Labour market and wage developments in Europe : annual review 2019.
Date: 2019-11-10
Institutions: Directorate-General for Employment, Social Affairs and Inclusion
Subjects: EU employment policy, Europe, euro area, labour market, labour mobility, report, wage cost, wage determination

Labour Market and 
Wage Developments 
in Europe

Annual Review 2019

Social Europe

LEGAL NOTICE 

Manuscript completed in November 2019

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for the use that might be made of the following information. More information on the European Union is 
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Luxembourg: Publications Office of the European Union, 2019 

PDF   ISBN: 978-92-76-11010-1  
© European Union, 2019  

    ISSN: 2443-6771           doi10.2767/399955  

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Reuse is authorised provided the source is acknowledged. The reuse policy of European Commission 
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ACKNOWLEDGEMENTS 

This report was prepared in the Directorate-General of Employment, Social Affairs and Inclusion under 
the supervision of Joost Korte (Director-General), Barbara Kauffmann (Director, Employment and Social 
Governance Directorate) and Nathalie Darnaut (Head of Unit – Country Reform). 

The production of the report was coordinated by Alfonso Arpaia (Deputy Head of Unit – Country Reform). 

The  main  contributors  were  Daniel  Alonso,  Katerina  Aristodemou,  Alfonso  Arpaia,  Marco  Cantalupi, 
Matteo Duiella, Andra Dusu, Giulia Filippeschi, Anita Halász, Natasja Hofstee, Áron Kiss, Kristine van 
Herck, Marie-Luise  Rud, Gilles Thirion and  Alkistis  Zavakou.  Adam Kowalski provided statistical and 
editorial assistance. Emilie Meurs provided secretarial support.  

The  report  has  benefited  from  useful  comments  and  suggestions  received  from  Olivier  Bontout,    Gelu 
Calacean,  Lucie  Davoine,  Fabio  Domanico,  Stefano  Filauro,  Federico  Lucidi,  Michael  Horgan,  Bettina 
Kromen,  Fabiana  Pierini,  Jorg  Peschner,  Marcello  Ranucci,  Sarah  Schinazi,  Frank  Siebern-Thomas, 
Loukas Stemitsiotis in the Directorate-General for Employment, Social Affairs and Inclusion and Reuben 
Borg and Anneleen Vandeplas in the Directorate-General for Economic and Financial Affairs. 

Comments  on 
EMPL-A3-UNIT@ec.europa.eu. 

the 

report  would  be  gratefully 

received  at 

the 

following  email  address:  

is  written  by  staff  of 

This  report 
for  Employment,  Social  Affairs  
and  Inclusion.  Views  expressed  in  the  report  do  not  necessarily  represent  the  official  position  
of the European Commission. 

the  Directorate-General 

ii 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Foreword 

Foreword 

This report shows a positive situation. The European labour market has so far proven to 
be  strong  and  resilient  to  the  weakening  of  the  economy.  Standing  at  241  million,  the 
number  of  people  employed  in  the  EU  is  at  its  highest  level  ever,  and  the  EU’s 
unemployment  rate  stands  at 
level  ever  recorded.  Moreover,  high 
unemployment  countries  experienced  high  employment  growth,  further  decreasing 
divergences  across  countries.  Wage  growth  has  been  higher  in  in  Central  and  Eastern 
European countries, contributing to wage convergence across the EU. 

lowest 

its 

Since  2008,  reforms  have  extended  the  coverage  and  increased  the  level  of  social 
benefits. These reforms have reinforced their effect on reducing poverty. Reforms of the 
tax  and  benefit  systems  enacted  after  2008  have  lowered  inequality  in  almost  all 
countries  and  the  proportion  of  people  with  income  below  the  poverty  line.  Yet, 
challenges remain as the poorest have experienced a worsening of their living conditions 
in about half of the Member States.  

Building skills, creating equal opportunities and access to the labour market and social protection is more important 
than ever before to foster social inclusion. Investment in education and training remains the key policy action for the 
years  to  come  to  respond  to  the  challenges  of  the  ongoing  job  polarisation  resulting  from  automation  and 
digitalisation. 

Marianne Thyssen 
Commissioner for Employment, 
Social Affairs, Skills and Labour Mobility 

3 

 
 
 
 
 
 
CONTENTS 

Summary and main findings 

Part I: 

Labour market and wage developments 

1. 

General labour market conditions in the euro area and the EU 
1.1. 

Introduction 

1.2. 

1.3. 

Setting the scene: the EU labour market from an international perspective 

Employment and activity rates, hours worked in the EU 

1.4.  Wages and labour costs 

1.5. 

Long-term unemployment and labour market matching 

1.6.  Conclusions 
Labour market developments in Member States 

2. 

2.1. 

2.2. 

2.3. 

2.4. 

2.5. 

Introduction 

Unemployment rates 

Employment, activity rates and hours worked 

Job market flows 

Trends in wages and labour costs 

2.6.  Cost competitiveness and external adjustment 

2.7.  Conclusions 

2.A1.  Appendix 
3. 

Policy developments: the poverty-reducing effects of social 
transfers 
3.1. 

Introduction 

3.2. 

3.3. 

3.4. 

3.5. 

3.6. 

Trends in social expenditure by expenditure categories 

Policy trends 

The poverty-reducing IMPACT of social transfers 

How do policies affect the poverty-reducing effect of social transfers: 
macro-analysis 

How do policy changes affect poverty and inequality: a micro-simulation 
analysis 

3.7. 

Impact on material deprivation of different categories of social expenditure 

3.8.  Conclusions 

3.A1.  Appendix - Quartile analysis 
3.A2.  Appendix - Definitions of the functions of social protection 

Part II:  Analytical chapter 

1. 

The relationship between polarisation and skills mismatches: 
evidence for the EU 
Introduction 
1.1. 

1.2. 

1.3. 

1.4. 

1.5. 

Literature review on the determinants of skills mismatches and polarisation 

Skills mismatch: measures and recent developments 

Job polarisation: measures and recent trends 

Does polarisation increase (reduce) skills mismatches? 

1.6.  Conclusions 

1.A1.  Appendix  
1.A2.  Empirical analysis: on-the-job mismatch and polarisation 

1 

5 

6 
6 

7 

13 

14 

17 

19 
23 

23 

23 

25 

31 

33 

38 

41 
49 

54 
54 

56 

64 

69 

73 

77 

81 

86 
89 
90 

91 

92 
92 

94 

98 

105 

112 

120 
121 
127 

iii 

 
References 

Statistical annex 

LIST OF TABLES 

128 

137 

I.1.1. 

I.1.2. 
I.2.1. 

I.2.2. 

I.2.3. 
I.2.4. 

I.2.5. 
I.2.6. 
I.3.1. 

I.3.2. 
I.3.3. 
I.3.4. 

I.3.5. 

Unemployment, compensation per employee and GDP growth in the euro area 
and EU 
GDP growth and unemployment in selected economies 
Employment and activity rates and shares of marginally attached and 
discouraged workers over all inactive workers, various time periods 
Employment growth in different sectors, cumulative % change over the years 
2013-2018 
Distribution of contract types among the employed, 2018, % and pps. 
Decomposition of the annual percentage change of nominal and real unit 
labour costs (NULC and RULC), 2018 
Contributions to the final demand deflator, 2018, annual % change 
Decomposition of tax wedge 
Correlation of growth of social protection spending with GDP growth and 
unemployment 
Responsiveness of the growth of social spending to changes in GDP growth 
Smoothing capacity of social spending components 
Determinants of the changes in the poverty-reducing effect of social transfers, 
EU28, 2007-2017 
Regression summary, drivers of AROP and severe material deprivation: 2000-
2016 

I.3.A1.1.  ESSPROS: all expenses 
I.3.A1.2.  Quartiles, 1996-2016 
I.3.A2.1.  Definitions of the functions of social protection 
II.1.1. 
II.1.2. 
II.1.3. 
II.1.5. 
II.1.6. 

Industry contributions to with- and between-industry polarisation, 2000 to 2018 
Correlations skills mismatch and polarisation indicators. EU 2002-2018 
Determinants of Macroeconomic skills mismatches in EU27, 2000-2018 
Job polarisation increases the probability of being mismatched 
Skills mismatches are more likely in jobs in increasing demand, while they are less 
likely in jobs at risk of disappearing 
Determinants of Macroeconomic skills mismatches in EU27, 2000-2018. Content 
of jobs 
Employment protection legislation and labour market policies on 
macroeconomic skill mismatches 

II.1.4. 

II.1.7. 

II.1.A1.1.  Determinants of Macroeconomic skills mismatches in EU27, 2000-2018 
II.1.A1.2.  Marginal effects on the probability of being mismatched by Member State, 

2000-2018 

iv 

7 
11 

26 

28 
29 

36 
38 
39 

60 
62 
63 

77 

85 
89 
89 
90 
107 
112 
113 
116 

116 

117 

119 
125 

126 

 
 
 
 
LIST OF GRAPHS 

I.1.1. 
I.1.2. 

I.1.3. 
I.1.4. 
I.1.5. 

Employment, GDP and productivity growth in the EU 
Employment and unemployment in the EU, million persons, 2001-2018, quarterly 
data 
Unemployment rates by quartile 
Unemployment rate and structural unemployment rate in the EU 
Extended measures of labour utilisation (as percentage of extended labour 
force) 
Unemployment rates in the EU, the US and the ‘Group of seven’ advanced 
economies, 2000-2019, monthly data 
The activity rate in the EU and selected advanced economies, 1996-2018 
Real wages and productivity growth in the euro area and selected advanced 
economies, 2017-2018 
I.1.9. 
Employment, unemployment and activity rates, EU28, 2000-2018 
I.1.10.  Cumulative change in GDP, employment and average hours worked per 

I.1.7. 
I.1.8. 

I.1.6. 

person, euro area, 2008Q1-2019Q2 

I.1.11.  Contribution to the change of the average hours worked of changes of the 

average hours within sectors and of the employment structure 

I.1.12.  Compensation per employee and unit labour costs in the euro area, annualised 

I.1.13. 
I.1.14. 
I.1.15. 
I.1.16. 

I.1.17. 

I.1.18. 
I.1.19. 

I.1.20. 
I.2.1. 
I.2.2. 
I.2.3. 

I.2.4. 
I.2.5. 

I.2.6. 
I.2.7. 

I.2.8. 
I.2.9. 
I.2.11. 

I.2.10. 
I.2.13. 

growth rates, 2005Q1-2019Q2 
Phillips curve for the euro area: 2000-2018 
Phillips curve for the euro area: 2008Q1-2019Q2 
Responsiveness of wage growth to labour market slack 
Responsiveness of wage growth to inflation and 1-year-ahead inflation 
expectations 
Long-term unemployed (for 1 year or more) in the EU, the euro area and the US 
(% of total labour force), 2005-2018, quarterly data 
Job finding and separation rates in the euro area, 2005-2018, quarterly data 
Job finding rate by duration of unemployment, euro area, 2005-2018, quarterly 
data 
Beveridge curve for the euro area, 1995-2018, quarterly data 
Unemployment rate, 2007, 2013, 2017 and 2018 
Dispersion in GDP growth and unemployment rates 
Changes in the unemployment rate unexplained by GDP growth, 2015-2018, 
pps 
Dispersion of measures of labour market reserves (‘slack’), EU28 
Change in number of employees and hours worked per employee, cumulative 
% change since the first quarter of 2009 
Employment growth by contract type, EU 28, 2004-2018, % 
Employment growth by contract type, 2008-2019Q2, cumulative % change 
since the 2008Q1 
Job finding and job separation rates, 2008-2018, quarterly data 
Average length of unemployment spells, 2005-2019Q2, quarterly data, months 
Nominal compensation per employee, 2017, 2018 and 2019Q2, annual % 
change 
Unemployment rates and structural unemployment rates in the EU 
Real compensation per employee and productivity, 2018 (left panel) and the 
average over 2016-2018 (right) 
Real wages per employee, 2017, 2018 and 2019Q2, annual % change 

I.2.12. 
I.2.14.  Change in the wage share 2013-2018 
I.2.15. 

Unemployment rate (2017) and change in real unit labour costs (RULC, 2018) 

8 

8 
8 
9 

10 

11 
11 

12 
13 

13 

14 

14 
15 
15 
16 

17 

17 
18 

18 
19 
24 
24 

24 
25 

27 
28 

29 
31 
31 

32 
33 

34 
35 
35 
36 

v 

 
 
I.2.16.  Compensation per employee in public and private sectors, 2018 and average 

I.2.17. 
I.2.19. 

I.2.20. 

over 2014-2017, % change 
Nominal compensation per employee by sector, 2018, annual % change 
NULC in deficit and surplus countries within the euro area, weighted average, 
1999-2018, annual % change 
Real appreciation in 2018 and current account balance in 2017, euro area 
countries 

I.2.21.  Wage and employment growth differential between tradable and non-
tradable sectors in the euro area, average annual % change, 2016-2018 

I.2.22.  Compensation per employee, tradable and non-tradable sectors, in 'deficit' 

I.2.18. 

and 'surplus' countries: 1999-2017, annual % change 
REERs based on various deflators, cumulative % change over the period 2016-
2018 

I.2.A1.1.  The Beveridge curve in EU Member States, 2000q1-2018q4, quarterly data 
I.2.A1.2.  The Beveridge curve in EU Member States, 2000q1-2018q4, quarterly data, cont. 
I.2.A1.4.  The gap between actual unemployment and the NAWRU 
I.2.A1.3.  The gap between actual unemployment and NRU 
I.2.A1.5.  The Phillips curve for EU countries: compensation per employee growth and 

unemployment rate, 2000-2018, Panel A 

I.2.A1.6.  The Phillips curve for EU countries: compensation per employee growth and 

I.3.1. 

I.3.2. 
I.3.3. 
I.3.4. 
I.3.5. 
I.3.6. 
I.3.7. 

I.3.8. 

I.3.9. 
I.3.10. 

I.3.11. 

I.3.12. 

I.3.13. 
I.3.14. 

I.3.15. 

unemployment rate, 2000-2018, Panel B 
Dispersion of measures of monetary and material poverty (coefficient of 
variation) 
Social Protection expenditure, 2000-2016 (as % of GDP) 
Social expenditure by quartile of GDP per capita 
Share of social expenditure category on GDP before and after 2008 in percent 
Composition of social protection spending by quartile of GDP per capita 
Growth of social expenditure categories and GDP growth 
Stabilisation effect of social spending by categories and quartile of GDP per 
capita 
Social assistance and housing: average number of reform measures per country 
per year by direction of reform measures, EU28 
Income support measures (as % of expenditure on social exclusion n.e.c.) 
Spending on family and children (2016) : break-down between means-tested 
and non-means tested 
Family-related benefits: average number of reform measures per country, per 
year by direction of reform measures, EU28 
Unemployment benefits: average number of reform measures per country per 
year by direction of reform measures, EU28 
Spending per unemployed (in percentage of GDP per capita) 
The impact of social transfers on the at-risk of poverty rate, by type of transfer, 
2008, 2013, 2017 
The impact of social transfers on the poverty gap, by type of transfer, 2008, 
2013, 2017 

I.3.16.  Correlation between change in the poverty-reducing impact of unemployment 

benefits and change in the unemployment rate (pps) (2008-13 vs. 2013-17) 

I.3.17.  Changes in the poverty-reducing effect of transfers by type (2013-2017, pps) 

and ‘reform stance’ in the relevant policy area (2012-2015) 

I.3.18.  Change in the Gini coefficient due to tax and benefit policies: 2008-2018 
I.3.19.  Changes in disposable income by income deciles due to tax-benefit policy 

changes: 2008-2018 

I.3.20.  Change in the at-risk-of-poverty rate due to tax and benefit policies and 

changes in the poverty line: 2008-2018 

38 
38 

40 

40 

40 

41 

41 
49 
50 
50 
51 

52 

53 

55 
57 
58 
59 
59 
62 

64 

65 
66 

67 

67 

68 
69 

71 

72 

75 

76 
78 

79 

79 

vi 

 
 
II.1.2. 

II.1.3. 
II.1.4. 

I.3.21.  Change in the poverty gap due to tax and benefit policies: 2008-2018 
I.3.22.  Correlation severe material deprivation and AROP, 2016 
I.3.23.  Median disposable income (at PPP) and AROP market income 
I.3.24.  Median disposable income (at PPP) and AROP disposable income 
I.3.25.  Median disposable income (at PPP) and AROP change after redistribution 
I.3.26. 
Severe material deprivation and social spending 
I.3.27.  Change in AROP after redistribution and social spending 
I.3.28. 
II.1.1. 

Efficiency of social protection spending in reducing AROP 
Relative dispersion of employment rates by education level (macroeconomic 
skills mismatch), 2010, 2015 and 2018 
Percentage of firms reporting difficulties in filling positions by company size 
(2018) 
Labour shortage index, 2008, 2013 and 2018, Composite indicator 
Over-qualification rate (vertical skills mismatch) in 2018 and 2008 across EU 
Member States 
Over-qualification rate (vertical skills mismatch) in 2018 in EU28 by sector 
Horizontal skills mismatch across EU Member States, 2014-2018 
The proportion of middle wage workers is shrinking 
Polarisation peaked at the height of the crisis 
After the crisis, intensity in polarisation decreased 
In all countries polarisation has largely reflected within-sector dynamics 

II.1.5. 
II.1.6. 
II.1.7. 
II.1.8. 
II.1.9. 
II.1.10. 
II.1.11.  Almost all countries have experienced a decline in routine tasks 
II.1.12. 
II.1.13. 
II.1.14.  Current and predicted macroeconomic skills mismatches if countries reach UK 

Skills mismatches vs JPI 
Skills mismatches vs RTI 

(de)routinisation levels 

II.1.A1.1.  Evolution of the task content of jobs in EU Member States, 2000-2018 
II.1.A1.2.  Evolution of the polarisation-related mearues in EU Member States, 2000-2018 
II.1.A1.3.  Evolution of the macroeconomic skill mismatch and JPI in EU Member States, 

2000-2018 

II.1.A1.4.  Evolution of the macroeconomic skill mismatch and RTI in EU Member States, 

2000-2018 

LIST OF BOXES 

I.1.1. 
I.2.1. 
I.2.2. 
I.3.1. 
I.3.2. 
I.3.3. 
II.1.1. 
II.1.2. 
II.1.3. 

II.1.4. 

A primer on the Phillips curve 
The impact of demographics on unemployment rate 
The dynamics of labour force participation rates in the EU 
Use of microsimulation models for assessing tax-benefit systems 
Indicators of poverty and social exclusion 
Access to social protection 
The task-based approach and construction of task measures 
Description of the variables used in the empirical analysis 
Empirical analysis: The link between macroeconomic skill mismatch and 
polarisation 
Decomposition of changes in macroeconomic skills mismatches 

81 
82 
82 
83 
83 
83 
83 
86 

100 

101 
102 

103 
103 
104 
105 
106 
107 
108 
109 
112 
112 

113 
121 
122 

123 

124 

20 
44 
48 
80 
84 
85 
110 
111 

113 
117 

vii 

 
 
 
 
 
SUMMARY AND MAIN FINDINGS 

The EU labour market 
has proved so far 
quite resilient to the 
weakening of the EU 
economy 

The steady decline in 
unemployment has 
supported 
convergence across 
countries  

The fall in 
unemployment partly 
reflects improvements 
in the structural 
features of the labour 
market  

The fall in structural 
unemployment 
reflects the ageing of 
the population and 
the effects of reforms 
enacted after the 
2008 crisis  

In  2018  and  2019,  improvements  in  the  EU  labour  market  continued  at  a 
steady  pace.  In  the  first  half  of  2018,  the  labour  market  delivered  robust 
employment  gains,  spurred  by  strong  domestic  demand.  The  economic 
slowdown  in  the  second  half  of  the  year  had  only  a  minor  impact  on  job 
creation,  despite  external  developments  becoming  less  favourable  and 
confidence indicators reaching a turning point. Compared to 2017, about 3.1 
million jobs were created in 2018 (almost 2.4 in the euro area); the yearly EU 
unemployment  rate  reached  6.8%  (8.2%  for  the  euro  area),  about  one 
percentage point below the rate of the previous year. In 2019, trade tensions 
escalated,  weighing  on  industrial  production  and  trade  flows.  As  the 
slowdown is primarily driven by external demand, the labour market has so 
far proved to be resilient. In the first half of 2019, EU employment expanded 
at 1%, slightly below the rate of one year earlier (1.3%)   for the euro area, 
1.3% against 1.5% in 2018. In September 2019, the EU unemployment rate 
reached 6.3%,  the lowest level since January 2000   and  7.5% for the euro 
area, close to the pre-crisis lows. 

As  in  previous  years,  the  fall  in  unemployment  was  stronger  than  expected 
based  on  the  pace  of  economic  growth.  High  unemployment  countries, 
including Croatia, Greece, Spain and Portugal, experienced high employment 
growth.  As  a  result,  the  gap  between  the  maximum  and  minimum 
unemployment  rates  dropped  from  22.3  percentage  points  in  the  second 
quarter of 2013 to 16.7 percentage points in the fourth quarter of 2018  – and 
further to 15.2 percentage points in the second quarter of 2019. Although the 
dispersion in unemployment rates across countries also declined, it remained 
high in 2018. 

Structural unemployment is a theoretical concept that relates the failure of the 
labour market to absorb jobless individuals to factors other than the business 
cycle.  Different  indicators  suggest  that  the  structural  unemployment  has 
declined  over  the  last  years.  First,  between  2006  and  2012,  the  NAWRU  – 
i.e. the unemployment rate at which wage growth is stable independently of 
the stage of cycle  hovered around 9%; it started falling in 2013 to get closer 
to  7%  in  2018.  Second,  the  EU  long-term  unemployment  rate  has  been 
steadily  declining,  reaching  2.8%  in  the  first  quarter  of  2019  (3.5%  for  the 
euro area). In the first quarter of 2019, 25 countries had an average duration 
of  unemployment  spells  below  the  2013-2018  average;  in  20  countries  – 
including Germany, France, Poland and Portugal – the duration fell below the 
pre-crisis average. Finally, since the second quarter of 2018, the proportion of 
firms reporting labour shortages has been falling from very high levels; and 
yet  the  unemployment  rate  has  kept  falling.  This  is  consistent  with  an 
improvement of the process of matching job seekers with available jobs. 

for 

this  decline 

Various  explanations  can  be  given 
in  structural 
unemployment.  First,  ageing  may  have  reduced  unemployment  as  younger 
cohorts    whose  unemployment  rates  are  usually  several  times  higher  than 
older  age  groups    become  smaller.  Second,  several  countries  enacted 
comprehensive  reforms  aimed  at  enhancing  labour  market  adjustment  that, 
together  with  reforms  of  the  unemployment  benefit  schemes  and  active 
labour market policies, have made the labour market more fluid and spurred 
labour demand and supply. 

1 

 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

The activity rates have 
kept rising  

Labour under-
utilisation is still 
significant; the 
average hours worked 
remain low 

Wages have 
continued to rise but 
wage growth remains 
moderate  

In all Member States, 
nominal wages rose at 
a higher pace in 2018 
than in 2017 

Total social protection 
expenditure as a 
percentage of GDP 
shifted up during the 
2008 crisis.  

In 2018, the EU activity rate hit a new historic high   73.7% in the EU and 
73.4%  in  the  euro  area.  This  trend  is  in  marked  contrast  with  that  in  the 
United  States,  where  the  decade-long  decline  in  labour  force  participation 
came  to  a  halt  only  in  2016.  Activity  rates  increased  in  nearly  all  Member 
States,  and  mostly  in  Cyprus,  Finland,  Latvia,  Lithuania  and  Malta.  Higher 
female  participation  and  education  levels  are  the  main  drivers  of  this 
increase. 

Hidden  unemployment  includes  both  people  willing  to  work  more  hours  – 
under-employed  part-time  workers  −  and  people  who  have  given  up  on 
searching for a job because they consider their chances of finding a job low − 
discouraged workers. In 2018, discouraged workers declined from 8.2 to 7.7 
million;  under-employed  part-time  workers  fell  from  9  to  8.3  million.  Yet, 
the  number  of  discouraged  workers  remains  high  in  some  countries, 
especially  in  Italy  and  Portugal.  The  hours  worked  per  person  employed 
reached a historical low, with no major differences across countries. Between 
1995  and  2018  the  average  hours  worked  dropped  by  about  26  hours 
quarterly; 70.6% of this decline is due to the fall of hours worked within each 
sector,  with  the  shift  of  employment  towards  services,  usually  less  hour-
intensive, accounting for the remaining 29.4%. 

In 2018, wages rose at a rate  higher than one  year earlier (for the EU 2.7% 
against 2.1%; for the euro area 2.1% against 1.6%), with a slight deceleration 
in the last quarter of the year. In the first half of 2019, they increased at about 
the  same  rate  as  in  the  second  half  of  2018.  Nonetheless,  the  response  of 
wages  to  unemployment  appears  subdued.  The  analysis  suggests  that  the 
response of wages to unemployment has not changed after the 2013 recovery. 
Wages  closely  follow  inflation  expectations  and  productivity,  two  variables 
that  have  been  growing  at  a  very  modest  rate  since  the  onset  of  the  2013 
recovery. 

Developments  in  2018  remained  consistent  with  the  external  rebalancing 
needs  within  the  euro  area.  Nominal  unit  labour  costs  continued  to  grow 
faster in countries characterised by a current account surplus before the crisis 
than in countries with previous current account deficits. Real wages increased 
in almost all countries supporting workers’ purchasing power. In 2018, they 
rose above productivity growth in the euro area as a whole after rising below 
productivity  growth  in  2013-2017.  As  in  previous  years,  wage  growth  was 
higher  in  Central  and  Eastern  European  countries,  contributing  to  wage 
convergence.  In  several  Central  and  Eastern  European  Member  States,  real 
wages rose faster than productivity. Consequently, the wage share increased 
in countries where it was low. 

Total  social  protection  expenditure  as  a  percentage  of  GDP  increased  from 
24%  in  2008  to 27.5%  in  2010  and  it  has  remained  at  about  this  level  ever 
since. Transfers typically paid to working age individuals accounted for about 
one-third of the increase in total spending, with the rest accounted mainly by 
spending  for  old  age  and  sickness.  High-income  Member  States  spend 
relatively  more on  social protection as percentage of GDP than low-income 
ones. At the end of 2016, per capita spending in high-income countries was 
more than five times larger than in low-income countries. The composition of 
social  spending  also  varies  with  the  level  of  GDP  per  capita;  wealthier 
countries spend more on sickness, family and unemployment benefits. 

2 

 
 
Summary and main findings 

Social protection 
spending provides 
insurance against 
asymmetric shocks  

Social protection 
spending is effective 
in reducing poverty 
risks 

Only  unemployment  benefits  are  clearly  anti-cyclical.  Results  suggest  that 
old  age,  unemployment  and  sickness  benefits  contribute  the  most  to 
stabilising households’ disposable incomes. The ability of social spending to 
insure  against  income  shocks  varies  with  the  level  of  GDP  per  capita.  It  is 
higher  for high-income countries, owing to the stronger  stabilising effect of 
spending on old age, unemployment and social exclusion. 

Overall,  social  benefits  reduce  the  incidence  of  poverty  in  the  EU  by  about 
one-third when measured as the proportion of people with income below the 
poverty  line  and  its  depth  –  i.e.  the  gap  between  the  median  income  of 
persons below the poverty threshold and the at-risk-of-poverty threshold  by 
more than one-half. Family, sickness and disability benefits have the largest 
effect  on  the  poverty  rate;  housing  and  social  inclusion  benefits  contribute 
less,  but  still  significantly.  Social  transfers  are  more  effective  in  reducing 
poverty  in  Nordic  and  Continental  countries  than  in  Southern  European  or 
Baltic countries. 

Reforms increasing the 
generosity of social 
benefits have 
reinforced their 
moderating effect on 
monetary poverty 

During  the  crisis,  social  transfers  have  mitigated  the  effect  of  the  increased 
joblessness on poverty. This effect was higher in countries that implemented 
reforms  increasing  the  level  and  coverage  of  benefits.  Those  that  reacted 
early in the recession coped better with the effects of a prolonged recession. 
Doing  reforms  on  time  strengthens  the  effect  of  social  benefits  on  poverty 
when  unemployment  increases.  However,  unemployment  benefits  provide 
only a temporary relief during a recession. 

The reforms of the tax 
and benefit systems 
enacted after 2008 
have lowered 
inequality in almost all 
countries  

The tax and benefits systems in place in 2018 achieved a greater reduction in 
income  inequality  as  compared  to  what  the  systems  of  2008  would  have 
produced. In 14 countries, the reforms enacted after 2008 protected the low-
income  earners.  In  Germany,  the  Netherlands  and  Latvia,  policy  changes 
benefitted relatively more the middle-income groups. For Spain, Portugal and 
Ireland, the disposable income of households at the lower end of the income 
distribution was negatively affected. 

Reforms of tax and 
benefit systems 
decreased poverty 
but not often its depth  

Tax and benefit reforms modify the at-risk-of-poverty rate as they affect the 
median income and the poverty lines. In eight countries (Belgium, Bulgaria, 
Denmark,  Estonia,  Luxembourg,  Poland,  Romania  and  Sweden),  policy 
changes led to a decline in the number of people at risk of poverty and to an 
increase  in  the  poverty  line.  In  a  few  countries,  including  Germany  and 
Slovakia,  both  the  poverty  line  and  the  share  of  people  at  risk  of  poverty 
increased. In the remaining countries, policy changes led to a decline in the 
median income  i.e. in the poverty line – but the proportion of people at risk 
of poverty fell. In these countries, lower income  groups benefitted from the 
changes in the tax and benefit systems. Nonetheless, the intensity of poverty 
–  i.e.  the  gap  between  the  median  income  of  those  below  the  poverty 
threshold and the poverty threshold – deepened in almost half of the Member 
States, indicating a relative worsening of living conditions of the poorest. 

There is a close 
relationship between 
job polarisation and 
skill mismatches  

Job polarisation – i.e. the increase of employment at the upper and lower ends 
of the wage (skill) distribution and the decline in the middle – is a long-term 
tendency  influenced by technological changes and shifts in the international 
division of labour. Skill mismatches refer to a broad imbalance between skills 
sought by employers and skills offered by job seekers. This imbalance  may 
be temporary, due to labour market  frictions, or more persistent,  because of 

3 

 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

The intensity of job 
polarisation declined 
after the 2008 crisis 

The task-based 
approach provides a 
better measure of the 
drivers of polarisation 

The decline in routine 
employment is 
associated with an 
increase in skill 
mismatches  

Addressing the 
consequences of job 
polarisation would 
also help to reduce 
skill mismatches  

structural  disequilibria  between  supply  and  demand  of  skills.  Only  few 
studies have assessed the link between job polarisation and skill mismatch.  

Between 2002 and 2018, the EU share of middle-paying jobs declined by 13 
percentage points, with some differences across countries. The drop was the 
largest  for  France,  Luxembourg,  Ireland  and  Portugal,  and  the  smallest  for 
Poland, Estonia, Slovakia and Bulgaria. In the EU as a whole, employment is 
largely  shifting  towards  high-paying  occupations.  Yet,  in  some  Member 
States  (e.g.  Bulgaria,  Slovakia,  Netherlands,  Romania,  Spain  and  Greece), 
employment increased more in low-paying occupations. After the crisis, only 
few  countries  (Bulgaria,  Czech  Republic,  Finland  and  the  Netherlands) 
continued  to  experience  job  polarisation;  the  opposite  trend  is  observed  in 
Germany, Greece, Romania and Slovenia. 

The introduction of technologies that replace human labour in routine tasks is 
one explanation of job polarisation. Most countries experienced a substantial 
shift from routine to non-routine tasks. While non-routine cognitive tasks are 
on the rise in all countries, in particular in Latvia, Luxembourg and Portugal, 
only  few  Member  States  experienced  a  simultaneous  increase  in  both  non-
routine  manual  and  non-routine  cognitive  tasks.  Therefore,  for  the  EU  as  a 
whole there seems to be a process of upskilling rather than one of genuine job 
polarisation. 

Technological  progress  is  reducing  the  demand  for  workers  performing 
routine  tasks.  The  analysis  suggests  that  this  trend  may  increase  skill 
mismatches. Middle-income countries are more exposed to this change than 
high-income  ones  that  have  largely  completed  the  transition  from  more 
routine  sectors  (e.g.  manufacturing)  to  less  routine  ones  (e.g.  services). 
Moreover,  middle-income  countries  might  find  it  more  difficult  to  meet  the 
fast changing labour demand towards more complex skills as it takes time to 
upskill their workforces. 

The demand for workers performing routine tasks is falling relative to that of 
workers performing non-routine tasks. There is a clear need for policies that 
smooth  the  transition  between  jobs.  Building  skills  is  more  important  than 
ever before. More effective spending on education and training and lifelong 
learning  is  associated  with  lower  skill  mismatches.  Workers  need  to  be  re-
the  challenges  spurred  by  ongoing 
skilled 
technological and climate change. 

to  effectively  deal  with 

4 

 
Part I 
Labour market and wage developments 

 
 
1.  GENERAL LABOUR MARKET CONDITIONS IN THE EURO 

AREA AND THE EU 

reduced 

fluid  and 

Structural  reforms  may  have  made  the  labour 
structural 
market  more 
unemployment,  possibly  more 
than  currently 
captured by the available estimations. If the fall in 
the  structural  unemployment  rate  is  larger  than 
indicated  by  available  measures,  then  cyclical 
unemployment  (i.e.  the  labour  market  slack), 
would  be  larger,  which  may  keep  wage  growth 
sluggish.  

1.1. 

INTRODUCTION  

In the second half of 2018, economic growth in 
the  EU  started  to  weaken.  The  softening  of 
external  demand,  amid  ongoing  trade  tensions, 
China's  slowdown  and 
the  uncertainty  of  a 
withdrawal  of  the  UK  from  the  EU  without  an 
agreement, were the main drivers of the slowdown. 
Conversely, domestic demand continued to benefit 
from the good labour market developments and the 
gradual pick-up in wage growth. Employment rose 
by  1.3%  in  2018,  while  wages  expanded  at  a  rate 
closer 
to  2.5%.  Employment  growth  slightly 
declined  in  the  second  half  of  the  year.  The 
unemployment  rate  continued  to  fall  and,  in 
September  2019,  reached  its  lowest  rate  since  the 
start of available EU time-series 2000 (6.3%).  

of 

of 

by 

that 

relevant 

features 

performance  with 

Against  this  background,  this  chapter  analyses 
labour  market 
the  main 
developments  in  the  EU  and  the  euro  area  in 
2018  and  early  2019.  It  compares  the  EU  labour 
market 
other 
industrialised  economies  and  assesses  the  role 
including 
played 
employment,  participation,  working  hours  and 
labour  costs.  Section  1.2  describes  the  recent 
labour  market  developments  in  the  EU  in  an 
international  perspective.  Section  1.3  analyses  the 
trends  in  employment,  activity  rates  and  hours 
worked.  Section  1.4  reviews  the  latest  trends  in 
wages  and  labour  costs.  Section  1.5  focuses  on 
of 
aggregate  movements 
unemployment, as well as job matching. 

variables 

and 

out 

in 

the 

first  half  of  2019, 

In  2018  and 
the 
improvements in the labour market continued at a 
steady  pace.  Unemployment  kept  falling  and,  in 
May 2019, reached its lowest rate since 2000. The 
decline  in  the  unemployment  rate  in  both  the  EU 
and the euro area was accompanied by a reduction 
in the divergences across countries. 

The labour market has proved so far quite resilient 
to the weakening of the EU economy in the second 
half of 2018. The fact that the slowdown has been 
driven  primarily  by  the  weakness  of  external 
demand  explains  why  the  effects  on  employment 
have  been  limited  so  far.  The  hours  worked  per 
worker remain at historical lows. This is consistent 
with  the  downward  trend  in  hours  of  the  last  two 
decades, and reflects the rising share of services in 
employment.  Involuntary  part-time  employment 
has  dropped  from  the  peak  of  2014,  when  it 
reached  almost  6%  and  7%  of  total  employment 
for the EU and the euro area, respectively. Yet, at 
about  5%  in  the  EU  and  6%  in  the  euro  area,  it 
remains high. 

Both a decline in job losses and an increase in the 
job  finding  rates  contributed  to  the  observed 
reduction  in  unemployment.  The  drop  in  the 
jobless rate is not only the outcome of a prolonged 
economic  expansion.  Structural  reforms  enacted 
since the 2008 crisis may also have contributed to 
improve  the  matching  between  vacant  jobs  and 
unemployed  people,  resulting  in  an  estimate  of 
structural unemployment at the lowest levels since 
the EU time series is available. 

Wage  growth  in  the  EU  and  the  euro  area 
continued  to  pick  up  at  a  steady  pace  consistent 
with  the  decline  in  unemployment.  Nonetheless, 
wage growth remains moderate. Wages are linked 
to  inflation  expectations,  which,  however,  remain 
subdued.  It  can  be  expected  that  the  slow  but 
persistent  acceleration  of  nominal  wages  will 
continue as inflation moves closer to the reference 
value  of  2%  and  unemployment  stays  below  its 
structural rate. Yet, in a low inflation environment, 
wage  growth  depends  more  on 
long-term 
productivity  growth,  which  has  been  on  a 
downward path since the mid-1990s.  

6 

 
Labour market and wage developments 

Part I 

Table I.1.1: 

Unemployment, compensation per employee and GDP growth in the euro area and EU 

(1) Seasonally adjusted data.  
(2) In the case of the unemployment rate, the table presents changes in percentage points, rather than percent. 
Source: Eurostat. 

1.2. 

SETTING  THE  SCENE:  THE  EU  LABOUR 
MARKET 
INTERNATIONAL 
PERSPECTIVE  

FROM  AN 

1.2.1.  Recent EU-level developments 

In  2018,  the  weakening  economic  expansion 
weighed only moderately on the labour market. 
Spurred  by  strong  domestic  demand  and  high  but 
rapidly  falling  levels  of  business  and  consumer 
confidence,  the  labour  market  delivered  robust 
employment  gains  in  the  first  half  of  2018  (Table 
I.1.1). The deceleration of economic growth in the 
second half of the year had only a small impact on 
job  creation,  despite  developments  becoming  less 
favourable  with  confidence 
indicators  rapidly 
reaching a turning point. (1) After hovering around 
a  growth  rate  of  0.4%  for  almost  8  quarters, 
employment  growth  temporarily  dropped  quarter 
on quarter in the third and fourth quarter of 2018  
to 0.3% and 0.4% in the EU and to 0.2% and 0.3% 
in the euro area (Table  I.1.1). Given that the drop 
of  GDP  growth  was  larger,  the  employment 
content  of  growth  increased;  this  implies  that, 
as compared  to  the  pre-crisis  period,  less  GDP 
growth is necessary to have job creation. (2)  

(1)  Graph  I.1.1  suggests  that  since  2013  employment  growth 

responded to GDP growth with little lags. 

(2)  This can be verified by testing the response of employment 
growth  to  GDP  growth.  A  regression  of  employment 
growth  on  a  constant  and  GDP  growth  over  the  period 
1996Q1-2018Q4  shows 
that  from  2014Q2  onwards, 
employment  growth  was  higher  than  expected.  Usually, 
GDP  growth  has  to  be  above  a  certain  threshold  to  offset 
the  employment  effect  of  trends  in  technology  and  the 
intensity  of  labour  in  production.  This  threshold  fell  from 

Because of these developments, employment in the 
EU rose on an annual basis by 1.3% (1.5% for the 
euro area), above the average growth of the period 
2000-2007 (1% for both the EU and the euro area). 
Currently  employment  is  about  3.4%  above  the 
pre-crisis level (2.7% in the euro area).  

Employment  has  so  far  been  resilient  to  the 
slowdown,  while  productivity  growth  has 
deteriorated.  At  the  early  stage  of  the  2013 
recovery,  GDP  expanded  at  a  higher  rate  than 
employment,  and  productivity  growth  increased 
from 0.5% in 2013 to 1.2% in 2015. The recovery 
was  mainly  driven  by  domestic  demand  and  was 
supported  by  the  strengthening  of  the  labour 
market. Since consumption goods and services are 
usually  labour  intensive,  economic  growth  had  a 
stronger impact on job creation. Between 2013 and 
2018, employment grew by about 6.5% with more 
than  two-thirds  of  the  growth  stemming  from  job 
creation  in  market  services  (growing  at  8%).  So 
far, the labour market in the EU and the euro area 
has  proved  to  be  resilient  to  the  weakening  of 
economic  growth,  as  domestic  demand,  the  main 
driver  of  employment  growth  has  held  up  fairly 
well  during 
robust 
the 
employment  growth 
deceleration  of  GDP  growth  in  the  second  half  of 
the year led to a further weakening of productivity 
growth (Graph I.1.1).  

slowdown.  Thus, 

in  2018 

amidst 

the 

0.7%  for  the  period  1996Q1-2007Q4  to  0.4%  during 
2013Q1-2018Q4. 

7 

Quarter over same quarter of previous year, %Quarter over previous quarter, %2016201720182018Q12018Q22018Q32018Q42019Q12019Q22018Q12018Q22018Q32018Q42019Q12019Q2EA10.09.18.2-1.0-0.8-1.0-0.8-0.8-0.7-0.2-0.2-0.3-0.1-0.2-0.1EU288.67.66.8-0.9-0.8-0.8-0.7-0.6-0.6-0.2-0.2-0.2-0.1-0.1-0.2EA-6.9-9.3-9.2-9.2-9.0-10.1-8.6-8.9-8.2-1.9-2.9-2.9-1.3-2.2-2.1EU28-8.6-10.3-10.1-10.3-10.3-10.5-9.1-8.8-7.9-2.5-3.0-2.4-1.6-2.1-2.1EA1.21.62.11.92.22.52.22.32.20.40.70.70.50.40.6EU281.72.12.72.12.22.72.72.82.70.60.70.50.90.70.6EA1.92.51.92.62.21.61.21.31.20.30.40.20.30.40.2EU282.02.62.02.42.21.81.51.71.40.30.50.30.40.50.2EA1.41.61.51.61.61.41.41.41.20.40.40.20.30.30.2EU281.31.51.31.51.41.31.21.11.00.40.40.20.20.30.3Unemployment rateUnemployment growthGrowth of nominal compen-sation per employeeEmployment growthGDP growth 
                                                           
                                                                                   
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.1.1:  Employment, GDP and productivity growth in 

the EU 

Source: Eurostat. 

Unemployment  has  been  steadily  falling.  In 
2018, employment in  the EU further increased by 
3.2  million  (2.3  million  in  the  euro  area).  It 
outpaced  the  increase  in  the  labour  force,  leading 
to  a  drop  in  unemployment  by  almost  2  million. 
The  number  of  unemployed  hovered  around  the 
lowest level reached before the 2008 crisis (Graph 
I.1.2).  The  unemployment  rate  has  fallen  steadily 
from almost 11% in early 2013 (12% for the euro 
area)  to  6.3%  (7.5%  for  the  euro  area)  in 
September  2019,  the  lowest  rate  since  2000.  The 
decline of the unemployment rate was observed at 
all  durations,  including  for  those  jobless  for  more 
than 12 months. At the onset of the 2013 recovery, 
the  number  of  unemployed  had  reached  26.2 
million (19 million in the euro area), but by 2018 it 
had  dropped  to  16.9  million  (13.4  million  in  the 
euro area). More than half of this decline (54% in 
the EU and 56% in the euro area) can be attributed 
to the fall in the long-term unemployed.  

Graph I.1.2:  Employment and unemployment in the EU, 
million persons, 2001-2018, quarterly data 

The  decline  of  unemployment  in  the  EU  has 
been  matched  by  a  falling  dispersion  across 
countries.  All  Member  States  have  benefitted 
from  the  favourable  labour  market  performance. 
Graph  I.1.3  shows  how 
the  distribution  of 
unemployment  rates  across  countries  has  evolved 
over time. The  fall in the jobless rate  involved all 
countries  and  was  comparatively  larger  for  high- 
than for low-unemployment countries. (3) With the 
broadening  of  the  recovery,  a  larger  number  of 
countries benefitted from economic growth and the 
diversity  in  unemployment  rates  across  countries 
declined. (4)  The  gap  between  the  maximum  and 
minimum  unemployment  rate  dropped  from  22.3 
pps in 2013Q2 to 16.7 pps in 2018Q4. During the 
same  period,  the  gap  between  the  unemployment 
rates  of  the  first  and  third  quartile  dropped  from 
about  6  pps  to  3  pps.  Thus,  both  unemployment 
rates at the extreme ends of the distribution and at 
the centre improved.  

Graph I.1.3:  Unemployment rates by quartile 

(1) A quartile divide data into four representative points. The 
first quartile is the middle number that falls between the 
minimum and the median (the second quartile). The third 
quartile is the point that lies between the median and the 
maximum. The median is the point where half of the values 
are greater and half are less than the value.  
Source: Eurostat, LFS. 

(3)  The 

in 

the  distribution  of 
ranking  of  countries 
unemployment  rates  varies  over 
time.  The  highest 
unemployment rate in the EU was in Poland in the period 
2002-2006;  in  Spain  in  2008-2009,  Latvia  in  2009-2010, 
Spain  in  2010-2012,  and  Greece  in  2012-2019.  Similarly, 
the  country  with  the  lowest  unemployment  rate  was 
Luxembourg  between  2000  and  2003,  and  the  Czech 
Republic between 2016 and 2019. 

(1) Employment is from National Accounts, domestic 
concept, ages 15 and over, seasonally adjusted. 
(2) Unemployment is from the Labour Force Survey, ages 15-
74, seasonally adjusted. 
Source: Eurostat. 

(4)  Looking at the characteristics over time of the distribution 
of unemployment rates across countries gives better insight 
of  how  the  dispersion across  countries has varied.  Indeed, 
the standard deviation and the average depend on countries 
with  extreme  values.  Based  on the  standard  deviation,  the 
dispersion started to fall in 2013Q2; yet, this indicator does 
not  tell  if  this decline  concerned several  countries  or  only 
few of them. Conversely, the coefficient of variation hints 
at  a  rising  dispersion  since  2008Q2  due  to  the  effect  of 
falling unemployment in Germany on the EU aggregate.  

8 

-6-4-20241996Q11998Q12000Q12002Q12004Q12006Q12008Q12010Q12012Q12014Q12016Q12018Q12019Q2Productivity growthGDP growthEmployment growth1996Q1-2007Q4 avg productivitygrowth0102030402002102202302402502000Q12002Q12004Q12006Q12008Q12010Q12012Q12014Q12016Q12018Q12019Q2Employment 15+ (lhs)Unemployment 15-74 (rhs)05101520252000Q12002Q12004Q12006Q12008Q12010Q12012Q12014Q12016Q12018Q12019Q2MinimumFirst quartileMedianThird quartileMaximumEU 28 
 
 
 
                                                           
the 

rooted 

The  fall  in  unemployment  reflects  both  the 
favourable  business  cycle  conditions  and  the 
decline  in  the  structural  unemployment  rate. 
level  of 
Structural  unemployment 
is 
unemployment 
the  microeconomic 
in 
functioning  of  the  labour  market.  (5)  It  is  a 
theoretical concept that needs to be estimated. Two 
notions  are  most  commonly  used.  The  non-
accelerating  wage  inflation  rate  of  unemployment 
(NAWRU)  is  the  unemployment  rate  at  which, 
controlling  for  supply  shocks,  wage  inflation 
remains stable. Frictional unemployment is due to 
the  normal  turnover  in  the  labour  market  and 
depends,  inter  alia,  on  the  process  of  matching 
vacant jobs with job seekers (Pissarides, 2000). (6)  

indicators  hint  at  a  decline 

in 
Different 
structural  unemployment.  According  to  official 
Commission  estimates  (AMECO),  the  NAWRU 
for the EU declined slowly between 2009 and 2013 
- by 0.1 and 0.2 pps for the EU and the euro area - 
and kept falling at a more rapid pace thereafter (1.5 
and 1.3 pps for the EU and the euro area) to reach 
7.2%  in  2018  (8.1%  for  the  euro  area). (7)  Graph 
reports  a  measure  of  structural 
I.1.4  also 
unemployment  close  to  the  notion  of  frictional 
unemployment. The  frictional  unemployment  rate 
is  the  rate  that  equates  inflows  into  and  outflows 
out of unemployment. When unemployment equals 
its frictional rate, the unemployment rate does not 
change.  Several  observations  stand  out.  First, 

and 

rigidities, 

retraining 

(5)  It reflects institutional factors (tax and benefit systems, real 
costs, 
recruitment 
wage 
availability  of  information  about  job  vacancies)  and 
structural 
skills, 
(e.g. 
technologies)  that  affect  demand  and  supply  of  labour 
beyond  the  normal  cyclical  swings  (Layard,  2005).  The 
effect  of  the  2008  crisis  on  structural  unemployment  is 
discussed in European Commission, 2013a, Labour Market 
and Wage Developments in Europe.  

demographics, 

elements 

(6)  Both  concepts  are  not  fully  unreactive  to  the  cycle.  The 
NAWRU  is  pro-cyclical  if  real  wages  adjust  slowly  to 
labour  demand  shocks  (Hristov  et  al.,  2017;  European 
Commission,  2013a,  Labour  Market  and  Wage 
Developments  in  Europe)  or  if  prolonged  periods  of 
unemployment  lead  to  a  deterioration  of  skills  and  weak 
labour  market  attachment,  which  permanently  affects 
employability  (Blanchard  and  Summers,  1988).  The 
frictional unemployment fluctuates over the cycle with the 
job finding and job separation rates (Elsby et al., 2010) or 
if  employers  adapt  their  recruitment  intensity  over  the 
cycle  (Diamond  and  Şahin,  2016).  The  NAWRU  is  a 
concept  of  structural  unemployment  that  is  more  closely 
related  to  the  wage  formation  mechanism;  the  frictional 
unemployment  instead  reflects  mainly  the  functioning  of 
the  labour  market,  in  particular  the  matching  of  the 
unemployed to vacant jobs. 
(7)  AMECO and Hristov et al., 2017. 

Labour market and wage developments 

Part I 

between  2006  and  2012,  both  measures  are  in  a 
ballpark  of  9%. (8)  Until  2009,  the  frictional 
unemployment rose by 0.8 pps (from 8.2% to 9%), 
while  the  NAWRU  rose  only  by  0.2  pps  (from 
8.7%  to  8.9%). (9).  Second,  both  measures  started 
falling  from  2013.  Given  that  both  the  actual  and 
the 
the 
structural  unemployment  declined, 
deviation of unemployment from its structural rate 
(i.e.  the  cyclical  unemployment)  dropped  more 
slowly.  Third, 
the  current 
unemployment  rate  and  the  NAWRU  virtually 
closed in 2018, while it remained open in the case 
of  frictional  unemployment.  Since  the  effects  of 
structural reforms on structural unemployment can 
be quantified only  with a lag, the effective size of 
the  labour  market  slack  (i.e.  how  far  is  the 
unemployment rate from its structural level) could 
be higher than the one currently observed. (10) 

the  gap  between 

Graph I.1.4:  Unemployment rate and structural 

unemployment rate in the EU 

(1) The frictional is computed as follows. Inflows into 
unemployment are the product of the stock of employed 
(N) times the job separation rate (s). Outflows out of 
unemployment are the product of the stock of unemployed 
(U) times the job finding rate (f). When the two flows are 
equal, unemployment does not change: sN-fU=0. The 
frictional unemployment is defined as the flow-steady state 
unemployment rate u=s/(s+f). Cyclical fluctuations are 
smoothed out with a Hodrick-Prescott filter.  
Source: Ameco and Commission services calculations 
based on LFS. 

Different  explanations  can  be  offered  for  the 
in  structural  unemployment.  First, 
decline 
ageing  may 
overall 
reduced 
unemployment rate, as young cohorts, usually with 

have 

the 

(8)  The  average  for  the  period  is  8.7%  for  the  NAWRU  and 

8.8% for the frictional unemployment.  

(9)  The deterioration in the probability of losing and finding a 
job and  the  greater  mismatch between labour demand and 
supply  were  the  main  factors  behind  the  increase  in  the 
frictional  unemployment  (European  Commission,  2013a, 
Labour Market and Wage Developments in Europe). 

(10)  Euro  area  estimates  of 

the  NAWRU  have  been 
continuously  revised  downwards  (Praet,  2018;  Cœuré, 
2018).  

9 

579112006200820102012201420162018NAWRUUnemployment rateUnemployment rate consistent with inflows equal to outflows 
                                                           
 
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

internal 

external 

high  unemployment  rates,  are  less  represented  in 
the  population.  The  evidence  provided  in  Chapter 
2 (11)  suggests  that  this  effect  may  have  played  a 
role  for  some  but  not  all  Member  States,  i.e.  in 
some  countries,  the  unemployment  rate  would 
have  been  higher  had  the  age  structure  of  the 
population  remained  unchanged.  Second, 
the 
reforms  enacted  after  the  2008  crisis  may  have 
brought down structural unemployment. Measures 
increasing 
flexibility 
and 
combined  with  reforms  of  the  tax  and  benefit 
systems  supporting  transitions  between  jobs  have 
made the labour market more fluid. This has led to 
better  matching  between  vacant 
jobs  and 
unemployed  people  and  spurred  demand  and 
supply of labour. Box 2.2 in Chapter 2 shows that 
the  rise  in  participation  rates  is  explained  by  the 
increase  in  the  probability  of  joining  the  labour 
force.  This  factor  does  not  depend  on  socio-
demographic  changes  and  can  be  related  to  the 
effect  of  structural  reforms.  Moreover,  the  2008 
financial  crisis  and  the  ensuing  deep  recession 
might have made workers more reluctant to ask for 
a pay rise. (12) This might have lowered the lowest 
wage at which unemployed are willing to accept a 
job  (the  reservation  wage),  encouraging  labour 
supply  with  a  positive  effect  on  structural 
unemployment. Finally, 
the 
reservation  wage  during  the  crisis  may  have 
strengthened the monopolistic power of employers 
in  wage  negotiations.  This,  in  turn,  is  usually 
associated  with  moderate  wage  growth  and  sub-
optimal employment outcomes. (13)  

the  decline 

in 

Labour under-utilisation is still significant. The 
drop in unemployment observed between 2013 and 
2018  has  been  remarkable  (a  decrease  of  9.2 
million  in  the  EU  or  35%).  Nonetheless,  the 
decline  in  unused  labour  resources  appears  less 
striking  for  those  partially  attached  to  the  labour 
force (a drop by around 13%) (Graph I.1.5). (14) In 
2018, the discouraged workers  those available to 

(11)  Box "The impact of demographics on unemployment". 
(12)  In particular, in countries were reforms of EPL reduced the 
increasing 

time  of  high  or 

dismissals'  cost  at  a 
unemployment (Duval and Furceri, 2018). 

(13)  The  effect  on  structural  unemployment  might  also  be 
weakened  by  the  prolonged  restraint of  aggregate  demand 
transforming a temporary increase in unemployment into a 
permanent increase.  

(14)  This  group  includes:  (a)  people  available  to  work  not 
seeking  a  job  because  they  consider  that  no  work  is 
available for them – the so-called discouraged workers and 
(b) those seeking work but not immediately available.  

10 

work  but  not  seeking  a  job  because  they  consider 
that no work is available for them  dropped in the 
EU from about 5% to 4% of the labour force (from 
5% to 4.7% for the euro area). Besides, the number 
of  part-time  workers  willing  and  ready  to  work 
more  hours  (underemployed)  amounted  to  8.3 
million or 3.4% of the labour force (6.2 million or 
3.8% in the euro area). Between 2013 and 2018, it 
decreased  from  10.3  to  8.3  million  (7.3  to  6.2  in 
the euro area). The number of discouraged workers 
and  underemployed  remains  high 
in  several 
countries (see Chapter 2). 

Graph I.1.5:  Extended measures of labour utilisation (as 

percentage of extended labour force) 

(1) The extended labour force is the active labour force plus 
those available, but not seeking, plus those seeking but not 
available to work. All figures refer to the age group between 
15 and 74 years. 
Source: Own calculations based on LFS. 

increasingly  favoured 

The growth of temporary jobs is still important. 
Since  the  onset  of  the  2013  recovery,  employers` 
temporary  hiring, 
have 
especially in the euro area.  Compared to previous 
years,  in  2018  temporary  employment  appeared 
less dynamic (2.4% against an average of 3.5% in 
the previous five years), but still above the growth 
rate  for  the  total  number  of  employees  (1.5%). 
Therefore,  the  share  of  temporary  employment  in 
the  euro  area  reached  16%.  The  increase  of 
temporary  work  involved  only  contracts  with  a 
duration  of  more  than  4  months,  while  shorter-
duration 
down. 
Temporary  employment 
the  EU  remained 
mostly unchanged, with a share in the total number 
of employees hovering around 14%. (15) 

actually 
in 

contracts 

inched 

(15)  This  is  about  the  same  rate  as  before  the  2008  crisis. 
Chapter  2  provide  an  overview  of  temporary  employment 
growth at the national level. 

01020304050607020082009201020112012201320142015201620172018UnemployedSeeking work, but not availableAvailable, but not seekingUnderemployed 
                                                           
 
                                                           
Labour market and wage developments 

Part I 

significantly. All labour market indicators are now 
well below the averages of  the period 2000-2008. 
The growth rate of average hourly wages, at 3.3% 
in  2018  and  3.5%  in  the  first  quarter  of  2019,  is 
higher than the 2.4% in 2015-2016. Wages picked 
up  also  at  the  lower  end  of  the  distribution, 
therefore  solidly  boosting  domestic  demand 
growth. (18) 

Table I.1.2:  GDP growth and unemployment in selected 

economies 

1.2.2.  Recent  labour  market  developments  in 

major world regions 

Unemployment continued to decline in the main 
industrialised countries. Unemployment rates are 
back  to  pre-crisis  levels  in  most  industrialised 
countries. The gap with the EU unemployment rate 
has also been falling (Graph I.1.6 and Table I.1.2). 
In July 2019, the EU unemployment rate was only 
2.6 pps and 2 pps above the rates of the US and the 
G7 countries. Yet, the lower unemployment rate in 
the  US  reflects  mainly  the  decline  in  the  activity 
rate.  Indeed,  the  gap  between  the  EU  and  the  US 
unemployment rates would disappear if the activity 
rate  in  this  country  had  stayed  unchanged  (Graph 
I.1.7). (16) 

Graph I.1.6:  Unemployment rates in the EU, the US and the 
‘Group of seven’ advanced economies, 2000-
2019, monthly data 

Source: Eurostat and OECD. 

Graph I.1.7:  The activity rate in the EU and selected 

advanced economies, 1996-2018 

(1) The activity rate is the ratio of active to total population. 
Active population includes those employed and 
unemployed, but excludes those inactive (e.g. not seeking 
work).  
(2) Age group: 15-64.  
Source: OECD. 

In Canada, a supportive economy underpinned 
labour  market  developments. 
favourable 
Employment  reached  high  levels  in  2017  and 
added  further  163  thousand  employed  in  2018. 
This  brought  the  yearly  employment  growth  to 

Source: OECD. 

The  US  labour  market  is  getting  increasingly 
tight  as  the  economy  continues  to  grow. 
Rebounding  from  a  sharp  negative  trend  in  1999-
2016,  the  employment  rate  hit  a  high  in  February 
2019 (60.7 %). (17) According to the US Bureau of 
Labor  Statistics  (BLS),  the  unemployment  rate 
since  March  2018  has  constantly  been  below  4% 
and  the  job  vacancies  (job  openings  in  the  BLS 
statistics) peaked in December 2018 at 7.5 million. 
A  large  fall  in  involuntary  part-time  employment 
along with a rebound in the hours worked suggests 
labour  market  slack  has  decreased 
that 

the 

(16)  European Commission, (2018b), Labour Market and Wage 
Developments 
the  US 
unemployment rate with activity rate unchanged at the start 
of  the  recovery  would  have  been  at  7.7%  against  an 
effective rate of 3.6%. 

In  2019Q2, 

in  Europe. 

(17)  Between  1999  and  2016,  the  employment  rate  in  the  US 
dropped by 4.5 percentage points, from 64.3 to 59.7. About 
3  pps  of  this  decline  is  due  to  demographic  effects, 
(Abraham and Kearney, 2018). 

(18)  According to the Federal Reserve of Atlanta Wage Tracker, 
an  indicator  based  on  micro  data  from  the  Current 
Population  Survey,  wages  of  the  first  decile  expanded  at 
4%.  

11 

2468101214Jan-2007Jul-2007Jan-2008Jul-2008Jan-2009Jul-2009Jan-2010Jul-2010Jan-2011Jul-2011Jan-2012Jul-2012Jan-2013Jul-2013Jan-2014Jul-2014Jan-2015Jul-2015Jan-2016Jul-2016Jan-2017Jul-2017Jan-2018Jul-2018Jan-2019Aug-2019USAEA19EU28G7%2000-2007201720182000-200720172018EA2.22.41.98.69.18.2EU2.52.52.08.77.66.8CAN2.83.01.87.06.35.8JPN1.51.90.84.72.82.4USA2.72.22.95.04.43.9OECD2.52.42.36.55.85.3BRIC:8.15.85.7:::BRA3.61.11.111.313.3:RUS7.21.62.38.15.2:IND7.26.97.4:3.5:CHN10.66.86.63.93.9:GDP growth %Unemployment rate %6668707274767880199619982000200220042006200820102012201420162018EU28USCanadaJapanG7 
 
                                                           
 
 
 
 
 
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

about  1%.  The  unemployment  rate  at  5.7%  in 
August 2019 hit a four-decade low in May (5.4 %). 
The  participation  (25-54  years)  continued  to  rise 
and  peaked  at  87%;  the  job  vacancy  rates  and 
labour  shortages  suggest  that  the  labour  market  is 
very  tight.  Nonetheless  and  similar  to  other 
countries,  wage  growth  remained  restrained  until 
the first quarter of 2019 while in the second it went 
up to 2.4 %. 

by 

growth 

fell  markedly 

to  2.4%  and 

The Japanese labour market is well beyond full 
employment.  All  indicators  point  to  a  degree  of 
tightness  unseen  since  the  early  1970s.  In  2018, 
inactivity 
2.7%  while 
employment  grew  by  2%,  boosted  especially  by 
employment 
the 
non-regular 
of 
(4.1 %). (19)  The  unemployment 
further 
rate 
decreased 
in  August  2019 
unexpectedly  hit  a  new  low  (2.2 %).  The  youth 
unemployment  rate  stands  at  3.6 %.  Real  wage 
growth  remains  low  at  1.4%,  partly  due  to  the 
increasing  share  of  non-regular  workers.  In  an 
attempt  to  tackle  specific  sectoral  shortages,  the 
National  Diet  (i.e.  the  Parliament)  passed  a  bill 
law.  By  making 
revising 
immigration  easier,  this  bill  marks  a  significant 
policy change for the country as in the past it had 
effectively  granted  working  visas  only  to  highly 
skilled professionals. 

immigration 

the 

Although the Chinese economy is slowing down, 
the  unemployment  rate  keeps  falling.  In  2018, 
economic growth slowed down mainly due to trade 
tensions  with  the  US  and  financial  regulatory 
tightening  to  rein  in  financial  stability  risks  and 
improve banking sector resilience. Yet, the official 
unemployment  rate  fell  slightly  from  4.1%  to 
3.9 %, hitting a historical low in July 2019 (3.6%). 
As  the  economy  has  been  losing  steam,  job 
openings have weakened. Massive dismissals from 
manufacturing  firms  are  no  longer  quickly  and 
fully  absorbed  by  the  service  sector.  The  gig 
economy  is  also  starting  to  suffer  from  saturation 
and, in the last months of 2018, the Internet sector 

(19)  In Japan, a regular employee is  an employee  who is hired 
directly by their employer  without a predetermined period 
of  employment  and  works  for  full  time  hours.  A  non-
regular  employee  is  an  employee  who  does  not  meet  the 
conditions  for  regular  employment.  See  Asao  (2010),  and 
IMF  (2019).  To 
the 
government  announced  guidelines  in  2018  for  the  equal 
treatment  between  regular  and  non-regular  employment 
(Jones and Seitani, 2019). 

labour  market  dualism, 

tackle 

12 

registered a large number of layoffs. The  ongoing 
structural  transition  of  the  labour  market  also 
involves  a  rising  highly  educated  labour  supply, 
with an estimated 8.3 million graduates in 2019. 

In Brazil, the gradual recovery from the 2015–
16  recession  continues.  Employment  and  wage 
growth have been modest and mostly limited to the 
informal  and  the  public  sectors.  Average  real 
wages  grew at a  modest 0.6% in 2018. Following 
the crisis, the unemployment rate increased steeply 
for the youngest age group (15-29). In 2018, with 
12.5 million of jobseekers, the unemployment rate 
was stuck at 12.5 %. The share of workers earning 
less  than  the  minimum  wage  is  rising  with 
unemployment. 
informal 
employment  is  also  particularly  harmful  for  the 
youngest  cohorts,  which  is  the  group  with  the 
highest risk of poverty. (20) 

expanding 

An 

Graph I.1.8:  Real wages and productivity growth in the 

euro area and selected advanced 
economies, 2017-2018 

Note: Real wages are wages adjusted for the change of 
prices in economic output (the GDP deflator) rather than 
consumption. This is the relevant indicator for determining 
the labour demand by firms (“real product wages”). 
Source: DG ECFIN AMECO database. 

In  2018,  real  wage  growth  in  industrialised 
countries picked up. In the euro area, real wages 

(20)  Informal  employment  is  common  in  developing  and 
emerging  countries.  According  to  the  ILO,  "the  term 
encompasses  a  variety  of  situations.  It  includes  small  or 
undefined  work  places,  unsafe  and  unhealthy  working 
conditions,  low  levels  of  skills  and  productivity,  low  or 
irregular incomes, long working hours and lack of access to 
information,  markets,  finance,  training  and  technology. 
Workers  in  the  informal  economy  are  not  recognized, 
registered,  regulated  or  protected  under  labour  legislation 
and social protection". See  ILO  (2019) "Women and Men 
in the Informal Economy: A statistical Picture". A specific 
global statistical indicator on informal employment (8.3.1) 
is used to monitor the Sustainable Development Goals. See 
ILO. 

-0.50.00.51.01.52.0JapanUnited StatesEuro areaCanadaJapanUnited StatesEuro areaCanadaReal wagesProductivity20172018 
                                                           
 
                                                           
increased at a higher rate than in 2017 (1% against 
0.5%),  while  productivity  growth  dropped  from 
0.8% to 0.6% (Graph I.1.8). This pattern represents 
a  change  with  respect  to  the  period  2013-2017, 
with  real  wages  rising  well  below  productivity 
growth. (21) In 2018, US real wages expanded at a 
lower rate than productivity in line with a decade-
long  pattern  of  a  falling  wage  share. (22)  This 
contrasts with Canada, where real wages regularly 
follow productivity developments with a lag of one 
year. The  pick-up in  wages in 2018 following the 
acceleration of productivity in 2017 contributed to 
keeping  the  US  wage  share  constant  at  around 
56%.  In  Japan,  real  wages  expanded  on  average 
below productivity between 2000 and 2015. Since 
2016, they are  growing above productivity,  which 
reversed the declining trend in the wage share. (23) 

1.3. 

EMPLOYMENT  AND  ACTIVITY  RATES, 
HOURS WORKED IN THE EU 

The  labour  market  improvements  along  with 
falling population have contributed to a firming 
of both employment and activity rates. In 2018, 
the EU employment rate increased by less than one 
percentage  point  –  from  67.7%  to  68.6%  (Graph 
I.1.9);  since  2013,  the  gain  has  been  around  4.5 
pps  (3.8  pps  for  the  euro  area).  The  participation 
rate hit a new historic high  73.7% in the EU and 
73.4%  in  the  euro  area.  This  upward  trend  in  the 
participation  rate  tempered  the  impact  of  the 
of 
employment 
unemployment. The  positive  dynamics  in  2018 
concerned  mainly  the  medium-  and  high-skilled 
workforce.  Another  relevant  factor  is  ageing, 
having  a  negative  impact  in  most  European 
countries,  with  the  exception  of  the  southern 
European  Member  States 
and 
Turegano, 2018). (24)  

(Fernandéz 

recovery 

fall 

the 

on 

(21)  Obviously, 

the 

real  wages  grow  below 
productivity does not mean that they do not follow changes 
in productivity growth.  

that 

fact 

(22)  Between  2000  and  2018,  the  wage  share  in  the  US  fell 

from 61% to 56.8%. 

(23)  The wage share in Japan fell from 62.4% in 2000 to 56.5% 

in 2015; in 2018, it stood at 58%.  

(24)  The  effect  of  ageing  on  the  activity  rate  is  positive  in 
countries  that  started  to  experience  a  change  in  the  age 
recently. 
structure  of 
large 
Demographic  changes  can  play  a  role  with 
differences across countries (see Chapter 2).  

the  population  only  more 

Labour market and wage developments 

Part I 

Graph I.1.9:  Employment, unemployment and activity 

rates, EU28, 2000-2018 

(1) Age group: 15-64 years old. 
Source: Eurostat, Labour Force Survey.  

The hours worked per person employed remain 
at  a  low  level.  In  the  aftermath  of  the  crisis,  the 
average  hours  worked  continued  to  fall  at  an 
accelerated  pace.  They  did  not  recover  when 
economic  growth  picked  up  again.  At  the  end  of 
2018,  they  stood  at  the  same  level  as  five  years 
earlier  (i.e.  3.5%  below  the  level  achieved  at  the 
beginning  of  the  2008  crisis)  (Graph  I.1.10). This 
feature  has  allowed  a  steeper  rise  in  hourly 
productivity during the recovery. (25) 

Graph I.1.10:  Cumulative change in GDP, employment and 
average hours worked per person, euro area, 
2008Q1-2019Q2 

Source: Eurostat, National Accounts. 

The  decline  in  the  average  hours  worked  is  a 
involves  all  sectors. 
long-term  trend  that 
Between  the  first  quarter  of  1995  and  the  fourth 
quarter of 2018, the average hours worked dropped 
by 25.7 hours quarterly– from 433.6 hours to 407.9 
hours,  about  2  hours  less  on  a  weekly  basis.  The 
decline  occurred  in  all  sectors,  in  particular  in 
services,  and  most  notably in  wholesale and retail 

(25)  The availability of more flexible working arrangements and 
of  short-time  working  schemes  has  made  hours  worked 
more  responsive  to  the  cycle  (European  Commission, 
2017a,  Labour  Market  and  Wage  Developments 
in 
Europe). 

13 

6789101112135257626772200220042006200820102012201420162018200220042006200820102012201420162018Employment rateActivity rateUnemployment rate - total (right scale)TotalWomen-6-4-202468102008Q12009Q12010Q12011Q12012Q12013Q12014Q12015Q12016Q12017Q12018Q12019Q2GDPHours per person employedEmployment 
                                                           
 
 
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

trade  (3.6  hours 
less  on  a  weekly  basis), 
professional  services  (2  hours  less  on  a  weekly 
basis) and construction (1.7 hours less on a weekly 
basis). The first two are the sectors where most of 
job  creation  was  concentrated;  their  share  in 
employment grew from 30% to 38%. Graph I.1.11 
isolates  the  change  in  the  average  hours  worked 
due  to  shifts  of  employment  toward  sectors  with 
low  average  hours  worked  (a  job  creation  effect) 
from  the  effect  due  to  the  change  in  the  average 
hours  worked  within  each  industry  for  a  given 
employment  structure  (hours  worked  effect).  The 
decline  in  the  hours  worked  within  sectors  (hours 
worked  effect)  led  to  a  reduction  in  total  hours 
worked  on  a  quarterly  basis  by  18.14  hours, 
accounting for about 70.6% of the total decline in 
the  average  hours  worked.  The  relatively  stronger 
creation  of  jobs  in  services  accounted  for  the 
remaining 29.4% (i.e. 7.6 hours less). The effect of 
the  decline  in  the  hours  worked  was  stronger  in 
wholesale  and  retail  trade.  The  declining  share  of 
employment in industries contributed negatively to 
the  change  in  the  total  average  hours  worked;  the 
opposite is observed for professional services. 

Graph I.1.11:  Contribution to the change of the average 

hours worked of changes of the average hours 
within sectors and of the employment structure 

about  1%  in  the  second  quarter  of  2016).  The 
economic  slowdown  has  started  to  have  a  visible 
impact  on  wages  only  at  the  end  of  2018,  when 
their  growth  rate  dropped  from  0.7%  (quarter  on 
quarter)  of  the  first  half  of  2018  to  0.4%  of  the 
second  half.  Thus,  on  an  annual  basis,  wages  still 
expanded  at  a  higher  rate  than  the  previous  year 
(2.7% and 2.1% against 2.1% and 1.6% for the EU 
and the euro area respectively); this is the  highest 
growth  rate  since  2008.  The  decline  in  labour 
productivity  growth  is  the  counterpart  of  the 
employment  resilience.  Because  of  the  lower 
in  2018 
productivity  growth,  wage  growth 
translated  into  accelerating  dynamics  of  nominal 
unit  labour  cost,  which  reached  an  annual  growth 
rate of almost 2%; in the first two quarters of 2019, 
it  grew  at  2.4%  and  2.2%,  respectively  (Graph 
I.1.12). 

Graph I.1.12:  Compensation per employee and unit labour 

costs in the euro area, annualised growth 
rates, 2005Q1-2019Q2 

Source: Commission Services. 

Although rising, wages expanded below the rate 
expected  based  on  the  pre-crisis  relation  with 
unemployment. Graph I.1.13 depicts the euro-area 
Phillips  curve  (see  Box  on  Phillips  curve).  At  the 
onset  of  the  2008  crisis  and  throughout  2012,  the 
unemployment  rate  rose  by  almost  4  pps,  while 
wage growth hovered around 2%. Wages remained 
initially  muted  as  the  recovery  gained  pace  and 
unemployment  started  to  fall.  The  fact  that  wage 
growth for the years 2015 to 2018 is below the pre-
crisis  line  hints  at  a  possible  flattening  of  the 
Phillips  curve  (i.e.  a  lower  response  of  wage 
growth  to  unemployment).  Indeed,  relative  to  the 
pre-crisis  relation,  there  is  a  shortfall  of  wage 
growth  in  2018  of  about  0.5  pp.  For  2019  and 
2020,  the  European  Commission’s  spring  forecast 
points  to  a  further  decline  of  the  unemployment 
rate to respectively 7.7% and 7.3% in the EU and 

(1)The chart decomposes the change in the hours worked 
into a within and between component. The former captures 
the change in the average hours within each sector; the 
latter the change in the employment share towards sectors 
with hours worked lower and higher than the average.  
Source: Own calculations based on Eurostat. 

1.4.  WAGES AND LABOUR COSTS  

On a yearly basis, wages have continued to rise 
at  a  steady  pace.  Nominal  wage  growth  in  the 
euro  area  has  been  on  an  accelerating  trend  since 
mid-2016,  albeit  from  very  low  rates  (i.e.  from 

14 

-25-15-551525Change in total hoursAgricultureIndustryConstructionWholesaleInformation andcommunicationFinanceReal estateProfessional servicesPublic administrationArts, entertainmenthoursHours worked effectJob creation effect-4-202462005q12006q12007q12008q12009q12010q12011q12012q12013q12014q12015q12016q12017q12018q12019q2Productivity (inverted sign)Compensation per employeeNominal unit labour costsLabour Cost Index 
 
 
the  euro  area,  with  wage  growth  expanding  at 
about the same rate as 2018.  

Graph I.1.13:  Phillips curve for the euro area: 2000-2018 

Source: DG ECFIN AMECO database and Eurostat, LFS. 

A closer look at the quarterly data suggests that 
the  Phillips  curve  may  have  shifted  leftward 
(Graph I.1.4). Between the second quarter of 2013 
and the second quarter of 2016, the unemployment 
rate  fell from 12% to 10%.  Yet,  wage growth did 
not accelerate  actually it declined by one-third of 
a percentage point. Thus, while unemployment and 
wage  growth  are  usually  related  negatively,  their 
correlation  became  positive  between  the  second 
quarter  of  2013  and  the  second  quarter  of  2016. 
This is a pattern commonly associated with supply 
shocks,  which  concurs  with  the  years  of  falling 
structural  unemployment.  (See  Box  "The  Phillips 
curve: a primer").  

Graph I.1.14:  Phillips curve for the euro area: 2008Q1-

2019Q2 

Source: Eurostat, LFS and National Accounts. 

Labour market and wage developments 

Part I 

growth 

is  mixed  evidence  on  whether  the 
There 
relationship  between  wage 
and 
unemployment  has  changed  after  the  2008 
crisis. Some studies (e.g. IMF, 2017) found that in 
several  countries  the  Phillips  curve  has  flattened. 
They  relate  this  to  the  low  inflation  environment 
and  downward  nominal  wage  rigidity. (26)  Others 
link the flattening of the Phillips curve to inflation 
expectations  being  firmly  anchored  (Blanchard  et 
al.,  2015;  Ball  and  Mazumder,  2019)  or  to  a 
decline  in  inflation  persistence  (Hooper  et  al., 
2019).  Conversely,  the  European  Commission 
(2018b)  finds  no  evidence  of  change  in  the 
relationship  between  wage  growth  and  cyclical 
unemployment. (27)  Fiercer  competition  in  global 
markets  and  outsourcing  of 
labour-intensive 
production  may  have  also  muted  the  response  of 
wage growth to the recovery. This effect may have 
been  reinforced  by  the  rising  employment  in 
services,  with 
lower  bargaining  coverage  as 
compared  to  the  more  unionised  manufacturing 
sector. This  weaker  collective  bargaining  capacity 
would manifest itself in flatter Phillips curves. (28) 

The rest of this section will try to shed light on 
two issues. First, it examines whether the Phillips 
curve  has  shifted  leftwards.  Second,  it  documents 
whether  last  year's  shortfall  in  wage  growth 
its 
reflects 
determinants. 

relationship  with 

changing 

a 

Wage  growth  depends  on  unemployment, 
productivity and price inflation. Estimates in the 
Box "The Phillips curve: a primer" show that wage 
growth  is  well  explained  by  the  cyclical  labour 
market  conditions,  productivity  growth  as  well  as 
past  and  expected  inflation.  The  evidence  in  the 
Box  also  suggests  that  there  is  no  shift  in  the 
Phillips curve. The relation linking wage growth to 
these  variables  may  change  over  time.  Graph 
I.1.14  suggests  that,  for  the  three  years  that 
followed the 2013 recovery, a closing gap between 

(26)  Downward  nominal  wage  and  price  rigidity  reduce  the 
slope  of  the  Phillips  curve  when  unemployment  exceeds 
the  NAIRU  and  inflation  is  very  low.  See  references  in 
Bobeica et al (2019). 

(27)  European  Commission,  2018b,  Labour  Market  and  Wage 
Developments  in  Europe  (Chapter  II.1)  shows  that  wage 
growth in the EU Member States closely followed inflation 
and  productivity  developments,  and  continued  to  respond 
to  unemployment  changes.  However,  the  relationship  was 
weaker in the low-inflation environment after 2010. 

(28)  For an analysis of sectoral price inflation Phillips curves in 

the US, see Seydl and Spittler (2016). 

15 

201320142017201801234681012Compensation per employee (annual growth)Unemployment rateLinear (Trend 2009-2018)Linear (Trend 2014-2020)2009Linear Trend 2000-20082010-11201220152016201920202008q12019q12010q12016q22017q12012q12013q22019q21236.57.58.59.510.511.512.5Compensation per employee(% change over same quarter of previous year)Unemployment rate % 
 
 
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

the  current  unemployment  rate  and  its  structural 
level  (the  unemployment  gap)  was  accompanied 
by  only  a  modest  increase  of  wage  growth  (i.e.  a 
flatter  Phillips  curve).  (29)  The  remainder  of  this 
section examines whether the relation linking wage 
growth  to  its  underlying  variables  has  changed 
over time.  

There  is  no  evidence  of  a  lower  response  of 
wage  growth  to  unemployment  after  the  2013 
recovery.  Graph  I.1.15  shows  the  responsiveness 
of  wage  growth  to  the  difference  between  current 
unemployment  and  the  structural  unemployment 
rate 
(unemployment  gap)  estimated  with  a 
technique  that  allows  this  response  to  vary  over 
time. (30)  After  2010,  wage  growth  has  become 
less responsive to unemployment with most of the 
change occurring before the 2013 recovery. (31) At 
the  end  of  2017,  it  temporarily  became  more 
responsive,  but,  thereafter,  returned  to  previous 
values of about 0.3. (32)  

Between  the  second  quarter  of  2014  and  the 
second  quarter  of  2018,  actual  wage  growth  was 
about  0.3  pps  lower  than  estimates  based  on  the 
unemployment  gap  and  trend  productivity  growth 
would suggest - i.e. predicting wage growth based 
on  these  variables  leads  to  an  overestimation  of 
wage  growth.  This  overestimation  is  substantially 
reduced  when  backward-  and  forward-looking 
measures  of  inflation  are  taken  into  account 
(compare  the  red  and  the  orange  dashed  lines  of 
the graph in the Box). The predicted wage growth 
gets closer to the effective one when past inflation 
is replaced with past wage growth, which indicates 
that  in  a  low  inflation  environment  wage  growth 
tends  to  be  highly  persistent.  This  suggests  that, 
without taking into account past wage growth, one 
would  obtain  a  biased  description  of  wage 
developments over time. This result remains valid 
when  replacing  inflation  expectations,  measured 
by the ECB Survey of professional forecasters, by 
a 2% reference for price inflation. (33) 

Graph I.1.15:  Responsiveness of wage growth to labour 

market slack 

Evidence suggests that wage growth is  strongly 
anchored  to  inflation  expectations,  but  less 
dependent  on past inflation. Graph  I.1.16 shows 
that  wage  growth  has  become  less  responsive  to 
past 
inflation 
that 
expectations  play  a  constant  stabilising  role  in 
guiding  wage  developments  (see  Box  and  Graph 
I.1.15).  

inflation  and 

long-term 

(29)  A  pattern  of  a  falling  unemployment  rate  and  low  price 
inflation  is  also  confirmed  by  the  ECB  Survey  of 
Professional Forecasters for 2009Q2. 

(30)  This  is  done  with  the  technique  of  the  rolling  regression, 
which  estimates  the  relation  over  the  period  2000Q1-
2010Q1. The regression is re-estimated for each successive 
quarter up to the end of the sample. Thus, the last 10 years 
sample  is  2009-2018.  The  chart  shows  the  estimated 
response  of  wage  growth  to  unemployment  over  the  38 
sample  periods.  The  date  in  the  figure  shows  the  end  of 
each sub-sample. This evidence is consistent with Bulligan 
and Viviano (2017). 

(31)  A weaker trade-off means that employers rise wages less in 

response to a tight labour market. 

(32)  In  other  words,  if  the  current  unemployment  rate  drops 
below  the  structural  level  by  1  pp,  wage  growth  would 
expand by 0.3 pp. 

(1) The chart shows the response of wage growth to a 1 
percentage point increase in the deviation of the 
unemployment rate from the NAWRU. Each point shows the 
coefficient estimated with the rolling regression on windows 
of 10 years on a quarterly basis. See footnote. Dotted lines 
represent the degree of statistical precision with a 90% 
confidence interval. 
Source: Own Calculations based on National Accounts, LFS. 

(33)  Same results have been found by Hooper, Mishkin and Sufi 

(2019) for the US. 

16 

-0.9-0.7-0.5-0.3-0.10.12000Q2-2010q12002Q2-2012q12004Q2-2014q12006Q2-2016q12009Q1-2018q4 Unemloyment gap 
                                                           
 
 
                                                           
Labour market and wage developments 

Part I 

Graph I.1.16:  Responsiveness of wage growth to inflation and 1-year-ahead inflation expectations 

(1) The chart shows the response of wage growth to a 1 pp increase in inflation (Panel a) and to 1-year-ahead inflation 
expectations from the ECB survey of professional forecasters based on Phillips curve regression in column 6 of the Box "Phillips 
curve: a primer". Each point shows the coefficient estimated by means of rolling regression on windows of 10 years on a 
quarterly basis. See footnote. Dotted lines represent the degree of statistical precision with a 90% confidence interval. 
Source: Owns calculations. 

1.5. 

LONG-TERM 
LABOUR MARKET MATCHING  

UNEMPLOYMENT 

AND 

Graph I.1.17:  Long-term unemployed (for 1 year or more) in 

the EU, the euro area and the US (% of total 
labour force), 2005-2018, quarterly data 

in 

the 

labour 

long-term  unemployment 
The  reduction 
continued.  The  share  of 
force 
unemployed  for  at  least  12  months  dropped 
significantly  from  a  peak  of  5.3%  (6.4%  for  the 
euro  area)  reached  in  the  first  quarter  of  2014 
down  to  2.6%  (3.4%  for  the  euro  area)  in  the 
second quarter of 2019 (Graph I.1.17). In 2018, the 
number  of  long-term  unemployed  fell  to  7.2 
million (6.3 million for the euro area). This decline 
accounts  for  60%  of  the  total  reduction  in  EU 
unemployment (70% for the euro area). In the euro 
area,  almost  half  of  the  2018  decline  can  be 
attributed to changes in the very long-term (at least 
24  months)  component.  As  a  percentage  of  total 
unemployment,  the  respective  EU  rate  fell  from 
28.1% to 26.8%. Rising job finding rates have led 
to  shortened  unemployment  spells.  The  expected 
duration  of  unemployment  fell  from  a  peak  of 
almost  19  months  at  the  end  of  2012  to  less  than 
13  months.  (34)  Overall,  the  yearly  EU  long-term 
unemployment rate fell further from 3.4% in 2017 
to 3% in 2018 and 2.6 in the first half of 2019. It is 
still higher than the corresponding US rate (0.5%), 
reflecting the much higher labour market churning 
in this country (Graph I.1.17).  

Source: Eurostat and U.S. Bureau of Labor Statistics. 

Changes  in  unemployment  reflect  different 
dynamics  of  job  finding  and  separation  rates. 
Job finding rates have gradually improved, gaining 
momentum  at  the  end  of  2015  (Graph  I.1.18). 
After  an  initial  surge  in  the  first  quarter  of  2009, 
separation rates declined steadily and in the second 
quarter  of  2018,  falling  well  below  pre-crisis 
levels. In the first quarter of 2019, job-finding rates 
kept  rising  while  job  separation  kept  falling.  This 
hints  at  a  further  decline 
long-term 
unemployment rate. 

the 

in 

(34)  The  expected  duration  of  unemployment  equals  the 

reciprocal of the job-finding rate.  

17 

02462005q12006q12007q12008q12009q12010q12011q12012q12013q12014q12015q12016Q12017Q12018Q12019Q2%EUEuro areaUS-10122010q12011q22012q32013q42015q12016q22017q32018q4inflation-10122010q12011q22012q32013q42015q12016q22017q32018q4inflation expectations 
                                                           
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.1.18:  Job finding and separation rates in the euro 
area, 2005-2018, quarterly data 

Source: Commission Services based on Eurostat data. 

The recovery has increasingly benefitted all the 
unemployed. This is evident from the evolution of 
job  finding  rates  computed  for  different  spells  of 
unemployment  (Graph  I.1.17).  In  the  first  half  of 
2018,  improvements  have  been  milder  than  in 
2017  while  in  the  third  quarter  of  2018,  they 
spiked  considerably,  hitting  a  high  not  seen  since 
2005. Corrected for volatility, job finding rates for 
all  spells  are  now  higher  than  in  the  pre-crisis 
period. 

Graph I.1.19:  Job finding rate by duration of unemployment, 

euro area, 2005-2018, quarterly data 

Source: Commission Services based on Eurostat data. 

While  the  labour  market  remains  tight,  recent 
developments  suggest  that  the  efficiency  of  the 
improved.  The 
job  matching  process  has 
provides 
(Graph 
curve 
Beveridge 
information on the labour market tightness and on 
possible  changes  in  the  job matching  process.  (35) 

I.1.20) 

(35)  In the standard framework, higher unemployment over the 
cycle  is  associated  with  a  lower  number  of  vacancies  per 
unemployed  person  (a  quantitative  margin).  Movements 
along the curve are associated with cyclical fluctuations for 
unchanged  efficiency  of  the  process  matching  job  seekers 

18 

At the onset of the 2008 crisis, vacancies dropped, 
layoffs  increased  and  the  probability  of  finding  a 
job  deteriorated. (36)  With  the  lengthening  of 
unemployment  durations,  the  matching  between 
demand  and  supply  of  skills  worsened.  (37)  Thus, 
both  a  weak  demand  for  labour  (a  quantitative 
margin) and a rising skills mismatch (a qualitative 
margin)  were  responsible  for  the  rightwards  shift 
of  the  Beveridge  curve  in  the  years  after  2008 
(European Commission, 2013a; ECB, 2019). Since 
2013,  vacancies  have  been  growing  in  line  with 
falling  unemployment,  an  atypical  pattern  at  the 
early  stage  of  a  recovery.  The  rapid rate  at  which 
the unemployed have found jobs together with the 
decline  of  the  separation  rate  below  the  pre-crisis 
average  suggest  that  the  efficiency  of  the  job 
matching  process  has  improved  in  the  EU.  (38) 
Starting  from  the  second  half  of  2017,  vacancies 
jumped abruptly and reached a peak in the second 
quarter of 2018, reflecting rising labour shortages. 
With  the  economic  slowdown,  labour  shortages 
have 
gradually  while 
recede 
unemployment has continued to drop, in line  with 
the  decline  in  structural  unemployment  discussed 
the 
in  Section  1.2 
improvements 
job  matching  process 
in 
(European Commission, 2018b).  

consistent  with 

and 
the 

started 

to 

and  vacant  posts.  A  parallel  shift  of  the  curve  means  that 
employers  are  more  (less)  reluctant  to  fill  their  job 
openings. A shift rightward means that employers need to 
post  more  vacancies  to  keep  unemployment  unchanged. 
This implies a change in the efficiency of the job matching 
process  (a  qualitative  margin).  However,  an  outward  shift 
of the curve is also possible at the beginning of a recovery 
as  employers  post  vacancies  at  a  faster  rate  than  they  are 
able to match jobs with jobseekers (European Commission, 
2013a; Elsby et al. 2013; Consolo, A. and A. Dias da Silva, 
2019).  The  Beveridge  curve  is  complementary  to  the 
Phillips  curve  and  adds  a  qualitative  margin  to  the  labour 
market analysis (Consolo, A. and A. Dias da Silva, 2019). 
(36)  This  deterioration  can  be  attributed  to  both  the  labour 
market  becoming  less  tight  and  the  matching  process 
becoming  less  efficient  (see  Petrongolo  and  Pissarides, 
2001).  

(37)  An 

in 

increase 

in 
the 
long-term  unemployment, 
geographical  dispersion  of  unemployment, 
in  skill 
mismatches  (Bova  et  al.  2018;  European  Commission, 
2018b)  are  expected  to  worsen  labour  market  efficiency. 
An  increase  in  Spending  on  ALMPs  per  unemployed 
improves  matching  efficiency  (European  Commission, 
2018b). 

(38)  European Commission (2018b) provides an estimate of the 
matching efficiency by country. For the euro area, see ECB 
(2019). 

0.00.20.40.60.81.057911132005Q12006Q12007Q12008Q12009Q12010Q12011Q12012Q12013Q12014Q12015q12016q12017q12018q12019q1Job finding rateUnemployment rateJob separation rate (rhs)%%46810122005Q12006Q12007Q12008Q12009Q12010Q12011Q12012Q12013Q12014Q12015Q12016Q12017Q12018Q12019Q1Less than one monthBetween 3 and 6 monthsBetween 6 and 12 monthsMore than 12 months% 
 
 
                                                           
                                                                                   
Labour market and wage developments 

Part I 

Nominal wage growth in the euro area has been on 
an  accelerating  path  since  mid-2016,  albeit  from 
very low rates. Nonetheless, since the onset of the 
recovery,  it  has  remained  below  what  one  would 
the  pre-crisis  relation  with 
expect  based  on 
unemployment.  The  analysis  of 
this  chapter 
suggests  that  there  is  little  evidence  that  the 
response  of  wage  growth  to  unemployment  has 
changed.  Wage  growth  is  linked  to  inflation 
expectations  and  is  determined  by  productivity 
growth and cyclical labour market conditions.  

linked 

Looking  ahead,  while  the  labour  market  has 
improved  considerably, 
its  developments  are 
increasingly 
to  medium-term  growth 
prospects,  which  are  highly  dependent  on 
productivity growth and structural reforms, as well 
as  uncertainties  surrounding  international  trade 
relations. 

Graph I.1.20:  Beveridge curve for the euro area, 1995-2018, 

quarterly data 

Note: The survey-based indicator of labour shortages in 
industry approximates Job vacancies (factors limiting 
production: labour). 
Source: European Commission, based on data from the 
Labour Force Survey and the Business and Consumer Survey. 

1.6.  CONCLUSIONS  

The  EU  labour  market  has  proved  resilient  to  the 
economic slowdown of the second half of 2018. In 
September 2019, the unemployment rate fell to its 
lowest level since the early 2000s - 6.3% and 7.5% 
in the EU and the euro area, respectively. Both the 
increase in the probability of finding a job and the 
fall of job destruction rates contributed to the drop 
in  unemployment.  The  unemployment  rate  has 
been  decreasing  in  conjunction  with  a  strong 
decline  of  the  structural  unemployment  rate  and, 
possibly,  with  an  improvement  in  labour  market 
matching. Compared across the region, the decline 
in  unemployment  has  been  accompanied  by  a  fall 
in the dispersion among Member States.  

19 

1995Q12001Q12006Q12007Q12008Q12009Q12010Q12011Q12012Q12013Q12014Q12015Q12019Q10246810121416187.07.58.08.59.09.510.010.511.011.512.012.5VacanciesUnemployment rate2016Q12017Q12018Q12019Q2 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

(Continued on the next page) 

20 

Box I.1.1: A primer on the Phillips curveThe Phillips curve (PC) is the standard tool to study the link between wage growth and unemployment. A tightening (loosening) labour market is expected to lead to higher (lower) wage growth. During the 1950s and the 1960s, the idea of a stable relation between unemployment and wage growth was undisputed. The consensus broke down during the 1970s and 1980s, when inflation and unemployment turned out positively correlated. This led to major theoretical and empirical controversies over the causes of inflation and the nature of unemployment.  Milton Friedman (1968) and Edmund Phelps (1970) noted that the original relation missed the link between wage growth and expected inflation and that economic institutions and long-term trends could entail long-lasting changes in structural unemployment (i.e. defined as the unemployment rate consistent with steady inflation in the absence of supply shocks or NAWRU). It then became common to assume that expected inflation was backward-looking (dependent on the past) and that structural unemployment changed slowly over time. The change in wage growth rather than its level was linked to the unemployment gap (deviations of unemployment from the NAWRU). Unemployment could stay below the NAWRU only with a rising wage growth. In the long run, wage growth is unrelated to unemployment (the PC is vertical).  Subsequently, the importance of forward-looking inflation expectations was emphasised, which implies that inflation may be reined in without major increases of unemployment. The PC is vertical also in the short-term. A credible policy commitment to low inflation would be possible without inducing high levels of unemployment. Some noticed that with anchored (i.e. stable) expectations, the relation between the level of wage growth and inflation becomes stable (e.g. Blanchard, 2016).  wage growth       long-run PC  wage growth              𝑤1       𝑤0              new short-run PC              𝑤0               Initial short-run PC     initial short-run PC  𝑤1                 New short-run PC           𝑢1 𝑢0  Unemployment rate     𝑢1    𝑢0    Unemployment rate The correlation between wages and unemployment can be positive in case of demand shocks (left panel) or negative in case of supply shocks (right panel). When a demand shock occurs, unemployment falls temporarily to 𝑢1, while the NAWRU remains unchanged at 𝑢0. As the labour market tightens, wage growth increases along the short-run Phillips curve, from point A to point B. Higher wage growth will trigger higher inflation and move workers’ expectations of future inflation further up, which shifts the short-run PC to the right, from B to C. The reason of this shift is that workers care about real wage growth and will revise upwards their wage claims. At higher wages, also production costs will rise and firms will demand less labour. At C, unemployment rate has gone back to the NAWRU, but wage inflation remains higher than its initial level.  A drop in the price of energy, trend productivity growth or structural reforms induce a fall in the NAWRU. In panel B, a long-lasting supply shock brings the NAWRU down from 𝑢0 to 𝑢1. Actual unemployment follows only with lags the decline in the structural unemployment. If the economy is at point A, a gap opens between the unemployment rate and the new NAWRU, which restrains wage growth. With inflation expectations credibly anchored, the new short-run PC moves leftwards; the wage growth consistent with the new PC drops from 𝑤0 to 𝑤1. The faster is the response of wages, the less time wages and unemployment moves in the same direction.  Table 1 shows that the correlation between unemployment and wage growth is usually negative, except between 2013Q2 and 2016Q2, which coincides with the years where the NAWRU declined the most.  A B  
 
 
 
 
 
Labour market and wage developments 

Part I 

Box (continued) 

(Continued on the next page) 

21 

  Table 1. Correlation between yearly wage growth and unemployment rate and unemployment gap  1996Q1-2018Q4 1996Q1-2007Q4 2008Q1-2018Q4 2008Q1-2013Q2 2013Q2-2016Q2 2016Q3-2018Q4 Unemployment rate  -0.67 -0.48 -0.71 -0.88 0.69 -0.96 Unemployment gap  -0.74 -0.47 -0.82 -0.85 0.66 -0.94 In empirical analysis, the triangle Phillips curve model (Gordon, 2013) has been used to identify the relation between wage growth and unemployment based on three elements: inertia, demand and supply. Inertia captures the idea that wages are set taking into account past price inflation. An increase in aggregate demand lowers unemployment below the NAWRU and increases wage inflation along the Phillips curve. Supply shocks shift the PC (leftwards or rightwards): the economy experiences both higher (lower) inflation and higher (lower) unemployment.  Table 2 reports estimates of the relation between wage growth and unemployment for the euro area for the period 2000Q1-2018Q4. The unemployment gap explains 67% of the variation of wage growth over time (Column 1). Column 2 adds a dummy variable that models a shift of the PC after 2013Q1; its negative coefficient suggests that for a given unemployment gap, after 2013 wage growth is 0.2% lower, i.e. the PC has shifted to the left. This effect is statistically significant when productivity growth is included among the determinants of wage growth (Column 3). Yet, when inflation lagged one year is added (Column 4), the coefficient turns statistically insignificant. This implies that there is no shift in the PC after 2013 and that the dummy variable captures the lagged response of wage growth to price inflation. Wages are not fully backward-looking. Column 6 adds the one-year-ahead inflation expectations, as measured by the ECB's Survey of Professional Forecasters (SPF). The fit improves while the estimated effects of the other variables remain fairly stable. Replacing the inflation expectations by a constant inflation expectation of 2% does not change the coefficients of the other explanatory variables or alter the fit, consistent with the view that inflation expectations are anchored. Estimates are robust to an alternative specification where possible endogeneity of inflation expectations with respect to inflation and unemployment is taken into account by replacing them with a residual of a regression of inflation expectations on current inflation (quarter over quarter) and the unemployment gap lagged four quarters.  Estimates based on column 6 suggest that a 1 pp decline in the unemployment gap (cyclical unemployment) is associated with an increase of wage growth of about 0.3 pps. (1) Between 2015 and 2018, the unemployment rate dropped by 3 pps, while wage expanded at 5%. According to this estimate, the decline of unemployment explains 0.9 pps (i.e. one fifth of the total increase of wages). Turning to the effect of productivity, only about half of the change in trend productivity growth is transferred into higher wages. Between 2015 and 2018, trend productivity growth declined from 0.7% to 0.5%; the fact that productivity changes are only partly reflected in wages, has contributed to smoothing out the effect of a falling productivity growth.  Thus, the decline in trend productivity growth of about 0.2 pps constrained wage growth by about 0.1 pp. Finally, an increase in current inflation by 1 percent leads to a 0.5 percentage point increase in nominal wage growth. If this increase is combined with an increase of inflation expectations of comparable magnitude, wage growth is expected to increase by further 0.2%. In a low inflation scenario, wage growth may depend more on past wage growth than on past inflation. This confirmed in column 7, which replaces inflation one year earlier with the previous quarter growth of wages. Accounting for past wage growth, allows capturing the dynamics of wages rather well, as proved by the higher R2 (see also Graph). All variables remain statistically significant (i.e. continue to drive wage growth) but their effect is smaller. The coefficient of the unemployment gap in column (7) is not comparable to that of columns 1 to 6. In fact, lagged wage growth incorporates the effect of past changes in the other determinants of wage growth. The full effect of a change in the unemployment gap on wages equals the coefficient of the unemployment gap (0.16) divided by one minus the coefficient of lagged inflation (0.49). The full change of wage growth after all lags are played out is 0.3.                                                             (1) This is in line with previous findings (e.g. Blanchard et al, 2017; Gali and Gambetti, 2018).    
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Box (continued) 

22 

 The Graph below reports the fit of wage growth for the specifications in columns 1, 6 and 7  Table 2 Phillips curve estimate for wage growth over the period 2000Q1-2018Q4(1)(2)(3)(4)(5)(6)(7)Wage growth lagged 1 quarter0.49***(0.08)Inflation lagged 4 quarters0.18**0.24***0.17**(0.09)(0.08)(0.07)Inflation expectations0.49***0.29**(0.13)(0.13)Unemployment gap -0.39***-0.33***-0.34***-0.35***-0.37***-0.29***-0.16***(0.03)(0.03)(0.04)(0.03)(0.03)(0.03)(0.04)Productivity growth trend 0.43***0.52***0.56***0.52***0.29**(HP of productivity growth)(0.17)(0.17)-0.17(0.16)(0.13)Constant2.2***2.3***2.0***1.7***1.5***0.8***0.5*(0.04)(0.05)(0.13)(0.19)(0.17)(0.29)(0.29)Dummy after 2013Q1-0.24***-0.22***-0.11(0.10)(0.09)(0.10)R-squared adjusted0.670.680.710.720.720.750.81White heteroskedasticity-consistent standard errors & covariance12342000Q12002Q12004Q12006Q12008Q12010Q12012Q12014Q12016Q12018Q1Actual wage growthWage growth estimated on the basis of:Constant and unemployment gapConstant, unemployment gap, productivity growth, inflation lagged 4 quarthers and 1-year ahead inflation expectationsConstant, wage growth of previous quarter, unemployment gap, productivity growth, 1-year ahead inflation expectations 
 
 
 
 
2. 

LABOUR MARKET DEVELOPMENTS IN MEMBER STATES 

Labour  markets  continued  to  improve  in  most 
countries  in  2018  and  the  first  half  of  2019.  In 
most Member States, employment levels surpassed 
pre-crisis levels. Employment rates were supported 
by  both  the  fall  in  unemployment  –  more  rapid 
than  would  have  been  expected  on  the  basis  of 
output  growth  –  and  the  rise  in  activity  rates.  At 
the same time, some countervailing forces are also 
emerging, 
from  population 
dynamics  or  from  a  slowdown  in  the  rate  of 
growth.  In  some  countries,  significant  labour 
market  reserves  remain,  including  discouraged 
and involuntary part-time workers.  

stemming  either 

Developments  in  activity  and  unemployment  rates 
partly  reflect  long-term  demographic  trends.  In 
particular,  higher  activity  rates  in  the  last  two 
decades  were  boosted  by  higher  education  rates 
and  higher  participation  of  women.  The  link 
between  demographic  change  and  unemployment 
appears 
complex.  While  a 
“mechanical” composition effect of a lower share 
of  young  people  would 
lower 
to  be  no 
unemployment  rate, 
systematic  relationship  between 
falling  youth 
shares and falling unemployment rates across EU 
Member  States.  Among  EU15  Member  States,  the 
statistical relationship appears to be the opposite. 

to  be  more 

there  seems 

to  a 

lead 

Wage growth continued to gain pace in 2018 and 
the first half of 2019. Nominal wages increased in 
all  Member  States  in  2018  while  real  wages 
increased in almost all. Nominal wage growth was 
fastest  in  some  Central  and  Eastern  European 
Member States, contributing to wage convergence. 
Over the last three years, real wages grew roughly 
in  line  with  productivity  on  average,  with  some 
variability across countries.  

in 

the  euro  area.  Unit 

In 2018, labour cost developments continued to be 
broadly  consistent  with  the  external  rebalancing 
needs 
labour  costs 
continued to grow faster in countries characterised 
by  a  current  account  surplus  before  the  crisis 
in  countries  with 
than 
(‘surplus  countries’) 
(‘deficit 
current  account  deficits 
previous 
countries’).  At 
external 
time, 
competitiveness  did  not  responded  to  current 
account  imbalances  in  all  countries  of  the  euro 
area.  

same 

the 

2.1. 

INTRODUCTION 

looked  at 

To  what  extent  do  Member  States  share  the 
same  labour  market trends? While the previous 
chapter 
labour  market  and  wage 
developments  at  the  level  of  the  EU  and  the  euro 
area  as  a  whole, 
into 
looks 
identifying 
developments 
common  trends  and  the  degree  of  variety  across 
them. 

this  chapter 
in  Member  States, 

in 

an 

chapter 

starts  with 

job  creation  and 

analysis  of 
The 
developments  in  unemployment  (Section  2.2), 
employment,  activity,  and  hours  worked  (Section 
2.3).  Fluctuations 
job 
destruction  behind  unemployment  developments 
are 
in  Section  2.4.  Wage  and 
productivity developments are analysed in Section 
2.5. 
external 
competitiveness  and  how  labour  market  outcomes 
relate  to  external  balances  and  adjustment  needs. 
Section 2.7 concludes. 

reviewed 

focuses 

Section 

2.6 

on 

2.2.  UNEMPLOYMENT RATES 

in 

growth. 

In  2018,  unemployment  rates  continued  to  fall 
in  all  Member  States  in  2018,  despite  the 
The 
economic 
slowdown 
unemployment rate reached 2% in Czechia and fell 
below  4%  in  Germany,  Hungary,  Malta,  the 
Netherlands  and  Poland.  At  the  same  time,  it  was 
still above 10% in Italy, 15% in Spain and 19% in 
Greece.  The  annual  decrease  exceeded  2  pps  in 
some  Member  States  with  a  high  unemployment 
rate, such as Croatia, Cyprus, Greece, Portugal and 
Spain (Graph I.2.1). Accordingly, the dispersion of 
unemployment rates, which had reached its peak in 
2013, fell further in 2018 (Graph I.2.2).  

Almost  all  Member  States  registered  further 
decreases in unemployment in the first semester 
of 2019. The fastest reductions continued to be in 
Member  States  with 
levels  of 
unemployment, including Croatia, Cyprus, Greece, 
and  Spain  by  at  least  1  percentage  point  as 
compared 
the  2018  average.  Meanwhile, 
unemployment  slightly  rose  in  Luxembourg  and 
Sweden.  

the  highest 

to 

23 

 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.2.1:  Unemployment rate, 2007, 2013, 2017 and 2018 

(1) Countries are ranked by ascending order of unemployment rate in 2018.  
Source: Eurostat, Labour Force Survey. 

Graph I.2.2:  Dispersion in GDP growth and unemployment 

Graph I.2.3:  Changes in the unemployment rate 

rates 

unexplained by GDP growth, 2015-2018, pps 

(1) Standard deviation; 2018 based on Commission forecast; 
For 2015, the Ireland is excluded due to revision in the 
measurement of GDP.  
Source: Eurostat, National Accounts, Ameco database.  

Unemployment continued to fall at a faster rate 
than  expected  based  on  the  pace  of  economic 
growth.  The  labour  market  recovery  that  had 
started in 2013 has been “job-rich”. Since 2016, on 
average unemployment fell by 3 percentage points 
faster  than  could  be  expected  based  on  the 
historical  relationship  between  economic  growth 
and unemployment (Graph I.2.3). The unexpected 
component in the fall in unemployment was more 
significant in some countries that still had double-
digit unemployment rates in 2016 (Cyprus, Greece, 
Spain,  and  Portugal).  In  a  few  countries,  the  gap 
was less than 1 percentage point (Sweden, Finland, 
Denmark, Italy, Romania, and Malta). 

(1) The graph shows the gap between the actual change in 
the unemployment rate and the change predicted on the 
basis of the relationship with GDP growth estimated for the 
period 1995-2007 on a panel of EU28 countries, with country 
specific fixed-effects. Negative values mean that the 
unemployment rate declined faster (or increased by less) 
than predicted on the basis of GDP growth. (2) A 1 pp of 
GDP growth reduces the unemployment rate by 0.22 pps, 
based on regression estimates with country fixed effects. 
Source: European Commission based on Eurostat data. 

Current unemployment rates are influenced by 
country-specific  demographic  trends.  The  role 
of demography in affecting unemployment rates is 
explored  in  Box  2.1,  with  a  focus  on  the  links 
between unemployment rates and the falling share 
of  active  population  15-24  years  over  the  period 
1995-2017. In  a  “mechanical”  composition  effect, 
falling  youth  population 
the  overall 
unemployment  rate  as  unemployment  is  higher 
among  younger  cohorts  than  other  age  groups. 
Between  1995  and  2007,  this  composition  effect 
reduced the unemployment rate especially in Italy, 
Spain and Croatia, while it increased it in Finland 
and Sweden.  

lowers 

24 

012345620082009201020112012201320142015201620172018GDPUnemployment rate-8-7-6-5-4-3-2-101ELCYHRPTSKLTESSICZLUBGLVIEPLNLBEHUEEUKFRATDESEFIDKITROMT201620172018051015202530CZDEMTHUNLPLUKROATDKSIBGEELUIEBELTSESKEU28PTFILVEACYHRFRITESEL201720182019q220072013% 
 
 
 
 
 
However, the regression analysis in Box 2.1 shows 
that this is not the only effect in operation. For the 
fifteen  Member  States  that  first  joined  the  EU,  a 
fall  in  youth  population  over  the  sample  period  is 
associated  to  bigger  increases  in  unemployment. 
Conversely,  the  opposite  seems  to  be  true  for  the 
13 Member States that became members in or after 
2004,  even  though  the  effect  is  not  statistically 
significant.  

Broader  measures  of 
labour  reserves  are 
falling,  yet  differences  across  countries  remain 
substantial. Since 2013, supplementary indicators 
of labour reserves (or ‘labour  market slack’) have 
been  falling  and  so  has  their  dispersion  across 
countries.  Graph  I.2.4  shows,  over  time,  the 
dispersion of a narrow measure of labour reserves 
(LS1,  based  on 
the  standard  unemployment 
definition)  and  of  a  broader  measure,  including 
discouraged workers and underemployed part-time 
workers  (LS4).  In  2018,  this  broadest  measure  of 
labour  slack  fell  in  the  EU  slightly  below  its  pre-
crisis level (13.7%), and in the first half of 2019 to 
13.1%.  In  the  second  quarter  of  2019,  Poland, 
Hungary  and  Czechia  recorded  the  lowest  values, 
while the labour market slack remained above 20% 
in Greece, Spain and Italy. 

Graph I.2.4:  Dispersion of measures of labour market 

reserves (‘slack’), EU28 

(1) LS1 is unemployment as percentage of the extended 
labour force. LS4 adds to the unemployed those available 
to work but not seeking, those seeking work but not 
immediately available, as well as the involuntary part-time 
workers (underemployed); LS4 is in percentage of the 
extended labour force (i.e. the sum of the labour force and 
all previous components).  
(2) Boxes represent the middle half of the distribution; the 
mark inside the box is the median. Dots are outliers. The 
upper and lower whiskers around the boxes show the 
minimum and maximum values excluding outliers.  
Source: European Commission based on Eurostat LFS. 

Labour market and wage developments 

Part I 

2.3. 

EMPLOYMENT,  ACTIVITY  RATES  AND 
HOURS WORKED  

2.3.1.  Employment and activity rates 

In  2018,  the  employment  rate  increased  in  all 
Member  States.  The  most  rapid  increases  were 
recorded in Cyprus (3.0 pps), Malta (2.2), Finland 
(2.1)  and  Lithuania  (2.0),  as  compared  to  the  EU 
average  growth  rate  of  1  pc  (Table  I.2.1). 
Compared to 2013, the employment rate in the EU 
grew  by  4.5  percentage  points,  with  the  highest 
increases recorded in Ireland (11.1 pps), Malta (9.2 
pps), Portugal (9.1 pps) and Lithuania (8.7 pps). In 
some  cases,  employment  rates  were  boosted  by 
double-digit 
(Malta, 
Luxembourg,  Ireland,  Hungary,  Portugal  and 
Spain). In Spain and to a lesser extent the UK, they 
were  outpacing  the  growth  of  the  working-age 
population. 

employment 

growth 

Improvements continued in the first semester of 
2019.  They  were  particularly  strong  in  Belgium, 
Bulgaria,  Luxembourg  and  Malta,  with  slightly 
negative  developments  only  in  Denmark,  Estonia, 
Sweden and Latvia. In the second quarter of 2019, 
the  seasonally  unadjusted  EU  average,  up  to 
69.3 %,  was  higher  than  the  average  of  the 
previous year (Table I.2.1).  

Increasing  activity  rates  continued  to  support 
employment growth. In 2018, the EU activity rate 
went  up  by  0.4  pp,  with  the  Netherlands  now 
joining  Sweden  at  above  80  per  cent.  Annual 
increases  higher  than  1  percentage  point  were 
registered  for  Malta,  Lithuania,  Cyprus  and 
Finland,  while  the  indicator  declined  in  Spain, 
Croatia and Greece (Table I.2.1). Over the last five 
years,  activity  rates  grew  significantly  in  all 
Member  States  with  the  exception  of  Spain.  The 
highest increases were recorded in Malta (7.9 pps) 
and Hungary (7.2 pps).  

Higher  female  participation  and  educational 
levels  are  the  main  drivers  of  increases  in 
activity  rates.  Box  2.2  presents  the  results  of  a 
statistical  decomposition  exercise  for  the  period 
(2000-2017).  Changes  in  activity  rates  have  been 
mostly  driven  by  changes  in  socio-demographic 
factors  in  Belgium,  Bulgaria,  Cyprus,  Greece, 
Ireland,  Italy,  Lithuania,  Latvia,  Poland,  Portugal, 
Slovakia  and  the  UK.  Higher  education  levels 
contributed  substantially,  while  the  impact  of 

25 

 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Table I.2.1: 

Employment and activity rates and shares of marginally attached and discouraged workers over all inactive 
workers, various time periods 

(1) Marginally attached workers are defined as inactive persons (aged 15-74) who are available to work but are not actively 
searching for a job, expressed as a share of the total inactive population.  
(2) Discouraged workers are marginally attached workers who are not seeking employment because they think no work is 
available. Employment is based on the resident concept. Employment and activity rates refer to age group 15-64. 
(3) Countries are ranked by descending order of the employment rate in 2018. 
(4) Break in time series: IE in 2016, BE, DK, IE in 2017. 
Source: Eurostat, Labour Force Survey. 

ageing  is  smaller  and  ambiguous.  On  the  other 
hand, changes in the probability of being active for 
the various socio-demographic groups – especially 
women  –  played  a  major  role  in  Denmark,  the 
Netherlands,  Germany,  France,  Luxembourg, 
Austria and Spain. 

As  the  recovery  progressed,  more  inactive 
people  started  to  search  for  a  job.  In  the  five 
years to 2018, the number of people not searching 
for  a  job,  despite  being  available  (‘marginally 
attached  workers’),  dropped  considerably  from 
16.5 
fell 
to  14.4  million.  Their  numbers 
substantially  in  the  UK,  Romania,  Poland,  Spain 
and  Italy,  while  increased  in  a  few  countries, 
including France, Ireland and Bulgaria. Table I.2.1 
reports information on the share of the  marginally 
attached as a share of the inactive population (15-
74  years).  Major  reductions  (of  at  least  5  pps) 
occurred in Romania, Latvia, Cyprus and Bulgaria. 

At the same time, the share of marginally attached 
workers increased in seven countries, in particular 
in  Estonia  (due  also  to  a  fall  in  the  inactive 
population), Denmark, Finland and Belgium.  

The  share  of  discouraged  workers  also  fell. 
Discouraged workers are those marginally attached 
workers  who  are  not  searching  for  a  job  because 
they  think  no  jobs  are  available  for  them  (See 
Table  I.2.1).  The  share  of  discouraged  workers  is 
lowest  in  Austria  and  the  UK  (0.2%  of  inactive) 
closely  followed  by  Czechia  and  Denmark.  Their 
share  among  inactive  workers  exceeds  12%  in 
Portugal and is close to 8% in Italy. 

26 

2013201720182019Q22013201720182019Q22013201820132018SE74.476.977.577.681.182.582.983.610.47.21.91.5NL73.675.877.278.179.479.780.380.815.511.93.72.3DE73.575.275.976.577.678.278.678.98.38.81.21.0DK72.574.275.475.078.178.879.478.812.414.90.30.4EE68.574.174.874.875.178.879.178.915.519.22.82.7CZ67.773.674.875.072.975.976.676.55.03.90.50.3UK70.574.174.775.076.477.677.977.914.611.60.30.2AT71.472.273.073.475.576.476.876.920.419.30.50.2LT63.770.472.473.072.475.977.377.94.74.41.71.2FI68.970.072.173.775.276.777.979.912.013.54.12.8MT62.269.271.973.166.372.274.775.714.311.81.20.4LV65.070.171.872.074.077.077.777.020.114.05.92.6SI63.369.371.172.570.574.275.075.713.19.62.81.0PT60.667.869.770.473.074.775.175.314.711.314.612.5HU58.168.269.270.064.771.271.972.411.78.54.82.2EU2864.167.668.669.372.073.373.774.012.310.94.02.8IE61.767.768.669.171.872.772.973.111.023.13.92.2CY61.765.668.671.273.673.975.076.313.36.74.91.9BG59.566.967.770.768.471.371.573.812.27.29.44.2SK59.966.267.668.169.972.172.472.25.95.40.71.3PL60.066.167.468.267.069.670.170.615.311.94.82.4EA1963.566.467.368.072.273.173.473.611.911.54.33.3LU65.766.367.168.369.970.271.172.218.417.11.10.8FR64.064.765.465.771.171.571.971.56.07.11.91.7RO60.163.964.866.464.967.367.869.112.05.38.24.0BE61.863.164.565.667.568.068.669.37.38.73.72.4ES54.861.162.463.574.373.973.773.913.810.14.82.6HR52.558.960.661.863.766.466.365.814.09.45.64.6IT55.558.058.559.463.465.465.666.019.519.59.27.9EL48.853.554.957.167.568.368.268.94.65.31.31.5Share of marginally attached workersShare of discouraged workersEmployment rateActivity rate  
 
 
 
 
Labour market and wage developments 

Part I 

Graph I.2.5:  Change in number of employees and hours worked per employee, cumulative % change since the first quarter 

of 2009 

(1) Countries are ranked by ascending order of % change in the number of employees between 2009q1 and 2018q4. Values 
for number of employees for Luxembourg and Malta are out of scale (+30 and +51%). Due to break in the series of hours 
worked data from HU are shown relative to 2010Q1. 
Source: Eurostat, National Accounts. 

2.3.2.  The adjustment of hours worked 

2.3.3.  Employment  developments  at  sectoral 

loss  more 

in  hours  worked  during 

Hours  worked  remained  flat  and  in  many 
countries  below  pre-crisis  levels.  In  the  EU,  the 
drop 
the  crisis 
compounded  an  already  existing  trend  (European 
Commission  2017a,).  In  the  period  2000-2007, 
hours  had  already  fallen  in  most  of  the  countries 
and  by  more  than  a  cumulative  4  pps  in  Austria, 
Malta,  Latvia  and 
the  Czech  Republic.  The 
the 
cumulative 
subsequent  period 2008-2012 (4.3 pps in  the euro 
area) and for several countries, the yearly pace was 
higher  than  1  percent  point  (Hungary,  Malta, 
Ireland). Starting from the cyclical low in the first 
quarter  of  2013,  developments  in  hours  worked 
remained  overall  flat  and  often  well  below  pre-
crisis  levels  (Graph  I.2.5).  Since  2013,  average 
hours  worked  increased  in  twelve  countries.  The 
increase  was  particularly  strong 
Ireland, 
Czechia, Italy, Germany and Spain.  

than  doubled 

in 

in 

level 

Job  creation  is  particularly  high  in  services. 
During the period 2013-2018, the strongest growth 
of  employment  occurred  in  the  market  services 
(9.4%)  and  in  public  administration,  health  and 
education (5.9%). Double-digit growth in industry 
was  recorded 
in  Portugal,  Slovakia,  Ireland, 
Slovenia,  Poland,  Hungary,  the  Czech  Republic 
and  Cyprus  (Table  I.2.2).  Strong  divergences 
emerge  in  the  construction  sector,  with  some 
countries  showing  record  growth  rates  (such  as 
Ireland) and others showing negative growth rates 
(such as Czechia, Italy and France).  

The  occupational  structure  of  employment  is 
changing.  The 
recovery  brought  about  an 
acceleration  in  the  change  of  the  professional 
structure  of  the  employment.  In  2018,  more  than 
80  per  cent  of  the  overall  increase 
in  EU 
two  broad  socio-
employment  occurred 

in 

27 

-20%-15%-10%-5%0%5%10%ELLVBGSIES-20%-15%-10%-5%0%5%10%PTCYHRFINL-15%-10%-5%0%5%10%LTDKROFRIT-15%-10%-5%0%5%10%CZEEBEPLUK-15%-5%5%15%25%2009Q12011Q12013Q12015Q12017Q12019Q22011Q12013Q12015Q12017Q12019Q22011Q12013Q12015Q12017Q12019Q22011Q12013Q12015Q12017Q12019Q2EmployeesHours worked per employeeIESKDEAT-15%-5%5%15%25%2009Q12011Q12013Q12015Q12017Q12019Q22011Q12013Q12015Q12017Q12019Q22011Q12013Q12015Q12017Q12019Q22011Q12013Q12015Q12017Q12019Q2EmployeesHours worked per employeeSEHULUMT51%30% 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

both 

employees  while 

economic  groups:  professionals  and  technicians 
and  associate  professional  employees.  In  the  euro 
area,  employment  growth  was  also  sustained  for 
the  group  of  managers  and  for  clerks  and  skilled 
service  employees.  It  was  milder  for  skilled 
industrial 
small 
entrepreneurs  and  unskilled  workers  registered  a 
contraction.  More  than  60%  of  the  increase  in 
employment  over  the  period  2013-2018  involved 
professionals and technicians, whose overall share 
has now passed from 32.7% to 34.4%, with much 
higher  shares  than  the  average  in  Luxemburg,  the 
Nordic Member States and the Netherlands. In the 
same time span, small entrepreneurship contracted 
on  average  by  5.8%  (1.3  million)  with  losses 
concentrated  in  Romania,  Italy,  Germany,  and 
Poland. (39)  

Table I.2.2: 

Employment growth in different sectors, 
cumulative % change over the years 2013-
2018 

(1) Countries are ranked by descending order of cumulative 
employment growth over the period 2013-2018. 
Source: Eurostat, National Accounts. 

(39)  Chapter  II.1  analyses  trends  in  job  polarisation  and  skills 

mismatches in detail. 

28 

2.3.4.  Employment  developments  by  contract 

type  

In 

by 

2018, 

I.2.6). 

(Graphs 

boosted 

full-time 

Permanent  employment  continues  to  grow, 
positions. 
mostly 
Employment  growth 
in  permanent  positions 
overtook that in temporary contracts in 2018 in the 
EU 
permanent 
employment increased in all countries except Italy, 
with an average growth rate of 1.9% (1.7% in the 
euro  area).  Its  increase  was  strongest  in  Cyprus, 
Denmark,  Slovakia  and  Slovenia  (Graph  I.2.7). 
total 
The  share  of  permanent  workers 
employment  slightly  increased  in  the  EU  (by 
0.4 pp in 2018) and in a majority of Member States 
(Table  I.2.3).  The  increase  reached  1.5  pps  in 
Denmark,  Poland,  Slovakia  and  Slovenia.  The 
share of permanent workers fell in a few countries, 
exceeding 0.5 pps in Estonia, Italy and Malta. Self-
employment  continues  to  follow  a  declining  path 
in most countries. 

in 

Graph I.2.6:  Employment growth by contract type, EU 28, 

2004-2018, % 

(1) Age group: 15-64. 
Source: Eurostat, Labour Force Survey. 

In  2018,  part-time  employment  accounted  for 
less  than  one  fifth  of  total  employment. 
Compared  to  pre-crisis  levels,  the  EU  average 
went up from 18% to 20%, with increases of more 
than  3  pps  in  Finland,  Greece,  Cyprus,  Italy,  the 
Netherlands,  Austria 
and  Estonia.  Under-
employment  -  involuntary  part-time  work  -  as  a 
share  of  employment  (3.6%  in  the  EU)  is  still 
slightly  above  its  pre-crisis  level,  and  grew  in 
almost  all  Member  States, 
the 
Netherlands  (3.3  pps),  Cyprus  (3.9  pps)  and 
Greece  (4.3  pps).  In  a  few  cases,  however,  it 
dropped,  most  notably  in  Sweden  (-1.7  pps)  and 
Germany (-3.5 pps). 

in  particular 

IndustryConstructionMarket servicesPublic admin, health, educationMT3.521.041.523.7IE12.867.317.115.5LU3.613.419.117.5HU11.221.622.510.3CY11.323.520.25.4ES9.218.613.58.8PT14.07.921.65.5SK13.24.312.16.7SI12.47.412.78.1SE-1.319.611.110.7HR8.56.117.916.3EE6.05.114.62.0UK4.916.09.53.5DK6.016.510.92.6NL2.61.912.40.9CZ9.5-5.96.69.8EU284.86.09.55.9AT4.87.57.39.0EA193.23.69.06.1LT7.81.07.95.6DE3.74.16.19.7PL11.33.89.34.2EL0.10.710.15.1BE-2.91.08.25.8IT0.3-6.38.22.8FR-3.0-4.27.32.4FI-3.213.95.12.7BG4.72.96.10.3LV-4.97.64.50.5RO8.47.417.27.3-6-4-202468102006200820102012201420162018%Permanent employeesTemporary employeesSelf employed 
 
 
 
                                                           
 
Labour market and wage developments 

Part I 

Graph I.2.7:  Employment growth by contract type, 2008-2019Q2, cumulative % change since the 2008Q1 

(1) Age group: 15-64. 
Source: European Commission based on Eurostat data, Labour Force Survey. 

Table I.2.3:  Distribution of contract types among the 

employed, 2018, % and pps. 

Open-ended  contracts  remain  the  predominant 
form of employment. As displayed in Table I.2.3, 
in Luxembourg, Hungary, the Baltic and two of the 
Nordic  countries,  the  shares  on  total  employment 
are the highest – ranging from about 82% to 89%. 
Temporary  contracts,  on  the  other  hand,  are  most 
widespread  in  Spain  (21.9%),  Poland  (19.2%), 
Portugal (17.9%) and Croatia (17.3%). Finally, for 
five  countries  (Greece,  Bulgaria,  Romania  and 
Italy,  Poland),  self-employment  remains  quite 
significant with shares above 20%.  

(1) Countries are ranked by descending share of open-
ended contracts in 2017.  
(2) “Change” refers to the change in the share compared 
with the previous year (in pps). 
Source: European Commission based on Eurostat LFS data. 

29 

2018chg2018chg2018chgLU88.5-0.45.50.56.0-0.1LT86.90.41.4-0.111.7-0.3EE86.2-0.83.00.310.80.5LV86.01.32.3-0.211.7-1.1HU84.61.26.2-1.49.20.2DK84.21.59.7-1.56.1-0.1SE82.40.613.7-0.53.9-0.2MT81.3-1.96.61.512.10.3UK80.40.44.5-0.115.1-0.2AT80.20.57.6-0.112.1-0.4DE80.20.610.4-0.39.4-0.2SK79.51.57.2-1.213.3-0.3CZ79.11.16.7-0.914.2-0.1CY77.41.211.2-1.111.4-0.2IE76.2-0.38.50.715.3-0.4FR75.70.214.1-0.110.20.0RO75.11.10.8-0.124.0-1.1FI75.0-0.413.50.311.50.0BE74.2-0.49.10.416.70.0EU2873.90.411.4-0.114.7-0.3EA1973.10.112.90.114.0-0.3BG70.91.03.1-0.326.0-0.7HR70.20.917.3-0.612.6-0.3SI68.21.912.7-1.819.1-0.1PT68.20.417.90.113.9-0.5NL67.30.616.3-0.316.4-0.3IT64.2-0.911.91.223.8-0.3ES64.00.121.90.314.1-0.4EL60.60.46.80.032.6-0.4PL60.61.519.2-1.420.2-0.1Open-endedTemporary  Self   contractscontractsemployed 
 
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.2.8:  Job finding and job separation rates, 2008-2018, quarterly data 

Source: European Commission based on Eurostat data. 

30 

 
 
 
Labour market and wage developments 

Part I 

Long-term  unemployment  fell  significantly.  In 
2018,  the  number  of  long-term  unemployed  – 
those  who  are  looking  for  a  job  for  at  least  more 
than  a  year  –  fell  from  8.3  to  7.2  million,  with 
notable  improvements  in  Spain,  France,  Greece, 
Poland and Italy. The overall reduction since 2013 
was significant, more than 5 million in the EU (3.3 
in  the  euro  area),  mainly  driven  by  a  reduction  in 
Spain (1.5 million), Poland, the UK, Germany and 
Portugal.  

Graph I.2.9:  Average length of unemployment spells, 2005-
2019Q2, quarterly data, months 

2.4. 

JOB MARKET FLOWS 

2.4.1.  Job finding and separation rates 

In  2018,  job  finding  and  job  separation  rates 
improved  further.  For  the  large  majority  of 
Member  States,  job-finding  rates  –  a  measure  of 
the  probability that an unemployed person finds a 
job  within  the  next  month  –  have  increased  or 
hovered around 2017 values (Graph I.2.8). Several 
Member  States 
(including  Czechia,  Finland, 
Ireland,  Malta,  the  Netherlands,  Poland,  Portugal, 
Slovenia)  further  improved  on  their  already  high 
finding rates, while the separation rate – a measure 
of 
that  an  employed  person 
the  probability 
the  next  month  –
in 
becomes  unemployed 
continued  on  a  declining  path.  For  another  group 
of countries (including Cyprus, Denmark, Estonia, 
Luxembourg),  job  finding  rates  improved  while 
the  downward  trend  in  the  job  separation  rate 
softened or changed sign. 

2.4.2.  The Beveridge curve and labour market 

matching  

(1) Data for Malta are not available. 
Source: European Commission based on Eurostat data. 

The  relation  between  unemployment  and 
labour market tightness suggests both a decline 
in  structural  unemployment  and  a  tightening 
labour  market.  (40)  From  the  end  of  2016,  the 
indicator  of  labour  market  tightness  increased 
rapidly. In some cases, such as Czechia, Germany, 
Slovakia and Spain, it reached a peak during 2018. 
In Southern Member States, such as Greece, Italy, 
Portugal  and  Spain,  the  shape  of  the  Beveridge 
curve  was  flatter  for  a  long  time,  before  recently 
steepening  in  Greece  and  Spain  as  the  tightness 
indicator  increased.  In  other  countries,  mostly  in 
Central  and  Eastern  Europe  (including  Bulgaria, 
Poland, Slovenia and Slovakia), the labour market 
has  been 
the 
slowdown  of  economic  growth,  unemployment 
continued  dropping,  suggesting  a  decline  in  the 
structural unemployment.  

tightening  since  2013.  With 

(40)  The  Beveridge  curve 

is 

the 

relationship  between 
unemployment and an indicator of labour market tightness 
(i.e.  the  abundance  of  job  vacancies).  Graphs  A1.1  and 
A.1.2in the  Annex of this Chapter  rely  on a proxy for the 
vacancy  rates,  namely  an  indicator  of  labour  market 
tightness, as calculated from the EU Business Survey. It is 
the share of manufacturing firms reporting that labour is a 
“factor limiting production”. 

the 

some 

duration 

countries, 

In 
of 
unemployment  remains  high.  It  is  inversely 
related to job finding rates and has been constantly 
improving, although it remains high (Graph I.2.9). 
In  2018,  the  average  duration  of  unemployment 
rose  –  with  increases  in  Belgium,  Bulgaria, 
Estonia,  Latvia,  Romania  and  the  UK  –  thus 
remaining  higher  (7.6  months)  than  the  pre-crisis 
(7.1  months). (41)  Wide  differences, 
value 
therefore,  persist  and  have  even  increased.  Some 
Member  States  (i.e.  Poland,  the  Czech  Republic, 
Croatia,  Romania,  Slovakia,  Lithuania  and 
Germany) recorded significant improvements even 
over  their  initial  2005-2007  high  averages;  others 
(Greece,  Spain  and  Italy)  are  still  far  from  their 
pre-crisis  values.  In  2018, 
the  Scandinavian 
countries  feature  both  much  lower  durations  of 
unemployment  and 
long-term  unemployment 
ratios.  On  the  other  hand,  Bulgaria,  Greece,  Italy 
and  Slovakia  have  significantly  higher  values  for 
both  indicators.  The  most  recent  data  (2019Q2) 
point  to  a  general  reduction  of  the  durations, 

(41)  However,  the  median  value  fell  by  almost  1  month, to  10 
months,  and  further  down  to  9.7  in  the  second  quarter  of 
2019 

31 

05101520ELSKITPTBGHRSIIEBEROESHUFRCYNLCZEEPLDELVLTLUATUKDKFISE2013Q1-2019q22005q1-2007q42019Q2 
                                                           
 
 
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.2.10:  Unemployment rates and structural unemployment rates in the EU 

(1) Information is missing for Croatia for data availability. 
Source: Ameco and Commission services calculations based on LFS. 

especially  in  Slovakia,  Belgium  the  Netherlands 
and Croatia. 

for  Austria,  Greece,  Spain,  Luxemburg,  Cyprus 
and  Italy  they  occurred  after  2013.  For  all  the 
others, they were reached in the period 2010-2013. 

in  unemployment  partly  reflects 
The  fall 
improvements  in  structural  features  of  the 
labour  markets.  Graph  I.2.10  compares  three 
indicators:  the  unemployment  rate,  an  estimate  of 
structural  unemployment  (NAWRU)  and 
the 
unemployment  rate  consistent  with  current  job 
finding  and  separation  rates  (a  measure  of 
‘frictional’  unemployment,  NRU).  According  to 
Commission  estimates,  the  NAWRU  for  the  EU 
peaked  in  2009  (8.9%)  and  has  kept  falling  since 
2013  at  a  rapid  pace,  reaching  7.2%  in  2018.  (42) 
Country  heterogeneity  in  the  EU  is  reflected  both 
in  the  wide  differences  in  levels  –  in  some  cases 
the NAWRU is even up to three times the median 
of  6.1%  –  and  in  the  temporal  evolution  of  the 
indicator.  For  Belgium,  Germany,  Malta,  Poland 
and  Slovakia  the  peaks  well  pre-dated  the  crisis; 

(42)  In  European  Commission  (LMWD,  2018b),  econometric 
evidence  suggested  that  an  improvement  in  matching  of 
vacant  jobs  with  unemployed  people  explains  part  of  the 
decline  in  the  structural  unemployment  in  a  large  number 
of  countries.  In  some  cases,  other  factors  seem  to  have 
played  a  role,  such  as  reforms  increasing  real  wage 
flexibility  or  technological  changes  and  automation  in 
Denmark. 

32 

of 

the 

countries, 

in  finding  rates  and  reductions 

In  most 
frictional 
unemployment  peaked  earlier  and  fell  faster 
than  actual  unemployment.  Since  2013,  the 
largest  decreases  in  the  frictional  unemployment 
can  be  observed  in  countries  where  both  the 
increases 
in 
separation  rates  were  stronger,  especially  where 
initial  values  were  particularly  unfavourable 
(Greece,  Croatia,  and  Spain).  Elsewhere  (Nordic 
countries  and  Luxemburg),  job  finding  rates  were 
already  quite  high  so  that  their  marginal  increase 
could  not 
significant 
into 
reductions of frictional unemployment. In 2018 the 
difference between the current unemployment rate 
and  the  NAWRU  was  closed  (Graph  I.2.10  and 
Graphs  A1.3  and  A1.4  in  the  Annex)  or  virtually 
so  for  the  great  majority  of  countries.  In  several 
the 
including  Belgium,  Malta, 
countries, 
the 
Netherlands, 
current 
the  UK, 
and 
unemployment  rate  fell  below 
the  structural 
unemployment.  (43)  In  the  case  of  frictional 

translate 

further 

(43)  Besides  Greece  (5.6  pps),  the  gap  is  higher  than  half  a 

percentage point in Sweden and Italy. 

 
                                                           
                                                           
 
Labour market and wage developments 

Part I 

Graph I.2.11:  Nominal compensation per employee, 2017, 2018 and 2019Q2, annual % change 

(1) Wages are measured by the indicator "Nominal compensation per employee", which is calculated as total compensation 
of employees divided by the total number of employees. The total compensation is defined as the total remuneration, in 
cash or in kind, payable by an employer to an employee in return for work done by the latter during the accounting period 
and it has two components: i) Wages and salaries payable in cash or in kind; and ii) Social contributions payable by 
employers. (2) All the data used are national accounts data. The indicators are based on national currency values. 
Aggregates are weighted averages. (3) Countries are ranked in descending order of nominal compensation growth in 2018. 
Source: European Commission, AMECO database. 

unemployment,  instead, and with the exception of 
Austria, the gap remains open at around 3 pps (it is 
marginal only in the Nordic countries and the UK). 

2.5. 

TRENDS IN WAGES AND LABOUR COSTS 

2.5.1.  Nominal wage developments  

Nominal  wage  growth  rose  in  many  Member 
States  despite  the  weakening  of  economic 
growth.  For  the  first  time  since  2008,  wages 
increased  in  all  Member  States  in  2018.  In  2017, 
nominal  wages  registered  a  decrease  in  Finland 
and  Croatia.  The  increases  in  2018  ranged  from 
0.5%  in  Cyprus  to  16.3%  in  Romania  (Graph 
I.2.11). (44)  

In  2018,  nominal  wage  growth  was  fastest  in 
some  Central  and  Eastern  European  Member 
States,  contributing 
to  wage  convergence. 
Romania  registered  the  most  rapid  growth  in 
nominal  compensation  per  employee;  the  increase 
was  even  greater  in  gross  wages  and  salaries, 
which exclude employer contributions, because of 
a  tax  shift  from  employees  to  employers.  Other 
Member  States  with  rapid  nominal  wage  growth 
include  Bulgaria,  Czechia,  Estonia,  Hungary, 

(44)  Wages  refer  to  compensation  per  employee;  it  includes 
gross wages as well as employer contributions, thus it is a 
useful  concept  of  labour  cost.  It  is  also  not  sensitive  to 
shifts  of 
tax  and  contribution  burden  between 
the 
employers and employees 

Latvia,  Lithuania  and  Poland,  all  above  6%.  In 
contrast, nominal wage growth was slowest, below 
2%,  in  some  Member  States  with  comparatively 
high unemployment, including Cyprus, Greece and 
Spain.  

Average  wage  growth  in  the  first  semester  of 
2019  was  close  to  that  in  2018,  with  some 
variation  across  countries.  Growth  in  nominal 
to  have 
compensation  per  employee  appears 
slowed somewhat in some countries with fast wage 
growth,  such  as  Czechia,  Hungary,  and  Romania 
but gained pace in Bulgaria, Cyprus, Latvia, Spain, 
Ireland and Slovakia. In contrast, it appears to have 
almost  stagnated  in  countries  with  the  slowest 
wage growth.  

In  many  countries,  nominal  wage  growth  has 
been  rising  as  unemployment  fell,  but  the 
relationship is not uniform. In the past few years, 
nominal  wage  growth  has  been  below  what  could 
be expected based on the past relationship between 
wage  growth  and  unemployment  (the  so-called 
Phillips-curve  relationship)  in  the  euro  area  (see 
Chapter  I.1).  There  is,  however,  a  variety  of 
patterns across countries (see Graphs in  Annex of 
this  chapter).  In  some  Member  States,  including 
Belgium,  Finland,  the  Netherlands  and  Spain, 
wage growth indeed appears to be below the pace 
that  could  be  observed  at  similar 
levels  of 
unemployment  before  2008.  In  other  countries, 
including  Austria,  France,  and  Germany,  wage 
developments  appear  to  be  in  line  with  the  pre-

33 

-2381318ROEEBGLVPLCZLTHUSKSISELUATDEUKPTMTHRITIEDKBEFRNLELESFICY201720182019q2 
                                                           
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.2.12:  Real wages per employee, 2017, 2018 and 2019Q2, annual % change 

(1) Real gross wages and salaries per employee, deflator private consumption. (2) Countries are ranked by descending order 
of real wage growth in 2018. (3) Real wage growth in Romania in 2018 (30,4%).  
Source: European Commission, AMECO database. 

crisis  relationship.  In  Greece  and  Italy,  wage 
growth  is  modest  but  above  the  historical  Phillips 
curve.  

Romania,  while 
Denmark, 
Netherlands, Greece, and Malta.  

it  was 

Italy,  and  even  negative 

lowest 

in  Belgium, 
the 
in 

2.5.2.  Real wage developments  

2.5.3.  Wages, productivity, unit labour costs 

In  2018,  real  wages  increased  in  almost  all 
Member  States,  supporting  the  employees’ 
purchasing  power.  As  nominal  wage  growth 
increased  ahead  of  inflation,  real  wages  increased 
except  in  Cyprus,  the  Netherlands,  and  Spain 
(Graph  I.2.12).  As  compared  to  2017,  real  wage 
growth turned positive in Croatia, Italy, and Malta.  

Real  wage  growth  continued  to  be  fastest  in 
some  Central  and  Eastern  European  Member 
States. It was fastest in Romania (the 2018 growth 
rate of nearly 30% is not shown in the graph as it is 
due to the effect on gross wages of a tax shift from 
employees  to  employers),  followed  by  Bulgaria,  
Hungary,  Poland,  Estonia,  and  Czechia  (all  above 
5%).  

is  heterogeneous.  It 

In contrast, the group of countries with low real 
includes 
wage  growth 
countries  with  comparatively  high  unemployment 
rates  (such  as  Cyprus  and  Spain),  but  also  those 
with low (such as Austria and the Netherlands) and 
intermediate rates (France and Sweden).  

In the first semester of 2019, real wage growth 
increased  in  most  countries.  As  nominal  wage 
growth  continued  to  rise  and  inflation  declined, 
real wages strengthened in the first two quarters of 
2019.  Real  wage  growth  was  fastest  in  the  Baltic 
countries,  Bulgaria,  Slovakia,  Hungary  and 

34 

faster 

In  2018,  real  wages  grew 
than 
productivity  in  a  majority  of  Member  States. 
This  included  notably  some  Eastern  European 
Member  States,  such  as  Romania  (where  real 
compensation  per  employee  growth  exceeded 
productivity  growth  by  almost  8  pps)  and  to  a 
lesser  degree  in  Czechia  and  Bulgaria  (Graph 
I.2.13,  left  panel,  countries  furthest  below  the 
diagonal line). In Denmark, moderate wage growth 
was  coupled  with  negative  productivity  growth. 
Among  large  euro-area  Member  States,  wages 
grew in line with productivity in France and Spain, 
but  somewhat  ahead  (by  about  a  pps  faster  than 
productivity)  in  Germany  and  Italy.  In  contrast, 
wages  grew  slower  than  productivity  in  Cyprus, 
Hungary,  Ireland  and  Malta  in  2018  (countries 
above and to the left of the diagonal line).  

some 

across 

variability 

Over  the  last  three  years,  real  wages  grew 
roughly  in  line  with  productivity  on  average, 
with 
countries. 
Comparing  wages  and  productivity  over  a  longer 
time  period  has  the  advantage  that  one-off  events 
and  unexpected  economic  shocks  have  a  smaller 
effect than in individual years. This is borne out in 
Graph I.2.13 as the variability between countries is 
smaller on average over the last three  years (right 
panel) than in 2018 alone (left panel). 

-4-2024681012ROBGPLEECZHULVLTSKMTDESIPTDKLUELHRITIEUKATSEBEFRFINLESCY201720182019q2 
 
Labour market and wage developments 

Part I 

Graph I.2.13:  Real compensation per employee and productivity, 2018 (left panel) and the average over 2016-2018 (right) 

(1)Real compensation is nominal compensation per employee deflated with the GDP deflator.  
(2) On the 45 degree line, real wage growth equals productivity growth. Point above the line represent countries where 
productivity growth is above real wage growth; the opposite for points below the line. 
Source: European Commission based on Eurostat data. 

Real  wage  growth  over  three  years  exceeded 
productivity  growth 
in  some  Central  and 
Eastern European Member States. This occurred 
in  particular  in  Romania  (by  almost  6  pps  on 
average) followed by Bulgaria, Czechia, Hungary, 
Latvia,  Lithuania,  Poland  and  Slovakia  (by  2  pps 
or  more).  In  contrast,  wage  growth  remained 
below productivity growth by at least 1 percentage 
point per year in Croatia, Cyprus, Finland, Ireland 
and Malta.  

recovery  in  2013  (Graph  I.2.14).  In  contrast,  the 
wage  share  has  receded  in  countries  in  which 
wages  grew  slower  than  productivity  including  in 
Belgium,  the  Netherlands  and  Slovenia,  where  its 
level  was  comparatively  high.  It  remained  fairly 
constant  in  large  euro-area  countries  such  as 
France  and  Italy  (slight  decrease)  while 
it 
increased  in  Germany.  In  Ireland,  the  wage  share 
decreased  because  of  a  revision  of  GDP  statistics 
in 2015.  

Graph I.2.14:  Change in the wage share 2013-2018 

Source: AMECO database of the European Commission; 
Adjusted wage share; total economy; as percentage of 
GDP at current prices [ALCD0]. 

Consequently,  the  wage  share  has  increased  in 
some  Eastern European Member States, where 
it  was  low.  When  real  wages  grow  faster  than 
productivity, the wage share (the share of national 
income  paid  to  labour)  increases.  This  is  the  case 
in a number of Eastern European Member States in 
which  the  labour  share  was  comparatively  low 
(including  Bulgaria,  Latvia,  Lithuania,  Romania 
and Slovakia), since the onset of the labour market 

in  real 
The  gap  between  developments 
is  weakly 
compensation  and  productivity 
negatively  related 
to  unemployment.  The 
relationship is expected to be negative: wages may 
lag  behind  productivity  in  countries  in  which 
excess unemployment needs to be absorbed, while 
they  may  grow  ahead  of  productivity  in  countries 
with  low  unemployment  and  labour  shortages.  In 
2018,  like  in  previous  years,  the  relationship  was 
weakly negative (Graph I.2.15). Wages grew most 
ahead  of  productivity  in  some  countries  with  a 
comparatively 
rate  and 
developing  labour  shortages  (Czechia,  Hungary, 
Romania).  In  contrast,  wages  rose  in  line  with 
productivity 
a 
comparatively  high  unemployment  rate  (Spain, 
Croatia),  lagged  behind  productivity  in  Cyprus, 
and  grew  somewhat  ahead  of  productivity  in 
Greece and Italy.  

low  unemployment 

countries  with 

some 

in 

35 

BEBGCZDKDEEEIEELESFRHRITCYLVLTLUHUMTNLATPLPTROSISKFISEUK-15-10-50510404550556065Change in wage share, 2013 to 2018, pptWage share in 2013, percentBEBGCZDKDEEEIEELESFRHRITCYLVLTLUHUMTNLATPLPTROSISKFISEUK-2-1012345-2024681012Productivity growth, average 2016-2018Real compensation growth, average 2016-201845 degree line BEBGCZDKDEEEIEELESFRHRITCYLVLTLUHUMTNLATPLPTROSISKFISEUK-2-1012345-2024681012Productivity growth, 2018Real compensation growth, 201845 degree line  
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.2.15:  Unemployment rate (2017) and change in real 

unit labour costs (RULC, 2018) 

I.2.4  provides  an  overview  of  the  components  of 
real unit labour costs by country.  

2.5.4.  Wage developments by sector 

In  most  countries  with  comparatively  rapid 
wage  growth  in  2018,  public  sector  wages 
increased  ahead  of  the  private  sector.  This  was 
the case in Bulgaria, Lithuania and Romania and to 
a  lesser  extent  in  Czechia  and  Latvia.  In  contrast, 
public  sector  wage  growth  lagged  behind  the 
private  sector  in  Estonia  and  Hungary  (Graph 
I.2.16).  

These patterns have not been constant over the 
last years. Over the average of 2014-2017, public 
sector wage growth was faster than wage growth in 
the  private  sector 
in  Estonia,  Hungary  and 
Romania (by 3 pps per  year  on average  or  more), 
but slower in Bulgaria, Latvia and Lithuania.  

In  most  countries  with  slow  wage  growth, 
public  sector  wages  grew  slower  than  wages  in 
the  private  sector.  The  gap  was  more  than  1 
percentage point in Croatia, Denmark and Portugal 
and  it  exceeded  half  a  percentage  point  in  Greece 
and  Spain.  In  contrast,  public  sector  wages  grew 
faster than in the private sector in Cyprus and Italy 
in  2018.  Looking  at  the  longer  period  of  2014-
2017, public wages lagged behind private wages in 
most  countries  with  comparatively  slow  wage 
growth,  including  in  Cyprus  and  Italy  (Graph 
I.2.16).  

In  some  countries  with  high  current  account 
surpluses,  public  wage  growth  has  been  slower 
than  wage  growth  in  the  private  sector.  This  is 
the case in Denmark, Germany and Slovenia (both 
in  2018  and  on  average  between  2014  and  2017). 
In  Malta,  public  wages  substantially  outpaced 
private  wage  growth  in  2018  after  growing  at  the 
same  pace  for  the  previous  four  years.  Finally,  in 
the  Netherlands,  public  sector  wage  growth 
outpaced  that  in  the  private  sector  wages  since 
2014, although the gap closed in 2018.  

In 2018, private sector wage growth was fastest 
in  market  services.  In  most  countries,  wage 
growth  in  market  services  was  faster  than  overall 
wage  growth  (Graph  I.2.17).  Exceptions  were 
Bulgaria,  Cyprus,  and  Malta,  where  wage  growth 
was driven by industry. Wages in the construction 
sector grew ahead of overall wage growth in a few 

Source: European Commission based on Eurostat data.  

Table I.2.4:  Decomposition of the annual percentage 

change of nominal and real unit labour costs 
(NULC and RULC), 2018 

(1) Countries are ranked in descending order of Real Unit 
Labour Costs (RULC). (2) The annual percentage change of 
Nominal unit labour costs (NULC) is calculated as the 
difference between the growth rate of nominal 
compensation per employee and labour productivity. The 
annual percentage change of Real Unit Labour Costs 
(RULC) is calculated as the difference between the growth 
rate of NULC and the GDP deflator.  
Source: European Commission, AMECO database. 

the 

same 

At 
lagged  behind 
time,  wages 
productivity in some Member States characterised 
low  unemployment  rate, 
by  a  comparatively 
including Ireland, Luxembourg and Malta and, to a 
lesser  extent,  Austria  and  the  Netherlands.  Table 

36 

ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUK-4-202460510152025RULC, annual change, 2018Unemployment rate 2017Compensation per employeeLabour productivityNULCGDP deflatorRULCRO18.43.914.05.97.7CZ8.01.66.32.53.7HU9.62.76.74.52.1LT8.02.45.43.32.0DK2.0-0.32.30.61.7PL7.84.82.81.11.7EE8.82.66.14.61.4SK5.42.13.32.11.2IT2.00.11.90.81.1DE3.00.12.81.91.0PT2.0-0.22.21.40.8SE3.40.52.92.20.7UK2.70.22.51.90.6EL1.30.21.10.50.5BE1.70.11.61.10.4FI1.4-0.82.32.00.3LV7.83.14.64.20.3FR1.80.81.00.80.2SI4.01.52.52.30.2HR1.90.21.71.70.0AT2.51.01.51.6-0.1ES0.80.10.81.0-0.2NL1.70.01.82.2-0.4LU2.1-1.13.33.8-0.5BG5.63.22.43.6-1.2CY0.1-0.20.31.6-1.3MT2.11.30.72.2-1.4IE2.14.8-2.60.8-3.4 
 
 
 
 
 
Labour market and wage developments 

Part I 

Graph I.2.16:  Compensation per employee in public and private sectors, 2018 and average over 2014-2017, % change 

(1) The public sector is defined as public administration and defence, education, health and social work, personal service 
activities. (2) Countries are ranked by ascending order of growth of compensation per employee in the public sector in the 
period 2014-2017. 
Source: Eurostat. 

countries, including Bulgaria and Romania and, to 
a  lesser  extent,  Estonia  and  Ireland,  suggesting 
emerging labour shortages in construction in these 
countries.  In  contrast,  construction  wages  lagged 
far  behind  general  wage  developments  in  Croatia, 
Latvia and the UK. 

2.5.5.  Contribution to inflation 

a  modest  but 

increasing 
Wages  had 
contribution  to  inflation  in  2018.  Table  I.2.5 
breaks down how various components contributed 
to  the  final  demand  deflator,  an  indicator  of 
inflation based on National Accounts. In 2018, the 
contribution of  nominal unit labour costs (NULC) 
was  positive 
in  all  countries,  but  remained 
moderate in most, indicating that wage growth has 

translated 

not  yet 
inflation  pressures 
into 
(European  Commission,  2019a).  In  two  thirds  of 
the  Member  States,  the  contribution  of  NULC 
remained below 1% while it exceeded 2% only in 
Romania.  Among  the  larger  euro-area  Member 
States,  it  reached  1.1%  in  Germany,  and  0.9%  in 
Italy, but remained at 0.5% in France and 0.3% in 
Spain.  In  an  analysis  looking  at  higher-frequency 
data,  the  ECB  (2019)  documents  that  NULC’s 
contribution  to  inflation  (in  particular,  the  GDP 
deflator)  was  increasing  in  the  course  of  2018, 
partly  counterbalanced  by  a  falling  contribution 
from profits (gross operating surplus). 

Graph I.2.17:  Nominal compensation per employee by sector, 2018, annual % change 

(1) Countries are ranked by ascending order of changes in average compensation per employee (total economy) in 2018. 
Source: Eurostat. 

37 

-2261014182226CYHRELIEFIITPTBEESFRDKSIATLUNLMTDEUKSEPLLTSKCZBGHULVEEROPublic sector 2018Private sector 2018Public sector 2014-2017Private sector 2014-2017-404812162024CYESELFIBENLFRHRDKITPTMTIELUATUKDESESISKBGPLLVLTCZEEHUROIndustryBuilding and constructionMarket servicesTotal 
 
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Table I.2.5:  Contributions to the final demand deflator, 

2018, annual % change 

Hungary  and  Belgium  (-1.1  pps).  The  average 
increase  in  the  tax  wedge  for  the  remaining 
countries  was  moderate  (around  0.2  pp),  with 
Bulgaria recording the largest increase (0.6 pp). 

Some convergence across Member States can be 
observed  in  the  tax  wedge  since  2008.  Over  the 
last  ten  years,  the  tax  wedge  at  the  average  wage 
has decreased in a  majority of Member States.  At 
the  same  time,  it  tended  to  decrease  most  in 
countries  with  high  initial  levels  and  vice  versa. 
The  largest  decrease  was  recorded  in  Hungary 
(-9.1  pps)  followed  by  Romania  (-4.1  pps), 
Belgium  (-3.2  pps)  and  Denmark  (-2.9  pps).  Of 
these countries, Belgium and Hungary had some of 
the highest levels in 2008. At the other end of the 
spectrum, the tax wedge increased by more than 3 
pps in countries (Ireland,  Luxembourg,  Malta and 
Portugal) where it was comparatively low in 2008. 

2.6.  COST  COMPETITIVENESS  AND  EXTERNAL 

ADJUSTMENT 

(1) Countries are ranked by ascending order the final 
demand deflator. 
Source: European Commission. 

2.6.1.  Real 

effective 

exchange 

rate 

developments  

of 

an 

rates, 

indicator 

Competitive  developments  have  continued  to 
reflect  the  catching-up  process  in  low-wage 
countries.  Since  2016,  Central  and  Eastern 
European  Member  States  with  rapid  wage  growth 
experienced  an  appreciation  of  their  real  effective 
cost 
exchange 
competitiveness based on the developments of real 
unit  labour  costs  as  compared  to  main  trading 
partners  (Graph  I.2.18).  The  magnitude  of  real 
appreciation depends on which deflator is used. In 
the  case  of  Czechia,  Estonia,  Hungary  and 
Romania,  real  appreciation  is  much  milder  with  a 
deflator based on export prices than with a deflator 
based on unit labour costs. This suggests that firms 
in  these  countries  are  not  able  to  pass  labour  cost 
increases  on  to  their  export  prices,  which  may 
compress profit margins.  

2.5.6.  The tax wedge 

In 2018, the total tax wedge at the average wage 
ranged  from  25.9%  in  Malta  to  52.7%  in 
Belgium. The tax wedge is defined as the ratio of 
all  the  taxes  and  benefits  paid  on  wages  to  total 
labour  cost.  Its  significance  is  that  it  drives  a 
wedge  between 
the  net  wages  (relevant  for 
workers) and total labour cost (relevant for firms). 
Table  I.2.6  shows  this  indicator  of  the  total  tax 
burden on labour, evaluated at the average wage in 
2018,  as  well  as  its  breakdown  into  various 
components and the changes  of these components 
over one year and the last ten years.  

Eleven countries reduced their  tax wedge  from 
2017  to  2018.  Most  significant  changes  occurred 
in  Romania  (-4.7  pps)  and  Estonia  (-2.5  pps), 
countries  with  below-median  levels  in  the  first 
place (the median was 40.7 in 2018). In the former, 
the  reduction  was  the  result  of  a  large  shift  from 
employers’  to  employees’  social  contributions, 
while in the latter it was a direct consequence of a 
reduction  in  the  personal  income  tax.  Lower 
reductions  in  the  tax  wedge  were  recorded  in 

38 

IE0.6-0.5-0.21.10.8CY0.00.10.40.50.9FR0.50.50.20.01.1CZ-0.31.80.0-0.31.2IT0.60.90.0-0.21.3DK1.00.90.2-0.71.4ES0.70.30.20.21.5HR0.40.60.40.11.5PT0.70.80.4-0.21.6PL0.90.90.4-0.51.7EL1.30.4-0.20.21.8DE0.51.20.10.11.8AT0.70.50.00.51.9BE1.40.50.2-0.12.0UK0.71.10.10.22.2SK1.10.80.20.12.2FI0.90.90.30.32.3MT1.40.10.20.62.3NL1.00.60.20.52.3SI1.10.90.00.42.4LU1.20.60.10.52.5EE0.81.90.50.23.0LV0.51.50.40.73.1BG1.10.80.50.93.4SE1.81.00.20.33.4LT2.01.50.20.23.9HU1.71.70.60.14.2RO1.14.70.6-1.25.2Import pricesNULCIndirect taxesGross oper. surplusFinal demand deflator 
 
 
Labour market and wage developments 

Part I 

Table I.2.6:  Decomposition of tax wedge 

(1) The tax wedge data refer to a single person, without children, earning the average wage.  
(2) Countries are ranked by ascending order of the tax wedge in 2018.  
(3) Data for Cyprus not available; data for Croatia not available before 2013. 
Source: European Commission based on OECD tax-benefit models. 

Some  Member  States  recorded  competitiveness 
gains  in  terms  of  real  depreciation.  In  Sweden 
and the UK, real depreciation was supported by the 
exchange  rate,  more  than  counterbalancing  the 
effect of wage growth. In Finland and Croatia, real 
appreciation  was  held  in  check  by  low  wage 
growth,  while  in  Ireland  comparatively  strong 
wage  growth  was  counterbalanced  by  strong 
productivity growth. 

2.6.2.  Competitiveness  and  adjustment  in  the 

euro area 

In 2018, labour cost developments continued to 
be broadly consistent with rebalancing needs in 
the  euro  area.  In  the  post-crisis  period,  nominal 
unit labour costs (NULC) have continued to grow 
faster  in  countries  characterised  by  a  current 
account 
(‘surplus 
countries’) than in countries with previous current 
countries’).  The 
(‘deficit 
account 

surplus  before 

the  crisis 

deficits 

Graph I.2.18:  REERs based on various deflators, cumulative % change over the period 2016-2018 

(1) Countries are ranked by ascending order of the variation in the ULC-based REER in 2016-2018. 
Source: European Commission based on Eurostat data. 

39 

Personal Income TaxSocial Contributions EmployeeSocial Contribution EmployerTotal Tax WedgePersonal Income TaxSocial Contribution EmployeeSocial Contribution EmployerTotal Tax WedgePersonal Income TaxSocial Contribution EmployeeSocial Contribution EmployerMT25.9 12.46.76.70.40.40.00.03.13.7-0.3-0.3UK30.9 12.68.59.8-0.1-0.10.00.0-1.9-2.20.20.1IE32.7 19.33.69.80.10.00.00.14.53.90.60.1BG34.9 7.211.616.10.6-0.10.30.4-0.20.00.8-1.0DK35.7 35.70.00.0-0.2-0.10.00.0-2.9-2.90.00.0PL35.8 6.315.314.10.10.10.00.01.10.0-0.21.2EE36.5 10.01.225.3-2.5-2.50.00.0-1.9-2.90.70.3NL37.7 15.611.610.40.30.2-0.20.2-1.51.6-4.10.9LU38.2 15.110.812.30.40.40.0-0.13.51.3-0.12.3RO38.3 6.929.22.2-4.7-4.015.8-16.5-4.1-2.616.9-18.4ES39.4 11.54.923.00.10.10.00.01.41.50.0-0.2HR39.5 7.717.114.70.20.20.00.0::::LT40.6 10.06.923.8-0.4-0.40.00.0-1.0-5.64.60.0PT40.7 12.68.919.2-0.7-0.70.00.03.83.80.00.0EL40.9 8.112.820.00.10.10.00.0-0.61.00.3-1.9SK41.7 8.010.323.50.10.30.0-0.12.90.5-0.42.7FI42.3 16.68.117.6-0.4-0.20.4-0.6-1.5-2.93.1-1.7LV42.3 14.08.919.4-0.6-1.30.40.31.0-0.61.60.0SE43.1 13.85.323.90.10.10.00.0-1.8-1.20.0-0.6SI43.3 10.319.013.90.30.30.00.00.41.00.2-0.8CZ43.8 10.28.225.40.40.40.00.00.31.9-1.1-0.6HU45.0 12.415.317.4-1.10.30.3-1.7-9.1-3.42.7-8.3AT47.6 11.614.022.10.20.30.0-0.1-1.4-1.00.0-0.4FR47.6 12.38.826.50.01.4-1.90.5-2.12.5-0.8-3.9IT47.9 16.77.224.00.20.20.00.01.21.50.0-0.3DE49.5 16.017.316.2-0.10.1-0.10.0-1.8-1.70.0-0.1BE52.7 20.311.021.3-1.1-0.40.1-0.8-3.2-1.50.3-1.9Total Tax Wedge 2018Of whichDifference 2017 - 2018Difference 2008 - 2018-505101520SEIEUKFIHRATESCYSINLITFRMTELBEDKPTSKDEPLLVHULULTEEBGCZROREER based on ULCREER based on GDP deflatorREER based on Exports deflator 
 
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

divergence narrowed in 2016 but widened again in 
2017 and remained above 1 percentage point while 
NULC  gained  pace  in  both  groups  of  countries 
(Graph  I.2.19).  In  particular,  in  2018,  nominal 
NULC  growth  increased  to  2.4%  in  ‘surplus 
countries’  (from  1.1%  in  2017)  and  to  1.4%  in 
‘deficit countries’ (from 0.4% in 2017). 

Graph I.2.19:  NULC in deficit and surplus countries within the 

euro area, weighted average, 1999-2018, 
annual % change 

current  account  in  a  low  (Latvia,  Lithuania)  or 
moderate surplus (Estonia close to 5% of GDP). In 
contrast,  Cyprus,  the  only  Member  State  with  a 
significant current account deficit, had a stable real 
effective exchange rate.  

Graph I.2.21:  Wage and employment growth differential 

between tradable and non-tradable sectors in 
the euro area, average annual % change, 
2016-2018 

(1) Surplus countries are Belgium, Germany, Luxembourg, 
the Netherlands, Austria and Finland. 'Deficit' countries are 
all other euro area Member States. This classification is 
based on the current account situation around 2008. 
Source: European Commission based on Eurostat data. 

Source: European Commission based on Eurostat data.  

the  same 

time,  changes 

At 
in  external 
competitiveness  were  not  systematically  linked 
to current account imbalances in the euro area 
in 2018. While Germany’s real effective exchange 
rate appreciated more than that of most other euro 
area  members,  this  has  not  happened  in  other 
large  current  account 
Member  States  with 
surpluses in 2017 (Graph I.2.20).  

Graph I.2.20:  Real appreciation in 2018 and current account 

balance in 2017, euro area countries 

in 

tradeable 

In  the  past  three  years,  employment  and  wage 
developments 
sectors 
outperformed  those  in  non-tradeable  sectors  in 
Greece,  Slovakia  and  Spain.  (45)  In  the  case  of 
Greece  and  Spain,  this  is  in  line  with  external 
rebalancing  needs  in  the  wake  of  the  euro  area 
crisis.  In  Portugal,  there  were  similar  patterns  in 
wage  developments,  but  employment  growth  was 
somewhat  stronger  in  the  non-tradable  sector.  In 
Slovakia,  the  strong  performance  of  tradable 
sectors  occurred  in  the  context  of  a  tightening 
labour market and strong wage growth. In contrast, 
non-tradeable sectors outperformed tradeable ones 
both in employment and in wage developments in 
Ireland  and  Malta  and  to  a  lesser  extent  in 
Belgium,  Cyprus,  Germany,  Lithuania  and 
Luxembourg  (Graph  I.2.21).  In  Ireland,  strong 
in 
employment 
construction were among the drivers.  

developments 

and  wage 

Source: AMECO. 

significant 

Member  States  with  a 
real 
appreciation of the real effective  exchange rate 
in 2018 did not have current account deficits in 
2017.  The  Baltic  Member  States  experienced  the 
most significant real appreciation in 2018 based on 
unit  labour  costs  (Graph  I.2.20).  All  three  had  a 

(45)  Tradable  sectors  include:  agriculture,  forestry  and  fishing; 
industry  (except  construction);  wholesale  and  retail  trade, 
transport, accommodation and food service activities. Non-
tradable  sectors  include:  construction;  information  and 
communication;  financial  and  insurance  activities;  real 
estate  activities;  professional,  scientific  and  technical 
activities;  administrative  and  support  service  activities; 
public  administration,  defence,  education,  human  health 
and  social  work  activities;  arts,  entertainment  and 
recreation;  other  service  activities;  activities  of  household 
and extra-territorial organizations and bodies. 

40 

-2024619992000200120022003200420052006200720082009201020112012201320142015201620172018'Surplus' countries'Deficit' countriesBEDEEEIEELESFRITCYLVLTLUMTNLATPTSISKFI036-10-5051015REER ULC (% change), 2018Current account balance, 2017BEDEEEIEELESFRITCYLVLTLUMTNLATPTSISKFI-2.0-1.5-1.0-0.50.00.5-3-2-1012Employment growth in tradable  relative to non-tradable Growth of compensation per employee in tradable relative to non-tradable 
 
 
 
                                                           
In  some  countries,  employment  and  wage 
developments  in  tradeable  and  non-tradeable 
sectors  moved in opposite directions. In Estonia 
and Lithuania, employment growth was somewhat 
stronger  in  tradeable  sectors,  but  wage  growth  in 
non-tradeables. The opposite pattern can be seen in 
Latvia, Finland and Austria. The different patterns 
in  the  Baltic  Member  States  appear  to  reflect,  at 
least to some degree, the situation in construction. 
For  instance,  Latvia  recorded  comparatively  fast 
employment growth in construction in the last five 
years  (see  Table  I.2.2),  while  lagged  behind 
general  wage  developments  in  2018  (see  Graph 
I.2.212).  In  contrast,  employment  growth  was 
slower in the  construction sector in Estonia in the 
last five years, while wage pressures seem to have 
appeared in the sector in 2018. When employment 
and wages move in the opposite direction (at least 
in comparative terms), it is possible that outcomes 
are driven by constraints in labour supply, causing 
labour shortages, in some sectors. 

Graph I.2.22:  Compensation per employee, tradable and 
non-tradable sectors, in 'deficit' and 'surplus' 
countries: 1999-2017, annual % change 

(1) Surplus countries are Belgium, Germany, Luxembourg, 
the Netherlands, Austria and Finland. Deficit countries are all 
other euro area Member States. This classification is based 
on the current account situation around 2008. 
(2) Data for some deficit countries (Greece, Italy) for 2016 
are not available. 
Source: European Commission based on Eurostat data. 

Wages  in  tradeable  and  non-tradeable  sectors 
had on aggregate similar dynamics in ‘surplus’ 
and  ‘deficit’  countries  in  2018.  Between  2017 
and  2018,  nominal  compensation  per  employee 
picked  up  across  both  country  groups  and  both 
sectoral  aggregates  (Graph  I.2.22).  Tradeable  and 
non-tradeable  sectors  showed  very  similar  wage 
growth  in  both  country  groups  in  2017  and  2018. 
However,  the  small  differences  between  sectors 
are  conducive to rebalancing. For the  first time in 
years,  wages  in  ‘surplus  countries’  grew  faster  in 
non-tradeable  than  in  tradeable  sectors.  In  turn, 
tradeable-sector wage growth in ‘deficit countries’ 

Labour market and wage developments 

Part I 

returned  to  be  slightly  ahead  of  that  in  non-
tradeable sectors in ‘deficit countries’. 

2.7.  CONCLUSIONS 

Six  years  after  the  start  of  the  economic 
recovery, the improvement of European labour 
markets  is  continuing.  In  most  of  the  countries, 
employment levels are now above pre-crisis levels. 
Both the fall in  unemployment  – on a faster track 
than  would  had  been  expected  on  the  basis  of 
output growth – and the rise in activity rates have 
boosted employment rates. In the large majority of 
Member  States,  job-finding  rates  have  further 
increased.  In  services,  employment  growth  has 
been  particularly  dynamic.  At  the  same  time,  in 
some countries some countervailing forces are also 
emerging,  stemming  either 
from  population 
dynamics  or  from  a  slowdown  in  the  rate  of 
growth. 

Average hours worked are  below the pre-crisis 
levels  in  almost  all  countries.  The  recovery  is 
accompanied by a significant process of structural 
change  in  employment,  generally  reducing  the 
weight  of  sectors  with  higher  shares  of  full-time 
work  in  favour  of  sectors  and  professions  with  a 
higher  concentration  of  part-time  work.  However, 
in  2018,  average  hours  rose,  boosted  by  the 
increase 
the  employment  with  permanent 
contracts  and  in  full-time  positions,  and  in  the 
average  number  of  usual  hours  worked  by  part-
timers.  

in 

Large differences persist in the magnitudes and 
forms  of  remaining  labour  market  reserves 
(slack) across countries. Both short- and long-run 
unemployment  fell  significantly.  After  peaking  in 
2013,  the  dispersion  of  unemployment  rates  fell 
further. Beveridge curves suggest a general decline 
in  structural  unemployment.  In  some  Member 
States,  however,  supplementary 
indicators  of 
labour  market  slack  point  to  significant  labour 
market  reserves,  with  discouraged  workers  still  a 
significant  share  among  the  inactive  population, 
while  the  recent  reduction  of  underemployed 
involuntary part-time workers was concentrated in 
just a few countries. 

and 
Current 
developments 
unemployment  rates  also  reflect 
long-term 
demographic  trends.  Since  2000,  higher  activity 

activity 

in 

41 

-10123452000200220042006200820102012201420162018Tradable 'surplus'Tradable 'deficit'Non-tradable 'surplus'Non-tradable 'deficit' 
 
In  most  countries  with  comparatively  rapid 
wage  growth  in  2018,  public  sector  wages 
increased  ahead  of  the  private  sector.  The 
opposite was the case in countries with slow wage 
growth.  In  some  countries  with  high  current 
account surpluses, public wage growth was slower 
than  wage  growth  in  the  private  sector.  In  the 
private  sector,  wage  growth  was  comparatively 
faster in market services than in industry. 

In 2018, labour cost developments continued to 
be broadly consistent with external rebalancing 
needs  in  the  euro  area.  In  the  post-crisis  period, 
nominal  ULC  have  continued  to  grow  faster  in 
countries  characterised  by  a  current  account 
surplus  before  the  crisis  (‘surplus  countries’)  than 
in countries with previous current account deficits 
(‘deficit  countries’).  At  the  same  time,  external 
competitiveness  did  not  systematically  responded 
to  current  account  imbalances  in  the  euro  area  in 
2018. 

European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

rates have been boosted by  higher education rates 
and  higher  participation  of  women  in  particular. 
The 
link  between  demographic  change  and 
unemployment appears to be more complex. While 
a  mechanical  composition  effect  of  fewer  young 
people  would  lead to a lower unemployment rate, 
there  seems  to  be  no  systematic  relationship 
falling 
between 
unemployment  rates  across  Member  States  in  the 
last  two  decades.  Among  the  EU15  Member 
States, the statistical relationship appears to be the 
opposite. 

falling  youth 

shares 

and 

Wage  growth  continued  to  gain  pace  in  2018 
and  the  first  quarter  of  2019,  affected  by 
differences  in  unemployment  and,  to  some 
extent,  external  imbalances  across  countries. 
Nominal  wages increased in  all Member States in 
2018  while  real  wages  increased  in  almost  all. 
Overall,  wages  made  a  modest  but  increasing 
contribution  to  inflation  in  2018.  Over  the  last 
three  years,  real  wages  grew  roughly  in  line  with 
productivity  on  average,  with  some  variability 
across countries. Since the start of the recovery in 
2013,  the  wage  share  is  increasing  the  most  in 
countries where it was the lowest.  

42 

 
Labour market and wage developments 

Part I 

(Continued on the next page) 

43 

 Box I.2.1: The impact of demographics on unemployment rateThe age structure of the EU labour force has changed significantly during the last two decades, mainly due to declining fertility trends and increase in longevity. The percentage of the active population aged 15-24 years of age in the euro area fell from 14.9% in 2000 to 12.5% in 2017 (Table 1). All countries experienced a drop in the share of young: yet, the size of this decline and its dynamics differ across countries (Bodnàr 2018). It is comparatively small (below 2.5 pps) for Austria, Belgium Germany, Finland, France, Luxembourg, and the Netherlands. Larger declines (exceeding 4 pps) are seen in the Southern, Central and Eastern European Countries (except Croatia). In Bulgaria and the Baltic States, the fall in the young population got sharper after 2010, mirroring the combined effect of ageing and outmigration of young cohorts. This Box quantifies the impact of these effects on total and age-specific unemployment rates. Table 1. Active population 15-24 years, shares   1995 2000 2007 2017 AT 15.3 13.8 14.3 13.0 BE 15.7 14.7 14.5 13.9 BG   16.6 15.3 10.7 CY   17.3 15.3 14.0 CZ   17.8 15.2 11.2 DE 12.9 12.9 13.6 12.0 DK 16.2 14.1 13.8 15.2 EA19   14.9 13.9 12.5 EE   17.1 17.2 11.6 EL 16.2 16.5 13.6 11.4 ES 19.7 17.1 13.4 11.5 EU28     14.7 12.8 FI 14.6 15.2 14.6 13.3 FR 16.2 15.4 15.3 14.2 HR     14.5 13.1 HU   18.3 14.8 12.7 IE 22.9 21.7 18.9 15.5 IT 16.5 13.8 11.9 11.3 LT   17.6 17.6 13.4 LU 14.8 14.0 14.2 14.4 LV   17.4 18.2 11.1 MT   18.8 17.1 13.5 NL 16.4 14.6 14.8 14.8 PL   18.7 17.9 12.5 PT 19.9 17.2 13.8 12.3 RO   19.5 17.5 12.9 SE 17.2 14.1 15.5 14.2 SI   17.3 14.7 11.2 SK   21.0 18.8 13.3 UK 15.6 14.5 15.6 14.1 Demographic change affects the unemployment rate directly and indirectly. An increase in the share of older workers reduces total unemployment directly, as younger cohorts - whose unemployment rate is several times higher than the unemployment rate of older age groups - become smaller. Moreover, higher educational levels may also modify the age structure of the active population. Indirectly, aging of the population reduces the labour supply of the young and increases that of older workers. According to the crowding hypothesis (Easterlin, 1966), the baby-boom generation has high risk of becoming unemployed if the increase in the labour supply is not matched by a comparable increase in labour demand (1): consequently, a "baby-bust", as it was experienced by many Member States in the 1980s and 1990s, would reduce youth unemployment. Yet, Shimer (2001) finds that firms have stronger incentives to create jobs in countries with younger populations. The effect on unemployment depends on how fungible are workers of different age and on the wage response to changes in labour supply (e.g. Biagi and Lucifora, 2008). Long-term trends, e.g. skill-biased technological change, may also affect the labour demand of young and old individuals differently.  The direct effect is quantifiable by looking at the gap between the actual unemployment rate and its “counterfactual” obtained by holding fixed (at a base year) the age composition of the labour force (Perry 1970, Flaim 1979, Fuchs 2016). (2) By construction, the gap reflects the composition effect, i.e. how much of the change in unemployment is due to demographics. The underlying assumption of building a counterfactual unemployment rate is that changes in the age structure of the labour force do not affect the age specific                                                            (1) See “Easterlin hypothesis” in the New Plagrave Dictionary of Economics, 2018, Macmillian Publishers. (2) Fuchs, M. and Weyh, A. 2014. "Demography and unemployment in East Germany: how close are the ties?," IAB Discussion Paper 2014 26.  
 
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Box (continued) 

44 

(Continued on the next page) 

 counterfactual unemployment rate is that changes in the age structure of the labour force do not affect the age specific unemployment rates. The indirect effect can be inferred only empirically. In principle, both effects can offset each other, i.e. ageing has no effect on total unemployment rate, even if it modifies age specific unemployment rates. Graph 1 shows the direct effect by country. (3) A negative (positive) gap means that demographic change contributed to keep high (low) the unemployment rate. For example, in Sweden and Finland, had the age structure of the population remained unchanged, unemployment would have been lower than actual unemployment. Conversely, in Italy, Spain, Croatia, Romania, Greece and Portugal the unemployment outlook is less positive than indicated by the current unemployment. In these countries, the impact of demographic change on the size of the age groups with higher unemployment rates mitigates the increase in unemployment. For example, Italy would have an unemployment rate almost 3 pps higher had the age structure of the population remained unchanged at the 1995 level. For Germany, on the other hand, there is no difference until 2001. From that year onward, demographic change explains about 1 percentage point of the almost 5 pps decline in the German unemployment rate.  Graph 1. The effect of ageing on the 1995-2017 change in the unemployment rate (15-59 years of age)  Note: The chart shows the gap between the actual unemployment rate and the simulated change in unemployment keeping fixed the structure of the active population. Due to data availability the starting year is 1996 for HU and SI; 1997 for CZ, PL and RO; 1998 for EE, LV and SK; 2000 for BG, CY; 2002 for HR; 2007 for MT. The indirect effect can be quantified only econometrically, by estimating how much of the change in age specific unemployment rates is due to changes in the share of young in total population. Table 2 presents the results. Across different specifications, the share of young workers has no statistically significant effect on total unemployment (Table 2, column 1). Yet, consistent with evidence of Chart 1, the relationship varies across countries.  For the EU15, an increase in the share of the youth by 1 per cent reduces the total unemployment rate between 1.3 and 1.5 percent (Table 2, Panel a, column 1), which is close to the estimate obtained by Shimer 2001 for the US States. This evidence is also consistent with the findings of Skans (2005) for Sweden and Ochsen (2009) for Germany; yet, they differ from Biagi and Lucifora (2008) who focus on a panel of 10 European countries only. Column 3 shows the effect of changes in the share of young on the unemployment rate of the young and older workers' unemployment rates. An increase in the youth population is associated with a decline in the unemployment rates for both the young and the older cohorts, with a stronger effect on the unemployment rate of the older age group. Consistently with Shimer (2001), this implies that the unemployment rate of young relative to older workers rises when the share of young in the active population rises. The estimates in column 1 assume changes in the young population are exogenous, while in practice they also reflect mobility decisions. When estimates net out the effect of mobility decisions on the youth population (column 2), estimates remain statistically significant and even stronger in size. (4)  For the EU13 (Table 2, Panel b), on the contrary, an increase in the share of youth in the population by 1 per cent increases the unemployment rate of those aged 25 and above more than it would for the 15-24 cohort.                                                            (3) Since EU LFS data are not available for all countries from the same year, the reference is the first year for which data are available. Five years age-groups are considered starting from 15-19 until to 55-59 years of age. (4) The share of young individuals in the labour force depends both on current migration decisions and past fertility rates; in order to isolate the effect of ageing, the fertility rate of 15 to 24 years earlier is used as instrument for the share of young in the labour force. In fact the fertility rate explains 84% of the variation within countries in the population of young and is an excellent predictor of the population about twenty years later and doesn’t influence mobility decisions.  
 
 
 
 
Labour market and wage developments 

Part I 

Box (continued) 

(Continued on the next page) 

45 

 This suggests that an increase in the youth population rises the unemployment rate of young relative to that of older cohorts. Nonetheless, the effects are imprecisely estimated and it cannot be excluded that they are different from zero.  Table 2. Effect on unemployment rate of 1% increase in the share of young workers Dependent variable OLS  IV  (1) (2) Total unemployment rate  -0.06 [-0.16] -0.46 [-1.06] Unemployment rate young  0.27 [0.67] -0.06 [-0.13] Unemployment rate 25 plus -0.16 [-0.40] -0.49 [-1.06]   EU15 Total unemployment rate  -1.27**  [-2.77] -1.54***  [-3.0] Unemployment rate young  -0.92*  [-1.93] -1.14**  [-2.09] Unemployment rate 25 plus -1.34**  [-2.83] -1.57**  [-2.85]  EU13 Total unemployment rate  1.11 [1.03] 0.78 [0.66] Unemployment rate young  1.39 [1.31] 1.35 [1.30] Unemployment rate 25 plus 0.96 [0.90] 0.92 [0.88] Panel estimates for 28 Member States from 1995 to 2017. All regressions include country and year fixed effects and a 25-54 dummy; t-statistics in parentheses; variables in logs. The instrument is the fertility rate computed as ten year average of the fertility rate lagged from 15 to 24 years earlier. Standard errors are clustered at country level. *** p>0.01, ** p>0.05, * p>0.1. These findings suggest that there is a link between demographic change and unemployment, working both directly and indirectly. Aging exerts a positive cohort crowding effect, meaning that it is associated with higher relative unemployment for the young age groups. However, this effect appears only for the EU15 member states; conversely, no significant effect is found on average for the EU13.    
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Box (continued) 

46 

 Graph 2. The unemployment rate and demographic change  4681012510152025010203040010203040051015205101546810121401020304010203040506010203040506010203040510152025301020304050510152025305101520253010203040500102030400510152025010203040510152005101501020304001020304051015202551015202551015202510203040510152025131415161414.51515.510121416141516171015201212.51313.514131415161214161812141618121416182013.51414.5151416131415161214161820161820222412141614161813.51414.51510121416181416182014.51515.51616.51214161820121416182012141618201415161710152014161820221414.51515.519952000200520102015199520002005201020151995200020052010201519952000200520102015ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKShare of young (15-24) in the population Youth unemployment rate Total unemployment rate year 
 
 
 
 
 
 
 
Labour market and wage developments 

Part I 

(Continued on the next page) 

47 

 Box I.2.2: The dynamics of labour force participation rates in the EUThis box analyses how shifts in the composition of the population, in particular increasing education and average age, affect the evolution of labour market participation (or activity) rates between 2000 and 2017. For each Member State, the analysis decomposes the change in the activity rate for the age group 15-64 into a component driven by changes in the average socio-demographic characteristics of the population (“composition effect”) and a part that represents changes in the probability of participating for the various socio-demographic groups (“coefficient effect”). The analysis is based on individual-level data obtained from the 2000 to 2017 EU Labour Force Survey. Labour market participation of individuals is explained, in a so-called logit model, by individual characteristics (gender, age and educational level). The Oaxaca-Blinder decomposition method allows breaking down changes in the activity rate into a composition effect and a coefficient effect. Graph 1 presents the breakdown of the cumulated change in the activity rate between 2000 and 2017 for each Member State. There are several findings worth noting. First, the activity rate increased in the last decade in almost all Member States except Denmark and Finland. The increase was the largest in countries with initial low levels (e.g. Hungary, Bulgaria and Spain). Second, the composition effect is positive in all Member States. The composition effect was comparatively low in France, Germany and the Netherlands.  Graph 1. Decomposition of the cumulative change in the activity rate between 2000 and 2017 – total, composition and coefficient effect (in pps)  Third, the composition effect is the main driver of trends in the activity rates in about half of the Member States. In a number of countries, including Belgium, Greece, Lithuania, Portugal, and the UK, shifts in the composition of the population explain about the total increase in activity rate. In Denmark, Ireland, Poland, Romania and Slovakia, the composition effect even exceeds the total increase in the activity rate. This is possible if the activity rate of some groups fell while population shares shifted towards groups with higher labour market participation rates (older and more educated groups). On the other hand, changes in the population’s composition explain less than half of the overall increase in activity rates in Estonia, Slovenia, Hungary, Spain, Austria, France, Germany, and the Netherlands.   
 
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Box (continued) 

48 

 Fourth, and consistent with the findings of De Philippis (2017) on selected EU countries, the composition effect is mostly driven by increased education, while the ageing impact is smaller and ambiguous in sign: increased educational attainment is by far the most important component of the positive trend in the composition effect (Graph 2). The effect of ageing is smaller and more ambiguous. In Belgium, Denmark, Finland, France, and the Netherlands, ageing of the population has a negative effect on labour market participation. The opposite is observed in the case of Bulgaria, Germany, Spain, Greece, Hungary and Romania. Unsurprisingly, there is almost no effect of changes in the gender composition of the population (the percentage of women in the population is indeed constant over time). Graph 2. Decomposition of the cumulative change in activity rate between 2000 and 2017 - composition effect (in pps)  Finally, the coefficient effect represents the combined effect of structural reforms and trends, and the impact of the business cycle. It explains a big deal of the change in the activity rate in Denmark, the Netherlands, Germany, France, Luxembourg, Austria, Spain and Hungary. In Italy, it drives most of the fluctuations in participation after 2008. In almost all Member States, the main driver of the composition effect has been increased labour participation of women. The impact of age is close to zero in most Member States as the increased participation rate of older workers is counterbalanced by a decline in the participation for young workers.  
 
 
 
 
APPENDIX 1 

Graph I.2.A1.1: 

The Beveridge curve in EU Member States, 2000q1-2018q4, quarterly data 

(1) Share of manufacturing firms indicating that labour is a “factor limiting production”, EU-Business and Economic survey. 
Source: European Commission based on Eurostat data. 

49 

 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.2.A1.2: 

The Beveridge curve in EU Member States, 2000q1-2018q4, quarterly data, cont. 

(1) Share of manufacturing firms indicating that labour is a “factor limiting production”, EU-Business and Economic survey. 
Source: European Commission based on Eurostat data. 

50 

 
 
 
Labour market and wage developments 

Part I 

Graph I.2.A1.3: 

The gap between actual unemployment and NRU 

(1) NRU:  A measure of frictional unemployment. In particular, it is the rate of unemployment consistent with current job 
finding and separation rates. 
Source: Ameco and Commission services calculations based on LFS. 

Graph I.2.A1.4: 

The gap between actual unemployment and the NAWRU 

(1) NAWRU: Non-accelerating wage rate of unemployment, an estimate of structural unemployment consistent with 
economic equilibrium.  
Source:  Ameco and Commission services calculations based on LFS. 

51 

 
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.2.A1.5: 

The Phillips curve for EU countries: compensation per employee growth and unemployment rate, 2000-

2018, Panel A 

Source:  Eurostat, LFS and National Accounts. 

52 

 
 
 
Labour market and wage developments 

Part I 

Graph I.2.A1.6: 

The Phillips curve for EU countries: compensation per employee growth and unemployment rate, 2000-

2018, Panel B 

Source:  Eurostat, LFS and National Accounts. 

53 

 
 
3. 

POLICY DEVELOPMENTS: THE POVERTY-REDUCING 

EFFECTS OF SOCIAL TRANSFERS 

social  protection 

Total 
expenditure  as  a 
percentage of GDP rose after the 2008 crisis. This 
pattern  is  common  to  spending  components  that 
are  relatively  stable  over  the  economic  cycle  and 
to  those  that  move  opposite  to  the  cycle  (counter-
cyclical).  

of 

being 

cyclical 

response 

spending 

pro-cyclical 

The 
to 
unemployment benefits increased on average after 
the  2008  crisis,  in  spite  of  social  expenditure 
2012. 
developments 
Nonetheless,  spending  on  unemployment as  a 
percentage of GDP started to fall earlier than the 
unemployment rate. This reflects the persistence of 
the crisis combined with the limited duration (and, 
in 
limited  coverage)  of 
the 
some  cases, 
unemployment benefits.  

in 

those  at  the  bottom  of  the  income  distribution. 
Only  in  Spain,  Ireland  and  Portugal,  policies 
protected  rather  the  middle-income  brackets  from 
the  extreme  effects  of  the  crisis.  Finally,  in 
Hungary  and, 
the  UK, 
significant  losses  were  observed  at  the  lower  end 
of  the  income  distribution.  However,  the  poverty 
gap  still  deepened  in  almost  half  of  the  Member 
States, 
living 
conditions of the poor population. 

indicating  a  worsening  of 

lesser  extent, 

to  a 

the 

varies 

Social  policy  spending  impacts  relative  poverty 
and  material  deprivation;  yet,  its  quantitative 
relevance 
poverty 
across 
in  social  protection 
indicators.  An 
spending  reduces  severe  material  deprivation  and 
the  at-risk-of-poverty  rate  with 
the  poverty 
threshold held constant.  

different 

increase 

Social  spending  contributes  to  smoothing  cyclical 
fluctuations.  This  contribution  increased  after  the 
2008  crisis.  High  income  countries  stabilise  a 
higher  proportion  of  GDP  shocks,  which  might 
reflect  their  higher  spending  on  some  counter-
cyclical 
items  and  more  effective  spending 
institutions. 

Overall,  social  benefits  reduce  the  incidence  of 
poverty  by  about  one  third  and  its  depth  by  more 
than one half. On average, family benefits have the 
largest  effect  on  the  poverty  rate  (17%),  followed 
by  sickness  and  disability  benefits  (15%)  and 
unemployment  benefits  (10%).  Nordic  countries 
tend  to  be  more  effective  in  reducing  both  the  at-
risk-of-poverty rate (the incidence of poverty) and 
the poverty gap (depth of poverty). Unemployment 
benefits  only  play  a  limited  role  in  reducing  the 
poverty rate and poverty gap; their effect is larger 
whenever coverage and generosity are higher and 
the incidence of long-term unemployment is lower. 
Social inclusion benefits have a stronger effect on 
the  poverty  gap.  For  all  benefits,  reforms 
increasing  their  generosity  are  associated  with  a 
higher  poverty-reducing  effect.  However,  reforms 
that  make  unemployment  benefits  more  generous 
have a temporary reducing effect on poverty. 

The  analysis  shows  that,  in  most  Member  States, 
the  policy  changes  in  the  tax  and  benefit  system 
introduced  between  2008  and  2018  were 
progressive, and in particular were able to protect 

54 

3.1. 

INTRODUCTION 

During  the  2008  crisis,  poverty  and  social 
exclusion  in  the  EU  increased  significantly. 
Between  2008  and  2012,  the  number  of  people  at 
risk of poverty or social exclusion in the EU rose 
by  6.4  million.  (46)  This  reflects  the  surge  in 
material deprivation and in the number of persons 
in 
low  work-intensity  households.  Although 
relative  poverty  did  not  increase  much,  low-wage 
and 
earners 
financial hardships. At the end of 2012, nearly one 
in  four  persons  was  at  risk  of  poverty  or  social 
exclusion (123.7 million people). (47)  

experienced 

economic 

serious 

The  at-risk-of-poverty  rate  has  declined,  but 
improvements  have  been  uneven  across 
countries. The number of people at risk of poverty 
stabilised in 2015 and 2016 and fell in 2017. At the 
end  of  2017,  the  number  of  people  at  risk  of 
poverty  or  social  exclusion  was  below  the  pre-
crisis  levels  (Joint  Employment  Report,  2017). 

(46)  See Box I.3.2.  
(47)  Between  2008  and  2012,  the  AROP  rate  increased  from 
16.6%  to  16.8%;  this  small  increase  took  place  in  the 
context  of  a  drop  of  median  incomes,  quite  substantial  in 
some  countries.  When  median  incomes  are  anchored  at 
their  2008  level,  the  AROP  rate  rises  from  16.6%  to 
19.4%. The  dispersion  in  the  anchored  measure  rises  also 
when the countries with the lowest and the highest rates are 
excluded from the sample.  

 
                                                           
 
There  are  signs  that  the  trend  continued  in  2018, 
with  less  than  85  million  people  being  at  risk  of 
poverty by the end of the year. Yet, the dispersion 
of  the  at-risk-of-poverty  (AROP,  thereafter)  rate 
with  median  income  anchored  at  the  2008  level 
increased  considerably 
In 
several  Member  States,  a  sizeable  portion  of  the 
population  remains  at  risk  of  poverty  and  social 
exclusion.  

I.3.1). (48) 

(Graph 

Graph I.3.1:  Dispersion of measures of monetary and 
material poverty (coefficient of variation) 

(1) The coefficient of variation is defined as the ratio of the 
standard deviation to the mean. 
Source: Own calculations based on Eurostat. 

These  differences  across  countries  reflect 
developments in household  disposable incomes. 
The number of people at risk of poverty or social 
exclusion  had  increased  during  the  2008  crisis, 
declining  only  when  the  economic  recovery  was 
broad  and  stable  and  improvements  in  the  jobless 
rate  and  disposable  incomes  became  persistent. 
Yet, disposable income did not increase equally for 
all  families  in  all  countries,  despite  the  economic 
recovery  being  broad-based  and  stable. 
In 
particular,  the  median  AROP  gap  –  i.e.  the 
distance  between  median  income  of  the  poor  and 
the at-risk-of-poverty threshold – continued to rise 
until  2016,  in  particular  in  Southern  European 
countries. (49)  Moreover,  the  dispersion  across 
countries  around  this  higher  average  was  larger 
compared to the pre-crisis period. 

Certain  features  of  the  labour  market  and 
emerging  trends  made  poverty  particularly 

(48)  Excluding catching up countries does not alter the picture, 
but  the  increase  in  the  dispersion  of  severe  material 
deprivation  starts  earlier  (in  2010).  The  EU  AROP  rate 
increased from 2009 to 2014 and started to fall thereafter. 
(49)  The  poverty  gap  is  a  measure  of  the  depth  of  poverty.  In 
2017, the median income of the poor was about one fourth 
lower than the threshold; it was one fifth before the crisis. 

Labour market and wage developments 

Part I 

reactive  to  the  increase  in  unemployment.  The 
increase  in poverty  was  mainly driven by cyclical 
factors. It was exacerbated by characteristics of the 
labour  market  (e.g.  the  divide  between  temporary 
and permanent contracts) or  emerging trends (e.g. 
labour  market  polarisation,  new  forms  of  work) 
that  exposed  the  most  vulnerable  workers  more 
strongly to poverty risks.  

Policies  to  tackle  the  increase  in  poverty  and 
social  exclusion  may  not  have  been  equally 
effective  for  all  countries.  The  crisis  also 
highlighted that not all countries were prepared to 
cope  with  the  consequences  of  a  prolonged  and 
deep  recession  for  households'  incomes  (Figari  et 
al.,  2011;  OECD,  2014). 
It  became  more 
challenging  for  social  policies  to  provide  support 
to those living in households without income from 
work.  In  response  to  this,  the  focus  shifted  from 
reforms  aiming  at 
labour  market 
adjustment to reforms enhancing social safety nets 
and  adapting  social  policy  settings  to  smoothly 
respond to change (European Commission, 2016). 
The European Pillar of Social Rights – proclaimed 
by EU leaders in Gothenburg in November 2017 – 
was  adopted  to  support  upward  convergence  in 
Europe  and  address  the  social  implications  of  the 
crisis,  by  reinforcing  social  rights  and  responding 
to economic and social insecurity.  

improving 

the 

against 

individuals 

Against this background this chapter focuses on 
the  role  of  social  spending  to  alleviate  poverty 
and  exclusion.  Social  protection  systems  smooth 
and redistribute incomes over the life  cycle. They 
protect 
financial 
implications  of  major  risks,  such  as  job  loss  and 
social  exclusion,  old  age  or  poor  health.  Social 
protection  systems  may  support  incomes  during  a 
recession. By raising the quality of human capital, 
social  policies  can  also  foster  economic  growth 
(Barr,  2012). (50)  That  is  why  social  protection 
systems,  together  with  education  policies,  are 
important 
economy’s 
resilience:  they  cushion  economic  shocks  in  the 
short-term and foster inclusive growth in the long-
term  by  investing  in  people.  Spending  on  social 
protection,  although  not  uniquely  a  guarantee  that 
programmes  will  meet  their  objectives,  represents 

determinants 

an 

of 

(50)  The insurance principle suggests that both too little and too 
much  risks  are  harmful  for  growth.  Social  protection  and 
welfare  policies  ensure  that  risks  are  shared  efficiently 
(Barr, 2012).  

55 

00.40.81.22005200720092011201320152017At risk of poverty rateAt  risk of poverty rate anchored at 2008 median incomeSevere material deprivation 
 
                                                           
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

a  metric by which it is possible to assess Member 
States'  commitments  to  coping  with  social  risks, 
including 
for  vulnerable  groups.  Obviously, 
spending  is  not  the  only  tool  at  the  disposal  of 
authorities  to  alleviate  such  risks.  The  design  of 
the tax and benefit systems may have an impact on 
disposable  income  and  therefore  on  poverty  and 
exclusion.  More  broadly,  poverty  and  social 
exclusion  are  a  multidimensional  problem  calling 
for  a  comprehensive  strategy.  It  requires  enabling 
policies    such  as  education  and  training,  early 
childhood intervention, stable home environments, 
and  access  to  health  care    to  ensure  access  to 
more and better jobs. These latter aspects, although 
important, will not be discussed in this chapter.  

Section  3.2  sets  the  scene  by  describing  trends  in 
social spending by type of expenditure and across 
countries. Subsequently, it looks at the response of 
social spending to GDP and the ability of different 
spending  categories  to  smooth  output  shocks. 
Section  3.3  reviews  the  main  policy  changes 
enacted after the crisis. The effectiveness of social 
transfers  in  relieving  poverty  may  differ  across 
categories of spending, as they cover very different 
risks  and  benefits  can  be  awarded  subject  to 
different  eligibility  conditions.  How  effective 
social  transfers  are  in  reducing  poverty  is  a 
question investigated in Section 3.4 by comparing 
measures  of  poverty  before  and  after  policy 
intervention.  The  effectiveness  of  social  transfers 
in reducing poverty documented in Section 4 may 
reflect both differences in labour market conditions 
and  changes 
in  policy  settings.  Section  3.5 
disentangles  these  two  effects  with  the  help  of 
statistical  methods  that  exploit  the  cross-country 
dimension of the data. Section 3.6 identifies,  with 
the  help  of  microsimulation  models,  whether  the 
reforms  of  tax  and  benefit  systems  enacted  after 
the  crisis  are  more  effective  in  delivering  social 
outcomes,  compared  to  the  pre-crisis  setting. 
Section 3.7 and Section 3.8 dig deeper into the role 
of spending on  social protection, and in particular 
to  what  extent  different  categories  of  spending 
contribute  to  reducing  monetary  poverty  and 
severe material deprivation. 

56 

3.2. 

TRENDS 
EXPENDITURE CATEGORIES 

IN  SOCIAL  EXPENDITURE  BY 

health, 

decisions, 

impact  health 

conditions 
disability 

Social  spending  varies  in  response  to  the  cycle, 
long-term  trends  and  policy  changes.  It  should 
be  noted  that  only  some  schemes  provide  greater 
assistance  during  a  recession.  Higher  spending  is 
for  programmes  where  eligibility 
expected 
depends on the employment or income status, e.g. 
unemployment  benefits  or  family  benefits. (51) 
Conversely,  to  the  extent  that  the  cycle  does  not 
significantly 
and 
or 
retirement 
retirement benefits are presumably less responsive 
to  the  cycle.  Likewise,  housing  programmes  may 
not entail an automatic increase in spending during 
a recession, if caseloads are constrained by limited 
financial  resources.  (52)  Nonetheless,  programmes 
with  a-cyclical  spending  –  i.e.  with  spending 
moving  independently  of  the  overall  state  of  the 
economy  –  can  still  be  income  stabilising,  if  they 
protect  those  workers  who  are  more  likely  to  be 
the beneficiaries of a specific spending item during 
a  recession. (53)  Long-term  trends  (e.g.  ageing  or 
the expansion of welfare programmes) have also a 
direct  bearing  on  social  spending.  Moreover, 
reforms  that  improve  labour  market  functioning 
(e.g.  reduce  unemployment,  accidents  at  work) 
ratio. 
indirectly  affect 
Finally,  reforms  phased  in  only  gradually  modify 
spending not only in the year in which the change 
occurred, but also in subsequent years.  

spending-to-GDP 

the 

This  section  first  describes  the  main  spending 
patterns  of  social  protection  over  time  and  across 
countries.  (54)  Subsequently,  it  identifies  whether 
their cyclical response and ability to smooth GDP 
shocks changed after the 2008 crisis.  

(51)  As  for  family  benefits,  this  is  the  case  to  the  extent  that 
their eligibility is based on income or employment status. 
(52)  Although in almost all countries access to social housing is 
subject  to  means-testing,  most  of  them  have  also  some 
queuing system; see Andrews et al. (2011). 

(53)  See  for  example  Fatas  (2019).  A-cyclical  means  moving 

independently of the overall state of an economy. 

(54)  In  the  European  System  of  integrated  Social  Protection 
Statistics  (ESPROSS),  social protection  includes  public  or 
private  interventions  intended  to  relieve  households  and 
individuals of the burden of a set of risks or needs. These 
are:  old  age,  survivors,  disability,  sickness/health  care, 
housing,  unemployment,  family/children,  social  exclusion 
not elsewhere classified (n.e.c.). Benefits included are cash 
payments,  reimbursements  of  expenditure  or  goods  and 
services directly provided.  

 
                                                           
3.2.1.  Analysis of trends and patterns 

the  outbreak  of 

Total  social  protection  expenditure  as  a 
percentage  of  GDP  increased  after  the  2008 
crisis.  During  a  recession  the  spending-to-GDP 
ratio rises as governments increase their efforts to 
protect  the  most  vulnerable  and/or  because  GDP 
the  2008-2009 
declines.  At 
recession, governments put in place large stimulus 
packages 
for  social 
measures  (European  Commission,  2009;  OECD, 
2013).  As  a 
total  social  protection 
expenditure  as  a  percentage  of  GDP  increased 
from 24% in 2008 to 27.5% in 2010 (Graph I.3.2). 
It hovered at this level throughout 2016, mirroring 
the  pattern  of  spending after  the economic  shocks 
of the early 1980s and 1990s. (55)  

resources 

included 

result, 

that 

Graph I.3.2:  Social Protection expenditure, 2000-2016 

 (as % of GDP) 

(1) Shaded area represents the 2008-2012 period. For 
definition of the variables see Manual. 
Source: ESSPROS and National Accounts. 

Most  categories  of  social  expenditure  shared 
this  pattern.  Transfers  paid  typically  to  working-
age  individuals  (unemployment,  housing,  family 
and social exclusion benefits) account for one sixth 
of 
total  social  protection  expenditure.  Yet, 
consistent  with  the  weakening  of  the  cycle,  they 
accounted  for  about  one  third  of  the  increase  in 
total spending between 2008 and 2010. Old age is 
the  largest  expenditure  category,  accounting  for 
40%  of  spending.  Relative  to  GDP,  old  age 
spending  increased  after  2008  and  remained  at  a 
high  level  throughout  the  recovery.  This  increase 
embodies the compound effect of the recession on 

(55)  However, during the recession that hit the EU economy in 
2012,  “social  protection  went  on  an  adjusting  downward 
path  despite  a  worsening  output  gap”  ESDE  2015  Cap 
III.2. OECD (2012); Adema et al (2014). 

Labour market and wage developments 

Part I 

GDP,  of  the  increase  in  spending  due  to  ageing 
and,  in  particular,  of  the  indexation  of  pension 
benefits to prices. (56) A similar pattern is observed 
for sickness  the second highest expenditure item 
  and  for  family  and  housing  benefits. (57) 
Although  the  increase  in  spending  on  family  and 
housing  reflects  the  higher  cash  transfers  in 
countries  where  benefits  are  income-tested,  the 
fact  that  after  2013  it  did  not  decline  as  a 
percentage  of  GDP  may  also  be  the  outcome  of 
reforms 
to 
vulnerable  groups.  Finally,  reforms  tightening 
the 
access 
respective  spending-to-GDP  ratio  after  its  drop 
during the pre-crisis period.  

to  disability  may  have  stabilised 

that  have  extended 

the  support 

the 

increase 

spells,  a 

fell  before 

I.3.2).  With 

During the crisis, spending on unemployment as 
a  percentage  of  GDP 
the 
unemployment rate started to decline. After the 
initial 
in  2008-2009,  spending  on 
unemployment  benefits  relative  to  GDP  started 
falling  well  before  the  decline  of  unemployment 
lengthening 
(Graph 
of 
unemployment 
larger  number  of 
individuals  exhausted  their  entitlements  while 
existing  social  assistance  schemes  provided  only 
limited coverage. (58) Other factors that  may have 
contributed  to  driving  down  the  coverage  of 
unemployment  benefits  include  the  shortening  of 
temporary 
the 
job 
to 
tenures  due 
employment  during 
the 
the 
strengthening  of  activation  measures  to  boost 
participation  of  individuals  with  limited  work 
experience  (OECD,  2018).  Yet  after  the  crisis,  as 
documented  in  the  next  section,  several  countries 
took measures aimed at extending the coverage of 
unemployment  insurance  and/or  introduced  more 

rise  of 
recovery  and 

(56)  Between  1998  and  2008,  the  old  age  dependency  ratio 
increased  by  3.5  pps;  between  2008  and  2018,  it  rose  by 
4.8 pps to 31.7%. Although reforms reduced incentives for 
early  retirement,  this  option  was  used  where  recessionary 
pressures forced job cuts on employers (Eurofound. 2013). 
For an analysis of indexation see ESDE (2015).  

(57)  Sick  and  old  age  expenditure  absorbs  a  larger  amount  of 
resources  because  benefits  are  partly  insurance-based  and 
accrue also to middle- and higher-income individuals rather 
than just to those with low incomes. 

(58)  The EU unemployment rate peaked in 2013; the long-term 
unemployment  rate  continued  to  increase  until  2015.  For 
the  2007-2014  period,  unemployment  rose  more  strongly 
than benefit recipients (OECD, 2018).  

57 

 
 
                                                           
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.3.3:  Social expenditure by quartile of GDP per capita 

(1) Member States are clustered in four quartiles based on their average income per capita over the period 1996-2016, in PPS. 
Bulgaria and Croatia are excluded due to lack of data. See Annex 1. 
Source: Own calculations based on ESSPROS. 

comprehensive 
assistance systems. (59)  

unemployment 

and 

social 

Countries  with  higher  GDP  per  capita  spend 
more  on  social  protection  than  countries  with 
low  GDP  per  capita.  (60)  Graph  I.3.3  shows  the 
evolution  of  total  expenditure  clustering  countries 

(59)  Unemployment  insurance  benefits  are  intended  to  smooth 
income by replacing a portion of an eligible  worker’s lost 
wages  attributable 
to  unemployment.  Unemployment 
assistance  benefits  are  intended  to  reduce  poverty  spells 
among  low-income  families  when  unemployment  occurs. 
While both make payments occasioned by unemployment, 
the  former  goes  to  persons  as  a  matter  of  right  while  the 
latter  is  paid  only  to  families  with  unemployed  whose 
income and assets fall below certain thresholds.  

(60)  The  Wagner  law  predicts  that  the  demand  of  social 
protection  rises  in  richer  societies.  Wealthier  countries 
spend more on social protection either because the demand 
for  social  services  rises  with  income  or  because  richer 
countries are more open economies and subject to external 
shocks, which rises the demand for social protection.  

58 

on  the  basis  of  their  GDP  per  capita. (61)  Real 
spending  per  capita  is  positively  correlated  with 
the  level  of  GDP  per  capita. (62)  At  the  end  of 
2016, per capita spending in high-income countries 
was  more  than  five  times  higher  than  in  low-
income  countries  (EUR  10000  vs.  about  EUR 
1800). (63) Between 2008 and 2009, the spending-
to-GDP ratio increased for all clusters. (64) 

is  convergence 

There 
in  real  per  capita 
spending  between  the  richest  and  the  poorest 

(61)  The composition of each cluster is fairly stable over time. 
Annex 1 reports selected socio-economic characteristics of 
each cluster. 

(62)  Similar  results  are  found  for  the  US  (Isaacs,  2004).  This 
pattern  does  not  change  if  spending  on  old  age  and 
survivors are excluded. 

(63)  This  gap  is  slightly  reduced  if  old-age  and  survivor 

expenditures are excluded. 
(64)  Human  capital  and  effective 

two  key 
determinants  of  total  factor  productivity  growth,  are 
positively  associated  with  GDP  per  capita  and  social 
protection  expenditure  (European  Commission,  2019c, 
Employment and Social Developments in Europe).  

institutions, 

 
                                                           
                                                           
 
Labour market and wage developments 

Part I 

Graph I.3.4:  Share of social expenditure category on GDP before and after 2008 in percent 

(1) Horizontal axis pre-crisis average; vertical axis post-crisis average. 
Source: Eurostat and DG EMPL calculations. 

EU countries. Real social expenditure increased in 
all  countries,  but  more  in  the  low-income  group 
(Graph  I.3.3),  where  it  went  from  EUR  975  in 
2000 to almost EUR 1800 per capita in 2016  an 
increase  of  more  than  80  percent. (65)  After  2008, 
the rate  of convergence slowed down as spending 
per  capita  kept  rising  in  rather  high-income 
countries (66) while it dropped or grew at a slower 
pace  respectively  in  the  low-  and  middle-income 
countries  (i.e.  those  belonging  to  the  second  and 
third  quartiles).  Nonetheless,  in  the  less  wealthy 
countries (see Graph quartile 1) the spending ratio 
increased  rapidly,  due 
the  dynamism  of 
expenditure  on  old  age,  sickness  and  family 
benefits. 

to 

(65)  Data suggests that most of this increase is due to old age, 
lesser  extent,  family  spending; 
sickness  and, 
conversely,  the  drop  of  the  spending  ratio  in  the  second 
quartile is mostly attributed to sickness spending.  
(66)  Real spending also increased in the US (Moffitt, 2012). 

to  a 

Graph I.3.5:  Composition of social protection spending by 

quartile of GDP per capita 

(1) Percentage of total social expenditure. Countries are 
clustered in four quartiles based on their average income 
per capita over the period 1996-2016, in PPS. See Annex 1.  
Source: Own calculation based on ESSPROS and Eurostat. 

The  composition  of  social  spending  varies  with 
the 
total 
level  of  GDP  per  capita.  When 
into  specific 
expenditures  are  disaggregated 
categories, it turns out that richest Member States 
allocate  a  relatively  lower  proportion  of  the  total 

59 

01020304050Quartile 1Quartile 2Quartile 3Quartile 4% 
 
 
                                                           
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

that 

social expenditure to old age than poorer ones and 
a  higher  proportion  on  sickness,  family  and 
unemployment  benefits  (Graph  I.3.5).  This  is 
the  optimal 
the  view 
consistent  with 
composition of total spending shifts toward health 
as  income  grows  (Hall  and  Jones,  2007)  or  that 
more  open  economies  tend  to  be  richer  and 
demand  more  protection  against  risks  than  poorer 
(Rodrick,  1998). (67) 
and  more  closed  ones 
Differences  are  small  for  the  other  spending 
categories. This remains valid also after 2008. 

figures  mask 

considerable 
Aggregate 
differences  across  countries.  In  2016,  aggregate 
spending  varied  from  less  than  15%  of  GDP  in 
Romania  to  more  than  30%  in  France  (Graph 
I.3.4).  Compared  to  the  pre-crisis  period,  all 
countries experienced an increase in the spending-
to-GDP  ratio,  in  particular  Greece  (6.4  pps), 
Ireland (5 pps), Portugal (4.8 pps), Spain (4.7 pps) 
and Italy (4.4pps). (68) It declined only in Sweden, 
from  a  ratio  above  the  average.  While  resources 
allocated to old age increased in all countries after 
2008, those for survivors and disability dropped in 
almost  all  (Graph  I.3.4). (69)  For  sickness,  the 
spending-to-GDP  ratio  grew  everywhere  –  except 
Hungary,  Cyprus  and  Slovakia  –  in  particular  in 
countries with a high share before the crisis. More 
heterogeneity  across  countries  is  observed  for 
housing.  In  a  first  group,  where  spending  is 
extremely low, it rose in Belgium and Luxembourg 
and  fell  in  Poland,  Malta  and  Greece.  (70)  In  a 
it 
second  group  with 
increased in Finland, Germany, Ireland and Cyprus 
and decreased in Hungary and Sweden. In general, 
spending  on  family  and  children  increased  in 
countries that allocated relatively few resources to 
this  category  before  2008. (71)  The  unemployment 
expenditure 
in  few 
countries, including Italy - as an effect of the 2015 
reform of  the  unemployment  benefit system  - and 

intermediate  spending, 

to  GDP  ratio 

increased 

(67)  See also footnote 15. 
(68)  In countries hit hard by the 2008 crisis, the increase in the 
spending  ratio  reflects  both a  more  moderate  or  a  drop  of 
social expenditure and a stronger decline of GDP. 

(69)  Exceptions are Denmark, where both items increased, and 
Slovenia, Greece, Spain, Portugal and Luxembourg, where 
only spending on survivors grew. However, there is a break 
in spending for Denmark in 2007 and for Greece in 2000. 
(70)  There  is  a  break  in  the  data  in  2000  and 2015  for  Greece 

and Hungary. 

(71)  However,  spending  as  a  percentage  of  GDP  fell  in  Malta 
and Romania. It also increased in Luxembourg and the UK, 
where  it  absorbed  a  higher  share  already  before  the  2008 
crisis. 

60 

in  Ireland  and  Spain,  where  the  high  jobless  rate 
may  have  contributed  to  higher  spending  after 
2008.  Finally,  for  social  exclusion,  spending 
that 
increased  mainly 
traditionally  put  more  resources  in  this  type  of 
spending category. 

in  Member  States 

3.2.2.  The cyclicality of social expenditure  

social 

in  aggregate 

Prima-facie  evidence  suggests  that  after  2008 
social  expenditures  move  counter-cyclically. 
Table  I.3.1  shows  the  correlation  between  the 
growth  of  different  spending  items  and  business 
cycle  indicators.  Over  the  period  1990-2016, 
changes 
spending  are 
uncorrelated  with  changes  in  unemployment  and 
GDP  growth,  i.e.  social  spending  is  a-cyclical. 
This is also valid for all spending categories except 
sickness 
and  unemployment.  Spending  on 
unemployment  is  anti-cyclical,  i.e.  it  rises  during 
downturns  and  falls  during  upturns,  and  therefore 
it  contributes  to  stabilising  income.  The  relation 
with  the  cycle  has  become  stronger  after  2008. 
This  is  mainly  due  to  spending  on  old  age,  social 
spending  on 
exclusion  and  unemployment; 
disability becomes countercyclical after 2008.  

Table I.3.1:  Correlation of growth of social protection 

spending with GDP growth and unemployment 

(1) Median correlation between the growth of different 
categories of expenditure and GDP growth/ unemployment 
rate change. A positive (negative) correlation with 
unemployment rate changes (GDP growth) means that 
spending rises (falls) during downturns (upturns). The median 
is not affected by extremely high or extremely low values. 
Results are unchanged for the mean correlation. 
Source: Eurostat and DG EMPL calculations. 

trends  vary  significantly  across 
Spending 
different categories. The growth of each spending 
item reflects factors that are either country-specific 
or  common  to  all  countries.  Among  the  latter  are 
demographic  trends  or  common  policy  responses, 
e.g. the expansionary measures put in place in the 
aftermath  of  the  2008  recession  or  the  fiscal 

Expenditure growth for Full samplebefore 2008after 2008Full samplebefore 2008after 2008All functions-0.010.17-0.110.02-0.090.13Disability0.050.09-0.07-0.10-0.100.10Family/ Children0.050.17-0.04-0.04-0.070.01Housing0.00-0.040.080.020.09-0.09Old age0.050.16-0.140.09-0.090.20Sickness/Health care0.230.270.23-0.25-0.25-0.28Social exclusion n.e.c.-0.03-0.04-0.130.090.070.17Survivors0.04-0.01-0.07-0.05-0.020.04Unemployment-0.48-0.14-0.570.570.240.55GDP growthUnemployment rate changes  
                                                           
 
 
 
less  over 

tightening  of  the  early  2010s.  In  contrast,  factors 
such  as  preferences  toward  redistribution  are 
country-specific  and  vary 
time. 
Econometric models allow disentangling the effect 
of  the  business  cycle  from  trends  in  expenditure, 
while  controlling  for  factors  that  account  for 
differences  across  countries  in  the  growth  of 
spending. (72)  Graph  I.3.6  reports  the  growth  of 
real  spending  for  each  category  after  netting  out 
the  effect  of  the  cycle  and  controlling  for  country 
specific  components  of  expenditure  growth. (73) 
Hence,  it  shows  the  variation  over  time  of  the 
component  of  social  spending  common  to  all  EU 
countries. A  number  of  stylised  facts  can  be 
identified:  

  Between  the  second  half  of  the  1990s  and  the 
first half of the 2000s, social expenditure grew 
at about 2% per  year (i.e. close  to the  average 
of  GDP  growth  over  the  period).  When  the 
crisis  hit,  expenditure  reacted  in  an  anti-
cyclical fashion: total social spending expanded 
at  a  rate  higher  than  that  of  GDP,  therefore 
exercising a stabilising effect on the  economy. 
However,  in  2012,  social  expenditure  growth 
dropped significantly. (74)  

  As  GDP  growth  resumed  in  2013,  the  growth 
of social expenditure picked up again although 
at a lower rate. A similar pattern is detected for 
spending  on  family  and  children,  although  it 
grew on average less than GDP over the whole 
period. From 2008, the increase in expenditure 
growth also reflects the reform intensity in this 
policy area (see next section).  

 

In  line  with  the  increase  in  life  expectancy, 
spending  on  old  age  increased  throughout  the 
period. The growth of spending is above that of 
GDP, except for the last three years. From the 
early  2000s  onwards,  the  intensity  of  pension 
reforms  in  the  EU  was  particularly  strong  and 
further accelerated during the 2008-2009 crisis 
(Carone et al., 2016). These changes influenced 
the  growth  rate  of  old  age spending,  which, 

(72)  This is done by estimating the effect of GDP growth on the 
growth of different spending items, controlling for country 
and year fixed-effects. 

(73)  The  graph  shows  for  each  spending  type,  the  year  fixed 

effects of the panel regression in Table I.3.2.  

(74)  See ESDE 2015 Chapter III.2. 

Labour market and wage developments 

Part I 

relative to GDP, first stabilised in 2009 before 
falling from 2013 onwards. (75) 

to 

reduce 

incentives 

to  move  workers 

  The  declining  trend  in  disability  spending  is 
linked  to  reforms  implemented  in  several 
for 
Member  States 
employers 
to  disability 
schemes  (e.g.  Denmark,  Estonia,  Sweden),  to 
increase gate keeping (Austria, Italy, Germany, 
Finland  and  the  Netherlands)  and  enhance 
prevention  and  health  care,  including  through 
occupational health-and-safety at work (Austria 
and Germany). (76)  

  Over 

in 

increased 

line  with 

lengthening 

the  period  1992-2011,  spending  on 
unemployment 
the 
expansion  of  the  coverage  of  unemployment 
benefits.  With 
of 
the 
unemployment  spells  after  the  2008  crisis, 
many  jobless  people  lost  access  to  benefits, 
which  led  to  a  decline  of  spending  in  real 
terms, including during the 2012 recession. As 
documented  in  chapter  1,  the  job  destruction 
rate  started  to  fall  in  the  first  half  of  2011, 
while  the  job  finding  rate  remained  low  until 
the second half of 2013. Thus, the non-cyclical 
component  of  spending  on  unemployment 
continued  to  fall  until  the  share  of  long-term 
unemployed  started  to  decline  at  the  end  of 
2014.  It  is  only  in  2015  that  the  fall  of  this 
spending  component  came  to  a  halt.  This 
reflected  both 
long-term 
the  effect  of  reforms 
unemployment  and 
scope  and  coverage  of 
broadening 
unemployment 
social 
benefits 
assistance. (77)  

the  drop  of 

and 

the 

 

In  response  to  the  expansion  of  means-tested 
programmes, spending on social exclusion rose 
continuously. The crisis-related measures taken 
between  2011  and  2013  were  accompanied  by 
a  flattening  of  the  growth  rate  of  spending  in 
real  terms,  which  nonetheless  continued  to  be 

(75)  Pension  reforms  typically  preserve  acquired  rights  of 
current  pensioners  and  are  implemented  only  gradually 
over  time.  This  implies  that  their  effects  on  spending  is 
only gradual. Yet some policy changes, e.g. in the statutory 
retirement  age,  may  have  a  more  immediate  impact  on 
aggregate old age spending.  
(76)  Boheim and Leoni (2015).  
(77)  Non-contributory  and  means-tested  public 

providing  minimum  entitlements 
unemployment  are 
spending in the unemployment functions. 

in 
in  ESSPROS 

included 

schemes 
the  case  of 
in  social 

61 

 
                                                           
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

higher  than  that  of  GDP.  Thus,  the  increased 
spending  on  social  exclusion  after  2008 
represents  a  shift  towards  measures  aimed  at 
alleviating  poverty  in  a  period  of  lengthening 
unemployment spells.  

  As 

in 

the  case  of  unemployment, 

the 
component of spending on housing adjusted for 
the  cycle  increased  from  1992  to  2008.  The 
spending-to-GDP  ratio  stabilised  until  2012 
and,  after  a  temporary  decline  in  2013,  kept 
growing again in the subsequent years.  

Graph I.3.6:  Growth of social expenditure categories and 

GDP growth 

(1) The graph shows year fixed effects from regression in 
table 3.2. GDP is the EU aggregate.  
Source: Own calculations based on regression in Table 3.2. 

The  cyclical  response  of  the  different  spending 
categories varies over time. For the total and for 
specific  categories,  table  I.3.2  investigates  the 
cyclical response of expenditure, while controlling 
for underlying trends. Over the period 1991-2016, 
total  expenditure  is  a-cyclical  (i.e.  doesn’t  follow 
the  economic  cycle).  Spending  on  sickness  is 
highly  pro-cyclical,  with  a  stronger  response  to 
GDP  growth  before  the  2008  crisis;  yet,  its  effect 
2008. 
is 
Unemployment  is  the  only  category  of  spending 
which  is  counter-cyclical  and  features  a  higher 
response  to  GDP  growth  after  the  2008  crisis.  A 
decline  of  GDP  growth  by  1%  is  on  average 
associated  with  an 
in  spending  on 
unemployment  by  1.6%  before  the  crisis  and  by 
almost double that amount after 2008. 

insignificant 

statistically 

increase 

after 

62 

3.2.3.  The  capacity  of  social  spending  to 

smooth GDP shocks: regression analysis 

The extent to which GDP  shocks are smoothed 
by  social  protection  spending  increased  after 
the  2008  crisis.  Output  shocks  do  not  necessarily 
translate into income shocks. Part of output shocks 
can  be  smoothed  via  financial  markets  or  through 
government  expenditure.  For  example,  banks  in 
one  country  may  lend  to  borrowers  in  other 
countries and the flow of interest stabilises income 
in the lending country. Similarly, individuals save 
when  working  and  dis-save  when  unemployed  or 
retired.  When  consumption  smoothing 
is  not 
achieved via national or international  risk  sharing 
mechanisms,  fiscal  policy  can  intervene.  Social 
protection spending is an  important component of 
total public expenditure, accounting for about 40% 
of total public expenditure for the EU as a  whole. 
Following  a  standard  approach  in  the  risk  sharing 
literature  (Asdrubali  et  al.,  1996;  Arreaza  et  al., 
1998; Furceri, 2010; Darby and Melitz, 2008), the 
fraction  of  the  output  shock  that  is  smoothed  via 
social spending is measured by the fraction of the 
cross-sectional  variance  of  GDP  absorbed  by 
government  transfers. (78)  In  other  words,  the 
smoothing capacity of government expenditure is a 
measure of how much it attenuates the volatility of 
consumption  and  disposable 
income  around 
average consumption growth in response to shocks 
to GDP growth relative to the average. Table I.3.3 
reports  the  extent  to  which  net  disposable  income 
is  being  smoothed  via  total  social  spending  and 
specific  social  spending  types  over  three  different 
periods. In comparison, it also reports the extent of 
smoothing  ensured  by  the  overall  government 
spending. (79) 

(78)  Formally, this is obtained by looking at the effect of GDP 
growth  on  the  difference  between  growth  of  disposable 
income  and  the  growth  of  disposable  income  inclusive  of 
the  government’s  transfers.  Government  transfers  absorb 
GDP  shocks,  if  the  cross-section  correlation  between  the 
disposable  income  inclusive  of  the  transfers  and  GDP  is 
lower  than  the  correlation  between  the  disposable  income 
and GDP. In this case, the coefficient of a panel regression 
of the growth rate of disposable income minus the growth 
rate of the sum of disposable income and each categories of 
spending  in  the  table  on  GDP  growth  should  be  positive 
(see Arreaza et al., 1998). 

(79)  Results  are  shown  using  country-fixed  effects  and  both 
time  and  country-fixed  effects.  In  the  specification  with 
country  and  time-fixed  effects,  the  latter  are  barely 
significant.  For  this  reason,  the  preferred  specification  is 
without time-fixed effects.  

 
 
                                                           
Labour market and wage developments 

Part I 

Table I.3.2: 

Responsiveness of the growth of social spending to changes in GDP growth 

Estimation method: Panel regression including country and year-fixed effects. Robust standard errors in brackets. * Statistically 
significant at 10% level ** Statistically significant at 5% level *** Statistically significant at 10% level. Robust standard errors in 
parentheses, *** p<0.01, ** p<0.05, * p<0.1.  
Source: European Commission. 

1995-2016, 

government 

spending  contributes 

Social  protection 
to 
stabilising  disposable  income.  For  the  overall 
period 
spending 
smoothens about 9.4% of GDP shocks, which is in 
line  with  previous  results  of  the  literature  (e.g. 
Furceri, 2010). After 2008, the proportion of GDP 
shocks 
spending 
absorbed  by  government 
increased  to  18.7%. Turning  to  the  role  of  social 
expenditure, the amount of smoothing provided by 
total  social  spending  (6.5%)  is  slightly  below  the 
proportion of GDP shock that is smoothed by total 
government  spending    i.e.  government  spending 
in  social  areas  stabilises  GDP  shocks  less  than 
other  items  of  total  government  expenditure.  This 
implies  that  other  public  spending  categories 
the 
contribute 
opposite of what is found by Furceri (2010) for the 
period 1980-2003. (80) As concerns  sub-categories 
of social spending, old age spending represents the 
category  that  accounts  for  the  largest  degree  of 
the  other 
smoothing 
in  all  periods.  Among 
components, 
unemployment 
benefits  and  sickness  contribute  the  most  to 
stabilising disposable incomes. Relative to the pre-
crisis  period,  after  2008  all  social  spending  items 
stabilise  income  more.  The  strongest  increase  in 

income,  which 

to  stabilise 

expenditure 

on 

is 

(80)  Furceri  (2010)  finds  for  the  period  1983-2003  that  some 
items  of  government  spending  provide  dis-smoothing  and 
relate  this  to  the  higher  cyclicality  of  unemployment 
benefits and the dis-smoothing of public investments. This 
study  covers  the  pre-crisis  period  and  therefore  doesn’t 
take  into  account  the  effect  of  the  large  and  coordinated 
fiscal  stimulus  undertaken  by  the  EU  in  2009  (see 
European  Commission,  2009,  Public  Finances  in  EMU, 
European  Economy  5),  nor  the  focus  on  unlocking 
investment  of  the  Juncker  plan  for  investments;  See 
European Commission Investment plan results.  

the  amount  of  insurance  from  income  shocks 
comes  from  spending  on  old  age,  family  and 
unemployment benefits. 

The  ability  of  social  spending  items  to  insure 
against GDP shocks varies with the level of per 
capita income. Graph I.3.7 shows the stabilisation 
effect of social spending for different categories of 
spending clustering countries according to their per 
capita  income.  The  chart  reveals  a  number  of 
findings. First, the richest Member States stabilise 
income shocks  more,  which is consistent  with the 
findings of the literature. (81) This may surprise as 
one would expect wealthier countries to have other 
effective  risk  sharing  mechanisms  (e.g.  credit 
those  provided  by 
markets), 
government 
spending.  However,  wealthier 
Member  States  have  also  larger  welfare  spending 
and  possibly  more  effective  policies,  which  allow 
them  to  rely  more  on  social  spending  when 
needed. (82)  Second,  in  all  quartiles,  the  largest 
amount  of  stabilisation  is  provided  by  old  age 
spending (slightly more in richer countries). Third, 
as  compared  to  low  income  Member  States, 
spending  on  unemployment  benefits  and  sickness 
and disability provide a larger smoothing effect in 
high income countries. 

in  addition 

to 

(81)  Furceri  and  Jalles  (2015)  show  that  fiscal  stabilisation  is 
level  of  economic 
trade  openness, 

positively  associated  with 
development, 
government size as well as political factors.  

financial  deepening, 

the 

(82)  The  correlation  between  social  expenditure  and  GDP  per 
capita is 0.29 (the max is 1); if Ireland and Luxembourg are 
excluded  from  the  sample,  the  correlation  reaches  0.78. 
The  rank  correlation  is  0.7  and  0.8,  if  these  countries  are 
excluded from the sample. 

63 

Explanatory variables Total expenditure DisabilityFamilyHousingOld age SicknessSocial Exclusion s.o.e.SurvivorsUnemployment-0.019-0.170.08-1.44-0.110.52**-0.50-0.37-1.9***[0.19][0.24][0.26][1.47][0.22][0.21][0.52][0.67][0.61]Observations559559559559559559559559559R-squared0.190.210.160.0860.1470.20.060.060.370.2-0.190.69-5.040.280.74**0.34-1.64-1.61*[0.32][0.34][0.51][5.01][0.35][0.35][1.69][2.24][0.81]Observations308308308275308308308308308R-squared0.4420.3610.2670.1200.3910.4150.0720.0860.248-0.23-0.23-0.4-0.16-0.310.33-0.59-0.08-2.8***[0.19][0.23][0.30][1.26][0.21][0.24][0.57][0.21][0.88]Observations251251251250251251251251251R-squared0.350.350.30.130.40.250.170.170.53GDP growth Whole sample: 1991-20161991-20072008-2016GDP growth GDP growth  
                                                           
                                                           
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Table I.3.3: 

Smoothing capacity of social spending components 

(1) The table shows the coefficient of the panel regression: ∆log⁡(𝐷𝑁𝐼𝑖)- ∆log⁡(𝐷𝑁𝐼𝑖+𝑔𝑜𝑣𝑒𝑟𝑛𝑚𝑒𝑛𝑡 𝑠𝑝𝑒𝑛𝑑𝑖𝑛𝑔 ) on the growth 
rate of GDP , where ∆log⁡(𝐷𝑁𝐼𝑖) is the growth rate of net disposable income of country i (see Arreaza, 1998 and Furceri, 
2010). Cofog manual provides a detailed description of the different categories.  
Source: Own calculations, based on COFOG and National accounts. 

Graph I.3.7:  Stabilisation effect of social spending by 

categories and quartile of GDP per capita 

(1) The table shows the coefficient of the panel regression 
estimated for each quartile: ∆log(𝐷𝑁𝐼𝑖)- ∆log(𝐷𝑁𝐼𝑖+x ) on the 
growth rate of GDP , where ∆log(𝐷𝑁𝐼𝑖) is the growth rate of 
net disposable income of country i and x is one of the 
spending items. The stabilisation effect is estimated for the 
group of countries that belong to the different quartiles; see 
Annex I. Sample period 1995-2016. 
Source: Own calculations based on COFOG and National 
accounts. 

3.3. 

POLICY TRENDS 

crisis,  many  countries  increased  the  generosity  of 
unemployment  and  other  welfare-related  benefits 
to protect the incomes of those newly unemployed 
and  of  vulnerable  groups.  Starting  from  2010,  the 
reform  activity  in  the  area  of  social  protection 
became  less  intense;  significant  reforms  were 
implemented  to  improve  the  adjustment  capacity 
of  the  labour  market  (mainly  in  2011  and  2012). 
Reforms  aimed  at  reinforcing  welfare  systems   
also  in  countries  which  were  hit  less  hard  by  the 
crisis  –  were 
implemented  from  2013/2014 
onwards. 

This  section  reviews  the  main  social  benefit 
reforms  enacted  after  the  2008  crisis,  making 
use of the LABREF database. (84) The focus is on 
the  design  of  cash  support  in  the  following  areas: 
social  assistance  and  housing  benefits,  child  and 
family  benefits  and  unemployment  benefits.  (85) 
These  areas  have  been  selected  for  their  potential 
in  mitigating  the  effect  of  economic  shocks  on 
poverty. 

Social  protection  systems  offer  benefits  to 
individuals or families in order to protect them 
from major life-cycle risks. These benefits can be 
provided in cash, to replace or supplement income 
from  labour,  or  in  kind  through  the  provision  of 
services, such as job search assistance, childcare or 
training.  

3.3.1.  Social assistance and housing benefits 

All European countries have some type of social 
assistance.  These  schemes  are  aimed  at  helping 
individuals  and  households  to  obtain  an  adequate 
standard  of  living  and  alleviate  the  impact  of 
poverty  and  social  exclusion.  Generally,  they  are 

As  highlighted  in  previous  editions  (83),  several 
countries  have  implemented  reforms  in  their 
social  protection  systems  since  the  start  of  the 
2008  crisis.  In  the  immediate  aftermath  of  the 

(83)  European Commission, 2017a and 2018b. 

(84)  The  LABREF  database  is  an  inventory  of  labour  market 
measures adopted in the EU Member States since 2000. It 
is  maintained  by  the  European  Commission  and  is 
available  online  at  the  link  LABREF.  The  number  of 
reforms is informative only of the policy stance.  

(85)  The 2018 LMWD Report looked at reforms in the area of 
unemployment  benefits.  This  section  focuses  on  the 
poverty-reducing effects of unemployment benefits.  

64 

-4%0%4%8%12%16%Quartile 1Quartile 2Quartile 3Quartile 4FamilyHousingOld ageSickness and disabilitySocial exclusion n.e.c.SurvivorsUnemploymentOthersOLSCountry&time fixed effectsOLSCountry&Time fixed effectsOLSCountry&time fixed effectsTotal Government Spending0.094 ***0.0430.0430.0280.19***0.09***Total Social Protection Spending0.065 ***0.031***0.029***0.018***0.12***0.06***Family0.009 ***0.005**0.0030.0040.02***0.011**Housing0.0000.0000.000.000.0010.001Old age 0.040 ***0.021***0.014**0.012**0.06***0.034***R&D Social Protection0.0000.0000.0000.0000.0000.000Sickness and disability0.006*0.003-0.0040.000.015***0.010**Social Exclusion n.e.c 0.002**-0.0010.0010.000.005***0.000Social Protection n.e.c.0.0020.0000.0040.004*0.003*-0.002Survivors0.004***0.002**0.0010.002**0.006***0.002**Unemployment0.014***0.006**0.005**0.0020.026***0.013**2008-20161995-20161996-2007 
 
 
                                                           
                                                           
 
 
 
Labour market and wage developments 

Part I 

Graph I.3.8:  Social assistance and housing: average 

number of reform measures per country per 
year by direction of reform measures, EU28 

means-tested and non-contributory. There are large 
differences between Member States in the level of 
support provided by these schemes as well as other 
features of their design, such as their coverage and 
the  conditionality  rules  attached  to  receiving  the 
benefits.  Most  social  assistance  benefits  are 
complemented  by  other  means-tested  benefits, 
such  as  rent  and  housing  benefits  and  child  or 
family allowances.  

Reform intensity in the area of social assistance 
and  housing  benefits  has  been  relatively  high 
since the start of the crisis. The  average number 
of annual measures increased during the first years 
of  the  crisis  (2008-2009),  as  illustrated  by  graph 
I.3.8. Most of the reforms increased the generosity 
or  coverage  of  benefits  to  cushion  the  impact  of 
the  crisis.  After  a  drop  in  2010,  the  average 
number  of  annual  measures  increased  again  in 
2011  and  2012.  Yet,  most  of  the  reforms  made 
systems less generous, reflecting fiscal constraints, 
notably  in  vulnerable  countries  and  in  countries 
under  financial  assistance  programmes.  As  of 
2013,  renewed  reform  efforts  to  improve  the 
generosity  and  coverage  of  social  assistance 
benefits  are  visible  and  reform  intensity  remains 
relatively  high  compared  to  the  post-crisis  period. 
These  developments  are  also  visible 
the 
in 
the  previous 
spending  patterns  discussed 
section.  The 
in  spending  on  social 
exclusion was mainly driven by the rise of income 
support  measures,  which  at  the  end  of  2016 
accounted  for  about  56%  of  total  spending  in  the 
social  exclusion  category  compared  to  about  50% 
in 2008. (86) 

increase 

in 

(1) Information for Croatia starts in 2012. Reform measures 
are classified as ''increasing'' (''decreasing'') if they lead to 
an increase (decrease) of the generosity and/or coverage 
of benefits.  
Source: European Commission, LABREF database. 

Since  2008,  several  Member  States  have 
implemented substantial reforms of their social 
assistance  systems.  New  minimum 
income 
schemes  have  been  introduced  in  Austria  (2009), 
the  United  Kingdom  (2012)  Croatia  (2013), 
Cyprus  (2014),  Greece  (2016),  Romania  (2016), 
Luxembourg  (2018)  and  Italy  (2015,  2017  and 
2018). The introduction of new  schemes  was also 
an  opportunity  to  improve  their  delivery.  Indeed, 
the  new  schemes  were  often  merging  several 
previous  benefits  into  one  scheme  and  in  most 
cases  aimed  at  combining  poverty  alleviation 
(through  means-tested  income  support)  with  the 
provision  of  social  services  to  support  social 
inclusion  and  labour  market  integration.  The 
responsibility  for  social  assistance  benefits  has 
been  decentralised 
in  both 
Lithuania (2012-2015) and the Netherlands (2013), 
with  the  aim  to  increase  the  effectiveness  and 
efficiency.  Similarly,  Hungary  created  a  new 
municipality  benefit 
for  which 
municipalities were put in charge of the generosity 
and eligibility criteria.  

to  municipalities 

in  2014, 

Many  Member  States  have 
increased  the 
generosity  of  social  assistance  benefits  by 
raising  levels  or  extending  their  coverage.  The 
level  of  social  benefits  was  raised  (sometimes 
temporarily)  in  several  countries.  For  example, 
Cyprus  increased  the  basic  allowance  of  welfare 
recipients  by  12%  in  2009;  the  minimum  income 
level  in  Slovenia  was  raised  by  over  25%  in 

(86)  As  part  of  the  implementation  of  the  European  Pillar  of 
Social  Rights,  the  European  Commission  adopted  a 
proposal for a council recommendation on access to social 
protection. See Box “Access to Social protection”. 

65 

-1.0-0.50.00.51.02000200220042006200820102012201420162018increasingdecreasing 
 
 
 
 
 
 
                                                           
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

transfers 

targeted  particularly  at 

2010. (87)  A  number  of  countries  have  provided 
one-off 
the 
poorest  or  most  vulnerable  groups  (e.g.  Italy  in 
2008,  Greece  and  Slovenia  in  2009,  Bulgaria  in 
2013,  Malta  in  2014).  Moreover,  to  improve 
coverage and incentives to work, several countries 
have raised the income threshold to be eligible for 
social  assistance  or  excluded  certain  types  of 
income,  assets  or  other  benefits  from  the  means 
test (e.g. Sweden in 2012, Latvia in 2014, Malta in 
2016, Slovenia in 2016, Finland in 2011 and 2013 
and  Latvia  in  2017).  In  addition,  in  a  number  of 
countries,  eligibility  criteria  were  widened  to 
support specific categories of people, such as part-
time  workers  (Spain  in  2013),  young  people 
(France in 2009), single parents (Malta in 2014) or 
the  self-employed  (the  Netherlands  in  2015).  As 
shown  in  Graph  I.3.9,  income  support  measures 
have  absorbed  a  rising  share  of  total  expenditure 
on social exclusion, including in countries where it 
accounted for a lower share of GDP in 2008.  

Graph I.3.9: 

Income support measures (as % of 
expenditure on social exclusion n.e.c.) 

(1) In parenthesis spending on social exclusion n.e.c. as 
percentage of GDP in 2008 and in 2016. Social exclusion 
n.e.c encompasses benefits in cash and in kind. The former 
includes incomes support and other benefits; the latter 
accommodation, rehabilitation of alcohol and drug abusers 
and other benefits in kind. For definition of the variables see 
Manual.  
Source: ESSPROS. 

In  a  few  countries  the  generosity  of  social 
assistance benefits has been reduced via cuts in 
benefit  levels  or  the  introduction  of  more 
stringent  eligibility  criteria.  The  level  of  social 
assistance benefits  was reduced in Cyprus (2011), 
Latvia  (2012),  Lithuania  (2011),  Portugal  (2012) 
and  Romania  (2008  and  2011).  In  addition, 
Denmark  (2016)  placed  a  ceiling  on  the  total 

(87)  Other  countries  include  Belgium  (2008),  Bulgaria  (2009 
and 2017), Denmark (2011), Estonia (2015), Ireland (2016 
and 2017), France (2013), Latvia (2017), Lithuania (2017), 
Portugal  (2016,  2017  and  2018),  Slovenia  (2018)  and 
Finland (2012 and 2018). 

66 

amount of benefits. In the Netherlands, a norm for 
sharing  costs  was  introduced  in  2014,  stipulating 
that  an  individual  welfare  benefit  will  decrease 
with  each  extra  person  living  in  a  household. 
These  measures  were  aimed  at  consolidating 
public  finances  and  reducing  disincentives  to 
work. More stringent means testing was applied in 
the  United  Kingdom  (2011)  and  Slovakia  (2012), 
while 
requirements  were 
introduced  as  a  condition  to  become  eligible  for 
social  assistance  benefits  in  Ireland  (2014)  and 
Denmark (2015). However, expenditure on income 
support as a percentage of GDP dropped slightly in 
only a few countries (e.g. Ireland and Romania). 

residence 

stricter 

incentives 

Several  Member  States  have 
implemented 
measures aimed at strengthening activation and 
incentives for job search. The obligation to work 
a minimum number of hours for those able to work 
has  been  introduced  as  a  condition  for  receiving 
social  assistance  benefits  in  Portugal  (2012), 
Denmark  (2013)  and  the  Czech  Republic  (2016). 
In  addition, 
for  social  assistance 
beneficiaries  to  participate  in  social  inclusion  or 
job-search programmes have been strengthened in 
several countries, either as obligatory requirements 
for the receipt of assistance (e.g. Romania in 2010 
and  2018,  Croatia  in  2013,  the  Netherlands  in 
in  2016  and  Greece  and 
2014,  Belgium 
Luxembourg  in  2018)  or  by  providing  a  special 
bonus  or  allowance  in  return  (Slovenia  and 
Slovakia in 2013).  

Several  countries  have 
increased  housing 
benefits.  These  benefits  or  subsidies  were 
introduced  or  increased  in  several  countries  (88), 
often  targeting  vulnerable  groups,  such  as  youth, 
pensioners, families with children, people living in 
extreme  poverty  or  people  with  long-term  work-
incapability. Moreover, some Member States took 
measures  to  improve  incentives  to  work,  for 
instance  by  making  the  withdrawal  of  housing 
benefits  more  gradual  with  increasing  income  or 
hours worked (e.g. Finland and Ireland in 2014). In 
addition,  heating  subsidies  or  allowances  targeted 
low-income  households  were  (temporarily) 
at 
introduced  or  increased  in  a  few  Member  States 
(e.g. Greece, Italy and Hungary in 2008, Bulgaria 
in  2016).  However,  in  a  few  countries,  reforms 

(88)  E.g.:  France  in  2011,  Portugal  in  2013,  Greece  in  2015, 
Sweden  in  2013  and  2017,  Denmark  in  2008,  Malta  in 
2016 and 2018, Lithuania and Poland in 2018.  

020406080100CY (1); (1.2)NL (1.2); (1.3)IE (0.4); (0.1)RO (0.3); (0.2)SK (0.4); (0.3)PT (0.3); (0.2)SI (0.4); (0.7)EE (0.1); (0.1)FR (0.8); (1)LU (0.4); (0.5)DK (0.7); (1.5)EU28 (0.5); (0.6)UK (0.9); (0.6)DE (0.1); (0.4)LT (0.2); (0.3)CZ (0.2); (0.3)PL (0.2); (0.1)BE (0.7); (0.7)ES (0.2); (0.2)HR (0.2); (0.3)FI (0.6); (1.1)MT (0.4); (0.2)AT (0.4); (0.8)LV (0.1); (0.1)SE (0.6); (1.4)BG (0.3); (0.3)IT (0.2); (0.3)EL (0); (0.2)HU (0.1); (0.1)20082016 
 
                                                           
                                                           
were  implemented  to  reduce  the  generosity  of 
housing benefits. (89) 

3.3.2.  Child and family benefits 

are 

income 

schemes 

Minimum 
often 
complemented by social transfers in the form of 
family  and  child  benefits.  Family  and  child 
benefits  play  an  important  role  in  providing 
adequate  income  support  to  families  and  are 
associated  with  reduced  risks  of  child  poverty. 
Benefits can be means-tested or universal and their 
level  and  design  differ  across  the  EU.  In  most 
countries, family spending is not means-tested and 
accounts  for  between  1%  of  GDP  in  Malta  and 
3.4% of GDP in Denmark (Graph I.3.10).  

Graph I.3.10:  Spending on family and children (2016) : 

break-down between means-tested and non-
means tested 

(1) Countries are shown in descending order of spending on 
family as percentage of GDP in 2016. In parenthesis 
spending on Family and children as percentage of GDP in 
2008 and in 2016. For definition of the variables see Manual. 
Source: ESSPROS. 

The  reform  intensity  in  the  area  of  family-
related  benefits  has 
increased  substantially 
since  the  start  of  the  crisis  and  has  remained 
relatively  elevated  in  recent  years.  The  average 
number  of  annual  measures  in  this  area  has 
followed  more  or  less  the  same  pattern  as  for 
social  assistance  benefits  (Graph  I.3.11).  An 
increase  is  visible  in  2008  and  2009,  with  most 
reforms leading to an increase in the generosity or 
coverage of benefits. After a drop in 2010, reform 
intensity  picked  up  again  in  2011,  although  most 

(89)  Ireland  (2011),  the  UK  (2012),  Cyprus  (2013),  the  Czech 
Republic  (2016)  and  Finland  (2017).  Within  this  group, 
spending  on  housing  as  a  percentage  of  GDP  fell  only  in 
Cyprus (by 0.4 pps between 2008 and 2016).  

Labour market and wage developments 

Part I 

the  average  number  of 

reforms  in  that  year  were  characterised  by  a 
‘decreasing’ direction, indicating a reduction in the 
generosity and/or coverage of benefits. In the years 
reforms 
2012-2018, 
remained relatively high and most of the measures 
taken  led  to  an  increase  in  the  generosity  and/or 
coverage  of  benefits.  Nonetheless,  spending  as  a 
percentage  of  GDP  fell  in  a  number  of  countries 
(see numbers in brackets of Graph I.3.10). 

Graph I.3.11:  Family-related benefits: average number of 

reform measures per country, per year by 
direction of reform measures, EU28 

(1) Information for Croatia starts in 2012. Reform measures 
are classified as ''increasing'' (''decreasing'') if they lead to 
an increase (decrease) in the generosity and/or coverage 
of family-related benefits.  
Source: European Commission, LABREF database. 

(2018) 

Since  2008,  many  Member  States  have 
increased their levels of child or family benefits. 
New means-tested child or family benefit schemes 
were  introduced  in  Estonia  (2012),  Greece  (2011 
and  2018),  Poland  (2016)  and  Romania  (2010), 
while  Cyprus 
(2018)  and 
(2017),  Slovenia 
Lithuania 
introduced  universal  child 
benefits independent of family income. To provide 
adequate income support to families with children, 
the level of child or family allowances was raised 
in several countries. (90) Some countries raised the 
benefit 
supplements 
large  families (91)  or 
particularly 
vulnerable  groups,  such  as  low-income  families 
(Portugal  in  2009,  Malta  in  2014  and  2018, 
Lithuania  in  2018),  single  parents  (Finland  in 

or 
targeted  at 

introduced 

level 

(90)  Bulgaria  (2009  and  2017),  Germany  (2008,  2009,  2016, 
2017 and 2018), Estonia (2014 and 2015), Finland (2018), 
Greece  (2018),  Latvia  (2013  and  2016),  the  Netherlands 
(2016), Poland (2012), Portugal (2016 and 2017), Romania 
(2014),  Slovenia  (2015),  Slovakia  (2016,  2017  and  2018), 
Sweden (2017) and the United Kingdom (2009). 

(91)  E.g.:  Estonia  (2012  and  2016),  Sweden  (2016),  Cyprus 
(2017), Latvia (2017), Lithuania (2018) and Poland (2014).  

67 

0%20%40%60%80%100%DK (3.8); (3.4)LU (4.1); (3.3)DE (2.8); (3.2)FI (2.8); (3.1)SE (2.9); (3)AT (2.9); (2.8)UK (2.6); (2.6)PL (1.2); (2.5)EU (2.1); (2.4)FR (2.4); (2.4)HU (2.7); (2.2)BE (2.1); (2.1)EE (1.7); (2.1)BG (1.3); (1.8)HR (1.5); (1.8)IT (1.1); (1.8)SI (1.7); (1.7)CZ (2); (1.6)LV (1.3); (1.6)SK (1.4); (1.6)RO (1.5); (1.4)IE (2.4); (1.3)ES (1.3); (1.3)CY (1.9); (1.3)PT (1.2); (1.2)LT (1.8); (1.1)NL (1.1); (1.1)EL (0.9); (1)MT (1.2); (1)Non means-tested benefitsMeans-tested benefits-1.0-0.50.00.51.02000200220042006200820102012201420162018increasingdecreasing 
 
                                                           
 
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

2008)  or  disabled  children  (Bulgaria  in  2013, 
Malta  and  Hungary  in  2018,  Poland  in  2012  and 
2014).  In  addition,  Germany  introduced  several 
education-related  support  measures  for  children 
(between  2008  and  2012).  As  a  percentage  of 
GDP, Poland, Italy and Bulgaria are the countries 
where  social  spending  on  families  has  increased 
the  most  (see  numbers  in  brackets  of  Graph 
I.3.10). 

Childcare  benefits  were  raised  in  a  number  of 
countries. To improve participation of children in 
early  childhood  education  and  care  services  and 
also support labour market participation of parents, 
childcare benefits were raised in Malta (2009), the 
Netherlands (2015) and Slovakia (2010 and 2015). 
introduced  means-tested 
In  addition,  Belgium 
subsidies  for  kindergarten  expenses  in  2008.  To 
the  affordability  of  early  childhood 
improve 
education  and  care,  Finland 
lowered  early 
childhood  education  fees  and  Austria  made 
childcare costs tax-deductible (both in 2017).  

Only a few countries reduced the level of family 
or child benefits, or abolished existing schemes. 
The  Czech  Republic  lowered  the  birth  grant  in 
2010, while child benefit rates were cut in Ireland 
(2011  and  2012),  Cyprus  (2011),  Latvia  (2009) 
and  Finland  (2014).  Poland  decreased  the  tax 
credit for children in 2009. Childcare benefits were 
reduced in the Netherlands in 2013 and the United 
Kingdom cut the childcare cost subsidy in 2011.  

Several countries changed eligibility criteria for 
family, child or parental allowances to improve 
coverage  and  incentives  to  work.  The  income 
threshold  of  means-tested  child  or  family  benefits 
was increased in Bulgaria (2016 and 2017), Cyprus 
(2017)  and  Poland  (2012)  and  abolished 
in 
Slovenia  (2018).  Incentives  to  work  were  raised 
through changes in  the design of these benefits in 
Ireland  (2014),  Hungary  (2013  and  2015)  and  the 
Netherlands (2014) and Austria (2013). New rules 
to  allow  for  combining  parental  allowances  with 
(part-time)  work  were 
in  several 
countries. (92) In 2016, Belgium introduced a  new 
flat-rate  child  benefit  no  longer  dependent  on  the 
status  of  the  parent,  who  previously  had  to  be 
working or actively searching for work (applicable 
as of 2019). 

introduced 

(92)  Bulgaria  (2016),  Germany  (2014),  Latvia  (2013),  Estonia 

(2013) and Romania (2014). 

A few countries restricted eligibility criteria for 
family  or  child  benefits,  or 
introduced 
additional criteria. In Bulgaria (2013), the family 
allowance  became  dependent  on  regular  school 
attendance of children. Estonia decided in 2009 to 
apply the child tax allowance only from the second 
child  onwards.  Ireland  (2011)  gradually  reduced 
the  upper  age  limit  for  receiving  the  One-Parent 
Family Benefit. An income ceiling was introduced 
for  family  benefits  in  Lithuania  (2009),  for  the 
childbirth allowance in Poland (2012) and for child 
benefits  in  the  United  Kingdom  (2013).  Cyprus 
(2011)  and  Portugal  (2011)  decreased  the  income 
ceiling for child benefits.  

3.3.3.  Unemployment benefits 

Unemployment  benefits  protect  unemployed 
people and their families from large reductions 
in  income.  (93)  Reform  intensity  in  this  area  has 
risen  after  the  crisis.  The  average  number  of 
reform measures per country increased in 2009 and 
remained elevated in 2010 (Graph  I.3.12). After a 
drop  in  2011,  the  reform  effort  resumed  in  2012, 
followed  by  more  moderate  intensity  after  the 
2013  recovery.  After  a  relative  slowdown  in  the 
number  of  reforms  in  2016,  reform  intensity 
increased  again  in  2017  and  stayed  relatively 
stable throughout 2018. 

Graph I.3.12:  Unemployment benefits: average number of 

reform measures per country per year by 
direction of reform measures, EU28 

(1) Information for Croatia starts in 2012. Reform measures 
are classified as ''increasing'' (''decreasing'') if they lead to 
an increase (decrease) in generosity and/or coverage of 
benefits.  
Source: European Commission, LABREF database. 

(93)  European  Commission  (2018b)  reviews  the  reforms  of 
unemployment benefit systems enacted after the crisis.  

68 

-1.0-0.50.00.51.02000200220042006200820102012201420162018increasingdecreasing 
                                                           
 
                                                           
Labour market and wage developments 

Part I 

Since  2008,  several  countries  have  aimed  at 
improving 
the  coverage  of  unemployment 
benefits. Eligibility conditions were eased, mainly 
by  reducing  the  minimum  years  of  experience  or 
contribution  periods  necessary  to  be  entitled  to 
unemployment benefits. Coverage was extended to 
previously  excluded  groups  (e.g.  self-employed, 
freelancers,  non-regular  workers  and  temporary 
agent workers). (94)  

Graph I.3.13:  Spending per unemployed (in percentage of 

GDP per capita) 

the risks of poverty. Moreover, in order to improve 
living  standards  of  low-income  families  while 
encouraging employment, an increasing number of 
countries  have  relied  on  in-work  benefits  and  tax 
credits. (96)  

Although  employment  is  the  key  way  out  of 
poverty,  social  transfers  may  play  a  role  in 
alleviating  the  risk  of  poverty  when  transitioning 
between  different  jobs.  Not  all  transfers  are 
equivalent  in  relieving  poverty.  Their  design  may 
imply  that  the  benefit  is  awarded  subject  to 
eligibility  conditions  that  are  differently  binding 
as,  for  example,  in  the  case  of  social  assistance, 
where  amounts  paid  generally  depend  on  income 
or  family  composition  or,  on  the  contrary,  in  the 
case  of  the  child  benefits  awarded  without  a 
contribution  or  income  test.  The  next  section  will 
assess how effective social transfers are in when it 
comes to poverty reduction.  

(1) GDP per capita is as percentage of 15-74 population. 
Source: ESPROSS and Eurostat National Accounts.  

3.4. 

THE  POVERTY-REDUCING 
SOCIAL TRANSFERS  

IMPACT  OF 

Although 

In  several  Member  States,  activation  strategies 
were  pursued  to  improve  the  cost  effectiveness 
of  unemployment  benefit  systems.  A  clear 
reform  pattern  emerges  regarding  the  maximum 
duration  of  unemployment  benefits,  which  was 
reduced in several Member States and raised only 
in a few. In several countries, net replacement rates 
were  reduced  and  eligibility  conditions  tightened 
through  stronger  job-search  and  work  availability 
requirements. 
measures 
strengthened  activation, 
they  may  also  have 
negatively  affected  poverty,  as  many  unemployed 
lost  their  entitlements  to  benefits. (95)  In  several 
Member  States,  this  tightening  contributed  to  the 
decline  of  social  spending  on  unemployment  per 
unemployed  persons,  in  particular  where  labour 
market  weakness  was persistent and  many jobless 
people  had  lost  access  to  unemployment  benefits 
(OECD,  2018  and  Graph  I.3.13).  In  response  to 
this  challenge,  unemployment  assistance  benefits 
were  increasingly  used  to  protect  individuals  not 
anymore eligible to unemployment insurance from 

these 

(94)  For  a  detailed  review  of  the  reforms  of  unemployment 
benefit  systems,  see  European  Commission  (2018a)  and 
Joint Employment Report (2019b). 

(95)  The lengthening of unemployment durations has also had a 
direct  impact  on  poverty,  as  the  net  replacement  rate 
typically declines over the unemployment spell. 

The  effect  of  social  transfers  on  monetary 
poverty  is  usually  measured  by  comparing  the 
at-risk-of-poverty  rate  before  and  after  all 
social  transfers.  (97) In  addition,  one  can  also 
analyse  the  effect  of  social  transfers  on  the  depth 
of poverty, by comparing the ‘poverty gap’ before 
and  after  all  social  transfers. (98)  This  section 
analyses the effect of each type of benefit on both 
the  incidence  and  the  depth  of  poverty.  The 
analysis  is  based  on  individual  data  from  the  EU-
SILC  for  the  years  2008-2017.  In  this  survey, 
benefits  are  divided  into  the  following  types: 
unemployment,  family,  sickness  and  disability, 
social inclusion, and housing benefits. (99)  

(96)  Greece  introduced  an  income  support  scheme  in  2009  for 
employees in non-standard forms of employment. In 2010, 
Denmark  introduced  a  tax-free  in-work  premium  for  lone 
parents  entering  employment.  Slovakia  introduced  an 
activation allowance for workers in low-income families in 
2013.  

(97)  See  box  I.3.2.  For  an  examination  of  the  effects  of  taxes 
and  transfers  on  inequality,  see  European  Commission 
(2019c), Employment and Social Developments in Europe. 

(98)  See box I.3.2 for a definition of poverty gap. 
(99)  Calculations  do  not  take  into  account  the  potential 
interactions  that  exist  between  different  types  of  benefits 
(for  instance  some  households  could  be  eligible  for  other 
benefits if they lost one particular type).  

69 

EUBEBGCZDKDEEEIEELESFRHRITCYLVLTLUHUMTNLATPLPTROSISKFISEUK02040608002040608020162008 
 
                                                           
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Overall,  social  benefits  (excluding  pensions) 
reduce  the  incidence  of  poverty  by  about  one 
third  and  its  depth  by  more  than  one  half. 
Graphs  I.3.14  and  I.3.15  show  the  effect  of  all 
benefits  (excluding  pensions),  as  well  as  that  of 
each  type  separately  on  the  AROP  rate  and  the 
poverty gap. (100) In 2017, social transfers reduced 
the  AROP  rate  by  34%,  and  the  poverty  gap  by 
55%, on average across the Member States. Family 
benefits have the largest effect on the poverty rate 
(17%), followed by sickness and disability benefits 
(15%)  and  unemployment  benefits  (10%).  The 
effect of housing and social inclusion benefits are, 
on  average,  smaller  at  around  5%;  this  is  a 
substantial  effect  considering  their  low  share  in 
social spending (see Section I.3.2). The ranking of 
benefits is the same in terms of their effect on the 
poverty gap.  

Social  transfers  reduce  the  poverty  rate  by 
more  than  one  half  in  Denmark,  Finland  and 
Ireland. In 2017, their effect also exceeded 40% in 
some  other  Nordic  (Sweden),  Anglo-Saxon  (UK), 
Continental  (France,  the  Netherlands)  and  Central 
European  (Czechia,  Hungary,  Slovenia)  welfare 
systems.  In  contrast,  they  reduce  poverty  by  less 
than  a  quarter  in  some  Central  and  Eastern 
European  countries  (Bulgaria,  Latvia,  Lithuania, 
Romania)  and  Southern  Member  States  (Greece, 
Italy,  Portugal,  Spain,  Graph  I.3.14).  Denmark, 
Finland and Ireland were also among the countries 
with  the  biggest  impact  of  social  transfers  on  the 
depth  of  poverty.  In  these  countries,  as  well  as  in 
Belgium and Sweden,  social  transfers reduced the 
poverty gap by 70% or more (Graph I.3.15). 

Family  benefits  reduce  the  poverty  rate  by 
more  than  a  third  in  Hungary  and  Ireland. 
More generally, it is high in some Eastern Member 
States that have relatively generous parental leave 
schemes  (Hungary  and  Poland),  but  also 
in 
Austria,  Finland,  Ireland  and  the  UK  (Graph 
I.3.14). In contrast, their effect on poverty remains 
below  10%  in  Greece,  Italy,  Malta,  Portugal  and 
Spain. The relative position of countries is similar 
in  terms  of  the  impact  of  benefits  on  the  poverty 
gap. However, these benefits have a comparatively 
larger  effect  on  the  depth  of  poverty  than  on  the 
poverty rate in Beveridge welfare models (Cyprus, 

(100) For a definition of poverty gap see Box I.3.2. 

70 

Ireland, Malta and the UK). (101) This suggests that 
benefits  are  targeted  to  the  poorest  in  these 
systems. 

Sickness and disability benefits reduce the rate 
and  depth  of  poverty  the  most  in  Czechia, 
Denmark,  Finland  and  Slovenia.  In 
these 
countries  they  reduced  the  poverty  rate  by  more 
than  25%  in  2017  (Graph  I.3.14).  They  have  a 
somewhat  smaller  but  still  substantial  effect  in 
Belgium,  the  Netherlands  and  Sweden.  Only  in  a 
few  countries,  sickness  and  disability  benefits 
reduce the poverty rate by less than 10%, including 
Germany, Greece, France, Italy and Romania.  

this 

There  is  a  large  variation  in  the  poverty-
reducing  impact  of  unemployment  benefits.  In 
the  majority  of  Member  States,  unemployment 
benefits  only  play  a  limited  role  in  reducing  the 
poverty  rate  and  poverty  gap; 
includes 
countries with relatively high unemployment rates, 
such as Greece and Croatia. In these two countries, 
this  may  be  explained  by  the  low  coverage  of 
unemployment  benefits  -  due  to  high  long-term 
unemployment  -  and  their  short  duration  and 
levels.  Unemployment  is  also  comparatively  high 
in  France,  Spain  and  Italy,  but  unemployment 
benefits  in  these  countries  play  a  larger  role  in 
reducing  poverty.  The  largest  poverty-reducing 
effect  of  unemployment  benefits  is  observed  in 
Denmark,  Finland  and  Ireland.  In  these  countries, 
the long-term unemployment rate is relatively low, 
while  the  coverage  and  net  replacement  rates  of 
unemployment benefits are comparatively high. 

impact  on 

the  poverty  rate, 

While social inclusion benefits have a relatively 
small 
they 
contribute  more  to  reducing  the  depth  of 
poverty. The  effect  of  social  inclusion  benefits  is 
the  largest  in  the  Netherlands,  especially  on  the 
poverty  gap.  This  is  explained  by  the  minimum 
income  benefit  being  above  the  poverty  threshold 
and the adequacy of the scheme being the  highest 
in the EU. (102) 

(101) Beveridge  welfare  models  provide  social 

insurance 
financed  out  of  general  taxation.  Bismarckian  systems 
insurance  based  primarily  on  social 
provide  social 
contributions.  Compared 
latter 
to 
provides fewer universal  social  rights  in  the  form  of 
benefits  entitlements  as  their  provisions  is linked  to  the 
employment status. See “CESifo DICE report 4/2008. 
(102) See,  for  instance,  Joint  Employment  Report  (2019).  The 
impact on the poverty gap is on average below the effect of 

former, 

the 

the 

 
                                                           
                                                           
Labour market and wage developments 

Part I 

Graph I.3.14:  The impact of social transfers on the at-risk of poverty rate, by type of transfer, 2008, 2013, 2017 

(1) The impact of each type of transfer is the percentage decrease in the AROP rate brought about by the given type of 
transfer in a given country and year. More precisely, it is measured as the percentage difference between a counterfactual 
AROP rate, calculated based on disposable household income including all transfers except the type analysed, and the 
actual AROP rate, taking into account all transfers. The sum of the effects of the different benefits is not equal to the total 
overall effect of all benefits.  
Source: European Commission calculations based on individual data from EU-SILC. 

other  types  of  transfers.  This  is  not  surprising  as  social 
exclusion  benefits  are  targeted  to  those  well  below  the 
poverty  line  and  are  not  sufficient  to  help  them  cross  the 
poverty line.  

71 

0204060ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKFamily benefits2017200820130204060ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKHousing benefits2017200820130204060ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKSickness and disability benefits2017200820130204060ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKSocial inclusion benefits2017200820130204060ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKAll benefits2017200820130204060ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKUnemployment benefit201720082013 
 
 
 
                                                                                   
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.3.15:  The impact of social transfers on the poverty gap, by type of transfer, 2008, 2013, 2017 

(1) The impact of each type of social transfer is the percentage decrease in the poverty gap ‘caused’ by the given type of 
transfer in a given country and year. More precisely, it is measured as the percentage difference between a counterfactual 
poverty gap rate, calculated based on disposable household income, including all transfers except the type analysed, and 
the actual poverty gap, taking into account all transfers. 
Source: European Commission calculations based on individual data from EU-SILC. 

Housing  benefits  play  a  significant  role  in 
poverty reduction in only a few Member States. 
In  particular,  in  Denmark,  Finland,  France,  the 
Netherlands,  Sweden  and  the  UK,  they  reduce 
poverty by 15% or more (Graph I.3.14). The social 
housing  market  represents  a  sizeable  share  of  the 
rental  market  in  these  Member  States.  However, 
the  impact  on  the  poverty  gap  remains  relatively 
low,  indicating  that  they  are  not  necessarily 
targeted at those with the lowest incomes. (103) 

(103) European Commission (2019) provides a detailed analysis 

of investing in affordable and adequate housing.  

In  the  initial  phase  of  the  crisis,  the  poverty-
reducing impact of social transfers increased in 
most  countries  but  fell  in  most  thereafter. 
Between  2008  and  2017,  the  poverty-reducing 
effect of benefits fell in Hungary and Slovakia (by 
more  than  10  pps),  Luxembourg,  the  Netherlands 
and  Sweden,  while  it  increased  in  Latvia  (8  pps), 
Estonia, Finland, and the UK (Graph I.3.14). In the 
recovery  period  between  2013  and  2017,  the 
biggest increase in the poverty-reducing effect was 
recorded  in  Poland  (13  pps),  while  the  effect 
receded  most  in  Croatia,  Luxembourg  and  the 
Netherlands (by 10 pps each).  

72 

0306090ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKFamily benefits2017200820130306090ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKHousing benefits2017200820130306090ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKSocial inclusion benefits2017200820130306090ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKSickness and disability benefits2017200820130306090ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKAll benefits2017200820130306090ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKUnemployment benefits201720082013 
                                                           
 
Labour market and wage developments 

Part I 

3.5.  HOW DO POLICIES AFFECT THE POVERTY-
SOCIAL 

REDUCING 
TRANSFERS: MACRO-ANALYSIS  

EFFECT 

OF 

Graph  I.3.17  provides  descriptive  evidence  for 
the most recent five-year period. The graph plots 
changes  in  the  poverty-reducing  effect  of  various 
benefits  between  2013  and  2017  against  an 
indicator  of  policy  action  over  roughly  the  same 
period.  ‘Reform  stance’,  the  indicator  of  past 
policy  action,  is  derived  from  the  Commission’s 
LABREF database of labour  market reforms. (104) 
The  reform  stance  is  defined  as  the  number  of 
reform  measures  increasing  the  generosity  or 
availability  of  a  certain  type  of  benefit  in  a  given 
country  and  time  period,  net  of  the  number  of 
reforms  decreasing  their  generosity.  It  provides 
information  on  the  direction  of  reforms.  Since 
social statistics from year t refer to incomes earned 
in  year  t-1,  the  relevant  time  period  for  reform 
is  between  2012  and  2015.  Social 
activity 
inclusion and  housing benefits are  included in the 
same LABREF policy area and, hence, an average 
of  the  poverty-reducing  effect  of  both  types  is 
calculated for this graph.  

effect 

The  poverty-reducing 
of  benefits 
increases  more  in  countries  that  adopted  a 
larger  number  of  reforms  increasing  benefit 
generosity. This statistical association is relatively 
weak,  but  it  can  be  seen  for  all  benefit  types 
(Graph  I.3.17). (105)  In  the  area  of  sickness  and 
disability  benefits,  the  change  in  the  poverty-
reducing  impact  is  correlated  with  the  reform 
stance in the policy area of disability schemes (as 
shown in the graph), but not sickness schemes (not 
shown).  This  is  consistent  with  the  fact  that 
disability benefits have more fiscal weight in most 
countries. 

(104) For a detailed description of this indicator, see Turrini et al. 

(2015).  
(105) However, 

it  must  be  acknowledged 

the  weak 
correlation  does  not  necessarily  prove  that  policy  reforms 
have  a  weak  effect;  there  is  some  measurement  error  as 
reform stance does not account for the size of the impact on 
benefit generosity.  

that 

The  impact  of  sickness  and  disability  benefits 
decreased  in  the  last  decade.  Their  poverty-
reducing  effect  fell  by  1  pp  in  the  crisis  period 
(2008-2013)  and  a  further  2  pps  in  the  recovery 
(2013-2017). The decline was most pronounced in 
Hungary,  where  both  sickness  and  disability 
systems were reformed over this period, but also in 
Croatia, Czechia, Denmark, Slovakia and Sweden. 
In  Belgium  and  Cyprus,  the  poverty-reducing 
effect of these benefits increased by 4 pps or more. 

the  downturn 

Social  transfers,  in  particular  unemployment 
benefits,  have  mitigated  the  poverty  effect  of 
increased  joblessness  during  the  2008  crisis. 
Graph  I.3.16  shows  the  cross-country  correlation 
of unemployment with the poverty-reducing effect 
of  unemployment  benefits  and  all  benefits  for 
periods  of  the  downturn  (2008-2013)  and  the 
following  recovery  (2013-2017).  There 
is  a 
positive  correlation  between  the  unemployment 
rate  and  the  poverty-reducing  effect  of  social 
transfers,  in  particular  that  of  unemployment 
benefits. Countries where unemployment increased 
the  most  were  those  where  the  poverty-reducing 
impact of unemployment benefits was the greatest. 
During 
(2008-2013),  a  one 
percentage  point  increase  in  the  unemployment 
rate was associated with an average increase in the 
poverty-reducing effect  of  unemployment benefits 
of roughly 0.6 pps and an increase in the effect of 
all  benefits  of  about  0.7  pps.  In  the  recovery,  the 
effect  of  unemployment  benefits  tended  to  fall  as 
unemployment  receded,  but  the  changes  in  the 
poverty-reducing  effect  of  all  benefits  was  no 
longer 
in 
unemployment across countries. This suggests that 
while  differences  across  countries  in  the  poverty-
reducing impact of all benefits are mostly cyclical, 
the intensity of the reforms enacted over the period 
also partly explains such differences. To gain more 
insight into the effects of policies and the business 
cycle,  the  next  section  conducts  an  econometric 
analysis,  whereby  the  poverty-reducing  effect  of 
social  transfers  is  explained  in  terms  of  policy 
changes  while  controlling  for  possible  cyclical 
the 
fluctuations.  This 
contribution  of  the  business  cycle  from  that  of 
changes in policy settings. 

allows  disentangling 

correlated  with 

change 

the 

73 

 
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.3.16:  Correlation between change in the poverty-reducing impact of unemployment benefits and change in the 

unemployment rate (pps) (2008-13 vs. 2013-17) 

Source: Commission calculations based on EU SILC data. 

in 

A  regression  analysis  allows  to  disentangle  the 
effect  of  labour  market  conditions  from  the 
effect  of  reforms.  Changes 
the  poverty-
reducing  impact  of  various  types  of  benefits  are 
regressed  both  on  changes  in  the  unemployment 
rate  and  on  the  corresponding  reform  stance,  the 
indicator of policy action. For example, in the case 
of  the  poverty-reducing  effect  of  unemployment 
benefits, the relevant area of reform is in the policy 
domain  of  unemployment  benefits.  (106)  Since 
social  statistics  from  year  t  are  based  on  incomes 
earned in year t-1, explanatory variables need to be 
lagged. The reform stance in a given year refers to 
reforms  adopted  in  that  year.  Since  these  reforms 
are  typically  implemented  in  the  following  year, 
the  most  recent  reform  stance  included  in  the 
regression is from year  t-2, the year preceding the 
income reference year of social statistics in year t. 

(106) The  same  LABREF  policy  field  (social  assistance)  is 
associated with both social inclusion and housing benefits. 
Sickness  and  disability  benefits  are  associated  with  the 
LABREF policy area of disability schemes. 

74 

(107) Three lags are included for both the change in 
unemployment  and  the  relevant  reform  stance. 
Additional  lags  did  not  prove  to  be  empirically 
relevant.  Regression  results  are  shown  in  Table 
I.3.4  for  the  total  effect  of  benefits  as  well  as  for 
all benefit types  separately.  Four  main results can 
be derived. 

increases, 

the 
First,  when  unemployment 
poverty-reducing 
effect  of  unemployment 
benefits increases too (and vice versa). The effect 
is substantial: a 1 percentage point increase in the 
unemployment  rate  is  on  average  associated  with 
an  increase  in  the  poverty-reducing  effect  of 
unemployment benefits by about three quarters of 
a  percentage  point  in  the  same  year  (column  2  in 
Table  I.3.4).  This  is  in  line  with  the  fact  that 
unemployment benefit spending increases in years 
when  unemployment  goes  up. 
the  year 
following an increase in unemployment, there is a 

In 

(107) The  contemporaneous  reform  stances  (from  year  t-1)  are 
the 
significant  when 

statistically 

included 

in 

not 
regressions. 

 
                                                           
                                                           
 
smaller  movement  in  the  opposite  direction:  the 
poverty-reducing effect of  unemployment benefits 
falls  by  about  a  quarter  of  a  percentage  point.  A 
in  most  cases, 
is 
possible  explanation 
jobseekers  may 
their 
have 
unemployment  benefit  eligibility  in  the  second 
year  after  the  job  loss.  This  also  implies  that 
temporarily 
unemployment  benefits  can  only 
relieve  the  effect  of  higher  unemployment  on 
monetary poverty. 

exhausted 

that, 

Second, unemployment also affects the poverty-
reducing  effect  of  social  inclusion  benefits,  but 
to a lesser degree. The estimated effect is smaller 
than  the  one  on  the  effect  of  unemployment 
benefits and it is estimated with less precision, but 
cumulates  over  time.  A  first  effect  can  be  seen  in 
the  year  of  the  unemployment  increase,  while  a 
second effect, of a similar magnitude, can be seen 
two years later. This is reasonable given that social 
inclusion  benefits  include  benefits  of  the  last 
resort.  Households  may  rely  on 
them  after 
exhausting  other  benefits  they  may  have  been 
eligible  for.  This  is  particularly  the  case  in 
prolonged times of high unemployment,  when job 
finding rates plummet, including for the long-term 
unemployed. The poverty-reducing effect of other 
types of benefits is not found to be associated with 
changes  in  the  unemployment  rate.  The  point 
estimate  suggests  that  increasing  unemployment 
may  also  increase  the  poverty-reducing  effect  of 
family benefits, but the effect is not estimated with 
sufficient precision to be statistically significant. 

Third,  reforms  increasing  the  adequacy  of  all 
types  of  benefits  are  associated  with  a  higher 
poverty-reducing effect of each specific benefit. 
It is estimated that on average an additional reform 
increasing  benefit  adequacy  raises  the  poverty-
reducing  effect  of  all  benefits  by  0.2  pps  in  the 
year following its adoption. Effects of a similar or 
greater magnitude appear in the regressions for all 
benefit types.  

Labour market and wage developments 

Part I 

Fourth,  policy  affects  poverty  with  a  delay  in 
the areas of family and disability benefits. In the 
areas  of  unemployment  benefits,  social  inclusion 
and  housing  benefits,  reform  activity  increasing 
benefit  generosity  is  associated  with  an  increased 
poverty-reducing impact in the  year  following the 
reform adoption. In the areas of family benefits as 
well  as  sickness  and  disability  benefits,  the  effect 
appears 
two  years  after  adoption,  with  an 
additional effect in the area of family benefits three 
years after adoption.  

rate 

increases).  The 

Reforms  of  social  transfers  enacted  after  2007 
have paid out. Overall, these findings suggest that 
social  transfers  are  an  effective  tool  to  reduce  the 
risk  of  poverty  during  downturns  (i.e.  when  the 
reforms 
unemployment 
enacted  after  2008  aiming  at  either  expanding  the 
coverage  of  benefits  or  improving  their  cost-
effective  design  have  been 
in 
strengthening  the  impact  of  social  transfers  on 
poverty,  on  top  of  the  ‘normal’  contribution 
usually built in the benefit system. Going one step 
further,  the  next  section  will  draw  on  micro-
simulation  models  to  isolate  the  redistributive 
impact of reforms of tax and benefit systems. 

successful 

75 

 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph I.3.17:  Changes in the poverty-reducing effect of transfers by type (2013-2017, pps) and ‘reform stance’ in the relevant 

policy area (2012-2015) 

(1) The ‘reform stance’ is calculated based on the European Commission’s LABREF database of labour market reforms. It is 
defined as the number of reform measures adopted in a given country and year increasing the generosity or availability of a 
given type of benefits minus the number of reforms decreasing their generosity or availability.  
Source: European Commission calculations, based on EU-SILC data and the LABREF database of labour market reforms. 

76 

 
 
 
Labour market and wage developments 

Part I 

Table I.3.4:  Determinants of the changes in the poverty-reducing effect of social transfers, EU28, 2007-2017 

(1) Pooled ordinary least squares estimations. (2) Standard errors in parentheses. (3) Estimated coefficients statistically 
significant at the 10%, 5% and 1% level are marked with (*), (**) and (***), respectively. (4) ‘Reform stance’ is calculated 
based on the LABREF database. It is defined as the number of reform measures adopted in a given country and year 
increasing the generosity or availability of a given type of benefits minus the number of reforms decreasing their generosity or 
availability. (5) The relevant reform stance is chosen as follows: the effect of (a) unemployment benefits; (b) family benefits; 
(c) sickness and disability benefits; (d) social inclusion benefits; (e) housing benefits; and (f) all benefits is explained by the 
reform stance in the LABREF policy area (a) unemployment benefits; (b) family benefits; (c) disability schemes; (d) and (e) 
social assistance benefits; (f) all of these areas.  
Source: European Commission calculations based on data from EU-SILC, Eurostat Labour Force Survey, and the Commission’s 
LABREF database of labour market reforms. 

3.6.  HOW  DO  POLICY  CHANGES  AFFECT 
POVERTY  AND  INEQUALITY:  A  MICRO-
SIMULATION ANALYSIS  

This  section  examines  the  impact  of  the  tax-
benefit  systems  of  today  on  poverty  and 
inequality as compared to before the crisis. The 
observed  effect  on  poverty  reduction  of  various 
types  of  social  transfers  of  Section  3.4  depends 
both  on  policy  changes  as  well  as  on  changes  in 
the  demographic  and  socio-economic  structure  of 
the population. Section 3.5 makes a first attempt at 
disentangling  the  role  of  policy  changes  from  the 
one  of  changing  labour  market  conditions.  This 
section makes a step further, isolating the effect of 
policy  changes  by  keeping  constant  all  the  other 
characteristics  of  the  underlying  population.  This 
is  achieved  with  the  use  of  EUROMOD,  the  EU 
tax-benefit  microsimulation  model,  which  allows 
producing  counterfactual  scenarios 
that  apply 
different  policy  settings  to  the  same  underlying 
income distribution. (108)  

(108) This  analysis  is  complementary  to  the  macro  approach  of 
Section 3.5 as it looks at specific policy settings and their 
changes over time. See Box I.3.1 for the methodology. 

to 

The effectiveness of the current policy setting is 
assessed assuming unchanged demographic and 
income  distributions.  For  each  Member  State, 
policy  settings  of  2008  and  2018  are  compared 
with regard to their effects on poverty by applying 
them 
income 
the  same  population  and 
distribution  (the  2016  wave  of  the  EU-SILC 
survey).  In  other  words,  a  counterfactual  scenario 
is  simulated  to  examine  what  the  situation  would 
have  been  in  2018,  if  no  policy  changes  had 
occurred  since  2008.  This  allows  assessing  the 
effectiveness of current tax and benefit systems in 
reducing  poverty  and  inequality  compared  to  the 
policy  systems  in  place  at  the  onset  of  the  crisis. 
The  analysis  looks  in  particular  at  the  impact  on 
the  distribution  of  disposable  income  by  deciles, 
overall  income  inequality  (measured  by  the  Gini 
coefficient) and the AROP rate. (109) 

A  number  of  caveats  must  be  considered  to 
interpret  microsimulation 
First, 
simulations  are  static  and  do  not  incorporate  the 
effect  of  possible  behavioural  responses  induced 
by policy changes. Also, the standard EUROMOD 
model  used  for  this  exercise  does  not  allow  to 

results. 

(109) For  a  cross-country  comparison  of  the  Gini  coefficient  of 
equivalised  disposable  income  after  social  transfers,  see 
European  Commission  (2019c),  Employment  and  Social 
Developments in Europe , Chapter 1.  

77 

(1)(2)(3)(4)(5)(6)Dependent vatiable: All benefitsUnemploy-ment benefitsFamily benefitsSickness and disability benefitsSocial inclusion benefitsHousing benefitsChange in unemployment rate, year t-10.512***0.757***0.2230.0550.153*0.092(0.137)(0.123)(0.136)(0.118)(0.078)(0.069)Change in unemployment rate, year t-2-0.139-0.285**-0.1430.018-0.072-0.067(0.151)(0.135)(0.149)(0.130)(0.085)(0.075)Change in unemployment rate, year t-3-0.023-0.0110.0370.0350.156**0.055(0.132)(0.117)(0.132)(0.111)(0.073)(0.065)Reform stance, relevant policy area, year t-20.208**0.329**0.2110.1060.308**0.241*(0.103)(0.142)(0.242)(0.463)(0.148)(0.131)Reform stance, relevant policy area, year t-30.047-0.270*0.589**0.747*0.0450.015(0.103)(0.140)(0.254)(0.425)(0.153)(0.136)Reform stance, relevant policy area, year t-40.233**0.0900.518*0.114-0.0730.128(0.110)(0.143)(0.277)(0.401)(0.167)(0.148)Constant-0.415**-0.217-0.423**-0.362**-0.103-0.118(0.187)(0.168)(0.202)(0.163)(0.112)(0.100)Observations287264264264264264R-squared0.1050.1720.0640.0170.0440.026Change in the poverty-reducing effect of social benefits, by type, ppts 
                                                           
                                                           
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

simulate  certain  policies,  such  as  in-kind  benefits 
or  indirect  taxation,  or  certain  features  (such  as 
asset criteria for eligibility or compliance with job-
search  requirements)  due  to  the  lack  of  necessary 
information in the  underlying  microdata  used (the 
EU-SILC survey). (110) 

In  the  vast  majority  of Member  States,  the  tax 
and  benefit  systems  of  2018  achieve  a  greater 
reduction in income inequality than in 2008. In 
20  Member  States,  the  tax  and  benefit  system  of 
2018  produces  a  lower  level  of  income  inequality 
compared to the 2008 policies applied to the same 
income  distribution  (Graph  I.3.18);  this  means 
that,  in  most  countries,  changes  implemented 
between  2008  and  2018  increase  the  overall 
progressivity  of  the  tax  and  benefit  systems. (111) 
The  highest  reduction  in  income  inequality  after 
redistribution  is  observed  in  Bulgaria,  Estonia, 
Poland, Romania and Greece. In Hungary, changes 
in  tax  and  benefit  policies  produce  a  significantly 
higher level of income inequality. 

Graph I.3.18:  Change in the Gini coefficient due to tax and 

benefit policies: 2008-2018 

The graph shows the difference (in pps) in the Gini 
coefficient computed on the same income distribution and 
with the simulated tax and benefit policies of 2008 and 2018. 
Source: Own calculations based on EUROMOD. 

In  general,  the  policy  changes  introduced  after 
2008  protected  lower-income  groups.  Graph 
I.3.19  shows  the  change  of  disposable  income  by 

(110) In  addition,  results  of  microsimulation  may  differ  from 
observed  data  due  to  partial  take-up  of  benefits  and  tax 
evasion. To the extent that these phenomena are persistent 
over time, however, this last issue appears less crucial for 
the  purpose  of  the  present  analysis,  which  focuses  on  the 
comparison between policy systems. 

(111) These  results  differ  from  the  actual  changes  in  income 
inequality  that  can  be  observed  over  the  period,  as 
illustrated for instance in European Commission (2019c). 

78 

in 

the 

latter 

increase 

the  policy  changes; 

income deciles due to tax-benefit policy changes in 
the  period  2008-2018. (112)  Among  the  countries 
that  saw  an 
in  average  disposable 
incomes,  this  was  higher  for  the  low  deciles  in 
Belgium,  Bulgaria,  Estonia,  Luxembourg,  Malta, 
Poland, Romania, Sweden, Slovenia, Slovakia and 
Austria.  In  Germany,  the  Netherlands  and  Latvia 
the  middle-income  groups  benefitted  the  most 
from 
two 
countries, these changes benefitted also the poorest 
(bottom  income  decile).  As  concerns  countries 
where  policy  changes  determined  a  fall 
in 
disposable  incomes,  the  changes  introduced  after 
2008 protected low-income earners in Italy, France 
and  Greece;  in  the  last  two  countries,  the  median 
income  increased  only  for  the  lowest  decile.  This 
pattern  is  close  to  that  of  Portugal,  Ireland  and 
Spain,  which  however  also  experienced  a 
considerable  fall  in  the  median  income  of  lower-
income earners. In Hungary, only the upper deciles 
benefitted from policy changes while sizable losses 
are  found  for  the  lowest  deciles  (driving  the 
increase  in  inequality  noted  above).  Finally,  the 
highest-income  brackets  benefitted  from  policy 
changes in Denmark. In the UK, lower disposable 
incomes  are  recorded  at  the  extremes  while  gains 
are  observed  in  the  middle-upper  half  of  the 
income distribution. 

The  at-risk-of-poverty  rate  is  reduced  in  the 
majority  of  Member  States.  Graph  I.3.20  shows 
the effect of policy changes on the AROP rate over 
the period 2008-2018, together with the change in 
the corresponding poverty threshold over the same 
period. In 16 Member States, policy changes result 
in a reduction in relative poverty, whereas in seven 
other  Member  States,  the  AROP  rate  remains 
unchanged. The largest increase in relative poverty 
is  observed  in  Hungary  and  the  UK,  where  the 
AROP rate increased by 3% and 2%, respectively. 
The increases in the poverty rates in both Hungary 
and  the  UK  seem  to  be  mostly  associated  with 
higher poverty among households with children. 

(112) Disposable income is the sum of gross market income and 
cash  benefits,  net  of  direct  taxes  and  social  insurance 
contributions.  

-5-4-3-2-1012345BGEEPLROELFRCYIEPTLVBESEITLUFICZSKNLSIUKMTATESDELTDKHUGini coefficient 
 
                                                           
 
 
                                                           
 
Graph I.3.19:  Changes in disposable income by income 
deciles due to tax-benefit policy changes: 
2008-2018 

The graph shows the percentage change in average 
disposable income by decile based on simulated changes 
in the tax and benefit systems between 2008 and 2018, 
keeping constant the underlying distribution. 
Source: Own calculations based on EUROMOD. 

in  the  poverty 

Once  movements 
line  are 
considered, policy changes improved the income 
situation  of  the  poor  only  in  a  subset  of 
countries.  The  effect  of  policy  measures  on  the 
AROP  rate  can  be  driven  both  by  their  effects  on 
households’  incomes  and  by  shifts  in  the  relative 
poverty  line.  (113)  As  illustrated  above,  in  16 
countries,  policy  changes  between  2008  and  2018 
led  to  an  increase  in  median  disposable  incomes, 
producing an upward shift of the poverty line.  

(113) The poverty line underpinning the AROP rate is defined as 
60%  of  the  median  of  equivalised  disposable  household 
income.  Shifts  are  caused  by  changes  in  the  median 
disposable  income,  as  a  result  of  tax  and  benefit  policy 
reforms and possible indexation of benefits.  

Labour market and wage developments 

Part I 

to 

In  half  of  these  (Belgium,  Bulgaria,  Estonia, 
Denmark,  Luxembourg,  Poland,  Romania  and 
Sweden),  this  shift  was  accompanied  by  a  fall  in 
the  number  of  people  at  risk  of  poverty,  pointing 
unequivocally  to  a  material  improvement  in  the 
conditions  for  the  poorest  households.  In  other 
countries  (e.g.  Germany,  Slovakia  and  the  UK), 
both the poverty line and the share of people at risk 
of  poverty 
the  new 
(defined  according 
threshold)  increased.  In  the  remaining  Member 
States,  policy  changes  produced  a  fall  in  median 
incomes  –  and  so  also  a  lowering  of  the  poverty 
line. This is the case of Greece (-15.9%), Slovenia 
(-11.4%)  and  France  (-7.4%);  yet,  the  AROP  rate 
for  these  countries  fell.  This  indicates  that  tax-
benefit  policies  had  a  stronger  (negative)  impact 
on median incomes than on those at the lower end 
of  the  distribution  (as  it  also  appears  from  Graph 
I.3.19).  Finally,  in  Hungary,  a  downward  shift  of 
the  poverty  line  was  accompanied  by  an  increase 
in  the  AROP  rate,  which  clearly  points  to  a 
worsening  of  the  situation  for  those  at  the  bottom 
of the income distribution. 

Graph I.3.20:  Change in the at-risk-of-poverty rate due to 
tax and benefit policies and changes in the 
poverty line: 2008-2018 

The graph shows the percentage change in the AROP rate 
and the AROP threshold based on simulated changes in the 
tax and benefit systems between 2008 and 2018, keeping 
constant the underlying distribution. 
Source: Own calculations based on EUROMOD. 

79 

-20%-15%-10%-5%0%5%10%15%20%25%ELSIFRIEHUITPTESCYFIATROMTNLUKCZLVDELUBESESKLTEEDKPLBGAt-Risk of poverty ratePoverty Line 
 
                                                           
 
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

80 

(Continued on the next page) 

 Box I.3.1: Use of microsimulation models for assessing tax-benefit systemsEUROMOD is a static tax-benefit microsimulation model for the European Union based on micro-data and national tax and benefit rules for a given year. For each Member State, it allows computing the effect of taxes and benefits on household incomes. With EUROMOD it is also possible to assess the effects of policies and simulate the effects of tax-benefit policy reforms on poverty, inequality, work incentives and government budgets. The model makes use of micro-data from nationally representative samples of households from the European Union Statistics on Income and Living Conditions (EU-SILC) and Family Resources Survey (FRS) for the UK, which contain detailed information on individual and household characteristics as well as income by source.  The model allows for standardised analysis across EU countries´ tax and benefits systems. For a comprehensive overview, see Sutherland and Figari (2013). Microsimulation models have been used to assess the performance of tax-benefit systems. A number of studies provide comparative assessments of the distributional implications of policy changes over time. For instance, Tammik (2019) studies the effects of various changes in tax and benefit systems on poverty risk, poverty gap and income inequality in the EU between 2015 and 2018. His findings suggest that public pensions are the most effective component in reducing inequality and poverty in market incomes; means-tested benefits are on average the second instrument in order of importance (though non-mean tested benefits dominates in some countries). Conversely, the role of taxes and social contribution is rather limited. De Agostini et al. (2016) provide a comprehensive assessment of distributional effects of tax-benefit reforms over the period 2008-2014. They find that tax-benefit policies increased household disposable incomes and helped to alleviate poverty and reduce inequality in the period 2008-11; overall, they had a negative impact on both incomes and poverty in 2011-14, with sizeable differences across countries. Alari et al. (2019), look at how changes in the tax and benefit policy settings affected the income distribution in 2001-2011 in seven EU countries, distinguishing between structural effects (i.e. those arising from the changes in the design of the tax-benefit system) and indexation effects (i.e. the periodic uprating of monetary parameters such as benefit amounts and eligibility thresholds, based on either automatic statutory indexation or discretionary adjustments). They show that the regular uprating of monetary parameters (indexation effect) not only has a positive effect on household incomes over the period considered (meaning that fiscal drag and benefit erosion were avoided), but that it generally contributed more to poverty and inequality reduction than structural changes. Methodology used EUROMOD is used to estimate the distributional effects of direct tax and cash benefit policies in EU Member States using EU SILC microdata for each country from the onset of the economic crisis (2008) and from the latest period available (2016). The analysis involves simulating the disposable income distribution and, subsequently, inequality and poverty indices, between the year 2008 and 2018. The results focus on changes between 2008 and 2018 and are based on the same input database (EU-SILC 2016, with income reference year in 2015).  The aim of the exercise is to quantify the effect of tax-benefit policy changes between 2008 and 2018 on a range of poverty and inequality measures. To achieve this is necessary to disentangle the pure effect of policy changes occurring over the period 2008–2018 from changes due to other factors, such as changes in the market income inequality - due to wage cuts, job losses or working time reductions, changes to the minimum wage, etc - and in other socio-demographic characteristics of the population. The applied methodology draws on the method in Bargain and Callan (2010). Consider a single household and denote its market income (and other characteristics) with 𝑦 and monetary values of tax-benefit parameters (e.g. maximum benefit amounts, threshold level of tax brackets, etc.)  𝑝. Let 𝑑 be the ‘tax-benefit function’ that calculates the household disposable income on the basis of its market income and monetary parameters, reflecting the structure of the tax-benefit system (e.g. tax rates, benefit eligibility rules). In period 𝑡, the household disposable income can be denoted as 𝑑𝑡 𝑝𝑡,𝑦𝑡  The aim is to estimate the direct effect of policy changes on household incomes in the period from t=1 to t=2, where subscripts 1 and 2 refer to start-period (2008) and end-period (2018) values, respectively.  
 
 
 
 
 
Labour market and wage developments 

Part I 

Box (continued) 

As  a  consequence  of  policy  changes  after  2008, 
the  depth  of  poverty  decreased 
in  eleven 
countries,  but  worsened  in  eight.  Graph  I.3.21 
presents  the  changes  of  the  poverty  gap  during 
2008-2018,  providing  a  measure  of  the  depth  of 
poverty.  In  almost  half  of  the  Member  States,  the 
intensity  of  poverty  increased,  most  notably  in 
Hungary,  Slovakia,  Ireland,  Italy,  Spain,  Sweden, 
Czechia  and  Portugal  (with  increases  of  the 
poverty gap ranging from 2% to 5%). By contrast, 
improvements in the standard of living of the poor 
were  recorded  in  eleven  Member  States,  most 
notably in Romania (-10%), Bulgaria (-6%) as well 
as Estonia, Austria and Greece (all -5%).  

Graph I.3.21:  Change in the poverty gap due to tax and 
benefit policies: 2008-2018 

3.7. 

IMPACT  ON  MATERIAL  DEPRIVATION  OF 
DIFFERENT  CATEGORIES  OF 
SOCIAL 
EXPENDITURE  

This section explores the links between relative 
or  material  deprivation  and  the  spending  on 
social protection. Section 3.4 looked at the effect 
of  various  types  of  benefits  on  monetary  poverty 
by comparing indicators before and after transfers 
received  at  the  household  level.  Section  3.5  and 
3.6 assessed the impact of reforms on the poverty-
reducing  effects  of  social  transfers.  Building  on 
these  insights,  the  following  section  investigates 
how social protection spending influences poverty 
indicators, while taking into account the effects of 
macroeconomic drivers. 

3.7.1.  Drivers  of  relative  poverty  and  material 

deprivation: descriptive analysis 

Source: Own calculations based on EUROMOD. 

that 

link  between 
Descriptive  evidence  on  the 
economic  development  and  poverty  highlights 
the importance of redistributive policies. Graph 
I.3.23  suggests 
relative  poverty  before 
redistribution (i.e. AROP based on market income) 
is unrelated to the level of income per capita. The 
correlation  with  economic  development 
(as 
measured  by  income  per  capita)  is  stronger  when 
AROP  is  based  on  disposable  income  (Graph 
I.3.24). This means that countries with higher GDP 
per  capita  have 
rates  after 
redistribution. These findings are not surprising as 

lower  AROP 

81 

-12%-10%-8%-6%-4%-2%0%2%4%6%8%ROBGEEATELFRPLLUCYNLSILTBEMTFIDKUKDELVPTCZSEESITIEHUSKPoverty Gap To isolate it from other changes in the income distribution (e.g. changes in household composition or market incomes), household disposable incomes under the two policy systems are assessed holding household characteristics and market incomes constant. Furthermore, to adjust for changes in nominal income levels over time, the monetary parameters of the tax-benefit system are adjusted with a factor alpha which reflects benchmark indexation. In the literature, this is generally done taking into account either changes in the consumer price index, or average income growth. For this chapter the CPI indexation was chosen as the benchmark indexation. This means that the effect of policy changes is calculated in real terms.  Specifically, the policy effect (for each household) is estimated as: ∆ = 𝑑2 1𝛼𝑝2,𝑦1 −𝑑1 𝑝1,𝑦1  Technically in EUROMOD, instead of scaling monetary policy parameters, the method scales monetary input variables with the factor alpha and monetary output variables with the factor 1/alpha: ∆ =1𝛼 𝑑2 𝑝2,𝛼𝑦1 −𝑑1 𝑝1,𝑦1  This relies on the assumption that tax-benefit systems are linearly homogenous, that is 𝑑𝑡 𝑝𝑡𝑐𝑦𝑡 =𝑐𝑑𝑡 𝑝𝑡𝑦𝑡 . Input variable adjustments are limited to market incomes, expenditures and assets. Output variable adjustments cover all monetary variables.  
 
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

is 

there 

income 

income. However, 

AROP captures income inequality at the bottom of 
the income distribution rather than the standard of 
living and while market income inequality is often 
high  in  richer  countries,  this  is  not  the  case  for 
disposable 
some 
diversity  between  countries  with 
low/medium 
disposable  income.  Member  States  with  similar 
levels  of  economic  development  can  have  starkly 
different AROP rates based on disposable income. 
Thus, beyond the stage of economic development, 
differences in social policy and in the structure of 
the  market 
a 
role. (114) Graph  I.3.25  illustrates  the  relationship 
between  economic  development  and  the  capacity 
of  redistributive  policies  to  lift  people  out  of 
poverty  through  the  tax-benefit  system.  Richer 
Member  States  achieve  higher  poverty  reduction 
through  redistribution.  Yet,  large  discrepancies 
exist  at  a  given  level  of  economic  development. 
For  instance,  poverty  before  transfers  (excluding 
pensions)  in  Czechia  and  Estonia  are  roughly 
similar 
I.3.23),  but 
redistribution  in  Estonia  only  achieves  a  poverty 
reduction  by  18  pps  compared  to  27  pps  in 
Czechia. 

(37%  vs.  39%;  Graph 

distribution 

play 

Graph I.3.22:  Correlation severe material deprivation and 

AROP, 2016 

Source: EU-SILC. 

Graph I.3.23:  Median disposable income (at PPP) and AROP 

market income 

social 

Countries  that  allocate  a  higher  share  of  GDP 
to 
spending  have  better  poverty 
outcomes.  The  AROP  rate  and  severe  material 
deprivation  are  negatively  associated  to  the  social 
spending-to-GDP  ratio.  Yet,  there  are  important 
exceptions.  For  example,  Graph  I.3.27  shows  that 
Italy  and  Greece  have  a  very  high  share  of  social 
spending  despite  having  a  severe  material 
deprivation indicator comparable to that of Latvia 
and  Romania,  which  are  the  countries  where  the 
intensity of social spending is among the lowest in 
the  EU.  Graph  I.3.28  reveals  that  countries  that 
allocate  a  higher  percentage  of  GDP  to  social 
spending  have  a  higher  poverty-alleviating  effect 
of  the  tax  and  benefit  system.  However,  the 
heterogeneity  across  countries  suggests  that  the 
effectiveness  in  reaching  lower  levels  of  relative 
poverty  goes  beyond  the  amount  of  resources 
spent.  

Source: EU-SILC and Eurostat. 

(114) There is a strong correlation between per capita income and 

severe material deprivation (-0.75).  

82 

ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKcorrelation: 0.64p value: 0.000205101520253005101520253035At-risk-of-Poverty (%)Severe material deprivation (% total population)ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKCorrelation: -0.195p value: 0.065354045505550001000015000200002500030000At-risk-of-poverty, market income (%)Median disposable income, Purchasing Power Parity 
 
 
                                                           
 
 
 
 
 
Labour market and wage developments 

Part I 

Graph I.3.24:  Median disposable income (at PPP) and AROP 

Graph I.3.26:  Severe material deprivation and social 

disposable income 

spending 

Source: EU-SILC. 

Source: COFOG database on government expenditures 
and EU-SILC. 

Graph I.3.25:  Median disposable income (at PPP) and AROP 

change after redistribution 

Graph I.3.27:  Change in AROP after redistribution and social 

spending 

Source: EU SILC and Eurostat. 

Source: COFOG database on government spending and EU 
SILC. 

83 

ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUK5101520253050001000015000200002500030000At-risk-of-poverty (%)Median disposable income, Purchasing Power ParityCorrelation: -0.534p value: 0.0034ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKCorrelation: 0.354p value: 0.32152025303550001000015000200002500030000Change in At-risk-of-poverty after transfers and taxes (%)Median disposable income, Purchasing Power ParityATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKCorrelation: -0.39p value: 0.0380816243210121416182022242628Severe material deprivation (%)Social spending (% GDP)ATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVMTNLPLPTROSESISKUKCorrelation: 0.67p value: 0.0001152025303510121416182022242628Change At-risk-of-poverty after redistribution (%)Social spending (% GDP) 
 
 
 
 
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

84 

 Box I.3.2: Indicators of poverty and social exclusionThe at-risk-of-poverty and social exclusion (AROPE) is a leading indicator and target in the EU 2020 strategy. People at risk of poverty or social exclusion are people at-risk-of-poverty (relative monetary poverty) and /or experiencing severe material deprivation (material deprivation) and/or living in households with very low work intensity. (1)  The at-risk-of-poverty rate (AROP) is a measure of (relative) monetary poverty. The at-risk-of-poverty rate is defined as the share of individuals with an equivalised disposable income below 60% of the median income. Median income is the point that separates the top half of earners from the bottom half. The median income changes over the cycle and the indicator can change little during recessions. AROP is a measure of the gap in income or the inequality between the median and the lower deciles of the income distribution. The AROP with a poverty line anchored in 2005 is not affected by changes over the time in the median income and provides a characterisation of the dynamics of monetary poverty keeping unchanged the median income. The effect of a downturn on the AROP, depends on the distribution of changes in income across the different sub-populations. A fall on the aggregate income, will generally lead to a reduction in the median level of equivalised disposable income and therefore a lowering of the poverty threshold. Therefore, during a recession it is possible that AROP falls even if a larger number of people find difficult to make ends meet. Similarly, the increase of median income during the recovery may lead to an increase in AROP if not all households’ benefit uniformly of the increase of aggregate income. For this reasons, it is standard to make also comparison of the AROP with poverty threshold anchored at a specific point in time.  The AROP does not take into account the income needed in order to enjoy decent living standards. For instance, the AROP rate may suggest that poverty is less prevalent even when the lowest levels of income do not grow. The AROP is one of the headline indicators of the Social Scoreboard, a tool to monitor the performance of Member States in relation to the European Pillar of Social Rights.  The poverty gap is the distance between the median equivalised total net income of persons below the at-risk-of poverty threshold and the at-risk-of poverty threshold itself, expressed a proportion of the at-risk-of poverty threshold. The at-risk-of-poverty rate measures the incidence of poverty, while the poverty gap its depth. Severe material deprivation is a measure of poverty defined on the basis of social and economic material needs. It is defined as the share of the population living in households that could not afford at least four out of the following nine items deemed to be needed to lead an adequate life (Eurostat, Statistics explained):  to pay their rent, mortgage or utility bills;  to keep their home adequately warm;  to face unexpected expenses;  to eat meat or proteins regularly;  to go on holiday;  a television set (if wanted);  a washing machine (if wanted);  a car (if wanted);  a telephone (if wanted). Households identified as being at risk of income poverty or as material deprived do not necessarily face unsatisfactory living conditions. For instance, more than half of those severely materially deprived have income above the first income quintile. This is the case for instance if households have sufficient savings, access to credit or other liquid assets they can use to maintain their standard of living. Material deprivation is also relevant to study various aspects of the wellbeing of groups of the population. It is also highly relevant for the self-employed, whose income not only tends to suffer from large underreporting but also to neglect the fact that the self-employed can often draw on its business assets (see Filauro and Thirion 2018 (2)).  Indicators of poverty and exclusions are derived from two successive EU-wide surveys, the European Survey on Income and Living Conditions (EU-SILC) and its predecessor, the European Community Household Panel. The severe material deprivation series covers the EU28 Member States for the period 2004 until 2016 for most Member States. The series available for at-risk-of-poverty is slightly longer, spanning across 2000-2016.                                                           (1) Note that this section does not consider the low-work intensity indicator. (2) Chapter 4 ‘Inequality of outcomes’, Employment and Social Developments in Europe 2018. DG EMPL.  
 
 
 
 
 
3.7.2.  Regression  analysis: 

the  drivers  of 
monetary  poverty  and  severe  material 
deprivation 

Econometric  techniques  allow  to  go  beyond 
simple correlation, isolating the contribution of 
different  factors  driving  poverty  indicators. 
to  gaining  a  better 
Thus,  with  a  view 
understanding  of 
the  relation  between  social 
spending  and  poverty,  this  section  estimates  the 
effects of different spending categories on poverty 
and  exclusion  indicators,  taking  into  account  the 
effect of disposable income and the unemployment 
rate.  (115)  The  poverty  and  exclusions  indicators 
are the following:  

  severe material deprivation, 

  AROP based on disposable income, 

  AROP based on market income, 

  AROP with an anchored poverty line in 2005. 

An increase in disposable per capita income and 
in  social  protection  spending  reduces  severe 
material  deprivation.  An  increase  in  median 
disposable income by 1% leads, on average, to an 
estimated  decline  by  0.23  pps  in  the  rate  of 
material  deprivation  (Table  I.3.5  column  a).  The 
effect of social protection spending is comparable. 
(116) 

The AROP based on market income is strongly 
influenced  by  the  unemployment  rate  and 
disposable per capita income. This confirms that 
higher unemployment and a drop in incomes leads 
to  a  higher  AROP  before  redistribution.  As 
expected,  social  spending  has  no  statistically 
significant  impact  on  AROP  based  on  market 
income (Table I.3.5 column c).  

Yet,  social  protection  spending  plays  a  role  in 
alleviating  poverty.  Estimates  suggest  that  an 

(115) In  order  to  control  for  unobserved  time-  and  country-
related factors, the regressions are estimated with OLS with 
time- and country-fixed effects. Estimates are indicative of 
the  average  response  to  a  change  in  social  spending.  This 
contrasts with the analysis of Section 3.5, where the focus 
is  on  how  much  of  the  differences  across  countries  in  the 
poverty  reducing  effect  of  social  benefits  is  explained  by 
the reform stance. 

(116) Using ESSPROS data instead of COFOG leads to a slightly 

higher and more significant coefficient. 

Labour market and wage developments 

Part I 

increase  in  social  protection  spending  by  1  pp  is 
associated with a 0.34 pps average decrease in the 
AROP  based  on  disposable  income. (117)  The  net 
median  income  does  not  have  a  statistically 
significant  impact  on  AROP  (Table  I.3.5  column 
b). (118)  The  relevance  of  this  effect  is  confirmed 
when the same estimate is done for the AROP with 
an  anchored  poverty  line  (Table  I.3.5  column  d). 
Since the poverty threshold is adjusted to take into 
account changes in consumer prices, an increase in 
unemployment  leads  to  a  loss  of  income  and  an 
increase  in  the  percentage  of  people  below  the 
fixed  poverty  line.  Indeed  in  this  case,  both  a 
decline  in  the  unemployment  rate  and  an  increase 
in  median  disposable  income  reduces  the  AROP 
rate.  The  negative  sign  for  the  median  disposable 
income suggests that as Member States get richer, 
they achieve a stronger level of poverty reduction, 
when  keeping  social  spending  and  unemployment 
rate  conditions  constant.  This  finding  may  be 
related  to  the  fact  that  European  countries  with 
higher  median  income  tend  to  be  also  more 
inclusive, leaving less scope for redistribution and 
poverty reduction to start with. 

Table I.3.5: 

Regression summary, drivers of AROP and 
severe material deprivation: 2000-2016 

(1) Robust standard errors in parentheses, *** p<0.01, ** 
p<0.05, * p<0.1. 
Source: Author's calculations based on EU-SILC, Eurostat, 
and Cofog. 

There  are  sizeable  differences 
in  Member 
States’  ability  to  lift  people  out  of  poverty 
through  redistributive  policies.  The  descriptive 
evidence  of  Section  7.2  suggested  that  the  ability 
of  tax  and  benefit  systems  to  reduce  monetary 
poverty  differ  for  countries  with  similar  levels  of 

(117) Yet,  this  effect  disregards  the  impact  of  redistributive 

taxation.  

(118) It  is  more  that  the  unemployment  rate  does  not  drive 
AROP, as unemployed individuals are likely to fall below 
the poverty line. Yet, the lack of such a link might hint at 
the poverty-reducing effect of social protection spending. 

85 

Severe material deprivation (a)AROP (b)AROP before benefits (c)AROP, anchored 2005 (d)-0.23***0.001-0.06***-0.20***(0.03)(0.01)(0.01)(0.03)0.050.0420.39***0.33***(0.07)(0.04)(0.05)(0.11)-0.26*-0.34***0.10-0.36**(0.13)(0.09)(0.09)(0.17)Observations348374374264Log median disposable incomeUnemployment rateSocial protection spending as % of GDP 
                                                           
 
 
 
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

social expenditure relative to GDP; this means that 
the  effectiveness  of  social  spending  to  alleviate 
monetary  poverty  does  not  only  depend  on  the 
amount  of  resources  committed.  In  order  to 
estimate  the  effectiveness  of  social  spending  to 
alleviate market poverty, a measure of efficiency is 
computed  as  the  component  of  the  change  in  the 
(excluding 
AROP  before  and  after  benefit 
pensions),  which  cannot  be  explained  by  social 
spending,  the  level  of  initial  AROP  and  the 
unemployment rate. This approach is based on the 
concept  of  production  efficiency  applied  to  the 
public sector. Social protection is considered to be 
effective,  if  the  reduction  in  market  poverty  goes 
beyond  what  is  expected  on  the  basis  of  social 
spending.  However, 
the 
the 
impact  of  social  protection  is  challenging  when  it 
has multiple social objectives (e.g. social inclusion 
and  labour  market  integration).  (119)  Moreover, 
results  depend  on  the  assumption  that  only  social 
reducing 
protection 
poverty.  In  practice,  other  policies  (such  as 
employment and educational policies) as well as a 
series  of  contextual  factors  (demographic  and 
economic)  also  contribute  to  reducing  the  risk  of 
poverty.  

spending  contributes 

identification  of 

to 

this 

limitation 

With 
in  mind,  cross-country 
analyses  can  be  used  to  identify  the  effectiveness 
of social spending in reducing AROP, i.e. the size 
of the uncertainty about “why countries perform so 
differently”  (Vandenbroucke  et  al.,  2013).  The 
results  suggest  that  in  Southern  and  some  Central 
and  Eastern  European  Member  States  there  is  a 
lower  poverty-reducing  effect  at  a  given  level  of 
social  spending,  after  controlling  for  initial  levels 
of  poverty.  In  contrast, 
in  Ireland,  Czechia, 
Slovakia,  the  Netherlands  and  Hungary,  welfare 
states  tend  to  achieve  above  average  levels  of 
poverty 
these 
findings provide some prima facie evidence of the 
reduce 
effectiveness  of  social  spending 
monetary poverty.  

reduction.  While 

suggestive, 

to 

Graph I.3.28:  Efficiency of social protection spending in 

reducing AROP 

(1) The graph reports the residual of a regression of the 
change in AROP before and after redistribution on the level 
of AROP, social spending as a percentage of GDP and the 
interaction between them; The regression is estimated 
without country fixed-effects over the period 2000-2017.  
Source: Authors’ calculation based on EU-SILC, Eurostat, 
Cofog. 

3.8.  CONCLUSIONS 

The  2008-2012  economic  and  financial  crisis 
was  accompanied  by  a  deterioration  of  living 
standards  and  by  an  increase  in  poverty  and 
social  exclusion  across  the  EU.  The  crisis  left 
many  unemployed  with  no  or 
insufficient 
protection. Thus, after the initial response aimed at 
ring-fencing  fiscal  risks  and  improving  labour 
market  adjustment,  the  focus  of  policy  action 
shifted 
the  social 
implications of the crisis. This chapter looks at the 
role  of  social  protection  spending  in  alleviating 
poverty  and  exclusion.  At  the  current  juncture, 
with  the  EU  economy  entering  a  more  uncertain 
period,  it  is  relevant  to  assess  whether  and  how 
spending  on  social  protection  provides  a  better 
coverage  against  the  risk  of  poverty  than  in  the 
past. 

towards  measures 

tackling 

increase 

the  crisis, 

During 
total  social  protection 
spending  as  a  percentage  of  GDP  shifted  up. 
involved  almost  all  spending 
This 
categories, in particular those addressed at working 
age  individuals.  Nonetheless,  spending  for  the 
unemployed  started  to decline  well before the  fall 
of  the  jobless  rate,  reflecting  the  exhaustion  of 
entitlements  of  many  long-term  unemployed,  the 
shortening of job tenures and the strengthening of 
activation measures.  

(119) It is also not obvious to uniquely identify the effects when 
a  broad  set  of  policies  influence  social  outcomes.  See 
Grigoli  and  Kapsoli  (2013)  and  SPC  and  European 
Commission joint report on social protection systems in the 
EU.  

Convergence  in  real  social  protection  spending 
per  capita  continued  during  the  crisis.  High-
income  countries  tend  to  spend  more  on  social 

86 

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Labour market and wage developments 

Part I 

protection  than  low-income  countries.  Over  time, 
there  has  been  a  convergence  in  the  resources 
allocated 
to  social  protection.  This  process 
continued  during  the  crisis,  although  at  a  lower 
rate.  While  spending  in  countries  with  the  lowest 
income continued to increase at a rate higher than 
the  average,  it  slowed  down  or  even  dropped  in 
countries  with  GDP  per  capita  closer  to  the  EU 
average.  

Social  protection  smooths  and  distributes 
income  over  the  business  cycle.  The  analysis 
suggests  that  only  sickness  and  unemployment 
benefits are specifically associated to the economic 
cycle.  After  2008,  total  expenditure  has  become 
more  responsive  to  the  business  cycle;  this  is 
mainly  due  to  the  increased  responsiveness  of 
spending  on  unemployment  and  social  exclusion. 
This  does  not  mean  that  spending  items  not 
systematically  associated  with  the  cycle  do  not 
contribute  to  support  incomes  during  recessions. 
Spending  categories  that  do  not  move  with  the 
cycle (i.e. that are a-cyclical) stabilise incomes by 
providing transfers to those workers more likely to 
be  liquidity-constrained  during  recessions  (e.g. 
during a recession, access to health care  stabilises 
incomes  of  workers  experiencing  lower  monthly 
wages  because  of  lower  working  hours).  The 
proportion  of  shocks  that  is  smoothed  via  social 
protection has increased after the 2008 crisis. The 
degree  of  stabilisation  of  incomes  varies  across 
countries;  those  with  relatively  higher  GDP  per 
capita stabilise income shocks via social spending 
– 
in  particular  unemployment,  sickness  and 
disability  benefits  –  more  than  countries  with 
lower incomes.  

Social  benefits  reduce  the  incidence  and  the 
depth  of  poverty.  Social  protection  spending 
reduces  the  share  of  the  population  with  incomes 
below  the  poverty  line  by  one  third.  Family, 
sickness  and  disability  benefits  are  the  categories 
contributing  most  to  the  reduction  of  the  poverty 
rate. Unemployment benefits mitigate the effect of 
higher  unemployment  on  monetary  poverty; 
nonetheless,  their  effect  is  only  temporary.  By 
contrast, spending on social inclusion benefits has 
a more moderate impact on monetary poverty, but 
it  tends  to  last  longer.  Social  benefits  also  reduce 
the  gap  between  income  of  the  poor  and  the 
poverty threshold by half. 

Reforms  increasing  the  generosity  of  benefits 
have  reinforced  their  moderating  effect  on 
monetary  poverty.  During 
the  crisis,  social 
benefits  have  contributed to  moderating the  effect 
of  higher  unemployment  on  poverty.  Estimates 
suggest  that  reforms  increasing  the  generosity  of 
all  benefits  had  an  impact  on  their  poverty-
reducing  effect  after  two  years.  This  means  that 
countries  enacting  reforms  early  in  the  recession 
were  better  prepared  to  cope with  the  effects  of  a 
persistent  increase  in  unemployment  on  poverty. 
This  also  implies  that  implementing  reforms  in 
good times, i.e. when unemployment is low, would 
strengthen  the  poverty-reducing  effect  of  benefits 
as unemployment increases.  

The reforms of tax and benefit systems enacted 
after 2008 have contributed to lower inequality 
and  monetary  poverty  in  many  countries.  With 
the  exception  of  Hungary,  the  at-risk-of-poverty 
in  all  Member  States  as  a 
rate  has  fallen 
consequence  of  the  policy  changes  enacted  after 
2008.  This  includes  both  countries  with  rising 
poverty  lines  and  countries  with  falling  poverty 
lines.  Thus,  reforms  of  tax  and  benefit  systems 
were  able  to  reduce  the  risk  of  poverty  in  almost 
all  countries.  Nonetheless,  only  few  were  able  to 
effectively  improve  the  relative  position  of  the 
most  vulnerable  individuals.  Indeed,  although  the 
changes of tax and benefit systems introduced after 
2008  increased  the  disposable  income  of  low-
income  groups,  the  depth  of  poverty  –  i.e.  the 
average  gap  between  the  income  of  poor  people 
and  the  poverty  line  –  dropped  only  in  a  few 
countries,  most  notably  Bulgaria,  Estonia,  Austria 
and Greece. In the latter case, this is most likely a 
result of the declining poverty line.  

Countries  that  allocate  a  higher  share  of  GDP 
to  social  spending  have  better  outcomes  in 
terms of poverty indicators. Beyond the stage of 
economic  development,  the  at-risk-of-poverty  rate 
and  severe  material  deprivation  are  lower  in 
countries, where social protection absorbs a higher 
percentage  of  GDP.  Estimates  suggest  that  a  one 
percentage  point  increase  in  social  protection 
spending is on average associated with a reduction 
of the  at-risk-of-poverty rate  (based on disposable 
income) by 0.35 pps. Yet, its effectiveness differs 
across  countries,  pointing  to  differences  in  the 
efficiency of social spending in reducing poverty.  

87 

 
 
 
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Labour Market and Wage Developments in Europe, Annual Review 2019 

88 

Box I.3.3: Access to social protectionSocial protection systems play a key role in supporting people facing key social risks over their life course and reducing their exposure to poverty. There are key differences in the protection provided to different groups of the society. In most Member States, social protection systems were geared towards workers with full-time open-ended contracts. Conversely, workers with non-standard forms of work and the self-employed were more marginally covered.  For example, some self-employed still do not have access to sickness benefits (in three Member States), unemployment benefits (in eleven Member States) or insurance against accidents (in ten Member States). These groups represent a significant share of the workforce (39% in 2018). (1) They usually faced higher economic uncertainty and are at higher risk of poverty. For example, for the EU as a whole, over 10% of working households are at risk of poverty or of being materially deprived, this rate exceeds 20% for the self-employed, thus being nearly three times higher than for standard employees. (2) Retired self-employed face a risk of poverty twice as high as that of retired employees on average. (3) With a view to tackle this gap in the protection of workers with different types of contracts, the Commission adopted a proposal for a Council Recommendation on Access to Social protection in March 2018. Following the proposal, the Council reached a political agreement in December 2018. (4) The objective of the reccomendation is to support people in non-standard forms of work and the self-employed who, due to their employment status, are not sufficiently covered by social security schemes. The recommendation applies to unemployment benefits; sickness and health care benefits; maternity and equivalent paternity benefits; invalidity benefits; old-age benefits and survivors' benefits; benefits in respect of accidents at work and occupational diseases. Member States are recommended to ensure that all workers and the self-employed:  can adhere to social protection schemes on a mandatory basis for workers and "at least on a voluntary basis and, where appropriate, on a mandatory basis for the self-employed" (closing formal coverage gaps),   can build up entitlements, which can be preserved, accumulated or transferred across schemes (improving effective coverage),   can receive sufficient and timely benefits, contribute in a proportionate manner (improving adequacy),  are informed about their rights and obligations (transparency). The EU will support Member States and stakeholders in achieving the objectives of the recommendation by launching mutual learning activities, establishing a monitoring framework and pursuing dialogue with the Member States, social partners and civil society. This will help social protections systems to better deliver poverty redutction, income smoothing and automatic stabilisation.                                                            (1) Source: Eurostat, Labour Force Survey, 2018. (2) Source: Eurostat, EU-SILC, 2016. (3) 2017 Share survey data: comparison available for all EU Member States except Bulgaria, Latvia, Lithuania, Portugal, Romania and Slovakia, due to a limited sample size, and Ireland, the Netherlands and the UK, which are not included in the survey. The largest gaps are in Denmark, Poland and Malta. Only in the Czech Republic and Estonia the self-employed have lower AROP rates. (4) http://data.consilium.europa.eu/doc/document/ST-15394-2018-INIT/en/pdf  
 
 
 
 
 
APPENDIX 1 

Quartile analysis 

Table I.3.A1.1: ESSPROS: all expenses 

Source: Eurostat 

Table I.3.A1.2: Quartiles, 1996-2016 

(1) Quartiles built on average income per capita (pps). 
Source: Eurostat 

89 

Quartile 1Quartile 2Quartile 3Quartile 4BGCZBEATEEELCYDEHRHUESDKLTMTFIIELVPTFRLUPLSIITNLROSKUKSESelected characteristicsQuartile 1Quartile 2Quartile 3Quartile 4Real per-capita income, 2000-2016EUR 7700EUR 14821EUR 28358EUR 33980Growth rate of real per-capita GDP, 2000-201685.4%22.1%9.2%21.5%Mean percentage of unemployment rate, 1996-201611.0%9.5%9.4%6.3%Max increase in unemployment rate after 2008 (duration in years)6.7 (2)6.1 (5)5.8 (5)2.3 (5)Decline in unemployment since peak 6.35.02.82.0At-risk of poverty rate, 2005-2016 average19.7%15.2%16.9%14.3%Real Expenditure per person, 2000-2016EUR 1357EUR 3104EUR 7479EUR 9242Real Expenditure per poor person, 2005-2016EUR 7662EUR 21948EUR 46446EUR 66829Expenditure over GDP, 2000-201617.6%20.9%26.4%27.2% 
 
 
 
 
 
 
 
APPENDIX 2 

Definitions of the functions of social protection 

Table I.3.A2.1: Definitions of the functions of social protection 

Source: Eurostat 

90 

FunctionBrief description1. Sickness/Health careIncome maintenance and support in cash in connection with physical or mental illness, excluding disability. Health care intended to maintain, restore or improve the health of the people protected irrespective of the origin of the disorder.2. DisabilityIncome maintenance and support in cash or kind (except health care) in connection with the inability of physically or mentally disabled people to engage in economic and social activities.3. Old ageIncome maintenance and support in cash or kind (except health care) in connection with old age.4. SurvivorsIncome maintenance and support in cash or kind in connection with the death of a family member.5. Family/childrenSupport in cash or kind (except health care) in connection with the costs of pregnancy, childbirth and adoption, bringing up children and caring for other family members.6. UnemploymentIncome maintenance and support in cash or kind in connection with unemployment.7. HousingHelp towards the cost of housing.8. Social exclusion not else-where classifiedBenefits in cash or kind (except health care) specifically intended to combat social exclusion where they are not covered by one of the other functions. 
 
 
 
Part II 
Analytical chapter 

 
 
1. 

THE RELATIONSHIP BETWEEN POLARISATION AND SKILLS 

MISMATCHES: EVIDENCE FOR THE EU 

Skills mismatches – i.e. discrepancies between the 
skills sought by employers and the skills offered by 
workers  –  increased  during  the  crisis  and  remain 
high in many Member States. This imbalance may 
be  temporary,  because  of  labour  market  frictions 
and the business cycle, or more persistent, because 
of  structural  disequilibria  between  supply  and 
demand of skills. 

At  the  same  time,  employment  is  largely  shifting 
from routine to non-routine occupations in the EU 
as  a  whole.  Known  as  job  polarisation,  this 
phenomenon captures the decline of employment in 
middle-skilled occupations and the increase of jobs 
requiring  lower  and  higher  skill  levels.  As  this 
chapter  will  explore,  polarisation  is  a  broadly 
influenced  by 
long-term  process 
observed, 
technological 
the 
in 
shifts 
and 
international division of labour.  

changes 

chapter 

job  polarisation  and 

Only  a  few  studies  have  so  far  tried  to  assess  the 
skills 
link  between 
this 
mismatches.  This 
the 
relationship.  The  analysis  suggests 
introduction  of 
the 
technologies 
demand  for  workers  performing  routine  tasks  has 
increased skills mismatches. This is consistent with 
technological  changes  driving  permanent  shifts  in 
the  demand  for  labour,  which  lead  to  imbalances 
between supply and demand.  

investigates 
that 
that  reduce 

The  increasing labour demand for high-skill tasks 
along  with  the  lower  labour  demand  for  routine 
tasks  have  increased  skill  mismatches  in  the  EU. 
This effect may be partly offset by improvements in 
economic  conditions  and  by  higher  levels  of 
education  and  labour  market  participation.  This 
highlights the scale of the challenge to promote the 
alignment  of  skills  supply  with  skills  demand  in  a 
growing  economy.  In  the  absence  of  an  adequate 
supply  response,  the  labour  market  trends  would 
lead  to  higher  skill  mismatches,  in  particular 
during weakening economic conditions. 

92 

1.1. 

INTRODUCTION  

the  share  of  middle-wage 

Much  has  been  written  about  job  polarisation, 
but  little  is  known  about  its  impact  on  skills 
mismatches.  Job  polarisation,  defined  as  the 
relative  job  growth  at  both  extremes  of  the  wage 
distribution  (lower  and  higher-skilled)  relative  to 
the  middle-wage  (middle-skilled)  ones,  is  a  well-
documented  phenomenon  in  advanced  economies 
(Autor  et  al.,  2006;  Goos  and  Manning,  2007; 
Acemoglu  and  Autor,  2011;  OECD,  2018a).  A 
large  strand  of  the  literature  has  focused  on  the 
causes of this phenomenon, pointing to technology 
and globalisation as the main drivers of the decline 
(middle-skill) 
in 
occupations.  Less  is  known,  however,  about  its 
consequences  and  more  precisely  its  impact  on 
skills  and  skills  mismatches.  Technology  can 
replace  workers  in  routine  tasks  that  are  easy  to 
automate  and  complement  workers  in  tasks  that 
require  creativity,  problem  solving  and  cognitive 
skills.  As  machine 
learning  and  artificial 
intelligence  advance  in  many  sectors,  a  growing 
number  of  workers  may  need  to  move  from 
declining occupations (concentrated in middle-skill 
routine 
to  growing  ones  (which  are 
characterised  by  high-  or  low-level,  non-routine 
cognitive skills). 

tasks) 

All  EU  Member  States  have  experienced  job 
polarisation  in  the  past  two  decades,  though 
unevenly.  Compared  to  the  pre-crisis  period,  the 
proportion of  workers in  middle-paid and  middle-
skill  occupations  is  shrinking  in  all  EU  Member 
States, albeit to different degrees. This chapter will 
consider  different  notions  of  polarisation  across 
Member  States  and  examine  whether  this  has 
occurred mostly within sectors or between them. In 
countries 
late  shift  of 
employment  from  agriculture  and  manufacturing 
to  service  sectors,  such  as  Southern  and  Central 
European  countries,  the  increase  in  polarisation 
observed  at  the  national  level  may  have  occurred 
reallocations,  both 
because  of  employment 
between and  within sectors.  This shift is expected 
to affect both the demand and the supply of skills: 
the types of tasks carried out in jobs as well as the 
types  of  professions  and  occupations  required  in 
the labour market. 

that  experienced  a 

 
 
Part II 

Analytical chapter 

to 

skills  mismatch), 

Skills  mismatches  refer 
to  a  discrepancy 
between the demand and the supply of skills in 
the labour market, a situation in which the skills 
sought  by  employers  are  different  from  the  skills 
offered  by  workers  in  general  or  job  seekers  in 
particular. The term skills mismatch is very broad. 
It can refer to the level of the economy as a whole 
skills 
(macroeconomic 
shortages  experienced  by  firms  when  filling  open 
vacancies (firm-level aggregates), or to on-the-job 
skills mismatches experienced by specific workers, 
i.e.  a  discrepancy  between  the  worker's  skills  and 
the  skills  needed for the  specific job. While skills 
shortages  refer  to  a  situation  whereby  employers 
are  unable  to  fill  vacant  posts  due  to  a  lack  of 
suitably  qualified  candidates,  on-the-job  skills 
mismatches  refers  to  the  degree  at  which  workers 
in  firms  possess  skill  or  education  levels  that  are 
above, below or poorly connected to those required 
at  their  current  job  (Quintini,  2011;  Cedefop, 
2015).  This  last  dimension  of  skills  mismatches 
does not capture individuals who are out of work. 
Every economy experiences some degree of skills 
mismatch  as  a  result  of  labour  market  frictions  in 
the  short-term  (Kiss  and  Vandeplas,  2015). 
However, when mismatches become structural and 
persistent,  they  can  have  considerable  economic 
and  social  repercussions,  requiring  appropriate 
structural policy responses to address them.  

Skills  mismatches remain high among Member 
States.  Today,  more  than  60  million  adults  in  the 
EU  lack  necessary  literacy,  numeracy  and  digital 
skills. Digital technologies are increasingly used in 
workplaces across the EU and nowadays most jobs 
require basic digital skills. In 2017, almost half of 
the EU population (43%) had basic or below basic 
digital  skills  with  only  a  slight  improvement 
observed  since  2015,  while  38%  of  employers 
reported  that  the  lack  of  digital  skills  had  an 
impact  on  their  performance,  notably  through 
productivity  losses.  In  2019,  every  Member  State 
received  a  Country-Specific  Recommendation 
related  to  skills,  out  of  which  twelve  explicitly 
mentioned their labour market relevance and skills 
mismatches  (Belgium,  Bulgaria,  Croatia,  Estonia, 
France,  Ireland,  Netherlands,  Poland,  Portugal, 
Romania, Slovenia, and Spain). This highlights the 
scale of the challenge to promote the alignment of 
skills supply with skills demand in the EU.  

In this context, it is relevant to examine whether 
job  polarisation  has  affected  skills  mismatches 

interest  given 

and,  if  so,  to  what  extent.  Both  job  polarisation 
and  skills  mismatches  have  become  topics  of 
increasing 
their  prevalence. 
Technological progress and automation have been 
offered  as  explanations  for 
job  polarisation. 
Furthermore,  there  is  substantial  evidence  on  the 
adverse effects of skills mismatches on wages and 
(Mavromaras  et  al.,  2009; 
job  satisfaction 
European  Commission, 
also 
productivity  and  output  (Nickell  and  Nicolatsis, 
1997; Bennett and McGuinness, 2009). In contrast, 
the effect of job polarisation on skills mismatches 
has  been  much  mess  analysed,  although  the 
literature on job polarisation suggests the existence 
of such a link (Goos and Manning, 2007). 

2015), 

but 

skills  mismatches 

From  a  theoretical  point  of  view,  the  effect  of 
job  polarisation  on 
is 
ambiguous. Starting from a hypothetical situation 
in the labour market, in which the supply of skills 
perfectly  matches  demand  and  adequately  trained 
workers  fill  all  jobs,  polarisation  could  increase 
different  dimensions  of  skills  mismatches.  In  the 
absence  of  a  supply  response,  it  could  increase 
macroeconomic  skills  mismatches,  as  it  would 
imply  a  fall  in  the  demand  for  middle-skilled 
workers and a rise in the demand of high- and low-
skilled  workers.  It  could  also  increase  labour 
shortages  for  the  latter  two  groups.  Furthermore, 
under-qualiﬁcation  could  rise  among  workers 
moving  to  the  growing  share  of  high-skill  jobs, 
while  over-qualiﬁcation  would  rise  for  those 
performing  low-skill  jobs,  if  workers  previously 
employed  in  middle-skill  jobs  take  up  these  jobs. 
Whether  such  effects  would  materialise 
is 
uncertain  for  at  least  three  reasons.  First,  the 
supply of skilled workers is on an upward trend in 
many  EU  countries,  which  may  or  may  not  be  in 
accordance  with  the  increasing  share  of  high-skill 
jobs.  Secondly, 
labour  markets  are  often 
characterised  by  a  certain  extent  of  skills 
mismatches  and  different  starting  positions  with 
regard to levels and types of such mismatches will 
clearly change how polarisation shapes them. In a 
country with a high incidence of under-skilling, for 
example, job polarisation could even reduce under-
skilling,  if  it  implies  a  shift  of  medium-skilled 
workers from medium- to low-skilled jobs. Finally, 
job  polarisation  may  interact  with  unemployment 
(i.e.  the  workers  previously  employed  in  middle-
skill  occupations  might  become  unemployed 
instead of moving immediately to low-skill and/or 
high-skill  occupations).  In  that  case,  it  might  not 

93 

 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

affect  skills  mismatches  at  all.  Overall,  this 
between 
illustrates 
that 
polarisation  and 
is  not 
straightforward, but rather ambiguous. 

the 
skills  mismatches 

relationship 

This chapter examines the relationship between 
job polarisation and skills mismatches as well as 
the  main  determinants  of  the  latter  across 
Member  States.  The  chapter  is  structured  as 
follows.  First,  it  starts  with  a  literature  review  of 
the determinants of both job polarisation and skills 
mismatches.  The  chapter  proceeds  with  an 
overview of the  main concepts and dimensions of 
skills  mismatches  and  job  polarisation  that  have 
been  employed  in  the  literature  as  well  as  the 
indicators  to  operationalise  them,  their  pros  and 
cons,  their  evolution  over  time  across  Member 
States  and  some  descriptive  statistics.  The 
subsequent  section  estimates  the  effect  of  job 
polarisation  on  skills  mismatches  across  Member 
States  between  2002  and  2017,  presenting  the 
empirical  findings.  Finally,  the  chapter  concludes 
with some policy recommendations. 

1.2. 

LITERATURE 
THE 
REVIEW 
DETERMINANTS  OF  SKILLS  MISMATCHES 
AND POLARISATION  

ON 

1.2.1.  The determinants of job polarisation 

important  determinant  of 

The  most 
job 
polarisation is technological change. For several 
years,  it  was  argued  that  technological  change 
benefits  the  more  skilled  workers  and  harms  the 
low-skilled workers, thereby creating a “skill bias” 
in the evolution and remuneration of labour (Katz 
and  Autor,  1999).  However,  several  studies  have 
challenged  this  view  arguing  that  the  relationship 
between new technologies and the employment or 
wages  of  different  skill  groups  is  more  complex 
(Card  and  DiNardo,  2002;  Lemieux,  2008).  In 
particular,  while  the  hypothesis  of  skill-biased 
technological change could account for the growth 
of  high-skilled  employment,  it  could  not  explain 
the  simultaneous  increase  in  the  demand  of  low-
skilled 
in  middle-skill 
the 
occupations.  In  their  seminal  study,  Goos  and 
Manning  (2007)  were  the  first  to  refer  to  this 
phenomenon as “job polarisation” putting forward 
an  alternative  link  between  technological  change 
and  job  polarisation.  Goos  and  Manning  (2007) 
used the US data from Autor et al. (2003) to show 

jobs  and 

fall 

94 

that the jobs that require non-routine tasks tend to 
be  at  the  top  and  bottom  of  the  skills  and  wage 
distribution,  while  the  jobs  that  require  routine 
tasks tend to be in the middle. Following Autor et 
al.  (2003),  they  attributed  polarisation  to  "task-
biased  technological  change"  which  leads  to  the 
replacement  of  middle-skill, 
tasks 
undertaken  by  workers  with  primarily  medium-
level  education  (such  as  clerical  and  craft 
occupations) and corresponding to tasks performed 
by  computers  and  associated  technologies  (the 
“routinisation hypothesis”).  

routine 

progress 

technological 

However, 
and 
automation  do  not  necessarily  lead  to  job 
destruction. Member States where automation and 
digitalisation  are  most  widespread,  for  example 
Germany  and  the  Czech  Republic,  are  also  those 
that have registered the lowest unemployment rates 
in  the  EU  (European  Commission,  2018a).  In 
particular, this study finds that the extent to which 
labour  can  be  replaced  by  technology  depends  on 
the  level  of  skills  required  by  the  tasks  to  be 
performed in each job. This explains the relatively 
high replacement rate of repetitive low-skill tasks. 
In contrast, skilled labour is needed to fully exploit 
the potential of modern technologies by operating, 
maintaining, 
them. 
Whether or not labour is substituted by technology 
ultimately depends on the ability of education and 
training  systems  to  adapt  to  the  fast-changing 
technological opportunities. 

repairing  and 

improving 

Furthermore,  although  there  is  evidence  of  job 
polarisation 
in  terms  of  employment,  the 
evidence  for  polarisation  in  terms  of  wages  is 
mixed.  According  to  the  theory,  along  with 
employment, also wages should be polarising with 
salaries  growing  relatively  faster  for  those  at  the 
poles. This pattern, however, is found only for the 
US  (Autor  et  al.,  2006;  Acemoglu  and  Autor, 
2011).  For  Europe,  the  evidence  is  mixed  with 
several  studies  finding  no  wage  polarisation 
(Dustmann et al., 2009; Mishel et al., 2013; Autor, 
2015; OECD, 2019). Goos et al. (2009) looked at 
ISCO 88 two-digit occupations between 1993 and 
2006  and  find  evidence  for  job  polarisation  in 
Europe as a whole: the four lowest paying and the 
eight  highest-paying  occupations  increase  their 
employment  share,  while  the  nine  middle-paying 
occupations  lose  jobs.  Kampelmann  and  Rycx 
(2011) find some evidence of wage polarisation in 
Germany  for  the  period  1985-2008.  In  particular, 

 
they find that occupations situated around the 40th 
percentile of the earnings distribution have lost and 
high-paid  occupations  have  gained  employment 
lowest  percentiles  stagnated  or 
shares.  The 
recorded minor employment losses.  

of 

structural 

international 

In  addition 

determinants 

Other 
job 
polarisation  comprise  international  trade,  off-
shoring,  deindustrialization  and  the  shift  from 
manufacturing 
to 
to  services. 
technological  change, 
trade  and 
oﬀ shoring  allow  respectively  to  substitute  home 
routine  productions  with  imports  and  to  move 
routine  activities  in  countries  with  lower  labour 
costs (Autor et al., 2013; 2015). Both international 
trade  and  offshoring  have  been  shifting 
the 
domestic  labour  demand  in  favour  of  non-routine 
occupations (both low skilled and high skilled) for 
the  last  three  decades.  However,  it  is  inherently 
difficult to disentangle the effects of technological 
change and international trade on job polarisation, 
since  these  two  megatrends  complement  and 
reinforce each other. Keller and Utar (2016) linked 
the rise in Chinese imports to the decline in Danish 
middle-paid manufacturing jobs, which has further 
aggravated job polarisation. Their study  finds that 
as  workers  are  pushed  out  of  middle-paid 
employment  in  manufacturing,  they  turn  to  low-
wage  and  high-wage  services. (120)  These  effects 
hold  when  controlling  for  off  shoring  and 
technology,  suggesting  that  international  trade 
competition  operates 
these 
channels.  

separately 

from 

initially 

The  increase  in  polarisation  observed  at  the 
national  level  can  result  from  polarisation 
within  individual  industries  or  employment 
less  polarised 
shifting  away  from 
industries  to  more  polarised  ones.  Polarisation 
between  industries  might  be  of  particular  interest 
given  the  decline  of  manufacturing  and  the  shift 
towards  services  in  developed  economies.  In  a 
recent study, Breemersch et al. (2017) find that, in 
the  EU  for  the  period  1997-2010,  polarisation 
the 
occurred  mostly  within 
industries,  while 
less 
reallocation  of  employment  away  from 
polarised industries towards more highly polarised 
industries  contributed  only  to  about  one  third  of 
the total change. They find that both technological 

(120) Overall, 

the  estimates  suggest 

import 
competition accounts for about a fifth of total middle-paid 
employment decline in the Danish national labour market. 

that  Chinese 

Part II 

Analytical chapter 

international 

change  and 
trade  competition 
contributed  to  job  polarisation  within  European 
manufacturing  industries. (121)  However,  they  find 
no  evidence  that  technological  change  played  any 
role  in  job  polarisation  between  industries.  This 
implies  that  raising  barriers  to  trade  seem  to  have 
limited effects on reducing job losses in declining 
industries. Finally, labour market institutions could 
also  play  a  crucial  role.  Kampelmann  and  Rycx 
(2011) 
declining 
evidence 
unionisation  has  also  led  to  job  polarisation:  the 
fall  in  union  membership  has  a  significant  effect 
in 
the  evolution  of  employment  shares 
on 
Germany  between  1985  and  2008.  Nevertheless, 
Breemersch  et  al.  (2017)  find  no  evidence  that 
labour  market  institutions  (namely  union  density, 
employment  protection  legislation  and  minimum 
wages) have affected job polarisation in the EU. 

provide 

that 

finds 

Several studies have attributed job polarisation 
to  changes  in  the  business  cycle;  yet,  the 
evidence is mixed. Jaimovich and Siu (2012) ﬁnd 
that in the US, much of the job loss in middle-skill 
occupations  occurs  during  economic  downturns, 
while  high-  and  low-skill  occupations  are  less 
affected. However, another US-based study (Foote 
and  Ryan,  2013) 
that  middle-skill 
occupations  have  traditionally  been  more  cyclical 
than  other  occupations,  in  part  because  of  the 
volatile industries that tend to employ middle-skill 
workers.  Unemployed  middle-skill  workers  also 
appear 
feasible 
employment  alternatives  outside  of  their  skill 
class.  On  the  contrary,  employment  of  the  low-
skill non-routine manual group never shows much 
cyclical  variance  and  their  relative  unemployment 
rate even improves in recessions (122).  

few  attractive  or 

to  have 

1.2.2.  The determinants of skills mismatches 

capital 

Human 
skills 
theory 
mismatches a short-term, cyclical phenomenon. 
Most  of  the  studies  on  the  determinants  of  skills 

considers 

(121) More  precisely,  technological  change  explains  a  third  of 
within-industry polarisation, while international net import 
competition  contributed  to  a  much  smaller  extent.  The 
process  of  between-industry  polarisation  is  driven  by 
widespread  deindustrialization  and  the  structural  shift  to 
the  services  sector  while 
import 
competition  explains  about  a  fifth  of  the  reallocation  of 
less  polarised 
labour  within  economies  away  from 
manufacturing industries. 

international  net 

(122) This  is  also  supported  by  the  recent  study  of  Baley  et  al. 

(2019).  

95 

 
                                                           
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

mismatches have focused on over-qualification. In 
his  seminal  work,  Becker  (1964)  assumes  that 
workers  will  always  be  paid 
their  marginal 
product,  which  is  determined  by  the  level  of 
human  capital  acquired  through  initial  formal 
training  and  on-the-job  training.  Thus,  human 
capital  theory,  under  the  assumption  of  perfect 
competition, predicts no mismatches assuming that 
employers  will  fully  utilise  the  skills  of  their 
employees.  However,  for  the  proponents  of  the 
human  capital  theory,  it  is  possible  that  in  the 
short-run  workers  are  overeducated,  as  workers 
themselves  may  be  seeking  a  job  that  suits  more 
their  skills  while  firms  may  require  time  to  adapt 
their  production  processes  to  human  capital  of 
existing  workers.  Several  economists  have 
questioned this view (Duncan and Hoffman, 1981; 
Hartog and Oosterbeek, 1988). Battu et al. (2000) 
examine the prediction that firms upgrade the tasks 
they give to their overeducated workers by testing 
for  the  hypothesis  that  graduates’  jobs  converge 
over  time,  rendering  over-qualification  a  short-
term  phenomenon.  They  find  no  evidence  that 
overeducated graduates have been able to upgrade 
their jobs (i.e. implying that over-qualification can 
survive in the long-term. 

Skills  mismatches  are  affected  by  the  business 
cycle. Whether and how labour supply responds to 
rapid 
the 
increases  of  demand  depends  on 
distribution  of  existing  skills,  the  type  of  skills 
mismatch, the extent of geographical mobility and 
the  signals  provided  by  wage  adjustments.  In 
downturns, there are two factors at play: on the one 
hand,  mismatches  decline,  because  low  quality 
matches  are  destroyed  but  high  quality  matches 
between  firms  and  employees  survive.  On  the 
other  hand,  mismatches  increase  because  firms 
post fewer vacancies and job seekers are willing to 
accept  less  desirable  jobs  because  of  the  higher 
competition  they  face  (“sullying  effect”).  When 
demand is buoyant and the labour market is tight, 
employers  may  be  forced  to  adjust  their  hiring 
standards  downward  to  cope  with  difficulties  in 
recruiting  skilled  labour,  which  increases  the 
incidence  of  under-skilling  (Healy,  Mavromaras 
and Sloane, 2015; Livanos and Nunez, 2017). Yet, 
over-qualification  has  been  found 
to  behave 
counter-cyclically:  the  highly  educated  crowd  out 
the  lower  educated  during  downturns  where  the 
selectivity  of  employers  and  workers  decreases 
(Croce and Ghignoni, 2012; Kiersztyn, 2013).  

96 

structural 

While  there  is  evidence  that  at  least  some  part 
of  skills  mismatches  is  cyclical,  there  are  also 
important 
determinants.  One 
important  structural  determinant  is  technological 
change  (Di  Pietro,  2002).  The  rapid  pace  of 
technological  change  may  require  skills  and 
qualifications  higher  than  those  possessed  by 
workers; it changes the demand for skills between 
sectors,  occupations  and  firms.  Technological 
change  can  lead  to  skill  shortages  by  creating  the 
need  for  new  skills  that  are  not  immediately 
available in the labour  market,  until the education 
system  at  large  (including  employer  training)  is 
able  to  respond  to  the  demand  of  new  skills.  In 
addition,  firms  may  wish  to  hire  high-skilled 
workers as these can adapt at a lower cost relative 
(Desjardins  and 
to 
Rubenson, 2011). Demographic trends are another 
structural  factor  that  can  add  to  skill  shortages 
through 
the 
composition  of  the  labour  force.  Demographic 
change  also  affects  the  demand  for  goods  and 
services,  and  hence  on  the  demand  for  the  skills 
necessary  to  provide  them  (e.g.  medical  services 
and personal care). Shifts in the demand for labour 
towards more skilled jobs and ageing – a long-term 
feature of European economies – can produce skill 
shortages  and  mismatches,  as  older  workers  are 
endowed with skills that do not necessarily match 
closely 
the  process  of 
digitalisation  of  modern  economies  (European 
Parliament, 2015).  

impact  on  size,  age  and 

the  unskilled  workers 

required  by 

those 

its 

labour  market 

institutions  and 

There  is  a  broad  consensus  on  the  importance 
of 
labour 
market  policies  in  reducing  skills  mismatches. 
Berton  et  al.  (2017)  study  whether  the  Italian 
reform  of  employment  protection  legislation  of 
2012  (Fornero  reform)  has  influenced  the  quality 
of the match between workers and firms. (123) They 
found that the reform increased the probability of a 
good  match  by  9.5%  and  that  the  main  driver  for 
this has been a reduction in under-education and an 
improvement of labour reallocation. Thus, reforms 
that  improve  labour  reallocation  may  lead  to  a 
more  efficient  match  between  existing  jobs  and 
skills  and  reduce  skills  mismatches.  Adalet 
the 
McGowan  and  Andrews  (2015)  explore 

(123) The study uses Labour Force Survey data and a difference-
in-difference approach and measures the quality of matches 
in  terms  of  dispersion  around  sector-  and  occupation-
specific median educational attainment. 

 
                                                           
for 

and 

individual 

controlling 

relationship between skills  mismatches and public 
policies  using  micro  data  for  22  OECD  countries 
from  the  OECD  Survey  of  Adult  Skills  (PIAAC). 
job 
After 
characteristics,  their  findings  suggest  that  well-
designed  product  and 
labour  markets  are 
associated  with  a  smaller  skills  mismatch.  More 
precisely,  greater  flexibility  in  wage  negotiations 
and  higher  participation  in  lifelong  learning  are 
associated with a better matching of skills to jobs. 
Higher  shares  of  temporary  contracts  have  been 
found  to  increase  over-qualification  via  lowering 
selectivity  of  employers  and  workers. 
the 
Increasing  participation  in  lifelong  learning  from 
the  lowest  level  (Italy)  to  the  median  (Estonia)  is 
associated with a 6 pps reduction in the mismatch. 
Less  cumbersome  product  market  regulations  and 
bankruptcy  legislation  that  does  not  excessively 
punish  business  failure  are  associated  with  lower 
skills  mismatches.  (124)  Finally,  skills  mismatches 
are lower in countries with housing policies that do 
not  impede  residential  mobility  (e.g.  transaction 
costs  on  buying  property  and  stringent  planning 
regulations). (125) 

important 

Individual  characteristics  are  also  important 
determinants  of  skills  mismatches.  Several 
studies  suggest  that  individual  characteristics  may 
than  macro-level  factors 
be  more 
(Ghignoni  and  Verashchagina,  2014).  At  the 
microeconomic  level,  three  factors  have  been 
found  to  significantly  affect  skills  mismatches: 
gender,  immigrant  background  and  education. 
Empirical  evidence  about  gender  effects  has  been 
mixed,  however,  most  studies  conclude 
that 
women  have  a  higher  skills  mismatches  risk  than 
men  (Aleksynska  and  Tritah,  2013;  Baert  et  al., 
2013;  Betti  et  al.,  2011;  Kiersztyn,  2013).  Using 
Dutch data, Renes and  Ridder (1995) find that on 
average  women  are  required  to  have  almost  six 
months  more  work  experience  than  men  to  be 
hired  for  the  same  job.  However,  Quintini  (2011) 
finds that women across the OECD are less likely 
to  be  over-skilled.  First-  and  second-generation 
immigrants  face  higher  risks  of  skills  mismatches 
(Aleksynska  and  Tritah,  2013;  Tarvid,  2012), 

(124) The role of bankruptcy law is particularly strong: reducing 
the cost of closing a business from its most restrictive level 
in Italy to the median level in Canada is associated with a 
10 pps decrease in the probability of mismatch. 

(125) However,  in  this  paper  a  caveat  applies  and  causation  is 
difficult to establish given that the data is available only at 
one point in time. 

Part II 

Analytical chapter 

whereas residence duration seems to have no effect 
on it (Aleksynska and Tritah, 2013; Fernández and 
Ortega, 2008). However, Nieto et al. (2014) do not 
find any significant difference in the probability of 
having skills mismatches between natives and EU-
movers while immigrants from outside the EU are 
less likely to match skills demand, once individual 
characteristics  are  controlled  for.  The  region  of 
origin, the native language, length of residence and 
work  experience  in  the  host  country,  place  of 
acquisition  of 
formal  qualifications,  age  at 
migration and migrant generation can be important 
determinants  of  whether  migrants  are  more  likely 
to  be  mismatched  (Battu  and  Sloane,  2004). 
Finally,  and  somewhat  counterintuitively,  Adalet 
McGowan  and  Andrews  (2015)  show  that  skills 
mismatches  are  more  likely  among  those  with 
higher levels of educational attainment, suggesting 
that policies to increase educational levels may not 
be sufficient to address mismatches. 

1.2.3.  The 

relationship 

job 
polarisation  and  skills  mismatches:  the 
literature 

between 

that 

there 

job  polarisation  decreases 
skills  demand  and 
the 

There  have  been  few  empirical  studies  on  the 
relationship between job polarisation and skills 
mismatch.  Sparreboom  and  Tarvid  (2016)  find 
the  mismatch 
that 
skills 
between 
supply, 
level  of  educational 
approximated  by 
attainment,  but 
is  no  statistically 
signiﬁcant effect on the reverse relationship (from 
skills mismatches to job polarisation). Zago (2018) 
examines  the  effect  of  polarisation  on  skills 
mismatches  in the US during the Great  Recession 
and finds that a 1 pp increase in polarisation leads 
to a rise in skills mismatch by 0.39 pps. The author 
provides evidence that when the economy is hit by 
a  negative  transitory  shock  and  the  labour  market 
polarises,  high-skilled  workers  are  mismatched 
only  temporarily  to  lower  paying  jobs,  i.e.  they 
typically  move  from  abstract  to  routine  (clerical) 
jobs  in  bad  times,  but  they  climb  up  the  ladder 
when  the  economy  goes  back  to  its  expansionary 
path.  On  the  contrary,  low-skilled  workers  are 
mismatched  permanently  or  remain  unemployed 
for  longer,  since  their  mobility  is  constrained  by 
their skill level and their employment opportunities 
are  critically  reduced  due  to  job  polarisation.  (126) 

(126) For  the  US,  the  paper  suggests  that  as  a  result  of  a  1  pp 
increase  in  polarisation  during  the  recession,  high-skilled 

97 

 
                                                           
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

is 

to 

This 
track 
the  only  empirical  study 
mismatched  workers  in  the  economy  and  account 
for the  efficiency of the reallocation patterns over 
the cycle, bridging the literature on job polarisation 
with  the  literature  on  the  cyclical  reallocation  of 
workers.  

effect 

a  displacement 

(2018)  develop  a 
Acemoglu  and  Restrepo 
theoretical  framework  to  analyse  the  implications 
of  automation  and  artificial  intelligence  on  the 
demand for labour, and find that the former lead to 
skills  mismatches.  Their  model  suggests  that 
in 
automation  has 
accordance  with  the  task-based  approach  and  a 
simultaneous  productivity  effect,  resulting  from 
the  cost savings generated,  which in turn increase 
the  demand  for  labour  in  non-automated  tasks. 
Therefore,  automation  is  found  to  lead  to  skills 
mismatches. To the extent that the workforce does 
not possess the new required skills, the adjustment 
significant 
process  will  be  hampered  with 
repercussions.  As 
the 
educational  system  is  not  up  to  providing  those 
skills (and if we are not even aware of the types of 
new  skills  that  will  be  required  so  as  to  enable 
investments  in  them),  the  adjustment  will  be 
greatly impeded. At stake is not only the speed of 
adjustment,  but  potential  gains 
from  new 
technologies. If certain skills are complementary to 
new technologies, their absence will imply that the 
productivity  of  these  new  technologies  will  be 
lower than otherwise. Thus, the mismatch between 
skills  and  technologies  not  only  slows  down  the 
adjustment  of  employment  and  wages,  but  holds 
back  potential  productivity  gains”  (Acemoglu  and 
Restrepo, 2018, pp.13). 

the  authors  argue  “If 

1.3. 

SKILLS  MISMATCH:  MEASURES  AND 
RECENT DEVELOPMENTS 

Skills mismatch refers to a discrepancy between 
the  demand  and  the  supply  of  skills  in  the 
labour  market,  whereby  the  skills  sought  by 
employers  are  different  from  the  skills  offered 
by workers. It is a broad, encompassing term and 
the 
three 
literature  has  distinguished  among 
different  concepts  of  skills  mismatch.  The  first  is 
the broader macroeconomic skills mismatch in the 

workers  are  3  pps  more  likely  to  temporarily  downgrade 
into routine jobs while low-skilled workers are 2 pps more 
likely  to  permanently  downgrade  into  manual  jobs  in  the 
upcoming recovery. 

98 

economy,  which  captures  the  gap  between  the 
skills that the working age population has and the 
skills  needed  in  the  economy.  This  has  been 
operationalised  through  the  dispersion  in  the 
employment rates of low,  middle and high-skilled 
workers.  The  second  concept  of  skills  mismatch 
are labour shortages, which refer to the difficulties 
filling  open 
experienced  by  employers 
vacancies.  Finally,  the  third  and  more  specific 
concept of skills mismatches in the literature is on-
the-job 
to 
differences  between  the  worker's  skills  and  the 
skills needed for his/her specific job.  

skills  mismatch,  which 

refers 

in 

in 

literature 

skills  mismatch 
refers 

skills  mismatches  have  been 
On-the-job 
operationalised 
through 
the 
indicators  of  vertical  and  horizontal  skills 
(over-
mismatch.  Vertical 
qualification,  under-qualification) 
to  a 
situation  in  which  there  is  an  ordinal  relationship 
between  the  skills  required  for  a  specific  job  and 
the  skills  possessed  by  the  worker,  i.e.  a  worker 
has more/less skills compared to those required by 
her/his job. Horizontal skills mismatch (mismatch 
by field of education) refers to a situation in which 
a  worker  possesses  skills  that  are  different  from 
those  that  are  required  to  adequately  perform 
her/his  job  tasks,  but  no  clear  ordinal  relationship 
emerges.  This  is  for  example  the  case  when  a 
worker  is  employed  in  an  occupation  different 
from  the  field  of  study  in  which  he/she  has  been 
trained.  

There  are 
significant  differences  across 
Member States in both the levels and the trends 
of all these indicators. The evidence suggests that 
certain  types  of  skills  mismatches  are  indeed  on 
the  rise  in  the  EU,  notably  labour  shortages  and 
over-qualification.  Other  types  are  on  a  long-term 
declining trend (e.g. under-qualification) or follow 
patterns  that  are  more  complex  over  time  (e.g. 
macro-economic skills mismatch) (127). 

1.3.1.  Macroeconomic skills mismatch 

The  macroeconomic  skills  mismatch  shows  the 
relative  dispersion  of  employment  rates  across 
population  groups  with  different  educational 
attainment.  While 
term  “macroeconomic 
skills  mismatch”  has  been  broadly  used  in  the 
literature, the  term “macroeconomic qualifications 

the 

(127) For a review see Vandeplas and Thum-Thysen (2019).  

 
                                                                                   
                                                           
in 

mismatch” would arguably be more accurate as in 
the  operationalisation  of  the  indicator  skills  are 
If  data  are 
approximated  by  qualifications. 
the 
compare 
can  directly 
available,  one 
composition of vacancies (in terms of qualification 
levels) with that of (un)employment (see e.g. Şahin 
et  al.,  2014).  Nevertheless,  as  reliable  data  on 
vacancies  at  the  EU  level  are  hard  to  obtain, 
simplified  measures  can  be  used  to  compare  the 
composition  of 
terms  of 
employment 
qualifications (as a proxy for labour demand) with 
that of the working age population (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).  Thus,  in  the 
absence 
the 
macroeconomic  skills  mismatches  indicator  is 
defined  as  the  relative  dispersion  of  employment 
rates across three population groups with different 
educational  attainment:  the  low,  middle  and  high 
skilled. (128)  In  general,  macroeconomic  skills 
mismatches will be high if the employment rates of 
low-  and/or  middle-skilled  workers  are  low  as 
compared  to  those  of  high-skilled  workers,  while 
the  former  make  out  a  substantial  share  of  the 
working age population. (129)  

vacancy 

reliable 

data, 

of 

Macroeconomic  skills  mismatches  can  have 
both  cyclical  and  structural  determinants.  On 
the one  hand, as low-skilled  employment tends to 
be  more  sensitive  to  the  cycle  than  high-skilled 
employment,  the  difference  in  employment  rates 
between qualification groups typically increases in 

(128)  

|

=

−

 𝑒𝑖

𝑖=𝐿,𝑀,𝐻

𝑖=𝐿,𝑀,𝐻

1
𝑒𝑡

𝑃𝑖
𝑃𝑡

𝐸𝑖
𝐸𝑡

∑ |

𝑆𝑀𝐼 = ∑ |

𝑃𝑖
𝑃𝑡
where  𝑖  equals  the  three  different  qualification  groups  (low-
skilled, middle-skilled and high-skilled), 𝐸𝑖, 𝑃𝑖 and 𝑒𝑖 equal 
the total employment, the working age population and the 
employment rate of group i respectively; and 𝐸𝑡, 𝑃𝑡 and 𝑒𝑡 
equal the aggregate employment, the aggregate population 
and  the  aggregate  employment  rate  respectively.  This 
indicator  have  been  calculated  by  Estevao  and  Tsounta 
(2011) for US states, by the ECB (2012) for the euro area 
as a whole and by European Commission (2013b, Chapter) 
and Arpaia et al. (2014) for all EU Member States. The use 
of dispersion indicators to measure mismatch in the labour 
market dates back to Lipsey (1960). 

(129) This measure takes on values between 0 and 2. It equals 0 
if the employment rates of all skill groups are equal to each 
other and hence to the aggregate employment rate. It equals 
2 if the employment rate is extremely low (0%) in two out 
of  three  skill  groups,  and  extremely  high  (100%)  in  the 
third group, and if the size of the former two is sufficiently 
high.  For  a  detailed  description  of  the  indicator,  see  Kiss 
and Vandeplas (2015). 

Part II 

Analytical chapter 

are 

economic  downturns.  As  a  result,  macroeconomic 
skills  mismatches  typically  increase  during  an 
economic  downturn  and  decline  again  during  the 
recovery. Nevertheless, changes in the index could 
to  structural  factors,  such  as 
also  be  due 
technological, 
demographic 
or 
occupational 
changes  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  highly-qualified  ones).  Some  skills 
mismatches 
dynamic, 
continuously  changing  economies,  as  there  are 
always  some  unfilled  positions,  even  if  some 
people  remain  unemployed.  Moreover,  there  can 
be individuals who are in a job that does not fully 
match their skills profile. Even  when an economy 
is “in equilibrium”, less skilled  workers are likely 
to experience higher unemployment rates than the 
highly  skilled  (Layard  et  al.,  2005).  Nevertheless, 
skills 
high 
mismatches  are  costly  for  firms,  workers,  the 
society  and  the  economy  as  a  whole  (see  for 
example  Velciu,  2017;  LaRochelle-Côté  and 
Hango, 2016; European Commission, 2015). (130)  

persistent  macroeconomic 

inevitable 

and 

in 

a 

is 

there 

although 

recovery, 

Over the last decade, the macro-economic skills 
mismatch  has 
followed  a  countercyclical 
pattern on average in the EU. It increased during 
the crisis and recession and declined again during 
the 
large 
heterogeneity among Member States. Graph  II.1.1 
presents  the  evolution  of  macro-economic  skills 
mismatches  across  EU  countries.  In  2018,  the 
highest  rates  were  recorded  in  Belgium,  Croatia, 
Bulgaria and Italy, with differences in employment 
rates  among  skill  groups  of  above  20%.  These 
substantial 
countries 
employment  gaps  (between  low-  and  highly-
qualified  workers,  and/or  between  medium-  and 
highly-qualified  workers)  with  substantial  shares 
of  low-  and/or  medium-qualified  workers  in  their 
population.  The 
lowest  macroeconomic  skills 
mismatches were observed in the United Kingdom 
(10%)  followed  by  Portugal  (12%).  In 
the 
recovery, 21 Member States have seen a reduction 
of 
largest  declines  were 
observed in  Lithuania, Cyprus, Hungary, Bulgaria 

the  mismatch.  The 

generally 

combine 

(130) Among other things, economic losses can take the form of 
employment,  productivity  or  income  losses,  while  social 
losses  can  be  reflected  in  inequality  of  opportunities, 
poverty, low quality of life or social exclusion. 

99 

 
                                                           
                                                           
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

and  Estonia.  In  these  countries,  employment  rates 
of medium- and high-qualified workers have been 
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 
the 
opposite 
employment  rate  of  low-qualified  workers,  who, 
however, made up a shrinking part of the working 
age  population.  On 
the  other  hand,  skills 
mismatches have increased in the recovery in some 
of  the  countries  particularly  hit  by  the  crisis 
(namely  Greece  and  Latvia),  but  also  in  Croatia 
and Luxembourg. 

applies 

to 

Graph II.1.1:  Relative dispersion of employment rates by 

education level (macroeconomic skills 
mismatch), 2010, 2015 and 2018 

Source: Own calculations based on Eurostat. Annual 
average based on the average of four quarters. 

1.3.2.  Labour shortages 

reflect 

shortages 

Labour 
a  market 
disequilibrium  where  the  demand  for  labour 
exceeds  the  available  supply,  at  the  particular 
wages  and  working  conditions.  According  to 
Quintini  (2011),  labour  shortages  arise  “when 
employers  are  unable  to  recruit  staff  with  the 
required skills in the accessible labour market and 
at the ongoing rate  of pay”.  Labour shortages can 
be  specific  to  certain  geographical  locations,  to 
occupations  with  special  skills  or  functional 
requirements  and  can  have  a  seasonal  pattern  in 
certain industries (such as services or agriculture). 
to  distinguish  quantitative  and 
It 
qualitative 
In  case  of  a 
quantitative  (or  aggregate)  labour  shortage,  the 
total  supply  of  labour  in  an  economy  (i.e.  for  all 
sectors and occupations) is below the total demand 
for  labour.  Qualitative  shortages  occur  if  labour 
demand exceeds labour supply in a specific sector, 
occupation  or  at  a  specific  skill  level  (Reymen  et 
al., 2015). 

labour  shortages. 

is  useful 

100 

Like  macroeconomic  skills  mismatch,  labour 
shortages can have both cyclical and structural 
determinants.  Cyclical  factors  include  increasing 
or  decreasing  labour  demand  due  to  shifts  in  the 
economic  business  cycle.  Employers  typically 
encounter  more  challenges  to  recruit  the  “right” 
workers  at  times  of  economic  growth  (Green  and 
Ashton,  1992;  Desjardins  and  Rubenson,  2011). 
The employment of low-qualified workers tends to 
be more sensitive to the cycle than the employment 
of the high qualified. 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  (Layard  et  al.,  1991;  Devereux,  2002; 
Büttner et al., 2010; Vandeplas and Thum-Thysen, 
2019).  Structural  skills  shortages  stem  from 
technological 
labour  market 
relevance  of  the  education  and  training  systems, 
demographic  factors  and  migration  flows,  while 
skills  mismatches  are  a  major  determinant  of 
labour 
(Shah  and  Burke,  2003; 
Kaminska  and  Kahancova,  2010;  Fuchs  and 
Söhnlein,  2013;  Quintini,  2011;  Cappelli,  2015). 
Organisational  and  sectoral  changes  may  be  a 
further  structural  reason  behind  the  emergence  of 
labour  shortages:  job  seekers  who  previously 
worked  in  declining  sectors,  such  as  agriculture 
and  manufacturing,  may not  be  easily employable 
in  jobs  in  growing  sectors  such  as  health  and 
education,  which  require  different  types  of  skills 
(CEDEFOP, 2014). 

shortages 

changes, 

low 

Labour  shortages  can  be  measured  either 
through Beveridge curves or based on business 
surveys. The Beveridge curve plots unemployment 
against  job  vacancies.  A  large  share  of  vacancies 
coupled  with  low  unemployment  is  indicative  of 
quantitative  labour  shortages,  likely  of  a  cyclical 
nature.  On  the  other  hand,  vacancies  coexisting 
indicative  of 
with  high  unemployment  are 
qualitative labour  shortages.  Business surveys can 
establish  the  existence  of  unfilled  or  hard-to-fill 
vacancies,  and  discern  the  employers’  views  for 
the reasons underlying the recruitment difficulties.  

Three  surveys  collect  data  from  employers  on 
their difficulties to fill vacancies. Their sampling 
methodologies, the questions asked and the results 
are  different.  First,  Eurofound's  European 
Company  Survey  (ECS),  implemented  every  four 
years,  asks  employers  whether  they  “encounter 

0.00.10.20.3UKNLPTATEEDKDECYLVCZFISIEU28MTSEESHUPLROLTLUELFRIESKITBGHRBE201820152010 
 
Part II 

Analytical chapter 

employers said that applicants lack either the hard 
skills or soft skills they needed to fulfil their roles. 
Graph II.1.2 depicts the percentage of firms facing 
difficulties in filling positions by company size. As 
can  be  seen,  across  the  whole  sample,  large  firms 
have  twice  as  much  difficulty  filling  vacancies: 
67% report hiring challenges as opposed to 45% of 
small firms. 

Graph II.1.2:  Percentage of firms reporting difficulties in 

filling positions by company size (2018) 

Source: Manpower Talent Shortage Survey. 

is 

last  survey  on 

labour  shortages 

The 
the 
European  Business  and  Consumer  (EU-BCS) 
survey.  Graph  II.1.3  depicts  the  evolution  of  a 
composite  indicator  on  labour  shortages  of  the 
three  biggest  sectors  (construction,  industry  and 
services) across Member States, based on the EU-
BCS.  The  2004  EU  enlargement  and 
the 
concomitant  emigration  can    at  least  partly   
account  for  the  high  labour  shortages  in  2007  in 
Estonia,  Latvia,  Lithuania  and  Romania.  Labour 
shortages  fell  significantly  during  the  crisis  in  all 
Member  States,  but  they  have  re-emerged  in  the 
post-crisis  period  and  are  currently  at 
their 
historical  peak  in  several  countries.  Since  2013, 
almost all Member States have seen an increase in 
labour shortages in all three sectors examined, with 
the  exceptions  of  Cyprus,  Greece,  Portugal  and 
Spain, which never experienced substantial labour 
shortages,  neither  before  the  crisis  nor  in  the 
recovery.  This  could  be  attributed  to  the  fact  that 
these  are  all  high  unemployment  countries.  The 
highest  labour  shortages  are  observed  in  Sweden, 
Malta, Hungary, Poland, Finland, Estonia, Finland 
and  the  United  Kingdom,  which  are  all  low 
unemployment countries. 

difficulties  in  finding  employees  with  the  right 
skills”.  Second,  the  annual  Manpower  Group 
Talent Shortage Survey asks employers about why 
the  open  positions  in  their  organizations  remain 
difficult to fill. Finally, the European Business and 
Consumer  Surveys  (EU-BCS)  collects  quarterly 
data  on  employers  in  manufacturing,  services  and 
construction  reporting  whether  labour  shortages 
are  a  major  factor  limiting  their  production.  Both 
the ECS and the EU-BCS have the same coverage, 
i.e.  all  EU  Member  States  and  the  five  candidate 
countries.  The  Manpower  Group  Talent  Shortage 
Survey has a broader, global coverage including 41 
countries out of which 20 are Member States. 

Both 
the  Beveridge  curve  approach  and 
business  surveys  can  reflect  cyclical  conditions 
and  structural  factors.  The  Beveridge  curve 
analysis  cannot  help  to  distinguish  the  cyclical 
from 
labour 
the  structural  determinants  of 
shortages. Similarly, when firms report recruitment 
difficulties,  this  can  be  due  to  a  genuine  lack  of 
labour  supply  to  meet  labour  demand,  or  this  can 
also  be  due  to  the  unwillingness  of  firms  to 
increase  wages  or  to  improve  their  working 
conditions  (CEDEFOP,  2015)  as  well  as  other 
factors such as geographical location.  

to 

the  macroeconomic 

skills 
Contrary 
mismatch,  labour  shortages  in  the  EU  moved 
counter-cyclically, i.e. they decreased during the 
crisis and increased again in the recovery, with 
many  Member  States  experiencing  today  high 
labour shortages. The Manpower Talent survey is 
an  annual,  global  survey  on  skills  shortages, 
although  it  does  not  cover  all  EU  Member  States 
(131).  In  2018,  45%  of  all  the  employers  surveyed 
stated  that  they  found  it  difficult  to  fill  positions, 
the  highest  rate  in  the  last  decade.  Nevertheless, 
this  high  percentage  could  be  attributed  to  other 
factors and not “genuine” skills shortages, such as 
preferences  (e.g.  individuals  not  willing  to  accept 
jobs  or  high  labour  turnover  induced  due  to  poor 
wage  and  working  conditions)  and  barriers  to 
geographical mobility. Thus, to identify “genuine” 
skill  shortages  the  Manpower  Talent  survey  asks 
for  the  reasons  why  employers  are  encountering 
these  difficulties.  Almost  one  third  of  employers 
said that the main reason why they cannot fill jobs 
is  a  lack  of  applicants  while  20%  said  that 
candidates  lack  the  necessary  experience;  27%  of 

(131) The latest available data are from 2013. 

101 

0%20%40%60%80%Large(250+)Medium(50-250)Small(10-49)Micro(<10) 
                                                           
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph II.1.3:  Labour shortage index, 2008, 2013 and 2018, 

Composite indicator 

(1) Annual data obtained from raw quarterly seasonally 
adjusted data. Data are missing for CY, DK, ES and HR for 
2007 and for IE for all years. 
Source: EU-BCS. 

1.3.3.  On-the-job mismatch 

The  concept  of  on-the-job  mismatch  reflects  a 
complementary  dimension  of  skills  mismatches 
that has received ample attention in the  micro-
economics literature. It captures the discrepancies 
between  the  skills/qualifications  of  individuals 
who  have  a  job  and  the  skills/qualifications 
required for their job. As opposed to the other two 
concepts  of  skills  mismatch,  this  dimension  does 
not take into account the unemployed and unfilled 
vacancies.  Ideally,  on-the-job  mismatch  should 
look  at  mismatches  between  the  skills  a  worker 
possesses and those that are needed for his/her job. 
However,  as 
to 
operationalize  and  measure,  all  indicators  so  far 
have focused on education, either in terms of years 
of  education/qualifications  (vertical  on-the-job 
mismatch)  or  in  terms  of  field  of  education 
(horizontal on-the-job mismatch). 

skills  are  very  difficult 

Skills  and  competencies  are  not  measured  by 
the regular statistical surveys of most countries 
and  this  is  why  qualifications  usually  serve  as 
proxy  for  skills,  although  the  two  terms  differ 
and  cannot  be  used  interchangeably.  Education 
is  often  used  as  a  proxy  for  skills;  yet,  the  two 
terms  differ.  Skills  are  defined  as  a  “bundle  of 
knowledge,  attributes  and  capacities  that  can  be 
learned  and 
to 
that  enable  an 
successfully  and  consistently  perform  an  activity 
or  task,  whether  broadly  or  narrowly  conceived, 
and  can  be  built  upon  and  extended  through 
learning”  (OECD,  2012).  While  this  definition  is 
not  undisputed, everyone agrees that  skills can be 
acquired  through  formal  and  informal  education, 
job  experience/practice.  Hence, 
training  and 

individual 

102 

that 

their 

formal education is just one of the components of 
skills acquisition, and its relevance might decrease 
as  workers  age,  leaving  space  to  training  (formal 
and informal) and work experience. For instance, a 
person qualified as a university professor working 
as  a  receptionist  is  clearly  overeducated,  but  may 
lack the communication skills that are necessary in 
this  job  and  may  therefore  also  be  under-skilled. 
Quintini  (2011)  shows  that  only  about  36%  of 
overqualified  workers  report  being  over-skilled. 
Furthermore,  only  about  12%  of  underqualified 
workers  report  being  under-skilled.  Hence,  a 
majority  of  over-  and  under-qualified  workers 
report 
jobs' 
requirements well. Based on an analysis of PIAAC 
data,  Flisi  et  al.  (2014)  even  find  a  negative 
skills 
correlation  between  qualification  and 
mismatch,  i.e.  the  higher  the  qualification  the 
lower  the  skills  mismatch.  More  precisely,  their 
findings  suggest  that  the  share  of  people  who  are 
simultaneously  mismatched  (both  overeducated 
and  over-skilled)  is  low  (15%  of  those  employed 
for  the  17  countries  covered  by  the  first  round  of 
the PIAAC survey). On the contrary, 30% of those 
employed  reported  being  overeducated  (but  not 
over-skilled),  while  roughly  17%  are  found  to  be 
over-skilled 
overeducated).  This 
demonstrates  that  the  two  indicators  cannot  be 
used interchangeably.  

skills  match 

their 

(but 

not 

1.3.3.1.  Vertical  skills  mismatch:  under-  and 

over-qualification  

Over-qualification  is  an  indicator  of  vertical 
skills mismatch, which measures the number of 
medium-  and  high-qualified  workers  holding  a 
job for which they are overqualified, as a share 
of  total  employment.  ILO  (2015)  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.  (132)(133)  As  a 
result,  a  high-qualified  worker  will  be  considered 

(132) ISCO  1-digit  categories  are  specified  as  follows:  1  - 
Managers; 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. 

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

01020304050HUMTPLSEEEFIUKNLBGSICZDEHRSKATFRLTBEDKLVROESPTITELCY201820082013 
 
                                                           
as overqualified if he/she has a job at ISCO levels 
4-9.  A  medium-qualified  worker  will  be 
considered  as  overqualified  if  he/she  has  a  job  at 
ISCO level 9, and as underqualified if he/she has a 
job at ISCO level 1-3. A low-qualified worker will 
be considered as underqualified if he/she has a job 
at  ISCO  levels  1-8.  As  a  natural  consequence, 
over-qualification  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 
total 
employment.  

in 

Graph II.1.4:  Over-qualification rate (vertical skills 

mismatch) in 2018 and 2008 across EU 
Member States 

(1) Over-qualification is defined as the % of people aged 20-
64 with tertiary education and working in ISCO 4-9. 
Source: EU-LFS. 

that  do  not 

While  the  incidence  of  over-qualification  has 
remained  roughly  constant  for  the  EU-28  as  a 
whole  in  the  last  decade,  there  is  a  large 
heterogeneity  across  Member  States.  Graph 
II.1.4  depicts  the  percentage  of  over-qualification 
across EU Member States in 2018 and 2008. This 
indicator  shows  how  many  high-skilled  persons 
(i.e.  persons  who  have  completed 
tertiary 
education level based on the ISCED classification) 
are  employed  in  occupations  (based  on  the  ISCO 
classification) 
tertiary 
education. In 2018, fourteen Member States had a 
share  of  over-qualification  above  20%.  Over-
qualification  was  the  highest  in  Spain  (37.3%), 
Cyprus  (33.3%)  and  Greece  (31.6%)  and  the 
lowest  in  Luxembourg  (5.3%),  Portugal  (13.7%) 
and Croatia (14.3%) – even if tertiary attainment in 
Luxembourg  is  relatively  high  (higher  than  in 
Spain).  The 
time  are 
recorded  in  Slovakia,  Malta  and  Greece,  where 
over-qualification  increased  by  more  than  10  pps 
between 2008 and 2018. In Denmark and Ireland, 
over-qualification  has  remained  relatively  stable 
during  the  last  decade,  while  only  in  Estonia, 

increases  over 

require 

largest 

Part II 

Analytical chapter 

Germany  and  Belgium  it  decreased  by  7,  2.3  and 
2.2  pps,  respectively.  Some  have  argued  that  the 
expansion of higher education necessarily leads to 
higher  rates  of  over  qualification.  Interestingly, 
however,  the  positive  correlation  between  over 
qualification  and  tertiary  attainment  is  driven  by 
those  countries  with  very  low  tertiary  attainment 
and  hardly  any  over  qualification  as  a  result 
(Vandeplas and Thum-Thysen, p. 16). Among the 
countries with high tertiary attainment, there is no 
longer  a  significant  correlation  between  tertiary 
attainment  and  over  qualification.  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 
in  over 
jobs, 
qualification.  Different  factors  may  account  for 
this,  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, 
policies 
growth, 
supporting  labour  mobility  and  innovation.  Graph 
II.1.5  depicts  over-qualification  by  sector  in  the 
EU-28  in  2018.  In  the  EU-28  overall,  the  highest 
in  Transport 
over-qualification  was  observed 
(51%)  and  in  Wholesale  (46%),  while  the  lowest 
rate  of  over-qualification  was  observed 
in 
Education (7%) and in Health (13%). 

reallocation 

resulting 

sectoral 

and 

Graph II.1.5:  Over-qualification rate (vertical skills 
mismatch) in 2018 in EU28 by sector 

Source: own calculations based on EU-LFS. 

Although this  indicator has several drawbacks, 
there  are  currently  no  better  alternative 
indicators  to  allow  cross-country  comparisons 
on vertical skills mismatch. A major drawback of 
this indicator is that the classification dates back to 
the latest ISCO update in 2008 and it is not being 

103 

010203040ESCYELIEATUKSKBGLTFRLVBEEEITPLMTDEFIROSENLSIDKHUCZHRPT20082018ManufacturingConstructionWholesaleTransportPublic AdministrationEducationHealth 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

the 

the 

have 

even 

occupational 

young  workers 

skills 
updated 
though 
then. 
requirements  may  have  evolved  since 
Furthermore,  within 
same  occupation, 
educational  attainments  of  workers  might  vary  in 
time (and by country) due to technological change 
and  educational  reforms  (e.g.  reforms  favouring 
same 
tertiary  education).  Also  within 
higher 
occupation, 
educational  attainment  levels  than  older  cohorts. 
This  implies  that  over-qualification  will  appear 
higher  in  occupations  and  sectors  in  which  there 
are  more  young  workers/rising sectors. While this 
indicator  of  over-  (under-)  qualification  has 
drawbacks,  there  is  currently  no  better  indicator 
available for cross-country comparisons. Indicators 
based  on  subjective  estimations  of  workers’ 
qualifications  suffer  from  measurement  error 
(workers  have  different 
the 
appropriate levels of education) and from response 
bias 
the 
educational  requirements  of  their  job  in  order  to 
gain 
job 
simply  equate 
requirements to their own level of education).  

interpretation  of 

(workers  might 

to  overstate 

status  or 

social 

tend 

1.3.3.2.  Horizontal  skills  mismatch:  on-the-job 
mismatch by field of education 

to 

vertical 

complement 

skills  mismatches  capture 

In  addition  to  vertical  skills  mismatches, 
the 
horizontal 
discrepancy  between the field of education and 
the  occupation.  Although  most  indicators  of  on-
the-job  mismatch  have  focused  on  the  vertical 
dimension,  Garcia-Espejo  and  Ibáñez  (2006) 
consider  horizontal  skills  mismatches  as  an 
important 
skills 
mismatches,  as  over-qualification  and  under-
qualification  do  not  take  into  account  the  field  of 
education and in  general the  heterogeneity among 
individuals who have the same qualification level. 
Skills  mismatches  by  field  of  education  may  be 
relevant  since  “non-matched”  persons  (i)  might 
experience  frustration  because  of  the  lack  of  a 
direct  return  to  the  effort  dedicated  to  study  and 
(ii)  may  generate  economic  losses  for  businesses 
because  of  lower  efficiency  and/or  the  additional 
costs of acquiring job specific skills. The indicator 
of horizontal skills mismatches presented below is 
also  based  on 
the  EU-LFS  and  aims  at 
understanding  how  many  employed  persons  are 
working  in  occupations  (based  on  the  ISCO 
classification) that do not correspond to the field of 
education they have attended (based on the ISCED 
classification).  After  having  matched  fields  of 

104 

education  (based  on  the  ISCED  classification)  to 
occupations  at  ISCO  2008  3-digit  level,  persons 
working  outside  their  field  of  education  are 
considered  as  individuals  with  horizontal  skills 
mismatch.  (134)  This  indicator,  however,  focuses 
only on employed persons aged 25 to 34 years who 
have attained tertiary education (ISCED level 5 to 
8) and currently covers only the  years 2014-2018. 
It cannot be calculated for all the employed people 
because 
the  field  of 
information  about 
education  is  only  collected  if  the  person  has 
successfully  completed  his/her  highest  level  of 
education within the last 15 years. 

the 

Although  horizontal  skills  mismatches  have 
remained  the  same  (30%)  in  the  EU-28  as  a 
whole  during  the  recovery,  there  is  a  large 
heterogeneity  among  Member  States.  The 
highest  rates  in  2018  were  observed  in  Poland, 
Slovenia and Slovakia, while the lowest rates were 
observed  in  Luxembourg,  Belgium  and  Malta 
(Graph II.1.6). Nevertheless, it is worth noting that 
both  Poland  and  Slovenia  have  educational 
systems  favouring  the  acquisition  of  general, 
transferable  skills  and  not  sector-specific  skills. 
Some  countries  have  experienced  large  increases 
(Latvia,  Ireland)  while  others  have  experienced 
large declines (France, Romania, Croatia). 

Graph II.1.6:  Horizontal skills mismatch across EU Member 

States, 2014-2018 

Fields of education are defined according to the ISCED1999 
classification for the years 2014 and 2015 and the ISCED 
2013 as from 2016 onwards. Occupations are based on the 
ISCO08 classification. In order to match fields of study to 
occupation the methodology from the article Job 
Mismatches and their Labour Market Effects among School-
leavers in Europe by Maarten Wolbers (European 
Sociological Review, Vol.19 No. 3, 249-266) has been used. 
Data for the UK and LU are not included due to low 
reliability. 
Source: EU-LFS. 

(134) The  indicator  is  defined  as  1-  (persons  employed  with 

matching field of education) / (persons employed). 

010203040BEMTHRFIFRHUDKDECYCZSEPTROEEBGLVNLLTESIEELITATSKSIPL20182014 
 
                                                           
Part II 

Analytical chapter 

1.4. 

JOB  POLARISATION:  MEASURES  AND 
RECENT TRENDS 

a clear pattern of a pervasive polarisation. The key 
differences stem from three factors.  

First,  some  studies  use  for  the  definition  of  jobs 
only  the  two-digit  occupational  codes  of  ISCO, 
while  others  an  occupation-industry  matrix 
approach.  Second,  a  key  component  of  the  jobs-
based approach is that the jobs are ranked by their 
median hourly wages (for constructing job quality 
tiers  later).  Some  studies  rank  jobs  based  on  the 
median  hourly  wage  of  a  specific  country.  For 
example,  Goos  et  al.  (2009)  used  the  median 
hourly  wages of the United Kingdom to rank jobs 
in all 15 European countries. Others, however, use 
country-specific  wage  levels  to  rank  jobs  by  their 
quality.  Third,  while  some  studies  classify  the 
ranked  jobs  in  three  categories,  which  may  have 
very  uneven  sizes 
terms  of  number  of 
occupations  and  in  terms  of  employment  shares, 
other studies group the jobs in equally sized groups 
ranked by their median hourly wages.  

in 

Graph II.1.7:  The proportion of middle wage workers is 

shrinking 

is 

the  well-known 

Job  polarisation  refers  to  the  relative  job 
growth in the lower and upper tail of the wage 
distribution  relative  to  the  middle-wage  ones. 
The  most  prominent  theory  used  to  explain  job 
routinisation 
polarisation 
hypothesis,  called  Routine  Biased  Technical 
Change. It argues that recent technological change 
is biased towards replacing labour in routine tasks 
(tasks  that  are  easy  to  codify  and  automate).  It 
claims  that  routine  tasks  are  more  frequent  in  the 
middle  of  the  skills  continuum,  while  non-routine 
ones  are  in  the  top  and  bottom,  hence  the 
polarising effect of technological change. (135) 

Studies define the low-, middle- and high-skilled 
groups  based  mainly  on 
two  different 
approaches:  the  jobs-based  and  the  task-based 
approach.  In  the  jobs-based  approach,  studies 
rank occupations based on the wage level by ISCO 
major (1 digit) or minor (two or three digit) groups 
and  then  assign  occupations  to  each  of  the  three 
aforementioned  groups.  Alternatively,  other 
authors  use  a  “task  content”  approach  to  analyse 
this  phenomenon,  which 
a  better 
understanding of occupational changes.  

allows 

1.4.1.  The jobs-based approach 

The jobs-based approach has been used to assess 
the  extent  to  which  employment  structures  are 
polarising. Evidence of polarisation has been found 
for  a  number  of  countries. (136)  OECD  (2018b) 
observed  that  during  the  1995-2015  period,  job 
polarisation  was a pervasive phenomenon affecting 
all countries with the exception of Hungary and the 
Czech Republic. 

Differences in methodology and/or data sources 
lead  to  different  and  sometimes  contradictory 
results.  For  example,  Oesch  et  al  Menes  (2011), 
Fernández-Macías  (2012)  and  Eurofound  (2017) 
find different results and conclude that there is not 

(135) For  a  discussion  of  how  technological  transformations  are 
labour  market  see  European 

the  European 

shaping 
Commission (2018a). 

(136) These  include  the  United  States  (Acemoglu  and  Autor, 
2011; Autor and Dorn, 2013; Autor, 2014), the UK (Goos 
and  Manning,  2007;  Salvatori,  2018),  Germany  (Spitz-
Oener,  2006;  Dustmann  et  al.,  2009;  Kampelmann  and 
Rycx,  2011),  Sweden  (Adermon  and  Gustavsson,  2015) 
and France (Harrigan, Reshef and Touba, 2016). 

High, middle and low-paying jobs in the EU - change from 
2002 to 2018 in pps. 
Source: Own calculations based on LFS. 

105 

-25-20-15-10-50510152025PLBGLVEESKDKLTCZSIFIDEHUNLEU27ROSECYBEHRITESATELPTUKFRIELUppsHighest PayingMiddle PayingLowest paying 
                                                           
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

The  proportion  of  workers  in  middle-paid 
occupations  is  shrinking  in  all  EU  Member 
States,  albeit  to  different  degrees.  To  establish 
the  relevance  of  job  polarisation  in  European 
economies  based  on  the  jobs-based  approach,  this 
report follows the methodology suggested by Goos 
et al. (2014) and recently applied by the European 
Commission  (2018a).  Using  EU-LFS  micro-data, 
occupations  are  grouped  according  to  their  mean 
wage 
high-paying 
occupations. (137)  On  average,  over  the  period 
2002-2018, the share of middle-paid jobs declined 
in  the  EU-27  (138)  by  about  13  pps,  while  the 
shares  of 
low-  and  high-paying  employment 
increased  by  5  and  7  pps,  respectively  (Graph 
II.1.7). However, this masks significant differences 
across  Member  States.  While  there  is  a  clear 
hollowing-out of middle-paid jobs across Member 
States, the pace of this process differs by country.  

low,  middle, 

and 

in 

in  most  countries, 

After  the  crisis,  the  intensity  of  the  fall  of 
middle-paid  jobs  decreased.  There  are  not  only 
differences  across  countries,  but  also  over  time. 
Breaking  down  the  period  2002-2018  into  two 
(2002-2010  and  2011-2018)  shows  that  while  the 
share  of  middle-paying  jobs  declined  in  both 
periods 
this  decline  has 
moderated  significantly  after  the  crisis.  More 
interestingly,  since  2011  only  Luxembourg,  the 
Netherlands,  Italy,  Bulgaria,  Finland  and  the 
Czech Republic show polarisation patterns (Graph 
II.1.9).  In  addition,  some  countries  (Slovenia, 
Hungary, Greece, Latvia and Romania) have even 
experienced 
in  middle-paying 
increase 
occupations  (“de-polarisation”).  These  countries 
might  be  benefiting  from  offshoring  within  the 
Single  Market.  Thus,  less  polarisation  in  these 

an 

(137) The  three  categories  are  defined  in  the  following  way. 
High-paying  occupations:  Corporate  managers;  Physical, 
mathematical,  and  engineering  professionals;  Life  science 
and health professionals; Other professionals; Managers of 
small  enterprises;  Physical,  mathematical  and  engineering 
associate  professionals;  Other  associate  professionals,  life 
science and health associate professionals.  Middle-paying 
occupations:  Stationary  plant and  related,  stationary  plant 
and  related  operators;  Metal,  machinery  and  related  trade 
work;  Drivers  and  mobile  plant  operators;  Office  clerks; 
Precision,  handicraft,  craft  printing  and  related  trade 
workers; Extraction and building trades workers; Customer 
service  clerks;  Machine  operators  and  assemblers;  Other 
craft and related trade workers.  Low-paying occupations: 
Labourers  in  mining,  construction,  manufacturing  and 
transport; Personal and protective service workers; Models, 
sales  persons  and  demonstrators;  Sales  and  service 
elementary occupations. 

(138) Malta is not included due to data limitations. 

106 

job 

creation 

countries  means 
routine 
occupations.  Conversely,  more  polarisation  in 
high-income  countries  might  be  the  consequence 
of middle-paying jobs displaced to certain middle-
income countries. 

in 

Graph II.1.8:  Polarisation peaked at the height of the crisis 

High, middle and low-paying jobs in the EU - change from 
2002 to 2010 in pps. 
Source: Own calculations based on LFS. 

Polarisation may be the result of within and/or 
shift  of 
sectors  dynamics.  The 
between 
employment  away  from  middle-skill  jobs  can 
occur  in  two  ways.  On  the  one  hand,  factors 
underlying  job  polarisation  may  contribute  to  a 
shift of employment within an industry, as middle-
skill jobs decline and the shares of high- and low-
skill jobs increase. Technological advances tend to 
be  the  primary  drivers  of  this  aspect  of  job 
polarisation, as middle-skill jobs becomes obsolete 
(OECD,  2017).  On  the  other  hand,  employment 
may shift between industries when some industries 
experience  increases  in  demand  for  their  products 
and  respond  by  hiring  workers,  while  other 
industries  contract  in  the  face  of  weakening 
demand  for  their  products.  Worker  reallocation 
contributes  to  job  polarisation  if  the  contracting 

-20-15-10-505101520EEBGPLNLHRSKCZDESEFRCYDKITLVUKLTEU27PTBEHUESFIIEATELROSILUppsHighest PayingMiddle PayingLowest paying 
                                                           
 
industries  have  a  larger  share  of  middle-skill  jobs 
and the expanding industries have a larger share of 
low- or high-skill jobs.  

Graph II.1.9:  After the crisis, intensity in polarisation 

decreased 

High, middle and low-paying jobs in the EU - change from 
2011 to 2018 in pps. 
Source: Own calculations based on LFS. 

Polarisation  mostly  occurs  within  sectors.  To 
understand the relative importance of between- and 
within-industry  effects,  one  can  apply  a  formal 
decomposition of the change in overall polarisation 
over the period analysed into between- and within-
industry  components  (Goos  et  al.,  2014). (139) 
Across  all  countries  considered,  the  share  of  top 
and  bottom  occupations  in  total  employment 
increased  on  average  by  about  13  pps  between 

(139) Overall  polarisation  is  the  sum  of  high-  and  low-paid 
workers over total employment. Within-sector polarisation 
is  the  increase  in  the  share  of  high-  and  low-paying  jobs 
within  an  industry;  between-sector  polarisation  is  the 
reallocation of employment towards more highly polarised 
industries.  Within-industry  polarisation  is  the  change  in 
polarisation by industry over the time period, multiplied by 
industry. 
the  average  share  of  employment  of 
Between-industry polarisation is change in the employment 
share of an industry over the time period, multiplied by the 
average polarisation of that industry. 

that 

Part II 

Analytical chapter 

industries,  while 

2000  and  2018  -  Table  II.1.1. The  last  row  shows 
that  79%  of  this  increase  is  explained  by  changes 
in  polarisation  within 
the 
remaining 21% is accounted for by changes in the 
relative  size  of  different  industries.  The  positive 
between-industry  component  is  the  result  of  the 
fact  that  overall  employment  has  shifted  towards 
industries  with  higher  polarisation,  in  particular 
from  agriculture  to  services,  reflecting  structural 
changes  in  the  labour  market.  On  top  of  that, 
within  most  sectors,  polarisation  has  increased. 
Because  of  these  two  forces,  the  real  estate  and 
business  services  sector  emerges  as  the  industry 
making 
to  aggregate 
largest  contribution 
polarisation (28% of the overall increase). 

the 

Table II.1.1: 

Industry contributions to with- and between-
industry polarisation, 2000 to 2018 

Average industry contributions expressed in percentage 
points. Total polarisation is the sum of both within and 
between components 
Source: Own calculations based on EU LFS. 

that 

II.1.10  shows 

In  all  EU  countries,  the  increased  polarisation 
within sectors has played a major role in overall 
polarisation.  Graph 
the 
prevalence  of  the  within-industry  component  is  a 
pattern observed in all EU countries. This suggests 
that  technological  advances  might  be  the  primary 
drivers  of  job  polarisation  in  EU  Member  States. 
However,  in  some  countries  like  Spain,  Portugal 
and Greece, the decline of specific sectors has also 
played  an  important  role,  suggesting  that  the 
polarisation observed in these countries is also the 
result  of  a  process  of  structural  change 
in 
employment. 

107 

-20-15-10-505101520ROLVELHUSICYDELTIEUKSKESCZFIEU27BGDKPLATHRBEITLUNLSEFREEPTppsHighest PayingMiddle PayingLowest payingSectorWithinBetweenAgriculture, Fishing, mining0.29-0.51Manufacturing1.85-2.36Electricity, gas water0.250.17Construction0.50-0.17Wholesale and retail0.85-0.56Hotels and restaurants0.170.76Transport, storage and communications2.070.95Financial services1.16-0.05Real estate and business activities0.992.79Public administration and defense0.91-0.16Education0.250.60Health and social work0.541.37Other service activities0.50-0.10Total10.322.74 
 
                                                           
 
 
 
High-skill  tasks  are  on  the  rise  while  routine 
tasks  are  falling.  This  report  follows  closely 
Acemoglu and Autor (2011) methodology to show 
the  overall  evolution  of  task  content  of  jobs  in 
Member  States  (Box  II.1.1).  Graph  II.1.11  shows 
that  the  EU  as  a  whole  has  experienced  a 
significant  growth  of  non-routine  cognitive  tasks 
(high-skilled  tasks)  and  a  steep  decline  of  routine 
tasks  (middle-skilled  tasks),  while  non-routine 
manual  tasks  (low-skilled  tasks)  remain  constant. 
Thus,  if  the  assumed  correspondence  between 
routine  and  skill  content  of  jobs  is  correct,  in  the 
EU  overall,  a  process  of  upskilling  seems  to  be 
ongoing 
polarisation 
phenomenon.  

rather 

than 

true 

a 

have 

countries 

experienced 

true 
Few 
polarisation.  While  almost  all  countries  have 
experienced  a  steep  decline  in  routine  tasks,  few 
countries have experienced a simultaneous growth 
in  high-  and  low-skilled  tasks  (true  polarisation). 
Only 
the  UK,  Denmark,  Belgium,  Estonia, 
Finland, Ireland and to a lesser extent Italy seem to 
be  experiencing  a  true  polarisation  process  based 
on the evolution of the task content of jobs. On the 
other  hand,  several  countries  have  experienced  an 
increase  in  high-skilled  tasks  relative  to  middle- 
and low-skilled tasks (upskilling).  

European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph II.1.10: In all countries polarisation has largely 

reflected within-sector dynamics 

Percentage-point change in polarisation between 2000 and 
2018. 
Source: Own calculations based on LFS. 

1.4.2.  The task-based approach 

disregarding 

routine-intensive, 
of 

The task-based approach is a richer alternative 
that  has  sparked  a  growing  literature.  One 
caveat of the jobs-based approach is its assumption 
that  broad  groups  of  “middle-skilled”  occupations 
the 
are 
heterogeneity 
intensity  within 
routine 
occupations,  potentially  overstating  the  decline  in 
routine-intensive employment. It is easy to see that 
underlying  this  alternative  model  is  a  richer 
and 
It 
concept  of 
multidimensional  one: 
single 
continuum of skills, there are different axes (such 
as  routine  or  cognitive  task  content)  affecting  the 
impact  of  technology  on  labour  demand.  This 
alternative  approach  has  sparked  a  growing 
literature, which has not only applied it to different 
periods and countries, but which has also discussed 
other  types  of  task  content  beyond  routine  and 
cognitive  (the  ones  proposed 
the  earlier 
literature; see Autor, Levy and Murnane, 2003). 

instead  of  a 

is  non-linear 

skills. 

in 

108 

0%5%10%15%20%BGPLLVSKEECZLTHUDKNLFIDEBESIROITHRSECYESATELPTLUFRIEUKWithinBetween 
 
 
 
Part II 

Analytical chapter 

Graph II.1.11: Almost all countries have experienced a decline in routine tasks 

(1) To make the results comparable the task indices were rescaled so that the initial value was 0. 
Source: Own calculations based on EU-LFS and O*NET data. 

109 

 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

110 

Box II.1.1: The task-based approach and construction of task measuresStudies following this approach are typically based on the job skill measures created by Acemoglu and Autor (2011) and combine Occupational databases or Workers’ surveys as a source of information on the task content of occupations with country-specific labour force survey data to analyse changes in task content of jobs over time. While few studies use workers’ surveys like the OECD Program for the International Assessment of Adult Competencies (PIAAC) or the European Working Conditions Survey (EWCS), many authors use an occupational database, in particular, the Occupational Information Network dataset (ONET). Both alternatives, workers’ surveys and occupational databases, have advantages and disadvantages to infer the task content of jobs and occupations. On the one hand, using workers’ surveys enables the study of variability in task content within each occupation or job type. However, gathering information on tasks from workers introduces a potential bias in measurement, since workers’ answers may be subjectively biased or just wrong (dissatisfied workers may exaggerate the amount of routine in their jobs, or new recruits may not be able to answer). Furthermore, there can be inconsistencies in the classification of workers across occupational levels and sectors. On the other hand, the Occupational Information Network dataset (ONET) is generally quite detailed in its measure of task content. However, it is only available for the US and although the task content of occupations should (in principle) be roughly the same across similarly developed economies, certain institutional and socioeconomic factors differ across countries, which could have an impact even at the level of task content.  This report follows the standard approach of combining the Occupational Information Network (O*NET) database as a source of information on the task content of occupations with the EU Labour Force Survey (EU-LFS) to analyse changes in task content of jobs over time. Using the Acemoglu and Autor (2011) methodology, six task content measures are constructed, namely: non-routine cognitive analytical, non-routine cognitive interpersonal, routine cognitive, routine manual, non-routine manual physical and non-routine manual personal. Each of these measures was created by adding up the appropriate standardised task items (listed in Table 1). These measures can be further grouped into three broader groupings - non-routine cognitive, routine, non-routine manual – approximating the top, middle and lower ends of the labour market respectively. In the final step, we standardise the content measures over time using the survey weights for each country separately so that the task indices represent the overall intensity in the use of each task by country and year. Table 1 displays the six task indices, paired by skill level, their corresponding tasks and an example of occupation that requires high levels of the relevant index.  Skill level Task content measure Tasks Example High Skilled: non-routine cognitive tasks Non-routine cognitive analytical Analysing data/information  Thinking creatively  Interpreting information for others Mathematicians, Actuaries and Statisticians Non-routine cognitive interpersonal Establishing and maintaining personal relationships  Guiding, directing and motivating subordinates  Coaching/developing others Managing Directors and Chief Executives Medium skilled: routine tasks Routine cognitive The importance of repeating the same tasks The importance of being exact or accurate Structured vs. unstructured work Rubber, plastics and paper products machine operators Routine manual Pace determined by the speed of equipment Controlling machines and processes Spending time making repetitive motions Cashiers and ticket clerks Low skilled: non-routine manual tasks Non-routine manual physical Operating vehicles, mechanized devices, or equipment Spending time using hands to handle, control or feel objects, tools or controls Manual dexterity Spatial orientation Heavy track and bus drivers Non-routine manual personal Social Perceptiveness  Face-to-face discussions  Assisting and Caring for Others Hairdressers, beauticians, and related workers Source: Own elaboration based on Acemoglu and Autor (2011).  
 
 
 
 
 
 
 
 
 
 
Part II 

Analytical chapter 

111 

Box II.1.2: Description of the variables used in the empirical analysisPolarisation-related variables: The Job Polarisation Index (JPI). Sparreboom and Tarvid (2016) developed a polarisation index using three groups of occupations categorised by skill level. The JPI traces what happens to “medium-level” jobs and measures what can be called imbalanced polarisation. The index points to higher polarisation at a point in time if the proportion of “medium-level” jobs, relative to its average in previous years, has fallen (and vice versa). The index also registers changes in the proportion of medium-level jobs that come about in a very imbalanced manner, e.g. if there is a strong change to either low- or high-level jobs. Such an “imbalance effect” would inflate the JPI. Using the three pay segments in Chart II.1.5, it is possible to calculate the JPI since 2001. The Job Polarisation Index (JPI) is composed of two terms. The first measures the growth in the share of “low-level” and “high-level” jobs (the negative of the change in the proportion of medium-level jobs). The second accounts for the imbalance, which is the greater the more the change in the proportions of low- and high-level jobs differ from each other. The Routine Task Intensity Index (RTI). The RTI is a polarisation-related measure based on the task-content approach. It is considered the best way to capture the impact of recent technological progress. The RTI index has become the standard practice for measuring the task content of occupations (Goos, Manning and Salomons, 2014). Routine tasks consist of a repeated sequence of actions and are more easily replaceable through technological innovations. A higher RTI score thus indicates that an occupation is more “routine-task intensive” and, thus, can more readily be automated. Following Autor and Dorn (2013), the RTI is calculated for each occupation as a relative intensity of routine tasks. In particular, from these three measures the Routine Task Intensity (RTI) index is constructed as the difference between the log of routine tasks (medium skill) and the sum of the log of abstract (high skill) and the log of manual tasks (low skill).  The off-shorability index. It is also a task-based measure related to polarisation, which measures the potential for outsourcing of jobs in the economy. Technological progress, particularly in the area of information and communication, has made it easier to outsource tasks previously performed by middle-skilled workers. In particular, jobs that require little face-to-face interaction, or other on-site requirements, are more at risk of outsourcing. While routinisation and off-shorability are related, they are different concepts. Blinder and Krueger (2013) find that off-shorability is conceptually distinct from, though related to, an occupation’s routineness. The authors argue that it is likely that jobs that can be broken down into simple, routine tasks are easier to offshore than jobs requiring complex thinking, judgment, and human interaction. However, a wide variety of complex tasks that involve high levels of skill and human judgment can also be offshored via telecommunication devices. The low-, middle- and high-skill task indices. They are based on the job skill measures created by Acemoglu and Autor (2011). They use O*NET skill description data to classify each occupation according to their propensity for use of five tasks. The skill characteristics form three broad groupings - non-routine cognitive, routine, non-routine manual – approximating the top, middle and lower ends of the labour market respectively (See Box II.1.1 for more details). Other variables used in the analysis:  Non-accelerating wage rate of unemployment (NAWRU). It is the rate of unemployment consistent with constant wage inflation (structural unemployment).  GDP per capita, in log.  Share of employment in agriculture and manufacturing over total employment.  The total expenditure in Research and Development (R&D) as a % of the GDP.  Activity rate by level of education (primary-ISCED 0-2, secondary-ISCED 3-4, tertiary- ISCED 5-8).  Ageing is captured by the average age of the workforce.   The supply of skills is captured by the share of the population with at least secondary education. 
 
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

1.5.  DOES POLARISATION INCREASE (REDUCE) 

SKILLS MISMATCHES?  

1.5.1.  Macroeconomic  skills  mismatches  and 

polarisation 

II.1.2  shows 

is  weak.  Table 

between  macroeconomic 

Descriptive  evidence  suggests  that  polarisation 
and  off-shorability  are  negatively  linked  to 
macroeconomic  skills  mismatches,  but  this 
the 
relation 
correlations 
skills 
mismatch  indicator  and  the  different  polarisation-
related variables. (140) While the three polarisation-
related measures are negative correlated with skills 
mismatches,  the  link  is  weak  and  only  significant 
for  the  job  polarisation  (JPI)  and  off-shorability 
indices.  

graphical  analysis  confirms 
the  descriptive 
assessment.  First,  while  there  seems  to  be  a 
negative 
link  between  polarisation  and  skills 
mismatches,  i.e.  more  polarisation  leads  to  less 
skills mismatches, this link seems weak. Secondly, 
the  link  differs  by  group  of  countries.  While  for 
high-income  Member  States  the  link  is  more 
evident,  for  middle-income  ones,  there  seems  not 
to be a clear link. Similarly, Graph  II.1.13 depicts 
changes  in  routinisation  and  skills  mismatches 
between  2002  and  2018.  It  also  suggests  that  the 
link  between  the  two  is  different  by  group  of 
countries. 

Graph II.1.12: Skills mismatches vs JPI 

Deroutinisation seems positively linked to skills 
mismatches  in  middle-income  Member  States 
and negatively in high-income ones. Table II.1.2 
also  suggests  that  the  routinisation  index  (RTI)  is 
related  to  skills  mismatches  differently  in  high-
income  Member  States  and  middle-income  ones. 
(141)  While  for  the  latter  the  link  is  negative,  i.e. 
more  routinisation  is  associated  to  less  skills 
mismatches,  the  opposite  is  true  for  the  high-
income countries.  

Source: Own calculations based on LFS. 

Table II.1.2:  Correlations skills mismatch and polarisation 

indicators. EU 2002-2018 

Graph II.1.13: Skills mismatches vs RTI 

(1)* Significant at 10%. 
Source: Own calculations based on EU-LFS and O*net. 

The 
link  between  polarisation  and  skills 
mismatches  is  weak  and  different  by  group  of 
countries.  To  graphically  analyse  how 
job 
polarisation  and  skills  mismatches  are  linked, 
Graph  II.1.12  plots  changes  in  polarisation  and 
skills  mismatches  between  2002  and  2018.  The 

(140) See  Box  II.1.2  for  details  on  the  polarisation-related 

variables (direct and indirect) 

(141) Middle-income  countries  =  Member  States  with  GDP 
below  90%  of  the  EU  average.  High-income  countries  = 
Member States with GDP above 90% of the EU average. 

Source: Own calculations based on EU-LFS and O*NET data. 

On  aggregate,  polarisation  and  deroutinisation 
are  associated  with  an 
in  skills 
mismatches.  To  more  formally  test  the  link 
between  polarisation  and  skills  mismatches  this 
section  provides  an  analysis  of  the  impact  of  the 
the 
different  polarisation-related  measures  on 

increase 

112 

Skill mismatchEUMore developed countriesLess developed countriesJPI-0.0769*0.0452-0.1099*RTI-0.03490.2269*-0.3419*Offshorability Index-0.2032*-0.0137-0.2044*Low-skill Index0.3245*-0.060.3717*Middle-skill Index0.2788*0.09390.1575*High-skill Index-0.0848*-0.04490.1820* 
 
 
 
                                                           
 
 
 
Part II 

Analytical chapter 

Table II.1.3:  Determinants of Macroeconomic skills mismatches in EU27, 2000-2018 

(1) Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. 
Source: Own calculations using LFS and O*net. 

to  have  a 

macroeconomic skills mismatches indicator (Table 
II.1.3). (142)  Among  the  three  polarisation-related 
measures, only the off-shorability and RTI indices 
significant  and  negative 
seem 
relationship  with  skills  mismatches.  Thus,  more 
deroutinisation  is  associated  with  an  increase  in 
skills mismatches. Technological change increases 
polarisation  by  reducing  routine-jobs,  and  this 
associated  with  more 
is 
deroutinisation 
macroeconomic 
skills  mismatches.  Similarly, 
technology  makes  possible  to  outsource  tasks 
previously  performed  by  middle-skilled  workers. 
As a result, skills mismatches seem to be driven by 
shifts  in  the  demand  spurred  by  technological 
changes. 

in 

skills  mismatches  due 

Most  Member  States  may  experience  an 
increase 
to 
deroutinisation.  To  better  explain  the  impact  of 
changes 
in  employment  structures  driven  by 
technology, Graph II.1.14 plots the current level of 
skills  mismatches  and  the  predicted  impact  of  a 
process  of  deroutinisation  that  would  bring  the 
routine  intensity  of  all  EU  Member  States  to  the 
level  currently  observed  in  the  UK,  the  country 
with  the  lowest  level  of  RTI.  The  graph  suggests 
that with the exception of countries such as Ireland 
and  Sweden  with  a  structure  of  employment 
similar  to  that  of  the  UK,  most  Member  States 
would  experience  a  rise  in  skills  mismatches,  as 
there  is  room  for  further  deroutinisation.  Yet, 
climate change policies might mitigate this process 
of  deroutinisation  by  adding  middle-skilled, 

middle-paying 
2019c). 

jobs 

(European  Commission, 

Graph II.1.14: Current and predicted macroeconomic skills 

mismatches if countries reach UK 
(de)routinisation levels 

Source: Own calculations using LFS and O*net. 

II.1.3). (143)  Fast-changing 

Middle-income  Member  States,  however,  are 
more  exposed  to  the  impact  of  employment 
deroutinisation on skills mismatches. The impact 
of  routinisation  (measured  by  RTI)  and  off-
shorability  on  skills  mismatches  seem  to  be 
significant  only  in  middle-income  Member  States 
(Table 
technology 
accelerates  the  demand  for  different  skills  and 
creates  mismatches  unless 
follows 
accordingly. At the same time, technology replaces 
more routine jobs and makes it easier to outsource 
middle-skilled ones. Finally, an ongoing process of 
shifting  employment  from  more  routine  sectors 
(e.g.  manufacturing)  to  less  routine  sectors  (e.g. 
services)  is  taking  place,  particularly  in  middle-
income  countries  as  high-income  countries  have 

supply 

(142) See Box II.1.3 for details on the empirical strategy. 

(143) The RTI coefficients is only significant for less developed 

member states (Last column of Table II.1.3). 

113 

00.050.10.150.20.250.3NLPTATEEDKDECYLVCZFISISEESHUPLROLTLUELFRIESKITBGHRBEObserved mismatch 2018Impact RTIVARIABLESJPIOff-shorabilityRTIJPIOff-shorabilityRTIJPIOff-shorabilityRTI-0.0017-0.2300-0.0541*-0.0017-0.0609-0.0085-0.0017-0.4084-0.0930**(0.0016)(0.0068)(0.0264)(0.0013)(0.0118)(0.0310)(0.0024)(0.0068)(0.0409)0.0038**0.0036*0.0034*0.00400.00400.0040*0.00370.00350.0032(0.0018)(0.0018)(0.0019)(0.0022)(0.0023)(0.0022)(0.0030)(0.0030)(0.0031)-0.1234***-0.1262***-0.1327***-0.0515*-0.0522*-0.0540**-0.1268***-0.1269***-0.1330***(0.0267)(0.0265)(0.0264)(0.0243)(0.0241)(0.0213)(0.0377)(0.0379)(0.0366)-0.2375*-0.2882**-0.2629**-0.1812-0.2068-0.2009-0.2241-0.2106-0.1240(0.0562)(0.0554)(0.0534)(0.0597)(0.0615)(0.0589)(0.1017)(0.0992)(0.0986)-0.0145*-0.0127*-0.0108-0.0064-0.0067-0.0061-0.0188**-0.0108-0.0087(0.0078)(0.0068)(0.0068)(0.0119)(0.0118)(0.0116)(0.0081)(0.0068)(0.0063)Observations436449449212220220224229229R-squared0.52070.54130.54740.30670.31560.31530.59180.62180.6362Countries272727131313141414Country FEYESYESYESYESYESYESYESYESYESHigh-income countriesMiddle-income countriesTotal R&D expenditure (%GDP)EUShare employment in agriculture + manufacturing over totalPolarisation measureNon-accelerating wage rate of unemploymentlngdp 
                                                           
 
                                                           
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

largely  completed 
this  process.  As  a  result, 
deroutinisation  in  middle-income  countries  might 
be  faster  and  more  intense,  potentially  increasing 
skills  mismatches  as 
labour  supply  in  these 
countries  might  find  it  more  difficult  to  meet  the 
fast changing labour demand. 

Workforce  upskilling  and  improvements  in 
economic  conditions  have  contributed 
to 
reducing  skills  mismatches  in  many  Member 
States.  Box  II.1.4  and  Table  II.1.4  show  that 
changes in macroeconomic skills mismatches were 
largely  driven  by  changes  in  general  economic 
conditions  and  improvements  in  the  supply  of 
skills  (upskilling).  General  economic  conditions 
are  captured  by  unemployment,  the  level  of  GDP 
per  capita  and  by  the  activity  rate  (by  level  of 
education). The supply of skills is captured by the 
share  of  the  population  with  at  least  secondary 
education.  In  particular,  upskilling  and  increasing 
participation rates seem to be important  factors to 
reduce  skills  mismatches  and  better  meet  the 
increasing demand for more complex skills.  

The rise in labour demand for high-skilled tasks 
leads to more skills mismatches. A more detailed 
look at the impact of the changing task content of 
jobs  on  skills  mismatches  shows  that,  for  the  EU, 
only  changes  in  the  labour  demand  for  high-  and 
low-skilled  tasks  have  a  significant  (and  positive) 
impact  on  skills  mismatches  (Table  II.1.4). Since 
overall trends in the task content of jobs suggest a 
significant  rise 
tasks  and  a 
in  high-skilled 
stagnation in low-skilled ones (Graph  II.1.11), the 
overall growth of skills  mismatches seems  mostly 
driven  by  changes  at  the  top  of  the  skill-content 
distribution.  

Trends  in  the  labour  demand  for  skills  play 
against the decline in skills mismatches. Graph 2 
in  Box  II.1.4  and  Table  II.1.4  suggest  that  the 
increasing  non-routine  cognitive  task  content  of 
jobs  has  been  associated  with 
increasing 
macroeconomic skills mismatches in EU countries, 
particularly  in  middle-income  ones.  Similarly,  the 
declining  routine  task  content  of  jobs  has  been 
associated  with  an  increase  in  macroeconomic 
skills mismatches in high-income Member States.  

114 

Box II.1.3: Empirical analysis: The link between macroeconomic skill mismatch and polarisationThe empirical model to study the links between macroeconomic skill mismatches and polarisation in the EU is based on a fixed-effects panel regression to control unobservable country-specific factors and estimated the model for each polarisation-related measure (see Box II.1.2), in the following formula:  𝑆𝑀𝑀𝑖𝑡=𝑢𝑖𝑡+𝛼1𝑃𝑖𝑡+𝛽𝐶𝑖𝑡+𝜀𝑖𝑡 Whereby: 𝑆𝑀𝑀𝑖𝑡 is the macroeconomic skill mismatch indicator during the period t ∈𝑇 in country 𝑖∈ C, where C is the set of countries and T={2012,…, 2018}; 𝛼1 is the estimated coefficients for the polarisation measure (P); 𝑃𝑖𝑡 is one of the polarisation measures in country 𝑖 and time t; 𝛽 is a vector of the estimated coefficients for the control variables (C); 𝐶𝑖𝑡 is a vector of controls in country 𝑖 and time t; 𝑢𝑖𝑡 is the unknown intercept for each country 𝑖.  𝜀𝑖𝑡 is the error term for country 𝑖 and time t. The basic set of controls included in the analysis consit of: the level of unemployment, the log of gdp per capita, the share of employment in manufacturing or agricultural sectors and the R&D expenditure as a proxy for technology adoption. In addition to this basic set of controls, in some specifications other controls are included: the average age of the workforce, the share of the population with at least secondary education and the activity rates by level of education (see Box II.1.2 for more details on the variable used in the analysis).  
 
 
 
 
 
Part II 

Analytical chapter 

Table II.1.4:  Determinants of Macroeconomic skills mismatches in EU27, 2000-2018. Content of jobs 

(1) Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. 
Source: Own calculations based on LFS and O*net. 

in 

low-paying 

Due to technological change, the skills demand is 
shifting  towards  higher  skills  and  qualifications. 
Besides,  there  is  an  increasing  demand  for  highly 
educated  workers,  even 
jobs 
(European  Commission,  2018a)  and  this  demand 
might  not  be  met  fully  and  immediately  by  the 
skills supply on the labour market, as it takes time 
for  the  education  system  and  for  adult  learning  to 
adjust.  This  is  more  apparent  in  middle-income 
countries,  while  in  high-income  countries,  the 
increase  in  the  supply  of  high-skill  workers  may 
have  contributed  to  mitigating  the  effects  of 
changes at the top of the skill-content distribution.  

1.5.2.  On-the-job mismatch and polarisation 

Across  EU  Member  States,  job  polarisation 
seems  to  have  a  positive  impact  on  individual-
level  skills  mismatches.  To  complement  the 
analysis  at  the  macro  level,  this  section  studies 
how  the  risk  of  mismatch  at  the  individual  level 
depends  on  individual  and  job  characteristics 
(including  its  task  content),  but  also  on  the 
polarisation  of  the  occupational  structure  (job 
polarisation).  Results  in  Table  II.1.5  suggest  that 
the  probability  of  being  mismatched  increases  if 
polarisation  increases. (144)  This  is  mainly  driven 
by  a  positive  impact  on  the  probability  of  being 

(144) See Appendix 1.A2 for details on the empirical strategy. 

under-skilled.  If  polarisation  increases,  under-
education  may  rise  if  middle-skilled  workers 
cannot  find  appropriate  jobs  and  increasingly 
compete  for  jobs  usually  taken  by  those  with  a 
higher level of education. 

Men  are  less  likely  to  be  over-skilled  than 
women, while the relationship between gender and 
under-skilling is not significant (Table II.1.5). This 
may  reflect  gender  discrimination,  the  effect  of 
career brakes on the working lives of women or a 
preference  towards  more  family  friendly  working 
arrangements. 

to  most  Member  States  except 

Foreign-born  workers  are  more  likely  to  be 
mismatched  in  terms  of  skills  than  the  native 
workforce. Table II.1.5 shows that the probability 
of skills mismatches is higher for immigrants. This 
in 
applies 
Luxembourg  and  Portugal,  where  the  opposite  is 
true (Table II.1.A1.2 in Appendix). Looking at the 
components of mismatches reveals that immigrants 
are  more  likely  to  be  over-skilled.  Among  the 
factors  that  might  explain  this  result  are  the  non-
recognition  of  qualifications,  discrimination  or 
more  difficult  labour  market  access,  as  well  as  a 
lack of language skills. 

Temporary  and  part-time  workers  are  more 
likely  to  be  overeducated.  Occupational  choices 

115 

VARIABLESEUEUHigh-income countriesHigh-income countriesMiddle-income countriesMiddle-income countriesLow skilled: Non-routine manual-0.12980.5803**0.7987*0.8285*-0.9044**0.1521(0.3263)(0.2742)(0.4436)(0.4213)(0.4286)(0.3871)Medium skilled: routine-0.22230.1227-0.4442**-0.8316***0.9366***0.2264(0.1633)(0.1549)(0.1962)(0.2192)(0.2728)(0.2602)High skilled: Non-routine cognitive0.3890***0.3966***0.21680.14971.2492***0.5855***(0.1280)(0.1065)(0.1480)(0.1202)(0.2137)(0.1876)Non-accelerating wage rate of unemployment0.0039***0.0048***0.0047***0.0048***0.0024**0.0028***(0.0007)(0.0005)(0.0008)(0.0007)(0.0010)(0.0008)lngdp-0.1176***-0.1339***-0.0369**-0.0826***-0.1553***-0.1367***(0.0084)(0.0083)(0.0167)(0.0153)(0.0113)(0.0115)Upskilling-0.0016***-0.0028***-0.0008(0.0003)(0.0003)(0.0005)Ageing0.0081***0.0136***-0.0037(0.0018)(0.0018)(0.0036)Activity rate: low-skilled-0.0048***-0.0034***-0.0051***(0.0003)(0.0003)(0.0004)Activity rate: middle-skilled0.0034***0.0038***0.0035***(0.0004)(0.0006)(0.0006)Activity rate: high-skilled0.0024***0.0015**0.0011*(0.0005)(0.0008)(0.0007)Observations478440234214244226R-squared0.55960.75720.31190.63490.68810.8338Countries272713131414Country FEYESYESYESYESYESYES 
                                                           
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

in  part-time  work  could  be  more  limited,  raising 
the  probability  of  over-skilling  and  a  switch  from 
full-time  to  part-time  employment  could  entail 
occupational downgrading (Connolly and Gregory, 
2009). 

Table II.1.5: 

Job polarisation increases the probability of 
being mismatched 

EU  labour  market  trends  favour  on-the-job 
skills  mismatches.  It  is  increasingly  demanding 
occupations  that  are  intense  in  non-routine  skills. 
Table II.1.6 shows that being in occupations with a 
high  use  of  non-routine  cognitive  tasks  increases 
the probability of being under-skilled, while being 
in occupations that are intensive in the use of non-
routine  manual  tasks  increases  the  probability  of 
being over-skilled. Moreover, workers in more off-
shorable  occupations  are 
to  be 
mismatched.  Thus,  in  the  absence  of  an  adequate 
supply  response,  EU  labour  market  trends  could 
increase  on-the-job  skills  mismatches  in  several 
Member States. 

likely 

less 

Table II.1.6: 

Skills mismatches are more likely in jobs in 
increasing demand, while they are less likely 
in jobs at risk of disappearing 

(1) All regressions weighted with EU-LFS weights, and 
standard errors clustered at country level. 
(2) High index occupations is a dummy that takes value 1 if 
the occupation is above the 66th percentile for the index in 
that year. 
Source: Own calculations based on EU-LFS and O*net. 

(1) All regressions weighted with EU-LFS weights, and 
standard errors clustered at country level. 
Source: Own calculations based on EU-LFS. 

116 

VARIABLESSkill mismatchOver-skilledUnder-skilled0.0171***-0.003260.0197***(0.00511)(0.00573)(0.00677)-0.00630-0.0169**0.00970(0.0104)(0.00756)(0.0153)0.00471-0.0124***0.0164***(0.00559)(0.00361)(0.00605)0.0296***0.0454***-0.0214(0.00796)(0.00720)(0.0197)0.0445*0.0297***0.0104(0.0266)(0.00815)(0.0332)0.009810.0434***-0.0400***(0.00629)(0.00403)(0.00746)-0.165***0.118***-0.308***(0.0438)(0.0440)(0.0714)-0.005980.00687***-0.0134***(0.00478)(0.00148)(0.00411)-0.0279***0.0118***-0.0398***(0.00720)(0.00381)(0.00881)-0.0554***0.00779*-0.0633***(0.0109)(0.00463)(0.0147)-0.0578**0.0289***-0.0889**(0.0283)(0.0101)(0.0376)-0.03050.0138-0.0460(0.0325)(0.0140)(0.0469)0.007020.004020.000483(0.0384)(0.0179)(0.0570)0.02700.0002860.0247(0.0415)(0.0211)(0.0636)Observations22,716,93422,716,93422,716,934Country and sector FEYESYESYESparttimefirmsize = 3, 50 persons or moreJob Polarisation Indexmalemarriedforeigntemporaryage_group = 3, 35-44age_group = 4, 45-54age_group = 5, 55-65lgdpfirmsize = 2, 11 to 49 personsage_group = 2, 25-34nawruVARIABLESSkill mismatchOver-skilledUnder-skilled0.003350.146***-0.162***(0.0251)(0.00938)(0.0195)-0.0188*-0.0128-0.00603(0.0104)(0.0137)(0.0183)0.01980.0315***-0.00221(0.0151)(0.0118)(0.0119)0.00696-0.131***0.143***(0.0373)(0.00439)(0.0315)Observations23 292 06423 292 06423 292 064Country and sector FEYESYESYESHigh routine occupationsHigh off-shorable occupationsNon-routine manual occupationsNon-routine cognitive occupations 
 
 
 
 
 
 
 
Part II 

Analytical chapter 

(Continued on the next page) 

117 

 Box II.1.4: Decomposition of changes in macroeconomic skills mismatchesTo delve further into factors driving changes in macroeconomic skill mismatches, contributions of selected explanatory variables to the total change in the macroeconomic skill mismatch indicator between 2002 and 2017 are estimated. Given C is the set of countries, the change in skill mismatch indicator may be approximated by the formula below: ∀𝑖∈𝐶𝑆𝑖2018−𝑆𝑖2002≈𝛽1 ∆𝑈𝑖+𝛽2 ∆𝐺𝑖+𝛽3 ∆𝐸𝑖+𝛽4 ∆𝐴𝑖+𝛽2 ∆𝐿𝑖+𝛽3 ∆𝑀𝑖+𝛽4 ∆𝐻𝑖 Whereby: 𝑆𝑖2018and 𝑆𝑖2002 are the macroeconomic skill mismatch indicator in 2018 and 2002, respectively; 𝛽𝑘  are the estimated coefficients (Table II.1.A1.1 in the Annex) for independent variables (k=1,2,3); ∆𝑈𝑖 is the change in unemployment rate in country 𝑖 between 2002 and 2018. ∆𝐺𝑖 is the change in log of GDP percapita in country 𝑖 between 2002 and 2018; ∆𝐸𝑖 is the change in the share of the population with at least secondary (ISCED 3-8) in country 𝑖 between 2002 and 2018; ∆𝐴𝑖 is the change in the average age of the workforce in country 𝑖 between 2002 and 2018; ∆𝐿𝑖, ∆𝑀𝑖 and ∆𝐻𝑖 is the change in the activity rate of those with primary (ISCED 0-2), secondary (ISCED 3-4)  and tertiary education (ISCED 5-8) respectively in country 𝑖 between 2002 and 2018. Graph 1 shows that the rise or fall in macroeconomic skill mismatches was largely driven by changes in the general economic conditions, particularly among middle-income Member States. General economic conditions are captured by unemployment, GDP per capita and by the activity rate (by level of education). Each of these variables capture to some extent the state of the economic business cycle, with the unemployment rate most influenced by short-term downturns and upswings. However, ageing and improvements in the supply of skills (captured by the share of the population with at least secondary education) have also significantly contributed to increase and decrease, respectively, skill mismatches in many Member States.  Graph 1: Decomposition of changes in macroeconomic skills mismatches between 2002 and 2018  In the next step, the analysis focuses on the impact of changes in the task content of jobs on Macroeconomic skill mismatches. In particular, the contributions of changes in the task content of jobs (low, medium and  
 
 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Box (continued) 

118 

high skill) to the total change in macroeconomic skills mismatches are estimated following a similar equation to the previous one:  ∀𝑖∈𝐶𝑆𝑖2018−𝑆𝑖2002≈𝛽1 ∆𝐿𝑖+𝛽2 ∆𝑀𝑖+𝛽3 ∆𝐻𝑖 Whereby: 𝑆𝑖2018and 𝑆𝑖2002 are the macroeconomic skill mismatch indicator in 2018 and 2002, respectively; 𝛽𝑘  are the estimated coefficients (Table II.1.4) for independent variables (k=1,2,3); ∆𝐿𝑖 is the change in the Low-skill: non-routine manual indicator in country 𝑖 between 2002 and 2018; ∆𝑀𝑖 is the change in the Medium-skill: routine indicator in country 𝑖 between 2002 and 2018;  ∆𝐻𝑖 is the change in the High-skill: non-routine cognitive indicator in country 𝑖 between 2002 and 2018. As results from table II.1.4 suggest that the impact differs by group of country, separate regressions for each group of countries are estimated (i.e. High-income and middle-income countries). Among the group of high-income Member States, the decrease in routine intensive (middle-skill) jobs was the main contributor related to changes in the task content of jobs. However, the rise in the use of non-routine cognitive skills (high skill) also played an important role in many high-income Member States. On the other hand, changes at the top of the skill distribution had a more prominent impact on skill mismaches in middle-income Member States. However, as Graph 1 and Table II.1.3 suggest, this change in the skill content of jobs that would have led to more skill mismacthes was compensated by other factors. In particular, some middle-income Member States benefited from an increase in routine skills and many more in general from improvements in economic conditions (less unemployment and higher GDP per capita), workforce upskilling and an increase in labour participation (Graph 1). Graph 2: Contribution of changes in the skill content of jobs to changes in skill mismatches: 2002-2018  
 
 
 
 
 
Part II 

Analytical chapter 

1.5.3.  The role of labour market institutions 

The  existence  of  structural  imbalances  in  the 
labour  market  makes  it  crucial  to  use  several 
types of policies in order to address frictions. As 
previous  sections  show,  cross-country  differences 
in  skills  mismatches  can  be  due  to  structural 
differences  in  supply  and  demand  as  well  as 
cyclical factors. 

market  services  as  not  necessarily  more  spending 
on those services reduces skill mismatches. 

Table II.1.7: 

Employment protection legislation and labour 
market policies on macroeconomic skill 
mismatches 

Employment  protection  legislation  (EPL)  and 
other aspects of labour market institutions may 
have  an  impact  on  skills  mismatches.  For 
example,  stringent  regulations  on  the  firing  of 
permanent  employees  make  it  more  difficult  for 
firms to adapt the labour force structure to address 
mismatches  between  the  demand  and  supply  of 
skills.  The  use  of  fixed-term  contracts  and 
temporary  work  contracts  may  help  avoid  strict 
firing  rules,  and  thus  reduce  mismatches.  In 
addition,  in  response  to  the  changing  demand  for 
skills,  firms  need  to  adjust  wages  or  their  labour 
force  composition  to  avoid  mismatches.  At  the 
same 
to 
institutional  settings 
education,  education  policies,  more  rigid  labour 
markets  and  higher  labour  market  segmentation) 
may  explain 
the  different  nature  of  skills 
mismatches  across  European  countries  (European 
Commission, 2013b; Boheim et al., 2008). 

(access 

time, 

(1) Clustered standard errors in parentheses, *** p<0.01, ** 
p<0.05, * p<0.1. 
Source: Own calculations based on EU-LFS. 

the 

that 

factors 

the  macro 

policy-related 

At  the  macro  level,  EPL  and  certain  labour 
market  policies  do  not  seem  to  affect  skills 
mismatches.  Table  II.1.7 explores  the  effects  of 
on 
different 
skills 
level. (145)  Results 
mismatches  at 
suggest 
strictness  of  employment 
protection  does  not  have  a  significant  impact  on 
skills  mismatches.  Similarly,  some  labour  market 
policies  such  as  the 
labour  market  services, 
supported  employment  and  rehabilitation,  direct 
job creation and out of  work  income  maintenance 
impact  on  skills 
do  not  seem 
mismatches  either.  While  employment  services, 
which  aim  to  match  workers  and  jobs,  should 
improve  skill  mismatches,  the  results  highlights 
the importance of well targeted and quality labour 

to  have  an 

can 

improve 

incentives 

training  and  opportunities 

However,  more  spending  on  training  and 
employment 
the 
matching  of  skills  to  jobs.  Higher  spending  on 
policies related to training and lifelong learning is 
associated  with  lower  skills  mismatches  (Table 
II.1.7), reinforcing the importance of skills gained 
beyond  formal  qualifications  through  both  on  the 
job 
lifelong 
education and training as important instruments to 
reduce  skills  mismatches.  Moreover,  employment 
incentives seem to be also important to reduce skill 
mismatches.  According  to  results  in  previous 
incentives  might  be 
sections, 
particularly 
the 
to  counterbalance 
increase  in  over  qualification  by  improving  the 
employment rates of low-skilled workers.  

employment 
important 

for 

(145) The  coefficients  reported  are  the  result  of  a  country  fixed 
effects linear regression controlling for routinisation (RTI), 
unemployment,  GDP  and  activity  rates  by  level  of 
education  (see  Box  II.1.2).  However,  only  the  estimated 
coefficients of policy-related variables are reported for the 
sake of brevity. 

Adult  learning  and  training  are  important  to 
address  new  skill  requirements  driven  by 
technological  change  as  well  as  skill  loss  over 
time.  Given  the  changing  face  of  the  labour 
market,  both  workers  who  are  just  entering  the 
labour  force  and  those  already  in  the  workforce 
have to be willing to learn new skills. Participation 

119 

VARIABLESSkill mismatch-0.0116(0.0078)-0.0007(0.0491)-0.0444**(0.0167)-0.0746**(0.0288)-0.1273(0.1033)-0.0574(0.0457)0.2231**(0.0859)-0.0002(0.0053)Country FEYESFull Controls + RTIYESDirect job creationStart-up incentivesOut-of-work income maintenance and supportStrictness of employment protectionLabour market services TrainingEmployment incentivesSupported employment and rehabilitation 
                                                           
 
 
 
 
mismatches are more likely in jobs with increasing 
demand, while they are less likely in jobs at risk of 
disappearing.  

While  most  Member  States  are  exposed  to  the 
impact  of  routinisation  and  off-shorability  on 
skills mismatches, middle-income ones are more 
at  risk.  First,  deroutinisation  in  middle-income 
countries might be more intense as they have more 
routine employment structures, potentially leading 
to more skills mismatches. Moreover, while in the 
short-term  certain  middle-income  countries  may 
potentially  benefit  from  job  creation  in  routine 
occupations outsourced from high-income Member 
these  countries  more 
States, 
vulnerable 
displacing 
technologies.  Finally,  middle-income  countries 
might  find  it  more  difficult  to  meet  the  fast 
changing  labour  demand  for  more  complex  skills, 
as it takes time to upskill their workforces.  

leave 
routine 

this  will 

labour 

to 

A  policy  response  to  address  the  consequences 
of  job  polarisation  can  also  help  to  address 
skills mismatches. Countries should put in place a 
comprehensive  package  of  policies  on  skills, 
lifelong learning, labour markets, social protection, 
as  well  as  research  and  innovation.  Training 
policies  will  need  to  facilitate  the  transition  of 
workers in jobs with a high risk of automation into 
new  and  better-quality  jobs  that  are  in  higher 
demand.  As  technology  advances  the  importance 
of  certain  jobs  in  the  labour  market,  governments 
will  need  to  foster  flexibility  and  labour  mobility 
as  well  as  invest  in  education  and  training.  This 
could  enable  workers  to  change  jobs  or  even 
occupation, allowing them to benefit from new job 
opportunities and reducing their risk of job loss. 

European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

in  lifelong  learning  can  be  especially  useful  to 
workers  who  need  to  adapt  to  rapid  changes  in 
labour  market  demand  and  the  arrival  of  new 
technologies. Finally, employment incentives seem 
to  be  also  important  to  reduce  skill  mismatches 
particularly by improving the employment rates of 
low-skilled workers, counterbalancing the increase 
technological 
in  over  qualification  driven  by 
change.  

1.6.  CONCLUSIONS 

of 

and 

considerably, 

This  chapter  examines  the  evolution  as  well  as 
the  relationship  between  the  well-documented 
process 
skills 
job  polarisation 
mismatches.  Different  concepts  and  measures  of 
skills  mismatches  and  polarisation  are  considered 
and  the  analysis  yields  a  number  of  interesting 
results  and  new  insights.  Firstly,  our  analysis 
suggests that certain types of skills mismatches are 
on  the  rise  in  the  EU  as  a  whole,  notably  labour 
shortages  and  over-qualification  (especially  in 
those  countries  where  tertiary  attainment  has 
being 
expanded 
accompanied  by  a  commensurate  rise  in  high-
skilled 
the 
macroeconomic  skills  mismatches  have  been 
declining  in  the  recovery,  or  followed  more 
complex  patterns  over  time  (e.g.  horizontal  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.  Polarisation  is  also  evident  in  all  EU 
Member States through the simultaneous shrinking 
of  employment 
in  middle-skilled  occupations 
(requiring routine tasks) and its rise in low-skilled 
and  particularly  high-skilled  occupations,  albeit 
with large differences across countries.  

jobs).  Other 

without 

types, 

such 

as 

is  shifting 

Current  EU 
labour  market  trends  hinder 
reducing  skills  mismatches.  Labour  demand  for 
towards  higher  skills  and 
skills 
qualifications. 
technology 
Fast-changing 
accelerates demand for different skills, particularly 
for  more  complex  skills  that  can  complement 
technology. At the same time, technology replaces 
more routine jobs and makes it easier to outsource 
middle-skilled 
the 
increases  in  the  high-skill  task  content  and  the 
declining routine task content of jobs are linked to 
an increase in macroeconomic skills mismatches in 
EU  Member  States.  Moreover,  on-the-job  skills 

jobs.  Results  suggest 

that 

120 

 
 
APPENDIX 1 

Graph II.1.A1.1: 

Evolution of the task content of jobs in EU Member States, 2000-2018 

(1)  To make the results comparable the task indices were rescaled so that the initial value was 0. 
Source: Own calculations based on EU-LFS and O*NET data. 

121 

 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph II.1.A1.2: 

Evolution of the polarisation-related mearues in EU Member States, 2000-2018 

Source:  Own calculations based on EU-LFS and O*NET data. 

122 

 
 
 
Part II 

Analytical chapter 

Graph II.1.A1.3: 

Evolution of the macroeconomic skill mismatch and JPI in EU Member States, 2000-2018 

Source:  Own calculations based on EU-LFS and O*NET data. 

123 

 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

Graph II.1.A1.4: 

Evolution of the macroeconomic skill mismatch and RTI in EU Member States, 2000-2018 

Source:  Own calculations based on EU-LFS and O*NET data. 

124 

 
 
 
Part II 

Analytical chapter 

Table II.1.A1.1: 

Determinants of Macroeconomic skills mismatches in EU27, 2000-2018 

(1) Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. 
Source: Own calculations based on LFS and O*net. 

125 

VARIABLESEUHigh-income countriesLow-income countriesNon-accelerating wage rate of unemployment0.0050***0.0051***0.0042***(0.0005)(0.0007)(0.0008)lngdp-0.1204***-0.0733***-0.1174***(0.0084)(0.0161)(0.0115)Upskilling-0.0018***-0.0019***-0.0012**(0.0003)(0.0003)(0.0005)Ageing0.0100***0.0138***0.0035(0.0018)(0.0019)(0.0032)Activity rate: Low-skill-0.0049***-0.0038***-0.0056***(0.0003)(0.0004)(0.0004)Activity rate: Middle-skill0.0030***0.0039***0.0029***(0.0004)(0.0007)(0.0006)Activity rate: High-skill0.0027***0.0016**0.0023***(0.0005)(0.0007)(0.0007)Observations443214229R-squared0.72970.57160.7936Countries271314Country FEYESYESYES 
 
 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

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VARIABLESATBEBGCYCZDEDKEEELESFIFRHRHUIEITLTLULVNLPLPTROSESISKUK(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)0.0129***-3.54e-050.009480.004250.0323***0.0371***0.0404***-0.004600.0113**0.0114**-0.0149**0.00348-0.002590.00809**-0.003520.0196***-0.00839**-0.00441**0.009910.0103**-0.00897***0.0161***-0.00230-0.0348***0.0138**0.0002570.00545*(0.00467)(0.00222)(0.00685)(0.00406)(0.0109)(0.0113)(0.00434)(0.00300)(0.00486)(0.00498)(0.00694)(0.00266)(0.00271)(0.00363)(0.00385)(0.00275)(0.00351)(0.00221)(0.00659)(0.00405)(0.00346)(0.00441)(0.00259)(0.0117)(0.00619)(0.00291)(0.00305)0.01150.0188*-0.00250-0.00243-0.0755***-0.0379**0.00883-0.005620.03160.0497***-0.01820.008560.00520-0.0723***0.0306***0.0387-0.0286*-0.01500.00183-0.00403-0.0517*0.0906***-0.0500*0.0416***-0.0212-0.0896***-0.0242***(0.0182)(0.0112)(0.0172)(0.0161)(0.0289)(0.0188)(0.0162)(0.0168)(0.0255)(0.00793)(0.0218)(0.00834)(0.0266)(0.0221)(0.00947)(0.0238)(0.0166)(0.0113)(0.0216)(0.0115)(0.0272)(0.0196)(0.0258)(0.0110)(0.0240)(0.0253)(0.00861)0.00489-0.002690.00467**-0.01080.00893***0.00386-0.00865-0.0227***0.0174***0.0188***-0.0334***-0.004140.0160***-0.0148***-0.0182**0.0314***-0.0221***0.0167**-0.0191***0.00733*-0.0161**0.0568***-0.0136**-0.00581-0.00503-0.00356-0.0128***(0.00461)(0.00376)(0.00220)(0.0155)(0.00316)(0.00509)(0.00700)(0.00554)(0.00562)(0.00565)(0.00793)(0.00552)(0.00392)(0.00393)(0.00908)(0.00495)(0.00446)(0.00654)(0.00693)(0.00422)(0.00721)(0.0113)(0.00567)(0.00856)(0.00618)(0.00673)(0.00460)0.0176**0.0494***-0.0001810.0375**0.01060.0848***0.113***0.0288**0.0276-0.01620.118***0.0513**0.01300.003880.0850***0.0189**0.0114-0.0832**0.0173**0.0299***0.0250***-0.0630***0.04760.0701***0.0242***0.01300.0126(0.00836)(0.00835)(0.0335)(0.0179)(0.0181)(0.0219)(0.00921)(0.0118)(0.0307)(0.0358)(0.0122)(0.0202)(0.0100)(0.0255)(0.0164)(0.00957)(0.0135)(0.0377)(0.00852)(0.00661)(0.00960)(0.0220)(0.0523)(0.0168)(0.00847)(0.0253)(0.0110)0.173***0.0569***0.0600***0.008690.008940.130***0.0960***0.0266***0.002800.005040.0714***0.0816***0.0244*0.0381***0.0238***-0.0523***0.0943***0.0438*0.0542***0.0214*0.0291**-0.0256**0.0624***0.0740***0.02200.0438***0.00465(0.0535)(0.0101)(0.0126)(0.0146)(0.00798)(0.0288)(0.0191)(0.00939)(0.0190)(0.0170)(0.0198)(0.0142)(0.0134)(0.00627)(0.00620)(0.0187)(0.0171)(0.0245)(0.0158)(0.0110)(0.0121)(0.0112)(0.0153)(0.0187)(0.0148)(0.0155)(0.0111)0.005260.0450***0.0801***0.0361***0.0343**0.006790.0356**0.0595***0.02240.009750.0580***0.02140.0813***0.0541***0.0371*-0.0340***0.0756***0.0292*0.0565***0.0299*0.0666***-0.0772***0.02640.0337***0.0604***0.0429***0.0124(0.0152)(0.0161)(0.0141)(0.0124)(0.0144)(0.0169)(0.0144)(0.0202)(0.0166)(0.0180)(0.0176)(0.0139)(0.0175)(0.0112)(0.0199)(0.00745)(0.0157)(0.0161)(0.0128)(0.0160)(0.00883)(0.0119)(0.0189)(0.0124)(0.0130)(0.0147)(0.0249)0.110*-0.512***-0.122***-0.0485-0.338***-0.1940.253***-0.02710.0607***-0.205***-0.394**-0.427***-0.0851-0.166***-0.127***0.0342-0.201***-0.147**-0.0679*-0.430***0.0151-0.435***-0.0922**-0.227*-0.148***-0.00542-0.429***(0.0612)(0.104)(0.0265)(0.0420)(0.106)(0.120)(0.0799)(0.0402)(0.0209)(0.0437)(0.198)(0.0686)(0.0560)(0.0366)(0.0264)(0.0537)(0.0355)(0.0742)(0.0356)(0.0430)(0.0446)(0.0942)(0.0408)(0.132)(0.0495)(0.0464)(0.0997)-0.0270**0.0380***-0.00203-0.00706*-0.00854**0.001810.0522**-0.00173-0.0174***-0.0121***0.00527-0.0287*0.00187-0.00577***-0.00510***-0.0491***-0.0274***-0.0128**0.000758-0.0372***0.00619*-0.0107***-0.007360.000850-0.02840.0141***-0.0506***(0.0122)(0.00579)(0.00201)(0.00414)(0.00382)(0.00842)(0.0243)(0.00287)(0.00646)(0.00302)(0.0222)(0.0158)(0.00435)(0.00175)(0.00154)(0.00549)(0.00401)(0.00552)(0.00242)(0.00913)(0.00355)(0.00247)(0.00540)(0.0163)(0.0319)(0.00453)(0.0122)-0.0196-0.00812-0.0360***-0.0334***-0.00558-0.0132-0.0285*-0.0181-0.0616***-0.0571***-0.0471***-0.00437-0.0283**0.0136-0.0209***-0.0230***-0.00127-0.0155-0.0126-0.0205-0.00248-0.0595***-0.0911***-0.0225**-0.0252**-0.00406-0.0228***(0.0159)(0.00708)(0.00915)(0.00981)(0.0216)(0.0194)(0.0159)(0.0111)(0.0130)(0.00849)(0.0104)(0.00768)(0.0119)(0.00927)(0.00759)(0.00681)(0.0123)(0.00981)(0.0105)(0.0128)(0.0127)(0.00935)(0.0350)(0.0114)(0.0120)(0.0260)(0.00586)-0.0125-0.0334***-0.0610***-0.0738***-0.00401-0.0144-0.0566***-0.0387**-0.0833***-0.109***-0.0705***-0.0398***-0.0527***0.00638-0.0540***-0.0846***-0.0212-0.0486***-0.0313**-0.0327-0.0230-0.131***-0.119***-0.0553***-0.0120-0.0124-0.0665***(0.0198)(0.00634)(0.0106)(0.0149)(0.0234)(0.0233)(0.0196)(0.0174)(0.0126)(0.0120)(0.00829)(0.0116)(0.0107)(0.0148)(0.0140)(0.0201)(0.0184)(0.0149)(0.0152)(0.0200)(0.0142)(0.0121)(0.0424)(0.0191)(0.0258)(0.0288)(0.0103)-0.0954***0.00679-0.00161-0.0166-0.00626-0.150***-0.0753***-0.0478*0.0306*-0.0167-0.0436-0.0244*0.0242-0.01510.0424**0.0171-0.0668***-0.0795***-0.0456**-0.0778***-0.0245-0.0185-0.0428***-0.0337-0.00646-0.005440.0223(0.0167)(0.0134)(0.0276)(0.0188)(0.0192)(0.0202)(0.0225)(0.0254)(0.0184)(0.0155)(0.0302)(0.0130)(0.0236)(0.0141)(0.0185)(0.0141)(0.0182)(0.0210)(0.0213)(0.0218)(0.0200)(0.0120)(0.0133)(0.0205)(0.0208)(0.0189)(0.0324)-0.0700***0.0478***-0.00843-0.0335**0.0130-0.138***-0.0407*-0.0483*0.0558**0.000879-0.01900.0262*0.0660**0.008080.0594***0.0779***-0.0718***-0.0405-0.0580***-0.0367*-0.007740.0354**-0.0681***0.02060.009840.01430.0565(0.0206)(0.0171)(0.0286)(0.0169)(0.0187)(0.0253)(0.0220)(0.0278)(0.0239)(0.0172)(0.0344)(0.0136)(0.0269)(0.0118)(0.0227)(0.0215)(0.0148)(0.0258)(0.0173)(0.0223)(0.0151)(0.0159)(0.0112)(0.0233)(0.0231)(0.0161)(0.0347)-0.0479**0.0956***0.00872-0.001550.0255-0.122***0.00447-0.04430.101***0.0400*0.02510.103***0.0914***0.02210.101***0.130***-0.0497**0.0111-0.0591***0.008130.0420***0.0917***-0.0517***0.0884***0.04140.03140.0880***(0.0219)(0.0229)(0.0284)(0.0198)(0.0201)(0.0267)(0.0216)(0.0294)(0.0318)(0.0223)(0.0336)(0.0172)(0.0304)(0.0144)(0.0249)(0.0296)(0.0198)(0.0278)(0.0169)(0.0265)(0.0148)(0.0197)(0.00958)(0.0261)(0.0281)(0.0201)(0.0335)-0.03170.122***0.0443*0.0543**0.0332-0.112***0.02940.004540.156***0.0643**0.0720*0.128***0.0807**0.02890.156***0.138***0.0007750.0154-0.01620.03230.0735***0.0992***0.008500.149***0.02380.0411*0.112***(0.0297)(0.0279)(0.0268)(0.0245)(0.0209)(0.0324)(0.0221)(0.0310)(0.0422)(0.0300)(0.0381)(0.0224)(0.0403)(0.0200)(0.0266)(0.0362)(0.0146)(0.0304)(0.0170)(0.0337)(0.0189)(0.0236)(0.0242)(0.0326)(0.0326)(0.0236)(0.0332)Observations124013453438226730623537977005723161816858961461701044697627811229385763997156936139688010300012724910347293159453169716775657142237487439010657172081856398208540246711902JPImaleforeignparttimenawrulgdptemporarymarriedage_group = 5, 55-65firmsize = 2, 11 to 49 personsfirmsize = 3, 50 persons or moreage_group = 2, 25-34age_group = 4, 45-54age_group = 3, 35-44 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
APPENDIX 2 

Empirical analysis: on-the-job mismatch and polarisation 

The  link  between  skill  mismatch,  individual  and  job  characteristics  is  investigated  by  estimating  the 
following cross-country binomial logit specification:  

𝑆𝑀𝑀𝑖,𝑐,𝑡 = Φ 𝛿1 + 𝛿2𝐼𝑖 + 𝛿3𝐶𝑐,𝑡 + 𝛿4𝑃𝑐,𝑡 + 𝜀𝑖,𝑐,𝑡  

where Φ is the normal distribution, i denotes an individual, c denotes country, t denotes year and SMMi 
denotes the probability that an individual i is mismatched (SMM=1 if an individual i is over- or under-
skilled),  and  C  denotes  country-specific  control  variables,  including  GDP  per  capita  and  level  of 
unemployment.  A country-by-country estimation is also considered to explore cross-country differences 
in the determinants of mismatch (Table II.1.A1.2 in Appendix 1.A1). 

The definition of mismatch used in the analysis is that of the ILO (2007). Thus, a high-qualified worker 
(tertiary education) is considered as overqualified if he has a job at ISCO levels 4-9. A medium- qualified 
worker  (upper  secondary)  is  considered  as  overqualified  if  he  has  a  job  at  ISCO  level  9,  and  as 
underqualified if he has a job at ISCO level 1-3. A low-qualified worker (lower sencondary) is considered 
as underqualified if he has a job at ISCO levels 1-8. 

The explanatory  variables include  a vector of individual characteristics, I,  which are likely to influence 
mismatch. These include: age category (15-24, 25-34, 35-44, 45-54 and 55-65), gender (1 if the person is 
male, 0 otherwise), nationality (0 if national, 1 if foreign-born), marital status (1 if married, 0 otherwise), 
firm size (1-10, 11-49 and 50+), contract type (1 if the person is temporary, 0 otherwise) and parttime (1 
if the person is employed part-time, 0 otherwise). In addition, the Job Polariation Index (𝑃𝑐,𝑡) for country 
c and year t is included to investigate the impact of the polarisation of the economy on the probability of 
being mismatched. 

The  reported  coefficients  are  marginal  effects  and  can  be  interpreted  as  the  average  impact  across  EU 
Member States of a unit change in the explanatory variable on the probability of skill mismatch. These 
changes are relative to the probability of skill mismatch of the excluded individual: single, female, native-
born, full-time young worker in a small firm on indefinite contracts. 

In a second step, to study the link between skill mismatches and the task content of jobs, the specification 
replaces the JPI by a binary indicator of whether an individual (i) in country (c) and time (t) is in a high 
index occupation. This high index occupation variable (𝐻𝑜𝑐,𝑡  takes value 1 if the occupation (o) is above 
the 66th percentile for the index in that year and country:  

𝑆𝑀𝑀𝑖,𝑐,𝑡 = Φ 𝛿1 + 𝛿2𝐼𝑖 + 𝛿3𝐶𝑐,𝑡 + 𝛿4𝐻𝑜,𝑐,𝑡 + 𝜀𝑖,𝑐,𝑡  

127 

 
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135 

 
 
Statistical annex 

 
 
 
APPENDIX 1 

Statistical annex 

138 

Belgium201420152016201720181-Population (LFS, total, 1000 pers.)11180112381129511349114050.5%2-Population (LFS, working age:15-64, 1000 pers.)726672817290726672890.3%(% of total population)65.064.864.564.063.9-0.1pps3-Labour force (15-64, 1000 pers.)492049214929494050001.2%Male264426402649265226640.5%Female227722812281228923352.0%4-Activity rate (% of population 15-64)67.767.667.668.068.60.6ppsYoung (15-24)30.230.028.528.129.61.5ppsPrime age (25-54)85.685.185.184.885.00.1ppsOlder (55-64)45.146.648.151.352.61.3ppsNationals (15-64)68.168.068.068.369.00.6ppsNon-nationals (15-64)65.064.865.065.765.90.2ppsMale72.472.272.372.872.80.1ppsYoung (15-24)32.332.830.730.631.40.8ppsPrime age (25-54)90.789.990.490.089.6-0.4ppsOlder (55-64)51.352.253.656.957.91.1ppsFemale63.063.062.963.264.31.1ppsYoung (15-24)28.127.126.125.427.82.3ppsPrime age (25-54)80.680.279.879.680.30.7ppsOlder (55-64)39.041.242.845.847.41.6pps5- Employment rate (% of population 15-64)61.961.862.363.164.51.3ppsYoung (15-24)23.223.422.722.725.02.3ppsPrime age (25-54)79.178.579.179.580.40.9ppsOlder (55-64)42.644.045.448.350.32.1ppsLow-skilled (15-64)37.336.036.035.535.50.0ppsMedium-skilled (15-64)63.864.064.465.166.61.5ppsHigh-skilled (15-64)82.081.882.282.283.41.3ppsNationals (15-64)62.962.863.364.165.41.3ppsNon-nationals (15-64)53.754.655.156.557.81.3ppsMale65.865.566.567.568.20.7ppsYoung (15-24)24.525.024.024.426.41.9ppsPrime age (25-54)83.282.583.884.484.50.2ppsOlder (55-64)48.548.950.753.855.11.3ppsFemale57.958.058.158.760.72.0ppsYoung (15-24)21.821.721.420.923.52.6ppsPrime age (25-54)75.074.574.374.676.21.6ppsOlder (55-64)37.039.340.242.845.62.8pps6-Employed persons (15-64, 1000 pers.)4497.34499.34540.64587.24699.42.4%7-Employment growth (%, National accounts)0.40.91.31.41.3-0.1ppsEmployment growth (%, 15-64, LFS)0.30.00.91.02.41.4ppsMale-0.7-0.21.51.21.40.2ppsFemale1.50.40.20.93.72.8pps8-Self employed (15-64, % of total employment )13.213.813.513.112.7-0.4ppsMale16.817.517.316.315.8-0.6ppsFemale9.19.59.29.39.1-0.2pps9-Temporary employment (15-64, % of total employment)8.69.09.110.410.70.3ppsMale7.68.38.39.79.80.1ppsFemale9.79.710.011.211.70.5pps10-Part-time (15-64, % of total employment )23.724.324.724.524.50.0ppsMale8.49.39.510.210.0-0.2ppsFemale41.241.442.141.241.0-0.2pps11Involuntary part-time (15-64, % of total employment)2.42.42.21.91.7-0.2pps12-Unemployment rate (harmonised:15-74)8.58.57.87.16.0-1.1ppsYoung (15-24)23.222.120.119.315.8-3.5ppsPrime age (25-49)7.67.77.16.25.4-0.8ppsOlder (55-64)5.45.65.75.94.3-1.6ppsLow-skilled (15-64)16.417.016.114.813.3-1.5ppsMedium-skilled (15-64)8.88.78.17.26.0-1.2ppsHigh-skilled (15-64)4.74.64.24.33.5-0.8ppsNationals (15-64)7.57.67.06.25.2-1.0ppsNon-nationals (15-64)17.315.815.214.012.3-1.7ppsMale9.09.18.17.16.3-0.8ppsFemale7.97.87.67.15.6-1.5pps13-Long-term unemployment (% of total unemployment)49.951.751.648.848.7-0.1pps14-Worked hours (full-time, average actual weekly hours)41.141.341.340.340.2-0.2%Male42.042.342.241.141.0-0.2%Female39.339.339.538.738.70.0%15-Sectoral employment growth (% change)Agriculture-0.31.0-1.5-1.40.92.3ppsBuilding and construction-1.5-0.50.71.31.0-0.3ppsServices1.01.81.71.81.6-0.3ppsManufacturing industry-2.6-2.20.00.51.20.7pps16-Indicator board on wage developments (% change)Compensation per employee0.90.00.51.91.7-0.2ppsReal compensation per employee based on GDP-0.2-0.31.21.51.70.2ppsLabour cost index (compens. of employees plus taxes minus subs.)0.90.10.11.21.60.4ppsLabour cost index (wages and salaries, total)0.90.10.51.92.20.3ppsLabour productivity (GDP/person employed)0.80.80.20.30.1-0.2pps2017-2018 
 
Statistical annex 

139 

Bulgaria201420152016201720181-Population (LFS, total, 1000 pers.)72247178712870767049-0.4%2-Population (LFS, working age:15-64, 1000 pers.)47964727465945954531-1.4%(% of total population)66.465.865.464.964.3-0.7pps3-Labour force (15-64, 1000 pers.)33093276320032783240-1.2%Male17631744171017511737-0.8%Female15461532149015261503-1.5%4-Activity rate (% of population 15-64)69.069.368.771.371.50.2ppsYoung (15-24)27.226.023.926.323.7-2.6ppsPrime age (25-54)83.383.282.084.384.30.0ppsOlder (55-64)56.658.058.861.863.71.9ppsNationals (15-64)69.069.368.771.471.50.2ppsNon-nationals (15-64)54.248.958.956.855.9-0.9ppsMale72.973.272.775.475.90.4ppsYoung (15-24)31.530.528.030.527.9-2.7ppsPrime age (25-54)86.286.485.788.088.30.3ppsOlder (55-64)62.562.763.466.869.12.4ppsFemale65.065.464.667.167.0-0.1ppsYoung (15-24)22.621.219.621.819.3-2.5ppsPrime age (25-54)80.279.878.280.580.2-0.3ppsOlder (55-64)51.453.854.657.358.71.4pps5- Employment rate (% of population 15-64)61.062.963.466.967.70.8ppsYoung (15-24)20.720.319.822.920.7-2.2ppsPrime age (25-54)74.576.176.279.480.10.8ppsOlder (55-64)50.053.054.558.260.72.6ppsLow-skilled (15-64)29.729.629.633.434.81.4ppsMedium-skilled (15-64)65.267.267.871.772.40.8ppsHigh-skilled (15-64)81.784.084.285.586.10.6ppsNationals (15-64)61.162.963.466.967.80.8ppsNon-nationals (15-64)52.145.553.352.353.91.7ppsMale63.965.966.770.671.50.9ppsYoung (15-24)24.024.023.126.524.2-2.3ppsPrime age (25-54)76.478.579.282.883.50.8ppsOlder (55-64)54.556.858.362.565.43.0ppsFemale58.259.860.063.163.90.8ppsYoung (15-24)17.316.516.319.117.0-2.1ppsPrime age (25-54)72.573.673.075.876.50.7ppsOlder (55-64)46.049.551.054.356.42.2pps6-Employed persons (15-64, 1000 pers.)2927.42973.52954.33073.43068.9-0.1%7-Employment growth (%, National accounts)0.40.40.51.8-0.1-1.9ppsEmployment growth (%, 15-64, LFS)1.31.6-0.64.0-0.1-4.2ppsMale1.71.8-0.24.4-0.1-4.5ppsFemale0.91.3-1.23.6-0.2-3.8pps8-Self employed (15-64, % of total employment )11.511.110.810.810.6-0.2ppsMale14.614.113.513.513.4-0.1ppsFemale8.17.77.87.67.3-0.3pps9-Temporary employment (15-64, % of total employment)5.34.44.14.44.0-0.4ppsMale5.64.74.54.94.4-0.5ppsFemale4.94.13.63.93.7-0.2pps10-Part-time (15-64, % of total employment )2.52.22.02.21.8-0.4ppsMale2.21.91.82.01.7-0.3ppsFemale2.82.52.22.42.0-0.4pps11Involuntary part-time (15-64, % of total employment)1.61.31.21.31.0-0.3pps12-Unemployment rate (harmonised:15-74)11.49.27.66.25.2-1.0ppsYoung (15-24)23.821.617.212.912.7-0.2ppsPrime age (25-49)10.58.57.15.95.0-0.9ppsOlder (55-64)11.78.77.35.94.6-1.3ppsLow-skilled (15-64)28.625.522.518.315.7-2.6ppsMedium-skilled (15-64)10.78.46.85.34.6-0.7ppsHigh-skilled (15-64)5.24.03.43.12.4-0.7ppsNationals (15-64)11.59.27.76.25.3-0.9ppsNon-nationals (15-64)0.00.00.00.00.00.0ppsMale12.39.88.16.45.7-0.7ppsFemale10.48.47.06.04.7-1.3pps13-Long-term unemployment (% of total unemployment)60.361.158.954.958.33.4pps14-Worked hours (full-time, average actual weekly hours)40.540.540.640.440.2-0.5%Male40.740.840.840.640.5-0.2%Female40.240.240.340.139.9-0.5%15-Sectoral employment growth (% change)Agriculture1.6-2.6-3.76.4-6.1-12.5ppsBuilding and construction-0.82.5-3.90.15.25.1ppsServices0.01.03.00.61.30.7ppsManufacturing industry0.52.31.31.10.5-0.6pps16-Indicator board on wage developments (% change)Compensation per employee5.65.65.810.55.6-4.9ppsReal compensation per employee based on GDP0.52.6-0.41.58.06.6ppsLabour cost index (compens. of employees plus taxes minus subs.)6.67.36.412.46.5-5.9ppsLabour cost index (wages and salaries, total)6.37.66.412.36.2-6.1ppsLabour productivity (GDP/person employed)1.53.13.42.03.21.2pps2017-2018 
 
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140 

Czech Republic201420152016201720181-Population (LFS, total, 1000 pers.)10525105431056510590106260.3%2-Population (LFS, working age:15-64, 1000 pers.)70817026696869176879-0.5%(% of total population)67.366.666.065.364.7-0.6pps3-Labour force (15-64, 1000 pers.)520652015226524852670.4%Male291429002906291229150.1%Female229223012321233623520.7%4-Activity rate (% of population 15-64)73.574.075.075.976.60.7ppsYoung (15-24)32.232.532.031.730.4-1.2ppsPrime age (25-54)88.888.688.989.189.30.2ppsOlder (55-64)56.858.060.863.666.52.8ppsNationals (15-64)73.473.974.975.776.40.7ppsNon-nationals (15-64)78.878.082.682.082.91.0ppsMale81.281.482.282.983.30.4ppsYoung (15-24)38.137.437.536.534.4-2.1ppsPrime age (25-54)95.695.495.495.795.90.2ppsOlder (55-64)67.968.370.973.275.32.1ppsFemale65.666.567.668.769.61.0ppsYoung (15-24)26.127.426.226.626.2-0.3ppsPrime age (25-54)81.681.482.182.182.30.2ppsOlder (55-64)46.348.351.254.558.03.4pps5- Employment rate (% of population 15-64)69.070.272.073.674.81.2ppsYoung (15-24)27.128.428.629.128.4-0.8ppsPrime age (25-54)83.884.585.786.787.50.8ppsOlder (55-64)54.055.558.562.165.13.0ppsLow-skilled (15-64)22.922.323.726.126.50.4ppsMedium-skilled (15-64)73.675.477.478.980.11.2ppsHigh-skilled (15-64)82.282.683.484.285.61.4ppsNationals (15-64)68.970.171.873.574.71.2ppsNon-nationals (15-64)74.174.479.379.881.21.4ppsMale77.077.979.380.981.80.9ppsYoung (15-24)32.333.133.833.832.2-1.6ppsPrime age (25-54)91.591.992.793.794.40.7ppsOlder (55-64)64.865.568.271.774.02.3ppsFemale60.762.464.466.267.61.5ppsYoung (15-24)21.623.423.224.324.30.1ppsPrime age (25-54)75.776.778.479.380.10.8ppsOlder (55-64)43.845.949.353.056.63.7pps6-Employed persons (15-64, 1000 pers.)4883.54934.35015.95093.95146.81.0%7-Employment growth (%, National accounts)0.61.41.61.61.3-0.3ppsEmployment growth (%, 15-64, LFS)0.81.01.71.61.0-0.5ppsMale0.80.41.11.30.7-0.7ppsFemale0.81.82.41.81.5-0.3pps8-Self employed (15-64, % of total employment )17.016.316.216.116.0-0.1ppsMale21.320.219.519.819.90.1ppsFemale11.511.411.911.611.1-0.4pps9-Temporary employment (15-64, % of total employment)9.710.09.79.68.4-1.2ppsMale8.48.48.17.86.5-1.3ppsFemale11.311.911.611.710.6-1.1pps10-Part-time (15-64, % of total employment )5.55.35.76.26.30.1ppsMale2.52.22.32.42.60.2ppsFemale9.59.310.010.910.90.0pps11Involuntary part-time (15-64, % of total employment)1.20.90.80.60.4-0.2pps12-Unemployment rate (harmonised:15-74)6.15.14.02.92.2-0.7ppsYoung (15-24)15.912.610.57.96.7-1.2ppsPrime age (25-49)5.64.63.52.72.0-0.7ppsOlder (55-64)4.94.43.82.42.0-0.4ppsLow-skilled (15-64)22.423.120.913.310.8-2.5ppsMedium-skilled (15-64)6.14.83.62.72.1-0.6ppsHigh-skilled (15-64)2.92.41.91.51.2-0.3ppsNationals (15-64)6.25.14.02.92.3-0.6ppsNon-nationals (15-64)6.14.54.12.62.1-0.5ppsMale5.14.23.42.31.8-0.5ppsFemale7.46.14.73.62.8-0.8pps13-Long-term unemployment (% of total unemployment)43.647.442.135.030.6-4.4pps14-Worked hours (full-time, average actual weekly hours)40.440.240.540.340.1-0.5%Male41.441.241.541.341.1-0.5%Female38.938.739.238.838.80.0%15-Sectoral employment growth (% change)Agriculture-0.9-1.4-1.40.9-0.9-1.8ppsBuilding and construction-4.6-0.5-1.7-0.10.91.0ppsServices0.61.01.41.32.10.8ppsManufacturing industry1.33.52.71.31.1-0.2pps16-Indicator board on wage developments (% change)Compensation per employee2.63.04.06.48.01.6ppsReal compensation per employee based on GDP3.93.85.22.52.0-0.5ppsLabour cost index (compens. of employees plus taxes minus subs.)2.54.03.87.87.5-0.3ppsLabour cost index (wages and salaries, total)2.74.13.87.87.5-0.3ppsLabour productivity (GDP/person employed)2.23.80.82.81.6-1.2pps2017-2018 
 
Statistical annex 

141 

Denmark201420152016201720181-Population (LFS, total, 1000 pers.)564356825729576757940.5%2-Population (LFS, working age:15-64, 1000 pers.)362636443669368436960.3%(% of total population)64.364.164.063.963.8-0.1pps3-Labour force (15-64, 1000 pers.)283128592934290529351.0%Male148215001532151715331.0%Female135013591402138714021.0%4-Activity rate (% of population 15-64)78.178.580.078.879.40.6ppsYoung (15-24)61.562.166.263.363.2-0.1ppsPrime age (25-54)87.187.187.486.286.50.4ppsOlder (55-64)66.467.670.671.673.31.7ppsNationals (15-64)78.679.180.379.480.10.6ppsNon-nationals (15-64)73.273.077.274.174.20.1ppsMale81.181.682.681.582.10.6ppsYoung (15-24)61.061.765.062.462.4-0.1ppsPrime age (25-54)90.390.890.889.689.90.3ppsOlder (55-64)72.672.874.975.677.72.0ppsFemale75.075.377.276.176.60.6ppsYoung (15-24)62.062.567.464.164.0-0.1ppsPrime age (25-54)83.883.483.882.783.10.4ppsOlder (55-64)60.362.666.467.669.01.3pps5- Employment rate (% of population 15-64)72.873.574.974.275.41.2ppsYoung (15-24)53.755.458.256.357.31.0ppsPrime age (25-54)82.082.182.581.782.71.0ppsOlder (55-64)63.264.767.868.970.71.7ppsLow-skilled (15-64)54.254.357.855.655.70.1ppsMedium-skilled (15-64)77.178.278.978.780.11.4ppsHigh-skilled (15-64)85.585.685.685.486.51.0ppsNationals (15-64)73.874.775.875.276.51.3ppsNon-nationals (15-64)63.363.667.066.066.10.1ppsMale75.876.677.776.978.01.2ppsYoung (15-24)52.754.656.555.355.80.5ppsPrime age (25-54)85.585.986.485.286.21.1ppsOlder (55-64)68.969.872.072.775.02.2ppsFemale69.870.472.071.572.61.1ppsYoung (15-24)54.956.260.057.258.81.6ppsPrime age (25-54)78.478.378.578.179.00.9ppsOlder (55-64)57.659.663.665.266.41.2pps6-Employed persons (15-64, 1000 pers.)2640.12678.32747.72734.02785.31.9%7-Employment growth (%, National accounts)0.91.41.51.71.80.1ppsEmployment growth (%, 15-64, LFS)0.71.42.6-0.51.92.4ppsMale1.41.72.3-0.71.82.5ppsFemale-0.11.12.9-0.31.92.2pps8-Self employed (15-64, % of total employment )8.07.87.77.37.2-0.2ppsMale10.810.510.29.89.6-0.2ppsFemale4.94.84.94.64.4-0.2pps9-Temporary employment (15-64, % of total employment)8.68.713.512.911.1-1.8ppsMale8.27.912.011.99.9-2.0ppsFemale9.09.415.113.912.5-1.4pps10-Part-time (15-64, % of total employment )24.624.726.425.324.8-0.5ppsMale15.215.616.816.215.6-0.6ppsFemale35.034.736.935.334.8-0.5pps11Involuntary part-time (15-64, % of total employment)4.23.93.63.53.0-0.5pps12-Unemployment rate (harmonised:15-74)6.66.26.25.75.0-0.7ppsYoung (15-24)12.610.812.011.09.4-1.6ppsPrime age (25-49)5.95.75.55.24.5-0.7ppsOlder (55-64)4.84.44.03.73.6-0.1ppsLow-skilled (15-64)10.610.09.49.38.2-1.1ppsMedium-skilled (15-64)6.15.45.34.74.0-0.7ppsHigh-skilled (15-64)4.84.95.04.84.1-0.7ppsNationals (15-64)6.15.65.65.34.4-0.9ppsNon-nationals (15-64)13.512.913.211.010.9-0.1ppsMale6.45.95.85.64.8-0.8ppsFemale6.86.46.65.95.2-0.7pps13-Long-term unemployment (% of total unemployment)25.226.922.322.521.2-1.3pps14-Worked hours (full-time, average actual weekly hours)39.439.638.938.938.6-0.8%Male40.640.740.140.139.6-1.2%Female37.737.836.937.237.1-0.3%15-Sectoral employment growth (% change)Agriculture2.42.0-2.4-1.30.61.9ppsBuilding and construction2.12.83.23.34.10.8ppsServices1.12.12.62.52.2-0.4ppsManufacturing industry0.51.30.91.61.5-0.1pps16-Indicator board on wage developments (% change)Compensation per employee1.51.71.51.72.00.3ppsReal compensation per employee based on GDP1.00.41.41.3-0.3-1.5ppsLabour cost index (compens. of employees plus taxes minus subs.)1.81.82.01.92.10.2ppsLabour cost index (wages and salaries, total)1.61.61.81.62.20.6ppsLabour productivity (GDP/person employed)0.71.00.90.6-0.3-0.9pps2017-2018 
 
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Germany201420152016201720181-Population (LFS, total, 1000 pers.)80983816878234982657829060.3%2-Population (LFS, working age:15-64, 1000 pers.)5272952964538025379753524-0.5%(% of total population)65.164.865.365.164.6-0.5pps3-Labour force (15-64, 1000 pers.)40990411174193242094420940.0%Male2188121926223992250422485-0.1%Female19109191911953319590196090.1%4-Activity rate (% of population 15-64)77.777.677.978.278.60.4ppsYoung (15-24)49.948.849.249.950.30.4ppsPrime age (25-54)87.687.687.387.387.70.4ppsOlder (55-64)69.169.471.372.673.61.0ppsNationals (15-64)78.878.779.479.880.10.3ppsNon-nationals (15-64)69.469.368.168.270.01.8ppsMale82.582.182.282.482.90.5ppsYoung (15-24)52.050.550.951.352.51.2ppsPrime age (25-54)92.692.591.991.992.30.4ppsOlder (55-64)75.575.376.977.978.70.8ppsFemale72.973.173.674.074.30.3ppsYoung (15-24)47.747.147.448.347.8-0.5ppsPrime age (25-54)82.582.582.682.582.90.3ppsOlder (55-64)62.963.865.967.568.61.2pps5- Employment rate (% of population 15-64)73.874.074.775.275.90.7ppsYoung (15-24)46.145.345.746.547.20.7ppsPrime age (25-54)83.583.783.984.284.90.6ppsOlder (55-64)65.666.268.670.171.41.3ppsLow-skilled (15-64)46.046.147.047.648.30.7ppsMedium-skilled (15-64)77.778.078.979.580.20.7ppsHigh-skilled (15-64)87.787.887.988.188.50.3ppsNationals (15-64)75.175.476.577.377.80.5ppsNon-nationals (15-64)62.862.962.262.664.82.1ppsMale78.178.078.478.979.70.8ppsYoung (15-24)47.746.546.947.448.81.4ppsPrime age (25-54)88.088.188.188.489.00.6ppsOlder (55-64)71.471.373.775.076.11.1ppsFemale69.569.970.871.572.10.6ppsYoung (15-24)44.344.044.545.545.4-0.1ppsPrime age (25-54)78.879.279.780.080.60.6ppsOlder (55-64)60.061.263.565.466.91.5pps6-Employed persons (15-64, 1000 pers.)38907.739175.940165.140481.640635.70.4%7-Employment growth (%, National accounts)0.90.91.21.41.40.0ppsEmployment growth (%, 15-64, LFS)0.70.72.50.80.4-0.4ppsMale0.60.52.70.80.3-0.6ppsFemale0.90.92.30.70.5-0.2pps8-Self employed (15-64, % of total employment )9.89.69.39.18.8-0.3ppsMale12.412.111.611.210.9-0.4ppsFemale6.96.86.76.66.3-0.2pps9-Temporary employment (15-64, % of total employment)13.113.213.212.912.6-0.3ppsMale13.113.113.213.012.9-0.1ppsFemale13.213.213.212.912.4-0.5pps10-Part-time (15-64, % of total employment )26.526.826.726.926.8-0.1ppsMale9.29.39.49.79.6-0.1ppsFemale46.346.646.546.446.3-0.1pps11Involuntary part-time (15-64, % of total employment)3.83.73.23.02.7-0.3pps12-Unemployment rate (harmonised:15-74)5.04.64.13.83.4-0.4ppsYoung (15-24)7.77.27.16.86.2-0.6ppsPrime age (25-49)4.74.43.93.53.2-0.3ppsOlder (55-64)5.14.73.93.42.9-0.5ppsLow-skilled (15-64)12.011.410.39.79.0-0.7ppsMedium-skilled (15-64)4.74.33.83.42.9-0.5ppsHigh-skilled (15-64)2.52.42.22.01.9-0.1ppsNationals (15-64)4.64.23.63.22.9-0.3ppsNon-nationals (15-64)9.49.28.68.17.5-0.6ppsMale5.35.04.54.13.8-0.3ppsFemale4.64.23.83.32.9-0.4pps13-Long-term unemployment (% of total unemployment)44.344.041.141.941.3-0.6pps14-Worked hours (full-time, average actual weekly hours)41.441.241.240.940.7-0.5%Male42.142.042.041.641.4-0.5%Female39.939.839.839.539.4-0.3%15-Sectoral employment growth (% change)Agriculture-0.5-0.8-1.6-1.3-1.10.2ppsBuilding and construction0.50.01.01.11.50.4ppsServices0.90.91.41.51.2-0.2ppsManufacturing industry0.60.40.30.81.70.9pps16-Indicator board on wage developments (% change)Compensation per employee2.92.82.32.62.90.3ppsReal compensation per employee based on GDP-0.9-0.40.7-0.81.32.1ppsLabour cost index (compens. of employees plus taxes minus subs.)2.52.52.23.42.1-1.3ppsLabour cost index (wages and salaries, total)2.22.52.13.22.4-0.8ppsLabour productivity (GDP/person employed)1.30.81.01.10.2-0.9pps2017-2018 
 
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Estonia201420152016201720181-Population (LFS, total, 1000 pers.)131613131316131613190.3%2-Population (LFS, working age:15-64, 1000 pers.)862853849844843-0.1%(% of total population)65.565.064.564.163.9-0.2pps3-Labour force (15-64, 1000 pers.)6486546586656660.2%Male3363383433463470.3%Female3133163153203200.1%4-Activity rate (% of population 15-64)75.276.777.578.879.10.2ppsYoung (15-24)39.241.843.246.147.31.2ppsPrime age (25-54)87.187.987.888.688.3-0.3ppsOlder (55-64)67.768.771.072.272.90.7ppsNationals (15-64)75.377.077.678.879.00.2ppsNon-nationals (15-64)74.975.076.679.279.40.3ppsMale79.380.481.982.782.6-0.1ppsYoung (15-24)41.345.846.249.749.5-0.2ppsPrime age (25-54)92.292.693.793.393.40.1ppsOlder (55-64)69.267.770.471.970.8-1.1ppsFemale71.373.073.275.175.60.5ppsYoung (15-24)37.037.840.442.545.12.6ppsPrime age (25-54)82.083.081.883.783.0-0.7ppsOlder (55-64)66.569.571.472.374.52.2pps5- Employment rate (% of population 15-64)69.671.972.174.174.80.6ppsYoung (15-24)33.436.337.540.541.71.2ppsPrime age (25-54)80.983.082.683.984.20.3ppsOlder (55-64)64.064.565.268.069.00.9ppsLow-skilled (15-64)40.439.941.844.945.00.1ppsMedium-skilled (15-64)71.074.074.076.277.41.2ppsHigh-skilled (15-64)83.285.284.185.585.2-0.2ppsNationals (15-64)70.372.572.974.675.30.6ppsNon-nationals (15-64)65.268.067.471.271.70.6ppsMale73.075.375.777.478.10.7ppsYoung (15-24)33.439.438.842.843.50.7ppsPrime age (25-54)85.687.787.988.589.51.0ppsOlder (55-64)65.263.063.866.765.9-0.8ppsFemale66.368.568.670.971.40.5ppsYoung (15-24)33.333.136.138.239.91.7ppsPrime age (25-54)76.178.277.279.278.7-0.5ppsOlder (55-64)63.165.866.569.371.52.2pps6-Employed persons (15-64, 1000 pers.)599.5613.1612.3625.6630.20.7%7-Employment growth (%, National accounts)0.82.90.32.71.2-1.5ppsEmployment growth (%, 15-64, LFS)0.52.3-0.12.20.7-1.4ppsMale1.32.60.22.01.3-0.7ppsFemale-0.41.9-0.52.40.1-2.2pps8-Self employed (15-64, % of total employment )8.89.39.59.910.40.5ppsMale12.111.912.113.314.00.7ppsFemale5.46.46.76.36.50.3pps9-Temporary employment (15-64, % of total employment)3.13.43.73.13.50.4ppsMale3.33.93.93.43.60.2ppsFemale3.03.03.52.93.30.4pps10-Part-time (15-64, % of total employment )8.39.59.99.511.11.6ppsMale5.76.06.86.07.21.2ppsFemale11.213.413.313.315.32.0pps11Involuntary part-time (15-64, % of total employment)1.31.31.00.70.6-0.1pps12-Unemployment rate (harmonised:15-74)7.46.26.85.85.4-0.4ppsYoung (15-24)15.013.113.412.111.8-0.3ppsPrime age (25-49)7.25.55.95.34.6-0.7ppsOlder (55-64)5.46.08.15.75.4-0.3ppsLow-skilled (15-64)13.212.813.411.410.7-0.7ppsMedium-skilled (15-64)8.36.78.06.85.9-0.9ppsHigh-skilled (15-64)4.94.03.83.33.50.2ppsNationals (15-64)6.65.86.15.24.7-0.5ppsNon-nationals (15-64)12.89.312.110.29.8-0.4ppsMale7.96.27.46.25.4-0.8ppsFemale6.86.16.15.35.30.0pps13-Long-term unemployment (% of total unemployment)45.238.831.633.223.7-9.5pps14-Worked hours (full-time, average actual weekly hours)39.739.740.140.339.8-1.2%Male40.240.240.840.940.6-0.7%Female39.139.239.339.638.8-2.0%15-Sectoral employment growth (% change)Agriculture-9.27.50.8-9.0-3.65.4ppsBuilding and construction1.78.1-12.13.15.52.4ppsServices2.00.84.56.10.5-5.6ppsManufacturing industry-2.35.80.73.5-1.1-4.6pps16-Indicator board on wage developments (% change)Compensation per employee6.53.36.36.98.81.9ppsReal compensation per employee based on GDP7.04.25.412.62.9-9.7ppsLabour cost index (compens. of employees plus taxes minus subs.)6.24.65.47.75.6-2.1ppsLabour cost index (wages and salaries, total)6.44.75.37.95.7-2.2ppsLabour productivity (GDP/person employed)2.1-0.93.22.12.60.5pps2017-2018 
 
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Ireland201420152016201720181-Population (LFS, total, 1000 pers.)465246964749480248611.2%2-Population (LFS, working age:15-64, 1000 pers.)306130813110314131761.1%(% of total population)65.865.665.565.465.3-0.1pps3-Labour force (15-64, 1000 pers.)219922202260228223161.5%Male119412061221122712411.2%Female100610141039105510751.9%4-Activity rate (% of population 15-64)71.872.172.772.772.90.3ppsYoung (15-24)48.147.450.546.746.70.0ppsPrime age (25-54)81.882.082.082.983.20.3ppsOlder (55-64)58.260.260.762.063.31.3ppsNationals (15-64)71.471.872.272.072.00.0ppsNon-nationals (15-64)74.473.675.676.178.01.9ppsMale78.679.079.278.878.80.1ppsYoung (15-24)50.250.152.647.848.40.5ppsPrime age (25-54)89.589.689.390.190.0-0.2ppsOlder (55-64)68.070.770.170.872.11.3ppsFemale65.265.266.366.667.10.5ppsYoung (15-24)45.844.648.345.545.0-0.5ppsPrime age (25-54)74.474.674.975.976.70.8ppsOlder (55-64)48.549.851.453.354.71.3pps5- Employment rate (% of population 15-64)63.164.866.467.768.61.0ppsYoung (15-24)36.837.842.040.040.30.3ppsPrime age (25-54)73.174.775.878.079.21.2ppsOlder (55-64)52.655.456.858.460.42.0ppsLow-skilled (15-64)34.936.137.537.037.0-0.1ppsMedium-skilled (15-64)63.364.667.167.569.41.9ppsHigh-skilled (15-64)80.581.982.584.284.60.4ppsNationals (15-64)63.064.766.167.167.90.8ppsNon-nationals (15-64)63.965.268.770.472.62.2ppsMale68.470.371.873.074.11.1ppsYoung (15-24)36.938.342.240.241.21.0ppsPrime age (25-54)79.481.182.384.585.71.2ppsOlder (55-64)60.864.665.166.568.52.0ppsFemale58.059.461.162.463.30.9ppsYoung (15-24)36.737.341.739.739.4-0.4ppsPrime age (25-54)67.068.569.671.772.91.2ppsOlder (55-64)44.446.448.550.352.32.0pps6-Employed persons (15-64, 1000 pers.)1932.81994.72066.42125.12180.02.6%7-Employment growth (%, National accounts)2.73.63.73.03.20.2ppsEmployment growth (%, 15-64, LFS)2.53.23.62.82.6-0.3ppsMale3.03.33.32.72.60.0ppsFemale2.03.13.93.12.5-0.6pps8-Self employed (15-64, % of total employment )14.714.314.013.412.9-0.4ppsMale21.320.519.919.118.3-0.8ppsFemale7.07.07.16.86.80.0pps9-Temporary employment (15-64, % of total employment)10.39.69.09.19.90.8ppsMale10.19.68.68.89.50.7ppsFemale10.59.79.49.410.41.0pps10-Part-time (15-64, % of total employment )23.022.221.920.119.5-0.6ppsMale13.712.912.910.910.6-0.3ppsFemale33.733.132.430.629.9-0.7pps11Involuntary part-time (15-64, % of total employment)9.48.36.74.73.5-1.3pps12-Unemployment rate (harmonised:15-74)11.910.08.46.75.8-0.9ppsYoung (15-24)23.420.216.814.413.8-0.6ppsPrime age (25-49)10.78.97.55.84.8-1.0ppsOlder (55-64)9.67.96.55.84.6-1.2ppsLow-skilled (15-64)21.218.415.612.610.8-1.8ppsMedium-skilled (15-64)14.912.610.28.67.1-1.5ppsHigh-skilled (15-64)7.05.75.14.13.8-0.3ppsNationals (15-64)11.89.98.56.85.7-1.1ppsNon-nationals (15-64)14.111.49.17.56.9-0.6ppsMale12.710.89.17.15.8-1.3ppsFemale10.98.97.66.35.7-0.6pps13-Long-term unemployment (% of total unemployment)56.155.052.246.437.1-9.3pps14-Worked hours (full-time, average actual weekly hours)40.139.940.140.240.61.0%Male42.041.842.042.142.51.0%Female37.036.737.137.137.61.3%15-Sectoral employment growth (% change)Agriculture-3.21.43.6-2.4-3.0-0.6ppsBuilding and construction9.016.29.38.411.43.0ppsServices3.72.23.83.03.40.4ppsManufacturing industry1.35.66.21.1-1.3-2.4pps16-Indicator board on wage developments (% change)Compensation per employee0.42.32.22.52.1-0.4ppsReal compensation per employee based on GDP4.62.10.94.54.2-0.3ppsLabour cost index (compens. of employees plus taxes minus subs.)0.60.81.82.23.10.9ppsLabour cost index (wages and salaries, total)1.00.81.72.23.41.2ppsLabour productivity (GDP/person employed)5.720.80.05.04.8-0.2pps2017-2018 
 
Statistical annex 

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Greece201420152016201720181-Population (LFS, total, 1000 pers.)1089210821107761075510728-0.3%2-Population (LFS, working age:15-64, 1000 pers.)70406987693768866831-0.8%(% of total population)64.664.664.464.063.7-0.3pps3-Labour force (15-64, 1000 pers.)47474738473247014657-0.9%Male26462621261326052590-0.6%Female21012117211920962068-1.3%4-Activity rate (% of population 15-64)67.467.868.268.368.2-0.1ppsYoung (15-24)28.026.024.625.123.3-1.8ppsPrime age (25-54)84.385.485.585.085.00.0ppsOlder (55-64)41.141.644.946.748.51.7ppsNationals (15-64)66.867.467.868.068.00.0ppsNon-nationals (15-64)75.073.873.971.970.9-1.0ppsMale76.075.976.276.476.60.1ppsYoung (15-24)30.027.726.426.225.1-1.1ppsPrime age (25-54)93.193.193.293.093.20.2ppsOlder (55-64)53.454.957.359.861.41.5ppsFemale59.059.960.460.359.9-0.3ppsYoung (15-24)26.124.322.923.921.5-2.5ppsPrime age (25-54)75.677.777.777.076.7-0.3ppsOlder (55-64)29.929.533.634.936.82.0pps5- Employment rate (% of population 15-64)49.450.852.053.554.91.4ppsYoung (15-24)13.313.013.014.114.0-0.2ppsPrime age (25-54)62.464.566.067.468.91.5ppsOlder (55-64)34.034.336.338.341.12.8ppsLow-skilled (15-64)39.039.739.439.839.90.1ppsMedium-skilled (15-64)47.048.850.151.853.11.3ppsHigh-skilled (15-64)67.667.969.670.873.32.5ppsNationals (15-64)49.350.852.053.655.11.5ppsNon-nationals (15-64)50.451.052.051.951.8-0.1ppsMale58.059.361.062.764.72.0ppsYoung (15-24)15.815.114.715.915.90.1ppsPrime age (25-54)71.773.776.077.579.62.1ppsOlder (55-64)44.044.946.249.653.33.8ppsFemale41.142.543.344.445.30.9ppsYoung (15-24)10.910.911.312.412.0-0.4ppsPrime age (25-54)53.155.455.957.258.21.0ppsOlder (55-64)25.024.727.228.030.02.0pps6-Employed persons (15-64, 1000 pers.)3479.53548.03610.33682.73751.11.9%7-Employment growth (%, National accounts)0.90.70.51.51.70.2ppsEmployment growth (%, 15-64, LFS)0.62.01.82.01.9-0.1ppsMale-0.51.62.12.22.40.2ppsFemale2.22.51.21.71.1-0.6pps8-Self employed (15-64, % of total employment )30.729.929.529.429.1-0.3ppsMale36.435.334.234.434.0-0.4ppsFemale22.922.522.922.422.1-0.3pps9-Temporary employment (15-64, % of total employment)11.611.911.211.411.3-0.1ppsMale11.011.410.39.99.5-0.4ppsFemale12.412.612.313.313.50.2pps10-Part-time (15-64, % of total employment )9.39.49.89.79.1-0.6ppsMale6.56.76.96.66.1-0.5ppsFemale13.013.113.714.113.2-0.9pps11Involuntary part-time (15-64, % of total employment)6.66.87.16.86.4-0.4pps12-Unemployment rate (harmonised:15-74)26.524.923.621.519.3-2.2ppsYoung (15-24)52.449.847.343.639.9-3.7ppsPrime age (25-49)26.024.422.820.718.9-1.8ppsOlder (55-64)17.217.519.218.115.3-2.8ppsLow-skilled (15-64)28.727.226.924.822.8-2.0ppsMedium-skilled (15-64)30.327.726.224.021.9-2.1ppsHigh-skilled (15-64)20.120.018.116.614.3-2.3ppsNationals (15-64)26.124.623.321.219.0-2.2ppsNon-nationals (15-64)32.830.929.627.826.9-0.9ppsMale23.721.819.917.815.4-2.4ppsFemale30.228.928.126.124.2-1.9pps13-Long-term unemployment (% of total unemployment)73.473.071.872.670.1-2.5pps14-Worked hours (full-time, average actual weekly hours)42.842.843.142.942.6-0.7%Male44.144.244.644.444.1-0.7%Female40.740.640.840.740.4-0.7%15-Sectoral employment growth (% change)Agriculture0.4-3.5-2.40.23.73.5ppsBuilding and construction1.2-4.31.31.51.0-0.5ppsServices2.52.31.62.01.3-0.7ppsManufacturing industry-3.01.1-1.82.80.1-2.7pps16-Indicator board on wage developments (% change)Compensation per employee-2.0-2.4-0.90.51.30.8ppsReal compensation per employee based on GDP1.36.1-0.41.1-0.7-1.8ppsLabour cost index (compens. of employees plus taxes minus subs.)-1.1-3.0-1.42.52.50.0ppsLabour cost index (wages and salaries, total)-1.2-2.7-0.71.42.00.6ppsLabour productivity (GDP/person employed)-0.2-1.2-0.70.00.20.2pps2017-2018 
 
European Commission 

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146 

Spain201420152016201720181-Population (LFS, total, 1000 pers.)46455464104645046533467290.4%2-Population (LFS, working age:15-64, 1000 pers.)30750306423053630531306710.5%(% of total population)66.266.065.765.665.60.0pps3-Labour force (15-64, 1000 pers.)22814227672265722558226070.2%Male12277122321212012064120890.2%Female10537105351053610495105180.2%4-Activity rate (% of population 15-64)74.274.374.273.973.7-0.2ppsYoung (15-24)35.734.733.033.333.0-0.3ppsPrime age (25-54)87.387.487.487.086.9-0.1ppsOlder (55-64)55.457.659.259.660.50.8ppsNationals (15-64)73.773.873.873.573.4-0.1ppsNon-nationals (15-64)77.778.077.276.876.1-0.8ppsMale79.579.579.278.978.8-0.1ppsYoung (15-24)37.336.234.735.135.10.0ppsPrime age (25-54)92.692.692.592.091.9-0.1ppsOlder (55-64)64.366.267.067.968.40.6ppsFemale68.869.069.268.868.6-0.2ppsYoung (15-24)34.033.231.331.530.8-0.7ppsPrime age (25-54)82.082.082.382.081.8-0.2ppsOlder (55-64)46.949.451.751.852.91.1pps5- Employment rate (% of population 15-64)56.057.859.561.162.41.3ppsYoung (15-24)16.717.918.420.521.71.2ppsPrime age (25-54)67.469.471.573.274.71.5ppsOlder (55-64)44.346.949.150.552.21.7ppsLow-skilled (15-64)44.046.248.149.651.31.6ppsMedium-skilled (15-64)56.057.558.759.860.60.8ppsHigh-skilled (15-64)75.376.777.979.480.10.7ppsNationals (15-64)56.658.359.961.462.81.4ppsNon-nationals (15-64)50.854.256.658.559.41.0ppsMale60.762.964.866.567.91.4ppsYoung (15-24)17.418.619.421.222.71.5ppsPrime age (25-54)72.575.177.479.280.81.5ppsOlder (55-64)51.254.055.757.859.72.0ppsFemale51.252.754.355.756.91.2ppsYoung (15-24)16.017.317.219.720.50.8ppsPrime age (25-54)62.363.765.667.168.61.5ppsOlder (55-64)37.840.142.843.544.91.4pps6-Employed persons (15-64, 1000 pers.)17210.517717.518182.718648.519136.32.6%7-Employment growth (%, National accounts)1.02.82.12.72.2-0.5ppsEmployment growth (%, 15-64, LFS)1.22.92.62.62.60.1ppsMale1.43.32.42.52.50.0ppsFemale1.12.52.92.62.70.1pps8-Self employed (15-64, % of total employment )16.716.416.115.715.2-0.5ppsMale20.720.219.719.318.6-0.6ppsFemale11.911.811.911.411.1-0.2pps9-Temporary employment (15-64, % of total employment)24.025.226.126.826.90.1ppsMale23.625.125.826.026.00.0ppsFemale24.625.326.527.627.80.2pps10-Part-time (15-64, % of total employment )15.815.615.114.914.5-0.4ppsMale7.77.87.67.26.7-0.5ppsFemale25.525.124.124.123.9-0.2pps11Involuntary part-time (15-64, % of total employment)10.19.99.39.18.1-1.0pps12-Unemployment rate (harmonised:15-74)24.522.119.617.215.3-1.9ppsYoung (15-24)53.248.344.438.634.3-4.3ppsPrime age (25-49)22.820.618.215.914.0-1.9ppsOlder (55-64)20.018.617.015.313.8-1.5ppsLow-skilled (15-64)34.031.228.225.222.3-2.9ppsMedium-skilled (15-64)24.221.619.217.015.5-1.5ppsHigh-skilled (15-64)14.813.311.710.09.0-1.0ppsNationals (15-64)23.221.018.816.414.4-2.0ppsNon-nationals (15-64)34.630.526.723.921.9-2.0ppsMale23.620.818.115.713.7-2.0ppsFemale25.423.621.419.017.0-2.0pps13-Long-term unemployment (% of total unemployment)52.851.648.344.441.7-2.7pps14-Worked hours (full-time, average actual weekly hours)40.740.640.440.140.30.5%Male41.741.541.341.041.20.5%Female39.339.139.038.838.90.3%15-Sectoral employment growth (% change)Agriculture0.80.04.52.8-0.3-3.1ppsBuilding and construction-2.96.71.55.07.52.5ppsServices2.03.91.83.02.0-1.0ppsManufacturing industry-1.12.23.53.41.5-1.9pps16-Indicator board on wage developments (% change)Compensation per employee0.11.0-0.10.71.30.6ppsReal compensation per employee based on GDP-0.6-0.50.01.44.63.3ppsLabour cost index (compens. of employees plus taxes minus subs.)0.10.20.20.81.81.0ppsLabour cost index (wages and salaries, total)0.40.80.40.81.91.1ppsLabour productivity (GDP/person employed)0.31.00.90.20.1-0.1pps2017-2018 
 
Statistical annex 

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France201420152016201720181-Population (LFS, total, 1000 pers.)66312665816682867063672740.3%2-Population (LFS, working age:15-64, 1000 pers.)4101040927408904094740924-0.1%(% of total population)61.861.561.261.160.8-0.2pps3-Labour force (15-64, 1000 pers.)29121291642920729288294380.5%Male15103151271512915194152040.1%Female14018140371407814094142351.0%4-Activity rate (% of population 15-64)71.071.371.471.571.90.4ppsYoung (15-24)36.537.136.936.937.70.8ppsPrime age (25-54)87.887.587.587.487.70.3ppsOlder (55-64)50.752.653.754.956.01.1ppsNationals (15-64)71.471.872.072.272.40.2ppsNon-nationals (15-64)65.464.164.163.966.42.4ppsMale75.175.375.475.675.80.2ppsYoung (15-24)39.740.239.840.341.20.9ppsPrime age (25-54)92.992.492.492.692.4-0.2ppsOlder (55-64)53.155.156.156.958.31.4ppsFemale67.167.367.667.668.20.6ppsYoung (15-24)33.233.934.033.434.00.6ppsPrime age (25-54)83.082.782.782.483.20.7ppsOlder (55-64)48.550.351.453.053.90.9pps5- Employment rate (% of population 15-64)63.763.864.264.765.40.6ppsYoung (15-24)27.627.927.828.729.91.2ppsPrime age (25-54)79.879.479.780.080.60.5ppsOlder (55-64)46.948.749.851.352.10.9ppsLow-skilled (15-64)41.139.738.839.739.6-0.2ppsMedium-skilled (15-64)65.565.966.166.266.70.4ppsHigh-skilled (15-64)81.081.482.482.982.8-0.1ppsNationals (15-64)64.564.865.265.866.20.4ppsNon-nationals (15-64)52.450.851.452.155.23.1ppsMale67.167.167.668.468.90.5ppsYoung (15-24)29.729.929.831.032.41.4ppsPrime age (25-54)84.483.784.285.085.20.2ppsOlder (55-64)48.850.751.652.754.01.3ppsFemale60.360.660.961.261.90.7ppsYoung (15-24)25.526.025.826.327.20.9ppsPrime age (25-54)75.475.275.375.276.10.9ppsOlder (55-64)45.246.948.249.950.40.5pps6-Employed persons (15-64, 1000 pers.)26108.626118.526243.426511.826744.50.9%7-Employment growth (%, National accounts)0.50.20.61.01.00.0ppsEmployment growth (%, 15-64, LFS)2.20.00.51.00.9-0.1ppsMale1.6-0.20.61.40.5-0.8ppsFemale2.90.30.30.71.30.6pps8-Self employed (15-64, % of total employment )10.910.811.010.911.00.1ppsMale14.214.114.313.814.10.3ppsFemale7.27.37.57.77.7-0.1pps9-Temporary employment (15-64, % of total employment)15.316.016.116.816.7-0.1ppsMale14.515.415.716.216.1-0.1ppsFemale16.116.616.617.417.3-0.1pps10-Part-time (15-64, % of total employment )18.618.418.318.218.0-0.2ppsMale7.47.47.57.77.80.1ppsFemale30.630.129.829.628.8-0.8pps11Involuntary part-time (15-64, % of total employment)7.98.08.17.87.6-0.3pps12-Unemployment rate (harmonised:15-74)10.310.410.19.49.1-0.3ppsYoung (15-24)24.224.724.622.320.8-1.5ppsPrime age (25-49)9.29.38.98.58.1-0.4ppsOlder (55-64)7.57.47.26.56.90.4ppsLow-skilled (15-64)17.317.818.317.316.5-0.8ppsMedium-skilled (15-64)10.710.910.710.19.8-0.3ppsHigh-skilled (15-64)6.46.45.75.25.50.3ppsNationals (15-64)9.79.89.58.88.6-0.2ppsNon-nationals (15-64)19.920.719.818.516.8-1.7ppsMale10.510.810.39.59.0-0.5ppsFemale10.09.99.99.39.1-0.2pps13-Long-term unemployment (% of total unemployment)44.244.245.845.342.0-3.3pps14-Worked hours (full-time, average actual weekly hours)38.838.839.139.039.00.0%Male39.839.940.239.940.00.3%Female37.237.337.537.637.5-0.3%15-Sectoral employment growth (% change)Agriculture0.7-0.3-0.8-0.3-0.10.2ppsBuilding and construction-0.7-2.5-2.30.01.31.3ppsServices0.50.81.52.32.1-0.2ppsManufacturing industry-0.5-2.0-0.6-0.30.10.4pps16-Indicator board on wage developments (% change)Compensation per employee1.21.11.21.91.8-0.1ppsReal compensation per employee based on GDP1.71.10.6-0.10.30.4ppsLabour cost index (compens. of employees plus taxes minus subs.)0.70.81.21.32.51.2ppsLabour cost index (wages and salaries, total)1.61.51.41.91.7-0.2ppsLabour productivity (GDP/person employed)0.40.90.51.30.7-0.6pps2017-2018 
 
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Croatia201420152016201720181-Population (LFS, total, 1000 pers.)4236420841724130::%2-Population (LFS, working age:15-64, 1000 pers.)28262786275327202689-1.2%(% of total population)66.766.266.065.9::pps3-Labour force (15-64, 1000 pers.)18681865180618071783-1.4%Male1003998968973953-2.0%Female865867838835829-0.7%4-Activity rate (% of population 15-64)66.166.965.666.466.3-0.1ppsYoung (15-24)33.633.237.235.733.5-2.2ppsPrime age (25-54)84.184.582.083.383.40.0ppsOlder (55-64)41.044.342.243.644.81.2ppsNationals (15-64)66.167.065.766.566.3-0.2ppsNon-nationals (15-64)53.844.437.843.767.523.8ppsMale70.971.670.371.570.9-0.6ppsYoung (15-24)38.538.241.940.937.9-3.0ppsPrime age (25-54)86.686.985.286.786.4-0.3ppsOlder (55-64)52.154.950.752.853.40.6ppsFemale61.362.360.961.461.70.3ppsYoung (15-24)28.528.032.330.228.8-1.4ppsPrime age (25-54)81.582.178.879.980.30.4ppsOlder (55-64)30.634.434.235.136.71.7pps5- Employment rate (% of population 15-64)54.656.056.958.960.61.7ppsYoung (15-24)18.319.125.625.925.6-0.4ppsPrime age (25-54)71.272.372.474.977.02.0ppsOlder (55-64)36.239.238.140.442.82.5ppsLow-skilled (15-64)26.728.027.424.425.81.3ppsMedium-skilled (15-64)57.058.059.562.663.91.2ppsHigh-skilled (15-64)78.478.779.781.581.50.0ppsNationals (15-64)54.656.057.059.060.61.7ppsNon-nationals (15-64)40.038.934.142.558.115.6ppsMale59.160.361.463.865.41.6ppsYoung (15-24)21.222.428.929.830.50.7ppsPrime age (25-54)74.575.476.378.780.41.7ppsOlder (55-64)45.848.245.149.051.02.0ppsFemale50.051.652.454.055.91.8ppsYoung (15-24)15.315.722.221.820.3-1.5ppsPrime age (25-54)67.969.368.571.173.52.4ppsOlder (55-64)27.330.731.632.335.22.9pps6-Employed persons (15-64, 1000 pers.)1541.81559.11566.61603.01630.21.7%7-Employment growth (%, National accounts)2.71.20.32.20.0-2.2ppsEmployment growth (%, 15-64, LFS)3.21.10.52.31.7-0.6ppsMale4.00.60.62.71.3-1.5ppsFemale2.31.80.41.92.20.3pps8-Self employed (15-64, % of total employment )13.412.911.810.510.2-0.3ppsMale16.716.414.912.612.2-0.4ppsFemale9.68.98.17.97.8-0.1pps9-Temporary employment (15-64, % of total employment)16.920.222.220.719.9-0.8ppsMale16.620.421.920.619.4-1.2ppsFemale17.119.922.420.720.6-0.1pps10-Part-time (15-64, % of total employment )5.36.05.64.85.20.4ppsMale4.24.84.43.83.80.0ppsFemale6.77.37.16.06.80.8pps11Involuntary part-time (15-64, % of total employment)1.41.61.71.71.80.0pps12-Unemployment rate (harmonised:15-74)17.216.113.411.08.4-2.6ppsYoung (15-24)45.542.331.327.423.7-3.7ppsPrime age (25-49)15.314.411.610.17.7-2.4ppsOlder (55-64)11.611.69.67.54.4-3.1ppsLow-skilled (15-64)26.522.518.120.512.1-8.4ppsMedium-skilled (15-64)18.818.114.711.79.2-2.5ppsHigh-skilled (15-64)9.69.47.97.26.1-1.1ppsNationals (15-64)17.416.413.311.38.5-2.8ppsNon-nationals (15-64)0.00.00.00.014.214.2ppsMale16.615.612.710.37.7-2.6ppsFemale18.016.714.211.99.3-2.6pps13-Long-term unemployment (% of total unemployment)58.563.150.641.040.2-0.8pps14-Worked hours (full-time, average actual weekly hours)40.439.639.739.939.7-0.5%Male40.840.140.240.440.1-0.7%Female39.838.939.239.339.30.0%15-Sectoral employment growth (% change)Agriculture-9.4-1.9-17.4-6.1::ppsBuilding and construction-3.85.02.8-4.6::ppsServices4.92.83.25.5::ppsManufacturing industry2.8-1.82.72.0::pps16-Indicator board on wage developments (% change)Compensation per employee-5.20.41.3-1.1::ppsReal compensation per employee based on GDP0.92.6-0.91.1-0.6-1.7ppsLabour cost index (compens. of employees plus taxes minus subs.)-0.51.7-9.65.06.51.5ppsLabour cost index (wages and salaries, total)-0.51.7-9.35.06.61.6ppsLabour productivity (GDP/person employed)-2.71.13.20.7 :   :pps2017-2018 
 
Statistical annex 

149 

Italy201420152016201720181-Population (LFS, total, 1000 pers.)6078960731606286053760438-0.2%2-Population (LFS, working age:15-64, 1000 pers.)3916139035388713872638588-0.4%(% of total population)64.464.364.164.063.8-0.1pps3-Labour force (15-64, 1000 pers.)25039249972524325340253270.0%Male1432714382144641446714450-0.1%Female10712106151077910873108770.0%4-Activity rate (% of population 15-64)63.964.064.965.465.60.2ppsYoung (15-24)27.126.226.626.226.1-0.1ppsPrime age (25-54)77.076.877.577.977.90.0ppsOlder (55-64)48.951.153.455.457.01.5ppsNationals (15-64)63.263.364.364.865.00.2ppsNon-nationals (15-64)70.470.370.470.871.20.4ppsMale73.674.174.875.075.10.1ppsYoung (15-24)31.030.430.230.029.90.0ppsPrime age (25-54)87.787.788.288.588.4-0.1ppsOlder (55-64)60.263.365.967.068.61.5ppsFemale54.454.155.255.956.20.3ppsYoung (15-24)23.121.722.822.121.9-0.2ppsPrime age (25-54)66.465.966.867.367.40.1ppsOlder (55-64)38.339.641.744.546.11.6pps5- Employment rate (% of population 15-64)55.756.357.258.058.50.6ppsYoung (15-24)15.615.616.617.117.70.6ppsPrime age (25-54)67.968.268.869.469.80.4ppsOlder (55-64)46.248.250.352.253.71.5ppsLow-skilled (15-64)41.842.242.943.443.80.4ppsMedium-skilled (15-64)62.662.963.764.164.30.2ppsHigh-skilled (15-64)75.576.377.578.278.70.5ppsNationals (15-64)55.456.057.057.758.20.6ppsNon-nationals (15-64)58.558.959.560.661.20.6ppsMale64.765.566.567.167.60.6ppsYoung (15-24)18.218.619.220.120.80.8ppsPrime age (25-54)78.278.679.379.980.30.4ppsOlder (55-64)56.559.361.762.864.21.4ppsFemale46.847.248.148.949.50.6ppsYoung (15-24)12.812.413.713.914.30.4ppsPrime age (25-54)57.657.958.559.059.40.4ppsOlder (55-64)36.637.939.742.343.91.6pps6-Employed persons (15-64, 1000 pers.)21809.521972.622241.122443.622585.70.6%7-Employment growth (%, National accounts)0.10.71.31.20.9-0.3ppsEmployment growth (%, 15-64, LFS)0.20.71.20.90.6-0.3ppsMale0.01.01.10.60.6-0.1ppsFemale0.50.41.41.30.7-0.6pps8-Self employed (15-64, % of total employment )22.221.921.520.820.6-0.2ppsMale26.726.225.625.224.8-0.4ppsFemale16.015.915.814.914.90.0pps9-Temporary employment (15-64, % of total employment)13.614.114.015.517.11.6ppsMale13.113.613.515.116.61.5ppsFemale14.214.614.716.017.71.7pps10-Part-time (15-64, % of total employment )18.118.318.518.518.4-0.1ppsMale7.88.08.28.38.0-0.3ppsFemale32.132.432.732.532.4-0.1pps11Involuntary part-time (15-64, % of total employment)11.812.011.911.612.10.5pps12-Unemployment rate (harmonised:15-74)12.711.911.711.210.6-0.6ppsYoung (15-24)42.740.337.834.732.2-2.5ppsPrime age (25-49)11.811.211.110.910.3-0.6ppsOlder (55-64)5.55.55.75.85.7-0.1ppsLow-skilled (15-64)17.015.916.015.814.9-0.9ppsMedium-skilled (15-64)12.011.511.210.610.2-0.4ppsHigh-skilled (15-64)8.07.26.96.56.1-0.4ppsNationals (15-64)12.411.611.411.110.4-0.7ppsNon-nationals (15-64)17.016.315.414.414.1-0.3ppsMale11.911.310.910.39.7-0.6ppsFemale13.812.712.812.411.8-0.6pps13-Long-term unemployment (% of total unemployment)61.458.958.358.759.00.3pps14-Worked hours (full-time, average actual weekly hours)39.639.739.940.040.10.2%Male40.840.941.141.241.30.2%Female37.537.537.737.737.80.3%15-Sectoral employment growth (% change)Agriculture-0.21.02.3-0.90.61.5ppsBuilding and construction-4.0-1.2-0.10.3-0.3-0.6ppsServices0.51.62.21.71.2-0.5ppsManufacturing industry-1.8-0.90.90.61.40.8pps16-Indicator board on wage developments (% change)Compensation per employee0.21.00.50.12.01.9ppsReal compensation per employee based on GDP1.01.00.60.01.21.1ppsLabour cost index (compens. of employees plus taxes minus subs.)0.7-0.3-0.80.61.61.0ppsLabour cost index (wages and salaries, total)0.60.5-0.10.40.90.5ppsLabour productivity (GDP/person employed)0.00.3-0.20.50.0-0.5pps2017-2018 
 
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Cyprus201420152016201720181-Population (LFS, total, 1000 pers.)8538488528608691.1%2-Population (LFS, working age:15-64, 1000 pers.)5725595565645680.7%(% of total population)67.065.965.265.665.4-0.3pps3-Labour force (15-64, 1000 pers.)4254134084174262.2%Male2182102092152202.4%Female2072021992022072.0%4-Activity rate (% of population 15-64)74.373.973.473.975.01.1ppsYoung (15-24)40.337.837.336.639.22.5ppsPrime age (25-54)88.487.986.887.587.2-0.3ppsOlder (55-64)56.057.459.060.064.74.6ppsNationals (15-64)73.272.973.073.775.31.7ppsNon-nationals (15-64)79.478.375.274.873.6-1.2ppsMale80.078.878.778.879.91.1ppsYoung (15-24)41.136.935.833.236.53.3ppsPrime age (25-54)93.592.692.293.092.8-0.2ppsOlder (55-64)69.970.070.571.675.23.5ppsFemale69.169.468.569.370.41.0ppsYoung (15-24)39.538.938.539.941.71.9ppsPrime age (25-54)83.983.881.882.582.1-0.4ppsOlder (55-64)42.345.347.848.954.75.8pps5- Employment rate (% of population 15-64)62.162.763.765.668.63.0ppsYoung (15-24)25.825.426.327.531.33.8ppsPrime age (25-54)76.276.576.678.480.42.0ppsOlder (55-64)46.948.552.255.360.95.5ppsLow-skilled (15-64)40.440.742.641.744.22.5ppsMedium-skilled (15-64)62.562.462.666.469.83.5ppsHigh-skilled (15-64)77.378.378.379.180.81.7ppsNationals (15-64)60.861.663.265.268.83.6ppsNon-nationals (15-64)68.167.565.767.167.50.4ppsMale66.166.768.670.073.33.2ppsYoung (15-24)25.924.026.524.227.33.1ppsPrime age (25-54)79.680.681.783.686.22.6ppsOlder (55-64)57.257.760.964.970.35.4ppsFemale58.659.059.261.464.22.7ppsYoung (15-24)25.826.726.330.735.14.4ppsPrime age (25-54)73.172.772.073.575.01.5ppsOlder (55-64)36.939.443.746.252.05.8pps6-Employed persons (15-64, 1000 pers.)355.1350.0353.9369.8389.75.4%7-Employment growth (%, National accounts)-1.81.54.64.34.0-0.3ppsEmployment growth (%, 15-64, LFS)-0.4-1.41.14.55.40.9ppsMale-2.4-0.92.44.65.71.1ppsFemale1.7-1.9-0.24.45.00.6pps8-Self employed (15-64, % of total employment )15.213.012.211.411.70.3ppsMale20.315.915.513.714.10.4ppsFemale10.09.98.68.99.10.3pps9-Temporary employment (15-64, % of total employment)19.018.416.515.313.8-1.5ppsMale13.113.211.712.010.5-1.5ppsFemale24.423.421.318.617.2-1.4pps10-Part-time (15-64, % of total employment )13.513.013.412.210.8-1.4ppsMale10.310.311.39.17.5-1.6ppsFemale16.815.815.615.614.4-1.2pps11Involuntary part-time (15-64, % of total employment)8.89.09.38.26.9-1.3pps12-Unemployment rate (harmonised:15-74)16.115.013.011.18.4-2.7ppsYoung (15-24)36.032.829.124.720.2-4.5ppsPrime age (25-49)13.913.111.710.47.8-2.6ppsOlder (55-64)16.315.611.57.85.8-2.0ppsLow-skilled (15-64)20.319.416.414.910.4-4.5ppsMedium-skilled (15-64)18.416.714.511.68.9-2.7ppsHigh-skilled (15-64)13.012.110.99.87.7-2.1ppsNationals (15-64)16.915.513.411.58.6-2.9ppsNon-nationals (15-64)14.113.712.610.58.3-2.2ppsMale17.115.112.710.98.1-2.8ppsFemale15.114.813.411.38.8-2.5pps13-Long-term unemployment (% of total unemployment)47.745.644.540.731.6-9.1pps14-Worked hours (full-time, average actual weekly hours)40.540.540.940.740.3-1.0%Male41.741.742.042.041.4-1.4%Female39.339.139.639.238.9-0.8%15-Sectoral employment growth (% change)Agriculture-3.92.12.0-1.9-0.51.4ppsBuilding and construction-9.50.07.812.710.1-2.6ppsServices0.52.65.74.73.9-0.8ppsManufacturing industry-4.21.55.24.54.70.2pps16-Indicator board on wage developments (% change)Compensation per employee-3.5-1.3-1.10.70.1-0.6ppsReal compensation per employee based on GDP-0.54.51.1-2.2-1.40.8ppsLabour cost index (compens. of employees plus taxes minus subs.)-2.5-1.00.41.13.42.3ppsLabour cost index (wages and salaries, total)-2.9-0.90.71.53.41.9ppsLabour productivity (GDP/person employed)0.50.50.20.2-0.1-0.3pps2017-2018 
 
Statistical annex 

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Latvia201420152016201720181-Population (LFS, total, 1000 pers.)19941977195919411926-0.8%2-Population (LFS, working age:15-64, 1000 pers.)12951275125412301216-1.1%(% of total population)65.064.564.063.363.2-0.2pps3-Labour force (15-64, 1000 pers.)966965957946945-0.1%Male4864864794754750.0%Female480479478471470-0.3%4-Activity rate (% of population 15-64)74.675.776.377.077.70.7ppsYoung (15-24)40.441.339.739.737.7-2.0ppsPrime age (25-54)87.287.687.888.589.10.5ppsOlder (55-64)62.665.567.667.970.82.9ppsNationals (15-64)74.976.176.977.578.20.7ppsNon-nationals (15-64)72.673.372.873.274.31.1ppsMale77.878.978.879.880.40.7ppsYoung (15-24)45.345.243.242.840.6-2.3ppsPrime age (25-54)90.590.790.291.892.10.4ppsOlder (55-64)63.768.069.569.272.53.3ppsFemale71.672.874.074.375.10.8ppsYoung (15-24)35.337.135.936.634.8-1.9ppsPrime age (25-54)84.084.685.585.486.00.6ppsOlder (55-64)61.763.666.166.969.42.5pps5- Employment rate (% of population 15-64)66.368.168.770.171.81.7ppsYoung (15-24)32.534.532.833.033.10.2ppsPrime age (25-54)78.279.279.781.282.71.4ppsOlder (55-64)56.459.461.462.365.43.1ppsLow-skilled (15-64)32.634.735.535.835.1-0.7ppsMedium-skilled (15-64)67.768.868.270.572.72.2ppsHigh-skilled (15-64)83.485.186.586.988.92.0ppsNationals (15-64)67.068.869.670.972.71.8ppsNon-nationals (15-64)61.963.663.564.465.91.5ppsMale68.469.970.071.973.61.7ppsYoung (15-24)36.537.134.035.035.50.5ppsPrime age (25-54)80.381.281.483.584.61.1ppsOlder (55-64)56.460.161.362.466.43.9ppsFemale64.466.467.668.470.11.7ppsYoung (15-24)28.231.931.630.930.6-0.3ppsPrime age (25-54)76.077.378.179.080.71.7ppsOlder (55-64)56.458.961.462.264.62.5pps6-Employed persons (15-64, 1000 pers.)858.6867.9862.3861.9873.31.3%7-Employment growth (%, National accounts)-1.31.4-0.30.01.61.6ppsEmployment growth (%, 15-64, LFS)-0.91.1-0.60.01.31.4ppsMale-0.31.0-1.40.71.50.9ppsFemale-1.51.20.0-0.81.21.9pps8-Self employed (15-64, % of total employment )10.611.611.811.811.0-0.9ppsMale13.214.714.713.912.9-1.0ppsFemale8.08.59.09.89.1-0.7pps9-Temporary employment (15-64, % of total employment)3.33.83.73.02.7-0.3ppsMale4.34.64.63.73.0-0.7ppsFemale2.43.02.82.42.40.0pps10-Part-time (15-64, % of total employment )6.87.28.57.77.3-0.4ppsMale4.74.56.14.84.7-0.1ppsFemale8.910.010.810.69.8-0.8pps11Involuntary part-time (15-64, % of total employment)2.62.43.12.72.4-0.2pps12-Unemployment rate (harmonised:15-74)10.89.99.68.77.4-1.3ppsYoung (15-24)19.616.317.317.012.2-4.8ppsPrime age (25-49)10.49.59.38.37.2-1.1ppsOlder (55-64)9.99.39.28.37.6-0.7ppsLow-skilled (15-64)24.522.321.119.216.8-2.4ppsMedium-skilled (15-64)11.911.111.610.48.7-1.7ppsHigh-skilled (15-64)5.75.04.44.03.8-0.2ppsNationals (15-64)10.59.69.58.57.1-1.4ppsNon-nationals (15-64)14.813.212.712.111.3-0.8ppsMale11.811.110.99.88.4-1.4ppsFemale9.88.68.47.76.4-1.3pps13-Long-term unemployment (% of total unemployment)43.045.541.537.442.04.6pps14-Worked hours (full-time, average actual weekly hours)40.039.840.339.939.90.0%Male40.340.140.640.340.30.0%Female39.739.539.939.539.50.0%15-Sectoral employment growth (% change)Agriculture-3.87.2-3.4-3.40.23.6ppsBuilding and construction3.3-1.8-8.05.49.44.0ppsServices0.52.3-0.1-0.22.02.2ppsManufacturing industry-5.0-1.30.40.01.11.1pps16-Indicator board on wage developments (% change)Compensation per employee8.67.77.38.07.8-0.2ppsReal compensation per employee based on GDP7.613.69.012.35.3-7.0ppsLabour cost index (compens. of employees plus taxes minus subs.)6.27.36.86.512.15.6ppsLabour cost index (wages and salaries, total)7.37.46.16.610.74.1ppsLabour productivity (GDP/person employed)3.31.52.44.73.1-1.6pps2017-2018 
 
European Commission 

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152 

Lithuania201420152016201720181-Population (LFS, total, 1000 pers.)29322905286828222800-0.8%2-Population (LFS, working age:15-64, 1000 pers.)19611935189918541828-1.4%(% of total population)66.966.666.265.765.3-0.4pps3-Labour force (15-64, 1000 pers.)144614341433140814130.3%Male7217107096977041.0%Female724724724711709-0.4%4-Activity rate (% of population 15-64)73.774.175.575.977.31.4ppsYoung (15-24)34.233.835.435.036.51.4ppsPrime age (25-54)89.789.389.389.389.60.4ppsOlder (55-64)63.066.270.071.373.92.6ppsNationals (15-64)73.774.175.576.077.31.3ppsNon-nationals (15-64)82.173.370.573.977.53.7ppsMale76.075.877.177.478.91.5ppsYoung (15-24)38.636.738.737.838.70.9ppsPrime age (25-54)90.890.490.290.491.00.6ppsOlder (55-64)68.269.873.673.376.23.0ppsFemale71.672.573.974.675.81.2ppsYoung (15-24)29.630.831.832.234.11.9ppsPrime age (25-54)88.788.288.588.188.30.1ppsOlder (55-64)58.963.367.269.672.02.3pps5- Employment rate (% of population 15-64)65.767.269.470.472.42.0ppsYoung (15-24)27.628.330.230.432.42.0ppsPrime age (25-54)80.881.682.783.384.61.3ppsOlder (55-64)56.260.464.666.168.52.4ppsLow-skilled (15-64)19.519.919.220.922.71.8ppsMedium-skilled (15-64)64.666.167.668.871.02.3ppsHigh-skilled (15-64)88.488.790.490.090.50.5ppsNationals (15-64)65.667.269.470.472.42.0ppsNon-nationals (15-64)72.667.564.871.273.22.0ppsMale66.668.070.070.673.32.7ppsYoung (15-24)31.030.932.532.334.11.8ppsPrime age (25-54)80.781.882.683.185.22.1ppsOlder (55-64)58.862.466.967.170.53.4ppsFemale64.966.568.870.271.61.4ppsYoung (15-24)24.025.727.828.430.62.2ppsPrime age (25-54)80.981.482.983.684.10.5ppsOlder (55-64)54.358.862.865.267.01.8pps6-Employed persons (15-64, 1000 pers.)1288.01300.61317.71305.61323.71.4%7-Employment growth (%, National accounts)2.01.32.0-0.51.11.6ppsEmployment growth (%, 15-64, LFS)1.91.01.3-0.91.42.3ppsMale1.90.90.9-1.12.94.0ppsFemale1.91.11.7-0.70.00.7pps8-Self employed (15-64, % of total employment )10.610.811.110.910.80.0ppsMale12.613.414.313.813.4-0.4ppsFemale8.68.48.18.18.30.2pps9-Temporary employment (15-64, % of total employment)2.82.12.01.71.6-0.1ppsMale3.62.42.22.11.7-0.4ppsFemale2.01.81.71.31.40.1pps10-Part-time (15-64, % of total employment )8.67.67.17.67.1-0.5ppsMale6.45.55.45.75.2-0.5ppsFemale10.69.78.89.48.9-0.5pps11Involuntary part-time (15-64, % of total employment)2.72.42.22.31.7-0.6pps12-Unemployment rate (harmonised:15-74)10.79.17.97.16.2-0.9ppsYoung (15-24)19.316.314.513.311.1-2.2ppsPrime age (25-49)9.98.67.46.65.6-1.0ppsOlder (55-64)10.78.77.77.37.2-0.1ppsLow-skilled (15-64)30.727.325.921.618.5-3.1ppsMedium-skilled (15-64)13.711.910.69.68.2-1.4ppsHigh-skilled (15-64)4.33.73.03.02.9-0.1ppsNationals (15-64)10.99.38.17.36.3-1.0ppsNon-nationals (15-64)0.00.00.00.00.00.0ppsMale12.210.19.18.66.9-1.7ppsFemale9.28.26.75.75.4-0.3pps13-Long-term unemployment (% of total unemployment)44.642.838.237.732.2-5.5pps14-Worked hours (full-time, average actual weekly hours)39.639.639.739.339.40.3%Male40.140.140.339.939.90.0%Female39.139.139.138.738.90.5%15-Sectoral employment growth (% change)Agriculture11.00.2-10.3-3.0-2.90.1ppsBuilding and construction0.05.8-1.4-4.51.35.8ppsServices2.6-0.13.50.11.61.6ppsManufacturing industry-0.42.13.6-0.84.25.0pps16-Indicator board on wage developments (% change)Compensation per employee4.75.96.78.78.0-0.7ppsReal compensation per employee based on GDP9.16.513.15.14.0-1.1ppsLabour cost index (compens. of employees plus taxes minus subs.)4.35.48.29.610.00.4ppsLabour cost index (wages and salaries, total)4.75.98.28.89.70.9ppsLabour productivity (GDP/person employed)1.50.70.44.72.4-2.3pps2017-2018 
 
Statistical annex 

153 

Luxembourg201420152016201720181-Population (LFS, total, 1000 pers.)5585695845976092.0%2-Population (LFS, working age:15-64, 1000 pers.)3643863964074151.9%(% of total population)65.367.867.768.268.1-0.1pps3-Labour force (15-64, 1000 pers.)2582742772862953.2%Male1431491511531582.9%Female1161251261331373.5%4-Activity rate (% of population 15-64)70.870.970.070.271.10.9ppsYoung (15-24)26.435.230.730.533.12.6ppsPrime age (25-54)88.087.787.288.088.40.4ppsOlder (55-64)44.440.441.741.041.90.9ppsNationals (15-64)66.366.866.165.766.10.4ppsNon-nationals (15-64)75.675.173.874.475.81.4ppsMale77.276.075.174.074.70.7ppsYoung (15-24)29.536.330.532.533.81.3ppsPrime age (25-54)95.093.993.091.992.20.3ppsOlder (55-64)52.045.449.146.747.40.7ppsFemale64.265.664.766.267.41.2ppsYoung (15-24)22.934.230.928.232.24.0ppsPrime age (25-54)80.981.481.184.084.50.4ppsOlder (55-64)36.535.134.035.236.21.0pps5- Employment rate (% of population 15-64)66.666.165.666.367.10.8ppsYoung (15-24)20.329.024.925.828.42.6ppsPrime age (25-54)83.882.682.583.783.90.2ppsOlder (55-64)42.538.439.639.740.50.8ppsLow-skilled (15-64)41.946.842.142.044.82.8ppsMedium-skilled (15-64)65.965.965.367.867.6-0.2ppsHigh-skilled (15-64)83.083.383.884.083.7-0.3ppsNationals (15-64)63.863.963.363.263.20.0ppsNon-nationals (15-64)69.768.467.769.270.81.5ppsMale72.671.370.569.970.60.7ppsYoung (15-24)21.929.524.326.828.51.6ppsPrime age (25-54)90.689.388.587.488.00.6ppsOlder (55-64)49.742.946.445.345.50.2ppsFemale60.560.860.462.663.40.8ppsYoung (15-24)18.828.925.524.728.43.7ppsPrime age (25-54)76.875.776.479.879.7-0.1ppsOlder (55-64)35.233.532.434.035.01.0pps6-Employed persons (15-64, 1000 pers.)242.8255.2259.4269.9278.43.1%7-Employment growth (%, National accounts)2.62.63.03.43.70.3ppsEmployment growth (%, 15-64, LFS)2.85.11.64.03.1-0.9ppsMale1.94.31.61.83.11.3ppsFemale4.16.11.76.73.1-3.6pps8-Self employed (15-64, % of total employment )7.88.69.08.97.5-1.3ppsMale9.09.410.39.78.4-1.3ppsFemale6.47.57.58.06.4-1.6pps9-Temporary employment (15-64, % of total employment)8.110.29.09.19.80.7ppsMale7.110.28.98.89.10.3ppsFemale9.210.29.19.410.71.3pps10-Part-time (15-64, % of total employment )18.518.519.219.617.8-1.8ppsMale4.75.66.26.15.8-0.3ppsFemale35.634.235.135.331.8-3.5pps11Involuntary part-time (15-64, % of total employment)2.42.72.22.62.2-0.4pps12-Unemployment rate (harmonised:15-74)6.06.56.35.65.5-0.1ppsYoung (15-24)22.617.318.915.414.2-1.2ppsPrime age (25-49)4.95.85.34.95.00.1ppsOlder (55-64)4.34.75.03.33.60.3ppsLow-skilled (15-64)10.210.79.98.98.4-0.5ppsMedium-skilled (15-64)6.36.36.85.35.60.3ppsHigh-skilled (15-64)4.04.74.03.94.30.4ppsNationals (15-64)3.84.34.23.94.40.5ppsNon-nationals (15-64)7.88.98.26.96.6-0.3ppsMale5.85.96.15.65.3-0.3ppsFemale6.47.16.55.65.70.1pps13-Long-term unemployment (% of total unemployment)27.328.434.938.124.7-13.4pps14-Worked hours (full-time, average actual weekly hours)41.541.341.140.840.6-0.5%Male42.142.242.041.641.3-0.7%Female40.339.739.539.539.4-0.3%15-Sectoral employment growth (% change)Agriculture-0.9-0.6-0.5-0.40.20.6ppsBuilding and construction1.41.83.02.44.11.7ppsServices2.92.93.94.04.10.1ppsManufacturing industry-0.90.60.71.41.1-0.3pps16-Indicator board on wage developments (% change)Compensation per employee3.51.60.93.32.1-1.1ppsReal compensation per employee based on GDP0.9-0.4-2.72.6-1.1-3.7ppsLabour cost index (compens. of employees plus taxes minus subs.)3.20.51.12.61.9-0.7ppsLabour cost index (wages and salaries, total)3.40.61.24.02.0-2.0ppsLabour productivity (GDP/person employed)1.71.3-0.6-1.8-1.10.7pps2017-2018 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

154 

Hungary201420152016201720181-Population (LFS, total, 1000 pers.)98669843981497889776-0.1%2-Population (LFS, working age:15-64, 1000 pers.)65886530647864156370-0.7%(% of total population)66.866.366.065.565.2-0.4pps3-Labour force (15-64, 1000 pers.)441344834543456545820.4%Male238424262465248525000.6%Female202920572079208020830.2%4-Activity rate (% of population 15-64)67.068.670.171.271.90.8ppsYoung (15-24)29.531.032.332.432.3-0.2ppsPrime age (25-54)85.085.886.186.987.00.2ppsOlder (55-64)44.648.152.153.655.82.3ppsNationals (15-64)66.968.670.171.272.00.8ppsNon-nationals (15-64)74.970.668.462.564.11.6ppsMale73.475.376.978.279.10.8ppsYoung (15-24)33.034.436.136.537.10.6ppsPrime age (25-54)91.292.092.493.393.30.0ppsOlder (55-64)53.257.862.464.567.12.5ppsFemale60.762.263.564.264.90.7ppsYoung (15-24)25.927.528.228.227.2-1.0ppsPrime age (25-54)78.879.679.880.480.70.3ppsOlder (55-64)37.439.943.544.346.32.0pps5- Employment rate (% of population 15-64)61.863.966.568.269.21.1ppsYoung (15-24)23.525.728.129.029.00.0ppsPrime age (25-54)79.280.682.283.784.10.4ppsOlder (55-64)41.845.349.851.754.42.7ppsLow-skilled (15-64)31.533.936.638.539.40.9ppsMedium-skilled (15-64)66.768.871.573.173.70.6ppsHigh-skilled (15-64)80.882.184.484.385.10.7ppsNationals (15-64)61.763.966.568.269.31.1ppsNon-nationals (15-64)71.067.565.360.660.3-0.2ppsMale67.870.373.075.276.31.1ppsYoung (15-24)26.428.131.532.933.40.5ppsPrime age (25-54)85.386.888.290.190.40.3ppsOlder (55-64)49.654.459.762.565.53.0ppsFemale55.957.860.261.362.31.1ppsYoung (15-24)20.523.124.624.824.3-0.5ppsPrime age (25-54)73.274.476.277.277.70.5ppsOlder (55-64)35.237.741.542.444.92.5pps6-Employed persons (15-64, 1000 pers.)4069.94175.84309.44373.44410.70.9%7-Employment growth (%, National accounts)4.82.43.12.02.20.2ppsEmployment growth (%, 15-64, LFS)5.42.63.21.50.9-0.6ppsMale5.72.83.22.20.9-1.3ppsFemale5.22.43.20.60.80.2pps8-Self employed (15-64, % of total employment )10.310.210.09.79.70.1ppsMale13.012.612.111.511.60.2ppsFemale7.17.47.57.57.40.0pps9-Temporary employment (15-64, % of total employment)10.811.49.78.87.3-1.5ppsMale11.211.69.48.26.7-1.5ppsFemale10.311.110.29.57.9-1.6pps10-Part-time (15-64, % of total employment )6.05.74.84.34.2-0.1ppsMale4.14.03.12.72.5-0.2ppsFemale8.37.76.86.36.30.0pps11Involuntary part-time (15-64, % of total employment)2.52.11.41.21.0-0.2pps12-Unemployment rate (harmonised:15-74)7.76.85.14.23.7-0.5ppsYoung (15-24)20.417.312.910.710.2-0.5ppsPrime age (25-49)6.86.04.53.73.4-0.3ppsOlder (55-64)6.45.84.43.62.6-1.0ppsLow-skilled (15-64)18.617.413.311.210.4-0.8ppsMedium-skilled (15-64)7.46.44.83.83.4-0.4ppsHigh-skilled (15-64)3.22.41.81.61.5-0.1ppsNationals (15-64)7.86.95.24.23.7-0.5ppsNon-nationals (15-64)0.00.00.00.00.00.0ppsMale7.66.65.13.83.5-0.3ppsFemale7.97.05.14.64.0-0.6pps13-Long-term unemployment (% of total unemployment)47.445.546.540.438.6-1.8pps14-Worked hours (full-time, average actual weekly hours)39.339.339.839.338.8-1.3%Male39.839.940.439.939.3-1.5%Female38.738.639.138.638.1-1.3%15-Sectoral employment growth (% change)Agriculture1.4-3.9-0.6-2.7-4.2-1.5ppsBuilding and construction3.31.93.04.37.53.2ppsServices6.52.94.43.03.90.8ppsManufacturing industry2.9-0.53.33.52.4-1.1pps16-Indicator board on wage developments (% change)Compensation per employee0.6-1.54.46.29.63.4ppsReal compensation per employee based on GDP5.75.21.7-0.22.22.3ppsLabour cost index (compens. of employees plus taxes minus subs.)2.93.54.89.39.0-0.3ppsLabour cost index (wages and salaries, total)3.33.95.113.411.3-2.1ppsLabour productivity (GDP/person employed)-0.61.1-0.72.12.70.6pps2017-2018 
 
Statistical annex 

155 

Malta201420152016201720181-Population (LFS, total, 1000 pers.)4354454564694822.9%2-Population (LFS, working age:15-64, 1000 pers.)2922993043133222.9%(% of total population)67.267.066.866.966.90.0pps3-Labour force (15-64, 1000 pers.)1982062152262395.8%Male1211251291351414.8%Female78818691987.3%4-Activity rate (% of population 15-64)67.868.970.672.274.22.0ppsYoung (15-24)53.051.651.852.955.42.5ppsPrime age (25-54)81.081.783.284.685.61.0ppsOlder (55-64)42.144.547.548.351.43.0ppsNationals (15-64)66.668.069.470.672.52.0ppsNon-nationals (15-64)80.975.578.380.781.91.2ppsMale80.881.582.583.484.51.1ppsYoung (15-24)53.453.754.554.354.90.6ppsPrime age (25-54)95.495.495.896.196.30.2ppsOlder (55-64)61.463.365.066.169.23.0ppsFemale54.455.558.060.263.12.9ppsYoung (15-24)52.549.448.851.455.94.5ppsPrime age (25-54)65.767.269.671.974.02.1ppsOlder (55-64)23.025.830.030.533.32.9pps5- Employment rate (% of population 15-64)63.965.167.269.271.42.1ppsYoung (15-24)46.845.646.247.350.33.0ppsPrime age (25-54)77.278.280.081.883.11.2ppsOlder (55-64)39.642.245.847.249.82.6ppsLow-skilled (15-64)51.553.155.656.457.91.5ppsMedium-skilled (15-64)70.669.970.873.076.03.0ppsHigh-skilled (15-64)87.688.189.090.290.30.1ppsNationals (15-64)62.764.266.067.970.12.3ppsNon-nationals (15-64)76.672.175.576.477.30.8ppsMale75.877.178.980.181.21.1ppsYoung (15-24)45.946.648.748.648.70.2ppsPrime age (25-54)90.891.592.393.193.40.2ppsOlder (55-64)56.959.662.764.567.22.7ppsFemale51.652.555.057.660.83.2ppsYoung (15-24)47.244.543.545.952.06.1ppsPrime age (25-54)62.864.066.769.471.82.4ppsOlder (55-64)22.025.229.029.832.02.2pps6-Employed persons (15-64, 1000 pers.)186.8194.4204.6216.8230.06.1%7-Employment growth (%, National accounts)5.44.14.38.15.3-2.8ppsEmployment growth (%, 15-64, LFS)4.64.15.26.06.10.1ppsMale3.14.34.45.04.8-0.2ppsFemale7.03.76.57.48.10.8pps8-Self employed (15-64, % of total employment )12.813.313.614.413.6-0.8ppsMale16.917.418.718.617.6-1.0ppsFemale6.66.95.98.17.8-0.3pps9-Temporary employment (15-64, % of total employment)7.77.57.66.07.71.7ppsMale6.26.56.65.37.21.9ppsFemale9.69.08.96.98.51.6pps10-Part-time (15-64, % of total employment )15.314.313.913.713.3-0.4ppsMale6.96.46.06.36.50.2ppsFemale28.126.525.924.623.0-1.6pps11Involuntary part-time (15-64, % of total employment)2.32.31.71.51.3-0.2pps12-Unemployment rate (harmonised:15-74)5.75.44.74.03.7-0.3ppsYoung (15-24)11.711.610.710.69.2-1.4ppsPrime age (25-49)4.64.33.93.23.0-0.2ppsOlder (55-64)6.15.23.42.53.20.7ppsLow-skilled (15-64)9.28.87.56.25.8-0.4ppsMedium-skilled (15-64)3.63.63.63.33.2-0.1ppsHigh-skilled (15-64)2.62.01.72.02.00.0ppsNationals (15-64)5.95.64.93.83.3-0.5ppsNon-nationals (15-64)5.54.63.65.35.70.4ppsMale6.15.44.43.83.80.0ppsFemale5.15.45.24.33.6-0.7pps13-Long-term unemployment (% of total unemployment)47.544.339.740.130.8-9.3pps14-Worked hours (full-time, average actual weekly hours)40.140.240.740.040.20.5%Male41.141.341.741.041.20.5%Female38.138.038.638.238.50.8%15-Sectoral employment growth (% change)Agriculture-4.5-0.52.45.94.7-1.2ppsBuilding and construction0.94.61.46.75.9-0.8ppsServices7.35.16.29.87.5-2.3ppsManufacturing industry2.02.1-0.83.42.2-1.2pps16-Indicator board on wage developments (% change)Compensation per employee1.45.52.9-0.72.43.0ppsReal compensation per employee based on GDP1.4-0.91.60.91.00.1ppsLabour cost index (compens. of employees plus taxes minus subs.)3.07.3-1.72.21.1-1.1ppsLabour cost index (wages and salaries, total)3.17.3-1.72.21.1-1.1ppsLabour productivity (GDP/person employed)3.26.51.3-1.21.42.6pps2017-2018 
 
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Netherlands201420152016201720181-Population (LFS, total, 1000 pers.)16865169401703017131172320.6%2-Population (LFS, working age:15-64, 1000 pers.)10980109501098811044110700.2%(% of total population)65.164.664.564.564.2-0.2pps3-Labour force (15-64, 1000 pers.)867787198754880588840.9%Male463846414645465946990.8%Female404040784109414641851.0%4-Activity rate (% of population 15-64)79.079.679.779.780.30.5ppsYoung (15-24)67.468.568.268.368.90.5ppsPrime age (25-54)87.187.186.986.787.00.3ppsOlder (55-64)64.967.168.469.570.81.3ppsNationals (15-64)79.680.280.380.481.00.6ppsNon-nationals (15-64)69.169.068.868.468.70.4ppsMale84.284.684.484.284.70.5ppsYoung (15-24)67.067.667.267.068.01.0ppsPrime age (25-54)92.292.191.791.391.70.3ppsOlder (55-64)75.577.678.279.080.01.0ppsFemale73.874.775.075.275.80.5ppsYoung (15-24)67.769.469.269.769.80.1ppsPrime age (25-54)81.982.182.282.082.40.4ppsOlder (55-64)54.356.758.660.261.81.7pps5- Employment rate (% of population 15-64)73.174.174.875.877.21.3ppsYoung (15-24)58.860.860.862.363.91.7ppsPrime age (25-54)81.782.282.983.584.61.1ppsOlder (55-64)59.961.763.565.767.72.0ppsLow-skilled (15-64)55.657.057.858.860.41.7ppsMedium-skilled (15-64)76.076.577.478.079.11.0ppsHigh-skilled (15-64)86.887.487.487.888.50.7ppsNationals (15-64)73.974.975.676.778.11.4ppsNon-nationals (15-64)60.559.861.562.863.81.0ppsMale78.179.079.680.481.61.2ppsYoung (15-24)58.759.959.661.062.81.7ppsPrime age (25-54)86.987.588.188.489.20.9ppsOlder (55-64)69.471.172.874.876.61.8ppsFemale68.169.270.171.372.81.5ppsYoung (15-24)58.861.762.163.665.21.6ppsPrime age (25-54)76.577.077.778.679.91.3ppsOlder (55-64)50.452.454.256.658.82.2pps6-Employed persons (15-64, 1000 pers.)8028.58115.58223.48376.48543.32.0%7-Employment growth (%, National accounts)-0.11.01.52.22.50.3ppsEmployment growth (%, 15-64, LFS)-0.91.11.31.92.00.1ppsMale-0.50.71.11.51.70.2ppsFemale-1.51.51.62.32.30.1pps8-Self employed (15-64, % of total employment )15.115.315.515.515.4-0.1ppsMale18.418.318.618.418.40.0ppsFemale11.412.012.112.212.0-0.2pps9-Temporary employment (15-64, % of total employment)21.120.020.621.721.4-0.3ppsMale20.218.819.320.419.9-0.5ppsFemale22.021.222.023.123.0-0.1pps10-Part-time (15-64, % of total employment )49.650.049.749.850.10.3ppsMale26.126.526.227.027.50.5ppsFemale76.776.976.475.875.6-0.2pps11Involuntary part-time (15-64, % of total employment)5.45.04.94.13.5-0.6pps12-Unemployment rate (harmonised:15-74)7.46.96.04.93.8-1.1ppsYoung (15-24)12.711.310.88.97.2-1.7ppsPrime age (25-49)6.25.64.63.72.8-0.9ppsOlder (55-64)7.78.17.25.54.5-1.0ppsLow-skilled (15-64)12.311.310.08.56.7-1.8ppsMedium-skilled (15-64)7.57.06.14.83.6-1.2ppsHigh-skilled (15-64)4.03.83.52.92.4-0.5ppsNationals (15-64)7.26.65.84.73.6-1.1ppsNon-nationals (15-64)12.413.310.68.27.2-1.0ppsMale7.26.55.64.53.7-0.8ppsFemale7.87.36.55.34.0-1.3pps13-Long-term unemployment (% of total unemployment)39.443.242.440.036.8-3.2pps14-Worked hours (full-time, average actual weekly hours)41.741.541.741.541.3-0.5%Male42.242.142.342.041.8-0.5%Female39.839.639.939.839.5-0.8%15-Sectoral employment growth (% change)Agriculture-0.5-0.51.01.51.0-0.5ppsBuilding and construction-2.8-0.90.72.22.80.6ppsServices1.02.42.52.92.90.0ppsManufacturing industry-0.70.10.50.91.91.0pps16-Indicator board on wage developments (% change)Compensation per employee1.6-0.31.61.01.90.9ppsReal compensation per employee based on GDP1.5-0.3-1.01.11.60.5ppsLabour cost index (compens. of employees plus taxes minus subs.)1.30.00.51.42.20.8ppsLabour cost index (wages and salaries, total)-0.31.90.61.61.90.3ppsLabour productivity (GDP/person employed)1.51.00.60.70.1-0.6pps2017-2018 
 
Statistical annex 

157 

Austria201420152016201720181-Population (LFS, total, 1000 pers.)854486308740879588440.6%2-Population (LFS, working age:15-64, 1000 pers.)567657215790580058090.2%(% of total population)66.466.366.365.965.7-0.3pps3-Labour force (15-64, 1000 pers.)427943194412443344610.6%Male226022872340235023690.8%Female201820322072208320920.4%4-Activity rate (% of population 15-64)75.475.576.276.476.80.4ppsYoung (15-24)58.057.457.556.156.60.6ppsPrime age (25-54)88.088.088.488.788.5-0.2ppsOlder (55-64)46.948.651.753.656.22.6ppsNationals (15-64)76.076.277.277.377.40.1ppsNon-nationals (15-64)71.671.571.372.474.01.7ppsMale80.080.180.781.081.60.6ppsYoung (15-24)60.760.760.258.459.51.1ppsPrime age (25-54)91.591.691.892.392.1-0.2ppsOlder (55-64)56.857.461.263.066.03.0ppsFemale70.870.971.771.872.00.1ppsYoung (15-24)55.454.154.653.753.80.1ppsPrime age (25-54)84.584.484.985.084.8-0.2ppsOlder (55-64)37.540.242.744.546.62.1pps5- Employment rate (% of population 15-64)71.171.171.572.273.00.8ppsYoung (15-24)52.151.451.050.651.30.7ppsPrime age (25-54)83.483.583.684.184.50.4ppsOlder (55-64)45.146.349.251.354.02.7ppsLow-skilled (15-64)47.547.247.346.948.21.3ppsMedium-skilled (15-64)73.873.573.874.575.40.9ppsHigh-skilled (15-64)83.383.384.084.684.5-0.1ppsNationals (15-64)72.372.573.373.874.40.6ppsNon-nationals (15-64)63.663.362.664.566.72.2ppsMale75.375.175.476.277.41.3ppsYoung (15-24)54.354.052.952.153.91.7ppsPrime age (25-54)86.686.686.687.287.80.6ppsOlder (55-64)54.354.157.660.163.53.4ppsFemale66.967.167.768.268.60.4ppsYoung (15-24)49.948.749.049.048.7-0.3ppsPrime age (25-54)80.380.380.681.081.30.3ppsOlder (55-64)36.438.841.142.844.82.0pps6-Employed persons (15-64, 1000 pers.)4034.24067.64142.74185.34241.11.3%7-Employment growth (%, National accounts)1.00.61.31.71.70.0ppsEmployment growth (%, 15-64, LFS)0.10.81.81.01.30.3ppsMale-0.30.92.01.01.80.8ppsFemale0.60.81.71.10.8-0.2pps8-Self employed (15-64, % of total employment )10.911.010.810.610.4-0.2ppsMale13.313.313.212.912.6-0.4ppsFemale8.38.48.17.97.9-0.1pps9-Temporary employment (15-64, % of total employment)9.29.19.09.29.1-0.1ppsMale9.29.18.99.28.8-0.4ppsFemale9.29.19.19.29.40.2pps10-Part-time (15-64, % of total employment )26.927.327.827.927.3-0.6ppsMale9.69.810.510.610.0-0.6ppsFemale46.346.847.147.246.9-0.3pps11Involuntary part-time (15-64, % of total employment)3.13.43.63.52.9-0.6pps12-Unemployment rate (harmonised:15-74)5.65.76.05.54.9-0.6ppsYoung (15-24)10.310.611.29.89.4-0.4ppsPrime age (25-49)5.25.25.45.14.4-0.7ppsOlder (55-64)3.84.75.04.23.9-0.3ppsLow-skilled (15-64)11.811.513.013.311.6-1.7ppsMedium-skilled (15-64)5.15.55.85.14.3-0.8ppsHigh-skilled (15-64)4.03.93.63.23.30.1ppsNationals (15-64)4.84.95.04.53.9-0.6ppsNon-nationals (15-64)11.311.412.110.910.0-0.9ppsMale5.96.16.55.95.0-0.9ppsFemale5.45.35.65.04.7-0.3pps13-Long-term unemployment (% of total unemployment)27.229.232.233.328.9-4.4pps14-Worked hours (full-time, average actual weekly hours)41.340.941.040.740.80.2%Male42.041.541.741.441.40.0%Female39.939.539.539.439.40.0%15-Sectoral employment growth (% change)Agriculture3.2-6.3-2.7-2.4-5.9-3.5ppsBuilding and construction1.3-0.31.32.22.70.5ppsServices0.90.61.52.22.20.0ppsManufacturing industry-0.10.50.51.43.01.6pps16-Indicator board on wage developments (% change)Compensation per employee1.91.92.41.52.51.1ppsReal compensation per employee based on GDP0.3-0.51.20.71.30.6ppsLabour cost index (compens. of employees plus taxes minus subs.)2.83.21.03.52.9-0.6ppsLabour cost index (wages and salaries, total)3.03.31.12.72.80.1ppsLabour productivity (GDP/person employed)-0.30.50.70.81.00.2pps2017-2018 
 
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158 

Poland201420152016201720181-Population (LFS, total, 1000 pers.)38484384553842738422384130.0%2-Population (LFS, working age:15-64, 1000 pers.)2527825128246492431723941-1.5%(% of total population)65.765.364.163.362.3-1.0pps3-Labour force (15-64, 1000 pers.)1715317112169611691916790-0.8%Male94199389931593049213-1.0%Female77347723764676167577-0.5%4-Activity rate (% of population 15-64)67.968.168.869.670.10.6ppsYoung (15-24)33.932.834.534.835.10.3ppsPrime age (25-54)85.185.184.984.985.20.3ppsOlder (55-64)45.646.948.350.150.30.2ppsNationals (15-64)67.868.168.869.570.10.5ppsNon-nationals (15-64)73.767.867.977.678.30.8ppsMale74.674.875.776.677.00.4ppsYoung (15-24)38.838.439.839.739.2-0.5ppsPrime age (25-54)90.590.690.891.191.00.0ppsOlder (55-64)57.257.558.660.861.91.1ppsFemale61.161.462.062.663.30.7ppsYoung (15-24)28.726.928.929.730.71.1ppsPrime age (25-54)79.679.679.078.779.30.6ppsOlder (55-64)35.237.339.040.539.9-0.6pps5- Employment rate (% of population 15-64)61.762.964.566.167.41.3ppsYoung (15-24)25.826.028.429.631.01.3ppsPrime age (25-54)78.479.580.381.482.41.0ppsOlder (55-64)42.544.346.248.348.90.6ppsLow-skilled (15-64)22.723.323.023.323.60.3ppsMedium-skilled (15-64)62.964.065.667.068.11.1ppsHigh-skilled (15-64)83.985.085.886.887.60.9ppsNationals (15-64)61.762.964.566.167.41.2ppsNon-nationals (15-64)66.062.460.571.274.12.9ppsMale68.269.271.072.874.01.2ppsYoung (15-24)30.030.532.933.934.70.8ppsPrime age (25-54)83.984.986.187.388.10.8ppsOlder (55-64)53.154.255.758.359.81.5ppsFemale55.256.658.159.560.81.3ppsYoung (15-24)21.421.323.725.227.01.8ppsPrime age (25-54)72.773.974.575.376.51.3ppsOlder (55-64)32.935.537.639.339.1-0.2pps6-Employed persons (15-64, 1000 pers.)15591.015811.615901.816078.816133.40.3%7-Employment growth (%, National accounts)1.71.50.81.30.3-1.0ppsEmployment growth (%, 15-64, LFS)1.81.40.61.10.3-0.8ppsMale1.41.00.51.20.1-1.1ppsFemale2.32.00.61.00.6-0.4pps8-Self employed (15-64, % of total employment )17.917.917.717.417.40.0ppsMale21.921.821.721.821.6-0.2ppsFemale13.013.112.712.012.30.3pps9-Temporary employment (15-64, % of total employment)28.328.027.526.124.3-1.8ppsMale28.528.027.325.623.5-2.1ppsFemale28.027.927.626.625.1-1.5pps10-Part-time (15-64, % of total employment )7.16.86.46.66.4-0.2ppsMale4.44.23.73.73.80.1ppsFemale10.39.99.710.09.7-0.3pps11Involuntary part-time (15-64, % of total employment)2.32.11.61.41.0-0.4pps12-Unemployment rate (harmonised:15-74)9.07.56.24.93.9-1.0ppsYoung (15-24)23.920.817.714.811.7-3.1ppsPrime age (25-49)7.96.65.44.23.4-0.8ppsOlder (55-64)6.85.44.43.72.8-0.9ppsLow-skilled (15-64)19.717.314.912.610.3-2.3ppsMedium-skilled (15-64)10.28.47.05.74.5-1.2ppsHigh-skilled (15-64)4.74.03.32.52.0-0.5ppsNationals (15-64)9.17.66.25.03.9-1.1ppsNon-nationals (15-64)0.00.011.08.25.4-2.8ppsMale8.57.36.14.93.9-1.0ppsFemale9.67.76.24.93.9-1.0pps13-Long-term unemployment (% of total unemployment)42.739.334.931.026.9-4.1pps14-Worked hours (full-time, average actual weekly hours)41.141.141.240.840.2-1.5%Male42.342.342.341.941.2-1.7%Female39.439.439.639.338.8-1.3%15-Sectoral employment growth (% change)Agriculture-2.62.1-7.6-2.4-6.0-3.6ppsBuilding and construction-0.91.91.3-0.21.61.8ppsServices3.61.91.71.60.1-1.5ppsManufacturing industry2.23.05.44.40.7-3.7pps16-Indicator board on wage developments (% change)Compensation per employee2.21.74.85.87.82.0ppsReal compensation per employee based on GDP-2.4-0.50.42.04.22.3ppsLabour cost index (compens. of employees plus taxes minus subs.)3.43.84.46.67.00.4ppsLabour cost index (wages and salaries, total)3.43.84.46.67.00.4ppsLabour productivity (GDP/person employed)1.62.32.23.44.81.4pps2017-2018 
 
Statistical annex 

159 

Portugal201420152016201720181-Population (LFS, total, 1000 pers.)1040110358103261030010284-0.2%2-Population (LFS, working age:15-64, 1000 pers.)67946743670066596623-0.5%(% of total population)65.365.164.964.664.4-0.2pps3-Labour force (15-64, 1000 pers.)497649494940497249760.1%Male25232501249825062499-0.3%Female245424482441246624770.4%4-Activity rate (% of population 15-64)73.273.473.774.775.10.5ppsYoung (15-24)34.333.533.234.034.20.2ppsPrime age (25-54)88.688.889.189.689.80.2ppsOlder (55-64)55.357.058.561.563.41.9ppsNationals (15-64)73.273.373.674.675.10.5ppsNon-nationals (15-64)76.376.778.779.377.1-2.2ppsMale76.776.777.277.978.10.2ppsYoung (15-24)34.834.235.035.636.61.0ppsPrime age (25-54)91.691.791.992.392.60.3ppsOlder (55-64)64.065.066.969.269.0-0.3ppsFemale70.070.370.571.672.40.7ppsYoung (15-24)33.832.831.332.331.7-0.7ppsPrime age (25-54)85.886.086.687.087.30.2ppsOlder (55-64)47.549.951.054.658.43.8pps5- Employment rate (% of population 15-64)62.663.965.267.869.71.9ppsYoung (15-24)22.422.823.925.927.21.4ppsPrime age (25-54)77.478.880.282.584.31.8ppsOlder (55-64)47.849.952.156.259.23.0ppsLow-skilled (15-64)55.456.357.059.861.31.5ppsMedium-skilled (15-64)65.966.968.370.572.01.4ppsHigh-skilled (15-64)79.480.481.883.585.52.0ppsNationals (15-64)62.764.065.367.869.71.9ppsNon-nationals (15-64)59.461.465.168.368.30.0ppsMale65.866.968.371.172.71.6ppsYoung (15-24)22.924.125.527.629.31.7ppsPrime age (25-54)80.681.883.085.687.51.8ppsOlder (55-64)54.356.058.563.064.51.5ppsFemale59.661.162.464.866.92.1ppsYoung (15-24)21.921.522.224.125.11.0ppsPrime age (25-54)74.376.177.679.781.41.8ppsOlder (55-64)42.044.546.350.254.64.3pps6-Employed persons (15-64, 1000 pers.)4254.54309.04371.24515.44615.02.2%7-Employment growth (%, National accounts)1.41.41.63.32.3-1.0ppsEmployment growth (%, 15-64, LFS)2.31.31.43.32.2-1.1ppsMale2.20.81.33.41.8-1.7ppsFemale2.41.71.63.22.6-0.5pps8-Self employed (15-64, % of total employment )15.514.513.913.413.1-0.4ppsMale19.317.817.116.616.2-0.4ppsFemale11.711.110.710.19.8-0.3pps9-Temporary employment (15-64, % of total employment)21.422.022.322.022.00.0ppsMale21.622.422.522.322.0-0.3ppsFemale21.121.522.121.722.00.3pps10-Part-time (15-64, % of total employment )10.19.89.58.98.1-0.8ppsMale7.67.16.86.15.7-0.4ppsFemale12.612.512.111.710.5-1.2pps11Involuntary part-time (15-64, % of total employment)5.04.94.64.23.7-0.6pps12-Unemployment rate (harmonised:15-74)14.112.611.29.07.0-2.0ppsYoung (15-24)34.832.028.023.920.3-3.6ppsPrime age (25-49)12.711.210.07.96.1-1.8ppsOlder (55-64)13.512.511.08.56.5-2.0ppsLow-skilled (15-64)16.214.212.710.27.7-2.5ppsMedium-skilled (15-64)15.314.012.310.08.3-1.7ppsHigh-skilled (15-64)10.19.38.46.65.4-1.2ppsNationals (15-64)14.312.711.49.17.1-2.0ppsNon-nationals (15-64)22.120.017.313.811.4-2.4ppsMale13.812.411.18.66.6-2.0ppsFemale14.512.911.39.57.4-2.1pps13-Long-term unemployment (% of total unemployment)59.557.255.249.643.4-6.2pps14-Worked hours (full-time, average actual weekly hours)41.541.440.740.640.4-0.5%Male42.442.441.741.641.5-0.2%Female40.440.339.639.439.3-0.3%15-Sectoral employment growth (% change)Agriculture-4.6-5.7-3.7-2.7-1.71.0ppsBuilding and construction-4.71.31.25.84.4-1.4ppsServices4.83.33.35.13.5-1.7ppsManufacturing industry2.33.11.73.83.0-0.8pps16-Indicator board on wage developments (% change)Compensation per employee-1.80.41.71.62.00.4ppsReal compensation per employee based on GDP0.41.3-1.30.50.90.4ppsLabour cost index (compens. of employees plus taxes minus subs.)-0.62.01.52.01.9-0.1ppsLabour cost index (wages and salaries, total)-0.72.31.92.11.9-0.2ppsLabour productivity (GDP/person employed)-0.50.40.3-0.5-0.20.3pps2017-2018 
 
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Romania201420152016201720181-Population (LFS, total, 1000 pers.)1991619820197071959219531-0.3%2-Population (LFS, working age:15-64, 1000 pers.)1352713404132631309512930-1.3%(% of total population)67.967.667.366.866.2-0.6pps3-Labour force (15-64, 1000 pers.)88838858869688128761-0.6%Male506150995006503450360.0%Female38223759369037783725-1.4%4-Activity rate (% of population 15-64)65.766.165.667.367.80.5ppsYoung (15-24)29.631.328.029.929.5-0.4ppsPrime age (25-54)82.182.581.983.483.60.2ppsOlder (55-64)44.642.744.246.047.51.5ppsNationals (15-64)65.766.165.667.367.80.5ppsNon-nationals (15-64)0.00.00.074.572.1-2.4ppsMale74.375.374.876.276.90.8ppsYoung (15-24)34.837.033.934.634.6-0.1ppsPrime age (25-54)90.591.691.092.292.50.3ppsOlder (55-64)55.453.855.157.459.72.2ppsFemale56.956.756.258.258.30.1ppsYoung (15-24)23.925.221.825.024.2-0.9ppsPrime age (25-54)73.372.972.474.274.20.0ppsOlder (55-64)35.032.834.435.736.40.7pps5- Employment rate (% of population 15-64)61.061.461.663.964.81.0ppsYoung (15-24)22.524.522.324.524.70.2ppsPrime age (25-54)77.177.477.679.980.60.7ppsOlder (55-64)43.141.142.844.546.31.7ppsLow-skilled (15-64)44.442.641.042.542.60.1ppsMedium-skilled (15-64)65.064.965.267.568.61.1ppsHigh-skilled (15-64)82.585.386.287.988.40.4ppsNationals (15-64)61.061.461.663.964.81.0ppsNon-nationals (15-64)0.00.00.068.268.60.4ppsMale68.769.569.771.873.21.4ppsYoung (15-24)26.629.427.228.429.00.6ppsPrime age (25-54)84.685.285.587.688.71.0ppsOlder (55-64)53.251.253.055.357.92.6ppsFemale53.353.253.355.856.20.4ppsYoung (15-24)18.019.317.120.420.3-0.1ppsPrime age (25-54)69.369.269.271.872.10.3ppsOlder (55-64)34.232.133.634.935.70.8pps6-Employed persons (15-64, 1000 pers.)8254.48234.88166.18363.28381.80.2%7-Employment growth (%, National accounts)0.8-1.3-1.12.60.2-2.4ppsEmployment growth (%, 15-64, LFS)0.9-0.2-0.82.40.2-2.2ppsMale1.20.6-0.81.61.0-0.6ppsFemale0.5-1.3-0.93.5-0.8-4.3pps8-Self employed (15-64, % of total employment )18.417.616.516.415.5-0.8ppsMale23.822.521.221.119.9-1.3ppsFemale11.511.110.210.19.8-0.4pps9-Temporary employment (15-64, % of total employment)1.51.41.41.21.1-0.1ppsMale1.71.61.71.41.2-0.2ppsFemale1.21.11.00.90.90.0pps10-Part-time (15-64, % of total employment )8.78.87.46.86.5-0.3ppsMale8.28.57.36.76.2-0.5ppsFemale9.59.27.76.96.90.0pps11Involuntary part-time (15-64, % of total employment)5.05.24.33.83.5-0.3pps12-Unemployment rate (harmonised:15-74)6.86.85.94.94.2-0.7ppsYoung (15-24)24.021.720.618.316.2-2.1ppsPrime age (25-49)6.16.25.34.23.6-0.6ppsOlder (55-64)3.33.73.23.22.5-0.7ppsLow-skilled (15-64)7.79.18.67.66.6-1.0ppsMedium-skilled (15-64)7.27.36.35.24.4-0.8ppsHigh-skilled (15-64)5.94.13.12.42.1-0.3ppsNationals (15-64)7.17.06.15.14.3-0.8ppsNon-nationals (15-64)0.00.00.00.00.00.0ppsMale7.37.56.65.64.7-0.9ppsFemale6.15.85.04.03.5-0.5pps13-Long-term unemployment (% of total unemployment)41.143.950.041.544.12.6pps14-Worked hours (full-time, average actual weekly hours)40.440.140.240.140.0-0.2%Male40.840.540.640.540.4-0.2%Female39.839.539.639.639.5-0.3%15-Sectoral employment growth (% change)Agriculture-2.4-11.0-10.71.2-1.8-3.0ppsBuilding and construction1.3-0.36.62.6-2.7-5.3ppsServices3.04.52.43.42.9-0.4ppsManufacturing industry4.1-2.82.94.40.5-3.9pps16-Indicator board on wage developments (% change)Compensation per employee6.91.915.012.618.45.7ppsReal compensation per employee based on GDP-1.415.21.9-6.215.421.7ppsLabour cost index (compens. of employees plus taxes minus subs.)5.45.010.414.310.5-3.8ppsLabour cost index (wages and salaries, total)6.87.610.514.233.118.9ppsLabour productivity (GDP/person employed)2.65.26.04.33.9-0.4pps2017-2018 
 
Statistical annex 

161 

Slovenia201420152016201720181-Population (LFS, total, 1000 pers.)206220632065206620720.3%2-Population (LFS, working age:15-64, 1000 pers.)13971382137113621352-0.7%(% of total population)67.867.066.465.965.3-0.7pps3-Labour force (15-64, 1000 pers.)991992982101110150.4%Male5355365245385441.2%Female456456458473470-0.6%4-Activity rate (% of population 15-64)70.971.871.674.275.00.8ppsYoung (15-24)33.635.333.739.138.5-0.6ppsPrime age (25-54)90.390.890.591.992.00.2ppsOlder (55-64)38.439.741.245.649.53.9ppsNationals (15-64)71.071.571.474.175.11.0ppsNon-nationals (15-64)67.877.676.776.174.4-1.6ppsMale74.375.474.577.178.21.1ppsYoung (15-24)36.638.936.942.942.4-0.5ppsPrime age (25-54)92.292.992.093.494.00.7ppsOlder (55-64)45.746.347.151.755.13.3ppsFemale67.267.968.671.271.70.5ppsYoung (15-24)30.531.730.534.934.4-0.5ppsPrime age (25-54)88.388.688.990.289.9-0.3ppsOlder (55-64)31.132.935.239.543.94.4pps5- Employment rate (% of population 15-64)63.965.265.869.371.11.9ppsYoung (15-24)26.829.628.634.735.10.4ppsPrime age (25-54)81.982.983.586.187.51.4ppsOlder (55-64)35.436.638.542.747.04.3ppsLow-skilled (15-64)36.135.732.335.436.30.9ppsMedium-skilled (15-64)64.965.967.470.772.82.1ppsHigh-skilled (15-64)82.083.184.086.288.01.8ppsNationals (15-64)64.265.265.869.371.32.0ppsNon-nationals (15-64)55.166.366.469.168.8-0.3ppsMale67.569.268.972.574.52.0ppsYoung (15-24)29.532.031.138.638.80.2ppsPrime age (25-54)84.686.185.688.590.11.5ppsOlder (55-64)41.742.643.648.052.24.2ppsFemale60.061.062.665.867.51.7ppsYoung (15-24)23.927.026.030.431.00.6ppsPrime age (25-54)79.179.581.283.584.81.3ppsOlder (55-64)29.030.533.437.441.94.5pps6-Employed persons (15-64, 1000 pers.)892.5901.6902.5943.5961.92.0%7-Employment growth (%, National accounts)0.41.31.83.03.20.2ppsEmployment growth (%, 15-64, LFS)0.51.00.14.52.0-2.6ppsMale0.31.2-1.64.62.5-2.1ppsFemale0.70.82.14.51.3-3.1pps8-Self employed (15-64, % of total employment )12.112.111.511.412.10.7ppsMale15.915.715.114.315.41.1ppsFemale7.77.87.48.18.30.2pps9-Temporary employment (15-64, % of total employment)16.517.816.917.615.7-1.9ppsMale16.017.015.916.414.4-2.0ppsFemale17.118.718.018.917.1-1.8pps10-Part-time (15-64, % of total employment )10.010.19.310.39.7-0.6ppsMale6.87.06.06.75.9-0.8ppsFemale13.713.713.114.514.3-0.2pps11Involuntary part-time (15-64, % of total employment)1.01.31.31.10.6-0.5pps12-Unemployment rate (harmonised:15-74)9.79.08.06.65.1-1.5ppsYoung (15-24)20.216.315.211.28.8-2.4ppsPrime age (25-49)9.38.77.76.34.9-1.4ppsOlder (55-64)7.87.86.56.44.9-1.5ppsLow-skilled (15-64)16.414.615.111.59.1-2.4ppsMedium-skilled (15-64)10.510.08.16.85.6-1.2ppsHigh-skilled (15-64)6.35.86.25.33.7-1.6ppsNationals (15-64)9.68.97.96.55.1-1.4ppsNon-nationals (15-64)18.814.613.49.27.6-1.6ppsMale9.08.17.55.84.6-1.2ppsFemale10.610.18.67.55.7-1.8pps13-Long-term unemployment (% of total unemployment)54.552.353.347.542.9-4.6pps14-Worked hours (full-time, average actual weekly hours)41.041.040.539.940.10.5%Male41.541.641.240.540.70.5%Female40.440.239.639.239.30.3%15-Sectoral employment growth (% change)Agriculture-1.7-0.9-1.3-1.0-0.50.5ppsBuilding and construction-1.10.6-0.82.36.54.2ppsServices1.01.82.24.03.2-0.8ppsManufacturing industry0.21.73.13.74.60.9pps16-Indicator board on wage developments (% change)Compensation per employee1.21.53.13.03.90.9ppsReal compensation per employee based on GDP4.24.43.11.92.60.7ppsLabour cost index (compens. of employees plus taxes minus subs.)2.41.41.85.63.3-2.3ppsLabour cost index (wages and salaries, total)2.51.01.45.13.6-1.5ppsLabour productivity (GDP/person employed)2.30.91.31.80.9-0.9pps2017-2018 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

162 

Slovak Republic201420152016201720181-Population (LFS, total, 1000 pers.)541954225431543854460.1%2-Population (LFS, working age:15-64, 1000 pers.)38533834381037813749-0.8%(% of total population)71.170.770.269.568.8-0.7pps3-Labour force (15-64, 1000 pers.)27072719273827262713-0.5%Male15011493149914891487-0.1%Female12061226123912371225-0.9%4-Activity rate (% of population 15-64)70.370.971.972.172.40.3ppsYoung (15-24)31.031.732.433.232.3-0.9ppsPrime age (25-54)87.387.387.686.686.50.0ppsOlder (55-64)50.151.853.956.457.20.9ppsNationals (15-64)70.270.971.872.172.30.3ppsNon-nationals (15-64)81.581.875.879.678.6-1.1ppsMale77.677.578.378.278.70.5ppsYoung (15-24)38.038.339.739.639.70.1ppsPrime age (25-54)94.093.693.593.193.20.0ppsOlder (55-64)58.958.460.160.061.11.1ppsFemale62.964.365.465.965.90.0ppsYoung (15-24)23.624.924.726.524.5-2.0ppsPrime age (25-54)80.480.881.579.879.7-0.1ppsOlder (55-64)42.245.848.253.053.70.6pps5- Employment rate (% of population 15-64)61.062.764.966.267.61.4ppsYoung (15-24)21.823.325.226.927.50.6ppsPrime age (25-54)76.878.280.080.081.21.2ppsOlder (55-64)44.847.049.053.054.21.3ppsLow-skilled (15-64)17.718.419.821.421.1-0.3ppsMedium-skilled (15-64)66.968.670.972.574.11.6ppsHigh-skilled (15-64)75.676.577.378.579.30.8ppsNationals (15-64)60.962.764.966.267.61.4ppsNon-nationals (15-64)77.877.369.775.070.4-4.6ppsMale67.669.571.472.073.91.9ppsYoung (15-24)26.928.431.932.434.01.6ppsPrime age (25-54)83.285.186.386.387.91.5ppsOlder (55-64)53.253.655.156.658.41.7ppsFemale54.355.958.360.361.20.9ppsYoung (15-24)16.518.018.221.120.6-0.6ppsPrime age (25-54)70.271.073.573.474.40.9ppsOlder (55-64)37.241.043.549.650.40.8pps6-Employed persons (15-64, 1000 pers.)2349.22405.12471.72502.12533.31.2%7-Employment growth (%, National accounts)1.42.02.42.22.0-0.2ppsEmployment growth (%, 15-64, LFS)1.42.42.81.21.20.0ppsMale1.52.32.20.21.81.6ppsFemale1.22.53.52.50.6-1.9pps8-Self employed (15-64, % of total employment )15.214.915.215.014.6-0.4ppsMale19.618.819.119.018.7-0.3ppsFemale9.710.010.410.29.6-0.7pps9-Temporary employment (15-64, % of total employment)8.810.59.99.48.1-1.3ppsMale9.09.89.79.17.5-1.6ppsFemale8.511.310.29.88.7-1.1pps10-Part-time (15-64, % of total employment )5.15.85.85.84.9-0.9ppsMale3.74.04.14.03.2-0.8ppsFemale6.88.07.98.07.0-1.0pps11Involuntary part-time (15-64, % of total employment)1.71.72.01.81.3-0.5pps12-Unemployment rate (harmonised:15-74)13.211.59.78.16.5-1.6ppsYoung (15-24)29.726.522.218.914.9-4.0ppsPrime age (25-49)12.010.58.77.66.1-1.5ppsOlder (55-64)10.69.39.06.05.3-0.7ppsLow-skilled (15-64)41.437.731.729.930.00.1ppsMedium-skilled (15-64)12.611.09.27.65.8-1.8ppsHigh-skilled (15-64)6.46.15.74.23.1-1.1ppsNationals (15-64)13.211.69.78.26.6-1.6ppsNon-nationals (15-64)0.00.00.00.00.00.0ppsMale12.810.38.87.96.1-1.8ppsFemale13.612.910.88.47.0-1.4pps13-Long-term unemployment (% of total unemployment)70.265.860.262.461.7-0.7pps14-Worked hours (full-time, average actual weekly hours)40.040.240.139.739.80.3%Male40.940.940.840.540.50.0%Female38.939.239.138.738.90.5%15-Sectoral employment growth (% change)Agriculture-2.11.3-1.1-0.3-0.6-0.3ppsBuilding and construction-1.4-0.61.62.33.91.6ppsServices1.62.82.51.82.10.3ppsManufacturing industry2.02.43.73.92.1-1.8pps16-Indicator board on wage developments (% change)Compensation per employee1.83.52.15.25.40.3ppsReal compensation per employee based on GDP2.16.54.97.53.7-3.8ppsLabour cost index (compens. of employees plus taxes minus subs.)5.23.42.96.76.90.2ppsLabour cost index (wages and salaries, total)5.43.72.86.16.70.6ppsLabour productivity (GDP/person employed)1.32.20.71.02.11.1pps2017-2018 
 
Statistical annex 

163 

Finland201420152016201720181-Population (LFS, total, 1000 pers.)546354815495550855160.1%2-Population (LFS, working age:15-64, 1000 pers.)34723455344534343421-0.4%(% of total population)63.663.062.762.362.0-0.3pps3-Labour force (15-64, 1000 pers.)261726192615263526651.1%Male134413431350136213750.9%Female127412771265127312901.4%4-Activity rate (% of population 15-64)75.475.875.976.777.91.2ppsYoung (15-24)52.152.252.253.253.1-0.2ppsPrime age (25-54)86.686.686.386.887.81.0ppsOlder (55-64)63.865.266.467.870.32.4ppsNationals (15-64)75.676.176.377.178.31.2ppsNon-nationals (15-64)68.867.967.368.768.90.2ppsMale76.877.277.778.579.51.0ppsYoung (15-24)51.551.151.252.351.5-0.9ppsPrime age (25-54)89.589.689.789.890.81.0ppsOlder (55-64)61.963.265.267.569.72.2ppsFemale73.974.474.174.976.31.4ppsYoung (15-24)52.653.353.254.254.70.5ppsPrime age (25-54)83.683.682.883.684.61.0ppsOlder (55-64)65.567.267.668.270.82.6pps5- Employment rate (% of population 15-64)68.768.569.170.072.12.1ppsYoung (15-24)41.440.541.742.544.01.5ppsPrime age (25-54)80.580.079.980.682.51.8ppsOlder (55-64)59.160.061.462.565.42.9ppsLow-skilled (15-64)39.337.938.638.539.51.0ppsMedium-skilled (15-64)70.670.270.671.173.22.0ppsHigh-skilled (15-64)83.382.982.984.486.21.8ppsNationals (15-64)69.269.069.770.572.72.2ppsNon-nationals (15-64)56.755.955.558.257.7-0.5ppsMale69.569.370.571.473.52.1ppsYoung (15-24)39.838.240.141.342.61.2ppsPrime age (25-54)82.782.583.083.385.32.0ppsOlder (55-64)56.857.459.861.664.32.6ppsFemale68.067.767.668.570.62.1ppsYoung (15-24)43.042.843.343.745.51.8ppsPrime age (25-54)78.177.376.777.979.51.7ppsOlder (55-64)61.462.563.063.466.53.1pps6-Employed persons (15-64, 1000 pers.)2385.92367.92379.52402.62464.82.6%7-Employment growth (%, National accounts)-0.5-0.10.51.02.51.5ppsEmployment growth (%, 15-64, LFS)-0.7-0.80.51.02.61.6ppsMale-1.1-0.71.61.02.61.5ppsFemale-0.4-0.8-0.60.92.61.7pps8-Self employed (15-64, % of total employment )12.612.712.411.611.6-0.1ppsMale16.516.716.415.014.8-0.2ppsFemale8.48.58.28.18.20.1pps9-Temporary employment (15-64, % of total employment)15.415.115.615.816.20.4ppsMale12.312.312.912.913.10.2ppsFemale18.217.818.218.619.20.6pps10-Part-time (15-64, % of total employment )14.114.114.915.115.10.0ppsMale9.29.710.09.910.00.1ppsFemale19.318.720.220.520.60.1pps11Involuntary part-time (15-64, % of total employment)4.14.45.14.84.80.1pps12-Unemployment rate (harmonised:15-74)8.79.48.88.67.4-1.2ppsYoung (15-24)20.522.420.120.117.0-3.1ppsPrime age (25-49)7.17.77.47.16.0-1.1ppsOlder (55-64)7.38.07.57.86.9-0.9ppsLow-skilled (15-64)18.018.717.618.916.7-2.2ppsMedium-skilled (15-64)9.510.49.79.68.4-1.2ppsHigh-skilled (15-64)5.16.15.95.34.3-1.0ppsNationals (15-64)8.59.38.78.67.2-1.4ppsNon-nationals (15-64)17.617.617.615.216.21.0ppsMale9.39.99.08.97.4-1.5ppsFemale8.08.88.68.47.3-1.1pps13-Long-term unemployment (% of total unemployment)22.424.625.924.421.9-2.5pps14-Worked hours (full-time, average actual weekly hours)38.438.538.838.738.5-0.5%Male39.840.040.240.039.8-0.5%Female36.736.737.137.037.00.0%15-Sectoral employment growth (% change)Agriculture-0.9-2.8-3.1-0.5-0.10.4ppsBuilding and construction-1.31.84.83.64.40.8ppsServices0.4-0.20.81.22.91.7ppsManufacturing industry-2.8-1.5-0.90.11.61.5pps16-Indicator board on wage developments (% change)Compensation per employee1.01.41.1-1.01.01.9ppsReal compensation per employee based on GDP2.92.52.50.51.20.6ppsLabour cost index (compens. of employees plus taxes minus subs.)1.71.40.6-0.71.52.2ppsLabour cost index (wages and salaries, total)1.51.20.20.42.11.7ppsLabour productivity (GDP/person employed)-0.20.62.32.0-0.8-2.8pps2017-2018 
 
European Commission 

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164 

Sweden201420152016201720181-Population (LFS, total, 1000 pers.)96969799992310058101751.2%2-Population (LFS, working age:15-64, 1000 pers.)614161706214629063470.9%(% of total population)63.363.062.662.562.4-0.2pps3-Labour force (15-64, 1000 pers.)500550445100519052641.4%Male261226242658270927441.3%Female239324202442248125191.5%4-Activity rate (% of population 15-64)81.581.782.182.582.90.4ppsYoung (15-24)55.455.154.854.754.2-0.5ppsPrime age (25-54)90.890.990.991.291.60.3ppsOlder (55-64)78.278.779.780.581.61.1ppsNationals (15-64)82.282.582.983.283.70.5ppsNon-nationals (15-64)73.573.173.775.975.90.0ppsMale83.683.583.984.384.60.3ppsYoung (15-24)54.953.854.254.153.1-1.0ppsPrime age (25-54)93.593.393.393.693.90.2ppsOlder (55-64)81.581.882.583.284.41.2ppsFemale79.379.980.280.781.20.5ppsYoung (15-24)56.156.555.555.455.40.0ppsPrime age (25-54)88.088.488.588.889.20.4ppsOlder (55-64)74.975.576.977.878.80.9pps5- Employment rate (% of population 15-64)74.975.576.276.977.50.7ppsYoung (15-24)42.843.944.544.945.10.2ppsPrime age (25-54)85.485.685.986.386.80.4ppsOlder (55-64)74.074.575.576.477.91.5ppsLow-skilled (15-64)45.946.045.846.546.60.0ppsMedium-skilled (15-64)80.280.981.682.683.40.9ppsHigh-skilled (15-64)87.387.788.188.188.90.8ppsNationals (15-64)76.277.078.078.679.61.0ppsNon-nationals (15-64)58.457.757.659.859.0-0.7ppsMale76.577.077.578.379.00.8ppsYoung (15-24)41.642.443.143.943.5-0.4ppsPrime age (25-54)87.987.988.188.589.10.7ppsOlder (55-64)76.576.877.578.480.01.6ppsFemale73.174.074.875.476.00.6ppsYoung (15-24)44.045.545.946.046.80.8ppsPrime age (25-54)82.883.383.784.184.30.1ppsOlder (55-64)71.572.173.574.475.81.3pps6-Employed persons (15-64, 1000 pers.)4597.54659.94735.64833.94921.31.8%7-Employment growth (%, National accounts)1.41.51.92.31.8-0.5ppsEmployment growth (%, 15-64, LFS)0.91.41.62.11.8-0.3ppsMale0.71.21.62.31.9-0.4ppsFemale1.21.51.71.81.7-0.1pps8-Self employed (15-64, % of total employment )9.18.98.78.68.4-0.2ppsMale12.412.111.811.811.6-0.2ppsFemale5.45.45.35.15.0-0.1pps9-Temporary employment (15-64, % of total employment)16.816.616.116.115.6-0.5ppsMale14.714.914.514.514.0-0.5ppsFemale18.818.317.717.717.2-0.5pps10-Part-time (15-64, % of total employment )24.624.323.923.322.8-0.5ppsMale12.813.213.013.113.10.0ppsFemale37.336.335.634.433.3-1.1pps11Involuntary part-time (15-64, % of total employment)7.37.16.86.35.4-0.8pps12-Unemployment rate (harmonised:15-74)7.97.46.96.76.3-0.4ppsYoung (15-24)22.920.418.917.916.8-1.1ppsPrime age (25-49)6.05.85.55.45.3-0.1ppsOlder (55-64)5.45.35.35.14.5-0.6ppsLow-skilled (15-64)20.019.719.719.419.90.5ppsMedium-skilled (15-64)7.16.45.85.24.6-0.6ppsHigh-skilled (15-64)4.44.34.14.13.6-0.5ppsNationals (15-64)7.26.65.95.54.9-0.6ppsNon-nationals (15-64)20.621.121.821.322.31.0ppsMale8.27.57.36.96.4-0.5ppsFemale7.77.36.56.46.3-0.1pps13-Long-term unemployment (% of total unemployment)19.020.819.419.619.90.3pps14-Worked hours (full-time, average actual weekly hours)39.239.139.439.139.0-0.3%Male39.939.840.139.839.7-0.3%Female38.137.938.338.038.00.0%15-Sectoral employment growth (% change)Agriculture-0.1-1.6-2.9-0.4-4.1-3.7ppsBuilding and construction2.52.91.85.54.2-1.3ppsServices1.52.51.82.22.60.3ppsManufacturing industry-1.1-4.3-1.81.71.0-0.7pps16-Indicator board on wage developments (% change)Compensation per employee2.22.72.52.03.41.4ppsReal compensation per employee based on GDP3.82.31.32.40.3-2.1ppsLabour cost index (compens. of employees plus taxes minus subs.)2.72.83.82.82.0-0.8ppsLabour cost index (wages and salaries, total)2.52.52.62.31.5-0.8ppsLabour productivity (GDP/person employed)1.22.90.8-0.20.50.7pps2017-2018 
 
Statistical annex 

165 

United Kingdom201420152016201720181-Population (LFS, total, 1000 pers.)64597651106564866040664660.6%2-Population (LFS, working age:15-64, 1000 pers.)41116412874143041539416560.3%(% of total population)63.663.463.162.962.7-0.2pps3-Labour force (15-64, 1000 pers.)31532317463202532215324420.7%Male16754168431698217003171020.6%Female14778149031504315212153400.8%4-Activity rate (% of population 15-64)76.776.977.377.677.90.3ppsYoung (15-24)57.858.558.357.557.1-0.4ppsPrime age (25-54)86.085.886.186.586.90.3ppsOlder (55-64)63.564.465.866.467.51.1ppsNationals (15-64)76.977.077.577.777.90.2ppsNon-nationals (15-64)74.975.975.976.577.51.1ppsMale82.282.282.482.382.60.3ppsYoung (15-24)59.560.059.258.258.50.4ppsPrime age (25-54)92.291.992.292.492.50.1ppsOlder (55-64)70.971.472.672.172.70.6ppsFemale71.371.772.272.973.20.4ppsYoung (15-24)56.157.057.556.855.6-1.2ppsPrime age (25-54)79.979.880.180.881.30.5ppsOlder (55-64)56.457.759.260.962.51.6pps5- Employment rate (% of population 15-64)71.972.773.574.174.70.6ppsYoung (15-24)48.050.050.750.550.60.1ppsPrime age (25-54)82.182.482.983.884.30.5ppsOlder (55-64)61.062.263.464.165.31.3ppsLow-skilled (15-64)55.055.958.359.661.11.6ppsMedium-skilled (15-64)72.773.373.874.274.40.1ppsHigh-skilled (15-64)84.384.784.985.085.30.3ppsNationals (15-64)72.272.973.774.374.80.4ppsNon-nationals (15-64)69.471.071.572.474.01.5ppsMale76.877.678.278.679.10.6ppsYoung (15-24)48.250.350.450.351.41.1ppsPrime age (25-54)88.088.389.089.689.80.2ppsOlder (55-64)67.868.669.569.270.31.0ppsFemale67.167.968.869.770.30.6ppsYoung (15-24)47.849.751.150.849.9-0.9ppsPrime age (25-54)76.276.677.078.178.80.7ppsOlder (55-64)54.456.057.459.160.61.5pps6-Employed persons (15-64, 1000 pers.)29558.830019.630443.630785.531112.01.1%7-Employment growth (%, National accounts)2.41.71.51.01.20.2ppsEmployment growth (%, 15-64, LFS)2.21.61.41.11.1-0.1ppsMale2.21.51.40.71.00.3ppsFemale2.21.61.41.61.1-0.5pps8-Self employed (15-64, % of total employment )14.013.614.114.013.8-0.2ppsMale18.017.417.917.717.4-0.3ppsFemale9.59.49.910.09.8-0.2pps9-Temporary employment (15-64, % of total employment)6.36.15.95.65.5-0.1ppsMale5.85.65.45.25.1-0.1ppsFemale6.86.56.56.15.8-0.3pps10-Part-time (15-64, % of total employment )25.425.225.224.924.6-0.3ppsMale11.211.211.311.111.10.0ppsFemale41.341.040.940.439.7-0.7pps11Involuntary part-time (15-64, % of total employment)4.84.54.03.63.4-0.3pps12-Unemployment rate (harmonised:15-74)6.15.34.84.34.0-0.3ppsYoung (15-24)17.014.613.012.111.3-0.8ppsPrime age (25-49)4.64.03.63.23.0-0.2ppsOlder (55-64)4.03.43.73.53.3-0.2ppsLow-skilled (15-64)11.710.08.67.66.6-1.0ppsMedium-skilled (15-64)7.06.15.54.94.8-0.1ppsHigh-skilled (15-64)3.23.03.02.82.5-0.3ppsNationals (15-64)6.25.34.84.34.0-0.3ppsNon-nationals (15-64)7.26.55.85.34.6-0.7ppsMale6.45.55.04.54.1-0.4ppsFemale5.85.14.74.24.0-0.2pps13-Long-term unemployment (% of total unemployment)35.730.627.125.926.30.4pps14-Worked hours (full-time, average actual weekly hours)41.341.341.441.240.9-0.7%Male42.642.642.742.542.2-0.7%Female39.139.039.238.938.8-0.3%15-Sectoral employment growth (% change)Agriculture16.6-8.60.84.7-7.8-12.5ppsBuilding and construction3.32.53.84.21.2-3.0ppsServices2.92.62.10.70.90.2ppsManufacturing industry0.60.9-0.41.61.80.2pps16-Indicator board on wage developments (% change)Compensation per employee0.61.12.83.12.7-0.4ppsReal compensation per employee based on GDP2.10.92.82.8-2.2-5.0ppsLabour cost index (compens. of employees plus taxes minus subs.)1.54.21.53.13.30.2ppsLabour cost index (wages and salaries, total)1.63.81.82.73.00.3ppsLabour productivity (GDP/person employed)0.60.60.30.80.2-0.6pps2017-2018 
 
European Commission 

Labour Market and Wage Developments in Europe, Annual Review 2019 

166 

European Union (28 countries)201420152016201720181-Population (LFS, total, 1000 pers.)5082935097535113085124015136910.3%2-Population (LFS, working age:15-64, 1000 pers.)329514329008328881328157327255-0.3%(% of total population)64.864.564.364.063.7-0.3pps3-Labour force (15-64, 1000 pers.)2382162386292398392406702411990.2%Male1282861284731290141293241294720.1%Female1099311101551108251113461117270.3%4-Activity rate (% of population 15-64)72.372.572.973.373.70.4ppsYoung (15-24)41.741.641.641.641.70.0ppsPrime age (25-54)85.585.485.585.785.90.2ppsOlder (55-64)55.957.359.160.661.91.4ppsNationals (15-64)72.372.673.173.573.80.3ppsNon-nationals (15-64)71.771.671.371.472.41.0ppsMale78.178.378.578.979.20.3ppsYoung (15-24)44.444.244.044.044.30.3ppsPrime age (25-54)91.591.491.491.691.70.1ppsOlder (55-64)63.965.066.667.869.11.3ppsFemale66.566.867.367.868.20.4ppsYoung (15-24)38.938.839.039.138.9-0.2ppsPrime age (25-54)79.579.479.679.780.10.3ppsOlder (55-64)48.450.052.053.855.21.4pps5- Employment rate (% of population 15-64)64.865.666.667.668.60.9ppsYoung (15-24)32.433.133.834.635.40.7ppsPrime age (25-54)77.478.078.779.680.40.8ppsOlder (55-64)51.853.355.257.158.71.6ppsLow-skilled (15-64)43.343.744.545.546.30.8ppsMedium-skilled (15-64)68.469.069.970.971.60.7ppsHigh-skilled (15-64)82.082.783.484.084.50.5ppsNationals (15-64)65.266.067.168.169.00.9ppsNon-nationals (15-64)59.960.861.562.564.11.5ppsMale70.170.871.872.973.80.9ppsYoung (15-24)34.234.935.536.337.31.0ppsPrime age (25-54)83.183.884.685.586.20.7ppsOlder (55-64)58.860.162.063.765.41.7ppsFemale59.660.461.462.463.30.9ppsYoung (15-24)30.631.332.032.833.30.4ppsPrime age (25-54)71.772.272.973.774.60.9ppsOlder (55-64)45.246.948.950.852.41.6pps6-Employed persons (15-64, 1000 pers.)213486.0215821.0218991.8221994.9224407.61.1%7-Employment growth (%, National accounts)1.11.11.31.51.3-0.2ppsEmployment growth (%, 15-64, LFS)1.31.11.51.41.1-0.3ppsMale1.11.11.51.31.0-0.3ppsFemale1.41.11.51.41.2-0.3pps8-Self employed (15-64, % of total employment )14.414.114.013.713.5-0.2ppsMale18.217.817.517.216.9-0.3ppsFemale9.99.99.99.79.6-0.1pps9-Temporary employment (15-64, % of total employment)13.914.114.214.314.2-0.1ppsMale13.513.813.813.913.6-0.3ppsFemale14.314.514.714.814.7-0.1pps10-Part-time (15-64, % of total employment )19.619.619.519.419.2-0.2ppsMale8.88.98.98.88.7-0.1ppsFemale32.232.131.931.731.3-0.4pps11Involuntary part-time (15-64, % of total employment)5.85.75.45.14.8-0.4pps12-Unemployment rate (harmonised:15-74)10.29.48.67.66.8-0.8ppsYoung (15-24)22.220.318.716.815.2-1.6ppsPrime age (25-49)9.48.77.97.06.3-0.7ppsOlder (55-64)7.47.06.55.85.2-0.6ppsLow-skilled (15-64)19.017.816.615.213.7-1.5ppsMedium-skilled (15-64)9.58.87.97.06.3-0.7ppsHigh-skilled (15-64)6.25.75.24.64.2-0.4ppsNationals (15-64)9.99.18.27.36.5-0.8ppsNon-nationals (15-64)16.515.213.712.511.5-1.0ppsMale10.19.38.47.46.6-0.8ppsFemale10.39.58.87.97.1-0.8pps13-Long-term unemployment (% of total unemployment)49.648.546.845.143.5-1.6pps14-Worked hours (full-time, average actual weekly hours)40.540.540.640.340.2-0.2%Male41.541.541.541.341.1-0.5%Female38.938.939.038.838.7-0.3%15-Sectoral employment growth (% change)Agriculture-0.5-3.2-4.10.0-2.5-2.5ppsBuilding and construction-0.40.81.22.12.20.1ppsServices1.71.92.01.91.7-0.2ppsManufacturing industry0.30.41.41.81.4-0.4pps16-Indicator board on wage developments (% change)Compensation per employee1.83.2-0.51.02.41.4ppsReal compensation per employee based on GDP0.50.60.70.60.60.1ppsLabour cost index (compens. of employees plus taxes minus subs.)1.62.01.52.62.70.1ppsLabour cost index (wages and salaries, total)1.62.31.62.62.80.2ppsLabour productivity (GDP/person employed)0.71.20.71.00.7-0.3pps2017-2018 
 
Statistical annex 

167 

Euro Area201420152016201720181-Population (LFS, total, 1000 pers.)3381053391153401953409403417430.2%2-Population (LFS, working age:15-64, 1000 pers.)218536218307218797218585218216-0.2%(% of total population)64.664.464.364.163.9-0.3pps3-Labour force (15-64, 1000 pers.)1580171581861593481597321601360.3%Male84895849528547285636857400.1%Female73123732357387674096743970.4%4-Activity rate (% of population 15-64)72.372.572.873.173.40.3ppsYoung (15-24)40.139.739.739.840.00.2ppsPrime age (25-54)85.485.385.585.585.60.2ppsOlder (55-64)56.458.059.861.362.61.3ppsNationals (15-64)72.472.773.173.473.60.2ppsNon-nationals (15-64)71.070.670.170.171.11.0ppsMale78.078.178.378.578.70.3ppsYoung (15-24)42.642.141.942.142.60.6ppsPrime age (25-54)91.591.491.491.491.50.1ppsOlder (55-64)63.765.266.968.169.31.2ppsFemale66.666.967.467.768.00.3ppsYoung (15-24)37.537.237.337.437.3-0.1ppsPrime age (25-54)79.379.379.679.679.80.3ppsOlder (55-64)49.551.153.154.856.31.5pps5- Employment rate (% of population 15-64)63.864.565.466.467.30.9ppsYoung (15-24)30.630.831.432.333.30.9ppsPrime age (25-54)76.076.677.478.179.00.8ppsOlder (55-64)51.753.355.357.158.81.6ppsLow-skilled (15-64)43.644.144.745.646.30.7ppsMedium-skilled (15-64)68.468.969.770.371.10.7ppsHigh-skilled (15-64)81.081.682.483.183.60.5ppsNationals (15-64)64.465.166.167.167.90.8ppsNon-nationals (15-64)57.858.559.260.261.91.7ppsMale68.969.670.571.572.50.9ppsYoung (15-24)32.332.433.033.935.21.3ppsPrime age (25-54)81.882.483.284.184.80.8ppsOlder (55-64)58.059.561.563.264.91.7ppsFemale58.759.460.361.262.10.9ppsYoung (15-24)28.829.229.730.631.20.6ppsPrime age (25-54)70.370.871.672.273.10.9ppsOlder (55-64)45.747.449.451.352.91.6pps6-Employed persons (15-64, 1000 pers.)139421.7140774.3143150.9145055.7146817.31.2%7-Employment growth (%, National accounts)0.61.01.41.61.5-0.1ppsEmployment growth (%, 15-64, LFS)0.91.01.71.31.2-0.1ppsMale0.70.91.71.31.1-0.2ppsFemale1.21.01.61.31.40.0pps8-Self employed (15-64, % of total employment )14.214.013.813.513.3-0.2ppsMale17.917.617.316.916.6-0.3ppsFemale9.99.99.99.79.5-0.2pps9-Temporary employment (15-64, % of total employment)15.115.415.616.116.20.1ppsMale14.615.115.215.615.70.1ppsFemale15.615.816.016.516.70.2pps10-Part-time (15-64, % of total employment )21.621.621.621.521.3-0.2ppsMale9.29.39.49.49.3-0.1ppsFemale36.036.035.835.735.3-0.4pps11Involuntary part-time (15-64, % of total employment)6.86.86.66.35.9-0.4pps12-Unemployment rate (harmonised:15-74)11.610.910.09.18.2-0.9ppsYoung (15-24)23.722.320.918.816.9-1.9ppsPrime age (25-49)11.010.39.58.67.8-0.8ppsOlder (55-64)8.48.17.66.86.2-0.6ppsLow-skilled (15-64)20.619.418.216.815.2-1.6ppsMedium-skilled (15-64)10.29.79.08.27.5-0.7ppsHigh-skilled (15-64)7.36.96.25.55.1-0.4ppsNationals (15-64)11.110.49.68.77.8-0.9ppsNon-nationals (15-64)18.617.215.614.113.0-1.1ppsMale11.510.79.78.77.9-0.8ppsFemale11.811.010.49.58.6-0.9pps13-Long-term unemployment (% of total unemployment)52.651.550.148.946.8-2.1pps14-Worked hours (full-time, average actual weekly hours)40.440.440.440.240.20.0%Male41.441.441.441.241.1-0.2%Female38.738.738.838.638.60.0%15-Sectoral employment growth (% change)Agriculture-0.2-1.0-0.3-0.6-0.40.2ppsBuilding and construction-1.30.20.31.82.60.8ppsServices1.11.71.92.11.8-0.2ppsManufacturing industry-0.40.20.91.21.50.3pps16-Indicator board on wage developments (% change)Compensation per employee1.41.41.31.62.10.5ppsReal compensation per employee based on GDP0.40.40.40.21.51.2ppsLabour cost index (compens. of employees plus taxes minus subs.)1.41.31.22.22.20.0ppsLabour cost index (wages and salaries, total)1.51.71.32.22.1-0.1ppsLabour productivity (GDP/person employed)0.81.00.50.90.4-0.5pps2017-2018 
 
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