Understanding Delegation through Machine Learning: A Method and Application to the European Union

Abstract

Delegation of powers represents a grant of authority by a legislature to one or more agents whose powers are determined by the conditions in enabling statutes. Extant empirical studies of this problem have relied on labor-intensive content analysis that ultimately restricts our knowledge of how delegation has responded to politics and institutional change in recent years. We present a machine learning approach to the empirical estimation of authority and constraint in European Union (EU) legislation and demonstrate its ability to accurately generate the same discretion measures used in an original study directly from all EU directives and regulations enacted between 1958-2017. We assess validity by training our classifier on a random sample of only 10% of hand-coded provisions and replicating an important substantive finding. While our principal interest lies in delegation, our method is extensible to any context in which human coding has been profitably produced.

Keywords: Delegation, Text Analysis, Machine Learning, European Union, Gridlock

JEL Classification: D72, D70, C40