Asset Ownership of the Elderly Across Europe: A Multilevel Latent Class Analysis to Segment Countries and Households

  • Omar PaccagnellaEmail author
  • Roberta Varriale
Part of the Studies in Theoretical and Applied Statistics book series (STAS)


Wealth is a useful measure of the socio-economic status of the elderly, because it might reflect both accumulated socio-economic position and potential for current consumption. A growing number of papers have studied household portfolio in old age, both from a financial point of view (i.e. in the framework of the life-cycle model) and from a marketing perspective. In this chapter, we aim at providing new evidence on this issue both at the household and country level, by investigating similarities and differences in the ownership patterns of several financial and real assets among elderly in Europe. To do so, we exploit the richness of information provided by SHARE (Survey of Health, Ageing and Retirement in Europe), an international survey on ageing that collects detailed information on several aspects of the socio-economic condition of the European elderly. Given the hierarchical structure of the data, the econometric solution we adopt is a multilevel latent class analysis, which allows us to obtain simultaneously country and household segments.


Ageing Latent class analysis Multilevel data analysis Segmentation 



The authors thank Dimitri Christelis and two anonymous referees for their helpful comments. This chapter uses data from SHARE wave 2 release 2.3.1, as of July 28, 2010. The SHARE data collection has been primarily funded by the European Commission through the 5th framework programme (project QLK6-CT-2001-00360 in the thematic programme Quality of Life), through the 6th framework programme (projects SHARE-I3, RII-CT-2006-062193, COMPARE, CIT5-CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812) and through the 7th framework programme (SHARE-PREP, 211909 and SHARE-LEAP, 227822). Additional funding from the U.S. National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, Y1-AG-4553-01 and OGHA 04-064, IAG BSR06-11, R21 AG025169) as well as from various national sources is gratefully acknowledged (see for a full list of funding institutions).


  1. 1.
    Bijmolt, T.H.A., Paas, L.J., Vermunt, J.K.: Country and consumer segmentation: multi-level latent class analysis of financial product ownership. Int. J. Res. Marketing 21, 323–340 (2004)CrossRefGoogle Scholar
  2. 2.
    Börsch-Supan, A., Brugiavini, A., Jürges, H., Kapteyn, A., Mackenbach, J., Siegrist, J., Weber, G. (eds.): First Results from the Survey of Health, Ageing and Retirement in Europe (2004–2007). Starting the Longitudinal Dimension. MEA, Mannheim (2008)Google Scholar
  3. 3.
    Christelis, D., Georgarakos, D., Haliassos, M.: Economic integration and mature portfolios. CSEF Working Paper n. 194, Centre for Studies in Economics and Finance (CSEF), University of Naples (2008)Google Scholar
  4. 4.
    Christelis, D., Jappelli, T., Padula, M.: Cognitive abilities and portfolio choice. Eur. Econ. Rev. 54, 18–38 (2010)CrossRefGoogle Scholar
  5. 5.
    Guiso, L., Haliassos, M., Jappelli, T. (eds.): Household Portfolios. MIT, Cambridge (2002)Google Scholar
  6. 6.
    Hagenaars, J.A., McCutcheon, A.L.: Applied Latent Class Analysis Models. Cambridge University Press, Cambridge (2002)CrossRefGoogle Scholar
  7. 7.
    Hurd, M.: Portfolio holdings of the elderly. In: Guiso, L., Haliassos, M., Jappelli, T. (eds.) Household Portfolios, pp. 431–472. MIT, Cambridge (2002)Google Scholar
  8. 8.
    Hurd, M., Shoven, J.B.: The economic status of the elderly. In: Bodie, Z., Shoven, J.B. (eds.) Financial Aspects of the United States Pension System, pp. 359–398. University of Chicago Press, Chicago (1983)Google Scholar
  9. 9.
    Lemmens, A., Croux, C., Dekimpe, M.G.: Consumer confidence in Europe: united in diversity? Int. J. Res. Marketing 24, 113–127 (2007)CrossRefGoogle Scholar
  10. 10.
    Lukočienė, O., Varriale, R., Vermunt, J.K.: The simultaneous decision(s) about the number of lower- and higher-level classes in multilevel latent class analysis. Sociol. Methodol. 40, 247–283 (2010)CrossRefGoogle Scholar
  11. 11.
    Paas, L.J., Bijmolt, T.H.A., Vermunt, J.K.: Acquisition patterns of financial products: a longitudinal investigation. J. Econ. Psychol. 28, 229–241 (2007)CrossRefGoogle Scholar
  12. 12.
    Skrondal, A., Rabe-Hesketh, S.: Generalized Latent Variable Modeling. Chapman and Hall, Boca Raton (2004)zbMATHCrossRefGoogle Scholar
  13. 13.
    Steenkamp, J.-B., ter Hofstede, F.: International market segmentation: issues and perspectives. Int. J. Res. Marketing 19, 185–213 (2002)CrossRefGoogle Scholar
  14. 14.
    Vermunt, J.K.: Multilevel latent class models. Sociol. Methodol. 33, 213–239 (2003)CrossRefGoogle Scholar
  15. 15.
    Vermunt, J.K., Magidson, J.: LG-Syntax Users Guide: Manual for Latent GOLD 4.5 Syntax Module. Statistical Innovations Inc., Belmont (2008)Google Scholar
  16. 16.
    Wedel, M., Kamakura, W.A.: Market Segmentation: Conceptual and Methodological Foundations, 2nd edn. Kluwer Academic, Dordrecht (2000)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Statistical SciencesUniversity of PaduaPadovaItaly
  2. 2.Istat - Italian National Statistical InstituteRomeItaly

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