AER (version 1.2-13)

CPS1988: Determinants of Wages Data (CPS 1988)

Description

Cross-section data originating from the March 1988 Current Population Survey by the US Census Bureau.

Usage

data("CPS1988")

Format

A data frame containing 28,155 observations on 7 variables.

wage

Wage (in dollars per week).

education

Number of years of education.

experience

Number of years of potential work experience.

ethnicity

Factor with levels "cauc" and "afam" (African-American).

smsa

Factor. Does the individual reside in a Standard Metropolitan Statistical Area (SMSA)?

region

Factor with levels "northeast", "midwest", "south", "west".

parttime

Factor. Does the individual work part-time?

Details

A sample of men aged 18 to 70 with positive annual income greater than USD 50 in 1992, who are not self-employed nor working without pay. Wages are deflated by the deflator of Personal Consumption Expenditure for 1992.

A problem with CPS data is that it does not provide actual work experience. It is therefore customary to compute experience as age - education - 6 (as was done by Bierens and Ginther, 2001), this may be considered potential experience. As a result, some respondents have negative experience.

References

Bierens, H.J., and Ginther, D. (2001). Integrated Conditional Moment Testing of Quantile Regression Models. Empirical Economics, 26, 307--324.

Buchinsky, M. (1998). Recent Advances in Quantile Regression Models: A Practical Guide for Empirical Research. Journal of Human Resources, 33, 88--126.

See Also

CPS1985, CPSSW

Examples

 if(!requireNamespace("quantreg")) {
  if(interactive() || is.na(Sys.getenv("_R_CHECK_PACKAGE_NAME_", NA))) {
    stop("not all packages required for the example are installed")
  } else q() }
## data and packages
library("quantreg")
data("CPS1988")
CPS1988$region <- relevel(CPS1988$region, ref = "south")

## Model equations: Mincer-type, quartic, Buchinsky-type
mincer <- log(wage) ~ ethnicity + education + experience + I(experience^2)
quart <- log(wage) ~ ethnicity + education + experience + I(experience^2) +
  I(experience^3) + I(experience^4)
buchinsky <- log(wage) ~ ethnicity * (education + experience + parttime) + 
  region*smsa + I(experience^2) + I(education^2) + I(education*experience)

## OLS and LAD fits (for LAD see Bierens and Ginter, Tables 1-3.A.)
mincer_ols <- lm(mincer, data = CPS1988)
quart_ols <- lm(quart, data = CPS1988)
buchinsky_ols <- lm(buchinsky, data = CPS1988)
# \donttest{
quart_lad <- rq(quart, data = CPS1988)
mincer_lad <- rq(mincer, data = CPS1988)
buchinsky_lad <- rq(buchinsky, data = CPS1988)
# }