Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments September 15, 2019 erstellt
Beschreibung:
Consumers and businesses rely on others' ratings of items when making choices. However, individual reviewers vary in their accuracy, and some are biased -- either systematically over- or under-rating items relative to others' tastes, or even deliberately distorting a rating. We provide a new technique that processes ratings by a group of reviewers over a set of items and simultaneously evaluates the individual reviewers' accuracies and biases, and provides unbiased and efficient estimates of the items' true qualities. We show that our technique generates significant improvements over an average of ratings in recovering qualities even with small data sets, and that this improvement increases as the number of items increases. Revisiting the famous 1976 wine tasting that compared Californian and Bordeaux wines, we find substantial variation in reviewers' accuracies and a ranking that differs from the original one based on average ratings. In addition, we apply our methodology to more than forty-five thousand ratings of Bordeaux wines. Our estimated wine qualities significantly predict prices when controlling for prominent experts' ratings and numerous fixed effects. We also find that the elasticity of a wine price in an expert's ratings increases with that expert's accuracy