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

Published online by Cambridge University Press:  11 November 2019

L. JASON ANASTASOPOULOS Open the ORCID record for L. JASON ANASTASOPOULOS [Opens in a new window]  and
ANTHONY M. BERTELLI Open the ORCID record for ANTHONY M. BERTELLI [Opens in a new window]
L. JASON ANASTASOPOULOS*
Affiliation:
University of Georgia
ANTHONY M. BERTELLI*
Affiliation:
Bocconi University and Pennsylvania State University
*
*L. Jason Anastasopoulos, Assistant Professor of Political Science and Assistant Professor of Public Administration and Policy, University of Georgia, ljanastas@uga.edu.
†Anthony M. Bertelli, Professor of Political Science, Bocconi University; Sherwin-Whitmore Professor of Public Policy and Political Science, Pennsylvania State University, bertelli@psu.edu.
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Abstract

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.

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Type
Letter
Information
American Political Science Review , Volume 114 , Issue 1 , February 2020 , pp. 291 - 301
Copyright
Copyright © American Political Science Association 2019 

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Footnotes

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: https://doi.org/10.7910/DVN/FF3DQM.

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