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Platforms and Data Privacy

Paper Session

Sunday, Jan. 7, 2024 8:00 AM - 10:00 AM (CST)

Convention Center, 304A
Hosted By: Industrial Organization Society
  • Chair: Tesary Lin, Boston University

Online Advertising, Data Sharing, and Consumer Control

Justin P. Johnson
,
Cornell University
Thomas Jungbauer
,
Cornell University
Marcel Preuss
,
Cornell University

Abstract

We examine how competition between advertising exchanges influences the targeting options that these exchanges make available to advertisers. When advertisers have strong property rights over data regarding consumers' active purchase interests, competition between ad exchanges leads to too little sharing of data. This may harm consumers, who receive too few pertinent ads, and advertisers themselves can also be harmed due to a situation resembling a prisoner's dilemma. We find that giving consumers the right to opt out of tracking may also benefit consumers who allow tracking, by altering the incentives of ad exchanges to offer improved targeting options. In addition, we show that initiatives by Apple and Google to limit third-party tracking and to introduce alternative tracking systems such as Topics, might benefit consumers by weakening the data property rights of advertisers. Because more data is shared by default under such systems, this can be true even if these systems are less accurate than third-party tracking systems.

Search, Data, and Market Power

Carl-Christian Groh
,
University of Bonn

Abstract

I study the relationship between data and market power in a duopoly model of price discrimination with search frictions. One firm receives a signal about the valuation of any arriving consumer while its rival receives no information. A share of consumers, referred to as searchers, have equal valuation for the good of either firm and optimally choose which firms to visit. The remaining consumers are captive. In equilibrium, a large majority of searchers will only visit the firm with data. The market share of the firm with data converges to one as the share of searchers in the market goes to one, regardless of the signal structure. Reductions of search frictions exacerbate the dominant position of the firm with data. The establishment of a right to data portability can address the competitive imbalances caused by data advantages.

Data, Privacy Laws, and Firm Production: Evidence from GDPR

Mert Demirer
,
Massachusetts Institute of Technology
Diego Jimenez Hernandez
,
Federal Reserve Bank of Chicago
Dean Li
,
Massachusetts Institute of Technology
Sida Peng
,
Microsoft

Abstract

In response to data’s increasingly important role in producing goods and services, many countries are enacting new privacy laws to regulate how firms use data. How do these regulations affect firms’ data and computation usage? We use seven years of confidential data from one of the largest cloud-computing providers to study the impact of the European Union’s (EU) General Data Protection Regulation (GDPR) on firm outcomes. Difference-in-differences estimates suggest that the GDPR decreased data storage by 26% and processing by 15% after two years, indicating that firms became less data-intensive as a response to the privacy regulation. We then propose a production framework that incorporates data and computation, and we structurally estimate the parameters of our model to quantify the distortion generated by the GDPR. Our estimates suggest that storage and computation are strong complements in production and that the effect of the GDPR was equivalent to an average of a 20% tax on the price of storing data, with substantial heterogeneity across firms.

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

Tesary Lin
,
Boston University
Avner Strulov-Shlain
,
University of Chicago

Abstract

We investigate how the choice architecture used during data collection influences the quality of collected data, including volume (how many people share) and representativeness (who shares data). 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% compared to opt-in, while a $0–50 price anchor decreases valuations by 53% compared to a $50–100 anchor. Moreover, some consumer segments respond more strongly to frame influence while having lower initial privacy valuation. As a result, conventional frame optimization practices that aim to maximize data volume can lead to less representative data. We demonstrate the magnitude of this volume-bias trade-off in our data and argue that it should be a key decision factor in choice architecture design.

Discussant(s)
Elisabeth Honka
,
University of California-Los Angeles
Thomas Jungbauer
,
Cornell University
Yizhou Jin
,
University of Toronto
Ben Leyden
,
Cornell University
JEL Classifications
  • L0 - General
  • L5 - Regulation and Industrial Policy