Transitions of low educated graduates
Arjen Edzes*
Marije Hamersma*
Viktor Venhorst*
and
Jouke van Dijk*
DRAFT PAPER
Do not quote without authorization of the author
Abstract**
In this paper we look at the impact of regional circumstances and school satisfaction in explaining
educational decisions and the position of low educated school graduates. We distinguish between
the lowest educated who, according to governmental policy, should not yet enter the labour
market (no ‘start qualification’) and low educated with an educational level just above this
intangible line. First we compare both groups on the choice between continuing education versus
entering the labour market. Secondly we focus in more detail on the labour market entry by
analysing the chance to get a job versus becoming unemployed. Finally, for those who find a job,
we analyse the quality of the job in terms of wages. Several explanatory regional circumstances
are considered like urbanization, regional economic growth and the interaction with higher
educated on the labour market. We use a satisfaction measure as indicator for school
environment. To answer the research questions we use a dataset based on a yearly survey under
school graduates in the period of 1996-2008 in the Netherlands. We find that both regional and
satisfaction factors have an impact on the decision to (not) continue education, the chance to get a
job as well as on the variation in wages of low educated graduates. Satisfaction is especially
important in the choice to continue education. Graduates who enter the labour market with no
‘start qualification’ seem to be less negatively influenced by the presence of high educated on the
labour market.
Groningen, 2011
*
University of Groningen, Faculty of Spatial Sciences, Department of Economic Geography, Urban and
Regional Studies Institute (URSI), P.O. Box 800, NL-9700 AV Groningen, The Netherlands. Corresponding
author: Arjen Edzes, tel. +31 50 363 8650; e-mail: [email protected]
**
The research is partly funded by The Netherlands Institute of City Innovation Studies (NICIS), The Hague
(www.nicis .nl)
1
1.
Introduction
Low educated school participants have a fundamental choice to make at a certain point in their school
career: to continue education at a higher level or to exit school and enter the labour market. From an
individual point of view both can be rational decisions dependent on the (expected) situation on the
labour market. The OECD (2010) shows that on an aggregated level the labour market position of low
educated over time is worsening. This stimulates governments to invest in educational programs and
stimulate continues educational participation of youth to the highest reachable level as possible. In the
short run educational participation could prevent that youths become unemployed because of a lack of
skills and make unwanted use of expensive social security benefits. In the long run a well-educated and
well trained labour force is considered to be essential for the social- and economic well-being of countries
(OECD, 2010: 28). It pushes innovation and stimulates economic growth while on the other hand skill
biased technology change demands an upgrading of skills and competencies of the labour force.
According to the Lisbon Treaty in 2000 European countries committed themselves to reduce the level of
early school-leavers before they reach the level of upper secondary education. According to the OECD
(2011: 44) upper secondary education provides the basis for advanced learning and training opportunities
and prepares some students for direct entry into the labour market. Indeed, OECD figures show (2011:
116) that higher education improves job prospects, in general, and the likelihood of remaining employed
in times of economic hardship. Nevertheless, the general minimum educational norm under which no one
should leave school is also disputable. Among other things because the labour market prospects for low
skilled persons will differ depending on the sector and occupational structure. The importance of reaching
a minimum education differs for example in agriculture and manufacturing versus financial institutions
(Cörvers & Lintjens, 2006). In this case a more differentiated norm would better reflect the demand on
the labour market (Raad voor Werk en Inkomen, 2006). Furthermore, the prospects on the labour market
would also depend on interaction effects with higher educated. For example, low educated could suffer
from displacement effects within the presence of medium en high skilled persons in a polarizing labour
market (Gesthuizen & Scheepers, 2010; Hensen, de Vries & Cörvers, 2009; de Beer, 2006).
The question we ask is twofould: what, first of all, determines educational choices and secondly, what are
the socio-economic consequences of these decisions for low educated graduates? Is it the regional labour
market that pulls low educated into jobs? Is it the school environment that determines whether low
educated continue their education? A better understanding of these outcomes can be helpful to assess
governmental programs that invest in preventing young adults to leave school (too) early. However,
explaining the decision and socioeconomic position of low educated persons is not easy considering the
overwhelming economic and sociological literature. Several determinants coming from even so many
theoretical frameworks explain the (persistence) of the weak economic position of low educated. In
general we can distinguish between individual-, socio-cultural- and (inter)generational aspects by which
the socioeconomic position is explained by family background and processes like social and biographical
reproduction (van Doorn, Pop & Wolbers, 2011; Tieben & Wolbers, 2010; Gesthuizen & Scheepers, 2010;
2
Breen & Johnsson, 2000); institutional and political aspects by which rules, regulations and welfare
arrangements hinder low educated from moving upwards into society (Traag, van der Valk, van der
Velden, de Vries & Wolbers, 2005; Gangl, 2006; Wolbers, 2007); organizational aspects by which quality
of schools and school environment determines whether low educated will be successful in their career
(Holter & Bruinsma, 2010; Oberon, 2008) and last but not least the labour market aspects by which the
socioeconomic position is the outcome of market processes.
In this paper we focus on two aspects, namely the impact of regional labour markets and the influence of
satisfaction with education on decisions and socio economic outcomes of low educated graduates. We
focus on three possible transitions. First we look at the choice between continuing education versus
entering the labour market. Secondly we analyze the chance to get a job versus becoming unemployed.
Finally we research the quality of the job in terms of wages of low educated young workers. We do not
only look at the individual effect of region and satisfaction, but also to their relative strength compared to
other relevant aspects explaining these transitions. To answer these questions, we use a dataset of
graduates from the Research Centre for Education and the Labour Market (ROA) for the period of 19962008 complemented by data from Statistics Netherlands (CBS). In the analysis, we split the respondents
in a group with and a group without ‘start qualification’, the educational norm set in the Netherlands.
In section 2 we describe the theoretical background and develop several hypotheses. In section 3 we will
describe the dataset and the adjustments that we made. In section 4 we will present the results of our
model estimations followed by some concluding remarks in section 5. Briefly we find that in explaining
the decision to (not) continue education both regional factors and especially aspects of satisfaction are
relevant.
Briefly we find both region and satisfaction to be relevant in explaining the choice to continue education
both, the latter being the most important. In explaining the chance to find a job and the wage outcomes,
regional factors are relevant for both those below and above the educational norm, while satisfaction
aspects are only relevant for the latter group. Graduates entering the labour market without a ‘start
qualification’ seem to be less negatively influenced by the presence of high educated.
2.
Theoretical background
Several studies have confirmed that especially low-educated people are vulnerable because of a lower
labour force participation, lower wages and working conditions (e.g. Gesthuizen & Scheepers, 2010; Layte,
Maitre, Nolan & Whelan, 2001; Tsakloglou & Papadopoulos, 2002; Muffels & Fouarge, 2004).
Consequently, in most modern western economies the policy aim is to upgrade the labour force by means
of stimulating school participants to continue education to the highest possible level (OECD, 2011). When
low educated have a bad position on the labour market this is according to this line of reasoning due to a
shortage in skills in relation to the demand and the occupational structure of the labour market.
3
Continuing education would be the only solution to anticipate on changing demand. It emphasizes the
productive aspects of education as assumed in the Human Capital Theory from Becker (1964).
There is also an alternative explanation. Although the number of low skilled jobs decline over the last
decades, recent developments show that these declines have mainly been in basic cognitive tasks which
can now be computerized and exported to other countries. In that sense the labour market is polarizing
indicating that the share of manually- and elementary jobs on the one hand and higher- and scientific jobs
on the other hand is staying constant or is even rising, while the share of low- and medium skilled jobs is
declining (e.g. Autor, Katz & Kearney, 2006; Autor, Levy & Murnane, 2003; Spitz-Oener, 2006; Goos &
Manning, 2007). This development is well documented in western economies. For the Netherlands a
recent study shows that although the number of low educated persons is declining since years, the
number of elementary skilled jobs stays rather stable (Josten, 2010). That, in spite of the developments in
the changing levels of jobs, the position of low educated is worsening, is according to this reasoning
attributed to the overeducation and displacement effects of low skilled by medium- and high skilled
(Gesthuizen & Scheepers, 2010; Hensen, de Vries & Cörvers, 2009; de Beer, 2006). It is an example of the
distributive aspects of education which are the core of among others the Job Competition Theory from
Thurow (1975). Nevertheless for the economic position of low educated this theoretical debate highlights
several relevant aspects in studying the regional labour market impacts on the economic position of low
educated, from the spatial development of the employment structure and regional unemployment to the
interaction effects with other groups on the regional labour market.
Concerning educational characteristics, there is an overwhelming evidence that investment in education
leads to better job opportunities and higher wages (e.g. Broersma, Edzes & van Dijk, 2010; Minne, van der
Steeg & Wibbink ,2007; Psacharopoulus & Patrinos, 2002; Groot & Maassen van den Brink, 2003;
Gesthuizen & Scheepers, 2010). Human Capital theory predicts that school participants continue
education for as long as their expected rate of return from further investments in education would out
weight the returns they receive on the labour market. Nevertheless regional differences in private returns
on education exist because of differences in occupational structure and supply of skills between regions
(Broersma, Edzes & van Dijk, 2010). If this is the case we would also expect regional differences in the
choices of low educated graduates to continue education, mainly influenced by the demand for low skilled
and the unemployment level at a regional scale. A high number of elementary jobs would pull low
educated to the labour market where high unemployment would stimulate continuing education.
When it comes to job opportunities, the chance to get a job and the earned wages, we especially focus on
the interaction effects with higher educated. First the higher educated are more concentrated in certain
regional labour markets such as urban en population dense areas. This makes that mutual relations are a
typical regional characteristic and we would expect regional differences in this respect. Second the
theoretical effect of the presence of higher educated on job opportunities for lower educated is an ongoing
dispute. On the one hand high shares of higher educated would improve regional productivity of low
educated (productivity spill over) which could at the end lead to employment effects from which low
4
educated could benefit (Broersma, Edzes & van Dijk, 2010; Moretti, 2004a; 2004b). Besides these
productivity spill overs this effect could also be reached by consumption spill overs (Broersma, Edzes &
van Dijk, 2010; Suedekum, 2006; Canton, 2009). On the other hand higher educated could substitute low
educated especially when there is an oversupply in relation to the demand on the local labour market
(Gesthuizen & Wolbers, 2010).
From a policy perspective is a possible way to influence graduates behaviour and their socio economic
position the investment in the quality of educational environments and the guidance of choice of
education and careers from school participants. As lower educated graduates are in the usual pathway still
in their adolescence when they graduate for the first time, they are more amendable by external factors
like parents, friends, teachers and their direct environment. Studies concerning early school leaving
mention the importance of the school environment in the decision to premature leaving education
(Oberon, 2008). According to Holter and Bruinsma (2010) the decision to continue education will be
more based on their current surroundings instead of their intention to have a better future by obtaining
diploma’s. School quality can therefore be a stimulus in continuing education. Besides that a proper
education of a good quality might positively influence a student’s position on the labour market.
To investigate the effects of regional circumstances and school satisfaction we control for characteristics
from which we know from the literature that they influence the educational choices, job search behaviour
and labour market economic outcomes like gender, ethnicity, age and field of study (see for instance
Tieben & Wolbers, 2010; van der Meer, 2008; Traag et al., 2005) and firm aspects (Broersma et al, 2010;
Canton, 2009).
3.
Data and method
Dataset used
We use a dataset from the Research Centre for Education and the Labour Market (ROA). The dataset is
based on an extended survey under graduates in the Netherlands approximately 18 months after finalizing
their education, covering the period 1996-2008. With the survey, data on demographics, followed
education, students’ opinions and information about the actual situation of the graduate (continue to
higher education or work situation) is gathered on a cross-sectional base.
Cleaning the data
As we focus on the low educated graduates, we first selected respondents who have graduated in prevocational secondary education (VMBO) or in the first two levels of secondary vocational education
(MBO). Note that we leave out the levels 3 and 4 of secondary vocational education and other educational
groups at the secondary level like senior general secondary education (HAVO) and pre-university
education (VWO). Within the selected group we identified whether a low educated person has a ‘start
qualification’ based on the successfully completed educational level 2 or not, see Table 1. This ‘start
5
qualification’ is comparable with the European Lisbon norm as the minimum educational level that
people should attain before they leave school.
Table 1 ‘Start qualification’
Education level
No ‘start qualification’
Pre-vocational secondary education (VMBO) ór Secondary
vocational education (MBO) level 1
‘Start qualification’
Secondary vocational education (MBO) level 2
We focus on all graduates between the age of 15 and 30. Based on the information given by the
respondents, there are four possible outcomes. First a group who is participating in further education 1,5
year after graduation. Second a group that is working 1,5 year after graduation. The third group is
unemployed 1,5 year after graduation. The fourth group consists of persons who cannot be classified in of
the other categories and are assumed not to participate. Figure 2 shows a summarizing flowchart which is
the basis for the group division.
Figure 1 Classification in groups
We excluded working graduates with an abnormal salary. Therefore we set a boundary on 75% of the
minimum wage per age per year. Working respondents with a wage per hour below this barrier are
6
deleted. Besides, we deleted workers with a wage exceeding 12x the minimum wage based on age1 per
year (based on Canton, 2009).
Variables in the dataset
For each individual, the dataset contains information on basic demographics, field of education and level
of education. Furthermore it contains information on educational experiences of respondents. We
measure education by the question whether graduates would choose the same education, a different
education or would choose not to study at all when they were able to start over again. As we don’t have
this information available for all respondents, we added a category ‘satisfaction unknown’ to prevent for a
loss of respondents and to make our modeling results more reliable. Next to that the dataset contains
information about (possible) job and company, which we will use as controlls. We also have locational
information of the respondent on different levels (education, residence and sometimes job) which we use
to link regional data about the labour market situation.
Regional variables
Because of our interest in the regional component, we linked regional data to the dataset. We collected
this data from statistics Netherlands for the period of 1996-2008 as well for on the RBA-level2 as the
Nuts-3 level3 and linked this to respectively the geographical location of the school (based on RBAlocation) and the geographical location of the company (based on Nuts-3 location). In order to select a
relevant set of explanatory regional variables, we controlled for multicollinearity issues between the
independents by checking correlations and inspecting the Verification of Inflation Factors (VIF) of the
model (e.g. Hair, Black, Babin, Anderson & Tatham, 2006)4. Finally we have chosen for a combination of
regional variables. To measure the effect of unemployment, we include the percentage of unemployment
under young people (15-25). Population density is included to measure urbanity. The number of working
people with an elementary or lower profession is added as an indication of the total absolute supply of
labour for low educated. Finally we include economic growth as we are interested in the effect of the
business cycle on the position of low educated graduates. To measure interaction effects between highand low educated people, we calculated the oversupply of high educated by dividing the share of working
people with a high education by the share of high skilled jobs. We also take along the percentage of higher
educated living within the area. Appendix 1 shows the average values per RBA-region and per Nuts-3
region on the regional variables we include in the model.
Under 21, minimum wage levels are age based.
The RBA (Regionale Bureaus Arbeidsvoorziening) area is a classification of the Netherlands originating from the 1990’s. It divides
the Netherlands in 18 regions and is a summary of labour market areas.
3 The Nuts-3 regions are 40 stable areas in the Netherlands, originally formed in 1971 based on a nodal classifying principle; each
with a central core and a surrounding area.
4 To check the VIF’s indicating the mutual correlation of a variable, we estimated the models as a linear regression, considering the
dependent variables being numeric variables.
1
2
7
Estimation technique
In the first analysis we perform a binary logistic regression analysis with the choice to (not) continue in
further education as a dependent variable, being 1 (yes) or 0 (no), based on the group division (Figure 1)
we have made. This means we set all respondents participating in further education against the others.
We split up between those having and not having a ‘start qualification’ to see whether we find differences
between both groups of low educated. As explanatory variables we include regional data based on the
location of the school and we control for demographic- and educational differences (M1). In a second
phase (M2) we include interaction with higher educated and in the third step (M3) we add satisfaction
with education to research in how far organizational aspects deliver an additional effect in explaining the
graduates’ choice.
In the second analysis we make a selection of school leavers who decide to enter the labour market (group
2 and 3 in Figure 2) and analyze the chance to get a job (versus getting unemployed). We perform a binary
logistic regression with being in a job equal to 1, and being unemployed equal to 0 and again split up
between those having and not having a ‘start qualification’. We use the same structure of model
composition. In the basic model we include regional aspects (M4) in the next step we include the
interaction with higher educated (M5) and we finally include satisfaction (M6). Again we control for
demographic- and educational differences.
Our third analysis looks into wage outcomes for low educated graduates who are classified as working
(Figure 2, group 3). We perform an Ordinary Least Squares (OLS) regression analysis with as dependent
variable the natural log of hourly wage per worker, for respectively those having and not having a ‘start
qualification’. Again we include regional factors in the first step (M7) and extend the model with
interaction with higher educated (M8) and satisfaction variables (M9). We control for demographics,
educational differences and for company characteristics.
4.
Results
Decision to continue in further education
The first analysis focuses on the decision to (dis)continue education. The descriptives for this analysis can
be found in Appendix 2. Table 2 shows the percentage of graduates choosing to continue education for
both groups. We can observe that on average, almost 83% of the graduates without a ‘start qualification’
choose to continue education, while for those with a ‘start qualification’ the percentage is almost 43%.
Table 2 Percentage continuing education per group
Continuing education
No ‘start qualification’
‘Start qualification’
82,74%
42,85%
The results of the binary logistic models are depicted in Table 3. Concerning the regional effects we
include, the first interesting difference we observe between graduates with and without ‘start
8
qualification’ is that for the former the effect of region in explaining educational choice is far more
marginal. The lowest educated without a ‘start qualification’ seem to base their educational decision more
on the attractiveness of the labour market. In a prospective economic situation, within more population
dense areas (with more jobs available), in areas with low unemployment and many low skilled jobs, there
is a significantly higher chance for graduates without a ‘start qualification’ to discontinue education within
the period of research. For the group with a ‘start qualification’, we only find a stable negative effect for
population density, meaning that lower educated with a ‘start qualification’ are more likely to discontinue
education in population dense areas. The percentage of higher educated in the area and the oversupply of
higher educated are not of particular importance in the choice whether or not to continue education, as
we can conclude in the second phase (M2).
In the third phase (M3) we include the satisfaction measure. We find a strongly significant relation
between satisfaction and the decision to continue education, especially for graduates without a ‘start
qualification’. Graduates indicating to choose the same study and school again when they could start over,
are more likely to participate in further education. Offering an attractive education and school
environment and focusing on choosing the right education seems to be of relevance in influencing the
decision of young graduates.
Concerning the other individual and educational aspects we control for, we can observe from table 3 that
continuing education strongly declines with age. Besides that during the years many more low educated
graduates decide to continue education. A more general education stimulates continuing in further
education. Females decide more often to exit compared to males, especially after having reached the level
for ‘start qualification’. Immigrants choose more often to continue in further education. This seems
surprising at first glance, but might be stimulated by the difficult position they still have on the labour
market compared to low educated native counterparts (e.g. Andriessen et al, 2010).
9
Table 3 Estimation results binary logistic regression choice to continue education
No ‘start qualification’
M2
M1
M3
‘Start qualification’
M2
M1
M3
Unst.B
Wald
Unst.B
Wald
Unst.B
Wald
Unst.B
Wald
Unst.B
Wald
Unst.B
Wald
-0,08
-0,37
0,13
0,12
-0,81
-0,89
0,05
-0,07
-0,13
0,95
5,24**
415,19***
6,08**
613,69***
53,85***
119,60***
0,92
1,09
4,46**
270,40***
-0,08
-0,37
0,13
0,12
-0,80
-0,89
0,05
-0,07
-0,13
0,95
5,28**
413,92***
6,28**
337,10***
53,58***
118,24***
0,91
1,05
4,29**
271,24***
-0,12
-0,37
0,14
0,07
-0,87
-0,93
0,02
-0,06
-0,15
0,89
11,29***
407,90***
6,77***
0,12
56,86***
119,33***
0,12
0,82
5,49**
236,95***
-0,35
-0,17
0,23
0,12
-0,71
49,08***
226,03***
12,23***
462,13***
242,22***
-0,35
-0,17
0,23
0,12
-0,71
48,94***
226,75***
12,61***
219,97***
235,97***
-0,35
-0,18
0,27
0,12
-0,73
47,56***
230,03***
15,97***
222,78***
243,91***
-0,18
0,35
-0,08
7,23***
21,47***
1,77
-0,17
0,34
-0,08
6,27**
20,97***
1,93
-0,13
0,35
-0,05
3,67*
22,05***
0,91
-0,15
-3,16
2,89
-0,12
13,51***
20,08***
32,12***
10,71***
-0,20
-3,16
3,03
-0,12
13,58***
20,08***
32,84***
10,18***
-0,19
-3,64
3,52
-0,12
10,74***
25,86***
42,40***
11,08***
-0,13
-1,47
0,67
-0,08
4,88**
2,73*
1,30
2,54
-0,14
-1,40
0,50
-0,07
3,14*
2,46
0,69
2,30
-0,15
-1,49
0,53
-0,09
3,58*
2,70*
0,73
2,94*
Oversupply high educated
-0,33
0,78
-0,51
1,84
0,51
1,13
0,42
0,74
Percentage high educated
Satisfaction(ref. would have chosen
different education)
Satisfaction: would have chosen same
education again
Satisfaction: would have chosen not
to study again
Satisfaction unknown
0,77
2,06
0,78
2,05
-0,08
0,01
0,11
0,02
0,90
310,76***
0,31
40,96***
-0,63
22,97***
0,06
1,22
-132,68
87,23***
Female
Age
Non-native
Year trend
Apprenticeship training (BBL)
Vocational training (BOL)
Sector of studies: agriculture
Sector of studies: healthcare
Sector of studies: economics
Sector of studies: general
Region
Population density
Economic growth
Percentage unemployed 15-25
Number of low skilled jobs
High educated
Constant
-230,18
571,01***
-229,42
321,85***
Nr of variables
14
16
19
N
31594
31594
31594
Chi Square
3170,94
3173,01
Year of graduation
1996-2007
1996-2007
-241,10
-1,47
-0,89
79,86***
447,70***
-228,28
220,13***
-0,02
0,02
2,73*
-1,40
2,46
-232,27
223,19
12
14
17
11725
11725
11725
3799,46
1362,46
1362,46
1996-2007
1996-2007
1364,01
1996-2007
1996-2007
10
Chance to find a job
In the second analysis we model the chance to find a job versus being unemployed for those who
enter the labour market. The descriptives can be found in
Appendix 3. Before we go to the estimation results, we will first look into the difference in the chance to
find a job for the two groups. Table 4 shows us that graduates without a ‘start qualification’ who decide to
enter the labour market have a higher chance to be unemployed 1.5 years after graduation.
Table 4 Percentage unemployed per group
Percentage of those entering the
labour market being unemployed
No ‘start qualification’
15,15%
‘Start qualification’
6,94%
Table 5 shows the estimation results of the binary logistic models. Concerning the regional characteristics,
we find comparable effects for both groups in explaining the chance to find a job. The chance to get a job
is positively influenced by economic growth and smaller in areas and times with more unemployment. In
the base model we do not find an effect of the number of low skilled jobs in the area for none of the groups
(it turns to marginally positively influencing the chance to get a job for those without a ‘start qualification’
after including extra variables in M5 and M6), but do find a positive effect of population density for
graduates with a ‘start qualification’.
In the second phase we include the variables indicating the presence of high educated in the area (M5).
We find for both groups a positive effect of the percentage of higher educated living in the area on the
chances to find a job, especially for the ones with a ‘start qualification’. It seems like the presence of
higher educated living in an area has a positive effect on the number of jobs in the area, also for the low
skilled, probably created by a consumption effect. It is interesting to see that with the inclusion of this
variable, the effect of population density turns to negatively significant for both groups. So, after
correction for the presence of higher educated, the effect of population density is negative; probably the
presence of higher educated comes together with high employment and economic growth. The oversupply
effect of higher educated on the job chances of lower educated is only marginally negatively influencing
the chance to find a job for graduates with a ‘start qualification’. Low educated young graduates with a
‘start qualification’ experience some negative competition of higher educated on the labour market. From
the fact that we cannot find an effect on the lowest educated entering the labour market, we might assume
that the lowest jobs are not really under competition of higher educated.
The inclusion of the satisfaction measure (M6), only results in a significant impact on the ones with a
‘start qualification’. Interesting is that both, those who indicate they would choose the same education
again as those who indicate not to study again, have a better chance to find a job compared to graduates
who would have chosen a different education. We assume the most satisfied graduates are more
motivated and dedicated to their field of study and therefore have a better chance to find a job. It might
also be that satisfaction tells us something about school quality and that the most satisfied therefore have
11
the most capabilities and the best chances on the labour market. Instead, the ones who indicate that they
would have chosen another education (the referent) might have a harder job in finding a satisfying job.
Graduates indicating not to study again are probably eager to earn money instead of learning more and
therefore might be less demanding in accepting a job they can get.
Concerning the controls we included, we find a strongly negative effect on the chance to find a job for
women and immigrants, especially within the group without a ‘start qualification’. Besides we find for the
group without a ‘start qualification’ a higher chance for older graduates, and for those with a ‘start
qualification’ a lower chance for older graduates to find a job. We find a negative trend effect meaning that
the chance to find a job has deteriorated a little bit over the years 1996-2008. Graduates who decide to
enter the labour market with a vocational training on level 1 (BOL) have a lower chance to find a job than
the ones with a pre-secondary vocational education (VMBO). For those with a ‘start qualification’ we find
a positive effect on the chance to find a job for graduates with apprenticeship training (BBL) compared to
vocational training (BOL). For both groups, people with an agricultural or healthcare education have a
smaller chance to find a job compared to people with a technical education. To conclude, although region
and satisfaction are relevant, some controlling variables on the educational- and individual level are more
important in explaining the chance to get a job (when we look at Wald statistics), especially for the group
without a ‘start qualification’.
12
Table 5 Estimation results binary logistic regression chance to find a job
No ‘start qualification’
M4
Unst. B
Female
Age
Non-native
Yeartrend
Apprenticeship training (BBL)
Vocational training (BOL)
Sector of studies: agriculture
Sector of studies: healthcare
Sector of studies: economics
Sector of studies: general
Region
Population density
Economic growth
Percentage unemployed 15-25
Number of low skilled jobs
‘Start qualification’
M5
Wald
Unst. B
M6
Wald
Unst. B
M4
Wald
Unst. B
M5
Wald
Unst. B
M6
Wald
Unst. B
Wald
-0,59
0,10
-1,40
-0,03
-0,09
-0,35
-0,39
-0,31
-0,28
0,10
32,48***
8,21***
136,68***
5,82**
0,14
3,89**
6,45**
3,05*
2,70*
0,26
-0,61
0,10
-1,37
-0,06
-0,06
-0,31
-0,37
-0,31
-0,26
0,14
33,98***
7,91***
128,45***
10,26**
0,07
3,09*
5,73**
3,03*
2,34
0,49
-0,61
0,10
-1,36
-0,09
-0,09
-0,36
-0,39
-0,31
-0,29
0,16
34,83***
7,97***
126,81***
15,48***
0,14
3,71**
6,60**
2,97*
2,91*
0,62
-0,46
-0,08
-0,96
-0,03
1,03
13,66***
12,21***
47,38***
3,08*
69,01***
-0,46
-0,09
-0,99
-0,05
1,05
13,38***
13,78***
49,97***
6,32**
70,39***
-0,44
-0,09
-0,93
-0,05
1,00
12,57***
16,11***
43,45***
7,31**
63,20***
-0,65
-0,34
-0,23
13,46***
3,03*
2,12
-0,72
-0,36
-0,25
16,16***
3,32*
2,56
-0,73
-0,34
-0,25
16,47***
2,97*
2,47
-0,05
0,22
-0,40
6,51**
-0,40
6,40**
0,32
3,89**
-0,43
4,40**
-0,42
4,20**
5,65
-6,54
0,13
8,39***
21,54***
1,65
5,96
-6,38
0,18
9,18***
18,42***
3,09*
5,97
-6,35
0,18
9,23***
18,25***
3,28*
8,51
-7,05
-0,08
13,37***
24,13***
0,36
7,13
-6,40
-0,02
9,09***
17,73***
0,03
7,21
-6,44
-0,02
9,23***
17,92***
0,02
-0,23
4,32
0,05
7,80**
-0,31
4,32
0,09
7,80**
-2,39
9,58
3,59*
23,54***
-2,19
9,40
3,01*
22,29***
0,09
0,45
0,74
44,01***
-0,26
1,17
0,57
8,66***
-0,22
2,06
0,64
3,03*
171,11
19
15,82***
113,73
17
8,17***
High educated
Oversupply high educated
Percentage high educated
Satisfaction(ref. would have chosen
different education)
Satisfaction: would have chosen
same education again
Satisfaction: would have chosen
not to study again
Satisfaction unknown
Constant
Nr of variables
65,87
14
5,99**
119,92
17
10,57***
55,53
12
3,67*
106,34
15
7,17**
N
4283
4283
4283
6282
6282
6282
Chi Square
Years of graduation
250,19
1996-2007
262,25
1996-2007
268,92
1996-2007
289,79
1996-2007
317,19
1996-2007
360,29
1996-2007
13
Wage of low educated young graduates
As a third analysis we model the wages of the young graduates who find a job. Error! Reference
source not found. shows the descriptives of this group of graduates, Table 6 shows the average wage of
both groups in our analysis.
Table 6 Average wage per group
Hourly wage when entering the labour
market and finding a job
No ‘start qualification’
5,14
‘Start qualification’
7,12
Table 7 shows the estimation results. Focusing first on the regional effects in the basic model, we can find
almost comparable results for both of the groups. Two of the four included regional variables show
significant results. The wage is significantly higher in population dense areas but significantly lower in
areas with more regional unemployment. We cannot find an effect of economic growth or the number of
low skilled jobs in the area on the hourly wage.
In the first extension we include the interaction with higher educated. Where the presence of higher
educated has a positive effect on the chance for low educated to find a job, it does not influence the wage
for any of the groups. We do find a strong negative effect of oversupply of high educated on a workers’
wage for the group with a ‘start qualification’. Like we saw already in the analysis on the chance to find a
job and now also for wage, oversupply of higher educated does not really seem to affect the ones with the
lowest education level.
In the last stage we include satisfaction measures. Again we find a difference between both groups. Once
more we find no effect of satisfaction for those without a ´start qualification´. For the group with a ´start
qualification´, we find a significant negative effect for those indicating not to study again. This finding is
in line with our explanation for the positive effect of this group on the chance to find a job. It seems like
this group is likely to accept all job offers, also on a lower level or with a lower wage.
Concerning the individual, educational and company specific control variables, we find, in line with other
research, that women earn significantly less than man and that wage increases with age. Besides we
logically find that wage increases over the years, but this effect is stronger within the group with a ‘start
qualification’. As the effect of apprenticeship- and vocational training is positively significant compared to
pre-secondary vocational education (no ‘start qualification’), we can conclude that wage increases with
education level. Besides we find for the group with ‘start qualification’ that those with a practical
education (apprenticeship training) earn significantly more. We already found before that they also have
better chances to find a job. With respect to sector of studies, we do not find a significant difference in
wage for the group without ‘start qualification’. For those with ‘start qualification’, we find that low
educated graduates with an education in engineering earn the most.
Finally concerning company
14
controls, we find a positive effect of company size on wage and find differences between sectors. The
control effects are more important in explaining wage than most of the regional and satisfaction effects.
Table 7 Estimation results linear regression wage of low educated working graduates
No ‘start qualification’
Female
Age
Non-native
Year trend
Apprenticeship training (BBL)
Vocational training (BOL)
Sector of studies: agriculture
Sector of studies: healthcare
Sector of studies: economics
Sector of studies: general
Company size
Agriculture/fishing (ref. Industry)
Mineral extraction
Construction
Reparation of consumer articles and trade
Hotel and catering industry
Mobility/storage/communication
Financial institutions
Trading estate/rent estate/services business
Education
Healthcare
Environmental/culture/recreation and
other services
Public management/governmental
business/obliged social insurance
Production of electricity./gas/water
‘Start qualification’
M7
M8
M9
M7
M8
M9
St.B.
St.B.
St.B.
St.B.
St.B.
St.B.
-0,10***
0,35***
0,01
0,20***
0,12***
0,10***
-0,03
-0,04
-0,02
0,04*
0,08***
0,02
0,00
0,01
-0,18***
-0,04*
0,05**
0,04**
0,06***
-0,02
0,08***
-0,04**
-0,10***
0,35***
0,00
0,21***
0,12***
0,10***
-0,03
-0,04
-0,02
0,04*
0,08***
0,02
0,00
0,01
-0,18***
-0,04*
0,05**
0,04**
0,06**
-0,02
0,08***
-0,04**
-0,10***
0,35***
0,00
0,20***
0,12***
0,10***
-0,03
-0,04
-0,02
0,04*
0,08***
0,02
0,00
0,01
-0,18***
-0,04*
0,05**
0,04**
0,06**
-0,02
0,08***
-0,04**
-0,12***
0,36***
0,00
0,28***
-0,12***
0,37***
0,00
0,33***
-0,12***
0,36***
0,01
0,33***
0,08***
-0,13***
-0,09***
-0,08***
0,07***
-0,13***
-0,08***
-0,07***
0,07***
-0,13***
-0,08***
-0,07***
0,09***
0,06***
0,01
0,06***
-0,11***
0,03**
0,07***
0,03**
0,04**
0,00
0,15***
0,03**
0,09***
0,06***
0,01
0,06***
-0,12***
0,03**
0,07***
0,03**
0,04**
0,00
0,14***
0,03**
0,09***
0,06***
0,01
0,06***
-0,11***
0,03**
0,07***
0,03**
0,04**
0,00
0,14***
0,03**
0,09***
0,09***
0,09***
0,02*
0,02*
0,02
0,02*
0,02
0,02*
Region
Population density
0,07***
0,05**
0,05**
0,06***
0,08***
0,08***
Economic growth
Percentage unemployed 15-25
0,01
-0,05***
0,01
-0,05***
0,01
-0,05***
0,01
-0,05***
0,02
-0,04***
0,01
-0,04***
Number of low skilled jobs
0,02
0,02
0,02
-0,02
-0,01
-0,01
Oversupply high educated
-0,03
-0,03
-0,07***
-0,07***
Percentage high educated
0,04
0,04
-0,01
-0,01
High educated
Satisfaction(ref. would have chosen different
education)
Satisfaction: would have chosen same
education again
0,01
0,01
Satisfaction: would have chosen not to study
again
-0,01
-0,04***
Satisfaction unknown
(Constant)
0,00
-45,47***
-46,47***
-44,91***
0,00
-56,01***
-64,91
-64,90***
15
Nr of variables
N
Adj. R2
Years of questionnaire
27
2284
0,41
1997-2008
29
2284
0,42
1997-2008
32
2284
0,42
1997-2008
26
1997-2008
28
4331
0,43
1997-2008
31
4331
0,43
1997-2008
5. Summary and conclusion
In the light of the worse socio-economic position of lower educated on the labour market and the
importance of education in improving one’s social and economic position, the central question of this
research was to investigate the impact of region, the presence of higher educated and school satisfaction
on educational choices and socio-economic outcomes for low educated graduates. We distinguish between
low educated below and above the minimum educational norm (‘start qualification’) that is the
international standard for entering the labour market. School-leavers who leave education below this
minimum norm have according to the literature a higher chance to become unemployed or end up in low
wage occupations. In this paper we investigated the educational decisions, chances to find a job and wage
outcomes of both groups, those with and without a ´start qualification´.
Concerning the choice to continue education, we find that especially those without a ‘start qualification’
seem to base their decisions partly on the labour market situation. For the period of research, we find that
a higher population density, more economic growth and a higher supply of low skilled jobs attracts this
group of graduates to discontinue education, while higher unemployment encourages them to continue
education. For those with a ‘start qualification’ only population density and a higher number of low skilled
jobs significantly leads to more discontinuation in education. Both groups are not extra stimulated by the
presence of more higher education in their area of living. Concerning the satisfaction measure we
included, we find for both groups a strong relation with educational choices. Those indicating to be more
satisfied with the direction of education choose more often to continue education. Especially the group of
graduates without a ‘start qualification’ seems to be influenced in their educational choice by their
experiences with education. Focusing on the attractiveness of education and better guidance on
educational and career choices might therefore be a useful tool to influence an adolescent student’s
behavior.
As we look at the economic outcomes in terms of job chances and earned wages, we find that the impact of
regional characteristics is of comparable importance for both groups of low educated graduates on both
aspects. Regional unemployment deteriorates, while economic growth stimulates the chances to find a
job. Furthermore low educated have a better chance to find a job when there are many low skilled jobs
available. Concerning wage a higher regional unemployment decreases, while a higher population density
increased wages of low educated workers. Concerning the presence of higher educated we find that job
chances for low educated are better in areas where a lot of high educated people live which may be due to
a consumption effect. Nevertheless when there is an oversupply of higher educated in relation to the
16
available high skilled jobs this seems to have a negative effect on the job chances as well on the earned
wage of low educated with a ´start qualification´, which may be due to a displacement effect. On the other
hand low educated without a ´start qualification´ do not seem to be affected by an oversupply of high
educated, presumably because they take the lowest and least wanted jobs on the labour market and are for
that reason not in competition with higher educated.
To conclude both regional circumstances and satisfaction with school and educational direction influence
choices of low educated as well as their socio-economic outcomes. However it seems that satisfaction with
school and educational direction is of greater importance than the regional labour market. It would be
interesting to do more research concerning the quality of the school in relation to the graduate’s behavior
and success of graduates on the labour market. A drawback of the satisfaction measure in our research is
the issue of causality between satisfaction on the one hand and educational choice, job chances and wages
on the other hand. However, as the effect on especially educational decisions is so strong, this might be a
key to influence even the lowest educated to continue, by finding out what they find most interesting and
thereby make education more attractive
6.
Literature
Andriessen, I., E. Nievers, Faulk, L. & Dagevos, J. (2010). Liever Mark dan Mohammed? Onderzoek
naar arbeidsmarktdiscriminatie van niet-westerse migranten via praktijktests. The Hague: The
Netherlands institute for Social Research (SCP).
Autor, D.H., Levy, F. & Murnane, R.J. (2003). The Skill Content of Recent Technological Change: An
Empirical Exploration. Quarterly Journal of Economics, 118 (4), pp. 1279-1333.
Autor, D.H., Katz, L.F. & Kearney, M.S. (2006). The Polarization of the US Labor Market. American
Economic Review, 96(2), pp. 189-194.
Becker, G. (1964). Human captial. A theoretical and empirical analysis, with special reference to
education. New York: National Bureau of Economic Research.
Beer, P. de (2006). Perspectieven voor laag opgeleiden. Tijdschrift voor Arbeidsvraagstukken, 22
(3), pp. 218-233.
Berg, G. van der (2007). Voortijdig van school. Jeugd & Co, 4, pp. 16-22.
Breen, R. & Johnsson, J.O. (2000). Analyzing educational careers: A Multinomial Transition Model.
American Sociological Review, 65, pp. 754-772.
Broersma, L., Edzes, A.J.E. & Dijk, J. van (2010). Human Capital externalities and proximity: effects
for low-skilled workers. Paper presented at the North American Regional Science Congress,
Denver.
Canton, E. (2009). Human captial externalities and proximity: evidence from repeated cross sectional
data. De Economist , 157, pp. 79-105.
17
Cörvers, F. & Lintjens, E. (2006). Het instroomniveau voor schoolverlaters op de arbeidsmarkt.
Maastricht: ROA.
Doorn, M. van, Pop, I. & Wolbers, M.H.J. (2011). Intragenerational Transmission of Education
across European Countries and Cohorts. European Societies, 13 (1), pp. 93-117.
Gangl, M. (2006). Scar effects of Unemployment: An Assessment of Institutional Complementarities.
American Sociological Review, 71 (6), pp. 986-1013.
Gesthuizen, M. & Scheepers, P. (2010). Economic Vulnerability among Low-Educated Europeans :
Resource, Composition, Labour Market and Welfare State Influences. Acta sociologica, 53 (3), pp.
247-267.
Gesthuizen, M. & Wolbers, M.H.J. (2010). Employment transitions in the Netherlands, 1980–2004:
Are low educated men subject to structural or cyclical crowding out? Research in Social
Stratification and Mobility, 28, pp. 437–451.
Goos, M. & Manning, A. (2007). Lousy and Lovely Jobs: The Rising Polarization of Work in Britain.
Review of Economics and Statistics, 89 (1), pp. 118-133.
Groot, W. & Maassen van den Brink, H. (2003). Investeren en terugverdienen: kosten en baten van
onderwijsinvesteringen. Amsterdam: University of Amsterdam (UVA).
Hair, J.F., Black, W.C.. Babin, B.J., Anderson, R.E. & Tatham, R.L. (2006). Multivariate data
analysis. Upper Saddle River: Prentice hall.
Hensen, M.M., Vries, M.R. de & Cörvers, F. (2009). The role of geographic mobility in reducing
education-job mismatches in the Netherlands. Maastricht: Research Centre for Education and the
Labour market (ROA).
Holter, N & Bruinsma, W. (2010). Wat werkt bij het voorkomen van voortijdig schoolverlaten?
Utrecht: Youth Institute Netherlands (NJI).
Josten, E. (2010). Minder werk voor laagopgeleiden. Ontwikkelingen in baanbezit en baankwaliteit
1992-2008. The Hague: The Netherlands institute for social research.
Layte, R., Maitre, B., Nolan, B. & Whelan, C.T. (2001). Persistent and consistent poverty in the 1994
and 1995 waves of the European community household panel survey. Review of income and
wealth,47(4), pp. 427-449.
Meer, P.H. van der (2008). Is the gender wage gap declining in the Netherlands? Applied
Economics, 40, pp. 149-160.
Meer, P.H. van der & Glebbeek, A.C. (2001). The return to formal overschooling: filling a research
gap. ‘ The Netherlands' Journal of Social Sciences, 37 (2), pp. 108-131.
Ministry of education, Culture and Science (2007). The educational system in the Netherlands. The
Hague.
Minne, B., Steeg, M. van der & Wibbink, D. (2007). De maatschappelijke opbrengsten van
onderwijs. CPB Memorandum, 2007.
Moretti, E. (2004a). Workers' Education, Spillovers, and Productivity: Evidence from Plant-Level
Production Functions. American Economic Review, 94 (3), pp. 656-690.
18
Moretti, E. (2004b). Human Capital Externalities in cities. Henderson, J.V. & J.F. Thisse. Handbook
of Regional and Urban Economics, Volume 4, Elsevier.
Muffels, R. & Fouarge, D. (2004). The Role of European Welfare States in Explaining Resources
Deprivation. Social Indicators Research, 68, pp. 299–330.
Nordin, M., Persson, I. & Rooth, D.O. (2010). Education-occupation mismatch: Is there an income
penalty? Economics of education review, 29, pp. 1047-1059.
Oberon (2008). De belevingswereld van voortijdig schoolverlaters. Een onderzoeksrapportage.
Utrecht: Oberon.
OECD (2010). Education at a glance 2010. Paris: OECD.
OECD (2011). Education at a glance 2011. Paris: OECD.
Psacharopoulus, G. & Patrinos, H.A. (2002). Returns to investment in Education: A Further Update.
World Bank Policy Research Working Paper 2881.
Raad voor Werk en Inkomen (2006). Advies Sectorstartniveau. The Hague: The councile for work
and income (RWI)
Sassen, S. (2000). Cities in a World Economy. London: Pine Forge Press.
Spitz-Oener, A. (2006). Technical Change, Job Tasks, and Rising Educational Demands: Looking
outside the Wage Structure. Journal of Labor Economics 24 (2), pp. 235-270.
Suedekum, J. (2006). Human capital externalities and growth of high- and low skilled jobs. IZA
Discussion Paper.
Tieben, N. & Wolbers, M.H.J. (2010). Transitions to post-secondary and tertiary education in the
Netherlands: a trend analysis of unconditional and conditional social-economic background effects.
Higher Education, 60, pp. 85-100.
Thurow, L. (1975). Generating Inequality. New York: Basic Books.
Traag, T., Valk, J. van der, Velden, R. van der, Vries, R. de & Wolbers, M.H.J. (2005). Waarom doet
opleiding ertoe? Een verklaring voor het effect van het hoogst bereikbare opleidingsniveau op de
arbeidsmarktpositie van schoolverlaters. Pedagogische studiën, 82, pp. 453-469.
Tsakloglou, P. & Papadopoulos, F. (2002). Aggregate level and determining factors of social exclusion
in twelve European countries. European social policy, 12 (3), pp. 211-226.
Vries, R. de, Wolbers, M.H.J. & Velden, R. van der (2004). De arbeidsmarktpositie van
schoolverlaters en werkenden zonder startkwalificatie. Maastricht: Research Centre for Education
and the Labour market (ROA).
19
20
Appendix 1 Regional variables
Corop region
Oost-Groningen
% Economic
growth
0,04
% unemployed
15-25
0,14
Population
density perM2
0,18
Number of low
skilled jobs
0,25
Oversupply high
educated
0,91
% High educated
in area
0,14
Delfzijl en omgeving
0,03
0,06
0,19
0,07
1,16
0,19
Overig Groningen
0,09
0,16
0,29
0,46
1,06
0,34
Noord-Friesland
0,04
0,14
0,2
0,45
0,97
0,22
Zuidwest-Friesland
0,06
0,11
0,17
0,15
1,1
0,2
Zuidoost-Friesland
0,07
0,1
0,18
0,27
0,93
0,21
Noord-Drenthe
0,05
0,11
0,17
0,22
0,95
0,27
Zuidoost-Drenthe
0,07
0,08
0,19
0,28
0,84
0,14
Zuidwest-Drenthe
0,04
0,09
0,18
0,2
0,87
0,17
Noord-Overijssel
0,05
0,06
0,23
0,49
0,91
0,22
Zuidwest-Overijssel
0,06
0,09
0,35
0,22
1
0,26
Twente
0,05
0,09
0,41
0,85
0,94
0,23
Veluwe
0,06
0,06
0,35
0,87
0,93
0,25
Achterhoek
0,05
0,07
0,25
0,61
0,95
0,2
Arnhem/Nijmegen
0,05
0,12
0,75
0,89
1,03
0,34
Zuidwest-Gelderland
0,06
0,08
0,32
0,36
0,92
0,21
Utrecht
0,06
0,08
0,82
1,35
1,01
0,37
Kop van Noord-Holland
0,06
0,06
0,33
0,55
0,9
0,19
Alkmaar en omgeving
0,05
0,09
0,82
0,29
0,92
0,27
IJmond
0,04
0,09
1,2
0,25
0,97
0,25
Agglomeratie Haarlem
0,03
0,11
1,67
0,24
1
0,39
Zaanstreek
0,05
0,09
1,34
0,23
0,94
0,23
Groot-Amsterdam
0,06
0,11
1,63
1,45
1,05
0,39
Het Gooi en Vechtstreek
0,05
0,05
1,26
0,26
0,95
0,37
Agglomeratie Leiden en
Bollenstreek
Agglomeratie sGravenhage
Delft en Westland
0,04
0,05
1,6
0,53
0,96
0,33
0,05
0,08
3,11
0,94
1
0,36
0,04
0,07
1,31
0,27
0,94
0,3
Oost-Zuid-Holland
0,05
0,06
0,64
0,43
0,92
0,27
Groot-Rijnmond
0,06
0,11
1,12
1,79
0,92
0,25
Zuidoost-Zuid-Holland
0,05
0,06
0,81
0,54
0,84
0,21
Zeeuwsch-Vlaanderen
0,05
0,07
0,15
0,17
1,07
0,2
Overig Zeeland
0,06
0,06
0,25
0,38
0,95
0,22
West-Noord-Brabant
0,05
0,07
0,49
0,87
0,95
0,25
Midden-Noord-Brabant
0,05
0,07
0,49
0,64
1,02
0,26
Noordoost-NoordBrabant
Zuidoost-Noord-Brabant
0,05
0,04
0,45
0,94
0,98
0,26
0,06
0,09
0,5
1,02
1,03
0,3
Noord-Limburg
0,05
0,08
0,33
0,46
0,96
0,19
21
Midden-Limburg
0,05
0,08
0,34
0,34
0,98
0,24
Zuid-Limburg
0,05
0,13
0,95
0,87
1
0,25
Flevoland
0,08
0,09
0,23
0,48
0,9
0,23
RBA region
Groningen
Percentage
Economic
growth
0,05
Percentage
unemployed
15-25
0,14
Population
density per
Meter2
0,24
Number of low
skilled jobs
Oversupply
high educated
0,77
1,04
% High
educated in
area
0,28
Friesland
0,06
0,14
0,19
0,85
0,97
0,21
Drenthe
0,06
0,1
0,18
0,66
0,89
0,2
IJssel-Vecht/Twente
0,06
0,08
0,32
1,44
0,93
0,22
IJssel/Veluwe
0,06
0,06
0,33
1,14
0,95
0,25
Arnhem-Oost
Gld./Nijm.
Rivierenland
Flevoland
0,06
0,09
0,44
1,67
0,99
0,28
0,09
0,09
0,23
0,46
0,88
0,23
Midden-Nederland
0,06
0,07
0,87
1,63
1
0,37
Noord-Holland Noord
0,06
0,07
0,43
0,84
0,92
0,23
Zuidelijk NoordHolland
Rijnstreek
0,06
0,11
1,54
2,11
1,02
0,35
0,06
0,06
0,96
0,99
0,94
0,3
Haaglanden
0,06
0,08
2,37
1,19
0,99
0,35
Rijnmond
0,06
0,1
1,02
2,17
0,91
0,24
Zeeland
0,05
0,08
0,23
0,61
0,94
0,21
Midden- en WestBrabant
Noordoost-Brabant
0,06
0,08
0,49
1,47
0,98
0,25
0,08
0,05
0,45
0,95
0,98
0,25
Zuidoost-Brabant
0,06
0,08
0,5
1,02
1,03
0,29
Limburg
0,06
0,1
0,53
1,66
0,97
0,23
Appendix 2 Descriptives model choice to continue education
No ‘start qualification’
‘Start qualification’
Mean/pct
Std dev
Min
Max
Mean/pct
Std dev
Min
Max
Age
17,71
1,10
16
30
20,69
2,09
16
30
Male
44,68%
53,64%
Immigrant
11,83%
11,51%
General
30,94%
0,00%
Agriculture
30,45%
13,12%
Engineering
13,42%
33,42%
Economics
11,96%
36,61%
Healthcare
13,22%
16,85%
Demographics
Sector of studies
Level of education
22
Pre-vocational secondary
education
Secondary vocational education
level 1
Secondary vocational education
level 2
94,91%
0,00%
5,09%
0,00%
0,00%
0,00%
Category of studies
Pre-vocational secondary
education
94,91%
0,00%
Vocational training (BOL)
3,25%
57,54%
Apprenticeship training (BBL)
1,85%
42,46%
Region
Population density per m2
0,71
0,49
0,17
2,46
0,61
0,45
0,17
2,46
Percentage economic growth
5,62%
0,03
-0,03
0,13
5,69%
0,02
-0,03
0,13
Percentage unemployed 15-25
Number of working people with
an elementary or lower
job(x100000)
9,51%
0,03
0,01
0,25
9,42%
0,04
0,01
0,25
142,69
57,09
36,00
232,41
135,66
52,28
36,00
232,41
Oversupply high educated
0,97
0,08
0,80
1,15
0,97
0,07
0,80
1,15
Percentage high educated
27,09%
0,06
16,67%
42,08%
26,02%
0,05
16,67%
42,08%
High educated
Satisfaction
Choose same education
50,87%
68,37%
Choose different education
10,71%
22,79%
Not study at all
1,13%
6,74%
Satisfaction unknown
Dependent: Continue
education
37,28%
2,11%
82,74%
42,85%
31594
11725
Yes continue education
Total N
Appendix 3 Descriptives of respondents in analysis on chance to get a job
No ‘start qualification’
‘Start qualification’
Mean/pct
Std dev
Min
Max
Mean/pct
Std dev
Min
Max
Demographics
Age
18,41
1,86
16
30
21,02
2,27
17
30
Male
47,75%
54,49%
Immigrant
11,16%
9,68%
General
10,72%
0,00%
Agriculture
34,98%
14,47%
Engineering
19,08%
35,58%
Economics
20,52%
36,61%
Healthcare
14,71%
13,34%
Sector of studies
Level of education
Pre-vocational secondary
education
Secondary vocational education
82,05%
17,95%
23
level 1
Secondary vocational education
level 2
Category of studies
Pre-vocational secondary
education
Vocational training (BOL)
Apprenticeship training (BBL)
Region
100%
82,05%
47,76%
10,09%
52,24%
7,87%
Population density per m2
0,74
0,49
0,17
2,46
0,62
0,45
0,17
2,46
Percentage economic growth
5,87%
0,02
-3,07%
13,13%
5,81%
0,02
-3,07%
13,13%
Percentage unemployed 15-25
9,19%
0,03
1,03%
25,05%
9,25%
0,04
1,03%
25,05%
Number of working people with
an elementary or lower
job(x100000)
146,01
58,42
36,00
232,41
136,83
52,42
36,00
232,41
Oversupply high educated
26,64%
0,06
16,67%
42,08%
25,75%
0,06
16,67%
42,08%
Percentage high educated
Satisfaction
0,96
0,08
0,80
1,15
0,96
0,07
0,80
1,15
Choose same education
33,97%
65,38%
Choose different education
14,52%
23,16%
Not study at all
4,58%
9,17%
Satisfaction unknown
46,93%
2,29%
Found a job
84,85%
93,06%
Total N
4283
6282
High educated
Dependent: Chance to get a
job
No ‘start qualification’
‘Start qualification’
Mean/pct
Std dev
Min
Max
Mean/pct
Std dev
Min
Max
Age
18,50
1,97
16
30
20,96
2,19
17
30
Male
47,95
0,48
53,88
0,54
Immigrant
9,33
0,09
8,46
0,08
Demographics
Sector of studies
General
12,75%
0,00%
Agriculture
30,01%
15,13%
Engineering
18,84%
34,74%
Economics
24,53%
37,03%
Healthcare
13,87%
13,10%
78,42%
0,00%
21,58%
0,00%
0,00%
100,00%
Level of education
Pre-vocational secondary
education
Secondary vocational education
level 1
Secondary vocational education
level 2
Category of studies
24
Pre-vocational secondary
education
Vocational training (BOL)
78,42%
0,00%
12,43%
50,47%
Apprenticeship training (BBL)
9,15%
49,53%
Region
Population density per m2
0,80
0,58
0,15
3,23
0,72
0,52
0,15
3,23
Percentage economic growth
5,30%
0,03
-9,31%
28,86%
5,53%
0,03
-8,52%
28,86%
Percentage unemployed 15-25
8,33%
0,04
0,00%
33,33%
8,84%
0,04
0,00%
33,33%
Number of working people with
an elementary or lower
job(x100000)
87,05
52,05
4,00
193,00
82,04
45,88
5,00
193,00
Percentage high educated
27,73%
0,07
9,38%
46,63%
26,69%
0,07
9,38%
46,63%
Oversupply high educated
Satisfaction
0,96
0,09
0,63
1,36
0,96
0,09
0,63
1,51
Choose same education
37,90%
66,13%
Choose different education
16,79%
22,65%
Not study at all
5,40%
8,98%
Satisfaction unknown
39,91%
2,25%
2,73
2,48
43,34
High educated
Dependent: Wage
Wage
5,14
Total N
2776
2,32
1,92
49,26
7,12
5334
25