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OECD ÜLKELERİNİN REFAH DÜZEYLERİNİN GRİ İLİŞKİSEL ANALİZ ile DEĞERLENDİRİLMESİ

Yıl 2020, Sayı: 41, 83 - 107, 19.10.2020
https://doi.org/10.30794/pausbed.697852

Öz

Son yıllarda ülkelerin refah düzeyinin tanımlanması ve ölçümü araştırmacılar tarafından ilgi ile çalışılan konular arasındadır. II. Dünya Savaşından sonra bu amaçla milli gelir kullanılmış, ancak sonraki dönemlerde küreselleşme sürecinin de etkisiyle bu anlayışın yetersizliği fark edilmiş ve insan merkezli bir kalkınma yaklaşımına geçilmiştir. Bu bağlamda 1990’da hesaplanan İnsani Gelişme Endeksi (HDI) önemli bir gösterge olarak sunulmuştur. Zaman içinde değişen koşullarla refahın tanımı ve ölçümünün daha detaylı bir biçimde yapılması gerekliliği, 2011’de Ekonomik İşbirliği ve Kalkınma Örgütü (OECD) tarafından Daha İyi Yaşam Endeksi (BLI)’nın hesaplanması sonucunu doğurmuştur. Bu çalışmada OECD üyesi 35 ülke ve üye olmayan 3 ülke için hesaplanan BLI 2017 verileri Gri İlişkisel Analiz (GRA) kullanılarak değerlendirilmiştir. Çok Kriterli Karar Verme (MCDM) analizlerinde ağırlık belirlemede oldukça önemli ve sonuçları direkt etkileyen kritik bir konudur. Bu nedenle araştırmada Eşit ağırlık (MW) and Standart sapma (SD), Entropy and CRITIC olmak üzere dört farklı objektif ağırlık belirleme metodu kullanılmıştır. Ülkeler analiz sonucu elde edilen gri katsayı skorlarına göre sıralanmıştır. Sonuç olarak en yüksek skorlara sahip olan ülkelerin Norveç, Avustralya, ABD, Kanada,İzlanda, İsviçre, Danimarka ve İsveç olduğu en düşük skorlara sahip olan ülkelerin Güney Afrika, Türkiye, Meksika ve Yunanistan olduğu saptanmıştır.

Kaynakça

  • Akar, S. (2014). “Türkiye’de Daha İyi Yaşam Endeksi: Oecd Ülkeleri ile Karşılaştırma”, Journal of Life Economics, Iss. 1, s. 1-12.
  • Balešentis, T. A. and & Brauers, W. K. M. (2011). “Multi–Objective Optimization of Well–Being in the Eu-Ropean Union Member STATES”, Ekonomska istraživanja, Vol. 24, No. 4 pp. 1-15.
  • Deng, J. (1989). “Introduction to Grey System Theory”, The Journal of Grey System, Vol. 1, Iss. 1; pp. 1-24.
  • Depren, S.K., and Kalkan, S.B. (2018). “Determination of Countries’ Position Using Better Life Index: The Entropy Based Multimoora Approach” Trakya Üniversitesi Sosyal Bilimler Dergisi, Vol. 20, No. 2, pp. 353-366.
  • Diakoulaki, D., Mavrotas, G. and Papayannakis, L. (1995). “Determining Objective Weights in Multiple Criteria Problems: The Critic Method”, Computers & Operations Research, Vol. 22, Iss. 7, pp. 763-770. Feng, C.M. ve Wang, R.T. (2000). “Performance Evaluation for Airlines Including the Consideration of Financial Ratios”, Journal of Air Transport Management, Vol. 6, pp. 133-142.
  • Gökdemir, Ö. and Veenhoven, R. (2014). Kalkınmaya Farklı Bir Bakış: İyi Oluş, Bölüm 17, Kalkınmada Yeni Yaklaşımlar, (Yayıma Hazırlayanlar: Ahmet Faruk ve Devrim Dumludağ), İmge Kitabevi, Ankara.
  • Gürses, D. (2009). “İnsani Gelişme ve Türkiye”, Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, C. 12, S. 21, s.339-350.
  • Ivaldi, E., Bonatti, G. and Soliani, R. (2016). “The Construction of a Synthetic Index Comparing Multidimensional Well-Being in the European Union”, Social Indicators Research, Vol. 125, pp. 397–430.
  • Kaya, P., İpekçi Çetin, E., and Kuruüzüm, A. (2011). “Çok Kriterli Karar Verme ile Avrupa Birliği ve Aday Ülkelerinin Yaşam Kalitesinin Analizi”, İktisat Fakültesi Ekonometri ve İstatistik Dergisi, Iss. 13, pp. 80-94.
  • Kerenyi, A., 2011. “The Better Life Index of the Organisation for Economic Co-operation and Development”, Public Finance Quarterly, Vol. 56, Iss. 4, pp. 518–538.
  • Liu, S., Yang, Y., Cao, Y. and Xie, N. (2013), “A Summary on the Research of GRA models”, Grey Systems: Theory and Application, Vol. 3, No 1, pp. 7-15.
  • Madıć, M. and Radovanovıć, M. (2015). “Ranking of Some Most Commonly Used Nontraditional Machining Processes Using Rov And Critic Methods”, U.P.B. Scientific Bulletin, Series D, Vol. 77, Iss. 2, pp. 193–204.
  • Mizobuchi, H., (2013), “Measuring World Better Life Frontier”, Discussion Paper Series, No. 13-01, pp.1-23.
  • Orakçı, E., and Özdemir, A. (2017). “Telafi Edici Çok Kriterli Karar Verme Yöntemleri İle Türkiye ve AB Ülkelerinin İnsani Gelişmişlik Düzeylernin Belirlenmesi”, Journal of Economics and Administrative Sciences, Vol. 19, Iss. 1, pp. 61-74.
  • Ömürbek, N., Eren, H., and Dağ, O. (2017). “Entropi-ARAS VE Entropi-MOOSRA Yöntemleri İle Yaşam Kalitesi Açısından Ab Ülkelerinin Değerlendirilmesi”, Ömer Halis Demir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Vol. 10, Iss. 2, s. 29-48.
  • Önay, O. (2016). “Multi-Criteria Assessment of Better Life via TOPSIS and MOORA Methods”, International Journal of Business and Social Science, Vol. 7, No. 1, pp. 225-234.
  • Peiro´-Palomino, J. and Picazo-Tadeo, A. J. (2018), “OECD: One or Many? Ranking Countries with a Composite Well-Being Indicator”, Social Indicators Research, Vol. 139, pp. 847–869.
  • Reig-Martı´nez, E. (2013). “Social and Economic Wellbeing in Europe and the Mediterranean Basin: Building an Enlarged Human Development Indicator”, Social Indicators Research, Vol. 111, pp. 527-547.
  • Shannon, C.E. (1948). “A Mathematical Theory of Communication”, The Bell System Technical Journal, Vol. 27, No. 3, pp. 379-423.
  • Segal, I. E. (1960). “A Note on the Concept of Entropy”, Journal of Mathematics and Mechanics, Vol. 9, No. 4, pp. 623-629.
  • Stiglitz, J., Sen, A. and Fitoussi, J.P. (2009). Report by the Commissionon the Measurement of Economic Performance and Social Progress.
  • Türe, H. (2019). “OECD Ülkeleri İçin Refah Ölçümü: Gri İlişkisel Analiz Uygulaması”, Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Vol. 21, No. 2, s. 310-327.
  • Veenhoven, R. (2002). “Why Social Policy Needs Subjective Indicators”, Social Indicators Research, Vol. 58, Iss.1-3, pp. 33-46.
  • Wang, T.C. and Lee, H.D. (2009). “Developing a Fuzzy TOPSIS Approach Based on Subjective Weights and Objective Weights”, Expert Systems with Applications, Vol. 36, pp. 8980–8985.
  • Wang, J. J., Jing, Y. Y., Zhang, C. F., & Zhao, J. H. (2009). “Review on multi-criteria decision aid in sustainable energy decision-making”, Renewable and Sustainable Energy Reviews, Vol. 13, pp. 2263–2278.
  • Wu, H. H., (2002). “A Comparative Study of Using Grey Relational Analysis in Multiple Attribute Decision Making Problems”, Quality Engineering, Vol. 15, No. 2, pp. 209-217.
  • Wu, J., Sun, J., Liang, L. and Zha, Y. (2011). Determination of Weights for Ultimate Cross Efficiency using Shannon Entropy, Expert Systems with Applications, Vol. 38, pp. 5162–5165.
  • Zardari, N.H., Ahmed, K., Shirazi, S.M. ve Yusop, Z.B. (2015). Weighting Methods and Their Effects on Multi-Criteria Decision Making Model Outcomes in Water Resources Management. Springer.
  • OECD, (2013). How’s Life? 2013: Measuring Well-being, OECD Publishing. (10.12.2019). http://www.oecd.org/sdd/3013071e.pdf.
  • OECD, (2019). Better Life Index: Definitions and Metadata. (10.12.2019). http://www.oecd.org/statistics/OECD-Better-Life-Index-definitions-2019.pdf.
  • www.oecdbetterlifeindex.org (10.12.2019).

THE ANALYSIS of THE WELL-BEING LEVELS of OECD COUNTRIES with GREY RELATIONAL ANALYSIS

Yıl 2020, Sayı: 41, 83 - 107, 19.10.2020
https://doi.org/10.30794/pausbed.697852

Öz

In recent years, the definition and measurement of the well-being levels of nations have been among the most studied topics by researchers. After World War II, national income was used for this purpose; however, due to the impact of globalization, the inadequacy of this approach was identified, and a human-oriented development approach was adopted. Thus, the Human Development Index (HDI), first introduced in 1990, was presented as an important indicator. The need to define and measure well-being in more detail based on changing conditions resulted in the calculation of the Better Life Index (BLI) by the Organization for Economic Cooperation and Development (OECD) in 2011. In the present study, BLI 2017 data for 35 OECD member countries and 3 non-member states were analyzed with Gray Relational Analysis (GRA). In Multicriteria Decision Making (MCDM) analyses, determination of the weight is an important and critical issue that directly affects the results. Therefore, four objective weight determination methods, including mean-weights (MW) and standard deviation (SD), entropy, and CRITIC, were used in the study. Countries were ranked based on gray coefficient scores determined in the analysis. Thus, it was determined that the countries with the highest scores included Norway, Australia, USA, Canada, Iceland, Switzerland, Denmark, and, Sweden while the countries with the lowest scores were South Africa, Turkey, Mexico, Greece, and.

Kaynakça

  • Akar, S. (2014). “Türkiye’de Daha İyi Yaşam Endeksi: Oecd Ülkeleri ile Karşılaştırma”, Journal of Life Economics, Iss. 1, s. 1-12.
  • Balešentis, T. A. and & Brauers, W. K. M. (2011). “Multi–Objective Optimization of Well–Being in the Eu-Ropean Union Member STATES”, Ekonomska istraživanja, Vol. 24, No. 4 pp. 1-15.
  • Deng, J. (1989). “Introduction to Grey System Theory”, The Journal of Grey System, Vol. 1, Iss. 1; pp. 1-24.
  • Depren, S.K., and Kalkan, S.B. (2018). “Determination of Countries’ Position Using Better Life Index: The Entropy Based Multimoora Approach” Trakya Üniversitesi Sosyal Bilimler Dergisi, Vol. 20, No. 2, pp. 353-366.
  • Diakoulaki, D., Mavrotas, G. and Papayannakis, L. (1995). “Determining Objective Weights in Multiple Criteria Problems: The Critic Method”, Computers & Operations Research, Vol. 22, Iss. 7, pp. 763-770. Feng, C.M. ve Wang, R.T. (2000). “Performance Evaluation for Airlines Including the Consideration of Financial Ratios”, Journal of Air Transport Management, Vol. 6, pp. 133-142.
  • Gökdemir, Ö. and Veenhoven, R. (2014). Kalkınmaya Farklı Bir Bakış: İyi Oluş, Bölüm 17, Kalkınmada Yeni Yaklaşımlar, (Yayıma Hazırlayanlar: Ahmet Faruk ve Devrim Dumludağ), İmge Kitabevi, Ankara.
  • Gürses, D. (2009). “İnsani Gelişme ve Türkiye”, Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, C. 12, S. 21, s.339-350.
  • Ivaldi, E., Bonatti, G. and Soliani, R. (2016). “The Construction of a Synthetic Index Comparing Multidimensional Well-Being in the European Union”, Social Indicators Research, Vol. 125, pp. 397–430.
  • Kaya, P., İpekçi Çetin, E., and Kuruüzüm, A. (2011). “Çok Kriterli Karar Verme ile Avrupa Birliği ve Aday Ülkelerinin Yaşam Kalitesinin Analizi”, İktisat Fakültesi Ekonometri ve İstatistik Dergisi, Iss. 13, pp. 80-94.
  • Kerenyi, A., 2011. “The Better Life Index of the Organisation for Economic Co-operation and Development”, Public Finance Quarterly, Vol. 56, Iss. 4, pp. 518–538.
  • Liu, S., Yang, Y., Cao, Y. and Xie, N. (2013), “A Summary on the Research of GRA models”, Grey Systems: Theory and Application, Vol. 3, No 1, pp. 7-15.
  • Madıć, M. and Radovanovıć, M. (2015). “Ranking of Some Most Commonly Used Nontraditional Machining Processes Using Rov And Critic Methods”, U.P.B. Scientific Bulletin, Series D, Vol. 77, Iss. 2, pp. 193–204.
  • Mizobuchi, H., (2013), “Measuring World Better Life Frontier”, Discussion Paper Series, No. 13-01, pp.1-23.
  • Orakçı, E., and Özdemir, A. (2017). “Telafi Edici Çok Kriterli Karar Verme Yöntemleri İle Türkiye ve AB Ülkelerinin İnsani Gelişmişlik Düzeylernin Belirlenmesi”, Journal of Economics and Administrative Sciences, Vol. 19, Iss. 1, pp. 61-74.
  • Ömürbek, N., Eren, H., and Dağ, O. (2017). “Entropi-ARAS VE Entropi-MOOSRA Yöntemleri İle Yaşam Kalitesi Açısından Ab Ülkelerinin Değerlendirilmesi”, Ömer Halis Demir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Vol. 10, Iss. 2, s. 29-48.
  • Önay, O. (2016). “Multi-Criteria Assessment of Better Life via TOPSIS and MOORA Methods”, International Journal of Business and Social Science, Vol. 7, No. 1, pp. 225-234.
  • Peiro´-Palomino, J. and Picazo-Tadeo, A. J. (2018), “OECD: One or Many? Ranking Countries with a Composite Well-Being Indicator”, Social Indicators Research, Vol. 139, pp. 847–869.
  • Reig-Martı´nez, E. (2013). “Social and Economic Wellbeing in Europe and the Mediterranean Basin: Building an Enlarged Human Development Indicator”, Social Indicators Research, Vol. 111, pp. 527-547.
  • Shannon, C.E. (1948). “A Mathematical Theory of Communication”, The Bell System Technical Journal, Vol. 27, No. 3, pp. 379-423.
  • Segal, I. E. (1960). “A Note on the Concept of Entropy”, Journal of Mathematics and Mechanics, Vol. 9, No. 4, pp. 623-629.
  • Stiglitz, J., Sen, A. and Fitoussi, J.P. (2009). Report by the Commissionon the Measurement of Economic Performance and Social Progress.
  • Türe, H. (2019). “OECD Ülkeleri İçin Refah Ölçümü: Gri İlişkisel Analiz Uygulaması”, Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Vol. 21, No. 2, s. 310-327.
  • Veenhoven, R. (2002). “Why Social Policy Needs Subjective Indicators”, Social Indicators Research, Vol. 58, Iss.1-3, pp. 33-46.
  • Wang, T.C. and Lee, H.D. (2009). “Developing a Fuzzy TOPSIS Approach Based on Subjective Weights and Objective Weights”, Expert Systems with Applications, Vol. 36, pp. 8980–8985.
  • Wang, J. J., Jing, Y. Y., Zhang, C. F., & Zhao, J. H. (2009). “Review on multi-criteria decision aid in sustainable energy decision-making”, Renewable and Sustainable Energy Reviews, Vol. 13, pp. 2263–2278.
  • Wu, H. H., (2002). “A Comparative Study of Using Grey Relational Analysis in Multiple Attribute Decision Making Problems”, Quality Engineering, Vol. 15, No. 2, pp. 209-217.
  • Wu, J., Sun, J., Liang, L. and Zha, Y. (2011). Determination of Weights for Ultimate Cross Efficiency using Shannon Entropy, Expert Systems with Applications, Vol. 38, pp. 5162–5165.
  • Zardari, N.H., Ahmed, K., Shirazi, S.M. ve Yusop, Z.B. (2015). Weighting Methods and Their Effects on Multi-Criteria Decision Making Model Outcomes in Water Resources Management. Springer.
  • OECD, (2013). How’s Life? 2013: Measuring Well-being, OECD Publishing. (10.12.2019). http://www.oecd.org/sdd/3013071e.pdf.
  • OECD, (2019). Better Life Index: Definitions and Metadata. (10.12.2019). http://www.oecd.org/statistics/OECD-Better-Life-Index-definitions-2019.pdf.
  • www.oecdbetterlifeindex.org (10.12.2019).
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ekonomi
Bölüm Makaleler
Yazarlar

Dilek Murat 0000-0002-5667-8094

Yayımlanma Tarihi 19 Ekim 2020
Kabul Tarihi 5 Haziran 2020
Yayımlandığı Sayı Yıl 2020 Sayı: 41

Kaynak Göster

APA Murat, D. (2020). THE ANALYSIS of THE WELL-BEING LEVELS of OECD COUNTRIES with GREY RELATIONAL ANALYSIS. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(41), 83-107. https://doi.org/10.30794/pausbed.697852
AMA Murat D. THE ANALYSIS of THE WELL-BEING LEVELS of OECD COUNTRIES with GREY RELATIONAL ANALYSIS. PAUSBED. Ekim 2020;(41):83-107. doi:10.30794/pausbed.697852
Chicago Murat, Dilek. “THE ANALYSIS of THE WELL-BEING LEVELS of OECD COUNTRIES With GREY RELATIONAL ANALYSIS”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 41 (Ekim 2020): 83-107. https://doi.org/10.30794/pausbed.697852.
EndNote Murat D (01 Ekim 2020) THE ANALYSIS of THE WELL-BEING LEVELS of OECD COUNTRIES with GREY RELATIONAL ANALYSIS. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 41 83–107.
IEEE D. Murat, “THE ANALYSIS of THE WELL-BEING LEVELS of OECD COUNTRIES with GREY RELATIONAL ANALYSIS”, PAUSBED, sy. 41, ss. 83–107, Ekim 2020, doi: 10.30794/pausbed.697852.
ISNAD Murat, Dilek. “THE ANALYSIS of THE WELL-BEING LEVELS of OECD COUNTRIES With GREY RELATIONAL ANALYSIS”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 41 (Ekim 2020), 83-107. https://doi.org/10.30794/pausbed.697852.
JAMA Murat D. THE ANALYSIS of THE WELL-BEING LEVELS of OECD COUNTRIES with GREY RELATIONAL ANALYSIS. PAUSBED. 2020;:83–107.
MLA Murat, Dilek. “THE ANALYSIS of THE WELL-BEING LEVELS of OECD COUNTRIES With GREY RELATIONAL ANALYSIS”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 41, 2020, ss. 83-107, doi:10.30794/pausbed.697852.
Vancouver Murat D. THE ANALYSIS of THE WELL-BEING LEVELS of OECD COUNTRIES with GREY RELATIONAL ANALYSIS. PAUSBED. 2020(41):83-107.