Disentangling Moral Hazard and Adverse Selection in Private Health Insurance

Moral hazard and adverse selection create inefficiencies in private health insurance markets and understanding the relative importance of each factor is critical for addressing these inefficiencies. We use claims data from a large firm which changed health insurance plan options to isolate moral hazard from plan selection, estimating a discrete choice model to predict household plan preferences and attrition. Variation in plan preferences identifies the differential causal impact of each health insurance plan on the entire distribution of medical expenditures. Our estimates imply that 53% of the additional medical spending observed in the most generous plan in our data relative to the least generous is due to adverse selection. We find that quantifying adverse selection by using prior medical expenditures overstates the true magnitude of selection due to mean reversion. We also statistically reject that individual health care consumption responds solely to the end-of-the-year marginal price.

Teaching for All? Teach For America's Effects across the Distribution of Student Achievement

This paper examines the effect of Teach For America (TFA) on the distribution of student achievement in elementary school. It extends previous research by estimating quantile treatment effects (QTE) to examine how student achievement in TFA and non-TFA classrooms differs across the broader distribution of student achievement. It also updates prior distributional work on TFA by correcting for previously unidentified missing data and estimating unconditional, rather than conditional QTE. Consistent with previous findings, results reveal a positive impact of TFA teachers across the distribution of math achievement. In reading, however, relative to veteran non-TFA teachers, students at the bottom of the reading distribution score worse in TFA classrooms, and students in the upper half of the distribution perform better.