From school to employment; the dilemma of youth in Sub–Saharan Africa

ABSTRACT

The present study compares the educational attainment level among the youth in two Sub-Saharan African countries, namely; the Democratic Republic of the Congo (DRC) and Madagascar, with their status in work. The study employed data from the school-to-work transition survey of the International Labour Office, 2015. This study reveals that in the two countries investigated, that youths face several challenges when negotiating their move to employment because the educational attainment of the majority of them is deplorable. Financial burdens and exam failures are the critical reasons why the youth of these two countries leave school early and engage in low-quality jobs to secure their living. Besides, young wage workers are most likely to be exposed to exploitation, since their employment is usually based on an oral contract.

KEYWORDS:

• Youth
• work
• employment

Introduction

Recent data on unemployment among youths shows that developing countries, in general, and in Africa, in particular, are the regions most affected by the youth unemployment phenomenon. The rate of youth unemployment in Africa is higher, as compared to the world as a whole. For example, in 2017, the youth unemployment rate in Africa, which stood at 12.5%, was about four times the rate of Southeast Asia. Even in Africa, the severity of this problem varies between the different sub-regions, between countries and also within individual countries over time (see Table A1 in the Appendix). Most importantly, this problem may be exacerbated in the future, given the expectation that 29% of the world’s youth population will reside in Africa by the year 2050 (World Bank, 2014).

High leakage in the early stages of the education cycle is one of the crucial causes of problems in the transition to the labour market for youth. Low levels of entrepreneurship worsen the situation, as well as, the unwillingness of youth to join vocational education to gain the skills necessary for widely available low- to mid-skilled jobs (Atchoarena & Delluc, 2001; Awogbenle & Iwuamadi, 2010; Oketch, 2007; World Economic Forum, 2017). As a result, youths face lengthy transitions from school to employment and are often discouraged from maintaining their job search. The move to work is considered as one of the most substantial changes for anyone in their entire life. A fruitful transition, or participation, in the workplace, often brings on an individual basis, a variety of benefits, such as financial self-sufficiency, social networks, or self-worth (Tilbury et al., 2011).

Moreover, with higher-quality jobs, it is expected that the labour market will achieve higher productivity. Thus, the transition to the labour market for youths is not only about them having something to do, as well as, the length of time between the end of schooling to their first entry into a job but also the quality of the jobs that they enter into is also worth considering. ILO (2013a) defines the transition to the labour market as the journey of young people, aged from 15 to 29, from the end of schooling (either upon graduation or early exiting without completion) to their first stable or satisfactory job. Permanent and adequate position, the qualitative ingredients in the transition to the labour market for youths, has turned out to be an emerging subject for discussion for academicians and policymakers in the literature.

Generally, transitions are recognized as complicated and challenging, and often require an initial drop in performance, especially when the transition is from a lower to a higher level (Tran, 2017; Van Geert, 1994). The process of moving from school or university to the processes of employment require youths to learn new tacit knowledge regarding the workplace. This knowledge, which comprises cognitive and noncognitive skills, is needed and required for youths to participate effectively in the labour market. (Cawley et al., 2000, 2001; Capatina, 2014; Heineck, 2011; Murnane et al., 1995; Sternberg et al., 2000; Weiss, 2010). However, without a specific and sufficient level of knowledge, learning is not an easy task. Therefore, adequate preparation and guidance for youths before starting to search for a job is essential (Artess et al., 2011; Chapple & Tolley, 2000; Creed et al., 2003; Harvey, 2005; Helyer et al., 2011; Lowden et al., 2011; Rust & Froud, 2011; Tran, 2017). Nonetheless, we have to keep in mind that many stakeholders (i.e. schools, universities, parents, families, and the social networks of friends and acquaintances) may get involved in preparation and guidance for youths. It seems that the transition to employment for youths appears to be a multi-faceted process comprising various factors, from personal aspects to the interference of several labour market factors and other contextual and environmental influences.

The term “employability“ appeared recently in the dialogue regarding the transition to employment or the engagement of young people in the labour market. Usually, employability is understood to be ‘the capability to gain initial employment, to maintain employment and to obtain employment, if required (Hillage & Pollard, 1998). Employability was defined by Moreland (2006), as a set of skills, knowledge, and personal attributes that let an individual be more likely to secure and be successful in their selected occupations. In general, employability is most likely associated with the level of knowledge and skills that one can reach before starting the transition to work. With the process of economic globalization and integration that shapes the region today, knowledge and ability turn out to be important factors related to the employability of youth.

Nonetheless, as mentioned before, in addition to knowledge, gaining employment opportunities is highly dependent on other factors comprising; labour market regulations (Clarke, 2007), personal circumstances (class, gender, ethnicity, place of living, individual characteristics and relationships), as well as the ability for job flexibility and mobility (McQuaid, 2006). In other words, the transition to employment involves not only the level of suitability, in terms of the knowledge and skills youths possess, but also their circumstances and characteristics

The issue of youth unemployment in the region has been addressed in several studies, however, all of these previous studies have investigated this matter by utilizing aggregated (macro) variables (Anyanwu, 2013, 2014; Awad, 2019; Kabbani & Ekta, 2005; Mabala, 2011; Thieme, 2010). It is important to note, however, that the youth unemployment rate does not provide a full description of youth’s difficulties in the labour market. Most importantly, even the data on employment among youths provides no information concerning whether their jobs are secure, stable, and decent. The present study will, instead, rely on updated microdata that describes, in detail, the movement of youths from school to work. More specifically, the present study will use data from the ‘school-to-work transition survey’ developed by the International Labour Office, in cooperation with the Mastercard Foundation, under the W4Y project (Work4Youth). This paper aims to examine the relationship of ‘human capital’, or the employability assets that young people in two sub-Saharan African countries (SSA) could develop, before making their transition to employment and the quality of jobs that they could gain access to. Specifically, this paper aims to look at educational attainment among youths in SSA countries and their status in the labour market. It also intends to illustrate the current economic conditions and labour markets in the countries examined to bring about a clearer image regarding the opportunities, as well as, the challenges for youths when preparing themselves to join the labour market.

The school-to-work transition survey covered several countries in SSA over different periods. Our criteria, in the selection of the countries to be investigated, was based on two conditions – first, the availability of harmonized data. Second, and to be consistent, the year of the survey should be the same across the selected countries. We found that only the DRC and Madagascar, met our criteria. Interestingly, the aggregate data on the unemployment rate showed that the DRC’s youth unemployment rate was relatively high (22.8%), while it was only 2.7% in Madagascar as per 2018 (World Bank Group, 2019). Therefore, the experience of these two countries with the matter of youth unemployment is to some extent relevant to all of the countries in the region.

The present study is intended for the use of policymakers and social partners involved in the implementation of national youth-related policies and programs, as well as for international and non-governmental organizations involved in the development of responses at the regional level. The remainder of this paper is organized as follows. Section 2 presents the economic and demographic structure of the two countries. Section 3 outlines the methodology. The results are presented in Section 4. Finally, Section 5 presents the discussion and conclusion of the present study and provides policy recommendations.

Socioeconomic context

Economic growth

Since the status of youth and the performance of the labour market are mainly affected by the structure of an economy, this section aims to highlight the relevant components of the economy in each country. Figure 1 shows the per capita annual growth rate for each country during the period 2000–2015. Figure 1 indicates that, overall, and before 2015, the DRC performed better than Madagascar. However, since 2010, significant changes have occurred in each country. More specifically, since 2010, Madagascar started to record positive and substantial growth in the per capita GDP. In contrast, during the same period, the economy of the DRC started to deteriorate significantly. By the end of 2015, both countries recorded the same growth rate in the per capita GDP.

Figure 1. GDP per capita growth (annual %).

Sources: World Bank Development Indicator, 2019

The contribution of the critical components of an economy to the GDP is considered as one of the factors that affect the structure of the labour market. In general, while the industrial sector is the dominant sector in the DRC, the services sector is the dominant sector in Madagascar. After 2010 significant changes in the structure of the DRC’s economy took place. More specifically, as shown in Figure 2, the share of the industrial sector decreased from 75% in 2010 to 55% in 2015, and in the same period, the share of the services sector increased from 20% in 2010 to 38% in 2015. The agricultural sector is the second-largest economic sector in Madagascar, which contributed, on average, 24% of the GDP during the period 2000–2015. With such economic structures, one can expect that most workers, including youths, are engaged in the services and the agriculture sectors in Madagascar, while in the DRC workers are concentrated in the industrial sector. Medina et al. (2016) estimated the size of the informal economy for Sub-Saharan African countries over 24 years using the Predictive Mean Matching (PMM) method. The results show that the informal economy in Sub-Saharan Africa remains among the largest in the world, although this share has been very gradually declining.

Figure 2. Value add (% GDP) per sector.

Sources: World Bank Development Indicator, 2019

Concerning the integration of each country with global markets, Figure 3 clearly indicates that the DRC is more open to the world economy than Madagascar. Over time, it is clear that the DRC has implemented the necessary policies to become more integrated with the global economy. The economic globalization index score for the DRC increased by 127% during 1970–2019, while during the same period, it increased by 77% only for Madagascar (Gygli et al., 2019). This index offers an alternative measurement to trade liberalization by accounting the various dimensions of trade policies. The methodology for computing the EGI mandates that the two indices used to construct the index are actual outflow and restrictions, weighted equally at 50%. The actual outflow index includes the following variables as a percentage of GDP: trade (weighted at 21%); FDI (weighted at 81%); stocks portfolio investment (weighted at 24%); and income payments to foreign nationals (weighted at 27%). The restriction index includes data on hidden import barriers (weighted at 24%); mean tariff rates (weighted at 27%); taxes on international trade (as a percentage of current revenue) (weighted at 26%); and capital account restrictions (weighted at 23%). The value of the EGI ranges from zero to 100 in which high score indicates more openness, and vice versa (Dreher et al., 2008). A growing amount of evidence has shown that openness to the worldwide market or to regional markets affects the rate of youth unemployment in several ways, depending on different factors (Awad, 2019).

Figure 3. Trade (%GDP).

Sources: World Bank Development Indicator, 2019.

Figure 4. Annual Population Growth (%).

Sources: World Bank Development Indicator, 2019.

Demographic

It’s recognized that population growth is one of the critical factors that affect the structure of the population. As per figure 4, before 2003, the population of Madagascar grew faster than that of the DRC, however, in 2004, the rate of population growth was the same in both countries. From 2005 until 2012, the rate of population growth was higher in the DRC, as compared to that of Madagascar. However, since 2013, the rate of population growth in the DRC has shown a decreasing trend while that of Madagascar has been relatively high and stable.

Concerning the unemployment rate among youths, Figure 5 shows that, in general, youths, regardless of gender, suffered more from unemployment in the DRC, as compared to Madagascar. However, the unemployment rate among youths of both genders decreased in the DRC during the period 2000–2015. In Madagascar, the unemployment rate for both males and females fluctuated at under 10% over the same period.

Figure 5. Unemployment, youth (% of labour force ages 15–24), by gender.

Sources: World Bank Development Indicator, 2019.

Regarding gender equality among waged and salaried workers, Figure 6 shows high inequality between males and females in Madagascar. The share of females in the DRC in the category of waged and salaried workers is larger, as compared to females in Madagascar. In Madagascar, during the period 2000–2015, approximately 33–35% of the employed people (working for wages or salary) were male. During the same period, only 15–17% of the employed people (working for wages or salary) were females. In the DRC, during the period 2000–2015, the share of females in this category of workers was marginally higher, as compared to males.

Figure 6. Wage and salaried workers, (% of employment), by gender.

Sources: World Bank Development Indicator, 2019.

During the period 2000–2015, Figure 7 indicates that the labour force participation rate (LFPR) for youths in Madagascar was relatively high, as compared to the DRC. More specifically, during this period the LFPR in Madagascar remained above 75%, but during the same period, this rate froze at 40% in the DRC. The relatively high growth in the population in the DRC during this period, as previously mentioned, may explain, even partly, the low share of youths in the LFPR.

Figure 7. Labour force participation rate for ages 15–24, total (%).

Sources: World Bank Development Indicator, 2019.

Research methods

This study uses data from the school-to-work transition surveys that were conducted in the two countries during 2015. The survey was developed by the International Labour Office, in cooperation with the Mastercard Foundation, under the W4Y Project (Work4Youth) to create more and detailed labour market information, specific to youths, in developing economies. The survey focused, in particular, on the transitional journey of youths to the labour market. In the DRC, the survey was conducted by the General Office of Qualifying Training and Employment, in Madagascar, the survey was performed by the National Institute of Statistics.

The W4Y survey in 2015 covered samples of 3276 and 5044 young people, aged from 15 to 29, in the DRC and Madagascar respectively. All of the data used in this study were gathered from the school-to-work surveys (STWS) carried out in these two countries. The STWS survey provided rich and reliable data, during the transition to work, for the youth in these two countries. The STWS data offered two types of information about youth; the first part described the educational attainment of youths, against their residence and gender. The second part compared the educational attainment of youths, as in the first part, with the potential jobs that they could consider when entering the labour market. Thus, the information in the first and second parts of the survey help in investigating the link between youth educational attainment and employment status. Firstly, this study explored the educational attainment of the youth in the two countries examined and their employment status, after that, the study compared the educational attainment of the youths with their status in work. Finally, a regression model, based on the standard Mincerian earnings equation, as used by several studies in research regarding the impact of education on wages (e.g. Blien & Phan, 2009; Tran, 2017), was implemented.

Findings

General information about the sample

The distribution of youth by sex, by area of residence and unemployment, is displayed in Table 1. The distribution of the sample, according to gender, is to some extent the same in both countries, where the ratio of males and females is equal. However, concerning the area of residence, rural youth represent the majority of the sample, for the case of Madagascar, while the opposite is true in the case of the DRC. For the unemployment rate, in Madagascar, the microdata from this survey supported the aggregate data that has been previously discussed, while for the DRC, the aggregate data may have overestimated the rate of unemployment among youths.

Table 1. Distribution of youth by sex, by area of residence and unemployment.

Statement	Congo	Madagascar
Male	48.4%	47%
Female	54.6%	53%
Urban	72.1%	29%
Rural	27.9%	71%
Unemployment	9.4%	2.2%
Total	3267	5044

Source: ILO, SWIT-2015.

Youth educational attainment

Table 2 reflects disappointing information regarding the status of youth education in both of the countries examined. In the DRC, only 18% of youths completed their education, while the rate for Madagascar was 27%. In Madagascar, approximately half of the youths (49%), either, left before graduation/completion of a training program or never attended school or training. In the DRC, this category of youth constituted 31.3%. This signalled that inadequate human capital had been formed from educational attainment, among the young, in both countries.

Table 2. Youth education status.

Statement	Congo	Madagascar
Currently attending schooling or training	1573 (48%)	1225 (24%)
No, I have completed my education	597 (18%)	1364 (27%)
No, I left before graduation or completion of a training program	1024 (31%)	1756 (35%)
Never attended school or training	82 (0.3%)	699 (14%)
Total	3276 (100%)	5044 (100%)

Source: ILO, SWIT-2015.

Nonetheless, when looking at educational attainment among youths who were not engaged in any course of study, the results were even more disappointing (see, Table 3 for the DRC, Table 4 for Madagascar). The proportion of youths whose highest qualification was the primary education level in each country (25.3% in the DRC, 48% in Madagascar). The highest drop-out rate was among youths during primary school (37.2% in the DRC, 67.4% in Madagascar) and lower secondary school, 30% in the DRC and 25% for the secondary level in Madagascar. This information reflects the deplorable situation of human capital formation in these countries. Also, the information reflects the high drop-out rate from the schools in the early stages of education.

Table 3. Highest qualification among youth who were not attending school or training, Congo.

Statement	No qualifications	Primary	Lower secondary	Upper secondary	Lower professional secondary	Upper professional secondary	Professional Certificate (secondary level)	Professional Certificate (post-secondary level)	University	Post Graduate	Total
No, I have completed my education	37 6.2%	151 25.3%	145 24.3%	59 9.9%	9 1.5%	40 6.7%	34 5.7%	23 3.9%	97 16.2%	1 0.2%	598
No, I left before graduation or completion of training programme	151 14.7%	381 37.2%	307 30%	75 7.3%	30 2.9%	32 3.1%	14 1.4%	4 0.4%	30 2.9%	0 0%	1024
Total	188	532	452	134	39	72	48	27	127	1	1622

Source: ILO, SWIT-2015.

Table 4. Highest qualification among youth who were not attending school or training, Madagascar.

Statement	No qualifications	Primary	Vocational school (secondary)	Secondary level	Vocational school (post-secondary)	University	Post-graduate, post-doctoral level	Total
No, I have completed my education	11 0.8%	661 48.5%	14 1%	614 45%	6 0.4%	57 4.2%	1 0.1%	1364
No, I left before graduation or completion of training programme	100 5.7%	1184 67.4%	16 0.9%	441 25.1%	2 0.1%	12 0.7%	1 0.1%	1756
Total	111	1845	30	1055	8	69	2	3120

Source: ILO, SWIT-2015.

Economic reasons, in both countries, justify why youths leave school/training early (Table 5). The financial burden that young people from low-income families bore was another layer of pressure that caused them to leave school early and start working to earn money. However, this problem was more chronic in Madagascar than in the DRC, as approximately half of the youths in Madagascar mentioned that economic reasons were the main problem, compared to 34.2% in the DRC. The second reason to leave school/training early in Madagascar was that youths were not interested in education/training (17.9%), while in the DRC it was due to failed examinations (20.7%). The lack of interest in education and training in Madagascar appeared to be one of the most significant obstacles which took youth out of the educational system early. This negative attitude towards education indicated, even partially, the lack of aspiration among youths. The issue of how aspirations shape future-oriented activities have already been discussed in different conceptual and theoretical frameworks (Appadurai, 2004; D. Ray, 2006). These concepts are equally crucial to decisions regarding schooling and employment. In a more recent study, Genicot and Ray (2017) showed that economy-wide outcomes determine aspirations, which in turn determine the incentives to invest in education. Thus, in each country, to help human capital formation and to encourage youths to attend and complete their educational journey, the best method is to improve their family’s economic condition.

Table 5. Youth left school before completion by reasons.

Statement	Congo	Madagascar
Failed examinations	212 20.7%	248 14.1%
Not interested in education/training	52 5.1%	315 17.9%
Wanted to start working	66 6.4%	58 3.3%
To get married	54 5.3%	95 5.4%
Parents did not want me to start schooling	46 4.5%	43 2.4%
Economic reasons (could not afford/too poor/needed to earn money to support family)	350 34.2%	877 49.9%
No school nearby	27 2.6%	38 2.2%
Maternity	143 14%	NA
Health reasons	43 4.2%	NA
Others (specify)	31 3%	82 4.7%
Total	1024 100%	1756 100%

Source: ILO, SWIT-2015.

Before moving on to look at youth employment and to what extent such jobs are decent and secure, we first looked at the expectations regarding employment, from the perspective of the youths that we were studying. Table 6 Show that, in both countries, a large proportion of youths were willing to work in the government/public sector (52.2% in the DRC, 74.2% in Madagascar). The main reasons for such a preference may have been; higher salaries and benefits, better working conditions, and greater job security in the government sector. In this respect, Bandara (2018) using School to Work Transition Surveys (SWTS), during the period 2012–2013, for eight Sub-Saharan African countries, investigated what factors influenced youth labour market expectations. Using multinomial logit analysis, the study found evidence that youth education significantly affected youth employment expectations and employment.

Table 6. Youth perspective for future job.

Statement	Congo	Madagascar
Myself (own business/farm)	187 11.9%	159 13%
Work for the government/public sector	916 58.2%	909 74.2%
Work for a public company	115 7.3%	128 10.4%
Work for a private company	307 19.5%	21 1.7%
Work for an international or non-profit organization	42 2.7%	7 0.6%
Work for family business/farm	6 0.4%	1 0.1%
Total	1573 100%	1225 100%

Source: ILO, SWIT-2015.

Youth status in employment

Madagascar indicated a high employment rate among youths (as per Table 1). 70.5% of youths were engaged in self-employment (own-account workers 26.9%) or doing unpaid family work (43.6%) (Table 7). Self-employment and unpaid family work are often considered different types of lowly productive, informal employment, which is popular in developing countries (Antonopoulos and Hirway, 2010; Campbell, 2013; Margolis, 2014). In the DRC, the story was not so different, as 50% of the employees were engaged in self-employment (own-account workers). However, in the DRC, 32.6% of youths were working for wages or in kind. Nonetheless, as we will see later, even the paid employment carried out by the participants of this study was not always decent employment. This finding implies that relying on aggregate data on employment may lead to misleading perceptions, since, as we mentioned previously, it doesn’t provide information regarding whether such jobs are secure, stable, and decent.

Table 7. Employment status among workers youth.

Statement	Congo	Madagascar
Employee (working for someone else for pay in cash or in kind)	371 32.6%	499 12.9%
Employer (employing one or more employees)	58 5.1%	95 2.5%
Own‐account worker (not employing any employee)	569 50%	1040 26.9%
Member of a producers’ cooperative	6 0.5%	-
Helping without pay in the business or farm of another household/family member	101 8.9%	2201 43.6%
Other (specify)	32 2.5%	32 0.8%
Total	1137 100%	3867 100%

Source: ILO, SWIT-2015.

The data indicate that among the young workers who received wages or salaries for their work, 68.3% and 58.2% of them worked purely under oral contracts in Madagascar and the DRC, respectively (Figure 8). These workers could not get access to a legal/formal employment contract, and, therefore, they could be made redundant any time without the protection of the law. Moreover, as indicated in Table 8, among the 499 wageworkers in Madagascar, only 13.2% received social security entitlements, 23.8% received paid sick leave, and 12.2% received pension insurance. The story is identical in the DRC where among the 371 wageworkers, only 25.1% received social security entitlements, 27.5% received paid sick leave, and 21.6% received pension insurance. This is undoubtedly not a good signal of stable, decent, and satisfactory employment.

Table 8. Fringe benefits young workers could get access to.

Statement	Congo	Madagascar
Transport or transport allowance	161 43.4%	50 10.1%
Meals or Meal allowance	125 33.7%	302 61.3%
Annual paid leave (holiday time)	97 26.1%	91 18.5%
Paid sick leave	102 27.5%	118 23.8%
Pension/old age insurance	80 21.6%	60 12.2%
Severance/end of service payment	83 21.6%	34 6.9%
Overtime pay	101 27.2%	73 14.8%
Medical insurance coverage	43 11.6%	148 30%
Bonus/reward for good performance	70 18.9%	27 8.5%
Social security contributions	93 25.1%	65 13.2%
Education or training Courses	90 24.3%	78 15.4%
Occupational safety/protective equipment or clothing	134 36.1%	64 13%
Childcare facilities	6 1.6%	19 3.9%
Maternity/paternity leave	109 29.4%	77 15.8%
Others	0 0%	73 14.8%
Total observations	371	493

Source: ILO, SWIT-2015.

Figure 8. Wage employees by type of contact (% of wage employees).

Sources: World Bank Development Indicator, 2019.

Education attainment and youth status in employment

In the DRC, only 59% of the employed young had a qualification at the secondary level of education or below which was a disappointing finding (see, as shown in Table 9). Besides, a large proportion of the young with relatively higher levels of education (upper secondary school and above) had been absorbed as employees (working for someone else either for cash or in-kind), followed by the category of own‐account workers. The opposite was true for a large proportion of the young with less than an upper secondary school level of education who were engaged as own‐account workers, followed by employees (working for someone else either for cash or in-kind).

Table 9. Employment status by Educational attainment, Congo.

Statement	No qualifications	Primary	Lower general secondary	Upper general secondary	Lower professional secondary	Upper professional secondary e	Professional Certificate (secondary level)	Professional Certificate (post-secondary level)	University	Others	Total
Employee (working for someone else for pay in cash or in kind)	29 24.6%	70 24.5%	83 22.8%	46 55.4%	10 62.5%	23 56.1%	22 71%	14 70%	41 60%	0 0%	338
Employer (employing one or more employees)	8 6.8%	14 4.9%	17 6.7%	5 6%	0 0%	2 4.9%	1 3.2%	0 0%	4 5.9%	0 0%	51
Own‐account worker (not employing any employee)	75 63.6%	167 58.4%	136 53.8%	26 31.3%	6 37.5%	14 34.1%	8 25.8%	4 20%	18 26.5%	0 0%	455
Member of a producers’ cooperative	0 0%	4 1.4%	1 0.4%	0 0%	0 0%	0 0%	0 0%	0 0%	0 0%	0 0%	5
Helping without pay in the business or farm of another household/family member	5 4.2%	20 7%	8 3.2%	6 7.2%	0 0%	1 2.4%	0 0%	1 5%	2 2.9%	0 0%	43
Other (specify)	1 0.8%	11 3.8%	8 3.2%	0 0%	0 0%	1 2.4%	0 0%	1 5%	3 4.4%	0 0%	25
Total	118	286	253	83	16	41	31	20	68	0	917

Source: ILO, SWIT-2015.

In Madagascar, as per Table 10, the situation was even worse, as the highest qualification for 65% of the employed was at the primary level of education or no qualification at all. However, on average, 58% of them were engaged in unpaid work (helping without pay in the business or farm of another household/family member). Most of the young workers at the secondary education level were absorbed as own‐account workers or were carrying out unpaid work. Most of the youths at the vocational, University and Post-graduate education level were working for someone else, either for cash or in-kind.

Tables 11 and 12 show waged workers, by type of contract and level of educational attainment in the DRC and Madagascar, respectively. Surprisingly, in both countries, and on average, 80% of the workers in this category were working, either under an oral contract or under no contract at all. This means that the education level had no significant effect on the type of contract being used. This finding indicated that due to the kind of agreement, waged workers are more exposed to risk if they stopped working, regardless of the reason, as they may not be able to get access to any benefits generally associated with formal employment, such as sick leave, annual leave or social security entitlements.

Table 10. Employment status by Educational attainment, Madagascar.

Statement	No qualifications	Primary	Vocational school (secondary)	Secondary level	Vocational (post-secondary)	University	Post-graduate,	Total
Employee (working for someone else for pay in cash or in kind)	10 9.8%	189 10.9%	5 18.5%	195 21.4%	3 42.9%	31 62%	1 50%	434
Employer (employing one or more employees)	2 2%	43 2.5%	3 11.3%	30 3.3%	0 0%	2 4%	0 0%	80
Own‐account worker (not employing any employee)	29 28.4%	495 28.6%	8 29.6%	278 30.4%	2 38.6%	8 16%	1 50%	821
Helping without pay in the business or farm of another household/family member	61 59.8%	988 57.2%	11 40.7%	405 44.4%	2 28.6%	9 18%	0 0%	1476
Other (specify)	0 0%	13 0.8%	0 0%	5 0.5%	0 0%	0 0%	0 0%	18
Total	102	1728	27	913	7	50	2	2829

Source: ILO, SWIT-2015.

Table 11. Wages Workers by type of contract and educational attainment, Congo.

Statement	No qualifications	Primary	Lower secondary	Upper secondary	Lower professional secondary	Upper professional secondary	Professional Certificate (secondary level)	Professional Certificate (post-secondary level)	University	Total
A written agreement	2 2.7%	10 4.5%	20 9.9%	6 10.2%	5 27.8%	7 23.3%	6 24%	9 64.3%	22 33.3%	87
An oral agreement	32 43.2%	69 31.4%	59 29.1%	14 23.7%	4 22.2%	11 36.7%	6 24%	2 14.3%	11 16.7%	208
No contract (self-employed, unpaid, ect.)	40 54.1%	141 64.1%	124 61.1%	39 66.1%	9 50%	12 40%	13 52%	3 21.4%	33 50%	414
Total	74 100%	220 100%	203 100%	59 100%	18 100%	30 100%	25 100%	14 100%	66 100%	709

Source: ILO, SWIT-2015.

Table 12. Wages Workers by type of contract and educational attainment, Madagascar.

Statement	No qualifications	Primary	Vocational school (secondary)	Secondary level	Vocational school (post-secondary)	University	Post-graduate, post-doctoral level	Total
A written agreement	0 0%	4 10.5%	2 5.3%	20 52.6%	1 2.6%	10 26.3%	1 2.6%	38 100%
An oral agreement	2.1.7%	64 52.9%	2 1.7%	49 40.5%	1 0.8%	3 2.5%	0 0%	121 100%
No contract (self-employed, unpaid, ect.)	56 4.1%	800 58.8%	11 0.8%	466 34.3%	2 0.1%	24 1.8%	1 0.1%	1360 100%
Total	58	868	15	535	4	37	2	1519

Source: ILO, SWIT-2015.

The wage regression model (Table 13) is based on the standard Mincerian earnings equation. For the DRC the results show that an increase in the number of workers with a qualification at the primary school education level will lead to a significant reduction in wages. However, an increase in workers with a qualification at the college-level will lead to a considerable increase in wages. For Madagascar, interestingly, the level of wages increased substantially with any level of education, even at the primary school level. The number of empirical studies that have tried to estimate the private return against different levels of education has grown significantly (see, Psacharopoulos & Patrinos, 2018, for updated literature). From these studies, it seems that there is a consensus on the fact that the returns are highest for primary education in a country with the lowest per capita income (Psacharopoulos & Patrinos, 2018). Theoretically, in the early stages of the development process, the demand for low skilled workers (those with primary education) will be relatively high compared to those with higher skills. However, in the last stage of development, the demand for workers will be the opposite. Thus, in our case, the difference between the impact of primary school education on wages between the two countries may be because of a difference in the level of development. A recent classification by the World Bank showed that while the DRC belonged to the lower-middle-income group, Madagascar was classified in the low-income group. This fact is consistent with the information indicated in Figure 1. Thus, the private return on primary education is positive in Madagascar because of the lower per capita income in this country, as compared to that of the DRC.

Table 13. Wage Regression, Dependent variable log Wage.

Explanatory variable	Congo	Madagascar
Primary	−0.25*** (0.07)	0.45* (0.000)
Lower secondary	0.07 (0.62)	0.98* (0.000)
Upper secondary	(0.24 0.19)	1.07 (0.15)
Vocational	0.03 (0.91)	1.37* (0.000)
College	0.37** (0.04)	1.60* (0.000)
Age	0.13 (0.42)	0.88 (0.85)
Age 2	−0.001*** (0.09)	0.06 (0.58)
Constant	10.25* (0.000)	2.77* (0.000)
Total No. Ob	330	354

(*), (**), (***) denotes significant at 1%, 5% and 10% level respectively.

p value in ().

Discussion and conclusion

The findings of this study reveal that the youths, in both of the countries examined, possessed low educational attainment or weak employability assets, and inadequate human capital when negotiating their transition from education to employment. Low educational attainment is a significant indicator of insufficient knowledge and skills that youth could develop to satisfy the increasingly demanding requirements of the labour market (Caraway, 2010; Tencati et al., 2010; Tran, 2017). Although the key factors that explain this situation vary between the two countries, economic reasons, as well as failed examinations, seem to be common elements in both countries. Tuition fees, and other school expenses (uniforms, Text books, transportation … … …, etc.) seem to be the main problem that prevents families from sending their kids to school. To minimize this burden, the government should allocate more resources to the education sector. NOGs should also contribute to this by initiating programs that encourage families to send their children to school.

Regarding failed examinations, several studies have tried to explain this phenomena and have identified several factors including, for example, family background (living with one parent, parental divorce, parents showing no interest in their child’s education and the school system, low income), frequent absences from school, school resources; incomplete homework (Rumberger, 2004, 2011; Williams Bost & Riccomini, 2006). Although there is no information in the survey about why youths failed their exams, there are a considerable proportion of youths who indicated that they are working while they studied. Thus, the consequences of combining work with study may result in frequents absences from school and incomplete homework and, hence, failure in examinations. Most of the literature on child labour in developing countries has identified family financial status as one of the key factors behind combining work with study phenomenon (Abebe & Bessell, 2011; Blunch & Verner, 2001; Bonnet, 1993; Kamlongera, 2011; R. Ray, 2003). Consequently, to prevent youths from this behaviour, there should be continued efforts to support families financially.

Most of the work that youths with low educational attainment carried out was of low productivity, and the money they could earn may not be sufficient for their everyday needs. This kind of work is not only bad for the workers (as it often does not provide any legal protection or social security for them), but it is also not good for the development of the economy in general. Economists suggest that to ease the development of labour-abundant developing economies that there is a need to reduce the share of low-productivity self-employment and increase the share of paid employment (Campbell, 2013; Tran, 2017).

Although the rate of unemployment among youths in both countries was relatively low, especially in Madagascar, the quality of work carried out by these young people is often low. The majority of the adolescent youth in both countries are engaged in either unpaid jobs or own-account work. These groups of youths were doing some form of informal employment, which could be identified as vulnerable employment, as they could not get access to any benefits generally associated with formal employment, such as paid annual leave, sick leave or social security protection and entitlements. Even for youths who worked for wages and salaries, the situation is a disaster. The working conditions for the majority of them were based on an oral contract, regardless of their education level. This finding indicated that youths were potentially exposed to exploitation by employers. Labour market regulations in these countries must be reformed to guarantee minimum rights for youths.

As we expected, the results of this study are substantial and provide significant policy implications for the economies examined in the study, as well as for all of the countries in SSA. The results are also imperative for future research, as it is anticipated that this study may open further research directions. As mentioned earlier, the STWS survey provides rich data on youth in their transition from education to employment for several developing countries, in general, and in Africa, in particular. Therefore, further studies may move to and answer several more questions regarding this matter, for instance, but not limited to; Does the situation of youth in the transition to employment vary between genders? Between urban and rural areas? What is the duration of the transition? Why do youth fail in examinations? Are these challenges the same for males, as well as for females? What are the common tools that are used by the young in their search for jobs?

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Notes on contributors

Atif Awad

Atif Awad is an associated professor of Finance and Economics at Sharjah University in the United Arab Emirates. Dr. Atif worked for Kassala University, Sudan since 2006 thereafter, he worked as post-doctoral researcher in the National University of Malaysia, Malaysia and in 2014  he joined Sharjah University, Department of Finance and Economics. Dr. Atif publishes several academic papers in reputable journals in addition, he is a reviewer for a number of journals including journal of Economic Modeling. Dr. Atif a warded several academic prizes as well as he a warded funds for more than three projects from international institute and NGOs. Dr. Atif has been invited as key note speakers for several international conference and forums. Dr. Atif lives in Sharjah city, UAE, with his wife and three kids.

Appendix

Table A1. Unemployment, youth total (% of total labour force ages 15–24), Selected African countries, 2005–2019.

Country Name	2005	2010	2015	2019	2005	2010	2015	2019
Algeria	30.713	21.843	29.73	29.506	Lesotho	42.46	37.383	34.927
Angola	8.48	19.466	16.875	15.982	Liberia	3.099	3.17	1.554
Benin	1.212	2.208	5.32	4.397	Madagascar	2.274	6.821	3.309
Botswana	36.766	35.28	35.969	37.35	Malawi	7.892	7.804	7.748
Burkina Faso	4.9	5.949	8.523	8.307	Mali	12.577	10.584	18.088
Cabo Verde	22.278	24.641	30.141	28.009	Mauritius	25.62	23.182	25.021
Burundi	3.031	3.005	2.86	2.693	Mauritania	14.288	14.881	15.101
Cameroon	6.555	6.297	6.009	5.819	Morocco	15.897	17.772	20.92
Chad	2.071	2.528	2.989	3.086	Mozambique	6.496	6.943	7.154
Central African Republic	6.23	6.222	6.01	5.706	Niger	5.443	0.843	0.67
Comoros	9.715	10.01	10.114	9.88	Nigeria	9.429	9.468	7.812
Congo, Rep.	39.953	29.798	22.177	21.367	Rwanda	1.325	1.664	1.894
Congo, Dem. Rep.	4.715	7.199	8.262	7.977	Sierra Leone	5.318	7.334	9.435
Cote d’Ivoire	7.589	9.38	4.558	5.138	Senegal	13.256	13.175	8.022
Egypt, Arab Rep.	31.242	24.534	34.286	31.053	Somalia	18.055	17.913	17.484
Equatorial Guinea	12.332	12.746	12.767	12.306	South Africa	55.944	50.527	50.321
Eritrea	8.538	8.82	8.966	8.763	Sudan	28.368	28.544	32.612
Ethiopia	3.583	3.499	3.355	3.231	Togo	6.913	3.54	3.757
Gabon	33.218	35.902	36.217	35.952	Tunisia	28.063	29.485	34.159
Gambia, The	12.998	12.862	12.8	12.465	Tanzania	6.015	5.807	3.693
Guinea	5.49	5.521	5.47	5.316	Uganda	3.066	5.491	2.618
Ghana	10.585	11.153	14.173	9.157	Zambia	28.211	26.839	19.731
Kenya	7.024	7.286	7.403	7.239	Zimbabwe	7.52	8.259	8.577

Source: World Bank Development Indicator