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Professor Yi Xin
Assistant Professor of Economics
Division of the Humanities and Social Sciences
California Institute of Technology

Traditional merger analysis emphasizes the trade-off between increased market concentration and efficiency gains from cost synergies. In the era of big data, however, information synergies have become an increasingly important motivation for mergers and acquisitions. This paper develops a framework to analyze information synergies as a distinct channel of merger effects in selection markets such as insurance and credit. We show that information synergies can reduce allocative efficiency and have distributional consequences for consumers. Using a structural model and merger simulations calibrated to the Italian auto insurance market, we find that a merger between two insurers generates large information advantages that enable them to cream-skim low-risk consumers, reducing their average expected claim payouts by 7.2–15.5%. Surprisingly, this can benefit high-risk consumers, who are pooled with intermediate-risk consumers after the merger and are effectively cross-subsidized. Moreover, if the merging firms are more efficient at serving high-risk consumers, overall market efficiency declines, since their informational advantage leads them to concentrate on low-risk consumers. This misallocation between consumers and insurers raises the average cost of serving the market by up to 1.4%, equivalent to about 12 euros per contract.