Magister Commercii - MCom / The literature on financial inclusion and poverty connections has received considerable attention recently. There exist a scarcity of local studies examining the relationship between financial inclusion (FI) and poverty. Precisely, there is a lack of local studies who previously used FinScope data to investigate the mentioned relationship in South Africa. This study is motivated to fill the gap. To achieve the aims, the study will source data from FinScope (a secondary data) for the periods of 2011 and 2016. The Foster-Greer-Thorbecke indices were used to measure the level of poverty, while the lower-bound poverty (LBPL) line was used to differentiate the poor from the non-poor. Principal Component Analysis (PCA) was also applied to derive the financial inclusion index (FII). Probit regressions were run to measure the likelihood of being poor and being financially excluded. Ordinary Least Squares were run to identify the nature of the relationship between the dependent and the independent variables. Lastly, bivariate regression was also run to test the relationship between poverty and financial exclusion.

Masters of Commerce / The literature on financial inclusion and poverty connections has received considerable attention recently. There exist a scarcity of local studies examining the relationship between financial inclusion (FI) and poverty. Precisely, there is a lack of local studies who previously used FinScope data to investigate the mentioned relationship in South Africa. This study is motivated to fill the gap. To achieve the aims, the study will source data from FinScope (a secondary data) for the periods of 2011 and 2016. The Foster-Greer-Thorbecke indices were used to measure the level of poverty, while the lower-bound poverty (LBPL) line was used to differentiate the poor from the non-poor. Principal Component Analysis (PCA) was also applied to derive the financial inclusion index (FII). Probit regressions were run to measure the likelihood of being poor and being financially excluded. Ordinary Least Squares were run to identify the nature of the relationship between the dependent and the independent variables. Lastly, bivariate regression was also run to test the relationship between poverty and financial exclusion. The empirical findings indicated that the South African financial system is inclusive. Unemployment and financial language restricted financial service access. The frequently used financial services were borrowing and funeral cover. Black African female with low education residing in rural areas and unemployed were poorer. The rich elderly white man from the urban areas of the Western Cape and Gauteng who are highly educated, were more likely to be financially included. The regression analysis showed that the female was more likely to be financially included yet poor. It is also found that Gauteng residents were less likely to be poor. Also, individuals from a bigger household were less likely to be excluded. The other results showed that individuals with higher real per capita income enjoyed much lower probability of being financially excluded, and they are mainly white individuals living in urban areas.