Life Expectancy Index: Age Structure of Population and Environment Evolution

  • C. Cosculluela-Martínez
  • R. Ibar-AlonsoEmail author
  • G. J. D. Hewings


Tolerance, Technology and Talent indexes that are found in the literature and used to compare cities tend to focus more on economic and technologic progress. However, time is important, this paper presents a Life Expectancy Index (LEI) for the 40 OECD countries computed as a weighted average of three dimensions: population pyramid base (such as fertility, dependency, population, life expectancy per sex, birth and fertility rates), enterprise contamination (methane and nitrogenous gases population density) and civil contamination (deaths and greenhouse emissions) obtained in a factorial analysis, where the weights are calculated with the IRFs of the estimated VECM. The ranking of the countries provides policy-makers with a sense of where improvements might be targeted. For each timespan, 1970–2012, 2000–2012 and 2008–2012, Mexico, Korea and Israel are the countries where the index is higher while Sub-Sahara, Russia and Hungary are the ones where the index is lower.


Dynamic multiequational methodology Life Expectancy Index OECD countries Environment variables Human capital 


  1. Álvarez, I., & Cárdenas, E. (2006). Índice de Vulnerabilidad Social en los Países de la OCDE (No. 2006/01) [Social Vulnerability Index in OECD countries]. Universidad Autónoma de Madrid (Spain), Department of Economic Analysis (Economic Theory and Economic History). Access 1 May 2016.
  2. Anderson, T. W., & Rubin, H. (1956). Statistical inference in factor analysis. In Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability (Vol. 5, pp. 111–150).Google Scholar
  3. Bernanke, B., Boivin, J., & Eliasz, P. (2005). Factor augmented vector autoregressions (FVARs) and the analysis of Monetary Policy. Quarterly Journal of Economics, 120(1), 387–422.Google Scholar
  4. Caballero, B., & Peña, D. (1987). Un Estudio Estadístico de la Investigaci6n Científica en los Países de la OCDE. Estadística Española, 29(114), 151–178.Google Scholar
  5. Caudill, S. B., Zanella, F. C., & Mixon, F. G. (2000). Is economic freedom one dimensional? A factor analysis of some common measures of economic freedom. Journal of Economic Development, 25(1), 17–40.Google Scholar
  6. Cosculluela-Martínez, C., & Flores de Frutos, R. (2015). The Macroeconomic Impact of Transportation Investment on the Spanish Economy. European Journal of Transport and Infrastructure Research (EJTIR), 15(4), 376–395.Google Scholar
  7. Durán Víquez, R., & Kikut Valverde, A. C. Pronóstico de Inflación Mediante el Uso de Análisis Factorial. [Inflation Forecast by Factorial Analysis]. Access 1 May 2016.
  8. Echavarría, G., & González, W. (2011). Un Modelo de Factores Dinámicos de Pequeña Escala para elImacec [Dynamic Factor Models]. Notas de Investigación Journal Economía Chilena (The Chilean Economy), 14(2), 109–118.Google Scholar
  9. Florida, R. (1999). Engine or Infrastructure?. Technology.Google Scholar
  10. Florida, R. (1999b). The role of the University: Leveraging talent, not technology. Issues in Science and Technology, 15(4), 67–73.Google Scholar
  11. Florida, R. (2002a). Bohemia and economic geography. Journal of Economic Geography, 2(1), 55–71.CrossRefGoogle Scholar
  12. Florida, R. (2002b). The rise of the creative class. New York: Basic Books.Google Scholar
  13. Florida, R., & Mellander, C. (2006). The creative class or human capital. Explaining Regional Development in Sweden. Royal Institute of Technology, CESIS, 79.Google Scholar
  14. Florida, R., Mellander, C., & King, K. (2015). The Global Creativity Index 2015. Access 2 May 2016.
  15. Florida, R., Mellander, C., & Stolarick, K. (2008). Inside the black box of regional development human capital, the creative class and tolerance. Journal of Economic Geography, 8(5), 615–649.CrossRefGoogle Scholar
  16. Glaeser, E. (2005). Edward L. Glaeser, review of Richard Florida’s the rise of the creative class. Regional Science and Urban Economics, 35(5), 593–596.CrossRefGoogle Scholar
  17. Glaeser, E. (2011). Triumph of the City: How our greatest invention makes us richer, smarter, greener, healthier, and happier. New York: Penguin.Google Scholar
  18. Glaeser, E. L., Kolko, J., & Saiz, A. (2001). Consumer City. Journal of Economic Geography, 1(1), 27–50.CrossRefGoogle Scholar
  19. Granger, C. W. J., & Engle, R. F. (1987). Co-integration and error correction: Representation, Estimation and Testing. Econometrica, 5, 251–276.Google Scholar
  20. Huppert, F. A., & So, T. T. (2013). Flourishing across Europe: Application of a new conceptual framework for defining well-being. Social Indicators Research, 110(3), 837–861.CrossRefGoogle Scholar
  21. Jenkins, G. M., & Alavi, A. S. (1981). Some aspects of modeling and forecasting multivariate time series. Journal of Time Series Analysis, 2, 1–47.CrossRefGoogle Scholar
  22. Johansen, S. (1988). Statistical analysis of co-integration vectors. Journal of Economic Dynamics and Control, 12, 231–254.CrossRefGoogle Scholar
  23. Johansen, S. (1991). Estimation and hypothesis testing of co-integration vectors in gaussian vector autoregressive models. Econometrica, 59, 1551–1580.CrossRefGoogle Scholar
  24. Marlet, G. A., & Van Woerkens, C. (2004). Skills and creativity in a cross-section of Dutch Cities. Discussion Paper Series/Tjalling C. Koopmans Research Institute, 4(29). Access 3 May 2016.
  25. Mateos Mora, C., & Navarro, C. J. (2014). La Localización de la Clase Creativa en los Municipios Españoles. Discusión Conceptual-operativa y Análisis Descriptivo [Creative Class allocation in Spanish Municipies. Conceptual Discussion and Descriptive Analysis]. Empiria: Revista de Metodología de Ciencias Sociales, 29, 123–153.Google Scholar
  26. Peña, D., & Galeano, P. (2001). Multivariate analysis in vector time series (No. ws012415). Universidad Carlos III de Madrid. Departamento de Estadística. Access 2nd May 2016.
  27. Prados De La Escosura, L. (2015). Economic freedom in the long run: Evidence from OECD countries (1850–2007). The Economic History Review, 69, 435–468.CrossRefGoogle Scholar
  28. Qian, H. (2010). Talent, creativity and regional economic performance: The case of China. The Annals of Regional Science, 45(1), 133–156.CrossRefGoogle Scholar
  29. Stolarick, K., & Adiarte, A. L. (2003). First ever rankings of the 50 states on the Creativity Index. Creative Intelligence, 1(4), 1–4.Google Scholar
  30. Vicent Arastey, P. (2011). La Clase Creativa en la Región Metropolitana de Valencia [The Creative Class in Valencia Region]. Access 1 may 2016.
  31. World Bank Access 1 May 2016.
  32. Zhang, Y., Huang, W., London, S. J., Song, G., Chen, G., Jiang, L., et al. (2006). Ozone and daily mortality in Shanghai, China. Environmental Health Perspectives, 114(8), 1227.CrossRefGoogle Scholar

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© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Departamento de Economía Aplicada I, Facultad de Ciencias Jurídicas y SocialesUniversidad Rey Juan CarlosMadridSpain
  2. 2.Departamento de Matemática Aplicada y Estadística, Facultad de Ciencias Económicas y EmpresarialesUniversidad San Pablo CEUMadridSpain
  3. 3.Regional Economics Applications LaboratoryUniversity of IllinoisUrbanaUSA

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