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ultrapollution_NO2.R
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# This script performs the econometric analyses for the ultrapollution project
# Loading libraries
library(stargazer)
library(sandwich)
library(lmtest)
# Specifying directories for data + results
direc <- paste('C:/Users/', username, '/Documents/Data/ultrapollution/ultra_data/', sep = '')
direc2 <- paste('C:/Users/', username, '/Documents/Data/ultrapollution/results/', sep = '')
# Reading in the data set
data <- read.csv(paste(direc, 'ultradata.csv', sep = ''))
# See which event types have at least 100 observations
event_types <- c()
for (d in unique(data$RACE_Distance)) {if (dim(data[which(data$RACE_Distance == d),][1])>100) {event_types <- c(event_types,d)}}
# Run OLS regressions
mod50k <- lm(log(Seconds+.001) ~ NO2 + factor(Gender) + Temperature*Humidity + Precipitation
+ WindSpeed + factor(FIPS_Race) + factor(RACE_Month)*factor(RACE_Year)
+ Total_Races + RACE_Finisher_Count + In_State
+ Travel_Distance + Ability + Population + Poverty_Rate + Unemployment_Rate + Income
+ Education_High_School + Education_Some_College + Education_Associates
+ Education_Bachelors + Education_Graduate + Altitude_Home + NO2_Home,
data = data[which(data$RACE_Distance == '50 KM'),])
mod50m <- lm(log(Seconds+.001) ~ NO2 + factor(Gender) + Temperature*Humidity + Precipitation
+ WindSpeed + factor(FIPS_Race) + factor(RACE_Month)*factor(RACE_Year)
+ Total_Races + RACE_Finisher_Count + In_State
+ Travel_Distance + Ability + Population + Poverty_Rate + Unemployment_Rate + Income
+ Education_High_School + Education_Some_College + Education_Associates
+ Education_Bachelors + Education_Graduate + Altitude_Home + NO2_Home,
data = data[which(data$RACE_Distance == '50 Miles'),])
mod100k <- lm(log(Seconds+.001) ~ NO2 + factor(Gender) + Temperature*Humidity + Precipitation
+ WindSpeed + factor(FIPS_Race) + factor(RACE_Month)*factor(RACE_Year)
+ Total_Races + RACE_Finisher_Count + In_State
+ Travel_Distance + Ability + Population + Poverty_Rate + Unemployment_Rate + Income
+ Education_High_School + Education_Some_College + Education_Associates
+ Education_Bachelors + Education_Graduate + Altitude_Home + NO2_Home,
data = data[which(data$RACE_Distance == '100 KM'),])
mod100m <- lm(log(Seconds+.001) ~ NO2 + factor(Gender) + Temperature*Humidity + Precipitation
+ WindSpeed + factor(FIPS_Race) + factor(RACE_Month)*factor(RACE_Year)
+ Total_Races + RACE_Finisher_Count + In_State
+ Travel_Distance + Ability + Population + Poverty_Rate + Unemployment_Rate + Income
+ Education_High_School + Education_Some_College + Education_Associates
+ Education_Bachelors + Education_Graduate + Altitude_Home + NO2_Home,
data = data[which(data$RACE_Distance == '100 Miles'),])
mod6h <- lm(log(Distance+.001) ~ NO2 + factor(Gender) + Temperature*Humidity + Precipitation
+ WindSpeed + factor(FIPS_Race) + factor(RACE_Month)*factor(RACE_Year)
+ Total_Races + RACE_Finisher_Count + In_State
+ Travel_Distance + Ability + Population + Poverty_Rate + Unemployment_Rate + Income
+ Education_High_School + Education_Some_College + Education_Associates
+ Education_Bachelors + Education_Graduate + Altitude_Home + NO2_Home,
data = data[which(data$RACE_Distance == '6 Hours'),])
mod12h <- lm(log(Distance+.001) ~ NO2 + factor(Gender) + Temperature*Humidity + Precipitation
+ WindSpeed + factor(FIPS_Race) + factor(RACE_Month)*factor(RACE_Year)
+ Total_Races + RACE_Finisher_Count + In_State
+ Travel_Distance + Ability + Population + Poverty_Rate + Unemployment_Rate + Income
+ Education_High_School + Education_Some_College + Education_Associates
+ Education_Bachelors + Education_Graduate + Altitude_Home + NO2_Home,
data = data[which(data$RACE_Distance == '12 Hours'),])
mod24h <- lm(log(Distance+.001) ~ NO2 + factor(Gender) + Temperature*Humidity + Precipitation
+ WindSpeed + factor(FIPS_Race) + factor(RACE_Month)*factor(RACE_Year)
+ Total_Races + RACE_Finisher_Count + In_State
+ Travel_Distance + Ability + Population + Poverty_Rate + Unemployment_Rate + Income
+ Education_High_School + Education_Some_College + Education_Associates
+ Education_Bachelors + Education_Graduate + Altitude_Home + NO2_Home,
data = data[which(data$RACE_Distance == '24 Hours'),])
xmod50k <- coeftest(mod50k, vcov = vcovCL, cluster = ~RACE_Name)
xmod50m <- coeftest(mod50m, vcov = vcovCL, cluster = ~RACE_Name)
xmod100k <- coeftest(mod100k, vcov = vcovCL, cluster = ~RACE_Name)
xmod100m <- coeftest(mod100m, vcov = vcovCL, cluster = ~RACE_Name)
xmod6h <- coeftest(mod6h, vcov = vcovCL, cluster = ~RACE_Name)
xmod12h <- coeftest(mod12h, vcov = vcovCL, cluster = ~RACE_Name)
xmod24h <- coeftest(mod24h, vcov = vcovCL, cluster = ~RACE_Name)
# Viewing results
write.csv(stargazer(xmod50k, xmod50m, xmod100k, xmod100m, xmod6h, xmod12h, xmod24h,
omit = c('FIPS_Race', 'RACE_Name', 'RACE_Month', 'RACE_Year')),
paste(direc2, 'NO2_results_tex.txt', sep = ''), row.names = FALSE)
write.csv(stargazer(xmod50k, xmod50m, xmod100k, xmod100m, xmod6h, xmod12h, xmod24h, type = 'text',
omit = c('FIPS_Race', 'RACE_Name', 'RACE_Month', 'RACE_Year')),
paste(direc2, 'NO2_results.txt', sep = ''), row.names = FALSE)