Skills mismatch & productivity in the EU.
Date: 2019-07-18
Institutions: Directorate-General for Economic and Financial Affairs
Subjects: European Union, labour shortage, macroeconomics, productivity, professional qualifications

6

ISSN 2443-8022 (online)

Skills Mismatch 
& Productivity 
in the EU

Anneleen Vandeplas
and Anna Thum-Thysen

DISCUSSION PAPER 100 | JULY 2019

EUROPEAN ECONOMY

EUROPEAN 
ECONOMY

Economic and 
Financial Affairs

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European Commission 
Directorate-General for Economic and Financial Affairs 

Skills Mismatch and Productivity in the EU 

Anneleen Vandeplas and Anna Thum-Thysen 

Abstract 

This  paper  analyses  different  dimensions  of  skills  mismatch  (notably  ‘macro-economic  skills  mismatch’, 
‘skills  shortages’,  and  ‘on-the-job  skills  mismatch’)  and  their  empirical  relationship  with  labour 
productivity.  Macro-economic  skills  mismatch  arises  when  the  skills  distribution  differs  between  the 
available  workers  and  those that get hired. Skills shortages occur when employers encounter difficulties to 
fill  their  vacancies.  On-the-job  skills  mismatch  (overqualification  or  underqualification)  refers  to  a 
discrepancy between the qualification level  of a jobholder and the requirements for that particular job.  

Our  data  suggest  that  certain  types  of  skills  mismatch  are  indeed  on  the  rise  in  the  EU,  notably  skills 
shortages  and  overqualification.  Other  types  are  on a long-term declining trend (e.g. underqualification) or 
follow  more  complex patterns over time (e.g. macro-economic skills mismatch). There are also significant 
differences  across  EU  Member  States  in  the  levels  of  these indicators. We further suggest that theoretical 
predictions  on  the  relationship  between  skills  mismatch and productivity depend on the dimension of skills 
mismatch  considered.  Our  empirical  analysis  suggests  a  negative  relationship  between  macro-economic 
skill  mismatch  and  labour  productivity  and  –  as  a  sign  of  a  buoyant  economy  -  a  positive  relationship 
between  skills  shortages  and  labour  productivity.  With  regard  to  on-the-job  skills  mismatch,  our  data 
confirm  earlier  findings  from  the  economic literature: when comparing a mismatched with a well-matched 
worker  within  the  same  occupation,  overqualification  raises  and  underqualification  reduces  productivity. 
When  comparing  a  mismatched  with  a  well-matched  worker  within  the  same  qualification  level, 
overqualification  reduces and underqualification increases productivity.  

Our  results  imply  a  positive  link  between  skills  supply  and  productivity.  However,  to  realise  the  full 
potential  of  higher  skills,  skills  should  be  labour  market  relevant  and  skilled  workers  need  to  be  matched 
with  jobs  that  use  these  skills. Therefore, upskilling policies should ideally be accompanied by policies that 
assure  quality  and  labour  market  relevance  of  acquired  skills,  policies  that  foster  a  general  upgrading  of 
jobs  such  as  business  regulations  allowing  for  firm  entry,  growth,  sectoral  reallocation,  and  policies 
supporting labour mobility  and innovation. 

JEL Classification: D40, E31, L51. 

Keywords: human capital, skills  mismatch, productivity, inclusive growth, structural policy.  

Acknowledgements:  We  would  like  to  thank  Alfonso  Arpaia,  Erik  Canton,  Nathalie  Darnaut,  Antonio 
Dias  Da  Silva,  Jorge  Duran  Laguna,  Atanas  Hristov,  Barbara  Kauffmann,  Emmanuelle  Maincent,  Dino 
Pinelli,  Simone  Rosini,  Mantas  Sekmokas,  Mary  Veronica  Tovsak  Pleterski,  Anne  Van  Bruggen,  Irene 
Vlachaki  and participants at EMCO and LIME  meetings for useful discussions and suggestions. 

This  note  has  been  jointly  elaborated  by  the  European  Commission's  Directorates  General  Economic  and 
Financial  Affairs (ECFIN) and Employment, Social Affairs & Inclusion (EMPL).   

Contact:  Anneleen  Vandeplas,  European  Commission,  Directorate-General  for  Economic  and  Financial 
Affairs,  anneleen.vandeplas@ec.europa.eu;  Anna  Thum-Thysen,  European  Commission,  Directorate-
General  for Economic and Financial Affairs, anna.thym-thysen@ec.europa.eu. 

EUROPEAN ECONOMY 

 Discussion Paper 100

CONTENTS 

1. 

Introduction 

2. 

Skills mismatch in the eu: an overview of different measures  

2.1. Macro-economic skills mismatch 

 2.2. Skills shortages 

2.3. Measures of on-the-job mismatch 

2.4. Are mismatches increasing ov er time in the EU? 

3. 

Relationship between skills mismatches and productivity 

3.1. Framework for analysis 

3.2. Macro-economic skills mismatch 

3.2.1. Theoritical arguments 

3.2.2. Empirical findings 

3.3. Skills shortages 

3.3.1. Theoritical arguments 

3.3.2. Empirical findings 

3.4. Measures of on-the-job skills mismatch 

3.4.1. Theoritical arguments 

3.4.2. Empirical findings 

4.  Conclusion and policy implications 

5. 

References 

ANNEX  I:  DATA  SOURCES  AND COVERAGE 

ANNEX  II: DESCRIPTIVE STATISTICS  AND GRAPHS 

3 

5 

6 

6 

10 

13 

17 

18 

20 

22 

22 

24 

27 

27 

27 

30 

30 

33 

38 

42 

 
 
 
 
 
 
 
 
ANNEX  III: THEORITICAL  APPROACHES  TO  MODELLING  THE  RELATIONSHIP  BETWEEN  SKILLS 
MISMATCH  AND  PRODUCTIVITY 

ANNEX  IV: EMPIRICAL  RELATIONSHIPS  BETWEEN  TOTAL  FACTOR  PRODUCTIVITY  AND  SKILLS 
MISMATCH 

ANNEX  V: EMPIRICAL  RELATIONSHIPS  BETWEEN  CAPITAL  INTENSITY  AND  SKILLS  MISMATCH 

TABLES 

GRAPHS 

4 

 
 
 
 
 
 
 
 
 
 
1.  INTRODUCTION   

Human  capital  is  a  major  driver  of  inclusive  growth.  As  a  consequence  of  ongoing  trends  such  as 
progressive  globalisation,  demographic  change,  technological  change  and  digitalisation,  jobs  are 
becoming  increasingly  skills-intensive.  Innovation,  which  heavily  relies  on  advanced  knowledge,  is  a 
key  contributing  factor  to  productivity  growth,  the  main  source  of  growth  for  EU  Member  States 
today.  These  developments  underline  the  increasing  importance  of  human  capital,  and  the  need  to 
foster a good match between the demand for and the supply of skills. 

Some  have  argued  that there are increasing mismatches between the skills or  qualifications the work 
force has on the one hand and the skills or qualifications required by the economy, as the demand for 
skills is changing rapidly as a result of the ongoing processes of structural change listed above. 1  There 
is  also  a concern that  the onset of the economic crisis in 2008-2009 has exacerbated skills mismatch, 
as previously rapidly expanding sectors employing less-skilled labour - such as the construction sector 
- experienced severe difficulties.2 Moreover, there are concerns that these mismatches are hampering 
productivity  growth, which has been sluggish since the mid-1990s.3     

This  note  reviews  different  dimensions  of skills mismatch (at the aggregate level) and describes how 
they  vary  across  EU Member States, as well as over time.  It then explores the empirical relationship 
between  these  skills  mismatch  measures  and  labour  productivity.  The  note  highlights  that  the 
relationship  between  skills  mismatch  and  productivity is not as  clear-cut as it may seem at first sight: 
the  various  different  concepts  of  skills  mismatch  that  are  being  discussed  in  the  literature  and  by 
policymakers  imply  very  different  theoretical  and  empirical  conclusions  on  both the sign and the size 
of  their  relationship  with  productivity.  We  find  a  positive  relationship  between  skills  shortages  and 
labour  productivity,  and  a  negative  relationship  between  macro-economic  skill  mismatch  and  labour 
productivity,  while  controlling  for  the  output  gap.  With  regard  to  on-the-job  skills mismatch, our data 
seem  to  confirm  earlier  findings  from  the  economic  literature.  Notably,  when  comparing  with  well-
matched  workers  in  the  same  occupation,  overqualification  raises  productivity  and  underqualification 
reduces  it.  When  comparing  with  well-matched  workers  with  the  same  qualification  level,  however, 
overqualification  reduces  and  underqualification  increases productivity.  Our work goes beyond earlier 
work  by  McGowan  and  Andrews  (2015a)  by  being  more  comprehensive  in  the  dimensions  of  skills 
mismatch  considered.  Moreover,  in  contrast  with  their  paper,  ours  concentrates  on aggregate labour 
productivity. 

To enhance a positive link between skills and productivity,  the note argues in favour of policies aiming 
at  upskilling  of the workforce, which should ideally be accompanied by a general upgrading of jobs in 
order to put these higher skills  in their best use.  

The  note  is  organised  as  follows:  Section  2  provides  an  overview  of  the  different  concepts  or 
dimensions  of  skills  mismatch  which  have  been  described  and  analysed  in  the  economic  literature. 
Section 3 explores the relationship (across countries and over time) between labour productivity and a 

1 For example, Toner (2011) argues, that economies of today need workers with a combination of technical competences and 
generic  or  transferable/trans-occupational  skills,  such  as  problem-solving,  creativity,  teamwork  and  communication  skills. 
Durbin  (2004)  argues  that  raising  productivity  requires  entrepreneurial  skills  (to  identify  and  respond  to  market  and 
innovation  opportunities);  managerial  skills  (to  provide  leadership and organise production processes in the most effective 
way); and technical skills (to increase the effectiveness of production and take advantage of technological  improvements). 

2 see e.g.  ECB (2012), European Commission (2016). 

3 e.g. M cGowan and Andrews (2015a) or M avromaras et al. (2009). 

5 

 
                                                                 
set  of four different measures of skills mismatch that can be easily constructed based on data readily 
available  through  Eurostat.  It  first  discusses  our  prior  expectations  on  the  sign  and  size  of  the 
relationship  with  productivity  based  on  economic  theory,  and  then  the  observed  empirical  results. 
Finally,  Section 5 concludes and provides a set of general policy implications. 

2.  SKILLS MISMATCH  IN THE  EU:  AN OVERVIEW  OF 

DIFFERENT  MEASURES   

Skills  mismatch  is  a  broad concept that is meant to reflect a suboptimal match between workers and 
jobs  in  terms of skills and/or qualifications.  While qualifications relate to formally certified undertaken 
education  and  training,  skills  are  the  abilities  an  individual  actually  possesses  and  may  have  been 
acquired  through  informal  learning  processes.  Notwithstanding  the  important  distinction  between 
skills  and  qualifications,  for  the  purpose  of  this  note  we  will  refer  to  “skills  mismatch”  rather  than 
“qualification mismatch” for reasons of brevity but also because the former is a more standard term in 
the research literature. Some degree of mismatch naturally occurs as a result of labour market frictions 
in  the  short  term (Kiss and Vandeplas, 2015).  When mismatch is more broad-based and persistent, it 
becomes a stronger concern to policymakers. 

Two major approaches have been used in the literature. On the one hand, the literature has considered 
mismatches  between  individuals  available for work and the available jobs. Such mismatches give rise 
to the coexistence of unemployed individuals and vacancies. On the other hand, there is a proliferating 
literature  on  mismatches  between  those  individuals  who  have  a  job,  and  the  job  they  are  in.  In  the 
latter approach, no attention is paid to individuals  who are out of work. 

This note will focus on two indicators that belong to the first approach, notably macro-economic skills 
mismatch  and  skills  shortages;  and  two  indicators  that  rather  fit  under  the  second  approach 
(underqualification  and  overqualification).4  The  considered  indicators  have  been  used  for  some  time 
by  now  for  EU-related  policy  analysis.  Kiss  and  Vandeplas  (2015)  provide  a  more  detailed 
justification of the choice of these indicators. An important criterion is the availability of cross-country 
comparable data for EU-28 countries over a sufficiently long time period. In what follows, we discuss 
the specific definitions  and indicators  that will  be used. 

2.1. 

MACRO-ECONOMIC SKILLS MISMATCH 

Macro-economists  have  typically  looked  at  skills  mismatches  between  individuals  available  for  work 
(the unemployed) and the available jobs (vacancies) and have compared them across time and across 
countries at the macro-level.5 For the purpose of this note, this type of skills mismatch is referred to as 
"macro-economic  skills  mismatch".  It  may  result  in a persistent coexistence of a pool of unemployed 
workers  (of  a certain skills type) with a relatively high vacancy rate (suggesting demand for workers 

4  Several  researchers  have  argued  that  overskilling  measures  are  more  significantly  associated  with  several  economic 
variables than overqualification measures (see e.g. Quintini, 2011; Sloane, 2014; M avromaras et al., 2012; Budria and M oro-
Egido, 2014), and that the former type of measures are therefore more relevant for analysis. However, data on the latter type 
of  measures  are  typically more easily available, which explains why they have been used more frequently, including in our 
own  analysis.  The  PIAAC  and  the  European  Skills  and  Jobs Survey, which currently provide the best measures on skills 
(including subjective reports of overskilling and underskilling) consist of only one data round. While it offers unique data on 
direct measures of skills, PIAAC currently covers an incomplete set of European M ember States: BG, HR, HU, LU, LV, M T, 
PT and RO are missing. 

5 See e.g.  Kiss and Vandeplas (2015), Sahin et al. (2014), Arpaia et al. (2014), Estevão and Tsounta (2011), and ECB (2012).  

6 

 
                                                                 
of  a  different  skills  type)  and  can  therefore  reflect  structural  labour  market  matching  inefficiencies, 
leading to structural unemployment or inactivity. As this measure of skills mismatch only accounts for 
whether  or  not  individuals  of  a  certain  skill  type  are  (un)employed  (or  not),  it  does  not  take  into 
account whether, if they are employed, their job is in line with their skills. 

Several inter-related measures are available to assess macroeconomic skills mismatch.6 For reasons of 
data  availability,  skills  are  in  this  context  usually  proxied  by  qualifications.  While  the  term 
"macroeconomic  qualifications  mismatch" would arguably be more correct, the term "skills mismatch" 
remains more commonly used in the macroeconomic literature.  If data are available, one can compare 
directly  the  composition  of  vacancies  (in  terms  of  qualification  levels)  with  that  of  (un)employment 
(see e.g. Şahin et al., 2014). As vacancy data are not always available or reliable, simplified measures 
can  be  used  that  compare  the  composition  of  employment  in  terms  of  qualifications  (as  a proxy for 
labour demand) with that of the population of working age (as a proxy for supply); or the composition 
of  unemployment  (as  a  proxy  for  the  lack  of  demand)  with  that  of  the  labour force (as a proxy for 
supply).  

The  use  of  dispersion  indicators  to  measure  mismatch  in  the  labour  market  dates  back  to  Lipsey 
(1960),  who  argued  that  such  mismatch  can  influence  the  trade-off  between  unemployment  and 
inflation. By now, their use for macro-economic analysis is widespread. One can consider absolute or 
relative  dispersion  measures;  one  can  look  at  sums  of  squares  or  at  sums  of  absolute  values  of 
deviations from the mean; one can weight different categories considered or not. Differences between 
the  resulting  measures  can  generally  be  explained  based  on  the  selected  methodology,  and different 
methodologies  can be defended based on the purpose of the analysis.  

For  the  purpose  of  this  paper,  the  following  specification  is  proposed  for  macro-economic  skills 
mismatch,  measured  as  the  relative  dispersion  of  employment  rates  across  three  population  groups 
with different educational attainment:   

𝑆𝑀𝐼 = ∑ |
𝑖=𝐿,𝑀,𝐻

𝐸𝑖
𝐸𝑡

−

𝑃𝑖
𝑃𝑡

|

=

1
𝑒𝑡

𝑃𝑖
𝑃𝑡

∑ |
𝑖=𝐿,𝑀,𝐻

(𝑒𝑖 − 𝑒𝑡)|

where    𝑖  stands  for  the  three  different  qualification  groups  L,  M  and  H,7  𝐸𝑖,  𝑃𝑖  and  𝑒𝑖  for  total 
employment,  the  working  age  population  and the employment rate of group  i respectively; and 𝐸𝑡, 𝑃𝑡 
and 𝑒𝑡 for aggregate (or total – indicated by the subscript 𝑡) employment, the aggregate population and 
the aggregate employment rate respectively.8  

We  selected this specification following Dixon (2006) who argues that a simple dispersion indicator is 
easier to interpret than e.g. a standard deviation or a coefficient of variation; we divide it however by 
aggregate  employment  to  obtain  a  relative  dispersion  indicator  that is more easily comparable across 
countries  and  years  with  considerable  variations  in  employment  rates.  This  measure  quantifies  the 
differences  in  employment  rates  between  qualification  groups,  weighted  by  their  importance  in  the 
working  age  population.  It  will  be  0  if  the  employment  rates  of  all  qualification  groups  are  equal  to 
each  other  and  hence  to  the  aggregate  employment  rate.  It  will  converge  to  its  upper  limit  if  the 

6  See  for  instance  Kiss  and  Vandeplas  (2015),  Arpaia  et  al.  (2014),  Estevão  and  Tsounta  (2011),  European Commission 
(2008:119); Peters (2000), ECB (2012:73). Similar  indicators can be derived for regional  or sectoral mismatch. 

7  Notably  low-qualified  individuals  (with  educational attainment at most at ISCED 0-2 level); medium-qualified individuals 
(with attainment at most at ISCED 3-4 level); and high-qualified  individuals  which hold a tertiary degree (ISCED 5-8 level) 

8 As a relative indicator, this measure takes on a value between 0 and 2. The 2 refers to n - 1 where n = 3 as three groups of 
workers are considered (L, M , and H) 

7 

 
 
                                                                 
employment  rate  is  extremely  low  (0%)  in  two  out  of  three  qualification  groups, and extremely high 
(100%)  in  the  third  group,  and  if  the  size  of  the  third  group  converges  to  0  such that the aggregate 
employment  rate  converges  to  0  as  well.  In  general,  a  relatively  high  mismatch  will  be  observed  if 
employment  rates  of  low-  and/or  medium-qualified  workers  are  low  as  compared  to  those  of  high-
qualified  workers, while the former make out a substantial share of the working age population. 

Macro-economic  skills  mismatch  can  reflect  both  cyclical and structural dynamics. On the one hand, 
as  low-qualified  (un)employment  tends  to  be  more  sensitive  to  the  cycle  than  high-qualified 
(un)employment,  the  difference  in  (un)employment  rates  between  qualification  groups  typically 
increases  in  economic  downturns.  As  a  result,  mismatch  typically  increases  during  an  economic 
downturn,  and declines again during  the recovery.  

However,  as  there  are  also  structural  reasons  underlying  broader  dynamics  in  (un)employment  rate 
differences between education groups, mismatch dynamics can also reflect those. Examples include a 
changing  relative  demand  for  low-  versus  high-qualified  workers  or  differences  in  the  impact  of 
changing  institutional  settings  and  of  demographics  across  education  groups  (e.g.  if  the  increasing 
generosity of benefit systems affects low-qualified workers more than  high-qualified ones; or if ageing 
is  proceeding  faster  among  low-qualified  workers  than  among  high-qualified  ones).  Some  of  these 
reasons are more concerning than others are, which underlines the importance of deeper investigation 
to assess the policy implications  of a deterioration of mismatch. 

Figure 1 shows that in terms of macro-economic skills mismatch, Belgium, Croatia, Bulgaria, Italy and 
Slovakia  show  differences  in  employment  rates  across  qualification  levels  of 20% and above. These 
countries  generally  combine  substantial  employment  gaps  (between  low-  and  high-qualified  workers, 
and/or  between  medium-  and  high-qualified  workers)  with substantial shares of low- and/or medium-
qualified workers in their population. At the other end of the spectrum, the best performer is the United 
Kingdom,  the  where  employment  rates  differ by below 10% across workers of different qualification 
levels, suggesting that supply and demand for workers of different qualification levels are more in line 
with each other. 

Figure  1:  Comparison  of  macro-economic  skills  mismatch  in  terms  of  employment  (%)  across  EU 
Member States 

35%

30%

25%

20%

15%

10%

5%

0%

2017
2007

K
U

L
N

T
P

E
E

K
D

T
A

E
D

V
L

I

Z S
C

I
F

8
2
U
E

E
S

Y
C

T
M

U
L

L
E

L
P

S
E

O
R

U
H

T
L

R
F

E
I

K
S

T
I

G
B

R
H

E
B

Source: Own calculations based on EU LFS according to methodology proposed by Kiss and Vandeplas (2015). 
Annual av erage based on the av erage of four quarters. 

8 

 
 
 
 
Over  the  last  decade,  macro-economic  skills mismatch has followed largely a countercyclical pattern 
on  average  in the European Union: rising with the economic downturn, and declining again during the 
recovery  (see  Figure  1  and  Figure  3).  At  the  individual  Member  State  level,  patterns  vary 
considerably.  

Fifteen  Member  States  saw  a  reduction  of  mismatch.  The  largest  (relative)  declines  over  the  past 
decade are observed in Estonia, Hungary, Slovakia and Bulgaria. In these countries, employment rates 
of  medium-  and  high-qualified  workers  have  been  structurally  converging  to  the  aggregate 
employment rate over recent decades, while the latter have been growing in importance as a share of 
the  working  age  population.  The  opposite  applies  to  the  employment  rate  of  low-qualified  workers, 
who  however  make  out  a  shrinking  part  of  the  working-age population. These dynamics have led to 
lower employment  rate dispersion and have not been visibly  interrupted by the crisis.  

On  the  other  hand, skills mismatch has gone up in some of the countries that were particularly hit by 
the  crisis  (such  as  Portugal,  Spain,  Greece,  or  Ireland)  but  also  in  France,  Croatia  and Denmark. It 
remained  relatively  stable  in  Belgium,  Cyprus,  Italy,  Luxemburg,  the  Netherlands,  Slovenia  and 
Sweden.9  

While there is no significant correlation between macro-economic skills mismatch and the educational 
attainment of the working age population or the  sectoral or occupational composition of employment 
in  a  cross-country  section,  some interesting correlation patterns can be distinguished over time within 
countries.  A  first  observation  is  that  macro-economic  skills  mismatch  has  increased  particularly  in 
countries  where  the  building  sector  saw  a  strong  decline  in  2008  (Figure  A2.7).  Since  then, 
employment  in  the  construction  sectors  has  not  returned  to  these  levels. 10  Macro-economic  skills 
mismatch  is  increasing in the share of high-skilled occupations (ISCO 1-3) in employment, suggesting 
that  the  process  of  occupational  upgrading  is  associated  with  an  increased  challenge  of  integrating 
less-qualified  workers  in  the  labour market (Figure A2.8). Finally, macro-economic skills mismatch is 
declining  in  the  share  of  tertiary  graduates  in  the  population,  highlighting  the  negative  relationship 
between mismatch and rising educational  attainment of the workforce (Figure A2.9).11 

A related mismatch indicator can be calculated based on the dispersion of unemployment (rather than 
employment)  rates  across  qualification  groups  (Figure  2).  In  general,  unemployment  dispersion rates 
are  more  volatile  than  employment  dispersion  rates.  Figure  2  shows  that  unemployment  rates  have 
diverged across qualification groups on average in the EU-28 over the period 2007-17, with important 
contributions  from  Portugal,  the  Baltic  countries,  Spain,  Poland,  but  also  Malta  and  Sweden. 
Unemployment  rates  became  more  similar across qualification groups in Bulgaria,  Cyprus, the Czech 
Republic,  Denmark, Slovakia,  the United Kingdom,  and the Netherlands. 

While relative dispersion of employment rates increased on average in the EU-28 over the crisis period 
(roughly from 2008-2014) and declined since then, relative dispersion of unemployment rates has seen 
a steady increase, which started even before the crisis (Figure 3).  

In  the  case  of  the  employment-based  indicator,  this  reflects  the  crisis-related  divergence  in 
employment  rates. The recovery brought a halt and a partial reversal to this process of divergence. The 
decline in the relative size of the low attainment group (as a share of the working age population) put 

9 Time trends by country are provided in Figure A2.5. 

10  One  could  of  course  wonder whether it would be desirable for employment in construction to fully recover to the 2008 
levels, as this was widely considered an unsustainable construction boom in many countries. 

11 Note that the macro-economic mismatch uses population weights of different qualification groups. If the group with more 
problematic labour market outcomes shrinks, mismatch will improve. 

9 

 
                                                                 
further downward pressure on the indicator. As a result, by 2017 the indicator has returned to its 2007 
level for EU-28. 

Figure  2:  Comparison  of  macro-economic  skills  mismatch  in  terms  of  unemployment  (%)  across  EU 
Member States 

2017

2007

60%

50%

40%

30%

20%

10%

0%

Y
C

T
P

I
S

L
E

R
H

O
R

U
L

T
I

K
D

L
N

K
U

Z
C

8
2
U
E

I

K F
S

S
E

R
F

L
P

E
E

E
B

T
A

E
I

V
L

E
D

U
H

G
B

T
M

T
L

E
S

Source: Own calculations based on EU LFS according to methodology proposed by Kiss and Vandeplas (2015). 
Annual av erage based on the av erage of four quarters. 

Figure 3: Evolution of macro-economic skills mismatch over time, EU-28 

38%
36%
34%
32%
30%
28%
26%
24%
22%
20%

18,6%

18,4%

18,2%

18,0%

17,8%

17,6%

17,4%

17,2%

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

Mismatch-UR (lhs)

Mismatch-ER (rhs)

Note: Mismatch-ER measures employment rate dispersion; Mismatch-UR measures unemployment rate dispersion 
across educational attainment groups. 

Source:  Own calculations based on EU LFS according to methodology proposed by Kiss and Vandeplas (2015). 
Annual av erage based on the av erage of four quarters. 

In  the  case  of  the  unemployment-based  indicator,  similar  patterns  can  be  discerned.  Unemployment 
rate  dispersion  was  already  increasing  before  the  onset  of  the  crisis  on  account  of  an  increasing 
deviation  of  the  unemployment rate of low-qualified vis-à-vis other educational groups (in spite of the 
fact  that  their  population  share  was  already  steadily  declining). 12  This  effect  was  reinforced  by  the 

12  Note  that  even  if  the  unemployment  rate  for  low-qualified  workers  reached  an  all-time  low  in  2007,  its  relative 
performance was still weaker than other education groups (who saw a stronger relative decline in unemployment rates in the 
“economic boom” period). 

10 

 
 
 
 
 
 
 
                                                                 
crisis.  The  coincidence  of  convergence  in  employment  rates  with divergence of unemployment rates 
can  be explained by the underlying dynamics in activity rates  – it can reflect either a widening gap in 
activity  rates or a general rise in activity rates (denominator effect). 

2.2. 

SKILLS SHORTAGES 

A  related  dimension  of  labour  market  mismatch  is  when  employers  encounter difficulties filling their 
vacancies (skills shortages). This also reflects an imbalance between demand and supply on the labour 
market, as there are seemingly no suitable workers for the jobs to be occupied.  

Skills  shortages  can  be  cyclical  and/or  structural  (European  Parliament,  2015).  Employers  typically 
encounter  more  challenges  to  recruit  the  "right"  workers  at  times  of  economic  growth  (Green  and 
Ashton,  1992;  Desjardins  and Rubenson, 2011) and technological or organisational change (Bosworth 
and Dutton, 1990). Empirical evidence supports the view that employers adjust wages upwards and/or 
recruitment  standards  down  (such  as  minimum  qualification  levels  for  hiring)  during  economic 
upturns when job applicants become scarcer (Reder 1955; Mortensen 1970; Devereux, 2002; Büttner 
et al., 2010).  

At  present,  there  is  no  perfect  way  to  measure  skills  shortages.  Three  surveys  collect  data  from 
employers  on  their  difficulties to fill vacancies, but the sampling methodologies, the questions that are 
asked,  and  the  results  are  different.  First,  Eurofound's  European  Company  Survey  (ECS)  asks 
employers whether they “encounter difficulties in finding employees with the right skills”. Second, the 
Manpower  Talent  Shortage  Survey  indicator  identifies  employers  with  difficulties  filling  jobs  “due  to 
lack  of  available  talent”. Both questions refer directly to skills or talent, but at the same time they do 
not exclude difficulties arising from some other sources (see further). Also, ECS data are not collected 
on  a  frequent  basis.  Finally,  the  European  Business  and  Consumer  Surveys  (EU-BCS)  collects 
(depending  on  the  sector)  quarterly  or  monthly  data  on  employers  in  manufacturing,  services  and 
construction reporting  whether labour shortages are a major factor limiting  their production.   

Recently, the OECD has started to develop new indicators for skills shortages ("Skills for jobs") based 
on more objective data (such as employment growth, wage pressure etc.). These are currently publicly 
available  as a single year-cross-sectional dataset (OECD.Stat, 2018).  

For  our  analysis,  we  construct  a  measure  of skills shortages drawing on data from the EBCS on the 
share  of  employers  in  manufacturing,  services  and  construction  reporting that labour shortages are a 
major  factor  limiting  their  production.  The  major  advantage  of  this survey is that it produces regular, 
cross-country comparable data (on a quarterly or monthly basis) and covers a relatively long time span. 
The  main  disadvantage  is  that  it  does  not  directly  allow  us  to  distinguish  skills  shortages  from  more 
general labour shortages (e.g. as a result of a shrinking or ageing population). 

A  composite  measure  of  shortages  was  constructed  as  the  weighted  average  of  three  sectors 
(construction, industry and services), weighting each sector by its share in value added. Unfortunately, 
data are missing for Bulgaria,  Ireland, Luxembourg,  and Malta.  

11 

 
Figure 4: Comparison of skills shortages (%) across EU Member States 

40

35

30

25

20

15

10

5

0

2018

2008

CY EL PT IT ES RO LV DK BE LT FR AT HR SK SI DE FI NL CZ UK EE SE PL HU

Source: Own calculations based on European Business Surv ey and AMECO  

Notes: Skills shortages data are missing for IE and LU and v alue added data missing for BG and MT.  

For  reasons  of  data  availability,  the  added  value  of  sectors  G-N  is  used  as  weight  for  the  EBCS 
services sector which covers sectors H-N and R-S. This implies the assumption that the added value of 
the wholesale and retail sector (G) is reasonably close to the added value of the arts and other service 
activities  sector (R-S). 

Figure  4  provides  an  overview  of  the indicator, showing that shortages are reportedly most imminent 
in countries such as Hungary and Poland with the incidence of skills shortages is reported to be above 
35% and 25%, respectively, and least problematic in Cyprus, Greece, Portugal, Italy and Spain where 
the  incidence  of  skill  shortages  is  reported  to  be  lower  than  5%.  Interestingly,  countries with higher 
levels  of  educational  attainment  (such  as  Finland,  the  United  Kingdom,  Sweden,  Lithuania  and 
Estonia)  tend  to  be  located  near  the  right  side  of  the  figure,  implying  that  they  are  more  likely  to 
encounter skills  shortages, not less.13 

An important caveat applies in interpreting data from employer surveys, as difficulties to fill vacancies 
can  result  from  unattractive  working  conditions  offered,  inefficiencies  in  recruitment  practices,  or 
broader  "labour  shortages"  following  from a general decline of the labour force currently observed in 
several  EU countries, rather than from skills mismatch as such (see e.g. CEDEFOP, 2015; Shah and 
Burke,  2003).  It  is  also  important  to  note  that  even  if  employers  are  looking  out  for  specific  skills, 
these  are  not  necessarily  technical  skills  that  relate  to  specific  qualifications.  For  instance,  Capelli 
(2015)  reports  that  only a third of employer reports of talent shortages in the Manpower data for the 
US related to a lack of hard skills that can be learned in school. 

Our  data  on  shortages  are  however  more  strongly  correlated  with  the  availability  of  high-qualified 
labour  than  with  that  of  low-qualified  labour,  suggesting  that  they  do  not  merely  reflect  labour 
shortages  in  most  countries.  In  particular,  our  indicator  for  skills  shortages  is  negatively  and 
statistically  significantly  correlated  with  unemployment  rates  of  medium-  and  high-qualified  workers, 
and less so with unemployment  of low-qualified  workers (see Figure A2.15 - Figure A2.17).  

13 A notable exception is Cyprus, which combines one of the highest rates of educational attainment with one of the lowest 
rates of skills shortages.  

12 

 
 
 
 
                                                                 
A  potential  explanation  could  be  that,  while  worker  reallocation  rates  tend to be higher in those jobs 
and  industries  that  generally  offer  lower  wages  and  more  difficult working schedules14 firms tend to 
find  new  hires relatively easily for these types of jobs. In other words, the least productive firms hire 
as  frequently  as  the  most  productive  ones,  but  their  separation  rates are considerably higher as well 
(more often because of quits than because of layoffs). Vacancies tend to persist longer for firms that 
are  more  productive  and/or  follow  higher-end  product  strategies,  and  for  jobs  that  require  higher 
qualification  or  more  experience  (Galenianos  et  al.,  2015;  Bennett  and  McGuinness,  2009;  UKCES, 
2012).  Interestingly,  countries  with  higher  levels  of  educational  attainment  (such  as  Finland,  the 
United  Kingdom,  Sweden,  Lithuania  and  Estonia) tend to be located near the right side of the figure, 
implying  that they are more likely to encounter skills  shortages, not less.15 

2.3. 

MEASURES OF ON-THE-JOB MISMATCH 

On-the-job  mismatch  indicators  reflect  a  complementary  dimension  of  labour  market  mismatch  that 
has received ample attention in the micro-economics literature. It considers the discrepancies between 
the  skills  and  qualifications  of  individuals  who  have  a job and the skills/qualifications requirements of 
their job. It does not take into account the unemployed.  

The  difficulty  in  pinning  down  the  appropriate  skills/qualifications  requirements  for  a  job  in  practice 
has  resulted  in  a  wide  range  of different indicators in this area that are often only weakly correlated 
with each other.  

On-the-job  mismatch  indicators  may  look  at  mismatches  between  the  skills  a  worker  possesses and 
those that are needed for his/her job; or rather at qualifications, he/she has and those needed for his/her 
job. Differences between skills and qualifications can arise for instance as a result of differences in the 
skills  content  (or  the  quality)  of  qualifications,  skills  developed  through  non-formal  or  informal 
training  (e.g.  training  on-the-job,  which  raises  one's  skills  beyond  his/her  qualifications)  or  skills 
depreciation  over  the  lifetime  (e.g.  because  of  changes in skills demands or insufficient maintenance 
or use of skills,  which can reduce one's skills as compared to his/her qualifications).   

For  each  of  these  dimensions,  the  literature has proposed a variety of measures 16 based on different 
data  sources17,  often  producing  diverging  results.18  Workers  experiencing  a mismatch with their jobs 
might  either  have  too  few  skills/qualifications  (underskilling/  underqualification)  or  too  many 
skills/qualifications  (overskilling/overqualification)  as  compared  to  what  their  job  requires  or  allows 
them  to  use.  Workers  may as well in some cases experience underskilling at the same time as being 

14 For example, OECD (2009) presents data on worker reallocation rates by industry, showing that rates  are generally higher 
in  the  hotels  and  restaurants  sector,  wholesale  and  retail,  and  construction.  Note  that  worker  reallocation  means  that  a 
different worker is hired for an existing job. If a worker is hired for a new job, this is usually referred to as  job reallocation. 
Usually job reallocation is (more so than worker reallocation) associated with productivity increases. Worker reallocation can 
for example be the result of worker quits, involving recruitment and training costs for the employer; a new worker with lower 
tenure may be less productive than a worker with longer tenure and more experience. 

15 A notable exception is Cyprus, which combines one of the highest rates of educational attainment with one of the lowest 
rates of skills shortages.  

16  E.g.,  qualification  mismatch  can  be  measured based on direct or indirect self-assessment, job analysis, or mean or modal 
empirical analysis. Similarly, skills mismatch can be measured based on self-assessment or on empirical analysis; and within 
these, there are still different methodological  options available.   

17 Such as Eurostat's Labour Force Survey (LFS) and Adult Education Survey (AES), OECD's survey of adult skills PIAAC, 
CEDEFOP's European Skills and Jobs Survey (ESJ) and so on 

18  see  e.g.  for  a  fairly  comprehensive  overview  Ramos  (2014)  on  qualification  mismatch;  Perry  et  al.  (2014)  on  skills 
mismatch 

13 

 
                                                                 
overqualified  (or  vice  versa);  see  e.g.  Perry  and  Maehler  (2017).  Another  dimension  of  on-the-job 
mismatch  is  horizontal  mismatch,  referring  to  a  mismatch  between  an  individual's  field  of  study  and 
the area (s)he is working in. This topic remains beyond the scope of this paper  – not only because of 
challenges  in  measurement,19  but  also  because  it  is  considered  less  problematic  from  a  policy 
perspective than vertical mismatch (see e.g. Sellami et al., 2018 and Montt, 2017 for a discussion).   

This note considers two measures of on-the job qualification mismatch that can be calculated based on 
regularly available and cross-country comparable data from Eurostat's Labour Force Survey (LFS), by 
comparing  the  qualification  level  of a particular worker with the required qualification level of his/her 
job,  based on the occupational classification  of that job.  

ILO  (2007)  proposes  that  jobs  classified  at  ISCO  levels  1-3  require  workers  that  are high-qualified, 
jobs  at  ISCO  levels  4-8  require  workers that are medium-qualified; and elementary jobs (ISCO level 
9)  do  not  require  workers  to  have  any  qualification.20,21  As  a  result,  a  high-qualified  worker  will  be 
considered  as  overqualified  if  (s)he has a job at ISCO levels 4-9. A medium qualified worker will be 
considered as overqualified if (s)he has a job at ISCO level 9, and as underqualified if (s)he has a job 
at  ISCO  level  1-3.  A  low-qualified  worker  will  be  considered  as  underqualified  if (s)he has a job at 
ISCO levels 1-8. 

Several  researchers  have  argued  that  overskilling  measures  are  more  significantly  associated  with 
several  economic  variables  than  overqualification  measures  (see  e.g.  Quintini,  2011;  Sloane,  2014; 
Mavromaras  et  al.,  2012;  Budria  and  Moro-Egido,  2014),  and  that  the  former  type  of measures are 
therefore  more  relevant  for  analysis.  However,  qualification-based  data  are  typically  more  easily 
available,  which  explains  why  they  have  been  used  more  frequently, including in our own analysis. 22 
Unfortunately,  the  link  between  skills  and  qualification  mismatch  is  rather  weak.  Quintini  (2011) 
shows  that  only  about  36%  of  overqualified  workers  report  being  overskilled.  Similarly,  only  about 
12%  of  underqualified  workers  report  being  underskilled.  Hence,  a  majority  of  over-  and  under-
qualified  workers  report  that  their  skills  match  their  jobs'  requirements  well.  Flisi  et  al.  (2017)  even 
find  a  negative  correlation  between qualification and skills mismatch based on an analysis of PIAAC 
data. 

A. Underqualification 

Underqualification  measures  the  number  of  low-  and  medium-qualified  workers that hold a job for 
which  they  are  underqualified,  as  a  share  of  total  employment.  Therefore,  underqualification  is  not 
only  determined  by  the  probability  that  a  low-  (or  medium-)  qualified  worker  is  underqualified,  but 
also by the share of low- and medium-qualified  workers in total employment.   

As shown in Figure 5,  Portugal, Malta, and Italy have the highest incidence of underqualification (with 
above  30%  of  total  employment),  most  likely  as  a  result  of  the  general  low  level  of  educational 

19 EUROSTAT has recently initiated work on the development of  indicators of horizontal mismatch based on the EU-LFS, 
but as yet, the available indicators remain  experimental  due to the remaining challenges  in the underlying methodology. 
20  ISCO  1-digit  categories  are  specified  as  follows:  1  -  M anagers;  2  -  Professionals;  3  -  Technicians  and  associate 
professionals;  4  -  Clerical  support  workers;  5  -  Service  and  sales  workers;  6  -  Skilled  agricultural,  forestry  and  fishery 
workers; 7 - Craft and related trades workers; 8 - Plant and machine operators, and assemblers; 9 - Elementary occupations. 

21 ECB (2012: 74) proposes a slightly different classification, notably assuming that jobs at ISCO levels 7-9 do not require 
any qualification.  

22  The  PIAAC  and  the  European  Skills  and  Jobs  Survey,  which  currently  provide  the  best  measures  on  skills (including 
subjective  reports  of  overskilling  and  underskilling)  consist  of  only  one  data  round.  While  it  offers  unique  data on direct 
measures of skills, PIAAC currently covers an incomplete set of European M ember States: BG, HR, HU, LU, LV, M T, PT 
and RO are missing. 

14 

 
                                                                 
attainment  in  the  working-age  population.  The  lowest  incidence  of  underqualification  is  found  in 
Lithuania,  Bulgaria  and  Slovakia  (at  below  15%  of  total  employment).  Of  these,  Lithuania  and 
Slovakia  are  amongst  the  countries  with  the  lowest  share  of  low-qualified  individuals  in  the 
population.   

Figure 5: Underqualification as a % of total employment 

2017

2007

0,7

0,6

0,5

0,4

0,3

0,2

0,1

0

T
L

G
B

K
S

Y
C

L
P

V
L

R
H

E
I

I

U S
H

I
F

E
B

Z
C

E
E

O
R

R
F

L
E

T
A

K
U

U
L

8
2
U
E

E
S

K
D

E
D

9
1
A
E

S
E

L
N

T
I

T
M

T
P

Source: Own calculations based on EU LFS data 

Over  time,  underqualification  has  declined  across  all  EU  Member  States,  with  the  exception  of 
Estonia,  where  it  has  remained  relatively  stable.  Estonia  is  one  of  the EU Member States that have 
made the least progress in reducing low attainment over the past decade. On the other hand, it also has 
one of the lowest shares of low attainment across the EU.  

In  cross-country  comparison  and  after  controlling  for  educational  attainment,  underqualification  is 
positively  correlated  with  the  share  of  jobs  with  high  skills  requirements  (Figure  A2.21).  This  is 
intuitive: 
if  job  skills  requirements  increase  and  educational  attainment  remains  the  same, 
underqualification  will  increase.  As  expected,  it  declines  with  rising  educational  attainment,  when 
taking job  skills  requirements as given (Figure A2.22).  

Underqualification  can  be  the  result  of  skills  shortages,  notably  the  non-availability  of  workers  with 
the  right  qualifications;  but  it  can  as  well  be  the  result  of  inefficient  recruitment  strategies  (e.g. 
employers  not  investing  sufficient  efforts  in  searching  for  the  right  workers,  unattractive  wage 
policies, and so on). Finally, it could as well reflect some measurement error, in the sense that workers 
may have the correct skills for the job, but this is not reflected by their qualification level (e.g. because 
they have relevant work experience or undertook additional  non-formal training  in a different way). 

B. Ov erqualification 

Overqualification  measures  the  number  of  medium-  and  high-qualified  workers  holding  a  job  for 
which  they  are  overqualified,  as  a  share  of  total  employment.  As  a  natural  consequence, 
overqualification  is  not  only  determined  by  the  probability  that  a high- (or medium-) qualified worker 
is overqualified,  but also by the share of high- and medium-qualified  workers in total employment. 

As  shown  in  Figure  6,  the  countries  with  the  highest  level  of  overqualification  is Cyprus with above 
20%  overqualification  in  total  employment,  followed  by  Ireland,  Spain  and  Lithuania  (with  above 

15 

 
 
 
 
18%  overqualification),  all  countries  with  above-average  tertiary  attainment.  The  lowest  level  of 
overqualification  is  found  in  Luxembourg,  Malta  and  Portugal  –  even  if  tertiary  attainment  in 
Luxembourg  is  relatively  high  (higher  than  for  Spain).  Over  the  last  decade,  overqualification  has 
increased significantly  in Greece, Cyprus, Ireland, Latvia and Austria.23  

Figure 6: Overqualification as a % of total employment 

2017

2007

0,25

0,20

0,15

0,10

0,05

0,00

U
L

T
M

T
P

Z
C

T
I

O
R

E
S

U
H

R
H

L
N

I
S

E
D

K
D

K
S

L
P

I
F

9
1
A
E

8
2
U
E

G
B

E
B

R
F

T
A

L
E

E
E

K
U

V
L

T
L

S
E

E
I

Y
C

Source: Own calculations based on EU LFS data 

Overqualification and underqualification are negatively correlated with each other (see  Figure A2.20). 
A  remarkable  exception  is  Spain,  which combines a high rate of overqualification with a high rate of 
underqualification.  Cyprus  and  Ireland  also  have  much  higher  rates  of  underqualification  than  what 
one would expect based on the incidence of overqualification  in these countries.  

Overqualification  declines  with  the  share  of  jobs  with  high  skills  requirements  (when  keeping 
educational  attainment constant) and increases with educational attainment (when controlling for jobs' 
skills  requirements (Figure A2.23 and Figure A2.24).  

Some  have  argued  that  the  expansion  of  higher  education  necessarily  leads  to  higher  rates  of 
overqualification.  Interestingly,  however,  the  positive  correlation  between  overqualification  and 
tertiary  attainment  is  driven  by  those  countries  with  very  low  tertiary  attainment  and  hardly  any 
overqualification  as  a  result.  Among  the  countries  with  high  tertiary  attainment  (for  instance  above 
30%  as  in  Figure  A2.19),  there  no  longer  is a significant correlation between tertiary attainment and 
overqualification. The reason is that in some countries, growing tertiary attainment has coincided with 
a  correspondingly  strong  growth  in  jobs  with  higher  skills  requirements,  while  in  other  countries  the 
growing  number  of  tertiary  graduates  has  seemingly  not  been  absorbed  into  higher  skilled  jobs, 
resulting in overqualification. Different factors may be at play here, including policy-related ones such 
as  quality  assurance  institutions  in  education,  policies  that  promote  the  alignment  between  education 
and  training  systems  and  the  labour  market,  but  also  business  regulations  allowing  for  firm  entry, 
growth, sectoral reallocation and policies  supporting  labour mobility  and innovation. 

To  a  certain  extent, overqualification can be the result of rational decision-making at the individual or 
the  household  level.  For  example,  Frank  (1978)'s  "differential  overqualification  theory"  argues  that 

23  M ost  of  the  increase  in  overqualification  in  Austria  was  due  to a methodological change, where several post -secondary 
qualifications (ISCED level 4) were reclassified as tertiary qualifications (ISCED level 5) as of 2014. 

16 

 
 
 
 
                                                                 
women are often constrained in their  job search by their husband's job choice.24 By facing a "smaller" 
labour  market  than  men,  they  have  a  higher  chance  of  being  overqualified.  These  predictions  have 
been  empirically  tested  by  McGoldrick  and  Robst  (1996)  and  Büchel  and  Battu  (2002).  While  the 
former rejected its validity, the latter did find some evidence that higher commuting distances strongly 
reduce the probability of being overqualified; and that individuals living in rural areas run a higher risk 
of  being  overeducated.  The  argument that spatial mobility constraints lead employees to accept a job 
with  lower  formal  requirements  has  broadly  been  accepted  in  the  economic  literature  (e.g.  Reichelt 
and Abraham, 2017). Furthermore, Sicherman (1991) suggests that some workers may accept jobs for 
which  they  are  overqualified  with  a  view  to  receiving  on-the-job  training  and experience needed for 
future  jobs.  Similarly,  recent  immigrants  or  mobile  workers  might  accept  jobs  with  lower  formal 
requirements  temporarily  whilst  building  up  the  necessary  language  skills  for  a  more  requiring  job. 
Some  employers  may  prefer  to  hire  overqualified workers,25 as they want to promote innovation and 
decentralised  decision-making.26  Individuals  tend  to  undertake  education  not  only  to  expand  their 
labour  market  opportunities,  but  also to some extent for personal development and fulfilment. 27 Some 
reports  of  overqualification  can  also  result  from  measurement  error:  there  is  some  evidence  that 
workers  with  lower  levels  of  skills  than  the average for their qualification level  are more likely to be 
overqualified  for  their  jobs  (LaRochelle-Côté and Hango 2016; Erdsiek 2017; Reichelt and Abraham, 
2017).  

However,  if  overqualification  is  widespread,  it  tends  to  occur  in  environments  where  labour  market 
demand  for  high-qualified  individuals  is  weak.  Furthermore,  it  tends  to  be  even  weaker  for  lower-
qualified  individuals,  suggesting  that  high-qualified  individuals  mostly  take  up  jobs  with  lower 
qualification  requirements  because  there  are  no  other  jobs  available  that  would  allow  them  to  fully 
exploit  their  skills;  and  that  low  labour  market  returns  to  low-  and  medium  qualification  levels 
strengthen incentives for tertiary educational attainment (Figure A2.27). 

2.4. 

ARE MISMATCHES INCREASING OVER TIME IN THE EU? 

Over the last decade, macro-economic skills mismatch has largely followed a countercyclical pattern in 
the EU-28, with a notable uptick over the crisis years (Figure 7, Figure 8). As noted earlier in Section 
2.2,  the  aggregate  picture  hides  diverging  evolutions  at  the  Member  State  level.  On  the one hand, a 
strong  declining  trend  is  observed  in  EU-13  countries.28  On  the  other  hand,  in  EU-15  countries,  a 
growing trend in macro-economic skills mismatch is observed, with a remarkable acceleration over the 
crisis period, and a slight  improvement  after 2014 (see Figure A2.10).  

24 Notably, Frank (1978) argues that women are often "tied stayers" or "tied movers", as they have to stay in a certain area or 
to move to a certain area where their husband has found a job. Note that at the time of his writing, men were typically better 
educated  and  had  higher  earning  power  than  women.  This  difference  is  slowly  fading  away  by  now. Still, optimising job 
decisions in a couple is bound to be subject to more constraints than optimising individual job decisions.   

25 See e.g.  Verhaest et al. (2018) 

26 See e.g.  Blundell et al. (2016) who consider decentralised decision-making  as a skills-biased innovation. 

27 See e.g.  Green and Henseke (2016) 

28 In this paper, EU-13 comprises the EU M ember States that joined the EU as of 2004 (Bulgaria, Cyprus, Czech Republic, 
Estonia,  Croatia,  Hungary,  Lithuania,  Latvia,  M alta,  Poland,  Romania,  Slovenia,  and  Slovakia).  EU-15  comprises  those 
countries which were already EU M ember States before 2004, notably Austria, Belgium, Germany, Denmark, Greece, Spain, 
Finland, France, Ireland, Italy, Luxembourg,  the Netherlands, Portugal, Sweden, and the United Kingdom.  

17 

 
 
                                                                 
Figure 7: Evolution of macro-economic skills 
mismatch  and skills shortages in the EU-28 

Figure 8: Evolution of underqualification and 
overqualification in the EU-28 

Note: The EU-28 aggregate reflects population-weighted averages. In the case of skills shortages, these av erages 
take into account the countries for which data are available in each respective year. 

For  most  of  the  EU-13  countries,  the  high  level  of  mismatch  is  a  legacy  from  transition  (Figure 
A2.10):  the  collapse  of  the  Soviet Union generated macro-economic skills mismatch at a large scale, 
as  economic  production  systems  were  being  thoroughly  disrupted.  Typically,  activation  support  and 
adult  learning  policies  in  these  countries  are  rather  weak,  resulting  in  relatively  little  support  being 
given  to  lower-qualified persons for labour market reintegration. Throughout the process of transition, 
however,  macroeconomic  mismatch  is  gradually declining in EU-13 countries. In contrast, the EU-15 
countries  saw  a  strong  upsurge  in  macroeconomic  mismatch  since  the  crisis  in  2008,  albeit  from  a 
much lower starting level. 

Skills shortages are also observed to move broadly with the economic cycle (Figure 7, Figure 8), and 
particularly  so in EU-13 countries. They increase during economic recoveries, and decline in times of 
economic  slack.  Skills  shortages  reached  a  peak  in  2007  and  a  trough  in  2009.  In  other  words, this 
mismatch  indicator  does  not  seem  to  show  a  consistent  upward  or  downward  structural  trend  over 
time.  

Underqualification (overqualification) is on a long term declining (increasing) trend, mostly driven by 
upward  trends  in  educational  attainment (Figure 8). The reduction in underqualification is driven by a 
decline  of  the  population  share  with  low  educational  attainment;  the  increase  in  overqualification  is 
driven by an increase of the population share with tertiary attainment. In some countries, however, the 
increase  in  tertiary  attainment  is  not  (e.g.  Germany, Estonia, Luxembourg) or hardly (e.g. Denmark) 
significantly  related with overqualification  (See Figure A2.6).29  

3.  RELATIONSHIP  BETWEEN  SKILLS MISMATCHES  AND 

PRODUCTIVITY 

The  question  of  how  human  capital  in  general  and  skills  mismatch  in  particular  is  related  with 
productivity  has  been  explored  previously  in  the  literature  (see  Section  3.1).  Most  of  these  studies 
have  considered  skills  mismatch  in  terms  of  on-the-job  mismatch  (under-  or  over-qualification  or 

29 This is all the more remarkable, as it implies that overqualification among tertiary graduates must decline to allow the share 
of overqualified  in employment to remain stable while tertiary attainment increases.  

18 

 
 
 
 
 
                                                                 
under- or over-skilling); and some studies have looked at skills shortages. The question of how macro-
economic skills mismatch and productivity are related is a relatively novel question, which has not yet 
attracted significant attention in the literature, possibly because the link is more indirect than for other 
dimensions  of skills  mismatch.  

Our  analysis  looks  at  labour  productivity,  a  productivity  measure  that  is  easy  to  calculate,  and 
intuitively  closely  related  to  per  capita  GDP  levels  and  living  standards  (per  capita  GDP  can  be 
decomposed  into  labour  productivity  (GDP  per  hour  worked)  and  labour  resource  utilisation  (hours 
worked per capita)). Figure 9 presents a view of how EU Member States compare against each other in 
terms  of  labour  productivity  levels.  The  best performing Member States are Luxemburg, Ireland and 
Denmark. At the other end of the spectrum are Bulgaria, Romania and Poland. As can be seen, labour 
productivity  increases  were  considerably  larger  over the period 1997-2007 than over the most recent 
decade,  reflecting  the  recent  slowdown  in  productivity  growth.  Figure  10  provides  a  longer  time 
perspective  on  labour  productivity  trends  in  the  EU,  showing  that  labour  productivity  has  been 
considerably  affected by the economic crisis (as of 2009).   

Figure 9: Labour productivity (€ per hour worked) across EU Member States   

90

80

70

60

50

40

30

20

10

0

2017

2007

1997

BG RO PL HU LV LT HR EE CZ PT SK EL MT SI CY ES IT UK AT FI DE FR SE NL BE DK IE LU

Source: AMECO 
Note: Labour productivity is measured as GDP per hour worked, expressed in Euro and in constant 2010 prices.  

19 

 
 
 
 
Figure 10: Trend over time in labour productivity – EU-28 

Note: Labour productivity is measured as GDP per hour worked, expressed in Euro per hour worked in constant 2010 
prices. 

3.1. 

FRAMEWORK FOR ANALYSIS 

This  section  will  describe  the  relationship  between  various  measures  of  skills  mismatch  and 
productivity based on (i) a review of theoretical arguments that can be derived from or have been put 
forward in the literature, (ii) descriptive evidence, and (iii) a set of panel data regressions that allow us 
to exploit both cross-country and cross-time variation. As the regression methods used may not suffice 
to  allow  for  a  causal  interpretation  (e.g.  there  may  be  important  omitted  variables  or  issues  of non-
stationarity),  we  will  interpret  our  results  as  correlations,  and  refrain  from  drawing  overly  strong 
conclusions  on causality.   
Estimates  from  both  random-  and  fixed-effects  panel  regressions  will  be  discussed,  taking  into 
consideration  between-  and  within-country  variation.  The  estimated  country-level  regressions  are  of 
the following  (reduced) form:  

𝐿𝑃𝑖,𝑡 = 𝛽𝐻𝑖,𝑡 + 𝛾𝑆𝐻𝑖,𝑡 + 𝛿𝑋𝑖,𝑡 + 𝑐𝑖 + 𝜀𝑖,𝑡  

with  LP  denoting  labour  productivity  (measured  as  GDP  per  hour  worked,  expressed  in  log  levels 
based  constant  2010 prices), H reflecting the level of human capital in the economy, and  𝑆𝐻 denoting 
skills mismatch in country  i at time t. In some specifications of the regression model, human capital is 
measured by educational attainment of the population at working age (25-64) (notably the % of tertiary 
qualified  individuals  in  the  population);  in  others  it  is  measured  by  the  skills  intensity  of  jobs  in  the 
economy  (notably  the  %  of  high-skilled  jobs  in  total  employment,  according  to  the  ILO  (2007) 
assumption).30  X  comprises  additional  variables  such  as  the  output  gap31  to  control  for  cyclical 

30 Unfortunately, it is not possible to control for educational attainment and the skills intensity of jobs at the same time, as  
these are too strongly correlated to allow for proper estimation. 

31 The output gap is measured as the deviation from potential GDP, in other words, it will be positive in economic upturns 
and negative  in economic  downturns. 

20 

 
 
 
                                                                 
influences,  and  𝑐𝑖  includes  country  fixed  effects.  Dependent  variables  are  expressed  in  logs  and 
independent variables in percentages.32 

This  reduced  form  model  can  be  derived  from a production function expressed in labour productivity 

𝑌
terms  as  in  (
𝐿

)
𝑖𝑡

𝐾
= 𝐴𝑖𝑡 (

𝐿

𝛼
)
𝑖𝑡

or,  in  logs  ln 𝑦𝑖𝑡 = ln 𝐴𝑖𝑡 + 𝛼 ln 𝑘𝑖𝑡,  where 
𝐾

𝑌

𝐿

  denotes  GDP  per  hour 

worked, 𝐴 denotes total factor productivity  (TFP), 

 denotes capital intensity and 𝛼 denotes the factor 

𝐿
elasticity  of  capital.  To  account  for  the  possibility  that  human  capital  (and  as  a  result  also  skills 
mismatch)  is  related  with labour productivity through capital accumulation and through innovation and 
total  factor  productivity,  we  use  the  following  theoretical  framework  to  underpin  our  regression 
analysis:  we  assume  that  both  ln 𝐴𝑖𝑡  and  ln 𝑘𝑖𝑡  depend  on  𝐻𝑖𝑡  (human  capital), 𝑆𝑖𝑡 (skills mismatch) 
and possible  other covariates (𝑋𝑖𝑡). The regressions reported in Annex 4 (regression TFP levels on 𝐻, 𝑆 
and 𝑋) and Annex 5 (regressing capital intensity on 𝐻, 𝑆 and 𝑋)) test the validity of these assumptions 
and  suggest  that  human  capital  and  skills  mismatch  indeed  correlate  with  both  TFP  and  capital 
intensity  in  a  similar  way  as  with  labour  productivity.  As  ln  𝑘𝑖𝑡 = 𝑓1 (𝐻𝑖𝑡,𝑆𝑖𝑡,𝑋𝑖𝑡)  and  ln 𝐴𝑖𝑡 =
𝑓2(𝐻𝑖𝑡,𝑆𝑖𝑡,𝑋𝑖𝑡),  this  implies  that  labour  productivity  can  be  written  as  𝑦𝑖𝑡 = 𝑓(𝐻𝑖𝑡, 𝑆𝑖𝑡,𝑋𝑖𝑡),  the 
empirical  specification  of which is described by the equation above.  

The  literature  examining  the  way  human  capital  accumulation  may  sustain  growth and motivating as 
such  the  positive  correlation  between  labour productivity and the level of education (see e.g.  Schultz, 
1962)  supports  the  setup  of  our  model  above.  Human  capital  can  have  a  positive  impact  on  labour 
productivity  both  by  raising  TFP  and  by  fostering  physical  capital  accumulation.    The  positive 
relationship  between  human  capital  and  TFP  -  implied  in our reduced form model by our assumption 
that ln 𝐴𝑖𝑡 = 𝑓2(𝐻𝑖𝑡,𝑆𝑖𝑡,𝑋𝑖𝑡) - arises from increasing knowledge raising the capacity to innovate and to 
adapt and operate new technologies (e.g. Benhabib and Spiegel, 1994; Miller  and Upadhyay, 2000). 

Complementarities  between  human  and  physical  capital,  which  we  imply  in  our  reduced form model 
by  our  assumption  that  ln  𝑘𝑖𝑡 = 𝑓1 (𝐻𝑖𝑡,𝑆𝑖𝑡,𝑋𝑖𝑡), have also been frequently emphasised. For example, 
Acemoglu  (1996)  argues  that  firms  are  willing  to  invest  more  in  physical  capital  if  the  workforce is 
raising  its  education  and  skills  levels.  Lucas  (1990)  hypothesises  that  lower  stocks  of  human  capital 
restrict  capital  inflow  (and  as  such,  growth)  in  poor  countries.  Barro  (1991)  discusses that if human 
capital  has  positive  spillover  effects  (as  hypothesised  in  Lucas  (1988)),  upskilling  will  lead  to  higher 
rates of investment, both in human and in physical capital, and as a result, to higher per capita growth. 
Goldin  and Katz (1998) conclude that the complementarity between physical and human capital is not 
a  new  phenomenon,  as  it  was  already observed in the early 20th century. It can even be argued that 
human  capital,  in  a  form  or  another,  is  behind  any  source  of  growth,  whether  it  has  been  explicitly 
accounted for or not.33 The complementarities between physical and human capital underlie our choice 
not  to  add  capital  intensity  as  a  control  variable,  as  this  would  possibly  cover  up  some  of  the 
relationship  between  human  capital  (and  skills  mismatch)  and  productivity  in  the  reduced  form 
equation.  

32  Note  that  correcting  the  standard  errors  for  heteroscedasticity  and  within-cluster  correlation at the country  level yields 
similar  results  in  terms  of  significance  in  most  cases.  However,  for  the  on-the-job  skills  mismatch  indicators  significance 
levels decrease. Since robust standard errors may be overestimated with less than 40 clusters (see Angrist and Pischke 2008), 
we conclude that there is still evidence for significant  results. 

33  For instance, in models of embodied technical progress (e.g. Greenwood et al., 1997) the increasing quality of equipment 
goods  could  well  be  interpreted  to  be  the  result  of  the  increasing  degree  of  sophistication  of  engineering.  M odels  of 
expanding product variety (e.g. Grossman and Helpman, 1991) consider that R&D in product innovation uses growing human 
capital to create new and better varieties. 

21 

 
                                                                 
Productivity regressions are run in levels, following the approach taken (amongst others) by McGowan 
and  Andrews  (2015)  and  in  Acemoglu  et  al.  (2014).  Education  (and  therefore  also  skills  mismatch) 
may generally affect economic and productivity growth through different channels (see Lucas, 1988 or 
Barro,  2001),  for  instance  by  increasing  the  innovation  capacity  and  the  general  quality  of  the 
workforce, which also leads to higher absorption of new techniques and technologies. Consequently, a 
country's  level  of  human  capital  (and  therefore  also  skills  mismatches)  can  have  an  impact  on 
productivity  levels  (referred  to  as  a  "level-level"  effect)  and/or  on  the  rate  of productivity growth (a 
"level-growth"  effect).  In  turn,  the  "level-growth"  literature  emphasises  the  interaction  between  the 
human  capital  stock  and  technological  change  (see  for  instance  Benhabib  and  Spiegel  1994).  The 
major difference between the two strands relates to the actual transmission channels at work: while the 
first  emphasises  the  productivity-enhancing effects of schooling (and the associated skill-upgrading of 
the  labour  force),  the  second  strand  stresses  the  adoption  and  innovation  channels  (shifting  the 
boundaries of the production possibilities outwards due to technological progress). Both channels work 
in parallel. Which one tends to be more conducive to growth appears to be country-specific.34 

The  reduced  form  model  described  in  this  section  allows  estimating  the  unconstrained  direct 
relationship between education and mismatch on the one hand, and productivity on the other hand, but 
is  little  informative  about  the  channels  of  these  relationships. To test empirically the channels at play 
in  determining  the  relationship between education and mismatch with productivity, one could resort to 
specific structural models. The education and growth literature has proposed different types of models 
that  integrate  human  capital  in  standard  production  functions  (see  De  la  Fuente  2011  for  a  brief 
review).  These  models  could  be  adjusted  to  incorporate  the  relationship  with  skills  mismatch. 
Developing a structural model is however beyond the scope of this exploratory note and left for future 
work.  

In  what  follows,  we  discuss  for each of the four measures of skills mismatch described in Section  2, 
their  expected  relationship  with  labour  productivity,  some  descriptive  results,  and  then  the  results  of 
our regressions.  

3.2. 

MACRO-ECONOMIC SKILLS MISMATCH 

3.2.1. 

Theoretical arguments 

Macro-economic skills mismatch points at the presence of substantial differences in employment rates 
between  different  qualification  groups.  In  practice,  this  implies  that employment rates of low- and/or 
medium-qualified  individuals  are  low  compared  to  those  of  other  groups,  while  they  make  out  a 
substantial share of the population; in other words, that there is a significant composition bias towards 
high-qualified  workers  in  employment.  Usually  this  is  associated  with  generally  low  aggregate 
employment  rates.  

From  a  theoretical  perspective,  it  is  not  straightforward  to  relate  this  type  of  mismatch  to  labour 
productivity.  In  a  growth  accounting  framework,  it  would  link  more  directly  to labour utilisation than 
to  labour  productivity  (see  for  example  European  Commission,  2008:117).  At  the  same  time,  the 
exclusion of low-qualified workers from employment can raise labour quality and as such raise labour 

34 We tested level-growth and growth-growth relationships but did not find evidence for significant relationships. Note that 
concerning  the  level-level  relationship  one  might  say  that,  since  the  variables  in  question  are  in  levels  with  a  trending 
behaviour - indicating non-stationarity, their relationship may be spurious if there is no co-integrating relationship. However, 
we somewhat control for possible residual non-stationarity by adding educational attainment or occupation indicators, which 
are typically also trending variables.  

22 

 
                                                                 
productivity,  as  high-qualified  workers  are  expected  to  be  more  productive  than  low-  or  medium-
qualified  workers  (see  e.g.  European  Commission,  2008:  44).  Given  the  stronger  link  with  labour 
utilisation  than  with  labour  productivity,  the  relationship  between  macro-economic  skills  mismatch 
and GDP per capita is expected to be negative.  

However,  other  factors  may  be  at  play  simultaneously,  adding  "noise"  to  the  relationship  between 
labour  productivity  and  macro-economic  skills  mismatch.  First  there  is  a  possible  concern  about 
reverse  causality.  Past  literature  has  argued  that  productivity  growth  tends  to  be  associated  with 
aggregate  employment  growth  (see  e.g.  Saltari  and  Travaglini  2009;  Chirinko  1995;  Christiano  et  al 
2003).  If  technological  progress  is  skill-biased35  and  wages  show  downward  rigidities,  productivity 
growth  will  reinforce  the  composition  bias  of  employment  towards  high-qualified  workers,  hence 
increasing  skills  mismatch  (Moreno-Galbis  and  Sneessens  2007).  This  would  strengthen  the  positive 
correlation between productivity and skills mismatch, but lead to an over-estimation of the relationship 
between skills mismatch and productivity.   

In  addition,  there  may  be  some  cyclical  factors  at play. Macro-economic skills mismatch behaves to 
some  extent  countercyclically.  As  low-qualified  employment is more sensitive to the cycle than  high-
qualified  employment,  macro-economic  skills  mismatch  will  increase  during  economic  downturns. 
Economic  studies  that  have  looked  specifically  into  the  cyclicality  of  labour  productivity  find striking 
discrepancies  between  theoretical  predictions  and  empirical  observations.  Notably,  theory  would 
predict  that  in  perfectly  competitive  markets,  profit-maximising  firms  will  produce  according  to  a 
production  function  with  decreasing  marginal  returns.  As  a  result,  during  an  economic  upturn,  firms 
expand  production  and  hire  marginally  less  productive  workers,  with  a  negative  impact  on  average 
labour  productivity.  During  an  economic  downturn,  less  productive workers are fired first, increasing 
average  labour  productivity.36  This  implies  that  labour  productivity  should  show  countercyclical 
movements (see e.g. Aizcorbe 1992).  

Interestingly,  however,  most  economists  have observed that labour productivity moves with the cycle 
instead (see e.g. Baily et al., 2001).37 The most popular explanation for why labour productivity moves 
with the cycle is the labour hoarding hypothesis, which argues that in the presence of transaction costs 
for  hiring  and  firing,  firms  adjust  factor  utilisation  during  downturns  (e.g.  by  letting  employees  work 
less  intensively),  resulting  in  lower  output  production  with  the  same  staff  –  while  the  intensity  of 
factor  utilisation  is  difficult  to  observe  (see  e.g.  Oi,  1962;  Solow,  1964;  Rotemberg  and  Summers, 
1990;  Bernanke  and  Parkinson,  1991;  Burnside,  Eichenbaum  and  Rebelo,  1993,  Basu,  1996;  Wen, 
2004; Barnichon, 2014). Some scholars have suggested as well that a drop in the speed of production 
(and  an  associated  drop  in  employment  at  the  firm  level)  leads  to  a  decline  in  the  degree  of 
specialisation  (as  the  remaining  workers  are  reorganised  along  the  assembly  line),  resulting  in lower 
labour  productivity  (Aizcorbe  1992).  These  theoretical  arguments  would  not  establish  a  causal  link 
between  macro-economic  skills  mismatch  and  productivity,  but  they  could  offer an explanation for a 
possible  negative  correlation  between  productivity  (which  is  procyclical)  and  skills  mismatch  (which 
is  countercyclical,  as  low-skilled  employment  is  more  sensitive  to  the cycle (Maré and Hyslop 2008; 

35 See e.g. Bound and Johnson (1995), Berman et al. (1994); Johnson (1997), M achin and Van Reenen (1998); Akerman et al. 
(2015) 

36  Along  the  same  lines,  Devereux  (2002)  finds  that  new  hires  are  more  qualified during economic downturns than during 
economic upturns; and Pollmann-Schult (2005) provides evidence of crowding out of low-qualified workers during economic 
downturns.   

37  However,  focussing  mainly  on  the  US,  a  number  of  scholars  finds  this  pro-cyclicality  of  labour  productivity  to  have 
become  more  subdued  since  the  early  1980s  (see  for  instance  Blanchard  and  Watson,  1986,  Galí  and  van  Rens,  2014  or 
Stiroh, 2009). 

23 

 
                                                                 
Abraham  and  Haltiwanger  1995;  Saint-Paul  1993).  In  our  regressions,  we  try  to  filter  out  these 
cyclical elements by controlling  for the output gap.  

There  could  as  well  be  some  dynamic impacts on productivity, through different channels. First, long 
unemployment  spells  may  lead  to  human  capital  depreciation  or  skill  loss,  including  because  the 
unemployed  lose  opportunities  to  learn  by  doing  (Martin  and  Rogers,  2000),  leading  to  a  permanent 
loss  of  productivity  and  potential  output  (hysteresis)  (Blanchard  and  Summers,  1986).  On  the  other 
hand,  a  wider  dispersion  in  employment  outcomes  between  workers  of  different  qualifications  may 
increase  the  incentive  to  invest  in  education.  Some  scholars  have  argued  that  economic  downturns 
may reduce incentives to drop out of school prematurely and raise educational attainment as such (see 
e.g.  Dellas  2003;  Alessandrini  et al., 2015). There is some evidence that this has indeed happened in 
the countries where the 2008 economic crisis hit hardest (Quarina 2017; European Commission 2015). 
These  issues  could  as  well  contribute  to  a positive hysteresis with a more skilled labour force, lower 
macro-economic skills  mismatch, and higher productivity  when the economy picks up.  

Finally,  there  is  a  strand  of  economic  literature  arguing  that  recessions  have  positive  impacts  on 
productivity  when  the  crisis  unwinds  and  recovery  takes  hold.  Saint-Paul  (1993)  provides  a  brief 
review  of  these  effects,  including  the  "lame  duck"  effect  (less  productive  firms  being  less  likely  to 
survive  recessions);  "discipline  effects"  (the  recession  would put pressure on firms to reorganise and 
raise productivity); and "intertemporal substitution effects", where firms allocate a larger share of their 
labour  force  to  productivity-improving  activities  (such  as  training  or  reorganisation)  during  a 
recession,  when  the  opportunity  cost  of  doing  so is lower than during upturns. Our analysis however 
focuses  on  contemporary  links  between  mismatch  and  productivity,  without  considering  dynamic 
relationships. 

As a result, our hypothesis on the link  between mismatch and productivity  is as follows:   

Macro-economic skills mismatch is expected to be positively related with productivity, 
after controlling for cyclical elements.   

3.2.2. 

Empirical findings 

The  data  however  show  a  relatively  strong  negative  correlation  between  macro-economic  skills 
mismatch  and  productivity,  both  in  a  cross-section  comparison and when considering variation within 
countries  over time, even after controlling for the output gap (Figure 11). This negative relationship is 
mostly  driven  by  EU-13  countries.  With  only  a  few  exceptions  such  as  Estonia  and  Latvia,  these 
countries  combine  a  relatively  high  level  of  skills  mismatch  with  a  relatively  low  level  of  labour 
productivity  –  but  while  skills  mismatch  is  on  a  structural  downward  trend  (mostly  as  a  result  of 
upskilling), labour productivity is on a structural upward trend as a result of catching up effects. When 
zooming  in  on  EU-15  countries,  we  do  find  the  expected  positive  relationship  between  labour 
productivity  and macro-economic skills  mismatch (Figure 12). 

24 

 
Figure 11: Fixed effects correlation between labour 
productivity and macro-economic skills mismatch 
(all MS), controlling for output  gap (2000-17) 

Figure 12: Fixed effects correlation between labour 
productivity and macro-economic skills mismatch 
(EU-15 only), controlling for output  gap (2000-17) 

Source: Labour productivity data are taken from AMECO and measured as GDP per hour worked, expressed in log 
lev els based on constant 2010 prices. Macro-economic skills mismatch data are based on own calculations drawing on 
LFS data. 
Note: In the Figures abov e, all countries appear 18 times – once for each year included in the sample. 

Regression  results,  controlling  for  cyclical  elements  by  the  output  gap,  educational  attainment  or 
occupation levels and country-specific effects, confirm the observed negative relationship both for the 
cross-sectional  and  the within-country dimensions. They suggest that an increase of 10 ppt in macro-
mismatch38 is associated with a reduction of 19% in labour productivity (or of 23% in the fixed effects 
regression  model).  The  magnitude  of  the  relationship  is  similar  in  size  (but  opposite  in  sign)  to  the 
relationship  between  raising  the  level  of  high-skilled  jobs  or  of tertiary attainment in the economy by 
10  ppt  and  labour  productivity.  The  sign  of  the  output  gap  is  positive,  suggesting  that  labour 
productivity  behaves  as  a  pro-cyclical  variable  (fitting  for  instance  the  labour  hoarding  hypothesis). 
The Hausman test suggests that the fixed effects model is the preferred one, in other words that time -
invariant  country-specific  variables  are  correlated  with  labour  productivity,  and  that  it  is  therefore 
better to control for these in the regression.  

If  the sample is split between EU-15 and EU-13 countries, a positive relationship is revealed for EU-
15  countries,  fitting  more  closely  our  prior  expectations  (either because mismatch ispositively related 
with  productivity  or  because  of skills-biased technological change). In EU-13 countries, however, the 
negative  relationship  remains,  possibly  reflecting  other  time-variant  factors  (omitted  variables)  that 
lead  both  to  reducing  macroeconomic  skills  mismatch  and  improving  labour  productivity. 39  It  is 
beyond the scope of this paper, however, to go deeper into these issues. 

38 A 10 percentage-point difference in macro-mismatch is roughly equivalent to the difference in mismatch between RO and 
UK in 2017 or to the rise observed in ES observed over 2007-17. 

39 Possible contributing factors could be FDI inflows or business regulations supporting firm entry and growth. 

25 

 
 
 
 
 
                                                                 
Table 1a: Random-effects regressions of labour productivity and macro-economic skills mismatch

40 

Table 1b: Fixed-effects regressions of labour productivity and macro-economic skills mismatch 

40 The reported R² value for this and other random-effects regressions in this paper is the overall-R². Note that this does not 
have the same properties as the usual OLS R².   

26 

(1)(2)(3)(4)(5)(6)VARIABLESrerere-EU-15re-EU-15re-EU-13re-EU-13Output gap0.0010.005***0.004**0.008***0.007***0.007***(0.002)(0.001)(0.002)(0.002)(0.002)(0.002)% High-skilled jobs0.019***0.011***0.021***(0.002)(0.002)(0.003)Macro-economic skills mismatch-0.025***-0.017***0.010***0.010***-0.035***-0.019***(0.002)(0.002)(0.003)(0.003)(0.003)(0.003)% Tertiary attainment0.017***0.009***0.021***(0.001)(0.001)(0.002)Constant2.935***3.083***3.147***3.330***2.582***2.511***(0.109)(0.112)(0.076)(0.077)(0.157)(0.150)Observations493489267263226226R-squared0.6080.4360.7350.5310.4720.234Number of geo282815151313Wald chi2258.587437.72981.302159.167243.368313.327Prob>chi20.0000.0000.0000.0000.0000.000Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1(1)(2)(3)(4)(5)(6)VARIABLESfefefe-EU-15fe-EU-15fe-EU-13fe-EU-13Output gap0.0010.005***0.003*0.008***0.007***0.007***(0.002)(0.001)(0.002)(0.002)(0.002)(0.002)% High-skilled jobs0.018***0.008***0.021***(0.002)(0.001)(0.003)Macro-economic skills mismatch-0.024***-0.017***0.009***0.009***-0.035***-0.019***(0.002)(0.002)(0.003)(0.003)(0.003)(0.004)% Tertiary attainment0.017***0.009***0.022***(0.001)(0.001)(0.002)Constant2.956***3.065***3.267***3.321***2.573***2.477***(0.076)(0.050)(0.063)(0.042)(0.138)(0.115)Observations493489267263226226R-squared0.3440.4860.1790.3850.5280.597Number of geo282815151313Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 
  
 
                                                                 
3.3. 

SKILLS SHORTAGES 

3.3.1. 

Theoretical arguments 

Skills  shortages  generally  reflect  the  lack  of  availability  of  (skilled)  labour.  Several  scholars  have 
assessed the relationship between skills shortages and productivity. From a theoretical perspective, one 
would  expect  skills  shortages  to  be  negatively  related  with  productivity,  as  they  may  lead  to  loss of 
production due to unfilled jobs for a certain period of time; or to the recruitment of workers with lower 
skills  or  qualifications  than  what  their  vacancies  would  ideally  require,  resulting  in  underskilling  and 
underqualification (Bennett and McGuinness, 2009). Another argument that is commonly made is that 
skills  shortages  inhibit  investment  and  the adoption of new technologies, reducing productivity growth 
(e.g.  Foley  and  Watts,  1994).  By  raising  labour  costs  (e.g.  because  firms  need  to  spend  more  on 
training,  recruitment  or  wages),  they  could  distort  the  optimal allocation of resources. Some scholars 
have  argued  as well that skills shortages put workers in a better bargaining position, allowing them to 
demand an easier pace at work (Haskel and Martin, 1993a). 41  

Empirical  evidence  on  the  magnitude  of  these relationships has however produced mixed results. On 
the  one  hand,  Forth  and  Mason  (2004)  and  McGuinness  and  Bennett  (2006)  identified  no  clear  link 
between  productivity  and  unfilled  vacancies  based  on  their  analysis  of  firm-level  data.  On  the other 
hand,  Haskel  and  Martin  (1993a)  find  that  skills  shortages  have  reduced  productivity  growth  in  the 
United  Kingdom  by  around  0.7  percentage-points  per  year  over  the  period  1983-86.  Furthermore, 
Nickell  and  Nicolatsis  (1997)  argue  that  a  10%  increase  in  the  number  of  firms  reporting  a  skills 
shortage  leads  to  a  (permanent)  10%  reduction  in  fixed  capital  investment  and  a  (temporary)  4% 
reduction in spending on research and development. Tang and Wang (2005) find a negative impact on 
the  performance  of  small-  and  medium-sized  companies;  and  Bennett  and  McGuinness  (2009)  find, 
after  controlling  for  selection  bias  arising  from the fact that more productive firms are more likely to 
experience skills  shortages, a substantial impact of those shortages on firm-level productivity.   

A  complicating  factor  pertains  to  cyclical  elements:  while  skills  shortages  may  prevail  during 
economic  expansions,  as  more  firms  will  be hiring new workers, we have seen above that economic 
expansions  are  typically  associated  with  higher  productivity.  This  relationship  may  mitigate  the 
observed  negative  relationship  of  skills  shortages  with  labour  productivity  and  underlines  the 
importance of controlling  for the cyclical elements. 

As a result, our hypothesis on the link  between skills shortages and productivity  is as follows:   

Skills shortages are expected to be negatively related with productivity, after controlling 
for cyclical elements.   

3.3.2. 

Empirical findings 

When  we  look  at  the  data,  however,  we  do  not  observable  any  relevant  link  between  labour 
productivity  and  skills  shortages  in  a  cross-sectional  comparison.  The  apparently  negative  link  in 
Figure 13 is entirely driven by an outlier (Hungary) and not statistically significant. In the fixed effects 
correlation  plot  shown  in  Figure  14  (which considers the variation over time within countries), a faint 
(but  significant)  positive  correlation  can  be  observed,  even  after  controlling  for  cyclical  elements; 
driven in particular by EU-15 countries.  

41 On the other hand, a paper by the same authors finds that firms often adapt to shortages by adjusting overtime – especially 
when shortages are perceived as cyclical  - which would have the opposite effect on the pace of work of workers in shortage 
positions (Haskel and M artin, 1993b)  

27 

 
                                                                 
Figure 13: Labour productivity in relation to skills 
shortages, cross-sectional comparison (2017) 

Figure 14: Fixed-effects correlation between 
labour productivity and skills shortages (2000-17) 

Source: Labour productivity data are taken from AMECO and measured as GDP per hour worked, expressed in log 
lev els based on constant 2010 prices. Macro-economic skills mismatch data are based on own calculations 
drawing on LFS data. 
Note: In Figure 14, all countries appear 18 times – once for each year included in the sample. 

Our  best  explanation of this counter-intuitive positive correlation is possible endogeneity as a result of 
omitted  variables  or  even  reverse  causality;  with  countries  who  are  performing  better  in  terms  of 
labour  market  outcomes  (on  a  structural  basis)42  and  have  higher  labour  productivity,  also  exhibiting 
higher  needs  for  high-skilled  labour,  and  running  into  shortages  when  supply  is  insufficient. 43  This 
relationship  seems  to  outweigh  the  potentially  negative  relationship  between  skills  shortages  and 
labour productivity.   

Countries  with  the highest skills shortages in EU-15 are Germany, Sweden, and the United Kingdom. 
These  have  good  labour  market  conditions  in  general  –  not  only  for  high-qualified  workers.  The 
countries  with  the  lowest  skills  shortages  in  EU-15  are  Greece,  Spain,  Italy,  and  Portugal.  These 
combine  low  productivity  with  high  unemployment  among  high-qualified  individuals  (as  well  as 
among  the  general  population).44  As  a  matter  of  fact,  in  none of the countries with the highest skills 
shortages (Hungary, the United Kingdom, Sweden, Poland, Germany,  and Estonia), the unemployment 
rate of high-skilled  workers exceeds 4%.45  

Regression  results  also  provide  evidence  for  a  positive  relationship  between  labour  productivity  and 
skills  shortages.  The  positive  relationship  persists  when  controlling  for  educational  attainment  and 
high-skilled occupations, and, as can be seen from split sample regressions, is mostly driven by results 
for  EU-15  countries.46  The  size  of  the  relationship  is  not  large,  however,  as  an  increase  in  skills 
shortages  by  10  percentage-points  is  associated  with  an  increase  of  about  4%  in labour productivity 

42 Figure A2.18 shows that skills shortages are strongly correlated with the cyclically adjusted unemployment rate measured 
as the non-accelerating  wage rate of unemployment (NAWRU) (Havik et al., 2014). 

43  E.g.  because  there  is  a  time  lag  between  new  needs  arising  in  the  economy  and  adequate  labour  supply  response (e.g. 
because new training programs need to be developed, students need to be trained). 

44 These countries have aggregate unemployment rates above 10% and unemployment rates of at least 8% for high-qualified 
workers. 

45 Interestingly, unemployment of high-qualified  workers has no link with tertiary attainment in the population: while HU, PL 
and DE have less than 30% tertiary attainment and are therefore on the lower side within the EU; UK, SE and EE are amongst 
the EU countries with the highest tertiary attainment (around 40% or more). 

46 The results are impacted by outliers HU and EL; if these outliers are dropped, the results become stronger and more robust 
across different regression  specifications. 

28 

 
 
 
 
                                                                 
(both  in  random  and  fixed-effects  regressions).  These  results  can  also  be  turned around, to say that 
relatively  small  increases  in  labour  productivity  can  already  go  hand  in  hand  with  a  sizeable 
relationship  with the perception of skills  shortages in an economy.  

Table 2a: Random-effects regressions of labour productivity and skills shortages 

29 

(1)(2)(3)(4)(5)(6)VARIABLESrerere-EU-15re-EU-15re-EU-13re-EU-13Output gap-0.0000.005***0.002*0.004***-0.0010.008***(0.002)(0.002)(0.001)(0.001)(0.003)(0.003)% High-skilled jobs0.014***0.005***0.022***(0.002)(0.001)(0.004)Skills shortages0.004***0.0000.004***0.003***0.004*-0.002(0.001)(0.001)(0.001)(0.001)(0.002)(0.002)% Tertiary attainment0.014***0.004***0.025***(0.001)(0.001)(0.002)Constant2.675***2.849***3.466***3.580***1.846***2.065***(0.115)(0.118)(0.074)(0.068)(0.173)(0.102)Observations323322174173149149R-squared0.3620.2130.5960.3850.1640.168Number of geo242413131111Wald chi249.735145.87056.74567.55429.614162.918Prob>chi20.0000.0000.0000.0000.0000.000Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 
 
  
Table 2b: Fixed-effects regressions of labour productivity and skills shortages 

Note  that  our  results  do  not  imply  that  skills shortages have a positive impact on labour productivity. 
Rather,  we  are  not  able  to  isolate  the  potentially  negative  impact  of  skills  shortages  based  on  our 
analysis  at  the  aggregate  level.  Micro-econometric  analyses  based  on  firm-level  data  may  be  more 
adequate  to  measure  this  impact.  The  correct  interpretation  of  our  findings  would  be  that  skills 
shortages tend to occur in an environment of economic expansion  and growing productivity.   

3.4. 

MEASURES OF ON-THE-JOB SKILLS MISMATCH 

We  now 
overqualification.   

turn 

to  our  two  measures  of  on-the-job  skills  mismatch:  underqualification  and 

3.4.1. 

Theoretical arguments 

In  the  existing  literature,  inferences  on  the  productivity  of  workers  have  mostly  been  drawn  from 
observations  on  their  wages  – which are considered as proxies for labour productivity. Workers with 
higher  qualifications  tend  to  fetch  higher  wages  than  workers  with  lower  qualifications.  Wages  also 
tend  to  grow  with  the  occupational  status  of  someone's  job:  managers,  professionals and technicians 
tend to earn more than clerks, sales workers, and  artisans. If wages correctly reflect productivity, this 
implies  that  productivity  is  increasing  with  a  worker's  qualification  level  and  with  the  occupational 
level  of  his/her  job.  Aggregate  data  indeed suggest a positive correlation between labour productivity 
and the share of high-skilled jobs on the one hand (Figure 15) and between labour productivity and the 
share of tertiary attainment on the other hand (Figure 16). 

30 

(1)(2)(3)(4)(5)(6)VARIABLESfefefe-EU-15fe-EU-15fe-EU-13fe-EU-13Output gap-0.0010.005***0.002*0.004***-0.0010.008***(0.002)(0.002)(0.001)(0.001)(0.003)(0.003)% High-skilled jobs0.012***0.004***0.022***(0.002)(0.001)(0.005)Skills shortages0.004***0.0000.004***0.003***0.005**-0.002(0.001)(0.001)(0.001)(0.001)(0.002)(0.002)% Tertiary attainment0.014***0.004***0.025***(0.001)(0.001)(0.002)Constant2.734***2.850***3.523***3.589***1.835***2.060***(0.083)(0.032)(0.054)(0.024)(0.164)(0.047)Observations323322174173149149R-squared0.1300.3260.2640.3020.1720.544Number of geo242413131111Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 
 
Figure 15: Linear fit between productivity and 
employment share of high-skilled occupations 
(2017)  

Figure 16: Linear fit  between productivity and 
qualification level of the population (2017) 

Note: High-skilled jobs are occupations with ISCO 1-digit codes 1-3. Tertiary attainment is measured as the share of 
indiv iduals holding a tertiary degree (ISCED levels 5-8) in age group 25-64. The R²adj-values of the linear models are 
respectiv ely 0.47 and 0.30. 

Countries  with  a  higher  share  of  high-skilled  occupations  typically  also  have  a  higher  labour 
productivity.  The  relationship  between  the  share  of  high-qualified  individuals  in  the  working 
population  and  productivity  is  a  bit  less  strong. In countries such as Lithuania and Cyprus, where, as 
we  will  see  in  subsequent  analysis,  the  degree  of  overqualification  is  relatively  high,  labour 
productivity  is below what one would expect in a country with such a high share of tertiary educated 
workers.  

A. Underqualification 

Underskilling  or  underqualification  implies  that  workers  do  not have the skills or  qualifications, which 
would  allow  them  to  attain  the  highest  possible  productivity  in  their  jobs.  The  argument  that  skilled 
workers are more capable of using new technologies has been made earlier by Katz and Autor (1999), 
Acemoglu  (2002)  and  Link  and  Siegel  (2003).  Acemoglu  and  Zilibotti  (2001)  argue  that  less 
developed  countries,  which  import technologies developed by more advanced countries, are unable to 
achieve  the  same  productivity  levels,  resulting  in  considerable  output  gaps.  They  attribute  this  to 
"skills  scarcity",  implying  that  in  less-developed  countries,  unskilled  workers  perform  the  tasks  that 
are carried out by skilled  workers in developed countries. 

Empirical  studies  on  the  contribution of workers' human capital to productivity find that workers with 
higher  educational  attainment  achieve  higher  sales  per  worker  (e.g.  Abowd  and  Kramaz, 2005) and 
contribute  to  raising  total  factor  productivity  (e.g.  Ilmakunnas  et  al.,  2004).  Based  on  an analysis of 
PIAAC  data,  both  McGowan  and  Andrews  (2015a)  and  Zira  (2016)  find  a  negative  relationship 
between  underskilling  and  firm-level  productivity.  Based  on  linked  employer-employee  panel  data, 
similar  results have been found for underqualification  (Kampelmann and Rycx, 2012; Grunau, 2016).   

The  relationship  between  underqualification  and  aggregate  productivity  depends  on  the  perspective 
taken.  If  one  compares  productivity  of  underqualified  workers  with  productivity  of  workers with the 
right  (higher)  educational  attainment  in  a  similar  job,  it  is  clear  that  underqualification  leads to lower 
aggregate  productivity.  On  the  other  hand,  if  one  compares  the  productivity  level  of  underqualified 
workers  with  the  productivity  level  of  workers  with  similar  educational attainment in a well-matched 
job (in other words, a job with lower skills requirements), the underqualified worker will have a higher 

31 

 
 
 
 
 
labour  productivity  as  jobs  with  higher  skills  requirements  tend  to  be  more  productive.  It  is  also 
important  to  mention  that  underqualification  might  actually  be  a  sign  of the successful labour market 
integration of low-qualified workers, strengthening effective labour resource utilisation, and as such be 
positively related with aggregate output. Figure A2.25  and Figure A2.26 in Annex 2 show the positive 
correlation  between  underqualification  and  employment outcomes for low-qualified workers. See also 
e.g.  Maré  et  al.  (2015)  for  a  paper  considering  the  impact  of  "skills  dilution"  as  a  result  of  strong 
employment  growth for workers with relatively  low skills  on firm-level  productivity  growth. 

B. Ov erqualification 

Overqualification  implies  that workers have a higher qualification level than their job requires, or than 
they  can  use  in  their  jobs.  The  literature  has  identified  several  theoretical  channels  through  which 
overqualification  (or  the  related  –  albeit  not  identical  –  phenomenon  of  overskilling)  can  be  related 
with  productivity  of  workers.  On  the  one  hand,  as  mentioned  in  Section  3.1  human  capital  theory 
predicts  that  education  and  skills  have  a  positive  impact  on productivity  – and this would apply even 
beyond the qualifications or skills that are strictly required for a job (although at a lesser rate). 47 On the 
other  hand,  overqualification  has  been  argued  to  reduce  job  satisfaction  and motivation and increase 
turnover,  reducing  productivity  (Tsang,  1987;  Tsang  et  al.,  1991;  Hersch  1991). 48  Over  time, 
overqualification  may  as  well  lead  to  cognitive  decline  (De  Grip  et  al.,  2008).  These  relationships 
mitigate  the positive relationship  between overqualification  and productivity  at the micro-level.  

Empirical  work  suggests  that  overqualified  workers  typically  earn  more  than  workers  with  a  similar 
job  and  matching  qualifications;  but  less  than  workers  with  a  similar  qualification  level  and  a 
matching  job  (see  e.g.  Sicherman,  1991;  Sloane  et  al.,  1999;  Cohn  and  Ng,  2000;  Dolton and Silles, 
2008;  Levels  et  al.,  2014). Hence, if wages reflect productivity,49 overqualification raises productivity 
compared  to  the  case where a worker would have a lower qualification in the same job; but reduces 
productivity compared to the case where a worker would have the same qualification level but a more 
requiring  job.   

Some researchers have directly assessed the impact of overqualification on firm-level productivity. For 
example, Rycx and co-authors find that overeducation has a positive impact on productivity (implicitly 
controlling  for  jobs'  skills  requirements);  and  in  particular  so  for  firms  with  a  higher  share  of  high-
skilled  jobs,  in high-tech industries, and in a more uncertain economic environment (Kampelmann and 
Rycx, 2012; Mahy et al., 2015).   

47  Note  that  some  scholars  have  argued  against  the  human  capital  theory,  arguing  that  the  positive  correlation  between 
education  and  productivity  reflects  to  a  certain  extent  the  signalling  value  of  education  for  individuals  who already had a 
higher  innate  ability (and hence productivity) before undertaking education (e.g. Arrow, 1973; Spence, 1973; 1974). Brown 
and Sessions (1999) and by Psacharopolous (1979) found some evidence in favour of this hypothesis; however, a substantial 
number  of  studies  refute  these  results  and point at a substantial impact of education on productivity (e.g. Chevalier et al.,  
2004;  Layard  and  Psacharopolous,  1974;  Groot  and  Oosterbeek,  1994;  Kroch and Sjoblom, 1994; Johnes, 1998; Dickson, 
2013).  An interesting argument in this context is made by Pericles Rospigliosi et al. (2014), who argue that most (40 -70%) 
graduate  job  vacancies  are  open  to  graduates of any subject; imply ing that employers are not interested in subject-specific 
knowledge per se (see as well Roberts 2006) as it rapidly becomes outdated; but rather in the fact that they are "specialists  in 
the practice of learning", reflecting a strong ability and willingness to "learn on-the-job". In their view, the propensity to learn 
benefits  graduates (who earn higher wages), employers, as well as economies at large (who benefit from increased resilience 
if the workforce has a higher capacity to adapt to rapid change).   

48 Some counterevidence  is however provided by Büchel (2002). 

49  Human  capital  theory  asserts  that  wage  differentials  reflect  differences  in  productivity  (Becker,  1964).  Labour  market 
regulations and bargaining institutions can however generate a mismatch between productivity and wages in the short run (see 
e.g.  Weeden, 2002). A recent study by Rycx et al. (2015) argues that qualifications have a stronger impact on productivity 
than on wages; as productivity dispersion tends to exceed wage dispersion.  

32 

 
                                                                 
McGowan  and  Andrews  (2015a)  argue  that,  while  firm-level  productivity  may  indeed  increase  with 
overqualification,  there  may  still  be  a  negative  association  between  aggregate  productivity  of  an 
economy  and  overqualification  as  a  result  of  a  suboptimal  allocation  of  resources  (referring  to  the 
"potential  productivity"  higher-skilled  workers  could  have  if  they  were  in  well-matched  jobs).  Their 
main  argument  is  that  if  (less  productive)  firms  recruit  highly  qualified  workers  for  jobs  that  do  not 
require  such  qualifications;  while  other  firms  face  a  shortage  of  or have difficulties accessing highly 
qualified workers for the (more productive) jobs they offer, such misallocation of resources constrains 
entry and expansion of more productive firms and as such reduces aggregate productivity. 50 Potential 
causes  include  policy  variables  such  as rigid labour market regulations, barriers to competition and to 
firm entry and exit, but also structural factors (such as housing policies etc.) leading to low geographic 
labour mobility  (McGowan and Andrews, 2015b). 

The  ensuing  hypotheses  on the sign of the  relationship between on-the-job mismatch and productivity 
are as follows: 

Underqualification  is  expected  to  be  negatively  related  with  productivity,  after 
controlling  for  jobs'  qualification  requirements  in  the  economy  (in which case the 
counterfactual consists of those with the right – and hence higher - qualification in the 
same job).  

Underqualification is expected to be positively related with productivity, after controlling 
for educational attainment in the economy (in which case the counterfactual con siders 
those with similar qualifications in less-requiring jobs).   

Overqualification is expected to be positively related with productivity, when controlling 
for jobs' skills requirements in the economy (in which case the counterfactual consists of 
those with the right – and hence lower - qualification in the same job. 

Overqualification is expected to be negatively related with productivity, when controlling 
for educational attainment in the economy (in which case the counterfactual considers 
those with similar qualifications in more-requiring jobs).  

3.4.2. 

Empirical findings 

Figure  17  shows  that  there  is  a  positive  cross-country  correlation  between  underqualification  and 
labour  productivity.  The  correlation  becomes  even  stronger  when  outliers  such  as  Italy,  Malta  and 
Portugal  are  dropped  from  the  plot.  Again, the relationship seems strongly influenced by divergences 
between EU-15 and EU-13 countries. While the former generally have higher labour productivity, they 
also  have  more  underqualification. The difference in underqualification results from the fact that EU-
13  countries  have  on  average  lower  shares  of  low-qualified  individuals (and higher levels of medium 
attainment).51  Moreover,  those  who  are  low-qualified  are  significantly  less  likely  to  be  employed 
(given  high  levels  of  macro-economic  skills  mismatch  in  EU-13  countries), again reducing the extent 
of underqualification  (as the latter only accounts for those individuals  who hold  a job).  

50  This  argument  is  also used by Berton et al. (2017) as a starting point for their analysis of the impact of deregulation of 
employment protection legislation on skills mismatch and productivity.  

51 The share of individuals with low educational attainment in the New M ember States is 13%, as compared to 20% in 2016 
in the Old M ember States (excluding  Portugal, M alta, Italy and Spain).  

33 

 
 
                                                                 
 
Figure 17: Linear fit  between labour productivity and underqualification (2017)  

Source: Own calculations based on LFS. The R²adj-values of the linear models are respectiv ely 0.06 (all MS included) 
and 0.37 (without PT, MT, and IT). 

Fixed-effects correlations in   

Figure  18  suggest  that,  as  one  would  expect,  the  relationship  between  labour  productivity  and 
underqualification  over  time  is  negative,  once  controls for job skills requirements are included. If one 
controls  for  educational  attainment  instead,  the  relationship  becomes insignificant, suggesting that the 
relationship  between  underqualification  and  labour  productivity  mostly  coincides  with  the  relationship 
between educational attainment and the latter.  

Figure 18: Fixed-effects correlations between labour productivity and underqualification (2000 -17) 

(a) controlling for % high-skilled jobs 

(b) controlling for tertiary attainment 

Note: In the Figures abov e, all countries appear 18 times – once for each year included in the sample. 

As  Figure  19  shows, there is no significant cross-country correlation between labour productivity and 
overqualification.  This  could  be  related  to  the  fact  that  overqualification  relates  positively  to  tertiary 
attainment  (which  relates  positively  with  labour  productivity)  on  the  one  hand,  and  it  relates 

34 

 
 
 
 
 
 
 
 
 
 
negatively  to  the  share  of  high-skilled  jobs  in  the  economy  (which  relates  positively  with  labour 
productivity  as well). 

Figure 19: Linear fit between labour productivity and overqualification (2017)  

Source: Own calculations based on LFS 

We  noted  earlier  the  positive  correlation  between  countries  with  high  tertiary  attainment  and 
overqualification.  Countries  that  combine  high  tertiary  attainment  with  low  overqualification  clearly 
have  a  higher  labour  productivity  on  average  than  those  countries combining high tertiary attainment 
with high  levels of overqualification  (Figure 20). 

Figure 20: The relationship between tertiary attainment,  overqualification and labour productivity (2017)  

The  R²adj-values  of  the linear models are respectively 0.28 (all MS included)  and ± 0 (only MS with 
high  tertiary attainment). 

35 

ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKATBEDKESFIFRIELUNLSEUKCYEEELLTLVSI20304050Tertiary attainment510152025% Overqualified workersLow tert. att.High tert. att. and high LPHigh tert. att. and low LPall MS includedonly MS with high tert. att 
 
 
 
 
 
When  exploiting  within  country  variation  over 
time,  we  find  a  positive  relationship  with 
overqualification  when  controlling  for  job  skills  requirements;  and  a  negative  relationship  when 
controlling  for educational attainment, in line  with what one would expect.  

Figure 21: Fixed- effects correlations between labour productivity and overqualification (2000 -17) 

(a) controlling for job skills requirements 

(b) controlling for educational attainment  

Note: In the Figures abov e, all countries appear 18 times – once for each year included in the sample. 

Regression  results  in  Table  3a  and  3b  confirm  the  expected  relationships  outlined  above,  i.e. 
overqualification  is  positively  correlated  with  labour  productivity  when  controlling  for  occupation 
levels  and  negatively  when  controlling  for  educational  attainment.  In  terms  of  the  sise  of  the 
relationship,  an  increase  of  10  percentage-points  in  overqualification  will  increase labour productivity 
by 24%, if we keep the share of high-skilled jobs equal. Note that this implicitly means that attainment 
is  increased,  while no additional jobs are generated that require a higher degree. If on the other hand 
one  keeps  the  share  of  tertiary  attainment  equal,  however,  an  increase  of  10  percentage-points  in 
overqualification  will  reduce  productivity  by  11%.52  Note  that  this  implicitly  means  that  jobs'  skills 
requirements are reduced.  

The  relationship  between  underqualification  and  productivity  has  the  opposite  sign.  An  increase  in 
underqualification  by  10  percentage-points reduces productivity by 10-12%, when controlling for jobs' 
skills  intensity.  When  controlling  for  attainment,  however,  this  relationship  loses  significance,  even  if 
it  has  the  expected  positive  sign.  This  again  aligns  with  our  earlier  finding  that  the  relationship 
between  underqualification  and  productivity  works  mainly  through  the  channel  of  educational 
attainment.  

52 These results refer to the random-effects regression estimations. The size of the relationship is similar (resp. +25% and -
10%  according  to the fixed-effects regression estimations. 

36 

 
 
 
 
 
 
                                                                 
Table 3a: Random-effects regressions of labour productivity and on-the-job mismatch  indicators 

Table 3b: Fixed-effects regressions of labour productivity and on-the-job mismatch  indicators 

37 

(1)(2)(3)(4)(5)(6)(7)VARIABLESrerererererereOverqualification0.023***-0.012***0.005-0.012**(0.003)(0.004)(0.005)(0.005)Underqualification-0.010***0.002-0.009***0.000(0.001)(0.002)(0.002)(0.002)Output gap0.008***0.008***0.009***0.008***0.009***0.008***0.009***(0.002)(0.001)(0.002)(0.001)(0.002)(0.001)(0.001)% High-skilled jobs0.012***0.009***0.009***(0.002)(0.002)(0.002)Tertiary attainment0.022***0.020***0.022***0.018***(0.002)(0.002)(0.002)(0.001)Constant2.531***2.760***3.167***2.615***3.073***2.745***2.748***(0.113)(0.101)(0.129)(0.133)(0.157)(0.141)(0.119)Observations586581586581586581585R-squared0.1240.3170.0050.2980.0100.3270.217Number of geo28282828282828Wald chi2237.698448.552261.698435.802260.751442.572453.796Prob>chi20.0000.0000.0000.0000.0000.0000.000Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1(1)(2)(3)(4)(5)(6)(7)VARIABLESfefefefefefefeOverqualification0.024***-0.010**0.003-0.011**(0.003)(0.004)(0.005)(0.005)Underqualification-0.012***0.001-0.011***-0.001(0.001)(0.002)(0.002)(0.002)Output gap0.008***0.008***0.009***0.009***0.009***0.008***0.009***(0.002)(0.001)(0.002)(0.001)(0.002)(0.001)(0.001)% High-skilled jobs0.011***0.007***0.007***(0.002)(0.002)(0.002)Tertiary attainment0.021***0.019***0.020***0.018***(0.002)(0.002)(0.002)(0.001)Constant2.593***2.798***3.325***2.728***3.276***2.867***2.790***(0.053)(0.022)(0.096)(0.094)(0.131)(0.110)(0.021)Observations586581586581586581585R-squared0.3030.4500.3400.4440.3410.4500.448Number of geo28282828282828Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 
 
 
 
4.  CONCLUSION  AND POLICY IMPLICATIONS 

In  conclusion,  we  have  provided  theoretical  considerations  on  the  relationship  between  skills 
mismatch and productivity and have shown a correlational analysis thereof. These considerations have 
shown  the  complexity  of  this  relationship  and  the  need  for  further  work  in  terms  of  developing  a 
structural model.  

In  terms  of  skills  mismatch  developments  our  data  suggest  that  certain  types  of  skills mismatch are 
indeed  on  the  rise  in  the  EU,  notably  skills  shortages  (especially  in  those countries with good labour 
market  performances)  and  overqualification  (especially  in  those  countries  where  tertiary  attainment 
has  expanded  considerably,  without  being  accompanied  by  a commensurate rise in high-skilled jobs). 
Other  types  are  on  a  long-term  declining  trend  (e.g.  underqualification)  or  follow  more  complex 
patterns  over  time  (e.g.  macro-economic  skills  mismatch).  Nevertheless,  there  are  significant 
differences  across  EU  Member  States  in  the  levels  of  these  indicators  as well as in their evolutions 
over time.  

In  terms  of  the  relationship  between  skills  mismatch  and  productivity,  our analysis suggests that it is 
not as clear-cut as it may seem.  

Table  presents a schematic overview of our findings.  

Table 4: Overview of our findings 

Skills mismatch indicator 

Expected  relationship 

Empirical  relationship   

Macro-economic skills  mismatch 

(+)  as high macro-economic  skills 
mismatch  is associated with low 
employment  rates of lower  qualified 
individuals and labour productivity 
is expected to be higher if 
employment  is biased towards the 
higher-qualified   

(-)  for the full  sample, possibly due 
to strong correlation with economic 
growth   

(+)  for the EU-15 

Skills  shortages 

(-) 

(+)  possibly due to strong correlation 
with economic growth 

Overqualification 

Underqualification 

(+)  within a given job category,  
(-)  within a given qualification 

(+)  within a given job category,  
(-)  within a given qualification 

(-)  within a given job category,  
(+)  within a given qualification 

(-)  within a given job category,  
(+)  within a given qualification 

In  the  case  of  macro-economic  skills  mismatch  and  skills  shortages,  our  results  contradict  our  prior 
hypotheses  –  potentially  pointing  to  other  factors at  play that may require some further investigation. 
In  the  case  of  macro-economic  skills  mismatch,  the  results  should  not  entirely  surprise us as similar 
inconsistencies  between  what  theory  predicts  under  common  assumptions  have  been  found  in  the 
existing  literature at the level of the cyclical behaviour of productivity, where it has for example been 
explained  by  the  labour  hoarding  hypothesis.  The  hypothesis  that  higher  macro-economic  skills 
mismatch  (i.e.  a  right-skewed  distribution  of  employment  towards  higher  qualified  workers)  is 
correlated with higher productivity  is, however, confirmed for EU-15 countries.  

For  skills  shortages,  while  one  would  theoretically  expect  a  negative  relationship  with  productivity, 
across  countries  and  across  time,  we  mostly  observe  that  those  countries  doing  better  in  terms  of 

38 

 
 
 
 
economic  growth  (beyond  what  is  captured  by  the output gap) generally combine labour productivity 
growth with a higher incidence of skills shortages. This by no means implies that skills shortages are a 
desirable feature. It rather means that they are often an undesirable side effect of an otherwise positive 
phenomenon  (economic  expansion).  To  accurately  analyse  their relationship with productivity, micro-
level analysis based on firm-level data might be more appropriate than the type of macro-level analysis 
we have pursued. 

Finally,  in  the  case  of  on-the-job  mismatch  indicators,  our  macro-level  findings  align  closely  with 
earlier  findings  from  micro-level  studies.  They  imply  that  educational  expansion  is  positively  related 
with  productivity,  but  to  be  exploited  to  its  full  potential,  it  needs  to  be  accompanied  by  a  general 
upgrading  of  jobs  in order to put these higher skills to their best use. Important framework conditions 
are quality assurance in education, policies that promote the alignment between education and training 
systems and the labour market, and a policy setting that encourages the creation of higher value-added 
jobs.     

What options are available to policymakers wanting to address skills mismatch?  

Macro-economic  skills  mismatch  is  typically  caused  by  a  weak  integration  in  the labour market of 
less-skilled  workers,  while  they  still  make  out  a  substantial  share  of  the  population.  Skills  mismatch 
can decline through a reduction in the share of less -skilled workers – e.g. through prevention of 
early  school  leaving,  by  making  available  appropriate  means  to  allow  individuals to upskill and reskill 
themselves  (including  through  work-based  learning)  and  fight  skills  obsolescence  (active  labour 
market policies,  the accessibility  of vocational  training  and higher education for adults and so on).  

When not caused by general labour shortages, skills shortages  often reflect the lack of specific skills 
or  profiles  among  individuals  available  for  work.  To  some  extent,  skills  shortages  may  result  from 
inertia  in  wage  adjustment  (for  certain  types  of  shortages  wage  increases  can  raise  labour  supply) 
and/or  reflect  the  unavoidable  lag  between  emerging  labour  market  demands (e.g. as a result of the 
development  of  new  sectors)  and  the  response  of  labour supply (as education and training programs 
typically  take  a  few  years).  It  may  be solved through market mechanisms. Economists argue indeed 
that the demand side issues such as wage setting practices are also important responses to mismatch 
(Rathelot and van Rens 2017). In view of the important role of wages, also the wider policy context 
such as labour market and welfare policies should be considered. However, to address structural 
skills  shortages,  measures  that  allow  individuals  to  upskill  and  reskill  themselves ,  including 
through  “Upskilling  pathways”53  and  work-based  learning,  are  crucial  here  too.54 In this context, the 
European Commission has been promoting the European Pillar of Social Rights 55 since 2017 – with as 
first  principle  that  "Everyone  [should  have]  the  right  to  quality  and  inclusive  education,  training  and 
life-long  earning  in  order  to  maintain and acquire skills that enable them to participate fully in society 
and  manage  successfully  transitions  in  the  labour  market". Furthermore, the “New Skills Agenda for 
Europe”56,  proposed  by  the  European  Commission  in  2016,  aims  at  improving  training,  skills  and 
support available to people in the EU as well as enhancing the skill matching process and proposes ten 
actions  (“Upskilling  Pathways”  is  one  of  them).  It  includes  dimensions  related  to  improving  skills 
matching,  also  by  developing  labour  market  intelligence  and  by  better aligning skills to labour market 

53  See  EC  (2016)  Council  Recommendation of 19 December 2016 on Upskilling Pathways: New Opportunities for Adults 
(2016/C 484/01). 
54 For a more detailed review of some policy options, see e.g. EP (2015) 
55 
rights_en  
56 See SWD(2016) 195 final. 

https://ec.europa.eu/commission/priorities/deeper-and-fairer-economic-and-monetary-union/european-pillar-social-

See 

39 

 
                                                                 
needs (such as for instance the “Europass”57 framework, the “European Alliance for Apprenticeships 
(EAfA)58”  or  the  “European  multilingual  classification  of  Skills,  Competences,  Qualifications  and 
Occupations  (ESCO)59”).  Labour  market  intelligence  and  skill  governance  are  measures  that 
inform  prospective  students  about  labour  market  outcomes  of  specific  training  programs  (based  for 
instance  on  graduate  tracking  surveys),  reports  on  expected  skills  needs  by  growing  sectors,  the 
involvement  of  social  partners  in  the  development  and  update  of  education  and  training  curricula  or 
effective tools for workforce planning in specific sectors (such as healthcare). Effective immigration 
policies for specific higher skills profiles can also help address skills shortages in a relative short-term. 
60  

When it comes to overqualification, some degree of overqualification can be acceptable. However, if 
overqualification  is  widespread,  persistent,  involuntary,  and  results  from  gaps  in  the  quality  of 
education or from a lack of labour demand (of graduates of a specific type), it is an important reason 
for  policy  concern.  Strategies  to  address  it  include  ensuring  the  quality  and  labour  market 
relevance  of  education  and  training  programs   (e.g.  if  graduates  are  lacking  specific  skills 
indispensable to employers),61 and broader initiatives to promote regional development of skills -
intensive industries 62 and reduce possible barriers to firm entry, exit and growth63 to foster the 
creation  of  jobs  that  can  effectively  exploit  available  human  capital.  Reducing  overqualification  by 
restricting  access  to  tertiary  attainment  may  in  the  end  not be an optimal policy response, given that 
our  results suggest a positive relationship between higher attainment and productivity, even in case of 
overqualification,  where  it  is  attenuated.  Moreover,  upskilling  of  the  population  can  reduce  macro-
economic  skills  mismatch,  address  skills  shortages,  and  reduce  underqualification  and  may  have  a 
positive  relationship  with productivity  and/or output  through these channels as well. 

Likewise,  underqualification  can  be  a  sign  of  successful  labour  market  integration  of  less-skilled 
workers,  for  example  through  non-formal  vocational  training  courses  that are not necessarily leading 
to  higher  formal  qualifications.  It  is  certainly  more  desirable  to  have  underqualified  workers  than  to 
have  high  unemployment  or  inactivity  rates  among  less-skilled  workers.  On  the  other  hand, 
underqualification  can  be  mitigated  by  reducing  the  share  of  low-qualified  workers  in  the 
population  by  preventing  early  school  leaving  and  providing  adequate  "Upskilling  pathways"  (see 
above).  A  considerable  share  of  the  underqualified  are  workers  that  left  school  without  at  least  an 
upper  secondary  school  degree.  However,  upper  secondary  education  is  nowadays  generally 
considered  as  the  minimum  qualification  for  a  successful  integration  in  the  labour  market  and  in 
society more generally. Educational requirements can be expected to strengthen further due to ongoing 
societal  trends  of  globalisation,  skill-biased technological change, and the need to raise productivity to 
counteract  demographic  challenges  in  Europe.  This  is  attested  by  the  observation  that  employment 
expansion  in  occupations  with  higher  skills  requirements  has  outpaced  employment  growth  in  other 
occupations over recent decades.  

57 See SWD(2016) 320 final 
58 See https://ec.europa.eu/social/main.jsp?catId=1147  
59 See https://ec.europa.eu/esco/portal/home  
60 See European M igration Network, EM N (2015) 
61  For  example,  Erdsiek  (2017)  finds  that  overqualification persistence is partly attributable to ability and choice of study 
field.  The  negative  impact  of  ability  on  the  probability  of  overqualification  has  been  highlighted  before  by  Büchel  and 
Pollmann-Schult  (2004) and Chevalier  and Lindley (2009). Erdsiek (2014) has found social capital to play a role as well.   
62 For example by strengthening local knowledge  conditions (Roper et al., 2017)  
63 See e.g. M cGowan and Andrews (2015b) on firm entry and exit regulations and mismatch. There are different reasons why 
barriers to firm growth may constrain the knowledge intensity of the economy. For example, large firms may invest more in 
R&D due to their easier access to external capital (Czarnitzki and Hottenrott, 2011). M oreover, growth options can mitigate 
the R&D investment dampening effect of uncertainty (Czarnitzki and Toole, 2013) 

40 

 
                                                                 
Note  that  in  this  paper,  we  have  not  looked  explicitly at  horizontal skills mismatches, notably that  a 
mismatch can exist between workers’ fields of study and the field of their jobs. The main reason is we 
have  not  been  able  to  identify  a  robust  indicator  of  horizontal  mismatch  that  covers  all  or  most  EU 
countries,  and  can  be  calculated  on  a  yearly  basis.  As  mentioned  above  in  Section  2.3,  horizontal 
mismatch  is  more difficult to measure than vertical mismatch (Sellami et al., 2018), and arguably less 
problematic  from  a  policy  perspective  as  horizontally  mismatched  workers  are  less  likely  to  suffer 
from  a  wage  penalty  than  overqualified  workers  (Montt,  2017).  Yet,  in  terms  of  policy implications, 
reskilling  workers  is  often  as  important  as  upskilling  them.  Reskilling  often  involves  shorter  and 
more  flexible  training  programs,  and  is  of  particular  importance  for  workers  who  have  left  initial 
education and are seeking to update their skills or reorient their skills set towards new demands of the 
labour market. 

41 

 
 
 
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48 

 
 
 
ANNEX I:  DATA SOURCES AND COVERAGE 

The  table  below  presents  in  more detail the sources and coverage of the data used in the analysis in 
this paper.  

Table A1.1: Data sources and coverage 

Indicator 

Data source  

Description 

Productivity 

Labour productivity  (LP) 
– GDP per hour worked   

AMECO  

(no sectoral 
information) 

Definition:  GDP in Euros and constant prices at 2010 
reference level per hours worked  

Frequency: Annual data 

Member states covered: all 

Time coverage: EU15: 1970-2018;  EU12: 1995-2018 

Total factor productivity 
(TFP ) 

DG ECFIN-B3 
based on 
AMECO (no 
sectoral 
information) 

Definition:  TFP is calculated as the Solow residual 
i.e. the residual of a log-linearised  Cobb-Douglas 
production  function  with assumed factor elasticities of 
0.65 for labour and 0.35 for capital and employment 
is expressed in terms of hours worked. European 
Economy Forecast 

Frequency: Annual data 

Member states covered:  all 

Time coverage: EU15: 1961-2018;  EU12: 1995-2018 

Capital intensity 

AMECO  

(no sectoral 
information) 

Definition:  Net capital stock in 1000 Euros and 
constant prices at 2010 reference level per hours 
worked  

Frequency: Annual data 

Member states covered: all 

Time coverage: EU15: 1970-2018;  EU12: 1995-2018 

Mismatch variables 

Macro-economic skills 
mismatch 
(mismatch_macro) – 
based on employment 
rates  

DG EMPL 
calculations 
based on LFS 
data 

Definition:  relative dispersion  of employment  rates 
(Eurostat code: lfsa_ergaed) by educational 
attainment, weighted by their importance in  the 
working  age population  (Eurostat code: lfsq_pgaed) 

Frequency: quarterly or annual data 

Member States covered: all 

Time coverage: as of 2007q2  for HR, as of 2005q1 
for all others; as of 1998q1 for some; up to 2017Q4 
(last update 07.12.2017) 

Macro-economic skills 
mismatch 
(mismatch_macro) – 

DG EMPL 
calculations 
based on LFS 

Definition:  relative dispersion  of unemployment  rates 
(Eurostat code: lfsa_urgaed) by educational 
attainment, weighted by their importance in the active 

49 

 
 
 
 
 
based on unemployment 
rates 

data 

population  (Eurostat code: lfsq_agaed) 

Frequency: quarterly or annual data 

Member States covered: all 

Time coverage: as of 2007q2  for HR, as of 2005q1 
for all others; as of 1998q1 for some; up to 2017Q4 
(last update 07.12.2017) 

Overqualification, 
normative measure 
(overqual) 

DG EMPL 
calculations 
based on LFS 
data 

Definition:  Share of overqualified  medium-  and high-
qualified  workers (as percentage of employment) 
(Eurostat code for employment  by occupation: 
lfsa_egised; Eurostat code for population  by 
educational attainment level:  edat_lfse_03) 

Frequency: quarterly data 

Member States covered: all 

Time coverage: as of around 2005 for quarterly data; 
as of around 2000 for annual data. 

Underqualification, 
normative measure 
(underqual) 

DG EMPL 
calculations 
based on LFS 
data 

Definition:  Share of underqualified  low- and medium-
qualified  workers (as percentage of employment) 
(Eurostat code for employment  by occupation: 
lfsa_egised; Eurostat code for population  by 
educational attainment level:  edat_lfse_03) 

Frequency: quarterly data 

Member States covered: all 

Time coverage: as of around 2005 for quarterly data; 
as of around 2000 for annual data. 

Skills  shortages – 
industry 

Business & 
Consumer 
Surveys; Eurostat 

Definition:  % companies in industry  sector reporting 
that labour is a factor constraining  their production 
(Eurostat code: ei_bsin_q_r2) 

Frequency: Quarterly data 

Member States covered: all  except for IE  

Time coverage: for some MS as of 1982;  as of 1996 
for all except for 5 (IE, HR, CY, LV, MT), for all 
except IE as of 2008 

Skills  shortages – 
services 

Business & 
Consumer 
Surveys; Eurostat 

Definition:  % companies in services sector reporting 
that labour is a factor constraining  their production 
(Eurostat code: ei_bsse_q_r2)  

Frequency: Quarterly data 

Member States covered: all  except for IE and LU 

Time coverage: as of around 2003 

Skills  shortages – 
construction 

Business & 
Consumer 
Surveys; Eurostat 

Definition:  % companies in construction  sector 
reporting that labour is a factor constraining  their 
production  (Eurostat code: ei_bsse_q_r2) 

Frequency: Monthly  data 

50 

 
Skills  shortages (LS_tot)  Business & 

Consumer 
Surveys; Eurostat 
and AMECO 

Possible control variables  

Educational  attainment 
(ISCED_58, ISCED_02) 

EU-LFS 

Share of occupations 
requiring  high  skills 
(ISCO_high) 

EU-LFS 

Output gap (OG) 

DG ECFIN-B3  
calculations 
based on 
AMECO data 

Member States covered: all  except for IE 

Time coverage: for some MS already as of 1985. At 
EU level:  as of 1993 

Weighted average of the three sectoral skills 
shortages indicators  above. Weights are constructed 
based on sectoral gross value added figures in 2010 
prices from AMECO. 

Definition:  % of 25-64 year old population  with 
tertiary educational attainment (as measured by 
ISCED64 levels 5-8) or with low educational 
attainment (as measured by ISCED levels 0-2) 
(Eurostat code for population  by educational 
attainment level:  edat_lfse_03) 

Frequency: Annual data 

Member states covered: all 

Time coverage: EU15: around 1992-2017;  EU12: 
around 2000-2017 

Definition:  % of employed population  with an 
occupation requiring  tertiary educational attainment 
according to ILO (2007), notably  occupations in 
ISCO levels 1-3. (Eurostat code for employment  by 
occupation:  lfsa_egised) 

Frequency: Annual data 

Member states covered: all 

Time coverage: EU15: around 1992-2017;  EU12: 
around 2000-2017 

Definition:  % difference between actual GDP and 
potential  GDP (for calculations of potential  GDP see 
Havik et al. 2014)  

Frequency: Annual data 

Member states covered: all 

Time coverage: EU15: around 1965-2018;  EU12: 
around 1997-2018 

64 http://uis.unesco.org/en/topic/international-standard-classification-education-isced  

51 

 
 
 
 
                                                                 
ANNEX II: DESCRIPTIVE STATISTICS AND GRAPHS 

Table A2.1: Summary statistics 

52 

ObsMeanSt. Dev.Min.Max.Log(Labour productivity)10523.2670.6371.3224.353Log(Total factor productivity)1065-7.2850.377-8.329-6.375Log(Capital intensity)1054-2.7770.789-4.849-1.632Output gap1133-0.2262.966-15.81016.868Overqualification5909.1504.4440.00022.763Underqualification59031.66112.15810.07173.794Macro-economic skills mismatch52118.3645.2024.30733.307Skills shortages3477.5496.5580.16236.800% High-skilled jobs60037.2647.28916.24862.834% Medium-skilled jobs60053.2437.09930.71977.477% Low-skilled jobs6009.4932.5344.59118.015% Tertiary attainment58824.5228.9825.40045.900% Upper secondary attainment59246.88015.6389.50076.900% Lower attainment59228.52716.9385.20082.200 
 
 
Table A2.2. Correlation table 

53 

Log(Labour productivity)1,00Log(Total factor productivity)0,87*1,000,00Log(Capital intensity)0,89*0,89*1,000,000,00Output gap0,010,07*-0,07*1,00-0,82-0,02-0,02Overqualification-0,03-0,01-0,02-0,10*1,00-0,45-0,73-0,60-0,02Underqualification0,23*0,16*0,25*0,08*-0,65*1,000,000,000,00-0,040,00Macro-economic skills mismatch-0,46*-0,40*-0,46*-0,050,14*-0,49*1,000,000,000,00-0,300,000,00Skills shortages0,060,12*-0,040,55*0,13*-0,25*-0,071,00-0,29-0,03-0,510,00-0,020,00-0,20% High-skilled jobs0,62*0,68*0,57*-0,050,18*-0,27*-0,080,28*1,000,000,000,00-0,190,000,00-0,080,00% Medium-skilled jobs-0,61*-0,69*-0,54*0,03-0,31*0,24*0,12*-0,25*-0,94*1,000,000,000,00-0,450,000,00-0,010,000,00% Low-skilled jobs-0,09*-0,02-0,14*0,070,35*0,13*-0,10*-0,11-0,25*-0,10*1,00-0,03-0,710,00-0,100,000,00-0,02-0,050,00-0,01% Tertiary attainment0,47*0,48*0,43*-0,12*0,69*-0,52*-0,17*0,27*0,70*-0,73*0,041,000,000,000,000,000,000,000,000,000,000,00-0,36% Upper secondary attainment-0,42*-0,36*-0,43*0,020,09*-0,68*0,46*0,090,010,13*-0,39*-0,14*1,000,000,000,00-0,57-0,040,000,00-0,10-0,890,000,000,00% Lower attainment0,16*0,10*0,18*0,04-0,44*0,90*-0,35*-0,28*-0,37*0,26*0,34*-0,40*-0,85*1,000,00-0,020,00-0,300,000,000,000,000,000,000,000,000,00UnderqualificationLog (Labour prod.)Log (Total factor prod.)Log (Capital intensity)Output gapOverqualification% Upper secondary att.% Lower att.Macro-economic skills mismatchSkills shortages% High-skilled jobs% Medium-skilled jobs% Low-skilled jobs% Tertiary att. 
 
Figure A2.1 Trends in labour productivity across the European Union 

Note: Labour productivity is expressed as logarithm (GDP in EUR (2010 prices) per hour worked). 

3.53.63.73.83.93.63.73.83.941.41.61.822.233.13.23.322.533.53.63.73.83.93.83.944.14.21.522.5333.13.23.33.43.453.53.553.43.63.843.63.842.42.52.62.72.82.42.52.62.72.833.544.53.43.53.63.71.522.534.154.24.254.34.351.522.532.82.933.13.23.63.73.83.9422.22.42.62.82.852.92.9531.822.22.43.63.73.83.9433.13.23.322.533.43.63.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 productivity 
 
 
 
 
Figure A2.2 Trends in total factor productivity and capital intensity  across the European Union 

Note: Total factor productivity is expressed as lograrithm. Capital intensity is expressed as logarithm (1000 EUR (2010 
prices) per hour worked). 

55 

-2.2-2.1-2-1.9-1.8-2.2-2.1-2-1.9-4.8-4.6-4.4-4.2-4-3.8-3.2-3-2.8-2.6-3.4-3.3-3.2-3.1-3-2.9-2.4-2.3-2.2-2.1-2-2.1-2-1.9-1.8-4.5-4-3.5-3-2.7-2.6-2.5-2.4-2.3-2.6-2.4-2.2-2-2.3-2.2-2.1-2-1.9-2.2-2.1-2-1.9-1.8-4-3.8-3.6-3.4-3.2-4-3.8-3.6-3.4-2.4-2.2-2-1.8-2.4-2.3-2.2-2.1-4.5-4-3.5-1.95-1.9-1.85-1.8-1.75-4.8-4.6-4.4-4.2-3.5-3.4-3.3-3.2-2.15-2.1-2.05-2-1.95-1.9-4.4-4.2-4-3.8-3.6-3.4-3.4-3.2-3-2.8-2.6-4.8-4.6-4.4-4.2-4-3.8-2-1.9-1.8-1.7-1.6-3.6-3.4-3.2-3-4-3.8-3.6-3.4-2.6-2.5-2.4-2.3-2.2-7.15-7.1-7.05-7-6.95-6.9-6.85-6.8-6.75-8.3-8.2-8.1-8-7.9-7.25-7.2-7.15-7.1-7.8-7.7-7.6-7.5-7.4-7.1-7-6.9-6.8-6.8-6.75-6.7-6.65-6.6-8-7.5-7.6-7.5-7.4-7.3-7.2-7.18-7.16-7.14-7.12-7.2-7.1-7-6.9-6.8-7-6.95-6.9-6.85-6.8-7.7-7.6-7.5-7.4-7.8-7.7-7.6-7.5-7.4-7.2-7-6.8-6.6-7.1-7.05-7-8.2-8-7.8-7.6-7.4-6.55-6.5-6.45-6.4-6.35-8.2-8-7.8-7.6-7.4-7.6-7.5-7.4-7.3-7.2-6.95-6.9-6.85-6.8-6.75-8-7.9-7.8-7.7-7.6-7.55-7.5-7.45-7.4-8.4-8.2-8-7.8-7.2-7-6.8-7.5-7.4-7.3-7.2-7.8-7.6-7.4-7.2-7.2-7.1-7-6.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 factor productivity (lhs)Capital intensity (rhs) 
 
 
 
Figure A2.3 Trends in the output gap across the European Union 

56 

-202-2024-1001020-10-505-505-4-2024-4-2024-1001020-15-10-505-10-505-10-505-4-2024-505-505-505-505-10-50510-505-10010-2024-4-202-4-202-4-2024-50510-505-50510-50510-4-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 gap 
 
Figure A2.4 Trends in jobs' skills requirements and tertiary attainment  across the European Union 

57 

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% High-skilled jobs (lhs)Tertiary attainment (rhs) 
 
Figure A2.5 Trends in macro-economic skills mismatch  and skills shortages across the European Union 

58 

0510155101520250510150510152005101520246810120510152025024612345101520468101214051015010203040012340510152025051015202505101520010203011.522.533.5051015051015202505101520051015205101520251314151623.52424.52525.520253035141618161820221214161810151015202512141618201214161820161718191718192019202122232025303518202224192021221520253017.51818.519152025161820221112131418202224265101510152025121416181618202220253081012141990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKMacro-economic skills mismatch (lhs)Skills shortages (rhs) 
 
Figure A2.6 Trends in overqualification and underqualification across the European Union 

59 

253035402025303540101520253015202530352022242628302628303226283032343618192021222320304050603035404550552025303540253035401520253015202530352025303540454045505560101214161820253035404550152025304050607080303540451520253035505560657075202530354045262830323420253035401520253025303540455101510121481012141015202556788.599.51010.578910141618205101551015206810125101556789468105101520246812141618123451012141602464681068101202464681067892468107891011510151990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKOverqualification (lhs)Underqualification (rhs) 
 
 
 
Additional graphs on macro-economic skills mismatch 

Figure A2.7 Fixed-effects correlation between macro-economic skills mismatch  and employment in the 
construction sector (2000-17) 

Figure A2.8 Fixed-effects correlation between macro-economic skills mismatch  and employment in 
high-skilled occupations (2000-17) 

60 

ESESSKSKESESMTESSKIEBGESIEPTPTPTIESEPTPLBGLTSEPTEESKSECYBGHRCYHRMTBGIECYIESESKBGSEHRITEEELESFIFIELLTIEHRSEPTEESIEEEECYFIROELLVFIBGLVLTLVELLVIEIEFRSIUKROEEROMTFISIEEROLTELDKHUNLHRSICZELDKSILTPLLVSEROITELITDKFRSIUKDKHUPLNLIEBECZHUBECYLTSKLVCYNLRONLCZLVLVUKMTLUITBEFIRODKPLBEESHUFRUKBEBENLSINLCZCZPLBGROLTLTLUDKBEROUKCZSIDEHUNLPLLVCZSKFRITROPLELLUHRUKFISILUATLTPLCZCZCZMTLULTDKDEUKNLDESILVDEUKDKLUSICZFRITSEEEPLHUHULUATATITATSIATPTATUKATATDKPLDEDEITDECZITATITUKATATUKNLDEPLLTDECZATLUNLNLDKEEBEMTSKDKROITNLDEEELUFRLUDEFIBEFRLUHUHULTDEATFRSIHULVFRROMTFRDKHUHUATBEDEPLUKCYMTITEEHUBEBEHUEEESROFRUKBGLVMTLUBEFRNLSEFIHUFRNLDKFRDKLTBEROMTBGMTBEUKELHULUELBEHUEEROFRFIUKEELTNLCZIEITFIUKPLPTELESMTPLFILUBEBGLVEEITFIHUFIFIITCYLVIEITSKBGITCZCYBGFIBESESIDKCZIENLMTELBGITCYELSIELROPTUKDKBGMTPLDKEESKELSKESPLCYUKHRNLPTSKPTDKNLELPTPLHRPTSEMTMTCZPTSKSEBGFIPLPTPTELELPTCYFILTIESKSKSECYCZPTSEESLVSKESSISESKSIBGSKSEBGHRHRSEHRCYESIESILVESBGLVIELTROROESEEESIEEELTIEES-10-505Macro-economic skills mismatch-4-2024Employment in constructioncoef = -1.0448912, se = .09880151, t = -10.58LULULULULUSKUKSKEESKSKUKSKITATELSEITATITCYITBGSIUKELPTCYELSIHUATPTSKATLVHUPTFRFRBGSELVFIROSIMTPTPTUKESEECYROFILTBGESUKSEBEFRUKBGELHRESBGROPTHRPTFRLVMTLTHUCZMTBEIEESBEESELPTITFRESBGNLITIEROITNLEESESIITSEBGSESESEESPTSIDKUKCZITSIEEITFIROITNLUKCZBGHRCZBGBGBEDEMTCZPLBGCZBGIEHUPLDKIEFRFIELPLCYCZFIATIENLUKHRLTDKCZPLDKLTDEBEELDEMTLTLTEEIENLBEMTLVATSEPLMTCZCZBENLNLDKPLMTLVEEDENLSEELPLFRIEFILVLTDKELLTLVIEPLNLPLIEDEHUDEHRUKCZPLCYPLPLHRFIATDKDEPLFIFIFIDKSEFRBENLFIROHUNLHUHUSIROLVSIEELVMTELPTPTATDEDKIENLHRHUHUDKDEATEEDKIESKMTATEESIATBEROUKLTCZCYIEDKELHUDKROIECYPTLVHRPLCYLVLVDKSKDECYLTESIEBGBEFINLBEEEROROROBECYHUFIBELTSKHUEERONLDEESEESEBEPLDKDKDKESDKLTBEEEFIHUSKROESLTDELTBESIATSIESMTBGESHULTNLLTROLVEEDEESEELVSISISIROEECYMTESMTLVHUROLTBECZLVEEFRATIENLFIELHRSECZSIROPLESMTELCZSIHUCYFRHUPLSIESFIFRPTFIFILUSINLFREEESFRIELUSKSKHRMTATFRCZATITCZCYNLATBEPLFRLUELSKHRELCYLVSEMTATSKCZELELELATUKPTUKUKUKSKITSESKSKIEBGITUKSKLULUUKPTLUUKITITPTPTPTITLUSEBGITSELUBGBGLU-6-4-2024Macro-economic skills mismatch-10-50510% High-skilled jobscoef = .20635715, se = .0416681, t = 4.95 
 
 
 
 
 
 
Figure A2.9 Fixed- effects correlation between macro-economic skills mismatch and the share of tertiary 
graduates in the working age population (2000-17) 

Source: Own calculations based on Kiss and Vandeplas (2015), LFS  

Note: The fixed-effects correlation reflects an added-value plot of the considered v ariable based on a regression with 
independent variables: the employment share in agriculture, in construction, and in serv ices, country dummies. For 
Figure A2.7 and Figure A2.8 Fixed-effects correlation between macro-economic skills mismatch and employment in 
high-skilled occupations the regression also includes the share of tertiary graduates in the working age population; for 
Figure A2.9 Fixed- effects correlation between macro-economic skills mismatch and the share of tertiary graduates in 
the working age population includes the employment share in high-skilled occupations instead. The share of tertiary 
graduates and of high-skilled occupations could not be included in the same regression due to their high degree of 
multicollinearity. 

61 

LULULUNLLUNLSENLCYIECYLUBGUKSEEEATATBGEEATUKCYBGPLIEUKPLEELTFRFRUKSIBELVSEELUKBGLTUKBEATELIEATROATATCZROFRBGDKBEPLSELVATDKCZMTDKLTELFIUKUKESPLNLESIEIEELDEESPTLVDEATBGBEFRUKSKCZATROEECZPLPTFIPLFILTBGSILVCZESFRCZESMTFRLTBGCZCZHUSKHUPTSIBGSIEEPLCZLVSECZESMTDKCZFIHRITSKFIFICYROITPTELESIESKPTIEDESIFIBENLELDKHRESESITNLSKLTSISEDKSIFIIEHUSELTBELVFIBESEROPLHRHUMTMTHUUKPLITFISESKLVBESKEEHUDKSKSIROPLPTEEPTSESILTSKFIHRHRHUELDKITEEHULTDKDELUHUESMTPTBEDEIEROROLVFRNLSIHUITITHRIEPLSKROSIMTHUITPTSECYDEBEMTITPTLVFRITFRPTHUSKHUSIDKLVROEEROITELDEDECYMTDKMTNLMTITHRITPTITCYELCZELSISKPLMTFRITROHRITFIRONLPTLUITLTBEELHUHRLTFICYDEROHUDKSEEEHRLVDKRODEROSKESITELDESKSIBGELCYLVCZDEPLPTMTBESKEEMTDKPTDEBGEEFILVSISKHRPTUKBEHUBEPTELBGEEEESEMTLTMTELBEBGHUCYBECYIEESHUDKELFRLTEEESSKESSKLVESIEESLUESCZFIDKNLBEPTLVNLLTELELLVNLCYLTIEDKIENLNLPLBGEEUKLTLUSIDKSINLESNLEELVCZEEROPLNLFRFIFIIEUKROFRPLFRNLLTFRSEFILUCYCYBGSEUKIESICZBEPLUKBGIEUKCZBGUKBGLUSEPLLUUKLUCZSEATATATATLU-6-4-2024Macro-economic skills mismatch-10-50510Tertiary attainmentcoef = -.04952987, se = .03604722, t = -1.37 
 
 
 
 
 
 
 
 
 
Figure A2.10 Broad trends in macro-economic skills mismatch  for EU-13 and EU-15 countries 

Macro-economic skills mismatch reflects weaknesses in labour resource utilisation. This is reflected in 
the  figures  below  through  a  negative  correlation  (in  cross-country  comparison)  between  macro-
economic  skills  mismatch  and  the  aggregate  employment  rate,  and  a  positive  one  between  macro-
economic  skills  mismatch  and  the  cyclically  adjusted  unemployment  rate  measured  as  the  non-
accelerating wage rate of unemployment (NAWRU) (Havik et al. 2014). 

Figure A2.11 Linear fit between macro-economic 
skills mismatch and aggregate employment rate, 
all Member States (2017) 

Figure A2.12 Linear fit between macro-economic 
skills mismatch and aggregate employment rate, 
excl. ES, PT, EL (2017) 

Note: R² adj of linear regression= 0.40 

Note: R² adj of linear regression= 0.62 

62 

152025303520002005201020152000200520102015EU-13EU-15Based on employmentBased on unemploymentMacro-economic skills mismatchyearGraphs by OLDMSATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUK10152025Macro-economic skills mismatch50556065707580Aggregate employment rateATBEBGCYCZDEDKEEFIFRHRHUIEITLTLULVMTNLPLROSESISKUK10152025Macro-economic skills mismatch556065707580Aggregate employment rate 
 
 
 
 
 
 
 
 
Figure A2.13 Linear fit between macro-economic 
skills mismatch and NAWRU, all Member States 
(2017) 

Figure A2.14 Linear fit between macro-economic 
skills mismatch and NAWRU, excluding ES, PT, EL 
(2017) 

Note: R² adj of linear regression= 0.09 

Note: R² adj of linear regression= 0.27 

63 

ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUK10152025Macro-economic skills mismatch051015NAWRUATBEBGCYCZDEDKEEFIFRHRHUIEITLTLULVMTNLPLROSESISKUK10152025Macro-economic skills mismatch24681012NAWRU 
 
 
 
 
Additional graphs on skills shortages 

Figure A2.15 Linear fit between composite 
measure of skills shortages and unemployment 
among low-qualified individuals (2017) 

Figure A2.16 Linear fit between composite 
measure of skills shortages and unemployment 
among medium-qualified individuals (2017) 

Note: R² adj of linear regression ± 0 

Note: R² adj of linear regression = 0.43 

Figure A2.17 Linear fit between composite 
measure of skills shortages and unemployment 
among high-qualified individuals (2017) 

Figure A2.18 Linear fit between composite 
measure of skills shortages and the NAWRU 
(2017) 

Note: R² adj of linear regression = 0.50 

Note: R² adj of linear regression = 0.37 

Source: Own calculations based on European Business Survey and AMECO 

64 

ATBECYCZDEDKEEESFIFRHRITLTLVNLPLPTSESISKUK0510152025Skills shortages5101520253035Unemployment of low-qualified individualsATBECYCZDEDKEEESFIFRHRITLTLVNLPLPTSESISKUK0510152025Skills shortages05101520Unemployment of medium-qualified individualsATBECYCZDEDKEEESFIFRHRITLTLVNLPLPTSESISKUK0510152025Skills shortages246810Unemployment of high-qualified individualsATBECYCZDEDKEEESFIFRHRITLTLVNLPLPTSESISKUK0510152025Skills shortages051015NAWRU 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Additional graphs on on-the-job mismatch 

Figure A2.19 Cross-country correlation between tertiary attainment  and overqualification (2017)  

Note: "High" tertiary attainment is defined as exceeding a share of 30% of the population in age group 25-64. 
Source: EC own elaborations based on EUROSTAT-LFS data 

Figure A2.20 Linear fit between overqualification and underqualification (2017)  

Source: Own calculations based on Eurostat-LFS data 
Note: R² adj. = 0.23 

65 

ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKATBECYDKEEELESFIFRIELTLULVNLSESIUK20304050Tertiary attainment510152025% Overqualified workersLow tert. att.High tert. att.all MS includedonly MS with high tert. attATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUK510152025Overqualification1020304050Underqualification 
 
 
 
 
 
 
Figure A2.21 Linear fit between underqualification 
and ISCO after controlling for ISCED (2017) 

Figure A2.22 Linear fit between underqualification 
and ISCED after controlling for ISCO (2017) 

Figure A2.23 Linear fit between overqualification 
and ISCO after controlling for ISCED (2017) 

Figure A2.24 Linear fit between overqualification 
and ISCED after controlling for ISCO (2017) 

66 

CYESELROBGIELTSKHUFIPLLVEEPTATHRUKDKBECZSIFRNLITSEDEMTLU-1001020Underqualification-10-5051015% High-skilled jobscoef = .62488042, se = .27821557, t = 2.25ITMTDECZLUHRPTHUSKROSIPLFRATNLLVSEDKBGBEEEUKELFILTESIECY-100102030Underqualification-10-5051015Tertiary attainmentcoef = -.68368019, se = .25672068, t = -2.66CYESELROBGIELTSKHUFIPLLVEEPTATHRUKDKBECZSIFRNLITSEDEMTLU-10-50510Overqualification-10-5051015% High-skilled jobscoef = -.53896913, se = .07631833, t = -7.06ITMTDECZLUHRPTHUSKROSIPLFRATNLLVSEDKBGBEEEUKELFILTESIECY-50510Overqualification-10-5051015Tertiary attainmentcoef = .63152549, se = .07042199, t = 8.97 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure A2.25 Linear fit between underqualification 
and employment rates of low-qualified (2017) 

Figure A2.26 Linear fit between underqualification 
and unemployment rates of low-qualified (2017) 

Note: R² adj of linear regression= 0.57 

Note: R² adj of linear regression= 0.12 

Figure A2.27 Linear fit between overqualification and the difference (in percentage-points) between 
unemployment rates for medium and for high-qualified individuals (2017) 

Note: R² adj of linear regression= 0.20. Source: Eurostat, based on LFS 

67 

ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUK1020304050Underqualification2030405060Employment rate low-qualifiedATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUK1020304050Underqualification51015202530Unemployment rate low-qualifiedATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUK510152025Overqualification02468Difference (ppt) in unemployment rates between medium and high-qualified 
 
 
 
 
 
 
 
 
 
ANNEX III: THEORITICAL APPROACHES TO MODELLING THE 
RELATIONSHIP BETWEEN SKILLS MISMATCH AND PRODUCTIVITY 

The literature has proposed several approaches that integrate human capital in production functions for 
growth  accounting,  or  as  a  basis  for  the  development  of  more  structural  models.  These  approaches 
typically  start out from a simple Cobb-Douglas production  function  framework: 

(1−𝛼) 

𝛼𝐿𝑖𝑡

𝑌𝑖𝑡 = 𝐴𝑖𝑡𝐾𝑖𝑡
with  𝑌𝑖𝑡,𝐾𝑖𝑡, 𝐿𝑖𝑡  and  𝐴𝑖𝑡  denoting  respectively  GDP,  the  capital  stock,  hours worked and total factor 
productivity (TFP) in country 𝑖 and time 𝑡. 𝛼 denotes the factor elasticity of capital. If both sides of the 
equation are multiplied  by 𝐿𝑖𝑡
𝐾
𝑌
= 𝐴𝑖𝑡 (
(
𝐿

−1, we obtain 

  (2) 

  (1) 

𝐿

𝛼
)
𝑖𝑡

)
𝑖𝑡

or, in logs: 

ln 𝑦𝑖𝑡 = ln 𝐴𝑖𝑡 + 𝛼 ln 𝑘𝑖𝑡  
  (3) 
implying that labour productivity (𝑦 = Y/L) is a function of TFP 65 (𝐴𝑖𝑡) and capital intensity (k = K/L). 
Human capital has been integrated in these equations in several ways, including by considering it as a 
labour-augmenting  factor in the following  way: 

𝛼(𝐻𝑖𝑡𝐿𝑖𝑡)(1−𝛼) such that ln 𝑦𝑖𝑡 = ln 𝐴𝑖𝑡 + (1 − 𝛼) ln 𝐻𝑖𝑡 + 𝛼 ln 𝑘𝑖𝑡 

𝑌𝑖𝑡 = 𝐴𝑖𝑡𝐾𝑖𝑡
where 𝐻𝑖𝑡 denotes human capital, or as a separate production factor (next to capital and labour):66 

 (4) 

𝛽𝐿𝑖𝑡

𝛼𝐻𝑖𝑡

(1−𝛼−𝛽) such that ln 𝑦𝑖𝑡 = ln 𝐴𝑖𝑡 + 𝛽 ln ℎ𝑖𝑡 + 𝛼 ln 𝑘𝑖𝑡 

𝑌𝑖𝑡 = 𝐴𝑖𝑡𝐾𝑖𝑡
where ℎ𝑖𝑡 = 𝐻𝑖𝑡/𝐿 is a measure of skills intensity. In both of these cases, human capital accumulation 
has  a  direct  positive  relationship  with  labour  productivity  as  well  as  an  indirect  positive  relationship 
through its complementary with physical capital accumulation. Using more generalised versions of the 
production  function  such  as  a  constant  elasticity  of  substitution  (CES)  specification  allows  assuming 
even stronger complementarities between human and physical capital accumulation (see e.g. Duffy et 
al., 2004). TFP – denoted by 𝐴𝑖𝑡 – is assumed to be exogenous, in the sense of not being affected by 
human capital accumulation. 

  (5) 

Alternatively,  human  capital  can  also  enter  more  simply  as  a  determinant  of total factor productivity 
(or TFP growth – as in De La Fuente (2011) and in Benhabib and Spiegel (1994)67), in which case the 
complementarity  between human and physical capital accumulation  becomes less explicit.   

Skills  mismatch  could  be  incorporated in these models as a factor correcting human capital (upwards 
or downwards, depending on  the type of mismatch and the measure of human capital), for instance as 
follows  (based on equation (5) above): 

𝛼(𝑆𝐻)𝑖𝑡
𝑌𝑖𝑡 = 𝐴𝑖𝑡𝐾𝑖𝑡
resulting  in labour productivity 

(𝐿𝑖𝑡)1−𝛼−𝛽   

𝛽

  (6) 

65 Total factor productivity measures the efficiency of all inputs used in the economy by capturing the growth contribution of 
disembodied65  technological  change  (stemming  e.g.  from  general  knowledge,  spillovers  or  organisational  structures  (OECD 
2001)). It is harder to measure than labour productivity and as it is typically measured as a residual of a production funct ion 
estimate  it includes measurement errors. The choice of the productivity measure to be used depends on the purpose of the 
analysis and on data availability. 

66 See e.g.  M ankiw et al. (1992) or De La Fuente (2011) 

67  Note  that  the  equations  below  represent  a  simplified  and  at  the  same  time  slightly  extended  version  of  Benhabib  and 
Spiegel's  (1994) model.  

68 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
                                                                 
𝑌
(
𝐿

)
𝑖𝑡

𝐾
= 𝐴𝑖𝑡 (

𝐿

𝛽

𝛼
)

𝑆𝐻
)
(

𝐿

𝑖𝑡

𝑖𝑡

or, in logs,  ln 𝑦𝑖𝑡 = ln 𝐴𝑖𝑡 + 𝛼 ln 𝑘𝑖𝑡 + 𝛽 (ln ℎ𝑖𝑡 + ln 𝑠𝑖𝑡) 

  (7) 

In  this  setting  𝑠𝑖𝑡  (measured  as  overqualification)  could  act  as  a  factor  correcting 𝐻𝑖𝑡  (measured  as 
educational  attainment)  downwards  as  those  with  high  qualifications  are  in  a  job that does not allow 
them to make full  use of their skills.   

Given that we prefer not to impose many restrictions on our model at this stage, we take a more ad-hoc 
approach in this paper (see Section 3.1).  

ANNEX IV: EMPIRICAL RELATIONSHIPS BETWEEN TOTAL FACTOR 
PRODUCTIVITY AND SKILLS MISMATCH 

A4.1   Macro-economic  skills mismatch 

Table A4.1: Random-effects regressions of TFP and macro-economic skills mismatch 

69 

(1)(2)(3)(4)(5)(6)VARIABLESrerere-EU-15re-EU-15re-EU-13re-EU-13Output gap0.007***0.010***0.006***0.008***0.011***0.011***(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)% High-skilled jobs0.013***0.006***0.018***(0.001)(0.001)(0.002)Macro-economic skills mismatch-0.011***-0.006***0.0020.003**-0.015***-0.002(0.001)(0.001)(0.002)(0.001)(0.002)(0.002)% Tertiary attainment0.011***0.006***0.018***(0.001)(0.000)(0.001)Constant-7.514***-7.384***-7.220***-7.125***-7.858***-7.909***(0.061)(0.067)(0.045)(0.055)(0.092)(0.092)Observations465461252248213213R-squared0.6440.3900.7580.4980.4430.106Number of geo282815151313Wald chi2309.822527.98098.885216.242248.414404.609Prob>chi20.0000.0000.0000.0000.0000.000Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 
   
 
 
 
 
Table A4.1: Fixed-effects regressions of TFP and macro-economic skills mismatch 

A4.2   Skills shortages 

Table A4.2: Random-effects regressions of TFP and skills shortages 

70 

(1)(2)(3)(4)(5)(6)VARIABLESfefefe-EU-15fe-EU-15fe-EU-13fe-EU-13Output gap0.007***0.010***0.005***0.008***0.011***0.011***(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)% High-skilled jobs0.013***0.005***0.018***(0.001)(0.001)(0.002)Macro-economic skills mismatch-0.011***-0.006***0.0020.003**-0.015***-0.001(0.001)(0.001)(0.002)(0.001)(0.002)(0.002)% Tertiary attainment0.011***0.006***0.018***(0.001)(0.000)(0.001)Constant-7.501***-7.396***-7.196***-7.138***-7.863***-7.927***(0.042)(0.027)(0.032)(0.021)(0.080)(0.062)Observations465461252248213213R-squared0.4050.5490.2820.4840.5520.674Number of geo282815151313Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1(1)(2)(3)(4)(5)(6)VARIABLESrerere-EU-15re-EU-15re-EU-13re-EU-13Output gap0.004***0.007***0.006***0.008***0.0020.006***(0.001)(0.001)(0.000)(0.000)(0.002)(0.001)% High-skilled jobs0.008***0.005***0.009***(0.001)(0.001)(0.002)Skills shortages0.002***-0.0000.001***0.0000.003**0.001(0.001)(0.001)(0.000)(0.000)(0.001)(0.001)% Tertiary attainment0.008***0.004***0.013***(0.001)(0.000)(0.001)Constant-7.524***-7.426***-7.182***-7.070***-7.859***-7.811***(0.059)(0.065)(0.041)(0.042)(0.088)(0.059)Observations300299161160139139R-squared0.3960.2250.6490.4390.1700.113Number of geo242413131111Wald chi2106.594310.487326.365422.04832.578254.872Prob>chi20.0000.0000.0000.0000.0000.000Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 
 
 
 
 
 
 
 
Table A4.3: Fixed-effects regressions of TFP and skills shortages 

A4.3.  Measures of on-the-job  skilss mismatch 

Table A4.4: Random-effects regressions of TFP and on-the-job skills mismatch 

71 

(1)(2)(3)(4)(5)(6)VARIABLESfefefe-EU-15fe-EU-15fe-EU-13fe-EU-13Output gap0.004***0.007***0.006***0.008***0.0020.006***(0.001)(0.001)(0.000)(0.000)(0.002)(0.001)% High-skilled jobs0.007***0.005***0.009***(0.001)(0.001)(0.002)Skills shortages0.002***0.0000.001***0.0000.003***0.001(0.001)(0.001)(0.000)(0.000)(0.001)(0.001)% Tertiary attainment0.008***0.004***0.013***(0.001)(0.000)(0.001)Constant-7.497***-7.428***-7.171***-7.068***-7.846***-7.812***(0.040)(0.015)(0.023)(0.010)(0.080)(0.021)Observations300299161160139139R-squared0.2830.5310.7220.7620.2020.670Number of geo242413131111Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1(1)(2)(3)(4)(5)(6)(7)VARIABLESrerererererereOverqualification0.016***-0.0010.002-0.006*(0.002)(0.002)(0.003)(0.003)Underqualification-0.008***-0.002**-0.007***-0.003***(0.001)(0.001)(0.001)(0.001)Output gap0.011***0.011***0.011***0.011***0.011***0.011***0.011***(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)% High-skilled jobs0.009***0.007***0.007***(0.001)(0.001)(0.001)Tertiary attainment0.012***0.010***0.011***0.012***(0.001)(0.001)(0.001)(0.001)Constant-7.708***-7.517***-7.238***-7.387***-7.269***-7.328***-7.521***(0.063)(0.059)(0.073)(0.081)(0.089)(0.085)(0.070)Observations560554560554560554558R-squared0.1830.2570.0280.1280.0310.1460.238Number of geo28282828282828Wald chi2385.781541.428450.714554.327449.216554.611579.835Prob>chi20.0000.0000.0000.0000.0000.0000.000Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 
 
 
 
 
 
 
Table A4.5: Fixed-effects regressions of TFP and on-the-job skills mismatch 

72 

(1)(2)(3)(4)(5)(6)(7)VARIABLESfefefefefefefeOverqualification0.017***-0.0000.001-0.005*(0.002)(0.002)(0.003)(0.003)Underqualification-0.008***-0.003***-0.008***-0.004***(0.001)(0.001)(0.001)(0.001)Output gap0.011***0.011***0.011***0.011***0.011***0.011***0.011***(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)% High-skilled jobs0.008***0.005***0.005***(0.001)(0.001)(0.001)Tertiary attainment0.012***0.009***0.010***0.012***(0.001)(0.001)(0.001)(0.001)Constant-7.673***-7.497***-7.147***-7.322***-7.159***-7.258***-7.499***(0.031)(0.013)(0.054)(0.056)(0.073)(0.065)(0.013)Observations560554560554560554558R-squared0.4260.5110.4830.5200.4830.5240.521Number of geo28282828282828Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 
 
 
 
 
 
 
 
 
 
 
 
 
 
ANNEX V: EMPIRICAL RELATIONSHIPS BETWEEN CAPITAL INTENSITY 
AND SKILLS MISMATCH 

A5.1  Macro-economic  skills mismatch 

Table A4.6: Random-effects regressions of capital intensity and macro-economic skills mismatch 

Table A4.7: Fixed-effects regressions of capital intensity and macro-economic skills mismatch 

73 

(1)(2)(3)(4)(5)(6)VARIABLESrerere-EU-15re-EU-15re-EU-13re-EU-13Output gap-0.013***-0.008***-0.013***-0.006***-0.005**-0.006***(0.002)(0.001)(0.002)(0.001)(0.002)(0.002)% High-skilled jobs0.031***0.014***0.044***(0.002)(0.001)(0.003)Macro-economic skills mismatch-0.022***-0.011***0.012***0.014***-0.028***-0.005(0.002)(0.002)(0.003)(0.002)(0.003)(0.004)% Tertiary attainment0.023***0.013***0.034***(0.001)(0.001)(0.002)Constant-3.595***-3.196***-2.874***-2.696***-4.559***-4.286***(0.134)(0.140)(0.071)(0.068)(0.191)(0.196)Observations493489267263226226R-squared0.5330.3350.6460.4890.3490.094Number of geo282815151313Wald chi2557.230727.363456.4221128.614471.314432.642Prob>chi20.0000.0000.0000.0000.0000.000Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1(1)(2)(3)(4)(5)(6)VARIABLESfefefe-EU-15fe-EU-15fe-EU-13fe-EU-13Output gap-0.013***-0.008***-0.013***-0.007***-0.005**-0.006***(0.002)(0.001)(0.001)(0.001)(0.002)(0.002)% High-skilled jobs0.031***0.013***0.044***(0.002)(0.001)(0.003)Macro-economic skills mismatch-0.022***-0.011***0.012***0.013***-0.028***-0.004(0.002)(0.002)(0.003)(0.002)(0.003)(0.004)% Tertiary attainment0.023***0.013***0.035***(0.001)(0.001)(0.002)Constant-3.589***-3.213***-2.854***-2.711***-4.578***-4.311***(0.076)(0.052)(0.055)(0.030)(0.131)(0.121)Observations493489267263226226R-squared0.5440.6140.6420.8200.6880.673Number of geo282815151313Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 
 
  
 
A5.2  Skills shortages 68 

Table A4.8: Random-effects regressions of capital intensity and skills shortages 

Table A4.9: Fixed-effects regressions of capital intensity and skills shortages 

68 Data are missing  for IE and LU (in EU-15) and BG and M T (in EU-13) 

74 

(1)(2)(3)(4)(5)(6)VARIABLESrerere-EU-15re-EU-15re-EU-13re-EU-13Output gap-0.018***-0.011***-0.014***-0.010***-0.018***-0.011***(0.002)(0.002)(0.001)(0.001)(0.003)(0.003)% High-skilled jobs0.023***0.009***0.039***(0.002)(0.001)(0.004)Skills shortages0.005***0.0000.003***0.001*0.007***0.000(0.001)(0.001)(0.001)(0.000)(0.002)(0.002)% Tertiary attainment0.018***0.009***0.028***(0.001)(0.001)(0.002)Constant-3.665***-3.248***-2.497***-2.360***-4.985***-4.217***(0.141)(0.158)(0.072)(0.071)(0.211)(0.173)Observations323322174173149149R-squared0.3220.1790.3340.1300.1330.101Number of geo242413131111Wald chi2281.622445.349335.309849.113189.507309.230Prob>chi20.0000.0000.0000.0000.0000.000Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1(1)(2)(3)(4)(5)(6)VARIABLESfefefe-EU-15fe-EU-15fe-EU-13fe-EU-13Output gap-0.018***-0.011***-0.014***-0.010***-0.018***-0.011***(0.002)(0.002)(0.001)(0.001)(0.003)(0.003)% High-skilled jobs0.022***0.009***0.040***(0.002)(0.001)(0.004)Skills shortages0.005***0.0000.003***0.001*0.007***0.000(0.001)(0.001)(0.001)(0.000)(0.002)(0.002)% Tertiary attainment0.018***0.008***0.028***(0.001)(0.001)(0.002)Constant-3.647***-3.247***-2.476***-2.349***-5.019***-4.222***(0.081)(0.032)(0.043)(0.014)(0.150)(0.050)Observations323322174173149149R-squared0.4950.6000.6840.8450.5830.693Number of geo242413131111Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 
 
 
 
                                                                 
A5.3  Measures of on-the-job  skills mismatch  

Table A4.10: Random-effects regressions of capital intensity  and on-the-job skills mismatch 

Table A4.11: Fixed-effects regressions of capital intensity  and on-the-job skills mismatch

75 

(1)(2)(3)(4)(5)(6)(7)VARIABLESrerererererereOverqualification0.033***0.011**0.016***0.008(0.003)(0.004)(0.005)(0.005)Underqualification-0.013***-0.004**-0.008***-0.002(0.001)(0.002)(0.002)(0.002)Output gap-0.004***-0.005***-0.004***-0.005***-0.004**-0.005***-0.006***(0.002)(0.002)(0.002)(0.002)(0.002)(0.002)(0.002)% High-skilled jobs0.017***0.015***0.015***(0.002)(0.002)(0.002)Tertiary attainment0.019***0.019***0.018***0.022***(0.002)(0.002)(0.002)(0.001)Constant-3.788***-3.401***-2.982***-3.183***-3.284***-3.282***-3.376***(0.143)(0.134)(0.152)(0.158)(0.176)(0.167)(0.152)Observations588582588582588582586R-squared0.1090.1220.0100.0940.0270.0920.182Number of geo28282828282828Wald chi2565.571700.657566.444690.329583.689690.882688.948Prob>chi20.0000.0000.0000.0000.0000.0000.000Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1(1)(2)(3)(4)(5)(6)(7)VARIABLESfefefefefefefeOverqualification0.034***0.012***0.015***0.008(0.003)(0.004)(0.005)(0.005)Underqualification-0.014***-0.005***-0.010***-0.003*(0.001)(0.002)(0.002)(0.002)Output gap-0.004***-0.005***-0.004**-0.005***-0.004**-0.004***-0.006***(0.002)(0.002)(0.002)(0.002)(0.002)(0.002)(0.002)% High-skilled jobs0.017***0.013***0.013***(0.002)(0.002)(0.002)Tertiary attainment0.018***0.017***0.016***0.022***(0.002)(0.002)(0.002)(0.001)Constant-3.725***-3.355***-2.849***-3.062***-3.125***-3.160***-3.325***(0.054)(0.023)(0.099)(0.100)(0.134)(0.117)(0.022)Observations588582588582588582586R-squared0.5080.5640.5200.5640.5280.5660.553Number of geo28282828282828Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1 
 
 
 
 
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