Volume 24 Issue 2 Article 4 
June 2022 
Determinants of Country-Level Employee Overqualification: A 
Configurational Approach 
Aleksandar Nikolovski 
University of Ljubljana, School of Economics and Business, PhD Student, Ljubljana, Slovenia, 
nikolovski.aleksandar@yahoo.com 
Robert Kaše 
University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia, robert.kase@ef.uni-lj.si 
Follow this and additional works at: https://www.ebrjournal.net/home 
 Part of the Human Resources Management Commons 
Recommended Citation 
Nikolovski, A., & Kaše, R. (2022). Determinants of Country-Level Employee Overqualification: A 
Configurational Approach. Economic and Business Review, 24(2), 111-131. https://doi.org/10.15458/
2335-4216.1301 
This Original Article is brought to you for free and open access by Economic and Business Review. It has been 
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Review. 
ORIGINAL ARTICLE
Determinants of Country-Level Employee
Overqualification: A Configurational Approach
Aleksandar Nikolovski a,*, Robert Ka
se b
a University of Ljubljana, School of Economics and Business, PhD Student, Ljubljana, Slovenia
b University of Ljubljana, School of Economics and Business, Ljubljana, Slovenia
Abstract
We build on the configurational approach to identify patterns of macro contextual factors leading to high country-level
employee overqualification. We differentiate between subjective and objective employee overqualification and establish
that each is caused by different configurations of macro factors. We also identify country-level overqualification ar-
chetypes and link specific countries with respective archetypes. We find that a country’s non-vocational education system
is a necessary condition for high objective overqualification, while objective overqualification itself is not a necessary
condition for perceived overqualification. We discuss theoretical implications and offer policy implications.
Keywords: Objective overqualification, Perceived overqualification, Context, QCA, Configurations, Archetypes
JEL classification: J2
Introduction
E mployee overqualification is a pervasive globalphenomenon and a growing problem, as many
countries will face shortages of qualified workers in
2030 (Starck et al., 2014). Already today more than
21% of workers in many OECD countries are over-
qualified for their current occupations (OECD,
2013c).
Overqualification has many negative conse-
quences at the level of the individual employee and
the organization. At the employee level, over-
qualification has been associated with low job satis-
faction (Alfes et al., 2016;Maltarich et al., 2011; Zhang
et al., 2015), effects on wages such as inequality
(Handel, 2003; Di Pietro&Urwin, 2006;OECD, 2013a)
or a wage penalty relative to qualification level
(Brynin, 2002), increased turnover intentions (Allen,
2001; Erdogan&Bauer, 2009;McKee-Ryan&Harvey,
2011), increased on-the-job search (Quintini, 2011)
and psychological stress (CEDEFOP, 2010). At the
organizational level, overqualification increases
turnover rates and reduces employee productivity
(Allen& vanderVelden, 2007). It also has amediating
effect on reducing competitive advantage (Quintini,
2011), because it reduces cumulative learning (Laz-
onick et al., 2014).
With so many negative effects, it is understand-
able that most of the literature on overqualification
(OQ) focuses on mitigating these effects. There are,
however, fewer studies on how OQ actually occurs,
which leads to a need to address the antecedents of
OQ. While antecedents of employee OQ can be
addressed at multiple levels, in this paper we focus
on antecedents at the macro level. We can easily
observe significant differences in country-specific
levels of employee overqualification (see OECD,
2013b), but previous research does not seem to be
able to explain why this happens. In particular,
there is a lack of research addressing this issue at
the cross-country level and including countries
outside the Western world (Erdogan & Bauer, 2009;
Zhang et al., 2015).
Instead of addressing the antecedents in isolation,
we adopt the configurational logic and examine
configurations of macro factors instead (Furnari
Received 29 September 2020; accepted 13 February 2021.
Available online 1 June 2022
* Corresponding author.
E-mail addresses: nikolovski.aleksandar@yahoo.com (A. Nikolovski), robert.kase@ef.uni-lj.si (R. Ka
se).
https://doi.org/10.15458/2335-4216.1301
2335-4216/© 2022 School of Economics and Business University of Ljubljana. This is an open access article under the CC-BY-NC-ND license (http://creativecommons.
org/licenses/by-nc-nd/4.0/).
et al., 2021). Our central research question is what
configurations of macro conditions determine employee
overqualification at a country level. Accordingly, the
ultimate goal of this study is to connect the inferred
knowledge of individual causal conditions with the
causal complexity underlying employee over-
qualification. Configurational theorizing (Furnari
et al., 2021) allows us determine the configurations
of macro factors that lead to the occurrence of OQ
by country and type.
The originality of this study also lies in its contri-
bution to the emerging overqualification literature by
acknowledging the existence of two types of OQ:
objective and perceived. In this study, these are not
just two different operationalizations of over-
qualification, but independent concepts with their
own theoretical domains, antecedents, and separate
literature (Erdogan et al., 2011a; Maltarich et al., 2011).
Because of their interdependence, we treat both with
the intention of providing a way to integrate the
related yet so far disparate literatures.
For these purposes, the study adopted the
configurational or set-theoretical approach (Dul,
2015; Furnari et al., 2021; Ragin, 2006; Rihoux &
Ragin, 2009) to represent the macro factors as con-
ditions under which the objective or perceived
overqualification of employees at the country level
actually occur. In this research, the multitude of
causal mechanisms that could explain OQ denotes a
context or propriety space from which we select
theoretically plausible macro factors and determine
the configurational patterns that link them and lead
to country-level employee OQ. To do this, we use
Qualitative Comparative Analysis (QCA).
This study considers OQ as a form of competence
mismatch resulting from imbalances between the
supply and demand of human capital in the labor
market (Handel, 2003) andderived frommacro factors
of the labor market, education, and culture. Thereby,
it includes the stances from assignment theory, career
mobility theory and institutionalism, which referred
to employee overqualification as temporary or per-
manent (Quintini, 2011) or both (Kiersztyn, 2013) or as
an exclusively individual phenomenon (Budría, 2010;
Duncan & Hoffman, 1981). We continue by intro-
ducing and integrating these aspects in our theoretical
framework in the next section.
1 Theoretical framework
1.1 Objective and perceived overqualification
Overqualification is traditionally defined as edu-
cation, skills and work experience that exceed those
required by the job post or by the employer
(Erdogan et al., 2017; Feldman & Maynard, 2011;
Khan & Morrow, 1991; Kuhn & Shen, 2012).
Acknowledging this general definition for this
study, we define objective overqualification (OOQ)
following the OECD’s qualification mismatch
approach where an employee is mismatched when
s/he is employed in a job that requires a lower or
higher level of qualification/education than s/he has
(Zhang et al., 2015). When the difference between
the employee’s qualification level and the qualifi-
cation level required in her/his job is positive, s/he
is overqualified (OECD, 2016b; Quintini, 2011).
A complete analysis of the antecedents of over-
qualification should consider both objective and
subjective overqualification. In the literature, OOQ
has typically been operationalized as over-educa-
tion (Liu & Wang, 2012), while the subjective,
“perceived” overqualification (POQ) concerned
employee awareness (Alfes et al., 2016), feelings
(Erdogan et al., 2011b; Maynard et al., 2006; Liu
et al., 2014) or perceptions (Harari et al., 2017;
Johnson & Johnson, 1996) that her/his qualifications
exceed his job requirements. Management and or-
ganization literature, in particular, has relied
extensively on POQ as antecedent. In this study, we
differentiate between objective and perceived OQ
by acknowledging that the two constructs are
linked, yet different (Liu & Wang, 2012).
Very few studies have addressed objective and
perceived overqualification simultaneously (Cheva-
lier&Lindley, 2009;Maltarich et al., 2011; Liu&Wang,
2012), although such integration is necessary to pro-
vide a full understanding of the phenomenon.
Namely, manyOQnegative effects are a consequence
of the perception of overqualification by employees
and are not due to the actual state of being objectively
overqualified. While OOQ may seem to be a more
accurate representation of overqualification, the em-
ployees’ perceptions of overqualification do not
necessarily reflect an accurate assessment of reality
(Alfes et al., 2016), as individuals may easily overstate
the requirements of their job to inflate the status of
their position (Flisi et al., 2016). TheOOQ, on the other
hand, does not take into account that people with the
same qualifications working in comparable positions
often experience diverse levels of POQ (Erdogan et al.,
2011b), possibly due to variation in actual job content
and the differences in the quality and type of their
education and experience (Hu et al., 2015).
1.2 Antecedents of overqualification
The configurational approach proposed in this
study was operationalized based on a scarce litera-
ture on OQ antecedents. Moreover, it is not always
112 ECONOMIC AND BUSINESS REVIEW 2022;24:111e131
clear from this literature whether it refers to
employee overqualification as OOQ or as POQ.
Therefore, antecedents derived from theory will be
considered for both types of OQ, unless a clear
distinction is made in the respective literature.
Fig. 1 depicts a theoretical model, an actual
intersection of relevant theoretical antecedents. It
first represents the temporal nature of OQ through
frictions between the education system and the
state of the labor market, leading to OQ vulnera-
bility of young (tertiary educated) workers or the
re-employment of prime-age workers during eco-
nomic downturns. Second, the regulatory
perspective sets OQ as an outcome when rigid
employment protection structures the demand for
human capital in the labor market. Finally, the
cultural antecedents combine to create a double-
headed appearance of OQ: from one point of view,
set as the norm by employers and to be accepted in
weak labor markets; from another, resulting from
aggregate and cognitive individual trade-offs be-
tween cultural, social, and human capital in the
same market.
1.2.1 Labor market aspects of OQ occurrence: the
theory of career mobility, assignment theory and
workforce dynamism during economic downturns
The OQ is designated as a temporary phenome-
non by the theory of career mobility (Sicherman &
Galor, 1990), as schooling increases the probability
of occupational upgrading by means of higher
wages or of higher chances of promotion to occu-
pations with higher wages. Thus, OQ usually affects
inexperienced labor market entrants, such as young
employees in early career stages (Abraham, 2015;
Erdogan et al., 2011a), the low-level jobs taken by
students to pay for their studies in time and money
(Erdogan et al., 2017) or tide-over jobs that are taken
by older workers to earn a sufficient pension income
(Erdogan & Bauer, 2009).
Further, during economic downturns the size of
labor markets deflates and unemployment grows
(ECB, 2014), limiting the availability of jobs, which
may lead to higher OQ (Erdogan et al., 2011b).
Namely, workers left or dismissed from unionized
jobs in manufacturing, many working in shrinking
industries and experiencing re-employment after
dismissal for lower-paid, less skilled service jobs
(Abraham, 2015; Liu & Wang, 2012; McKee-Ryan
& Harvey, 2011) are likely to become over-
qualified. As a consequence, the structure of the
economy changes and there is certain mobility
between industrial and service sectors leading to
OOQ.
In addition, according to assignment theory (Sat-
tinger, 1995), objective OQ is a function of labor
market responses to the problem of assigning
newcomers to less demanding jobs, which could be
attributed to the increasing number of college
graduates entering the labor market (Liu & Wang,
2012). Here, we emphasize the rapid population
growth in developing and transition countries,
Demand for human capital
Vocational/general education Structure/state of the LM                                 Government regulation of labor costs during crises
Economic downturn (Inadequate) unemployment support
Decline in industry employment Permanent and temporary employment protection
Augmentation in services employment            Wage loss support programs
Job loss/reemployment due to layoff
Employer hiring decisions/
perceptions of OQ
Rising college graduate population                            Inexperienced young workers Trading human capital for job security
Migrant employment                                Careerist attitudes
Older (boomer) workers Trading human capital for social capital
Personal demand for social status
Supply of human capital
EDUCATION CULTURELABOR MARKET
Permanence/temporality Regulative        Normative Cognitive – cultural
EMPLOYEE OVERQUALIFICATION
Fig. 1. Theoretical model of overqualification antecedents. Source: Own work.
ECONOMIC AND BUSINESS REVIEW 2022;24:111e131 113
reflecting an oversupply of educated young em-
ployees, which is also reflected in their lower bar-
gaining power and high wage dispersion (Liu &
Wang, 2012).
1.2.2 Overprotective labor legislation and OQ
occurrence
Given the imbalance between employees’ earn-
ings and productivity even before the 2009 recession
(Eichhorst et al., 2010), these employees have suf-
fered a further decline in their negotiated wages and
incomes (ECB, 2014). To tackle this problem, gov-
ernments addressed labor costs (McKee-Ryan &
Harvey, 2011) and regulated inactivity and
employment through unemployment and wage loss
support programs and staffed retraining programs
to meet the new demands of the labor market
(Eichhorst et al., 2010).
Next, in segmented labor markets, where a per-
manent contract is a particularly valuable asset for
employees, human capital can be traded for job
security, and paradoxically, OQ is more likely to
occur among permanent than temporary workers
(Ortiz, 2010). Nonetheless, OQ also likely occurs
among directly hired temporary workers (Liu &
Wang, 2012), suggesting that during an economic
crisis, this kind of work arrangements can also lead
to OQ. The rigidity of labor legislation relates to
both permanent and temporary employment re-
lationships, as there are measures to protect each
of them, as well as measures against mass dis-
missals. However, this may have exacerbated OQ,
as the initial rigidity of laws against the dismissal
of employees and the hiring of temporary workers
could increase the unemployment rate in crisis
times.
Diamond (2011) stressed that the question of
whether a person who is considered qualified for a
particular job may depend on the situation on the
labor market. For example, employers who may not
consider a candidate for a particular job in a weak
labor market would hire him and provide him with
the necessary training in a tight labor market.
Although employers consider qualifications and
value OQ (Akkoyunlu et al., 2012), managers often
questionwhy candidates apply for jobs forwhich they
are overqualified (Maynard et al., 2006). Paradoxi-
cally, employers who expect layoffs to be curtailed
may be reluctant to hire at all, even in good times,
given overly protective legislation, by reducing hiring
rates, although, in a weak labor market, the unem-
ployed could reject job offers that they would other-
wise find acceptable (Tasci & Zenker, 2011).
To sum up, the OQ results due to rigidity as dif-
ferences between labor market institutions and the
policies that regulate the interactions between labor
markets. Flexible labor markets keep unemploy-
ment lower in the long run (Tasci & Zenker, 2011),
but also act as a deterrent for employers who
anticipate labor market conditions and its rigidity in
hiring. Indeed, since the mismatch between educa-
tion and skills is pervasive (Kulkarni et al., 2015),
persistent (CEDEFOP, 2010) and linked to labor
market structure (Brynin, 2002), the policies that
focus only on the current employment situation
ignore the dynamics of growing economies (Hampf
& Woesman, 2017).
1.2.3 Institutionalism, normative and educational
perspectives on OQ occurrence
The institutionalist stance on OQ (Scott, 2014)
considers qualification (mis)match occurring when
its antecedents cumulate in both human systems
and organizational contexts of organizations. This
approach stresses that organizational practices
achieve legitimacy through social processes by
which individuals come to accept shared definitions
of reality (Swiss, 1982) or which take on rule-like
status in social thought and action (Meyer & Rowan,
1977).
Correspondingly, OQ could also be such an
accumulation of employees’ beliefs and values
resulting from such common way of thinking
(Palthe, 2014). Thus, labor market rigidity could
affect OOQ through employers’ perceptions and
hiring decisions, and it could influence POQ
through employees’ perceptions of quality through
which they choose a job for which they are over-
qualified. Employers’ requirements may have risen
faster than graduation trends (Handel, 2003), they
may have re-categorized jobs that require a degree
(Di Pietro & Urwin, 2006), or they may ultimately
not hire workers, because of job search and with-
drawal. In all of these cases, employers set over-
qualification as the norm that must be accepted by
jobseekers and employees.
The literature which pays attention to the extent to
which the mismatch between employees in general
varies according to the type of education (Allen &
van der Velden, 2007) adds to this argument.
Namely, vocational training is seen as improving
the transition from school to work and an initial
employment advantage, but it becomes a disad-
vantage later in life compared to people with gen-
eral education (Hanushek et al., 2016). It also
reduces the adaptability of older employees to
114 ECONOMIC AND BUSINESS REVIEW 2022;24:111e131
economic change (Hampf & Woessmann, 2017).
Also, while general education only ensures good
horizontal match, vocational qualifications reduce
vertical or horizontal mismatch (Allen & van der
Velden, 2007). Therefore, the classification of a
country’s education system as general or less
vocational seems to precede OOQ.
1.2.4 Cultural macro-factors preceding the OQ
The cultural antecedents designate the OQ
occurrence due to co-occurrence of multiple indi-
vidual characteristics such as belongingness to a
particular population group (Erdogan et al., 2011a)
or discrimination on grounds of demography (Alfes
et al., 2016) as are migrant workers and older people
(OECD, 2013a). Migrant employees, for example,
are prone to OQ due to the extent to which their
foreign education is recognized or to the existing
language barriers (Allen & van der Velden, 2007;
OECD, 2016a, b), while older employees rarely
benefit from HR practices across organizations
(Dychtwald et al., 2004).
Individual characteristics can aggregate and
reflect OQ at the macro level from a cultural and
cognitive point of view. From a cultural perspective,
OQ arises at macro-level from pent-up individual
personalities (Zhang et al., 2015), particularly
careerist attitudes (De Vos et al., 2009) or narcissism
(Liu & Wang, 2012). These lead the personal desire
for social status to override the rational calculus of
the value of education (Brynin, 2002) or to the
achievement of an individual psychological contract
(Erdogan et al., 2017) by adopting OQ jobs based on
individual career history (Alfes et al., 2016) or per-
sonal work preferences (McKee-Ryan & Harvey,
2011).
From a cognitive perspective, OQ often occurs in
aggregation due to imperfect information about job
requirements (Erdogan & Bauer, 2009). OQ in
particular may also be due to multiple individual
trade-offs between benefits and costs (Stojanov,
2012), such as the degree of freedom of choice of the
OQ candidate (Erdogan et al., 2011b) to tradeoff
between the attractiveness of a current job and the
availability of alternatives (Shaw et al., 1998). Also,
POQ is generally believed to be caused by OOQ
(Feldman et al., 2002; Liu & Wang, 2012).
Finally, we stress the employee’ gender as an
ambiguous OQ antecedent (Liu & Wang, 2012;
McKee-Ryan & Harvey, 2011). It is initially
considered that women are more likely to be more
often overqualified than men (Luksyte et al., 2011)
by settling for low salary to balance family duties
(McKee-Ryan & Harvey, 2011), which exemplifies
the “trading” human capital for social benefits and
social capital (Erdogan et al., 2011a). Inversely,
Erdogan and Bauer (2009) and Maynard et al. (2006)
stressed that male employees actually perceived
higher levels of OQ. This paradox also ranges from
both genders being equally prone to OQ (OECD,
2016b), up to a nearezero correlation between
gender and POQ (Johnson & Johnson, 1996; Luk-
syte et al., 2011).
1.3 Configurational approach to country-level
employee overqualification
1.3.1 Configurational view on overqualification
antecedents
According to Cress and Snow (2000, p.1077), it is
important to establish the general context of OQ
occurrence: “One of the main difficulties in assess-
ing the factors that theoretically influence the
achievement of outcomes is to operationalize them
in a way that is consistent with the literature and yet
relevant to the local context”.
Employee overqualification (as OOQ and POQ)
occurs in different countries (OECD, 2015c) and
affects the way individuals experience their work in
organizations (Oldham & Hackman, 2010). Ac-
cording to Selznick (1948, p. 256e257) "all organi-
zational action is driven by cultural-cognitive,
normative, and regulatory elements of in-
stitutions". Action is a product of the institutional
context and not context-free, but constrained
(bounded) and shaped by the setting in which it
occurs. This is also in line with Johns’ (2006) defi-
nition of context. If we apply his definition to our
specific case, macro context can be seen as a set of
situational opportunities and constraints that in-
fluence the occurrence and meaning of employee
overqualification (objective, subjective).
If one sees country context in this way, set-theo-
retic logic along with configurational approach are
well suited to explain such a causally complex
phenomenon (Furnnari et al., 2021). Using this
approach, we can build on a preselected set of
theoretically plausible factors (see previous section
and Fig. 1 as a summary) to explain the occurrence
of OQ by examining the role of combinations of
macro factors or configurations. The configurational
(set-theoretic) approach seems to be superior to the
correlational approach in accomplishing this.
Indeed, while the net effects/correlation statistics
approach focuses on the independent effects of
causal conditions on the outcome and cannot un-
ravel the causally complex nature (Pustovrh &
Jakli
c, 2014), the configurational approach estab-
lishes clear links between specific combinations of
conditions and the outcome (Ragin, 2009).
ECONOMIC AND BUSINESS REVIEW 2022;24:111e131 115
1.3.2 Qualitative comparative analysis (QCA)
QCA starts from the premise that causation is not
easily unraveled, because outcomes of interest
rarely have any single cause, rarely operate in
isolation, and a specific causal attribute may have
different and even opposite effects depending on
the other factors (Greckhammer et al., 2007).
Developed by Charles Ragin (2009) QCA seeks to
identify the common causal conditions underlying a
particular outcome by examining the attributes of
cases exhibiting that outcome.
The causality in QCA is complex, equifinal, asym-
metric and conjectural and it is based on set-theoretic
relationships, making use of the concepts of neces-
sary and sufficient conditions. It uses combinatorial
logic relying on Boolean algebra and considers only
the relations of necessity and sufficiency between
antecedents and an outcome. With necessity, the
outcome is a subset of the causal condition; with
sufficiency, the causal condition is a subset of the
outcome. A causal condition is necessary if it appears
in all the configurational patterns producing the
outcome, but cannot predict the outcome by itself.
Alternatively, the sufficient conditions are able to
solely predict the outcome, but might not be the only
ones with this propriety (Legewie, 2013).
To assess necessity and sufficiency, QCA uses two
parameters. Consistency refers to the degree towhich
a relation of necessity or sufficiency between a causal
condition and an outcome is met within a given data
set (Ragin, 2006) or the degree towhich solution terms
and the solution as a whole are subsets of the
outcome. Coverage, on the other hand, assesses the
degree to which a cause or causal combination ‘‘ac-
counts for’’ instances of an outcome (Ragin, 2006;
Rihoux& Ragin, 2009) or howmuch of the outcome is
covered (or explained) by each solution term and by
the solution as a whole.
2 Methodology
2.1 The data and the sources
We build on the OECD and international labor
organization (ILO) data, primarily PIAAC (Program
for international assessment of adult competencies,
OECD, 2013a), but also resorted to national reports
where needed. In the two rounds of PIAAC (2013
and 2015), more than 255,000 respondents from 33
countries provided rich data adults’ proficiency in
key information-processing skills and in various
“generic” skills (OECD, 2013a). They also provided
individual socio-economic data, among which they
reported whether their qualifications match their
work requirements. For this study, we considered
working age population (aged 15e64) from 27
participating countries. We excluded six countries
due to the unavailability of data on key causal
conditions. In addition, country-level data was
queried from the ILO and annual OECD reports on
education (OECD, 2017; OECD, 2018), migration
(OECD, 2015a), employment protection legislation
for the year 2013 or the nearest available.
2.2 Variables
The theoretical antecedents of the over-
qualification from Fig. 1 were streamlined into a
new, macro-configurational model (Fig. 2) of
employee OQ, considering apart six causal condi-
tions for the OOQ and five for the POQ as outcomes.
2.2.1 Outcomes
The outcome POQ is created from the PIAAC
questionnaire as a sum of the cross-tabulations of
the respondent’s stated (perceived) necessity of his/
her highest level of education in order to satisfac-
torily do his/her current job. To put it differently, all
the respondents who stated that a lower qualifica-
tion than the one they have is necessary for doing
their job satisfactorily were set as “perceiving
themselves as overqualified”.
The OOQ was operationalized as any positive dif-
ference between the highest acquired level of educa-
tion and the job-required educational level, both
measured on the ISCED scale (OECD, 1999;
UNESCO, 1976). For the purpose, all the formal edu-
cation levels were collapsed into four clusters: lower-
and upper-secondary, post-secondary (non-tertiary)
and tertiary education (see Appendix, Table A1).
2.2.2 Causal conditions
2.2.2.1 Labor market. The Risk categories causal
condition (causal condition RISK) embeds the em-
ployees prone to overqualification due to labor
market discrimination. It is computed as a sum, a
cumulative percent of labor force participation (LFP)
of the labor market categories: young workers aged
15e24, “boomer” workers aged 55e64 and migrant
(or foreign-born) workers (OECD, 2015b).
The LFP migrant percent was firstly counted as
number (in 1000s) of foreign population share in total
population from the ILOSTAT databases and then
multiplied by its LFP rate from the OECD database
(LFP rate/100). The migrant LFP rate along with other
secondary data were sourced for the year 2013, except
Chile with 2014 data (OECD, 2015a), France (2014),
Israel (2014), Lithuania (2014), Ireland (2012), Poland
and Turkey as in 2005. In Japan, the labor force
116 ECONOMIC AND BUSINESS REVIEW 2022;24:111e131
participation rate of foreign population was not
known, so their LFP is calculated from the aged 15e64
working population overall LFP rate in the country.
Next, the OECD publishes an Employment pro-
tection legislation (EPL) index that categorizes
countries as rigid or flexible in terms of measures
against individual or collective dismissal (OECD,
2013b). This Rigidness of employment protection
legislation (RGTY) considers the rigidity against
individual dismissal of permanent and temporary
workers, with accenting rigidity against temporary
employment or collective dismissal, again for the
year 2013. The exceptions years are for Slovenia
(2014), Great Britain (2014) and Lithuania (2015).
The Dynamism (causal condition DYNA) or
workforce turnover from industry to services sector
between 2007 (the Economic crisis debut) and 2013
(the crisis end), is the next causal condition. In a
general pattern, besides Turkey, during the Crisis,
all other countries from the sample had a decline in
industry employment and an augmentation in ser-
vices employment. For Estonia, the data for the
constituting causal condition are taken for 2012 and
as for Lithuania, the data are drawn from The World
bank database.
2.2.2.2 Education. Two causal conditions were
created for education. The first one is the rising
labor force participation rate as LFP of tertiary
educated population entering the labor market for
the period 2007e2013 (OECD, 2018) (TERT). The
second causal condition is the vocational or general
nature of a country’s educational system (VOCA).
However, as the definition of vocational education
differs across countries, the Hanushek et al. (2016)
approach was implemented. If a country’s educa-
tional system has a score of at least 50 in vocational
EAG scores (OECD, 2017) or if it’s apprenticeship
programs score was substantively high (like for
Germany and Denmark), the country’s education
system was designated as “vocational”. Also, the
OOQ was used as a causal condition for the POQ.
The causal conditions DYNA and VOCA were used
solely in the configurations for the OOQ.
2.2.2.3 Culture. The culture itself is a candidate for the
precursors of overqualification levels and types; here
is the emphasizedmasculinity versus femininity index
(MAS) or the aggregated emotional implications of
beingbornas a girl or boy (Schooler&Hofstede, 1983).
It is the index of a country’s preference for perfor-
mance, heroism, assertiveness and material rewards
for success rather than cooperation, modesty, caring
for the weak and quality of life.
It is primarily concerned with gender differences
in the propensity to over-qualify, the trade-off be-
tween human and social capital/benefits or
discrimination against older people and migrants in
the labor market. Reference is also made to the
personal psychological contract when faced with
work or the search for a job for which one is over-
qualified. MAS also includes the trade-off between
personal costs and benefits, or individual work
preferences embedded in quality perceptions.
DYNA:
Structure/state of the LM
Job loss/reemployment due to layoffs
Augmentation in services employment
Decline in industry employment
Economic downturn
POQ
RGTY: 
Changes in employer hiring perceptions/decisions
Wage loss support programs     
Permanent and temporary employment protection
(Inadequate) unemployment support
Government regulation of labor costs during crisis
VOCA:
Vocational/general 
educational system
MAS:
Trading human capital for social capital and job 
security
Careerist attitudes
Personal demand for social status 
RISK:
Inexperienced young workers
Migrant employment
Older (boomer) workers
TERT:
Rising college graduate
population
OOQ
Fig. 2. Macro-configurational model on overqualification antecedents. Source: own work. Note: Legend: VOCA ¼ country’s education system is
vocational; DYNA ¼ workforce turnover between industrial and services sectors; RGTY ¼ Rigid employment protection legislature;
TERT ¼ augmentation of tertiary educated workers on the labor market; RISK ¼ labor force participation rate of oldest, youngest, and migrant
workers, combined; MAS ¼ country’s masculinity index.
ECONOMIC AND BUSINESS REVIEW 2022;24:111e131 117
2.3 The procedure and analysis
The purpose of crisp-set QCA (hereafter csQCA)
was to identify the necessary or sufficient subset
conditions between our data set and the attributes
relying on the theoretical background of the over-
qualification antecedents. All the causal conditions’
values were dummy-coded or dichotomized upon a
conservative threshold value (mainly an OECD
average was used, see Table 1) or upon the mean
values of the data set for the causal condition we
have created.
The causal condition with values below the set
QCA threshold value were coded as "0" (or in
Boolean logic as "missing", "not member" of a
configuration), and those with values above the
threshold value were coded as "1". The result is the
"truth table" (Table 2), in which 15 countries had
high POQ (low POQ was referred to as ~ POQ),
while 11 countries had high OOQ (low OOQ was
referred to as ~ OOQ). Once the causal conditions
were entered, the data were analyzed as a crisp-set
(cs) QCA (Ragin, 2009). There, in a three stage
process of csQCA, the first step considered the
causal conditions (macro factors) for the both out-
comes OOQ and POQ to be present or absent. The
threshold for the factors to be included into the
analysis is a coverage benchmark of 0.5 and a
consistency benchmark of 0.9 (Ragin, 2006).
In a second stage, as the csQCA software gener-
ally assesses relations of sufficiency, it unveils ne-
cessity relations in very peculiar circumstances.
Furthermore, for all the outcomes (OOQ, ~OOQ,
POQ and ~POQ) there exist “remainder” and
“contradictory” rows (Appendix, Tables A2eA5.1),
which signal that the respective truth tables are not
fully specified. Therefore, not all the causal condi-
tions are to be designated in QCA as “necessary”
and a necessity analysis is by rule the first one to be
manned (Schneider &Wagemann, 2010). In result, it
was established that solely ~ VOCA is the necessary
condition for high OOQ (~VOCA ) OOQ, Table 3).
In a third stage, csQCA performs a truth table and
counterfactual analyses and then selects the
Table 1. Descriptive overview of the dataset.
COUNTRY OOQ POQ RISK RGTY DYNA VOCA TERT MAS
AUT 20.979 16.502 31.729 2.369 7.511 70 4.65 79
BEL 15.798 23.514 24.276 1.892 7.062 60 3.44 54
CAN 26.841 21.061 44.643 0.920 5.958 8 4.79 52
CHI 16.345 15.794 15.657 2.626 21.447 29 3.97 28
CZE 20.623 25.686 13.038 2.924 5.959 73 6.73 57
DNK 18.386 15.455 25.371 2.198 5.550 50 4.53 16
EST 26.507 17.290 37.079 1.809 6.600 36 4.20 30
FIN 16.762 23.874 17.823 2.166 5.454 71 4.19 26
FRA 31.330 24.256 19.208 2.384 4.997 41 5.46 43
GBR 25.733 26.855 24.296 1.095 8.361 40 6.36 66
GER 23.165 21.808 24.513 2.678 6.822 51 4.17 66
GRE 20.954 15.548 19.109 2.119 22.177 30 4.53 57
IRL 27.172 20.554 30.680 1.396 11.787 27 9.08 68
ISR 32.489 19.304 62.313 2.035 28.262 41 3.83 47
ITA 13.265 15.852 19.923 2.678 6.219 56 2.71 70
JPN 31.069 22.301 10.198 1.369 3.718 23 6.29 95
KOR 21.221 28.176 8.822 2.369 7.965 18 8.50 39
LTU 26.473 23.933 10.337 2.448 35.040 27 6.27 19
NDL 14.848 15.448 21.935 2.821 6.300 69 3.07 14
NZL 33.823 28.626 53.456 1.392 5.949 32 4.80 58
NOR 19.756 18.466 27.875 2.333 6.844 50 5.57 8
POL 16.370 26.856 10.026 2.230 4.845 50 7.07 64
SVK 18.034 21.173 8.656 1.841 7.694 69 5.44 100
SVN 11.752 14.188 16.356 2.603 6.882 67 5.65 19
SPA 21.741 17.792 27.548 2.047 16.055 35 4.46 42
SWE 18.741 23.466 22.336 2.607 7.351 38 5.71 5
TUR 11.587 13.100 15.934 2.309 18.151 49 4.62 45
mean 21.547 20.625 23.820 2.068 10.406 44.815 5.188 46.926
QCA threshold 21.698 20.625 23.820 2.044 10.406 ≥50 5.188 50.166
Note: * Legend: VOCA ¼ country’s education system is vocational; DYNA ¼ workforce turnover between industrial and services sectors;
RGTY ¼ Rigid EPL; TERT ¼ augmentation of tertiary educated workers on the labor market; RISK ¼ LFP rate of oldest, youngest, and
migrant workers, combined; MAS ¼ country’s masculinity index.
OOQ, POQ, RISK, DYNA and TERT are expressed as a percentage, RGTY, VOCA and MAS are index numbers.
Source: Own work.
118 ECONOMIC AND BUSINESS REVIEW 2022;24:111e131
smallest number of combinations that will cover all
the positive instances of the outcome (Boolean
minimization). This minimization provided certain
“primitive expressions” leading to a final configu-
ration-patterns that explain OOQ and POQ. During
minimization, however, discrepancies arose be-
tween the primitive expressions of a final solution-
configuration. For this reason, by relying on the
extant literature, the causal conditions were set both
as “present” and absent for the POQ/~POQ as
outcomes. This was done in order to isolate “prime
implicants” that are logically redundant. As for the
high OOQ, we inputted only the causal condition
VOCA to be absent.
Table 2. Overview of the csQCA truth table.
COUNTRY OOQ POQ RISK RGTY DYNA VOCA TERT MAS
AUT 0 0 1 1 0 1 0 1
BEL 0 1 0 0 0 1 0 1
CAN 1 1 1 0 0 0 0 1
CHI 0 0 0 1 1 0 0 0
CZE 0 1 0 1 0 1 1 1
DNK 0 0 0 1 0 1 0 0
EST 1 0 1 0 0 0 0 0
FIN 0 1 0 1 0 1 0 0
FRA 1 1 0 1 0 0 1 0
GBR 1 1 0 0 0 0 1 1
GER 1 1 0 1 0 1 0 1
GRE 0 0 0 1 1 0 0 1
IRL 1 1 1 0 1 0 1 1
ISR 1 0 1 0 1 0 0 0
ITA 0 0 0 1 0 1 0 1
JPN 1 1 0 0 0 0 1 1
KOR 0 1 0 1 0 0 1 0
LTU 1 1 0 1 1 0 1 0
NDL 0 0 0 1 0 1 0 0
NZL 1 1 1 0 0 0 1 1
NOR 0 0 1 1 0 1 1 0
POL 0 1 0 1 0 1 1 1
SVK 0 1 0 0 0 1 1 1
SVN 0 0 0 1 0 1 1 0
SPA 1 0 1 1 1 0 0 0
SWE 0 1 0 1 0 0 1 0
TUR 0 0 0 1 1 0 0 0
Table 3. Necessity relationship analysis.
OutcomeCausal condition
OOQ ~ OOQ POQ ~ POQ
consistency coverage consistency coverage consistency coverage consistency coverage
OOQ / / / / 0.533333 0.727273 0.250000 0.272727
~ OOQ / / / / 0.466667 0.437500 0.750000 0.562500
TERT 0.454545 0.416667 0.437500 0.583333 0.666667 0.833333 0.166667 0.166667
~ TERT 0.545455 0.400000 0.562500 0.600000 0.333333 0.333333 0.833333 0.66666
RISK 0.727273 0.666667 0.250000 0.333333 0.400000 0.500000 0.500000 0.500000
~ RISK 0.272727 0.200000 0.750000 0.800000 0.600000 0.600000 0.500000 0.400000
RGTY 0.363636 0.222222 0.875000 0.777778 0.533333 0.444444 0.833333 0.555556
~ RGTY 0.636364 0.777778 0.125000 0.222222 0.466667 0.777778 0.166667 0.222222
MAS 0.545455 0.461538 0.437500 0.538462 0.666667 0.769231 0.250000 0.230769
~ MAS 0.454545 0.357143 0.562500 0.642857 0.333333 0.357143 0.750000 0.642857
DYNA 0.363636 0.571429 0.187500 0.428571 / / / /
~ DYNA 0.636364 0.350000 0.812500 0.650000 / / / /
VOCA 0.090909 0.083333 0.687500 0.916667 / / / /
~ VOCA 0.909091 0.666667 0.312500 0.333333 / / / /
ECONOMIC AND BUSINESS REVIEW 2022;24:111e131 119
Finally, csQCA presents three1 solutions to each
truth table analysis: “complex” solution (Appendix,
Tables A2eA5.1), a “parsimonious” solution and an
“intermediate” solution. The latter was used for
dealing with logical remainders and for interpreting
the resulting configurations patterns.
All causal conditions appearing in both the
parsimonious and intermediate solutions were
designated as “core” conditions, whereas the causal
conditions appearing only in intermediate solutions
are “complementary” conditions (Ragin, 2009). The
conditions appearing in all the configurations lead-
ing to the same outcome were designated as
necessary conditions.
3 Results
The configurational patterns (Table 4) for the high
OOQand lowOOQ (~OOQ) relied on configurations
which cover 9 of the OOQ countries and 13 of the low
(~) OOQ countries. These respective configurations
relied incorporated 3 and 5 configuration patterns
respectively (named “a” to “c” and “a” to “e”), which
are showed in detail in Tables 5 and 6.
The high OOQ at the country level is coherently
explained by three configuration patterns (a, b and
c, Table 4. This illustrates equifinality as a feature of
QCA when multiple configurations (patterns) lead
to the same result. Among them, configuration
Table 4. Configurations for OOQ.
1 The “complex” solution avoids using any rows without cases e “remainders” (counterfactual cases were eliminated upon our own input), a “parsi-
monious” solution, uses only the remainders that will yield simpler (or fewer) patterns and an “intermediate” solution, which uses only the remainders that
survive counterfactual analysis (Legewie, 2013).
120 ECONOMIC AND BUSINESS REVIEW 2022;24:111e131
pattern “a” (Table 5) has the highest raw/unique
coverage and covers five countries and designates
the high OOQ as occurring in countries with high
LFP of risk categories and non-vocational education
systems.
The ~ OOQ at the country level is explained by
five configuration patterns (aee, Table 4). Among
these equifinal patterns, the configuration pattern
“c” (Table 6) has the highest raw/unique coverage
and covers seven country relationships and denotes
the low OOQ to occur in countries with low risk
categories LFP, low dynamism of intersectoral
workforce turnover, rigid EPL and vocational edu-
cation system.
As for the POQ, the configurational patterns
(Table 7) relied on configurations which cover 11 of
the high POQ countries and 8 of the low (~) POQ
countries. These configurations incorporated 4 and 3
configuration patters respectively (a to c and aee),
which are shown in detail in Tables 7 and 8 Before
proceeding to the standard QCA analyses, prime
implicants emerged for the high POQ, according to
which a decision in the prime implicants chart was
made (Appendix, Table А6).
The high POQ is explained by four configuration
patterns (aed, Table 7). Among them, the
configuration pattern “a” (Table 8) has the highest
raw/unique coverage value and covers seven coun-
tries by designating for the high POQ to occur in
countries having high augmentation of LFP of ter-
tiary educated workers and strong masculinity index.
The ~ POQ is explained by three configuration
patterns (aec, Table 7). Among them, the configu-
ration pattern “c” (Table 9) has the highest fit
parameter values and covers three countries by
designating for the low POQ to occur in countries
having low share of overeducated employees, rigid
EPL, low augmentation of LFP of tertiary educated
workers and strong masculinity index.
From this section, it is evident that the resulting
QCA configurations made a distinction on OQ
configurations of antecedents in three ways: be-
tween high and low OOQ and POQ, on a country
level and between countries.
4 Discussion
In this paper, we aimed to identify configurational
patterns composed of macro factors that lead to the
occurrence of employee overqualification at the
country level. Our study was based on a macro
factorial, contextual and set-theoretic approach in
Table 5. Configurations for high OOQ.
Model: OOQ ¼ f(RISK, RGTY, DYNA, VOCA, TERT, MAS)
INTERMEDIATE SOLUTION: frequency cutoff: 1.0000 consistency cutoff: 1.0000
Assumptions: RISK (present) RGTY (present) DYNA (present) ~VOCA (absent) TERT (present)
RISK*~VOCA þ ~RGTY*~VOCA*TERT*MAS þ RGTY*DYNA*~VOCA*TERT*~MAS / OOQ
Raw
coverage
Unique
coverage
Consistency Cases with greater than
0.5 membership in term
RISK*~VOCA 0.636364 0.454545 1 CAN, EST, GBR, IRL,
ISR, NZL, SPA
~RGTY*~VOCA*TERT*MAS 0.272727 0.090909 1 GBR, IRL, JPN
RGTY*DYNA*~VOCA*TERT*~MAS 0.090909 0.090909 1 LTU
solution coverage: 0.818182
solution consistency: 1
Table 6. Configurations for low OOQ.
Model: ~OOQ* ¼ f (RISK, RGTY, DYNA, VOCA, TERT, MAS)
INTERMEDIATE SOLUTION: frequency cutoff: 1.000000 consistency cutoff: 1.000000
Assumptions: TERT (present) ~VOCA (absent) DYNA (present) RGTY (present) RISK (present)
RGTY*VOCA*~MAS þ VOCA*TERT*MAS þ ~RISK*RGTY*~DYNA*VOCA þ RISK*~RGTY*VOCA*MAS þ ~RISK*RGTY*
DYNA*~VOCA*~TERT / ~OOQ
Raw
coverage
Unique
coverage
Consistency Cases with greater than
0.5 membership in term
RGTY*VOCA*~MAS 0.3125 0.125 1 DNK, FIN, NDL, NOR,
SVN
VOCA*TERT*MAS 0.1875 0.0625 1 CZE, POL, SVK
~RISK*RGTY*~DYNA*VOCA 0.375 0.0625 1 CZE
FIN, ITA, NDL,
POL, SVN
RISK*~RGTY*VOCA*MAS 0.0625 0.0625 1 BEL
~RISK*RGTY*DYNA*~VOCA*~TERT 0.1875 0.1875 1 CHI, GRE, TUR
solution coverage: 0.8125
solution consistency: 1.000000
ECONOMIC AND BUSINESS REVIEW 2022;24:111e131 121
consideration of the sparse existing literature on
country-level employee overqualification. Using
configurational logic and QCA procedures, we
showed how antecedents embedded in the labor
market, education, and culture act as causal condi-
tions that explain the occurrence and absence of
overqualification, both objectively and as perceived
by employees.
Considering the configurational patterns leading
to high OOQ, we observed that it is specific for the
countries with general educational systems. This
causal condition appears in all the configurations
and in all the solutions (complex, intermediary,
parsimonious), and it was also confirmed during the
initial necessity analysis. Hence, a first key propo-
sition from of our research is that non-vocational or
Table 7. Configurations for perceived overqualification (POQ).
Table 8. Configurations for high POQ.
Model: POQ ¼ f (MAS, TERT, RGTY, RISK, OOQ)
INTERMEDIATE SOLUTION: frequency cutoff: 1 consistency cutoff: 1
Assumptions: OOQ (present) RISK (present) RGTY (present) TERT (present)
TERT*MAS + RISK*~RGTY*MAS + OOQ*RGTY*TERT + OOQ*RISK*MAS / POQ
Causal conditions Raw coverage Unique coverage Consistency Cases with greater than 0.5 membership in term
TERT*MAS 0.4 0.266667 1 CZE, GBR, IRL, JPN, POL, SVK
RISK*~RGTY*MAS 0.333333 0.0666667 1 BEL, CAN, GBR, IRL, NZL
OOQ*RGTY*TERT 0.1333333 0.133333 1 FRA, LTU,
OOQ*RISK*MAS 0.333333 0.0666667 1 CAN, GBR, GER, IRL, NZL
solution coverage: 0.8
solution consistency: 1
122 ECONOMIC AND BUSINESS REVIEW 2022;24:111e131
general education system is a necessary condition for
high country-level objective overqualification. For the
low OOQ, the high dynamism of inter-sectoral
workforce turnover was established as a comple-
mentary condition, which on its hand, led to fewer
and simpler configurational patterns.
The configurations for high POQ do not feature
a joint necessary condition. However, the high share
of risk categories LFP is a core condition for the low
POQ and a complementary one for high POQ. This
means that this high share of risk categories LFP ap-
pears every time when there is at least one low POQ
country explained by our configurations. As for the
high POQ, the high risk categories LFP led to fewer
and simpler configurational patterns.
What is also evident is that although OOQ is a
factor included in configurations predicting POQ,
high country-level objective overqualification is not a
necessary condition for high perceived overqualification
(our second key proposition). Put differently, the
POQ can take high or low values without being
conditioned by a high or low value of the OOQ at
the country level or even without its membership in
the configurations explaining POQ. This also ex-
emplifies the asymmetrical nature of the configu-
rational approach, where the causal conditions
generating a presence of certain outcome, do not
generate its absence, if they take opposite values. If
configurational approach was not used, we would
probably not be able to establish this pattern.
4.1 Overqualification archetypes at a county level
By juxtaposing the configurations leading tohighand
low OOQ and POQ, we distinguish four types of
countries: two “genuine” and two “apparent” arche-
types (Table 10).
Genuine Archetype 1 countries that have high
overall country-level overqualification (high
OOQ and high POQ). Prototypical countries include
Canada, Great Britain, Ireland, Japan, Lithuania and
New Zealand. By applying the rules of Boolean
algebra,2 we found that a common configuration
leading to Genuine Archetype 1 includes macro
factors: non-vocational education system and
masculinity.
Genuine Archetype 2 includes countries with low
overall country-level overqualification, where both
OOQ and POQ are low. Prototypical countries are
Denmark, Greece, Italy and Norway. Again, by
applying common Boolean algebra rules, their com-
monconfigurationwas found to include risk categories
and vocational education systems in all its patterns.
ApparentArchetype1countriesarecharacterizedby
low OOQ and high POQ. Prototypical countries
include Czech Republic, Poland and Slovakia. The
additional algebra of the sets computation revealed
that the common configuration for this archetype en-
velops vocational education and almost always con-
tainsahighaugmentationof tertiaryeducatedentrants
to the labor market. All countries adhering to this
Table 9. Configurations for low POQ.
Model: ~POQ ¼ f (OOQ, RISK, RGTY, TERT, MAS)
COMPLEX SOLUTION: frequency cutoff: 1 consistency cutoff: 1
Raw coverage Unique coverage Consistency Cases with greater than
0.5 membership in term
OOQ*RISK*~MAS 0.25 0.166667 1 EST, ISR, SPA
RISK*RGTY*~MAS 0.25 0.166667 1 DNK, NOR, SPA
~OOQ*RGTY*~TERT*MAS 0.25 0.25 1 AUT, GRE, ITA
solution coverage: 0.666667
solution consistency: 1
Table 10. Cross-country overview of objective and perceived overqualification.
Objective overqualification
High Low
Perceived
overqualification
High CAN, GBR, IRL, JPN, LTU, NZL CZE, POL, SVK
(RISK*~VOCA*MAS þ RISK*~VOCA*~RGTY*MAS þ
RGTY*~VOCA*OOQ*RGTY*TERT þ RISK*~VOCA
*OOQ*MAS þ ~VOCA*TERT*MAS*DYNA þ ~VOCA
*TERT*MAS*~RGTY)
(RGTY*VOCA*~MAS*OOQ*TERT þ
VOCA*TERT*MAS þ ~RISK*RGTY*~DYNA
*VOCA*PPQ*TERT þ RISK ~ RGTY*VOCA*MAS)
Low EST, ISR, SPA DNK, GRE, ITA, NOR
(RISK*~VOCA*OOQ*~MAS þ RISK*~VOCA*
RGTY*~MAS þ RISK*~VOCA*~OOQ*~TERT*MAS)
(RGTY*VOCA*~MAS*RISK þ RISK ~ RGTY
*VOCA*MAS*~OOQ*~TERT)
2 Combining the configurations leading to the occurrence of presence and absence of OOQ/POQ as presented in results section.
ECONOMIC AND BUSINESS REVIEW 2022;24:111e131 123
archetype are characterized by low dynamics of
mobility from industrial to service sectors.
Apparent Archetype 2 envelops countries with
high OOQ and low POQ. Prototypical countries are
Estonia, Israel and Spain. The common configura-
tion for this archetype contains a high proportion of
risk categories in the labor market and non-voca-
tional education system. In general, all apparent
archetype 2 countries are characterized by high LFP
of migrants, young and "boomer" workers, general
education systems, low increase in LFP of tertiary
educated workers and a strong feminine culture.
4.2 Theoretical implications
This research contributes to the existing knowledge
base by adopting a configurational approach to bridge
the knowledge of antecedents of objective and
perceived overqualification at a country level. Indeed,
the simultaneous consideration of OOQ and POQ in
the elaboration of employee overqualification isworth
highlighting. We showed that these two concepts
envelop different nomological networks and accumu-
late different predictors. The challenging work of
isolating theoretical antecedents from the sparse
literature was empirically supported by our reference
to higher-level, contextual causal conditions that sur-
round them. We encourage such a set-theoretic
approach and recourse to QCA when addressing
theoretical scarcities and inconsistencies in the other
areas of existingmanagement literature too.
The contribution of this study is reflected in the
fact that the OOQ and the POQ have been linked
and paired, although one does not imply the other.
Nevertheless, when both were considered, it
became clear that among the macro factors, some of
them were common to the specific archetypes in the
selected countries, although no necessity relation-
ship was generally established between them.
The originality of this study also lies in the fact
that it puts the term "contextual conditions" in a
positive light; here were the macro factors that did
not bound, but actually caused the occurrence of the
OQ, by level and type, within a country, allowing an
analysis between countries on the same ground. It is
certainly important to highlight the new proposed
aspect of genuine and apparent overqualification as
it was previously related to OOQ and one’s satis-
faction with the job (Chevalier & Lindley, 2009).
Next, a benefit of this study is to illustrate a
normative approach (Flisi et al., 2016) to measuring
worker country-level overqualification by estab-
lishing the OOQ score (Appendix, Table A1). We
also stress that the present study was conducted at
the cross-national level and outside the Western
world (Erdogan & Bauer, 2009), unlike the previous
studies which were seen as less diversified (Erdogan
& Bauer, 2009), case-oriented and based on a
smaller sample (Alfes et al., 2016; Zhang et al., 2015;
Erdogan et al., 2011a).
In addition to the positions from the assignment
theory, occupational mobility theory and institu-
tionalism, this research shows that OQ is not
just individual or temporal. This is equally due to
the new "theorizing tool" of the configuration the-
ory as proposed by Furnari et al. (2021) which has
also made clear that overqualification does not only
arise at the managerial level, because there are
macro-contextual patterns that imply it (Abraham,
2015; Hu et al., 2015; Zhang et al., 2015). Thereby, it
also matters to address this issue at a county level.
4.3 Implications for practice
The differences between the patterns explaining
objective and perceived overqualification suggest
that the concepts of “being” and “feeling” over-
qualified are different for actual work. This could be
essential for understanding skill mismatch (Alfes
et al., 2016), as qualification has been used as a proxy
for skills, another core element of skill mismatch.
Another advantage is that the identified configura-
tions capture the contextual embeddedness of POQ,
which in turn is seen as a major factor in lower job
satisfaction leading to voluntary turnover (Shaw et al.,
1998), a basis for other negative employee outcomes.
We emphasize that national policies to address
objective and perceived overqualification at a
country level should focus on its nomological di-
versity. In our first main proposition derived from
this study, we emphasize that high level of OOQ
necessarily occurs in countries with general educa-
tion systems, although we do not arbitrarily assume
that the return to vocational education could reduce
it. What is in its favor is that the rigid EPL legislation
is observed at lower levels for both OOQ and POQ
when compared among the OECD countries.
Next, the typology of genuine and apparent ar-
chetypes provides policy makers with a tool to
identify the macro conditions that contribute to the
context of high levels of OQ across countries.
Seemingly, within these archetypes there are com-
mon labels for the causal conditions e macro factors
that characterize or cluster highand low levels ofOOQ
and POQ, overall and vice versa, but are not attribut-
able to propositions derived.
The configuration patterns of causal conditions
that generate OQ have labels that characterize them
and that might shed light on many of the OQ-
related problems that practitioners are trying to
124 ECONOMIC AND BUSINESS REVIEW 2022;24:111e131
solve. For example, in genuine archetype 1 countries
there are observed high OOQ and high POQ levels
are high in all but three countries, whereas among
the countries with high POQ, only half of them have
high OOQ as well. Seemingly, on a country-level,
the overeducation is more often accompanied by the
perceived OQ, unlike vice-versa.
Nevertheless, these high levels of both OOQ and
POQ are observed in countries with general educa-
tional system (all but one of the high POQ countries).
To sum up, the general context of high OQ is often
describedbynon-vocationaleducationssystem;onthe
opposite end, the lowOQ is evident in countries clus-
tered by rigid EPL. From this stance, for the policy
makers, the recurrence to measures against mass
dismissal might be beneficial in tackling the highOQ.
As for the apparent type 1 country (low OOQ, high
POQ), the low dynamics of the mobility of employees
from industrial to service sector after the economic
crisis of 2009 is common. Moreover, in the apparent
archetype 2, high OOQ and low POQ is common to
countries that have a strong LFP of risk categories,
along with non-vocational education systems, a low
increase in the LFP of workers with tertiary education,
andaweakmasculineculture.Insummary,there isone
factor that simultaneously keeps OOQ at a low level
andPOQat a high level,while there are several factors
that lead to the occurrence of the reverse case.
5 Limitations and future research
As any other study, this study also has its downsides.
The first is that, contrary to what was suggested by Liu
andWang (2012), thedesignof theOOQresult grouped
all academic education levels together rather than
separating them as master’s, doctoral and bachelor’s
degrees. We proceeded in this way because of the dif-
ferences in country-specific levels of education in terms
of duration and level of secondary education. Secondly,
the UK hasmore than 2400 respondents in the individ-
ual PIAAC data that do not belong to any specific cate-
gory of higher academic education, as do 18
respondents from France. This can be seen as prob-
lematic from two perspectives. First, because although
the 1997 ISCEDmanual gives a clear picture of educa-
tional levels (Table A1, Appendix), such a distinction
between academic levels of education bringswith it the
challenge of dealing in the same way with lower/sec-
ondary levels of education. Although this can be dealt
with at the individual country level (see Domadenik
et al., 2015), it is virtually impossible across countries.
Second is the use of crisp-set QCA, which relies on
dichotomizing variables, but such a designation for a
variable as "fully in" or "fully out" of a set leads to the
risk of losingmore complex information.We adopted
csQCA to avoid problems with calibration of more
nuanced variables in fuzzy sets (fsQCA). With even
less empirical data on these OQ’ macro-level out-
comes, this prompted us to use more conservative
thresholds for dichotomizing causal conditions.
For example, as can be debatable, which exact
country levels of OOQ or POQ actually reduce
worker productivity at the country level to use later
as a calibrated measure, as they are not always well
suited to be dichotomized. Hence, the key question
might be what level of employee overqualification
should be considered relevant at the country level.
Therefore, once such data are available, we
encourage future research to calibrate the causal
condition into a fuzzy set QCA; or set its values as
the degree of belonging to such a specific OOQ/
POQ threshold that should provide more fine-
grained, diversified results. Perhaps the net effects
resulting from the regression analysis of the inci-
dence of overqualification could respond and com-
plement our results, as QCA did to them, in return.
Conducting this study relied on a caution to
ecological fallacy which can be observed in the
manner of streamlining causal conditions and out-
comes and in the analysis and interpretation sec-
tions. The resulting conclusions bear on country and
cross-country aspects of OQ occurrence, by level
and type, where these relationships observed for
countries do not necessarily hold for individuals,
what is more, neither did this study imply it.
Nevertheless, our macro-configurational approach to
OQ occurrence did not explain all the OOQ/POQ levels.
Known as "limited diversity" (typical of QCA), our solu-
tion consistencies have values below one, and the exis-
tence of the "remainder" rows indicates that our
theoretical and configurational models are not able to
explainallthediversityofthecasesinthedataset(Legewie,
2013). Furthermore, not all configurations cover theentire
27 countries in our sample either. High OOQ is not
explained for Germany and France, low OOQ is not
explainedfor fourothercountries; samegoesfor fourhigh
POQ and four low POQ countries. Moreover, Austria,
France, Korea and Sweden are not explained by any
pattern in any configuration. In conclusion, the ecological
fallacy and limited diversity left room for additional theo-
reticallyplausibleconfigurationsleadingtotheoccurrence
of overqualification, possibly based on individual or
organizational factors or with the addition of other coun-
tries outside theOECD sample.
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ECONOMIC AND BUSINESS REVIEW 2022;24:111e131 127
Appendix
Table A1. Detailed overview of the measure of objective overqualification.
ISCED (1997 levels) * EDCAT 7 Value labels D_Q12a Chapter 1 Chapters 2,3 and 4 (as in Domadenik et al., 2015)
No formal education (ISCED 0,
less than ISCED 1)
1 Primary or less 1 Lower secondary Primary or less No formal education + Incomplete basic
education
1
2
3C short or less
2
2
2
Lower secondary 2
3
4
Lower secondary Completed basic education
Secondary short-term vocational edu-
cation (1e2 years)
3C long
3 A-B
3 A-B, C long
(without distinction)
3
3
3
Upper secondary 5
6
7
Upper secondary Secondary vocational education (2e3
years)
Technical and professional secondary
education (4 years)
General secondary education (gymna-
sium) (4 years) Master craftsman cour-
ses and further vocational education
courses, such as foreman and sales
manager courses
4C
4 A-B
4 A-B, C (without distinction)
4
4
4
Postsecondary, non-tertiary 8
9
10
Postsecondary,
non-tertiary
Short-term higher education
(former) + professional higher
education
5 B
5 A (bachelor)
5
6
Tertiary-professional 11
12
Tertiary/professional
/bachelor
master
PhD
Tertiary/professional
/bachelor
Academic higher education
Tertiary-bachelor
5 A (master) 7 Tertiary-master/research 13 Tertiary master/research Specialization after professional higher
education, specialization after academic
higher education +
Master degree, 2nd level professional
degree
6 7 14 Doctorate of science and similar
education
Source: Own work.
128
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Table A2. Truth table for high OOQ.
RISK RGTY DYNA VOCA TERT MAS number OOQ cases raw consist. PRI consist. SYM consist.
1 0 0 0 0 1 2 1 1 1 1
1 0 0 0 0 0 1 1 1 1 1
1 0 1 0 0 0 1 1 1 1 1
1 1 1 0 0 0 1 1 1 1 1
0 1 1 0 1 0 1 1 1 1 1
0 0 0 0 1 1 1 1 1 1 1
1 0 0 0 1 1 1 1 1 1 1
1 0 1 0 1 1 1 1 1 1 1
1 1 0 1 0 1 2 0 0.5 0.5 0.5
0 1 0 0 1 0 3 0 0.333333 0.333333 0.333333
0 1 1 0 0 0 2 0 0 0 0
0 1 0 1 0 0 2 0 0 0 0
0 1 0 1 1 1 2 0 0 0 0
1 1 0 1 0 0 1 0 0 0 0
0 1 0 1 1 0 1 0 0 0 0
1 1 0 1 1 0 1 0 0 0 0
0 1 1 0 0 1 1 0 0 0 0
1 0 0 1 0 1 1 0 0 0 0
0 1 0 1 0 1 1 0 0 0 0
0 0 0 1 1 1 1 0 0 0 0
Source: Own work.
Table A2.1. Configurations for high OOQ, complex solution.
Model: OOQ ¼ f (RISK, RGTY, DYNA, VOCA, TERT, MAS)
COMPLEX SOLUTION: frequency cutoff: 1 consistency cutoff: 1
Raw coverage Unique coverage Consistency Cases with greater than
0.5 membership in term
RISK*~RGTY*~DYNA*~VOCA*~TERT 0.272727 0.272727 1 CAN, EST
RISK*DYNA*~VOCA*~TERT*~MAS 0.181818 0.181818 1 ISR, SPA
~RGTY*~DYNA*~VOCA*TERT*MAS 0.181818 0.0909091 1 GBR, JPN
RISK*~RGTY*~VOCA*TERT*MAS 0.181818 0.0909091 1 GBR, IRL
~RISK*RGTY*DYNA*~VOCA*TERT*~MAS 0.0909091 0.0909091 1 LTU
solution coverage: 0.818182
solution consistency: 1
Source: Own work.
Table A3. Truth table for low OOQ.
RISK RGTY DYNA VOCA TERT MAS number ~OOQ cases raw consist. PRI consist. SYM consist.
0 1 1 0 0 0 2 1 1 1 1
0 1 0 1 0 0 2 1 1 1 1
0 1 0 1 1 1 2 1 1 1 1
1 1 0 1 0 0 1 1 1 1 1
0 1 0 1 1 0 1 1 1 1 1
1 1 0 1 1 0 1 1 1 1 1
0 1 1 0 0 1 1 1 1 1 1
1 0 0 1 0 1 1 1 1 1 1
0 1 0 1 0 1 1 1 1 1 1
0 0 0 1 1 1 1 1 1 1 1
0 1 0 0 1 0 3 0 0.666667 0.666667 0.666667
1 1 0 1 0 1 2 0 0.5 0.5 0.5
1 0 0 0 0 1 2 0 0 0 0
1 0 0 0 0 0 1 0 0 0 0
1 0 1 0 0 0 1 0 0 0 0
1 1 1 0 0 0 1 0 0 0 0
0 1 1 0 1 0 1 0 0 0 0
0 0 0 0 1 1 1 0 0 0 0
1 0 0 0 1 1 1 0 0 0 0
1 0 1 0 1 1 1 0 0 0 0
Source: Own work.
ECONOMIC AND BUSINESS REVIEW 2022;24:111e131 129
Table A3.1. Configurations for low OOQ, complex solution.
Model: OOQ ¼ f (RISK, RGTY, DYNA, VOCA, TERT, MAS)
COMPLEX SOLUTION: frequency cutoff: 1 consistency cutoff: 1
Raw coverage Unique coverage Consistency Cases with greater than
0.5 membership in term
RGTY*~DYNA*VOCA*~MAS 0.3125 0.125 1 DNK, FIN, NDL, NOR, SVN
~RISK*RGTY*~DYNA*VOCA 0.375 0.0625 1 CZE, FIN, ITA, NDL, POL, SVN
~RISK*RGTY*DYNA*~VOCA*~TERT 0.1875 0.1875 1 CHI, GRE, TUR
~RISK*~DYNA*VOCA*TERT*MAS 0.1875 0.0625 1 CZE, POL, SVK
RISK*~RGTY*~DYNA*VOCA*~TERT*MAS 0.0625 0.0625 1 BEL
solution coverage: 0.8125
solution consistency: 1
Source: Own work.
Table A4. Truth table for high POQ.
OOQ RISK RGTY TERT MAS number POQ cases raw consist. PRI consist. SYM consist.
1 0 1 1 0 2 1 1 1 1
1 1 0 0 1 2 1 1 1 1
1 1 0 1 1 2 1 1 1 1
0 0 1 1 1 2 1 1 1 1
0 1 0 0 1 1 1 1 1 1
1 1 1 0 1 1 1 1 1 1
0 0 0 1 1 1 1 1 1 1
1 0 0 1 1 1 1 1 1 1
0 0 1 1 0 3 0 0.666667 0.666667 0.666667
0 0 1 0 0 4 0 0.25 0.25 0.25
1 1 0 0 0 2 0 0 0 0
0 0 1 0 1 2 0 0 0 0
0 1 1 0 0 1 0 0 0 0
1 1 1 0 0 1 0 0 0 0
0 1 1 1 0 1 0 0 0 0
0 1 1 0 1 1 0 0 0 0
Source: Own work.
Table A4.1. Configurations for high POQ, complex solution.
Model: POQ ¼ f (OOQ, RISK, RGTY, TERT, MAS)
COMPLEX SOLUTION: frequency cutoff: 1 consistency cutoff: 1
Raw coverage Unique coverage Consistency Cases with greater than 0.5 membership in term
RISK*~RGTY*~TERT*MAS 0.2 0.0666667 1 BEL, CAN, NZL
~OOQ*~RISK*TERT*MAS 0.2 0.2 1 CZE, POL, SVK
OOQ*RISK*~TERT*MAS 0.2 0.0666667 1 CAN, GER, NZL
OOQ*~RGTY*TERT*MAS 0.2 .0.2 1 GBR, IRL, JPN
OOQ*~RISK*RGTY*TERT*~MAS 0.13333 0.13333 1 FRA, LTU
solution coverage: 0.8
solution consistency: 1
Source: Own work.
Table A5. Truth table for low POQ.
OOQ RISK RGTY TERT MAS number ~POQ cases raw consist. PRI consist. SYM consist
1 1 0 0 0 2 1 1 1 1
0 0 1 0 1 2 1 1 1 1
0 1 1 0 0 1 1 1 1 1
1 1 1 0 0 1 1 1 1 1
0 1 1 1 0 1 1 1 1 1
0 1 1 0 1 1 1 1 1 1
0 0 1 0 0 4 0 0.75 0.75 0.75
0 0 1 1 0 3 0 0.333333 0.333333 0.333333
1 0 1 1 0 2 0 0 0 0
1 1 0 0 1 2 0 0 0 0
1 1 0 1 1 2 0 0 0 0
0 0 1 1 1 2 0 0 0 0
0 1 0 0 1 1 0 0 0 0
1 1 1 0 1 1 0 0 0 0
0 0 0 1 1 1 0 0 0 0
1 0 0 1 1 1 0 0 0 0
Source: Own work.
130 ECONOMIC AND BUSINESS REVIEW 2022;24:111e131
Table A5.1. Configurations for low POQ, complex solution.
Model: ~POQ ¼ f (OOQ, RISK, RGTY, TERT, MAS)
COMPLEX SOLUTION: frequency cutoff: 1 consistency cutoff: 1
Raw coverage Unique coverage Consistency Cases with greater than
0.5 membership in term
OOQ*RISK*~MAS 0.25 0.166667 1 EST, ISR, SPA
RISK*RGTY*~MAS 0.25 0.166667 1 DNK, NOR, SPA
~OOQ*RGTY*~TERT*MAS 0.25 0.25 1 AUT, GRE, ITA
solution coverage: 0.666667
solution consistency: 1
Source: Own work.
Table A6. Prime implicants chart.
OOQ* ~RISK* RGTY* TERT* ~MAS ~OOQ* RISK *~RGTY* ~TERT* MAS
~ RGTY * MAS √
OOQ * TERT √
Source: Own work.
ECONOMIC AND BUSINESS REVIEW 2022;24:111e131 131