Bio
I’m an Assistant Professor of Economics at the U.S. Military Academy at West Point. I completed my PhD in Economics at the University of California, Davis in September 2021.
My primary research interests are related to the design of social insurance and safety net programs. I am also interested in the economics of health behaviors. My recent work on the US Unemployment Insurance program has been funded by the Washington Center for Equitable Growth, the Bilinski Educational Foundation, and the W.E. Upjohn Institute for Employment Research.
Curriculum Vitae (Updated October 2024)
Fields: Public, Labor, Health
Email: geoffrey.schnorr@westpoint.edu, gcschnorr@gmail.com
Twitter: @GeoffSchnorr
Publications
(with Lester Lusher and Rebecca Taylor)
American Economic Journal: Applied Economics, 2022
Abstract (click to expand): We provide causal evidence of an ex-ante moral hazard effect of Unemployment Insurance (UI) by matching plausibly exogenous changes in UI benefit duration across state-weeks during the Great Recession to high-frequency productivity measures from individual supermarket cashiers. Estimating models with day and cashier-register fixed effects, we identify a modest but statistically significant negative relationship between UI benefits and worker productivity. This effect is strongest for more experienced and less productive cashiers, for whom UI expansions are especially relevant. Additional analyses from the American Time Use Survey reveal a similar increase in shirking during periods with increased UI benefit durations.
(with Alex Bell, T.J. Hedin, Peter Mannino, Roozbeh Moghadam, Carl Romer, and Till von Wachter)
American Economic Association Papers & Proceedings, 2022
Abstract (click to expand): To better measure the full extent of the impact of the COVID-19 crisis on workers and the labor market, this paper estimates three measures of the cumulative impact of the pandemic on workers across intensive and extensive margins using longitudinal administrative unemployment insurance (UI) data from California. During the first year of the crisis, 30 percent of the labor force filed a UI claim, over 50 percent of recipients spent more than 6 months on the program, and the mean work time lost was 13 weeks. Less advantaged workers and counties saw much higher rates of claiming and long-term unemployment.
(with Alex Bell, T.J. Hedin, Peter Mannino, Roozbeh Moghadam, and Till von Wachter)
RSF: The Russell Sage Foundation Journal of the Social Sciences, 2023
Abstract (click to expand): To what extent did jobless Americans benefit from unemployment insurance (UI) during the COVID-19 pandemic? This paper documents geographic disparities in access to UI during 2020. We leverage aggregated and individual-level UI claims data to perform an integrated analysis across four measures of access to UI. In addition to the traditional UI recipiency rate, we construct rates of application among the unemployed, rates of first payment among applicants, and exhaustion rates among paid claimants. Through correlations across California counties and across states, we show that areas with more disadvantaged residents had less access to UI during the pandemic. While these disparities are large in magnitude, cross-state analysis suggests that policy can play a salient role in mitigating these disparities.
(with Alex Bell, T.J. Hedin, and Till von Wachter)
Journal of Labor Economics, 2024
Abstract (click to expand): This paper provides estimates of the effect of unemployment insurance benefits on labor supply outcomes over the business cycle using 20 years of administrative claims, earnings, and employer data from California. A regression kink design exploiting nonlinear benefit schedules provides experimental estimates of behavioral labor supply responses throughout the unemployment spell that are comparable over time. For a given unemployment duration, the behavioral effect of UI benefit levels on labor supply is unchanged over the business cycle from 2002 to 2019. However, due to increased coverage from extensions in benefit durations, the duration elasticity of UI benefits rises during recessions. The behavioral effect during the start of the COVID-19 pandemic is substantially lower at all weeks of the unemployment spell.
(with Eunju Lee)
American Journal of Health Economics, forthcoming
Abstract (click to expand): We use data on siblings near the minimum drinking age to provide causal estimates of peer effects in alcohol consumption, exploiting the increase in consumption of the older sibling in a regression discontinuity design. Preferred point estimates imply that younger sibling binge drinking decreases at the cutoff. These negative reduced form spillover effects are concentrated in subgroups where the first stage discontinuity is largest, among siblings who are likely to spend more time together, and for measures of excessive alcohol consumption. While our results are somewhat imprecise, we argue that these patterns of heterogeneity are consistent with younger siblings learning from the costs of their older siblings' drinking behavior. Our results are directly interpretable as the effect of peer alcohol consumption, whereas most prior work identifies the effect of exposure to the peer. We explain how this distinction matters for policy.
Online Appendix
Working Papers
(with Serena Canaan, Stefanie Fischer, and Pierre Mouganie)
Revise & Resubmit, Journal of Political Economy: Microeconomics
Abstract (click to expand): To boost college graduation rates, policymakers often advocate for academic supports such as coaching or mentoring. Proactive and intensive coaching interventions are effective, but are costly and difficult to scale. We evaluate a relatively lower-cost group coaching program targeted at first-year college students placed on academic probation. Participants attend a workshop where coaches aim to normalize failure and improve self-confidence. Coaches also facilitate a process whereby participants reflect on their academic difficulties, devise solutions to address their challenges, and create an action plan. Participants then hold a one-time follow-up meeting with their coach or visit a campus resource. Using a difference-in-discontinuity design, we show that the program raises students' first-year GPA by 14.6% of a standard deviation, and decreases the probability of first-year dropout by 8.5 percentage points. Effects are concentrated among lower-income students who also experience a significant increase in the probability of graduating. Finally, using administrative data we provide the first evidence that coaching/mentoring may have substantial long-run effects as we document significant gains in lower-income students' earnings 7-9 years following entry to the university. Our findings indicate that targeted, group coaching can be an effective way to improve marginal students' academic and early career outcomes.
W.E. Upjohn Institute Policy Brief
(with Jonathan Cohen)
Early Career Research Award, W.E. Upjohn Institute
Abstract (click to expand): Approximately 10% of Unemployment Insurance (UI) claimants in the US are denied benefits after being deemed at-fault for their job loss by a government examiner. Using administrative data from California and an examiner leniency design, we estimate the causal effects of extending eligibility to marginally at-fault claimants---whose separation-reason would have been considered eligible by a different examiner group. Approving a marginally at-fault claimant increases UI benefits paid by over $3,000 and lengthens the nonemployment spell by just under 2 weeks, but it does not decrease total earnings. Combining these estimates to calculate the total fiscal externality of expanding eligibility on this margin, we find that these social costs are 16% of the total cost of the eligibility expansion. Using different research designs in the same data, we show that other types of UI benefit expansions have significantly higher efficiency costs. We provide suggestive evidence that lower efficiency costs for the at-fault eligibility expansion are driven by smaller responses among lower-income claimants who are disproportionately affected by at-fault eligibility criteria.
W.E. Upjohn Institute Policy Brief
Postdoctoral Grant, Washington Center for Equitable Growth
Abstract (click to expand): Unemployment Insurance replaces a percentage of prior earnings while a claimant is out of work. To implement the program, policymakers must define a base period from which prior earnings are measured. I analyze two implications of this previously unexamined policy choice. First, for claimants with volatile enough earnings, a commonly used base period structure creates ''benefit risk''---a job loss at the wrong time implies lower benefit amounts. Second, since base periods are determined by the claim filing date, claimants can partially avoid the negative effects of this risk by strategically timing their claims. Using several new sources of administrative data from California’s Unemployment Insurance program, I make three contributions. First, I use a simple dynamic model of job search and Unemployment Insurance to show that the private welfare costs of benefit risk are large. The average claimant would trade 5% of their expected Unemployment Insurance benefits to eliminate exposure to benefit risk and this number rises substantially among young and especially low-income claimants. Second, I demonstrate that claim-timing responses can act as an effective solution to this problem. Some claimants strategically delay their claims after a job loss in order to receive higher benefits. Third, I provide suggestive evidence that information frictions are a key barrier to this mitigating behavior.
The Disparate Long-run Impacts of Academic Probation
(with Serena Canaan, Stefanie Fischer, and Pierre Mouganie)
Draft coming soon
Explaining the Rise in Unemployment Insurance Take-Up Rates During the COVID-19 Pandemic
(with Till von Wachter)
Abstract (click to expand): We study incomplete take-up of Unemployment Insurance (UI) benefits using administrative micro-data from the State of California (CA). We show that take-up rates increased sharply during the pandemic, from just under 30% in 2019q4 to just over 70% in 2020q2, and that the severity of the economic downturn explains the majority of this increase. Relying on variation in pandemic-related employment losses across local labor markets, we estimate that 56% of the pandemic-era increase in the take-up rate is explained by the severity of the recession. Using variation in replacement rates across earnings groups created by the Federal Pandemic Unemployment Compensation program, we estimate that benefit generosity explains no more than 17% of the rise in take-up rates. Detailed payment-level administrative UI data allows us to further estimate that the PUA program explains 11% of the increase. Our data also allows us to provide a uniquely detailed assessment of predictors of take-up over the past three business cycles---local economic conditions, industry, geography, and prior earnings levels are strong predictors of take-up throughout this longer time period.
Selected Pre-PhD Publications
Overspending driven by oversized single dose vials of cancer drugs
(with Peter Bach, Rena Conti, Raymond Muller, and Leonard Saltz)
British Medical Journal, 2016
Policy Work
California Unemployment Insurance Claims During the COVID-19 Pandemic
(with Alex Bell, T.J. Hedin, Roozbeh Moghadam, and Till von Wachter)
California Policy Lab Policy Brief, 2020-2022 (updated frequently)
I am part of a team of researchers at the California Policy Lab working with the State of California’s Employment Development Department to analyze nearly real-time administrative Unemployment Insurance claims data during the Coronavirus crisis. Our findings have been released in a series of Policy Briefs, which you can find at the link above. This work has received media coverage from The New York Times, the Los Angeles Times, and The Washington Post, among others.
Employment and Earnings Among LA County Residents Experiencing Homelessness
(with Nefara Riesch and Till von Wachter)
California Policy Lab Policy Brief, February 2020
Website: Thanks to Gautam Rao and Xinyue Lin for making their code publicly available.
