About

Eduardo Azevedo is a Professor of Business Economics and Public Policy at the University of Pennsylvania's Wharton School, holding the titles of John M. Bendheim Professor and Thomas L. Bendheim Professor. He specializes in economic theory and its applications to various areas of economics, science, and business. His research interests include market design, selection markets, social science genetics, and how organizations can utilize online and offline experimentation to enhance productivity. Azevedo received his Ph.D. in Economics from Harvard University, where he was advised by Alvin Roth, and was recognized as a Sloan Foundation Fellow in 2016. His work focuses on applying microeconomic theory to real-world problems, including the design and analysis of markets and institutions, with an emphasis on understanding strategic interactions, market failures, and mechanisms for improving economic efficiency.

Research topics

• Computer Science
• Mathematics
• Mathematical economics
• Econometrics
• Microeconomics
• Economics
• Algorithm
• Mathematical optimization
• Marketing
• Statistics
• Macroeconomics
• Industrial organization
• Business
• Telecommunications

Selected publications

• Collusive Content in Corporate Communications

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Dilution vs. Risk Taking: Capital Gains Taxes and Entrepreneurship

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Dilution vs. risk taking: capital gains taxes and entrepreneurship

  Universität Zürich, ZORA · 2025-12-01

  articleOpen access1st authorCorresponding

  Recent proposals to tax unrealized capital gains or wealth have sparked a debate about their impact on entrepreneurship. We show that accrual-based taxation creates two opposing effects: successful founders face greater dilution from advance tax payments, whereas unsuccessful founders receive tax credits that effectively provide insurance. Using comprehensive new data on U.S. venture capital deals, we find that founder returns remain extremely skewed, with 84% receiving zero exit value while the top 2% capture 80% of total value. Moving from current realization-based to accrual-based taxation would reduce founder ownership at exit by 25% on average but would also increase the fraction receiving positive payoffs from 16% to 47% when tax credits are refunded. Embedding these distributions in a dynamic career choice model, we find that founders with no or moderate risk aversion prefer the current realization-based tax system, while more risk-averse founders prefer accrual-based taxation. We estimate that a 2% annual wealth tax has a similar impact on dilution as taxing unrealized capital gains, but produces no risk-sharing benefits due to the absence of tax credits in case of down rounds.

  PublisherDOI

• Genetic Prediction and Adverse Selection

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Genetic prediction and adverse selection

  medRxiv · 2025-01-20

  preprintOpen access1st author

  Abstract The predictive power of genetic data has increased dramatically and is reaching levels of clinical utility for many diseases. Meanwhile, many countries have banned insurers from utilizing genetic information, despite concerns about adverse selection. We make three contributions. First, we develop a method to measure the amount of selection in an insurance market where consumers have access to the current genetic prediction technology. Second, we extend the method to measure selection under expected future prediction technology. Third, using the rich UK Biobank dataset with nearly 500,000 genotyped individuals linked to electronic health records, we apply the method to the critical illness insurance market. We find potentially crippling selection under expected future technology. A robustness analysis using a calibrated equilibrium model of adverse selection yields similar results, with the equilibrium quantity unraveling to zero under future genetic prediction technology.

  PublisherOA PDFDOI

• The A/B testing problem with Gaussian priors

  Journal of Economic Theory · 2023 · 5 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Econometrics
  PublisherDOI

• Channel Auctions

  Management Science · 2020 · 6 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Microeconomics

  Standard auction formats feature either an upper bound on the equilibrium price that descends over time (as in the Dutch auction) or a lower bound on the equilibrium price that ascends over time (as in the English auction). We show that in some settings with costly information acquisition, auctions featuring both (viz., a narrowing channel of prices) outperform the standard formats. This Channel auction preserves some of benefits of both the English (truthful revelation) and Dutch (security for necessary information acquisition) auctions. Natural applications include housing, online auction sites like eBay, recording transactions on blockchains, and spectrum rights. This paper was accepted by Joshua Gans, business strategy.

  PublisherDOI

• A/B Testing with Fat Tails

  Journal of Political Economy · 2020 · 53 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Econometrics

  We propose a new framework for optimal experimentation, which we term the “A/B testing problem.” Our model departs from the existing literature by allowing for fat tails. Our key insight is that the optimal strategy depends on whether most gains accrue from typical innovations or from rare, unpredictable large successes. If the tails of the unobserved distribution of innovation quality are not too fat, the standard approach of using a few high-powered “big” experiments is optimal. However, if the distribution is very fat tailed, a “lean” strategy of trying more ideas, each with possibly smaller sample sizes, is preferred. Our theoretical results, along with an empirical analysis of Microsoft Bing’s EXP platform, suggest that simple changes to business practices could increase innovation productivity.

  PublisherDOI

• Empirical Bayes Estimation of Treatment Effects with Many A/B Tests: An Overview

  AEA Papers and Proceedings · 2019-05-01 · 9 citations

  article1st authorCorresponding

  The use of large-scale experimentation to screen product innovations is increasingly common. This is a practical guide on how to use treatment effect estimates from a large number of experiments to improve estimates of the effects of each experiment. When thousands of new features are A/B tested by internet companies, the winners tend to be a combination of good features and features that got lucky experimental draws. Empirical Bayes methods are a commonly used tool in statistics to separate good features from lucky draws. We give a user-friendly overview of both classic and recent approaches to this problem.

  PublisherDOI

• Market Failure in Kidney Exchange

  American Economic Review · 2019-10-29 · 62 citations

  articleOpen access

  We show that kidney exchange markets suffer from market failures whose remedy could increase transplants by 30 to 63 percent. First, we document that the market is fragmented and inefficient; most transplants are arranged by hospitals instead of national platforms. Second, we propose a model to show two sources of inefficiency: hospitals only partly internalize their patients’ benefits from exchange, and current platforms suboptimally reward hospitals for submitting patients and donors. Third, we calibrate a production function and show that individual hospitals operate below efficient scale. Eliminating this inefficiency requires either a mandate or a combination of new mechanisms and reimbursement reforms. (JEL D24, D47, I11)

  PublisherOA PDFDOI

Frequent coauthors

• Ömer Karaduman

  39 shared

• Clayton R. Featherstone

  Baylor University

  35 shared

• Itai Ashlagi

  Stanford University

  22 shared

• Nikhil Agarwal

  19 shared

• Nikhil Agarwal

  16 shared

• E. Glen Weyl

  13 shared

• Daniel Gottlieb

  7 shared

• Justin M. Rao

  5 shared

Awards & honors

• 2016 Sloan Foundation Fellow