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Non-Economic Quality of Life and Population Density in South Africa

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Abstract

The purpose of this study is to investigate the relationship between population density and non-economic quality of life. Popular opinion has generally been that population density can be seen as beneficial for economic growth, as it allows for greater productivity, greater incomes and can be translated into higher levels of quality of life. Recently though, growing evidence tends to suggest the exact opposite in that increases in productivity and incomes are not translated into better quality of life. As economic or income variables have always played a significant role in this research, questions regarding the relationship between population density and non-economic quality of life has largely remained unanswered. In this light, the paper utilises a panel data set on the eight metropolitan cities in South Africa for the period 1996–2014 to determine the relationship between population density and non-economic quality of life in the South African context. In the analyses we make use of panel estimation techniques which allows us to compare changes in this relationship over time as well as adding a spatial dimension to the results. This paper contributes to the literature by firstly studying the aforementioned relationship over time and secondly conducting the analyses at a sub-national level in a developing country. Our results show that there is a significant and negative relationship between population density and non-economic quality of life. Based on our findings policy measures to encourage urbanisation should not be supported if the ultimate outcome is to increase non-economic quality of life.

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Notes

  1. Centripetal are the three classic Marshallian sources of external economies; market size effects, thick labour markets and pure external economies.

  2. Centrifugal forces include immobile factors, land rents and pure external diseconomies.

  3. To see the formal definition of South Africa’s upper poverty line please visit Statistics South Africa at www.statssa.gov.za/publications/Report-03-10-06/Report-03-10-06March2014.pdf.

  4. City of Cape Town, EThekwini, Ekurhuleni, City of Johannesburg, Nelson Mandela Bay, City of Tshwane, Mangaung and Buffalo city.

  5. Sahel is the semiarid region of western and north-central Africa extending from Senegal eastward to the Sudan.

  6. For the universe of 150 countries with population greater than 1 million, the correlation between population density (population per \(km^{2}\)) and GDP per capita in 1995 is 0.32.

  7. In an urban setting, children are net economic costs: they are likely to attend school rather than contribute to household production, and because of urban mortality, are much less reliable as social security for aged parents. Moreover, the opportunity costs of raising children are much higher, especially if women are part of the urban labour force.

  8. Due to a lack of environmental variables in our data set we were unable to test the effect of environmental factors on non-economic quality of life, though this is a very important matter that should be addressed in future research.

  9. McGillivray (2005) uses a method explained in the Handbook on Constructing Composite Indicators (OECD 2008) to construct the initial composite index of non-economic quality of life, to be used in further analyses, by applying PCA and saving the first extracted principal component which represents a weighted summary index of the original indicators.

  10. Heterogeneity is the likelihood that there are important independent variables that are not included in a regression model but which are correlated with the dependent variable.

  11. Panel data analysis can be divided into FE and RE methods. The FE method is designed to study the causes of changes within an entity such as a metropolitan city. The model estimates change in the dependent variable from changes in the independent variables (within group variation) and removes estimates of any variables that are time invariant being either observed or unobserved. In this manner the FE model, in particular, deals with unobserved heterogeneity. The main limitation of the FE method is that it can only incorporate the effect of variables that change over time, such as population density or the GDP per region, and not variables that are time invariant. Time invariant variables, however, can be estimated using RE techniques, as it uses both within group and between group variation.

  12. We ran diagnostic tests for homoscedasticity and autocorrelation. To address heteroscedasticity, we made use of robust standard error estimations. No autocorrelation was detected. To test for multicollinearity we correlated all independent variables and found no correlation of more than 0.3.

  13. The relationship between population density and number of households in a geographical region was also highlighted in among other Beckmann (1969), Cardillo et al. (2004) and Carlino and Mills (1987).

  14. Accepted level of distortion.

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Acknowledgments

We thank Economic Research Southern Africa (ERSA) for their financial support.

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Authors and Affiliations

  1. Department of Economics and Econometrics, University of Johannesburg, PO Box 524, Auckland Park, Johannesburg, 2006, South Africa

    Talita Greyling

  2. Faculty of Business, Economics and Law, Auckland University of Technology, Private Bag 92006, Auckland, 1142, New Zealand

    Stephanié Rossouw

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  1. Talita Greyling
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Correspondence to Stephanié Rossouw.

Appendix: Testing the Robustness of the Estimation Results

Appendix: Testing the Robustness of the Estimation Results

In the Appendix we include the estimation results as pertaining to Mc Gillivray’s TNEQoL index (see Table 6) based on the selection of variables used by McGillivray (2005) and adult literacy rate (see Table 7) as dependent variables to test the robustness of our regression results in which our own TNEQoL index is the dependent variable (see Sect. 5.2). The regression results using our own TNEQoL index, the McGillivray composite index and adult literacy rate respectively as dependent variables are very similar Therefore, we can conclude that our results are robust.

Table 6 Estimation results for McGillivray’s composite index of TNEQoL.
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Table 7 Estimation results for adult literacy rate as the dependent variable.
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Greyling, T., Rossouw, S. Non-Economic Quality of Life and Population Density in South Africa. Soc Indic Res 134, 1051–1075 (2017). https://doi.org/10.1007/s11205-016-1468-1

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