Date of Submission


Date of Award


Institute Name (Publisher)

Indian Statistical Institute

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

Subject Name

Quantitative Economics


Economics and Planning Unit (EPU-Delhi)


Chowdhury, Prabal Roy (EPU-Delhi; ISI)

Abstract (Summary of the Work)

The digital transformation and rise of online platforms have improved consumer welfare and business opportunities through better consumer choice and increased efficiency in trade. In this light, the authorities must weigh the concerns regarding the concentration of market power in the hands of a few large firms against the substantial benefits that they offer. In particular, user data is seen as a competitive resource in digital markets. In addition to better matches and information learning, activities of online firms in data collection and exploitation have raised concerns about erosion in user privacy and abuse of market power in digital markets. This raises the question of whether an intervention to reduce the data advantage of firms will improve or reduce social gains. In this light, this thesis analyzes the competitive and welfare effects of a specific form of data advantage arising from sharing consumer data among affiliated and unaffiliated firms. In addition, it also examines the competitive framework that should be part of antitrust intervention in such markets.In doing so, Chapter 2 analyses the strategic and welfare impact of voluntary data sharing in platform markets. It is shown that, under data sharing, the upstream firm can invest higher in data collection, especially in markets with lower improvement in its advertising targeting rates. However, social welfare rises. Moreover, the exclusive technology sharing regime paradoxically improves the welfare of all users.Chapter 3 examines the competitive and welfare implications of alternative regulatory approaches to protect privacy namely i) restricting access to data owned by the firm’s subsidiaries, and ii) empowering users to control data collection activities. It is shown that the former is always welfare reducing in the absence of any change in privacy level. Whereas, the latter can enhance user and social welfare in markets with large advertising targeting rates. Chapter 4 examines the private and social incentives to bundle when one firm can collect data regarding users from another market. It is shown that bundling is not profitable when investment in data collection and adveriv List of Figures tising targeting rate are small. Moreover, user welfare and social welfare can move in opposite direction when both data collection and targeting rate are large, leading to a policy dilemma. Chapter 5 is a policy paper that discusses the nature of antitrust enforcement required in platform markets. It argues that the authorities should not pursue an ex-ante agenda and exclusively target objectionable activities that hurt consumers (not protecting some competitors) leaving other pro-competitive conducts that benefit consumers unregulated.As a result, the theoretical models developed in the thesis contribute to the literature on platform markets by analyzing data sharing among firms. In the literature, so far, the current focus is on understanding the data collection on a platform. However, in our models, one firm can benefit from the functioning of another firm in a different market through data sharing.


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Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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