Research

Published

Information Economics and Policy 65 (2023): 101063

Market design for personal data

Coauthors: Dirk Bergemann, Jacques Cremer, David Dinielli, Carl-Christian Groh, Paul Heidhues, Monika Schnitzer, Fiona Scott Morton, Katja Seim, Michael Sullivan2023-08-31
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Yale J. on Reg. 40, 1056

Journal of European Competition Law & Practice, 2022

Working Paper

Airbnb and Rents: Evidence from Berlin

Coauthors: Kevin Ducbao Tran, Tomaso Duso, Claus Michelsen

Cities worldwide have regulated peer-to-peer short-term rental platforms claiming that those platforms remove apartments from the long-term housing market, causing an increase in rents. Establishing and quantifying such a causal link is, however, challenging. We investigate two policy changes in Berlin to first assess how effective they were in regulating Airbnb, the largest online peer-to-peer short-term rental platform. We document that the policy changes reduced the number of entire homes listed on Airbnb substantially, by eight to ten listings per square kilometer. In particular the introduction of limitations on the misuse of regular rental apartments as short-term accommodations, also strongly reduced the average number of days per year that Airbnb listings are available for booking. In a second step, we then use this policy-induced change in Airbnb supply to assess the impact of Airbnb on rents in the city. Our results suggest that each nearby apartment on Airbnb increases average monthly rents by at least seven cents per square meter. This effect is larger for Airbnb listings that are available for rent for a larger part of the year. Further analyses suggest some effect heterogeneity across the city. In particular, areas with lower Airbnb density tend to be affected more by additional Airbnb listings.

DIW Berlin Discussion Paper No. 1890 (R&R at Regional Science and Urban Economics)

Value for Money and Selection: How Pricing Affects Airbnb Ratings

Coauthors: Christoph Carnehl, Kevin Ducbao Tran, André Stenzel

We investigate the impact of prices on ratings using Airbnb data. We theoretically illustrate two opposing channels: higher prices reduce the value for money, worsening ratings, but they increase the taste-based valuation of the average traveler, improving ratings. Results from panel regressions and a regression discontinuity design suggest a dominant value-for-money effect. In line with our model, hosts strategically complement lower prices with higher effort more when ratings are relatively low. Finally, we provide evidence that, upon entry, strategic hosts exploit the dominant value-for-money effect. The median entry discount of seven percent improves medium-run monthly revenues by three percent.

On the Emergence of Cooperation in the Repeated Prisoner's Dilemma

Coauthors: Single Author

Using simulations between pairs of ϵ-greedy q-learners with one-period memory, this article demonstrates that the potential function of the stochastic replicator dynamics (Foster and Young, 1990) allows it to predict the emergence of error-proof cooperative strategies from the underlying parameters of the repeated prisoner's dilemma. The observed cooperation rates between q-learners are related to the ratio between the kinetic energy exerted by the polar attractors of the replicator dynamics under the grim trigger strategy. The frontier separating the parameter space conducive to cooperation from the parameter space dominated by defection can be found by setting the kinetic energy ratio equal to a critical value, which is a function of the discount factor, f(δ)=δ/(1−δ), multiplied by a correction term to account for the effect of the algorithms' exploration probability. The gradient at the frontier increases with the distance between the game parameters and the hyperplane that characterizes the incentive compatibility constraint for cooperation under grim trigger.
Building on literature from the neurosciences, which suggests that reinforcement learning is useful to understanding human behavior in risky environments, the article further explores the extent to which the frontier derived for q-learners also explains the emergence of cooperation between humans. Using metadata from laboratory experiments that analyze human choices in the infinitely repeated prisoner's dilemma, the cooperation rates between humans are compared to those observed between q-learners under similar conditions. The correlation coefficients between the cooperation rates observed for humans and those observed for q-learners are consistently above 0.8. The frontier derived from the simulations between q-learners is also found to predict the emergence of cooperation between humans.

Facebook Shadow Profiles

Coauthors: Hannes Ullrich, Christian Peukert, Luis Aguiar

Data is often at the core of digital products and services, especially when related to online advertising. This has made data protection and privacy a major policy concern. When surfing the web, consumers leave digital traces that can be used to build user profiles and infer preferences. We quantify the extent to which Facebook can track web behavior outside of their own platform. The network of engagement buttons, placed on third-party websites, lets Facebook follow users as they browse the web. Tracking users outside its core platform enables Facebook to build shadow profiles. For a representative sample of US internet users, 52 percent of websites visited, accounting for 40 percent of browsing time, employ Facebook's tracking technology. Small differences between Facebook users and non-users are largely explained by differing user activity. The extent of shadow profiling Facebook may engage in is similar on privacy-sensitive domains and across user demographics, documenting the possibility for indiscriminate tracking.

Airbnb, Hotels, and Localized Competition

Coauthors: Kevin Ducbao Tran

The rise of online platforms has disrupted numerous traditional industries. A prime example is the short-term accommodation platform Airbnb and how it affects the hotel industry. On the one hand, consumers can profit from Airbnb due to an increased number of choices and lower prices. On the other hand, critics of the platform argue that it allows professional hosts to operate de facto hotels while being subject to much laxer regulation. Understanding the nature of competition between Airbnb and hotels as well as quantifying consumer welfare gains from Airbnb is important to inform the debate on necessary platform regulation. In this paper, we analyze competition between hotels and Airbnb listings and the effect of Airbnb on consumer welfare. For this purpose, we use granular daily-level data from Paris for the year 2017. We estimate a nested logit model of demand that allows for consumer segmentation along accommodation types and the different districts within the city. We extend prior research by modeling the localized nature of competition taking place within districts of the city. Our results suggest that demand is segmented by district as well as accommodation type. Based on the parameter estimates, we calibrate a supply-side model to assess how Airbnb affects hotel revenues and consumer welfare. Our simulations imply that Airbnb increases average consumer surplus by 4.3 million euro per night and reduces average hotel revenues by 1.8 million euro. Furthermore, we find that 25 percent of Airbnb travelers would choose hotels if Airbnb did not exist.

Work in Progress