Education, Labour Market Opportunities and Mismatch in the European Union Before and

After the 2008-2009 Crisis

Fabrizio Pompei* and Ekaterina Selezneva **

Abstract : This paper investigates whether general formal education still helps youth avoid

situations of unemployment and inactivity in favour of other labour force statuses (dependent

employment, self-employment, education) across EU countries over the period 2006-2010. In a

second step, we analyse whether the relationship between education and the labour statuses above

is affected by different degrees of country-level education mismatch. Our results show that after

the outbreak of the crisis and in countries with high educational mismatch there is an additional

reduction in unemployment risk for highly educated people that is accompanied by a higher

probability of being an employee than of remaining in education.

Keywords: youth unemployment; education mismatch; multinomial logit model; multilevel analysis

JEL Classification: I20; J24; Z13

*Corresponding Author: University of Perugia, Perugia, Italy, email: fabrizio.pompei@unipg.it.**Institute for East and Southeast European Studies, Regensburg, Germany, email: eka.selez@gmail.com.

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Education, Labour Market Opportunities and Mismatch in the European Union Before and

After the 2008-2009 Crisis

1 Introduction and policy relevance of the study

The construction of effective education systems, especially those promoting higher education, is at

the heart of the European Union (EU) initiatives addressing young citizens and their access to the

labour market. The European Commission (2011) issued an urgent statement that 35% of all jobs in

the EU would require high-level qualifications by 2020, but only 26% of the workforce had

achieved such qualifications in 2011. The Europe 2020 strategy explicitly aims to reduce a share of

early leavers from education and to bring the share of the highly educated among those aged 30-34

up to 40% (Rogge, 2019). This quantitative target, i.e., higher tertiary attainment levels, remains

one of the EU’s priorities despite some emerging challenges. For example, technological changes

and their role in shaping the skill demand, along with the various forms of mismatch, are alarming

the EU institutions (European Commission, 2017). Indeed, both the last few years’ changes in the

skilled labour demand and labour mismatch could hinder access to the labour market for young

people, bringing into question education investments over the life-cycle. While higher education

expansion continues and politicians place the accessibility of (higher) education at the top of their

agenda, not much is known about the impact of an aggregated country-level educational mismatch

on the relationship between education and labour status at the individual level in times of crisis. For

this reason, the official EU documents call for the collection of contemporary evidence on the role

of education in promoting welfare and economic growth in the EU economies (European

Commission, 2017, p. 3).

This paper shares the listed concerns and questions whether general formal education still helps

youth avoid situations of unemployment and inactivity in favour of other labour force statuses

(employment, education) across EU countries over the period 2006-2010. Second, we analyse

whether the relationship between education and labour status is affected by different degrees of

country-level education mismatch, which is often identified as being responsible for the severe

youth unemployment across Europe (European Commission, 2013; European Central Bank, 2012).

Whether the 2008-2009 crisis has had a direct or indirect impact (via the education mismatch) on

the relationship between education and labour status is the third important issue investigated in this

study.

To the best of our knowledge, there is scanty evidence on the relationship between formal education

and labour status when the moderator is a form of country-level education mismatch that basically

reflects the skill shortages (ILO, 2013, European Central Bank, 2012). Understanding whether

improving the access to higher education may still reduce unemployment among youth, even in the

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aftermath of the crisis and in countries affected by high education mismatch, is important for the

policy debate. This especially holds true when policy makers have to justify tax-financed increases

in education expenditure (Annabi, 2017).

We perform in this paper a multinomial logit model to study the effect of incremental education on

the probability of maintaining an alternative labour status and address the potential endogeneity of

this relationship by applying the two-stage residual inclusion approach suggested by Terza et al.

(2008) and Bollen et al. (1995). In addition, we take into account the multilevel character of the data

and the possible cross-level effects (interactions between the country and individual levels). To do

so, we implement the methodology described by Bryan and Jenkins (2015) to avoid the problems

affecting multilevel models when the number of second-level units (countries) is low.

Two results make this study worth noting in terms of policy implications. First, between 2006 and

2010, acquiring more formal education reduces the unemployment risk for youth on the one hand

but stimulates additional investments in education on the other hand. This strong option for further

education observed in the EU countries could be explained by a perception of overall weak labour

markets (higher unemployment prospects) that leads many young people to remain in education

(Clark, 2011). Second, our results also show that after the outbreak of the crisis and in countries

with high education mismatch (i.e., higher skill shortages), there is an additional reduction in

unemployment risk for highly educated people that is accompanied by a higher probability of being

an employee than of remaining in education. This means that a higher country-level educational

mismatch might act as a catalyst by further improving labour market opportunities for youngsters

who accumulate education.

The paper is structured as follows. In the next section, we discuss the theoretical and empirical

background for our working hypotheses. Section 3 presents the econometric strategy, whereas

section 4 presents the data sources and variables used in the estimations. After a brief summary of

statistics (section 5), the econometric results are given in section 6. The last section is dedicated to a

detailed discussion of econometric results, policy implications and some final remarks.

2 Conceptual Framework and Policy-Guided Working Hypotheses

Human capital theory, as well known, argues that an individual invests in education if and only if

the expected future stream of benefits exceeds the total costs to be borne to acquire that education

(Becker, 1964; 1993; Mincer, 1974). While the majority of studies taking the human capital theory

perspective concentrate on the impact of education on wages (Card, 1999; Peracchi, 2006), there are

other important contributions, on both theoretical and empirical levels, that emphasize the positive

effect of human capital accumulation on reducing unemployment and inactivity risk (Ashenfelter

and Ham, 1979; Mincer, 1991; Cairò and Cajner, 2018). From a broader perspective, some

investigations highlight a clear relation between a rising share of highly educated individuals and

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the reduction in the unemployment rate in the European Union and OECD countries (Biagi and

Lucifora, 2008). Especially in times of crisis, higher education is also important to escape inactivity

and the NEET status, that is young people neither in employment nor in education and training

(Scarpetta et al., 2012; OECD, 2016).

Other studies of interest for our case are those focusing on individual choices made in the first part

of the life cycle and concerning education and labour outcomes alternative to

employment/unemployment. Trostel and Walker (2006) stress the endogeneity of the choice to

invest in human capital to the labour market status (employee, unemployed, etc.) for young people

and demonstrate that education helps one escape inactivity. The endogeneity problem in the

relationship between education and labour outcomes has also been extensively addressed by Riddell

and Song (2011). Interestingly, Keane and Wolpin (1997) implemented a model in which school

attendance, employment and inactivity are jointly analysed as potential choices for 1,400 white

males born in 1979 in the United States and aged from 16 to 26. These authors also highlighted that

for young people at the end of compulsory school, further school attendance, searching for a job

(and related occupational options) or staying at home are alternative individual choices driven by

the accumulation of schooling or work experience.

In more detail, different effects of secondary and tertiary educational attainment are established

for self-employment (Millàn et al., 2014; Simoes et al., 2016; Caballero, 2017) and the continuation

of studies/education (Cunha and Heckman, 2007; Clark, 2011; Styczynska, 2013; Kramer and

Tamn, 2016;), depending on the specific environment or the individual probability of entering

unemployment or dependent employment. According to Caballero (2017) and Millan et al. (2014)

theory does not predict clear-cut effects of higher education on self-employment choice. This is also

confirmed by a large survey of the literature conducted by Simoes et al. (2016), which includes

empirical analyses covering EU countries. On the one hand, higher education affects self-

employment quality and its duration due its strong contribution to building entrepreneurial

competences. On the other hand, more formal years of education could play a signalling role for

employers concerning the worker’s good quality (Garcia-Mainar and Montuenga, 2019) but no role

in entering self-employment. In addition, increasing years of schooling might be related to higher

expected wage earnings (human capital theory), that is, a higher opportunity cost of being self-

employed. For these reasons, the potential effects of education on self-employment remain

ambiguous.

As with education status, an important conclusion drawn about the choice to continue studies is

that education begets additional education, and hence, those with higher levels of human capital

early in life are more likely to continue studies later in life (Cunha and Heckman, 2007; Kramer and

Tamn, 2016). According to Kramer and Tamn (2016), this dynamic complementarity between the

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educational investment propensity in different stages of life persists throughout adulthood and

influences young people who must choose among alternative labour statuses. Hence, in this case,

theory predicts a positive impact of early formal education on the decision to continue studies.

Besides the level of educational attainment, the literature on the determinants of youth labour

market outcomes highlights household structure and income (marital status, presence of children,

capital versus labour income as main income source), political rights, civic engagement and social

activities (citizenship, association memberships, meeting friends) as potential explanatory variables

(Dietrich, 2012). All these factors not only influence unemployment and inactivity risk but also

could orientate youth towards self-employment or continuation of studies, as alternatives to

dependent employment (Millàn et al., 2012; Pfeiffer and Seiberlich, 2010).

The micro-level relationship between education and labour market outcomes could be affected

by macro-determinants of the individual labour status, such as the degree of the educational

mismatch across countries and over time. To date, only labour market institutions, unemployment

rate and international trade have been considered as possible moderators of the relationship under

scrutiny (De Lange et al., 2014; Millan et al., 2012; Pastore, 2012; Scherer, 2004; Giannelli and

Monfardini, 2003). To the best of our knowledge, no study has investigated educational mismatch

as a country-level moderator of the probability of maintaining an alternative labour market status

for young individuals with different years of formal education. However, in the wake of the recent

crisis, country-level matching problems in labour markets stand out as one of the reasons for

structural youth unemployment (Pissarides, 2013; European Central Bank, 2012; ILO, 2013;

European Commission, 2013; OECD, 2014). The crisis has aggravated the education mismatch

between labour demand and supply in some EU countries. These imbalances in the educational

composition of the labour supply and demand actuate frictions in the process of labour reallocation

across sectors and cause an overall decline in the job-finding rates. The macro-level increase in

youth unemployment conceals extensive heterogeneity, however, that we can observe across youth

with different education attainment. For example, Woessman (2014) notes that across the 28 EU

countries, the unemployment rate is, on average, 17.9, 8.6 and 5.9 percent among those with low,

medium, and high levels of education, respectively.

The labour supply side is, in fact, characterized by an excess of less-educated people in

opposition to a shortage of individuals with higher educational attainment (de Weert, 2011;

European Commission, 2013). Among young European workers, under-education was prevalent

between 2006 and 2010. In particular, the share of youngsters with a lower level of education than

required by the job they performed largely exceeded the share of over-educated employees in

almost all EU countries (ILO, 2015, p.87). Under such conditions of education mismatch, when

highly educated young people are lacking on the supply side of the labour market, one can expect

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these relatively rare individuals to be in an advantageous position in terms of job-finding chances.

Therefore, a country-level education mismatch mainly characterized by skill shortages should

strengthen the positive relationship between additional years of education and the probability of

being employed compared to the probability of being unemployed/inactive. A corollary of this

proposition is that the greater the number of years of education, the lower the probability of still

being in education. The latter follows from better employability, making the choice of further

education less appealing.

As for the relationship between education and self-employment, again, theory does not give us

unambiguous predictions for environments characterized by high education mismatch. According to

Lee et al. (2011), contexts shaped by educational mismatch in which highly educated people do not

find adequate organizational conditions within the firms that are recruiting them, positively

influence the relationship between education and self-employment. This means that the probability

of entering self-employment, especially when it corresponds to entrepreneurial intentions, is

favoured by higher levels of educational attainment. By contrast, Brixiova et al. (2009)

demonstrated that when the educational mismatch mainly relies on skill shortages, a lower number

of skilled workers reduces the average profitability of operating firms at the country level; hence,

there will be lower incentives to enter self-employment.

Based on the discussion above, the following interrelated working hypotheses (WH) that also

maintain direct policy implications, drive the investigation of the relationship between educational

attainment and the probability of being in an alternative labour status for youth across EU countries

over 2006-2010.

WH1: The greater the length of individual education, the lower the risk of being unemployed or

inactive and the higher the probability of being employed or still in education.

It is worth noting that there is no a priori expectation for self-employment due to the inconclusive

results of theoretical and empirical studies in this case. WH1 will be rejected, for example, if the

risk of being unemployed/inactive is no longer linearly decreasing with years of education, as

human capital theory predicts, or if additional years of education for people aged between 15 and 34

do not favour the option of continuing studies, as the theory of dynamic complementarities in

educational investments states.

Our second and third working hypotheses connect the macro- and micro-dimensions. We

wonder whether this aggregated education mismatch favours—in terms of labour market status—

youth with a greater number of accumulated years of education and, thus, with higher levels of

human capital and a higher potential for skill acquisition.

WH2: The higher the country-level education mismatch, the stronger the positive effect of the

number of years of education on reducing unemployment / inactivity risk and on attenuating the

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choice to continue studies at the individual level.

As we discussed above, when the aggregate education mismatch basically relies on skill shortages,

this moderator could strengthen the positive relationship between highly educated youth and the

probability of being employed. For self-employment, no prediction is possible, and the answer is a

pure empirical question.

Finally, because the crisis increased the education mismatch in Europe, it might also have

favoured more-educated youngsters in the labour market for the reasons mentioned above. Different

results could emerge for the different number of years of education accumulated. Therefore,

WH3: The higher the country-level education mismatch at the moment of the crisis, the stronger

the positive effect of education on attenuating unemployment/inactivity risk and on reducing the

continuation of studies choice at the individual level.

These predictions should be also considered for their direct policy implications. WH1 is formulated

to provide evidence on effectiveness of secondary and tertiary education systems in contrasting the

emergence of NEETs across Europe (Scarpetta and Sonnet, 2012; OECD, 2016). Moreover, WH1 is

useful to learn about how education policies are currently boosting the likelihood to enter the

employee status compared to self-employment or entrepreneurship. Finally, WH2 and WH3 inform

policy makers, at least in quantitative terms, whether outcomes of education systems are weakened

in contexts with important country-level education mismatch or rather the highly educated people

are even favoured in such circumstances.

3 Econometric Strategy

Based on the ideas discussed above, we assume that human capital stock approximated by years of

education determines probabilities for young people (15-34) to fall within five mutually exclusive

unordered labour market statuses: 1) Employee; 2) Self-employed; 3) Unemployed; 4) In

Education; and 5) Inactive. Models in which school attendance, work and occupational choices are

examined together have been developed by Keane and Wolpin (1997). Wooldridge (2010, p. 645)

made these alternative choices testable on the empirical level by means of the multinomial logit

model (MNL). To ensure that the alternative-specific errors are uncorrelated and that the odds-ratios

for pairs of alternatives are invariant with respect to the expansion (and contraction) of the

alternatives set, we test the validity of the irrelevant alternatives assumption. The Small-Hsiao test,

does not reject this hypothesis for any set of outcomes1.

Our baseline specification aims to test the validity of working hypothesis 1 (WH1). Therefore,

by taking the log odds version2, we estimate the following equation (1) on a pooled sample of data:

1 Results are available upon request.

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ln Ωm∨b=α m∨b+EduYrsβ1, m∨b+Pβ2 , m∨b+Fβ3 ,m∨b+Sβ4 , m∨b+δ t , m∨b+ηc ,m∨b(1)

where m are the four outcomes alternative to the base category b (Employee). EduYrs is the

main variable of interest at the individual level (years of education); P, F and S are matrices,

including other personal, family and socio-political characteristics of young people (see section 4

for more details on these characteristics), δt,m|b are time dummies (t = 2006, 2008, 2010), and

ηc,m|b are country-fixed effects (c =1..., 21). It is worth noting that this country-fixed effects

specification allows us to represent the EduYrs coefficient as the parameter of within-country

regressions.

As for the interpretation, a positive value of the estimated parameters βm|b means that the higher

the value of the regressor, the higher the likelihood of being in labour status m in comparison to the

probability of being employed (b, base category). The interpretation is complicated by the fact that

these coefficients only indicate a change in the relative probability of an outcome and not the

probability of the outcome itself. In order to reduce misinterpretations, we follow Cameron and

Trivedi (2009, p.478) and always calculate specific discrete changes of our predicted probabilities.

This also allow us to get a much finer-grained understanding of the effects of our key explanatory

variable, EduYrs. Despite EduYrs is a continuous variable, we can calculate the marginal effects of

people aged 26 and compare outcomes of having years of education corresponding with the end of

tertiary education (18 years) to outcomes associated to less EduYrs (13 and 8) and lower levels of

educational attainment.

To test WH2, i.e., the impact of the educational mismatch index (EMI) on the relationship

between individual-level education and the relative probabilities of the outcomes, we augment

equation (1) with the interaction term of EMI and years of education completed by individuals:

ln Ωm∨b=α m∨b+( EMI∗EduYrs ) β1 ,m∨b+EduYrsβ2 ,m∨b+Pβ 3 ,m∨b+Fβ4 ,m∨b+Sβ5 ,m∨b+δt ,m∨b+ηc, m∨b (2)

In this equation, the coefficient of EduYrs is intended to describe the within-country effects of

education, whereas the interaction EMI∗EduYrs captures the additional cross-country effect of the

country-level variable educational mismatch. For example, if the β2 ,m∨b, referring to unemployment,

is negative, it means that additional years of education reduce the probability of being unemployed

compared to that of being an employee. A negative coefficient β1 ,m∨b signals that this negative

relationship is even strengthened in countries with higher educational mismatch, that is, highly

educated individuals are favoured in contexts in which the educational mismatch presumably

corresponds to a skills shortage.

Also note that equation (2) is a country-fixed effects specification in which the country

2 We use Stata as statistical software package. By default, Stata produces the parameters βm|b as log-odds ratios. For this reason, we also refer to log-odds ratios as raw coefficients.

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intercepts ηc , m∨b absorb all other country-level factors. Because the variance of country-level EMI

as a stand-alone variable is already encapsulated in the country dummies, we follow the

recommendations of Bryan and Jenkins (2015), Allison (2009) and Snijders and Bosker (1999) and

omit the main effect of EMI from our specification with a cross-level interaction (EMI∗EduYrs).

To disentangle a potential impact of crisis on the interrelations described above (WH3), we also add

the year-specific (2010) interactions:

ln Ωm∨b=α m∨b+( EMI∗EduYrs∗2010 ) β1 , m∨b+¿(3)

where X is a matrix including all personal, family and socio-political characteristics that enter P, F

and S in the previous equations.

The computation of predicted probabilities and their discrete changes for EduYrs, after and before

the outbreak of the crisis and for different levels of education mismatch adds, to that of specific

amounts of EduYrs (8, 13 and 18) discussed for the equation 1 and allows us to test in which

specific conditions the human capital investments corresponding to higher education are still

playing a positive role.

Two potential problems undermine these specifications, the endogeneity of education with

respect to labour status (Riddell and Song, 2011; Trostel and Walker, 2006) and the multilevel

nature of the data (Bryan and Jenkins, 2015).

Regarding endogeneity, unobserved factors such as innate and noncognitive abilities of young

people (perseverance, motivation, self-esteem) might be simultaneously correlated with both

additional years of formal schooling and our dependent variables (Riddell and Song, 2011). This

means that the estimated coefficients for β1 ,m∨b and β2 ,m∨b might be seriously biased and the causal

effect of education on labour market outcomes questioned. To fix this problem we follow several

authors (Ivlevs and King, 2012; Wooldridge, 2010; Terza et al., 2008; Bollen et al., 1995) and

prefer a 2-stage residual inclusion regression (2RSI) over the conventional 2-stage predictor

substitution approach (2SPS). At the first stage of the 2RSI method, we set out an OLS regression

in which, similarly to the 2SPS, we regress our continuous endogenous variable years of education

on instrumental variables.

EduYrs=α+ IVβ1+Pβ2+Fβ3+Sβ4+δ t+ηc (4)

where IV is a matrix containing a set of excluded instruments, which we thoroughly discuss in the

next section; P , F and S are the same matrices of equations (1) and (2), containing all the

individual-level control variables (included instruments); and δt and ηc are time and country

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dummies, respectively.

In the second-stage regression of 2RSI, the endogenous variables are not replaced by their

predicted values. Instead, the first-stage residuals are included in the second-stage regression

alongside the actual value of EduYrs.

ln Ωm∨b=α m∨b+EduYrsβ1, m∨b+1 stage Resid β2 , m∨b+Pβ3 , m∨b+Fβ4 , m∨b+Sβ5 , m∨b+δ t ,m∨b+ηc ,m∨b (5)

where 1-stage Resid are the residuals stemming from the first stage of the 2RSI.

Following Bollen et al. (1995) and Ivlevs and King (2012), we test the relevance of instruments,

that is, the correlation between EduYrs and instruments, in the first stage by means of an F test, and

discuss the endogeneity of EduYrs by reporting the Wald test for the coefficients of 1stage Resid. If

the latter is not statistically significant, then the endogeneity of education is not an issue. In

contrast, if endogeneity is detected, we also need additional guarantees for the quality of

instruments, which are obtained by testing their orthogonality with respect to labour market

outcomes, that is, the exclusion restrictions. To test these exclusion restrictions, we compare the

reduced form, in which we replace EduYrs with the set of instruments IV, and the structural

equation, in which we only include the predicted value for EduYrs and omit the instruments. If the

instruments only influence the labour status indirectly, through their effects on EduYrs, the log-

likelihood of the reduced and structural equations should be similar (Bollen et al., 1995). This test

on the identifying assumptions proves the exogeneity of the instruments.

Finally, since we find that EduYrs is endogenous, we include the residuals in specifications (1), (2)

and (3).

As with the second problem, Bryan and Jenkins (2015) highlighted difficulties arising from data

structures in which individual-level observations are nested within a higher level (countries). On the

one hand, the cross-level effects (EMI∗EduYrs) provide us with useful information about the

influence of aggregate educational mismatch on the individual relationship between education and

labour market outcomes. On the other hand, the EMI variable only varies across 21 countries and

three years (2006, 2008 and 2010), in our case. The authors above demonstrated that the estimated

properties of the coefficient for cross-level effects (EMI∗EduYrs, in our case) might suffer from

consistency and efficiency problems when the number of countries is below 30. Put differently, the

properties of the parameters estimated cannot be ameliorated by the large size of the sample at the

individual level (thousands of observations for EduYrs) when there are few observations at the

country level (EMI).

To solve this issue, we follow Bryan and Jenkins (2015), and in addition to omitting EMI as a

stand-alone term in equations (2) and (3), we perform a robustness check to disclose its statistical

significance as a country-level variable. In doing so, we run an additional regression at the country

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level that uses the country intercepts estimated in equation (5) as dependent variables, that is,

equation (1) corrected for endogeneity. More precisely, we separately estimate three regressions of

equation (5) for each year (2006, 2008, 2010) and therefore regress the estimated country intercepts

resulting from equation (5) (that is, the country-level means of the relative probabilities of

maintaining a given labour status) on the country-level variable EMI using OLS. Of course, this is a

regression based on many fewer observations (21 countries * 3 years) that uses as inputs the results

from our baseline regression, i.e., the country-level means of the relative probabilities of

maintaining a given labour status coming from equation (5). We repeat this regression for each

outcome m (Unemployed, Self-employed, Education, Inactive) of equation (5).

η̂c , t=α+EMI c ,t β1+LabMarkLiberalc, t β2+GDPShockc , t β3+δt+εc ,t (6)

where c =1, ...21 countries and t = the years 2006, 2008, 2010 (this leads to have approximately 60

observations or slightly under, due to missing data); η̂c , t are the estimated parameters from equation

(5) referring to the country intercept c and year t, capturing the relative probability to be in labour

status m; EMI is the same proxy for the education mismatch used in equations (2) and (3); and

Lab.MarketLiberalc,t and GDP_Shockc,t are two country-level control variables that take into account

labour market institutions and business cycles, respectively.

This supplementary step offers two advantages to our econometric analysis: (i) we have a

preliminary assessment of the reliability of EMI as a country-level effect, where a significant

coefficient for EMI means that its main effect on the average relative probability of being in a labour

status is binding; and (ii) we have useful information on the sign (direction) of the main effect of

EMI in order to clarify the interpretation of cross-level effects (interaction terms) in the main

specifications (equations 2 and 3).

4 Data sources and variables

We selected the individual-level variables from the European Social Survey (ESS), that is an

academically-driven multi-country survey repeated every two years and aiming at developing a

series of European socio-economic indicators. Our sample includes 21 European Union member

states, with the exclusion of Italy, Austria, Malta, Luxembourg, Latvia, Lithuania and Romania, due

to missing data. The cumulative data files integrate cross-section information gathered in 2006,

2008, and 2010; therefore, we have not a sample with a panel data structure. Unfortunately, the

distributed editions of the latest rounds exclude a large number of countries we intend to investigate

in this study, so we limited our investigation to the years 2006-2010, which is a time frame that

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covers before and during the crisis.

The key variable of interest is self-reported labour status at the moment of the interview for

young individuals aged 15-34. The status Employee is our base outcome, and it includes all young

employees (contracts with limited and unlimited durations). Self-employment is the second status,

and it includes self-employed and persons working for their own family business. Unemployed

actively looking for a job is the third status (Unemployed), and youngsters still in education is the

fourth (Education). Inactive is a residual category that includes unemployed young people who are

not actively looking for a job and are not in education and young people who are inactive for

different reasons (permanently sick or disabled, community or military service, housework, looking

after children, or other).

As regards the key explanatory variable at the individual level, we use completed years of

education, which includes compulsory schooling.

To accommodate the relevant literature on youth labour market outcomes, we introduce as

controls a set of variables describing personal characteristics (age and gender), family

characteristics (number of family members, presence of children, labour/capital income as main

source of household income), political rights (citizenship), social relationships (frequency of

meetings with friends or colleagues, participating in events with other people, membership in trade

unions). In more detail, the conventional wisdom is that personal characteristics and the family

legacy strongly affect the probability of alternative labour outcomes for youths (see Caroleo et al.,

2017, for an updated review of the literature). Besides variables that capture the family structure

(family members and income, Rees and Gray, 1982), we also introduce the presence of children.

This is because we study young adults up to the age of 34; they could have children and according

to many authors the presence of children works as important driver of labour market participation

(De la Rica et al., 2008; Heckman, 1979). Political rights and social relationships are important

dimensions of social exclusion; according to Dietrich (2013) the latter shows a complementary

explanatory power in analysing youth unemployment and inactivity.

Additionally, we drew five binary variables from ESS to instrument years of education at the

first stage of the 2SRI approach. These variables are the father’s primary education level and four

proxy variables for tolerance, altruism, egalitarianism and environmentalism from the section of

the ESS database dedicated to human scale values1. We assume that these five binary variables are

correlated with years of education while not having an impact on the probability of being in any of

the five labour statuses considered. In the literature, there is a growing consensus on defining basic

11These four proxy variables are coded as 1 when an individual responds i) very much like me; ii) like me; iii) somewhat like me to a relevant question, and zero in case of other answer. We deduced a) tolerance from the question important to listen and understand people even when she/he disagrees with them; b) altruism from the question important to help people and care for others’ well-being; c) egalitarianism from important that people are treated equally and have equal opportunities; and d) environmentalism from important to care for nature and environment.

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values as cognitive representations of desirable goals that serve as guiding principles in the life of a

person. Tolerance, altruism, egalitarianism and environmentalism orientations - which, according

to Piurko et al. (2011), enter the broader categories of benevolence and universalism - belong to

self-transcendence values. It is plausible to assume that sharing these values is correlated with

increasing years of education through mental openness and the desire to understand the world,

while it is unrelated to pursuing self-interest. Indeed, the self-enhancement values (power,

achievement) located at the opposite end of the scale should be correlated with the probability of

being employed, unemployed, inactive or self-employed (Piurko et al., 2011).

Parental educational attainment is largely used in the literature as an instrument for children’s

education (Ivlevs and King, 2012; Parker and Van Praag, 2006), even though the instrument’s

exogeneity with respect to income and labour status has been questioned (Card, 1993; 1999). To

take into account Card’s criticisms on parents’ formal skills as instrument variables for their

offspring’s education, we use low education of the father2 interacted with the self-transcendence

values discussed above. In Card’s paper (1993), the father’s primary education is a proxy for a poor

family background; in the set of instruments used, it plays a role as a control variable when it is

interacted with college proximity, the latter being the main instrument for education. In other

words, geographical college proximity is the main source of exogenous variation (that is, the main

instrument) for educational choices; however, its potential positive relationship with the

endogenous variable (education) is even stronger whether the positive sign persists after interacting

with a poor family background as proxied by the low education of the father. Similarly, we

conjecture that self-transcendence values are formed with the support of a social context that is

wider than the family environment. The idea is that self-transcendence values alone are the main

source of exogenous variation and are positively correlated with years of education, whereas the

opposite holds for father’s primary education status. We use the low education of father both as a

stand-alone term and as an interaction term with tolerance, environmentalism, altruism and

egalitarianism. Positive and significant coefficients of the interaction terms tell us that these human

scale values will foster the choice of continuing education despite the poor family background of

young people.

Concerning EMI, we followed the approaches of ILO (2013), the European Commission (2013)

and the European Central Bank (2012) and constructed country-level education mismatch as a

dissimilarity index. This index compares the differences in educational attainment (coded as three

levels of education completed) between two groups, employed and unemployed (or labour force).

The index is estimated separately for two proxies of the labour supply, namely, the pools of

unemployed (EMIun) and of the labour force (EMIlf ):

22 We only use the father’s education due to the excessive number of missing data for the mother’s education.

13

EM I un=12∑i=1

3 |Ei

E−

U i

U |

where i is the level of education coherent with the International Classification of Education 2011

(ISCED, 2012)3; Ei

E is the proportion of employed with education level i; and U i

U is the proportion of

unemployed with education level i;

EM I lf=∑j=1

N LF j

LF ( EMI lf , j )

where EMI lf , j=LF ij

LF j∑i=1

3 |Eij

E j−

LF ij

LF j|;

Eij

E j∧LF ij

LF j

are respectively the proportion of employed and of the labour force with education level i

in region j (NUTS2 level). The sum over education groups (i) is weighted with the group’s share in

the population ( LFij

LF j) at regional level (j). EM I lf at the country level is a weighted average of

EM I lf , j at the regional level, and j=1…N are the regions (NUTS2 level) within a given country.

According to ILO (2013), if the unemployment rate in EMIun is the same among the primary, sec-

ondary and tertiary education-level graduates, the index equals zero. The index equals unity in the

case of complete dissimilarity among groups, for example, when all primary and tertiary education

graduates are employed, while those with secondary education are unemployed. The index can also

be interpreted as the percentage of unemployed individuals who should be reallocated across skill

levels to balance labour supply and demand.

EMIlf, instead, does not range from zero to one, although the score of the indicator is low if the

educational composition of the employed reflects the labour force’s educational composition, while

the value is high if the education groups that are highly represented in the labour force are not in the

employment pool (European Commission, 2013). In addition, we follow the European Central Bank

(2012) in the EMIlf case and calculate a country-level weighted average of regional level EMIlf,j . If

there is a lack of certain skills in some regions and an excess in others, then EMIlf will be higher

compared to an EMIlf calculated on the country-level skill distribution aggregates. In other words,

this version of EMIlf incorporates the size of mismatch caused by cross-region mobility problems,

besides the skill imbalances between labour demand and supply.

33 1) Primary or less and lower secondary education (levels 1-2); 2) Upper secondary and post-secondary, non-tertiary education (levels 3 and 4); 3) from short-cycle tertiary education on, i.e., bachelor, master (levels 5-8).

14

Our calculations show that the ranking of countries differs for these two definitions of EMI;

therefore, we use both indices later to test the robustness of the results. The data on shares of

employed, unemployed and labour force come from the Eurostat database (country- and regional-

level labour force survey). In addition, we assume a one-year delay for the impact of aggregate

mismatch on individual outcomes, as all macro-level phenomena usually take time to impact on

individuals. Therefore, EMIs referring to 2006, 2008 and 2010 are calculated for 2005, 2007 and

2009 respectively.

As regards the country-level controls in equation 6, GDP−Shock was calculated from Eurostat

data as the difference between the annual variation of GDP (e.g., 2005-2004 for the first year) and

the 5-year annual average of GDP (chain-linked volumes, reference year 2005). The proxy for

labour market liberalization comes from the Fraser Institute database and combines six different

components of country-level labour market institutions: i) hiring regulation and minimum wage; ii)

hiring and firing regulations; iii) centralized collective bargaining; iv) regulation of hours; v)

mandated cost of worker dismissal; and vi) military conscription. Similarly to EMIs, also GDP-

Shock and proxy for labour market liberalization have been used with one-year delay.

We obtain a pooled sample of 29,008 observations. These observations reduce to 27,201 because

missing data in the instrumental variables. Since we always use and compare models with the

endogeneity treatment (27,201 observations), problems related to changes in the sample size have

been kept to a minimum.

5 Descriptive statistics

Summary statistics referring to the aggregate ESS sample (21 EU countries over 3 rounds: 2006,

2008, 2010) are reported in Table 1. The overall number of individual-level observations varies, on

average, from approximately 9,000 in 2006 to more than 10,000 in 2008 and 2010. The first five

rows of the Table describe the five labour statuses of interest, whereas the remaining rows refer to

the explanatory variables. As expected, over the five years that include the outbreak of the recent

crisis, the employment rate of people aged 15-34 decreased from 48.6 to 43.53%, whereas the

percentage of unemployed of the total population, that is the youth unemployment ratio, increased

from 5.27 to 7.64%. At the same time, the percentage of young people in education grew from

29.20 to 32.92% and the share of inactivity slightly decreased from 12.08 to 11.42%. Both average

age and average years of education remained stable, at approximately 24.7 and 13.30, respectively.

According to ISCED (2012), the latter number corresponds approximately to the end of upper

secondary education. Indeed, approximately half of all the young people in the sample have a

secondary level of educational attainment, one-third have only primary education, and the share of

highly educated varies between 16.26 and 18.20%.

15

Table 1 also shows that the majority of youngsters in the sample live in 3-member households, in

which reward for labour is the main source of income. The share of young people with children is

not negligible, even though it decreased from 26.70 to 24.33%.

Eventually, we observe a fluctuation in dynamics of the two indices of the educational mismatch

(lagged one year), where a downturn in 2007 precedes a further increase in 2009. According to the

ILO (2013) interpretation of the EMIun, in 2005, 19.64% of youth unemployment would need to be

reallocated according to the educational attainment composition of employment to reduce the

mismatch to zero. This figure drops to 18.64% in 2007 and increases again to approximately 20% in

2009. As noted by the European Commission (2013), the crisis might have interrupted a decreasing

trend in the educational mismatch, at least for some countries.

In all countries under scrutiny, an education mismatch means that, besides an excess of low-

educated people, there is an important deficit of highly educated people on the labour supply side,

as the graphical representation of EMIun for 2009 clearly discloses (see Figure A1 in the appendix).

A very similar pattern has been obtained for EMIlf (the results are available upon request).

6 Estimation results

Table 2 shows the coefficients for the MNL model of the baseline specification (equation 2) es-

timated on the pooled sample with time and country dummies (29,008 observations)4. As already

explained in section 3, coefficients are log-odds ratios and only report relative probabilities to be in

a given status. In order to clarify the dominant effect and its magnitude we calculate predicted

probabilities and their discrete changes, that will be discussed in the next section. These results

partially support working hypothesis 1 (WH1), namely, that an extra year of formal education

reduces the probability of being unemployed or inactive compared to the probability of being

employed, while it has a remarkable positive impact on the probability of staying in education.

However, this baseline estimation is inconclusive on self-employment, that is, the years of

education have no effect on the relative probability of being self-employed. Nearly all other control

variables are significant with the sign predicted by the literature on youth labour market outcomes

(Dietrich, 2013).

As discussed in section 3, we implemented the 2SRI method to tackle the endogeneity problem, and

we present the respective estimation results in Table 3. To save space we omit hereinafter the

44 The number of observations slightly reduces in the subsequent estimations (Tables 3-8), due to missing data in the instrumental variables. We are aware that inference, p_values, and the statistical significance of coefficients (signalled by asterisks), could simply be driven by the large sample size. However, in the specifications with cross-level effects (Table 6 and 8), it is the low number of countries*years (21*3) that affects the efficiency of coefficients, as explained in the methodological section (Bryan and Jenkins, 2015). Therefore, for the sake of coherence, the conventional significance levels (0.1; 0.05; 0.01) have been reported in all the Tables. As for results without cross-level effects (Tables 2, 3 and 7) we follow the recommendations of Wasserstein and Lazar (2016), Lin et al. (2013) and Smart (2005, p.473). It means that we always pay attention to the practical significance of the estimated coefficients and report confidence intervals when we estimate the changes in predicted probabilities (see tables attached to Figures 3 and 4).

16

coefficients referring to the control variables; they are available upon request. First, the instruments

are jointly statistically significant, as the F-statistic value suggests, and hence, they are relevant and

are correlated with years of education, the endogenous variable. The first stage of this specification

is reported in the appendix (Table A.1) and shows that tolerance and environmentalism stimulate

investment in education, whereas the father’s low education discourages this choice. The negative

influence of a poor family background is likely to be compensated by the impact of altruism when

educational choice is concerned (see the significant coefficient of the interaction term Father’s

Primary Education*Altruism). This evidence is coherent with the findings from Card’s strategy

(1993) discussed above and led us to complete the implementation of the 2SRI method. The test for

the validity of exclusion restrictions signals that the instruments are exogenous with respect to the

outcomes in the second stage (H0 of equivalence between the reduced and structural equations

cannot be rejected; see at the bottom of Table 3). In addition, the significant coefficients for the first-

stage residuals indicate that the variable years of education is endogenous for two key alternatives,

Unemployed and Education. As we can see, controlling for endogeneity makes stronger and highly

significant the impact of education on reducing the probability of unemployment with respect to the

probability of being an employee (the coefficient changes from -0.068 to -0.162 and remains at the

0.01 level of significance). The positive cumulative effect of years of education on the choice to

continue studies is confirmed (0.524, at 0.01 level of significance), as is the impact on the

probability of being self-employed, which is not significantly higher than the probability of being

an employee. Unlike in Table 2, after correcting for endogeneity, the significant influence of

education on reducing inactivity disappears. However, in Table 3, the coefficient of 1-stage Resid.,

referring to inactivity, is not significantly different from zero. This could signal that education is not

endogenous for the inactive status in our specification.

Now that we have established a beneficial impact of human capital, in the form of duration of

education, on youth employability (and on the decision to continue studies) as hypothesized in

WH1, the question remains whether this strategy is successful for young people in countries heavily

plagued by educational mismatch (WH2). In addition, we want to investigate whether the current

crisis introduced any changes in this relationship, given that in the majority of countries, there was a

surge in the level of education mismatch (WH3).

As Bryan and Jenkins (2015) suggest, we start by presenting the main effects of education

mismatch at the country level. Tables 4 and 5 report the results of OLS estimates. We regressed the

country intercepts of the MNL model in Table 3 – in total, approximately 60 time-country-specific

cases from 21 countries times three rounds of surveys (2006, 2008 and 2010) – on EMIun and EMIlf,

respectively. In accordance with the macro-level theoretical and empirical evidence discussed in

section 2, EMIs positively affect the average probability of being unemployed at the country level.

17

This is seen most clearly for EMIun (see Table 4), where a one-point increase in the mismatch index

boosts the average relative probability of being unemployed by 0.014 (at a 0.05 level of significance

in the specification with macro-level control variables, i.e., GDPshock and labour market

institutions). This result is less pronounced for EMI-lf, as the coefficients show the expected sign,

although not statistically different from zero (Table 5). Regarding other labour statuses, EMIlf exerts

a significant and positive impact only on the average relative probability of staying in education,

whereas no significant influence has been found on self-employment and inactivity.

In the rest of this section, we concentrate on the combined effects of macro- and micro-level

variables for which we obtained significant results. We focus here on the interactions of micro

variables with EMIun, whereas the respective results for EMIlf will be only briefly reviewed (the

Table reporting cross-level effects for EMIlf is available upon request).

Although both the country-level effects of educational mismatch (Table 4) and the individual

effects of years of education are statistically significant, at least for the unemployed and education

statuses, their cross-level effects (Table 6) are not. This conclusion holds for all alternative labour

statuses. In particular, for unemployment, the interaction term EMI-un* Years of Education shows

the expected negative sign, but it is not significantly different from zero. That the coefficient of

EMI-un* Years of Education lacks statistical significance means that higher levels of individual

human capital help lower the risk of unemployment (the main effect of Years of Education remain

negative, -0.154, and significant at the 0.01 level), irrespective of the levels of education mismatch

in a country. Therefore, this cross-level evidence suggests to reject WH2; educational mismatch

does not amplify the beneficial effect of further education on reducing the unemployment risk.

Conversely, as assumed in WH3, the crisis could have affected the relationships above through

education mismatch. Indeed, if we combine these three terms in the interaction EMI-un*Years of

Education*Year-2010, significant coefficients emerge for unemployment, education and inactivity

(see Table 7)5. More precisely, an extra year of education in countries that experienced higher levels

of mismatch after the beginning of the crisis adds (-0.141) to the direct impact of the years of

education in reducing the relative probability of being unemployed. This result is significant at the

0.05 level. However, as we explained in section 3, the inference in this case is affected by the small

number of countries. If we consider EMI_un as a stand-alone term that was significant in the

country-level specification of Table 4, the raw coefficient we obtain for EMI-un*Years of

Education*Year-2010 in the unemployment equation of Table 7 is rather reliable.

It is also worth noting that an extra year of education, in conditions of higher mismatch and

crisis, significantly reduces the relative probability of being in education (-0.204, at 0.01

55 We also obtain very similar findings with EMI-lf; the results are available upon request.

18

significance level). Finally, Table 7 shows a complex picture for the inactivity option that we must

consider with caution because the stand-alone term of EMI_un in the country-level specification of

Table 4 was not significant (see the column headed ‘inactive’ status). Indeed, additional years of

education reduce the relative probability of being inactive after the crisis, represented by a negative

coefficient for Years of Education*Year-2010. This effect is reversed when we aim to capture the

additional effect of the country-level education mismatch (positive sign of EMI-un*Years of

Education*Year-2010).

7. Discussion of results, policy implications and conclusions

7.1 Discussion of results

Undoubtedly, a notable result we obtain from the investigation of the individual-level relationship

between the education of young people and their potential labour market status over the period 2006

and 2010 is that additional years of schooling still reduce the risk of being unemployed when

compared to the probability of being an employee. It is coherent with the human capital theory

framework and especially corresponds with the ideas of Mincers (1991) and Cairò and Cajner

(2018) that formal education likely boosts the ability of workers to acquire firm-specific knowledge

and invest in training. In doing so, the value of the job (i.e., worker productivity) increases, whereas

job turnover and unemployment spells decline.

However, the first real value added provided by this study is the joint analysis of other statuses,

alternative to unemployment, that may be held by people aged 15-34 (WH1). An important result

that parallels what we have found for unemployment is the reduction in the probability of remaining

inactive as the years of education increase. We obtain this evidence only if we do not control for

endogeneity (Table 2). However, the test we performed tells us there is no significant endogeneity

between education and inactivity (no significant coefficient for 1-Stage.Resid. in Table 3). This

finding somehow validates the evidence reported in Table 2 for inactivity. Instead, additional years

of schooling are not crucial for self-employment choice, or entrepreneurial orientation, compared to

dependent employment. This is probably because higher educational levels, by supporting crucial

managerial abilities, increase the ability to identify high-quality self-employment opportunities but

also boosts the opportunity costs of self-employment in terms of potential higher earnings in the

wage sector (Simoes et al., 2016). The latter counteracts the former and, in absence of adequate

entrepreneurship policies or start-up incentives, it could neutralize the chances for a self-

employment entry compared to dependent employment status

Finally, the only alternative status in which young people are included with higher probability than

dependent employment is the continuation of education, as signalled by positive and significant log-

odds ratios (raw coefficients) of years of education for education outcome (Tables 2 and 3). Recall

that these raw coefficients only tell us about relative probabilities, not the real magnitude and sign

19

of the impact of years of education (Long and Freese, 2006; Cameron and Trivedi, 2009).

Therefore, by using the regression results from Table 3, we calculate the predicted probabilities of

outcomes and their discrete changes associated with an additional year of education. Figure 1

displays these features for individuals at three particular levels of cumulative years of education: i)

8 years of education, which approximately correspond to the end of primary (lower secondary)

education and the beginning of upper-secondary education; ii) 13 years of education, which is the

end of upper-secondary and the beginning of tertiary education; and iii) 18 years of education,

corresponding to the completion of a master’s or equivalently long first degree programmes and the

beginning of doctoral or equivalent-level programmes (ISCED, 2012). To avoid confounding

effects caused by age and cohort composition (our sample includes young people 15-34), we plotted

the predicted probabilities of the four labour statuses alternative to our benchmark (employee) and

calculated their discrete changes for individuals aged 26. Young adults aged 26 are in a stage of life

in which they have potentially completed at least the first two cycles of tertiary education and are

ready to enter the labour market. Figure 1 suggests that for these individuals, the predicted

probability of being unemployed will monotonically decrease from 0.167 to 0.019 as accumulated

years of education rise from 8 to 18. Moreover, an additional completed year of education always

causes a statistically significant reduction in the unemployment risk. As the Table attached to

Figure 1 reports, the discrete change for the predicted probability is -0.024 (2.4 p.p.) starting at 8

years of education and is smaller but still negative, at -0.009 (0.9 p.p.), for 18 years of education. It

is also worth noting that on average, between 2006 and 2010, the most likely state among the four

alternatives to dependent employment for people aged 26 with only 8 years of schooling was

unemployment (see Figure 1). Additional years of schooling for people in these conditions hold the

highest impact in reducing the probability of being unemployed. Indeed, for individuals of the same

age and 18 years of education, unemployment becomes their least likely outcome. In contrast, it is

confirmed that for highly educated individuals aged 26, the most likely state, among the alternatives

to dependent employment, is continuing in education. The predicted probability for this condition is

slightly above 0.5, and additional years of schooling increase the probability of persisting in this

state (13.1 p.p. is the discrete change estimated; see the Table at the bottom of Figure 1). As

discussed in section 2, this result could be explained by the weak conditions of the labour market

during the period under scrutiny. An overall high youth unemployment rate increases the

attractiveness of the education choice (Clark, 2011). This is especially true for highly educated

individuals because of the higher complementarity between educational investments (Kramer and

Tamn, 2016). Individuals who opted for tertiary educational attainment are more likely to invest in

additional education if their perception of labour market opportunities is not encouraging.

The second value added of this paper resides in the evidence it offers on country-level

educational mismatch. The latter seems to act as moderator that in times of crisis fosters labour

20

market entry for young people with a high number of years of schooling. The result emerging from

Table 7 tells us that not only can an additional negative effect on the probability of being

unemployed be observed when individuals accumulate years of education in countries with higher

educational mismatch, but also these conditions reduce the probability of being in education. This

only happens if we interact the country-level education mismatch with the dummy for crisis and

individual years of education (EMI-un*Years of Education*Year-2010). However, if we consider a

pure country-level specification, the mismatch as a stand-alone term is found to increase the

country-level average probability of being unemployed or of continuing education (see Tables 4 and

5), as macro-economic theories discussed in section 2 predict. The evidence shown in Tables 4 and

5 relies on a composition effect of aggregated data driven by the higher share of low- and medium-

educated young people in the labour supply pool (see Figure A.1). By contrast, if years of education

of young people increase, we observe an additional negative effect of aggregate mismatch on both

unemployment and choice to continue education. This means that in environments characterized by

educational mismatch, in times of crisis, the perceptions of labour market opportunities for highly

educated young people change. They realize that their skills are rare in the labour supply and the

crisis likely pressures employers to create high value jobs by demanding more highly skilled

youngsters. For this reason, the choice to continue education becomes less attractive.

Recall again that the coefficients we present in Table 7 for EMI-un*Years of Education*Year-

2010 only give us information concerning relative probabilities with respect to employment as the

base outcome. Predicted probabilities and their marginal effects show a clearer picture about the

magnitude of the impact of additional education on labour status. Due to the high number of factors

involved, we only concentrate here on the probability of being unemployed. Unfortunately, the

computation of marginal effects in predicted probabilities for interaction terms in non-linear models

is affected by many drawbacks that severely limit their interpretation (Greene, 2010; Ai and Norton,

2003). For this reason, we follow Greene (2010) and use a graphical representation of predicted

probabilities and their discrete changes for Years of Education on unemployment, based on the

model of Table 7 and conditional on EMI-un and Year-2010. Figure 2 depicts this representation by

adopting the approach used to construct Figure 1, where we take into consideration young people

aged 26 with years of education equal to 8, 13, and 18. In this particular case, the predicted

probabilities and their discrete changes are calculated at three different values of the distribution of

EMIun, the bottom decile (5.3), the median (16.3) and the top decile (21.4), in both the pre-crisis

(Year-2010=0) and post-crisis (Year-2010=1) periods.

In the case of unemployment, the pattern we observe for young people 26 years old with 8 years

of education (who presumably only completed primary education) is very different from the

experience of those with secondary (13 years of studies) and tertiary (18 years of studies) education.

21

Those with only primary education are exposed to the highest risk of being unemployed, which

worsens after the crisis and in the case of severe educational mismatch. Their predicted probabilities

of unemployment grow from 0.156 to 0.262 at the turn of the crisis for a mild educational mismatch

of 5.3 and from 0.172 to 0.285 for a severe mismatch of 21.4 points. An additional year of study for

individuals with primary education remarkably reduces the risk: in countries with severe education

mismatch, the unemployment probability is reduced by -0.032, while in the presence of mild

mismatch, it is reduced by -0.030 (see the table below Figure 2). It is worth noting that for

individuals at the end of secondary (13 years of education) and tertiary (18 years of education)

education, the risk of exiting the labour market is lower in countries with higher mismatches. The

risk of being unemployed decreases further as an individual adds years of education in countries

with severe educational mismatch (see the table below Figure 2, columns EMIun= 5.3 and EMI-

un=21.4 for 18 years of education). The strength of this impact even increases after the crisis; for

example, an additional year of education for young people aged 26 with 18 years of education in

countries with higher mismatch (EMIun=21.4) reduces the unemployment risk by -0.012 (1.2 p.p.)

before the crisis and -0.021 (2.1 p. p.) after the crisis.

We performed a similar plot to study the effects of additional years of education on the education

equation. The results displayed by the raw coefficients in Table 7 are basically confirmed.

Especially for individuals aged 26 with 18 years of education, the probability of continuing studies

notably reduces as the educational mismatch increases (predicted probabilities and their discrete

changes for the education equation of Table 7 are available upon request).

This evidence contributes to clarifying the complex picture emerging from previous studies

according to which additional years of education reduce the probability of being unemployed but

maintain the probability of continuing education rather than fostering their employability as an

employee or self-employed (Clark, 2011; Plümper and Schneider, 2007). In fact, in countries

characterized by strong skill shortages in the labour supply, accumulated years of education

corresponding to a university degree favour employment entry. Therefore, especially after the crisis,

the higher educational mismatch acted as a sort of catalyst by favouring the still-limited offering of

educated young people in the labour market.

7.2 Policy implications and concluding remarks

Our results are in line with the goals of the current European agenda and have clear policy

implications. Increasing years of education remains the main road to tackling unemployment caused

by education mismatch, and, on the whole, education still fosters the welfare of the young European

citizen. Even under the condition of technological change and job polarization, formal (higher)

education can still help minimize the negative effects of country-level education mismatch

exacerbated by the recent crisis.

22

Regardless of the positive impact of higher levels of education on better individual opportunities in

the labour market, we would like urge caution about the mechanical expansion of tertiary education.

This process should be complemented by a careful assessment of the skill and qualification

demands at regional and national levels, the establishment of institutions providing timely labour

market information, early involvement of students in practicum and apprentice programs, study

plans encouraging the use of methods and tools across disciplines, and similar factors. The labour

market success of youth who enter the labour market in times of crisis also requires tracking and,

consequently, a conscious effort to help youngsters avoid being trapped in relatively poor jobs they

are overqualified for. This will improve their individual welfare, labour productivity, and the

efficiency of distribution of resources in the economy.

However, these general considerations only partially fit the heterogeneous institutional contexts

across Europe. In countries with high educational mismatch (skill shortages in the labour supply),

the high employability of more-educated young people likely favours youth in attending upper-

secondary school and vocationally oriented tertiary education or universities, without excessively

prolonging the continuation of studies. This happens in Sweden, Finland, the UK, Ireland, Belgium,

the Netherlands, France and Germany, which report high values in the educational mismatch

indexes used in our analysis. Indeed, the majority of these countries in 2010 showed good rates of

graduation from upper-secondary programmes and good rates of entry into vocationally oriented or

university-level tertiary education according to OECD (2012, pp. 17-19). In addition, these

countries already possess well-developed systems to monitor higher education and systematically

implement active programmes to bridge the gap between higher education and the labour market

(de Weert, 2011).

In contrast, in countries with low educational mismatch, such as Cyprus, Greece, Portugal, Croatia,

Slovak Republic, Poland and Hungary, young adults with tertiary education experience more

problems entering the labour markets, as also signalled by their higher unemployment rates in 2010

(ILO, 2013). The option to continue education could be the only way to escape unemployment and

inactivity. In the long run, this process negatively affects the quality of education (Plümper and

Schneider, 2007) and does not make effective higher education systems. Structural problems rooted

in the composition and quality of labour market demand could be among the main bottlenecks that

hinder the reform and restructuring of education systems. In this case, defining the role of different

stakeholders such as higher education institutions, governments and employers is often requested to

align the skills composition of the labour supply to those of in demand. In particular, the

involvement of employers and businesses in higher education programmes and courses is crucial to

improving the employability of young adults who successfully complete tertiary education.

23

Tables and figures

Table 1: Summary Statistics for Variables used in the econometric analysis

.

2006 2008 2010 N Mean Std.Dev. N Mean Std.Dev. N Mean Std.Dev.

Employees 8933 48.60 49.98 10789 47.12 49.92 10882 43.53 49.58 Self-employed 8933 4.84 21.45 10789 5.33 22.47 10882 4.50 20.72 Unemployed 8933 5.27 22.35 10789 6.07 23.88 10882 7.64 26.57 In Education 8933 29.20 45.47 10789 30.13 45.89 10882 32.92 46.99 Inactive 8933 12.08 32.60 10789 11.35 31.72 10882 11.42 31.80 Age 9050 24.75 5.75 10862 24.75 5.68 10952 24.55 5.73 Gender(male=1) 9041 49.70 50.00 10858 48.77 49.99 10944 48.44 49.98 Years of Education 8949 13.30 3.37 10794 13.32 3.29 10836 13.39 3.31 Primary Ed. 5731 30.91 46.22 7294 31.02 46.26 10899 33.52 47.21 Secondary Ed. 5647 53.20 49.90 7153 51.95 49.97 10899 48.31 49.97 Tertiary Ed. 5647 16.26 36.91 7153 17.56 38.05 10899 18.20 38.59 Citizenship 9044 94.59 22.62 10857 93.94 23.85 10949 93.30 25.01 Disconnected 9036 3.67 18.79 10848 3.72 18.92 10940 4.16 19.98 No Social Activ. 8924 27.46 44.63 10734 27.95 44.88 10876 28.03 44.92 Children(yes=1) 9002 26.70 44.24 10826 24.36 42.93 10946 24.33 42.91 H.Labour Income 8730 88.26 32.19 10625 88.74 31.62 10594 86.98 33.65 H. Capit. Income 8730 2.75 16.36 10625 2.75 16.34 10594 2.89 16.77 Trade Un. Member 8994 13.26 33.91 10815 11.99 32.49 10906 11.71 32.16 Family Members 9038 3.41 1.50 10856 3.37 1.47 10947 3.41 1.44 EMI-un 18 19.64 4.26 21 18.64 6.34 21 19.98 4.64 EMI-lf 18 1.41 0.45 21 1.21 0.55 21 1.49 0.56GDP_shock 16 42.60 50.50 21 29.73 92.07 21 -605.66 309.17 Lab.Market Liberal. 16 6.03 1.46 21 5.95 1.33 21 6.46 1.13

Notes: Weighted statistics according to the ESS sample weights. All variables are percentages, with the exception of Age, Family Members, Years of Education and Labour Market Liberal. EMI-un, EMI-lf, GDP_shock and Lab.Market Liberal are country-level variables that vary across 21 observations (with the exception of missing values in 2006). All these macro-level variables are also lagged one year; hence, they refer to 2005, 2007 and 2009, respectively.

24

Table 2: Effects of

education on labour status of young people aged 15-34. MLN Model: baseline specification; raw coefficients. (Base category: Employee)

Self-employed Unemployed Education Inactive Years of Education -0.003 -0.068*** 0.206*** -0.057***

(0.010) (0.011) (0.025) (0.012)Age -0.488*** -0.255*** -1.677*** -0.788***

(0.126) (0.082) (0.118) (0.132)AgeSquared 0.010*** 0.003** 0.025*** 0.013***

(0.002) (0.002) (0.002) (0.002)Gender(male=1) 0.667*** -0.14 -0.540*** -1.169***

(0.073) (0.094) (0.051) (0.084)Citizenship 0.398** -0.055 0.006 0.006

(0.191) (0.158) (0.164) (0.092)Disconnected -0.211 -0.316* -0.727*** -0.077

(0.171) (0.168) (0.153) (0.062)No Social Activities -0.013 0.240*** -0.108* 0.322***

(0.071) (0.053) (0.061) (0.042)Children (yes=1) 0.312*** -0.096 -0.680*** 1.374***

(0.090) (0.098) (0.098) (0.087)H.Labour Income -0.294 -3.598*** -2.809*** -3.096***

(0.225) (0.290) (0.311) (0.262)H.Capital Income 1.202*** -0.329 1.404*** 0.378

(0.331) (0.373) (0.324) (0.261)Trade Un. Member -0.947*** -0.385*** -0.677*** -0.651***

(0.152) (0.142) (0.158) (0.115)Family Members 0.014 0.200*** 0.159*** 0.223***

(0.037) (0.034) (0.034) (0.033)Year-2008 0.073 0.245* 0.036 0.02

(0.083) (0.130) (0.093) (0.085)Year-2010 0.127 0.667*** 0.435*** 0.053Country dummies Yes Yes Yes YesObs 29008

p-value-Overall Model 0.000

25

Table 3: Effects of education on labour status of young people aged 15-34. MNL model: endogeneity control with 2-Stages Residual Inclusion method; raw coefficients. (Base category: Employee)

Test for

validity of exclusion restrictions Log-Likelihood Reduced-Form Equation(a)= -23977.495

Log-Likelihood Structural Equation(b)= -23997.445 Ho: a=b; p-value=0.159

Notes: *** significant at 1% level; ** significant at 5% level; *significant at 10% level. Control Variables: all control variables already shown in Table 2 have been included. Clustered standard errors in parentheses. Significant t-test for the coefficients of 1-stage Resid. indicates that education is endogenous

Table 4: Education Mismatch effects at country level. EMI unemployment version (OLS regression)

Panel a Panel bUnempl. Self-empl. Educ. Inact. Unempl. Self-empl. Educ. Inact.

EMI-un 0.012* -0.003 0.012 0.003 0.014** 0.004 0.021* 0.007(0.007) (0.013) (0.012) (0.018) (0.006) (0.013) (0.011) (0.019)

Year-2008 0.644*** 1.904*** -0.353 2.412*** 0.632*** 1.895*** -0.37 2.404***(0.140) (0.274) (0.244) (0.372) (0.136) (0.272) (0.237) (0.376)

Year-2010 0.15 0.952*** 1.135*** 1.532*** -0.213 0.796** 0.727** 1.356***(0.139) (0.277) (0.226) (0.371) (0.237) (0.369) (0.337) (0.474)

GDPshock -0.056* -0.028 -0.067 -0.029(0.033) (0.043) (0.047) (0.054)

Lab.Mark.Lib. -0.028 -0.128 -0.146* -0.08(0.040) (0.103) (0.082) (0.142)

Constant 4.407*** 5.136*** 19.831*** 8.902*** 4.407*** 5.136*** 19.831*** 8.902***(0.164) (0.368) (0.274) (0.483) (0.164) (0.368) (0.274) (0.483)

Adj. R2 0.337 0.542 0.468 0.53 0.362 0.549 0.504 0.517Obs. 60 60 60 60 52 52 52 52

Notes: *** significant at 1% level; ** significant at 5% level; *significant at 10% level. Robust standard errors in parentheses.

Table 5: Education mismatch effects at country level. EMI labour force version (OLS

26

Self-employed Unemployed Education InactiveYears of Education 0.028 -0.162*** 0.524*** -0.042

(0.053) (0.050) (0.052) (0.052)1-stage Resid. -0.032 0.102*** -0.330*** -0.014

(0.050) (0.005) (0.042) (0.051)Control Variables Yes Yes Yes YesCountry dummies Yes Yes Yes YesObs. 27201p-value-Overall Model Pseudo-R2

0.0000.334

F test for the relevance of instruments in the first stageF(9, 20)=28.40; p-value=0.000.

regression) Panel a Panel b

Unempl. Self-empl. Educ. Inact. Unempl. Self-empl. Educ. Inact.EMI-lf 0.047 -0.047 0.331*** 0.056 0.046 -0.054 0.324*** 0.052

(0.062) (0.121) (0.080) (0.157) (0.067) (0.117) (0.093) (0.152)Year-2008 0.664*** 1.895*** -0.299 2.422*** 0.652*** 1.893*** -0.31 2.418***

(0.143) (0.277) (0.239) (0.373) (0.138) (0.273) (0.233) (0.376)Year-2010 0.152 0.960*** 1.063*** 1.521*** -0.227 0.809** 0.642** 1.342***

(0.142) (0.278) (0.212) (0.371) (0.239) (0.358) (0.319) (0.465)GDPshock -0.057* -0.029 -0.068 -0.03

(0.033) (0.042) (0.048) (0.054)Lab.Mark.Lib. -0.002 -0.122 -0.103 -0.066

(0.038) (0.098) (0.081) (0.134)Constant 4.570*** 5.133*** 19.664*** 8.883*** 4.608*** 5.901*** 20.338*** 9.306***

(0.147) (0.308) (0.233) (0.416) (0.280) (0.693) (0.549) (0.964)Adj. R2 0.309 0.543 0.522 0.53 0.328 0.549 0.544 0.517Obs 60 60 60 60 52 52 52 52

Notes: *** significant at 1% level; ** significant at 5% level; *significant at 10% level. Robust standard errors in parentheses.

Table 6: Combined effects of country-level EMI-un and individual education on labour status of young people aged 15-34. MNL model: endogeneity control with 2-Stages Residual Inclusion method; raw coefficients. (Base category: Employee)

Self-employed Unemployed Education Inactive EMI-un*Education Years -0.093 -0.043 -0.055 0.083

(0.067) (0.069) (0.053) (0.057)Years of Education 0.042 -0.154*** 0.535*** -0.057

(0.046) (0.045) (0.037) (0.036)1-stage Resid. -0.03 0.102** -0.331*** -0.014

(0.045) (0.045) (0.036) (0.035)(0.077) (0.077) (0.057) (0.060)

Control Variables Yes Yes Yes YesCountry dummies Yes Yes Yes YesObs 27201p-value-Overall Model Pseudo-R2

0.0000.334

Notes: *** significant at 1% level; ** significant at 5% level; *significant at 10% level. Robust standard errors in parentheses. Control Variables: all control variables already shown in Table 2 have been included. Significant t-test for the coefficients of the first-stage resid. indicates that education is endogenous.

27

Table 7: Country-level EMI-un and individual education in crisis time, effects on labour status of young people aged 15-34. MNL model: endogeneity control with 2-Stages Residual Inclusion method; raw coefficients. (Base category: Employee)

Self-employed Unemployed Education InactiveEMI-un*Education Years*Year-2010 -0.075 -0.141** -0.204*** 0.116**

(0.058) (0.064) (0.053) (0.051)EMI-un*Education Years -0.07 -0.005 -0.016 0.043

(0.069) (0.071) (0.054) (0.058)Years of Education*Year-2010 0.031 0.014 0.026 -0.038**

(0.022) (0.021) (0.018) (0.018)Years of Education 0.033 -0.156*** 0.533*** -0.045

(0.047) (0.046) (0.038) (0.036)1-stage Resid. -0.031 0.101** -0.331*** -0.013

(0.045) (0.045) (0.036) (0.035)Control Variables Yes Yes Yes YesCountry dummies Yes Yes Yes YesObs. 27201 p-value-Overall Model Pseudo-R2

0.000 0.334

Notes: *** significant at 1% level; ** significant at 5% level; *significant at 10% level. Robust standard errors in parentheses. Control Variables: all control variables already shown in Table 2 have been included. Significant t-test for the coefficients of 1-stage Resid. indicates that education is endogenous.

Figure 1: Predicted Probabilities and their changes for labour outcomes in Table 3 at years of education = 8, 13 and 18; age=26 and at the sample mean for other regressors.

8 Years 13 Years 18 Years0

0.1

0.2

0.3

0.4

0.5

0.6

Self-Employed Unemployed Education Inactivity

Pred

icte

d Pr

obab

ility

Discrete changes of the predicted probability (Years of Education = +1) and confidence intervals8 Years 95% CI 13 Years 95% CI 18 Years 95% CI

Self-Employed 0.003* -0.000; 0.005 0.001 -0.004; 0.006 -0.008*** -0.010; -0.006

Unemployed -0.024** -0.046; -0.002 -0.015*** -0.023; -0.007 -0.009*** -0.011; -0.007

Education 0.002*** 0.001; 0.003 0.028*** 0.024; 0.032 0.131*** 0.105; 0.158

Inactive -0.001 -0.011; 0.009 -0.005 -0.014; 0.004 -0.013*** -0.015; -0.079Notes: Changes in predicted probabilities are calculated with delta method. *** significant at 1% level; ** significant at 5% level; *significant at 10%

28

Figure 2: Predicted probabilities for outcome “Unemployment” according EMI-un and crisis effect (at years of education = 8, 13 and 18; age=26 and at the sample mean for other regressors)

Bottom Decile (5.3) Median (16.3) Top Decile (21.4)0

0.05

0.1

0.15

0.2

0.25

0.3

0.1560.161

0.172

0.2620.269

0.285

0.0820.077 0.075

0.1430.136 0.133

0.024 0.024 0.023

0.039 0.039 0.036

EduYrs=8 before crisis EduYrs=8 after crisisEduYrs=13 before crisis EduYrs=13 after crisisEduYrs=18 before crisis EduYrs=18 after crisis

Educational Mismatch Index (EMI_un)

Pred

icte

d Pr

obab

ility

Discrete changes of the predicted probability (Years of Education = +1) and confidence intervalsEMI-un (5.3) 95% CI EMI-un (16.3) 95% CI EMI-un (21.4) 95% CI

8 Years_Before -0.020** -0.039, -0.001 -0.021** -0.040, -0.001 -0.022 -0.044, 0.0018 Years_After -0.030** -0.056, -0.004 -0.031** -0.057, -0.004 -0.032** -0.060, -0.00413 Years_Before -0.014*** -0.024, -0.004 -0.012*** -0.010, -0.005 -0.012*** -0.019, -0.00513 Years_After -0.024*** -0.034, -0.008 -0.022*** -0.034-0.001 -0.021*** -0.033, -0.00918 Years_Before -0.008*** -0.011, -0.004 -0.008*** -0.009, -0.006 -0.012*** -0.019, -0.00518 Years_After -0.013*** -0.019, -0.008 -0.013*** -0.017, -0.010 -0.021*** -0.033, -0.009

APPENDIX

29

Table A.1: 2SRI-First Stage estimation for results in table 3. Reduced Form for Years of Education (OLS)

Dependent Variable: Years of Education

Altruism 0.007(0.076)

Environmentalism 0.245***(0.068)

Equalitarianism 0.064(0.103)

Tolerance 0.390***(0.080)

Father’s Primary Education * Altruism 0.276** (0.117)

Father’s Primary Education * Environmentalism -0.002(0.105)

Father’s Primary Education * Equalitarianism -0.053(0.149)

Father’s Primary Education * Tolerance 0.175(0.117)

Father’s Primary Education -1.453***(0.298)

Control Variables (Included Instruments) YesCountry dummies Yes

Adj. R2 0.322

Obs. 27376Notes: excluded instruments in bold. Robust standard errors in parentheses.*** significant at 1% level; ** significant at 5% level; *significant at 10% level. Control Variables: all control variables already shown in Table 2 have been included.

30

Figure A.1: Education Mismatch composition in 2009 (Employment vs Unemployment)

Youth Unemployment by Education Youth Employment by Education

31

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