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    Big data and competition policy / Maurice E. Stucke, Allen P. Grunes.

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    • Title:Big data and competition policy / Maurice E. Stucke, Allen P. Grunes.
    •    
    • Author/Creator:Stucke, Maurice E., author.
    • Other Contributors/Collections:Grunes, Allen P., author.
    • Published/Created:Oxford, United Kingdom : Oxford University Press, 2016.
      ©2016

    • Holdings

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    • Library of Congress Subjects:Antitrust law.
      Big data--Government policy.
      Privacy, Right of.
    • Edition:First edition.
    • Description:xx, 371 pages : illustrations ; 25 cm
    • Notes:Includes bibliographical references and index.
    • ISBN:9780198788133 hardback
      0198788134 hardback
      9780198788140 paperback
      0198788142 paperback
    • Contents:Machine generated contents note: 1. Introduction
      A. Myth 1: Privacy Laws Serve Different Goals from Competition Law
      B. Myth 2: The Tools that Competition Officials Currently Use Fully Address All the Big Data Issues
      C. Myth 3: Market Forces Currently Solve Privacy Issues
      D. Myth 4: Data-Driven Online Industries Are Not Subject to Network Effects
      E. Myth 5: Data-Driven Online Markets Have Low Entry Barriers
      F. Myth 6: Data Has Little, If Any, Competitive Significance, Since Data is Ubiquitous, Low Cost, and Widely Available
      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
      H. Myth 8: Competition Officials Should Not Concern Themselves with Data-Driven Industries because Competition Always Comes from Surprising Sources
      I. Myth 9: Competition Officials Should Not Concern Themselves with Data-Driven Industries Because Consumers Generally Benefit from Free Goods and Services
      J. Myth 10: Consumers Who Use these Free Goods and Services Do Not Have Any Reasonable Expectation of Privacy
      I. GROWING DATA-DRIVEN ECONOMY
      2. Defining Big Data
      A. Volume of Data
      B. Velocity of Data
      C. Variety of Data
      D. Value of Data
      3. Smartphones as an Example of How Big Data and Privacy Intersect
      A. Why the Odds Favoured the Government in Riley
      B. Surprising Unanimous Decision
      C. Reflections
      4. Competitive Significance of Big Data
      A. Six Themes from the Business Literature Regarding the Strategic 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. Twitter Firehose
      E. 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
      B. Why Hasn't the Market Responded to the Privacy Concerns of So Many Individuals?
      C. Are Individuals Concerned About Privacy?
      D. Problem with the Revealed Preference Theory
      E. Lack of Viable Privacy-Protecting Alternatives
      II. 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. US's and EU's Mixed Record in Assessing Data-Driven Mergers
      A. European Commission's 2008 Decision Not to Challenge the TomTom/Tele Atlas Merger
      B. Facebook/WhatsApp
      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. DOJ's 2014 Win against Bazaarvoice/PowerReviews
      G. Synopsis of Merger Cases
      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. Push Towards Price-Centric Antitrust
      B. What the Price-Centric Approach Misses
      C. Elusiveness of Assessing a Merger's Effect on Quality Competition
      D. Why Quality Competition is Paramount in Many Data-Driven Multi-Sided Markets
      E. Challenges in Conducting an SSNDQ on Privacy
      F. Using SSNIP for Free Services
      G. How a Price-Centric Analysis Can Yield the Wrong Conclusion
      H. Reflections
      8. Data-Driven Mergers Often Fall Outside Competition Policy's Conventional Categories
      A. Categorization of Mergers
      B. Belief that Similar Products/Services Compete More Fiercely than Dissimilar Products/Services
      C. Substitutability of Data
      D. Defining a New Category
      9. Belief that Privacy Concerns Differ from Competition Policy Objectives
      A. Defining Privacy in a Data-Driven Economy
      B. Whether and When There Is a Need to Show Harm, and If So, What Type of Harm
      C. How Should the Competition Agencies and Courts Balance the Privacy Interests with Other Interests?
      D. Courts' Acceptance of Prevailing Defaults, in Lieu of Balancing
      E. Setting the Default in Competition Cases
      F. Conclusion
      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
      B. Looking Beyond Traditional Entry Barriers
      11. Entry Barriers Can Be Higher in Multi-Sided Markets, Where One Side Exhibits Traditional Network Effects
      A. Traditional Network Effects in Facebook/WhatsApp
      B. Commission's Reasoning Why the Merger Was Unlikely to Tip the Market to Facebook
      C. Strengths and Weaknesses of the Commission's Analysis of Network Effects
      12. Scale of Data: Trial-and-Error, `Learning-by-Doing' Network Effects
      A. Waze's Turn-by-Turn Navigation App
      B. Search Engines
      C. Facebook
      D. Reflections
      13. Two More Network Effects: Scope of Data and Spill-Over Effects
      A. Scope of Data
      B. Spill-Over Effects: How Networks Effects on One Side of Multi-Sided Platforms Can Increase Market Power on the Other Sides
      14. Reflections on Data-Driven Network Effects
      A. Ten Implications of Data-Driven Network Effects
      B. Why Controlling the Operating System Gives the Platform a Competitive Advantage Over an Independent App
      C. Independent App Developers' Dependence on Google and Apple
      D. How Google Benefits from These Network Effects
      E. Domination is not Guaranteed
      15. Risk of Inadequate Merger Enforcement
      A. Prediction Business
      B. Most Mergers are Cleared
      C. Big Mystery: How Often Do the Competition Agencies Accurately Predict the Mergers' Competitive Effects?
      D. Ex-Post Merger Reviews Paint a Bleak Picture
      E. High Error Costs When the Agencies Examine Only One Side of a Multi-Sided Platform
      F. How Data-Driven Mergers Increase the Risks of False Negatives
      16. Price of Weak Antitrust Enforcement
      A. Chicago School's Fear of False Positives
      B. United States as a Test Case of Weak Antitrust Enforcement
      C. Costs of Weak Antitrust Enforcement in the Agricultural Industry
      D. Costs of Weak Antitrust Enforcement in the Financial Sector
      E. Consumers' Overall Welfare
      F. Why Ignoring Big Data Will Compound the Harm
      G. Competition Agencies Cannot Assume that Other Agencies will Repair Their Mistakes
      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
      B. Some Simple Examples Where Privacy and Competition Law Intersect
      C. Looking Beyond Privacy's Subjectivity
      D. Developing Better Economic Tools to Address Privacy
      E. Why Competition Policy Does Not Have an Efficiency Screen
      F. Using a Consumer Well-Being Screen
      G. Media Mergers as an Example of a Consumer Well-Being Screen
      H. Conclusion
      18. Data-opoly: Identifying Data-Driven Exclusionary and Predatory Conduct
      A. In False Praise of Monopolies
      B. Debunking the Myth that Competition Law is Ill-Suited for New Industries
      C. How the `Waiting for Dynamic Competition' Argument Ignores Path Dependencies
      D. How (Even Failed) Antitrust Enforcement Can Open Competitive Portals
      E. Nowcasting Radar
      -Why Some Data-opolies are More Dangerous than Microsoft in the 1990s
      F. Keeping the Competitive Portals Open
      G. Object All Sublime, the Competition Authority Shall Achieve in Time
      -to Let the Punishment Fit the Crime
      19. Understanding and Assessing Data-Driven Efficiencies Claims
      A. Efficiencies Benefit Consumers
      B. Efficiencies Must Be Merger-Specific
      C. Efficiencies Must Be Verifiable
      D. Balancing Efficiency and Privacy
      E. Challenges Ahead
      20. Need for Retrospectives of Data-Driven Mergers
      A. Waiting for the Right Data-Driven Merger
      B. Debiasing Through Ex-Post Merger Reviews
      C. FTC's Retrospectives of Hospital Mergers
      D. Benefits in Conducting Merger Retrospectives
      21. More Coordination among Competition, Privacy, and Consumer Protection Officials
      A. Moving Beyond Notice-and-Consent
      B. Several Preconditions to Spur Privacy Competition
      22. Conclusion.
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