Characteristics and Macroeconomic Determinants of Youth Employment in Africa

Anyanwu, John C.

doi:10.1111/j.1467-8268.2013.12019.x

Cited by 108 publications

(110 citation statements)

References 59 publications

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“…The size of a country is observed to have a decreasing effect on youth unemployment even though the statistical significance of the coefficient is weak. The level of a country's development measured by per capita real GDP is observed to have no statistically significant effect on youth unemployment in Africa, contrary to findings by Anyanwu (2013) that per capita real GDP has negative and statistically significant effects on youth employment, indicating a positive effect of per capita GDP on youth unemployment in Africa.…”

Section: Estimation Strategy and Analysis Of Resultscontrasting

confidence: 88%

The youth unemployment challenge in Africa: What are the drivers?

Baah‐Boateng

2016

Econ. labour relat. rev.

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Youth unemployment remains a major political and socioeconomic challenge in Africa despite the recent strong growth performance of many African countries. The study undertakes an empirical assessment of the main sources of youth unemployment in Africa. Based on panel data of 41 African countries covering the period 2000-2010, the study finds a demographic youth bulge and poor economic growth from both supply and demand sides of the market to be key drivers of youth unemployment in Africa. Employment-to-population ratio as a measure of country's job creation ability and vulnerable employment as a proxy for informality are observed to have had a decreasing effect on youth unemployment. The empirical findings also suggest higher youth employment rates among females than males and a higher concentration in urban than rural areas. Investment in the high labour absorption sectors of agriculture and manufacturing is advocated as job creation strategies, along with population control measures to slow the growing youth population in Africa. High growth in the low employment sectors of mining and extractive industries could serve as resource generating avenues to promote investment in education and skill training, along with infrastructure to facilitate growth in high labour absorption sectors.JEL Codes: J 13, N37, N57, N67, O17, O55

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Section: Estimation Strategy and Analysis Of Resultscontrasting

confidence: 88%

The youth unemployment challenge in Africa: What are the drivers?

Baah‐Boateng

2016

Econ. labour relat. rev.

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“…Sackey and Osei () argued that younger people are more likely to be unemployed due to the fact that they have lower labour market skills relative to older cohorts. Anyanwu () cited a number of reasons that accounts for the labour market bias against young people including the fact that younger workers tend to be easier and less expensive to dismiss than older ones; they are likely to face the brunt of layoffs due to the perceived lower cost to establishments of releasing them relative to their older counterparts.…”

Section: Theoretical Framework and Empirical Literaturementioning

confidence: 99%

“…Anyanwu () used available cross‐sectional data over a period of 1991–2009 in Africa to show an increasing effect of a nation's domestic investment rate on youth employment and by implication a decreasing effect on youth unemployment in sub‐Saharan Africa (SSA) with the reverse reported in North Africa. He further found a statistically significant positive effect of real GDP growth on youth employment in SSA and North Africa suggesting a decreasing effect of economic growth on youth unemployment in Africa.…”

Section: Theoretical Framework and Empirical Literaturementioning

confidence: 99%

Determinants of Unemployment in Ghana

Baah‐Boateng¹

2013

African Development Review

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Unemployment is often cited as a measure of the low employment content of Ghana's strong growth performance over the past three decades. The paper presents evidence to suggest that employment growth in Ghana continues to trail economic growth due to high growth of low employment generating sectors against sluggish growth of high labour absorption sectors. A cross-sectional estimation of a probit regression model also indicates a strong effect of demand factors on unemployment, indicating a weak employment generating impact of economic growth. Empirical analysis also confirms higher vulnerability of youth and urban dwellers to unemployment with education and gender explaining unemployment in some instances. Reservation wage is also observed to have an increasing effect of unemployment. The paper recommends policies that promote investment in agriculture and manufacturing which is associated with higher employment elasticity of output. High incidence of unemployment among the youth and secondary school leavers in the most recent period requires targeted intervention including support for entrepreneurial training and start-up capital to attract young school leavers to become 'creators' rather than 'seekers' of jobs. A downward review of expectations on the part of jobseekers in terms of their reservation wage could help reduce unemployment in Ghana.

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“…Based on the above review and following the frameworks posited by Chen (), Tseloni et al . (), Eastin and Prakash () and Anyanwu (, ), the relationship that we want to estimate can be written as:

\begin{matrix} left \\ log G E_{i t} = α_{i} + β_{1} log (r g d p_{i t}) + β_{2} log (r g d p^{2}) + β_{3} (d e m o c_{i t}) + β_{4} (d e m o c^{2}) + β_{5} (X_{i t}) + β_{6} (Z_{i t}) + ϵ_{i t} \\ (i = 1,... ., N; t = 1,.... ., T) \end{matrix}

where GE is the measure of gender equality in country i at time t ; α i is a fixed effect reflecting time differences between countries; β 1 is the elasticity of gender equality with respect to real per capita income in 2000, rgdp ; β 2 is the gender equality elasticity with respect to quadratic real per capita GDP; β 3 is the coefficient of democracy, domec ; β 4 is the coefficient of the quadratic of democracy; X is the control variables, including domestic investment (percentage of GDP) ( inv ), foreign direct investment (percentage of GDP) ( fdi ), primary school enrolment ratio ( primedu ), secondary school enrolment ratio ( secedu ), urban population share ( urban ), and sex population ratio ( popratio ); Z represents year, sub‐regional and oil effects dummies used as fixed effects; and ϵ is an error term that includes errors in the gender equality measure. We use the North African dummy with its separate estimation to check if, indeed, North Africa is different.…”

Section: The Model and Datamentioning

confidence: 99%

Gender Equality in Employment in Africa: Empirical Analysis and Policy Implications

Anyanwu¹,

Augustine²

2013

African Development Review

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Gender equality in employment is currently one of the greatest development challenges facing countries globally, including those in Africa. In 2011, the male employment-to-population ratio, globally, was estimated at about 72.7 per cent compared to the female employment-to-population ratio of only 47.9 per cent. For Africa as a whole, the male employment-topopulation ratio was estimated at about 69.2 per cent compared to the female employment-to-population ratio of only 39.2 per cent. In addition to analysing the characteristics of gender equality in employment in Africa, this paper empirically studies the key drivers of gender equality in employment (proxied by the ratio of female employment rate to male employment rate for the age group 15-64 over the period, 1991 and 2009), using cross-sectional data. Our results suggest that for the all-Africa and sub-Saharan African samples, increased democracy (and its quadratic form), higher gross domestic investment, more primary education, and higher urban share of the population increase gender equality in employment while higher level of real GDP per capita, higher foreign direct investment, sex population ratio, and being a net oil-exporting country tend to lower it. However, North Africa is different. Apart from a negative and highly significant North African dummy in the overall results, the North African specific sample result indicates that while the quadratic element of real GDP per capita, higher gross domestic investment, higher urban share of the population, more secondary education, and being an oil-exporting country increase gender equality in employment, higher levels of real GDP per capita, more primary education, and sex population ratio tend to lower gender equality in employment in the sub-region. The policy implications and lessons of these results are discussed. These policies are directed at making the African labor market more inclusive and hence enhancing women's employment for the purpose of greater economic empowerment, household welfare and poverty reduction, in particular.

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Characteristics and Macroeconomic Determinants of Youth Employment in Africa

Cited by 108 publications

References 59 publications

The youth unemployment challenge in Africa: What are the drivers?

The youth unemployment challenge in Africa: What are the drivers?

Determinants of Unemployment in Ghana

Gender Equality in Employment in Africa: Empirical Analysis and Policy Implications

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