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A Multidimensional Dynamic Measure of Child Disadvantage: A Methodological Tool for Policymakers


This paper demonstrates the applicability of an innovative approach towards examining child disadvantage, using a holistic, dynamic measure that not only accounts for multiple sources of disadvantage but also for the recurrence and persistence of disadvantage throughout a child’s life. We analyse child disadvantage using two longitudinal surveys of the Australian child population, one of which is specific to Indigenous children, who experience notably higher rates of disadvantage. Among Australian children, we detect that poor body weight and bullying—representative of the broad dimensions of health and emotional wellbeing—should be of significant concern to policymakers. Among Indigenous children, housing conditions, schooling and exposure to risky behaviours stand out as areas of concern. By identifying the dimensions in which rates of child disadvantage are most severe, this methodological approach can help steer targeted policy actions.

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Fig. 1
Fig. 2
Fig. 3

Source: Longitudinal Study of Australian Children (LSAC) collected biennially from 2004 to 2012, following children from the age of 4–5 years to 12–13 years

Fig. 4

Source: Longitudinal Study of Indigenous Children (LSIC) collected annually from 2008 to 2013, following children from the age of 3½–5 years to 8 ½–10 years

Fig. 5

Source: Longitudinal Study of Australian Children (LSAC) collected biennially from 2004 to 2012, following children from the age of 4–5 years to 12–13 years

Fig. 6

Source: Longitudinal Study of Indigenous Children (LSIC) collected annually from 2008 to 2013, following children from the age of 3½–5 years to 8½–10 years

Fig. 7

Source: Longitudinal Study of Australian Children (LSAC) collected biennially from 2004 to 2012, following children from the age of 4–5 years to 12–13 years

Fig. 8

Source: Longitudinal Study of Indigenous Children (LSIC) collected annually from 2008 to 2013, following children from the age of 3½–5 years to 8½–10 years


  1. 1.

    See Saunders (2015) for an explanation of the ‘social inclusion agenda’ that formed the centrepiece of the social policy agenda of the Australian Government between 2007 and 2013.

  2. 2.

    Examples of recent contributions in the unidimensional context include Calvo and Dercon (2007), Foster (2007), Bossert et al. (2010) and Gradin et al. (2012).

  3. 3.

    See, also, Alkire et al. (2015).

  4. 4.

    See Bossert et al. (2010) for a similar distinction in the unidimensional context.

  5. 5.

    In Australia, the ‘Indigenous’ population refers to the Aboriginal and Torres Strait Islander people who are the original inhabitants of the land.

  6. 6.

    Given that \( \mu_{i} \) takes as its input the (T × K) matrix \( \varvec{D}_{i} \), there can in principle be a maximum of \( 2^{{\left( {T*K} \right)}} \) different types of child disadvantage scores, one for each possible permutation of the child disadvantage profile.

  7. 7.

    Equation (4) moves beyond a simple counting approach since it uses information on permutations of disadvantage across the time dimension, and not simply combinations.

  8. 8.

    The three parameters used in this study, \( \alpha ,{\kern 1pt} \beta , {\text{and}} \gamma \), correspond to the same parameters in Gradin et al. (2012) unidimensional model, except that \( \alpha \) only applies to disadvantage across time in their specification, whereas \( \alpha \) applies to both time and indicators here.

  9. 9.

    As a numerical example, consider a child’s disadvantage profile for one indicator (\( K = 1 \)) over four time periods (\( T = 4 \)): let us suppose this child’s profile is denoted as \( \varvec{D}_{i} = \left( {\begin{array}{*{20}c} {1,} & {1,} & {0,} & 0 \\ \end{array} } \right) \) indicating that they are disadvantaged for the first 2 of the 4 possible time periods. Using Eq. (4) and \( s = \left( {c_{ijt} /T} \right) \), we compute the child’s individual disadvantage score as \( \mu_{i} = \left( {\frac{1*2/4 + 1*2/4 + 0*2/4 + 0*2/4}{4}} \right)^{\alpha } \), where each of the two periods of disadvantage (t = 1, 2) is multiplied by (2/4), giving weight to the fact that they belong to a spell of 2 out of a possible of 4 periods.

  10. 10.

    For more details on the properties of these disadvantage measures, refer to Nicholas and Ray (2012).

  11. 11.

    The CRC was signed on 20 November 1989 and came into force on 2 September 1990 (Office of the High Commissioner, United Nations Human Rights (undated)). The notion of ‘child poverty’ did not explicitly appear in the CRC, although we note that a universally accepted definition of child poverty was not adopted within the UN until 2006.

  12. 12.

    To test the robustness of our results to different weighting patterns, we repeat the calculations by varying the weighting schemes over the seven dimensions of child wellbeing. Results are available on request from the authors.

  13. 13.

    As stated on the LSAC website

  14. 14.

    As stated on the LSIC website

  15. 15.

    For more information on survey design and sampling methodologies, refer to the LSAC Data User Guide (available through the ‘Growing Up in Australia’ website and the LSIC User Guide (available through the Australian Department of Social Services website

  16. 16.

    To identify the level of geographic remoteness of the area in which a child is living, we use a variable contained in the LSIC dataset capturing the ‘level of relative isolation categorised as none, low, moderate or high/extreme. We convert into binary subgroups: ‘low’ (none and low categories) and ‘high’ (moderate and high/extreme categories). Since a small number of children changes location over time, we base these categorisation on the location in which the child spent the majority of their years. In each age group of the balanced LSIC panel, we have a sample of 276 Indigenous children in the ‘low’ isolated areas and 45 Indigenous children in the ‘high’ isolated areas.

  17. 17.

    To assess whether these computations are affected by respondent attrition in the sample over time, the headcount rates are also calculated using the unbalanced panel, which are reported in parentheses in Table 3. The consistency of the numbers between the balanced and unbalanced panel calculations alleviates our concern about this potential attrition bias.


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This paper uses unit-record data from ‘Growing Up in Australia’ (the Longitudinal Study of Australian Children conducted in partnership between the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS), and the Australian Bureau of Statistics (ABS)) and from ‘Footprints in Time’ (the Longitudinal Study of Indigenous Children which was initiated and is funded and managed by the Australian Government Department of Social Services (DSS)). The findings and views presented in this paper are those of the authors and should not be attributed to the DSS, the AIFS, the ABS, nor the Indigenous people and their communities involved in the study. Helpful comments from two anonymous referees, and from seminar participants in several presentations of earlier versions of the paper, are gratefully acknowledged. The usual disclaimer applies.

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Corresponding author

Correspondence to Ankita Mishra.


Appendix 1

Survey Questionnaire Items

See Tables 6, 7.

Table 6 Survey questionnaire items and parameters to classify child’s state disadvantage, using Longitudinal Study of Australian Children (LSAC)
Table 7 Survey questionnaire items and parameters to classify child’s state disadvantage, using Longitudinal Study of Indigenous Children (LSIC)

Appendix 2

Pairwise Correlations Between Indicators in Duration and Persistence

To further explore the multidimensional nature of disadvantage, we extend our analysis by examining the degree of correlation between the various indicators. Specially, we compute the extent to which the average duration of time that a child experiences disadvantage in one indicator is associated with their average duration of disadvantage in another indicator, measured by pairwise correlation values.

Within the LSAC sample of children, we observe that many, though not all, of the disadvantage indicators are significantly correlated in duration, and that most these pairwise correlation values are positive (Appendix Table B1). This denotes that a longer spell of disadvantage in one indicator is associated with a longer spell in the other. The strongest duration correlations exist among the indicators relating to the dimensions of family relationships (outdoor activities), community connectedness (community activities) and material wellbeing (extra cost activities and access to computer). Additionally, health (use of medical care) is found to be strongly associated in duration with indicators relating to educational wellbeing (school performance) and emotional wellbeing (bullying). In some instances, indicators are found to be negatively correlated to each other. Namely, shorter spells of disadvantage in one of the measures of educational wellbeing (talk about school) are found to be correlated with longer spells of disadvantage in indicators of health (use of medical care) and of emotional wellbeing (bullying). Within the LSIC sample, we detected relatively fewer pairwise correlations of statistical significance among the various indicators (Appendix Table B2). Where significant correlations are detected, they relate to housing quality, housing size, bullying, educational development, and community safety and suitability.

When we extend this correlation analysis to the persistence-augmented measures of disadvantage, we observe for the LSAC sample mostly no change in the indicators of the correlated in duration, although the degree of correlation among them weakens (Appendix Table B3). The only exception to this observation is the school performance indicator which intensifies in its correlation of duration with the other indicators. For the LSIC sample, the use of the persistence-augmented measures of disadvantage results in fewer of the indicators being correlated in duration (Appendix Table B4).

As noted in the main text, correlation values do not necessarily imply causality or a commonality of causal factors, yet can offer value in pointing towards potential interconnections which can be further investigated with appropriate analysis. Given the inherently multi-faceted nature of disadvantage, this type of analysis illustrates how a multidimensional methodology generates a picture of children’s experiences of disadvantage that is not only more comprehensive but also more informative in guiding the next steps of effective policy design.

See Tables 8, 9, 10, 11.

Table 8 Pairwise correlation between durations in disadvantage for Australian children in LSAC
Table 9 Pairwise correlation between durations in disadvantage for Indigenous children in LSIC
Table 10 Pairwise correlation between persistence-augmented durations in disadvantage for all Australian children in LSAC
Table 11 Pairwise correlation between persistence-augmented durations in disadvantage for Indigenous children in LSIC

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Mishra, A., Ray, R. & Risse, L. A Multidimensional Dynamic Measure of Child Disadvantage: A Methodological Tool for Policymakers. Soc Indic Res 139, 1187–1218 (2018).

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  • Multidimensional deprivation
  • Child disadvantage
  • Persistence
  • Longitudinal study
  • Indigenous children

JEL Classifications

  • D63
  • I12
  • I31
  • I32
  • J15