Sarah LEMAIRE 's PhD Thesis, July 8th

July 08, 2022 Research

 Sarah LEMAIRE will defend her thesis on Friday 8 July at 2:30 PM Auditorium 3 and Zoom
« Essays in digital economics »
Supervisor: Professor Wilfried SAND-ZANTMAN

To attend the conference, please contact the secretariat Elvire Jalran

Memberships are:

  • Wilfried SAND-ZANTMAN, ESSEC, Supervisor
  • Catherine TUCKER, MIT Sloan, Rapporteure
  • Marc BOURREAU, Telecom Paris, Rapporteur
  • Renato GOMES, CNRS, Toulouse School of Economics, Examinateur
  • Grazia CECERE , Institut Mines Telecom, Business School, Examinatrice
  • Daniel ERSHOV, Toulouse School of Economics, Examinateur 

Abstract :

This thesis is composed of four independent chapters. Each of these chapters uses empirical or theoretical methods to answer questions related to digital economics, regulation, two-sided platforms, data economics, and privacy.
The first chapter, co-authored with Grazia Cecere, looks into the effect of privacy protection rules on targeting efficiency and ad prices. We use a change in Apple's privacy policy the App Tracking Transparency option, as a natural experiment. Introduced in Spring 2021, this new feature of the iOS 14 aims at providing smartphone users with more control over their data. It requires app developers to request explicit permission to track users beyond the app in use. However,  this privacy policy is expected to reduce ad effectiveness on mobile devices. To assess the effect of the policy, we use an original database of estimated Facebook ad performances in the US market. We compare the outcomes of ad campaigns targeting iOS users versus Android users. The results suggest a reduction in targeting efficiency and ad prices for ads aimed at iOS users compared to Android users.
The second chapter, co-authored with Grazia Cecere and Wilfried Sand-Zantman, investigates how a monopoly ad network should extract information to price its advertising campaigns for commercial firms. This information, provided by firms and consumers, is key in display advertising markets to provide ads to the right target.
We assume that the network is better able to identify the right target audiences but this requires knowing the firm's heterogeneous characteristics, both in audience and value. We show how the ad network copes with this heterogeneity by altering the efficiency of the ad targeting process and the prices charged to different types of firms. In particular, we show that a narrower audience may be associated with lower prices to induce truthful information disclosure by firms. 
In the third chapter, I develop a model in which two platforms are substitutable on one side but can be all the range from perfect complements to perfect substitutes on the other side. I analyze the optimal strategies and cooperation incentives of the platforms and assess whether cooperation is socially desirable, depending on the degree of differentiation between platforms on this second side.
This chapter shows that when platforms are rather differentiated or complementary on this second side, they are able to choose a price that optimizes their demand-margin trade-off, leaving some surplus to consumers. Moreover, the cooperation incentives of platforms are aligned with the best interest of consumers. However, when platforms are close substitutes on this second side, they are constrained in their choice of price and choose to extract all the consumer surplus. In this case, even though firms have an incentive to cooperate, this is detrimental to consumers.
The fourth chapter investigates a rationale for websites with business models based on data collection to engage in self-regulation. I develop a theoretical model in which users do not independently consider the collecting behavior of websites. Instead, they opt-out of data collection based on the total amount of data collected on them. Websites self-regulate by optimizing jointly data collection. They end up collecting less data than when they do not coordinate, improving not only their outcome but also total welfare.