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.

AI and algorithms are being used in new workplace technologies to automate and augment production, service, and management tasks. Companies in the information and communications technology industry are at the forefront of both developing new AI-based tools and adopting them in their workplaces. This mixed-method study will examine how these companies in the telecommunications and video game development industries are applying AI and algorithm-based technologies in different service and technical occupations. These include call-center agents and technicians (telecoms) and quality-assurance workers and software engineers (game development). They will compare the role of management strategy, occupational characteristics, and collective worker voice through labor unions in these decisions, as well as their impacts on workers’ job quality. Findings will help to inform policies and labor union strategies to encourage productive and socially sustainable approaches to workplace AI adoption and deployment.

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.

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.