wages: Wages and Hours

In armstrtw/rrcov: Scalable Robust Estimators with High Breakdown Point

Description

The data are from a national sample of 6000 households with a male head earning less than USD 15,000 annually in 1966. The data were clasified into 39 demographic groups for analysis. The study was undertaken in the context of proposals for a guaranteed annual wage (negative income tax). At issue was the response of labor supply (average hours) to increasing hourly wages. The study was undertaken to estimate this response from available data.

Usage

data(wages)

Format

A data frame with 39 observations on the following 10 variables:

HRS

Average hours worked during the year

RATE

Average hourly wage (USD)

ERSP

Average yearly earnings of spouse (USD)

ERNO

Average yearly earnings of other family members (USD)

NEIN

Average yearly non-earned income

ASSET

Average family asset holdings (Bank account, etc.) (USD)

AGE

Average age of respondent

DEP

Average number of dependents

RACE

Percent of white respondents

SCHOOL

Average highest grade of school completed

Source

DASL library http://lib.stat.cmu.edu/DASL/Datafiles/wagesdat.html

References

D.H. Greenberg and M. Kosters, (1970). Income Guarantees and the Working Poor, The Rand Corporation.

Examples

    data(wages)
    names(wages)
    x <- as.matrix(wages)
    ok <- is.finite(x %*% rep(1, ncol(x)))
    wages <- wages[ok, , drop = FALSE]
    wages.lm <- lm(HRS~AGE, data=wages)
    plot(HRS ~ AGE, data = wages)
    abline(wages.lm)
    class(wages.lm)
    names(wages.lm)
    summary(wages.lm)
    
    wages.mm <- lmrob(HRS~AGE, data=wages)
    plot(HRS ~ AGE, data = wages)
    abline(wages.mm)
    class(wages.mm)
    names(wages.mm)
    summary(wages.mm)   

armstrtw/rrcov documentation built on May 10, 2019, 1:43 p.m.