Research Projects Using Growing Up Data

Dimensions of wellbeing across the early life course

Publication Date:
Lead Organisation:
Victoria University of Wellington
Lead Researcher:
Kate Prickett
Access Type:
Primary Classification:
Family and Whanau
Secondary Classification:

A more holistic understanding of the factors that may be contributing to, and coexisting with, poverty are important for supporting families and whānau, buffering children from the effects of those various factors and disrupting the intergenerational transmission of disadvantage. Despite a recognition that elements of wellbeing tend to co-occur across multiple domains, there is a dearth of information on families’ and children’s experiences of multi-dimensional advantages and disadvantages across the early life course, nor whether different types of benefits are more likely to co-exist than others. In short, we do not know whether and how different domains of wellbeing cluster together, and how they do so at critical time points in children’s lives.

Identifying how these dimensions of wellbeing cluster together across early childhood—and the depth and length of children’s exposure to these dimensions—is important for understanding the range of factors that contribute to children’s wellbeing. Early childhood specifically is considered a sensitive period of development—one that shapes children’s lifelong health and wellbeing trajectories. Importantly, early childhood has also been identified as a crucial policy investment period, where intervention has been shown to have long-term fiscal benefits. This is also a period of significant economic and social change and instability for families and whānau, as they adjust to a new set of expenses and family stressors.

Thus, this project asks the following questions:

1. How do domains of disadvantage (e.g., material hardship, parents’ employment circumstances, housing conditions) cluster together across early-to-middle childhood for New Zealand children and tamariki?

2. What are the key sociodemographic predictors of these different experiences?

3. Are these multiple disadvantage trajectories associated with child wellbeing?

We will use data from the antenatal, 9-month, 2-year, 4.5-year, and 8-year waves. Our elements of wellbeing will include those constructs that measured at each time point, that the literature provides evidence are important for child wellbeing, and that are potential policy 'levers.' These include: household income, material hardship, home ownership, residential mobility, overcrowding, parental work status, parental depression, and neighbourhood deprivation. A wide range of covariates will included in the analyses.

Latent class analysis will be used to construct the 'profiles' of wellbeing at each wave. The consistency in 'profiles' across waves will determine the approach to constructing trajectories of experience across time. Multinomial regression will be used to examine associations between sociodemographic variables and profile membership. The analytical approach to answer research question #3 will also be determined by the consistency in classes across waves (i.e., multinomial regression in a random effects framework versus repeated regressions across waves versus SEM but with latent variable construct for multidimensional wellbeing).

The anticipated output is a report for the Productivity Commission to inform their inquiry into persistent disadvantage.