The Classification of OECD Countries in Terms of Life Satisfaction Using Partial Least Squares Discriminant Analysis

Abstract

Life satisfaction (LS) measures how people assess their lives as a whole, not their present emotions. Measuring emotions can be very subjective, but it is still a useful completion to more objective data when comparing quality of life across countries. Many questionnaires are used to measure especially LS and happiness. The Partial Least Squares Discriminant Analysis (PLSDA) is a statistical method for classification and includes an ordinary Partial Least Squares Regression, where the dependent variable is categorical that represents each observation's class membership. In this study, the purpose is to classify 35 OECD countries correctly to their predefined classes (above or below the average LS level of OECD) by using year 2017 Better Life Index data. In the analyses PLSDA, a flexible supervised classification method, is used. PLSDA is a preferable alternative method in case of some assumptions not satisfied for classical discriminant analysis. The results showed that PLSDA has a satisfying classification performance and self-reported health (SH) is only effective variable in determining the LS levels of countries

Keywords

• : Better Life Index,classification,life satisfaction,OECD countries,Partial Least Squares Discriminant Analysis

References

1. https://www.oecd.org/forum/issues/forum-issue-better-life-index.htm.
2. OECD (2017), How’s Life? 2017: Measuring Well-being, OECD Publishing, Paris. http://dx.doi.org/10.1787/how_life-2017-en
3. https://www.oecd.org/social/the-path-to-happiness-lies-in-good-health-and-a-good-job-the-better-life-index-shows.htm
4. http://www.oecdbetterlifeindex.org/topics/life-satisfaction/
5. E. Gundogan Aşık, A. Altın Yavuz, “Investigatıon of life satisfactıon in OECD countries with multivariate analysis method”, Journal of Social and Humanities Sciences Research (JSHSR), Vol. 5, no. 26, pp. 2547-2561, 2018.
6. L. Osberg and A. Sharpe, “An index of economic well-being for selected OECD countries”, Review of Income and Wealth, vol. 48, pp. 291-316, 2012. A. Kerenyi, “The better life index of the organisation for economic co-operation and development”, Public Finance Quarterly, vol. 56, pp. 518–538, 2011.
7. J. Kasparian and A. Rolland, “OECD’s better life index: Can any country be well ranked?”, Journal of Applied Statistics, vol. 39, pp. 2223– 2230, 2012.
8. B. Stevenson and J. Wolfers, “Subjective Well-Being and Income: Is There Any Evidence of Satiation”, American Economic Association, vol. 103, pp. 598-604, 2013.

9. H. Mizobuchi, “Measuring world better life frontier: a composite indicator for OECD better life index”, Soc Indic Res, vol. 118, pp. 987–1007, 2014.
10. S. Akar, “Türkiye’de daha iyi yaşam endeksi: OECD ülkeleri ile karşılaştırma”. Journal of Life Economics, vol. 1, no. 1, pp. 1-12, 2014.
11. M. Durand, “The OECD better life initiative: how’s life and the measurement of well-being”, Review of Income and Wealth, vol. 61, no. 1, pp.4-17, 2015.
12. O. Başol, “An evaluation on life satisfaction in OECD countries “, “IS, GUC” Industrial Relations and Human Resources Journal, vol. 20, no. 3, pp. 71-86, 2018.
13. E. Polat, “Determination of the effective economic and/or demographic indicators in classification of European Union member and candidate countries using Partial Least Squares Discriminant Analysis”, Journal of Data Science, vol. 16, no. 1, pp. 79-92, 2018.
14. M. Barker, W.S. Rayens, “Partial least squares for discrimination”, Journal of Chemometrics, vol. 17 , pp. 166 – 173, 2003.
15. M.R. Almeida, D.N. Correa, W.F.C. Rocha, and F.J.O. Scafi, “Discrimination between authentic and counterfeit banknotes using Ramanspectroscopy and PLS-DA with uncertainty estimation”, Microchemical Journal vol. 109, pp. 170-177, 2013.
16. H. Martens and M. Martens, “Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR)”. Food Quality and Preference, vol. 11, no. 1-2, pp. 5-16, 2000.
17. S. Wold, M. Sjöström, and L. Eriksson, “PLS-regression: a “basic tool of chemometrics”, Chemometrics and Intelligent Laboratory Systems, vol. 58, pp. 109-130, 2001.
18. B.M. Wise, N.B. Gallagher, R. Bro, J.M. Shaver, W. Windig, and R.S. Koch, R.S. “PLS Toolbox 4.0 for use with Matlab”, 3905 West Eaglerock Drive, Wenatchee, WA, Eigenvector Research Inc. http://www.eigenvector.com, 2006.
19. D. Ballabio, F. Grisoni, and R. Todeschini, “Multivariate comparison of classification performance measures”, Chemometrics and Intelligent Laboratory Systems, vol. 174, pp. 33-44, 2018.
20. D. Ballabio, R. Todeschini, “Chapter 4 - Multivariate Classification for Qualitative Analysis” in Book: “Infrared Spectroscopy for Food Quality Analysis and Control” , pp.83-104, 2009. https://doi.org/10.1016/B978-0-12-374136-3.00004-3
21. Classification toolbox for MATLAB. file:///C:/Program%20Files/MATLAB/MATLAB%20Production%20Server/R2015a/classification_toolbox_5.2/help/index.htm
22. D. Ballabio and V. Consonni, “Classification Tools in Chemistry. Part 1: Linear Models. PLS-DA”, Analytical Methods, vol. 5, pp. 3790-3798, 2013.
23. XLSTAT, Partial Least Squares Discriminant Analysis PLSDA Tutorial. https://help.xlstat.com/s/article/partial-least-squares-discriminant-analysis-plsda-tutorial?language=en_US