Youth labor market expectations and job matching: evidence from Sub- Saharan Africa Abstract In this paper we investigate factors that influence youth labor market expectations and outcomes. We also perform a job matching exercise to understand youth labor market dynamics in Sub-Saharan Africa. Our results show that youth education is an influential factor of youth employment expectations and employment, ceteris paribus. Higher educational achievements have a great impact on expecting and securing better jobs, particularly in the technical and professional fields. Youth with low educational achievements, particularly primary education and lower, have a higher tendency to expect to be employed in occupations with low job complexity. Our results indicate a severe job-skill mismatch in all occupational categories, both before and after the youth’s transition into the labor market. Using education as the only selection criterion, we found that less than 10 per cent of employment expectations match with skills required while 55 per cent and 34 per cent are under or over educated for the jobs expected, respectively. Over and under education is a notable feature in youth labor markets in Sub-Saharan Africa. About 47 per cent of employed youth in the sample are over qualified for their respective jobs while 28 per cent are under qualified. 1
Transcript
Youth labor market expectations and job matching: evidence from Sub-Saharan Africa
Abstract
In this paper we investigate factors that influence youth labor market expectations and outcomes.
We also perform a job matching exercise to understand youth labor market dynamics in Sub-Saharan
Africa. Our results show that youth education is an influential factor of youth employment
expectations and employment, ceteris paribus. Higher educational achievements have a great
impact on expecting and securing better jobs, particularly in the technical and professional fields.
Youth with low educational achievements, particularly primary education and lower, have a higher
tendency to expect to be employed in occupations with low job complexity. Our results indicate a
severe job-skill mismatch in all occupational categories, both before and after the youth’s transition
into the labor market. Using education as the only selection criterion, we found that less than 10 per
cent of employment expectations match with skills required while 55 per cent and 34 per cent are
under or over educated for the jobs expected, respectively. Over and under education is a notable
feature in youth labor markets in Sub-Saharan Africa. About 47 per cent of employed youth in the
sample are over qualified for their respective jobs while 28 per cent are under qualified.
Youth will be the most dynamic segment of the world population in upcoming years, particularly in
Sub-Saharan African countries that harbor very young populations. As seen during the Arab Spring in
2011, their influence, directly and indirectly, in the political, economic and social spheres will be
profound in the years ahead. Youth years are also a crucial time in life as young people start realizing
their aspirations, assuming economic independence and finding their place in society. However, this
phenomenon depends on numerous factors including adequate skill development prior to transition
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from school to work and a functioning local economy, among others. Recent global developments, in
particular, have made youth vulnerable to shocks. For example, the 2008 global financial crisis has
put further pressure on youth unemployment, reduced the quality of jobs, exacerbated labor market
inequalities among different demographics, particularly youth, made school to work transition both
longer and more insecure and increased detachment from the labor market. According to the Global
Employment Trends for Youth (ILO 2013), the weakening of the global recovery from the financial
crisis has further aggravated the youth employment crisis. Queues for available jobs have become
even longer for some unfortunate young jobseekers.
While having a young population enables countries to reap demographic dividends, it also becomes
a burden if sufficient policies are not enforced to ensure that the young are both employable and
employed. Youth unemployment is a critical issue not only in Sub-Saharan Africa but in most
emerging economies and to a certain extent, in developed countries. Understanding youth behavior
and the youth labor market is vital in order to implement policies that will best address youth
unemployment. Two particular issues that contribute to youth unemployment, among others, are
the limited access to learning opportunities as well as the mismatch of skills with respect to market
demand. The objective of this research is to investigate what influences youth labor market
expectations and if these expectations match with market demand. Investigation into what drives
youth labor market expectations and if these expectations match with market demand would help
us to understand the forces behind successful school to labor market transitions. Such an
investigation would also allow us to gauge into policies that may be necessary for strengthening
human capital and addressing youth unemployment.
We use the “School-to-work Transition Surveys (SWTS) 2012-2013” recently conducted by the ILO
for eight countries in Sub-Saharan Africa to investigate the relationship between youth labor market
expectations and outcomes. We combine the SWTS data sets with the “Labor Demand Enterprise
Surveys (LDES) 2012-2013” conducted by the ILO in our labor demand–supply matching exercise. To
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our knowledge, this is the first study that investigates school to labor market transition in Sub-
Saharan Africa using the aforementioned new data sets. The new data sets give this research an
edge over past studies and enable us to shed light on recent trends on the subject.
The remaining sections of the paper are organized as follows. Section II provides a brief literature
review of youth labor markets in Sub Saharan Africa. Section III will describe the model. Section IV
describes the data, and outlines the estimation methodology. An analysis of the estimation results is
provided in Section V, along with a robustness check of our results. Section VI discusses policy
implications of findings, and Section VII concludes the investigation.
II. Literature review
Literature on youth labor market expectations is very limited. The theory and empirical literature
that relate to the present research derives from two fronts. First is the theory on the impact of
cognitive and non-cognitive skills on employment and wages. In many studies, it is commonly
accepted that measured cognitive abilities are important determinants of educational and labor
market outcomes (e.g. Murnane et al. (1995), Cawley et al. (2000) and Cawley et al. (2001)). Using a
dynamic setting, Heineck (2011) investigated how cognitive abilities affect unemployment entry and
exit rates. According to Heineck, cognitive skills only weakly affect unemployment propensity and
contribute little to individual heterogeneity. However, they can aid employed males to stay out of
unemployment. Cognitive skills can also impact wages. For example, Capatina (2014), in a study of
the United States, found that returns to cognitive skills increase very sharply for high skill levels,
more gradually around mean levels, and decrease at low levels. Hanusek and Woessman (2008),
using a variety of data sources, including information on international tests conducted by the
International Association for the Evaluation of Educational Achievement (IAE) and from the OECD,
found that average levels of cognitive abilities are also associated with differences in economic
growth across countries. In this research, we will use the educational achievements of youth as a
reflection of outcomes of cognitive skills.
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Past research also used family background as a control variable. For example, in a study of the British
education system, using longitudinal data from the National Child Development Study (NCDS), and
controlling for family background, school system and educational attainment, Weiss (2010) found
that both cognitive and non-cognitive abilities have an impact on earnings.1 The effects are especially
large at the bottom and top ends of the earning distribution. Calvès, Kobiané, and N’Bouké (2013),
using data from a retrospective survey conducted in Ouagadougou, found that private schools
accelerate chances of entry into the wage labor market due to differentials in the education
attainment and socio-economic origins of school-leavers from private and public schools. Lindquist,
Sol and Praag (2015) found parental entrepreneurship to increase the probability of children’s
entrepreneurship by 60 per cent. Findings from Dunn and Holtz-Eakin (2000) indicate that second
generation entrepreneurs are two to three times more likely to work in the same occupation as their
fathers.
Another strand of the literature related to this study is the growing empirical evidence on the
importance of non-cognitive abilities in determining labor market outcomes. For example, Heckman
and Masterov (2007), in an investigation of American children who grew up in disadvantageous
environments, established that parents play an important role in stimulating both the cognitive and
non-cognitive skills of their children. Cunha et al. (2006) found similar results using data from the
1979 National Longitudinal Survey of Youth in the United States. Both Cunha et al. (2006) and
Carneiro and Heckman (2003) showed that non-cognitive abilities are more malleable than cognitive
abilities in later stages of youth life. More able and engaged parents have greater success in
stimulating both types of skills. In a study of Britain, using controls for family structure, the home
1 The study uses two measures of non-cognitive ability. One measure is based on 8 questions asked to the child’s mother relating to whether her child is bullied by other children, destroys own or others’ belongings, is miserable or tearful, is squirmy or fidgety, is irritable or quick to fly off the handle, fights with other children, and is disobedient. The other measure is based on questions asked to the teacher of the child relating to 12 different syndromes, namely, anxiety for acceptance by children, hostility towards children, hostility towards adults, “writing off” adults and adult standards, withdrawal, unforthcomingness, depression, anxiety for acceptance by adults, restlessness, inconsequential behavior, miscellaneous symptoms, and miscellaneous nervous symptoms.
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learning environment and parent characteristics, Carneiro et al. (2007) found that an overall
measure of non-cognitive skills is important for a host of later outcomes, including education
attainment, employment status and wages.2 A significant aspect of their findings is that social skills
are more important for individuals from low socio-economic backgrounds than it is for individuals
from high socio-economic backgrounds, suggesting that investment in non-cognitive skills may
reduce income inequality. On the other hand, Macmillan (2013), using data from the British Cohort
Study (BCS), found non-cognitive skills and behavioral outcomes to play a more important role in the
intergenerational transmission of joblessness.3
One of the key issues concerning youth unemployment, especially in developing countries, is the
mismatch between the supply of and demand for jobs. This is mainly due to the prevalence of skill
mismatch, which is costly to employers, workers and the society at large (World Economic Forum
2014). For example, additional years of under-education, an issue that is prevalent particularly in
low-income countries, are detrimental to firm productivity (Kampelmann and Rycx 2012). According
to the ILO (2013), skill mismatch in youth labor markets has become a persistent and growing trend.
Lack of proper preparation for the transition from school to work underlies this mismatch.
Mengistae (1998), in a study on Ethiopian manufacturing firms, found on-the-job skill formation and
job matching to be significant sources of growth for both productivity and wages. Here, job matching
is by far the most important source of growth, both for productivity and wages. Acemoglu (1999)
and Mortensen and Pissarides (1999) examined the effects of skill-biased technical change using a
labor market matching process. Albrecht and Vroman (2002) applied a similar method for the United
States, United Kingdom and Europe in a matching model with endogenous skill requirements. They
found that skill-biased technical change increases wage dispersion both within and in between skill
groups, while increasing unemployment, particularly among low-skill workers. Shi (2002),
2 The study also uses data from NCDS to construct a measure of non-cognitive skills similar to the second non-cognitive measure used by Weiss (2010). 3 This study uses agreeableness, emotionality, extroversion, conscientiousness and intelligence as key non-cognitive measures.
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establishing similar results, found that skill-biased technological programs in the United States
generate concurrent increases in within-group inequality and skill premium, with the latter rising
more sharply. Kok (2014), in a study of the Netherlands, also found evidence for higher job-match
quality to be associated with higher wages.
Market behavior and demand could also influence the potential labor market entrants to invest in
their human capital. For example, in a labor market model that incorporates the possibility that
agents may invest in human capital before matching, Hatfield, Kojima and Kominers (2014) showed
that the worker-optimal stable matching mechanism incentivizes workers to make efficient human
capital investments.
III. The Model
One of the key takeaways from the literature review above is the influence of cognitive and non-
cognitive skills on educational and labor market outcomes, further strengthened by the family
background of labor market entrants. Taking these into account, and following Hanushek and
Woessmann (2008), as well as Carneiro et al. (2007), we developed a model that captures the effects
of parental attributes (such as level of education and occupation) as well as youth attributes (such as
level of education and area of residence) on youth labor market expectations, especially on
preferred occupations and type of work.
Consider for example, the model of the following form:
U ki=∝k+β' Xki+γ
' Y ki+εki (1)
where k = 1, ……., K is the economic sector (field) and i = 1, …….. N k is the number of individuals in a
specific sector (field). U i is a dummy variable that takes a value of 1 if the youth’s employment
expectation is in a given field, or 0 otherwise. The vector X ki includes attributes that relate to the
youth (such as education, skills, health and area of residence) while vector Y ki includes attributes of
parents (such as level of education and occupation). ε ki is a random error term. This model enables
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us to establish links between parental and youth attributes, and employment expectations of the
youth in a particular field.
Once such links are established, we conduct a matching exercise to investigate whether youth
expectations, in terms of future occupation, are consistent with labor market demand. Such an
exercise would enable us to understand if youth have undue expectations or if they are well
prepared for the transition from school to work, especially considering the skills they have acquired.
While the results from the model in (1) would give a better understanding of the importance of
youth and parental attributes on youth employment expectations, a job-skill matching exercise will
allow us to further investigate the criticalities of such attributes in better preparing youth to secure
jobs that best match the skills they possess.
Consider a labor market where wage (w) depends on skills required for a particular job (β ) and
location (l ¿, while employer’s revenue depends on job complexity (α ) and the location(l). Given
their skills (s), including education level (e) and parental attributes (p), workers always want to
maximize their expected nominal wage in segment z relative to the minimum they could earn by
engaging in the least complex job in segment z. Thus, workers maximize their expected nominal
wage according to:
w (β , l )=max ¿¿ (2)
Where α reflects job complexity and c l represents the location cost.4 w ¿ (>0 for all z) is the wage for
the least complex job in segment z. The relation described in (2) indicates that the more complex
the job, the higher the wages.5 A worker’s skills (s¿ comprise of both education (e) and non-
cognitive skills. Workers and employers face the same location cost. We also assume the location
4 z could be job categories5 Workers’ nominal wage is given by: w (a ,l )=f (α)−cl. Revenue earned by the employer is given by: r (α ,l )=f (β )−c l
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cost to be higher in cities than in rural areas, as living costs are higher in cities. Similarly, the
employers maximize expected revenue, given the skill level of the workers (s¿, according to:
r (α ,l )=max ¿¿ (3)
r ¿ is the revenue generated by the least skilled worker in segment z. r ¿ > 0 for all z . From an
employer’s point of view, better skilled workers generate more revenue.
Job Matching Requirements
For a worker to agree to accept a job, the expected nominal wage, net of location costs, should be at
least equal to the minimum he can expect from the least complex job in a particular segment, given
the skills he possesses. On the other hand, the employer will hire a worker with the required skills
only if its return from hiring the worker is at least as high as that from employing the least skilled
worker in the given segment.
The aforementioned mutual agreements for job matching would require that two conditions are
satisfied:
Condition 1: Elw (α z|s , p )−c l≥w ¿ (4)
Condition 2: El r (βz|s )−c l≥r ¿ (5)
Since both the worker and the employer maximize their respective utilities (wages and revenue,
respectively, as discussed above), the gap between a worker’s skills and job complexity becomes as
small as possible. In fact when βz ,l=α z ,l, a perfect job-skill match occurs. Thus we assume the
relationship between the lowest paid wage, w ¿ for z at l, and that of lowest revenue, r ¿, is given
such that:
r ¿ (6)
Taking the above two conditions in (4) and (5) along with the relationship in (6) implies:
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El r (βz|s )−Elw (α z|s , p )=θ+ϵ (7)
Where θ ≥ 0 is a constant and ϵ is a random error term. We define match quality,Q z ,l, in segment z
such that E(Q z , l)=1 /E (βz , l−α z ,l) (i.e. the smaller the gap between skills and job complexity, the
better the quality of the match). Assume r and w are related in such a way that we can represent
(7) as:
El f (β z−α z|s , p )=δ+ϵ 1 (8)
where δ ≥0 is a constant and ϵ 1 is a random error term. We make the assumption that
f ( βz−α z|s , p ) is a multiplicative function of the form g (βz−α z ) . h(s , p).6 From (8), this would
imply:
El f ¿ (9)
where ϑ ≥0 is a constant. Or El(1Q z ,l
) . El h(s , p)=ϑ+ϵ 2. Rearranging the above, we have:
El (Q z ,l )=γ+Elh (s , p )+ε (10)
Match quality, Q z ,l is a function of workers’ skills, s, (cognitive, that includes the level of education (
e ¿ ,and non-cognitive) and parental attributes ( p¿. γ is a constant while ε is a random error term.
The relationship described in (10) allows us to investigate the relationship between workers’ skills, as
well as worker and parental attributes. It also allows us to match jobs and expectations on a certain
level of job complexity.
IV. Data and empirical strategy
This study uses cross-sectional data from “School-to-Work Transition Surveys (SWTS) 2012-2013”
recently conducted by the ILO in eight Sub-Saharan African countries (i.e. Benin, Liberia,
6 Workers maximize wages against job complexity, which in turn is a function of the wage rates. The more complex the job, the higher the wage rate.
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Madagascar, Malawi, Togo, Uganda, United Republic of Tanzania and Zambia). Thus we have
observations for variables from only one point in time. A sample of 26,228 observations of employed
and unemployed youth is available for the analysis from these surveys. Our particular interest is on a
sub-sample of 7,114 observations of employed and unemployed youth who have expressed their
employment expectations. We also use another sub-sample of 2,549 observations of unemployed
youth and compare our results with the previous samples.
For cognitive skills, we use educational achievements of the youth while for non-cognitive skills we
use a measure of the health of the youth which indicates 1 if the youth does not have health
problems and 0 if the youth has any health issues.7 For parental characteristics, we use educational
and occupational status. A summary of basic statistics of the sub-sample is given in Table 1.
Approximately 52 percent of the sample is urban and 48 per cent is rural. The average age of the
youth is approximately 21.8 years. Male youths accounted for 58 per cent of the sample while the
remaining 42 per cent was represented by female youth. 17.7 per cent of the youth did not have any
education while 20.4 per cent and 30.5 per cent had primary and secondary education, respectively.
Only 2.4 per cent had a tertiary education. On average, 24 per cent of fathers and 41 per cent of
mothers had not received an education. Amongst the parents with education, fathers had an
average of secondary level education while mothers’ education lied in between primary and
secondary level.
Table 1 around here
The second cross sectional data set we use is the “Labor Demand Enterprise Survey (LDES)” for 2012
to 2013, conducted by the ILO in five countries in Sub-Saharan Africa (i.e. Benin, Liberia, Malawi,
United Republic of Tanzania and Zambia). The primary use of LDES is to understand the
characteristics of demand for labor by employers. Given the limitations of the survey, we are only
interested in two key variables, the most important characteristics that an employer looks for when
7 While the assumption of a multiplicative function makes transformation of (9) into (10) straight forward, we could also assume it to be an additive function with no significant impact on the final outcome.
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hiring a worker and job complexity, both of which enable us to compute skills and characteristics
necessary for a particular job, which in turn will be used to investigate match quality. The LDES
indicates several criteria used by employers in hiring managers, professionals and production
workers. For managers and professionals, the most important characteristics are job experience
(39.5%), education (32.6%), attitude (7.4%), expectations (5.1%), age (5.0%), gender (3.3%) and
marital status. Fairly similar expectations are reflected for production workers, with attitude and
expectations gaining little more weight than for managers and professionals. The SWTS data set does
not have information on job experience (since the target population is primarily youth undergoing
school to work transition), attitudes and expectations. As such, for the skills and other characteristics
required for a particular job in a specific job segment, βz, we use age, gender, education or training
and marital status characteristics of the preferred hiring criteria used by employers. We use “hard to
fill vacancies” as a proxy for job complexity. We classify the top 10 “hard to fill vacancies” into six
occupational categories: professional, services, administration, clerical, technical and other. The last
category, “other”, includes agriculture, craft, operational, elementary and armed forces. For this
analysis, we use a total of 3066 observations. Despite the limitations that arise from a small data set,
it nonetheless provides useful information for our purposes. The required educational level is coded
as follows: 1 = no education, 2 = primary, 3 = secondary (vocational), 4 = post- secondary
(vocational), 5 = secondary and 6 = university and postgraduate. We merge university and
postgraduate education into one category, as their importance in terms of “hard to fill vacancies” is
very low. The rankings of job complexity (“hard to fill”) and skills demanded are given in Table 2.
Since professional-level occupations demand higher qualifications, they are also the hardest to fill.
On the other hand, the most preferred skill level for production and elementary level occupations is
secondary education.
Table 2 around here
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We use the above information from the LDES to define a variable that corresponds to job complexity
as well as skills required for a particular occupation in the SWTS data set. Job complexity in the SWTS
data set is coded as follows: 6 = professional, (i.e. if the expected occupation of the youth is
professional), 5 = services, 4 = administration, 3 = clerical, 2 = technical and 1 = other (Table 2). For
skills required for managers and professionals, we code the preferred skills as follows: 6 = university
and postgraduate (i.e. if the youth has a university or post graduate education), 5 = secondary, 4 =
post-secondary (vocational), 3 = secondary (vocational), 2 = primary and 1 = no education. Similarly,
for skills required for production workers and elementary occupations, we code the preferred skills
as follows: 6 = secondary education (i.e. if the youth has a secondary education), 5 = secondary
(vocational), 4 = primary, 3 = post-secondary (vocational), 2 = university and postgraduate and 1 = no
education. Note that in this category of workers, undergraduate or postgraduate qualifications only
allow a score of 2. Essentially, such a level of education is not required for this category of work.
As discussed earlier, match quality occurs when the gap between skills and job complexity is
smallest. Match quality is computed as a weighted average of the gaps in employer expectations and
youth characteristics in relation to age, gender, education and marital status. The weights for each of
these characteristics based on employer preferences are: education (0.747), age (0.115), gender
(0.076) and marital status (0.062) for managers and professionals. The weights with respect to
production workers are: education (0.717), age (0.115), gender (0.095) and marital status (0.073).
V. Estimation results and analysis
We begin by investigating the relationship in (1) 8 using the sub-sample of employed and
unemployed youth who have expressed their employment expectations. Table 3 presents
multinomial logit regression results for the impact of youth characteristics and parental attributes on
each of the youth employment expectation categories. Given that agriculture is the primary source
of income for the majority, we use the employment expectation in agriculture as the base outcome.
8 These health issues relate to difficulties in eye sight, hearing, walking, remembering or concentrating and self-caring such as washing or dressing.
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Since coefficients from multinomial logit regressions can be difficult to interpret, as they are relative
to the base outcome, we also compute marginal effects of changing their values on the probability of
observing each outcome. The results are given in Table 4. Estimation results point to several key
conclusions9:
(i) Low level of education (i.e. below secondary) or no education has significant and
positive effects on employment expectations in job categories that have low level of
complexity and low pay, such as the elementary, craft, operational and service sectors
but a negative effect on high complexity occupations such as those in the administration,
professional and technical sectors. For example, a one-unit increase in the probability of
a youth having no education will increase employment expectations by 1.3 units in the
operations job category and by 1.2 units in the elementary category, relative to
agriculture and holding all other variables constant. Such low education levels also tend
to reduce employment expectations in high complexity jobs, particularly in
administrative, professional and technical sectors. Similarly, a one-unit increase in the
probability of youth having a primary education will increase employment expectations
by 1.02 units in the crafts job category. Average marginal effects in Table 4 confirm the
direction of these results.
(ii) Secondary education has an even larger positive impact on job expectations in almost all
sectors, except administrative and technical. Post-secondary education has positive
effects on expectations in high complexity jobs but a negative impact on expectations in
low complexity jobs, although they are not significant. In general, average marginal
effects confirm these results and also highlight the impact of post-secondary education
on employment expectations in the high complexity professional and technical sectors.
For example, at the means of all covariates, post-secondary education is likely to
9 We remove the non-cognitive skill variable from estimations as our preliminary estimations indicated its weakness as a proxy.
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increase job expectations in the professional and technical categories by 19.6% and
6.6%, respectively.
(iii) Fathers with secondary education or higher seem to influence job expectations of their
children in high complexity jobs. A one-unit increase in the number of fathers with
secondary education or higher will increase employment expectations of youth in
occupations in the professional, technical, clerical and services categories by 1.3, 1.0, 0.9
and 0.7 units, respectively. The impact of their higher educational achievements on
employment expectations in low complexity jobs is insignificant. In contrast, mothers’
education does not exhibit a clear trend in influencing the employment expectations of
their children. This could be due to the low average level of education of mothers.
(iv) Parents seem to dislike their children expecting a job in the same profession in which
they are employed, perhaps because the majority of parents have low complexity and
low wage jobs and hold high expectations for their children, though average marginal
effects do not support this phenomenon.
We also estimated the model in (1) using the sub-sample of 2,549 observations from unemployed
youth. The multinomial logit regression results, with agriculture as the base category, are given in
Table 5. The results more or less confirm our original findings discussed above. Youth with low levels
of education, primary and below, tend to have a negative effect on employment expectations in the
administrative, professional and technical categories but a positive impact on expectations in low
complexity jobs, particularly in the operations and craft sectors. As previously established, secondary
education seems to have a highly significant impact on job expectations across the board, except in
the administrative sector. Marginal effects reported in Table 6 confirm this finding. Post-secondary
education has a positive and significant effect on employment expectations in the professional
category, but marginal effects in Table 6 do not confirm this relationship. Parents with post-
secondary education seem to influence employment expectations of their children in high
complexity jobs, especially in the professional, technical and administrative sectors. Once again, as
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previously concluded, parents do not want their children to expect jobs in the same profession in
which they are employed, most likely due to low pay.
Job matching and match quality
As per the discussion earlier on job market requirements, we define a job match as when a youth’s
level of skill, defined by their level of education, other characteristics and the type of employment
expectation or employment correspond with the skills and characteristics favored by the employer
as well as the job complexities identified by them.10 A perfect match occurs when the level of skill,
including other characteristics, and the level of job complexity are the same. This occurs when the
gap between skills required and job complexity is nearly zero. A close match occurs when the level of
skill and job complexity is one step higher or lower than the perfect matching score. This occurs
when the skill level has a score that is one step lower than that for job complexity or when the score
for job complexity is one step lower than that for skills.
We begin by using education as the only criterion considered by employers when hiring workers.
This means that the gap between the level of education a youth possesses and the level demanded
by an employer for a given level of job complexity will determine a job match. In Table 7, we provide
results for job matching for youth employment expectations. The results indicate a low perfect
match between youth employment expectations and job skills in almost all employment categories,
except low complexity sectors, particularly the “other” category which includes agriculture, crafts,
operations, elementary and armed forces. Lowest job matching occurs in the administrative sector.
Overall, only 10 per cent of youth job expectations match the skills required. About 42 per cent of
employment expectations could be considered either a match or a close match. A larger majority
(92%) of youth with expectations of securing a job in the professional sector does not have the
required skills or qualifications. Both over and under expectation relative to skills required seem to
dominate all employment categories. Overall, 34 per cent of youth are over educated in comparison
10 Similar results were found with the full sample.
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to their job expectations. This could very well be due to tight labor market conditions that compel
youth to adopt lower job expectations relative to the skills they possess. Similarly, over 55 per cent
of youth do not have the required skills or education demanded by employers.
Table 7 and 8 around here
We compare our results with actual job matching in the youth labor market using a sub-sample for
employed youth consisting of 13,213 observations. Table 8 provides estimates for the employed
youth, which indicates a job-matching rate of almost 25 per cent, much higher than the 9.7 percent
seen in relation to job market expectations.11 Youth seem to invest and prepare for job market skill
requirements despite having higher or lower expectations during the school-to-work transition
period given their level of skills before securing jobs. This is also reflected in a lower level of overall
under education (28%) for any given job. While almost 31 per cent of youth employment in the
professional sector indicates a perfect match, only 2.5 per cent in the service sector exhibits a
match. In the technical field, the job match level is only 15.4 per cent, similar to jobs in the
administration sector. The match for clerical jobs is also at a low of 5.8 per cent. In contrast, the
“other” category has a match of 32.7 per cent. Almost a quarter of jobs in the service sector are held
by youth with a slightly lower education level than is required while about 23 per cent is filled by
youth with a slightly higher level of education than required. More than 27 per cent of jobs in the
professional sector are held by youth with a slightly lower level of education than required. In fact,
the service sector has the most severe job mismatch, whereby nearly 75 per cent of jobs are held by
youth with lower skills than required. However, most occupational categories also indicate a heavy
presence of over-educated youth. More than 50 per cent of youth with technical and clerical jobs
are over qualified. Overall, about 40 per cent of youth in the sample are over qualified for their
respective jobs.
11 See Sparrenboom and Staneva (2014) for a detailed analysis of qualification mismatch.
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We now extend the “skills and other characteristics” variable to include age, gender and marital
status in addition to education when computing match quality, as described above. Our sample
indicated a mean of 0.07 for match quality with a standard deviation of 0.12, indicating a large gap
between skills and other characteristics demanded by employers and those actually possessed by
youth. To further investigate, we conducted an OLS regression for the model in (10) on sectoral
match quality using the whole sample (Table 9). Our results confirm the previously computed
results in Tables 3 through 6. Lower educational levels tend to increase job match quality in low
complexity jobs. While no education and primary education have negative effects on match quality
in the professional, technical, clerical and administrative fields, they have positive effects on match
quality in the craft, operational and elementary job categories. Contrary to the negative relationship
for primary education or lower, tertiary education tends to contribute to better job matching in high
complexity jobs. For example, tertiary education has a significant positive effect on job match quality
in the professional, clerical and administration job categories, in that order of impact. A 1 per cent
increase in the probability of a youth having a tertiary education increases job match quality by 0.14
per cent, 0.04 per cent, and 0.01 per cent in the professional, clerical and administrative fields,
respectively. While a father’s level of education has no significant impact on match quality in any
employment category, a mother’s level of education has a positive effect on job match quality in the
services sector but a negative effect in the agricultural sector. Interestingly, while fathers employed
in agriculture tend to negatively influence match quality in that sector, mothers tend to positively
affect match quality in that same sector. Mothers employed in the administrative and professional
sectors also tend to positively impact their children’s match quality in the same job category. Urban
residency has a significant and positive relationship with job match quality in the technical, clerical,
and operational sectors while having a negative effect in agriculture, with a one per cent increase in
the probability of being in urban areas leading to a 0.003-0.023 per cent increase in match quality.
Overall, job match quality tends to be very low, except in agriculture and services, as demonstrated
by means of 0.38 and 0.24, respectively. The lowest match quality is observed in the administration
17
(mean of 0.0008), clerical (mean of 0.0054) and professional (mean of 0.0056) fields. The trend of
low match quality for high complexity job categories is somewhat similar to the characteristics we
have seen in Table 9 using only education as the selection criterion.
Table 9 around here
VI. Policy implications
The results of this study point to several key conclusions. Firstly, youth education can significantly
influence youth employment expectations and employment outcomes at later stages in life. Higher
educational achievements have a great impact on expectations and securing better jobs, particularly
in the technical and professional sectors. Employment expectations in the professional and technical
fields are mostly influenced by higher level of education (in this case post-secondary (tertiary)
education). Youth with low educational achievements (primary education or lower) have a higher
tendency to expect to be employed in agriculture or other elementary occupations. Secondly,
mothers’ level of education has no clear effects on youth employment expectations, though fathers
with secondary education or higher have a positive effect on youth expectations in high complexity
jobs.
Thirdly, a parent’s occupation has a negative influence on a youth’s employment expectations in the
same field. Fourthly, there is severe expected job-skill mismatch in every occupational category,
although this reduces slightly when the youth actually enters the labor market and secures a job. As
a result, most occupational categories indicate a heavy presence of under and over-educated youth.
Job-skill mismatch is a prevalent issue not only in Sub-Saharan Africa but also in other low-income
countries. Yet, Sparrenboom and Staneva (2014) found that job-skill mismatch is much wider in
scope in Sub-Saharan African countries.
These findings have several policy implications. The fact that youth education tends to significantly
influence youth employment expectations and employment clearly indicates the need for greater
18
access to quality education12. While most sample countries seem to have a higher level of enrolment
in primary school, enrolment in secondary and post-secondary education is low, albeit improving.
Given the fact that secondary and tertiary education has a greater impact on youth employment in
the technical and professional fields, increased public investment in these sectors may result in
greater societal benefits.
There are three major issues related to job-skill mismatch. First is the lack of appropriate
qualifications that has led to a significant presence of youth with subpar skills, especially in the
professional, administrative and service sectors. Youth skill development, particularly in specialized
occupational categories and professional fields, is paramount to addressing this problem. The
second issue relates to the limited availability of suitable jobs as reflected by the heavy presence of
over educated youth in most occupational categories. Our results indicated that many educated
youths are employed in low complexity jobs relative to the skills they possess. Creation of jobs that
target the youth is equally as important as skill development for better youth employment. The third
issue relates to the limited impact of mothers’ level of education on children’s educational outcomes
and job-match quality. This may be due to the lower levels of educational attainment of mothers, on
average, slightly above primary education, as well as the lagging nature of parental education in Sub-
Saharan Africa, highlighting the importance of improved adult education programs.
VII. Conclusion
Sub-Saharan Africa has a very young population that will be the most dynamic demographic segment
in the coming years. Youth years are a crucial time in life as young adults begin to realize their
aspirations, assume economic independence and find their place in society. However, realizing this
depends on numerous factors including proper skill development prior to the transition from school
to work and a functioning economy, among others. This study investigated factors that drive youth
12 Note that there is no one to one relationship between the two sub-samples as they relate to employed and unemployed youth.
19
labor market expectations and outcomes, with a particular focus on youth and parental attributes,
such as education, among others.
Using multinomial logit analysis, we found evidence that youth education greatly influences youth
employment expectations and employment, with higher educational achievements having a great
impact on expecting and securing better jobs, particularly in the technical and professional fields.
There is a higher tendency for youth with low educational achievements, primary education or
lower, to expect low complexity jobs. Our results do not give a clear indication on the impact of
mothers’ education on youth employment expectations. This could perhaps be due to low education
levels of parents as well as predominantly low complexity jobs held by parents. Educated fathers, on
the other hand, tend to influence youth employment expectations in high complexity jobs.
Meanwhile, a parent’s occupation has a negative effect on employment expectations in the same
field.
There is a severe job-skill mismatch in every occupational category, both before and after entering
the labor market. Youth employment expectations seem to be higher relative to the skills they
possess. Less than 10 per cent of youth employment expectations match with the skills required,
while 55 per cent of total youth are under educated for the jobs they expect. Similarly, youth also
seem to have lower expectations relative to their actual capabilities, with 34 per cent indicating
employment expectations below what they could have attained given the skills they possess. In the
sample with employed youth, more than 27 per cent of the jobs in the professional category are
held by youth with slightly lower education than required. The service sector has the most severe
job mismatch with nearly 75 per cent of jobs held by youth with subpar skills. Most occupational
categories indicate a heavy presence of over-educated youth, with about 47 per cent of youth in the
sample over qualified for their respective jobs. Increasing access to quality education and skill
development would effectively address these problems. An important aspect in skill development is
the importance of matching market expectations. Education and skills alone will not resolve youth
20
employment issues, as they only address the supply side. A robust economy is key in attracting new
entrants to the labor market. Parents also have an important role in shaping the employment status
of their children. Thus, increasing the knowledge of parents on youth employment, particularly
parents with no or limited education, should be considered at the community level.
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