Drawing on surveys of small business owners and employees, we present three main findings about the evolution of remote work after the onset of COVID-19. First, uptake of remote work was abrupt and widespread in jobs suitable for telework according to the task-based measure from Dingel and Neiman (2020). The initial adoption lead to a persistent shift in work arrangements that both firms and workers forecast would continue into the future. Second, business leaders’ perceptions of how remote work affected productivity shifted over time. In early 2020, 70 percent of small business owners reported a productivity dip due to remote work. By contrast, the median business owner reported a positive productivity impact of remote work by 2021. Third, 21 percent of workers report being willing to accept a pay cut in excess of 10 percent if it allowed them to continue working from home, but the median worker in a teleworkable job would not tradeoff any compensation for the option of continued remote work. Taken together, our evidence points to perceived productivity gains and some workers’ preferences as reasons for the persistence of remote work in the years following the onset of COVID-19.

What happens when public resources are allocated by private companies whose objectives may be imperfectly aligned with policy goals? We study this question in the context of the Paycheck Protection Program (PPP), which relied on private banks to disburse aid to small businesses rapidly. In our model, delegation is attractive when delay is sufficiently costly, variation across firms in the impact of funds is mall, and the alignment between government and private objectives is high. We use novel firm-level survey data that contains information on banking relationships to measure heterogeneity in the impact of PPP and to assess whether banks targeted loans to high- impact firms. Banks did target loans to their most valuable pre-existing customers. However, using an instrumental variables approach that exploits variation in banks’ loan processing speeds, we find that treatment effect heterogeneity is sufficiently moderate, delay is sufficiently costly, and bank and government objectives are sufficiently aligned that delegation was likely superior from the government’s perspective to delaying loans to improve targeting.

We study the removal of information from a market, such as a job-applicant screening tool. We characterize how removal harms groups with relative advantage in that information: typically those for whom the banned information is most precise relative to alternative signals. We illustrate this using recent bans on employers' use of credit report data. Bans decrease job-finding rates for Black job-seekers by 3 percentage points and increase involuntary separations for Black new hires by 4 percentage points, primarily because other screening tools, such as interviews, have around 60% higher standard deviation of signal noise for Black relative to white job-seekers.

We study the removal of information from a market, such as a job-applicant screening tool. We characterize how removal harms groups with relative advantage in that information: typically those for whom the banned information is most precise relative to alternative signals. We illustrate this using recent bans on employers' use of credit report data. Bans decrease job-finding rates for Black job-seekers by 3 percentage pTo explore the impact of coronavirus disease 2019 (COVID-19) on small businesses, we conducted a survey of more than 5,800 small businesses between March 28 and April 4, 2020. Several themes emerged. First, mass layoffs and closures had already occurred—just a few weeks into the crisis. Second, the risk of closure was negatively associated with the expected length of the crisis. Moreover, businesses had widely varying beliefs about the likely duration of COVID-related disruptions. Third, many small businesses are financially fragile: The median business with more than $10,000 in monthly expenses had only about 2 wk of cash on hand at the time of the survey. Fourth, the majority of businesses planned to seek funding through the Coronavirus Aid, Relief, and Economic Security (CARES) Act. However, many anticipated problems with accessing the program, such as bureaucratic hassles and difficulties establishing eligibility. Using experimental variation, we also assess take-up rates and business resilience effects for loans relative to grants-based programs.

We use traditional and non-traditional data sources to measure the collapse and subsequent partial recovery of the U.S. labor market in Spring 2020. Using daily data on hourly workers in small businesses, we show that the collapse was extremely sudden -- nearly all of the decline in hours of work occurred between March 14 and March 28. Both traditional and non-traditional data show that, in contrast to past recessions, this recession was driven by low-wage services, particularly the retail and leisure and hospitality sectors. A large share of the job loss in small businesses reflected firms that closed entirely. Nevertheless, the vast majority of laid off workers expected, at least early in the crisis, to be recalled, and indeed many of the businesses have reopened and rehired their former employees. There was a reallocation component to the firm closures, with elevated risk of closure at firms that were already unhealthy, and more reopening of the healthier firms. At the worker-level, more disadvantaged workers (less educated, non-white) were more likely to be laid off and less likely to be rehired. Worker expectations were strongly predictive of rehiring probabilities. Turning to policies, shelter-in-place orders drove some job losses but only a small share: many of the losses had already occurred when the orders went into effect. Last, we find that states that received more small business loans from the Paycheck Protection Program and states with more generous unemployment insurance benefits had milder declines and faster recoveries. We find no evidence so far in support of the view that high UI replacement rates drove job losses or slowed rehiring substantially.

Since 1970, the share of Black individuals living in suburbs of large cities has risen from 16 to 36 percent. This shift is as large as the post-World War II wave of the Great Migration. We first show that Black suburbanization has led to major changes in neighborhoods, accounting for a large share of recent increases in both the average Black individual’s neighborhood quality and within-Black income segregation. We then show that changes in relative suburban amenities and housing prices explain about 60 and 30 percent, respectively, of Black suburbanization, while regional reallocation, changing educational attainment, and gentrification play only minor roles.

We present a novel method called "generative regulatory measurement" that uses Large Language Models (LLMs) to interpret statutes and administrative documents. We demonstrate its effectiveness in analyzing municipal zoning codes, achieving 96% accuracy in binary classification tasks and a 0.92 correlation in predicting minimum lot sizes. Applying this method to U.S. zoning regulations, we establish five facts about American zoning: (1) Housing production disproportionately happens in unincorporated areas without municipal zoning codes. (2) Density in the form of multifamily apartments and small lot single family homes is broadly limited. (3) Zoning follows a monocentric pattern with regional variations, with suburban regulations particularly strict in the Northeast. (4) Housing regulations can be clustered into two main principal components, the first of which corresponds to housing complexity and can be interpreted as extracting value in high demand environments. (5) The second principal component associates with exclusionary zoning.

Exploiting geological variation and timing in the initiation of hydraulic fracturing, we find that fracking leads to sharp increases in oil and gas recovery and improvements in a wide set of economic indicators. There is also evidence of deterioration in local amenities, which may include increases in crime, noise, and traffic and declines in health. Using a Rosen-Roback-style spatial equilibrium model to infer the net welfare impacts, we estimate that willingness-to-pay (WTP) for allowing fracking equals about $2,500 per household annually (4.9 percent of household income), although WTP is heterogeneous, ranging from more than $10,000 to roughly 0 across 10 shale regions.

We provide new evidence on the causal effect of unearned income on consumption, balance sheets, and financial outcomes by exploiting an experiment that randomly assigned 1000 individuals to receive $1000 per month and 2000 individuals to receive $50 per month for three years. The transfer increased measured household expenditures by at least $300 per month. The spending impact is positive in most categories, and is largest for housing, food, and car expenses. The treatment increases housing unit and neighborhood mobility. We find noisily estimated modest positive effects on asset values, driven by financial assets, but these gains are offset by higher debt, resulting in a near-zero effect on net worth. The transfer increased self-reported financial health and credit scores but did not affect credit limits, delinquencies, utilization, bankruptcies, or foreclosures. Adjusting for underreporting, we estimate marginal propensities to consume non-durables between 0.44 and 0.55, durables and semi-durables between 0.21 and 0.26, and marginal propensities to de-lever of near zero. These results suggest that large temporary transfers increase short-term consumption and improve financial health but may not cause persistent improvements in the financial position of young, low-income households.

We study the causal impacts of income on a rich array of employment outcomes, leveraging an experiment in which 1,000 low-income individuals were randomized into receiving $1,000 per month unconditionally for three years, with a control group of 2,000 participants receiving $50/month. We gather detailed survey data, administrative records, and data from a custom mobile phone app. The transfer caused total individual income to fall by about $1,500/year relative to the control group, excluding the transfers. The program resulted in a 2.0 percentage point decrease in labor market participation for participants and a 1.3-1.4 hour per week reduction in labor hours, with participants’ partners reducing their hours worked by a comparable amount. The transfer generated the largest increases in time spent on leisure, as well as smaller increases in time spent in other activities such as transportation and finances. Despite asking detailed questions about amenities, we find no impact on quality of employment, and our confidence intervals can rule out even small improvements. We observe no significant effects on investments in human capital, though younger participants may pursue more formal education. Overall, our results suggest a moderate labor supply effect that does not appear offset by other productive activities.

This paper provides new evidence on the causal relationship between income and health by studying a randomized experiment in which 1,000 low-income adults in the United States received $1,000 per month for three years, with 2,000 control participants receiving $50 over that same period. The cash transfer resulted in large but short-lived improvements in stress and food security, greater use of hospital and emergency department care, and increased medical spending of about $20 per month in the treatment relative to the control group. Our results also suggest that the use of other office-based care—particularly dental care—may have increased as a result of the transfer. However, we find no effect of the transfer across several measures of physical health as captured by multiple well-validated survey measures and biomarkers derived from blood draws. We can rule out even very small improvements in physical health and the effect that would be implied by the cross-sectional correlation between income and health lies well outside our confidence intervals. We also find that the transfer did not improve mental health after the first year and by year 2 we can again reject very small improvements. We also find precise null effects on self-reported access to health care, physical activity, sleep, and several other measures related to preventive care and health behaviors. Our results imply that more targeted interventions may be more effective at reducing health inequality between high- and low-income individuals, at least for the population and time frame that we study.