Research

Abstract

This paper develops a new method for identifying econometric models with partially latent covariates. Such data structures arise in industrial organization and labor economics settings where data are collected using an input-based sampling strategy, e.g., if the sampling unit is one of multiple labor input factors. We show that the latent covariates can be nonparametrically identified, if they are functions of a common shock satisfying some plausible monotonicity assumptions. With the latent covariates identified, semiparametric estimation of the outcome equation proceeds within a standard IV framework that ac- counts for the endogeneity of the covariates. We illustrate the usefulness of our method using a new application that focuses on the production functions of pharmacies. We find that differences in technology between chains and independent pharmacies may partially explain the observed transformation of the industry structure.

Abstract

This paper studies how occupational flexibility shapes married couples’ labor supply and the gender pay gap around childbirth. I estimate a dynamic discrete choice model of couples’ joint labor supply and occupational choices using NLSY79 data combined with Goldin’s (2014) measure of time flexibility. A key implication is that spousal flexibility matters more than own flexibility for married women’s labor market outcomes: switching a husband’s occupation from low to high flexibility increases his wife’s labor participation by 10 percentage points after childbirth, compared to 4 percentage points from switching her own occupation. Policies targeting women reduce the long-run gender pay gap, whereas extending benefits to both spouses weakens these gains and can expand the gap.

Abstract

Structural models of time allocation typically rely on detailed time-use diaries, which are costly and often unavailable. We develop an alternative identification and estimation strategy using qualitative survey responses, including ordinal reports of time use, stated valuations of activities, and a few quantitative anchors. We show these data identify preferences, the production technology, and the opportunity cost of time within a penalized maximum likelihood framework. Monte Carlo experiments demonstratethat our estimator performs well relative to traditional approaches. Applied to news

consumption, the method reveals information gaps by race, ethnicity, and education,

driven primarily by differences in wages and preferences.

Abstract

Children who grow up in two-parent households score 0.2-0.4 standard deviations higher on covariate-adjusted cognitive skill measures than children in single-parent households. This paper analyzes the extent to which these differences reflect causal effects of marriage versus selection. We develop a dynamic Roy model that allows for selection into and out of marriage, with cognitive skill evolving according to a dynamic latent factor model. A long panel of marriage decisions permits identification of unobserved heterogeneity jointly affecting both marriage and skill development. Estimates indicate positive sorting on gains into marriage that is more pronounced at later ages. A decomposition analysis suggests that differences in household income explains 19% of the skill difference between children growing up in two-parent and one-parent households while differences in the technology of skill formation explain an additional 30%.