Factors Explaining Life Expectancy at Age 65: A Panel Data Approach Applied to European Union Countries

Abstract

The aim of this paper is to explain the determinants of longevity in 20 European Union countries for which comparable data are available for the period 1990–2016. We use a health equation to explain life expectancy at age 65 for total population considering socioeconomic factors, population structure, health resources, lifestyles and environment as the main determinants of health status. Panel estimation techniques are implemented to estimate the health equation with lagged explanatory variables to attenuate the endogeneity problems of regressors. Our evidence shows that per capita income, education and pharmaceutical expenditures positively affect life expectancy at the age of 65. As expected, risky lifestyles and air pollution have a significant negative impact on health. The interaction between population age structure and pharmaceutical expenditures proves also to be important for explaining longevity. Our evidence reinforces the idea that investing in education and health care provision is the way to achieve healthier longevity allowing a more active participation of the elderly in society.

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Notes

  1. 1.

    See, for instance, Or et al. (2005), Shaw et al. (2005), Ramesh and Mirmirani (2007), Ricci and Zachariadis (2009), Joumard et al. (2008), Fayissa and Traian (2011), among others.

  2. 2.

    It must be noticed that more resources do not reflect necessarily better health outcomes since they depend also on the efficiency of the use of health care resources.

  3. 3.

    CO2 emissions can be understood as a proxy for climate change, due to their close link, as discussed in IPCC (2018), in the context of consolidating a response at the global level, in order to react to the threat of climate change and promote sustainable development and poverty eradication. Cumulative CO2 emissions are responsible for global warming, independently from where they occur. Thus, to restrict climate change it is essential to reduce greenhouse gas emissions continuously (Knutti 2013).

  4. 4.

    PM (particle pollution) is the term used to define a mixture of solid particles and liquid droplets found in the air exposures to fine particulate matter (inhalable particles measuring < 2.5 μm in aerodynamic diameter [PM2.5]).

  5. 5.

    Participants were followed for 22 years and were 30 years old or over at baseline (1982).

  6. 6.

    Participants were followed for 16 years and were 25 years old or over at baseline (1991) and usual residents of Canada.

  7. 7.

    Although the UK is currently under the Brexit process for leaving the European Union (EU), we still include it in our sample for being an historical EU-member. The list of the 20 EU countries is given in Table 5 in the “Appendix”.

  8. 8.

    The log is used for this variable for the sake of data scale normalization.

  9. 9.

    According to Shaw et al. (2005), ignoring the correlation between pharmaceutical consumption and a country’s age distribution creates an omitted variable bias in the elasticity of pharmaceutical consumption, thus undervaluing the marginal effect of drug consumption on health. That bias calls the attention for the relevance of the age structure in the design of public macroeconomic policies.

  10. 10.

    OECD (2016a, b) defines alcohol consumption as annual consumption of pure alcohol in litters, per person, aged 15 years and over. However, it is important to mention that the methodology to convert alcoholic drinks to pure alcohol may differ across countries: typically, beer is weighted as 4–5%, wine as 11–16% and spirit drinks as 40% of pure alcohol equivalent.

  11. 11.

    The log transformation is used as in per capita income. We also used per capita consultations as a proxy for health system’s supply, but no reasonable results were found.

  12. 12.

    For the explanation of the variables, their description and corresponding data sources see Table 6 in the “Appendix”.

  13. 13.

    Since we have cross-section and time-series data the most appropriate manner to estimate the health Eq. (1) is using panel data estimation techniques. In this way we have gains in information (within groups and between groups) and in estimation efficiency due to a large size sample, ensuring the asymptotic properties of the estimates.

  14. 14.

    The country dummies are not reported due to space limitations.

  15. 15.

    For detailed information on the panel data estimation methods see Baltagi (2005).

  16. 16.

    This parameter is given by \(\theta = 1 - \sqrt {\frac{{\sigma_{\varepsilon }^{2} }}{{\left( {T\sigma_{u}^{2} + \sigma_{\varepsilon }^{2} } \right)}}}\) with 0 < θ < 1.

  17. 17.

    Regression results referred to cases (1) and (2) can be provided upon request.

References

  1. Baltagi, B. (2005). Econometric analysis of panel data (3rd ed.). New York: Willey.

    Google Scholar 

  2. Beard, J., Officer, A., Carvalho, I., Sadana, R., Pot, A., Michel, J., et al. (2016). The World report on ageing and health: A policy framework for healthy ageing. Lancet,387(10033), 2145–2154.

    Article  Google Scholar 

  3. Cakmak, S., Hebberna, C., Pinaultb, L., Lavignec, E., Vanos, J., Crousee, L., et al. (2018). Associations between long-term PM2.5 and ozone exposure and mortality in the Canadian Census Health and Environment Cohort (CANCHEC), by spatial synoptic classification zone. Environment International,111, 200–211. https://doi.org/10.1016/j.envint.2017.11.030.

    Article  Google Scholar 

  4. Chen, C., Goldman, D., Zissimopoulos, J., Rowe, J., & Research Network on an Aging Society. (2018). Multidimensional comparison of countries’ adaptation to societal aging. PNAS,115(37), 9169–9174. https://doi.org/10.1073/pnas.1806260115.

    Article  Google Scholar 

  5. Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N., et al. (2016). The association between income and life expectancy in the United States, 2001–2014. Journal of the American Medical Association,315(16), 1750–1766. https://doi.org/10.1001/jama.2016.4226.

    Article  Google Scholar 

  6. Christensen, K., Doblhammer, G., Rau, R., & Vaupel, J. (2009). Ageing populations: The challenges ahead. The Lancet,374, 1196–1208.

    Article  Google Scholar 

  7. Corsini, V. (2010). Highly educated men and women likely to live longer: Life expectancy by educational attainment. Eurostat Statistics in Focus 24/2010, http://ec.europa.eu/eurostat/documents/3433488/5565012/KS-SF-10-024-EN.PDF/f2caf9d2-3810-4088-bdbe-2f636e6ecc48.

  8. Crouse, D., Peters, P., Hystad, P., Brook, J., van Donkelaar, A., Martin, R., et al. (2015). Ambient PM2.5, O3, and NO2 exposures and associations with mortality over 16 years of follow-up in the Canadian Census Health and Environment Cohort (CanCHEC). Enviromental Health Perspecives,123, 1180–1186. https://doi.org/10.1289/ehp.1409276.

    Article  Google Scholar 

  9. Cylus, J., Normand, C., & Figueras, J. (2018). Will population ageing spell the end of the Welfare State? A review of evidence and policy options. Copenhagen: World health Organization (acting as the host organization for, and secretariat of, the European Observatory on Health Systems and Policies).

    Google Scholar 

  10. Deguen, S., & Zmirou-Navier, D. (2010). Social inequalities resulting from health risks related to ambient air quality—A European review. European Journal of Public Health,20(1), 27–35.

    Article  Google Scholar 

  11. Di, Q., Wang, Y., Zanobetti, A., Wang, Y., Koutrakis, P., Choirat, C., Dominici, F., Schwartz, J. (2017). Air Pollution and Mortality in the Medicare Population. The New England Journal of Medicine,376(26), 2513–2522. https://doi.org/10.1056/NEJMoa1702747

    Article  Google Scholar 

  12. European Environment Agency. (2018). Air quality in Europe—2018 report. Retrieved August 12, 2019 from https://www.eea.europa.eu/publications/air-quality-in-europe-2018.

  13. Eurostat. (2018). Quality of life indicators—Health. Statistics explained. Retrieved April 24, 2018 from https://ec.europa.eu/eurostat/statisticsexplained/index.php/Quality_of_life_indicators_-_health. ISSN 2443-8219.

  14. Fayissa, B., & Traian, A. (2011). Estimation of a health production function: Evidence from East-European countries. Working papers 201104, Middle Tennessee State University, Department of Economics and Finance.

  15. Gilligan, A., & Skrepnek, G. (2015). Determinants of life expectancy in eastern mediterranean countries. Health Policy and Planning,30, 624–637.

    Article  Google Scholar 

  16. Hallgren, M., Högberg, P., & Andréasson, S. (2009). Alcohol consumption among elderly European Union citizens Health effects, consumption trends and related issues. In Expert conference on alcohol and health, 21–22 September 2009, Stockholm, Sweden. http://www.antoniocasella.eu/archila/alcol_Hallgren_2009.pdf.

  17. IPCC. (2018). Summary for policymakers. In V. Masson-Delmotte, P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P. R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J. B. R. Matthews, Y. Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, & T. Waterfield (Eds.), Global warming of 1.5 °C. An IPCC special report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Geneva: World Meteorological Organization.

  18. James, C., Devaux, M., & Sassi, F. (2017). Inclusive growth and health. OECD Health working papers, No. 103, OECD Publishing, Paris. https://doi.org/10.1787/93d52bcd-en.

  19. Joumard, I., André, C., Nicq, C., & Chatal, O. (2008). Health status determinants: Lifestyle, environment, health care resources and efficiency. OECD Economic Department working paper, no. 627.

  20. Kiuila, O., & Mieszkowski, P. (2007). The effects of income, education and age on health. Health Economics,16, 781–798. https://doi.org/10.1002/hec.1203.

    Article  Google Scholar 

  21. Knutti, R. (2013). Relationship between global emissions and global temperature rise. Working group I contribution to the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment report. CLA Chapter 12. Retrieved July 30, 2019 from https://unfccc.int/sites/default/iles/7_knutti.reto.3sed2.pdf.

  22. Lugo, A., La Vecchia, C., Boccia, S., Murisic, B., & Gallus, S. (2013). Patterns of smoking prevalence among the elderly in Europe. International Journal of Environmental Research and Public Health,10(9), 4418–4431. https://doi.org/10.3390/ijerph10094418.

    Article  Google Scholar 

  23. Mackenbach, J., Meerding, W., & Kunst, A. (2010). Economic costs of health inequalities in the European Union. Journal of Epidemiology and Community Health,65, 412–419. https://doi.org/10.1136/jech.2010.112680.

    Article  Google Scholar 

  24. Majer, I., Nusselder, W., Mackenbach, J., & Kunst, A. (2011). Socioeconomic inequalities in life and health expectancies around official retirement age in 10 Western-European countries. Journal of Epidemiology and Community Health,65(11), 972–979. https://doi.org/10.1136/jech.2010.111492.

    Article  Google Scholar 

  25. Monsef, A., & Mehrjardi, A. (2015). Determinants of life expectancy: A panel data approach. Asian Economic and Financial Review,5(11), 1251–1257.

    Article  Google Scholar 

  26. Murtin, F., Mackenbach, J., Jasilionis, D., & d’Ercole, M. (2017). Inequalities in longevity by education in OECD countries. OECD Statistics working paper, no. 2017/02.

  27. Nash, S., Liao, L., Harris, T., & Freedman, N. (2017). Cigarette smoking and mortality in adults aged 70 years and older: Results from the NIH-AARP cohort. American Journal of Preventive Medicine,52(3), 276–283. https://doi.org/10.1016/j.amepre.2016.09.036.

    Article  Google Scholar 

  28. National Institute on Alcohol Abuse and Alcoholism. (2008). Alcohol Research: a lifespan perspective. Alcohol Alert, 74, January. Retrieved August 8, 2019, from https://pubs.niaaa.nih.gov/publications/aa74/AA74.pdf.

  29. OECD. (2016a). Health at a glance 2016: OECD indicators. Paris: OECD Publishing. https://doi.org/10.1787/9789264265592-en.

    Book  Google Scholar 

  30. OECD. (2016b). The economic consequences of outdoor air pollution. Paris: OECD Publishing. https://doi.org/10.1787/9789264257474-en.

    Book  Google Scholar 

  31. OECD. (2017). Health at a glance 2017: OECD indicators. Paris: OECD Publishing. https://doi.org/10.1787/health_glance-2017-en.

    Book  Google Scholar 

  32. Or, Z. (2000). Determinants of health outcomes in industrialised countries: A pooled, cross-country time series analysis. OECD Economic studies, no. 30, 2000/I.

  33. Or, Z., Wang, J., & Jamison, D. (2005). International differences in the impact of doctors on health: A multilevel analysis of OECD countries. Journal of Health Economics,24(3), 531–560.

    Article  Google Scholar 

  34. Poças, A., & Soukiazis, E. (2010). Health status determinants in the OECD countries. A panel data approach with endogenous regressors, Estudos do GEMF, 4, FEUC. Grupo de Estudos Monetários e Financeiros. http://hdl.handle.net/10316/13325

  35. Ramesh, M., & Mirmirani, S. (2007). An assessment of OECD health care system using panel data analysis. MPRA paper, no. 6122, University Library of Munich.

  36. Rau, R., Soroko, E., Jasilionis, D., & Vaupel, J. (2008). Continued reductions in mortality at advanced ages. Population and Development Review,34(4), 747–768.

    Article  Google Scholar 

  37. Ricci, F., & Zachariadis, M. (2009). Longevity and education externalities: A macroeconomic perspective. TSE working papers, no. 09-009, Toulouse School of Economics.

  38. Schroeder, S. (2007). We can do better—Improving the health of the American people. New England Journal of Medicine,357, 1221–1228.

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. Turner, M., Jerrett, M., Pope, C., Krewski, D., Gapstur, S., Diver, W., et al. (2016). Long-term ozone exposure and mortality in a large prospective study. American Journal of Respiratory and Critical Care Medicine,93(10), 1134–1142. https://doi.org/10.1164/rccm.201508-1633OC.

    Article  Google Scholar 

  41. WHO. (2017a). Risk factors of ill health among older people. Retrieved June 1, 2017, from http://www.euro.who.int/en/health-topics/Life-stages/healthy-ageing/data-and-statistics/accessed.

  42. WHO (2017b). WHO report on the global tobacco epidemic, 2017: Monitoring tobacco use and prevention policies. Executive Summary, Geneva: World Health Organization. Licence: CC BY-NC-SA 3.0 IGO. Retrieved June 30, 2018, from https://www.who.int/tobacco/global_report/2017/executivesummary/en/.

  43. WHO. (2018). Factsheet on alcohol consumption. Retrieved May 2, 2019, from https://www.who.int/news-room/fact-sheets/detail/alcohol.

  44. Zaidi, A. (2014). Life cycle transitions and vulnerabilities in old age: A review. Human Development Report Office Occasional Paper.

  45. Zaidi, A., Gasior, K., Hofmarcher, M., Lelkes, O., Marin, B., Rodrigues, R., Schmidt, A., Vanhuysse, P., & Zolyomi, E. (2013). Active ageing index 2012: Concept, methodology and final results. EC/UNECE, Active Ageing Index Project. UNECE Grant ECE/GC/2012/003. European Centre for Social Welfare Policy and Research, Vienna.

  46. Zaidi, A., & Howse, K. (2017). The policy discourse of active ageing: Some reflections. Population Ageing,10, 1. https://doi.org/10.1007/s12062-017-9174-6.

    Article  Google Scholar 

  47. Zheng, H. (2014). Aging in the context of cohort evolution and mortality selection. Demography,51(4), 1295–1317. https://doi.org/10.1007/s13524-014-0306-9.

    Article  Google Scholar 

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Appendix

Appendix

See Tables 5 and 6.

Table 5 List of the 20 European Union countries involved in the regressions
Table 6 Variables, description and data sources

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Poças, A., Soukiazis, E. & Antunes, M. Factors Explaining Life Expectancy at Age 65: A Panel Data Approach Applied to European Union Countries. Soc Indic Res 150, 265–288 (2020). https://doi.org/10.1007/s11205-020-02290-2

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Keywords

  • Life expectancy at age 65
  • Socioeconomic status
  • Panel data

JEL Classification

  • C23
  • Q53
  • I14
  • I15