Michael Luca

Assistant Professor of Business Administration

Michael Luca is an assistant professor of business administration in the Negotiation, Organizations, and Markets Unit. He teaches the Negotiations course in the MBA elective curriculum.

Professor Luca applies econometric methods to field data in order to study the impact of information in market settings. He investigates the types and features of information disclosure that are most effective, the way in which information disclosure is produced and designed, and how these phenomena affect market structure. In his research, Professor Luca considers rankings, expert reviews, online consumer reviews, and quality disclosure laws.

His current work focuses on crowdsourced reviews, analyzing a variety of companies including Yelp, Amazon, and Airbnb. His findings have been written and blogged about in such media outlets as The Wall Street Journal, The New York Times, The Washington Post, The Huffington Post, Chicago Tribune, Harvard Business Review, PC World Magazine, and Salon.

Professor Luca received his Ph.D. in economics from Boston University and a bachelor’s degree in economics and mathematics from SUNY Albany. Before beginning his doctoral studies, he worked as a health-care actuary for major insurers.

  1. Digital Discrimination: The Case of Airbnb.com

    Online marketplaces often contain information not only about products, but also about the people selling the products. In an effort to facilitate trust, many platforms encourage sellers to provide personal profiles and even to post pictures of themselves. However, these features may also facilitate discrimination based on sellers’ race, gender, age, or other aspects of appearance. In this paper, we test for racial discrimination against landlords in the online rental marketplace Airbnb.com. Using a new data set combining pictures of all New York City landlords on Airbnb with their rental prices and information about quality of the rentals, we show that non-black hosts charge approximately 12% more than black hosts for the equivalent rental. These effects are robust when controlling for all information visible in the Airbnb marketplace. These findings highlight the prevalence of discrimination in online marketplaces, suggesting an important unintended consequence of a seemingly-routine mechanism for building trust.

    Read the recent covereage by The Boston Globe and Forbes.com.

  2. Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud

    Consumer reviews are now a part of everyday decision-making. Yet the credibility of reviews is fundamentally undermined when business-owners commit review fraud, either by leaving positive reviews for themselves or negative reviews for their competitors. In this paper, we investigate the extent and patterns of review fraud on the popular consumer review platform Yelp.com. Because one cannot directly observe which reviews are fake, we focus on reviews that Yelp's algorithmic indicator has identified as fraudulent. Using this proxy, we present four main findings. First, roughly 16 percent of restaurant reviews on Yelp are identified as fraudulent, and tend to be more extreme (favorable or unfavorable) than other reviews. Second, a restaurant is more likely to commit review fraud when its reputation is weak, i.e., when it has few reviews, or it has recently received bad reviews. Third, chain restaurants - which benefit less from Yelp – are also less likely to commit review fraud. Fourth, when restaurants face increased competition, they become more likely to leave unfavorable reviews for competitors. Taken in aggregate, these findings highlight the extent of review fraud and suggest that a business's decision to commit review fraud responds to competition and reputation incentives rather than simply the restaurant's ethics.

    Read the Wall Street Journal’s recent blog coverage.
  3. Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online Reviews

    This paper offers an approach for governments to harness the information contained in social media in order to make public inspections and disclosure more efficient. As a case study, we turn to restaurant hygiene inspections – which are done for restaurants throughout the United States and in most of the world and are a frequently cited example of public inspections

    and disclosure. We present the first empirical study that shows the viability of statistical models that learn the mapping between textual signals in restaurant reviews and the hygiene inspection records from the Department of Public Health. The learned model achieves over 82% accuracy in discriminating severe offenders from places with no violation, and provides insights into salient cues in reviews that are indicative of the restaurant’s sanitary conditions. Our study suggests that public disclosure policy can be improved by mining public opinions from social media to target inspections and to provide alternative forms of disclosure to customers.


    Read the Atlantic’s recent coverage.

  4. When 3+1>4: Gift Structure and Reciprocity in the Field

    Do higher wages elicit reciprocity and hence higher effort? In a field experiment with 266 employees, we find that paying above-market wages, per se, does not have an effect on effort relative to paying market wages. However, structuring a portion of the wage as a clear and unexpected gift (by offering a raise with no further conditions after the employee has accepted the contract – with no future employment) does lead to higher effort for the duration of the job. Targeted gifts are more efficient than hiring more workers. However, the mechanism makes this unlikely to explain persistent above-market wages.


    Read the Washington Post’s recent blog coverage.