Using machine learning for measuring democracy: An update

Klaus Gründler () and Tommy Krieger

No 21-012, ZEW Discussion Papers from ZEW - Leibniz Centre for European Economic Research

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.

Keywords: Data aggregation; Democracy indicators; Machine Learning; Measurement Issues; Regime Classifications; Support Vector Machines (search for similar items in EconPapers)
JEL-codes: C38 C43 C82 E02 P16 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-mac and nep-pol
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Working Paper: Using Machine Learning for Measuring Democracy: An Update (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:zewdip:21012

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