Cross-sectional data on 753 married white women from the Panel Study of
Income Dynamics (PSID), 1975. Of these, 428 were working (inlf == 1)
with observed wages. Used by Mroz (1987) to study married women's labor
force participation and hours of work. A classic IV application instruments
education with parental education (motheduc, fatheduc).
Format
A data frame with 753 observations and 22 variables:
- inlf
In the labor force in 1975 (binary).
- hours
Hours worked in 1975.
- kidslt6
Number of children younger than 6.
- kidsge6
Number of children aged 6–18.
- age
Age in years.
- educ
Years of education.
- wage
Estimated hourly wage (1975 dollars).
- repwage
Reported hourly wage at interview (1976).
- hushrs
Husband's hours worked in 1975.
- husage
Husband's age.
- huseduc
Husband's years of education.
- huswage
Husband's hourly wage (1975 dollars).
- faminc
Family income (1975 dollars).
- mtr
Federal marginal tax rate facing the woman.
- motheduc
Mother's years of education.
- fatheduc
Father's years of education.
- unem
Unemployment rate in county of residence.
- city
Lives in SMSA (binary).
- exper
Years of labor market experience.
- nwifeinc
Non-wife household income (
faminc - wage * hours, in thousands of 1975 dollars).- lwage
Log estimated hourly wage.
- expersq
Experience squared (
exper^2).
Source
Mroz, T.A. (1987). "The Sensitivity of an Empirical Model of Married Women's Hours of Work to Economic and Statistical Assumptions." Econometrica, 55(4), 765–799.
Obtained from Stata's bcuse archive (Boston College),
bcuse mroz.
Redistribution basis: system.file("COPYRIGHTS", package = "ivreg2r").
Examples
data(mroz)
# Restrict to working women (observed wages)
mroz_work <- subset(mroz, inlf == 1)
# IV regression: instrument education with parental education (Example 15.5,
# "Return to Education for Working Women", in Wooldridge (2020),
# Introductory Econometrics).
# These instruments (both parents' education) differ from the specification
# in ?ivreg2, which instruments educ with fatheduc alone (just-identified)
# and with age + kidslt6 + kidsge6 (overidentified).
fit <- ivreg2(lwage ~ exper + expersq | educ | motheduc + fatheduc,
data = mroz_work)
summary(fit)
#>
#> 2SLS Estimation
#>
#> Call:
#> ivreg2(formula = lwage ~ exper + expersq | educ | motheduc +
#> fatheduc, data = mroz_work)
#>
#> Observations: 428
#> VCV type: Classical (iid)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.0481003 0.3984530 0.121 0.903915
#> educ 0.0613966 0.0312895 1.962 0.049737 *
#> exper 0.0441704 0.0133696 3.304 0.000954 ***
#> expersq -0.0008990 0.0003998 -2.249 0.024543 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> ---
#> R-squared: 0.1357
#> Adj. R-squared: 0.1296
#> Wald chi2(3): 8.1 (p = 0.0000)
#> Root MSE: 0.6716
#>
#> Underidentification test (Anderson canon. corr. LM statistic):
#> Chi-sq(2) = 88.84 (p < 2.2e-16)
#>
#> Weak identification test:
#> Cragg-Donald Wald F: 55.40
#> Stock-Yogo critical values (IV size):
#> 10% maximal IV size 19.93
#> 15% maximal IV size 11.59
#> 20% maximal IV size 8.75
#> 25% maximal IV size 7.25
#>
#> Overidentification test (Sargan):
#> Chi-sq(1) = 0.38 (p = 0.5386)
#>
#> Weak-instrument-robust inference:
#> H0: B1=0 and orthogonality conditions are valid
#> Anderson-Rubin Wald F(2,423) = 1.90 (p = 0.1505)
#> Anderson-Rubin Wald Chi-sq(2) = 3.85 (p = 0.1459)
#> Stock-Wright LM S Chi-sq(2) = 3.81 (p = 0.1485)
#>
#> Endogeneity test:
#> Chi-sq(1) = 2.81 (p = 0.0938)
#> Tested: educ
#>
#> First-stage diagnostics:
#> Endogenous F-stat p-value Partial R2 Shea PR2 SW F AP F
#> educ 55.40 0.0000 0.2076 0.2076 55.40 55.40
#>
#> Instrumented: educ
#> Included instruments: exper, expersq
#> Excluded instruments: motheduc, fatheduc
#>