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Democracy and growth : evidence from a machine learning indicator

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2016

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https://doi.org/10.1016/j.ejpoleco.2016.05.005
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European Journal of Political Economy. 2016, 45(suppl.), pp. 85-107. ISSN 0176-2680. eISSN 1873-5703. Available under: doi: 10.1016/j.ejpoleco.2016.05.005

Zusammenfassung

We present a novel approach for measuring democracy based on Support Vector Machines, a mathematical algorithm for pattern recognition. The Support Vector Machines Democracy Index (SVMDI) is continuous on the [0,1] interval and enables very detailed and sensitive measurement of democracy for 185 countries in the period between 1981 and 2011. Application of the SVMDI yields results which highlight a robust positive relationship between democracy and economic growth. We argue that the ambiguity in recent studies mainly originates from the lack of sensitivity of traditional democracy indicators. Analyzing transmission channels through which democracy exerts its influence on growth, we conclude that democratic countries feature better educated populations, higher investment shares, and lower fertility rates, but not necessarily higher levels of redistribution.

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320 Politik

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Democracy Economic growth Panel data Machine learning Support Vector Machines

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ISO 690GRÜNDLER, Klaus, Tommy KRIEGER, 2016. Democracy and growth : evidence from a machine learning indicator. In: European Journal of Political Economy. 2016, 45(suppl.), pp. 85-107. ISSN 0176-2680. eISSN 1873-5703. Available under: doi: 10.1016/j.ejpoleco.2016.05.005
BibTex
@article{Grundler2016-12Democ-40446,
  year={2016},
  doi={10.1016/j.ejpoleco.2016.05.005},
  title={Democracy and growth : evidence from a machine learning indicator},
  number={suppl.},
  volume={45},
  issn={0176-2680},
  journal={European Journal of Political Economy},
  pages={85--107},
  author={Gründler, Klaus and Krieger, Tommy}
}
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