Assistant Professor
Boston University Questrom School of Business
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Digital Fellow @ MIT Initiative on the Digital Economy
I am a professor who studies the design of digital platforms, quantitative marketing, the digitization of the economy, and search behavior in markets. You can find additional information about me and my work
here, on
Twitter, and on
Google Scholar.
I am now blogging and podcasting on Substack at
Empiricrafting.
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Algorithmic Auditing Using Revealed Preference and Experimental Variation
We propose a novel methodology for auditing algorithmic systems by treating them as decision-makers optimizing specific objectives. Combining revealed preference with experimental variation, our approach disentangles the priorities embedded within algorithms. Applying this framework to real-world ranking systems at a leading technology company, we estimate latent utility functions and infer algorithmic trade-offs. Our methodology has implications for fairness, transparency, and accountability in algorithmic decision-making.
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Data Sharing and Website Competition: The Role of Dark Patterns
Regulations like the GDPR require firms to obtain consumer consent before using data. In response, some firms employ "dark patterns" — interface designs that nudge consumers to share data. We study the causal effects of these designs and how they vary across individuals and firms. To do so, we run a field experiment in which users download a browser extension that randomizes cookie consent interface designs as users browse the Internet. We find that consumers accept all cookies more than half of the time in the absence of dark patterns. Hiding consent options behind an additional click is the most effective dark pattern, while designs that only manipulate visual elements have smaller effects. Larger and better-known firms have moderately higher consent rates than other firms, giving them a slight competitive advantage. However, the effects of dark patterns do not vary systematically across site popularity. We find no evidence that frequent pop-ups increase choice fatigue.
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Vertical Integration and Consumer Choice: Evidence from a Field Experiment
Please email for a copy.
Many firms, from retailers to investment management companies, offer their own products alongside products sold by competitors. This form of vertical integration has been the target of regulation in digital markets based on concerns of harm to consumers. We study the effects of this practice on consumer choice in the context of Amazon.com. We run a field experiment using a custom browser extension that allows us to generate random variation in the set of products observable to consumers. When Amazon brands are not available, consumers substitute toward products that are comparable along most observable dimensions, including price, average rating, and shipping speed. One exception is that the substitute products have many fewer reviews---often considered a proxy for quality---compared to Amazon brands. We collect supplemental evidence from surveys which shows that consumers view Amazon branded products similarly to other products. On the supply side, we do not find evidence that Amazon discriminates in favor of its own products in search results. In addition, the absence of Amazon brands does not noticeably change consumer search behavior. The results are consistent with consumer preferences that prioritize low prices, high ratings, and fast delivery over other product characteristics, such as brand or seller reputation.
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Webmunk: A New Tool for Studying Online Behavior and Digital Platforms
Understanding the behavior of users online is important for researchers, policymakers, and companies. But measuring behavior online and conducting experiments is difficult for independent researchers, who do not have access to the user bases or software of technology companies. We introduce Webmunk, an open-source tool designed to make conducting online studies much easier. The user facing side of Webmunk is a browser extension that can track consumer browsing behavior and experimentally modify consumers experiences as they browse the Internet. It can be installed just like any other browser extension. Through this extension, researchers can collect a host of consumer data, from URLs to web page HTML elements, clicks, and scroll positions. The extension can also modify information and change the look of a web page, allowing for researchers to implement interventions that vary across study participants. A key advantage of this approach is that interventions occur while participants are engaging in real world activities such as shopping, browsing the news, using social media, or searching for information. We demonstrate the power of Webmunk by discussing two studies in progress.
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Competition Avoidance vs Herding in Job Search: Evidence from Large-scale Field Experiments on an Online Job Board
Accepted at Management Science
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.
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Consumer Protection in an Online World: An Analysis of Occupational Licensing
AEJ:Applied Economics (July 2024)
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.
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Dog Eat Dog: Balancing Network Effects and Differentiation in a Digital Platform Merger
Management Science (January 2024)
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.
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Do Incentives to Review Help the Market? Evidence from a Field Experiment on Airbnb
Marketing Science (September 2023)
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.
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Self-Preferencing at Amazon: Evidence from Search Rankings
AEA Papers and Proceedings (May 2023)
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.
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The Welfare Effects of Peer Entry: The Case of Airbnb and the Accommodation Industry
American Economic Review (June 2022)
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.
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Reciprocity and Unveiling in Two-sided Reputation Systems: Evidence from an Experiment on Airbnb
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".
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.
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Blame the Parents? How Parental Unemployment Affects Labor Supply and Job Quality for Young Adults
Journal of Labor Economics (January 2019)
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.
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The Impact of Unemployment Insurance on Job Search: Evidence from Google Search Data
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.
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The Welfare Economics of Default Options in 401(k) Plans
American Economic Review (September 2015)
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.
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What Does Banning Short-Term Rentals Really Accomplish?
with Sophie Calder-Wang and Chiara Farronato
Harvard Business Review - Digital (2024)
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Understanding the Tradeoffs of the Amazon Antitrust Case
with Chiara Farronato, Andrei Hagiu, and Dionne Lomax
Harvard Business Review - Digital (2024)
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Platform Papers: Do Incentives to Review Help the Market?
Platform Papers (2023)
Commentary on reputation design, with reference to the paper "Do Incentives to Review Help the Market?".
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The balance between platform variety and network effects
VoxEU (2021)
Summary of our Dog Eat Dog paper.
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Tit for Tat? The Difficulty of Designing Two-Sided Reputation Systems
NIM Marketing Intelligence Review (2020)
This article provided a practitioner overview of recent work on reputation system design.
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Digital Marketplaces
New Palgrave Dictionary of Economics (2017).
This article provides an overview of the economics of digital marketplaces.
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Digital Market Design and Inequality
Oxford University Press Volume IV: "More Equal by Design: Economic Design Responses to Inequality". Eds. Scott Duke Kominers and Alex Teytelboym. (2017)
This article discusess how privacy and the design of digital marketplaces may affect inequality.
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Search, Matching, and the Role of Digital Marketplace Design in Enabling Trade: Evidence from Airbnb
This paper supercedes the first half of "Search Frictions and the Design of Online Marketplaces". (Most Recent Version 2018)
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.
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A Simulation Approach to Designing Digital Matching Platforms
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.
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.