Cilt 13 Sayı 4 (2025): Business & Management Studies: An International Journal
Makaleler

G-7 ülkelerinde gelir, yaşlı nüfus ve kamu sağlık harcamaları arasındaki eş bütünleşme ilişkisi: Panel ARDL yaklaşımı

Cuma Çakmak
Dr. Öğr. Üyesi, Dicle Üniversitesi, Diyarbakır, Türkiye
Biyografi

Yayınlanmış 25.12.2025

Anahtar Kelimeler

  • Ageing Population, Income, Public Health Expenditures, Panel ARDL, G-7
  • Yaşlı Nüfus, Gelir, Kamu Sağlık Harcamaları, Panel ARDL, G-7

Nasıl Atıf Yapılır

G-7 ülkelerinde gelir, yaşlı nüfus ve kamu sağlık harcamaları arasındaki eş bütünleşme ilişkisi: Panel ARDL yaklaşımı. (2025). Business & Management Studies: An International Journal, 13(4), 1699-1715. https://doi.org/10.15295/bmij.v13i4.2636

Nasıl Atıf Yapılır

G-7 ülkelerinde gelir, yaşlı nüfus ve kamu sağlık harcamaları arasındaki eş bütünleşme ilişkisi: Panel ARDL yaklaşımı. (2025). Business & Management Studies: An International Journal, 13(4), 1699-1715. https://doi.org/10.15295/bmij.v13i4.2636

Öz

Sağlık harcamaları, gelir ve yaşlı nüfusun artması ile artış eğilimi göstermektedir. Modern ekonomiler ve sağlık sistemleri kamu sağlık harcamaları üzerindeki bu baskıyı ciddi bir şekilde hissetmekte ve çözüm arayışlarında bulunmaktadır. Bu çalışmada gelir, yaşlı nüfus ve kamu sağlık harcamaları arasındaki kısa ve uzun dönemli ilişkinin analizi amaçlanmıştır. Bu çalışmada gelişmiş ülkelerden olan G-7 ülkeleri analiz kapsamına alınmıştır. G-7 ülkelerinin gelir, yaşlı nüfus ve kamu sağlık harcamaları Panel ARDL yöntemi ile analiz edilmiştir. Elde edilen bulgulara göre kısa dönemde gelir değişkeni kamu sağlık harcamaları ile negatif yönlü bir ilişki gösterir iken yaşlı nüfus değişkeninin kamu sağlık harcamaları üzerinde herhangi bir etkisi bulunmamaktadır. Uzun dönemli analiz ilişkisine göre ise hem gelir hem de yaşlı nüfus oranı kamu sağlık harcamaları üzerinde pozitif etki göstermektedir. Yaşlı nüfus uzun dönemde sağlık harcamaları üzerinde ciddi bir baskı oluşturmadır. Hükümetlerin optimal sağlık sistemlerini yeniden dizayn etmeleri ve koruyucu sağlık hizmetlerine yönelik talebi artıcı önlemlerin alması gerektiği yönünde önerilerde bulunulabilir.

Referanslar

  1. Atella, V., Piano Mortari, A., Kopinska, J., Belotti, F., Lapi, F., Cricelli, C., & Fontana, L. (2019). Trends in age‐related disease burden and healthcare utilisation. Aging Cell, 18(1), e12861. https://doi.org/10.1111/acel.12861
  2. Bai, J., & Ng, S. (2007). Panel unit root tests with cross-section dependence: A further investigation. Working Paper, Department of Economics, New York University.
  3. Baltagi, B. H., Feng, Q., & Kao, C. (2012). A Lagrange multiplier test for cross-sectional dependence in a fixed effects panel data model. Journal of Econometrics, 170(1), 164–177. https://doi.org/10.1016/j.jeconom.2012.04.004
  4. Bedir, S. (2016). Healthcare expenditure and economic growth in developing countries. Advances in Economics and Business, 4(2), 76–86. https://doi.org/10.13189/aeb.2016.040203
  5. Behera, D., & Dash, U. (2016). Nexus between public health expenditure and income: Empirical evidence from Indian states. Nexus, 11(6), 44–52.
  6. Biçer, İ., Konca, M., & Sarıgül, S. S. (2025). Sağlık harcamalarının belirleyicileri: Bir kantil regresyon uygulaması. Trakya Üniversitesi Sosyal Bilimler Dergisi, 27(Ek), 63-92.
  7. Bloom, D. E., Cafiero, E. T., Jané-Llopis, E., Abrahams-Gessel, T., Bloom, L. R., Fathima, S., ... & Sweet, S. (2011). The global economic burden of noncommunicable diseases. World Economic Forum.
  8. Brucker, D. L., Lauer, E., & Boege, S. (2023). Americans aging with disabilities are more likely to have multiple chronic conditions. Journal of Disability Policy Studies, 34(1), 52–60. https://doi.org/10.1177/10442073221134665
  9. Cutler, D. M. (2001). The contribution of better health to economic growth. In J. M. Poterba (Ed.), Health and welfare policy in the United States (pp. 91–125). University of Chicago Press.
  10. De Meijer, C., Wouterse, B., Polder, J., & Koopmanschap, M. (2013). The effect of population aging on health expenditure growth: A critical review. European Journal of Ageing, 10, 353–361. https://doi.org/10.1007/s10433-013-0276-2
  11. Deaton, A. (2003). Health, income, and inequality. NBER Working Paper Series, No. 9809. National Bureau of Economic Research. https://doi.org/10.3386/w9809
  12. Economou, C., Kaitelidou, D., Kentikelenis, A., Maresso, A., & Sissouras, A. (2015). The impact of the crisis on the health system and health in Greece. In B. Rechel, E. Jakubowski, M. McKee, & E. Nolte (Eds.), Economic crisis: Health systems and health in Europe (pp. xx–xx). World Health Organization.
  13. European Commission. (2024). 2024 ageing report: Economic and budgetary projections for the EU Member States (2022–2070). Publications Office of the European Union.
  14. Farag, M., Nandakumar, A. K., Wallack, S. S., Hodgkin, D., & Gaumer, G. (2013). Health expenditures, income, and health status in a new panel of countries. Review of Economics and Statistics, 95(1), 1-14.
  15. Getzen, T. E. (2000). Health care is an individual necessity and a national luxury: Applying multilevel decision models to the analysis of health care expenditures. Journal of health economics, 19(2), 259-270.
  16. Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80(2), 223–255. https://doi.org/10.1086/259880
  17. Hall, R. E., & Jones, C. I. (2007). The value of life and the rise in health spending. The Quarterly Journal of Economics, 122(1), 39–72. https://doi.org/10.1162/qjec.122.1.39
  18. Han, J. W., Kim, D. J., Min, I. S., & Hahm, M. I. (2019). Association of supplementary private health insurance type with unmet health care needs. Health Policy and Management, 29(2), 184–194. https://doi.org/10.4332/KJHPA.2019.29.2.184
  19. Kallestrup-Lamb, M., Marin, A. O., Menon, S., & Søgaard, J. (2024). Aging populations and expenditures on health. The Journal of the Economics of Ageing, 29, 100518.
  20. Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics, 90(1), 1–44. https://doi.org/10.1016/S0304-4076(98)00023-2
  21. Kao, C., & Chiang, M. H. (2000). On the estimation and inference of a cointegrated regression in panel data. Advances in Econometrics, 15, 179–222. https://doi.org/10.1016/S0731-9053(00)15007-8
  22. Kar, M., Nazlıoğlu, Ş., & Ağır, H. (2011). Financial development and economic growth nexus in the MENA countries: Bootstrap panel Granger causality analysis. Economic Modelling, 28(1–2), 685–693. https://doi.org/10.1016/j.econmod.2010.05.015
  23. Lama, P. (2023). The Economic Impact of Ageing on Healthcare. In The Ageing Population: Impact Analysis on'Societal and Healthcare Cost' (pp. 69-81). Singapore: Springer Nature Singapore.
  24. Lau, M. K., & Pung, H. K. (2016). Aging population and public health expenditure in the G7 countries: A dynamic panel analysis. Journal of Public Economics, 138, 26-38.
  25. Navarro, F. H. (2014). Patterns of health-related behavior as predictors of medical expenditures (Doctoral dissertation, Walden University).
  26. Noy, S., & Sprague-Jones, J. (2016). Comparative dynamics of public health spending: Re-conceptualising delta-convergence to examine OECD and Latin America. International Journal of Comparative Sociology, 57(6), 425–448. https://doi.org/10.1177/0020715216674262
  27. OECD. (2005). Long-term care for older people (The OECD Health Project). OECD Publishing. https://doi.org/10.1787/9789264015852-en
  28. Özyilmaz, A., Bayraktar, Y., Işık, E., Toprak, M., Er, M. B., Besel, F., ... & Collins, S. (2022). The relationship between health expenditures and economic growth in EU countries: empirical evidence using panel fourier toda–yamamoto causality test and regression models. International journal of environmental research and public health, 19(22), 15091.
  29. Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61(S1), 653–670. https://doi.org/10.1111/1468-0084.0610s1653
  30. Pedroni, P. (2000). Fully modified OLS for heterogeneous cointegrated panels. Advances in Econometrics, 15, 93–130. https://doi.org/10.1016/S0731-9053(00)15004-2
  31. Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory, 20(3), 597–625. https://doi.org/10.1017/S0266466604203073
  32. Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics, No. 0435.
  33. Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967–1012. https://doi.org/10.1111/j.1468-0262.2006.00692.x
  34. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616
  35. Pesaran, M. H., Shin, Y., & Smith, R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94(446), 621–634. https://doi.org/10.1080/01621459.1999.10474156
  36. Pesaran, M., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50–93. https://doi.org/10.1016/j.jeconom.2007.05.010
  37. Pesaran, M., Ullah, A., & Yamagata, T. (2008). A bias-adjusted LM test of error cross-section independence. Journal of Econometrics, 11(1), 105–127. https://doi.org/10.1016/j.jeconom.2007.05.001
  38. Phillips, P. C. B., & Hansen, B. E. (1990). Statistical inference in instrumental variables regression with I(1) processes. Review of Economic Studies, 57(1), 99–125. https://doi.org/10.2307/2297545
  39. Rechel, B., Jakubowski, E., McKee, M., & Nolte, E. (Eds.). (2018). Organisation and financing of public health services in Europe (No. 50). World Health Organization.
  40. Ryan, C., Feldman, R., & Parente, S. (2022). The demand for individual insurance: Evidence from a private online marketplace. American Journal of Health Economics, 8(2), 275–299. https://doi.org/10.1086/718324
  41. Sarıgül, S. S., Konca, M., & Biçer, İ. (2024). Yatak kapasitesinin ve kamu sağlık harcamalarının hasta memnuniyet düzeyi üzerindeki etkisi: Gelişmiş ve gelişmekte olan ülkeler örneği. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (2024), 269-284.
  42. Varabyova, Y., & Schreyögg, J. (2013). International comparisons of the technical efficiency of the hospital sector: panel data analysis of OECD countries using parametric and non-parametric approaches. Health Policy, 112(1-2), 70-79.
  43. Wang, L., & He, J. (2019). The relationship between health expenditure and economic growth in G7 countries: A panel causality analysis. Sustainability, 11(18), 5006.
  44. World Health Organization. (2010). The world health report 2010: Health systems financing: The path to universal coverage. World Health Organization.
  45. Yıldırım, Z., & Cebeci, A. (2021). Gelişmiş ülkelerde kamusal sağlık harcamaları ve sermaye birikimi İlişkisi: Panel eşbütünleşme analizi. Yaşar Üniversitesi E-Dergisi, 16(62), 982-1004.