This paper documents three stylized facts about the demand for Large Language Models (LLMs) using data from OpenRouter, a prominent LLM marketplace. First, new models experience rapid initial adoption that stabilizes within weeks. Second, model releases differ substantially in whether they primarily attract new users or substitute demand from competing models. Third, multi-homing—using multiple models simultaneously—is common among apps. These findings suggest significant horizontal and vertical differentiation in the LLM market, implying opportunities for providers to maintain demand and pricing power despite rapid technological advances.

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.

We study the welfare consequences of choice architecture for online privacy using a field experiment that randomizes cookie consent banners. We study three ways in which firms or policymakers can influence choices: (1) nudging users through banner design to encourage acceptance of cookie tracking; (2) setting defaults when users dismiss banners; and (3) implementing consent decisions at the website versus browser level. Absent design manipulation, users accept all cookies more than half of the time. Placing cookie options behind extra clicks strongly influences choices, shifting users toward more easily accessible alternatives. Many users dismiss banners without making an explicit choice, underscoring the importance of default settings. Survey evidence further reveals substantial confusion about default settings. Using a structural model, we find that among consent policies requiring site-specific decisions, consumer surplus is maximized when consent interfaces clearly display all options and default to acceptance in the absence of an explicit choice. However, the welfare gains from optimizing banner design are much smaller than those from adopting browser-level consent, which eliminates the time costs of repeated decisions.

Platforms, retailers, and other firms often offer their own products alongside products sold by competitors. We study the effects of this practice by combining a field experiment that hides brands owned by Amazon (i.e., private labels) from shoppers on Amazon.com with model-based counterfactuals and welfare analysis. In the absence of private labels, consumers substitute toward products that are similar along most observable dimensions. Removing Amazon brands does not change consumers' search effort or their propensity to shop at other retail websites. Despite the ample availability of observably similar alternatives, our welfare estimates imply that, for the categories we study, removing Amazon brands would reduce consumer surplus by 5.4 percent in the short run, with approximately 10 percent of the impact due to equilibrium price increases by other sellers. The effects are heterogeneous, with consumer surplus reductions exceeding 10 percent in some categories, while other categories realize much smaller decreases when Amazon brands are removed. Demoting private labels in search results to counteract potential self-preferencing does not lead to gains in consumer surplus. This outcome arises because a subset of consumers derive greater utility from private labels and benefit from their high placement in search results.

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.

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.

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.

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

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.

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.

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.

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.

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.

Summary of our Dog Eat Dog paper.

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

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.

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.

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.

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.

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

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

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.

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.