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Shedding Light on the Doing Business Index: a Machine Learning Approach

Shedding Light on the Doing Business Index: a Machine Learning Approach AbstractBackground: The World Bank (WB) acknowledged the importance of business regulatory environment and therefore created a metric which ranks 190 countries based on their level of business regulation for domestic firms measured by the Doing Business Index (DBI).Objectives: The question which attracted our attention is whether all the observed entities should be given the same weighting scheme.Methods/Approach: The approach we propose as an answer is two-fold. First, we cluster the countries covered by the DBI. In the next step, we apply the statistical multivariate Composite I-distance Indicator (CIDI) methodology to determine new, data-driven weights for each of the retained clusters.Results: The obtained results show that there is a difference between the weighting schemes proposed by the CIDI methodology.Conclusions: One can argue that one weighting scheme does not fit all the observed countries, meaning that additional analyses on the DBI are suggested to explore its stability and its weighting scheme. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Business Systems Research Journal de Gruyter

Shedding Light on the Doing Business Index: a Machine Learning Approach

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Publisher
de Gruyter
Copyright
© 2019 Milica Maričić et al., published by Sciendo
ISSN
1847-9375
eISSN
1847-9375
DOI
10.2478/bsrj-2019-019
Publisher site
See Article on Publisher Site

Abstract

AbstractBackground: The World Bank (WB) acknowledged the importance of business regulatory environment and therefore created a metric which ranks 190 countries based on their level of business regulation for domestic firms measured by the Doing Business Index (DBI).Objectives: The question which attracted our attention is whether all the observed entities should be given the same weighting scheme.Methods/Approach: The approach we propose as an answer is two-fold. First, we cluster the countries covered by the DBI. In the next step, we apply the statistical multivariate Composite I-distance Indicator (CIDI) methodology to determine new, data-driven weights for each of the retained clusters.Results: The obtained results show that there is a difference between the weighting schemes proposed by the CIDI methodology.Conclusions: One can argue that one weighting scheme does not fit all the observed countries, meaning that additional analyses on the DBI are suggested to explore its stability and its weighting scheme.

Journal

Business Systems Research Journal – de Gruyter

Published: Sep 1, 2019

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