Multidimensional Poverty Indicator and Its Determinants in Rural South Africa

  • B.C. Mosasane North-West University
  • Abayomi Oyekale North-West University
Keywords: Rural Households; Multidimensional Poverty Indicator; Tobit Regression; South Africa

Abstract

Poverty is prevalent among South African households and it is multidimensional in nature. This study therefore constructed indicator of multidimensional poverty and analyzed its determinants in rural South Africa. Data were obtained from the South African Demographic and Health Survey (SADHS) for 2016. The Alkire-Foster (AF) was used to compute multidimensional poverty indicator (MPI) and Tobit regression method was used to analysis its determinants. The results revealed that majority of the rural dwellers in KwaZulu-Natal were poor (93%). Eastern Cape and Limpopo provinces had 92% and 90% poverty rates respectively, while Western Cape had 61%. In addition, the results also showed that an average rural dweller in KwaZulu-Natal and Limpopo provinces were deprived in 76% of the weighted indicators, while North West and Mpumalanga provinces were each deprived in 70% of the weighted indicators. The Tobit regression results indicated that as size of household, male household headship, age and some provincial variables significantly explained MPI. Conclusively, a good percentage of the South African rural population were living in poverty. It was recommended that government should prioritise alleviating rural poverty with focus on regional, age and maternal fertility.

Author Biography

B.C. Mosasane, North-West University

Department of Agricultural Economics and Extension

References

Alkire, S., Roche, J.M., Ballon, P., Foster, J., Santos, M.E. & Seth, S. (2015). Multidimensional poverty measurement and analysis. Oxford University Press, USA.

Alkire, S., and Foster, J, 2011. Understandings and misunderstandings of multidimensional poverty
measurement. Journal of Economic Inequality 9(2), 289–314.

Chen, K.M., Leu, C.H. and Wang, T.M. (2019). Measurement and Determinants of Multidimensional Poverty: Evidence from Taiwan. Social Indicators Research, pp.1-20.

Fransman, T. and Yu, D. (2019). Multidimensional poverty in South Africa in 2001–16. Development Southern Africa, 36(1):50-79.

Ghalib, A.K., Malki, I. and Imai, K.S. (2015). Microfinance and household poverty reduction: Empirical evidence from rural Pakistan. Oxford Development Studies, 43(1), pp.84-104.

Hurlbut, W.B. (2018). Overcoming poverty and inequality in South Africa: An assessment of drivers, constraints and opportunities: World Bank.

Katumba, S. (2018). Spatial statistical analyses to assess the spatial extent and concentration of multidimensional poverty in Gauteng using the South African Multidimensional Poverty Index. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42.

Lloyd-Sherlock, P., Barrientos, A., Moller, V. and Saboia, J. 2012. Pensions, poverty and wellbeing in later life: Comparative research from South Africa and Brazil. Journal of Aging Studies, 26(3):243-252.

Mushongera, D., Zikhali, P. and Ngwenya, P. (2017). A multidimensional poverty index for Gauteng province, South Africa: evidence from Quality of Life Survey data. Social Indicators Research, 130(1), pp.277-303.

National Department of Health (NDoH), Statistics South Africa (Stats SA), South African Medical Research Council (SAMRC), and ICF. 2018. South Africa Demographic and Health Survey 2016 Key Findings. Pretoria, South Africa, and Rockville, Maryland, USA: NDoH, Stats SA, SAMRC, and ICF.
Pauw, K. (2007). Agriculture and poverty: Farming for food or farming for money? Agrekon, 46(2):195-218.

Saunders, P. (2011). Down and out: Poverty and exclusion in Australia. Bristol: Policy Press.
Schoch, M. and Lakner, C. (2020). The number of poor people continues to rise in Sub-Saharan Africa, despite a slow decline in the poverty rate. Available online: https://blogs.worldbank.org/opendata/number-poor-people-continues-rise-sub-saharan-africa-despite-slow-decline-poverty-rate (Accessed 23rd June 2021).

Stats SA (2017). Poverty trends in South Africa: An examination of absolute poverty between 2006 and 2015. Pretoria: Statistics South Africa. www.statssa.gov.za

Victor, B., Blevins, M., Green, A.F., Ndatimana, E., González-Calvo, L., Fischer, E.F., Vergara, A.E., Vermund, S.H., Olupona, O. & Moon, T.D. (2014). Multidimensional poverty in rural Mozambique: a new metric for evaluating public health interventions. PLoS One, 9(9), p.e108654.

Wang, W. and Griswold, M.E. (2017). Natural interpretations in Tobit regression models using marginal estimation methods. Statistical Methods in Medical Research. 26(6) 2622–2632 DOI: 10.1177/0962280215602716
World Bank, 2019. Understanding Poverty. https://www.worldbank.org/en/topic/poverty/over view
Published
2021-10-13
Section
Articles