Intern
Chair of Labour Economics

SVMDI Dataset

Democracy in the World, post-2010 period, SVMDI dataset

 

This Page provides data access to the Support Vector Machines Democracy Indicator (SVMDI) as proposed by Gründler and Krieger (2015).

The approach exploits Support Vector Machines (SMVs), a mathematical algorithm for pattern recognition, which is a nonlinear generalization of the Generalized Portrait algorithm.

Data is available here

SVMDI dataset.xlsx

SVMDI dataset.dta

SVMDI dataset.csv

A documentation of the method can be found here

Gründler and Krieger (2015): Democracy and Growth: Evidence of a New Measurement, CESifo Working Paper No. 5647, December 2015.