AI and Economic Measurement
Artificial intelligence, combined with increasingly rich datasets, is reshaping how economists measure key aspects of the economy as well as how we collect data, construct statistics, and evaluate policy. To support research on the methods, applications, and policy implications of AI for economic measurement, the National Bureau of Economic Research (NBER), with the generous support of the National Science Foundation (NSF) and Alfred P. Sloan Foundation and in collaboration with the Journal of Financial Economics (JFE), will host a conference on Thursday, May 7, 2026 on the Stanford University campus. The meeting will be organized by NBER Research Associates Erik Brynjolfsson of Stanford University and Karen Dynan of Harvard University.
The organizers welcome submissions of empirical, theoretical, and methodological papers, and encourage work at early stages with compelling measurement contributions as well as using novel data. Topics of particular interest include, but are not limited to:
Economic Foundation Models: LLMs or multimodal models for economic data; retrieval and tool-use over statistical databases; evaluation on forecasting, policy analysis, and imputation.
LLM Agents for Economic Research: Use of LLMs as synthetic respondents and research assistants in survey design, data collection, and experimentation; implications for bias, validation, and methodological innovation.
Labor Markets and Skills: Measuring AI exposure by task/occupation; new skill taxonomies; effects on wages, skills, and job quality; and differential impacts across workers and regions.
Prices, Inflation, and Quality Adjustment: AI for hedonics and quality adjustment; nowcasting inflation from web data; and use of unstructured, high-frequency multimodal data (text, image, audio, video) in economic measurement.
Productivity and Intangible Capital: Measuring firm- and sector-level productivity in the presence of AI; value of data, models, and algorithmic capital; and organizational complements to AI.
Policy & Governance for Measurement: Transparency, explainability, and auditability of AI-generated statistics; privacy and confidentiality.
Welfare and Economic Contribution of AI and Digital Technologies: Approaches to measuring the welfare impacts and economic value of AI and digital innovations, including distributional effects.
Researchers who wish to submit papers should use the following link:
https://conference.nber.org/confsubmit/backend/cfp?id=AIEMs26
The deadline for submitting papers and proposals is 11:59 pm (EST) on Monday, February 2, 2026. Papers from researchers with and without NBER affiliations are welcome, as are papers from early career scholars. All submissions should include a disclosure statement indicating whether any of the authors have financial or other ties related to the research project. Decisions about which papers are accepted will be announced in late March 2026.
Papers submitted to this conference are eligible for dual submission to the Journal of Financial Economics. Authors who submit papers will receive a separate email for the JFE dual submission after the NBER conference submission deadline. To be eligible to be considered by the JFE, a paper must not have been previously rejected by the JFE and it cannot be under review at another journal. If a paper submitted to the conference is not invited for JFE submission, it may still be submitted later to the JFE through the normal submission process and without prejudice. Submission of a paper to the conference does not guarantee an invitation to submit to the JFE.
The NBER has a zero-tolerance policy for any form of discrimination or harassment at both in-person and virtual meetings. All invitees will be required to agree and comply with the NBER Conference Code of Conduct. Please direct questions about the subject matter of the conference to the meeting organizers, and questions about conference logistics to confer@nber.org.