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library(tidyverse)
library(haven)
library(glue)
library(jtools)
library(lubridate)
library(huxtable)
library(multcomp)
library(lfe)
}
# Data merging code copied in from kavanagh_g66z_data_merge.R
# load input files
# 5% Sample
set.seed(2982)
county_variables <- read_csv('replication_data/county_variables.csv') %>%
sample_frac(.05)
transportation <- read_csv('replication_data/transportation.csv')
# changes in distancing
flat_data <- transportation %>%
mutate(prop_home = pop_home/(pop_home + pop_not_home),
# Define the three time periods
time_period = case_when(
between(date, ymd('2020-02-16'),ymd('2020-02-29')) ~ 'AAA Reference',
between(date, ymd('2020-03-19'),ymd('2020-04-01')) ~ 'March',
between(date, ymd('2020-08-16'),ymd('2020-08-29')) ~ 'August')
) %>%
filter(!is.na(time_period), !is.na(pop_home)) %>%
group_by(time_period, fips, state) %>%
# Average over county, time period
summarize(prop_home = mean(prop_home, na.rm = TRUE)) %>%
arrange(state, fips, time_period) %>%
group_by(fips, state) %>%
# Scale to 100
mutate(prop_home_change = 100*(prop_home/first(prop_home) - 1)) %>%
filter(time_period != 'AAA Reference') %>%
# Reshape to get one variable for March and one for August
pivot_wider(id_cols = c('fips','state'),
names_from = 'time_period',
values_from = c('prop_home','prop_home_change')) %>%
# Bring in county-level data
right_join(county_variables, by = 'fips')
# IQR of Trump support
trumpIQR <- county_variables %>%
dplyr::select(fips, trump_share) %>%
unique() %>%
pull(trump_share) %>%
quantile(c(.25, .75), na.rm = TRUE) %>%
{.[2] - .[1]} %>%
unname()
# Variable construction
flat_data <- flat_data %>%
mutate(state = factor(state)) %>%
dplyr::select(prop_home_change_March,
prop_home_change_August,
income_per_capita,
trump_share,
male_percent,
percent_black,
percent_hispanic,
percent_college,
percent_retail,
percent_transportation,
percent_hes,
prop_rural,
ten_nineteen,
twenty_twentynine,
thirty_thirtynine,
forty_fortynine,
fifty_fiftynine,
sixty_sixtynine,
seventy_seventynine,
over_eighty,
state,
fips) %>%
ungroup() %>%
# These are stored as 0-1 but everything else is 0-100
mutate(across(starts_with('percent_'),function(x) x*100)) %>%
mutate(male_percent = male_percent*100,
percent_college = percent_college/100) %>%
mutate(income_per_capita = income_per_capita/1000)
# Create regression formulae
formula_maker <- function(depvar, data) {
vnames <- data %>%
dplyr::select(-fips, -prop_home_change_March, -prop_home_change_August, -state) %>%
names()
form <- paste0(depvar,'~',
paste(vnames, collapse ='+'),
' | state')
return(as.formula(form))
}
# Run fixed effect regressions
m1 <- felm(formula_maker('prop_home_change_March',flat_data), data = flat_data)
m2 <- felm(formula_maker('prop_home_change_August',flat_data), data = flat_data)
# Regression table
results_tab <- export_summs(m1, m2,
digits = 3,
model.names = c('March 19-April 1','August 16-29'),
coefs = c('Income per Capita (Thousands)' = 'income_per_capita',
'Share of Trump Voters' = 'trump_share',
'Percent Male' = 'male_percent',
'Percent Black' = 'percent_black',
'Percent Hispanic' = 'percent_hispanic',
'Percent with College Degree' = 'percent_college',
'Percent in Retail' = 'percent_retail',
'Percent in Transportation' = 'percent_transportation',
'Percent in Health / Ed / Soc. Svcs' = 'percent_hes',
'Percent Rural' = 'prop_rural',
'Percent Age 10-19' = 'ten_nineteen',
'Percent Age 20-29' = 'twenty_twentynine',
'Percent Age 30-39' = 'thirty_thirtynine',
'Percent Age 40-49' = 'forty_fortynine',
'Percent Age 50-59' = 'fifty_fiftynine',
'Percent Age 60-69' = 'sixty_sixtynine',
'Percent Age 70-79' = 'seventy_seventynine',
'Percent Age 80+' = 'over_eighty'),
statistics = c(N = 'nobs',
R2 = 'r.squared')) %>%
add_footnote('More-positive numbers indicate more stay-at-home activity. State fixed effects included.')
quick_html(results_tab, file = 'regression_table.html')
# Effect of a one-IQR change in Trump share
summary(glht(m1, paste0(trumpIQR,'*trump_share = 0')))
summary(glht(m2, paste0(trumpIQR,'*trump_share = 0')))
## Additional analysis: spatial autocorrelation
{
library(tigris)
library(spdep)
library(sphet)
library(spatialreg)
}
# Get information on central county latitude/longitude
counties <- counties()
counties <- as_tibble(counties[,c('STATEFP','COUNTYFP','INTPTLAT','INTPTLON')]) %>%
mutate(fips = as.numeric(STATEFP)*1000 + as.numeric(COUNTYFP)) %>%
dplyr::select(-geometry, -STATEFP, -COUNTYFP) %>%
rename(lat = INTPTLAT, lon = INTPTLON) %>%
mutate(lat = as.numeric(lat),
lon = as.numeric(lon))
# Bring in to data
flat_data <- left_join(flat_data, counties)
# K nearest neighbors for spatial spillovers
kn <- knearneigh(as.matrix(flat_data[,c('lon','lat'), with = FALSE]), 5)
nb <- knn2nb(kn)
listw <- nb2listw(nb)
# Create regression formulae
formula_maker <- function(depvar, data) {
vnames <- data %>%
dplyr::select(-fips, -prop_home_change_March, -prop_home_change_August) %>%
names()
form <- paste0(depvar,'~',
paste(vnames, collapse ='+'))
return(as.formula(form))
}
# Run models with spatial autocorrelation term
m3 <- lagsarlm(formula_maker('prop_home_change_March',flat_data), data = flat_data, listw = listw)
m4 <- lagsarlm(formula_maker('prop_home_change_August',flat_data), data = flat_data, listw = listw)
# Regression table
results_tab <- export_summs(m3, m4,
digits = 3,
model.names = c('March 19-April 1','August 16-29'),
coefs = c('Income per Capita (Thousands)' = 'income_per_capita',
'Share of Trump Voters' = 'trump_share',
'Percent Male' = 'male_percent',
'Percent Black' = 'percent_black',
'Percent Hispanic' = 'percent_hispanic',
'Percent with College Degree' = 'percent_college',
'Percent in Retail' = 'percent_retail',
'Percent in Transportation' = 'percent_transportation',
'Percent in Health / Ed / Soc. Svcs' = 'percent_hes',
'Percent Rural' = 'prop_rural',
'Percent Age 10-19' = 'ten_nineteen',
'Percent Age 20-29' = 'twenty_twentynine',
'Percent Age 30-39' = 'thirty_thirtynine',
'Percent Age 40-49' = 'forty_fortynine',
'Percent Age 50-59' = 'fifty_fiftynine',
'Percent Age 60-69' = 'sixty_sixtynine',
'Percent Age 70-79' = 'seventy_seventynine',
'Percent Age 80+' = 'over_eighty',
'rho' = 'rho'),
statistics = c(N = 'nobs',
R2 = 'r.squared')) %>%
add_footnote('More-positive numbers indicate more stay-at-home activity.\nState fixed effects included.\nSpatial autocorrelation included with 5-nearest-neighbor neighbors.')
quick_html(results_tab, file = 'spatial_regression_table.html')
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