Content:
Abbreviations xiii Table of Cases xv Table of Legislation xix
1. Introduction;
A. Myth 1: Privacy Laws Serve Different Goals from Competition Law 1.07;
B. Myth 2: The Tools that Competition Officials Currently Use Fully Address All the Big Data Issues 1.11;
C. Myth 3: Market Forces Currently Solve Privacy Issues 1.12;
D. Myth 4: Data-Driven Online Industries Are Not Subject to Network Effects 1.17;
E. Myth 5: Data-Driven Online Markets Have Low Entry Barriers 1.18;
F. Myth 6: Data Has Little, If Any, Competitive Significance, Since Data is Ubiquitous, Low Cost, and Widely Available 1.20;
G. Myth 7: Data Has Little, If Any, Competitive Significance, as Dominant Firms Cannot Exclude Smaller Companies' Access;
to Key Data or Use Data to Gain a Competitive Advantage 1.22;
H. Myth 8: Competition Officials Should Not Concern Themselves with Data-Driven Industries because Competition Always Comes from Surprising Sources 1-24 ;
I. Myth 9: Competition Officials Should Not Concern Themselves with Data-Driven Industries Because Consumers Generally Benefit from Free Goods and Services 1-26;
J. Myth 10: Consumers Who Use these Free Goods and Services Do Not Have Any Reasonable Expectation of Privacy 1.28;
I THE GROWING DATA-DRIVEN ECONOMY;
2. Defining Big Data; A. Volume of Data 2.04 B. Velocity of Data 2.11 Vll Contents;
C. Variety of Data 2.16 D. Value of Data 2.19;
3. Smartphones as an Example of How Big Data and Privacy Intersect;
A. Why the Odds Favoured the Government in Riley 3.05;
B. The Surprising Unanimous Decision 3.14; C. Reflections 3.21;
4. The Competitive Significance of Big Data;
A. Six Themes from the Business Literature Regarding the Strategies Implications of Big Data;
B. Responding to Claims of Big Data's Insignificance for Competition Policy;
C. If Data is Non-Excludable, Why are Firms Seeking to Preclude Third Parties from Getting Access to Data?;
D. The Twitter Firehose;
E. The Elusive Metaphor for Big Data;
5. Why Haven't Market Forces Addressed Consumers' Privacy Concerns?;
A. Market Forces Are Not Promoting Services that Afford Great Privacy Protections 5.02;
B. Why Hasn't the Market Responded to the Privacy Concerns of So Many Individuals? 5.11;
C. Are Individuals Concerned About Privacy? 5.13;
D. The Problem with the Revealed Preference Theory 5.15;
E. The Lack of Viable Privacy-Protecting Alternatives 5.24 4.02 4.14 4.27 4.29 4.32;
II THE COMPETITION AUTHORITIES MIXED RECORD IN RECOGNIZING DATA'S
IMPORTANCE AND THE IMPLICATIONS OF A FEW FIRMS' UNPARALLELED SYSTEM OF HARVESTING AND MONETIZING THEIR DATA TROVE;
6. The US's and EU's Mixed Record in Assessing Data-Driven Mergers;
A. The European Commission's 2008 Decision Not to Challenge the TomTom/Tele Atlas Merger 6.03;
B. Facebook/WhatsApp 6.15; viu Contents;
C. FTC's 'Early Termination' of its Review of the Alliance Data Systems Corp/Conversant Merger;
D. Google/Nest Labs and Google / Dropcam;
E. Google/Waze;
F. The DOJ's 2014 Win against Bazaar voice / Power Reviews;
G. Synopsis of Merger Cases 6.49 665 6.75 6.93 6.104
III WHY HAVEN'T MANY COMPETITION AUTHORITIES CONSIDERED THE IMPLICATIONS
OF BIG DATA?;
7. Agencies Focus on What Is Measurable (Price), Which Is Not Always Important (Free Goods)
A. The Push Towards Price-Centric Antitrust 7.04;
B. What the Price-Centric Approach Misses 7.12;
C. The Elusiveness of Assesse ng a Merger's Effect on Quality Competition 7.16;
D. Why Quality Competition is Paramount in Many Data-Driven Multi-Sided Markees 7.20;
E. Challenges in Conducting an SSNDQon Privacy 7.28;
F. Using SSNIP for Free Services 7.41;
G. How a Price-Centric Analysis Can Yield the Wrong Conclusion 7.45;
H. Reflections 7.51;
8. Data-Driven Mergers Often Fall Outside Competition Policy's Conventional Categories
A. Categorization of Mergers 8.02;
B. Belief that Similar Products/Services Compete More Fiercely than Dissimilar Products/Services 8.08;
C. Substitutability of Data 8.22;
D. Defining a New Category 8.31;
9. Belief that Privacy Concerns Differ from Competition Policy Objectives A. Defining Privacy in a Data-Driven Economy 9.02;
B. Whether and When There Is a Need to Show Harm, and If So, What Type of Harm 9.04;
C. How Should the Competition Agencies and Courts Balance the Privacy Interests with Other Interests? 9.14;
ix Contents
D. Courts ‘Acceptance of Prevailing Defaults, in Lieu of Balancing 9.21;
E. Setting the Default in Competition Cases 9.31;
F. Conclusion 9.39;
IV WHAT ARE THE RISKSIF COMPETITION AUTHORITIES IGNORE OR DOWNPLAY BIG DATA?
10. Importance of Entry Barriers in Antitrust Analysis;
A. Entry Barriers in Data-Driven Markets 10.03;
B. Looking Beyond Traditional Entry Barriers 10.06;
11. Entry Barriers Can Be Higher in Multi-Sided Markets,;
Where One Side Exhibits Traditional Network Effects;
A. Traditional Network Effects in Facebook/WhatsApp 11.04;
B. The Commission's Reasoning Why the Merger Was Unlikely;
to Tip the Market to Facebook 11.11;
C. Strengths and Weaknesses of the Commission's Analysis of Network Effects 11.17;
12. Scale of Data: Trial-and-Error, 'Learning-by-Doing' Network Effects A. Waze's Turn-by-Turn Navigation App 12.03;
B. Search Engines 12.07 ;
C. Facebook 12.27;
D. Reflections 12.31;
13. Two More Network Effects: Scope of Data and Spill-Over Effects A. Scope of Data 13.01;
B. Spill-Over Effects: How Networks Effects on One Side of Multi-Sided Platforms Can Increase Market Power on the Other Sides 13.10;
14. Reflections on Data-Driven Network Effects;
A. Ten Implications of Data-Driven Network Effects 14.03;
B. Why Controlling the Operating System Gives the Platform;
a Competitive Advantage Over an Independent App 14.20;
C. Independent App Developers' Dependence on Google and Apple 14.29;
D. How Google Benefits from These Network Effects 14.32;
E. Domination is not Guaranteed 14.40 x Contents;
15. Risk of Inadequate Merger Enforcement;
A. The Prediction Business 15.02;
B. Most Mergers are Cleared 15.06;
C. The Big Mystery: How Often Do the Competition Agencies Accurately Predict the Mergers' Competitive Effects? 15.11;
D. The Ex-Post Merger Reviews Paint a Bleak Picture 15.17;
E. The High Error Costs When the Agencies Examine Only One Side of a Multi-Sided Platform 15.20
F. How Data-Driven Mergers Increase the Risks of False Negatives 15.32;
16. The Price of Weak Antitrust Enforcement;
A. The Chicago School's Fear of False Positives 16.02;
B. The United States as a Test Case of Weak Antitrust Enforcement 16.10;
C. Costs of Weak Antitrust Enforcement in the Agricultural Industry 16.15;
D. Costs of Weak Antitrust Enforcement in the Financial Sector 16.22;
E. Consumers ‘Overall Welfare 16.45;
F. Why Ignoring Big Data Will Compound the Harm 16.51;
G. The Competition Agencies Cannot Assume that Other Agencies will Repair Their Mistakes 16.58
V ADVANCINGA RESEARCH AGENDA FOR THE AGENCIES AND ACADEMICS;
17. Recognizing When Privacy and Competition Law Intersect;
A. Promoting Consumers' Privacy Interests Can Be an Important Part of Quality Competition 17.03;
B. Some Simple Examples Where Privacy and Competition Law Intersect 17.08;
C. Looking Beyond Privacy's Subjectivity 17.14;
D. Developing Better Economic Tools to Address Privacy 17.19;
E. Why Competition Policy Does Not Have an Efficiency Screen 17.27;
F. Using a Consumer Well-Being Screen 17.30;
G. Media Mergers as an Example of a Consumer Well-Being Screen 17.36;
H. Conclusion 17.40; xi Contents;
18. Data-opoly: Identifying Data-Driven Exclusionary and Predatory Conduct;
A. In False Praise of Monopolies 18.03;
B. Debunking the Myth that Competition Law is Ill-Suited for New Industries 18.08;
C. How the 'Waiting for Dynamic Competition' Argument Ignores Path Dependencies 18.14;
D. How (Even Failed) Antitrust Enforcement Can Open Competitive Portals 18.20;
E. The Now casting Radar—Why Some Data-opolies are More Dangerous than Microsoft in the 1990s 18.28;
F. Keeping the Competitive Portals Open 18.33;
G. An Object All Sublime, the Competition Authority Shall Achieve in Time—to Let the Punishment Fit the Crime 18.65;
19. Understanding and Assessing Data-Driven Efficiencies Claims A. Efficiencies Benefit Consumers 19.04;
B. Efficiencies Must Be Merger-Specific 19.10;
C. Efficiencies Must Be Verifiable 19.19;
D. Balancing Efficiency and Privacy 19.21;
E. Challenges Ahead 19.28;
20. Need for Retrospectives of Data-Driven Mergers A. Waiting for the Right Data-Driven Merger 20.04;
B. Debasing Through Ex-Post Merger Reviews 20.08;
C. FTC's Retrospectives of Hospital Mergers 20.14;
D. The Benefits in Conducting Merger Retrospectives 20.24;
21. More Coordination among Competition, Privacy and Consumer Protection Officials;
A. Moving Beyond Notice-and-Consent 21.06;
B. Several Preconditions to Spur Privacy Competition 21.11;
22. Conclusion 335;
Index 339