git add 4

.gitattributes

@@ -57,7 +57,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
 *.csv filter=lfs diff=lfs merge=lfs -text
-
 *.dta filter=lfs diff=lfs merge=lfs -text
 *.sav filter=lfs diff=lfs merge=lfs -text
 *.xls filter=lfs diff=lfs merge=lfs -text
@@ -66,3 +65,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.rds filter=lfs diff=lfs merge=lfs -text
 *.txt filter=lfs diff=lfs merge=lfs -text
 *.dat filter=lfs diff=lfs merge=lfs -text
+*.pdf filter=lfs diff=lfs merge=lfs -text
+4/paper.pdf filter=lfs diff=lfs merge=lfs -text

4/paper.pdf

@@ -0,0 +1,3 @@
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+oid sha256:17d81e44bfd9a2a11d4566c6631e04fca53554344f9146321fd9e1d3858dbfab
+size 1651200

4/replication_package/acs.RData

@@ -0,0 +1,3 @@
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+size 590945

4/replication_package/an_blacks.R

@@ -0,0 +1,149 @@
+## 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())

4/replication_package/an_descriptives.R

@@ -0,0 +1,78 @@
+## Descriptive statistics
+## Brian T. Hamel and Bryan Wilcox-Archuleta
+## First: 28 April 2019  
+## Last: 19 March 2020
+
+## Loading packages
+## install.packages(c("tidyverse", "estimatr", "ggpubr", "gridExtra", "texreg", "scales"))
+library(tidyverse)
+library(estimatr)
+library(ggpubr)
+library(gridExtra)
+library(texreg)
+library(scales)
+
+## Loading Racial Flux data
+load("01_data/lodes/racial_flux.RData")
+
+## Loading ACS data
+load("01_data/acs/acs.RData")
+
+## Merging Racial Flux and ACS data
+dta = racial_flux %>%
+  left_join(., acs, by = c("zcta" = "zip"))
+
+## Plotting Racial Flux vs. % white and % black
+###############
+## FIGURE 1 ##
+###############
+pct_white = ggplot(dta, aes(x = pct_white, y = racial_flux)) + 
+  geom_point(color = "black", shape = 1, alpha = .25) + 
+  stat_smooth(method = "lm_robust", se = FALSE, lty = 2, color = "blue", show.legend = TRUE) +
+  stat_smooth(method = "loess", se = FALSE, color = "red", show.legend = TRUE) + 
+  labs(x = "% White", y = "Racial Flux") + 
+  scale_y_continuous(labels = number_format(accuracy = 0.01)) +
+  theme(legend.title = element_blank()) +
+  stat_cor(method = "pearson", label.x = 2, label.y = 85, size = 2)
+
+pct_black = ggplot(dta, aes(x = pct_black, y = racial_flux)) + 
+  geom_point(color = "black", shape = 1, alpha = .25) + 
+  stat_smooth(method = "lm_robust", se = FALSE, lty = 2, color = "blue", show.legend = TRUE) +
+  stat_smooth(method = "loess", se = FALSE, color = "red", show.legend = TRUE) + 
+  labs(x = "% Black", y = "") +
+  scale_y_continuous(labels = number_format(accuracy = 0.01)) +
+  theme(legend.title = element_blank()) +
+  stat_cor(method = "pearson", label.x = 2, label.y = 85, size = 2)
+
+## Combine plots, and save
+flux_res = grid.arrange(pct_white, pct_black, ncol = 2, nrow = 1)
+ggsave(flux_res, file = "03_output/flux_res.png", height = 2, width = 4, units = "in", dpi = 600)
+
+## Correlates of Racial Flux
+correlates = lm_robust(racial_flux ~ pct_white + pct_black
+                       + pct_unemployed + pct_college + log_per_cap_inc + gini + south
+                       + non_rural + log_pop_density, data = dta, se_type = "stata")
+
+## Save table of coefficients
+##############
+## TABLE A2 ##
+##############
+texreg(correlates,
+       file = "03_output/correlates.tex", 
+       label = "correlates",
+       caption = "Multivariate Correlates of Racial Flux", 
+       custom.model.names = c("(1)"),
+       custom.coef.names = c("Intercept", "% 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, 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)
+
+## Clear R 
+rm(list = ls())

4/replication_package/an_main.R

@@ -0,0 +1,150 @@
+## Voting and racial attitudes (whites only)
+## Brian T. Hamel and Bryan Wilcox-Archuleta
+## First: 5 May 2019  
+## Last: 19 March 2019
+
+## 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(white == 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(white == 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(white == 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(white == 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(white == 1),
+                   clusters = zipcode, se_type = "stata")
+
+pred_affirm = cbind(predict(affirm, shell, 
+                            se.fit = TRUE, type = "response"), 
+                    shell)
+
+## Table of coefs., and save
+############################
+## TABLE 1  AND TABLE A3 ###
+############################
+texreg(list(pres_dem, house_dem, rr, affirm),
+       file = "03_output/main.tex", 
+       label = "main",
+       caption = "Racial Flux, Voting Behavior, and Racial Attitudes (Whites)", 
+       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,
+       scalebox = 0.7)
+
+## 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(white == 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 2 ##
+##############
+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(white == 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/main.png", height = 4, width = 4, units = "in", dpi = 600)
+
+## Clear R 
+rm(list = ls())

4/replication_package/an_non_racial.R

@@ -0,0 +1,118 @@
+## Non-racial attitudes (whites 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(white == 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
+abortion = lm_robust(abortion ~ 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(white == 1),
+                     clusters = zipcode, se_type = "stata")
+
+pred_abortion = cbind(predict(abortion, shell, 
+                              se.fit = TRUE, type = "response"), 
+                      shell)
+
+climate = lm_robust(climate ~ 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(white == 1),
+                    clusters = zipcode, se_type = "stata")
+
+pred_climate = cbind(predict(climate, shell, 
+                             se.fit = TRUE, type = "response"), 
+                       shell)
+
+guns = lm_robust(guns ~ 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(white == 1),
+                 clusters = zipcode, se_type = "stata")
+
+pred_guns = cbind(predict(guns, shell, 
+                          se.fit = TRUE, type = "response"), 
+                  shell)
+
+## Table of coefs., and save
+##############
+## TABLE A5 ##
+##############
+texreg(list(abortion, climate, guns),
+       file = "03_output/non_racial.tex", 
+       label = "non_racial",
+       caption = "Racial Flux and Non-Racial Attitudes (Whites)", 
+       custom.model.names = c("\\textit{Abortion}", "\\textit{Climate Change}", 
+                              "\\textit{Gun Control}"),
+       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_abortion = cbind(pred_abortion, outcome = "Abortion")
+pred_climate = cbind(pred_climate, outcome = "Climate Change")
+pred_guns = cbind(pred_guns, outcome = "Gun Control")
+pred_att = bind_rows(pred_abortion, pred_climate, pred_guns) %>% 
+  mutate(upper = fit + 1.96 * se.fit,
+         lower = fit - 1.96 * se.fit)
+
+###############
+## FIGURE A1 ##
+###############
+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 = 2, ncol = 2, scales = "free") +
+  labs(y = "Predicted Attitude", 
+       x = "Racial Flux") +
+  geom_rug(data = dta %>% filter(white == 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()) +
+  ggsave(file = "03_output/non_racial.png", height = 4, width = 4, units = "in", dpi = 600)
+
+## Clear R 
+rm(list = ls())

4/replication_package/an_retired.R

@@ -0,0 +1,152 @@
+## Voting and racial attitudes (retired whites only)
+## Brian T. Hamel and Bryan Wilcox-Archuleta
+## First: 5 May 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(white == 1 & retired == 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(white == 1 & retired == 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(white == 1 & retired == 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(white == 1 & retired == 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(white == 1 & retired == 1),
+                   clusters = zipcode, se_type = "stata")
+
+pred_affirm = cbind(predict(affirm, shell, 
+                            se.fit = TRUE, type = "response"), 
+                    shell)
+
+## Table of coefs., and save
+##############
+## TABLE A7 ##
+##############
+texreg(list(pres_dem, house_dem, rr, affirm),
+       file = "03_output/retired.tex", 
+       label = "retired",
+       caption = "Racial Flux, Voting Behavior, and Racial Attitudes (Retired Whites)", 
+       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,
+       scalebox = 0.7)
+
+## 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(white == 1 & retired == 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 A3 ##
+###############
+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(white == 1 & retired == 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/retired.png", height = 4, width = 4, units = "in", dpi = 600)
+
+## Clear R 
+rm(list = ls())

4/replication_package/an_robust.R

@@ -0,0 +1,650 @@
+## 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)

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