Vol. 8 No. 3 (2020): Business & Management Studies: An International Journal


Hacı Hayrettin TIRAŞ
Asisst. Prof. Dr., Niğde Ömer Halisdemir University
Res. Asisst., Kahramanmaraş Sütçü İmam University

Published 2020-09-25


  • Health,
  • Life Expectancy,
  • Human Capital,
  • Panel Cointegration,
  • OECD Countries
  • Sağlık,
  • Yaşam Beklentisi,
  • Beşeri Sermaye,
  • Panel Eşbütünleşme,
  • OECD ülkeleri

How to Cite

TIRAŞ, H. H., & ÖZBEK, S. (2020). ECONOMETRIC ANALYSIS OF THE DETERMINANTS OF LIFE EXPECTANCY AT BIRTH IN OECD COUNTRIES. Business & Management Studies: An International Journal, 8(3), 2893–2923. https://doi.org/10.15295/bmij.v8i3.1542


    Life expectancy at birth indicates how many years an individual born in society will live on a particular year. Life expectancy at birth is one of the most important indicators of the health status and welfare level of society. It is frequently used in comparing health levels among countries and is one of the most important indicators of development. Increasing life expectancy accelerates economic growth and development by making significant contributions to human capital. Many economic, social, cultural, environmental and demographic factors affect life expectancy. In this respect, life expectancy at birth is of great importance for many countries today. Therefore, it becomes essential to know what factors affect life expectancy at birth in terms of health, social and economic policies to be implemented by countries.
    While doing the literature review, it was paid attention to the studies that the life expectancy at birth was the dependent variable. When the related literature is analyzed, studies with economic, social, demographic, health and even political variables affecting life expectancy at birth are found. It has been observed that recent domestic and foreign studies are generally directed towards socio-economic variables. In this section, the literature on the subject is examined, and the related studies are tried to be summarized.
    Life expectancy at birth is frequently used in comparing the health levels of countries and while life expectancy increases in the societies where a healthy lifestyle is maintained, it is accepted that both the quality of life and health services are useful in these societies. Many economic, social, cultural, environmental and demographic factors affect life expectancy.
    Due to the income increase of the countries and the improvements in health, life expectancy at birth increases. Increasing the resources allocated to health decreases diseases and deaths while increasing the quality of life and extending the average life.
    Urbanization is seen as one of the factors affecting life expectancy. Urbanization makes life more comfortable with its proximity to the goods and services that individuals need. Urbanization can have positive effects on life expectancy by facilitating life with the increased educational opportunities, increased access to medicines, medicines, clean water and food when needed.
    One of the factors that may affect the life expectancy at birth is the rate of crude birth. The fact that the births at very high rates in a country do not mean that the population growth rate will be very high. Increasing the health level of society and increasing the awareness of health decrease the fertility rate and positively affect the increase in the average life.
    In this section, the countries included in the study in order to determine the determinants of life expectancy at birth, data of these indicators and data sources of these countries are included.
    Variable Description Source
    Life Expectancy at Birth (LDYB) Total in Years WB Databank (WB, 2020)
    GDP Per Capita (LGDP) Per Capita, Current US Dollar WB Databank (WB, 2020)
    Urbanization (LURBAN) % of Total Population WB Databank (WB, 2020)
    Crude Birth Rate (LKDO) Per 1000 People WB Databank (WB, 2020)
    Carbon Dioxide Emission (LCO) Per Person in Tons OECD Database (OECD, 2020)
    Countries included in the analysis; USA, UK, Turkey, Sweden, Spain, Portugal, Norway, New Zealand, Mexico, Ireland, Iceland, France, Germany, Finland, Greece, Denmark, Chile, Austria, Luxembourg, Australia, South Korea, Belgium, Japan, Canada, Israel Designated as the Netherlands, Italy and Switzerland
    In order to determine the determinants of life expectancy at birth, the full logarithmic model created with variables whose logarithmic transformations are made is shown in Equation 1.
    LDYBit=αi+ β1i LGDPit+ β2i LURBANit+ β3i LKDOit+ β4i LCOit + εit (1)
    (i= 1,…39) and (t= 1980,…, 2018)
    The letters i and t in Equation 1 show the cross-section size and time dimension of the mentioned variables, respectively. In this study, dynamic panel econometric forecasts are made with data sets from 28 countries. Long-term coefficients of the variables were estimated using the CommonCorelated Effects (CCE) method developed by Pesaran (2006), assuming horizontal cross-section dependency and heterogeneity.
    When the co-integration estimator results are analyzed, in 28 OECD countries, the effect of per capita income and urbanization on life expectancy at birth is statistically significant; however, co-integration coefficients of coarse birth rate and carbon dioxide emissions were found to be statistically insignificant. The findings in these countries, which make up the panel, show that a 1% increase in per capita income reduces the life expectancy at birth by approximately 0.007%. This result is in parallel with the result obtained by Sede and Ohemang (2015). On the other hand, a 1% increase in urbanization increases life expectancy at birth by approximately 0.27%. This result is Ecevit (2013), and Shahbaz et al. (2015) support the results they obtained in their work.
    The presence of a co-integration relationship between variables is analyzed by the test proposed by Westerlund (2006), which can be used in cases where horizontal cross-section dependency is present or not, taking into account structural breaks.
    In this study, the effects of GDP per capita, crude birth rate, urbanization rate and carbon dioxide emission per capita in life expectancy were analyzed by dynamic panel econometric estimates with data from 1980-2018 in 28 OECD countries. Findings, in 28 OECD countries, the impact of per capita income and urbanization on life expectancy at birth was statistically significant; however, co-integration coefficients of crude birth rate and carbon dioxide emission are statistically insignificant. Across the panel, a 1% increase in per capita income reduces life expectancy at birth by about 0,007%, while a 1% increase in urbanization increases life expectancy at birth by about 0,27%. Also, the impact of the factor affecting life expectancy at birth in 28 OECD countries is estimated to differ from country to country.


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  1. Akın, A. ve Ersoy, K. (2012). 2050'ye Doğru Nüfusbilim ve Yönetim: Sağlık Sistemine Bakış, TÜSİAD, Yayın No: TÜSİAD-T/2012-11/533
  2. Baltagi, B. (2008). Econometric Analysis of Panel Data. John Wiley & Sons.
  3. Bayın, G., (2016), Doğuşta ve İleri Yaşta Beklenen Yaşam Sürelerine Etki Eden Faktörlerin Belirlenmesi, Türkiye Aile Hekimliği Dergisi (Turkish Journal of Family Practice), Cilt; 20, Sayı; 3, ss 93-103 doi: 10.15511/tahd.16.21693
  4. Bilir, B. Ö. ve Gökdemir, T., (2018), Kalkınma Göstergeleri Çerçevesinde Yaşam Beklentisinin Yapısal Eşitlik Modeli İle İncelenmesi, Anemon Muş Alparslan Üniversitesi Sosyal Bilimler Dergisi, 6 (ICEESS’ 18) 163-167
  5. Breusch, T. S. ve Pagan, A. R. (1980). The Lagrange Multiplier Test And Its Applications To Model Specification In Econometrics. The Review of Economic Studies, 47(1), 239-253.
  6. Dağdemir, Ö. (2009). “Sağlık ve Ekonomik Büyüme: 1960-2005 Döneminde Gelişmekte Olan Ülkelerde Sağlık ve Ekonomik Büyüme Arasındaki Karşılıklı İlişkinin Analizi”, Ankara Üniversitesi SBF Dergisi, 64-2, ss. 76-96.
  7. Ecevit, E., (2013), Türk Cumhuriyetlerinde Yaşam Beklentisinin Belirleyicileri ve Ampirik Bir Analiz Yönetim ve Ekonomi Araştırmaları Dergisi, Sayı: 21, ss 349-363 Doi: http://dx.doi.org/10.11611/JMER220 349
  8. Erdoğan, S. ve Bozkurt, H., (2008). “Türkiye’de Yaşam Beklentisi-Ekonomik Büyüme İlişkisi: ARDL Modeli İle Bir Analiz,” The Journal of Knowledge Economy & Knowledge Management, Vol: 3, ss. 25-38.
  9. Gilligan, A. M. ve Skrepnek, G. H., (2015), Determinants of life expectancy in the Eastern Mediterranean Region, Health Policy and Planning, Vol: 30, pp. 624-637 doi:10.1093/heapol/czu041
  10. Girum, T., MuktarR, E. ve Shegaze, M., (2018), Determinants of Life Expectancy in Low And Medium Human Development İndex Countries, Medical Studies/Studia Medyczne, 34 (3), pp 218-225, DOI: https://doi.org/10.5114/ms.2018.78685
  11. Hassan, F. A., Minato, N., Ishida, S. ve Nor, N. M., (2017), Environment Determinants of Life Expectancy in Developing Countries: A Panel Data Analysis, Global Journal of Health Science, Vol: 9, No: 5, pp 105-117.
  12. Kabir, M., (2008), Determinants of Life Expectancy in Developing Countries, The Journal of Developing Areas, Vol: 41, No: 2, pp. 185-204 https://www.jstor.org/stable/40376184
  13. Lin, R. T., Chen, Y. M., Chien, L. C. ve Chan, C. C., (2012), Political And Social Determinants of Life Expectancy in Less Developed Countries: A Longitudinal Study, BMC Public Health, 12: 85, doi:10.1186/1471-2458-12-85 http://www.biomedcentral.com/1471-2458/12/85
  14. McCoskey, S. and Kao, C. (1998). A Residual-Based Test of Thenull of Cointegration in Panel Data. Econometricreviews, 17(1), 57-84.
  15. Miladinov, G., (2020), Socioeconomic Development and Life Expectancy Relationship: Evidence From the EU Accession Candidate Countries, Genus, Journal of Population Sciences, 76, 2.https://doi.org/10.1186/s41118-019-0071-0
  16. Narayan, P. K. ve Narayan, S. (2008). Does Environmenta lQuality Influence Health Expenditures? Empirical Evidence From a Panel of Selected OECD Countries. Ecological Economics, 65(2), 367-374. https://www.sciencedirect.com/science/article/abs/pii/S0921800907003941
  17. Nazlıoğlu, Ş. (2010). Makro iktisat politikalarının tarım sektörü üzerindeki etkileri: Gelişmiş ve gelişmekte olan ülkeler için bir karşılaştırma. Yayınlanmamış Doktora Tezi, TC Erciyes Üniversitesi Sosyal Bilimler Enstitüsü, Kayseri.
  18. Nazlıoğlu, S. ve Karul, C. (2017). Panel LM Unit Root Test With Gradual Structural Shifts.
  19. OECD, (2020), OECD Data, Air and GHG Emissions (indicator). doi: 10.1787/93d10cf7-en (Accessed on 09 May 2020) https://data.oecd.org/air/air-and-ghg-emissions.htm
  20. Pesaran, M. H. (2004). General Diagnostic Tests For Cross Section Dependence in Panels.
  21. Pesaran, M. H. ve Yamagata, T. (2008). Testing Slope Homogeneity in Large Panels. Journal of econometrics, 142(1), 50-93.
  22. Pesaran, M. H., Ullah, A. ve Yamagata, T. (2008). A Bias-Djusted LM Test of Error Cross-Section Independence. The Econometrics Journal, 11(1), 105-127.
  23. Ranabhat, C. L., Atkinson, J., Park, M-B., Kim, C-B. and Jakovljevic, M., (2018), The Influence of Universal Health Coverage on Life Expectancy at Birth (LEAB) and Healthy Life Expectancy (HALE): A Multi-Country Cross-Sectional Study, Frontiers in Pharmacology, September, Volume: 9, Article 960, doi: 10.3389/fphar.2018.00960
  24. Sede, P. I. ve Ohemeng, W., (2015), Socio-economic Determinants of Life Expectancy in Nigeria (1980-2011), Health Economics Review, ISSN 2191-1991,Springer, Heidelberg, Vol:5, Iss: 2, pp. 1-11, http://dx.doi.org/10.1186/s13561-014-0037-z
  25. Shahbaz, M., Loganathan, N., Mujahid, N., Ali, A. and Nawaz, A., (2015), Determinants of Life Expectancy and its Prospects under the Role of Economic Misery: A Case of Pakistan, MPRA (Munich Personal RePEc Archive) Paper No: 67167, https://mpra.ub.uni-muenchen.de/67167/ E. Tarihi:12.03.2020
  26. Shaw, J. W. Horrace, W. C. and VogelL, R. J., (2005), The Determinants of Life Expectancy: An Analysis of the OECD Health Data, Southern Economic Journal, 71(4), pp 768-783
  27. Şahin, D., (2018), Doğumda Yaşam Beklentisinin Belirleyicilerinin Analizi: APEC Ülkeleri Örneği Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi Cilt-Sayı: 11(1) ss: 1-7, ISSN: 2564-6931, DOI: 10.25287/ohuiibf.303281
  28. Tafran, K., Tumin, M. ve Osman, A. F., (2020), Poverty, Income and Unemployment as Determinants of Life Expectancy: Empirical Evidence From Panel Data of Thirteen Malaysian States, Iran J Public Health, Vol. 49, No.2, pp.294-303
  29. Tatar, V. veÖzer, M. B. (2018). Sera Gazı Emisyonlarının İklim Değişikliği Üzerindeki Etkileri: Türkiye’de Mevcut Durum Analizi, Journal of Social And Humanities Sciences Research (JSHSR), Vol: 5, Issue: 30, pp 3993-3999
  30. Teker, D., Teker, S. ve Sönmez, M., (2012), Ekonomik Değişkenlerin Kadın ve Erkeğin Yaşam Süresine Etkisi, İşletme Araştırmaları Dergisi, Cilt: 4, Sayı: 3, ss 118-126.
  31. Tıraş, H. H., (2019), Türkiye İçin İnsani Gelişmişlik Göstergeleri, Bilgi Ekonomisi ve Yönetimi Dergisi (The Journal of Knowledge Enonomy & Knowledge Management), Cilt: 14, Sayı: 1, ss 15-31.
  32. WB (World Bank), (2020). Databank, World Development Indicators, https://databank.worldbank.org/source/world-development-indicators.
  33. Westerlund, J., (2006). TestingFor Panel Cointegration With Multiple Structural Breaks. Oxford Bulletin of Economics and Statistics, 68(1), 101-132.
  34. WHO (World Health Organization). (2015). 2015 Global Reference List of 100 Core Health Indicators, https://apps.who.int/iris/bitstream/handle/10665/173589/WHO_HIS_HSI_2015.3_eng.pdf;jsessionid=F177D9AA08FE942142262E31E0397FAB?sequence=1 Erişim Tarihi: 05.05.2020.