A For Effort? Using The Crowd To Identify Moral Hazard In New York City Restaurant Hygiene Inspections

From an upset stomach to a life-threatening foodborne illness, getting sick is all too common after eating in restaurants. Although health inspection programs are designed to protect consumers, such inspections typically occur at wide intervals of time, allowing restaurant hygiene to remain unmonitored in the interim periods. Information provided in online reviews may be effectively used in these interim periods to gauge restaurant hygiene. In this paper, we use textual information from online reviews of restaurants to effectively identify cases of hygiene violations in restaurants, even after the restaurants have been inspected and certified, thereby identifying moral hazard. Using a data set of restaurant hygiene inspections in New York City from 2010 through 2016 and the associated set of online reviews for the same set of restaurants from Yelp, we use supervised machine learning techniques to develop a hygiene dictionary specifically crafted to identify hygiene-related concerns. The use of this dictionary and the related word counts in online reviews allows us to identify systematic instances of moral hazard, wherein restaurants with positive hygiene inspection scores are seen to regress in their hygiene maintenance within 90 days of receiving the inspection scores. To the extent that social media provide some visibility into the hygiene practices of restaurants, we argue that the effects of information asymmetry that lead to moral hazard may be partially mitigated in this context. Based on our work, we also provide strategies for how cities and policymakers may design effective restaurant inspection programs, through a combination of traditional inspections and the appropriate use of social media.