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Working Paper | HBS Working Paper Series | 2018

Digitizing Disclosure: The Case of Restaurant Hygiene Scores

by Weijia (Daisy) Dai and Michael Luca

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Abstract

Collaborating with Yelp and the City of San Francisco, we revisit a canonical example of quality disclosure by evaluating—and helping to redesign—the posting of restaurant hygiene scores on Yelp.com. Implementing a difference-in-differences strategy, we find that posting restaurant hygiene scores on Yelp leads to a 12% decrease in purchase intentions for restaurants with low scores (as predefined by the City) relative to those with higher scores. We then create a “hygiene alert”—a message that appears only for restaurants identified by the City as having “poor” operating conditions with “high-risk” hygiene violations (using the same low score threshold as above)—and find a further 9% decrease in purchase intentions. Moreover, the presence of an alert reduces the restaurant’s likelihood of getting a second alert. We conclude that disclosure policy should focus not only on what information to disclose, but also on how and where to design disclosure.

Keywords: Information; Web; Quality; Safety; Food; Consumer Behavior; Outcome or Result; Food and Beverage Industry;

Language: English Format: Print 30 pages SSRN Read Now

Citation:

Dai, Weijia (Daisy), and Michael Luca. "Digitizing Disclosure: The Case of Restaurant Hygiene Scores." Harvard Business School Working Paper, No. 18-088, February 2018.

About the Author

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Michael Luca
Lee J. Styslinger III Associate Professor of Business Administration
Negotiation, Organizations & Markets

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    Beshears, John, Michael Luca, Alister Martin, and Simin Gharib Lee. "Nudging Hand Hygiene Compliance at the Brigham and Women's Hospital." Harvard Business School Case 918-035, March 2018.  View Details
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  • Working Paper | HBS Working Paper Series | 2019

    Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change

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    Data from digital platforms have the potential to improve our understanding of gentrification and enable new measures of how neighborhoods change in close to real time. Combining data on businesses from Yelp with data on gentrification from the Census, Federal Housing Finance Agency, and Streetscore (an algorithm using Google Streetview), we find that gentrifying neighborhoods tend to have growing numbers of local groceries, cafes, restaurants, and bars, with little evidence of crowd-out of other types of businesses. For example, the entry of a new coffee shop into a zip code in a given year is associated with a 0.5 percent increase in housing prices. Moreover, Yelp measures of local business activity provide leading indicators for housing price changes and help to forecast which neighborhoods are gentrifying.

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

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    Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change." Harvard Business School Working Paper, No. 18-077, February 2018.  View Details
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