An assessment of migrant/non-migrant differentials in socio-demographic predictors of employment status, South Africa

Abstract

Background: Despite being one of the richest countries in Africa, South Africa has one of the highest unemployment rates in the world. Factors such as corruption, poor education system, and structural changes in the economy are attributable to high unemployment. As a result of high unemployment, the country has experienced xenophobic attacks. These attacks are based on the assertion that migrants take jobs from South Africans. Furthermore, not much has been done to understand the ways in which migrants and non-migrants differ in relation to employment status. Therefore, this study compared socio-demographic factors associated with employment status between migrants and non-migrants in South Africa. Methods and Data: The study used the Statistics South Africa Census 2011 10% dataset. The study population included all economically-active individuals (15-64 years) in South Africa. The final sample size for the current study was 1 406 730 individuals of working age. The sample was disaggregated into 84319 migrants and 1 322 411 non-migrants. The predictor variables included sex, age, ethnicity, level of education, type of place of residence, and marital status. The outcome variable is employment status, which had three categories; formal employment, informal employment, and unemployment. Data analysis was performed using the statistical software Stata. Statistical analysis included univariate analysis, bivariate analysis, Pearson’s chi squared test and multinomial logistic regression models. Results were presented using graphs and tables. Results: Overall, the association between socio-demographic factors and employment status was found to be statistically significant [p≤0.05]. Results indicated that levels of informal employment are very low among South Africans (9%) compared to migrants (24%). Tertiary education was found to offer more employment opportunities for South Africans than it does for migrants. Females were found to have fewer employment opportunities compared to males. However, South African females were found to have more employment opportunities compared to migrant females. Individuals who had been married were found to have more employment opportunities compared to those who had never been married. Migrants who were based in rural areas were found to have more employment opportunities compared to those based in urban areas. Conclusion: The comparison between migrants and non-migrants revealed that there are important differences in the employment status of these two sub-populations groups. Recommendations: The study recommended that the South African government should consider investing more resources on female education, and encourage entrepreneurship through effective channels in order to boost self-employment among South Africans.