Data Privacy and Data Markets
Paper Session
Monday, Jan. 4, 2021 10:00 AM - 12:00 PM (EST)
- Chair: Daron Acemoglu, Massachusetts Institute of Technology
The Economic Consequences of Data Privacy Regulation: Empirical Evidence from GDPR
Abstract
This paper studies the effects of the EU’s General Data Protection Regulation (GDPR) on the ability of firms to collect consumer data, identify consumers over time, accrue revenue via online advertising, and predict their behavior. Utilizing a novel dataset by an intermediary that spans much of the online travel industry, we perform a difference-in-differences analysis that exploits the geographic reach of GDPR. We find a 12.5% drop in the intermediary-observed consumers as a result of the new opt-in requirement of GDPR. At the same time, the remaining consumers are observable for a longer period of time. We provide evidence that this pattern is consistent with the hypothesis that privacy-conscious consumers substitute away from less efficient privacy protection (e.g, cookie deletion) to explicit opt out, a process that would reduce the number of artificially short consumer histories. Further in keeping with this hypothesis, we observe that the average value of the remaining consumers to advertisers has increased, offsetting most of the losses from consumers that opt out. Finally, we find that the ability to predict consumer behavior by the intermediary’s proprietary machine learning algorithm does not significantly worsen as a result of the changes induced by GDPR. Our results highlight the externalities that consumer privacy decisions have both on other consumers and for firms.From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising
Abstract
This paper analyzes the impact of intermediaries' concentration on the allocation of revenues in online platforms. We study sponsored search - the sale of ad space on search engines through online auctions - documenting how advertisers increasingly bid through a handful of specialized intermediaries. This enhances automated bidding and data pooling, but lessens competition whenever the intermediary represents competing advertisers. Using data on nearly 40 million Google's keyword-auctions, we rst apply machine learning algorithms to cluster keywords into thematic groups serving as relevant markets. Then, through an instrumental variable strategy, we quantify a negative and sizeable impact of intermediaries' concentration on platform's revenues.The Economics of Social Data
Abstract
A data intermediary pays consumers for information about their preferences and sells the information so acquired to firms that use it to tailor their products and prices. The social dimension of the individual data-whereby an individual's data are predictive of the behavior of others-generates a data externality that reduces the intermediary's cost of acquiring information. We derive the intermediary's optimal data policy and show that it preserves the privacy of the consumers' identities while providing precise information about market demand to the firms. This enables the intermediary to capture the entire value of information as the number of consumers grows largeDiscussant(s)
Joshua Gans
,
University of Toronto
Avi Goldfarb
,
University of Toronto
Fiona Scott Morton
,
Yale University
Hal Varian
,
Google Inc.
JEL Classifications
- D8 - Information, Knowledge, and Uncertainty
- D6 - Welfare Economics