# Replication File for # Figure 1 (Effects of Excess Males on Prob of Hate Crime) # Appendix: Figure C8 (Effects of Male Diadvantage on Prob of Hate Crime) # 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 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 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$Hate_all_muni_1517 > 0, 1, 0) ## ######################### ## Main Figure (Figure 1) ## ######################### # Remove Extreme Value of Excess Males range_x <- quantile(dat_2015$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], ] # sum bin_1_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), id = "ags_county", data = dat_2015_s) # annual bin_1_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), id = "ags_county", data = dat_s) # Excess Males # Effect Estimation bin_1_sum_effect <- marginal_effect(bin_1_sum, newdata = dat_2015_s, family = "logit", main_var = "pop_15_44_muni_gendergap_2015", difference = TRUE, treat_range = c(1, 1.2)) bin_1_p_effect <- marginal_effect(bin_1_p, newdata = dat_s, family = "logit", main_var = "pop_15_44_muni_gendergap_2015", difference = TRUE, treat_range = c(1, 1.2)) # Dose function bin_1_sum_dose <- marginal_effect(bin_1_sum, newdata = dat_2015_s, family = "logit", main_var = "pop_15_44_muni_gendergap_2015") bin_1_p_dose <- marginal_effect(bin_1_p, newdata = dat_s, family = "logit", main_var = "pop_15_44_muni_gendergap_2015") # Male Diadvantage # Effect Estimation bin_1_sum_gap <- marginal_effect(bin_1_sum, newdata = dat_2015_s, family = "logit", main_var = "unemp_gendergap_2015", difference = TRUE, treat_range = c(1, 1.15)) bin_1_p_gap <- marginal_effect(bin_1_p, newdata = dat_s, family = "logit", main_var = "unemp_gendergap_2015", difference = TRUE, treat_range = c(1, 1.15)) # Dose Function bin_1_sum_gap_dose <- marginal_effect(bin_1_sum, newdata = dat_2015_s, family = "logit", main_var = "unemp_gendergap_2015") bin_1_p_gap_dose <- marginal_effect(bin_1_p, newdata = dat_s, family = "logit", main_var = "unemp_gendergap_2015") # ##################### # Plot Effects (Figure 1) # ##################### point <- c(bin_1_sum_effect$out_main[2], bin_1_p_effect$out_main[2]) high <- c(bin_1_sum_effect$out_main[3], bin_1_p_effect$out_main[3]) low <- c(bin_1_sum_effect$out_main[1], bin_1_p_effect$out_main[1]) ## Short Panel marginal_list <- list() marginal_list[[1]] <- bin_1_sum_dose marginal_list[[2]] <- bin_1_p_dose title_c <- c("Predicted Probability: Sum", "Predicted Probability: Annual") # Plot Dose function pdf("figure_1.pdf", height = 4, width = 11) par(mfrow = c(1,3), mar = c(4.5, 2, 4, 1), oma = c(0, 2, 0, 0)) for(i in 1:2){ plot_coef_all <- do.call("rbind", marginal_list[[i]]$out_main) plot_x <- marginal_list[[i]]$treat_range if(i == 1){ ylim_u <- c(0.14, 0.23) ylab_u <- "" } if(i == 2){ ylim_u <- c(0.06, 0.11) ylab_u <- "" } plot(plot_x, plot_coef_all[ ,2], pch = 19, main = paste("", title_c[i], sep = ""), ylim = ylim_u, xlab = "Excess Males", ylab = ylab_u, col = "black", cex = 2, type = "l", lwd = 3, cex.lab = 1.5, cex.main = 1.5, cex.axis = 1.5) lines(plot_x, plot_coef_all[ ,1], col = "black", lty = 2) lines(plot_x, plot_coef_all[ ,3], col = "black", lty = 2) abline(h = marginal_list[[i]]$sample, lty = 2, col = "red", lwd = 2) polygon(c(plot_x, rev(plot_x)), c(plot_coef_all[ ,1], rev(plot_coef_all[ ,3])), col = adjustcolor("black", 0.2), border = NA) par(new=TRUE) hist(dat_2015_s$pop_15_44_muni_gendergap_2015, freq = FALSE, breaks = seq(from = 0, to = 6, by = 0.01), xlim = c(min(plot_x), max(plot_x)), xaxt = "n", yaxt = "n", xlab = "", ylab = "", ylim = c(0, 40), main ="") } par(mar = c(4.5, 5, 4, 1)) plot(seq(1:2), point, ylim = c(-0.005, 0.045), ylab = "Effects on Prob (hate crime)", main = "Effects of Excess Males", xlim = c(0.5, 2.5), xlab = "Outcome Types", xaxt = "n", pch = c(19, 15), cex.lab = 1.5, cex.axis = 1.5, cex.main = 1.5) segments(seq(1:6), low, seq(1:6), high, lwd = 2, c(rep("black",2), rep("black",2), rep("black",2))) Axis(side = 1, at = c(1, 2), labels = c("Sum", "Annual"), cex.axis = 1.5) abline(h = 0, lty = 2) mtext(side = 2, at = 0.5, "Prob (hate crime)", outer = TRUE, line = 0.5) dev.off() # ######################## # Figure C.8 in Appendix # ######################## # Plot Effects point_g <- c(bin_1_sum_gap$out_main[2], bin_1_p_gap$out_main[2]) high_g <- c(bin_1_sum_gap$out_main[3], bin_1_p_gap$out_main[3]) low_g <- c(bin_1_sum_gap$out_main[1], bin_1_p_gap$out_main[1]) ## Short Panel marginal_list_g <- list() marginal_list_g[[1]] <- bin_1_sum_gap_dose marginal_list_g[[2]] <- bin_1_p_gap_dose title_c <- c("Predicted Probability: Sum", "Predicted Probability: Annual") # Plot Dose function pdf("figure_C8.pdf", height = 4, width = 11) par(mfrow = c(1,3), mar = c(4.5, 2, 4, 1), oma = c(0, 2, 0, 0)) for(i in 1:2){ plot_coef_all <- do.call("rbind", marginal_list_g[[i]]$out_main) plot_x <- marginal_list_g[[i]]$treat_range if(i <=1) ylim_u <- c(0.14, 0.22) if(i > 1) ylim_u <- c(0.06, 0.11) plot(plot_x, plot_coef_all[ ,2], pch = 19, main = paste("", title_c[i], sep = ""), ylim = ylim_u, xlab = "Male Disadvantage", ylab = "", col = "black", cex = 2, type = "l", lwd = 3, cex.lab = 1.5, cex.axis = 1.5, cex.main = 1.5) lines(plot_x, plot_coef_all[ ,1], col = "black", lty = 2) lines(plot_x, plot_coef_all[ ,3], col = "black", lty = 2) abline(h = marginal_list_g[[i]]$sample, lty = 2, col = "red", lwd = 2) polygon(c(plot_x, rev(plot_x)), c(plot_coef_all[ ,1], rev(plot_coef_all[ ,3])), col = adjustcolor("black", 0.2), border = NA) par(new=TRUE) hist(dat_2015_s$unemp_gendergap_2015, freq = FALSE, breaks = seq(from = 0, to = 6, by = 0.01), xlim = c(min(plot_x), max(plot_x)), xaxt = "n", yaxt = "n", xlab = "", ylab = "", ylim = c(0, 40), main ="") } par(mar = c(4.5, 5, 4, 1)) plot(seq(1:2), point_g, ylim = c(-0.01, 0.035), ylab = "Effects on Prob (hate crime)", main = "Effects of Male Disadvantage", xlim = c(0.5, 2.5), xlab = "Outcome Types", xaxt = "n", pch = c(19, 15), cex.lab = 1.5, cex.axis = 1.5, cex.main = 1.5) segments(seq(1:6), low_g, seq(1:6), high_g, lwd = 2, c(rep("black",2), rep("black",2), rep("black",2))) Axis(side = 1, at = c(1, 2), labels = c("Sum", "Annual"), cex.axis = 1.25) abline(h = 0, lty = 2) mtext(side = 2, at = 0.5, "Prob (hate crime)", outer = TRUE, line = 0.5) dev.off()