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## 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()) |