About

Jason Abaluck is a Professor of Economics at Yale School of Management. His work lies at the intersection of public finance, behavioral economics, health economics, and industrial organization. His research focuses on the detection of mistakes and the design of institutions when consumers or providers make mistakes in contexts such as health plan choice, dietary choice, or the provision of medical care.

Research topics

• Computer Science
• Medicine
• Surgery
• Actuarial science
• Sociology
• Political Science
• Econometrics
• Economics
• Psychiatry
• Psychology
• Microeconomics
• Finance
• Environmental health
• Demography
• Nursing
• Business
• Economic growth

Selected publications

• Forecasting the Economic Effects of AI

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Forecasting the Economic Effects of AI

  National Bureau of Economic Research · 2026-04-01

  reportOpen access

  We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public.The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and privatesector forecasters.Conditional on a "rapid" AI progress scenario, in which AI systems surpass human performance on many cognitive and physical tasks, experts forecast substantial, though not historically unprecedented, economic shifts: annualized GDP growth rising to around 4% and the labor force participation rate falling from its current level of 62% to 55% by 2050, with roughly half of that decline-equivalent to around 10 million lost jobs-attributable to AI.A variance decomposition suggests that expert disagreement about these effects is driven primarily by different beliefs about the economic effects of highly capable AI systems rather than by disagreement about the pace of AI progress.These forecasts map onto notably different policy preferences across groups: experts strongly favor targeted measures such as worker retraining, whereas the general public supports both targeted programs and broader interventions, including a job guarantee and universal basic income.

  PublisherDOI

• Forecasting the Economic Effects of AI

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• A Method to Estimate Discrete Choice Models That Is Robust to Consumer Search

  Journal of Political Economy · 2026-01-07 · 1 citations

  articleOpen access1st authorCorresponding

  We state a sufficient condition under which choice data alone suffices to identify consumer preferences when choices are not fully informed. Suppose that: (i) the data generating process is a search model in which the attribute hidden to consumers is observed by the econometrician; (ii) if a consumer searches good j, she also searches goods which are better than j in terms of the non-hidden component of utility; and (iii) consumers choose the good that maximizes overall utility among searched goods. Canonical models will be biased: the value of the hidden attribute will be understated because consumers will be unresponsive to variation in the attribute for goods that they do not search. Under the conditions above and additional mild restrictions, an alternative method of recovering preferences using cross derivatives of choice probabilities succeeds regardless of the search protocol and is thus robust to whether consumers are informed. The approach nests several standard models, including full information. Our methods suggest natural tests for full information and can be used to forecast how consumers will respond to additional information. We verify in a lab experiment that our approach succeeds in recovering preferences when consumers engage in costly search.

  PublisherOA PDFDOI

• Reproducibility package for Can LLMs Improve Healthcare Delivery? Evidence From Physician Review And Objective Testing

  Reproducibility catalog · 2026-01-27

  otherOpen access
  PublisherDOI

• Does LLM Assistance Improve Healthcare Delivery? An Evaluation Using On-Site Physicians and Laboratory Tests

  Washington, DC: World Bank eBooks · 2026-01-15

  bookOpen access1st authorCorresponding

  This study tests the effects of large language model (LLM) decision support on patient care at two outpatient clinics in Nigeria. Health workers were given the option to make revisions to their initial care plan based on LLM feedback. The unassisted and assisted plans are evaluated using (1) comparisons with independent care plans created by on-site physicians, (2) laboratory tests for malaria, anemia, and urinary tract infections, and (3) a blinded randomized assessment by the on-site physician who saw the same patient. In response to LLM feedback, health workers changed their prescribing for more than half of the patients and reported high satisfaction with the recommendations. In a selected sample, retrospective review by academic physicians also suggested improvements in care related to long-term risk management. However, the three metrics show mixed effects of LLM-assistance, with on average no significant improvement in diagnostic alignment with physicians, detection rates for the tested conditions, or physician subjective assessments. Health workers follow LLM recommendations that agree with the physician's decisions only slightly more often than those that do not. These results suggest that, despite some benefits, LLM-based frontline health worker support is not yet a public health priority in low- and middle-income countries.

  PublisherOA PDFDOI

• Forecasting the Economic Effects of AI

  2026-01-01

  report

  We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public. The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and private-sector forecasters. Conditional on a “rapid” AI progress scenario, in which AI systems surpass human performance on many cognitive and physical tasks, experts forecast substantial, though not historically unprecedented, economic shifts: annualized GDP growth rising to around 4% and the labor force participation rate falling from its current level of 62% to 55% by 2050, with roughly half of that decline—equivalent to around 10 million lost jobs—attributable to AI. A variance decomposition suggests that expert disagreement about these effects is driven primarily by different beliefs about the economic effects of highly capable AI systems rather than by disagreement about the pace of AI progress. These forecasts map onto notably different policy preferences across groups: experts strongly favor targeted measures such as worker retraining, whereas the general public supports both targeted programs and broader interventions, including a job guarantee and universal basic income.

  PublisherDOI

• Does LLM Assistance Improve Healthcare Delivery? An Evaluation Using On-site Physicians and Laboratory Tests

  National Bureau of Economic Research · 2026-01-01

  reportOpen access1st authorCorresponding

  We deployed large language model (LLM) decision support for health workers at two outpatient clinics in Nigeria.For each patient, health workers drafted care plans that were optionally revised after LLM feedback.We compared unassisted and assisted plans using blinded randomized assessments by on-site physicians who evaluated and treated the same patients, as well as results from laboratory tests for common conditions.Academic physicians performed blinded retrospective reviews of a subset of notes.In response to LLM feedback, health workers changed their prescribing for more than half of the patients and reported high satisfaction with the recommendations, and retrospective academic reviewers rated LLM-assisted plans more favorably.However, on-site physicians observed little to no improvement in diagnostic alignment or treatment decisions.Laboratory testing showed mixed effects of LLM-assistance, which removed negative tests for malaria but added them for urinary tract infection and anemia, with no significant increase in the detection rates for the tested conditions.

  PublisherDOI

• Does LLM Assistance Improve Healthcare Delivery? An Evaluation Using On-site Physicians and Laboratory Tests

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Can Feedback from a Large Language Model Improve Health Care Quality?

  AEA Randomized Controlled Trials · 2025-01-23

  dataset
  PublisherDOI

Frequent coauthors

• Jonathan Gruber

  Massachusetts Institute of Technology

  26 shared

• Leila Agha

  Harvard University

  16 shared

• Laura H. Kwong

  University of California, Berkeley

  12 shared

• Giovanni Compiani

  University of Chicago

  12 shared

• Stephen P. Luby

  Stanford University

  11 shared

• Ahmed Mushfiq Mobarak

  Yale University

  11 shared

• Jade Benjamin‐Chung

  Stanford University

  9 shared

• Arjun K. Venkatesh

  9 shared