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



Delegation of powers represents a grant of authority by politicians 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 discretionary measures used in an original study directly using 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.


Corresponding author

*L. Jason Anastasopoulos, Assistant Professor of Political Science and Assistant Professor of Public Administration and Policy, University of Georgia,
Anthony M. Bertelli, Professor of Political Science, Bocconi University; Sherwin-Whitmore Professor of Public Policy and Political Science, Pennsylvania State University,


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We thank Fabio Franchino for providing the data and for helpful comments. Nicola Palma, Maulik Shah and Giulia Leila Travaglini provided excellent assistance in data collection and preparation. We thank Moritz Osnabrugge, Matia Vannoni, Arthur Spirling, Erik Voeten and Michael Bailey for helpful comments. Replication files are available at the American Political Science Review Dataverse:



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Type Description Title
Supplementary materials

Anastasopoulos and Bertelli Dataset

Supplementary materials

Anastasopoulos and Bertelli supplementary material
Anastasopoulos and Bertelli supplementary material

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



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