Power in Text: Implementing Networks and Institutional Complexity in American Law

Abstract

How should social scientists measure institutional complexity? Formal (textually defined) institutional design—and particularly the complexity of formal institutions—is an important object of study across political science, law, and public administration. However, because of measurement constraints, existing work on formal institutional design focuses either on single policy areas or “important” legislation, creating clear selection problems. In this article, I propose and validate a novel natural language processing approach designed to extract networks of institutional relationships from legal texts scalably. These “implementing networks” offer a straightforward way to represent the institutional content of law and naturally suggest measures for quantities like institutional complexity. I then apply this method to measure institutional complexity in all American laws enacted from 1993 to 2014. This approach reveals a surprising disconnect between partisan disagreement and institutional complexity among lower-profile legislation, which would have been difficult to detect without this approach.

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