b/23/replication_package/Tables/table_A5.tex new file mode 100644 index 0000000000000000000000000000000000000000..b1b72222c92b9b910dc04cf1d3c1729842aa8c9b --- /dev/null +++ b/23/replication_package/Tables/table_A5.tex @@ -0,0 +1,19 @@ +% latex table generated in R 4.1.0 by xtable 1.8-4 package +% Thu Jul 22 18:44:19 2021 +\begin{table}[ht] +\centering +\begin{tabular}{rrrrrll} + \hline + & predicted & std.error & conf.low & conf.high & tactic & Status \\ + \hline +1 & 0.27 & 0.07 & 0.14 & 0.40 & Non-violent & Not excluded \\ + 2 & -0.00 & 0.07 & -0.14 & 0.14 & Non-violent & Excluded \\ + 3 & 0.02 & 0.02 & -0.02 & 0.06 & Violent & Not excluded \\ + 4 & 0.05 & 0.01 & 0.02 & 0.08 & Violent & Excluded \\ + 5 & 0.27 & 0.07 & 0.13 & 0.41 & Non-violent & Group size $>$= mean \\ + 6 & 0.02 & 0.06 & -0.09 & 0.14 & Non-violent & Group size $<$ mean \\ + 7 & 0.04 & 0.02 & 0.01 & 0.08 & Violent & Group size $>$= mean \\ + 8 & 0.04 & 0.01 & 0.01 & 0.07 & Violent & Group size $<$ mean \\ + \hline +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A6.tex b/23/replication_package/Tables/table_A6.tex new file mode 100644 index 0000000000000000000000000000000000000000..ae86098196875da9c8c17e554b65d3928b31b29b --- /dev/null +++ b/23/replication_package/Tables/table_A6.tex @@ -0,0 +1,31 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:19 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lcc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] + & \multicolumn{2}{c}{\textit{Dependent variable:}} \\ +\cline{2-3} +\\[-1.8ex] & \multicolumn{2}{c}{success} \\ +\\[-1.8ex] & (1) & (2)\\ +\hline \\[-1.8ex] + Nonviolent campaign & 0.030 & 0.516$^{***}$ \\ + & (0.053) & (0.058) \\ + & & \\ + Constant & 0.074$^{***}$ & 0.017 \\ + & (0.018) & (0.020) \\ + & & \\ +\hline \\[-1.8ex] +Observations & 246 & 130 \\ +R$^{2}$ & 0.001 & 0.383 \\ +Adjusted R$^{2}$ & $-$0.003 & 0.378 \\ +Residual Std. Error & 0.268 (df = 244) & 0.211 (df = 128) \\ +F Statistic & 0.315 (df = 1; 244) & 79.340$^{***}$ (df = 1; 128) \\ +\hline +\hline \\[-1.8ex] +\textit{Note:} & \multicolumn{2}{r}{$^{*}$p$<$0.1; $^{**}$p$<$0.05; $^{***}$p$<$0.01} \\ +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A7.tex b/23/replication_package/Tables/table_A7.tex new file mode 100644 index 0000000000000000000000000000000000000000..62e4cfe593e87d7f2505f3ed399ab27ec1b64c8e --- /dev/null +++ b/23/replication_package/Tables/table_A7.tex @@ -0,0 +1,60 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:19 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] +Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Max} \\ +\hline \\[-1.8ex] +age & 2,269 & 47.374 & 17.544 & 18 & 91 \\ +female & 2,269 & 0.539 & 0.499 & 0 & 1 \\ +degree\_violence & 2,269 & 4.064 & 3.190 & 0 & 10 \\ +police\_action\_required & 2,269 & 5.081 & 3.295 & 0 & 10 \\ +recall\_violence2 & 2,269 & 0.293 & 0.524 & 0 & 2 \\ +education\_No HS & 2,269 & 0.052 & 0.221 & 0 & 1 \\ +education\_High school graduate & 2,269 & 0.343 & 0.475 & 0 & 1 \\ +education\_Some college & 2,269 & 0.211 & 0.408 & 0 & 1 \\ +education\_2-year & 2,269 & 0.114 & 0.318 & 0 & 1 \\ +education\_4-year & 2,269 & 0.183 & 0.387 & 0 & 1 \\ +education\_Post-grad & 2,269 & 0.097 & 0.297 & 0 & 1 \\ +income\_Less than \$10,000 & 2,269 & 0.076 & 0.265 & 0 & 1 \\ +income\_\$10,000 - \$19,999 & 2,269 & 0.111 & 0.314 & 0 & 1 \\ +income\_\$20,000 - \$29,999 & 2,269 & 0.118 & 0.323 & 0 & 1 \\ +income\_\$30,000 - \$39,999 & 2,269 & 0.103 & 0.304 & 0 & 1 \\ +income\_\$40,000 - \$49,999 & 2,269 & 0.084 & 0.278 & 0 & 1 \\ +income\_\$50,000 - \$59,999 & 2,269 & 0.071 & 0.258 & 0 & 1 \\ +income\_\$60,000 - \$69,999 & 2,269 & 0.063 & 0.244 & 0 & 1 \\ +income\_\$70,000 - \$79,999 & 2,269 & 0.056 & 0.229 & 0 & 1 \\ +income\_\$80,000 - \$99,999 & 2,269 & 0.056 & 0.230 & 0 & 1 \\ +income\_\$100,000 - \$119,999 & 2,269 & 0.046 & 0.210 & 0 & 1 \\ +income\_\$120,000 - \$149,999 & 2,269 & 0.035 & 0.183 & 0 & 1 \\ +income\_\$150,000 - \$199,999 & 2,269 & 0.029 & 0.167 & 0 & 1 \\ +income\_\$200,000 - \$249,999 & 2,269 & 0.012 & 0.108 & 0 & 1 \\ +income\_\$250,000 - \$349,999 & 2,269 & 0.003 & 0.055 & 0 & 1 \\ +income\_\$350,000 - \$499,999 & 2,269 & 0.003 & 0.055 & 0 & 1 \\ +income\_\$500,000 or more & 2,269 & 0.003 & 0.055 & 0 & 1 \\ +income\_Prefer not to say & 2,269 & 0.130 & 0.336 & 0 & 1 \\ +partyID\_D & 2,031 & 0.462 & 0.499 & 0.000 & 1.000 \\ +partyID\_I & 2,031 & 0.282 & 0.450 & 0.000 & 1.000 \\ +partyID\_R & 2,031 & 0.256 & 0.437 & 0.000 & 1.000 \\ +partyID\_NA & 2,269 & 0.105 & 0.306 & 0 & 1 \\ +ideology\_Very liberal & 2,269 & 0.120 & 0.325 & 0 & 1 \\ +ideology\_Liberal & 2,269 & 0.188 & 0.391 & 0 & 1 \\ +ideology\_Moderate & 2,269 & 0.291 & 0.454 & 0 & 1 \\ +ideology\_Conservative & 2,269 & 0.184 & 0.387 & 0 & 1 \\ +ideology\_Very conservative & 2,269 & 0.118 & 0.322 & 0 & 1 \\ +ideology\_Not sure & 2,269 & 0.100 & 0.300 & 0 & 1 \\ +race\_White & 2,269 & 0.561 & 0.496 & 0 & 1 \\ +race\_Black & 2,269 & 0.241 & 0.428 & 0 & 1 \\ +race\_Hispanic & 2,269 & 0.126 & 0.332 & 0 & 1 \\ +race\_Asian & 2,269 & 0.025 & 0.155 & 0 & 1 \\ +race\_Native American & 2,269 & 0.010 & 0.098 & 0 & 1 \\ +race\_Mixed & 2,269 & 0.022 & 0.147 & 0 & 1 \\ +race\_Other & 2,269 & 0.014 & 0.116 & 0 & 1 \\ +race\_Middle Eastern & 2,269 & 0.002 & 0.042 & 0 & 1 \\ +\hline \\[-1.8ex] +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A8.tex b/23/replication_package/Tables/table_A8.tex new file mode 100644 index 0000000000000000000000000000000000000000..be9b9046ca233025fc83cf7a4c8aea42cf02dd75 --- /dev/null +++ b/23/replication_package/Tables/table_A8.tex @@ -0,0 +1,67 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:20 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] +Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Max} \\ +\hline \\[-1.8ex] +female & 2,538 & 0.506 & 0.500 & 0 & 1 \\ +education & 2,527 & 2.998 & 1.691 & 0.000 & 6.000 \\ +income & 2,140 & 3.075 & 1.689 & 0.000 & 8.000 \\ +ideology & 2,538 & 3.024 & 1.531 & 1 & 7 \\ +interest\_news & 2,538 & 3.120 & 0.999 & 1 & 4 \\ +degree\_violence & 2,538 & 5.346 & 2.673 & 0 & 10 \\ +police\_action\_required & 2,538 & 5.825 & 2.861 & 0 & 10 \\ +recall\_violence2 & 2,538 & 0.284 & 0.576 & 0 & 2 \\ +age\_18-22 & 2,538 & 0.117 & 0.322 & 0 & 1 \\ +age\_23-29 & 2,538 & 0.167 & 0.373 & 0 & 1 \\ +age\_30-39 & 2,538 & 0.223 & 0.416 & 0 & 1 \\ +age\_40-49 & 2,538 & 0.177 & 0.382 & 0 & 1 \\ +age\_50-70 & 2,538 & 0.315 & 0.465 & 0 & 1 \\ +age\_71 ומעלה & 2,538 & 0.0004 & 0.020 & 0 & 1 \\ +age\_ & 2,538 & 0.000 & 0.000 & 0 & 0 \\ +age\_18-24 & 2,538 & 0.000 & 0.000 & 0 & 0 \\ +age\_25-34 & 2,538 & 0.000 & 0.000 & 0 & 0 \\ +age\_35-40 & 2,538 & 0.000 & 0.000 & 0 & 0 \\ +age\_41-65 & 2,538 & 0.000 & 0.000 & 0 & 0 \\ +religiosity\_Haredi & 2,520 & 0.031 & 0.172 & 0.000 & 1.000 \\ +religiosity\_Religious & 2,520 & 0.146 & 0.353 & 0.000 & 1.000 \\ +religiosity\_Secular & 2,520 & 0.505 & 0.500 & 0.000 & 1.000 \\ +religiosity\_Traditional & 2,520 & 0.319 & 0.466 & 0.000 & 1.000 \\ +religiosity\_NA & 2,538 & 0.007 & 0.084 & 0 & 1 \\ +ethnicity\_Ashkenazi & 2,520 & 0.364 & 0.481 & 0.000 & 1.000 \\ +ethnicity\_Ethiopia & 2,520 & 0.004 & 0.063 & 0.000 & 1.000 \\ +ethnicity\_Mixed & 2,520 & 0.129 & 0.336 & 0.000 & 1.000 \\ +ethnicity\_Mizrachi & 2,520 & 0.405 & 0.491 & 0.000 & 1.000 \\ +ethnicity\_Other & 2,520 & 0.025 & 0.155 & 0.000 & 1.000 \\ +ethnicity\_Soviet Union & 2,520 & 0.073 & 0.260 & 0.000 & 1.000 \\ +ethnicity\_NA & 2,538 & 0.007 & 0.084 & 0 & 1 \\ +education\_0 & 2,527 & 0.013 & 0.115 & 0.000 & 1.000 \\ +education\_1 & 2,527 & 0.226 & 0.418 & 0.000 & 1.000 \\ +education\_2 & 2,527 & 0.243 & 0.429 & 0.000 & 1.000 \\ +education\_3 & 2,527 & 0.079 & 0.269 & 0.000 & 1.000 \\ +education\_4 & 2,527 & 0.279 & 0.449 & 0.000 & 1.000 \\ +education\_5 & 2,527 & 0.025 & 0.155 & 0.000 & 1.000 \\ +education\_6 & 2,527 & 0.135 & 0.342 & 0.000 & 1.000 \\ +education\_NA & 2,538 & 0.004 & 0.066 & 0 & 1 \\ +income\_0 & 2,140 & 0.083 & 0.276 & 0.000 & 1.000 \\ +income\_1 & 2,140 & 0.098 & 0.298 & 0.000 & 1.000 \\ +income\_2 & 2,140 & 0.178 & 0.382 & 0.000 & 1.000 \\ +income\_3 & 2,140 & 0.223 & 0.417 & 0.000 & 1.000 \\ +income\_4 & 2,140 & 0.236 & 0.425 & 0.000 & 1.000 \\ +income\_5 & 2,140 & 0.117 & 0.321 & 0.000 & 1.000 \\ +income\_6 & 2,140 & 0.039 & 0.194 & 0.000 & 1.000 \\ +income\_7 & 2,140 & 0.018 & 0.132 & 0.000 & 1.000 \\ +income\_8 & 2,140 & 0.008 & 0.089 & 0.000 & 1.000 \\ +income\_NA & 2,538 & 0.157 & 0.364 & 0 & 1 \\ +partyID\_C & 2,198 & 0.046 & 0.209 & 0.000 & 1.000 \\ +partyID\_L & 2,198 & 0.430 & 0.495 & 0.000 & 1.000 \\ +partyID\_R & 2,198 & 0.524 & 0.500 & 0.000 & 1.000 \\ +partyID\_NA & 2,538 & 0.134 & 0.341 & 0 & 1 \\ +\hline \\[-1.8ex] +\end{tabular} +\end{table} diff --git a/23/replication_package/Tables/table_A9.tex b/23/replication_package/Tables/table_A9.tex new file mode 100644 index 0000000000000000000000000000000000000000..c39612024420c00322913e3a250f44f2d4254c20 --- /dev/null +++ b/23/replication_package/Tables/table_A9.tex @@ -0,0 +1,60 @@ + +% Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu +% Date and time: Thu, Jul 22, 2021 - 18:44:21 +\begin{table}[!htbp] \centering + \caption{} + \label{} +\begin{tabular}{@{\extracolsep{5pt}}lccccc} +\\[-1.8ex]\hline +\hline \\[-1.8ex] +Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Max} \\ +\hline \\[-1.8ex] +female & 525 & 0.619 & 0.486 & 0 & 1 \\ +education & 522 & 2.973 & 1.479 & 0.000 & 6.000 \\ +income & 418 & 1.921 & 1.489 & 0.000 & 8.000 \\ +ideology & 525 & 4.653 & 1.778 & 1 & 7 \\ +interest\_news & 525 & 2.415 & 1.106 & 1 & 4 \\ +degree\_violence & 525 & 4.284 & 2.647 & 0 & 10 \\ +police\_action\_required & 525 & 6.659 & 2.839 & 0 & 10 \\ +recall\_violence2 & 525 & 0.145 & 0.407 & 0 & 2 \\ +age\_18-22 & 525 & 0.000 & 0.000 & 0 & 0 \\ +age\_23-29 & 525 & 0.000 & 0.000 & 0 & 0 \\ +age\_30-39 & 525 & 0.000 & 0.000 & 0 & 0 \\ +age\_40-49 & 525 & 0.000 & 0.000 & 0 & 0 \\ +age\_50-70 & 525 & 0.000 & 0.000 & 0 & 0 \\ +age\_71 ומעלה & 525 & 0.000 & 0.000 & 0 & 0 \\ +age\_ & 525 & 0.000 & 0.000 & 0 & 0 \\ +age\_18-24 & 525 & 0.337 & 0.473 & 0 & 1 \\ +age\_25-34 & 525 & 0.453 & 0.498 & 0 & 1 \\ +age\_35-40 & 525 & 0.166 & 0.372 & 0 & 1 \\ +age\_41-65 & 525 & 0.044 & 0.205 & 0 & 1 \\ +religiosity\_Christian & 519 & 0.079 & 0.270 & 0.000 & 1.000 \\ +religiosity\_Druze & 519 & 0.089 & 0.284 & 0.000 & 1.000 \\ +religiosity\_Muslim & 519 & 0.832 & 0.374 & 0.000 & 1.000 \\ +religiosity\_NA & 525 & 0.011 & 0.106 & 0 & 1 \\ +ethnicity\_Arab & 525 & 1.000 & 0.000 & 1 & 1 \\ +education\_0 & 522 & 0.025 & 0.156 & 0.000 & 1.000 \\ +education\_1 & 522 & 0.184 & 0.388 & 0.000 & 1.000 \\ +education\_2 & 522 & 0.170 & 0.376 & 0.000 & 1.000 \\ +education\_3 & 522 & 0.205 & 0.404 & 0.000 & 1.000 \\ +education\_4 & 522 & 0.308 & 0.462 & 0.000 & 1.000 \\ +education\_5 & 522 & 0.044 & 0.205 & 0.000 & 1.000 \\ +education\_6 & 522 & 0.063 & 0.244 & 0.000 & 1.000 \\ +education\_NA & 525 & 0.006 & 0.075 & 0 & 1 \\ +income\_0 & 418 & 0.199 & 0.399 & 0.000 & 1.000 \\ +income\_1 & 418 & 0.258 & 0.438 & 0.000 & 1.000 \\ +income\_2 & 418 & 0.189 & 0.392 & 0.000 & 1.000 \\ +income\_3 & 418 & 0.179 & 0.384 & 0.000 & 1.000 \\ +income\_4 & 418 & 0.141 & 0.349 & 0.000 & 1.000 \\ +income\_5 & 418 & 0.026 & 0.160 & 0.000 & 1.000 \\ +income\_6 & 418 & 0.002 & 0.049 & 0.000 & 1.000 \\ +income\_7 & 418 & 0.002 & 0.049 & 0.000 & 1.000 \\ +income\_8 & 418 & 0.002 & 0.049 & 0.000 & 1.000 \\ +income\_NA & 525 & 0.204 & 0.403 & 0 & 1 \\ +partyID\_C & 225 & 0.018 & 0.132 & 0.000 & 1.000 \\ +partyID\_L & 225 & 0.871 & 0.336 & 0.000 & 1.000 \\ +partyID\_R & 225 & 0.111 & 0.315 & 0.000 & 1.000 \\ +partyID\_NA & 525 & 0.571 & 0.495 & 0 & 1 \\ +\hline \\[-1.8ex] +\end{tabular} +\end{table} diff --git a/23/should_reproduce.txt b/23/should_reproduce.txt new file mode 100644 index 0000000000000000000000000000000000000000..f4ce5e86a2ffc1b7f46d3213801e851cf8e2f0b9 --- /dev/null +++ b/23/should_reproduce.txt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab2a736d96407e981ad3d471e4cf6d48b1c0d72d1adea6dec0bb480264ee450a +size 33