PSID1976 | R Documentation |

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

`data("PSID1976")`

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?

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).

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

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

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.

`Greene2003`

, `WinkelmannBoes2009`

```
## 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)
```