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# Replication File for Appendix Survey Analyses
# Table C1 in Appendix C1: The Effect of Excess Males on the Probability of Observing at least One Hate Crime
# Table C2 in Appendix C2: The Effect of Excess Males on the Probability of Observing at least One Hate Crime (Different Definition of “Excess Males”)
# Table C3 in Appendix C3: The Effect of Excess Males on the Probability of Observing at least One Hate Crime (linear probability model)
# Table C4 in Appendix C4: The Effect of Excess Males on the Probability of Observing at least One Physical Attack
# Table C5 in Appendix C5: Negative Binomial Regression
# Table C6 in Appendix C6: Interaction between Excess Males and East/West Germany
# Table C7 in Appendix C7: Interaction between Excess Males and Refugee Inflow
# Table C9 in Appendix C9: Placebo Analysis
# Appendix C10: Descriptive Statistics
# R version 4.0.2 (2020-06-22)
rm(list=ls())
# install.packages("readstata13") # readstata13_0.9.2
# install.packages("MASS") # MASS_7.3-51.6
# install.packages("sandwich") # sandwich_2.5-1
# install.packages("lmtest") # lmtest_0.9-37
# install.packages("stargazer") # stargazer_5.2.2
require(readstata13) # readstata13_0.9.2
require(MASS) # MASS_7.3-51.6
require(sandwich) # sandwich_2.5-1
require(lmtest) # lmtest_0.9-37
require(stargazer) # stargazer_5.2.2
source("Help.R")
dat <- read.dta13("context.dta")
dat_2015 <- dat[dat$year == 2015, ]
dat_2016 <- dat[dat$year == 2016, ]
dat_2017 <- dat[dat$year == 2017, ]
dat_2015$Hate_all_muni_1517 <- dat_2015$Hate_all_muni + dat_2016$Hate_all_muni + dat_2017$Hate_all_muni
dat_2015$Hate_all_muni_1517_bin <- ifelse(dat_2015$Hate_all_muni_1517 > 0, 1, 0)
# Remove Extreme Value of Excess Males
range_x <- quantile(dat_2015$pop_15_44_muni_gendergap_2015, c(0.025, 0.975), na.rm = TRUE)
dat_2015_s <- dat_2015[dat_2015$pop_15_44_muni_gendergap_2015 >= range_x[1] &
dat_2015$pop_15_44_muni_gendergap_2015 <= range_x[2], ]
dat_s <- dat[dat$pop_15_44_muni_gendergap_2015 >= range_x[1] &
dat$pop_15_44_muni_gendergap_2015 <= range_x[2], ]
# ##########################################
# Main Table (Table C1 in Appendix C1)
# ##########################################
bin_1_sum <- bin.summary(Hate_all_muni_1517_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
as.factor(ags_county), # county fixed effects
id = "ags_county", data = dat_2015_s)
bin_1_p <- bin.summary(Hate_all_muni_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
as.factor(ags_county) + as.factor(year), # county + year fixed effects
id = "ags_county", data = dat_s)
bin_2_sum <- bin.summary(Hate_all_muni_1517_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
as.factor(ags_county), # county fixed effects
id = "ags_county", data = dat_2015_s)
bin_2_p <- bin.summary(Hate_all_muni_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
as.factor(ags_county) + as.factor(year), # county + year fixed effects
id = "ags_county", data = dat_s)
bin_3_sum <- bin.summary(Hate_all_muni_1517_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 +
as.factor(ags_state), # state fixed effects
id = "ags_county", data = dat_2015_s)
bin_3_p <- bin.summary(Hate_all_muni_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 +
as.factor(ags_state) + as.factor(year), # state + year fixed effects
id = "ags_county", data = dat_s)
fit_list <- list(bin_1_sum$fit, bin_1_p$fit,
bin_2_sum$fit, bin_2_p$fit,
bin_3_sum$fit, bin_3_p$fit)
se_list <- list(sqrt(diag(bin_1_sum$vcov)), sqrt(diag(bin_1_p$vcov)),
sqrt(diag(bin_2_sum$vcov)), sqrt(diag(bin_2_p$vcov)),
sqrt(diag(bin_3_sum$vcov)), sqrt(diag(bin_3_p$vcov)))
star_out(stargazer(fit_list, se = se_list,
covariate.labels = c("Excess Males (Age 15 - 44)",
"Log (Population)","Log (Population Density)",
"Log (Unemployment Rate)",
"% of population change (2011 vs 2015)",
"Vote share for AfD (2013)",
"Log (Refugee Inflow) (2014 vs 2015)", "Log (Refugee Size) (2014)",
"Log (General Violence per capita)",
"% of High Education",
"Change in Manufacturing Share (2011 vs 2015)",
"Share of Manufacturing", "Male Disadvantage"),
keep=c("pop_15_44_muni_gendergap_2015",
"log_population_muni_2015",
"log_popdens_muni_2015", "log_unemp_all_muni_2015",
"d_pop1511_muni", "vote_afd_2013_muni",
"log_ref_inflow_1514", "log_pop_ref_2014",
"log_violence_percap_2015",
"pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015")),
name = "table_C1.tex")
# ###############################################
# Replicate Table with 25-44 (Table C2 in C2)
# ###############################################
range_x2 <- quantile(dat_2015$pop_25_44_muni_gendergap_2015, c(0.025, 0.975), na.rm = TRUE)
dat_2015_s2 <- dat_2015[dat_2015$pop_25_44_muni_gendergap_2015 >= range_x2[1] &
dat_2015$pop_25_44_muni_gendergap_2015 <= range_x2[2], ]
dat_s2 <- dat[dat$pop_25_44_muni_gendergap_2015 >= range_x2[1] &
dat$pop_25_44_muni_gendergap_2015 <= range_x2[2], ]
bin_r_1_sum <- bin.summary(Hate_all_muni_1517_bin ~
pop_25_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
as.factor(ags_county),
id = "ags_county", data = dat_2015_s2)
bin_r_1_p <- bin.summary(Hate_all_muni_bin ~
pop_25_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
as.factor(ags_county) + as.factor(year),
id = "ags_county", data = dat_s2)
bin_r_2_sum <- bin.summary(Hate_all_muni_1517_bin ~
pop_25_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
as.factor(ags_county),
id = "ags_county", data = dat_2015_s2)
bin_r_2_p <- bin.summary(Hate_all_muni_bin ~
pop_25_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
as.factor(ags_county) + as.factor(year),
id = "ags_county", data = dat_s2)
bin_r_3_sum <- bin.summary(Hate_all_muni_1517_bin ~
pop_25_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 +
as.factor(ags_state),
id = "ags_county", data = dat_2015_s2)
bin_r_3_p <- bin.summary(Hate_all_muni_bin ~
pop_25_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 +
as.factor(ags_state) + as.factor(year),
id = "ags_county", data = dat_s2)
## Table C2 in Appendix C2
fit_list2 <- list(bin_r_1_sum$fit, bin_r_1_p$fit,
bin_r_2_sum$fit, bin_r_2_p$fit,
bin_r_3_sum$fit, bin_r_3_p$fit)
se_list2 <- list(sqrt(diag(bin_r_1_sum$vcov)), sqrt(diag(bin_r_1_p$vcov)),
sqrt(diag(bin_r_2_sum$vcov)), sqrt(diag(bin_r_2_p$vcov)),
sqrt(diag(bin_r_3_sum$vcov)), sqrt(diag(bin_r_3_p$vcov)))
star_out(stargazer(fit_list2, se = se_list2,
covariate.labels = c("Excess Males (Age 25 - 44)",
"Log (Population)","Log (Population Density)",
"Log (Unemployment Rate)",
"% of population change (2011 vs 2015)",
"Vote share for AfD (2013)",
"Log (Refugee Inflow) (2014 vs 2015)", "Log (Refugee Size) (2014)",
"Log (General Violence per capita)",
"% of High Education",
"Change in Manufacturing Share (2011 vs 2015)",
"Share of Manufacturing", "Male Disadvantage"),
keep=c("pop_25_44_muni_gendergap_2015",
"log_population_muni_2015",
"log_popdens_muni_2015", "log_unemp_all_muni_2015",
"d_pop1511_muni", "vote_afd_2013_muni",
"log_ref_inflow_1514", "log_pop_ref_2014",
"log_violence_percap_2015",
"pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015")),
name = "table_C2.tex")
# #####################################
# Linear Probability Model (Table C3 in Appendix C3)
# #####################################
lm_1_sum <- lm.summary(Hate_all_muni_1517_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
as.factor(ags_county),
id = "ags_county", data = dat_2015_s)
lm_1_p <- lm.summary(Hate_all_muni_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
as.factor(ags_county) + as.factor(year),
id = "ags_county", data = dat_s)
lm_2_sum <- lm.summary(Hate_all_muni_1517_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
as.factor(ags_county),
id = "ags_county", data = dat_2015_s)
lm_2_p <- lm.summary(Hate_all_muni_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
as.factor(ags_county) + as.factor(year),
id = "ags_county", data = dat_s)
lm_3_sum <- lm.summary(Hate_all_muni_1517_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 +
as.factor(ags_state),
id = "ags_county", data = dat_2015_s)
lm_3_p <- lm.summary(Hate_all_muni_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 +
as.factor(ags_state) + as.factor(year),
id = "ags_county", data = dat_s)
fit_list_lm <- list(lm_1_sum$fit, lm_1_p$fit,
lm_2_sum$fit, lm_2_p$fit,
lm_3_sum$fit, lm_3_p$fit)
se_list_lm <- list(sqrt(diag(lm_1_sum$vcov)), sqrt(diag(lm_1_p$vcov)),
sqrt(diag(lm_2_sum$vcov)), sqrt(diag(lm_2_p$vcov)),
sqrt(diag(lm_3_sum$vcov)), sqrt(diag(lm_3_p$vcov)))
star_out(stargazer(fit_list_lm, se = se_list_lm,
covariate.labels = c("Excess Males (Age 15 - 44)",
"Log (Population)","Log (Population Density)",
"Log (Unemployment Rate)",
"% of population change (2011 vs 2015)",
"Vote share for AfD (2013)",
"Log (Refugee Inflow) (2014 vs 2015)", "Log (Refugee Size) (2014)",
"Log (General Violence per capita)",
"% of High Education",
"Change in Manufacturing Share (2011 vs 2015)",
"Share of Manufacturing", "Male Disadvantage"),
keep=c("pop_15_44_muni_gendergap_2015",
"log_population_muni_2015",
"log_popdens_muni_2015", "log_unemp_all_muni_2015",
"d_pop1511_muni", "vote_afd_2013_muni",
"log_ref_inflow_1514", "log_pop_ref_2014",
"log_violence_percap_2015",
"pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015")),
name = "table_C3.tex")
## ############################################
## Physical Attacks (Table C4 in Appendix C4)
## ############################################
dat_2015$Physical_muni_1517 <- dat_2015$Physical_muni + dat_2016$Physical_muni + dat_2017$Physical_muni
dat_2015$Physical_muni_1517_bin <- ifelse(dat_2015$Physical_muni_1517 > 0, 1, 0)
range_x <- quantile(dat_2015$pop_15_44_muni_gendergap_2015, c(0.025, 0.975), na.rm = TRUE)
dat_2015_s <- dat_2015[dat_2015$pop_15_44_muni_gendergap_2015 >= range_x[1] &
dat_2015$pop_15_44_muni_gendergap_2015 <= range_x[2], ]
dat_s <- dat[dat$pop_15_44_muni_gendergap_2015 >= range_x[1] &
dat$pop_15_44_muni_gendergap_2015 <= range_x[2], ]
bin_phys_1_sum <- bin.summary(Physical_muni_1517_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
as.factor(ags_county),
id = "ags_county", data = dat_2015_s)
bin_phys_1_p <- bin.summary(Physical_muni_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
as.factor(ags_county) + as.factor(year),
id = "ags_county", data = dat_s)
bin_phys_2_sum <- bin.summary(Physical_muni_1517_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
as.factor(ags_county),
id = "ags_county", data = dat_2015_s)
bin_phys_2_p <- bin.summary(Physical_muni_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
as.factor(ags_county) + as.factor(year),
id = "ags_county", data = dat_s)
bin_phys_3_sum <- bin.summary(Physical_muni_1517_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 +
as.factor(ags_state),
id = "ags_county", data = dat_2015_s)
bin_phys_3_p <- bin.summary(Physical_muni_bin ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 +
as.factor(ags_state) + as.factor(year),
id = "ags_county", data = dat_s)
## Table C4 in Appendix C4
fit_list_bin_phys <- list(bin_phys_1_sum$fit, bin_phys_1_p$fit,
bin_phys_2_sum$fit, bin_phys_2_p$fit,
bin_phys_3_sum$fit, bin_phys_3_p$fit)
se_list_bin_phys <- list(sqrt(diag(bin_phys_1_sum$vcov)), sqrt(diag(bin_phys_1_p$vcov)),
sqrt(diag(bin_phys_2_sum$vcov)), sqrt(diag(bin_phys_2_p$vcov)),
sqrt(diag(bin_phys_3_sum$vcov)), sqrt(diag(bin_phys_3_p$vcov)))
star_out(stargazer(fit_list_bin_phys, se = se_list_bin_phys,
covariate.labels = c("Excess Males (Age 15 - 44)",
"Log (Population)","Log (Population Density)",
"Log (Unemployment Rate)",
"% of population change (2011 vs 2015)",
"Vote share for AfD (2013)",
"Log (Refugee Inflow) (2014 vs 2015)", "Log (Refugee Size) (2014)",
"Log (General Violence per capita)",
"% of High Education",
"Change in Manufacturing Share (2011 vs 2015)",
"Share of Manufacturing", "Male Disadvantage"),
keep=c("pop_15_44_muni_gendergap_2015",
"log_population_muni_2015",
"log_popdens_muni_2015", "log_unemp_all_muni_2015",
"d_pop1511_muni", "vote_afd_2013_muni",
"log_ref_inflow_1514", "log_pop_ref_2014",
"log_violence_percap_2015",
"pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015")),
name = "table_C4.tex")
## ########################################
## Appendix C5: Count Model
## ########################################
rm(list=ls())
dat <- read.dta13("context.dta")
source("Help.R")
dat_2015 <- dat[dat$year == 2015, ]
dat_2016 <- dat[dat$year == 2016, ]
dat_2017 <- dat[dat$year == 2017, ]
dat_2015$Hate_all_muni_1517 <- dat_2015$Hate_all_muni + dat_2016$Hate_all_muni + dat_2017$Hate_all_muni
# Remove Extreme Value of Excess Males
range_x <- quantile(dat_2015$pop_15_44_muni_gendergap_2015, c(0.025, 0.975), na.rm = TRUE)
dat_2015_s <- dat_2015[dat_2015$pop_15_44_muni_gendergap_2015 >= range_x[1] &
dat_2015$pop_15_44_muni_gendergap_2015 <= range_x[2], ]
dat_s <- dat[dat$pop_15_44_muni_gendergap_2015 >= range_x[1] &
dat$pop_15_44_muni_gendergap_2015 <= range_x[2], ]
for_s <- as.formula(Hate_all_muni_1517 ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 +
as.factor(ags_state)) # state fixed effects
for_p <- as.formula(Hate_all_muni ~
pop_15_44_muni_gendergap_2015 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 + as.factor(ags_state) + as.factor(year)) # state + year fixed effects
## Use boostrap to compute standard errors (Use the "out_count.rdata" to get the exact same estimates)
## Note: the following codes take a while to run
full_run <- FALSE
if(full_run == TRUE){
# Sum
nb_1_sum_b <- glm.boot(for_s, family = "negative-binomial",
data = dat_2015_s,
cluster_id = dat_2015_s$ags_county)
# Panel
nb_1_p_b <- glm.boot(for_p, family = "negative-binomial",
data = dat_s,
cluster_id = dat_s$ags_county)
fit_list_nb <- list(nb_1_sum_b$fit, nb_1_p_b$fit)
se_list_nb <- list(nb_1_sum_b$se, nb_1_p_b$se)
out_count_table <- list(fit_list_nb, se_list_nb)
save(out_count_table, file = "out_count_table.rdata")
}
load(file = "out_count_table.rdata")
fit_list_nb <- out_count_table[[1]]
se_list_nb <- out_count_table[[2]]
star_out(stargazer(fit_list_nb, se = se_list_nb,
covariate.labels = c("Excess Males (Age 15 - 44)",
"Log (Population)","Log (Population Density)",
"Log (Unemployment Rate)",
"% of population change (2011 vs 2015)",
"Vote share for AfD (2013)",
"Log (Refugee Inflow) (2014 vs 2015)", "Log (Refugee Size) (2014)",
"Log (General Violence per capita)",
"% of High Education",
"Change in Manufacturing Share (2011 vs 2015)",
"Share of Manufacturing", "Male Disadvantage"),
keep=c("pop_15_44_muni_gendergap_2015",
"log_population_muni_2015",
"log_popdens_muni_2015", "log_unemp_all_muni_2015",
"d_pop1511_muni", "vote_afd_2013_muni",
"log_ref_inflow_1514", "log_pop_ref_2014",
"log_violence_percap_2015",
"pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015")),
name = "table_C5.tex")
## ########################################################################
## Replicate Tables with East/West Interaction (Table C6 in Appendix C6)
## ########################################################################
rm(list=ls())
dat <- read.dta13("context.dta")
source("Help.R")
dat_2015 <- dat[dat$year == 2015, ]
dat_2016 <- dat[dat$year == 2016, ]
dat_2017 <- dat[dat$year == 2017, ]
dat_2015$Hate_all_muni_1517 <- dat_2015$Hate_all_muni + dat_2016$Hate_all_muni + dat_2017$Hate_all_muni
dat_2015$Hate_all_muni_1517_bin <- as.numeric(dat_2015$Hate_all_muni_1517 > 0)
# Remove Extreme Value of Excess Males
range_x <- quantile(dat_2015$pop_15_44_muni_gendergap_2015, c(0.025, 0.975), na.rm = TRUE)
dat_2015_s <- dat_2015[dat_2015$pop_15_44_muni_gendergap_2015 >= range_x[1] &
dat_2015$pop_15_44_muni_gendergap_2015 <= range_x[2], ]
dat_s <- dat[dat$pop_15_44_muni_gendergap_2015 >= range_x[1] &
dat$pop_15_44_muni_gendergap_2015 <= range_x[2], ]
dat_2015_s$west <- 1 - dat_2015_s$east
dat_s$west <- 1 - dat_s$east
bin_2_sum_ew <- bin.summary(Hate_all_muni_1517_bin ~
pop_15_44_muni_gendergap_2015 + west +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015,
id = "ags_county", data = dat_2015_s)
bin_2_p_ew <- bin.summary(Hate_all_muni_bin ~
pop_15_44_muni_gendergap_2015 + west +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 + as.factor(year),
id = "ags_county", data = dat_s)
bin_3_sum_ew <- bin.summary(Hate_all_muni_1517_bin ~
pop_15_44_muni_gendergap_2015*west +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015,
id = "ags_county", data = dat_2015_s)
bin_3_p_ew <- bin.summary(Hate_all_muni_bin ~
pop_15_44_muni_gendergap_2015*west +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 + as.factor(year),
id = "ags_county", data = dat_s)
fit_list_ew <- list(bin_2_sum_ew$fit, bin_2_p_ew$fit,
bin_3_sum_ew$fit, bin_3_p_ew$fit)
se_list_ew <- list(sqrt(diag(bin_2_sum_ew$vcov)), sqrt(diag(bin_2_p_ew$vcov)),
sqrt(diag(bin_3_sum_ew$vcov)), sqrt(diag(bin_3_p_ew$vcov)))
star_out(stargazer(fit_list_ew, se = se_list_ew,
covariate.labels = c("Excess Males (Age 15 - 44)", "West",
"Log (Population)","Log (Population Density)",
"Log (Unemployment Rate)",
"% of population change (2011 vs 2015)",
"Vote share for AfD (2013)",
"Log (Refugee Inflow) (2014 vs 2015)",
"Log (Refugee Size) (2014)",
"Log (General Violence per capita)",
"% of High Education",
"Change in Manufacturing Share (2011 vs 2015)",
"Share of Manufacturing", "Male Disadvantage",
"Excess Males x West"),
keep=c("pop_15_44_muni_gendergap_2015", "west",
"log_population_muni_2015",
"log_popdens_muni_2015", "log_unemp_all_muni_2015",
"d_pop1511_muni", "vote_afd_2013_muni",
"log_ref_inflow_1514",
"log_pop_ref_2014",
"log_violence_percap_2015",
"pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015",
"pop_15_44_muni_gendergap_2015:west")),
name = "table_C6.tex")
## ##############################################################
## Interaction with Refugee Inflow (Table C7 in Appendix C7)
## ##############################################################
bin_sum_int <- bin.summary(Hate_all_muni_1517_bin ~
pop_15_44_muni_gendergap_2015*log_ref_inflow_1514 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 +
as.factor(ags_state),
id = "ags_county", data = dat_2015_s)
bin_p_int <- bin.summary(Hate_all_muni_bin ~
pop_15_44_muni_gendergap_2015*log_ref_inflow_1514 +
log_population_muni_2015 + log_popdens_muni_2015 +
log_unemp_all_muni_2015 + d_pop1511_muni + vote_afd_2013_muni +
log_ref_inflow_1514 + log_pop_ref_2014 + log_violence_percap_2015 + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_2015 +
as.factor(ags_state) + as.factor(year),
id = "ags_county", data = dat_s)
## Table C7 in Appendix C7
fit_list_int <- list(bin_sum_int$fit, bin_p_int$fit)
se_list_int <- list(sqrt(diag(bin_sum_int$vcov)), sqrt(diag(bin_p_int$vcov)))
star_out(stargazer(fit_list_int, se = se_list_int,
covariate.labels = c("Excess Males (Age 15 - 44)",
"Log (Refugee Inflow) (2014 vs 2015)",
"Log (Population)","Log (Population Density)",
"Log (Unemployment Rate)",
"% of population change (2011 vs 2015)",
"Vote share for AfD (2013)",
"Log (Refugee Size) (2014)",
"Log (General Violence per capita)",
"% of High Education",
"Change in Manufacturing Share (2011 vs 2015)",
"Share of Manufacturing", "Male Disadvantage",
"Excess Males × Log (Refugee Inflow)"),
keep=c("pop_15_44_muni_gendergap_2015", "log_ref_inflow_1514",
"log_population_muni_2015",
"log_popdens_muni_2015", "log_unemp_all_muni_2015",
"d_pop1511_muni", "vote_afd_2013_muni",
"log_pop_ref_2014",
"log_violence_percap_2015",
"pc_hidegree_all2011", "d_manuf1115", "pc_manufacturing_2015", "unemp_gendergap_2015",
"pop_15_44_muni_gendergap_2015:log_ref_inflow_1514")),
name = "table_C7.tex")
# ################################################################
# Appendix C9. Placebo Analysis
# ###############################################################
rm(list=ls())
dat_pl <- read.dta13("context_placebo.dta") # data for placebo analysis
source("Help.R")
dat_2015_s <- dat_pl[dat_pl$year == 2015, ]
dat_2016_s <- dat_pl[dat_pl$year == 2016, ]
dat_2017_s <- dat_pl[dat_pl$year == 2017, ]
# ##########################################
# 2015
# ##########################################
# main model + Placebo Treatment
bin_15_sum_pl <- bin.summary(Hate_all_muni_bin ~
pop_15_44_muni_gendergap_future +
pop_15_44_muni_gendergap_anu +
log(population_muni_anu) + log(popdens_muni_anu) +
log_unemp_all_muni_anu + d_pop_muni_anu + vote_afd_2013_muni +
log_ref_inflow_anu + log(pop_ref_anu) + log(violence_percap_anu) + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_anu +
as.factor(ags_state), # state fixed effects
id = "ags_county", data = dat_2015_s)
# ##########################################
# 2016
# ##########################################
# Main model + Placebo
bin_16_sum_pl <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_future +
pop_15_44_muni_gendergap_anu +
log(population_muni_anu) + log(popdens_muni_anu) +
log_unemp_all_muni_anu + d_pop_muni_anu + vote_afd_2013_muni +
log_ref_inflow_anu + log(pop_ref_anu) + log(violence_percap_anu) + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_anu +
as.factor(ags_state), # state fixed effects
id = "ags_county", data = dat_2016_s)
# ##########################
# 2017
# #########################
# Main model + Placebo
bin_17_sum_pl <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_future +
pop_15_44_muni_gendergap_anu +
log(population_muni_anu) + log(popdens_muni_anu) +
log_unemp_all_muni_anu + d_pop_muni_anu + vote_afd_2013_muni +
log_ref_inflow_anu + log(pop_ref_anu) + log(violence_percap_anu) + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_anu +
as.factor(ags_state), # state fixed effects
id = "ags_county", data = dat_2017_s)
## ####################
## Pooled Analysis
## ####################
# Final model + Placebo
bin_pool_sum_pl <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_future +
pop_15_44_muni_gendergap_anu +
log(population_muni_anu) + log(popdens_muni_anu) +
log_unemp_all_muni_anu + d_pop_muni_anu + vote_afd_2013_muni +
log_ref_inflow_anu + log(pop_ref_anu) + log(violence_percap_anu) + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_anu +
as.factor(ags_state) + as.factor(year), # state + year fixed effects
id = "ags_county", data = dat_pl)
# Repeat the analysis for Large Counties
dat_pool_s_l <- dat_pl[dat_pl$population_muni_anu >
quantile(dat_pl$population_muni_anu, prob = 0.5), ]
# Main model + Placebo
bin_pool_sum_pl_l <- bin.summary(Hate_all_muni_bin ~ pop_15_44_muni_gendergap_future +
pop_15_44_muni_gendergap_anu +
log(population_muni_anu) + log(popdens_muni_anu) +
log_unemp_all_muni_anu + d_pop_muni_anu + vote_afd_2013_muni +
log_ref_inflow_anu + log(pop_ref_anu) + log(violence_percap_anu) + ## county level
pc_hidegree_all2011 + d_manuf1115 + pc_manufacturing_2015 + ## county level
unemp_gendergap_anu +
as.factor(ags_state) + as.factor(year), # state + year fixed effects
id = "ags_county", data = dat_pool_s_l)
# Table
pl_fit_list_m <- list(bin_15_sum_pl$fit,
bin_16_sum_pl$fit,
bin_17_sum_pl$fit,
bin_pool_sum_pl$fit,
bin_pool_sum_pl_l$fit)
pl_se_list_m <- list(sqrt(diag(bin_15_sum_pl$vcov)),
sqrt(diag(bin_16_sum_pl$vcov)),
sqrt(diag(bin_17_sum_pl$vcov)),
sqrt(diag(bin_pool_sum_pl$vcov)),
sqrt(diag(bin_pool_sum_pl_l$vcov)))
star_out(stargazer(pl_fit_list_m, se = pl_se_list_m,
covariate.labels = c("Future-Treatment"),
keep=c("pop_15_44_muni_gendergap_future")),
name = "table_C9.tex")
# ##############################
# Appendix C10. Descriptive Statistics
# ##############################
rm(list=ls())
dat <- read.dta13("context.dta")
min15 <- round(min(dat$pc_ref_male[dat$year == 2015], na.rm = TRUE),2)
min16 <- round(min(dat$pc_ref_male[dat$year == 2016], na.rm = TRUE),2)
pdf("figure_C10.pdf", height = 5, width = 10)
par(mfrow = c(1, 2))
plot(density(dat$pc_ref_male[dat$year == 2015], na.rm = TRUE),
main = "Proportion of Male Refugees (2015)",
xlim = c(50, 100), xlab = "Percent of Male Refugees Among Refugees (county)")
text(x = 90, y = 0.08, paste0("min = ", min15), font = 2)
polygon(density(dat$pc_ref_male[dat$year == 2015], na.rm = TRUE)$x,
density(dat$pc_ref_male[dat$year == 2015], na.rm = TRUE)$y,col='grey80')
plot(density(dat$pc_ref_male[dat$year == 2016], na.rm = TRUE),
main = "Proportion of Male Refugees (2016)", xlim = c(50, 100),
xlab = "Percent of Male Refugees Among Refugees (county)")
text(x = 90, y = 0.12, paste0("min = ", min16), font = 2)
polygon(density(dat$pc_ref_male[dat$year == 2016], na.rm = TRUE)$x,
density(dat$pc_ref_male[dat$year == 2016], na.rm = TRUE)$y,col='grey80')
dev.off()
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