Demand and Supply of Skilled Labour and Overeducation in Europe: A Country-level Analysis

Symposium Article

Demand and Supply of Skilled Labour andOvereducation in Europe: A Country-levelAnalysis

GIUSEPPE CROCE & EMANUELA GHIGNONI

Department of Economics and Law, Sapienza University of Rome, Via del CastroLaurenziano, 9, 00161, Rome, Italy.E-mail: [email protected]

This paper analyses the incidence of overeducation of university graduates. To this

end, we estimate a random effects model for a panel of European countries. Our

results do not confirm that the increase of the supply of qualified labour per se can

be seen as a relevant factor fuelling overeducation. The relative wage of university

graduates is inversely related to overeducation. This finding suggests a role for the

demand for qualified labour. Cyclical conditions also matter, as overeducation

operates as a short-term adjustment mechanism. This result sheds new light on the

possible effects of the recession currently hitting the industrialized countries and

on policy measures needed to foster economic recovery.

Comparative Economic Studies (2012) 54, 413 – 439. doi:10.1057/ces.2012.12;

published online 12 April 2012

Keywords: overeducation, university graduates, skill-biased technical change,

output gap, panel data

JEL Classifications: I24, I28, J24

INTRODUCTION

An imbalance between the demand and supply of educated labour is the mostintuitive and captivating explanation for cross-country differences in theincidence of overeducation. According to this view, overeducation ariseswhen demand lags behind the growth of supply and its incidence should beproportional to the relative overall excess of the supply of qualified labour. It

Comparative Economic Studies, 2012, 54, (413–439)r 2012 ACES. All rights reserved. 0888-7233/12

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can be seen as a possible side-effect of what has been called the race betweentechnology – which represents the main factor behind the evolution of thedemand for qualified labour – and education, whose rise is the dominantfactor driving the growth of supply of qualified labour (Tinbergen, 1975;Goldin and Katz, 2008).

A worker is considered overeducated if the educational level requiredby his/her job is below the level he/she attained. This paper provides ananalysis of the incidence of overeducation of tertiary graduates on the basis ofcountry-level aggregate data in order to assess the relative importance ofhighly educated labour supply and demand as determinants of overeducationin a group of European countries.

While the majority of studies considers the determinants and effects ofovereducation at an individual level, this paper deals with a macro perspec-tive and cross-countries comparison along the lines of Daly et al., 2000;Wieling and Borghans, 2001; Di Pietro, 2002; Davia et al., 2010; Verhaest andvan der Velden, 2010.

Since the seminal contribution of Duncan and Hoffman (1981), literaturebased on individual data has yielded many estimates of the extent of over-education and the consequences of being overeducated on individual’swages. At the same time, aggregate analyses have been much less developed.

In our analyses, we test whether the supply–demand framework fits theevidence arising from European countries. First, we verify whether data supportthe hypothesis that there are too many highly educated individuals in thesecountries, leading to aggregate overeducation. Moreover, we analyse the effectsof additional variables that can be seen as factors characterizing the demand forhighly educated workers. We consider the role of the relative wage of tertiarygraduates as a way to choose between two competing explanations ofovereducation; one based on technological or institutional rigidities on thedemand side and the other arising from the behaviour of labour supply. We alsoinvestigate whether overeducation acts as a short-term adjustment mechanismwhen labour demand experiences a cyclical downturn.

We estimate a panel model in which the dependent variable is the logof the incidence of overeducation in 26 European countries from 1998 to2006. We use instrumental variables techniques to cope with endogeneityproblems. Our main results show that the relative wage of universitygraduates is inversely related to overeducation. This can be interpreted as aconfirmation at the macro level of the job assignment hypothesis (Sattinger,1993). Furthermore, it suggests a possible role for the demand for skilledlabour in explaining the spread of overeducation among tertiary graduates.Our findings reject the idea that the increase of the supply of skilled labourper se can be seen as a relevant factor fuelling overeducation. Cyclical

G Croce & E GhignoniDemand and Supply of Skilled Labour and Overeducation in Europe

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Comparative Economic Studies

conditions also matter, as overeducation appears to operate as a sort of short-term adjustment mechanism. Even though the period covered by our dataends just before the beginning of the global financial turmoil in 2007–2008,this result can be helpful in understanding the possible role of mismatch inthe current crisis, and it suggests that labour policies for recovery shouldbe targeted not only at the unemployed, but also at mismatched workers.In addition, we find that the diffusion of temporary employment andthe reduction of long-term unemployment experienced in most Europeancountries may have contributed to the incidence of overeducation.

The next section provides a brief review of the relevant literature andthe theoretical background. The third section summarizes data sources andpresents descriptive evidence. The fourth presents our model and the under-lying hypotheses. In the fifth section, the results of estimations are shownwith some comments. The last section draws up the conclusions.

OVEREDUCATION IN A SUPPLY–DEMAND FRAMEWORK: PREVIOUSRESULTS AND THEORETICAL BACKGROUND

When the growth in the supply of highly educated workers outpaces thegrowth in demand, overeducation of the workforce is a likely result. Accor-ding to this view, in their meta-analysis of data collected from a large numberof studies, Groot and Maassen van den Brink (2000) estimate that an increaseof the labour force growth rate by 1 percentage point increases the incidenceof overeducation by 2 percentage points. Even Hartog (2000) suggests thatthe trend in the incidence of overeducation in Europe could reflect an excessof supply.

On the other hand, both studies by Groot and Maassen van den Brink(2000) and Leuven and Oosterbeek (2011) report that, contrary to what couldbe expected from the rapid increase in the educational level of the populationin the advanced countries, the rate of overeducation appears to have declinedrather than increased during the last decades. Leuven and Oosterbeek (2011)point to skill-biased technical change as a possible explanation for this trend.A slight decrease is also found by McGuinness (2006) on the basis of studiesbased on a subjective assessment of educational mismatch. After consideringobjective measures as well, he concludes that the overall evidence supports arelatively stable incidence of overeducation across time. These results suggestthat potential overeducation brought about by the expansion of skilled laboursupply can be absorbed by upward shifts of demand for skilled labour drivenby structural changes. Nevertheless, the data can hide differences betweenEurope and the United States, as in the latter the demand for skilled labour


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has increased substantially from the 1980s, while the relative importance ofsupply and demand effects on overeducation in Europe is less clear (Hartog,2000).

Overall, a large number of papers refers to the supply–demand frame-work (among them, eg, Borghans and de Grip, 2000; Mendes de Oliveiraet al., 2000; Walker and Zhu, 2005; Chun-Hung and Chun-Hsuan, 2005;Barone and Ortiz, 2010). However, in spite of that, very few papers carry outa macro-level analysis and attempt to verify whether this framework can behelpful in explaining cross-country variability in the incidence of over-education. One reason for the scarcity of such analyses is the lack of adequatemeasures on the demand side. Indeed, unlike supply, there is no straight-forward way to account for the demand for qualified labour and its evolutionsover time. If, as a consequence, in cross-country comparisons the emphasisis placed on the supply side while differential features of labour demand arenot taken into account, only partial conclusions can be reached.1

As to the papers dealing with aggregate analyses, the data reported byDi Pietro (2002) show that, based on information from the 1995 wave of theECHP survey of 13 European countries, the incidence of overeducation,measured by workers’ self-assessment, rises with educational level and isbetween 52% and 78% for tertiary graduates. Using a simple pooled cross-country regression, he finds that the higher the increase of the share of thepopulation with a given level of educational attainment, the higher the shareof workers holding that educational level consider themselves to be over-educated. The explanation put forward for this result relies on the role ofinstitutional factors, such as employment protection legislation (EPL), thatraise the costs of adjusting the workforce and, as a consequence, may dis-courage firms from adopting new technologies.

These results are not confirmed by Verhaest and van der Velden (2010)who carry out a multilevel analysis on a sample of tertiary graduates of16 European countries based on information from the REFLEX survey.Indeed, they do not find that the absolute share of highly educated peopleexerts a significant effect on the incidence of overeducation, nor do theyconfirm that the strictness of EPL plays a role in determining overeducation.

1 In the literature on overeducation based on individual-level data, a number of explanations of

overeducation have been proposed. One of these maintains that it can derive from substitution

between human capital acquired through formal learning at school and skills learnt through

experience in the labour market (Sicherman, 1991) or from a poor quality of schooling (Ordine and

Rose, 2009). In an analogous way, overeducation can be associated with lower individual ability

(McGuinness, 2006). Otherwise, it can be caused by imperfect matching in the labour market due to

a wrong distribution of educated workers by field of study, to spatial mismatch or to other

imperfections. For a survey, see also Leuven and Oosterbeek (2011) and Quintini (2011b).


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On the contrary, according to their results, cross-country differences inovereducation are affected by the relative oversupply of educated labour,which is defined as the excess of supply, measured by the share of highlyeducated population, over demand, proxied by gross domestic expenditure onR&D. In their view, there is no reason to expect that overeducation increasesfollowing supply growth as, according to theory (Snower, 1996; Acemoglu,2002), supply can create its own demand. Moreover, they find that alsothe business cycle, as measured by the output gap, is relevant. Indeed,a recession tends to worsen the prospects for workers to have a well-matchedposition and, as a consequence, it may raise the risk of overeducation.Similarly, the findings of Quintini (2011a) on a panel of European countriesshow that workers losing their job during a recession face a higher probabilityof being overeducated in their subsequent employment.

In a study on Dutch data, Wieling and Borghans (2001) estimate thedemand for labour with a given educational level through the method firstproposed by Katz and Murphy (1992), and the supply as the sum of employedand unemployed workers with the same educational attainment. Theirfindings confirm that a higher excess of supply of workers with a given levelof education over the corresponding demand increases the percentage ofpeople working in jobs requiring education below that level.

Finally, Davia et al. (2010) consider as a measure of the excess of educatedlabour supply the ratio of tertiary graduates to employment in professional andmanagerial positions and show that this measure positively affects theincidence of overeducation of both males and females in European countries.

From a theoretical point of view, depending on the underlying assump-tions, it is possible to distinguish different approaches to overeducation. First,in a standard competitive labour market, flexibility of wages allows adjust-ments of equilibrium following shocks on the supply or demand side.Downward wage flexibility ensures that any increase in the supply of skilledlabour is rapidly absorbed through the movement along the demand curveending up in a proper matching of workers and jobs. Similarly, adjustmentsof earnings may accommodate shifts in labour demand driven by techno-logical innovations or other structural changes. Thus, in a competitive labourmarket, overeducation can only be a transitory phenomenon.

On the other hand, overeducation can arise if the structure of labourdemand by educational level is rigid due to technological reasons and doesnot respond to the increase of supply of the skilled labour as predicted bythe job competition model (Thurow, 1975) or if the adjustment of the firms istoo slow. Similarly, overeducation can result from rigidities impeding a properallocation of available skills to jobs due to institutional factors that do notallow a sufficient flexibility of wages (McGuinness, 2006). Under these


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hypotheses, the equilibrium can be characterized by an excess of supplyof skilled labour and overeducation can arise as a consequence of thisimbalance. Under these assumptions, the labour supply would represent amajor determinant of the amount of overeducation.

When skilled wages are influenced by institutional factors that makethem downward inflexible, it can be expected that the incidence of over-education increases with the level of the relative wage of the highly educated.It is worth recalling that institutions can affect the relative wage in differentdirections. Indeed, on the one hand, it can be argued that, if institutions suchas collective bargaining impede downward flexibility of skilled earnings, thehigh educated–low educated relative wage differential might be higher thanits competitive level. On the other hand, the opposite effect could emerge ifunions favour wage compression (Agell and Lommerud, 1992).

A further approach that can help explain overeducation is job assignmenttheory, according to which overeducation can arise even with completeflexibility of wages as a consequence of the decline in the relative wage ofskilled labour. This result relies on the allocative role of the wage structure inthe labour market as put in evidence by this class of models (Sattinger, 1993;McGuinness, 2006). This contrasts with the result obtained under thealternative hypothesis of technological or institutional rigidity. Indeed, it canbe expected that, following a rapid expansion of skilled labour supply, therelative wage will decrease and, as a result, educated workers could be forcedor prefer to accept a low-skilled job based on their heterogeneous preferencesand constraints. This can happen, for example, as long as we admit hetero-geneity in workers’ preferences and in job characteristics, with skilled jobsimplying more effort. Then, the decline in the relative earnings can increaserather than decrease the incidence of overeducation. This is also in line withthe results obtained by Wieling and Borghans (2001) who find that differentmechanisms of adjustment such as wage reduction and overeducation canoperate together.

Gottschalk and Hansen (2003) develop a theoretical model with twotypes of labour, college and non-college workers, and two correspondingtypes of jobs, where college and non-college workers are perfect substitutesand wages adjust following demand and supply shifts. College workers areassumed to react to changes in their relative wage by choosing which job toenter, whether skilled or low skilled. Such a model generates overeducationas an equilibrium phenomenon and predicts that overeducation’s incidenceincreases when the relative college wage declines. Thus skill-biased tech-nical change can be expected to reduce the incidence of overeducation byincreasing the relative wage of highly educated workers. This prediction fitsboth the facts of the decline of overeducation and the increase of wage


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inequality experienced in the United States during the 1980s and 1990s.Conversely, it is likely that in economies where the pace of technologicaladvancement is less rapid in comparison with the expansion of skilled laboursupply, a lower wage premium can be observed for skilled workers as well asa larger share of overeducated workers. On this basis, even thoughovereducation entails a productivity loss due to incomplete utilization ofhuman capital, it can be seen as a voluntary status, conditioned by theavailable jobs in the labour market, from an individual point of view.

Similarly, in a study concerning UK tertiary graduates, McGuinness andSloane (2011) find that overeducated workers suffer a large wage penalty but, atthe same time, tend to report a less severe loss of job satisfaction. This quitesurprising result can be explained by assuming that workers trade off earningsfor other job attributes such as, for example, an easier conciliation of work andfamily duties or higher employment security, according to their preferences.These results are consistent also with the assumption of Gottschalk andHansen (2003) maintaining that workers choose between a right or a wrongmatch according to the relative wage and other job characteristics.

Assignment models, which assume that individual productivity dependson both worker’s schooling and job characteristics, can be seen as anintermediate case between human capital theory and job competition theoryand are consistent with the bulk of empirical evidence. Indeed, theestimations of wage equations where the years of schooling are decomposedinto required, deficit and surplus years (as first proposed by Duncan andHoffman, 1981) confirm that even the surplus component is rewarded but lessthan the required one (Dolton and Vignoles, 2000; Hartog, 2000; Ghignoni,2001; Sloane, 2003; McGuinness, 2006; Leuven and Oosterbeek, 2011).

OVEREDUCATION AND GRADUATES’ SUPPLY: DATA AND SOMEPRELIMINARY FACTS

In this section, we measure overeducation incidence by exploiting individualinformation derived from the European Labour Force Survey (ELFS), whichprovides highly comparable data for a large panel of European countries.Given this source of information, we use the average years of education as acriterion to identify overeducated workers. According to this method, workersare defined as overeducated if the number of years of schooling correspond-ing to their school degree is more than one standard deviation above themean of all individuals in their occupation (Verdugo and Verdugo, 1988).2

2 Correspondences between certificate of education and years of schooling are reported in Table 1.


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In order to calculate the percentage of overeducated by country and year,we consider all employees in occupations defined at the ISCO88 3-digit levelincluded in ELFS from 1998 to 2006 in 26 countries.3 In our estimates, werestrict ourselves to the percentage of overeducated with a tertiary degree (ormore) in order to deal with a more homogeneous sample of workers.

Figure 1 displays the average level of the incidence of overeducation oftertiary graduates in each country over the entire period. There are largedifferences, ranging from countries such as Romania, Finland, Sweden andothers below 30%, to others such as Austria, Germany and Czech Republicwhich are above 50%, with the largest number of countries concentratedbetween 30% and 40%.

As is well documented in the literature, different methods of measure-ment yield different measures of overeducation. Overall, estimates obtainedthrough the statistical approach tend to be lower than those based on theworkers’ self-assessment (Leuven and Oosterbeek, 2011). In practice, as noneof the available methods proved to be more reliable than the others, the

Figure 1: Graduates’ overeducation, averages 1998–2006

3 Austria, Belgium, Czech Republic, Germany, Denmark, Estonia, Spain, Finland, France,

Greece, Hungary, Ireland, Iceland, Italy, Lithuania, Luxemburg, Latvia, Netherlands, Norway,

Poland, Portugal, Romania, Sweden, Slovenia, Slovakia, the United Kingdom.


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choice depends on the data available.4 To check the robustness of ourincidence measures, we compared them with data provided by Quintini(2011a) that refer to overeducation of employees with any educational level in23 European countries.5 Despite major differences in data sources anddefinitions adopted, we find a rank correlation of 0.5788, which can beregarded as an adequate confirmation of the validity of our data.6

Moreover, the scatter diagram (Figure 2), which represents the ave-rage share of overeducated among employees holding a tertiary degree in1998–1999 and in 2005–2006 for each country, shows that the ranking ofEuropean countries is persistent over the observed period. However, despitethe overall stability of the relative positions, in some countries the incidenceof education increased rapidly while others experienced a large decrease, asshown by Figure 3. Then, we can ask whether data exhibit an associationbetween such variations and changes in supply of graduates. Indeed, over-education of workers with a tertiary educational degree might rise, Ceterisparibus, following the expansion of the supply of workers with this level ofeducation.

As revealed by Figure 4, nearly all countries, with the exceptions ofAustria and Lithuania, which was excluded as an outlier, saw an increase in

Figure 2: Persistence of graduates’ overeducation

4 For thorough discussions of the different methods, see Borghans and de Grip, 2000; Hartog,

2000; McGuinness, 2006; Leuven and Oosterbeek, 2011.5 In Quintini (2011a), the share of overeducated employees are derived from data collected by

the European Survey of Working Condition (2005), by applying the statistical method based on the

modal qualification in each occupation.6 The Spearman test proves that the probability of independence between the ranks of the two

distributions is equal to 0.6%.


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Figure 3: Incidence of overeducation (from 1998–1999 to 2005–2006)

Figure 4: Overeducation and graduates supply (changes 1998–2006)


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the share of population holding a tertiary degree, and yet no evidentrelationship between increases of supply and changes in overeducation arisesfrom the data as the R2 is very small.7

To achieve a better understanding of this issue, additional data arereported in Table 2. Overall, the incidence of overeducation of tertiarygraduates has slightly declined in Europe over the period, as its averagefell from 36.8% to 35.4% according to Table 2, column ‘Overeducation-Allcountries’. On the other hand, the share of population holding a tertiarydegree increased substantially for the whole set of countries. At the sametime, the decline of the standard deviations points out a cross-countryconvergence both in overeducation and educational attainment.

In the same Table, countries have been grouped according to the changein overeducation they experienced during the period. The group of countrieswith a sharp rise in overeducation, a 5% increase or more, includes Finland,Italy, Portugal, Sweden and Poland. Despite their lower overeducation at the

Table 1: Correspondence between certificate of education and years of schooling

Certificate of education Years ofschooling

Isced 0 – Pre-primary education 0Isced 1 – Primary education or first stage of basic education 5Isced 2 – Lower secondary education or second stage of basic education 8Isced 3 – Upper secondary education:Isced 3.a – Programme designed to provide direct access to Isced 5.a 13Isced 3.b – Programme designed to provide direct access to Isced 5.b 13Isced 3.c – Programme not designed to lead to Isced 5.a or Isced 5.b 12Isced 4 – Post-secondary non-tertiary education:Isced 4.a: See Isced 3.a 13Isced 4.b: See Isced 3.b 13Isced 4.c: See Isced 3.c 12Isced 5 – First stage of tertiary education, not leading directly to an advanced researchqualification:ISCED 5.a: Programmes that are largely theoretically based and are intended to providesufficient qualifications for gaining entry into advanced research programmes andprofessions with high skills requirements

17

ISCED 5.b: Programmes that are practically oriented/occupationally specific and aremainly designed for participants to acquire the practical skills and know-how neededfor employment in a particular occupation or trade or class of occupations or trades,the successful completion of which usually provides the participants with a labour-market relevant qualification

15

Isced 6 – Second stage of tertiary education (leading to an advanced researchqualification)

20

7 We use the percentage of individuals aged between 25 and 64 with tertiary educational

attainment in European countries provided by Eurostat.


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Table 2: Overeducation and the supply of graduates

Time Overeducation Graduates supply

Allcountries

Countrieswhere

overeducationincreased(D>+5%)

Countrieswhere

overeducationwas almost

stable(�5%oDo+5%)

Countrieswhere

overeducationdecreased

(Do�5 p.p.)

Allcountries

Countrieswhere

overeducationincreased(D>+5%)

Countrieswhere

overeducationwas almost

stable(�5%oDo+5%)

Countrieswhere

overeducationdecreased

(Do�5 p.p.)

Start of the perioda averagec 36.8 25.3 39.3 40.0 21.0 18.4 20.3 23,8(10.7) (6.1) (10.4) (8.1) (8.3) (10.4) (7.6) (7,1)

End of the periodb averagec 35.4 34.8 38.1 31.3 24.3 22.0 22.9 28,2(8.5) (5.6) (9.5) (6.4) (7.4) (9.1) (5.8) (7,2)

Change (average) �1.5 9.5 �1.2 �8.7 3.3 3.6 2.6 4.4

Standard deviations in parenthesis.a Overeducation: values refer to 1998–1999; graduates supply: values refer to 2000.b Overeducation: values refer to 2005–2006; graduates supply: values refer to 2006.c Unweighted averages.

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start of the period, they suffered an increase of more than 9 percentage pointsin subsequent years. Countries such as the United Kingdom, Belgium andDenmark, among others, where overeducation has fallen by at least 5%, werecharacterized by the highest initial level of overeducation and, later, expe-rienced a decline of almost 9 percentage points. The last group includes theremaining countries, where overeducation was almost stable over the sampleperiod, with a variation smaller than 5% in absolute value. On average, over-education of this group has somewhat fallen by 1.2 percentage points. Table 2also reports the changes in the share of population with a tertiary degree forthe same groups. Supply increased in every group and, what is more relevant,the increase was the largest in the countries where the most significantdecline of overeducation took place. This fact clearly contradicts the hypo-thesis of a positive relationship between overeducation and the supply ofgraduates. This is also confirmed by simple cross-country correlationsbetween the incidence of overeducated workers and the share of graduatesin the population: indeed, their sign is negative both at the start and at theend of the period and their absolute value increases from �0.21 to �0.39.Thus, other factors have to be considered to make progress in our search foran explanation of the variability of overeducation among European countries.This is what we are going to tackle in the next sections.

MODEL SPECIFICATION

In the following econometric estimates, we use information on our panelof European countries in order to detect major factors affecting the incidenceof overeducation of tertiary graduates.

Our estimated model is as follows:

Yit ¼ aþ b1Sit þ b2Dit þ b3oit þ b4Xit þ eit

where the dependent variable is the (log of the) incidence of overeducationby country (i) and year (t), S represents alternative measures of skilled laboursupply, D is a vector of indicators of skilled labour demand, o is the relativegraduate/undergraduate wage and X is a vector of some further controls.

As for the supply side, we test two alternative measures of supply ofuniversity graduates. The first measure (poplau) is given by the percentageof individuals with tertiary education (or more) between 25 and 64 years.Moreover, in line with the model usually adopted in the wage inequalityliterature (eg, Katz and Autor, 1999; Goldin and Katz, 2008), we considerthe relative supply of university graduates, which, under the hypothesis of


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a competitive labour market, corresponds to the ratio of tertiary graduates tosecondary graduates in employment (emphm).8 Thus, we can check therobustness of our results by using these two measures.

Moreover, when either the average or the mode of years of schooling isused to identify overeducated workers, a spurious effect might bias the mea-surement of the incidence of overeducation, because in each occupationalgroup the number of overeducated tertiary graduates declines mechanicallyas the number of tertiary graduates in the group increases just because theaverage years increase accordingly without regard to the actual quality oftheir job matching. In order to control for this possible distortion, we includein the estimated equation the relative share of secondary graduates in thepopulation (popdip).9 As will be shown later, according to our predictions,the coefficient of this variable turns out to be positive and significant.

Measuring the demand for highly educated labour appears more difficultthan measuring skilled labour supply. Some authors resort to proxies suchas R&D expenditures (Verhaest and van der Velden, 2010). In line withDi Pietro (2002), this variable may be correlated with educational level.Hence, we use the relative weight of employment in specific occupationalcategories or industries that are characterized by a large presence of workerswith a university degree. More precisely, we consider the percentagesof employees in education (empedu) and health (emphea) industries,as well as the percentage of employees in high-skilled occupations (ISCO88code: 1, 2, 3; Isco_1_2_3).10

Moreover, we insert in our model the relative earnings of university tosecondary graduates (wagehm). Given the supply of skilled labour, thisvariable allows us to test the two alternative hypotheses drawn in the pre-vious section. On the one hand, the ‘rigidity’ hypothesis maintains that, dueto technological or institutional factors, the demand for skilled labour canbe prevented from adjusting to the expansion of supply. In this case, a higherrelative wage will be associated with a higher rate of overeducation. Onthe other hand, under the ‘job assignment’ hypothesis, a decline in the rela-tive wage, which is likely to occur when supply increases, will widen

8 In our definition, emphm corresponds to the ratio of the number of employees with tertiary

degree to the number of employees with upper secondary degree (15–74 years).9 Indeed, if the percentage of upper secondary graduates (in the population and in each

occupation) is higher, also the percentage of overeducated tertiary graduates can be expected to be

higher, just as a consequence of the spurious effect stemming from the definition of overeducation

that we have adopted. We can exclude multicollinearity between poplau and popdip, as the

correlation between them is only �0.2515 and not statistically significant.10 ISCO 88 is coded on one digit as follows: (1) Legislators, Senior Officials and Managers; (2)

Professionals; (3) Technicians and Associate Professionals.


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overeducation. At the same time, if demand for skilled labour shifts upwards,as is the case when a skill-biased technical change occurs, an increase of therelative wage and a reduction of overeducation will result. Thus, a higherrelative wage should be associated with a lower incidence of overeducation.

The inclusion of the relative earnings as an explanatory variable inour model poses a problem of endogeneity as earnings and overeducationare both determined in the labour market. Moreover, as well known,overeducated workers earn less than well-matched ones with the sameeducational level. Then, the average earnings of tertiary graduates are likelyto decline as overeducation increases among them. This may distort ourmeasure of the relative earnings, which is calculated as the ratio of the ave-rage earnings of all tertiary graduates, no matter if employed in a high-skilledor in a poor-skilled job, to the average earnings of secondary graduates.As shown in more detail below, we take this drawback into account by meansof an instrumental variables approach.

In addition to structural imbalances, we also consider the discrepancybetween demand and supply due to cyclical fluctuations (for a discussion oftheir implications on skilled and unskilled employment, see Sloane, 2003). Todo this, we include in our model a measure of the output gap (outgap).11 Thisvariable also provides some insight into the possible effects of the currenteconomic crisis on overeducation. More generally, we investigate whetherovereducation has to be considered an adjustment mechanism operatingduring a cyclical shock alongside increased unemployment and labour forcewithdrawals (Wieling and Borghans, 2001; Verhaest and van der Velden, 2010).

Furthermore, we take into account more structural labour marketconditions through the percentage of long-term unemployment (lonune). Tocontrol for the relative weight of youth in the labour market, we insert thepercentage of young people (25–34 years) in total employment (young_emp).The rationale for including this control derives from the concern that youngerworkers face a higher risk of being in a job below their educational level aspredicted by several theories (Leuven and Oosterbeek, 2011). We consideralso the percentage of engineers in the flow of newly graduates by year andcountry (engineers). As reported by Walker and Zhu (2005), graduates inengineering are less likely to be overeducated, thus they can be regarded as avalid control in order to take into account the distribution of tertiarygraduates by fields of education.

Finally, we include some institutional controls, a measure of the EPLstrictness (epl2_oecd) and the percentage of temporary contracts (temrate).

11 The output gap is derived from the OECD Economic Outlook 2010 (issue 2) and is defined as

the deviation of actual GDP from potential GDP as a percent of potential GDP.


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Indeed, firing costs introduced by EPL increase the cost of labour turnover forthe firm, and this can make it more difficult to absorb the skilled labour forcewhen innovations push up the demand for skilled labour (Mendes de Oliveiraet al., 2000). Moreover, under a strict EPL regime, firms face longer employmentrelationships and, if specific experience complements education, employers canbe encouraged to hire young workers with a high level of education evenwithout experience to allow future internal promotions (Sicherman, 1991).Thus, new entrants can be particularly affected by overeducation if they acceptlow-level jobs offering more promising career prospects. Nevertheless, previousempirical results do not clearly confirm the impact of EPL on overeducation.As noted above, Di Pietro (2002) reports a positive effect while Verhaest andvan der Velden (2010), on the basis of a larger sample and a more accurateeconometric analysis, do not find any significant effect.

Even the features of the unemployment benefit system can affect over-education. A generous subsidy may favour a better matching between the skillspossessed by the unemployed and those required by the filled vacancy, thusreducing the incidence of overeducation. Nevertheless, on empirical grounds itis not clear whether the overall institutional differences between countries doaffect educational mismatch in a significant way (Daly et al., 2000).

The operative variables included in the model are listed and describedin Tables 3 and 4. In order to calculate the variable wagehm, since the ELFS

Table 3: Variables description and sources

Abbreviation Description Source

l_oe_lau Percentage of overeducated with tertiary degree (log) ELFS, various yearsWagehm Relative wage tertiary graduates/upper secondary graduates ECHP – EU-SILC,

various yearsPoplau Percentage of individuals with tertiary education between 25

and 64 yearsWebsite Eurostat

Emphm Ratio between the number of employees with tertiary degree andthe number of employees with upper secondary degree (15–74 years)

Website Eurostat

Popdip Percentage of individuals with upper-secondary education between25 and 64 years

Website Eurostat

Engineers Percentage of engineers on the flow of new graduates OECD Stat ExtractsYouth_emp Percentage of young workers (25–34 years) on total employment OECD Stat ExtractsIsco_1_2_3 Percentage of employees in occupations ISCO88 1–2–3 on total

employment 20–64 yearsWebsite Eurostat

Temrate Percentage of employees with temporary contracts Website EurostatLonune Long-term unemployment in percentage of active population Website EurostatEmpedu Employment in education sector Website EurostatEmphea Employment in health sector Website EurostatEpl2_oecd EPL version 2 (OECD) Website EurostatOutgap Output gap OECD Economic

Outlook 2/2010Union_cov Union coverage (instrumental variable) Jelle Visser databaseUbrepl Unemployment benefit replacement rate fRDB database


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does not contain any earnings information, we calculate time-varyingcountry-specific labour incomes from microdata provided by the EuropeanCommunity Household Panel (ECHP) and European Union Statistics onIncome and Living Conditions (EU-SILC). The ECHP contains data ongross and net monthly earnings for individuals in 15 countries for the period1994–2001, whereas the EU-SILC reports (only) gross monthly earnings for allthe 26 countries for the period 2004–2008. Then, to match our data set, weuse ECHP for the period 1998–2001 and EU-SILC for the period 2004–2006.Given that the levels and rankings of the earnings are rather stable withincountries over time, we impute earnings for missing years (2002 and 2003) byinterpolation. All earnings are calculated in terms of gross hourly earningsand are converted into 2000 Euros using harmonized price indices and 2000real exchange rates (Goos et al., 2010).

ESTIMATIONS: METHODOLOGY AND RESULTS

As a starting point, we perform a pooled OLS regression for the log of thepercentage of overeducated tertiary graduates using data for all countries inall years. Obviously, the error uit is likely to be correlated over time for a givencountry, so we use cluster-robust standard errors that cluster on countries.Results (Table 5, column 1) show a significant negative coefficient of the wageratio (wagehm) pointing out an inverse relationship between the relativewage of graduates and the share of graduates who occupy a job below their

Table 4: Descriptive statistics

Variable Observations Mean Standard deviation Minimum Maximum

Oe_2la 226 35.620 9.648 16.38 65.52Wagehm 144 1.510 0.256 1.057 2.358Poplau 178 22.316 7.755 8.8 42.4Emphm 229 0.551 0.307 0.123 1.566Popdip 178 48.373 15.927 10.5 76.9Engineer 166 0.128 0.046 0.051 0.241Youth_emp 221 35.496 6.745 21.798 50.959Isco_1_2_3 234 36.088 6.167 18.74 48.59Lonune 226 3.638 2.607 0.2 12.2Temrate 224 10.75 6.709 1 34Epl2_oecd 171 2.3 0.619 0.98 3.53Empedu 205 6.899 1.572 3.6 11.5Emphea 205 8.676 4.320 2.9 19.6Outgap 189 0.195 1.572 �3.1 4.8Union_cov 159 70.976 25.524 12 100Ubrepl 179 0.401 0.181 0.08 0.74


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educational level. According to the theoretical discussion above, this outcomefavours the job assignment hypothesis predicting that, if relative wagedecreases, some highly educated workers could prefer or be forced to accept alow-skilled job according to their heterogeneous preferences and individualconstraints. Our second step is the estimation of a Pooled FGLS model, whichcan lead to more efficient estimators of the pooled model parameters. Weassume an AR(1) correlation structure for the errors. To guard against amisspecified model of the error correlation, cluster-robust standard errorsare computed. Results are given in column 2 of Table 5 and show that thecoefficient of the wage ratio remains negative although less significant andthe other coefficients do not vary much from those pooled OLS.

At this stage of our analysis, we perform a Breusch-Pagan LagrangeMultiplier (LM) test to verify the presence of country-specific effects. The nullhypothesis in the LM test is that there is no significant difference acrosscountries, ie, no panel effect. As the null hypothesis is rejected (Table 6), wehave to account for the presence of significant country-specific effects. Con-sequently, we run a Hausman test to decide between fixed or random effects.The result (Table 6) does not lead to the rejection of the null hypothesis thatRE provides consistent estimates. In this case, possible unobserved variablesare not correlated with the regressors included in the models.

To test whether time fixed effects are needed, we also run a joint testascertaining if the dummies for all years are equal to 0. We fail to reject thenull that all years coefficients are jointly equal to 0, therefore we concludethat yearly dummies are not needed in our final model (see again Table 6).

Table 5: Pooled OLS, pooled FGLS and random effects models

Variable Pooled OLS Pooled FGLS RE

Wagehm �0.478*** �0.415* �0.435***Poplau �0.011* �0.013** �0.003Popdip 0.011*** 0.010*** 0.011***Engineers �0.081 �0.112 �0.391Youth_emp 0.002 0.002 0.000Isco_1_2_3 �0.013 �0.013* �0.018Lonune �0.003 �0.006 �0.006Temrate �0.003 �0.002 �0.000Epl2_oecd 0.062 0.057 0.054Empedu �0.100*** �0.098*** �0.082**Emphea 0.007 0.010 0.004Outgap �0.019 �0.019 �0.025*_cons 5.029*** 4.999*** 4.974***N 82 77 83

*po0.1; **po0.05; ***po0.01.


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Finally, we perform a Modified Wald test for groupwise heteroskedasticityand a Wooldridge test for autocorrelation (Table 6) and we cannot reject thepresence of heteroskedasticity and serial correlation. Thus, we have to usecluster-robust standard errors.

Our final model is a Random Effects model without yearly dummies andwith cluster-robust standard errors to cope with heteroskedasticity and serialcorrelation. Results are given in column 3 of Table 5 and confirm a signifi-cantly negative effect of the wage ratio on graduates’ overeducation. The sizeof the coefficient is stable and its statistical significance high.

As mentioned above, an endogeneity problem could bias our estimates.Indeed, both overeducation and earnings arise as equilibrium outcomes inthe labour market. Furthermore, on the one hand, according to the job assign-ment hypothesis, an increase of the ratio between the earnings of tertiary andupper secondary graduates is expected to be associated with a reduction ofovereducation. On the other hand, when graduates’ overeducation decreases,the wage ratio tends to increase as it is likely that overeducated tertiarygraduates earn less than well-matched tertiary graduates (Dolton andVignoles, 2000; Leuven and Oosterbeek, 2011). Thus, we can have a reversecausality problem.

As a solution, we resort to an instrumental variables approach. To thisend, we choose to use an index of union coverage as it appears to be correla-ted with the earnings differential, while we have no reason to suppose thatit has a direct influence on overeducation incidence. The first stage of the

Table 6: Tests for model specification

Breusch-Pagan Lagrange Multiplier (panel effects)w2 6.76Probability (w2) 0.018

Hausman test (random versus fixed effects)w2 11.88Probability (w2) 0.455

Time-fixed effectsF 1.28Probability (F) 0.284

Modified Wald test for groupwise heteroskedasticityw2 4.9e+29Probability (w2) 0.000

Wooldridge test for autocorrelationF 7.054Probability (F) 0.022


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instrumental variable procedure, not shown here, proves that union coverageis strongly and negatively correlated with the wage ratio.12

The estimation results of the second stage IV model with robust clusterstandard errors are reported in Table 7. Even in this case, the coefficient ofwagehm is significantly negative and its magnitude stays stable, whereas thecoefficient of the supply variable (poplau) turns out to be negative but notsignificant. Moreover, the share of upper secondary graduates has a positivecoefficient, which is consistent with the spurious effect discussed above.

As for the percentage of temporary employment, our estimations yielda positive and significant coefficient showing that a rise in the share of short-term jobs in the labour market occurs along with a deterioration of thematching. This contradicts the conjecture that a larger diffusion of short-termemployment relationships would be compensated by an improved qualityof the matches. On the contrary, such result seems to suggest that, as longas temporary contracts imply lower turnover costs, both firms and workersare likely to be less selective in accepting a proposal in the labour market and,as a consequence, the quality of matching deteriorates.13 This, in turn, willresult in a higher incidence of overeducation.

Table 7: RE with IV, second stage estimations (with cluster-robust standard errors)

Variable Skilled labour supply: poplau Skilled labour supply: emphm

Wagehm^ �0.437** �0.417*Poplau �0.010 FEmphm F 0.259Popdip 0.010* 0.007Engineers �1.322 �1.118Youth_emp �0.007 �0.006Isco_1_2_3 0.005 �0.000Lonune �0.051*** �0.045**Temrate 0.018*** 0.018***Epl2_oecd 0.103 0.095Empedu 0.065* 0.060Emphea �0.045* �0.048*Outgap �0.034*** �0.033**_cons 3.951*** 4.146***N 102 102

*po0.1; **po0.05; ***po0.01.

12 More formal tests on the quality and validity of the instrument are reported in Appendix A.13 Actually, firms would be prone to hire overeducated workers when returns to overeducation

are low (Di Pietro and Urwin, 2006), besides the contractual arrangement. The possibility of hiring

temporary workers could strengthen this behaviour.


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At the same time, the coefficient of the percentage of long-termunemployment turns out to be negative and significant. On theoreticalgrounds, the sign of the effect of this variable on overeducation is ambiguous.Indeed, on the one hand, we can assume that, in a labour market affectedby widespread long-term unemployment, job seekers are forced to acceptemployment opportunities for which they are overqualified. As a conse-quence, a positive effect of long-term unemployment on overeducation isexpected. On the other hand, the opposite result should be found if weassume that both workers who stay unemployed longer and those facinga higher risk of being overeducated when in employment are characterizedby lower abilities or other adverse individual constraints. To this regard,Ordine and Rose (2011) find that mismatched workers experience longerunemployment spells and possess lower individual abilities than their well-matched peers. Under this assumption, it is likely that in a period when thejob finding rate increases and long-term unemployment shrinks, the rate ofovereducation also increases as less able workers disproportionally enteremployment.14 Thus, out of the steady-state level of long-term unemploy-ment, a negative relationship between long-term unemployment and over-education incidence can arise at the aggregate level as pointed out by ourfindings. As a matter of fact, the long-term unemployment rate, measuredas a percentage of active population, declined in 15 countries out of the 26 inour panel, throughout the period 1998–2006 considered in our analysis.Overall, it fell by more than 20% of its initial average level.15

Like in Verhaest and van der Velden (2010), we find that the EPL doesnot have a significant impact on overeducation (but see Mendes de Oliveiraet al., 2000 and Di Pietro, 2002). Finally, our findings show that, when the IVapproach is used, the percentage of overeducated graduates is found to reactsignificantly to cyclical movements in GDP as measured by the output gap.This result is in line with the evidence reported by Quintini (2011a), showingthat workers losing their job during a recession are more likely to find a jobrequiring less education than they actually possess. The same holds truefor young school leavers entering their first job when unemployment is aboveits long-term average level. In the context of the deep and prolongedeconomic crisis currently affecting advanced countries, it can be expectedthat such adverse effects on the quality of the matching might further worsen

14 Similarly, Dew-Becker and Gordon (2008) find that labour market reforms in Europe

favoured a rise of employment of less experienced, less skilled components of the labour force and

argue that this contributed to the productivity slowdown.15 The unweighted average rate of long-term unemployment at the end of the period was equal

to 3.24% compared with 4.13% in 1998.


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the earnings and the job satisfaction prospects of the weaker categories ofworkers.

As for the other variables, those controlling for the composition oflabour supply (the relative weight of graduates in engineering and the shareof young population), as well as the share of employed in high occupationsdo not strongly affect overeducation. On the contrary, the relative weight ofemployment in the educational and in the health industries are significant at10%.

To check the robustness of these findings, we replicate the estimationwith a different measure of skilled labour supply (emphm). The results areshown in Table 7 (column 2) and confirm almost all the previous results.In particular, the relative wage is still negative even if slightly less significant.The supply measure turns positive, but remains not significant. The share ofupper secondary graduates loses its statistical significance.

Finally, a further robustness check is performed by inserting the replace-ment rate, which represents a measure of the generosity of the unemploymentsubsidy, in combination with both the supply variables. In principle, theunemployment insurance system can affect overeducation since a more gene-rous subsidy allows the job seeker to refuse a job that he/she considersa wrong match and prolong the search. Thus, we insert this variable totest the stability of our results. As reported in Table 8, the replacement ratedoes not affect the dependent variable. At the same time, the wage ratiois still negative and statistically significant even though at a lower level.

Table 8: Robustness checks: RE with IV, second stage estimations (with cluster-robust standard errors)

Variable Skilled labour supply: poplau Skilled labour supply: emphm

Wagehm^ �0.600* �0.623*Poplau �0.016 FEmphm F �0.162Popdip 0.013* 0.014Engineers �1.340 �0.900Youth_emp �0.012 �0.014*Isco_1_2_3 0.008 �0.003Lonune �0.057** �0.045*Temrate 0.023*** 0.024***Epl2_oecd 0.289* 0.222Empedu 0.095* 0.101**Emphea �0.043* �0.059*Outgap �0.040** �0.034*Ubrepl 0.018 0.391_cons 3.540*** 3.709***N 84 84

*po0.1; ** po0.05; *** po0.01.


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Moreover, when the supply is measured by emphm, the negative effect of theshare of young workers on total employment on overeducation becomessignificant. However, as it is only 10% significant in one out of four estima-tions, we can conclude that it does not exert a relevant effect on the incidenceof overeducation among tertiary graduates. The same conclusion can bereached (in line with Verhaest and van der Velden, 2010) for the strictness ofEPL, whose coefficient has a 10% level of statistical significance in the modelwith the replacement rate, but only when combined with one of the twosupply variables. Both the relative weight of employment in the educationaland in the health industries are significant at 10%, although with an oppositesign. On the basis of results shown in Tables 7 and 8, we can conclude that, asexpected, the size of the health sector negatively affects the share of over-education among university graduates. Indeed, graduates in fields of studyrelated to medical and health occupations are less likely to be overeducated.It is more difficult to explain the positive effect of the size of the employmentin the education sector. As a tentative explanation, it can be argued that ineconomies where the share of employment in education has grown faster,a higher proportion of the young may have enroled in fields of studies (suchas the humanities) that are related to teaching but, at the same time, tendto increase the risk of overeducation as documented in a number of studies(eg, Quintini, 2011b; Dolton and Vignoles, 2000; Ortiz and Kucel, 2008).

CONCLUDING REMARKS

The paper undertakes an analysis of overeducation of tertiary graduates ina group of European countries to assess the role of demand and supply factorsin determining its incidence. To this end, at first we presented some pre-liminary descriptive evidence showing that no evident relationship betweenthe increase of the share of labour force holding a tertiary degree and over-education incidence is evident in the data. In the subsequent econometricestimations, we included variables capturing the effects of skilled labourdemand. Among them, we also considered the ratio between the earnings oftertiary and upper secondary graduates. The effect of skilled labour supplywas taken into account by means of two alternative measures. To cope withendogeneity problems affecting the wage ratio, we applied an instrumentalvariables approach.

Our results, obtained from a random effects model with instrumentalvariables, highlight that none of the supply measures exert a major impact onovereducation. As for the wage ratio, we found that it has a negative andsignificant effect, which proved to be stable in subsequent robustness checks.


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This result is consistent with the job assignment hypothesis, predicting thatthe incidence of overeducation is likely to rise with declining relativeearnings. Although we are cautious in interpreting this as a true causal effect,it can be taken as a signal of the role of demand for skilled labour. Indeed,demand shifts are the most natural explanations for the level of the wage ratioaffecting overeducation.

The share of temporary employment appears to increase overeducation,as the reduction of hiring and firing costs allowed by short-term contractsmakes firms and workers less selective. Quite interestingly, long-termunemployment is found to be inversely related to overeducation. This resultsuggests that when long-term unemployment declines substantially, as it isthe case in the period we consider, overeducation can increase as a largenumber of less experienced and less skilled individuals enter employment.Institutional variables measuring aspects of unemployment benefits andstrictness of EPL in each country do not have a significant impact.

Besides structural determinants, our results show that when a cyclicaldownturn hits the economy, we can expect the incidence of overeducationto increase. This evidence points to the role of overeducation as an oftenneglected short-term adjustment mechanism in the labour market. As aconsequence, in policies aimed at bringing about an economic recovery,there should be room for labour market policies favouring a proper labourreallocation. By keeping mismatched workers from being trapped in theircurrent jobs, aggregate productivity growth and higher workers’ jobsatisfaction could be attained. Further comparative research extended tothe years of the current global crisis should be undertaken to cast morelight on this issue.

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APPENDIX

Tests on the quality and the validity of the instrument

As pointed out in preceding sections, the estimation of a model withinstrumental variable needs at least one instrument that affects the wage ratioand not the incidence of overeducation. In this section, we test the qualityand the validity of the instrumental variable we use in this paper (unioncoverage).

Instrumental quality is ensured if there is a strong correlation betweenthe instrument and the wage ratio. A statistic commonly used in order to testthis condition (Bound et al., 1995) is the R2 of the first stage regression withthe included instrument ‘partialled-out’, or Shea partial R2 (for an applicationto the analysis of overeducation, see Di Pietro and Cutillo, 2006). In our firststage regressions, the partial R2 on the excluded instrument range from 0.47 to0.63, suggesting that the instrument make a relevant contribution inexplaining the wage ratio (see Table A1).

Instrumental validity is ensured if the instrument can be legitimatelyexcluded from the overeducation equation. This assumption is often checked


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through the Sargan test. Nevertheless, this test is valid only in case of over-identification (ie, the number of valid instruments exceeds the number ofendogenous variables), which is not our case. Following the suggestion ofCutillo and Ceccarelli (2010), we checked the validity of the instrumentthrough the approach of Dolton and Vignoles (2002). According to theseauthors, a valid instrument must be uncorrelated with the error term of theoutcome equation, and thus it should not affect the incidence of over-education conditional on the included explanatory variables. When theresiduals from the overeducation equations were regressed on the instru-ment, we obtained R2 ranging from 0.0002 to 0.0041 (see again Table A1).This indicates that the instrument does not explain any significant variationin the residual variability and hence is valid.

Table A1: Tests on the quality and the validity of the instrument

Model Test Skilled laboursupply: poplau

Skilled laboursupply: emphm

RE with IV (Table 7) Partial R2 0.4677 0.5244Dolton-Vignoles 0.0041 0.0031

Robustness control: Ubrepl (Table 8) Partial R2 0.4891 0.6363Dolton-Vignoles 0.0002 0.0006


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Demand and Supply of Skilled Labour and Overeducation in Europe: A Country-level Analysis

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