In the first comprehensive academic analysis of court-ordered wage garnishment, this study finds that garnishment rates nearly doubled from 2014 to 2019 due largely to increases in student loan collections. On average, 11 percent of gross earnings were remitted to creditors, raising concerns about whether unexpected garnishments may perpetuate a cycle of debt. Garnishments are most frequent in neighborhoods with higher percentages of Black residents and fewer college-educated workers, even controlling for income.

This paper evaluates the reliability of neural networks in actively managed mutual fund applications. The authors conclude that neural networks identify important interaction effects that are not apparent to linear models and offer predictability that is “real-time, out-of-sample, long-lived, and economically meaningful.”

This study finds that nearly 30% of total debt relief in response to the COVID-19 pandemic was provided by the private sector, with the balance provided pursuant to government mandates focusing on mortgage and student loans. Households with lower incomes and lower creditworthiness were more likely to obtain forbearance relief, as were households who live in areas with higher Black or Hispanic populations, high infection rates, and more severe economic deterioration. The authors caution that the winding down of forbearance measures and subsequent structuring of debt repayments may have a significant impact on household debt distress and the aggregate economy given the amount of accumulated postponed repayments.

This study of loan-level Paycheck Protection Program data finds that despite a lag in approving several fintech lenders to participate in the program, such lenders provided disproportionate amounts of PPP funds in ZIP codes with fewer bank branches, lower incomes, and a larger minority share of the population, as well as in industries with little ex ante small-business lending. Fintechs’ role in PPP provision was also greater in counties where the economic effects of the COVID-19 pandemic were more severe.

This paper analyzes cell phone data from March through mid May, finding that private, self-regulating changes in behavior explained about 75% of the decline in foot traffic across most industries, while restrictive regulation (including school closures) had more influence on essential retail foot traffic and the fraction of cell phones that remained at home all day.

This paper provides the first analysis of impacts of the pandemic on the number of active small businesses in the United States using nationally representative data from May 2020. The number of active business owners rebounded 7% since the low in April but remained 15% down from February. Drops in business activity from pre-pandemic levels are disproportionately concentrated among African-American (26%), Latino (19%), Asian (21%), and immigrant (25%) business owners.

Using real time anonymized data from private companies, this paper focuses on the ripple effects of a sharp decrease in spending by high-income households on both small businesses and low-income workers.

Using data from the nation’s largest payroll processor from February through May, this paper finds that employment losses were disproportionately concentrated among low-income workers while wage cuts were disproportionately concentrated among workers in the top two deciles of the wage distribution. The percent of workers receiving wage cuts was roughly twice that reported during the Great Recession.