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Consumer review websites such as Yelp.com leverage the wisdom of the crowd, with each product being reviewed many times (some with more than 1000 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 (some reviewers tend to leave worse reviews on average) and accuracy (some reviewers are more erratic than others), (2) reviewers to be influenced by existing reviews, and (3) product quality to change over time. We apply this approach to reviews from Yelp.com to derive optimal ratings for each restaurant (in contrast with the arithmetic average displayed by Yelp). Because we have the history of reviews for each restaurant and many reviews left by each reviewer, we are able to identify these factors using variation in ratings within and across reviewers and restaurants. Using our estimated parameters, we construct optimal ratings for all restaurants on Yelp, and compare them to the arithmetic averages displayed by Yelp. As of the end of our sample, a conservative finding is that roughly 25-27% of restaurants are more than 0.15 stars away from the optimal rating, and 8-10% of restaurants are more than 0.25 stars away from the optimal rating. This suggests that large gains could be made by implementing optimal ratings. Much of the gains come from our method responding more quickly to changes in a restaurant's quality. Our algorithm can be flexibly applied to many different review settings.

Zonder onderwerpscode: economie

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This paper examines reputation formation in intra-familial interactions. We consider parental reputation in a repeated two-stage game in which adolescents decide whether to give a teen birth or drop out of high school, and given adolescent decisions, the parent decides whether to house and support his children beyond age 18. Drawing on the work of Milgrom and Roberts (1982) and Kreps and Wilson (1982), we show that the parent has, under certain conditions, the incentive to penalize older children for their teenage risky behaviors in order to dissuade the younger children from the same risky behaviors. The model generates two empirical implications: the likelihood of teen risky behaviors and parental transfers to a child who engaged in teen risky behaviors will decrease with the number of remaining children at risk. We test these two implications, using data from the National Longitudinal Survey of Youth, 1979 Cohort (NLSY79). Exploiting the availability of repeated observations on individual respondents and of observations on multiple siblings, we find evidence in favor of both predictions.

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The recent withdrawal of Cox-2 Inhibitors has generated debate on the role of information in drug diffusion: can the market learn the efficacy of new drugs, or does it depend solely on manufacturer advertising and FDA updates? In this study, we use a novel data set to study the diffusion of three Cox-2 Inhibitors ? Celebrex, Vioxx and Bextra ? before the Vioxx withdrawal. Our study has two unique features: first, we observe each patient?s reported satisfaction after consuming a drug. This patient level data set, together with market level data on FDA updates, media coverage, academic articles, and pharmaceutical advertising, allows us to model individual prescription decisions. Second, we distinguish across-patient learning of a drug?s general efficacy from the within-patient learning of the match between a drug and a patient. Our results suggest that prescription choice is sensitive to many sources of information. At the beginning of 2001 and upon Bextra entry in January 2002, doctors held a strong prior belief about the efficacy of Celebrex, Vioxx, and Bextra. As a result, the learning from patient satisfaction is gradual and more concentrated on drug-patient match than on across-patient spillovers. News articles are weakly beneficial for Cox-2 drug sales, but academic articles appear to be detrimental. The impact of FDA updates is close to zero once we control for academic articles, which suggests that FDA updates follow academic articles and therefore deliver little new information to doctors. We find that drug advertising also influences the choice of a patient?s medication. A number of counterfactual experiments are carried out to quantify the influence of information on market shares.