This project will investigate how the Advanced Manufacturing Production Tax Credit (45X MPTC) and the Advanced Energy Project Investment Tax Credit (48C ITC) affect manufacturing activity throughout the solar photovoltaic supply chain, with a focus on its impacts in the United States. The central research questions are: How do the Inflation Reduction Act’s domestic manufacturing and content-based tax credits affect investment, production, and employment across different stages of the solar manufacturing supply chain? How do these effects propagate downstream to influence solar technology adoption and environmental outcomes? The author will do a reduced form analysis of manufacturing investment and output, develop a structural model of the global solar photovoltaic supply chain, and conduct simulations using the structural model. By focusing on the entire supply chain, from polysilicon and wafers to cells and modules, this research aims to provide the first comprehensive assessment of the Inflation Reduction Act’s economic and environmental implications in the solar industry.

This study seeks to compare the Inflation Reduction Act and tariff approaches by quantifying how each policy affects the new vehicle market and domestic manufacturing activity. The authors will evaluate how these policies affect purchase decisions and welfare of different consumer groups (by income, geography, and other demographics), as well as the production and profits of different manufacturer groups (based in the United States versus abroad) and the incentives each policy creates to expand U.S. industrial capacity. The authors will use an existing model of the electric vehicle market to examine the impact of switching from IRA subsidies to tariffs on EV prices, the impact of switching from IRA subsidies to tariffs on short-run domestic production and profits, and the impact of IRA subsidies on long-run investments in capacity and production.

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.

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.

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.