M.Sc., Faculty of Science, University of the Witwatersrand, 2011 / The need for Official Statistics to assist in the planning and monitoring of development projects is becoming more intense, as the country shifts toward better service delivery by local government. It is evident that the demand for statistics at small area level (municipal rather than provincial) is high. However, the statistics with respect to employment status at municipal level is limited by the poor estimation of unemployment in 2001 Census and by changes in boundaries in local government areas. Estimates are judged to be reliable only at provincial level (Stats SA, 2003) The aim of this study is to investigate possible methods to resolve the problem of the misclassification of employment status in Census 2001 by readjusting the data with respect to the classification of people as employed, unemployed or economically inactive, to that of Labour Force Survey of September 2001. This report gives an overview of the different methods of small area estimation proposed in the literature, and investigates the use of these methods to provide better estimates of employment status at a small area (municipal) level. The application of the small area estimation methods to employment status shows that the choice of the method used is dependent on the available data as well as the specification of the required domain of estimation. This study uses a two-stage small area model to give estimates of unemployment at different small areas of estimation across the geographical hierarchy (i.e. District Council and Municipality). Even though plausible estimates of the unemployment rate were calculated for each local municipality, the study points out some limitations, one of which is the poor statistical representation (very few people) living in some specific municipalities (e.g. District Management Areas used for national parks). Another issue is the poor classification of employment status in rural areas due to poor data with respect to economic activities, mostly with respect to family businesses, and the non-availability of additional auxiliary data at municipal level, for the validation of the results. The inability to incorporate the time difference factors in the small area estimation model is also a problem. In spite those limitations, the small area estimation of unemployment in South Africa gives the reference estimates of unemployment at municipality level for targeted policy intervention when looking at reducing the gap between those who have jobs and those who do not. Hence, the outcome of the small area estimation investigation should assist policy makers in their decision-making. In addition, the methodological approach used in this report constitutes a technical contribution to the knowledge of using Small Area Estimation techniques for South African Employment statistics.

Unemployment in South Africa has continued to be consistently high as indicated by the various reports published by Statistics South Africa. Unemployment is a global problem where in Organisation for Economic Co-operation and Development (OECD) countries it is related to economic condition. The economic conditions are not solely responsible for the problem of unemployment in South Africa. Consistently high unemployment rates are observed irrespective of the level of economic growth, where unemployment responds marginally to changes Gross Domestic Product (GDP). To understand factors that influence unemployment in South Africa, we need to understand the dynamics of the unemployed population. This study aims at providing a statistical tool useful in improving the understanding of the labour market and enhancing of the labour market policy relevancy. Survival techniques are applied to determine duration dependence, probabilities of exiting unemployment, and the association between socio-demographic factors and unemployment duration. A labour force panel data from Statistic South Africa is used to analyse the time it takes an unemployed person to find employment. The dataset has 4.9 million people who were unemployed during the third quarter of 2013. The data is analysed by computing non-parametric and semi-parametric estimates to avoid making assumption about the functional form of the hazard. The results indicate that the hazard of finding employment is reduced as people spend more time in unemployment (negative duration dependence). People who are unemployed for less than six months have higher hazard functions. The hazards of leaving unemployment at any given duration are significantly lower for people in the following categories - females, adults, education level of lower than tertiary, single or divorced, attending school or doing other activities prior to job search and no work experience. The findings suggest an existence of association between demographics and the length of stay in unemployment; which reflect the nature of the labour market. Due to lower exit probabilities young people spent more time unemployed thus growing out of the age group which is more likely to be employed. Seasonal jobs are not convenient for pregnant women and for those with young kids at their care thus decreasing their employment probabilities. Analysis of factors that affect employment probabilities should be based on datasets which have no seasonal components. The findings suggest that the seasonal components on the labour force panel impacted on the results. According to the findings analysis of unemployment durations can be improved by analysing men and women separately. Men and women have different challenges in the labour market, which influence the association between other demographic factors and unemployment duration / Statistics / M. Sc. (Statistics)