AER (version 0.2-2)

Fatalities: US Traffic Fatalities

Description

US traffic fatalities panel data for the lower 48 US states (i.e., excluding Alaska and Hawaii), annually for 1982 through 1988.

Usage

data("Fatalities")

source

Online complements to Stock and Watson (2007).

http://wps.aw.com/aw_stock_ie_2

Details

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.

References

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.

See Also

StockWatson2007

Examples

## data from Stock and Watson (2007)
data("Fatalities")
## 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,]
coeftest(fm_sfe2, vcov = vcovHC(fm_sfe2, type = "HC1"))[1,] ## uses different df in sd and pval
coeftest(fm_sfe3, vcov = pvcovHC(fm_sfe3, type = "HC1", method = "white1")) ## uses different df in pval


## 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 = pvcovHC) ## 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, pvcovHC)
coeftest(fm2, pvcovHC)
coeftest(fm3, pvcovHC)
coeftest(fm4, pvcovHC)
coeftest(fm5, pvcovHC)
coeftest(fm6, pvcovHC)
coeftest(fm7, pvcovHC)

## TODO: Testing exclusion restrictions