with Chiara Farronato, Brad Larsen and Erik Brynjolfsson

Forthcoming at AEJ:Applied Economics (July 2024).

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We study the demand and supply implications of occupational licensing using transaction-level data from a large online platform for home improvement services. We find that demand is more responsive to a professional's reviews than to the professional's platform-verified licensing status. We show some evidence that consumers view licenses and reviews as substitutes. We confirm the generality of our findings off the platform in an independent consumer survey. Combining state-level licensing regulation data with platform micro-data, we find that more stringent requirements are associated with less competition, higher prices, and no increase in demand or consumer satisfaction.

with Monica Bhole and John Horton

Minor Revision at Management Science

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We study how information that may simultaneously signal the degree of competition and vacancy quality affects job search. To do so, we conduct three experiments on a large online job platform in which the treatment varies what information is shown to job seekers. Information about the number of prior applicants to a vacancy increases the number of applications and redirects them to vacancies with few prior applications. Information about vacancy age increases application rates, especially to new vacancies. To further investigate the causal mechanisms, we conduct and analyze a survey choice experiment. We conclude that job seekers prefer to avoid competition rather than using the popularity of a vacancy as a signal of quality.

with Chiara Farronato and Alexander MacKay

AEA Papers and Proceedings (May 2023)

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We study whether Amazon engages in self-preferencing on its marketplace by favoring its own brands (e.g., Amazon Basics) in search. To address this question, we collect new micro-level consumer search data using a custom browser extension installed by a panel of study participants. Using this methodology, we observe search positions, search behavior, and product characteristics. We find that Amazon branded products are indeed ranked higher than observably similar products in consumer search results. The prominence given to Amazon brands is 30% to 60% of the prominence granted to sponsored products.

with Chiara Farronato and Jessica Fong

Management Science (2023)

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Mergers among digital platforms are increasingly receiving public and regulatory attention. These mergers may benefit users if network effects from a combined platform are large enough or may hurt users if the two platforms are differentiated and one of the platforms is shut down. We study the net effect of this trade-off in the context of the merger between the two largest platforms for pet-sitting services. We exploit geographic variation in pre-merger market shares and a difference-in-differences approach to causally estimate network effects. We find that users of the acquiring platform benefited from the merger because of network effects. However, users of the acquired platform were more likely to exit the market, for reasons including switching costs, coordination failures, and disintermediation. Network effects and attrition offset each other such that at the market level consumers are, on average, not substantially better off with a single combined platform than with two separate and competing platforms. Our results highlight the importance of platform differentiation even when platforms enjoy network effects, which has important implications for antitrust authorities and platform owners.

with David Holtz

Marketing Science (2023)

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Many online reputation systems operate by asking volunteers to write reviews for free. As a result, a large share of buyers do not review, and those who do review are self-selected. This can cause the reputation system to miss important information about seller quality. We study the extent to which a platform can improve market outcomes by attempting to increase the amount and quality of information collected by its reputation system. We do so by analyzing a randomized experiment conducted by Airbnb. In the treatment, buyers were offered a coupon to review listings that had no prior reviews. In the control, buyers were not offered any incentive to review. We find that although the treatment induced additional reviews that were more negative on average, these reviews did not affect the number of nights sold or total revenue. Furthermore, we find that, contrary to the treatment's intended effect, Airbnb's incentivized program caused transaction quality for treated sellers to fall. We examine how the quality of the induced reviews, market conditions, and the design of Airbnb's reputation system can explain our findings.

(with Chiara Farronato)

American Economic Review (June 2022)

Abstract | PDF | Appendix | Code

We study the effects of enabling peer supply through Airbnb in the accommodation industry. We present a model of competition between flexible and dedicated sellers - peer hosts and hotels - who provide differentiated products. We estimate this model using data from major US cities and quantify the welfare effects of Airbnb on travelers, hosts, and hotels. The welfare gains are concentrated in locations (New York) and times (New Years Eve) when hotels are capacity constrained. This occurs because peer hosts are responsive to market conditions, expand supply as hotels fill up, and keep hotel prices down as a result.

(with Elena Grewal and David Holtz )

Marketing Science (October 2021)
This is a substantially revised version of a paper presented at EC'15 as: "Bias and Reciprocity in Online Reviews: Evidence from Field Experiments on Airbnb".

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Reputation systems are used by nearly every digital marketplace, but designs vary and the effects of these designs are not well understood. We use a large-scale experiment on Airbnb to study the causal effects of one particular design choice — the timing with which feedback by one user about another is revealed on the platform. Feedback was hidden until both parties submitted a review in the treatment group and was revealed immediately after submission in the control group. The treatment stimulated more reviewing in total. This is due to users' curiosity about what their counterparty wrote and/or the desire to have feedback visible to other users. We also show that the treatment reduced retaliation and reciprocation in feedback and led to lower ratings as a result. The effects of the policy on feedback did not translate into reduced adverse selection on the platform.

(with Frederic Panier and Ilan Tojerow)

Journal of Labor Economics (January 2019)

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We study the role of shocks to parental income in determining the labor market outcomes of children entering the labor market. We find that a child whose parent loses a job prior to the child’s labor market entry is, on average, induced to work 9 percent more in the 3 years following labor market entry than a child whose parents lose a job after the child’s entry. This effect is concentrated on the extensive margin and decreases in magnitude over time. We find no evidence that parental support affects the quality of the initial job that entrants find.

(with Scott R. Baker)

Review of Economics and Statistics (December 2017)
Also see "On Event Study Designs and Distributed-Lag Models in Two-Way Fixed Effect Models: Identification, Equivalence, and Generalization" for an extension of our event study results. This paper applies an improved methodology to our results and shows that our estimates about potential benefit duration are consistent with an improved estimation procedure for the event study.

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Job search is a key choice variable in theories of labor markets but is difficult to measure directly. We develop a job search activity index based on Google search data, the Google Job Search Index (GJSI). We validate the GJSI with both survey- and web-based measures of job search. Unlike those measures, the GJSI is high-frequency, geographically precise, and available in real time.We demonstrate the GJSI’s utility by using it to study the effects of changes in the unemployment insurance (UI) system between 2008 and 2014 on job search, finding no evidence of economically meaningful decreases in aggregate search activity.

(with Douglas Bernheim and Igor Popov)

American Economic Review (September 2015)

Abstract | PDF | Appendix

Default contribution rates for 401(k) pension plans powerfully influence workers’ choices. Potential causes include opt-out costs, procrastination, inattention, and psychological anchoring. We examine the welfare implications of defaults under each of these theories. We show how the optimal default, the magnitude of the welfare effects, and the degree of normative ambiguity depend on the behavioral model, the scope of the choice domain deemed welfare-relevant, the use of penalties for passive choice, and other 401(k) plan features. Depending on which theory and welfare perspective one adopts, virtually any default contribution rate may be optimal. Still, our analysis provides reasonably robust justifications for setting the default either at the highest contribution rate matched by the employer or – contrary to common wisdom – at zero. We also identify the types of empirical evidence needed to determine which case is applicable.

with Chiara Longoni, Luca Cian, and Gordon Pennycook

Fairness, Accountability, and Transparency (ACM FAccT 2022)

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Artificial Intelligence (AI) can generate text virtually indistinguishable from text written by humans. A key question, then, is whether people believe news generated by AI as much as news generated by humans. AI is viewed as lacking human motives and emotions, suggesting that people might view news written by AI as more accurate. By contrast, two pre-registered experiments on representative U.S. samples (N=4,034) showed that people rated news written by AI as less accurate than news written by humans. People were more likely to incorrectly rate news written by AI (vs. a human) as inaccurate when it was actually true, and more likely to correctly rate it as inaccurate when it was indeed false. Our findings are important given the increasing adoption of AI in news generation, and the associated ethical and governance pressures to disclose it use and address standards of transparency and accountability.

(with Elena Grewal and David Holtz )

Proceedings of the Sixteenth ACM Conference on Economics and Computation (EC '15)
This paper is superseded by 'Reciprocity and Unveiling in Two-sided Reputation Systems' and 'Do Incentives to Review Help the Market?'

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Reviews and other evaluations are used by consumers to decide what goods to buy and by firms to choose whom to trade with, hire, or promote. However, because potential reviewers are not compensated for submitting reviews and may have reasons to omit relevant information in their reviews, reviews may be biased. We use the setting of Airbnb to study the determinants of reviewing behavior, the extent to which reviews are biased, and whether changes in the design of reputation systems can reduce that bias. We find that reviews on Airbnb are generally informative and 97% of guests privately report having positive experiences. Using two field experiments intended to reduce bias, we show that non-reviewers tend to have worse experiences than reviewers and that strategic reviewing behavior occurred on the site, although the aggregate effect of the strategic behavior was relatively small. We use a quantitative exercise to show that the mechanisms for bias that we document decrease the rate of reviews with negative text and a non-recommendation by just .86 percentage points. Lastly, we discuss how online marketplaces can design more informative review systems.

with Sophie Calder-Wang and Chiara Farronato

Harvard Business Review - Digital (2024)

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with Chiara Farronato, Andrei Hagiu, and Dionne Lomax

Harvard Business Review - Digital (2024)

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Platform Papers (2023)

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Commentary on reputation design, with reference to the paper "Do Incentives to Review Help the Market?".

with Chiara Farronato and Jessica Fong

VoxEU (2021)

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Summary of our Dog Eat Dog paper.

(with David Holtz)

NIM Marketing Intelligence Review (2020)

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This article provided a practitioner overview of recent work on reputation system design.

New Palgrave Dictionary of Economics (2017).

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This article provides an overview of the economics of digital marketplaces.

Oxford University Press Volume IV: "More Equal by Design: Economic Design Responses to Inequality". Eds. Scott Duke Kominers and Alex Teytelboym. (2017)

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This article discusess how privacy and the design of digital marketplaces may affect inequality.

This paper supercedes the first half of "Search Frictions and the Design of Online Marketplaces". (Most Recent Version 2018)

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Two-sided marketplaces are distinguished by the fact that both sides have preferences regarding each others' non-price characteristics. This paper studies how digital platform design affects transaction costs and volume in these markets by analyzing the decisions of guests and hosts to search and match with each other on Airbnb. I show that the two-sided nature of the market is important. Through 2014, rejections of guests by hosts occur for 42% of inquiries regarding booking and these rejections causally decrease the rate at which guests eventually book on the platform by 43% to 70%. Rejections are primarily caused by stale vacancies and the screening of guests by hosts. I use data on search and communication to estimate a model of guest and host choices. I apply this model to study the effects of search engine design and find that, by tracking listing availability, Airbnb reduces rejections by 59%. I then show that incorporating host preferences into rankings can further increase match rates and discuss how Airbnb's subsequent innovations reflect these findings.

This paper supercedes the second half of "Search Frictions and the Design of Online Marketplaces". I've decided to retire this paper since I no longer enjoy working on it by myself, even though it received a reject and resubmit at management science. Most recent version from 2019.

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Digital matching marketplaces are characterized by user heterogeneity, limited capacity, and dynamic market clearing. These features create spillovers between users. For example, an Airbnb listing booked by one guest cannot be booked by another guest for the same night. Spillovers limit the applicability of many experimental and observational methods for evaluating the effects of marketplace policies. In this paper, I show how to use marketplace simulations as an input into the design of user acquisition strategies and ranking algorithms. I calibrate a marketplace simulation using data on searches and transactions from Airbnb and use it to address three topics: the returns to scale in matching, the heterogeneity in returns to user acquisition, and the size of bias in experimental designs. I find that returns to scale are initially increasing due to market thickness effects and then decreasing due to availability frictions in search. Furthermore, heterogeneity in the value of listings to the platform is large – the effect of acquiring 25% more listings on bookings varies between -4.1% and 5.4% depending on the quartile of listing quality. I then measure the extent of bias in experimental treatment effects due to spillovers. The treatment effect of a better ranking algorithm on conversion rates is overstated by 53% when a quarter of users are randomized into treatment.