AI and Automation in US Census Data

Our research studies how AI, automation, and robotics are transforming U.S. businesses, drawing on large-scale, nationally representative data collected by the U.S. Census Bureau.

The core data source is the Annual Business Survey (ABS), which is the first comprehensive federal survey to measure firm-level AI use across the U.S. economy, covering more than 300,000 employers. We further complement this with the Annual Survey of Manufactures (ASM), which provides detailed information on automation and robotics in U.S. manufacturing, and the Management and Organizational Practices Survey (MOPS), which captures how firms are organized, managed, and monitored internally.  Together, these surveys allow researchers in our lab to move beyond anecdotes and case studies to systematically measure where AI and robotics are being adopted, which firms face the greatest barriers, and how new technologies interact with organizational practices and workforce structure at scale. 

Across these Census datasets, a clear pattern emerges: adoption of AI and robotics is widespread but highly uneven. Larger firms are significantly more likely to report using advanced technologies, reflecting their greater access to capital, data, and specialized technical talent. Meanwhile, small and medium-sized businesses, which account for the majority of U.S. firms and a significant share of employment, often face constraints related to cost, skills, data, and implementation complexity. These patterns echo broader evidence from recent economic research showing that AI and automation can reshape productivity, market structure, and competitive dynamics, potentially amplifying differences between firms if access remains unequal. By leveraging the Census Bureau’s unique combination of technology, manufacturing, and management surveys, our work helps clarify how AI and robotics are diffusing through the U.S. economy and why organizational capabilities and complementary investments matter as much as the technologies themselves for broad-based economic impact.

Featured DEL Researchers

Wang Jin

Digital Fellow

Wang Jin is an Associate Professor of Management Science at Chapman University and a Digital Fellow at the Stanford Digital Economy Lab. His research spans the Economics of Digitization, IT Productivity, Organizational Design, and the Future of Work. Using large-scale administrative, platform, and regulatory datasets, he examines how digital technologies—including cloud computing, machine learning, AI adoption, and modern data infrastructure—reshape firm performance, organizational structure, innovation, and labor-market dynamics. His work focuses on the microeconomics of digital transformation, particularly how data-centric technologies alter coordination costs, managerial practices, and productivity. His work has appeared in leading outlets such as Management Science, MIS Quarterly, and Harvard Business Review. He leads and contributes to multiple projects using confidential U.S. Census microdata (Title 13 & 26) and collaborates with industry partners on cloud transformation, digital architecture, and GenAI deployment.

Before joining Chapman, Jin served as a Research Scientist at the Stanford Digital Economy Lab, a Research Associate at the MIT Initiative on the Digital Economy and MIT Sloan, and a Research Fellow at Harvard’s Institute for Quantitative Social Science. He holds a PhD in Economics from Clark University.

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J. Frank Li

Digital Fellow

I am an assistant professor of Management Information Systems at the UBC Sauder School of Business, a digital fellow at Stanford University Institute for Human-Centered Artificial Intelligence Digital Economy Lab and a visiting fellow at NYU Stern Center for the Future of Management.

My research stands at the nexus of the economics of information technology, focusing on AI and robots, technical skills and future of work, and organizational changes. I also study how uncertainty shocks and competition affect firm investment, innovation, and hiring decisions.

​I received my Ph.D. in Business Administration from Ross School of Business at the University of Michigan. I hold a BA in Math and Economics from the University of Michigan and an MA in Economics from Duke University.

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Kristina McElheran

Digital Fellow

Kristina McElheran is an Associate Professor at the University of Toronto, where she researches how digital technologies shape firms, their performance, and the future of work. After six years on the Harvard Business School faculty, she joined the University of Toronto in 2014. Her research spans the rise of the commercial internet through the rise of cloud computing and AI-related technologies, with a focus on productivity, entrepreneurship, and worker-level impacts. Her research has appeared in Management Science, the Journal of Economics and Management Strategy, the Journal of Econometrics, the American Economic Review Papers & Proceedings, Harvard Business Review, Sloan Management Review, Communications of the ACM, The Economist, and other leading outlets in the U.S. and Canada. She is a Faculty Fellow at the Schwartz Reisman Institute for Technology and Society, which serves as a research and solutions hub at the University of Toronto dedicated to deepening understanding of technologies, societies, and what it means to be human. She is also a Digital Fellow at the MIT Initiative on the Digital Economy and a Fellow at the Technology and Policy Research Initiative at Boston University.

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