78 Pages Posted: 28 Mar 2021 Last revised: 23 Jun 2022
Date Written: March 22, 2021
Conventional wisdom portrays contracts as static distillations of parties’ shared intent at some discrete point in time. In reality, however, contract terms evolve in response to their environments, including new laws, legal interpretations, and economic shocks. While several legal scholars have offered stylized accounts of this evolutionary process, we still lack a coherent, general theory that broadly captures the dynamics of real-world contracting practice. This paper advances such a theory, in which the evolution of contract terms is a byproduct of several key features, including efficiency concerns, information, and sequential learning by attorneys who negotiate several deals over time. Each of these factors contributes to the underlying evolutionary process, and their relative prominence bears directly on the speed, direction, and desirability of how contractual innovations diffuse. Using a formal model of bargaining in a sequence of similar transactions, we demonstrate how different evolutionary patterns can manifest over time, in both desirable and undesirable directions. We then take these insights to real-world dataset of over 2,000 merger agreements negotiated over the last two decades, tracking the adoption of several contractual clauses, including pandemic-related terms, #MeToo provisions, CFIUS conditions, and reverse termination fees. Our analysis suggests that there is not a “one size fits all” paradigm for contractual evolution; rather, the constituent forces affecting term evolution appear manifest in varying strengths across differing circumstances. We highlight several constructive applications of our framework, including the study of contract negotiation unfolds when price cannot easily be adjusted, and how to incorporate other forms of cognitive and behavioral biases into our general framework.
Keywords: Mergers and Acquisitions; COVID-19; MAEs; CFIUS; #MeToo; Contracts; Contract Design; Network Effects
JEL Classification: G30, G34, K12, K22
Suggested Citation: Suggested Citation