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• Article | Quantitative Marketing and Economics | September 2018

  Aggregation of Consumer Ratings: An Application to Yelp.com

  Weijia Dai, Ginger Jin, Jungmin Lee and Michael Luca

  Because consumer reviews leverage the wisdom of the crowd, the way in which they are aggregated is a central decision faced by platforms. We explore this "rating aggregation problem" and offer a structural approach to solving it, 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 to restaurant reviews from Yelp.com, we construct an adjusted average rating and show that even a simple algorithm can lead to large information efficiency gains relative to the arithmetic average.

  Keywords: user generated content; crowdsourcing; e-commerce; learning; Yelp; Social and Collaborative Networks; Information; Internet; Mathematical Methods;

  Citation:

  Dai, Weijia, Ginger Jin, Jungmin Lee, and Michael Luca. "Aggregation of Consumer Ratings: An Application to Yelp.com." Quantitative Marketing and Economics 16, no. 3 (September 2018): 289–339.  View Details

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• Working Paper | 2018

  Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change

  Edward L. Glaeser, Hyunjin Kim and Michael Luca

  We demonstrate that data from digital platforms such as Yelp have the potential to improve our understanding of gentrification, both by providing data in close to real time (i.e., nowcasting and forecasting) and by providing additional context about how the local economy is changing. Combining Yelp and Census data, we find that gentrification, as measured by changes in the educational, age, and racial composition within a zip code, is strongly associated with increases in the numbers of grocery stores, cafes, restaurants, and bars, with little evidence of crowd-out of other categories of businesses. We also find that changes in the local business landscape is a leading indicator of housing price changes and that the entry of Starbucks (and coffee shops more generally) into a neighborhood predicts gentrification. Each additional Starbucks that enters a zip code is associated with a 0.5% increase in housing prices.

  Keywords: Geographic Location; Local Range; Transition; Data and Data Sets; Measurement and Metrics; Forecasting and Prediction;

  Citation:

  Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change." NBER Working Paper Series, No. 24952, August 2018.  View Details

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• Working Paper | HBS Working Paper Series | 2018

  Complex Disclosure

  Ginger Zhe Jin, Michael Luca and Daniel Martin

  Disclosure policies have the potential to help consumers and make markets more efficient. Yet, the effectiveness of disclosure policies can be undermined if firms strategically make unfavorable information unnecessarily complicated to understand. To explore the incentives for using complexity in disclosure, we implement a game of mandatory disclosure where senders are required to report their private information truthfully but can choose how complex to make their reports. We find that senders use complex disclosure over half the time, and most of this obfuscation is profitable because receivers make systematic mistakes in assessing complex reports. Stated beliefs suggest that receivers correctly infer the strategic implications of complexity but are overconfident about their ability to assess complex reports.

  Keywords: Corporate Disclosure; Policy; Information; Complexity; Strategy;

  Citation:

  Jin, Ginger Zhe, Michael Luca, and Daniel Martin. "Complex Disclosure." Harvard Business School Working Paper, No. 18-105, May 2018.  View Details

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