MurderRates {AER} | R Documentation

## Determinants of Murder Rates in the United States

### Description

Cross-section data on states in 1950.

### Usage

    data("MurderRates")

### Format

A data frame containing 44 observations on 8 variables.

rate

  Murder rate per 100,000 (FBI estimate, 1950).

convictions

  Number of convictions divided by number of murders in 1950.

executions

  Average number of executions during 1946–1950 divided by convictions in 1950.

time

  Median time served (in months) of convicted murderers released in 1951.

income

  Median family income in 1949 (in 1,000 USD).

lfp

  Labor force participation rate in 1950 (in percent).

noncauc

  Proportion of population that is non-Caucasian in 1950.

southern

  Factor indicating region.

### Source

Maddala (2001), Table 8.4, p. 330

### References

Maddala, G.S. (2001). Introduction to Econometrics, 3rd ed. New York: John Wiley.

McManus, W.S. (1985). Estimates of the Deterrent Effect of Capital Punishment: The Importance of the Researcher's Prior Beliefs. Journal of Political Economy, 93, 417–425.

Stokes, H. (2004). On the Advantage of Using Two or More Econometric Software Systems to Solve the Same Problem. Journal of Economic and Social Measurement, 29, 307–320.

### Examples

    data("MurderRates")

    ## Maddala (2001, pp. 331)
    fm_lm <- lm(rate ~ . + I(executions > 0), data = MurderRates)
    summary(fm_lm)

    model <- I(executions > 0) ~ time + income + noncauc + lfp + southern
    fm_lpm <- lm(model, data = MurderRates)
    summary(fm_lpm)

    ## Binomial models. Note: southern coefficient
    fm_logit <- glm(model, data = MurderRates, family = binomial)
    summary(fm_logit)

    fm_logit2 <- glm(model, data = MurderRates, family = binomial,
      control = list(epsilon = 1e-15, maxit = 50, trace = FALSE))
    summary(fm_logit2)

    fm_probit <- glm(model, data = MurderRates, family = binomial(link = "probit"))
    summary(fm_probit)

    fm_probit2 <- glm(model, data = MurderRates , family = binomial(link = "probit"),
      control = list(epsilon = 1e-15, maxit = 50, trace = FALSE))
    summary(fm_probit2)

    ## Explanation: quasi-complete separation
    with(MurderRates, table(executions > 0, southern))

[Package AER version 1.2-4 Index]