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dc.contributor.authorDEUTSCHER, Elias
dc.date.accessioned2018-08-30T13:23:01Z
dc.date.available2018-08-30T13:23:01Z
dc.date.issued2018
dc.identifier.issn1725-6739
dc.identifier.urihttps://hdl.handle.net/1814/58064
dc.description.abstractThis study contributes to the current debate on how privacy concerns can and should be integrated into merger analysis. First, I contend that while competition authorities increasingly account for the role of personal data as a source of market power and entry barriers, privacy-related consumer harm still remains a blind spot in merger analysis. Second, I discuss how this analytical gap can be filled by mapping out three potential theories of privacy-related consumer harm: namely, privacy as an element of product quality, privacy as a feature of consumer choice, and privacy as non-monetary price. Third, and this is my major claim and contribution, this study proposes willingness-to-pay studies in the form of conjoint analysis as a methodology that enables competition authorities to quantify privacy-related consumer harm in monetary terms. In a fourth section, this study discusses potential objections to this approach. In so doing, it shows that the widespread opposition against the incorporation of privacy into merger analysis is based on a ‘privacy fallacy’. This ‘privacy fallacy’ derives from the erroneous assumption that deteriorations in the level of privacy protection as a consequence of a merger automatically amount to a breach of data-protection rules which should be addressed by data protection authorities, but which do not constitute an antitrust concern.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.relation.ispartofseriesEUI LAWen
dc.relation.ispartofseries2018/13en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.subjectBig dataen
dc.subjectCompetition lawen
dc.subjectPrivacyen
dc.subjectMerger controlen
dc.subjectConjoint analysisen
dc.titleHow to measure privacy-related consumer harm in merger analysis? : a critical reassessment of the EU Commission's merger control in data-driven marketsen
dc.typeWorking Paperen


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