R: Determinants of Wages Data (CPS 1988)

CPS1988 {AER} | R Documentation

## 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

http://www.personal.psu.edu/hxb11/MEDIAN.HTM

### 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)
    mincer_lad <- rq(mincer, data = CPS1988)
    quart_ols <- lm(quart, data = CPS1988)
    quart_lad <- rq(quart, data = CPS1988)
    buchinsky_ols <- lm(buchinsky, data = CPS1988)
    buchinsky_lad <- rq(buchinsky, data = CPS1988)

[Package AER version 1.2-5 Index]