During the COVID-19 lockdowns, forgivable loans or grants to firms became a large-scale countercyclical income support strategy. This project studies the effect of such programs on short- and medium-run outcomes for U.S. firms and workers using $125 million in grants to 12,129 small businesses administered by the state of Minnesota via random lottery. The dataset will link the full set of program applicants and awardees to business and individual tax records and to credit histories. The authors will use the random assignment of grants to investigate the causal impact of loans on firm employment and payroll, borrowing and delinquency, and worker attachment to recipient firms. The Minnesota program closely mirrors the design of the federal Paycheck Protection Program but provides for a much cleaner research design due to the random lottery for recipients. Supporting small businesses during economic downturns is critical, but more evidence is needed to inform effective policy design of direct support to firms. This research promises to provide such evidence.

In contrast to the wide literature on the effects of permanent business wage subsidies on employment and wages, which generally finds small effects, less research has examined the effects of temporary wage subsidies on labor markets. In response to the economic shock induced by the COVID-19 pandemic, the U.S. federal government created the Employee Retention Tax Credit, or ERTC, to aid businesses adversely affected by the pandemic and stay-at-home orders. The tax credit provided businesses a maximum of $26,000 per worker over the 2020–2021 period, depending on firm size and the fiscal quarters in which the worker was furloughed vs. employed. Using matched employer-employee data from a payroll processing company covering one-twelfth of the U.S. private-sector workforce, the author will study the effects of the Employee Retention Tax Credit on employment, payroll, and small business reopening. We know relatively little about how to efficiently and effectively help firms during recessions. This research can inform policy design intended to preserve job matches, both to protect workers from the consequences of a recession and to lead to a strong economic recovery.

Unemployment Insurance was a critical component of the federal fiscal response to the COVID-19 recession. Yet one of the UI system’s most promising job retention programs used in many other countries—Short-Time Compensation—was underutilized in the United States, despite substantial federal subsidies to states. Through Short-Time Compensation, employers reduce hours for a group of workers who receive prorated UI benefits and maintain job benefits. Despite having bipartisan support, little is known about this program’s effectiveness due to a lack of data on it. This project harnesses unique administrative data and an ongoing partnership in California to evaluate the impact of Short-Time Compensation on different workers and employers during the pandemic. The outcomes the research seeks to address are: STC awareness and use, partial UI use and full UI use, worker outcomes, and firm outcomes. To evaluate heterogeneity in outcomes, the authors will analyze outcomes by worker characteristics, including age, gender, and prior earnings, and firm characteristics, including firm size, industry, geography, and wage bill size. To help improve take-up during the next recession and obtain the first causal impacts of Short-Time Compensation, the authors will use an encouragement design experiment to estimate the effect of randomized promotion on employer awareness of the program and its impact on worker and firm outcomes.

Governments around the world provided substantial support to individuals and firms throughout the COVID-19 pandemic. This project will study the effects of the stimulus payments provided to U.S. individuals through the Coronavirus Aid, Relief, and Economic Security Act, or CARES, Act. Comprising $2.2 trillion in spending, this bill included $300 billion in stimulus payments to individuals at the onset of the pandemic. Using data on the universe of U.S. federal tax returns for individuals and firms, the authors plan to estimate the effects of stimulus payments on labor market outcomes. The data allow the authors to link stimulus payments with labor market outcomes, including salaried employment, contract jobs, unemployment, and entrepreneurship. The authors plan to implement a regression kink design to identify the effects of stimulus payments on labor market outcomes. The project is poised to contribute to our understanding of the effects of fiscal stimulus during the COVID-19 pandemic on the labor market choices of individuals.

The COVID-19 recession was unique in that it was marked by a labor shortage in many U.S. sectors, leading policymakers to experiment with new tools to mitigate these losses. This project will study the impact of the child care spending provisions of the American Rescue Plan Act of 2021 on women's labor supply and child care utilization. The American Rescue Plan included $24 billion in child care stabilization grants, which were direct block grants to states to support child care services. The law also included the largest-ever expansion of the Child and Dependent Care Tax Credit, which increased by 400 percent the child-care-related transfers paid to some U.S. households in 2022, relative to past years. Unlike other child-related transfers, only children ages 13 and under are eligible for the Child and Dependent Care Tax Credit . Utilizing unique tax record data from the universe of all U.S. tax filers—which includes care providers, as well as CDCTC claims—the authors will use a regression discontinuity design in children's age to estimate the causal impact of this credit on household choices and how the impact varies with socioeconomic factors.

This project will examine whether there are heterogenous inflation rates across the United States. If so, the authors plan to identify which areas face higher rates of inflation and what mechanisms drive the observed differences. More specifically, the authors will use regional Nielsen Retail Scanner data from 2006–2016 to measure food inflation. This project will also analyze the effects of local market concentration on inflation, adding to the evidence base needed to effectively measure the heterogeneous effects of inflation.