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

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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.

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Correspondence to Micaela Antunes.

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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 (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