Using machine learning for measuring democracy: An update


Gründler, Klaus ; Krieger, Tommy


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URL: https://madoc.bib.uni-mannheim.de/59001
URN: urn:nbn:de:bsz:180-madoc-590019
Document Type: Working paper
Year of publication: 2021
The title of a journal, publication series: ZEW Discussion Papers
Volume: 21-012
Place of publication: Mannheim
Publication language: English
Institution: Sonstige Einrichtungen > ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung
MADOC publication series: Veröffentlichungen des ZEW (Leibniz-Zentrum für Europäische Wirtschaftsforschung) > ZEW Discussion Papers
Subject: 330 Economics
Classification: JEL: C38 , C43 , C82, E02 , P16,
Keywords (English): Data aggregation , democracy indicators , machine learning , measurement issues , regime classifications , support vector machines
Abstract: We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016, European Journal of Political Economy). Four improvements are particularly notable: First, we produce a continuous and a dichotomous version of the Machine Learning democracy indicator. Second, we calculate intervals that reflect the degree of measurement uncertainty. Third, we refine the conceptualization of the Machine Learning Index. Finally, we largely expand the data coverage by providing democracy indicators for 186 countries in the period from 1919 to 2019.

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