1
Is Education Becoming a Weaker Determinant of Occupation? Educational Expansion
and Occupational Returns to Education in 30 European Countries
Abstract: This article examines the relationship between education and
occupation over a course of educational expansion. We analyse European Union
Labour Force Survey (EU-LFS) data from 2014, 2015, and 2016 from 30
European countries. We work with 12 graduated cohorts defined by the year in
which they left the educational system (2003-2014). We use a multilevel model
approach. Education is measured in both absolute and relative terms. The results
show that during a time of educational expansion there was no change in the
relationship between education and occupation if education is conceptualized in
absolute terms. However, there was a change in this relationship if we
conceptualize education as a positional good. Many previous studies posing a
similar research question did not consider the study fields. Our results show that
the role of the study field changed during this time of educational expansion, with
natural science, computer, and IT study fields growing stronger than other fields
of study. We interpret the strengthening of education as a positional good in terms
of the theory of task-biased technological change.
Sociologists working in the field of social stratification agree that the level of
education is a strong determinant of labour market position in modern societies (Blau and
Duncan, 1967; Erikson and Goldthorpe, 1992; Breen, 2004). Education is considered to be
one of the strongest predictors of occupation and income, and also of behaviour, values,
attitudes, and opinions. Different levels of education then imply different positions in the
labour market and different income levels. From this perspective, education is a good that can
be utilized. Returns to education are a topic of interest in sociology. From an economic
perspective, a return can be expressed as the ratio between the benefits related to higher
education and the costs necessary to obtain it. The returns to education are both individual and
social (Dickson and Harmon, 2011; Hout, 2012). The individual returns reflect the fact that
different levels of education result in different occupations, incomes, and economic security.
The social returns describe the social contributions of education, such as the health of the
population, life expectancy, the rates of crime and suicides, and the size of the GDP.
This article focuses on the individual returns to education in 30 European countries.
Dickson and Harmon (2011) and Vila (2000) distinguish between monetary and non-monetary
individual returns to education. Monetary returns are expressed by income; non-monetary
returns include the quality of occupation. This analysis concerns non-monetary returns to
education; specifically, we analyse the relationship between education and occupation and
talk about occupational returns to education. Our analysis focuses on the period between 2003
and 2014, when European countries experienced educational expansions. We focus on the
position in the labour market of the people who left the educational system during this time,
and we study whether and how the educational expansion influenced their transitions into the
labour market. What is the change in the relationship between educational achievement and
occupation in the time of educational expansion in Europe? How strongly does education
determine occupation during a period of rising educational levels? Is this effect stronger or
weaker? These are our research questions.1
1
The answers to these questions in the context of social stratification research, without relation to educational
expansion, cf. Shavit and Müller (1998).
2
Studies that have recently asked similar questions have shown that one of the
consequences of educational expansion is a change in the role of education in the labour
market (cf. Rotman, Shavit, and Shavel, 2016; Fujihara and Ishida, 2016; Bol, 2015; Ortiz and
Rodriguez-Menés, 2016). Education ceases to be an absolute category (nominal) and becomes
a relative category (positional good) that is influenced by time and place. In our analysis, we
build on these conclusions, and we conceptualize education in absolute as well as in relative
terms. In addition, we take into account the fields of study – i.e. the horizontal differentiation
of education, which has not been considered in most previous studies. We start from the
assumption that the study fields are as important for the school-to-work connection as the
level of education. It is known that educational expansion is not universal, but field-specific.
The data we analyse were obtained from the European Union Labour Force Survey (EU-LFS)
between 2014 and 2016. As the data contain information on graduated cohorts in 30 European
countries, we analyse it using multilevel models.
The results support the theory that education is changing into a positional good. The
occupational returns to absolute education did not change dramatically during the period of
educational expansion; the occupational returns to relative education increased. For absolute
education, the effect of study fields was diminished by the educational expansion; for relative
education, this effect was strengthened by educational expansion. The most significant growth
is observed in the field of natural science/computer/IT and engineering/construction. We
interpret the change of education to a positional good as a function of the deployment of new
technologies, computerization, and robotization on the labour market, which increased the
demand for employees in these fields, and which provided them with a relative advantage in
comparison with other fields (task-biased technological change theory).
Role of education on the labour market
Sociologists and economists conceptualize education most commonly as human
capital (Barone and Van de Werfhorst, 2011).2
In this perspective, education indicates
acquired competences that make it possible to work efficiently, i. e. with higher productivity
and obtain an appropriate position in the labour market (Mincer, 1958; Becker, 1964). In
meritocratic societies, people receive various financial compensations according to their
occupation (their different positions in the labour market), and they have different work
benefits and social securities. In short, they belong to different social positions (Jackson,
Goldthorpe, and Mills, 2005; Goldthorpe, 2014). According to Becker (1964), these
differences happen because the modern labour market works on the economic principle of
supply and demand. A higher level of education brings better financial compensation, because
in the context of ongoing modernization processes there is a higher demand for people with
such an education. A lower level of education brings lower financial compensation, because
there is not such a demand for people with lower levels of education. In this perspective, the
monetary and non-monetary benefits of education are determined by the level of education
attainment. These benefits are not influenced by the number of people with the same level of
education, and these benefits do not change much over time. Credentials have intrinsic value
(Mincer, 1974; Becker, 1985; Kerckhoff, Raudenbush, and Glennie, 2001). This is the
absolute (nominal) value of education (Rotman, Shavit, and Shavel, 2015).
Human capital theory originated at a time when the number of tertiary education
graduates in the labour market was not changing (Brown, 2001). When sociologists and
economists talked about educational expansion, they anticipated two social stratification
consequences.
2
The OECD defines human capital as ‘the knowledge, skills, competencies and attributes that allow people to
contribute to their personal and social well-being, as well as that of their countries’ (Keeley 2007).
3
The first consequence is a decrease in unequal chances for education according to
social origin. This anticipated consequence is based on the assumption that in industrial
societies, the labour market rewards only skills (proven by attained education), and not
ascriptive characteristics (social origin, gender, age, or ethnicity). Education-based
meritocracy theory was formulated on this basis. However, this theory was apparently too
optimistic: employers do not make hiring decisions strictly based on the qualification of job
applicants; employers also consider applicants’ social origins and the soft skills that strongly
correlate with social origins. Even though this happens implicitly, this effect of social origin is
still valid. Therefore, this theory was replaced by the theory of education as a great equalizer,
a theory that anticipates that if the accessibility of education increases due to educational
expansion, educational inequalities based on social origin will at least partly decrease
(Bernardi and Ballarino, 2016).
The second consequence is credential inflation (Berg, 1971; Collins, 1979; Collins,
2002). If we understand education as an achieved variable that people utilize in the labour
market, then its value and its monetary and non-monetary benefits are determined by the
relationship between the supply and demand for it. If demand is low and supply is high, the
value of education decreases, and vice versa. Therefore, if the educational expansion increases
the number of people with higher education, while at the same time the number of appropriate
job opportunities does not increase, then the monetary and non-monetary benefits of
education decrease. Ulrich Beck (2011) stated that a university diploma no longer means
automatically getting a good job, but it is a necessary ticket when applying to compete for
such a job. Other authors developing the inflation theory have assumed that members of the
expanding cohorts of graduates must show additional personal competitive advantages in
order to reach similar positions as previous cohorts with identical level of education
(Bourdieu and Passeron, 1990; Bourdieu, 1996; Van de Werfhorst and Andersen, 2005).
Otherwise, graduates must accept less prestigious occupations, while pushing less educated
employees out of the labour market (Burris, 1983; Kivinen and Ahola, 1999; Morrison Paul
and Siegel, 2001; Nelson and Phelps, 1966; Welch, 1970).
However, empirical studies from the 1980s showed no inflation in tertiary education
(c.f. Card and Dinardo, 2002). One explanation was the increasing use of information
technologies across all domains of human activity, which requires a qualified workforce. In
response, the skill-biased technological change theory was formulated in the 1990s.
According to this theory, the labour market gives preference to job applicants with higher
education, while employees with lower education are excluded from the labour market
because their work is outsourced to other countries or is being done by machines (Ábrahám,
2008; Blinder, 2009; Card and Dinardo, 2002; Morrison Paul and Siegel, 2001; Oesch, 2013).
Berman, Somanathan, and Tan (2005) see technology as the main reason for the higher
demand for university graduates. Acemoglu (2002) states that technological changes have
been taking place since the industrial revolution and divides these changes into two periods.
First, the demand for unskilled workers for mass production in factories increased due to
Fordism. After the 1950s, thanks mainly to the introduction of information technology, the
demand for highly skilled workers started to increase. While some studies challenge the
theory of technological change connected with the persistent demand for people with higher
education (cf. Aamodt and Arnesen, 1995; Kivinen and Ahola, 1999), according to this theory
the labour markets of modern societies are not yet saturated with university graduates to the
extent that supply outstrips demand. Educational expansion does not mean decreasing the
value of education as human capital. Moreover, the polarization hypothesis – emerging at the
beginning of the new millennium – provides an empirical evidence that while the income
returns to education do indeed increase for workers with tertiary attainment (as expected by
the skilled-base technological change theory), they also do so for workers of the lowest
4
attainment due to growth of the service sector. The labour market becomes polarized (Autor,
Levy, Murnare 2003; Manning 2004; Oesch, Rodriguez Menés 2010). In our first hypothesis,
we therefore expect that the value of education as human capital in a time of educational
expansion does not change. The occupational return to absolute education remains constant.
Education as a positional good
The theory of education as a positional good assumes that the value of education is
contextually contingent (Thurow, 1975; Hirsch, 1977). The returns to education are
determined by the fraction of individuals at a given level of education relative to the fraction
of individuals at other levels of education. The sociological literature often presents the
example of a car: its benefit as a means of transportation from point A to point B varies for the
user according to the number of cars around. If a majority owns a car, traffic jams result and
the benefit of a car decreases (Hirsch, 1978; Ultee, 1980). Therefore, owning a car is not a
value in itself; rather, the time and place define the value of a car. The value of education as a
positional good varies in relation to its scarcity. The value does not come from the level of
education alone. In a time of educational expansion, the number of people attaining higher
education increases, and the individual return to education can be directly affected by this
number. People with the same level of education are in the same position when entering the
labour market; however, if their number is larger than the number of relevant job
opportunities, the return to higher levels of education can approach that of lower levels of
education. Employers start to distinguish among people according to other, ‘softer’ criteria
(Thurow, 1975). According to signalling theory (Arrow, 1973; Spence, 1973), the level of
education is then only one of a set of a person’s characteristics (albeit the most important one)
for taking up a position in the labour market. Other non-directly observable characteristics
include the motivation to work; the ability to learn new things; the ability to express one’s
opinion; the ability to lead and motivate people, to pursue a certain goal over a longer period,
and to be loyal to the work and the employer (Weiss, 1995; Jackson, Goldthorpe, and Mills,
2005; Goldthorpe, 2014).
The change of education in a positional good can be conceptualized on a macro as well
micro societal level. On the macro level, the benefits of different educational levels change.
This can happen ‘from the bottom’ or ‘from the top’ of the educational structure. In the first
case, lower educational levels become redundant in the labour market, their returns to
education decrease, and higher levels of education thus receive a relative advantage over
them. In the second case, the demand for higher levels of education grows thanks to
technological changes, while the technology does not change for lower levels. In both cases,
the positional advantages for some levels of education are created.
On the micro level, employers create job opportunities connected with specific
requirements. For these jobs, they do not expect applicants to have specific knowledge
obtained in the educational system. On the contrary, they choose candidates who were
prepared by the educational system to be able to obtain this knowledge after taking up the
position. This is expected to happen in the shortest time possible and using the least financial
resources possible. The employer has to bear the time and financial burden connected with the
imperfect work of an untrained employee. Therefore, it is not the absolute value of education
that guarantees a position in the labour market, but the ability, readiness, and facility of a
person to successfully master the requirements connected with a particular work position
(Goldthorpe, 2009). Job applicants are then lined up by the employer into an imaginary labour
queue not only according to the level of education, but also according to a number of other
characteristics (Thurow, 1975).
5
The change in the value of education from absolute to relative has been tested by a
number of sociological studies (e.g. Ultee, 1980; Olneck and Kim, 1989; Bol, 2015; Bukodi
and Goldthorpe, 2016; Rotman, Shavit, and Shalev, 2016; Fujihara and Ischida, 2016; Ortiz
and Rodriguez-Menés, 2016). Even these studies focused on different social phenomena; in
relation to the monetary or the non-monetary benefits of education, they share a common
question: to what extent is the change in analysed phenomenon caused by the shift in
education from human capital to a positional good? The results show that each concept of
education produces different results. While the explanatory potency of education
conceptualized in absolute terms decreased, the explanatory power of education as a
positional good increased.3
Following these conclusions, in our second hypothesis we expect
that the value of education, conceptualized as positional good, increases in a time of
educational expansion. The occupational returns to relative education increase.
Educational expansion and horizontal differentiation of study fields
In 1999 the Bologna declaration was signed, which can be seen as jumpstarting the
most recent wave of educational expansion in Europe.4
This declaration recommended that
the signatory nations should divide university education into three levels: a widely accessible
Bachelor’s level, more selective Master’s (level 5 of the International Standard Classification
of Education ISCED), and a scientific post-graduate (level 6 ISCED).5
The separation of fiveyear
Master’s degrees enabled universities to substantially open the educational system to a
wider range of applicants. As another reason for educational expansion, Kogan (2012) cites
the establishing of new study fields and new institutions for higher education (private, and
particularly smaller regional ones). In the Czech Republic, the number of higher education
institutions increased from 23 in 1999 to 67 in 2015. Private universities started to appear
after 2000. By 2015, there were 41 private universities.
Figure 1 shows the educational expansion in 30 European countries between 2003 and
2015 for the age group 25 to 34 years.6
The X axis shows the proportion of people with
tertiary education in 2003 in each country. The Y axis shows how much this proportion
increased between 2003 and 2015. The expansion is path dependent and is negatively
contingent upon the proportion of people with tertiary education (Pearson correlation is 0.55).
The increase between 2003 and 2015 is lower in countries where the proportion with tertiary
education was already higher in 2003, and vice versa. The average proportion of people with
tertiary education across the countries was 26.5 % in 2003, and 40.4 % in 2015. Among the
Western European countries that in 2003 fell below the European Union average,
3
Cf. the monothematic issue of Research in Social Stratification and Mobility 43 (2016), which focuses on the
change in the value of education as a positional good, and the effects of this change on social stratification
outcomes.
4
The first educational expansion occurred in Western European countries in the 1970s, when the number of
university students more than doubled (Throw, 1973). In the socialist countries, the class and political affiliations
of the parents, as well as the low capacity of universities, limited access to higher education before 1989.
Moreover, in these countries, studying at a university was subject to central planning (similar to other aspects of
society). After the fall of the communist regimes in 1989, most of the limits were eased and the number of
university students in the former socialist countries began to rise; the Bologna declaration started the massive
educational expansion in these countries, together with the countries of Western Europe.
5
This classification comes from ISCED version 1997, i.e. before the Bologna declaration. A new version, ISCED
2011, makes a distinction between the levels of tertiary education (Bachelor’s – ISCED 6, Master’s – ISCED 7,
Ph.D. – ISCED 8); however, this classification became relevant to empirical research only after 2014.
6
The analysed countries are (abbreviations in parentheses): Austria (AT), Belgium (BE), Bulgaria (BG), Croatia
(HR), Cyprus (CY), Czechia (CZ), Denmark (DK), Estonia (EE), Finland (FI), France (FR), Germany (DE),
Greece (GR), Hungary (HU), Iceland (IS), Ireland (IE), Italy (IT), Latvia (LV), Lithuania (LT), Luxembourg
(LU), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Slovakia (SK), Slovenia
(SI), Spain (ES), Sweden (SE), Switzerland (CH), and United Kingdom (UK).
6
Luxembourg and Greece reached parity with the EU average by 2015 (Luxembourg 18.8% +
31.5%, Greece 23.1% + 17%). Among the former socialist countries, which were below
average in 2003, Poland (20.3% + 22.9%), Slovenia (23.7% + 17.1%), and Latvia (18.5 % +
21.4 %) reached the EU average. The other former socialist countries did not reach the EU
average by 2015, despite intense expansion. The situation was similar in four Western
European countries: Austria (20.1% + 18.5 %), Portugal (16.7 % + 16.4 %), Germany (21.8 %
+ 7.7%), and Italy (13.0 % + 13.2 %). These countries were still below the EU average in
2015.
Insert Figure 1
Martin Trow (1973) makes a distinction among three types of massification of higher
education, which differ in their social functions and goals: elite, mass, and universal. The
transition between the elite and mass phases takes place when more than 15 % of a birth
cohort enters the given educational phase. The mass system becomes universal when at least
50 % of a birth cohort enters higher education.7
In terms of the goals tertiary education is
supposed to move from building narrow elites (in the elite phase), through the training for
new and demanding types of occupations (in the mass phase), to increasing the adaptability of
wide strata of the population to conditions that are constantly changing (in the universal
phase).
From the perspective of this typology, no country in this analysis can be described as
elite. The lowest proportions of the population as students in higher education in 2015 were
found in Italy (25.2 %), Romania (25.5 %), and Germany (29.6 %). The group of universal
countries, where the proportion of higher education students in the age group 25-34 is higher
than 50 %, comprised four countries: Lithuania (54.8 %), Cyprus (54.7 %), Ireland (52 %),
and Luxembourg (50.3 %).
Previous research concerned with the changing role of education in the labour market
(cf. Ultee, 1980; Olneck and Kim, 1989; Bol, 2015; Bukodi and Goldthorpe, 2016; Rotman,
Shavit, and Shalev, 2016; Fujihara and Ischida, 2016) did not take into account the fields of
study. People with different fields of study have been seen as competing for the same
positions in the labour market. It was supposed that they stand in one labour queue (Thurow
1975). However, if we assume that graduates of different study fields compete for different
jobs, as recently suggested by Ortiz and Rodriguez-Menés (2016), we also have to assume
that they stand in different labour queues. In his typology of educational expansion, Trow
(1973) assumed that the gradual massification also means a differentiation among schools.
Tertiary education, which was originally offered by a limited number of elite universities,
strictly separated from the influences of the labour market, is, in a time of educational
expansion, also obtained at institutions of higher education that are not of the university type.
These institutions do not share the academic values of elite universities, and often they focus
primarily on developing skills that can be used in the labour market. The consequence is that
there are differences among study fields; these differences are not affected by the expansion to
the same extent. According to Thurnow (1975), there is a job queue in the labour market
wherein job applicants distinguish among types of jobs, ranking the jobs according to their
technical complexity. Engineers with technical training apply for jobs that are different from
those sought by dentists with medical training or journalists and sociologists with education in
the humanities. Heckman et al. (2006) confirm this when they show that the benefits of
education in American society are contingent primarily upon the chosen field. According to
7
The massification of the tertiary level is associated with progressive secularization and the simultaneous
dismantling of social elites (Trow, 1973).
7
their findings, more important than the diploma is the field of study in which the diploma has
been obtained.8
When considering the occupational returns to education in a time of educational
expansion, it is therefore necessary to consider not only the level of education (empirically
measured in absolute and relative terms), but also the field of study. Following this argument,
we expect that educational expansion changes both the relationship between level of
education and occupation and the relationship between study field and occupation. If this
occurs, then the identification of the study fields that gain a relative advantage in the labour
market compared to other study fields during an educational expansion should contribute to
the interpretation of the increase in educational positionality, as we assume in hypothesis 2.
Data and variables
We analyse data from the European Union Labour Force Survey (EU-LFS) from 2014,
2015, and 2016 (EU Labour Force Survey…, 2017).9
The data come from 30 European
countries (Figure 1). Because we are interested in the school-work connection, which is
conceptualized as a non-monetary benefit of education (Vila, 2000), we constrain our analysis
to the age group of 25-34. These are the individuals who had already left the educational
system and who are working.. Within this group, we distinguish 12 graduated cohorts (defined
by the year in which they left school). The first cohort graduated in 2003, the second in 2004,
and so on, until the final cohort of 2014. The total number of analyzed respondents is 991,922
(Table A1 in Appendix shows the number of analyzed respondents for each country and year).
The dependent variable is current occupation, indicated in the data as ISCO08
(International Standard Classification of Occupations) (cf. ILO, 2008). From this variable, we
constructed the International Socio-Economic Index of Occupational Status (ISEI)
(Ganzeboom, De Graff, and Treiman, 1992; Ganzeboom and Treiman, 1996; 2003). The
‘philosophy’ of ISEI is that occupation transforms education into income. ISEI is a continuous
scale in which the higher the value, the higher the socio-economic position in the labour
market. ISEI is typically constructed from a 4-digit ISCO, which, however, is not present in
the EU-LFS data. A 3-digit ISCO is available instead. We multiplied it by 10, thereby
extending it to a 4-digit ISCO. It is possible to carry out such an operation, since ISCO is
hierarchically collapsible at the expense of losing accuracy (ILO, 2008).10
Education is indicated by ISCED11 categories (0, 100, 200… 800).11
We have
transformed these categories into an interval variable of years in the educational system: 12,
8
The structural changes in labour markets also speak for the consideration of the fields of study. If, for instance,
the number of job opportunities in agriculture dramatically decreases due to a transition from being a primary to
a secondary industry, as described by Breen et al. (2004), it will be more difficult for graduates in agriculture to
find relevant jobs (it is known that the labour markets of developed countries first shift from agricultural to
industrial production, and then from industrial production to social services).
9
EU-LFS is a survey carried out by European Union member states, which is harmonised by Eurostat, so that the
results are comparable among countries and time periods. The sample represents all people living in households
who are older than 15 years at the time of the survey. The respondents living in ‘institutional households’, i.e.
army barracks, hospitals, or prisons, are not included. For instance in year 2016, data on 1.5 million respondents
was available from 28 European Union members plus Iceland, Norway, and Switzerland.
10
For instance, transforming the four-digit ISCO code 2211 to 221 means alternation from the group ‘general
practitioners’ to the more general group, ‘medical doctors’. The comparison of ISEI as calculated from a 4-digit
ISCO, with ISEI calculated from a 3-digit ISCO shows that the average deviation caused by using a 3-digit ISCO
is 2.05 ISEI scores. The errors for individual ISCO codes range from 0 to 21.96 ISEI scores, with more than half
of the codes showing an error less than 1 ISEI score.
11
International Standard Classification of Education (ISCED) is indicated according to the ISCED97 standard in
the EU-LFS data until 2013, which does not enable a distinction between levels of tertiary education (BA, MA).
After 2014, the ISCED11 is incorporated in the data, and it is possible to distinguish between graduates of
secondary education, as well as BA, MA, and doctoral programmes of tertiary education. Since the ISCED97
8
14, 16, 18 and 20 years (for this transformation, see the Appendix). In this way we
operationalize education in absolute terms that remain unadjusted across graduated cohorts
and countries. As our goal is to compare the effects of absolute and relative educations, we
created standardized value for this variable: z-scores (mean = 0, SD = 1), and work with it.
We indicate the relative education by proportion measure (percentile scores of
education from 0 to 100) for cohorts and countries. A number of previous studies dealing with
the relative concept of education have used identical indications (cf. Ultee, 1980; Olneck and
Kim, 1989; Bol, 2015; Ortiz and Rodriguez-Menés, 2016). When constructing the percentile
scores for education, we first connected the ISCED11 categories with years spent in the
educational system (see the Appendix for this transformation) and then converted these into a
ranked variable. In this way, we determined the position of each respondent relative to others
in the graduated cohort and country. These positions are influenced by the number of years in
the educational system of other respondents, i.e. by the composition of each graduated cohort.
To analyse absolute education, we also created standardized values for this variable (z-scores,
mean = 0, SD = 1) and work with them.
Studies that have posed similar questions in the past have assumed that the effect of
educational expansion is not horizontally (by study fields) differentiated (cf. Ultee, 1980; Bol,
2015), with one exception (cf. Ortiz and Rodriguez-Menés, 2016). In our analysis, we reject
the assumption of one-dimensionality, and we explicitly control the results for the fields of
study. In the data, the highest fields of educational attainment are indicated by the ISCED97
and ISCED11 codes. We have recoded these into six categories: 1) unspecified and general; 2)
education, social, and services; 3) natural, computers, and IT; 4) engineering, construction; 5)
agriculture, forestry, and veterinary; 6) health and welfare (for more on this, see the
Appendix).
Further variables for which we control the effect of absolute and relative education on
occupation are gender, family status, and full time/part time job. We consider the effect of
these three variables because it is known that the occupational returns to education are
different for men and women (Manning and Saidi, 2010; Peet, Fink and Fawzi, 2015), for
single and married people (DiPrete and Buchman, 2006; Hout, 2012) and for the case of full
and part time job positions (Bol, 2015).
The dataset is characterized by a hierarchical structure: at the first (micro) level,
individuals are settled. These individuals are nested in the cohorts that constitute the second
(macro) level of analysis. The cohorts are nested in the countries that constitute the third
(macro) level of analysis. Because of this structure, multilevel modelling is employed to
estimate the effect of the predictors on ISEI (for details on this method, see Gelman and Hill,
2006; Rabe-Hesketh and Skrondal, 2012). The major advantage of multilevel models is their
ability to combine characteristics from the micro level with those from the macro levels,
assuming that the variation in the dependent variable comprises two parts, within- and
between-group components.
At the contextual levels, we are interested in the effect of educational expansion on the
effect of education on occupation. Educational expansion is a macro variable. The effect of
this variable should therefore be qualitatively different from the effects of individual
variables. We have operationalized the educational expansion as the proportion of people with
tertiary education (indicated by ISCED11, 5-8 levels) aged 25-34 in each country and cohort
(together, 360 numbers given by 12 cohorts in 30 countries). This variable defines our 360
analytical macro contexts and enables us to change the 3-level hierarchical structure of the
data (individuals in cohorts and cohorts in countries) into a 2-level hierarchical structure
(individuals in cohorts by countries). With this transformation, we lose one level in the data
and the ISCED11 categories are not unambiguously transformable, we analysed only data from 2014, 2015, and
2016.
9
but increase the number of contexts in the second level. The estimation of the effect of
educational expansion should then be more reliable (cf. Bryan and Jenkins, 2016). Table 1
presents the descriptive statistics of all variables (a detailed description is available in Table
A2 in the Appendix).
Insert Table 1
Methods and statistical analysis
In order to identify the trends in the effect of education on occupation and test our
hypotheses, we estimated two sets of models. The first set includes five two-level random
effect models for absolute education and study fields. The second set includes five two-level
random effect models for relative education and study fields. The models are identical; they
differ only in the standardized variable of education. The general equation for these models is:
c cic ic icy X Z e     
where yic is the ISEI score for each respondent i and context c; Xic are observed individual
variables with estimated parameters β; and Zc is observed contextual variable of educational
expansion (that does not vary on an individual level) with estimated parameter γ. The
unobserved individual effect is eic and the contextual effect uc (both assumed to be normally
distributed and uncorrelated with observed individual and contextual variables). We start with
the null random intercept model (in which no covariates are taken into account) and continue
by adding relevant covariates and adding random-slope models for education and study fields
with and without a cross-level interaction term.
Estimated models are reported in Table 2 for absolute education and in Table 3 for
relative education. The interclass coefficient (ICC) in Model 0 reveals the degree of variance
in the dependent variable (ISEI) that is attributable to the context level. While the model has
no explanatory variables, it suggests that occupation status is likely shaped by variation at the
individual level (not much variance in the ISEI stems from the context level: 10.18%).
When individual-level factors are added to the null model (Model 1 in Table 2 and
Table 3), we see a decrease in ICC for absolute education, but an increase in ICC for relative
education.12
This confirms that ISEI is mostly determined by micro-level factors (at the
individual level). We can also see a considerable improvement in the LL statistic, meaning
that these variables markedly increase model fit. We evaluated the significance of the change
by using a likelihood-ratio test, the value of which can easily be derived from the difference in
the LL criteria of the compared models.
All the individual-level variables are statistically significant.13
Compared to men,
women have a slightly higher average ISEI (by 0.74 in absolute education, by 0.88 in relative
education). Similarly, those who are married have a slightly higher average ISEI than others
(by 0.15 in absolute education, by 0.22 in relative education). A distinctly higher average ISEI
can be found in the comparison of full-time jobs with part-time jobs (by 4.43 in absolute
education, by 4.54 in relative education). Both variables on education are standardized (z-
12
An increase in ICC happens in cases ‘if the estimated level-1 variance decreases more than the level-2 variance
does when covariates are added’ (Rabe-Hesketh and Skrondal 2012:137).
13
In the models, we report significance because we understand it within multilevel models as ‘model-based’. In
this case, the statistical model can be understood as a ‘data-generating mechanism’, and the randomness of the
parameters results from a distribution of responses and not from sampling units of a finite population (for more
on this, cf. Rabe-Hesketh and Skrondal 2012).
10
scores), and they increase the average ISEI. Specifically: if absolute education increases by 1
SD, the average ISEI increases by 13.34. If relative education increases by 1 SD, the average
ISEI increases by 11.52.
In absolute education, natural/computer/IT, and health/welfare fields increase the
average ISEI when compared to unspecified/general fields (by 2.32 and 3.51, respectively).
The other fields decrease the average ISEI when compared to the reference category (the
strongest negative effect is in the agriculture/forestry/veterinary field). The same conclusions
apply also for relative education; only the effects are slightly different. The effects of all
individual variables remain almost identical across the other more complex models (Models 2
to 4) for absolute as well as relative education. We therefore consider them to be robust across
the models.
Model 1 for both absolute and relative education assumes a fixed effect for the
education and study fields. But because it cannot be ruled out that these variables influence
ISEI differently by countries and cohorts (cf. Shavit and Müller, 1998), in the next step we
allowed the slopes of education and study fields to vary randomly across contexts. As reported
for Model 2 in Tables 2 and 3, we found that the effects are random. In the case of absolute
education, the fixed effect is 13.46 and its random effect is 3.81. In the case of relative
education, the fixed effect of education is 12.11 and the random effect is 8.43.14
This
corresponds with the findings of Shavit and Müller (1998), who showed that the association
between education and occupation is influenced by the institutional context. They talk about
stratification and standardization of educational systems, the occupational specificity of
vocational education, and the relative size of the tertiary sector. Countries that differ in these
characteristics also differ in the effect of education on occupation (cf. Shavit and Müller
1998). The variance in relative education in Model 2 is significantly higher than in absolute
education. This indicates that relative education reacts to changes due to educational
expansions more sensitively than absolute education. The downside of using the random
effect model is that it is not possible to calculate the interclass correlation (c.f. Kreft and De
Leeuw, 1998). This is too minor a downside to require considering a better specified model.
Insert Table 2
Insert Table 3
In Model 3 in Tables 2 and 3, we added a context-level variable to the previous model:
educational expansion. This variable would play a role in the relationship between education
and occupational achievement. Its effect is significant only for relative education. Moreover,
in the first case it is zero; in the second case it is positive. This means that in absolute
education, educational expansion does not have an effect on average ISEI; in relative
education, it works in favour of higher average ISEI. Furthermore, while the LL criterion
improved after including the context-level variable, the change is rather small compared to the
shift in this criterion from Model 0 to Model 1. This again confirms that ISEI is mostly
determined by micro-level variables.
Model 4 (in Tables 2 and 3) includes cross-level interactions that expresses the joint
effect of education and educational expansion and joint effect of study fields and educational
expansion. Hypotheses 1 and 2 predicted that educational expansion should influence the
14
It can be assumed that the effect of education is normally distributed around the fixed effect. This means that
95% of its values lie in the range of ± 2σ around the mean.
11
absolute and the relative effects of education on occupation in different ways. Educational
expansion does not change the effect of absolute education, while it strengthens the effect of
relative education. Figure 2 shows the effects of absolute and relative education on ISEI (Y
axis) in relation to the rate of educational expansion (X axis). These are the margins from
Model 4, presented in Tables 2 and 3. The educational expansion does not change the effect of
absolute education on ISEI; it strengthened the effect of relative education. When the
proportion of people with tertiary education (aged 25-34) is low (10%), the effect of absolute
education is a little bit stronger than the effect of relative education. When the proportion of
educated people is 50%, relative education is the stronger determinant of occupation. Both
variables are standardized (z-scores), and their effects are therefore comparable to each other.
Based on Model 4, we do not reject hypotheses 1 and 2, and conclude: education changes its
role in the labour market from absolute to relative as a result of educational expansion.
Insert Figure 2
Why did this happen? Figures 3 and 4 show the average ISEI (Y axis) by study field in
relation to the educational expansion with regard to absolute and relative education
(respectively). These are again margins from Model 4, presented in Tables 2 and 3. In both
cases, study fields differentiate positions in the labour market. At the beginning of the
expansion (10 % of people with tertiary education), the effect of study field on ISEI for
absolute education was significantly higher than the effect of study field for relative
education. For instance, agriculture/forestry/veterinary in the absolute model is about 42 ISEI
scores; in the relative model, it is about 32 ISEI scores. The educational expansion decreases
the average ISEI in agriculture/forestry/veterinary and health/welfare fields in the absolute
model. In the relative model, the effect of study fields strengthens. The fields that rose the
most in the relative model are natural/computer/IT and engineering/construction.
Agriculture/forestry/veterinary strengthened only a little bit. The distances between the effects
of other fields remains constant.
These findings suggest that the shift of education should be interpreted as shifting into
a positional good during educational expansion as a consequence of the deployment of new
technologies in production, computerization, and robotization in the labour market. Such
changes increase the demand for employees in the computing and IT fields, and give
individuals with education in these fields a relative advantage over those who study other
fields. This is probably a period effect that would correspond to skill-biased technological
change theory (Bernardi and Ballarino, 2014). According to this theory, people at the other
end of the spectrum of the educational structure should be at a disadvantage when compared
to other fields. However, this was not observed in our data. In Figure 4, we see this with the
category ‘unspecified/general’; the effect of this field on occupation was rather strong at the
beginning of the expansion, and the educational expansion made it even stronger. Therefore,
Goos, Manning, and Salomons (2014) speak of a task-biased technological change theory.
According to this theory, the positionality of education is not in some advanced skills; the
occupational returns depends on the tasks an employee can carry out as a result of those skills
and how routine these tasks are.
Insert Figure 3
Insert Figure 4
12
Conclusion
A number of sociological analyses (cf. Ross and Wu, 1995; Pallas, 2006; Hout, 2012)
show that more highly educated people tend to be healthier, to have higher life expectancy,
and to experience better well-being. They see their occupation as a form of self-fulfilment, not
as a routine activity necessary to obtain financial resources for living. They differ in patterns
of assortative mating; their political votes are rather stable, without swings to extremist
political parties; and subjectively these people are also happier than people with lower
education.
We explored the relationship between education and occupation during a period of
educational expansion in 30 European countries. In the introduction, we posed a question:
how does educational expansion influence the effect of education on occupation? We
conceptualized education as an individual variable in absolute as well as in relative terms (as a
nominal and positional good). We started with the assumption that occupational returns to
education are not absolute, as was long expected in the social sciences, but rather that they are
contingent upon time and place. We therefore expected that they change according to social
context. In contrast to previous studies focused on similar topics, we additionally included the
effect of study fields in the analysis. The identification of this effect means a
multidimensionality of the education-occupation relationship, and also contributes to the
interpretation of the findings.
We suggest interpreting the change from absolute education to relative education on
the basis of study fields using the task-biased technological change theory. According to the
original skill-biased technological change theory, those in possession of university diplomas
are rewarded by the labour market for their higher qualification, which enables them to take
advantage of the quickly evolving sophisticated technologies. People with lower education
should be pushed out of the labour market, and their work should be taken over by machines
or employees in countries with lower labour costs (Bernardi and Ballarino, 2014). However,
the empirical findings contradict this theory. They show that the occupational returns to
education also increase at the lowest end of the educational spectrum during the educational
expansion. Lower-educated employees are able to obtain well-paid work in the developing
services sector, in jobs that cannot be outsourced to other countries. In contrast, a number of
relatively qualified workers with routine tasks lose jobs due to computers taking over their
tasks. In this respect, Goos, Manning, and Salomons (2014) formulated the task-biased
technological change theory. According to this theory, the tasks are more important than the
skills, and the distinguishing criterion of the occupational return to education is the level of
routine (c. f. Autor, Levy, Murnare 2003; Manning 2004; Oesch, Rodriguez Menés 2010).
Employees with more routine jobs are more easily replaceable by computers even when
having relatively high educational level obtained. In our data, this specifically means that the
effect of the computer and IT study field on occupation grew stronger, the health and welfare
study field weakened, and the effect of the general and unspecified study field did not
weaken.
The positional change of education should be understood as a period effect. In times of
technological decline or economic crisis, these changes would be probably different. It is
possible that under different economic circumstances educational expansion could lead to a
decline in the labour-market value of tertiary diplomas as described by the theory of inflation
(Berg, 1971; Collins, 1979; Bourdieu and Passeron, 1990). It is also possible that the role of
education would shift in a way not yet described by social sciences. All of this leads to the
conclusion that the merit of education with respect to one’s labour market performance should
not be seen as fixed but rather as context-dependent.
13
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Source: OECD (2019), Population with tertiary education (indicator). doi: 10.1787/0b8f90e9-en.
Note: Tertiary education indicated by ISCED 2011 (5-8 levels) for age group 25-34.
AT
BE
BG
CH
CY
CZ
DE DK
EE
ES
FI
FR
GR
HR
HU
IE
IS
IT
LT
LU
LV
NL
NO
PL
PTRO
SE
SI
SK
UK
y = 23.87 - 0.38x
correlation= -0.55
0
5
10
15
20
25
30
35
40
45
0 5 10 15 20 25 30 35 40 45
increaseofpeoplewithtertiaryeducation2003-2015(%)
people with terrtiary education in 2003 (%)
Figure 1. Educational expansion in 30 European countries
between 2003 and 2015 (%)
18
Table 1. Descriptive statistics
Variable Mean Std. Dev. Min. Max. Categories
Individual variables
ISEI 49.40 21.28 11.01 88.70
Education in years (standardized) 0.00 1.00 -1.07 2.16
Education in percentiles (standardized) 0.00 1.00 -1.25 2.10
Study fields 2.92 1.48 1 6 1 - unspecified/general; 2 education/social/services;
3 - natural/computer/IT;
4 - engineering/construction; 5 agriculture/forestry/veterinary;
6 -health/welfare
Gender 1.50 0.50 1 2 1 – man; 2 – woman
Marriage 1.31 0.46 1 2 1 – other; 2 – married
Full-time job 1.84 0.36 1 2 1 – part-time; 2 – full-time job
Contextual level
Country by Cohort - - 1 360
Contextual variable
Educational expansion by countries and cohorts 31.62 10.21 10.70 54.00 360 numbers for 12 cohorts in 30 countries
Source: Labour Force Survey (LFS) from years 2014, 2015 and 2016; contextual variable comes from OECD (2019), Population with tertiary
education (indicator). doi: 10.1787/0b8f90e9-en. There were 991,922 respondents.
Table 2. Multilevel models for occupational status attainment in age group 25-34 - education is measured as absolute variable
β SE β SE β SE β SE β SE
Individual variables
Gender
man ref. ref. ref. ref.
woman 0.743 *** ( 0.035 ) 0.753 *** ( 0.035 ) 0.753 *** ( 0.035 ) 0.757 *** ( 0.035 )
Marriage
other ref. ref. ref. ref.
married 0.150 *** ( 0.040 ) 0.127 *** ( 0.040 ) 0.127 *** ( 0.040 ) 0.129 *** ( 0.040 )
Full-time job
part-time ref. ref. ref. ref.
full-time 4.430 *** ( 0.042 ) 4.335 *** ( 0.042 ) 4.335 *** ( 0.042 ) 4.316 *** ( 0.042 )
Education in years (z-scores) 13.341 *** ( 0.017 ) 13.458 *** ( 0.088 ) 13.456 *** ( 0.088 ) 11.190 *** ( 0.305 )
Study fields
unspecified/general ref. ref. ref. ref.
education/social/services -3.242 *** ( 0.052 ) -3.236 *** ( 0.053 ) -3.236 *** ( 0.053 ) -3.876 *** ( 0.184 )
natural/computer/IT 2.320 *** ( 0.081 ) 2.351 *** ( 0.082 ) 2.351 *** ( 0.082 ) 0.123 ( 0.285 )
engineering/construction -4.623 *** ( 0.057 ) -4.630 *** ( 0.058 ) -4.629 *** ( 0.058 ) -6.091 *** ( 0.190 )
ag./forestry/veterinary -11.470 *** ( 0.105 ) -11.526 *** ( 0.106 ) -11.525 *** ( 0.106 ) -9.315 *** ( 0.347 )
health/welfare 3.512 *** ( 0.069 ) 3.524 *** ( 0.069 ) 3.524 *** ( 0.069 ) 7.848 *** ( 0.256 )
Constant 50.796 *** ( 0.225 ) 47.819 *** ( 0.192 ) 47.916 *** ( 0.194 ) 47.156 *** ( 0.647 ) 47.716 *** ( 0.667 )
Contextual variable
Educational expansion 0.002 ( 0.018 ) -0.035 ( 0.168 )
Interaction
Edu expansion*Edu in years (z-scores) 0.035 *** ( 0.009 )
Edu expansion*Study fields
education/social/services 0.022 *** ( 0.005 )
natural/computer/IT 0.033 *** ( 0.008 )
engineering/construction 0.050 *** ( 0.005 )
ag./forestry/veterinary -0.073 ( 0.010 )
health/welfare -0.124 *** ( 0.007 )
Variance constant 46.818 13.605 29.095 29.069 29.210
Variance education in years (z-scores) 3.811 3.810 3.657
Variance study fields 0.873 0.873 0.890
ICC
LL
N / Groups
Sources: Labour Force Survey (LFS) from years 2014, 2015 and 2016.
Random effects parameters
Model 4Model 0 Model 1 Model 2 Model 3
-3058598.4
10.18% 5.08%
-3240371.5 -3062316.9 -3058942.6 -3058941.9
Note: Dependent variable is ISEI (11,01-88,70); standard errors are in parentheses; 360 contexts = 12 cohorts by 30 countries; xtmixed command in Stata 15 used to
obtain this table; * p ≤ ,10, ** p ≤ ,05, *** p ≤ ,01.
731099 / 360 731099 / 360 731099 / 360 731099 / 360 731099 / 360
19
Table 3. Multilevel models for occupational status attainment in age group 25-34 - education is measured as relative variable
β SE β SE β SE β SE β SE
Individual variables
Gender
man ref. ref. ref. ref.
woman 0.881 *** ( 0.036 ) 0.839 *** ( 0.035 ) 0.840 *** ( 0.035 ) 0.843 *** ( 0.035 )
Marriage
other ref. ref. ref. ref.
married 0.215 *** ( 0.041 ) 0.162 *** ( 0.041 ) 0.162 *** ( 0.041 ) 0.168 *** ( 0.040 )
Full-time job
part-time ref. ref. ref. ref.
full-time 4.536 *** ( 0.043 ) 4.349 *** ( 0.043 ) 4.350 *** ( 0.043 ) 4.347 *** ( 0.043 )
Education in percentiles (z-scores) 11.517 *** ( 0.018 ) 12.107 *** ( 0.144 ) 12.104 *** ( 0.144 ) 10.784 *** ( 0.487 )
Study fields
unspecified/general ref. ref. ref. ref.
education/social/services -2.204 *** ( 0.054 ) -2.795 *** ( 0.055 ) -2.796 *** ( 0.055 ) -4.923 *** ( 0.186 )
natural/computer/IT 3.566 *** ( 0.083 ) 2.902 *** ( 0.084 ) 2.902 *** ( 0.084 ) -0.695 ( 0.288 )
engineering/construction -3.774 *** ( 0.058 ) -4.271 *** ( 0.059 ) -4.270 *** ( 0.059 ) -7.153 *** ( 0.192 )
ag./forestry/veterinary -10.926 *** ( 0.108 ) -11.296 *** ( 0.108 ) -11.295 *** ( 0.108 ) -10.155 *** ( 0.352 )
health/welfare 3.779 *** ( 0.070 ) 3.454 *** ( 0.071 ) 3.453 *** ( 0.071 ) 4.182 *** ( 0.262 )
Constant 50.796 *** ( 0.225 ) 47.919 *** ( 0.226 ) 48.182 *** ( 0.255 ) 40.504 *** ( 0.842 ) 42.763 *** ( 0.835 )
Contextual variable
Educational expansion 0.233 *** ( 0.024 ) 0.160 *** ( 0.024 )
Interaction
Edu expansion*Edu in percen. (z-scores) 0.039 *** ( 0.014 )
Edu expansion*Study fields
education/social/services 0.070 *** ( 0.005 )
natural/computer/IT 0.086 *** ( 0.008 )
engineering/construction 0.096 *** ( 0.006 )
ag./forestry/veterinary -0.004 ( 0.010 )
health/welfare 0.015 ( 0.007 )
Variance constant 46.818 49.585 50.645 54.206 45.693
Variance education in percentiles (z-scores) 8.428 8.434 8.287
Variance study fields 0.913 0.914 0.957
ICC
LL
N / Groups
Sources: Labour Force Survey (LFS) from years 2014, 2015 and 2016.
Model 4Model 0 Model 1 Model 2 Model 3
Random effects parameters
10.18% 15.71%
-3240371.5 -3079324.7 -3070730.4 -3070712.2 -3070494.4
Note: Dependent variable is ISEI (11,01-88,70); standard errors are in parentheses; 360 contexts = 12 cohorts by 30 countries; xtmixed command in Stata 15 used to
obtain this table; * p ≤ ,10, ** p ≤ ,05, *** p ≤ ,01.
731099 / 360 731099 / 360 731099 / 360 731099 / 360 731099 / 360
20
Note: Absolute education effect is estimated from Model 4 in Table 2, relative education effect is estimated from Model 4 in Table 3.
10,0
10,5
11,0
11,5
12,0
12,5
13,0
13,5
14,0
14,5
15,0
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
lineareffect
people withtertiaryeducation inage 25-34 (%)
Figure2. Marginaleffects of absoluteand relative education by
educational expansion (model 4 estimation)
absolute education relative education
21
Note: Education effect is estimated from Model 4 in Table 2.
25
30
35
40
45
50
55
60
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
averageISEI
people withtertiary educationin age 25-34 (%)
Figure 3. Marginal effects of study fields by educational
expansion (absolute education, model 4 estimation)
unspecified/general education/social/services
natural/computer/IT engineering/construction
agriculture/forestry/veterinary health/welfare
22
Note: Education effect is estimated from Model 4 in Table 3.
25
30
35
40
45
50
55
60
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
averageISEI
people withtertiary educationin age 25-34 (%)
Figure 4. Marginal effects of study fields by educational
expansion (relative education, model 4 estimation)
unspecified/general education/social/services
natural/computer/IT engineering/construction
agriculture/forestry/veterinary health/welfare
23
Table A1. The numbers of analyzed respondents from individual countries and years.
Country 2014 2015 2016 Total
Austria (AT) 13 193 13 645 14 680 41 518
Belgium (BE) 8 319 8 600 8 983 25 902
Bulgaria (BG) 1 775 1 702 2 211 5 688
Croatia (HR) 6 189 6 252 5 591 18 032
Cyprus (CY) 3 694 3 861 3 919 11 474
Czechia (CZ) 2 792 3 007 3 164 8 963
Denmark (DK) 42 427 44 940 48 553 135 920
Estonia (EE) 8 904 9 022 7 214 25 140
Finland (FI) 2 168 2 322 2 389 6 879
France (FR) 6 475 7 054 6 428 19 957
Germany (DE) 4 258 4 398 4 190 12 846
Greece (GR) 33 586 35 666 37 930 107 182
Hungary (HU) 2 455 2 732 2 260 7 447
Iceland (IS) 16 300 17 270 17 100 50 670
Ireland (IE) 17 610 15 840 13 706 47 156
Italy (IT) 1 649 1 658 1 296 4 603
Latvia (LV) 27 705 30 586 31 807 90 098
Lithuania (LT) 3 912 3 998 4 204 12 114
Luxembourg (LU) 1 044 2 926 2 381 6 351
Netherlands (NL) 2 974 3 023 3 217 9 214
Norway (NO) 1 654 1 788 1 869 5 311
Poland (PL) 6 433 5 895 5 537 17 865
Portugal (PT) 2 357 2 412 2 491 7 260
Romania (RO) 32 104 30 835 29 314 92 253
Slovakia (SK) 9 425 9 766 9 422 28 613
Slovenia (SI) 13 007 16 893 17 178 47 078
Spain (ES) 30 856 30 147 25 712 86 715
Sweden (SE) 5 809 5 690 5 636 17 135
Switzerland (CH) 7 174 7 771 8 005 22 950
United Kingdom (UK) 6 567 6 414 6 607 19 588
Total 322 815 336 113 332 994 991 922
Year
Source: Labour Force Survey (LFS)
24
Table A2. Variables included in the analysis
Variable EU LFS item Question wording / description Values after transformation
ISEI ISCO3D Occupation coded on 3 digits ISCO-08 Transformation into ISEI codes (range: 11,01 -
88.70).
Gender SEX 1 – man; 2 – woman
Marriage MARSTAT 1 – other; 2 – married
Full-time job FTPT Full time / part-time distinction 1 – part-time; 2 – full-time job
Education in years
(standardized)
HATLEVEL Highest educational attainment level: ISCED 11
codes (0, 100, 200, 300… 800).
Transformation ISCED 11 codes into years in
educational system (0-304=12; 400-500=14;
600=16; 700=18; 800=20); standardization of
variable (mean = 0; SD = 1)
Education in
percentiles
(standardized)
HATLEVEL Highest educational attainment level: ISCED 11
codes (0, 100, 200, 300… 800).
Transformation ISCED 11 codes into years in
educational system (0=1; 100=7; 200=9; 302=12;
300, 303, 304=13; 400=14; 500=15; 600=16;
700=18; 800=22); transformation into proportional
scores (percentiles) for each country-cohort
combination; standardization of proportion variable
(mean = 0; SD = 1)
Study fields HATFIELD Highest educational attainment field: ISCED 97
and ISCED 11 codes (0, 10, 20... 100, 200,
300… 888).
Recode into 6 categories: (0 888 900 . = 1,
unspecified/general); (10/40 100/300 301/399 800
801/863 = 2, education/social/services); (50/60
400/482 = 3, natural/computer/IT); (70 500 501/599
= 4, engineering/construction); (80 600 601/699 =
5, agriculture/forestry/veterinary); (90 700 701/799
= 6, health/welfare)
Educational
expansion by
cohorts
-- Proportion of people with tertiary education for
each country and cohort (defined by year of
leaving educational system) indicated by
ISCED 2011 (5-8 levels) for age group 25-34;
data come from Eurostat.
Proportion of people with tertiary education in
analyzed 30 countries and 12 graduated cohorts
2003-2014 indicated by ISCED 2011 (5-8 levels) for
age group 25-34.
Contextual level
Individual level