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SUMMARY:Off-Platform Tracking and Data Externalities - Christian Peukert\,
  Associate Professor\, HEC Lausanne\, University of Lausanne
DTSTART:20251022T130000Z
DTEND:20251022T140000Z
UID:TALK239485@talks.cam.ac.uk
CONTACT:Emily Brown
DESCRIPTION:Many online platforms generate revenue through targeted advert
 ising\, which depends on access to detailed consumer data. While platforms
  collect information directly from their users\, they can also track both 
 users' and non-users' off-platform browsing behaviour\, allowing them to b
 uild consumer profiles that are valuable to advertisers. In this context\,
  data externalities arise when user-provided data (e.g. demographic inform
 ation) enables platforms to infer non-users' characteristics\, raising imp
 ortant privacy concerns. Focusing on Meta’s Facebook\, we provide empiri
 cal evidence on the extent of off-platform tracking and quantify data exte
 rnalities. We first demonstrate that Facebook monitors a substantial porti
 on -- over 40% -- of online activity for both users and non-users of the p
 latform. We then train a machine learning model using only data from Faceb
 ook users to identify patterns that link individuals' browsing behaviour w
 ith their demographic attributes. Applying this model to non-users' browsi
 ng data\, we find that Facebook can infer non-users' demographic character
 istics with significantly higher accuracy than random guessing\, providing
  evidence of data externalities. We further examine the impact of the Euro
 pean General Data Protection Regulation (GDPR) and find that while the reg
 ulation reduced Facebook’s tracking of non-users by over a third\, it on
 ly marginally decreased data externalities by 1.4 percent. Our findings su
 ggest that even with stricter privacy regulations\, platforms can infer pe
 rsonal information from off-platform tracking\, limiting the effectiveness
  of policies aimed at enhancing consumer privacy.
LOCATION:LT1\, Cambridge Judge Business School
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