CPS1985: 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")

Arguments

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?

References

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

Examples

Run this code

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"))