Izvorni znanstveni članak
https://doi.org/10.2478/bsrj-2019-019
Shedding Light on the Doing Business Index: a Machine Learning Approach
Milica Maričić
orcid.org/0000-0003-0441-9899
; University of Belgrade, Faculty of Organizational Sciences, Department of Operational Research and Statistics, Belgrade, Serbia
Milica Bulajić
; University of Belgrade, Faculty of Organizational Sciences, Department of Operational Research and Statistics, Belgrade, Serbia
Zoran Radojičić
; University of Belgrade, Faculty of Organizational Sciences, Department of Operational Research and Statistics, Belgrade, Serbia
Veljko Jeremić
; University of Belgrade, Faculty of Organizational Sciences, Department of Operational Research and Statistics, Belgrade, Serbia
Sažetak
Background: 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.
Ključne riječi
CIDI methodology; Doing Business Index; international business; machine learning algorithms
Hrčak ID:
224561
URI
Datum izdavanja:
1.9.2019.
Posjeta: 905 *