Regulating Data Monopolies: A Law and Economics Perspective 981168765X, 9789811687655

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Jingyuan Ma

Regulating Data Monopolies A Law and Economics Perspective

Regulating Data Monopolies

Jingyuan Ma

Regulating Data Monopolies A Law and Economics Perspective

Jingyuan Ma School of Law Central University of Finance and Economics Beijing, China

Ministry of Education 18YJC820047 ISBN 978-981-16-8765-5 ISBN 978-981-16-8766-2 (eBook) https://doi.org/10.1007/978-981-16-8766-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The aim of this book is to conduct an in-depth analysis of the anti-competitive behaviour of companies that monopolize data, and propose the necessity of regulating data monopoly by exploring the causes and characteristics of their anti-competitive behaviour. The book studies four aspects of the differences between data monopoly and traditional monopolistic behaviour, namely defining the relevant market for data monopolies, the entry barrier, the problem of determining the dominant position of data monopoly and the influence on consumer welfare. It points out the limitations of traditional regulatory tools and discusses how new regulatory methods could be developed within the competitive legal framework to restrict data monopolies. The book analyses the business model of enterprises in the digital economy by taking an economic and comparative perspective. Applying the methodology of law and economics, comparative legal studies, behavioural and business and entrepreneurship studies, the author proposes to show how economic analytical tools used in traditional anti-monopoly law are facing challenges and how competition enforcement agencies could adjust regulatory methods to deal with new anti-competitive behaviour by data monopolies. The book will be valuable to academic as well as public sector and professional audiences. The book will attract scholars, researchers and students with interest in economic law and policy in the digital economy. Readers will benefit from the comprehensive analysis of relevant market definition, abuse of dominant position and consumer welfare analysis in the book. It will help professionals and business practitioners to understand distinct features of monopolistic behaviour in traditional and digital markets, and how the substances and enforcement of the data law in China could be compared with competition regulation in the USA and EU from an economic perspective. The book can also be used as reading material for courses such as Competition Law and Regulation in the Digital Economy, Comparative Competition Law, Competition Law and Big Data for students studying competition law and policy at the undergraduate, postgraduate and doctoral levels. This book is supported by the Humanities and Social Sciences Fund of the Ministry of Education (Project Title: Regulation on Data Monopolies v

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Preface

(2018 年度教育部人文社会科学研究青年基金 《数据垄断的法律规制研究》 ,项 目批准号18YJC820047) and Research on the Rule of Law of Data Opening and Trading in the Digital Era Project (智能时代数据开放与交易的法治研究) at the Central University of Finance and Economics. Beijing, China

Jingyuan Ma

Acknowledgements

The author is grateful to Prof. Niels Philipsen from Maastricht Institute for Transnational Legal Research (METRO) and Rotterdam Institute of Law and Economics (RILE) for his kind encouragement and guidance in this book project. Profound thanks go to Prof. Daniel Sokol for the inspiring discussions during the visit to Levin School of Law at Florida University in autumn 2015. The author also wishes to thank Dean Wu Tao (CUFE), Prof. Han Wei (CASS), Prof. Michael Faure (Maastricht) and Prof. Stefan Weishaar (Groningen) for their continuous support in writing this book, to students participating in the Comparative Competition Law class at CUFE for their kind encouragement and insightful discussions. The author is grateful to the editorial assistance provided by Graham Sedgley and to Rajasekar Ganesan and Lydia Wang at Springer Nature for their professional assistance in the publication process.

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Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Law and Economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Comparative Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Behavioural Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.4 Business and Entrepreneurship Studies . . . . . . . . . . . . . . . . 1.3 Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Academic and Social Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 5 6 6 6 8 8 9 12 13

2

Emerging Digital Markets and Regulation . . . . . . . . . . . . . . . . . . . . . . . 2.1 Global Trend of Digital Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 The Development of Online Business . . . . . . . . . . . . . . . . . 2.1.2 Global Market Capitalization . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Digital Consumers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Online Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Defining Online Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 “Free Service” Supplier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Gatekeepers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Platform Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Digital Giants and Market Power . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Regulations in Digital Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Policy Developments in the EU on Regulating Digital Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Regulations on the Digital Economy in Other Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 General Policy Recommendations . . . . . . . . . . . . . . . . . . . . 2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17 17 17 18 21 25 25 26 30 31 34 36 36 44 46 49 49

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3

Online Markets and Nonprice Competition . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Characteristics of Online Market . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Dynamic Efficiency and Innovation . . . . . . . . . . . . . . . . . . . 3.2.2 Multi-sided Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Indirect Network Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Switching-Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Attention-Based Nonprice Competition . . . . . . . . . . . . . . . . . . . . . . 3.4 New Organizational Forms and Revenue Models . . . . . . . . . . . . . . 3.5 Winner-Take-All Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55 55 57 57 66 67 69 70 72 73 74 76 77

4

Data Monopolies and Competition Law . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.2 Definition of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4.2.1 4 V . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4.2.2 Data Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.2.3 The Use of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4.2.4 Data Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.3 Characteristics of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 4.3.1 Low Cost of Collecting and Storing . . . . . . . . . . . . . . . . . . 95 4.3.2 Non-exclusive and Non-rivalrous . . . . . . . . . . . . . . . . . . . . . 96 4.3.3 The Value Decreases Over Time . . . . . . . . . . . . . . . . . . . . . 98 4.4 Data Driven Productivity Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.5 Data and Online Business . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.6 Implications for Competition Law . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5

Defining the Relevant Market for Data Monopoly . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 The Pricing Structure of Two-Sided Markets . . . . . . . . . . . . . . . . . 5.3 Avoid Defining Relevant Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Defining Relevant Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Adapting the SSNIP Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Critical Loss Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.3 SSNDQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.4 Other Market Definition Techniques . . . . . . . . . . . . . . . . . . 5.5 Relevant Market for Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

109 109 110 112 113 114 116 117 118 118 120 121

Contents

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6

Entry Barriers of Data Monopoly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Entry Analysis in Traditional Markets . . . . . . . . . . . . . . . . . . . . . . . 6.3 Barriers to Entry in Online Markets . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Economies of Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Defining Entry Barriers—Data Substitutability . . . . . . . . . 6.3.3 Technical, Legal and Behavioural Barriers . . . . . . . . . . . . . 6.3.4 Data Portability and Data Interoperability . . . . . . . . . . . . . 6.3.5 Data Standardization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.6 Data Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

125 125 129 130 130 133 134 135 140 142 144 145

7

Market Power Assessment in Online Markets . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Assessing Market Power—Traditional Method . . . . . . . . . . . . . . . 7.3 Assessing Market Power in Online Markets . . . . . . . . . . . . . . . . . . 7.3.1 Economies of Scale and Data Portability . . . . . . . . . . . . . . 7.3.2 Essential Facility Doctrine . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.3 Strategic Market Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.4 Abusive Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.5 Concentrations in Digital Market . . . . . . . . . . . . . . . . . . . . . 7.4 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 Google Search Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Microsoft/Skype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.3 Facebook/WhatsApp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.4 SAMR/Alibaba Group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Implications of the Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

151 151 152 154 155 156 157 158 160 162 162 165 166 168 169 169 170

8

Data Monopoly and the Impact on Consumer Welfare . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Improved Quality and Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Reducing Search Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Consumer Self-confidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Targeted Advertisement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Price Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6.1 Definition of Price Discrimination . . . . . . . . . . . . . . . . . . . . 8.6.2 Personalizing Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6.3 Economic Effects of Price Discrimination . . . . . . . . . . . . . 8.6.4 Empirical Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7 Consumer Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7.1 Consumer Privacy—General Rights . . . . . . . . . . . . . . . . . . 8.7.2 Consumer Privacy Paradox . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7.3 Broader Goals of Consumer Welfare . . . . . . . . . . . . . . . . . .

175 175 177 179 182 183 184 184 186 187 189 191 192 192 195

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Contents

8.7.4 Consumer Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7.5 Consumer Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

199 203 205 206

Data Regulation, Consumer Protection and Competition Law . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Data Authority and Data Regulation . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Data Authority . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 Data Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.3 Digital Competence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Public and Private Regulation on Data Sharing . . . . . . . . . . . . . . . 9.3.1 An Ex-ante Regulatory Framework . . . . . . . . . . . . . . . . . . . 9.3.2 Public and Private Regulation and Self-regulation in Data Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Data Protection and Personal Data . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Coordinating Data Regulation, Consumer Protection and Competition Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

217 217 218 218 223 223 224 224

10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Economic Theory of Online Platform and Data Monopolies . . . . 10.3 Quality Competition in Zero-Price Markets . . . . . . . . . . . . . . . . . . 10.4 Market Power Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 Impact on Consumer Welfare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Implications for Competition Law . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7 Conclusions and Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

233 233 234 235 236 238 238 240 241

9

224 225 227 230 230

About the Author

Jingyuan Ma is an Assistant Professor of Law at Central University of Finance and Economics in Beijing, China, and visiting professor at Levin School of Law, University of Florida. Jingyuan holds an LLM in Law and Economics from the University of Hamburg and the University of Ghent (2010) and a BA in Economics from Beijing Foreign Studies University (2009). She obtained Ph.D. from the University of Hamburg, Erasmus University Rotterdam and the University of Bologna, taking part in the European Doctorate in Law and Economics programme (EDLE). Her teaching and research interests include comparative law, economic analysis of competition law and law and society in East Asia. Her recent publications include Comparative Analysis of Merger Control Policy—Lessons for China (Intersentia, 2014), Competition Law in China—A Law and Economics Perspective (Springer, 2020) and Confucian Culture and Competition Law in East Asia (Cambridge, forthcoming 2022).

xiii

Abbreviations

ABA ACCC ACM ACPD AI AML API B2B B2C B2G C2C C4 CAICT CASS CCPA CDR CMA CNNIC CPA CPC CPM CPPA CRS CUFE DaTA DDI DESI DMA DMU DOJ DRCF

American Bar Association Australian Competition and Consumer Commission The Netherlands Authority for Consumers and Markets Algorithmic Consumer Price Discrimination Artificial intelligence Anti-monopoly Law Application Programming Interfaces Business-to-business Business-to-consumer Business-to-government Consumer-to-consumer Four-firm concentration ratio China Academy of Information and Communications Technology China Academy of Social Science California Consumer Privacy Act Consumer data right Competition and Markets Authority China Internet Network Information Center Cost per action Cost per click Cost per mile Consumer Privacy Protection Act Congressional Research Service Central University of Finance and Economics Data, Technology and Analytics Data-driven innovation Digital Economy and Society Index Digital Markets Act Digital Markets Unit Department of Justice Digital Regulation Cooperation Forum xv

xvi

DSA DSI DTP EDPS EPRS ETSI FTC G2C GAFA GDPR GDS GUPPI ICN ICO ICT IDC IDCTF IEC IETF IMSB INCITS IoT ISO ITIF ITU IWGDPT JFTC METRO MIT NGO OASIS OCCP OECD OFT PDPB PIMS PIPEDA POPIA R&D RILE SAMR SDK SME SMS SSNDQ

Abbreviations

Digital Service Act Digital Skills Indicator Data Transfer Project European Data Protection Supervisor European Parliamentary Research Service European Telecommunications Standards Institute Federal Trade Commission Government-to-consumer Google, Apple, Facebook and Amazon General Data Protection Regulation Government Digital Service Gross Upward Pricing Pressure Index International Competition Network Information Commissioner’s Office Information and Communication Technology Informational Data Corporation International Developments and Comments Task Force International Electrotechnical Commission Internet Engineering Task Force Information Management Steering Board International Committee for Information Technology Standards Internet of Things International Organization for Standardization Information Technology & Innovation Foundation International Telecommunication Union International Working Group on Data Protection in Technology Japan Fair Trade Commission Maastricht Institute for Transnational Legal Research Massachusetts Institute of Technology Nongovernmental organization Organization for the Advancement of Structured Information Standards The Office of Competition and Consumer Protection Organization for Economic Cooperation and Development Office of Fair Trading India Personal Data Protection Bill Personal Information Management System Personal Information Protection and Electronic Document Act South Africa Protection of Personal Information Act Research and Development Rotterdam Institute of Law and Economics State Administration of Market Regulation Software Development Kits Small and medium enterprises Strategic market status Small but Significant Non-Transitory Decrease in Quality

Abbreviations

SSNIP SSO TFEU TIA UNCTAD UPP USITC

xvii

Small but Significant Non-Transitory Increase in Price Standard Setting Organization Treaty on the Functioning of the European Union Telecommunications Industry Association United Nations Conference on Trade and Development Upward pricing pressure United States International Trade Commission

List of Charts

Chart 2.1

Chart 2.2

Chart 2.3

Chart 2.4

Chart 2.5

Chart 2.6 Chart 2.7

Chart 2.8

World’s top 20 companies by market capitalization by sector 2009 versus 2018 (per cent). Source UNCTAD (2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Main global platforms in the world in 2018—America (market capitalization in billions of dollars). Source UNCTAD (2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Main global platforms in the world in 2018—Asia (market capitalization in billions of dollars). Source UNCTAD (2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cumulative number of apps downloaded from the Apple app store from July 2008 to June 2017 (in billions). Source https://www.statista.com/statistics/263794/number-ofdownloads-from-the-apple-app-store/ . . . . . . . . . . . . . . . . . . . . . . The number of users of online products and services in China (million). Source The 44th Statistical Report on Internet Development in China, China Internet Network Information Center (CNNIC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social media use in the US in 2016 and 2018. Source Gans (2018, at p. 9) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Online population and E-shoppers in Europe in 2018 (million). Source A guide to E-commerce in Europe, Enterprise Europe Network, http://een.ec.europa.eu/about/ branches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Most popular social networks worldwide ranked by number of active users in January 2021 (in millions). Source https://www.statista.com/statistics/272014/globalsocial-networks-ranked-by-number-of-users/ . . . . . . . . . . . . . . . .

19

19

20

20

22 22

24

24

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List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 2.4 Table 2.5 Table 2.6 Table 3.1 Table 4.1 Table 4.2 Table 5.1 Table 6.1 Table 6.2 Table 7.1 Table 9.1 Table 9.2 Table 9.3

Functional categories of online platforms . . . . . . . . . . . . . . . . . . . Examples of peer platform marketplaces . . . . . . . . . . . . . . . . . . . Summary of most frequented platforms in the US . . . . . . . . . . . . Abusive conduct that may be undertaken by dominant gatekeepers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Policy options in regulating gatekeeper conduct . . . . . . . . . . . . . EU legislations on digital markets . . . . . . . . . . . . . . . . . . . . . . . . . Strength of competitive forces for different types of platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Examples of categories of personal data collected online . . . . . . User data tracked online and offline . . . . . . . . . . . . . . . . . . . . . . . Big data relevant market structure . . . . . . . . . . . . . . . . . . . . . . . . . Technical, legal and behavioural barriers in data supply chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EU legal framework for technical standards . . . . . . . . . . . . . . . . . Sources of evidence to be evaluated for establishing dominance in digital markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Established digital authorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . EU commission data governance levels . . . . . . . . . . . . . . . . . . . . Advocacy measures to improve privacy competition in zero-price markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27 28 29 32 33 40 76 89 96 120 136 142 161 221 222 229

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Chapter 1

Introduction

Abstract When competition analysis is focused on online markets and the business strategies between digital platforms, economic arguments have indicated the multidimensional paradox. As the basis of the analysis, online markets are argued to have its particular characteristics, and indirect network effects make the traditional understanding of pricing structure and welfare analysis inapplicable to multi-sided markets. On the one hand, data collection is seen as the key input for producing online products and services, and platforms are motivated by obtaining attention and data as the “currency” to subsidize advertisers to gain profits when online services are provided at zero price. On the other hand, the use of personal data may harm consumer welfare, although consumers may suffer from bounded rationality and do not learn to apply privacy techniques. Chapter 1 is an introduction to the development of the digital economy and the definition of a data monopoly.

1.1 Introduction Competition law has been debated by law and economics scholars for the inherent paradox it creates. It is known that competition law is to promote competition and to impose restraints on monopolists. In the process of enforcement, however, it leads to different decisions when the ultimate goal is to protect the total welfare of consumers; distributional fairness or maximizing efficiency, and the considerations of non-economic goals all add additional difficulties. When the competition analysis is focused on online markets and the business strategies between digital platforms, the paradox is multidimensional: as the basis of the analysis, online markets are argued to have their particular characteristics, that the indirect network effects make the traditional understanding of pricing structure and welfare analysis inapplicable to multi-sided markets. On the one hand, data collection is seen as the key input for producing online products and services, and platforms are motivated by obtaining attention with data as the “currency” to subsidize advertisers, to gain profits when online services are provided at zero price. On the other hand, the use of personal data may harm consumer welfare, although consumers may suffer from bounded rationality and do not learn to apply privacy techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 J. Ma, Regulating Data Monopolies, https://doi.org/10.1007/978-981-16-8766-2_1

1

2

1 Introduction

Economic arguments in many ways respond to the multidimensional paradox, and it has general implications for competition law that data monopolies cannot be prohibited at all costs and must take into account heterogenous consumer preferences and different contexts of online business operations. Competition regulators are influenced by both the proponents on competition scrutiny to force platforms to provide data for rivals or prohibit them from collecting excessive data and opponents who are against government intervention by arguing the low entry barriers of data economy and the irrelevancy of competition law in dealing with privacy concerns.1 The economic analysis of online markets focuses on the characteristics of a twosided market, which makes the analysis of the relevant market and market power of online businesses different from traditional price competition based “one-sided” markets. The novel work by economists2 on multi-sided markets has raised interest for academics studying the particular features of platforms and the competition between online service providers, and economists have provided strong arguments that the abusive behaviour of online platforms cannot be easily assessed through market shares, and thus this is against an intervention by competition agencies. Competition authorities and public organs at the national3 and EU4 levels have also devoted resources to the understanding of indirect network effects and the interdependent relationships between users of the platform.5 Another economic aspect of the analysis of competition issues in the online market is the effects of innovation on dynamic efficiency. The data-driven economy has been characterized by fast speed innovation and through dynamic competition it creates substantial value for society, as the OECD suggested: “The exploitation of data promises to create added value in a variety of operations ranging from optimizing the value chain and manufacturing production to more efficient use of labour and better customer relationships.”6 When data giants have reached significant market power, it is critical to review whether such market power will harm competition in the two-sided market pricing structure framework, and whether such market power has efficiency benefits, and a tradeoff between anti-competitive harm and pro-competitive values could be made. The rapid development of information and communication technologies (ICTs), the Internet of Things (IoT), computing algorithms and artificial intelligence (AI) has changed business strategies, pricing models and competing structures in the market. Structural-based analysis has become outdated, and replacement effects are of great importance in destructive competition. Competition agencies and courts have also expressed different views on whether data monopolies should be monitored by competition law. It became hot news that 1

See for example, Tucker and Wellford (2014) and Kennedy (2017). Caillaud and Jullien (2001, pp. 797–808, 2003), Rochet and Tirole (2003, 2006), Evans and Schmalensee (2007, pp. 151, 152, 2008), Armstrong (2006), Katz and Shapiro (1994, pp. 93–115) and Parker and Van Alstyne (2005). 3 Bundeskartellamt (2016). 4 European Commission (2016). 5 Wismer et al. (2017, p. 257). 6 OECD (2013). 2

1.1 Introduction

3

online platforms such as Google, Skype, Microsoft, Yahoo!, Apple, Facebook,7 ebay, Amazon,8 have raised anti-competitive concerns about their dominant market positions. The emergence of internet giants has been named “data-oplies”, as Stucke defined “companies that control a key platform, which, like a coral reef, attracts users, sellers, advertisers, software developers, apps, and accessory makers to its ecosystem.”9 The European Competition authorities named the big four data-oplies (Google, Apple, Facebook and Amazon) GAFA,10 and The Economist gave them the acronym BAADD (“too big, anti-competitive, addictive and destructive to democracy”11 ). Similarly, the digital companies Baidu, Alibaba and Tencent are named BAT companies. With the help of technology developments and successful acquisitions, these data giants have obtained substantial market power during the past decade and have become the largest business firms both in market capitalization and the population of daily and monthly users. In 2018, Facebook had more than 2.2 billion monthly active users globally.12 As of January 2021, the global population of active internet users reached 4.66 billion, and the number of active social media users reached 4.2 billion.13 The large population of digital consumers has generated substantial market value for internet service providers. In December 2020, the combined market capitalization of those four companies (Alphabet-Google, Amazon, Apple and Facebook) exceeded 5.7 trillion USD.14 When consumer data is regarded as the “price” of consuming products and services online, the market value of online platforms is also considered to be the monetarization of consumer data. While recognizing the anti-competitive risks that internet giants may produce, applying competition law and policy to regulate online platforms has long been debated, as Daniel Rubinfeld argued in the Microsoft case in 1998, “[The Sherman Antitrust Act] doesn’t allow one to just break you up because you’re big and you’re powerful.”15 For the same type of conduct, even the same case, competition agencies may have very different opinions and decisions. Whereas the FTC held that Google had committed no infringement of competition law,16 in June 2017, after seven years of investigation, Google was fined 2.42 billion EURO for abusing its dominant position and providing favourable treatment to shopping services.17 The

7

Bundeskartellamt (2017). See for example, European Commission (2020). 9 Stucke (2018, p. 275). 10 See Moore and Tambini (2018). 11 Smith (2018). 12 Facebook (2018). 13 https://www.statista.com/statistics/617136/digital-population-worldwide/. 14 EPRS (2021, at p. 1). “This amount is greater than the market capitalisation of the entire Euronext stock exchange and a third of the value of the whole Standard and Poor’s 100 index of Unites States stocks”. 15 Luckerson (2018). 16 FTC (2013). 17 European Commission (2017a). 8

4

1 Introduction

ability to collect and use the massive data collected from users has also been criticized in the context of consumer protection, privacy and competition law.18 After the FTC approved the Facebook/WhatsApp merger,19 in May 2017, Facebook was fined by European Commission 122 million USD (110 million EURO) for providing misleading information in the acquisition of WhatsApp in 2014.20 In April 2021, the competition authority in China, SAMR, imposed the highest fine of 18.23 billion RMB on the internet provider Alibaba Group. In a number of jurisdictions, online markets have already been subject to antitrust review in merger or conduct cases. In other jurisdictions, these issues are in a nascent stage of policy. A number of lessons can be learned from the cases to date involving online markets with regard to optimal antitrust policy. What these cases tend to share are some basic features as to how online markets work. Some jurisdictions understand the particular dynamics of multi-sided online markets. Other competition authorities sometimes may misidentify these markets. In the new decade, competition agencies and government authorities have taken more active actions to regulate online platforms. In September 2018, the Federal Minister for Economic Affairs and Energy Peter Altmaier set up the EU Commission ‘Competition Law 4.0’, which proposes the revision of EU competition law to incorporate the new developments of the digital economy. On 15 December 2020, the European Union published the Digital Services Act Package, including two significant proposals: a proposal for a Regulation on a Single Market for Digital Services (Digital Services Act or “DSA”), the proposed ePrivacy Regulation (ePR), the Regulation on Promoting Fairness and Transparency for Business Users of Online Intermediation Services,21 and the amending Directive 2000/31/EC (the “e-Commerce Directive”). As an important part of the EU’s digital strategy, these two bills provide the legal basis for the Commission to strengthen regulation in the digital sector while reflecting the Commission’s ambition to reform the digital sector. The Digital Market Act imposes specific obligations outside the existing antitrust regulatory framework for platforms that meet the “gatekeeper online platform” criteria, including prohibiting bundling/self-preferential treatment, ensuring interoperability and data migration, and ensuring merchant selection and conversion platform rights. The introduction of the two bills reflects the Commission’s preference for preregulation of data-intensive platform companies. The UK Competition and Markets Authority (CMA) has recommended the application of “GDPR plus” requirements to specific platforms, requiring them to plan their platforms on a “fair” basis when dealing with user choices and services, and the UK government has indicated that such a level of intervention is required and that further consultation is needed. In the German draft, the Ministerial 18

Newman (2014) and EDPS (2014). FTC (2014). 20 European Commission (2017b). 21 Regulation (EU) 2019/1150 of the European Parliament and of The Council of 20 June 2019 on Promoting Fairness and Transparency for Business Users of Online Intermediation Services, L 186/57, July 11, 2019. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32019R 1150&from=EN. 19

1.1 Introduction

5

bill on the 10th amendment to the German Act Against Restraints of Competition (GWB-Digitalisierungsgesetz) requires a competition agency (Bundeskartellamt) to impose stricter competition rules on digital platforms.22 On February 7, 2021, the Antimonopoly Committee of the State Council of China officially issued the Antimonopoly Guidelines in the Field of Platform Economy,23 the first systematic antitrust guide specifically for the Internet platform economy in major jurisdictions in the world. This book is written to respond to the debate among academics and public policy makers on the regulation of data monopolies. It outlines areas in which online markets may be different from traditional markets for antitrust purposes. The book also explores why such markets require a more careful consideration from antitrust authorities and courts in their respective antitrust analyses.

1.2 Methodology The development of the digital economy is considered to be a revolutionary process that changes the function of an economic system. When it is observed that new competition acts are promulgated and competition laws are revised, it is also important to understand that such reform is not only taken in the field of competition law—it is a reform of almost the entire legal system in all aspects. Thus, the study of laws on data monopolies has to incorporate multiple changes in law, business, commercial strategies, marketing theory and behavioural studies. It is important to link public regulation and private business conduct and between commercial strategies and consumer behaviour. It is important to understand the function of e-commerce and the digital industry. Therefore, this book makes its contribution to combining studies in economic theory, regulation, comparative law and behavioural science in the understanding of data-driven competition issues. By applying the law and economics research method, it investigates the most recent economic literature on the characteristics of online platforms, data-driven economies and the features of data. It discusses the economic theory of the competition between online platforms and how the use of data may affect the intensity of competition, and it therefore raises antitrust concerns. The benchmark for assessing regulations on data monopolies is economic theory, although economists may not have reached consensus on some types of conduct concerning whether government intervention will generate social welfare or whether such intervention should be avoided. In general, the book takes a positive view of describing the characteristics of online platforms and the data economy, the business strategies that data monopolies take and the potential challenges for competition law. It also studies the reactions 22

Ritz and Schöning (2019, p. 2). Guidelines of the Anti-monopoly Commission of the State Council for Anti-monopoly in the Field of Platform Economy (国务院反垄断委员会关于平台经济领域的反垄断指南) issued on February 7, 2021. http://gkml.samr.gov.cn/nsjg/fldj/202102/t20210207_325967.html.

23

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1 Introduction

by competition agencies and courts and summarizes how they answer the questions of how a relevant market could be defined, the assessment of market entry, the understanding of abuse of dominant position and consumer welfare. It presents the conflicts of theory and the challenges of applying economic arguments to case studies.

1.2.1 Law and Economics A proper design of regulatory tools to prevent anti-competitive harm caused by data monopolies has to be consistent with developments in economic theory and empirical evidence. As discussed in the introduction, the paradox of regulating data monopolies is centered on the unclear understanding of platform competition and the uncertain effects of market power caused by internet giants. Applying the method of law and economics in this research is crucial: economic theory will provide the benchmark for a further detailed discussion in case law and multiple disciplines are to be incorporated in the economic analysis to give proper guidance in the shaping of law and policy in competition cases. From a global perspective, the law and economic method is also applicable for comparative legal analysis for case studies in the US, EU and China.

1.2.2 Comparative Law Economists and lawyers have not reached consensus on the economic characteristics, competitive effects and regulatory methods on online platforms, and regulators in the US, EU and China have also demonstrated different views on competition rules on regulating the market power of online platforms. A comparative view of studying the experiences and lessons of the theory and practice in data regulation is important to enhance mutual learning between academics and regulators in the US, EU and China and to facilitate international cooperation in regulating the expanding market power of internet firms in the global market.

1.2.3 Behavioural Studies Whereas most studies on data monopolies and regulations on data monopolies focus on the theories of regulation, this book investigates the literature of consumer behaviour and marketing theories, to understand how a particular business strategy has an impact on consumer behaviour. It then discusses how competition regulation could be better designed to protect consumer welfare. As things are perceived regulations on protecting competition in digital economy may have been trapped in the paradox that consumer welfare is often improved when there is less intervention on

1.2 Methodology

7

the online platform, when consumers complain that their personal information is at risk of violations of privacy, they have benefited significantly when online service suppliers provide personalized products. When data and the technology of the internet are by nature neutral, it is often the business strategy of using data that has raised competition concerns. At the same time, behaviour studies show that consumers are very often cognitively biased and strongly manipulated by the marketing strategies of online sellers. While they are charged a higher price because of the marketing strategies, they are in fact subjectively willing to lock in their purchasing behaviour, and are thus cognitively locked in by the website. The behavior studies indicate that it is the cognitive behaviour of consumers, not the de facto market power, which makes it possible that online retailers could manipulate marketing strategies, including charging personalized prices. When consumers are cognitively locked in by a famous brand or by a particular website that offers multimedia feedback and reviews, it is likely that new and less popular brands are facing entry barriers in the online market. Such barriers to entry or the difficulties in marketing new products, are not the result of market power exploitation but because of consumers’ behavioural preferences and biases. The effects of those biases are hard to generalize: they may objectively harm the welfare of a particular consumer group (for example, those less experienced in internet searching) but may also subjectively increase the welfare of another group (those that are loyal to a brand or with more internet searching experience). Even when consumer welfare is reduced, it may not be a wise decision to intervene in the market by increasing the penalty of competition law, but to implement behavioural tools, such as nudging to protect consumer welfare. Behavioural studies also show the heterogeneity of consumers’ preferences and explain why generalizing consumer preferences would be impractical. There is a case for learning consumers’ searching behaviour, as well as consumer privacy issues. It is particularly important in the analysis of the effects on consumer welfare, because the observed anti-competitive behaviour may in fact have positive consumer surplus when the economic model takes into account consumer preferences, and such preferences may also have strong economic proof from behavioural literature. Thus, a more scientific judgment on anti-competitive conduct has to incorporate behavioural studies and develop new methods to improve consumer welfare when competition law and policy are facing the paradox of interventionist regulation for competition, which might not in fact promote competition because it may have negative economic effects on consumer welfare in theory. Price discrimination is an example, and consumer privacy issues are another. These topics will be discussed in detail in Chap. 8. By incorporating the rich literature in marketing, consumer neuroscience and consumer behaviour, it analyses why consumers’ attention can be attracted and how the supply of attention varies among different consumer groups. It helps to understand how consumers allocate scarce attention across various online products. This book contributes to the current discussion by showing how business pricing strategies could create market power and therefore provides implications on how competition law should be adapted to balance the interest of facilitating innovation and technology, on the one hand, and restricting market power on exploiting consumers’ attention to protect consumer welfare, on the other. Studying the differences between

8

1 Introduction

consumer groups also shows how online antitrust enforcement should be differentiated, even personalized, to target different consumer groups in particular industries. It is worth carefully categorizing the differences among consumer preferences and the characteristics of heterogenous nonprice product competition and differentiating the types of regulation to address the infringements of competition law in the data economy.

1.2.4 Business and Entrepreneurship Studies When discussing the anti-competitive effects of online platforms, it is crucial to understand the new business model and organizational forms of platforms. Distinct from the traditional linear relationship between suppliers and buyers in traditional manufacturing markets, multi-sided platforms develop networks to facilitate the transaction of resources (including products, skills, services and ideas) to create value for users on multiple sides. Because of indirect network effects, online platforms have applied new organizational structures and pricing strategies. It is logical to charge zero price for users on one side to attract the demand from the other side. This book investigates the literature in business and entrepreneurship studies to understand the business model of online platforms, and the theory and empirical evidence of the new business model has laid theoretical foundations for the analysis of pro- and anti-competitive effects in case studies.

1.3 Structure The main content of this topic is to conduct a more in-depth and specific analysis of data monopoly and its anti-competition behaviour, and propose the necessity of regulating data monopoly by exploring the causes and manifestations of data monopoly anti-competitive behaviour. The research objectives of this topic include two levels of problems, the first of which is to study how a data monopoly is produced and the necessity of analysing and regulating a data monopoly. This includes studying how the market forces of data monopolies are formed, especially how the market forces of data monopolies form (e.g., total price discrimination) compared to traditional forms of monopolies. The second aspect is how to realize the purpose of regulating data monopoly through competition law, and discussion of competition law and policy should respond to the problem of data monopoly. It will discuss four aspects of the obvious differences between data monopoly and traditional monopoly form, namely, the related market definition problem brought about by data monopoly, the entry barrier formed by data monopoly, the problem of determining the dominant position of data monopoly enterprise, and the influence of consumer welfare. The book will focus on analysing the application limitations of traditional regulatory tools

1.3 Structure

9

and discussing new regulatory methods and paths for data monopolists on issues such as market definition, market share measurement and market power comparison. The book consists of ten chapters. Chapter 1 is the introduction. Chapter 2 discusses the global trend of the digital economy, the definition of online platforms and regulations in digital markets. Chapter 3 discusses the characteristics of online platforms and online markets. These features include the strong focus of dynamic efficiency, the online platform as multi-sided markets and indirect network effects, switching costs and attention-based nonprice competition. Chapter 4 discusses the characteristics of data-driven market competition. There are economic arguments that the costs of collecting, storing and analysing data are low. Data has non-exclusive and non-rivalrous feature, and the value of data decreases over time. Chapters 5–9 discuss the economic analysis of competition cases in the era of big data. Chapter 5 analyses the issue of the relevant market, and Chap. 6 discusses the entry barriers of data monopoly. Chapter 7 describes the economic rationale used in abuse of dominant position cases, and Chap. 8 deals with the issue of consumer welfare effects. Chapter 9 studies the relationship between data regulation, consumer protection and competition law. The last chapter concludes.

1.4 Academic and Social Relevance Seeing the political debate on whether there is a need to intervene in the digital economy, it becomes of great importance to examine the rich literature in law and economics to understand the economic rationale that provides the theoretical basis for this debate. Policy makers, courts and sector regulators, on the one hand, promulgate a series of laws and regulations to identify abusive behaviour and to set up blacklists to block the abusive use of market power with the goal of protecting consumer welfare; on the other hand, they express the worries that those interventions would have adverse effects that impede innovation and the development of the digital economy. As the G20 Ministerial statement expressed, “Digital divide should be addressed with a commitment to evidence-based policy approaches together with the efforts to improve the measurement of the digital economy that enable the widest possible adoption and use of innovative technology.”24 This book is written to examine the economic evidence that is for or against the policy intervention on data monopolies, and to discuss the challenges of regulating data giants and internet service providers when pro-competitive innovation-based arguments and anti-competitive concerns about the abuse of market power are both presented. The book focuses on the competition regulation of data monopolies, and in particular discusses the key methods applied in competition assessment, including defining relevant markets, 24

G20 (2019). Under the chairmanship of H. E. Mr. Hiroshige Seko, Minister of Economy, Trade and Industry, H. E. Mr. Masatoshi Ishida, Minister for Internal Affairs and Communications, and H. E. Mr. Taro Kono, Minister for Foreign Affairs, of the Government of Japan, to further strengthen G20 trade and digital economic policy cooperation.

10

1 Introduction

market power assessments, entry barrier analyses and impacts on consumer welfare. It does not separate the types of anti-competitive conduct, such as agreements, abuse of dominant position or concentration regulations, and does not discuss in detail the sector regulations. It aims to provide the analytical tools and analytical framework that is applied in competition law and has combined the discussion of cases in all types of conduct in each chapter. A close look at the dominance and merger case has been included in Chap. 7—the market power assessment. Having the background of a law and economics researcher being educated both in China and European universities, the author takes a comparative view when discussing developments of competition regulation in China and Europe. It also included a study of policy discussion in the US. Therefore, this book provides a unique comparative view of data monopolies and competition law. The study of these three main antitrust jurisdictions is of great importance, as they have formulated the main models for data protection, privacy and competition policy in the digital market.25 The contribution of this book is that it has a deep analysis of those analytical tools by incorporating theoretical discussion in various disciplines and has combined recent studies in economics, behavioural science, management studies, business and entrepreneurship studies. It provides an unbiased, up-to-date study of regulations on online platforms and data monopolies from a multidisciplinary and interdisciplinary perspective. Moreover, it has a comparative view by investigating policy changes and more recent cases in the digital market in the European Union, United States and China. As the debate on regulations in digital markets continues, the literature on data monopolies is also evolving. This book provides the most up-to-date information on the economic and legal debate on regulating data giants from the perspective of competition law. By reviewing the most up-to-date economic literature and public policy debate in online platforms and digital markets, this book aims to provide a comprehensive view of the economic theory and policy implications for regulating data monopolies. There are three main contributions to this topic. First, I study the relationship between big data and competition law at the theoretical level and explore the industrial economic background and the reasons for the commercial system formed by data monopoly to clarify the necessity of regulating data monopoly. Second, I study the main forms of implementation of data monopolies, that is, data possession enterprises by restraining consumers’ alternative choices, and thus harming consumer welfare. Third, it points out what specific explanations and improvements competition law should make on the issue of regulating data monopoly, that is, on the basis of summarizing the four key issues of market judgment, entry barrier, market dominance and consumer welfare impact of data monopoly by economists, lawyers and practice workers. 25

Wang (2020, p. 662) “The current discourse on global data privacy reform has tended to focus on the European Union (“EU”), the U.S., and the Chinese models of data privacy. The “privatized” approach favoured by the United States sharply contrasts with China’s state-controlled framework, with its expansive surveillance powers. German Chancellor Angela Merkel argues that the EU occupies a middle ground between these two opposing models”.

1.4 Academic and Social Relevance

11

The goals of this study are: 1.

2.

3.

To study the characteristics of online platforms and to understand how online service providers facilitate buy-sell transactions in the digital environment. The indirect network effect has changed the function of how price increases will affect the demand, thus how a substitutability test could be applied, and has become the key for the analysis of relevant market definition, market power and the effects on consumer welfare. To study how online platforms utilize data as their crucial input. When online products and services are provided at zero price, consumer personal information has become the new currency for the product. Although data itself have proven to be cheap and easily collected, the technology of computing algorithms makes it possible to analyse data in large volumes to make predictions on consumer behaviour and preferences. Thus, sellers can tailor contract offers, send targeted advertisements and charge discriminated prices based on their predictions. Dominant firms holding large volumes of data can therefore achieve economies of scale, as more data makes better predictions. From the supply side, the possibility of replicating data and the ability to access to a large volume of data are the key issues to measure barriers to entry and to judge whether the firm has obtained market power. From the demand side, whether the data have been collected with consumer consent and how consumers behave in their willingness to provide complete data, is the key issue in designing policy infrastructure for privacy and data protection. The traditional analysis of market structure as the proxy for measuring market power has become outdated, because consumers are no longer passive actors in digital transactions. Consumer protection goals have to be achieved by motivating consumers to be aware of data collection, and training consumers to be able to switch among different online service providers when the right to data portability is implemented. To understand how the study of consumer behaviour is relevant for competition policy in digital era. As online services are most commonly provided at zero price, the effects of their business conduct on consumers have to take consumer behaviour and preferences into account. Economic models have proven that unlike in traditional markets, the effects on consumer welfare in digital markets are determined by the characteristics of consumers. Price discrimination, for example, may increase or reduce consumer surplus when sellers charge prices based on consumer loyalty or their searching behaivour. Moreover, the importance of the heterogeneity of consumers has been neglected in traditional competition economic analysis, as aggregated consumer welfare is still the main goal of competition law today. Digital platforms no longer sell products to consumer groups as a whole; machine learning technologies have made it possible to track each individual’s behaviour, send targeted advertisements to each individual consumer, and charge different prices to each individual. Both the demandside consumer groups and the supply side products become heterogeneous. Economic models are still not able to incorporate the presumption of perfect consumer heterogeneity, but the business strategy of perfectly differentiated

12

4.

5.

1 Introduction

consumer groups has been largely implemented. Without taking distribution issues as the main objective concern, competition enforcement thus has to take into account the different preferences, switching costs, searching experiences, and searching costs of different consumer groups, and these characteristics will replace the proxy of market power and market shares in the assessment of the anti-competitive effects of firms’ conduct. To understand the boundaries of competition law in regulating data monopolies. Competition law in the traditional market has evolved from per se rules to rule of reason analysis. A per se illegality can hardly be applied in the digital environment, as empirical reports have clearly shown the benefits of data-driven innovation that the digital economy has brought in the last decade. A tradeoff has to be made between static efficiency and dynamic efficiency, market power and abusive conduct, economies of scale and entry barriers, consumer privacy protection and algorithmic data analysis. Amendments of competition law are about making boundaries for competition intervention that accelerate the development of the digital economy without being detrimental to consumer welfare and getting consumers involved in law enforcement without favouring one group of consumers over another. The study will try to set the baselines for such boundaries and make some suggestions on the issues the competition authorities are facing in the making of digital competition law. To study the global trend in the convergence of competition policy in the digital era. The process of competition modernization has called for a convergence of competition law when economic analysis has become the basic benchmark and when comparing legislative developments and case decisions in different antitrust jurisdictions. Such a benchmark becomes more important in the development of digital competition law because the competing models and the effects of competition become more ambiguous and uncertain. Any good regulation of market intervention has to take economic analysis as a prerequisite and has to carefully evaluate the pros and cons of the effects that the regulation may cause. This research also points out that empirical data, and studies on consumer behaviour and preferences should also be incorporated as the benchmark, and the antitrust cases in the US, EU and China should be compared to understand how the intervention of competition authorities will be beneficial for improving economic efficiency, enhancing consumer welfare and achieving fairness.

1.5 Limitations The book has limitations in terms of both theory, content and methodology. Having chosen the title of “regulating data monopolies”, the book mainly focuses on competition regulation and has not extended the discussion to other regulatory methods such as public and private regulation, self-regulation and deregulation in the internet industry. It narrows the debate within competition law itself and has not addressed

1.5 Limitations

13

the relationship between competition law, consumer protection and industry regulations. It explains the regulatory tools that are commonly applied in the analysis of competitive effects, and hopes to take competition law as a way through the debate on promoting efficiency and innovation and restraining the market power of data giants. The core focus of the data economy applied in this book is on competition issues and with less focus on consumer protection, privacy and sharing economies. Furthermore, the transformation from a traditional economy to a digital economy includes broad technological changes, including infrastructure issues such as 5G networks, Internet-of-Things and machine-to-machine networks, cloud computing; platform issues such as data analytics, digital identity, blockchain technology, and quantum computing; and application issues including cryptocurrencies, artificial intelligence, robotics, 3D printing and autonomous vehicles.26 This book focuses on competition issues of data-driven online platforms, and hopes to take further research on other areas of the digital economy by applying the methodology of law and economics at a later stage after this book project. Third, the study applies the research results from various disciplines and assumes the law and economics analytical framework as the benchmark in the comparison of legal acts in different countries. Obviously, it is not to say there is no cost for choosing such a benchmark, and when there are conflicts between philosophical foundations, social welfare, consumer welfare, efficiency and fairness goals may not be achieved at the same time. This book applies the methodology of law and economics when discussing the design of regulations, and the suggestions on regulations are based on the benchmark of economic theory and empirical findings. Those suggestions may have to be adjusted when another methodology is applied to design regulatory plans.

References Armstrong, M. (2006). Competition in two-sided markets. Rand Journal of Economics, 37(3), 668–691. Bundeskartellamt. (2016). Working paper on market power of platforms and networks. German Competition Authority, Ref. B6-113/15, June 2016. Bundeskartellamt. (2017). Preliminary assessment in Facebook proceeding: Facebook’s collection and use of data from third-party sources is abusive. Press Release, December 19, 2017. https://www.bundeskartellamt.de/SharedDocs/Publikation/EN/Pressemitteilungen/2017/ 19_12_2017_Facebook.html?nn=3591568 Caillaud, B., & Jullien, B. (2001). Competing cybermediaries. European Economic Review, 45(4–6), 797–808. Caillaud, B., & Jullien, B. (2003). Chicken and egg: Competition among intermediation service providers. Rand Journal of Economics, 34(2), 309–328. EDPS. (2014). Privacy and competitiveness in the age of big data: The interplay between data protection, competition law and consumer protection in the Digital Economy. Preliminary Opinion of the European Data Protection Supervisor, March 2014. Available at https://edps. europa.eu/sites/default/files/publication/14-03-26_competitition_law_big_data_en.pdf

26

Lovelock (2018, pp. 7–12).

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1 Introduction

EPRS. (2021). Online platforms: Economic and societal effects. Panel for the Future of Science and Technology, European Parliamentary Research Service (EPRS), Scientific Foresight Unit (STOA). https://www.europarl.europa.eu/RegData/etudes/STUD/2021/656336/EPRS_STU(202 1)656336_EN.pdf European Commission. (2016). Commission staff working document on Online Platforms, accompanying the document ‘Communication on Online Platforms and the Digital Single Market’, COM (2016) (288), SWD (2016) 172 final, Brussels, May 25, 2016. https://eur-lex.europa.eu/ legal-content/EN/TXT/PDF/?uri=CELEX:52016SC0172 European Commission. (2017a). Antitrust: Commission fines Google e2.42 billion for abusing dominance as search engine by giving illegal advantage to own comparison shopping service. Press Release, June 27, 2017. http://europa.eu/rapid/press-release_IP-17-1784_en.htm European Commission. (2017b). Mergers: Commission fines Facebook e110 million for providing misleading information about WhatsApp takeover. Press Release, May 18, 2017. http://europa. eu/rapid/press-release_IP-17-1369_en.htm European Commission. (2020) Antitrust: Commission sends statement of objections to Amazon for the use of non-public independent seller data and opens second investigation into its e-commerce business practices. Press Release, November 10, 2020. https://ec.europa.eu/commission/pressc orner/detail/en/ip_20_2077 Evans, D. S., & Schmalensee, R. (2007). Industrial organization of markets with two-sided platforms. Competition Policy International, 3(1), 150–179. Evans, D. S., & Schmalensee, R. (2008). Markets with two-sided platforms. Issues in Competition Law & Policy, 1, 667–693. FTC. (2013). Google agrees to change its business practices to resolve FTC competition concerns in the markets for devices like smart phones, games and tablets, and in online search. https://www.ftc.gov/news-events/press-releases/2013/01/google-agrees-changeits-business-practices-resolve-ftc FTC. (2014). Letter from Jessica L. Rich, Director at the Bureau of consumer protection, federal trade commission, concerning Facebook’s proposed acquisition of WhatsApp, April 10, 2014. https://epic.org/privacy/internet/ftc/whatsapp/FTC-facebook-whatsapp-ltr.pdf Facebook. (2018). Facebook reports second quarter 2018 results, 25 July 2018. G20. (2019). Ministerial statement on trade and digital economy (Vol. 11). G20 Trade Ministers and Digital Economy Ministers meeting on 8–9 June 2019 in Tsukuba City, Ibaraki Prefecture, Japan. Kennedy, J. (2017). The myth of data monopoly: Why antitrust concerns about data are overblown. Information Technology and Innovation Foundation, March 2017. Katz, M., & Shapiro, C. (1994). Systems competition and network effects. Journal of Economic Perspectives, 8(2), 93–115. Lovelock, P. (2018). Framing policies for the digital economy—Towards policy frameworks in the Asia-Pacific. Singapore e-Government Leadership Centre at Institute of Systems Science, National University of Singapore and Global Centre for Public Service Excellence, United Nations Development Programme. https://www.undp.org/publications/framing-policies-digital-economy Luckerson, V. (2018). Monopoly money: How to break up the biggest companies in tech. https://www.theringer.com/tech/2018/6/7/17436870/apple-amazon-google-facebookbreak-up-monopoly-trump Moore, M. & Tambini, D. (Eds). (2018). Digital dominance, the power of Google, Amazon, Facebook, and Apple. Oxford University Press. Newman, N. (2014). Search, antitrust and the economics of the control of user data (Working Paper). NYU Information Law Institute. OECD. (2013). Exploring data-driven innovation as a new source of growth: Mapping the policy issues raised by “big data”. In OECD digital economy papers (Vol. 222). OECD Publishing. Parker, G., & Van Alstyne, M. (2005). Two-sided network effects: A theory of information product design. Management Science, 51(10), 1494–1504.

References

15

Ritz, C., & Schöning, F. (2019). Digital avant-garde: Germany’s proposed “digital antitrust law. In CPI antitrust chronicle, December 2019. Rochet, J. C., & Tirole, J. (2003). Platform competition in two-sided markets. Journal of the European Economic Association, 1, 990–1029. Rochet, J. C., & Tirole, J. (2006). Two-sided markets: A progress report. Rand Journal of Economics, 37, 645–667. Smith, E. (2018). The techlash against Amazon, Facebook, and Google—And what they can do. Economist, January 20, 2018. https://www.economist.com/briefing/2018/01/20/the-techlash-aga inst-amazon-facebook-and-google-and-what-they-can-do Stucke, M. E. (2018). Should we be concerned about data-opolies? 2 Georgetown Law Technology Review, 2(2), 275–324. Tucker, D. S., & Wellford, H. B. (2014). Big mistakes regarding big data. The Antitrust Source, 1–12. Wang, F. Y. (2020). Cooperative data privacy: The Japanese model of data privacy and the EU-Japan GDPR adequacy agreement. Harvard Journal of Law & Technology, 33(2), 661–691. Wismer, S., Bongard, C., & Rasek, A. (2017). Multi-sided market economics in competition law enforcement. Journal of European Competition Law & Practice, 8(4), 257–262.

Chapter 2

Emerging Digital Markets and Regulation

Abstract This chapter introduces the global trend of the growth of the digital economy, and the changes it has brought to online business models and consumer behaviours. With the help of technological development and successful acquisitions, data giants such as Google, Facebook. Amazon, Apple and Microsoft have obtained substantial market power. It has also created a large group of consumers who purchase internet products and services online. Emerging digital markets have changed the industry structure and business patterns. This chapter discusses those changes and the responses from regulators in the EU, US, and China. It will discuss the public concerns of internet giants in the aspect of market power and the collection of user data. It will summarize the challenges that regulators are facing and describe the recent developments of regulation in the digital market.

2.1 Global Trend of Digital Economy 2.1.1 The Development of Online Business The rapid development of telecommunication technology has reduced the costs for internet use and smartphones, and business models have been changed to facilitate the growth of online transactions in industries including communication, transportation, grocery shopping, media, education and others. The development of digital markets and the internet economy has become a global trend in recent years. The development of internet technology gave rise to the transaction of business models online. The value created through online transactions is substantial: In 2018, the value of the digital economy in China reached 31.3 trillion CNY, accounting for 34.8% of GDP.1 UNCTAD estimated that the Global Internet Protocol Traffic (a proxy for data flows) has grown from 100 gigabytes (GB) of traffic per day in 1992 to 100 GB percent in 2002, 46,600 GB/s in 2017 and 150,700 GB percent in 1

CAICT (2019).

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 J. Ma, Regulating Data Monopolies, https://doi.org/10.1007/978-981-16-8766-2_2

17

18

2 Emerging Digital Markets and Regulation

2022.2 In 2019, the size of the digital economy accounted for 4.5–15.5% of global GDP. In the information and communications technology sector, the US and China together accounted for approximately 40% of world value added total.3 The US held the largest share of value added in the global computer service industry, accounting for almost as much of the next nine largest economies combined.4 In 2019, the US Census Bureau estimated that e-commerce retail sales reached 33 billion USD in 2001 and 600 billion USD in 2019.5 In 2019, online retail sales in China reached 1.5 trillion USD, representing 24–25% of the global retail market and larger than the next ten markets combined, and the number of digital consumes in China reached 855 million.6

2.1.2 Global Market Capitalization Digital platform companies have also dramatically changed global market capitalization. In 2009, among the top 20 companies in the world, seven of them were oil and gas and mining sectors (35%) and only three companies were from the technology and consumer service sector. In 2018, the number of technology and consumer service companies increased to eight (40%), and only two companies were from the oil and gas and mining sectors (see Chart 2.1).7 Most digital platforms are concentrated in the US (see Chart 2.2) and China (see Chart 2.3). On the one hand, there are numerous competitors in the market of mobile apps. In the first quarter of 2020, there were approximately 2.56 million apps on the Google Play Store, and 1.847 million apps on the Apple’s App Store8 ; 669,000 apps on the Windows Store; and 489,000 apps on the Amazon store.9 Chart 2.4 shows the cumulative number of apps downloaded from the Apple App Store from July 2008 until June 2017. On the other hand, data giants such as Facebook, Google, Amazon, and Apple have obtained significant market power. Google has obtained a large market share in all general search queries (81% on desktop and 94% on mobile) in the US.10 The market share 2

UNCTAD (2019, p. 2). UNCTAD (2019, xvi). 4 UNCTAD (2019, xvii). 5 U.S. Department of Commerce, U.S. Census Bureau Press Release, Quarterly Retail E-Commerce Sales 4th Quarter 2019; Press Release, U.S. Department of Commerce, U.S. Census Bureau, Retail E-Commerce Sales in Fourth Quarter 2001 Were $10.0 Billion, Up 13.1 Percent from Fourth Quarter 2000, Census Bureau Reports, February 20, 2002. 6 Bu et al. (2019). 7 UNCTAD (2019, p. 17). 8 Statista, Number of Apps Available in Leading App Stores as of 1st Quarter 2020. https://www.statista.com/statistics/276623/number-of-apps-available-in-leading-app-stores/. 9 Statista, Number of apps available in leading app stores. https://www.statista.com/statistics/276623/number-of-apps-available-in-leading-app-stores/. 10 Statcounter, Search Engine Market Share United States of America: Sept. 2019–Sept. 2020. http://gs.statcounter.com/search-engine-market-share/all/united-states-of-america. 3

2.1 Global Trend of Digital Economy

19

basic materials health care consumer goods telecommunicaƟons technology and consumer services financial services oil and gas

0

10

20 2018

30

40

50

60

2009

Chart 2.1 World’s top 20 companies by market capitalization by sector 2009 versus 2018 (per cent). Source UNCTAD (2019) 900 800 700 600 500 400 300 200 100 0

Chart 2.2 Main global platforms in the world in 2018—America (market capitalization in billions of dollars). Source UNCTAD (2019)

of Amazon is approximately eight times larger than that of eBay and Walmart,11 and approximately 60% of online sales in the US begin on Amazon.12 UNCTAD estimated that Google, Facebook, and Amazon held dominant market shares in relevant markets: Google reached 90% of the Internet search market; in more than 90% of the global economies, Facebook is the top social media platform; and Amazon has reached 40% share of the online retail markets around the world. The Congressional Research Service (CRS) report13 showed that the Google search engine processes more than 3.5 billion searches per day, and it is the owner of the 11

Lipsman (2020). Koch (2019). 13 Freeman and Sykes (2019). 12

20

2 Emerging Digital Markets and Regulation

400 350 300 250 200 150 100 50 0

Chart 2.3 Main global platforms in the world in 2018—Asia (market capitalization in billions of dollars). Source UNCTAD (2019) 200 180 160 140 120 100 80 60 40 20 Jun'17

Sep'16

Jun'15

Jun'16

Jun'14

Oct'14

Oct'13

Jan'13

May'13

Jun'12

Sep'12

Mar'12

Jul'11

Oct'11

Jan'11

Jun'11

Oct'10

Jun'10

Sep'10

Jan'10

Apr'10

Jul'09

Apr'09

Sep'08

July'08

0

Chart 2.4 Cumulative number of apps downloaded from the Apple app store from July 2008 to June 2017 (in billions). Source https://www.statista.com/statistics/263794/number-of-downloadsfrom-the-apple-app-store/

2.1 Global Trend of Digital Economy

21

largest video platform (YouTube) and is the largest seller of online advertising.14 In 2018, Google, Amazon, Facebook and Apple generated over 690 billion USD in revenue.15 The UK Competition Authority Competition and Markets Authority (CMA) estimated that in 2019 Google reached a market share of 93% in the search market overall (97% in the mobile devices search market and 84% on desktop devices), and the competitors Bing and Yahoo Search had the next highest shares of 5% and 1%, respectively. The next most used search engines were DuckDuckGo and Ecosia, with less than 1% of share in the search market in the UK in 2019.16 In India, Google controlled nearly 96% of the Internet search market.17 WeChat in China has more than 1 billion active users, and Alipay dominates almost all markets for mobile payments.18

2.1.3 Digital Consumers A large group of digital consumers have therefore been formed. In 2010, only approximately 12% of the world’s population had smartphones, and penetration was expected to grow at more than 20% each year.19 UNCTAD estimated that in 2017, the global value of e-commerce reached 29 trillion USD, accounting for 36% of GDP. The top 10 countries by total e-commerce sales were the US, Japan, China, Germany, the Republic of Korea, the UK, France, Canada, India and Italy. In 2017, China had the largest number of online consumers (440 million) (see Chart 2.5).20 In a survey conducted by McKinsey in 2019, consumers in China spent 358 min online per day, and 33% of the total online time was spent on social media applications (e.g., WeChat, Weibo), 11% on short video (e.g. Douyin/Tik Tok), 9% on news, 8% on online video (e.g., iQiyi), 8% on gaming, 4% on online shopping, 3% on online music, and 24% on others.21 In India, it is estimated that in 2018, users in India downloaded 12.3 billion applications on average, each user had 68 mobile applications and 35 of them were active used each month. There are 294 million active Indian users on social media platforms and over 200 million active users on instant messaging platforms.22 Chart 2.6 shows the main social media players in the US. This shows that Facebook had the largest market share in 2016 and 2018, and when users were multi-home, Facebook attracted users from other social media, but such multi-homing was not

14

Enberg (2019). Freeman and Sykes (2019, p. 1). 16 CMA (2020a, p. 81). 17 Bhusan (2019, p. 5). 18 UNCTAD (2019: xvii). 19 McKinsey Global Institute (2011, at p. 2). 20 UNCTAD (2019, p. 15). 21 Bu et al. (2019, p. 7). 22 Bhusan (2019, p. 4). 15

22

2 Emerging Digital Markets and Regulation

900 800 700 600 500 400 300 200 100 0 online government services

online car hailing

online video

online payment

online takeaway

online shopping

internet news

instant communicaƟons

Chart 2.5 The number of users of online products and services in China (million). Source The 44th Statistical Report on Internet Development in China, China Internet Network Information Center (CNNIC) 120 100 80 60 40 20 0 Facebook

Instagram

TwiƩer

LinkedIn

Percent daily users 2016

percent of US using 2016

Facebook Users 2016

percent daily user 2018

percent of US using 2018

Facebook users 2018

Chart 2.6 Social media use in the US in 2016 and 2018. Source Gans (2018, at p. 9)

reciprocal.23 In Australia, for each month, among the 25 million population of whom 21 million were over the age of 13, approximately 19.2 million users used Google Search, 17.3 million users used Facebook, 17.6 million users accessed YouTube and 11.2 million users accessed Instagram.24

23 24

Gans (2018, p. 9). ACCC (2019, p. 6).

2.1 Global Trend of Digital Economy

23

From 2010 until 2015, investment in e-commerce platforms (including B2B and B2C) was concentrated in China (10 billion USD), the US (9.8 billion USD) and India (5.6 billion USD), and the three countries accounted for approximately two-thirds of the e-commerce invested globally. In Europe, the largest e-commerce markets were Germany (2.8 billion USD) and the UK (1.2 billion USD).25 According to China Internet Network Information Center (CNNIC), up to June 2020, the population of online shopping users in China reached 749.39 million, taking up 79.7% of total Internet users, and the user size of online payment reached 805 million, accounting for 85.7% of total Internet users. Starting in 2013, China has become the largest online retail market in the world for seven consecutive years.26 A survey conducted by the University of Canberra’s News and Media Research Centre showed that online sources have become the primary source of news for 47% of Australians, 45% in the UK, 45% in Canada and 51% in the US.27 In Latin America, the e-commerce revenues reached 45.4 billion USD in 2017. The largest e-commerce platforms in Latin America include MercadoLibre, Amazon, B2W Digital, Alibaba, eBay, CNova, Apple, Walmart, Google Shopping, and Buscape.28 Headquartered in Argentina and operated in 18 countries in Latin America, MercadoLibre reached over 56 million unique visitors and generated 1.4 billion USD revenue in 2018.29 B2W Digital is based in Brazil and offers products including computer games, electronic devices, appliances, books, furniture, and consumer financing plans.30 In Europe, the largest online markets are the UK, Germany and France, which together account for 81.5% of European online sales (see Chart 2.7).31 In the UK, internet users, on average, spend 3 h 24 min each day online. During February 2020, 96% of the total UK population accessed a Google site, and 87% of them accessed a Facebook site.32 Eurostat data showed that in 2009, approximately 50% of internet users in the EU purchased online, and this number increased to 71% in 2019.33 In Australia, 68% of the total population (17 million) accessed Facebook on a monthly basis in 2018, and both Google Search and YouTube have 19 million users. Among the digital platforms that users use daily, Google Search reached 70% and Facebook held 58%.34 As of January 2021, Facebook had over 1 billion registered accounts and more than 2.6 billion monthly active users in total. The four platforms that are owned by Facebook, including the Facebook core platform, WhatsApp, Facebook Messenger and Instagram all have more than 1 billion monthly 25

USITC (2017, p. 149). CNNIC (2020, at p. 37). 27 Fisher et al. (2018, p. 29). See also ACCC (2018, p. 28). 28 OECD (2019b, p. 12). 29 OECD (2019b, p. 13). 30 OECD (2019b, p. 13). 31 Twenga Solutions: https://www.twenga-solutions.com/en/insights/e-commerce-europe-2016facts-figures/. 32 CMA (2020a, p. 153). 33 Eurostat, E-commerce Statistics for Individuals, September 17, 2020. 34 ACCC (2018, pp. 24–25). 26

24

2 Emerging Digital Markets and Regulation

60 50 40 30 20 10 0 Belgium

France

Greece

Ireland

online populaƟon

Poland

UK

Italy

e-shoppers

Chart 2.7 Online population and E-shoppers in Europe in 2018 (million). Source A guide to E-commerce in Europe, Enterprise Europe Network, http://een.ec.europa.eu/about/branches 3000 2500 2000 1500 1000 500 0

Chart 2.8 Most popular social networks worldwide ranked by number of active users in January 2021 (in millions). Source https://www.statista.com/statistics/272014/global-social-networks-ran ked-by-number-of-users/

active users.35 Chart 2.8 shows in January 2021 the most popular social networks worldwide, ranked by number of active users. A more recent survey shows that the COVID-19 pandemic and lockdowns have significantly expanded online retail marketplaces, and in May 2021 nonfood online sales form 14.2% of total nonfood sales in Australia, and the proportion of total food sales made online was 4.9% in

35

https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/.

2.1 Global Trend of Digital Economy

25

May 2021.36 Online retail marketplaces that are widely used by Australians include eBay, Amazon, Catch.com.au and Kogan.37

2.2 Online Platforms 2.2.1 Defining Online Platforms The development of the digital economy is mainly driven by the use of the internet and online platforms. Business transactions in various industries are facilitated by platforms today. The OECD defined online platforms as “digital services that facilitate interactions between two or more distinct but interdependent sets of users (whether firms or individuals) who interact through the service via the Internet”.38 The European Commission suggested that the definition of online platforms is: “an undertaking operating in two (or multi)-sided markets, which uses the Internet to enable interactions between two or more distinct but interdependent groups of users to generate value for at least one of the groups. Certain platforms also qualify as intermediary service providers”.39 The European Commission also defined the main characteristics of online platforms as having “(1) capacity to facilitate, and extract value, from direct interactions or transactions between users; (2) the ability to collect, use and process a large amount of personal and non-personal data in order to optimize, inter alia, the service and experience of each user. (3) capacity to build networks where any additional user will enhance the experience of all existing users—so-called “network effects”; (4) ability to create and shape new markets into more efficient arrangements that bring benefits to users but may also disrupt traditional ones. (5) Reliance on information technology as the means to achieve all of the above”.40 Based on the role that online platforms play, EdiMA has provided categories such as e-commerce platforms, social networks, search providers, entertainment services, and comparison tools/agents.41 Researchers gave categories based on the transaction act that the platform serves. Evans provided three categories to classify platforms: market makers that enable different user groups to transact with each other, and each side of the user values the service more when there is more demand at the other side; audience-makers, whose platforms are designed to match advertisers to audiences; demand-coordinators, whose platforms do not belong to market makers (based on transactions) or audience-makers (based on messages).42 In a later paper published in 36

Australian Bureau of Statistics (2021). ACCC (2021b, p. 6). 38 OECD (2019c). 39 European Commission (2015b). 40 European Commission (2016b). 41 Thelle et al. (2015, p. 7). 42 Evans (2003). 37

26

2 Emerging Digital Markets and Regulation

2005, Evans and Schmalensee categorize platforms as exchanges, advertiser-support media, transaction devices and software platforms.43 Filistrucchi simplified the categories into two: nontransaction markets where the transaction is not observable (such as online media, where the audiences are not aware of the transaction of the advertisements for the advertiser) and the platforms charging membership fees, and transaction markets where the transactions are observable (such as payment cards) and the platform charges transaction and adoption fees.44 The OECD provided 20 functional categories for online platforms (Table 2.1). In addition, the OECD Digital Economy Report 2016 has further defined the term “peer platform markets” to describe the online platform markets facilitating the commercial exchange of goods and services, including sale or auction of goods, rental of short-term accommodation and transportation or mobility services, and those business models have also been named the sharing economy.45 The OECD’s report has listed examples of peer platform marketplaces in accommodation, transportation, food consumption, retail, skills and financial services (see Table 2.2). Peer platforms create revenue through subscription or membership fees, charging commissions, surcharges or transactions and booking fees.46 Peer platforms take the responsibility of building trusts and reputation by providing monitoring and feedback mechanisms, reducing information asymmetry and developing mediation and communication channels. The distinct characteristic of this type of business model is fairly allocating cooperative responsibilities among peer platform participants.47 The OECD defined the economic role of online platforms as providing infrastructure as an improvement in convenience and lower transaction costs; collecting, organizing and evaluating information to reduce search costs; facilitating social communication and information exchange such as improving consumer feedback and improving consumer and social benefits; aggregating supply and demand to increase the variety of products and expand available markets; facilitating market processes to offer greater choice, more relevance and reduced prices; providing trust and improve consumer engagement; and taking into account the needs of buyers and sellers by improving the collection and incorporation of information contained in consumer feedback.48

2.2.2 “Free Service” Supplier Although most platforms provide products or services free or charge, it does not mean that those services are entirely “free”. Consumers have to exchange their personal data 43

Evans and Schmalensee (2005). Filistrucchi (2008). 45 OECD (2016a, p. 7). 46 OECD (2016a, p. 10). 47 OECD (2016a, p. 25). 48 OECD (2010, p. 15). 44

2.2 Online Platforms

27

Table 2.1 Functional categories of online platforms Categories of online platform

Examples

1

Ad-supported Internet Search

Google, Baidu

2

Social media

Facebook, WeChat, Twitter, Microsoft Linkedin

(1) Ad-supported general social media

Facebook, WeChat

(2) Ad-supported microblogging

Twitter

(3) Ad-supported photo/video sharing

Instagram, Flickr, TikTok, YouTube

3

App stores

Apple Apple Store, Google Play, Amazon Appstore for Android

4

Third-party business-to-business (B2Bs)

Alibaba

5

Third-party business-to-consumer (B2Cs)

Amazon Marketplace, MercadoLibre Classifieds, Rakuten, Tmall

6

Ad-supported music streaming

Deezer, Spotify

7

Ad-supported print media

National Geographic, ParisMatch

8

Consumer-to-consumer platforms (C2Cs)

MercadoLibre, Taobao

9

Maps

Baidu Maps, Bing Maps, Google Maps

10

Repositories for scholarly research

SSRN

11

Labour Freelancing/crowdsourcing

Freelancer, Amazon Mechanical Turk, Ikea/TaskRabbit, Upwork

12

Crowdsourcing (1) Competitive

TopCoder

(2) Non-competitive

Google Waze

13

Food delivery

Deliveroo, UberEats

14

Language education

Duolingo

15

Gaming

Amazon Twitch, Huya

16

Fintech

17

18

(1) Currency exchange platforms, crowdfunding

Currency Fair, Indiegogo, Kickstarter

(2) Mobile payments

AliPay, PayPal, WeChat Pay

(3) Online brokers

Fidelity, RobinHood, SaxoBank

Transportation (1) On-demand ride services

Uber, Lyft, Kapten

(2) Long-distance car-pooling

BlablaCar

Travel booking (1) Rental cars, air flights and hotels

Bookings.com, Expedia, Opodo

(2) Cruises

Vacationstogo.com

(3) Short-term rentals

Airbnb, Atraveo, Homeaway

19

Mobile payments

WeChat Pay, Alipay

20

Dating

Meetic, Tinder, Grindr

Source OECD (2011) and EPRS (2021).

28

2 Emerging Digital Markets and Regulation

Table 2.2 Examples of peer platform marketplaces Type

Online platform

Accommodation Airbnb (short term vacation stays), HomeAway, HomeStay, FlipKey, Wimdu, and physical Villas.com, FlatClub, onefinestay, HouseTrip, Guesthop (support services for space home sharers), DesksNearMe (workspace), Landshare (land, gardens) Transportation and mobility

Uber, Hitch, Lyft, BlaBlaCar, Getaround, ParkingPanda (parking spots), Freecycle Network

Food consumption

Feastly (connects diners with chefs), LeftoverSwap, EatWith (matches diners and hosts), MamaBake (homecooked cakes), EatWithMe (homecooked food)

Retail and Ziplok, Tradesy, Neighboorgoods, eBay, Poshmark, Yerdle, Spinlister (sports consumer goods equipment), Kidizen (kids clothing and toys); Rockbox (jewellery rental service); StubHub, viagogo, GetMeIn, Seatwave (secondary tickets) Skills and services

TaskRabbit (all kinds of tasks)

Financial services

Prosper (lending), Kickstarter (funding)

Source OECD (2016a, at p. 9)

or ‘attention’ in the transaction and those data are monetized by platforms to generate profits. Evans shows the paid participants of the most frequented platforms in the US (Table 2.3). UNTCAD has listed four ways to monetize data: advertising platforms such as Facebook, Google, Twitter and Snapchat store personal data for targeted advertising business; e-commerce platforms such as Amazon, Alibaba, eBay, Uber, and Apple generate profits by charging a commission for each transaction or app sale; product platforms such as Mobike, Rolls-Royce’s jet engine, generate profits from the use of traditional products; cloud platforms such as Alibaba Cloud, Amazon Web Services, Google Cloud Platform and Microsoft Azure provide software, hardware and AI tools and that infrastructure is the basis for their services.49 By 2019, Google and Facebook has owned 84% of the online advertising market. For Google, 83% of Google’s revenue was through digital advertising, and 61% of its total sales were from search advertising.50 By sending targeted messages based on the personalized profile that users share online, Facebook has grown its advertising revenue 600% during the first five years from 2012 to 2016.51 From 2014 until 2017, the total online advertising market in Australia grew by 3.1 billion USD and 70% of that growth was contributed by Google and Facebook.52 In 2017, Google earned 110 billion USD of revenue globally, and approximately 80% of the advertising revenue earned in Australia was from selling advertisements that are shown with Google Search results.53 The competition authority CMA in the UK found that in 2019, both 49

UNCTAD (2019, p. 30). Alphabet, Annual report (Form 10-K), 3 February 2020. 51 McCann (2018, p. 8). 52 ACCC (2018, p. 33). 53 ACCC (2018, p.38). 50

2.2 Online Platforms

29

Table 2.3 Summary of most frequented platforms in the US Webpage

Category

Free participants

Facebook.com

Social media/social networking

People and many app Advertisers and some app developers developers

Paid participants

Google.com

Search/navigation

Searchers and websites

Advertisers

Youtube.com

Entertainment/ multimedia

Video’s uploaders and viewers

Advertisers

Yahoo.com

Portals

Viewers

Advertisers

Amazon.com

Retail

Buyers

Sellers (for sales and advertising)

Bing.com

Search/navigation

Searchers and websites

Advertisers

Craigslist.org

Directories/ resources—classifieds

Viewers and many listers for ads

Certain categories of listers for ads

MSN

Portals

Viewers

Advertisers

Ebay.com

Retail

Buyers

Sellers (for sales and advertising)

Aol.com

Portals

Viewers

advertisers

Espn.com

Sports

None

Viewers and advertisers

Swagbucks.com

Services—coupons, services

People

Advertisers/marketers

Linkedin.com

Social media/social networking

People for basic service

Advertisers and people for premium service

Paypal.com

Business/finance, personal finance

Receivers of funds

Senders of funds

Groupon.com

Services—coupons, services

people

Business for marketing and advertising services

Imgur.com

Social media

Uploaders and viewers

Advertisers

Answers.com

Directories/resources

People looking for information

Advertisers

Twitter.com

Social media/social networking

People who send and read tweets

Advertisers

Indeed.com

Career services and development/career resources

People looking for jobs

Employers advertising jobs

CNN.com

News/information

Viewers

Advertisers

Source Evans (2016, at p. 18)

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Google and Facebook provided 400–500 billion personalized advertisements, and Facebook users on average see 50–60 advertisements per hour.54 Over the past ten years, the search advertising revenues of Google increased from 2.1 billion GBP in 2010 to 6.8 billion GBP in 2019. Google earns 0.04–0.05 GBP per search and Facebook earns 50–60 GBP per user through its platform in 2019.55

2.2.3 Gatekeepers Having studied the economic performance of online platforms, the EU commission has identified the main concerns that digital services raised and has provided a new concept of “gatekeepers”. They share the features of (1) maintaining a high concentration level and usually one or very few large digital platforms has the autonomy to set commercial conditions, (2) they serve as the gateway between business and consumers in the transaction, and (3) gatekeepers often misuse their market power by imposing unfair conditions.56 The most prominent large online platforms that have raised concerns of contestability and unfair practices are also defined as “core platform services”, including online intermediation services, online search engines, social networking, video sharing platform services, number-independent interpersonal electronic communication services, operating systems, cloud services and advertising services.57 Core platform providers are gatekeepers when they (1) have a significant impact on the internet market; (2) operate one or more important gateways to customers; and (3) enjoy or are expected to enjoy an entrenched and durable position in their operations.58 In the EU Digital Market Act, platforms are also named “gatekeepers” in the digital sector, with the definition that “These are platforms that have a significant impact on the internal market, serve as an important gateway for business users to reach their customers, and which enjoy, or will foreseeably enjoy, an entrenched and durable position. This can grant them the power to act as private rule-makers and to function as bottlenecks between businesses and consumers”.59 In addition to the economic characteristics, this term has further clarified the legal liabilities that online platforms should perform In the Digital Market Act, business practices such as prohibiting access to data or restrictions on consumers to switch to alternative platforms have all been considered to impose unfair conditions.60 The European Parliament’s briefing has clearly identified three issues where online gatekeepers with market power will become detrimental to fair competition.61 The 54

CMA (2020a, p. 153). CMA (2020a, p. 180, p. 232). 56 European Commission (2020c, at p. 2). 57 European Commission (2020c, at p. 2). 58 European Commission (2020c, at p. 2). 59 European Commission (2020b). 60 European Commission (2020b). 61 EPRS (2020, p. 2). 55

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31

first issue is that traditional businesses may become dependent on the limited number of large online platforms, causing imbalances in bargaining power between those large platforms and rivals. The second issue is that large online platforms will control online ecosystems, making it difficult to innovate alternative products and services. The third issue is that dominant firms may leverage their position to manipulate business rankings and reputations, and after growing beyond a tipping point, they will almost automatically strengthen their market power and dominance by gaining more users.62 The briefing listed nine types of abusive exploitative and exclusive practices that dominant gatekeepers may implement (see Table 2.4), and this behaviour can translate into lock-in effects on users that create high barriers to entry and prevent potential competitors from entering the market. It thus requires regulatory intervention to correct negative anti-competitive effects and to promote competition and innovation in digital markets. The goal of defining the concept of gatekeepers and identifying their anticompetitive conduct is to design proper policy instruments to establish a competitive, contestable and fair digital internal market. The European Commission Proposal for a Regulation of the European Parliament and of the Council on Contestable and Fair Markets in the Digital Sector (Digital Markets Act)63 discussed three policy options to regulate gatekeepers with market power. Options 1–3 are given in accordance with the flexibility of the regulation, and Option 1 is the strictest one by listing the gatekeepers and self-executing obligations. Option 3 is the most flexible option and is based exclusively on qualitative scoping thresholds. Option 2 is considered to be the most effective way to regulatory intervention because the four institutional tools listed in Option 2 provide timely, flexible and effective tools to address market failures in the changing market situation. Those options are summarized in Table 2.5 below.

2.2.4 Platform Ecosystem In 1993, Moore used the term ‘ecosystem’ in competition law.64 In 1997, Moore et al.65 developed the idea of using biological ecosystem metaphors in the analysis of business ecosystems, and Iansiti and Levien’s work66 further discussed how enterprises can play a key role in developing such an ecosystem.67 Jacobides, Cennano and Gawer defined an ecosystem as “a group of interacting firms that depend on

62

EPRS (2020, pp. 2–3). European Commission (2020c, at p. 9). 64 Moore (1993). 65 Moore et al. (1997). 66 Iansiti and Levien (2004a, 2004b). 67 de Reuver et al. (2018, p. 126). 63

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Table 2.4 Abusive conduct that may be undertaken by dominant gatekeepers Meaning

Examples

1 Self-preferencing

Behaviour

Unfairly favouring own products and services to the detriment of competing businesses

Preinstalling and default settings exclusively of its own products

2 Preferencing of a third party

Unfairly favouring a third Discriminating between trade party’s products or services partners to the detriment of competing businesses

3 Unjustified denial of access to Denying the access to key the platform or functionalities facilities necessary to conduct business

Denial of access to the platform’s payment services

4 Unjustified denial of access to collected data

Denying the access to data

Data that end users allow the platforms to share

5 Imposition of exclusionary terms and conditions for access

Imposing exclusive terms and Unfair blocking of certain conditions functionalities

6 Unjustified tying and bundling practices

Selling or offering together distinct goods/services without proper justification

7 Imposing unclear or unreasonable terms and conditions on business users or on end-users

Imposing unreasonable terms On business users: Excessive and conditions pricing for access to the platform On end-users: excessive gathering of end-user data

8 Unduly restricting or refusing Impeding individuals from data portability obtaining and reusing their personal data for their own purposes across different services

Adding applications to the primary service

Consumer lock-in

9 Unduly restricting or refusing Interoperability: the ability of Making it difficult or interoperability a system, product or service impossible for businesses and to communicate and function end-users to switch platforms with other systems, products or services Source EPRS (2020, at p. 3)

each other’s activities”.68 Adner argued that the ecosystem is defined “by the alignment structure of the multilateral set of partners that need to interact in order for a focal value proposition to materialize”.69 By taking a “hub and spoke” structure, peripheral firms are connected to the central platform through open-source technologies. Complementors connected to the platform also gain access to the platform’s

68 69

Jacobides et al. (2018). Adner (2017).

2.2 Online Platforms

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Table 2.5 Policy options in regulating gatekeeper conduct Policy options

Contents

(1) Predefined list of gatekeepers and self-executing obligations (2) Partially flexible framework of designation and updating of obligations, including regulatory dialogue for the implementation of some

(a) A closed list of core platform services (b) A combination of quantitative and qualitative criteria to designate providers of core platform services as gatekeepers (c) Directly applicable obligations, including certain obligations where a regulatory dialogue may facilitate their effective implementation (d) A possibility for the Commission to update the instrument, following a market investigation, as regards the obligations for gatekeepers, by way of delegated acts insofar as new practices are identified that are equally unfair and likely to impair contestability and through amending proposals in the other cases

(3) Flexible option based exclusively on qualitative scoping thresholds Source European Commission (2020c, at pp. 9–10)

consumers.70 The platform leader, often the owner of the platform core, plays the role of a central hub, and complementors can add to the codebase and through which they interoperate.71 The hub defines the hierarchical roles of members, establishes interfaces, IP rights and standards, and forms the tools to govern and motivate the members.72 The platform ecosystem literature discusses how a platform coordinates the platform, the platform owner, platform provider, complementors and end users within the system and how market failures (including positive externalities such as indirect network effects) that occur in the transaction could be addressed by platform governance.73 In ecosystems, platforms are vertically integrated or have conglomerate business models, and dominant firms may exercise anti-competitive conduct through conglomerate mergers that leverage market power into related markets or conduct vertical anti-competitive practices such as self-preferencing, foreclosure and margin squeeze.74 Andres Hein et al. proposed the definition that: “a digital platform ecosystem comprises a platform owner that implements governance mechanisms to facilitate value-creating mechanisms on a digital platform between the platform owner and an ecosystem of autonomous complementors and consumers”.75 Bakos 70

Jacobides et al. (2018, p. 2258). Gawer and Cusumano (2008). 72 Jacobides et al. (2018, pp. 2258–2259). 73 Boudreau and Hagiu (2009, pp. 163–191), Cennamo and Santaló (2018), Cennamo (2019) and Zhang et al. (2020) and Zhu and Iansiti (2012). 74 OECD (2021a, p. 7). 75 Hein et al. (2020, p. 90). 71

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and Katsamakas,76 Tiwana et al.77 proposed that platform ownership plays an essential role in platform ecosystems governance. Platform ownership can be centralized by a single owner, owned by a group of actors or decentralized by peer-to-peer communities.78 The ecosystem creates value through facilitation of transactions and developing innovation capabilities and enabling complementors to cocreate valueadding complements. The platform owner exercises a different level of control over complementors with different levels of autonomy.79

2.3 Digital Giants and Market Power Digital platforms have obtained substantial market power through their diversified online products and the successful acquisition of other market players. From 2015 until 2017, Google, Amazon, Facebook, Apple and Microsoft acquired 175 companies in total. From 2008 until 2018, Google acquired 168 companies, Facebook purchased 71 companies and Amazon acquired 60 companies.80 Over the last 20 years, Google has strengthened its market position through acquisitions of over 260 companies, and this number has not included purchases that were not reported.81 From its foundation in 1998 until December 10, 2019, Google acquired 214 business entities with a value exceeding 17 billion USD.82 From 2004 to 2014, Google spent over 23 billion USD purchasing 145 companies, including Nest Labs (2014), Waze (2013), Motorola (2011), ITA Software (2011), Admeld (2011), AdMob (2009), DoubleClick (2008)83 and YouTube (2006).84 Since 2020, Google has been the leading provider of nine products, including Android, Chrome, Gmail, Google Search, Google Drive, Google Maps, Google Photos, Google Play Store and YouTube, and each has more than a billion users.85 Since 2009, Google has become the dominant general search engine in Australia and has maintained a market share of between 93 and 95% from 2009 until 2018, and the next closest competitor, Bing, only had 2 to 4%.

76

Bakos and Katsamakas (2008, pp. 171–202). Tiwana et al. (2010, pp. 675–687). 78 Hein et al. (2020, p. 90). 79 Hein et al. (2020, p. 92). 80 Argentesi et al. (2019). 81 US Subcommittee on Antitrust, Commercial and Administrative Law of the Committee on the Judiciary (2020). 82 https://acquiredby.co/google-acquisitions/. 83 Case No COMP/M.4731—Google/DoubleClick, March 11, 2008, FTC. (2007a, p. 12). See also FTC (2007b, p. 9 and footnote 23). 84 ACCC (2018, p. 49). 85 McCracken (2019). 77

2.3 Digital Giants and Market Power

35

From 2006 until 2018, Facebook spent over 23 billion USD purchasing 66 companies, including WhatsApp (2015), and Instagram (2012).86 By October 2020, Facebook had 1.82 billion daily active users and through advertising via mobile devices, Facebook reached 70 billion USD in revenue in 2019.87 Facebook and Instagram together hold 46% of display advertising revenue, and in Australia, no other competitor has a market share of more than 5%.88 The products operated by Amazon include e-commerce, consumer electronics, television and film production, groceries, cloud services, book publishing and logistics.89 By 2020, Apple has more than 100 million iPhone users around the world, and Apple’s product has expanded to iPhone, iPad, Mac, Apple TV and Air Pods. Apple services include the Apple Store, iCloud, AppleCare, Apple Arcade, Apple Music, Apple TV+ and other software applications.90 In 2019, the reported revenue of Apple reached 260 billion USD. In China, Baidu has obtained 66% of market share in the search market, and by 2018, the Baidu app had 150 million daily active users.91 The fast growth of internet companies has raised two main public concerns. The first is the concentration of market power. When internet firms have developed key technology in online transactions or have invested in business concentration strategies and succeeded in competition at an earlier stage, they will be able to attract a large number of users. Thus, they obtained technological advantages and can be extremely profitable by exercising economies of scale. When internet firms have obtained large market shares, it is more likely that they conduct exclusive behaviour to prevent competition from other competitors. This exclusive behaviour includes leveraging their dominant power to increase entry barriers, imposing exclusive terms in deals with suppliers, and charging monopoly prices to consumers. However, because of the strong arguments made by economists that data has a non-exclusive and nonrivalry nature, and users can conduct “multi-homing” (choose multiple online service providers at the same time), online platforms do not have de facto exclusivity over user data92 ; regulating expanding market power is a challenging task. Competition authorities have to cooperate with public regulators in telecommunication, banking, transportation, and other industries to understand the market structure and to utilize both ex-ante and ex-post regulatory tools as well as to stimulate private regulation and self-regulation by the industry itself. The technical issues of how a relevant market could be defined, the measure of market power and the potential harm to consumer welfare, require continuous learning and cooperation between experts at the competition agency, and when contradicting evidence coexists, it is the task of 86

ACCC (2018, p. 52). US Subcommittee on Antitrust, Commercial and Administrative Law of the Committee on the Judiciary (2020). 88 ACCC (2018, p. 4, p. 42). 89 EPRS (2021, p. 6). 90 Apple Annual Report (Form 10-K), September 28, 2019. 91 Baidu, Baidu App Reaches 150 million DAUs after Launching Ads-Free Search, 5 June 2018. 92 Lerner (2014). 87

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the agency to analyse the evidence in accordance with the priority of competition goals. The second concern is the overcapacity of obtaining data and using algorithms and other technical tools to harm consumer welfare (for example, through price discrimination, targeted advertisement or privacy abuse), as the European Data Protection Supervisor once stated: “The collection and control of massive amounts of personal data are a source of market power for the biggest players in the global market for internet services”.93 In nonprice competition, consumers exchange their personal data to ‘purchase’ internet products and services. The proper collection and use of personal data must be monitored by regulators at various agencies. In addition, online platforms also maintain their dominance and leverage their business models to keep their market power through lobbying for both international and national regulations. UNCTAD showed that technology companies have already replaced financial sectors as the largest lobbyists. Google, Amazon, Facebook, Microsoft and Apple spent more than 60 million USD lobbying in 2018 in the US.94 As has been made clear in the introduction of this book, regulating data monopolies has to face a full range of paradoxes. As the start of the discussion, economists, lawyers, data scientists and regulators have not reached consensus on whether internet giants should be regulated, or how. When advocators emphasize the danger of market power and the ever-increasing capacity to collect data, opponents point out that the ‘size’ of internet firms is a sign of the success of their technology innovation. Moreover, it is unclear whether traditional regulatory tools, such as divestiture, or imposing high fines, will indeed have the effects of restricting market power and benefit the development of a market economy. While the next section will introduce the developments of regulators in digital markets and regulatory tools used in online market case analysis, this book will mainly focus on the economic theory and methods that are applied in competition law, and will discuss competition terms such as defining relevant markets, the use of market power, entry barriers and effects on consumer harm.

2.4 Regulations in Digital Markets 2.4.1 Policy Developments in the EU on Regulating Digital Economy The development of the digital economy and the new business model of online platforms gives rise to challenges in the design of legal frameworks to accommodate such development. Regulations on data and digital business are entirely lacking, but 93 94

EDPS (2014). UNCTAD (2019).

2.4 Regulations in Digital Markets

37

the rapid development of technology and multi-sided markets makes it necessary to make reforms and to build new legal infrastructures. In the EU, the first data protection act was Directive 95/46/EC, the European Data Protection Directive on the protection of individuals with regard to the processing of personal data and on the free movement of such data. In 2000, the European Commission adopted the e-Commerce Directive,95 which identified four crucial topics in the implementation: (1) transparency and information requirements for digital service providers; (2) commercial communications; (3) electronic contracts and limitations of liability of intermediary service providers; and (4) cooperation between member states and the role of self-regulation. On June 22, 2011, the European Data Protection Supervisor (EDPS) issued the Opinion on the European Commission’s Communication ‘A Comprehensive Approach to Personal Data Protection in the EU’. On January 25, 2012, the European Commission’s proposal to strengthen online privacy rights and the digital economy was published, and on March 7, 2012, the European Data Protection Supervisor issued another Opinion on EC data protection reform package. The EU Parliament adopted the General Data Protection Regulation on March 12, 2014. On December 15, 2015, the European Parliament, European Commission and European Council reached the agreement on the GDPR, and on April 27, 2016, Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data was promulgated and it came into force on May 25, 2018. EU adopted Regulation No. 2016/679 (General Data Protection Regulation, GDPR) to protect the processing of personal data and the free movement of such data.96 It replaced the European Data Protection Directive (95/46) in 1995. The EDPS has also addressed the critical issue of the regulatory relationship between data protection, consumer privacy and competition enforcement.97 At present, the regulatory framework of data protection has been laid by the GDPR, with two additional regulations covering non-personal data: the Digital Content Directive applicable in a B2C relationship and the Free Flow of Data Regulation applicable in B2B relationship. In 2015, the Digital Single Market (DSM) was published.98 On May 6, 2015, the European Commission published the Digital Single Market Strategy, which is based on three pillars: (1) better access for consumers and businesses to online goods and services across Europe; (2) creating the right conditions for digital networks and services to flourish; and (3) maximizing the growth potential of the European 95

Directive 2000/31/EC of 8 June 2000 on certain legal aspects of information society services, in particular electronic commerce, in the internal market (Directive on electronic commerce). 96 Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) [2016] OJ L119/1. 13. 97 European Data Protection Supervisor Executive Summary of the Opinion of the European Data Protection Supervisor on effective enforcement in digital society economy, OJ C 463/09 [2016]. https://edps.europa.eu/sites/edp/files/publication/17-01-13_big_data_ex_summ_en.pdf. 98 European Commission (2015a).

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Digital Economy.99 The three pillars are to take action to “enhance the use of digital technologies and online services should become a horizontal policy, covering all sectors of the economy and of the public sector”. The 2011–2015 Digital Europe Framework also introduced the Digital Economy and Society Index (DESI)100 and the Digital Skills Indicator (DSI) to measure the development of digital policy. The DESI is composed of five dimensions: connectivity (25%), human capital (25%), the use of Internet (15%), integration of digital technology (20%) and digital public services (15%). The result of the indicator calculations will be published on the open access Digital Agenda Scoreboard,101 which also has information including annual reports,102 repository of studies,103 and an interactive visualization tool.104 In the Communication on Online Platforms105 issued in May 2016, the European Commission has identified the guiding policy principles in regulating online platforms: (1) A level playing field for comparable digital services; (2) Ensure responsible behaviour of online platforms to protect core values; (3) Foster trust, transparency and ensure fairness on online platforms; (4) Keep markets open and nondiscriminatory to foster a data-driven economy. In February 2020, the European Commission announced in the Digital Strategy Communication106 the proposal of the Digital Services Act package. In June 2020, the European Commission published the Inception Impact Assessment, which mentioned the consideration of three policy reforms: (1) revising the horizontal legal framework on Platform-to-Business Regulation107 (2) implementing a horizontal legal framework to make it possible to collect information from large gatekeepers (3) adopting an ex ante regulatory framework on regulating gatekeepers to prohibit or restrict unfair trading practices (“blacklisted” practices) and adopting tailor-made remedies on a case-by-case basis.108 99

European Commission, A Europe fit for the digital age—Empowering people with a new generation of technologies. https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-fit-digital-age and European Commission (2015a). See also https://ec.europa.eu/digital-single-market/en/policies/ shaping-digital-single-market. 100 See Digital Economy and Society Index (2020), available at https://digital-strategy.ec.europa. eu/en/library/digital-economy-and-society-index-desi-2020. See also. https://digital-agenda-data.eu/datasets/desi/indicators. 101 http://ec.europa.eu/digital-agenda/en/digital-agenda-scoreboard. 102 http://ec.europa.eu/digital-agenda/en/download-scoreboard-reports. 103 http://ec.europa.eu/digital-agenda/en/newsroom/smart-studies. 104 http://digital-agenda-data.eu. 105 European Commission (2016c, 2018c). 106 European Commission (2020d). 107 This policy suggestion has been implemented by the promulgation of Regulation 2019/1150 of the European Parliament and of the Council of 20 June 2019 on promoting fairness and transparency. for business users of online intermediation services, OJ [2019] L 186/55. 108 Inception Impact Assessment on Digital Services Act package: Ex ante regulatory instrument for large online platforms with significant network effects acting as gate-keepers, available at https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/12418-Digital-ServicesAct-package-ex-ante-regulatory-instrument-of-very-large-online-platforms-acting-as-gatekeepers.

2.4 Regulations in Digital Markets

39

On December 15, 2020, the European Commission published the Digital Services Act package, composing two items of legislation of the Digital Services Act and the Digital Markets Act. The Digital Service Act (DSA) and Digital Markets Act (DMA) have two main goals: to create a safer digital space in which the fundamental rights of all users of digital services are protected; and to establish a level playing field to foster innovation, growth, and competitiveness, both in the European Single Market and globally.109 The package is aimed at developing the legal framework necessary to create the market value of the digital economy in the common market environment, as the European Commission’s website states: “The internet and digital technologies are transforming our world. However, existing barriers online mean citizens miss out on goods and services, internet companies and start-ups have limited horizons, and businesses and governments cannot fully benefit from digital tools. It’s time to make the EU’s single market fit for the digital age—tearing down regulatory walls and moving from 28 national markets to a single one. This would contribute 415 billion EUROs per year to our economy and create hundreds of thousands of new jobs”.110 In 2020, the Communication of ‘Shaping Europe’s Digital Future’111 and ‘A European Strategy for Data’112 were adopted. Both the GDPR on fundamental rights and the Digital Services Act package have laid the legal framework on the protection of data and the free flow of data within the common market. In February 2020, the Communication from the Commission to the European Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions—A European Strategy for Data113 stressed the vision and principles of data regulation in the European Union by stating that common European rules and efficient enforcement mechanisms should ensure that data can flow within the EU and across sectors; European rules and values, in particular personal data protection, consumer protection legislation and competition law, are fully respected; and rules for access to and use of data are fair, practical and clear, and there are clear and trustworthy data governance mechanisms in place, and there is an open, but assertive approach to international data flows, based on European values. Table 2.6 summarizes the relevant legislation on digital markets in the EU.

109

https://digital-strategy.ec.europa.eu/en/policies/digital-services-act-package. https://ec.europa.eu/commission/priorities/digital-single-market_en. 111 European Commission (2020d). 112 European Commission (2020a. 113 European Commission (2020a, at p. 5). 110

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Table 2.6 EU legislations on digital markets Legislation

Soft law Relevant initiatives

Directive 95/46/EC, the European Data Protection Directive on the protection of individuals with regard to the processing of personal data and on the free movement of such data Directive 2000/31/EC of 8 June 2000 on certain legal aspects of information society services, in particular electronic commerce, in the internal market (‘Directive on electronic commerce’)114 OJ L 167, 22.6.2001

Public Consultation on the Regulatory Environment for Platforms, Online Intermediaries, Data and Cloud Computing and the Collaborative Economy, September 24, 2015115 Online Platforms and the Digital Single Market—Opportunities and Challenges for Europe. COM (2016) 288 final European Parliament resolution of 15 June 2017 on online platforms and the digital single market Communication from the Commission on Tackling Illegal Content online COM(2017) 555 final Commission Recommendation on measures to effectively tackle illegal content online C (2018) 1177 final

Directive 2002/19/EC on Access to, and Interconnection of, electronic Communications Networks and Associated Facilities (Access Directive)116 Directive 2002/58/EC concerning the processing of personal data and the protection of privacy in the electronic communications sector (ePrivacy Directive)

Commission Proposal for a Regulation concerning the respect for private life and the protection of personal data in electronic communications (continued)

114

Directive 2000/31/EC of 8 June 2000 on certain legal aspects of information society services, in particular electronic commerce, in the internal market (‘Directive on electronic commerce’). 115 https://ec.europa.eu/digital-single-market/en/news/public-consultation-regulatory-environme ntplatforms-online-intermediaries-data-and-cloud. 116 Directive 2002/19/EC of the European Parliament and of the Council of 7 March 2002, on Access to, and Interconnection of, Electronic Communications Networks and Associated Facilities (Access Directive).

2.4 Regulations in Digital Markets

41

Table 2.6 (continued) Legislation

Soft law Relevant initiatives

Directive 2003/98/EC of the European A proposal for a review of the Directive on the Parliament and of the Council of 17 November re-use of public sector information (PSI 2003 on the re-use of public sector Directive), COM (2018) 234 information117 Regulation (EC) No 2006/2004 on cooperation between national authorities responsible for the enforcement of consumer protection laws, OJ L 337/11, 18 December 2009 Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE)118 Directive 2009/136/EC of the European Parliament and of the Council of 25 November 2009 amending Directive 2002/22/EC on universal service and users’ rights relating to electronic communication networks and services Regulation (EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 on European Statistics and repealing Regulation (EC, Euratom) No 1101/2008 of the European Parliament and of the Council on the Transmission of Data Subject to Statistical Confidentiality to the Statistical Office of the European Communities, Council Regulation (EC) No 322/97 on Community Statistics, and Council Decision 89/382/EEC, Euratom establishing a Committee on the Statistical Programmes of the European Communities (OJ L 87, 31.03.2009) (continued)

117

Directive 2003/98/EC of the European Parliament and of the Council of 17 November 2003 on the Re-use of Public Sector Information, L 345/90, 31.12.2003. Directive 2013/37/EU of the European Parliament and of the Council of 26 June 2013 amending Directive 2003/98/EC on the re-use of public sector information, L 175/1, 27.6.2013. 118 Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE) 25.4.2007, L 108/1.

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Table 2.6 (continued) Legislation

Soft law Relevant initiatives

Regulation (EU) 2016/679 of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data119 (General Data Protection Regulation, GDPR) OJ L 119, 4.5. 2016

Communication from the Commission to the European Parliament, the Council, The European Economic and Social Committee and the Committee of the Regions—a European Strategy for Data, COM (2020) 66 final Communication to the Commission, Data, Information and Knowledge Management at the European Commission120 Digitising the European Industry Initiative, COM (2016) 180 final

Directive (EU) 2016/680 on the protection of natural persons with regard to the processing of personal data by competent authorities for the purposes of the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties, and on the free movement of such data, OJ L 119, 4.5. 2016 Directive (EU) 2016/943 of the European Parliament and of the Council of 8 June 2016 on the protection of undisclosed know-how and business information (trade secrets) against their unlawful acquisition, use and disclosure (OJ L 157, 15.6. 2016)

Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, Online Platforms and the Digital Single Market Opportunities and Challenges for Europe121

Regulation (EU) 2018/1807 of the European Parliament and of the Council of 14 November 2018 on a Framework for the Free Flow of Non-Personal Data in the European Union122

Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, Towards a Common European Data Space123 Communication Building a European Data Economy, COM (2017) 9 final Communication to the Commission, European Commission Digital Strategy124 (continued)

119

Regulation (EU) 2016/679 of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data. 120 European Commission (2016a). 121 European Commission (2016b). 122 Regulation (EU) 2018/1807 of the European Parliament and of the Council of 14 November 2018 on a Framework for the Free Flow of Non-personal Data in the European Union, OJ L 303, 28.11.2018. 123 European Commission (2018a). 124 European Commission (2018b).

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Table 2.6 (continued) Legislation

Soft law Relevant initiatives

Directive (EU) 2019/770 of the European Parliament and of the Council of 20 May 2019 on Certain Aspects Concerning Contracts for the Supply of Digital Content and Digital Services125 Directive (EU) 2019/1024 of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information OJ L 172, 26.6.2019 Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2009 on Copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC, OJ L 130, 17.5.2019 Regulation (EU) 2019/1150 of 20 June 2019 on promoting fairness and transparency for business users of online intermediation services (P2B Regulation)126 Commission Proposal for a Regulation of the European Parliament and of the Council on Contestable and Fair Markets in the Digital Sector (Digital Markets Act)127

An update of the Recommendation on access to and preservation of scientific information, C (2018) 2375 Guidance on Sharing Private Sector data, SWD (2018) 125 Communication ‘Maximising the benefits of Artificial Intelligence for Europe’ Communication on enabling the digital transformation of health and care in the Digital Single Market

Directive (EU) 2019/2161 of the European Parliament and of the Council of 27 November 2019 amending Council Directive 93/13/EEC and Directives 98/6/EC, 2005/29/EC and 2011/83/EU of the European Parliament and of the Council as regards the better enforcement and modernization of Union consumer protection rules Source EPRS (2021, at p. V)

125

Directive (EU) 2019/770 of the European Parliament and of the Council of 20 May 2019 on Certain Aspects Concerning Contracts for the Supply of Digital Content and Digital Services L136/1. 126 Regulation (EU) 2019/1150 of 20 June 2019 on promoting fairness and transparency for business users of online intermediation services, OJ L 186, 11.7.2019. 127 European Commission (2020c).

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2.4.2 Regulations on the Digital Economy in Other Countries Similar to the GDPR in Europe, other countries have also enacted legislation on data protection. From 2011 to 2013, Australia launched the Cyber Security Strategy, Advancing Australia as a Digital Economy—An updated National Digital Economy Strategy, and Australian Public Service Information and Communications Technology Strategy. In the media release on May 6, 2021, Prime Minister Scott Morrison announced that the Digital Economy Strategy128 has received 1.2 billion AUD as part of the year’s Federal Budget, which aims at building Australia’s modern and leading economy by 2030.129 The strategy includes investment in supporting digital skills, building capacity in artificial intelligence, enhancing government services, supporting business growth, helping small and medium businesses, supporting emerging aviation technologies, unlocking the value of data, and strengthening safety, security and trust.130 The strategy is built around three pillars: the government is to create policy foundations for the digital economy to grow (including investment in digital infrastructure, skilled workforce, digital inclusion, digital trade agreements, cyber security and safety, and world-class systems and regulations); the government is to build capacities and develop its understanding of emerging technologies; and the government is to set digital growth priorities across the economy. These priorities include lifting the digital capability of small to medium enterprises (SMEs), supporting sectors of manufacturing, agriculture, mining and construction, building a dynamic technology sector and delivering simple and secure digital government services.131 In the UK, the competition authority Competition and Markets Authority (CMA) published the Digital Markets Strategy in July 2019,132 in which the CMA developed five strategic aims, including using existing tools effectively and efficiently, building knowledge and capability, adapting tools to the digital economy, considering the case and options for regulation, and considering potential remedies in digital markets.133 Those five strategic aims are supported by seven priority focus areas, namely, consumer and antitrust enforcement and merger assessment, the work of the Data, Technology and Analytics (DaTA) unit, market study on online platforms and digital advertising, review merger approach to digital markets as necessary, policy work to consider a possible ‘digital markets unit’, proposals to reform enforcement tools, and international cooperation.134 Article 29 of the second Amendment to the Law on Protection of Consumers’ Rights and Interests in China was promulgated in November 2013; it specified that the internet service providers need to obtain the consumers’ consent and abide by principles of legality, propriety and necessity when collecting and using consumers’ 128

https://digitaleconomy.pmc.gov.au/strategy/foreword. https://www.pm.gov.au/media/modern-digital-economy-secure-australias-future. 130 https://digitaleconomy.pmc.gov.au/sites/default/files/2021-05/digital-economy-strategy.pdf. 131 ACCC (2021a, p. 3). 132 CMA (2019). 133 CMA (2019, at p. 8). 134 CMA 2019, at pp. 9–12). 129

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personal information. Articles 40–45 of the Cybersecurity Law that came into force in June 2017 provide regulations on the processing of personal information by internet operators. Article 18 of the E-commerce Law implemented in January 2019 requires e-commerce operators to provide both personalized and nonpersonalized goods and services recommendations. In Japan, the Japan Fair Trade Commission (JFTC) published the Report of Study Group on Data and Competition Policy in June 2017.135 Furthermore, JFTC, METI and the MIC launched the Study Group on Improvement of Trading Environment Regarding Digital Platforms in July 2018, and two important documents Fundamental Principles for Improvement of Rules Corresponding to the Rise of Digital Platform Businesses and Fact-Finding Survey regarding Trade Practices on Digital Platforms were published in December 2018 and January 2019.136 On August 29, 2019, the JFTC issued the draft Guidelines Concerning Abuse of a Superior Bargaining Position in Transactions between Digital Platform Operators and Consumers that Provide Personal Information (request for public comments),137 and this Guideline came into effect in December 2019.138 In September 2019, Japan established the “Headquarters for Digital Market Competition” to facilitate the transparency of digital platform businesses and the issue of privacy protection. In June 2020, an interim report on ex ante regulation on digital platforms was released.139 The legislative development of the digital economy in main antitrust jurisdictions includes the promulgation of legal acts on data protection and online platforms. Legislation progress on data protection, privacy and consumer protection includes the US California Consumer Privacy Act (CCPA) 2018 at both the federal and state levels,140 the General Data Protection Law (Law. N. 13.709/2018) in Brazil,141 Russia Law on Personal Data,142 India Personal Data Protection Bill (PDPB), South Africa Protection of Personal Information Act, No 4 of 2013 (POPIA), Personal Information Protection and Electronic Document Act (PIPEDA) 2004 and the Consumer

135

English translation available at http://www.jftc.go.jp/en/pressreleases/yearly-2017/June/170 606.html. 136 JFTC (2019b). 137 JFTC (2019c). 138 JFTC (2019a). 139 List of publications from the Headquarters of Digital Markets Competition including establishment notice and Report on Medium-Term Vision on Competition in the Digital Market, for details, see JFTC Headquarters for Digital Market Competition official website (kantei.go.jp). https://www. kantei.go.jp/jp/singi/digitalmarket/index_e.html. See also Guidelines Concerning Abuse of a Superior Bargaining Position in Transactions between Digital Platform Operators and Consumers that Provide Personal Information. https://www.jftc.go.jp/en/legislation_gls/imonopoly_guidelines_f iles/191217DPconsumerGL.pdf. 140 http://www.caprivacy.org. 141 Law n. 13.709/2018, known in Portuguese as Lei Geral de Proteção de Dados—“LGPD”. 142 The Federal Law ‘On Personal Data’ dated 7 July 2006 No. 152-FZ.

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Privacy Protection Act (CPPA) in Canada, Act on Protection of Personal Information, Act on Specified Commercial Transactions, Electronic Consumers Contract Act and Consumers Contract Act in Japan, Philippines Data Privacy Act of 2012, Law of Concerning Electronic Information and Transactions 2008 (Amended 2016) and Law on Consumers’ Protection 1999 in Indonesia, Act on the Consumer Protection in Electronic Commerce Transactions in Republic of Korea, Electronic Commerce Act 2006 and Consumer Protection Act in Malaysia, Consumer Protection (Fair Trading) Act 2003 in Singapore, and Privacy Act 2017 and Electronic Transactions Act 2002 and Fair Trading Act in New Zealand. Regulations on digital platforms include ACCC’s Preliminary Report on Digital Platforms in Australia, the Dutch Ministry of Economic and Climate Affairs Consultation on Online Platforms and Competition Law. Seeing the efforts made by antitrust jurisdictions across the globe, it is important to call for international cooperation in building consensus on how law and policy should be designed to regulate internet giants. General principles and goals of competition law are to be defined, effective competition tools are to be adapted and a broad collaboration between competition and consumer protection regulators is to be established. A global digital market is also expected to develop as technological innovation reduces barriers in international business, and cross-border enforcement is needed to reduce regulatory barriers to promote sustainable competition.

2.4.3 General Policy Recommendations In addition to the legislative developments in making new laws and regulations on data protection and prohibiting abusive behaviour by online platforms, competition authorities include Australian Competition & Consumer Commission (ACCC),143 the EU Commission,144 Japan Fair Trade Commission,145 Authority for Consumers and Markets in The Netherlands,146 Digital Competition Expert Panel147 and the Digital Markets Taskforce148 in the UK, the House Judiciary Committee (Subcommittee on Antitrust, Commercial, and Administrative Law)149 and the Stigler Committee on Digital Platforms in the US150 and the OECD Competition Commission151 have taken initiatives to study the business models of the digital economy 143

ACCC (2019). Crémer et al. (2019). 145 JFTC (2017). 146 ACM (2020). 147 Digital Competition Expert Panel (2019). 148 CMA (2020b). 149 US House Judiciary Committee, Subcommittee on Antitrust, Commercial and Administrative Law of the Committee on the Judiciary (2020). 150 Stigler Center (2019). 151 For example, OECD (2016a, 2016b, 2018, 2019a, 2020a, 2020b, 2020c, 2021a, 2021b, 2021c). 144

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and published various reports and recommendations to clarify their policy agenda of building legal frameworks for the regulation of digital giants. To make a general overview, those recommendations of facing the challenges in the development of a digital economy, and how competition law should be adjusted focus on the following aspects: First, competition authorities are facing the challenges of formulating rules or standards in regulatory intervention, and understanding that both have the risk of type 1 or type 2 errors in enforcement. Product types, consumer preferences and market structures are proven to be heterogeneous, and a standard supply–demand perfect competition model becomes inapplicable. Theories of harm, innovation and data value have to be taken into account in the balance of pro-and anti-competitive effects in each case. Given the continuous debate on economic theories and the limited sources of empirical evidence, knowledge and relevant information on the reason and effects of new business models on consumer welfare is unclear; an open, evolving, international and multidisciplinary approach should be applied in the discussion of competition law in digital markets. As the book will show, there are more questions than answers to the paradoxes of digital competition law. Competition authorities are expected to learn the specific characteristics of the data economy, business strategies and pricing models of online platforms to adapt the existing tools and develop new economic methods as well as an analytical framework in judging competition cases, particularly in abuse of dominant positions and concentrations. Competition authorities are also expected to collaborate with data authorities to promote investment in the infrastructure of developing data resources and to build digital competence by recruiting data professionals, improving the skills and knowledge in machine learning, computing algorithms and data governance to learn how empirical evidence on data use and reuse could be incorporated when analysing the market power of data companies and their impact on consumer welfare and societal public interests. Second, online service providers take different forms, and online platforms are categorized as advertising platforms, transaction platforms, B2B platforms and B2C platforms. Named “multi-sided platforms”, they facilitate the interaction between different sides of users, and the direct and indirect network effects distinguish themselves from other markets. Competition authorities have to understand the business models and the pricing strategies of different types of platforms. Linked with online service providers and suppliers, online platforms build digital ecosystems and conglomerates, and the impact of network effects becomes more complex. Given the new characteristics of online platforms, the assessment methods used in measuring market power and relevant markets have to be adapted, and a separate guideline on defining relevant market and market power is to be published. The drafting of a manual of assessing market power in competition cases, for example, in abuse of dominance and concentration cases, is also on the policy agenda. Because of the network effects, the substitutability of the demand change has to take multiple sides into account, and charging below-cost prices cannot be considered predatory pricing or abuse of a dominant position.

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Third, whereas online products are often provided at zero price, data has been frequently used as the input of digital transactions. Personal data has been collected in visible or nonvisible ways and have been used for generating profits for platforms through computer algorithms. Understanding the value of data in nonprice competition in online markets is the key for competition authorities to formulate rules to prevent abusive behaviour. It has been argued that data has a diminishing value over time, and dominant firms have advantages of economies of scope in terms of fixed costs but not marginal costs; the strength of market power is also assessed from the viewpoint of entry barriers. The right of data portability and data interoperability has been applied as a remedy to ensure access to data by consumers and competitors, and the goal of protecting consumer welfare is achieved through the protection of consumer autonomy. Fourth, given the non-exclusive and non-rivalry nature of data, new entrants and incumbents are involved in dynamic competition to compete for the ‘attention’ of consumers. Competition authorities have to understand that protecting dynamic efficiency is not always consistent with the goal of productive efficiency and allocative efficiency, and theories of innovation are under debate among economists in history. It requires cautious investigation on whether a given level of market dominance could be counterbalanced by investment in R&D. Fifth, economists have to work with data scientists to understand the business model of online platforms, and law makers alone cannot formulate guidelines without close cooperation with economists, data scientists, and technicians. Classic antitrust paradoxes show the inconsistency of competition goals and the challenges of cooperating with sector regulators in competition enforcement, and such a paradox becomes more complex when consumer protection agencies and competition authorities have to work together on data protection. When it is argued that an independent data authority is required to monitor the transaction and trading of data, it is a separate topic to discuss how data authority could work with other agencies in the enforcement of data protection law and competition law. Traditional mechanisms on the regulatory framework of economic activities may have to be adjusted, for example to facilitate public and private regulation, self-regulation, and shifting ex-post regulation to ex-ante monitoring and supervision. Competition authorities are also expected to collaborate with sectoral regulators in developing remedies for correcting market failure in digital markets. With criticisms of their limitations, data portability and data interoperability are two crucial remedies that have been widely discussed among competition regulators. Chapters 6 and 7 of this book will address this issue from a law and economics perspective. Sixth, due to the transformation of the digital economy in social media, communications, manufacturing, agriculture, finance, e-commerce and global trading systems, industry authorities are facing the challenge of building digital competence in data governance. Competition agencies have to cooperate with sectoral regulators to establish digital authority and to train skilled staff and officials in dealing with data related issues. On the other hand, digital competence is also needed for business and private sectors, and practical guidelines have to be implemented to help vulnerable consumers cope with information asymmetry and equipped with debiasing tools

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in the digital environment. Chapter 8 of this book discusses the complex issue of consumer protection and Chap. 9 will introduce the concept of data governance and how data authorities could work with competition authorities to achieve the goal of consumer protection.

2.5 Conclusions The rapid development of the digital economy has raised concerns about rebuilding a legal framework for regulating online platforms. This chapter has studied different forms of platforms and has discussed different views on how to regulate digital platforms. The OECD provided the definitions of advertising platform, transaction platform, B2B platform and B2C platform. The EU gave a legal definition of gatekeepers to study abusive behaviour by online platforms. The feature of providing zero price services by online platforms has changed the structure of competition in online markets. The price competition of products has changed to quality competition, and the personal data of users have been used as the currency for online product transactions. The collection of data may be necessary for improving product quality and providing personalized services, and it may also serve as an economic justification for personalized advertisements. Understanding how data create value for platforms thus becomes one of the crucial issues in online competition. Many of the competition issues in traditional markets have been challenged: how market power is measured, whether defining a relevant market is required, how a substitutability assessment tool is adopted in quality competition, when collecting personal information is required for improving the quality of products, and how competition law could be coordinated with data protection and consumer protection law. These issues will be discussed in detail in the following chapters. This chapter reviewed the characteristics of online platforms and summarized the legislative developments in the EU and other countries. It studied regulations in the EU on data protection, the promotion of the digital economy and regulations on platforms. It also summarized the policy recommendations for the development of the digital economy made by various institutions.

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McCracken, H. (2019). How google photos joined the billion-user club. Fast Company. July 24, 2019. https://www.fastcompany.com/90380618/how-google-photos-joined-the-billion-user-club McKinsey Global Institute. (2011). Big data: The next frontier for innovation, competition, and productivity, McKinsey & Company. Moore, J. (1993). Predators and prey: A new ecology of competition. Harvard Business Review, 71(3), 75–86. Moore, J. C., Rao, H. R., Whinston, A., Nam, K., & Raghu, T. S. (1997). Information acquisition policies for resource allocation among multiple agents. Information Systems Research, 8(2), 151–170. OECD. (2010). The economic and social role of Internet intermediaries. OECD Digital Economy Papers, April 2010. Available at https://www.oecd.org/digital/ieconomy/44949023.pdf. OECD. (2011). Digital identity management: Enabling innovation and trust in the Internet economy. OECD. https://www.oecd.org/sti/ieconomy/49338380.pdf OECD. (2016a). Protecting consumers in peer platform markets, exploring the issues. 2016 Ministerial Meeting on the Digital Economy Background Report, OECD Digital Economy Papers No. 253. Available at https://unctad.org/system/files/non-official-document/dtl-eWeek2017c05oecd_en.pdf OECD. (2016b). Big data: Bringing competition policy to the digital era: Background paper by the secretariat. https://one.oecd.org/document/DAF/COMP(2016)14/en/pdf OECD. (2018). Personalised pricing in the digital era: background note by the secretariat. https:// one.oecd.org/document/DAF/COMP(2018)13/en/pdf OECD. (2019a). Enhancing access to and sharing of data: Reconciling risks and benefits for data re-use across societies. https://www.oecd.org/going-digital/enhancing-access-to-and-sharing-ofdata.pdf OECD. (2019b). Latin American and caribbean competition forum-Session III: Practical approaches to assessing digital platform markets for competition law enforcement. Background Note by the Secretariat, Directorate for Financial and Enterprise Affairs Competition Committee, August 5, 2019. OECD. (2019c). An introduction to online platforms and their role in the digital transformation. OECD Publishing. https://www.oecd.org/innovation/an-introduction-to-online-platformsand-their-role-in-the-digital-transformation-53e5f593-en.htm OECD. (2020a). Abuse of dominance in digital markets: Background note by the Secretariat. http:// www.oecd.org/daf/competition/abuse-of-dominance-in-digital-markets-2020.pdf OECD. (2020b). Conglomerate effects of mergers—Background note by the Secretariat. https:// one.oecd.org/document/DAF/COMP(2020)2/en/pdf OECD. (2020c). Consumer data rights and competition: Background note by the Secretariat. https:// one.oecd.org/document/DAF/COMP(2020)1/en/pdf OECD. (2021a). Data portability, interoperability and digital platform competition. OECD Competition Committee Discussion Paper. OECD. (2021b). Data portability: Analytical report, mapping data portability initiatives and their opportunities and challenges. OECD. (2021c). The role of online marketplaces in enhancing consumer protection (Going Digital Toolkit Note No. 7). https://goingdigital.oecd.org/data/notes/No7_ToolkitNote_ConsumerProt ection.pdf Park, S., Fisher, C., Fuller, G., & Lee, L. (2018). Digital news report: Australia 2018. News and Media Research Centre, University of Canberra, June 2018. Available at https://apo.org.au/node/ 174861 Stigler Center. (2019). Stigler committee on digital platforms: Final report. Available at https://www.chicagobooth.edu/research/stigler/news-and-media/committee-on-digital-platfo rms-final-report Thelle, M. H., Sunesen, E. R., Basalisco, B., la Cour Sonne, M., & Fredslund, N. C. (2015), Online intermediaries—Impact on the EU economy. Copenhagen Economics, EDiMa, October 2015.

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Tiwana, A., Konsynski, B., & Bush, A. A. (2010). Platform evolution: Coevolution of platform architecture, governance, and environmental dynamics. Information Systems Research, 21(4), 675–687. UNCTAD. (2019). Digital economy report 2019: Value creation and capture: Implications for developing countries. UNCTAD, United Nations Publication. Available at https://unctad.org/en/ PublicationsLibrary/der2019_en.pdf USITC. (2017). Global digital trade 1: Market opportunities and key foreign trade restrictions. United States International Trade Commission, August 2017. https://www.usitc.gov/publicati ons/332/pub4716_0.pdf US House Judiciary Committee, Subcommittee on Antitrust, Commercial and Administrative Law of the Committee on the Judiciary. (2020). Investigation of competition in digital markets, majority staff report and recommendations. Available at https://judiciary.house.gov/uploadedfiles/compet ition_in_digital_markets.pdf US Subcommittee on Antitrust, Commercial and Administrative Law of the Committee on the Judiciary. (2020). Investigation on competition in digital markets, October 20, 2020. Available at https://judiciary.house.gov/uploadedfiles/competition_in_digital_markets.pdf Zhang, Y., Li, J., & Tong, T. W. (2020). Platform governance matters: How platform gatekeeping affects knowledge sharing among complementors. Strategic Management Journal, 1–28. Zhu, F., & Iansiti, M. (2012). Entry into platform-based markets. Strategic Management Journal, 33, 88–106.

Chapter 3

Online Markets and Nonprice Competition

Abstract Economists have argued that there are particular characteristics of online markets, and the pricing structure and models have to be understood differently from traditional one-sided offline markets. The predominant features of innovation and dynamic efficiency, indirect network effects in a multi-sided market, the savings of transaction costs, information costs, the lock-in effects that consumers have to suffer and the costs of attention given in the nonprice competition. This chapter discusses those characteristics and the implications for competition law. It presents the tension of encouraging innovation and protecting consumers’ privacy and the unsolved economic debate on whether antitrust scrutiny and intervention are necessary when there is sufficient competition between the users of the platform. The next chapter will continue the debate by focusing on the use of data and will discuss the characteristics of data and how data monopolies raise antitrust concerns.

3.1 Introduction In recent years, with the rise of the digital economy and the intelligence of economic development models, the legal regulation of digital monopolies has become an important issue for anti-monopoly law enforcers. Under the influence of the digital economy, goods and services have gradually changed from physical market transactions to free access to online communication platforms, such as reading books through online readers, chatting with free communication tools and watching TV dramas, movies, etc. using video sites. Online platforms are the marketplace connecting different aspects of users in the environment of digital transactions. According to the FTC, a platform is defined as an “online market that provides a discrete set of services to the parties using it, facilitating their efforts to transact effectively and efficiently, including searching for potential transacting partners, agreeing to terms with them, and performing the contract”.1 Online platforms have been widely used for businesses ranging from hotels, office and parking spaces, transportation, broadcasting, restaurants, grocery shopping, laundry, flower and package delivery

1

FTC (2016).

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to capital, legal services, medical and academic services.2 In the emerging industrial model created by the digital economy revolution, companies offer products and services free of charge, while consumers with their “attention” and “personal data” accompany advertisements for software, websites and communication tools. Vendors move from a business model that sells a single product to one that monetizes user attention and data. Under different terminologies,3 online markets have become the main focus of research. In commercial activities under the influence of the digital economy, the competition of market forces changed from restricting consumers’ choice of alternative products to obtain monopoly profits after price increases to controlling the consumer data behind free goods. In particular, consumers who use one online service multiple times will find it difficult to switch data to another service provider because of the huge conversion costs, creating a dependency on software vendors or platforms. Internet vendors can increase advertising prices based on the attractiveness of free services to users, and at the same time, they can strengthen market control over consumer data through the development of service networks and the accompanying software, increase the likelihood of obtaining specific data, and obtain commercial benefits by reselling classified users’ consumption data to advertisers. The anti-competitive behaviour of data monopolists, such as abuse of market dominance, conspiracy to anti-competitive agreements and interenterprise mergers and acquisitions, is aimed at maintaining a monopoly position in user data, reselling data for profit by expanding their dominant position to occupy more specific user data, thereby undermining the competitive order and consumer welfare. Therefore, in the digital economy era, the control of information and data enterprises becomes a “new trust”, data itself has become a “commodity” for enterprises to compete for the market and exchange of interests, data can create economic benefits and has its own value, such as Internet enterprises can be installed in search engines, video sites, mail and chat tool software advertising and obtain high profits, when users are “locked” by Internet enterprises, the dominant position of enterprises in the industry can be solidified. At the same time, enterprises with a large amount of classified user data are able to provide personalized goods to different users and charge personalized prices, creating total price discrimination and differential treatment. When user information is monopolised by enterprises in large numbers, there is a huge information gap between consumers and operators. Therefore, there is an urgent need to regulate the abuse of market dominance consisting of data monopoly by using anti-monopoly law to protect the interests of consumers and build a fair competition order, while the definition and calculation of data monopoly needs to be carefully, systematically and deeply analysed.

2

Lobel (2015, p. 6). The terms defined for new digital economy and online technologies are extensive, as Lobel has listed: the sharing economy, the disaggregated economy, the peer-2-peer economy (P-2-P), humanto-human (H-2-H), the community marketplace, the on-demand economy, the app economy, mesh economy, gig economy, etc. See Lobel (2015).

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3.2 Characteristics of Online Market Economic analysis helps to explain how to conceptualize competitive effects in online market antitrust cases. However, static analytical tools may have limitations when applied to multi-sided models simply because the price mechanism has been changed. Given these dynamics, a proper understanding of economics is needed to play a role in online markets to determine any justifications for antitrust legal intervention.4 Online markets challenge traditional antitrust analysis because online services are often available to users for free. Instead of money, consumers provide attention and information that is often used to direct relevant advertising to those consumers. The economics literature refers to markets with more than one “side”. A firm (or “platform”) that brings together distinct types of economic actors together to interact (e.g., online auctions, dating, search engines, and payment systems) operates in a multi-sided market.5

3.2.1 Dynamic Efficiency and Innovation The first predominant feature of the online market is the effect of dynamic efficiency. The introduction of the concept of dynamic efficiency has two implications for competition enforcement in the digital market: the first is the debate on whether data giants’ massive investment in R&D could be perceived as benefiting consumers and society, and thus be applied in efficiency defense when analysing their anticompetitive effects caused by their market power. The economic theory of dynamic efficiency has stimulated the century-long debate on whether holding a monopoly position is the prerequisite for innovation. While the large market share is often clear evidence for proving the market power of internet giants, their superior capacity in investing in research and development (R&D) and the benefits of dynamic efficiency have also served as clear counterarguments. For example, in 2017 Google spent over 16 billion USD (approximately 15% of their revenue) on global R&D.6 In 2020, among the world’s top 1000 publicly owned enterprises, Amazon and Alphabet are the top two largest investors in R&D. Microsoft ranked sixth, Apple ranked seventh, and Facebook ranked 14th.7

4

See Evans (2013, pp. 35–36). Rochet and Tirole (2003, pp. 990, 991–993). For an overview of the multi-sided market literature, see Evans and Schmalensee (2014). 6 ACCC (2018, p. 47). 7 Strategy& (2020). 5

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The second implication is the change of understanding in antitrust analysis as the competition structure between online platforms is distinct from traditional markets. In conventional antitrust analysis, competition in product pricing competes for market share, but in digital markets, the potential rival with new business strategies will supplant the incumbent and the cycles of innovation will change the structure of competition from simultaneous to sequential.8 Evans and Schmalensee defined the dynamic competition in platform markets as “sequential winner-take-all” battles, as the new entrants will replace the incumbents in the competition process.9 Dynamic efficiency has become a crucial factor when competition authorities consider the competitive effects of innovation. Competition agencies have incorporated the factor of innovation as a crucial element in case decisions. For example, in the settlement issued by the FTC on Intel, the agency claimed that the settlement will allow Intel to “innovate and offer competitive pricing”.10 In the Microsoft/Yahoo! Merger, the DOJ approved the acquisition by emphasizing the potential benefits of innovation: “This larger data pool may enable more effective testing and thus more rapid innovation of potential new search-related products, changes in the presentation of search results and paid search listings, other changes in the user interface, and changes in the search or paid search algorithms. This enhanced performance, if realized, should exert correspondingly greater competitive pressure in the marketplace”.11 After the 2011 Guidelines on the Applicability of Article 101 TFEU to Horizontal Cooperation Agreements incorporated the concept of competition in innovation, the SAMR draft revision of the AML added the goal of “promoting innovation” in Article 1 of the AML. The US 2010 Horizontal Merger Guidelines had a section (Sect. 6.4) addressing the effects on innovation.

3.2.1.1

The Debate on Monopoly and Innovation

The concept of dynamic efficiency incorporates the economic theory of innovation. It has been advocated by economists that innovation is the driving force of economic growth,12 and competition is regarded as a process that generates innovation. The classic debate on the topic of competition versus innovation is held between Joseph Schumpeter and Kenneth Arrow.13 Arrow advocates that competition will promote innovation, whereas Schumpeter argues innovation is better achieved by monopolists.14 Schumpeter’s logic is that monopolists are better incentivized to finance R&D 8

Shelanski (2013, p. 1669). Evans and Schmalensee (2001). 10 FTC (2010). 11 DOJ (2010). 12 Brodley claimed that ‘[i]nnovation efficiency or technological progress is the single most important factor in the growth of real output in the United States and the rest of the industrialized world.’ See Brodley (1987, p. 1026). See also Solow (1957, p. 312) and Sidak and Teece (2009, p. 581). 13 Baker (2007, p. 575). 14 Schumpeter (2008). 9

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when the benefit of innovation is returned to the firm itself.15 Compared with competitive firms, monopolists are more likely to implement innovation plans because of their superior experience and the control of financial resources.16 For Arrow, this incentive is not obvious because there is no additional business that the monopolist can obtain from the market. If a monopolist invests in new technology, it will lose the flow of profits generated by the old technology; therefore, the monopolist bears an opportunity cost of continuing to earn monopoly profits when they innovate.17 However, competitors who do not hold monopolistic positions can take business away from monopolists. Therefore, they are more incentivized to invest in R&D to make improvements. This is named as “Arrow Effect” or the “Replacement Effect”.18 There are a very large number of empirical studies focusing on the relationship between competition and innovation. Aghion and Tirole call the empirical test between industry concentration and R&D the “second most tested hypothesis in industrial organization”.19 The most important finding of empirical tests is that the relationship between competition and innovation follows a pattern of an invertedU; that is, competition will accelerate innovation up to a certain level, and after this turning point, innovation will decrease. Innovation reaches the highest level in industries with an oligopolistic market structure.20 The inverted-U relationship was initially observed by Scherer21 and was proven by Levin et al., who found that the turning point is when the four-firm concentration ratio (C4)22 equals 52, and at this point, the R&D intensity is maximized.23 Other theoretical studies on the inverted-U relationship include Kamien and Schwartz (1976),24 De Bondt (1977),25 and De Bondt and Vandekerckhove (2012).26 Recently, this finding was confirmed by 15

Schumpeter (2008) and Baker (2007, p. 575). Schumpeter (2008) and Carrier (2008, p. 403). Schumpeter perceives monopolists are superior to competitors because they have a higher control of methods and resources. See Schumpeter (2008, p. 100) (‘There are superior methods available to the monopolist which either are not available at all to a crowd of competitors or are not available to them so readily.’). 17 Gilbert (2006a, p. 165) and Baker (2007, p. 578). 18 Baker (2007, p. 578) and Arrow (1962). 19 The first most tested hypothesis is the relationship between firm size and profits. In most of the empirical studies, ‘R&D expenditures’ or ‘patent counts’ are often taken as the proxy of ‘innovation’, and ‘industry concentration’ is used for measuring the level of ‘competition’. See Gilbert (2006a, pp. 187, 191–193) and Aghion and Tirole (1994). 20 Baker (2007, p. 583). 21 Scherer (1967a, pp. 524–531, 1967b, p. 359). For an overview of the empirical evidence, see Gilbert (2006a, pp. 188–189). 22 Four-firm Concentration Ratio (C4) standard is defined as the market concentration level can be determined by the first four firms with the largest market shares in the market. 23 Levin et al. (1985, pp. 20–24). 24 Kamien and Schwartz (1976). 25 De Bondt (1977). 26 De Bondt and Vandekerckhove (2012). 16

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Aghion et al. in their study on the relationship between innovation and product market competition. In their model, competition may encourage innovation by increasing the incremental profits, and firms are incentivized to invest in R&D to escape from competition, and this is named the “escape-competition effect”, which is demonstrated by the first part of the inverted-U. The decreasing part of the inverted-U model explains the Schumpeterian effect, which refers to the situation where competition reduces the incentives for innovation by reducing the monopoly rents.27 By using firm-level (95,544 observations) and industry-average data (2580 observations) with sixty industries from 1964 to 2006 in Japan, Yagi and Managi concluded that the inverted-U relationship exists between competition and innovation.28 By applying a three-stage, least square estimation (3-SLS) and using the panel data of Swiss firms observed across four periods (1999, 2002, 2005, and 2008) collected from the Swiss Economic Institute (KOF) at the ETH Zurich, Peneder proved the robust and nonlinear inverted-U relationship of the level of competition and R&D activities.29 Empirical studies conducted by Alder,30 Hashmi,31 and Polder and Veldhuizen32 have also proved the inverted-U relationship. Meanwhile, there are studies by other scholars that did not show that the relationship between competition and innovation follows such an inverted-U nonlinear pattern. For example, Nickel,33 Blundell et al.,34 Gottshalk and Janz,35 Carlin et al.36 and Okada37 found the positive relationship between competition and innovation, whereas Salop, Dixit and Stiglitz argued that it is a negative relationship because competition will discourage innovation by reducing post-entry rents.38 Similarly, Mansfield,39 Artés,40 Hashmi and Van Biesebroeck,41 Santos,42 and Czarnitzki et al.43 also proved that such negative relationship exists. With respect to the study of monopolies, a recent paper by Evans and Hylton proved the positive relationship between monopolies and innovation.44 This finding reflects Greenstein and Ramey’s theoretical model ten years earlier, which concluded 27

See Aghion et al. (2005, p. 720). Yagi and Managi (2013). 29 Peneder (2013). 30 Alder (2010). 31 Hashmi (2005). 32 Polder and Veldhuizen (2012). 33 Nickell (1996, pp. 724–746). 34 Blundell et al. (1999). 35 Gottschalk and Janz (2001). 36 Carlin et al. (2004). 37 Okada (2005). 38 Salop (1977, pp. 393–406) and Dixit and Stiglitz (1977, pp. 297–308). 39 Mansfield (1968). 40 Artes (2009). 41 Hashmi and Van Biesebroeck (2010). 42 Santos (2010). 43 Czarnitzki et al. (2011). 44 Evans and Hylton (2008, pp. 203–241). 28

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that monopolists profit more from innovation.45 The ambiguity of the economic evidence lies in the simple fact that empirical studies of competition and innovation often focus on a particular industry and rely on the particular characteristics of the market and the technological conditions.46 For example, Gilbert argued that Schumpeter’s view could be justified when intellectual property rights are nonexclusive, in which case competition in R&D will reduce the value of innovation. However, even if Arrow’s conclusion could be applied to the situation where intellectual property rights are exclusive, his arguments should be treated with caution for product innovation, particularly when products are differentiated and when product innovation will change the ability to discriminate among consumers. The reason is that the replacement effects for product innovation are less obvious than those for process innovation.47 In recent years, a new methodology called experimental economics has been applied to assess the impact of innovation on competition in a direct manner.48 By using an experiment, Darai et al.49 concluded the negative effect on innovation when the number of players on R&D investments increases, and Castellacchi proved the negative effects of competition on R&D.50 In merger enforcement, innovation effects have been incorporated as an important factor when merger decisions are issued. For example, in the US, between 1990 and 1994, among the 135 mergers challenged by the DOJ and the FTC, only 4 cases were considered to concern innovation effects, accounting for 3%. This number increased between 1995 and 1999, when the cases challenged for the reason of ‘innovation effects’ represented 18% of the total merger cases.51 From 2000 to 2003, this number increased to 38%.52 In Europe, Magdalena Laskowska’s research shows that among the 155 merger decisions made by the European Commission from 1989 to 2008, during the phase-II in-depth investigation, innovation played a relatively important role in 23 decisions, and in 29 decisions, the Commission briefly mentioned innovation when assessing the competitive effects.53 Although there is no doubt that innovation should be considered a factor in the merger assessment, it is still not clear how antitrust law should be adjusted according to the economic effects of innovation. The first unsolved issue is whether antitrust law 45

Greenstein and Ramey (1998, pp. 285–311). The indeterminacy problem of economic evidence, however, should be dealt with carefully. The contradicting results from empirical tests do not lead to a conclusion of economic analysis is of little use—as Gilbert said: ‘It is not that we don’t have a model of market structure and R&D, but rather that we have many models and it is important to know which model is appropriate for each market context.’ Gilbert (2006a, p. 165). 47 Gilbert (2006a, p. 162, p. 167). 48 Østbye and Roelofs (2013, pp. 153–176). Østbye and Roelofs list literature on experimental economics which examine the relationship between competition and innovation. For example, Darai et al. (2010), Sacco and Schmutzler (2011) and Sørensen et al. (2010). 49 Darai et al. (2010). 50 Castellacci (2010). 51 Gilbert (2006b, p. 2). 52 Gilbert (2006a, p. 160). 53 Laskowska (2013, p. 3). 46

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should be modified to have innovation as a goal because, as Markham put it, ‘[e]ven if all could agree with this general conclusion, however, it is not clear that this would argue for a significant reorientation of antitrust policy goals or modification of the present standards for attaining them.’54 The second issue is whether the view that monopolists are more incentivized to innovate would justify a lenient enforcement of antitrust laws, as the conduct of charging monopoly prices might be justified by the arguments of “taking risks to produce innovation and other efficiencies”.55 In contrast, a more active enforcement of antitrust law would be conducted if economic theories show the opposite, as Arrow proves that innovation may come from smaller firms. In this case, it is more likely that small firms would cooperate in innovating new technologies whereas monopolists often act unilaterally.56

3.2.1.2

Innovation Cycles and Dynamic Competition

Geroski has identified the challenge of introducing the concept of dynamic efficiency into competition analysis if competition in a digital market is to compete for creating a new market, whereas competition in a traditional market is the competition between incumbents and entrants in well-established markets.57 Katz and Shelanski defined competition in dynamic markets as sequential competition for the whole market through “cycles of innovation”, which is different from competition through static prices or output in a traditional market.58 Therefore, the analysis of market structure would be outdated because incumbents and rivals are involved in a constant transition, and even achieving temporary dominance is necessary in that dynamic process.59 The feature that online platforms are involved in constant dynamic competition can also be explained by the fact that platforms compete for users’ attention but not prices. Consumers pay their attention (or data, as discussed in the next chapter) to new online products, and platforms must invest in new technology to win competition. The digital competition model changes that speed of competition and cycles of innovation. Firms with temporary market power may easily be replaced by new entrants when their business models can attract consumers’ attention, and in this aspect, the size of the firm and market share per se cannot indicate the real market power when their revenues are generated from attention-based advertisements and other online products. To attract consumers’ attention, online service providers must constantly invest in new models and products, as David Evans explained: “Attention seekers cannot profitably raise prices above zero, must improve the quality of their services 54

Markham (1974, p. 268). This argument was made by Thomas Barnett, the Assistant Attorney General at the Department of Justice Antitrust Division in 2008. Barnett (2008, p. 1191, 1291) Additionally see Baker (2008, p. 3). 56 Hovenkamp (2012, p. 6, p. 9). 57 Geroski (2003). 58 Katz and Shelanski (2005, pp. 47, 49). 59 Shelanski (2013, p. 1671). 55

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through frequent introduction of new features to prevent users from switching to rivals, face constant threats of entry by new attention seekers that will divert traffic from them, face continual threats that new or existing attention seekers will develop a drastic innovation that diverts massive amounts of traffic from them, and operate in a business that has low barriers to entry and exit”.60 Economists Carl Shapiro and Hal Varian argued that those digital monopolies are “temporary” and “fragile” because hardware and software firms are competing for ever-changing leading technologies.61 Clayton Christensen argued that technology change is the greatest fear for leading internet firms.62

3.2.1.3

Technological Regimes

Along with the structural debate on whether a temporary monopoly position is necessary to encourage investment in R&D and the dynamic view of innovation cycles, scholars such as Pavitt and Breschi et al. proposed the “technological regimes” approach arguing that the structural view of innovation should be abandoned,63 and proposed that sectoral technology is the determinant of market structure and performance. In their opinion, market structure is endogenous.64 Malerba and Orsenigo categorized innovative firms into “Schumpeter Mark I”, where technology conditions are low and the market observes changes in leaders, and “Schumpeter Mark II”, where technology conditions are appropriate and the accumulation of knowledge becomes important, and the market observes a few large and stable innovators.65

3.2.1.4

Innovation in Digital Markets

Bourreau and de Streel defined two types of innovations that are used in the analysis of the digital economy.66 The first type is incremental and breakthrough, which refers to technological processes. Incremental innovation is a small improvement of a characteristic of a technological paradigm, and breakthrough innovation is a significant technological change. The second type of innovation is sustaining and disruptive, which refers to the relationship between innovation and the value network. Sustaining innovation takes place within the value network, and disruptive innovation takes place from the outside of the network and replaces such a network.67 They gave 60

Evans (2013). Shapiro and Varian (1999, at p. 173). 62 Christensen (2013). 63 Pavitt (1984), Malerba and Orsenigo (1997), Malerba and Orsenigo (1996) and Breschi et al. (2000). 64 Nuccio and Guerzoni (2018, p. 15). 65 Malerba and Orsenigo (1997). 66 Bourreau and de Streel (2020, p. 5). 67 Bourreau and de Streel (2020, p. 6). 61

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the example that replacing DVD with Blu-ray could be considered sustaining innovations, and video streaming is a disruptive innovation that completely replaced the network of DVD/Blu-ray. Examples of disruptive innovation also include replacing horse-drawn carriages with automobiles, replacing mail with email and replacing live performances with a phonograph.68 Furthermore, the development of technology has also brought industrial disruption, such as browser-centric computing disrupting the PC industry, smartphones and tablets disrupting the PC and microprocessor industries, and digital content and online distribution disrupting the traditional content industries.69 Furthermore, the debate of Schumpeter versus Arrow has recalled the competition theory of contestable markets. Economists and competition scholars70 have argued that certain digital markets are sufficiently, not to say perfectly, contestable because incumbent monopolists are not able to exploit their market power because a rival can quickly adapt the business model to replace them. The digital market meets the assumptions of a contestable market because the sunk costs for market entry are comparably low, and the necessary technical infrastructure can be scaled dynamically or outsourced to the cloud. The Schumpeterian “creative destruction” is fully driven by successful innovation, and the result of winning innovation competition is often the market power of the incumbent. The contestable market theory shows that because the digital market meets the underlying assumptions of the contestable market, monopolists with market power cannot exploit its market power; thus, it lacks a theoretical basis to draw the link between high market share and the abusive behaviour of market power by digital monopolists.71 Of course, the crucial aspect of contestable theory is whether market conditions can restrain monopolists from exploiting their market power, and this condition depends on whether the assumptions are met, such as whether monopolists are under pressure to keep pace in innovating and do not impose restrictions on market entry for rivals; in this situation, monopolists are less likely to exploit their dominant position.72 Such assumptions have been challenged by counterarguments on the high costs of collecting data and theories of economies of scale. When there is a “feedback loop” on firms with superior data and better data leads to more consumer interaction and results in better data quality, the entry barriers will be increased, and whether the market is still contestable will become doubtful.73 Nonetheless, the characteristics of dynamic efficiency and the impact of innovation on direct and indirect network effects are the starting point to discuss the competition process in digital markets, and from this view, contestable market theory also provides a perspective for assessing dominant firms’ market power, and

68

OECD (2015, p. 3). OECD (2015, p. 3). 70 Argenton and Prüfer (2012, pp. 73–105), Krämer and Wohlfarth (2015, pp. 71–90) and Krämer and Wohlfarth (2018). 71 Krämer and Wohlfarth (2018, p. 159). 72 Krämer and Wohlfarth (2018, p. 166). 73 Krämer and Wohlfarth (2018, p. 166). 69

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the extent to which monopolists are under competitive restraints to invest in innovation is also the extent to which they can or cannot exploit their dominant market power, although those conditions and assumptions are under debate and subject to particular cases and empirical evidence.

3.2.1.5

Incorporating Innovation in Competition Law

Seeing the particular importance of innovation in the digital market, competition law has to make the change of switching from focusing on price changes to the effects on innovation and dynamic efficiency. Innovation becomes one of the important goals of competition law, effects on innovation become the benchmark to evaluate the anti-competitive effects of a particular conduct, and those effects include potential harm on innovating new products. As Professor Richard Gilbert argued, “Antitrust enforcement should evolve from being price-centric to innovation-centric. Pricecentric antitrust enforcement prevents mergers that are likely to raise prices and prevents firm conduct that excludes competition for existing products and services. Innovation-centric antitrust enforcement does not abandon these concerns, but it augments them by challenging a merger and firm’s conduct that are likely to harm innovation and competition for products that do not presently exist. Innovationcentric competition policy will achieve goals that price-centric enforcement neglects, such as ensuring opportunities for entrepreneurs to compete and thrive.74 Innovation has been incorporated in competition laws as one of the main goals. In the SAMR draft version of the revised Anti-monopoly Law of China published on January 2, 2020, the goal of encouraging innovation has clearly been incorporated as one of the six goals of the AML.75 Competition in innovation was also introduced in the Guidelines on the Applicability of Article 101 TFEU to Horizontal Cooperation Agreements in 2011. The implication of introducing competition in innovation is to emphasize the importance of innovation in dynamic markets and in potential competition.76 The US Horizontal Merger Guidelines 2010 revision included a section on innovation effects. Competition authorities have also identified innovation and technology as the main characteristic of the digital economy, as the CMA gave the definition of digital markets as “those where companies develop and apply new technologies to existing businesses, or create brand new services using digital capabilities”.77

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Gilbert (2020). The Article 1 of the AML lists six goals: to prevent and restrain monopolistic behaviour, to protect fair competition, to encourage innovation, improve economic efficiency, protect consumer interest and social public interest, to promote a healthy development of socialist market economy. 76 European Commission (2011). 77 CMA (2019, p. 5). 75

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3.2.2 Multi-sided Market The concept of two-sided platforms was first named by Rochet and Tirole in 2001.78 It refers to the situation where an online platform facilitates two interdependent groups of consumers. In 2006, they proposed a clear definition of a two-sided market as “a market is two-sided if the platform can affect the volume of transactions by charging more to one side of the market and reducing the price paid by the other by an equal amount; in other words, the price structure matters, and platforms must design it to bring both sides on board”.79 Their definition indicates that a distribution of prices, for example charging more to one group of consumers to increase the demand of the other group, is important for two-sided markets. Veljanovski defines that there are three conditions to define a “two-sided” market: (1) have two or more consumer groups; (2) there is an interconnection between the demand of the two or more consumer groups; and (3) by coordinating the consumer demands, an intermediary can make each group better off.80 Evans and Schmalensee proposed four key features of multi-sided platforms: (1) has two or more groups of customers; (2) who need each other in some way; (3) but who cannot capture the value from their mutual attraction on their own; (4) rely on the catalyst to facilitate value-creating interaction between them”.81 The crucial function of the platform is to solve the coordination problem and thus reduce transaction costs to create value.82 Furthermore, Evens defined three necessary conditions for a platform to increase social benefits83 : “(a) there are distinct groups of customers; (b) a member of one group benefits from having his demand coordinated with one or more members of another group; (c) an intermediary can facilitate that coordination more efficiently than bilateral relationships between the members of the group”. The distinct feature of indirect network effects and the interdependencies among users on different sides were clearly mentioned when competition authorities gave the definition of online platforms. For example, The German Monopolies Commission defines online platforms as “intermediation services which allow for direct interaction between two or more distinct groups of users that are connected by indirect network effects”.84 Section 18(3a) of the German Competition Act has made the concept of “networks” and “multi-sided markets” legal terms. The European Commission gave the definition that “undertaking operating in two (or multi-) sided markets, which uses 78

Rochet and Tirole (2003). Rochet and Tirole (2006). 80 Veljanovski (2007). 81 Evans and Schmalensee (2007a). 82 Evans and Schmalensee (2012, p. 7). 83 Evans (2003b, p. 192), see also Armstrong (2006), Parker and Van Alstyne (2002), Rochet and Tirole (2003) and Rochet (2003). 84 Bundeskartellamt (2016). 79

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the Internet to enable interactions between two or more distinct but interdependent groups of users to generate value for at least one of the groups”.85 The most commonly used example for a two-sided market is the dating club for men and women; there are other examples, such as academic journals, auctions, flea markets, debit and credit banking card payments, employment and real estate agencies, entertainment and video game platforms, magazines, newspapers, search engines and media web portals, and stock markets.86 In online markets, consumer groups on each side of the platform are differentiated, and as the “multi-sided market” defines, one group of consumers will benefit from the platform when more consumers join the group on the other side of the platform.87 The concept of “a multi-sided market” also has the same meaning as “multi-sided platform”,88 as Rochet and Tirole defined two-sided or multi-sided markets are “markets in which one or several platforms enable interactions between end-users and try to get the two (or multiple) sides ‘on board’ by appropriately charging each side”.89 Competition in a multi-sided market may emerge not from one platform but across different types of platforms to attract users. For instance, Facebook competes with Google, Twitter and Apple for ad revenue, but it is also in direct competition with offline advertising such as TV and print ads. In this way, competition is not always like-for-like online. Often the free services being offered to the user may differ, although the advertisers consider these various services to be substitutes.90 A multi-sided market is the place where multisided platforms operate and compete, and the OECD defined multi-sided markets as “a market in which a firm acts as a platform and sells different products to different groups of consumers, while recognizing that the demand from one group of customers depends on the demand from the other group(s)”.91

3.2.3 Indirect Network Effects The crucial characteristic of online platforms that distinguishes them from other types of trade facilitators, is the network effects that demand externalities on one side are internalized between other sides of the users.92 Multi-sided platforms have both direct and indirect network effects, referring to the direct and indirect impact of the use of products by users at one side on the value of products to users at other sides.93 85

European Commission (2015). Wright (2004). 87 Evans (2003a, pp. 331–333), Filistrucchi et al. (2013, pp. 37–39) and Armstrong and Wright (2007, p. 353). 88 Evans and Schmalensee (2011, p. 3). 89 Rochet and Tirole (2006, p. 645). 90 Evans (2013, pp. 313, 316). 91 OECD (2018, p. 10). 92 Evans and Schmalensee (2007a, pp. 151–79). 93 Lasserre and Mundt (2017, p. 95). 86

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In the economic literature, the level of competition of online markets is decided by indirect network effects and switching costs, and indirect network effects often occur in the two-sided market94 McKnight and Bailey defined “network externality” as “the benefit gained by incumbent users of a group when an additional user joins the group. The group can be thought of as a ‘network’ of users”.95 Direct network effects refer to the situation where the utilities of the user increase with the increasing number of other users,96 where indirect network effects mean the utility of the user indirectly benefits from the increase of other users in the internet market. Therefore, there is an interrelationship between the users on each side of the market.97 Direct network effects often occur when users can internalize the effect among themselves, and the platform becomes more attractive when the demand on one side of the platform grows, particularly for social media and communication apps such as Facebook, LinkedIn, WhatsApp and Skype.98 Indirect network effects have to be internalized through the interaction between users on the two sides of the platform, meaning that when the demand on one side increases, the platform becomes more attractive for the users on the other side, such as Amazon or Bookings.com, which facilitate transactions or platforms such as YouTube operating in an advertisement revenue model.99 One side of the consumers will benefit from the platform when more customers join the group at the other side.100 It is also called the “chicken and egg problem” defined by Caillaud and Jullien,101 that is, the willingness of consumers on one side of the platform depends on sufficient participation of the consumers on the other side of the market, as Evans defined that “The demand on each side tends to vanish if there is no demand on the other side—regardless of what the price is”.102 The network effects are not internalized by the two groups of consumers; rather, they are internalized by the platform which provides intermediary services that coordinate the interaction among consumer groups.103 Thus, in a two-sided market, because of indirect network effects, competition between platforms may not always be efficient, as a monopoly platform in a concentrated market could be more efficient in coordinating users.104 Evens proved that the pricing strategy of platforms is not necessarily proportional to marginal costs, the total benefits of the platform depend on the magnitude of the indirect network externalities generated from one side to another, and the amount of costs contributed to the common costs of production may not be symmetric. The 94

See Haucap and Heimeshoff (2014, p. 50) and Evans and Schmalensee (2007a). Bailey and McKnight (1997). 96 Shapiro and Katz (1985, pp. 424–440). 97 Haucap and Heimeshoff (2014, p. 51). 98 Nooren et al. (2018, p. 7). 99 Nooren et al. (2018, p. 7). 100 Rochet and Tirole (2003, p. 995) and Evans (2003a, pp. 331–333). 101 Caillaud and Jullien (2003, pp. 309–328); Caillaud and Jullien (2001, pp. 797–808). 102 Jullien (2005, p. 3) and Evans (2003b). 103 Evans (2003a, pp. 331–333). 104 Haucap and Heimeshoff (2014, p. 52) and Caillaud and Jullien (2003, pp. 309–328) and Jullien (2005). 95

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effects of demand from one side to the other side are often called the “chicken-and-egg problem”.105 The key making profits on multi-sided platforms is not the price itself but the demands driven by indirect network effects. For example, audience-makers such as newspapers and magazines, television networks and online media receive revenues from advertising when there is greater audience react to their posts; demandcoordinators earn profits by making goods and services to coordinate transactions across two or more groups through indirect network effects, as software such as Microsoft earns revenues from licensing packaged software to end-users, and in banking industry payment cards receive revenues from merchants.106

3.2.4 Switching-Costs According to Barroso and Picón,107 switching costs are the real or psychological costs that consumers perceive when choosing different platforms and include monetary costs of losing commission paid to the previous firm, relational costs, and psychological costs such as dissatisfaction, risk and uncertainty about switching. Jones et al. defined switching costs as including sunk costs, continuity cost, learning cost before and after switching and setup costs.108 High switching costs often lead to lock-in effects by the incumbent. Traditionally, antitrust analysis is concerned about switching costs from one platform to another. However, in online markets, switching costs are often low because consumers can use more than one platform at the same time (“multi-homing”). That is, consumers use multiple search methods online in undertaking web searching.109 In doing so consumers switch easily from a general search engine to specialized vertical search engines and apps. As Wagner von Papp explains, “A significant proportion of searches are done on vertical search sites or apps (such as Amazon, Booking.com, eBay, Expedia, Kayak, TripAdvisor, etc.) or social networks (especially Facebook)–either instead of using general web search or in addition to it (multi-homing)”.110 Take the example of someone who needs to book a flight from Hong Kong to Madras. A consumer can easily switch from a general search engine (e.g., Baidu) to another search engine (e.g., Google or Bing), a social network (Facebook or Tencent), and a specialized travel search engine (Ctrip, Expedia, or Kayak) via websites and/or apps. Thus, any incentive that a firm may have to bias its search results would be significantly limited.111

105

Gawer and Cusumano (2002) and Evans and Schmalensee (1999). Evans (2003b, p. 194). 107 Barroso and Picón (2012, pp. 531–543). 108 Jones et al. (2002, pp. 441–450). 109 Edlin and Harris (2013, pp. 169, 194). 110 Wagner-von Papp (2015, p. 631). 111 Bork and Sidak (2013, p. 663, 676). 106

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3.3 Attention-Based Nonprice Competition An online market challenges neoclassical economic analysis in antitrust cases because online services are often available to users for free. Services provided through internet platforms are largely based on information; therefore, the marginal costs of production, which is a form of intellectual property product, are reduced to zero.112 Internet search engines provide free service to users but sell the data collected from users when they enter their query to earn revenue from advertisements.113 Advertisers take part in a so-called keyword auction, and the highest bids could win a better placement at the search engine’s webpage reserved for sponsored links. The advertiser only needs to pay when the user clicks the advertisement. This pricing strategy is named as Cost Per Click (CPC) price. Inge Graef, SihYuliana Wahyuningtyas & Peggy Valcke Argued that the services provided by online business, such as the search engine of Google, are not completely free because the consumer also has to see advertisements or to provide certain personal data, although those costs are hardly calculated in price.114 Once online platforms are able to charge differentiated prices to different groups of consumers, the prices charged to a group do not reflect the real costs of the service, and the platform could control the transactions by imposing differentiated prices on different consumers. Therefore, it is incorrect to define online markets as “free” because services are provided at no price.115 In 1999, George Franck defined attention as “the new currency of business”, as attention could be perceived as a new kind of capital and could generate income or wages.116 Evans defined the nonprice competition between online platforms in industries including search engines, social networking media, ecommerce, entertainment, mobile games and instant messaging as “attention rivals”.117 The competition between suppliers focuses on the availability of new products and services that attract the ‘attention’ of consumers, which is a dynamic process; thus, product differentiation is the key for analysing competition constraints, and the competition of securing ‘attention’ is the main issue when deciding market definition, market power and competitive effects.118 When services are provided at zero-prices, online platforms selling different products and services become competitors because the attention they obtained from consumers could ‘substitute’ each other although their products and services are different, and the traditional analysis of product substitutability becomes inapplicable to online markets.119 112

Piraino (2002, p. 65, p. 96). Manne and Wright (2011). 114 Graef et al. (2015, pp. 375–387). 115 Graef et al. (2015, pp. 380–381). 116 Franck (1999). 117 Evans (2013). 118 Evans (2013, p. 314). 119 Evans (2013, p. 357). 113

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Davenport and Beck defined the causal relationship between attention and the constant supply of information: “Attention is focused mental engagement on a particular item of information. Items come into our awareness, we attend to a particular item, and then we decide whether to act”.120 Back to 1971, Herbert Simon pointed out the fact that the increasing supply of information would cause a limited supply of attention—“[information] consumes the attention of its recipients. Hence, a wealth of information creates a poverty of attention”.121 Marazzi defined a new economy as “limitless growth in the supply of information” and “limited human demand”. As the attention of humans is a scarce resource, there is also a contradiction between the working time that generates economic benefits and living time when using communication technology; thus there is a diminution of attention time when the information is increasingly spreading.122 Terranova argued that when attention is measured for an economic entity, attention is featured by scarcity (the limited resource of attention), poverty (the economic value is reduced when the volume of information is increasing) and finite capacity of the brain to analyse information.123 The economic model proved by Falkinger shows that when consumers’ attention becomes a scarce economic resource, firms have to send stronger signals to attract consumers’ attention, and the increased signal affects consumers’ behaviour through the elasticity of substitution. When the signal (advertising) becomes stronger, the perceived products become less substitutable, thus the price elasticity of demand decreases. When price elasticity is low, signal strength rises; thus, reinforcing the firms’ incentives to advertise. Moreover, the competition of companies to attract consumers’ scarce attention in an information-rich economy may become wasteful.124 Economists Prat and Valletti defined digital platforms that have two crucial capabilities: obtaining the preferences of each individual user, and sending targeted advertisements to each individual user in the retail product industries are ‘attention brokers’,125 and specifically emphasize the value of at-the-moment data for advertisers. They argue that consumer data have been used for creating economic value in two steps: first, users provide proprietary data that could be used by machine learning techniques to infer real-time consumption preferences126 ; and second, the platforms as attention brokers can sell targeted advertising space to the exact supplier of the product that consumers are interested in. Those advertisements have market value because at that moment, users’ attention has already been captured by the platform.127 Their model shows that when attention brokers are becoming concentrated, such as after mergers between platforms, the market will create attention bottlenecks that lead to higher advertisement prices, fewer advertisements sold to entrants and 120

Davenport and Beck (2001, p. 20). Simon (1971, p. 40). 122 Marazzi (2008, p. 146). 123 Terranova (2012). 124 Falkinger (2008, pp. 1596–1620). 125 Prat and Valletti (2021, p. 2). 126 Prat and Valletti (2021, p. 2). 127 Prat and Valletti (2021, p. 2). 121

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reduced consumer welfare. Their study shows that when there is no real currency for price competition in platform ecosystems or between platforms, attention, which generates significant economic profit through online advertising, becomes the new currency, and anti-competitive conduct will equally reduce consumer welfare.

3.4 New Organizational Forms and Revenue Models In competition cases related to online markets, it is crucial to learn the new competition structure between platforms, the function of business models and the pricing strategy of multi-sided platforms. The understanding of competitive effects between online platforms has to rely on the understanding of new organizational forms that internet companies have developed.128 Traditional business transactions are organized through the linear relationship between buyers and suppliers along the value chain,129 while multi-sided platforms conduct transactions through the controlled resources (including skills and ideas) provided by agents on different sides.130 Thus multi-sided platforms do not earn profits through the manufacturing or reselling of products but through the connection of agents on different sides.131 Because online platforms create value by facilitating transactions and developing networks for producers, as the indirect network effects and nonprice competition features discussed in the previous sections, it is logical to charge zero prices on one side to attract demand on the other side.132 It is more important for policy makers to understand that such a pricing strategy has been commonly applied and that internet companies have developed a new organizational form and business model that significantly differ from traditional markets. Peitz and Valletti’s research133 defined three pricing structures and revenue models of digital platforms. The first is direct payment, in which the platform charges a transaction fee or subscription fee for products (including software and hardware) sold on the platform. It is also possible that charging zero price is for basic service but consumers have to pay for using advanced premium services. The second model is the advertisement model, in which online platforms collect personal data from consumers and send personalized advertisements when providing services. The third model is charging content developers and app suppliers for selling the products on the platform, such as happens in the Apple’s app store. In this model the platform serves as the mediator between app developers and multiple sides of users. Charging high prices for one group of users may lead to the reduction of user groups on other sides. 128

Eckhardt et al. (2018, pp. 369–391), Iansiti and Lakhani (2017, pp. 74–81), Parker and Van Alstyne (2018) and Teece (2018, pp. 40–49). 129 Van Alstyne et al. (2016). 130 Zhao et al. (2020, at p. 3). See also Adner and Kapoor (2010) and Thomas et al. (2014). 131 Zhao et al. (2020, p. 3). 132 Eisenmann et al. (2006). 133 Peitz and Valletti (2015).

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In addition to these three models, newly founded platforms or after the acquisition of a platform, new owners may also not generate current revenues but aim at creating future revenues by testing the current business model or technology.134 Choi and Mela defined the three types of revenue models of online platforms, including charging fees for impressions delivered to consumers (cost-per-mile, CPM), charging fees for clicks made by consumers (cost-per-click, CPC) or charging fees per completed transaction (cost-per-action, CPA).135 A typical example of CPA is charging commissions on sales, such as asking the platform to pay approximately 6% to 25% per item sold. Advertising fees are typical examples based on CPM or CPC, and platform revenues are affected by item prices and transactions (CPA) and advertising (display ranking algorithms, CPC).136 Their model shows that consumer preferences are most affected by price and the number of pictures, and the average marginal cost of browsing is 0.89 USD and the average marginal cost of clicking is 3.90 USD, with heterogeneity in search costs among consumer groups.137 Advertisers have high value for clicks but low estimated margins for sales transactions: when sellers opt in for advertising, the seller’s evaluation for demand will become negative. This means consumers’ searching behaviour when purchasing is interrupted by advertising, but the median valuation from a click for sells is 0.13 USD, and clicks contribute to brand value and can generate future demand for advertisers’ products. Their research shows that platforms’ profits are related to the pricing mechanism and the product ranking algorithm. The platform benefits from the policy that lowers CPA and charges an advertising fee based on CPC.138

3.5 Winner-Take-All Markets Although Schumpeter’s dynamic competition theory shows that a temporary monopoly position might be necessary for encouraging competition and such a position is only temporary because potential entrants can replace the incumbents with new business strategies, monopoly platforms may have the tendency to create a winnertake-all (or tipping) market. Gawer and Cusumano’s research139 showed that such a market will have negative effects on competition. According to Eisenmann et al., a winner-take-all situation is more likely to occur when network effects are positive and strong, multi-homing costs are high, and there are no differentiation opportunities in the markets.140 The extensive integration between platforms may also be due to non-standard development toolkits or application programming interfaces, leading 134

Nooren et al. (2018, p. 6). Choi and Mela (2019, p. 1). 136 Choi and Mela (2019, p. 1). 137 Choi and Mela (2019, p. 4). 138 Choi and Mela (2019, p. 1). 139 Gawer and Cusumano (2014). 140 Eisenmann et al. (2006). 135

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to high multi-homing costs.141 The OECD concluded that “Data-driven markets can lead to a ‘winner takes all’ result where concentration is a likely outcome of market success”.142 The network effects can also explain why winner-takes-all occurs. Data platforms’ network effects have changed the productivity effects of investment in technology. From the demand side, when there is an additional user joining the platform, the value of the platform to other uses increases. Positive network externalities indicate that the value of the platform to one user is positively affected by other users participating. From the supply side, the additional user at the platform will reduce the marginal costs and increase the quality of the product, improve the algorithmic analysis and benefit the users at all sides. The positive feedback loop indicates an increasing return to scale, and often leads to the “winner-takes-all” effect.143

3.6 Policy Implications Multi-sided platforms produce substantial positive effects, such as promoting dynamic efficiency, lowering transaction costs, creating new products and services and reducing product prices by facilitating exchanges between different groups of users. Those benefits would not be generated without the function of the platform.144 Because of the difficult tradeoff between the positive effects and the negative effects that are not clearly defined, for example, the abusive conduct of using market power, enforcing antitrust law in high-tech industries and online markets has become one of the most challenging legal tasks today.145 Economic methods and techniques that are commonly used in the analysis of competitive effects are inapplicable. The economist J. Wright146 has clearly defined that because of the particular characteristics of multisided markets, there are eight statements in industrial organization theories that are not applicable to multi-sided markets: (1) An efficient price structure should be set to reflect relative costs (2) A high price–cost margin indicates market power (3) A price below marginal cost indicates predation (4) An increase in competition necessarily results in a more efficient structure of prices (5) An increase in competition necessarily results in a more balanced price structure (6) In mature markets, price structures that do not reflect costs are no longer justified (7) Where one side of a two-sided market receives services below marginal cost, it must be receiving a crosssubsidy from users on the other side (8) Regulating prices set by a platform in a two-sided market is competitively neutral.

141

Ruutu et al. (2017, p. 120). OECD (2014, p. 7). 143 Lianos (2019, p. 33). 144 Evans and Schmalensee (2007a, 2007b). 145 Rosch (2010). 146 Wright (2004). 142

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Therefore, although it seems to be apparent that data giants have obtained large market shares, or charge below-cost prices (or zero price) on one side, they cannot be defined as conducting monopolistic behaviour (such as predatory pricing). The analysis of the relevant market and market power of multi-sided markets requires a thorough refinement of traditional methods or the development of completely new methods. These issues will be discussed in detail in Chaps. 5 and 7 of this book. In contrast with traditional markets, online markets bring together multiple groups of users who can interact with each other within the platform. The platform is often multi-sided because the value to one group of consumers often depends on another group of users. The pricing structure of the two-sided markets will be different from a one-sided market because the value to a one-sided user when additional members of the other side’s users join may be different from when the situation is reversed. Setting prices below costs is often an efficient pricing structure when platforms compete to attract users on the other side, so price–cost margins may not indicate market power, and below-costs do not represent predatory pricing.147 The traditional analysis of market power and the definition of a relevant market will not be applicable. (See more discussions in Sect. 5.2). Online markets may have different network effects, such as direct and indirect network effects. Each type of network effect has its own attributes. Direct network effects may matter for purposes of scaling up, such as Facebook or Skype where more users create larger scale.148 However, indirect network effects work differently. Indirect network effects take place in situations where additional users improve the use of a product or service better, although not due to direct interaction across users. Rather, additional users allow a platform to determine what its users want via trial and error in search results. This in turn improves the quality of search results.149 Understanding the difference between direct and indirect effects helps antitrust enforcers to better understand a multi-sided market. In a multi-sided market, all sides of the market need to be analysed because the benefits of indirect network effects can only be achieved when multiple agents are coordinated, and participation of each agent is ensured.150 For example, in a one-sided market, consumers and producers are often considered as a whole, whereas in multisided platforms consumers with different preferences could be separated and treated as independent groups. The increasing use of the platform of one consumer group would create an externality to other groups; therefore, particular attention has to be paid to the indirect network externality at the demand side. Multi-sided platforms could substantially reduce transaction costs. Without a multi-sided platform, the “value-creating” interaction among multiple agencies could be extremely costly.151

147

Wright (2004). Even with direct network effects, such effects do not always prevent successful entry as Facebook was challenged by Instagram, WhatsApp and Snapchat. 149 Lerner (2014). 150 Evans and Schmalensee (2014). 151 Evans and Schmalensee (2007a, pp. 151, 158). 148

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Table 3.1 Strength of competitive forces for different types of platforms Forces

Platform type Marketplaces

App stores

Social networks

Online advertisement

Examples

Amazon, ebay, booking.com

Google Play, Facebook, Apple App Store LinkedIn, Instagram

Doubleclick, AppNexus, Rubicon project

Direct network effects

Low for sellers and buyers

High for users, low for developers

High for users, low for advertisers

Low for users, publishers and advertisers

Indirect network effects

High for sellers and buyers

High for users and developers

Low for users, high for advertisers

Low for users, high for publishers and advertisers

Economics of scale

High

High

Medium

High

Capacity constraints

Medium

Medium

High

High

Differentiation

High

Medium

High

High

Multi-homing

Medium for buyers, low/medium for sellers

Low for users, medium for developers

High for users and advertisers

High for users and advertisers, medium for publishers

Source Duch-Brown (2017, at p. 9).

Such effects do not create consumer lock-in in the presence of multi-homing and low switching costs. Moreover, although platforms share the common features of being dynamic innovation driven, multi-sided, having direct and indirect network effects and multihoming effects, there are significant differences between different types of platforms in the degree of strength of those effects, which implies competition analysis on those effects for each platform has to follow a case-by-case approach. Table 3.1 summarizes the strength of competitive forces for different types of platforms.

3.7 Conclusions The case for antitrust intervention in online markets requires great caution because of a number of factors: proper market definition, accounting for possible low entry barriers, multi-homing and low switching costs, and the need for a proper analysis of all sides of a market. Often, multi-sided markets produce significant benefits to consumer welfare in dynamic and fast-moving markets. Platforms serve as the intermediator that brings users from different sides on board and facilitates the transactions between them, thus receiving revenues through advertisements or premium

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fees. The characteristics of dynamic efficiency and indirect network effects make the structural analysis of competition effects inapplicable for the online market. Deviations between price and cost cannot identify the extent to which firms hold market positions because price changes not only affect the demand on one side, the feedback effects cause the demand elasticity of two sides a “chicken-and-egg” problem. Consumers can easily switch to other platforms when products and services supplied by new entrants attract their attention. Although investment in the internet infrastructure leads to high fixed costs, the marginal costs of running online software are low and the data collected by the platforms have a diminishing value. The new competition framework and pricing strategy of online enterprises must be carefully analysed before antitrust authorities make decisions on regulatory intervention, since a divestiture-based remedy may threaten innovation and a simple calculation of market share makes no sense for assessing the competition level of the given market. In Schumpeter’s words, a temporary market dominant position might be necessary for large firms to invest in R&D, and from Evan’s perspective of attention economy, innovation cycles are constant and consumers can easily switch their attention to competitors at low cost. Given these significant concerns, antitrust authorities and courts should closely examine the facts of a particular case to ensure that facts and economic analysis are aligned with legal theories in multi-sided markets before bringing such cases. Furthermore, the nature of multi-sided markets suggests that before deciding on potential remedies, an antitrust authority should re-examine the market to see if its particular dynamics have already changed.

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Bailey, J. P., & McKnight, L. W. (Eds.). (1997). Internet economics. MIT Press. Baker, J. B. (2007). Beyond Schumpeter vs. Arrow: How antitrust fosters innovation. Antitrust Law Journal, 74(3), 575–602. Baker, J. B. (2008). ‘Dynamic competition’ does not excuse monopolization. Competition Policy International, 4(2), 243–251. Barnett, T. O. (2008). Maximizing welfare through technological innovation. George Mason Law Review, 15. Available at https://www.justice.gov/atr/speech/maximizing-welfare-through-techno logical-innovation Barroso, C., & Picón, A. (2012). Multi-dimensional analysis of perceived switching costs. Industrial Marketing Management, 41, 531–543. Blundell, R., Griffith, R., & Van Reenen, J. (1999). Market share, market value and innovation in a panel of British manufacturing firms. Review of Economic Studies, 529–554. Bork, R. H., & Sidak, J. G. (2012). What does the Chicago School teach about Internet search and the antitrust treatment of Google? Journal of Competition Law and Economics, 8(4), 663–700. Bourreau, M., & de Streel, A. (2020). Big tech acquisitions: Competition and innovation effects and EU merger control. In Centre on regulation in Europe (CERRE) issue paper, February 2020. Breschi, S., Malerba, F., & Orsenigo, L. (2000). Technological regimes and Schumpeterian patterns of innovation. The Economic Journal, 110(463), 388–410. Brodley, J. F. (1987). The economic goals of antitrust: Efficiency, consumer welfare and technological progress. New York University Law Review, 62, 1020–1053. Bundeskartellamt. (2016). Working paper—The market power of platforms and networks, executive summary, BKartA, B6-113/15, June 2016. Caillaud, B., & Jullien, B. (2001). Competing cybermediaries. European Economic Review, 45(4), 797–808. Caillaud, B., & Jullien, B. (2003). Chicken & egg: Competition among intermediation service providers. RAND Journal of Economics, 309–328. Carlin, W., Schaffer, M., & Seabright, P. (2004). A minimum of rivalry: Evidence from transition economies on the importance of competition for innovation and growth. Contributions to Economic Analysis and Policy, 3(1), 1–43. Carrier, M. A. (2008). Two puzzles resolved: Of the Schumpeter-Arrow stalemate and pharmaceutical innovation markets. Iowa Law Review, 93(2), 393–450. Castellacci, F. (2010). How does competition affect the relationship between innovation and productivity? Estimation of a CDM model for Norway. Economics of Innovation and New Technology, 19, 1–22. Choi, H. & Mela, C. F. (2019). Monetizing online marketplaces, April 28, 2019. http://ssrn.com/ abstract=2839802 Christensen, C. (2013). The innovator’s Dilemma: When new technologies cause great firms to fail. Harvard Business Review Press. CMA. (2019). Competition and markets authority (CMA), The CMA’s digital markets strategy, July 2019. Czarnitzki, D., Etro, F., & Kraft, C. (2011). Endogenous market structures and innovation by leaders: An empirical test (Working Paper No. 04/WP/2011). University of Venice Department of Economics. Darai, D., Sacco, D. & Schmutzler, A. (2010). Competition and innovation: An experimental investigation. Experimental Economics, 13, 439–460. Davenport, T. H., & Beck, J. C. (2001). The attention economy: Understanding the new currency of business. Harvard Business Press. De Bondt, R. (1977). Innovative activities and barriers to entry. European Economic Review, 10, 95–109. De Bondt, R., & Vandekerckhove, J. (2012). Reflections on the relation between competition and innovation. Journal of Industry, Competition and Trade, 12(1), 7–19. Dixit, A., & Stiglitz, J. (1977). Monopolistic competition and optimum product diversity. American Economic Review, 67(3), 297–308.

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Chapter 4

Data Monopolies and Competition Law

Abstract In addition to the main characteristics discussed in Chap. 3, online markets differ from traditional markets in that online platforms may apply business strategies by collecting and utilizing a significant amount of data. This chapter discusses the characteristics of data, the types of online business using consumer data and implications for competition law. It is argued that data have the characteristics of low cost of collecting and storing; data have a non-exclusive and non-rivalrous nature, and the value of data decreases over time. Data have valuable assets and input for online services, and they improve the quality of the services for consumers through personalization and are essential for producing new products and services. These arguments indicate that government intervention in data-driven business should be treated with caution. In some cases, the competition agency held that the evidence of economies of scale may not hold, so incumbents in the data industry do not necessarily have a comparative advantage. Moreover, the efficiency defense of incorporating the arguments of data-driven innovation has been accepted by competition agencies in the US and the EU.

4.1 Introduction The impact of the pricing strategies of online business could be revolutionary, since the function of currency has been replaced by the information and data collected or the time and attention that consumers devote to watching advertisements on search engine webpages. Thus, the hidden costs of the ‘free goods’ in online transactions include time, attention, and consumer information.1 When search engines can collect data from users and sell them to advertisers, data information becomes a profitable product.2 Companies have been changing their business models, and consumer data have been increasingly used as a valuable input. Trading consumer information between search engines and advertisers could be seen as a profit-driven business. The current theoretical debate supports the view of regulating Big Data through

1 2

Gal and Rubinfeld (2015). Grunes and Stucke (2015).

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consumer protection law, but not antitrust law.3 Sokol and Comerford’s research shows that empirical evidence to support the active use of antitrust law on big data is largely lacking, and antitrust policy should be cautiously applied when analysing the economic effects of an industry with rapid innovation.4 David Evans proposed a new method to assess the trading mechanism in online businesses in which online service providers compete for the attention of consumers, so ‘attention’ itself becomes a type of product and service.5 Evans’ research indicates that economic tools, in particular empirical methods, could be developed to specifically deal with the assessment problem in multi-sided platforms, for example, to provide applicable measurement to calculate the benefits and losses of consumers when switching their attention among online service providers. The economic feature of the attention economy is that the profit of online platforms, which are gained through advertisers, depends upon the amount of attention that it can attract from consumers.6 There are also particular characteristics of the attention market, such as the amount of attention that consumers spend on consuming online products creating substantial market value. The greater the volume of time that consumers are willing to provide, the more profitable the platform will be. In other words, when internet firms compete for consumers’ scarce attention, the winner takes all. Therefore, although online services are often provided free of charge, they do not indicate that the services have no costs and that there is no “market” for competition. Consumers have to exchange their attention and personal data for the same time-consuming online products, and firms gain profits by subsidizing one side of the users to get the other side advertisers on board, as the European Data Protection Supervisor concluded, “Through the supply of payment-free services, these companies compete for the attention and loyalty of individuals whose use of those services will generate personal data with a high commercial value”.7

4.2 Definition of Data 4.2.1 4 V De Mauro et al. defined big data “is the information asset characterized by a high volume, velocity and variety such as requires specific technology and analytical methods for its transformation into value”.8 The US Federal Trade Commission understood big data as the continuous influence of information technology, including 3

Cooper (2013). Sokol and Comerford (2016). 5 Evans (2013). 6 Evans (2017). 7 EDPS (2014, p. 10). 8 De Mauro et al. (2016). 4

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acquisition, transmission, compilation and storage, processing and analysis on business and commercial developments,9 and they gave the definition that “the term ‘big data’ refers to a confluence of factors, including the nearly ubiquitous collection of consumer data from a variety of sources, and plummeting cost of data storage, and powerful new capabilities to analyse data to draw connections and make inferences and predictions”.10 Data have been characterized by the OECD as volume, velocity, variety, and value.11 The “4 V” definition of data refers to the amount, the different types, the speed of data transfer and the hidden values discovered from the data.12 Tucker defines that data are inexpensive, ubiquitous and easy to collect, and data have been created by consumers continuously.13 The term “big data” refers to the massive collection of data and is often defined from the perspective of the value it creates. For example, Mayer-Schönberger and Cukier defined that “big data refers to things one can do on a large scale that cannot be done on a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments, and more”.14 The European Data Protection Supervisors’ Opinion 03/2013 on Purpose Limitation (2013) defined big data as “the exponential growth both in the availability and in the automated use of information: it refers to gigantic digital datasets held by corporations, governments and other large organizations, which are then extensively analysed using computer algorithms”.15 The European Commission Communication Towards a Thriving Data-Driven Economy pointed out the need to use new technologies in big data analysis: “The term ‘Big Data’ refers to large amounts of different types of data produced with high velocity from a large number of various types of sources. Handling today’s highly variable and real-time datasets requires new tools and methods, such as powerful processors, software and algorithms, going beyond traditional ‘data mining’ tools designed to handle mainly low variety, small scale and static datasets, often manually”.16 Robertson,17 Kemp18 and the OECD19 classified data in accordance with the type of information collected and named these classifications user-generated content (including blogs and commentary, photos, videos, and communications with others), activity or behavioural data (including searching behaviour, websites visited, apps used, shopping and paying behaviour, habits and preferences), social data (including 9

IDCTF (2020, at p. 13). FTC (2016). 11 OECD (2013b) and The White House (2014). 12 See Zikopoulos et al. (2012) and Berman (2013, xix–xxvi). 13 Tucker (2013), Lambrecht and Tucker (2015) and Sokol and Comerford (2016, p. 6). 14 Mayer-Schönberger and Cukier (2013, p. 6). 15 European Commission (2013). See also https://ec.europa.eu/justice/article-29/documentation/ opinion-recommendation/files/2013/wp203_en.pdf. 16 European Commission (2014). 17 Robertson (2020). 18 Kemp (2019). 19 OECD (2013a, 2020a, p. 7). 10

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contacts through social networks), locational data (e.g., residential addresses, GPS, IP address), demographic data (e.g., age, gender, race, income, sexual preferences, political affiliations), identifying data of an official nature (name, financial information, health information, social security numbers, police records) and biometric data (fingerprints, face, eye and voice recognition). The UK Competition authority CMA defined the contents of data that could be collected by firms for commercial purposes including financial data, contact information, sociodemographic data, transactional data, contractual, locational behavioural and technical data, communications, social relationships, open data and public records, usage data and documentary data, and the type of data that can be classified into personal data and non-personal data. Non-personal data include anonymous data, pseudonymous data and aggregate meta data.20 The economic term of ‘consumer data’ has the characteristics of simultaneous use (the same consumer data could be used by multiple collectors at the same time), the cost structure (high fixed costs in the collection, storage, processing and analysis of consumer data and low marginal costs), diversity in value (some has value over a longer time while some is relevant over shorter time).21 Based on the criteria provided by GSMA (2018),22 the OECD (2019b),23 and theSwedish National Board of Trade (2014),24 UNTCAD listed the categories of data including: personal or non-personal data, private and public data, data for commercial purposes or governmental purposes; data used by companies (including corporate data, human resources data, technical data and merchant data); non-structured and structured data, instant and historic data; volunteered, observed and inferred data; sensitive and non-sensitive data; B2B, B2C, government to consumer (G2C) or consumer to consumer (C2C) data.25 Schepp and Wambach defined data as being created through the interaction between the consumers (users) and online service providers; therefore, data are created either voluntarily (such as sociodemographic characteristics) or through the interaction between the user and the online platform and are beyond the knowledge of the user (such as locational data).26 The World Economic Forum defined the type of data into three categories: volunteered data, observed data and inferred data. Volunteered data means users are willing to share the data so it could be collected directly and statically; observed data means data is collected by tracking the dynamic consumer activities online; and inferred data is collected by analysing the volunteered and observed data.27 Similar to the definition by the World Economic Forum, Graef defined the data provided by the users to online platforms include information inserted directly in the search engines such as profile information (‘volunteered data’ 20

CMA (2015, pp. 24–26). CMA (2015, p. 75). 22 GSMA (2018). 23 OECD (2019b). 24 Swedish National Board of Trade (2014). 25 UNCTAD (2019, p. 29). 26 Schepp and Wambach (2016, pp. 120–124, p. 120). 27 World Economic Forum (2011). 21

4.2 Definition of Data Table 4.1 Examples of categories of personal data collected online30

89 Volunteered data

Observed data

Inferred data

Name

IP address

Income

Phone number

Operating system

Health status

Email address

Past purchases

Risk profile

Date of birth

Website visits

Responsiveness to ads

Address for delivery

Speed of click through

Consumer loyalty

Responses to surveys

User’s location

Political ideology

Professional occupation

Search history

Behavioural bias

Level of education

“Likes” in social networks

Hobbies

Sources OECD (2018, at p.11)

and ‘observed data’) and the data created by the platform through using cookies to analyse the behaviour and habits of the users (‘inferred data’, or ‘metadata’, that is data which describes other data).28 This type of classification is also perceived as using the category of data origin, as the OECD classified data into four types: volunteered data, observed data, derived or inferred data, and acquired (purchased or licensed) data that are obtained from third parties.29 The OECD provided examples of volunteered data, observed data, and inferred data (see Table 4.1). In addition, data could also be collected by third party agencies such as data brokers. Englehardt and Narayanan calculated that third-party web trackers were deployed at the top 1 million websites, Alphabet/Google’s trackers were deployed at approximately 70% of all websites and Facebook’s trackers were deployed at 30% of all websites.31 In addition, online businesses also use deterministic and probabilistic methods to track individuals when consumers use multiple devices to access the Internet. Deterministic methods refer to tracking consumers through identifiable characters such as log-ins.32 Probabilistic methods refer to the methods that infer consumers’ identity through IP addresses, geolocation information, browser or device fingerprinting, and general usage patterns.33

28

Graef (2015, p. 475). OECD (2019a). 30 OECD (2018). 31 Englehardt and Narayanan (2016). 32 FTC (2017). 33 Boerman, Kruikemeier and Borgesius (2017, pp. 363–376), Shakeel (2016). See also OECD (2020b, at p. 11). 29

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4.2.2 Data Values “Data” as the economic input, has also been named the “new oil”34 and “the new currency”35 of the information economy. In 2009, the European Consumer Commissioner Meglena Kuneva made clear that data is the “new oil of the information economy”.36 In 2010, The Economist defined consumer data as “the new raw material of business: an economic input almost on par with capital and labour”.37 In 2017, The European Commission stated that data “rapidly becoming the lifeblood of the global economy”.38 In the World Economic Forum 2019, Japanese Prime Minister Shinzo Abe stated that digital data are “the engine for growth”.39 IDC Italia and the Lisbon Council estimated that the direct value of the data market in the EU was 50 billion Euros in 2017, and has the potential to grow to 77 billion Euros in 2020 and 110 billion Euros in 2025.40 The use of data produces productivity that reduces transaction costs and improves product and dynamic efficiency in various aspects. The OECD estimated that by 2020, the use of data to track mobile devices could save time and fuel to the value of 500 billion USD in the transport sector, and the adoption of smart grid applications in households can reduce the cost of CO2 emissions by 79 billion EUROs. In the US health-care sector, using electronic health records could improve efficiency in management, reduce medical errors, improve diagnosis and foster R&D that reaches savings of approximately 300 billion USD.41 In the UK, it is estimated that the average cost of a digital transaction of government services is about 20 times lower than the cost of a telephone transaction, and about 30 times lower than the cost of a postal transaction and about 50 times lower than that of a face-to-face transaction.42 The MIT study showed that it would cost 17,530 USD for one internet user to give up search engines for one year, and the estimated compensation for giving up email is 8,414 USD and for giving up digital maps will be 3648 USD.43 The European Commission estimated that big data used by 100 EU manufacturers alone will save 425 billion Euros.44 In 2018, the value of the data economy in the EU reached 301 billion Euros, accounting for 2.4% of GDP,45 and it is expected that the value of the 34

Newman (2014, at pp. 3–4). Kuneva (2009). 36 Kuneva (2009). 37 Cukier (2010). 38 European Political Strategy Centre, European Commission, Enter the Data Economy: EU Policies for a Thriving Data Ecosystem, Jan. 11, 2017, https://ec.europa.eu/epsc/sites/epsc/files/strategic_ note_issue_21.pdf. 39 Abe (2019). 40 IDC Italia and the Lisbon Council (2018). See also Marcus, Petropoulos, and Yeung (2019, p. 27). 41 OECD (2013b). 42 Falk et al. (2017, p. 7). See also Central Digital and Data Office (2012). 43 Brynjolfsson et al. (2019) and Church (2019). 44 European Commission (2016). 45 European Commission (2020c, at p. 16). 35

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data economy will increase to over 829 billion Euros in 2025, accounting for 5.8% of GDP in the EU.46 In addition to improving productivity and the efficiency of business and government operations, the value and benefits that big data could create for consumers are substantial.47 Data may generate great value for society, as Massachusetts Institute of Technology (MIT)’s research shows that “data-directed decision making” in business may generate a 5–6% increase in productivity.48 In 2011, McKinsey calculated that the potential value of the US medical industry, if big data could be creatively and effectively utilized, could reach USD 300 billion, reducing US healthcare expenditures by over 8%. Retailers could improve their profits by more than 60%. The improved efficiency of government operations in Europe could save more than 100 billion euros.49 In 2013, McKinsey estimated that the benefits generated by big data and analytics for the health care industry are more than 300 billion USD annually, and for the overall economy, production and cost savings can reach 610 billion USD.50 In 2013, the OECD argued that “Big Data now represents a core economic asset that can create significant competitive advantage for firms and drive innovation and growth”.51 In 2015, the OECD estimated that companies benefited from 5 to 10% faster productivity growth than companies that did not use data-driven innovation.52 In 2017, Fortune estimated that Google and Facebook held approximately 20% of global advertising revenue, and 65% of digital advertising revenue.53 The combined market capitalization of Google, Amazon, Apple, Facebook and Microsoft exceeds 3.5 trillion USD.54 The OECD 2019 estimated that data access and sharing in the public sector generates social and economic benefits of 0.1–1.5% of GDP, and in the private sector, it generates between 1 and 2.5% of GDP.55 As it is often argued that data itself does not create value, online platforms use computers to conduct deep learning, and through analysis by algorithm computing, digital companies can make predictions and send personalized information; thus, a large dataset can generate substantial value for the platform. The OECD defined that a large dataset is used for companies to invest in creative innovation to improve product quality (named data-driven innovation, DDI) and better meet individual consumer needs. Through cloud computing and data analytics, consumer data can be used for improving the quality or functionality of products, providing personalized pricing,

46

European Commission (2020b). Chen et al. (2014, p. 5). 48 Brynjolfsson et al. (2011) and World Economic Forum (2011). 49 Manyika et al. (2011). 50 McKinsey Global Institute (2013). 51 OECD (2013b, p. 319). 52 OECD (2015). 53 See Handley (2017). 54 Cohen (2017, p. 142). 55 OECD (2019a). 47

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training machine learning and AI systems, and selling targeted advertising products,56 as Kemp argued, “suppliers have been enjoined to ‘measure everything’ in the interests of customer profiling, targeted marketing, customization, price discrimination, risk analysis and to support other potential applications of artificial intelligence in their businesses. For these purposes, from one point of view, more data are better. Machine learning is data hungry. Competitors are benefiting from millions of ‘insights’ about consumers in the market and possibilities of extending into other markets”.57 Machine learning algorithms work better with large, and good quality datasets to improve the usefulness of search engines, which leads to more users joining the platform and has feedback loop effects on improving the supporting algorithm.58 This argument has been commonly used when discussing entry barriers caused by economies of scale, the high fixed costs to invest in building a large and good quality dataset, and the feedback loop effects, which are often perceived as entry barriers that are held by powerful incumbents. Because companies can use large volumes of data to improve their products and services, and data-based targeted advertising increases consumption, there are studies trying to measure the monetary value of consumer data; instead of calculating the market capitalization of an internet company and the revenue they could earn by using consumer data, they calculate the value of personal data being sold. For example, the OECD reported that personal data of a street address is worth 0.50 USD, and birth date is valued at 2 USD, a social security number is worth 8 USD, a driving license number is valued at 3 USD and a military record is worth 35 USD.59 The report by the Orange Organization shows that the value of personal data varies depending on the familiarity with the institution that collects the data. Sharing the information of full name and date of birth with a familiar organization has a value of 12.14 GBP, compared with 15.02 GBP with an unfamiliar organization. The location data has a the monetary value of 13.99 for a familiar organization and 17.66 for an unfamiliar organization.60

4.2.3 The Use of Data In the Competition Policy for the Digital Era submitted to the EU Commission authored by Jacques Crémer, Yves-Alexandre de Montjoye, Heike Schweitzer,61 the use of data has been defined in four categories of how data is used: non-anonymous use of individual-level data, anonymous use of individual level data, aggregated data, and contextual data. Non-anonymous use of individual-level data refers to the data 56

OECD (2020a, p. 19). Kemp (2019, p. 10). See also OECD (2020a, p. 19). 58 OECD (2016, p. 8). 59 OECD (2013c). See also CMA (2015, p. 62). 60 Orange (2014) and CMA (2015, p. 62). 61 Crémer, de Montjoye and Schweitzer (2019, p. 25). 57

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that a platform collects from users to provide a service, including data volunteered, observed or inferred, such songs a user has listened to in a music app. The anonymous use of individual data refers to the collection of personal data with the purpose of training machine-learning algorithms that are often not directly related to the supply of online service, although an algorithm may improve the quality of the service at a later stage. Aggregated data refers to the collection of standardized data, such as sales data and national statistical information. Contextual data refers to data that is not derived at an individual level, including road network information and satellite and mapping data. Aggregated and contextual data are often defined as non-personal data, although they were originally collected from individuals.62 When defining the method by which data is collected, it could be generalized that either data is collected directly from the same device or indirectly from related browsers or related devices. In earlier studies, first- and third-party data collection was often distinguished. First-party data collection refers to the data that is collected directly from users as part of the online business, as Google’s first-party data is the data collected directly from Google Search, Google Photos, Gmail and other services owned by Google. Third-party tracking of data refers to the data collected from a variety of (non-Google) websites and apps across different devices.63 CMA has listed some sources of third parties that could collect data, including credit reference agencies, fraud prevention agencies, firms performing demographic modeling, data brokers, lead generation firms, public bodies that provide information for commercial use, price comparison websites and switching services.64 Powerful internet companies have dominant control over third-party trackers.65 The term “cookies” refers to digital codes that record certain user behaviour, and cookies can be sent to websites that the user is viewing or an unrelated website. Cookies are commonly used for search engines but are less common for mobile apps because some mobile browsers block third-party cookies by default.66 The OECD introduced the business models that are now used to track individual data on mobile devices and there are mainly two methods: deterministic methods refer to the tracking that is used by identifying consumer characteristics such as log ins; and probabilistic methods refer to the tracking that infers a consumer’s identity through other means including IP address, geolocation information, browser or device fingerprinting and general usage patterns.67 Krämer defined the four measurements that are used to assess data quality: fitness for use, accuracy, completeness and timeliness.68 It has been empirically proven that a larger dataset has greater predictive accuracy, and the quality of inferred data will

62

Crémer, de Montjoye and Schweitzer (2019, p. 25). OECD (2020a, p. 16). 64 CMA (2015, pp. 38–39). 65 Ezrachi and Roberston (2019, pp. 5–20). 66 OECD (2020a, p. 17). 67 OECD (2020a, p. 17). 68 Krämer (2020, p. 8). 63

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increase; thus, in data analysis economies of scale exist.69 Article 6(1) of the EU Data Protection Directive and Article 5(1) of the General Data Protection Regulation in the EU have listed data quality requirements that specified the controllers’ obligations on data processing, purpose, data minimization, accuracy, limitations on storage, integrity and confidentiality.70

4.2.4 Data Governance When there is little dispute on the economic value that data creates, the debate on how data should be used and regulated continues. Whereas economists have categorized data into observed and inferred data, public policy makers generally separate them into personal and non-personal data. Furthermore, in February 2020, the Communication from the Commission to the European Parliament, The Council, The European Economic and Social Committee and the Committee of the Regions—A European Strategy for Data71 defined data for the public good, referring to “Data is created by society and can serve to combat emergencies, such as floods and wildfires, to ensure that people can live longer and healthier lives, to improve public services, and to tackle environmental degradation and climate change, and, where necessary and proportionate, to ensure more efficient fight against crime”. Data for the public good can be generated by public and private sectors and should be accessible by researchers, public institutions, SMEs or start-ups, and social media. This definition extended the discussion on data sharing between online platforms to the use of public sector information by business (government-to-business, G2B data sharing), the sharing and use of privately-held data by other companies (business-to-business, B2B data-sharing) and the use of privately held data by government authorities (business-to-government, B2G data-sharing), and the sharing of data between public authorities.72 The communication has suggested four pillars in building the legislative infrastructure of a single data market: (A) developing a cross-sectoral governance framework for data access and use, including strengthening the governance mechanisms at the EU and the Member States level relevant for cross-sector data use and for data use in the common sectoral data spaces by harmonizing description and overview of datasets, data objects to foster data interoperability, and in compliance with the GDPR to facilitate the use of data for the public good, and providing incentives for horizontal data sharing across sectors; (B) investing in technologies to build infrastructures for hosting, processing and using data, including investing in cloud infrastructures, edge computing, high-performance/quantum computers, cyber-security, lower-power processors and 6G networks. (C) Building competences and investing 69

de Fortuny, Martens and Provost (2013, pp. 215–26), Martens et al. (2016, pp. 869–88) and Lewis and Rao (2015, pp. 1941–73). 70 Bourreau et al. (2017, pp. 23–24). 71 European Communication (2020a, at p. 6). 72 European Communication (2020a, at p. 7).

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in data skills and general data literacy, for example, to fill in the gap of digital specialists of 1 million population and increase the proportion of the EU population with basic digital skills.73 The above legislative framework should also be complemented by (D) common sectoral legislation for data access and use. The Communication suggested common European data spaces in nine sectors: manufacturing, Green Deal (climate change), mobility and transportation, health industry, finance, energy, agriculture, public administration and education, training and labour markets.74

4.3 Characteristics of Data Economists have argued that there are a few characteristics of data: the costs of collecting, storing and analysing data are low, and because data has non-exclusive and non-rivalry nature, no firm could have exclusive control of data. Because the value of data has a diminishing return, the arguments for economies of scale do not hold: new entrants could invest in innovation and new business strategies and collect new data from consumers. The following Sects. 4.3.1–4.3.3 will discuss those characteristics in more detail.

4.3.1 Low Cost of Collecting and Storing Economists have argued that the cost of collecting of data, as well as providing the data for third parties for analysis, is very low. Shapiro and Varian argued that the marginal cost of production and distribution of data is nearly zero.75 Bourreau et al. defined that data could be collected in three different ways: (1) data is collected through publicly observed channels such as mobile devices, computer operating systems and IP addresses; (2) data is provided by consumers voluntarily, including their personal data and purchasing information; and (3) data is collected by tracking consumers on the internet, such as by tracking cookies, browsers and device fingerprinting, history sniffing, cross-device tracking and through the use of applications.76 Autorité de la concurrence77 identified six ways which platforms often use to collect user data for online advertising: (1) data provided by users in sign-in/subscription (2) cookies that are automatically collected through websites (3) web tags that are found within webpages to track users when browsing the webpage (4) ad tags that are used to measure the performance of advertisement (5) Pixels to collect technical information such as IP address, device used and track how long users visit the webpage (6) 73

European Communication (2020a, at p. 20). European Communication (2020a, at p. 23). 75 Shapiro and Varian (1999). 76 Bourreau et al. (2017, pp. 11–12). 77 Autorité de la Concurrence (2018, pp. 30–31). 74

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Table 4.2 User data tracked online and offline Data tracked online

Offline

Computer

Mobile

Loyalty cards and rewards programs

Online transactions

Online transactions

Payment card transactions

Online browsing behaviour and search history

Online browsing behaviour and Registration for services (e.g. search history mobile phone contracts)

Social media and emails

Social media and emails

IP addresses and device specs

Location and movement data, Participation in questionnaires device specs, sensors and usage and surveys behaviour

Registration with public authorities (e.g. electoral roll, births, deaths and marriages)

Use of mobile apps Source ACCC (2018, at p. 170).

mobile apps that are used to analyse users’ behaviour. The ACCC listed the various channels through which user data could be collected and tracked online and offline (see Table 4.2). In practice, search engines and advertisers can collect various types of data directly from users from different platforms and devices, including personal data, contextual data, campaign data and search data. They can also collect data through technologies such as tags, cookies, and software development kits (SDKs) or from thirdparty applications when consumers sign into that application or collect device and browser information, bid information and event information from advertisements.78 For example, Google has tags on over 80% of websites and over 85% of apps on the Play Store, and through their own sites and non-Google websites they could collect various data from users to target advertisements.79

4.3.2 Non-exclusive and Non-rivalrous When it is possible for consumers to use multiple online platforms at the same time (“multi-homing”), one platform’s practice of data collection cannot exclude the use of data by another platform. Sokol and Comerford defined that data is non-exclusive and non-rivalrous—the users can use multiple different internet providers (“multihoming”) and thus share data with multiple online platforms. Thus, no firm can control the spread of data and cannot prevent the use of data by another firm.80 The data collected by one platform does not exclude the use of data by other platforms, 78

CMA (2020, p. 156). CMA (2020, p. 228). 80 Sokol and Comerford (2016, p. 6). 79

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and different platforms may create the same search result by analysing data collected from different sources. Garef provided the example that a search engine may know the music preferences of a user through search queries, and a social network provider can also obtain this knowledge by collecting information from the user’s profile information.81 It also means that the same dataset can be (re)used by different actors an unlimited number of times, and such reuse will not reduce the dataset’s quality or quantity. After the data is collected at one time, it can be used for various purposes and can produce different innovative outputs. As a type of experience good, the value that data generates for different purposes will not be the same, and datasets from different sources can also be combined for analysis.82 Therefore, data becomes the infrastructural resource that can be used by “an unlimited number of users” for “an unlimited number of purposes”.83 The sharing of a dataset will also save costs and generate social benefits. This argument has also been accepted by competition regulators in several cases and claimed that the proposed merger does not raise anticompetitive concerns because the enterprises do not have exclusive control of user data. In Telefonica UK/Vodafone UK/Everything Everywhere joint venture case, the EU Commission confirmed the non-rivalry nature of data by stating: “customers generally tend to give their personal data to many market players, which gather and market it. Therefore, this type of data is generally understood to be a commodity”. In the Facebook/WhatsApp merger, the EU Commission held that: “there are currently a significant number of market participants that collect user data alongside Facebook. These include Google, which accounts for a significant portion of the Internet user data and companies such as Apple, Amazon, eBay, Microsoft, AOL, Yahoo, Twitter, IAC, LinkedIn, Adobe and Yelp, among others. There will continue to be a large amount of Internet user data that are valuable for advertising purposes and that are not within Facebook’s exclusive control”.84 The non-exclusive and non-rivalrous nature of data is also linked to the argument that data itself is cheap and it is not about the ownership but the strategy or technology (or algorithm) that is applied to use data matters in the creation of market power. The Economist calculated that consumers’ ownership of data on Facebook and Google is only worth 8 USD per person, less than 1% of the value of the service they receive in return.85 There are also arguments that the scale of data itself does not increase market value, and having more data does not improve the quality of the online product.86

81

Graef (2015, p. 479). European Commission (2020c, at p. 17). See also Jones and Tonetti (2019). 83 OECD (2015). 84 Case No COMP/M.7217—Facebook/WhatsApp, 3 October 2014, paras 188–189. 85 The Economist (2018). See also Kennedy (2017, p. 9). 86 Kennedy (2017, p. 9). 82

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4.3.3 The Value Decreases Over Time Economists argued that because of the high fixed costs, there are economies of scale effect in data collection and analysis, but the marginal costs of the dataset are nearly zero because the value of data decreases over time. The game theory model developed by Ichihashi87 and Gu et al.88 showed that the value of data collected from consumers decreases to zero when data are nonexclusive. The creation and usage of data is dynamic, as new, personalized data are created constantly, and the value of data decreases over time. Therefore, economists have argued that the entry barrier in the data market is low and that incumbents do not have the advantages of storing data, but new entrants could easily collect new, differentiated data through new products.89 In addition, the entrants do not need to create the same scale of data as the incumbents, as all they need is to design new products to collect timely data.90 Holding data itself does not grant any advantages for incumbents or entrants; rather, business entities are encouraged to innovate and create new products to gain competitive advantage by using data.91 Therefore, earlier research showed the arguments that because data is nonrivalrous and non-exclusive, and has a diminishing return over time, platforms with a large dataset do not naturally have market power and those arguments against that forcing platforms to share their dataset. More recent literature also shows that the scale of a dataset has value for platforms to create a social network and is conducive to a predictive algorithm and sending targeted advertisements.92 Thus, the scope of the dataset might be relevant when comparing market power between incumbents and entrants. Because of the large fixed costs in collecting, storing and analysing data, platforms may obtain market power through economies of scale and such market power is often strengthened through indirect network effects and feedback loops, so that when the number of users on one side increases, the quality of the platform is also improved as it improves investment in targeting sales and in its algorithm. The critical issue of analysing such power is to discuss whether there are barriers to entry so that dominant platforms prevent the access of data by entrants such as imposing costs of switching. The importance of economies of scale is relevant for the analysis of market power, but holding economies of scale on datasets does not indicate that firms are abusing their market power and legal intervention is necessary only when there are barriers to accessing data created by incumbents. When entrants face difficulties accessing data because of the high costs of switching (user lock-in), it is suggested that users initiate data portability instead of forcing platforms to share datasets. These issues will be further discussed in Chaps. 6 and 7 of this book. 87

Ichihashi (2021). Gu et al. (2021). 89 Sokol and Comerford (2016, p. 7) and Chiou and Tucker (2014). 90 Sokol and Comerford (2016, p. 7) and Schepp and Wambach (2016). 91 Lambrecht and Tucker (2015). 92 OECD (2021, p. 15). 88

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4.4 Data Driven Productivity Effects Parallel with the research on innovation and market power discussed in Sect. 3.2.1 of this book, economic literature on dynamic efficiency has also shown a positive relationship between investment in digital technologies and productivity growth. Productivity effects are also one of the key arguments in the analysis of data-driven online markets that regulatory intervention has to be justified by promoting competition as well as dynamic efficiency. The arguments that data-driven technologies will improve industry efficiency have been supported by the large amount of economic literature on the productivity effects of the data economy.93 The literature includes a general econometric study on returns to investment in digital technologies94 and the positive relationship between digital technologies and economic growth, although the level of effect varies across countries and sectors. Investment in digital technologies is also positively associated with economic performance at the country level.95 Some of the empirical results include data quality and access: an improvement of 10% can increase labour productivity by 14% on average,96 and autonomous drill rigs can increase productivity by 30–60%.97 When the “industrial internet” brought a 1% increase in maintenance efficiency in the aviation industry, the savings in commercial airlines globally reached 2 billion USD per year.98

4.5 Data and Online Business Data-driven online platforms have significantly changed the pattern of doing business in the digital environment; this is not to say that with the importance of understanding the effects of the scale of aggregation of datasets, the flow of data itself has become a threat to the competition process. Commissioner Margrethe Vestager said in her speech at the European Internet Forum: “The giants of the Internet have worked out how to turn those huge flows of data in their favour. As the Internet has become a central part of our lives, their power to control the flow of information has become power over the way our economies and societies work. (…) Today, the decisions that big platforms make, about how to rank different sellers in search results, or how to use the data they collect about their users, have a huge influence on the fate

93

OECD (2017, p. 13). Brynjolfsson (1993, pp. 66–77, 1996, pp. 281–283), Brynjolfsson and Hitt (1995, p. 183, 1996, pp. 541–58, 1997, pp. 54–61), Oliner and Sichel (2000, pp. 3–22), Jorgenson (2001); Jorgenson and Stiroh (2000, pp. 161–167). 95 Schreyer (2000), Colecchia and Schreyer (2002, pp. 153–171), Jorgenson (2003, pp. 139–169) and Van Ark et al. (2002). 96 Barua et al. (2010). 97 Citigroup-Oxford Martin School (2015). 98 Evans and Anninziata (2012). 94

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of the millions of businesses which rely on those platforms to connect with their customers”.99 While holding data for marketing personalized products raises concerns of privacy and abuse of dominant positions, there are arguments that data are used by online forums to provide high-quality services. For example, Salinger and Levinson argued that “click-and-query” data that are collected when users search queries and clicks could help search engines deliver search results with high quality.100 Sokol and Comerford argue that data collected by search engines are crucial to providing “valueadded” services to users, such as online shops, social networking platforms and online media outlets. This could provide personalized recommendations for users and such recommendations have great value.101 Schepp and Wambach argued that data collected have the value of improving working processes, improving the quality of services through personalization and developing new products.102 Research by Mckinsey showed that data can create significant value in the global economy by increasing the productivity and competitiveness of companies in both public and private sectors. For example, the potential value created by data in US health care could reach more than 300 billion USD every year, and two-thirds of the value created is through the reduction of national health care expenditure. In private sectors, a retailer can increase their operating margin by more than 60% by using data.103 Data can create value by reducing search and processing time and enhancing transparency, enabling experimentation to discover needs by collecting more accurate and detailed performance data, segmenting the population to customize consumer goods and services by deploying more sophisticated data, improving decision making and minimizing risks with automated algorithms, and innovating new business models and services.104 Economists generally believe that data itself has a non-rivalrous and non-exclusive nature and a diminishing value over time. Data itself is not valuable, and it is only valuable to firms when the data is analysed through managerial, engineering and analytical skills in experiments or algorithms.105 It is the skills that are more valuable not the data itself, so it is crucial for firms to develop the data strategy and systematic tools to analyse the data and adapt it to organizational capabilities.106

99

Speech by Margrethe Vestager at the European Internet Forum, 17 March 2021. https://ec.europa.eu/commission/commissioners/2019-2024/vestager/announcements/compet ition-digital-age_en. 100 Salinger and Levinson (2015, pp. 25–57). 101 Sokol and Comerford (2016, p. 4). 102 Schepp and Wambach (2016, p. 120). 103 McKinsey Global Institute (2011, at p. 2). 104 McKinsey Global Institute (2011, at pp. 2–4). 105 Lambrecht and Tucker (2015, p. 11). 106 McAfee et al. (2012, pp. 61–67) and Bughin et al. (2010, 75–86).

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4.6 Implications for Competition Law Grunes and Stucke argued that the features of big data have important implications for competition policy, such as companies in two-sided markets that may perceive consumers’ personal data as an input, to attract advertisers in a particular consumer group; data-driven strategies may assist companies in outperforming their rivals; the value of data may increase the number of strategic acquisitions; companies may limit access to their database by competitors and obtain data-related advantages; companies with data advantages may raise efficiency defenses.107 The OECD defined three challenges of data collection for competition law: the challenges of defining the relevant market, challenges in assessing market concentration and challenges of assessing consumer harm.108 In addition to the viewpoint that platforms may use the data to produce better products and services, based on the characteristics of data discussed in the previous sections, there are other strong arguments against government intervention in data collection, and those arguments have been adopted in antitrust agencies (for example, the FTC in the US) in case decisions. Those arguments include online suppliers who do not hold data exclusively, so both users and competitors could choose to provide and collect data from other online services (user multi-homing), and no online seller would have exclusive control of the data collected. Since the use of data is non-rivalrous and non-exclusive, and there are many different sources to obtain data, one platform’s use of data does not reduce the value of data to others. The competition between data-driven companies relies on developing strategies and technologies but not relying on collecting more data.109 The economies of scale effect that large online platforms hold do have reducing returns, so this could not guarantee a competitive advantage because the possession of data itself does not create a barrier to entry, and they could easily be replaced by new entrants who have invested in online product innovation and new business strategies; therefore, the competition concerns of market dominance do not hold. It is argued that the barriers to collect data are low and data itself is non-exclusive and non-rivalrous in nature, and data-driven companies generate sufficient efficiency gains to benefit consumers and create sources for innovation and improved products and services, as Joe Kennedy argued that “data-rich companies are not an economic threat, but rather an important source of innovation, which policymakers should encourage, not limit”.110 Competition agency officials have paid attention to these arguments and have partially adopted them in case decisions. For example, in the US DOJ’s investigation in the Microsoft v. Yahoo! Case,111 the DOJ accepted the efficiency defense argument that access to data may produce efficiency and improvement in online searching services. In merger cases, the EU Commission does not recognize the 107

Grunes and Stucke (2015, pp. 3–4). OECD (2014, at pp. 58–60). 109 Lambrecht and Tucker (2015). 110 Kennedy (2017). 111 DOJ (2010). 108

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significant market power that the two data-driven companies would raise after the merger, because datasets were replicated. For example, in Google/DoubleClick, the Commission argued that search behaviour information is available to competitors, so the merger does not raise anti-competitive concerns.112 In Facebook/Whatsapp, the Commission concluded that Facebook does not have exclusive control of the internet user data, so the merger would not give Facebook an additional competition advantage of targeted advertising.113 Similarly, in Microsoft/Linkedin, the Commission argued that after the concentration, the user’s data will still be available for competitors, so the merger does not raise competition concerns.114 The combination of datasets has raised antitrust concerns for welfare effects on consumers. In the discussion of the Google/DoubleClick merger, it has been argued that the quality of consumer privacy will be reduced after the combination of the tracking results.115 In the Microsoft/Yahoo! merger case, Lande argued that the merger will reduce the variety of nonprice competition, including privacy protection.116

4.7 Conclusions This chapter discussed the economic arguments of data monopolies, including the low costs of collecting and storing, the non-exclusive and non-rivalry features and the diminishing values over time. The fixed costs of collecting and analysing data are high, particularly when technological infrastructure, such as computing algorithms, is needed to produce high-quality predictions based on the dataset analysis. A larger volume of data will generate greater feedback loop effects so that more users who join the platform will improve the quality of data analysis in return, so the use of data has an increasing return of scale. Because of the function of machine learning in data analysis, powerful platforms that have achieved economies of scale would produce better quality prediction at lower costs than small firms. They can also leverage their market power in one market to another through product tying. Unlike high fixed costs, the marginal costs and distribution costs of digital products are low. Economists have strong reasons against antitrust intervention and those arguments have been partially adopted by agencies and have been used, for example, in merger efficiency defense. There are counterarguments that the use of data may harm consumer welfare by violating consumer privacy and will obtain market dominance by imposing restrictions on market entry. The following chapters will discuss those issues in detail and will discuss the applications of the counterarguments in cases.

112

Commission Decision of 11 March 2008, Case M. 4731 Google/DoubleClick, para 364–366. Commission Decision of 3 October 2014, Case M. 7217 Facebook/Whatsapp, para 167–189. 114 Commission Decision of 6 December 2016, Case M. 8124 Microsoft/Linkedin. 115 See Swire (2007). 116 Lande (2008). 113

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Chapter 5

Defining the Relevant Market for Data Monopoly

Abstract Because of the multi-sided nature of online markets and indirect network effects, traditional economic methods (such as SSNIP) are not applicable for analysing data-driven online markets. There are arguments to support avoiding defining a relevant market and focusing on the anti-competitive effects of the particular business conduct. There are also discussions on how new economic methods could be developed to replace SSNIP when defining a relevant market is necessary. The fifth chapter analyses the definition of the relevant market, which includes determining the elements of the data monopoly-related market, limitations of the traditional relevant market determination methods, and the feasibility of the application of new economic analytical tools.

5.1 Introduction Because of the distinct features of digital markets and online platforms, there is no doubt that traditional methodologies in measuring market power face challenges. In traditional markets, market power has been indirectly measured by market shares in the relevant market. In online markets, because of indirect network effects, it becomes a highly complex issue to define whether a relevant market should include more than one side of the platform. Economists have expressed the clear view that traditional methods in defining a relevant market1 and the reliance of market shares2 may not be applicable to online markets. Learning from the literature on modern industrial economics, there are three ways to understand the problem of defining a relevant market: one is to ‘abandon’ the approach of defining the relevant market and to adopt modern empirical methods and measure market effects directly; another is to amend the SSNIP approach or to apply new techniques, such as Critical Loss Analysis in defining the relevant market. A third-way approach is to go beyond traditional analysis in the market of products and services, and to define a separate market for data. Overall, the analysis has to be context based and applied with caution,

1 2

See for example, Evans (2016, pp. 29–32), Argentesi and Filistrucchi (2007) and Pike (2018). Crane (2014).

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and the baseline of the analysis is to understand the business model and the pricing structure of the online platforms. In the following Sect. 5.2 will first summarize the economic literature on the pricing structure of two-sided markets, and Sects. 5.3 and 5.4 will discuss how the approach of a relevant market could be amended, either by avoiding a definition of the relevant market or by revising the traditional methods in defining the market. Section 5.5 will discuss a ‘third-way’ approach to define the relevant market for data.

5.2 The Pricing Structure of Two-Sided Markets To have a clear analysis of the relevant market of the two-sided markets, it is necessary to understand the pricing structure of the online platform. Industrial organization economists have clearly studied the pricing structure of multi-sided platforms and have argued that equilibrium prices are different from traditional markets. Evens clearly argued that platforms function to “get both sides on board”. As discussed in Sect. 3.2.3, because of the existence of indirect network effects, the key to determining optimal prices for multi-sided platform markets is to balance the demand between customer groups on both sides. Because it is undecided which side of the demand affects the demand on the other side (the “chicken-and-egg problem”), the business strategy that gives the product or services for free, or reduces the prices on one side to encourage the customer to participate in the market.3 This strategy is named by Caillaud and Jullien “divide-and-conquer.”4 Thus, prices are no longer proportional to marginal costs, as it is defined by using the Lerner index5 or multiproduct variants.6 Therefore, charging prices below marginal cost on one side would be possible based on economic analysis,7 although it may be considered predatory pricing in a traditional market. Evans claimed that because of the necessity of considering the pricing effects on all sides, traditional methods of defining relevant markets are not applicable to two-sided markets, including the SSNIP test, diversion ratios, and conditional logit demand analysis.8 Rochet and Tirole defined the behaviour of charging different fees to each side (one side pays and the other side subsidizes) as the consequence of “the Seesaw Principle”, that “A factor that is conducive to a high price on one side, to the extent that it raises the platform’s margin on that side, also tends to call for a low price on the other side as attracting members on that other side becomes more profitable.”9 Armstrong explained that the Seesaw principle would indicate that the externalities created by 3

Evans (2003a, pp. 193, 196). Caillaud and Jullien (2001). 5 Lerner (1934). 6 Baumol et al. (1982). 7 Parker and Van Alstyne (2002). 8 Evans (2011a). 9 Rochet and Tirole (2003). 4

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one side on the other side are asymmetric, such as in nightclubs men value more the presence of women than women values the presence of men.10 Julian Wright summarized eight fallacies when applying the one-sided market logic to analyse twosided markets by giving the example of men and women nightclubs.11 In particular, the prices set for both sides of the users could not be symmetric, and competition between platforms could not necessarily bring prices equal to costs, because users at both sides may have a different value matching with the other side, and the pricing structure has to take into account both the relevant costs (as it is in the one-side market) and the additional surplus that the user obtains when there is an additional user at the other side who joins the platform. The additional surplus for men when meeting more female members may be different from the additional surplus for women when meeting more male members. If the surplus for men when attracting additional women to the club is greater than the surplus that women obtain when additional men attend, it would be efficient to set the price lower for women than for men. Therefore, an efficient pricing structure does not indicate prices are set equal to costs; instead, an efficient pricing structure will be where the prices charged for men are above cost, and prices charged for women are below cost. Thus, the above-cost margin does not indicate market power, and below-cost prices do not indicate the behaviour of predatory pricing.12 Evans and Schmalensee13 have identified the three important implications of the pricing structure on the analysis of market definition and market power. The first issue is that the degree of connection between users on different sides affects the price elasticity of demand and whether the price increase will be profitable. An increase in the price at side A will reduce the demand at this side, and reduce the value of the platform to the other side B; thus, it reduces the price that side B pays and then the demand at side B. In this way, the reduction of the demand at side B will in turn reduce the demand and price at side A, therefore creating feedback effects. The price increase on one side that is profitable under the SSNIP test would be unprofitable when the SSNIP test is performed on both sides. The second issue is that the profits of the platform depend on the competition level on both sides. When there is limited competition on side A when consumers cannot switch competitors, but on side B consumers are able to indulge in multi-homing, and increasing prices at side A will lead to the increase of competition on side B and the profits of the platform will be lost in competition. The third issue is that “price equals cost” (marginal cost or average variable cost) can no longer be taken as the benchmark for assessing market power, especially for predatory pricing or excessive pricing. Because of the indirect network effects and the connection of demand elasticities on both sides, deviations between price and cost cannot indicate the exploitation of market power.14 Unlike single-sided businesses where the equilibrium occurs when the marginal revenue 10

Armstrong (2006, pp. 668–691). Wright (2003). 12 Wright (2003, p. 4). 13 Evans and Schmalensee (2011, p. 24). 14 Evans and Schmalensee (2011, pp. 24, 149). 11

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equals marginal cost, the two sided platforms have to take into three distinct results: first, the optimal prices depend on the price elasticities of demand on both sides, the nature and intensity of the indirect network effects between each side, and the marginal costs that result from changing the output of each side; second, the profitmaximizing, non-predatory prices may be below the marginal cost of supply for that side, or even negative; third, based on the demand assumption made by Rochet and Tirole (2003),15 the ratio of the profit-maximizing prices charged the two sides may become independent of side-specific marginal costs, and an increase in marginal cost on one side will not necessarily lead to an increase in price on that side relative to price on the other.16 Competition authorities have acknowledged the limitations of traditional methods of SSNIP in defining relevant markets for multi-sided platforms. For example, the UK competition authorities have named three difficulties in adopting SSNIP in multisided markets: (1) no single price to both sides of users, (2) the effects of SSNIP on the demand of one side could be exacerbated by externalities, and (3) the constraints on competing products not only from other platforms but also ‘one-sided’ firms serving one set of consumers.17 The OECD Policy Roundtable on Two-sided Markets concludes that because of the different profit maximizing models that a two-sided platform faces, the traditional methods of market definition may “lead to errors”. Most antitrust enforcement agencies have also accepted the concept that the competitive effects of multi-sided platforms cannot easily be assessed through price competition, and one of the most important limitations of SSNIP tests is that products are often provided for free of charge, as the EU Commission Director-General for Competition, Johannes Laitenberger stated: “Now in many digital markets, price—as we used to understand it—plays no decisive role since the services are not monetized on the consumer side, or at least there is no price expressed in monetary terms.”18

5.3 Avoid Defining Relevant Market Defining the relevant market has been regarded as an indirect tool to measure market power, and modern industrial economics has developed other methods to measure competitive effects. Those direct measures, including empirical and market simulation tools, could possibly avoid the step of defining a relevant market in competition analysis.19 Seeing the weakness and difficulties in traditional measurement of relevant market, economists represented by Louis Kaplow, Jonathan Baker and Timothy F. Bresnahan have written extensively on the new models to measure market power

15

Rochet and Tirole (2003). Evans and Schmalensee (2005). 17 Office of Fair Trading/Competition Commission (2010, p. 34). 18 EU Commission (2017, p. 6). 19 Hovenkamp (2021, p. 7). 16

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directly and using those models defining relevant market would be unnecessary and the market-definition approach could be “abandoned”.20 In addition, it has been argued that in digital markets, the concept of relevant markets has to be extended and does not play a determinant role as it is in traditional markets. The EU Competition Policy for the Digital Era Report argued that: “In digital markets, we should put less emphasis on analysis of market definition, and more emphasis on theories of harm and identification of anti-competitive strategies. At the same time, even if in some consumer-facing markets—according to their own accounting- firms compete to draw consumers into more or less comprehensive ecosystems, markets for specific products or services will persist from a consumer’s perspective, and should continue to be analysed separately, alongside competition on (possible) markets for digital ecosystems.”21

5.4 Defining Relevant Market Although there are arguments that defining relevant markets could be avoided, lawyers and regulators may still rely on the method of the relevant market in assessing market power in competition cases.22 In the US Merger Guidelines 2010, defining a relevant market is still required.23 Thus, those analysts have to trust the amended methods or new techniques that were developed by economists to replace classic SSNIP in assessing relevant markets.24 US FTC Commissioner J. Thomas Rosch argued in In the Matter of Evanston Northwestern Healthcare Corp. that defining a relevant market should refer to competitive effects.25 Online markets are distinct from traditional markets in the aspects of the interdependencies between different sides of the market. The issue of indirect network effects and zero-price competition has to be considered seriously, and the traditional methods in defining relevant markets may not be applicable, as Franck and Peitz argued that when defining relevant markets for online platforms is necessary, there are two challenges: one is whether treating each side of the platform separated or the platform could be considered as a single market; the other is how to measure relevant markets when users do not pay.26

20

Kaplow (2010, 2013, 2017) and Baker and Bresnahan (1992, 2008). Crémer et al. (2019, p. 3, pp. 42–48). 22 Bamberger and Lobel (2017, p. 1063). 23 FTC (2010). 24 Farrell and Shapiro (1990, pp. 107–126), Willig (1991, pp. 292–293) and Werden and Froeb (1994, 407–426). 25 FTC (2007a). 26 Franck and Peitz (2019, p. 21). 21

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5.4.1 Adapting the SSNIP Test Market definition is the first step for traditional antitrust analysis. In one-sided markets, an increase in price or decrease in output provides guidance on how to undertake antitrust analysis using the traditional Small but Significant Non-Transitory Increase in Price (SSNIP) test. However, market definition is more complicated in a multi-sided market. In multi-sided markets, the use of market shares for market definition purposes is something that should be carried out very cautiously. Understanding the multi-sided nature of internet markets is very important to market definition analysis. If an antitrust authority (or court) wrongly identifies the multi-sided nature of the market, this creates problems in analysing the competitive effects in such a multi-sided market. Consequently, market definition can be conducted incorrectly, as it may focus only on one side of the market or may implicate a free product or service. In markets where the product or service is free, it is not possible to calculate a traditional market share as in a one-sided market because in a multi-sided market, one side of the market may subsidize the other side of the market.27 In such multi-sided markets, services provided through internet platforms are largely based on information.28 For example, internet search engines provide free service to users, but monetize free service through advertising related to user queries.29 How much value to put on personal attention may be different across users, as consumers are not homogenous in their preferences.30 Such a focus on calculating market shares and drawing conclusions about competitive effects from only one side of the markets leads to suboptimal outcomes because of inefficiencies that it may create. This may include situations where the competitive analysis does not consider the welfare of all groups on both sides of the market.31 The effects may be such that one side of the market might harm the other side of the market. Alternatively, one side of the market might help the other side of the market.32 As an example given by the OECD: when readers do not like advertisements, a price increase in the newspaper will reduce readership and the newspaper will become less attractive to advertisers, but less advertisement will attract additional readers. The price increase becomes profitable. In the SSNIP test, after the price increase, both the impact on readership, newspaper sales and adverting profits must be taken into account. Unlike in a traditional market where there is only one price, in multi-sided markets, there are at least two prices that will be changed. The SSNIP test has to consider that one price is increased and the other price is unchanged, reduced, or increased.33 The nature of interdependencies across the multiple sides of the market 27

Argentesi and Filistrucchi (2007, pp. 1247–48). Salinger and Levinson (2015, pp. 32–35). 29 See e.g., Manne and Wright (2011, pp. 192–93). 30 Benndorf et al. (2015, at p. 52). 31 Evans and Schmalensee (2014, at p. 20). 32 Song (2013, at pp. 3–5) and Chandra and Collard-Wexler (2009, at p. 1045, 1046). 33 OECD (2018a, pp. 14–15). 28

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has implications for traditional antitrust tools for market definition when not modified for a multi-sided market. Thus, a traditional SSNIP test that focuses on only one side of the market leads to the conclusion that market definition and market power analysis will lead to errors in antitrust analysis.34 Instead, antitrust authorities and courts need to consider the interdependencies on the multi-sided platform. In doing so, such decision-makers need to provide particular attention to nonprice competition when participation on one side of the multi-sided market is free. For example, if a platform raised its price on one side, this might have an effect not merely on how many customers would leave that side of the platform but could also impact customers on the other side of the platform.35 In markets categorized by dynamic competition, competitive forces on one side of the market can have feedback effects on the other sides of the market. Evans argued that the traditional SSNIP test does not take into account the feedback effects, the estimates of the elasticity of demand would be too small, and the one-sided relevant market will be defined too narrowly.36 The reason is that, as Filistrucchi explained, a one-sided SSNIP test will only consider the price increase of side A on the demand and profits of side A, and will not consider the resulting reduction of demand on side B, and in turn the reduction of demand of side A. Therefore, one-sided SSNIP underestimates the profit loss of side A.37 On the other hand, if the network effects are one positive and one negative, the one-sided SSNIP test will define the relevant market too broadly. That, when the price of side A increases, the demand of side A decreases, which leads to an increase in demand on side B, and higher participation of side B will increase the demand of side A. In this situation, the one-sided SSNIP will overestimate the loss of profits on side A, and the relevant market will be defined too broadly.38 As Evans and Noel explain, “The possibility of obtaining supracompetitive profits through certain business actions depends on the relationship between the two sides due to their interlinked demand and the nature of the competition on both sides. Profits on one side can be dissipated on the other side.”39 Thus, by not considering the positive feedback effects, this may potentially serve to either significantly overstate or understate the size of the market. The OECD proposed three ways to adapt the SSNIP test: (1) assess the change in demand on one side, (2) predict the change in demand on other sides, and (3) determine that the market-balancing prices on other sides would be in response to the change of demand on one side.40 Courts and antitrust authorities around the world have recognized that the antitrust economics of multi-sided markets require a more nuanced market definition. For example, in the Microsoft/Skype merger, the General Court of the European Union found that the market share of 90% of the combined entity was unlikely to create 34

Evans (2003b, pp. 357–58). Evans and Schmalensee (2014, p. 3). 36 Evans (2011b, p. 153). 37 Filistrucchi (2018, p. 46). 38 Filistrucchi (2018, p. 46). 39 Evans and Noel (2005, pp. 667, 671). 40 OECD (2018b, 2020, p. 16). 35

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anticompetitive effects.41 It did so in part because of the fast-moving nature of the market and the fact that there was no exclusion of competitors. Similarly, in Qihoo 360 v. Tencent,42 China’s Supreme People’s Court rejected an SSNIP analysis (where Tencent possessed a market share of over 85%) because an increase in price to the free side of the platform would mean a fundamental change to the business model of Tencent. A number of mergers that antitrust agencies have approved such as Facebook/WhatsApp43 and Microsoft/Skype44 also suggest that a high market share in a multi-sided market setting is not indicative of market power. The lesson from multi-sided market cases around the world is that antitrust authorities and courts should proceed cautiously with the use of market definition and market shares in the assessment of multi-sided platforms.45

5.4.2 Critical Loss Analysis In the analysis of market definition in traditional market, the Critical Loss Test was first developed by Harris and Simons in 1989,46 and this method is defined by Evans and Noel as ‘one-sided critical loss’. When there is a given price increase in all products in the market and keeps the joint profits unchanged, the test measures the reduction in quantity. Critical loss is a threshold that is used to compare the loss of quantity under critical loss and the actual loss. If the actual loss is smaller, the price increase will be profitable.47 As defined by Evans and Noel,48 It compares “critical loss” (CL) – the percentage loss in quantity of a hypothetical monopolist’s products that would be exactly enough to make an X percent price increase in the price of all its products unprofitable – to “actual loss” (AL) – the predicted percentage loss in quantity that the monopolist would suffer if it did increase prices on all its products by X percent.

When actual loss equals the critical loss, the relevant market can be defined. When the actual loss exceeds the critical loss, more substitutes should be included.49 For multi-sided platforms, however, Evans and Noel argued that the traditional one-sided critical loss method used for symmetric platforms suffers from two biases: estimation bias and Lerner bias. When the price of one side of the platform is 41

Case T-79/12, Cisco Systems Inc. and Messagenet SpA v Commission, 11 December 2013. Beijing Qihoo Technology Co. Ltd. v. Tencent Technology (Shenzhen) Co. Ltd. and Shenzhen Tencent Computer System Co., Ltd., Civil Judgment of Supreme People’s Court of China. 43 European Commission Case No COMP/M.7217—Facebook/WhatsApp (2014). 44 European Commission Case No Comp/M.6281—Microsoft/Skype (2011). 45 Wagner-von Papp (2015) and Evans (2016). 46 Harris and Simons (1989, 207–226), O’Brien and Wickelgren. (2003) and Katz and Shapiro (2003, 49–56). 47 Daljord, Sørgard and Thomassen (2008, p. 266). 48 Evans and Noel (2007, p. 10) and Evans (2011b, p. 151). 49 Evans and Noel (2007, p. 10). 42

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increased, the demand on this side will be reduced, while at the same time, it will also affect the demand on the other side, and in turn, it will affect the demand on this side (because platform externalities create feedback effects). Estimation bias: if the impact of a price increase does not take into account the feedback effects, including the indirect responses and the demand changes of both sides of the users, the change in demand is underestimated, the relevant market will be defined too narrowly, and the market power of the firm will be overstated.50 On the other hand, when measuring the elasticity of demand based on a one-sided Lerner Index, without taking into account the feedback effects and the changes of platform size, such an estimate overstates the short-run price elasticity of demand, the market will be defined as too broad, and the market power of the firm will be understated.51

5.4.3 SSNDQ Evans argued that in multi-sided markets, the SSNIP test is not applicable because competition between platforms is not based on price but to attract attention from users. The exercise of market power could be assessed through the extent to which a reduction in quality on one side can decrease the attractiveness of the platform to the other side of the platform (advertisers). When users do not divert attention to other online platforms (including those offering completely different services), the reduction in quality is profitable and such a platform holds market power.52 When online services are offered at zero price, Evans argued that the substitutability of products and services is assessed by whether users will switch to a different platform given a small but significant absolute increase (not a percentage increase) in price or a small but significant decrease in quality (Small but Significant Non-Transitory Decrease in Quality, SSNDQ), both of which are perceived as a decrease of platform value for users.53 Competition authorities tend to acknowledge quality as a factor to assess market power, and in merger decisions on Microsoft/Skype the European Commission has acknowledged the importance of quality in assessing competition. In practice, the SSNDQ test can hardly replace the SSNIP test, as the quality measurement is more difficult than the indictor of price. However, it provides a general framework in the analysis of nonprice competition, as the OECD states: “SSNDQ is more useful as a loose conceptual guide than as a precise tool that courts and competition authorities should actually attempt to apply.”54

50

Evans and Noel (2007, p. 12). Evans and Noel (2007, p. 12). 52 Evans (2016, pp. 25–26). 53 Evans (2016, p. 26). 54 OECD (2013). 51

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5.4.4 Other Market Definition Techniques Tremblay55 proposed the adjusted Lerner Index: total platform profit plus fixed costs of the platform, divided by the total revenue of the platform. Farrell and Shapiro proposed the upward pricing pressure (UPP)56 and the gross upward pricing pressure index (GUPPI)57 to calculate the incentives of increasing prices after a merger.58 Brekke adjusted the UPP index and the GUIPPI indicators for competition authorities to use in merger cases.59 Brekke argues that to use UPP, a full analysis of the price impact on side A of the platform has to be analysed, including (1) the effect on demand from users on side A; (2) the effect on demand from users on side B; (3) the effect on the price on side B. It requires the analysis of the situations of (1) When the price of side A increases the demand of side A will fall, and it is the elasticity of side A’s demand when the price of side A increases, and this effect is negative. (2) When the price of side A increases, the demand for side B will fall (responding to the reduced demand of side A). When there is a positive cross-platform network externality, this effect is negative. (3) When the price of side A increases, the price on side B will fall, leading to an increase of demand on side B and consequently increasing the demand of side A. This happens because when the price of side A increases, the margin on side A will increase, which incentivizes the participation of side B, which attracts more high-margin sales on side A. If the cross-platform network externality is positive, this effect will be positive.60 The European Commission report in 2019 proposed a “characteristics-based” approach in market definition to compare the functions of digital services in the relevant market.61

5.5 Relevant Market for Data Economists have argued that a data market could be created where individuals have ownership over personal data and are granted the rights to sell or reuse their data to different platforms.62 Costa-Cabral and Lynskey proposed a relevant market for data.63 Since online platforms take data as an important input and the benefits of online business are generated through the collection, usage and transaction of data, 55

Tremblay (2017). Farrell and Shapiro (2010). 57 Moresi (2010). 58 Farrell and Shapiro (2010). 59 OECD (2018a, p. 17). 60 OECD (2018a, p. 17). 61 Crémer, de Montjoye and Schweitzer (2019, pp. 41–42). 62 Samuelson (2000). 63 Costa-Cabral and Lynskey (2017, pp. 11–50). 56

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economists have argued for defining a separate relevant market for data.64 In the Google/DoubleClick merger decision, the US Federal Trade Commissioner Pamela Jones Harbour proposed that a relevant market should be defined for data, and such a market should be separate from markets for products and services.65 Harbour and Koslov argued that in the data market definition, data collection and expanded data usage should be separated, and the product market definition should include not only current data usage but also the value derived from user data.66 Forrest argued that data and the algorithm’s ability to analyse data are both products, and the commoditization of a data set is the proxy in the definition of a relevant market.67 Graef argued that although the current competition law standard has not formally accepted defining a relevant market because data are not traded, developing such a concept will contribute to the understanding of potential competition. This is because when data are a specialized asset that can be deployed to make economic revenue, online platforms also compete in the market for data.68 Engaging in competition for data, platforms are motivated to derive economic benefits for the current online product market, as well as to leverage their market position in other markets, so the traditional methods of defining a product market may neglect competition in the expanding dataset.69 When data are a specialized asset to develop products and open new markets, a dynamic view should be taken to understand that platforms not only compete in prices and products of the current market but also potential new markets. Thus, defining a market for data would be necessary to understand the competitive constraints in potential new markets.70 Vicente Bagnoli developed the theory of defining relevant markets for big data by segmenting the data market into three parts: big data capture (otherwise called collection, access or acquisition of data), big data storage and big data analytics, and each phase involves a productive chain or economic activities with the participation of consumers, entrepreneurs, public institutions, nonprofit organizations, governments and others. The proposed big data relevant market (BDRM) identifies the competitive constraints between parties at each stage. Table 5.1 shows the structure of BDRM and the stakeholders involved.71

64

Graef (2015, pp. 473–506). See also Butts (2010, pp. 275, 290), Waller (2012, 1784–1785) and Thépot (2013, pp. 195, 217–218). 65 FTC (2007b, p. 9). See also Harbour and Koslov (2010, pp. 769–797). 66 Harbour and Koslov (2010, p. 773). 67 Forrest (2019, p. 12). 68 Graef (2015, p. 492). 69 Graef (2015, p. 493). 70 Graef (2015, p. 495). 71 Bagnoli (2018, p. 26).

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Table 5.1 Big data relevant market structure Data generation

Data capture

Data storage

Data analysis

Data usage

Start

1st stage

2nd stage

3rd stage

End

• Structured data • Semi structured data • Unstructured data • Scientific research • Personal data

• Cell • Blogs • Loyalty programs • Apps • Censors

• Phone companies • Government agencies • Social networks • Medical services • Retailers • Service providers

• Retailers • Service Providers • Public administration • Financial institutions • Health insurance companies • Marketing companies • Data analytics • Data brokers

• Companies • Governments • Public agencies • Final consumer

Source Bagnoli (2018, pp. 21–29, at p. 26)

5.6 Conclusions While having extensively discussed the particular features of online platforms, digital markets and competition in digital environments, economists do not agree that competition law regulating online transactions is completely separate from those for brick-and-mortar traditional markets. Theories on competition structure, harm and market power are applicable, although techniques and methods are to be amended by taking those specific features into account, in particular switching the analytical view from static competition to dynamic competition. Likewise, although the SSNIP method used for assessing relevant markets has been criticized as inapplicable for online markets, it should not be concluded that this method should be completely abandoned. This chapter has reviewed the criticisms on why SSNIP is inapplicable, how it could be amended and whether there are alternatives to be used for relevant market measurement. In addition, it is also possible to avoid defining relevant markets, and changing the assessment of relevant markets for products to a market for data. The temporary conclusion is that economists and lawyers have enhanced their understanding of the limitations of SSNIP and are on their way to developing new methods. Competition authorities may benefit from the current discussion and take multiple factors into account in particular cases.

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Lerner, A. (1934). The concept of monopoly and the measurement of monopoly power. Review of Economic Studies, 1, 157–175. Manne, G. A., & Wright, J. D. (2011). Google and the limits of antitrust: The case against the case against Google. Harvard Journal of Law and Public Policy, 34, 171–244. Moresi, S. (2010). The use of upward price pressure indices in merger analysis. The Antitrust Source, 1–12. https://www.crai.com/sites/default/files/publications/the-use-of-UPPIs-in-mergeranalysis%20-%20Moresi%20-%20Feb%202010.pdf O’Brien, D. P., & Wickelgren, A. L. (2003). A critical analysis of critical loss (FTC Working Paper 254). https://www.ftc.gov/reports/critical-analysis-critical-loss-analysis OECD. (2013). The role and measurement of quality in competition analysis. Policy Roundtables. https://www.oecd.org/competition/Quality-in-competition-analysis-2013.pdf OECD. (2018a). Rethinking antitrust tools for multi-sided platforms. www.oecd.org/competition/ rethinking-antitrust-tools-for-multi-sided-platforms.htm OECD. (2018b). Non-price effects of mergers: Background note by the secretariat. https://one.oecd. org/document/DAF/COMP(2018)2/en/pdf OECD. (2020). Abuse of dominance in digital markets. www.oecd.org/daf/competition/abuse-ofdominance-in-digital-markets-2020.pdf Office of Fair Trading/Competition Commission. (2010). Merger assessment guidelines (p. 34). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/ file/284449/OFT1254.pdf Parker, G. & Van Alstyne, M. W. (2002). Unbundling the presence of network externalities (Working Paper). http://www.idei.asso.fr/Commun/Conferences/Internet/Janvier2003/Papiers/VanAl.pdf Pike, C. (2018). Rethinking antitrust tools for multi-sided platforms. OECD, April 6, 2018. http:// www.oecd.org/daf/competition/Rethinking-antitrust-tools-for-multi-sided-platforms-2018.pdf Rochet, J.-C., & Tirole, J. (2003). Platform competition in two-sided markets. Journal of the European Economic Association, 1(4), 990–1029. Salinger, M. A., & Levinson, R. J. (2015). Economics and the FTC’s google investigation. Review of Industrial Organization, 46, 25–57. Samuelson, P. (2000). Privacy as intellectual property? Stanford Law Review. Available at http:// papers.ssrn.com/sol3/papers.cfm?abstract_id=239412 Song, M. (2013). Estimating platform market power in two-sided markets with an application to magazine advertising. http://ssrn.com/abstract=190861 Thépot, F. (2013). Market power in online search and social networking: A matter of two-sided markets. World Competition, 36(2), 195–221. Tremblay, M. (2017). Market power and mergers in multi-sided markets. Available at https://pap ers.ssrn.com/sol3/papers.cfm?abstract_id=2972701 Wagner-von Papp, F. (2015). Should Google’s secret sauce be organic. Melbourne Journal of International Law, 16, 608–646. Waller, S. W. (2012). Antitrust and social networking. North Carolina Law Review, 90(5), 1771– 1806. Werden, G. J., & Froeb, L. M. (1994). The effects of mergers in differentiated products industries: Logit demand and merger policy. Journal of Law, Economics, and Organization, 194, 407–426. Willig, R. (1991). Merger analysis, industrial organization theory, and merger guidelines. Brookings Papers on Economic Activity: Microeconomics, 281–312. Wright, J. (2003). One-sided logic in two-sided markets (Working Paper 03-10). AEI-Brookings Joint Center for Regulatory Studies. Available at http://ssrn.com/abstract=459362

Chapter 6

Entry Barriers of Data Monopoly

Abstract Economists have argued that data monopolies do not impose barriers to entry because of the characteristics of data: the low cost of collecting and storing, the non-exclusive and non-rivalrous nature and the diminishing value over time. Incumbents do not have monopoly power in holding existing datasets because the data itself does not have economic value. New entrants could obtain market shares when new products and services are designed through technology or new business strategies. Schumpeter’s dynamic efficiency theory is also commonly applied to discuss the continuous competition process between data-driven companies. This chapter deals with the issue of market entry in competition analysis and discusses the arguments in support of or against data monopolies that create barriers to competition, so intervention is required or not required, as the case may be. It will address the issue of economies of scale, the definition of entry barriers and the analysis of entry issues when the effects of innovation are taken into account.

6.1 Introduction Online markets are constantly transforming. Indeed, online markets typically have innovative challengers who are a threat to incumbents. Challengers may overtake incumbent firms through new ideas and technologies. In such settings, there are low entry barriers. Digital competition offers many examples, such as Facebook, Snapchat, and Tinder, where simple insight into customer needs enabled entry and rapid success.1 Other examples include WhatsApp and Instagram. They were far behind Facebook but made inroads without a large user base. In addition, Yahoo leapfrogged AltaVista and Google leapfrogged Yahoo. Each search engine was declared the “winner” of the search at one time.2

1 2

Lambrecht and Tucker (2015, pp. 11–14). Stross (1998).

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In online markets that are driven by a data economy, it has been argued that the collection and storage of data has high fixed costs and small marginal costs. Incumbents may obtain economies of scale when the large dataset requires high investment, since it is essential to improve product quality and is difficult to replicate. When focusing on high fixed costs, it is argued that a larger dataset will improve the quality of algorithmic analysis, and with better quality products, incumbents will obtain a larger market share. Particular attention is given to the entry barriers that are created by companies with market power, and such a dataset becomes an essential facility. It is an empirical question to assess how much investment is required to build the dataset and how much value such a dataset can create for the incumbents and whether the market data are accessible for potential entrants. Such empirical evidence is lacking and often varies in each case. On the other hand, there are strong arguments against the view of entry barriers when attention is given to the low marginal cost. Because of the fast growth of innovation in the dynamic market, data-driven markets are perceived to have low entry barriers.3 The most famous argument on data with low entry barriers is made by Sokol and Comerford: in innovation-driven markets, data are often collected at low costs, and entrants are motivated to invest in innovation to improve products and services in accordance with consumer needs.4 Tucker and Welford argued that because new firms could innovate with new products and collect consumer data through the new products, the advantages of the incumbent are not significant. Hovenkamp argued that low consumer switching costs, multi-homing and product differentiation encourage new entry, which could offset the entry deterrent created by the network effects or the accumulation of large amounts of consumer data or large intellectual property portfolios.5 In fact, entry barriers are low in a number of markets. For example, Facebook has become a major competitor to YouTube in video viewing in a very short period of time.6 Even one year ago, such an outcome seemed unlikely. This type of competition with low entry barriers is not unique. Another set of markets with low entry barriers often involve those that utilize so-called “Big Data.” Tinder, a company so new it had less data than competing dating websites, overtook its competitors with an innovation of swiping left or right to connect people. This was a simple innovation in the user interface. Tinder succeeded despite having less data than its rivals because it developed a product that attracted users who shared their information with the company. In the Facebook/WhatsApp merger, the European Commission argued that the transaction does not create barriers to entry because “consumers can and do 3

Tucker and Welford (2014). See Sokol and Comerford (2017) (“little, if any, user data is required as a starting point for most online services. Instead, firms may enter with innovative new products that skillfully address customer needs, and quickly collect data from users, which can then be used towards further product improvement and success.”). 5 Hovenkamp (2021, pp. 28–29). 6 See Gesenhues (2014). 4

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use multiple apps at the same time and can easily switch from one to another.”7 In the Facebook/WhatsApp case, the European Commission concluded that “the use of one particular messaging app does not exclude the use of a competing messaging app because multi-homing was common, and the users of messaging apps are not locked-in to the network”8 ; “[the network effects] are unlikely to shield the merged entity from competition from new and existing consumer communications apps.”9 Consumers can share information with as many services as they like, but firms must develop valuable products and services to attract consumers. In some cases, more data do not translate into a better product, as data are most relevant when it is about how a company and its consumers use its own product. Netflix was able to overtake Blockbuster even though Blockbuster initially had more customer data. Netflix used its data more effectively because Blockbuster operated each of its stores independently and did not focus its data efforts on the collection of customer preferences. As such, Blockbuster could not effectively use customer preferences and information (because for example, even with all of its data, it did not have a good sense of what a customer’s first choice was if a movie was out, merely the choice of what the customer would ultimately rent). These are but a few examples of how competition may emerge quickly and in doing so displace incumbents. For all these companies, it was not the quantity of data that was relevant but rather the insights that they derived from the data they had and their ability to innovate. The examples above suggest an important policy lesson, which is that—low entry barriers are a common attribute in online data markets.10 The data requirements of new competitors are far more modest and qualitatively different than those of more established markets.11 Little, if any, user data is required as a starting point for most online services. Instead, firms may enter with innovative new products that skilfully address customer needs and quickly collect data from users, which can then be used toward further product improvement and success.12 As such, new entrants are unlikely to be at a significant competitive disadvantage relative to incumbents in terms of data collection or analysis.13 For example, in Facebook/WhatsApp, the European Commission noted that data sets should not have an impact in a market for online advertising because there are so many different sources of user data available on the web.14 7

Sokol and Comerford (2017, p. 15), Tucker and Wellford (2014, p. 8), Case COMP/M.7217— Facebook/WhatsApp, European Commission. 8 Case COMP/M.7217, Facebook/WhatsApp, at para 135. 9 See also Sokol (2014, p. 4). 10 Sokol and Comerford (2017) (“[T]he unique economic characteristics of data mean that its accumulation does not, by itself, create a barrier to entry, and does not automatically endow a firm with either the incentive or the ability to foreclose rivals, expand or sustain its own monopoly, or harm competition in other ways.”). 11 Tucker and Wellford (2014, pp. 6–9). 12 Tucker and Wellford (2014, pp. 6–9). 13 Tucker and Wellford (2014, pp. 6–9). 14 Case COMP/M.7217, Facebook/WhatsApp.

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Low entry barriers are not limited to the most developed high tech markets. Evans and coauthors have noted the importance of the development of smart apps in the developing world context because of poor telecom and cable infrastructure. In India they identified that the development of smart apps creates significant market disruptions against more established internet companies, which in turn have disrupted traditional brick and mortar companies.15 Changes in technology such as cloud computing and the low cost of developing apps also decrease barriers to entry.16 These technological changes reflect that new competitors are springing up all of the time across a number of different platforms, such as social media, instant messaging, and general and vertical searches to name just a few. The overall enforcement takeaway regarding barriers to entry is that they appear to be low in many online markets. Furthermore, barriers differ from market to market. Thus, any generalization about barriers to entry across online markets may lead to mistaken inferences that may lead to overenforcement and quash innovation. The arguments of low entry barriers strongly focus on the ease of replicating the dataset and entering the market with innovative strategies to collect new data from consumers. Those arguments that neglect the opposite view of the high fixed costs to build dataset and the difficulties that the entrants are facing to replicate the dataset. In reality, the feedback loop effect often grants a substantial advantage to incumbents who have a large advantage over algorithms and can produce much better analytical results when the dataset volume is larger. Thus, Sect. 6.3.3 of this chapter reviewed the arguments made by Gal and Rubinfeld that the entry barriers include technical, legal and behavioural barriers, and the empirical evidence of whether datadriven incumbents have obtained market power. Sections 6.3.3–6.3.5 will discuss the remedies to provide a level playing field for incumbents and entrants in a data market without creating adverse effects such as forcing incumbents to share data. Efforts have been made to improve consumer preferences and consumer choices and to provide the possibility to persuade online service providers to reduce switching costs and mitigate locked-in effects. The right to portability and the right to interoperability have both been introduced in the GDPR, and the anti-competitive effects must be consistent with the goals of data protection and consumer protection policy from the perspective of consumer autonomy.

15

Bhargava et al. (2016, p. 172) (“These changes in consumer shopping behavior are resulting in a revolution in retail. Retail stores are developing ‘omnichannel’ approaches that integrate physical stores, mobile apps, and websites to provide consumers with multiple choices of how to shop and buy.”) 16 Small and medium businesses using cloud technology to overcome their growth challenges grow 26% faster and deliver 21% higher gross profits. 85% believe that cloud enables their business to scale and grow faster. See Deloitte (2014).

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6.2 Entry Analysis in Traditional Markets The issue of entry is one of the very important aspects in the analysis of market power, as the market power of incumbents will be restricted if there are potential entrants.17 Before the emergence of game theory, which assesses the interaction between incumbents and entrants in a strategic framework,18 the two dominant views on the conditions of entry were held by Bain and Stigler, who have different understandings of the barriers to entry.19 By relying on the SCP framework, Bain argues that barriers to entry are established through a series of structural factors, such as economies of scale, product differentiation and cost advantages of the incumbent firm.20 Bain’s structural analysis of barriers has policy implications for intervention,21 whereas the Chicago School scholar Stigler questioned the view of imposing regulations, patents and tariffs to prevent entry barriers.22 For Stigler, the barriers to entry are additional long-term costs that should be borne by the new entrant.23 If incumbents had an advantage and were able to enter the market first, the potential entrants had to make investments to compete. In situations where potential entrants are able to achieve low costs, or to develop differentiated products at lower costs than incumbents, Stigler would disagree with Bain on the inclusion factors of scale economies and product differentiation as barriers to entry.24 The recent development of game theory extended the debate on barriers to entry to the likelihood of entry. The strategic approach to entry analysis focuses the situations where entry requires significant sunk costs.25 Economists generally agree that sunk costs could deter entry by making entry riskier.26 The analysis of entry barriers in traditional markets is not applicable to online markets in two respects: the first is that in innovation-driven digital markets, the structural analysis would be outdated, and when firms do not compete for prices, the anti-competitive effects have to be assessed through quality, and the value of the platform is based on the value of data. Economies of scale are not understood from the perspective of product value but the platform value after using computer algorithms to analyse the dataset. Therefore, a static view of market structure has to be shifted to a dynamic view of innovation, and product competition has to be shifted to data 17

Motta (2004, p. 120). See for example, Salop (1979, pp. 335–338) and Farrell (1987, pp. 34–39). 19 Baker (2003, p. 191); For an overview of the discussion of ‘barriers to entry’, see for example, Gilbert (1989, pp. 476–535). 20 Bain (1949, pp. 448–464). 21 Baker (2003, p. 192). 22 Baker (2003, p. 192). 23 Stigler (1968). 24 Baker (2003, p. 193). 25 Baker (2003, p. 196). 26 Schmalensee (2004, p. 8). 18

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competition. The second aspect is that the remedy dealing with essential facilities in traditional markets is forcing companies to divest their assets, as the European Commission did in the Thomson/Reuters merger that forced the companies to divest their competing data products. Such a remedy may have adverse effects that harm the incentives for companies to invest in innovation, as building a dataset requires high fixed costs. In digital markets, new remedies have to be created, and at the moment, the most applicable remedy is introducing the right to data portability and the right to data interoperability.

6.3 Barriers to Entry in Online Markets 6.3.1 Economies of Scale The economic arguments for the creation of entry barriers by platforms holding market power are based on economies of scale, including the high quality of services and products that are provided, based on the large bases of users from whom data could be collected. The European Data Protection Supervisor stated in 2014 that “the collection and control of massive amounts of personal data are a source of market power for the biggest players in the global market for internet services.”27 However, the effects of economies of scale on entry barriers are uncertain. It depends on the empirical question of how easily the potential entrants can obtain access to market data, how important the current dataset held by the incumbents is for improving product quality, and how much investment is needed to build such a data set. It does not seem to be the volume of data matters but the value of the dataset to the incumbents and the entrants. Thus, to understand the competitive effects created by economies of scale, the UK competition authority Competition & Market Authority claimed, “We received mixed evidence about barriers to entry across a range of data markets. However, where concerns were raised, the most common were whether firms could gain access to consumer data, and the difficulties experienced by small and potential new entrants in some markets that arise from the economies of scale and scope.”28 There are strong arguments that the value of data decreases with the amount of data collected, only a small search scale is necessary to start a new search engine, and the additional search data provided have limited value.29 In the previous section, it was argued that the costs of collecting data are low, and the competition agency 27

The European Data Protection Supervisor stated that “[t]he collection and control of massive amounts of personal data are a source of market power for the biggest players in the global market for internet services.” See EDPS (2014). 28 CMA (2015, at p. 9). 29 Manne and Wright (2010, p. 212) and Bork and Sidak (2012, pp. 688–691).

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argued that the incumbents do not hold a competition advantage because the new entrants could build a database at very low cost. There are different views on how difficult it is to build the dataset necessary and how much value it can produce, as economists may argue that because of the network feedback loop, the dataset has significant value and cannot be easily replicated, whereas there are other arguments that data has zero marginal cost and diminishing value and can be replaced by new data that are collected by the entrants. Although all agree that it is the algorithm technology that produces value for the platform, not the data itself, data, especially in large volumes, remain an important input for the production of online services.30 For example, Manne and Wright31 and Bork and Sidak’s research32 both argued that the new search engine only has to conduct a minimum scale of searches to start the process of learning by doing, and to compete with the incumbents, their investment in fixed costs is not high. Lerner33 argued that there are many sources of inputs to provide high-quality results and that the scale of data is not unique for improving the search results.34 The empirical study conducted by Chiou and Tucker showed that storing historical data on users’ searches does not affect the accuracy of search results and that possessing historical data grants little advantage in market share. Their results showed that historic data are less useful for accurately predicting current news.35 It is similar to the argument made by the ABA association in the US, which argued that data can be collected and purchased from various sources, and in a dynamic market with changing technologies, holding a dataset at a given time cannot grant long-term advantage for the incumbent, and the dataset cannot be defined as an “essential” input or facility.36 Those arguments are taken in the European Commission’s decision on the Microsoft/Yahoo merger37 in which it is stated that the evidence to prove the positive influence of scale on search results is lacking.38 The US Supreme Court held the opinion that forcing the incumbent to share their dataset would have a negative effect on innovation, and the duty of data sharing should not be imposed.39 In contrast, given that the economic literature on the theory of better data quality will improve business performance, there are arguments on the positive causal relationship between data volume and the competitive advantage of online platforms 30

Graef (2015, p. 488) and Lambrecht and Tucker (2015). Manne and Wright (2010, p. 212). 32 Bork and Sidak (2012, pp. 688–691). 33 Lerner (2014). 34 Graef (2015, pp. 486–488). 35 Chiou and Tucker (2017). 36 IDCTF (2020, at p. 14). 37 Case No COMP/M.5727—Microsoft/Yahoo! Search Business, 18 Feb. 2010. 38 Graef (2015, p. 486). 39 Nigro (2017). 31

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from the demand side economies of scale. When the platform is holding a competitive advantage and there are more users joining the platform, more data are available for algorithm analysis, and thus the platform can enhance its dominant position. The study of algorithmic learning may prove the causal feedback loop in which the economies of scale on data collection can yield network effects, enhancing the dominant position of incumbents and creating entry barriers.40 As Colangelo and Maggiolino argued, “the more data a firm gathers and analyses, the better its products, the more users it attracts, the more data it collects and processes, and so on.”41 The empirical study by Schaefer showed such network effects in the algorithmic learning of search engines.42 Stucke and Grunes made similar arguments: “the more people who actively or passively contribute data, the greater the company can improve the quality of its product, the more attractive the product is to other users, and the more data the company has to further improve its product, which becomes more attractive to prospective users.”43 The impact of a demand side feedback loop on economies of scale is twofold: one is that the increased user data on demand will, in turn, influence the value of the platform and improve the quality of products for the supply side; the other is the increased value of a dataset when more data are available and different data sources are combined.44 When the platform is able to collect more data from users, algorithms could perform better analytical results, leading to better personalized services and recommendations, and therefore attract more consumers to use the platform. More data available also makes it easier to stimulate innovation. Furthermore, Gal and Aviv argued that in addition to the economies of scale and scope through which the volume, variety, veracity and velocity of data affect the quality of the algorithms, there is also a “transfer learning” effect in which algorithms will learn from the high-quality dataset to perform the task on other datasets, so there are positive externalities created by the qualities of datasets.45 In the previous section, it was argued that when the entrants were able to provide new business strategies and invest in innovation, the entry barriers would be considered low. In 2010, the DOJ approved Microsoft/Yahoo!’s merger case because the merger would increase the data pool of Microsoft and “This larger data pool may enable more effective testing and thus more rapid innovation of potential new searchrelated products”.46 In this context, the barrier of accessing data creates the barrier to

40

Krämer (2020, p. 13) and Lerner (2014). Colangelo and Maggiolino (2018, p. 2). 42 Schaefer et al. (2018); See also Krämer (2020, p. 14). 43 Stucke and Grunes (2016, p. 170). 44 Mayer-Schönberger and Padova (2016, pp. 315, 320) (“It is like a single puzzle piece that taken by itself offers little value, but when combined with others to complete an image is turned into something precious.”) 45 Gal and Aviv (2020, at p. 8); see also Mihalkova et al., (2007, p. 608). 46 DOJ (2010). 41

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entry, because the entrants are unable to compete on data analytics because the indirect network effects prevent them from accessing the massive user data.47 The importance of scale has also been accepted in other cases, in which the competition agency argued that building a fundamental dataset is costly, such as in Thomson/Reuters (2008)48 and the Bazaarvoice/Power Reviews merger case.

6.3.2 Defining Entry Barriers—Data Substitutability Competition agencies define whether merged parties have a competitive advantage with data control based on several factors. The first is the capability of competitors to provide substituted data.49 In the Thomson/Reuters case and Bazaarvoice/Power Reviews merger case, the court found that the merger created barriers to entry because the data would not be substituted. The Thomson/Reuters case was about two merging parties in a horizontal relationship that hoped to combine financial data. The US DOJ and European Commission found that entry barriers are high because creating fundamental datasets is costly.50 In the Bazaarvoice/Power Reviews merger case,51 the court held that the merger would provide Bazaarvoice with access to consumer behaviour data for syndication, advertising data that could give Bazaarvoice the advantage of control data. In United States v. Bazaarvoice,52 the court found that the company’s ability to “leverage the data from its customer base” was as a key barrier to entry. In contrast, in the merger case Microsoft/LinkedIn,53 the European commission found that substitutable data were available for competitors. In the Google/DoubleClick cases, the FTC argued that data is not an “essential input” in the competition market of search advertising.54

47

Krämer (2020, p. 14). United States v. Thomson Corp. & Reuters Grp., No. 1:08-cv-00262, 2008 WL 2910467 (D.D.C. June 17, 2008) (financial information); see also Commission Decision of 19/2/2008 (Case No COMP/M.4726—Thomson Corporation/Reuters Group), 2008 O.J. (C 212). 49 Sivinski et al. (2017, p. 215). 50 European Commission, Mergers: Commission clears acquisition of Refinitiv by London Stock. Exchange Group, subject to conditions, Press Release, Brussels, 13 January 2021; see also Complaint, U.S. v. Thomson Corp. and Reuters Group, D.D.C., 19 February 2008. 51 U.S. v. Bazaarvoice, Inc., No. 13-cv-00133-WHO, 2014 WL 203966, para 83 (N.D. Cal., 8 January 2014). 52 United States v. Bazaarvoice, Inc., Case No. 13-cv-00133-WHO, 2014 WL 203966 (N.D. Cal. Jan. 8, 2014). 53 Case M.8124, Microsoft/LinkedIn, Commission Decision. 54 FTC (2007). 48

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6.3.3 Technical, Legal and Behavioural Barriers Economists have different views on the impact of economies of scale of data on barriers to entry. It has been agreed that a data set has substantial fixed costs but a close to zero marginal cost, and the data itself has decreasing value over time. In addition, the data itself does not have economic benefits, and only after analysis of data, such as through computer algorithms, will data produce economic value. Economists who focus on the decreasing marginal value of data would not support the view that having obtained a large dataset will create barriers to entry for potential entrants, because historical data itself does not have value, but data’s non-rivalrous nature, dispersed ownership and the entrants can reproduce a dataset over time.55 However, those who focus on the substantial fixed costs expressed the arguments that because recreating the data will incur substantial sunk cost, and some of the dataset is difficult to recreate, the collection of data already incurs barriers to entry.56 Data giants invest in building good quality datasets, and when more users are using the platform, the feedback loop effect will in turn increase the value of the platform. Such a business advantage is difficult to catch up for potential entrants, as the ACCC’s Digital Platforms Inquiry stated: “The ubiquity of these platforms and their presence in related markets enable them to build particularly valuable data sets. This enables them to offer highly targeted or personalized advertising opportunities to advertisers. The advertising revenue can in turn be used to invest in the functionality and services provided, improving the consumer experience and attracting greater numbers of users to their platforms, as well as improving data gathering techniques.”57 At the same time, there are other economists who argued that holding large amounts of dataset is beneficial for companies to improve product quality. In this way, the anticompetitive effects of holding a large dataset are also offset by efficiency gains, and thus, the quality of the product also includes the efficiency benefits from data collection.58 In particular, the larger volume, variety, velocity and veracity of data will improve the quality of the algorithm, and algorithms perform better from datasets with higher accuracy and can transfer learning from different datasets, thus making better predictions.59 As the Google Chief Scientist Peter Norvig said: “we don’t have better algorithms than anyone else; we just have more data.”60 Geradin and Kuschewsky made the argument that: “the acquisition of large volumes of data by ‘first mover’ provides may, however, raise barriers to entry and thus deprive users from the benefits of competition.”61 Therefore, it is up to an empirical question of 55

Chiou and Tucker (2017), Körber (2016), and Gilbert and Pepper (2015). Rubinfeld and Gal (2017, p. 339), Pecman et al. (2020, p. 21), and ACCC (2019, p. 11). 57 ACCC (2019, p. 7). 58 Manne and Sperry (2015). 59 Gal and Rubinfeld (2019, p. 744). 60 Smichowski (2016). 61 Geradin and Kuschewsky (2013). 56

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whether the potential entrant has the ability to recreate the dataset and whether such recreation is important from a competition point of view, and barrier to entry should be analysed not only on a case-by-case approach, but also include a consistent cost and benefit evaluation using multidimensional factors in the supply chain of data collection, storage and use. Rubinfeld and Gal argued that barriers to entry in digital markets should be analysed in the chain of data collection, storage, synthesis and analysis, and barriers to entry include technical, legal and behavioural barriers. In the chain of collection, storage and use of data, when the sunk costs are declining, the entry barriers are lower, and when there are high switching costs because of technical barriers, the incumbents also hold advantages over potential rivals. Technological supply-side barriers arise when there are sunk costs to invest in economies of scale or scope. Demand-side barriers refer to the data-driven network effects that will positively affect barriers at the supply side, and an increased sunk expenditure is needed to counter the feedback loop.62 Technological barriers can be created by the uniqueness of the data or at the gateway in which data cannot be replicated because data is created although distinctive interaction or the collector has unique knowledge or because the data collection has to use specific devices or service applications.63 Legal barriers refer to the costs of data collections created by legislations on property or liability rules, such as data protection and privacy regulation. Behavioural barriers are those contractual limitations imposed by a data collector on the use and transfer of data (Table 6.1).64

6.3.4 Data Portability and Data Interoperability In economic theory, entry barriers are created by the high switching costs and lockin effects on the consumer side. When consumers choose one platform, they cannot transfer their personal data to another platform, or they cannot interact with another online service supplier because they have chosen this platform. The current platform can enhance its dominant position and can exercise its market power to prevent competition from new entrants. Legal scholars have developed two concepts to tackle these two problems: the right of data portability to reduce switching costs and the right of data interoperability to prevent consumer lock-in.

62

Rubinfeld and Gal (2017, p. 355). Rubinfeld and Gal (2017). 64 Rubinfeld and Gal (2017). 63

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Table 6.1 Technical, legal and behavioural barriers in data supply chain65 Technical barriers

Legal barriers

Behavioural barriers

Collection

(1) Uniqueness of the data, or access to data (2) Supply side: economies of scale, scope, learning by doing, speed (3) Demand side: network effects and two-sided markets

(1) Data protection and privacy laws (2) Data ownership

(1) Exclusivity agreements (2) Access prices and conditions (3) Disabling data and collecting software

Storage

Storage costs

Data protection and privacy laws

Lock-in and switching costs

Synthesis and analysis

Lack of interoperability (including a lack of standardization)

Use

(1) Inability to locate and reach relevant consumers (2) Lack of interoperability (including a lack of standardization)

(1) Data protection and privacy laws (2) Anti-discrimination laws

Contractual limitations

6.3.4.1

Data Portability

In legal theory, data portability is similar to access rights that individuals have the right to access their data in a reusable digital form and transfer their data to a third party.66 It is defined as the ability of consumers to submit their personal data to different platforms. The OECD gave the definition of data portability as “the ability (sometimes described as a right) of a natural or legal person to request that a data holder transfers to the person, or to a specific third party, data concerning that person in a structured, commonly used and machine-readable format on an ad hoc or continuous basis.”67 Data portability is the fundamental right of privacy to maintain individual autonomy in digital markets, and the right of data portability has been defined in Article 20 of the GDPR.68 The right of data portability has also been mentioned 65

Source OECD (2020, p. 33), Rubinfeld and Gal (2017, p. 339), Gal and Rubinfeld (2019, pp. 737– 770), Gal and Aviv (2020), and CMA (2016). 66 IWGDPT (2021, at p. 2). 67 OECD (2021a, p. 9). 68 Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), OJ L 119/1 of 4.5.2016. Article 20 of the GDPR: Right to Data Portability:

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in the proposed Directive on Certain Aspects Concerning Contracts for the Supply of Digital Content,69 and in Article 13(2)(c) and Article 16 (4)(b), it was required that after consumers terminated their contract with one platform, the “supplier shall provide the consumer with the technical means to retrieve all content provided by the consumer and any other data produced or generated through the consumer’s use of the digital content.” The right of data portability has also been included in the EU Commission Proposal for a Regulation on Cross Border Portability of Online Content Services, and the Augmenting Compatibility and Competition by Enabling the Service Switching Act of 2019 (the ACCESS Act) in the US.70 As Europe did in Article 20 of the GDPR,71 data portability rights have been recognized in Australia’s Consumer Data Right (CDR) and the California Consumer Privacy Act (CCPA) as a type of consumer right that empowers consumers to obtain access to data and be able to switch between online service providers.72 The US government has also set up examples of voluntary data portability initiatives to facilitate the access to data held by government agencies, such as “My Data” initiatives, “Get Transcript” initiative (access to the Internal Revenue Service data), “My student Data” initiative (access to federal student data), “Green Butten” initiative (access to electricity utility data), and “Blue Button” initiative (access to health data). The report made by the House Judiciary Committee (Subcommittee on Antitrust, Commercial, and Administrative Law)73 also requires platforms to make their services compatible 1. The data subject shall have the right to receive the personal data concerning him or her, which he or she has provided to a controller, in a structured, commonly used and machine-readable format and have the right to transmit those data to another controller without hindrance from the controller to which the personal data have been provided, where: (a) the processing is based on consent pursuant to point (a) of Article 6(1) or point (a) of Article 9(2) or on a contract pursuant to point (b) of Article 6(1); and (b) the processing is carried out by automated means. 2. In exercising his or her right to data portability pursuant to paragraph 1, the data subject shall have the right to have the personal data transmitted directly from one controller to another, where technically feasible. 3. The exercise of the right referred to in paragraph 1 of this Article shall be without prejudice to Article 17. That right shall not apply to processing necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller. 4. The right referred to in paragraph 1 shall not adversely affect the rights and freedoms of others. 69 Art. 13 (2) (c) and Art. 16 (4) (b) of the Proposal for a Directive of the European Parliament and of the Council on certain aspects concerning contracts for the supply of digital content, COM (2015) 634 of 9.12.2015. 70 Proposal for a Regulation of the European Parliament and of the Council on ensuring the crossborder portability of online content services in the internal market, COM (2015). 71 Article 20 of GDPR: “The data subject shall have the right to receive the personal data concerning him or her, which he or she has provided to a controller, in a structured, commonly used and machinereadable format and have the right to transmit those data to another controller without hindrance from the controller to which the personal data have been provided.” 72 OECD (2020, p. 14). 73 US House Judiciary Committee, Subcommittee on Antitrust, Commercial and Administrative Law of the Committee on the Judiciary (2020).

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with competing networks to allow for interoperability and data portability. Similarly, the UK government introduced the Midata data portability initiative (access to electronic information), and the UK banks have implemented the open banking rules to facilitate data portability in the financial sector.74 The Consumer Privacy Protection (CPPA) in Canada suggested the establishment of “data mobility frameworks” in which consumers can request the transfer of personal data between different organizations. CPPA Section 120 states that the cabinet has the authority to define parameters for the technical means for ensuring interoperability and specifying organizations that are subject to a data mobility framework. The EU Commission has clearly identified that the difficulty in transferring data to another platform creates entry barriers and causes lock-in effects for consumers.75 Competition authorities often consider the costs of switching as barriers to entry and assess how easily consumers can access another platform without worrying about losing their data. Legal provisions on data portability have been considered a practical mechanism to enhance competition between platforms by reducing lock-in and switching costs, and the cost of switching is an important factor for estimating entry barriers and market power. For example, in Facebook/WhatsApp, the EU commission noted that consumers cannot be locked in the network of communication apps, and data portability was “unlikely to result in a lock-in of users who typically retain access to message history on their handset even if they start using another consumer communication app.”76 In practice, to make use of the platform data, there is also a need to further define what kind of data (observed, inferred data) is to be transferred and how data controlling intermediaries such as personal information management systems (PIMS) or data unions could be developed to facilitate data transfer between digital services. In the centralized management system, PIMS plays the role of providing authentication services, giving permission on data transaction, connection and managing semantic conversations, accounting the value of data, providing data governance support, storing data, keeping historic logs of data access and secure data exchanges between data sources.77 The crucial benefits of data portability are to promote competition on the use of data sets by encouraging users to initiate the action, as economists believe that data is non-rivalrous and the storage of data does not have economic value, and data portability can encourage firms to invest in data analytics and usage.78 These arguments are based on empirical proofs that first, data portability can lead to more investment 74

OECD (2020, p. 13). European Commission (2012, at p. 28) (“With increasing use of a certain online service, the amount of personal data collected in this service becomes an obstacle for changing services, even if better, cheaper or more privacy friendly services become available…This situation effectively creates a lock-in with the specific service for the user and makes it effectively very costly or even impossible to change provider and benefit from better services available on the market.”) 76 COMP/M.7217, Facebook/WhatsApp, 3 October 2014, para 137. 77 Krämer (2020, p. 20). 78 Krämer et al. (2020). 75

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in innovation of data processing and thus increase competition in the use of datasets, and second, when the switching costs are reduced, entry barriers could be lifted and hence increase consumer welfare and social welfare. With limited literature on those empirical studies, the first aspect has been proved by Florez Ramos and Blind that data portability increases investment in data processing79 ; and the second issue has contradictory findings as Krämer and Stüdlein’s game theory model80 shows that when consumers are able to switch data, the ‘old’ consumers of the incumbents will lower their switching costs and increase the competitiveness of the markets between incumbents and entrants, but for the ‘new’ consumers who are not previously the consumers of the incumbents, they will become worse off because the new entrants are less incentivized to compete to attract them. Their model shows that not all consumers will benefit from the right of data portability. Lam and Liu’s game theory model81 shows that data portability will encourage consumers to reveal more data to the incumbents because they do not have to worry about switching to another platform at later stage. This strengthens the data analytics network effects and market position of the incumbents, thus creating entry barriers. Their model shows the adverse effect of data portability on competition and consumer welfare, although it can indeed reduce switching costs.82 Krämer, Senellart and de Streel have a similar argument that in markets with strong network effects, when switching costs are reduced through data portability, the incumbents may in fact become the monopoly because the network effects may prevent users from switching and so keep using the dominant service.83 Krämer and Stüdlein’s research both showed that data portability will incentivize firms to collect more user data and increase prices.84 The empirical study conducted by Florez Ramos and Blind in the telecommunication sector showed that data portability affects market competition by decreasing switching costs between online platforms in competing markets, and the extent of this effect depends on the market and the value of personal data. In competitive markets such as Spotify, firms will be motivated to invest in innovation and artificial intelligence to improve users’ engagement, and in non-competitive markets (winnertakes-all markets) such as Facebook and Google, they will not have such a need to increase investment in innovation due to data portability.85

6.3.4.2

Data Interoperability

The concept of data portability is also often heard with comparison of the concept of data interoperability, which is defined as an ongoing transfer of data from one service 79

Florez Ramos and Blind (2020). Krämer and Stüdlein (2019, pp. 99–103). 81 Lam and Liu (2020). 82 Krämer (2020, p. 15). 83 Krämer et al. (2020, pp. 58–59). 84 Krämer and Stüdlein (2019, pp. 99–103). 85 Florez Ramos and Blind (2020). 80

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with another without leaving the original platform, and allows users to multi-home.86 It also refers to the ability to communicate and interconnect with competing product services, or platforms horizontally (horizontal interoperability) or vertically (vertical interoperability).87 Vertical interoperability refers to the choice of making connections in incorporating data content or function between digital services and upstream providers. Horizontal interoperability refers to choosing rival services to communicate with.88 Both concepts are taken as the elements to estimate the competitiveness between platforms. In the Impact Assessment Report by the European Commission, it is stated that “The possibility to move data from one service provider to another would increase competition in some sectors, e.g. between social networks, and could also make data protection an element in this competition, when users decide to move away from a service they do not consider appropriate in terms of data protection.”89 The UK report submitted to the Treasury claimed that data portability and/or interoperability is crucial for smaller platforms to compete with larger, established platforms.90 The right of data interoperability empowers consumers to interact with different networks and communicate with one another, and when the user switches to a new platform, they can still communicate with the users who stay in the old network.91 In practice, to implement interoperability, the technology to connect digital platforms and to facilitate the interaction between one another needs to be developed. This covers web services or application programming interfaces (API), and such technology can ensure that the user or third parties can obtain information and functionality from the digital service. APIs may serve as the standard format that can transfer their data from one platform to another.92 Using API, Apple, Facebook, Google, Microsoft and Twitter are building data portability platforms named the Data Transfer Project (DTP),93 and this project can indeed facilitate data portability and interoperability between different digital service providers.94

6.3.5 Data Standardization Seeing the value of data analysis through modern technologies in data science, Gal and Rubinfeld argued that data standardization is the key to creating data synergies, promoting efficient data operation, lifting barriers to data sharing, broadening the 86

Manne and Bowman (2020, at p. 2). Source: EPRS (2020, p. 7). 88 OECD (2021b, p. 12). 89 European Commission (2012). 90 Digital Competition Expert Panel (2019, at p. 87). 91 Krämer (2020, p. 12). 92 OECD (2021b, pp. 12, 17). 93 https://datatransferproject.dev/. 94 OECD (2021b, p. 17). 87

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use of data and increasing data portability and interoperability.95 Data standardization provides the technological infrastructure that supports the diffusion of data, and such standards include the attributes of the data collected, measurement and identification related to measurement units, product codes, the terminology, structure and organization of the dataset, and the aspects of data storage (such as location) or use (including protocols for data portability).96 As mentioned above, APIs are the most commonly used computer protocols to connect software components and are used as data standards.97 The API supports the open data ecosystem, and the automated download system saves costs, which also facilitates the reuse of data for more products and services. It provides a correct and secure channel to create added value for data actors in the value chain.98 In the process of setting efficient data standards, private solutions are also possible, for example establishing standard-setting organizations (SSOs) and learning law and economics literature99 in determining standards by public and private players, because standards in industries with different types of data may be adopted in different regulatory models, and SSOs with industry data scientists and professionals are expected to cooperate with government agencies in performing standard setting tasks100 The OECD and the EU Commission101 have provided a list of standard-setting organizations at national and regional level, such as the European Committee for Standardization, European Committee for Electrotechnical Standardization (CEN-CENELEC), European Telecommunications Standards Institute (ETSI),102 Telecommunications Industry Association (TIA), World Wide Web Consortium (W3C),103 Internet Engineering Task Force (IETF)104 InterNational Committee for Information Technology Standards (INCITS), International Electrotechnical Commission (IEC), International Organization for Standardization (ISO) and International Telecommunication Union (ITU). In addition, there are also international standard setting organisations with direct memberships, including IEEE, Internet Engineering Task Force (IETF), Organization for the Advancement of Structured Information Standards (OASIS) and World Wide Web Consortium (W3C).105 Standard-setting organizations are expected to cooperate at the national or international level to build an open ecosystem to achieve standardization and interoperability. EU legislation has also provided the legal framework for technical standards (see Table 6.2). 95

Gal and Rubinfeld (2019, pp. 737–770). Gal and Rubinfeld (2019, p. 749). 97 Gal and Rubinfeld (2019, p. 750). 98 European Commission (2018, at pp. 5–6). 99 Farrell and Saloner (1988, pp. 235–236), Lemley (2002, p. 1896, 2002), and Kerber and Schweitzer (2017, p. 54). 100 Gal and Rubinfeld (2019, p. 767). 101 European Commission (2020). 102 https://www.etsi.org/about. 103 https://www.w3.org/standards. 104 https://www.ietf.org/standards. 105 OECD (2017, p. 73). 96

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Table 6.2 EU legal framework for technical standards Directive 2002/21/EC of the European Parliament and of the Council of 7 March 2002 on a common regulatory framework for electronic communications networks and services (Framework Directive) Directive (EU) 2018/1972 of the European Parliament and of the Council of 11 December 2018 establishing the European Electronic Communications Code (Recast) Directive 2002/19/EC of the European Parliament and of the Council of 7 March 2002 on access to, and interconnection of, electronic communications networks and associated facilities (Access Directive) Regulation (EU) No 1025/2012 of the European Parliament and of the Council of 25 October 2012 on European standardization, amending Council Directives 89/686/EEC and 93/15/EEC and Directives 94/9/EC, 94/25/EC, 95/16/EC, 97/23/EC, 98/34/EC, 2004/22/EC, 2007/23/EC, 2009/23/EC, and 2009/105/EC of the European Parliament and of the Council and repealing Council Decision 87/95/EEC and Decision No 1673/2006/EC of the European Parliament and of the Council Directive (EU) 2015/1535 of the European Parliament and of the Council of 9 September 2015 laying down a procedure for the provision of information in the field of technical regulations and of rules on Information Society services Source The Digital Markets Act: European Precautionary Antitrust, Information Technology & Innovation Foundation (ITIF), May 2021, at p. 57

6.3.6 Data Sharing When data has become the ‘input’ for online production and the market power of dominant firms is assessed through their ability to control data, entry barriers of the market have been created by the dominant firms that manipulate the use of data to create economic benefits for themselves. Economists focus on the need for scale to make such economic profits and the possibility for other entrants to obtain data from other sources. As discussed extensively in the previous sections, there are differing views on whether dominant firms have a particular advantage of holding good quality datasets, and if the consumer autonomy of voluntarily providing data for various sources could improve the availability of data, this would improve competition in the market. As it is said, although entrants may still face technological barriers in the collection of data, mandatory data sharing is rarely imposed by competition authorities for not having provided sufficient reasons to prove mandatory sharing is necessary: for example, taking counterarguments on the ease of obtaining data, and the necessities of economies of scale into account. Data sharing has been criticized for being detrimental for dominant firms to invest in innovation and improve product quality, and data protection authorities tend to impose legal barriers in the processing and use of data. With little recent academic literature on the controversial issue of mandatory data sharing, Gal and Aviv106 called for a more balanced integration between competition and data protection rules on lifting the restrictions on data sharing and criticized the 106

Gal and Aviv (2020).

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strict data protection rules of GDPR, which limits the flow and transfer of datasets that restrict competition when small and medium firms are in need of large volumes of data for production. Data protection law should incorporate the goal of promoting competition rather than only focusing on the consumer privacy goal. They suggested that concentrations between small and medium-sized companies could be treated more leniently as they may enable them to benefit from data synergies to compete with dominant firms.107 Detailed rules of implementing GDPR, such as specifying technological standards for data portability and interoperability, can reduce uncertainty, and develop faster tools for verifying GDPR compliance, improving the certification of data management and vetting process which will reduce risks and compliance costs in data sharing, thus helping small firms to obtain data from external sources and grow their dataset to benefit from data synergies.108 Gal and Aviv’s research called for regulatory intervention, instead of being precautious about intervention in the digital market to facilitate data transfer, and sharing and delivery in the digital environment. To achieve this goal, the interest of small firms must be taken into account, and particular efforts must also be made to improve the organization of governmental agencies in the operation of data-oriented tasks. Their idea is consistent with the European Commission 2016 Communication on data, information and knowledge management, to emphasize the need to maximize the use of data and to improve data retrieval and delivery. The Communication has pointed out the challenges for the optimal use of data, including legal considerations of intellectual property rights of third-party information providers, the early identification and governance of data sources, data analytics, data quality and metadata management, and the proper insertion of datadriven insights into the policy cycle.109 Moreover, data sharing between public and private sectors has also become a recent development named “B2G data sharing for the public interest”, “data-driven social partnerships”, “data collaboratives”, “data trusts” or “data philanthropy”. This promising trend is aimed at building consensus to maximize the use of data and to foster interoperability between public and private sectors.110 The EU Communication “Building a European Data Economy” proposed that the regulatory framework should be designed to ensure data markets could be developed on their own, with the freedom of contract as the basis. At the same time, non-regulatory measures, including fostering the use of APIs for simpler and more automated access to and use of datasets, developing recommended standard contract terms and the provision of EU-level guidance must also be developed.111

107

Gal and Aviv (2020). Gal and Aviv (2020). 109 European Commission (2016). 110 European Commission (2020, at p. 15). 111 European Commission (2018, at p. 9). 108

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6.4 Conclusions The starting point of understanding the market power of data-driven online platforms is to understand the entry barriers that are created by the incumbents, and because the powerful dominant firms have invested in obtaining a large dataset, it may become easier for them to obtain access to new information and to create market value by applying technologies such as computer algorithms. The network feedback loop effect also increases the value of the incumbent platform significantly because the more users join the platform, the better the algorithm works. Economists have different views on whether a large volume of data (the scale) is an important input and become the prerequisite for algorithmic analysis to improve product qualities. It is not clear whether competitors can obtain access to such data from other sources, and how costly it is for new entrants to replicate the necessary dataset. These factors are also to be taken into account by competition authorities when determining whether incumbent platforms have anti-competitive advantages and have abused their market power. In earlier chapters, it has been discussed that data has a non-rivalrous and nonexclusive nature, and the volume of data does not create value itself and has a diminishing return; thus, obtaining datasets does not mean that dominant firms create barriers to entry because data can be collected and purchased from various sources, the value of data decreases over time, so holding a dataset does not have long-term advantages, and potential entrants can develop new technologies or business strategies to replicate new sets of data. This argument, however, has also been challenged when there is a lack of empirical evidence on how easily the dataset can be replicated. Economists such as Gal and Rubinfeld argued that economies of scale may create entry barriers when reviewing economic literature on the causal relationship between quality of data and business performance, and there is also a feedback loop on the demand side. In the dynamic digital environment, traditional methods that are applied to mitigate entry barriers become inapplicable because forcing platforms to share a dataset will have adverse negative effects on encouraging innovation. Attention has to be paid to granting consumers the possibility to switch their data and make data more available for multiple sources. The economic logic is that barriers to entering the market may come from the lock-in effect and the costs of switching from the user side. When consumers cannot switch their data to another platform because of lock-in effects or cannot use multiple platforms at the same time (multi-homing) because they are not connected with one another, the entrants are facing disadvantages in the competition with powerful incumbents. The first problem (the cost of switching) has been dealt with by the legal concept of portability, and the second issue (multi-homing) has been addressed by the concept of interoperability. Data portability grants users the right to request data transfer from one platform to another, and using data for multiple portals needs legal action for standardization. From the perspective of technology, technologies such as PIMS have to be developed to facilitate such transfer. Data interoperability makes it possible to communicate

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and connect different platforms and such connections are often facilitated by application programming interfaces (APIs). At the same time, the legislation process of facilitating standard setting by public and private agencies, as well as international cooperation between standard-setting organizations are all also crucial elements. Although there are strong economic arguments against the necessity of highquality datasets to achieve economies of scale, and competition authorities have taken data portability as an effective remedy for the judgment of barriers to entry, the technological, legal and behavioural barriers that lead to competitive advantages for dominant firms cannot be neglected. Neither competition law nor data protection law can single out one goal without considering the effect of harming competition which it may cause in the market. Gal and Aviv called for a more balanced integration between competition and data protection rules, asked for lenient treatment of the concentration of datasets between small and medium firms, and improved implementation rules of GDPR to reduce risks and compliance costs in data collection and data sharing. In this way, small firms can grow their dataset to benefit from data synergies. Their research indicates that a one-sided view on entry barriers is not appropriate in datadriven economies where data has become the crucial input for production and trading. Proper incentives for granting choices in a competitive environment should not only be imposed on the consumer side but also the restraints for data sharing on firms that are detrimental to competition deserve attention.

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FTC. (2007, December 20). Statement of the Federal Trade Commission concerning Google/DoubleClick FTC File No. 071-0170. Available at https://www.ftc.gov/system/files/doc uments/public_statements/418081/071220googledc-commstmt.pdf Gal, M. S., & Aviv, O. (2020, forthcoming). The competitive effects of the GDPR. Journal of Competition Law and Economics. Available at http://ssrn.com/abstract=3548444 Gal, M. S., & Rubinfeld, D. L. (2019). Data standardization. New York University Law Review, 94, 737–770. Available at https://www.nyulawreview.org/wp-content/uploads/2019/10/NYULAW REVIEW-94-4-GalRubinfeld-1.pdf Geradin, D., & Kuschewsky, M. (2013). Competition law and personal data: Preliminary thoughts on a complex issue. Available at http://ssrn.com/abstract=2216088 Gesenhues, A. (2014). Facebook, helped by Autoplay, passes YouTube for desktop video views for first time. Marketing Land, October 14, 2014. http://marketingland.com/facebook-delivered-a-bil lionmore-desktop-video-views-than-facebook-103778 Gilbert, R. J. (1989). Mobility barriers and the value of incumbency. In R. Schmalensee, & R. D. Willig (Eds.), Handbook of industrial organization (Vol. 1, pp. 476–535). Elsevier. Gilbert, P., & Pepper, R. (2015). Privacy considerations in European Merger Control: A square peg for a round hole. Competition Policy International. Available at https://www.competitionpolicyi nternational.com/assets/Uploads/PepperGilbertMay-152.pdf Graef, I. (2015). Market definition and market power in data: The case of online platforms. World Competition, 38(4), 473–506. Hovenkamp, H. J. (2021). Antitrust and platform monopoly. Yale Law Journal, 130, 1952–2050. Available at https://www.yalelawjournal.org/pdf/130.Hovenkamp_mawopj7e.pdf IDCTF. (2020, February 27). Common issues relating to the digital economy and competition. Report of the International Developments and Comments Task Force (IDCTF) on Positions Expressed by the ABA Antitrust Law Section between 2017 and 2019. Available at https://assets.publishing. service.gov.uk/media/5fce08ece90e07562e8feed7/200727_Response_to_CFI_ABA_.pdf IWGDPT. (2021). The case for data portability as a privacy protection in the digital age. Written procedure prior to 67th (virtual) meeting on April 24, 2021, The International Working Group on Data Protection in Technology. Available at https://www.datenschutz-berlin.de/fileadmin/ user_upload/pdf/publikationen/working-paper/2021/2021-IWGDPT-Working_Paper_Data_Por tability.pdf Kerber, W., & Schweitzer, H. (2017). Interoperability in the digital economy. Journal of Intellectual Property, Information Technology and Electronic Commerce Law, 8(1), 39–58. Krämer, J. (2020). Personal data portability in the platform economy: Economic implications and policy recommendations. Journal of Competition Law & Economics, 1–46. Krämer, J., & Stüdlein, N. (2019). Data portability, data disclosure and data-induced switching costs: Some unintended consequences of the general data protection regulation. Economics Letters, 181(C), 99–103. Krämer, J., Senellart, P., & de Streel, A. (2020, January 15). Making data portability more effective for the digital economy—Economic implications and regulatory challenges. Centre on Regulation in Europe (CERRE). Available at https://cerre.eu/publications/report-making-data-portabilitymore-effective-digital-economy/ Körber, T. (2016). Is knowledge (market) power?—On the relationship between data protection, ‘data power’ and competition law. NZKart (pp. 303–348). Available at https://papers.ssrn.com/ sol3/papers.cfm?abstract_id=3112232 Lam, W. M. W., & Liu, X. (2020). Does data portability facilitate entry? International Journal of Industrial Organization, 69, 102564. Lambrecht, A., & Tucker, C. (2015). Can big data protect a firm from competition? Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2705530 Lerner, A. V. (2014). The role of “big data” in online platform competition. Available at https:// papers.ssrn.com/sol3/papers.cfm?abstract_id=2482780 Lemley, M. A. (2002). Intellectual property rights and standard-setting organizations. California Law Review, 90(6), 1889–1980.

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Manne, G. A., & Wright, J. D. (2010). Google and the limits of antitrust: The case against the case against Google. Harvard Journal of Law and Public Policy, 34, 171. Manne, G., & Sperry, R. (2015). The problems and perils of bootstrapping privacy and data into an antitrust framework. CPI Antitrust Chronicle, 2. Available at https://www.competitionpolicyinte rnational.com/assets/Uploads/ManneSperryMay-152.pdf Manne, G. A., & Bowman, S. (2020, September 10). Issue brief: Data portability and interoperability, the promise and perils of data portability mandates as a competition tool. International Center for Law & Economics (ICLE). Mayer-Schönberger, V., & Padova, Y. (2016). Regime change? Enabling big data through Europe’s new data protection regulation. Columbia Science and Technology Law Review, 17(2), 315–335. Mihalkova, L., et al. (2007). Mapping and revising Markov logic networks for transfer learning. In Proceedings of the 22nd Conference on Artificial Intelligence (AAAI-07) (pp. 608–608). Motta, M. (2004). Competition policy—Theory and practice. Cambridge University Press. Nigro, B. A. Jr. (2017). “Big Data” and competition for the market. Remarks as Prepared for Delivery at The Capitol Forum and CQ: Fourth Annual Tech, Media & Telecom Competition Conference New York, NY, Deputy Assistant Attorney General Antitrust Division U.S. Department of Justice, December 13, 2017. Available at https://www.justice.gov/opa/speech/file/101 7701/download OECD. (2017). Key issues for digital transformation in the G20. Report Prepared for a Joint G20 German Presidency/OECD conference, Berlin, Germany, 12 January 2017. OECD. (2020). Consumer Data Rights and Competition—Background note by Secretariat, June 10–12, 2020. Directorate for Financial and Enterprise Affairs Competition Committee. http:// www.oecd.org/daf/competition/consumer-data-rights-and-competition.htm OECD. (2021a). Data portability: Analytical report, mapping data portability initiatives and their opportunities and challenges. OECD. (2021b). Data portability, interoperability and digital platform competition. OECD Competition Committee Discussion Paper. Pecman, J., Johnson, P. A., & Reisler, J. (2020). Essential facilities fallacy: Big tech, winner-take-all markets, and anticompetitive effects. Competition Policy International, 1–12. Rubinfeld, D., & Gal, M. (2017). Access barriers to big data. Arizona Law Review, 59, 339–381. https://arizonalawreview.org/pdf/59-2/59arizlrev339.pdf Schmalensee, R. (2004). Sunk costs and antitrust barriers to entry (MIT Sloan School of Management Working Paper No. 4457-04). Stigler, G. J. (1968). The organization of industry. Irwin. Salop, S. C. (1979). Strategic entry deterrence. The American Economic Review, 69(2). Papers and Proceedings of the Ninety-First Annual Meeting of the American Economic Association (May 1979) (pp. 335–338). Schaefer, M., Sapi, G., & Lorincz, S. (2018). The effect of big data on recommendation quality: The example of internet search (DIW Berlin Discussion Paper No. 1730). http://www.dice.hhu.de/fileadmin/redaktion/Fakultaeten/Wirtschaftswissenschaftliche_F akultaet/DICE/Discussion_Paper/284_Schaefer_Sapi_Lorincz.pdf Sivinski, G., Okuliar, A., & Kjolbye, L. (2017). Is big data a big deal? A competition law approach to big data. European Competition Journal, 13(2–3), 199–227. Smichowski, B. C. (2016). Data as a common in the sharing economy: A general policy proposal. Available at https://hal.archives-ouvertes.fr/hal-01386644, preprint submitted on October 24, 2016 Sokol, D. D., & Comerford, R. (2017). Does antitrust have a role to play in regulating big data? In R. D. Blair, & D. D. Sokol (Eds.), Cambridge handbook of antitrust, intellectual property and high tech. Cambridge University Press. Available at https://papers.ssrn.com/sol3/papers.cfm?abs tract_id=2723693 Sokol, D. (2014). The broader implications of merger remedies in high technology markets. CPI Antitrust Chronicle, 1–7.

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Stross, R. E. (1998). How Yahoo! Won the search wars. Fortune, March 2, 1998. http://archive.for tune.com/magazines/fortune/fortune_archive/1998/03/02/238576/index.htm Stucke, M. E., & Grunes. (2016). Big data and competition policy. Oxford University Press. Tucker, D. S., & Wellford, H. B. (2014). Big mistakes regarding big data. Antitrust Source, December 2014. Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2549044 US House Judiciary Committee, Subcommittee on Antitrust, Commercial and Administrative Law of the Committee on the Judiciary. (2020). Investigation of competition in digital markets, majority staff report and recommendations. Available at https://judiciary.house.gov/uploadedfiles/compet ition_in_digital_markets.pdf

Chapter 7

Market Power Assessment in Online Markets

Abstract Traditional antitrust laws use market power as the primary analytical tool to measure the anti-competitive impact of monopolies, which are market forces when manufacturers have the ability to raise product prices above equilibrium prices. However, in the data monopoly market, the monopoly power of the manufacturer is not shown in the “price increase”, because the goods and services provided are “free”, and the market power may be reflected in the consumers’ attention cost and transfer cost increase, so that the traditional price analysis and price elasticity calculation methods will have significant limitations. This chapter is the judgment of data monopoly enterprises abusing their dominant position in the relevant market, including the formation of the dominant market position by companies monopolizing data, the characteristics of the abuse of market dominance, and the case study of Facebook and Google in the US and EU.

7.1 Introduction Traditional methods in assessing firms’ market power rely on the structural view of defining a relevant market and measuring market share. Static analysis has been challenged by the dynamic changes of digital markets and the replacement effects led by innovation. The assessment of the market power of online platforms and data-driven companies is often conducted through the scrutiny of mergers and the definition of abusive behaviour by a dominant firm. Mergers and acquisitions between internet firms often raise concerns about increased control of user data and a high level of concentration. The economic analysis of the potential anti-competitive effects is more difficult than in a traditional market because of indirect network effects and the particular pricing strategy of the platform. For example, the practice of charging below cost prices or even zero-prices may be judged as predatory pricing in a traditional market, but in multi-sided markets such practice is common. The crucial assessment of the effects of price changes on one side of the platform is to measure the change of demand and the price elasticity of the users at the other side of the platform, whereas traditionally such effects are assessed within the same side of the market. The pricing strategy is to attract the number of users on the other side © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 J. Ma, Regulating Data Monopolies, https://doi.org/10.1007/978-981-16-8766-2_7

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of the market, and such practice may increase the level of competition in the market. Thus, the traditional methods in assessing market power are often not applicable in analysing the competitive effects of data monopolies.1 Traditional methods that are used to assess market power rely on indirect indicators such as market shares in the relevant market, barriers to entry and the extent of lock-in effects, contestability of the market (concentration level), and market shares in the relevant market. This structure-based static view has been challenged when digital platforms are engaged in dynamic innovation-driven competition and market share cannot indicate market power because the monopolistic position may only be temporary and if there are sufficient competition constraints and low barriers to entry, it cannot prove that the platform has created anti-competitive effects. Market power assessment in digital markets tends to include broad factors and pay more attention to the analysis of barriers to entry, that are to be created for legal, technological and behavioural reasons. Section 18(3a) of the German Competition Act listed five factors to be considered when assessing the market position of an undertaking2 : (1) direct and indirect network effects; (2) the parallel use of services from different providers and the switching costs for users; (3) the undertaking’s economies of scale arising in connection with network effects; (4) the undertaking’s access to data relevant for competition; and (5) innovation-driven competitive pressure. This chapter is written to discuss the assessment of market power by reviewing the cases in merger and abuse of dominant position. Section 7.4 will discuss the Google search case, the merger cases of Microsoft/Skype, Facebook/WhatsApp, and the abuse of dominance case of Alibaba Group. It will address the issue of market power assessment and will discuss some of the contradictory arguments by competition authorities.

7.2 Assessing Market Power—Traditional Method The concept of market power as defined by economists is broader than that used by antitrust policy makers. Economists define market power as the ability of the seller to set prices above marginal costs, and this definition is made by setting perfect competition as the benchmark. Firms do not exert market power in a perfectly competitive market, where prices are set equal to marginal cost and no firm can affect market prices.3 Both allocative and productive efficiency are achieved in a perfect competitive market. According to this benchmark, firms will hold market power as soon as they are able to raise prices above marginal costs. The assessment of market power should therefore be conducted by examining the extent to which firms are able to

1

For economic analysis, see Evans (2003) and Wright (2004). Act against Restraints of Competition (Bundesgesetzblatt, June 26, 2013). https://www.gesetzeim-internet.de/englisch_gwb/englisch_gwb.html#p0024. 3 Neven et al., (1993, p. 17). 2

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raise prices above marginal costs without losing buyers.4 In the industrial economics literature, measuring market power by calculating to what extent the price deviates from the firm’s marginal costs is demonstrated by the Lerner Index (P-MC/P),5 which was first proposed by Abba Lerner in 1934.6 However, in practice, market power is usually defined as the ability of the seller to set prices above the competitive level for a significant period of time.7 This is because estimating market power by focusing on the marginal cost may not be realistic in antitrust practices as marginal cost pricing is based on the ideal perfect competition model.8 In reality, almost all firms exert a certain level of market power if it is defined as the ability to set the price above the marginal cost.9 Hence, the price under competitive conditions is often taken as the practical benchmark. In merger analysis, the market power of the merged firm can be assessed through an indirect or a direct way.10 The indirect approach relies on several indicators, such as market share and the level of market concentration, to estimate the market power of the merged firm. The economic reason for this indirect approach is provided by the SCP framework developed by Joe Bain in 1951.11 Following his pioneering work, a large volume of empirical studies on the interaction between market performance and structural variables has been conducted among industrial organization scholars.12 The empirical work with a strong focus on market structure provided economic justification for an indirect approach to merger analysis. In particular, by following this approach, postmerger firms with a high market share or a large concentration level would be more likely to exert market power.13 In recent years, a new trend has been observed in the industrial organization literature that applies econometric techniques to measure market power in a direct manner. The structure-based view of market power is also outdated in calculating market share and measuring entry barriers in static status. The traditional method of using market share to assess market power also becomes inapplicable in digital markets. The Policy Department for Economic and Scientific Policy of the European Parliament argued that “Market shares or profit margins are less useful for determining market power. It is better to use indicators that inform about contestability, such as the presence of entry barriers, the availability of alternative routes to reach end-users, and 4

Neven et al., (1993, p. 17). Landes and Posner (1981, p. 939). 6 Lerner (1934, pp. 157–175); For a study on the history of the Lerner Index, see Giocoli (2012, pp. 181–191). 7 Motta (2004, p. 235); see for example, US Horizontal Merger Guidelines 1992, para 0.1: ‘Market power to a seller is the ability profitably to maintain prices above competitive levels for a significant period of time’. Lindsay (2006, p. 5). 8 Bisshop and Walker (2002). 9 Bisshop and Walker (2002). 10 Motta (2004, p. 117). 11 Bresnahan (1989, p. 1012). 12 Bresnahan (1989, p. 1013). 13 Baker and Bresnahan (1992, p. 4). 5

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the extent to which both incumbents and challengers are trying to create new markets by engaging in innovation in unexplored technologies/services.”14 Similarly, the UK OFT/CMA Guidelines on the Assessment of Market Power held the Schumpeterian view that a temporary monopolistic position may indicate the success of innovation, and high market share cannot be interpreted as anti-competitive. The guidelines stated that “In a market where undertakings compete to improve the quality of their products, a persistently high market share might indicate persistently successful innovation and so would not necessarily mean that competition is not effective. For example, effective competition in innovation might mean that, in order to stay ahead of its rivals, the market leader must improve its products and processes on a regular basis.”15 The European Commission adopted this argument in the Microsoft/Skype case by stating that “market shares only provide a limited indication of competitive strength in the consumer communications services markets.”16 In the Facebook/WhatsApp case, the Commission held that the market share indicator cannot indicate market power because of the character of frequent market entry and short innovation cycles in the consumer communications sector.17

7.3 Assessing Market Power in Online Markets In traditional markets, market power is defined as the ability to charge excessive prices, while in digital markets, market power refers to the ability to control data. The OECD provides the definition of market power in a digital market as “to supply products or services of reduced quality, to impose large amounts of advertising or even to collect, analyse or sell excessive data from consumers.”18 As has been discussed extensively in previous chapters, the traditional methods of assessing market power are not applicable in online markets because of the characteristics of multi-sided platforms and their pricing structure. The change of choosing market power assessment tools is evolutionary: both the concepts, the framework and the techniques are to be changed, as the Policy Department for Economic and Scientific Policy of the European Parliament stated: “Because of the strong feedback effects in digital markets, market power and dominance are fleeting attributes that depend on the 14

European Parliament (2015a, 2015b); see also GSMA (2016, at pp. 19–20). Office of Fair Trading (2004), paragraph 4.4 and footnote 19; see also GSMA (2016, at pp. 19–20). 16 Case No COMP/M.6281 Microsoft/Skype, at paragraph 78. 17 Case No COMP/M.7217—Facebook/WhatsApp, 3 October 2014, para 99 (“the Commission notes that the consumer communications sector is a recent and fast-growing sector which is characterised by frequent market entry and short innovation cycles in which large market shares may turn out to be ephemeral. In such a dynamic context, the Commission takes the view that in this market high market shares are not necessarily indicative of market power and, therefore, of lasting damage to competition.”) 18 OECD (2016), para 48. 15

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behaviour of the firm and the behaviour of others. As such, market shares or profit margins are less useful for determining market power. It is better to use indicators that inform about contestability, such as the presence of entry barriers, the availability of alternative routes to reach end-users, and the extent to which both incumbents and challengers are trying to create new markets by engaging in innovation in unexplored technologies/services.”19 In addition to the limitations of using the Lerner Index (price-marginal cost) as the indicator of assessing market power, it has also been concluded that market share is not a reliable indicator as it may imply that the incumbent has succeeded in promoting innovation.20 In the Microsoft/Skype merger case, the European Commission argued that because in dynamic markets the market shares can change quickly, market shares “only provide a limited indication of competitive strength.”21 In Facebook/Whatsapp, the Commission stated that “the consumer communications sector is a recent and fast-growing sector which is characterized by short innovation cycles in which large market shares may turn out to be ephemeral.”22 In the traditional market, monopoly market power is defined as the ability to raise market prices, while in online markets, firms with market power can effectively exclude competition in a dynamic data-driven environment. The structural analysis may become outdated because dynamic innovation indicates that a high market share or a dominant position is only temporary. However, the substitutability test also works to assess how easily the incumbents could be replaced by entrants that have developed technological innovations, and such a substitutability test has to consider the entry barriers created by economies of scale and the likelihood that the entrants can overcome the barriers to displace the established firms. An ABA report suggested that the market power of online platforms should be assessed from five aspects: the degree of scale economies in production, the ability to differentiate the network from other products and networks, the existence of alternatives to the network and the possibility of duplicating the incumbent network by entrants, superior technology and the intensity of network usage, which may facilitate entry into the network.23

7.3.1 Economies of Scale and Data Portability Because of the high fixed costs of data collection, storage and analysis, platforms with large datasets have the advantage of obtaining market power through economies of scale, and such benefits are often enhanced by feedback loops through network effects because when more users join the platform, the number of existing users 19

European Parliament (2015a, 2015b). Office of Fair Trading (2004). 21 European Commission, Decision of 7 October 2011, Case M.6281, Microsoft/Skype. Case No COMP/M.6281 Microsoft/Skype, at paragraph 78. 22 Facebook/WhatsApp, October 3, 2014, para 99. 23 IDCTF (2020, at p. 6). 20

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also increases, and the increased user volume contributes to the improvement of algorithm quality and the investment in platform quality and targeting. In this way, potential entrants can hardly challenge the market power obtained through economies of scale and feedback loops, and such market power can also be leveraged into other markets.24 The discussion of market power obtained through economies of scale is closely connected to the discussion of entry barriers in the previous chapter. The economic argument for market power assessment is that dominant platforms have better quality data analysis not only because of the large scope of the dataset but also because of the network effects that the value of the platform increases when there are more data available as more users join the platform. Potential entrants face difficulties accessing the datasets obtained by the incumbents and data provided by the users who join platforms of the incumbents. Section 6.3.4 of the previous chapter discussed the possibility of implementing data portability as a remedy to encourage competition on the use of datasets, and when users can switch their data to new entrants, the barriers of entry could be reduced. Data portability has been widely acknowledged by policy makers in the EU and has been discussed by technicians from internet firms in the format of APIs. In practice, data portability faces significant challenges in filling in the gap of technical knowledge between users and internet service providers, although data portability is aimed at impeding the market power of dominant platforms that is created through network effects. In practice it may hardly achieve this goal and reducing switching costs may have adverse effects of enhancing the monopoly position of the dominant firm, as users are unwilling to switch when they do not have worries about data access.25 Therefore, defining whether online platforms have market power and how such power is created is crucial before discussing the abusive behaviour of platforms in exercising such market power.

7.3.2 Essential Facility Doctrine Similar to the analytical logic of substitutability, whether the data has become an “essential facility” can also be discussed from the perspective of how “indispensable” the data is. When it is proven that there are barriers to entry because competitors cannot obtain access to market data in other places, it raises the theoretical debate on whether the incumbent firm has abused its dominant position.26 Like the debate on economies of scale in entry barriers, there are strong arguments against the view that

24

OECD (2020a). Krämer et al. (2020). 26 Diker Vanberg and Ünver argued that: “if a dominant company holds specific data that are indispensable for other undertakings to enter a new market, and the dominant company’s refusal to transfer that data eliminates all potential competition, then, in the absence of objective justifications, Article 102 TFEU could be relied on.” Vanberg and Ünver (2017, p. 9). 25

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the dataset is “indispensable” and the entrants cannot create their own,27 and against the legislative progress of applying essential facility doctrine to data. As CMA argued, “large online platforms may have some competitive advantage if there is proprietary data to which they have access. To the extent that such data is inaccessible to rivals, it may confer a form of ‘unmatchable advantage’, making it hard for competitors to compete although this depends on the facts of the particular case.”28 The US MCI Communications Corp. case29 formulated the principle that a plaintiff bringing an essential-facilities claim must prove that (1) a monopolist controls access to an “essential” facility; (2) competitors cannot “practically or reasonably” duplicate that facility; (3) the monopolist has denied access to the facility to a competitor and (4) the monopolist can feasibly share access to the facility.30 The court has to accept the claim that duplicating the facility is “not economically feasible”,31 and the plaintiff does not have “reasonable alternatives” to the facility.32 When firms hold competitive advantages in market entry, they can also leverage their dominance in other markets. Once online platforms have obtained market dominance, they can extend their market power to other markets through leveraging. It is often done by tying monopoly products with another product in the new market.33 They can also foreclose the sales of tied products and reduce their prices to force competitors to leave tied markets.34 In digital markets where the rate of technological progress is high, the monopoly platform is incentivized to accelerate the innovation progress and to rapidly bundle the new products with monopoly products to extend its dominance in the tied product markets and deter the entry of competitors.35 Leveraging strategy also contributes to the ‘winner-takes-all’ effect.

7.3.3 Strategic Market Status Starting in March 2020, the UK competition authority Competition & Markets Authority (CMA) led a Digital Markets Taskforce to provide advice to the government on the formulation and implementation of legal infrastructure for competition in digital markets.36 The Taskforce proposed the Strategic Market Status (SMS) to assess a firm’s market power. It is based on economic evidence to assess whether a 27

For example, Gilbert and Pepper (2015). CMA (2015, p. 6). 29 708 F.2d at 1132–33. 30 Freeman and Sykes (2019, at p. 14). 31 Hecht, 570 F.2d at 992. 32 See Laurel Sand & Gravel, Inc. v. CSX Transp., Inc., 924 F.2d 539, 544–45 (4th Cir. 1991); see also Freeman and Sykes (2019, at p. 14). 33 Ciriani and Lebourges (2018). 34 Ciriani and Lebourges (2018). 35 Ciriani and Lebourges (2018) and Carlton and Waldman (2002, p. 194). 36 CMA (2020). 28

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firm has “substantial, entrenched” market power and whether the firm has a strategic position (widespread and/or significant market power) in at least one digital activity from an ex-ante perspective. To consider whether the firm holds a strategic position, a variety of factors should be identified, including if the firm has achieved very significant size or scale, the firm is an important access point to customers (as a gateway) or the activity is an important input for a diverse range of other businesses, the firm can use the activity to extend market power to other activities and/or has developed an ‘ecosystem’ to protect market power, the firm can use the activity to determine the rules of the game, and the activity has significant impact on markets that have broader social or cultural importance.37 The SMS test provided a new threshold to assess whether a particular conduct of the gateway has a dominant position and whether such a position is strategic. The SMS test is also relevant to providing compliance guidance and preventing abusive behaviour ex-ante, as when the firm has met the SMS test, the Digital Markets Unit (DMU) gave the firm a code of conduct ex-ante to follow to prevent the business from engaging in abusive conduct.38

7.3.4 Abusive Behaviour The concept of abuse of dominant position has been defined by the OECD as “anticompetitive business practices in which a dominant firm may engage in order to maintain or increase its position in the market”39 To identify a behaviour as abuse of dominance, it is generally required to define the infringer as having obtained market dominance in the relevant market, and its behaviour has fallen within a category of abuse, and such behaviour must have anti-competitive effects and is not counter balanced by efficiency gains.40 In Europe, the categories of abusive behaviour include exclusionary and exploitative abuses. Akman defined that “‘Exclusionary’ abuses refer to those practices of a dominant undertaking which seek to harm the competitive position of its competitors or to exclude them from the markets, whereas ‘exploitative’ abuses can be defined as attempts by a dominant undertaking to use the opportunities provided by its market strength in order to harm customers directly.”41 The OECD’s empirical study shows that from 2000 till 2017, 93% of the abuse of dominance cases were exclusionary, and only 7% were exploitative. Exclusionary practices include refusal to supply (35%), exclusionary pricing (18%), exclusivity agreements (12%), tying or bundling (11%), abuses of IPR and regulation (9%), and others (7%).42 Exploitative abuses have also rarely been implemented in Brazil,

37

CMA (2020, pp. 30–31). CMA (2020, p. 36). 39 OECD (1993) 40 OECD (2018). 41 Akman (2008). 42 OECD (2018, p. 27) and Dethmers and Blondeel (2017). 38

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Australia, South Korea, Turkey and Japan, and are not regulated by competition rules in North and Latin America (including the US, Canada and Mexico).43 The economic analytical framework and assessment tools that are used in abuse of dominance cases are the examples to study to learn how market power is measured in digital markets. Firms’ ability to engage in abusive behaviour is clearly the exercise of market power. In most antitrust jurisdictions, an effect-based approach has been applied, and both pro- and anti-competitive effects are considered when considering abusive behaviour. In traditional markets, dominance is established when competition authorities can decide on firms’ market power, which is often identified through indirect assessment tools such as market share. In view of the dynamic changes of digital markets and the characteristics of the data economy, both measurement tools for establishing dominance and assessing abusive behaviour must be reconsidered. The starting point for the revisions might be theories of harm and reducing risks of error in competition enforcement. The law and economics literature categorizes two risks of error: Type 1 errors refer to over-enforcement that finding conducts is not anti-competitive (false positive), and Type 2 errors refer to under-enforcement when not finding conduct that is anti-competitive (false negative). Using the benchmark of risks of error and theory of harm, Condorelli and Padilla proposed that an effect-based approach is the most appropriate in the justification of abuse of dominance cases, and antitrust authorities should measure the costs of harm and efficiencies: conduct should be judged to be per se illegal when the risk of type 1 errors is extremely unlikely.44 In cases where there are risks of both type 1 and type 2 errors but the expected costs of type 2 errors are higher and the costs of potential harm are larger, the efficiency defense should be applied with a rebuttable assumption of illegality. On the other hand, if the costs of type 1 errors are higher and the potential efficiency gains are higher, efficiency defense should be applied with a rebuttable assumption of legality. Finally, conduct is permitted when the risk of type 1 error is extremely likely.45 In abuse of dominance cases, Padilla argued that a rebuttable presumption of legality should be applied for predatory pricing, refusals to deal, margin squeeze and tying and bundling. A rebuttable presumption of illegality should be used for exclusive dealing.46 Type 1 and Type 2 errors show the paradox in assessing abusive dominance cases: competition authorities may have overintervened dominant firms that do not have anti-competitive effects or have overlooked firms with market power exercising exclusionary behaviour. Condorelli and Padilla’s approach of categorizing abusive behaviour in accordance with costs of harm and risks of error simplified the complex weighing of pro- and anti-competitive effects and limited the scope of use of the rule of reason. Extending their analysis, the OECD provided broad, full indictors in the assessment of dominance including both direct indictors (such as an assessment of substitutability) and indirect indictors (including entry barriers, profitability 43

OECD (2018, p. 27). Condorelli and Padilla (2019, p. 38). 45 Condorelli and Padilla (2019, 2020). 46 OECD (2020b, p. 10). 44

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and market shares).47 The OECD’s list of indictors proved that a simplified indirect approach in traditional markets, as discussed in Sect. 7.2, to assess market power, such as through a simple SSNIP or product characteristics to define relevant market, or to use a simple market share factor in assessing market power should be replaced by a broadly-based full list of factors to directly assess market power for digital firms. The OECD argued that the assessment of substitutability is the fundamental, key, and direct factor in assessing dominance because when there are limitations to substitution imposed on consumers and competitors, on the demand side there is no access to substitutes, and on the supply side, there are significant barriers to entry. Thus, the dominant firm can make decisions independently of its competitors, customers and final consumers.48 Substitutability can be quantitatively calculated through elasticity of demand or using event studies to examine the effect of a change on the demand of the market.49 Although the lists of sources of evidence (see Table 7.1) tends to be extensive and broad, and the list has included many separate issues that are discussed in the book, their analytical framework summarizes the factors that are discussed in different sources and sets the basis for applying an “effect-based” approach in digital competition cases.

7.3.5 Concentrations in Digital Market The assessment of market power is also evident in the analysis of mergers and acquisitions in the digital market. As discussed in the introduction, dominant platforms expanded their market power through large scale acquisitions in recent years. In 2017, Alphabet, Amazon, Apple, Facebook and Microsoft spent 31.6 billion USD on the acquisitions of start-up companies.50 From 2015 to 2017, Alphabet, Amazon, Apple, Facebook, and Microsoft made 175 acquisitions in total.51 The leading concentration cases of Facebook/WhatsApp (2014), and Microsoft/LinkedIn (2016) have attracted much attention from both public and private sectors. Concentration cases in digital markets often take a rule of reason approach by evaluating the efficiency gains, taking into account the potential trade-offs between allocative, productive and dynamic efficiencies, and the anti-competitive effects. In evaluating the efficiency gains of merger cases, two methods have been developed and adopted by competition authorities: Data Development Analysis (DEA) and Compensating Marginal Cost Reduction (CMCR). DEA is a tool to use crosssectional or panel data on multiple inputs and outputs of production to measure technical, allocative and scale efficiency effects.52 It uses the data of costs and outputs 47

OECD (2020b, p. 20). OECD (2020b, p. 16). 49 OECD (2020b, p. 17). 50 The Economist (2018). 51 Gautier and Lamesch (2020). 52 OECD (2012), Kaur and Kaur (2010, pp. 27–50), Kwoka and Pollitt (2010, pp. 645–656). 48

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Table 7.1 Sources of evidence to be evaluated for establishing dominance in digital markets Issues to be evaluated

Sources of evidence

Market definition (one multi-sided platform or (1) Platforms’ business model (internal firm two interlinked markets?) documents, analyst reports, information requests from market participants) (2) Externalities (information requests from paying side) (3) Pricing strategies (evidence of cross-subsidizing between business units, internal documents) Market definition (incorporating nonprice dimensions of competition)

(1) Consumer preferences (surveys, analyst reports, interviews) (2) Vies of the relevant dimensions of competition (internal documents, interviews) (3) Information on innovation (R&D spending, past patterns of product changes or new products, internal firm documents, analyst reports)

Demand-substitutability

(1) Substitutes for consumers and limitations to substitution due to e.g., switching costs (2) Sales data to calculate elasticity of demand and estimate diversion ratios, and conduct event studies

Entry barriers and potential competition

(1) Interviews from recent, potential or failed entrants regarding costs, regulatory burden, network effects, technological factors and demand-side characteristics on the incumbents’ advantages (2) Information requests to validate potential entry barriers (such as existence of regulatory exemptions for incumbents, and internal documents including business cases for investments, email correspondence and research reports) (3) Information on the role of data and network effects in the markets

Profitability

(1) Revenue and cost data to calculate Lerner Index (2) Commentary by investment analysts, internal documents of documents from financial advisors

Market shares

(1) Data from firms and competitors (2) Data from third-party data providers (3) Information on nonprice competition

OECD (2020b, at p. 20)

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of companies in the same industry to estimate the lowest level of costs achievable for different levels of output to determine how much the costs that the merging parties can reduce.53 CMCR is a tool using price–cost margins and diversion ratios to estimate how much cost efficiency is required to offset the market power effect of a merger.54 The data required for CMCR are the premerger data on markups and diversion ratios of the merging parties. This method has been used by the Swedish Competition Authority in merger cases.55

7.4 Case Studies This section studies cases of abuse of dominant positions and concentrations to understand the assessment of market power in digital markets. This section will discuss the case decisions of Google search, Facebook/WhatsApp,56 Microsoft/Skype57 and the SAMR/Alibaba Group. The Google Search case shows the different approaches taken by the US and EU, Microsoft/Skype cases show the merger cases in the EU, and the SAMR/Alibaba case is the first case of online platforms in China. The case decisions in the US, EU and China showed that efficiency gains and innovation effects are taken as the priority in the US, consumer behaviour has been extensively discussed by the European Commission, and the Chinese competition authorities focus on discussing the reasons to support the argument of market dominance and the abusive conduct of the dominant firm.

7.4.1 Google Search Case In Google Search (Shopping)58 case, the European Commission held that Google abused its dominant position because it favours its own comparison-shopping service by giving it a prominent placement, and such favours are considered to be an “illegal advantage”.59 The European Commission started its investigation on November 30, 2010, for Google’s abuse of a dominant position. Before the decision made by the Commission in June 2017, Google provided commitments to the Commission in October 2013 and proposed guarantees against earlier commitments on the portability

53

GSMA (2016, p. 35). OECD (2020c, p. 28) and OECD (2012). 55 GSMA (2016, p. 35). 56 COMP/M. 7217, Facebook/WhatsApp, 3 October 2014. 57 COMP M. 6281, Microsoft/Skype, 7 October 2011. 58 Case AT.39740, Google Search (Shopping), 2017 E.C. 1/2003, http://ec.europa.eu/competition/ antitrust/cases/dec_docs/39740/39740_14996_3.pdf. 59 European Commission (2012, 2017). 54

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of advertising campaigns.60 In February 2014, Google provided new commitments on the search bias concern raised by the EU Commission.61 In April 2015, the Commission issued the Statement of Objections (SO) to Google,62 and in July 2016, the Commission issued a Supplementary Statement of Objections.63 The Google search case raised anti-competitive concerns because Google favours its affiliates (the comparison shopping product, also called “Google Shopping”) on general search result pages. Google shopping options are presented at the top, showing detailed information such as products and prices and placing rivals of comparison-shopping services’ search results at a lower-ranked position without showing detailed features of products and merchants. The Google shopping case raised competition issues of how to define firms’ abusive behaviour through self-referencing, and in particular this case responds to the competition concerns in digital markets of (1) defining relevant market; (2) defining the harmful conduct based on the theory of market power leveraging (from Google’s dominant position in the general search market into the comparative shopping services market) (3) analysing the impact of consumer harm (such as reduced consumer choices, higher prices, less innovation and efficiency gains). The US Federal Trade Commission (FTC) conducted an investigation in Google for 19 months and closed the case in January 2013. The FTC held that Google had not violated Section 5 of the FTC Act through search self-preferencing over competitors.64 With limited sources on the process of reasoning by the FTC, it seems that economic arguments on innovation and efficiency had played an important role, as The Commissioner Maureen Ohlhausen65 held in the Statement that “technology industries are notoriously fast-paced, particularly industries involving the Internet. (…) The decision to close the search preferencing part of this investigation, in my view, is evidence that this agency understands the need to tread carefully in the Internet space.” The theory of leveraging market power was not recognized and the FTC accepted the economic argument that Google introduced the “Universal Search” box to promote its own vertical properties as a product innovation.66 The FTC statement held that introducing this product is to “quickly answer, and better 60

European Commission (2013b) and FTC (2013a); see also The letter from Goolge Inc. to the FTC, File No. 111-0163, December 27, 2012, available at of David Drummond in Google Inc. File No. 111-0163, 27 December 2012, available at https://www.sec.gov/Archives/edgar/data/1288776/ 000119312513002492/d461279dex101.htm. 61 Commissioner Almunia, Statement on the Google investigation, Press Conference Brussels, 5 February 2014, available at http://europa.eu/rapid/press-release_SPEECH-14-93_en.htm. 62 European Commission Press Release IP/15/4780, Antitrust: Commission Sends Statement of Objections to Google on Comparison Shopping Service, Opens Separate Formal Investigation on Android (Apr. 15, 2015). 63 European Commission Press Release IP/16/2532, Antitrust: Commission Takes Further Steps in Investigations Alleging Google’s Comparison Shopping and Advertising-Related Practices Breach EU Rules (July 14, 2016). 64 FTC (2013b). 65 FTC (2013c). 66 Salinger and Levinson (2015, pp. 25–57).

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satisfy, its users’ search queries by providing directly relevant information”, and this improvement of product design is “competition on the merits”. Although it may have a “byproduct effect” in which rivals may lose sales, competition law protects product innovation and the competitive process, and the rivals are not in a disadvantaged position as the FTC concluded that “we have not found sufficient evidence that Google manipulates its search algorithms to unfairly disadvantage vertical websites that compete with Google-owned vertical properties”.67 The FTC decision did not define dominant position and did not provide an indicator to measure the market power of Google. The FTC’s decision has positively responded to the economic argument that against antitrust intervention on tech companies as Google’s business strategy is beneficial to promote competition, efficiency and innovation,68 and economists such as Salinger and Levinson have openly supported the FTC arguments.69 The European Commission denied the argument of “competition on the merits”, and proposed that leveraging was an independent form of abuse by stating that “conduct consisting in the use of a dominant position on one market to extend that dominant position to one or more adjacent markets… constitutes a well-established, independent, form of abuse falling outside the scope of competition on the merits.”70 The European Commission held that because Google has abused its dominant position in general searches and its rivals do not have an incentive to innovate. The Commission identified three markets, the markets for web search, search advertising and comparison shopping, and defined Google as having a dominant position in the first two markets. The Commission also observed that consumers are likely to click the results on the first page and that self-preferencing behaviour will reduce consumer choice. This argument could be supported by the consumer behaviour literature, which shows that consumers pay most attention to the top links of the search result, and a higher ranking in the search results is also attracting more attention.71 Hotchkiss et al.’s experiment on eye-tracking found that consumers’ attention focused on the first three organic listings and they called it “The Golden Triangle”.72 In the research by De Los Santos and Koulayev, in the sample of 23, 959 search histories on travel and accommodation websites, 44 percent of the total clicks were on the top three links.73 Based on a dataset from the UK website Kelkoo, Baye et al. proved that 65% of total clicks were on the top two links.74 The dataset of 1 million consumer searches from November 2008 to January 2009 studied by Ghose et al. showed that one position higher on the search results on average has a 7.31% increase in clicks and a 4.56% increase in actual purchases.75 67

FTC (2013b). See for example, Bork and Sidak (2012, pp. 663–700) and Manne and Wright (2011, pp. 171–244). 69 Salinger and Levinson (2015, pp. 25–57). 70 European Commission June 27, 2017, Case AT.39740, Google Search (Shopping), para 649. 71 CMA (2017, at p. 43). 72 Hotchkiss et al. (2005) 73 De los Santos and Koulayev (2012). 74 Baye et al., (2009, pp 935–975). 75 Ghose et al. (2011). 68

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7.4.2 Microsoft/Skype On September 2, 2011, the European Commission received notification of the proposed concentration by Microsoft and Skype.76 Microsoft provided communication services under the brand “Windows Live Messenger” (WLM) for consumers and Lync for enterprises, Skype provided instant messaging (IM), voice and video communications over the Internet, and the Commission assessed the relevant market for this case was consumer and enterprise communications markets.77 The key issue of this case is to separate the market of consumer communications services and enterprise communications services. Based on market investigation and the opinions from original equipment manufacturers (OEMs), the Commission held that those two markets are different for three reasons.78 (1) Differences in the willingness to pay for the service: enterprises purchase communication services at high prices, and consumers are less willing to pay for the communication service; (2) enterprise communications services are more sophisticated and reliable in feature and quality; (3) There are very few communications application providers that design products for both consumers and enterprise users. The Commission concluded that the main functionalities of consumer communications services, including IM, voice calls and video calls have been integrated by social networking websites or online social environments, and consumers can switch with little cost among IM, voice calls and video calls. By taking the study with data from three consumer communication companies, the Commission concluded that setting up a new consumer communication service does not involve high costs.79 The Commission also denied the economies of scale argument by network effects because consumer communication services make a majority of calls to close family and friends in the “inner circle”.80 It is also the reason that consumers are more likely to multi-home and switching to a new consumer communications services provider is not costly.81 The entry barriers to the market are low because of the fast growth of new entrants in the consumer communications services markets. The Commission also concluded that Skype does not have business functions and does not have collaboration, management and administration tools, so it is not a market player in the enterprise communications services market.82 There are numerous competitors in the enterprise communications services market, including 76

Case No. COMP/M. 6281, Microsft/Skype, October 7, 2011, C (2011) 7279. Case No. COMP/M. 6281, Microsft/Skype, October 7, 2011, paras 2–7. 78 Case No. COMP/M. 6281, Microsft/Skype, October 7, 2011, paras 13–15. 79 Case No. COMP/M. 6281, Microsft/Skype, October 7, 2011, paras 88–89. 80 Case No. COMP/M. 6281, Microsft/Skype, October 7, 2011, paras 92. 81 Case No. COMP/M. 6281, Microsft/Skype, October 7, 2011, paras 92. 82 Case No. COMP/M. 6281, Microsft/Skype, October 7, 2011, paras 192. 77

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Citrix, Adobe, Intercall, Alcatel-Lucent, Avaya and Google. Because of the fast growth of the market, transactions cannot raise anti-competitive concerns.83

7.4.3 Facebook/WhatsApp On October 29, 2014, the European Commission received the notification of a proposed concentration of Facebook/WhatsApp and concluded that the transaction was compatible with the internal market and with the EEA Agreement.84 This case can show how the European Commission selects and uses the economic arguments that are extensively discussed in the previous chapters of this book on, for example, market shares in the relevant market, entry barriers, switching costs and consumer choices. The Commission held that the large market share obtained by the firm cannot indicate market power, and the competitiveness of the market can be observed from the perspective of consumer behaviour. This case showed the general analytical framework taken by the European Commission on competition issues in digital markets. In this case, the Commission defined the relevant market as a consumer communications service, which are “multimedia communications solutions that allow people to reach out to their friends, family members and other contacts in real time.” These services were developed as software applications for personal computers (PCs) and later shifted toward smart mobile devices, including smartphones and tablets, and provided through consumer communication apps.85 Consumer communications services can be provided as a separate app (such as WhatsApp, Viber, Facebook Messenger and Skype) or as a part of a social networking platform (such as Facebook or LinkedIn). In this case, the Commission did not use SSNIP or SSNDQ or other economic methods to define the relevant market of consumer communications services, but argued that three product characteristics differentiate between consumer communications services and enterprise communications services based on previous cases of Microsoft/Skype86 and Microsoft/Nokia87 : (1) enable one-to-one and/or group realtime communications in various forms; (2) in terms of the operating platform, some consumer communications apps are only available on one system (proprietary apps), and others are offered for download on multiple operating systems (cross-platform apps); (3) in terms of the operating devices, some apps are available for all types of devices, some only operate on smartphones, or can also be used on tablets and PCs.88

83

Case No. COMP/M. 6281, Microsft/Skype, October 7, 2011, paras 200. Case No COMP/M.7217—Facebook/WhatsApp, 3 October 2014, C (2014) 7239 final. 85 Case No COMP/M.7217—Facebook/WhatsApp, 3 October 2014, paras 13–14. 86 Commission decision of 7 October 2011 in Case M.6281—Microsoft/Skype. 87 Commission decision of 4 December 2013 in Case M.7047—Microsoft/Nokia. 88 Case No COMP/M.7217—Facebook/WhatsApp, 3 October 2014, paras 16–18. 84

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Because WhatsApp operates only on smartphones, the relevant product market will be consumer communications apps for smartphones. Facebook provides online services of online (non-search) advertising services on Facebook’s social networking platform on PCs and mobile devices, and in this online service Facebook collects and analyses user data on its social networking platform, to provide targeted advertising. WhatsApp does not sell advertising, and thus has not collected any user data.89 The Commission mentioned the SSNIP test in deciding the market of online and offline advertising, and search and non-search advertising on PC versus mobile devices and concluded that online advertising is a separate product market. In the analysis of competition effects, the Commission focused on consumer behaviour, and argued that many consumers are multi-homing, in that they use more than one consumer communications app at the same time, and the improvement of the app in innovation is investigated through (1) the reliability of the communications service, and (2) privacy and security.90 These two issues could both be interpreted as product quality. The Commission also mentioned the network effects in consumer communications services, the importance of being perceived as “trendy and cool” when choosing a communication app and the fact that the consumer communications app is price sensitive and often provided for free. The conclusion of consumers’ multi-homing is important because it indicates that the barriers to entry and switching costs will be low. The arguments of consumers’ multi-homing are also the key for this case. Based on the results from the consumer survey, the Commission provided the reasons for the low switching costs that (1) consumer communications apps are offered free of charge or at a very low price; (2) all consumer communications apps can be downloaded easily and function properly on the same device; (3) consumers can pass from one to another app easily after they are installed; (4) the learning costs of switching to a new app are minimal; and (5) given the large number of consumer reviews on app stores, information about new apps is easily accessible.91 The Commission also denied behaviour arguments of the status quo bias when software are preinstalled because consumers can download both Facebook Messenger and WhatsApp at the same time and can switch to another in case of dissatisfaction.92 Similarly, the Commission also argued that the messaging history is accessible on a phone so switching costs cannot come from data portability. The Commission also concluded that there will be sufficient competitors offering targeted advertising and whether after the merger WhatsApp will introduce advertising will not raise anti-competitive concerns,93 and because there are a number of Internet firms which collect user data besides Facebook, such as Google, Apple, Amazon, eBay, Microsoft, AOL, Yahoo!, Twitter, IAC, LinkedIn, Adobe and Yelp,

89

Case No COMP/M.7217—Facebook/WhatsApp, 3 October 2014, paras 70–71. Case No COMP/M.7217—Facebook/WhatsApp, 3 October 2014, para 87. 91 Case No COMP/M.7217—Facebook/WhatsApp, 3 October 2014, para 109. 92 Case No COMP/M.7217—Facebook/WhatsApp, 3 October 2014, para 111. 93 Case No COMP/M.7217—Facebook/WhatsApp, 3 October 2014, para 179. 90

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the issue of whether Facebook will strengthen its market dominance on online advertising services by using WhatsApp to collect user data will not raise anti-competitive concerns either.94

7.4.4 SAMR/Alibaba Group On December 24, 2020, the competition authority in China (SAMR) announced its investigation into Alibaba Group Holding for “choose one from two/either or” practice.95 In April 2021, in accordance with Article 17(4) of the Anti-monopoly Law (AML), the SAMR imposed a fine of 18.228 billion RMB (4% of the sales in China in 2019) administrative fines on Alibaba Group, ordering Alibaba Group to stop the illegal conduct, and issued an Administrative Guidance Letter, requiring it to make rectification and to submit self-examination compliance reports to the SAMR for three consecutive years.96 It is the first case decided by the SAMR on monopolistic behaviour in digital markets. The SAMR’s decision on relevant markets is based on the Anti-monopoly Law of China (AML) and the Guide of the Anti-monopoly Committee of the State Council for the Definition of Relevant Markets. There are six steps in determining the dominance of the Alibaba Group97 : (1) investigating market shares and gross merchandise volume (GMV), (2) understanding the market power of the firm in four aspects: formulating platform service rules and transaction agreements, controlling service prices, and the ability to obtain traffic and sales channels, (3) evaluating financial and technical conditions from sales accounts, net profit, market value, the power of controlling data, algorithms, and computing, (4) determining the firms’ dependence on the platform through the number of active users, average user consumption, cross-year consumer retention rate, data portability and lock-in effect, (5) entry barrier analysis (cost of entry, difficulty in obtaining critical scale), and (6) understanding the advantages of the relevant market through logistics, payment and cloud computing. The SAMR concluded that Alibaba has abused its dominant position by imposing “choosing one from two” requirements of the firm using its platform, which prohibits firms from operating on other competing platforms and prevents them from participating in promotional activities on other competing platforms. The SAMR held 94

Case No COMP/M.7217—Facebook/WhatsApp, 3 October 2014, paras 180–190. SAMR (2020). SAMR Investigates Alibaba for Alleged Monopolistic Behavior ( ) (December 24, 2020), available at http://www.samr.gov.cn/xw/zj/202012/t20201224_324638.html. 96 SAMR, China’s Initiatives to Identify and Address Competition Issues in the Digital Market, Anti-monopoly Bureau of State Administration for Market Regulation, April 28, 2021. 97 SAMR, China’s Initiatives to Identify and Address Competition Issues in the Digital Market, Anti-monopoly Bureau of State Administration for Market Regulation, April 28, 2021. 95

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that such a requirement has four negative effects98 : (1) deviating from the idea of open, inclusive and shared development of the platform economy; (2) excluding and restricting competition from the relevant markets; (3) harming the interests of operators and consumers on the platform; and (4) weakening the drive to innovation and development vitality of the platform economy.

7.5 Implications of the Cases Within the limited resources for understanding the reasoning process taken by the competition authorities, it seems to be clear that competition agencies in China and the EU rely on the principle of defining market dominance and the abusive conduct of market dominance. It has a different, albeit opposite, perspective from the US when the step of defining market dominance has been abandoned. The European Commission denied the argument that consumers benefit from product innovation and the introduction of the “universal search” box by Google is “competition on the merits”. Although competition authorities have acknowledged the importance of dynamic efficiency and the extensive use of market definition, the limitations of market share, relying on the market dominance principle, may weaken the efficiency argument and the competition effects may even become negative once market dominance has been determined. Effects on consumers may also have a different conclusion depending on whether efficiency or consumer behaviour is analysed at the first stage. The analytical framework taken by competition authorities in the US and EU could reflect the long debate of the meaning of consumer welfare, as the US acknowledged Bork’s interpretation of total welfare, and increased dynamic efficiency can directly increase total welfare, whereas in the EU, narrowly defined consumer welfare will take distribution effects into account. Efficiency gains are considered at the second stage, and consumer behaviour and consumer welfare effects are analysed in a narrower sense.

7.6 Conclusions The complexity of assessing market power in competition law may come from the complexity of competition goals: the goals of promoting efficiency, consumer welfare, innovation, social welfare and fairness are often in conflict with each other. When a dominant firm is efficient in promoting innovation and increasing investment in R&D, it may impede competition and lead to price increases for consumers. The second layer of complexity comes from the insufficient development of competition 98

SAMR, China’s Initiatives to Identify and Address Competition Issues in the Digital Market, Anti-monopoly Bureau of State Administration for Market Regulation, April 28, 2021.

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instruments and the ongoing debate on competition theories. Distinct from traditional markets, competition between digital platforms is technologically driven. While the Schumpeter versus Arrow debate shows that a temporary monopoly position may become necessary for firms to earn profits and invest in R&D, technological regimes and innovation cycles also show that technological change is highly dynamic and that a ‘replacement effect’ is predominant. As stated in the introduction, the paradox of antitrust lies in the fact that economic arguments are often two-sided and uncertain economic tools are designed to achieve undecided competition goals. The two opposite views of whether antitrust intervention in the tech market is also reflected in the debate of economies of scale of data, market entry, market share, market power and the definition of relevant market. While the traditional debate on Chicago’s versus structure-based Harvard’s approach poses different weights on the assessment of market share and relevant markets in determining market power, the US versus EU approach on deciding the Google case reflected the different views of the stage at which dynamic innovation-driven competition should be taken into account. Given the often mixed and contradicting economic evidence, economic arguments on innovation, consumer behaviour and consumer welfare may be manipulated to support a particular competition goal and the attitude of policy makers on whether to intervene in the market with data giants. When it is determined that defining market dominance is necessary, the principle of innovation may be applied at a later stage, and the analysis of competition effects goes in a different direction. The case decisions on Google Search, Facebook/WhatsApp and the SAMR/Alibaba Group in this chapter are also examples of this debate and reflect the broader view of competition goals and dynamic versus structure-based analysis of market dominance.

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FTC. (2013b, January 3). Statement of the Federal Trade Commission Regarding Google’s Search Practices In the Matter of Google Inc. FTC File Number 111-0163. Available at https://www.ftc.gov/sites/default/files/documents/public_statements/statement-commis sion-regarding-googles-search-practices/130103brillgooglesearchstmt.pdf FTC. (2013c). Statement of Commissioner Maureen K. Ohlhausen in the Matter of Google, Inc. Available at https://www.ftc.gov/sites/default/files/documents/public_statements/statement-com missioner-maureen-ohlhausen/130103googlesearchohlhausenstmt.pdf Giocoli, N. (2012). Who invented the Lerner Index? Luigi Amoroso, the dominant firm model, and the measurement of market power. Review of Industrial Organization, 41, 181–191. Gilbert, P., & Pepper, R. (2015). Privacy considerations in European Merger Control: A square peg for a round hole. Competition Policy International. Available at https://www.competitionpolicyi nternational.com/assets/Uploads/PepperGilbertMay-152.pdf Gautier, A., & Lamesch, J. (2020, February 3). Mergers in the digital economy (CESifo Working Paper No. 8056). Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3529012 GSMA. (2016, October). Resetting competition policy frameworks for the digital ecosystem. Ghose, A., Ipeirotis, P., & Li, B. (2011). Examining the impact of search engine ranking and personalization on consumer behavior: Combining Bayesian modeling with randomized field experiments. In Workshop on Information Systems and Economics (WISE), Shanghai, December. Hotchkiss, G., Alston, S., & Edwards, G. (2005, June). Eye tracking study: An in depth look at interactions with Google using eye tracking methodology, released by Enquiro, EyeTools and DidIt. Available at https://searchengineland.com/figz/wp-content/seloads/2007/09/hotchkiss-eye-tra cking-2005.pdf IDCTF. (2020, February 27). Common issues relating to the digital economy and competition. Report of the International Developments and Comments Task Force (IDCTF) on Positions Expressed by the ABA Antitrust Law Section between 2017 and 2019. Available at https://assets.publishing. service.gov.uk/media/5fce08ece90e07562e8feed7/200727_Response_to_CFI_ABA_.pdf Krämer, J., Senellart, P., & de Streel, A. (2020). Making data portability more effective for the digital economy. Centre on Regulation in Europe (CERRE). Available at https://cerre.eu/public ations/report-making-data-portability-more-effective-digital-economy Kaur, P., & Kaur, G. (2010). Impact of mergers on the cost efficiency of Indian commercial banks. Eurasian Journal of Business and Economics, 3(5), 27–50. http://ejbe.org/EJBE2010Vol03No 05p27KAUR-KAUR.pdf Kwoka, J., & Pollitt, M. (2010). Do mergers improve efficiency? Evidence from restructuring the U.S. electric power sector. International Journal of Industrial Organization, 28(6), 645–656. Landes, W. M., & Posner, R. A. (1981). Market power in antitrust cases. Harvard Law Review, 94(5), 939–996. Lindsay, A. (2006), The EC merger regulation: Substantive issues. Sweet & Maxwell Lerner, A. P. (1934). The concept of monopoly and the measurement of monopoly power. Review of Economic Studies, 1(3), 157–175. Manne, G., & Wright, J. (2011). Google and the limits of antitrust: The case against the case against Google. Harvard Journal of Law and Public Policy, 34(1), 171–244. Motta, M. (2004), Competition policy—Theory and practice. Cambridge University Press Neven, D., Nuttall, R., & Seabright, P. (1993). Mergers in daylight, the economics and politics of European merger control. The Centre for Economic Policy Research. Office of Fair Trading. (2004). Assessment of market power—Understanding competition law. OFT415. Available at https://www.gov.uk/government/publications/assessment-ofmarket-power; https://assets.publishing.service.gov.uk/government/uploads/system/uploads/att achment_data/file/284400/oft415.pdf OECD. (1993). Glossary of industrial organisation economics and competition law. Compiled by R. S. Khemani, & D. M. Shapiro, commissioned by the Directorate for Financial, Fiscal and Enterprise Affairs. Available at https://www.oecd.org/regreform/sectors/2376087.pdf OECD. (2012, October 11). The role of efficiency claims in antitrust proceedings. Background note by the Secretariat. http://www.oecd.org/competition/EfficiencyClaims2012.pdf

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OECD. (2014). Data-driven innovation for growth and well-being: Interim synthesis report. Available at https://www.oecd.org/sti/data-driven-innovation-9789264229358-en.htm OECD. (2016, October 27). Big data: Bringing competition policy to the digital era. DAF/COMP (2016)14. https://www.oecd.org/competition/big-data-bringing-competition-pol icy-to-the-digital-era.htm OECD. (2018, November 20). Personalised pricing in the digital era, background note by the Secretariat. OECD Directorate for Financial and Enterprise Affairs Competition Committee, DAF/COMP (2018)13. www.oecd.org/daf/competition/personalised-pricing-in-the-digital-era. htm OECD. (2020a). Conglomerate effects of mergers—Background note by the Secretariat. https://one. oecd.org/document/DAF/COMP(2020)2/en/pdf OECD. (2020b). Abuse of dominance in digital markets. www.oecd.org/daf/competition/abuse-ofdominance-in-digital-markets-2020.pdf OECD. (2020c). Merger control in dynamic markets. http://www.oecd.org/daf/competition/mergercontrol-in-dynamic-markets.htm Salinger, M. A., & Levinson, R. J. (2015). Economics and the FTC’s Google investigation. Review of Industrial Organization, 46, 25–57. The Economist. (2018, October 26). American tech giants are making life tough for startups. Vanberg, A. D., & Ünver, M. B. (2017). The right to data portability in the GDPR and EU competition law: Odd couple or dynamic duo? European Journal of Law and Technology, 8(1), 1–7. Wright, J. (2004). The determinants of optimal interchange fees in payment systems. Journal of Industrial Economics, 52(1), 1–26.

Chapter 8

Data Monopoly and the Impact on Consumer Welfare

Abstract The impact of data-driven business conduct on consumer welfare is multidimensional. From an economic point of view, data collected by firms will be used to improve the quality of the services and thus benefit consumers. Studies have also shown that consumers are aware of the benefits of personalized products that are provided through collecting consumers’ personal data. Economic studies of price discrimination have also shown that the effects on consumers are uncertain: for price discrimination based on consumer preferences, the effects increase the intensity of competition and thus benefit total consumer welfare, although the distribution of wealth among different consumer groups may not be fair. For price discrimination based on searching habits, the overall effects on consumer welfare are negative, but with exceptions. The strongest arguments that data-driven conduct may also violate consumer privacy have raised counterarguments, and the enforcement of competition law may not be the best instrument to protect consumer privacy. This chapter discusses the impact on consumer welfare, including the loss of consumer welfare, the measurement of the impact of consumer welfare in the context of a data economy, a comparative case study and trade-off between consumer welfare and other objectives in data monopoly cases.

8.1 Introduction Economists have agreed that the development of online platforms has provided a way to solve market failure in traditional markets. The improvement of delivery systems and logistics has reduced the transaction costs of delivery, and consumers benefit from a wider range of choices of retailers. Consumer surveys also show that consumer welfare increases with the availability of improved product quality and services. The review and rating systems on retail platforms have also reduced information asymmetry and thus improved consumer confidence in the purchase of experience goods.

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On the other hand, the increase in consumer welfare is often calculated as total welfare, the distribution effects among consumer groups are more complex because of the differences in search behaviour and heterogeneous preferences among consumer groups. The effects of targeted advertisement, price discrimination and privacy protection have to be analysed on a case-by-case approach. As Johannes Laitenberger, the former Director General of the European Commission’s Directorate General for Competition, has made clear: we must take an empirical driven view of consumer welfare and recognize that some consumer harm is not readily visible in price and output effects”,1 the welfare effects that online platforms impose on consumers are much more complex. When online services are provided at zero price, consumers provide their personal data or attention for the use of digital goods. While economists have argued that online platforms have significantly improved quality and services, and thus increased consumer welfare, there are also strong arguments that consumers are manipulated by digital giants and suffer consumer harm when platforms are able to charge personalized prices or collect extensive data from consumers. The analysis is more difficult when the literature on consumer behaviour shows there is a paradox that consumers ignore or are unaware of, learning privacy techniques, while at the same time showing their strong concerns for privacy protection. The analysis of consumer welfare effects of competition cases in online markets has to take into account the literature of economic studies, as well as behavioural law and economics and cognitive science studies, especially when the concept of consumer welfare has to be extended to nonprice aspects such as privacy, consumer choice and attention economy. When it is evident that consumers suffer from behavioural bias, new policies such as nudging could be a new way to measure consumer loss and to protect consumer welfare. Moreover, when markets become digital, there are groups of consumers who are more experienced in online transactions, and those who are less experienced become “‘vulnerable consumers”.2 Searching theory which is discussed in Sect. 8.3 shows that informed consumers may take advantage of less informed consumers. In such cases, amendments in consumer protection laws and public policies have to be considered,3 such as for more technical areas such as energy, financial services and telecommunications.4

1

European Commission (2018). Coscelli (2018). 3 European Consumer Consultative Group (2013). 4 London Economics (2016). 2

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8.2 Improved Quality and Services There is little doubt that the data economy, driven by innovations applied by online platforms, has significantly improved the quality and services and increases consumer welfare in various aspects. The World Bank has listed five main benefits that digital technologies have brought for individuals, companies and public sectors: (1) reduce information costs and lower transaction costs; (2) promote innovation; (3) improve efficiency through quicker and more convenient activities and services; (4) increase inclusion, as services that were previously inaccessible come within the reach of more consumers; and (5) create job opportunities.5 Oxera’s research showed that 97% of Internet users in Europe thought there were benefits of using online platforms, including improved convenience, greater choice, increased transparency, higher engagement, monetary benefits and enhanced relationships.6 Their survey also showed that the most popular tasks that consumers use the Internet to do are: accessing information about goods and services, reading online newspapers and magazines, participating in social networks, accessing games, images, files or music, using eGovernment services, accessing information about education and training, telephoning or making video calls, uploading self-created content, using internet storage space, looking for a job, sending a job application and doing an online course.7 The benefits for collecting data from users by online platforms are mainly to improve quality and services. The OECD defined the products sold online as three categories: tangible consumer goods, services for offline consumption and digital content services. The sales of tangible consumer goods involve physical delivery and the most common examples are clothing and footwear, cosmetics and healthcare products, and consumer electronics. Examples of sales of services for offline consumption include transport (e.g., plane or train tickets), accommodation (e.g., hotel bookings), tourist services (e.g., museum tickets) and cultural events (e.g., concert or cinema tickets). The sale of services may involve physical delivery or through e-ticket mechanisms. Examples for digital content services are films, television programmes, e-books and recorded music, and in this category the transaction is entirely delivered online.8 When more data are collected, the online platform can use and analyse the data to provide better search results and therefore improve

5

World Bank (2016). Oxera (2015, p. 5). 7 Oxera (2015, p. 24); see also Eurostat, Community survey on ICT usage in Households and by Individuals, available at https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glo ssary:Community_survey_on_ICT_usage_in_households_and_by_individuals. 8 OECD (2018a, at pp. 7–8). 6

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their services (“click-and-query” data).9 It will generate personalized research results and the recommendations sent by online sellers are considered a way of improving product quality and benefit consumers. The benefits of sending anticipated search results and recommendations of particular products by book sellers, social media and travel agencies are that they can provide more valuable services,10 as a survey in 2014 shows that 81% of users found the reviews at the Hotel agency website were “important”11 ; therefore, they are willing to allow online retailers to collect their data.12 Respondents to the interim report made by the UK competition authority CMA noted that digital services have produced “large gains” in consumer wellbeing and surplus—“enabling users to find information, connect, communicate and share relevant information in ways that did not exist two decades ago.”13 The George Mason University Mercatus Center reported that internet platforms create value by improving the use of resources, increasing competition by bringing new buyers and sellers, reducing transaction costs, reducing asymmetric information, and improving regulation for new business models and innovative sources.14 For consumers, on average individuals need 17,530 USD to give up using search engines for a year; and for emails, it would need 8414 USD; and for maps, it would be 3648 USD.15 Starting on June 3, 2019, the US House Judiciary Committee, led by the Subcommittee on Antitrust, Commercial and Administrative Law, initiated an investigation into competition in digital markets and examined the dominance of Amazon, Apple, Facebook, and Google. The report concluded that “Amazon, Apple, Facebook, and Google play an important role in our economy and society as the underlying infrastructure for the exchange of communications, information, and goods and services. As of September 2020, the combined valuation of these platforms is more than 5 trillion USD—more than a third of the value of the S&P 100.”16 This report acknowledged the value of market capitalization by the data giants in digital markets, and how these internet companies have created market power and at the same time increased market value during the past decades.

9

Lerner (2014, at p. 11). Lerner (2014, at p. 11). 11 For instance, a recent survey found that 81% of travel searchers found user reviews “important.” (“Internet Travel Hotel Booking Statistics,” Statistic Brain, citing research by eSearch, eMarketer, and Alexa.com as of May 25, 2014, available at http://www.statisticbrain.com/internet-travel-hotelbooking-statistics/.) BrightLocal’s survey showed that in 2020, 72% of US consumers have written a review, and 87 percent of consumers read online reviews for local businesses. https://www.bright local.com/research/local-consumer-review-survey/. 12 Nasri (2012). 13 CMA (2020, p. 154). 14 Koopman et al. (2015, pp. 2–3). 15 Kennedy (2020, at p. 6). 16 US House Judiciary Committee, Subcommittee on Antitrust, Commercial and Administrative Law of the Committee on the Judiciary (2020), Investigation of Competition in Digital Markets, Majority Staff Report and Recommendations, available at https://judiciary.house.gov/uploadedfiles/ competition_in_digital_markets.pdf, at p. 10. 10

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8.3 Reducing Search Costs The collection of data will reduce the costs of collecting information and making choices. As has been extensively argued in the previous chapters, most literature supports the arguments that online services are provided at zero price, although it was not completely “free”. There are also studies showing that the estimated price of searching products online was not zero but a much lower price than searching offline. For example, Brynjolfsson, Dick, and Smith found that the maximum cost of viewing additional pages in bookshops was $6.45.17 Hong and Shum showed that the median search costs for textbooks were less than $3.00.18 There are two directions of economic research on search costs, and both areas of study are relevant to understanding the effects of online purchasing on consumer welfare. Studies of search costs are also important to analyse the effects of price discrimination, which will be discussed in the next section. The first direction of the study is the theoretical debate on search costs and the price level of the market. Economists have extensively studied the impact of reduced searching costs on price levels and market efficiency. Smith19 defined the three dimensions that reduced searching costs on prices: price level, price elasticity and price dispersion. Economists generally agreed that lower search costs will reduce the price level and therefore improve market efficiency.20 Smith21 concluded that reduced search costs will increase price elasticity. Stahl22 found that reduced search costs will reduce price dispersion.23 The other direction is to study the reaction from consumers when purchasing products online, compared to the purchasing behaviour offline, to understand the impact of online platforms on consumer welfare from the perspective of search costs. As a rule of thumb, it is generally believed that online purchasing has reduced the information asymmetry between sellers and buyers, and the search costs to obtain information about different types of goods, such as between search goods and experience goods are both significantly reduced.24 Internet retailers could facilitate information sharing, and because consumers could learn from reviews, product ratings, feedback 17

Biyalogorsky and Naik (2003, pp. 21–32). Hong et al. (2006). 19 Smith et al. (1999). 20 Alba et al. (1997, pp. 38–53), Bailey et al. (1999), Bakos (1997, pp. 1676–1708), Smith (2001), Smith and Brynjolfsson (2001, pp. 541–558), Malone et al. (1987, pp. 484–497), and Spink (2002, pp. 401–426). 21 Smith et al. (1999). 22 Stahl (1989, pp. 700–712) and Stahl (1996, pp. 243–268). 23 See also Kumar et al. (2005, pp. 87–102, p. 89). 24 Search goods and experience goods are defined by Nelson: “Goods can be classified by whether the quality variation was ascertained predominantly by search or by experience.” Search goods refer to the products that consumers have the ability to obtain information on product quality prior to purchase. Experience goods are products which quality could be evaluated after purchasing. Credence goods are those the quality cannot be evaluated either before or after purchasing. See Nelson (1970, pp. 311–329) and Nelson (1974, pp. 729–754). 18

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and criticisms from other consumers, the differences in search costs to obtain information on different types of goods, such as search goods and experience goods, are also reduced.25 Consumers can learn from reviews posted by other consumers, so the costs of obtaining, collecting and sharing information are significantly reduced.26 It became easier to find information on alternative products and to compare those items.27 At the same time, behaviour scientists argued that the process of analysing information and making decisions on purchasing products involve cognitive effort, and the level of effort required is different for different types of products and different types of information.28 Experience goods require a greater level of effort in research, and product quality is often understood after consumers’ experience in using the product. Research shows that consumers’ behaviour in obtaining and analysing information during the purchasing process online is different for experience goods and search goods. These differences include the amount of time spent on webpages, the number of pages searched, the behaviour of free riding, and the role of consumer reviews and recommendations.29 Sénécal and Nantel found that online product recommendations of experiential products significantly influence consumer choices more than other types of products.30 Huang et al. conducted empirical research on the online browsing behaviour of consumers for both search (such as shoes, home furniture, garden and patio implements) and experience goods (such as automotive parts and accessories, health and beauty products, and camera equipment).31 They found that the amount of time spent gathering information for search and experience goods is similar, but the browsing and purchasing behaviour are different: experience goods involve more depth (time per page) and lower breadth (total number of pages) of search, free riding behaviour is less frequent for experience goods, and consumer reviews have a greater impact on purchasing behaviour for experience goods than search goods. Because of the efforts involved when consumers learn feedback from a particular website, it is more likely that internet information transformation, including consumer feedback, authoritative third-party information and experience simulation, has greater effects on experience goods, and because of the time and efforts devoted in the interfaces with a particular website, consumers are more likely to have cognitive lock-in by that website.

25

See for example, Alba et al. (1997, pp. 38–53), Klein (1998, pp. 195–203), and Peterson et al. (1997, pp. 329–346). 26 See for example Hoffman and Novak (1996, 50–68) and Zettelmeyer et al. (2006, pp. 168–181). 27 Bakos (1997, pp. 1676–1692), Brynjolfsson and Smith (2000, pp. 563–585), and Clemons et al. (2002, pp. 534–549). 28 See for example Bettman et al. (1993, pp. 931–951) and Ha and Hoch (1989, pp. 354–360). 29 Huang et al. (2009, pp. 55–69), see also Hoch and Deighton (1989), Hoch and Ha (1986, pp. 221– 233), and Weathers et al. (2007, pp. 393–401). 30 Sénécal and Nantel (2004, pp. 159–169). 31 Huang et al. (2009, pp. 55–69).

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Nonetheless, the fact that consumers trust internet reviews has also been manipulated by firms, as marketing studies show that companies have already used online reviews as an effective marketing tool32 and have manipulated the review mechanism to induce consumers to give positive feedback to influence other consumers’ purchasing decisions.33 Empirical studies support the argument that consumer ratings influence product sales in the book34 and movie35 industries. Again, such influence could be differentiated for different types of products, as Zhu and Zhang’s research shows that online reviews may affect the sales of less popular games more than more popular games.36 The empirical results for the causal relationship between the influence of online reviews on products become different when products are categorized as more popular and less popular. It therefore raises the paradox that the use of internet retailers and online platforms could reduce information asymmetry and increase consumer welfare by providing more channels to learn the quality of the products and reduce the price dispersion among different types of products. At the same time, behaviour studies show that consumers are cognitively locked in by the marketing strategy manipulated by internet retailers. They have strong preferences to purchase experience goods online and learn from internet reviews before purchasing. Consumers may become cognitively biased during the online purchasing decision making process, for example, to trust online reviews to reduce risk,37 to feel more confident,38 to trust the brand and a product’s popularity as a signal of good quality,39 or simply to follow the crowd (herding) to avoid taking responsibility for feeling regret when the purchasing results are negative.40 Marketing studies show that word-of-mouth communications (such as written consumer reviews) significantly influence consumer decisions.41 Consumers even optimally ignore private signals and entirely trust the information of the aggregate behaviour of others.42 The strength of bias varies across consumer groups and product types. When such reviews are manipulated by the internet supplier as a marketing strategy, consumers subjectively have increased consumer welfare, although such an increase was manipulated and subject to a negative effect on 32

Dellarocas (2003, pp. 1401–1424). Zhu and Zhang (2010, p. 133), see also Dellarocas (2006, pp. 1577–1593). 34 Chevalier and Mayzlin (2006, pp. 345–354). 35 Zhang and Dellarocas (2006); see also Liu (2006). 36 Zhu and Zhang (2010, pp. 133–148). 37 Bolton et al. (2004, pp. 1587–1602), Chen et al. (2009), Clemons et al. (2006), Forsyth and Shi (2003, pp. 867–875), and Pavlou and Gefen (2004, pp. 37–59). 38 Tversky and Kahneman (1974) argued that individuals feel more confident when the availability of reasons increased. Tversky and Kahneman (1974). 39 Ba and Pavlou (2002, pp. 243–268), Pavlou and Gefen (2004, pp. 37–59), Caminal and Vives (1996, pp. 221–239), and Hellofs and Jacobson (1999, pp. 16–25). 40 See for example Banerjee (1992), Bikhchandani et al. (1992), Zeelenberga and Beattieb (1997), Zeelenberga et al. (1996), and Simonson (1992). 41 Leonard-Barton (1985, pp. 914–926) and Feick and Price (1987). 42 Chen et al. (2011, p. 5); see also Banerjee (1993, pp. 309–327), Ellison and Fudenberg (1995), McFadden and Train (1996). 33

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consumer surplus. These findings are challenged by behaviour studies that found that the behaviour of consumers on obtaining and analysing information is different for different types of goods.43

8.4 Consumer Self-confidence While online shopping makes it difficult for consumers to observe and experience the real quality of the product, the availability of information could be increased when consumers could post reviews and feedback online. As information (or data) itself has great market value and in addition to the benefits of reducing the costs for consumers to seek information (searching costs), the availability of information on internet platforms also increases consumer welfare by increasing consumer self-confidence. The behaviour literature on efforts to acquire marketplace information and consumer self-confidence indicates that online purchasing could increase consumer welfare, as consumers could become more self-confident when obtaining more information on product knowledge,44 be able to pay attention to product labels,45 provide market expertise46 and express scepticism toward market claims.47 Consumer confidence and purchasing decision making have been influenced by the online platform review and rating system, and empirical studies have shown that consumer reviews have also affected sales. For example, Chevalier and Mayzlin’s research showed that for books, the products that consumers are unsure about their quality, positive reviews have an impact of increasing book sales on Amazon.com and other websites, while negative reviews reduce sales.48 Consumer confidence has been influenced by consumer behavioural biases, such as myopia, framing effects, endowment effects and overoptimism; consumers that are heterogeneous in age, gender, race, education, employment, and personal characteristics and computation skills may also lack confidence and become vulnerable to potential risks and challenges in the changing online environment. The OECD proposed that vulnerable consumers are at risk of detriment and are subject to legal protection in the digital economy,49 and the OECD has defined vulnerable consumers as “consumers who are susceptible to detriment at a particular point in time, owing to the characteristics of the market for a particular product, the product’s qualities, the nature of a transaction or the consumer’s attributes or circumstances.”50 Consumer 43

See for example, Bettman et al. (1993), Ha and Hoch (1989, pp. 354–360), Lurie (2004), Lynch and Ariely (2000), and Shugan (1980). 44 Park et al. (1994, pp. 71–82). 45 Marber et al. (2006) and Barber et al. (2007). 46 Chelminski and Coulter (2007a, pp. 94–118) and Chelminski and Coulter (2007b, pp. 69–91). 47 Brown and Krishna (2004, pp. 529–539) and Tan and Tan (2007, pp. 59–82). 48 Chevalier and Mayzlin (2006, pp. 345–354). 49 OECD (2019, p. 32). 50 OECD (2014).

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vulnerability may also be caused by market failures such as lack of competition, asymmetric information and/or market complexity,51 and consumers are more likely to be affected by behavioural biases and heuristics in highly complex markets.52 When online businesses are able to identify consumer behaviour including their biases and the time of being vulnerable to risks, they are able to leverage their control of consumer decision making,53 which is named by Calo “digital market manipulation”.54 In view of the OECD, vulnerable consumers are protected by legal actions, and market regulatory agencies have to study consumer biases in different consumer groups and to learn how to correct heuristic biases by policy intervention.55 The OECD introduced the vulnerable consumer challenge established by the UK government in 201956 and the guidelines issued by Canada’s Office of Consumer Affairs (OCA) in 2019.57

8.5 Targeted Advertisement Online advertising consists of search advertising and non-search advertising. The two most commonly used forms of non-search advertising are display advertising, including banner ads, plain text ads, and audio and video ads, and classified advertising.58 Search advertising is also named targeted advertisement and is one of the aspects of target-oriented business models that online platforms commonly adopt. By choosing a group of consumers who are to receive targeted advertisements, the advertising costs have been significantly reduced. When platform users are able to collect data on consumer behaviours, advertisements could be sent to consumers in accordance with their browsing history or behaviour traits—named “behaviour targeting”.59 Online behavioural advertising is also referred to as online profiling and behavioural targeting. Boerman et al. defined online behavioural advertising as “the practice of monitoring people’s online behaviour and using the collected information to show people individually targeted advertisements.”60 The browsing and search query data collected from consumers are valuable for advertisers, and in this way, online platforms can monetize their online services, although for consumers, they are available “for free”. Boerman et al. argued that data collected and used for online behaviour advertisements are extensive, including age, gender, location, 51

UNCTAD (2018) and CMA (2019). OECD (2010). 53 OECD (2019, p. 33). 54 Calo (2014). 55 OECD (2019, p. 35). 56 UK Government (2019). 57 OCA (2019). 58 Bourreau et al. (2017, p. 49). 59 Beales (2010, p. 1). 60 Boerman et al. (2017, pp. 363–376). 52

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education level, political persuasions, sexual preferences, online shopping behaviour and search history.61 Businesses use first-party and third-party cookies, deterministic and probabilistic methods, artificial intelligence and machine learning technologies to send targeted advertisements, track how users interact with advertisements to learn whether advertising campaigns are effective, and track consumers’ reactions, including clicks, webpage visits or purchases.62 Such technologically driven behavioural tracking has also been named “programmatic advertising”, as Geradin and Katsifis defined as “dedicated software and complex algorithms fuelled by various categories of user data (behavioural, demographic, etc.) are used to sell and purchase adverting inventory within fragments of a second, avoiding “human” negotiation between publishers and advertisers.”63 The evidence of how the practice of targeted advertisement affects consumer welfare is mixed: the profits that are earned from advertisers are the input to improve online products and services and thus increase consumer welfare; for example, McKinsey estimated that in 2014, the consumer surplus generated from advertisingfunded online services in the US and Europe was 100 million Euros.64 On the other hand, consumers may be unwilling to receive targeted advertisements.

8.6 Price Discrimination 8.6.1 Definition of Price Discrimination Richard Posner gave a definition of price discrimination: “Price discrimination is a term that economists use to describe the practice of selling the same product to different customers at different prices even though the cost of sale is the same to each of them. More precisely, it is selling at a price or prices such that the ratio of price to marginal costs is different in different sales.”65 Geradin and Petit66 summarized the three preconditions for price discrimination to occur: (1) A firm must have market power to be able to set supra-competitive prices. (2) The firm must have information over its consumers be able to sort consumers in accordance with their willingness to pay for each unit. (3) The firm must be able to prevent or limit the resale of products and arbitrage between consumers (increasing transaction costs or imposing contractual terms). When firms are able to charge up to the maximum that consumers are willing to pay, they can perfectly obtain individual information and characteristics

61

Boerman et al. (2017, pp. 363–376). OECD (2020a, p. 11). 63 OECD (2020a, p. 11) and Geradin and Katsifis (2019). 64 IAB Europe (2010). 65 Posner (2001, pp. 79–80). 66 Geradin and Petit (2007, p. 4). 62

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and discriminate between consumers, and in this situation, first-degree price discrimination occurs. Consumer surplus will be extracted to the maximum level, consumer welfare decreases, and total welfare increases. When firms set different prices in accordance with the quantity of units that each consumer buys (such as through volume discounts), second-degree price discrimination occurs. Third-degree price discrimination occurs when firms have general information on consumer groups but not individuals and can charge different prices to different consumer groups in accordance with their demand elasticity.67 Second- and third-degree price discrimination makes it possible to supply the products to new categories of consumers because without price discrimination (such as rebates or tariff-based tickets), those groups of consumers would not be able to afford the standard price, so the key to increasing welfare is to expand output and to spread the large fixed costs that firms face over units to offset their investments in the long run.68 Townley, Morrison and Yeung pointed out that price discrimination made by datadriven digital retailers is different from price discrimination in a traditional market, as it is in much more precise, targeted and dynamic forms.69 First, big data technology makes it possible to track the online behaviour of consumers and collect detailed information on the tastes, habits and preferences of consumers at a highly personal level; second, big data can perform personalized online experiments to acquire information on preferences, behaviours and potential willingness to pay. Third, firms can mine the resulting data to collect information on the choice environments of each digital consumer and predict individual willingness to pay in each purchase.70 Based on the algorithmically predicted willingness to pay, the online retailer can charge different prices for the same product to different consumers, although they may not even be aware that the figure displayed online is a discriminatory price.71 Online price discrimination has the specific feature of using technology to mine consumers’ information and using machine learning algorithms to make predictions on each individual consumer’s willingness to pay, and they give such a phenomenon a new definition of ‘algorithmic consumer price discrimination (ACPD)’. When online suppliers can observe the preferences and behaviour of consumers and make predictions on their willingness to pay, their predictions are based on rather imperfect information and it is close to third degree discrimination; however, it differs from the classic model of price discrimination in monopolistic markets, ACPD occurs in imperfectly competitive markets, and the effects on consumer welfare will be different than those of the classic models.72

67

Geradin and Petit (2007, p. 5). Geradin and Petit (2007, p. 6). 69 Townley et al. (2017a, p. 2). 70 Townley et al. (2017a, p. 2). 71 Townley et al. (2017a, p. 2). 72 Townley et al. (2017a, p. 2, pp. 6–7). 68

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8.6.2 Personalizing Pricing When online platforms are able to collect information and data from users, it will be possible for them to charge different prices based on consumer shopping preferences and search history, thus charging “personalized” prices instead of standard prices as in the offline shopping markets. It raises anti-competitive concerns as charging different prices for the same products is often defined as price discrimination. When the monopolist has perfect information about each consumer’s willingness to pay, she becomes able to charge different prices to each consumer. In this aspect, the concept of personalized pricing has also been applied as an alternative to first-degree price discrimination. Nonetheless, the Office of Fair Trade (OFT) 2013 report defined personalized pricing as “the practice where businesses may use information that is observed, volunteered, inferred, or collected about individuals’ conduct or characteristics, to set different prices for different consumers (whether on an individual or group basis), based on what the business thinks they are willing to pay.”73 This definition identifies the two elements that distinguish personalized pricing from the general concept of price discrimination, in that it defines the practice of personalized pricing as business-to-consumer relationships where the target of discriminating prices is consumers, and the discrimination is based on consumers’ characteristics and conduct.74 In practice, to implement personalized pricing, firms must collect sufficient data (including volunteered data and data inferred from consumer behaviour) on consumers’ individual characteristics and conduct, through which firms have to estimate consumers’ willingness to pay and then choose the optimal price for individual consumers.75 In online shopping, price insensitivity may occur because consumers may have a strong preference for a particular brand (consumers do not want to switch to another brand), or the search costs are very high (consumers do not like spending time searching).76 The effects of price discrimination on consumers are mixed: some consumers may benefit from the lower prices charged than nondiscriminatory situations, as the White House Report on Big Data and Differential Pricing has concluded: “If historically disadvantaged groups are more price-sensitive than the average consumer, profit-maximizing differential pricing should work to their benefit.”77

73

Office of Fair Trading (2013). OECD (2018b). 75 OECD (2018b). 76 Townley et al. (2017b, p. 692). 77 The White House (2015). 74

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8.6.3 Economic Effects of Price Discrimination Economic literature on the effects of price discrimination with consumer surplus includes two negative effects: rent transfer (when the monopoly charge discriminates against prices, they have more price options than uniform pricing and can increase its rents and profits) and misallocate output to consumers who do not value it most; and one positive effect: price discrimination can lower prices to some consumers and increase its overall output because it may make the product accessible to consumers who cannot afford it with uniform prices.78 This positive effect also leads to higher motivation for firms to invest in innovation and improve product qualities. Combining the two negative and one positive effects, economists had mixed conclusions on the general effects of degree price discrimination, and whether consumers will be better off depends on the effects on the intensity of competition in the market. For firstdegree price discrimination, consumer surplus is transferred to producer surplus, and total output increases. For third-degree price discrimination, Aguirre et al.79 and Bergemann et al.’s80 research showed that it generally reduces consumer surplus but has a few exceptional circumstances that increase consumer surplus.81 The economic findings of price discrimination in online markets are that price discrimination will change the level of competition in the market. However, the extent to which competition will be harmed requires a more cautious analysis because in many situations price discrimination has efficient effects. As the OECD summarized, “price discrimination is common in many markets. In many instances, price discrimination enhances market competition. In the United States, price discrimination is often viewed as efficient. In certain limited circumstances, price discrimination might be an aspect of an exclusionary strategy meant to enhance or protect market power. Intervention should be limited to preventing these exclusionary abuses.”82 It is often argued that price discrimination could make the products accessible by users with lower willingness to pay, and when the total output is expanded, price discrimination can increase social welfare, and thus become efficient.83 Townley, Morrison and Yeung’s research summarized two important conditions to analyse the effects of price discrimination on consumer welfare in imperfectly competitive digital markets. One is the switching costs that the additional value consumers receive from purchasing the product from an alternative brand, and the search costs that are incurred when consumers are comparing different brands.84 Thus, charging higher prices becomes possible for consumers who are price inelastic, meaning they either have strong preferences for a particular product brand or there 78

Townley et al. (2017a, p. 7). Aguirre et al. (2010). 80 Bergemann et al. (2015). 81 Additionally, see Townley et al. (2017a, p. 7). 82 OECD (2018c); See also FTC/DOJ Submission to Roundtable on Price Discrimination, DAF/COMP/WD (2016)69 at 6. 83 Armstrong (2008) and Varian (1985). 84 Townley et al. (2017a, p. 8). 79

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are switching costs when choosing alternative brands. Likewise, consumers become price insensitive when they have higher search costs, or are reluctant to search and economically unsophisticated, and it becomes possible for firms to charge higher prices to them than consumers who search more widely. The general conclusion on price discrimination and competition effects is that price discrimination based on brand preferences will increase competition and increase total consumer surplus, where price discrimination based on search costs will reduce competition and reduce consumer surplus. In the brand preferences model, the seller faces ‘strong’ consumers who prefer its brand and ‘weak’ consumers who prefer the brand of its rival. The ‘strong’ consumer of one brand is also the ‘weak’ consumer for the rival brand, and this is named ‘best-response asymmetry’.85 When online suppliers charge higher prices to those loyal to their brand and charge discount prices to attract consumers of preferring rival brands, this will intensify competition, reduce prices and benefit consumers.86 However, in the model of price discrimination based on search costs, sellers are willing to charge low prices to win (‘weak’) consumers with more search experiences, and high prices to (‘strong’) consumers with higher search costs. However, because consumers with high search costs are insensitive to prices in all brands, this leads to the situation of ‘best-response symmetry’, and sellers lose the incentive to compete to win consumers by lowering prices, which in this situation will reduce competition compared to uniform pricing.87 This is proved by Armstrong and Vickers that when the market has sufficient competition, best-response symmetry price discrimination reduces aggregate consumer surplus.88 Their research indicates that it is the market character, whether it is best-response symmetry or asymmetry, whether it is consumer behaviour and loyalty to a brand, or their searching behaviour and the ability to reveal information, rather than the firms’ market power, that matters for the effects of price discrimination. Similar to their terminology, other economists also found that in markets where consumers have strong preferences for a particular brand or it is difficult to switch to another brand because of switching costs (best-response asymmetry), the competition intensity will be increased in price discrimination and thus increase aggregate consumer surplus. Market power has positive effects on consumers. In the situation when the costs of searching are high (best-response symmetry), price discrimination will weaken competition and therefore reduce consumer surplus.89 However, the latter situation is not decisive—in Armstrong’s model,90 market power weakens consumer surplus, whereas in Armstrong and Zhou’s model,91 market power increases consumer surplus. Therefore, the effects of price discrimination do not depend on whether the seller has market power, but rather whether consumers have 85

Corts (1998). Townley et al. (2017a, p. 8). 87 Corts (1998). 88 Armstrong and Vickers (2001). 89 Townley et al. (2017b, pp. 683–748). 90 Armstrong (2006). 91 Armstrong and Zhou (2016). 86

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(strong) brand preferences and searching habits and history. This is also because in monopoly markets, the seller could collect accurate and refined consumer preference data (perfect information). Therefore, this reduces aggregate consumer surplus, where as in imperfectly competitive markets, collecting consumer data on their preferences and searching history and charging personalized prices may have the effects of intensifying competition. From an efficiency point of view, price discrimination does not necessarily harm total consumer welfare and does not raise anticompetitive concerns, although a group of consumers, such as those who have difficulties searching, will have to pay more when sellers charge discriminatory prices.92 Townley, Morrison and Yeung’s research also summarized the economic models of price discrimination when there is more detailed information on consumer preferences. The classic monopoly model on price discrimination shows that when more accurate and detailed information on consumer preferences is accessible to suppliers, the firm can extract more profits and reduce consumer surplus.93 In the situation of imperfect competition, Esteves’s model94 shows that the more accurate information the consumers have, the more intense the competition becomes, and although there are rent transfer effects from consumers to suppliers, the competition effects are larger, so more accurate information leads to more competition and increased consumer surplus.95 Shy and Stenbacka’s models96 show that consumer surplus is increased only when information on consumers’ preferences is shared with all suppliers in the market.97

8.6.4 Empirical Evidence Their research findings in theoretical models have important implications as empirical studies conducted by Danaher et al. found that better-known brands with a higher market share have greater-than-expected brand loyalty, whereas unknown brands have lower-than-expected loyalty in online shopping.98 In a traditional offline shopping environment, brand loyalty is not related to brand market share. In other words, online brand loyalty for brands with high market share is stronger than that in the traditional shopping market, whereas online brand loyalty for brands with low market share is weaker in online shopping than in a traditional market. Their result is similar to Degeratu et al. who argued that a “strong brand” sells better in an online

92

Townley et al. (2017b, p. 700). Townley et al. (2017a, p. 10). 94 Esteves (2014). 95 Townley et al. (2017a, p. 10). 96 Shy and Stenbacka (2015). 97 Townley et al. (2017a, pp. 10–11). 98 Danaher et al. (2003, pp. 461–476). 93

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environment than a “weak brand”.99 Moore and Andradi also found that consumers pay more attention to the brand name when shopping online.100 Pozzi found that consumers are less often inclined to try new products when choosing an online shopping channel. Because of consumers’ tendency to save time spent browsing, new brands may face higher barriers to entry into the Internet distribution channel.101 Other empirical research has concluded that consumers do not search many different brands online.102 Zhang et al.’s research showed that among 100,000 US households from July to December 2002, the average number of websites visited for purchasing music, hardware and travel products was 2.1, 3.3 and 3.3, respectively.103 The more recent study published in 2014 by Holland and Jacobs showed that only 26% of consumers visited more than one airline website before purchasing airline tickets, and in that 26% of consumers the average number of websites they browsed was 2.46.104 For other sectors, Holland and Mandry found that the number of suppliers searched by consumers when purchasing flights, phones, cars, banking products and groceries in the US and UK ranged between 2.1 and 2.8 in those markets.105 In South Korea, Jerath, Ma and Park found that for 120 keywords of products and services searched online, the average number of links clicked by consumers was only 1.44 among all search results.106 Those empirical findings may indicate that consumers are more likely to stick to famous brands when shopping online, and such behaviour (not de facto market power) may make it possible that “strong brand” online sellers could charge personalized prices for consumers. Based on the economic models discussed above, such price discrimination could intensify competition between online sellers and increase total consumer welfare. Such a conclusion could make the analysis of competition effects different for online platforms than for sellers in the traditional market. Moreover, the empirical study by Huang et al. mentioned in Sect. 7.2 also implies that for experience goods that require greater efforts to acquire information and learn interfaces from a particular website, it is more likely that those interfaces increase consumers’ intention to purchase and have cognitive lock-in effects during the online purchasing process. Thus, the impact of price discrimination on consumer welfare has to take into account consumers’ preferences and their search behaviour for different types of products. When companies invest in diversifying their production line and products are becoming more heterogeneous today, we are likely to observe that there are more types of experience goods than decades ago. Internet retailers could also invest in technological infrastructure to improve the online review system to interact with consumers during the purchasing process. While the information is becoming 99

Degeratu et al. (2000, pp. 55–78). Moore and Andradi (1996, pp. 57–64). 101 Pozzi (2012, pp. 96–120). 102 CMA (2017). 103 Zhang et al. (2007, pp. 71–95). 104 Holland and Jacobs (2014). 105 Holland and Mandry (2012). 106 Jerath et al. (2014, pp. 480–486). 100

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more accessible for all groups of consumers, the preferences of each group may also become more diverse, and the cognitive efforts devoted to learning, comparing and evaluating the information for each consumer are still different. Consumers with strong preferences for a particular brand of experience goods may become more loyal to a particular website. As discussed in Huang et al.’s research, communication mechanisms and experience simulation, including consumer feedback, reviews and multimedia, will increase consumers’ willingness to buy (preferences) experience goods. When taking into account Danaher et al.’s research, consumers are more likely to be loyal to strong brands with high market shares, and it is also possible that consumers are locked in to a particular website for a particular brand when the website has the best review mechanism. In addition, Zhu and Zhang’s study showed that consumers may be manipulated by online reviews for less popular products in their purchasing decision-making process, and the extent to which online reviews influence sales revenue varies across different types of products. In this situation, judging the effects of price discrimination cannot only focus on the percentages of market shares. It is important to understand the economic models of platform competition when consumers have strong preferences, and such differentiated product preferences are supported by the literature on consumer behaviour and marketing studies. Furthermore, when firms gain profits from price discrimination, they may have higher incentives to invest in innovation and increase product variety. In a static model, third-degree price discrimination makes it possible for some buyers to choose to purchase large quantities with lower prices, and some buyers choose to purchase lower quantities with higher prices. In a dynamic model, price discrimination will generate higher profits that incentivize incumbents and entrants to invest in innovation and will increase competition in all groups of consumers, including those who are not reached without price discrimination, and thus have a pro-competitive outcome.107 In a dynamic model, price discrimination may increase not only competition between firms but also competition among consumers.108 At present, further research is still needed to understand the effects on the pricing structure and consumer surplus when different thresholds are used to categorize consumer groups and product types. Without a solid economic model and empirical or experimental evidence, it lacks persuasive power when a regulation is implemented to prohibit price discrimination.

8.7 Consumer Privacy Consumer privacy is an often-heard argument to support competition intervention in the collection of data by online platforms. The previous section has already presented the economic arguments that data has non-exclusive and a non-rivalrous nature, and the market power of exploiting consumers by data-driven platforms could not be 107

Manne and Sperry (2015, p.7). See Landes and Posner (1981, pp. 937, 977); See also Antitrust Law Journal. vol. 70(2003) (symposium on competitive price discrimination); Wright (2005–2006, p. 348). 108

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proven by measuring the size of the dataset it holds. There are additional arguments that privacy is beyond the control of competition law and should be better addressed by consumer protection law. Competition regulators have shown both active and passive attitudes.

8.7.1 Consumer Privacy—General Rights The EU General Data Protection Regulation (GDPR) and US California Consumer Privacy Act (CCPA) set up general rights on consumer privacy protection, including empowering consumer rights to control their data through consent-based policy, the right to correct false information, the right to delete data, and data portability.109 The OECD (2013) suggested eight privacy principles, including limiting the collection of personal data, ensuring that the use of data collected is accurate, specifying the purposes of data collected, limiting the use of data, protecting data by security safeguards, establishing the existence and nature of data use (openness of data collection), individuals have the right to participate in data protection, and the data controller should be accountable for complying with data protection measures.110

8.7.2 Consumer Privacy Paradox From the perspective of consumer behaviour, there are also counterarguments for applying competition law to protect consumer privacy, which are also not clearcut. While the economic theory of price discrimination shows that personalized pricing strategy may increase or decrease consumer welfare, empirical research finds that the “privacy paradox” exists, which makes consumers’ own evaluation of data disclosure more complex. The “privacy paradox”, named by law and economics scholar Wolfgang Kerber,111 means that empirical studies have shown that consumers are very concerned about data protection during online purchasing, but at the same time, the behaviour findings show that consumers are often not cautious about disclosing information and do not apply privacy-enhancing technologies to protect their data.112 The phenomenon of the “consumer paradox” has also been studied by other scholars.113 There is clear evidence showing that consumers are concerned about their data protection, while at the same time, they have spent little time reading and 109

OECD (2020, p. 11). OECD (2013). 111 Kerber (2016, p. 6). 112 Norberg et al. (2007, pp. 100–126), Berendt et al. (2005, pp. 101–106); Athey et al. (2017). 113 Norberg et al. (2007, pp. 100–126) and Kokolakis (2017, pp. 122–134). 110

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studying the privacy terms provided by the business platform and do not consider privacy issues when making decisions. This may be explained by the behaviour pattern that consumers ‘rationally’ ignore the long and complex terms or are unwilling to trade-off better privacy protection and more expensive or less speedy technological devices, but it can also be explained by the institutional barriers that the limited choices (to click “content” or to exit) provided by the platforms have prevented consumers from making more ‘rational’ choices. For example, in 2019, the Centre for International Governance Innovation (CIGI) and Ipsos collaborated with UNCTAD, and the Internet Society conducted a survey in 25 economies, and found that 78% of internet users were “at least somewhat concerned” about their online privacy.114 On September 24, 2020, the survey published by the US Consumer Report shows that 85% of Americans are concerned about the amount of data stored by the online platform, and 81% of them are concerned about the use of data by platforms to build more comprehensive consumer profiles.115 An empirical study conducted by the German company vzbv showed that 53% of the responding consumers “always” (27%) or “mostly” (26%) agreed to the privacy terms without reading, and 72% of the consumers answered that the reason for not reading the privacy policy terms was that they were “too long and complex”.116 The Inquiry provided by the Australian Competition & Consumer Commission (ACCC) shows that in 2018, the users in Australia spent, on average, less than two minutes on the Google Privacy Policy webpage, and only 0.03% of users spent more than ten minutes on those pages.117 In the study of Digital Platform Inquiry in 2019, the ACCC concluded that “few consumers are fully informed of, fully understand, or effectively control, the scope of data collected and the bargain they are entering into with digital platforms when they sign up for, or use, their services.”118 McDonald and Cranor’s research calculated that reading the privacy policy of each website they view would take internet users on average 244 h per year, and it is more than 50% of the time the average user spends on the internet.119 Although the survey shows that the majority of consumers are concerned about privacy protection, their attitudes on the preferences of privacy are subjective, context-specific,120 heterogeneous,121 and suffer from bounded rationality and behavioral decision-making biases.122 The consumer privacy paradox showed that consumers suffer from the bias of neglecting privacy protection techniques in practice while expressing their attitude of caring about privacy in the survey; on the other hand, 114

CIGI-Ipsos, UNCTAD and Internet Society (2019). Consumer Reports (2020). 116 https://www.vzbv.de/sites/default/files/downloads/studie-digitalisierung-grafikreport-emnid2014.pdf. 117 ACCC (2018, p. 182). 118 ACCC (2019, p. 2). 119 McDonald and Cranor (2008, p. 17). 120 Acquisti et al. (2016, p. 446) and Choi et al. (2019). 121 Walters, Zeller and Trakman (2018). 122 Kerber (2016, p. 7); see also Borgesius (2015, p. 192). 115

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consumers also suffer from the myopic bias of neglecting the unknown future costs when there is an immediate benefit of sharing their personal data.123 Thus, consumer privacy attitudes are inconsistent and subject to time and context. In addition, the tendency of sticks with default rules can also be explained by status quo bias.124 Empirical studies also show that consumers may have lower preferences for privacy protection compared with product prices and search experiences. Consumers are unwilling to pay even a few cents more for ad-free experiences and better protected search options.125 When there is an option of purchasing the product at a lower price, the majority of consumers are willing to choose to purchase from this company even if its privacy policy is less friendly.126 The behavioural findings add one more layer of paradox to the discussion of consumer privacy protection: it is in fact difficult to distinguish when consumers suffer from behaviour bias, or it is their rational answer because of the institutional obstacles that online devices provide very limited choices. As DotEveryone’s survey showed, 45% of respondents answered that there is no need to read privacy terms since “companies would do what they want anyway.”127 For behavioural scientists, the tendency to ignore privacy terms could be reasonable, as Acquisti and Grossklags described: “Especially in the presence of complex, ramified consequences associated with the protection or release of personal information, our innate bounded rationality limits our ability to acquire, memorize and process all relevant information, and it makes us rely on simplified mental models, approximate strategies, and heuristics.”128 When behavioural and institutional reasons coexist and are causally interconnected, it is a policy issue that the extent to which improving privacy options can remedy consumer behaviour biases therefore improves consumer welfare. The direction of improving consumer privacy protection is to empower consumers with the ability to control their data and to increase the bargaining power on privacy notices, to increase competition among online service providers as privacy competition is also a type of product quality.129 General consumer privacy rights, discussed in the previous section, have to be implemented within enforcement agencies by taking consumer biases into account.

123

Acquisti et al. (2016, p. 446) and Choi et al. (2019). Costa-Cabral and Lynskey (2017). 125 See, e.g., Beresford et al. (2011) and Grossklags and Acquisti (2007). 126 Preibusch et al. (2013, pp. 423–455). 127 DotEveryone (2020, p. 19). 128 Acquisti and Grossklags (2007, p. 369). 129 As Peter Swire argued: “privacy harms can lead to a reduction in the quality of a good or service, which is a standard category of harm that results from market power. Where these sorts of harm exist, it is a normal part of antitrust analysis to assess such harms and seek to minimize them.” See Swire (2007). Behavioral Advertising: Tracking, Targeting, and Technology: Town Hall Before the FTC, October 18, 2007 (testimony of Peter Swire, Professor, Moritz College of Law of the Ohio State University), available at http://www.americanprogress.org/issues/regulation/news/ 2007/10/19/3564/protecting-consumers-privacymatters-in-antitrust-analysis/. 124

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8.7.3 Broader Goals of Consumer Welfare Although the analysis of competitive effects in traditional markets focuses on the ultimate goal of protecting consumer welfare, the meaning of consumer welfare has been under debate for decades, and a consensus concerning the interpretation of the term consumer welfare has not been achieved among lawyers and economists.130 The confusion lies in the fact that Robert Bork has misused the term consumer welfare for total welfare, and it has been a century long debate on whether consumer welfare should take distributional issues into account. Whereas narrowly defined consumer welfare denies the criteria of Kaldor-Hicks efficiency, a total welfare standard focuses on maximizing dynamic efficiency and calls for potential compensation among consumer groups. Promoting efficiency to maximize social welfare does not have a different meaning from maximizing consumer welfare if it is believed that “all of us are consumers”.131 Following this logic, Bork argued that the efficiency goal will not conflict with the consumer welfare goal. Nonetheless, maximizing social welfare does not necessarily mean maximizing every individual consumer’s welfare.132 There is still a distributive concern regarding which consumer is entitled to be made better off, if it will require a welfare loss from another consumer. In practice, when courts refer to the concept of consumer welfare, they tend to give superior treatment to some individuals and allow them to enjoy welfare gains at the cost of others.133 This could show that the goal of promoting consumer welfare does not refer to promoting the welfare of every individual in society. Since the consumer welfare goal provides a clear focus for distributive concerns, the definition of consumer welfare would be of vital importance. One common way of measuring consumer welfare is to use its proxy consumer surplus,134 which is economically defined as the difference between the market price and the consumers’ willingness to pay. Consumer surplus of the market as a whole is estimated by adding up the value of each individual consumer surplus. The sum of consumer surplus and producer surplus equals total surplus. The economic justification of using consumer surplus to measure consumer welfare is neoclassical price theory. Marshall explains that when the market price is increased, assuming the income level remains constant, the individual consumer’s utility will be reduced when the consumption level is decreased, and the term “surplus” is the monetary measure of the utility gain that consumers obtained from purchasing the product, as he wrote in the Principles of Economics that consumer surplus is defined as the excess of price which he (a consumer) would be willing to pay rather than go without the thing, over that which he actually does pay, is the economic measure of this surplus 130

As Brodley claimed, “Consumer welfare is the most abused term in modern antitrust analysis.” Brodley (1987. p. 1032). 131 Hovenkamp (1982, p. 5) (‘The answer, of course, is that all of us are consumers at one time or another.’) 132 Hovenkamp (1982, p. 6). 133 Hovenkamp (1982, p. 6). 134 Brodley (1987, p. 1033); see also Currie, Murphy and Schmitz (1971).

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satisfaction.135 The use of consumer surplus as the proxy of consumer welfare raises controversies from both economic and legal perspectives. From an economic perspective, interpreting consumer welfare as the maximization of consumer surplus and the reduction of prices, however, does not satisfy the Pareto criterion or the Kaldor-Hicks criterion.136 Pareto optimality will not be satisfied, as the price reduction is often at the expense of the profits earned by the firms. Nor does it satisfy the Kaldor-Hicks criterion because consumers must be made better off before being compensated by the producers.137 From a legal perspective, the consumer surplus standard in economic terms can hardly capture the real meaning of consumer welfare. In their book Fairness versus Welfare,138 Kaplow and Shavell argued that only a welfarebased normative approach should be employed in the evaluation of legal rules. The reason is that any government decision that is not based on individual welfare will, in some circumstances, violate the Pareto Principle. Nonwelfare goals, such as notions of fairness or corrective justice, should not play a role. The concept of the welfare standard they use, however, is broader than in the conventional economic approach, that is, the maximization of wealth. They argue that the concept of welfare should include all aspects of an individual’s well-being. As Stucke pointed out, the definition of the consumer welfare goal contains broad social, political, economic and moral values.139 A decision based on estimating the change of price and quality will not fully satisfy the consumer welfare goal, as other factors that may affect the gains to consumers, such as variety and innovation, should also be incorporated.140 The broad, vague definition of consumer and consumer welfare leads to a gap between the policy statement and what has been applied in practice.141 Lianos pointed out that there are two problems using consumer surplus as the proxy: the first is the confusion of mixing consumer welfare with total welfare. The second problem is the narrow understanding of price effects. Consumer surplus means the deadweight loss that a consumer suffers because of the quantity reduction due to a price increase. If focusing on consumer harm, the reduction of consumer surplus is the result of the exploitation of market power, and such a loss would not occur in competitive markets. However, if taking a total welfare perspective, the wealth transfer from consumers to suppliers needs to be taken into account, and the supplier may potentially compensate the consumers who suffer a loss; in this situation, it is Kaldor-Hicks efficient.142

135

Alfred Marshall, Principles of Economics, 8th edition 2009. Van den Bergh (2007, p. 29). 137 Van den Bergh (2007, p. 29). 138 Kaplow and Shavell (2002, p. 3). 139 Hayek (2007, p. 101) and Stucke (2012, p. 572). 140 Stucke (2012, p. 576). 141 Stucke (2012, p. 577). 142 Lianos (2019, pp. 15–16). 136

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The proxy of consumer surplus has also been criticized for narrowly using price increases as the measurement of consumer loss; it ignores the impact of innovation and the development of new ideas in the future143 and neglects the fact that consumers do not always benefit from lower prices, and their assessment of satisfaction is a complex set of capabilities. Economists such as Robert Lande have argued for incorporating consumer choice,144 or renamed it consumer sovereignty,145 as the ultimate goal of competition law, and Lande argued that the consumer choice standard is particularly useful in situations where firms compete through other variables such as quality, variety, and creativity of the product, rather than price,146 and consumer choice approach is superior in three categories of cases: (1) where the market has little or no price competition, and consumers become price non-sensitive, because of prices are regulated, or the market is dominated by legal joint ventures or by thirdparty payors; (2) where consumers’ search costs are high because of market-specific conduct, or such conduct has impeded the effectiveness of price competition; (3) where innovation and creativity, instead of price, are the main forms of competition.147 There are also arguments on considering nonprice factors such as quality, variety and innovation,148 as mentioned in the previous section consumer privacy may be considered a factor of quality. The 2010 Merger Guidelines in the US do indeed emphasize nonprice factors, such as quality, service and new products.149 Economists such as Wright and Ginsburg argued that nonprice factors can be transferred into price measurement by using economic methods of adjusting nonprice factors.150 For example, Shapiro suggested that the concept of consumer welfare is broad and flexible enough to encompass nonprice factors such as product variety and innovation.151 Kenney argued that consumer welfare standard can well address 143

Lande (2013, p. 2397). Lande (2001, p. 50). 145 Averitt and Lande (1997, pp. 713, 715). 146 Lande (2013, p. 2396). 147 Averitt and Lande (2007, p. 196). 148 Lianos (2019, p. 16). 149 Merger Guidelines 2010 §10, ‘A primary benefit of mergers to the economy is their potential to generate significant efficiencies and thus enhance the merged firm’s ability and incentive to compete, which may result in lower prices, improved quality, enhanced service, or new products.’ Some economists have expressed their doubt on how to assess this ‘improved quality, enhanced service, or new products’, see e.g., Blair and Haynes (2011, pp. 63–67). 150 Wright and Ginsburg put a long list of economic literature to illustrate how quality-adjusted prices are used in antitrust analysis. He argued that the discussion on quality-adjusted prices dated back to early 1900s. See Wright and Ginsburg (2013, p. 2410). 151 Testimony of Carl Shapiro Before the United States Senate Committee on the Judiciary, Subcommittee on Antitrust, Competition and Consumer Rights, Hearing on: The Consumer Welfare Standard in Antitrust: Outdated or a Harbor in a Sea of Doubt?, December 13, 2017, at p. 3 (“I have seen no evidence whatsoever that the “consumer welfare” standard is somehow outdated, so long as one accepts that the goal of antitrust is to promote competition. One of the wonderful things about our antitrust laws is that they express very broad concepts and principles – promoting competition and protecting consumers – and have proven extremely flexible over more than 100 years to address new situations, as our entire economy has evolved, with economic activity shifting over a long period 144

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nonprice consumer harms, and broader social issues such as inequality, data privacy and the concentration of political power are better dealt by other policy tools.152 There are counterarguments, for example, Lande argued that some factors of the product, such as quality, variety, product safety, convenience and product innovation, cannot be reflected by price.153 In the survey conducted by the ICN in 2011, 38 competition authorities worldwide stated that they did not have an explicit definition of consumer welfare. Competition authority of Australia stated that in static analysis, consumer welfare has the same meaning of consumer surplus, and in dynamic analysis it is close to total surplus.154 Lianos proposed that when innovation and quality factors are a part of dynamic efficiency, the loss of consumer welfare and the effects of wealth transfer could be compared with the efficiency gains by taking a cost and benefit analysis.155 When privacy could be transferred to an element of product quality, consumer choice, or non-monetary price,156 the definition of consumer welfare certainly should be extended.157 Claassen and Gerbrandy argued that the European Commission primarily consider the narrow consumer welfare approach, meaning an increase in price, a limitation in output or a limitation of innovation are considered detrimental to consumer welfare.158 A broad consumer welfare standard, as adopted by the Dutch competition authority in some cases, incorporates calculable non-economic interests that are not directly related to the specific product, but indirectly affect consumer welfare.159 They propose a third consumer welfare standard, named as inclusive welfare standard, which considers non-economic interests directly, should be adopted by the competition authority. The inclusive welfare standard takes subjective preference satisfaction as the starting point and can incorporate any factor that affects consumer preferences and can be quantified and compared.160 Further, to fully take into account of non-economic interests, a non-welfarist standard, named as the capability approach, is a possible alternative and is more desirable in assessing competition cases.161 Lianos cited the work of Amartya Sen162 to argue that the traditional welfarist standard should be redefined as “well-being”, and the economic effects of the distribution and allocation of products should be replaced by the analysis of a function of the

of time from agriculture toward manufacturing and then toward services.”) https://www.judiciary. senate.gov/download/12-13-17-shapiro-testimony. 152 Kennedy (2018, p. 14). 153 Lande (2001, p. 515) and Averitt and Lande (2007, p. 184). 154 ICN(2011, p. 19). 155 Lianos (2019, p. 16). 156 Deutscher (2017). 157 Lianos (2019, p. 30). 158 Claassen and Gerbrandy (2016, p. 2). 159 Claassen and Gerbrandy (2016, p. 3). 160 Claassen and Gerbrandy (2016, p. 3). 161 Claassen and Gerbrandy (2016). 162 Sen (1995).

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combination of capability sets.163 This argument recalls the work written by Orbach, who was against the view that the definition of welfare should also be extended to the evaluation of well-being, even to the individual’s subjective assessment of satisfaction, and Stucke’s argument that competition law is to promote happiness.164 Orbach claimed that welfare differs from surplus because welfare refers to the effects on an individual’s well-being as a result of the activity. For example, the negative effects on health must be taken into account when a consumer purchases cigarettes. If the goal of an antitrust law is to protect consumer surplus, it is equivalent to the goal of protecting low prices.165 Therefore, Orbach pointed out that consumers cannot benefit from price reduction in all situations because the increased consumption of some products will harm consumers and will result in an undesirable outcome. These products are known as “bads”, and examples include tobacco, alcohol, abortions, firearms, gambling, pornography, junk food, guns and sex services.166 Moreover, consumer preferences are not always elastic to prices. Consumers have personalized preferences toward the special features of products, such as the age of wine, which is the exclusivity of status goods.167 Low price does not necessarily change consumers’ tastes. Another important limitation of the conventional consumer welfare goal is that it underestimates how intensive legal regimes affect innovation. Consumers today are more willing to upgrade their products, and this decision is more affected by companies’ strategic decisions.168 Therefore, an extension of the welfarist approach indicates that it is a complex system to discover consumer preferences and consumer needs. In privacy protection, consumer preferences are highly heterogeneous and sometimes suffer from behavioural biases; thus, protection of consumer welfare also has to be extended to a multiple use of legal tools, nudging and consumer education. It further calls for coordination between competition law and consumer protection law to achieve the goal of consumer welfare.

8.7.4 Consumer Behaviour Behavioural economists and cognitive psychologists argue that people have limited brainpower to calculate the utility pay-off of their conduct. Herbert Simon defined it as “bounded rationality”.169 Jolls, Sunstein and Thaler argued that “real people” 163

Lianos (2019, p. 30). Stucke (2013, p. 2585). 165 Orbach (2013, p. 2155). 166 Orbach (2011, p. 152). 167 Orbach (2011, p. 158). 168 Orbach (2011, p. 158). 169 Simon (1979, at p. 493). 164

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differ from homo economicus and people display bounded rationality, bounded will power and bounded self-interest.170 The cognitive biases were mainly discovered by a series of experiments conducted by both psychologists and economists. For example, Tversky and Kahneman defined the anchoring effects that a randomly chosen starting point will influence the estimates.171 Anchor effects suggest that individuals do not easily make adjustments. Anchoring will, for example, be very important in the behaviour of judges in relation to the sanction that has been claimed by the prosecutor and that will hence function as an “anchor” from which it may become difficult to deviate. Daniel Kahneman and Aversky proposed their prospect theory172 and suggested that people value losses more than gains. This loss aversion bias173 leads to the framing effects,174 meaning that an individual’s decisions are affected by how future losses and gains are framed. Because there is an asymmetry between the losses and gains, people suffer more utility loss by giving up an object than the amount paid to acquire it; in other words, people are less willing to give up the things that they own, and this bias is named as endowment effect.175 Samuelson and Zeckhauser extended the discussion on the endowment effect to situations where even the framing of gains and losses is absent. They argue that people tend to retain the status quo.176 For example, people tend to maintain the current or previous decision, such as choosing products of the same brands or staying in the same job177 ; few people would also like to switch to Dvorak keyboards from QWERTY keyboards, although it is more efficient to use.178 Status quo effects could explain the consumer behaviour of single homing and high switching costs. In addition, representative effects refer to ignoring the actual statistical information and making a biased judgment between reality and its appearance. Egocentric bias refers to overestimating one’s own abilities and the tendency of interpreting information favourably toward oneself. Individuals tend to show a strong bias when assessing their capacity compared with others, which leads to a result of selective optimism and overconfidence. Psychologists’ research

170

Jolls et al. (1998). Anchor effects suggest that individuals are not easily to make adjustments. See Tversky and Kahneman (1974). 172 Prospect theory: people value losses more than the gains. Kahneman and Tversky (1979, p. 263). 173 Loss aversion: there is an asymmetry between the losses and gains, people suffer more utility loss to give up an object than the amount to acquire it. See Amos Tversky and Kahneman (1991, pp. 1055–1056); Novemsky and Kahneman (2005, pp. 119–120). This effect may affect decision making process in many domains of policy issues. See examples provided by Samuelson and Zeckhauser (1988, pp. 7–59). 174 Framing: individual’s decisions are affected by how future losses and gains are framed. Kahneman and Tversky (1984, at p. 343). 175 Endowment effect: people give a higher value to the things that they have obtained and are less willing to give up the things that they own. Kahneman et al. (1990, at p. 1325). Knetsch (1989, pp. 1277–1284); Kahneman et al. (1990, pp. 1325–1348). 176 Samuelson and Zeckhauser (1988). 177 Samuelson and Zeckhauser (1988, p. 8). 178 Camerer and Kunreuther (1989, p. 577). 171

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shows that overconfidence exists when people consider their own activities and their futures.179 Unrealistic optimism may lead to underestimation of the probability that unpleasant events will happen to oneself.180 For example, the well-known research by Ola Svenson shows that approximately 90% of drivers believe that their driving capacity is above the average.181 Viscusi and Magat’s research shows that 97% of interviewed consumers believe that they are better than average in avoiding accidents.182 In Baker and Emery’s study, none of the respondents replied that they would encounter a risk of divorce, although they estimated that the divorce rate in the US at that time was approximately 50%.183 Similarly, experiments conducted by DeJoy,184 Arnould and Grabowski,185 and Camerer and Kunreuther186 have all showed that most people thought their chances of experiencing a negative event (such as an auto accident) were significantly lower than the average person. In the experiment conducted by Hastorf and Cantril in 1954,187 students from Princeton and Dartmouth were asked to count the number of infractions during a football game. The results showed that the opponents counted more infractions than the supporters for each team, and this result could be considered to be evidence of self-serving bias in the real world. Their research had similar conclusion as Babcock, Loewenstein and Issacharoff’s work which suggesting self-serving judgments exist in both bargaining and non-bargaining situations.188 In general, consumer heuristic biases

179

Weinstein (1980, pp. 806–820) and Taylor and Brown (1988, pp. 193–210). Jolls (1998, pp. 1658–1663); Sunstein (1997, pp. 1182–1184). 181 Svenson (1981, pp. 143–148). 182 Viscusi and Magat (1987). 183 Baker and Emery (1993, p. 439). 184 DeJoy (1989). 185 Arnould and Grabowski (1981). 186 Camerer and Kunreuther (1989). 187 Hastorf and Cantril (1954, pp. 129–134). 188 Babcock et al. (1997, p. 915). 180

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include anchoring effects,189 prospect theory,190 loss aversion,191 framing,192 endowment effects,193 representativeness,194 egocentric bias,195 availability heuristics,196 status quo bias,197 extremeness aversion,198 and overconfidence.199 Traditional economic models of consumer welfare analysis are based on consumer surplus maximization, and consumer preferences are rational, observable, homogenous and consistent. When reliable data on purchases become available, economists can estimate the elasticities of demand200 and understand the substitutability of one product by another. Consumer behaviour in traditional markets is more difficult to observe, and behavioural literature has largely been neglected by competition authorities. In the digital environment, it is proven that consumers are biased by 189

A randomly chosen starting point that will influence the estimates. People value losses more than the gains. 191 There is an asymmetry between the losses and gains, people suffer more utility loss to give up an object than the amount to acquire it. 192 Kahneman and Tversky (1984), at p. 343. 193 People give a higher value to the things that they have obtained and are less willing to give up the things that they own. 194 People tend to make a judgment on whether an item belongs to a category or a group based on judging whether its characteristics belong to that group. See Cass Sunstein (1997, pp. 1188–1189) and Hanson and Kysar (1999, at p. 630). 195 Egocentric bias refers to overestimating one’s own abilities and the tendency of interpreting information favourable toward oneself. Individuals tend to show a strong bias when assessing their own capacity compared with others, which leads to selective optimism and overconfidence Psychologists’ research shows that overconfidence exists when people consider their own activities and their futures. See Weinstein (1980) and Taylor and Brown (1988). Unrealistic optimism may lead to underestimation of the probability that unpleasant events will happen to one self. See Jolls (1998, pp. 1658–1663) and Sunstein (1997, pp. 1182–1184). 196 People are likely to make judgments on the frequency of an event based on how available this type of event is, and how easily they could imagine the occurrence of such an event. See Tversky and Kahneman (1973, p. 207). 197 The status quo will be taken as the reference point when people make choices and choose alternatives. People have a high tendency of adhering to the status quo. See Samuelson and Zeckhauser (1988, p. 7). 198 People are likely to avoid extreme answers. People tend to change to choose a modest answer when a third, extreme option is added. See Simonson and Tversky (1992, p. 281, 290). 199 Leonid Rozenblit and Frank Keil developed a study to test the effect of overconfidence bias when people deal with complex problems. They used a phrase “illusion of explanatory depth” to describe that the overconfidence effect is the worst when people are asked a question to explain complex phenomena, compared with other questions such as the knowledge of facts, explaining the procedure issue, or reproducing a narrative story. Their study indicates that, even for experts, their capacity to explain causations or make predictions may be limited, and such capacity advantage may even become an “illusion”. Kruger and Dunning found that people are often overconfident about what they do not know and tend to develop a superficial understanding of the subjects to show their competence. 200 Lianos (2019, p. 104). 190

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framing, anchoring, endowment, and overconfidence. Consumers are easily influenced by online comments and recommendations, biased by the framing and availability heuristic, and locked into one platform because of endowment effects and status quo bias. These biases lead to behavioural entry barriers and the negative effects of price discrimination such as high switching costs. With the help of AI and computing algorithms, consumer behaviour has been manipulated by firms through targeted advertisement and price discrimination. While defining goals of consumer welfare becomes difficult, consumer protection is more challenging because of the difficulty of educating consumers to correct their bias. Because of status quo bias, endowment effects and loss aversion, consumers are unwilling to switch to new platforms or change privacy terms by default. Given the importance of consumer behaviour in the analysis of competition effects of price discrimination, targeted advertisement, lock-in effects, switching costs, privacy protection and data protection, heuristic biases of consumers have become another source of market failure. It is important to incorporate the study of nudging201 and to focus on choice architecture and default settings. Consumer education is also a way to provide remedies on consumer harm in digital environments.

8.7.5 Consumer Education Similar to the concept of consumer autonomy that is applied in the design of data portability rights, consumer education has also been stressed as a crucial aspect in improving consumer protection law in the digital economy. As discussed in the consumer paradox section, consumers may lack the knowledge, motivation and skills to protect their privacy during online transactions, and they may also suffer from behavioural biases of neglecting the benefits of long-term privacy protection when there are short-term benefits of receiving price discounts for the product. In parallel with the efforts of improving default rules and nudging policies to correct consumer biases, regulators and policy makers also have to be aware of the benefits of consumer education and improving consumer empowerment and skills.202 UNCTAD suggested that consumer education can stimulate innovation, productivity and competition in the market.203 When consumers have information and knowledge on privacy terms, they will be more motivated to protect themselves, as The Office of Competition and Consumer Protection (OCCP) advocated that when consumers have information on products and brands, they will have the opportunity to become “prosumers”, and play a more active role in market transactions.204 V˘at˘am˘anescu et al. introduced the Consumer Empowerment Index, written by Nardo et al. (2011),205 201

Thaler and Sunstein (2008). V˘at˘am˘anescu et al. (2017), at p. 357. 203 UNCTAD (2014). 204 OCCP (2009). 205 Nardo et al. (2011). 202

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which suggested an index to measure consumer skills, awareness of legislation on consumer rights and consumer engagement. The empirical study by Aridor et al. further shows that informing consumers to implement the opt-out options of privacy requirements by GDPR will result in a 12.5% reduction in total cookies, and although that leads to a drop in the total number of advertisements clicked, their bids for the remaining consumers increased. Thus, consumer privacy decisions will have positive externalities on the welfare of both consumers and firms.206 More broadly, consumer education has become a global issue in the development of the digital economy and the scope of education is far beyond teaching consumers the knowledge of privacy. The OECD has raised the global concern of improving information and communication technology (ICT) literacy and asked education departments to take responsibility for enhancing digital competencies and called for more countries to teach ICT skills in schools.207 To build the framework in improving consumer awareness and consumer privacy protection, such programmes of increasing consumer digital competencies should include three stages: the action before purchasing products and services (focus on information search, comparing information, evaluation of alternatives, dealing with commercial communication, managing digital identity, and making responsible and sustainable consumption choices); the purchasing process (focus on participation in collaborative economy platforms, managing payments, understanding copyrights, licenses and contracts for digital content, protecting data and health); and the postpurchase stage (focusing on sharing information, asserting consumer rights, updating digital consumer competences).208 Antitrust authorities have implemented policy plans for developing consumer education and awareness campaigns for young and elderly consumers. The OECD introduced the “Pass it on” initiative209 proposed by the US FTC that is aimed at sharing knowledge and dialogues among family and friends to protect against scams and fraud, US FTC publications such as Living Life Online210 and Net cetera,211 online resources such as Admongo212 and You Are Here213 that are available for young and elderly people,214 and the Better Internet for Kids portal developed by the European Commission with similar goals.215

206

Aridor et al. (2020). OECD (2019, at p. 33). 208 OECD (2017, at p. 108). 209 FTC, Pass it on, https://www.consumer.ftc.gov/features/feature-0030-pass-it-on; FTC (2018). 210 FTC (2014b). 211 FTC (2014a). 212 FTC, Admongo, https://www.consumer.ftc.gov/Admongo/index.html. 213 FTC, You Are Here, https://www.consumer.ftc.gov/sites/default/files/games/off-site/youarehere/ index.htm. 214 OECD (2019, p. 36). 215 EC, Better Internet for Kids, https://www.betterinternetforkids.eu/web/portal. 207

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8.8 Conclusions To achieve the goal of protecting consumers and increasing consumer welfare, competition authorities have to be informed of the more complex effects that online platforms would impose on consumers. Online transactions have generated significant benefits for consumers, including improved quality in products and services, reduced transaction costs and information asymmetry. However, when products and services are provided at zero price, consumers’ personal information becomes the ‘currency’ for digital transactions, and firms collect large volumes of consumer data, with or without consumers’ awareness to send targeted advertisements, charge discriminatory prices for the same product, tailor their purchase contracts based on the tracking of consumer behaviour, and make algorithmic predictions on their references. Economic evidence on the welfare effect of such conduct is mixed: consumers may benefit from more personalized advertisements and discriminated prices that are charged based on consumer loyalty to a brand; at the same time, consumers often suffer from behavioural biases when they are not willing to invest in improving their skills to protect privacy, although they claim in the survey that they are aware of data protection. Moreover, consumer protection in the digital environment faces distribution issues. Because there are consumer groups who are more experienced and less experienced in online searching, the effects on consumers are hard to generalize. Similarly, charging personalized prices could benefit consumers who are more experienced in searching while harming consumers who are more “vulnerable”. Consumers may be privacy-sensitive or less sensitive. The effects of price discrimination depend on whether consumers are price sensitive, and on their search habits. Moreover, when consumers are psychologically careful about privacy but unwilling to learn techniques to protect their privacy in online searching, regulators have to take into account the literature in behavioural science and diversify their regulatory tools to include new policy methods such as nudging. When there are disputes on whether competition law or consumer protection law is better equipped to protect consumer welfare and there is no solution per se to approve or prohibit price discrimination, it is possible to look for ‘third way’ solutions including the establishment of a “digital authority”, and call for more cooperation between experts in economics, data science, behavioural studies and business and management studies. An ongoing learning process will be required as online services become more complex and when products are heterogeneous, consumers are differentiated and platforms are different from one another. The concept of consumer welfare has also been extended from good quality and low prices to consumer choices and various individual concerns including privacy concerns. When competition authorities are amending their policy for regulating data monopolies, the diverse needs of consumers, the mixed evidence in theory and empirical studies, and the difficulties of coordinating different tools among various subjects are all to be taken into account.

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The OECD has proposed the possible solution of incorporating data and consumer protection in competition assessment, which incentivizes business enterprises to engage in privacy quality competition and to correct the market failure of information asymmetry and correct adverse selection in privacy protection. Technical mechanisms could be implemented to improve consumer knowledge and consumer choices. More disclosed information and improved default rules could provide behavioural remedies and tackle the consumer privacy paradox from a policy perspective. The ease of access to, transfer, control and switch data is also taken as an important parameter to assess the market power of online service providers; the right to data portability is being taken as a remedy to entry barriers, and the benchmark of quality assessment could replace the price effect measurements in traditional markets. Therefore, it is not the issue of whether consumer protection or competition law is more appropriate to protect consumer privacy, but how competition assessment can incorporate consumer protection goals by designing a cooperative policy framework for digital markets.

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Chapter 9

Data Regulation, Consumer Protection and Competition Law

Abstract Antitrust authorities in various jurisdictions have suggested the establishment of a unified data authority to improve data governance to better coordinate the enforcement of competition law and data protection and consumer protection law. It is generally agreed that such an authority should have specific knowledge and sufficient information to monitor the collection and transaction of data. At the international level, it has been highlighted that data governance is the growth engine in the digital area. This chapter reviews the discussion on the relationship between data governance and competition law, and summarizes the arguments of data governance and the possibility of promoting private regulation and self-regulation by online platforms in digital environments.

9.1 Introduction As discussed in Sect. 4.2.4, antitrust authorities in various jurisdictions have suggested the establishment of a unified data authority to improve data governance to better coordinate the enforcement of competition law and data protection and consumer protection law. It is generally agreed that such an authority should have specific knowledge and sufficient information to monitor the collection and transaction of data. At the international level, it is clear that data governance should be a crucial issue that requires collaboration between jurisdictions. For example, the Japanese Prime Minister Shinzo Abe proposed the “Osaka Track” data governance framework at the G20 Summit in 2019, and stressed the importance of data governance that “rule-making on data flow and e-commerce, which are the growth engines in the digital area, is an urgent mission.”1 A common standard and principle is needed to harmonize data protection and privacy rules in antitrust jurisdictions.

1

Sugiyama (2019).

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 J. Ma, Regulating Data Monopolies, https://doi.org/10.1007/978-981-16-8766-2_9

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This chapter reviews the different arguments for coordinating data governance and competition law and summarizes the legal framework on data governance that has already been proposed. It will also discuss the pros and cons of self-regulation and private regulation by online platforms, and aims to contribute to the discussion of facilitating competition enforcement in a multidimensional regulatory framework in a digital environment.

9.2 Data Authority and Data Regulation 9.2.1 Data Authority Regulators and academics have even argued for the establishment of an independent enforcement authority for the digital economy—“a digital authority” that is composed of a multidisciplinary team of experts that could monitor the collection and use of data and in this way competition policy could be better coordinated with consumer protection law to better adapt to the changing environments in digital markets.2 In the UK report written by antitrust experts in March 2019, it is suggested that a digital market unit has to be established and that the unit should perform three functions: (1) developing a code of competitive conduct for firms designated as having a strategic market status (those in a position to exercise market power over a gateway or bottleneck in a digital market wherein they control others’ market access), (2) enabling greater personal data mobility and systems with open standards, and (3) advancing data openness wherever access to non-personal data will tackle the key barrier to entry.3 The George J. Stigler Center for the Study of the Economy and the State at the University of Chicago Booth School of Business’s report suggested that it is the duty of the Digital Authority to create behavioural nudges to ensure the competitiveness of the market, for example, to routinely collect data on digital transactions to monitor the performance of the sector and to set up rules to ensure data portability.4 In the testimony for the Hearing for the US House of Representatives,5 Jason Furman from Harvard Kennedy School argued that the establishment of a Digital Markets Unit is highly recommended and has three main functions: establishing the transparent criteria and code of conduct that is applied to companies, promoting a system with open technical standards to enable consumers to multi-home and use multiple systems simultaneously, and encouraging the open access of data. 2

Digital Competition Expert Panel (2019) and .Stigler Center (2019). Digital Competition Expert Panel (2019). 4 Stigler Center (2019). 5 Furman (2019). 3

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The Commission ‘Competition Law 4.0’ set up by the Federal Minister for Economic Affairs and Energy in 20186 recommends the establishment of a European digital institution or a European digital agency. The EU-level authority would play the role of collecting information on the digital market, submitting the information to specialized authorities (including authorities in competition, data protection, media supervision, consumer protection, sectoral regulations), government and legislative policymakers, and monitoring the development of digitalization, but not to intervene in the market on its own initiative.7 The European Data Protection Supervisor’s Opinion 8/2016 suggested that a network (Digital Clearing House) should be established to facilitate the sharing of information in the investigation between competition and consumer protection regulators. The network would implement data protection and consumer protection standards in the investigation of merger control cases and exploitative abuse cases when determining theories of harm.8 Since 2018–2019, the UK competition authority CMA has established the Data, Technology and Analytics (DaTA) unit to work with CMA in particular to assist the technical knowledge on data and algorithms. The team members at the unit have expertise in data engineering, data science, and data and technology market intelligence and thus can use machine learning and artificial intelligence techniques to analyse small and large data sets, and provide technology remedies.9 In addition, CMA would cooperate with the Information Commissioner’s Office (ICO), and Ofcom formed the Digital Regulation Cooperation Forum (DRCF) to support the implementation of a coherent approach to digital regulation.10 In March 2020, the government commissioned the CMA to work with ICO and Ofcom to lead the Digital Markets Taskforce. The role of the taskforce is to provide expert suggestions on the functions, processes, and powers needed to promote competition and innovation in digital markets in a “proportionate and efficient” way.11 Based on the proposal of the Digital Competition Expert Panel led by Jason Furman,12 a digital market unit (DMU) should be established. It has three core functions: establishing and overseeing an enforceable code of conduct for firms that are designated as having strategic market status, pursuing personal data mobility and systems with open standards, and using data openness to promote competition. Their sources of information that support their advice include responses to call for information and questionnaires, existing literature of reports and studies, discussions with sector regulators and international counterparts, information from large digital firms, and interviewing experts.13 Furthermore, CMA, Ofcom and the ICO have established the Digital Regulation Cooperation Forum (DRCF)14 to work with the 6

German Federal Ministry for Economic Affairs and Energy (2019). German Federal Ministry for Economic Affairs and Energy (2019, pp. 78–79). 8 EDPS (2016, p. 15). 9 CMA (2019, at p. 9). 10 CMA (2021, at p. 27). 11 CMA (2020b, p. 13). 12 CMA (2020a). 13 CMA (2020b, p. 14). 14 https://www.gov.uk/government/publications/digital-regulation-cooperation-forum. 7

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government and deal with coordination and cooperation between sectoral regulators in digital markets, and the DRCF is responsible for the coordination, capability and clarity of digital regulation.15 When there is a market for data, data authorities are to be established to be responsible for data regulation, and such regulation is in a broad sense: by taking the definition given by Keohane and Nye, governance implies both formal and informal processes and institutions, and not only governments, but also private firms, associations of firms, nongovernmental organizations, and associations of NGOs all engage in the process of governance.16 This indicates that not only is a multilayer system of public and private co-regulation is to be established in e-governance but also data authorities that are established by either public or private actors are to play a role in coordination in data regulation and monitoring. Scholars have made the suggestion of creating a digital platform agency17 that is in charge of data-related tasks. In practice, many antitrust jurisdictions have already established a digital authority to coordinate the transaction of data, some of the organizations are a branch of the government agency, and some of them are separated from the government institution to play a coordination role. After the Australian Competition & Consumer Commission (ACCC) published the Digital Platform Inquiry in 2019,18 the Australian Government Treasury published the Government Response and Implementation Roadmap for the Digital Platforms Inquiry,19 which suggested a special ACCC unit to monitor and report on digital platform markets (Table 9.1). In addition, the EU commission has set up a data strategy agency to implement digital strategy at the corporate organizational level and to transform the Commission into a data-driven administration.20 Communication to the EU Commission on the European Commission Digital Strategy 2018 stated that the Commission’s departments are to be supported by internal, corporate, and IT-related actions to develop digital solutions and become more trustworthy, effective, efficient, transparent, and secure.21 The vision of this strategy is that “By 2022, the Commission will be a digitally transformed, user-focused and data-driven administration—a truly

15

CMA (2020b. p. 8). Keohane and Nye (2000): Governance implies the processes and institutions, both formal and informal, that guide and restrain the collective activities of a group. Government is the subset that acts with authority and creates formal obligations. Governance need not necessarily be conducted exclusively by governments. Private firms, associations of firms, nongovernmental organizations (NGOs), and associations of NGOs all engage in it, often in association with governmental bodies, to create governance; sometimes without governmental authority.” Keohane and Nye (2000). 17 Wheeler et al. (2020). 18 ACCC (2019). 19 Australian Government Treasury (2019). 20 https://ec.europa.eu/info/publications/EC-Digital-Strategy_en. 21 European Commission (2018, at p. 3). 16

9.2 Data Authority and Data Regulation

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Table 9.1 Established digital authorities Country

Digital authority

Organization structure

USA

Digital Services Innovation Center

A branch of GSA

Digital Services Advisory Group CIO council

Coordinate across states

UK

Government Digital Service (GDS)

Appoint Government CIO at departments

EU

Appoint Chief Digital Office Chief Data Official

Source Svenja Falk, Andrea Römmele, and Michael Silverman, The Promise of Digital Government, in Svenja Falk, Andrea Römmele Michael Silverman (eds) Digital Government, Leveraging Innovation to Improve Public Sector Performance and Outcomes for Citizens, Springer International Publishing Switzerland 2017, at p. 8

digital Commission. It will be endowed with a new generation of trusted and personalised digital solutions supporting its digitalised policies, activities and administrative processes. These solutions will increase the Commission’s efficiency, effectiveness, transparency and security and will deliver EU-wide, borderless, digital public services that are indispensable for the functioning of the European Union.”22 The Commission is to develop to be a world-class administration and be open, trusted, secure, connected, digitalized, data driven and to have the culture of collaborative working practices, sharing of data and personalized solutions, and be able to support the interoperable, digital public services across public sectors in Europe and help to build a digital single market.23 The sub-organization of the Corporate Management Board, the Information Management Steering Board (IMSB), is in charge of the Commission’s data strategy implementation, coordinating the policy actions on data strategy and data governance and enhancing synergies in policy implementation.24 The IMSB will oversee the operation of data coordination groups and the function of voluntarily established data governance boards, which are comprised of the Local Data Correspondent, the Local Security Officer, the Local Informatics Security Officer, the Data Protection Coordinator, the Document Management Officer, the Statistical Correspondent and the Information Resource Manager.25 Overall, there are three levels of data governance: strategic (defines long-term vision and makes strategic decisions), managerial (responsible for implementing data policies at the corporate and local levels, monitoring the progress) and operational (where data policies and data decisions are made). The organization among the three levels is listed in Table 9.2.

22

European Commission (2018, at p. 3). European Commission (2018, at p. 4). 24 European Commission (2020a, at p. 11). 25 European Commission (2020, at p. 12). 23

Groups

Groups Individual

Data Coordination Groups

Data Governance Boards

Data owners

Individual (appointed by the data owner)

individual

Data Stewards

Data Users

Including policy staff, data analysts, data architects, data engineers, data scientists, software developers; communicate to data stewards or data owner on issues related to the quality, reliability and integrity of the data assets

Support data owners in defining data quality rules, facilitate reuse of data assets, maintain quality metadata, collect business and policy needs

Managers or staff assigned by the strategic level; being responsible and accountable for a data domain or data asset

Promote data governance, management, use and literacy at all levels within their DG/Service

A formal or informal network, body, committee or community deals with data-related matters, develop data policies in their domain

Coordinate with data governance partners and other actors to implement data policies, monitor and report the progress,

Oversee the implementation of Commission’s strategy on data governance, providing opinions on local data policies

Roles26

26 For the full list of responsibilities for each data governance actor, see European Commission (2020, at pp. 12–14).

Source European Commission (2020a, at pp. 10–14)

Level 3 operational

Individual (more than one person)

Local Data Correspondents (LDC)

Level 2 managerial

Groups

Information Management Steering Board (IMSB)

Level 1 strategic

Groups or individual

Governance actor

Levels

Table 9.2 EU commission data governance levels

222 9 Data Regulation, Consumer Protection and Competition Law

9.2 Data Authority and Data Regulation

223

9.2.2 Data Governance Data governance is about designing a multiorganization system to ensure the collection, management, use and sharing of data between organizations. Janssen et al.27 take a multi-organizational view of the use of data and algorithms and define data governance as “organizations and their personnel defining, applying and monitoring the patterns of rules and authorities for directing the proper functioning of, and ensuring the accountability for, the entire life-cycle of data and algorithms within and across organizations.” The European Commission’s Report on Data Governance and Data Policy defined data governance as defining, implementing and monitoring strategies, policies and shared decision-making over the management and use of data assets.28 Data policies refer to broad, high-level principles that form the guiding framework in which data assets could be managed. Data policies govern data management, data interoperability and standards, data quality, data protection and information security.

9.2.3 Digital Competence As discussed in Chap. 8, successful consumer protection in a digital environment needs successful training of digital competence for consumers. Likewise, skills in information and communication technologies (ICT) are also of crucial importance for data governance. The International Data Corporation (IDC) report showed that in 2018, there were 7.2 million data professionals in the EU, accounting for 4.3% of the EU workforce, and it is expected that the number will reach 13 million in 2025.29 Data authorities are expected to train specialized workers in data management, and particularly ICT skills in computer algorithms and analysis. The OECD has listed four layers of system changes in which digital technology skills are in demand. The first layer is that works are in demand to acquire generic ICT skills to be able to use such technologies in work (such as access information online or use software). The second layer is that workers need to use special skills to programme, develop applications and manage networks to produce ICT-related products and services, such as software, webpages, e-commerce, clouds and big data. The third layer of transformation is changing the way of working, such as using ICT skills to process complex information, communicate with co-workers and clients, solve problems and make plans. The last layer of change is that the development of proficient ICT generic, specific and complementary skills needs the development of sound foundational skills as a prerequisite.30

27

Janssen et al. (2020). European Commission (2020, at p. 6). 29 European Commission (2020). 30 OECD (2017, at p. 104). 28

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9.3 Public and Private Regulation on Data Sharing 9.3.1 An Ex-ante Regulatory Framework The enforcement pattern of competition administrative agencies in the EU has been named a “precautionary approach”.31 The precautionary principle is defined based on four elements: uncertainty (when there is a lack of scientific certainties or of full knowledge), lack of harm (the potential of future harm), shift of the burden of proof (informing the regulator that the conduct does not have negative effects or to prove efficiencies), and urgency to regulate.32 It has been argued that when network effects are perceived as market failures in the digital economy, precautionary exante intervention should be preferred.33 The Digital Markets Strategy34 published by the UK Competition and Markets Authority includes the study of online platforms and digital advertising,35 which calls for the development of an ex-ante regulatory mechanism for regulating online platforms. In the Digital Services Act Package,36 the EU Commission proposed ex-ante rules on large online platforms that are gatekeepers to information and marketplaces. The task force established by the CMA in the UK also proposed the ex-ante codes of conduct implemented by the Digital Markets Unit (DMU) to regulate powerful platforms, and expressed the benefits of providing clear-cut guidelines on the platforms with regard to competition compliance.

9.3.2 Public and Private Regulation and Self-regulation in Data Sharing Given the non-exclusive and non-rivalic nature of data, the same dataset can be used by different actors for different purposes, and the combination of datasets from different sources can improve the quality of data. When data has become the ‘input’ of the digital economy, it has become an infrastructural resource that is valuable for improving the quality of online products, and the sharing of data between public and private sectors is also important to tackle societal challenges. The Expert Group Report to the EU Commission argued that data serve public interests in at least five aspects: to improve situational awareness, to better understand the causes and variables behind the current situation, to more accurately predict and forecast, to run more rigorous impact assessments and evaluations (of any intervention) to better define the policy problem and identify the most effective policy options, and to 31

Portuese (2020). Portuese (2020, p. 2). 33 Yun (2021). 34 CMA (2019). 35 CMA (2020a). 36 European Commission (2020b). 32

9.3 Public and Private Regulation on Data Sharing

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guide public management decisions.37 This report also suggested that B2G datasharing collaboration needs a governance framework including dedicated structures, legal frameworks, best practices and standard scenarios, in particular to define the responsibility of the person who shares inaccurate or biased data that leads to an incorrect or discriminatory decision and causes damage.38 In addition, successful collaboration also needs data professionals, including data scientists, engineers and data-protection officials in the public and private sectors, to work in the B2G data sharing community.39 In addition, data protection laws also have to be coordinated with regulations on the reuse of public and private data. Directive 2003/98/EC on the reuse of public sector information40 has laid the legal framework on the flow of publicly funded data across borders, and it has been implemented by the measure of launching the European open Data Portal.41 Commission Decision 2011/833/EU on the reuse of Commission documents42 laid the legal framework for reusing the data of the European Commission, and public data from EU institutions could be accessed through the Open Data portal.43 The proposal for a review of the Directive on the reuse of public sector information (PSI Directive)44 further requires reducing market entry barriers to obtain access to public sector information, particularly for small and medium-sized enterprises, and encourages the use of application programming interfaces (APIs) to increase business opportunities.

9.4 Data Protection and Personal Data In legal terms, data protection focuses on the collection, processing and usage of personal data. Article 2(a) of Directive 95/46/EC (Data Protection Directive) defines that personal data means “any information relating to an identified or identifiable natural person.” The protection of consumer privacy mainly focuses on the proper collection and processing of personal data. For example, Article 6 (1)(b) of the EU Data Protection Directive states that personal data must be “collected for specific, 37

European Commission (2020c, at p. 19). European Commission (2020c, at p. 26). 39 European Commission (2020c), at p. 33). 40 Directive 2003/98/EC of the European Parliament and of the Council of 17 November 2003 on the re0use of public sector information. 41 http://www.europeandataportal.eu. 42 Commission Decision of 12 December 2011 on the reuse of Commission Documents (2011/833/EU), L330/39, 14.12.2011 (Article 5 on Data Portal: The Commission shall set up a data portal as a single point of access to its structured data so as to facilitate linking and reuse for commercial and non-commercial purposes. Commission services will identify and progressively make available suitable data in their possession. The data portal may provide access to data of other Union institutions, bodies, offices and agencies at their request. 43 http://data.europa.eu/euodp/en/home. 44 COM (2018) 234. 38

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explicit and legitimate purposes and not further processed in a way incompatible with those purposes.” The legal basis provided in Article 7 (a) for the collection of personal data is “unambiguous consent”. In Article 2 (h) of the Data Protection Directive, it explains that “unambiguous consent” is that “freely given specific and informed indication of his wishes by which the data subject signifies his agreement to personal data relating to him being processed.” When consumers have no control over how their data are collected and where the data have been used, they have no choice but to click ‘consent’ to use the application; even when they are unwilling to share their data to target advertisements, they are in a position of deep vulnerability. As the study by the Centre for Data Ethics and Innovation suggested, there was a “deep concern about the potential for people’s vulnerabilities to be exploited; an expectation that organizations using targeting systems should be held to account for harm they cause; and a desire to be able to exercise more control over the way they are targeted.”45 These economic findings lead to the myth of consumer protection and competition law: data disclosure might have positive effects of enhancing competition, while at the same time disclosing data may harm consumers’ privacy. From a competition law point of view, if firms do not provide sufficient options for data collection or collect excessive data, it could be regulated as a type of “abusive behaviour”.46 Although it was argued that consumer protection law, not competition law, is more suitable for protecting consumer privacy.47 For example, in the Facebook/Whatsapp case, the EU Commission stated that “Any privacy-related concerns flowing from the increased concentration of data within the control of Facebook as a result of the Transaction do not fall within the scope of the EU competition law rules but within the scope of the EU data protection rules.”48 More recent literature shows that there is a need to coordinate and integrate competition law and consumer protection law in data protection.49 The European Data Protection Supervisor (EDPS) has argued that competition law should be enforced to control market power to protect the rights of consumers.50 The European Commission also considered that competition law should incorporate the concerns of data protection and consumer privacy, as the EU competition commissioner Margrethe Vestager stated in the proposal submitted to the European Parliament: “When firms collect personal data, a degradation of data protection may result in harm to competition that can be addressed by EU competition law. Data accumulation and data protection have already been taken into account in EU competition cases, such as the recent Apple/Shazam and Microsoft/LinkedIn merger cases. The Commission will continue investigating any such data-related

45

Centre for Data Ethics and Innovation, Review of Online Targeting, February 2020. Kerber (2016a, at p. 7). 47 Ohlhausen and Okuliar (2015, pp. 37–38) and Tucker (2015, pp. 2–4). 48 European Commission, Facebook/Whatsapp, COMP/M.7217, http://ec.europa.eu/competition/ mergers/cases/decisions/m7217_20141003_20310_3962132_EN.pdf. 49 Costa-Cabral and Lynskey (2017). 50 EDPS (2014, 2016). 46

9.4 Data Protection and Personal Data

227

concerns in future merger and antitrust cases.”51 In 2017, the European Data Protection Supervisor established the ‘Big Data and Digital Clearing House” to coordinate enforcement agencies in competition and consumer and data protection to better enforce rules to protect consumers in the digital market.52

9.5 Coordinating Data Regulation, Consumer Protection and Competition Law From the perspective of consumer protection law, the main issue is improving the transparency of data collection and ensuring that consumers are well informed. When there is an information asymmetry between the online platform and consumers, consumer protection law has to correct the market failure and ensure that the online platform should provide privacy choices more clearly and explicitly. When it is understood that consumers suffer from bounded rationalities and are unaware of the data being collected,53 consumer protection law should incorporate educational functions to give consumers the knowledge and nudge consumers (for example to provide opt-out options) to improve their privacy protection techniques. Moreover, studies of consumer behaviour have indicated that when consumers are at a vulnerable position to understand privacy terms and to learn privacy protection techniques, platforms are in a superior position to build privacy protection architecture, to change their default settings or to change the framing of information to provide clearer and transparent interpretations for consumers.54 In the Facebook/WhatsApp merger, the European Commission stated that “Any privacy-related concerns flowing from the increased concentration of data within the control of Facebook as a result of the Transaction do not fall within the scope of the EU competition law rules but within the scope of the EU data protection rules.”55 This view of clearly separate competition and consumer protection expressed by the EU authority in 2014 seems to be outdated, as more recent policy papers by European competition authorities concluded that consumer protection standards should be internalized in competition assessment. The paper jointly published by German and French competition authorities on Competition Law and Data expressed the view that “Generally speaking, statutory requirements stemming from other bodies of law may be taken into account, if only as an element of context, when conducting a legal

51

European Commission (2016, 2019). http://www.digitalclearinghouse.org. 53 Kerber (2016a, p. 14). 54 CMA (2020a, p. 195). 55 European Commission (2014), Case No COMP/M.7217—Facebook/WhatsApp, https://ec.eur opa.eu/competition/mergers/cases/decisions/m7217_20141003_20310_3962132_EN.pdf. 52

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assessment under competition law.”56 This view has been recognized in the German Bundeskartellamt investigation in the Facebook case.57 In 2008, the OECD raised the issue that both competition and consumer protection law share the common goal of improving consumer welfare.58 The OECD Privacy Guidelines,59 the OECD Security Risk Recommendation60 and the OECD E-commerce Recommendation61 provided the framework guidelines for a regulatory reform to coordinate digital management in online transactions, digital insurance, data access and portability, and algorithmic discrimination.62 Specifically, the OECD has provided the solution of interconnecting consumer protection and competition policy that two general measures could be taken to correct market failures in digital transactions: one is to improve consumer knowledge, and thus to correct information asymmetries, and the other is to provide consumer choices to correct behavioural biases.63 These measures are aimed at establishing consumer-friendly legal frameworks to encourage consumers to engage in the protection of privacy. As the European Data Protection Supervisor (EDPS)64 suggested, similar to the notion of corporate social and environmental responsibility, privacy is an opportunity for competitive advantage, and in this sense, a joint policy benchmark could be built between data protection and competition law. In zero-price markets, consumer protection concerns are centred on quality competition in advertising, data acquisition, privacy and data security, and consumer choices. Consumer privacy competition is one type of quality competition, that includes control over how their data are collected and used by collecting agency and third parties and how it is safeguarded from unauthorized or inappropriate uses.65 In quality competition, competition authorities cooperate with agencies from consumer protection and data protection to build consumer awareness. When there is improved transparency in privacy terms, business operators will compete in privacy quality, and consumers are encouraged to take better control over their data in zero-price markets. Dimensions of privacy quality competition include privacy and data security, advertising content, switching costs and simplicity, and choice associated with complement.66 These suggested measures are briefly summarized in Table 9.3. User choice and control has also been categorized as one of the main synergies between competition and data protection objectives by the UK Competition &

56

Bundeskartellamt and Autorité de la Concurrence (2016), p. 23. Bundeskartellamt (2017, p. 2). 58 OECD (2008). 59 OECD (2013). 60 OECD (2015). 61 OECD (2016). 62 OECD (2018, at p. 33). 63 OECD (2018, at p. 33). 64 EDPS (2014, at p. 34). 65 OECD (2018, at p. 7). 66 EDPS (2014, pp. 33–36). 57

67

Use data portability to support competition between intermediaries Improve the easiness of using data portability by private companies Provide individuals with full choice on data disclosure and the ability to sell or license them directly on the market

Promoting data portability

Recognising property rights over personal data

Using distinctive background shading or text colours in online advertisement

Establish Certification schemes to provide user-friendly signal of privacy standards

Adopting a labeling system of terms and conditions to provide key information about privacy protection

Using innovative techniques to ensure only relevant information is provided

Improve consumers’ predictability of how their data will be used

Information about the quality of the zero-price service

Whether algorithms will be used to process data

Duration of the storage of data

Mandate active collection opt-in versus opt-out policies to establish a higher level of privacy protection as a default option

Streamlining the information available to consumers

Setting minimum standards for clarity of contractual clauses

Enhancing consumer knowledge of technical aspects of privacy and data processing through information campaigns

Contents Value of personal information collected (including monetary price charged to purchasers of the collected data)

Source OECD (2018, at pp. 33–35), European Data Protection Supervisor (2014), de Streel and Sibony (2017), and Kerber (2016b).

Improve consumer choice

Mandate the disclosure of specific pieces of information of data collection

Improve consumer knowledge

Provide consumers with opportunities to revise their data collection consent or provide consumers with timely notices

Measurement

Type of measurement

Table 9.3 Advocacy measures to improve privacy competition in zero-price markets67

9.5 Coordinating Data Regulation, Consumer Protection … 229

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Markets Authority (CMA) and Information Commissioner’s Office (ICO).68 The other two categories are standards and regulations to protect privacy and data-related interventions to promote competition. The CMA and ICO argued that providing consumer choice and control over their data is crucial to reduce power asymmetry and to improve trust and confidence, to incentivize responsible innovation and to develop alternative, privacy-protective business models, and to achieve this goal, clear regulation and standards are crucial to building choice architecture.69 Furthermore, data-related intervention is required to build a level playing field in access to data and restrict the ability to combine or access data for firms with market power.70

9.6 Conclusions While seeing that the traditional analytical framework of market power is unapplicable in digital markets, economists have made suggestions on the analysis of the data market and called for the establishment of a data authority in charge of the coordination between competition law and data protection law. Seeing the benefits of data sharing the current economic and legal barriers on data sharing, the authority is also expected to take the responsibility of reducing the transaction costs for access to datasets and to facilitate the sharing of datasets between public and private sectors. This chapter reviews the arguments on the suggestion of establishing a separate market for data, the need to establish a data authority, and the legislative requirements to improve the skills needed for data governance.

References ACCC. (2019, July 26). Digital platforms inquiry—Final report. Australian Competition & Consumer Commission. https://www.accc.gov.au/publications/digital-platforms-inquiry-finalreport Australian Government Treasury. (2019, December 12). Government response and implementation roadmap for the digital platforms inquiry. https://treasury.gov.au/publication/p2019-41708 Bundeskartellamt and Autorité de la Concurrence. (2016, May 10). Competition law and data. https://www.bundeskartellamt.de/SharedDocs/Publikation/DE/Berichte/Big%20Data%20P apier.html?nn=3591568 Bundeskartellamt. (2017, December 19). Background information on the Facebook proceeding. https://www.bundeskartellamt.de/SharedDocs/Publikation/EN/Diskussions_Hintergrundpapi ere/2017/Hintergrundpapier_Facebook.html CMA. (2019, July). The CMA’s digital markets strategy. Available at https://www.gov.uk/govern ment/publications/competition-and-markets-authoritys-digital-markets-strategy

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CMA (2021, p. 18). CMA (2021, p. 20). 70 CMA (2021, pp. 22–23). 69

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CMA. (2020a, July 1). Online platforms and digital advertising, market study final report. https:// www.gov.uk/cma-cases/online-platforms-and-digital-advertising-market-study CMA. (2020b, December). A new pro-competition regime for digital markets, advice of the digital markets taskforce. Available at https://assets.publishing.service.gov.uk/media/5fce7567e90e075 62f98286c/Digital_Taskforce_-_Advice.pdf CMA. (2021, May 19). CMA-ICO joint statement on competition and data protection law. https://www.gov.uk/government/publications/cma-ico-joint-statement-on-competition-anddata-protection-law Costa-Cabral, F., & Lynskey, O. (2017). Family ties: The intersection between data protection and competition in EU Law. Common Market Law Review, 54(1), 11–50. Digital Competition Expert Panel. (2019, March). Unlocking digital competition: Report of the Digital Competition Expert Panel. Available at https://www.gov.uk/government/publications/unl ocking-digital-competition-report-of-the-digital-competition-expert-panel de Streel, A., & Sibony, A.-L. (2017). Towards smarter consumer protection rules for the digital society—Project report. http://www.cerre.eu/sites/cerre/files/171005_CERRE_DigitalCo nsumerProtection_FinalReport.pdf EDPS. (2014). Privacy and competitiveness in the age of big data: The interplay between data protection, competition law and consumer protection in the digital economy, March 2014. Preliminary Opinion of the European Data Protection Supervisor. https://edps.europa.eu/sites/edp/files/ publication/14-03-26_competitition_law_big_data_en.pdf EDPS. (2016). Opinion 8/2016 on coherent enforcement of fundamental rights in the age of big data. European Data Protection Supervisor. https://edps.europa.eu/sites/edp/files/publication/1609-23_bigdata_opinion_en.pdf European Commission, Commission Decision of 12 December 2011 on the reuse of Commission Documents (2011/833/EU), L330/39, 14.12.2011. European Commission. (2016, January 17). Commissioner Vestager, ‘Competition in a Big Data World’. https://ec.europa.eu/commission/2014-2019/vestager/announcements/competition-bigdata-world_en European Commission, Communication to the Commission, European Commission Digital Strategy, A Digitally Transformed, User-focused and data-driven Commission, Brussels 21.11.2018, C (2018) 7118 final. European Commission. (2019). Answer given by Ms Vestager on behalf of the European Commission. https://www.europarl.europa.eu/doceo/document/E-8-2019-000001-ASW_EN.pdf European Commission. (2020a), Data Governance and Data Policies at the European Commission, Secretariat-General, July 2020 European Commission. (2020b). The Digital Services Act package. https://ec.europa.eu/digital-sin gle-market/en/digital-services-act-package European Commission. (2020c). Towards a European strategy on business-to-government data sharing for the public interest. Final Report Prepared by the High-Level Expert Group on Business-to-Government Data Sharing, European Union 2020. European Commission. (2020d). The European data market monitoring tool, key facts & figures, first policy conclusions, data landscape and quantified stories. D2.9 Final Study Report. https://datala ndscape.eu/sites/default/files/report/D2.9_EDM_Final_study_report_16.06.2020_IDC_pdf.pdf Furman, J. (2019). Prepared Testimony for the Hearing “Online Platforms and Market Power, Part 3: The Role of Data and Privacy in Competition”, October 18, 2019. German Federal Ministry for Economic Affairs and Energy. (2019, September). A new competition framework for the digital economy, report by the commission ‘Competition Law 4.0’. German Federal Ministry for Economic Affairs and Energy. https://www.bmwi.de/Redaktion/EN/Publik ationen/Wirtschaft/a-new-competition-framework-for-the-digital-economy.pdf?__blob=public ationFile&v=3 Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Tomasz Janowski, T. (2020). Data governance: Organizing data for trustworthy artificial intelligence. Government Information Quarterly, 37(3), 141–184.

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Keohane, R. O., & Nye, J. S. (2000). Introduction. In J. S. Nye & J. D. Donahue (Eds.), Governance in a globalization world (p. 2000). Brookings Institution Press. Kerber, W. (2016a). Digital markets, data, and privacy: Competition law, consumer law, and data protection. http://www.uni-marburg.de/fb02/makro/forschung/magkspapers/index_html% 28magks%29 Kerber, W. (2016b). Digital markets, data, and privacy: Competition law, consumer law and data protection. Journal of Intellectual Property Law & Practice, 11(11), 856–866. OECD. (2008). The interface between competition and consumer policies. DAF/COMP/GF (2008)10. www.oecd.org/regreform/sectors/40898016.pdf OECD. (2013). Guidelines on the protection of privacy and transborder flows of personal data. http://www.oecd.org/sti/ieconomy/oecd_privacy_framework.pdf OECD. (2015). Recommendation on digital security risk management for economic and social prosperity. http://www.oecd.org/publications/digital-security-risk-management-for-eco nomicand-social-prosperity-9789264245471-en.htm OECD. (2016). Consumer protection in E-commerce: OECD recommendation. OECD Publishing. https://doi.org/10.1787/9789264255258-en OECD. (2017). Key issues for digital transformation in the G20, Report Prepared for a joint G20 German Presidency/OECD Conference, Berlin, Germany, 12 January 2017. OECD. (2018, November 28). Quality considerations in digital zero-price markets, background note by the secretariat. DAF/COMP (2018)14. Ohlhausen, M. K., & Okuliar, A. (2015). Competition, consumer protection, and the right (approach) to privacy. Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2561563 Portuese, A. (2020, November 11). European competition enforcement and the digital economy: The birthplace of precautionary antitrust. The Global Antitrust Institute (GAI) Report on the Digital Economy. Available at https://ssrn.com/abstract=3733715 Stigler Center. (2019, May 15). Stigler Committee on Digital Platforms Report. George J. Stigler Center for the Study of the Economy and the State, The University of Chicago Booth School of Business, Committee for the Study of Digital Platforms Market Structure and Antitrust Subcommittee, Report. Available at https://research.chicagobooth.edu/-/media/res earch/stigler/pdfs/market-structure---report-as-of-15-may-2019.pdf; https://www.publicknowle dge.org/wp-content/uploads/2019/09/Stigler-Committee-on-Digital-Platforms-Final-Report.pdf Sugiyama, S. (2019). Abe heralds launch of “Osaka Track” framework for free cross border data flow at G20. Japan Times, June 28, 2019. https://www.japantimes.co.jp/news/2019/06/28/nat ional/abe-heralds-launch-osaka-track-framework-free-cross-border-data-flow-g20 Tucker, D. S. (2015). The proper role of privacy in merger review. CPI Antitrust Chronicle. Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2614046 Wheeler, T., Verveer, P., & Kimmelman, G. (2020). New digital realities; new oversight solutions: The case for a digital platform agency and a new approach to regulatory oversight. Harvard Kennedy School—Shorenstein Center Discussion Paper, August 20. https://shorensteincenter. org/new-digital-realities-tom-wheeler-phil-verveer-gene-kimmelman/ Yun, J. M. (2021). Does antitrust have digital blind spots? George Mason Law & Economics Research Paper No. 20-16. Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=359 3467

Chapter 10

Conclusions

Abstract Because of the distinct features of data and online platforms and the diverse characteristics that different platforms have, economists have different views on whether and how data monopolies should be regulated. There are strong arguments for and against governmental intervention, while there are also strong opponents to ask for the divestiture of data giants. When online platforms do not compete for lower costs but to attract attention from consumers, platforms have to invest in new technologies and business strategic models, and a temporary market position may become necessary to facilitate large investments in R&D. This chapter summarizes the main arguments of this book and proposes directions for future studies on data monopolies.

10.1 Introduction Competition law in the era of a data-driven economy has faced a full range of challenges in making trade-offs. Online platforms function differently than in the traditional economy when they are characterized by attention-based zero-price competition, indirect network effects in multi-sided markets, the existence of switching-costs and multi-homing effects. Data collected by online platforms have been considered valuable input (or currency) in the data economy; therefore, collecting and storing data is necessary. As data are considered by economists to have non-exclusive and non-rivalrous features and a diminishing value over time, it is argued that holding a dataset itself does not violate the clauses of abuse of dominant position. Anti-competitive behaviour is mainly driven by charging personalized prices (price discrimination) and the abuse of consumer privacy by excessively collecting consumer data without a proper information system. Competition agencies and courts have to understand the difficult trade-offs of economic models on collecting heterogeneous consumer data, and using the data for charging personalized data may or may not harm consumer welfare, hence making the tradeoff between encouraging innovation and competition in dynamic markets and preventing the excessive collection of data from consumers that harm consumer welfare.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 J. Ma, Regulating Data Monopolies, https://doi.org/10.1007/978-981-16-8766-2_10

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While economists and lawyers have acknowledged the specific characteristics of online platforms that make digital competition distinct from competition analysis in traditional markets, they have also shown the attitude that those characteristics are not ‘specific’ enough to replace the entire neoclassical competition theory. While the presumption of structure-based competition has been changed, the analytical framework should be adapted, and adjustments should be made in the methods for assessing relevant market and market shares rather than abandoning the existing competition tools as a whole. Static structure-based indirect analysis must be adapted to a dynamic, behavioural-based analysis. The goal of competition to reduce prices has to be adapted to improving the quality of innovation and meeting heterogenous consumer needs. A new legal framework focusing on dynamic efficiency, nonprice indicators, heterogeneous preferences of consumers, economics of scale on data volume, and direct and indirect network effects of multi-sided platforms should all be developed to improve competition law and policy theory, and legal remedies should be designed based on a growing understanding of the economic insights on digital markets. This chapter will summarize the main arguments of this book, and it will have seven parts: the second section summarizes economic theory on online platforms and data monopolies; the third section discusses quality competition in zero-price markets. The fourth section will summarize the debate on market power assessment, and the fifth section discusses the impact of digital platforms on consumer welfare. The sixth section draws conclusions on the implications of data monopolies for competition law, and the last section concludes and points out the direction of future research.

10.2 Economic Theory of Online Platform and Data Monopolies Economic theory of the online platform is centred on the interdependencies between users on different sides of the platform. Indirect network effects and externalities in the platform make it profitable to offer a “zero price” for the services on one side to attract users on the other side, as statistics show that the revenue generated from search advertising contributes to 60–70% of the total revenue of Google and Facebook. The feature of interdependencies has brought the changes in business models and pricing strategies, and it is also the reason why competition authorities are facing obstacles to apply traditional tools of market definition and market power assessment to regulate data giants even if they have obtained over 90 percent market share in the given market. Chapter 3 of this book has extended the discussions on the specific characteristics of online platforms, and there are three issues that competition authorities must take into account to make good judgments on the assessment of market power. The first issue is dynamic efficiency in digital competition. The Schumpeterian view of the

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relationship between temporary market dominance and the large investment in R&D is a strong argument against government intervention. Innovation-driven competition has changed the analytical framework of competitive effects, as temporary market dominance may not indicate the presence of market power, and in the process of competition for the market, constant innovation cycles impose competitive pressure on market players. The second issue is the indirect network effects that different sides include in online transactions. Being first defined by Jean Charles Rochet and Jean Tirole in 2001, the concept of two-sided platforms has been identified as the starting point to analyse competition in digital markets. Users on different sides of the platform are connected and because of the indirect network effects, the demand on one side becomes interdependent on the demand on the other side. The platform serves as an intermediary to bring both sides on board, and charging below-cost prices on one side is reasonable as it will increase the demand on the other side. Traditional analysis of predatory pricing may not be applicable when there are feedback effects on demand elasticities. The third issue is that when internet firms provide products at zero price, the attention from users becomes the ‘price’ of the transaction, and advertisers have to design new products to compete for attracting consumers’ attention. When online platforms take data as an input of their business and products are supplied at zero price, the ability to control data and create business revenues through the analysis of data becomes the central focus of competition judgement on data monopolies. When an online platform charges zero price on one side to attract users on the other side, users often consume online products and services “for free” in exchange for providing their personal data. In this way, data become a crucial input for platforms to generate revenues and it becomes possible to send personalized advertisements and charge personalized prices when platforms have the ability to collect massive information on different groups of consumers. Data create substantial revenue when platforms use algorithms to send personalized advertisements or practice targeting sales. The ability to obtain valuable data or prohibit access to data raises concerns about creating data monopolies and harming consumer welfare by abusing their market power. When data are defined as non-exclusive and non-rivalic, the value of business firms cannot be assessed through a pure static calculation of market shares and the scale of the dataset, as the value of business is created through the use of data.

10.3 Quality Competition in Zero-Price Markets In many ways, the traditional methods of analysing competition structures based on price competition have their limitations in digital markets. Platforms provide products for free to increase the number of users. The benefits of positive indirect network effects are often not measured by price values. More users with more attention and more personal data will contribute to the quality of prediction, as good-quality data

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will improve algorithms, leading to the success of the firm in dynamic efficiency and can replace the incumbents in the long run. The benefits of nonprice competition are often not monetized in data markets,1 and their success in innovation cycles is also not easily measured by the simple price–cost margin. They charge zero price or price below cost to attract the attention of users, and win the market in the ‘winnertakes-all’ competition. When there is pressure to be replaced by innovative entrants, incumbents with high market share cannot exploit their market power as the market is contestable, and such market power might be economically efficient because it is beneficial for product innovation. The temporary market structure, product prices and costs cannot measure the true competitive restraints in the market. When online products and services are provided at zero price, the assessment of competitive effects has shifted from price to quality. It is not a new argument from the perspective of consumer protection goals that quality is also an important factor, but the shift from price competition to quality competition is rather fundamental in digital markets. One observable change in academic debate is that the SSNIP method used in market power measurement has been replaced by SSDNQ, as discussed in the next section. Another important area of research is the discussion of quality competition in consumer privacy protection. The quality of consumer data protection is an important benchmark when comparing product and service providers and the abuse of consumer privacy or blocking access to consumer data is also considered anti-competitive conduct. Online enterprises compete in the quality of their privacy terms, and consumers’ benefits do not include the quality of the online service itself but also the ability to switch their data, control their data and be informed of the value of their data. In this aspect, data protection law, consumer protection law and competition law are interconnected and have the responsibility of improving the legal framework to facilitate privacy quality competition, as discussed in Sect. 9.5 of this book.

10.4 Market Power Assessment The Schumpeterian view of dynamic efficiency shows that in an innovative digital market, high market shares and high profit margins cannot indicate market powers of the firm because high market shares may show the success of innovation and monopolistic positions are only temporary. Indirect measurements, including market definition and market shares are based on the structure-conduct-performance paradigm, and structural analysis may become inapplicable in highly dynamic digital competition. Online platforms no longer compete for lower prices, but compete for winning the market. They may charge below-cost prices or zero price in exchange for gaining large scale consumer data, and by using the data in charging personalized prices or sending targeted advertisements, they can increase the volume of users at the demand side and advertisers at the supply side. They increase the value of the platform. The 1

Lianos (2019, at p. 104).

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price–cost margin is no longer the source of market power, but the capability of collecting and analysing consumer data by adopting new technologies and business models becomes a concern. Given the continuing debate on economies of scale, it is an empirical question of whether firms hold market power as the data are or are not replicable, and the importance of data volume is in improving the quality of online products. Potential entrants are facing barriers in accessing data, and the reason for such barriers may be technological, legal or behavioural. The question of whether the firm holds market power depends on how the competition authority decides on whether the dataset was unique, and how easily competitors can access data. Therefore, it makes a judgment on whether the firm has obtained a competitive advantage and whether such advantage will increase when the dataset is expanded. The direct assessment of market power and dominance tends to include multiple factors and elements to understand the competitive restraints on the incumbents to keep their motivation to invest in innovation and to prevent them from abusing their dominant position. A crucial factor is the analysis of entry barriers. However, economists and lawyers may have different views on these factors, and one crucial example is the debate on economies of scale of data. It has been argued that the substantial volume of data that the platform holds does not raise anti-competitive effects per se because of the non-rivalry and non-exclusive feature of data itself and the diminishing value of data over time, so the incumbents do not have any illegal advantage. It is also empirically proven that historical data is not crucial for product quality, and the collection of new data is not economically costly. The main difficulty in designing regulatory strategies to assess market power and to prevent abusive behaviour by data giants is to understand the indirect network effects and interdependencies between different sides of the platform. The participation of users at one side generates externalities on the other side, and thus, it becomes a widely applied pricing strategy in which the platform offers zero price services on one side and charges prices on the other side of the market. Because of the link between the demands at two sides charging below cost prices at the users’ side cannot be judged as predatory pricing behaviour. Platforms no longer compete for price itself but compete for users’ attention. The traditional methods used in evaluating market power have been challenged because of the indirect network effects and the externalities that have been created during the transaction between different sides. Market share on one side itself can no longer be used because the platforms have adopted business models and pricing strategies distinct from traditional markets. Chapter 4 of this book reviewed the three ways to measure market power in digital markets: one is to avoid defining the relevant market; the method used for assessing substitutability in market power is also to be modified as assessing the possibility for users to switch their attention given the increase of absolute prices or reduction in quality (SSDNQ), or develop new techniques (such as critical loss analysis); and the third way is to define a separate market for data itself. Chapter 6 of this book discusses the barriers to entry created by dominant firms with market power. Potential entrants are placed in a disadvantageous position when there are high costs of switching and lead to users being locked-in or platforms preventing the communication of data with rivals or upstream service providers thus prohibiting user multi-homing. Data

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portability and data interoperability are the legal concepts developed to address these two problems.

10.5 Impact on Consumer Welfare The crucial difference between offline and online e-commerce is that online platforms take consumer data as the ‘input’ of their business and either directly or indirectly collect data on consumer preferences and draw a comprehensive picture of consumer behaviour. Online platforms have received substantial revenue through targeting advertisements and price discrimination. The ability to store and analyse massive data has also been considered a barrier to entry for competitors. Although consumers have acknowledged the convenience of using platforms to search for information and make use of the online retail delivery system, they have expressed strong concern at losing control of their personal data and the exploiting behaviour of the platform on consumer privacy. When competition authorities tend to impose more interventionist regulatory instruments on data giants with the goal of protecting consumers and more attention is given to the coordination between competition policy, economic regulation and privacy law, the literature on consumer behaviour and marketing studies is often neglected. Consumer protection and competition law has to be tailored to mitigate consumer biases and to meet the needs of the ever-increasing population of digital consumers. Chapter 8 of this book has listed the positive influences of online platforms on consumer welfare as they have significantly improved product quality and services; they also have reduced information asymmetry by reducing the search costs. The impact of price discrimination and sending personalized advertisements has to be analysed in the context: the behaviour of online consumers is heterogeneous: their behaviour of searching varies among groups and products. Consumers who have more search experience tend to be loyal to certain brands. Consumers are more likely to focus on the top links of search results and are likely to be influenced by consumer reviews.

10.6 Implications for Competition Law New technologies have driven market changes for internet devices, software, the online transaction value chain and business strategies and pricing structures. Multisided market platforms are operated by facilitating network effects among users on different sides, and data-driven technology has changed the pricing models of platforms. When products and services are provided with zero-price, it is critical to understand that first, the ‘currency’ of the data economy has switched from real prices to consumer data, and the personal information of users has been monetized in the digital environment; second, platforms earn revenue through capitalizing data

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from users to advertisers, and the feedback loop effects enhance the profit-making process when there are more users participating in the transaction. The focus of price competition thus switched to the focus on information collection. When consumer data are the ‘oil’ of the digital economy, data have been utilized by algorithms to make economic value. Data collection becomes essential in the value-producing chain of online platforms. Market power is assessed through the ability to control data. Dominant firms achieve economies of scale and can leverage their dominance to tied product markets. The ability to selectively collect data and to charge personalized prices based on data has raised concerns about consumer privacy, data protection and competition law. When economists have proven the efficiencyenhancing effects of price discrimination, there is a difficult trade-off to be made with economic efficiency, consumer protection and privacy. As mentioned in the introduction, economists and lawyers have not reached a consensus on whether government intervention is needed and how an effective regulatory framework could be designed in the era of digital economy. When there are strong public concerns about the abuse of consumer privacy in collecting massive data, there are also strong counterarguments on limiting government intervention to encourage technology innovation and investment in R&D by internet giants. This book has extensively reviewed the literature on both arguments and has identified the paradoxes in the study of competition law and policy, for example, the paradox of monopoly and innovation, switching costs and entry barriers, and consumer privacy. There is no clear-cut conclusion for the debate—all requires a caseby-case analysis. Competition authorities have to understand the distinct features of data and online platforms, carefully amend the competition tools in traditional markets that they are familiar with, and even invent new methods when necessary. Chapters 4 and 5 of this book have summarized the characteristics of online platforms and data monopoly, and Chapters 6–8 have discussed the implications of the analysis of market power, relevant markets and analysis of the impact on consumer welfare. There are two main issues that competition authorities could learn from these discussions. The first issue is the change of competition structure and competition process. The structure-based view of the competition process and the structurebased methods in regulating competition may both become outdated. Competition authorities have to understand that online markets are technologically driven and that potential entrants and incumbents are involved in innovation cycles. Data monopolies generate substantial market value and social benefits by utilizing their data assets and invest in AI technology and business strategies to analyse datasets. Although from the number of market shares they have already obtained a dominant position, such dominance may not be detrimental from the perspective of dynamic efficiency. Furthermore, economists have also emphasized the characteristics of indirect network effects and argued that the pricing structure (such as charging below cost prices) may not be considered anti-competitive (such as predatory pricing in traditional markets) because indirect network effects make the overall market effects efficient. Regulators have to focus on dynamic efficiency and competition for the market. Innovation creates business value and brings about an increase in consumer welfare and social welfare. Data itself does not have value, but the technology of using data can generate

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significant value. The second issue is the change of pricing structure and the assessment of market power in multi-sided markets. When internet giants have adopted a new pricing model and the Lerner Index can no longer be taken as the benchmark to assess market power, competition authorities will have to be cautious about the use of the SSNIP test and to discuss how to measure the demand changes on both sides but not only one side.

10.7 Conclusions and Future Research Recalling the classic debate of the antitrust paradox in the century-long debate, there is no clear black-and-white answer to the question of regulating data monopolies. The dynamic efficiency and innovation arguments questioned the necessity of obtaining a monopolistic position and investing in R&D. Digital platforms are the leaders in the global digital economy and have provided social benefits by reducing transaction costs and facilitating the development of new business models and services. When classic structural analysis is challenged, economic theories and empirics provide mixed evidence on the effects of digital market power on innovation cycles, entry barriers, price discrimination, consumer harm and privacy concerns. Practical methods for assessing relevant market and market power are even more a myth. The book has reviewed such a paradox and provided a preliminary analytical framework in regulating digital monopolies, and has identified the key issues that are under debate in this field. Cases in the EU, US and China are examples to show the difficulties in the practice of competition law in digital markets. As mentioned in the introduction, the competition law of data monopolies is comprised of several paradoxes. The first paradox is that while online platforms have obtained a clear large market share and can leverage their market dominance from one market to another, until the “winner-takes-all” tipping effect emerges, theories on innovation and dynamic efficiency replace the SCP structural analysis and against a command-and-control intervention. Market dominance is not at fault but the abuse of dominance, which becomes an antitrust concern. Theories on exploitative abuse have not been fully developed. Economic theories have shown the non-exclusive and non-rivalrous nature of data, and data have a diminishing return over time. However, the use of data creates massive economic value, and the use of data has an increasing return over scale. It becomes the paradox that regulating data lacks the economic reason, since data could be collected from various sources and can be re-used unlimited times without costs, and the holding of a dataset itself is not at fault. The use of data involves computing algorithms that create economic value, and regulating the holding of large datasets of dominant firms lacks economic reasons. The key is to let competitors to obtain access to datasets from multiple sources: from the consumer side, changing single homing behaviour to multi-homing behaviour by granting portability; from the business side reducing the costs of accessing data and improving the standard use of data through

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interoperability. Improving the sharing of data from multiple sources, i.e., public and private data, and encouraging public–private agreements on the common use of data. Future research on competition and regulation in digital markets could include the following: First, the amendments of competition act in antitrust jurisdictions. After the enactment of the GDPR and Digital Markets Act in the EU, major antitrust jurisdictions around the world implemented amendments of their antitrust laws. The amended Anti-monopoly Law in China is currently under review and is about to be promulgated in due course. Those amendments of competition laws and their enforcements require further research and follow-up discussions. Second, there are new developments and discussions of competition tools in assessing relevant market and market power. The limitations of SSNIP have been extensively discussed even before the emergence of digital markets; however, the consensus on how SSNIP could be revised or replaced by other methods such as SSNDQ or UPP has not been made. Discussions between economists, data scientists and legal scholars on relevant market definitions deserves more attention in the years to come. Third, the issue of how competition law could be coordinated with consumer protection law and sector regulations should be investigated in more detail. While economists often argue for a clear separation between competition and consumer protection law, there seems to be a global tendency to consolidate enforcement power and to establish a unified enforcement agency in regulating the market economy. When there is a conflict between competition, consumer protection and market regulation in protecting consumer privacy, there is a clear argument to establish one digital authority and to empower the digital authority to enforce data related competition laws. Whether a unified common standard should be given by public authorities to develop a common technology mechanism to facilitate data portability and data interoperability and thus to promote competition between platforms is also a crucial issue in the discussion of PIMSs and APIs. It recalls the classic coordination problem in the antitrust paradox, and requires continuous dialogue between competition regulators and academics in competition law.

Reference Lianos, I. (2019). Competition law for the digital era: A complex systems’ perspective (Research Paper Series 6/2019). UCL Centre for Law, Economics and Society (CLES).