Youth Employment Challenges in Africa: Policy Options and Agenda for Future Research

Youth Employment Challenges in Africa: Policy Options and Agenda for Future Research William Baah-Boateng Department of Economics, University of Ghana Legon for AERC BIANNUAL CONFERENCE IN LUSAKA, ZAMBIA December 2014 1. Introduction Africa s population is very young with more than half aged below 25 years. It is estimated that each year, between 2015 and 2035, there will be half a million more 15-year-olds than the year before (World Bank, 2014). This is against the backdrop of the fact that the population in the rest of the world will soon be aging. Estimates by Population Reference Bureau (2013) put Africa s youth population aged between 10 and 24 years at 344.4 million in 2013, representing 31% of total population making the continent the youngest region in the world. Besides the rapid growth of Africa s youth population, it is also getting better educated though weak in employable skills. According to AfDB et al (2012) about 59% of 20-24 year olds will have had secondary education in 2030, compared to the current figure of 42%. Considering the concerns about quality gap in the education of these young people, the growing educated youth population constitutes a potential resource for growth and development of the region if the quality of education is improved to make them gainfully and productively employed. On the dark side, this reservoir of human capital could also be a source of civil conflict and social tension if Africa fails to create adequate economic and employment opportunities to support them to have decent living. Indeed, disaffected youth without education, jobs or prospect of a meaningful future may fuel future instability, migration, radicalisation and violent conflict. Africa s growth performance has been quite remarkable particularly over the last decade. Between 2000 and 2012, Sub-Saharan Africa (SSA) grew more than 4.5% a year on average, compared to about 2% in the prior 20 years and that the region s growth rate in 2012 was estimated at 4.7% and 5.8% if South Africa is excluded (World Bank, 2014). Although, many jobs have been created, it has not been sufficient to meet the increasing number of young people in search for employment. Between 2000 and 2008, Africa created 73 million jobs but only 16 million went to young people aged 15-24 years (ILO 2008). Consequently, many young people on the continent find themselves unemployed or more frequently in vulnerable employment and/or informal jobs with low productivity and earnings (AfDB et al., 2012). Besides, the rate of 1

unemployment and low quality of employment among the youth is the long duration of unemployment among young Africans (Baah-Boateng, 2012). Even though youth employment challenge is a global phenomenon, the youth population bulge in Africa makes the phenomenon more acute in Africa. The growing concern about youth employment challenges on national, regional and global development agenda hinges largely on the fact that they face specific challenges in accessing the labour market. Indeed, lack of experience of the youth in the labour market poses specific barriers to securing decent jobs and this exacerbate their chances of getting jobs subsequently when they face early unemployment. 1 Additionally, they also stand the highest chance of losing their jobs in times of economic downturn. The challenge facing these young people in securing quality jobs after school increases their vulnerability in society and makes them susceptible to social vices and source of conflicts and civil disorders. As noted by OECD (2009) lack of employment opportunities may result in social conflicts such as violence and juvenile delinquency which in turn incur high social cost. The increasing incidence of street hawking and migration of the youth in Africa across the Mediterranean with its attendant risk are not only symptoms of labour market challenges facing these people but also a reflection of a sense of hopelessness. The prime objective of this paper is to explore issues in youth employment and in Africa and identify challenges and opportunities confronting the youth in the labour market. This will provide understanding of key constraints and opportunities faced by the youth, show research and policy gaps in the labour market and make policy recommendations to guide policy makers in Africa. The paper reviews relevant concepts as part of the survey of the theory and evidence related to unemployment. The paper outlines the sources of youth employment challenges concludes with some policy recommendations and a reflection of further research. Labour market research in Africa is often hampered by absence of regular and consistent data. Employment and unemployment data is generally sources from national population census, and household surveys such as living standards and labour force surveys. The main source of data for discussion in this paper is the ILO Key Indicators of the Labour Market (KILM) and the Global Employment Trends complemented by available published labour market indicators of some countries. The paper is structured into five sections. A survey of the theory and evidence of unemployment are discussed in section two after the introductory section followed by an overview of youth employment situation in Africa in section three. Section four outlines sources of youth 1 See also Clark and Summers (1982), Freeman and Wise (1982) 2

employment challenges after which conclusion and policy recommendations with agenda for future research are presented in section five. 2. Theory and Evidence: A Survey 2.1 Definitions and Concepts Employment and unemployment are the outcomes of demand for and supply of labour. Lack of jobs has implication for higher unemployment and vice versa. The concept employment refers to the outcome of demand for and supply of labour at a prevailing wage rate during a specified period of time. Within the context of the ILO, employment is defined as any economic activity that generates income through wages, profit or family gain in kind or in cash during a specified period of time. Generally, employment could be categorised into different forms: fulltime or part-time, temporary or permanent, decent or vulnerable. It can also be classified from a statistical angle based on international standard classification into occupation (professional, technical, clerical etc.), industry/economic sector (agriculture, industry and service); or type/status (i.e. wage or regular employment, self-employment etc.) or institutional sector formal (i.e. public and private) or informal. Labour statistics also differentiates between current employment and usual employment. The former refers to any lawful economic activity engaged by an individual above a specific age for pay in kind or in cash or for family gain for at least an hour during seven days as against the latter that looks at 12 months prior to the survey period. Unemployment on the other hand is a phenomenon of job-seeking arising out of joblessness. According to ILO (1982), a person who has attained the minimum age of employment (e.g. 15 years 2 ) who in a reference week was without work, available for work and actively seeking work is classified as unemployed. By implication, a jobless individual who is available for work but fails to make the effort to seek work for various reasons cannot be classified as unemployed and can only be referred to as a discouraged worker. In addition, an employed person who decides to seek (additional) work can only be termed as moonlighting (Baah-Boateng, et al, 2013). This definition of unemployment based on job-seeking has come under scrutiny particularly in relation to its application to the labour market in developing countries. It is increasingly seen as 2 The minimum working age convention (ILO Convention No. 138 stipulates 15 years which is the age of generally completing compulsory basic education. Some countries provide upper limit of 64. However, due to high informality, a considerable number of people above 64 years still remain active in the labour market particularly in agriculture. Thus, providing upper limit excludes a considerable number of people actively participating in the labour market. 3

inadequate to characterize low income countries labour markets (see Cling et al. 2006; Fares et al. 2006; World Bank 2006, inter alia). In Africa, a considerable number of jobless people may be available for work but fail to look for work for various reasons including the perception of no job or in some cases, jobs are mostly seasonal thereby creating high discourage worker effect (see Baah-Boateng, 2015). This has prompted some researchers to suggest a broader definition that relaxes the seeking work criterion. In addition, Not in Employment, Education and Training (NEET) or joblessness rate has also been suggested as a better measure of the problem of joblessness in Africa (see AFDB et al. 2012; Baah-Boateng and Ewusi, 2013; Aryeetey et al., 2014; Baah-Boateng, 2015). The section that follows surveys the theory and evidence of unemployment. Obviously, unemployment arises as a result of employment generation challenges. Therefore, a discussion of the theory and evidence of unemployment largely reflects employment generation concerns in the literature. 2.2 Theory The phenomenon of unemployment has been explained from different perspectives in the economic literature. Within the neoclassical framework, the labour market is deemed to always clear on the basis of the assumption of flexible wages and perfect information. In classical labour market, marginal productivity of labour has to be equal to the real wage within the firm s profit maximising behaviour. If this rule is distorted by wage rigidity due to institutional factors (e.g. minimum wage legislation) the labour market cannot clear causing classical involuntary unemployment to occur. Shackleton (1985) defines involuntary unemployment to exist if individuals cannot obtain work even if they are prepared to accept lower real wages or poorer conditions than similar qualified workers who are currently in employment. From the efficiency wage model, unemployment also arises when firms decide to pay higher wages above the equilibrium wage as incentive to increase efficiency of employees. According to the efficiency wage model, wages are kept higher above the market clearing wage with the view to averting shirking behaviour of employees (see Shapiro and Stiglitz, 1984); reducing labour turnover (see Salop, 1979 and Stiglitz, 1974); avoid adverse selection of job applicants (see Akerlof, 1970); or as a gift of exchange for high productivity from workers (see Akerlof, 1982). In such a situation, potential employees are kept out since the increased wage bills and workers high productivity would not make it appealing to engage more hands. The insider-outsider model of wage setting behaviour of firms also provides institutional explanation of involuntary unemployment (Lindbbeck and Snower, 1988). The model explains how unemployment can arise when wages are determined by taking into account only the interests of those employed known as insiders, without regard to the interests of those seeking to be employed referred to as the outsiders (see e.g. Bentolila et al., 2011). The concern by firms to reduce cost of labour turnover which prevents them from hiring outsiders gives some kind of protection to insiders even in the midst of their higher wage demands and creates limited avenues 4

for outsiders to get employed. Besides, insiders may resist competition with outsiders by refusing to cooperate with or harassing outsiders who try to underbid the wages of incumbent workers to escape unemployment. Even in times of economic recovery when more outsiders are expected to gain access to employment, insiders set higher wages and prevent underbidding from outsiders causing hysteresis, i.e. unemployment becomes permanently higher after recession (see Blanchard and Summers, 1987). From the Keynesian perspective, unemployment arises out of deficiencies in aggregate demand over certain periods in the business cycle such that jobs created are not enough for everyone who wants to work (Keynes, 1936). This type of unemployment is cyclical or demand deficient unemployment which is involuntary since the unemployed is constrained with limited job availability. Related to demand deficient unemployment is seasonal unemployment which is created by predictable seasonal variation in demand often corresponding with climatic seasons. A mismatch between demand for labour and the skills and location of jobseekers which tends to cause structural unemployment provides another explanation to the phenomenon of unemployment. This is related to unemployment created by technological advancement that makes skills of some workers obsolete. Unemployment can also result from the time it takes the individual to find and move into a new job or the time and resources it takes an employer to identify and recruit suitable workers to fill vacancies. This type of unemployment is called frictional search unemployment which is of short duration. Search theory has been used to analyse frictional unemployment resulting from job hunting by workers (see Stigler, 1962; Phelps, 1970). In contrast to neoclassical claim of perfect market information, jobseekers invest in job search due to imperfect information over one s best job opportunity while employers also search for availability of desired skill. Indeed, the basis of this theory is premised on the assumption that individuals searching for jobs do not get them instantly and therefore must make time (remain unemployed) to search in order to get better jobs. At the same time, employers looking for certain calibre of workers must also engage in the search trade. Since hires are not instantaneous and are actually preceded by job vacancies, search theory should be the standard theory for explaining unemployment caused by job vacancies (Farm, 2012). Unemployment has also been explained under the implicit contract framework. The theory argues that a rational worker will choose an unstable job, that is, a job with a higher probability of layoff, if that job offers higher wages during employment than choose a job which offers stable but lower wages, in a situation where unemployment insurance benefits or other forms of social security exist and are high, so as to maximise lifetime earnings. Consequently, more unemployment may be created in an unstable labour market if such benefits (social safety nets for the unemployed) are increased (see Azariadis, 1975; and Buddet and Hool, 1983). Rational jobseekers would prefer present high and certain wages over the short-run to low but stable income spread well into the future. 5

2.3 Evidence Empirical research on the sources of unemployment has often focused on the supply-side of the phenomenon. Most empirical literature has shown that unemployment tends to be relatively higher for the young than the old (see e.g. Johnson and Layard, 1993, AfDB, 2012, UNECA, 2005; Sackey and Osei, 2006; Baah-Boateng, 2013). Using age and the square of age in estimating unemployment, Blackaby et al., (1999) show how this may reflect job shopping by younger workers and the lack of job opportunities as workers approach retirement age. Unemployment is also identified to be an urban phenomenon (see e.g. Dikkens and Lang, 1996, Boateng, 1994; Baah-Boateng, 2013 and 2014). According to AfDB et al, (2012), in some countries in Africa, urban youth unemployment rate is estimated to be more than six times than the rate in rural areas. Gender differences of unemployment have also been established in Sri Lanka by Dikkens and Lang (1996) who found higher unemployment rates among women than men. In Ghana however, Baah-Boateng (2012) found unemployment to be a bigger labour market challenge for men than women from 1960 until 2000 when the reverse occurred. He attributed the higher unemployment rate among women than men in recent times to the increasing participation of women in market work on account of improved education of women. Naudé and Serumaga-Zake (2001) finds that gender, location (rural or urban), education level and family structure and relations are significant determinants of unemployment in South Africa s North-West province. Another emerging concern about unemployment is the increasing unemployment rate among the educated than uneducated. For example, Dikkens and Lang (1996) found unemployment as being highest among the highly educated Sri Lankan youth aged between 15 and 24 years and declining rapidly thereafter. In addition, according to AfDB et al (2012) unemployment tends to be high among educated than the uneducated particularly in middle income countries than low income countries. However, they contend that the educated unemployed are more likely to eventually escape unemployment than those with lower levels of qualification. Okun s argument of linking unemployment to the difference between potential and GDP provides some empirical understanding of demand side explanation to unemployment. According to Okun (1962) there is direct correlation between a percentage GDP gap (i.e. difference between potential and actual GDP) and unemployment rate in excess of natural rate of unemployment. However, the slow response of employment to GDP growth in some countries have rendered Okun s argument a bit outdated since growth driven by high capital and technologically driven sectors of mining and oil might not cause employment growth in line with the GDP growth. Using the data from the Current Population Survey (CPS), a monthly poll of 60,000 households conducted by the U.S. Census Bureau for the Bureau of Labour Statistics (BLS), Valletta and Kuang (2011) came out with results that strongly suggest that weak labour demand plays a key 6

role in prolonged unemployment duration. While mismatches may cause available jobs to go unfilled and thereby hold down employment growth, they (Valletta and Kuang, 2011) could not directly disprove explanations related to the supply of labour, such as mismatches between worker skills and employer skill needs. Baah-Boateng (2013) found strong effect of demand factors on unemployment in the midst of higher economic growth in Ghana. In particular, weak growth in the high labour absorption sectors of agriculture and manufacturing as against strong growth in the low labour intensive sectors of mining and oil and financial subsectors. With weak employment oriented growth, fulltime jobseekers and those who are specific on the job they are seeking are more likely to remain unemployed relative to part-time jobseekers and those seeking any type of jobs. He thus suggested investment in infrastructure, education and training by government in high labour absorption sectors to promote high employment oriented growth in Ghana. 3. Youth Employment and unemployment: the current situation 3.1 Who are the Youth? The youth are often cited as the demographic group that tends to bear the brunt of labour market challenges but the definition of the demographic group classified as youth needs to be put in context. Curtain, 2001) defines youth as an economic and social concept that refers to a separate stage in the lifecycle between childhood and adulthood. Generally, the classification of a section of the population as youth differs from country to country and the purpose of the classification such as marriage, criminal responsibility, voting right, access to alcoholic beverages, consent to medical treatment, and military service etc. In demographic analysis of population, the United Nations (UN) refers to individuals within the age range of 15-24 as youth while African Union regards those aged within 15-35 years as youth. The Commonwealth defines the youth as individuals within the age range of 15-29 years compared with the African Youth Charter s classification of youth as persons who fall within the age bracket of 18 35 years. In Ghana, Kenya and Tanzania, the AU definition of 15-35 years for youth is adopted for policy purposes compared with 12-30 years in Nigeria while South Africa s National Youth Policy defines youth as any person between the ages of 14 and 35 (see Mkandawire, 2000). The paper adopts the UN definition of 15-24 years for youth which is also adopted by the ILO in its statistical analysis of youth employment for the purpose of ensuring consistencies with many similar studies. 3.2 Youth Unemployment Rates Higher in Developed than Less Developed Regions Table 1 presents youth unemployment rates by regional groupings in the world and suggests generally higher youth unemployment rates in developed regions than less developed regions. 7

Less developed regions such as SSA, South-east Asia and South Asia recorded youth unemployment rates below the global average while higher youth unemployment rates relative to the global average were reported in developed regions including developed economies and EU, non-eu and Commonwealth of Independence States (CIS), and South-east Asia and Pacific. The less developed regions recorded average youth unemployment rates of between 9.2% in East Asia and 12.0% in SSA over 2006-2013 compared with global average of 12.6 %. In contrast, average youth unemployment rate of 14.2% in South-east Asia and Pacific and 16.1% and 18.2% in developed and EU and Non-EU and CIS regions respectively. Table 1: Youth Unemployment Rates by Regions and World, 2005-2013 Region 2006 2007 2008 2009 2010 2011 2012 2013* Sub-Saharan Africa (SSA) 12.2 11.7 12.1 12.1 12.0 11.9 11.9 11.9 North Africa 25.5 24.2 23.7 23.9 23.7 28.1 29.2 29.4 Middle East 25.1 23.9 24.1 23.7 26.2 26.0 26.6 27.2 Latin America & the Caribbean 15.1 14.1 13.6 15.5 15.0 14.3 13.8 13.6 South Asia 9.8 9.2 9.5 9.8 9.7 9.7 10.1 10.2 East Asia 8.4 8.0 9.2 9.4 9.1 9.4 9.7 10.1 South-East Asia & the Pacific 17.0 14.8 14.1 13.9 14.5 12.9 12.7 13.0 Non-EU and CIS 18.4 17.5 16.9 20.0 19.0 17.9 17.5 18.0 Developed Economies & EU 13.3 12.5 13.3 17.4 18.1 17.6 18.0 18.3 World 12.4 11.6 12.0 12.9 12.9 12.7 12.9 13.1 * 2013 are preliminary estimates Source: ILO (2014) Global Employment Trends 2014 The Middle East which is generally endowed with oil resources and thus classified as developed region based on per capita income with North Africa recorded the highest youth unemployment rates. The low youth unemployment rates in the less developed region could be explained by high level of informality which hides the face of unemployment while higher rates in the developed regions could largely be linked to the limited degree of informality in these regions (see Baah-Boateng, 2015). Latin America and Pacific which could be considered as moderately developed relative to less developed regions of SSA, South Asia and East Asia showed youth unemployment rates of between 13.6% and 15.1% over 2009 2013, which is marginally higher than the global average. 3.3 Higher Unemployment rates of youth than adults Youth unemployment rates vary between the two regions in Africa with SSA recording lower rates than North Africa. Generally, while SSA has the third lowest youth unemployment rate behind East Asia and South Asia, North Africa has consistently recorded the highest youth unemployment rate globally (Table 1). The rates are also estimated to be higher among the youth 8

Unemployment Rates (%) than adults to the extent that youth unemployment rate in 2011 is twice and about four times higher than that of adults in SSA and North Africa respectively (see Figure 1). This observation is explained by a number of factors. First, the youth are more vulnerable in times of economic challenges than their older counterparts considering their limited labour market experience. In addition, they also lack job search experience and labour market information to facilitate their job acquisition. In times of economic upturn, lack of work experience combined with lack of social capital puts the youth at a disadvantage for new job opportunities. In economic downturns the last-in, first-out of hiring and firing disproportionately affects young people (UNECA, 2005). The situation becomes worse in Africa where many countries do not have properly functioning employment and placement centres making the youth resort to job search through friends and family members since they often lack previous employment contacts and networking. From the perspective of the youth, the most pressing issue facing them is lack of jobs. Although they also recognise that lack of training constitutes another obstacle to getting employed they feel that jobs are given to people who have connections (AfDB et al., 2012). Figure 1: Relative Unemployment Rates of Youth and Adults in North Africa and SSA 35 30 25 20 28.1 Figure 1: Relative Unemployment Rates 29.2 29.4 15 10 5 11.9 6.0 7.9 11.9 6.0 8.1 11.9 6.0 8.2 Youth Adults 0 SSA N. Africa SSA N. Africa SSA N. Africa 2011 2012 2013* * 2013 are preliminary estimates Source: ILO (2012) Global Employment Trends 2012 3.4 Varying Rates of Unemployment by Countries Generally, youth unemployment rates vary among countries in Africa. Figure 2 presents youth unemployment rate as high as 54.2% in Reunion in 2012 as against less than 1 per cent in, 9

Congo DR. In all, six countries most of which are located in Sothern Africa recorded youth unemployment rates of between 30.7% and 54.2%. Similarly, seven countries out of which three are in Southern Africa, and two each in the North and East Africa recorded youth unemployment rates of between 20% and 30%. On the other hand, eleven countries, all located in the south of the Sahara recorded youth unemployment rates of less than 10 per cent with Malawi, Rwanda, Congo DR, and Benin reporting rate of less than 2% (see Figure 2). Figure 2: Youth unemployment rates of selected African countries Réunion, 2012 South Africa, 2012 Lesotho, 2008 *Botswana, 2006 Namibia, 2012 Tunisia, 2005 Ethiopia, 2006 Egypt, 2010 Mauritius, 2012 Zambia, 2005 Sudan, 2008 Algeria, 2011 Seychelles, 2002 Morocco, 2012 Zimbabwe, 2011 *Mali Senegal, 2006 Kenya, 2009 Ghana, 2013 *Niger Tanzania, 2011 Uganda, 2009 Sierra Leone, 2004 Liberia, 2010 Burkina Faso, 2006 Madagascar, 2005 *Malawi, 2004 *Rwanda, 2004 *Benin, 2010 24.9 24.8 23.7 23.4 22.9 22.4 20.3 18.6 17.0 14.9 14.8 14.5 10.9 9.1 7.1 5.4 5.2 5.1 3.8 2.3 1.9 1.6 1.0 34.8 34.5 34.3 30.7 * Computed by Author from Country s Survey data or Report Source: Constructed from Key Indicators of the Labour Market (ILO, 2013) 54.2 51.5 0.0 10.0 20.0 30.0 40.0 50.0 60.0 Among the remaining six countries that reported youth unemployment rates between 10% and 20%, three hail from West Africa, with one each from the North, East and Southern Africa. The 10

relatively higher youth unemployment rates in the four North African countries compared with very low rates in many countries in SSA largely explains the disparities in youth unemployment rates between North Africa and SSA. In addition, countries with low youth unemployment rates have high proportion of employment in the informal sector or high vulnerable unemployment rates (mostly in West Central and East Africa) and vice versa (see AfDB, et al 2012). 3.5 Unemployment Rates Vary by Sex, Location and Education Gender differences in youth unemployment generally show higher rate for females than males in the two sub-regions of Africa and the world. An estimated ratio of female-male unemployment rates of 1.1 was recorded in SSA and the world and 2.2 in North Africa in 2010 suggesting higher unemployment rates for females than males (Figure 3). The differences in youth unemployment between the two sexes however show mixed outcomes across countries on the continent. Available data indicates that out of 26 selected African countries, 16 experienced a higher youth unemployment rate for females than males at different periods with Nigeria, Mauritius, Madagascar, Senegal, Liberia, Algeria and Egypt recording female-male ratio of youth unemployment rate of between 1.5 and 3.7. Nine countries recorded higher youth unemployment rate for males than females as indicated by female-male youth unemployment ratio of less than one while one country, Zimbabwe reported equal youth unemployment rates for both sexes. Three countries namely Congo DR, Niger and Sierra Leone recorded gender ratio of less than 0.5 suggesting a higher youth unemployment rates for males than females in these three countries. This could be explained by the fact that women would rather work in lower-earning informal sector rather than remain unemployed or stay out of the labour market and committed to domestic production. The two highest gender ratios of youth unemployment rates of 3.68 and 2.01 were recorded in two of the four North African countries of Egypt and Algeria respectively. This may largely explain the high gender ratio of youth unemployment rate in North Africa compared to SSA and the world given a narrow gender differences in the rate in two other countries of Morocco and Tunisia indicated by a gender ratio of youth unemployment rate of 0.96 and 0.93 respectively. Generally, young females presence in the labour market as unemployed relative to their male counterpart may suggest difficulties faced by these young females in securing employment. This tends to undermine the effort of empowering women towards the attainment of gender equality. Generally, female-male differences in youth unemployment rates in various countries clearly requires careful but thorough study to ascertain the reasons accounting for the disparity. Youth unemployment rate is also estimated to be an urban phenomenon. Out of 14 countries represented in Table 2, youth unemployment rates in urban areas are far higher than in rural areas and this is explained by the dominance of agriculture activities in rural areas that keeps 11

underutilisation of labour force to take the face of unemployment. In addition, the youth are often attracted to the cities in search for non-existing jobs as they find life in the rural areas unattractive considering low earnings of agriculture being the main economic activity. In some countries like Ethiopia, Rwanda, Tanzania and Uganda, youth unemployment rate in urban areas is reported to be more than 7 times higher than the rate in rural areas (Table 2). With the increasing urbanisation and limited job opportunities, the problem of urban unemployment among the youth may worsen. Figure 3: Ratio of female-to-male of unemployment rates in selected African countries Egypt, 2010 Algeria, 2010 Liberia, 2010 Senegal, 2006 Madagascar, 2005 Mauritius, 2011 *Nigeria, 2003 Lesotho, 2008 Uganda, 2009 *Ethiopia, 2005 *Tanzania, 2006 *Botswana, 2006 South Africa, 2011 Namibia, 2008 *Ghana, 2010 *Malawi, 2004 Zimbabwe, 2004 Morocco, 2011 Tunisia, 2005 *Mali B. Faso, 2006 *Rwanda; 2004 Benin, 2002 Sierra Leone, 2004 *Niger *Congo DR World, 2010 SSA, 2010 North Africa, 2010 2.01 1.94 1.69 1.65 1.62 1.53 1.44 1.38 1.37 1.35 1.26 1.21 1.17 1.12 1.11 1.00 0.96 0.93 0.84 0.63 0.63 0.55 0.48 0.47 0.27 1.1 1.1 2.2 3.68 0 0.5 1 1.5 2 2.5 3 3.5 4 * computed and constructed by Author from countries household surveys and censuses Source: Key Indicators of the Labour Market (KILM) The relationship between education and youth unemployment appears to suggest higher youth unemployment rate among the educated than the less or uneducated. Table 2 reports relatively higher youth unemployment rate among youth with secondary school education or better in all the selected African countries except Niger and South Africa which showed marginally higher 12

rate among those with basic education than those with vocational and secondary education respectively. Higher youth unemployment rate among secondary school leavers than other levels is reported in six countries (i.e. Botswana, Egypt, Ethiopia, Rwanda, Senegal and Tanzania). Six other countries (Cong DR, Ghana, Mali, Malawi, Nigeria and Uganda) in contrast indicated higher youth unemployment rate among university graduates than all other levels. Table 2: Youth Unemployment Rates by Location and Education Country Location Education Urban Rural No Educ. Basic Sec Voc/Tech Tertiary Botswana, 2006 40.5 26.2 24.4 33.7 37.8 29.7 33.0 Congo DR, 2005 0.19 0.03 --- --- --- --- --- Egypt, 9.4 6.8 4.9 9.7 51.2 --- 34.2 Ethiopia, 2005 25.0 0.9 1.9 6.9 37.0 21.6 13.5 Ghana, 2006 18.2 8.0 3.2 6.2 14.6 17.2 46.1 Mali, 2006 33.6 8.1 10.2 18.5 54.1 65.1 85.3 Malawi, 2004 11.5 1.0 1.3 0.6 4.5 11.7 23.2 Niger, 9.8 8.8 7.9 16.9 --- 16.1 --- Nigeria, 2003 7.5 3.2 1.2 2.1 11.1 11.8 --- Rwanda, 2006 0.24 0.03 4.6 5.1 20.2 10.7 --- Senegal, 2006 19.5 10.7 14.1 25.2 30.2 14.3 6.8 South Africa, 2010 --- --- 31.4 54.9 54.3 49.7 34.9 Tanzania, 2006 19.2 1.6 2.3 8.1 32.8 23.4 23.2 Uganda, 2005 14.0 1.8 0.9 2.1 6.3 6.6 19.0 Source: Computed or constructed from countries household surveys and censuses With secondary school education, these young people do not have the skills to enable them secure regular or formal sector jobs which often require a minimum of diploma or university degree against the backdrop of considerable number of degree holders seeking jobs. On the other hand, they do not also find informal economy attractive enough and coupled with the difficulty in progressing to the next level on educational ladder, they become unemployed. Limited formal sector jobs for graduates with no consideration for employment in the informal economy continues to be the major driving force for the high and increasing youth unemployment among university and other tertiary graduates in Africa. Young people with no education recorded the lowest unemployment rate due to the fact that they have limited or no access to formal employment and clearly have no choice than to settle with informal agriculture and non-technical jobs which do not require any education. Similar explanation can be adduced to the lower youth unemployment rate among the youth with basic or primary education. The observed high unemployment rate among young people with vocational and technical training raises concerns about the need for a shift in education and training towards vocational 13

and technical education to improve their job acquisition prospects. Ghana reformed its educational system in 1987 with the introduction of technical and vocational skill training into its education curricula at the basic level as the major element of the reform. The weakness however was the inability of authorities to provide skill training workshops for schools and absence of instructors to teach vocational and technical skills rendering the whole exercise a failure. Obviously, one major challenge facing trainees of vocational and technical education has often been lack of start-up capital which tends to render most of these skilled young people jobless after school. Indeed, Frazer (2006) reports that for former apprentices one of the principal constraints is obtaining finance in order to start up their own business which corroborates Aryeetey et al (1994) that credit for start-up in Ghana is rare. 3.6 Lower Jobs Creation for Youth than Adults A country s ability to generate employment could be measured by employment-to-population ratio (or employment rate) (see Sparreboom and Baah-Boateng, 2011) and with an improved ratio of employment-to-population suggests improved performance in the labour market in Africa. The ratio is often estimated to be higher for adults than the youth on the account that the youth are more likely to be inactive due to schooling or unemployed. Figure 4: Employment-to-population ratio of youth & adults in selected African countries 100 90 80 70 60 50 40 30 20 10 90 89 89 85 62 63 82 38 76 58 75 74 33 25 70 69 69 60 54 52 54 51 33 24 25 25 41 39 13 14 0 Youth Adults Source: Computed from Nationally Representative Household Surveys As shown in Figure 4, employment rate for adults is significantly higher than their younger counterpart in all the 14 countries. Thus, besides high inactivity rate, employment prospects of the youth are very weak compared to the older counterparts in Africa. For instance, with the exception of Tanzania, Uganda, Rwanda, Ethiopia and Mali, 9 other countries reported that 5 or 14

less out of 10 young people were in employment. This tends to push many of these youth into unemployment or discouragement out of frustration. The rate among the youth also varies across countries with Tanzania recording the highest rate of 85 per cent compared with the lowest of 13 per cent in South Africa. In addition, the rate is higher in the rural areas than urban areas with rural-to-urban ratio of employment rate of more than 1 except Niger which reported a ratio of less than 1. 3.7 Low Quality of Youth Employment than Adults The employment-to-population ratio only measures quantity of employment generated in a country but unable to show the type and nature of employment created. For instance, high employment rate in rural than urban areas may give a misleading indication of better job creation performance in the former than the latter because it falls short of showing the quality of jobs being created. Indeed, the youth face the challenge of accessing regular and high remunerated jobs as evident in high share of vulnerable employment 3 in SSA of 77.5 per cent in 2011 compared with 41 per cent in North Africa and the world average of 50 per cent (see Table 3). Vulnerable employment rate is the sum of own account and contributing family work as a percentage of total employment. Workers in vulnerable employment are less likely to have formal work arrangements and are therefore more likely to lack elements associated with decent employment such as adequate social security and recourse to effective social dialogue mechanism (Sparreboom and Baah-Boateng, 2011). Clearly, the youth are generally the worst affected in terms of poor quality of employment considering the barriers they face in the labour market including lower education and skills as well as limited labour market experience. In most African countries, the share of youth in jobs considered vulnerable and informal with little or no social protection is relatively high. Selfand/or informal employments as well as contributing family work are more prevalent among the youth workers in both rural and urban areas in Africa which may be a sign of labour market entry difficulties for young people. In addition, it symbolizes low employment quality since informal jobs are generally less secure in which labour and safety regulations do not apply. Table 3 reports employment status of the youth and adults of selected African countries and confirms poor quality of youth employment relative to older individuals in terms of the rate of vulnerable employment. Indeed, over 70 per cent of young workers in Congo, Congo DR, Ethiopia, Ghana, Malawi, Mali, Rwanda, Senegal, Uganda and Tanzania are either self-employed or contributing family worker with no safety regulation, social protection and are widely exposed to vulnerability related to unstable income. 3 Vulnerable employment is the sum of own account and contributing family work 15

Table 3: Employment Status of Youth and Adults in selected African Countries (%) Country/ Region Wage employment Self-employment 16 Contributing family work Vulnerable employment rate Youth Adults Youth Adults Youth Adults Youth Adults Botswana 62.4 62.4 7.2 (1.4) 13.7 (4.4) 29.9 23.5 35.7 32.8 Congo 20.1 28.7 55.3 59.7 17.2 2.5 72.5 62.2 Congo DR 10.2 19.5 49.1 63.2 36.3 15.5 85.4 78.8 Egypt 64.9 69.5 4.1 7.0 31.0 23.5 35.1 30.5 Ethiopia 17.9 15.3 24.1 65.7 58.0 18.9 82.1 85.6 Ghana 13.3 18.4 26.2 (1.0) 62.2 (4.6) 50.4 13.7 75.6 75.9 Malawi 14.9 22.8 18.9 22.8 56.0 48.1 74.9 70.9 Mali 5.4 9.0 41.6 72.6 53.0 18.4 94.6 91.0 Rwanda 27.7 21.4 16.8 48.9 55.5 29.7 72.3 78.6 Senegal 12.3 20.3 41.7 67.4 46.3 12.3 88.0 79.7 South Africa 84.8 74.3 7.1 (1.2) 15.5 (5.9) 5.9 9.5 11.8 19.1 Tanzania 8.0 10.4 71.6 (1.0 79.5 (2.0) 20.2 8.1 90.8 85.6 Uganda 14.0 17.3 20.9 61.0 63.6 20.9 84.4 81.9 SSA, 2011 21.1 50.5 (1.4) 27.0 77.5 N. Africa, 2011 49.3 25.8 (9.4) 15.5 41.3 World, 2011 48.0 34.8 (2.4) 14.8 49.6 Note: 1. Youth covers those aged 15-24 years and adults covers those 25 years and above 2. Self-employment figures in brackets represent self-employed with employees. Source: Computed from Nationally Representative Household Surveys; Regional figures from KILM All countries represented in table 3 showed lower proportion of wage employment for youth than adults except South Africa and Rwanda which reported the reverse and Botswana which indicated equal proportion of wage employment for youth and adults. A higher proportion of contributing family work was also reported in all the countries for the youth than adults except in South Africa which showed the reverse. In all, vulnerable employment rate is generally higher among the youth than the adult workers (except South Africa and Rwanda), indicating poor quality of youth employment in Africa. Even in the two countries a greater proportion of youth than adults were in contributing family work with no remuneration. Clearly, limited access of youth to wage and regular employment tends to expose them to high levels of poverty which has implication for their confidence level in the labour market and ability to escape from that situation. 4. Youth Employment Challenges: Consequences and underlying reasons 4.1 Consequences of Labour Market Challenges of Young People The discouraging situation of youth in African labour markets has many economic, social and political consequences. Absence of decent and productive jobs and prospects of a meaningful

future for the youth make them feel excluded. This may cause them to use their energy to foment trouble and fuel instability and violent conflicts that can have regional and international repercussions. Social vices including commercial sex, arm robbery, HIV/AIDs, drug abuse, violent conflict etc. prevalent particularly among the youth are often blamed on labour market difficulties the youth face. Undoubtedly, unemployed youth are at much higher risk of contracting HIV/AIDS than are employed young people (UNAIDS 2004). The frequent reports of migration of low skilled youth across the Mediterranean and the Atlantic in search for greener pastures with its attendant problems including death can readily be linked to youth employment challenges. The prevalence of civil conflicts in Africa has been linked to underutilization of the skills of the youth on the continent. In Liberia, Sierra Leone, Cote d Ivoire, Rwanda, Burundi and many other conflict inflicted countries, the combatants were mostly young people who were easily swayed into joining the rebellion. The Arab spring which erupted in 2011 in North Africa was deemed to have its root from the problem of youth unemployment and hopelessness. Indeed, poor quality of jobs and low labour market incomes of the youth tend to negatively affect their welfare and create a sense of vulnerability. This tends to expose them to negative shocks which increase their probability of staying in poverty for a long time. 4.2 Causes of Youth Employment Challenges and Unemployment The direction of demand for and supply of labour determines the level of employment or unemployment. The phenomenon of youth unemployment and low employment quality are indications of lack of job opportunities which is a demand side problem. It is also a reflection of high rates of population growth and low quality of human resource related to poor quality of education from the supply-side. As pointed out by Blanchflower and Freeman (1999), insufficient aggregate demand, lack of skills among the youth and relative size of the youth labour force are the most commonly cited causes of youth unemployment. The section that follows discusses the underlying reasons of youth employment challenges from demand and supply angle of the market and also look at constraints emanating from the presence or absence of institutional measures for effective functioning of the labour market. Demand-Side Concerns Poor Quality of Growth The combination of both slower economic growth and high population growth results in scarcity of jobs, meaning that hiring is based more on experience and education, the very assets young people are struggling to acquire (UNECA, 2005). Indeed, lower employment-friendly economic growth manifested in economic activities that do not generate sufficient employment is one of the fundamental sources of youth unemployment and poor quality of employment in Africa. 17

Africa s growth performance over the last decade has been fairly good but the concern has often been the source of this strong growth and its implication for job creation. This is reflected in the weak response of employment generation to economic growth in many African countries with low estimated employment elasticity. Between 2004 and 2008, employment elasticity of growth of 14 out of 17 African countries stood below 0.6 (see Figure 5) indicating that a 1% growth was accompanied by less than 0.6% employment growth. Ironically, higher growth has evidently been accompanied by low employment elasticity and vice versa. For instance, three countries namely South Africa, Senegal and Cameroun which reported employment elasticity of between 0.7 and 0.84 were also among the four countries that recorded low economic growth of at most 4.6%. In contrast, five best growth performing countries (Ethiopia, Uganda, Rwanda, Tanzania and Malawi) were among the countries that recorded low employment elasticity. Figure 5: Employment elasticity and annual average GDP growth over 2004-2008 in Selected African Countries 14 12 10 8 6 4 2 0 11.8 8.8 8.4 0.54 0.52 0.57 0.46 0.50 0.52 7.2 7.1 6.7 6.5 6.4 6.4 0.34 0.40 0.33 6.0 5.8 0.41 5.3 5.2 0.40 0.40 0.27 0.70 0.53 0.84 4.6 4.4 3.8 3.1 0.72 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Average GDP Growth Employment elasticity Source: Key Indicators of the Labour Market, 6 th Edition, (ILO) The weak employment effect of growth is largely explained by the fact that economic growth in most countries on the continent has been driven by low labour absorption extractive and technological advanced and capital intensive services sectors. In countries like Ghana, Tanzania, Mali, Nigeria, Gabon, Equatorial Guinea and Botswana among others, mining and oil have been the main driving force of growth. In Nigeria for instance oil accounts for over 70% of GDP while Ghana s 15% growth in 2011 on the back of oil production is the highest in the history of the country. These countries are among the countries that recorded low employment elasticities of 18

below 0.6 (Figure 5). The danger is that, besides the environmental consequences from economic activities in the extractive sector, resource endowed countries find it difficult to escape from Dutch disease which largely reflects the collapse of employment friendly productive sectors of manufacturing, agriculture and tourism. The consequence is labour market challenges of high incidence of unemployment and high rate vulnerable employment in the region. Neglect of Employment as Core of Country s development agenda Lack of quality and productive jobs in the wake of improved growth performance in Africa supports the assertion that employment has not been the focus of economic policies in many countries. Most often, employment is treated as residual outcome of economic policies. An analysis of 21 African countries Poverty Reduction Strategy Papers (PRSP) reveals that only 11 could boast of at least one core section devoted to youth employment (UNECA, 2005). As noted by Baah-Boateng (2008), Ghana has failed to translate its remarkable growth performance into the generation of quality and productive jobs because employment is often treated as the residual outcome of macroeconomic and sectoral policies. Consequently, no effort is made to monitor the employment impact of national policies as evident in the non-availability of labour market data in most countries of Africa. Even at the global level, employment was not considered in the design of the MDG until 2008 when employment target and indicators were introduced to monitor country s employment performance within the MDG framework. However, monitoring employment performance of countries in the MDG in Africa has been a challenge due to data problem. Clearly, the general focus of economic policy on macroeconomic stability and the quest for high growth by policy makers without considering the job creation effect cannot escape blame for the problem of unemployment and high rate vulnerable employment particularly among the youth in Africa. Supply side argument Youth Population Budge The increasing young population in Africa and its effect on labour market inflows against the backdrop of limited job opportunities is often cited a major cause of labour market challenges facing the youth in Africa. As shown figure 6, ten out of the 14 countries have youth population of at least 20% of total national population. The situation is likely to worsen over the next decade considering that children below 15 years constitute between 32% and 52% of the population of these countries. It is estimated that every year, about 9 million youth enter the labour market in Africa (see UNECA, 2002). According to ILO (2008) as the youth population grew faster than the total population and youth employment, the share of employed youth in total youth population declined and the total number of unemployed increased, particular in SSA. Indeed, the increasing labour force particularly in SSA which has been projected at 28% between 2003 and 2015 would boost the supply of young people in the labour market to further constrain job creation (UNECA, 2005). Related to the youth population explosion is the rural-urban movement 19

of youth which tends to put undue pressure on limited jobs in the formal labour market in urban areas. The symptom of this situation is the higher youth unemployment rate and street hawking in many African cities. It is however important to note that the explosion of youth population would not be a major concern of they have the skills that would make them useful in the economic development process. Figure 6: Age Distribution of Population of Selected African Countries (%) Uganda, 2006 51.7 19.5 28.8 Niger 50.8 16.2 33.0 Mali, 2007 50.7 14.7 34.6 Congo DR 46.2 18.9 34.9 Malawi, 2005 46.2 19.6 34.2 Ethiopia, 2005 45.0 20.2 34.8 Tanzania, 2006 43.9 17.3 38.8 Rwanda, 2006 43.0 23.0 34.0 Senegal, 2005 42.2 21.3 36.4 Nigeria, 2003 40.5 18.5 41.0 Ghana, 2006 39.9 19.5 40.6 Botswana, 2006 36.0 20.4 43.6 Congo, 2005 34.3 21.0 44.7 Zambia, 2003 33.0 26.3 40.7 South Africa, 2010 31.5 20.6 47.9 0.0 20.0 40.0 60.0 80.0 100.0 Children (0-14) Youth (15-24) Adults (25+) Source: Authors Calculation from Countries Household and Labour Force Survey Education and Skills Constraints Generally, the youth are often at the receiving end of labour market challenges and found at the end of the job queue in the formal labour market largely as a result of low skills and education and labour market experience. The low level of education and skills of the youth in Africa is a critical factor contributing to high and longer youth unemployment spells on the continent. Although education is acknowledged as the main factor of high productivity and increases one s chance of securing gainful employment, youth education in Africa is found to fall below average. According to UNDP (2004), youth literacy in SSA was estimated at 77% compared with 95% in Latin America and the Caribbean and 98% in East Asia and pacific with limited improvement if any since then. 20