Michael Luca

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.  

Journal Articles

  1. Strategic Disclosure: The Case of Business School Rankings

    Michael Luca and Jonathan Smith

    We empirically analyze disclosure decisions made by 240 MBA programs about which rankings to display on their websites. We present three main findings. First, consistent with theories of countersignaling, top schools are least likely to disclose their rankings, whereas mid-ranked schools are most likely to disclose. Second, schools that do poorly in the U.S. News rankings are more likely to disclose their Princeton Review certification, suggesting that schools treat different certifications as substitutes. Third, conditional on displaying a ranking, the majority of schools coarsen information to make it seem more favorable. The stark patterns in the data help to provide empirical evidence on the strategic elements of voluntary disclosure and marketing decisions.

    Keywords: Voluntary Disclosure; Shrouded Attributes; Information Unraveling; Rankings;

    Citation:

    Luca, Michael, and Jonathan Smith. "Strategic Disclosure: The Case of Business School Rankings." Journal of Economic Behavior & Organization (forthcoming). View Details
  2. Evolution of Land Distribution in West Bengal 1967–2004: Role of Land Reform and Demographic Changes

    Pranab Bardhan, Michael Luca, Dilip Mookherjee and Francisco Pino

    This paper studies how land reform and population growth affect land inequality and landlessness, focusing particularly on indirect effects owing to their influence on household divisions and land market transactions. Theoretical predictions of a model of household division and land transactions are successfully tested using household panel data from West Bengal spanning 1967–2004. The tenancy reform lowered inequality through its effects on household divisions and land market transactions, but its effect was quantitatively dominated by inequality-raising effects of population growth. The land distribution program lowered landlessness, but this was partly offset by targeting failures and induced increases in immigration.

    Keywords: inequality; land reform; household division; land markets; Equality and Inequality; Residency Characteristics; Property; Household Characteristics; West Bengal;

    Citation:

    Bardhan, Pranab, Michael Luca, Dilip Mookherjee, and Francisco Pino. "Evolution of Land Distribution in West Bengal 1967–2004: Role of Land Reform and Demographic Changes." Journal of Development Economics 110 (September 2014): 171–190. View Details
  3. What Makes a Critic Tick? Connected Authors and the Determinants of Book Reviews

    Loretti I. Dobrescu, Michael Luca and Alberto Motta

    This paper investigates the determinants of expert reviews in the book industry. Reviews are determined not only by the quality of the product, but also by the incentives of the media outlet providing the review. For example, a media outlet may have the incentive to provide favorable coverage to certain authors or to slant reviews toward the horizontal preferences of certain readers. Empirically, we find that an author's connection to the media outlet is related to the outcome of the review decision. When a book's author also writes for a media outlet, that outlet is 25% more likely to review the book relative to other media outlets, and the resulting ratings are roughly 5% higher. Prima facie, it is unclear whether media outlets are favoring their own authors because these are the authors that their readers prefer or simply because they are trying to collude. We provide a test to distinguish between these two potential mechanisms and present evidence that this is because of tastes rather than collusion—the effect of connections is present both for authors who began writing for a media outlet before and after the book release. We then investigate other determinants of expert reviews. Relative to consumer reviews, we find that professional critics are less favorable to first time authors and more favorable to authors who have garnered other attention in the press (as measured by number of media mentions outside of the review) and who have won book prizes.

    Keywords: Quality; Media; Relationships; Marketing Reference Programs; Books; Publishing Industry;

    Citation:

    Dobrescu, Loretti I., Michael Luca, and Alberto Motta. "What Makes a Critic Tick? Connected Authors and the Determinants of Book Reviews." Journal of Economic Behavior & Organization 96 (December 2013): 85–103. View Details
  4. Salience in Quality Disclosure: Evidence from the U.S. News College Rankings

    Michael Luca and Jonathan Smith

    How do rankings affect demand? This paper investigates the impact of college rankings, and the visibility of those rankings, on students' application decisions. Using natural experiments from U.S. News and World Report College Rankings, we present two main findings. First, we identify a causal impact of rankings on application decisions. When explicit rankings of colleges are published in U.S. News, a one-rank improvement leads to a 1-percentage-point increase in the number of applications to that college. Second, we show that the response to the information represented in rankings depends on the way in which that information is presented. Rankings have no effect on application decisions when colleges are listed alphabetically, even when readers are provided data on college quality and the methodology used to calculate rankings. This finding provides evidence that the salience of information is a central determinant of a firm's demand function, even for purchases as large as college attendance.

    Keywords: Rank and Position; Demand and Consumers; Quality; Decisions; Newspapers; United States;

    Citation:

    Luca, Michael, and Jonathan Smith. "Salience in Quality Disclosure: Evidence from the U.S. News College Rankings." Journal of Economics & Management Strategy 22, no. 1 (Spring 2013): 58–77. View Details
  5. Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online Reviews

    Jun Seok Kang, Polina Kuznetsova, Yejin Choi and Michael Luca

    Restaurant hygiene inspections are often cited as a success story of public disclosure. Hygiene grades influence customer decisions and serve as an accountability system for restaurants. However, cities (which are responsible for inspections) have limited resources to dispatch inspectors, which in turn limits the number of inspections that can be performed. We argue that Natural Language Processing (NLP) can be used to improve the effectiveness of inspections by allowing cities to target restaurants that are most likely to have a hygiene violation. In this work, we report the first empirical study demonstrating the utility of review analysis for predicting restaurant inspection results.

    Keywords: Safety; Food; Governance Compliance; Mathematical Methods; Software; Public Administration Industry; Retail Industry; Food and Beverage Industry;

    Citation:

    Kang, Jun Seok, Polina Kuznetsova, Yejin Choi, and Michael Luca. "Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online Reviews." Proceedings of the Conference on Empirical Methods in Natural Language Processing (2013). View Details

Working Papers

  1. Strategic Disclosure: The Case of Business School Rankings

    Michael Luca and Jonathan Smith

    We empirically analyze disclosure decisions made by 240 MBA programs about which rankings to display on their websites. We present three main findings. First, consistent with theories of countersignaling, top schools are least likely to disclose their rankings, whereas mid-ranked schools are most likely to disclose. Second, schools that do poorly in the U.S. News rankings are more likely to disclose their Princeton Review certification, suggesting that schools treat different certifications as substitutes. Third, conditional on displaying a ranking, the majority of schools coarsen information to make it seem more favorable. The stark patterns in the data help to provide empirical evidence on the strategic elements of voluntary disclosure and marketing decisions.

    Keywords: Voluntary Disclosure; Shrouded Attributes; Information Unraveling; Rankings; Journals and Magazines; Strategy; Corporate Disclosure; Web Sites; Rank and Position; Business Education; Education Industry; United States;

    Citation:

    Luca, Michael, and Jonathan Smith. "Strategic Disclosure: The Case of Business School Rankings." Working Paper. (Revised and resubmitted, Journal of Economic Behavior and Organization.) View Details
  2. Optimal Aggregation of Consumer Ratings: An Application to Yelp.com

    Weijia Dai, Ginger Jin, Jungmin Lee and Michael Luca

    Consumer review websites leverage the wisdom of the crowd, with each product being reviewed many times (some with more than 1,000 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 and accuracy, (2) reviewers to be influenced by existing reviews, and (3) product quality to change over time.
    Applying this approach to restaurant reviews from Yelp.com, we construct optimal ratings for all restaurants and compare them to the arithmetic averages displayed by Yelp. Depending on how we interpret the downward trend of reviews within a restaurant, we find 19.1-41.38% of the simple average ratings are more than 0.15 stars away from optimal ratings, and 5.33-19.1% are more than 0.25 stars away at the end of our sample period. Moreover, the deviation grows significantly as a restaurant accumulates reviews over time. This suggests that large gains could be made by implementing optimal ratings, especially as Yelp grows. Our algorithm can be flexibly applied to many different review settings.

    Keywords: crowdsourcing; social network; e-commerce; Yelp; learning; Information; Demand and Consumers; Competition; Internet; Reputation; Social and Collaborative Networks; Retail Industry; Food and Beverage Industry;

    Citation:

    Dai, Weijia, Ginger Jin, Jungmin Lee, and Michael Luca. "Optimal Aggregation of Consumer Ratings: An Application to Yelp.com." Working Paper. (October 2014.) View Details
  3. Digital Discrimination: The Case of Airbnb.com

    Benjamin Edelman and Michael Luca

    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.

    Keywords: Prejudice and Bias; Online Technology; Race Characteristics; Trust; Renting or Rental; Accommodations Industry; Real Estate Industry;

    Citation:

    Edelman, Benjamin, and Michael Luca. "Digital Discrimination: The Case of Airbnb.com." Harvard Business School Working Paper, No. 14-054, January 2014. View Details
  4. When 3+1>4: Gift Structure and Reciprocity in the Field

    Duncan Gilchrist, Michael Luca and Deepak Malhotra

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

    Keywords: Wages; Employees;

    Citation:

    Gilchrist, Duncan, Michael Luca, and Deepak Malhotra. "When 3+1>4: Gift Structure and Reciprocity in the Field." Working Paper. (November 2014. Revised and resubmitted, Management Science.) View Details
  5. Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud

    Michael Luca and Georgios Zervas

    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.

    Keywords: Information; Competition; Internet; Ethics; Reputation; Social and Collaborative Networks; Retail Industry; Food and Beverage Industry;

    Citation:

    Luca, Michael, and Georgios Zervas. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud." Working Paper. (September 2013. Revise and resubmit, Management Science.) View Details
  6. Reviews, Reputation, and Revenue: The Case of Yelp.com

    Michael Luca

    Do online consumer reviews affect restaurant demand? I investigate this question using a novel dataset combining reviews from the website Yelp.com and restaurant data from the Washington State Department of Revenue. Because Yelp prominently displays a restaurant's rounded average rating, I can identify the causal impact of Yelp ratings on demand with a regression discontinuity framework that exploits Yelp's rounding thresholds. I present three findings about the impact of consumer reviews on the restaurant industry: (1) a one-star increase in Yelp rating leads to a 5% to 9% increase in revenue, (2) this effect is driven by independent restaurants; ratings do not affect restaurants with chain affiliation, and (3) chain restaurants have declined in market share as Yelp penetration has increased. This suggests that online consumer reviews substitute for more traditional forms of reputation. I then test whether consumers use these reviews in a way that is consistent with standard learning models. I present two additional findings: (4) consumers do not use all available information and are more responsive to quality changes that are more visible and (5) consumers respond more strongly when a rating contains more information. Consumer response to a restaurant's average rating is affected by the number of reviews and whether the reviewers are certified as "elite" by Yelp, but is unaffected by the size of the reviewer's Yelp friends network.

    Keywords: Revenue; Network Effects; Reputation; Social and Collaborative Networks; Food and Beverage Industry; Service Industry; Washington (state, US);

    Citation:

    Luca, Michael. "Reviews, Reputation, and Revenue: The Case of Yelp.com." Harvard Business School Working Paper, No. 12-016, September 2011. (Revise and resubmit at the American Economic Journal - Applied Economics.) View Details

Cases and Teaching Materials