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Using Machine Learning for measuring democracy: A practitioners guide and a new updated dataset for 186 countries from 1919 to 2019

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  • Gründler, Klaus
  • Krieger, Tommy

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). 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 significantly expand the data coverage by providing democracy indices for 186 countries in the period from 1919 to 2019.

Suggested Citation

  • Gründler, Klaus & Krieger, Tommy, 2021. "Using Machine Learning for measuring democracy: A practitioners guide and a new updated dataset for 186 countries from 1919 to 2019," European Journal of Political Economy, Elsevier, vol. 70(C).
    • Handle: RePEc:eee:poleco:v:70:y:2021:i:c:s017626802100046x
    DOI: 10.1016/j.ejpoleco.2021.102047
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    Keywords

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    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E02 - Macroeconomics and Monetary Economics - - General - - - Institutions and the Macroeconomy
    • P16 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Capitalist Institutions; Welfare State

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