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

Anastasopoulos, Jason; Bertelli, Anthony M.

doi:10.2139/ssrn.3207821

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Automated text analysis provides an avenue for solving this measurement problem similarly to the approach in Anastasopoulos and Bertelli (2018). Researchers might interview bureaucrats or agency heads to identify bills exemplifying discretion or delegation and then collect the texts of those bills.…”

Section: Delegation Versus Discretionmentioning

confidence: 99%

Measuring the Content of Presidential Policy Making: Applying Text Analysis to Executive Branch Directives

Kaufman¹

2019

Pres Stud Q

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The executive branch produces huge quantities of text data: policy‐driving documents such as executive orders, national security directives, and agency regulations; procedural documents such as notices of proposed rule making, meeting transcripts, and presidential daily schedules; and public relations documents including press releases, veto statements, and social media posts. Political science is increasingly turning to methods of automated text analysis to rigorously interrogate such corpora. These tools have proved invaluable to scholars of the judiciary, of the legislature, and of political behavior; they hold great promise for the study of the presidency and bureaucracy. These methods' value lies in measuring complex, nuanced, yet theoretically critical quantities of interest like power, agenda setting, policy significance, ideology, and diplomatic resolve: by categorizing individual documents into a researcher‐defined schema, automated text methods produce large‐N data sets with fine temporal granularity. Recent work using text analysis to study unilateral actions illustrates these methods' promise for upturning conventional wisdom, settling long‐standing debates, and illuminating new puzzles in executive politics in the United States.

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Section: Delegation Versus Discretionmentioning

confidence: 99%

Measuring the Content of Presidential Policy Making: Applying Text Analysis to Executive Branch Directives

Kaufman¹

2019

Pres Stud Q

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“…Examples of the first case include Bonica (2016), which uses supervised machine learning to infer ideological scores (outcome) based on roll call voting in Congress using campaign contribution data (features), and Anastasopoulos and Bertelli (2018), which predicts delegation and constraint ratios (outcomes) using the texts of European Union legislation (features). 1…”

Section: Supervised Techniquesmentioning

confidence: 99%

Machine Learning for Public Administration Research, With Application to Organizational Reputation

Anastasopoulos¹,

Whitford²

2018

Journal of Public Administration Research and Theory

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Machine learning methods have gained a great deal of popularity in recent years among public administration scholars and practitioners. These techniques open the door to the analysis of text, image and other types of data that allow us to test foundational theories of public administration and to develop new theories. Despite the excitement surrounding machine learning methods, clarity regarding their proper use and potential pitfalls is lacking. This paper attempts to fill this gap in the literature through providing a machine learning "guide to practice" for public administration scholars and practitioners. Here, we take a foundational view of machine learning and describe how these methods can enrich public administration research and practice through their ability develop new measures, tap into new sources of data and conduct statistical inference and causal inference in a principled manner. We then turn our attention to the pitfalls of using these methods such as unvalidated measures and lack of interpretability. Finally, we demonstrate how machine learning techniques can help us learn about organizational reputation in federal agencies through an illustrated example using tweets from 13 executive federal agencies.

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Machine Learning for Public Administration Research, with Application to Organizational Reputation

Anastasopoulos

Whitford

2018

SSRN Journal

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

Cited by 3 publications

References 12 publications

Measuring the Content of Presidential Policy Making: Applying Text Analysis to Executive Branch Directives

Measuring the Content of Presidential Policy Making: Applying Text Analysis to Executive Branch Directives

Machine Learning for Public Administration Research, With Application to Organizational Reputation

Machine Learning for Public Administration Research, with Application to Organizational Reputation

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