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View translated passages only
Oxford Law Citator
Contents
Expand All
Collapse All
Preliminary Material
Acknowledgements
Contents
Abbreviations
Table of Cases
Chinese Cases
European Cases
US Cases
Table of Legislation
European Legislation and Merger Guidelines
US Legislation and Merger Guidelines
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
1.07
1.08
1.09
1.10
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
1.13
1.14
1.15
1.16
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
1.19
F Myth 6: Data Has Little, If Any, Competitive Significance, Since Data is Ubiquitous, Low Cost, and Widely Available
1.20
1.21
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
1.23
H Myth 8: Competition Officials Should Not Concern Themselves with Data-Driven Industries because Competition Always Comes from Surprising Sources
1.24
1.25
I Myth 9: Competition Officials Should Not Concern Themselves with Data-Driven Industries Because Consumers Generally Benefit from Free Goods and Services
1.26
1.27
J Myth 10: Consumers Who Use these Free Goods and Services do not have any Reasonable Expectation of Privacy
1.28
1.29
1.30
1.31
1.32
Part I The Growing Data-Driven Economy
2 Defining Big Data
2.01
2.02
2.03
A Volume of Data
2.04
2.05
2.06
2.07
2.08
2.09
2.10
B Velocity of Data
2.11
2.12
2.13
2.14
2.15
C Variety of Data
2.16
2.17
2.18
D Value of Data
2.19
2.20
2.21
2.22
2.23
2.24
2.25
2.26
2.27
2.28
2.29
3 Smartphones as an Example of How Big Data and Privacy Intersect
3.01
3.02
3.03
3.04
A Why the Odds Favoured the Government in Riley
3.05
3.06
3.07
3.08
3.09
3.10
3.11
3.12
3.13
B The Surprising Unanimous Decision
3.14
3.15
3.16
3.17
3.18
3.19
3.20
C Reflections
3.21
3.22
3.23
3.24
3.25
4 The Competitive Significance of Big Data
4.01
A Six Themes from the Business Literature Regarding the Strategic Implications of Big Data
4.02
4.03
4.04
4.05
4.06
4.07
4.08
4.09
4.10
4.11
4.12
4.13
B Responding to Claims of Big Data’s Insignificance for Competition Policy
4.14
4.15
4.16
4.17
4.18
4.19
4.20
4.21
4.22
4.23
4.24
4.25
4.26
C If Data is Non-Excludable, Why are Firms Seeking to Preclude Third Parties from Getting Access to Data?
4.27
4.28
D The Twitter Firehose
4.29
4.30
4.31
E The Elusive Metaphor for Big Data
4.32
4.33
4.34
5 Why Haven’t Market Forces Addressed Consumers’ Privacy Concerns?
5.01
A Market Forces Are Not Promoting Services that Afford Great Privacy Protections
5.02
5.03
5.04
5.05
5.06
5.07
5.08
5.09
5.10
B Why Hasn’t the Market Responded to the Privacy Concerns of So Many Individuals?
5.11
5.12
C Are Individuals Concerned About Privacy?
5.13
5.14
D The Problem with the Revealed Preference Theory
5.15
5.16
5.17
5.18
5.19
5.20
5.21
5.22
5.23
E The Lack of Viable Privacy-Protecting Alternatives
5.24
5.25
5.26
5.27
5.28
5.29
5.30
5.31
5.32
5.33
5.34
5.35
5.36
5.37
5.38
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
6.03
6.04
6.05
6.06
6.07
6.08
6.09
6.10
6.11
6.12
6.13
6.14
B Facebook/WhatsApp
6.15
6.16
6.17
6.18
6.19
6.20
6.21
6.22
6.23
6.24
6.25
6.26
6.27
6.28
6.29
6.30
6.31
6.32
6.33
6.34
6.35
6.36
6.37
6.38
6.39
6.40
6.41
6.42
6.43
6.44
6.45
6.46
6.47
6.48
C FTC’s ‘Early Termination’ of Its Review of the Alliance Data Systems Corp/Conversant Merger
6.49
6.50
6.51
6.52
6.53
6.54
6.55
6.56
6.57
6.58
6.59
6.60
6.61
6.62
6.63
6.64
D Google/Nest Labs and Google/Dropcam
6.65
6.66
6.67
6.68
6.69
6.70
6.71
6.72
6.73
6.74
E Google/Waze
6.75
6.76
6.77
6.78
6.79
6.80
6.81
6.82
6.83
6.84
6.85
6.86
6.87
6.88
6.89
6.90
6.91
6.92
F The DOJ’s 2014 Win against Bazaarvoice/PowerReviews
6.93
6.94
6.95
6.96
6.97
6.98
6.99
6.100
6.101
6.102
6.103
G Synopsis of Merger Cases
6.104
6.105
6.106
6.107
6.108
Part III Why Haven’t Many Competition Authorities Considered the Implications of Big Data?
Preliminary Material
III.01
III.02
III.03
III.04
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
7.04
7.05
7.06
7.07
7.08
7.09
7.10
7.11
B What the Price-Centric Approach Misses
7.12
7.13
7.14
7.15
C The Elusiveness of Assessing a Merger’s Effect on Quality Competition
7.16
7.17
7.18
7.19
D Why Quality Competition is Paramount in Many Data-Driven Multi-Sided Markets
7.20
7.21
7.22
7.23
7.24
7.25
7.26
7.27
E Challenges in Conducting an SSNDQ on Privacy
7.28
7.29
7.30
7.31
7.32
7.33
7.34
7.35
7.36
7.37
7.38
7.39
7.40
F Using SSNIP for Free Services
7.41
7.42
7.43
7.44
G How a Price-Centric Analysis Can Yield the Wrong Conclusion
7.45
7.46
7.47
7.48
7.49
7.50
H Reflections
7.51
7.52
7.53
7.54
7.55
8 Data-Driven Mergers Often Fall Outside Competition Policy’s Conventional Categories
8.01
A Categorization of Mergers
8.02
8.03
8.04
8.05
8.06
8.07
B Belief that Similar Products/Services Compete More Fiercely than Dissimilar Products/Services
8.08
8.09
8.10
8.11
8.12
8.13
8.14
8.15
8.16
8.17
8.18
8.19
8.20
8.21
C Substitutability of Data
8.22
8.23
8.24
8.25
8.26
8.27
8.28
8.29
8.30
D Defining a New Category
8.31
8.32
8.33
8.34
8.35
8.36
8.37
8.38
8.39
8.40
9 Belief that Privacy Concerns Differ from Competition Policy Objectives
9.01
A Defining Privacy in a Data-Driven Economy
9.02
9.03
B Whether and When There Is a Need to Show Harm, and If So, What Type of Harm
9.04
9.05
9.06
9.07
9.08
9.09
9.10
9.11
9.12
9.13
C How Should the Competition Agencies and Courts Balance the Privacy Interests with Other Interests?
9.14
9.15
9.16
9.17
9.18
9.19
9.20
D Courts’ Acceptance of Prevailing Defaults, in Lieu of Balancing
9.21
9.22
9.23
9.24
9.25
9.26
9.27
9.28
9.29
9.30
E Setting the Default in Competition Cases
9.31
9.32
9.33
9.34
9.35
9.36
9.37
9.38
F Conclusion
9.39
9.40
Part IV What are the Risks if Competition Authorities Ignore or Downplay Big Data?
Preliminary Material
IV.01
IV.02
IV.03
10 Importance of Entry Barriers in Antitrust Analysis
10.01
10.02
A Entry Barriers in Data-Driven Markets
10.03
10.04
10.05
B Looking Beyond Traditional Entry Barriers
10.06
10.07
10.08
10.09
10.10
10.11
10.12
10.13
10.14
10.15
10.16
11 Entry Barriers Can Be Higher in Multi-Sided Markets, Where One Side Exhibits Traditional Network Effects
11.01
11.02
11.03
A Traditional Network Effects in Facebook/WhatsApp
11.04
11.05
11.06
11.07
11.08
11.09
11.10
B The Commission’s Reasoning Why the Merger Was Unlikely to Tip the Market to Facebook
11.11
11.12
11.13
11.14
11.15
11.16
C Strengths and Weaknesses of the Commission’s Analysis of Network Effects
11.17
11.18
11.19
11.20
11.21
11.22
11.23
11.24
11.25
12 Scale of Data: Trial-and-Error, ‘Learning-by-Doing’ Network Effects
12.01
12.02
A Waze’s Turn-by-Turn Navigation App
12.03
12.04
12.05
12.06
B Search Engines
12.07
12.08
12.09
12.10
12.11
12.12
12.13
12.14
12.15
12.16
12.17
12.18
12.19
12.20
12.21
12.22
12.23
12.24
12.25
12.26
C Facebook
12.27
12.28
12.29
12.30
D Reflections
12.31
12.32
12.33
12.34
12.35
12.36
13 Two More Network Effects: Scope of Data and Spill-Over Effects
A Scope of Data
13.01
1 Facebook’s digital assistant ‘M’
13.02
13.03
13.04
13.05
13.06
2 Search engines
13.07
13.08
13.09
B Spill-Over Effects: How Networks Effects on One Side of Multi-Sided Platforms Can Increase Market Power on the Other Sides
13.10
1 Traditional spill-over effects in multi-sided online platforms
13.11
13.12
13.13
13.14
2 Data-driven spill-over effects
13.15
3 Coupons.com
13.16
13.17
13.18
13.19
13.20
4 Facebook
13.21
13.22
13.23
13.24
13.25
13.26
13.27
13.28
13.29
5 Search engines
13.30
13.31
13.32
13.33
13.34
13.35
13.36
13.37
13.38
13.39
14 Reflections on Data-Driven Network Effects
14.01
14.02
A Ten Implications of Data-Driven Network Effects
14.03
14.04
14.05
14.06
14.07
14.08
14.09
14.10
14.11
14.12
14.13
14.14
14.15
14.16
14.17
14.18
14.19
B Why Controlling the Operating System Gives the Platform a Competitive Advantage Over an Independent App
14.20
14.21
14.22
14.23
14.24
14.25
14.26
14.27
14.28
C Independent App Developers’ Dependence on Google and Apple
14.29
14.30
14.31
D How Google Benefits from These Network Effects
14.32
14.33
14.34
14.35
14.36
14.37
14.38
14.39
E Domination is not Guaranteed
14.40
14.41
14.42
14.43
14.44
14.45
14.46
14.47
14.48
14.49
14.50
14.51
15 Risk of Inadequate Merger Enforcement
15.01
A The Prediction Business
15.02
15.03
15.04
15.05
B Most Mergers are Cleared
15.06
15.07
15.08
15.09
15.10
C The Big Mystery: How Often Do the Competition Agencies Accurately Predict the Mergers’ Competitive Effects?
15.11
15.12
15.13
15.14
15.15
15.16
D The Ex-Post Merger Reviews Paint a Bleak Picture
15.17
15.18
15.19
E The High Error Costs When the Agencies Examine Only One Side of a Multi-Sided Platform
15.20
15.21
15.22
15.23
15.24
15.25
15.26
15.27
15.28
15.29
15.30
15.31
F How Data-Driven Mergers Increase the Risks of False Negatives
15.32
15.33
15.34
15.35
15.36
15.37
15.38
15.39
15.40
16 The Price of Weak Antitrust Enforcement
16.01
A The Chicago School’s Fear of False Positives
16.02
16.03
16.04
16.05
16.06
16.07
16.08
16.09
B The United States as a Test Case of Weak Antitrust Enforcement
16.10
16.11
16.12
16.13
16.14
C Costs of Weak Antitrust Enforcement in the Agricultural Industry
16.15
16.16
16.17
16.18
16.19
16.20
16.21
D Costs of Weak Antitrust Enforcement in the Financial Sector
16.22
16.23
16.24
16.25
16.26
16.27
16.28
16.29
16.30
16.31
16.32
16.33
16.34
16.35
16.36
16.37
16.38
16.39
16.40
16.41
16.42
16.43
16.44
E Consumers’ Overall Welfare
16.45
16.46
16.47
16.48
16.49
16.50
F Why Ignoring Big Data Will Compound the Harm
16.51
16.52
16.53
16.54
16.55
16.56
16.57
G The Competition Agencies Cannot Assume that Other Agencies will Repair Their Mistakes
16.58
16.59
16.60
16.61
Part V Advancing a Research Agenda for the Agencies and Academics
Preliminary Material
V.01
V.02
V.03
V.04
V.05
V.06
V.07
V.08
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
17.03
17.04
17.05
17.06
17.07
B Some Simple Examples Where Privacy and Competition Law Intersect
17.08
17.09
17.10
17.11
17.12
17.13
C Looking Beyond Privacy’s Subjectivity
17.14
17.15
17.16
17.17
17.18
D Developing Better Economic Tools to Address Privacy
17.19
17.20
17.21
17.22
17.23
17.24
17.25
17.26
E Why Competition Policy Does Not Have an Efficiency Screen
17.27
17.28
17.29
F Using a Consumer Well-Being Screen
17.30
17.31
17.32
17.33
17.34
17.35
G Media Mergers as an Example of a Consumer Well-Being Screen
17.36
17.37
17.38
17.39
H Conclusion
17.40
17.41
17.42
17.43
17.44
17.45
18 Data-opoly: Identifying Data-Driven Exclusionary and Predatory Conduct
18.01
18.02
A In False Praise of Monopolies
18.03
18.04
18.05
18.06
18.07
B Debunking the Myth that Competition Law is Ill-Suited for New Industries
18.08
18.09
18.10
18.11
18.12
18.13
C How the ‘Waiting for Dynamic Competition’ Argument Ignores Path Dependencies
18.14
18.15
18.16
18.17
18.18
18.19
D How (Even Failed) Antitrust Enforcement Can Open Competitive Portals
18.20
18.21
18.22
18.23
18.24
18.25
18.26
18.27
E The Nowcasting Radar—Why Some Data-opolies are More Dangerous than Microsoft in the 1990s
18.28
18.29
18.30
18.31
18.32
F Keeping the Competitive Portals Open
18.33
18.34
18.35
1 Exclusive dealing to prevent rivals from accessing critical data
18.36
18.37
18.38
2 Exclusionary practices to prevent rivals from achieving scale
18.39
18.40
18.41
3 Dominant firm leverages its data-advantage in a regulated market to another market
18.42
18.43
18.44
4 Increasing customers’ switching costs
18.45
18.46
18.47
18.48
18.49
5 Vertical integration by a dominant platform operator
18.50
18.51
18.52
18.53
18.54
18.55
18.56
18.57
18.58
18.59
18.60
18.61
18.62
18.63
18.64
G An Object All Sublime, the Competition Authority Shall Achieve in Time—to Let the Punishment Fit the Crime
18.65
18.66
18.67
18.68
18.69
18.70
18.71
19 Understanding and Assessing Data-Driven Efficiencies Claims
19.01
19.02
19.03
A Efficiencies Benefit Consumers
19.04
19.05
19.06
19.07
19.08
19.09
B Efficiencies Must Be Merger-Specific
19.10
19.11
19.12
19.13
19.14
19.15
19.16
19.17
19.18
C Efficiencies Must Be Verifiable
19.19
19.20
D Balancing Efficiency and Privacy
19.21
19.22
19.23
19.24
19.25
19.26
19.27
E Challenges Ahead
19.28
19.29
19.30
19.31
20 Need for Retrospectives of Data-Driven Mergers
20.01
20.02
20.03
A Waiting for the Right Data-Driven Merger
20.04
20.05
20.06
20.07
B Debiasing Through Ex-Post Merger Reviews
20.08
20.09
20.10
20.11
20.12
20.13
C FTC’s Retrospectives of Hospital Mergers
20.14
20.15
20.16
20.17
20.18
20.19
20.20
20.21
20.22
20.23
D The Benefits in Conducting Merger Retrospectives
20.24
20.25
20.26
20.27
20.28
20.29
20.30
20.31
20.32
20.33
20.34
20.35
20.36
21 More Coordination among Competition, Privacy, and Consumer Protection Officials
21.01
21.02
21.03
21.04
21.05
A Moving Beyond Notice-and-Consent
21.06
21.07
21.08
21.09
21.10
B Several Preconditions to Spur Privacy Competition
21.11
21.12
21.13
21.14
21.15
21.16
21.17
21.18
21.19
21.20
21.21
21.22
21.23
21.24
22 Conclusion
22.01
22.02
22.03
22.04
22.05
22.06
22.07
22.08
22.09
22.10
22.11
22.12
22.13
Further Material
Index
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Table of Legislation
From:
Big Data and Competition Policy
Maurice Stucke, Allen Grunes
Content type:
Book content
Product:
Oxford Competition Law [OCL]
Published in print:
01 June 2016
ISBN:
9780198788133
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