R: Labor Force Participation Data

PSID1976 {AER} | R Documentation

## Labor Force Participation Data

### Description

Cross-section data originating from the 1976 Panel Study of Income Dynamics (PSID), based on data for the previous year, 1975.

### Usage

    data("PSID1976")

### Format

A data frame containing 753 observations on 21 variables.

participation

  Factor. Did the individual participate in the labor force in 1975? (This is essentially `wage > 0` or `hours > 0`.)

hours

  Wife's hours of work in 1975.

youngkids

  Number of children less than 6 years old in household.

oldkids

  Number of children between ages 6 and 18 in household.

age

  Wife's age in years.

education

  Wife's education in years.

wage

  Wife's average hourly wage, in 1975 dollars.

repwage

  Wife's wage reported at the time of the 1976 interview (not the same as the 1975 estimated wage). To use the subsample with this wage, one needs to select 1975 workers with `participation == "yes"`, then select only those women with non-zero wage. Only 325 women work in 1975 and have a non-zero wage in 1976.

hhours

  Husband's hours worked in 1975.

hage

  Husband's age in years.

heducation

  Husband's education in years.

hwage

  Husband's wage, in 1975 dollars.

fincome

  Family income, in 1975 dollars. (This variable is used to construct the property income variable.)

tax

  Marginal tax rate facing the wife, and is taken from published federal tax tables (state and local income taxes are excluded). The taxable income on which this tax rate is calculated includes Social Security, if applicable to wife.

meducation

  Wife's mother's educational attainment, in years.

feducation

  Wife's father's educational attainment, in years.

unemp

  Unemployment rate in county of residence, in percentage points. (This is taken from bracketed ranges.)

city

  Factor. Does the individual live in a large city?

experience

  Actual years of wife's previous labor market experience.

college

  Factor. Did the individual attend college?

hcollege

  Factor. Did the individual's husband attend college?

### Details

This data set is also known as the Mroz (1987) data.

Warning: Typical applications using these data employ the variable `wage` (aka `earnings` in previous versions of the data) as the dependent variable. The variable `repwage` is the reported wage in a 1976 interview, named RPWG by Greene (2003).

### Source

Online complements to Greene (2003). Table F4.1.

http://pages.stern.nyu.edu/~wgreene/Text/tables/tablelist5.htm

### References

Greene, W.H. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.

McCullough, B.D. (2004). Some Details of Nonlinear Estimation. In: Altman, M., Gill, J., and McDonald, M.P.: Numerical Issues in Statistical Computing for the Social Scientist. Hoboken, NJ: John Wiley, Ch. 8, 199–218.

Mroz, T.A. (1987). The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions. Econometrica, 55, 765–799.

Winkelmann, R., and Boes, S. (2009). Analysis of Microdata, 2nd ed. Berlin and Heidelberg: Springer-Verlag.

Wooldridge, J.M. (2002). Econometric Analysis of Cross-Section and Panel Data. Cambridge, MA: MIT Press.

### See Also

`Greene2003`, `WinkelmannBoes2009`

### Examples

    ## data and transformations
    data("PSID1976")
    PSID1976$kids <- with(PSID1976, factor((youngkids + oldkids) > 0,
      levels = c(FALSE, TRUE), labels = c("no", "yes")))
    PSID1976$nwincome <- with(PSID1976, (fincome - hours * wage)/1000)
    PSID1976$partnum <- as.numeric(PSID1976$participation) - 1

    ###################
    ## Greene (2003) ##
    ###################

    ## Example 4.1, Table 4.2
    ## (reproduced in Example 7.1, Table 7.1)
    gr_lm <- lm(log(hours * wage) ~ age + I(age^2) + education + kids,
      data = PSID1976, subset = participation == "yes")
    summary(gr_lm)
    vcov(gr_lm)

    ## Example 4.5
    summary(gr_lm)
    ## or equivalently
    gr_lm1 <- lm(log(hours * wage) ~ 1, data = PSID1976, subset = participation == "yes")
    anova(gr_lm1, gr_lm)

    ## Example 21.4, p. 681, and Tab. 21.3, p. 682
    gr_probit1 <- glm(participation ~ age + I(age^2) + I(fincome/10000) + education + kids,
      data = PSID1976, family = binomial(link = "probit") )
    gr_probit2 <- glm(participation ~ age + I(age^2) + I(fincome/10000) + education,
      data = PSID1976, family = binomial(link = "probit"))
    gr_probit3 <- glm(participation ~ kids/(age + I(age^2) + I(fincome/10000) + education),
      data = PSID1976, family = binomial(link = "probit"))
    ## LR test of all coefficients
    lrtest(gr_probit1)
    ## Chow-type test
    lrtest(gr_probit2, gr_probit3)
    ## equivalently:
    anova(gr_probit2, gr_probit3, test = "Chisq")
    ## Table 21.3
    summary(gr_probit1)

    ## Example 22.8, Table 22.7, p. 786
    library("sampleSelection")
    gr_2step <- selection(participation ~ age + I(age^2) + fincome + education + kids,
      wage ~ experience + I(experience^2) + education + city,
      data = PSID1976, method = "2step")
    gr_ml <- selection(participation ~ age + I(age^2) + fincome + education + kids,
      wage ~ experience + I(experience^2) + education + city,
      data = PSID1976, method = "ml")
    gr_ols <- lm(wage ~ experience + I(experience^2) + education + city,
      data = PSID1976, subset = participation == "yes")
    ## NOTE: ML estimates agree with Greene, 5e errata.
    ## Standard errors are based on the Hessian (here), while Greene has BHHH/OPG.

    #######################
    ## Wooldridge (2002) ##
    #######################

    ## Table 15.1, p. 468
    wl_lpm <- lm(partnum ~ nwincome + education + experience + I(experience^2) +
      age + youngkids + oldkids, data = PSID1976)
    wl_logit <- glm(participation ~ nwincome + education + experience + I(experience^2) +
      age + youngkids + oldkids, family = binomial, data = PSID1976)
    wl_probit <- glm(participation ~ nwincome + education + experience + I(experience^2) +
      age + youngkids + oldkids, family = binomial(link = "probit"), data = PSID1976)
    ## (same as Altman et al.)

    ## convenience functions
    pseudoR2 <- function(obj) 1 - as.vector(logLik(obj)/logLik(update(obj, . ~ 1)))
    misclass <- function(obj) 1 - sum(diag(prop.table(table(
      model.response(model.frame(obj)), round(fitted(obj))))))

    coeftest(wl_logit)
    logLik(wl_logit)
    misclass(wl_logit)
    pseudoR2(wl_logit)

    coeftest(wl_probit)
    logLik(wl_probit)
    misclass(wl_probit)
    pseudoR2(wl_probit)

    ## Table 16.2, p. 528
    form <- hours ~ nwincome + education + experience + I(experience^2) + age + youngkids + oldkids
    wl_ols <- lm(form, data = PSID1976)
    wl_tobit <- tobit(form, data = PSID1976)
    summary(wl_ols)
    summary(wl_tobit)

    #######################
    ## McCullough (2004) ##
    #######################

    ## p. 203
    mc_probit <- glm(participation ~ nwincome + education + experience + I(experience^2) +
      age + youngkids + oldkids, family = binomial(link = "probit"), data = PSID1976)
    mc_tobit <- tobit(hours ~ nwincome + education + experience + I(experience^2) + age +
      youngkids + oldkids, data = PSID1976)
    coeftest(mc_probit)
    coeftest(mc_tobit)
    coeftest(mc_tobit, vcov = vcovOPG)

[Package AER version 1.2-5 Index]