momentfit (version 0.5)

Mroz: Labour data on married women

Description

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

Usage

data("Mroz")

Format

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

(\(faminc - wage*hours)/1000\)

References

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.

Examples

## 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 <- momentModel(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)