Michael Luca

Assistant Professor of Business Administration

Michael Luca is a faculty member at Harvard Business School. Professor Luca works closely with companies and cities to help them become more data-driven, and has ongoing collaborations with Yelp, Facebook, the UK government, and the City of Boston, in addition to other partners.   

Professor Luca teaches The Online Economy, an elective course about the strategic and operational decisions faced when designing and launching an online platform. He also teaches an elective course in which student teams develop behavioral interventions and experimental designs for government and company clients, called IFC: Behavioral Insights.  

Professor Luca's current work focuses on digital data and platforms, analyzing a variety of companies including Yelp, Amazon, and Airbnb. Professor Luca also works on issues related to the design of information disclosure. Focusing on 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.

His work has been written about in a variety of media outlets including The Wall Street Journal, New York Times, Washington Post, Boston Globe, Guardian, Telegraph, Huffington Post, Harvard Business Review, Atlantic, Quartz, Vox, and Forbes.

Journal Articles

  1. Fixing Discrimination in Online Marketplaces

    Ray Fisman and Michael Luca

    Online marketplaces such as eBay, Uber, and Airbnb have the potential to reduce racial, gender, and other forms of bias that affect the off-line world. And in the early days of internet commerce, the relative anonymity of transactions did make it harder for participants to discriminate. But as listings began to include photos, names, and other means of identification, bias emerged in areas ranging from labor markets to credit applications to housing—sometimes made worse by a lack of regulation, the absence of in-person interactions, and the use of automation and big data. How can companies reverse the tide? The key lies in more-intentional platform design, say the authors, who offer a framework for creating a thriving marketplace while minimizing the risk of discrimination. For starters, they say, companies must track and report on potential problems and carefully test choices that may influence the extent of discrimination. And they should thoroughly examine four design decisions, asking themselves: • Are we providing too much information? In many cases, the simplest, most effective change a platform can make is to withhold information such as race and gender until after a transaction has been agreed to. • Could we further automate the process? Features such as “instant book,” allowing a buyer to sign up for a rental, say, without the seller’s prior approval, can reduce discrimination while increasing convenience. • Can we make discrimination policies more top-of-mind? Presenting them during the actual transaction process, rather than burying them in fine print, makes them less likely to be broken. • Should we make our algorithms discrimination-aware? To ensure fairness, designers need to track how race or gender affects the user experience and set explicit objectives. Seemingly small design features can have an outsize impact on discriminatory behavior. Smart choices and transparent experimentation can create markets that are both more efficient and more inclusive.

    Keywords: Prejudice and Bias; Market Platforms; Online Technology; Race; Gender;

    Citation:

    Fisman, Ray, and Michael Luca. "Fixing Discrimination in Online Marketplaces." Harvard Business Review 94, no. 12 (December 2016): 88–95. View Details
  2. Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment

    Benjamin G. Edelman, Michael Luca and Daniel Svirsky

    In an experiment on Airbnb, we find that applications from guests with distinctively African-American names are 16% less likely to be accepted relative to identical guests with distinctively White names. Discrimination occurs among landlords of all sizes, including small landlords sharing the property and larger landlords with multiple properties. It is most pronounced among hosts who have never had an African-American guest, suggesting only a subset of hosts discriminate. While rental markets have achieved significant reductions in discrimination in recent decades, our results suggest that Airbnb’s current design choices facilitate discrimination and raise the possibility of erasing some of these civil rights gains.

    Keywords: discrimination; field experiment; race; bias; Airbnb; Prejudice and Bias; Race; Accommodations Industry;

    Citation:

    Edelman, Benjamin G., Michael Luca, and Daniel Svirsky. "Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment." American Economic Journal: Applied Economics (forthcoming). View Details
  3. Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life

    Edward L. Glaeser, Scott Duke Kominers, Michael Luca and Nikhil Naik

    New, "big" data sources allow measurement of city characteristics and outcome variables at higher frequencies and finer geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big data has the most value for the study of cities when it allows measurement of the previously opaque, or when it can be coupled with exogenous shocks to people or place. We describe a number of new urban data sources and illustrate how they can be used to improve the study and function of cities. We first show how Google Street View images can be used to predict income in New York City, suggesting that similar image data can be used to map wealth and poverty in previously unmeasured areas of the developing world. We then discuss how survey techniques can be improved to better measure willingness to pay for urban amenities. Finally, we explain how Internet data is being used to improve the quality of city services.

    Keywords: Data and Data Sets; Urban Scope; City;

    Citation:

    Glaeser, Edward L., Scott Duke Kominers, Michael Luca, and Nikhil Naik. "Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life." Economic Inquiry (forthcoming). View Details
  4. Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy

    Edward Glaeser, Andrew Hillis, Scott Duke Kominers and Michael Luca

    The proliferation of big data makes it possible to better target city services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to crowdsource competence by making data public and offering a reward for the best algorithm. A simple model suggests that open tournaments dominate consulting contracts when cities can tolerate risk and when there is enough labor with low opportunity costs. We also report on an inexpensive Boston-based restaurant tournament, which yielded algorithms that proved reasonably accurate when tested "out-of-sample" on hygiene inspections.

    Keywords: user-generated content; operations; tournaments; policy-making; Machine learning; online platforms; Data and Data Sets; Mathematical Methods; City; Infrastructure; Business Processes; Government and Politics;

    Citation:

    Glaeser, Edward, Andrew Hillis, Scott Duke Kominers, and Michael Luca. "Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 114–118. View Details
  5. Algorithms Need Managers, Too

    Michael Luca, Jon Kleinberg and Sendhil Mullainathan

    Algorithms are powerful predictive tools, but they can run amok when not applied properly. Consider what often happens with social media sites. Today many use algorithms to decide which ads and links to show users. But when these algorithms focus too narrowly on maximizing click-throughs, sites quickly become choked with low-quality content. While clicks rise, customer satisfaction plummets. The glitches, say the authors, are not in the algorithms but in the way we interact with them. Managers need to recognize their two major limitations: First, they're completely literal; algorithms do exactly what they're told and disregard every other consideration. While a human would have understood that the sites' designers wanted to maximize quality as measured by clicks, the algorithms maximized clicks at the expense of quality. Second, algorithms are black boxes. Though they can predict the future with great accuracy, they won't say what will cause an event or why. They'll tell you which magazine articles are likely to be shared on Twitter without explaining what motivates people to tweet about them, for instance. To avoid missteps, you need to be explicit about all your goals—hard and soft—when formulating your algorithms. You also must consider the long-term implications of the data the algorithms incorporate to make sure they're not focusing nearsightedly on short-term outcomes. And choose the right data inputs, being sure to gather a wide breadth of information from a diversity of sources.

    Keywords: Machine learning; algorithms; predictive analytics; management; big data;

    Citation:

    Luca, Michael, Jon Kleinberg, and Sendhil Mullainathan. "Algorithms Need Managers, Too." Harvard Business Review 94, nos. 1/2 (January–February 2016): 96–101. View Details
  6. Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud

    Michael Luca and Georgios Zervas

    Consumer reviews are now part of everyday decision making. Yet, the credibility of these reviews is fundamentally undermined when businesses commit review fraud, creating fake reviews for themselves or their competitors. We investigate the economic incentives to commit review fraud on the popular review platform Yelp, using two complementary approaches and datasets. We begin by analyzing restaurant reviews that are identified by Yelp's filtering algorithm as suspicious or fake—and treat these as a proxy for review fraud (an assumption we provide evidence for). We present four main findings. First, roughly 16% of restaurant reviews on Yelp are filtered. These reviews tend to be more extreme (favorable or unfavorable) than other reviews, and the prevalence of suspicious reviews has grown significantly over time. 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 receive unfavorable fake reviews. Using a separate dataset, we analyze businesses that were caught soliciting fake reviews through a sting conducted by Yelp. These data support our main results and shed further light on the economic incentives behind a business's decision to leave fake reviews.

    Keywords: Ethics; Marketing Reference Programs;

    Citation:

    Luca, Michael, and Georgios Zervas. "Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud." Management Science (forthcoming). View Details
  7. When 3+1>4: Gift Structure and Reciprocity in the Field

    Duncan S. 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 higher wages, per se, does not have a discernible effect on productivity (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. Gifts are roughly as efficient as hiring more workers.

    Keywords: Wages; Performance Productivity;

    Citation:

    Gilchrist, Duncan S., Michael Luca, and Deepak Malhotra. "When 3+1>4: Gift Structure and Reciprocity in the Field." Management Science (forthcoming). View Details
  8. 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; Higher Education; Corporate Disclosure; Rank and Position;

    Citation:

    Luca, Michael, and Jonathan Smith. "Strategic Disclosure: The Case of Business School Rankings." Journal of Economic Behavior & Organization 112 (April 2015): 17–25. View Details
  9. 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; Property; Household; 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
  10. 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
  11. 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
  12. 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

Book Chapters

  1. User-Generated Content and Social Media

    Michael Luca

    This paper documents what economists have learned about user-generated content (UGC) and social media. A growing body of evidence suggests that UGC on platforms ranging from Yelp to Facebook has a large causal impact on economic and social outcomes ranging from restaurant decisions to voting behavior. These findings often leverage unique data sets and methods ranging from regression discontinuity to field experiments, and researchers often work directly with the companies they study. I then survey the factors that influence the quality of UGC. Quality is influenced by factors including promotional content, peer effects between contributors, biases of contributors, and self-selection into the decision to contribute. Nonpecuniary incentives, such as “badges” and social status on a platform, are often used to encourage and steer contributions. I then discuss other issues including business models, network effects, and privacy. Throughout the paper, I discuss open questions in this area.

    Keywords: user-generated content; social media; crowdsourcing; design economics; Internet; Marketing; Economics; Media;

    Citation:

    Luca, Michael. "User-Generated Content and Social Media." Chap. 12 in Handbook of Media Economics. Vol. 1B, edited by Simon Anderson, Joel Waldfogel, and David Strömberg. North-Holland Publishing Company, 2016. View Details

Working Papers

  1. Effectiveness of Paid Search Advertising: Experimental Evidence

    Weijia (Daisy) Dai and Michael Luca

    Paid search has become an increasingly common form of advertising, comprising about half of all online advertising expenditures. To shed light on the effectiveness of paid search, we design and analyze a large-scale field experiment on the review platform Yelp.com. The experiment consists of roughly 18,000 restaurants and 24 million advertising exposures—randomly assigning paid search advertising packages to more than 7,000 restaurants for a three-month period, with randomization done at the restaurant level to assess the overall impact of advertisements. We find that advertising increases a restaurant’s Yelp page views by 25% on average. Advertising also increases the number of purchase intentions—including getting directions, browsing the restaurant’s website, and calling the restaurant—by 18%, 9%, and 13%, respectively, and raises the number of reviews by 5%, suggesting that advertising also affects the number of restaurant-goers. All advertising effects drop to zero immediately after the advertising period. A back-of-the-envelope calculation suggests that advertising would produce a positive return on average for restaurants in our sample.

    Keywords: Search Technology; Performance; Online Advertising; Service Industry;

    Citation:

    Dai, Weijia (Daisy), and Michael Luca. "Effectiveness of Paid Search Advertising: Experimental Evidence." Harvard Business School Working Paper, No. 17-025, October 2016. View Details
  2. Designing Online Marketplaces: Trust and Reputation Mechanisms

    Michael Luca

    Online marketplaces have proliferated over the past decade, creating new markets where none existed. By reducing transaction costs, online marketplaces facilitate transactions that otherwise would not have occurred and enable easier entry of small sellers. One central challenge faced by designers of online marketplaces is how to build enough trust to facilitate transactions between strangers. This paper provides an economist’s toolkit for designing online marketplaces, focusing on trust and reputation mechanisms.

    Keywords: Market Design; Online Technology; Reputation; Trust;

    Citation:

    Luca, Michael. "Designing Online Marketplaces: Trust and Reputation Mechanisms." Harvard Business School Working Paper, No. 17-017, September 2016. (Forthcoming in NBER IPE book.) View Details
  3. The Impact of Campus Scandals on College Applications

    Michael Luca, Patrick Rooney and Jonathan Smith

    In recent years, there have been a number of high profile scandals on college campuses, ranging from cheating to hazing to rape. With so much information regarding a college’s academic and nonacademic attributes available to students, how do these scandals affect their applications? To investigate, we construct a dataset of scandals at the top 100 U.S. universities between 2001 and 2013. Scandals with a high level of media coverage significantly reduce applications. For example, a scandal covered in a long-form news article leads to a 10% drop in applications the following year. This is roughly the same as the impact on applications of dropping 10 spots in the U.S. News and World Report college rankings. Moreover, colleges react to scandals—the probability of another incident in the subsequent years falls—but this effect dissipates within five years. Combined, these results suggest important demand-side and supply-side responses to incidents with negative media coverage.

    Keywords: Media Economics; College Choice; reputation; Economics of Information; Crime and Corruption; Higher Education; Ethics; Media; Decision Choices and Conditions; Reputation; Education Industry; United States;

    Citation:

    Luca, Michael, Patrick Rooney, and Jonathan Smith. "The Impact of Campus Scandals on College Applications." Harvard Business School Working Paper, No. 16-137, June 2016. View Details
  4. The Impact of Mass Shootings on Gun Policy

    Michael Luca, Deepak Malhotra and Christopher Poliquin

    There have been dozens of high-profile mass shootings in recent decades. This paper presents three main findings about the impact of mass shootings on gun policy. First, mass shootings evoke large policy responses. A single mass shooting leads to a 15% increase in the number of firearm bills introduced in a state in the year after a mass shooting. Second, mass shootings account for a small portion of all gun deaths but have an outsized influence relative to other homicides. Our estimates suggest that the per-death impact of mass shootings on bills introduced is about 80 times as large as the impact of individual gun homicides on non-mass shooting incidents. Third, when looking at enacted laws, the impact of mass shootings depends on the party in power. A mass shooting increases the number of enacted laws that loosen gun restrictions by 75% in states with Republican-controlled legislatures. We find no significant effect of mass shootings on laws enacted when there is a Democrat-controlled legislature.

    Keywords: Crime and Corruption; Policy;

    Citation:

    Luca, Michael, Deepak Malhotra, and Christopher Poliquin. "The Impact of Mass Shootings on Gun Policy." Harvard Business School Working Paper, No. 16-126, May 2016. (Revised October 2016.) View Details
  5. Does Google Content Degrade Google Search? Experimental Evidence

    Michael Luca, Timothy Wu, Sebastian Couvidat, Daniel Frank and William Seltzer

    While Google is known primarily as a search engine, it has increasingly developed and promoted its own content as an alternative to results from other websites. By prominently displaying Google content in response to search queries, Google is able to use its dominance in search to gain customers for this content. This may reduce consumer welfare if the internal content is inferior to organic search results. In this paper, we provide a legal and empirical analysis of this practice in the domain of online reviews. We first identify the conditions under which universal search would be considered anticompetitive. We then empirically investigate the impact of this practice on consumer welfare. To investigate, we implement a randomized controlled trial in which we vary the search results that subjects are shown - comparing Google’s current policy of favorable treatment of Google content to results in which external content is displayed. We find that users are roughly 40% more likely to engage with universal search results (which receive favored placement) when the results are organically determined relative to when they contain only Google content. To shed further light on the underlying mechanisms, we show that users are more likely to engage with the OneBox when there are more reviews, holding content constant. This suggests that Google is reducing consumer welfare by excluding reviews from other platforms in the OneBox.

    Keywords: Prejudice and Bias; Search Technology;

    Citation:

    Luca, Michael, Timothy Wu, Sebastian Couvidat, Daniel Frank, and William Seltzer. "Does Google Content Degrade Google Search? Experimental Evidence." Harvard Business School Working Paper, No. 16-035, September 2015. (Revised August 2016.) View Details
  6. Is No News (Perceived as) Bad News? An Experimental Investigation of Information Disclosure

    Ginger Zhe Jin, Michael Luca and Daniel Martin

    This paper uses laboratory experiments to directly test a central prediction of disclosure theory: that market forces can lead businesses to voluntarily provide information about the quality of their products. This theoretical prediction is based on unraveling arguments, which require that consumers hold correct beliefs about non-disclosed information. Instead, we find that receivers are insufficiently skeptical about nondisclosed information, and as a consequence, senders do not always disclose their private information. However, when subjects are informed about non-disclosed information after each round, behavior slowly converges to full unraveling. This convergence appears to be driven by an asymmetric response in receiver actions after learning that they were profitably deceived. Despite the change in receiver behavior, stated beliefs about sender strategies remain insufficiently skeptical, which suggests that while direct and immediate feedback induces equilibrium behavior, it does not reduce strategic naïveté.

    Keywords: communication games; disclosure; unraveling; experiments; Information; Quality; Corporate Disclosure; Consumer Behavior; Product;

    Citation:

    Jin, Ginger Zhe, Michael Luca, and Daniel Martin. "Is No News (Perceived as) Bad News? An Experimental Investigation of Information Disclosure." Harvard Business School Working Paper, No. 15-078, April 2015. (Revised August 2016. Revise and Resubmit at the Review of Economic Studies.) View Details
  7. Curbing Adult Student Attrition: Evidence from a Field Experiment

    Raj Chande, Michael Luca, Michael Sanders, Xian‐Zhi Soon, Oana Borcan, Netta Barak-Corren, Elizabeth Linos, Elspeth Kirkman and Sean Robinson

    Roughly 20% of adults in the OECD lack basic numeracy and literacy skills. In the UK, many colleges offer fully government-subsidized adult education programs to improve these skills. Constructing a unique dataset consisting of weekly attendance records for 1179 students, we find that approximately 25% of learners stop attending these programs in the first ten weeks and that average attendance rates deteriorate by 20% in that time. We implement a large-scale field experiment in which we send encouraging text messages to students. Our initial results show that these simple text messages reduce the proportion of students that stop attending by 36% and lead to a 7% increase in average attendance relative to the control group. The effects on attendance rates persist through the three weeks of available data following the initial intervention.

    Keywords: Behavioral economics; field experiment; education; Education; Economics; United Kingdom;

    Citation:

    Chande, Raj, Michael Luca, Michael Sanders, Xian‐Zhi Soon, Oana Borcan, Netta Barak-Corren, Elizabeth Linos, Elspeth Kirkman, and Sean Robinson. "Curbing Adult Student Attrition: Evidence from a Field Experiment." Harvard Business School Working Paper, No. 15-065, February 2015. View Details
  8. 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
  9. 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; 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
  10. 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 5-9 percent 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 reviewers’ 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. (Revised March 2016. Revise and resubmit at the American Economic Journal - Applied Economics.) View Details

Cases and Teaching Materials

  1. Launching Yelp Reservations

    Michael Luca, Kevin Mohan and Patrick Rooney

    This teaching note accompanies "Launching Yelp Reservations (A) and (B)," which present a multi-party negotiation among Yelp, current partner OpenTable, and two startups in the online restaurant reservation industry.

    Keywords: Technology; Negotiation; Business Startups; Acquisition; Technology Industry; United States;

    Citation:

    Luca, Michael, Kevin Mohan, and Patrick Rooney. "Launching Yelp Reservations." Harvard Business School Teaching Note 917-005, July 2016. View Details
  2. Advertising Experiments at RestaurantGrades

    Michael Luca, Weijia Dai and Hyunjin Kim

    This exercise provides students with a data set consisting of results from a hypothetical experiment, and asks students to make recommendations based on the data. Through this process, the exercise teaches students to analyze, design, and interpret experiments. The context is an experiment in a hypothetical restaurant review company called RestaurantGrades (RG) whose main source of revenue comes from advertising. Like Yelp and TripAdvisor, RG advertisements are shown above the organic search results when someone searches on the page. RG is trying to understand whether its current advertising package is effective in practice. To do this, RG has run an experiment with two treatment arms and a control group of restaurants. The control group has no advertising, the first treatment arm consists of giving restaurants RG's current advertising package, and the second treatment arm is an alternative package that RG designed with a different approach to consumer targeting. Students are given the data to analyze, and asked to make a recommendation about which, if either, advertising package is effective.

    Keywords: Analysis; Online Advertising;

    Citation:

    Luca, Michael, Weijia Dai, and Hyunjin Kim. "Advertising Experiments at RestaurantGrades." Harvard Business School Exercise 916-038, March 2016. View Details
  3. Launching Yelp Reservations (A)

    Michael Luca, Kevin Mohan and Patrick Rooney

    This case presents a multi-party negotiation among Yelp, current partner OpenTable, and two startups in the online restaurant reservation industry.

    Keywords: Technology; Negotiation; Business Startups; Acquisition; Technology Industry; United States;

    Citation:

    Luca, Michael, Kevin Mohan, and Patrick Rooney. "Launching Yelp Reservations (A)." Harvard Business School Case 916-003, July 2015. (Revised April 2016.) View Details
  4. Behavioural Insights Team (A)

    Michael Luca and Patrick Rooney

    The Behavioural Insights Team case introduces students to the concept of choice architecture and the value of experimental methods (sometimes called A/B testing) within organizational contexts. The exercise provides an opportunity for students to apply these principles to solve a managerial problem – increasing tax compliance rates among delinquent taxpayers. Students are asked to rewrite the letter that the UK tax department (HMRC) sends to delinquent taxpayers; this exercise is based on a successful behavioral field experiment run by the UK government.

    Keywords: Behavioral economics; experiments; choice architecture; public entrepreneurship; Decision Choices and Conditions; Consumer Behavior; Taxation; Economics; Public Administration Industry; United Kingdom;

    Citation:

    Luca, Michael, and Patrick Rooney. "Behavioural Insights Team (A)." Harvard Business School Case 915-024, March 2015. View Details
  5. Behavioural Insights Team (A) and (B)

    Michael Luca and Patrick Rooney

    The Behavioural Insights Team case introduces students to the concept of choice architecture and the value of experimental methods (sometimes called A/B testing) within organizational contexts. The exercise provides an opportunity for students to apply these principles to solve a managerial problem – increasing tax compliance rates among delinquent taxpayers. Students are asked to rewrite the letter that the UK tax department (HMRC) sends to delinquent taxpayers; this exercise is based on a successful behavioral field experiment run by the UK government.

    Keywords: Behavioral economics; experiments; choice architecture; public entrepreneurship; Decision Choices and Conditions; Mathematical Methods; United Kingdom;

    Citation:

    Luca, Michael, and Patrick Rooney. "Behavioural Insights Team (A) and (B)." Harvard Business School Teaching Note 916-050, March 2016. View Details

Other Publications and Materials

  1. Productivity and Selection of Human Capital with Machine Learning

    Aaron Chalfin, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig and Sendhil Mullainathan

    Keywords: Data and Data Sets; Selection and Staffing; Performance Productivity; Mathematical Methods; Policy;

    Citation:

    Chalfin, Aaron, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig, and Sendhil Mullainathan. "Productivity and Selection of Human Capital with Machine Learning." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 124–127. View Details