A good starting point in the discourse on youth is how to define the population group that is considered youth. This is because there exists no globally accepted definition of the youth population group.[1] The concept is contextual and varies. For example, while the UN, for statistical purposes, defines youth as people between the ages of 15 and 24 years; the pdf Africa Youth Charter (234 KB) defines youth as those aged between 15 and 35 years.

UNESCO suggests a ‘practical’ definition of youth as the “period a person develops capabilities and social skills required to be ready for financial gain and the responsibility to be brought by the status of adulthood”. This article defines youth population as those aged between 18 and 35 years old. It uses the AU Youth Charter (AYC) upper limit of 35 years and a lower age limit of 18 years. This is both because of information availability (the Afrobarometer surveys data used has the lower age limit of 18 years old) and also for practicality as it is also a generally accepted that 18 years old is the onset of adulthood (legally).

The outcome document of the sustainable development goals (SDGs) recognizes young people as critical for sustainable development both as beneficiaries and as decision makers. Additionally, Africa’s Agenda 2063 (AA-2063), the continent’s long-term blueprint for transforming African societies into inclusive and sustainably developing societies, also identifies youth as critical decision makers and primary beneficiaries of the continent’s sustainable development ambitions.

The AA-2063 is distinguishable from SDGs as it defines its raison d'être as ensuring “the continent benefits form its demographic dividends”. Demographic dividends refer to the accelerated economic gains that can result from the expansion of the proportion of an economy’s working age population relative to its non-working age population.

The growth of a country’s working age population offers an opportunity for the growth of productivity and this is what facilitates accelerated economic growth. According to an IMF study, demographic dividends tend to be highest in low income countries as they generally rely more on labour intensive strategies. While the demographic dividend effect is unambiguous globally, the IMF warns that the full benefits accrue only to countries that make the necessary investments in human capital.

Seen against a background in which Africa is currently on track to be the only continent with a growing working age population beyond 2050 and may account up to 65 percent of the global working age population by 2090; the role of the youth – Africa’s majority of working age population – in achieving both the SDGs and the AA-2063 is practically indisputable.

SDGs with their focus on the elimination of inequalities and ensuring that no one is left behind, form a basis on which countries can ensure they are better prepared to fully reap the benefits of a growing working age population through offering human capital investment pathways. This, in turn, facilitates the attainment of the AA-2063 broad objective – reaping of demographic dividends.

This implies that an initial indication of Africa’s preparedness to reap demographic dividends can be deduced through the analysis of the trends in the youth that are unemployed and are also not looking for a job – the discouraged youth. The higher the proportion of discouraged youth, the less prepared to reap demographic dividends a country is. This is because, demographic dividends are dependent on gains in economic productivity which are in turn dependent on an economy’s ability to absorb labour supply.

Afrobarometer survey data covering 16 African countries between 2003 to 2018[2], suggests that the average youth is most likely (61 percent) to be employed or actively seeking employment. This illustrates the diverse stages of development among African countries. While a primary school educated female living in semi-urban Mali is most likely (62 percent) to be discouraged, a female of similar profile in Botswana in most likely to not be discouraged (80%). This, in part, explains why 6 in 10 youth are likely to be out of the labour market while only 2 out of 10 are in the same situation in Botswana.

Within countries, educational attainment is the strongest determinant of whether youth are active in the labour market. The majority of discouraged youth (72 percent) have primary education as their highest education. Illustrating how unequal access to education can drive inequality is the fact that education attainment is a major determinant of whether a respondent lives in an area with quality public bulk services (portable water and sewage system). Nine percent more discouraged youth live in areas without a sewage system than those that are not.

An overarching message from this is the importance of quality education as an accelerator of both the SDGs and harnessing demographic dividends in Africa. That said, the diversity in the development stages of African countries means that a ‘one size fits all’ approach to sustainable development in Africa is unworkable. Rather, approaches that are in sync with the country’s development realities are bound to be more effective. At the same time, the different development levels that African economies are provides an opportunity for African countries to learn from each other’s experience to distill important ingredients for success.

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[1] Gyimah-Brempong & Kimenyi (2013), Mengistu (2017) and Tekinda (2017)

[2] The countries are Botswana, Cape Verde, Ghana, Kenya, Lesotho, Malawi, Mali, Mozambique, Namibia, Nigeria Senegal, South Africa, Tanzania, Uganda, Zambia and Zimbabwe. Ten variables (Country, educational attainment, the combined effect of gender and locality (rural, urban, peri-urban and semi-urban), presence/absence of piped water services, presence/absence of sewage system as well as how respondents felt government was performing as regards; (a) managing the economy, (b) job creation, (c) addressing education needs and (d) improving basic health services were analysed using machine learning algorithms to determine the characteristics (and hence factors affecting/influencing) discouraged youth in Africa.