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Big data and competition policy (Record no. 41826)

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000 -LEADER
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003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20201205114458.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 160316s2016 xxu||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780198788140
040 ## - CATALOGING SOURCE
Transcribing agency .
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 343.072100
Item number STU
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Stucke Maurice E
245 ## - TITLE STATEMENT
Title Big data and competition policy
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Oxford
Name of publisher, distributor, etc. Oxford University Press
Date of publication, distribution, etc. 2016
300 ## - PHYSICAL DESCRIPTION
Extent 371p
Dimensions xix
365 ## - TRADE PRICE
Price amount Rs. 2,964
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note Content:<br/>Abbreviations xiii Table of Cases xv Table of Legislation xix<br/>1. Introduction;<br/>A. Myth 1: Privacy Laws Serve Different Goals from Competition Law 1.07;<br/>B. Myth 2: The Tools that Competition Officials Currently Use Fully Address All the Big Data Issues 1.11;<br/>C. Myth 3: Market Forces Currently Solve Privacy Issues 1.12;<br/>D. Myth 4: Data-Driven Online Industries Are Not Subject to Network Effects 1.17;<br/>E. Myth 5: Data-Driven Online Markets Have Low Entry Barriers 1.18;<br/>F. Myth 6: Data Has Little, If Any, Competitive Significance, Since Data is Ubiquitous, Low Cost, and Widely Available 1.20;<br/>G. Myth 7: Data Has Little, If Any, Competitive Significance, as Dominant Firms Cannot Exclude Smaller Companies' Access;<br/>to Key Data or Use Data to Gain a Competitive Advantage 1.22;<br/>H. Myth 8: Competition Officials Should Not Concern Themselves with Data-Driven Industries because Competition Always Comes from Surprising Sources 1-24 ;<br/>I. Myth 9: Competition Officials Should Not Concern Themselves with Data-Driven Industries Because Consumers Generally Benefit from Free Goods and Services 1-26;<br/>J. Myth 10: Consumers Who Use these Free Goods and Services Do Not Have Any Reasonable Expectation of Privacy 1.28;<br/>I THE GROWING DATA-DRIVEN ECONOMY;<br/>2. Defining Big Data; A. Volume of Data 2.04 B. Velocity of Data 2.11 Vll Contents;<br/>C. Variety of Data 2.16 D. Value of Data 2.19;<br/>3. Smartphones as an Example of How Big Data and Privacy Intersect;<br/>A. Why the Odds Favoured the Government in Riley 3.05;<br/>B. The Surprising Unanimous Decision 3.14; C. Reflections 3.21;<br/>4. The Competitive Significance of Big Data;<br/>A. Six Themes from the Business Literature Regarding the Strategies Implications of Big Data;<br/>B. Responding to Claims of Big Data's Insignificance for Competition Policy;<br/>C. If Data is Non-Excludable, Why are Firms Seeking to Preclude Third Parties from Getting Access to Data?;<br/>D. The Twitter Firehose;<br/>E. The Elusive Metaphor for Big Data;<br/>5. Why Haven't Market Forces Addressed Consumers' Privacy Concerns?;<br/>A. Market Forces Are Not Promoting Services that Afford Great Privacy Protections 5.02;<br/>B. Why Hasn't the Market Responded to the Privacy Concerns of So Many Individuals? 5.11;<br/>C. Are Individuals Concerned About Privacy? 5.13;<br/>D. The Problem with the Revealed Preference Theory 5.15;<br/>E. The Lack of Viable Privacy-Protecting Alternatives 5.24 4.02 4.14 4.27 4.29 4.32;<br/>II THE COMPETITION AUTHORITIES MIXED RECORD IN RECOGNIZING DATA'S<br/>IMPORTANCE AND THE IMPLICATIONS OF A FEW FIRMS' UNPARALLELED SYSTEM OF HARVESTING AND MONETIZING THEIR DATA TROVE;<br/>6. The US's and EU's Mixed Record in Assessing Data-Driven Mergers;<br/>A. The European Commission's 2008 Decision Not to Challenge the TomTom/Tele Atlas Merger 6.03;<br/>B. Facebook/WhatsApp 6.15; viu Contents;<br/>C. FTC's 'Early Termination' of its Review of the Alliance Data Systems Corp/Conversant Merger;<br/>D. Google/Nest Labs and Google / Dropcam;<br/>E. Google/Waze;<br/>F. The DOJ's 2014 Win against Bazaar voice / Power Reviews;<br/>G. Synopsis of Merger Cases 6.49 665 6.75 6.93 6.104 <br/>III WHY HAVEN'T MANY COMPETITION AUTHORITIES CONSIDERED THE IMPLICATIONS<br/>OF BIG DATA?;<br/>7. Agencies Focus on What Is Measurable (Price), Which Is Not Always Important (Free Goods)<br/>A. The Push Towards Price-Centric Antitrust 7.04;<br/>B. What the Price-Centric Approach Misses 7.12;<br/>C. The Elusiveness of Assesse ng a Merger's Effect on Quality Competition 7.16;<br/>D. Why Quality Competition is Paramount in Many Data-Driven Multi-Sided Markees 7.20;<br/>E. Challenges in Conducting an SSNDQon Privacy 7.28;<br/>F. Using SSNIP for Free Services 7.41;<br/>G. How a Price-Centric Analysis Can Yield the Wrong Conclusion 7.45;<br/>H. Reflections 7.51;<br/>8. Data-Driven Mergers Often Fall Outside Competition Policy's Conventional Categories<br/>A. Categorization of Mergers 8.02;<br/>B. Belief that Similar Products/Services Compete More Fiercely than Dissimilar Products/Services 8.08;<br/>C. Substitutability of Data 8.22;<br/>D. Defining a New Category 8.31;<br/>9. Belief that Privacy Concerns Differ from Competition Policy Objectives A. Defining Privacy in a Data-Driven Economy 9.02;<br/>B. Whether and When There Is a Need to Show Harm, and If So, What Type of Harm 9.04;<br/>C. How Should the Competition Agencies and Courts Balance the Privacy Interests with Other Interests? 9.14;<br/>ix Contents<br/>D. Courts ‘Acceptance of Prevailing Defaults, in Lieu of Balancing 9.21;<br/>E. Setting the Default in Competition Cases 9.31;<br/>F. Conclusion 9.39;<br/>IV WHAT ARE THE RISKSIF COMPETITION AUTHORITIES IGNORE OR DOWNPLAY BIG DATA?<br/>10. Importance of Entry Barriers in Antitrust Analysis;<br/>A. Entry Barriers in Data-Driven Markets 10.03;<br/>B. Looking Beyond Traditional Entry Barriers 10.06;<br/>11. Entry Barriers Can Be Higher in Multi-Sided Markets,;<br/>Where One Side Exhibits Traditional Network Effects;<br/>A. Traditional Network Effects in Facebook/WhatsApp 11.04;<br/>B. The Commission's Reasoning Why the Merger Was Unlikely;<br/>to Tip the Market to Facebook 11.11;<br/>C. Strengths and Weaknesses of the Commission's Analysis of Network Effects 11.17;<br/>12. Scale of Data: Trial-and-Error, 'Learning-by-Doing' Network Effects A. Waze's Turn-by-Turn Navigation App 12.03;<br/>B. Search Engines 12.07 ;<br/>C. Facebook 12.27;<br/>D. Reflections 12.31;<br/>13. Two More Network Effects: Scope of Data and Spill-Over Effects A. Scope of Data 13.01;<br/>B. Spill-Over Effects: How Networks Effects on One Side of Multi-Sided Platforms Can Increase Market Power on the Other Sides 13.10;<br/>14. Reflections on Data-Driven Network Effects; <br/>A. Ten Implications of Data-Driven Network Effects 14.03; <br/>B. Why Controlling the Operating System Gives the Platform;<br/>a Competitive Advantage Over an Independent App 14.20;<br/>C. Independent App Developers' Dependence on Google and Apple 14.29;<br/>D. How Google Benefits from These Network Effects 14.32;<br/>E. Domination is not Guaranteed 14.40 x Contents;<br/>15. Risk of Inadequate Merger Enforcement; <br/>A. The Prediction Business 15.02;<br/>B. Most Mergers are Cleared 15.06;<br/>C. The Big Mystery: How Often Do the Competition Agencies Accurately Predict the Mergers' Competitive Effects? 15.11;<br/>D. The Ex-Post Merger Reviews Paint a Bleak Picture 15.17;<br/>E. The High Error Costs When the Agencies Examine Only One Side of a Multi-Sided Platform 15.20<br/>F. How Data-Driven Mergers Increase the Risks of False Negatives 15.32;<br/>16. The Price of Weak Antitrust Enforcement;<br/>A. The Chicago School's Fear of False Positives 16.02;<br/>B. The United States as a Test Case of Weak Antitrust Enforcement 16.10;<br/>C. Costs of Weak Antitrust Enforcement in the Agricultural Industry 16.15;<br/>D. Costs of Weak Antitrust Enforcement in the Financial Sector 16.22;<br/>E. Consumers ‘Overall Welfare 16.45;<br/>F. Why Ignoring Big Data Will Compound the Harm 16.51;<br/>G. The Competition Agencies Cannot Assume that Other Agencies will Repair Their Mistakes 16.58<br/>V ADVANCINGA RESEARCH AGENDA FOR THE AGENCIES AND ACADEMICS;<br/>17. Recognizing When Privacy and Competition Law Intersect;<br/>A. Promoting Consumers' Privacy Interests Can Be an Important Part of Quality Competition 17.03;<br/>B. Some Simple Examples Where Privacy and Competition Law Intersect 17.08;<br/>C. Looking Beyond Privacy's Subjectivity 17.14;<br/>D. Developing Better Economic Tools to Address Privacy 17.19;<br/>E. Why Competition Policy Does Not Have an Efficiency Screen 17.27;<br/>F. Using a Consumer Well-Being Screen 17.30;<br/>G. Media Mergers as an Example of a Consumer Well-Being Screen 17.36;<br/>H. Conclusion 17.40; xi Contents;<br/>18. Data-opoly: Identifying Data-Driven Exclusionary and Predatory Conduct;<br/>A. In False Praise of Monopolies 18.03;<br/>B. Debunking the Myth that Competition Law is Ill-Suited for New Industries 18.08;<br/>C. How the 'Waiting for Dynamic Competition' Argument Ignores Path Dependencies 18.14;<br/>D. How (Even Failed) Antitrust Enforcement Can Open Competitive Portals 18.20;<br/>E. The Now casting Radar—Why Some Data-opolies are More Dangerous than Microsoft in the 1990s 18.28;<br/>F. Keeping the Competitive Portals Open 18.33;<br/>G. An Object All Sublime, the Competition Authority Shall Achieve in Time—to Let the Punishment Fit the Crime 18.65;<br/>19. Understanding and Assessing Data-Driven Efficiencies Claims A. Efficiencies Benefit Consumers 19.04;<br/>B. Efficiencies Must Be Merger-Specific 19.10;<br/>C. Efficiencies Must Be Verifiable 19.19;<br/>D. Balancing Efficiency and Privacy 19.21;<br/>E. Challenges Ahead 19.28;<br/>20. Need for Retrospectives of Data-Driven Mergers A. Waiting for the Right Data-Driven Merger 20.04;<br/>B. Debasing Through Ex-Post Merger Reviews 20.08;<br/>C. FTC's Retrospectives of Hospital Mergers 20.14;<br/>D. The Benefits in Conducting Merger Retrospectives 20.24;<br/>21. More Coordination among Competition, Privacy and Consumer Protection Officials;<br/>A. Moving Beyond Notice-and-Consent 21.06;<br/>B. Several Preconditions to Spur Privacy Competition 21.11;<br/>22. Conclusion 335;<br/>Index 339<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element 1. Antitrust Law - Compeition 2. Privacy - Right Of
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Grunes Allen P
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
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        . .   30.05.2017 2963.00 8 10 343.0721 STU 35104 09.12.2025 06.12.2025 30.05.2017 BOOKs