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Research Note—Improving the Efficiency of Course Bidding at Business Schools: Field and Laboratory Studies

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  • Aradhna Krishna

    (Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)

  • M. Utku Ünver

    (Department of Economics, University of Pittsburgh, Pittsburgh, Pennsylvania 15260)

Abstract

Registrars' offices at most universities face the daunting task of allocating course seats to students. Because demand exceeds supply for many courses, course allocation needs to be done equitably and efficiently. Many schools use bidding systems in which student bids are used both to infer preferences over courses and to determine student priorities for courses. However, this dual role of bids can result in course allocations not being market outcomes, and in unnecessary efficiency loss, which can potentially be avoided with the use of an appropriate market mechanism. We report the result of field and laboratory studies that compare a typical course-bidding mechanism with the alternate Gale-Shapley Pareto-dominant market mechanism. Results from the field study (conducted at the Ross School of Business, University of Michigan) suggest that using the latter could vastly improve efficiency of course allocation systems while facilitating market outcomes. Laboratory experiments with greater design control confirm the superior efficiency of the Gale-Shapley mechanism. The paper tests theory that has important practical implications because it has the potential to affect the learning experience of very large numbers of students enrolled in educational institutions.

Suggested Citation

  • Aradhna Krishna & M. Utku Ünver, 2008. "Research Note—Improving the Efficiency of Course Bidding at Business Schools: Field and Laboratory Studies," Marketing Science, INFORMS, vol. 27(2), pages 262-282, 03-04.
  • Handle: RePEc:inm:ormksc:v:27:y:2008:i:2:p:262-282
    DOI: 10.1287/mksc.1070.0297
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    1. Kojima, Fuhito, 2013. "Efficient resource allocation under multi-unit demand," Games and Economic Behavior, Elsevier, vol. 82(C), pages 1-14.
    2. Li, Mengling, 2020. "Ties matter: Improving efficiency in course allocation by allowing ties," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 354-384.
    3. Hoyer, B. & Stroh-Maraun, N., 2020. "Matching strategies of heterogeneous agents under incomplete information in a university clearinghouse," Games and Economic Behavior, Elsevier, vol. 121(C), pages 453-481.
    4. Eric Budish & Estelle Cantillon, 2012. "The Multi-unit Assignment Problem: Theory and Evidence from Course Allocation at Harvard," American Economic Review, American Economic Association, vol. 102(5), pages 2237-2271, August.
    5. Mariagiovanna Baccara & Ayse Imrohoroglu & Alistair J. Wilson & Leeat Yariv, 2012. "A Field Study on Matching with Network Externalities," American Economic Review, American Economic Association, vol. 102(5), pages 1773-1804, August.
    6. Wilfred Amaldoss & Sanjay Jain, 2010. "Reference Groups and Product Line Decisions: An Experimental Investigation of Limited Editions and Product Proliferation," Management Science, INFORMS, vol. 56(4), pages 621-644, April.
    7. Ortega, Josué, 2020. "Multi-unit assignment under dichotomous preferences," Mathematical Social Sciences, Elsevier, vol. 103(C), pages 15-24.
    8. Eric Budish & Gérard P. Cachon & Judd B. Kessler & Abraham Othman, 2017. "Course Match: A Large-Scale Implementation of Approximate Competitive Equilibrium from Equal Incomes for Combinatorial Allocation," Operations Research, INFORMS, vol. 65(2), pages 314-336, April.
    9. Franz Diebold & Haris Aziz & Martin Bichler & Florian Matthes & Alexander Schneider, 2014. "Course Allocation via Stable Matching," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(2), pages 97-110, April.
    10. Martin Bichler & Soeren Merting, 2021. "Randomized Scheduling Mechanisms: Assigning Course Seats in a Fair and Efficient Way," Production and Operations Management, Production and Operations Management Society, vol. 30(10), pages 3540-3559, October.
    11. Yan Chen & Peter Cramton & John A. List & Axel Ockenfels, 2021. "Market Design, Human Behavior, and Management," Management Science, INFORMS, vol. 67(9), pages 5317-5348, September.
    12. Pichl, Benjamin, 2019. "RAM: A collection of mechanisms for (indivisible) resource allocation in oTree," Journal of Behavioral and Experimental Finance, Elsevier, vol. 23(C), pages 133-137.
    13. Scott Fay & Juliano Laran, 2009. "Implications of Expected Changes in the Seller's Price in Name-Your-Own-Price Auctions," Management Science, INFORMS, vol. 55(11), pages 1783-1796, November.
    14. Eric Budish & Judd B. Kessler, 2016. "Can Market Participants Report their Preferences Accurately (Enough)?," NBER Working Papers 22448, National Bureau of Economic Research, Inc.
    15. Hoda Atef Yekta & Robert Day, 2020. "Optimization-based Mechanisms for the Course Allocation Problem," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 641-660, July.
    16. Yu Wang & Ernan Haruvy, 2013. "Tiers in One-Sided Matching Markets: Theory and Experimental Investigation," Management Science, INFORMS, vol. 59(6), pages 1458-1477, June.

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