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Social Indicators Research

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28-04-2017

Multidimensional Child Poverty: From Complex Weighting to Simple Representation

Authors: Maryam Abdu, Enrique Delamonica

Published in: Social Indicators Research | Issue 3/2018

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Abstract

Within the debates on poverty measurement among experts as well as the discussions about Sustainable Development Goals (SDGs) an interesting shift has taken place in recent years, away from uni-dimensional measurements (based on a poverty line) and towards multi-dimensional approaches. Any multi-dimensional approach is, by definition, dealing with the complexity of poverty across a range of aspects which need to be assessed separately before they can be combined (weighted) to produce an overall, synthetic measure. This measure, in turn, if it is going to be more than a theoretical curiosum, must be translated for and presented to the public at large and decision-makers in order to impact programs and policies to reduce and eliminate poverty. In this paper, all the steps involved in the last two sentences are explored. This is done in the context of the measurement of Child Poverty that was initiated over a decade ago by UNICEF. After a brief review of the history and evolution of the measurement of Child Poverty, three consecutive sections dealing with the issues raised above are introduced. First, based on the experience of over 70 countries from all developing continents, the selection of indicators is discussed. This is followed by a simple simulation showing the pitfalls of endogenous weights. The third of these sections explores the challenges in presenting these results to a wide, lay audience which are shown to be less intractable than the issues faced by weighted composite indexes and the “dollar-a-day” uni-dimensional metric. An additional section deals with the problem of embedding the Child Poverty measurement within the larger poverty picture of the country (i.e. comparing and complementing metrics of adult and overall poverty). The final section summarizes the main results and conclusions of the paper.

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Lack of material resources and wealth (monetary or otherwise) is the lay persons’ intuitive understanding of poverty as well as the typical dictionary definition. Other problems people might face are distinct from poverty. Saying people are poor because they are sad, tired, victims of rape, religiously discriminated, caught in a traffic accident, traumatized from witnessing a crime, kidnapped, afraid to walk alone at night, worried about the future, etc., would fly in the face of any reasonable understanding of the meaning of poverty. Of course, there is also various streams of literature on subjective well-being and poverty, but they addresses different issues ranging from alternative estimates of the poverty line to inclusion of children’s perception of feeling pressured at school as part of their well-being (Groedhart et al. 1977; Marks 2007; Bradshaw et al. 2007; Hoelscher et al. 2012). Similarly, authors working within the Capabilities Approach have measured multi-dimensional capabilities deficiencies (which is different from poverty) by including non-material elements of child welfare such as parental affection or feeling of autonomy which go beyond poverty (Apablaza and Yalonetzky 2011; Ballet et al. 2011; Trani and Biggeri 2013; Stoecklin and Bonvin 2014).

There is a conceptual shift from traditional economics here. For instance, we do not say the cause of a person having no house (the right to housing being violated) is poverty, nor do we say that we identify as monetary poor as those without a house. Instead, we say he or she who has no house is poor (irrespective and independently of whether they have monetary resources or not). Housing deprivations “makes” them poor (“constitutes” being poor).

The issue of teen pregnancy provides an interesting illustration of the relationship between deprivations and problems which do not constitute poverty. While teen pregnancy is the result of (and also causes) many problems and child rights violations, it is not in itself a material deprivation. However, lack of access to (reproductive) health care for teen agers (male and in particular female) is indeed a material deprivation that constitutes poverty (irrespective of the availability of income).

Or linking this issue to the analysis of time-poverty (Antonopoulos and Memis 2010; Boyden and Bourdillon 2012).

This provides the basis for sensitivity analysis, as is mentioned below.

And established as such by international experts (e.g. WHO for moderate and severe malnutrition in each of the anthropomorphic nutrition indicators and the WHO-UNICEF Joint Monitoring Programme on water and sanitation access).

For instance, for nutrition, instead of using two standard deviations from the norm (i.e. the international definition of moderate malnutrition), they used three standard deviations (the international norm for severe malnutrition), deprivation in education was determined by the child having never attended any type of formal schooling.

This is not the only convention dealing with these rights. The African Charter on the Rights and Welfare of the Child and the Covenant on the Rights of the Child in Islam also stipulate these rights. Earlier international law instruments (e.g. Declaration) also included them. The virtues of the CRC are its universality (all but two countries have ratified it) and, being a convention, is stronger than a declaration.

In the Human Rights literature it is said rights cannot be given up (even voluntarily, i.e. rights are inalienable) and they are interrelated (UNDP, 2003).

It is also conceivable to assign arbitrary weights or rely on experts’ opinions. These options are not worthwhile addressing here. However, it is important to clarify that, in this case, equal weighting is different from arbitrary weights. The two are equated in the otherwise clear and elucidating discussion of weighting mechanisms by Decanq and Lugo (2012) as both are clustered together under the category they label “Normative”. The notion that rights are inalienable, non-tradeable, and equally valuable is part of the theoretical framework which establishes the equal weights and would be acceptable (actually the ideal situation) in Decanq and Lugo’s classification. It is not an imposition of arbitrary equality or one based on ignorance. Similarly, Nussbaum (2001) and Dixon and Nussbaum (2012) provide arguments for equal weighting across capabilities which confirms the argument in the previous section about the convergence of approaches.

Of course, as weights usually are made to add up to 1, they can be endogenously adjusted. Moreover, there are other methods (e.g. principal component analysis, multiple correlation, etc) which, although a bit more convoluted, basically follow the same logic. Ferreira and Lugo (2013) propose using Venn diagrams, copula functions, and multivariate stochastic dominance analysis to avoid having to use weights and yet be able to explore correlations and juxtapositions across dimensions to compare across time and space. However, these comparisons are relative and do not provide a clear and unequivocal “headcount” or incidence of poverty.

Hagerty and Land (2007) also found the best approach in the construction of composites indexes is to avoid weighting dimensions. Gordon et al. (2012), following Ghiselli et al. (1981), make similar arguments based on the empirical evidence that when weights across items are similar, there are many variables, and they are correlated (as it is the case in multi-dimensional poverty analysis) weighting does not improve estimates.

For instance, if the threshold indicates that children are deprived of health when they have received less than 3 vaccines out of the mandatory immunization protocol, the bars in the figure would indicate the number of vaccines received by each child. Those children below the line representing the threshold would be considered deprived of their right to health.

In the Alkire-Foster formulae for multi-dimensional poverty among adults, this is the cut-off parameter k.

Theoretically, the cut off of deprivations to be considered poor (whether as a number or a proportion), the parameter k, could be any rational number (see, among others, Alkire and Foster 2011; Santos and Alkire 2011).

Besides being consistent with the discussion above on the fact that each dimension corresponds to a right, and all rights are equally important, there is an additional advantage of avoiding weights. While some of the indicators are measured on an interval scale (e.g. weigh or height of the child), most of them are ordinal (e.g. in-house piped water is better than a well outside which in turn is better than getting water from a river or other open source). This means that individual deprivations cannot be numerically manipulated. However, combinations of achievement/deprivation in the various dimensions can be (partially) ordered and evaluated (Fattore and Maggino 2014; Fattore 2016).

This measurement is just the prevalence (equivalent to the headcount in monetary poverty). For the analysis of depth and severity there are additional formulae and mechanisms (which are compatible with this headcount) ranging from very simple ones to more elaborate ones (see Delamonica and Minujín 2007; ECLAC-UNICEF 2010; Alkire et al. 2015).

I.e. the cost of the basic goods and services every person should be able to consume in order not to be considered poor (see Desai 1986; Laderchi et al. 2003; Lipton and Ravallion 1994; Orshansky 1965; among many others). Freeman (2007) clearly explains the PPP conversation exaggerates the reduction of poverty because the conversion hides the decline of consumption good prices vis-à-vis intermediate and capital goods. As the latter are under-represented in the PPP calculations, it seems that households are able to afford more goods when, actually, it is not the case at all.

In some cases more than 20 percentage points which means many millions of persons (just taking a few large and small countries form around the world like Brazil, Cameroon, Colombia, Cote d’Ivoire, Egypt, Guatemala, Niger, Pakistan, and Thailand, results in over 120 million not properly counted as monetary poor).

The quotes are from an article by the Assistant Director in the IMF’s External Relations Department (Callen 2012). The article was published in the most important information dissemination outlet of the IMF for a wide audience: the Finance and Development magazine website.

Deaton and Paxton (1997), use various levels and adjustments of monetary poverty lines to estimate monetary poverty among children and the elderly.

This issue is not germane only to multi-dimensional Child Poverty. “Separating” those under 18, while it visualizes Child Poverty, is not sufficient to understand properly what is happening to children and their poverty if the same indicators are used for adults and children. This is certainly the case with monetary poverty because, as mentioned above, children are not supposed to have an independent source of income to be pooled with the rest of the household. Thus, estimating monetary Child Poverty from the percentage of children living in monetary poor household assumes that monetary resources are shared equally within the family (Cockburn et al. 2009). We know from the gender literature that resources are definitely not shared equally within the household. Thus, it is not possible to estimate the percentage of children who are monetary poor. What can be calculated is the proportion of children who live on households where the level of monetary resources is less than the poverty line (for that number of household members). This is important information. It can and should be cross-tabulated with Child Poverty (e.g. CONEVAL 2010, 2015 and ECLAC-UNICEF 2010, carried out this type of analysis).

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Title: Multidimensional Child Poverty: From Complex Weighting to Simple Representation
Authors: Maryam Abdu
Enrique Delamonica
Publication date: 28-04-2017
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/s11205-017-1620-6

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