I am an economist at the University of Pennsylvania's Wharton School. I specialize in economic theory and its applications to other areas of economics, to science, and to business. I am particularly interested in market design, selection markets, social science genetics, and how organizations can use online and offline experimentation to increase productivity.
I received my Ph.D. in Economics from Harvard University, advised by Alvin Roth, and was a 2016 Sloan Foundation Fellow.
Eduardo Azevedo
John M. Bendheim, W'40 and Thomas L. Bendheim,
WG'90 Professor of Business Economics and Public Policy
Recent proposals to tax unrealized capital gains 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 full 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 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 limited risk-sharing benefits due to the absence of tax credits in case of down rounds.
The predictive power of genetic data has been increasing rapidly and is reaching levels of clinical utility for many diseases. Meanwhile, many jurisdictions have banned insurers from utilizing genetic information. This has led to concerns that further improvements in genetic prediction will lead to adverse selection. We make three contributions to this debate. First, we develop a method to measure the amount of selection in an insurance market where consumers have access to current genetic prediction technology. Second, we extend the method to estimate the amount of selection given expected improvements in genetic prediction technology. Third, using the UK Biobank dataset with nearly 500,000 genotyped individuals, we apply the method to the critical illness insurance market. We find that expected improvements in genetic prediction are likely to lead to unsustainably high levels of selection and thus threaten the viability of the market. We discuss policy implications.
The first-order approach (FOA) is the main tool in the study of the pure moral hazard principal-agent problem. Although many existing results rely on the FOA, its validity has been established only under relatively restrictive assumptions. We contribute three main findings. First, we demonstrate in a broad array of examples that the FOA frequently fails when the agent’s reservation utility is low (such as in principal-optimal contracts). However, the FOA holds when the agent’s reservation utility is at least moderately high (such as in competitive settings where agents receive high rents). Second, our main theorem formally shows that the FOA is valid in a standard limited liability model when the agent’s reservation utility is sufficiently high. The theorem also establishes existence and uniqueness of the optimal contract. Third, we use the FOA to derive tractable optimal contracts across a broad array of settings. These contracts are both simple and intuitive, and under log utility, they are piecewise linear for numerous common output distributions (including Gaussian, exponential, binomial, Gamma, and Laplace).
