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

Open access

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.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1. Adams S. (2009) “Foreign Direct investment domestic investment and economic growth in Sub-Saharan Africa” Journal of Policy Modeling Vol. 31 No. 6 pp. 939-949.

  • 2. Amado C. A. F. São José J. M. S. Santos S. P. (2016) “Measuring active ageing: A Data Envelopment Analysis approach” European Journal of Operational Research Vol. 255 No. 1 pp. 207-223.

  • 3. Arruñada B. (2007) “Pitfalls to avoid when measuring institutions: Is Doing Business damaging business?” Journal of Comparative Economics Vol. 35 No. 4 pp. 729-747.

  • 4. Basu P. Guariglia A. (2007) “Foreign Direct Investment inequality and growth” Journal of Macroeconomics Vol. 29 No. 4 pp. 824-839.

  • 5. Becker W. Saisana M. Paruolo P. Vandecasteele I. (2017) “Weights and importance in composite indicators: Closing the gap” Ecological Indicators Vol. 80 pp. 12-22.

  • 6. Bird S. M. Sir David C. Farewell V. T. Harvey G. Tim H. Peter C. S. (2005) “Performance indicators: good bad and ugly” Journal of the Royal Statistical Society: Series A (Statistics in Society) Vol. 168 No. 1 pp. 1-27.

  • 7. Booysen F. (2002) “An overview and evaluation of composite indices of development” Social Indicators Research Vol. 59 No. 2 pp. 115-151.

  • 8. Brunetti A. Kisunko G. Weder B. (1997) Institutional Obstacles to Doing Business: Region-by-Region Results from a Worldwide Survey of the Private Sector World Bank Publications.

  • 9. Büthe T. Milner H. V. (2008) “The Politics of Foreign Direct Investment into Developing Countries: Increasing FDI through International Trade Agreements?” American Journal of Political Science Vol. 52 No. 4 pp. 741-762.

  • 10. Cavusgil S. T. (1997) “Measuring the potential of emerging markets: An indexing approach” Business Horizons Vol. 40 No. 1 pp. 87-91.

  • 11. Celebi M. E. Kingravi H. A. Vela P. A. (2013) “A comparative study of efficient initialization methods for the k-means clustering algorithm” Expert Systems with Applications Vol. 40 No. 1 pp. 200-210.

  • 12. Charrad M. Ghazzali N. Boiteau V. Niknafs A. (2014) “NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set” Journal of Statistical Software Vol. 61 No. 6.

  • 13. Cherchye L. Moesen W. Rogge N. Van Puyenbroeck T. (2007) “An introduction to ‘benefit of the doubt’ composite indicators” Social Indicators Research Vol. 82 No. 1 pp. 111-145.

  • 14. Cherchye L. Moesen W. Rogge N. Van Puyenbroeck T. Saisana M. Saltelli A. Liska R. Tarantola S. (2008) “Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index” Journal of the Operational Research Society Vol. 59 No. 2 pp. 239-251.

  • 15. Davis K. E. Kingsbury B. Merry S. E. (2012) “Indicators as a Technology of Global Governance” Law & Society Review Vol. 46 No. 1 pp. 71-104.

  • 16. Decancq K. Lugo M. A. (2013) “Weights in Multidimensional Indices of Wellbeing: An Overview” Econometric Reviews Vol. 32 No. 1 pp. 7-34.

  • 17. Despotis D. K. (2005) “A reassessment of the human development index via data envelopment analysis” Journal of the Operational Research Society Vol. 56 No. 8 pp. 969-980.

  • 18. Djankov S. La Porta R. Lopez-de-Silanes F. Shleifer A. (2002) “The Regulation of Entry” The Quarterly Journal of Economics Vol. 117 No. 1 pp. 1-37.

  • 19. Dobrota M. Bulajic M. Bornmann L. Jeremic V. (2016) “A new approach to the QS university ranking using the composite I-distance indicator: Uncertainty and sensitivity analyses” Journal of the Association for Information Science and Technology Vol. 67 No. 1 pp. 200-211.

  • 20. Dobrota M. Martic M. Bulajic M. Jeremic V. (2015) “Two-phased composite I-distance indicator approach for evaluation of countries’ information development” Telecommunications Policy Vol. 39 No. 5 pp. 406-420.

  • 21. Gohou G. Soumaré I. (2012) “Does Foreign Direct Investment Reduce Poverty in Africa and are There Regional Differences?” World Development Vol. 40 No. 1 pp. 75-95.

  • 22. Hartigan J. A. Wong M. A. (1979) “Algorithm AS 136: A K-Means Clustering Algorithm” Applied Statistics Vol. 28 No. 1 pp. 100-108.

  • 23. Hoyland B. Moene K. Willumsen K. (2008) Be Careful When Doing Business Report to the Norwegian Ministry of Foreign Affairs.

  • 24. Huang M.-H. (2012) “Opening the black box of QS World University Rankings” Research Evaluation Vol. 21 No. 1 pp. 71-78.

  • 25. Independent Evaluation Group (2008) Doing Business: An Independent Evaluation. Taking the Measure of the World Bank-IFC Doing Business Indicators World Bank Washington DC.

  • 26. Ivanovic B. (1977) Teorija Klasifikacije Institut za ekonomiku industrije Beograd.

  • 27. Jain A. K. (2010) “Data clustering: 50 years beyond K-means” Pattern Recognition Letters Vol. 31 No. 8 pp. 651-666.

  • 28. Jeremic V. Bulajic M. Martic M. Radojicic Z. (2011) “A fresh approach to evaluating the academic ranking of world universities” Scientometrics Vol. 87 No. 3 pp. 587-596.

  • 29. Jeremic V. Jovanovic Milenkovic M. Radojicic Z. Martic M. (2013) “Excellence with Leadership: the crown indicator of Scimago Institutions Rankings Iber report” El Profesional de La Información Vol. 22 No. 5 pp. 474-480.

  • 30. Jovanovic M. Jeremic V. Savic G. Bulajic M. Martic M. (2012) “How does the normalization of data affect the ARWU ranking?” Scientometrics Vol. 93 No. 2 pp. 319-327.

  • 31. Kasim A. Shkedy Z. Kaiser S. Hochreiter S. Talloen W. (2016) Applied Biclustering Methods for Big and High-Dimensional Data Using R Chapman and Hall.

  • 32. Konings J. (2001) “The effects of foreign direct investment on domestic firms Evidence from firm-level panel data in emerging economies” The Economics of Transition Vol. 9 No. 3 pp. 619-633.

  • 33. Maricic M. Bulajic M. Martic M. Dobrota M. (2015) “Measuring the ict development: the fusion of biased and objective approach” Naval Academy Scientific Bulletin Vol. 18 No. 2 pp. 326-334.

  • 34. Maricic M. Bulajic M. Radojicic Z. Jeremic V. (2016) “Multivariate approach to imposing additional constraints on the Benefit-of-the-Doubt model: The case of QS World University Rankings by Subject” Croatian Review of Economic Business and Social Statistics Vol. 2 No. 1 pp. 1-14.

  • 35. Maricic M. Zornic N. Jeremic V. (2016) “Ranking European Universities Based on Their Level of Collaboration with the Industry: The Univesity-Industry Research Connections Index” in proceedings of the International Conference on Education and New Learning Technologies IATED pp. 6095-6105.

  • 36. Maricic M. Zornic N. Pilcevic I. Dacic-Pilcevic A. (2017) “ARWU vs. Alternative ARWU Ranking: What are the Consequences for Lower Ranked Universities?” Management: Journal of Sustainable Business and Management Solutions in Emerging Economies Vol. 22 No. 1 pp. 1-14.

  • 37. Miyamoto S. (2012) “An Overview of Hierarchical and Non-hierarchical Algorithms of Clustering for Semi-supervised Classification” in Torra V. Narukawa Y. López B. V. M. (Eds.) Modeling Decisions for Artificial Intelligence Springer Berlin Heidelberg pp. 1-10.

  • 38. Munda G. (2008) Social Multi-Criteria Evaluation for a Sustainable Economy Springer Berlin Heidelberg Berlin Heidelberg.

  • 39. Nardo M. Saisana M. Saltelli A. Tarantola S. Hoffman A. Giovannini E. (2005) Handbook on Constructing Composite Indicators OECD Statistics Working Papers.

  • 40. Neumayer E. De Soysa I. (2011) “Globalization and the Empowerment of Women: An Analysis of Spatial Dependence via Trade and Foreign Direct Investment” World Development Vol. 39 No. 7 pp. 1065-1075.

  • 41. Paruolo P. Saisana M. Saltelli A. (2013) “Ratings and rankings: voodoo or science?” Journal of the Royal Statistical Society: Series A (Statistics in Society) Vol. 176 No. 3 pp. 609-634.

  • 42. Radojicic M. Savic G. Radovanovic S. Jeremic V. (2015) “A novel bootstrap dba-dea approach in evaluating efficiency of banks” Naval Academy Scientific Bulletin Vol. 18 No. 2 pp. 375-384.

  • 43. Russell L. B. Bhanot G. Kim S.-Y. Sinha A. (2017) “Using Cluster Analysis to Group Countries for Cost-Effectiveness Analysis: An Application to Sub-Saharan Africa” Medical Decision Making Vol. 38 No. 2 pp. 139-149.

  • 44. Saisana M. D’Hombres B. Saltelli A. (2011) “Rickety numbers: Volatility of university rankings and policy implications” Research Policy Vol. 40 No. 1 pp. 165-177.

  • 45. Savic D. Jeremic V. Petrovic N. (2016) “Rebuilding the Pillars of Sustainable Society Index: A Multivariate Post Hoc I-Distance Approach” Problemy Ekorozwoju - Problems of Sustainable Development Vol. 12 No. 1 pp. 125-134.

  • 46. Singh R. K. Murty H. R. Gupta S. K. Dikshit A. K. (2007) “Development of composite sustainability performance index for steel industry” Ecological Indicators Vol. 7 No. 3 pp. 565-588.

  • 47. Škrabuľáková E. F. Ivanova M. Michaeli E. (2016) “Usage of clustering methods in mathematics geoinformatics and related fields of university study” in proceedings of 17th International Carpathian Control Conference (ICCC) IEEE pp. 723-728.

  • 48. Soh K. (2014) “Nominal versus attained weights in Universitas 21 Ranking” Studies in Higher Education Vol. 39 No. 6 pp. 944-951.

  • 49. Tibshirani R. Walther G. Hastie T. (2001) “Estimating the number of clusters in a data set via the gap statistic” Journal of the Royal Statistical Society: Series B (Statistical Methodology) Vol. 63 No. 2 pp. 411-423.

  • 50. World Bank (2017) Doing Business 2018: Reforming to Create Jobs Washington D.C available at: http://www.doingbusiness.org/~/media/WBG/DoingBusiness/Documents/Annual-Reports/English/DB2018-Full-Report.pdf (06 November 2018).

  • 51. Zhou P. Ang B. W. Zhou D. Q. (2010) “Weighting and aggregation in composite indicator construction: A multiplicative optimization approach” Social Indicators Research Vol. 96 No. 1 pp. 169-181.

Search
Journal information
Impact Factor


CiteScore 2018: 0.57

SCImago Journal Rank (SJR) 2018: 0.165
Source Normalized Impact per Paper (SNIP) 2018: 0.388

Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 38 38 23
PDF Downloads 33 33 13