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.

Source

Personal web page of Herman J. Bierens.

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

## 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)

quart_lad <- rq(quart, data = CPS1988)
mincer_lad <- rq(mincer, data = CPS1988)
buchinsky_lad <- rq(buchinsky, data = CPS1988)