Abstract
Although the UN Millennium Development Goals (MDGs) have contributed to substantial progress in reducing the number of people living on less than $1.25 a day since 1990, it is now generally accepted that poverty goes beyond the simple lack of income. The new Sustainable Development Goals (SDGs) adopted by all member states of the United Nations (UN) in September 2015 call for “reducing 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” by 2030 (UNDESA 2016). Although there must be numerous ways of achieving this goal, research evidence on reducing multidimensional poverty is scarce. This paper investigates possible strategies for halving MD poverty among children using the case studies of Armenia and Bosnia and Herzegovina (BiH). Both Armenia and BiH conducted multidimensional child poverty studies in 2014–2016 based on nationally agreed definitions and using secondary data from representative household budget surveys. This analysis compares two approaches to halving the rate of multidimensional poverty among children aged 5–15 by employing static simulations: 1) reducing deprivation headcount in two to three key dimensions and 2) increasing monetary transfers to the consumption-poor.
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Notes
Although this method is based on strong assumptions, it is the first attempt to simulate the impact of social transfers on the material deprivation status of the recipient households using EU household survey data. Similarly to the comparison of pre- and post-transfer income poverty rates, this method ignores behavioral consequences of withdrawing social transfers. It does not fully account for differences in access to savings or credit, or in-kind benefits or services.
http://ec.europa.eu/eurostat/documents/1012329/6071326/2009_Module_Material_deprivation.pdf/8a3e135d-f926-41bd-9f0c-3f4f87fd5356 (last accessed on 09/06/2016). The EU-SILC questions about children’s needs are based on the results of a Eurobarometer survey conducted in 2007 across the then 27 European Union states plus Croatia which asked respondents aged 15 and over how necessary it is for a child in their country to enjoy each item in order to be able to live and develop in good conditions (European Commission 2007). These questions were originally derived from the UK Poverty and Social Exclusion Survey 1999.
To simulate the reduction of the deprivation rate in one or more dimensions by a certain proportion x we switch the deprivation status of a random sub-sample of children from 1 to 0. The random sub-sample is composed of m observations, where m/D = x and D is the number of children originally deprived in that dimension.
A notable exception is a higher deprivation rate in the education dimension in urban than in rural areas in Armenia. It is driven by higher rates of children lacking ‘a suitable place to do homework’ in urban (41.3%) than in rural (26.5%) areas, likely due to higher rates of overcrowding in urban areas (see Ferrone and Chzhen 2016).
For a random 10% of children who are deprived in information (and in leisure), we change their deprivation status in information from deprived to non-deprived (so that they are still deprived in leisure but no longer in information), while switching deprivation in information from non-deprived to deprived for the same number of children (picked randomly) who were originally deprived in neither information nor leisure. This reduces the Pearson correlation between information and leisure from 0.21 to −0.01.
In a slight departure from the methodology adopted for BiH, where we picked the three dimensions with the highest headcounts, in Armenia we pick housing (46%) over social relations (48%) because, while the rates of deprivation in these dimensions are very similar, it is important to pick two household-based dimensions and one child-specific dimension for consistency with the BiH results.
Swapping deprivation status for 10% of children deprived in information reduces the correlation between utilities and information from 0.38 to 0.12.
The transfer is constructed using a random variable distributed as a Beta, with mean 0.20 and variance derived from the original variable.
It would cost approximately $9 million based on current (July 2016) exchange rates, compared with $2.9 million it would cost to transfer $20 per capita to the consumption-poor.
Approximately $3.4 million and $2.6 million for simulations 1 and 3, respectively.
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Acknowledgements
The authors are grateful to the UNICEF offices in Armenia and Bosnia and Herzegovina as well as their partners at the statistical agencies for giving access to the data for studying multidimensional child poverty. The views and analysis in this paper are the responsibility of the authors alone.
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Ferrone, L., Chzhen, Y. How to Reach the Sustainable Development Goal 1.2? Simulating Different Strategies to Reduce Multidimensional Child Poverty in Two Middle-Income Countries. Child Ind Res 11, 711–728 (2018). https://doi.org/10.1007/s12187-017-9485-4
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DOI: https://doi.org/10.1007/s12187-017-9485-4