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

This paper studies how individuals allocate time to news acquisition. Despite the growing importance of information for economic decision-making, this topic has not gained much attention in the literature, partly due to data limitations. We exploit rarely used data from the Pew Research Center’s Local News Survey, complemented with quantitative time-use data from the Media Consumption Survey. We document substantial differences in news consumption across race, ethnicity, and skill. Minority and low-skill individuals devote significantly more time to local news, while white and high-skill individuals consume more national and international news. We develop and estimate a structural model of time allocation and news acquisition that combines qualitative and quantitative survey data. Our results show that observed gaps are driven primarily by differences in wages and preferences, rather than access to news providers. These findings have important implications for inequality and welfare analysis.