About

Thayer Morrill is a Professor of Economics at NC State University, serving within the Department of Economics. He holds a Ph.D. in Economics from the University of Maryland, earned in 2008, and has academic backgrounds in Mathematics from the University of Wisconsin, Madison, and Miami University. His areas of expertise include Market Design, School Assignment, and Auction Theory. Morrill's work bridges theoretical and applied economics to address real-world problems, particularly in the context of school choice and market mechanisms. He is actively involved in graduate faculty activities and contributes to the academic community through his research and teaching.

Research topics

• Computer Science
• Mathematics
• Political Science
• Microeconomics
• Psychology
• Law
• Quantum mechanics
• Operations research
• Economics
• Mathematics education
• Physics
• Developmental psychology

Selected publications

• The attraction of magnet schools: Evidence from embedded lotteries in school assignment

  Economics of Education Review · 2025-07-08

  article
  PublisherDOI

• Desirable Rankings

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Advancing Early Disease Detection through Convolutional Neural Network Architectures in Medical Image Analysis

  Research Square · 2025-09-19

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Top trading cycles

  Journal of Mathematical Economics · 2024-04-16 · 6 citations

  article1st authorCorresponding
  PublisherDOI

• Partitionable choice functions and stability

  Social Choice and Welfare · 2024-07-09 · 3 citations

  article
  PublisherDOI

• The Attraction of Magnet Schools: Evidence from Embedded Lotteries in School Assignment

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Interview hoarding

  Theoretical Economics · 2023-01-01 · 5 citations

  articleOpen accessSenior author

  Many centralized matching markets are preceded by interviews between participants, including the residency matches between doctors and hospitals. Due to the COVID‐19 pandemic, interviews in the National Resident Matching Program were switched to a virtual format, which resulted in a dramatic and asymmetric decrease in the cost of accepting interview invitations. We study the impact of an increase in the number of doctors' interviews on their final matches. We show analytically that if doctors can accept more interviews, but hospitals do not increase the number of interviews they offer, then no doctor who would have matched in the setting with more limited interviews is better off and many doctors are potentially harmed. This adverse effect is the result of what we call interview hoarding . We characterize optimal mitigation strategies for special cases and use simulations to extend these insights to more general settings.

  PublisherDOI

• The Attraction of Magnet Schools: Evidence from Embedded Lotteries in School Assignment

  2023 · 6 citations

  • Computer Science
  • Physics
  • Computer Science
  PublisherDOI

• Desirable Rankings

  arXiv (Cornell University) · 2022-05-24

  preprintOpen access

  We study the problem of aggregating individual preferences over alternatives into a collective ranking. A distinctive feature of our setting is that agents are matched to alternatives. Applications include rankings of colleges or academic journals. The foundation of our approach is that alternatives agents desire -- that is, those they rank above their match -- should also be ranked higher socially. We introduce axioms to formalize this idea and call rankings that satisfy them desirable. We develop an algorithm to construct desirable rankings and prove that, as the market becomes large, desirable rankings converge to the true underlying ranking of the alternatives by quality. We support this convergence result through simulations and demonstrate the practical usefulness of our approach by ranking Chilean medical programs with data from their centralized admission system. Finally, we compare performance and show that our approach outperforms two benchmarks: revealed preference rankings and Borda counts.

  PublisherOA PDFDOI

• Desirable Rankings

  Proceedings of the 23rd ACM Conference on Economics and Computation · 2022-07-12

  article1st authorCorresponding

  We consider the problem of aggregating individual preferences over alternatives into a social ranking. A key feature of the problems that we consider---and the one that allows us to obtain positive results, in contrast to negative results such as Arrow's Impossibililty Theorem---is that the alternatives to be ranked are outcomes of a competitive process. Examples include rankings of colleges or academic journals. The foundation of our ranking method is that alternatives that an agent desires---those that they have been rejected by---should be ranked higher than the one they receive. We provide a mechanism to produce a social ranking given any preference profile and outcome assignment, and characterize this ranking as the unique one that satisfies certain desirable axioms.

  PublisherDOI

Frequent coauthors

• Umut Dur

  North Carolina State University

  11 shared

• Robert G. Hammond

  10 shared

• Peter Troyan

  University of Virginia

  5 shared

• William Phan

  3 shared

• Melinda Sandler Morrill

  North Carolina State University

  3 shared

• Lawrence M. Ausubel

  University of Maryland, College Park

  3 shared

• Vikram Manjunath

  3 shared

• Colleen Paeplow

  Wake County Public School System

  2 shared