with Seula Kim [Kilts Center at Chicago Booth Marketing Data Center Paper]
Abstract: We study how inflation varies across regions with different income levels and the role of retailer market structure. Using NielsenIQ Retail Scanner and Business Dynamics Statistics data, we document new stylized facts of spatial heterogeneity in food inflation and retailer market structure. From 2006 to 2020, poorer MSAs experienced annualized food inflation that was 0.46 percentage points higher than that of richer ones—amounting to a cumulative difference of 8.8 percentage points over the period. Poorer areas also had fewer goods, fewer retailers, and higher market concentration. Using a triple-difference estimator during the 2014–2015 bird flu outbreak, we identify a causal link between market concentration and inflation.
Doctoral Research Grant by the Washington Center for Equitable GrowthAbstract: The US experienced an unprecedented increase in unemployment insurance (UI) claims beginning 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, resulting in longer delays in benefit disbursement. These antiquated 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. Furthermore, these antiquated systems experienced at least a 2.1-percentage-point increase in the share of claims that were delayed by over 70 days.
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.
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.
Businesses, individuals, and government policymakers rely on accurate and timely measurement of nominal sales, inflation, and real output, but current official statistics face challenges on a number of dimensions. First, these key indicators are derived from surveys conducted by multiple agencies with different time frames, yielding a complex integration process. Second, some of the source data needed for the statistics (e.g., expenditure weights) are only available with a considerable lag. Third, response rates are declining, especially for high-frequency surveys. Focusing on retail trade statistics, we document important discrepancies between official statistics and measures computed directly from item-level transactions data. The long lags in key components of the source data delay recognition of economic turning points and lead to out-of-date information on the composition of output. We provide external data sources to validate the transactions data when their nominal sales trends differ importantly from official statistics. We then conduct counterfactual exercises that replicate the methodology that official statistical agencies use with the transactions data in the construction of nominal sales indices. These counterfactual exercises produce similar results to the official statistics even when the official nominal sales and item-level transactions data exhibit different trends.