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Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy

Author

Listed:
  • Edward L. Glaeser
  • Andrew Hillis
  • Scott Duke Kominers
  • Michael Luca

Abstract

Can open tournaments improve the quality of city services? The proliferation of big data makes it possible to use predictive analytics to better target services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to crowdsource competence by making data public and offering a reward for the best algorithm. This paper provides a simple model suggesting that open tournaments dominate consulting contracts when cities have a reasonable tolerance for risk and when there is enough labor with low opportunity costs of time. We also illustrate how tournaments can be successful, by reporting on a Boston-based restaurant hygiene prediction tournament that we helped coordinate. The Boston tournament yielded algorithms—at low cost—that proved reasonably accurate when tested “out-of-sample” on hygiene inspections occurring after the algorithms were submitted. We draw upon our experience in working with Boston to provide practical suggestions for governments and other organizations seeking to run prediction tournaments in the future.

Suggested Citation

  • Edward L. Glaeser & Andrew Hillis & Scott Duke Kominers & Michael Luca, 2016. "Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy," NBER Working Papers 22124, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:22124
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    References listed on IDEAS

    as
    1. Lazear, Edward P & Rosen, Sherwin, 1981. "Rank-Order Tournaments as Optimum Labor Contracts," Journal of Political Economy, University of Chicago Press, vol. 89(5), pages 841-864, October.
    2. Atila Abdulkadiroğlu & Parag A. Pathak & Alvin E. Roth, 2005. "The New York City High School Match," American Economic Review, American Economic Association, vol. 95(2), pages 364-367, May.
    3. Ron Siegel, 2009. "All-Pay Contests," Econometrica, Econometric Society, vol. 77(1), pages 71-92, January.
    4. Yeon-Koo Che & Ian Gale, 2003. "Optimal Design of Research Contests," American Economic Review, American Economic Association, vol. 93(3), pages 646-671, June.
    5. Atila Abdulkadiroğlu & Parag A. Pathak & Alvin E. Roth & Tayfun Sönmez, 2005. "The Boston Public School Match," American Economic Review, American Economic Association, vol. 95(2), pages 368-371, May.
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    Cited by:

    1. repec:gam:jsusta:v:9:y:2017:i:12:p:2160-:d:120044 is not listed on IDEAS
    2. Kevin J. Boudreau, 2018. "Amateurs Crowds & Professional Entrepreneurs as Platform Complementors," NBER Working Papers 24512, National Bureau of Economic Research, Inc.
    3. Wagner Piazza Gaglianone & Raffaella Giacomini & João Victor Issler & Vasiliki Skreta, 2018. "Incentive-driven Inattention," Working Papers Series 485, Central Bank of Brazil, Research Department.
    4. repec:kap:policy:v:50:y:2017:i:4:d:10.1007_s11077-017-9303-3 is not listed on IDEAS

    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D04 - Microeconomics - - General - - - Microeconomic Policy: Formulation; Implementation; Evaluation
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • L88 - Industrial Organization - - Industry Studies: Services - - - Government Policy
    • M50 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - General
    • R5 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis

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