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Homing in on the Core – Households Incomes, Income Sources and Geography in South Africa


  • Sten Dieden

    () (Riso National Laboratory)


The focus of this study is on household income generation among previously disadvantaged households in South Africa. Previous research has found that poverty among South African households was associated with the extent to which workers and their dependants were integrated into the South African core economy. This study investigates whether a similar conception can be ascertained in multivariate regression analysis. Households’ income sources are divided into categories that reflect differing extents of association with the core economy. Ensuing further justification by results from descriptive analyses, the income source categories are utilised as explanatory variables to investigate whether inter-household variation in income sources can explain variation in income levels. For the latter purposes, the results from the estimation of three reduced form models are compared. All three models have households’ log-income levels as dependent variables and share a set of household characteristics as explanatory variables. Two of the models are two-stage specifications that use provincial locations in the construction of instruments for income source categories. The third specification contains no income source variables but includes provincial locations as explanatory variables. The results show that, as compared to the specification with provincial locations, income sources can be incorporated as explanatory variables into multivariate regression analyses without considerable loss of explanatory power. Controls for endogeneity must however be applied. The partial impacts from income sources are statistically significant and their signs are in accordance with expectations. For some income sources the magnitudes of the impacts are not in correspondence with what may be expected from the descriptive analysis. The latter results suggest that households in different main income source categories also differ systematically in their demographic and educational endowments. When assimilated with results from the descriptive analyses, the estimated partial impacts from the different provinces support this interpretation.

Suggested Citation

  • Sten Dieden, 2004. "Homing in on the Core – Households Incomes, Income Sources and Geography in South Africa," Working Papers 04090, University of Cape Town, Development Policy Research Unit.
  • Handle: RePEc:ctw:wpaper:04090

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    References listed on IDEAS

    1. Carter, Michael R. & May, Julian, 1999. "Poverty, livelihood and class in rural South Africa," World Development, Elsevier, vol. 27(1), pages 1-20, January.
    2. Geda, A. & de Jong, N. & Mwabu, G. & Kimenyi, M.S., 2001. "Determinants of poverty in Kenya : a household level analysis," ISS Working Papers - General Series 19095, International Institute of Social Studies of Erasmus University Rotterdam (ISS), The Hague.
    3. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    4. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    5. Ellis, Frank, 2000. "Rural Livelihoods and Diversity in Developing Countries," OUP Catalogue, Oxford University Press, number 9780198296966.
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    More about this item


    South Africa: log-income levels; household income; multivariate regression analyses;

    JEL classification:

    • A1 - General Economics and Teaching - - General Economics


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