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Social Indicators Research

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04-09-2017 | Original Research

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

Authors: Ankita Mishra, Ranjan Ray, Leonora Risse

Published in: Social Indicators Research | Issue 3/2018

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Abstract

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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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.

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

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

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

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

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.

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

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.

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.

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

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.

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.

As stated on the LSAC website www.growingupinaustralia.gov.au.

As stated on the LSIC website www.dss.gov.au/about-the-department/national-centre-for-longitudinal-studies/overview-of-footprints-in-time-the-longitudinal-study-of-indigenous-children-lsic.

For more information on survey design and sampling methodologies, refer to the LSAC Data User Guide (available through the ‘Growing Up in Australia’ website www.growingupinaustralia.gov.au/data/docs/userguide.pdf) and the LSIC User Guide (available through the Australian Department of Social Services website www.dss.gov.au/sites/default/files/documents/04_2015/data_user_guide_-_release_6.0.pdf).

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.

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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Title: A Multidimensional Dynamic Measure of Child Disadvantage: A Methodological Tool for Policymakers
Authors: Ankita Mishra
Ranjan Ray
Leonora Risse
Publication date: 04-09-2017
Publisher: Springer Netherlands
Published in: Social Indicators Research / Issue 3/2018
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-017-1742-x

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