Institutional Affiliation: Harvard University
NBER Working Papers and Publications
|June 2020||Socioeconomic Network Heterogeneity and Pandemic Policy Response|
with Mohammad Akbarpour, Cody Cook, Aude Marzuoli, Simon Mongey, Abhishek Nagaraj, Matteo Saccarola, Pietro Tebaldi, Shoshana Vasserman: w27374
We develop a heterogeneous-agents network-based model to analyze alternative policies during a pandemic outbreak, accounting for health and economic trade-offs within the same empirical framework. We leverage a variety of data sources, including data on individuals' mobility and encounters across metropolitan areas, health records, and measures of the possibility to be productively working from home. This combination of data sources allows us to build a framework in which the severity of a disease outbreak varies across locations and industries, and across individuals who differ by age, occupation, and preexisting health conditions.
We use this framework to analyze the impact of different social distancing policies in the context of the COVID-19 outbreaks across US metropolitan areas. Our...
|February 2020||What Determines Consumer Financial Distress? Place- and Person-Based Factors|
with Benjamin J. Keys, Neale Mahoney: w26808
We use credit report data for a representative sample of 35 million individuals over 2000-2016 to examine consumer financial distress in the United States. We show there are large, persistent geographic disparities in consumer financial distress, with low levels in the Upper Midwest and high levels in the Deep South. To better understand these patterns, we conduct a "movers" analysis that examines how financial distress evolves when people move to places with different levels of financial distress. For collections and default, there is only weak convergence following a move, suggesting these types of financial distress are not primarily caused by place-based factors (such as local economic conditions, loan supply, and state laws) but instead reflect person-based characteristics (such as fi...
|January 2020||Recovering Investor Expectations from Demand for Index Funds|
with Mark L. Egan, Alexander MacKay: w26608
We use a revealed-preference approach to estimate investor expectations of stock market returns. Using data on demand for index funds that follow the S&P 500, we develop and estimate a model of investor choice to flexibly recover the time-varying distribution of expected returns. Our analysis is facilitated by the prevalence of “leveraged” funds that track the same underlying asset: by choosing between higher and lower leverage, investors trade off higher return against less risk. Although generated from a different method (realized choices) and a different population, our quarterly estimates of investor expectations are positively and significantly correlated with the leading surveys used to measure stock market expectations. Our estimates suggest that investor expectations are heterogene...
|May 2019||Nonparametric Estimates of Demand in the California Health Insurance Exchange|
with Pietro Tebaldi, Alexander Torgovitsky: w25827
We estimate the demand for health insurance in the California Affordable Care Act marketplace (Covered California) without using parametric assumptions about the unobserved components of utility. To do this, we develop a computational method for constructing sharp identified sets in a nonparametric discrete choice model. The model allows for endogeneity in prices (premiums) and for the use of instrumental variables to address this endogeneity. We use the method to estimate bounds on the effects of changing premium subsidies on coverage choices, consumer surplus, and government spending. We find that a $10 decrease in monthly premium subsidies would cause between a 1.6% and 7.0% decline in the proportion of low-income adults with coverage. The reduction in total annual consumer surplus woul...