The paper analyses poverty in South Africa, using the GHS-data of 2018. STATSSA designates three poverty lines, the upper-bound poverty line is employed in the methodology, which makes the analysis more detailed and unique. The methodology uses descriptive statistics, multivariate analysis and inferential approaches specifically the binary-logistic regression model. The results of the model indicate that socioeconomic factors such as income, and household size, on the household level and head of household characteristics such as gender, marital status, age and population group were significant determinants of poverty. Policies that target girl education would go a long way in addressing poverty.

Orientation: The increasing number of young people not in employment, education or training (NEET) is a concerning global trend. Research purpose: This study aimed to identify the factors that contribute to young individuals being classified as NEET in South Africa. Motivation for the study: Given the rising NEET rates in South Africa and the potential loss of human capital, this study addresses a critical gap in understanding the drivers of youth unemployment and disengagement from education and/or training. Research approach/design and method: Employing cross-sectional data analysis techniques, the study used the Quarterly Labour Force Survey data with a sample of 14 338 youths aged 15–34 years from Statistics South Africa. Main findings: The study identified education level, race and age as significant predictors of NEET status. The research highlighted the role of social capital and geographical barriers in determining the NEET status of young individuals, with location and gender having an impact. Practical/managerial implications: The study’s findings have important implications for policymakers. Possible strategies can target the specific factors identified, such as improving access to education and training, addressing racial disparities and reducing gender-related barriers. Contribution/value-add: This study reveals the causes of NEET among South African youth. Policymakers can use this information to create targeted interventions, reduce the NEET population and promote socio-economic development. The research provides a valuable resource to guide efforts in reducing the NEET population and harnessing the potential of the country’s youth.

In South Africa, youth (15–34 years) are the most vulnerable age group, assuming a large share of unemployment and NEET rates. This has raised concerns of their living standards, which have a great influence on their overall development. Although much has been written on poverty in South Africa, poverty specifically among the youth remains under-researched. This study analyses factors that determine poverty among youth-headed households in South Africa using the 2018 General Household Survey data from Statistics South Africa. Using a binary logistic regression, the paper provides findings and makes recommendations and imperative insights to policy-makers in advancing the socio-economic status of young people.

Background: Globalisation has brought significant benefits to countries’ industrialisation, but its impact has not been uniform. Policymakers in developing countries have increasingly focused on sound institutions’ role in offsetting the potential negative effects of an integrated economic structure. However, evidence of the mediating role of institutional quality in South Africa’s unique economic globalisation and industrial development relationship has been limited. Objective: This study had the primary objective to assess the mediating role of institutional quality within the globalisation and industrialisation nexus in South Africa. Method: The analysis used a quantitative research approach through a time series analysis, based on secondary data from 1980 to 2021. The long-run relationships between the variables were assessed through novel cointegration tests, while three different cointegration regression estimation techniques were employed to estimate the elasticities. Moreover, Toda-Yamamoto causality tests were utilised to evaluate the causal linkages between the variables. Results: The findings suggest that South Africa’s unique integration path has benefited industrial output significantly since its move to a more liberal and democratic structure. However, the level of institutional quality has contributed to a diminishing effect in this regard. This emanates primarily from a misalignment between institutional frameworks and the demands of globalisation, including increased competition and regulatory challenges. Conclusion: Based on this, the study recommends that policymakers prioritise developing a comprehensive industrial policy to promote key strategic sectors, regional integration, and public–private partnerships. Moreover, they should improve the business environment and infrastructure by means of reducing the costs of doing business, enhance the rule of law, ensure a stable financial system to attract foreign investment and foster sound regional partnerships.

Traditional approaches to predicting business cycles are limited by their omission of financial variables, which, in turn, leads to failures to signal financial-sector crises and to misestimate the duration and intensity of economic events. This study addresses this challenge by constructing a real-financial activity gap for South Africa and utilising it to predict the occurrence of economic recoveries. The study examines the period from 1970Q1 to 2023Q4, using real GDP, domestic credit, house prices, and share prices. The dynamic factor model and the Hodrick–Prescott filter are employed to construct the real-financial activity gap. The recursive ADF unit root test is used to assess the presence, frequency, and duration of economic recoveries. To validate the results, a Markov switching dynamic regression model is applied. The results reveal that the new gap tends to produce economic recovery predictions that are less frequent but longer in duration. In contrast, predictions based on real GDP lead to more frequent but shorter recoveries. The new gap suggests that financial variables contribute to stabilising growth over extended periods, whereas real GDP reflects quicker but more volatile economic adjustments. The latest gap offers a more stable basis for forecasting recoveries, aiding policymakers in better anticipating and mitigating economic downturns. Accordingly, the output gap and the real-financial activity gap should be used as complements.

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