Using Meta-goal Programming for a New Human Development Indicator with Distinguishable Country Ranks
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This paper builds on the extensive literature of the benefit-of-the-doubt (BoD) methodology to set weights for composite indicators (CIs). The proposed methodology, meta-goal programming benefit of the doubt (MGP-BoD), proved to overcome some of the BoD shortcomings and enhance its performance. MGP-BoD belongs to the family of common-weights BoD models. It comprises of two sets of goals and two meta-goals. Among other merits, results prove two additional benefits of the MGP-BoD over older BoD. First, it enhances BoD discriminating power by eliminating all ties in CI values and, hence, country ranks. This high discriminating power is achieved in only one totally endogenous step. Second, MGP-BoD weights add up to one. This makes weights more insightful, interpretable, comparable to weights from other weighting systems, and easier to interpret compared to BoD. These additional merits favor MGP-BoD over previous weighting methods. In the meantime, the proposed method preserves a significantly high correlation with previous methods results as shown a set of Pearson and Spearman correlation tests to compare it to various previous methodologies. To validate the proposed methodology, it has been thoroughly tested using sensitivity and classification analyses. All tests are found highly significant. Nevertheless, the method has its own limitations that suggest future research points. Finally, the paper offers a new human development indicator (HDI) using 2012 data using MGP-BoD. The proposed HDI offers an alternative to the currently used equal-weights HDI that offers distinguishable country ranks and more policy-guiding weights. The highest weights are assigned to education variables.
KeywordsMeta-goal programming (MGP) Common weights Composite indicators (CIs) Discriminating power Benefit-of-the-doubt (BoD) Human development indicator (HDI)
The authors would like to extend their thanks to the anonymous reviewers for their constructive remarks for improving the manuscript and clarifying its ideas. Thanks are also due to Dr. Mohamed Ossman, Dr. Hesham Abdel Meguid, and Dr. Mahmoud Rashwan for their helpful technical advice.
- Cooper, W., Seiford, L., & Zhu, J. (Eds.). (2011). Handbook on data envelopment analysis (2nd ed.). Berlin: Springer.Google Scholar
- Hosny, S. (2007). Modelling the relationship between poverty and female labor force participation in Egypt. Unpublished MSc thesis, Department of Statistics, Faculty of Economics and Political Science, Cairo University.Google Scholar
- Makui, A., Alinezhad, A., Mavi, R., & Zohrehbandian, M. (2008). A goal-programming method for finding common weights in DEA with an improved discriminating power for efficiency. Journal of Industrial and Systems Engineering, 1(4), 293–303.Google Scholar
- Mishra, S. K. (2008). On construction of robust composite indices by linear aggregation.Google Scholar
- OECD. (2008). Handbook on constructing composite indicators: Methodology and user guide. http://www.oecd.org/std/42495745.pdf. Accessed November 1, 2013.
- Sharpe, A., & Andrews, B. (2012). An assessment of weighting methodologies for composite indicators: The case of the index of economic wellbeing. Center for the study of living standards, CSLS Research Report No. 2012-10. http://www.csls.ca/reports/csls2012-10.pdf. Accessed October 13, 2013.
- UNDP. (2010). The real wealth of nations: Pathways to human development. Human development report 2010.Google Scholar
- UNDP. (2013a). The rise of the south: Human progress in a diverse world. Human development report 2013 Technical Notes. http://hdr.undp.org/en/statistics. Accessed September 20, 2013.
- UNDP. (2013b). International human development indicators database. http://hdrstats.undp.org/en/tables/. Accessed September 20, 2013.