AER (version 0.2-2)

PSID1976: 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")

source

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

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

Details

This data set is also known under the name Mroz (1987) data. Note that the variable earnings is called wage in many versions of this data set.

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.

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

See Also

Greene2003, PSID1982

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 * earnings)/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 * earnings) ~ 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 * earnings) ~ 1, data = PSID1976, subset = participation == "yes")
anova(gr_lm1, gr_lm)

## Example 21.4, p. 681
gr_probit1 <- glm(participation ~ age + I(age^2) + fincome + education + kids,
  data = PSID1976, family = binomial(link = "probit") )
gr_probit2 <- glm(participation ~ age + I(age^2) + fincome + education,
  data = PSID1976, family = binomial(link = "probit"))
gr_probit3 <- glm(participation ~ kids/(age + I(age^2) + fincome + 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, 
  earnings ~ experience + I(experience^2) + education + city,
  data = PSID1976, method = "2step")
gr_ml <- selection(participation ~ age + I(age^2) + fincome + education + kids, 
  earnings ~ 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: huge discrepancies for ML and OLS, source unclear


#######################
## 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)