Abstract: The United States experienced an unprecedented increase in unemployment insurance (UI) claims starting in March 2020. State UI-benefit systems were inadequately prepared to process these claims. In states that used an antiquated programming language, COBOL, to process claims, potential claimants experienced a larger increase in administrative difficulties, which led to longer delays in benefit disbursement and may have led to an increase in discouraged filers. Using daily debit and credit card consumption data from Affinity Solutions, I employ a two-way fixed-effects estimator to measure the causal impact of having an antiquated UI benefit system on aggregate consumption. Such systems led to a 2.8-percentage-point decline in total credit and debit card consumption relative to card consumption in states with more modern systems. I identify two factors that likely contributed to this outcome: delays in benefit disbursement and an increase in discouraged filers. I estimate that the share of claims whose processing was delayed by over 70 days rose by 1.4 percentage points more in COBOL states relative to non-COBOL states. I also find suggestive evidence that the increase in administrative burden for claimants in COBOL states led to an additional 4.5 million discouraged filers relative to the number that would have been expected had they faced the same increase in administrative burden as claimants in non-COBOL states. Based on a back-of-the-envelope calculation using 2019 data, my results suggest that the decline in consumption in COBOL states in 2020 after the pandemic-emergency declaration corresponds to a real-GDP decline of at least $90 billion (in 2012 dollars).
with Andrew H. McCallum
We introduce a new theory and new estimation method for optimizing frictions with a piecewise linear constraint. Allowing frictions to depend on observables, we estimate why agents do not behave as standard frictionless models predict. Our methods are not limited to public finance and apply to a general class of mixture models and any of the four possible piecewise linear constraints, 1) slope increase (convex kink), 2) slope decrease (concave kink), 3) intercept increase (convex notch), or 4) intercept decrease (concave notch). We demonstrate these methods in three of these four settings. Individual income tax returns with a, 1) convex kink and, 2) concave kink implied by the EITC. New Jersey real estate transfer taxes with a 3) convex notch. We document which covariates account for a substantial share of optimizing frictions and provide elasticity estimates that explicitly control for optimizing frictions.
Inflation is an important economic indicator that is typically measured and reported at the national level. However, there is systematic geographical variation in inflation across metropolitan statistical areas (MSAs), which can affect spatial inequality. This paper studies how spatial variation in inflation rates affect income inequality and examines the role of retailer market power in driving these differences. Using Nielsen Retail Scanner dataset, we uncover that the poorest MSAs experienced higher inflation rates than the richest MSAs from 2006 to 2016 at both disaggregated and aggregate food item levels. Accumulating the differences in inflation rates over this decade, the poorest decile experiences an approximately 10 percentage points higher inflation rate than the richest decile. Furthermore, we observe variations in retailer dynamics across MSAs with different income levels: poorer MSAs have fewer retailers and varieties of goods, but exhibit a higher fraction of larger retailers and a greater degree of retailer market concentration relative to richer MSAs. To explore the causal link between inflation and market concentration, we use a triple difference estimator, with a particular focus on the egg market during the 2015 bird flu episode. Our analysis underscores that retailers' market concentration and power can be a contributing factor to higher inflation rates and lead to spatial divergence in price inflation. This channel can act as a potential catalyst for amplifying inequality among regions with varying income levels and create important policy implications.
New business creation surged after the pandemic recession, but its causes are not well understood. In this paper, we establish evidence for a positive impact of unemployment insurance (UI) expansion on rising business formation. The expansion of UI benefits and relaxation of work search requirement under the CARES Act provided unemployed potential entrepreneurs with money and time. We exploit that the actual increase in UI payment per unemployed varied across states partly due to whether states use an outdated technology, COBOL, to process UI claims. We implement an instrumented difference-in-differences design and estimate that a one percent increase in UI benefits led to a 0.22 percent increase in new business applications, implying that more than half of the rise in business formation in 2020 can be attributed to the UI expansion.