How governments influence mental health

How governments influence mental health

Sub-Saharan Africa: mental health

This paper sets out to investigate mental health disorders in sub-Saharan Africa, and the extent to which they are influenced by various aspects of the government or state. Mental health disorders, in this context, are classed the mainstream way: depressive disorders, anxiety disorders, eating disorders, schizophrenia, and bipolar disorder; drug and alcohol use are included for purposes of comparison. The aim is to identify the extent to which government/state related variables predict mental health in sub-Saharan Africa. In other words, the aim is to obtain some indication of the extent to which government policies might help to “create” mental ill health in the population.

One might, from this perspective, understand mental health as consisting of two factors. The first of these is an innate predisposition to mental ill health that individuals might “carry with them”. These are theorised to be either genetic or socialised into individuals at a very young age. Factors such as a family history of mental ill health, as well as early adversity such as domestic abuse, bad parenting and bullying, among others, might play a role here.

The second major component consists of precipitating factors, which are external influences, or stressors. When paired alongside the predisposition, these can cause mental ill health to become manifest. This predisposition-threshold model is widely accepted within the discipline of psychology. The analysis seeks to identify some of the precipitating factors, rather than the prevalence of the innate aspects. If government policies or state bureaucracies have an impact on mental ill health, it is most obviously at this external point that they come to do so.

First, the prevalence of various categories of mental ill health is described. Secondly, those afflictions with the greatest prevalence were selected. Thirdly, these were correlated with contextual variables, to identify any government or state-related variables that influence mental ill health in the region.

The mental health data was obtained from various sources, including the Institute for Health Metrics and Evaluation (IHME), a research institute focusing on global health statistics at the University of Washington in Seattle.

The extent of mental ill health was measured in Disability Adjusted Life Years (DALYs). One DALY can be thought of as one lost year of healthy life. The sum of DALYs throughout the population can be thought of as the disease burden or the gap between actual disease situation and the ideal, where the population lives to an advanced age free of ill health. This is adapted from the WHO’s definition.

The external, government related variables were obtained from World Bank indicators and the United Nations Development Program (UNDP).

The IHME provides an indication of disease burden globally. Using their data, Richie and Roser (2019) created a series of visualisations of mental health and substance abuse disease burden. Their mappings are used in figures 1, 3 and 4. Considering figure 1, which maps global mental disorders and substance abuse, it appears that Africa is relatively devoid of these phenomena, and that these appear to be afflictions of the more advanced economies. Note, however, that the identification of psychological disorders is to an extent a western practice, and a number of arguments can be made about the validity of measuring western notions of mental health in an African context.

[Figure 1]

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Figure 1: Global mental disorders and substance abuse (from Richie and Roser, 2019)

If we break mental ill health in sub-Saharan Africa down into its constituent afflictions, the prevalence of mental health issues in that region shows a high degree of variation.

[Figure 2]

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Figure 2: Various classes of mental disorder in sub-Saharan Africa

It is evident that depressive and anxiety disorders are the major mental health challenges in this region. The IHME displays this graphically:

[Figure 3]

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Figure 3: Global depressive disorders (from Richie and Roser, 2019)

The prevalence of depressive disorders in Africa is heterogeneous, and is closer to the severe end of the spectrum, globally. The heterogeneity suggests some complexity to the aetiology of depression throughout the region. Anxiety is far less prevalent across the region.

[Figure 4]

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Figure 4: Global anxiety disorders (from Richie and Roser, 2019)

The question is: to what extent these patterns can be explained by factors at least partially under government control or as related to states’ service provision?

The following graph illustrates the risk factors associated with depressive and anxiety disorders. It is evident that problems in family dynamics account for a large portion of depressive aetiology – a pattern seen uniformly across the world. Of course, these family dynamics are very likely caused by contextual factors themselves. In the following sections, the analysis will move beyond these immediate family/household–related factors. As mentioned, we want to focus on the factors that push people over the threshold into mental ill health. This puts the focus onto the contextual stressors rather than on innate and biological factors or early life socialisation (such as bullying and childhood maltreatment).

[Figure 5]

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Figure 5: Risk factors associated with depressive disorders in sub-Saharan Africa (from IHME data)

There is some indication in the literature of the broader contextual factors that might predispose individuals to depressive episodes. The most obvious of these are income and education. Both of these variables have been shown to have some relevance to depression globally. These are therefore included in the analysis.

But this analysis goes further by investigating additional variables obtained from World Bank data. These include air pollution, access to basic services, migration and refugees, rural-to-urban shifts, gender equality and government expenditure on health. The choice of which variables to include in the analysis was constrained, to a large degree, by the available data. It was hypothesised, however, that all of these contextual factors would have some impact on the prevalence of depression and/ or anxiety disorders in the country.

In order to do so, a correlation matrix was created for the variables in question. Ideally, a multiple regression would have been used, but the available data precluded this. From this, the most influential external factors were selected. In our analysis, only the rural urban shift proved to have a measurable impact on depression, alongside income and education.

The government/state factor was included in the analysis in the way that the measures were constructed. While services were not found to be clearly related to depression, investment in education was found to be related. The significant outcomes were plotted on a graph, which also reflected gender differences.

[Figure 6]

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Figure 6: Education, income and rural-to-urban migration as related to depressive disorders in sub-Saharan Africa.

Figure 6 presents data for three stressors: education, income and rural-to-urban migration when related to depressive disorders, measured in number of DALYs lost. Each dot represents a sub-Saharan country, broken down by gender (DALYs lost by females and DALYs lost by males in that country). The position of the dot is then determined by the coordinates of the female and male score. The vertical line indicates the switch from female to male bias in the impact of depression.

The graph, therefore, allows one to see gender bias in depression burden by noting whether the dot falls above or below the vertical line. It also allows for an understanding of the severity of the burden. Dots further towards the top right represent higher burdens. Not all sub-Saharan countries are plotted – only those in which depression could be reliably linked to the stressors in the analysis were included. Also, the names of the countries were not included. Although this would be an interesting addition, it was felt that this might detract from the overall message/pattern in the data. For a similar reason, a broad indication of the severity of the burden was included on the x and y axes, rather than the exact DALY burden.

It is evident from the graph that there is some variation in the influence of gender, depending on the predisposing factor. Education is seen to be more of a factor for females than for males. This effect was found to be of moderate strength. Income, on the other hand, was found to be more influential for male depression. The third factor – rural-to-urban migration – did not display a noticeable effect between gender, and was less influential overall

These findings suggest that government investment in education has an unintended consequence of boosting mental health within the population.

The same methodology was applied to anxiety disorders. As with the depressive disorders, a correlation matrix was constructed for the variables in question. Once again, from this, the most influential external factors were selected and included in the analysis. Only one variable emerged, alongside education and income. This was access to services. To preserve some comparability, the rural-urban shift was also included in the plot.

[Figure 7]

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Figure 7: Traditional, family and household factors as related to anxiety disorders in sub-Saharan Africa.

From this graph it is evident that there is no single overarching causal factor regarding anxiety disorders. Is it possible, however, that broader socio-political issues also have an impact on the high anxiety disorder burden in sub-Saharan Africa? To gain some idea of this, a number of contextual factors were investigated; air pollution, access to basic services, migration and refugees, rural-to-urban shifts, gender equality and government expenditure on health.

[Figure 8]

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Figure 8: Education, income, rural-to-urban migration and services as related to anxiety disorders in sub-Saharan Africa.

In the case of anxiety disorders, investment in education was clearly more influential than the other factors. In addition, there seemed to be less of a gender bias in this effect, with no noticeable difference being exhibited between females and males. Income showed no clear correlation with anxiety, and neither did rural-to-urban migration. Services, however, showed a noticeable effect on anxiety disorders. The services scale included factors such as access to sanitation and the provision of safe drinking water. It is worth noting, however, that the services included in the analysis were not exhaustive; rather, they were constrained by the available data. Anxiety related to access to services showed a noticeable bias towards females.

Figure 9

Figure 9: Education, income, rural-to-urban migration and services as related to depressive and anxiety disorders in sub-Saharan Africa.

It is possible, merely by superimposing the depressive and anxiety graphs, to observe the combined effect of the four variables that came out as significant. When combined, the gender biases are less obvious, except in the case of services – which is unsurprising, since services appeared as significant in anxiety disorders only. The other variables show very little bias.

The analysis suggests that Africa is not as badly affected by mental health issues as more westernised/industrialised countries. Although the terminology is difficult, it might be said that this state of affairs may be expected to change as Africa moves more in line with globalisation. Depression and anxiety are, currently, the major disorders regarding mental ill health. These disorders have a large social component – as opposed to a condition like schizophrenia, which appears to be more biologically/genetically based.

The IHME data indicated that the risk factors associated with these two classes of disorder are largely family/household related, and include factors such as having an abusive partner or being bullied, among others. Beyond these factors – which are typical of global mental ill health aetiology – the broader context was found to have some impact on the expression of these disorders. Income and education – the latter was measured via government-related variables, such as investment in education, among others – were found to be related to both depression and anxiety, although in different ways. This suggests that mental health is affected by the environment in which individuals find themselves.

More importantly, these mental disorders are to a significant part affected by the diligence of government. The state’s underinvestment in education and services, and the failure to protect a basic level of income has a noticeable impact on their citizens’ levels of depression and anxiety.

This is a bold claim, and it opens up some questions for future research and clarification. Most obvious is the suitability of the data collected. Critics of mainstream psychology have noted that these disorders emerged from a western, industrialised context. Some powerful critiques have questioned the extent to which these are valid concepts in an African context. One might suggest that the more industrialised/westernised/globalised Africa becomes, the less the importance that should be attached to this question. The problem of whether these are appropriate constructs, however, remains. Future research might be directed toward this issue.

Another area for further investigation relates to the issue of how these issues should be addressed. A strong critique of psychology has been that it offers no effective cure for the ills that it describes. Typically, psychotherapy has been seen as the remedy to psychological problems. The effectiveness of psychotherapy has been questioned, but the real problem with psychotherapy probably has more to do with the fact that it is slow and typically expensive.

It seems unlikely, at least for the foreseeable future, that Africa’s mental health challenges will be addressed by armies of psychotherapists working at affordable rates. How, then, should these issues be tackled? One way is by conducting this kind of investigation into the uniquely African predisposing factors, and then addressing these factors via activism or some other means. Considered from this perspective, the most important message of this brief exploration is that a much more thorough investigation of the contextual and state-related predisposing factors to mental ill health needs to be carried out.

Vaughan Dutton is a chartered research psychologist and associate fellow of the British Psychological Society, with 18 years’ social research experience gained at senior levels in commercial, government and academic contexts. He has taught social research methods, social theory and psychological theory at a number of universities in South Africa and the UK. Currently a consultant, he is also completing a doctorate in psychotherapy. He studied research psychology (applied social research) in South Africa, and holds a research doctorate from the University of Oxford.

A material difference

A material difference


Entrenched family values and practices appear to play an important role in perpetuating gender inequalities in sub-Saharan Africa

Being a woman in Africa means being exposed to a range of prejudices and biases that are encoded in institutions, discourses and ways of relating, and experiencing the effects of these in material conditions that are divided along gender lines. The struggle has always been not only to minimise material biases, but also to eliminate the non-material, institutional and discursive prejudices from which these arise.

One way to conceive of this is to understand the material conditions of women’s lives as indicators of progress: more equality in material things means more progress has being made. The non-material institutional and discursive prejudices that affect women’s lives can be seen as challenges or obstacles to more progress being made. These non-material discriminatory social institutions pervade the lifespan of African women, limiting their access to justice, rights and empowerment, and compromising their agency and decision-making ability over important life choices.

Such a model does, of course, make a range of assumptions about the drivers of social change, in particular that non-material changes will result in material changes. From this view, these discriminatory social institutions can be understood as underlying drivers of gender inequalities that perpetuate gender inequalities in diverse areas such as education, employment and health, as well as stifling transformation that reduces inequalities.

This article adopts this model and seeks to describe areas in which material progress has been made, as well as areas in which non-material challenges and obstacles persist. It focuses specifically on countries in sub-Saharan Africa and presents an analysis of material and non-material conditions at country level. It will also provide an indication of the size of the problem by simply indicating the size of the female, and especially young female, population in each country.

It is not easy to locate data that spans sub-Saharan Africa. There are typically large gaps in data series, and some countries are notoriously data poor (Somalia and Eritrea are examples of this). This means that analyses and models have to be adapted according to the data that can be gathered. For this article, I have prioritised gathering as full a sample of sub Saharan countries as possible. If an indicator did not cover the vast majority of countries in the region, it was dropped and replaced with another. Somalia, Eritrea, and South Sudan had to be eliminated from the analysis because of a lack of data.

The lack of reliable and consistent gender disaggregated data for sub-Saharan Africa was especially evident with regards to income, GDP, expenditure, or similar. While income statistics such as GDP per capita are relatively easily obtained, these were unavailable by gender. Because of this, as well as the notoriously low validity of income measures even when they do exist, a measure of income has not been included in the model.

Education was proxied by youth literacy, adult literacy and upper secondary school completion rates. In four cases – Botswana, Angola, Equatorial Guinea and Uganda – the data was unavailable and upper secondary school enrolment was used instead. For these countries, this aspect is therefore slightly exaggerated. Life expectancy/health was proxied by life expectancy at birth, HIV prevalence, and mortality rate. All of these variables were disaggregated by gender and obtained from the World Bank Indicators site.

Figure 1: Gender differences across various domains across sub-Saharan Africa

Non-material conditions were modelled using data from the Organisation for Economic Co-operation and Development’s (OECD) Gender, Institutions and Development dataset. This dataset covers five aspects of female-discriminatory social institutions. The first of these is “discriminatory family code”, and covers issues related to marriage age, parental rights after dissolution of marriage, daughter inheritance, and divorce and unpaid care work. The second is “restricted physical integrity”, and relates to social institutions that restrict women’s control over their bodies. Included in this are laws and norms that fail to protect women’s physical integrity and reproductive autonomy, and those that promote gender-based violence, such as FGM.

“Son bias” describes ways in which sons are promoted or offered preferential treatment over daughters; indicators include missing women, preference of sons in educational opportunities, and fertility preference. “Restricted resources and assets” refers to gender-based biases regarding access and ownership of land and financial services. Finally, “restricted civil liberty” relates to issues of access to public space, workplace rights, and having political voice and representation.

All variables were obtained either as percentages or as a score on a range from 0 to 1. Aggregate scores were therefore obtained by ensuring that all variables were scored in the same direction, and then taking mean scores for each domain. Finally, a grand mean was calculated for material and non-material variables for each country. The model also made use of population-related variables, which were collected from the World Bank Indicators site. These indicated country female population size and young female population size (ages five to 30 years).

There were four stages to the analysis. Firstly, major gender gaps in the sub-Saharan region (limited, of course, by the available data) were identified by simply comparing males and females across each of the progress variables. A Mann-Whitney U test was used to identify which of these gaps were significant. All analysis was conducted using R and RStudio statistical software. The results of the analysis are presented in Figure 1.

From Figure 1 it is clear that four of the six variables showed a significant difference between genders. Adult literacy and HIV prevalence were found to be higher for females than males, while life expectancy at birth was found to be higher for women. Unsurprisingly, adult mortality rate was found to be higher for females as well.

When these biases were considered across sub-Saharan countries it was found that the adult literacy rate ranged from much higher for males (in Liberia, Mozambique, the Central African Republic, Togo and Senegal) to higher for females (in Lesotho, Namibia and Botswana).

HIV prevalence was found to display much variation when compared across gender. The greatest differences were found to be concentrated towards the south of the continent. Eswatini (Swaziland), South Africa and Namibia were joined by Malawi and Zambia in the highest ranges.

Like HIV prevalence, adult mortality was found to exhibit the greatest gap between the genders in southern African countries. This is likely due in part to the high HIV prevalence, and to an extent these variables may be measuring the same thing. South Africa was at the top of the list, followed by Eswatini, Botswana, Angola and Namibia.

Figure 2: Non-material gender biases; 0 = low bias , 1 = high bias

Life expectancy at birth was the only variable that showed a bias in favour of females; this is, in fact, a global trend. The variable was retained in the analysis, however, because the variation in the gap across the region is still indicative of environmental influences.

These four variables, which all show a marked gender gap, were used to calculate a material bias score. After appropriate adjustments, they were averaged to yield a grand mean, which was then treated as a material bias score for each country.

All variables relating to non-material biases were included in the analysis. Figure 2 presents these on a radar plot. It is evident that the highest biases relate to workplace rights and the legal provision of quotas for women’s participation in politics.

Material biases were then plotted on an x axis and non-material biases on a y axis by country (see Figure 3). This placed each country as a point in a scatterplot. In addition, each country’s female population size was used to adjust the size of the country’s corresponding point. Finally, the proportion of female youth in each country was represented by the colour of the point, with more yellow = more female youth.

Figure 3: Material v non-material biases

By creating different size points, with differing shades of yellow, it becomes possible to isolate higher priority countries. Those countries that have greater female population sizes, and higher proportions of female youth (for example, circles that are bigger and yellower) have the most urgent call to address issues affecting gender.

Finally, a regression line was fitted to the plot; the curve is shown as a blue-dotted line. This line was split by region. The one on the right-hand side of the graph reflects southern Africa (Angola, Botswana, Lesotho, Malawi, Mozambique, Namibia, South Africa, Eswatini, Zambia and Zimbabwe). The one on the left-hand side is the rest of sub-Saharan Africa. These lines provide a summary of the relationship between the x and y axes. It is evident from these that there is a different relationship between non-material and material factors in the two regions.

Overall, however, one could expect that if the reduction of non-material discrimination leads to a move towards material equality, then there should be a negative slope to the data. This is not, however, the case. In fact, in southern African countries the data suggests that increasing non-material challenges are related to higher material equality. Why this is the case is not evident. It may be that the data has not adequately captured the complexity of this relationship. Or it may be that the nature of the relationship changes in the various parts of Africa. Or it may be that the two are simply unrelated.

Putting these issues of interpretation aside, and aiming merely to describe how countries are doing, we highlighted two important regions within the plot.  These are presented in Figure 4. The red area represents high challenges and low progress toward alleviating material inequality – where it is not good to be, if you’re a country. The green area refers to low challenge and high progress towards alleviating material inequality –where it is good to be if you’re a country. From this we can see that there are relatively few countries that have substantially reduced their material inequalities, on the right-hand side. Still fewer fall into the green area, in which they have both made high progress towards alleviating material inequality and removed most non-material challenges.

Figure 4: Material v non-material biases with challenge and progress areas


Countries on this green area (especially towards the bottom right) tend to have done what they can to eliminate discrimination and also have low material inequality. Countries on the lower left-hand side have done what they can to eliminate challenges but have not reduced material inequality. Those on the top right of the plot are doing well but could do better if they address some of the challenges; while those on the top left have high material inequality and many challenges still to overcome. Larger and yellower circles are more important than smaller, blacker ones.

From this, we can see that the majority of countries fall in the middle range of the challenge variable. They have work to do, but are not extreme. There are a group of countries at the lower edge of the y axis that have done much to eliminate challenges but for some reason still experience material inequality, or are perhaps in the process of making progress. South Africa, Botswana, Uganda, Namibia and Lesotho are in a good position, according to this model.

Figure 5: Material v non-material biases with high priority details

Of more concern are those countries in the top left, red, area of the plot. Sudan, Chad, Niger and Mali are in the worst position. Their having a high proportion of female youth in their population compounds this; the fact that there are young women who are empowered introduces issues of youth and youth bulge problems, in addition to those purely related to women.

Another group of concerning countries consists of Tanzania, Togo and Rwanda, falling high up on the challenges axis and also showing a high proportion of female youth. Nigeria is also concerning. Although it does not display a high proportion of young females, the size of its population means that one cannot ignore its precarious position, edging towards the upper half of the non-material challenges axis.

A closer look at these “problem countries” gives some indication of where they are falling short regarding non-material challenges. These are presented in Figure 5. The Mali, Niger, Sudan and Chad group all lose points on variables related to discriminatory family codes. More specifically, they fall short on the legal age of marriage and whether women are as entitled as men for parental authority during and after marriage. In short, these countries exhibit cultural patterns that discriminate against women through the family. The Tanzania, Rwanda, Togo group exhibits similar sets of issues; discriminatory family codes are once again an issue, albeit to a lesser extent. In addition to this, however, this group falls back with regards to restrictions on women’s access to financial services.

The only other country that falls towards the high end of the challenges scale is Zimbabwe. The challenges here are very different to the previous countries; in Zimbabwe, non-material challenges relate to women being deprived of a political voice and not having adequate political representation. In Zimbabwe, the number of missing women and the low legal age of marriage are also issues.

This brief little analysis of material inequalities and non-material discriminatory factors in the sub-Saharan region has provided some preliminary insights and raised some interesting questions. Preliminary insights are that Sudan, Chad, Mali and Niger have much to achieve regarding the reduction of non-material discrimination. These challenges appear to be embedded in family practices and values and relate to women’s agency around the institution of marriage. Whether these issues are religiously derived is unclear.

Tanzania, Rwanda, Togo is another group faced with high levels of non-material discrimination; once again family practices and values are the issue, although less severely so than is the case with the previous group. Access to financial services is also an issue with this group. With the exception of Zimbabwe, which has its own unique set of challenges, it appears that family is the main point of discrimination in sub-Saharan Africa. It must be said that a more thorough analysis would be required to state this point with certainty.

An interesting question has also been raised; there appears to be no relationship – or the opposite relationship than was expected – between non-material and material phenomena. Yet it seems intuitively obvious that the two should be related. This provides a modification of the age-old social theory question: do material conditions influence non-material conditions? Or do non-material conditions influence material conditions? Or, indeed, are they unrelated?

VAUGHAN DUTTON is a chartered research psychologist and associate fellow of the British Psychological Society, with 18 years’ social research experience gained at senior levels in commercial, government and academic contexts. He has taught social research methods, social theory and psychological theory at a number of universities in South Africa and the UK. Currently a consultant, he is also completing a doctorate in psychotherapy. He studied research psychology (applied social research) in South Africa, and holds a research doctorate from the University of Oxford.
Ready for the youth

Ready for the youth

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.

Figure 1: Key policy actions for Generation 2030

Source: Adapted from Key Policy Actions for Generation 2030, UNICEF, 2017

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%).

Figure 2: Essential services: Health

Only Gabon rivals South Africa in this regard. The other countries trail off with significantly lower scores.

Figure 2: Essential services: Health

Level of employment represents the other variable measuring the provision of essential services. In this context, South Africa falls way behind other countries, with the average for all the countries included in this study being 7% unemployment. South Africa has the lowest level of employment, with some 24% of the workforce unemployed, while Nigeria has the highest level of employment, with 0.32% of the total employable workforce unemployed.

Figure 4: Essential services: Employment


Figure 5: Essential services: Employment


Skills enhancement was represented by the provision of education, more specifically government expenditure on education as a percentage of GDP. The results are represented in Figure 6 below.

Figure 6: Skills enhancement: Education

Overall, all the countries included in this study had similar employment figures, as indicated in Figure 5.

Education represents an area in which there is a large range across the countries, as indicated in Figure 7.

Figure 7: Skills enhancement: Education

The final component of the model was “social protection”. In our analysis, this was represented by gender equality. Once again, this variable displayed a wide range within the country sample.

Figure 8: Protection: Gender equality

Figure 9 provides an indication of the range of scores across the sample of countries. The staggered results reflect the crude scoring system underlying the measure, with many countries having scored the same as each other.

Figure 9: Protection: Gender equality

To add to this component, we also included the number of new HIV infections acquired as part of “social protection”. It was felt that new infections might better represent protection than HIV prevalence, as HIV prevalence is affected by historic trends while new infections are not. The results are represented below:

Figure 10: Protection: New HIV infections

It is evident that new HIV infections occur in significant numbers in only a few countries, with South Africa being by far the most severely affected, as represented by a score of O in Figure 10, which indicates that it is the worst-performing.


The findings for each component were combined to provide an overall picture of “readiness” for deriving a dividend from the youth demographic. When combined, a picture emerges in which Ghana fares particularly well, followed by Rwanda, Burundi, and Senegal.

Figure 12: Overall readiness for youth bulge

The model encapsulates the need for countries to perform well in all three components if they are to score highly in youth readiness. The message is that an all-round approach to readiness is essential.

Countries that fared very well on some variables but not on others cannot claim readiness based on one or two dimensions. South Africa (ZAF) is a case in point: despite good educational and health investments, the country still fared relatively badly because of high rates of HIV infection. 

Key data

ILO. (2018). Key Indicators of the Labour Market 9th edition. International Labor Organization. Retrieved from

The World Bank. (2018). World Development Indicators. The World Bank. Retrieved from

UNICEF. (2017). Generation 2030 Africa 2.0: Prioritising investments in children to reap the demographic dividend.

How long is too long?

How long is too long?

President of Zimbabwe Robert Mugabe in Addis Ababa, Ethiopia, January 31, 2008. (U.S. Air Force photo by Tech. Sgt. Jeremy Lock)

Data analysis shows that the longer current heads of state rule, the higher the levels of corruption and the more extensive their powers become