Working Papers

"Household Self-Insurance and the Value of Disability Insurance in the United States" [link (Draft: Jan 4, 2021)]
NEW DRAFT - available upon request


The debate over whether and how to curtail growth in the U.S. Disability Insurance (DI) system stems from a trade-off between the system's public costs and the economic value it provides to individuals. This paper studies the implications of household self-insurance behaviors, such as family savings and spousal labor supply, for this trade-off and for the value of alternative reforms to the DI system. Using panel data on disability onset in U.S. households, I provide evidence that married workers benefit from both higher self-insurance capacity and higher utilization of DI compared to unmarried workers—who are left, by contrast, more exposed to the costs of disability. Guided by this evidence, I develop and estimate a life cycle model to infer the value of DI. Importantly, the model I develop takes into account household self-insurance capacity and the manner in which it may reduce implicit costs of acquiring DI benefits. Although model-based results suggest that expansions to DI tend to be welfare-improving, accounting for spousal labor supply reduces their value by as much as 28 percent. Reforms that disproportionately benefit individuals with worse household self-insurance capacity by reducing the implicit costs of acquiring DI benefits, such as implementing a national temporary DI program, are especially valuable.

"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.

Pre-Doctoral Research

"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