Assessing the Impact of Algorithmic Quantity Regulations on Sharing Platforms: Evidence from Airbnb in Paris

Shagun Tripathi, Georgios Petropoulos and Harris Kyriakou

No 11811, CESifo Working Paper Series from CESifo

Abstract: In recent years, several automated caps, or algorithmic quantity regulations (AQRs), have been deployed to police supply conditions in sharing economy platforms. AQRs constitute a paradigm shift in platform regulation, as they enable exhaustive, and low-cost enforcement, thus comprehensively influencing interactions both within and outside the focal platform. However, their actual impact is not known, and has not been studied so far. In this work, we employ a series of difference-in-differences analyses to provide causal evidence on the impact of AQR both within, as well as outside a focal sharing platform - Airbnb. First, within Airbnb, we find that the quality of platform offerings was negatively affected after the introduction of an algorithmic quantity regulation - marked by 6% decline in ratings. Additionally, we find that the AQR affected certain platform participants disproportionately. Providers without organic and designated trust building signals, i.e., inexperienced hosts and non-superhosts, bore the cost of the AQR, ending up worse off than their counterparts. Second, we find that the occupancy rates of providers in the competing platform Vrbo declined by 3.6% as a result to Airbnb’s AQR. Third, we find a reduction in housing prices by 0.402 units after the introduction of the AQR. This research provides novel empirically grounded insights on the implications of AQRs that can shape the future of sharing economy platforms’ regulation.

Keywords: sharing platforms; algorithmic quantity regulation; anticipatory effect; spillover effect; causal inference. (search for similar items in EconPapers)
JEL-codes: L51 L86 R31 (search for similar items in EconPapers)
Date: 2025
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