AER (version 1.2-14)

Guns: More Guns, Less Crime?

Description

Guns is a balanced panel of data on 50 US states, plus the District of Columbia (for a total of 51 states), by year for 1977--1999.

Usage

data("Guns")

Format

A data frame containing 1,173 observations on 13 variables.

state

factor indicating state.

year

factor indicating year.

violent

violent crime rate (incidents per 100,000 members of the population).

murder

murder rate (incidents per 100,000).

robbery

robbery rate (incidents per 100,000).

prisoners

incarceration rate in the state in the previous year (sentenced prisoners per 100,000 residents; value for the previous year).

afam

percent of state population that is African-American, ages 10 to 64.

cauc

percent of state population that is Caucasian, ages 10 to 64.

male

percent of state population that is male, ages 10 to 29.

population

state population, in millions of people.

income

real per capita personal income in the state (US dollars).

density

population per square mile of land area, divided by 1,000.

law

factor. Does the state have a shall carry law in effect in that year?

Details

Each observation is a given state in a given year. There are a total of 51 states times 23 years = 1,173 observations.

References

Ayres, I., and Donohue, J.J. (2003). Shooting Down the ‘More Guns Less Crime’ Hypothesis. Stanford Law Review, 55, 1193--1312.

Stock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.

See Also

StockWatson2007

Examples

 if(!requireNamespace("lattice")) {
  if(interactive() || is.na(Sys.getenv("_R_CHECK_PACKAGE_NAME_", NA))) {
    stop("not all packages required for the example are installed")
  } else q() }
## data
data("Guns")

## visualization
library("lattice")
xyplot(log(violent) ~ as.numeric(as.character(year)) | state, data = Guns, type = "l")

## Stock & Watson (2007), Empirical Exercise 10.1, pp. 376--377
fm1 <- lm(log(violent) ~ law, data = Guns)
coeftest(fm1, vcov = sandwich)

fm2 <- lm(log(violent) ~ law + prisoners + density + income + 
  population + afam + cauc + male, data = Guns)
coeftest(fm2, vcov = sandwich)

fm3 <- lm(log(violent) ~ law + prisoners + density + income + 
  population + afam + cauc + male + state, data = Guns)
printCoefmat(coeftest(fm3, vcov = sandwich)[1:9,])
            
fm4 <- lm(log(violent) ~ law + prisoners + density + income + 
  population + afam + cauc + male + state + year, data = Guns)
printCoefmat(coeftest(fm4, vcov = sandwich)[1:9,])