Person:

Luca, Michael

A central prediction of information economics is that market forces can lead businesses to voluntarily provide information about the quality of their products, yet little voluntary disclosure is observed in the field. In this paper, we demonstrate that the inconsistency between theory and reality is driven by a fundamental failure in consumer inferences when sellers withhold information. Using a series of laboratory experiments, we implement a simple disclosure game in which senders can verifiably report quality to receivers. We find that senders disclose less often than equilibrium would predict. Receivers are not sufficiently skeptical about undisclosed information – they underestimate the extent to which no news is bad news. Senders generally take advantage of receiver mistakes. We find that providing disclosure rates by quality score helps to improve receiver inferences.

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.

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

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. The evidence suggests that higher minimum wages increase overall exit rates for restaurants. However, lower quality restaurants, which are already closer to the margin of exit, are disproportionately impacted 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), but has no discernible impact for a 5-star restaurant (on a 1 to 5 star scale).

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.

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.

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.

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.

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.

Economists have become increasingly interested in studying the nature of production functions in social policy applications, with the goal of improving productivity. Traditionally models have assumed workers are homogenous inputs. However, in practice, substantial variability in productivity means the marginal productivity of labor depends substantially on which new workers are hired—which requires not an estimate of a causal effect, but rather a prediction. We demonstrate that there can be large social welfare gains from using machine learning tools to predict worker productivity, using data from two important applications—police hiring and teacher tenure decisions.