Skip to main content

Health expenditure and gross domestic product: causality analysis by income level

International Journal of Health Economics and Management volume 20, pages 55–77 (2020)Cite this article

Abstract

The empirical findings on the relationship between gross domestic product (GDP) and health expenditure are diverse. The influence of income levels on this causal relationship is unclear. This study examines if the direction of causality and income elasticity of health expenditure varies with income level. It uses the 1995–2014 panel data of 161 countries divided into four income groups. Unit root, cointegration and causality tests were employed to examine the relationship between GDP and health expenditure. Impulse-response functions and forecast-error variance decomposition tests were conducted to measure the responsiveness of health expenditure to changes in GDP. Finally, the common correlated effects mean group method was used to examine the income elasticity of health expenditure. Findings show that no long-term cointegration exists, and the growth in health expenditure and GDP across income levels has a different causal relationship when cross-sectional dependence in the panel is accounted for. About 43% of the variation in global health expenditure growth can be explained by economic growth. Income shocks affect health expenditure of high-income countries more than lower-income countries. Lastly, the income elasticity of health expenditure is less than one for all income levels. Therefore, healthcare is a necessity. In comparison with markets, governments have greater obligation to provide essential health care services. Such results have noticeable policy implications, especially for low-income countries where GDP growth does not cause increased health expenditure.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3

References

  • Abrigo, M. R., & Love, I. (2016). Estimation of panel vector autoregression in Stata. Stata Journal,16, 778–804.

    Article  Google Scholar 

  • Acemoglu, D., & Johnson, S. (2007). Disease and development: The effect of life expectancy on economic growth. Journal of Political Economy,115(6), 925–985.

    Article  Google Scholar 

  • Adriana, D. (2014). Revisiting the relationship between unemployment rates and shadow economy. A Toda–Yamamoto approach for the case of Romania. Procedia Economics and Finance,10, 227–236.

    Article  Google Scholar 

  • Amiri, A., & Ventelou, B. (2012). Granger causality between total expenditure on health and GDP in OECD: Evidence from the Toda–Yamamoto approach. Economics Letters,116(3), 541–544. https://doi.org/10.1016/j.econlet.2012.04.040.

    Article  Google Scholar 

  • Asteriou, D. (2009). Foreign aid and economic growth: New evidence from a panel data approach for five South Asian countries. Journal of Policy Modeling,31(1), 155–161.

    Article  Google Scholar 

  • Atilgan, E., Kilic, D., & Ertugrul, H. M. (2016). The dynamic relationship between health expenditure and economic growth: Is the health-led growth hypothesis valid for Turkey? European Journal of Health Economics,18(5), 567–574. https://doi.org/10.1007/s10198-016-0810-5.

    PubMed  Article  Google Scholar 

  • Baltagi, B. H., Lagravinese, R., Moscone, F., & Tosetti, E. (2017). Health care expenditure and income: A global perspective. Health Economics,26(7), 863–874.

    PubMed  Article  Google Scholar 

  • Baltagi, B. H., & Moscone, F. (2010). Health care expenditure and income in the OECD reconsidered: Evidence from panel data. Economic Modelling,27(4), 804–811. https://doi.org/10.1016/j.econmod.2009.12.001.

    Article  Google Scholar 

  • Bloom, D. E., Canning, D., & Sevilla, J. (2004). The effect of health on economic growth: A production function approach. World Development,32(1), 1–13.

    Article  Google Scholar 

  • Carrion-i-Silvestre, J. L. (2005). Health care expenditure and GDP: Are they broken stationary? Journal of Health Economics,24(5), 839–854. https://doi.org/10.1016/j.jhealeco.2005.01.001.

    PubMed  Article  Google Scholar 

  • Chen, W., Clarke, J. A., & Roy, N. (2013). Health and wealth: Short panel Granger causality tests for developing countries. The Journal of International Trade and Economic Development,23(6), 755–784. https://doi.org/10.1080/09638199.2013.783093.

    Article  Google Scholar 

  • Chudik, A., Pesaran, M. H., & Tosetti, E. (2011). Weak and strong cross‐section dependence and estimation of large panels. The Econometrics Journal, 14(1), C45–C90. https://doi.org/10.1111/j.1368-423X.2010.00330.x.

    Article  Google Scholar 

  • Clarke, J. A., & Mirza, S. (2006). A comparison of some common methods for detecting Granger noncausality. Journal of Statistical Computation and Simulation,76(3), 207–231.

    Article  Google Scholar 

  • Clemente, J., Marcuello, C., Montañés, A., & Pueyo, F. (2004). On the international stability of health care expenditure functions: Are government and private functions similar? Journal of Health Economics,23(3), 589–613.

    PubMed  Article  Google Scholar 

  • Dolado, J. J., & Lütkepohl, H. (1996). Making Wald tests work for cointegrated VAR systems. Econometric Reviews,15(4), 369–386.

    Article  Google Scholar 

  • Dumitrescu, E. I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling,29(4), 1450–1460.

    Article  Google Scholar 

  • Erdil, E., & Yetkiner, I. H. (2009). The Granger-causality between health care expenditure and output: A panel data approach. Applied Economics,41(4), 511–518. https://doi.org/10.1080/00036840601019083.

    Article  Google Scholar 

  • Everaert, G., & De Groote, T. (2016). Common correlated effects estimation of dynamic panels with cross-sectional dependence. Econometric Reviews,35(3), 428–463.

    Article  Google Scholar 

  • Farag, M., Nandakumar, A., Wallack, S., Hodgkin, D., Gaumer, G., & Erbil, C. (2013). Health expenditures, health outcomes and the role of good governance. International Journal of Health Care Finance and Economics,13(1), 33–52.

    PubMed  Article  Google Scholar 

  • Gengenbach, C., Palm, F. C., & Urbain, J. P. (2006). Cointegration testing in panels with common factors. Oxford Bulletin of Economics and Statistics,68(1), 683–719.

    Article  Google Scholar 

  • Glied, S., & Smith, P. C. (2011). The Oxford handbook of health economics. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Granados, J. A. T. (2012). Economic growth and health progress in England and Wales: 160 years of a changing relation. Social Science and Medicine,74(5), 688–695.

    Article  Google Scholar 

  • Halici-Tuluce, N. S., Dogan, I., & Dumrul, C. (2016). Is income relevant for health expenditure and economic growth nexus? International Journal of Health Economics and Management,16(1), 23–49. https://doi.org/10.1007/s10754-015-9179-8.

    PubMed  Article  Google Scholar 

  • Hall, S. G., & Jones, C. I. (2007). The value of life and the rise in health spending. The Quarterly Journal of Economics,122(2007), 39–72.

    Article  Google Scholar 

  • Hall, S. G., Swamy, P. A. V. B., & Tavlas, G. S. (2011). Generalized cointegration: A new concept with an application to health expenditure and health outcomes. Empirical Economics,42(2), 603–618. https://doi.org/10.1007/s00181-011-0483-y.

    Article  Google Scholar 

  • Hansen, P., & King, A. (1996). The determinants of health care expenditure: A cointegration approach. Journal of Health Economics,15, 127–137.

    CAS  PubMed  Article  Google Scholar 

  • Harris, R. D., & Tzavalis, E. (1999). Inference for unit roots in dynamic panels where the time dimension is fixed. Journal of Econometrics,91(2), 201–226.

    Article  Google Scholar 

  • Hartwig, J. (2008). What drives health care expenditure? Baumol’s model of ‘Unbalanced growth’ revisited. Journal of Health Economics,27, 603–623.

    PubMed  Article  Google Scholar 

  • Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics,115(1), 53–74.

    Article  Google Scholar 

  • Kapetanios, G., Pesaran, M. H., & Yamagata, T. (2011). Panels with non-stationary multifactor error structures. Journal of Econometrics,160(2), 326–348.

    Article  Google Scholar 

  • Ke, X., Saksena, P., & Holly, A. (2011). The determinants of health expenditure: A country-level panel data analysis. Working paper of the Results for Development Institute (R4D). Geneva: World Health Organization. www.resultsfordevelopment.org. Accessed on August 11th, 2017.

  • Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics,74(1), 119–147.

    Article  Google Scholar 

  • Lago-Peñas, S., Cantarero-Prieto, D., & Blázquez-Fernández, C. (2013). On the relationship between GDP and health care expenditure: A new look. Economic Modelling,32, 124–129. https://doi.org/10.1016/j.econmod.2013.01.021.

    Article  Google Scholar 

  • Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics,108(1), 1–24.

    Article  Google Scholar 

  • Liddle, B., & Messinis, G. (2015). Which comes first-urbanization or economic growth? Evidence from heterogeneous panel causality tests. Applied Economics Letters,22(5), 349–355.

    Article  Google Scholar 

  • Lütkepohl, H., & Krätzig, M. (2004). Applied time series econometrics. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • MacDonald, G., & Hopkins, S. (2002). Unit root properties of OECD health care expenditure and GDP data. Health Economics,11(4), 371–376. https://doi.org/10.1002/hec.657.

    PubMed  Article  Google Scholar 

  • McCoskey, S. K., & Selden, T. M. (1998). Health care expenditures and GDP: Panel data unit root test results. Journal of Health Economics,17, 369–376.

    CAS  PubMed  Article  Google Scholar 

  • Menard, A.-R., & Weill, L. (2016). Understanding the link between aid and corruption: A causality analysis. Economic Systems,40(2), 260–272.

    Article  Google Scholar 

  • Mladenović, I., Milovančević, M., Sokolov Mladenović, S., Marjanović, V., & Petković, B. (2016). Analyzing and management of health care expenditure and gross domestic product (GDP) growth rate by adaptive neuro-fuzzy technique. Computers in Human Behavior,64, 524–530. https://doi.org/10.1016/j.chb.2016.07.052.

    Article  Google Scholar 

  • Moscone, F., & Tosetti, E. (2010). Health expenditure and income in the United States. Health Economics,19(12), 1385–1403. https://doi.org/10.1002/hec.1552.

    CAS  PubMed  Article  Google Scholar 

  • Okunade, A. A., & Karakus, M. C. (2001). Unit root and cointegration tests: Timeseries versus panel estimates for international health expenditure models. Applied Economics,33(9), 1131–1137. https://doi.org/10.1080/00036840122612.

    Article  Google Scholar 

  • Panopoulou, E., & Pantelidis, T. (2012). Convergence in per capita health expenditures and health outcomes in the OECD countries. Applied Economics,44(30), 3909–3920. https://doi.org/10.1080/00036846.2011.583222.

    Article  Google Scholar 

  • Persyn, D., & Westerlund, J. (2008). Error-correction-based cointegration tests for panel data. Stata Journal,8(2), 232–241.

    Article  Google Scholar 

  • Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. CESifo GmbH, CESifo working paper series: CESifo Working Paper No. 1229. http://ezproxy.usq.edu.au/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=ecn&AN=0906171&site=ehost-live. http://www.cesifo.de/DocCIDL/1229.pdf. Accessed January 14th, 2018.

  • Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica,74(4), 967–1012.

    Article  Google Scholar 

  • Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics,22(2), 265–312.

    Article  Google Scholar 

  • Pesaran, M. H., & Tosetti, E. (2011). Large panels with common factors and spatial correlation. Journal of Econometrics,161(2), 182–202.

    Article  Google Scholar 

  • Rafiq, S., Salim, R., & Bloch, H. (2009). Impact of crude oil price volatility on economic activities: An empirical investigation in the Thai economy. Resources Policy,34(3), 121–132.

    Article  Google Scholar 

  • Self, S., & Grabowski, R. (2003). How effective is public health expenditure in improving overall health? A cross-country analysis. Applied Economics,35(7), 835–845. https://doi.org/10.1080/0003684032000056751.

    Article  Google Scholar 

  • Shahbaz, M. (2012). Does trade openness affect long run growth? Cointegration, causality and forecast error variance decomposition tests for Pakistan. Economic Modelling,29(6), 2325–2339.

    Article  Google Scholar 

  • Shaw, J. W., Horrace, W. C., & Voge, R. J. (2005). The determinants of life expectancy: An analysis of the OECD health data. Southern Economic Journal,71(4), 768–783.

    Article  Google Scholar 

  • Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 48(1), 1–48.

    Article  Google Scholar 

  • Swanson, N. R., & Granger, C. W. (1997). Impulse response functions based on a causal approach to residual orthogonalization in vector autoregressions. Journal of the American Statistical Association,92(437), 357–367.

    Article  Google Scholar 

  • Tamakoshi, T., & Hamori, S. (2015). Testing cointegration between health care expenditure and GDP in Japan with the presence of a regime shift. Applied Economics Letters,23(2), 151–155. https://doi.org/10.1080/13504851.2015.1058901.

    Article  Google Scholar 

  • Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics,66(1), 225–250.

    Article  Google Scholar 

  • van der Gaag, J., & Stimac, V. (2008). Towards a new paradigm for health sector development. Amsterdam: Amsterdam Institute for International Development. https://www.r4d.org/wpcontent/uploads/Toward-a-New-Paradigm-for-Health-Sector-Development.pdf. Accessed on November 7th, 2017.

  • Villaverde, J., Maza, A., & Hierro, M. (2014). Health care expenditure disparities in the European Union and underlying factors: A distribution dynamics approach. International Journal of Health Care Finance and Economics,14(3), 251–268.

    PubMed  Article  Google Scholar 

  • Wang, K. M. (2011). Health care expenditure and economic growth: Quantile panel-type analysis. Economic Modelling,28(4), 1536–1549. https://doi.org/10.1016/j.econmod.2011.02.008.

    Article  Google Scholar 

  • Wang, Z. (2009). The determinants of health expenditures: Evidence from US state-level data. Applied Economics,41(4), 429–435. https://doi.org/10.1080/00036840701704527.

    Article  Google Scholar 

  • Wang, Z., & Rettenmaier, A. J. (2007). A note on cointegration of health expenditures and income. Health Economics,16(6), 559–578. https://doi.org/10.1002/hec.1182.

    PubMed  Article  Google Scholar 

  • Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics,69(6), 709–748.

    Article  Google Scholar 

  • Wooldridge, J. (2002). Econometric analysis of cross section and panel data. Cambridge: MA: MIT Press.

    Google Scholar 

  • World Bank. (2016). World development indicators. http://databank.worldbank.org/data/reports.aspx?source=world-development-indicators. Accessed on May 21st, 2017.

  • World Health Organization. (2016). Global health observatory data repository. http://apps.who.int/gho/data/view.main.healthexpratioglobaL?lang=en. Accessed on May 21st, 2017.

Download references

Acknowledgements

The paper was part of the first author’s Ph.D. study. The Ph.D. program was financed by the University of Southern Queensland, Australia [USQ International Stipend Research Scholarship and USQ International Fees Research Scholarship].

Author information

Affiliations

  1. School of Commerce, University of Southern Queensland, Toowoomba, Australia

    Rezwanul Hasan Rana, Khorshed Alam & Jeff Gow

  2. School of Accounting, Economics and Finance, University of KwaZulu-Natal, Durban, South Africa

    Jeff Gow

  3. School of Commerce, University of Southern Queensland, Room: T230, West Street, Toowoomba, QLD, 4350, Australia

    Rezwanul Hasan Rana

Authors
  1. Rezwanul Hasan Rana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khorshed Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeff Gow
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rezwanul Hasan Rana.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables 6, 7, 8, 9, 10 and 11.

Table 6 Unit root tests—Harris–Tsavalis and Im–Pesaran–Shin
Full size table
Table 7 Westerlund cointegration test per capita health expenditure and per capita GDP
Full size table
Table 8 Dumitrescu and Hurlin (2012) Granger non-causality test results; Z-bar tilde value
Full size table
Table 9 Panel vector auto regressive Granger causality Wald test, Chi square-value
Full size table
Table 10 VAR Granger causality/block exogeneity Wald Tests, Chi square-value
Full size table
Table 11 Cross-sectional dependence test
Full size table

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rana, R.H., Alam, K. & Gow, J. Health expenditure and gross domestic product: causality analysis by income level. Int J Health Econ Manag. 20, 55–77 (2020). https://doi.org/10.1007/s10754-019-09270-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10754-019-09270-1

Keywords

  • Health expenditure
  • Gross domestic product
  • Westerlund cointergration
  • Causality analysis
  • Impulse response function
  • Common correlated effects

JEL Classification

  • C55
  • I10
  • I15
  • O1