The Sale of Data Learning Synergies Before M&As

Working Paper: CEPR ID: DP17404

Authors: Patrick Legros; Antoine Dubus

Abstract: Firms may share information to discover potential synergies between their data sets and algorithms, which eventually may lead 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. Because more sharing of information may increase industry expected profits both when there is head-to-head competition and when there is an M&A, the presence of a regulator who can prevent or allow the M&A can decrease or increase the level of information sharing, as well as consumer surplus, with respect to the no-regulator case. A regulator who can also control the level of information sharing will allow firms to share information.

Keywords: synergies; mergers; sale of data; incomplete information; antitrust; privacy

JEL Codes: K21; L1; L21; L24; L41; L5


Causal Claims Network Graph

Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.


Causal Claims

CauseEffect
Information sharing (O36)Increased expected profits (D84)
Information sharing (O36)Efficiency of M&A decisions (G34)
Regulatory actions (G18)Information sharing (O36)
Information sharing (O36)Consumer surplus (D11)
Regulatory actions (G18)Firms' incentives to share information (D82)
Information sharing (O36)Competition intensity (L13)
Information sharing (O36)Privacy losses for consumers (D18)

Back to index