NBER Working Papers by Michael Luca

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Working Papers

March 2016Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy
with Edward L. Glaeser, Andrew Hillis, Scott Duke Kominers: w22124
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 coordi...
December 2015Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life
with Edward L. Glaeser, Scott Duke Kominers, Nikhil Naik: w21778
New, “big” data sources allow measurement of city characteristics and outcome variables higher frequencies and finer geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big data has the most value for the study of cities when it allows measurement of the previously opaque, or when it can be coupled with exogenous shocks to people or place. We describe a number of new urban data sources and illustrate how they can be used to improve the study and function of cities. We first show how Google Street View images can be used to predict income in New York City, suggesting that similar image data can be used to map wealth and poverty in previously unmeasured areas of the developing world. We then discuss how survey techniques can b...
April 2015Is No News (Perceived as) Bad News? An Experimental Investigation of Information Disclosure
with Ginger Zhe Jin, Daniel Martin: w21099
A central prediction of information economics is that market forces can lead businesses to voluntarily provide information about the quality of their products, yet little voluntary disclosure is observed in the field. In this paper, we demonstrate that the inconsistency between theory and reality is driven by a fundamental failure in consumer inferences when sellers withhold information. Using a series of laboratory experiments, we implement a simple disclosure game in which senders can verifiably report quality to receivers. We find that senders disclose less often than equilibrium would predict. Receivers are not sufficiently skeptical about undisclosed information – they underestimate the extent to which no news is bad news. Senders generally take advantage of receiver mistakes. We find...
November 2012Optimal Aggregation of Consumer Ratings: An Application to
with Weijia Dai, Ginger Z. Jin, Jungmin Lee: w18567
Consumer review websites leverage the wisdom of the crowd, with each product being reviewed many times (some with more than 1,000 reviews). Because of this, the way in which information is aggregated is a central decision faced by consumer review websites. Given a set of reviews, what is the optimal way to construct an average rating? We offer a structural approach to answering this question, allowing for (1) reviewers to vary in stringency and accuracy, (2) reviewers to be influenced by existing reviews, and (3) product quality to change over time. Applying this approach to restaurant reviews from, we construct optimal ratings for all restaurants and compare them to the arithmetic averages displayed by Yelp. Depending on how we interpret the downward trend of reviews within a r...

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