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Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy

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  • 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

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    Cited by:

    1. Susan Athey & Michael Luca, 2019. "Economists (and Economics) in Tech Companies," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 209-230, Winter.
    2. Juan Manuel Ponce Romero & Stephen H. Hallett & Simon Jude, 2017. "Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector," Sustainability, MDPI, vol. 9(12), pages 1-19, November.
    3. Gaglianone, Wagner Piazza & Giacomini, Raffaella & Issler, João Victor & Skreta, Vasiliki, 2022. "Incentive-driven inattention," Journal of Econometrics, Elsevier, vol. 231(1), pages 188-212.
    4. Stevenson, Megan T. & Doleac, Jennifer, 2019. "Algorithmic Risk Assessment in the Hands of Humans," IZA Discussion Papers 12853, Institute of Labor Economics (IZA).
    5. Kevin J. Boudreau, 2018. "Amateurs Crowds & Professional Entrepreneurs as Platform Complementors," NBER Working Papers 24512, National Bureau of Economic Research, Inc.
    6. Jane Andrew & Max Baker, 2021. "The General Data Protection Regulation in the Age of Surveillance Capitalism," Journal of Business Ethics, Springer, vol. 168(3), pages 565-578, January.
    7. Araz Taeihagh, 2017. "Crowdsourcing, Sharing Economies and Development," Journal of Developing Societies, , vol. 33(2), pages 191-222, June.
    8. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
    9. SJ, Balaji & Babu, Suresh Chandra & Pal, Suresh, 2021. "Understanding Science and Policy Making in Agriculture: A Machine Learning Application for India," 2021 Conference, August 17-31, 2021, Virtual 315227, International Association of Agricultural Economists.
    10. Maria R. Ibanez & Michael W. Toffel, 2020. "How Scheduling Can Bias Quality Assessment: Evidence from Food-Safety Inspections," Management Science, INFORMS, vol. 66(6), pages 2396-2416, June.
    11. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    12. Fiona Burlig & Christopher Knittel & David Rapson & Mar Reguant & Catherine Wolfram, 2020. "Machine Learning from Schools about Energy Efficiency," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 7(6), pages 1181-1217.
    13. Galdo, Virgilio & Li, Yue & Rama, Martin, 2021. "Identifying urban areas by combining human judgment and machine learning: An application to India," Journal of Urban Economics, Elsevier, vol. 125(C).
    14. Juan Carlos Perdomo, 2023. "The Relative Value of Prediction in Algorithmic Decision Making," Papers 2312.08511, arXiv.org.
    15. Araz Taeihagh, 2017. "Crowdsourcing: a new tool for policy-making?," Policy Sciences, Springer;Society of Policy Sciences, vol. 50(4), pages 629-647, December.
    16. Suying Gao & Xiangshan Jin & Ye Zhang, 2021. "User Participation Behavior in Crowdsourcing Platforms: Impact of Information Signaling Theory," Sustainability, MDPI, vol. 13(11), pages 1-19, June.
    17. Hayakawa, Kazunobu & Keola, Souknilanh & Urata, Shujiro, 2022. "How effective was the restaurant restraining order against COVID-19? A nighttime light study in Japan," Japan and the World Economy, Elsevier, vol. 63(C).
    18. Xiaoyan Wang & Xi Lin & Meng Li, 2021. "Aggregate Modeling and Equilibrium Analysis of the Crowdsourcing Market for Autonomous Vehicles," Papers 2102.07147, arXiv.org.
    19. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.
    20. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    21. Brandt, Tobias & Wagner, Sebastian & Neumann, Dirk, 2021. "Prescriptive analytics in public-sector decision-making: A framework and insights from charging infrastructure planning," European Journal of Operational Research, Elsevier, vol. 291(1), pages 379-393.

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    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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