Democracy and growth: Evidence from a machine learning indicator

Klaus Gründler () and Tommy Krieger

European Journal of Political Economy, 2016, vol. 45, issue S, 85-107

Abstract: 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.

Keywords: Democracy; Economic growth; Panel data; Machine learning; Support Vector Machines (search for similar items in EconPapers)
JEL-codes: C43 H11 O11 O47 P16 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (99)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:poleco:v:45:y:2016:i:s:p:85-107

DOI: 10.1016/j.ejpoleco.2016.05.005

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