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I investigate this question using a novel dataset combining reviews [from the website Yelp.com](from the website Yelp.com) and restaurant data from the Washington State Department of Revenue. Because Yelp prominently displays a restaurant's rounded average rating, I can identify the causal impact of Yelp ratings on demand with a regression discontinuity framework that exploits Yelp’s rounding thresholds. I present three findings about the impact of consumer reviews on the restaurant industry: (1) a one-star increase in Yelp rating leads to a 5-9 percent increase in revenue, (2) this effect is driven by independent restaurants; ratings do not affect restaurants with chain affiliation, and (3) chain restaurants have declined in market share as Yelp penetration has increased. This suggests that online consumer reviews substitute for more traditional forms of reputation. I then test whether consumers use these reviews in a way that is consistent with standard learning models. I present two additional findings: (4) consumers do not use all available information and are more responsive to quality changes that are more visible and (5) consumers respond more strongly when a rating contains more information. Consumer response to a restaurant’s average rating is affected by the number of reviews and whether the reviewers are certified as “elite” by Yelp, but is unaffected by the size of the reviewers’Yelp friends network.  \n† Harvard Business School, [mluca@hbs.edu](mluca@hbs.edu)  \n1 Introduction  \nTechnological advances over the past decade have led to the proliferation of consumer review websites such as [Yelp.com](Yelp.com), where consumers can share experiences about product quality. These reviews provide consumers with information about experience goods, which have quality that is observed only after consumption. With the click of a button, one can now acquire information from countless other consumers about products ranging from restaurants to movies to physicians. This paper provides empirical evidence on the impact of consumer reviews in the restaurant industry.  \nIt is a priori unclear whether consumer reviews will significantly affect markets for experience goods. On the one hand, existing mechanisms aimed at solving information problems are imperfect: chain affiliation reduces product differentiation, advertising can be costly, and expert reviews tend to cover small segments of a market.1 Consumer reviews may therefore complement or substitute for existing information sources. On the other hand, reviews can be noisy and difficult to interpret because they are based on subjective information reflecting the views of a non-representative sample of consumers. Further, consumers must actively seek out reviews, in contrast to mandatory disclosure and electronic commerce settings. 2  \nHow do online consumer reviews affect markets for experience goods? Using a novel data set consisting of reviews from the website [Yelp.com](Yelp.com) and revenue data from the Washington State Department of Revenue, I present three key findings: (1) a one-star increase in Yelp rating leads to a 5-9 percent increase in revenue, (2) this effect is driven by independent restaurants; ratings do not affect restaurants with chain affiliation, and (3) chain restaurants have declined in  \n1 For example, Zagat covers only about 5% of restaurants in Los Angeles, according to Jin and Leslie (2009) .  \n2 For an example of consumer reviews in electronic commerce, see Cabral and Hortacsu (2010) . For an example of the impact of mandatory ","cbCairvksYqqL9p8","https://ap.wps.com/l/cbCairvksYqqL9p8","pdf",934576,41,"English","# Abstract\n# Introduction\n## Mechanisms and uncertainty\n## Research design and data\n## Evidence and findings","[{\"question\":\"How does the paper measure the causal effect of Yelp ratings on restaurant demand?\",\"answer\":\"It combines Yelp review data with restaurant revenue records and uses a regression discontinuity framework around Yelp’s rating rounding thresholds to isolate exogenous rating variation.\"},{\"question\":\"What is the estimated impact of a one-star increase in Yelp rating on revenue?\",\"answer\":\"A one-star increase is associated with a 5%–9% increase in revenue, with the effect concentrated among independent restaurants.\"},{\"question\":\"How do consumers use Yelp information according to the paper’s learning tests?\",\"answer\":\"Consumers do not use all available information; they react more to quality changes that are more visible, and they respond more strongly when a rating contains more information.\"}]","Reviews, Reputation, and Revenue - The Case of Yelp.com - Working Paper 12-016 | PDF",103]