Gina WEIR-SMITH, Human Sciences Research Council, South Africa
Dlamini SMANGA, Human Sciences Research Council, South Africa
Rapid urbanisation and poor economic growth following the 2008 global market downturn, has lead to increased numbers of unemployment in South Africa (Statistics SA 2013). In an attempt to understand some of the labour market changes which took place over this time in the municipalities of the country, data on economic growth, employment, economic vulnerability and demographics is investigated. This research steers away from the cyclical and structural nature of unemployment (Weir-Smith 2017), to focus on one of the summary measures of the labour market, namely the labour absorption rate.
Data was obtained from secondary statistical sources and exploratory data analysis was undertaken together with spatial grouping. The latter is a multivariate geographical clustering tool using a K Means algorithm to group municipalities based on their labour absorption rate. The tool looks for features within each group that are as similar as possible, while the groups themselves are as different as possible. The grouping analysis tool utilizes unsupervised machine learning methods to determine natural groupings in the data. The outcome will provide an index of labour absorption per municipality.
It is expected that the findings will illustrate regional imbalances and dissimilar labour absorption rates across municipalities. Since the growth in jobs in Gauteng has outpaced other provinces for the last two decades (Turok 2017), this research will illustrate which municipalities had low absorption rates.
As a suggested way forward, the research concludes that the growth of cities and regions need to be managed in a way to encourage local economic development (LED) (World Bank 2009). In a developing world context, temporal change in labour absorption rates is an additional indicator of labour market health to consider since traditional unemployment statistics can be under-estimated in developing countries (Ndongo Samba 2013).
Mots clés : Labour absorption rate|South Africa|grouping analysis|developing countries
A105635GW