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A Multi-dimensional Individual Well-Being Framework: With an Application to Older Australians

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Abstract

This paper constructs a multi-dimensional individual well-being (MIW) indicator framework to explicitly recognise the inter-relationship between economic and non-economic dimensions in encapsulating the totality of an individual’s life. The MIW framework treats individual well-being as a multi-dimensional concept disaggregable into uniquely defined but latent well-being dimensions with observable indicators attached to each. A composite well-being index applicable to individual level analysis is developed through a series of intra-personal aggregative procedures. The results are based on person-level data from the household, income and labour dynamics in Australia survey and applied to an assessment of the individual well-being of older Australians, aged 65 years and over. The findings indicate that, although older Australians have slightly lower overall well-being compared to non-older adults, driven primarily by declining physical health and to a lesser extent mental health, they maintain strong personal relationships, and engage actively in their communities. More generally, the approach outlines how to quantify objective multi-dimensional assessments of individual well-being.

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Notes

  1. The HDI for instance is the geometric mean of three normalised indices: average life expectancy at birth, an education index (based on two-third weighted mean years of schooling and one-third weighted expected years of schooling) and gross national income per capita.

  2. Two other examples worth mentioning are the micro-data child well-being index (Moore et al. 2008) which is specific to child well-being, and the Personal Well-being Index (International Wellbeing Group 2013) which utilises a subjective well-being approach based on global and domain-specific satisfaction questions, as opposed to the more ‘objective’ well-being approach proposed in this paper.

  3. It is worth noting that while well-being is ubiquitously referred to in social science discourse, there is general agreement that it is a broad, somewhat elusive protean concept without a universally accepted definition (Frønes 2007; McAllister 2005; McGillivray and Clarke 2006). It is often used interchangeably with terms such as happiness, human development, quality of life and life satisfaction (Kahn and Juster 2002; McAllister 2005; McGillivray and Clarke 2006). The empirical evidence, however, suggests that its key contribution is as an umbrella term that is adaptable to and inclusive of a range of applications. For example, it can be defined at the person level or from a society viewpoint; it can be subjectively determined or researcher observed; it can be applied within a psychological dimension as life satisfaction or within a health dimension as the prevalence of disease and disability (McAllister 2005). This paper implicitly adopts Sen’s (1993) evaluative space of ‘well-being achievement’, that is as ‘the constituent elements of the person’s being seen from the perspective of her own welfare’ (pp. 36–37).

  4. A movement either up or down reflects improvement or deterioration.

  5. This recognises that while indicators may have different degrees of importance, they should not be ‘grossly different’ (ibid).

  6. The specification of ‘models of measurement’ in the MIW framework forms only one part of SEM. It is beyond the scope of this paper to use more complex investigations testing the casual relationship between variables and/or latent variables.

  7. Further details comparing the range of well-being studies is available on request from the author.

  8. In Sect. 5 the indicators collectively regarded as manifestations of the economic stability dimension are direct measures of well-being outcomes—they include questions on an individual’s ability to afford socially identified necessities and their self-assessed prosperity.

  9. This distinction aligns with the World Health Organisation’s definition acknowledging the all-encompassing scope of health as three separate constructs; ‘A state of complete physical, mental and social wellbeing, and not merely the absence of disease or infirmity’. In the MIW framework the social functioning aspects of health are reflected in the personal relationships dimension.

  10. Full details of the EFA is available from the author. Briefly, for each dimension a set of indicators was initially chosen based on their face validity and according to their initial placement in HILDA. Four pre-requisites to perform EFA is established: (high sample to variable ratio (148:1); the majority of polychoric correlations coefficients are > 0.30; a Kaiser–Meyer–Olkin measure of 0.945 > 0.6; and a significant Bartlett’s test of sphericity (χ2 (3240) = 364,000, p < 0.001) (OECD 2008; Costello and Osborne 2005). The factor solution using a principal axis factoring extraction method with promax rotation produced a clean structure with 6 factors aligned with the well-being dimensions: high factor loadings on most items (90.1% of items have factor loadings above 0.45 and none below the minimum accepted criterion of 0.32 (Costello and Osborne 2005); zero percentage of cross-loading items; a low percentage of items with high uniqueness (8.6%); and a low eigenvalue range (5.70–16.15).

  11. Factor scoring coefficients are different from the factor loadings in Table 2. A factor loading is the regression coefficient of the factor (latent variable) in explaining the item. Once the relationship between the factor and each item is established, the relationship can be ‘inverted’ so that the factor loading is now used as the basis for calculating a standard regression coefficient of the item in predicting the latent variable. The factor scoring coefficient hence represents the weight of the item in predicting the factor (DiStefano et al. 2009).

  12. While alternative aggregating mechanisms such as geometric aggregation and multi-criteria aggregation exist, they are not yet capable of fully overcoming compensability issues, are computationally complex and lacking in empirical applications relating to micro-level well-being assessments (Maggino and Zumbo 2012; OECD 2008; Mazziotta and Pareto 2013). While this area of research progresses, additive aggregation for micro-level well-being assessments remains a common method offering simplicity and transparency in estimation and interpretation.

  13. Transformation of z-scores (with mean 0 and standard deviation 1) is done by multiplying the z-score by the standard deviation (10) and adding the mean of 100 (Mertler 2007). The standardisation of MIW metrics using z-scores is permissible because this paper presents a cross-sectional analysis with relative comparisons to non-older adult (ibid). However, if the approach is to be applied longitudinally it would necessitate a different normalization not based on a multi-variate distribution and one that allowed the changing relative position of individuals to be assessed (Moeller 2015; Mazziotta and Pareto 2013).

  14. Pairwise comparison across demographic categories beyond two in Table 3 may be statistically significant even if the overall Wald test is not statistically significant. For example, despite statistical insignificance by birthplace for the mental health dimension, means are statistically different between those born in Australia compared to a non-English speaking country at p < 0.05 (p = 0.03).

  15. Results available on request.

  16. For example, a recent study by Baker et al. (2014) using the pooled data across 10 waves of HILDA found a bi-directional relationship between housing affordability and health in Australia. They report that a prior condition of mental health can predict current affordable housing outcomes and that current housing affordability influences current individual health.

  17. In making this statement, the assumption is that the housing tenure position for many older tenants occurred before retirement and continued post-retirement.

  18. There is a growing body of gerontological literature investigating the existence of cumulative advantage/disadvantage over the life course to explain increasing diverging well-being outcomes with age (Dannefer 2003; Ross and Wu 1996).

  19. The presumption is that, for this cohort of older Australians, the majority with tertiary qualifications graduated, at least, 40 to 50 years earlier. It is also understood that amongst the older Australians born in non-English speaking backgrounds, this includes a wide time span from those recently arriving elderly migrants to those who arrived in Australia as early as the 1950s.

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    Yuvisthi Naidoo

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Naidoo, Y. A Multi-dimensional Individual Well-Being Framework: With an Application to Older Australians. Soc Indic Res 146, 581–608 (2019). https://doi.org/10.1007/s11205-019-02132-w

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