A recent report by the United Nations Children's Emergency Fund (UNICEF), Generation 2030 Africa 2.0 (2017), refers to the vast number of Africa's children, or the so-called "youth bulge", as a potential "demographic dividend" for the continent. But which of the continent's countries are ready to take advantage of this "dividend"? And which have the correct conditions in place to turn the bulge into a dividend?
The UNICEF report indicates that getting Africa's large proportion of young people work to a country's advantage requires certain conditions. These include the presence of essential services, skills enhancement measures, as well as measures that enhance social protection. These various measures can be represented in a simple model, as in Figure 1, below.
Essential services include health and social welfare systems. Skills enhancement includes the provision of good quality educational systems, and social protection involves measures to protect women and children from abuse, discrimination and violence.
This simple model provides a way of conceptualising the readiness of countries to deal positively with the youth demographic. The more countries are able to invest in these requirements, the greater the probability that the continent's large proportion of young people will be an economic asset.
It is therefore possible, using this model, to gain some insight into countries' current readiness for dealing with the youth demographic, and to identify areas in which they might improve. This paper sets out to do so; that is, to apply the model to current African data to assess the continent's readiness for meeting this challenge.
There are several challenges to doing so. Firstly, the paucity of African data means that validity will be in question (that is, that the scarcity of data means that we will have to use variables that approximate the model components in only the broadest terms). Secondly, not all African countries and regions are represented. This article focuses only on sub-Saharan Africa, and within that, only the countries for which we could obtain complete data.
This aside, the analysis provides a useful starting point for discussion on the issue that is based, to an extent, on real-world data.
The model provides three overarching components that together represent the likelihood of a country reaping a benefit from the youth demographic. These include essential services, skills enhancement, and social protection. In our analysis, we approximated these components in the following way.
Essential services: for this component, we used the World Bank Indicators "current health expenditure per capita, PPP (current international $)" and "unemployment". The World Bank describes current health expenditure as "current expenditures on health per capita expressed in international dollars at purchasing power parity (PPP)". Unemployment is described as "the share of the labour force that is without work but is available for and seeking employment".
Skills enhancement: as a proxy for skills enhancement, we made use of the World Bank Indicators "government expenditure on education, total (% of GDP)", defined as the "general government expenditure on education (current, capital, and transfers) expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments".
Social protection was approximated by making use of the World Bank Indicator, "CPIA gender equality rating". This variable "assesses the extent to which the country has installed institutions and programmes to enforce laws and policies that promote equal access for men and women in education, health, the economy, and protection under law". Social protection was additionally assessed by the World Bank Indicator, "adults (ages 15+) and children (ages 0-14) newly infected with HIV", which refers to the "number of adults (ages 15+) and children (ages 0-14) newly infected with HIV".
It is, of course, possible to question the validity of these variables. How well do they measure the model components? Any claim that they offer a close or complete picture would be misleading. They do, however, offer some indication of youth-readiness of the countries included, and offer some indication of where improvements can be made.
To plot the data, all variables were converted to a percentage of the highest score. In each variable, therefore, one country scored 0% and another 100%, with all other countries scoring between these two extremes. Once again, there are strengths and weaknesses to this approach. While conversion to percentage means that variables are directly comparable, it also means that the range of scores are always 100, which might artificially extend or reduce the differences in the raw data.
The variables of interest were arrayed against the youth population size for each country, which was represented on the y-axis. There was little variation in the size of the youth demographic relative to the overall population (between 18% and 22%) and this might be considered to be uniform across the sample - as the International Labor Organization (ILO) does, for example, with its indicators.
The analysis includes 27 sub-Saharan countries. The high number of countries included is due to the lack of detail in the data; using few, broadly applicable variables means that the data exists in more countries. The list of countries, as well as their codes, is included in Table 1:
Investment in essential services was represented by government investment in health and employment. The spread of health provisions was dominated by South Africa (ZAF), which at 100% is the country spending the most on health per capita, expressed in international dollars at purchasing power parity (PPP). This is in spite of it spending significantly less of its GDP on health than several of the other countries (8% compared to, for example, Zimbabwe's 10%, Liberia's 15%, or Sierra Leone's 18%).