Skip to main content

Advertisement

SpringerLink
Log in

Multistate projections by level of education for Portugal, 2011–2031

  • Published:
Journal of Population Research Aims and scope Submit manuscript

Abstract

Educational attainment is an attribute that leads to a great distinction between the members of a population, including when considering their health and well-being, which are features to pursue within an aging society. The aim of this work was to produce demographic projections for the Portuguese population by sex, age group and level of educational attainment, for the period 2011–2031. Considering fertility, mortality and migration differentials by level of education, the population was projected using the multistate cohort-component method with a block Leslie matrix. Two scenarios were considered, one where educational attainment before 2011 remains constant and another in which educational attainment will follow the trend observed over the last decade, being the trend in the state proportion modelled using continuation ratio models. The results show an increase in the proportion of individuals who complete higher educational levels in almost all age groups of both sexes. Among women, only 13.6 % had completed some level of higher education in 2011, a figure that will rise to approximately 23.4 % in 2031, whereas among men this value was only 9.7 % and will also rise by 2031, reaching 15.5 %. We can expect the proportion of people with higher educational levels to continue to rise as the education of younger cohorts seems to evolve positively. This work will be particularly useful to study how the aging population and the rising levels of education can contribute to the planning and monitoring of public policies, although these findings can also be used in other research contexts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. Dustmann and Glitz (2011) classify as deliberate migrations those due to individual decisions, based on seeking better economic conditions in other regions.

  2. There are no published results from the projections produced by Statistics Portugal, concerning either age-specific fertility rates or infant mortality rates, which led to the need for using the results from the United Nations projections.

  3. In the two first age groups (0–4 and 5–9 years), this state includes only individuals who have no education or, for a reduced proportion of children between 5 and 9 years, those who completed the first 4 years of schooling: according to the structure of the Portuguese education system, it is not possible to complete the second cycle (6 years) before age 10.

  4. Considering the limitations associated with the use of more common model life tables systems, which were created several decades ago; for instance, the Coale–Demeny system (Coale 1966) does not allow us to consider extremely low infant mortality levels, since this kind of mortality pattern did not exist at that time, we chose to use a system based on contemporary data.

  5. It was assumed that no transitions occurred between education levels; it is also admitted that the deaths within a given time range occurred in the mid-interval.

  6. The estimated number of births was divided by the two sexes according to the sex ratio at birth: 0.512 male births and 0.488 female.

  7. The trend modelling for state proportions is based on data from Labour Surveys (Statistics Portugal), regarding individuals aged above 15 years, by sex, age group and educational attainment level, for years 1998–2010. Since we intend to project this trend to 2031, we can expect the prediction error to be high, given the small number of observations used for modelling, in relation to the number of periods ahead for prediction.

  8. The remaining proportions are not comparable, since KC et al. (2010) presented a different state structure: 1. No education; 2. Primary; 3. Secondary and 4. Tertiary and only the last class of education is defined as our higher education state.

References

  • Ahuja, V., & Filmer, D. (1995). Educational attainments in developing countries: New estimates and projections disaggregated by gender. Policy Research Working Paper Series 1489. World Bank. doi:10.1596/1813-9450-1489. http://elibrary.worldbank.org/content/workingpaper/.

  • Alho, J., & Keilman, N. (2010). On future household structure. Journal of the Royal Statistical Society, 173, 117–143.

    Article  Google Scholar 

  • Anderson, T. W., & Goodman, L. A. (1957). Statistical inference about Markov Chains. The Annals of Mathematical Statistics, 28(1), 89–110.

    Article  Google Scholar 

  • Barro, R. J., & Lee, J. (1993). International comparisons of educational attainment. NBER Working Papers 4349.Cambridge, MA: National Bureau of Economic Research.

  • Barro, R. J., & Lee, J. (2000). International data on educational attainment updates and implications. NBER Working Papers 7911. Cambridge, MA: National Bureau of Economic Research.

  • Bernardelli, H. (1941). Population waves. Journal of the Burma Research Society, 31, 1–18.

    Google Scholar 

  • Billari, F. C., & Philipov, D. (2004). Education and the transition to motherhood: A comparative analysis of Western Europe. Vienna: Vienna Institute of Demography.

    Google Scholar 

  • Brewster, K. L. (2000). Fertility and women’s employment in industrialized nations. Annual Review of Sociology, 26, 271–296.

    Article  Google Scholar 

  • Carrilho, M. J., & Craveiro, M. L. (2013). A situação demográfica recente em Portugal. Revista de Estudos Demográficos, INE, I.P., 50(3), 45–79.

  • Clark, D., & Royer, H. (2010). The effect of education on adult health and mortality: Evidence from Britain. NBER Working Paper 16013. Washington, DC: World Bank.

  • Clark, S. J., & Sharrow, D.J. (2011). Contemporary model life tables for developed countries: An application of model-based clustering. Working Paper 107. Center for Statistics and the Social Sciences. Seattle: University of Washington.

  • Cleland, J. (2002). Education and future fertility trends, with special reference to mid-transitional countries. In Completing the fertility transition. Part 2. Background papers (pp. 187–202). New York: United Nations, Population Division, Department of Economic and Social Affairs.

  • Coale, A. (1966). Regional model life tables and stable populations. Princeton: Princeton University Press.

    Google Scholar 

  • Crowder, M., & Stephens, D. (2011). On inference from Markov chain macro-data using transforms. Journal of Statistical Planning and Inference, 141(9), 3201–3216.

    Article  Google Scholar 

  • Dustmann, C., & Glitz, A. (2011). Migration and education. Norface Discussion Paper Series 2011–11. Norface Research Programme on Migration, Department of Economics, University College London.

  • Eurostat. (2010). Regional population projections EUROPOP2008: Most EU regions face older population profle in 2030. http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-SF-10-001/EN/KS-SF-10-001-EN.PDF. Accessed 5 April 2012.

  • Fernandes, A. A. (2007). Determinantes da mortalidade e da longevidade: Portugal numa perspectiva europeia (UE15, 1991–2001). Análise Social, 42(183), 419–443.

    Google Scholar 

  • Goujon, A. (2008). Report on projections by level of education (Future human capital: Estimates and projections of education transition probabilities). Deliverable D3 in Work Package 1. Bridging the micro-macro gap in population forecasting-MicMac Project. Vienna Institute of Demography, Austrian Academy of Sciences.

  • Goujon, A., Alkitkat, H., Lutz, W., & Prommer, I. (2007). Population and human capital growth in Egypt: Projections for governorates to 2051. Interim Report IR-07-010. Laxenburg, Austria: International Institute for Applied Systems Analysis.

  • Goujon, A., & Wils, A. (1996). The importance of education in future population. Global trends and case studies on Cape Verde, Sudan, and Tunisia. Working Paper WP-96-138. Laxenburg, Austria: International Institute for Applied Systems Analysis.

  • Gustavsson, S. (2006). Education and postponement of maternity. Economic analyses for industrialized countries. Dordrecht: Kluwer Academic Publishers.

    Book  Google Scholar 

  • Henriques, F. C., Rodrigues, T. F., & Martins, M. F. O. (2009). Ageing, education and health in Portugal: Prospective from the 19th to the 21st century. Hygiea Internationalis, 8(1), 81–96.

    Article  Google Scholar 

  • Huisman, M., Kunst, A. E., Andersen, O., et al. (2004). Socioeconomic inequalities in mortality among elderly people in 11 European populations. Journal of Epidemiology and Community Health, 58(6), 468–475.

    Article  Google Scholar 

  • Huisman, M., Kunst, A. E., Bopp, M., et al. (2005). Educational inequalities in cause-specific mortality in middle-aged and older men and women in eight western European populations. The Lancet, 365(9458), 493–500.

    Article  Google Scholar 

  • Imhoff, E., & Keilman, N. (1991). Lipro 2.0: An application of a dynamic demographic projection model to household structure in the Netherlands. Lipro 2.0. Swets & Zeitlinger.

  • Instituto Nacional de Estatística (INE), IP, (ed.) (2009). Projecções de população residente em Portugal 2008–2060. Lisbon.

  • Instituto Nacional de Estatística (INE). (2012). Censos 2011 resultados definitivos -Portugal. Estudo. http://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_publicacoes&PUBLICACOESpub_boui=73212469&PUBLICACOEStema=55466&PUBLICACOESmodo=2&xlang=en.

  • Jejeebhoy, S. J. (1995). Women’s education, autonomy and reproductive behaviour: Experience from developing countries. Oxford: Clarendon Press.

    Google Scholar 

  • Jones, M. T. (2005). Estimating Markov transition matrices using proportions data: An application to credit risk. IMF Working Papers 05/219. International Monetary Fund. http://ideas.repec.org/p/imf/imfwpa/05-219.html.

  • KC, S., et al. (2010). Projection of populations by level of educational attainment, age, and sex for 120 countries for 2005–2050. Demographic Research, 22(15), 383–472.

    Article  Google Scholar 

  • Keyfitz, N., & Caswell, H. (2005). The matrix model framework. In Applied mathematical demography. Statistics for biology and health (pp. 48–70). New York: Springer.

  • Leslie, P. H. (1945). On the use of matrices in certain population mathematics. Biometrika, 33, 183–212.

    Article  Google Scholar 

  • Lewis, E. G. (1942). On the generation and growth of a population. Sankhya, 6, 93–96.

    Google Scholar 

  • Lleras-Muney, A. (2005). The relationship between education and adult mortality in the United States. The Review of Economic Studies, 72(1), 189–221.

    Article  Google Scholar 

  • Lutz, W. (1999). Demographic dimensions in forecasting: Adding education to age and sex. In W. Lutz (Ed.), Frontiers of population forecasting. Supplement to Volume 24, 1998, Population and Development Review (pp. 42–58). New York: Population Council.

  • Lutz, W., & Goujon, A. (2001). The world’s changing human capital stock: Multi-State population projections by educational attainment. Population and Development Review, 27(2), 323–339.

    Article  Google Scholar 

  • Lutz, W., Goujon, A., & Wils, A. (2005). Forecasting human capital: Using demographic multi-state methods by age, sex, and education to show the long-term effects of investments in education. Working Paper WP-07-03. Washington DC: Education Policy and Data Center, Academy for Educational Development.

  • Lutz, W., & KC, S. (2010). Dimensions of global population projections: What do we know about future population trends and structures? Philosophical Transactions of the Royal Society B, 365, 2779–2791.

    Article  Google Scholar 

  • Mahlmann, T. (2006). Estimation of rating class transition probabilities with incomplete data. Journal of Banking & Finance, 30(11), 3235–3256.

    Article  Google Scholar 

  • Nehru, V., Swanson, E., & Dubey, A. (1995). A new database on human capital stock in developing and industrial countries: Sources, methodology, and results. Journal of Development Economics, 46(2), 379–401.

    Article  Google Scholar 

  • Oliveira, I. T. (2009). Fertility differentials and educational attainment in Portugal: A non-linear relationship. Canadian Studies in Population, 36(3–4), 347–362.

    Google Scholar 

  • O’Neill, B. C., Balk, D., Brickman, M., & Ezra, M. (2001). A guide to global population projections. Demographic Research, 4(8), 203–288.

    Article  Google Scholar 

  • Organisation for Economic Co-operation and Development (OECD). (2012). Health at a glance 2012: OECD indicators. Paris: OECD Publishing.

    Google Scholar 

  • Population Reference Bureau. (2012). 2012 World population data sheet. Washington, DC.

  • Psacharopoulos, G. and Arriagada, A.M. (1992). The educational composition of the labor force: An international update. PHREE background paper series. Washington DC: World Bank.

  • Rogers, A. (1975). Introduction to multiregional mathematical demography. New York: Wiley-Interscience.

    Google Scholar 

  • Rogers, A. (1995). Multiregional demography: Principles, methods and extensions (1st ed.). Chichester: Wiley.

    Google Scholar 

  • Rogers, A. (2008). Demographic modeling of the geography of migration and population: A multiregional perspective. Geographical Analysis, 40(3), 276–296.

    Article  Google Scholar 

  • Schoen, R. (1988). Modeling multigroup populations. Perspectives in Social Psychology. New York: Springer.

    Book  Google Scholar 

  • Schoen, R. (2006). Population models with constant rates. In Dynamic population models (Vol. 17). The Springer Series on Demographic Methods and Population Analysis (pp. 1–26). Dordrecht: Springer.

  • Sharpe, F. R., & Lotka, A. J. (1911). A problem in age-distribution. Philosophical Magazine, 21(124), 435–438.

    Article  Google Scholar 

  • Sharrow, D. J., Sevcikova, H. (2011). LifeTables: A package to implement HMD model life table system. R package version 0.1. http://cran.r-project.org/web/packages/LifeTables/index.html.

  • Sleebos, J. (2003). Low fertility rates in OECD countries. Labour Market and Social Policy Occasional Papers 15. Paris: OECD.

  • Sobotka, T. (2004). Postponement of childbearing and low fertility in Europe. PhD thesis. University of Groningen.

  • United Nations (UN). (1995). Women’s education & fertility eehaviour: Recent evidence from the Demographic and Health Surveys. New York.

  • United Nations (UN) (2011a). Trends in international migrant stock: Migrants by age and sex. Department of Economic and Social Affairs, Population Division. New York.

  • United Nations (UN). (2011b). World population prospects: The 2010 revision, highlights and advance tables. Department of Economic and Social Affairs Population Division. New York.

    Google Scholar 

  • Willekens, F. J. (1985). Multiregional demography. Working paper (Nederlands Interuniversitair Demografsch Instituut). The Hague: Netherlands Interuniversity Demographic Institute.

  • Willekens, F. J. (1999). The life course: Models and analysis. English. In L. J. G. Wissen & P. A. Dykstra (Eds.) Population issues. The plenum series on demographic methods and population analysis (pp. 23–51). Dordrecht: Springer.

  • Willekens, F. J. (2005). Biographic forecasting: Bridging the micro–macro gap in population forecasting. New Zealand Population Review, 31(1), 77–124.

    Google Scholar 

  • Willekens, F. J. (2006). Description of the multistate projection model (Multistate model for biographic analysis and projection). Deliverable D1 in Work Package 1. Bridging the micro-macro gap in population forecasting—MicMac Project. Netherlands Interdisciplinary Demographic Institute. The Hague.

  • Wils, A. (2007). Window on the future: 2025—Projections of education attainment and its impact. Washington, DC: Education Policy and Data Center.

    Google Scholar 

  • Wilson, T., & Rees, P. (2005). Recent developments in population projection methodology: A review. Population, Space and Place, 11, 337–360.

    Article  Google Scholar 

  • World Bank. (2013). Population growth (annual %). http://data.worldbank.org/indicator/SP.POP.GROW/countries/PT?display=default. Accessed 6 August 2013.

Download references

Acknowledgments

This work was co-funded by FEDER funds through the Operational Programme for Competitiveness Factors (COMPETE) and by National funds through Fundação para a Ciência e a Tecnologia (FCT) as part of the project Aging and Health in Portugal: Policies and Practices, FCOMP-01-0124-FEDER-PTDC/CS-DEM/109967/2009.

Author information

Authors and Affiliations

  1. Unidade de Parasitologia e Microbiologia Médicas, Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, Rua da Junqueira, 100, 1349-008, Lisbon, Portugal

    Maria Oliveira Martins

  2. Centro de Estudos da População, Economia e Sociedade, Universidade do Porto, Rua do Campo Alegre, 1021, 4169-004, Porto, Portugal

    Inês Rodrigues

  3. Departamento de Estudos Políticos, Faculdade de Ciências Sociais e Humanas, Universidade Nova de Lisboa, Av. de Berna, 26 C, 1069-061, Lisbon, Portugal

    Teresa Rodrigues

Authors
  1. Maria Oliveira Martins
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Inês Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Teresa Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maria Oliveira Martins.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Martins, M.O., Rodrigues, I. & Rodrigues, T. Multistate projections by level of education for Portugal, 2011–2031. J Pop Research 31, 317–343 (2014). https://doi.org/10.1007/s12546-014-9136-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12546-014-9136-2

Keywords

Search

Navigation