Please use this identifier to cite or link to this item: https://rfos.fon.bg.ac.rs/handle/123456789/1960
Title: Shedding Light on the Doing Business Index: a Machine Learning Approach
Authors: Maričić, Milica 
Bulajić, Milica
Radojičić, Zoran 
Jeremić, Veljko 
Keywords: machine learning algorithms;international business;Doing Business Index;CIDI methodology
Issue Date: 2019
Publisher: Sciendo, Warsaw
Abstract: 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.
URI: https://rfos.fon.bg.ac.rs/handle/123456789/1960
ISSN: 1847-8344
Appears in Collections:Radovi istraživača / Researchers’ publications

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