Analysing Multidimensional Child Poverty in Sub-Saharan Africa: Findings Using an International Comparative Approach

Abstract

This study provides with a first indication on the number of multidimensionally poor children in sub-Saharan Africa. It presents a methodology measuring multidimensional child deprivation within and across countries, and it is in line with the Sustainable Development Goal 1 focusing on multidimensional poverty by age and gender. Using the Multiple Overlapping Deprivation Analysis (MODA) methodology, the study finds that 67% or 247 million children are multidimensionally poor in the thirty sub-Saharan African countries included in the analysis. Multidimensional poverty is defined as missing two to five aspects of basic child well-being captured by dimensions anchored in the Convention on the Rights of the Child, namely nutrition, health, education, information, water, sanitation, and housing. The analysis also predicts the multidimensional child poverty rates for the whole sub-Saharan African region estimating 64% or 291 million children to be multidimensionally poor. In comparison, monetary poverty rates measured as less than USD 1.25 PPP per capita spending a day and weighted by the child population size finds 48% poor children. The results of this study highlight the extent of multidimensional poverty among children in sub-Saharan Africa and the need for children to have a specific poverty measure in their own right.

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Notes

  1. 1.

    SDG 1 – Target 1.2: By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions (UNDESA 2016).

  2. 2.

    SDG 1 – Target 1.1: by 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day (UNDESA 2016).

  3. 3.

    While many of the indicators are collected at a household level, the analysis is child-centred using children as unit of analysis.

  4. 4.

    The eighth dimension – Protection from violence – is included in the standard CC-MODA method whenever the violence module is available in the dataset. Since it was unavailable for several of the 30 countries in sub-Saharan Africa, the Violence dimension has been excluded from this specific study to ensure comparability of results in the region.

  5. 5.

    Although nutrition and health are crucial for child well-being regardless of children’s age, these dimensions are not included in the analysis for children aged 5–17 due to lack of adequate indicators for this age-group in the DHS and MICS surveys.

  6. 6.

    The analysis has also been carried out using all thresholds; the results can be found in de Milliano and Plavgo (2014).

  7. 7.

    To calculate child population, the percentage of children in a country (authors’ calculations based on MICS/DHS data; See Annex 1) is multiplied with the total population of each country in 2012 (World Bank Databank 2014).

  8. 8.

    Alkire and Foster (2011) methodology has been applied to calculate the average deprivation intensity (A) and the adjusted multidimensional poverty headcount rate (M0).

  9. 9.

    Monetary poverty has been included in other MODA studies such as the studies on Mali (de Milliano and Handa 2014) and the European Union (Chzhen et al. 2016).

  10. 10.

    International monetary poverty rates of USD 1.25 PPP a day are applied, retrieved from the World Bank Databank.

  11. 11.

    Although it would be informative to analyse the differences in deprivation rates within urban areas focusing on slum areas, the data used for this analysis do not permit this.

  12. 12.

    Analysis by gender using CC-MODA is only possible at indicator level; it is not done for the multidimensional poverty analysis because five out of seven dimensions are constructed using indicators that are applied to all children of the same household.

  13. 13.

    While gender is an important expected correlate to deprivation, results are not presented by gender given that the inclusion of indicators measured at the household level would mask any gender differences.

  14. 14.

    The average deprivation intensity (A) for Figure 5 is calculated using a cut-off of one dimension to avoid censoring the deprivations that may be experienced in isolation from other deprivations.

  15. 15.

    See De Milliano and Plavgo (2014) for more details on results by threshold and age-group.

  16. 16.

    Additional comparisons between multidimensional child poverty, national poverty, and national child poverty have been carried out (see de Milliano and Plavgo 2014), but are not included in this paper due to space limitations.

  17. 17.

    Data on monetary poverty and multidimensional poverty are, where possible, used from the same year. There are, however, time lags for some of the countries, so the comparison should be interpreted with caution due to discrepancies in the year of measurement. Annex 1 specifies the year by country and data source.

  18. 18.

    Calculations are made for 44 developing countries in Sub-Saharan Africa as classified by the World Bank, excluding Mauritius, Seychelles, Somalia and South Sudan, while adding Equatorial Guinea and Sudan.

  19. 19.

    Both the HDI and the multidimensional child poverty measure contain dimensions related to living standards, health, and education, suggesting a certain degree of endogeneity. However, the indicators that have been used for constructing the two measures differ, allowing the use of HDI when predicting the multidimensional poverty rates for the purpose of this analysis.

  20. 20.

    The HDI, the share of urban population, and the population size in 2012 are retrieved from the World Bank Databank (2014).

  21. 21.

    As a robustness check the last column in Annex 6 estimates the deprivation rates for the thirty countries in the sample using the HDI predictive model.

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Acknowledgements

We are grateful for the valuable contribution of many UNICEF colleagues, as well as the researchers working on multidimensional poverty measurement in OPHI, the University of Bristol, the University of Maastricht, and the University of Sussex, for their advice and inspiration. We are especially thankful to Chris de Neubourg, Jingqing Chai, Ziru Wei, Sudhanshu Handa, and Goran Holmqvist for their substantive engagement throughout the project. Many thanks to the anonymous reviewers providing useful comments to an earlier draft of this paper.

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Correspondence to Ilze Plavgo.

Appendices

Annex 1

Table 5 List of countries and data sources

Annex 2

Table 6 Definitions and thresholds of deprivation indicators

Annex 3

Table 7 Deprivation headcount rate by indicator and age-group

Annex 4

Table 8 Adjusted multidimensional deprivation ratio by country and the contribution of each country to the total adjusted deprivation ratio across thirty countries (children deprived in 2–5 dimensions)

Annex 5

Table 9 OLS Model and coefficients for the prediction of multidimensional deprivation

Annex 6

Table 10 Estimates and predictions of multidimensional child poverty and monetary poverty

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de Milliano, M., Plavgo, I. Analysing Multidimensional Child Poverty in Sub-Saharan Africa: Findings Using an International Comparative Approach. Child Ind Res 11, 805–833 (2018). https://doi.org/10.1007/s12187-017-9488-1

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Keywords

  • Child poverty
  • Multidimensional deprivation
  • Child rights
  • Sub-Saharan Africa