AER (version 1.2-2)

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 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).

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)