Michael Luca

Article | Harvard Business Review

Geoff Donaker, Hyunjin Kim and Michael Luca

Online reviews are transforming the way consumers choose products and services of all sorts. We turn to TripAdvisor to plan a vacation, Zocdoc to find a doctor, and Yelp to choose a new restaurant. Reviews can create value for buyers and sellers alike, but only if they attain a critical level of quantity and quality. The authors describe principles for setting the incentives, design choices, and rules that help review platforms thrive.
To address a shortage of reviews, companies can seed them by hiring reviewers or drawing reviews from other platforms, offering incentives, or pooling products. To address selection bias, they can require reviews, allow private comments, and design prompts carefully. To combat fraudulent and strategic reviews, they can set rules for reviewers and call in moderators—whether employees, the community, or algorithms.

Keywords: reviews; Web Sites; Design; Quality; Reputation;

Citation:

Donaker, Geoff, Hyunjin Kim, and Michael Luca. "Designing Better Online Review Systems." Harvard Business Review 97, no. 6 (November–December 2019): 122–129. View Details

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Article | Journal of Public Economics

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 within a state in the year after a mass shooting. This effect increases with the extent of media coverage. Second, mass shootings account for a small portion of all gun deaths but have an outsized influence relative to other homicides. Third, when looking at bills that were actually enacted into law, the impact of mass shootings depends on the party in power. The annual number of laws that loosen gun restrictions doubles in the year following a mass shooting in states with Republican-controlled legislatures. We find no significant effect of mass shootings on laws enacted when there is a Democrat-controlled legislature, nor do we find a significant effect of mass shootings on the enactment of laws that tighten gun restrictions.

Keywords: gun violence; gun policy; Crime and Corruption; Governance; Policy; United States;

Citation:

Luca, Michael, Deepak Malhotra, and Christopher Poliquin. "The Impact of Mass Shootings on Gun Policy." Art. 104083. Journal of Public Economics 181 (January 2020). View Details

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Article | Management Science

Product Quality and Entering Through Tying: Experimental Evidence

Hyunjin Kim and Michael Luca

Dominant platform businesses often develop products in adjacent markets to complement their core business. One common approach used to gain traction in these adjacent markets has been to pursue a tying strategy. For example, Microsoft pre-installed Internet Explorer into Windows, and Apple set Apple Maps as the iOS default. Policymakers have raised concerns that dominant platforms may be leveraging their market power to gain traction for lower quality products when they use a tying strategy. In this paper, we empirically explore this question by examining Google’s decision to tie its new reviews product to its search engine. We experimentally vary the reviews displayed above Google’s organic search results to show either exclusively Google reviews (Google’s current tying strategy) or reviews from multiple platforms determined to be the best-performing by Google’s own organic search algorithm. We find that users prefer the version that does not exclude competitor reviews. Furthermore, looking at observational data on user traffic to Yelp from search engines, we find that Google’s exclusion of downstream competitors may have been effective. The share of Yelp’s traffic coming from Google has declined over this period, relative to traffic from Bing and Yahoo (which do not exclude other companies’ reviews), and Google reviews has grown quicker than Yelp and TripAdvisor during the period in which they excluded these (and other) reviews providers. Overall, these results shed light on platform strategy: tying has the potential to facilitate entry in complementary markets, even when the tied product is of worse quality than competitors.

Keywords: tying; platform strategy; google; Product; Quality; Market Platforms; Strategy; Market Entry and Exit;

Citation:
Kim, Hyunjin, and Michael Luca. "Product Quality and Entering Through Tying: Experimental Evidence." Management Science 65, no. 2 (February 2019): 596–603. View Details

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Article | AEA Papers and Proceedings

Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change

Edward L. Glaeser, Hyunjin Kim and Michael Luca

Data from digital platforms have the potential to improve our understanding of gentrification and enable new measures of how neighborhoods change in close to real time. Combining data on businesses from Yelp with data on gentrification from the Census, Federal Housing Finance Agency, and Streetscore (an algorithm using Google Streetview), we find that gentrifying neighborhoods tend to have growing numbers of local groceries, cafés, restaurants, and bars, with little evidence of crowd-out of other types of businesses. For example, the entry of a new coffee shop into a zip code in a given year is associated with a 0.5% increase in housing prices. Moreover, Yelp measures of local business activity provide leading indicators for housing price changes and help to forecast which neighborhoods are gentrifying.

Keywords: Forecasting Models; Simulation Methods; Regional Economic Activity: Growth, Development, Environmental Issues, and Changes; Geographic Location; Local Range; Transition; Data and Data Sets; Measurement and Metrics; Economic Growth; Forecasting and Prediction;

Citation:
Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change." AEA Papers and Proceedings 108 (May 2018): 77–82. View Details

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Article | Proceedings of the National Academy of Sciences of the United States of America

Handgun Waiting Periods Reduce Gun Deaths

Michael Luca, Deepak Malhotra and Christopher Poliquin

Handgun waiting periods are laws that impose a delay between the initiation of a purchase and final acquisition of a firearm. We show that waiting periods, which create a “cooling off” period among buyers, significantly reduce the incidence of gun violence. We estimate the impact of waiting periods on gun deaths, exploiting all changes to state-level policies in the Unites States since 1970. We find that waiting periods reduce gun homicides by roughly 17%. We provide further support for the causal impact of waiting periods on homicides by exploiting a natural experiment resulting from a federal law in 1994 that imposed a temporary waiting period on a subset of states.

Keywords: gun policy; gun violence; waiting period; injury prevention; Policy; Safety; Governing Rules, Regulations, and Reforms; United States;

Citation:
Luca, Michael, Deepak Malhotra, and Christopher Poliquin. "Handgun Waiting Periods Reduce Gun Deaths." Proceedings of the National Academy of Sciences of the United States of America 114, no. 46 (November 14, 2017). View Details

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Article | Quantitative Marketing and Economics

Aggregation of Consumer Ratings: An Application to Yelp.com

Weijia Dai, Ginger Jin, Jungmin Lee and Michael Luca

Because consumer reviews leverage the wisdom of the crowd, the way in which they are aggregated is a central decision faced by platforms. We explore this "rating aggregation problem" and offer a structural approach to solving it, 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 to restaurant reviews from Yelp.com, we construct an adjusted average rating and show that even a simple algorithm can lead to large information efficiency gains relative to the arithmetic average.

Keywords: user generated content; crowdsourcing; e-commerce; Yelp; Social and Collaborative Networks; Information; Internet; Learning; Mathematical Methods;

Citation:

Dai, Weijia, Ginger Jin, Jungmin Lee, and Michael Luca. "Aggregation of Consumer Ratings: An Application to Yelp.com." Quantitative Marketing and Economics 16, no. 3 (September 2018): 289–339. View Details

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Article | Harvard Business Review

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 the following questions: 1) 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; 2) 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; 3) 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; 4) 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

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Article | American Economic Journal: Applied Economics

Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment

Benjamin 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, Michael Luca, and Daniel Svirsky. "Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment." American Economic Journal: Applied Economics 9, no. 2 (April 2017): 1–22. View Details

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Article | Economic Inquiry

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 56, no. 1 (January 2018): 114–137. View Details

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Article | American Economic Review: Papers and Proceedings

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

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Article | Harvard Business Review

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

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Article | Management Science

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 62, no. 12 (December 2016). View Details

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Article | Management Science

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 62, no. 9 (September 2016). View Details

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Article | Journal of Economic Behavior & Organization

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

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Article | Journal of Development Economics

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

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Article | Journal of Economic Behavior & Organization

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

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Article | Journal of Economics & Management Strategy

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

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Article | Proceedings of the Conference on Empirical Methods in Natural Language Processing

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

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Book Chapters

Chapter | Handbook of Media Economics | 2016

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

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Working Papers

Working Paper | HBS Working Paper Series | 2020

The Impact of COVID-19 on Small Business Outcomes and Expectations

Alexander Bartik, Marianne Bertrand, Zoë B. Cullen, Edward L. Glaeser, Michael Luca and Christopher Stanton

To explore the impact of COVID on small businesses, we conducted a survey of more than 5,800 small businesses between March 28 and April 4, 2020. Several themes emerged. First, mass layoffs and closures had already occurred – just a few weeks into the crisis. Second, the risk of closure was negatively associated with the expected length of the crisis. Moreover, businesses had widely varying beliefs about the likely duration of COVID-related disruptions. Third, many small businesses are financially fragile: the median business with more than $10,000 in monthly expenses had only about two weeks of cash on hand at the time of the survey. Fourth, the majority of businesses planned to seek funding through the CARES act. However, many anticipated problems with accessing the program, such as bureaucratic hassles and difficulties establishing eligibility. Using experimental variation, we also assess take-up rates and business resilience effects for loans relative to grants-based programs.

Keywords: COVID-19; stimulus; Health Pandemics; Small Business; Surveys; Insolvency and Bankruptcy;

Citation:
Bartik, Alexander, Marianne Bertrand, Zoë B. Cullen, Edward L. Glaeser, Michael Luca, and Christopher Stanton. "The Impact of COVID-19 on Small Business Outcomes and Expectations." Harvard Business School Working Paper, No. 20-102, April 2020. (Revised May 2020.) View Details

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Working Paper | HBS Working Paper Series | 2017

Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity

Edward L. Glaeser, Hyunjin Kim and Michael Luca

Can new data sources from online platforms help to measure local economic activity? Government datasets from agencies such as the U.S. Census Bureau provide the standard measures of economic activity at the local level. However, these statistics typically appear only after multiyear lags, and the public-facing versions are aggregated to the county or ZIP code level. In contrast, crowdsourced data from online platforms such as Yelp are often contemporaneous and geographically finer than official government statistics. In this paper, we present evidence that Yelp data can complement government surveys by measuring economic activity in close to real time, at a granular level, and at almost any geographic scale. Changes in the number of businesses and restaurants reviewed on Yelp can predict changes in the number of overall establishments and restaurants in County Business Patterns (CBP). An algorithm using contemporaneous and lagged Yelp data can explain 29.2% of the residual variance after accounting for lagged CBP data, in a testing sample not used to generate the algorithm. The algorithm is more accurate for denser, wealthier, and more educated ZIP codes.

Keywords: Economy; Data and Data Sets; Local Range; Social and Collaborative Networks;

Citation:
Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity." Harvard Business School Working Paper, No. 18-022, September 2017. (Revised October 2017.) View Details

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Working Paper | HBS Working Paper Series | 2018

Survival of the Fittest: The Impact of the Minimum Wage on Firm Exit

Dara Lee Luca and Michael Luca

We study the impact of the minimum wage on firm exit in the restaurant industry, exploiting recent changes in the minimum wage at the city level. We find that the impact of the minimum wage depends on whether a restaurant was already close to the margin of exit. Restaurants with lower ratings are closer to the margin of exit at all observed minimum-wage levels and are disproportionately driven out of business by increases to the minimum wage. Our point estimates suggest that a one-dollar increase in the minimum wage leads to a 14% increase in the likelihood of exit for a 3.5-star restaurant (which is the median rating on Yelp) but has no discernible impact for a 5-star restaurant (on a 1 to 5 star scale). Looking at data from delivery orders, we find that lower-rated restaurants also increase prices in response to minimum-wage increases. Overall, our analysis also highlights how digital data can be used to shed new light on labor policy and the economy.

Keywords: Wages; Business Exit or Shutdown; Food and Beverage Industry;

Citation:
Luca, Dara Lee, and Michael Luca. "Survival of the Fittest: The Impact of the Minimum Wage on Firm Exit." Harvard Business School Working Paper, No. 17-088, April 2017. (Revised August 2018.) View Details

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Working Paper | HBS Working Paper Series | 2016

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

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Working Paper | HBS Working Paper Series | 2017

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 strategic forces can lead those who possess private information to voluntarily provide it. In a simple two-person disclosure game, we find that senders disclose favorable information, but withhold less favorable information. The degree to which senders withhold information is strongly related to their stated beliefs about receiver actions, and their stated beliefs are accurate on average. Receiver actions are also strongly related to their stated beliefs, but receiver actions and beliefs suggest they are insufficiently skeptical about non-disclosed information. As a result, senders increase expected returns by strategically withholding unfavorable information, in contrast with classic theoretical predictions.

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 November 2017.) View Details

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Working Paper | HBS Working Paper Series | 2015

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

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Working Paper | HBS Working Paper Series | 2014

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

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Working Paper | HBS Working Paper Series | 2016

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

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Working Paper | HBS Working Paper Series | 2019

Digitizing Disclosure: The Case of Restaurant Hygiene Scores

Weijia (Daisy) Dai and Michael Luca

Collaborating with Yelp and the city of San Francisco, we revisit a canonical example of quality disclosure by evaluating and helping to redesign the posting of restaurant hygiene scores on Yelp.com. We implement a two-stage intervention that separately identifies consumer response to information disclosure and a disclosure design with improved salience—a consumer alert. We find score posting is effective, but improving salience further increases consumer response. Moreover, the presence of an alert for a low-score restaurant reduces its probability of getting a low score again.

Keywords: Information; Web; Quality; Safety; Food; Consumer Behavior; Outcome or Result; Food and Beverage Industry;

Citation:
Dai, Weijia (Daisy), and Michael Luca. "Digitizing Disclosure: The Case of Restaurant Hygiene Scores." Harvard Business School Working Paper, No. 18-088, February 2018. (Revised March 2019. Forthcoming at American Economic Journal: Microeconomics.) View Details

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Working Paper | 2018

Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change

Edward L. Glaeser, Hyunjin Kim and Michael Luca

We demonstrate that data from digital platforms such as Yelp have the potential to improve our understanding of gentrification, both by providing data in close to real time (i.e., nowcasting and forecasting) and by providing additional context about how the local economy is changing. Combining Yelp and Census data, we find that gentrification, as measured by changes in the educational, age, and racial composition within a zip code, is strongly associated with increases in the numbers of grocery stores, cafes, restaurants, and bars, with little evidence of crowd-out of other categories of businesses. We also find that changes in the local business landscape is a leading indicator of housing price changes and that the entry of Starbucks (and coffee shops more generally) into a neighborhood predicts gentrification. Each additional Starbucks that enters a zip code is associated with a 0.5% increase in housing prices.

Keywords: Geographic Location; Local Range; Transition; Data and Data Sets; Measurement and Metrics; Forecasting and Prediction;

Citation:
Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change." NBER Working Paper Series, No. 24952, August 2018. View Details

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Working Paper | HBS Working Paper Series | 2018

Economists (and Economics) in Tech Companies

Susan Athey and Michael Luca

As technology platforms have created new markets and new ways of acquiring information, economists have come to play an increasingly central role in tech companies—tackling problems such as platform design, strategy, pricing, and policy. Over the past five years, hundreds of PhD economists have accepted positions in the technology sector. In this paper, we explore the skills that PhD economists apply in tech companies, the companies that hire them, the types of problems that economists are currently working on, and the areas of academic research that have emerged in relation to these problems.

Keywords: technology companies; economists; Technology; Business Ventures; Economics; Research; Technology Industry;

Citation:
Athey, Susan, and Michael Luca. "Economists (and Economics) in Tech Companies." Harvard Business School Working Paper, No. 19-027, September 2018. View Details

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Working Paper | HBS Working Paper Series | 2018

Complex Disclosure

Ginger Zhe Jin, Michael Luca and Daniel Martin

We present evidence that complex disclosure can result from the strategic incentives to shroud information. We implement an experiment where senders are required to report their private information truthfully but can choose how complex to make their reports. We find that senders use complex disclosure more than half the time. Most of this obfuscation is profitable because receivers make systematic mistakes in assessing complex reports. Receivers understand that senders are using complexity to hide bad news. However, strategic complexity is still effective, which can be attributed to receivers being overconfident in their ability to process complex information.

Keywords: Corporate Disclosure; Policy; Information; Complexity; Strategy;

Citation:
Jin, Ginger Zhe, Michael Luca, and Daniel Martin. "Complex Disclosure." Harvard Business School Working Paper, No. 18-105, May 2018. (Revised October 2018.) View Details

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Working Paper | HBS Working Paper Series | 2017

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 non-academic 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 ten percent drop in applications the following year. This is roughly the same as the impact on applications of dropping ten spots in the U.S. News and World Report college rankings. This impact on applications persists for two years following the high-profile scandal. We find little evidence to suggest that this drop in applications is associated with longer-term negative effects for the school such as a less competitive applicant pool or a more dangerous campus environment.

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. (Revised November 2017.) View Details

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Working Paper | HBS Working Paper Series | 2016

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

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Cases and Teaching Materials

Teaching Note | HBS Case Collection | March 2020

Behavior Change for Good

Michael Luca and Max Bazerman

Teaching Note for HBS No. 920-049.

Citation:
Luca, Michael, and Max Bazerman. "Behavior Change for Good." Harvard Business School Teaching Note 920-041, March 2020. View Details

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Teaching Note | HBS Case Collection | March 2020

Racial Discrimination on Airbnb: The Role of Platform Design

Michael Luca

Teaching Note for HBS No. 920-051.

Citation:
Luca, Michael. "Racial Discrimination on Airbnb: The Role of Platform Design." Harvard Business School Teaching Note 920-039, March 2020. View Details

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Case | HBS Case Collection | March 2020

Racial Discrimination on Airbnb: The Role of Platform Design

Michael Luca, Scott Stern, Devin Cook and Hyunjin Kim

Facing mounting criticism and evidence of widespread racial discrimination on the platform, apartment rental platform Airbnb needed to decide a path forward. For years, Airbnb had given hosts extensive discretion about whether to reject a guest after seeing little more than a name and a picture, believing this was the best way for the company to build trust. While Airbnb ran thousands of experiments per year looking at ways to grow the user base and short-run profit, they failed to track or account for the possibility of discrimination. Should they become more proactive about identifying discrimination on the platform? Should they change the design of the platform to reduce discrimination? If so, how would they decide whether the changes were successful?

Keywords: Market Platforms; Design; Prejudice and Bias; Trust; Problems and Challenges;

Citation:
Luca, Michael, Scott Stern, Devin Cook, and Hyunjin Kim. "Racial Discrimination on Airbnb: The Role of Platform Design." Harvard Business School Case 920-051, March 2020. View Details

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Module Note | March 2020

The Role of Experiments in Organizations

Michael Luca

This note outlines the structure and content of a four-class module—The Role of Experiments in Organizations—that is designed to introduce students to the role of experimental methods in managerial decisions.

Keywords: experiments; experimental methods; Decision Making; Information;

Citation:
Luca, Michael. "The Role of Experiments in Organizations." Harvard Business School Module Note 920-044, March 2020. View Details

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Case | HBS Case Collection | December 2018

Creating the French Behavioral Insights Team

Michael Luca, Ariella Kristal and Emilie Billaud

This case explores how neuroscientist Mariam Chammat helped set up the first behavioral insights team at the center of the French government, and encouraged French administrations to innovate and create policy initiatives based on psychological theories of influence and persuasion. Students are asked to assess 35 projects ripe for behavioral intervention and pick the winning proposals.

Keywords: choice architecture; Behavioral economics; experiments; Negotiation; Decision Making; Economics; Taxation; Entrepreneurship; Consumer Behavior; Public Administration Industry; Europe; France; Paris;

Citation:
Luca, Michael, Ariella Kristal, and Emilie Billaud. "Creating the French Behavioral Insights Team." Harvard Business School Case 919-015, December 2018. View Details

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Case | HBS Case Collection | March 2020

Behavior Change for Good

Max Bazerman, Michael Luca and Marie Lawrence

In 2017, Katy Milkman and Angela Duckworth created Behavior Change for Good (BCFG)—a behavioral science initiative founded with the goal of helping people achieve long-term behavior change in the areas of personal health, financial decisions (savings), and education. The initiative centered around a simple idea—by partnering with large customer-facing organizations such as gyms and banks, BCFG could design interventions aimed at changing habits of these organizations’ customers. The earliest BCFG experiments focused on increasing exercise, partnering with two major gym chains—24 Hour Fitness and Blink Fitness. The gyms were on board with the idea of nudging members to exercise more and continue their memberships. But what interventions might lead their gym members to have better exercise habits? And how will they decide if an intervention is successful?

Keywords: behavioral science; interventions; Behavior; Change; Health;

Citation:
Bazerman, Max, Michael Luca, and Marie Lawrence. "Behavior Change for Good." Harvard Business School Case 920-049, March 2020. View Details

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Case | HBS Case Collection | March 2018

Nudging Hand Hygiene Compliance at the Brigham and Women's Hospital

John Beshears, Michael Luca, Alister Martin and Simin Gharib Lee

Citation:
Beshears, John, Michael Luca, Alister Martin, and Simin Gharib Lee. "Nudging Hand Hygiene Compliance at the Brigham and Women's Hospital." Harvard Business School Case 918-035, March 2018. View Details

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Case | HBS Case Collection | August 2017

Managing Diversity and Inclusion at Yelp

Michael Luca, Joshua Schwartzstein and Gauri Subramani

This case explores the industry-wide lack of employee diversity in the technology sector and Yelp’s decision to take a leadership position in identifying strategies to increase diversity. The goal of the case is to provide an opportunity for students to develop a framework for understanding the factors that might lead to a less diverse workforce and for evaluating approaches to increase diversity. The case opens in 2014, when Yelp hired Rachel Williams into a newly created position—the Head of Diversity and Inclusion. Rachel was tasked with developing strategies to ensure that Yelp was attracting and retaining a diverse, productive workforce and creating a welcoming environment for all employees. The case puts students in Rachel’s footsteps upon being hired and tasks them to propose a set of changes for Yelp to make in order to increase the diversity of the workforce.

Keywords: Diversity; Employees; Leading Change; Strategy; Organizational Culture; Technology Industry;

Citation:
Luca, Michael, Joshua Schwartzstein, and Gauri Subramani. "Managing Diversity and Inclusion at Yelp." Harvard Business School Case 918-009, August 2017. View Details

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Case | HBS Case Collection | August 2017 (Revised November 2017)

Paktor: Designing a Dating App

Michael Luca, Stephanie Chan and Essie Alamsyah

Paktor is a popular mobile-based online dating app from Singapore, where a user can swipe right or left on a profile to indicate her interest in a potential match. The case is designed to explore issues related to pricing, market design, and launch strategies in the context of online marketplaces. Students are asked to evaluate Paktor’s existing design features and pricing and formulate recommendations on design choices, pricing, and global expansion.

Keywords: Mobile Technology; Design; Price; Product Launch; Global Strategy;

Citation:
Luca, Michael, Stephanie Chan, and Essie Alamsyah. "Paktor: Designing a Dating App." Harvard Business School Case 918-005, August 2017. (Revised November 2017.) View Details

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Teaching Note | HBS Case Collection | July 2016

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

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Teaching Note | HBS Case Collection | March 2016 (Revised February 2020)

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: Advertising Campaigns; Marketing; Online Advertising; Analysis; Performance Effectiveness;

Citation:
Luca, Michael, Weijia Dai, and Hyunjin Kim. "Advertising Experiments at RestaurantGrades." Harvard Business School Teaching Note 916-039, March 2016. (Revised February 2020.) View Details

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Supplement | HBS Case Collection | March 2016

Advertising Experiments at RestaurantGrades

Weijia Dai, Hyunjin Kim and Michael Luca

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: marketing; digital marketing; experimental methods; analytics; social media; web technology; Marketing; Online Advertising; Analysis; Performance Effectiveness;

Citation:
Dai, Weijia, Hyunjin Kim, and Michael Luca. "Advertising Experiments at RestaurantGrades." Harvard Business School Spreadsheet Supplement 916-702, March 2016. View Details

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Exercise | March 2016 (Revised March 2020)

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. (Revised March 2020.) View Details

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Background Note | HBS Case Collection | January 2016

Designing a Dating App

Michael Luca

Citation:
Luca, Michael. "Designing a Dating App." Harvard Business School Background Note 916-030, January 2016. View Details

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Case | HBS Case Collection | July 2015 (Revised April 2016)

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

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Supplement | HBS Case Collection | August 2015

Launching Yelp Reservations (B)

Michael Luca, Kevin Mohan and Patrick Rooney

Citation:
Luca, Michael, Kevin Mohan, and Patrick Rooney. "Launching Yelp Reservations (B)." Harvard Business School Supplement 916-004, August 2015. View Details

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Case | HBS Case Collection | March 2015 (Revised January 2020)

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. (Revised January 2020.) View Details

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Supplement | HBS Case Collection | March 2015 (Revised January 2020)

Behavioural Insights Team (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; United Kingdom;

Citation:
Luca, Michael, and Patrick Rooney. "Behavioural Insights Team (B)." Harvard Business School Supplement 915-025, March 2015. (Revised January 2020.) View Details

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Teaching Note | HBS Case Collection | March 2016 (Revised January 2020)

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. (Revised January 2020.) View Details

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Case | HBS Case Collection | December 2011 (Revised March 2012)

Airbnb (A)

Benjamin Edelman and Michael Luca

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

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Supplement | HBS Case Collection | December 2011 (Revised March 2012)

Airbnb (B)

Benjamin Edelman and Michael Luca

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

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Teaching Note | HBS Case Collection | December 2011 (Revised March 2015)

Airbnb (A) and (B)

Benjamin Edelman and Michael Luca

Citation:
Edelman, Benjamin, and Michael Luca. "Airbnb (A) and (B)." Harvard Business School Teaching Note 912-021, December 2011. (Revised March 2015.) View Details

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Other Publications and Materials

Article | American Economic Review: Papers and Proceedings

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

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Books

Book | 2020

The Power of Experiments: Decision-Making in a Data-Driven World

Michael Luca and Max H. Bazerman

Have you logged into Facebook recently? Searched for something on Google? Chosen a movie on Netflix? If so, you've probably been an unwitting participant in a variety of experiments—also known as randomized controlled trials—designed to test the impact of changes to an experience or product. Once an esoteric tool for academic research, the randomized controlled trial has gone mainstream—and is becoming an important part of the managerial toolkit. In The Power of Experiments: Decision-Making in a Data Driven World, Michael Luca and Max Bazerman explore the value of experiments and the ways in which they can improve organizational decisions. Drawing on real world experiments and case studies, Luca and Bazerman show that going by gut is no longer enough—successful leaders need frameworks for moving between data and decisions. Experiments can save companies money—eBay, for example, discovered how to cut $50 million from its yearly advertising budget without losing customers. Experiments can also bring to light something previously ignored, as when Airbnb was forced to confront rampant discrimination by its hosts. The Power of Experiments introduces readers to the topic of experimentation and the managerial challenges that surround them. Looking at experiments in the tech sector and beyond, this book offers lessons and best practices for making the most of experiments.

In The Power of Experiments: Decision-Making in a Data Driven World, Michael Luca and Max Bazerman explore the value of experiments, and the ways in which they can improve organizational decisions. Drawing on real world experiments and case studies, Luca and Bazerman show that going by gut is no longer enough—successful leaders need frameworks for moving between data and decisions. Experiments can save companies money—eBay, for example, discovered how to cut $50 million from its yearly advertising budget without losing customers. Experiments can also bring to light something previously ignored, as when Airbnb was forced to confront rampant discrimination by its hosts.

The Power of Experiments introduces readers to the topic of experimentation and the managerial challenges that surround them. Looking at experiments in the tech sector and beyond, this book offers lessons and best practices for making the most of experiments.

Keywords: experiments; randomized controlled trials; Organizations; Decision Making; Data and Data Sets; Management Analysis, Tools, and Techniques;

Citation:
Luca, Michael, and Max H. Bazerman. The Power of Experiments: Decision-Making in a Data-Driven World. Cambridge, MA: MIT Press, 2020. View Details

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