Working Papers
Marital Stability and Intrahousehold Inequality with Jacob Penglase and Tomoki Fujii, March 2025
We examine which factors are predictive of unstable marriages using a unique panel data set of Japanese couples. We employ several machine learning and econometric techniques to identify characteristics of the couple pertaining to their consumption allocations, labor supply, savings decisions, and stated satisfaction that are associated with a higher divorce probability. We find time-varying characteristics of the couple, such as the wife's income and labor supply, are most predictive, while characteristics of the couple at the time of marriage are less so. We further show that marriage market conditions are highly predictive of divorce. We relate these findings to the theoretical literature on the drivers of divorce.
Short- and Long-term Child Penalty: Evidence from NLSY79 revised July 2026
I study ``child penalties'' -- the negative impacts of motherhood on women's earnings, labor force participation, and wage in the short- and long-term (until retirement age) in the U.S.. By estimating a joint model of Callaway & Sant’Anna (2020) and Sun & Abraham (2021), I allow observed heterogeneity in treatment effects by women's age at first birth, as well as unobserved heterogeneity in treatment propensity by using NLSY79 data on individual's fertility and education preferences and subjective work-family choices. The results are very different from those under the conventional event studies approach. I find substantial heterogeneity in mothers' labor market outcomes by their age at first birth, and the patterns can be different in the short- versus long-term. The findings suggest that younger (older) cohorts of mothers experience a larger penalty in the short-term (long-term). The short-term penalty is mainly driven by lower labor force participation, while the long-term penalty is mainly driven by the accumulated loss of human capital and the resulting lower hourly rate. The last cohort (age > 28) rarely has earnings back to the pre-birth level, whereas earnings of younger cohorts return and surpass the pre-birth level at retirement age.
The Demand for Soft Drinks: Evidence from Purchases At-Home and Away-From-Home with Linqi Zhang, revised and resubmitted, Journal of Health Economics, December 2025
Using a novel dataset that includes at-home and away-from-home food purchases, we study who is affected by soda taxes. We nonparametrically estimate a random coefficient nested logit model to exploit the rich heterogeneity in preferences and price elasticities across households, including SNAP participants and non-SNAP-participant poor. By simulating its impacts, we find that soda taxes are less effective away-from-home while more effective at-home, especially by targeting the total sugar intake of the poor, those with high total dietary sugar, and households without children. Our results suggest that ignoring either segment can lead to biased policy implications.
Publications
Scanner Data, Food Consumption, and SNAP, forthcoming in the Elgar Encyclopedia of Consumption, 2025, edited by José M. Labeaga and José Alberto Molina.
Food Demand and Cash Transfers: A Collective Household Approach with Homescan Data, 2023 Journal of Economic Behavior and Organization.
Identification of Semiparametric Model Coefficients, With an Application to Collective Households with Arthur Lewbel, 2022 Journal of Econometrics.