The Basic Health Program (BHP) is an optional provision of the Affordable Care Act (ACA) that allows states to create a state-administered coverage program for low-income individuals. To date, only three states have adopted it. This paper asks whether adoption is welfare-improving and what the optimal income-eligibility threshold should be, depending on state characteristics. I build a life-cycle model of households facing uninsurable risks, with endogenous labor supply, savings, and insurance choices, as well as stochastically evolving household composition. The state balances its budget through a consumption tax. Calibrating the model to New York, the largest adopter, and to Missouri, a non-adopter, we find that adoption is welfare-improving and that both optimal thresholds lie far above the current 200% of the Federal Poverty Level (FPL)—295% in New York and 275% in Missouri—at a modest fiscal cost (0.4 pp in NY and 0.6 pp in MO). Holding household composition fixed overstates New York's optimal threshold by 60 percentage points.
with Juan Carlos Conesa and Qian Li (Under Review)
We propose an overlapping generations model with marriage/divorce decisions, fertility choices, education, and labor supply to quantify the role of targeted transfers on family composition. Converting those into a transfer to all adults (of 9% of GDPpc) increases marriage and divorce rates among young low-skilled individuals. For high-skilled individuals, it increases marriage, decreases divorce and increases the assortative mating of high-skilled wealthier individuals. Our counterfactual implies a reduction in fertility and single motherhood and higher female employment, particularly among young, low-skilled women. Doubling the universal transfer (around 19% of GDPpc) moderates these effects. Both counterfactuals improve welfare.
Since the 1990s, the share of working-age adults not in the labor force (NILF) has risen from 33.4% in 1994 to 37.5% in 2024, but unevenly across gender and education. This paper adopts a three-state search model with heterogeneity in gender and educational attainment to explore the distinct evolution of NILF rates. My analysis quantitatively evaluates three possible mechanisms: labor demand, labor supply, and matching efficiency. Results show that the labor demand channel is the primary driver behind the increasing NILF rate among low-skilled men, which explains 63% of the increase. The declining NILF rate of low-skilled women mainly comes from their decreasing labor supply. In contrast, for both high-skilled men and women, the slight increase in their NILF rate results from the declining matching efficiency.
with Juan Carlos Conesa and Qian Li