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Pré-Publication, Document De Travail Année : 2024

The Sale of Data: Learning Synergies Before M&As

Résumé

Firms may share information to discover potential synergies between their data sets and algorithms, eventually leading to more efficient mergers and acquisitions (M&A) decisions. However, as pointed out by Arrow, information sharing also modifies the competitive balance when companies do not merge, and a firm may be reluctant to share information with potential rivals. Under general conditions, we show that firms benefit from (partially) sharing information. By doing so, they can merge conditionally based on high synergies. If the firms’ best alternative is to compete, information sharing allows M&As to occur when synergies are high. In contrast, if their best alternative is to merge under incomplete information, information sharing allows firms to avoid M&As when synergies are low. Compared to a laissez-faire situation, the presence of a regulator allowing or refusing the M&A may decrease or increase information sharing and consumer surplus.
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Dates et versions

hal-03698203 , version 1 (17-06-2022)
hal-03698203 , version 2 (07-12-2022)
hal-03698203 , version 3 (16-10-2023)
hal-03698203 , version 4 (02-05-2024)

Identifiants

  • HAL Id : hal-03698203 , version 4

Citer

Antoine Dubus, Patrick Legros. The Sale of Data: Learning Synergies Before M&As. 2024. ⟨hal-03698203v4⟩
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