Abstract
The ubiquity of algorithms has profound implications for the way businesses compete, perhaps most critically, through their use to harness and extract value from big data. Data has been variously described as “the new oil” and “the new currency”, amongst other metaphors. Both these descriptions help to conceptualise data as a valuable, fungible asset, which can be leveraged to the competitive advantage of the firm holding it. But if data is the new oil, it is the crude oil and algorithms make up the rest of the supply chain necessary to obtain value from it. It would therefore seem obvious that the data held, and algorithms used by, merging parties would come under scrutiny in merger reviews of what we describe in this chapter as “algorithm-driven businesses”. The purpose of this chapter is to catalogue the various ways in which analysis of algorithms (and big data) have featured in merger control decisions thus far, in the hope it may shed light on how practice may evolve going forwards.
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Notes
- 1.
- 2.
- 3.
EC (2016a).
- 4.
- 5.
IMS / Cegedim (EC 2004a, para. 110).
- 6.
Caffarra (2019).
- 7.
Lear (2019, para. II.264).
- 8.
OFT (2013).
- 9.
Lear (2019, pp. 117–118).
- 10.
EC (2014b, para. 179).
- 11.
Gov.uk. (2019).
- 12.
Lear (2019, p. iv).
- 13.
- 14.
EC (2014b).
- 15.
EC (2014b, para. 164).
- 16.
EC (2014b).
- 17.
EC (2016c).
- 18.
EC (2014b).
- 19.
BEUC (2020, p. 11).
- 20.
Newman (2020a).
- 21.
Newman (2020b).
- 22.
Chazan and Espinoza (2020).
- 23.
EC (2014b).
- 24.
Digital Competition Expert Panel (2019, paras. 3.164 to 3.165).
- 25.
US Counsel of Economic Advisers (2015, p. 2).
- 26.
CMA (2019).
- 27.
EC (2011, paras. 145–150).
- 28.
EC (2016a).
- 29.
OFT (2012).
- 30.
CMA (2020b).
- 31.
Paragraph 286.
- 32.
OFT (2012, para. 37).
- 33.
Lear (2019, section II.81).
- 34.
Facebook (2020).
- 35.
Oxera (2018).
- 36.
EC (2012).
- 37.
EC (2012, para. 544).
- 38.
EC (2016a, para. 247).
- 39.
CMA (2020b).
- 40.
Paragraph 245.
- 41.
Id.
- 42.
See for example, Apple / Shazam, paragraph 328.
- 43.
Id.
- 44.
Cunningham et al. (2020).
- 45.
Digital Competition Expert Panel (2019).
- 46.
CMA (2020c).
- 47.
Id.
- 48.
CMA (2019).
- 49.
Crémer et al. (2019).
- 50.
Parts (2020).
- 51.
Id.
- 52.
ACCC (2019).
- 53.
Caffarra et al. (2020).
- 54.
Espinoza and Fleming (2019).
- 55.
OFT (2013).
- 56.
Lear (2019, para. II.267).
- 57.
EC (2010b).
- 58.
Chirita (2018, p. 21).
- 59.
EC (2004c).
- 60.
EC (2009).
- 61.
Sisco and Ebersole (2020).
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Egerton-Doyle, V., Ford, J. (2022). Algorithms, Big Data, and Merger Control. In: Portuese, A. (eds) Algorithmic Antitrust. Economic Analysis of Law in European Legal Scholarship, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-85859-9_4
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