IDEAS home Printed from https://ideas.repec.org/a/aea/aecrev/v106y2016i5p114-18.html
   My bibliography  Save this article

Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy

Author

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

Abstract

The proliferation of big data makes it possible to better target city 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. A simple model suggests that open tournaments dominate consulting contracts when cities can tolerate risk and when there is enough labor with low opportunity costs. We also report on an inexpensive Boston-based restaurant tournament, which yielded algorithms that proved reasonably accurate when tested "out-of-sample" on hygiene inspections.

Suggested Citation

  • Edward L. Glaeser & Andrew Hillis & Scott Duke Kominers & Michael Luca, 2016. "Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy," American Economic Review, American Economic Association, vol. 106(5), pages 114-118, May.
  • Handle: RePEc:aea:aecrev:v:106:y:2016:i:5:p:114-18
    Note: DOI: 10.1257/aer.p20161027
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/articles?id=10.1257/aer.p20161027
    Download Restriction: no

    File URL: https://www.aeaweb.org/aer/app/10605/P2016_1027_app.pdf
    Download Restriction: no

    File URL: https://www.aeaweb.org/aer/ds/10605/P2016_1027_ds.zip
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ron Siegel, 2009. "All-Pay Contests," Econometrica, Econometric Society, vol. 77(1), pages 71-92, January.
    2. 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.
    3. 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.
    4. Kevin J. Boudreau & Karim R. Lakhani & Michael Menietti, 2016. "Performance responses to competition across skill levels in rank-order tournaments: field evidence and implications for tournament design," RAND Journal of Economics, RAND Corporation, vol. 47(1), pages 140-165, February.
    5. Yeon-Koo Che & Ian Gale, 2003. "Optimal Design of Research Contests," American Economic Review, American Economic Association, vol. 93(3), pages 646-671, June.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    3. 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.
    4. Gaglianone, Wagner Piazza & Giacomini, Raffaella & Issler, João Victor & Skreta, Vasiliki, 2022. "Incentive-driven inattention," Journal of Econometrics, Elsevier, vol. 231(1), pages 188-212.
    5. 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.
    6. Stevenson, Megan T. & Doleac, Jennifer, 2019. "Algorithmic Risk Assessment in the Hands of Humans," IZA Discussion Papers 12853, Institute of Labor Economics (IZA).
    7. Kevin J. Boudreau, 2018. "Amateurs Crowds & Professional Entrepreneurs as Platform Complementors," NBER Working Papers 24512, National Bureau of Economic Research, Inc.
    8. 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).
    9. 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.
    10. 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.
    11. Araz Taeihagh, 2017. "Crowdsourcing, Sharing Economies and Development," Journal of Developing Societies, , vol. 33(2), pages 191-222, June.
    12. 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.
    13. 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.
    14. Xiaoyan Wang & Xi Lin & Meng Li, 2021. "Aggregate Modeling and Equilibrium Analysis of the Crowdsourcing Market for Autonomous Vehicles," Papers 2102.07147, arXiv.org.
    15. 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.
    16. 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.
    17. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.
    18. 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.
    19. 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.
    20. Juan Carlos Perdomo, 2023. "The Relative Value of Prediction in Algorithmic Decision Making," Papers 2312.08511, arXiv.org.
    21. Araz Taeihagh, 2017. "Crowdsourcing: a new tool for policy-making?," Policy Sciences, Springer;Society of Policy Sciences, vol. 50(4), pages 629-647, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pavel Kireyev, 2016. "Markets for Ideas: Prize Structure, Entry Limits, and the Design of Ideation Contests," Harvard Business School Working Papers 16-129, Harvard Business School.
    2. Kaplan, Todd R. & Zamir, Shmuel, 2015. "Advances in Auctions," Handbook of Game Theory with Economic Applications,, Elsevier.
    3. Toomas Hinnosaar, 2016. "Dynamic common-value contests," Carlo Alberto Notebooks 479, Collegio Carlo Alberto.
    4. Daniel P. Gross, 2017. "Performance feedback in competitive product development," RAND Journal of Economics, RAND Corporation, vol. 48(2), pages 438-466, May.
    5. Shanglyu Deng & Hanming Fang & Qiang Fu & Zenan Wu, 2023. "Information Favoritism and Scoring Bias in Contests," PIER Working Paper Archive 23-002, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    6. John A. List & Daan van Soest & Jan Stoop & Haiwen Zhou, 2020. "On the Role of Group Size in Tournaments: Theory and Evidence from Laboratory and Field Experiments," Management Science, INFORMS, vol. 66(10), pages 4359-4377, October.
    7. Basteck, Christian & Klaus, Bettina & Kübler, Dorothea, 2021. "How lotteries in school choice help to level the playing field," Games and Economic Behavior, Elsevier, vol. 129(C), pages 198-237.
    8. Scott Duke Kominers & Alexander Teytelboym & Vincent P Crawford, 2017. "An invitation to market design," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 33(4), pages 541-571.
    9. Ehlers, Lars & Hafalir, Isa E. & Yenmez, M. Bumin & Yildirim, Muhammed A., 2014. "School choice with controlled choice constraints: Hard bounds versus soft bounds," Journal of Economic Theory, Elsevier, vol. 153(C), pages 648-683.
    10. Flip Klijn & Joana Pais & Marc Vorsatz, 2013. "Preference intensities and risk aversion in school choice: a laboratory experiment," Experimental Economics, Springer;Economic Science Association, vol. 16(1), pages 1-22, March.
    11. Yuen Leng Chow & Isa E. Hafalir & Abdullah Yavas, 2015. "Auction versus Negotiated Sale: Evidence from Real Estate Sales," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(2), pages 432-470, June.
    12. Segev, Ella & Sela, Aner, 2014. "Sequential all-pay auctions with noisy outputs," Journal of Mathematical Economics, Elsevier, vol. 50(C), pages 251-261.
    13. Afacan, Mustafa Og̃uz & Dur, Umut Mert, 2017. "When preference misreporting is Harm[less]ful?," Journal of Mathematical Economics, Elsevier, vol. 72(C), pages 16-24.
    14. Moldovanu, Benny & Sela, Aner, 2006. "Contest architecture," Journal of Economic Theory, Elsevier, vol. 126(1), pages 70-96, January.
    15. Christian Terwiesch & Yi Xu, 2008. "Innovation Contests, Open Innovation, and Multiagent Problem Solving," Management Science, INFORMS, vol. 54(9), pages 1529-1543, September.
    16. Yu, Han, 2020. "Am I the big fish? The effect of ordinal rank on student academic performance in middle school," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 18-41.
    17. Kóczy Á., László, 2009. "Központi felvételi rendszerek. Taktikázás és stabilitás [Central admission systems. Stratagems and stability]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 422-442.
    18. James Boudreau & Vicki Knoblauch, 2013. "Preferences and the price of stability in matching markets," Theory and Decision, Springer, vol. 74(4), pages 565-589, April.
    19. Kräkel, Matthias & Szech, Nora & von Bieberstein, Frauke, 2014. "Externalities in recruiting," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 123-135.
    20. Todd Kaplan & David Wettstein, 2015. "The optimal design of rewards in contests," Review of Economic Design, Springer;Society for Economic Design, vol. 19(4), pages 327-339, December.

    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D86 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Economics of Contract Law
    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • R51 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Finance in Urban and Rural Economies

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:aecrev:v:106:y:2016:i:5:p:114-18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.