Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy

Edward  Glaeser; Andrew  Hillis; Scott Duke Kominers; Michael  Luca

Article | American Economic Review: Papers and Proceedings

Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy

by Edward Glaeser, Andrew Hillis, Scott Duke Kominers and 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.

Keywords: user-generated content; operations; tournaments; policy-making; Machine learning; online platforms; Data and Data Sets; Mathematical Methods; City; Infrastructure; Business Processes; Government and Politics;

Format: Print

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Citation:

Glaeser, Edward, Andrew Hillis, Scott Duke Kominers, and Michael Luca. "Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 114–118.

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Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy

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