The Aggregate Implications of Mergers and Acquisitions

Mergers and acquisitions can play a transformative role in the evolution of firms and industries and have become an important feature of the US economy, representing about 5% of GDP and 80% of total capital reallocation among large US firms. In this paper, I develop a search-theoretic model of mergers and acquisitions in a dynamic general equilibrium setting and assess the implications for aggregate economic performance. I use a transaction-level dataset to document a number of empirical patterns in US merger activity: (1) acquiring firms are generally larger and more profitable than their targets; (2) there is a large degree of positive assortative matching between transacting firms; and (3) acquirers tend to be the largest and most profitable firms, but targets are not the smallest or least profitable. I build a parsimonious model that is able to address these facts and nests several existing theories of merger activity as special cases. I explore the merger patterns predicted by these theories and show that each meets difficulties in fitting the full set of empirical facts. I calibrate the model to match moments from the transaction-level data, as well as other salient features of the US economy. The calibrated model is capable of replicating the stylized facts quite closely and sheds new light as to how surplus is generated from merger and how the gains are split. I find that merger activity generates potentially large long-run gains in aggregate performance, measuring about 30% in aggregate productivity and output, and about 11% in welfare.