Social Indicators Research

, Volume 108, Issue 1, pp 111–130 | Cite as

Monitoring Socio-demographic Risk: A Cohort Analysis of Families Using Census Micro-Data

  • Peter Davis
  • Mervyl McPherson
  • Mark Wheldon
  • Martin von Randow


We apply cohort techniques to monitor four indicators of socio-demographic risk crucial to family wellbeing; namely, income, employment, education, and housing. The data were derived from New Zealand’s five-yearly Census for the period 1981–2006. This allowed us to track birth cohorts of mothers (and their families) over six successive New Zealand censuses focusing on the main childrearing ages of 20–59. This produced ten cohorts—termed “open familial cohorts”—ranging from mothers born in the period 1932–1937 through to 1977–1981. We present age, period and cohort analyses. Families in which the mother is in her early 20s were the most vulnerable, with the lowest incomes, the greatest risk of worklessnes, and the lowest levels of home ownership. Of particular interest is that those in the most recent cohorts—born since 1967—were worse off compared to earlier cohorts. The period from the mid-1980s to the mid-to-late 1990s was one of greatest “socio-demographic risk”, with the lowest work, income and education prospects over the 25 years. The picture on generational profiles was mixed. Contrary to popular mythology the “baby-boomer” cohorts did not enjoy an unqualified advantage over others; indeed the most recent cohorts were doing well, with relatively high incomes, education and work levels. The analysis is successful in identifying age and period effects over a period of major social change, and in documenting cohort experiences for each indicator, thus demonstrating the potential of constructing cohorts from routinely-collected census micro-data for monitoring and policy purposes.


Socio-demographic risk Family wellbeing Cohort analysis Age, period and cohort effects Social monitor Census micro-data 



The data for this paper were generated under the Family Whānau and Wellbeing Project (FWWP) which was funded by the Foundation for Research, Science and Technology. Access to the data used in this study was provided by Statistics New Zealand in a secure environment designed to give effect to the confidentiality provisions of the Statistics Act 1975. This study would not have been possible without the generous assistance of Statistics New Zealand. The views expressed in this occasional paper are the personal views of the authors and should not be taken to represent the views or policy of Statistics New Zealand or the Government. We are grateful to Professor Natalie Jackson, Roy Lay-Yee and Dr. Gerry Cotterell for comments on earlier drafts of this paper.


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Peter Davis
    • 1
  • Mervyl McPherson
    • 1
  • Mark Wheldon
    • 1
  • Martin von Randow
    • 1
  1. 1.Centre of Methods and Policy Application in the Social Sciences (COMPASS)The University of AucklandAucklandNew Zealand

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