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Using support vector machines for measuring democracy

Author

Listed:
  • Gründler, Klaus
  • Krieger, Tommy
Abstract
We present a novel approach for measuring democracy, which enables a very detailed and sensitive index. This method is based on Support Vector Machines, a mathematical algorithm for pattern recognition. Our implementation evaluates 188 countries in the period between 1981 and 2011. The Support Vector Machines Democracy Index (SVMDI) is continuously on the 0-1-Interval and robust to variations in the numerical process parameters. The algorithm introduced here can be used for every concept of democracy without additional adjustments, and due to its exibility it is also a valuable tool for comparison studies.

Suggested Citation

  • Gründler, Klaus & Krieger, Tommy, 2015. "Using support vector machines for measuring democracy," Discussion Paper Series 130, Julius Maximilian University of Würzburg, Chair of Economic Order and Social Policy.
    • Handle: RePEc:zbw:wuewwb:130
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    File URL: https://www.econstor.eu/bitstream/10419/110276/1/82471878X.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. José Cheibub & Jennifer Gandhi & James Vreeland, 2010. "Democracy and dictatorship revisited," Public Choice, Springer, vol. 143(1), pages 67-101, April.
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    Cited by:

    1. Bougharriou, Nouha & Benayed, Walid & Gabsi, Foued Badr, 2019. "The democracy and economic growth nexus: Do FDI and government spending matter? Evidence from the Arab world," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 13, pages 1-29.
    2. Gründler, Klaus & Krieger, Tommy, 2015. "Democracy and growth: Evidence from SVMDI indices," Discussion Paper Series 131, Julius Maximilian University of Würzburg, Chair of Economic Order and Social Policy.
    3. Florian Dumpert & Martin Beck, 2017. "Einsatz von Machine-Learning-Verfahren in amtlichen Unternehmensstatistiken [Use of machine learning in official business statistics]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(2), pages 83-106, October.

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    More about this item

    Keywords

    Democracy; Support Vector Machines; Democracy Index;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • H11 - Public Economics - - Structure and Scope of Government - - - Structure and Scope of Government
    • P16 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Capitalist Institutions; Welfare State

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