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"fueltax" = "Fuel Tax, Cents per Gallon", "state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban", tex = F)
# Statewide Variables
# ----------------------------------------------------------
s1 = feols(c(totfat, occfat, noccfat) ~ hha + milrur * rurdense + milurb * urbdense | state + year,
cluster = ~ state, data)
etable(s1,
headers = c("Types of roads"), drop = c("totpop", "avgage", "pcinc", "lim70", "fueltax"),
dict = c("totfat" = "Total Fatalities", "hha" = "Handheld Device Ban", "milrur" = "Total length of rural roads (miles)",
"milurb" = "Total length of urban roads (miles)",
"rurdense" = "Density of cars in rural roads (VMT / Total Length)",
"urbdense" = "Density of cars in urban roads (VMT / Total Length)",
"state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban", tex = F)
s2 = feols(c(totfat, occfat, noccfat) ~ hha + totpop + avgage + pcinc + lim70 + fueltax +
milrur * rurdense + milurb * urbdense | state + year, cluster = ~ state, data)
etable(s2,
drop = c("totpop", "avgage", "pcinc", "lim70", "fueltax"),
dict = c("totfat" = "Total Fatalities", "occfat" = "Occupant Fatalities",
"noccfat" = "Nonoccupant Fatalities", "hha" = "Handheld Device Ban",
"milrur" = "Total length of rural roads (miles)", "milurb" = "Total length of urban roads (miles)",
"rurdense" = "Density of cars in rural roads (VMT / Total Length)",
"urbdense" = "Density of cars in urban roads (VMT / Total Length)",
"state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban on Types of Roads with Control Variables", tex = T)
# FE Regression for Different Types of Fatalities
# ----------------------------------------------------------
t1 = feols(totfat ~ hha + totpop + avgage + pcinc + lim70 + fueltax | state + year, cluster = ~ state, data)
t2 = feols(occfat ~ hha + totpop + avgage + pcinc + lim70 + fueltax | state + year, cluster = ~ state, data)
t3 = feols(noccfat ~ hha + totpop + avgage + pcinc + lim70 + fueltax | state + year, cluster = ~ state, data)
etable(t1, t2, t3,
headers = c("Effect on Total Fatalities", "Effect on Total Occupant Fatalities", "Effect on Total Non-occupant Fatalities"),
dict = c("totfat" = "Total Fatalities", "hha" = "Handheld Device Ban",
"totpop" = "Total Population", "avgage" = "Average Age",
"pcinc" = "Per-Capita Personal Income", "lim70" = "70MPH or Greater Speed Limit",
"fueltax" = "Fuel Tax, Cents per Gallon", "state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban", tex = F)
# DID Regression per Year
# ----------------------------------------------------------
d1 = feols(totfat ~ i(tau, ref = -1) | state + year, data = data, cluster = ~ state)
d2 = feols(totfat ~ i(tau, ref = -1) + totpop + avgage + pcinc + lim70 + fueltax | state + year, data = data, cluster = ~ state)
etable(d1, d2)
# Plotting Results for DID Regression
# ----------------------------------------------------------
iplot(d1, xlab = "Relative Handheld Device Ban Year", main = "Effect on Total Traffic Fatalities")
iplot(d2, xlab = "Relative Handheld Device Ban Year", main = "Effect on Total Traffic Fatalities")
# Sun and Abraham Estimator
# ----------------------------------------------------------
d3 = feols(totfat ~ sunab(mintreatyear, year) | state + year, data = data, cluster = ~ state)
d4 = feols(totfat ~ sunab(mintreatyear, year) + totpop + avgage + pcinc + lim70 + fueltax | state + year, data = data, cluster = ~ state)
etable(d3, d4)
# Plotting Results for Sun and Abraham Estimator
# ----------------------------------------------------------
iplot(d3, xlab = "Year", main = "Effect on Total Traffic Fatalities")
iplot(d4, xlab = "Year", main = "Effect on Total Traffic Fatalities")
# ----------------------------------------------------------
# New Regression Analysis
# ----------------------------------------------------------
d5 = feols(
totfat ~ sunab(mintreatyear, year) +
totpop + avgage + pcinc + lim70 + fueltax +
milrur * milurb + rurdense * urbdense | state + year,
data = data,
cluster = ~ state
)
# Plotting the Results of the New Regression
# ----------------------------------------------------------
iplot(
list(d5, d4),
xlab = "Relative Handheld Device Ban Year",
main = "Effect on Total Traffic Fatalities"
)
# Analyze the Effects of the Handheld Device Ban
# ----------------------------------------------------------
data_c = data %>%
filter(treatment_group == 1) %>% # Filter for treated group
group_by(tau) %>%
summarise(mean_fat = mean(totfat, na.rm = TRUE), .groups = "drop") %>%
ungroup()
# Plotting Mean Traffic Fatalities Over Time Since Treatment
# ----------------------------------------------------------
ggplot(data_c, aes(x = tau, y = mean_fat)) +
geom_point() +
geom_smooth(method = "loess") +
xlab("Years Relative to Treatment") +
ylab("Mean Traffic Fatalities") +
ggtitle("Mean Traffic Fatalities Over Time Since Treatment")
# End of Script
# ----------------------------------------------------------
# Load Packages and Data
# ----------------------------------------------------------
setwd(/Applications/Ω/UCSD/29-GPEC446-SP23/final_poster/Traffic-Fatalities-Policy-Analysis) # Set your working directory
# Load Packages and Data
# ----------------------------------------------------------
setwd("/Applications/Ω/UCSD/29-GPEC446-SP23/final_poster/Traffic-Fatalities-Policy-Analysis") # Set your working directory
library(plm)
library(tidyverse)
library(stargazer)
library(wooldridge)
library(estimatr)
library(fixest)
library(lmtest)
library(sandwich)
library(broom)
# Data Load
# ----------------------------------------------------------
load("traffic_fat.rda")
data = traffic_fat
data_b = data
# Initial Data Manipulation
# ----------------------------------------------------------
data = data %>%
mutate(
treatyear = if_else(hha > 0 & !is.na(hha), year, NA) # Define treatment year
) %>%
group_by(state) %>%
mutate(
mintreatyear = min(treatyear, na.rm = TRUE), # Treatment year for each row
mintreatyear = if_else(is.infinite(mintreatyear), NA, mintreatyear), # Handle untreated areas
treatment_group = if_else(!is.na(mintreatyear), 1, 0) # Treatment group indicator
) %>%
ungroup() %>%
mutate(
treat = if_else(year >= mintreatyear, 1, 0), # Treated variable
tau = year - mintreatyear # Relative treatment year
) %>%
replace_na(list(treat = 0, tau = 0, mintreatyear = 0))
# Check for NAs
# ----------------------------------------------------------
test = is.na(data)
summary(test)
# Treat vs Policy
# ----------------------------------------------------------
p1 = feols(totfat ~ hha | state + year, cluster = ~ state, data)
p2 = feols(totfat ~ treat | state + year, cluster = ~ state, data)
etable(p1, p2) # Compare treatment and policy effects
# Initial Data Preview
# ----------------------------------------------------------
ggplot(data, aes(x = year, y = totfat, group = state, color = state)) +
geom_line() +
xlab("Year") + ylab("Total Fatalities") +
geom_vline(aes(xintercept = 1990, linetype = "1990 Recession"), colour= 'red') +
geom_vline(aes(xintercept = 2008, linetype = "2008 Recession"), colour= 'black') +
scale_linetype_manual(name = "Events", values = c(2, 2),
guide = guide_legend(override.aes = list(color = c("red", "black")))) +
theme_bw() + ggtitle("Traffic Fatalities", "From 1983 - 2012") +
labs(caption = "Figure 1: Total traffic fatalities")
ggplot() +
geom_smooth(data = data, mapping = aes(x = year, y = totfat), method = "loess", se = FALSE)
# Univariate
# ----------------------------------------------------------
m1 = feols(totfat ~ hha | state + year, cluster = ~ state, data)
# With Control Variables
# ----------------------------------------------------------
m2 = feols(totfat ~ hha + totpop | state + year, cluster = ~ state, data)
m3 = feols(totfat ~ hha + totpop + avgage | state + year, cluster = ~ state, data)
m4 = feols(totfat ~ hha + totpop + avgage + pcinc | state + year, cluster = ~ state, data)
m5 = feols(c(totfat, occfat, noccfat) ~ hha + totpop + avgage + pcinc + lim70 | state + year, cluster = ~ state, data)
etable(m5,
headers = c("Total Fatality", "Total Occupant Fatality", "Total Nonoccupant Fatality"),
dict = c("totfat" = "Total Fatalities", "occfat" = "Occupant Fatalities", "noccfat" = "Nonoccupant Fatalities",
"hha" = "Handheld Device Ban", "totpop" = "Total Population", "avgage" = "Average Age",
"pcinc" = "Per-Capita Personal Income", "lim70" = "70MPH or Greater Speed Limit",
"fueltax" = "Fuel Tax, Cents per Gallon", "state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban", tex = F)
# Statewide Variables
# ----------------------------------------------------------
s1 = feols(c(totfat, occfat, noccfat) ~ hha + milrur * rurdense + milurb * urbdense | state + year,
cluster = ~ state, data)
etable(s1,
headers = c("Types of roads"), drop = c("totpop", "avgage", "pcinc", "lim70", "fueltax"),
dict = c("totfat" = "Total Fatalities", "hha" = "Handheld Device Ban", "milrur" = "Total length of rural roads (miles)",
"milurb" = "Total length of urban roads (miles)",
"rurdense" = "Density of cars in rural roads (VMT / Total Length)",
"urbdense" = "Density of cars in urban roads (VMT / Total Length)",
"state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban", tex = F)
s2 = feols(c(totfat, occfat, noccfat) ~ hha + totpop + avgage + pcinc + lim70 + fueltax +
milrur * rurdense + milurb * urbdense | state + year, cluster = ~ state, data)
etable(s2,
drop = c("totpop", "avgage", "pcinc", "lim70", "fueltax"),
dict = c("totfat" = "Total Fatalities", "occfat" = "Occupant Fatalities",
"noccfat" = "Nonoccupant Fatalities", "hha" = "Handheld Device Ban",
"milrur" = "Total length of rural roads (miles)", "milurb" = "Total length of urban roads (miles)",
"rurdense" = "Density of cars in rural roads (VMT / Total Length)",
"urbdense" = "Density of cars in urban roads (VMT / Total Length)",
"state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban on Types of Roads with Control Variables", tex = T)
# FE Regression for Different Types of Fatalities
# ----------------------------------------------------------
t1 = feols(totfat ~ hha + totpop + avgage + pcinc + lim70 + fueltax | state + year, cluster = ~ state, data)
t2 = feols(occfat ~ hha + totpop + avgage + pcinc + lim70 + fueltax | state + year, cluster = ~ state, data)
t3 = feols(noccfat ~ hha + totpop + avgage + pcinc + lim70 + fueltax | state + year, cluster = ~ state, data)
etable(t1, t2, t3,
headers = c("Effect on Total Fatalities", "Effect on Total Occupant Fatalities", "Effect on Total Non-occupant Fatalities"),
dict = c("totfat" = "Total Fatalities", "hha" = "Handheld Device Ban",
"totpop" = "Total Population", "avgage" = "Average Age",
"pcinc" = "Per-Capita Personal Income", "lim70" = "70MPH or Greater Speed Limit",
"fueltax" = "Fuel Tax, Cents per Gallon", "state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban", tex = F)
# DID Regression per Year
# ----------------------------------------------------------
d1 = feols(totfat ~ i(tau, ref = -1) | state + year, data = data, cluster = ~ state)
d2 = feols(totfat ~ i(tau, ref = -1) + totpop + avgage + pcinc + lim70 + fueltax | state + year, data = data, cluster = ~ state)
etable(d1, d2)
# Plotting Results for DID Regression
# ----------------------------------------------------------
iplot(d1, xlab = "Relative Handheld Device Ban Year", main = "Effect on Total Traffic Fatalities")
iplot(d2, xlab = "Relative Handheld Device Ban Year", main = "Effect on Total Traffic Fatalities")
# Sun and Abraham Estimator
# ----------------------------------------------------------
d3 = feols(totfat ~ sunab(mintreatyear, year) | state + year, data = data, cluster = ~ state)
d4 = feols(totfat ~ sunab(mintreatyear, year) + totpop + avgage + pcinc + lim70 + fueltax | state + year, data = data, cluster = ~ state)
etable(d3, d4)
# Plotting Results for Sun and Abraham Estimator
# ----------------------------------------------------------
iplot(d3, xlab = "Year", main = "Effect on Total Traffic Fatalities")
iplot(d4, xlab = "Year", main = "Effect on Total Traffic Fatalities")
# ----------------------------------------------------------
# New Regression Analysis
# ----------------------------------------------------------
d5 = feols(
totfat ~ sunab(mintreatyear, year) +
totpop + avgage + pcinc + lim70 + fueltax +
milrur * milurb + rurdense * urbdense | state + year,
data = data,
cluster = ~ state
)
# Plotting the Results of the New Regression
# ----------------------------------------------------------
iplot(
list(d5, d4),
xlab = "Relative Handheld Device Ban Year",
main = "Effect on Total Traffic Fatalities"
)
etable(d3, d4)
# DID Regression per Year
# ----------------------------------------------------------
d1 = feols(totfat ~ i(tau, ref = -1) | state + year, data = data, cluster = ~ state)
d2 = feols(totfat ~ i(tau, ref = -1) + totpop + avgage + pcinc + lim70 + fueltax | state + year, data = data, cluster = ~ state)
etable(d1, d2)
# Plotting Results for DID Regression
# ----------------------------------------------------------
iplot(d1, xlab = "Relative Handheld Device Ban Year", main = "Effect on Total Traffic Fatalities")
iplot(d2, xlab = "Relative Handheld Device Ban Year", main = "Effect on Total Traffic Fatalities")
# Sun and Abraham Estimator
# ----------------------------------------------------------
d3 = feols(totfat ~ sunab(mintreatyear, year) | state + year, data = data, cluster = ~ state)
d4 = feols(totfat ~ sunab(mintreatyear, year) + totpop + avgage + pcinc + lim70 + fueltax | state + year, data = data, cluster = ~ state)
etable(d3, d4)
# Plotting Results for Sun and Abraham Estimator
# ----------------------------------------------------------
iplot(d3, xlab = "Year", main = "Effect on Total Traffic Fatalities")
iplot(d4, xlab = "Year", main = "Effect on Total Traffic Fatalities")
# ----------------------------------------------------------
# Farrel Azhar
# ----------------------------------------------------------
# UCSD School of Global Policy and Strategy, QM3 Final Poster
# ----------------------------------------------------------
# Load Packages and Data
# ----------------------------------------------------------
setwd("/Applications/Ω/UCSD/29-GPEC446-SP23/final_poster/Traffic-Fatalities-Policy-Analysis") # Set your working directory
library(plm)
library(tidyverse)
library(stargazer)
library(wooldridge)
library(estimatr)
library(fixest)
library(lmtest)
library(sandwich)
library(broom)
# Data Load
# ----------------------------------------------------------
load("traffic_fat.rda")
data = traffic_fat
data_b = data
# Initial Data Manipulation
# ----------------------------------------------------------
data = data %>%
mutate(
treatyear = if_else(hha > 0 & !is.na(hha), year, NA) # Define treatment year
) %>%
group_by(state) %>%
mutate(
mintreatyear = min(treatyear, na.rm = TRUE), # Treatment year for each row
mintreatyear = if_else(is.infinite(mintreatyear), NA, mintreatyear), # Handle untreated areas
treatment_group = if_else(!is.na(mintreatyear), 1, 0) # Treatment group indicator
) %>%
ungroup() %>%
mutate(
treat = if_else(year >= mintreatyear, 1, 0), # Treated variable
tau = year - mintreatyear # Relative treatment year
) %>%
replace_na(list(treat = 0, tau = 0, mintreatyear = 0))
# Check for NAs
# ----------------------------------------------------------
test = is.na(data)
summary(test)
# Treat vs Policy
# ----------------------------------------------------------
p1 = feols(totfat ~ hha | state + year, cluster = ~ state, data)
p2 = feols(totfat ~ treat | state + year, cluster = ~ state, data)
etable(p1, p2) # Compare treatment and policy effects
# Initial Data Preview
# ----------------------------------------------------------
ggplot(data, aes(x = year, y = totfat, group = state, color = state)) +
geom_line() +
xlab("Year") + ylab("Total Fatalities") +
geom_vline(aes(xintercept = 1990, linetype = "1990 Recession"), colour= 'red') +
geom_vline(aes(xintercept = 2008, linetype = "2008 Recession"), colour= 'black') +
scale_linetype_manual(name = "Events", values = c(2, 2),
guide = guide_legend(override.aes = list(color = c("red", "black")))) +
theme_bw() + ggtitle("Traffic Fatalities", "From 1983 - 2012") +
labs(caption = "Figure 1: Total traffic fatalities")
ggplot() +
geom_smooth(data = data, mapping = aes(x = year, y = totfat), method = "loess", se = FALSE)
# Analysis
# ----------------------------------------------------------
# FE Regression for pre/post
# Univariate
# ----------------------------------------------------------
m1 = feols(totfat ~ hha | state + year, cluster = ~ state, data)
# With Control Variables
# ----------------------------------------------------------
m2 = feols(totfat ~ hha + totpop | state + year, cluster = ~ state, data)
m3 = feols(totfat ~ hha + totpop + avgage | state + year, cluster = ~ state, data)
m4 = feols(totfat ~ hha + totpop + avgage + pcinc | state + year, cluster = ~ state, data)
m5 = feols(c(totfat, occfat, noccfat) ~ hha + totpop + avgage + pcinc + lim70 | state + year, cluster = ~ state, data)
etable(m5,
headers = c("Total Fatality", "Total Occupant Fatality", "Total Nonoccupant Fatality"),
dict = c("totfat" = "Total Fatalities", "occfat" = "Occupant Fatalities", "noccfat" = "Nonoccupant Fatalities",
"hha" = "Handheld Device Ban", "totpop" = "Total Population", "avgage" = "Average Age",
"pcinc" = "Per-Capita Personal Income", "lim70" = "70MPH or Greater Speed Limit",
"fueltax" = "Fuel Tax, Cents per Gallon", "state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban", tex = F)
# Statewide Variables
# ----------------------------------------------------------
s1 = feols(c(totfat, occfat, noccfat) ~ hha + milrur * rurdense + milurb * urbdense | state + year,
cluster = ~ state, data)
etable(s1,
headers = c("Types of roads"), drop = c("totpop", "avgage", "pcinc", "lim70", "fueltax"),
dict = c("totfat" = "Total Fatalities", "hha" = "Handheld Device Ban", "milrur" = "Total length of rural roads (miles)",
"milurb" = "Total length of urban roads (miles)",
"rurdense" = "Density of cars in rural roads (VMT / Total Length)",
"urbdense" = "Density of cars in urban roads (VMT / Total Length)",
"state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban", tex = F)
s2 = feols(c(totfat, occfat, noccfat) ~ hha + totpop + avgage + pcinc + lim70 + fueltax +
milrur * rurdense + milurb * urbdense | state + year, cluster = ~ state, data)
etable(s2,
drop = c("totpop", "avgage", "pcinc", "lim70", "fueltax"),
dict = c("totfat" = "Total Fatalities", "occfat" = "Occupant Fatalities",
"noccfat" = "Nonoccupant Fatalities", "hha" = "Handheld Device Ban",
"milrur" = "Total length of rural roads (miles)", "milurb" = "Total length of urban roads (miles)",
"rurdense" = "Density of cars in rural roads (VMT / Total Length)",
"urbdense" = "Density of cars in urban roads (VMT / Total Length)",
"state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban on Types of Roads with Control Variables", tex = T)
# FE Regression for Different Types of Fatalities
# ----------------------------------------------------------
t1 = feols(totfat ~ hha + totpop + avgage + pcinc + lim70 + fueltax | state + year, cluster = ~ state, data)
t2 = feols(occfat ~ hha + totpop + avgage + pcinc + lim70 + fueltax | state + year, cluster = ~ state, data)
t3 = feols(noccfat ~ hha + totpop + avgage + pcinc + lim70 + fueltax | state + year, cluster = ~ state, data)
etable(t1, t2, t3,
headers = c("Effect on Total Fatalities", "Effect on Total Occupant Fatalities", "Effect on Total Non-occupant Fatalities"),
dict = c("totfat" = "Total Fatalities", "hha" = "Handheld Device Ban",
"totpop" = "Total Population", "avgage" = "Average Age",
"pcinc" = "Per-Capita Personal Income", "lim70" = "70MPH or Greater Speed Limit",
"fueltax" = "Fuel Tax, Cents per Gallon", "state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban", tex = F)
# DID Regression per Year
# ----------------------------------------------------------
d1 = feols(totfat ~ i(tau, ref = -1) | state + year, data = data, cluster = ~ state)
d2 = feols(totfat ~ i(tau, ref = -1) + totpop + avgage + pcinc + lim70 + fueltax | state + year, data = data, cluster = ~ state)
etable(d1, d2)
# Plotting Results for DID Regression
# ----------------------------------------------------------
iplot(d1, xlab = "Relative Handheld Device Ban Year", main = "Effect on Total Traffic Fatalities")
iplot(d2, xlab = "Relative Handheld Device Ban Year", main = "Effect on Total Traffic Fatalities")
# Sun and Abraham Estimator
# ----------------------------------------------------------
d3 = feols(totfat ~ sunab(mintreatyear, year) | state + year, data = data, cluster = ~ state)
d4 = feols(totfat ~ sunab(mintreatyear, year) + totpop + avgage + pcinc + lim70 + fueltax | state + year, data = data, cluster = ~ state)
etable(d3, d4)
# Plotting Results for Sun and Abraham Estimator
# ----------------------------------------------------------
iplot(d3, xlab = "Year", main = "Effect on Total Traffic Fatalities")
iplot(d4, xlab = "Year", main = "Effect on Total Traffic Fatalities")
# ----------------------------------------------------------
# New Regression Analysis
# ----------------------------------------------------------
d5 = feols(
totfat ~ sunab(mintreatyear, year) +
totpop + avgage + pcinc + lim70 + fueltax +
milrur * milurb + rurdense * urbdense | state + year,
data = data,
cluster = ~ state
)
# Plotting the Results of the New Regression
# ----------------------------------------------------------
iplot(
list(d5, d4),
xlab = "Relative Handheld Device Ban Year",
main = "Effect on Total Traffic Fatalities"
)
# Analyze the Effects of the Handheld Device Ban
# ----------------------------------------------------------
data_c = data %>%
filter(treatment_group == 1) %>% # Filter for treated group
group_by(tau) %>%
summarise(mean_fat = mean(totfat, na.rm = TRUE), .groups = "drop") %>%
ungroup()
# Plotting Mean Traffic Fatalities Over Time Since Treatment
# ----------------------------------------------------------
ggplot(data_c, aes(x = tau, y = mean_fat)) +
geom_point() +
geom_smooth(method = "loess") +
xlab("Years Relative to Treatment") +
ylab("Mean Traffic Fatalities") +
ggtitle("Mean Traffic Fatalities Over Time Since Treatment")
# End of Script
# ----------------------------------------------------------
etable(s2,
drop = c("totpop", "avgage", "pcinc", "lim70", "fueltax"),
dict = c("totfat" = "Total Fatalities", "occfat" = "Occupant Fatalities",
"noccfat" = "Nonoccupant Fatalities", "hha" = "Handheld Device Ban",
"milrur" = "Total length of rural roads (miles)", "milurb" = "Total length of urban roads (miles)",
"rurdense" = "Density of cars in rural roads (VMT / Total Length)",
"urbdense" = "Density of cars in urban roads (VMT / Total Length)",
"state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban on Types of Roads with Control Variables", tex = T)
# FE Regression for Different Types of Fatalities
# ----------------------------------------------------------
t1 = feols(totfat ~ hha + totpop + avgage + pcinc + lim70 + fueltax | state + year, cluster = ~ state, data)
t2 = feols(occfat ~ hha + totpop + avgage + pcinc + lim70 + fueltax | state + year, cluster = ~ state, data)
t3 = feols(noccfat ~ hha + totpop + avgage + pcinc + lim70 + fueltax | state + year, cluster = ~ state, data)
etable(t1, t2, t3,
headers = c("Effect on Total Fatalities", "Effect on Total Occupant Fatalities", "Effect on Total Non-occupant Fatalities"),
dict = c("totfat" = "Total Fatalities", "hha" = "Handheld Device Ban",
"totpop" = "Total Population", "avgage" = "Average Age",
"pcinc" = "Per-Capita Personal Income", "lim70" = "70MPH or Greater Speed Limit",
"fueltax" = "Fuel Tax, Cents per Gallon", "state" = "State", "year" = "Year"),
title = "Effects of Handheld Device Ban", tex = F)
# DID Regression per Year
# ----------------------------------------------------------
d1 = feols(totfat ~ i(tau, ref = -1) | state + year, data = data, cluster = ~ state)
d2 = feols(totfat ~ i(tau, ref = -1) + totpop + avgage + pcinc + lim70 + fueltax | state + year, data = data, cluster = ~ state)
etable(d1, d2)
# Plotting Results for DID Regression
# ----------------------------------------------------------
iplot(d1, xlab = "Relative Handheld Device Ban Year", main = "Effect on Total Traffic Fatalities")
iplot(d2, xlab = "Relative Handheld Device Ban Year", main = "Effect on Total Traffic Fatalities")
# Plotting Results for DID Regression
# ----------------------------------------------------------
iplot(d1, xlab = "Relative Handheld Device Ban Year", main = "Effect on Total Traffic Fatalities")
iplot(d2, xlab = "Relative Handheld Device Ban Year", main = "Effect on Total Traffic Fatalities")
# Sun and Abraham Estimator
# ----------------------------------------------------------
d3 = feols(totfat ~ sunab(mintreatyear, year) | state + year, data = data, cluster = ~ state)
d4 = feols(totfat ~ sunab(mintreatyear, year) + totpop + avgage + pcinc + lim70 + fueltax | state + year, data = data, cluster = ~ state)
etable(d3, d4)
# Plotting Results for Sun and Abraham Estimator
# ----------------------------------------------------------
iplot(d3, xlab = "Year", main = "Effect on Total Traffic Fatalities")
iplot(d4, xlab = "Year", main = "Effect on Total Traffic Fatalities")
# ----------------------------------------------------------
# New Regression Analysis
# ----------------------------------------------------------
d5 = feols(
totfat ~ sunab(mintreatyear, year) +
totpop + avgage + pcinc + lim70 + fueltax +
milrur * milurb + rurdense * urbdense | state + year,
data = data,
cluster = ~ state
)
# Plotting the Results of the New Regression
# ----------------------------------------------------------
iplot(
list(d5, d4),
xlab = "Relative Handheld Device Ban Year",
main = "Effect on Total Traffic Fatalities"
)
# Analyze the Effects of the Handheld Device Ban
# ----------------------------------------------------------
data_c = data %>%
filter(treatment_group == 1) %>% # Filter for treated group
group_by(tau) %>%
summarise(mean_fat = mean(totfat, na.rm = TRUE), .groups = "drop") %>%
ungroup()
# Plotting Mean Traffic Fatalities Over Time Since Treatment
# ----------------------------------------------------------
ggplot(data_c, aes(x = tau, y = mean_fat)) +
geom_point() +
geom_smooth(method = "loess") +
xlab("Years Relative to Treatment") +
ylab("Mean Traffic Fatalities") +
ggtitle("Mean Traffic Fatalities Over Time Since Treatment")
# Plotting the Results of the New Regression
# ----------------------------------------------------------
iplot(
list(d5, d4),
xlab = "Relative Handheld Device Ban Year",
main = "Effect on Total Traffic Fatalities"
)
# Plotting Mean Traffic Fatalities Over Time Since Treatment
# ----------------------------------------------------------
ggplot(data_c, aes(x = tau, y = mean_fat)) +
geom_point() +
geom_smooth(method = "loess") +
xlab("Years Relative to Treatment") +
ylab("Mean Traffic Fatalities") +
ggtitle("Mean Traffic Fatalities Over Time Since Treatment")