Assistant Professor of Business Administration
Michael Luca is an assistant professor of business administration at Harvard Business School.
Professor Luca’s research applies econometric methods to field data in order to study the impact of information in market settings. He works closely with governments and companies to identify, design, implement, and experiment with different approaches to information disclosure, taking into account the behavioral foundations of how people make decisions. He has done work on rankings, expert reviews, online consumer reviews, and quality disclosure laws, among other types of information provision. Professor Luca has ongoing field research with Facebook, Yelp, several colleges, the UK government, and several U.S. cities, in addition to other partners.
His current work focuses on crowdsourced reviews, analyzing a variety of companies including Yelp, Amazon, and ZocDoc. 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.
Keeping it Fresh: Predict Restaurant Inspections
My collaborators and I are co-sponsoring a competition with Yelp and support from the City of Boston to explore ways to use Yelp review data to improve the city's health inspection process. We are looking for your help to achieve this goal.
The goal for this competition is to use data from social media to narrow the search for health code violations in Boston. Competitors will have access to historical hygiene violation records from the City of Boston — a leader in open government data — and Yelp's consumer reviews. The challenge: Figure out the words, phrases, ratings, and patterns that predict violations, to help public health inspectors do their job
Winning algorithms will be awarded financial prizes — but the real prize is the opportunity to help the City of Boston, which is committed to examining ways to integrate the winning algorithm into its day-to-day inspection operations.
Click here for more information, and to enter the competition.
City Governments Are Using Yelp to Tell You Where Not to Eat
On the Facebook and OkCupid experiments
Should companies run experiments?
How to update credit card disclosures for the digital age.
The High School Senior's Dilemma: Where Should I Go to College?
on choosing the right college.
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.
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
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.
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.