The Effect of Privacy Regulation on the Data Industry: Empirical Evidence from GDPR

Aridor, Guy; Che, Yeon‐Koo; Salz, Tobias

doi:10.3386/w26900

Cited by 21 publications

(10 citation statements)

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“…highlights the equilibrium inference from consumers' sign-up decisions in open banking. Using data from an online travel intermediary, Aridor, Che, and Salz (2020) offer evidence that this type of inference is well founded. They show that letting privacy-conscious consumers opt out of data sharing under GDPR increases the average value of the remaining consumers to advertisers.…”

Section: Related Literaturementioning

confidence: 90%

Open Banking: Credit Market Competition When Borrowers Own the Data

He¹,

Huang²,

Zhou³

2020

SSRN Journal

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Open banking facilitates data sharing consented by customers who generate the data, with a regulatory goal of promoting competition between traditional banks and challenger fintech entrants. We study lending market competition when sharing banks' customer data enables better borrower screening or targeting by fintech lenders. Open banking could make the entire financial industry better off yet leave all borrowers worse off, even if borrowers could choose whether to share their data. We highlight the importance of equilibrium credit quality inference from borrowers' endogenous sign-up decisions. When data sharing triggers privacy concerns by facilitating exploitative targeted loans, the equilibrium sign-up population can grow with the degree of privacy concerns.

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Section: Related Literaturementioning

confidence: 90%

Open Banking: Credit Market Competition When Borrowers Own the Data

He¹,

Huang²,

Zhou³

2020

SSRN Journal

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“…Our project also contributes to the literature on the value of consumer data to firms (Rossi et al 1996, Trusov et al 2016, Miller & Skiera 2017, Bajari et al 2019, Aridor et al 2021, Rafieian & Yoganarasimhan 2021, Sun et al 2021, Wernerfelt et al 2022, Lei et al 2023 forthcoming) by highlighting the importance of considering data composition when evaluating its value. Iansiti (2021) theorizes that the marginal value of data is initially very high as firms try to overcome the model's "cold-start" phase.…”

Section: Introductionmentioning

confidence: 92%

Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data

Lin

Strulov-Shlain

2023

Proceedings of the 24th ACM Conference on Economics and Computation

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We study how choice architecture that companies deploy during data collection influences consumers' privacy valuations. Further, we explore how this influence affects the quality of data collected, including both volume and representativeness. To this end, we run a large-scale choice experiment to elicit consumers' valuation for their Facebook data while randomizing two common choice frames: default and price anchor. An opt-out default decreases valuations by 14-22% compared to opt-in, while a $0-50 price anchor decreases valuations by 37-53% compared to a $50-100 anchor. Moreover, in some consumer segments, the susceptibility to frame influence negatively correlates with consumers' average valuation. We find that conventional frame optimization practices that maximize the volume of data collected can have opposite effects on its representativeness. A bias-exacerbating effect emerges when consumers' privacy valuations and frame effects are negatively correlated. On the other hand, a volume-maximizing frame may also mitigate the bias by getting a high percentage of consumers into the sample data, thereby improving its coverage. We demonstrate the magnitude of the volume-bias trade-off in our data and argue that it should be a decision-making factor in choice architecture design.

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“…Data processors have also apparently adapted to new GDPR requirements 27 . Disclosure requirements have changed, but as has been well established empirically, people tend to automatically accept such disclosures without reading them 28 .…”

Section: Potential Objectionsmentioning

confidence: 99%

Genetic Data Aren't So Special: Causes and Implications of Reidentification

Kasperbauer¹,

Schwartz²

2020

Hastings Center Report

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Genetic information is widely thought to pose unique risks of reidentifying individuals. Genetic data reveals a great deal about who we are and, the standard view holds, should consequently be treated differently from other types of data. Contrary to this view, we argue that the dangers of reidentification for genetic and nongenetic data—including health, financial, and consumer information—are more similar than has been recognized. Before different requirements are imposed around sharing genetic information, proponents of the standard view must show that they are in fact necessary. We further argue that the similarities between genetic and nongenetic information have important implications for communicating risks during consent for health care and research. While patients and research participants need to be more aware of pervasive data‐sharing practices, consent forms are the wrong place to provide this education. Instead, health systems should engage with patients throughout patient care to educate them about data‐sharing practices.

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The Effect of Privacy Regulation on the Data Industry: Empirical Evidence from GDPR

Cited by 21 publications

References 0 publications

Open Banking: Credit Market Competition When Borrowers Own the Data

Open Banking: Credit Market Competition When Borrowers Own the Data

Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data

Genetic Data Aren't So Special: Causes and Implications of Reidentification

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