# Replication File for Appendix Survey Analyses # Table C1 in Appendix C1: The Effect of Excess Males on the Probability of Observing at least One Hate Crime # Table C2 in Appendix C2: The Effect of Excess Males on the Probability of Observing at least One Hate Crime (Different Definition of “Excess Males”) # Table C3 in Appendix C3: The Effect of Excess Males on the Probability of Observing at least One Hate Crime (linear probability model) # Table C4 in Appendix C4: The Effect of Excess Males on the Probability of Observing at least One Physical Attack # Table C5 in Appendix C5: Negative Binomial Regression # Table C6 in Appendix C6: Interaction between Excess Males and East/West Germany # Table C7 in Appendix C7: Interaction between Excess Males and Refugee Inflow # Table C9 in Appendix C9: Placebo Analysis # Appendix C10: Descriptive Statistics # R version 4.0.2 (2020-06-22) rm(list=ls()) # install.packages("readstata13") # readstata13_0.9.2 # install.packages("MASS") # MASS_7.3-51.6 # install.packages("sandwich") # sandwich_2.5-1 # install.packages("lmtest") # lmtest_0.9-37 # install.packages("stargazer") # stargazer_5.2.2 require(readstata13) # readstata13_0.9.2 require(MASS) # MASS_7.3-51.6 require(sandwich) # sandwich_2.5-1 require(lmtest) # lmtest_0.9-37 require(stargazer) # stargazer_5.2.2 source("Help.R") dat <- read.dta13("context.dta") dat_2015 <- dat[dat$year == 2015, ] dat_2016 <- dat[dat$year == 2016, ] dat_2017 <- dat[dat$year == 2017, ] dat_2015$Hate_all_muni_1517 <- dat_2015$Hate_all_muni + dat_2016$Hate_all_muni + dat_2017$Hate_all_muni dat_2015$Hate_all_muni_1517_bin <- ifelse(dat_2015$Hate_all_muni_1517 > 0, 1, 0) # Remove Extreme Value of Excess Males range_x <- quantile(dat_2015$pop_15_44_muni_gendergap_2015, c(0.025, 0.975), na.rm = TRUE) dat_2015_s <- dat_2015[dat_2015$pop_15_44_muni_gendergap_2015 >= range_x[1] & dat_2015$pop_15_44_muni_gendergap_2015 <= range_x[2], ] dat_s <- dat[dat$pop_15_44_muni_gendergap_2015 >= range_x[1] & dat$pop_15_44_muni_gendergap_2015 <= range_x[2], ] # ########################################## # Main Table (Table C1 in Appendix C1) # ########################################## bin_1_sum <- bin.summary(Hate_all_muni_1517_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + as.factor(ags_county), # county fixed effects id = "ags_county", data = dat_2015_s) bin_1_p <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + as.factor(ags_county) + as.factor(year), # county + year fixed effects id = "ags_county", data = dat_s) bin_2_sum <- bin.summary(Hate_all_muni_1517_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + as.factor(ags_county), # county fixed effects id = "ags_county", data = dat_2015_s) bin_2_p <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + as.factor(ags_county) + as.factor(year), # county + year fixed effects id = "ags_county", data = dat_s) bin_3_sum <- bin.summary(Hate_all_muni_1517_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(ags_state), # state fixed effects id = "ags_county", data = dat_2015_s) bin_3_p <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(ags_state) + as.factor(year), # state + year fixed effects id = "ags_county", data = dat_s) fit_list <- list(bin_1_sum$fit, bin_1_p$fit, bin_2_sum$fit, bin_2_p$fit, bin_3_sum$fit, bin_3_p$fit) se_list <- list(sqrt(diag(bin_1_sum$vcov)), sqrt(diag(bin_1_p$vcov)), sqrt(diag(bin_2_sum$vcov)), sqrt(diag(bin_2_p$vcov)), sqrt(diag(bin_3_sum$vcov)), sqrt(diag(bin_3_p$vcov))) star_out(stargazer(fit_list, se = se_list, covariate.labels = c("Excess Males (Age 15 - 44)", "Log (Population)","Log (Population Density)", "Log (Unemployment Rate)", "% of population change (2011 vs 2015)", "Vote share for AfD (2013)", "Log (Refugee Inflow) (2014 vs 2015)", "Log (Refugee Size) (2014)", "Log (General Violence per capita)", "% of High Education", "Change in Manufacturing Share (2011 vs 2015)", "Share of Manufacturing", "Male Disadvantage"), keep=c("pop_15_44_muni_gendergap_2015", "log_population_muni_2015", "log_popdens_muni_2015", "log_unemp_all_muni_2015", "d_pop1511_muni", "vote_afd_2013_muni", "log_ref_inflow_1514", "log_pop_ref_2014", "log_violence_percap_2015", "pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015")), name = "table_C1.tex") # ############################################### # Replicate Table with 25-44 (Table C2 in C2) # ############################################### range_x2 <- quantile(dat_2015$pop_25_44_muni_gendergap_2015, c(0.025, 0.975), na.rm = TRUE) dat_2015_s2 <- dat_2015[dat_2015$pop_25_44_muni_gendergap_2015 >= range_x2[1] & dat_2015$pop_25_44_muni_gendergap_2015 <= range_x2[2], ] dat_s2 <- dat[dat$pop_25_44_muni_gendergap_2015 >= range_x2[1] & dat$pop_25_44_muni_gendergap_2015 <= range_x2[2], ] bin_r_1_sum <- bin.summary(Hate_all_muni_1517_bin ~ pop_25_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + as.factor(ags_county), id = "ags_county", data = dat_2015_s2) bin_r_1_p <- bin.summary(Hate_all_muni_bin ~ pop_25_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + as.factor(ags_county) + as.factor(year), id = "ags_county", data = dat_s2) bin_r_2_sum <- bin.summary(Hate_all_muni_1517_bin ~ pop_25_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + as.factor(ags_county), id = "ags_county", data = dat_2015_s2) bin_r_2_p <- bin.summary(Hate_all_muni_bin ~ pop_25_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + as.factor(ags_county) + as.factor(year), id = "ags_county", data = dat_s2) bin_r_3_sum <- bin.summary(Hate_all_muni_1517_bin ~ pop_25_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(ags_state), id = "ags_county", data = dat_2015_s2) bin_r_3_p <- bin.summary(Hate_all_muni_bin ~ pop_25_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(ags_state) + as.factor(year), id = "ags_county", data = dat_s2) ## Table C2 in Appendix C2 fit_list2 <- list(bin_r_1_sum$fit, bin_r_1_p$fit, bin_r_2_sum$fit, bin_r_2_p$fit, bin_r_3_sum$fit, bin_r_3_p$fit) se_list2 <- list(sqrt(diag(bin_r_1_sum$vcov)), sqrt(diag(bin_r_1_p$vcov)), sqrt(diag(bin_r_2_sum$vcov)), sqrt(diag(bin_r_2_p$vcov)), sqrt(diag(bin_r_3_sum$vcov)), sqrt(diag(bin_r_3_p$vcov))) star_out(stargazer(fit_list2, se = se_list2, covariate.labels = c("Excess Males (Age 25 - 44)", "Log (Population)","Log (Population Density)", "Log (Unemployment Rate)", "% of population change (2011 vs 2015)", "Vote share for AfD (2013)", "Log (Refugee Inflow) (2014 vs 2015)", "Log (Refugee Size) (2014)", "Log (General Violence per capita)", "% of High Education", "Change in Manufacturing Share (2011 vs 2015)", "Share of Manufacturing", "Male Disadvantage"), keep=c("pop_25_44_muni_gendergap_2015", "log_population_muni_2015", "log_popdens_muni_2015", "log_unemp_all_muni_2015", "d_pop1511_muni", "vote_afd_2013_muni", "log_ref_inflow_1514", "log_pop_ref_2014", "log_violence_percap_2015", "pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015")), name = "table_C2.tex") # ##################################### # Linear Probability Model (Table C3 in Appendix C3) # ##################################### lm_1_sum <- lm.summary(Hate_all_muni_1517_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + as.factor(ags_county), id = "ags_county", data = dat_2015_s) lm_1_p <- lm.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + as.factor(ags_county) + as.factor(year), id = "ags_county", data = dat_s) lm_2_sum <- lm.summary(Hate_all_muni_1517_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + as.factor(ags_county), id = "ags_county", data = dat_2015_s) lm_2_p <- lm.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + as.factor(ags_county) + as.factor(year), id = "ags_county", data = dat_s) lm_3_sum <- lm.summary(Hate_all_muni_1517_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(ags_state), id = "ags_county", data = dat_2015_s) lm_3_p <- lm.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(ags_state) + as.factor(year), id = "ags_county", data = dat_s) fit_list_lm <- list(lm_1_sum$fit, lm_1_p$fit, lm_2_sum$fit, lm_2_p$fit, lm_3_sum$fit, lm_3_p$fit) se_list_lm <- list(sqrt(diag(lm_1_sum$vcov)), sqrt(diag(lm_1_p$vcov)), sqrt(diag(lm_2_sum$vcov)), sqrt(diag(lm_2_p$vcov)), sqrt(diag(lm_3_sum$vcov)), sqrt(diag(lm_3_p$vcov))) star_out(stargazer(fit_list_lm, se = se_list_lm, covariate.labels = c("Excess Males (Age 15 - 44)", "Log (Population)","Log (Population Density)", "Log (Unemployment Rate)", "% of population change (2011 vs 2015)", "Vote share for AfD (2013)", "Log (Refugee Inflow) (2014 vs 2015)", "Log (Refugee Size) (2014)", "Log (General Violence per capita)", "% of High Education", "Change in Manufacturing Share (2011 vs 2015)", "Share of Manufacturing", "Male Disadvantage"), keep=c("pop_15_44_muni_gendergap_2015", "log_population_muni_2015", "log_popdens_muni_2015", "log_unemp_all_muni_2015", "d_pop1511_muni", "vote_afd_2013_muni", "log_ref_inflow_1514", "log_pop_ref_2014", "log_violence_percap_2015", "pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015")), name = "table_C3.tex") ## ############################################ ## Physical Attacks (Table C4 in Appendix C4) ## ############################################ dat_2015$Physical_muni_1517 <- dat_2015$Physical_muni + dat_2016$Physical_muni + dat_2017$Physical_muni dat_2015$Physical_muni_1517_bin <- ifelse(dat_2015$Physical_muni_1517 > 0, 1, 0) range_x <- quantile(dat_2015$pop_15_44_muni_gendergap_2015, c(0.025, 0.975), na.rm = TRUE) dat_2015_s <- dat_2015[dat_2015$pop_15_44_muni_gendergap_2015 >= range_x[1] & dat_2015$pop_15_44_muni_gendergap_2015 <= range_x[2], ] dat_s <- dat[dat$pop_15_44_muni_gendergap_2015 >= range_x[1] & dat$pop_15_44_muni_gendergap_2015 <= range_x[2], ] bin_phys_1_sum <- bin.summary(Physical_muni_1517_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + as.factor(ags_county), id = "ags_county", data = dat_2015_s) bin_phys_1_p <- bin.summary(Physical_muni_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + as.factor(ags_county) + as.factor(year), id = "ags_county", data = dat_s) bin_phys_2_sum <- bin.summary(Physical_muni_1517_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + as.factor(ags_county), id = "ags_county", data = dat_2015_s) bin_phys_2_p <- bin.summary(Physical_muni_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + as.factor(ags_county) + as.factor(year), id = "ags_county", data = dat_s) bin_phys_3_sum <- bin.summary(Physical_muni_1517_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(ags_state), id = "ags_county", data = dat_2015_s) bin_phys_3_p <- bin.summary(Physical_muni_bin ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(ags_state) + as.factor(year), id = "ags_county", data = dat_s) ## Table C4 in Appendix C4 fit_list_bin_phys <- list(bin_phys_1_sum$fit, bin_phys_1_p$fit, bin_phys_2_sum$fit, bin_phys_2_p$fit, bin_phys_3_sum$fit, bin_phys_3_p$fit) se_list_bin_phys <- list(sqrt(diag(bin_phys_1_sum$vcov)), sqrt(diag(bin_phys_1_p$vcov)), sqrt(diag(bin_phys_2_sum$vcov)), sqrt(diag(bin_phys_2_p$vcov)), sqrt(diag(bin_phys_3_sum$vcov)), sqrt(diag(bin_phys_3_p$vcov))) star_out(stargazer(fit_list_bin_phys, se = se_list_bin_phys, covariate.labels = c("Excess Males (Age 15 - 44)", "Log (Population)","Log (Population Density)", "Log (Unemployment Rate)", "% of population change (2011 vs 2015)", "Vote share for AfD (2013)", "Log (Refugee Inflow) (2014 vs 2015)", "Log (Refugee Size) (2014)", "Log (General Violence per capita)", "% of High Education", "Change in Manufacturing Share (2011 vs 2015)", "Share of Manufacturing", "Male Disadvantage"), keep=c("pop_15_44_muni_gendergap_2015", "log_population_muni_2015", "log_popdens_muni_2015", "log_unemp_all_muni_2015", "d_pop1511_muni", "vote_afd_2013_muni", "log_ref_inflow_1514", "log_pop_ref_2014", "log_violence_percap_2015", "pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015")), name = "table_C4.tex") ## ######################################## ## Appendix C5: Count Model ## ######################################## rm(list=ls()) dat <- read.dta13("context.dta") source("Help.R") dat_2015 <- dat[dat$year == 2015, ] dat_2016 <- dat[dat$year == 2016, ] dat_2017 <- dat[dat$year == 2017, ] dat_2015$Hate_all_muni_1517 <- dat_2015$Hate_all_muni + dat_2016$Hate_all_muni + dat_2017$Hate_all_muni # Remove Extreme Value of Excess Males range_x <- quantile(dat_2015$pop_15_44_muni_gendergap_2015, c(0.025, 0.975), na.rm = TRUE) dat_2015_s <- dat_2015[dat_2015$pop_15_44_muni_gendergap_2015 >= range_x[1] & dat_2015$pop_15_44_muni_gendergap_2015 <= range_x[2], ] dat_s <- dat[dat$pop_15_44_muni_gendergap_2015 >= range_x[1] & dat$pop_15_44_muni_gendergap_2015 <= range_x[2], ] for_s <- as.formula(Hate_all_muni_1517 ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(ags_state)) # state fixed effects for_p <- as.formula(Hate_all_muni ~ pop_15_44_muni_gendergap_2015 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(ags_state) + as.factor(year)) # state + year fixed effects ## Use boostrap to compute standard errors (Use the "out_count.rdata" to get the exact same estimates) ## Note: the following codes take a while to run full_run <- FALSE if(full_run == TRUE){ # Sum nb_1_sum_b <- glm.boot(for_s, family = "negative-binomial", data = dat_2015_s, cluster_id = dat_2015_s$ags_county) # Panel nb_1_p_b <- glm.boot(for_p, family = "negative-binomial", data = dat_s, cluster_id = dat_s$ags_county) fit_list_nb <- list(nb_1_sum_b$fit, nb_1_p_b$fit) se_list_nb <- list(nb_1_sum_b$se, nb_1_p_b$se) out_count_table <- list(fit_list_nb, se_list_nb) save(out_count_table, file = "out_count_table.rdata") } load(file = "out_count_table.rdata") fit_list_nb <- out_count_table[[1]] se_list_nb <- out_count_table[[2]] star_out(stargazer(fit_list_nb, se = se_list_nb, covariate.labels = c("Excess Males (Age 15 - 44)", "Log (Population)","Log (Population Density)", "Log (Unemployment Rate)", "% of population change (2011 vs 2015)", "Vote share for AfD (2013)", "Log (Refugee Inflow) (2014 vs 2015)", "Log (Refugee Size) (2014)", "Log (General Violence per capita)", "% of High Education", "Change in Manufacturing Share (2011 vs 2015)", "Share of Manufacturing", "Male Disadvantage"), keep=c("pop_15_44_muni_gendergap_2015", "log_population_muni_2015", "log_popdens_muni_2015", "log_unemp_all_muni_2015", "d_pop1511_muni", "vote_afd_2013_muni", "log_ref_inflow_1514", "log_pop_ref_2014", "log_violence_percap_2015", "pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015")), name = "table_C5.tex") ## ######################################################################## ## Replicate Tables with East/West Interaction (Table C6 in Appendix C6) ## ######################################################################## rm(list=ls()) dat <- read.dta13("context.dta") source("Help.R") dat_2015 <- dat[dat$year == 2015, ] dat_2016 <- dat[dat$year == 2016, ] dat_2017 <- dat[dat$year == 2017, ] dat_2015$Hate_all_muni_1517 <- dat_2015$Hate_all_muni + dat_2016$Hate_all_muni + dat_2017$Hate_all_muni dat_2015$Hate_all_muni_1517_bin <- as.numeric(dat_2015$Hate_all_muni_1517 > 0) # Remove Extreme Value of Excess Males range_x <- quantile(dat_2015$pop_15_44_muni_gendergap_2015, c(0.025, 0.975), na.rm = TRUE) dat_2015_s <- dat_2015[dat_2015$pop_15_44_muni_gendergap_2015 >= range_x[1] & dat_2015$pop_15_44_muni_gendergap_2015 <= range_x[2], ] dat_s <- dat[dat$pop_15_44_muni_gendergap_2015 >= range_x[1] & dat$pop_15_44_muni_gendergap_2015 <= range_x[2], ] dat_2015_s$west <- 1 - dat_2015_s$east dat_s$west <- 1 - dat_s$east bin_2_sum_ew <- bin.summary(Hate_all_muni_1517_bin ~ pop_15_44_muni_gendergap_2015 + west + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015, id = "ags_county", data = dat_2015_s) bin_2_p_ew <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_2015 + west + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(year), id = "ags_county", data = dat_s) bin_3_sum_ew <- bin.summary(Hate_all_muni_1517_bin ~ pop_15_44_muni_gendergap_2015*west + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015, id = "ags_county", data = dat_2015_s) bin_3_p_ew <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_2015*west + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(year), id = "ags_county", data = dat_s) fit_list_ew <- list(bin_2_sum_ew$fit, bin_2_p_ew$fit, bin_3_sum_ew$fit, bin_3_p_ew$fit) se_list_ew <- list(sqrt(diag(bin_2_sum_ew$vcov)), sqrt(diag(bin_2_p_ew$vcov)), sqrt(diag(bin_3_sum_ew$vcov)), sqrt(diag(bin_3_p_ew$vcov))) star_out(stargazer(fit_list_ew, se = se_list_ew, covariate.labels = c("Excess Males (Age 15 - 44)", "West", "Log (Population)","Log (Population Density)", "Log (Unemployment Rate)", "% of population change (2011 vs 2015)", "Vote share for AfD (2013)", "Log (Refugee Inflow) (2014 vs 2015)", "Log (Refugee Size) (2014)", "Log (General Violence per capita)", "% of High Education", "Change in Manufacturing Share (2011 vs 2015)", "Share of Manufacturing", "Male Disadvantage", "Excess Males x West"), keep=c("pop_15_44_muni_gendergap_2015", "west", "log_population_muni_2015", "log_popdens_muni_2015", "log_unemp_all_muni_2015", "d_pop1511_muni", "vote_afd_2013_muni", "log_ref_inflow_1514", "log_pop_ref_2014", "log_violence_percap_2015", "pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015", "pop_15_44_muni_gendergap_2015:west")), name = "table_C6.tex") ## ############################################################## ## Interaction with Refugee Inflow (Table C7 in Appendix C7) ## ############################################################## bin_sum_int <- bin.summary(Hate_all_muni_1517_bin ~ pop_15_44_muni_gendergap_2015*log_ref_inflow_1514 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(ags_state), id = "ags_county", data = dat_2015_s) bin_p_int <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_2015*log_ref_inflow_1514 + log_population_muni_2015 + log_popdens_muni_2015 + log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni + log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_2015 + as.factor(ags_state) + as.factor(year), id = "ags_county", data = dat_s) ## Table C7 in Appendix C7 fit_list_int <- list(bin_sum_int$fit, bin_p_int$fit) se_list_int <- list(sqrt(diag(bin_sum_int$vcov)), sqrt(diag(bin_p_int$vcov))) star_out(stargazer(fit_list_int, se = se_list_int, covariate.labels = c("Excess Males (Age 15 - 44)", "Log (Refugee Inflow) (2014 vs 2015)", "Log (Population)","Log (Population Density)", "Log (Unemployment Rate)", "% of population change (2011 vs 2015)", "Vote share for AfD (2013)", "Log (Refugee Size) (2014)", "Log (General Violence per capita)", "% of High Education", "Change in Manufacturing Share (2011 vs 2015)", "Share of Manufacturing", "Male Disadvantage", "Excess Males × Log (Refugee Inflow)"), keep=c("pop_15_44_muni_gendergap_2015", "log_ref_inflow_1514", "log_population_muni_2015", "log_popdens_muni_2015", "log_unemp_all_muni_2015", "d_pop1511_muni", "vote_afd_2013_muni", "log_pop_ref_2014", "log_violence_percap_2015", "pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015", "pop_15_44_muni_gendergap_2015:log_ref_inflow_1514")), name = "table_C7.tex") # ################################################################ # Appendix C9. Placebo Analysis # ############################################################### rm(list=ls()) dat_pl <- read.dta13("context_placebo.dta") # data for placebo analysis source("Help.R") dat_2015_s <- dat_pl[dat_pl$year == 2015, ] dat_2016_s <- dat_pl[dat_pl$year == 2016, ] dat_2017_s <- dat_pl[dat_pl$year == 2017, ] # ########################################## # 2015 # ########################################## # main model + Placebo Treatment bin_15_sum_pl <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_future + pop_15_44_muni_gendergap_anu + log(population_muni_anu) + log(popdens_muni_anu) + log_unemp_all_muni_anu + d_pop_muni_anu + vote_afd_2013_muni + log_ref_inflow_anu + log(pop_ref_anu) + log(violence_percap_anu) + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_anu + as.factor(ags_state), # state fixed effects id = "ags_county", data = dat_2015_s) # ########################################## # 2016 # ########################################## # Main model + Placebo bin_16_sum_pl <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_future + pop_15_44_muni_gendergap_anu + log(population_muni_anu) + log(popdens_muni_anu) + log_unemp_all_muni_anu + d_pop_muni_anu + vote_afd_2013_muni + log_ref_inflow_anu + log(pop_ref_anu) + log(violence_percap_anu) + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_anu + as.factor(ags_state), # state fixed effects id = "ags_county", data = dat_2016_s) # ########################## # 2017 # ######################### # Main model + Placebo bin_17_sum_pl <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_future + pop_15_44_muni_gendergap_anu + log(population_muni_anu) + log(popdens_muni_anu) + log_unemp_all_muni_anu + d_pop_muni_anu + vote_afd_2013_muni + log_ref_inflow_anu + log(pop_ref_anu) + log(violence_percap_anu) + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_anu + as.factor(ags_state), # state fixed effects id = "ags_county", data = dat_2017_s) ## #################### ## Pooled Analysis ## #################### # Final model + Placebo bin_pool_sum_pl <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_future + pop_15_44_muni_gendergap_anu + log(population_muni_anu) + log(popdens_muni_anu) + log_unemp_all_muni_anu + d_pop_muni_anu + vote_afd_2013_muni + log_ref_inflow_anu + log(pop_ref_anu) + log(violence_percap_anu) + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_anu + as.factor(ags_state) + as.factor(year), # state + year fixed effects id = "ags_county", data = dat_pl) # Repeat the analysis for Large Counties dat_pool_s_l <- dat_pl[dat_pl$population_muni_anu > quantile(dat_pl$population_muni_anu, prob = 0.5), ] # Main model + Placebo bin_pool_sum_pl_l <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_future + pop_15_44_muni_gendergap_anu + log(population_muni_anu) + log(popdens_muni_anu) + log_unemp_all_muni_anu + d_pop_muni_anu + vote_afd_2013_muni + log_ref_inflow_anu + log(pop_ref_anu) + log(violence_percap_anu) + ## county level pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level unemp_gendergap_anu + as.factor(ags_state) + as.factor(year), # state + year fixed effects id = "ags_county", data = dat_pool_s_l) # Table pl_fit_list_m <- list(bin_15_sum_pl$fit, bin_16_sum_pl$fit, bin_17_sum_pl$fit, bin_pool_sum_pl$fit, bin_pool_sum_pl_l$fit) pl_se_list_m <- list(sqrt(diag(bin_15_sum_pl$vcov)), sqrt(diag(bin_16_sum_pl$vcov)), sqrt(diag(bin_17_sum_pl$vcov)), sqrt(diag(bin_pool_sum_pl$vcov)), sqrt(diag(bin_pool_sum_pl_l$vcov))) star_out(stargazer(pl_fit_list_m, se = pl_se_list_m, covariate.labels = c("Future-Treatment"), keep=c("pop_15_44_muni_gendergap_future")), name = "table_C9.tex") # ############################## # Appendix C10. Descriptive Statistics # ############################## rm(list=ls()) dat <- read.dta13("context.dta") min15 <- round(min(dat$pc_ref_male[dat$year == 2015], na.rm = TRUE),2) min16 <- round(min(dat$pc_ref_male[dat$year == 2016], na.rm = TRUE),2) pdf("figure_C10.pdf", height = 5, width = 10) par(mfrow = c(1, 2)) plot(density(dat$pc_ref_male[dat$year == 2015], na.rm = TRUE), main = "Proportion of Male Refugees (2015)", xlim = c(50, 100), xlab = "Percent of Male Refugees Among Refugees (county)") text(x = 90, y = 0.08, paste0("min = ", min15), font = 2) polygon(density(dat$pc_ref_male[dat$year == 2015], na.rm = TRUE)$x, density(dat$pc_ref_male[dat$year == 2015], na.rm = TRUE)$y,col='grey80') plot(density(dat$pc_ref_male[dat$year == 2016], na.rm = TRUE), main = "Proportion of Male Refugees (2016)", xlim = c(50, 100), xlab = "Percent of Male Refugees Among Refugees (county)") text(x = 90, y = 0.12, paste0("min = ", min16), font = 2) polygon(density(dat$pc_ref_male[dat$year == 2016], na.rm = TRUE)$x, density(dat$pc_ref_male[dat$year == 2016], na.rm = TRUE)$y,col='grey80') dev.off()