Big data and competition policy/

por STUCKE, Maurice E.
[ Livros ]
Autores adicionais: GRUNES, Allen P. ; Autor
Publicado por : Oxford University Press, (Nova York:) Detalhes físicos: 371 p. ISBN:9780198788133. Ano: 2016 Tipo de Material: Livros
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Biblioteca Agamenon Magalhães
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CONTENTS

Abbreviations
Tabk of Cases
Table of Legislation

1. Introduction
A. Myth 1: Privacy Laws Serve Different Goals from Competition Law
B. Myth 2: lhe Tools that Competition Officials Currently Use FullyAddress Ali the Big Data Issues
C. Myth 3: Market Forces Currently Solve Privacylssues
D. Myth 4: Data-Driven Online Industries Are Not Sub ject to Network Effects
E. Myth 5: Data-Driven Oriline Markets Have Low Entry Barriers
F. Myth 6: Data Has Little, IfAny, Competitive Sigriificance, Since Data is Ubiquitous, Low Cost, and WidelyAvailable
G. Myth 7: Data Has Little, IfAny, 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 Generaily 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 THE GROWING DATA-DRIVEN ECONOMY

2. Defining Big Data
A. Volume of Data
B. Velocity of Data
C. Variety of Data
D. Va!ue of Data
3. Smartphones as an Example of How Big Data and Privacy Intersect
A. Why the Odds Favoured the Government in Riley
B. The Surprising Unanimous Decision
C. Reflections
4. lhe Competitive Significance of Big Data
A. Six ilhemes 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. lhe Twitter Firehose
E. The Elusive Metaphor for Big Data
5. Why Haven't Market Forces Addressed Consumers' Privacy Concerns?
A. Market Forces Are Not Promoring Services that Afford Great Privacy Protections
B. Why Hasn't the Market Responded to the Privacy Concerns Of So Many Individuais?
C. Are Individuais Concerned About Privacy?
D. lhe Problem with the Revealed Preference Theory
E. lhe Lack ofViable Privacy-Protecting Alternatives

II THE COMPETITION AUTHORITIES' MIXED RECORD IN RECOGNIZING DATAS IMPORTANCE AND THE IMPLICATIONS OF A FEW FIRMS' UNPARALLELED SYSTEM OF HARVESTING AND MONETIZING THEIR DATA TROVE

6. lhe US's and EU's Mixed Record in Assessing Data-Driven Mergers
A. lhe European Com mission's 2008 Decision Nor to Challenge the TomTom/Tele Atlas Merger
B. Facebook/WhatsApp
C. FTC's 'EarlyTermination' of lts Review of theAlliance 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

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
B. What the Price-Centric Approach Misses
C. The Elusiveness of Assessing a Merger's Effect on Quality Competition
D. Why Qualiry 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 Fali Outside Competition Policy's Conventional Categories
A. Categorization ofMergers
B. Belief that Similar Products/Services Compete More Fiercely than Dissimilar Products/Services
C. Substirutability of Data
D. Defining a New Category
9. Belief that PrivacyConcerns Differ from Competition Poilcy Objectives
A. Defining Privacy in a Data-Driven Econorny
B. Whether and When ]Ihere Is a Need to Show Harrn,and IfSo, What Type oU Harm
C. How Shouid the Competition Agencies and Courts Balance the Privacy Interests with Other Interests?
D. Courts' Acceptance ofPrevailing Defaults, in Lieu ofBalancing
E. Setring 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.EntryBarriers Can Be Higher in Multi-Sided Markets, Where One Side Exhibits Traditional Network Effects
A. Traditional Network Effects in Facebook/WhatsApp
B. The Commission's Reasoning Why the Merger Was Urilikely 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 Navigarion 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 Coritrolling the Operating Sysrem Gives the Platform a Comperitive Advantage Over an Independent App
C. Independent App Developers' Dependence on Google and Apple
D. How Google Benefits from Tbese Network Effects
E. Domination is not Guaranteed
15. Risk of lnadequate Merger Enforcement
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-PostMerger Reviews Paint a B!eak Picture
E. The High Error Costs When the Agencies Examine Only One Side of a Multi-Sided P!atform
F. How Data-Driven Mergers Increase the Risks ofFalse Negatives
16. The Price ofWeakAntitrust Enforcement
A.'lhe Chicago School's Fear of False Positives
B. The Unired States as a Test Case of WeakAntitrust 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 Igrioring Big Data Wi!! Compound the Harm
G. The Competition Agencies Cannot Assume that Other Agencies wil! 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 Inrerests Can Be an Important Part of Quality Competition
B. Some Simp!e Examples Where Privacy and Competition Law lntersecr
C. Looking Beyond Privacy's Sub jectivity
D. Deve!oping Berter Economic Too!s to Address Privacy
E. Why Competition Po!icy Does Not Have an Efficiency Screen
F. Using a Consumer Wel!-Being Screen
G. Media Mergers as an F.xamp!e of a Consumer WeIl-Being Screen
H. Conclusion
18. Data-opoiy: Identifying Data-Driven Exclusionary and Predatory Conduct
A. In False Praise ofMoriopolies
B. Debunking the Myth that Competition Law is Ili-Suited for New Industries
C. How the 'Waiting for Dynamic Competition' Argument Ignores Path Dependencies
D. How (Even Failed) Antitrust Enforcement Can Open Competitive Portais
E. The Nowcasting Radar—Why Some Data-opolies are More Dangerous than Microsoft in the 1990s
F. Keeping the Cotnpetitive Portais Open
G. An Object Ali Sublime, the Competition Authority Shail Achieve in Time—to Ler 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 Efflciency and Privacy
E. Challenges Ahead
20. Need for Retrospectives ofData-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. The Benefits in Conducting Merger Retrospectives
21. More Coordination among Competition, Privacy, and Consumer Protection Officials
A. Moving Bcyond Notice-and-Consent
B. Several Preconditions to Spur Privacy Competition
22. Conclusion
Index


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