R: US Traffic Fatalities

Fatalities {AER} | R Documentation

## 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")

### Format

A data frame containing 336 observations on 34 variables.

state

  factor indicating state.

year

  factor indicating year.

spirits

  numeric. Spirits consumption.

unemp

  numeric. Unemployment rate.

income

  numeric. Per capita personal income in 1987 dollars.

emppop

  numeric. Employment/population ratio.

beertax

  numeric. Tax on case of beer.

baptist

  numeric. Percent of southern baptist.

mormon

  numeric. Percent of mormon.

drinkage

  numeric. Minimum legal drinking age.

dry

  numeric. Percent residing in “dry” countries.

youngdrivers

  numeric. Percent of drivers aged 15–24.

miles

  numeric. Average miles per driver.

breath

  factor. Preliminary breath test law?

jail

  factor. Mandatory jail sentence?

service

  factor. Mandatory community service?

fatal

  numeric. Number of vehicle fatalities.

nfatal

  numeric. Number of night-time vehicle fatalities.

sfatal

  numeric. Number of single vehicle fatalities.

fatal1517

  numeric. Number of vehicle fatalities, 15–17 year olds.

nfatal1517

  numeric. Number of night-time vehicle fatalities, 15–17 year olds.

fatal1820

  numeric. Number of vehicle fatalities, 18–20 year olds.

nfatal1820

  numeric. Number of night-time vehicle fatalities, 18–20 year olds.

fatal2124

  numeric. Number of vehicle fatalities, 21–24 year olds.

nfatal2124

  numeric. Number of night-time vehicle fatalities, 21–24 year olds.

afatal

  numeric. Number of alcohol-involved vehicle fatalities.

pop

  numeric. Population.

pop1517

  numeric. Population, 15–17 year olds.

pop1820

  numeric. Population, 18–20 year olds.

pop2124

  numeric. Population, 21–24 year olds.

milestot

  numeric. Total vehicle miles (millions).

unempus

  numeric. US unemployment rate.

emppopus

  numeric. US employment/population ratio.

gsp

  numeric. GSP rate of change.

### 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.

### Source

Online complements to Stock and Watson (2007).

### 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", 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)

    ## TODO: Testing exclusion restrictions

[Package AER version 1.2-5 Index]