US Traffic Fatalities
US traffic fatalities panel data for the “lower 48” US states (i.e., excluding Alaska and Hawaii), annually for 1982 through 1988.
data("Fatalities")A data frame containing 336 observations on 34 variables.
factor indicating state.
factor indicating year.
numeric. Spirits consumption.
numeric. Unemployment rate.
numeric. Per capita personal income in 1987 dollars.
numeric. Employment/population ratio.
numeric. Tax on case of beer.
numeric. Percent of southern baptist.
numeric. Percent of mormon.
numeric. Minimum legal drinking age.
numeric. Percent residing in “dry” countries.
numeric. Percent of drivers aged 15–24.
numeric. Average miles per driver.
factor. Preliminary breath test law?
factor. Mandatory jail sentence?
factor. Mandatory community service?
numeric. Number of vehicle fatalities.
numeric. Number of night-time vehicle fatalities.
numeric. Number of single vehicle fatalities.
numeric. Number of vehicle fatalities, 15–17 year olds.
numeric. Number of night-time vehicle fatalities, 15–17 year olds.
numeric. Number of vehicle fatalities, 18–20 year olds.
numeric. Number of night-time vehicle fatalities, 18–20 year olds.
numeric. Number of vehicle fatalities, 21–24 year olds.
numeric. Number of night-time vehicle fatalities, 21–24 year olds.
numeric. Number of alcohol-involved vehicle fatalities.
numeric. Population.
numeric. Population, 15–17 year olds.
numeric. Population, 18–20 year olds.
numeric. Population, 21–24 year olds.
numeric. Total vehicle miles (millions).
numeric. US unemployment rate.
numeric. US employment/population ratio.
numeric. GSP rate of change.
Traffic fatalities are from the US Department of Transportation Fatal Accident Reporting System. The beer tax is the tax on a case of beer, which is an available measure of state alcohol taxes more generally. The drinking age variable is a factor indicating whether the legal drinking age is 18, 19, or 20. The two binary punishment variables describe the state's minimum sentencing requirements for an initial drunk driving conviction.
Total vehicle miles traveled annually by state was obtained from the Department of Transportation. Personal income was obtained from the US Bureau of Economic Analysis, and the unemployment rate was obtained from the US Bureau of Labor Statistics.
Online complements to Stock and Watson (2007).
Ruhm, C. J. (1996). Alcohol Policies and Highway Vehicle Fatalities. Journal of Health Economics, 15, 435–454.
Stock, J. H. and Watson, M. W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.
## data from Stock and Watson (2007)
data("Fatalities", package = "AER")
## add fatality rate (number of traffic deaths
## per 10,000 people living in that state in that year)
Fatalities$frate <- with(Fatalities, fatal/pop * 10000)
## add discretized version of minimum legal drinking age
Fatalities$drinkagec <- cut(Fatalities$drinkage,
breaks = 18:22, include.lowest = TRUE, right = FALSE)
Fatalities$drinkagec <- relevel(Fatalities$drinkagec, ref = 4)
## any punishment?
Fatalities$punish <- with(Fatalities,
factor(jail == "yes" | service == "yes", labels = c("no", "yes")))
## plm package
library("plm")
## for comparability with Stata we use HC1 below
## p. 351, Eq. (10.2)
f1982 <- subset(Fatalities, year == "1982")
fm_1982 <- lm(frate ~ beertax, data = f1982)
coeftest(fm_1982, vcov = vcovHC(fm_1982, type = "HC1"))
## p. 353, Eq. (10.3)
f1988 <- subset(Fatalities, year == "1988")
fm_1988 <- lm(frate ~ beertax, data = f1988)
coeftest(fm_1988, vcov = vcovHC(fm_1988, type = "HC1"))
## pp. 355, Eq. (10.8)
fm_diff <- lm(I(f1988$frate - f1982$frate) ~ I(f1988$beertax - f1982$beertax))
coeftest(fm_diff, vcov = vcovHC(fm_diff, type = "HC1"))
## pp. 360, Eq. (10.15)
## (1) via formula
fm_sfe <- lm(frate ~ beertax + state - 1, data = Fatalities)
## (2) by hand
fat <- with(Fatalities,
data.frame(frates = frate - ave(frate, state),
beertaxs = beertax - ave(beertax, state)))
fm_sfe2 <- lm(frates ~ beertaxs - 1, data = fat)
## (3) via plm()
fm_sfe3 <- plm(frate ~ beertax, data = Fatalities,
index = c("state", "year"), model = "within")
coeftest(fm_sfe, vcov = vcovHC(fm_sfe, type = "HC1"))[1,]
## uses different df in sd and p-value
coeftest(fm_sfe2, vcov = vcovHC(fm_sfe2, type = "HC1"))[1,]
## uses different df in p-value
coeftest(fm_sfe3, vcov = vcovHC(fm_sfe3, type = "HC1", method = "white1"))[1,]
## pp. 363, Eq. (10.21)
## via lm()
fm_stfe <- lm(frate ~ beertax + state + year - 1, data = Fatalities)
coeftest(fm_stfe, vcov = vcovHC(fm_stfe, type = "HC1"))[1,]
## via plm()
fm_stfe2 <- plm(frate ~ beertax, data = Fatalities,
index = c("state", "year"), model = "within", effect = "twoways")
coeftest(fm_stfe2, vcov = vcovHC) ## different
## p. 368, Table 10.1, numbers refer to cols.
fm1 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "pooling")
fm2 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "within")
fm3 <- plm(frate ~ beertax, data = Fatalities, index = c("state", "year"), model = "within",
effect = "twoways")
fm4 <- plm(frate ~ beertax + drinkagec + jail + service + miles + unemp + log(income),
data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways")
fm5 <- plm(frate ~ beertax + drinkagec + jail + service + miles,
data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways")
fm6 <- plm(frate ~ beertax + drinkage + punish + miles + unemp + log(income),
data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways")
fm7 <- plm(frate ~ beertax + drinkagec + jail + service + miles + unemp + log(income),
data = Fatalities, index = c("state", "year"), model = "within", effect = "twoways")
## summaries not too close, s.e.s generally too small
coeftest(fm1, vcov = vcovHC)
coeftest(fm2, vcov = vcovHC)
coeftest(fm3, vcov = vcovHC)
coeftest(fm4, vcov = vcovHC)
coeftest(fm5, vcov = vcovHC)
coeftest(fm6, vcov = vcovHC)
coeftest(fm7, vcov = vcovHC)
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