An analysis of the South African social security system

MCom (Economics), North-West University, Vanderbijlpark Campus, 2019 In the ever-changing global market environment, it is crucial for national governments to establish an enabling social economic atmosphere for the upliftment of individuals’ quality of life and well-being. International institutions state that social security has become one of the most important forms of assistance provided by a national government, which is now seen as an overall human right. Economic theory expresses a viewpoint of justice and a theoretical agenda for instituting radical organisations designed to maintain social justice and individual independence. Factors constituting the social security system include the number of social security recipients, the amount spent on social security, the economic environment, the level of human development, the amount of individuals without a job, the amount of individuals within a household, distribution of income and the number of individuals in income poverty. Empirical declarations have presented mixed findings on the subject matter and thus no single empirical consensus has been presented. Meanwhile, international institutions combined with the economic theory argue that potential effects of the aforementioned social factors on an economy’s social security patterns vary according to the orientation of the economy’s condition of need. This study examined the South African social security system, stated in the Republic of South Africa Constitution, Section 27 (1c) as, “every individual has the right to have access to social security, including if they are unable to support themselves and their households, proper social assistance” and requires legislative measures which should be employed by the national government to create awareness of this right in order to alleviate poverty and income inequality as well as to uplift individuals standard of living, included in Section 27 (2). In order to investigate the aforementioned Constitutional statements, the study employed both a descriptive and an econometric analysis on the South African social security system. Throughout the descriptive section, trends and graphs are used to analyse the different social security systems found across the globe and it culminates with an in-depth analysis of the different social security assistance types in South Africa. The econometric analysis ascertains the long-run and short-run relationship between three econometric models, namely the number of individuals in poverty, income inequality (as measured by the GINI coefficient) and human development (as measured by the Human Development Index (HDI)), with the number of social security recipients, the amount spent on social security, the number of individuals without a job, economic activity and the number of individuals within a household from 1996 to 2017. The study also establishes a causal direction between the aforementioned factors. The models employed under the econometric section include the correlation matrix, ARDL bounds test to co-integration and the Toda-Yamamoto Granger causality test. The study made use of a quantitative research methodology and includes time series macro-economic variables from 1996 to 2017. The correlation matrix found that the correlation between poverty and the aforementioned independent variables, indicates that two (social security recipients and the number of individuals within a household) out of the five variables have a positive relationship with the alleviation of poverty; however, these positive relationships are not significant at any significance level. Three (amount spent on social security, economic activity, number of individuals without a job) out of the five variables have a negative relationship with poverty and only social security expenditure and economic activity has a significant impact on poverty at 1 percent significance level. On the other hand, the correlation matrix for human development found that four (number of social security recipients, amount spent on social security, economic activity, unemployment) out of the five independent variables have a positive relationship with HDI and these positive relationships are all statistically significant at 1 and 5 percent significance level, except for the number of social security recipients (not significant) and one (number of individuals within a household) of the five independent variables having a negative relationship with the improvement of HDI and is statistically significant at the 1 percent significance level. The correlation matrix for income inequality found that two (number of social security recipients and the number of individuals within a household) of the five sectors under the study have a positive relationship with income inequality. However, these positive relationships are only statistically significant for the number of social security recipients and three (amount spend on social security, the number of individuals without a job and economic activity) of the five variables have a negative relationship with income inequality where only the number of individuals without a job is not statistically significant. The long-run relationships were insignificant between the number of individuals in poverty, income inequality (GINI) and human development (HDI) with the number of individuals receiving social security benefits, total amount spent on social security, number of individuals within a household, number of individuals without a job and economic activity. The short-run Toda-Yamamoto Granger causality found a unidirectional causal relationship between human development and the number of individuals without a job. This means that human development Granger causes unemployment but not the other way around. There is also a causal relationship between HDI and all the independent variables combined, at 5 percent significance level. A unidirectional causality exists between the number of social security recipients and the number of individuals in poverty. This means that the total number of social grant recipients Granger causes poverty but not the other way around. There is also a causal relationship between the number of social security recipients and all the independent variables combined, at 5 percent significance level. A unidirectional causal relationship exists between the number of individuals in poverty and the number of individuals without a job. This means that poverty Granger causes unemployment but not the other way around. Furthermore, there is a causal relationship between the number of individuals in poverty and all the independent variables combined, at 1 percent significance level. A unidirectional causal relationship exists between the income inequality and the human development. This means that income inequality Granger causes HDI but not the other way around. Furthermore, there is evidence of a unidirectional relationship between income inequality and economic activity, indicating income inequality Granger causes GDP but not the other way around. Lastly, there is evidence of a unidirectional relationship between income inequality and the number of individuals within a household, indicating income inequality Granger causes household size but not the other way around. There is also a causal relationship between income inequality and all the independent variables combined, at 1 percent significance level. No causal relationship exists between the number of individuals within a household and all the independent variables combined. Whereas, there is evidence of a causal relationship between the amount spent on social security systems and all the independent variables combined, at 5 percent significance level. There is also evidence that a causal relationship exists between economic activity and all the independent variables combined, at 1 percent significance level, however, not individually. A unidirectional causality exists between the number individuals without a job and the amount spent on social security. This means that unemployment Granger causes social security expenditure but not the other way around. There is also a causal relationship between the number of individuals without a job and all the independent variables combined, at 1 percent significance level. Masters