Understanding delegation through machine learning: a method and application to the European Union

<jats:p>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./jats:p

Originally Published at 10.1017/s0003055419000522

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Work Title Understanding delegation through machine learning: a method and application to the European Union
Access
Open Access
Creators
  1. L. JASON ANASTASOPOULOS
  2. ANTHONY M. BERTELLI
License CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
Work Type Article
Publisher
  1. Cambridge University Press (CUP)
Publication Date November 11, 2019
Publisher Identifier (DOI)
  1. 10.1017/s0003055419000522
Source
  1. American Political Science Review
Deposited September 09, 2021

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