Michael Luca

Assistant Professor of Business Administration

Unit: Negotiation, Organizations & Markets

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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.

Featured Work

Publications

Journal Articles

  1. Evolution of Land Distribution in West Bengal 1967–2004: Role of Land Reform and Demographic Changes

    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 (forthcoming). View Details
  2. What Makes a Critic Tick? Connected Authors and the Determinants of Book Reviews

    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
  3. Salience in Quality Disclosure: Evidence from the U.S. News College Rankings

    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
  4. Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online Reviews

    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. 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.

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

    Citation:

    Edelman, Benjamin G., and Michael Luca. "Digital Discrimination: The Case of Airbnb.com." Harvard Business School Working Paper, No. 14-054, January 2014. View Details
  2. 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.

    Keywords: Wages; Employees;

    Citation:

    Gilchrist, Duncan, Michael Luca, and Deepak Malhotra. "When 3+1>4: Gift Structure and Reciprocity in the Field." Harvard Business School Working Paper, No. 14-030, September 2013. (Revised May 2014.) View Details
  3. 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.

    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
  4. Strategic Disclosure: The Case of Business School Rankings

    Using a novel data set, we present three findings about the rankings that business schools choose to display on their websites. First, the data strongly rejects patterns predicted by classic models of voluntary disclosure. In contrast with the traditional unraveling hypothesis, top schools are least likely to display their rankings. 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.

    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." Harvard Business School Working Paper, No. 14-010, July 2013. View Details
  5. Evolution of Land Distribution in West Bengal 1967-2004: Role of Land Reform and Demographic Changes

    This paper examines the indirect effect of land reform and demographic changes on land inequality operating through induced household divisions and land market transactions. We develop an intra-household model of joint production where divisions, out-migration or land purchases arise to avoid inefficient free-riding arising from demographic growth. Land reform affects divisions and land transactions owing to induced effects on farm profitability and anticipation of future reforms. These predictions are successfully tested in data from a West Bengal household survey spanning 1967-2004, where we find the quantitative effect of the land reforms were dwarfed by demographic changes.

    Keywords: inequality; land reform; household division; land markets; Equality and Inequality; Property; Household Characteristics; Change; 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." (conditionally accepted, Journal of Development Economics.) View Details
  6. Optimal Aggregation of Consumer Ratings: An Application to Yelp.com

    Consumer review websites such as Yelp.com 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 (some reviewers tend to leave worse reviews on average) and accuracy (some reviewers are more erratic than others), (2) reviewers to be influenced by existing reviews, and (3) product quality to change over time. We apply this approach to reviews from Yelp.com to derive optimal ratings for each restaurant (in contrast with the arithmetic average displayed by Yelp). Because we have the history of reviews for each restaurant and many reviews left by each reviewer, we are able to identify these factors using variation in ratings within and across reviewers and restaurants. Using our estimated parameters, we construct optimal ratings for all restaurants on Yelp and compare them to the arithmetic averages displayed by Yelp. As of the end of our sample, a conservative finding is that roughly 25%–27% of restaurants are more than 0.15 stars away from the optimal rating, and 8%–10% of restaurants are more than 0.25 stars away from the optimal rating. This suggests that large gains could be made by implementing optimal ratings. Much of the gains come from our method responding more quickly to changes in a restaurant's quality. Our algorithm can be flexibly applied to many different review settings.

    Keywords: crowdsourcing; social network; e-commerce; Yelp; 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." Harvard Business School Working Paper, No. 13-042, November 2012. (NBER Working Paper Series, No. 18567, December 2012.) View Details
  7. Reviews, Reputation, and Revenue: The Case of Yelp.com

    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

  1. Airbnb (A)

    Citation:

    Edelman, Benjamin, and Michael Luca. "Airbnb (A)." Harvard Business School Case 912-019, December 2011. (Revised March 2012.) (request a courtesy copy.) View Details
  2. Airbnb (B)

    Citation:

    Edelman, Benjamin, and Michael Luca. "Airbnb (B)." Harvard Business School Supplement 912-020, December 2011. (Revised March 2012.) View Details
  3. Airbnb (A) and (B)

    Citation:

    Edelman, Benjamin, and Michael Luca. "Airbnb (A) and (B)." Harvard Business School Teaching Note 912-021, December 2011. View Details

    Research Summary

  1. Research overview

    The growth of consumer review websites over the past decade has revolutionized the way in which consumers learn about product quality. The centrality of information to consumer welfare has also been underscored in public policy debates, where quality disclosure has become an increasingly popular policy instrument. How do these new sources of information affect consumer decisions and firm incentives? Professor Luca uses econometric methods to investigate this question, with a research agenda at the intersection of public policy and industrial organization.
  2. Crowdsourced reviews

    To determine whether online consumer reviews influence the way that reputation is formed, Professor Luca has combined reviews from the website Yelp.com with public restaurant data. He has shown that a one-star increase in Yelp ratings results in a 5- to 9-percent increase in an independent restaurant’s revenue. Further, while chain restaurants are unaffected by rating changes, their market shares decline as Yelp penetrates the market.

    Professor Luca also examines which features of review websites have the largest impact on consumer decision making. He has found that while consumers do not use all available information, they respond more strongly when a rating contains more information (number of reviews overall and number of “elite” reviewers).

  3. The limits of reviews

    Consumer reviews are an important source of information in the digital age. Yet there are limits to the role that reviews can play. In a case study, Professor Luca discusses the limits of reviews and how companies can create more comprehensive reputation systems geared toward facilitating trust in online marketplaces. In ongoing research, he is analyzing the role of information in online marketplaces such as Airbnb.

  4. Quality disclosure and consumer behavior

    Professor Luca has investigated the relationship among quality disclosure, salience, and consumer behavior. He has found that when colleges are presented by rank in U.S. News & World Report, a one-rank improvement for an institution causes nearly a percentage point increase in the number of applications it receives. Conversely, rankings have no effect on application decisions when colleges are listed alphabetically, even though the quality data and methodology to calculate the rank are provided.

      13 Oct 2013
      Boston Globe
      24 Oct 2013
      Harvard Gazette
      03 Oct 2013
      US News & World Report
      13 Dec 2012
      SmartMoney
      01 Jan 2012
      New York Times
      04 Oct 2011
      Pay Dirt (Wall Street Journal blog)