M.Comm. / Each year, in an attempt to alleviate poverty, government invests large parts of the budget to provide infrastructure to poor households in South Africa. This not only necessitates an understanding of the effectiveness of government’s infrastructure delivery rate to address poverty in South Africa, but also raises important questions on how the poor can be identified. In recent years, countries have moved away from traditional broad poverty measures such as gross national income (GNI) per capita and Human Development Index (HDI). Information on poverty and other household information are more often collected through household surveys. From these surveys, monetary and non-monetary poverty measures can be used to identify the poor. By making use of a monetary poverty measure such as expenditure, per capita household expenditure can be calculated. Households are divided into quintiles based on their per capita household expenditure, and the bottom 20 and 40 per cent are usually the benchmark for households to be identified as being poor. This is analysed in terms of the poor’s access to services and other household characteristics. Qualitative regression models have gained more recognition in econometrics, especially in the social sciences field. Information collected from household surveys is often qualitative, or binary in nature. Due to the non-linear nature of binary-dependent variable models, logit and probit models were appropriate for this study. The maximum likelihood method, within the binary choice framework, was employed to determine the extent to which infrastructure delivery and other household characteristics have an impact on poverty. The results provided empirical evidence that infrastructure investment can significantly reduce the likelihood that a household will be poor, given a set of characteristics.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:6914 |
| Date | 04 October 2010 |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0019 seconds