Michael Luca

Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud

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 Yelps 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 businesss decision to leave fake reviews.

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

Restaurant hygiene inspections are often cited as a success story of public disclosure. Hygiene grades influence customer decisions and serve as an accountability system for restaurants. However, cities (which are responsible for inspections) have limited resources to dispatch inspectors, which in turn limits the number of inspections that can be performed. We argue that 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.

Optimal Aggregation of Consumer Ratings: An Application to Yelp.com

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

When 3 + 1 > 4: Gift Structure and Reciprocity in the Field

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.

User-Generated Content and Social Media

This paper documents what economists have learned about 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.

Is No News (Perceived as) Bad News? An Experimental Investigation of Information Disclosure

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 naivete.

BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE: BIG DATA AND BIG CITIES

New, “big” data sources allow measurement of city characteristics and outcome variables 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.

Curbing Adult Student Attrition: Evidence from a Field Experiment

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.

Designing Online Marketplaces: Trust and Reputation Mechanisms

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 tool kit for designing online marketplaces, focusing on trust and reputation mechanisms.

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

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 percent 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 minimum wage increases. Overall, our analysis also highlights how digital data can be used to shed new light on labor policy and the economy.