Mismatch between education and occupation: a case study of Pakistani graduates.
Farooq, Shujaat
In this study, an attempt has been made to estimate the incidences
of job mismatch in Pakistan. The study has divided the job mismatch into
three categories; education-job mismatch, qualification mismatch and
field of study and job mismatch. Both the primary and secondary datasets
have been used in which the formal sector employed graduates have been
targeted. This study has measured the education-job mismatch by three
approaches and found that about one-third of the graduates are facing
education-job mismatch. In similar, more than one-fourth of the
graduates are mismatched in qualification, about half of them are
over-qualified and the half are under-qualified. The analysis also shows
that 11.3 percent of the graduates have irrelevant and 13.8 percent have
slightly relevant jobs to their studied field of disciplines. Our
analysis shows that women are more likely than men to be mismatched in
field of study.
JEL classification: I23, I24, J21, J24
Keywords: Education and Inequality, Higher Education, Human
Capital, Labour Market
1. INTRODUCTION
The developed economies especially the US and UK started to invest
heavily to expand the supply of graduates before 1980s and Freeman
(1976) was the first who raised his concerns over this expansion in his
research entitled 'Overeducated Americans'. Since the late
1980s, the research on job mismatch has mushroomed in the US as well as
in other developed countries.
The initial studies perceived the education-job mismatch as a
temporary phenomenon [Freeman (1976)]; however, later the empirics did
not support it. The empirical text in various developed regions has
mainly focused the 'over-education' (1) which range from 10
percent to 40 percent in various developed countries [Groot and Maassen
(2000a)]. These estimates raised serious questions over the validity of
conventional views of the labour market; consequently a good debate has
started with the emergence of some new theories i.e., the job
competition theory and the job assignment theory in which the
institutional rigidities, allocation problems and skill heterogeneities
were dealt.
Both economists and sociologists have consigned the job mismatch
phenomenon as a serious efficiency concern with its pertinent
socio-economic costs at individual, firm and national level. At
individual level, it would let down the individual's marginal
product, though the estimated wage differential differs across the
countries. (2) The lower returns to education may also incur some
non-transitory costs i.e. lower level of job satisfaction, frustration
and higher turnover rate. At the firm level, job mismatch is associated
with lower productivity and lower level of job involvement; and in case
of high turnover rates, firms may have to prevail with extra costs on
screening, recruiting and training [Tsang (1987); Sloane, et al.
(1999)]. At the macro level, the national welfare in terms of monetary
welfare and non-monetary would be lowered by underutilisation of skills
[McGuinnes (2006)]. It is also possible that previously well-matched
graduates in the economy will be 'bumped down' in the labour
market as over-educated workers move into lower occupations thus raising
the educational requirements within these occupations [Battu, et al.
(2000)]. Thus, the rapid educational expansionary policies may not yield
the desired real economic benefits [Budria and Egido (2007)].
The subsequent section shows that no study on job mismatch exists
in Pakistan; however, Pakistan is also facing this dilemma. Keeping in
view the importance for researchers and policy-makers in Pakistan, the
present study aims to contribute in knowledge by analysing the
incidences of the various types of job mismatches among Pakistani
graduates. The rest of the study is organised as follows. Section 2
presents a review of potential presence of job mismatch in Pakistan,
followed by measurement issues, theoretical and empirical literature in
Section 3. A discussion on data sources and methodology is given in
Section 4. The results over the incidences of job mismatch are presented
in Sections 5, followed by conclusion and policy considerations in final
section.
2. POTENTIAL OF JOB MISMATCH IN PAKISTAN
In Pakistan, no direct study on job mismatch has been prepared;
however, there exist awareness about this issue] A variety of barriers
including the poor level of information about job opportunities,
institutional barriers, geographical barriers, race or gender etc., are
causing the job mismatch. Various socio-demographic characteristics and
customs are also regarded as a constraint to female's labour market
participation [Nazli (2004)]. The gender gap is still high with skewed
distribution in terms of economic sector and status [Pakistan (2007b)].
Despite the recent socio-economic developments, the educational
system in Pakistan is not coping with the right demand of labour market
by mainly imparting education in conventional subjects. Educational
policies have been suffered from frequent political undulations and the
educational system with outdated curricula is draining the human capital
accumulation when Pakistan is hardly investing only 2 percent of its GDP
on education [Pasha (1995); Nasir (1999)]. At school and college level,
the educational system follows variety of tiers [Haq, et al. (2007)].
During 2001-2008 periods, the rapid expansion at the higher education
has raised the participation especially among female graduates, (4)
while heterogeneity of skills across the regions and institutes also
rose. The return to education has a declining trend in Pakistan, implies
that the country has failed to produce high demand for education
[Qayyum, et al. (2007)].
Despite rising labour force, the unemployment rates also remained
high in the range of 6-10 percent during 2001-10 periods, suggesting
that employment generation has not kept pace with the labour force.
Meanwhile, quality of jobs and the access to modest earning
opportunities still remained an issue. As shown in Table 1, the educated
unemployment (matric and above) has increased during FY00 and FY06. It
could indicate the poor choice of educational fields [Pakistan (2007b)].
With rising employment participation, the labour market imperfections
and imbalances have also rose; with rising job search periods, rising
share of informal sector, lower productivity and high risk of
vulnerability especially for youth and female [Pakistan (2007a, 2007b,
2008b)]. (5) The market is skewed toward influential peoples where job
opportunities are predominately reference-oriented rather than skill
oriented.
Keeping in view this importance, the on-going study would provide
the information on job mismatch and would lay foundation for further
detailed studies. It would also help the educational and labour
policy-makers to make better decisions especially for the youth which is
the greatest asset of Pakistan.
3. DEFINITION, THEORETICAL FOUNDATIONS AND EMPIRICAL REVIEW ON JOB
MISMATCH
3.1. Definition and Measurement Issues of Job Mismatch
The phenomenon of job mismatch can be viewed in three dimensions;
education-job mismatch, qualification-job mismatch and field of study
and job mismatch. Education-job mismatch compares the acquired education
by a worker with that required by his/her current job. The empirical
work so far has relied on three main methods to measure the required
education for education-job mismatch. The first method pertains to
'Job analysts (JA) Method' (objective approach), in which the
professional job analysts grade the jobs and recommend the minimum
educational requirements for a certain job/occupation [Haratog (2000);
Battu, et al. (2000)]. The second method refers to 'Self-assessment
method' (subjective approach), where workers are asked directly to
give information on the minimum educational requirements for their
current job [Sicherman (1991); Alba (1993)]. The third 'Realised
match (RM)' method was found by Verdugo and Verdugo (1989) that
measure the degree of education-job mismatch by two variables; years of
schooling and occupational group of a job holder. The distribution of
education is calculated for each occupation; employees who depart from
the mean by more than some ad-hoc value (generally one) standard
deviation are classified as mismatched workers [Verdugo and Verdugo
(1989); Kiker, et al. (1997) and NG (2001)].
Qualification is a broad signal of human capital competences
because it assimilates the other constituents of human capital (skills,
experience) and also the formal education. Educated workers can
compensate their skill deficiencies by additional training and learning
during their jobs; therefore, the formal education is the part of
overall qualification [Ishikawa and Ryan (2002); Neumark and Wascher
(2003)]. The attained qualification possessed by the workers, may be
lower or higher than the required qualification in their perspective
jobs. When this happens, the worker is said to be mismatched in
qualification. The two measurement approaches of qualification mismatch
have been emerged from the literature; the first, the overall
qualification approach (subjective approach) is based on worker's
perception [Green and McIntosh (2002); Lourdes, et al. (2005)], while
the second, the specific approach is based by measuring the various
specific attained skills possessed by the workers and the required
skills in their current job as Lourdes, et al. (2005), Jim and Egbert
(2005) and Chevalier and Lindley (2006) did.
The field of study and job mismatch analyses the level of match
between the individual's field of study and his/her contents of
job. The existing three studies have adopted both subjective and
education-occupation combination to measure the field of study and job
mismatch [Jim and Robert (2004); Robst (2007) and Martin, et al.
(2008)].
The validity and choice of various measures of education-job
mismatch depend on data availability with limitations as well [Leuven
and Oosterbeek (2011)]. The JA approach ignores the ability and possible
deviation of job levels within a given occupation within same
occupational titles [Halaby (1994); Dolton and Siles (2003)]. Second,
the required level might change due to new technologies or reforms of
workplace organisations [McGuiness (2006)]. Third, the categories of
training requirements must be translated into equivalent years of
schooling with some consensus [Rumberger (1987)].
The 'subjective' measure might overestimate and/or
underestimate the job mismatch in the presence of qualification
inflation. Workers in less structured organisations may not always have
a good insight about the required level [Cohn and Khan (1995); McGuiness
(2006)]. Respondents may also apply different criteria for job
requirements, i.e., the actual level of education required to do
specific tasks or the formal educational requirements necessary to get
the job. However, Green, et al. (2002) found that in majority cases, the
assessment of education levels needed to do the job tended to match
those needed to get the job, suggesting consistency between two
subjective approaches.
The third method, the realised method is very sensitive to labour
market changes and for cohort analysis. In case of excess supply, it
will underestimate the level of over-education and will overestimate in
case of excess demand [Kiker, et al. (1997); Mendes et al. (2000)].
Therefore, the method based on realised matches is the least adequate
one for determining over-education and under-education [Chevalier
(2003); McGuiness (2006)].
Both the JA and RM measure imply that all the jobs within the same
occupational titles require identical skills. These assumptions are
obviously naive in those occupations where workers are hired for
flexible tasks [Groot (1996)]. Chevalier (2003) argued that widening
access to higher education has increased the heterogeneity of skills;
whereas, Green, et al. (2002) highlighted the potential heterogeneity
effects that may arise because of grade drift in UK. (6)
Some studies have measured the level of consistency between
different measures of education-job mismatch. Battu, et al. (2000)
analysed the consistency between WSA and JA measures on panel datasets
(1985 and 1990) and found high correlation for females between the two.
Jim and Velden (2001), Green and McIntosh (2002) and Lourdes, et al.
(2005) found the poor correlation between the education-job mismatch and
qualification-job mismatch. It is worth noting that the choice of
definition has a large effect on the incidence of education-job
mismatch. As reported few studies in appendix table 1, majority of the
studies have used the job analyst and self-assessment approach and found
mixed findings. In some studies, the incidences of education-job
mismatch are in close [Battu, et al. (2000); Dieter and Omey (2006,
2009)]; whereas, a lot of inconsistency exists in some studies [Hersch
(1995); Chevalier (2003)].
3.2. Theoretical Foundations of Job Mismatch
A significant segment of literature on job mismatch considers how
this phenomenon be positioned within the context of existing views of
labour market; however, there is no unified accepted theory on
education-job mismatch.
According to Human Capital Theory (HCT), wages and productivity are
fixed in relation to prospective jobs; therefore, over-educated workers
have same productivity and thus receive the same wages as compared to
those who are on matched jobs [Schultz (1962); Becker (1964)]. The
education-job mismatch phenomenon may not necessarily reject the HCT in
case of short run existence; however, if it appears to be a long run
phenomenon, then no one can save the HCT [McGuiness (2006)]. The
opponents of HCT argue that it fails to explain the underutilisation of
skills, institutional rigidities and noncompetitive labour market
[Carnoy (1994)]. Tsang (1987) suggested that the relationship between
education and productivity is more multifaceted than the direct and
positive relationship as suggested by HCT. World Bank (1999) in
"Knowledge for Development" pointed that the private rate of
return to higher education was similar to that for secondary schooling.
Psacharopoulos and Patrinos (2002) reviewed 98 countries for the period
1960 to 1997 and concluded that higher education gives less return than
that on secondary schooling. In Pakistan, Faheem (2008) shows that rate
of return for MPhil and a PhD degree is less than that for a master and
a professional bachelor degree.
In contrast to HCT, the Job Competition Theory (JCT) highlights the
institutional rigidities where marginal products and consequently
earnings are associated with job characteristics, and not by individual
characteristics [Thurow (1975)]. The allocation on job is based on
available supplies of both workers and jobs, workers may possess more
education and skills than their jobs necessitate. In the extreme,
education simply serves to obtain the job, and there is a zero return to
human capital beyond that required to do the job, as all workers in a
given job are paid the same wage. Therefore, Mincer model (1974) and the
Thurow's model (1975) are two extreme cases, being the first purely
supply side driven and the second purely demand side driven.
A third strand of the literature is based on the Assignment Theory
[Sattinger (1993)] which asserts that there is an allocation problem in
assigning heterogeneous workers to jobs which differ in their
complexity. Where the frequency distributions on the demand and supply
side are unlikely to match and education mismatches may be a persistent
problem if the job structure is relatively unresponsive to changes in
relative supplies of educated labour. The majority of studies on
education-job mismatch have supported the job assignment theory by
rejecting both the HCT and the JCT. (7)
According to the Theory of Occupational Mobility, individuals may
choose a lower entry level than those in other feasible entry levels
with the higher probability of promotion [Sicherman and Galor (1990)].
According to Job Screening Model, education is used as a signal to
identify more able and productive workers when labour market is not
perfect [Spence (1973)]. Workers, therefore, invest more on education in
order to provide good signals with the hope that it will permit them to
be distinguished from other job applicants. The Matching Theory assumes
that labour market is not opaque [Rosen (1972); Jovanovic (1979)]. The
search cost exists to find a perfect match. Therefore, both employees
and employer might have a mutual incentive to agree on a non-optimal
match. However, overtime, workers are expected to obtain an improved
job.
Some other explanations have also been put forward which appear to
be largely unrelated to any major theoretical framework. Theory of
differential over-qualification explains the higher probability of being
over-education among married women in lesser labour market [Frank
(1978)]. McGoldrick and Robst (1996) and Buchel and Ham (2003) suggest
that ethnic minorities are likely to be more severely affected. Robest
(1995) notes, "those who attend the lowest quality schools may be
over-educated throughout their career. Those who attend a better school
may be able to work their way upward during their career." Battu,
et al. (1999) and Dolton and Silles (2001) found a positive influence of
regional mobility on the quality of the match while Piracha and Vadean
(2012) found higher over-education among the immigrants. Green and
McIntosh (2002) argued that if the quality of education falls, this too
may encourage employers to upgrade the educational requirements of job,
referred as grade drift.
3.3. The Empirical Literature on Job Mismatch
As noted earlier, the wave of supply of fresh graduates in the U.S.
triggered first research on education-job mismatch in 1970s. According
to Freeman (1976), the excessive number of graduates would trim the
return on education, resulting lower investment on higher education.
However, his prediction has never materialised in US and in other
developed countries. Similarly, in U.K., the over-education ranges from
29 percent to 47 percent with stable rate of return from 1978 to 1996
[Green, et al. (2002)]. Through cohort analysis of UK graduates, Dolton
and Vignoles (2000) found that 62 percent of the male graduates, who
were over-educated in their first job, remained in a sub-graduate
position six years after graduation. Despite the increased mobility of
over-educated workers, Sloane, et al. (1999) found that 40 percent of
the graduates were over-educated six years after graduating using survey
carried out by the University of Birmingham. Further, the author
concludes that the quality of the match not improves with the change of
employer.
A number of studies in the developed countries have documented the
extent of education-job mismatch. Describing the results very broadly,
about a quarter to a third of a nation's employees tend to work in
jobs for which they are over-educated, with a somewhat smaller
proportion working in jobs for which the required education level
exceeds their actual education [Battu, et al. (1999); Dolton and
Vignoles (2000); and Green, et al. (2002)]. Groot and Maassen (2000a)
and McGuiness (2006) have catalogued these studies on the basis of
methodology used. For job analyst measure, the incidences of
over-education range between 11 percent and 40 percent, and
under-education between 20 percent and 44 percent. Appendix Table 2 also
summarises a number of empirical studies conducted in different
developed countries
The literature specifically on qualification mismatch is scarce as
existing studies mainly has used the formal education as a substitute of
qualification as Hersch (1995), Groot (1996), Battu, et al. (1999), Ng
(2001), and Frenette (2004) did. A few studies, however, has measured
the qualification mismatch conducted by Lourdes, et al. (2005) in Spain,
Jim, and Egbert (2005) in five developed countries (Spain, Germany, UK,
the Netherlands and Japan) and Brynin, et al. (2006) in four European
countries (Britain, Italy, Germany and Norway).
Few studies have so far been conducted on the field of study and
job mismatch. The pioneer research by Robst (2007) in US has estimated
the field of study and job mismatch by subjective approach and found
that 28 percent of men and 21 percent of women have somewhat related and
19 percent of men and 21 percent of women have complete mismatch between
field of study and occupation. In Sweden, Martin, et al. (2008) used the
various datasets (Swedish Register of Education, Enlistment data from
Pliktverket, National Tax Board) and found that 23 percent of men and 19
percent of women are matched, while 16 percent of men and 10 percent of
women are weakly matched. Using the data of five countries (Spain,
Germany, UK, the Netherlands and Japan), Jim and Egbert (2005) have
found that 6 percent of the employees in Spain, 10.4 percent in Germany,
11.1 percent in Netherlands, 18.6 percent in UK and 24.2 percent in
Japan were on jobs with matched education but mismatched in field of
study.
4. METHODOLOGICAL FRAMEWORK AND DATA DESCRIPTION
4.1. Data Description
The present study has used both the secondary and primary datasets
by targeting the employed graduates working in the formal sector who
hold fourteen and above year education, named as 'employed
graduates'. The rational to choose the graduates and above
employees is that the job mismatch phenomenon persists usually at the
higher education level. Regarding the secondary dataset, this study has
used the two Labour Force Survey (LFS) carried out in 2006-07 and
2008-09. The LFS, 2006-07 comprises of 2,839 employed graduates, while
the LFS 2008-09 comprises of 3,896 employed graduates. Across the
gender, about 84-85 percent are males while the rests are females in
both LFS datasets.
Keeping in view the data limitations in secondary dataset, the
primary survey, the Survey of Employed Graduates (SEG) has been
conducted in early 2010 in two major cities of Pakistan, Islamabad and
Rawalpindi to study the job mismatch phenomenon in depth. It would be
more enviable if such study has been conducted at national level;
however, time constraints and financial constraints were the most
difficult impediment.
At broad level, the targeted universe in the SEG dataset has been
divided into the three major groups; graduates in federal government,
graduates in autonomous/semiautonomous bodies under federal government
and graduates in the private sector. Table 2 shows the estimated
targeted population in the SEG dataset which is 100,386 employed
graduates. The Thirteenth Census Report of Federal Government Civil
Servants (2003-04) and Annual Statistical Bulletin of Federal Government
and Semi-government (2007-08) were used to estimate the graduate
employees in the federal government and semi-government. For private
sector, the relevant information were gathered from the few private
departments i.e. banks, hotels, telecom companies, international donor
offices, media (newspaper and broadcasting) from their documented
record. For the remaining private sector like hospitals, educational
institutions, NGOs, manufacturing and Industry etc, the internet and the
other sources were used to know the total numbers of units located in
Islamabad/Rawalpindi and then through rapid sample survey, the
information were obtained to estimate the employed graduates.
To avoid the sampling bias and errors, the proportional stratified
random sampling technique was adopted where the published BPS grades for
the government and semi-government sectors have been considered as
'strata' while the 3-digit occupational codes were used as
'strata' for the private sector. Figure 1 shows the
distribution of complete sample of 514 graduates across the three major
groups according to their relative employment share. All the
questionnaires have been conducted by face-to-face interviews.
4.2. The Methodological Framework for the Estimation of Job
Mismatch
4.2.1. The Measurement of Education-Job Mismatch
As discussed in Section 3, the empirical work has relied on three
main methods to measure the degree of education-job mismatch which are
job analyst (JA) method, worker self-assessment (WSA) method and
realised match (RM) method. As the present study has used both the
secondary and primary datasets; the secondary dataset (LFS 2006-07,
2008-09) fulfil the requirement of only RM measure. However, the
education-job mismatch in this study has been estimated by all the three
measures (JA, WSA, and RM) on the basis of SEG dataset.
If E is the actual number of year of education and [E.sup.r] is
number of years of education required for a job, thus over-education
([E.sup.o]) is represented by;
[E.sup.o] = 1 if E > [E.sup.r] and (1)
[E.sup.o] = 0 otherwise
Similarly, under-education ([E.sup.u]) is determined as;
[E.sup.u] = 1 if [E.sup.r] > E and (2)
[E.sup.u] = otherwise
If [E.sup.rj] is the estimated required education level by JA
measure and the [E.sup.rs] is estimated required level by WSA measure,
then qualification inflation (QI) can be measured as;
QI = [E.sup.rj] - [E.sup.rs] (3)
A positive value of QI indicates the qualification inflation which
means that due to excess supply, the employer has raised the required
education level [Green, et al. (2002)].
To capture the issues of skill heterogeneity among over-educated
graduates, we relax the assumption that graduates with same education
level are perfect substitutes and hypothesise that graduates with same
education may not be same in their skill endowment. This assumption
would also capture the widening access of education at higher level in
Pakistan which has increased the heterogeneity of the skills among the
fresh graduates. Following Chevalier (2003), a measure of education-job
mismatch and occupation-satisfaction has been adopted to capture the
idiosyncratic characteristics by segregating the over-educated graduates
into two categories; those over-educated graduates who are satisfied
over their mismatch are defined as apparently over-educated
([E.sup.oa]), whereas those who are dissatisfied are genuinely
over-educated ([E.sup.og]). (8)
4.2.2. The Measurement of Qualification Mismatch
The qualification mismatch can be assessed by comparing the
attained qualification/competences and required
qualification/competences by each worker and workers are typically
classified into over-qualified, under-qualified, and adequately
qualified. Unlike to existing subjective methodologies as adopted by
Green and McIntosh (2002) and Lourdes, et al. (2005) this study has
followed the specific approach where initially, the level of nine
specific attained and required skills has been estimated in SEG survey
on five-point scale, ranging from 1 'not at all' to 5 'a
lot'. Through Principal Component Analysis (PCA) method, the
weights has been estimated on attained skills and required skills on the
basis of mean required level of nine skills by assuming that the workers
in same occupations at two-digit occupational coding require the similar
types of skills in their jobs. The qualification mismatch has been
estimated by comparing the attained skill index and required skill index
with (+/-) 0.08 standard deviation (SD) of the mean (0.075 SD for SEG
weights). (9)
4.2.3. The Measurement of Field of Study and Job Mismatch
One of the most significant type of mismatch in Pakistan, the field
of study and job mismatch analyses the level of match between the
individual's field of studied discipline and his/her contents of
job. Since, no nationally representative dataset of Pakistan provides
the information about the field of study and job mismatch; therefore,
the field of study and job mismatch has been estimated in SEG dataset by
subjective approach with the question: 'how much your current job
is relevant to your areas of education?' The four possible options
were; irrelevant field of study, slightly relevant, moderately relevant
and completely relevant field of study.
5. THE INCIDENCES OF JOB MISMATCH
5.1. The Incidence of Education-Job Mismatch
Table 3 presents data on the incidence of education-job mismatch by
applying the three methods (JA, WSA and RM) on the SEG dataset and the
sampled graduates have been classified into three categories;
over-educated, under-educated and matched graduates. The LFS datasets by
RM measure shows that 30-31 percent of the graduates are mismatched at
the national level with the rising incidences of over-education and the
falling incidences of under-education between 2006-07 and 2008-09. In
both rounds, the female graduates are facing more education-job mismatch
than males with more over-education among females and more
under-education among males (Table 3).
Regarding the SEG dataset, the estimates show that the incidence of
education-job mismatch varies by the three measures (Table 3). First
take the case of over-education; it is 15 percent under RM measure, 25
percent under WSA measure and 26 percent under JA measure. Both the WSA
and JA show the level of over-education in close as compared to RM
measure. The incidence of under-education ranges from 4.5 percent under
JA criteria, 10 percent under WSA and to 22 percent under RM approach.
Again, the JA and WSA yield lower results than the RM approach.
The close estimates of over-education by WSA and JA approach
suggest that graduates have not overstated or understated the
educational requirements. It is the pioneer study in a developing
country; therefore, it may not be desirable to compare the incidences of
education-job mismatch to studies conducted in the developed countries.
However, estimates of this study are consistent with the earlier
findings that RM method reports the lower incidence of over-education as
compared to the WSA and JA methods [Meta-analysis of Groot and Maassen
(2000a) and reviewed study by McGuinnes (2006)].
Though it is not rational to compare the LFS dataset with the SEG
dataset; however, the higher incidence of under-education in SEG while
the lower incidence in LFS through RM measure reflects the excess supply
of graduates in the SEG dataset which has overestimated the level of
under-education and underestimated the level of over-education in SEG
dataset. Similarly, the higher incidence of under-education and lower
incidence of over-education also indicate variation in educational
distribution within the occupations which, in case of structural changes
usually overestimate the required level of education as suggested by
earlier studies [McGuiness (2006)].
While dividing the over-educated workers into 'apparent
over-educated' and 'genuine over-educated', Table 4 shows
that under WSA and JA approaches, about 57 to 63 percent of the
over-educated respondents in non-graduate jobs are not too dissatisfied
with their mismatch, therefore, they are defined as apparently
over-educated graduates and the rest (37 percent to 43 percent) who are
dissatisfied, are defined as genuinely overeducated graduates. These
results are consistent to the earlier studies which has captured the
issue of heterogeneity [Chevalier (2003); Chevalier and Lindley (2006)].
In a flexible labour market, the majority of workers should have
suitable education for their jobs where the job mismatch just explains
the searching and matching situation [Borghans and Grip (2000)].
However, in Pakistan, the labour market is not flexible; the structural
mismatch may exist also. Assuming that the JA and WSA truly measure the
education-job mismatch, the respondents who are mismatched on the basis
of JA, WSA, or RM are called frictional mismatched graduates, while who
are mismatched on the basis of JA and WSA only are called structural
mismatched graduates [Dieter and Omey (2006)]. Table 5 shows that
over-education is mainly a structural phenomenon as it is about 16
percent, while frictional over-education ranges from 9 percent to 10
percent. However; under-education is a frictional phenomenon as it is
more than structural undereducation with a range from 4 percent to 8
percent. The structural over-education reflects that the education-job
mismatch may not be a temporary phenomenon in Pakistan.
Contrary to existing empirical text [Pollet, et al. (1999); Dieter
and Omey (2006)], the results of SEG dataset show higher level of
qualification deflation (13 percent) than the qualification inflation (8
percent) in Pakistan where employees are reporting a higher educational
requirement than the employer's requirement level. This points to
the occurrence of up-gradation of educational requirements in the
private sector in lower occupations as well as the need of revising the
contents of jobs especially the professional jobs in the government
sector.
To check out the significance of differences between JA, WSA and RM
estimates on the required level of education, the parametric t-test
shows that two theoretic build methods, the JA and WSA are consistent
over the required level of education for a particular job; however, the
third statistical method (RM) significantly differ over the measurement
of required level of education as compared to the both WSA and JA
measures. Regarding the estimation of education-job mismatch, there
exist poor correlation between RM and JA (0.25), again a poor
correlation between RM and WSA (0.26), while the high correlation
between the JA and WSA (0.64) measure.
5.2. The Incidence of Qualification Mismatch
The representative datasets of the Pakistan labour market are
unable to provide the relevant information regarding the attained and
required skills of job holders; therefore, this study has measured the
qualification mismatch from the SEG 2010 dataset. As discussed earlier,
the nine skills possessed by the graduates and required in their current
job have been measured at 5 point-likert scale and weights have been
assigned to each attained and required skills on the basis of demanded
skills. Taking the difference of attained and required skill index, the
qualification mismatch has been measured after calculating the zero mean
and 0.08 standard deviation and the results have been reported in Table
6 which shows that more than one-fourth of the graduates are mismatched
in qualification either in terms of over-qualification or in terms of
under-qualification. The phenomenon of 'matched graduates' is
considerably higher among males (73 percent--74 percent) than among
females (67 percent). A lesser proportion of female graduates are
under-qualified (11 percent) as compared to the male graduates (13
percent--14 percent); however, there are more over-qualified female
graduates (22 percent) as compared to the male graduates (12.7
percent--13.4 percent). It reflects the scenario of relatively more
under-utilisation of females' skills in their jobs probably because
of the concentration of female graduates in the lower occupations.
A recent debate exists whether the formal education should be used
as a proxy of qualification or not. Green and McIntosh (2002) found a
moderate correlation between overeducation and over-qualification;
whereas no relationship could be found between undereducation and
under-qualification. The majority of studies found poor correlation
between the two by arguing that education and qualification mismatch are
different aspects with respect to incidence and their consequences on
the labour market [Jim and Egbert (2005); Lourdes, et al. (2005)]. Table
7 reports the marginal and joint distribution with poor level of
association between education-job mismatch and qualification mismatch.
Under the JA criteria by education-job mismatch, 59 percent of the
graduate workers are consistent to qualification criteria also; whereas,
under WSA criteria, 57 percent of the graduates are rightly classified
to both education and qualification mismatch.
To go one step further, the statistical association between
education-job mismatch and qualification mismatch has been checked by
non-parametric tests. Both the Spearman and Kendall tau rank correlation
coefficients in Table 8 show the lower level of correlation between the
two measures of education-job mismatch and qualification mismatch.
Regarding the Kruskal Wallis Rank test, the estimated Chi-square tie
values also show the poor association between the qualification mismatch
and education-job mismatch, as the calculated values of Chi-square are
less than the tabulated values (124.3 at 5 percent), thus supporting the
null hypothesis that a significant difference exists between the
education-job mismatch and qualification mismatch.
5.2. The Incidence of Field of Study and Job Mismatch
The existing studies on field of study and job mismatch, carried
out in the US and Sweden have used the national survey datasets which
provided them detailed information about the relevance of field of study
to the contents of current job [Robst (2007); Martin, et al. (2008)].
But, the national datasets in Pakistan have no such information about
this type of job mismatch. Following, Jim and Robert (2004) and Robst
(2007), this study has measured the field of study and job mismatch by
subjective approach from SEG 2010 dataset. Table 9 shows that 11 percent
of the graduates consider that their current jobs are totally irrelevant
to their studied field of discipline, while another 14 percent reported
their jobs are slightly relevant, followed by the moderate relevant with
38 percent and complete relevant with 37 percent. An important
information is that the female graduates are facing more field of study
and job mismatch than the male graduates as one-third of the female
graduates are mismatched either with irrelevant or weak relevant
category; however, less than one-fourth of the male graduates are
falling in these first two categories (Table 9).
6. CONCLUSIONS AND POLICY IMPLICATIONS
The main focus of this study is to estimate the three types of job
mismatch and the analysing the determinants of job mismatch. About
one-third of the graduates are mismatched either in over-education or in
under-education category. The overeducated graduates are further
classified into 'apparent over-educated and 'genuine
over-educated categories. Approximately 60 percent of the graduates are
in the former category while the rests are in later category. More than
one-fourth of the graduates are mismatched in qualification; about half
of them are over-qualified and the half are under-qualified. More than
one-tenth of the graduates consider that their current jobs are totally
irrelevant to their studied field of discipline, while 14 percent of the
sampled graduates reported that their jobs are slightly relevant to the
field of study. The female graduates are facing more field of study and
job mismatch than the male graduates.
Overall, the incidences of job mismatch do not support the Human
Capital Theory [Becker (1964); Schultz (1962)] which assumed the
competitive labour market and in a pure human capital framework, the
concept of job mismatch may be meaningless when wages are linked with
the productivity. However, this study cannot necessarily reject the
Human Capital Theory on the basis of cross-sectional dataset as the
mismatch phenomenon might be temporary. Similarly, more qualification
mismatch among female graduates support the theory of differential
over-qualification. Additional research is required with a dynamic
perspective to explore its length and the societal losses as well. In
the present analysis, the incidences of job mismatch do not mean that
the level of education should be lowered: it rather suggests the need
for more quality of education and skills as well as reforms in the
labour market. Our findings lead to the following policy implications
and recommendations primarily in two areas; reforms in human resource
development and labour market institutions;
* The phenomenon of job mismatch highlights the weak coordination
among various demand and supply side stakeholders. A close coordination
among these is prerequisite for better understanding of issues in order
to formulate the right policies.
* The estimates of over-education and under-qualification suggest
that the educational system is either providing inadequate skills or
creating more graduates in those disciplines which have relatively less
demanded in the labour market. A sound occupational-specific education
would ensure the matching jobs. There is a need to strengthen the
vocational education and training (TVET) policies at the province and
district levels.
* The statistics of under-qualification and qualification inflation
highlights that the education system is not coping with right demands of
the labour market. There is a need to conduct some tracer type studies
and/or occupational census (GED and DOT in US, SOC in UK, ARBI in
Netherlands) to understand the employment patterns and skills demanded
by the various sectors and occupations. It would not only guide the
planners and enrolled youth about the labour market opportunities and
type of skill needed, but also would help to project future educational
needs.
* For females, the rapid rising enrollment with limited
participation in the labour market and more job mismatch issues suggest
to address the socio-cultural constraints and labour market
discriminations. There is a need of policies and programs which would
not only breach the 'glass wall' and 'glass ceiling'
barriers, but also provide them the entrepreneur's opportunities
and care-taking skills.
* The estimates of job mismatch, especially the field of study and
job mismatch highlights the labour market rigidities and imperfections.
There is a need to design and promote policies which would ensure the
six dimensions of decent work; opportunities of work, conditions of
freedom, productive work, equity in work, security at work and dignity
at work. The 'merit' norms and equal job opportunities should
be ensured for the various segments of the society. There is a need of
strategies and programmes to improve the social relations between the
employers and employees to raise the level of job satisfaction and
productivity. Further, the macroeconomic policies i.e. fiscal, monetary
and trade policies can also be used to achieve the decent work
objectives.
* The existing labour market information system is inadequate. It
mainly depends on the Labour Force Survey (LFS) which is not sufficient
to provide up-to-date and adequate information to job seekers. There is
a need to improve the LFS questionnaire for skill assessment, labour
market opportunities and job mismatches. A module about the history of
employment may also be made part of the LFS.
Appendix Table 1
A Reviewed Summary of Incidence of Education-Job Mismatch with
Variation in Estimates by Various Approaches
Author(s) Country Type of Definition
Hartog and Netherlands Job Analyst
Oosterbeek (1988) Subjective (WSA)
Hersch (1995) US Subjective and Job
Analyst
Cohn and Khan (1995) US Subjective and
Realised Match (RM)
Battu, et al. (2000) UK Subjective-satisfaction
Job Analyst
Subjective- degree
requirement
Chevalier and Walker UK Job Analyst
(2001) Subjective
Groot and Maassen Holland Subjective
(2000b) Job Analyst
Realised Match
Bauer (2002) Germany Realised Match using
Mean and Modal Values
Chevalier (2003) UK Job Analyst
Subjective
Subjective- Job
requirements
Kler (2005) Australia Realised Match
Job Analyst
Lourdes, et Spain Subjective approach to
al. (2005) measure Education and
Qualification Mismatch
Dieter and Omey Belgium Subjective and Job
(2006) Analyst
Estimated Results of Education-Job
Author(s) Mismatch
Hartog and JA: 7% OE, 35.6% UE for 1960;
Oosterbeek (1988) 13.6%OE, UE 27.1% UE for 1971
WSA: 17% OE, 30% UE for 1974
Hersch (1995) WSA: 29% OE, 13% UE;
JA: 33% OE, 20% UE
Cohn and Khan (1995) WSA: 33% OE, 20% UE;
RM: 13% OE, 12% UE
Battu, et al. (2000) WSA-satisfaction: 40.4% OE
JA: 40.7% OE
WSA- degree requirement: 21.75%
OE
Chevalier and Walker JA: 13% OE in 1985, 18.9% (Male):
(2001) 14.7% OE in 1985, 21.6% (Female)
WSA
33.8% OE in 1985, 33.8% (Male):
30.9% OE in 1985, 30.9% (Female)
Groot and Maassen WSA
(2000b) 8.7% OE, 3.8% UE (male), 13.6% OE,
2.1% UE (female)
JA
12.3% OE, 13.3% UE (male), 19.5%
OE, 5.7% UE (female)
RM
11.5% OE, 16.7% UE (male), 12.2%
OE, 14.2% UE (female)
Bauer (2002) Mean Index: 12.3% OE, 10.4% UE
(male), 10.7% OE, 15.6% UE (female)
Mode Index: 30.8% OE, 20.6% UE
(male), 29.9% OE, 37% UE (female)
Chevalier (2003) JA: 17% OE
WSA: 32.4% OE
WSA-Jab requirements: 16.2% OE
Kler (2005) RM: 19% OE, 11% UE (male), 17%
OE, 13% (female)
JA: 7% OE, 45% UE (male), 10% OE,
50% UE (female)
Lourdes, et Education Mismatch: 35% OE, 26%
al. (2005) UE
Qualification Mismatch: 34% OQ,
44% UQ
Dieter and Omey WSA: OE 39.2%, UE 3.4%; JA: OE
(2006) 26.4%, UE 4.9%
Note: OE for over-education, UE for under-education, AE for adequate
education, OQ for Over-qualification and UQ for under-qualification
Appendix Table 2
A Reviewed Summary of Studies Over the Incidence of Education-Job
Mismatch
Author Country Time Frame Definition Type
Duncan and Hoffman us 1976 Subjective
(1981)
Rumberger (1987) US 1969.1973 Job Analyst
and 1977
Verdugo and Verdugo us 1980 Realised Match
(1989)
Sicherman (1991) US 1976.1978 Subjective
Alba-Ramirez (1993) Spain 1985 Subjective
Groot (1993) Netherlan 1983 Realised Match
ds
Robest (1995) US 1976.1978 Subjective
and 1985
Battu, et al. (1999) UK 1986.1991 Degree required
and 1996 (yes/no)
Cohn and Ng (2000) Hong 1986 and Realised Match
Kong 1991
Dolton and Siles UK 1998 Subjective
(2001)
Jim and Velden Holland 1998 Subjective
(2001)
McGuinness (2003) Northern 1997-2000 Subjective
Ireland
Decker, et al. Holland 1992 Subjective
(2002)
Voon and Miller Australia 1996 Realised Match
(2005)
Budria, et al. 12 European Subjective
(2007) European Community
Countries Household
Panel
2001
Author Estimates
Duncan and Hoffman 42.0%3E, 11.9% UE, 46.1%AE
(1981)
Rumberger (1987) 1969: 35% OE, 1973: 27% OE and
1977: 32% OE
Verdugo and Verdugo 10.9% OE, 9.9% UE and 79.2% AE
(1989)
Sicherman (1991) 40% OE, 16% UE, 44% AE
Alba-Ramirez (1993) 17% OE, 23% UE, 60% AE
Groot (1993) 16.1% OE, 16.3% UE, 67.5% AE
Robest (1995) 36% OE, 20% UE and 44% AE
(Pooled estimates for 3 years)
Battu, et al. (1999) For 1985: 37.6% OE (males), 46.4%
(females)
For 1991: OE 41.6% (male) 45.3%
(female)
For 1996: OE 41.3% (male) 39.3%
(females)
Cohn and Ng (2000) For 1986: 38% OE (male) 32% OE
(female); 28% UE (male) 24% OE
(female)
For 1991: 37% OE (male) 31 % OE
(female); 28% UE (male) 23% UE
(female)
Dolton and Siles 42% OE first job in terms of degree
(2001) being
22% OE current job necessary to do
the work
33% did not require a degree to get
job
Jim and Velden 33% OE(male), 10.7%
(2001) OE(female), 10.4%
UE(male), I5.6%UE(female)
McGuinness (2003) 29% OE first job, 24% OE current
job
Decker, et al. For 15-19 age 41.7% OE; For 30.44
(2002) age 27.0% OE
For 49-64 age 18.0% ; Overall
30.6% OE
Voon and Miller 15.8% OE(male), 13.6% (female);
(2005) UE 13.7% (male), 18.53% (female);
AE 70.53%for male, 67.86% for
female
Budria, et al. In Europe 21.92% OE; In Australia
(2007) 15.61% OE, 19.13 for Belgium,
19.33% for Denmark, 20.09% for
Finland, 23.68% for France, 14.29%
for Germany, 29.81% for Greece,
16.26% for Ireland, 30.35% for Itlay,
25.47% for Portugal, 25.01% for
Spain, 19.42% for UK
Note: OE for over-education, UE for under-education,
AE for adequate education.
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Comments
I appreciate the remarkable efforts of the researcher in this
paper, which open new avenues for researchers. It is a great
contribution from the scholar to address such an important Topic in this
age of rapid growth and competition. The scholar investigated the
mismatch between education and occupation for Pakistan by considering
the following:
(i) Education--Job-mismatch
(ii) Qualification--Job-mismatch
(iii) Field of Study--Job-mismatch Researcher may consider the
following observations/suggestions for improvement of the research work:
(i) There is a lack of latest literature review, most studies are
upto 2006. The researcher should review the latest studies as there are
paid development regarding human capital over the last 5-years and
update study accordingly.
(ii) The concept of education and qualification as used by the
researcher interchangeably which need to be clarified in some more
detail.
(iii) The reasons of qualification mismatch have not explained in
the paper, author may like to add the possible root cause of the
mismatch.
(iv) As this is an excellent research paper, author should
reconsider the recommendations and report only those recommendations,
which are based on the analysis and may be drawn from the study. The
recommendations should not be wish list rather they should be doable.
The author may like to incorporate these comments to improve the
quality of research work.
Imtiaz Ahmad
Planning Commission, Islamabad.
(1) Over-education explains the extent to which a worker possesses
a level of education in excess of that which is required for a
particular job.
(2) For UK, 12 percent by Dolton and Vignoles (2000), 18 percent by
Dolton and Silles (2003), 23.2 percent by Chevalier and Lindley (2006).
For US, 13 percent by Verdugo and Verdugo (1989), 11 percent by Cohn and
Khan (1995). For Holland, 26 percent by Groot (1993), 8 percent in
Kiker, et al. (1997) for Portugal and 27 percent in Budria and Edigo
(2007) for Spain.
(3) Statistics from various rounds of Labour Force Survey, Pakistan
(2007a, 2007b, 2008a, 2008b).
(4) In 1947, there were only two universities which jumped up to 54
in 1999 and 132 at present.
(5) 60.6 percent were considered vulnerable, meaning "at risk
of lacking decent work" in 2006-2007 [Pakistan (2007a)].
(6) Grade drill is drop in quality of education. It will be evident
if employers are found to be increasing educational requirements for
younger workers. The concept of grade drift is related to heterogeneity
as individuals with similar education potentially have significantly
different ability levels [McGuiness (2006)].
(7) Alba (1993); Groot (1996); Kiker, et al. (1997); Sloane, et al.
(1999); Dolton and Silles (2001); Kler (2005); Chevalier and Lindley
(2006); Martin, et al. (2008), etc.
(8) Job satisfaction has been measure at five point likert scale
range from very dissatisfied to very satisfied. For apparent
over-educated workers, range 1 (very dissatisfied) and range 2
(dissatisfied) was used while for genuine over-educated workers range 3
to 5 has been used.
(9) Standard deviation has been calculated after comparing the both
attained and required skill index.
(10) Herscb (1998) used the same measure of disability risks for
different industries.
Shujaat Farooq <
[email protected]> is Assistant
Professor, National University of Science and Technology (NUST),
Islamabad.
Author's Note: The author has completed PhD in Economics from
Pakistan Institute of Development Economics (PIDE), Islamabad in 2011.
This paper is the part of PhD Dissertation; therefore, a greatest
appreciation goes to my worthy supervisors Dr G. M. Arif, Joint Director
P1DE of and Dr Abdul Qayyum, PIDE, Islamabad for their valuable
suggestions and guidance during the process of dissertation.
Table 1
Educational Attainment of the Unemployed (Age 15+) (1)
Education Level FY00 FY02 FY04 FY06
Illiterate and Pre-primary 47.7 45.7 42.3 44.7
Primary and Middle 28.6 27.4 25.8 26.1
Matric and Intermediate Education
Overall 19.3 21.2 24.8 22.4
Male 23.2 23.7 28.8 26.1
Female 12.2 15.7 15.7 14.3
Degree Level Education
Overall 4.3 5.8 7.2 6.8
Male 5 6.4 7.1 6.9
Female 2.9 4.4 7.3 6.6
Change b/w FY00
and FY06
Education Level (Percentage Point)
Illiterate and Pre-primary -3.0
Primary and Middle -2.5
Matric and Intermediate Education
Overall +3.1
Male +2.9
Female +2.1
Degree Level Education
Overall +2.5
Male +1.9
Female +3.7
Source: Pakistan (2007a).
Table 2
Estimated Graduate Employees in SEG Dataset
Sector Total Male Female
Government Sector 25,828 22,389 3,439
Semi-government Sector 38,424 35,535 2,889
Private Sector 36,134 28,317 7,817
Total 100,386 86,241 14,145
Table 3
The Level of Education-Job Mismatch by Various Approaches (%)
Under- Over
Datasets Measures Matched education education N
RM Method on Female 65.7 4.4 30.0 457
LFS 2006-07 Male 69.4 9.7 20.9 2,382
Total 68.8 8.9 22.3 2,839
RM Method on Female 60.5 4.2 35.4 577
LFS 2008-09 Male 71.2 2.3 26.6 3,319
Total 69.6 2.5 27.9 3,896
SEG, 2010 WSA Method 65.4 9.9 24.7 514
JA Method 69.5 4.5 26.1 514
RM Method 63.4 21.6 15.0 514
Table 4
The Level of Genuine and Apparent Over-education (lo)
WSA JA RM
Education-Job Mismatch Approach Approach Approach
Matched 65.4 69.5 63.4
Under-educated 9.9 4.5 21.6
Genuine Over-educated 10.7 9.7 4.7
Apparent Over-educated 14.0 16.3 10.3
Table 5
The Level of Frictional and Structural Mismatch (0 of total)
Frictional Mismatch Structural Mismatch
JA WSA JA WSA
Type of Mismatch and RM and RM and RM and RM
Under-educated 4.3 7.8 0.2 2.1
Over-educated 10.3 8.8 15.8 16.0
Table 6
The Distribution of Respondents by the Level of Qualification
Mismatch (%)
Matched Under- Over
Graduates qualified qualified
Weights Estimated by PCA
Female 66.7 11.1 22.2
Male 72.8 13.9 13.4
Total 71.8 13.4 14.8
Table 7
Marginal and Joint Distribution of Education and Qualification
Match (%)
Under- Over- Education
Matched qualified qualified Match
Job Analyst Method
(JA)
Matched 52.0 10.3 7.2 69.5
Under-educated 3.5 0.4 0.6 4.5
Over-educated 16.3 2.7 7.0 26.1
Qualification Match 71.8 13.4 14.8 100
Worker Self
Assessment Method
(WSA)
Matched 48.8 9.0 7.6 65.4
Under-educated 6.8 2.1 1.0 9.9
Over-educated 16.2 2.3 6.2 24.7
Qualification Match 71.8 13.4 14.8 100
Table 8
The Level of Association between Education and Qualification Mismatch
Qualification Mismatch
Kendall tau rank
Spearman Correlation Kruskal
Education Correlation Wallis
Mismatch Coefficients tau-a tau-b Chi-squared ties
JA 0.13 0.06 0.13 10.88
WSA 0.11 0.05 0.1 6.20
Table 9
The % Distribution of the Respondents by Field of Study
and Job Mismatch
Level of Mismatch Female Male Total
Irrelevant 14.8 10.6 11.3
Slightly Relevant 18.5 12.9 13.8
Moderately Relevant 33.3 39.3 38.3
Completely Relevant 33.3 37.2 36.6
Fig. 1. Sector-wise Sample Distribution
Total Male Female
Government 131 110 21
Semi-government 196 177 19
Private 187 146 41
Note: Table made from bar graph.
