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.

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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
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Conveying Value Via Categories

(with Paula Onuchic), October 2019, revised December 2022. Forthcoming, Theoretical Economics.

A sender is about to come into possession of an object of heterogeneous quality. Prior to knowing that quality, she commits to a categorization. That is, she partitions the set of qualities into  subsets — some possibly singletons — and verifiably commits to reveal the element in which the quality belongs. The categories  must be monotone. Our main results fully describe the profit-maximizing categorization  for any pair of priors over object quality held by sender and receiver. We apply these results to the design of educational grades.

Maximality in the Farsighted Stable Set

(with Rajiv Vohra)   Econometrica 87(5), 1763–1779 Online Appendix.

Summary. The stable set of von Neumann and Morgenstern can be extended to cover farsighted coalitional deviations, as proposed by Harsanyi (1974), and more recently reformulated by Ray and Vohra (2015). However,  while coalitional deviations improve on existing outcomes, coalitions might do even better by moving elsewhere. Or other coalitions might intervene to impose their favored moves. We show that every farsighted stable set satisfying some reasonable, and easily verifiable, properties is unaffected by the imposition of this stringent maximality requirement. 

Gender Differentials in Eye Care: Access and Treatment

(with Rajshri Jayaraman and Shing-Yi Wang), Economic and Political Weekly 49 No. 25, June 21, 2014. 

Summary. Two potential sources of gender bias in health care are (a) females access treatment later than males and (b) they receive differential care at the medical facility. We explore both of these for eye care at a large Indian medical facility.  At presentation, women have worse diagnoses than men for indicators of symptomatic illness, such as myopia and cataract. There is no difference in treatment.