Algorithmic Hub-and-Spoke Cartels: A Japanese Perspective

Steven Van Uytsel ()
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Steven Van Uytsel: Kyushu University

A chapter in The Digital Economy and Competition Law in Asia, 2021, pp 193-219 from Springer

Abstract: Abstract The debate on algorithmic collusion has put the hub-and-spoke cartel in the picture. In Japan, a hub-and-spoke cartel, a cartel in which there is a vertical facilitator of horizontal collusion, has long been in existence. Kansei dango, bid-rigging on public procurement projects, have been assisted by bureaucrats. The problem with this kind of cartel was that the Japanese Antimonopoly Act (AMA) is not designed to make the vertical facilitator accountable for its cooperation with the cartel. This resulted in a legislative initiative to deal with the bureaucrats participating in the cartel. A similar evolution has been noticeable in relation to trade associations. This triggers the question of how the AMA can respond to situations in which an algorithm enters the role of facilitator. This contribution argues that an easy answer cannot be given. If the implementation of the algorithm is the result of communication between the enterprises participating in the collusion, the unreasonable restraint provision of the AMA, the provision relevant to price fixing, may be applicable. The only caution that remains is whether the algorithm is implemented at enterprises competing with each other. When an algorithm is able to impose a price on enterprises at another level in the market, and there is thus no communication, even indirectly, between competing enterprises, the Japan Fair Trade Commission may shift to the unfair trade practices provision. More specifically, the unfair trade practice of conditional dealing may apply to a situation in which an algorithm fixes the prices of sellers using the algorithm.

Keywords: Japanese antimonopoly law; Algorithmic collusion; Cartel; Price fixing; Hub and spoke; Antimonopoly law; Trade association; Kansei dango; Accountancy firm; Supplier; Algorithm developer; Uber (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-981-16-0324-2_7

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