"Household Self-Insurance and the Value of Disability Insurance in the United States." 2022. [link]
This paper uses a life cycle model to study the welfare implications of reforms to U.S. Disability Insurance (DI) while accounting for household self-insurance. In addition to crowding out the insurance value of DI, household self-insurance may drive negative selection into DI by reducing implicit application costs. Allowing for such interactions, I find that revenue-neutral expansionary DI reforms do not necessarily improve welfare. However, an asset test reduces negative selection and improves the welfare effects of DI expansions. Household self-insurance crowds out the value of DI expansions, but abstracting away from the insurance value of DI can still deliver erroneous policy recommendations.
"Explaining Geographic Differences in Young Disability Insurance Rates"
Co-authors John Friedman, Ithai Lurie, and Magne Mogstad
Published and Forthcoming
"Combining Matching and Synthetic Controls to Trade off Biases from Extrapolation and Interpolation" [link]
Co-authors Magne Mogstad, Guillaume Pouliot, and Alex Torgovitsky
Journal of the American Statistical Association, 2021, 116, 536, 1804-1816
The synthetic control method is widely used in comparative case studies to adjust for differences in pre-treatment characteristics. A major attraction of the method is that it limits extrapolation bias that can occur when untreated units with different pre-treatment characteristics are combined using a traditional adjustment, such as a linear regression. Instead, the SC estimator is susceptible to interpolation bias because it uses a convex weighted average of the untreated units to create a synthetic untreated unit with pre-treatment characteristics similar to those of the treated unit. More traditional matching estimators exhibit the opposite behavior: they limit interpolation bias at the potential expense of extrapolation bias. We propose combining the matching and synthetic control estimators through model averaging to create an estimator called MASC. We show how to use a rolling-origin cross-validation procedure to train the MASC to resolve trade-offs between interpolation and extrapolation bias.
"Marketing-level exposure to state antismoking media campaigns and public support for tobacco control policy in the United States, 2001-2002."
Co-authors Jeff Niederdeppe, Christofer Skurka, and Rosemary Avery
2018, 27, 177-184, Tobacco Control
"Mixed Messages, Mixed Outcomes: Exposure to Direct-to-Consumer Advertising for Statin Drugs is Associated with More Frequent Visits to Fast Food Restaurants and Exercise"
Co-authors Jeff Niederdeppe, Rosemary Avery, and Alan Mathios
2017,32:7, 845-856 , Health Communication