2025 Zayira Ray
Julius Silver Professor, Faculty of Arts and Science,
Professor of Economics, New York University
Research Associate, NBER
Part-Time Professor, University of Warwick
Research Fellow, CESifo
Spool Member, ThReD

Department of Economics
New York University,
19 West 4th Street
New York, NY 10012, U.S.A.
debraj.ray@nyu.edu, +1 (212)-998-8906.

Or use navbar and search icon at the top of this page to look for specific research areas and papers.
Oxford University Press, 2008. This book is now open-access; feel free to download a copy, and to buy the print version if you like the book.
Three Randomly Selected Papers
⟳ Re-randomize

What’s New in Development Economics?

The American Economist 44, 3-16, 2000.

Summary. This essay is meant to describe the current frontiers of development economics, as I see them. I might as well throw my hands up at the beginning and say there are too many frontiers. In recent years, the subject has made excellent use of economic theory, econometric methods, sociology, anthropology, political science and demography and has burgeoned into one of the liveliest areas of research in all the social sciences.

Decoding India’s Low Covid-19 Case Fatality Rate

(with Minu Philip and S. Subramanian),  Journal of Human Development and Capabilities 2227-51 (2021).

Summary. India’s case fatality rate (CFR) under covid-19 is strikingly low, trending from 3% or more, to a current level of under 1.8%. The world average rate is far higher. Several observers have noted that this difference is at least partly due to India’s younger age distribution. In this paper, we use age-specific fatality rates from comparison countries, coupled with India’s distribution of covid-19 cases to “predict” what India’s CFR would be with those age-specific rates. In most cases, those predictions are lower than India’s actual performance, suggesting that India’s CFR is, if anything, too high rather than too low.

Reinforcement Learning in Repeated Interaction Games

(with Jon Bendor and Dilip Mookherjee), Advances in Theoretical Economics 1, Issue 1, Article 3. Additional notes on extending the model to the probabilistic choice framework of Luce.

Summary. We study long run implications of reinforcement learning when two players repeatedly interact with one another over multiple rounds to play a finite action game. Within each round, the players play the game many successive times with a fixed set of aspirations used to evaluate payoff experiences as successes or failures. The probability weight on successful actions is increased, while failures result in players trying alternative actions in subsequent rounds. The learning rule is supplemented by small amounts of inertia and random perturbations to the states of players. Aspirations are adjusted across successive rounds on the basis of the discrepancy between the average payoff and aspirations in the most recently concluded round. We define and characterize pure steady states of this model, and establish convergence to these under appropriate conditions.