From Policy Confusion to Doctrinal Clarity: Successor Liability from the Perspective of Big Data

Abstract

Few corporate law doctrines have undergone more radical change but remain without clear restatement than successor liability. While other doctrines have received sustained empirical and theoretical attention, successor liability remains confused and understudied. This Article presents for the first time a large-scale empirical study of approximately 2,100 successor liability decisions. Employing quantitative machine learning and “big data” processing techniques, the Article develops a new theoretical taxonomy that reflects the underlying textual structure manifested across each of the 2,100 decisions’ full texts. The new taxonomy postulates that courts apply successor liability doctrine to effectuate four aims: (1) to carry out a federal statutory mandate, (2) to rectify liability evasion, (3) to allocate risk, and (4) to support bankruptcy values. The taxonomy is used to clarify the normative ambiguity surrounding application of de facto merger and to mitigate between the competing policies of asset alienability on the one hand and protection for involuntary tort creditors or satisfaction of contract law principles on the other.