rrcov: wages – R documentation

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wages

Wages and Hours

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)

rrcov

Scalable Robust Estimators with High Breakdown Point

v1.5-5

GPL (>= 2)

Authors

Valentin Todorov [aut, cre] (<https://orcid.org/0000-0003-4215-0245>)

Initial release

2020-07-31