Mroz: Labour data on married women
In gmm4: S4 Generalized Method of Moments

Description Usage Format Source References Examples

The dataset was used by Mroz (1987) and in examples in Wooldridge (2016)

1	data("Mroz")

A data frame with 753 observations on the following 22 variables.

inlf: =1 if in lab frce, 1975
hours: hours worked, 1975
kidslt6: number of kids < 6 years
kidsge6: number of kids 6-18
age: woman's age in years
educ: years of schooling
wage: Estimated wage from earnings and hours
repwage: reported wage at interview in 1976
hushrs: hours worked by husband, 1975
husage: husband's age
huseduc: husband's years of schooling
huswage: husband's hourly wage, 1975
faminc: family income, 1975
mtr: federal marginal tax rate facing woman
motheduc: mother's years of schooling
fatheduc: father's years of schooling
unem: unemployment rate in county of residence
city: =1 if live in SMSA
exper: actual labor market experience
nwifeinc: (faminc - wage*hours)/1000

From Wooldridge (2016) online resources.

Mroz, T.A. (1987), The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions, Econometrica, 55, 657–678. 387–405.

Wooldridge, J.M. (2016). Introductory Econometrics, A Modern Approach, 6th edition, Cengage Learning.

## Example 15.1 of Wooldridge (2016)

data(Mroz)
Mroz <- subset(Mroz, hours>0)
## I guess IID is assumed (That's how we get the same s.e.)
## By default a sandwich vcov is computed because it is 
## a just-identified model.
res4 <- gmm4(log(wage)~educ, ~fatheduc, vcov="iid", data=Mroz)
summary(res4)

## If we adjust the variance of the residuals, however,
## we are a little off (very little)

summary(res4, df.adj=TRUE)


## Example 15.5 of Wooldridge (2016)
## Need to adjust for degrees of freedom in order
## to get the same s.e.
## The first stage F-test is very different though
## Cannot get the same even if do it manually
## with the linearHypothesis from the car package
model <- gmmModel(log(wage)~educ+exper+I(exper^2),
~exper+I(exper^2)+fatheduc+motheduc, vcov="iid", data=Mroz)
res <- tsls(model)
summary(res, df.adj=TRUE)