Sensitivity analysis as a tool to optimise Human Development Index
DOI:
https://doi.org/10.24136/eq.2019.020Keywords:
Human Development Index, composite indicators, sensitivity analysisAbstract
Research background: Composite indicators are commonly used as an approximation tool to measure economic development, the standard of living, competitiveness, fairness, effectiveness, and many others being willingly implemented into many different research disciplines. However, it seems that in most cases, the variable weighting procedure is avoided or erroneous since, in most cases, the so-called ?weights by belief? are applied. As research show, it can be frequently observed that weights do not equal importance in composite indicators. As a result, biased rankings or grouping of objects are obtained.
Purpose of the article: The primary purpose of this article is to optimise and improve the Human Development Index, which is the most commonly used composite indicator to rank countries in terms of their socio-economic development. The optimisation will be done by re-scaling the current weights, so they will express the real impact of every single component taken into consideration during HDI?s calculation process.
Methods: In order to achieve the purpose mentioned above, the sensitivity analysis tools (mainly the first-order sensitivity index) were used to determine the appropriate weights in the Human Development Index. In the HDI?s resilience evaluation process, the Monte Carlo simulations and full-Bayesian Gaussian processes were applied. Based on the adjusted weights, a new ranking of countries was established and compiled with the initial ranking using, among others, Kendall tau correlation coefficient.
Findings & Value added: Based on the data published by UNDP for 2017, it has been shown that the Human Development Index is built incorrectly by putting equal weights for all of its components. The weights proposed by the sensitivity analysis better reflect the actual contribution of individual factors to HDI variability. Re-scaled Human Development Index constructed based on proposed weights allow for better differentiation of countries due to their socio-economic development.
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References
Aguna, C. G., & Kovacevic, M. (2011). Uncertainty and sensitivity analysis of the human development index. Human Development Research Paper, 47.
View in Google Scholar
Balcerzak, A. P., & Pietrzak, M. B. (2017a). Digital economy in Visegrad countries. multiple-criteria decision analysis at regional level in the years 2012 and 2015. Journal of Competitiveness, 9(2). doi: 10.7441/joc.2017.02.01.
DOI: https://doi.org/10.7441/joc.2017.02.01
View in Google Scholar
Balcerzak, A. P., & Pietrzak, M. B. (2017b). Human development and quality of institutions in highly developed countries. In M. H. Bilgin, H. Danis, E. Demir, & U. Can (Eds.), Financial environment and business development. Proceedings of the 16th Eurasia Business and Economics Society. Springer International Publishing. doi: 10.1007/978-3-319-39919-5_18.
DOI: https://doi.org/10.1007/978-3-319-39919-5_18
View in Google Scholar
Balcerzak, A. P., & Pietrzak, M. B. (2017c). TOPSIS with generalized distance measure gdm in assessing poverty and social exclusion at regional level in Visegrad countries. In: P. Pražák (Ed.). 35th international conference mathematical methods in economics MME 2017 conference proceedings. Hradec Králové: University of Hradec Králové.
View in Google Scholar
Bandura, R. (2008). Measuring country performance and state behavior: a survey of composite indices. New York: Office of Development StudiesUnited Nations Development Programme.
View in Google Scholar
Bartkowiak-Bakun, N. (2017). The diversity of socioeconomic development of rural areas in Poland in the western borderland and the problem of post-state farm localities. Oeconomia Copernicana, 8(3). doi: 10.24136/oc.v8i3.26.
DOI: https://doi.org/10.24136/oc.v8i3.26
View in Google Scholar
Becker, W., Saisana, M., Paruolo, P., & Vandecasteele, I. (2017). Weights and importance in composite indicators: closing the gap. Ecological Indicators, 80. doi: 10.1016/j.ecolind.2017.03.056.
DOI: https://doi.org/10.1016/j.ecolind.2017.03.056
View in Google Scholar
Becker, W., Paolo, P., E., Saisana, M., & Saltelli, A. (2016). Weights and importance in composite indicators : mind the gap. In. R. Ghanem, D. Higdon & H. Owhadi (Eds.). Handbook of uncertainty quantification. Springer International Publishing. doi: 10.1007/978-3-319-11259-6_40-1.
DOI: https://doi.org/10.1007/978-3-319-12385-1_40
View in Google Scholar
Bellido, H., Gimenez-Nadal, J., & Ortega, R. (2011). Measuring satisfaction of the unemployed: a composite indicator and policy implications. Applied Economics Letters, 18(17). doi: 10.1080/13504851.2011.558475.
DOI: https://doi.org/10.1080/13504851.2011.558475
View in Google Scholar
Cherchye, L., Lovell, C. A. K., & Mosen, W. (2007). One market, one number? A composite indicator assessment of EU internal market dynamics. European Economic Review, 51(3). doi: 10.1016/j.euroecorev.2006.03.011.
DOI: https://doi.org/10.1016/j.euroecorev.2006.03.011
View in Google Scholar
Despotis, D. (2005). A reassessment of the human development index via data envelopment analysis. Journal of Operational Research Society, 56(8). doi: 10.1057/palgrave.jors.2601927.
DOI: https://doi.org/10.1057/palgrave.jors.2601927
View in Google Scholar
Diefenbach, T. (2009). New public management in public sector organizations: the dark side of managerialistic 'enlightenment'. Public Administration, 87(4). doi: 10.1111/j.1467-9299.2009.01766.x.
DOI: https://doi.org/10.1111/j.1467-9299.2009.01766.x
View in Google Scholar
Dobrota, M., Jeremic, V., Bulajic, M., & Radojicic, Z. (2015). Uncertainty and sensitivity analyses of PISA efficiency: distance-based analysis approach. Acta Polytechnica Hungarica, 12(3). doi: 10.12700/APH.12.3.2015.3.3.
DOI: https://doi.org/10.12700/APH.12.3.2015.3.3
View in Google Scholar
Dominiquez-Serrano, M., & Blancas, F. (2011). A gender wellbeing composite indicator: the best-worst global evaluation approach. Social Indicators Research, 102(3). doi: 10.1007/s11205-010-9687-3.
DOI: https://doi.org/10.1007/s11205-010-9687-3
View in Google Scholar
Floridi, M., Pagni, S., & Falorni, S. (2011). An exercise in composite indicators construction: assessing the sustainability of Italian regions. Ecological Economics, 70(8). doi: 10.1016/j.ecolecon.2011.03.003.
DOI: https://doi.org/10.1016/j.ecolecon.2011.03.003
View in Google Scholar
Giambona, F., & Vassallo, E. (2014). Composite indicator of social inclusion for European countries. Social Indicators Research, 116(1). doi: 10.1007/s11205-013-0274-2.
DOI: https://doi.org/10.1007/s11205-013-0274-2
View in Google Scholar
Gnaldi, M., & Del Sarto, S. (2018). Variable weighting via multidimensional IRT models in composite indicators construction. Social Indicators Research, 136(3). doi: 10.1007/s11205-016-1500-5.
DOI: https://doi.org/10.1007/s11205-016-1500-5
View in Google Scholar
Gomez-Vega, M., & Picazo-Tadeo, A. J. (2019). Ranking world tourist destinations with a composite indicator of competitiveness: to weigh or not to weigh? Tourism Management, 72. doi: 10.1016/j.tourman.2018.11.006.
DOI: https://doi.org/10.1016/j.tourman.2018.11.006
View in Google Scholar
Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2019). On the methodological framework of composite indices: a review of the issues of weighting, aggregation, and robustness. Social Indicators Research, 141(1). doi: 10.1007/ s11205-017-1832-9.
DOI: https://doi.org/10.1007/s11205-017-1832-9
View in Google Scholar
Hoskin, B., & Mascherini, M. (2009). Measuring active citizenship through the development of a composite indicator. Social Indicators Research, 90(3). doi: 10.1007/s11205-008-9271-2.
DOI: https://doi.org/10.1007/s11205-008-9271-2
View in Google Scholar
Kruk, H., & Waśniewska, A. (2017). Application of the Perkal method for assessing competitiveness of the countries of Central and Eastern Europe. Oeconomia Copernicaca, 8(3). doi: 10.24136/oc.v8i3.21.
DOI: https://doi.org/10.24136/oc.v8i3.21
View in Google Scholar
Kuc, M. (2017a). Social convergence in Nordic countries at regional level. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(1). doi: 10.24136/eq.v12i1.2.
DOI: https://doi.org/10.24136/eq.v12i1.2
View in Google Scholar
Kuc, M. (2017b). Is the regional divergence the price for the international convergence? the case of Visegrad Group. Journal of Competitiveness, 9(4). doi: 10.7441/joc.2017.04.04.
DOI: https://doi.org/10.7441/joc.2017.04.04
View in Google Scholar
Łyszczarz, B. (2016). Public-private mix and performance of health care system in CEE and CIS countries. Oeconomia Copernicaca, 7(2). doi: 10.12775/OeC. 2016.011.
DOI: https://doi.org/10.12775/OeC.2016.011
View in Google Scholar
Mann, J., & Shideler, D. (2015). Measuring Schumpeterian activity using a composite indicator. Journal of Entrepreneurship and Public Policy, 4(1). doi: 10.1108/JEPP-07-2013-0029.
DOI: https://doi.org/10.1108/JEPP-07-2013-0029
View in Google Scholar
Mazouch P., Vltavska K., & Stnaek T. (2016). Towards the unnecessity of human development index: the case of sensitivity analysis. Statistika, 96(4).
View in Google Scholar
Mcgillivray, M. (1991). The human development index - yet another redundant composite development indicator. World Development, 19(10). doi: 10.1016/ 0305-750X(91)90088-Y.
DOI: https://doi.org/10.1016/0305-750X(91)90088-Y
View in Google Scholar
Mizobuchi, H. (2014). Measuring world better life frontier: a composite indicator for OECD better life index. Social Indicators Research, 118(3). doi: 10.1007/s 11205-013-0457-x.
DOI: https://doi.org/10.1007/s11205-013-0457-x
View in Google Scholar
Neumayer, E. (2001). The human development index and sustainability - a constructive proposal. Ecological Economics, 39(1). doi: 10.1016/S0921-8009(01)00201-4.
DOI: https://doi.org/10.1016/S0921-8009(01)00201-4
View in Google Scholar
Nikulin, D., & Sobiechowska-Ziegert, A. (2018). Informal work in Poland - a regional approach. Papers in Regional Science, 97(4). doi: 10.1111/pirs.12306.
DOI: https://doi.org/10.1111/pirs.12306
View in Google Scholar
Pääkkönen, J., & Seppälä, T. T. (2014). Using composite indicators to evaluate the efficiency of health care system. Applied Economics, 46(19). doi: 10.1080/0003 6846.2014.899675.
DOI: https://doi.org/10.1080/00036846.2014.899675
View in Google Scholar
Paruolo, P., Saisana, M., & Saltelli, A. (2013). Rating and rankings: voodoo or science? Journal of Royal Statistical Society Series A - Statistics in Society, 3. doi: 10.1111/j.1467-985X.2012.01059.x.
DOI: https://doi.org/10.1111/j.1467-985X.2012.01059.x
View in Google Scholar
Peiro-Palomino, J., & Picazo-Tadeo, A. J. (2018). OECD: one or many? ranking countries with a composite well-being indicator. Social Indicators Research, 139(3). doi: 10.1007/s11205-017-1747-5.
DOI: https://doi.org/10.1007/s11205-017-1747-5
View in Google Scholar
Perisic, A. (2015). Data-driven weights and restrictions in the construction of composite indicators. Croatian Operational Research Review, 6(1). doi: 10.17 535/crorr.2015.0003.
DOI: https://doi.org/10.17535/crorr.2015.0003
View in Google Scholar
Pietrzak, M. B., & Balcerzak, A. P. (2017). Economic development of Polish voivodeships in the years 2010-2014. application of taxonomic measure of development with entropy weights. In M. Papież & S. Śmiech (Eds.). The 11th professor Aleksander Zelias international conference on modelling and forecasting of socio-economic phenomena. Conference proceedings. Cracow: Foundation of the Cracow University of Economics.
View in Google Scholar
Pietrzak, M. B., Balcerzak, A. P., Gajdos, A., & Arendt, Ł. (2017). Entrepreneurial environment at regional level: the case of Polish path towards sustainable socio-economic development. Entrepreneurship and Sustainability Issues, 5(2). doi: 10.9770/jesi.2017.5.2(2).
DOI: https://doi.org/10.9770/jesi.2017.5.2(2)
View in Google Scholar
Pietrzak, M. B. (2016). The problem of the inclusion of spatial dependence within the TOPSIS method. Montenegrin Journal of Economics, 12(3). doi: 10.14254/ 1800-5845.2016/12-3/5.
DOI: https://doi.org/10.14254/1800-5845.2016/12-3/5
View in Google Scholar
Ravallion, M. (2010). Mashup indices of development. Policy Research Working Paper Series. The World Bank, 5432. doi: 10.1596/1813-9450-5432.
DOI: https://doi.org/10.1596/1813-9450-5432
View in Google Scholar
Reig-Martinez, E., Gomez-Limon, J., & Picazo-Tadeo, A. J. (2011). Ranking farms with a composite indicator of sustainability. Agricultural Economics, 42(5).
DOI: https://doi.org/10.1111/j.1574-0862.2011.00536.x
View in Google Scholar
Sagar, A., & Najam, A. (1998). The human development index: a critical review. Ecological Economics, 25(3). doi: 10.1111/j.1574-0862.2011.00536.x.
DOI: https://doi.org/10.1016/S0921-8009(97)00168-7
View in Google Scholar
Saisana, M., & Saltelli, A. (2010). Uncertainty and sensitivity analysis of the 2010 environmental performance index. Retrieved from https://ec.europa.eu/jrc/en/ publication/eur-scientific-and-technical-research-reports/uncertainty-and-sensiti vity-analysis-2010-environmental-performance-index (16.12.2018).
View in Google Scholar
Saisana, M., Saltelli, A., & Tarantola, S. (2005). Uncertainty and sensitivity analysis as tools for the quality assessment of composite indicators. Journal of the Royal Statistical Society, 168(2). doi: 10.1111/j.1467-985X.2005.00350.x.
DOI: https://doi.org/10.1111/j.1467-985X.2005.00350.x
View in Google Scholar
Sobiechowska-Ziegert, A., & Mikulska, A. (2013). Measure of the level of socio-economic development in provinces. Quantitative Methods in Economics, 14(2).
View in Google Scholar
United Nations Development Programme (2019). Human development index (HDI). Retrieved from http://hdr.undp.org/en/content/human-development-index-hdi (15.01.2019).
View in Google Scholar
Weziak-Bialowolska, D. (2015). Poverty in the regions of the European Union - measurement with a composite indicator. Contemporary Economics, 9(2). doi: 10.5709/ce.1897-9254.163.
DOI: https://doi.org/10.5709/ce.1897-9254.163
View in Google Scholar
Yang, F., Kao, R., & Chen, Y. (2018). A common weight approach to construct composite indicators: the evaluation of fourteen emerging markets. Social Indicators Research, 137(2). doi: 10.1007/s11205-017-1603-7.
DOI: https://doi.org/10.1007/s11205-017-1603-7
View in Google Scholar
Zavaleta, D., & Tomkinson, J. (Eds.) (2015). Training material for producing National Human Development Reports. New York: UNDP Human Development Report Office.
View in Google Scholar
Żelazny, R., & Pietrucha, J. (2017). Measuring innovation and institution: the creative economy index. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(1). doi: 10.24136/eq.v12i1.3.
DOI: https://doi.org/10.24136/eq.v12i1.3
View in Google Scholar
Zhou, P., Ang, B. W., & Zhou, D. Q. (2010). Weighting and aggregation in composite indicator construction: a multiplicative optimization approach. Social Indicators Research, 96(1). doi: 10.1007/s11205-009-9472-3.
DOI: https://doi.org/10.1007/s11205-009-9472-3
View in Google Scholar
Zizka, M. (2013). Construction of composite indicator based on factor analysis. In Proceedings of 31st international conference mathematical methods in economics 2013. Part II. Jihlava: The College of Polytechnics Jihlava.
View in Google Scholar
