Abstract
Drawing on the 2007 and 2010 Swaziland Labor Force Surveys, this chapter provides first systematic evidence on youth employment challenges in Swaziland, a small, land-locked country with one of the highest youth unemployment rates in Africa. The chapter first documents the labor market disadvantages faced by the Swazi youth, including discouragement. The multinomial logit regression analysis is then carried out to analyze the main socio-economic drivers of the youth labor market outcomes. Since the factors that could unlock the employment potential of the Swazi youth are also on the demand side of the labor market, the chapter examines the country’s barriers to private job creation and youth entrepreneurship. It concludes with experiences of other countries that could inform design of more effective interventions towards youth employment in Swaziland.
‘Over the long term, disadvantaged youth will confront steeper obstacles along the path to progress… Our challenge is clear: we must pay more attention to education and, in particular, to the transition from education to employment. And the ability of youth to find full and productive employment must be a central objective of national development strategies, including poverty reduction policies.’
UN Secretary-General Kofi Annan on the International Day of Youth—August 12, 2006
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Notes
- 1.
Felipe (2012) classified Swaziland among 22 countries currently caught in the low middle-income trap. Swaziland and Africa’s growth during 2000–2012 was calculated based on data in the AfDB database.
- 2.
Data is un-weighted and reflects the information in the surveys, where the urban population is slightly over-represented relative to its actual share in the total population. The re-weighted unemployment is even higher, amounting to 28 % of the labor force in both 2007 and 2010 according to the Swaziland Ministry of Labor.
- 3.
Kangoye and Brixiová (2013) elaborate on scope and the drivers of the gender gap in the Swazi labor market.
- 4.
At the same time, unemployment rate for women over 55 years is lower than for their male counterparts, even though women’s labor force participation gap this age group is smaller than for the younger cohorts.
- 5.
In Tunisia and other North African countries, the unemployment pool contained a disproportionate share of the educated youth (Stampini and Verdier-Chouchane 2011).
- 6.
Striking is also the notably greater involvement of women in the informal private sector than among men.
- 7.
Controlling for vocational training was not feasible due to the lack of data on this indicator.
- 8.
One question in our regression model is whether the controls may be collinear, i.e. if there may be statistical dependencies among them. We therefore use of variance inflation factor to identify multicollinearity. The results in the last column of Table 9.5 show that except for Age and Age2, all controls have VIF lower than 10, implying that multicollinearity is not an issue in our regression analysis. The high collinearity of the Age variables was expected and is not a source of biased inference. The VIF was performed after standard OLS regressions.
- 9.
Attending school reduces probability of being employed in the informal sector or self-employed in 2007.
- 10.
- 11.
No collateral is required. Young entrepreneurs have up to 3 months to start their business upon receiving the funds; they have to repay loans within 24 months. Interest rate is about 10 %, well below the commercial rates.
- 12.
Given the emphasis on high and inclusive growth and growth entrepreneurship, the experiences may be particularly relevant to slow growing middle income countries in Southern Africa. For Swaziland, particularly notable lessons are: (i) careful screening of applicants for support to identify high potential entrepreneurs and (ii) provide more support to few entrepreneurs rather than spreading resources thinly. In the case of the Youth Employment Fund, screening of applicants—before and after the funding was disbursed was not adequate, as indicated by low repayment rates. Further discussions with the young entrepreneurs indicated that the size of the loans was small, usually below the requested amounts.
- 13.
- 14.
A discussion of a broader development path for the Swazi economy is beyond the scope of this chapter.
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Acknowledgments
The authors are grateful to Mthuli Ncube, the Chief Economist and Vice President of the AfDB, for suggesting this topic and stimulating discussions. Special thanks go to Musinga T. Bandora, the United Nations Resident Coordinator and to Robert Fakudze from the Swaziland Ministry of Labor for data and discussing policies. The authors also thank Temi Abimbola, Pedro Conceição, Marva Corley-Coulibaly, Nomusa Dlamini-Tibane, Louise Fox, Kumiko Imai, Thandy Khumalo, Zodwa Mabuza, Mateus Magala, and Neil Rankin for comments. This research started when Zuzana Brixiová was Economic Advisor at UNDP Swaziland. Earlier versions were presented at the Wits University, the 2012 CSAE Conference, the 2012 African Economic Conference and the 2013 ASSA meetings. Financial support from the Wits University/IDRC is gratefully acknowledged. The views expressed are those of the authors and not necessarily those of the AfDB or UNDP.
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Brixiová, Z., Kangoye, T. (2014). Youth Employment in Africa: New Evidence and Policies from Swaziland. In: Malo, M., Sciulli, D. (eds) Disadvantaged Workers. AIEL Series in Labour Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-04376-0_9
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