Rebecca Jack (University of Nebraska, Lincoln) will use data from the competitive 21st Community Learning Center Grants – federal funding targeted at providing afterschool care – to estimate how expanded afterschool care affects mothers’ employment and earnings. Co-funded with the Russell Sage Foundation. Additional support provided by the Washington Center for Equitable Growth.

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.

As AI transforms health care, workers must have a say in its development, design, and implementation to ensure fair outcomes for both employees and patients. The proposed study examines the Labor Management Partnership at Kaiser Permanente, a unique collaboration between front-line workers and their employer, as a test case for integrating worker voice into AI decision-making. Through action research and a structured case study, the researchers will assess whether jointly negotiated AI strategies improve job quality and patient care. This work builds on a four-phase model of worker participation, addressing AI from its initial development to workforce training and transition planning. Given that U.S. labor law often limits workers' input on technology adoption, this study has broader policy implications, demonstrating pathways to stronger worker engagement in AI governance.

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.

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.