ssmodels (version 2.0.1)

Mroz87: U.S. Women's Labor Force Participation

Description

The Mroz87 data frame contains data about 753 married women. These data are collected within the "Panel Study of Income Dynamics" (PSID). Of the 753 observations, the first 428 are for women with positive hours worked in 1975, while the remaining 325 observations are for women who did not work for pay in 1975. A more complete discussion of the data is found in mroz1987;textualssmodels. It also includes the following explanatory variables:

• lfp: Dummy variable for labor-force participation.

• hours: Wife's hours of work in 1975.

• kids5: Number of children 5 years old or younger.

• kids618: Number of children 6 to 18 years old.

• Age: Wife's age.

• Educ: Wife's educational attainment, in years.

• wage: Wife's average hourly earnings, in 1975 dollars.

• repwage: Wife's wage reported at the time of the 1976 interview.

• hushrs: Husband's hours worked in 1975.

• husage: Husband's age.

• huseduc: Husband's educational attainment, in years.

• huswage: Husband's wage, in 1975 dollars.

• faminc: Family income, in 1975 dollars.

• mtr: Marginal tax rate facing the wife.

• motheduc: Wife's mother's educational attainment, in years.

• fatheduc: Wife's father's educational attainment, in years.

• unem: Unemployment rate in county of residence, in percentage points.

• city: Dummy variable = 1 if live in large city, else 0.

• exper: Actual years of wife's previous labor market experience.

• nwifeinc: Non-wife income.

• wifecoll: Dummy variable for wife's college attendance.

• huscoll: Dummy variable for husband's college attendance.

Usage

Mroz87

Format

An object of class data.frame with 753 rows and 22 columns.

References

mroz1987ssmodels ssmrobssmodels sampleSelectionssmodels wooldridge2016ssmodels

Examples

# Wooldridge(2016): page 247
data(Mroz87)
attach(Mroz87)
Mroz87$lwage <- ifelse(Mroz87$wage>0,log(Mroz87$wage), NA)
selectEq <- lfp ~ nwifeinc + educ + exper + I(exper^2) + age + kids5 + kids618
outcomeEq <- lwage ~ educ + exper + I(exper^2)
outcomeS <- cbind(educ, exper)
outcomeC <- 1
outcomeBS <- wage ~ educ + exper + I(exper^2)
outcomeBS <- wage ~ educ + exper + I(exper^2)
HeckmanCL(selectEq, outcomeEq, data = Mroz87)
HeckmanBS(selectEq, outcomeBS, data = Mroz87)
HeckmanSK(selectEq, outcomeEq, data = Mroz87, lambda = 1)
HeckmantS(selectEq, outcomeEq, data = Mroz87, df=5)
HeckmanGe(selectEq, outcomeEq, outcomeS, outcomeC, data = Mroz87)