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Digital Markets
Luís Cabral
140th Meeting of the Competition Committee
June 2023
Digital Markets
Luís Cabral
140th Meeting of the
Competition Committee
June 2023
1. Motivation
What is special about the digital space?
• Uncertain business model
– Will startup provide synergy to incumbent or will it rather be a potential
competitor? Uncertainty resolved over time
– Exhibit A: Google and Altavista
• Innovation-for-buyout strategy
– Most startups’ dream is to IPO or being acquired by Google. Beliefs
about merger policy influence investment effort
– Exhibit B: NYU’s Endless Frontier Lab
• Merger policy
– Traditional framework (HHI, SNIPP, etc) difficult to apply.
– Exhibit C: past twenty years
Digital Markets
Luís Cabral
140th Meeting of the
Competition Committee
June 2023
1. Motivation
GAFA acquisitions
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018
0
5
10
15
20
25
30
35
40
45
50
55
60
Year
Acquisitions count
Google
Amazon
Facebook
Apple
Source: Wikipedia
Digital Markets
Luís Cabral
140th Meeting of the
Competition Committee
June 2023
1. Motivation
A broader approach to merger analysis
[Merger policy should] reflect the features and dynamics of modern digital
markets, (...) [taking] into account the scale as well as the likelihood of
harm in merger cases involving potential competition and harm to
innovation.
— Furman et al, Unlocking Digital Competition (2019)
Digital Markets
Luís Cabral
140th Meeting of the
Competition Committee
June 2023
1. Motivation
Motivating example: Giphy
• In May 2020, Meta buys GIF company Giphy for $400 million. Meta to
integrate Giphy into the Instagram app “so that people can find just the
right way to express themselves.”
• Scenario A: Taking Meta’s words at face value, this seems to be a
value-increasing acquisition: Giphy complements Instagram.
• Scenario B: However, one can also think of Giphy as a potential
competitor to Instagram or Facebook
Digital Markets
Luís Cabral
140th Meeting of the
Competition Committee
June 2023
1. Motivation
A simplified scenario analysis
• Big tech company wants to acquire small firm
• Two possible scenarios
– target complements incumbent, merger adds value to consumers
– target is potential competitor, merger would be a “killer acquisition”
• Let α be the agency’s belief that target is a potential competitor
• Merger policy can be thought of as minimum threshold b
α beyond which
the merger is blocked
• What are current policies and proposals in terms of this framework
Digital Markets
Luís Cabral
140th Meeting of the
Competition Committee
June 2023
1. Motivation
Expected welfare as a function of b
α
Calibration exercise
based on various
studies as well as on
GAFAM aggregate
data. Of note:
1. αo > αh, a general
theoretical result.
2. Welfare levels from
αp and αl not very
different
3. Ban of mergers
clearly worst policy.
0 αh αo .25 αp = .5 .75 1
0.3
0.4
0.5
No enforcement (US?)
•
Merger ban (Warren et al)
•
•
Optimal threshold
•
Balance of probabilities (EU?)
•
Balance of harms (UK?)
b
α
change in welfare ($m per startup)
Digital Markets
Luís Cabral
140th Meeting of the
Competition Committee
June 2023
1. Motivation
Further reading
Luís Cabral, “Merger Policy in Digital Industries,” Information Economics
and Policy 54 (2021), 100866 (special issue on the digital economy)
Luís Cabral, “Big Tech Mergers” (2023), available at
http://luiscabral.net/economics/workingpapers/

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Theories of Harm for Digital Mergers – Luís Cabral – June 2023 OECD discussion

  • 1. Digital Markets Luís Cabral 140th Meeting of the Competition Committee June 2023
  • 2. Digital Markets Luís Cabral 140th Meeting of the Competition Committee June 2023 1. Motivation What is special about the digital space? • Uncertain business model – Will startup provide synergy to incumbent or will it rather be a potential competitor? Uncertainty resolved over time – Exhibit A: Google and Altavista • Innovation-for-buyout strategy – Most startups’ dream is to IPO or being acquired by Google. Beliefs about merger policy influence investment effort – Exhibit B: NYU’s Endless Frontier Lab • Merger policy – Traditional framework (HHI, SNIPP, etc) difficult to apply. – Exhibit C: past twenty years
  • 3. Digital Markets Luís Cabral 140th Meeting of the Competition Committee June 2023 1. Motivation GAFA acquisitions 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 0 5 10 15 20 25 30 35 40 45 50 55 60 Year Acquisitions count Google Amazon Facebook Apple Source: Wikipedia
  • 4. Digital Markets Luís Cabral 140th Meeting of the Competition Committee June 2023 1. Motivation A broader approach to merger analysis [Merger policy should] reflect the features and dynamics of modern digital markets, (...) [taking] into account the scale as well as the likelihood of harm in merger cases involving potential competition and harm to innovation. — Furman et al, Unlocking Digital Competition (2019)
  • 5. Digital Markets Luís Cabral 140th Meeting of the Competition Committee June 2023 1. Motivation Motivating example: Giphy • In May 2020, Meta buys GIF company Giphy for $400 million. Meta to integrate Giphy into the Instagram app “so that people can find just the right way to express themselves.” • Scenario A: Taking Meta’s words at face value, this seems to be a value-increasing acquisition: Giphy complements Instagram. • Scenario B: However, one can also think of Giphy as a potential competitor to Instagram or Facebook
  • 6. Digital Markets Luís Cabral 140th Meeting of the Competition Committee June 2023 1. Motivation A simplified scenario analysis • Big tech company wants to acquire small firm • Two possible scenarios – target complements incumbent, merger adds value to consumers – target is potential competitor, merger would be a “killer acquisition” • Let α be the agency’s belief that target is a potential competitor • Merger policy can be thought of as minimum threshold b α beyond which the merger is blocked • What are current policies and proposals in terms of this framework
  • 7. Digital Markets Luís Cabral 140th Meeting of the Competition Committee June 2023 1. Motivation Expected welfare as a function of b α Calibration exercise based on various studies as well as on GAFAM aggregate data. Of note: 1. αo > αh, a general theoretical result. 2. Welfare levels from αp and αl not very different 3. Ban of mergers clearly worst policy. 0 αh αo .25 αp = .5 .75 1 0.3 0.4 0.5 No enforcement (US?) • Merger ban (Warren et al) • • Optimal threshold • Balance of probabilities (EU?) • Balance of harms (UK?) b α change in welfare ($m per startup)
  • 8. Digital Markets Luís Cabral 140th Meeting of the Competition Committee June 2023 1. Motivation Further reading Luís Cabral, “Merger Policy in Digital Industries,” Information Economics and Policy 54 (2021), 100866 (special issue on the digital economy) Luís Cabral, “Big Tech Mergers” (2023), available at http://luiscabral.net/economics/workingpapers/