with Seula Kim [Kilts Center at Chicago Booth Marketing Data Center Paper]
Abstract: This paper studies how spatial variation in inflation rates affects real income inequality and examines the role of retailer dynamics in driving these differences. Using the NielsenIQ Retail Scanner dataset and the Business Dynamic Statistics, we document several stylized facts about the spatial heterogeneity in inflation and retailer dynamics. We find that poorer MSAs experienced higher inflation than richer MSAs on average from 2006 to 2020. The differences are substantial: the annualized difference in inflation between the poorest MSAs and the richest MSAs is 0.46 percentage points (10 p.p. in total over the period). Poorer MSAs have fewer retailers and less variety of goods with a larger fraction of large retailers; these poorer MSAs have higher retailer market concentration relative to richer MSAs. To explore a potential causal link between inflation and market concentration, we use a triple-difference estimator, with a particular focus on the egg market during the 2014-2015 bird flu episode. Our analysis suggests that retailer market concentration contributes to the difference in inflation between poor and rich MSAs.
Doctoral Research Grant by the Washington Center for Equitable GrowthAbstract: 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. 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 estimate that the share of claims whose processing was delayed by over 70 days rose by at least 2.1 percentage points more in COBOL states relative to 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 $105 billion (in 2019 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.
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.