Abstract
Ph.D. (Economics)
When measuring poverty, much of the theoretical and empirical work has focused mainly on money-metric measures of poverty. The conventional approach has been the use of a poverty line − often derived from consumption, expenditure or income levels − sufficient to meet primary human needs. However, the money-metric approach to poverty analysis in South Africa is not an appropriate measure, given that the South African environment has a very different outlook, possibly even arriving at a wrong measure of poverty with subsistence farming, where money is not a good measure of poverty. In order to measure poverty accurately in South Africa we need to consider the assets of households and compute an asset-poverty index. Assets are an important indicator of well-being and a measure that is based on assets is likely to capture an important dimension of economic well-being. While there are a few studies that have investigated asset poverty in South Africa, there are serious gaps in the literature. These should be addressed in order to improve policy designed to reduce poverty. Firstly, these studies have mainly relied on cross-sectional data rather than panel data at a national level. The reason for this is mainly the absence of national representative longitudinal data. However, this type of data has become available and is used in this thesis. Secondly, none of these studies have compared results among subsamples of urban and rural areas. This is very important as the areas are structurally very different, with different characteristics. Thus, it is likely that poverty, asset poverty and the determinants of asset poverty in these areas will differ. Thirdly, previous literature has not investigated the uniqueness of subsistence-farming communities in the measurement of poverty, in which monetary measures have limited application. In these types of economies, monetary measures of poverty are likely to overestimate poverty. Furthermore, the saving behaviour in these communities differs vastly from that of other communities.
To address these gaps, the thesis uses a newly-available panel data set named the National Income Dynamics Study (NIDS) observed over the period 2008-2015 in bi-annual waves to study asset poverty in South Africa. The panel nature of the NIDS data also allows us to overcome common estimation issues of endogeneity. The NIDS contains a comprehensive set of questions relevant to the analysis of asset poverty. However, the NIDS is not without shortcomings. Although it is a national representative...