Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/176949
Authors: 
Gründler, Klaus
Krieger, Tommy
Year of Publication: 
2018
Series/Report no.: 
CESifo Working Paper 6930
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.
Subjects: 
democracy
development
economic growth
estimation bias
indices
institutions
machine learning
support vector machines
JEL: 
C26
C43
N40
O10
P16
P48
Document Type: 
Working Paper

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