Currently, no government surveys collect worker-level data on generative AI adoption and use. This proposal seeks to continue and enhance the Real-Time Population Survey, the first nationally representative survey tracking genAI adoption in U.S. workplaces. The Real-Time Population Survey, first launched in April 2020, is benchmarked to the Current Population Survey, allowing researchers to validate outcomes against the larger sample size. It builds upon previous technology adoption surveys, enabling comparisons to other information and communication technologies. Given the rapid pace of AI adoption thus far, adoption rates are expected to change substantially in the coming years. Funding would cover three additional surveys in the August 2025–July 2027 grant period and support the development of innovative questions on how genAI interacts with work tasks. The resulting data will provide insights into which workers use genAI, how often, and which tasks it complements or automates, helping to discipline and test theories of the labor market impact of genAI. Findings will also inform workforce development and social insurance policies with the goal of maximizing aggregate productivity gains while simultaneously ensuring that benefits are broadly distributed.
This grant was co-funded by the Russell Sage Foundation.
This study will examine the emerging role of artificial intelligence in ongoing “modernization” initiatives at the California Department of Motor Vehicles and the impacts these changes have had on the agency’s workforce. Public-sector employment has long provided a dependable pathway to the middle class for workers otherwise less likely to attain such job security, wages, and benefits based on their race, gender, geography, or educational attainment. The rapid ascendance of public-sector AI initiatives in California raises significant questions about the future of this longstanding opportunity for middle-class mobility. Through mixed methods analysis of public and private datasets, the team will assess the demographic and economic outcomes associated with specific AI technologies in use at the Department of Motor Vehicles. This will provide policymakers and labor advocates with a clearer sense of how to meaningfully intervene to bolster worker protections and sustain a diverse middle class amid widespread technological uncertainty.
Firms in the generative AI ecosystem offer their products with strings attached: terms and conditions that purport to impose legal restrictions on user behavior. This project will study the terms and conditions of more than 100 genAI firms and would be the first large-scale effort to document this issue systematically. Research in other digital markets and exploratory research in the genAI space indicate that these terms could pose at least two significant competition problems. First, by effectively depriving users of the right to bring private antitrust claims against genAI firms, genAI terms and conditions could erode one of the three pillars of an effective antitrust enterprise. Second, genAI firms have begun to impose noncompete restrictions on users. These restrictions could raise entry barriers and lead to more highly concentrated markets—a recipe for less dynamism and dampened innovation. Yet policymakers and researchers currently know very little about how ubiquitous or restrictive these genAI terms actually are in practice. This research will offer data-driven analysis and responsive policy prescriptions for these nascent, critically important markets.
This project proposes to estimate a structural equilibrium model of the U.S. child care sector to use for counterfactual subsidy design, with the goal of finding an optimal cost-neutral subsidy design. The project consists of two parts. First, the author will evaluate the effect of reimbursement rate policies on local maternal labor force participation, child care worker wages, child care prices, and quality of care. Second, the author will use the estimated model to simulate the effects of counterfactual subsidy policies on parent utility, worker wages, mark-ups, and the distribution of quality.
This project aims to provide new causal evidence on the economic implications of
supply chain disruptions. The identification strategy leverages the fact that global supply chains rely on maritime trade, which depends on a few critical choke points. The author will identify disruptive incidents, which are plausibly exogenous to the U.S. economy, and then isolate the market impact of the disruption using high-frequency financial data. The author will then use these shipping cost surprises as an instrument in a structural VAR model of the U.S. economy to identify a structural supply chain shock.
This project brings together two important U.S. economic policy areas: the housing shortage and market power. The author will use cutting-edge tools from industrial organization to test firm conduct and answer the question of whether market power among homebuilders can explain the under-supply of new housing, particularly entry-level units, or whether their economies of scale reduce costs.
This project will utilize third-party scheduling data that is well-suited to investigate schedule volatility. Research in this area has been limited to surveys of workers, but with detailed time and attendance data from a payroll provider, this project seeks to document novel facts about worker schedules, evaluate the effect of predictive scheduling and minimum wage laws on schedule-related outcomes for firms and workers, and understand the welfare effects of schedule regulation on workers.
Federal Housing Administration and Veterans Administration policies are understood to have contributed to racial disparities in homeownership, wealth, and neighborhood opportunity in the United States, but systematic data on their mortgage activity is scarce. This project proposes to digitize and publicly release a dataset of FHA-insured and VA-guaranteed mortgages issued between 1935 and 1975 to assess the demographic and spatial distribution of these loans. Addresses will be geocoded, and names of borrowers matched to full-count Census data from 1930, 1940, and 1950 to identify borrowers’ demographic and socioeconomic backgrounds. This project will assess who received these loans; how they were distributed across neighborhoods; and whether FHA and VA insurance accelerated White flight and exacerbated segregation.
The declining relative earnings of workers constitutes an important macroeconomic trend. This project will study a new potential explanation: changes in corporate governance. To do so, the author will use the Longitudinal Employer-Household Dynamics, Longitudinal Business Databases, Census of Manufacturers, and the Annual Survey of Manufacturers to analyze changes generated by activist hedge fund investors, then changes in equity-based compensation of managers, and their impacts on worker outcomes.
Building on past research on the role of the Earned Income Tax Credit in supporting migration decisions, this research will evaluate the subsequent outcomes for both parents and children. Leveraging detailed linked administrative data—including the American Community Survey, Current Population Survey, and individual tax records—the author will conduct a longitudinal analysis of U.S. families’ migration patterns and economic outcomes. High-resolution geographic information provides information on the quality of neighborhoods families move to and from, with variables such as school quality, local poverty rates, incarceration rates, labor market opportunities, and measures of economic mobility. Linking individual tax records with survey data allows for an assessment of children’s educational attainment, employment, and earnings over time. Tax records provide information on family income, employment, and geographic mobility.
