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Using machine learning for measuring democracy: An update

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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, 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.

Suggested Citation

  • Gründler, Klaus & Krieger, Tommy, 2021. "Using machine learning for measuring democracy: An update," ZEW Discussion Papers 21-012, ZEW - Leibniz Centre for European Economic Research.
    • Handle: RePEc:zbw:zewdip:21012
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    References listed on IDEAS

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    1. Martin Gassebner & Michael J. Lamla & James Raymond Vreeland, 2013. "Extreme Bounds of Democracy," Journal of Conflict Resolution, Peace Science Society (International), vol. 57(2), pages 171-197, April.
    2. Daron Acemoglu & Suresh Naidu & Pascual Restrepo & James A. Robinson, 2019. "Democracy Does Cause Growth," Journal of Political Economy, University of Chicago Press, vol. 127(1), pages 47-100.
    3. Dorsch, Michael T. & Maarek, Paul, 2020. "Economic downturns, inequality, and democratic improvements," European Journal of Political Economy, Elsevier, vol. 62(C).
    4. Dorsch, Michael T. & Maarek, Paul, 2019. "Democratization and the Conditional Dynamics of Income Distribution," American Political Science Review, Cambridge University Press, vol. 113(2), pages 385-404, May.
    5. José Cheibub & Jennifer Gandhi & James Vreeland, 2010. "Democracy and dictatorship revisited," Public Choice, Springer, vol. 143(1), pages 67-101, April.
    6. Christian Bjørnskov & Martin Rode, 2020. "Regime types and regime change: A new dataset on democracy, coups, and political institutions," The Review of International Organizations, Springer, vol. 15(2), pages 531-551, April.
    7. Vanessa A Boese, 2019. "How (not) to measure democracy," International Area Studies Review, Center for International Area Studies, Hankuk University of Foreign Studies, vol. 22(2), pages 95-127, June.
    8. TokeS. Aidt & Martin Daunton & Jayasri Dutta, 2010. "The Retrenchment Hypothesis and the Extension of the Franchise in England and Wales," Economic Journal, Royal Economic Society, vol. 120(547), pages 990-1020, September.
    9. de Haan, Jakob & Sturm, Jan-Egbert, 2003. "Does more democracy lead to greater economic freedom? New evidence for developing countries," European Journal of Political Economy, Elsevier, vol. 19(3), pages 547-563, September.
    10. Gründler, Klaus & Krieger, Tommy, 2016. "Democracy and growth: Evidence from a machine learning indicator," European Journal of Political Economy, Elsevier, vol. 45(S), pages 85-107.
    11. Klaus Gründler & Tommy Krieger, 2019. "Should We Care (More) About Data Aggregation? Evidence from Democracy Indices," CESifo Working Paper Series 7480, CESifo.
    12. Toke S. Aidt & Gabriel Leon, 2016. "The Democratic Window of Opportunity," Journal of Conflict Resolution, Peace Science Society (International), vol. 60(4), pages 694-717, June.
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    Cited by:

    1. Clemens Fuest & Klaus Gründler & Niklas Potrafke & Fabian Ruthardt, 2021. "Read My Lips? Taxes and Elections," CESifo Working Paper Series 9401, CESifo.
    2. Gründler, Klaus & Hillman, Arye L., 2021. "Ambiguous protection," European Journal of Political Economy, Elsevier, vol. 68(C).
    3. Jennifer Elena Feichtmayer & Klaus Gründler, 2021. "Global Evidence on Misperceptions and Preferences for Redistribution," CESifo Working Paper Series 9381, CESifo.
    4. Batinti, Alberto & Costa-Font, Joan, 2022. "Does democracy make taller men? Cross-country European evidence," Economics & Human Biology, Elsevier, vol. 45(C).
    5. Klaus Gründler & Armin Hackenberger & Anina Harter & Niklas Potrafke, 2021. "Covid-19 Vaccination: The Role of Crisis Experience," CESifo Working Paper Series 9096, CESifo.

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    More about this item

    Keywords

    Data aggregation; Democracy indicators; Machine Learning; Measurement Issues; Regime Classifications; Support Vector Machines;
    All these keywords.

    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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