{ 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')