The Language of Delegation: An NLP Analysis of Congressional Bill Text

Abstract

Delegation of powers from the legislature to the executive branch is a nearly ubiquitous feature of modern lawmaking. However, much of what scholars know about delegation is gleaned from an exclusive focus on landmark legislation. We introduce a method to identify delegating language across a larger universe of legislation. Using an active learning convolutional neural network on bill text, we classify bill sections by their delegation content, applying an iteratively improving coding scheme that enhances existing supervised learning approaches. We develop a novel dataset that allows us to answer important questions about interbranch relations. First, we find that legislator ideology, partisanship, and institutional position affect the delegatory content of introduced legislation. We then explore the role of delegation in the advancement of bills through the legislative process. Finally, we evaluate the ally principle, finding that variation in delegation is driven by cross-agency differences in ideology and structural independence.