R: Determinants of Wages Data (CPS 1985)

CPS1985 {AER} | R Documentation

## Determinants of Wages Data (CPS 1985)

### Description

Cross-section data originating from the May 1985 Current Population Survey by the US Census Bureau (random sample drawn for Berndt 1991).

### Usage

    data("CPS1985")

### Format

A data frame containing 534 observations on 11 variables.

wage

  Wage (in dollars per hour).

education

  Number of years of education.

experience

  Number of years of potential work experience (`age - education - 6`).

age

  Age in years.

ethnicity

  Factor with levels `"cauc"`, `"hispanic"`, `"other"`.

region

  Factor. Does the individual live in the South?

gender

  Factor indicating gender.

occupation

  Factor with levels `"worker"` (tradesperson or assembly line worker), `"technical"` (technical or professional worker), `"services"` (service worker), `"office"` (office and clerical worker), `"sales"` (sales worker), `"management"` (management and administration).

sector

  Factor with levels `"manufacturing"` (manufacturing or mining), `"construction"`, `"other"`.

union

  Factor. Does the individual work on a union job?

married

  Factor. Is the individual married?

### Source

StatLib.

http://lib.stat.cmu.edu/datasets/CPS_85_Wages

### References

Berndt, E.R. (1991). The Practice of Econometrics. New York: Addison-Wesley.

### See Also

`CPS1988`, `CPSSW`

### Examples

    data("CPS1985")

    ## Berndt (1991)
    ## Exercise 2, p. 196
    cps_2b <- lm(log(wage) ~ union + education, data = CPS1985)
    cps_2c <- lm(log(wage) ~ -1 + union + education, data = CPS1985)

    ## Exercise 3, p. 198/199
    cps_3a <- lm(log(wage) ~ education + experience + I(experience^2),
      data = CPS1985)
    cps_3b <- lm(log(wage) ~ gender + education + experience + I(experience^2),
      data = CPS1985)
    cps_3c <- lm(log(wage) ~ gender + married + education + experience + I(experience^2),
      data = CPS1985)
    cps_3e <- lm(log(wage) ~ gender*married + education + experience + I(experience^2),
      data = CPS1985)

    ## Exercise 4, p. 199/200
    cps_4a <- lm(log(wage) ~ gender + union + ethnicity + education + experience + I(experience^2),
      data = CPS1985)
    cps_4c <- lm(log(wage) ~ gender + union + ethnicity + education * experience + I(experience^2),
      data = CPS1985)

    ## Exercise 6, p. 203
    cps_6a <- lm(log(wage) ~ gender + union + ethnicity + education + experience + I(experience^2),
      data = CPS1985)
    cps_6a_noeth <- lm(log(wage) ~ gender + union + education + experience + I(experience^2),
      data = CPS1985)
    anova(cps_6a_noeth, cps_6a)

    ## Exercise 8, p. 208
    cps_8a <- lm(log(wage) ~ gender + union + ethnicity + education + experience + I(experience^2),
      data = CPS1985)
    summary(cps_8a)
    coeftest(cps_8a, vcov = vcovHC(cps_8a, type = "HC0"))

[Package AER version 1.2-5 Index]