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Big Data and Competition Policy by Stucke, Maurice; Grunes, Allen (1st June 2016)
- Preliminary Material
- Main Text
- 1 Introduction
- 1.01
- 1.02
- 1.03
- 1.04
- 1.05
- 1.06
- 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
- Part I The Growing Data-Driven Economy
- 2 Defining Big Data
- 3 Smartphones as an Example of How Big Data and Privacy Intersect
- 4 The Competitive Significance of Big Data
- 4.01
- 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 The Twitter Firehose
- E The Elusive Metaphor for Big Data
- 5 Why Haven’t Market Forces Addressed Consumers’ Privacy Concerns?
- 5.01
- 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 The Problem with the Revealed Preference Theory
- E The Lack of Viable Privacy-Protecting Alternatives
- Part 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
- 6.01
- 6.02
- A The 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 The DOJ’s 2014 Win against Bazaarvoice/PowerReviews
- G Synopsis of Merger Cases
- 6 The US’s and EU’s Mixed Record in Assessing Data-Driven Mergers
- Part III Why Haven’t Many Competition Authorities Considered the Implications of Big Data?
- Preliminary Material
- 7 Agencies Focus on What is Measurable (Price), Which is Not Always Important (Free Goods)
- 7.01
- 7.02
- 7.03
- A The Push Towards Price-Centric Antitrust
- B What the Price-Centric Approach Misses
- C The 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
- 9 Belief that Privacy Concerns Differ from Competition Policy Objectives
- 9.01
- 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
- Part IV What are the Risks if Competition Authorities Ignore or Downplay Big Data?
- Preliminary Material
- 10 Importance of Entry Barriers in Antitrust Analysis
- 11 Entry Barriers Can Be Higher in Multi-Sided Markets, Where One Side Exhibits Traditional Network Effects
- 12 Scale of Data: Trial-and-Error, ‘Learning-by-Doing’ Network Effects
- 13 Two More Network Effects: Scope of Data and Spill-Over Effects
- 14 Reflections on Data-Driven Network Effects
- 14.01
- 14.02
- 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
- 15.01
- A The Prediction Business
- B Most Mergers are Cleared
- C The Big Mystery: How Often Do the Competition Agencies Accurately Predict the Mergers’ Competitive Effects?
- D The Ex-Post Merger Reviews Paint a Bleak Picture
- E The 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 The Price of Weak Antitrust Enforcement
- 16.01
- A The Chicago School’s Fear of False Positives
- B The 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 The Competition Agencies Cannot Assume that Other Agencies will Repair Their Mistakes
- Part V Advancing a Research Agenda for the Agencies and Academics
- Preliminary Material
- 17 Recognizing When Privacy and Competition Law Intersect
- 17.01
- 17.02
- 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
- 18.01
- 18.02
- 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 The Nowcasting Radar—Why Some Data-opolies are More Dangerous than Microsoft in the 1990s
- F Keeping the Competitive Portals Open
- 18.33
- 18.34
- 18.35
- 1 Exclusive dealing to prevent rivals from accessing critical data
- 2 Exclusionary practices to prevent rivals from achieving scale
- 3 Dominant firm leverages its data-advantage in a regulated market to another market
- 4 Increasing customers’ switching costs
- 5 Vertical integration by a dominant platform operator
- G An 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
- 20 Need for Retrospectives of Data-Driven Mergers
- 21 More Coordination among Competition, Privacy, and Consumer Protection Officials
- 22 Conclusion
- 1 Introduction
- Further Material
