Jinglin Wang

Many social programs provide public benefits that can reduce demand for private options. Expanding eligibility for these programs can create a trade-off between broadening access to public benefits and preserving the demand that sustains private competition and consumer choice. I study this trade-off in the U.S. health insurance market. Using administrative enrollment and claims data from Colorado, I develop and estimate a structural model of insurance demand, pricing, and insurer participation to evaluate an expansion of Medicaid eligibility from 138 to 200 percent of the federal poverty level. I find the expansion removes relatively low-cost enrollees from the private market. Adverse selection is limited, however, because premium subsidies largely offset higher gross premiums. The policy does reduce private demand, lowering insurer participation and leaving remaining consumers with fewer coverage options. Welfare losses among these consumers are concentrated in smaller markets, where expansion leads to a greater decline in the probability of having multiple insurers. These findings highlight the role of insurer participation in shaping the welfare effects of public coverage expansions and inform recent proposals to extend Medicaid eligibility.

Most hospitals have financial assistance programs for low-income patients. We use administrative data from Kaiser Permanente to study the effects of financial assistance on health care utilization. Using a regression discontinuity design based on an income threshold for program eligibility, we find that financial assistance increases the likelihood of inpatient, ambulatory, and emergency department encounters by 3.6 pp (59 percent), 13.4 pp (20 percent), and 6.7 pp (53 percent), respectively, though effects dissipate three quarters after program receipt. Financial assistance also increases the detection and management of treatment-sensitive conditions (e.g., drugs treating diabetes), suggesting that financial assistance may increase receipt of high-value care.

Economics research increasingly combines multiple data sources, such as administrative records, surveys, and field experiments, to improve identification of policy-relevant parameters. However, these datasets are often not directly comparable: samples may differ in population coverage, and outcomes may be missing or selected in systematically different ways. We develop a general, data-driven weighting framework that aligns datasets by minimizing the distance between potentially high-dimensional distributions. Our approach leverages optimal transport with debiased Sinkhorn divergence and flexible, nonparametric weight functions, allowing the data to determine which features are most relevant for alignment. The method is computationally scalable and designed for broad applicability in applied work. Monte Carlo simulations show that our method performs well in high-dimensional settings and in the presence of limited overlap, offering a flexible alternative to existing parametric and moment-based reweighting approaches. We demonstrate the performance of our approach in three common empirical settings. First, the resulting weights correct for selection induced by attrition in randomized controlled trials. Second, they improve efficiency when combining micro-level data with macro-level moments. Finally, our main empirical application combines experimental and observational data to estimate long-run treatment effects; we show that our method corrects bias from attributing treatment-effect heterogeneity to differential selection into treatment.