. Information , Bias , and Efficiency in Expert Evaluation :

Experts may have more information about the potential of projects in their area, but may also be biased. This paper develops a framework for separately identifying the effects of bias and information on expert evaluation and applies it in the context of peer review at the National Institutes of Health (NIH). I find that while reviewers are biased in favor of applications from their own subfield, they are also more informed about their quality. On net, the benefits of information tend to dominate, indicating that policies designed to reduce conflicts of interest may also reduce the quality of funding decisions. ∗I am very grateful to Pierre Azoulay, Michael Greenstone, and especially David Autor, for detailed feedback on this project. I also thank Jason Abaluck, Daron Acemoglu, Leila Agha, Josh Angrist, David Berger, David Chan, Esther Duflo, Brigham Frandsen, Richard Freeman, Bob Gibbons, Nathan Hendren, Xiao Yu May Wang, Ziad Obermeyer, Amanda Pallais, Chris Palmer, Michael Powell, Pian Shu, Scott Stern, Heidi Williams, and numerous participants at Berkeley Haas, Boston University, Columbia GSB, Dartmouth, Harvard Business School, Harvard Kennedy School, the London School of Economics, MIT, the NBER Productivity Lunch, and Northwestern Kellogg, for helpful comments and suggestions. I am grateful to George Chacko, Raviv Murciano-Goroff, Joshua Reyes, and James Vines for assistance with data. All errors are my own. †[email protected]