Skip to main content
Log in

Avoiding Ranking Contradictions in Human Development Index Using Goal Programming

  • Published:
Social Indicators Research Aims and scope Submit manuscript

Abstract

This paper builds on the extensive literature of the rank reversal issue in multi-criteria decision making (MCDM) techniques. It is a continuation of the study of Sayed et al. (Soc Indic Res 123(1):1–27, 2015) that exhibited this problem in the human development index (HDI) framework. The proposed methodology, the Goal Programming Benefit-of-the-Doubt (GP-BOD), aims to overcome this problem and obtain consistent and stable rankings. For investigating the credibility of the proposed method in solving this issue, it has been applied to the HDI dataset in 2012. The resulted HDI rankings are compared with those evaluated from eleven overlapping sub-groups that are internationally categorized based on geographic regions and income levels. The results show a solution to the ranking contradictions problem. Among other merits, the results prove two additional features of the proposed GP-BOD model. First, the resulted countries’ rankings are distinguishable and absolutely tie-free. This enhances the discriminating power of the proposed rank preservation model. Second, the GP-BOD weights are evaluated on a common base to compare all countries on the same scale. Moreover, a lower bound is endogenously imposed on these weights to avoid the problem of zero weights. Finally, the validity of the proposed GP-BOD technique has been thoroughly examined using sensitivity tests. The results show stability in the rankings when different methods of normalization and weighting are applied.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. The alternative term is equivalent to a country in the CI context.

  2. The criteria term is equivalent to sub-indicators in the CI context.

  3. The transitivity property in CI context indicates that if country A1 is superior to another country A2 and A2 is better than country A3, then to verify this property country A1 should have a higher ranking than A3.

  4. The value 0.001 has been tested, among other values, as an aspiration level of the GP-BOD model in the HDI application. The results showed ranking contradictions in some of the studied sub-groups when compared to the rankings of the full list of countries.

  5. These tests are mentioned with other three tests, explained in Sect. 2, for checking the occurrence of the rank reversal problem.

References

  • Belton, V., & Gear, A. E. (1983). On a short-coming of Saaty’s method of analytic hierarchies. Omega, 11(3), 228–230.

    Article  Google Scholar 

  • Bernini, C., Guizzardi, A., & Angelini, G. (2013). DEA-like model and common weights approach for the construction of a subjective community well-being indicator. Social Indicators Research, 114(2), 405–424.

    Article  Google Scholar 

  • Cherchye, L., Moesen, W., Rogge, N., & Van Puyenbroeck, T. (2007). An introduction to ‘benefit of the doubt’composite indicators. Social Indicators Research, 82(1), 111–145.

    Article  Google Scholar 

  • Cook, W. D., Kress, M., & Seiford, L. M. (1996). Data envelopment analysis in the presence of both quantitative and qualitative factors. Journal of the Operational Research Society, 47(7), 945–953.

    Article  Google Scholar 

  • Cook, W. D., & Zhu, J. (2007). Within-group common weights in DEA: An analysis of power plant efficiency. European Journal of Operational Research, 178(1), 207–216.

    Article  Google Scholar 

  • Desai, M. (1991). Human development: Concepts and measurement. European Economic Review, 35(2), 350–357.

    Article  Google Scholar 

  • Despotis, D. K. (2005). A reassessment of the human development index via data envelopment analysis. Journal of the Operational Research Society, 56(8), 969–980.

    Article  Google Scholar 

  • Dyer, J. S. (1990). Remarks on the analytic hierarchy process. Management Science, 36(3), 249–258.

    Article  Google Scholar 

  • García-Cascales, M. S., & Lamata, M. T. (2012). On rank reversal and TOPSIS method. Mathematical and Computer Modelling, 56(5), 123–132.

    Article  Google Scholar 

  • Golany, B., & Yu, G. (1995). A goal programming-discriminant function approach to the estimation of an empirical production function based on DEA results. Journal of Productivity Analysis, 6(2), 171–186.

    Article  Google Scholar 

  • Hatefi, S. M., & Torabi, S. A. (2010). A common weight MCDA–DEA approach to construct composite indicators. Ecological Economics, 70(1), 114–120.

    Article  Google Scholar 

  • Ignizio, J. P. (1978). A review of goal programming: A tool for multiobjective analysis. Journal of the Operational Research Society, 29(11), 1109–1119.

    Article  Google Scholar 

  • Kao, C., & Hung, H. T. (2005). Data envelopment analysis with common weights: The compromise solution approach. Journal of the Operational Research Society, 56(10), 1196–1203.

    Article  Google Scholar 

  • Kong, F., Wei, W., & Gong, J. H. (2016). Rank reversal and rank preservation in ANP method. Journal of Discrete Mathematical Sciences and Cryptography, 19(3), 821–836.

    Article  Google Scholar 

  • Maleki, H., & Zahir, S. (2013). A comprehensive literature review of the rank reversal phenomenon in the analytic hierarchy process. Journal of Multi-Criteria Decision Analysis, 20(3–4), 141–155.

    Article  Google Scholar 

  • OECD. (2008). Handbook on constructing composite indicators: Methodology and user guide. http://www.oecd.org/std/42495745.pdf. Accessed April, 20 2015.

  • Reig-Martínez, E. (2013). Social and economic wellbeing in Europe and the Mediterranean Basin: Building an enlarged human development indicator. Social Indicators Research, 111(2), 527–547.

    Article  Google Scholar 

  • Roll, Y., Cook, W. D., & Golany, B. (1991). Controlling factor weights in data envelopment analysis. IIE Transactions, 23(1), 2–9.

    Article  Google Scholar 

  • Saaty, T. L., & Sagir, M. (2009). An essay on rank preservation and reversal. Mathematical and Computer Modelling, 49(5), 1230–1243.

    Article  Google Scholar 

  • Saaty, T. L., & Vargas, L. G. (1993). Experiments on rank preservation and reversal in relative measurement. Mathematical and Computer Modelling, 17(4–5), 13–18.

    Article  Google Scholar 

  • Sagar, A. D., & Najam, A. (1998). The human development index: A critical review. Ecological Economics, 25(3), 249–264.

    Article  Google Scholar 

  • Sayed, H., Hamed, R., Ramadan, M. A. G., & Hosny, S. (2015). Using meta-goal programming for a new human development indicator with distinguishable country ranks. Social Indicators Research, 123(1), 1–27.

    Article  Google Scholar 

  • Senouci, M. A., Mushtaq, M. S., Hoceini, S., & Mellouk, A. (2016). TOPSIS-based dynamic approach for mobile network interface selection. Computer Networks, 107, 304–314.

    Article  Google Scholar 

  • Shin, Y. B., & Lee, S. (2013). Note on an approach to preventing rank reversals with addition or deletion of an alternative in analytic hierarchy process. US-China Education Review A, 3(1), 66–72.

    Google Scholar 

  • Soltanifar, M., & Shahghobadi, S. (2014). Survey on rank preservation and rank reversal in data envelopment analysis. Knowledge-Based Systems, 60, 10–19.

    Article  Google Scholar 

  • Tofallis, C. (2013). An automatic-democratic approach to weight setting for the new human development index. Journal of Population Economics, 26(4), 1325–1345.

    Article  Google Scholar 

  • Tofallis, C. (2014). Add or multiply? A tutorial on ranking and choosing with multiple criteria. INFORMS Transactions on Education, 14(3), 109–119.

    Article  Google Scholar 

  • Triantaphyllou, E. (2001). Two new cases of rank reversals when the AHP and some of its additive variants are used that do not occur with the multiplicative AHP. Journal of Multi-Criteria Decision Analysis, 10(1), 11–25.

    Article  Google Scholar 

  • Troutt, M. D. (1988). Rank reversal and the dependence of priorities on the underlying MAV function. Omega, 16(4), 365–367.

    Article  Google Scholar 

  • UNDP (2013a). The Rise of the South: Human Progress in a Diverse World. Human Development Report 2013 Technical Notes. UNDP. http://hdr.undp.org/en/statistics. Accessed September, 9 2015.

  • UNDP (2013b). International Human Development Indicators Database http://hdrstats.undp.org/en/tables/. Accessed September, 20 2013.

  • Velasquez, M., & Hester, P. T. (2013). An analysis of multi-criteria decision making methods. International Journal of Operations Research, 10(2), 56–66.

    Google Scholar 

  • Wang, Y. M., & Elhag, T. M. (2006). An approach to avoiding rank reversal in AHP. Decision Support Systems, 42(3), 1474–1480.

    Article  Google Scholar 

  • Wang, Y. M., & Luo, Y. (2009). On rank reversal in decision analysis. Mathematical and Computer Modelling, 49(5), 1221–1229.

    Article  Google Scholar 

  • Wang, Y. M., Luo, Y., & Lan, Y. X. (2011). Common weights for fully ranking decision making units by regression analysis. Expert Systems with Applications, 38(8), 9122–9128.

    Article  Google Scholar 

  • Wang, X., & Triantaphyllou, E. (2008). Ranking irregularities when evaluating alternatives by using some ELECTRE methods. Omega, 36(1), 45–63.

    Article  Google Scholar 

  • Zhou, P., Ang, B. W., & Poh, K. L. (2007). A mathematical programming approach to constructing composite indicators. Ecological Economics, 62(2), 291–297.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to show their gratitude to the anonymous reviewers for their helpful remarks to improve the manuscript and clarify its ideas. Technical notes from Dr. Mohamed Ossman are gratefully acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alyaa Hegazy Abdelhamid.

Appendix

Appendix

See Tables 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 and 18.

Table 7 Data, HDI values, and GP-BOD index values using three different normalization methods for year 2012.
Table 8 Comparing African Union rankings with the full list of countries in 2012.
Table 9 Comparing Arab League rankings with the full list of countries in 2012.
Table 10 Comparing European Union rankings with the full list of countries in 2012.
Table 11 Comparing OECD rankings with the full list of counties in 2012.
Table 12 Comparing South Asia rankings with the full list of countries in 2012.
Table 13 Comparing Latin America and the Caribbean rankings with the full list of countries in 2012.
Table 14 Comparing Middle East and North Africa rankings with the full list of countries in 2012.
Table 15 Comparing low-income rankings with the full list of countries in 2012.
Table 16 Comparing lower-middle income rankings with the full list of countries in 2012.
Table 17 Comparing upper-middle income rankings with the full list of countries in 2012.
Table 18 Comparing high income rankings with the full list of countries in 2012.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sayed, H., Hamed, R., Hosny, S.H. et al. Avoiding Ranking Contradictions in Human Development Index Using Goal Programming. Soc Indic Res 138, 405–442 (2018). https://doi.org/10.1007/s11205-017-1663-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11205-017-1663-8

Keywords

Navigation