A Multidimensional Poverty Index for Gauteng Province, South Africa: Evidence from Quality of Life Survey Data

Abstract

This paper estimates a Multidimensional Poverty Index for Gauteng province of South Africa. The Alkire–Forster method is applied on Quality of Life survey data for 2011 and 2013 which offer an excellent opportunity for estimating poverty at smaller geographical areas. The results suggest that the Multidimensional Poverty Index for Gauteng is low but varies markedly by municipality and by ward, as well as across income groups. Not only are low income households more likely to be multidimensionally poor, they also suffer from higher intensities of poverty. Multidimensional poverty is highest in areas of low economic activity located on the edges of the province. However, pockets of multidimensional poverty do prevail even in better performing municipalities. Government, at all spheres, needs to devise policies that channel investments into lagging areas and avoid approaches that are indifferent to the heterogeneities that exist across localised geographical extents.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Notes

  1. 1.

    For a concise history and use of unidimensional measures see Alkire and Foster (2011).

  2. 2.

    The Premier for Gauteng province, Mr. David Makhura stressed during the State of Province Address, that the province was adopting evidence-based planning.

  3. 3.

    Social wage refers to monetary and in-kind support given to vulnerable households. Four components make up the social wage in South Africa (i) housing and community amenities, (ii) health, (iii) education, and (iv) social protection. The first three replace or subsidise day-to-day expenses for housing, education and health hence reducing the cost of living. The fourth is income paid directly to vulnerable groups.

  4. 4.

    For example, for the Housing-adjusted standard of living dimension, the weight is calculated as: \(\frac{1}{4}\times\frac{1}{6} = \frac{1}{24}\).

  5. 5.

    The uncensored headcount of an indicator refers to the proportion of total households deprived in that indicator. The censored headcount, on the other hand, refers to proportion of all household that are multidimensionally poor (i.e. households that fall within the cut-off point of k = 33 percent) and deprived in that indicator at the same time.

  6. 6.

    Table 6 in the “Appendix” presents the results for 2011.

References

  1. Alkire, S., & Foster, J. (2008). Counting and multidimensional poverty measurement. Oxford Poverty & Human Development Initiative (OPHI) Working Paper No. 7.

  2. Alkire, S., & Foster, J. (2011). Understandings and misunderstandings of multidimensional poverty measurement. Oxford Poverty & Human Development Initiative (OPHI) Working Paper No. 43 ISSN 2040-8188; ISBN: 978-1-907194-27-6

  3. Alkire, S., & Santos, M. E. (2010). Acute multidimensional poverty: A new index for developing countries. OPHI Working paper No. 38. Oxford University.

  4. Alkire, S., Santos, M. E., Seth, S., & Yalonetzky, G. (2010). Is the multidimensional poverty index robust to different weights? OPHI Research in Progress 22a. Oxford University.

  5. Anand, S., & Sen, A. (1997). Concepts of human development and poverty: A multidimensional perspective. In Poverty and human development: Human development papers (pp. 1–20). New York: United Nations Development Programme.

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

    Article  Google Scholar 

  7. Bhorat, H., van der Westhuizen, C., Goga, S. (2007). Welfare shifts in post-apartheid South Africa: A comprehensive measurement of changes. Development Policy Research Unit (DPRU) Working Paper 07/128. ISBN: 978-1-920055-54-7

  8. Bhorat, H., van der Westhuizen, C., Jacobs, T. (2009). Income and non-income inequality in post-apartheid South Africa: What are the drivers and possible policy interventions? Trade & Industrial Policy Strategies (TIPS).

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

    Article  Google Scholar 

  10. Everatt, D. (2009). Dispatches from the War on Poverty in South Africa’s 21 Poorest Rural and Urban Nodes, 1996––2006. In A. McLennan, & B. Munslow, (Eds.), The politics of service delivery (pp. 155–189). Johannesburg.

  11. GCRO. (2012). Key findings from Statistics South Africa’s 2011 National Census for Gauteng. GCRO Data Brief No. 1 of 2012.

  12. GPG. (2014a). Gauteng News, SOPA Special Edition.

  13. GPG. (2014b). Gauteng News, Aug/Sept 2014.

  14. Hoogeveen, J., & Özler, B. (2006). Not separate, not equal: Poverty and inequality in post-apartheid South Africa. In H. Bhorat & R. Kanbur (Eds.), Poverty and policy in post-apartheid South Africa. Pretoria: HSRC Press.

    Google Scholar 

  15. Kakwani, N., and Silber, J. (Eds.). (2008). The many dimensions of poverty. In N. Kakwani & J. Silber, (Eds.), The many dimensions of poverty (pp. xiv–xxii). New York: Palgrave McMillan. ISBN-13: 978-0-230-00490-0

  16. Landau, L. B., & Gindrey, V. (2008). Migration and population trends in Gauteng Province 1996–2055. Migration Studies Working Paper Series No. 42 Forced Migration Studies Programme, University of the Witwatersrand, Johannesburg.

  17. Leibbrandt, M., & Levinsohn, J. (2011). Fifteen years on: Household incomes in South Africa. NBER Working Paper 16661. National Bureau of Economic Research, Cambridge, MA.

  18. Leibbrandt, M., Woolard, I., Finn, A., Argent, J. (2010). “Trends in South African income distribution and poverty since the fall of apartheid”. OECD Social, Employment and Migration Working Papers, No. 101, OECD. doi:10.1787/5kmms0t7p1ms-en

  19. Leventhal, T., & Newman, S. (2010). Housing and child development. Children and Youth Services Review, 32, 1165–1174.

    Article  Google Scholar 

  20. Lund, C., De Silva, M., Plagerson, S., Cooper, S., Chisholm, D., Das, J., et al. (2011). Poverty and mental disorders: breaking the cycle in low-income and middle-income countries. The Lancet, 378, 1502–1514. doi:10.1016/S0140-6736(11)60754-X.

    Article  Google Scholar 

  21. Martins, J. H. (2003). Minimum living level and consumer price index: What’s in a name? Development Southern Africa, 20, 197–212.

    Article  Google Scholar 

  22. Ngepah, N. (2011). Production, inequality and poverty linkages in South Africa. Working Paper 206, Competition Commission of South Africa.

  23. Noble, M., Babita, M., Barnes, H., Dibben, C., Magasela, W., Noble, S., et al. (2006). The provincial indices of multiple deprivation for South Africa 2001. Oxford: University of Oxford.

    Google Scholar 

  24. RSA (1994). White paper on reconstruction and development of 1994.

  25. RSA. (2013). Budget Review 2013. RP: 344/2012. National Treasury, Republic of South Africa. ISBN: 978-0-621-41455-4.

  26. Schiel, R. (2000). Money metric versus non money metric measures of well‐being.

  27. Seekings, J., & Nattrass, N. (2005). Class, race, and inequality in South Africa USA. London: Yale University Press.

    Google Scholar 

  28. Sekhampu, T. J. (2013). Determinants of poverty in a South African township. Journal of Social Science, 34, 145–153.

    Google Scholar 

  29. Sen, A. (2000). A decade of human development. Journal of Human Development, 1, 17–23.

    Article  Google Scholar 

  30. Stats, S. A. (2011). National census 2011. Pretoria: Statistics South Africa.

    Google Scholar 

  31. Stats, S. A. (2014). Poverty trends in South Africa: An examination of absolute poverty between 2006 and 2011. Pretoria: Statistics South Africa.

    Google Scholar 

  32. Thorbecke, E. (2008). “Multidimensional poverty: Conceptual and measurement issues.” In N. Kakwani, & J. Silber (Eds.), The many dimensions of poverty (pp. 3–19). Palgrave McMillan, New York. ISBN-13: 978-0-230-00490-0

  33. Tregenna, F. (2011). What are the distributional implications of halving poverty in South Africa when growth alone is not enough? Applied Economics, 44(20), 2577–2596. doi:10.1080/00036846.2011.566186.

    Article  Google Scholar 

  34. Woolard, I., & Leibbrandt, M. (2001). Measuring Poverty in South Africa. In H. Bhorat (Ed.), Fighting poverty: Labour markets and inequality in South Africa (pp. 41–73). Cape Town: UCT Press.

    Google Scholar 

  35. Woolard, I., & Leibbrandt, M. (2006). Towards a poverty line for South Africa: A background note. Southern Africa Labour and Development Research Unit University of Cape Town.

  36. World Bank. (2012). South Africa economic update: Focus on inequality of opportunity. Washington: The International Bank for Reconstruction and Development/THE WORLD BANK.

    Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the invaluable review comments by Prof. Ingrid Woolard. Ingrid is a Professor in the School of Economics and a Research Associate of SALDRU at the University of Cape Town (UCT). She holds a Ph.D. in economics from UCT and is currently one of the Principal Investigators of the National Income Dynamics Study. A special thanks to participants of the 2015 Oxford Poverty & Human Development Initiative (OPHI) Summer School held at Georgetown University, Washington D.C. USA, who also gave us invaluable comments and suggestions for deepening our analysis. Special thanks to Samy Katumba, a Junior Researcher at GCRO for assisting with the GIS mapping.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Darlington Mushongera.

Additional information

GCRO is a partnership of the University of the Witwatersrand, Johannesburg, University of Johannesburg, Gauteng Provincial Government and Organised Local Government.

Appendix

Appendix

See Tables 6 and 7.

Table 6 Multidimensional poverty indicators by income group, 2013
Table 7 Proportion of households that are poor at k = 33 % per indicator, per municipality, 2011

Poorest Wards

See Figs. 5, 6 and 7.

Fig. 5
figure5

Multidimensional headcount for poorest wards, 2013, Source: Authors’ calculations based on GCRO’s 2013 Quality of Life survey

Fig. 6
figure6

Multidimensional poverty intensity for poorest wards, 2013, Source: Authors’ calculations based on GCRO’s 2013 Quality of Life survey

Fig. 7
figure7

Multidimensional Poverty Index (MPI) for poorest wards, 2013, Source: Authors’ calculations based on GCRO’s 2013 Quality of Life survey

Robustness Checks

The Pearson correlation coefficient ( r) is a measure of the strength of a linear association between two variables. In this case it was used to measure the strength between the MPI with equal weights versus the MPIs using 50 % weights per successive dimension.

$$r = \frac{{n\left( {\sum\nolimits_{i = 1}^{n} {x_{i} y_{i} } } \right) - \left( {\sum\nolimits_{i = 1}^{n} {x_{i} } } \right)\left( {\sum\nolimits_{i = 1}^{n} {y_{i} } } \right)}}{{\sqrt {\left[ {n\sum\nolimits_{i = 1}^{n} {x_{i}^{2} - \left( {\sum\nolimits_{i = 1}^{n} {x_{i} } } \right)^{2} } } \right]\left[ {n\sum\nolimits_{i = 1}^{n} {y_{i}^{2} - \left( {\sum\nolimits_{i = 1}^{n} {y_{i} } } \right)^{2} } } \right]} }}$$
(6)

Spearman’s rho (ρ) measures the strength of the association between two ranked variables. In this case the ranked variables were the MPI using equal weights and the MPI using 50 % weights successively per dimension. It is estimated using

$$\rho = 1 - \frac{{6\sum\nolimits_{i = 1}^{n} {x_{i} - y_{i} } }}{{n(n^{2} - 1)}}$$
(7)

where x i is the municipal rank using equal weights, y i is the municipal rank using 50 % weight on each dimension, and n is the number of municipalities.

Kendall’s Tau (τ) measures the association between two quantities, in this case, ranks. It is based on the number of concordant and discordances in paired observations. The ranks of the two sets of MPIs are concordant if they are in the same order with respect to each variable and discordant if they are arranged such that they are in opposite directions. The value of Kendall’s τ ranges between −1 and 1. If all pairs are discordant, τ = −1, if they are all concordant, τ = 1. Kendall’s Tau (τ) is calculated as

$$\tau = \frac{{N_{c} - N_{d} }}{n(n - 1)/2}$$
(8)

Varying the weights assigned to dimensions results in a change in the MPI and therefore the ranking of municipalities. The estimated correlation coefficients, as illustrated in Table 8 show that the municipal rankings are stable with respect to all dimensions except for the dimension on economic activity.

Table 8 Correlations between MPI and adjusted MPIs having 50 % weight on each dimension in turn

Furthermore, the analysis was also done to test the sensitivity of the choice of k i.e. the number of deprivations that a household must experience in order for them to be considered multi-dimensionally poor (Table 9). The Pearson and Kendall coefficients indicate stability with respect to choice of k, even though the Spearman coefficient is weak.

Table 9 Correlations between MPI and adjusted MPI for different choices of k

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mushongera, D., Zikhali, P. & Ngwenya, P. A Multidimensional Poverty Index for Gauteng Province, South Africa: Evidence from Quality of Life Survey Data. Soc Indic Res 130, 277–303 (2017). https://doi.org/10.1007/s11205-015-1176-2

Download citation

Keywords

  • Multidimensional Poverty Index
  • Headcount
  • Intensity
  • Quality of Life survey
  • Gauteng
  • South Africa