Big data and competition policy:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford
Oxford University Press
[2016]
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| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XX, 371 Seiten Diagramme |
| ISBN: | 9780198788133 9780198788140 |
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Datensatz im Suchindex
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| adam_text | Titel: Big data and competition policy
Autor: Stucke, Maurice E
Jahr: 2016
BIG DATA AND COMPETITION POLICY Maurice E. Stucke Allen P. Gruñes OXFORD UNIVERSITY PRESS
CONTENTS Abbreviations xiii Table of Cases xv Table ofLegislation 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 vii
C. Variety of Data D. Value of Data Contents 2.16 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 Strategic Implications of Big Data 4.02 B. Responding to Claims of Big Data’s Insignificance for Competition Policy 4.14 C. If Data is Non-Excludable, Why are Firms Seeking to Preclude Third Parties from Getting Access to Data? 4.27 D. The Twitter Firehose 4.29 E. The Elusive Metaphor for Big Data 4.32 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 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 viii
Contents C. FTC’s ‘Early Termination’ of Its Review of the Alliance Data Systems Corp/Conversant Merger 6.49 D. Google/Nest Labs and Google/Dropcam 6.65 E. Google/Waze 6.75 F. The DOJ’s 2014 Win against Bazaarvoice/PowerReviews 6.93 G. Synopsis of Merger Cases 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 Assessing a Merger’s Effect on Quality Competition 7.16 D. Why Quality Competition is Paramount in Many Data-Driven Multi-Sided Markets 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 RISKS IF 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, ‘Leaming-by-Doing’ Network Effects A. Waze’sTurn-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
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 ofWeak Antitrust Enforcement 16.10 C. Costs ofWeak Antitrust Enforcement in the Agricultural Industry 16.15 D. Costs ofWeak 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 ADVANCING A 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 Nowcasting 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. Debiasing 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 xii
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| title | Big data and competition policy |
| title_auth | Big data and competition policy |
| title_exact_search | Big data and competition policy |
| title_full | Big data and competition policy Maurice E. Stucke, Allen P. Grunes |
| title_fullStr | Big data and competition policy Maurice E. Stucke, Allen P. Grunes |
| title_full_unstemmed | Big data and competition policy Maurice E. Stucke, Allen P. Grunes |
| title_short | Big data and competition policy |
| title_sort | big data and competition policy |
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