Economics of Digitization
The Digital Economics and AI initiative aims to advance and disseminate cutting-edge research on the intersection of AI, digitization, and economic outcomes. Building on the foundations of two longstanding NBER projects—the Economics of Digitization and the Economics of AI—this initiative seeks to foster a collaborative and diverse research community that spans multiple subfields of economics, including both empirical work and economic theory. The initiative’s objectives are to strengthen the research community and define a comprehensive agenda for digital economics and AI research. Annual events will include a major meeting at the NBER Summer Institute, at least two additional conferences, and a PhD workshop emphasizing key themes such as the impact of digital data and AI on labor markets, productivity and economic growth, political economy, societal inequality, and market competition. The initiative will generate new research findings, and provide information to policymakers and practitioners about the broader societal and economic implications of both AI and digital economics.
Investigators
Catherine Tucker is the Sloan Distinguished Professor of Management Science and Professor of Marketing at MIT’s Sloan School of Management. Her research investigates the consequences of the rise of digital data for issues including privacy and algorithmic bias, and the broader impacts of social media and online advertising.
Avi Goldfarb holds the Rotman Chair in AI and Healthcare at the University of Toronto's Rotman School of Management. His research spans a wide range of issues concerning the digital economy, including the economics of AI, online marketing, privacy economics, and the interaction between on-line and brick-and-mortar markets.
Supported by the Alfred P. Sloan Foundation grants #2010-10-17, #2014-3-13, #G-2017-9684, #G-2018-10104, #G-2020-12662, #G-2021-16971, and #G-2023-19618, the National Science Foundation grants #1441413, #1547832, and #1643267, the Tides Foundation grant #1171-48957, Amazon grant #2D-00885552, Microsoft Github , and Microsoft
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