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Guiding School-Choice Reform through Novel Applications of Operations Research

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  • Peng Shi

    (MIT Operations Research Center, Cambridge, Massachusetts 02139)

Abstract

In January 2012, Boston initiated a school-assignment reform. After attending several community meetings, I formulated the reform as an optimization problem of finding school-choice menus and priorities that induce the best combination of equity of access, proximity to home, predictability, and community cohesion. Using previous school-choice data, I fit a discrete choice model of how families select schools, and created a simulation engine that estimated a variety of outcome measures for any plan. I also proposed several new plans, and helped Boston Public Schools analyze a short list of plans in detail. In March 2013, Boston adopted one of these plans, which will affect the 9,500 children who apply to Boston elementary schools each year.

Suggested Citation

  • Peng Shi, 2015. "Guiding School-Choice Reform through Novel Applications of Operations Research," Interfaces, INFORMS, vol. 45(2), pages 117-132, April.
    • Handle: RePEc:inm:orinte:v:45:y:2015:i:2:p:117-132
    DOI: 10.1287/inte.2014.0781
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    References listed on IDEAS

    as
    1. Itai Ashlagi & Peng Shi, 2014. "Improving Community Cohesion in School Choice via Correlated-Lottery Implementation," Operations Research, INFORMS, vol. 62(6), pages 1247-1264, December.
    2. Atila Abdulkadiroglu & Parag A. Pathak & Alvin E. Roth & Tayfun Sönmez, 2006. "Changing the Boston School Choice Mechanism," Levine's Bibliography 122247000000001022, UCLA Department of Economics.
    3. Hausman, Jerry A. & Ruud, Paul A., 1987. "Specifying and testing econometric models for rank-ordered data," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 83-104.
    4. Beggs, S. & Cardell, S. & Hausman, J., 1981. "Assessing the potential demand for electric cars," Journal of Econometrics, Elsevier, vol. 17(1), pages 1-19, September.
    5. Parag A. Pathak & Peng Shi, 2014. "Demand Modeling, Forecasting, and Counterfactuals, Part I," NBER Working Papers 19859, National Bureau of Economic Research, Inc.
    6. Alvin E. Roth, 1982. "The Economics of Matching: Stability and Incentives," Mathematics of Operations Research, INFORMS, vol. 7(4), pages 617-628, November.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Estelle Cantillon, 2017. "Broadening the market design approach to school choice," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 33(4), pages 613-634.
    2. Pathak, Parag A. & Shi, Peng, 2021. "How well do structural demand models work? Counterfactual predictions in school choice," Journal of Econometrics, Elsevier, vol. 222(1), pages 161-195.
    3. Hai Nguyen & Thành Nguyen & Alexander Teytelboym, 2021. "Stability in Matching Markets with Complex Constraints," Management Science, INFORMS, vol. 67(12), pages 7438-7454, December.
    4. Biró, Péter & Gudmundsson, Jens, 2021. "Complexity of finding Pareto-efficient allocations of highest welfare," European Journal of Operational Research, Elsevier, vol. 291(2), pages 614-628.
    5. Peng Shi, 2022. "Optimal Priority-Based Allocation Mechanisms," Management Science, INFORMS, vol. 68(1), pages 171-188, January.
    6. Ali Aouad & Jacob Feldman & Danny Segev, 2023. "The Exponomial Choice Model for Assortment Optimization: An Alternative to the MNL Model?," Management Science, INFORMS, vol. 69(5), pages 2814-2832, May.
    7. Nikhil Agarwal & Eric Budish, 2021. "Market Design," NBER Working Papers 29367, National Bureau of Economic Research, Inc.
    8. Johnson, Michael P. & Midgley, Gerald & Chichirau, George, 2018. "Emerging trends and new frontiers in community operational research," European Journal of Operational Research, Elsevier, vol. 268(3), pages 1178-1191.
    9. Jiafeng Chen, 2021. "Nonparametric Treatment Effect Identification in School Choice," Papers 2112.03872, arXiv.org, revised Oct 2023.

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