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This study explores the nexus between population growth and unemployment in South Africa from 2000 to 2023, a period marked by significant demographic changes and economic challenges. South Africa has experienced rising unemployment alongside rapid population growth, which has led to concerns about the country’s ability to absorb its expanding labour force. The aim of this research is to examine how population growth influences unemployment, considering additional factors like foreign direct investment (FDI), gross domestic product (GDP), and inflation. Using time series data from the World Bank, Statistics South Africa, and the South African Reserve Bank, the study employs the Augmented Dickey-Fuller unit root test for stationarity, Johansen cointegration for long-term relationships, and Ordinary Least Squares (OLS) regression to estimate the impact of population growth on unemployment. Key findings indicate that population growth has a positive and statistically significant relationship with unemployment, meaning higher population growth increases unemployment levels. Foreign direct investment also shows a positive effect on unemployment, while GDP and inflation exhibit negative but statistically insignificant relationships. Based on these findings, the study recommends strategic urban planning, family planning initiatives, and targeted policies to attract labour-intensive FDI. Additionally, the government should focus on economic diversification, supporting SMEs, and investing in skill development to create sustainable jobs. This research contributes to understanding the dynamics between population growth and unemployment in South Africa and offers insights for more effective policy interventions.

This study investigates the relationship between sectoral labour productivity and unemployment in South Africa by answering the question how has labour productivity across different sectors affected employment rates in South Africa? Both descriptive statistics and inferential statistics (Augmented Dickey–Fuller and Phillips-Perron tests and Autoregressive Distributed Lag Bound test and estimation techniques) were applied on obtained timeseries data for the period 1991-2022. The analysis surprisingly shows that none of the sectoral labour productivity variables contribute to unemployment reduction in the country. Plausible reasons for this finding include the adoption of automation and technological advancements in labour intensive, imbalance between rate of job loss in labour intensive sector and rate of job creation in the service sector during sectoral shifts and economic transition, and general lower purchasing due to unequal distribution of labour productivity gain and income inequality that could have boosted consumer demand, these measures can stimulate economic growth and job creation. Various recommendations are suggested to reduce unemployment in the light of these findings.