## Voting and racial attitudes (blacks only) ## Brian T. Hamel and Bryan Wilcox-Archuleta ## First: 24 September 2019 ## Last: 19 March 2020 ## Loading packages ## install.packages(c("estimatr", "tidyverse", "magrittr", "texreg", "gridExtra", "scales")) library(estimatr) library(tidyverse) library(magrittr) library(texreg) library(gridExtra) library(scales) ## Loading data load("01_data/dta.RData") ## Create shell shell = dta %>% filter(black == 1) %$% expand.grid(racial_flux = seq(min(racial_flux, na.rm = TRUE), max(racial_flux, na.rm = TRUE), by = 1), pid7 = round(mean(pid7, na.rm = TRUE), digits = 2), ideo5 = round(mean(ideo5, na.rm = TRUE), digits = 2), female = round(mean(female, na.rm = TRUE), digits = 2), age = round(mean(age, na.rm = TRUE), digits = 2), faminc = round(mean(faminc, na.rm = TRUE), digit = 2), educ = round(mean(educ, na.rm = TRUE), digits = 2), pct_white = round(mean(pct_white, na.rm = TRUE), digits = 2), pct_black = round(mean(pct_black, na.rm = TRUE), digits = 2), pct_unemployed = round(mean(pct_unemployed, na.rm = TRUE), digits = 2), pct_college = round(mean(pct_college, na.rm = TRUE), digits = 2), log_per_cap_inc = round(mean(log_per_cap_inc, na.rm = TRUE), digits = 2), gini = round(mean(gini, na.rm = TRUE), digits = 2), south = round(mean(south, na.rm = TRUE), digits = 2), non_rural = round(mean(non_rural, na.rm = TRUE), digits = 2), log_pop_density = round(mean(log_pop_density, na.rm = TRUE), digits = 2)) %>% na.omit() ## Models and predicted probabilities pres_dem = lm_robust(pres_dem ~ racial_flux + pid7 + ideo5 + female + age + faminc + educ + pct_white + pct_black + pct_unemployed + pct_college + log_per_cap_inc + gini + south + non_rural + log_pop_density, data = dta %>% filter(black == 1), clusters = zipcode, se_type = "stata") pred_pres_dem = cbind(predict(pres_dem, shell, se.fit = TRUE, type = "response"), shell) house_dem = lm_robust(house_dem ~ racial_flux + pid7 + ideo5 + female + age + faminc + educ + pct_white + pct_black + pct_unemployed + pct_college + log_per_cap_inc + gini + south + non_rural + log_pop_density, data = dta %>% filter(black == 1), clusters = zipcode, se_type = "stata") pred_house_dem = cbind(predict(house_dem, shell, se.fit = TRUE, type = "response"), shell) rr = lm_robust(mean_rr ~ racial_flux + pid7 + ideo5 + female + age + faminc + educ + pct_white + pct_black + pct_unemployed + pct_college + log_per_cap_inc + gini + south + non_rural + log_pop_density, data = dta %>% filter(black == 1), clusters = zipcode, se_type = "stata") pred_rr = cbind(predict(rr, shell, se.fit = TRUE, type = "response"), shell) affirm = lm_robust(affirm ~ racial_flux + pid7 + ideo5 + female + age + faminc + educ + pct_white + pct_black + pct_unemployed + pct_college + log_per_cap_inc + gini + south + non_rural + log_pop_density, data = dta %>% filter(black == 1), clusters = zipcode, se_type = "stata") pred_affirm = cbind(predict(affirm, shell, se.fit = TRUE, type = "response"), shell) ## Table of coefs., and save ############## ## TABLE A6 ## ############## texreg(list(pres_dem, house_dem, rr, affirm), file = "03_output/blacks.tex", label = "blacks", caption = "Racial Flux, Voting Behavior, and Racial Attitudes (Blacks)", custom.model.names = c("\\textit{President}", "\\textit{U.S. House}", "\\textit{Racial Resentment}", "\\textit{Affirmative Action}"), custom.coef.names = c("Intercept", "Racial Flux", "Party ID", "Ideology", "Female", "Age", "Family Income", "Education", "% White", "% Black", "% Unemployed", "% College", "log(Per Capita Income)", "Gini Coef.", "South", "Non-Rural", "log(Pop. Density)"), reorder.coef = c(2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1), custom.gof.names = c(NA, NA, "Observations", NA, NA), stars = c(0.05, 0.01, 0.001), digits = 3, center = TRUE, include.ci = FALSE, caption.above = TRUE) ## Plot, and save pred_pres_dem = cbind(pred_pres_dem, outcome = "President") pred_house_dem = cbind(pred_house_dem, outcome = "U.S. House") pred_vote = bind_rows(pred_pres_dem, pred_house_dem) %>% mutate(upper = fit + 1.96 * se.fit, lower = fit - 1.96 * se.fit) vote_plot = ggplot(pred_vote, aes(x = racial_flux, y = fit, ymin = lower, ymax = upper)) + geom_line(color = "red4") + geom_ribbon(alpha = .2, fill = "red1") + facet_wrap(~ outcome, nrow = 1, scales = "free") + labs(y = "Pr(Vote Democrat)", x = "") + geom_rug(data = dta %>% filter(black == 1), aes(x = racial_flux), inherit.aes = FALSE, sides = "b") + scale_y_continuous(labels = number_format(accuracy = 0.01)) + theme(legend.title = element_blank(), panel.spacing = unit(1, "lines"), axis.line.y = element_blank()) pred_rr = cbind(pred_rr, outcome = "Racial Resentment") pred_affirm = cbind(pred_affirm, outcome = "Affirmative Action") pred_att = bind_rows(pred_rr, pred_affirm) %>% mutate(upper = fit + 1.96 * se.fit, lower = fit - 1.96 * se.fit) pred_att$outcome = factor(pred_att$outcome, levels = c("Racial Resentment", "Affirmative Action")) ############### ## FIGURE A2 ## ############### att_plot = ggplot(pred_att, aes(x = racial_flux, y = fit, ymin = lower, ymax = upper)) + geom_line(color = "red4") + geom_ribbon(alpha = .2, fill = "red1") + facet_wrap(~ outcome, nrow = 1, scales = "free") + labs(y = "Predicted Attitude", x = "Racial Flux") + geom_rug(data = dta %>% filter(black == 1), aes(x = racial_flux), inherit.aes = FALSE, sides = "b") + scale_y_continuous(labels = number_format(accuracy = 0.01)) + theme(legend.title = element_blank(), panel.spacing = unit(1, "lines"), axis.line.y = element_blank()) main = grid.arrange(vote_plot, att_plot, ncol = 1, nrow = 2) ggsave(main, file = "03_output/blacks.png", height = 4, width = 4, units = "in", dpi = 600) ## Clear R rm(list = ls())