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<title>2020 - 2029</title>
<link>http://hdl.handle.net/11090/965</link>
<description>Working Papers</description>
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<rdf:li rdf:resource="http://hdl.handle.net/11090/1061"/>
<rdf:li rdf:resource="http://hdl.handle.net/11090/1059"/>
<rdf:li rdf:resource="http://hdl.handle.net/11090/1058"/>
<rdf:li rdf:resource="http://hdl.handle.net/11090/1057"/>
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<dc:date>2026-08-22T02:03:59Z</dc:date>
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<item rdf:about="http://hdl.handle.net/11090/1061">
<title>From Profiling to Prediction: A Multidimensional Vulnerability Index for NEET Triage in South Africa</title>
<link>http://hdl.handle.net/11090/1061</link>
<description>From Profiling to Prediction: A Multidimensional Vulnerability Index for NEET Triage in South Africa
Grotte, Joanna; De Lannoy, Ariane; Tatham, Claire
This paper develops and validates a Youth Multidimensional Vulnerability Index (YMVI) for South Africa using nationally representative panel data from the National Income Dynamics Study (NIDS). Applying the Alkire-Foster dual cut-off methodology, the YMVI measures intersecting deprivations across household financial burden, labour market orientation, mental wellbeing, and social networks. Nearly 90 percent of NEET youth are identified as multidimensionally vulnerable at baseline, at substantially higher prevalence and intensity than peers in employment, education, or training. This gap is statistically significant and invariant across a comprehensive sensitivity analysis encompassing alternative domain structures, weighting schemes, and deprivation cut-offs. Critically, baseline vulnerability is a robust predictor of NEET status nine to ten years later, with vulnerability coefficients remaining stable and significant even after controlling for baseline NEET status. It is the depth and breadth of intersecting capability deprivations, not NEET status at a point in time, that primarily determines whether disengagement is transient or chronic. The YMVI thereby offers an empirically grounded basis for multidimensional triage in youth employment programming, identifying young people facing structural barriers to labour market and education and training engagement, rather than those who merely happen to be disengaged when assessed.
</description>
<dc:date>2026-06-01T00:00:00Z</dc:date>
</item>
<item rdf:about="http://hdl.handle.net/11090/1059">
<title>From school exit to degree completion: Gender differences along the SET pipeline in South Africa</title>
<link>http://hdl.handle.net/11090/1059</link>
<description>From school exit to degree completion: Gender differences along the SET pipeline in South Africa
Whitelaw, Emma; Branson, Nicola
South Africa has experienced substantial expansion in higher education participation over the past three decades, alongside marked shifts in the demographic and academic composition of National Senior Certificate (NSC) cohorts. Specifically, females are increasingly outperforming their male counterparts in both general NSC achievement and higher education access. These patterns coexist with a continued under-representation of women in science, engineering, and&#13;
technology (SET) fields. This raises important questions about where gaps in the pipeline emerge, how they relate to readiness for SET study at the point of school exit, and how the changing profile of recent NSC cohorts will shape future gender gaps and/or the production of SET skills in South African universities.&#13;
Using linked student-level administrative data on NSC examinations (2011–2021) and public higher education records (2012–2022), this paper examines gender gaps in SET-study readiness (based on NSC subject choices and performance) at secondary school exit, entry into SET qualifications, and graduation in SET fields. By considering multiple progression points, we distinguish between differences arising at the point of access to higher education and those emerging from within the system. Specifically, we locate at which stages gender gaps widen or attenuate; and assess the contribution of each stage to the overall gap. Finally, we simulate expected patterns in SET qualification completion accounting for recent trends in the performance and profile of NSC cohorts, providing insight into how current schooling dynamics may shape future SET graduate output, overall and between men and women.
</description>
<dc:date>2026-04-01T00:00:00Z</dc:date>
</item>
<item rdf:about="http://hdl.handle.net/11090/1058">
<title>School closures and parental labour market responses during COVID-19: Evidence from South Africa</title>
<link>http://hdl.handle.net/11090/1058</link>
<description>School closures and parental labour market responses during COVID-19: Evidence from South Africa
Eyal, Katherine; Daniels, Reza C.; Mlatsheni, Cecil; Brophy, Timothy; Ingle, Kim
This paper investigates the effects of COVID-19 induced school and daycare closures on the labour market outcomes of working-age South Africans (SA). This&#13;
aggregate, exogenous shock to the labour market had profound implications for the world of work around the globe. We apply the theoretical underpinnings of&#13;
human capital theory and time allocation models to examine the socio-economic impacts of school closures on parental labour supply. What sets the SA context&#13;
apart is the high levels of unemployment pre-COVID, which leads to more readily available caregivers both before and during the pandemic. We postulate that this leads to an excess supply of caregivers and a low substitutability between parental and educator time, depending on the age of the child. However, we find persistent negative associations between school closures and hours worked that are not minor in size, with larger effects for parents and those living with pre-school-age children, in both a balanced and unbalanced panel of the SA National Income Dynamics Study-Coronavirus Rapid Mobile Survey (NIDS-CRAM). No impact is found for the probability of being employed, or on broad labour force participation, or income. These effects are attenuated to insignificance in households where a grandparent is present or where grants constitute the main household income source, consistent with the availability of alternative caregivers in larger, multigenerational households. The hours effect is driven almost exclusively by changes in female labour supply. Our results are robust to a number of specifications, placebo checks, and variations in covariates.
JEL Codes: J13, J16, J22, J23, J61, I38, O55
</description>
<dc:date>2026-04-01T00:00:00Z</dc:date>
</item>
<item rdf:about="http://hdl.handle.net/11090/1057">
<title>Why has decreasing schooling inequality not led to decreasing earnings inequality in South Africa?</title>
<link>http://hdl.handle.net/11090/1057</link>
<description>Why has decreasing schooling inequality not led to decreasing earnings inequality in South Africa?
Lam, David; Leibbrandt, Murray; Finn, Arden; Branson, Nicola
Inequality in education has declined substantially in South Africa since the end of apartheid, with inequality in years of completed education declining by all standard measures of inequality. At the same time, inequality in earnings has not shown significant declines, and has increased by some measures. Given the strong positive relationship between earnings and years of education, why hasn’t the decline in education inequality led to declines in earnings inequality? This paper explores this puzzle from both a theoretical and empirical perspective. We analyse how earnings inequality is affected by changes in returns to schooling when returns increase at some levels of schooling and decrease at other levels. We show that changes in the distribution of education over the 1994-2019 period would have significantly reduced earnings inequality in and of themselves. This was offset by disequalizing changes in the earnings-education gradient, including an increase in relative earnings for those with post-secondary education and a decrease in relative earnings for those with incomplete secondary education. The net result is a combination of decreasing schooling inequality and persistently high earnings inequality.
JEL Classification: D310 (Income inequality), I240 (Education and inequality), I260 (Returns to education)
</description>
<dc:date>2026-02-01T00:00:00Z</dc:date>
</item>
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