Machine Learning Indices, Political Institutions, and Economic Development

Klaus Gründler () and Tommy Krieger

No 6930, CESifo Working Paper Series from CESifo

Abstract: We present a new aggregation method - called SVM algorithm - and use this technique to produce novel measures of democracy (186 countries, 1960-2014). The method takes its name from a machine learning technique for pattern recognition and has three notable features: it makes functional assumptions unnecessary, it accounts for measurement uncertainty, and it creates continuous and dichotomous indices. We use the SVM indices to investigate the effect of democratic institutions on economic development, and find that democracies grow faster than autocracies. Furthermore, we illustrate how the estimation results are affected by conceptual and methodological changes in the measure of democracy. In particular, we show that instrumental variables cannot compensate for measurement errors produced by conventional aggregation methods, and explain why this failure leads to an overestimation of regression coefficients.

Keywords: democracy; development; economic growth; estimation bias; indices; institutions; machine learning; support vector machines (search for similar items in EconPapers)
JEL-codes: C26 C43 N40 O10 P16 P48 (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pol
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

Downloads: (external link)
https://www.cesifo.org/DocDL/cesifo1_wp6930.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_6930

Access Statistics for this paper

More papers in CESifo Working Paper Series from CESifo Contact information at EDIRC.
Bibliographic data for series maintained by Klaus Wohlrabe ().