men, in the absence of such behavior, the earnings of wives of low-wage men would not have increased at all.
II. Literature Review
Blundell and MaCurdy 1999 provide a summary of empirical work on family labor supply. There is a large cross-sectional literature that tends to treat
wages as exogenous Ransom 1987 or to use age or education as instruments for wages Kooreman and Kapteyn 1986. Juhn and Murphy 1997 estimate cross-
sectional labor supply equations and instrument individual wages with an indicator of the wage decile they are in. They find small wage and income effects for married
women.
Pencavel 1998 provides a thorough study of the correlations between wages and hours worked by women between 1975 and 1994. He uses education, cohort, and
trade variables, and interactions of these variables as instruments for the wages of wives and husbands. Thus, the identification strategy uses a mixture of cross-sectional
sources education and cohort and time-varying elements trade variables. He does not report a first stage regression so the extent to which time-varying variables play a
role in the identification is unclear.
Cross-sectional strategies require strong assumptions to consistently estimate labor supply parameters. If taste for work is related to wages or to education, then cross-
sectional estimation provides inconsistent estimates of own-wage elasticities. Obtaining consistent estimates is likely to be particularly difficult in models of fam-
ily labor supply because assortative mating implies that unobservable characteristics of both spouses are likely to be correlated and also be related to the wages of each
spouse.
Lundberg 1988 uses longitudinal data from the Panel Study of Income Dynamics PSID and takes a fixed effect simultaneous equation approach to the labor supply of
husbands and wives. She finds evidence of compensatory behavior for families with young children but not for other families. A potential problem with this approach is
that changes in wages are not random and may be related to fundamental changes to individual skills or attitudes. In contrast, my approach uses wage changes of the age-
education group that are assumed to result from changes in the returns to skills in the economy, and hence are unrelated to tastes for work of husbands and wives.
Blundell, Duncan, and Meghir BDM 1998 take a somewhat similar strategy to the approach used in this paper. They estimate labor supply functions for married and
cohabiting women in the United Kingdom over the period 1978–92. Unlike cross-sec- tional approaches, they allow for differences in preferences across education and age
groups that are assumed to remain constant over time. They identify labor supply elas- ticities using changes in relative wages across groups of women defined by education
and birth cohort. This paper differs from BDM in several ways. First, I use U.S. data rather than U.K. data and there are differences in the pattern of changes in wage
inequality between the two countries Gosling and Lemieux 2001. Second, the groups used by BDM have relatively few observations an average of 142 per group
and their estimates may suffer from small sample bias. The groups I use are much larger an average of 1,175 persons per group and I utilize two estimators that cor-
The Journal of Human Resources 698
rect for small sample bias in different ways. Third, unlike BDM, I use variation across groups defined both by the type of husband and the type of wife and so I exploit vari-
ation that arises because conditional on characteristics of the wife, there are pre- dictable differences in wage growth of husbands that are explained by husband
characteristics such as age and education. This addresses the possibility that declin- ing wages of men occurring alongside increasing hours of women may be purely a
demand-side phenomenon as suggested by Juhn and Kim 1999 rather than resulting from compensatory labor supply behavior. Fourth, I use regional variation in relative
wage changes and labor supply behavior to relax the assumption that any changes in unobserved factors over time are the same for all education and age groups.
III. Data
