## Voting and racial attitudes (whites only) -- robustness checks for JOP ## Brian T. Hamel and Bryan Wilcox-Archuleta ## First: 11 March 2020 ## Last: 16 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) library(lme4) ## Loading data load("01_data/dta.RData") ## Number of people per zipcode people_per_zip = dta %>% group_by(zipcode) %>% mutate(n = 1) %>% summarise(tot_people = sum(n, na.rm = TRUE)) mean(people_per_zip$tot_people) sd(people_per_zip$tot_people) people_per_zip %>% filter(tot_people >= 30) ## Re-estimating the main models with random slope and intercept pres_dem = lmer(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 + (1 | zipcode), data = dta %>% filter(white == 1)) house_dem = lmer(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 + (1 | zipcode), data = dta %>% filter(white == 1)) rr = lmer(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 + (1 | zipcode), data = dta %>% filter(white == 1)) affirm = lmer(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 + (1 | zipcode), data = dta %>% filter(white == 1)) ## Table of coefs., and save ############## ## TABLE A4 ## ############## texreg(list(pres_dem, house_dem, rr, affirm), file = "03_output/mlm.tex", label = "mlm", caption = "Racial Flux, Voting Behavior, and Racial Attitudes (Whites) --- Models with Random Intercept for Zip Code", 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), include.loglik = FALSE, custom.gof.names = c(NA, NA, "\\# of Individuals", "\\# of Zip Codes", NA, NA), stars = c(0.05, 0.01, 0.001), digits = 3, center = TRUE, include.ci = FALSE, caption.above = TRUE, scalebox = 0.9) ## Adding past racial segregation -- 90 pres_dem_zseg90 = 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 + zipcode_dissim_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") pres_dem_cseg90 = 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 + county_dissim_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") house_dem_zseg90 = 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 + zipcode_dissim_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") house_dem_cseg90 = 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 + county_dissim_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") rr_zseg90 = 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 + zipcode_dissim_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") rr_cseg90 = 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 + county_dissim_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") affirm_zseg90 = 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 + zipcode_dissim_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") affirm_cseg90 = 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 + county_dissim_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") ## Table of coefs., and save ############## ## TABLE A8 ## ############## texreg(list(pres_dem_zseg90, pres_dem_cseg90, house_dem_zseg90, house_dem_cseg90, rr_zseg90, rr_cseg90, affirm_zseg90, affirm_cseg90), file = "03_output/seg90.tex", label = "seg90", caption = "Racial Flux, Voting Behavior, and Racial Attitudes (Whites) --- Controlling for Racial Segregation in 1990", custom.model.names = c("\\textit{President}", "\\textit{President}", "\\textit{U.S. House}", "\\textit{U.S. House}", "\\textit{Racial Resentment}", "\\textit{Racial Resentment}", "\\textit{Affirmative Action}", "\\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)", "Zipcode Dissimilarity", "County Dissimilarity"), reorder.coef = c(2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 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, scalebox = 0.7) ## Adding past racial segregation -- 00 pres_dem_zseg00 = 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 + zipcode_dissim_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") pres_dem_cseg00 = 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 + county_dissim_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") house_dem_zseg00 = 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 + zipcode_dissim_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") house_dem_cseg00 = 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 + county_dissim_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") rr_zseg00 = 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 + zipcode_dissim_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") rr_cseg00 = 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 + county_dissim_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") affirm_zseg00 = 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 + zipcode_dissim_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") affirm_cseg00 = 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 + county_dissim_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") ## Table of coefs., and save ############## ## TABLE A9 ## ############## texreg(list(pres_dem_zseg00, pres_dem_cseg00, house_dem_zseg00, house_dem_cseg00, rr_zseg00, rr_cseg00, affirm_zseg00, affirm_cseg00), file = "03_output/seg00.tex", label = "seg00", caption = "Racial Flux, Voting Behavior, and Racial Attitudes (Whites) --- Controlling for Racial Segregation in 2000", custom.model.names = c("\\textit{President}", "\\textit{President}", "\\textit{U.S. House}", "\\textit{U.S. House}", "\\textit{Racial Resentment}", "\\textit{Racial Resentment}", "\\textit{Affirmative Action}", "\\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)", "Zipcode Dissimilarity", "County Dissimilarity"), reorder.coef = c(2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 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, scalebox = 0.7) ## Adding past racial conflict pres_dem_conflict = 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 + goldwater + protest, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") house_dem_conflict = 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 + goldwater + protest, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") rr_conflict = 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 + goldwater + protest, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") affirm_conflict = 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 + goldwater + protest, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") ## Table of coefs., and save ############## ## TABLE A10 ## ############## texreg(list(pres_dem_conflict, house_dem_conflict, rr_conflict, affirm_conflict), file = "03_output/conflict.tex", label = "conflict", caption = "Racial Flux, Voting Behavior, and Racial Attitudes (Whites) --- Controlling for Past Racial and Political Conflict", 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)", "Support for Goldwater", "Civil Rights Protest"), reorder.coef = c(2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 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, scalebox = 0.7) ## Adding past racial income gap -- 90 pres_dem_zinc90 = 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 + zipcode_inc_gap_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") pres_dem_cinc90 = 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 + county_inc_gap_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") house_dem_zinc90 = 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 + zipcode_inc_gap_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") house_dem_cinc90 = 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 + county_inc_gap_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") rr_zinc90 = 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 + zipcode_inc_gap_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") rr_cinc90 = 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 + county_inc_gap_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") affirm_zinc90 = 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 + zipcode_inc_gap_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") affirm_cinc90 = 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 + county_inc_gap_90, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") ## Table of coefs., and save ############### ## TABLE A11 ## ############### texreg(list(pres_dem_zinc90, pres_dem_cinc90, house_dem_zinc90, house_dem_cinc90, rr_zinc90, rr_cinc90, affirm_zinc90, affirm_cinc90), file = "03_output/inc90.tex", label = "inc90", caption = "Racial Flux, Voting Behavior, and Racial Attitudes (Whites) --- Controlling for the Racial Income Gap in 1990", custom.model.names = c("\\textit{President}", "\\textit{President}", "\\textit{U.S. House}", "\\textit{U.S. House}", "\\textit{Racial Resentment}", "\\textit{Racial Resentment}", "\\textit{Affirmative Action}", "\\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)", "Zipcode White-Black Income Gap", "County White-Black Income Gap"), reorder.coef = c(2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 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, scalebox = 0.7) ## Adding past racial income gap -- 00 pres_dem_zinc00 = 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 + zipcode_inc_gap_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") pres_dem_cinc00 = 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 + county_inc_gap_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") house_dem_zinc00 = 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 + zipcode_inc_gap_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") house_dem_cinc00 = 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 + county_inc_gap_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") rr_zinc00 = 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 + zipcode_inc_gap_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") rr_cinc00 = 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 + county_inc_gap_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") affirm_zinc00 = 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 + zipcode_inc_gap_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") affirm_cinc00 = 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 + county_inc_gap_00, data = dta %>% filter(white == 1), clusters = zipcode, se_type = "stata") ## Table of coefs., and save ############### ## TABLE A12 ## ############### texreg(list(pres_dem_zinc00, pres_dem_cinc00, house_dem_zinc00, house_dem_cinc00, rr_zinc00, rr_cinc00, affirm_zinc00, affirm_cinc00), file = "03_output/inc00.tex", label = "inc00", caption = "Racial Flux, Voting Behavior, and Racial Attitudes (Whites) --- Controlling for the Racial Income Gap in 2000", custom.model.names = c("\\textit{President}", "\\textit{President}", "\\textit{U.S. House}", "\\textit{U.S. House}", "\\textit{Racial Resentment}", "\\textit{Racial Resentment}", "\\textit{Affirmative Action}", "\\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)", "Zipcode White-Black Income Gap", "County White-Black Income Gap"), reorder.coef = c(2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 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, scalebox = 0.7) ## Subsetting to above median % white pres_dem_median = lm_robust(pres_dem ~ wac_pct_black + 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(white == 1 & pct_white >= median(dta$pct_white, na.rm = TRUE)), clusters = zipcode, se_type = "stata") house_dem_median = lm_robust(house_dem ~ wac_pct_black + pid7 + ideo5 + female + age + faminc + educ + pct_white + pct_black + pct_unemployed + pct_college + log_per_cap_inc + gini + south + non_rural + log_pop_density, data = dta %>% filter(white == 1 & pct_white >= median(dta$pct_white, na.rm = TRUE)), clusters = zipcode, se_type = "stata") rr_median = lm_robust(mean_rr ~ wac_pct_black + 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(white == 1 & pct_white >= median(dta$pct_white, na.rm = TRUE)), clusters = zipcode, se_type = "stata") affirm_median = lm_robust(affirm ~ wac_pct_black + pid7 + ideo5 + female + age + faminc + educ + pct_white + pct_black + pct_unemployed + pct_college + log_per_cap_inc + gini + south + non_rural + log_pop_density, data = dta %>% filter(white == 1 & pct_white >= median(dta$pct_white, na.rm = TRUE)), clusters = zipcode, se_type = "stata") ## Table of coefs., and save ############### ## TABLE A13 ## ############### texreg(list(pres_dem_median, house_dem_median, rr_median, affirm_median), file = "03_output/median.tex", label = "median", caption = "Racial Flux, Voting Behavior, and Racial Attitudes (Whites) --- % White > Median", custom.model.names = c("\\textit{President}", "\\textit{U.S. House}", "\\textit{Racial Resentment}", "\\textit{Affirmative Action}"), custom.coef.names = c("Intercept", "% Black Workers", "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, scalebox = 0.7) ## Subsetting to above 75th percentile % white pres_dem_75 = lm_robust(pres_dem ~ wac_pct_black + 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(white == 1 & pct_white >= quantile(dta$pct_white, 0.75, na.rm = TRUE)), clusters = zipcode, se_type = "stata") house_dem_75 = lm_robust(house_dem ~ wac_pct_black + pid7 + ideo5 + female + age + faminc + educ + pct_white + pct_black + pct_unemployed + pct_college + log_per_cap_inc + gini + south + non_rural + log_pop_density, data = dta %>% filter(white == 1 & pct_white >= quantile(dta$pct_white, 0.75, na.rm = TRUE)), clusters = zipcode, se_type = "stata") rr_75 = lm_robust(mean_rr ~ wac_pct_black + 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(white == 1 & pct_white >= quantile(dta$pct_white, 0.75, na.rm = TRUE)), clusters = zipcode, se_type = "stata") affirm_75 = lm_robust(affirm ~ wac_pct_black + pid7 + ideo5 + female + age + faminc + educ + pct_white + pct_black + pct_unemployed + pct_college + log_per_cap_inc + gini + south + non_rural + log_pop_density, data = dta %>% filter(white == 1 & pct_white >= quantile(dta$pct_white, 0.75, na.rm = TRUE)), clusters = zipcode, se_type = "stata") ## Table of coefs., and save ############### ## TABLE A14 ## ############### texreg(list(pres_dem_75, house_dem_75, rr_75, affirm_75), file = "03_output/p75.tex", label = "p75", caption = "Racial Flux, Voting Behavior, and Racial Attitudes (Whites) --- % White > 75th Percentile", custom.model.names = c("\\textit{President}", "\\textit{U.S. House}", "\\textit{Racial Resentment}", "\\textit{Affirmative Action}"), custom.coef.names = c("Intercept", "% Black Workers", "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, scalebox = 0.7) ## Subsetting to above 90th percentile % white pres_dem_90 = lm_robust(pres_dem ~ wac_pct_black + 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(white == 1 & pct_white >= quantile(dta$pct_white, 0.9, na.rm = TRUE)), clusters = zipcode, se_type = "stata") house_dem_90 = lm_robust(pres_dem ~ wac_pct_black + pid7 + ideo5 + female + age + faminc + educ + pct_white + pct_black + pct_unemployed + pct_college + log_per_cap_inc + gini + south + non_rural + log_pop_density, data = dta %>% filter(white == 1 & pct_white >= quantile(dta$pct_white, 0.9, na.rm = TRUE)), clusters = zipcode, se_type = "stata") rr_90 = lm_robust(mean_rr ~ wac_pct_black + 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(white == 1 & pct_white >= quantile(dta$pct_white, 0.9, na.rm = TRUE)), clusters = zipcode, se_type = "stata") affirm_90 = lm_robust(affirm ~ wac_pct_black + pid7 + ideo5 + female + age + faminc + educ + pct_white + pct_black + pct_unemployed + pct_college + log_per_cap_inc + gini + south + non_rural + log_pop_density, data = dta %>% filter(white == 1 & pct_white >= quantile(dta$pct_white, 0.9, na.rm = TRUE)), clusters = zipcode, se_type = "stata") ## Table of coefs., and save ############### ## TABLE A15 ## ############### texreg(list(pres_dem_90, house_dem_90, rr_90, affirm_90), file = "03_output/p90.tex", label = "p90", caption = "Racial Flux, Voting Behavior, and Racial Attitudes (Whites) --- % White > 90th Percentile", custom.model.names = c("\\textit{President}", "\\textit{U.S. House}", "\\textit{Racial Resentment}", "\\textit{Affirmative Action}"), custom.coef.names = c("Intercept", "% Black Workers", "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, scalebox = 0.7)