Consumer Privacy and the Market for Customer Information

Taylor, Curtis R.

doi:10.2307/1593765

Cited by 345 publications

(179 citation statements)

References 15 publications

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“…An ostensible reason for firms to acquire customer records is to predict consumer preferences or offer customized services (Garfinkel et al, 2007;Li et al, 2011;Tsai et al, 2011). Our analysis suggests they may use such records simply for static price discrimination (Taylor, 2004;Fudenberg and Villas-Boas, 2006). …”

Section: H L} If and Only Ifmentioning

confidence: 90%

“…This powerful pricing mechanism results from the dynamic change in consumer valuation, not consumer identification instruments such as purchase history (Levinthal and Purohit, 1989;Fudenberg and Tirole, 1998;Lee and Lee, 1998) or cross-market information sales (Taylor, 2004;Hermalin and Katz, 2006). To our knowledge, that a vendor can achieve perfect intertemporal price discrimination without identifying consumers is a novel result.…”

Section: No Upgrade Policymentioning

confidence: 97%

“…In any case, the assumption of myopic or naive consumers is customary in many studies of dynamic consumer choices (see, e.g., Taylor, 2004;Acquisti and Varian, 2005;Liu and Zhang, 2013).…”

Section: Strategic Playersmentioning

confidence: 99%

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Technology Mergers and Acquisitions in the Presence of an Installed Base: A Strategic Analysis

Wang

Hui

2017

Information Systems Research

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We study the strategic benefits of M&A when competing IT vendors sell different generations of the same product with different quality. We assume the new product arrives unexpectedly when an installed base of old product exists. We show that the combination of consumers' purchase history and heterogeneity leads to new demand complexity that gives rise to innovative product strategies. We find that shelving the old product is an important motivation for M&A. The acquirer may exercise static or intertemporal price discrimination depending on whether it can exercise upgrade pricing. M&A may speed up or slow down new product consumption, and it can lead to delayed new product introduction in some markets. However, it always increases the acquirer's profit and can sometimes help maximize social welfare. We discuss relevant managerial and policy implications.

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Section: H L} If and Only Ifmentioning

confidence: 90%

Section: No Upgrade Policymentioning

confidence: 97%

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Technology Mergers and Acquisitions in the Presence of an Installed Base: A Strategic Analysis

Wang

Hui

2017

Information Systems Research

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“…As seen in Varian (2009), this approach allows researchers to analyze the release of private data in the context of the trade-off between the network effects created by the data release and the utility loss associated with this release. The network effect can be associated with the loss of competitive advantage of the owner of personal data, as discussed in Taylor (2004), Acquisti and Varian (2005), and Calzolari and Pavan (2006). Consider the setting where firms obtain a comparative advantage due to the possibility of offering prices that are based on past consumer behavior.…”

Section: Related Literaturementioning

confidence: 99%

Identification, data combination, and the risk of disclosure

Komarova¹,

Nekipelov

Yakovlev

2018

Quantitative Economics

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It is commonplace that the data needed for econometric inference are not contained in a single source. In this paper we analyze the problem of parametric inference from combined individual-level data when data combination is based on personal and demographic identifiers such as name, age, or address. Our main question is the identification of the econometric model based on the combined data when the data do not contain exact individual identifiers and no parametric assumptions are imposed on the joint distribution of information that is common across the combined data set. We demonstrate the conditions on the observable marginal distributions of data in individual data sets that can and cannot guarantee identification of the parameters of interest. We also note that the data combination procedure is essential in a semiparametric setting such as ours. Provided that the (nonparametric) data combination procedure can only be defined in finite samples, we introduce a new notion of identification based on the concept of limits of statistical experiments. Our results apply to the setting where the individual data used for inferences are sensitive and their combination may lead to a substantial increase in the data sensitivity or lead to a "de-anonymization" of the previously "anonymized" information. We demonstrate that the point identification of an econometric model from combined data is incompatible with restrictions on the risk of individual disclosure. If the data combination procedure guarantees a bound on the risk of individual disclosure, then the information available from the combined data set allows one to identify the parameter of interest only partially, and the size of the identification region is inversely related to the upper bound guarantee for the disclosure risk. This result is new in the context of data combination as we notice that the quality of links that need to be used in the combined data to assure point identification may be much higher than the average link quality in the entire data set, and thus point inference requires the use of the 396 Komarova, Nekipelov, and Yakovlev Quantitative Economics 9 (2018) most sensitive subset of the data. Our results provide important insights into the ongoing discourse on the empirical analysis of merged administrative records as well as discussions on the "disclosive" nature of policies implemented by the datadriven companies (such as internet services companies and medical companies using individual patient records for policy decisions).

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“…Laudon assumes that customer data is stored by companies and institutions which may wish to use it for different purposes later. Taylor [2] names individual pricing and targeted advertising, among others, as reasons for letting stored customer (and consumer) data become highly valuable for companies and institutions. In order to keep in line with the legislation, institutions that intend to re-use data for another purpose have to return to the individual who will have to give consent and will claim compensation for the new usage of his data.…”

Section: Introductionmentioning

confidence: 99%

Towards a Formal Language for Privacy Options

Berthold

2011

IFIP Advances in Information and Communication Technology

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Describing complex ideas requires clear and concise languages. Many domains have developed their specific languages for describing problem instances independently from solutions and thus making a reference model of the domain available to solution developers. We contribute to the zoo of domain-specific languages within the privacy area with a language for describing data disclosure and usage contracts. Our Privacy Options Language is defined by a small number of primitives which can be composed to describe complex contracts. Our major contribution is the notion of contract rights which is based on the notion of obligations and therefore establishes both concepts as first-class language citizens in a new coherent model for privacy policy languages. Our model overcomes the traditional separation of the right and obligation notions known from access control based policy language approaches. We compare our language to the PrimeLife Policy Language and provide rules for the translation from our language to PrimeLife's language. Then, we present a canonical form of our contracts. It is used to ensure that contracts with equal semantics have the same syntax, thus eliminating the possibility of a covert channel in the syntax revealing information about the originator. Finally, we show different ways of how to extend our language.

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Consumer Privacy and the Market for Customer Information

Cited by 345 publications

References 15 publications

Technology Mergers and Acquisitions in the Presence of an Installed Base: A Strategic Analysis

Technology Mergers and Acquisitions in the Presence of an Installed Base: A Strategic Analysis

Identification, data combination, and the risk of disclosure

Towards a Formal Language for Privacy Options

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