OECD Ülkeleri İçin Refah Ölçümü: Gri İlişkisel Analiz Uygulaması

Son yıllarda refah değerlendirmesine olan ilginin artması, bölgesel ve ulusal düzeyde refahın ölçülmesinde çok çeşitli istatistiksel ve optimizasyon tekniklerinin kullanılmasını sağlamıştır. Bu çalışmada, gri ilişkisel analizini (GRA) entropy formülü ile birleştiren metodolojik bir yaklaşım kullanılarak, 34 OECD ülkesinin refah düzeyi skorlarının hesaplanması amaçlanmıştır. Refah ölçümü OECD'nin iki boyutlu çerçevesi göz önüne alınarak incelenmiştir. Bu boyutlardan ilki olan yaşam kalitesi boyutu sağlık durumu, iş - yaşam dengesi, eğitim - beceriler, sosyal bağlantılar, sivil katılım - yönetişim, çevresel kalite, kişisel güvenlik, öznel refah gibi göstergeleri ile incelenmektedir. Diğer boyut olan fiziki durum ise gelir - servet, iş - kazanç ve barınma göstergelerini kapsamaktadır. OECD Bölgesel Refah Veri tabanından toplanan, yaşam kalitesi ve fiziki duruma ilişkin göstergeler 15 yıllık dönem (2000-2014) içerisindeki en güncel verilerden oluşmaktadır. Sonuç olarak, İzlanda, Avustralya, Norveç ve İsviçre'nin en yüksek refah seviyesine ulaştığı tespit edilmiştir. Sıralamanın diğer ucunda yer alan Macaristan, Yunanistan, Türkiye ve Meksika’nın ise nispeten daha düşük refah seviyesine sahip oldukları gözlenmiştir.

Anahtar Kelimeler:

Refah seviyesi, entropy, OECD, çok amaçlı karar verme

Measuring Wellbeing in OECD: An Application of Grey Relational Analysis

There has been growing interest in the assessment of well-being in recent years and a wide range of statistical and optimization techniques have been used to measure well-being at regional and national level. This paper aims at computing the well-being scores of 34 OECD countries, using a methodological approach that combines grey relational analysis (GRA) with entropy formula. The measurement of well-being process was examined by considering the two-dimensional framework of the OECD. The dimension of quality of life is examined by the indicators such as health status, work – life balance, education – skills, social connections, civic engagement – governance, environmental quality, personal security, subjective well-being. And the dimension of material living and conditions covers income – wealth, jobs – earnings and housing indicators. The data were collected from the OECD Regional Well-Being Database. The data used in this study refer to last available year in 15-year period (2000–2014), belonging to various quality of life and material conditions. Consequently, it was revealed that Iceland, Australia, Norway and Switzerland have achieved the highest level of well–being. At the other end of ranking, Hungary, Greece, Turkey, and Mexico have been observed to have relatively lowest well–being level.

Keywords:

Well-being, entropy, OECD, multi-objective optimization,

PDF

___

Balazentis, T., Balazentis, A., & Brauers, W. K. M. (2011). Multi–Objective Optimization of Well–Beıng In The European Unıon Member States, Ekonomska istraživanja, Vol. 24(4), 1-15.
Benzeval, M., Judge, K., & Whitehead, M. (1995). Tackling Inequalities in Health. Kings Fund, London.
Boarini, R., Kolev, A., & McGregor, A. (2014). Measuring well-being and progress in countries at different stages of development: Towards a more universal conceptual framework. Working Paper No. 325. OECD Development Centre.
Clark, A. E. (2009). Work, Jobs and Well-Being across the Millennium, IZA Discussion Paper, No. 3940.
Cloninger, C. R. (2004). Feeling good: the science of well-being. Oxford University Press.
Costanza R., Hart M., Posner S., & Talberth J. (2009). Beyond GDP: The Need for New MEasures of Progress, The Pardee Papers, No:4, Jauary, Boston University The Frederick S. Pardee Center for the Study of the Longer-Range Future.
Deng, J-L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288-294.
Diener, E. (1984). Subjective Well-Being, Psychological Bulletin, 95(3), 542-575.
Dolan, P., Peasgood, T., & White, M. (2008). Do we really know what makes us happy? A review of the economic literature on the factors associated with subjective well-being, Journal of Economic Psychology, 29, 94–122.
Durand, M. (2015). The OECD Better Life Initiative: How’s Life? And The Measurement of Well-Being, Review of Income and Wealth, 61(1), 4-17.
Evans, G., W., Kantrowitz, E., & Eshelman, P. (2002). Housing Quality and Psychological Well-Being Among the Elderly Population, The Journals of Gerontology: Series B, 57(4), 381–383.
Fleurbaey, M. (2009). Beyond GDP: The Quest for a Measure of Social Welfare. Journal of Economic Literature, 47 (4), 1029-75.
Ginevičius, R., Podvezko, V. (2009). Evaluating the changes in economic and social development of Lithuanian counties by multiple criteria methods, Technological and Economic Development of Economy 15(3), 418–436.
Greenhaus, J. H., Collins, K. M., & Shaw, J. D. (2003). The relation between work–family balance and quality of life. Journal of Vocational Behavior, 63, 510–531.
Gröpel, P., & Kuhl, J. (2009). Work-Life balance and subjective well-being: the mediating role of need fulfilment. The British Journal of Psychology, 100, 365-375.
Headey, B., Holmstrom, E., & Wearing, A. (1985). Models of well-being and ill-being, Social Indicator Research, 17 (3), 211-234.
Helliwell, J. F., & Putnam, R. D. (2004). The social context of well-being, The Royal Society Publishing, Published online 31 August 2004. e.t. 22. 10.2018.
Hills, J. (1995). Inquiry into Income and Wealth, vol. 2. Joseph Rowntree Foundation, York.
Huang, J. T. & Liao, Y. S. (2003). Optimization of machining parameters of wire-EDM based on grey relational and statistical analyses. International Journal of Production Research, 41 (8), 1707–1720
Hussain, M. A. (2016). EU Country Rankings’ Sensitivity to the Choice of Welfare Indicators, Social Indicators Research, 125, 1–17
Inglehart, R. (1997). Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies. Princeton, New Jersey: Princeton University Press.
Ivaldi, E., Bonatti, G., & Soliani, R. (2016) The Construction of a Synthetic Index Comparing Multidimensional Well-Being in the European Union, Social Indicators Research, 125, 397–430.
Khan, H. (1991). Measurement and determinants of socioeconomic development: A critical conspectus. Social Indicators Research, 24, 153–175.
Koçak, D., & Türe, H. (2018). Sürdürülebilir Kalkınma Hedefleri 4 Gündemi Doğrultusunda Ülkelerin Değerlendirilmesi. Türe, H. (Ed.), Nicel Karar Yöntemlerinde Güncel Konular: Teori ve Uygulama. Gazi Kitabevi, Ankara.
Kuo, Y., Yang, T., & Huang, G. W. (2008). The Use of Grey Relational Analysis in Solving Multiple Attribute Decision-Making Problems. Computers & Industrial Engineering, 55, 80-93.
Li, X., Wang, K., Liu, L. & Xin, J. (2011). Application of the Entropy Weight and TOPSIS Method in Safety Evaluation of Coal Mines, Procedia Engineering, 26, 2085-2091.
Mazziotta, M., & Pareto, A. (2019). Use and Misuse of PCA for Measuring Well-Being, Social Indicators Research, 142, 451-475.
Mon, D. L., Cheng, C. H., & Lin, J. C. (1994). Evaluating Weapon System Using Fuzzy Analytic Hierarchy Process Based on Entropy Weight, Fuzzy Sets and Systems, 62, 227-134.
Nissi, E., & Sarra, A. (2018). A Measure of Well-Being Across the Italian Urban Areas: An Integrated DEA-Entropy Approach, Social Indicators Research, 136, 1183–1209.
Nordhaus, W. D., & Tobin, J. (1972). Is growth obsolete? In Economic research: Retrospect and prospect, Economic growth, NBER, 5,1–80.
OECD. (2001). The Well-Being of Nations: The Role of Human and Socal Capital, http://www.oecd.org/site/worldforum/33703702.pdf.
OECD. (2011), Society at a Glance 2011: OECD Social Indicators, OECD, Paris.
OECD. (2011a). Compedium of OECD well-being indicators, http://www.oecd.org/sdd/47917288.pdf, e.t. 21.10.2018
OECD. (2011b). Doing Better for Families, OECD, Paris. www.oecd.org/social/family/doingbetter
OECD. (2015). How’s life? 2015: Measuring well-being. Paris: OECD Publishing.
Osberg, L., & Sharpe, A. (2002). An Index of Economic Well-Being for Selected OECD Countries, Review of Income and Wealth, 48(3), 291- 316. Peiro´-Palomino, J., & Picazo-Tadeo, A., J. (2018). OECD: One or Many? Ranking Countries with a Composite Well-Being Indicator, Social Indicators Research, 139, 847–869.
Pinar, M. (2018). Multidimensional Well‑Being and Inequality across the European Regions with Alternative Interactions between the Well‑Being Dimensions, Social Indicators Research, https://doi.org/10.1007/s11205-018-2047-4
Pukeliene, V., & Starkauskiene, V. (2011). Quality of Life: Factors Determining its Measurement Complexity, Inzinerine Ekonomika-Engineering Economics, 22(2), 147-156.
Sen, A. (1985). Commodities and capabilities. Oxford: Oxford University Press.
Shafer, S. S., Lee, B. K., & Turner, S. (2000). A tale of three greenway trails: user perceptions related to quality of life Landscape Urban Plann, 49, 163-178.
Stewart, K. (2005). Dimensions of well-being in EU regions: Do GDP and unemployment tell us all we need to know? Social Indicators Research, 73, 221-246.
Stiglitz, J., Sen, A. K. & Fitoussi, J. P. (2009). Report of the Commission on the measurement of economic performance and social progress. http://www.stiglitz-sen-fitoussi.fr/documents/rapport_anglais.pdf
Tang, C-W., & Young, H-T. (2013). Using Grey relational analysis to determine wet chemical etching parameters in through-silicon-via etching application, Materials Science in Semiconductor Processing, 16, 403–409.
Tzeng, G. H., & Huang, J.J. (2011). Multiple Attribute Decision Making: Methods and Applications, CRC Press Taylor&Francis Group, FL.
Türe, H., Koçak, D., & Doğan, S. (2016). MULTIMOORA yöntemi ile Ülke Riski Değerlendirmesi. Gazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 18(3), 824–844.
Ture, H., Dogan, S., & Kocak, D. (2019). Assessing Euro 2020 Strategy Using Multi criteria Decision Making Methods: VIKOR and TOPSIS. Social Indicators Research, 142, 645-665.