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Multidimensional Measurement of Poverty Among Women in Sub-Saharan Africa

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Abstract

Since the seminal work of Sen, poverty has been recognized as a multidimensional phenomenon. The recent availability of relevant databases renewed the interest in this approach. This paper estimates multidimensional poverty among women in fourteen Sub-Saharan African countries using the Alkire and Foster multidimensional poverty measures, whose identification method is based on a counting approach. Four dimensions are considered: assets, health, schooling and empowerment. The results show important differences in poverty among the countries of the sample. The multidimensional poverty estimates are compared with some alternative measures such as the Human Development Index, income poverty, asset poverty and the Gender-related Development Index. It is found that including additional dimensions into the analysis leads to country rankings different from those obtained with the mentioned four measures. Decompositions by geographical area and dimension indicate that rural areas are significantly poorer than urban ones and that a lack of schooling is, in general, the highest contributor to poverty. The paper also conducts robustness and sensitivity analyses of the multidimensional estimates with respect to the number of dimensions in which deprivation is required in order to be considered poor, as well as to the poverty lines within each dimension. Several cases of dominance between countries are found in the first robustness test.

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Notes

  1. For instance, Foster et al. (1984) and Watts (1968) indices.

  2. For example, the demography and health surveys (DHS) in Africa.

  3. The dominance analysis here comes down to a simple one-dimensional dominance since all dimensions are aggregated in a vector c of deprivation counts.

  4. With composite indicators, a third kind of robustness could be defined through the weights assigned to each dimension. Foster et al. (2009) examine such a variable-weight robustness criterion where a comparison is considered as robust if the ranking is not reversed at any weight vector within a given set. This type of robustness is not discussed here.

  5. Here we ignore health deprivation that could derive from obesity.

  6. Such estimates are available upon request to the author.

  7. By definition, H ≥ M 0 > M 1 for all k. M 0 is derived from H by multiplying the latter by the average deprivation share. Thus, unless all poor are deprived in all dimensions for some k, in which case M 0 = H for this k, M 0 will be always lower than H. M 1 is computed by multiplying M 0 by the average poverty gap.

  8. Asset poverty is estimated from the current method by considering only the eight indicators of Assets. Each indicator is weighted to 1 and the poverty measured by M 0 for k = 4.

  9. Since 2010, the human development report replaced the Gender-related Development Index (GDI) by a different gender index named gender inequality index (GII).

References

  • Alkire, S. & Foster, J. (2007). Counting and multidimensional poverty measurement. OPHI working paper series no. 07. The University of Oxford.

  • Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics, 95, 476–487.

    Article  Google Scholar 

  • Anderson, G., Crawford, I., & Leicester, A. (2008). Efficiency analysis and the lower convex hull approach. In N. Kakwani & J. Silber (Eds.), Quantitative approaches to multidimensional poverty measurement (pp. 176–191). New York: Palgrave-MacMillan.

    Google Scholar 

  • Atkinson, A. B. (1987). On the measurement of poverty. Econometrica, 55, 749–764.

    Article  Google Scholar 

  • Atkinson, A. B. (2003). Multidimensional deprivation: Contrasting social welfare and counting approaches. Journal of Economic Inequality, 1, 51–65.

    Article  Google Scholar 

  • Batana, Y. M., & Duclos, J.-Y. (2010). Multidimensional poverty among West African children: testing for robust poverty comparisons. In J. M. Cockburn & J. Kabubo-Mariara (Eds.), Child welfare in developing countries (pp. 95–122). New York: Springer/PEP/CRDI.F.

    Chapter  Google Scholar 

  • Batana, Y. M., & Duclos, J.-Y. (2011). Comparing multidimensional poverty with qualitative indicators of well-being. In J. Deutsch & J. Silber (Eds.), The measurement of individual well-being and group inequalities: Essays in memory of Z. M. Berrebi. New York: Routledge.

    Google Scholar 

  • Booysen, F., Von Maltitz, M., Van Der Berg, S., Burger, R., & Du Rand, G. (2008). Using an asset index to assess trends in poverty in seven Sub-Saharan African countries. World Development,. doi:10.1016/j.worlddev.2007.10.008.

    Google Scholar 

  • Bourguignon, F., & Chakravarty, S. R. (2003). The measurement of multidimensional poverty. Journal of Economic Inequality, 1, 25–49.

    Article  Google Scholar 

  • Cerioli, A., & Zani, S. (1990). A fuzzy approach to the measurement of poverty. In C. Dagum & M. Zenga (Eds.), Income and wealth inequality and poverty (pp. 272–284). Berlin: Spring.

    Chapter  Google Scholar 

  • Cheli, B., & Lemmi, A. (1994). A “totally” fuzzy and relative approach to the multidimensional analysis of poverty. Economic Notes, 24, 115–134.

    Google Scholar 

  • Chiappero-Martinetti, E. (2006). Capability approach and fuzzy set theory: Description, aggregation and inference issues. In A. Lemmi & G. Betti (Eds.), Fuzzy set approach to multidimensional poverty measurement. New York: Spring.

    Google Scholar 

  • Davidson, R., & Duclos, J.-Y. (2006). Testing for restricted stochastic dominance. IZA discussion paper no. 2047, Institute for the Study of Labor (IZA).

  • Deaton, A. (1997). The analysis of household surveys: A microeconometric approach to development policy. Baltimore: Johns Hopkins University Press for the World Bank.

    Book  Google Scholar 

  • Deutsch, J., & Silber, J. (2005). Measuring multidimensional poverty: An empirical comparison of various approaches. Review of Income and Wealth, 51, 145–174.

    Article  Google Scholar 

  • Duclos, J.-Y., Sahn, D. E., & Younger, S. D. (2006a). Robust multidimensional spatial poverty comparisons in Ghana, Madagascar and Uganda. World Bank Economic Review, 20, 91–113.

    Article  Google Scholar 

  • Duclos, J.-Y., Sahn, D. E., & Younger, S. D. (2006b). Robust multidimensional poverty comparison. Economic Journal, 113, 943–968.

    Article  Google Scholar 

  • Foster, J. E., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica, 52, 761–766.

    Google Scholar 

  • Foster, J. E., McGillivray, M., & Seth, S. (2009). Rank robustness of composite indices. OPHI working paper 26, The University of Oxford.

  • Foster, J. E., & Shorrocks, A. F. (1988a). Poverty orderings and welfare dominance. Social Choice Welfare, 5, 179–198.

    Article  Google Scholar 

  • Foster, J. E., & Shorrocks, A. F. (1988b). Poverty orderings. Econometrica, 56, 173–177.

    Article  Google Scholar 

  • Ibrahim, S., & Alkire, S. (2007). Agency and empowerment: A proposal for internationally comparable indicators. Oxford Development Studies, 35, 379–403.

    Article  Google Scholar 

  • Klasen, S. (2000). Measuring poverty and deprivation in South Africa. Review of Income and Wealth, 46, 33–58.

    Article  Google Scholar 

  • Lovell, C. A. K., Richardson, S., Travers, P., & Wood, L. (1994). Resources and functionings: A new view of inequality in Australia. In W. Eichhorn (Ed.), Models and measurement of welfare and inequality. Heidelberg: Springer.

    Google Scholar 

  • Maasoumi, E. (1993). A compendium to information theory in economics and econometrics. Econometric Reviews, 12, 1–49.

    Article  Google Scholar 

  • Maasoumi, E., & Lugo, M. A. (2008). The information basis of multivariate poverty assessments. In N. Kakwani & J. Silber (Eds.), Quantitative approaches to multidimensional poverty measurement (pp. 1–29). New York: Palgrave-MacMillan.

    Google Scholar 

  • Sahn, D. E., & Stifel, D. C. (2000). Poverty comparisons over time and across countries in Africa. World Development, 28, 2123–2155.

    Article  Google Scholar 

  • Sahn, D. E., & Stifel, D. C. (2003a). Exploring alternative measures of welfare in the absence of expenditure data. Review of Income and Wealth, 49, 463–489.

    Article  Google Scholar 

  • Sahn, D. E., & Stifel, D. C. (2003b). Urban-rural inequality in living standards in Africa. Journal of African Economies, 12, 564–597.

    Article  Google Scholar 

  • Samman, E., & Santos, M. E. (2009). Agency and empowerment: A review of concepts, indicators and empirical evidence. Prepared for the 2009 Human Development Report in Latin America and the Caribbean, May.

  • Sen, A. (1976). Poverty: An ordinal approach to measurement. Econometrica, 44, 219–231.

    Article  Google Scholar 

  • Sen, A. (1985). Commodities and capabilities. Amsterdam: Elsevier.

    Google Scholar 

  • Sen, A. (1992). Inequality reexamined. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Sen, A. (1995). The political economy of targeting. In D. van der Walle & K. Nead (Eds.), Public spending and the poor: Theory and evidence. Baltimore: Johns Hopkins University Press.

    Google Scholar 

  • Tsui, K.-Y. (2002). Multidimensional poverty indices. Social Choice and Welfare, 19, 69–93.

    Article  Google Scholar 

  • Von Maltzahn, R., & Durrheim, K. (2008). Is poverty multidimensional? A comparison of income and asset based measures in five Southern Africa countries. Social Indicators Research, 86, 149–162.

    Article  Google Scholar 

  • Watts, H. (1968). An economic definition of poverty. In D. P. Moynihan (Ed.), On understanding poverty. New York: Basic Books.

    Google Scholar 

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Acknowledgments

I am grateful to OPHI for financial support. I am also grateful for significant comments from Sabina Alkire and three anonymous referees, and for the input of Maria Emma Santos and David Vazquez-Guzman.

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Authors and Affiliations

  1. World Bank, Washington, DC, USA

    Yélé Maweki Batana

  2. OPHI, Queen Elizabeth House, University of Oxford, Oxford, UK

    Yélé Maweki Batana

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  1. Yélé Maweki Batana
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Correspondence to Yélé Maweki Batana.

Appendix

Appendix

See Tables 6, 7, 8, 9, 10, 11, 12 and 13.

Table 6 DHS samples’ characteristics
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Table 7 Deprivation rates by dimension
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Table 8 Deprivation rates by dimension (cont’d)
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Table 9 Country ranking according to different measures (M 0 using k = 1)
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Table 10 Country ranking according to different measures (M 0 using k = 2)
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Table 11 Country ranking according to different measures (M 0 using k = 3)
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Table 12 Decomposing M 0 by dimension and country for k = 2
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Table 13 Decomposing M 0 by dimension and country for k = 2 (cont’d)
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Batana, Y.M. Multidimensional Measurement of Poverty Among Women in Sub-Saharan Africa. Soc Indic Res 112, 337–362 (2013). https://doi.org/10.1007/s11205-013-0251-9

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