The 2021 ageing report : underlying assumptions and projection methodologies.
Date: 2021-01-13
Institutions: Directorate-General for Economic and Financial Affairs
Subjects: EU Member State, EU expenditure, economic forecasting, health expenditure, labour force, long-term forecast, macroeconomics, non-working population, pension scheme, population ageing, population forecast, public finance, report, unemployment

ISSN 2443-8014 (online)

The
2021
Ageing Report

Underlying Assumptions 
& Projection Methodologies

INSTITUTIONAL PAPER 142 | NOVEMBER 2020

EUROPEAN ECONOMY

Economic and 
Financial Affairs

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European Commission 
Directorate-General for Economic and Financial Affairs 

The 2021 Ageing Report 
Underlying Assumptions and Projection Methodologies 

EUROPEAN ECONOMY                                                                                    Institutional Paper 142 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ACKNOWLEDGEMENTS 

This  report  has  been  prepared  as  part  of  the  mandate  the  Economic  and  Financial  Affairs  (ECOFIN) 
Council gave to the Economic Policy Committee (EPC) in 2018 to update and further deepen its common 
exercise of age-related expenditure projections, on the basis of a new population projection by Eurostat.   

The  forthcoming  2021  Ageing  Report  analyses  the  long-term  projections  of  the  budgetary  impact  of 
demographic ageing in the European Union in the period 2019–2070. It will be calculated on the basis of 
the macroeconomic assumptions and methodologies described in this report. The 2021 vintage will be the 
sixth  edition  of  the  Ageing  Report  and  is  envisaged  to  be  presented  to  the  ECOFIN  Council  in  spring 
2021.  

In  response  to  the  mandate,  the  EPC  mandated  a  working  group,  the  Ageing  Working  Group  (AWG) 
under the chairmanship of Godwin Mifsud, to take forward the work needed to discharge this remit.  

This  report  is  presented  by  the  EPC  and  the  European  Commission  (Directorate-General  for  Economic 
and Financial Affairs - DG ECFIN) after full discussion on the basis of the AWG’s comprehensive work. 
DG ECFIN provided the necessary analysis and calculations underpinning the report. Eurostat prepared 
the demographic projections.  

The report was prepared under the supervision of Lucio Pench (Director in DG ECFIN), Wolfgang Merz 
(Chair of the EPC), Godwin Mifsud (Chair of the AWG), Giuseppe Carone (Head of Unit in DG ECFIN). 
The main contributors were Pedro Arevalo, Santiago Calvo Ramos, Ben Deboeck, Per Eckefeldt, Nicola 
Gagliardi, Boriana Goranova, Fabrice Orlandi,  Anda Patarau and the  members of the  AWG (see list of 
Members below). The EPC and DG ECFIN would like to thank all those concerned. 

Comments on the report would be gratefully received at the following addresses: 

DG ECFIN - Unit C2 

ecfin-secretariat-c2@ec.europa.eu 

Secretariat of the Economic Policy Committee 

epc-secretariat@ec.europa.eu 

ii 

 
 
MEMBERS OF THE AGEING WORKING GROUP 

CHAIRMAN 

Mr. Godwin MIFSUD  

Ministry of Finance, Malta 

BELGIQUE-BELGIË 

Ms. Nicole FASQUELLE  

Bureau fédéral du Plan - Federaal Planbureau 

Mr. Peter WILLEME 

Bureau fédéral du Plan - Federaal Planbureau 

BULGARIA 

Mr. Kaloyan KOLEV  

Ministry of Finance 

Ms. Penka TANEVA 

National Social Security Institute 

CZECH REPUBLIC 

Mr. Zbynek STORK 

Ministry of Finance 

Mr. Jindrich MARVAL 

Ministry of Finance 

DANMARK 

Mr. Soren ARNBERG 

Ministry of Finance 

Mr. Sandru SURENDRAN 

Ministry of Finance 

BUNDESREPUBLIK DEUTSCHLAND 

Ms. Clara WELTEKE  

Federal Ministry of Finance 

Mr. François PEGLOW 

Federal Ministry of Labour and Social Affairs 

ESTONIA 

Mr. Risto KAARNA 

Ministry of Finance 

Ms. Tonu LILLELAID 

Ministry of Finance 

ELLAS 

Ms. Angeliki ZOULAKI   

National Actuarial Authority  

Ms. Eirini ANDRIOPOULOU 

Ministry of Finance 

ESPAÑA 

Mr. Javier HERNÁNDEZ  

Ministry of Economic Affairs and Digital Transformation 

Mr. Alfonso SAHUQUILLO 

Ministry of Finance 

iii 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
FRANCE 

Mr. Misha KHODABIN    

French Ministry for the Economy and Finance  

Mr. Hamza ZAKRAOUI   

French Ministry for the Economy and Finance  

CROATIA 

Ms. Josipa MESTROVIC  

Ministry of Labour and Pension System 

Ms. Ljiljana MARUSIC 

Croatian Pension Insurance Institute 

IRELAND 

Mr. Diarmaid SMYTH  

Department of Finance 

Mr. David HUGHES 

Department of Finance 

ITALIA 

Mr. Marco CACCIOTTI   

Ministero dell'Economia e delle Finanze 

Ms. Elena FABRIZI 

Ministero dell'Economia e delle Finanze 

CYPRUS 

Mr. Costas STAVRAKIS  

Ministry of Labour, Welfare and Social Insurance 

Ms. Maria MATSI 

Ministry of Finance 

LATVIA 

Ms. Jūlija BURĢE  

Ministry of Finance  

Ms. Sandra STABIŅA 

Ministry of Welfare 

LITHUANIA  

Ms. Rasa SLIOGERIENE 

Ministry of Finance 

Ms. Vidija PASTUKIENE 

Ministry of Social Security and Labour 

LUXEMBOURG 

Mr. Tom HAAS   

STATEC 

Mr. Kevin EVERARD 

Inspection Générale de la Securité Sociale 

HUNGARY 

Ms. Renáta NÉMETH-SZŰCS  

State Treasury 

Mr. Krisztián LOSONCZI  

Ministry of Finance 

iv 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
MALTA 

Mr. Andrew CASSAR OVEREND  

Ministry of Finance 

Ms. Anna BONNICI  

Ministry of Finance 

NEDERLAND 

Ms. Iris VAN TILBURG   

Centraal Planbureau 

Mr. Sam PEEPERKORN  

Ministry of Finance 

ÖSTERREICH 

Mr. Peter GRAFENEDER 

Bundesministerium für Finanzen 

Mr. Armin HEINRICH 

Bundesministerium für Finanzen 

POLAND 

Ms. Joanna STACHURA   

Ministry of Finance 

Mr. Pawel STRZELECKI 

National Bank of Poland 

PORTUGAL 

Ms. Conceicao NUNES 

Ministry of Finance  

Ms. Rita FIGUEIRAS 

Ministry of Labour, Solidarity, and Social Security  

ROMANIA 

Mr. Daniel VALCU 

Ministry of Public Finance 

Mr. Lucian NOVAK 

National Commission for Strategy and Prognosis  

SLOVENIA 

Ms. Lejla FAJIC  

Institute of Macroeconomic Analysis and Development 

Mr. Franci KLUZER 

Ministry of Finance 

SLOVAKIA 

Mr. Jakub FODOR  

Ministry of Finance 

Ms. Alexandra SALAMONOVA 

Ministry of Finance 

SUOMI FINLAND 

Mr. Olli PALMEN 

Ministry of Finance 

Mr. Ilari AHOLA 

Ministry of Finance 

v 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
SVERIGE 

Mr. Jonas NORLIN 

Ministry of Finance 

Mr. Olle SUNDBERG 

Ministry of Health and Social Affairs  

OBSERVERS 

NORWAY 

Mr. Yngvar DYVI 

Ministry of Finance 

ECB 

Mr. Marien FERDINANDUSSE 

Eurostat 

Mr. Didier DUPRE 

Mr. Giampaolo LANZIERI 

EPC Secretariat 

Ms. Laura RUUD 

Mr. Balazs HORVATH 

European Commission  

Mr. Giuseppe CARONE  

Ms. Stéphanie PAMIES 

vi 

 
 
 
 
 
 
 
 
CONTENTS 

Executive Summary 

1. 

2. 

2021 Ageing Report: mandate and general principles 

Main results: the economic impact of population ageing 

Part I:  Underlying assumptions and projection methodologies 

1.  Population 

1.1. 

1.2. 

1.3. 

1.4. 

Introduction 

Developments and assumptions for fertility rates 

Developments and assumptions for life expectancy 

Developments and assumptions for net migration 

1.5.  Overall results of the 2019-based population projections 

1.6. 

Demographic ageing in a global context 

1.7.  Comparison with the demographic scenario underlying the 2018 Ageing 

2. 

Report projections 
Labour force projections 
2.1. 

Introduction 

2.2. 

2.3. 

2.4. 

2.5. 

2.6. 

2.7. 

2.8. 

Past trends and main drivers of labour market developments 

Legislated pension reforms in EU Member States 

The impact of pension reforms on the participation rate of older workers 

Results of the participation rate and labour supply projections 

Employment projections 

Economic dependency ratios 

Projection of total hours worked 

2.9.  Comparing the 2021 and 2018 labour market projections 
Labour productivity and potential GDP 
3.1. 

Introduction 

3.2. 

Long-term potential GDP projections 

3.3.  Comparing the 2021 and 2018 GDP projections 
Interest rates 
4.1. 

Background 

4.2. 

interest rate assumptions in the 2021 ageing report 

Economic rationale for the downward revision 

4.3. 
Sensitivity tests and alternative scenarios 
5.1. 

Introduction 

3. 

4. 

5. 

5.2. 

Projections under different sensitivity tests and alternative scenarios 

Part II:  Age-related expenditure items: coverage, projection 

methodologies and data sources 

1.  Pensions 

1.1.  Main features of pension projections 

1.2.  Coverage of pension projections 

1.3. 

Definitions of the variables 

2.  Healthcare 

2.1.  Overview of the projection methodology 

2.2. 

Different scenarios for projecting public healthcare expenditure 

1 

1 

3 

13 

14 
14 

14 

16 

18 

22 

25 

26 
30 
30 

30 

35 

44 

47 

56 

59 

60 

61 
66 
66 

66 

76 
78 
78 

78 

79 
81 
81 

81 

91 

92 
92 

92 

94 
105 
105 

107 

vii 

3. 

Long-term care 
Introduction 
3.1. 

3.2.  Overview of the projection methodology and model structure 

3.3. 

Different scenarios projecting public long-term care expenditure 

4.  Education 

Short overview of the methodology used to project expenditure on education  124 

Introduction 

Sensitivity analysis 

4.1. 

4.2. 

4.3. 

Annexes 
A1. 

A2. 
A3. 

A4. 
A5. 
A6. 
A7. 
A8. 

Projecting labour force developments using the cohort simulation model 
(CSM) 
Estimation of the average exit age from the labour market 
Methodology underpinning potential GDP growth projections 

A3.1.  Description of the production function framework 

A3.2.  Potential GDP projections for the first ten years ('t+10' projections) 

Pension projection reporting sheet 
Overview of pension systems in the Member States 
Coverage and specification of pension schemes 
Long-term care model structure 
Sources of data to compute healthcare and long-term care 

A8.1.  Data sources healthcare 

A8.2.  Data sources long-term care 

Mathematical illustration of the healthcare scenarios 

A9. 
A10.  Mathematical illustration of the long-term care scenarios 
A11.  Organisational structure of secondary education 
A12. 
A13. 

Sources of data for education expenditure 
Estimating the education enrolment rate 

Part III:  Statistical Annex 

Spain 

1.  Belgium 
2.  Bulgaria 
3.  Czech Republic 
4.  Denmark 
5.  Germany 
6.  Estonia 
7. 
Ireland 
8.  Greece 
9. 
10.  France 
11.  Croatia 
12.  Italy 
13.  Cyprus 
14.  Latvia 
15.  Lithuania 
16.  Luxembourg 
17.  Hungary 
18.  Malta 

viii 

115 
115 

115 

119 
124 
124 

127 
129 

129 
131 
132 

132 

134 
135 
140 
145 
158 
159 

159 

160 
166 
174 
181 
182 
183 

185 

186 
187 
188 
189 
190 
191 
192 
193 
194 
195 
196 
197 
198 
199 
200 
201 
202 
203 

 
19.  The Netherlands 
20.  Austria 
21.  Poland 
22.  Portugal 
23.  Romania 
24.  Slovenia 
25.  Slovakia 
26.  Finland 
27.  Sweden 
28.  Norway 
29.  European Union 
30.  Euro area 

Part IV:  Resources 

1.  References 

LIST OF TABLES 

1. 
2. 

3. 
4. 
I.1.1. 
I.1.2. 
I.1.3. 
I.1.4. 
I.1.5. 

Population projections, 2021 Ageing Report and 2018 Ageing Reports 
Population projection assumptions, 2021 Ageing Report and 2018 Ageing 
Reports 
Labour force projections, 2021 Ageing Report and 2018 Ageing Reports 
Potential GDP projections, 2021 Ageing Report and 2018 Ageing Reports 
Past trends in total fertility rates (1960-2018) 
Projection of total fertility rates (2019-2070) 
Past trends in life expectancy at birth (1960-2018) 
Projection of life expectancy at birth and at 65 (2019-2070) 
Average annual net migration flows (including statistical adjustment; 
1960-2018) 
Projection of net migration flows (2019-2070) 
Total population projections (2019-2070) 

I.1.6. 
I.1.7. 
I.1.8.  Composition of the population by age group 
I.1.9. 
I.1.10.  Geographical distribution of world population (1960-2070, % of total 

Dependency ratios (%, 2019-2070) 

world population) 

I.1.11.  Global demographic dependency ratios (19, 1960-2070, (%) 
I.1.12.  Population – difference between EUROPOP2019 and ESSPOP2015 ('000) 
I.1.13.  Old-age dependency ratio – difference between EUROPOP2019 and 

ESSPOP2015 (percentage points) 

I.1.14.  Fertility and net migration – differences between EUROPOP2019 and 

ESSPOP2015 
Life expectancy at birth – difference between EUROPOP2019 and 
ESSPOP2015 (years) 
Historical participation rates: total 
Historical participation rates: men 
Historical participation rates: women 
Automatic adjustment mechanisms 

I.1.15. 

I.2.1. 
I.2.2. 
I.2.3. 
I.2.4. 

204 
205 
206 
207 
208 
209 
210 
211 
212 
213 
214 
215 

217 

218 

8 

9 
10 
11 
15 
15 
15 
17 

18 
20 
22 
23 
24 

25 
26 
27 

28 

28 

29 
31 
32 
32 
36 

ix 

 
 
Participation rate projections by age group – total 
Participation rate projections – 65-74y, total 
Participation rate projections by age group – men 
Participation rate projections by age group – women 
Labour supply projections - total 

I.2.6. 
I.2.5. 
I.2.7. 
I.2.8. 
I.2.9. 
I.2.10.  Contribution to the overall change in participation rates, 2019-2070 (in 

pps) 

I.2.11.  Employment rate projections by age group – total 
I.2.12.  Employment rate projections by age group – men 
I.2.13.  Employment projections by age group – women 
I.2.14.  Share of older workers (55-64y) in total employment (20-64y) 
I.2.15.  Economic old-age dependency ratio 
I.2.16. 
I.2.18. 
I.2.17.  Projection of total weekly hours worked (thousands) and breakdown in 

Total economic dependency ratio 
Labour force projections: revisions AR2021 vs. AR2018 (thousands) 

full- and part-time work 
Labour force projections: revisions AR2021 vs. AR2018 (pps) 

I.2.19. 
I.2.20.  Breakdown of revisions in employment projection for 2070 (AR2021 vs. 

AR2018) (%) 

I.2.21.  Participation rate projections for 2070: revisions AR2021 vs. AR2018 (pps) 
I.3.1. 
I.3.2. 

Potential GDP annual growth rate - Period average (%) 
Labour input (total hours worked), annual growth rate - Period average 
(%) 
Labour productivity per hour, annual growth rate - Period average (%) 
Annual total factor productivity growth rate - Period average (%) 
Annual contribution of capital deepening - Period average (%) 
Breakdown of potential GDP growth (baseline), 2019-70 
Breakdown of potential GDP growth (TFP risk scenario), 2019-70 
Difference between 2021 and 2018 baseline scenarios, annual average 
GDP growth, 2019-2070 (pps) 
Difference between 2021 and 2018 TFP risk scenarios, annual average 
GDP growth, 2019-2070 (pps) 
Selected macroeconomic assumptions in subsequent projection rounds 
(baseline scenario) 

I.3.3. 
I.3.4. 
I.3.5. 
I.3.6. 
I.3.7. 
I.3.8. 

I.3.9. 

I.4.1. 

Sensitivity test: Higher life expectancy 
Sensitivity test: Higher migration 
Sensitivity test: Lower migration 
Sensitivity test: Lower fertility 
Sensitivity test: Higher employment rate of older workers 
Sensitivity test: TFP risk 
Sensitivity test: Higher TFP growth 
Policy scenario: Linking retirement age to life expectancy 

I.5.1.  Overview of the sensitivity tests and alternative scenarios 
I.5.2. 
I.5.3. 
I.5.4. 
I.5.5. 
I.5.6. 
I.5.7. 
I.5.8. 
I.5.9. 
I.5.10.  Policy scenario: Unchanged retirement age 
I.5.11.  Adverse macroeconomic scenario – Lagged recovery 
I.5.12.  Adverse macroeconomic scenario – Adverse structural 
II.1.1. 
II.2.1.  Overview of scenarios to project healthcare expenditure 

Pension schemes across Member States 

x 

48 
48 
49 
50 
51 

53 
56 
57 
57 
58 
59 
59 
62 

63 
63 

64 
65 
67 

67 
70 
70 
71 
72 
73 

77 

77 

79 
82 
84 
85 
85 
86 
86 
87 
87 
88 
88 
89 
89 
100 
108 

 
II.3.1.  Overview of the different scenarios to project long-term care 

expenditure 
Education expenditure, % of GDP 
Total public expenditure on education per pupil in EUR PPS (1) in 2016 
Pension projection reporting sheet: blocks common to all 

II.4.1. 
II.4.2. 
II.A4.1. 

schemes 

II.A4.2. 

Pension projections reporting sheet: disaggregation of new 

public pensions expenditure – earnings-related for defined benefit 
(DB) schemes 

II.A4.3. 

Pension projection reporting sheet: disaggregation of new 

public pension expenditure - earnings-related for notional defined 
contribution (NDC) schemes 

II.A4.4. 

Pension projection reporting sheet: disaggregation of new 

public pension expenditure - earnings-related for point systems (PS) 
Reporting sheet for special pension schemes (voluntary 

II.A4.5. 

reporting) 

II.A5.1.  Pension schemes in EU Member States and projection coverage 
II.A5.2.  Statutory retirement ages, early retirement ages (in brackets) and 

incentives to postpone retirement 

II.A5.3.  Key indexation and valorisation parameters of pension systems (old-age 

pensions) 

119 
124 
126 

135 

137 

138 

138 

139 
140 

141 

142 

II.A5.4.  Automatic balancing mechanisms, sustainability factors and links to life 

expectancy in pension systems 

143 
144 
II.A5.5.  Contribution rates to the public pension system 
145 
II.A6.1.  Pension schemes included in the projections 
II.A8.1.  Data sources for the sector-specific indexation components 
160 
II.A8.2.  LTC public expenditure base data requirements according to availability  162 

LIST OF GRAPHS 

1. 
2. 
3. 
I.1.1. 
I.1.2. 
I.2.1. 
I.2.2. 
I.2.3. 

I.2.4. 

I.2.5. 

I.3.1. 

2 
Overview of the 2021 projection exercise 
3 
EU – Population by age groups and gender, 2019 and 2070 (thousands) 
6 
Baseline and COVID-19 crisis scenarios, EU27 
19 
Net migration flows (including statistical adjustment; 1960-2018) 
23 
EU population by age group and gender (‘000) 
Legislated changes in the statutory retirement age 
44 
Impact of pension reforms on the average exit age from the labour force  45 
Impact of pension reforms on the participation rate of the age group 55-
64 
Impact of pension reforms on the participation rate of the age group 65-
74 
Age profiles of participation (PR) and employment rates (ER) by gender 
for the EU27 (2019 and 2070) 

46 

46 

I.2.6.  Change in the labour force (20-64y; %) 
I.2.7. 

Population and employment projections: revisions AR2021 vs. AR2018, 
2070 (percentage change) 
Average annual cyclical and potential GDP growth projections (2019-
2029) 

47 
52 

63 

73 

xi 

 
 
76 

79 
79 
106 

109 

117 
128 
133 
158 

21

33 

37 
54 
68 

74 

I.4.1. 

I.3.2. 

I.4.2. 
II.2.1.
II.2.2.

Annual GDP growth rates 2019-70 (%) in 2021 and 2018 baseline scenario 
projections - period average 
(R-g) based on market long-term interest rates, aggregates and selected 
countries, % 
Interest rate decline 
Schematic presentation of the projection methodology 
Stylised illustration of the constant healthy ageing scenario using 
age-profiles of healthcare costs 
Schematic presentation of the projection methodology / in-kind long-
term care benefits 
II.4.1.
Implicit breakdown of expenditure per student 
II.A3.1.  Overview of the production function approach
II.A7.1.  Long-term care model structure

II.3.1.

LIST OF BOXES 

I.1.1.  Methodology for the migration assumptions in the EUROPOP2019 

projections

I.2.1.  Main features of the Cohort Simulation Model (CSM) and assumptions for 

I.2.2. 

I.2.3. 
I.3.1. 
I.3.2. 

the 2021 projections 
Pension reforms legislated in Member States incorporated in the labour 
force projections 
Assumptions on structural unemployment 
The COVID-19 crisis and macroeconomic prospects 
Assumptions on the components of the production function used for 
long-run potential growth projections 

xii 

EXECUTIVE SUMMARY 

1. 

2021 AGEING REPORT: MANDATE AND GENERAL PRINCIPLES

With a view to ensuring the sustainability of public finances in the EU, the ECOFIN Council charged the 
Economic  Policy  Committee  (EPC)  with  producing  a  new  set  of  long-term  budgetary  projections  by 
2021,  on  the  basis  of  new  population  projections  to  be  provided  by  Eurostat.  Safeguarding  the 
sustainability of public finances requires that the analysis is based on reliable, comparable information on 
possible  challenges  to  fiscal  sustainability,  including  strains  caused  by  future  demographic  changes,  in 
particular population ageing. 

The  EPC  and  the  Commission’s  Directorate-General  for  Economic  and  Financial  Affairs  (DG ECFIN) 
subsequently  developed  a  work  programme  with  arrangements  for  the  budgetary  projections  and  the 
preparation of the underlying assumptions and methodologies (see Graph 1 for details). 

This  report  sets  out  the  macroeconomic  assumptions  and  methodologies  to  be  used  for  the  age-related 
expenditure  projections  for  all  Member  States.  These  will  form  the  basis  for  calculating  projected 
expenditure  on  pensions,  healthcare,  long-term  care  and  education  in  the  sixth  Ageing  Report,  to  be 
presented to the ECOFIN Council in spring 2021. 

The long-term projections show where (in which countries), when (2019-2070) and to what extent ageing 
pressures will accelerate as the baby boom generation retires and life expectancy in the EU continues to 
increase.  They  are  thus  helpful  in  highlighting  the  immediate  and  future  policy  challenges  that 
governments  face  as  a  result  of  demographic  trends.  The  report provides  a  very  rich  set  of  comparable 
information,  including  alternative  assumptions  (sensitivity  scenarios),  at  country  level.  Comparable  and 
reliable underlying assumptions are crucial, since the projections cover a long time span (until 2070). 

The  Ageing  Report  projections  feed  into  a  range  of  policy  debates  and  processes  at  EU  level.  In 
particular,  they  are  used  in  the  coordination  of  economic  policies  (the  European  Semester,  multilateral 
budgetary  surveillance  and  the  assessment  of  public  finance  sustainability)  to  identify  relevant  policy 
challenges  and  options (1).  In  addition,  the  projections  assist  the  analysis  of  the  impact  of  population 
ageing, including on the labour market and potential economic growth. 

Coverage and overview of the 2021 long-term projection exercise 

The  long-term  projections  take  Eurostat’s  2019-2070  population  projections  as  a  starting  point.  In 
addition, on the basis of proposals from DG ECFIN and the EPC’s Ageing Working Group (EPC-AWG), 
the  EPC  agreed  common  assumptions  and  methodologies  for  all  Member  States  to  project  a  set  of 
exogenous  macroeconomic  variables  covering 
(participation,  employment  and 
unemployment),  labour  productivity  and  interest  rates  (see  Graph 1).  The  combined  set  of  projections 
make it possible to calculate GDP figures for all Member States up to 2070. 

labour 

force 

(1) They  will  also  feed  into  the  Recovery  and  Resilience  Facility,  which  is  rooted  in  the  EU’s  aim  of  achieving  competitive

sustainability and cohesion through a new growth strategy, the European Green Deal.

1 

European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Graph 1: 

Overview of the 2021 projection exercise 

Source: European Commission, EPC. 

On  the  basis  of  the  assumptions  presented  in  this  report,  budgetary  projections  will  be  made  for  four 
government  expenditure  items:  pensions,  healthcare,  long-term  care  and  education.  Using  national 
models,  Member  States  will  make  projections  for  pensions  in  a  peer-reviewed  process  overseen  by  the 
EPC-AWG.  The  projections  will  capture  country-specific  circumstances  (e.g. different  pension 
legislation),  while ensuring consistency by  using the common agreed  macroeconomic  assumptions. The 
healthcare, long-term care and education projections will be prepared by DG ECFIN in collaboration with 
the EPC-AWG, on the basis of a common projection model for each expenditure item. The results of all 
these projections provide an overall projection of age-related public expenditure (see Graph 1). 

The long-term projections are not forecasts. Projecting economic developments over the next half century 
is  a  daunting  analytical  task,  subject  to  considerable  uncertainty.  The  projections  are  made  under  a  ‘no 
policy change’ assumption. They do not aim to predict the future but to illustrate  what the future could 
hold  if  current  policies  remain  unchanged.  As  the  results  are  strongly  influenced  by  the  underlying 
assumptions, a set of tests are carried out to gauge the extent of this sensitivity. 

This  report  is  structured  in  two  parts.  The  first  part  describes  the  underlying  assumptions  (as  regards 
population, labour force, potential GDP and other macroeconomic factors) and the sensitivity tests. The 
second  part  presents  the  methodologies  for  projecting  future  expenditure  on  pensions,  healthcare,  long-
term  care  and  education.  A  statistical  annex  gives  an  overview  of  the  main  assumptions  and 
macroeconomic projections by country. 

2 

 
Executive Summary 

2. 

MAIN RESULTS: THE ECONOMIC IMPACT OF POPULATION AGEING 

The EU population is projected to fall over the long term 

The total population of the EU is projected to decline over the long term and the age structure will change 
significantly  in  the  coming  decades  (see  Graph 2).  According  to  Eurostat,  the  overall  population  is 
projected to shrink by 5% between 2019 (447 million) and 2070 (424  million) (see Table 1). However, 
there are wide differences in national population trends, with increases in 11 Member States and falls in 
the others. 

The old-age dependency ratio is set to continue to rise sharply over the coming decades 

The  EU’s  demographic  old-age  dependency  ratio  (i.e. the  ratio  between  people  aged  65  years  and  over 
and  those  aged 20-64)  is  projected  to  increase  significantly  in  the  coming  decades.  From  about  29%  in 
2010, it had risen to 34% in 2019 and is projected to rise further, to 59% in 2070, i.e. a shift from less 
than four working-age people for every person aged 65 years and over in 2010 to below two in 2070. 

Graph 2: 

EU – Population by age groups and gender, 2019 and 2070 (thousands) 

Male

2070

2019

2070

2019

Female

90+

85-89

80-84

75-79

70-74

65-69

60-64

55-59

50-54

45-49

40-44

35-39

30-34

25-29

20-24

15-19

10-14

5-9

0-4

20000

15000

10000

5000

0

0

5000

10000

15000

20000

Source: European Commission, EPC. 

Projected changes in the size and age profile of the population are determined by assumptions regarding 
fertility rates, mortality rates and migration (see Table 2): 

(cid:129)  The total fertility rate (TFR) is projected to rise from 1.52 in 2019 to 1.65 by 2070 for the EU as a 
whole, with an increase of similar magnitude projected for the euro area (see Table 2). This follows 
from  an  assumed  process  of  convergence  across  Member  States  to  the  country  with  the  highest 
fertility rate over the very long term. 

(cid:129)  Life  expectancy  at  birth  for  males  is  expected  to  increase  by  7.4  years  over  the  projection  period, 
from 78.7 in 2019 to 86.1 in 2070 in the EU. For females, it is projected to increase by 6.1 years, from 
84.2  in  2019  to  90.3  in  2070,  implying  continued  convergence  between  males  and  females.  The 
biggest increases, for both males and females, are projected for the Member States with the lowest life 
expectancies in 2019. 

3 

 
  
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

(cid:129)  For the EU as a  whole, it is  assumed that annual  net  migration inflows  will  fall  gradually over the 
very  long  term;  they  are  projected  to  decrease  from  about  1.3 million  people  in  2019  to  about 
1 million  in  2070  (0.2%  of  the  EU  population) (2).  However,  there  are  large  differences  between 
Member States. 

Labour force participation is projected to increase, in particular among older workers on account of 
implemented pension reforms 

The labour force projections are made using a cohort simulation model, capturing the specific situation in 
each  country  and  assuming  no  further  policy  changes  beyond  already-legislated  pension  reforms.  They 
expect  an  increase  in  labour  force  participation  rates,  especially  among  older  workers,  reflecting  the 
combined effect of the estimated impact of pension reforms and rising participation by younger women. 

Total labour force participation (for the 20-64 age group) in the EU is projected to increase by about 3 pps 
(from  more than 78% in 2019 to close to 81% in 2070) and similar developments are projected for the 
euro area (see Table 3).  

The  biggest  increase  in  participation  rates  is  projected  for  older  workers  (55-64  age  group):  almost 
10 pps. As this is the result of increases of 13 pps for women and only 6 pps for men, the participation 
gender gap is projected to narrow substantially in the period to 2070. 

However, labour supply is still set to decline  

Still, given the projected shrinkage of the prime-age population (25-54 age group) in many countries, total 
labour  supply  in  the  EU  is  projected  to  decrease  over  the  projection  horizon,  by  an  average  of  0.3%  a 
year, corresponding to a reduction of 16% (32 million people) in the period to 2070. Male labour supply 
is projected to fall by 17% (around 19 million) and female labour supply by 14% (almost 13 million). In 
the  euro  area,  total  labour  supply  is  projected  to  fall  between  2019  and  2070  by  13%,  equivalent  to 
20 million people. 

Further rises in employment rates projected, although the number of employed is declining 

Employment  rate  projections  are  determined  on  the  basis  of  the  population  projections,  assumed 
participation  and  unemployment  rates.  Unemployment  is  projected  to  decline  slightly  in  the  EU  (from 
6.8%  in  2019  to  5.8%  in  2070),  under  the  general  assumption  that  the  rate  will  converge  to  estimated 
‘NAWRUs’ (3). Euro area unemployment is assumed to fall from 7.7% in 2019 to 6% in 2070. 

The total employment rate (among people aged 20-64) in the EU is projected to increase from 73.1% in 
2019  to  76.2%  in  2070. In  the  euro  area,  a  somewhat  bigger  increase  is  expected.  Employment  among 
women is projected to rise by 5.1 pps (from 67.2% to 72.3%), compared to 1.1 pps for men. The rate for 
older  workers  (aged  55-64)  is  expected  to  rise  by  almost  10 pps  (from  59.1%  to  68.7%),  reflecting  the 
expected impact of pension reforms in many Member States that are aimed at raising people’s effective 
retirement age. 

The EU’s effective economic old-age dependency ratio (inactive persons aged 65 years and over relative 
to the number of employed 20-64 year-olds) is projected to rise significantly, from 45% in 2019 to 72% in 
2070.  This  reflects  how  the  growth  in  the  number  of  older  people  is  not  offset  by  higher  employment 
considering  that  the  higher  employment  rate  combines  with  a  declining  working-age  population.  In  the 

(2)  The immigration and emigration projections, which combine into net migration, rest on a number of technical assumptions, e.g. 
total  non-EU  immigration  is  assumed  constant  as  of  2024  for  the  EU  as  a  whole.  For  more  information,  see  Box  I.1.1  in 
Chapter 1. 

(3)  NAWRU stands for ‘non-accelerating wage rate of unemployment’. For countries with a high estimated NAWRU, it is assumed 

that structural unemployment will fall further to reach the EU median. 

4 

                                                           
Executive Summary 

euro area, a similar deterioration is projected (from 46% to 71%). The total economic dependency ratio 
(total  inactive  population  versus  employment)  is  also  projected  to  rise  further,  from  119%  in  2019  to 
141% in 2070, with a similar change for the euro area. 

Stable potential GDP growth projected over the long term 

Average annual potential GDP growth of 1.3% in 2019-2070 is projected for the EU as a whole under the 
baseline scenario (see Table 4). Growth will average 1.2% up to 2030, rise slightly to 1.3% in the 2030s 
and further to 1.4% in the 2040s, where it is expected to remain through to 2070. The projections for the 
euro area follow a similar (though slightly lower) trajectory, with annual growth of 1% up to 2030, 1.2% 
in  2031-2040  and  1.4%  in  2041-2070.  Overall,  the  average  euro  area  growth  rate  in  2019-2070  is 
projected at 1.3%. 

As labour growth turns negative in the 2020s, only labour productivity is expected to drive GDP growth 
over the long term 

The contribution of labour input (total hours worked) to potential growth in the EU and the euro area is 
projected to be negative from the early 2020s onwards. Demographic developments result in a decline of 
the working-age population and by extension a negative contribution of labour input to potential growth 
for most EU Member States. 

As a result, potential GDP growth projections are driven almost entirely by labour productivity. Annual 
growth in productivity per hour worked is projected to increase from less than 1% to 1.5% by the 2030s 
and  to  remain  fairly  stable  at  around  1.6%  throughout  the  remaining  projection  period.  As  a  result, 
average  annual  productivity  growth  equals  1.6%  in  2019-2070.  A  similar  trajectory  is  envisaged  in  the 
euro area, though with average productivity growth of only 1.4% (see Table 4). 

A risk scenario takes on board the downward trend in TFP growth 

The  projected  increase  in  labour  productivity  rests  on  the  assumption  that  TFP  growth  in  all  Member 
States will converge to 1% by 2070 at the latest. It is assumed that those with relatively high per capita 
GDP will converge by 2045, while those with per capita GDP below the EU average will go through a 
period of economic catching-up, with higher TFP growth of up to 1.5% until 2045. 

Given  the  downward  trend  in  TFP  growth  in  recent  decades,  it  is  important  to  assess  the  impact  on 
age-related  expenditure  and  fiscal  sustainability.  To  this  end,  a  ‘TFP  risk  scenario’  was  run,  which 
entailed convergence to a lower assumed TFP growth rate of 0.8% (while still allowing for catching-up 
by countries with below-average per capita GDP). In that scenario, average annual potential GDP growth 
in  2019-2070  is  projected  at  1.1%  for  the  EU  and  the  euro  area,  compared  to  1.3%  in  the  baseline 
scenario. 

The fallout from the COVID-19 crisis presents considerable risks to the future GDP growth outlook  

The  COVID-19  pandemic  and  the  containment  measures  it  has  necessitated  have  profoundly  disrupted 
people’s lives and the economy at large. Global demand, supply chains, labour markets, industrial output, 
commodity  prices,  foreign  trade  and  capital  flows  have  all  been  affected.  Given  the  severity  of  this 
unprecedented worldwide shock, it was clear that the EU had entered the deepest economic recession in 
its  history  when  this  report  was  finalised.  The  baseline  scenario  in  this  report  takes  the  Commission’s 

5 

 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

spring 2020 forecast as a starting point, reflecting the impact of the crisis and assuming recovery as per 
May 2020 (4). 

Given the current uncertainty, economic growth developments may be less buoyant than those set out in 
the  baseline  scenario.  For  this  reason,  without  assigning  any  ex-ante  probability  of  materialisation,  two 
additional – and more adverse scenarios – were prepared (see Graph 3 and Box I.3.1 for details): 

(cid:129)  The lagged recovery scenario, which maintains the assumption of a relatively limited impact of the
COVID-19  crisis  on  potential  GDP  growth  (with  only  a  slightly  greater  impact  on  potential  GDP
growth  than  in  the  baseline  scenario)  but  with  a  much  more  pronounced  cyclical  downturn  and  a
longer recovery phase, resulting in a wide ‘U-shaped’ recovery; and

(cid:129)  The  adverse  structural  scenario:  in  addition  to  the  stronger  cyclical  downturn  in  the  ‘lagged
recovery’ scenario, this scenario assumes that potential growth will be lower over the next decade, so
potential  output  growth  will  be  permanently  lower  than  in  the  baseline.  Labour  productivity  growth
recovers  to  lower  trend  growth,  through  lower  investment  and/or  total  factor  productivity  (TFP)
growth stemming from a long period of reduced business activity, with the crisis contributing to the
historical  downward  trend.  In  addition,  the  deeper  recession  and  slower  recovery  lead  to  lower
business activity, producing a hysteresis effect and permanently higher unemployment.

Graph 3: 

Baseline and COVID-19 crisis scenarios, EU27 

Actual GDP level (billion EUR)

Potential GDP growth (%)

16000

15500

15000

14500

14000

13500

13000

12500

12000

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

baseline

lagged recovery

adverse structural

baseline

lagged recovery

adverse structural

Source: European Commission, EPC. 

However, the COVID-19 crisis is still running its course and its full medium/long-term consequences are 
uncertain. Continued vigilance is therefore called for if policymakers are to be able to adjust and adapt 
economic policies to mitigate the short-, medium- and long-term impact. The recovery path will depend 
on what policies are implemented. The Commission has tabled a comprehensive proposal for a durable, 
inclusive and sustainable recovery from the crisis with the Recovery and Resilience Facility (RRF). 

Comparison with the long-term budgetary projections in the 2018 Ageing Report 

In  the  base  year  2019,  the  EU  population  counted  762 000  fewer  people  than  anticipated  in  the  2015-
based demographic projections. The old-age dependency ratio was 0.1 pps lower in 2019 than anticipated 
in  the  previous  Ageing  Report  at  EU/EA  aggregate  level.  In  most  countries,  the  total  labour  force  and 
total employment were larger in 2019 than anticipated in the 2018 Ageing Report. In the EU as a whole, 
3.3 million more people were employed in 2019 than expected in the previous exercise, with the labour 
force  showing  1.1 million  more  people  as  two  thirds  of  the  higher  employment  stems  from  people 
previously presumed unemployed. Real GDP growth was slightly higher in the EU in 2019 (at 1.5%) than 

(4) The spring 2020 forecast assumed a rebound of growth in 2021, broadly  resulting in a narrow ‘U-shaped’ recovery scenario.
Also,  it  incorporates  the  ‘t+10’projections  according  to  the  methodology  agreed  by  the  EPC’s  Output  Gap  Working  Group,
which assumes a rather small impact on potential growth (see Box I.3.1 in Chapter 3 for details). 

6 

Executive Summary 

expected in the 2018 Ageing Report (at less than 1.4%), while similar figures are found for the euro area 
(1.2%).  

The total EU population is projected to shrink by about 15.2 million  more by 2070 as compared to the 
2018 Ageing Report estimate, given reductions in the younger age groups and the working-age population 
(see Table 1). The population in the euro area is projected to be 12.4 million lower. As a result, the new 
Eurostat population projections lead to a 2070 old-age dependency ratio that, for the EU as a  whole, is 
1.7 pps higher than the previous assumption and 2 pps higher for the euro area.  

For the EU, the annual contribution from labour productivity growth during 2019-2070 is 0.1 pps higher 
than in the 2018 projection, while for the euro area, labour productivity growth is the same. Labour input 
growth (hours worked) is projected to be the same as in the 2018 Ageing Report for both the EU and the 
euro area.  

At 1.3% on average over the period 2019-2070, potential GDP growth in both the EU and the euro area is 
projected to remain unchanged from the baseline projection in the 2018 Ageing Report. For the EU as a 
whole, annual potential GDP growth is projected to average 1.2% in 2019-2035 and 1.4% in 2036-2070, 
similar rates as in the 2018 Ageing Report. For the euro area, the 2021 Ageing Report projects average 
annual potential GDP growth of 1.1% in 2019-2035, against 1% in the 2018 Ageing Report, but the same 
average of 1.4% for 2036-2070. 

7 

 
8

Table 1: 

Population projections, 2021 Ageing Report and 2018 Ageing Reports 

Projection exercise 2021 Ageing Report

2021 AR - 2018 AR (2019-70)

Total population (million)

Old-age dependency ratio (%)

Total population (thousand)

Old-age dependency ratio (pps)

2019

2070

% change 
2019-2070

2019

2070

pps change 
2019-2070

2019

2070

2070 difference 
as % of 2070 
population 
(ESSPOP2015)

2019

2070

pps change 
2019-2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

11.5
7.0
10.7
5.8
83.1
1.3
4.9
10.7
47.1
67.1
4.1
60.3
0.9
1.9
2.8
0.6
9.8
0.5
17.3
8.9
38.0
10.3
19.3
2.1
5.5
5.5
10.3
5.3
342.4
447.2

11.8
5.0
10.2
6.2
81.7
1.2
6.5
8.6
47.0
69.4
3.0
53.9
1.1
1.2
1.8
0.8
8.9
0.7
18.0
9.2
30.8
8.5
13.7
1.9
4.7
5.0
13.1
6.7
333.1
424.0

3.1%
-27.8%
-4.4%
6.0%
-1.6%
-10.2%
31.7%
-19.8%
-0.2%
3.5%
-25.5%
-10.7%
24.7%
-38.4%
-34.8%
27.0%
-8.7%
41.4%
3.7%
4.1%
-18.8%
-17.7%
-29.4%
-7.3%
-13.6%
-8.9%
27.3%
25.6%
-2.7%
-5.2%

32.5
36.0
33.0
34.1
36.1
33.8
24.2
37.9
32.1
36.5
34.8
38.9
26.2
34.6
32.9
22.6
32.2
29.7
32.9
30.7
29.0
37.3
31.1
33.2
25.9
38.9
35.2
29.4
35.3
34.4

53.3
60.8
53.7
53.8
54.6
59.4
53.0
65.2
62.5
56.9
64.6
65.6
50.7
63.6
66.0
56.1
57.4
62.4
55.2
55.9
67.8
67.3
62.1
58.8
63.1
62.5
49.8
52.4
58.9
59.2

20.8
24.8
20.6
19.7
18.5
25.6
28.7
27.3
30.5
20.4
29.8
26.7
24.6
29.0
33.1
33.6
25.1
32.7
22.4
25.2
38.8
30.0
31.0
25.5
37.2
23.6
14.6
23.0
23.6
24.7

-63
-6
31
-56
-555
10
105
120
585
-572
-34
-406
15
-5
27
-2
-21
50
-14
-85
11
57
30
14
-1
-30
34
-32
-751
-762

-2070
178
236
-673
2488
16
458
925
-2813
-7604
-368
-986
80
-161
101
-250
46
186
-1561
-922
-58
479
-1333
-19
-186
-592
-788
-297
-12430
-15190

-14.9%
3.7%
2.4%
-9.9%
3.1%
1.4%
7.6%
12.1%
-5.6%
-9.9%
-10.8%
-1.8%
7.8%
-12.0%
5.9%
-24.1%
0.5%
35.8%
-8.0%
-9.1%
-0.2%
6.0%
-8.9%
-1.0%
-3.8%
-10.5%
-5.7%
-4.2%
-3.6%
-3.5%

0.0
-0.2
-0.3
0.3
0.1
-0.2
-0.8
-0.4
-0.9
0.3
0.3
0.1
0.1
-0.3
-0.6
-0.6
-0.4
-4.8
-0.2
0.2
-0.3
0.0
0.0
-0.4
0.1
0.0
-0.3
0.2
-0.1
-0.1

3.5
-1.0
-1.2
-1.6
-6.7
1.4
7.0
-3.5
11.0
7.2
3.3
0.1
-14.8
3.6
7.2
2.6
0.0
0.9
1.9
-3.7
-0.5
-5.6
3.7
3.5
0.6
5.3
1.8
0.4
2.0
1.7

3.5
-0.8
-0.9
-1.8
-6.8
1.6
7.8
-3.1
11.8
6.9
3.0
0.0
-14.9
3.9
7.8
3.2
0.4
5.7
2.1
-3.9
-0.2
-5.6
3.7
3.9
0.5
5.3
2.1
0.2
2.1
1.8

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

(1)  Population is defined as per 1 July, and EU/EA averages are constructed accordingly (weighted with population per 1 July).  

Source: European Commission, EPC. 

r

E
u
o
p
e
a
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o
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s
s
i
o
n

i

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0
2
1
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g
e
n
g
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e
p
o
r
t
:

U
n
d
e

i

r
l
y
n
g
a
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s
u
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p

j

r

t
i
o
n
s
a
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e
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i
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s

i

l

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 2: 

Population projection assumptions, 2021 Ageing Report and 2018 Ageing Reports 

Projection exercise 2021 Ageing Report

Fertility rate 
(live births/woman)

Life expectancy at birth (y)

Males

Females

Net migration ('000)

Fertility rate 
(live births/woman)

2021 AR - 2018 AR (2019-70)

Life expectancy at birth (y)

Males

Females

Net migration ('000)

2019

2070

change 
2019-70

2019 2070

change 
2019-70

2019 2070

change 
2019-70

2019

2070

cum. 
change 
2019-70 
(1)

2019

2070

change 
2019-70

2019 2070

change 
2019-70

2019 2070

change 
2019-70

2019

2070

cum. 
change 
2019-70 
(2)

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

1.58

1.68

0.10

79.8

86.3

6.5

84.3

90.3

1.58

1.71

0.13

71.5

82.9

11.4

78.8

87.7

1.71

1.78

0.07

76.5

84.8

1.72

1.77

0.05

79.5

86.1

1.53

1.67

0.13

79.1

86.0

1.51

1.70

0.18

74.9

84.3

1.78

1.81

0.02

81.1

86.8

1.34

1.54

0.20

79.0

86.4

1.27

1.49

0.22

81.2

87.1

1.85

1.84

-0.01

80.1

86.7

HR

1.43

1.59

0.16

75.3

84.3

1.31

1.52

0.21

81.3

87.0

1.33

1.53

0.20

80.8

86.6

8.3

6.6

6.9

9.4

5.7

7.4

5.9

6.6

9.0

5.7

5.8

82.3

89.2

83.3

89.8

83.7

89.9

83.4

89.9

84.8

90.4

84.3

90.3

86.8

91.4

86.3

91.4

81.6

88.8

85.7

90.9

85.1

90.2

1.58

1.71

0.13

70.6

82.6

12.0

80.2

88.5

1.61

1.70

0.09

71.3

82.9

11.6

81.1

88.8

1.34

1.56

0.22

80.3

86.6

6.3

85.0

90.8

1.51

1.70

0.18

72.9

83.6

10.7

79.8

88.5

1.14

1.47

0.33

80.5

86.8

1.58

1.68

0.10

80.7

86.6

1.45

1.60

0.15

79.8

86.3

6.3

5.9

6.5

84.5

90.6

83.6

89.9

84.3

90.2

1.36

1.56

0.20

74.1

84.3

10.2

82.0

89.5

1.43

1.59

0.16

78.6

85.7

7.1

84.8

90.4

RO

1.65

1.74

0.10

71.9

83.5

11.6

79.5

88.5

SI

SK

FI

SE

1.55

1.68

0.13

78.7

85.9

1.56

1.67

0.11

74.4

84.1

1.35

1.53

0.19

79.5

86.1

1.71

1.78

0.07

81.4

86.8

NO

1.53

1.65

0.12

81.4

86.9

EA

1.51

1.65

0.13

79.9

86.5

EU27

1.52

1.65

0.14

78.7

86.1

7.2

9.7

6.6

5.4

5.5

6.6

7.4

84.5

90.4

81.2

89.0

84.8

90.4

84.7

90.3

84.6

90.3

6.0

8.9

6.9

6.5

6.2

6.5

5.6

6.0

4.6

5.1

7.2

5.2

5.1

8.3

7.7

5.8

8.7

6.1

6.3

5.9

7.5

5.6

9.0

5.9

7.8

5.6

5.6

5.7

45.0

20.5

9.7%

-0.15

-0.14

0.01

0.5

0.1

-0.4

0.1

0.1

-3.9

10.0

2.9%

-0.03

-0.09

-0.05

-0.9

-0.4

44.2

18.2

9.2%

0.04

-0.04

-0.08

-0.1

-0.1

0.5

0.0

-0.2

-0.1

-0.2

-0.1

0.0

0.1

0.1

-9.2

-5.7

-7.8%

7.7

21.9

8.7

9.6

1.7

4.4%

1.9%

-6.2%

-1.6

11.0

9.9%

0.01

-0.04

-0.06

0.2

0.0

-0.2

-0.1

-0.2

-0.1

-36.4

277.4

214.2

14.9%

0.04

-0.01

-0.05

-0.1

-0.1

0.0

-0.3

-0.2

0.1

-107.8

70.7

1.2%

6.6

2.6

8.7%

-0.14

-0.12

0.02

1.3

0.4

-0.9

1.0

0.4

-0.6

4.0

2.3

4.9%

32.7

10.5

18.0%

-0.18

-0.16

0.02

1.1

0.4

-0.7

0.8

0.1

-0.7

21.7

-0.4

6.4%

13.7

26.0

8.7%

0.01

-0.11

-0.11

-0.4

-0.1

0.3

-0.1

0.0

0.1

33.6

15.0

6.5%

438.5

169.0

21.3%

-0.27

-0.39

-0.13

0.4

0.2

-0.2

0.6

0.2

-0.4

399.4

32.2

5.9%

38.1

80.2

5.6%

-0.16

-0.14

0.01

0.1

0.1

-3.8

6.0

1.7%

-0.04

-0.06

-0.03

-0.3

-0.1

0.0

0.2

0.3

0.3

0.0

-35.1

24.9

0.1%

0.0

-0.1

-0.1

0.4

1.5

-3.7%

HR

134.7

206.6

18.3%

-0.05

-0.14

-0.09

0.2

0.1

-0.1

0.0

0.0

0.0

-19.9

42.8

1.9%

7.8

-3.9

10.1

10.2

2.3

0.7

2.6

2.5

18.3%

-0.01

-0.09

-0.08

-0.5

-0.4

0.1

0.2

0.0

-0.2

-10.1%

-0.25

-0.16

0.09

0.2

-0.1

-0.3

0.0

-0.1

-0.1

6.4

3.8

-6.0%

-0.09

-0.13

-0.04

0.7

0.1

-0.6

0.3

0.0

-0.3

34.0

-1.5

-4.0%

0.6

2.6

-4.2%

7.2%

30.3%

-0.19

-0.13

0.06

0.4

0.2

-0.2

-0.2

-0.1

36.3

23.5

12.7%

-0.08

-0.10

-0.02

-0.6

-0.3

0.3

-0.4

-0.1

12.8

3.8

57.4%

-0.38

-0.28

0.10

0.2

0.0

-0.2

-0.1

0.0

105.4

33.2

11.0%

-0.15

-0.14

0.01

0.2

0.1

-0.1

-0.4

-0.2

44.3

25.5

16.7%

-0.04

-0.06

-0.02

0.1

0.0

-0.1

-0.1

0.0

3.3

72.4

5.8%

-0.07

-0.15

-0.08

-0.6

-0.1

40.1

18.6

7.4%

0.16

0.00

-0.16

-0.2

-0.2

-73.5

21.0

-4.4%

-0.06

-0.15

-0.09

-0.8

-0.1

13.4%

-0.07

-0.13

-0.06

0.0

0.1

0.5

0.0

0.7

0.1

-0.2

0.0

0.0

0.0

-0.2

0.2

0.2

0.3

15.7

3.4

5.2

7.4

5.1%

0.10

-0.16

-0.26

0.0

-0.1

-0.1

0.0

-0.1

-0.1

-2.4

17.6

13.2

11.6%

-0.36

-0.27

0.09

0.6

0.2

-0.4

0.3

0.2

-0.1

1.5

66.7

30.3

22.5%

-0.16

-0.25

-0.09

0.4

0.1

-0.3

0.0

0.0

25.3

23.4

24.9%

-0.21

-0.19

0.03

0.8

0.3

-0.5

-0.1

-0.1

-0.3

-1.4

-24.5%

15.5

12.4

4.2%

9.4

37.1

-26.6

2.8

8.7

4.9

35.2%

-1.6%

-5.9%

13.0

65.1

4.5%

40.1

4.4

0.3%

11.7

-3.0

-2.1

2.7

4.1

6.5

5.9

7.3

4.3%

0.2%

1.9%

1.2%

3.5%

401.5

216.2

1.4%

-9.2

18.4

0.2%

RO

0.1

0.3

0.1

0.2

0.1

0.2

0.0

0.4

0.1

0.0

0.0

0.0

0.0

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

SI

SK

FI

SE

NO

EA

85.0

90.6

5.6

1,249.9 844.5

13.4%

-0.09

-0.15

-0.06

0.1

0.0

-0.1

0.1

0.0

84.2

90.3

6.1

1,317.5 1,036.8

11.8%

-0.08

-0.14

-0.06

0.0

0.0

0.0

0.0

0.0

(1) Cumulated net migration as % of total population in 2019 (EUROPOP2019) 
(2) Cumulated difference as % of total population in 2019 (ESSPOP2015). 

Source: European Commission, EPC. 

9

411.2

339.5

1.6%

EU27

E
x
e
c
u
t
i
v
e
S
u
m
m
a
r
y

 
 
 
 
   
 
 
 
1
0

Table 3: 

Labour force projections, 2021 Ageing Report and 2018 Ageing Reports 

Employment rate (%)

Participation rate (%)

Unemployment rate (%)

Employment rate (pps)

Participation rate (pps)

Unemployment rate (pps)

(20-64y)

(55-64y)

(20-64y)

(55-64y)

(15-64y)

(20-64y)

(55-64y)

(20-64y)

(55-64y)

(15-64y)

Projection exercise 2021

2021 AR - 2018 AR (2019-70)

2019

2070

pps 
change

2019

2070

pps 
change

2019

2070

pps 
change

2019

2070

pps 
change

2019

2070

pps 
change

2019

2070

pps 
change

2019

2070

pps 
change

2019

2070

pps 
change

2019

2070

pps 
change

2019

2070

pps 
change

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

BE

70.6

70.9

0.3

52.4 60.8

8.5

74.5

75.7

1.1

54.6

64.0

9.4

BG

75.2

73.5

-1.7

64.5 64.5

0.0

78.5

77.5

-0.9

67.1

67.7

0.6

CZ

80.4

78.5

-1.9

67.1 68.3

1.3

82.0

81.3

-0.7

68.4

70.7

2.3

DK

78.4

80.9

2.5

71.9 79.7

7.8

82.3

83.7

1.4

74.4

81.7

7.3

DE

80.6

80.7

0.2

72.6 73.4

0.7

83.2

84.2

1.0

74.6

76.0

1.4

80.2

82.5

2.3

72.7 82.7 10.0

83.8

88.0

4.1

75.7

87.6

11.9

75.1

75.9

0.7

61.8 66.8

5.0

78.8

81.1

2.3

64.1

70.4

6.3

60.9

76.5 15.6 43.7 76.4 32.7

73.8

82.2

8.4

50.4

80.8

30.4

17.6

68.1

76.2

8.2

53.9 73.5 19.6

79.0

81.8

2.8

61.7

78.3

16.6

14.2

71.6

74.5

2.9

53.0 63.3 10.2

78.0

80.0

1.9

56.9

67.0

10.1

HR

66.8

69.6

2.8

44.3 52.5

8.2

71.4

74.6

3.2

45.8

54.5

8.6

63.6

69.8

6.2

54.4 73.2 18.8

70.5

74.9

4.4

57.5

75.9

18.4

10.2

75.1

80.1

5.0

61.2 75.4 14.2

80.9

85.9

4.9

65.3

80.1

14.8

77.6

77.4

-0.2

67.7 64.7

-3.0

82.9

83.0

0.1

72.5

69.4

-3.1

78.3

80.4

2.2

68.7 69.1

0.4

83.6

86.4

2.8

73.8

74.5

0.7

72.7

74.1

1.4

43.3 43.6

0.3

76.8

77.5

0.8

45.2

45.2

0.0

HU

75.4

81.9

6.5

56.9 81.4 24.5

77.9

85.3

7.3

58.2

83.7

25.5

MT

77.3

82.7

5.5

51.5 67.5 16.0

79.7

86.0

6.4

52.3

69.2

16.8

NL

AT

PL

PT

80.2

80.7

0.6

69.7 74.7

4.9

82.6

84.6

1.9

72.0

78.5

6.5

76.8

79.5

2.7

54.6 62.1

7.5

80.3

82.9

2.6

56.5

63.9

7.4

73.3

72.1

-1.2

49.9 53.1

3.2

75.7

75.9

0.2

51.1

55.1

4.0

76.2

80.4

4.2

60.4 73.8 13.3

81.4

85.7

4.3

64.5

78.4

14.0

RO

71.0

72.7

1.7

47.9 55.7

7.8

73.7

76.0

2.2

49.0

57.2

8.2

SI

76.4

78.3

1.9

48.0 60.7 12.8

79.9

83.0

3.1

50.3

64.6

14.3

SK

73.6

71.3

-2.3

57.7 56.4

-1.3

78.0

76.4

-1.6

60.5

59.6

-0.9

FI

77.1

79.7

2.6

66.8 76.0

9.2

82.2

85.0

2.8

71.5

81.4

9.9

SE

82.1

83.0

0.8

77.9 76.0

-1.9

87.3

87.1

-0.3

81.7

78.9

-2.8

NO 79.4

78.7

-0.7

72.8 69.2

-3.6

82.1

81.2

-0.9

73.9

70.3

-3.6

EA

72.6

76.3

3.7

60.0 70.2 10.2

78.4

81.0

2.5

63.7

73.7

10.0

EU27 73.1

76.2

3.1

59.1 68.7

9.6

78.2

80.7

2.5

62.3

71.9

9.6

Source: European Commission, EPC. 

5.4

4.3

2.1

5.1

3.2

4.6

5.1

8.6

6.7

7.5

6.6

6.5

5.7

3.5

3.4

3.4

4.6

3.3

6.7

4.0

4.5

5.8

6.9

7.0

3.8

7.7

6.8

6.4

5.4

3.7

3.6

4.2

6.6

7.0

7.0

7.0

7.0

7.0

7.0

7.0

7.0

7.0

4.9

4.2

4.4

5.0

4.3

5.2

6.4

4.8

5.8

7.0

6.9

5.6

3.6

6.0

5.8

1.0

1.1

1.6

-7.2

-1.6

0.3

-3.2

-0.5

0.4

0.5

-0.9

0.7

1.0

1.7

-0.3

1.8

-0.2

0.7

1.3

1.2

0.0

-1.4

-0.2

-1.7

-1.0

1.6

-0.4

-2.1

2.2

-1.5

-3.8

-0.1

-1.7

-1.6

-1.8

-3.4

-2.3

-1.5

5.4

2.5

5.7

1.9

0.3

-0.6

9.0

8.9

5.1

-3.9

3.4

-5.5

4.5

1.6

-3.9

-1.4

-1.3

-5.1

-1.1

-0.5

-1.6

-0.7

1.0

1.7

-0.4

4.8

5.2

-0.8

5.0

-0.1

-1.0

-0.9

5.0

1.5

0.3

1.5

6.1

5.0

0.5

-0.1

1.1

0.2

4.9

2.4

1.8

1.6

8.3

8.4

-0.3

3.1

4.4

3.3

4.7

1.9

2.2

2.7

4.6

5.5

-1.2

-0.5

-0.5

4.9

16.6

11.7

-2.9

-1.3

2.4

2.8

0.0

-0.4

0.5

-0.9

-0.9

-2.2

-0.9

1.0

2.0

1.9

-10.6

1.7

3.6

3.0

0.4

1.9

6.8

4.6

2.2

0.2

3.3

1.5

1.7

3.5

2.4

-1.1

7.8

17.7

9.9

2.6

3.1

4.2

5.8

1.6

1.3

1.2

2.6

2.7

-0.3

1.6

0.4

-1.4

-1.8

-2.1

-3.1

-1.0

-1.6

-2.3

-0.7

-3.5

-3.5

0.6

-0.3

-0.8

0.3

-1.1

-1.4

0.1

-1.0

-1.1

3.4

-0.3

-3.7

4.6

0.6

-4.1

0.4

3.8

2.5

2.1

-1.1

2.7

3.8

1.5

-2.4

5.7

2.7

-3.0

0.1

0.0

0.8

-1.0

-1.1

2.0

2.3

2.0

1.5

3.9

-0.2

-4.1

8.3

-2.7 -11.1

1.9

-1.2

-3.1

2.9

1.1

1.5

5.4

2.1

-0.8

8.8

0.8

-8.0

2.9

2.5

1.8

1.0

0.6

6.6

2.3

1.7

3.4

-3.1

2.0

-3.4

8.1

-0.5

-8.6

2.3

-0.3

-2.6

5.8

0.2

-5.6

0.9

1.5

3.4

3.1

3.9

0.9

0.0

1.5

1.4

-0.1

1.3

4.6

6.1

1.2

3.0

3.8

2.5

2.8

2.3

9.4

6.5

1.4

1.0

5.7

4.0

3.6

-0.3

3.0

2.6

-0.4

2.5

1.1

1.1

4.7

1.1

0.1

0.4

0.8

2.4

2.2

1.4

2.9

1.8

1.0

0.1

0.5

1.0

3.6

5.4

3.6

-1.1

1.8

0.8

-3.7

-1.0

0.4

0.6

2.7

2.9

1.4

0.5

2.7

-1.0

3.9

7.7

9.3

0.9

6.1

7.5

4.2

1.0

0.8

2.0

2.4

2.7

-1.1

-1.6

-0.7

-0.9

-0.2

-0.3

-3.0

-4.9

-0.9

4.0

HR

3.8

-0.7

-0.9

-0.2

2.8

3.4

-0.6

-3.8

0.9

-3.0 -10.7

-2.5

-0.9

0.8

2.7

2.3

-8.6

-0.6

-0.9

-0.3

1.8

0.0

-0.1

-0.2

-3.8

-0.5

-0.8

-0.3

-0.9

-8.4

-1.2

-1.2

-0.2

-4.4

-1.7

0.5

2.6

2.2

9.1

6.5

3.7

1.7

1.3

7.1

4.1

1.0

-1.0

-0.5

-1.5

-0.7

-3.3

-1.5

-2.3

-0.1

-1.1

-1.4

-0.4

RO

1.9

-4.3

-6.2

6.9

-14.9 -21.8 -0.1

-5.5

-5.4

5.5

-16.7 -22.2

-2.6

-0.9

2.4

2.5

-0.2

0.7

0.1

0.9

3.9

2.6

1.2

-2.7

1.5

-1.1

2.1

0.5

2.1

0.6

0.0

0.1

-0.4

-1.6

-1.2

1.0

-2.6

-3.6

-0.1

-1.5

-1.4

1.2

1.4

1.0

-0.2

1.5

0.9

-0.6

1.2

-0.1

1.9

1.4

-0.5

0.3

0.5

0.4

0.7

0.1

0.2

4.6

2.9

1.2

1.0

1.5

1.8

1.2

-2.8

-0.4

-0.7

-0.2

-1.8

-2.5

-3.7

1.0

0.5

-0.2

-1.2

0.2

-0.2

0.7

1.2

-0.3

-1.1

-0.8

-0.3

-1.1

-0.8

0.4

0.4

0.8

0.1

0.6

0.0

1.6

0.9

0.0

1.3

4.8

1.6

0.0

2.2

0.5

0.8

1.8

2.2

1.7

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

SI

SK

FI

SE

NO

EA

EU27

r

E
u
o
p
e
a
n
C
o
m
m

i
s
s
i
o
n

i

2
0
2
1
A
g
e
n
g
R
e
p
o
r
t
:

U
n
d
e

i

r
l
y
n
g
a
s
s
u
m
p

j

r

t
i
o
n
s
a
n
d
p
o
e
c
t
i
o
n
m
e
t
h
o
d
o
o
g
e
s

i

l

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 4: 

Potential GDP projections, 2021 Ageing Report and 2018 Ageing Reports 

Projection exercise 2021

Due to growth in (pps)

2021 AR - 2018 AR (2019-70)
Due to growth in (pps)

GDP 
growth 
2019-70 
(pps)

Labour 
prod. 
(GDP/ 
hours 
worked)

TFP

Capital 
deepening

Labour 
input

Total 
population

Employment 
rate

Share of 
working 
age 
population

Change in 
average 
hours 
worked

GDP 
growth 
2019-70 
(%)

Labour 
prod. 
(GDP/ 
hours 
worked)

1=2+5

2=3+4

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

HR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

NO

EA

EU27

1.2

1.2

1.6

1.7

1.2

1.9

1.8

1.2

1.4

1.3

1.1

1.0

1.9

1.2

1.2

1.8

1.8

2.2

1.3

1.3

1.5

1.2

1.7

1.6

1.3

1.2

1.8

1.7

1.3

1.3

1.2

2.1

2.0

1.5

1.4

2.2

1.5

1.5

1.5

1.3

1.8

1.3

1.5

2.3

2.2

1.1

2.1

1.8

1.3

1.4

2.3

1.7

2.6

1.9

2.1

1.5

1.4

1.5

1.4

1.6

TFP

Capital 
deepening

Labour 
input

Total 
population

Employment 
rate

Share of 
working 
age 
population

Change in 
average 
hours 
worked

3

0.8

1.3

1.3

1.0

0.9

1.4

1.1

1.0

1.0

0.8

1.1

0.8

0.9

1.4

1.3

0.7

1.3

1.2

0.9

0.9

1.5

1.1

1.6

1.3

1.3

0.9

0.9

0.9

0.9

1.0

4

0.4

0.8

0.7

0.5

0.5

0.8

0.5

0.4

0.5

0.5

0.7

0.4

0.6

0.9

0.9

0.4

0.7

0.6

0.5

0.5

0.9

0.6

0.9

0.6

0.8

0.5

0.5

0.5

0.5

0.5

5=6+7+8+9

0.0

-0.9

-0.3

0.2

-0.2

-0.3

0.3

-0.3

0.0

0.1

-0.7

-0.2

0.4

-1.1

-1.0

0.7

-0.3

0.4

0.0

0.0

-0.8

-0.5

-0.9

-0.3

-0.7

-0.3

0.4

0.2

-0.1

-0.2

6

0.1

-0.6

-0.1

0.1

0.0

-0.2

0.6

-0.4

0.0

0.1

-0.6

-0.2

0.5

-0.9

-0.8

0.5

-0.2

0.7

0.1

0.1

-0.4

-0.4

-0.7

-0.1

-0.3

-0.2

0.5

0.5

0.0

-0.1

7

0.1

0.0

0.0

0.3

0.0

0.1

-0.1

0.3

0.2

0.1

0.1

0.2

0.2

0.1

0.1

0.4

0.2

0.1

0.1

0.1

-0.1

0.1

0.0

0.1

-0.1

0.1

0.1

-0.1

0.1

0.1

8

-0.2

-0.3

-0.3

-0.2

-0.2

-0.2

-0.1

-0.2

-0.2

-0.2

-0.2

-0.2

-0.2

-0.2

-0.2

-0.2

-0.2

-0.3

-0.2

-0.2

-0.3

-0.3

-0.2

-0.2

-0.3

-0.2

-0.1

-0.1

-0.2

-0.2

9

0.0

0.0

0.0

0.0

0.0

-0.1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

-0.1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

GDP per 
capita 
growth 
2019-70 
(%)

10=1-6

1.2

1.9

1.7

1.6

1.3

2.1

1.3

1.6

1.4

1.3

1.7

1.3

1.4

2.2

2.1

1.3

2.0

1.5

1.2

1.2

1.9

1.6

2.4

1.7

1.6

1.4

1.3

1.2

1.3

1.4

Source: European Commission, EPC. 

1
1

1=2+5

-0.3

-0.1

0.2

0.1

0.1

0.4

-0.1

0.3

-0.1

-0.2

-0.1

0.2

0.4

-0.7

0.2

-0.4

0.2

0.1

-0.2

-0.1

0.2

0.3

0.0

0.1

-0.5

-0.1

-0.1

-0.1

0.0

0.0

2=3+4

-0.1

-0.2

0.1

0.1

-0.1

0.3

0.0

0.2

0.1

0.0

0.1

0.2

0.2

-0.4

0.2

-0.3

0.1

-0.1

0.0

-0.1

0.2

0.1

0.1

0.1

-0.2

0.1

-0.1

0.0

0.0

0.1

3

-0.1

-0.1

0.1

0.1

0.0

0.2

0.0

0.2

0.1

0.0

0.1

0.1

0.1

-0.3

0.1

-0.2

0.0

-0.1

0.0

-0.1

0.1

0.1

0.0

0.1

-0.2

0.1

-0.1

0.0

0.0

0.1

4

5=6+7+8+9

6

0.0

-0.1

0.0

0.0

0.0

0.1

0.0

0.0

0.0

0.0

0.1

0.0

0.1

-0.1

0.1

-0.1

0.1

-0.1

0.0

0.0

0.0

0.0

0.1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

-0.3

-0.3

0.2

0.1

0.0

0.2

0.1

0.0

0.1

-0.2

-0.2

-0.3

0.0

0.2

-0.3

0.1

0.0

0.1

0.2

-0.2

-0.1

0.0

0.2

-0.1

0.0

-0.3

-0.2

0.0

-0.1

0.0

0.0

0.1

0.0

-0.2

0.1

0.0

0.1

0.2

-0.1

-0.2

-0.2

0.0

0.1

-0.2

0.1

-0.5

0.0

0.4

-0.2

-0.2

0.0

0.1

-0.2

0.0

-0.1

-0.2

-0.1

-0.1

-0.1

-0.1

7

0.0

0.1

0.0

0.2

0.0

0.1

-0.1

-0.1

-0.1

0.0

-0.1

0.0

0.0

0.0

0.0

0.5

0.1

-0.2

0.0

0.1

0.0

0.1

0.1

0.0

-0.2

0.0

0.1

0.0

0.0

0.0

8

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

GDP per 
capita 
growth 
2019-70 
(pps)

10=1-6

0.0

-0.1

0.2

0.3

0.0

0.4

-0.2

0.1

0.1

0.0

0.1

0.2

0.3

-0.4

0.1

0.2

0.2

9

0.0

0.0

0.0

0.0

0.0

-0.1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

-0.1

-0.3

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.2

0.2

0.2

0.1

-0.4

0.1

0.0

0.0

0.0

0.1

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

HR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

NO

EA

EU27

E
x
e
c
u
t
i
v
e
S
u
m
m
a
r
y

 
 
 
 
 
 
 
Part I 
Underlying assumptions and projection 
methodologies 

 
 
1. 

POPULATION 

1.1. 

INTRODUCTION 

faster in countries with lower current levels of life 
expectancy.  

the  2019-based  population 
EUROPOP2019, 
projections,  was  released  by  Eurostat  in  April 
2020.  It  provides  the  demographic  basis  for  the 
age-related  expenditure  projections  in  the  2021 
Ageing  Report  for  the  27  EU  Member  States  and 
Norway.  The  fertility  rates,  mortality  ratesand  net 
migration projections for the period 2019-2100, as 
well as the underlying  methodologies used can be 
the  Eurostat  dedicated  website (5). 
found  on 
National  statistical  institutes  collaborated  with 
Eurostat  for  the  data  collection  and  had  the 
some  methodological 
opportunity 
these 
suggestions  during 
population projections (6).  

the  preparation  of 

to  make 

EUROPOP2019  applies  a  ‘partial  convergence’ 
approach,  meaning  that  the  country-specific  key 
demographic  determinants  converge  in  the  very 
long  term.  Setting  the  convergence  point  far  into 
the  future  –  even  beyond  the  endpoint  of  the 
projections  –  has  the  advantage  of  taking  due 
account  of  recent  country-specific  trends  at  the 
start,  while  at  the  same  time  assuming  that 
Member  States’  demographic  drivers  will 
converge 
demographic 
determinants  are  the  fertility  rate  (impacting  the 
number of births), the mortality rate (impacting the 
number  of  deaths)  and  the  level  of  net  migration 
(the  population  growth  beyond 
the  ‘natural’ 
growth due to births and deaths).  

time.  These 

over 

The  projection  methodology  assumes  that  fertility 
and  mortality  tend  to  converge  to  that  of  the  best 
performing  Member  States.  As  a  result,  fertility 
rates  would  rise  in  almost  all  Member  States 
during  2019-2070,  though  faster  in  the  countries 
that currently have the lowest rates. Similarly, life 
convergent 
expectancy 
increasing  relatively 
trajectory  with 

an  upward 

longevity 

follows 

(5)  The 

datasets 

can 

be 

found 

on  

http://ec.europa.eu/eurostat/web/population-demography-
migration-projections/population-projections-data; Eurostat 
(2020), 
the  Eurostat  population 
projections  2019-based  (EUROPOP2019)’,    available  at 
https://ec.europa.eu/eurostat/cache/metadata/Annexes/proj_
esms_an1.pdf. 

‘Methodology  of 

(6)  This does not preclude national statistical institutes having 
their  own 

different  population  projections  based  on 
assumptions and methodologies. 

14 

is  estimated 

through  separate 
Net  migration 
emigration  and  immigration  flows,  based  on  past 
trends,  the  latest  empirical  evidence,  long-term 
partial 
flows 
consistency.  Moreover,  when  the  working-age 
population 
‘partially 
shrinks, 
compensating’ immigration is assumed.  

convergence 

additional 

intra-EU 

and 

1.2.  DEVELOPMENTS AND ASSUMPTIONS FOR 

FERTILITY RATES 

The total fertility rate (TFR) is assumed to rise in 
almost  all  Member  States  between  2019  and 
2070, increasing from 1.52 to 1.65 on average in 
the EU. 

1.2.1.  Past trends 

Total  fertility  rates  (TFR (7))  declined  sharply  in 
the  EU  Member  States  following  the  post-war 
‘baby boom’. From an EU average of 2.6 in 1960, 
the  number  of  live  births  per  woman  declined 
steadily in nearly all countries, to two children on 
average 
the  natural 
replacement rate of 2.1 (see Table I.1.1). By 1980, 
fertility  had  fallen  to  1.6  or  less  in  Denmark, 
Germany, Luxembourg and the Netherlands.  

thus  below 

in  1980, 

By  the  mid-1990s,  fertility  rates  had  fallen  below 
the natural replacement rate in all Member States. 
In  2000,  fertility  rates  were  below  1.4  in  fifteen 
Member  States,  with  women 
the  Czech 
Republic, Spain and Latvia giving birth to just 1.2 
children on average.  

in 

Fertility rates seemed to have reached their nadir in 
most  Member  States  by  the  turn  of  the  century, 
with  a  slightly  upward  trend  in  general.  Total 
fertility  rates  increased  in  17  of  them  between 
2000  and  2018.  However,  this  increase  mostly 
reflects  developments  during  the  2000s,  when 
fertility  rates  increased  in  all  but  five  Member 
States.  Since  2010,  the  trend  has  reversed  again, 

(7)  Fertility  rates  are  reflected  by  the  average  number  of 
children a woman would have, should she at each bearing 
age  have  the  fertility  rates  of  the  year  under  review.  This 
number  is  obtained  by  summing  the  fertility  rates  by  age 
and is called the Total Fertility Rate, or TFR. 

                                                           
 
                                                           
Underlying assumptions and projection methodologies 

Part I 

1.2.2.  Latest Eurostat population projections  

The  new  projections  assume  a  country-specific 
trend  extrapolation  up 
to  2020.  Afterwards, 
convergence to the long-term anchor starts gaining 
weight. This anchor equals 1.83 live births, which 
is  the  maximum  fertility  rate  projected  by  the 
United  Nations  in  2100  for  the  countries  covered 
Full 
in  EUROPOP2019, 
convergence  to  this  common  anchor  is  assumed 
beyond EUROPOP2019’s own 2100 horizon. 

France. 

namely 

Table I.1.2: 

Projection of total fertility rates (2019-2070) 

2019

2030

2050

2070

change 
2019-
2070

avg 
2019-
2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

1.58
1.58
1.71
1.72
1.53
1.51
1.78
1.34
1.27
1.85
1.43
1.31
1.33
1.58
1.61
1.34
1.51
1.14
1.58
1.45
1.36
1.43
1.65
1.55
1.56
1.35
1.71
1.53
1.51
1.52

1.59
1.65
1.75
1.74
1.57
1.59
1.80
1.39
1.33
1.84
1.48
1.37
1.38
1.64
1.63
1.40
1.61
1.26
1.60
1.49
1.40
1.47
1.66
1.59
1.59
1.38
1.75
1.55
1.55
1.55

1.64
1.70
1.78
1.76
1.63
1.68
1.80
1.47
1.41
1.84
1.54
1.45
1.46
1.69
1.67
1.49
1.69
1.39
1.64
1.55
1.49
1.53
1.72
1.65
1.63
1.46
1.78
1.60
1.60
1.61

1.68
1.71
1.78
1.77
1.67
1.70
1.81
1.54
1.49
1.84
1.59
1.52
1.53
1.71
1.70
1.56
1.70
1.47
1.68
1.60
1.56
1.59
1.74
1.68
1.67
1.53
1.78
1.65
1.65
1.65

0.10
0.13
0.07
0.05
0.13
0.18
0.02
0.20
0.22
-0.01
0.16
0.21
0.20
0.13
0.09
0.22
0.18
0.33
0.10
0.15
0.20
0.16
0.10
0.13
0.11
0.19
0.07
0.12
0.13
0.14

(1) EU27 and EA are weighted averages. 
Source: EUROPOP2019 (Eurostat). 

1.6
1.7
1.8
1.8
1.6
1.6
1.8
1.4
1.4
1.8
1.5
1.4
1.4
1.7
1.7
1.5
1.7
1.3
1.6
1.5
1.5
1.5
1.7
1.6
1.6
1.4
1.8
1.6
1.6
1.6

with  decreases  in  16  Member  States.  The  largest 
declines  between  2000  and  2018  were  in  Cyprus, 
Finland, Luxembourg and Malta, with a decline in 
live  births.  Conversely, 
the  TFR  of  0.3-0.5 
Bulgaria,  Estonia,  Slovenia,  Latvia,  Romania  and 
the  Czech  Republic  saw  increases  of  0.3-0.6  in 
their fertility rate during the same period. 

Table I.1.1: 

Past trends in total fertility rates (1960-2018) 

1960

1980

2000

2018

1960-
2018

2000-
2018

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2.54
2.31
2.09
2.57
2.37
1.98
3.78
2.23
2.86
2.73
2.38
2.40
3.51
1.95
2.60
2.29
2.02
3.62
3.12
2.69
2.98
3.16
2.74
2.18
3.04
2.72
2.20
2.90
2.72
2.63

1.68
2.05
2.08
1.55
1.56
2.02
3.21
2.23
2.22
1.95
1.90
1.64
2.48
1.88
1.99
1.50
1.91
1.99
1.60
1.65
2.28
2.25
2.43
2.11
2.32
1.63
1.68
1.72
2.00
1.99

1.67
1.26
1.15
1.77
1.38
1.36
1.89
1.25
1.22
1.89
1.46
1.26
1.64
1.25
1.39
1.76
1.32
1.68
1.72
1.36
1.37
1.55
1.31
1.26
1.30
1.73
1.54
1.85
1.50
1.47

1.62
1.56
1.71
1.73
1.57
1.67
1.75
1.35
1.26
1.88
1.47
1.29
1.32
1.60
1.63
1.38
1.55
1.23
1.59
1.47
1.46
1.42
1.76
1.60
1.54
1.41
1.76
1.56
1.50
1.54

-0.9
-0.8
-0.4
-0.8
-0.8
-0.3
-2.0
-0.9
-1.6
-0.9
-0.9
-1.1
-2.2
-0.3
-1.0
-0.9
-0.5
-2.4
-1.5
-1.2
-1.5
-1.7
-1.0
-0.6
-1.5
-1.3
-0.4
-1.3
-1.2
-1.1

0.0
0.3
0.6
0.0
0.2
0.3
-0.1
0.1
0.0
0.0
0.0
0.0
-0.3
0.4
0.2
-0.4
0.2
-0.5
-0.1
0.1
0.1
-0.1
0.5
0.3
0.2
-0.3
0.2
-0.3
0.0
0.1

(1) EU27 and EA are simple averages. 
(2) DE: time series does not include the former GDR until 
1991; HR: 2000 value is from 2001, 1960 and 1980 based on 
UN data; CY: 1980 value is from 1982; LV: 1960 based on UN 
data; RO: 1960 based on UN data. 
Source: European Commission based on Eurostat/UN data. 

In  2018,  the  Czech  Republic,  Denmark,  Ireland, 
Romania, Sweden and France had fertility rates of 
at  least  1.7,  with  a  maximum  of  around  1.9  in 
France. At 1.2 children per woman, Malta had the 
lowest  fertility  rate  in  2018,  followed  by  Spain, 
Italy,  Cyprus,  Greece  and  Luxembourg,  which  all 
had fertility rates below 1.4. For Luxembourg and 
Malta,  the  2018  rates  are  the lowest  on  record,  as 
rates have continued to decline in recent years.  

The total fertility rate would rise from 1.52 in 2019 
to 1.65 by 2070 for the EU as a whole (see Table 
I.1.2).  The  fertility  rate  increases  in  all  Member 
States, with the exception of a stable rate in France 
(the  frontrunner,  with  the  highest  current  and 
prospective TFR). Considering a long-term anchor 
of 1.83 live births per woman implies that fertility 
in  all  Member  States  would  remain  below  the 
natural replacement rate over the entire projection 
period.  The  difference  in  fertility  between  the 

15 

 
    
 
 
    
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.1.3: 

Past trends in life expectancy at birth (1960-2018) 

Males

Females

1960

1980

2000

2018

1960-
2018 (2)

2000-
2018

1960

1980

2000

2018

1960-
2018 (2)

2000-
2018

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

66.8
67.5
67.8
70.4
66.5
64.7
68.1
67.3
67.4
66.9
:
67.2
:
65.2
64.9
66.5
65.9
66.5
71.5
66.2
64.9
61.1
:
66.1
67.9
65.5
71.2
71.6
66.5
66.8

69.9
68.4
66.9
71.2
69.6
64.2
70.1
73.0
72.3
70.2
:
70.6
72.3
63.6
65.4
70.0
65.5
68.0
72.7
69.0
66.9
67.9
66.6
67.4
66.7
69.2
72.8
72.4
69.1
68.9

74.6
68.4
71.6
74.5
75.1
65.6
74.0
75.9
75.8
75.3
:
76.9
75.4
65.0
66.7
74.6
67.5
76.3
75.6
75.2
69.6
73.3
67.7
72.2
69.2
74.2
77.4
76.0
73.2
72.6

79.4
71.5
76.2
79.1
78.6
74.0
80.5
79.3
80.7
79.7
74.9
81.2
80.9
70.1
70.9
80.1
72.7
80.4
80.3
79.4
73.7
78.3
71.7
78.5
73.9
79.1
80.9
81.1
78.2
77.3

12.6
4.0
8.4
8.7
12.1
9.3
12.4
12.0
13.3
12.8
:
14.0
8.6
4.9
6.0
13.6
6.8
13.9
8.8
13.2
8.8
17.2
5.1
12.4
6.0
13.6
9.7
9.5
11.7
10.4

4.8
3.1
4.6
4.6
3.5
8.4
6.5
3.4
4.9
4.4
:
4.3
5.5
5.1
4.2
5.5
5.2
4.1
4.7
4.2
4.1
5.0
4.0
6.3
4.7
4.9
3.5
5.1
5.0
4.7

72.8
71.1
73.5
74.4
71.7
73.1
71.9
72.4
72.2
73.6
:
72.3
:
72.4
71.4
72.2
70.2
70.5
75.5
72.7
70.6
66.7
:
72.0
72.7
72.5
74.9
76.0
72.1
72.2

76.7
73.9
74.0
77.3
76.2
74.3
75.6
77.5
78.4
78.4
:
77.4
77.0
74.2
75.4
75.6
72.8
72.8
79.3
76.1
75.4
74.9
71.9
75.2
74.4
78.0
79.0
79.3
76.2
75.8

81.0
75.0
78.5
79.2
81.2
76.4
79.2
81.3
82.8
83.0
:
82.8
80.1
76.1
77.4
81.3
76.2
80.5
80.7
81.2
78.0
80.4
74.8
79.9
77.5
81.2
82.0
81.5
80.2
79.5

83.9
78.6
82.0
82.9
83.3
82.7
84.1
84.4
86.3
85.9
81.5
85.6
84.8
79.7
80.7
84.6
79.6
84.6
83.4
84.1
81.7
84.5
79.2
84.4
80.8
84.5
84.3
84.5
83.8
83.0

11.1
7.5
8.5
8.5
11.6
9.6
12.2
12.0
14.1
12.3
:
13.3
7.8
7.3
9.3
12.4
9.4
14.1
7.9
11.4
11.1
17.8
7.3
12.4
8.1
12.0
9.4
8.5
11.7
10.8

2.9
3.6
3.5
3.7
2.1
6.3
4.9
3.1
3.5
2.9
:
2.8
4.7
3.6
3.3
3.3
3.4
4.1
2.7
2.9
3.7
4.1
4.4
4.5
3.3
3.3
2.3
3.0
3.6
3.5

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

(1) EU27 and EA are simple averages. 
(2) 1980-2019 for CY and RO. 
Source:  European Commission based on Eurostat data. 

countries with the highest and lowest rates shrinks 
from  0.7  live  births  per  woman  in  2019  to  0.4  in 
2070,  with  a  minimum  of  1.47  in  Malta  versus 
1.84  in  France.  Aside  from  Malta,  also  Spain, 
Italy,  Cyprus,  Greece,  Luxembourg,  Poland, 
Croatia  and  Portugal  would  have  a  fertility  rate 
below 1.6 in 2070. 

1.3.  DEVELOPMENTS AND ASSUMPTIONS FOR 

LIFE EXPECTANCY 

projections 

demographic 

Eurostat 
show 
continued  increases  in  life  expectancy  both  at 
birth  and  at  the  age  of  65  for  both  males  and 
females over the period 2019-2070. For the EU as 
a  whole,  life  expectancy  at  birth  would  increase 
by  7.4  years  for  males  and  by  6.1  years  for 
females,  with  the  largest  increases  in  Member 

that  currently  have 

States 
expectancy. 

the 

lowest 

life 

1.3.1.  Past trends 

Life  expectancy  has  been  increasing  in  most 
developed  countries  for  very  long  periods.  Since 
1960,  there  have  been  significant  gains  in  life 
expectancy  at  birth  in  all  EU  Member  States  (see 
Table  I.1.3).  The  average  life  expectancy  at  birth 
increased  by  more  than  ten  years  between  1960 
and  2018  in  the  EU;  from  66.8  years  in  1960  to 
77.3 years in 2018 for males, and from 72.2 years 
to 83 years for females.  

The  gap  between  the  average  female  and  the 
average  male  life  expectancies  at  birth  rose  from 
5.4 years in 1960 to 7 years in 1980 and remained 
level  until  2000,  with  diverging 
at 

that 

16 

 
      
 
Underlying assumptions and projection methodologies 

Part I 

developments  across  Member  States.  Since  2000, 
the  increase  in  life  expectancy  has  been  3.5  years 
for females and 4.7 years for males, resulting in a 
narrowing of the gender longevity gap to 5.8 years 
in 2018.  

historical trends. This is because mortality rates at 
younger ages are already very low and future gains 
in  life  expectancy  would  require  improvements  in 
mortality  rates  at  older  ages,  which  statistically 
have a smaller impact on life expectancy at birth.  

The  general  trend  of  rising  life  expectancy  and  a 
smaller  gender  gap  differs  across  countries. 
Between  1960  and  2018,  females  gained  12  years 
or  more  in  life  expectancy  in  Ireland,  Greece, 
Spain, France, Italy, Luxembourg, Malta, Portugal, 
Slovenia and Finland. Gains however amounted to 
less  than  eight  years  in  Bulgaria,  Latvia  and  the 
Netherlands (8). Gains in male life expectancy over 
the  same  period  exceeded  12  years  in  Belgium, 
Germany,  Ireland,  Greece,  Spain,  France,  Italy, 
Luxembourg,  Malta,  Austria,  Portugal,  Slovenia 
and Finland. Male longevity rose by at most eight 
years  in  Bulgaria,  Latvia,  Lithuania,  Hungary  and 
Slovakia.  

Up to 2000, the gender  gap  widened compared to 
1960  in  Bulgaria,  the  Czech  Republic,  Estonia, 
Spain,  Latvia,  Lithuania,  Hungary,  Malta,  Poland, 
to  1980)  and 
Portugal,  Romania  (compared 
Slovakia,  as  gains  in  female  life  expectancy 
exceeded 
at 
developments since 2000, the gender gap widened 
only in Bulgaria and Romania. 

for  males.  Looking 

those 

There  is  no  consensus  among  demographers  on 
very  long-term  trends,  e.g.  whether  there  is  a 
natural biological limit to longevity, the impact of 
future  medical  breakthroughs,  and  the  long-term 
effect  of  public  health  programmes  and  societal 
behaviour  such  as  the  reduction  of  smoking  rates 
or  a  higher  prevalence  of  obesity.  Past  population 
projections  have,  however,  generally  under-
estimated  the  gains  in  life  expectancy  at  birth  as 
the  reduction  of  mortality  was  not  assumed  to 
continue  at  the  same  pace  in  the  long  run. 
Therefore,  previous  estimates  on  the  budgetary 
impact  of  ageing  populations  may  have  been  too 
low in certain cases.  

Most  official  demographic  projections  by 
institutes 
international  and  national  statistical 
nevertheless  still  assume 
life 
expectancy at birth will slow down compared with 

that  gains 

in 

(8)  Also CY and RO gained less than eight years (both in the 
case  of  men  and  women  in  the  case  of  RO),  but  over  a 
shorter period (1980-2018), see Table I.1.3. 

On  the  other  hand,  the  current  wide  range  of  life 
expectancies  across  EU  Member  States,  also 
compared  with  non-EU  developed  countries, 
points  to  considerable  scope  for  future  gains.  In 
2018,  life  expectancy  at  birth  for  females  ranged 
from  78.6  in  Bulgaria  to  86.3  years  in  Spain  and 
from  70.1  in  Latvia  to  81.2  in  Italy  for  males. 
These  gaps  of  7.7  and  11.1  years  entail  a 
narrowing compared to 2000, when the difference 
between the highest and lowest life expectancy in 
the EU still amounted to 8.2 years for woman and 
12.4 years for men. 

1.3.2.  Latest Eurostat population projections  

The  projected  changes  in  life  expectancy  at  birth 
and  at  the  age  of  65  for  males  and  females 
underlying  the  2019-based  population  projections 
are  shown  in  Table  I.1.4.  The  projections  assume 
that  increases  in  life  expectancy  at  birth  are 
sustained  during  the  projection  period,  albeit  with 
considerable diversity across Member States (9).  

to 

increase  by  7.4  years  over 

In  the  EU,  life  expectancy  at  birth  for  males  is 
expected 
the 
projection  period,  from  78.7  in  2019  to  86.1  in 
2070.  Female  life  expectancy  at  birth  would  rise 
by  6.1  years,  from  84.2  in  2019  to  90.3  in  2070, 
leading to a further convergence between genders. 
The  largest  increases  in  life  expectancies  at  birth, 
for  both  males  and  females,  are  projected  to  take 
place in the Member States that currently have the 
lowest life expectancies.  

In  2019,  male  life  expectancy  at  birth  was  the 
lowest  in  Bulgaria,  Estonia,  Latvia,  Lithuania, 
Hungary,  Poland,  Romania  and  Slovakia,  ranging 
between 70 and 75 years. In these countries, male 
life  expectancy  would  increase  by  9-12  years  by 

(9)  Mortality  patterns  are  assumed  to  partially  converge  from 
the latest observed values towards a common (sex-specific) 
life table (the 'ultimate' life table), which incorporates some 
information  from  previous  mortality  trends  of  selected 
countries.  The  initial  mortality  patterns  are  derived  from 
the period-cohort age-  and  sex-specific  deaths  reported by 
the  country  for  the  last  three  years  (2016-2018).  See 
https://ec.europa.eu/eurostat/cache/metadata/Annexes/proj_
esms_an1.pdf 

17 

                                                           
                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.1.4: 

Projection of life expectancy at birth and at 65 (2019-2070) 

Life expectancy at birth

Life expectancy at 65

Males

Females

Males

Females

2019

2050

2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

79.8
71.5
76.5
79.5
79.1
74.9
81.1
79.0
81.2
80.1
75.3
81.3
80.8
70.6
71.3
80.3
72.9
80.5
80.7
79.8
74.1
78.6
71.9
78.7
74.4
79.5
81.4
81.4
79.9
78.7

83.9
79.0
81.8
83.7
83.5
80.8
84.6
83.8
84.9
84.3
81.1
84.9
84.5
78.4
78.8
84.4
79.8
84.6
84.4
83.9
80.7
83.2
79.5
83.3
80.6
83.7
84.8
84.8
84.1
83.5

86.3
82.9
84.8
86.1
86.0
84.3
86.8
86.4
87.1
86.7
84.3
87.0
86.6
82.6
82.9
86.6
83.6
86.8
86.6
86.3
84.3
85.7
83.5
85.9
84.1
86.1
86.8
86.9
86.5
86.1

change 
2019-
70
6.5
11.4
8.3
6.6
6.9
9.4
5.7
7.4
5.9
6.6
9.0
5.7
5.8
12.0
11.6
6.3
10.7
6.3
5.9
6.5
10.2
7.1
11.6
7.2
9.7
6.6
5.4
5.5
6.6
7.4

2019

2050

2070

84.3
78.8
82.3
83.3
83.7
83.4
84.8
84.3
86.8
86.3
81.6
85.7
85.1
80.2
81.1
85.0
79.8
84.5
83.6
84.3
82.0
84.8
79.5
84.5
81.2
84.8
84.7
84.6
85.0
84.2

88.2
84.6
86.7
87.5
87.7
87.5
88.3
88.1
89.7
89.6
86.2
89.0
88.3
85.6
86.0
88.7
85.4
88.4
87.6
88.1
86.9
88.3
85.3
88.2
86.2
88.4
88.2
88.2
88.6
88.2

90.3
87.7
89.2
89.8
89.9
89.9
90.4
90.3
91.4
91.4
88.8
90.9
90.2
88.5
88.8
90.8
88.5
90.6
89.9
90.2
89.5
90.4
88.5
90.4
89.0
90.4
90.3
90.3
90.6
90.3

change 
2019-
70
6.0
8.9
6.9
6.5
6.2
6.5
5.6
6.0
4.6
5.1
7.2
5.2
5.1
8.3
7.7
5.8
8.7
6.1
6.3
5.9
7.5
5.6
9.0
5.9
7.8
5.6
5.6
5.7
5.6
6.1

2019

2050

2070

18.9
14.2
16.5
18.5
18.4
16.5
19.6
18.8
19.9
20.0
15.8
19.6
19.2
14.5
15.0
19.1
14.8
19.6
19.0
18.8
16.1
18.4
14.9
18.1
15.6
18.9
19.7
19.7
19.1
18.4

21.8
18.8
20.3
21.5
21.5
20.2
22.1
22.1
22.5
22.6
19.7
22.3
21.9
19.0
19.3
22.0
19.3
22.3
21.8
21.8
20.2
21.4
19.5
21.3
19.7
21.7
22.2
22.2
22.0
21.6

23.6
21.4
22.5
23.3
23.4
22.6
23.8
23.9
24.1
24.2
22.1
23.9
23.5
21.7
21.9
23.7
21.9
23.9
23.5
23.6
22.6
23.2
22.1
23.2
22.1
23.5
23.7
23.8
23.7
23.5

change 
2019-
70
4.7
7.2
6.0
4.8
5.0
6.1
4.2
5.1
4.2
4.2
6.3
4.3
4.3
7.2
6.9
4.6
7.1
4.3
4.5
4.8
6.5
4.8
7.2
5.1
6.5
4.6
4.0
4.1
4.6
5.1

2019

2050

2070

22.2
18.1
20.0
21.1
21.4
21.5
22.1
21.8
23.9
24.1
19.4
22.9
22.1
19.4
20.0
22.5
18.7
22.4
21.4
21.8
20.5
22.2
18.6
22.0
19.6
22.3
22.0
21.9
22.6
22.0

25.1
22.3
23.6
24.4
24.6
24.6
24.9
24.9
26.2
26.5
23.1
25.6
24.7
23.3
23.5
25.4
23.0
25.3
24.5
24.8
24.2
25.0
22.9
25.0
23.4
25.1
24.8
24.8
25.4
25.0

26.8
24.7
25.7
26.3
26.4
26.5
26.7
26.7
27.7
27.9
25.3
27.2
26.4
25.5
25.7
27.1
25.4
27.0
26.3
26.6
26.2
26.7
25.4
26.8
25.7
26.8
26.6
26.6
27.1
26.8

change 
2019-
70
4.6
6.6
5.7
5.2
5.0
5.0
4.6
4.9
3.8
3.8
5.9
4.3
4.3
6.1
5.7
4.6
6.7
4.6
4.9
4.8
5.7
4.5
6.8
4.8
6.1
4.5
4.6
4.7
4.5
4.8

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

(1) EU27 and EA are weighted averages. 
Source: EUROPOP2019 (Eurostat). 

2070. For females, gains in life expectancy of 8-9 
years  are  expected  in  Bulgaria,  Latvia,  Hungary 
and  Romania,  with  life  expectancy  of  around  80 
years  in  2019.  As  a  result  of  this  catching-up,  the 
difference  between  the  countries  with  the  highest 
and  the  lowest  life  expectancy  narrows  from  10.8 
and  8  years  in  2019  for  men  and  women, 
respectively, to 4.5 and 3.7 years in 2070. 

When  looking  at  the  remaining  life  expectancy  at 
the  age  of  65,  average  increases  of  5.1  and  4.8 
years  are  expected  respectively  for  males  and 
females  in  the  EU  over  the  projection  period, 
implying  a  more  modest  narrowing  of  the  gender 
gap than for the life expectancy at birth. 

By 2070, male life expectancy at 65 is expected to 
increase by around seven years in Bulgaria, Latvia, 
these 
Lithuania,  Hungary  and  Romania. 
countries,  remaining  life  expectancy  at  the  age  of 
65 was 15 years or less in 2019, compared with an 
EU  average  of  more  than  18  years.  Gains  of 
between  six  and  seven  years  are  projected  for 
females  aged  65  in  Bulgaria,  Latvia,  Hungary, 
Romania and Slovakia. In 2019, life expectancy at 

In 

65  was  less  than  20  years  in  these  countries, 
compared to 22 years on average in the EU.  

1.4.  DEVELOPMENTS AND ASSUMPTIONS FOR 

NET MIGRATION 

Because of high historical volatility over time and 
between countries, assumptions on migration are 
the  most  difficult  when 
methodologically 
preparing demographic projections. On the basis 
of  the  latest  projections,  annual  net  migration 
inflows into the EU are expected to decrease from 
about  1.3  million  people  in  2019  (0.3%  of  the 
population)  to  around  1  million  people  (0.2%) 
during most of the projection period.  

1.4.1.  Past trends and driving forces 

Graph  I.1.1  shows  the  net  migration  flows  to  the 
EU  and  the  euro  area  during  the  last  six  decades. 
From  1960  through  the  mid-1980s,  net  migration 
was  mostly  positive  with  annual  net  inflows 
averaging  around  118 000,  though  certain  years 
saw  large  net  outflows.  Since  1985,  annual  net 

18 

 
                  
 
Underlying assumptions and projection methodologies 

Part I 

Table I.1.5:  Average annual net migration flows (including statistical adjustment; 1960-2018) 

1960-1979 
(%1960 pop)

1980-1999 
(%1980 pop)

2000-2018 
(%2000 pop)

total 1960-2018 
(%1960 pop)

highest value in 
2009-2018 (year)

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

11.857 (0.1)
-7.717 (-0.1)
-9.108 (-0.1)
2.567 (0.1)
142.347 (0.2)
7.603 (0.6)
-3.821 (-0.1)
-11.493 (-0.1)
-30.654 (-0.1)
136.844 (0.3)
(0)

-1.140

-45.135 (-0.1)
-3.703 (-0.6)
12.376 (0.6)
4.847 (0.2)
2.067 (0.7)
(0)

-191

-3.606 (-1.1)
18.990 (0.2)
6.745 (0.1)
-35.689 (-0.1)
-51.042 (-0.6)
-11.167 (-0.1)
3.282 (0.2)
(0)

969

-9.030 (-0.2)
14.869 (0.2)
(0)
189.627 (0.1)
142.050
(0)

1.767

7.705 (0.1)
-25.036 (-0.3)
42

(0)
8.433 (0.2)
275.275 (0.4)
-3.965 (-0.3)
-6.238 (-0.2)
41.774 (0.4)
55.646 (0.1)
36.617 (0.1)
-9.615 (-0.2)
8.372
(0)
3.794 (0.7)
-4.541 (-0.2)
-5.446 (-0.2)
2.623 (0.7)
(0)
1.014 (0.3)
28.458 (0.2)
18.383 (0.2)
-23.546 (-0.1)

-699

1.734

(0)

-40.519 (-0.2)
953 (0.1)
-3.670 (-0.1)
4.922 (0.1)
17.848 (0.2)
7.689 (0.2)
463.410 (0.2)
390.317 (0.1)

5.290

45.182 (0.4)
-22.353 (-0.3)
20.806 (0.2)
17.570 (0.3)
277.538 (0.3)
-1.750 (-0.1)
22.568 (0.6)
(0)
285.307 (0.7)
79.137 (0.1)
-3.769 (-0.1)
240.927 (0.4)
6.148 (0.9)
-14.859 (-0.6)
-26.562 (-0.8)
7.421 (1.7)
14.271 (0.1)
4.650 (1.2)
31.007 (0.2)
43.034 (0.5)
-9.738
(0)
8.268 (0.1)
-112.411 (-0.5)
4.849 (0.2)
(0)
11.891 (0.2)
55.905 (0.6)
28.041 (0.6)
1.029.787 (0.3)
990.067 (0.2)

-258

1.249.688 (13.7)
-1.079.771 (-13.7)
213.977 (2.2)
553.816 (12.1)
13.625.651 (18.7)
39.497 (3.3)
227.596
(8)
706.147 (8.5)
5.920.662 (19.4)
4.972.821 (10.9)
-286.717 (-6.9)
3.842.365 (7.7)
122.326 (21.4)
-125.631 (-5.9)
-516.650 (-18.6)
234.816 (74.8)
253.357 (2.5)
36.484 (11.2)
1.538.079 (13.4)
1.320.212 (18.7)
-1.369.729 (-4.6)
-829.068 (-9.4)
-3.169.517 (-17.2)
176.834 (11.2)
-58.929 (-1.4)
143.776 (3.2)
1.716.529 (22.9)
721.893 (20.2)
32.626.676 (12.4)
29.458.622 (8.3)

86.413 (2010)
-1.108 (2013)
38.629 (2018)
41.886 (2015)
1.165.772 (2015)
7.071 (2018)
43.835 (2018)
17.290 (2018)
332.939 (2018)
98.939 (2013)
888 (2009)
1.183.877 (2013)
18.142 (2011)
-4.905 (2018)
-3.292 (2018)
11.159 (2015)
32.165 (2018)
17.102 (2018)
85.917 (2018)
114.237 (2015)
22.147 (2018)
15.408 (2009)
-13.887 (2013)
14.928 (2018)
3.955 (2018)
17.934 (2013)
117.693 (2016)
47.142 (2012)
1.473.824 (2013)
1.518.409 (2013)

(1) CY: 1961-1979 average. 
Source: European Commission based on Eurostat data. 

migration  into  the  EU  has  been  consistently 
positive.  Despite  high  volatility,  it  rose  signifi-
cantly: annual net entries averaged 622 000 people 
in  1990-1999  and  around  1.1 million  in  2000-
2008.  Following  a  slowdown  to  around  500 000 
people  in  2009-2011  in  the  wake  of  the  global 
economic  crisis,  net  migration  started  to  rise  
again,  peaking  at  more  than  1.5 million  in  2013, 
because  of  record  inflows  in  Italy due  to  the 
statistical  adjustment  linked  to  the  post-2011 
census  corrections  (+966 000).  In  2015,  several 
Member  States  countries  saw 
inflows 
because  of  instability  in  North  Africa  and  the 
Middle  East,  and  in  2018,  net  inflows  surpassed 
1 million people.  

large 

Graph I.1.1:  Net migration flows (including statistical 

adjustment; 1960-2018) 

)
0
0
0
'
(

s
w
o

l
f
n
o

i
t
a
r
g
m

i

t
e
N

2.000

1.500

1.000

500

0

-500

-1.000

0
6
9
1

5
6
9
1

0
7
9
1

5
7
9
1

0
8
9
1

EU27

5
8
9
1

0
9
9
1

5
9
9
1

0
0
0
2

euro area

5
0
0
2

0
1
0
2

5
1
0
2

Source: European Commission based on Eurostat data. 

19 

 
    
 
 
 
 
           
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Net migration flows per country (10) between 1960 
and  2018  are  shown  in  Table  I.1.5,  in  absolute 
terms and relative to the population size. Over this 
period, Germany, Spain, France, Italy and Sweden 
recorded  the  largest  total  net  inflows  in  absolute 
terms.  When  relating  migration  flows  to  the  1960 
population,  the  largest  overall  inflows  were  in 
Luxembourg  (+75%),  Sweden  (+23%),  Cyprus 
(+21%),  Norway  (+20%),  Spain  (+19%),  Austria 
(+19%) and Germany (+19%). At the opposite end 
are  Romania,  Poland,  Bulgaria,  Portugal  and 
Lithuania,  which  saw  the  largest  outflows  in 
absolute  terms.  Relative  to  population  size,  net 
outflows  were  the  largest  in  Lithuania  (-19%), 
Romania  (-17%),  Bulgaria  (-14%)  and  Portugal 
(-9%). Between 2000 and 2018, net migration was 
negative  on  average  in  Lithuania  (with  annual 
outflows 
the  2000 
representing  0.8%  of 
population),  Latvia  (-0.6%),  Romania  (-0.5%), 
Bulgaria  (-0.3%),  Estonia  (-0.1%)  and  Croatia 
(-0.1%), with very small outflows in Slovakia and 
Poland.  In  the  latter  two  countries,  as  well  as  in 
Estonia, net migration has been positive and rising 
for several years. 

Following  the  2009  crisis,  net  migration  turned 
temporarily negative in several countries. This was 
the  case  in,  for  example,  Ireland,  Greece,  Spain, 
Cyprus and Portugal. For France, net migration has 
been  negative  since  2015.  For  eleven  Member 
States, net migration in 2018 was the highest since 
2009. 

1.4.2.  Latest Eurostat population projections 

Table I.1.6 presents the net migration flows in the 
EUROPOP2019  projections.  The  methodology 
is 
underlying 
summarised in Box I.1.1.  

the  net  migration  projections 

(10)  Due 

to  difficulties 

in  producing  good  statistics  on 
migration  flows  for  all  Member  States,  net  migration  is 
measured  as  the  difference  between  the  total  population 
stocks on 31 December and 1 January for a given calendar 
year,  minus  the  natural  increase  (the  difference  between 
births  and  deaths).  The  population  stocks  that  Member 
States transmit to Eurostat represents the number of person 
with usual  residence in  the  country  for  at  least 12  months 
(or legal  or registered persons), including refugees and, in 
some cases, asylum seekers who are resident in the country 
for  at  least  12  months.  This  method  is  different  from  the 
approach  of  subtracting  recorded  emigration  flows  from 
immigration flows, which not only incorporates errors due 
to  the  difficulty  of  registering  migration  flows,  but  also 
includes  all  possible  errors  and  adjustments  in  other 
demographic variables.  

20 

Table I.1.6: 

Projection of net migration flows (2019-2070) 

Net migration ('000)

Net migration (% of population)

2019

2030

2050

2070

2019

2030

2050

2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA

45
-4
44
-2
277
7
33
14
439
38
-4
135
8
-4
10
10
36
13
105
44
3
40
-74
16
3
18
67
25
1250
EU27 1318

20
1
16
12
248
2
19
12
185
68
-1
224
3
-7
-10
4
24
6
33
31
25
10
-40
5
5
11
52
27
871
960

20
6
17
11
227
2
14
21
179
75
3
214
3
-2
-2
3
23
5
33
27
48
14
-2
5
5
12
40
25
856
1001

21
10
18
11
214
3
10
26
169
80
6
207
2
1
3
3
24
4
33
25
72
19
21
5
7
13
30
23
844
1037

0.4
-0.1
0.4
0.0
0.3
0.5
0.7
0.1
0.9
0.1
-0.1
0.2
0.9
-0.2
0.4
1.6
0.4
2.6
0.6
0.5
0.0
0.4
-0.4
0.8
0.1
0.3
0.6
0.5
0.4
0.3

0.2
0.0
0.2
0.2
0.3
0.1
0.3
0.1
0.4
0.1
0.0
0.4
0.4
-0.4
-0.4
0.6
0.2
1.0
0.2
0.3
0.1
0.1
-0.2
0.2
0.1
0.2
0.5
0.5
0.3
0.2

0.2
0.1
0.2
0.2
0.3
0.2
0.2
0.2
0.4
0.1
0.1
0.4
0.3
-0.2
-0.1
0.4
0.3
0.7
0.2
0.3
0.1
0.2
0.0
0.2
0.1
0.2
0.3
0.4
0.2
0.2

0.2
0.2
0.2
0.2
0.3
0.2
0.2
0.3
0.4
0.1
0.2
0.4
0.2
0.1
0.1
0.3
0.3
0.5
0.2
0.3
0.2
0.2
0.2
0.3
0.2
0.3
0.2
0.3
0.3
0.2

2019-70 
(%2019)(1)
9.7
2.9
9.2
9.9
14.9
8.7
18.0
8.7
21.3
5.6
1.7
18.3
18.3
-10.1
-6.0
30.3
12.7
57.4
11.0
16.7
5.8
7.4
-4.4
13.4
5.1
11.6
22.5
24.9
13.4
11.8

(1) Cumulative net migration as % of 2019 population. 
Source: EUROPOP2019 (Eurostat). 

For  the  EU  as  a  whole,  annual  net  inflows  are 
expected to decrease from about 1.3 million people 
in  2019 (0.3%  of  the  EU  population)  to  around 
1 million  as  of  the  mid  2020s  (0.2%  of  the  EU 
population). Total net migration in the period up to 
to  52.6 million  people, 
2070  would  amount 
equivalent  to  almost  12%  of  the  2019  population. 
Net migration would broadly converge to a level of 
about 0.2% of population across Member States. 

The  countries  with  the  highest  cumulative  net 
migration as a share of population are expected to 
be  Malta,  Luxembourg,  Norway,  Sweden  and 
Spain,  with cumulative inflows of at least 20% of 
the  2019  population  over  the  projection  period. 
While net migration is assumed to turn positive in 
all  countries  during 
the  projection  period, 
cumulative  net  migration  in  2019-2070  would 
nevertheless be negative for Latvia, Lithuania and 
Romania,  the  only  countries  with  net  migration 
outflows after 2035. 

                                                           
 
   
 
Underlying assumptions and projection methodologies 

Part I 

Box I.1.1: Methodology for the migration assumptions in the EUROPOP2019 projections

The  models  used by  Eurostat to produce  immigra-
tion  and  emigration  projections,  which  combine 
into  net  migration,  take  account  of  past  migration 
trends,  the  most  recent  data,  underlying  demo-
graphic factors as well as assumptions about future 
developments  in  migration  flows.  The  models  are 
built  around  a  nowcast  component,  a  trend  extra-
polation  and  a  long-term  convergence  module.  In 
the case of the immigration model, also a working-
age population feedback mechanism is applied (1). 

The nowcast components determine the emigration 
and  immigration  figures  for  base  year  2019.  They 
are  based  on  the  latest  empirical  evidence  as 
provided  by  the  national  statistical  institutes.  For 
the  EUROPOP2019  exercise,  finalised  in  April 
2020,  all  countries  transmitted  nowcast  data  for 
emigration  and  immigration  for  2019,  with  the 
exceptions  of  Belgium,  Estonia,  Greece,  France, 
Romania  and  Portugal  (though  Portugal  provided 
the  immigration  nowcast).  For  these  countries, 
2019  figures  were  produced  using  the  models’ 
other components.  

Emigration assumptions 

Emigration  values  for  2020-2023  are  obtained  by 
linear  interpolation  between  the  nowcast  emigra-
tion value for the base year (2019) and the midterm 
year (2024), when emigration is assumed to return 
to its 2000-2018 average. The latter is ‘winsorized’ 
by replacing the maximum and minimum values by 
the  second  highest  and  second  lowest  values, 
respectively, in order to correct for extreme values 
that might bias the average. 

Next,  the  midterm  value  is  linearly  interpolated 
with  the  ultimate  emigration  distribution  derived 
from the aggregation of all emigration events in the 
EU.  To  this  end,  the  country  profiles  converge 
towards  a  common  probabilities profile,  progressi-
vely removing national peculiarities.  

Immigration assumptions 

The immigration assumptions are the sum of three 
components.  First,  the  immigration  from  other EU 
Member  States,  which  is  generated  from  the 

(1) This box is based on Eurostat (2020), ‘Methodology
of  the  Eurostat  population  projections  2019-based
(EUROPOP2019)’. 

corresponding  sex-  and  age-specific  emigration 
probabilities. This way, labour migration and post-
retirement migration flows can be distinguished.  

Second,  there  is  the  non-EU  immigration  before 
applying  the  working-age  feedback  mechanism 
(see  below).  These  assumptions  are  derived  from 
the latest observed data. As non-EU immigration is 
available  only  for  2013-2018,  values  for  earlier 
years  are  estimated  by  applying  the  average  share 
of non-EU immigration in the total inflow in 2013-
2018 
the 
immigration nowcast provides an immigration time 
series covering 20 years, i.e. 2000-2019.  

immigration.  Adding 

to  past 

total 

Given  the  high  volatility  and  general  unpredicta-
bility  of  immigration,  the  assumptions  for  non-EU 
migration  are  based  on  the  average  value  of  the 
available  time  series.  To  confine  the  profound 
impact in some countries of the 2015 refugee crisis 
and  the  economic  crisis,  the  immigration  time 
series  are  winsorised.  Immigration  for  the  first 
projection years is computed by linear interpolation 
between  the  nowcast  for  2019  and  2024,  when  the 
average of the winsorised 2000-2019 time series is 
assumed to be reached.  

The  2024  immigration  figure  is  then  converted  in 
immigration  per  capita  and  starts  converging 
towards  a  common  value,  namely  total  non-EU 
immigration  per  capita  for  the  EU  as  a  whole  in 
2024.  This  means  that,  making  abstraction  of  the 
working-age feedback mechanism discussed below, 
the inflow into the EU is kept constant as of 2024. 
This  inflow  is  nevertheless  redistributed  across 
countries  in  function  of  a  partial  convergence 
towards  equal  attractiveness  between  countries  for 
non-EU immigrants. 

Third,  there  is  the  potential  additional  non-EU 
immigration  generated  by 
the  working-age 
feedback  mechanism.  For  all  years  in  which  the 
size  of  the  population  aged  between  15  and  64  is 
projected to shrink, a 'feedback' correction factor is 
triggered, assuming an additional non-EU immigra-
tion flow in the same year. This extra immigration 
is assumed to be 10% of the decline in the working-
age  population,  distributed  by  age  and  sex  in 
accordance  with  the  country-specific  immigration 
pattern for the applicable year.  

(Continued on the next page) 

21 

European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Box (continued) 

Methodological changes 

The  methodology 
to  determine  net  migration 
projections  is  fundamentally  different  from  that  of 
the  population  projections  underlying  the  2018 
Ageing  Report.  Whereas  these  directly  projected 
net migration(2), EUROPOP2019 provides separate 

(2)  See  2018  Ageing  Report  –  Underlying  Assumptions 

& Projection Methodologies, Box I.1.1.  

1.5.  OVERALL RESULTS OF THE 2019-BASED 

POPULATION PROJECTIONS 

The  EU  population  is  projected  to  decline  from 
447 million people in 2019 to 424 million in 2070. 
During  this  period,  Member  States’  population 
will  age  dramatically  given  the  dynamics  in 
fertility,  life  expectancy  and  migration.  The 
median age would rise by five years over the next 
decades. 

Table I.1.7 presents an overview of the population 
projections  for  the  period  2019-2070 (11).  These 
provide  the  basis  for  the  age-related  expenditure 
projections in the 2021 Ageing Report. 

According  to  the  baseline  demographic  projec-
tions,  the  EU  population  would  reach  a  peak 
during  the  next  decade.  It  would  rise  from  about 
447  million  people  in  2019  to  a  little  over  449 
million people in 2026. Thereafter, the population 
would start to shrink, falling back to 424 million in 
2070.  This  is  a  decline  by  5%  compared  to  the 
base year level, most of which would take place in 
the  second  half  of  the  projection  period.  The 
overall downward trend comprises rather heteroge-
neous developments at the country level. 

For  11  Member  States  and  Norway  the  total 
population  would  increase  between  2019  and 
2070,  while  16  Member  States  would  see  the 
number  of  inhabitants  shrink.  Compared  to  the 
base  year,  the  sharpest  declines  are  expected  in 
Latvia, Lithuania, Romania,  Bulgaria and Croatia, 

(11)  The  population  projections  published  by  Eurostat  refer  to 
the population  on 1  January  of  each year.  The  projections 
in this table (and used throughout this report) for year t are 
calculated  as  the  average  of  the  Eurostat  projections  on  1 
January  for  year  t  and  those  for  year  t+1,  as  done  in 
previous projection exercises. 

22 

flows for emigration and immigration as migration 
data  has  improved.  The  new  migration  model 
ensures  consistency  for  intra-EU  flows  and  takes 
better  into  account  sex  differentials.  It  works  on 
immigration levels and emigration probabilities. 

with a fall of between 26% and 38%. Declines by 
around  20%  are  projected  in  Greece,  Poland  and 
Portugal.  In  these  countries,  the  population  is 
expected 
the 
projection period. 

to  dwindle  steadily 

throughout 

Table I.1.7: 

Total population projections (2019-2070) 

Total population 
(annual average - millions)

2019

2045

2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

11.5
7.0
10.7
5.8
83.1
1.3
4.9
10.7
47.1
67.1
4.1
60.3
0.9
1.9
2.8
0.6
9.8
0.5
17.3
8.9
38.0
10.3
19.3
2.1
5.5
5.5
10.3
5.3
342
447

11.9
5.8
10.6
6.1
83.0
1.3
6.1
9.7
49.5
70.0
3.5
58.8
1.0
1.5
2.2
0.8
9.3
0.7
18.2
9.3
34.8
9.6
16.0
2.1
5.2
5.4
12.0
6.2
346
444

11.8
5.0
10.2
6.2
81.7
1.2
6.5
8.6
47.0
69.4
3.0
53.9
1.1
1.2
1.8
0.8
8.9
0.7
18.0
9.2
30.8
8.5
13.7
1.9
4.7
5.0
13.1
6.7
333
424

Source: EUROPOP2019 (Eurostat). 

% change

0%
3%

2019-
2045
4%

2019-
2070
3%

2045-
2070
-1%
-17% -13% -28%
-4%
-3%
-1%
6%
1%
5%
-2%
-2%
0%
-10%
-6%
-4%
32%
23%
7%
-11% -20%
-9%
-5%
5%
-1%
4%
-14% -13% -26%
-11%
-8%
-3%
17%
25%
6%
-24% -19% -38%
-20% -18% -35%
27%
4%
22%
-9%
-5%
-4%
41%
8%
31%
4%
-1%
5%
-1%
5%
4%
-8%
-12% -19%
-12% -18%
-7%
-17% -14% -29%
-6%
-1%
-7%
-10% -14%
-4%
-9%
-6%
-3%
27%
9%
17%
26%
8%
17%
-3%
-4%
1%
-5%
-5%
-1%

Among 
the  countries  with  rising  population 
between  2019  and  2070,  Cyprus,  Luxembourg, 
Sweden,  Ireland  and  Malta  would  see 
their 
inhabitants  increase  by  between  25%  and  41%, 
mainly in the first part of the projection period. In 
the 
the  cases  of  Belgium,  Spain,  France, 

                                                           
 
    
 
 
    
 
 
                                                           
Underlying assumptions and projection methodologies 

Part I 

Netherlands  and  Austria,  the  initial  population 
increase  would  be  mitigated  in  the  second  half  of 
the projection horizon.  

would rise from 6% in 2019 to 13% in 2070. The 
projected  increase  is  the  highest  in  Poland,  and 
Slovakia.  

In  2019,  Germany  (83.1  million  people)  was  the 
Member  State  with 
largest  population, 
the 
followed  by  France  (67.1  million),  Italy  (60.3 
(47.1  million)  and  Poland 
million),  Spain 
(38 million). In 2070, this order would remain the 
same  despite  the  depopulation  in  Germany,  Italy 
and Poland, and population growth in France. 

In  all  Member  States,  the  share  in  the  overall 
population  of  the  age  cohorts  above  65  years  is 
expected  to  rise  by  2070  (see  Table  I.1.8),  from 
20% in 2019 to 30% in 2070 for the EU. Increases 
range from six pps in Sweden to 16 pps in Poland, 
where  people  aged  65  or  more  would  represent 
34%  of  the  population  in  2070.  Shares  in  Greece, 
Croatia, Italy, Latvia and Poland would be similar, 
with  one  in  three  persons  being  at  least  65  years 
old at the end of the projection period.  

Table I.1.8:  Composition of the population by age group 

2019

2070

(80+) (0-19) (20-64) (65+)

(0-19) (20-64) (65+)
(80+)
22% 59% 19% 6% 20% 52% 28% 12%
19% 60% 21% 5% 18% 51% 31% 14%
20% 60% 20% 4% 20% 52% 28% 13%
22% 58% 20% 5% 21% 52% 28% 11%
18% 60% 22% 7% 20% 52% 28% 12%
21% 59% 20% 6% 18% 51% 30% 14%
27% 59% 14% 3% 21% 52% 27% 12%
19% 58% 22% 7% 17% 50% 33% 15%
20% 61% 20% 6% 17% 51% 32% 15%
24% 56% 20% 6% 21% 50% 29% 13%
19% 60% 21% 5% 17% 51% 33% 14%
18% 59% 23% 7% 16% 51% 33% 15%
22% 62% 16% 4% 19% 53% 27% 11%
21% 59% 20% 6% 18% 50% 32% 15%
20% 60% 20% 6% 17% 50% 33% 14%
21% 64% 14% 4% 17% 53% 30% 12%
20% 61% 20% 4% 19% 52% 30% 12%
18% 63% 19% 4% 16% 52% 32% 13%
22% 59% 19% 5% 20% 52% 29% 11%
19% 62% 19% 5% 18% 52% 29% 12%
20% 62% 18% 4% 16% 50% 34% 16%
19% 59% 22% 6% 18% 49% 33% 15%
21% 60% 19% 5% 18% 51% 32% 14%
20% 60% 20% 5% 18% 52% 30% 14%
21% 63% 16% 3% 18% 50% 32% 15%
21% 57% 22% 6% 17% 51% 32% 13%
23% 57% 20% 5% 21% 53% 26% 11%
23% 59% 17% 4% 19% 53% 28% 11%
20% 59% 21% 6% 19% 51% 30% 13%
EU27 20% 59% 20% 6% 18% 51% 30% 13%

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA

Source: European Commission based on EUROPOP2019 
(Eurostat). 

The  population  share  of  the  age  cohorts  above  80 
would  more  than  double  in  all  Member  States 
between  2019  and  2070,  with  the  exception  of 
Germany.  For  the  EU  as  a  whole,  their  share 

The population share of the age group 0-19 would 
shrink  in  all  Member  States  during  the  projection 
horizon,  aside  from  Germany  where  it  would 
increase  slightly.  The  share  in  the  EU  population 
of  this  youngest  group  would  decrease  from  20% 
in  2019  to  18%  in  2070. The  dwindling  –  also  in 
absolute numbers for most countries – of the 0-19 
age group is the sharpest in Ireland and Finland.  

Finally, the population at working age (20-64 year 
olds),  would  shrink  in  all  Member  States  relative 
to the overall population. Whereas in 2019, people 
at  working  age  represented  59%  of  the  EU 
population,  this  share  would  fall  to  51%  in  2070. 
The  decrease  exceeds  10 pps 
in  Lithuania, 
Luxembourg, Malta, Poland and Slovakia.  

The drivers of these trends are manifold. First, the 
increasing share of the population in the higher age 
cohorts is due to the combination of the numerous 
cohorts born in the 1950s and 1960s and continu-
ing gains in life expectancy. Second, the size of the 
groups  aged  25-59  (the  bulk  of  the  working-age 
population,  see  Graph  I.1.2)  shrinks  significantly 
between 2019 and 2070 due to fertility rates below 
the natural replacement rate and shrinking cohorts 
of  women  in  childbearing  ages.  Finally,  net 
migration  flows  would  not  suffice  to  offset  the 
trends towards an ageing population.  

Graph I.1.2:  EU population by age group and gender (‘000) 

Males

2070

2019

2070

2019

Female

90+

85-89

80-84

75-79

70-74

65-69

60-64

55-59

50-54

45-49

40-44

35-39

30-34

25-29

20-24

15-19

10-14

5-9

0-4

20000

15000

10000

5000

0

0

5000

10000

15000

20000

Source: European Commission based on EUROPOP2019 
(Eurostat). 

23 

 
    
 
    
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

The  strong  upward  shift  in  the  age  distribution 
over  the  next  decades  is  shown  in  Graph  I.1.2. 
While  in  2019,  the  largest  cohort  for  both  males 
and  females  was  that  of  people  aged  50-54,  in 
2070  the  55-59  and  60-64  age  brackets  would  be 
the  largest  cohorts.  Overall,  the  median  age  will 
rise  from  43.7  year  in  2019  to  48.8  year  in  2070, 
most of which occurs by around 2040. For men it 
goes  from  42.2  to  47.3,  for  women  from  45.2  to 
50.3.  

Because of the demographic shift from younger to 
older  age  groups,  demographic  dependency  ratios 
are  expected 
in  all 
countries (see Table I.1.9). 

increase  significantly 

to 

The old-age dependency ratio (OADR), i.e. people 
aged  at  least  65  relative  to  those  aged  20-64) 
expresses  the  presumed  number  of  pensioners  in 
terms  of  the  theoretical  number  of  contributors.  It 
provides  a  gauge  of  how  demographic  ageing 
alters  the  beneficiary-contributor  balance.  The 
OADR is projected to increase from 34% in 2019 
to  59%  in  2070  for  the  EU  as  a  whole.  This 
increase  would  predominantly  take  place  during 

Table I.1.9:  Dependency ratios (%, 2019-2070) 

the  first  half  of  the  projection  period. The  change 
in the OADR means that the EU would move from 
having, for every person aged over 65 years, nearly 
three (2.9)  people  at  working-age  to  less  than 
two (1.7).  

in 

The  Member  States  with  the  highest  projected 
the  OADR  are  Portugal,  Spain, 
increase 
Romania, Malta, Lithuania, Luxembourg, Slovakia 
and  Poland,  with  increases  of  at  least  30 pps.  In 
2070,  the  OADR  would  surpass  65%  in  Greece, 
Italy,  Lithuania,  Portugal  and  Poland:  for  every 
two  retirees,  there  would  be  only  three  potential 
contributors. In Sweden, Cyprus, Ireland, Belgium, 
the  Czech  Republic,  Denmark  and  Germany,  the 
OADR would stay below 55% in 2070. 

Similarly, 
the  very  old-age  dependency  ratio 
(people aged 80 or above relative to those aged 20-
64) is projected to rise considerably, from 10% to 
25.7%  on  average  in  the  EU.  The  same  countries 
come  to  the  fore  as  for  the  standard  old-age 
dependency ratio. 

Finally, the total dependency ratio (people younger 

Old-age dependency ratio
(65+/20-64)

Very old-age dependency ratio
(80+/20-64)

Total dependency ratio 
((0-19 & 65+)/20-64)

2019

2045

2070

2019-2070 
(pps change)

2019

2045

2070

2019-2070 
(pps change)

2019

2045

2070

2019-2070 
(pps change)

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

HR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

NO

EA

EU27

32.5

36.0

33.0

34.1

36.1

33.8

24.2

37.9

32.1

36.5

34.8

38.9

26.2

34.6

32.9

22.6

32.2

29.7

32.9

30.7

29.0

37.3

31.1

33.2

25.9

38.9

35.2

29.4

35.3

34.4

47.7

55.8

51.3

48.2

52.2

49.5

42.2

64.0

61.2

53.1

53.7

65.4

36.7

57.4

58.9

41.6

49.6

39.3

49.3

49.5

49.5

65.9

55.6

55.9

49.9

49.9

41.8

42.6

55.8

54.6

53.3

60.8

53.7

53.8

54.6

59.4

53.0

65.2

62.5

56.9

64.6

65.6

50.7

63.6

66.0

56.1

57.4

62.4

55.2

55.9

67.8

67.3

62.1

58.8

63.1

62.5

49.8

52.4

58.9

59.2

20.8

24.8

20.6

19.7

18.5

25.6

28.7

27.3

30.5

20.4

29.8

26.7

24.6

29.0

33.1

33.6

25.1

32.7

22.4

25.2

38.8

30.0

31.0

25.5

37.2

23.6

14.6

23.0

23.6

24.7

9.7

8.1

6.8

8.0

11.1

9.7

5.8

12.2

10.0

11.1

9.0

12.3

6.0

9.7

9.7

6.2

7.3

6.8

7.9

8.4

7.1

11.0

7.8

8.9

5.3

9.8

9.1

7.2

10.6

9.9

17.8

16.9

15.5

17.2

20.3

17.5

13.3

22.7

21.1

20.8

18.7

23.7

13.4

20.6

21.5

13.4

14.4

13.7

18.5

18.1

17.2

23.4

15.9

19.8

15.5

19.8

14.8

14.7

20.7

19.7

22.2

27.5

24.1

21.1

22.9

27.0

22.2

30.3

28.5

25.0

26.7

28.5

19.6

29.9

28.7

23.3

23.5

25.4

21.7

23.3

31.4

30.0

28.2

26.7

29.1

26.1

20.1

20.6

25.5

25.7

Source:  European Commission based on EUROPOP2019 (Eurostat). 

12.5

19.4

17.3

13.2

11.8

17.3

16.4

18.0

18.5

13.9

17.7

16.2

13.7

20.2

19.0

17.1

16.2

18.7

13.8

14.9

24.3

19.0

20.4

17.8

23.8

16.2

11.0

13.4

14.9

15.8

70.8

67.7

67.1

72.7

66.9

69.5

70.0

71.1

64.4

79.8

67.1

69.2

61.0

69.5

66.0

55.9

64.4

58.5

69.9

62.1

61.5

69.4

66.0

65.7

58.5

76.4

76.2

69.0

69.7

68.8

84.8

89.8

88.1

88.3

87.1

83.3

82.6

96.3

93.1

95.1

85.0

95.8

71.4

92.0

91.5

72.9

84.1

65.7

87.0

82.8

78.6

90.5

96.7

92.1

94.0

92.1

94.9

92.6

98.7

95.3

98.1

97.5

96.8

86.9

100.2

100.8

89.2

93.6

92.3

93.2

91.1

99.5

100.7

103.3

89.6

88.9

82.8

81.7

81.3

78.8

91.1

89.4

97.1

93.2

99.1

94.7

89.4

88.6

95.2

95.3

19.8

29.0

25.0

21.3

25.2

25.4

22.7

27.6

30.9

18.3

30.4

27.6

26.0

30.7

34.8

33.4

29.2

33.9

23.2

29.0

38.0

33.9

31.1

27.5

40.6

18.3

13.2

19.6

25.5

26.5

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

HR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

NO

EA

EU27

24 

 
             
 
Underlying assumptions and projection methodologies 

Part I 

Table I.1.10:  Geographical distribution of world population (1960-2070, % of total world population) 

Africa
Asia

China
Japan
India

Europe

EU27
EA
Russian Federation

Latin America and the Caribbean
Northern America

US

1960

1980

2000

2020

2045

2070

9.3%
56.2%
21.8%
3.1%
14.8%
19.9%
11.7%
8.7%
3.9%
7.3%
6.7%
6.2%

10.7%
59.4%
22.4%
2.6%
15.7%
15.6%
9.1%
6.8%
3.1%
8.1%
5.7%
5.1%

13.2%
60.9%
21.0%
2.1%
17.2%
11.8%
6.9%
5.2%
2.4%
8.5%
5.1%
4.6%

17.2%
59.5%
18.5%
1.6%
17.7%
9.6%
5.7%
4.4%
1.9%
8.4%
4.7%
4.2%

24.1%
55.4%
15.1%
1.2%
17.1%
7.6%
4.5%
3.5%
1.4%
8.0%
4.4%
3.9%

31.6%
49.8%
12.0%
0.9%
15.6%
6.4%
3.7%
2.9%
1.2%
7.3%
4.3%
3.9%

(pps change)

1960-2020 2020-2070

7.9
3.4
-3.3
-1.5
2.9
-10.4
-6.0
-4.3
-2.1
1.1
-2.0
-1.9

14.4
-9.8
-6.4
-0.8
-2.1
-3.2
-2.0
-1.5
-0.6
-1.1
-0.4
-0.4

Source: UN World Population Prospects 2019. 

than 20 or older than 64 relative to the population 
aged 20-64) is projected to rise from 69% in 2019 
to  95%  in  2070  for  the  EU  as  a  whole.  This 
measure relates the theoretical inactive population 
– people that have not yet entered or have already 
left 
theoretical 
the 
labour  market  – 
the  same 
contributory  base.  Again,  broadly 
countries  record  the  largest  changes  for  the  total 
dependency 
the  more  narrow 
definitions.  

ratio  as 

the 

for 

to 

In  2070,  it  is  expected  that  the  number  of  young 
and old people will have surpassed the number of 
people  at  working  age  in  Latvia,  Lithuania  and 
Portugal,  with  a  total  dependency  ratio  of  more 
than 100%. In contrast, the ratio would stay below 
90% 
in  Cyprus,  Norway,  Luxembourg  and 
Sweden.  This  compares  to  a  maximum  value  of 
80% in 2019. 

1.6.  DEMOGRAPHIC AGEING IN A GLOBAL 

CONTEXT 

The  EU's  share  of  the  world  population  is 
forecast to shrink from 5.7% in 2020 to 3.7% by 
2070.  The  projected  increase  in  dependency 
ratios  is  comparatively  high  for  the  EU,  in 
particular given the current demographic balance 
compared to ‘younger’ continents.  

The  UN  population  statistics  and  projections 
provide  a  global  perspective  of  demographic 
trends (12).  The  combined  share  of  EU  Member 
States  in  the  world  population  halved  since  1960, 

(12)  The United Nations Population Division updates its global 
latest 

population  projections  every 
projections are the UN World Population Prospects 2019. 

two  years.  The 

when the EU represented almost 12% of the world 
population  (see  Table  I.1.10).  While  the  EU 
population grew by 25% over the past six decades, 
demographic growth was faster outside of Europe, 
with the global population increasing by more than 
150%  over  the  same  period. The  shares  of  China, 
Japan, Russia and the US in the global population 
also  declined  compared  to  1960,  in  contrast  with 
the  rising  shares  in  India,  Latin  America  and, 
particularly, Africa.  

Given  that  fast  population  growth  is  expected  to 
continue,  the  African  continent’s  share  in  the 
world  population  would  increase  further,  to  about 
32%  in  2070.  While  staying  the  most  populous 
continent, Asia’s share would decline over the next 
five decades to around 50%. This fall is driven by 
China,  India  and  Japan  –  in  particular  China, 
whose  share  would  decrease  by  a  third  in  2020-
2070  –,  with  a  broadly  stable  share  of  about  22% 
for the other Asian countries.  

By 2070, the EU’s share in the global population is 
expected to reach 3.7%, shrinking by 2 pps relative 
to  the  current  situation (13).  This  is  comparable  to 
the projected share of the US,  whose  share  would 
remain broadly stable. 

Looking at the age structure in the UN projections, 
the EU currently has already a comparatively high 
OADR (see Table I.1.11). At 35%, it is below the 
Japanese ratio of 52% but considerably above that 
in  other  large  countries,  with  a  higher  increase  in 

(13)  The  UN  projections  and  Eurostat’s  EUROPOP2019 
projections  differ  notably.  The  former  expect  the  EU27 
population  to  peak  in  2021  before  falling  to  392  million 
people in 2070, i.e. 32 million below the Eurostat baseline 
(see Table I.1.8). The UN figures show a larger decline (or 
a smaller increase) for a majority of Member States. 

25 

                                                           
                                                           
 
              
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.1.11:  Global demographic dependency ratios (19, 1960-2070,  (%) 

World
Africa
Asia

China
Japan
India

Europe

EU27
EA
Russian Federation

Latin America and the Caribbean
North America

US
Oceania

Old-age dependency ratio
(65+/20-64)

Very old-age dependency ratio
(80+/20-64)

1960

2000

2020

2070

(pps change)

1960-2020 2020-2070

1960

2000

2020

2070

(pps change)

1960-2020 2020-2070

10.1
7.0
7.6
7.6
10.4
6.4
15.3
16.7
17.8
10.5
8.1
17.1
17.3
14.3

12.8
7.8
10.6
11.3
27.3
8.6
24.3
25.7
26.7
20.4
10.9
20.9
20.9
17.4

16.3
7.7
14.8
18.5
52.0
11.3
32.0
35.2
36.0
25.3
15.2
28.5
28.4
22.6

34.3
14.8
41.1
58.2
81.9
35.5
55.6
62.5
63.7
40.1
48.0
48.7
48.3
36.7

6.2
0.6
7.2
10.9
41.6
4.9
16.7
18.5
18.2
14.8
7.1
11.5
11.1
8.3

18.0
7.2
26.3
39.7
30.0
24.2
23.6
27.3
27.7
14.8
32.7
20.2
19.9
14.1

1.2
0.6
0.7
0.4
1.3
0.7
2.2
2.4
2.7
1.5
0.9
2.6
2.7
2.1

2.2
0.8
1.5
1.6
5.9
1.0
4.9
5.4
5.9
3.3
1.9
5.4
5.5
3.8

3.3
1.0
2.6
2.9
16.4
1.7
8.9
10.2
11.0
6.3
3.2
6.8
6.8
5.4

11.1
2.7
13.5
24.0
42.2
8.7
24.2
28.0
28.6
17.2
16.9
18.9
18.7
13.6

2.1
0.4
1.9
2.5
15.1
1.0
6.6
7.8
8.3
4.9
2.3
4.2
4.1
3.2

7.9
1.7
10.9
21.2
25.7
7.1
15.4
17.7
17.6
10.9
13.7
12.0
11.9
8.2

Source: UN World Population Prospects 2019. 

recent  decades.  Ratios  and  projected  changes  for 
the  EU  are  comparable  to  those  based  on  the 
Eurostat projections (see Table I.1.9).  

Globally,  the  UN  demographic  projections  expect 
the OADR to rise by 18 pps, from 16% in 2020 to 
34%  in  2070.  The  EU  ratio  would  increase  by 
27 pps,  reaching  63%  in  2070.  All  continents  are 
expected to see an increase and in some cases, e.g. 
Asia  and  Latin  America,  the  projected  change  in 
the balance between potential retirees and potential 
contributors is similar to what is anticipated for the 
EU.  However,  given  that  the  current  situation  is 
generally  more  favourable  in  other  regions,  it  can 
be concluded that European Member States will on 
average  undergo  a  more  radical  ageing  process 
than the rest of the world, notable exceptions such 
as  Japan  and  China  aside.  Developments  for  the 
very  old-age  dependency  ratio  lead  to  the  same 
conclusion. 

Global  ageing  shows  in  expected  changes  of 
median  ages.  In  2020,  the  median  age  in  the  EU 
was  43  years,  up  from  30  years  in  1960.  This 
compares  to  39  years  in  Northern  America,  32 
years  in  Asia  and  31  years  in  Latin  America.  By 
2070, half of the EU population would be above 49 
years  according  to  the  UN  projections,  compared 
to 46 years in Latin America, 44 years in Northern 
America and 43 years in Asia. 

26 

1.7.  COMPARISON WITH THE DEMOGRAPHIC 
SCENARIO UNDERLYING THE 2018 
AGEING REPORT PROJECTIONS 

In 2070, the EU would count 15.2 million people 
less  than  assumed  in  the  2018  Ageing  Report. 
This  is  due  to  lower  projections  for  people  aged 
less  than  65  years,  with  the  upward  revision  in 
net migration insufficient to offset the downward 
revision  in  the  average  fertility  rate.  As  a  result, 
the  new  demographic  projections  entail  a  larger 
increase in the old-age dependency ratio between 
2019 and 2070. 

section  compares 

This 
latest  Eurostat 
demographic  projections,  EUROPOP2019,  with 
those  underlying 
the  2018  Ageing  Report, 
ESSPOP2015. 

the 

In the base  year 2019, the EU population counted 
762 000 fewer people than anticipated in the 2015-
based  demographic  projections  (see  Table  I.1.12). 
This  difference  was  mainly  due 
to  France, 
Germany and Italy, reflecting how net migration in 
than  expected  under 
2016-2018  was  smaller 
ESSPOP2015. 

In  2070,  the  total  EU  population  would  be  some 
15.2 million people smaller (-3%) than previously 
projected. Among the sixteen Member States with 
a  downward  revision,  the  latter  corresponds  to  at 
least  10%  of 
the  previous  2070  population 
projection  for  Luxembourg,  Belgium,  Latvia, 
Croatia,  Finland,  Denmark  and  France.  Upward 
revisions  are  fewer  in  number  and  generally 

 
              
 
Underlying assumptions and projection methodologies 

Part I 

Table I.1.12:  Population – difference between EUROPOP2019 and ESSPOP2015 ('000) 

Total population

Population 0-19

Population 20-64

Population 65+

2019

2070

-63
-6
31
-56
-555
10
105
120
585
-572
-34
-406
15
-5
27
-2
-21
50
-14
-85
11
57
30
14
-1
-30
34
-32
-751
-762

-2.070
178
236
-673
2.488
16
458
925
-2.813
-7.604
-368
-986
80
-161
101
-250
46
186
-1.561
-922
-58
479
-1.333
-19
-186
-592
-788
-297
-12.430
-15.190

Difference in 
2070 (%) (1)
-15%
4%
2%
-10%
3%
1%
8%
12%
-6%
-10%
-11%
-2%
8%
-12%
6%
-24%
1%
36%
-8%
-9%
0%
6%
-9%
-1%
-4%
-11%
-6%
-4%
-4%
-3%

2019

2070

-27
0
8
-9
240
1
-48
59
21
-334
-16
-93
11
-3
2
-2
-4
4
-14
3
73
50
80
1
15
-23
10
-10
-137
5

-646
-4
43
-134
1.102
-16
-52
168
-2.981
-3.113
-100
-777
57
-67
-31
-69
-114
8
-556
-190
-620
225
-687
-47
-84
-284
-467
-182
-7.352
-9.433

Difference in 
2070 (%) (1)
-22%
0%
2%
-9%
7%
-7%
-4%
13%
-27%
-18%
-16%
-8%
37%
-24%
-9%
-33%
-6%
8%
-14%
-10%
-11%
18%
-22%
-12%
-9%
-25%
-15%
-12%
-11%
-11%

2019

2070

-26
2
30
-43
-634
8
140
63
614
-256
-18
-254
2
1
26
1
4
45
18
-76
2
7
-37
14
-14
-6
32
-23
-327
-355

-1.097
128
165
-315
2.619
15
187
539
-1.631
-4.694
-198
-146
66
-72
42
-125
101
109
-770
-347
377
281
-569
-4
-71
-284
-302
-86
-5.383
-5.995

Difference in 
2070 (%) (1)
-15%
5%
3%
-9%
7%
3%
6%
14%
-6%
-12%
-11%
-1%
13%
-11%
5%
-23%
2%
42%
-8%
-7%
3%
7%
-8%
0%
-3%
-10%
-4%
-2%
-3%
-3%

2019

2070

-10
-7
-8
-5
-161
1
13
-2
-50
18
0
-59
1
-3
-1
-2
-21
0
-18
-12
-63
0
-13
0
-1
-1
-7
1
-287
-411

-328
54
28
-224
-1.233
17
323
218
1.799
203
-71
-62
-44
-22
90
-56
59
70
-235
-385
185
-28
-78
33
-31
-24
-20
-29
305
238

Difference in 
2070 (%) (1)
-9%
4%
1%
-12%
-5%
5%
22%
8%
14%
1%
-7%
0%
-13%
-5%
18%
-19%
2%
44%
-4%
-12%
2%
-1%
-2%
6%
-2%
-1%
-1%
-2%
0%
0%

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

(1) Difference in 2070 as percentage of ESSPOP2015 projection for 2070. 
Source: European Commission based on EUROPOP2019 and ESSPOP2015 (Eurostat). 

Luxembourg.  Conversely,  upward  revisions 
exceed  10%  of  the  previous  projection  for 
Spain, Lithuania, Ireland and, notably, Malta. 

These changes result in a higher projected increase 
in  the  old-age  dependency  ratio  for  18  Member 
States and Norway (see Table I.1.13). For the EU 
as  a  whole,  the  OADR  would  increase  by  an 
additional  two pps  as  compared  to  the  2018 
Ageing  Report’s  demographic  projections.  The 
largest  upward  revisions  are  for  Spain  (+12 pps), 
Ireland  and  Lithuania  (+8 pps),  France  (+7 pps), 
Malta  (+6 pps)  and  Finland  (+5 pps).  Projections 
for  Cyprus  (-15 pps),  Germany  (-7 pps)  and 
Portugal  (-6 pps)  show  smaller  increases  in  the 
OADR than assumed in the 2018 Ageing Report. 

smaller,  with the exceptions of Malta (+36%) and 
Greece (+12%). 

Developments  for  broad  age  groups  (0-19,  20-64 
and  above  65),  show  that  the  average  revision  is 
driven by the 0-19 and 20-64 age brackets,  which 
in 2070 would on average be 11% and 3% smaller, 
respectively, than projected in ESSPOP2015.  

-  The  2070  projection  for  people  below  20  was 
revised  down  by  at  least  20%  of  the  previous 
number  in  the  cases  of  Luxembourg,  Spain, 
Finland,  Latvia,  Romania  and  Belgium. 
Upward revisions for this age group are limited 
to  six  Member  States,  led  by  Cyprus  and 
Portugal. 

-  For  the  working-age  population  (20-64  year-
olds),  downward  revisions  surpass  20%  of  the 
previous  projection  only  for  Luxembourg 
(-23%).  Malta  has  by  far  the  largest  upward 
revision for this age bracket (+42%), with most 
other revisions below 10%.  

-  Downward  revisions  for  the  population  cohort 
above  65  year  are  mostly  small,  surpassing 
10%  only  for  Denmark,  Austria,  Cyprus  and 

27 

 
 
         
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.1.13:  Old-age dependency ratio – difference 
between EUROPOP2019 and ESSPOP2015 
(percentage points) 

2019

2045

2070

2019-2070

these  countries,  the  fertility  rate  in  2019  was  on 
average  0.2  lower  than  what  was  assumed  in  the 
previous  demographic  projections,  thus  lowering 
the starting point.  

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

0.0
-0.2
-0.3
0.3
0.1
-0.2
-0.8
-0.4
-0.9
0.3
0.3
0.1
0.1
-0.3
-0.6
-0.6
-0.4
-4.8
-0.2
0.2
-0.3
0.0
0.0
-0.4
0.1
0.0
-0.3
0.2
-0.1
-0.1

2.9
-2.7
-1.8
3.9
-2.2
-0.2
-4.1
-8.0
-6.1
2.8
2.1
-1.2
-3.6
-2.8
-5.7
2.8
-2.2
-8.0
1.7
2.3
-2.6
-1.8
-0.8
-1.7
0.1
1.4
0.6
0.5
-0.9
-0.9

3.5
-1.0
-1.2
-1.6
-6.7
1.4
7.0
-3.5
11.0
7.2
3.3
0.1
-14.8
3.6
7.2
2.6
0.0
0.9
1.9
-3.7
-0.5
-5.6
3.7
3.5
0.6
5.3
1.8
0.4
2.0
1.7

3.5
-0.8
-0.9
-1.8
-6.8
1.6
7.8
-3.1
11.8
6.9
3.0
0.0
-14.9
3.9
7.8
3.2
0.4
5.7
2.1
-3.9
-0.2
-5.6
3.7
3.9
0.5
5.3
2.1
0.2
2.1
1.8

(1) Old-age dependency ratio: persons aged 65 and over in 
relation to persons aged 20-64. 
Source: European Commission based on EUROPOP2019 and 
ESSPOP2015 (Eurostat). 

The broad differences in the population projections 
described  above  can  be  related  to  changes  in  the 
assumptions regarding fertility, life expectancy and 
net migration.  

Fertility 

Fertility rates are lower than those assumed in the 
previous  demographic  projections,  by  0.1  on 
average  (see  Table  I.1.14).  This  is  caused  by  the 
combined effect of the fertility rate in 2019 being 
in most countries lower than what was assumed in 
the  previous  demographic  projections  and  the 
lower anchor point for long-term convergence.  

The sole exceptions to the decrease in the average 
number  of  live  births  in  2019-2070  are  Portugal, 
increase,  and  Germany,  with  a 
with  a  0.1 
rate.  Downward 
fertility 
marginally  higher 
revisions are the largest for Spain (-0.4 live births 
per  woman),  Malta  and  Finland  (-0.3),  Norway, 
Sweden,  Latvia,  Ireland  and  France  (-0.2).  For 

Table I.1.14:  Fertility and net migration – differences 

between EUROPOP2019 and ESSPOP2015 

Fertility rate

Net migration ('000)

2019 2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA

-0.2
0.0
0.0
0.0
0.0
-0.1
-0.2
0.0
-0.3
-0.2
0.0
0.0
0.0
-0.2
-0.1
-0.2
-0.1
-0.4
-0.1
0.0
-0.1
0.2
-0.1
-0.1
0.1
-0.4
-0.2
-0.2
-0.1
EU27 -0.1

-0.1
-0.1
0.0
0.0
0.0
-0.1
-0.2
-0.1
-0.4
-0.1
-0.1
-0.1
-0.1
-0.2
-0.1
-0.1
-0.1
-0.3
-0.1
-0.1
-0.2
0.0
-0.1
-0.1
-0.2
-0.3
-0.2
-0.2
-0.1
-0.1

avg 2019-
2070
-0.1
-0.1
0.0
0.0
0.0
-0.1
-0.2
0.0
-0.4
-0.2
0.0
-0.1
0.0
-0.2
-0.1
-0.1
-0.1
-0.3
-0.1
0.0
-0.1
0.1
-0.1
-0.1
-0.1
-0.3
-0.2
-0.2
-0.1
-0.1

2019 2070

-9
8
22
-36
-108
4
22
34
399
-35
0
-20
6
4
34
0
15
9
37
-27
13
40
-9
12
-2
2
-3
-2
402
411

-6
9
10
2
71
2
0
15
32
25
1
43
-1
1
3
-1
12
3
9
5
65
4
18
3
4
6
6
7
216
339

sum 2019-
2070
-903
310
207
-369
957
65
327
699
2.884
12
-154
1.070
-32
-81
199
-153
413
187
-288
-545
1.697
39
42
90
9
100
128
183
4.637
6.911

sum 2019-
2070(1)
-6.5%
6.4%
2.1%
-5.4%
1.2%
5.5%
5.4%
9.1%
5.8%
0.0%
-4.5%
2.0%
-3.1%
-6.0%
11.6%
-14.7%
4.7%
36.0%
-1.5%
-5.4%
5.5%
0.5%
0.3%
4.6%
0.2%
1.8%
0.9%
2.6%
1.3%
1.6%

(1) Difference in total net migration (2019-2070) as 
percentage of ESSPOP2015 population projection for 2070. 
Source: European Commission based on EUROPOP2019 and 
ESSPOP2015 (Eurostat). 

Net migration 

In  2019,  net  migration  flows  were  generally 
slightly  higher  than  the  numbers  projected  under 
ESSPOP2015  (see  Table  I.1.14).  The  higher  net 
migration for the EU as a whole (411 000 persons) 
– mostly accounted for by Spain – corresponded to 
0.1%  of  the  EU  population.  The  difference  was 
larger  than  0.5%  of  the  2019  population  in  the 
cases  of  Slovenia,  Cyprus,  Spain,  Lithuania  and 
Malta with higher net migration representing up to 
2% of the overall population. Among the countries 
for  which  net  migration  in  2019  was  lower  than 
previously expected, the difference surpasses 0.5% 
of  the  population  only  in  the  case  of  Denmark 
(-0.6%). 

Over  the  entire  projection  period  2019-2070, 
cumulative  net  migration  is  expected  to  count 

28 

 
    
 
 
    
 
Underlying assumptions and projection methodologies 

Part I 

Table I.1.15:  Life expectancy at birth – difference between 

EUROPOP2019 and ESSPOP2015 (years) 

Males

Females

2019

2070

change 
2019-2070

2019

2070

change 
2019-2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

0.5
-0.9
-0.1
0.2
-0.1
1.3
1.1
-0.4
0.4
0.1
-0.3
0.2
-0.5
0.2
0.7
0.4
-0.6
0.2
0.2
0.1
-0.6
-0.2
-0.8
0.0
0.0
0.6
0.4
0.8
0.1
0.0

0.1
-0.4
-0.1
0.0
-0.1
0.4
0.4
-0.1
0.2
0.1
-0.1
0.1
-0.4
-0.1
0.1
0.2
-0.3
0.0
0.1
0.0
-0.1
-0.2
-0.1
0.1
-0.1
0.2
0.1
0.3
0.0
0.0

-0.4
0.5
0.0
-0.2
0.0
-0.9
-0.7
0.3
-0.2
0.0
0.2
-0.1
0.1
-0.3
-0.6
-0.2
0.3
-0.2
-0.1
-0.1
0.5
0.0
0.7
0.1
-0.1
-0.4
-0.3
-0.5
-0.1
0.0

0.1
-0.2
-0.2
-0.1
-0.3
1.0
0.8
-0.1
0.6
0.3
0.0
0.0
0.2
0.0
0.3
-0.2
-0.4
-0.1
-0.4
-0.1
-0.2
0.0
-0.2
0.2
0.0
0.3
0.0
-0.1
0.1
0.0

0.1
-0.1
-0.1
-0.2
-0.2
0.4
0.1
0.0
0.2
0.3
-0.1
0.0
0.0
-0.1
0.0
-0.1
-0.1
0.0
-0.2
0.0
0.0
0.0
0.2
0.3
-0.1
0.2
0.0
-0.1
0.0
0.0

0.0
0.1
0.1
-0.1
0.1
-0.6
-0.7
0.1
-0.4
0.0
-0.1
0.0
-0.2
-0.1
-0.3
0.1
0.3
0.1
0.2
0.1
0.2
0.0
0.4
0.1
-0.1
-0.1
0.0
0.0
0.0
0.0

Source: European Commission based on EUROPOP2019 and 
ESSPOP2015 (Eurostat). 

about  seven  million  more  people  as  compared  to 
the ESSPOP2015 projections. This corresponds to 
1.6%  of  the  previous  population  projection  for 
2070.  For  most  countries,  the  new  migration 
projections  entail  an  upward 
revision.  This 
revision exceeds 5% of the former 2070 population 
size in the cases of Ireland, Estonia, Poland, Spain, 
(+9%),  Lithuania 
Bulgaria 
(+12%)  and  Malta  (+36%).  At  the  opposite  end, 
downward  revisions  in  the  total  net  migration 
during 2019-2070 correspond to at least 5% of the 
old  2070  population  figure  for  Denmark,  Austria, 
Latvia,  Belgium  (minus  5-7%)  and  Luxembourg 
(-15%). 

(+5-7%),  Greece 

Life expectancy 

For the EU as a whole, life expectancy at birth in 
2019  remained  unchanged  between 
two 
projections  for  males  and  females  (see  Table 
I.1.15). The expected average increase in male and 
female longevity was also confirmed.  

the 

Considering 
that  Eurostat  projections  assume 
upward  convergence,  lower/higher  values  than  in 
the previous round of projections for the base year 
in  a  country,  implies  respectively  higher/lower 
increases  in  life  expectancy  by  2070  than  in  the 
previous round. 

When looking at the projections for males, upward 
revisions for 2019 exceed one year for Estonia and 
Ireland,  and  ranges  between  0.5  and  one  year  for 
Belgium,  Finland,  Lithuania 
and  Norway. 
Downward  revisions  of  between  0.5  and one  year 
are  found  for  Hungary,  Poland,  Romania  and 
Bulgaria. This results in smaller differences for life 
expectancy  compared  to  the  previous  projections 
in 2070 than in 2019. 

For females, the biggest upward revisions in 2019, 
of  between  0.5  and  one  year,  are  for  Estonia, 
Ireland  and  Spain.  Downward  revisions  for  the 
base year are generally small, with a maximum of 
0.4  years  for  Hungary  and  the  Netherlands.  As  to 
the  overall  gain  in  life  expectancy  at  birth  in  the 
period up to 2070, revisions range from -0.7 years 
for Ireland to +0.4 years for Romania. 

29 

 
    
 
 
 
2. 

LABOUR FORCE PROJECTIONS 

The  total  participation  rate  in  the  EU  is  projected 
to rise from 78% in 2019 to 81% in 2070, mostly 
as  a  result  of  higher  labour  force  participation  of 
older  workers.  Over  the  same  period,  the  total 
employment rate is projected to increase from 73% 
to 76%. This change is driven largely by increases 
in  the  employment  of  older  people  (+10 pps)  and 
women (+6 pps). The total labour supply in the EU 
is  projected  to  fall  by  16%  during  the  period  in 
question,  as  higher  labour  force  participation  can 
only  partly  offset  the  expected  reduction  in  the 
working-age population.  

2.1. 

INTRODUCTION 

The  macroeconomic  implications  of  the  demo-
graphic  trends  described  in  the  previous  chapter 
will  depend  in  large  part  on  the  future  growth  of 
the  labour  force  and  on  how  long  people  remain 
part  of  it.  Working  longer  can  provide  more 
resources to finance the higher social security and 
healthcare costs associated with population ageing. 
This  would  also  enable  the  propor¬tion  of  total 
resources  allocated 
the  older 
population  to  be  reduced.  More  resources  would 
thereby  be  freed  up  to  provide  education  and 
training for the young and the unemployed. 

to  supporting 

How  long  people  work  will  depend,  among  other 
factors,  on  the  incentive  emanating  from  public 
and  private  pension  programmes.  It  is  therefore 
important to take into account the future effects of 
any  pension  reforms  adopted.  Other  aspects  that 
may  affect  the  labour  supply  are  trends  in  health 
and  disability  and  the  implementation  of  active 
labour  market  policies  that  may  increase  the 
demand  for  older  workers  and  the  flexibility  of 
work at older ages. 

This  chapter  starts  with  a  comparison  of  recent 
trends in labour forces, followed by an overview of 
the estimated effects of legislated pension reforms. 
Thereafter,  the  participation  rate  and  employment 
rate  projections  are  discussed (14).  An  analysis  of 
the  economic  dependency  ratio  and  a  comparison 

(14)  To project participation rates by gender and single age, the 
the 
cohort  simulation  model 
European Commission (DG ECFIN) is used. Labour force 
projections  are  based  on  a  'no-policy-change'  assumption 
(see Box I.2.1). 

(CSM)  developed  by 

with the 2018 Ageing Report conclude the chapter. 
The  boxes  and  annexes  focus  on  the  underlying 
assumptions and on methodological aspects of the 
projections. 

2.2. 

PAST TRENDS AND MAIN DRIVERS OF 
LABOUR MARKET DEVELOPMENTS 

Labour force composition has undergone profound 
changes in recent decades. While younger cohorts 
tend  to  enter  the  labour  market  later,  women  and 
older  people  have  steadily  increased  their  labour 
market  participation.  There  are 
four  broad 
dynamics driving these changes, namely: 

(cid:129)  Social  factors,  such  as  longer  schooling  or  a 
change in the role women play in households; 

(cid:129)  Demographic  factors,  including  the  decline  of 

fertility rates and delays in childbearing; 

(cid:129) 

Institutional factors, in particular changes in the 
early and statutory retirement ages; 

(cid:129)  Economic  factors,  such  as  substitution  and 
income  effects  of  labour  taxation  (particularly 
relevant  for  second  earners),  a  higher  preva-
lence  of  part-time  employment  and  the  shift 
towards a service-based economy. 

The  labour  market  participation  of  people  of 
working  age  (20-64)  rose  from  72%  in  2000  to 
78%  in  2019  for  the  EU  as  a  whole  (see  Table 
I.2.1).  Aside  from  Romania,  all  Member  States 
saw an increase. In the cases of Bulgaria, Hungary 
and  Malta,  the  overall  participation  rate  has  risen 
by more than 10 pps since the start of the century.  

Although  labour  forces  show  large  cross-country 
variabilities, some common features stand out and 
need  to  be  catered  for  in  any  projection  exercise. 
They can be summarised as follows: 

(cid:129)  At 92% in the EU and more than 90% in nearly 
all  countries,  the  participation  rates  of  prime-
age male workers (aged 25-54), are the highest 
of all groups.  

(cid:129)  The participation rates of men aged between 55 
and  64  years  have  risen  considerably  since 
2000,  mostly  as  a  result  of  pension  reforms 

30 

                                                           
 
Underlying assumptions and projection methodologies 

Part I 

Table I.2.1:  Historical participation rates: total 

20-64

20-24

25-54

55-64

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2000
70.8
67.1
77.4
81.4
74.6
77.6
73.0
69.6
69.8
74.9
67.6
63.6
75.6
73.7
78.6
69.0
65.0
60.5
76.0
74.1
72.9
76.4
75.9
73.4
76.5
79.6
80.7
82.9
72.0
72.4

2010
73.5
71.9
75.7
80.6
80.6
80.2
76.2
73.0
77.8
75.8
69.9
66.3
80.0
79.6
78.2
73.8
67.4
64.0
79.7
77.5
71.1
79.1
69.7
75.8
75.2
79.0
84.5
82.1
75.8
75.0

2019
74.4
78.3
81.9
82.2
83.2
83.8
78.7
74.0
78.9
78.0
71.3
70.5
81.4
82.6
83.5
76.8
77.9
79.6
82.6
80.3
75.4
81.4
73.6
79.9
77.8
82.2
87.3
82.2
78.4
78.2

2000
60.7
48.5
69.3
79.1
71.1
64.7
73.6
63.1
60.9
59.3
63.1
55.8
72.6
64.8
64.6
56.3
57.6
79.5
80.6
71.7
63.7
63.7
60.9
59.4
70.1
77.7
61.3
74.6
64.1
63.5

2010
55.2
51.3
51.5
75.2
69.9
60.8
78.1
51.4
64.6
61.6
56.5
46.8
69.4
65.0
52.3
40.8
44.7
73.8
77.1
73.4
57.9
59.1
47.2
57.7
52.7
69.7
72.0
72.3
62.3
60.5

2019
49.0
42.4
52.3
72.4
71.0
72.3
71.9
42.1
55.0
62.2
52.3
44.4
63.7
63.8
61.6
52.4
53.6
77.3
75.7
73.9
61.0
58.2
47.9
58.6
50.2
70.8
71.5
70.8
60.6
59.9

2000
82.8
81.6
88.5
87.9
85.4
88.0
78.4
78.3
78.0
86.4
80.2
74.2
81.6
85.5
89.3
79.8
77.3
64.2
83.6
85.3
82.7
84.6
84.4
87.7
88.3
88.1
86.8
87.7
82.2
82.6

2010
86.3
82.9
87.8
88.4
87.3
88.3
80.7
83.2
85.7
88.7
80.8
76.9
86.9
88.6
88.4
85.7
80.9
72.9
87.8
87.1
84.1
88.7
81.9
90.0
86.9
87.5
89.8
87.3
85.2
85.0

2019
84.8
85.8
89.1
86.5
88.0
87.8
83.5
85.4
87.0
87.4
83.6
78.1
88.3
88.3
90.3
88.5
87.0
87.7
87.4
89.0
85.3
90.3
84.1
92.4
86.5
87.7
91.3
86.3
85.8
85.9

2000
25.9
25.1
38.1
56.9
42.9
47.3
46.3
40.9
40.8
31.7
24.5
28.6
51.2
39.0
45.6
27.6
22.6
29.5
38.6
31.4
32.1
53.2
52.5
23.7
24.6
45.5
68.4
66.2
37.3
37.9

2010
39.2
49.3
49.7
58.9
62.6
64.3
55.1
45.2
50.7
42.2
41.8
37.9
59.1
56.9
56.5
40.6
36.5
33.3
55.3
42.2
36.7
54.3
42.1
36.5
45.1
60.2
74.8
69.6
49.3
48.1

2019
54.3
66.9
68.0
73.8
74.7
75.5
64.1
49.8
61.6
56.9
45.5
57.4
65.3
72.1
73.4
45.0
58.0
52.4
72.0
56.4
50.7
64.4
48.9
50.9
59.8
71.5
81.5
74.0
63.6
62.3

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2000 figures for Croatia show 2002 values; EU27 figures for 2000 do not include Croatia. 
Source: Eurostat, LFS. 

raising  the  early  and  statutory  retirement  ages. 
For  women  aged  55-64  there  has  also  been  a 
strong  increase,  though  from  a  lower  starting 
in 
point,  given 
retirement conditions to those for men; 

the  general  convergence 

(cid:129)  Female overall participation rates have steadily 
increased  in  recent  decades,  largely  reflecting 
societal trends; 

(cid:129)  The  participation  rates  of  young  people  (aged 
20-24) have declined, mostly because more are 
now  spending  longer  in  education,  as  average 
educational attainment has risen; 

(cid:129)  Given these trends, the main drivers of changes 
in the total labour market participation rate will 
be  changes  in  the  labour  force  attachment  of 
prime-age  women,  older  workers  (especially 
men) and, to a lesser extent, young people.  

31 

 
 
     
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.2.2:  Historical participation rates: men 

20-64

20-24

25-54

55-64

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2000
80.1
73.4
86.2
85.7
82.9
83.3
86.2
85.1
84.4
81.9
75.2
78.6
89.2
80.5
82.8
82.2
73.6
85.8
85.8
83.2
79.4
84.8
82.6
78.0
84.7
82.6
83.1
87.4
82.3
81.9

2010
79.8
76.8
84.9
84.5
86.8
83.8
83.9
84.4
85.4
80.7
75.8
78.4
87.2
82.5
80.6
82.1
74.0
83.2
85.9
83.0
78.6
83.8
79.1
79.9
83.4
81.4
88.0
85.2
83.1
82.3

2019
78.9
83.0
89.2
85.7
87.7
87.4
85.5
82.9
84.2
82.0
76.5
80.6
87.1
85.4
85.0
81.6
85.9
89.3
87.4
84.9
83.2
84.8
83.7
83.0
84.4
84.1
89.8
85.1
84.1
84.2

2000
65.5
58.3
77.3
84.4
74.6
75.8
79.2
69.3
65.2
63.2
69.5
61.9
78.2
74.7
70.0
61.5
66.0
81.7
82.5
75.3
68.3
70.0
67.2
63.4
78.0
82.2
64.8
78.8
68.7
68.6

2010
59.5
58.2
60.0
77.0
72.5
67.2
81.0
56.3
67.4
65.9
63.8
54.1
68.8
68.7
57.4
42.6
49.5
77.9
77.6
76.5
65.3
61.4
54.7
63.3
62.3
72.4
75.3
74.6
66.2
65.1

2019
51.8
48.1
59.3
73.8
73.6
77.3
74.6
43.5
58.3
66.5
59.9
50.2
61.0
67.9
64.5
57.5
61.1
78.0
76.1
76.4
67.5
61.0
57.2
63.6
62.8
73.5
74.0
72.4
64.2
64.1

2000
92.1
84.4
95.0
91.5
93.7
91.6
92.0
94.5
93.2
94.3
86.9
90.4
95.3
88.5
90.4
94.2
84.3
93.5
93.8
93.6
88.4
92.4
91.0
90.7
94.0
91.1
88.6
91.7
92.9
92.0

2010
92.2
86.1
95.5
91.8
93.2
91.8
88.9
94.2
92.4
94.2
84.1
89.4
93.4
91.0
89.0
94.8
87.3
94.5
93.3
91.9
89.6
92.7
90.9
91.7
92.9
90.5
92.9
90.1
92.4
91.8

2019
89.3
90.0
95.9
90.1
92.7
92.5
90.6
93.2
91.7
91.9
86.9
88.5
93.4
91.2
91.4
92.8
93.4
96.8
91.5
92.4
91.5
92.7
93.1
94.4
93.2
90.3
93.7
88.9
91.4
91.6

2000
36.3
39.9
54.5
64.5
52.5
54.4
64.6
57.7
60.3
35.5
34.2
42.2
69.5
53.8
59.0
38.6
34.3
52.9
50.8
44.5
41.1
64.6
58.4
33.5
41.0
46.4
72.1
72.7
48.5
48.8

2010
47.6
56.6
62.5
65.1
70.8
64.3
64.3
60.2
63.7
45.0
54.4
49.5
74.3
58.5
62.6
48.8
42.2
52.3
66.2
51.4
48.9
62.0
52.3
47.5
59.7
60.1
79.3
73.5
58.1
57.2

2000 figures for Croatia show 2002 values; EU27 figures for 2000 do not include Croatia. 
Source: Eurostat, LFS. 

Table I.2.3:  Historical participation rates: women 

20-64

20-24

25-54

55-64

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2000
61.3
61.0
68.8
77.1
66.2
72.3
59.9
54.6
55.2
68.1
60.4
48.9
62.8
67.6
74.7
55.5
56.7
35.1
66.0
65.1
66.7
68.4
69.4
68.8
68.5
76.6
78.3
78.3
61.7
63.1

2010
67.1
67.0
66.4
76.7
74.5
76.8
68.5
61.8
70.1
71.0
64.1
54.6
73.4
77.0
76.0
65.3
61.0
44.3
73.5
71.9
63.6
74.7
60.2
71.5
67.0
76.6
81.0
79.0
68.6
67.8

2019
69.8
73.5
74.4
78.5
78.7
80.2
72.1
65.3
73.7
74.1
66.1
60.5
76.1
80.0
82.1
71.9
70.0
69.0
77.8
75.6
67.7
78.3
63.3
76.7
71.0
80.2
84.8
79.1
72.8
72.2

2000
55.8
38.5
61.5
74.2
67.8
52.8
67.9
57.1
56.6
55.7
56.5
49.9
68.0
54.7
59.1
51.0
49.0
77.1
78.7
68.1
59.2
57.4
54.9
55.1
62.3
73.3
57.7
70.4
59.5
58.6

2010
51.0
43.9
42.5
73.3
67.3
54.3
75.3
46.6
61.8
57.3
49.0
39.2
70.1
61.2
47.0
39.0
39.6
69.5
76.5
70.5
50.1
56.9
39.1
50.8
42.8
66.9
68.5
70.0
58.3
55.7

2019
46.2
36.3
44.9
70.9
68.2
67.3
69.2
40.7
51.5
58.0
44.4
38.2
66.1
59.4
58.5
47.1
45.7
76.6
75.3
71.4
54.2
55.2
38.3
52.7
37.0
68.0
68.8
69.1
56.9
55.5

2000
73.2
78.9
81.9
84.3
77.0
84.5
64.9
62.2
62.7
78.6
73.7
57.9
68.6
82.7
88.3
64.9
70.5
34.5
73.0
76.8
77.1
77.1
77.9
84.7
82.5
85.1
84.9
83.5
71.4
73.1

2010
80.4
79.6
79.8
85.0
81.3
84.8
72.6
72.4
78.8
83.4
77.4
64.5
81.0
86.3
87.8
76.4
74.6
50.6
82.2
82.4
78.6
84.9
72.7
88.1
80.9
84.4
86.6
84.3
78.1
78.1

2019
80.3
81.4
81.8
82.8
83.3
82.8
76.7
77.6
82.3
83.1
80.2
67.8
83.5
85.5
89.2
84.0
80.6
77.5
83.3
85.7
79.0
88.0
74.6
90.4
79.6
84.9
88.7
83.5
80.2
80.2

2000
15.8
12.5
23.3
48.2
33.4
41.9
27.7
25.9
22.5
28.2
16.0
15.9
33.6
28.0
35.4
16.8
13.2
8.6
26.4
18.9
24.4
43.1
47.5
14.8
11.1
44.6
64.6
59.7
26.6
27.8

2010
30.9
42.9
38.0
52.8
54.6
64.3
45.7
31.1
38.4
39.5
30.2
26.9
44.3
55.7
51.7
32.0
31.7
14.6
44.4
33.6
25.9
47.4
33.1
25.5
32.3
60.3
70.2
65.5
41.1
39.6

2000 figures for Croatia show 2002 values; EU27 figures for 2000 do not include Croatia. 
Source: Eurostat, LFS. 

2019
59.8
72.0
76.2
78.4
79.5
73.3
72.5
63.8
69.2
59.4
54.2
68.6
76.7
73.0
74.6
51.2
70.6
67.6
81.0
65.6
62.6
70.9
61.6
55.7
62.8
70.5
84.1
78.4
70.1
69.6

2019
48.9
62.2
60.1
69.3
70.0
77.4
55.9
37.3
54.4
54.6
37.5
47.0
54.2
71.4
72.5
38.4
47.2
36.7
63.1
47.4
40.0
58.8
37.3
46.0
56.9
72.4
78.9
69.5
57.5
55.4

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

32 

 
 
    
 
 
    
 
Underlying assumptions and projection methodologies 

Part I 

Box I.2.1: Main features of the Cohort Simulation Model (CSM) and assumptions for the 
2021 projections 

The cohort simulation model (CSM) as developed by the European Commission (DG ECFIN) (1) 
is used to project participation rates by gender and single age, as was the case in the 2006, 2009, 
2012,  2015  and  2018  long-term  projection  exercises.  This  methodology  is  based  on  the 
calculation  of  the  average  probability  of  labour  force  entry  and  exit  observed  over  the  last  ten 
years (2).  The  average  entry  and  exit  rates  are  then  used  to  project  future  participation  rates  as 
older generations are progressively replaced by younger ones.  

For those Member States having legislated pension reforms, average exit rates for the age group 
51-74  are  adjusted  to  account  for  the  reform  impact,  based  on  a  best-reasoned  judgment. 
Otherwise, both average entry and exit rates are kept constant throughout the projection period, 
reflecting a no-policy-change assumption (3). 

The rationale for using the CSM is to reflect the substantial changes in labour market behaviour 
in  recent  decades  across  different  cohorts  and  gender  groups.  This  methodology  is  particularly 
suited  to  take  into  account  the  significant  rise  in  female  labour  force  participation  in  recent 
decades,  as  younger  women,  with  a  much  stronger  attachment  to  the  labour  force,  gradually 
replace  older  women  with  relatively  low  participation  rates.  Simultaneously,  the  cohort 
methodology also caters for a – relatively small – decline in the participation rate of men in recent 
generations in a majority of countries, opposite to the trend observed for women.  

The 2021 projection is made using the latest Eurostat demographic projections (see Chapter 1), 
prepared  independently  by  Eurostat  in  collaboration  with  National  Statistical  Institutes. 
Population projections are the major driving force of labour force projections.  

The following assumptions were made: 

(cid:129)  The base year for labour market projections is 2019, 2020 is the first year of projections and 

the projection horizon ends in 2070; 

(cid:129)  Average entry/exit rate are calculated, as a ten-year average (2010-2019), using participation 
rates by single age (15-74) and sex from the harmonised EU Labour Force Survey of Member 
States (as compiled by Eurostat);  

(cid:129)  Labour market participation rates are calculated, by single age and sex, using average labour 

force entry/exit rates for 2010-2019;  

(cid:129)  A  corrective  mechanism  for  young  cohorts  (15-29)  is  applied,  in  order  to  avoid  that  any 
increase  in  education  enrolment  rates  (and  the  corresponding  decline  in  participation  rates) 
feeds  into  future  declines  of  participation  rates  for  prime-age  workers.  This  assumption 
implies  that  participation  rates  at  each  single  year  of  age  between  age  15  and  19  remain 
constant at the last observed level, i.e. 2019. Participation rates between ages 20 and 29 are 
allowed to increase if this is the outcome of the cohort simulation model. Otherwise, the rates 
are kept constant at the level observed in 2019; 

(1)  The methodology was initially developed at the OECD, see Burniaux, Duval & Jaumotte (2003). 
(2)  A more detailed description of the methodology can be found in Carone (2005). 
(3)  For a given set of exogenous macroeconomic assumptions and using partial equilibrium methodologies, a no-policy-
change assumption projects future outcomes at unchanged current policies. It should not be interpreted as a forecast, 
because no assumptions are made regarding (entry/exit) probability distributions, but rather as an 'unbiased' estimate.  

(Continued on the next page) 

33 

 
 
 
 
   
 
                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Box (continued) 

(cid:129)  Pension reforms were modelled through their estimated impact on the labour market exit rates 
of  older  workers  (aged  51-74) (4).  This  is  largely  a  judgemental  approach,  using  the 
probabilistic  nature  of  the  CSM.  Specifically,  the  historical  average  exit  rates  of  older 
workers,  calculated  separately  for  both  genders,  are  adjusted  to  account  for  the  expected 
effects  of  enacted  pension  reforms.  The  estimation  of  the  adjustment  takes  into  account 
country-specific  information  about  the  relationship  between  retirement  behaviour  and  the 
parameters of the pension system, as well as cross-country evidence of the impact of changes 
in  the  implicit  tax  rate  on  retirement  decisions.  This  framework  for  analysis  is  able  to 
incorporate  a  broad  typology  of  measures,  inter  alia,  increases  in  the  statutory  or  early 
retirement age, the convergence of fower female statutory retirement ages to that of men, the 
linking  of  the  statutory  retirement  age  to  changes  in  life  expectancy,  and  changes  in  (price) 
incentives affecting the retirement decision. Moreover, policy changes can be incorporated as 
one-off measures or be phased in progressively within a specified period.  

Steps to project the labour force/supply 

First,  participation  rates  by  single  age  and  gender  are  projected  up  to  2070  using  the  CSM. 
Aggregate  values  for  participation  rates  are  a  weighted  average  of  participation  rates  by  single 
age  and  gender  using  population  shares  as  weights.  For  example,  the  average  participation  rate 
PR for age groups (cid:1853) (lower age) to (cid:1853) (upper age) in period t is calculated as: 

(cid:1853)
(cid:1842)(cid:1844)(cid:3435)(cid:1853), (cid:1853), (cid:1872)(cid:3439) = (cid:3533) (cid:3533) (cid:1842)(cid:1844)(cid:1853),(cid:1859)
(cid:1853)=(cid:1853)

(cid:1859)=(cid:1865) ,(cid:1858)

(cid:1872)

(cid:1872)
∗ (cid:1868)(cid:1853),(cid:1859)

where (cid:1868)(cid:1853),(cid:1859)

(cid:1872) =

(cid:1853)
∑
(cid:1853) =(cid:1853)

(cid:1872)
(cid:1868)(cid:1867)(cid:1868) (cid:1853) ,(cid:1859)
∑

(cid:1859)=(cid:1865) ,(cid:1858)

(cid:1872)
(cid:1868)(cid:1867)(cid:1868) (cid:1853) ,(cid:1859)

where a is the age index, g is the gender index, (cid:1842)(cid:1844)(cid:1853),(cid:1859)

(cid:1872)

 is the participation rate for single 

age a and gender g in period t, pop is the population; and p is the structure of the population.  

(cid:1872) ) or labour supply (for each single age and gender combination) is 
Second, the labour force ((cid:1838)(cid:1832)(cid:1853),(cid:1859)
calculated  by  multiplying  the  age/gender  labour  force  participation  rate  by  the  corresponding 
population projection: 

(cid:1838)(cid:1832)(cid:1853),(cid:1859)

(cid:1872) =   (cid:1842)(cid:1844)(cid:1853),(cid:1859)

(cid:1872)

∗ (cid:1868)(cid:1867)(cid:1868)(cid:1853),(cid:1859)

(cid:1872)

The  total  labour  supply  for  age  groups  (cid:1853)  (lower  age)  to  (cid:1853)  (upper  age)  in  period  t  is 

calculated as: 

(cid:1853)

(cid:1853)

(cid:1838)(cid:1832)(cid:3435)(cid:1853), (cid:1853), (cid:1872)(cid:3439) = (cid:3533) (cid:3533) (cid:1838)(cid:1832)(cid:1853),(cid:1859)

(cid:1872) =

(cid:1853)=(cid:1853)

(cid:1859)=(cid:1865),(cid:1858)

(cid:1872)

(cid:3533) (cid:3533) (cid:1842)(cid:1844)(cid:1853),(cid:1859)
(cid:1853)=(cid:1853)
(cid:1859)=(cid:1865),(cid:1858)

(cid:1872)

∗ (cid:1868)(cid:1867)(cid:1868)(cid:1853),(cid:1859)

Age aggregates commonly used are the groupings 15-64, 20-64, 25-54, 55-64, 20-71 and 20-74. 

Additional assumption on labour input 

In addition, the production function methodology is used to project GDP growth (see Chapter 3), 
using total hours worked as the labour input variable. The split between full- and part-time work 

(4)  A more detailed description of the methodology can be found in Carone (2005).  

(Continued on the next page) 

34 

 
 
   
 
 
 
 
 
 
 
 
 
                                                           
Underlying assumptions and projection methodologies 

Part I 

Box (continued) 

(for the age groupings 15-24, 25-54, 55-64 and 65-74), as well as the corresponding weekly hours 
worked, is fixed at the average values for the last available year (2019) for the entire projection 
period. 

Although  part-time  vs.  full-time  rates  and  the  corresponding  average  weekly  hours  of  work  are 
frozen per age group over the projection period, total hours worked change due to compositional 
effects  that  mostly  reflect  the  projected  increase  in  female  labour  force  participation,  given  the 
higher incidence of part-time work among women. 

2.3. 

LEGISLATED PENSION REFORMS IN EU 
MEMBER STATES 

Over the past decade, many Member States have 
adopted gradual and substantial pension reforms. 
However, 
introduced 
previously  have  been  made  in  several  recent 
cases. 

reversals  of 

reforms 

As discussed in Carone et al. (2016), the intensity 
of pension reforms  was particularly high in 2000-
2015 (15). Especially in the slipstream of the 2008-
2009 financial crisis and the  subsequent euro area 
crisis, many Member States reformed their pension 
systems.  Box  I.2.2  provides  an  overview  of  those 
reforms that have an impact on effective retirement 
ages  in  the  27  Member  States  and  Norway  and 
the  Cohort 
which  are 
incorporated 
Simulation  Model  when  projecting 
future 
participation rates (see Box I.2.1). 

thus 

in 

While  in  some  cases  reforms  were  systemic  –  for 
example  a  shift  from  a  defined  benefit  public 
scheme  to  a  point  system  –  in  most  cases 
parametric changes were implemented. Two broad 
types  can  be  distinguished: 
the 
eligibility  requirements  for  pension  benefits  and 
making pension benefits less generous by adapting 
the  pension  formula  and  indexation  rules.  Some 
countries  also  increased  the  contributions  for 
current  workers in order to strengthen the internal 
balance of their pension systems.  

tightening 

(15)  For  an  extensive  review  of  the  pension  reforms  legislated 
in  the  last  decades  see  Carone,  G.,    Eckefeldt,  P.,  
Giamboni,  L.,  Laine,  V.  and    S.  Pamies-Sumner  (2016). 
‘Pension  reforms  in  the  EU  since  the  early  2000s: 
Achievements and challenges ahead’, European Economy, 
Discussion paper No 42. 

In  more  recent  years,  the  reform  drive  has 
generally  abated  and  several  Member  States  have 
even reversed already legislated reforms. This was 
for  example  the  case  in  Poland (16),  the  Czech 
Republic (17),  Croatia and  Slovakia (18).  In  other 
cases, 
legislated  reforms  was 
suspended or postponed (e.g. the application of the 
‘index 
the 
sustainability  factor  in  Spain)  or  new,  temporary 
possibilities to retire early were created, which was 
the case in Italy. 

revaluation’ 

impact  of 

pension 

and 

the 

for 

The most frequent reform over the past decades is 
the  decision  to  raise  the  statutory  and  early 
retirement ages. It is probably the measure with the 
largest direct impact on the retirement decision and 
on  the  labour  supply  and  therefore  needs  to  be 
accounted  for  when  projecting  participation  rates. 
Indeed,  nearly  all  Member  States  have  increased 
their  early  and  statutory  retirement  ages  or  have 
legislation  in  place  to  do  so  in  the  coming  years. 
Some countries have opted for the introduction of 
an  automatic  link  between  retirement  ages  and 
changes  in  life  expectancy  to  make  their  pension 
system  more 
the  effect  of 
robust  against 
continuous  demographic  ageing  (see  Table  I.2.4). 
The 
automatic  balancing 
an 
adoption  of 
mechanism  within 
the  pension  system  or  a 
sustainability  factor  also  adjust  certain  parameters 
in function of longevity gains but they do at most 
have an indirect impact on the labour supply. 

(16)  The 2016 reform lowered the statutory retirement age to 60 
for women and to 65 for men, restoring the situation from 
before  the  2012  reform,  which  increased  the  statutory 
retirement  age  to  67  years  for  both  men  (by  2020)  and 
women (by 2040). 

(17)  The  2011  reform  linked  the  retirement  age  to  changes  in 
life expectancy. The 2017 reform reintroduced a ceiling at 
65 years, undoing the automatic link. 

(18)  See  Box  I.2.2  for  the  recent  changes  concerning  Croatia 

and Slovakia. 

35 

                                                           
                                                           
 
   
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.2.4:  Automatic adjustment mechanisms 

Sustainability factor 
(benefit link to life 
expectancy)(5)
X

X

X

X

X

X

X

X

Country

Automatic balancing 
mechanism

IT

LV

SE

PL
FR(1)
DE

FI
PT(2)
EL(3)
DK(4)
ES
NL(2)
CY

LT

EE

X

X

X

X

Retirement age linked to 
life expectancy

X

X

X

X

X

X

X

X

Legislated 

1995 & 2010

1996

1998 & 2001

1999

2003

2004

2005 & 2015

2007 & 2013

2010

2011

2011 & 2013

2012

2012

2016

2018

(1) Pension benefits evolve in line with life expectancy through the 'proratisation' coefficient; it has been legislated until 2035. 
(2) Only two thirds of the increase in life expectancy is reflected in the retirement age. 
(3) An automatic balancing mechanism is applied in the auxiliary pension system. 
(4) Subject to Parliamentary decision. 
(5) In NDC systems, the benefit is linked to changes in life expectancy through the annuity factor. 
Source: European Commission, EPC. 

36 

 
 
             
 
Underlying assumptions and projection methodologies 

Part I 

Box I.2.2: Pension reforms legislated in Member States incorporated in the labour force 
projections 

This box lists recent pension reforms legislated by 
the  Member  States  that  have  a  direct  impact  on 
labour  market  participation  rates  and  that  are  thus 
relevant for the participation rates as projected with 
the  CSM  model  for  the  baseline  scenario.  For  a 
more  exhaustive  overview  of  Member  States’ 
pension  systems,  see  Annexes  5  and  6.  This  box 
describes the situation per 30 September 2020. 

Belgium 

The 2015 pension reform raised the minimum early 
retirement  age  to  63  years  as  of  2018  and  the 
minimum required  career  length  to  42  years  as  of 
2019. Exceptions are still possible for people aged 
60/61  with  a  career  of  at  least  44/43  years.  The 
reform  also  raised  the  statutory  retirement  age  in 
the  three  main  public  old-age  pension  schemes 
(wage  earners,  self-employed  and  civil  servants), 
from 65 for both men and women to 66 in 2025 and 
to  67  in  2030.  A  career  of  45  years  remains 
required to obtain a full pension. 

The  system  of  unemployment  with  company 
allowance was  also  modified:  the  minimum  age 
was raised from 60 to 62 in 2015 (for restructuring 
companies it went from 55 in 2015 to 60 in 2021). 
Moreover,  since  2015,  new  beneficiaries  of  the 
scheme need  to  remain  available  to  the  labour 
market  and  are  thus  included  in  the  labour  force. 
The pension bonus for people working beyond the 
age  of  60  (while  meeting  the  early  retirement 
requirements) was abolished as of 2015.  

Bulgaria 

With  the  entering  into  force  of  the  2015  pension 
reform, the  statutory  retirement  age  for  both  men 
and  women 
increased  and 
is  being  gradually 
equalised  to  65  years  by  2037  (2029  for  men). 
the  statutory 
After  2037, 
is 
increase  in  line  with  gains  in  life 
supposed 
to 
rule  has  been 
expectancy,  though  no 
legislated 
is  not 
so 
included in the projections.  

such  mechanism 

retirement  age 

clear 

that 

The  required  career  length  for  workers  in  normal 
work  conditions  to  qualify  for  retirement  is  rising 
by two  months  annually,  until  it  reaches  40  years 
for  men  and  37  years  for  women  in  2027  (from  38 
years for men and 35 years for women in 2015). 

The  retirement  age  in  case  of  insufficient  insurance 
years  is  gradually  increased  to  67  years,  while  the 
minimum 
remains 
unchanged  at  15  years  of  actual  service  (i.e. 
excluding  periods  of  military  service,  maternity 
leave and unemployment).  

length  of  service 

required 

A possibility for granting a reduced early retirement 
pension is introduced for persons who are within 12 
months  from  the  statutory  retirement  age,  with  a
lifetime reduction in the pension benefit of 0.4% for 
each month of anticipation.  

The  retirement  age  for  workers  in  strenuous  and 
hazardous work conditions is gradually increased to 
55 years for the former group and to 60 years for the 
latter category.  

In  2016,  the  minimum  retirement  age  was  set  at  52 
years and 10 months for workers in the defence and 
security sector, with a minimum career length of 27 
years.  The  minimum  retirement  age 
is  being
increased  by  two  months  annually,  until  it  reaches
55 years.  

Czech Republic  

To  be  entitled  to  an  old-age  pension,  one  has  to 
either reach an insurance period of at least 35 years 
and a retirement age specified by law, or at least 20 
years of insurance with an age five years above the 
statutory  retirement  age.  Non-contributory  periods 
are also included in the insurance period.  

retirement  ages  are  determined 

Statutory 
in 
function of a person’s birth year and, in the case of 
women,  the  number  of  children  raised.  In  June 
2017, a reform was legislated to cap the increase in 
the  statutory  retirement  age  at  65,  reversing  the 
2011  reform  that  entailed  increases  in  line  with 
gains in life expectancy.  

that 

the  condition 

Early  retirement  is  possible  prior  to  the  statutory 
retirement  age  under 
the 
applicable  statutory  retirement  age  is  at  least  63 
years. The  moment as of which early retirement is 
possible, is gradually increasing to five years prior 
to the statutory retirement age, under the conditions 
that the latter is lower than 63 years and people are 
at least 60 years old.  

(Continued on the next page) 

37 

European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Box (continued) 

Denmark 

in 

The  2011  “Retirement  Reform”  brought  forward 
the  discretionary  increase  in  the  retirement  ages 
agreed 
the  2006  “Welfare  Reform”.  The 
retirement  age  for  the  voluntary  early  retirement 
pension (VERP) rose from 60 to 62 years in 2014-
2017,  while  the  public  old-age  pension  is  rising 
from 65 to 67 years in 2019-2022. Furthermore, the 
VERP period is reduced from five to three years in 
2018-2023.  The  minimum  contribution  period  to 
VERP  rose  from  25  to  30  years.  Private  pension 
wealth  also  lowers  the  VERP  amount  to  a  higher 
degree  than  before  the  reform,  making  the  VERP 
scheme less favourable to people with large private 
pension wealth. 

As of 2015, retirement ages are indexed to the life 
expectancy for a 60-year-old, with updates every 5 
years  and  a  maximum  jump  by  one  year.  Changes 
have to be confirmed by Parliament 15 years before 
they take effect (12 years for changes in the VERP 
age),  so  that  the  first  increase  will  apply  in  2030 
(2027 for the VERP).  

Germany 

As  a  result  of  the  2007  reform,  the  statutory 
retirement age is gradually increasing, reaching  an 
age of 67 by 2029, with annual steps of one or two 
months depending on the year of birth. 

In  July  2014,  a  pension  reform  was  legislated  that 
aimed  at  improving  pension  benefits  and  early 
retirement conditions for certain groups:  

- 

‘Rente  mit  63’: 
the  possibility  of  early 
retirement without pension reduction two years 
ahead of the statutory retirement age in case of 
a  contributory  period  of  45  years  (including 
periods of unemployment). As of 2016, the age 
will rise by 2 months a year until it reaches 65; 

-  Continuation  of 

agreement 

after 
labour 
reaching  statutory  retirement  age:  employers 
and  employees  can  continue  the  employment 
relationship  for  a  certain  period  after  the 
statutory  pensionable  age  has  been  reached. 
The  agreement  to  postpone  retirement  must  be 
reached before the pensionable age. 

38 

Estonia 

The  retirement  age  for  men  and  women  was 
equalised in 2016 at 63y, rising to 65 by 2026. The 
2018 reform introduces a link to changes in the 5-
year average life expectancy at the age of 65 as of 
2027,  with  a  maximum  annual  increase  of  three 
months.  The  change  in  the  retirement  age  will  be 
known two years in advance.  

The  old  bonus/malus  system  (-0.4%/+0.9%  per 
month)  will  be  replaced  by  an  actuarially  neutral 
system  as  of  2021.  Early  retirement  (previously 
three year prior to the statutory retirement age) will 
be  possible  1-2-3-4-5  year  before  reaching  the 
the 
statutory 
years 
contribution 
respectively). 

retirement  age,  depending  on 

(20-25-30-35-40 

period 

Ireland 

The  State  Pension  Transition  payment  was 
abolished  in  2014.  In  the  social  security  pension 
system  the  earliest  retirement  age  rose  to  66  in 
2014, rising to 67 in 2021 and to 68 in 2028. 

Greece 

In November 2012, Parliament approved a pension 
law, increasing the statutory retirement age from 65 
to 67 to receive a full pension. The full contributory 
career rose to 40 years and the early retirement age 
from  60  to  62.  In  addition,  as  of  2021,  retirement 
ages are linked to changes in life expectancy, with 
updates  every  three  years.  In  August  2015,  an 
to  early 
additional  reform  reduced  pathways 
retirement. 

Spain 

The 2013 pension reform entails a gradual increase 
in  the  statutory  retirement  age  from  65  in  2013  to 
67  in  2027.  The  contributory  career  for  a  full 
pension  will  be  gradually  increased  from  35  to  37 
years.  Workers  with  contributory  careers  of  more 
than  38.5  years  are  allowed  to  retire  at  65  with  a 
full pension. 

Early retirement for involuntary retirees (collective 
dismissals) require a minimum retirement age of 63 
years in 2027 (increasing progressively from 61 in 
2013)  and  a  minimum  contributory  period  of  33 
years (unchanged).  

(Continued on the next page) 

 
 
   
 
Box (continued) 

Early  retirement  for  voluntary  retirees  requires  a 
minimum  age  of  65 
(increasing 
progressively  from  63  in  2013),  a  minimum 
contributory  period  of  35  years  (unchanged)  and 
the  computed  benefit  must  be  greater  than  the 
minimum pension. 

in  2027 

In  case  a  worker  with  a  40-year  career  decides 
voluntarily to retire at the earliest possible age (63 
years),  the  penalty  to  the  pension  at  retirement  is 
15%, 7.5% for each of the two years remaining to 
reach  the  statutory  retirement  age.  For  involuntary 
retirement,  the  earliest  retirement  age  is  61  years 
and 
the 
corresponding penalty is 28% when retiring at 61. 

the  annual  penalty  7%, 

that 

so 

Access  to  early  partial  retirement  is  restricted. For 
longer  careers  (beyond  36.5  years),  the  minimum 
age is increasing progressively to 63 years, up from 
61  in  2013.  For  careers  of  33-36.5  years  the 
minimum age being raised progressively from 61 to 
65 years. For careers  shorter than 33 years, partial 
retirement  is  not  possible;    only  30  years  were 
required before the reform. 

France 

The  2010  reform  (Law  2010-1330)  led  to  the 
following changes: 

-  The 

standard  pension  age 

is  gradually 
increasing, for all pension schemes, from 60 to 
62  years  of  age.  Simultaneously,  the  full  rate 
pensionable age will rise from 65 to 67. These 
two  rises  imply  a  4  months  increase  in  age 
limits  every  year  from  generation  1951  to 
generation 1955 (e.g., people born in 1956 will 
be  able  to  claim  pension  at  62  in  2018  and  a 
full rate pension at 67 in 2023); 

-  The  early  retirement  age  for  long  contributory 

careers is also increasing by 2 years; 

-  Closing  down  of  early  retirement  pathways  in 
the public sector: for parents with a career of 15 
years  and  three  children;  provisions  in  the 
‘Cessation Progressive d'Activité’ programme;  

-  Some  categories/groups  will  still  be  granted  a 

full rate pension at 65 years of age; 

-  People suffering from a professional disease or 
in  a  permanent 
that  results 

an  accident 

Underlying assumptions and projection methodologies 

Part I 

incapacity of at least 10% can continue to retire 
at 60 with a full rate pension.  

increases 

In 2013, a public pension reform was adopted that 
gradually 
required  number  of 
the 
contribution years for a full retirement benefit. The 
number  of  required  contribution  years  for  a  full 
benefit will rise gradually from 41.5 to 43 years in 
2020-2035. 

In 2015, an agreement was reached on the comple-
mentary  schemes  Agirc  and  Arrco.  The  agreement 
introduces  a  system  of  incentives  to  postpone 
retirement: 

-  For 

less 

individuals  who  retire 

than  one 
calendar  year  after  the  age  at  which  they  are 
entitled  to  a  full  basic  pension,  the  Agirc  and 
Arrco  benefits  are  reduced  by  a  solidarity 
coefficient of 10% for three years or until they 
reach the age of 67; 

- 

Individuals  who  retire  between  one  and  two 
years  after  that  age  receive  their  full  pension, 
with no solidarity coefficient; 

-  For  each  additional  year  that  the  individual 
delays  retirement,  the  benefit  is  increased  for 
one year by 10% (with a maximum of 30%). 

Croatia 

In  December  2018, 
the  Croatian  Parliament 
adopted  a  pension  reform  package,  which  came 
into  force  in  2019.  The  reforms  included  an 
accelerated increase of the statutory retirement age 
to 67 and higher penalties/bonus for early/deferred 
retirement.  

In  2019,  Parliament  adopted  amendments  to  the 
pension  law,  including  capping  the  early/statutory 
retirement  age  at  60/65  years  as  of  2030, 
decreasing  penalisation  for  early  retirement  and 
reversing  the  acceleration  of  the  equalisation  of 
retirement  ages  between  men  and  women.  These 
changes,  which  entered  into  force  as  of  January 
2020, reversed features of the 2018 reform as well 
as  the  earlier  2014  reform,  which  introduced  the 
gradual  increase  in  the  early/statutory  retirement 
age to 62/67 years by 2038. 

(Continued on the next page) 

39 

 
 
   
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Box (continued) 

Italy 

the  statutory 

Given  the  fact  that  the  Italian  Statistical  Institute 
(ISTAT) estimates no gains in life expectancy were 
made in the past two years, in the period 2021-2022, 
the automatic update of the statutory retirement age 
(SRA)  and  all  pension  requirements  will  be  nil.  In 
retirement  age  will 
2021-2022, 
therefore  remain  at  67  years  for  men  and  women. 
indexation 
Starting  from  2023, 
mechanism 
to 
changes  in  life  expectancy  at  65  –  as  applied  in 
2013-2018  –  is  projected  to  increase  all  age  and 
contributory  requirements  by  a  maximum  of  3 
months every 2 years. 

linking  eligibility 

the  automatic 

requirements 

to 

retirement  window 

life  expectancy  for 

Adjustments 
the  early 
retirement  scheme  based  only  on  a  minimum 
contribution  requirement  regardless  of  age,  are 
frozen until 2026 (Decree Law 4/2019, as converted 
into Law 26/2019). As of 2027, there is an increase 
of  three  months  every  two  years.  As  a  result,  early 
retirement  remains  possible  until  2026  with  a 
contribution  period  of  42  years  and  10  months  for 
men  and  41  years  and  10  months  for  women, 
irrespective  of  their  age  (plus  3  months  for  all  as  a 
shifting 
foreseen). 
Precocious  workers  can  retire  with  41  years  of 
contributions  until  2026  (plus  3  months  for  all). 
Thereafter there is an increase of three months every 
two  years.  Those  enrolled  in  the  pension  system 
after  1995  (i.e.  those  fully  covered  by  the  NDC 
scheme) may retire at the statutory retirement age as 
long  as  they  have  at  least  20  years  of  contributions 
and a monthly pension of at least 1.5 times the old-
age  allowance.  They  can  retire  retire  up  to  a 
the  statutory 
three  years  before 
maximum  of 
retirement  age,  as  long  as  they  have  20  years  of 
contributions  and  a  monthly  pension  of  at  least  2.8 
times the old-age allowance. 

is  also 

Decree  Law  4/2019  (converted  into  Law  26/2019) 
introduced  a  temporary  early  retirement  channel. 
This  ‘Quota  100’  scheme  allows  early  retirement 
with  62  years  of  age  and  38  years  of  contribution 
during  2019-2021.  A  shifting  retirement  window  of 
3  months  for  private  sector  workers  and  6  months 
for  public  sector  workers  is  also  established.  For 
women,  ‘Opzione  Donna’  was  extended  to  2019, 

40 

allowing  retirement  at  58  (59  for  self-employed) 
with 35 years of contribution. It has to be considered 
though  that  the  law  foresees  a  12  months  shifting 
retirement  window  for  female  employees,  whereas 
for  female  self-employed  such  shifting  retirement 
window is extended to 18 months. 

Cyprus 

The 2012 pension reform introduced a link between 
the  statutory  retirement  age  and  changes  in  life 
expectancy,  following  the  increase  to  65  years  by 
2016.  The  mechanism  applies  since  2018,  with 
updates every five years. 

Latvia 

As  a  result  of  the  2012  pension  reform,  the 
retirement  age  increases  by  three  months  every 
year,  until  reaching  65  years  and  a  minimum 
contributory period of 20 years in 2025. The early 
retirement age – with an insurance record of at least 
30 years – is two years less than the statutory one, 
thus  rising  to  63  years  by  2025.  The  early 
retirement  benefit  is  50%  of  the  normal  benefit. 
The  full  pension  is  restored  upon  reaching  the 
statutory retirement age. 

Lithuania 

In  2011,  a  law  was  passed  that  lifts  the  statutory 
retirement  age  to  65  years  by  2026,  with  annual 
increases  of  4  and  2  months  for  women  and  men 
respectively.  

In  2018,  the  2016  pension  reform  entered  into 
force, increasing the eligibility requirements for the 
full general pension component from 30 years to 35 
years by 2027. 

Early  retirement  is  possible  five  years  prior  the 
statutory  retirement  age  on 
the  condition  of 
meeting the eligibility requirements for full general 
pension.  The  pension  benefit  is  reduced  by  0.4% 
for each month of anticipation. 

Hungary 

The  2009  reform  entails  a  gradual  increase  in  the 
statutory  retirement  age  between  2014  and  2022, 
when it reaches 65 years for both men and women.  

Subsequent reforms  eliminated all early retirement 
possibilities,  except  for  women  with  40  eligibility 

(Continued on the next page) 

 
 
   
 
Box (continued) 

(including  years 

years 
in  employment,  paid 
maternity  leave,  childcare  fee  or  child  homecare 
allowance). 

Malta 

The 2006 reform entailed a gradual increase in the 
statutory  retirement  age  to  65  years  by  2027.  For 
people  born  before  1956,  the  pension  age  is  62 
years; for people born in 1956-1958, it is 63 years; 
for people born in 1959-1961, the pension age is 64 
years.  In  addition,  the  contributory  period  in 
increased from 30 years to 35/40 years, depending 
on the cohort. 

The  2016 
incentives: 

reform 

introduced 

the 

following 

-  The number of contribution years required for a 
full retirement benefit was increased from 35 to 
41 years. Stricter rules on the ability to exit the 
labour  force  prematurely  were  introduced  by 
capping  the  number  of  credited  contributions 
for persons born as of 1969. 

People  eligible  for  retirement  at  the  age  of  61  are 
awarded a progressive bonus for each year that they 
continue working up to the age of 65.  Those who 
continue working beyond the retirement age can do 
so  without  forfeiting 
in 
employment. 

their  pension  while 

The Netherlands 

The 2012 reform involved a gradual increase in the 
statutory  retirement  age  to  67  in  2023  and  the 
adoption of a full link of the retirement age to gains 
in life expectancy thereafter. The duration of social 
security  arrangements 
the 
retirement  age  (disability  pensions,  survivors' 
pensions,  unemployment  schemes  and  social 
assistance)  will  be  extended  in  line  with  the 
increase in the statutory retirement age.  

for  people  below 

The  Law  of  2  July  2019  modified  the  speed  at 
which  the  statutory  retirement  age  will  rise  to  67. 
The  latter  will  be  reached  in  2024.  Thereafter,  as 
will  be  covered  by  separate  legislation,  it  will  be 
linked  to  2/3th  of  the  rise  in  the  remaining  life 
expectancy at 65, instead of the full link envisaged 
under the 2012 reform. 

Underlying assumptions and projection methodologies 

Part I 

Austria 

The  statutory  retirement  age  is  65  years  for  men 
and  all  civil  servants  (also  females)  and  60  years 
for  women.  As of 2024, the female retirement age 
will increase by six months every year, reaching 65 
years in 2033. 

In  2014,  reform  measures  came  into  effect  that 
tightened  access  to  early  retirement  and  modified 
invalidity pension schemes.  

-  While  the  early  retirement  scheme  ‘Korridor-
pension’ can still be accessed by men at the age 
of 62 years, it requires 40 insurance years since 
2017, when the penalty for early retirement was 
increased  from  4.2%  to  5.1%  per  year  for 
people born as of 1955. 

contributors 
by 

-  The  early  old-age  pension  scheme  for  long-
term 
‘Hacklerregelung’  was 
the  minimum 
increasing 
tightened 
retirement  age  to  62  for  men  born  as  of  1954 
and to 57 for women born as of 1959, rising to 
62. 

-  For  the  heavy  worker  ‘Schwerarbeitspension’, 
the  early  retirement  age  is  60  (for  women  this 
gets relevant only by 2024), with a minimum of 
45  insurance  years  (at  least  10  years  of  hard 
labour in the last 20 years before retirement). 

-  For  the  early  old-age  pension  for  long-term 
contributors in combination with heavy worker 
the 
regulation 
minimum retirement age is 55 years for women 
(born  in  1959-1963)  and  60  years  for  men 
(born in 1954-1958). Required insurance years 
are 40 for women and 45 for men. 

(‘Hackler-Schwerarbeit’), 

replaced  by  medical 

In  2014,  comprehensive  new  regulations  for 
invalidity  and  occupational  disability  pensions 
came into effect. The temporary invalidity pension 
job-related 
was 
rehabilitation  and  was  completely  abolished  for 
people  born  after  1963.  These  people  will  receive 
special  unemployment  benefits  (‘Rehabilitations-
geld’)  instead.  Therefore,  the  temporary  invalidity 
pension is phased out. 

and 

Portugal 

In  2007,  Portugal  introduced  a  ‘sustainability 
factor’  linking  initial  benefits  to  average  life 

(Continued on the next page) 

41 

 
 
   
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Box (continued) 

expectancy  at  retirement.  Individuals  have  the 
possibility to postpone retirement beyond the legal 
retirement  age  as  a  way  to  compensate  benefit 
reductions due to the sustainability factor.  

servants  within  the  national  defence,  public  order 
and  national  security  systems, 
the  standard 
retirement age is increasing gradually, reaching 60 
years in 2030.  

Since  2015,  the  statutory  retirement  age  varies  to 
two-thirds  of  the  change  in  life  expectancy  at  the 
age  of  65.  The  statutory  retirement  age  is  reduced 
by four months for each contributory year above 40 
years, with a minimum retirement age of 65 years. 
In  2019,  the  minimum  retirement  age  for  certain 
specific schemes (special pensions) was indexed to 
life expectancy at the age of 65. 

In  2007,  early  retirement  was  possible  for  people 
with at least 30 contribution years at the age of 55; 
between  2012  and  2015  it  was  suspended  for  the 
Social  Security  Scheme;  in  2016  it  was  re-
introduced,  allowing  people  with  at  least  40 
contribution  years  to  apply  for  an  old-age  early 
pension  as  of  the  age  of  60.  Initially,  under  this 
scheme,  the  pension  benefit  was  reduced  by  0.5% 
for  each  month  of  anticipation  to  the  statutory 
retirement  age  and  multiplied  by  the  sustainability 
factor.  As  the  penalties  were  quite  severe,  it  only 
was  in  force  between  January  and  March  2016. 
Since  then,  people  aged  60  years  or  more  or  that 
have a career of at least 40 years can apply  for an 
early pension.  

Since  October  2017,  the  government  implemented 
a  set  of  reforms  related  to  early  retirement. 
Contributors with very long careers could apply to 
a  pension  benefit  without  application  of 
the 
sustainability  factor  and  penalty.  The  first  phase 
concerned  workers  aged  60  or  more  with  a 
contribution record of at least 48 years and people 
who  started  working  at  an  early  age  (14  or 
younger) an have a record of at least 46 years. The 
second  phase 
in  October  2018, 
extending  access 
to  early  retirement  without 
penalties  to  workers  aged  60  or  more  with  a 
contribution record of at least 46 years who started 
working  at  the  age  of  16  or  younger.  In  2019,  the 
sustainability factor was eliminated for contributors 
with  a  career  of  40  years  at  the  age  of  60  and  for 
difficult conditions jobs. 

took  place 

Slovenia 

The  2012  pension  reform  included  the  following 
elements with an impact on exit behaviour: 

-  a  gradual  increase  in  the  statutory  retirement 
age  to  65,  as  of  2016  for  men  and  as  of  2020 
for women; 

-  higher penalties for early retirement, as well as 

bonuses for prolonging working lives; 

- 

the  lengthening  of  the  definition  of  a  full 
career.  

Changes  in  the  pension  system  adopted  at  the  end 
of 2019 included the following relevant elements: 

-  higher  incentives  for  people  who  continue 
working after they reach the required retirement 
conditions.  For  men:  gradual  increase  in  the 
total  accrual  rate  from  57.25% 
in  2019 
(previous  pension  law)  to  63.5%  in  2025.  For 
women:  following  the  previous  law  the  total 
accrual  rate  would  decrease  from  63.5%  in 
2019 to 60.25% in 2023 and onwards. With the 
new  pension  law  it  remains  at  63.5%  in  2020 
and onwards; 

-  a favourable valuation of 3% per year has been 
set  for  a  maximum  of  3  additional  years  of 
insurance for individuals who have already met 
the  conditions  for old-age  retirement  (60  years 
of  age  and  40  years  of  pensionable  service 
without purchased periods); 

-  people  who  continue  working  after  they  reach 
the  required  retirement  age  are  entitled  to 
receive 40% of pension for the first three years 
and  20%  from  the  fourth  year  onwards  (20% 
according to the previous pension law). 

Slovakia  

Romania 

The  statutory  female  retirement  age  is  being 
increases to 63 in 2030 (already 65y for men). For 
active  military  police  corps  and  special  civil 

The  2019  reform  introduced  two  main  changes  to 
the  universal  pension  system.  First,  the  retirement 
age will continue to increase, but only to 64 years. 
For women with children, the maximum retirement 
age is  further decreased by six  months for each of 

(Continued on the next page) 

42 

 
 
   
 
Underlying assumptions and projection methodologies 

Part I 

Box (continued) 

the  first  three  children.  If  the  mother  is  unable  to 
benefit  from  such  early  retirement  possibility,  the 
right  is  transferred  to  the  father.  Second,  the 
automatic adjustment of retirement ages to changes 
in  life  expectancy  was  abolished.  Pensioners  are 
allowed  to  retire  two  years  before  reaching  the 
statutory retirement age. In that case, their old-age 
pension is reduced by approximately 6.5% per year 
or 12.5% per two years. On the other hand, benefits 
are increased by 6% for every working year above 
the retirement age. 

Sweden 

As a first step of a more substantial pension reform, 
the  earliest  age  to  draw  an  old-age  pension  was 
increased  from  61  years  to  62  years  in  2020  for 
both women and men. In 2019, only 11 000 people 
drew an old-age pension at the age of 61, of whom 
more  than  50%  continued  to  work,  so  the  positive 
effect on the labour supply is small. 

From 1 September 2021, old-age pensioners with a 
monthly pension income between 9 000 and 17 000 
SEK (ca. 850 – 1 610 EUR) will receive a monthly 
pension  supplement  of  at  most  600  SEK  (ca.  570 
EUR).  The  supplement  is  taxed  and  will  be 
financed via the central government budget. 

Finland 

The  2017  reform  of  the  earnings-related  pension 
system,  increases  the  retirement  age  for  most 
workers  and  amends 
the  early  and  partial 
retirement options. 

- 

In  2018,  the  lowest  old-age  retirement  age 
started  to  rise  by  three  months  for  each  age 
cohort,  to  reach  65  years  in  2027.  The  upper 
age limit of the old-age pension is rising to 69 
for those born in 1958–1961 and to 70 for those 
born after 1961; 

-  The  lowest  old-age  retirement  age  will  be 
linked to life expectancy as of 2030 so that the 
time spent working in relation to the time spent 
in  retirement  remains  at  the  2025  level.  The 
annual increase of the retirement age is limited 
to two months. 

43 

 
   
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

2.4. 

THE IMPACT OF PENSION REFORMS ON 
THE PARTICIPATION RATE OF OLDER 
WORKERS 

By  changing  eligibility  criteria  and  retirement 
incentives,  reforms  will  affect  the  behaviour  of 
older  workers  in  the  coming  decades.  Effective 
labour market exit ages for men and women are 
projected  to  increase  by  0.9  and  1.3  years 
respectively on average in the EU by 2070. 

As  already  underlined  in  the  previous  section, 
Member  States  have  legislated  a  considerable 
number  of  reforms  to  change  the  qualifying 
conditions  for  retirement.  The  age  group  55-64  is 
the most affected by measures aimed at postponing 
retirement.  Table 
the 
I.2.1 
participation  rates  for  this  age  bracket  increased 
from 38% in 2000 to 64% in 2019.  

showed  how 

In  many  Member  States,  legislated  measures  
envisage  additional  increases  in  retirement  ages. 
As a result, the average statutory retirement age for 
men/women  rises  from  65/60.4  years  today  to 
around  67/63  years  in  2070  (see  Graph  I.2.1). 
These  changes  in  legal  retirement  ages  will  affect 
people’s 
together  with 
changes  in  qualifying  conditions  (i.e.  minimum 
contributory periods) or disincentives to retire (i.e. 
penalties  for  early  retirement  and  bonuses  for 
postponing retirement). The impact of these factors 
is  included  in  the 
on  future  exit  behaviour 
projected participation rates.  

retirement  decisions, 

Graph I.2.3 shows the estimated impact of pension 
reforms on participation rates in the age group 55-
64 by 2070. In most of the 24 Member States with 
relevant  legislated  pension  reforms,  the  latter  are 
projected  to  have  a  sizeable  impact  on  the  labour 
market  participation  of  older  workers.  For  the 
countries  concerned,  the  reforms  alone  lift  the 
participation  rate  of  people  aged  55-64  by  about 
9 pps  for  men  and  by  10 pps  for  women  on 
average  by  2070.  Also  when  considering  the  age 
group  65-74,  adopted  reforms  are  estimated  to 
push  up  participation  rates  by  2070;  by  9  pps  for 
men and by 8 pps for women (see Graph I.2.4).  

It  should  be  recalled  that  total  participation  rates 
(20-64)  are  mainly  driven  by  changes  in  the 
participation rate of prime-age workers (25-54), as 
this  group  accounts  for  about  60%  of  the  total 

labour  force.  Therefore,  the  significant  projected 
increases 
labour  market 
participation  only  partly  reflect  in  the  overall 
participation rates, as discussed in the next section. 

in  older  workers’ 

Graph I.2.1:  Legislated changes in the statutory retirement 

age 

men

73

72

71

70

69

68

67

66

65

64

63

62

61

60

59

58

73

72

71

70

69

68

67

66

65

64

63

62

61

60

59

58

72.1

67.1

65.0

69.9

66.5

65.0

67.0

65.0

63.1

2019

2045

2070

average of three highest member states
average of three lowest member states
EU27

women

72.1

66.8

63.3

69.9

66.2

63.3

67.0

64.1

60.4

73

72

71

70

69

68

67

66

65

64

63

62

61

60

59

58

73

72

71

70

69

68

67

66

65

64

63

62

61

60

59

58

2019

2045

2070

For a comprehensive overview, see Table II.A5.2 in Annex 5. 
Source: European Commission, EPC. 

Based  on  the  participation  rates  of  older  age 
brackets,  average  male  and  female  exit  ages  from 
the  labour  market  can  be  calculated.  Changes  in 
these exit ages between 2019 and 2070 are shown 
in  Graph  I.2.2,  providing  a  summary  measure  of 
the long-term impact of enacted pension reforms. 

The  projections  show  an  average  increase  of 
around two years in the effective retirement age for 

44 

   
Underlying assumptions and projection methodologies 

Part I 

that 

both  men  and  women.  For  some  countries,  e.g. 
Austria  and  Ireland,  the  increase  for  women  is 
higher  than  that  for  men  because  of  a  progressive 
convergence  of  the  retirement  age  for  women  to 
that  of  men.  Countries 
introduced  an 
automatic  link  between  retirement  ages  and  gains 
in  life  expectancy  show  the  highest  increases;  in 
the  cases  of  Greece,  Denmark  and  Estonia,  exit 
ages  rise  by  between  4  and  5  years  for  both  men 
and women. The other countries with a full link – 
Finland,  Cyprus  and  Italy  –  have  increases  of 
between  3  and  3.5  years.  The  Netherlands  and 
link,  show 
Portugal,  which  apply  a  partial 
increases of around 3 and 2 years respectively.  

Graph I.2.2: 

Impact of pension reforms on the average exit 
age from the labour force 

men

E
S

T
A

O
R

GI
E
B

R
H

K
S

Z
C

I
S

T
L

E
B

E
D

T
M

V
L

T
P

U
H

R
F

L
N

T
I

Y
C

S
E

I
F

E
E

K
D

L
E

women

6.0

5.0

4.0

3.0

2.0

1.0

0.0

6.0

5.0

4.0

3.0

2.0

1.0

0.0

-1.0

O
R

E
S

K
S

E
D

T
M

V
L

G
B

I

ES
B

R
H

T
L

T
A

Z
C

T
P

E
I

R
F

U
H

L
N

S
E

I
F

Y
C

T
I

E
E

L
E

K
D

Based on the age group 51-74. LU, PL and NO are not shown 
as there are no legislated pension measures affecting 
retirement behaviour in 2019-2070. 
Source: European Commission, EPC. 

45 

    
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Graph I.2.3: 

Impact of pension reforms on the participation rate of the age group 55-64 

Participation rate 55-64y in 2070, men

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

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

30

40

50

60

70

80

PR 55-64y (no reform)

PR 55-64y (impact of pension reform)

Participation rate 55-64y in 2070, women

30

40

50

60

70

80

PR 55-64y (no reform)

PR 55-64y (impact of pension reform)

LU, PL, and NO are not shown as there are no legislated pension measures affecting retirement behaviour in 2019-2070. 
Source: European Commission, EPC. 

Graph I.2.4: 

Impact of pension reforms on the participation rate of the age group 65-74 

Participation rate 65-74y in 2070, men

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

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

0

5

10

15

20

25

30

35

40

45

PR 65-74y (no reform)

PR 65-74y (impact of pension reform)

Participation rate 65-74y in 2070, women

0

5

10

15

20

25

30

35

40

45

PR 65-74y (no reform)

PR 65-74y (impact of pension reform)

LU, PL and NO are not shown as there are no legislated pension measures affecting retirement behaviour in 2019-2070. 
Source: European Commission, EPC. 

46 

 
         
        
Underlying assumptions and projection methodologies 

Part I 

Graph I.2.5:  Age profiles of participation (PR) and employment rates (ER) by gender for the EU27 (2019 and 2070) 

PR EU Males 2019

PR EU Males 2070

ER EU Males 2019

ER EU Males 2070

100%

80%

60%

40%

20%

0%

100%

80%

60%

40%

20%

0%

100%

80%

60%

40%

20%

0%

15

20

25

30

35

40

45

50

55

60

65

70

15

20

25

30

35

40

45

50

55

60

65

70

PR EU Females 2019

PR EU Females 2070

ER EU Females 2019

ER EU Females 2070

100%

80%

60%

40%

20%

0%

15

20

25

30

35

40

45

50

55

60

65

70

15

20

25

30

35

40

45

50

55

60

65

70

Source: European Commission, EPC. 

2.5. 
RESULTS OF THE PARTICIPATION 
RATE AND LABOUR SUPPLY PROJECTIONS  

Social  and  institutional  factors  such  as  a  higher 
attachment  to  the  labour  market  of  younger 
women  and  pension  reforms  lead  to  higher 
participation rates. These partly offset the impact 
of  the  projected  decrease  in  the  working-age 
population on the labour supply. 

2.5.1.  Projection of participation rates 

The total participation rate in the EU is projected 
to increase by 2.5 pps, with female labour market 
participation  anticipated  to  rise  by  4.4 pps  and 
generally  the  largest  increases  among  older  age 
groups. 

As  discussed  in  Chapter  1,  the  population  at 
working age is projected to decline substantially in 
the  coming  decades  as  large  cohorts  of  retiring 
people are replaced by smaller cohorts of younger 
workers.  Other  things  being  equal  and  given  the 
age  profile  of  participation  rates,  the  increasing 
share  of  older  workers  in  the  labour  force  puts 
downward pressure on the total participation rate.  

The  projections  nevertheless  reveal  a  rightward 
shift  in  the  age  profile  of  both  male  and  female 

participation rates, particularly visible at 60+ ages 
(see Graph I.2.5). For female participation, there is 
in  addition  a  general  upward  shift.  These  broad 
trends  reflect  the  combined  effect  of  pension 
reforms  and  the  rising  attachment  of  younger 
generations of women to the labour market. 

for 

Tables I.2.6, I.2.7 and I.2.8 provide an overview of 
the  main  projected  developments 
the 
participation  rates,  broken  down  by  broad  age 
groups  and  gender.  In  the  EU,  the  overall  labour 
participation by the population at working age (20-
64y) would increase by 2.5 pps between 2019 and 
2070: from 78.2% in 2019 to 80.7% in 2070. This 
increase  is  predominantly  the  result  of  rising 
female  participation;  an 
increase  of  4.4 pps 
compared  to  0.5 pps  for  men.  With  77%  of  the 
female  working-age  population  expected  to  be 
active  in  the  labour  market  in  2070,  female 
participation  would  nevertheless  remain  8 pps 
below male participation. 

Only  in  Bulgaria,  the  Czech  Republic,  Slovakia, 
Sweden and Norway, the overall participation rate 
is  expected  to  decrease,  with  the  largest  fall  of 
1.6 pps in Slovakia. The largest projected increases 
in labour market participation range between 4 and 
8 pps  in  Greece,  Hungary,  Malta,  Cyprus,  Italy, 
Estonia  and  Portugal.  The  difference  between  the 

47 

           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

countries  with  the  highest  and  lowest  rates  would 
narrow  somewhat,  from 17 pps in 2019 to 13 pps 
in 2070. 

When  comparing  projections  for  male  and  female 
participation  for  the  20-64  age  group,  nearly  all 
Member States show a higher increase for women. 
Only  Bulgaria  and  Slovakia  are  expected  to  see 
female 
labour  market  participation  fall  from 
current levels. Among men, more countries would 
show  a  decline  in  participation.  In  spite  of  this 
convergence, 
participation  would 
nevertheless  remain  lower  than  that  of  men  in  all 
Member States throughout the projection period. 

female 

Participation among the youngest age bracket (20-
24y)  is  expected  to  rise  in  all  Member  States,  by 
1.7 pps on average. Only for Bulgaria and Poland, 
a  very  limited  decrease  in  female  and  male 
participation,  respectively,  would 
take  place. 
Differences  between  countries  as  well  as 
differences between genders remain substantial for 
this age group. 

For  the  prime-age  group  (25-54y),  a  further 
increase  from  the  current  levels  is  generally 
expected, with a maximum rise of 5.4 pps in Malta 
and  decreases  of  up  to  1.5 pps  in  Belgium, 
Denmark,  Slovakia,  France  and  Spain.  In  2070, 
only  Italy  would  have  a  participation  rate  below 
80%  and  eleven  Member  States  would  have  a 
participation  rate  of  at  least  90%  among  people 
aged 25-54.  

(+4.1 pps). 

The  picture  for  males  in  the  25-54  age  bracket  is 
generally one of broad stabilisation, with projected 
changes  limited  to  ±2  pps  with  the  exception  of 
female 
Lithuania 
participation is anticipated to rises for all countries 
aside  from  small  decreases  in  Belgium,  Denmark, 
Slovakia  and  Bulgaria.  In  the  other  countries, 
prime-aged  women’s  participation  would  increase 
by  about  3 pps  on  average,  with  the  largest 
increases in Malta, Luxembourg and Greece. 

contrast, 

In 

Participation  among  the  group  of  older  workers 
(55-64y)  is  expected  to  increase  substantially  for 
most  countries.  The  EU  average  rises  by  10 pps 
between  2019  and  2070:  from  62%  to  72%. 
Projected  increases  exceed  15 pps  in  Greece, 
Hungary,  Italy,  Malta  and  Spain.  In  2070,  only 
Luxembourg,  Croatia,  Poland,  Romania  and 
Slovakia would still have participation rates below 

48 

60% for this age group. With higher increases for 
female  (+13 pps)  than  for  male  older  workers 
(+6 pps),  the  gender  gap  is  projected  to  narrow 
substantially.  

rate  doubles  over 

As  Graph  I.2.5  showed,  the  increase  in  labour 
market participation extends beyond the age of 65. 
Indeed,  when  considering  the  65-74  age  group, 
there  is  a  similar  upward  trend  in  participation 
between  2019  and  2070  (see  Table  I.2.5).  The 
average  participation 
the 
projection  period,  to  20%  in  2070,  with  large 
differences  between  Member  States.  The  largest 
increases  occur  for  countries  with  a  link  to  life 
expectancy  (e.g.  Denmark,  Italy,  Greece,  the 
Netherlands,  Estonia,  Finland  and  Cyprus)  and 
countries  that  currently  have  comparatively  low 
participation rates (e.g. Spain, Austria and France). 
Also  for  this  age  group,  increases  in  labour 
participation  are  generally  higher  among  women 
than what is projected for men. 

Table I.2.5: 

Participation rate projections – 65-74y, total 

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2019
4.3
11.0
10.9
14.6
13.9
28.1
16.7
8.0
4.5
5.5
5.0
9.1
13.8
20.7
17.5
2.9
7.1
8.7
14.4
7.1
8.5
16.1
13.4
4.6
7.0
11.5
17.8
19.0
9.5
9.8

2070
10.8
15.9
12.5
38.4
18.5
40.9
24.1
25.7
21.2
14.6
8.1
32.6
24.9
13.6
10.7
3.9
11.5
7.7
27.9
17.9
12.6
21.6
17.9
9.4
5.8
24.8
17.4
18.2
21.1
20.0

change
6.5
4.9
1.6
23.9
4.6
12.7
7.4
17.7
16.7
9.0
3.1
23.5
11.1
-7.1
-6.7
1.0
4.5
-1.0
13.5
10.9
4.1
5.5
4.5
4.8
-1.2
13.3
-0.4
-0.8
11.7
10.2

Source: European Commission, EPC. 

 
          
 
 
Underlying assumptions and projection methodologies 

Part I 

Table I.2.6: 

Participation rate projections by age group – total 

Total

20-64

Young

20-24

Prime-age

25-54

Older

55-64

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2019
74.5
78.5
82.0
82.3
83.2
83.8
78.8
73.8
79.0
78.0
71.4
70.5
80.9
82.9
83.6
76.8
77.9
79.7
82.6
80.3
75.7
81.4
73.7
79.9
78.0
82.2
87.3
82.1
78.4
78.2

2070
75.7
77.5
81.3
83.7
84.2
88.0
81.1
82.2
81.8
80.0
74.6
74.9
85.9
83.0
86.4
77.5
85.3
86.0
84.6
82.2
75.9
85.4
76.0
83.0
76.4
85.0
87.1
81.2
81.0
80.7

2019
49.7
44.2
52.5
72.3
71.1
72.3
72.3
42.4
55.5
62.6
52.4
44.7
62.5
66.2
63.1
51.9
54.4
77.7
75.7
74.0
61.3
58.3
48.4
59.3
50.4
70.7
71.5
70.7
61.0
60.3

2070
52.8
44.4
53.5
74.7
71.3
74.3
72.9
45.8
56.2
63.6
55.5
45.4
66.9
69.9
65.7
54.5
57.4
79.2
78.5
75.0
61.6
58.8
49.9
59.5
52.3
74.6
75.2
72.3
62.4
62.0

2019
84.8
85.8
89.1
86.5
88.0
87.8
83.5
85.4
87.0
87.4
83.6
78.2
88.3
88.4
90.1
88.5
87.1
87.5
87.4
89.0
85.3
90.3
84.1
92.4
86.6
87.6
91.2
86.3
85.8
85.9

2070
83.3
86.4
89.4
85.7
88.8
90.2
86.1
88.2
86.9
87.2
85.0
78.5
90.5
90.1
93.8
92.2
90.0
93.0
87.5
90.3
86.1
92.4
86.8
93.5
86.2
87.8
91.7
86.3
86.4
86.7

2019
54.6
67.1
68.4
74.4
74.6
75.7
64.1
50.4
61.7
56.9
45.8
57.5
65.3
72.5
73.8
45.2
58.2
52.3
72.0
56.5
51.1
64.5
49.0
50.3
60.5
71.5
81.7
73.9
63.7
62.3

2070
64.0
67.7
70.7
81.7
76.0
87.6
70.4
80.8
78.3
67.0
54.5
75.9
80.1
69.4
74.5
45.2
83.7
69.2
78.5
61.7
55.1
77.0
57.2
64.6
59.6
81.4
78.9
70.3
73.7
71.9

Source: European Commission, EPC. 

Table I.2.7: 

Participation rate projections by age group – men 

Total

20-64

Young

20-24

Prime-age

25-54

Older

55-64

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2019
79.1
83.2
89.3
85.8
87.6
87.4
85.5
82.5
84.2
82.1
76.6
80.6
86.5
85.6
85.2
81.5
85.9
89.4
87.4
84.9
83.5
84.9
83.9
83.0
84.6
84.2
89.8
85.0
84.1
84.2

2070
79.3
82.3
87.4
87.5
86.8
91.1
86.3
85.5
84.8
82.7
78.3
81.9
89.8
84.7
87.9
78.4
90.1
89.6
87.3
84.9
82.9
87.0
85.1
84.8
83.9
86.6
88.9
82.9
84.5
84.7

2019
52.5
49.9
59.8
73.9
73.6
76.6
74.9
43.8
58.8
67.0
59.9
50.4
60.8
70.6
65.8
56.9
61.8
78.0
76.1
76.2
68.0
61.2
57.7
64.4
62.9
73.3
74.1
72.2
64.5
64.5

2070
54.5
50.5
60.2
76.5
73.6
79.1
75.5
49.1
59.7
67.6
63.4
51.1
67.3
73.8
69.0
58.0
64.5
80.7
78.7
78.3
67.8
61.6
58.8
64.7
63.7
76.5
76.3
73.7
65.8
65.9

2019
89.3
90.1
95.9
90.1
92.6
92.5
90.6
93.3
91.7
91.9
86.8
88.5
93.3
91.3
91.3
92.9
93.4
96.7
91.5
92.4
91.6
92.7
93.0
94.3
93.2
90.3
93.7
89.0
91.4
91.6

2070
87.6
91.5
96.1
89.9
92.1
93.9
92.3
91.4
90.6
89.8
88.1
86.4
94.5
91.6
95.4
92.6
95.2
96.4
89.9
92.7
91.6
92.9
95.4
95.0
93.6
90.0
93.3
87.4
90.3
90.9

2019
60.1
72.2
76.5
78.7
79.4
73.6
72.5
64.5
69.1
59.3
54.8
68.7
77.0
73.0
75.1
51.6
70.7
67.8
81.0
65.9
63.0
71.0
61.9
54.9
63.5
70.8
84.3
78.3
70.2
69.7

2070
66.9
70.7
74.8
85.3
77.2
88.7
74.2
84.1
79.3
69.4
58.4
81.3
85.8
70.6
75.2
46.7
86.6
73.8
83.5
65.0
66.1
81.4
67.1
65.7
65.3
81.5
81.8
73.9
76.3
75.5

Source: European Commission, EPC. 

change 2019-2070 (pps)

Young

Prime-age Older

20-24
3.0
0.2
0.9
2.4
0.2
2.0
0.6
3.5
0.7
1.0
3.1
0.6
4.4
3.8
2.6
2.6
3.0
1.5
2.8
1.0
0.3
0.5
1.6
0.3
1.9
3.9
3.6
1.6
1.5
1.7

25-54
-1.5
0.6
0.4
-0.7
0.8
2.4
2.6
2.8
-0.1
-0.3
1.4
0.3
2.2
1.7
3.7
3.7
2.9
5.4
0.1
1.3
0.8
2.1
2.7
1.1
-0.4
0.2
0.5
0.0
0.6
0.8

55-64
9.4
0.6
2.3
7.3
1.4
11.9
6.3
30.4
16.6
10.1
8.6
18.4
14.8
-3.1
0.7
0.0
25.5
16.8
6.5
5.1
4.0
12.6
8.2
14.3
-0.9
9.9
-2.8
-3.6
10.0
9.6

change 2019-2070 (pps)

Young

Prime-age Older

20-24
2.0
0.6
0.5
2.7
0.0
2.5
0.6
5.3
0.9
0.6
3.5
0.7
6.5
3.2
3.2
1.1
2.7
2.6
2.6
2.0
-0.3
0.4
1.1
0.3
0.9
3.2
2.2
1.5
1.3
1.4

25-54
-1.7
1.5
0.1
-0.2
-0.5
1.4
1.8
-1.9
-1.1
-2.0
1.3
-2.1
1.2
0.3
4.1
-0.3
1.7
-0.3
-1.6
0.4
0.0
0.2
2.4
0.7
0.4
-0.3
-0.3
-1.6
-1.1
-0.7

55-64
6.8
-1.4
-1.7
6.6
-2.2
15.1
1.6
19.7
10.1
10.1
3.6
12.6
8.8
-2.3
0.1
-4.9
15.9
6.0
2.6
-0.9
3.0
10.4
5.2
10.8
1.7
10.7
-2.5
-4.5
6.1
5.8

Total

20-64
1.1
-0.9
-0.7
1.4
1.0
4.1
2.3
8.4
2.8
1.9
3.2
4.4
4.9
0.1
2.8
0.8
7.3
6.4
1.9
1.9
0.2
4.0
2.2
3.1
-1.6
2.8
-0.3
-0.9
2.5
2.5

Total

20-64
0.3
-0.9
-1.9
1.7
-0.8
3.8
0.8
3.0
0.6
0.6
1.7
1.3
3.3
-0.9
2.7
-3.1
4.2
0.2
-0.1
0.0
-0.6
2.2
1.2
1.8
-0.8
2.4
-0.9
-2.1
0.4
0.5

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

49 

 
 
         
 
 
         
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.2.8: 

Participation rate projections by age group – women 

Total

20-64

Young

20-24

Prime-age

25-54

Older

55-64

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2019
70.0
73.7
74.5
78.7
78.6
80.3
72.1
65.4
73.8
74.1
66.1
60.5
75.7
80.4
82.1
71.8
70.0
68.8
77.8
75.6
68.0
78.3
63.3
76.6
71.3
80.1
84.8
79.1
72.8
72.2

2070
71.9
72.6
74.9
79.8
81.4
84.6
75.9
78.7
78.9
77.3
70.6
67.3
82.2
81.2
84.7
76.6
80.1
81.8
81.8
79.4
68.5
83.9
65.8
81.0
68.6
83.2
85.1
79.4
77.4
76.6

2019
46.9
38.0
44.9
70.7
68.3
67.8
69.6
40.8
52.1
58.1
44.4
38.5
64.0
61.4
60.1
46.5
46.4
77.3
75.3
71.6
54.3
55.3
38.5
53.5
37.4
67.9
68.7
69.1
57.2
55.8

2070
51.0
37.9
46.3
72.8
68.8
69.2
70.2
42.3
52.5
59.6
47.1
39.1
66.5
65.8
62.0
50.7
49.8
77.6
78.4
71.6
55.2
55.9
40.3
53.9
40.3
72.6
74.0
70.8
58.9
58.0

2019
80.3
81.4
81.8
82.8
83.3
82.7
76.7
77.8
82.3
83.1
80.3
67.8
83.5
85.5
89.0
84.0
80.5
77.1
83.3
85.7
79.0
88.0
74.6
90.4
79.6
84.8
88.7
83.5
80.3
80.2

2070
78.9
81.0
82.4
81.5
85.5
86.3
80.1
84.8
83.2
84.6
81.8
69.9
86.7
88.6
92.1
91.8
84.5
88.9
85.1
87.9
80.4
91.9
77.0
91.8
78.3
85.5
90.0
85.2
82.5
82.4

2019
49.2
62.4
60.5
70.2
70.0
77.6
55.9
38.0
54.5
54.6
37.6
47.0
53.9
72.2
72.7
38.4
47.4
36.4
63.1
47.4
40.3
58.7
37.2
45.6
57.7
72.1
79.1
69.4
57.5
55.4

2070
61.2
64.6
66.3
78.0
74.9
86.5
66.8
77.3
77.3
64.8
50.4
70.2
75.2
68.2
73.7
43.6
80.6
63.5
73.6
58.3
44.0
73.3
46.3
63.2
53.8
81.3
75.9
66.5
71.1
68.4

Source: European Commission, EPC. 

change 2019-2070 (pps)

Young

Prime-age Older

20-24
4.1
-0.1
1.4
2.0
0.5
1.4
0.6
1.5
0.5
1.5
2.7
0.6
2.5
4.4
2.0
4.2
3.4
0.3
3.1
0.0
0.9
0.6
1.8
0.4
2.9
4.7
5.2
1.7
1.7
2.2

25-54
-1.4
-0.4
0.5
-1.3
2.3
3.5
3.3
7.0
0.9
1.4
1.5
2.1
3.3
3.1
3.1
7.9
4.0
11.8
1.8
2.2
1.4
3.9
2.4
1.4
-1.3
0.7
1.3
1.7
2.2
2.2

55-64
12.0
2.2
5.7
7.8
4.9
8.9
10.9
39.2
22.8
10.2
12.8
23.3
21.3
-3.9
1.0
5.2
33.2
27.1
10.5
10.9
3.7
14.6
9.0
17.6
-3.9
9.2
-3.3
-2.9
13.6
12.9

Total

20-64
2.0
-1.1
0.4
1.1
2.8
4.3
3.8
13.3
5.1
3.1
4.5
6.8
6.6
0.9
2.6
4.8
10.1
13.0
4.0
3.8
0.6
5.7
2.5
4.4
-2.7
3.1
0.4
0.3
4.6
4.4

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2.5.2. 

Projection of labour supply 

The  size  of  the  EU  labour  supply  is  expected  to 
decrease by 16% over the projection horizon, with 
the largest decline of labour supply of males. 

six  countries.  In  the  cases  of  Poland,  Croatia, 
Romania, Bulgaria, Lithuania and Latvia, it would 
shrink  by  a  third  or  more.  Such  steep  declines  in 
the labour force reverberate in the potential growth 
estimates, discussed in Chapter 3.  

the 

over 

substantially 

Total  labour  supply  in  the  EU  is  expected  to 
decrease 
projection 
horizon. (19)  By  2070,  the  labour  force  would 
shrink  by  almost  16%  as  compared  to  2019,  with 
an  average  annual  decrease  of  0.3%  (see  Table 
I.2.9).  This  entails  a  total  loss  in  the  number  of 
workers  in  the  EU  of  32.1  million  people;  18.6 
million  fewer  male  workers  (-17%)  and  13.5 
million fewer female workers (-14%).  

The general decline in the labour supply expected 
in  the  first  half  of  the  projection  period  (2019-
2045)  is  expected  to  continue  thereafter  (2045-
2070), though at a slower pace in  most cases (see 
Graph I.2.6). The countries with the sharpest fall in 
the  labour  supply  in  2019-2045  also  show  the 
steepest declines in 2045-2070, both for males and 
females. 

Graph I.2.6 highlights the substantial differences in 
labour  supply  projections  across  Member  States, 
ranging  from  an  increase  of  26%  in  Malta  to  a 
decrease  of  48%  in  Latvia  (2019-70).  The  labour 
force would be larger in 2070 than in 2019 for only 

(19)  Labour  supply  projections  by  single  age  and  gender  are 
obtained  by  multiplying  the  participation  rates  with  the 
demographic projections. 

50 

                                                           
 
 
         
 
Underlying assumptions and projection methodologies 

Part I 

Table I.2.9: 

Labour supply projections - total 

2.5.3.  Breaking down changes in participation 

Total labour force 
(20-64y, '000 persons)

2019

5.011

3.264

5.239

2.768

2070

4.699

1.984

4.320

2.655

41.389

35.800

657

2.287

4.622

22.639

29.127

1.737

538

2.735

3.552

19.705

28.024

1.143

25.139

20.490

443

936

1.407

305

4.634

251

8.435

4.399

505

489

783

323

3.928

316

7.875

3.978

17.798

11.719

4.942

8.594

1.007

2.683

2.572

5.094

2.599

3.555

5.264

832

1.809

2.196

6.015

2.893

158.252

138.202

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

HR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

NO

EA

EU27

207.378

175.231

Change 
(2070/2019)

Avg annual 
growth rate 
(2019-2070)

Impact on 
potential 
output 
growth(1)

-6.2%

-39.2%

-17.5%

-4.1%

-13.5%

-18.0%

19.6%

-23.1%

-13.0%

-3.8%

-34.2%

-18.5%

13.9%

-47.8%

-44.3%

5.7%

-15.2%

25.7%

-6.6%

-9.6%

-34.2%

-28.1%

-38.8%

-17.4%

-32.6%

-14.6%

18.1%

11.3%

-12.7%

-15.5%

-0.1%

-1.0%

-0.4%

-0.1%

-0.3%

-0.4%

0.4%

-0.5%

-0.3%

-0.1%

-0.8%

-0.4%

0.3%

-1.3%

-1.1%

0.1%

-0.3%

0.5%

-0.1%

-0.2%

-0.8%

-0.6%

-1.0%

-0.4%

-0.8%

-0.3%

0.3%

0.2%

-0.3%

-0.3%

0.1%

-0.4%

0.0%

0.2%

0.0%

0.0%

0.4%

-0.1%

0.0%

0.2%

-0.3%

0.0%

0.4%

-0.6%

-0.5%

0.3%

0.0%

0.5%

0.1%

0.1%

-0.3%

-0.2%

-0.4%

0.0%

-0.3%

0.0%

0.4%

0.4%

0.0%

0.0%

(1) Impact of labour force growth differential relative to the 
EU average. 
Source: European Commission, EPC. 

rates and labour force 

Table  I.2.10  applies  a  shift-share  analysis  to 
changes  in  the  total  participation  rate  over  the 
and  gender 
period  2019-2070 
along 
dimensions.  The  overall  participation  rate 
is 
algebraically  broken  down  in  a  participation  rate 
effect,  a  demographic  effect  and  a  residual 
interaction effect (20). 

age 

The  participation  rate  effect,  capturing  changes  in 
participation  rates  of  specific  age  and  gender 
groups,  is  positive  for  most  Member  States.  This 
basically reflects the trend rise in the participation 
rates  of  women  and  older  workers,  with  a  small 
decrease in participation among prime-aged people 
for several countries. 

the  working-age  population, 

The  demographic  effect,  capturing  changes  in  the 
is 
structure  of 
negative  in  nearly  all  many  Member  States,  as  a 
result  of  shrinking  cohorts  of  prime-aged  people 
and women in general. Women are thus associated 
with  both  positive  participation  and  negative 
demographic  effects.  The  former  reflects  the 
upward 
cohorts’ 
partitcipation rates, as embedded in the CSM. The 
latter  reflects  the  ageing  of  the  population,  with  a 
stronger  impact  on  women,  mostly  because  they 
leave the labour force on average earlier than men. 
For some countries, the interaction effect between 
both  dimensions  is  also  important  (e.g.  EL,  ES, 
HU and MT). 

transmission 

younger 

of 

(20)  This breakdown is based on the rule for approximating the 
difference of a product: y1x1  – y0x0 = x0Δy + y0Δx + ΔyΔy. 
For more details, see Carone (2005). 

51 

 
    
 
 
 
 
 
 
 
 
 
 
 
                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Graph I.2.6:  Change in the labour force (20-64y; %) 

Total

T
M

E
I

E
S

Y
C

O
N

U
L

R
F

K
D

E
B

L
N

T
A

S
E

I

E F
D

I

U S
H

Z
C

E
E

T
I

L
E

T
P

K
S

L
P

R
H

O
R

G
B

T
L

V
L

2019-2045

2045-2070

total: 2019-2070

Men

T
M

E
S

E
I

O
N

Y
C

U
L

K
D

R
F

E
B

L
N

I

T F
A

E
D

S
E

U
H

E
E

I
S

Z
C

T
I

L
E

T
P

K
S

L
P

R
H

O
R

G
B

T
L

V
L

2019-2045

2045-2070

total: 2019-2070

Women

50%

40%

30%

20%

10%

0%

-10%

-20%

-30%

-40%

-50%

50%

40%

30%

20%

10%

0%

-10%

-20%

-30%

-40%

-50%

50%

40%

30%

20%

10%

0%

-10%

-20%

-30%

-40%

-50%

T
M

E
I

Y
C

E
S

O
N

U
L

R
F

L
N

K
D

E
B

T
A

S
E

E
D

I

U F
H

Z
C

T
I

I
S

E
E

L
E

T
P

R
H

K
S

L
P

G
B

O
R

T
L

V
L

Countries are ranked in descending order of total change over the period 2019-2070. 
Source: European Commission, EPC. 

2019-2045

2045-2070

total: 2019-2070

52 

7
2
U
E

7
2
U
E

7
2
U
E

 
    
Underlying assumptions and projection methodologies 

Part I 

Table I.2.10:  Contribution to the overall change in participation rates, 2019-2070 (in pps) 

Contribution of group-specific changes in participation rates
Men 
Total

Women 

Demographic effect 

Participation 
rates in 2070

Change in 
participation 
rates 2019-
2070 (pps)

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

HR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

NO

EA

EU27

75.7
77.5
81.3
83.7
84.2
88.0
81.1
82.2
81.8
80.0
74.6
74.9
85.9
83.0
86.4
77.5
85.3
86.0
84.6
82.9
75.9
85.7
76.0
83.0
76.4
85.0
87.1
81.2
81.0
80.7

1.1
-0.9
-0.7
1.4
1.0
4.1
2.3
8.4
2.8
1.9
3.2
4.4
4.9
0.1
2.8
0.8
7.3
6.4
1.9
2.6
0.2
4.3
2.2
3.1
-1.6
2.8
-0.3
-0.9
2.5
2.5

)
4
6
-
0
2
(
e
g
a
-
g
n
i
k
r
o
w

l

a
t
o
T

1.4
0.5
0.8
1.3
0.9
4.5
3.1
9.0
3.6
2.2
3.3
4.6
4.8
0.7
2.8
2.9
7.7
7.3
1.9
2.8
1.5
4.7
3.8
4.1
-0.3
2.9
0.1
-0.5
2.8
2.9

)
4
6
-
0
2
(
e
g
a
-
g
n
i
k
r
o
w

l

a
t
o
T

)
4
5
-
5
2
(
e
g
a
-
e
m

i
r
P

)
4
6
-
5
5
(

r
e
d
O

l

0.3
2.1
-1.0
0.4
0.1
0.4
-0.1
0.5
0.3
0.8
1.6
-0.5
-0.4
0.3
0.5
2.1
2.6
1.7
0.8
1.2
1.8
1.7
6.8
1.9
0.7
3.6
-0.1
0.4
2.3
-0.2
1.0
2.1
1.0
0.7
4.3
0.2
1.6
2.8
1.5
0.0
-0.7
1.2
1.5
0.2
2.4
-0.5
0.0
2.7
2.3
5.4
2.0
0.6
3.3
3.8
-0.1
1.5
0.1
0.0
1.7
1.0
0.3
0.9
0.6
1.2
3.2
1.4
1.4
1.7
1.9
1.5
0.7
3.3
0.4
-0.3 -0.2
1.3
2.3
0.1
-0.6 -0.3
0.3
-0.7 -0.9
0.0
0.4
2.3
0.4
0.5
2.2
0.5

)
4
2
-
0
2
(
g
n
u
o
Y

0.3
0.0
0.1
0.3
0.0
0.2
0.1
0.3
0.1
0.1
0.3
0.1
0.5
0.3
0.2
0.3
0.3
0.2
0.3
0.1
0.0
0.0
0.1
0.0
0.2
0.4
0.4
0.2
0.1
0.2

)
4
6
-
0
2
(
e
g
a
-
g
n
i
k
r
o
w

l

a
t
o
T

1.1
0.1
0.8
0.5
1.4
2.3
2.3
7.1
2.9
1.8
2.2
3.6
3.3
0.7
1.3
3.4
5.3
6.5
2.0
2.8
0.9
3.5
1.9
2.5
-0.8
1.5
0.4
0.4
2.4
2.4

)
4
2
-
0
2
(
g
n
u
o
Y

0.2
0.0
0.1
0.1
0.0
0.1
0.0
0.1
0.0
0.1
0.1
0.0
0.2
0.2
0.1
0.2
0.2
0.0
0.2
0.0
0.0
0.0
0.1
0.0
0.1
0.2
0.3
0.1
0.1
0.1

)
4
6
-
0
2
(
e
g
a
-
g
n
i
k
r
o
w

l

a
t
o
T

)
4
5
-
5
2
(
e
g
a
-
e
m

i
r
P

)
4
6
-
5
5
(

r
e
d
O

l

-0.4
1.4
-0.5
-1.5
0.3
-0.1
-1.6
0.6
0.2
0.0
0.8
-0.4
0.1
0.6
0.7
-0.6
1.1
1.2
-0.9
1.0
1.2
-1.3
4.6
2.4
-1.3
2.5
0.3
-0.4
1.2
0.5
-0.2
1.6
0.5
-0.7
2.8
0.7
2.0
-0.2
1.2
-0.5 -0.7
1.0
-0.1
0.1
1.0
-1.9
0.5
2.8
-0.9
3.7
1.4
-1.5
2.6
3.9
0.1
1.2
0.6
-0.2
2.0
0.8
-1.4
0.4
0.5
-0.5
2.1
1.4
-1.7
1.0
0.8
0.5
-1.1
2.1
-0.4 -0.4 -1.3
-0.3
1.1
0.2
-0.3 -0.3
0.5
-0.3 -0.2
0.6
-0.4
1.6
0.7
-0.5
1.5
0.8

)
4
5
-
5
2
(
e
g
a
-
e
m

i
r
P

)
4
2
-
0
2
(
g
n
u
o
Y

-1.0
0.2
-4.0
1.1
-4.8
1.4
0.0
-0.6
1.0
0.8
-3.9
1.2
-4.0
0.1
-3.0
0.4
-4.3
0.8
0.3
-1.2
-0.3 -0.4
0.2
-2.3
-0.9 -0.8
-2.8
1.7
0.2
-0.2
-0.3 -3.9
0.3
-2.7
-1.2 -2.7
0.6
-0.3
-0.8
0.3
-3.7
0.2
-1.5
0.4
-4.1
0.5
-2.9
1.1
0.7
-3.9
-0.7 -1.6
0.4
-2.7
-0.7 -1.9
-1.4
0.5
-1.8
0.5

)
4
5
-
5
2
(
e
g
a
-
e
m

i
r
P

)
4
6
-
5
5
(

r
e
d
O

l

0.8
-0.6
-0.2
0.5
-0.2
0.0
-0.1
0.7
-0.2 -0.3
1.5
0.5
0.2
0.6
2.1
-0.7
1.1
-0.4
1.1
-0.7
0.4
0.4
1.4
-0.7
0.8
0.4
-0.3
0.1
1.4
0.0
-0.1 -0.5
1.6
0.6
0.6
-0.1
0.3
-0.5
-0.3
0.2
0.3
0.0
1.2
0.1
0.5
0.9
1.3
0.2
0.2
0.1
1.2
-0.1
-0.1 -0.3
-0.6 -0.5
0.7
-0.4
0.6
-0.3

)
4
2
-
0
2
(
g
n
u
o
Y

0.1
0.0
0.0
0.2
0.0
0.1
0.0
0.2
0.0
0.0
0.2
0.0
0.4
0.1
0.2
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.0
0.2
0.1
0.1
0.1
0.1

Source: European Commission, EPC. 

t
c
e
f
f
e
n
o
i
t
c
a
r
e
t
n

I

)
4
6
-
5
5
(

r
e
d
O

l

n
e
m
o
W

n
e
M

0.1
0.5
0.5
1.4
0.5
1.8
0.6
0.2
-1.7 -0.2
1.0
2.1
-0.1
3.1
2.4
1.3
-0.1
2.2
0.1
0.5
1.0
0.5
1.6
1.4
-0.7
1.6
2.3
0.5
3.0
0.0
0.1
2.2
1.3
1.5
2.4
1.2
-0.2 -0.1
0.4
0.3
1.0
2.0
-0.2
0.6
1.9
1.9
1.0
0.7
0.7
1.9
0.5
2.0
0.3
1.9
0.2
2.4
0.4
0.5
0.5
0.8

IT

IE

EL

ES

EE

LV

FR

CY

CZ

BE

DE

DK

HR

BG

0.1
-0.1
0.0
-0.5
0.1
-0.4
0.0
-0.2
0.0
0.1
0.2
-0.9
0.2
0.1
0.7
-1.9
0.6
0.1
0.1
-0.1
0.1
-0.8
0.4
-1.2
0.3
0.6
0.0
-2.2
-2.9
0.0
-0.1 -0.2
0.6
-1.0
HU
0.6 MT
-1.0
0.0
0.1
0.0
-0.4
0.1
-0.8
0.1
0.2
0.2
-1.4
0.2
-0.9
0.0
-0.6
0.2
-0.4
-0.3 -0.1
-0.2 -0.1
0.1
-0.3
0.1
-0.4

NO

RO

NL

LU

EA

AT

PT

SK

SE

PL

LT

FI

SI

EU27

53 

 
 
        
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Box I.2.3: Assumptions on structural unemployment

The  structural  unemployment  rate  estimates  (NAWRU) (1),  based  on  the  methodology  developed  by  the 
Output  Gap  Working  Group  (OGWG)  attached  to  the  Economic  Policy  Committee  (EPC),  are  used  as  a 
proxy for the structural unemployment rate in the baseline scenario.  

As  a  general  rule,  actual  unemployment  rates  are  assumed  to  converge  to  NAWRU  rates  in  5  years 
(currently 2024), corresponding to the closure of the output gap. On their turn, NAWRU rates are assumed 
to  gradually (2)  converge  to  the  minimum  of  country-specific  ‘anchors’ (3)  or  the  median  of  national 
‘anchors’, whichever is the lowest.  

Anchor  values  are  country-specific  values  for  the  NAWRU  that  are  calculated  on  the  basis  of  the 
coefficients  of  a  panel  estimation  model  in  which  the  short-term  NAWRU  for  old  EU  member  states  is 
regressed  on  a  set  of  structural  variables  (unemployment  benefit  replacement  rates,  expenditure  on  active 
labour market policies, the power of unions proxied by union density and the tax wedge), together with a set 
of  cyclical  variables  (TFP,  fraction  of  employment  in  construction  and  the  real  interest  rate).  To  derive 
country-specific  anchors,  it  is  assumed  then  that  the  non-structural  variables  are  set  at  their  average 
values (4).  

Capping  country-specific  NAWRU  values  to  the  non-weighted  median (5)  is  done  in  order  to  avoid 
extrapolating  very  high  unemployment  rates  into  the  far  future.  It  should  be  noted  that  this  cap  on 
unemployment rates is a crucial assumption for some countries that currently register high unemployment. 
Higher  long-term  unemployment  assumptions  would,  through  weaker  employment  growth,  lead  to  lower 
potential output growth. Capping unemployment rates, as done in some cases, leads to higher employment, 
employment growth and GDP growth, and essentially assumes the implementation of future policy measures 
in the labour market. Therefore, this approach is not aligned with a 'no-policy-change' approach. 

In order to avoid changes in total/average unemployment rates as a result of the interaction between cohort-
specific structural unemployment rates and the structure of the labour force, the age-specific unemployment 
rates (by gender) for each projection year are calculated as follows: 

(cid:1872) =

(cid:1873)(cid:1853),(cid:1859)

where 

(cid:1872)
(cid:1873)(cid:1872)(cid:1867)(cid:1872)(cid:1853)(cid:1864)
2019 ∗ (cid:1864)(cid:1853),(cid:1859)

(cid:1872)

∑ (cid:3419)(cid:1873)(cid:1853),(cid:1859)

(cid:1853),(cid:1859)

∗ (cid:1873)(cid:1853),(cid:1859)

2019 

(cid:3423)

(cid:1872) =
(cid:1864)(cid:1853),(cid:1859)

(cid:1872)
(cid:1838)(cid:1832)(cid:1853),(cid:1859)
(cid:1872)
(cid:1838)(cid:1832)(cid:1872)(cid:1867)(cid:1872)(cid:1853)(cid:1864)

(cid:1872)

where  (cid:1873)(cid:1853),(cid:1859)
unemployment rate in period t; and (cid:1864)(cid:1853),(cid:1859)

(cid:1872)

  is  the  unemployment  rate  in  age  group  a  with  gender  g  in  period  t;  (cid:1873)(cid:1872)(cid:1867)(cid:1872)(cid:1853)(cid:1864)

  is  the  total 

(cid:1872)

 is the fraction of the total labour force.  

(1)  Non-accelerating wage rate of unemployment. For further details on the Commission’s NAWRU methodology, see 

Havik et al. (2014). 

(2)  In addition, if the estimated NAWRU ten years ahead (2029) is lower than the country-specific anchor, the former is 
assumed to replace the anchor. The gradual convergence, for countries whose NAWRU is higher than the EU median, 
is assumed to be completed by 2050.  

(3)  Under the guidance of the EPC-OGWG and with the twin objectives of improving the medium-term framework for 
fiscal  surveillance  up  to  T+10  (currently  2029),  DG  ECFIN  carried  out  some  econometric  work  (Orlandi,  2012) 
leading to the estimation of anchor values for the NAWRU.  

(4)  Over the estimation sample. 
(5)  The use of weighted or non-weighted averages makes a difference of around 0.2-0.3 pps. Weights are based on the 

labour force. 

(Continued on the next page) 

54 

  
 
 
 
   
 
 
 
 
 
 
                                                           
Underlying assumptions and projection methodologies 

Part I 

Box (continued) 

This  means that the unemployment rate structure (by age and gender) observed in the base year (2019) is 
kept unchanged throughout the projection period, with age/gender values adjusted proportionally in order to 
satisfy a given total unemployment rate target. 

Table 1  presents  the  unemployment  rate  assumptions.  In  the  EU,  the  unemployment  rate  is  assumed  to 
decline from 6.0% in 2019 to 5.8% in 2070. In the euro area, the unemployment rate is expected to fall from 
6.7% in 2019 to 6.2% in 2070. 

Table 1: Unemployment rate assumptions (15-64y, %) 

Table 1:
BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2019
5.4
4.2
2.0
5.0
3.2
4.4
5.0
17.3
14.1
8.5
6.6
10.0
7.1
6.3
6.3
5.6
3.4
3.4
3.4
4.5
3.3
6.5
3.9
4.5
5.8
6.7
6.8
3.7
6.7
6.0

2029
6.4
5.4
3.7
3.6
4.2
6.6
7.6
12.5
14.6
8.5
8.2
9.4
8.6
9.4
7.2
4.9
4.2
4.4
5.0
4.3
5.2
6.4
4.8
5.8
8.3
6.9
5.6
3.7
7.4
6.7

2050
6.4
5.4
3.7
3.6
4.2
6.6
7.0
7.2
7.2
7.0
7.0
7.1
7.0
7.1
7.0
4.9
4.2
4.3
5.0
4.3
5.2
6.4
4.8
5.8
7.0
6.9
5.6
3.6
6.2
5.9

2070
6.4
5.4
3.7
3.6
4.2
6.6
7.0
7.0
7.0
7.0
7.0
7.0
7.0
7.0
7.0
4.9
4.2
4.3
5.0
4.3
5.2
6.4
4.8
5.8
7.0
6.9
5.6
3.6
6.2
5.8

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

Source: European Commission, EPC. 

55 

 
 
   
 
 
 
 
 
 
   
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.2.11:  Employment rate projections by age group – total 

Total

20-64

Young

20-24

Prime-age

25-54

Older

55-64

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2019
70.6
75.2
80.4
78.4
80.6
80.2
75.1
60.9
68.1
71.6
66.8
63.6
75.1
77.6
78.3
72.7
75.4
77.3
80.2
76.8
73.3
76.2
71.0
76.4
73.6
77.1
82.1
79.4
72.6
73.1

2070
70.9
73.5
78.5
80.9
80.7
82.5
75.9
76.5
76.2
74.5
69.6
69.8
80.1
77.4
80.4
74.1
81.9
82.7
80.7
79.5
72.1
80.4
72.7
78.3
71.3
79.7
83.0
78.7
76.3
76.2

2019
43.0
40.9
50.2
66.3
67.3
65.6
65.3
27.9
39.1
51.1
45.2
33.1
52.9
59.1
56.2
44.3
49.1
72.6
71.4
68.3
55.9
48.7
41.7
55.3
43.9
61.3
61.5
65.4
51.8
51.9

2070
44.4
40.3
49.4
70.3
66.4
64.7
63.1
39.6
48.2
54.2
47.3
37.1
57.0
62.2
57.9
47.5
50.6
71.8
71.9
69.6
53.0
49.6
42.0
54.5
44.0
64.5
66.9
67.0
55.0
54.7

2019
80.8
82.3
87.4
82.6
85.4
84.3
80.1
70.8
75.8
80.9
78.4
70.5
82.6
83.1
84.9
84.3
84.5
85.1
85.2
85.3
82.9
85.2
81.4
88.6
82.0
83.2
86.4
83.6
79.7
80.6

2070
78.6
81.9
86.6
83.0
85.4
85.1
81.1
82.1
81.3
81.8
79.4
72.9
85.0
84.5
87.9
88.6
86.7
89.6
84.2
87.0
82.3
87.3
83.5
88.5
80.9
83.4
87.9
83.7
81.7
82.2

2019
52.4
64.5
67.1
71.9
72.6
72.7
61.8
43.7
53.9
53.0
44.3
54.4
61.2
67.7
68.7
43.3
56.9
51.5
69.7
54.6
49.9
60.4
47.9
48.0
57.7
66.8
77.9
72.8
60.0
59.1

2070
60.8
64.5
68.3
79.7
73.4
82.7
66.8
76.4
73.5
63.3
52.5
73.2
75.4
64.7
69.1
43.6
81.4
67.5
74.7
62.1
53.1
73.8
55.7
60.7
56.4
76.0
76.0
69.2
70.2
68.7

Source: European Commission, EPC. 

change 2019-2070 (pps)

Young

Prime-age Older

20-24
1.4
-0.6
-0.8
4.0
-0.9
-0.9
-2.2
11.7
9.1
3.1
2.1
4.0
4.1
3.1
1.8
3.2
1.4
-0.7
0.5
1.2
-2.8
0.9
0.3
-0.7
0.1
3.3
5.4
1.7
3.2
2.8

25-54
-2.2
-0.4
-0.9
0.5
0.0
0.9
1.0
11.3
5.6
0.9
1.0
2.4
2.4
1.4
3.0
4.2
2.3
4.5
-1.0
1.6
-0.6
2.1
2.1
0.0
-1.1
0.2
1.6
0.1
2.0
1.6

55-64
8.5
0.0
1.3
7.8
0.7
10.0
5.0
32.7
19.6
10.2
8.2
18.8
14.2
-3.0
0.4
0.3
24.5
16.0
4.9
7.5
3.2
13.3
7.8
12.8
-1.3
9.2
-1.9
-3.6
10.2
9.6

Total

20-64
0.3
-1.7
-1.9
2.5
0.2
2.3
0.7
15.6
8.2
2.9
2.8
6.2
5.0
-0.2
2.2
1.4
6.5
5.5
0.6
2.7
-1.2
4.2
1.7
1.9
-2.3
2.6
0.8
-0.7
3.7
3.1

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2.6. 

EMPLOYMENT PROJECTIONS 

The total employment rate in the EU is projected 
to  increase  from  73%  in  2019  to  76%  in  2070. 
This  change  mostly  reflects  higher  employment 
rates among older people and women in general. 

The  Ageing  Report  methodology  calculates 
employment as a residual variable. It is determined 
on  the  basis  of  the  population  projections  from 
Eurostat,  future  participation  rates  derived  using 
the  CSM and the  unemployment rate assumptions 
(see Box I.2.3).  

The employment rate among people aged 20-64 is 
expected to rise from around 73% in 2019 to about 
76%  in  2070  (see  Table  I.2.11).  This  overall 
increase in the employment rate by 3 pps includes 
increases  by  5 pps  in  the  female  employment  rate 
and  a  small  increase  of  1 pp  in  the  male  one  (see 
Tables  I.2.12  and  I.2.13).  In  spite  of  the  strong 
increase  in  female  employment,  a  higher  share  of 
the  male  population  aged  20-64  would  neverthe-

less be employed in 2070: 80% compared to 72% 
for women. 

Employment  among  older  workers  is  expected  to 
rise  considerably  over  the  projection  horizon  for 
both  sexes,  with  increases  of  6 pps  and  13 pps  in 
the  respective  employment  rates  of  men  and 
women  aged  55-64.  This  reflects  how  recent 
pension  and  labour  market  reforms  in  many 
Member  States  incentivise  older  workers  to  retire 
later. Only few Member States show a decrease in 
the employment rate for this age bracket. By 2070, 
the employment rate for people aged 55-64 would 
be  inferior  to  60%  in  Croatia,  Luxembourg, 
Poland,  Romania  and  Slovakia,  which  under 
current  legislation  all  cap  the  statutory  retirement 
at  65  years  –  less  in  the  case  of  Slovakia  and  for 
Romanian  and  Polish  women  –  with  early 
retirement possible several years earlier (see Table 
II.A5.2 in Annex 5). 

56 

 
 
             
 
Underlying assumptions and projection methodologies 

Part I 

Table I.2.12:  Employment rate projections by age group – men 

Total

20-64

Young

20-24

Prime-age

25-54

Older

55-64

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2019
74.6
79.4
87.8
82.0
84.6
84.0
81.4
70.9
74.0
75.2
72.2
73.3
80.9
79.4
79.1
77.2
83.2
86.8
84.8
81.2
81.0
79.9
80.5
79.7
80.0
78.5
84.5
81.9
78.1
78.9

2070
74.0
77.6
84.8
84.8
82.9
86.0
80.4
80.6
79.6
77.0
73.5
76.7
84.3
78.1
81.2
74.8
86.6
86.4
83.4
81.1
79.0
82.2
81.0
80.4
78.5
80.7
84.8
80.1
79.6
80.1

2019
44.2
46.2
57.3
67.5
69.0
70.4
66.2
29.5
42.3
53.4
52.9
37.9
49.9
61.6
57.0
47.9
55.4
72.3
71.3
70.0
62.2
53.1
50.0
60.7
55.7
62.1
63.1
65.9
54.4
55.3

2070
44.4
45.7
55.9
71.9
67.6
70.1
63.3
42.7
51.7
56.5
55.4
42.3
55.9
64.0
59.1
50.0
56.2
72.3
71.4
72.1
58.6
53.9
49.8
60.0
55.0
64.6
67.5
67.5
57.4
57.6

2019
84.7
86.1
94.5
86.3
89.5
89.5
86.7
80.9
81.6
85.1
81.7
80.8
88.3
85.1
85.3
88.6
90.8
94.2
89.3
88.5
89.3
88.1
89.6
90.9
88.4
85.6
88.9
86.0
85.3
86.3

2070
82.3
86.4
93.6
87.3
88.1
89.6
86.8
86.4
85.6
84.4
82.6
80.9
89.7
85.1
88.6
89.0
92.0
93.2
86.6
89.3
88.0
88.4
91.4
90.6
87.9
85.3
89.7
84.6
85.5
86.4

2019
57.6
69.3
75.0
76.1
77.0
70.0
69.9
56.7
61.1
55.3
53.2
64.8
72.2
67.6
69.9
49.2
69.0
67.0
78.3
63.4
61.4
66.6
60.3
52.4
60.9
65.1
80.0
76.8
66.1
66.1

2070
63.5
67.3
72.4
83.3
74.2
82.8
70.3
80.0
74.9
65.5
56.5
78.1
80.8
65.3
69.6
44.8
84.2
72.5
79.3
61.1
63.4
76.8
65.1
61.7
62.2
74.9
78.5
72.5
72.6
72.0

Source: European Commission, EPC. 

Table I.2.13:  Employment projections by age group – women 

Total

20-64

Young

20-24

Prime-age

25-54

Older

55-64

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2019
66.6
70.8
72.7
74.8
76.5
76.3
69.1
51.3
62.2
68.1
61.5
53.8
69.6
75.9
77.5
68.0
67.6
66.6
75.5
72.4
65.5
72.7
61.3
72.8
67.1
75.7
79.7
76.8
67.1
67.2

2070
67.8
69.1
71.8
77.0
78.6
78.9
71.5
72.1
72.8
72.2
65.5
62.3
76.2
76.5
79.6
73.2
76.9
78.4
78.1
77.8
64.8
78.8
63.2
75.9
63.8
78.7
81.1
77.3
72.9
72.3

2019
41.7
35.4
42.7
65.1
65.5
60.5
64.4
26.2
35.7
48.7
37.0
27.8
55.7
56.4
55.2
40.5
42.5
72.9
71.4
66.6
49.2
44.2
32.8
49.2
31.5
60.4
59.7
64.8
49.0
48.3

2070
44.4
34.7
42.5
68.6
65.1
59.1
62.9
36.3
44.6
51.9
38.7
31.5
58.2
60.3
56.7
44.9
44.6
71.3
72.4
66.8
47.1
45.3
33.6
48.6
32.3
64.5
66.3
66.5
52.4
51.5

2019
76.8
78.3
79.9
78.8
81.1
78.7
73.7
60.8
69.9
76.8
74.9
60.2
77.1
81.1
84.5
79.9
77.9
74.8
81.1
82.1
76.3
82.5
72.6
86.1
75.4
80.7
83.7
81.1
74.0
74.7

2070
74.8
77.2
79.1
78.7
82.6
80.4
75.6
77.3
77.1
79.3
75.9
64.2
80.5
83.8
87.2
88.1
81.2
85.3
81.7
84.6
76.3
86.3
74.6
86.2
73.5
81.3
86.1
82.9
77.8
77.9

2019
47.3
60.0
59.3
67.7
68.3
75.0
53.9
32.2
47.0
50.9
36.0
44.6
50.4
67.7
67.8
37.1
46.4
35.7
61.2
46.0
39.4
55.0
36.5
43.6
54.6
68.4
75.8
68.6
54.2
52.6

2070
58.2
61.6
64.1
76.0
72.5
82.7
63.5
72.6
72.2
61.2
48.3
68.0
70.8
64.1
68.5
42.3
78.5
61.5
70.1
63.1
42.6
71.2
45.2
59.7
50.4
77.1
73.4
65.8
67.8
65.4

Source: European Commission, EPC. 

change 2019-2070 (pps)

Young

Prime-age Older

20-24
0.2
-0.5
-1.3
4.4
-1.4
-0.4
-2.8
13.2
9.3
3.1
2.6
4.4
6.0
2.4
2.0
2.1
0.8
0.0
0.0
2.2
-3.6
0.8
-0.3
-0.7
-0.6
2.5
4.4
1.6
3.0
2.3

25-54
-2.4
0.4
-0.9
1.0
-1.4
0.0
0.0
5.5
4.0
-0.8
0.9
0.1
1.4
0.0
3.3
0.4
1.2
-1.0
-2.6
0.8
-1.2
0.3
1.7
-0.3
-0.5
-0.3
0.7
-1.4
0.2
0.1

55-64
5.9
-2.1
-2.7
7.2
-2.8
12.8
0.5
23.3
13.8
10.2
3.3
13.4
8.6
-2.3
-0.2
-4.4
15.2
5.5
1.0
-2.4
2.0
10.2
4.9
9.3
1.2
9.8
-1.5
-4.3
6.5
6.0

change 2019-2070 (pps)

Young

Prime-age Older

20-24
2.6
-0.7
-0.2
3.6
-0.3
-1.4
-1.5
10.1
8.9
3.2
1.7
3.7
2.5
3.9
1.5
4.3
2.1
-1.6
0.9
0.3
-2.1
1.1
0.7
-0.6
0.8
4.2
6.6
1.8
3.4
3.2

25-54
-2.0
-1.1
-0.9
-0.1
1.5
1.7
2.0
16.5
7.2
2.5
1.0
4.0
3.4
2.7
2.6
8.2
3.3
10.6
0.6
2.5
0.0
3.8
2.0
0.1
-1.9
0.6
2.4
1.8
3.8
3.2

55-64
11.0
1.6
4.8
8.3
4.2
7.6
9.6
40.4
25.2
10.3
12.3
23.4
20.3
-3.7
0.7
5.2
32.1
25.8
8.9
17.1
3.2
16.1
8.7
16.1
-4.2
8.7
-2.4
-2.9
13.6
12.8

Total

20-64
-0.6
-1.8
-3.0
2.8
-1.7
2.0
-0.9
9.6
5.6
1.8
1.4
3.4
3.4
-1.3
2.1
-2.3
3.5
-0.4
-1.4
0.0
-2.0
2.2
0.6
0.8
-1.6
2.1
0.3
-1.9
1.6
1.1

Total

20-64
1.2
-1.8
-1.0
2.2
2.1
2.5
2.4
20.8
10.7
4.1
4.0
8.4
6.6
0.6
2.1
5.3
9.3
11.8
2.6
5.4
-0.7
6.1
2.0
3.1
-3.3
3.0
1.4
0.4
5.8
5.1

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

57 

 
 
        
 
 
        
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.2.14:  Share of older workers (55-64y) in total employment (20-64y) 

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2019
16.7
19.6
17.1
19.8
22.2
20.1
15.3
16.0
17.1
16.7
16.1
20.1
15.4
20.9
21.5
11.4
15.9
13.1
20.0
16.0
15.0
18.4
13.9
14.7
16.4
20.2
19.1
18.4
19.1
18.3

Total
2045
19.2
24.7
22.0
20.3
21.5
25.3
19.4
23.0
23.3
19.6
19.1
24.4
17.8
23.5
22.6
13.9
23.8
20.2
19.4
18.2
20.5
21.6
20.4
19.8
21.7
21.4
19.8
18.8
21.5
21.4

2070
20.0
22.0
20.1
22.1
20.2
24.9
20.6
24.8
24.2
20.0
19.1
27.1
20.0
20.6
21.1
14.1
23.4
19.8
21.1
18.0
19.1
22.1
18.8
19.2
19.0
24.8
20.6
20.5
22.0
21.6

2019
17.2
19.0
16.9
19.8
21.9
16.9
16.0
17.0
17.4
16.3
17.2
20.1
17.0
18.7
19.7
12.2
16.2
14.5
21.1
17.2
15.9
18.8
14.6
14.9
15.2
18.8
18.8
18.6
19.1
18.4

Men
2045
18.9
23.9
21.5
20.5
20.7
24.6
18.9
22.2
22.2
19.3
19.3
22.9
17.9
23.1
22.5
14.2
23.1
20.9
20.1
17.2
21.9
21.7
20.8
19.9
21.4
20.6
19.9
19.7
20.9
21.0

2070
19.8
21.4
19.7
22.0
19.8
24.0
20.2
24.5
23.3
19.6
19.4
26.1
19.7
20.4
21.1
14.4
22.8
20.5
21.5
17.2
20.5
21.8
19.6
19.1
18.9
24.0
20.8
21.1
21.5
21.3

2019
16.1
20.4
17.2
19.8
22.4
23.6
14.5
14.6
16.7
17.2
14.8
20.0
13.5
23.2
23.2
10.3
15.4
10.9
18.7
14.5
13.8
18.0
13.0
14.5
17.8
21.7
19.5
18.1
19.0
18.2

Women
2045
19.5
25.7
22.6
20.1
22.3
26.1
19.9
24.1
24.5
19.8
18.9
26.5
17.7
23.8
22.6
13.6
24.7
19.3
18.6
19.3
18.6
21.5
19.8
19.7
22.1
22.2
19.6
17.9
22.2
21.9

2070
20.2
22.6
20.7
22.2
20.7
26.1
21.1
25.2
25.2
20.3
18.7
28.5
20.4
20.8
21.0
13.8
24.2
18.8
20.6
18.8
17.2
22.4
17.7
19.3
19.1
25.7
20.3
19.8
22.4
21.9

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

longer 

staying 

the  need 

The  share  of  the  older  workers  rises  generally 
more  for  women,  due  to  cohort  effects  and 
reflecting 
in 
for 
employment  to  fulfil  qualifying  conditions  for 
labour  market 
retirement  because  of  a 
entrance 
careers. 
or 
Exceptions  are  countries  were  older  women  are 
working  currently  more  often  than  men,  e.g.  the 
Baltic countries, or countries with more favourable 
retirement  conditions  for  women,  e.g.  Poland  and 
Slovakia. 

interrupted  working 

later 

Source: European Commission, EPC. 

Mainly  as  a  result  of  the  ageing  process,  the  age 
structure of the working population will undergo a 
number of significant changes. The share of older 
workers  in  total  employment  at  EU  level  is 
projected to rise from 18% in 2019 to around 21% 
in 2045 and to remain around this level thereafter 
(see Table I.2.14). The highest increase is expected 
in  Greece,  with  older  workers’  share  rising  by 
more  than  half  by  2070,  followed  by  Malta, 
Hungary,  Spain,  Italy  and  Romania.  At  27%, 
Italian older workers would have the highest share 
in  total  employment  in  2070.  At  the  other  end 
stands  Luxembourg,  where people aged 55-64 are 
projected 
total 
to 
workers (21). 

represent  only  14%  of 

(21)  See  Part  III  -  Statistical  Annex  for  employment  rates  for 

the age group 15-74. 

58 

                                                           
 
 
 
 
 
        
 
Underlying assumptions and projection methodologies 

Part I 

2.7. 

ECONOMIC DEPENDENCY RATIOS 

the  first  half  of 

The economic old-age dependency ratio (inactive 
elderly  versus  employed  people)  is  projected  to 
rise significantly in all Member States, especially 
in 
the  projection  period. 
Similarly,  the  ratio  between  the  total  inactive 
(economic 
population  and  employed  people 
dependency  ratio)  would  rise  strongly  amid 
demographic ageing with large variability across 
countries. 

An  important  indicator  to  assess  the  impact  of 
ageing  on  budgetary  expenditure,  particularly  on 
its  pension  component,  is  the  economic  old-age 
dependency  ratio.  This  indicator  expresses  the 
inactive elderly population (+65) as a share of total 
employment (aged 20-64 or 20-74). The economic 
old-age  dependency  ratio  is  projected  to  rise 
significantly in the EU: from 45% in 2019 to 67% 
in 2045 and to 72% in 2070 (from 44% to 64% and 

68%  in  terms  of  employment  in  the  20-74  age 
group)  (see  Table  I.2.15).  This  means  that  there 
will be less than 6 employed persons for 4 inactive 
persons aged  more than 65 in 2070, down  from 9 
employed persons in 2019. 

Across EU Member States, the projected economic 
old-age  dependency  ratio  for  2070  ranges  from  a 
minimum  of  56%  in  Sweden  and  Denmark  to  a 
maximum  of  90%  in  Poland.  The  bulk  of  the 
expected  increase  is  generally  concentrated  in  the 
first  half  of  the  projection  period,  2019-2045, 
though  with  some  notable  exceptions.  The  largest 
overall  increases  would  be  in  Slovakia,  Poland, 
Luxembourg,  Lithuania,  Romania,  Croatia,  Latvia 
and Malta. 

The  economic  old-age  ratio  is  expected  to  be 
above  or  equal  to  80%  in  2070  (less  than  5 
employed  persons  for  4  inactive  persons  aged 
more  than  65)  in  Croatia,  Italy,  Poland,  Romania 

Table I.2.15:  Economic old-age dependency ratio 

(Inactive population +65) / (employment 20-64y)

(Inactive population +65) / (employment 20-74y)

2019

2045

2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

44.9
44.8
38.3
39.9
41.8
35.9
29.1
59.7
46.0
49.4
50.6
58.4
31.9
39.9
38.3
30.5
41.0
36.3
37.5
38.5
37.5
44.8
40.5
42.3
33.6
47.1
38.7
33.0
46.3
44.7

63.7
70.5
61.4
53.5
60.1
53.8
49.1
80.2
73.9
67.6
74.6
86.1
42.8
71.0
70.6
55.0
56.8
45.7
56.6
59.8
65.3
76.3
70.7
68.7
69.1
59.3
46.4
49.6
68.3
66.9

71.9
78.1
65.3
56.0
62.7
61.4
63.0
77.2
75.6
72.0
89.8
82.2
56.9
78.4
78.9
74.6
66.9
73.2
60.5
67.5
90.0
76.4
79.8
72.4
86.7
70.9
55.9
61.6
71.0
71.7

Source: European Commission, EPC. 

Change 
2019-2045
18.8
25.7
23.1
13.6
18.3
17.9
20.0
20.5
27.8
18.2
24.0
27.7
10.9
31.2
32.2
24.5
15.8
9.4
19.0
21.4
27.8
31.5
30.2
26.3
35.5
12.2
7.7
16.6
22.0
22.2

Change 
2045-2070
8.2
7.6
3.9
2.5
2.6
7.6
13.9
-3.0
1.7
4.5
15.2
-3.9
14.0
7.4
8.3
19.6
10.0
27.5
3.9
7.7
24.7
0.1
9.1
3.8
17.6
11.6
9.5
12.0
2.7
4.9

2019

2045

2070

44.5
43.5
37.3
38.5
40.7
33.9
28.3
58.4
45.6
48.7
49.9
56.9
31.1
38.1
37.0
30.4
40.3
35.6
36.3
37.9
36.8
43.1
39.2
41.9
33.1
45.6
37.2
31.7
45.3
43.6

61.8
66.6
58.9
50.3
57.4
50.3
46.1
75.8
68.9
65.1
72.5
79.4
41.2
68.0
68.3
54.4
54.8
44.8
54.0
58.3
62.5
71.2
66.3
66.6
67.6
57.0
44.7
47.5
64.8
63.5

69.8
74.7
63.4
50.8
59.9
55.8
59.0
71.7
71.1
69.2
87.2
73.6
53.5
75.6
76.5
73.8
64.8
71.6
56.3
65.7
86.4
71.3
75.5
70.6
85.2
66.0
53.7
58.7
66.9
67.8

Change 
2019-2045
17.4
23.1
21.6
11.7
16.8
16.4
17.8
17.3
23.3
16.4
22.6
22.5
10.1
29.9
31.4
24.1
14.5
9.3
17.7
20.4
25.8
28.2
27.1
24.7
34.5
11.4
7.5
15.8
19.5
19.9

Change 
2045-2070
7.9
8.1
4.5
0.5
2.4
5.5
13.0
-4.0
2.2
4.1
14.7
-5.7
12.3
7.6
8.1
19.3
10.0
26.7
2.3
7.4
23.9
0.0
9.2
4.0
17.6
8.9
9.0
11.1
2.1
4.3

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

59 

 
        
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.2.16:  Total economic dependency ratio 

(Total inactive population) / (employment 20-64y)

(Total inactive population) / (employment 20-74y)

2019

133.6
115.0
102.5
105.2
98.0
99.1
114.8
156.4
123.0
138.4
140.2
151.5
102.1
105.8
100.6
106.4
112.0
97.5
97.2
101.2
114.4
110.3
125.1
110.0
107.2
114.2
99.5
100.3
120.9
119.2

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2045

147.7
148.2
132.5
118.3
120.1
110.8
124.7
149.2
138.6
149.2
155.3
166.3
105.5
138.8
128.7
125.3
116.2
92.5
115.9
119.7
140.9
139.3
151.0
132.1
151.2
116.2
104.5
114.2
137.9
136.9

2070

157.1
156.8
137.3
119.5
125.2
117.3
137.0
143.7
141.1
152.2
172.1
162.1
118.2
146.3
138.1
147.7
128.1
123.9
118.7
129.0
166.6
137.9
159.5
136.7
169.4
126.1
114.3
126.8
140.9
141.9

Change 
2019-2045
14.1
33.2
30.0
13.1
22.1
11.7
10.0
-7.2
15.6
10.8
15.0
14.8
3.4
33.0
28.1
18.9
4.2
-5.0
18.7
18.5
26.5
29.0
25.9
22.2
44.1
2.1
5.0
13.9
16.9
17.7

Change 
2045-2070
9.4
8.6
4.8
1.2
5.1
6.5
12.3
-5.5
2.5
3.0
16.8
-4.2
12.7
7.5
9.4
22.4
11.9
31.4
2.8
9.3
25.7
-1.4
8.4
4.6
18.2
9.9
9.8
12.6
3.0
5.1

2019

132.2
111.6
99.8
101.6
95.2
93.4
111.4
153.0
121.8
136.3
138.3
147.6
99.3
101.2
97.0
105.9
110.1
95.6
94.1
99.8
112.1
106.1
121.1
108.8
105.6
110.6
95.7
96.5
118.2
116.4

2045

143.2
140.0
127.0
111.1
114.7
103.6
117.0
140.9
129.2
143.7
150.8
153.3
101.5
133.0
124.6
124.0
112.1
90.9
110.7
116.7
135.0
130.2
141.6
128.1
147.9
111.8
100.7
109.5
130.8
130.0

2070

152.3
150.0
133.3
108.4
119.4
106.5
128.4
133.4
132.7
146.1
167.1
145.1
111.1
141.1
133.9
146.0
124.2
121.2
110.5
125.5
160.1
128.8
150.9
133.3
166.5
117.5
109.8
120.8
132.8
134.2

Change 
2019-2045
11.1
28.3
27.2
9.5
19.5
10.1
5.6
-12.1
7.5
7.3
12.5
5.7
2.1
31.8
27.5
18.1
2.0
-4.7
16.5
16.8
22.8
24.1
20.6
19.3
42.3
1.2
5.0
13.0
12.6
13.6

Change 
2045-2070
9.1
10.0
6.3
-2.8
4.7
2.9
11.4
-7.4
3.5
2.4
16.3
-8.2
9.6
8.1
9.3
22.0
12.1
30.4
-0.2
8.9
25.2
-1.4
9.3
5.1
18.6
5.7
9.2
11.3
2.0
4.2

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

Source: European Commission, EPC. 

and  Slovakia.  For  Italy,  this  would  be  already  the 
case  in  2045,  when  also  for  Greece  the  ratio 
exceeds  80%  according  to  the  projections.  The 
ratio would slightly decrease in the second part of 
the projections in both countries. 

Another  relevant  indicator  is  the  total  economic 
dependency  ratio,  calculated  as  the  ratio  between 
the  total  inactive  population  and  employment.  It 
the  average  number  of 
gives  a  measure  of 
individuals  that  each  employed  person  'supports' 
economically, which is relevant for potential GDP 
per  capita  growth.  This  broadest  definition  of  the 
dependency  ratio  is  expected  to  constantly  grow 
over  the  projection  period,  from  an  average  of 
119% in 2019 to 142% in 2070 (see Table I.2.16).  

indicator 
The  projected  development  of 
reflects 
the 
changes in life expectancy and fertility rates during 

the  profound  societal 

impact  of 

this 

60 

are, 

large 

however, 

the next few decades in nearly all Member States. 
There 
cross-country 
differences.  While  Bulgaria,  Latvia,  Luxemburg 
Poland  and  Slovakia  have  increases  of  more  than 
40 pps  and  up  to  62 pps  in  the  case  of  Slovakia, 
other  countries  show  more  limited  increases,  with 
even  a  steady  decrease  projected  for  Greece. 
Overall,  a  smaller  increase  is  projected  when 
considering employment in the age group 20-74. 

2.8. 

PROJECTION OF TOTAL HOURS WORKED 

The number of hours worked are expected to fall 
by 12% in the EU over the projection period, with 
decreases projected for most countries (22). 

(22)  The projection of weekly hours in Table I.2.17 is calculated 
using the CSM described in this chapter, which is different 
from  the  projection  of  hours  worked  in  Chapter  3.  To 

                                                           
 
        
 
Underlying assumptions and projection methodologies 

Part I 

Table I.2.17:  Projection of total weekly hours worked (thousands) and breakdown in full- and part-time work 

2019

2045

2070

% change (total)

Total

Full-time

Part-time

Total

Full-time

Part-time

Total

Full-time

Part-time

2019-2045

2045-2070

2019-2070

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

HR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

NO

175.400

128.070

205.756

96.518

1.481.429

25.349

83.503

157.946

733.760

989.060

64.331

856.456

16.144

35.125

52.625

11.080

175.723

9.830

286.207

155.909

686.111

185.303

337.791

37.677

100.429

91.305

180.703

90.643

EA
EU27

5.484.536

7.359.539

83.7%

99.0%

96.6%

86.8%

84.7%

93.8%

89.9%

95.4%

92.5%

89.2%

97.6%

89.0%

94.8%

95.4%

96.5%

89.4%

97.6%

93.1%

65.3%

84.1%

96.8%

96.4%

96.2%

95.6%

97.8%

91.8%

85.3%

84.3%

87.7%

89.6%

16.3%

172.703

1.0%

3.4%

13.2%

15.3%

6.2%

10.1%

4.6%

7.5%

93.879

178.171

97.630

1.340.503

22.803

98.601

156.570

767.696

10.8%

1.005.073

2.4%

11.0%

5.2%

4.6%

3.5%

10.6%

2.4%

6.9%

34.7%

15.9%

3.2%

3.6%

3.8%

4.4%

2.2%

8.2%

14.7%

15.7%

12.3%

10.4%

52.382

827.109

18.688

23.067

36.912

12.472

165.421

13.034

273.941

151.473

564.413

154.701

254.354

33.925

79.707

88.055

207.854

99.645

5.277.033

6.891.138

83.1%

99.0%

96.4%

86.8%

84.4%

93.7%

89.5%

95.4%

92.3%

88.9%

97.5%

89.1%

94.6%

95.3%

96.5%

88.8%

97.3%

92.7%

64.9%

83.6%

96.6%

96.2%

95.9%

95.4%

97.7%

92.1%

85.1%

84.5%

87.5%

89.1%

16.9%

165.359

1.0%

3.6%

13.2%

15.6%

6.3%

10.5%

4.6%

7.7%

11.1%

2.5%

10.9%

5.4%

4.7%

3.5%

11.2%

2.7%

7.3%

35.1%

16.4%

3.4%

3.8%

4.1%

4.6%

2.3%

7.9%

14.9%

15.5%

12.5%

10.9%

78.163

167.550

100.038

1.293.597

21.134

100.220

143.874

725.531

988.928

42.804

785.281

19.070

18.123

29.044

11.837

149.602

12.204

270.521

144.499

452.278

137.555

209.774

31.130

67.137

80.599

216.910

101.811

5.045.643

6.462.763

82.6%

99.0%

96.3%

86.8%

84.3%

93.5%

89.1%

95.4%

92.2%

88.7%

97.4%

89.1%

94.6%

95.2%

96.5%

88.4%

97.2%

92.4%

64.4%

83.6%

96.5%

96.1%

95.8%

95.2%

97.7%

91.7%

84.7%

84.3%

87.3%

88.7%

17.0%

1.0%

3.6%

13.1%

15.6%

6.4%

10.6%

4.6%

7.8%

11.1%

2.5%

10.8%

5.4%

4.7%

3.4%

11.2%

2.7%

7.3%

35.3%

16.3%

3.3%

3.8%

4.0%

4.7%

2.3%

8.1%

15.1%

15.6%

12.7%

11.1%

-1.5

-26.7

-13.4

1.2

-9.5

-10.0

18.1

-0.9

4.6

1.6

-18.6

-3.4

15.8

-34.3

-29.9

12.6

-5.9

32.6

-4.3

-2.8

-17.7

-16.5

-24.7

-10.0

-20.6

-3.6

15.0

9.9

-3.8

-6.4

-4.3

-16.7

-6.0

2.5

-3.5

-7.3

1.6

-8.1

-5.5

-1.6

-18.3

-5.1

2.0

-21.4

-21.3

-5.1

-9.6

-6.4

-1.2

-4.6

-19.9

-11.1

-17.5

-8.2

-15.8

-8.5

4.4

2.2

-4.4

-6.2

-5.7

-39.0

-18.6

3.6

-12.7

-16.6

20.0

-8.9

-1.1

0.0

-33.5

-8.3

18.1

-48.4

-44.8

6.8

-14.9

24.2

-5.5

-7.3

-34.1

-25.8

-37.9

-17.4

-33.2

-11.7

20.0

12.3

-8.0

-12.2

BE

BG

CZ

DK

DE

EE

IE

EL

ES

FR

HR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

NO

EA
EU27

Hours worked by people aged 15-74y. 
Source: European Commission, EPC. 

Total  hours  worked  are  projected  to  decrease  by 
6% in 2019-2045 in the EU (see Table I.2.17) (23). 
A  similar  decrease  is  expected  in  the  second  half 
of  the  projection  period  so  that  the  number  of 
hours worked would fall by 12% in 2019-2070.  

There are major differences across Member States, 
reflecting  different  demographic  outlooks.  A 
total  hours  worked  of  30-50% 
reduction 

in 

calculating  potential  GDP,  the  estimated  potential  hours 
worked using the production function approach were used 
(see Chapter 3 and Annex 3). Specifically, for the potential 
GDP  projections  until  2029,  the  growth  rates  of  hours 
worked  were  estimated  using  the  production  function 
approach; thereafter the growth rates estimated by the CSM 
are used (see Table I.3.2 in Chapter 3). Due to the different 
data  sources  and  projection  models,  there  may  be  some 
differences between the two projections. 

(23)  The  total  number  of  hours  worked  is  the product  between 
employment  and  hours  worked  per  person.  Regarding 
hours worked, the following assumptions are made: i) total 
amount of hours worked per person (in the base year 2019) 
are  kept  constant  by  gender  and  type  of  work  (part-time 
versus  full-time);  and  ii)  the  part-time  share  of  total  work 
by  gender  and  age  groups  (20-24,  25-54  and  55-74)  are 
kept constant over the entire projection period. 

between  2019  and  2070  is  projected  for  Bulgaria, 
Croatia,  Latvia,  Lithuania,  Poland,  Romania  and 
Slovakia. Only seven countries are expected to see 
an increase in the number of hours people work in 
total,  with  increases  of  20-25%  in  Ireland,  Malta 
and  Sweden.  The  share  of  part-time  work  would 
remain broadly unchanged so that the current large 
differences between countries would remain. Part-
time  labour  accounts  for  less  than  4%  of  total 
hours  worked  in  Bulgaria,  the  Czech  Republic, 
Croatia, Lithuania, Hungary, Poland, Portugal and 
Slovakia, as compared to 35% of total hours in the 
case of the Netherlands. 

2.9.  COMPARING THE 2021 AND 2018 

LABOUR MARKET PROJECTIONS 

Labour  market  figures  for  the  base  year  were 
better  than  assumed  in  the  2018  Ageing  Report. 
The  improved  starting  point  for  employment, 
participation 
rates 
reverberates  in  the  labour  market  assumptions, 

unemployment 

and 

61 

                                                                                   
 
     
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

which are generally more favourable than in the 
previous  exercise.  Yet,  the  revised  population 
projections 
lower 
considerably 
in 
employment for several countries in 2070. 

result 

This  section  provides  a  summary  comparison  of 
the  main  labour  market  assumptions  discussed 
higher  with  those  underlying  the  2018  Ageing 
Report.  In  most  countries,  the  total  labour  force 
and  total  employment  were  larger  in  2019  than 
anticipated  in  the  2018  Ageing  Report  (see  Table 
I.2.18).  In  the  EU  as  a  whole,  3.3  million  more 
people  were  employed  in  2019  than  expected  in 
the  previous  exercise,  with  the  labour  force 
showing  1.1 million  more  people  as  two  thirds  of 
the  higher  employment  stems 
from  people 
previously presumed unemployed.  

The  employment  rate  –  which  also  accounts  for 
difference  in  the  population  size  –  was  1.4  pps 
higher  on  average  in  2019  than  projected  in  the 
2018  Ageing  Report  (see  Table  I.2.19).  Only  in 
Denmark,  Sweden  and  Norway  was  it  lower.  The 
biggest upward revisions for the base year were for 
Bulgaria, Malta, Latvia, Slovenia, Cyprus, Estonia, 
Croatia  and  Portugal.  Actual  employment  rates 
among people aged 55-64 were also higher in 2019 
for  most  countries,  especially  in  the  Eastern  and 
Central European Member States. 

As shown in Table I.2.19, participation rates were 
higher than previously projected in most countries 
in  2019,  which  is  again  driven  by  the  oldest  age 
bracket.  Unemployment  came 
than 
assumed in the 2018 Ageing Report on average. 

lower 

in 

Using  a  simple  identity (24), Table  I.2.20  provides 
a  breakdown  of  the  change  in  the  employment 
projection  for  2070  between  the  2021  and  2018 
Ageing  Reports.  For  the  EU  as  a  whole,  total 
employment  in  2070  was  revised  downward  by 
1.1%.  This  revision  is  due  to  the  downward 

(24)  The  labour  force  identity:  L  =  E  +  U  can  be  written  as 
follows: E = P * PR * [1-UR] where L is the labour force; 
E is employment; U is unemployment; P is population; PR 
is the participation rate; and UR the unemployment rate.  

Taking  the  logarithm  of  the  above  expression,  revisions  in 
employment  level  projections  can  be  approximated  as 
follows: 
E
E

PR
PR
where  indices  0  and  1  refer  to  two  distinct  projection 
exercises. 

(
UR

P
1
P
0

)0

log(

log(

log(

UR

-

-

»

+

)

)

)

0

1

1

1

0

62 

rates 

(+0.8%)  and 

revision  of  population  projections  (-2.7%),  which 
is  only  partly  offset  by  the  upward  revision  in 
participation 
lower 
unemployment rate (+0.8%). There are significant 
downward  revisions  in  the  2070  employment 
projections  for  Luxembourg,  Belgium,  France, 
Croatia and  Latvia because of the  new population 
projections.  

the 

the 

close 

The breakdown of revisions in employment levels 
underscores 
between 
employment/labour force and population variables. 
In  fact,  there  is  a  high  cross-country  correlation 
between revisions in employment and those for the 
population projections (see Graph I.2.7). 

link 

Table I.2.18:  Labour force projections: revisions AR2021 vs. 

AR2018 (thousands) 

Labour force (20-64)
2019
2070
-952
-26
221
190
213
128
-254
-63
2817
133
49
16
311
182
502
31
-1916
38
-4155
-169
-165
-11
440
-196
69
6
-67
22
48
64
-81
6
168
67
96
49
-642
129
-261
-53
431
96
381
57
-30
258
33
38
-188
-16
-182
60
-221
57
-126
-23
-3698
369
-3334
1090

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

Employment (20-64)
2019
2070
-808
92
232
226
226
181
-220
-57
2883
358
53
34
287
188
494
65
-1598
537
-3616
24
-143
70
581
-14
61
23
-57
45
52
68
-77
5
194
91
95
50
-650
251
-230
-8
480
348
404
210
45
339
32
59
-156
55
-155
71
-198
14
-127
-31
-2404
2113
-1788
3326

Source: European Commission, EPC. 

                                                           
 
 
 
 
    
 
 
Underlying assumptions and projection methodologies 

Part I 

Table I.2.19:  Labour force projections: revisions AR2021 vs. AR2018 (pps) 

Employment rate

Participation rate

(20-64)

(55-64)

(20-64)

(55-64)

Unemployment rate
(20-64)

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

2019
1.6
5.4
2.5
-0.7
1.7
3.6
3.0
0.4
0.4
0.6
3.4
0.4
3.8
3.9
2.9
1.1
1.5
5.4
2.3
0.9
1.5
3.4
3.1
3.9
1.9
2.4
-0.2
-0.4
1.2
1.4

2070
-0.4
5.7
1.9
1.0
1.9
6.8
4.6
2.2
-1.4
-0.3
-0.3
2.5
1.5
-0.2
2.1
2.9
2.5
2.0
-0.3
0.9
1.4
4.6
6.1
3.6
-4.3
2.5
0.7
-1.6
1.0
1.2

2019
2.2
9.0
8.9
-0.4
3.5
7.8
2.6
3.1
-2.1
0.3
4.6
-1.1
5.7
8.3
8.8
0.6
6.6
8.1
5.8
1.5
1.3
3.8
2.5
3.0
6.9
3.9
2.6
1.0
1.5
1.9

2070
-1.5
5.1
3.4
4.8
2.4
17.7
4.2
5.8
-3.1
-1.1
0.6
2.7
2.7
-2.7
0.8
2.3
3.4
-0.5
0.2
2.8
2.3
9.4
6.5
2.6
-14.9
1.2
1.5
-2.6
0.9
1.4

2019
-0.1
4.5
1.6
-0.8
1.3
1.2
2.6
-0.3
-1.6
0.1
0.1
0.0
0.8
1.9
2.5
1.1
1.1
4.7
1.1
0.1
0.4
0.8
2.4
2.2
-0.1
2.1
0.5
-0.1
0.3
0.5

2070
-1.7
5.0
1.5
0.3
1.5
6.1
5.0
1.6
-2.3
-1.0
-1.0
2.0
2.3
-1.2
1.4
2.9
1.8
1.0
0.1
0.5
1.0
3.6
5.4
3.6
-5.5
2.1
0.6
-1.5
0.4
0.7

2019
1.6
8.3
8.4
-0.3
3.1
4.9
2.2
2.7
-3.5
0.5
2.7
-1.0
3.9
7.7
9.3
0.9
6.1
7.5
4.2
1.0
0.8
2.0
2.4
2.7
5.5
4.6
2.9
1.2
1.0
1.5

2070
-1.8
4.4
3.3
4.7
1.9
16.6
4.6
5.5
-3.5
-1.1
-0.3
2.8
3.4
-3.0
0.8
2.7
2.3
-0.9
-0.2
2.6
2.2
9.1
6.5
3.7
-16.7
1.8
1.2
-2.5
0.7
1.2

2019
-2.3
-1.4
-1.1
-0.1
-0.6
-2.9
-0.7
-0.9
-2.2
-0.6
-4.6
-0.6
-3.9
-2.6
-0.7
0.0
-0.6
-1.2
-1.5
-1.0
-1.4
-3.2
-1.1
-2.3
-2.6
-0.6
0.8
0.3
-1.1
-1.1

2070
-1.5
-1.3
-0.5
-0.9
-0.5
-1.4
0.2
-0.9
-0.9
-0.8
-0.8
-0.9
0.8
-1.1
-0.9
-0.1
-0.9
-1.2
0.4
-0.5
-0.6
-1.5
-1.4
-0.2
-1.0
-0.7
-0.2
0.1
-0.8
-0.8

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

The downward revisions in the employment and participation rates for SK are driven by the reforms adopted since the 2018 
Ageing Report (see Box I.2.2). 
Source: European Commission, EPC. 

Graph I.2.7:  Population and employment projections: 

revisions AR2021 vs. AR2018, 2070 (percentage 
change) 

50%

40%

30%

20%

10%

0%

-10%

-20%

-30%

t
n
e
m
y
o
p
m
E

l

-30%

-10%

10%

30%

50%

Source: European Commission, EPC. 

Population

Table I.2.21 provides an overview of the revisions 
in  the  participation  rates  in  2070  by  age  group. 
Average EU participation rates are revised slightly 
upward  for  young  people  (20-24),  prime-age 
workers  (25-54)  and  older  workers  (55-64). 
However,  for  ‘very  old’  workers  (65-74),  there  is 
on average a downward revision. Pension reforms 
legislated  since  the  previous  Ageing  Report  (see 
Box I.2.2) clearly come to the fore in some cases. 
For  Estonia,  which  will  start 
the 
statutory retirement age in line with changes in life 
expectancy  as  of  2027,  there  is  a  large  upward 
revision  in  the  2070  participation  rates  for  people 
aged  55-64  and  65-74.  Conversely,  Slovakia 
abandoned  the  life  expectancy  link,  capping  the 
statutory  retirement  age  at  64  years  so  that 
participation rates are expected to be considerably 
lower  than  assumed  in  the  2018  Ageing  Report. 
Croatia  withdrew  a  planned 
the 
early/statutory  retirement  age  to  62/67  years, 

increasing 

increase 

in 

63 

 
    
 
    
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.2.20:  Breakdown of revisions in employment projection for 2070 (AR2021 vs. AR2018) (%) 

Employment

Population

Participation rate

(20-64)
(1)»(2)+(3)-(4)
-16.8%
13.2%
5.6%
-8.2%
8.8%
11.1%
11.9%
16.2%
-8.3%
-13.0%
-12.6%
3.1%
13.8%
-11.8%
7.4%
-22.3%
5.3%
37.4%
-8.3%
-5.8%
4.4%
12.9%
0.9%
4.2%
-8.8%
-7.2%
-3.4%
-4.4%
-1.8%
-1.1%

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

(20-64)
(2)
-16.3%
5.1%
3.2%
-9.5%
6.4%
2.5%
5.7%
13.3%
-6.6%
-12.6%
-12.1%
-0.5%
11.9%
-11.5%
4.7%
-26.3%
2.2%
35.0%
-7.9%
-6.9%
2.5%
7.0%
-7.9%
-0.4%
-2.9%
-10.4%
-4.3%
-2.4%
-3.1%
-2.7%

(20-64)
(3)
-2.2%
6.7%
1.9%
0.3%
1.8%
7.1%
6.4%
1.9%
-2.7%
-1.3%
-1.3%
2.7%
2.7%
-1.4%
1.7%
3.8%
2.2%
1.2%
0.1%
0.6%
1.3%
4.3%
7.3%
4.4%
-6.9%
2.5%
0.7%
-1.9%
0.5%
0.8%

Unemployment 
rate
(20-64)
(4)
1.5%
1.3%
0.5%
0.9%
0.5%
1.4%
-0.2%
0.9%
0.9%
0.8%
0.8%
0.9%
-0.8%
1.1%
0.9%
0.1%
0.9%
1.2%
-0.4%
0.5%
0.6%
1.5%
1.4%
0.2%
1.0%
0.7%
0.2%
-0.1%
0.8%
0.8%

Discrepancy
(1)-(2)-(3)+(4)

0.1%
0.1%
0.0%
0.0%
0.0%
0.1%
0.0%
0.1%
0.1%
0.1%
0.1%
0.1%
-0.1%
0.1%
0.1%
0.0%
0.0%
0.1%
0.0%
0.0%
0.0%
0.1%
0.1%
0.0%
0.1%
0.0%
0.0%
0.0%
0.1%
0.0%

Source: European Commission, EPC. 

limiting  the  increase  instead  to  60/65  years.  As  a 
result,  projected  participation  rates  among  people 
aged 65-74 are lower. 

64 

 
 
     
 
Underlying assumptions and projection methodologies 

Part I 

Table I.2.21:  Participation rate projections for 2070: revisions AR2021 vs. AR2018 (pps) 

20-74
-1.9
3.9
1.1
1.5
2.1
8.2
2.8
-0.5
-4.0
-1.7
-2.6
1.7
2.7
-3.9
-1.6
2.3
1.1
0.4
-1.0
-0.3
0.1
1.7
4.1
2.2
-8.8
0.2
0.1
-1.6
-0.2
0.0

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

20-64
-1.7
5.0
1.5
0.3
1.5
6.1
5.0
1.6
-2.3
-1.0
-1.0
2.0
2.3
-1.2
1.4
2.9
1.8
1.0
0.1
0.5
1.0
3.6
5.4
3.6
-5.5
2.1
0.6
-1.5
0.4
0.7

20-24
1.8
1.4
0.3
0.4
2.0
3.4
6.0
-1.1
0.9
0.3
-7.9
-0.5
2.9
5.0
4.2
4.3
2.9
4.2
0.6
0.2
3.4
-0.2
3.6
2.9
-2.6
3.5
1.7
1.9
1.3
1.5

25-54
-2.0
5.6
0.7
-1.1
1.4
2.5
4.9
0.0
-2.8
-0.8
-0.1
1.9
2.0
-1.7
1.3
3.6
1.3
1.6
0.2
0.0
0.4
1.8
5.5
3.6
-2.0
2.0
0.2
-1.6
0.2
0.4

55-64
-1.8
4.4
3.3
4.7
1.9
16.6
4.6
5.5
-3.5
-1.1
-0.3
2.8
3.4
-3.0
0.8
2.7
2.3
-0.9
-0.2
2.6
2.2
9.1
6.5
3.7
-16.7
1.8
1.2
-2.5
0.7
1.2

65-74
-0.3
1.0
0.6
4.7
0.8
22.0
1.1
-9.0
-1.7
-0.3
-6.3
1.4
-3.2
-7.4
0.6
0.0
0.4
2.5
-4.9
-7.4
-0.5
-9.4
1.9
1.8
-23.4
-3.3
0.5
-0.8
-1.0
-0.8

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

Source: European Commission, EPC. 

65 

 
   
 
3. 

LABOUR PRODUCTIVITY AND POTENTIAL GDP 

3.1. 

INTRODUCTION 

3.1.1.  A production function approach for the 

long-term GDP projections  

To project potential GDP growth in the long term, 
a production function framework with the standard 
specification  of 
the  Cobb-Douglas  production 
function  with  constant  returns  to  scale  is  used.  In 
this framework, potential GDP growth is driven by 
long-term developments in labour input and labour 
productivity.  

namely 

determinants, 

Projections  of  labour  productivity  are  based  on 
assumptions  about  long-run  developments  in  its 
underlying 
labour-
augmenting  total  factor  productivity  and  capital 
stock  per  worker  (also  referred  to  as  capital 
deepening).  The  long-run  projection  is  based  on 
the  central  assumption  of  convergence  of  all 
Member  States  towards  the  same  value  of  labour 
productivity  by  the  end  of  the  projection  horizon. 
Labour input projections are based on assumptions 
taken from Eurostat's latest population projections. 

are 

summarised 

A  detailed  description  of  the  production  function 
framework  and  the  key  assumptions  underpinning 
the  long-term  GDP  projections  presented  in  this 
section 
in  Annex 3.  All 
assumptions  have  been  approved  by  the  EPC, 
including the methodology developed by the EPC's 
Output Gap Working Group (OGWG) to calculate 
potential  GDP  over  a  time  horizon  of  T+10,  and 
are  used 
their  work  by  other  Council 
committees.  

in 

Following  the  practice  used  for  the  2018  Ageing 
Report, the OGWG T+10 methodology is used for 
projecting  potential  growth  and  its  components 
over  the  medium  term  –  namely  up  to  2029 
(Annex 3).  The  long-term  projections,  and  T+10 
projections,  in  this  report  are  based  on  the 
Commission spring 2020 forecast. Thus, the EPC's 
working  groups,  the  OGWG  and  the  AWG,  are 
fully aligned (25)(26).  

(25)  The  output  gap  estimates  are  used  to  calculate  structural 
budgetary  developments,  which  are  used  within  the 
framework of the Stability and Growth Pact (SGP). 

(26)  Yet, the T+10 methodology relies on population growth in 
the age bracket 15-74, which is based on Europop2018 (the 

66 

The  rest  of  this  section  summarises  (i)  the  long-
term  GDP  projections  in  the  baseline  and  risk 
scenario;  (ii)  cross-country  differences  within  the 
EU;  (iii)  the  main  differences  between  these 
projections and those of the 2018 Ageing Report.  

3.2. 

LONG-TERM POTENTIAL GDP 
PROJECTIONS 

Relatively stable potential annual GDP growth of 
almost 1½% is projected over the long term in the 
EU in the baseline scenario. This is much lower 
than  in  previous  decades  and  involves  downside 
risks  should  future  TFP  growth  develop  less 
favourably than assumed. 

3.2.1.  Baseline scenario 

Annual  potential  GDP  growth  in  the  EU  is 
projected to average 1.3% over the period 2019-70 
under  the  baseline  scenario.  It  will  average  1.2% 
up to 2030, rising slightly to 1.3% during 2031-40 
and  further  to  1.4%  in  the  2040s.  It  is  then 
expected  to  remain  at  this  level  up  to  2070  (see 
Table I.3.1).  

The projections for the euro area follow a similar, 
though  slightly  lower,  trajectory  at  the  beginning 
of  the  projection  horizon,  with  annual  growth  of 
1% through 2030, 1.2% in 2031-40, rising to 1.4% 
during  2041-70.  Overall,  the  average  growth  rate 
over the period 2019-70 is projected to be 1.3%. 

However, there is currently exceptional uncertainty 
concerning  GDP  growth  in  the  wake  of  the 
this  reason,  additional 
COVID-19  crisis.  For 
scenarios are run, described in Box I.3.1. 

The  contribution  of  labour  input  –  total  hours 
worked  –  to  potential  growth  in  the  EU  and  the 
euro area is projected to be negative as of the early 
2020s.  The  demographic  assumptions  result  in  a 
decline  in  the  working-age  population  and,  by 
extension,  a  negative  contribution  of  labour  input 
to potential growth for most EU countries. Hence, 
the  increase  in  the  participation  rate  will  not  be 
sufficient to counterbalance the decline of working 
age population (see Chapter 2).  

latest  one  available  at  the  spring  2020  forecast),  whereas 
the long-term projections adopted by the Ageing Working 
Group are based on Eurostat Europop2019. 

                                                           
                                                                                   
Underlying assumptions and projection methodologies 

Part I 

Table I.3.1: 

Potential GDP annual growth rate - Period 
average (%) 

Table I.3.2: 

Labour input (total hours worked), annual 
growth rate - Period average (%) 

2019-
2030

2031-
2040

2041-
2050

2051-
2060

2061-
2070

2019-
2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

1.0
1.3
2.0
1.9
0.9
2.9
2.4
0.2
1.5
1.0
0.8
0.3
1.7
2.0
2.2
2.0
2.7
3.7
0.9
1.2
2.7
1.0
3.0
2.5
1.7
1.0
1.8
1.8
1.0
1.2

1.1
1.2
1.6
1.5
1.1
2.0
1.8
1.4
1.6
1.3
1.2
0.9
2.1
1.3
1.2
2.1
1.9
2.8
1.1
1.4
1.8
1.0
1.7
1.6
1.5
1.4
1.9
1.7
1.2
1.3

1.3
1.1
1.3
1.7
1.4
1.4
1.6
1.6
1.4
1.5
1.4
1.3
2.3
0.7
0.9
1.8
1.5
1.7
1.6
1.4
1.0
1.2
1.1
1.1
1.1
1.2
1.8
1.8
1.4
1.4

1.3
1.1
1.5
1.7
1.3
1.3
1.6
1.5
1.4
1.5
1.1
1.5
1.8
0.7
0.7
1.6
1.5
1.2
1.5
1.3
0.9
1.4
1.2
1.3
1.1
1.2
1.7
1.6
1.4
1.4

1.4
1.3
1.7
1.5
1.5
1.5
1.6
1.4
1.4
1.5
1.0
1.4
1.6
1.1
1.0
1.5
1.4
1.4
1.4
1.4
1.1
1.3
1.1
1.4
1.3
1.2
1.7
1.6
1.4
1.4

Source: European Commission, EPC. 

1.2
1.2
1.6
1.7
1.2
1.9
1.8
1.2
1.4
1.3
1.1
1.0
1.9
1.2
1.2
1.8
1.8
2.2
1.3
1.3
1.5
1.2
1.7
1.6
1.3
1.2
1.8
1.7
1.3
1.3

2019-2070 
(TFP risk 
scenario)
1.0
0.9
1.3
1.3
1.0
1.3
1.4
1.2
1.2
1.2
0.9
1.0
1.7
0.7
0.7
1.4
1.3
1.5
1.1
1.1
0.9
1.0
1.0
1.1
0.9
1.0
1.5
1.3
1.1
1.1

After a recovery in the first years of the projection, 
total hours worked will fall in the EU and  the euro 
area.,  by  0.2%  and  0.1%,  respectively.  The 
negative  contribution  of  labour  input  will  become 
slightly lower by the 2060s (Table I.3.2). 

2019-
2030

2031-
2040

2041-
2050

2051-
2060

2061-
2070

2019-
2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

0.5
-0.9
-0.2
0.4
-0.2
-0.4
0.7
-0.1
0.6
0.3
-0.5
0.0
1.0
-1.3
-0.8
2.1
0.5
1.5
0.1
0.3
-0.5
0.0
-0.9
0.1
-0.6
-0.2
0.9
0.5
0.1
0.0

-0.1
-1.1
-0.6
0.0
-0.3
-0.2
0.4
-0.3
0.0
0.1
-0.7
-0.4
0.6
-1.2
-1.3
0.8
-0.5
0.9
-0.3
0.0
-0.7
-0.9
-1.1
-0.5
-0.8
-0.1
0.5
0.2
-0.2
-0.3

-0.2
-1.2
-0.7
0.2
-0.1
-0.5
0.1
-0.5
-0.4
0.0
-0.9
-0.4
0.4
-1.3
-1.2
0.3
-0.7
0.0
0.1
-0.2
-1.1
-0.8
-1.2
-0.7
-1.0
-0.3
0.3
0.2
-0.2
-0.4

-0.2
-0.8
-0.3
0.1
-0.2
-0.4
0.1
-0.4
-0.2
-0.1
-0.9
-0.2
0.1
-1.2
-1.2
0.1
-0.5
-0.4
0.0
-0.2
-1.0
-0.4
-0.8
-0.4
-0.8
-0.4
0.1
0.1
-0.2
-0.3

-0.1
-0.4
0.0
0.0
0.0
-0.1
0.0
-0.2
-0.2
-0.1
-0.7
-0.2
0.0
-0.5
-0.6
-0.1
-0.3
-0.2
-0.1
-0.1
-0.6
-0.4
-0.5
-0.1
-0.3
-0.3
0.2
0.0
-0.1
-0.2

0.0
-0.9
-0.3
0.2
-0.2
-0.3
0.3
-0.3
0.0
0.1
-0.7
-0.2
0.4
-1.1
-1.0
0.7
-0.3
0.4
0.0
0.0
-0.8
-0.5
-0.9
-0.3
-0.7
-0.3
0.4
0.2
-0.1
-0.2

For Luxembourg, an adjustment has been made to take 
account of the non-resident work force (cross-border 
workers). 
Source: European Commission, EPC. 

67 

 
    
 
 
   
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

(Continued on the next page) 

68 

 
 
 
   
 
Underlying assumptions and projection methodologies 

Part I 

Box (continued) 

Graph 2:  Baseline and COVID-19 crisis scenarios, medium-term impact (EU27) 

Actual GDP growth (%)

Potential GDP growth (%)

8
6
4
2
0
-2
-4
-6
-8
-10
-12
-14

16000

15500

15000

14500

14000

13500

13000

12500

12000

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

baseline

lagged recovery

adverse structural

baseline

lagged recovery

adverse structural

Actual GDP level (billion EUR)

Potential GDP level (billion EUR)

16000

15500

15000

14500

14000

13500

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

baseline

lagged recovery

adverse structural

baseline

lagged recovery

adverse structural

Source: European Commission, EPC. 

in 

than 

the  baseline.  First, 

Adverse  structural  scenario:  on  top  of  the 
stronger  cyclical  downturn  in  the  lagged  recovery 
scenario  described  above,  this  adverse  structural 
scenario  additionally  assumes  that  the  growth 
potential would be lower over the next decade and 
potential  output  growth  will  thus  be  permanently 
lower 
labour 
productivity growth would recover to a lower trend 
growth,  through  lower  investment  and/or  TFP 
growth  stemming  from  reduced  business  activity 
for  a 
the  crisis 
contributing  to  the  historical  downward  trend. 
Second,  the  deeper  recession  and  slower  recovery 
would 
becoming 
permanently higher due to lower business activity, 
leading  to  a  hysteresis  effect  and  permanently 
higher unemployment. 

long  period  of 

unemployment 

time,  with 

lead 

to 

labour 

impact 

the  structural 

Specifically, 
is 
assumed  to  lead  to  25%  higher  country-specific 
NAWRU  anchors  (or  country-specific  NAWRU 
t+10  values)  for  all  Member  States  (leading  to  a 
25%  higher  EU  median  NAWRU 
(baseline 
NAWRU  EU  anchor  median  7%,  crisis  scenario: 
8.75%).  The  structural  labour  productivity  impact 
labour 
is  assumed 
productivity  growth  vs.  the  baseline  (technically 
done by  30%  lower  TFP  target  growth  rate  for  all 
Member  States,  (baseline  TFP  target  growth:  1%, 
crisis scenario: 0.7%). Aside of the new values, the 
convergence assumptions for the NAWRUs and the 
TFP  growth  are  the  same  as  in  the  baseline 
scenario. 

to  30% 

lower 

lead 

to 

Graph 3:  Baseline and COVOD-19 crisis scenarios, long-term impact (EU27) 

Potential GDP growth (%)

Potential GDP level (billion EUR)

1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0

29500
27500
25500
23500
21500
19500
17500
15500
13500

9
1
0
2

1
2
0
2

3
2
0
2

5
2
0
2

7
2
0
2

9
2
0
2

1
3
0
2

3
3
0
2

5
3
0
2

7
3
0
2

9
3
0
2

1
4
0
2

3
4
0
2

5
4
0
2

7
4
0
2

9
4
0
2

1
5
0
2

3
5
0
2

5
5
0
2

7
5
0
2

9
5
0
2

1
6
0
2

3
6
0
2

5
6
0
2

7
6
0
2

9
6
0
2

9
1
0
2

1
2
0
2

3
2
0
2

5
2
0
2

7
2
0
2

9
2
0
2

1
3
0
2

3
3
0
2

5
3
0
2

7
3
0
2

9
3
0
2

1
4
0
2

3
4
0
2

5
4
0
2

7
4
0
2

9
4
0
2

1
5
0
2

3
5
0
2

5
5
0
2

7
5
0
2

9
5
0
2

1
6
0
2

3
6
0
2

5
6
0
2

7
6
0
2

9
6
0
2

baseline

lagged recovery

adverse structural

baseline

lagged recovery

adverse structural

Source: European Commission, EPC. 

69 

 
   
 
 
 
   
   
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

thereafter 

As  a  result,  potential  GDP  growth  in  the  EU  and 
the  euro  area  will  be  driven  almost  entirely  by 
labour  productivity.  Annual  growth  in  labour 
productivity  per  hour  worked  is  projected  to 
increase  in  the  period  to  the 2030s  from  less  than 
1%  to  1.5%  and  to  remain  fairly  stable  at  around 
1.6% 
remaining 
throughout 
projection  period.  As  a  result,  the  average  annual 
growth  rate  is  projected  to  be  equal  to  1.6% 
throughout  the  projection  period  (2019-2070).  A 
similar  trajectory  is  envisaged  in  the  euro  area, 
with labour productivity rising from an average of 
0.9% up to 2030 to about 1.6% in the 2040s, with 
an  overall  average  of  1.4%  over  the  entire  period 
(Table I.3.3).  

the 

Table I.3.3: 

Labour productivity per hour, annual growth 
rate - Period average (%) 

2019-
2030

2031-
2040

2041-
2050

2051-
2060

2061-
2070

2019-
2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

0.5
2.2
2.2
1.5
1.1
3.2
1.6
0.3
0.9
0.7
1.3
0.3
0.7
3.3
2.9
0.0
2.2
2.2
0.8
0.9
3.2
1.1
3.9
2.3
2.3
1.2
0.9
1.3
0.9
1.2

1.3
2.4
2.2
1.5
1.5
2.3
1.4
1.7
1.6
1.3
2.0
1.3
1.5
2.5
2.4
1.3
2.3
1.9
1.4
1.4
2.6
1.9
2.8
2.1
2.3
1.5
1.4
1.5
1.5
1.6

1.5
2.3
2.0
1.5
1.5
1.8
1.5
2.1
1.8
1.5
2.3
1.7
1.9
2.1
2.1
1.5
2.2
1.6
1.5
1.5
2.2
2.1
2.3
1.8
2.1
1.5
1.5
1.5
1.7
1.8

1.5
2.0
1.8
1.5
1.5
1.7
1.5
1.9
1.7
1.5
2.0
1.7
1.7
1.9
1.9
1.5
1.9
1.6
1.5
1.5
1.9
1.9
2.0
1.7
1.9
1.5
1.5
1.5
1.6
1.7

1.5
1.7
1.6
1.5
1.5
1.6
1.5
1.6
1.6
1.5
1.7
1.6
1.6
1.6
1.6
1.5
1.7
1.6
1.5
1.5
1.7
1.6
1.7
1.6
1.7
1.5
1.5
1.5
1.6
1.6

Source: European Commission, EPC. 

1.2
2.1
2.0
1.5
1.4
2.2
1.5
1.5
1.5
1.3
1.8
1.3
1.5
2.3
2.2
1.1
2.1
1.8
1.3
1.4
2.3
1.7
2.6
1.9
2.1
1.5
1.4
1.5
1.4
1.6

2019-2070 
(TFP risk 
scenario)
1.0
1.9
1.7
1.3
1.2
1.9
1.3
1.3
1.3
1.1
1.6
1.1
1.3
2.1
1.9
0.8
1.8
1.5
1.1
1.1
2.0
1.5
2.4
1.6
1.8
1.2
1.1
1.1
1.2
1.3

Total  factor  productivity  (TFP)  growth  explains 
around  two-thirds  of  labour  productivity  growth 
during  the  projection  period.  Annual  TFP  growth 
converges  to  1%  by  2070  at  the  latest  for  all 
Member States.  

For the EU as a whole, TFP growth averages 0.8% 
per  year  over  2019-30,  rising  to  just  above  1% 
over 2031-40 and converging to 1% by the end of 
the  projection  horizon.  The  resulting  average 

70 

annual growth rate over 2019-70 is 1%, just under 
two-thirds  of  average  annual  labour  productivity 
growth during this period (Table I.3.4).  

The  annual  TFP  growth  rate  in  the  euro  area 
follows a similar path, albeit from a lower starting 
point over 2019-30 (0.6%) and rising more slowly 
in  the  coming  decades,  with  an  average  growth 
rate of 0.9% over 2016-70, just under two-thirds of 
labour  productivity  growth  over  the  projection 
period.  

Table I.3.4:  Annual total factor productivity growth rate - 

Period average (%) 

2019-
2030

2031-
2040

2041-
2050

2051-
2060

2061-
2070

2019-
2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

0.3
1.3
1.5
1.1
0.7
1.9
1.3
0.5
0.6
0.4
0.5
0.3
0.2
1.9
1.6
0.0
1.3
1.4
0.5
0.5
1.9
0.8
2.4
1.9
1.3
0.6
0.7
0.7
0.6
0.8

0.8
1.4
1.4
1.0
1.0
1.4
1.0
1.2
1.0
0.9
1.3
0.9
1.0
1.6
1.5
0.8
1.5
1.2
0.9
0.9
1.6
1.2
1.7
1.4
1.5
1.0
0.9
1.0
1.0
1.1

1.0
1.5
1.3
1.0
1.0
1.2
1.0
1.4
1.1
1.0
1.5
1.1
1.2
1.3
1.3
1.0
1.4
1.1
1.0
1.0
1.4
1.3
1.5
1.1
1.4
1.0
1.0
1.0
1.1
1.2

1.0
1.3
1.2
1.0
1.0
1.1
1.0
1.2
1.1
1.0
1.3
1.1
1.1
1.2
1.2
1.0
1.3
1.0
1.0
1.0
1.2
1.2
1.3
1.1
1.2
1.0
1.0
1.0
1.0
1.1

1.0
1.1
1.1
1.0
1.0
1.0
1.0
1.1
1.0
1.0
1.1
1.0
1.0
1.1
1.1
1.0
1.1
1.0
1.0
1.0
1.1
1.1
1.1
1.0
1.1
1.0
1.0
1.0
1.0
1.0

Source: European Commission, EPC. 

0.8
1.3
1.3
1.0
0.9
1.4
1.1
1.0
1.0
0.8
1.1
0.8
0.9
1.4
1.3
0.7
1.3
1.2
0.9
0.9
1.5
1.1
1.6
1.3
1.3
0.9
0.9
0.9
0.9
1.0

2019-2070 
(TFP risk 
scenario)
0.7
1.2
1.1
0.9
0.8
1.2
0.9
0.9
0.8
0.7
1.0
0.7
0.8
1.2
1.1
0.5
1.1
1.0
0.7
0.7
1.3
1.0
1.5
1.1
1.1
0.7
0.7
0.6
0.8
0.9

The  contribution  of  capital  deepening  to  labour 
productivity  for  the  EU  averages  0.5%  per  year 
during 2019-2070 but starts  from a lower level of 
0.4%  on  average  over  2019-30  (see  Table  I.3.5). 
For  countries  whose  GDP  per  capita  is  below  the 
EU  average,  the  capital  deepening  contribution  is 
projected  to  be  considerably  higher  than  the  EU 
average  in  the  first  part  of  the  projection  period, 
reflecting  the  assumed  catching-up  process  of 
converging economies. 

For  the  euro  area,  the  contribution  from  capital 
deepening  averages  just  0.3%  per  year  during 

 
    
 
 
    
 
2019-30  but  rises  to  0.5-0.6%  thereafter,  with  an 
average of 0.5% for the entire projection period.  

Table I.3.5:  Annual contribution of capital deepening - 

Period average (%) 

2019-
2030

2031-
2040

2041-
2050

2051-
2060

2061-
2070

2019-
2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA

0.2
0.9
0.7
0.4
0.4
1.3
0.3
-0.1
0.3
0.3
0.8
0.1
0.5
1.4
1.4
0.0
0.9
0.8
0.3
0.4
1.3
0.2
1.5
0.4
1.0
0.6
0.2
0.6
0.3
EU27 0.4

0.4
0.9
0.8
0.5
0.5
0.9
0.4
0.5
0.6
0.4
0.7
0.4
0.6
1.0
0.9
0.4
0.8
0.7
0.5
0.5
1.0
0.6
1.1
0.8
0.8
0.5
0.5
0.5
0.5
0.6

0.5
0.8
0.7
0.5
0.5
0.6
0.5
0.7
0.6
0.5
0.8
0.6
0.7
0.7
0.7
0.5
0.8
0.6
0.5
0.5
0.8
0.7
0.8
0.6
0.8
0.5
0.5
0.5
0.6
0.6

0.5
0.7
0.6
0.5
0.5
0.6
0.5
0.7
0.6
0.5
0.7
0.6
0.6
0.7
0.7
0.5
0.7
0.6
0.5
0.5
0.7
0.7
0.7
0.6
0.7
0.5
0.5
0.5
0.6
0.6

0.5
0.6
0.6
0.5
0.5
0.6
0.5
0.6
0.6
0.5
0.6
0.6
0.6
0.6
0.6
0.5
0.6
0.5
0.5
0.5
0.6
0.6
0.6
0.6
0.6
0.5
0.5
0.5
0.6
0.6

0.4
0.8
0.7
0.5
0.5
0.8
0.5
0.4
0.5
0.5
0.7
0.4
0.6
0.9
0.9
0.4
0.7
0.6
0.5
0.5
0.9
0.6
0.9
0.6
0.8
0.5
0.5
0.5
0.5
0.5

Source: European Commission, EPC. 

A summary of the relative contribution to potential 
GDP  growth  of  labour  productivity  and  labour 
utilisation  (and  their  determinants)  in  the  baseline 
scenario over the entire projection horizon 2019-70 
is  provided  by  the  standard  growth  accounting 
framework reported in Table I.3.6. 

For  the  EU  and  for  the  euro  area,  the  total 
population  and  the  change  in  total  hours  worked 
over  the  entire  projection  period  are  projected  to 
remain  quite  stable  while  an  assumed  increase  in 
employment rates makes a positive contribution to 
potential  growth  (0.1 pps).  However,  this  is  more 
than  offset  by  a  decline  in  the  share  of  the 
working-age  population,  which  is  a  substantial 
negative  drag  on  growth,  with  an  annual  average 
of -0.2 percentage points. As a result, labour input 
contributes  negatively  to  annual  potential  output 

Underlying assumptions and projection methodologies 

Part I 

growth  on  average  over  the  projection  period  (by 
0.2 pps, in the EU and by 0.1 pps in the euro area). 
Hence,  growth  in  labour  productivity  (production 
per  hour  worked)  becomes  the  sole  source  of 
potential  output  growth  in  both  the  EU  and  the 
euro  area,  averaging  1.6  pps  and  1.4  pps 
respectively.  As  a  result,  over  the  projection 
horizon  annual  potential  GDP  growth  in  the  EU 
and  euro  area  will  average  1.4%  and  1.3% 
respectively.   

While  most  EU  Member  States  are  projected  to 
experience  a  slowdown  in  the  contribution  of 
labour  input  (total  hours  worked)  to  potential 
growth  rates  due 
impact  of 
demographic  developments,  overall  potential 
growth  rates  differ  substantially  across  countries 
over the projection horizon. 

the  adverse 

to 

Specifically,  under  the  baseline  scenario,  average 
labour  input  growth  is  positive  for  Denmark, 
Ireland,  France,  Cyprus,  Luxembourg,  Malta, 
Sweden and Norway (see also Table I.3.2).  

By  contrast,  particularly  for  countries  with  GDP 
per capita below the EU average, growth rates are 
projected to be higher. This is because in the first 
half  of  the  projection  period,  TFP  growth  is  the 
main  source  of  discrepancy  across  countries, 
reflecting different productivity growth rates at the 
outset  of  the  projection  and  the  assumed  different 
future  paths  given    the  catching-up  potential  (see 
description in Box I.3.2 . TFP growth is above 1% 
for those countries with GDP per capita below the 
EU average, which are thus assumed to have high 
catch-up potential. For these countries, annual TFP 
growth  peaks  during  the  2040s  before  gradually 
falling  to  1%.  For  countries  with  GDP  per  capita 
above  the  EU  average,  annual  TFP  growth  is 
generally  below  1%,  before  converging  to  1%  by 
2045 and remaining at that level until 2070. 

71 

 
   
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.3.6: 

Breakdown of potential GDP growth (baseline), 2019-70 

GDP growth 
in 2019-
2070

Labour 
prod. (GDP 
per hour 
worked)

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

1=2+5
1.2
1.2
1.6
1.7
1.2
1.9
1.8
1.2
1.4
1.3
1.1
1.0
1.9
1.2
1.2
1.8
1.8
2.2
1.3
1.3
1.5
1.2
1.7
1.6
1.3
1.2
1.8
1.7
1.3
1.3

2=3+4
1.2
2.1
2.0
1.5
1.4
2.2
1.5
1.5
1.5
1.3
1.8
1.3
1.5
2.3
2.2
1.1
2.1
1.8
1.3
1.4
2.3
1.7
2.6
1.9
2.1
1.5
1.4
1.5
1.4
1.6

TFP

3
0.8
1.3
1.3
1.0
0.9
1.4
1.1
1.0
1.0
0.8
1.1
0.8
0.9
1.4
1.3
0.7
1.3
1.2
0.9
0.9
1.5
1.1
1.6
1.3
1.3
0.9
0.9
0.9
0.9
1.0

Source: European Commission, EPC. 

Capital 
deepening

Labour 
input

Total 
population

Employment 
rate

Share of 
working-age 
population

change in 
average 
hours worked

GDP per 
capita 
growth in 
2019-2070

4
0.4
0.8
0.7
0.5
0.5
0.8
0.5
0.4
0.5
0.5
0.7
0.4
0.6
0.9
0.9
0.4
0.7
0.6
0.5
0.5
0.9
0.6
0.9
0.6
0.8
0.5
0.5
0.5
0.5
0.5

5=6+7+8+9
0.0
-0.9
-0.3
0.2
-0.2
-0.3
0.3
-0.3
0.0
0.1
-0.7
-0.2
0.4
-1.1
-1.0
0.7
-0.3
0.4
0.0
0.0
-0.8
-0.5
-0.9
-0.3
-0.7
-0.3
0.4
0.2
-0.1
-0.2

6
0.1
-0.6
-0.1
0.1
0.0
-0.2
0.6
-0.4
0.0
0.1
-0.6
-0.2
0.5
-0.9
-0.8
0.5
-0.2
0.7
0.1
0.1
-0.4
-0.4
-0.7
-0.1
-0.3
-0.2
0.5
0.5
0.0
-0.1

7
0.1
0.0
0.0
0.3
0.0
0.1
-0.1
0.3
0.2
0.1
0.1
0.2
0.2
0.1
0.1
0.4
0.2
0.1
0.1
0.1
-0.1
0.1
0.0
0.1
-0.1
0.1
0.1
-0.1
0.1
0.1

8
-0.2
-0.3
-0.3
-0.2
-0.2
-0.2
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
-0.2
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.2
-0.1
-0.1
-0.2
-0.2

9
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6
1.2
1.9
1.7
1.6
1.3
2.1
1.3
1.6
1.4
1.3
1.7
1.3
1.4
2.2
2.1
1.3
2.0
1.5
1.2
1.2
1.9
1.6
2.4
1.7
1.6
1.4
1.3
1.2
1.3
1.4

72 

 
 
 
 
 
 
 
 
    
 
Underlying assumptions and projection methodologies 

Part I 

Graph I.3.1:  Average annual cyclical and potential GDP growth projections (2019-2029) 

)

%

(

s
e
t
a
r
h
t
w
o
r
G

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

T
I

R
H

E
D

L
E

L
N

I
F

E
B

A
E

T
P

R
F

T
A

7
2
U
E

G
B

S
E

Y
C

K
S

E
S

V
L

O
N

Z
C

U
L

T
L

K
D

I
S

E
I

L
P

U
H

E
E

O
R

T
M

Cyclical GDP growth

Potential GDP growth

Source: European Commission, EPC. 

Taking account of the cyclical position of the 
economy in the long-term projections 

To  bridge  the  current  situation  and  the  assumed 
longer-term prospects under the baseline scenario, 
there  is  a  need  to  take  account  of  the  cyclical 
position  of  the  economy  in  the  short-  to  medium-
term horizon.  

In  making  actual  and  potential  growth  rate 
projections, the general rule is that the output  gap 
is  closed  at  the  latest  three  years  after  the  end  of 
the  2020  spring  forecast,  that  is,  by  2024.  Taking 
the  small  positive  output  gaps 
account  of 
prevailing  in  most  Member  States  in  2019,  actual 
growth  is  assumed  to  be  lower  than  potential 
growth  until the output gap is closed in 2024 (see 
Graph I.3.1). 

3.2.2.  TFP risk scenario 

A  risk  scenario  reflecting  more  conservative 
assumptions  regarding  TFP  growth  rates  is  also 
examined, in the light of the  trend decline in TFP 
growth over the last decades (see Box I.3.2).  

risk  scenario 

forecasts  annual  average 
The 
potential  GDP  growth  during  2019-70  of  1.1%  in 
the EU and euro area (Table I.3.7), as opposed to  
1.3%  in  the  baseline.  This  is  driven  by  average 
annual TFP growth over 2019-2070 under the risk 
scenario  of  0.9%  and  0.8%  respectively,  as 

opposed  to  1%  and  0.9%  respectively  in  the 
baseline.   

Table I.3.7: 

Breakdown of potential GDP growth (TFP risk 
scenario), 2019-70 

GDP growth 
in 2019-
2070

1=2+5
1.0
1.0
1.4
1.5
1.0
1.6
1.6
1.0
1.2
1.1
0.9
0.8
1.7
0.9
0.9
1.5
1.6
1.9
1.1
1.1
1.3
1.0
1.5
1.4
1.0
0.9
1.6
1.3
1.1
1.1

Labour 
prod. (GDP 
per hour 
worked)
2=3+4
1.0
1.9
1.7
1.3
1.2
1.9
1.3
1.3
1.3
1.1
1.6
1.1
1.3
2.1
1.9
0.8
1.8
1.5
1.1
1.1
2.0
1.5
2.4
1.6
1.8
1.2
1.1
1.1
1.2
1.3

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

TFP

3
0.7
1.2
1.1
0.9
0.8
1.2
0.9
0.9
0.8
0.7
1.0
0.7
0.8
1.2
1.1
0.5
1.1
1.0
0.7
0.7
1.3
1.0
1.5
1.1
1.1
0.7
0.7
0.6
0.8
0.9

Source: European Commission, EPC. 

Capital 
deepening

Labour 
input

4
0.4
0.7
0.6
0.4
0.4
0.7
0.4
0.4
0.4
0.4
0.6
0.4
0.5
0.8
0.8
0.3
0.7
0.6
0.4
0.4
0.8
0.5
0.9
0.5
0.7
0.5
0.4
0.4
0.4
0.5

5
0.0
-0.9
-0.3
0.2
-0.2
-0.3
0.3
-0.3
0.0
0.1
-0.7
-0.2
0.4
-1.1
-1.0
0.7
-0.3
0.4
0.0
0.0
-0.8
-0.5
-0.9
-0.3
-0.7
-0.3
0.4
0.2
-0.1
-0.2

73 

 
    
 
          
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Box I.3.2: Assumptions on the components of the production function used for long-run 
potential growth projections

the  period  2019-2029 

the  medium-term 
For 
potential growth estimation was based on the T+10 
methodology  described  in  Annex  3.  The  long-run 
projection is based on convergence rules toward the 
same value of labour productivity growth at the end 
of the projection horizon. There is therefore a need 
to  ensure  consistency  between  the  medium  term 
projection based on country-specific trends and the 
long-run  projection  based  on  horizontal  conver-
gence  assumptions.  There  is  also  an  overriding 
constraint  to  ensure  comparability  across  the  EU 
through  the  use  of  a  common  methodology  for  all 
Member States. 

The key assumptions on Total Factor Productivity 

Concerning  total  factor  productivity  growth,  the 
AWG  and  EPC  decided  that  the  long-run  level  of 
annual TFP growth in the baseline scenario should 
remain as in the 2018 Ageing Report, namely 1%. 
As  in  the  previous  report,  convergence  to  this 
annual TFP growth rate is reached at the earliest by 
2045.  In  addition,  due  visibility  and  prominence 
should  also  be  given  to  the  risk  of  lower  TFP 
growth in the future, in light of the trend decline on 
TFP  growth  performance  over  the  last  decades. 
Thus,  a  risk  scenario  should  be  included,  with  a 
lower  TFP  growth  rate  (0.8%).  In  both  cases, 
allowance for higher TFP growth for countries with 
below  average  GDP  per  capita  is  factored  in  for  a 
period  of  time,  as  in  the  previous  projection 
exercise,  to  cater  for  catching-up  potential  (see 
Table  1).  Moreover,  in  order  to  avoid  undue 
changes  in  relative  GDP  per  capita  terms,  the 
catching up TFP growth potential is revaluated (in 
2037). Similarly, in both scenarios, the labour share 
is  assumed  to  stay  constant  at  0.65  over  the 
projection horizon (see Annex 3 for details). 

Baseline scenario 

The  assumption  for  TFP  is  that  country-specific 
TFP  growth  rates  converge  to  1%  in  the  baseline 
scenario.  Likewise,  the  speed  and  the  year  of 
convergence to the long-run TFP growth rate are to 
be  determined  by  the  relative  income  position  in 
the  different  Member  States  (Table  2),  and  it  is 

74 

the 

real  catching  up  potential 

assumed  that  the  lower  the  GDP  per  capita,  the 
higher 
(real 
convergence  process).  In  the  long  term,  labour 
productivity  broadly  coincides  with  TFP  growth 
divided by labour share, equalling 1.5%. 

Table 1:  GDP per capita, % of EU27 (2029) 

LU
IE
DK
SE
NL
AT
FI
DE
BE
FR
EA
EU27
IT
MT
ES
SI
CY
EE
PT
CZ
LT
EL
SK
LV
PL
HU
RO
HR
BG

GDP per capita 
(% of EU27)
310.8
227.4
186.8
161.6
139.0
138.9
138.7
128.4
125.4
113.4
109.0
100.0
90.9
87.5
84.3
81.1
76.1
72.7
65.0
64.1
59.8
59.1
57.6
57.3
52.5
51.9
44.3
41.8
27.2

Source: AMECO, European Commission. 

The  specific  assumptions  agreed  for  the  baseline 
scenario by the EPC are as follows: 

(cid:129) 

(cid:129) 

the 'leader' is the group of countries that have a 
GDP  per  capita  above  the  EU27  average.  For 
these  countries,  TFP  growth  is  assumed  to 
converge from the estimated value in 2029 to a 
1% growth rate by 2045; 

the 'follower' group of countries are those with 
GDP  per  capita  below  the  EU27  average,  for 
whom  a  differentiation  is  made  depending  on 
the distance to the EU average. 

(Continued on the next page) 

 
 
 
 
   
 
 
 
  
 
Underlying assumptions and projection methodologies 

Part I 

Box (continued) 

Table 2: 

Baseline scenario TFP (1%): assumptions on speed of convergence and criteria for selection - 2021 AR 

GDP per 
capita
(% of 
EU27)

Countries, 2029

Years 
(from/to)

Values

Countries, 2037

Years 
(from/to)

Values

Years 
(from/to)

Values

"Leaders" (per capita GDP higher than the EU average)

Above 
100%

LU, IE, DK, SE, NL, 
AT, FI, DE, BE, FR 

2029 to 
2037

From value in 2029 to 1%, by linear interpolation

LU, IE, DK, SE, FI, 
AT, NL, DE, BE, FR 

2037 to 
2045

From value in 2038 to 1%, by linear interpolation

2046 to 
2070

1.0%

Below 
100%

IT, MT, ES, SI, CY, 
EE, PT, CZ, LT, EL, 
SK, LV, PL, HU, 
RO, HR, BG

2029 to 
2037

From value in 2029 to 

by linear interpolation

Source: European Commission, EPC. 

"Followers" (per capita GDP lower than the EU average) 

MT, IT, ES, SI, EE, 
CY, CZ, PT, LT, LV, 
SK, EL, PL, HU, 
RO, HR, BG

2037 to 
2045

From value in 2038 to 

by linear interpolation

2046 to 
2070

From value in 
2045 to 1%, by 
linear 
interpolation

Table 3: 

Risk scenario TFP (0.8%): assumptions on speed of convergence and criteria for selection - 2021 AR 

GDP per 
capita
(% of 
EU27)

Countries, 2029

Years 
(from/to)

Values

Countries, 2037

Years 
(from/to)

Values

Years 
(from/to)

Values

"Leaders" (per capita GDP higher than the EU average)

Above 
100%

LU, IE ,NL, DK, AT, 
DE, SE, MT, BE, FI, 
UK 

2029 (t+1) 
to 2037

Below 
100%

FR, SK, SI, CZ, LT, 
PL, IT, HU, EE,ES, 
LV, RO,CY, PT, EL, 
HR, BG

2029 (t+1) 
to 2037

From value in 2029 (t+1) to 0.8%, by linear interpolation

From value in 2038 to 0.8%, by linear interpolation

LU, IE , MT, NL, DK, 
AT, SE, DE,  SK,  
FI,BE, SI, PL, UK, 
LT
"Followers" (per capita GDP lower than the EU average) 

2038 to 
2045

From value in 2029 (t+1) to 

From value in 2038 to 

by linear interpolation

CZ, FR, LV, EE, 
HU, RO, ES, IT, CY, 
PT, EL, BG, HR

2038 to 
2045

by linear interpolation

2046 to 
2070

0.8%

2046 to 
2070

From value in 
2045 to 0.8%, by 
linear 
interpolation

Source: European Commission, EPC. 

TFP risk scenario 

The core assumptions for the risk scenario are that 
(i)  country-specific  TFP  growth  rates  converge  to 
0.8%,  and  (ii)  the  lower  the  GDP  per  capita,  the 
higher the catch-up potential (Table 3). 

In the long term, labour productivity broadly coin-
cides with TFP growth divided by the labour share, 
namely 1.2%. 

Specifically,  the  assumptions  agreed  for  the  risk 
scenario by the EPC are as follows: 

(cid:129)  For  the  'leader'  group,  TFP  growth  is  assumed 
to converge from the estimated value in 2019 to 
0.8% by 2045;  

(cid:129)  For  the  'follower'  group,  a  differentiation  is 
made  depending  on  the  distance  to  the  EU27 
average. 

Key assumptions regarding capital formation 

With regard to capital deepening, the assumption in 
the  previous  exercises  to  keep  the  long-run  capital 
to  labour  ratio  in  efficiency  units  constant  (the 
‘capital rule’) is kept. It is assumed therefore that in 
the long run, the capital stock adjusts to the steady 

state path according to the ‘capital rule’: the growth 
rate  of  capital  is  equal  to  the  sum  of  labour  and 
labour augmenting technical progress.  

This fulfils the steady state property, as the ratio of 
capital  to  labour  expressed  in  efficiency  unit 
remains  constant  over  time.  Consequently,  labour 
productivity  growth  coincides  with  that  of  labour-
augmenting technical progress.  

However, the application of this rule would lead to 
sharp  shifts  in  investment  rates  for  the  year  in 
which  it  is  applied.  For  example,  introducing  the 
rule  in  2029  results  in  pessimistic  productivity 
projections  for  many  of  the  catching-up  countries, 
while making little difference for countries that are 
already close to their long-run TFP growth rate.  

A  transition  between  the  investment  rule  and  the 
capital  rule  is  therefore  applied  to  smooth  the 
investment profile:  

(cid:129)  First, 

the 

transition 

constant 
capital/labour  (in  efficiency  units)  ratio  is 
introduced  gradually  in  the  period  2030-2039 
in a linear manner (‘transition rule’); 

the 

to 

(cid:129)  Second,  the  capital/labour  (in  efficiency  units) 
ratio is constant from 2040 (‘capital rule’). 

75 

 
   
 
 
 
  
 
 
  
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

3.3.  COMPARING THE 2021 AND 2018 GDP 

Graph I.3.2:  Annual GDP growth rates 2019-70 (%) in 2021 

and 2018 baseline scenario projections - 
period average 

EU27

euro area

2018 Ageing Report

2021 Ageing Report

2.0

1.5

1.0

0.5

0.0

)

%

(

e
t
a
r

h
t
w
o
r
g

l

a
u
n
n
a
g
v
a

2019-35

2036-70

2019-35

2036-70

Source: European Commission, EPC. 

For the EU, annual potential GDP growth over the 
period 2019-35 is now projected to average 1.18% 
(as compared  with 1.22% in the 2018 projection). 
Over  the  period  2036-70,  average  GDP  growth  is 
projected at 1.42% and 1.39% respectively. For the 
euro  area,  annual  potential  GDP  growth  over  the 
period  2019-35  is  projected  in  the  2021  Ageing 
Report to average 1.1% (compared with 1% in the 
2018 Ageing Report). Over the period 2036-70, it 
is projected to remain the same at 1.42%. 

A  comparison  between  the  current  TFP  risk 
scenario  projection  and  that  in  the  2018  Ageing 
Report  shows  that  for  the  EU,  annual  potential 
GDP  growth  is  forecast  to  be  on  average  0.1  pps 
higher  than  in  the  2018  projection.  For  the  euro 
area,  the  projections  coincide  (Table  I.3.9).  The 
upward  revisions  for  the  EU  vis-à-vis  the  2018 
Ageing Report are driven entirely by higher labour 
productivity  growth  forecasts,  with  only  marginal 
differences in the labour input contribution.  

PROJECTIONS 

Over  the  whole  projection  period,  potential  GDP 
growth is similar in the 2021 and 2018 exercises; 
however,  there  are  some  differences  by  sub-
periods, especially during 2019-2035. 

Under  the  baseline  scenario  of  the  2021  Ageing 
Report,  the  annual  average  potential  GDP  growth 
rate  over  the  period  2019-2070  in  the  EU  and  in 
the euro area is projected to be 1.3%, the same as 
in the 2018 Ageing Report (Table I.3.8).  

For  the  EU,  the  annual  contribution  from  labour 
productivity  growth  during  2019-70  is  0.1  pps 
higher  than  in  the  2018  projection,  while  for  the 
euro area, labour productivity growth is the same. 
Labour  input  growth  (hours  worked)  is  projected 
to  be  the  same  as  in  the  2018  Ageing  Report  for 
both the EU and the euro area.  

However,  there  is  substantial  variation  across 
countries in the differences between the 2021 and 
2018  potential  GDP  growth  projections  under  the 
baseline scenario. The largest downward revisions 
in  average  annual  potential  GDP  growth  rates  are 
for  Latvia  (-0.7  pps)  and  Slovakia  (-0.5  pps),  due 
to the contributions of both labour productivity and 
labour input being notably lower than in the 2018 
exercise.  The  largest  upward  revisions  concern 
Estonia  and  Cyprus  (+0.4  pps),  both  of  which 
benefiting  from  stronger  labour  productivity  and 
labour input.  

The  differences  between  the  2021  and  2018 
potential  GDP  growth  projections  under 
the 
baseline  scenario  materialise  primarily  in  the  first 
part  of  the  projections  (2019-35),  particularly  for 
the euro area (Graph I.3.2).  

76 

 
    
 
 
 
 
 
Underlying assumptions and projection methodologies 

Part I 

Table I.3.8:  Difference between 2021 and 2018 baseline scenarios, annual average GDP growth, 2019-2070 (pps) 

GDP growth 
in 2019-
2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

1=2+5
-0.3
-0.1
0.2
0.1
0.1
0.4
-0.1
0.3
-0.1
-0.2
-0.1
0.2
0.4
-0.7
0.2
-0.4
0.2
0.1
-0.2
-0.1
0.2
0.3
0.0
0.1
-0.5
-0.1
-0.1
-0.1
0.0
0.0

Labour 
prod. (GDP 
per hour 
worked)
2=3+4
-0.1
-0.2
0.1
0.1
-0.1
0.3
0.0
0.2
0.1
0.0
0.1
0.2
0.2
-0.4
0.2
-0.3
0.1
-0.1
0.0
-0.1
0.2
0.1
0.1
0.1
-0.2
0.1
-0.1
0.0
0.0
0.1

TFP

3
-0.1
-0.1
0.1
0.1
0.0
0.2
0.0
0.2
0.1
0.0
0.1
0.1
0.1
-0.3
0.1
-0.2
0.0
-0.1
0.0
-0.1
0.1
0.1
0.0
0.1
-0.2
0.1
-0.1
0.0
0.0
0.1

Capital 
deepening

Labour 
input

Total 
population

Employment 
rate

Share of 
working-age 
population

change in 
average 
hours worked

GDP per 
capita 
growth in 
2019-2070

4
0.0
-0.1
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.1
0.0
0.1
-0.1
0.1
-0.1
0.1
-0.1
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0

5=6+7+8+9
-0.3
0.2
0.1
0.0
0.2
0.1
0.0
0.1
-0.2
-0.2
-0.3
0.0
0.2
-0.3
0.1
0.0
0.1
0.2
-0.2
-0.1
0.0
0.2
-0.1
0.0
-0.3
-0.2
0.0
-0.1
0.0
0.0

6
-0.3
0.1
0.0
-0.2
0.1
0.0
0.1
0.2
-0.1
-0.2
-0.2
0.0
0.1
-0.2
0.1
-0.5
0.0
0.4
-0.2
-0.2
0.0
0.1
-0.2
0.0
-0.1
-0.2
-0.1
-0.1
-0.1
-0.1

7
0.0
0.1
0.0
0.2
0.0
0.1
-0.1
-0.1
-0.1
0.0
-0.1
0.0
0.0
0.0
0.0
0.5
0.1
-0.2
0.0
0.1
0.0
0.1
0.1
0.0
-0.2
0.0
0.1
0.0
0.0
0.0

8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

9
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6
0.0
-0.1
0.2
0.3
0.0
0.4
-0.2
0.1
0.1
0.0
0.1
0.2
0.3
-0.4
0.1
0.2
0.2
-0.3
0.0
0.0
0.2
0.2
0.2
0.1
-0.4
0.1
0.0
0.0
0.0
0.1

Source: European Commission, EPC. 

Table I.3.9:  Difference between 2021 and 2018 TFP risk scenarios, annual average GDP growth, 2019-2070 (pps) 

GDP growth 
in 2019-
2070

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

1=2+5
-0.3
0.1
0.3
0.2
0.1
0.5
-0.3
0.5
0.0
-0.2
-0.1
0.2
0.5
-0.4
0.5
-0.3
0.4
0.1
-0.1
-0.1
0.4
0.3
0.2
0.2
-0.3
0.0
-0.1
-0.2
0.0
0.1

Labour 
prod. (GDP 
per hour 
worked)
2=3+4
0.0
-0.1
0.3
0.2
-0.1
0.4
-0.2
0.4
0.1
0.0
0.1
0.2
0.3
-0.2
0.4
-0.3
0.3
-0.1
0.1
0.0
0.3
0.2
0.2
0.2
-0.1
0.2
-0.1
0.0
0.0
0.1

TFP

3
0.0
0.0
0.2
0.2
-0.1
0.3
-0.1
0.3
0.1
0.0
0.1
0.1
0.1
-0.2
0.3
-0.2
0.2
-0.1
0.0
0.0
0.2
0.1
0.1
0.2
-0.1
0.1
-0.1
-0.1
0.0
0.1

Capital 
deepening

Labour 
input

Total 
population

Employment 
rate

Share of 
working-age 
population

change in 
average 
hours worked

GDP per 
capita 
growth in 
2019-2070

4
0.0
-0.1
0.1
0.1
0.0
0.1
-0.1
0.1
0.0
0.0
0.1
0.0
0.1
0.0
0.1
-0.1
0.1
-0.1
0.0
0.0
0.1
0.0
0.1
0.0
0.1
0.1
0.0
0.0
0.0
0.0

5=6+7+8+9
-0.3
0.2
0.1
0.0
0.2
0.1
0.0
0.1
-0.2
-0.2
-0.3
0.0
0.2
-0.3
0.1
0.0
0.1
0.2
-0.2
-0.1
0.0
0.2
-0.1
0.0
-0.3
-0.2
0.0
-0.1
0.0
0.0

6
-0.3
0.1
0.0
-0.2
0.1
0.0
0.1
0.2
-0.1
-0.2
-0.2
0.0
0.1
-0.2
0.1
-0.5
0.0
0.4
-0.2
-0.2
0.0
0.1
-0.2
0.0
-0.1
-0.2
-0.1
-0.1
-0.1
-0.1

7
0.0
0.1
0.0
0.2
0.0
0.1
-0.1
-0.1
-0.1
0.0
-0.1
0.0
0.0
0.0
0.0
0.5
0.1
-0.2
0.0
0.1
0.0
0.1
0.1
0.0
-0.2
0.0
0.1
0.0
0.0
0.0

8
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

9
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6
0.0
0.0
0.3
0.4
0.0
0.5
-0.4
0.3
0.1
0.0
0.1
0.2
0.3
-0.2
0.4
0.2
0.4
-0.3
0.0
0.1
0.4
0.2
0.3
0.2
-0.3
0.2
0.0
-0.1
0.1
0.1

Source:  European Commission, EPC 

77 

 
 
 
 
 
          
 
 
          
 
4. 

INTEREST RATES 

In the 2021 Ageing Report, long-term interest rates 
will  first  converge  to  country-specific  forward 
market rates at T+10 (2030), then converge to 2% 
real  by  T+30  (2050)  (4%  nominal  for  most  EU 
thereafter. (27) 
countries)  and  remain  constant 
Compared 
this 
approach  revises  the  assumption  on  the  level  of 
long-term  interest  rates  downward,  in  line  with 
recent  changes 
fiscal 
sustainability analysis. 

to  previous  Ageing  Reports, 

the  Commission’s 

in 

4.1. 

BACKGROUND 

Assumptions regarding the evolution of long-term 
interest rates over the projection period until 2070 
influence  the  projections  of  private  pensions, 
which  countries  report  voluntarily.  The  Ageing 
Working Group (AWG) agreed that projections of 
(expenditure  and 
privately 
revenue) are voluntary.  

funded  pensions 

long-term 

interest  rate  assumptions 

that 
The 
(AR) 
underpinned  previous  Ageing  Report 
projections contained features dating back to 2006. 
In the 2018 AR, the long-term interest rate used to 
project private pensions until 2070 was assumed to 
converge  linearly  to  3%  real  (5%  nominal,  given 
an inflation rate of 2% (28)) in  10 years' time (i.e. 
by  T+10),  from  current  country-specific  levels, 
staying  constant  thereafter  (Table  I.4.1).  This 
approach  was  identical  to  the  one  applied  in  the 
AR  projection  rounds  since  2006,  with  some 
differences.  In  particular,  in  all  AR  rounds  since 
2006,  the  target  value  for  the  long-term  interest 
rate  was  3%  real  (5%  nominal).  Yet  in  the  2006 
and 2009 AR rounds, this value remained constant 
throughout the projection period, whereas from the 
2012  AR  onwards  a  gradual  convergence  to  this 
value  was  agreed,  with  faster  linear  convergence 
the  2012  AR  and  slower 
(up 
convergence  (up  to  T+10)  in  the  2015  and  2018 
ARs.  In  all  cases,  long-term  interest  rates  were 
kept constant beyond the convergence year.   

to  T+5) 

in 

A  conventional  approach  of  this  kind  was  also 
used  by  the  Commission  in  the  past  to  assess  the 
Member  States’  fiscal  sustainability,  but  was 

(27)  4.5%  nominal  for  Poland  and  Romania,  and  5%  nominal 
for Hungary, given these countries’ higher inflation targets) 
(28)  Inflation  was  assumed  to  reach  2%  progressively  from 

current country specific levels.  

78 

recently  revised  to  reflect  market  expectations. 
Given  that  conventional  assumptions  contrasted 
with  interest  rate  trends  in  recent  decades  (see 
Section  4.3  below),  in  2019  the  Commission  
adjusted the interest rate assumption underpinning 
the  Commission’s  debt  projections  up  to  T+10 
(2030) to reflect market expectations. In particular, 
the long-term interest rates on new and rolled-over 
debt  now  converge  linearly  from  country-specific 
current  values  to  country-specific  market-based 
forward  (nominal)  rates  by  T+10 (29).  Beyond 
T+10,  fiscal  sustainability  analysis  currently  uses 
the  former  conventional  target  of  3%  real  (5% 
nominal),  deferred  to  T+30  (2050).  This  target 
stays constant thereafter (Table I.4.1). 

4.2. 

INTEREST RATE ASSUMPTIONS IN THE 2021 
AGEING REPORT  

first  converge 

Interest rates used to project private pensions  will 
be  lower  this  round  in  comparison  with  those  in 
past ageing reports. In this report, nominal interest 
to  country-specific 
rates  will 
forward  market  rates  at  T+10  (2030),  in  line  with 
the  Commission’s  fiscal  sustainability  analysis 
framework.  Then, 
interest  rates  will  have  a 
common target of a 2% long-term real rate for all 
EU  countries  by  T+30  (2050).  This  would  imply 
convergence  to  4%  nominal  rates  by  the  same 
year,  for  all  EU  countries  except  Poland  and 
Romania  (4.5%  nominal)  and  Hungary  (5% 
nominal),  given  higher  inflation  targets  in  these 
countries (30).  All  interest  rates  would  remain 
constant  thereafter,  until  T+50  (2070).  (Table 
I.4.1). 

of 

downward 

The 
rate 
revision 
assumptions  in  this  Ageing  Report  has  a  double 
rationale. On the one hand, the linear convergence 
principle  maintains  the  advantage  of  accounting 
for  country-specific  situations  in  the  short  run, 

interest 

(29)  This  approach  is  similar  to  that  used  in  the  Commission 
Forecasts. For more details of the new and previous interest 
rate assumptions used for fiscal sustainability analysis, the 
rationale  for  the  change  and  the  impact  on  debt  ratio 
projections,  see  Chapter  3  and  Box  3.1  of  the  European 
Commission  (2020),  ‘Debt  Sustainability  Monitor  2019’, 
European Economy, Institutional Paper, No. 120. 

(30)  Inflation  is  still  assumed  to  reach  2%  for  most  countries 
(see  footnote  1).  In  a  few  non-euro  area  countries, 
however, the convergence value is now higher, in line with 
the  respective  national  central  banks’  inflation  targets 
(2.5% in Poland and Romania, and 3% in Hungary). 

                                                           
                                                           
Underlying assumptions and projection methodologies 

Part I 

Table I.4.1: 

Selected macroeconomic assumptions in subsequent projection rounds (baseline scenario) 

Column1

Long-term market interest rate 

Inflation target Potential GDP growth

Ageing Report 2021 

Linear convergence to:
- country-specific forward market rates at T+10 (2030)
- 2% real (4% nominal in all EU27 except PL, RO (4.5%) and HU (5%)), 
   by T+30 (2050)
- maintained thereafter, until the end of projection period T+50 (2070).

2% for all EU27, 
except PL, RO 
(2.5%) and HU (3%)

1.4% real, EU27 average at 
the end of projection period 
T+50 (2070)(1)

Ageing Report 2018 

Linear convergence to: 
- 3% real (5% nominal) by T+10
- maintained thereafter, until the end of projection period T+50 (2070).

2% for all EU28

1½% real, EU28 average at 
the end of projection period 
T+50 (2070)(1)

Debt Sustainability 
Monitor 2019

Linear convergence to: 
- country-specific forward market rates at T+10 (2030)
- 3% real in all EU28 except PL, RO (2.5%) and HU (2%), 
  (5% nominal in all EU28) by T+30 (2050)
- maintained thereafter, until the end of projection period T+50 (2070).

2% for all EU28, 
except PL, RO 
(2.5%) and HU (3%)

1½% real, EU28 average at 
the end of projection period 
T+50 (2070)

(1) Individual Member States' growth rates are country-specific. See Chapter 3 of this report and Chapter 3 of the 2018 
Ageing Report, Underlying Assumptions & Projection Methodologies. 
Source: European Commission  

while  still  maintaining 
the  assumption  of  a 
common  real  interest  rate  in  the  long  run.  On  the 
other  hand,  compared  with  the  previous  Ageing 
Reports,  the  proposal  would  entail  a  significant 
reduction  in  the  EA/EU-27  interest  rate-growth 
rate (r-g) differential by 2070, reflecting declining 
and even negative past trends (Graph I.4.1) and in 
line  with  the  recent  literature  (31).  Under  the  new 
assumptions, the EU economy would tend towards 
a  modest  (0.5)  positive  interest  rate  –  growth  rate 
differential in 2070. 

4.3. 

ECONOMIC RATIONALE FOR THE 
DOWNWARD REVISION 

The  conventional  assumptions  about  long-term 
interest rates that have underpinned the work of the 
AWG  over  the  past  15  years  reflect  historical 
averages  in  some  countries  (32).  However,  the 
macroeconomic 
changed 
environment 
substantially  over 
the  past  few  decades,  as 
acknowledged in a vast amount of literature.  

has 

Even before the start of the COVID crisis, risk-free 
nominal  interest  rates  in  advanced  economies  had 

been  trending  downward  for  several  decades 
(Graph  I.4.2).  Real  rates  declined  in  parallel, 
though to a slightly lesser extent. Persistently low  

Graph I.4.1: 

(R-g) based on market long-term interest 
rates, aggregates and selected countries, % 

(r-g) market 

0
0
0
2

1
0
0
2

2
0
0
2

3
0
0
2

4
0
0
2

5
0
0
2

6
0
0
2

7
0
0
2

8
0
0
2

9
0
0
2

0
1
0
2

1
1
0
2

2
1
0
2

3
1
0
2

4
1
0
2

5
1
0
2

6
1
0
2

7
1
0
2

8
1
0
2

9
1
0
2

EA

EU27

(r-g) market 

10.0

5.0

0.0

-5.0

10.0

5.0

0.0

-5.0

-10.0

0
0
0
2

1
0
0
2

2
0
0
2

3
0
0
2

4
0
0
2

5
0
0
2

6
0
0
2

7
0
0
2

8
0
0
2

9
0
0
2

0
1
0
2

1
1
0
2

2
1
0
2

3
1
0
2

4
1
0
2

5
1
0
2

6
1
0
2

7
1
0
2

8
1
0
2

9
1
0
2

NL

DE

IT

FR

ES

(1) (r-g) series are based on historical long-term nominal 
interest rates (market) and the growth rate of nominal GDP 
in national currency, between 2010 and  2019; 
Source: AMECO and Commission staff calculations 

(31)  See for example Abbas, S. A., Pienkowski, A., and Rogoff, 
K.  (Editors)  (2020).  ‘Sovereign  Debt:  A  Guide  for 
Economists and Practitioners’, Oxford University Press and 
Blanchard,  O.  (2019).  ‘Public  Debt  and  Low  Interest 
Rates’, American Economic Review 190(4). 

(32)  See  European  Commission  (2017),  ‘The  2018  Ageing 
Report:  Underlying  Assumptions 
Projection 
Methodologies’,  European  Economy  Institutional  Paper, 
No. 065, Part I.4. Table I.4.1. 

and 

inflation  and  sluggish  economic  growth  suggest  a 
secular  decline  of  the  real  equilibrium  rate  to 
historically  low  levels,  as  reflected  in  market 
expectations of persistently low interest rates in the 
years  to  come.  This  global  phenomenon  is  well 
documented in the literature. It is attributed both to 

79 

                                                           
 
   
 
   
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Graph I.4.2: 

Interest rate decline 

(1) 2a: Estimates provided by the New York Fed, following Holston, Laubach, Williams (2017). 
(2) 2b: Simple averages of available data for selected countries. 
Source:  DG ECFIN based on New York Fed (2a), Macrobond, and national sources (2b). 

investment  and 

include  ageing  and 

‘structural  factors’  having  triggered  an  excess 
ofreal  savings  over 
to  more 
circumstantial  or  policy-related  drivers.  The 
former 
low  productivity, 
sluggish invention and innovation, low investment 
profitability, 
emerging 
economies, rising income or wealth inequality, and 
deleveraging. The latter include the scarcity of safe 
assets  and  increased  demand  for  them  amidst 
global uncertainty, especially in the euro area after 
the sovereign debt crisis. 

income 

growth 

in 

Currently,  several  economists  take  the  view  that 
the 
the  COVID  crisis  could  further  depress 
equilibrium real interest rate, possibly for decades 
ahead.  Recent  evidence  indicates  that  the  real 
natural rate of interest could decline for decades in 
the  aftermath  of  pandemics,  as  the  latter  induce 
labour 
to  greater 
precautionary  savings  (Jordà  et  al,  2020)(33). 
Similarly,  the  channel  of  excess  savings,  which 
played  an  important  role  in  past  decades,  is 
expected  to  play  a  significant  role  again  in  the 
context of falling private-sector demand associated 
with 
the 
the  current  crisis,  driving  down 
equilibrium real interest rate (Goy, G. and van den 

scarcity  and/or  a 

shift 

(33)  Jordà, O, S R Singh and A M Taylor (2020), ‘Longer-Run 
Economic  Consequences  of  Pandemics’,  Federal  Reserve 
Bank  of  San  Francisco  Working  Paper  2020-09. 
https://doi.org/10.24148/wp2020-09.  This  study,  focused 
on European countries, relies on a dataset covering France 
(1387–2018),  Germany  (1326–2018),  Italy  (1314–2018), 
the  Netherlands  (1400–2018),  Spain  (1400–1729,  1800–
2018), and the UK (1314–2018).  

80 

En,  2020).  Some  upward  effects  on  interest  rates 
could  be  noted  if  there  was  persistent  supply  of 
‘safe’  government  bonds.  However,  if  potential 
growth  falls  and  risk  premia  remain  elevated,  or 
even rise as a result of increased risk aversion, the 
crisis  would  have  an  additional  downward  effect 
on the equilibrium rate (34). 

the  European 
European  authorities  such  as 
Insurance  and  Occupational  Pensions  Authority 
(EIOPA) have already weighed the implications of 
low and even negative long-term interest rates  for 
certain  financial  sectors.  In  this  context,  EIOPA 
proposed  a  review  of  its  long-term  risk-free 
interest  rates  modelling  used  to  estimate  insurers’ 
solvency  and  calculate  their  capital  requirements. 
EIOPA  considers  options  for  extrapolating  the 
structure 
basic 
(government  bond  yields)  for  periods  of  up  to  60 
years ahead (or the last liquid point + 40 years, for 
non-EA  countries)  and  making  this  term  structure 
more market-consistent (35). 

risk-free 

interest 

term 

rate 

(34)  Goy,  G.  and  van den En,  J.W.  (2020),  ‘The  impact  of the 
COVID-19 crisis on the equilibrium interest rate’, Vox 20 
April 
https://voxeu.org/article/impact-covid-19-
crisis-equilibrium-interest-rate. 

2020, 

(35) EIOPA (2019), ‘Consultation Paper on the Opinion on the 
2020  review  of  Solvency  II’,  EIOPA-BoS-19/465,  15 
October 
‘Technical 
documentation of the methodology to derive EIOPA’s risk-
free  interest  rate  term  structures’,  EIOPA-BoS-19/408,  12 
September 2019.  

and  EIOPA 

(2019), 

2019 

                                                           
                                                           
5. 

SENSITIVITY TESTS AND ALTERNATIVE SCENARIOS 

5.1. 

INTRODUCTION 

the 

term.  However,  given 

The  Ageing  Report  baseline  projections  attempt 
to measure how population ageing can influence 
economic  and  budgetary  developments  over  the 
long 
inherent 
uncertainty of the assumptions underpinning any 
long-run projections, it is essential to carry out a 
number  of  sensitivity 
the 
responsiveness of projection results to changes in 
assumptions.  Moreover, 
key 
additional  scenarios  are  included  given  the  high 
level  of  uncertainty  about  the  magnitude  and 
duration of the COVID-19 crisis. 

to  quantify 

underlying 

tests 

in 

The sensitivity tests introduce a change or shock to 
an  underlying  assumption/parameter 
the 
projection  framework.  For  each  sensitivity  test,  a 
uniform shock is applied to all Member States. The 
presentation  and  assessment  of  the  impact  of 
ageing  populations  on  particular  age-related 
expenditure  items  should  be  made  with  reference 
to all scenarios (baseline plus sensitivity tests): this 
is needed so that a clear picture emerges of the key 
factors  driving  the  projection  results  and  the 
potential  sources  of 
future  public 
expenditure  developments.  In  addition,  alternative 
scenarios  are  simulated,  in  order  to  reflect  the 
impact of potential future policy changes.  

risk 

to 

is 

run 

scenario 

The  sensitivity  scenarios  do  not  provide  a  stress 
test  of  the  baseline  projections  as  they  consider 
both  unfavourable  and  favourable  changes  in  the 
underlying  assumptions.  Moreover,  no  overall 
adverse 
the 
to 
impact  of  several  unfavourable 
compounded 
circumstances  occurring  simultaneously.  Rather, 
the  different  sensitivity  tests  rather  provide  useful 
information  on  the  dynamics  of  the  projection 
results  with  respect  to  possible  changes  in  the 
underlying  assumptions.  The  relative  impact  can 
also be read as an 'elasticity' parameter.  

evaluate 

5.2. 

PROJECTIONS UNDER DIFFERENT 
SENSITIVITY TESTS AND ALTERNATIVE 
SCENARIOS 

The  macroeconomic  projections  under 
the 
different  sensitivity  tests  are  provided  in  Tables 
I.5.2  to  I.5.10.  The  assumptions  are  described  in 
the  following  section  and  summarised  in  Table 
I.5.1. 

To  produce  the  overall  set  of  assumptions,  a 
i.e.  from 
bottom-up  approach  was  followed, 
population projections through labour input and to 
GDP growth projections. Each sensitivity test may 
therefore involve recalculating all assumptions and 
re-running  of  the  labour  force  and  productivity 
function-based  models, 
to  keep  a 
consistent macroeconomic framework.  

in  order 

tests  draws  on 
The  selection  of  sensitivity 
experience  from  previous  rounds  of  Ageing 
Report.  In  general,  the  set  of  scenarios  applied  in 
the  2018  Ageing  Report  was  appropriate 
to 
conducting a sensitivity analysis of changes in age-
related  expenditure.  Reproducing 
tests 
ensures  consistency  and  enables  comparison 
between  projection  exercises.  At  the  same  time, 
experience  and  the  need  to  assess  new  types  of 
risks warrant a number of modifications.  

these 

round, 

the  previous 

Compared  with 
the 
higher/lower  total  employment  rate  tests  are  not 
carried out. Nevertheless, the scenario assuming a 
higher employment rate among older workers (age 
55-74)  is  maintained.  The  latter  had  a  more 
profound  estimated  impact  in  2018,  highlighting 
increasing 
how 
employment  among  older  people  can  be  benefit  
the  economy.  The  2018  Ageing  Report  also 
included 
labour  productivity 
scenarios.  In  this  round,  a  ‘TFP  risk  scenario’  is 
performed,  as  well  as  a  ‘Higher  TFP  growth’ 
scenario. 

three  alternative 

challenge 

policy 

the 

of 

The  tests  would  also  be  applied  to  the  other  age-
related public expenditure items, as was the case in 
the 2018 Ageing Report.  

Sensitivity tests  

following 

The 
formulated. 

sensitivity 

tests  have  been 

Life expectancy: mortality rates are adjusted so as 
to achieve an increase in life expectancy at birth of 
about two years by 2070 compared to the baseline. 

81 

European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.5.1:  Overview of the sensitivity tests and alternative scenarios 

Population

Labour force

Productivity

Policy risk scenarios

Higher life 
expectancy

Lower/higher 
net migration

Lower fertility

Higher employment 
rate older workers

Higher TFP growth and 
TFP risk scenario

Linking 
retirement age 

Unchanged 
retirement age

Offset declining 
pension benefit 
ratio

Increase in life 
expectancy at 
birth of two 
years by 2070 
compared with 
the baseline 
projection.

33% less/more 
net migration 
compared with the 
baseline over the 
entire projection 
horizon.

20% lower 
fertility 
compared with 
the baseline over 
the entire 
projection 
horizon.

Employment rate of 
older workers (55-74y) 
10 pps higher 
compared with the 
baseline projection. 

TFP growth assumed to 
converge to 
0.8%/1.2% (instead of 
1%). As done for the 
baseline scenario, a period 
of fast convergence for 
'followers' is assumed (i.e. 
rising by up to 
0.8%+0.5% and 
1.2%+0.5%, 
respectively).

The increase is 
introduced linearly 
over the period 2021-
2033 and remains 10 
pps higher thereafter. 

Convergence to the target 
rate in 2045 from the 
latest outturn year, i.e. 
2019.

The higher 
employment rate of 
this group of workers 
is assumed to be 
achieved through a 
reduction of the 
inactive population.

The retirement 
age is shifted year-
over-year in line 
with 3/4 of the 
change in life 
expectancy at 
current retirement 
ages (in the 
Cohort Simulation 
Model).

The early and 
statutory 
retirement ages, 
as well as career 
requirements, are 
frozen at the 
situation in the 
base year.

When the earnings-
related public 
pension benefit 
ratio declines by 
10% as compared 
to the base year 
level, measures 
are taken to 
stabilise the 
benefit ratio.

Source: European Commission, EPC. 

Specifically, this would be introduced by reducing 
the  age-specific  mortality  rates  linearly  over  the 
2018-2070 period. 

Net  migration:  in  line  with  the  2018  Ageing 
Report, it is decided to run sensitivity tests for both 
higher  and  lower  migration.  Compared  to  the 
baseline,  net  migration  flows  are  assumed  to  be 
the  entire  projection 
33%  higher/lower  over 
horizon.  This  highlights 
the  economic  and 
alternative  migratory 
budgetary 
population developments.  

impact  of 

Lower  fertility:  under  this  scenario,  the  fertility 
rate  is  assumed  to  be  20%  lower  than  under  the 
baseline  scenario  throughout  the  entire  projection 
horizon.  As  with  the  net  migration  tests,  this 
scenario  allows  the  impact  of  alternative  natural 
population  developments  on  economic  and 
budgetary systems to be highlighted. 

in 

through  a 
Older  workers’  employment  rate: 
the 
the 
reduction 
employment  rate  of  older  workers  (55  to  74)  is 
increased  by  10  pps  for  the  remainder  of  the 
projection period. 

inactive  population, 

of 0.8%/1.2%. The convergence speed follows the 
same  principles  and  time  points  as  under  the 
baseline 
fast 
convergence  for  'followers'  for  which  TFP  can 
grow  by  up  to  0.8%+0.5%  and  1.2%+0.5%  under 
the respective scenarios. 

scenario,  with  a  period  of 

Policy scenarios 

to 

addition 

In 
the 
testing 
macroeconomic  and  demographic  assumptions, 
three policy scenarios are formulated:  

changes 

in 

the  Cohort  Simulation  Model), 

Linking  retirement  age  to  life  expectancy:  this 
scenario  considers  the  adoption  of  an  automatic 
mechanism  to  revise  the  effective  retirement  age 
(the  exit  age  from  the  labour  market  as  estimated 
by 
thereby 
changing the statutory and early retirement ages in 
line  with  changes  in  life  expectancy.  For  those 
countries  where a link between the retirement age 
and  the  increase  in  life  expectancy  is  already  laid 
down  by  law  (and  is  thus  an  integral  part  of  the 
baseline),  no  deviations  are  expected  in  terms  of 
the pension expenditure-to-GDP ratio (36).   

Higher  TFP  growth  and  TFP  risk:  under  these 
scenarios, 
is 
assumed to converge to a steady-state growth rate 

total  factor  productivity  growth 

(36)  The same applies if the legislation provides for increases in 
statutory  retirement  that  are  higher  than  the  gains  in  life 
expectancy. 

82 

                                                           
 
  
 
retirement  age,  career 

Unchanged  retirement  age:  this  scenario  assumes 
that  the  main  eligibility  requirements  (early  and 
statutory 
requirement) 
remain  unchanged  from  the  starting  point  and 
throughout  the  projection  horizon.  This  makes  it 
possible to isolate the expected impact of reforms 
already  adopted  in  law  but  which  have  not  yet 
taken effect (as reflected in the baseline projection) 
and the risk of such reforms being reversed. 

Offset declining pension benefit ratio: this scenario 
assumes  policy  measures  are 
the 
(earnings-related)  public  pension  benefit  ratio 
would start to decrease by more than 10% relative 
to the base year. In this  scenario the benefit ratio 
is  kept  constant  at  this  10%  lower  point  for  the 
remainder of the projection period. 

taken 

if 

Additional adverse macroeconomic scenarios 
due to COVID-19 related risks  

The  2021  Ageing  Report  also  includes  additional 
scenarios  relating  to  the  COVID-19  crisis,  given 
the  high  level  of  uncertainty  about  its  magnitude, 
duration and economic impact.  

Hence,  two  adverse  macroeconomic  scenarios  are 
proposed  in  addition  to  the  baseline  scenario  (37), 
both of which are described below. An illustration 
of  these  scenarios  (for  the  EU27)  and  a  detailed 
description  are  provided  in  Chapter  3,  Box  I.3.1. 
The  macroeconomic  projections  under  the  two 
adverse scenarios are provided in Tables I.5.11 and 
I.5.12. 

Lagged recovery scenario: this scenario maintains 
the  assumption  of  a  relatively  limited  impact  on 
potential  growth  (slightly  higher  than  in  the 
baseline  scenario),  but  with  a  much  more 
pronounced  cyclical  downturn  and  a 
longer 
recovery  phase,  resulting  in  a  wide  ‘U-shaped’ 
recovery instead (38).  

(37)  The baseline scenario in the 2021 Ageing Report takes the 
Commission’s  spring  2020  forecast  as  a  starting  point, 
reflecting the impact of the crisis and assuming recovery as 
per  May  2020  and  a  rebound  of  growth  in  2021,  broadly 
resulting  in  a  narrow  ‘U-shaped’  recovery  scenario.  In 
addition, it incorporates the‘t+10’ projections according to 
the methodology agreed by the OGWG.  

(38)  More severe COVID crisis scenarios were estimated in the 
available 

ECB 
here:https://www.ecb.europa.eu/pub/economic-

Economic 

Bulletin 

3/2020, 

Underlying assumptions and projection methodologies 

Part I 

Adverse structural scenario: on top of the stronger 
cyclical  downturn  in  the  lagged  recovery  scenario 
described  above,  this  adverse  structural  scenario 
additionally assumes that the growth potential will 
be lower over the next decade and potential output 
growth will thus be permanently lower than under 
the  baseline  scenario.  First,  labour  productivity 
growth  would  recover  to  a  lower  trend  growth, 
through  lower  investment  and/or  TFP  growth 
stemming  from  reduced  business  activity  for  a 
long period of time, with the crisis contributing to 
the historical downward trend. Second, the deeper 
recession  and  slower  recovery  would  lead  to 
unemployment  becoming  permanently  higher  as  a 
result  of  lower  business  activity,  leading  to  a 
hysteresis 
higher 
unemployment.  

permanently 

effect 

and 

bulletin/focus/2020/html/ecb.ebbox202003_01~767f86ae9
5.en.html  

83 

                                                           
                                                                                   
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.5.2: 

Sensitivity test: Higher life expectancy 

GDP growth in 
2019-2070

Labour 
productivity 
(GDP per hour 

worked)

1=2+5

2=3+4

1.2
1.2
1.6
1.7
1.2
1.9
1.8
1.2
1.5
1.3
1.1
1.1
1.9
1.2
1.2
1.8
1.8
2.2
1.3
1.3
1.6
1.2
1.7
1.6
1.3
1.2
1.8
1.7
1.3
1.4

1.2
2.1
2.0
1.5
1.4
2.2
1.5
1.5
1.5
1.3
1.8
1.3
1.5
2.3
2.2
1.1
2.1
1.8
1.3
1.4
2.3
1.7
2.6
1.9
2.1
1.4
1.4
1.5
1.4
1.6

Country
BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

Source: European Commission, EPC. 

TFP

Capital 
deepening

3

0.8
1.3
1.3
1.0
0.9
1.4
1.1
1.0
1.0
0.8
1.1
0.8
0.9
1.4
1.3
0.7
1.3
1.2
0.9
0.9
1.5
1.1
1.6
1.3
1.3
0.9
0.9
0.9
0.9
1.0

4

0.4
0.8
0.7
0.5
0.5
0.8
0.5
0.4
0.5
0.5
0.7
0.4
0.6
0.9
0.9
0.4
0.7
0.6
0.5
0.5
0.9
0.6
0.9
0.6
0.8
0.5
0.5
0.5
0.5
0.5

Due to:

Labour input

5=6+7+8+9

0.0
-0.9
-0.3
0.2
-0.2
-0.3
0.3
-0.2
0.0
0.1
-0.7
-0.2
0.5
-1.1
-1.0
0.7
-0.2
0.4
0.0
0.0
-0.8
-0.4
-0.9
-0.3
-0.7
-0.2
0.4
0.2
-0.1
-0.2

Total 
population

Employment 
rate

Share of 
Working age 
population

change in 
average 

hours worked

GDP per 
capita growth 
in 2019-2070

6

0.1
-0.6
0.0
0.2
0.0
-0.2
0.6
-0.4
0.1
0.1
-0.5
-0.2
0.5
-0.9
-0.8
0.5
-0.1
0.8
0.1
0.1
-0.4
-0.3
-0.6
-0.1
-0.2
-0.1
0.5
0.5
0.0
-0.1

7

0.1
0.0
0.0
0.3
0.1
0.2
-0.1
0.4
0.2
0.1
0.1
0.2
0.2
0.1
0.1
0.4
0.2
0.1
0.1
0.1
-0.1
0.2
0.0
0.1
-0.1
0.1
0.1
-0.1
0.1
0.1

8

-0.2
-0.3
-0.3
-0.2
-0.2
-0.2
-0.2
-0.3
-0.3
-0.2
-0.3
-0.2
-0.2
-0.3
-0.3
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.3
-0.3
-0.3
-0.4
-0.2
-0.2
-0.2
-0.2
-0.2

9

0.0
0.0
0.0
0.0
-0.1
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6

1.1
1.8
1.7
1.5
1.2
2.0
1.2
1.6
1.4
1.2
1.6
1.3
1.4
2.1
2.0
1.3
1.9
1.4
1.2
1.2
1.9
1.6
2.3
1.7
1.6
1.4
1.3
1.2
1.3
1.4

84 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   
 
Underlying assumptions and projection methodologies 

Part I 

Table I.5.3: 

Sensitivity test: Higher migration 

GDP growth in 
2019-2070

Labour 
productivity 
(GDP per hour 

worked)

1=2+5

2=3+4

1.3
1.2
1.7
1.8
1.4
1.9
1.9
1.3
1.6
1.4
1.1
1.2
2.1
1.1
1.1
2.0
1.9
2.5
1.4
1.5
1.6
1.3
1.6
1.7
1.4
1.3
2.0
1.9
1.4
1.5

1.2
2.1
2.0
1.5
1.4
2.2
1.5
1.5
1.5
1.3
1.8
1.3
1.4
2.3
2.2
1.1
2.1
1.7
1.3
1.3
2.3
1.7
2.6
1.9
2.1
1.4
1.3
1.5
1.4
1.6

Country
BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

TFP

Capital 
deepening

3

0.8
1.3
1.3
1.0
0.9
1.4
1.1
1.0
1.0
0.8
1.1
0.8
0.9
1.4
1.3
0.7
1.3
1.2
0.9
0.9
1.5
1.1
1.6
1.3
1.3
0.9
0.9
0.9
0.9
1.0

4

0.4
0.8
0.7
0.5
0.5
0.8
0.4
0.4
0.5
0.5
0.7
0.4
0.6
0.9
0.9
0.4
0.7
0.6
0.5
0.5
0.9
0.5
1.0
0.6
0.8
0.5
0.4
0.5
0.5
0.5

Source: European Commission, EPC. 

Table I.5.4: 

Sensitivity test: Lower migration 

GDP growth in 
2019-2070

Labour 
productivity 
(GDP per hour 

worked)

1=2+5

2=3+4

1.1
1.2
1.5
1.6
1.1
1.8
1.7
1.1
1.2
1.3
1.1
0.9
1.7
1.4
1.3
1.6
1.7
1.9
1.2
1.1
1.5
1.1
1.7
1.5
1.3
1.1
1.6
1.5
1.1
1.2

1.2
2.1
2.0
1.5
1.4
2.2
1.6
1.5
1.5
1.3
1.8
1.3
1.5
2.3
2.2
1.2
2.1
1.8
1.4
1.4
2.3
1.7
2.6
1.9
2.1
1.5
1.4
1.5
1.4
1.6

Country
BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

Source: European Commission, EPC. 

TFP

Capital 
deepening

3

0.8
1.3
1.3
1.0
0.9
1.4
1.1
1.0
1.0
0.8
1.1
0.8
0.9
1.4
1.3
0.7
1.3
1.1
0.9
0.9
1.5
1.1
1.6
1.3
1.3
0.9
0.9
0.9
0.9
1.0

4

0.5
0.8
0.7
0.5
0.5
0.8
0.5
0.4
0.5
0.5
0.7
0.5
0.6
0.9
0.9
0.4
0.8
0.7
0.5
0.5
0.9
0.6
0.9
0.6
0.8
0.6
0.5
0.6
0.5
0.6

Due to:

Labour input

5=6+7+8+9

0.1
-0.9
-0.2
0.3
0.0
-0.2
0.4
-0.2
0.2
0.1
-0.7
-0.1
0.7
-1.3
-1.1
0.9
-0.1
0.7
0.1
0.2
-0.7
-0.4
-1.0
-0.2
-0.7
-0.1
0.6
0.4
0.0
-0.1

Due to:

Labour input

5=6+7+8+9

-0.2
-0.9
-0.5
0.1
-0.4
-0.4
0.2
-0.4
-0.3
0.0
-0.8
-0.4
0.2
-0.9
-0.9
0.4
-0.4
0.0
-0.2
-0.2
-0.9
-0.6
-0.8
-0.4
-0.8
-0.4
0.2
0.0
-0.3
-0.4

Total 
population

Employment 
rate

Share of 
Working age 
population

change in 
average 

hours worked

GDP per 
capita growth 
in 2019-2070

6

0.2
-0.6
0.0
0.2
0.1
-0.1
0.7
-0.3
0.2
0.1
-0.6
-0.1
0.6
-1.1
-0.9
0.7
-0.1
1.0
0.2
0.2
-0.3
-0.3
-0.8
0.0
-0.2
-0.1
0.6
0.6
0.1
0.0

7

0.1
0.0
0.0
0.3
0.1
0.1
-0.1
0.3
0.2
0.1
0.1
0.2
0.2
0.1
0.1
0.4
0.2
0.1
0.1
0.1
-0.1
0.2
0.0
0.1
-0.1
0.1
0.1
-0.1
0.1
0.1

8

-0.1
-0.3
-0.2
-0.2
-0.2
-0.2
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
-0.3
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.2
-0.1
-0.1
-0.2
-0.2

9

0.0
0.0
0.0
0.0
-0.1
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6

1.2
1.9
1.7
1.6
1.3
2.1
1.2
1.6
1.4
1.3
1.7
1.3
1.5
2.1
2.1
1.3
2.0
1.5
1.2
1.3
2.0
1.6
2.4
1.8
1.6
1.4
1.3
1.2
1.3
1.5

Total 
population

Employment 
rate

Share of 
Working age 
population

change in 
average 

hours worked

GDP per 
capita growth 
in 2019-2070

6

0.0
-0.7
-0.2
0.0
-0.2
-0.3
0.4
-0.5
-0.2
0.0
-0.6
-0.4
0.3
-0.8
-0.7
0.3
-0.3
0.4
0.0
-0.1
-0.5
-0.5
-0.6
-0.3
-0.3
-0.3
0.3
0.3
-0.2
-0.2

7

0.1
0.0
0.0
0.2
0.1
0.1
-0.1
0.3
0.2
0.1
0.1
0.2
0.2
0.1
0.1
0.4
0.2
0.1
0.1
0.1
-0.1
0.1
0.0
0.1
-0.1
0.1
0.1
-0.1
0.1
0.1

8

-0.2
-0.3
-0.3
-0.2
-0.2
-0.2
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
-0.3
-0.3
-0.2
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.2
-0.1
-0.2
-0.2
-0.2

9

0.0
0.0
0.0
0.0
-0.1
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6

1.1
1.9
1.7
1.5
1.2
2.1
1.3
1.6
1.4
1.3
1.7
1.3
1.4
2.2
2.1
1.3
2.0
1.5
1.2
1.2
1.9
1.6
2.3
1.7
1.6
1.4
1.3
1.2
1.3
1.4

85 

 
 
 
      
 
 
      
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.5.5: 

Sensitivity test: Lower fertility 

GDP growth in 
2019-2070

Labour 
productivity 
(GDP per hour 

worked)

1=2+5

2=3+4

1.0
1.0
1.4
1.4
1.0
1.6
1.6
1.0
1.3
1.1
0.8
0.9
1.7
0.9
0.9
1.6
1.6
2.0
1.0
1.1
1.3
0.9
1.4
1.4
1.1
1.0
1.6
1.5
1.1
1.1

1.2
2.1
2.0
1.5
1.4
2.2
1.5
1.5
1.5
1.3
1.8
1.3
1.5
2.3
2.2
1.1
2.1
1.8
1.3
1.4
2.3
1.7
2.6
1.9
2.1
1.5
1.4
1.5
1.4
1.6

Country
BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

TFP

Capital 
deepening

3

0.8
1.3
1.3
1.0
0.9
1.4
1.1
1.0
1.0
0.8
1.1
0.9
0.9
1.4
1.3
0.7
1.3
1.2
0.9
0.9
1.5
1.1
1.6
1.3
1.3
0.9
0.9
0.9
0.9
1.0

4

0.4
0.8
0.7
0.5
0.5
0.8
0.5
0.4
0.5
0.5
0.7
0.4
0.6
0.9
0.9
0.4
0.8
0.6
0.5
0.5
0.9
0.6
0.9
0.6
0.8
0.5
0.5
0.5
0.5
0.5

Due to:

Labour input

5=6+7+8+9

-0.3
-1.2
-0.6
-0.1
-0.4
-0.6
0.0
-0.5
-0.2
-0.2
-1.0
-0.4
0.2
-1.4
-1.3
0.5
-0.5
0.2
-0.3
-0.3
-1.1
-0.7
-1.2
-0.5
-1.0
-0.5
0.2
0.0
-0.4
-0.5

Total 
population

Employment 
rate

Share of 
Working age 
population

change in 
average 

hours worked

GDP per 
capita growth 
in 2019-2070

6

-0.2
-0.9
-0.3
-0.2
-0.3
-0.5
0.3
-0.6
-0.2
-0.2
-0.8
-0.4
0.2
-1.2
-1.1
0.3
-0.4
0.6
-0.2
-0.1
-0.6
-0.6
-1.0
-0.4
-0.5
-0.4
0.2
0.2
-0.3
-0.3

7

0.0
0.0
0.0
0.2
0.0
0.1
-0.1
0.3
0.2
0.1
0.0
0.2
0.2
0.1
0.1
0.4
0.2
0.1
0.0
0.1
-0.1
0.1
0.0
0.0
-0.1
0.1
0.1
-0.1
0.1
0.1

8

-0.1
-0.3
-0.2
-0.2
-0.2
-0.2
-0.1
-0.2
-0.2
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
-0.2
-0.2
-0.3
-0.2
-0.2
-0.2
-0.3
-0.2
-0.1
-0.1
-0.2
-0.2

9

0.0
0.0
0.0
0.0
-0.1
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6

1.2
1.9
1.7
1.6
1.3
2.1
1.3
1.6
1.4
1.3
1.6
1.3
1.5
2.1
2.0
1.3
2.0
1.5
1.2
1.2
1.9
1.6
2.3
1.7
1.6
1.4
1.3
1.2
1.3
1.4

Source: European Commission, EPC. 

Table I.5.6: 

Sensitivity test: Higher employment rate of older workers 

GDP growth in 
2019-2070

Labour 
productivity 
(GDP per hour 

worked)

1=2+5

2=3+4

1.3
1.3
1.7
1.7
1.3
1.9
1.9
1.3
1.5
1.4
1.2
1.1
2.0
1.3
1.3
1.9
1.9
2.3
1.4
1.4
1.6
1.3
1.8
1.7
1.4
1.3
1.8
1.8
1.3
1.4

1.2
2.1
2.0
1.5
1.4
2.2
1.5
1.4
1.4
1.3
1.8
1.2
1.4
2.3
2.2
1.1
2.0
1.8
1.3
1.3
2.3
1.6
2.5
1.9
2.0
1.4
1.3
1.4
1.4
1.5

Country
BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

Source: European Commission, EPC. 

TFP

Capital 
deepening

3

0.8
1.3
1.3
1.0
0.9
1.4
1.1
1.0
1.0
0.8
1.1
0.8
0.9
1.4
1.3
0.7
1.3
1.2
0.9
0.9
1.5
1.1
1.6
1.3
1.3
0.9
0.9
0.9
0.9
1.0

4

0.4
0.8
0.7
0.5
0.5
0.8
0.4
0.4
0.5
0.4
0.7
0.4
0.6
0.8
0.8
0.4
0.7
0.6
0.4
0.5
0.8
0.5
0.9
0.6
0.8
0.5
0.4
0.5
0.5
0.5

Due to:

Labour input

5=6+7+8+9

0.1
-0.8
-0.3
0.2
-0.1
-0.2
0.4
-0.2
0.1
0.1
-0.6
-0.1
0.5
-1.0
-0.9
0.8
-0.1
0.5
0.0
0.1
-0.7
-0.4
-0.8
-0.2
-0.6
-0.1
0.5
0.3
0.0
-0.1

Total 
population

Employment 
rate

Share of 
Working age 
population

change in 
average 

hours worked

GDP per 
capita growth 
in 2019-2070

6

0.1
-0.6
-0.1
0.1
0.0
-0.2
0.6
-0.4
0.0
0.1
-0.6
-0.2
0.5
-0.9
-0.8
0.5
-0.2
0.7
0.1
0.1
-0.4
-0.4
-0.7
-0.1
-0.3
-0.2
0.5
0.5
0.0
-0.1

7

0.2
0.1
0.1
0.3
0.2
0.2
0.0
0.4
0.3
0.2
0.2
0.3
0.3
0.2
0.2
0.5
0.3
0.2
0.2
0.2
0.1
0.3
0.2
0.1
0.0
0.2
0.2
0.0
0.2
0.2

8

-0.2
-0.3
-0.3
-0.2
-0.2
-0.2
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
-0.2
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.2
-0.1
-0.1
-0.2
-0.2

9

0.0
0.0
0.0
0.0
-0.1
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6

1.2
1.9
1.8
1.6
1.3
2.1
1.3
1.7
1.5
1.3
1.8
1.3
1.5
2.2
2.1
1.4
2.1
1.6
1.3
1.3
2.0
1.6
2.4
1.8
1.7
1.4
1.4
1.3
1.4
1.5

86 

 
 
 
      
 
 
      
 
Underlying assumptions and projection methodologies 

Part I 

Table I.5.7: 

Sensitivity test: TFP risk 

GDP growth in 
2019-2070

Labour 
productivity 
(GDP per hour 

worked)

1=2+5

2=3+4

1.0
1.0
1.4
1.5
1.0
1.6
1.6
1.0
1.2
1.1
0.9
0.8
1.7
0.9
0.9
1.5
1.6
1.9
1.1
1.1
1.3
1.0
1.5
1.4
1.0
0.9
1.6
1.3
1.1
1.1

1.0
1.9
1.7
1.3
1.2
1.9
1.3
1.3
1.3
1.1
1.6
1.1
1.3
2.1
1.9
0.8
1.8
1.5
1.1
1.1
2.0
1.5
2.4
1.6
1.8
1.2
1.1
1.1
1.2
1.3

Country
BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

TFP

Capital 
deepening

3

0.7
1.2
1.1
0.9
0.8
1.2
0.9
0.9
0.8
0.7
1.0
0.7
0.8
1.2
1.1
0.5
1.1
1.0
0.7
0.7
1.3
1.0
1.5
1.1
1.1
0.7
0.7
0.6
0.8
0.9

4

0.4
0.7
0.6
0.4
0.4
0.7
0.4
0.4
0.4
0.4
0.6
0.4
0.5
0.8
0.8
0.3
0.7
0.6
0.4
0.4
0.8
0.5
0.9
0.5
0.7
0.5
0.4
0.4
0.4
0.5

Source: European Commission, EPC. 

Table I.5.8: 

Sensitivity test: Higher TFP growth 

GDP growth in 
2019-2070

Labour 
productivity 
(GDP per hour 

worked)

1=2+5

2=3+4

1.5
1.5
1.9
1.9
1.5
2.1
2.1
1.5
1.7
1.6
1.4
1.3
2.2
1.4
1.4
2.0
2.0
2.4
1.6
1.6
1.7
1.5
1.9
1.8
1.5
1.4
2.0
1.8
1.5
1.6

1.5
2.4
2.2
1.8
1.7
2.4
1.8
1.8
1.7
1.6
2.1
1.6
1.8
2.5
2.4
1.3
2.3
2.0
1.6
1.6
2.5
2.0
2.8
2.1
2.2
1.7
1.6
1.5
1.7
1.8

Country
BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

Source: European Commission, EPC. 

TFP

Capital 
deepening

3

1.0
1.5
1.5
1.2
1.1
1.5
1.3
1.3
1.2
1.0
1.3
1.0
1.1
1.6
1.4
0.9
1.5
1.3
1.0
1.0
1.6
1.3
1.8
1.5
1.4
1.0
1.1
0.9
1.1
1.2

4

0.5
0.9
0.8
0.6
0.6
0.9
0.5
0.5
0.6
0.5
0.8
0.5
0.7
1.0
1.0
0.5
0.8
0.7
0.6
0.6
0.9
0.6
1.0
0.7
0.9
0.6
0.5
0.6
0.6
0.6

Due to:

Labour input

5=6+7+8+9

0.0
-0.9
-0.3
0.2
-0.2
-0.3
0.3
-0.3
0.0
0.1
-0.7
-0.2
0.4
-1.1
-1.0
0.7
-0.3
0.4
0.0
0.0
-0.8
-0.5
-0.9
-0.3
-0.7
-0.3
0.4
0.2
-0.1
-0.2

Due to:

Labour input

5=6+7+8+9

0.0
-0.9
-0.3
0.2
-0.2
-0.3
0.3
-0.3
0.0
0.1
-0.7
-0.2
0.4
-1.1
-1.0
0.7
-0.3
0.4
0.0
0.0
-0.8
-0.5
-0.9
-0.3
-0.7
-0.3
0.4
0.2
-0.1
-0.2

Total 
population

Employment 
rate

Share of 
Working age 
population

change in 
average 

hours worked

GDP per 
capita growth 
in 2019-2070

6

0.1
-0.6
-0.1
0.1
0.0
-0.2
0.6
-0.4
0.0
0.1
-0.6
-0.2
0.5
-0.9
-0.8
0.5
-0.2
0.7
0.1
0.1
-0.4
-0.4
-0.7
-0.1
-0.3
-0.2
0.5
0.5
0.0
-0.1

7

0.1
0.0
0.0
0.3
0.1
0.1
-0.1
0.3
0.2
0.1
0.1
0.2
0.2
0.1
0.1
0.4
0.2
0.1
0.1
0.1
-0.1
0.1
0.0
0.1
-0.1
0.1
0.1
-0.1
0.1
0.1

8

-0.2
-0.3
-0.3
-0.2
-0.2
-0.2
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
-0.2
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.2
-0.1
-0.1
-0.2
-0.2

9

0.0
0.0
0.0
0.0
-0.1
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6

1.0
1.7
1.5
1.3
1.0
1.8
1.1
1.4
1.2
1.1
1.5
1.1
1.3
1.9
1.7
1.0
1.7
1.2
1.0
1.0
1.7
1.4
2.1
1.5
1.3
1.1
1.1
0.8
1.1
1.2

Total 
population

Employment 
rate

Share of 
Working age 
population

change in 
average 

hours worked

GDP per 
capita growth 
in 2019-2070

6

0.1
-0.6
-0.1
0.1
0.0
-0.2
0.6
-0.4
0.0
0.1
-0.6
-0.2
0.5
-0.9
-0.8
0.5
-0.2
0.7
0.1
0.1
-0.4
-0.4
-0.7
-0.1
-0.3
-0.2
0.5
0.5
0.0
-0.1

7

0.1
0.0
0.0
0.3
0.1
0.1
-0.1
0.3
0.2
0.1
0.1
0.2
0.2
0.1
0.1
0.4
0.2
0.1
0.1
0.1
-0.1
0.1
0.0
0.1
-0.1
0.1
0.1
-0.1
0.1
0.1

8

-0.2
-0.3
-0.3
-0.2
-0.2
-0.2
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
-0.2
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.2
-0.1
-0.1
-0.2
-0.2

9

0.0
0.0
0.0
0.0
-0.1
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6

1.4
2.1
2.0
1.8
1.5
2.3
1.5
1.9
1.7
1.6
2.0
1.5
1.7
2.4
2.2
1.5
2.2
1.7
1.5
1.5
2.1
1.9
2.6
2.0
1.8
1.6
1.6
1.3
1.6
1.7

87 

 
 
      
 
 
      
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table I.5.9: 

Policy scenario: Linking retirement age to life expectancy 

GDP growth in 
2019-2070

Labour 
productivity 
(GDP per hour 

worked)

1=2+5

2=3+4

1.3
1.4
1.8
1.7
1.3
1.9
1.9
1.2
1.5
1.4
1.3
1.0
1.9
1.4
1.4
2.0
1.9
2.3
1.3
1.4
1.7
1.2
1.9
1.7
1.5
1.2
1.9
1.8
1.3
1.4

1.2
2.1
2.0
1.5
1.4
2.2
1.5
1.5
1.5
1.3
1.8
1.3
1.5
2.3
2.2
1.1
2.1
1.8
1.3
1.3
2.3
1.7
2.6
1.9
2.0
1.5
1.3
1.5
1.4
1.6

Country
BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

TFP

Capital 
deepening

3

0.8
1.3
1.3
1.0
0.9
1.4
1.1
1.0
1.0
0.8
1.1
0.9
0.9
1.4
1.3
0.7
1.3
1.2
0.9
0.9
1.5
1.1
1.6
1.3
1.3
0.9
0.9
0.9
0.9
1.0

4

0.4
0.8
0.7
0.5
0.5
0.8
0.5
0.4
0.5
0.5
0.7
0.4
0.6
0.9
0.9
0.4
0.7
0.6
0.5
0.5
0.8
0.5
0.9
0.6
0.8
0.5
0.4
0.5
0.5
0.5

Source: European Commission, EPC. 

Table I.5.10:  Policy scenario: Unchanged retirement age 

GDP growth in 
2019-2070

Labour 
productivity 
(GDP per hour 

worked)

1=2+5

2=3+4

1.2
1.2
1.6
1.5
1.2
1.7
1.8
1.0
1.4
1.3
1.1
0.9
1.8
1.2
1.2
1.8
1.7
2.2
1.2
1.3
1.5
1.1
1.7
1.6
1.3
1.1
1.8
1.7
1.2
1.3

1.3
2.1
2.0
1.5
1.4
2.2
1.5
1.5
1.6
1.3
1.8
1.3
1.5
2.3
2.2
1.1
2.1
1.8
1.4
1.4
2.3
1.7
2.6
1.9
2.1
1.5
1.4
1.5
1.5
1.6

Country
BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

TFP

Capital 
deepening

3

0.8
1.3
1.3
1.0
0.9
1.4
1.1
1.0
1.0
0.8
1.1
0.8
0.9
1.4
1.3
0.7
1.3
1.2
0.9
0.9
1.5
1.1
1.6
1.3
1.3
0.9
0.9
0.9
0.9
1.0

4

0.5
0.8
0.7
0.5
0.5
0.8
0.5
0.5
0.6
0.5
0.7
0.5
0.6
0.9
0.9
0.4
0.8
0.6
0.5
0.5
0.9
0.6
0.9
0.6
0.8
0.6
0.5
0.5
0.5
0.6

Due to:

Labour input

5=6+7+8+9

0.1
-0.7
-0.2
0.2
-0.1
-0.3
0.4
-0.3
0.0
0.1
-0.6
-0.2
0.4
-1.0
-0.8
0.8
-0.1
0.5
0.0
0.1
-0.6
-0.4
-0.7
-0.2
-0.5
-0.3
0.6
0.3
-0.1
-0.2

Due to:

Labour input

5=6+7+8+9

-0.1
-0.9
-0.4
0.0
-0.2
-0.5
0.2
-0.5
-0.2
-0.1
-0.8
-0.4
0.3
-1.2
-1.0
0.7
-0.4
0.4
-0.1
-0.1
-0.8
-0.6
-0.9
-0.3
-0.7
-0.4
0.4
0.2
-0.2
-0.3

Total 
population

Employment 
rate

Share of 
Working age 
population

change in 
average 

hours worked

GDP per 
capita growth 
in 2019-2070

6

0.1
-0.6
-0.1
0.1
0.0
-0.2
0.6
-0.4
0.0
0.1
-0.6
-0.2
0.5
-0.9
-0.8
0.5
-0.2
0.7
0.1
0.1
-0.4
-0.4
-0.7
-0.1
-0.3
-0.2
0.5
0.5
0.0
-0.1

7

0.2
0.2
0.1
0.3
0.2
0.1
0.0
0.3
0.2
0.2
0.2
0.2
0.2
0.3
0.2
0.6
0.3
0.2
0.1
0.2
0.1
0.2
0.2
0.2
0.1
0.1
0.2
0.1
0.2
0.2

8

-0.2
-0.3
-0.3
-0.2
-0.2
-0.2
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
-0.2
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.2
-0.1
-0.1
-0.2
-0.2

9

0.0
0.0
0.0
0.0
-0.1
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6

1.3
2.0
1.8
1.6
1.4
2.1
1.4
1.6
1.5
1.3
1.8
1.3
1.4
2.3
2.2
1.5
2.1
1.6
1.2
1.3
2.1
1.6
2.5
1.9
1.8
1.4
1.4
1.3
1.4
1.5

Total 
population

Employment 
rate

Share of 
Working age 
population

change in 
average 

hours worked

GDP per 
capita growth 
in 2019-2070

6

0.1
-0.6
-0.1
0.1
0.0
-0.2
0.6
-0.4
0.0
0.1
-0.6
-0.2
0.5
-0.9
-0.8
0.5
-0.2
0.7
0.1
0.1
-0.4
-0.4
-0.7
-0.1
-0.3
-0.2
0.5
0.5
0.0
-0.1

7

0.0
0.0
-0.1
0.1
0.0
0.0
-0.2
0.1
0.0
0.0
0.0
0.0
0.1
0.1
0.0
0.4
0.1
0.0
0.0
0.1
-0.1
0.1
0.0
0.0
-0.1
0.0
0.1
-0.1
0.0
0.0

8

-0.2
-0.3
-0.3
-0.2
-0.2
-0.2
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
-0.2
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.2
-0.1
-0.1
-0.2
-0.2

9

0.0
0.0
0.0
0.0
-0.1
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6

1.1
1.8
1.7
1.4
1.2
1.9
1.2
1.4
1.3
1.2
1.6
1.1
1.3
2.1
2.0
1.3
1.9
1.4
1.1
1.2
1.9
1.5
2.4
1.7
1.6
1.2
1.3
1.2
1.3
1.4

Source: European Commission, EPC. 

88 

 
 
 
      
 
 
   
 
Underlying assumptions and projection methodologies 

Part I 

Table I.5.11:  Adverse macroeconomic scenario – Lagged recovery 

GDP growth in 
2019-2070

Labour 
productivity 
(GDP per hour 

worked)

1=2+5

2=3+4

1.2
1.2
1.6
1.7
1.2
1.9
1.8
1.2
1.5
1.3
1.1
1.0
1.9
1.2
1.2
1.8
1.8
2.2
1.3
1.3
1.5
1.2
1.7
1.6
1.3
1.2
1.8
1.7
1.3
1.3

1.2
2.1
2.0
1.5
1.4
2.2
1.5
1.5
1.5
1.3
1.8
1.3
1.5
2.3
2.2
1.1
2.1
1.8
1.3
1.4
2.3
1.7
2.6
1.9
2.1
1.5
1.4
1.5
1.4
1.6

Country
BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

Source: European Commission, EPC. 

TFP

Capital 
deepening

3

0.8
1.3
1.3
1.0
0.9
1.4
1.1
1.0
1.0
0.8
1.1
0.8
0.9
1.4
1.3
0.7
1.3
1.2
0.9
0.9
1.5
1.1
1.6
1.3
1.3
0.9
0.9
0.9
0.9
1.0

4

0.4
0.8
0.7
0.5
0.5
0.8
0.5
0.4
0.5
0.5
0.7
0.4
0.6
0.9
0.9
0.4
0.7
0.6
0.5
0.5
0.9
0.6
0.9
0.6
0.8
0.5
0.5
0.5
0.5
0.5

Due to:

Labour input

5=6+7+8+9

0.0
-0.9
-0.4
0.2
-0.2
-0.3
0.3
-0.3
0.0
0.0
-0.7
-0.2
0.4
-1.1
-1.0
0.7
-0.3
0.4
0.0
0.0
-0.8
-0.5
-0.9
-0.3
-0.7
-0.3
0.4
0.2
-0.1
-0.2

Total 
population

Employment 
rate

Share of 
Working age 
population

change in 
average 

hours worked

GDP per 
capita growth 
in 2019-2070

6

0.1
-0.6
-0.1
0.1
0.0
-0.2
0.6
-0.4
0.0
0.1
-0.6
-0.2
0.5
-0.9
-0.8
0.5
-0.2
0.7
0.1
0.1
-0.4
-0.4
-0.7
-0.1
-0.3
-0.2
0.5
0.5
0.0
-0.1

7

0.1
0.0
0.0
0.2
0.0
0.1
-0.1
0.3
0.2
0.1
0.1
0.2
0.2
0.1
0.1
0.4
0.2
0.1
0.1
0.1
-0.1
0.1
0.0
0.1
-0.1
0.1
0.1
-0.1
0.1
0.1

8

-0.2
-0.3
-0.3
-0.2
-0.2
-0.2
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
-0.2
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.2
-0.1
-0.1
-0.2
-0.2

9

0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6

1.1
1.9
1.7
1.5
1.3
2.0
1.3
1.6
1.5
1.3
1.7
1.3
1.4
2.2
2.1
1.3
2.0
1.5
1.2
1.2
1.9
1.6
2.4
1.7
1.6
1.4
1.3
1.2
1.3
1.4

Table I.5.12:  Adverse macroeconomic scenario – Adverse structural 

GDP growth in 
2019-2070

Labour 
productivity 
(GDP per hour 

worked)

1=2+5

2=3+4

0.8
0.9
1.3
1.3
0.9
1.5
1.4
0.9
1.1
1.0
0.7
0.7
1.5
0.8
0.8
1.4
1.4
1.8
0.9
1.0
1.2
0.8
1.3
1.2
1.0
0.8
1.4
1.3
0.9
1.0

0.9
1.8
1.6
1.2
1.1
1.8
1.2
1.2
1.1
1.0
1.5
1.0
1.1
2.0
1.9
0.8
1.7
1.5
1.0
1.0
2.0
1.3
2.2
1.6
1.7
1.1
1.0
1.1
1.1
1.2

Country
BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

Source: European Commission, EPC. 

TFP

Capital 
deepening

3

0.6
1.1
1.1
0.8
0.7
1.1
0.8
0.8
0.7
0.6
0.9
0.6
0.7
1.2
1.1
0.5
1.1
0.9
0.6
0.6
1.2
0.9
1.4
1.1
1.1
0.7
0.7
0.7
0.7
0.8

4

0.3
0.7
0.6
0.4
0.4
0.7
0.3
0.3
0.4
0.3
0.6
0.3
0.5
0.8
0.8
0.3
0.6
0.5
0.4
0.4
0.8
0.4
0.8
0.5
0.7
0.4
0.3
0.4
0.4
0.4

Due to:

Labour input

5=6+7+8+9

-0.1
-0.9
-0.4
0.1
-0.2
-0.4
0.2
-0.3
0.0
0.0
-0.7
-0.3
0.4
-1.2
-1.0
0.6
-0.3
0.3
-0.1
-0.1
-0.8
-0.5
-1.0
-0.4
-0.8
-0.3
0.4
0.2
-0.2
-0.3

Total 
population

Employment 
rate

Share of 
Working age 
population

change in 
average 

hours worked

GDP per 
capita growth 
in 2019-2070

6

0.1
-0.6
-0.1
0.1
0.0
-0.2
0.6
-0.4
0.0
0.1
-0.6
-0.2
0.5
-0.9
-0.8
0.5
-0.2
0.7
0.1
0.1
-0.4
-0.4
-0.7
-0.1
-0.3
-0.2
0.5
0.5
0.0
-0.1

7

0.0
0.0
0.0
0.2
0.0
0.1
-0.2
0.3
0.2
0.1
0.0
0.2
0.2
0.1
0.0
0.4
0.1
0.0
0.0
0.1
-0.1
0.1
0.0
0.0
-0.1
0.1
0.0
-0.1
0.1
0.1

8

-0.2
-0.3
-0.3
-0.2
-0.2
-0.2
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.3
-0.2
-0.2
-0.3
-0.3
-0.2
-0.2
-0.3
-0.2
-0.1
-0.1
-0.2
-0.2

9

0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

10=1-6

0.8
1.5
1.3
1.2
0.9
1.7
0.9
1.3
1.1
0.9
1.3
0.9
1.1
1.8
1.7
0.9
1.6
1.1
0.8
0.9
1.6
1.2
2.0
1.4
1.2
1.0
0.9
0.9
1.0
1.1

89 

 
 
 
   
 
 
   
 
Part II 
Age-related expenditure items: coverage, 
projection methodologies and data sources 

 
 
1. 

PENSIONS 

1.1.  MAIN FEATURES OF PENSION 

PROJECTIONS 

To  gauge  the  fiscal  impact  of  population  ageing, 
long-term  projections  of  public  pension  expen-
diture  are  made  based  on  the  commonly  agreed 
underlying assumptions described in Part I of this 
report,  using  national  models.  These  projections 
reflect  in  detail  the  institutional  features  of  the 
pension systems in each country.  

Despite  the  different  arrangements  in  national 
healthcare,  long-term  care  and  education  systems, 
the Commission (DG ECFIN) and the AWG have 
been able to develop common models to carry out 
long-term projections for these public expenditure 
items.  For  pensions,  a  different  approach 
is 
followed,  as  the  specificities  of  pension  systems 
across  the  EU  proved  difficult  to  capture  in  a 
single  framework.  Given  this  diversity  in  pension 
system design, pension expenditure projections are 
prepared  by  the  Member  States  using  national 
models  and  the  commonly  agreed  demographic 
and  macroeconomic  assumptions  discussed  in 
Part I.  

features  and 

On  the  one  hand,  this  decentralised  approach 
makes  it  possible  to  incorporate  the  specific 
institutional 
settings  of 
individual  countries (39).  On  the  other  hand,  using 
different,  country-specific  projection  models  may 
introduce  an  element  of  non-comparability  to  the 
results,  despite  relying  on  an  agreed,  common 
methodology. 

legal 

the 
To  ensure  high  quality  and  strengthen 
comparability of the projection results, an in-depth 
peer  review  is  being  carried  out  by  the  AWG 
members and the Commission. The discussion and 
validation  of  the  projection  results  is  intended  to: 
(i)  verify  whether  the  Member  States  are  all 
following 
the 
macroeconomic  assumptions;  and  (ii)  check  how 
Member  States  interpret  the  pension  legislation  in 
force. When deemed necessary, the peer review by 
the  AWG  can  ask  the  Member  State  to  revise  the 
projections. 

the  agreed  methodology  and 

1.2.  COVERAGE OF PENSION PROJECTIONS 

The  core  of 
the  pension-projection  exercise 
continues to be government expenditure on public 
pension  schemes  (see  Annex  6  for  a  comprehen-
sive description of the pension schemes covered by 
the  projections).  Building  on 
the  previous 
exercises,  the  members  of  the  AWG  agreed  to 
provide  pension  projections  for  the  following 
items:  gross  pension  expenditure; 
taxes  on 
pensions;  benefit 
ratio  and  gross  average 
replacement rates; number of pensions/pensioners; 
revenues  from  contributions  and  the  number  of 
contributors; 
pension 
expenditure  (earnings-related);  and  assets  and 
reserves of public and private pension schemes. 

breakdown 

new 

of 

The  reporting  framework  for  the  2021  Ageing 
Report builds on the framework used for the 2018 
exercise.  Changes  remain  limited  in  number  and 
scope.  The  main  changes,  as  agreed  by  the 
AWG/EPC, are listed below. 

Firstly,  the  updated  reporting  framework  puts  all 
tax-related  variables  in  a  separate  block,  which 
also  includes  net  pension  expenditure,  i.e.  gross 
spending  minus  reported  taxes  for  the  relevant 
scheme. In the past, these indicators were included 
in  the  block  covering  gross  pension  expenditure. 
This  change,  which  does  not  entail  any  new 
reporting,  clarifies  the  structure  of  the  pension 
questionnaire. The reporting  by Member States of 
taxes  on  public  pensions  makes  it  possible  to 
analyse  developments  in  net  pension  expenditure. 
Moreover,  the  Commission’s  fiscal  sustainability 
indicators  consider  ageing  costs  net  of  taxes  and 
contributions paid on pensions. 

Secondly,  the  updated  framework  clarifies  that 
public pension contributions by the State (line 124 
in  the  pension  questionnaire)  and  other  revenues 
if  such 
(line  125)  should  only  be  reported 
contributions  are  based  on 
(the 
questionnaire is provided in Annex 4). This way, a 
better distinction can be made between, on the one 
hand, contributions based on a specific rate and, on 
the  other  hand,  de  facto  transfers  to  make  up  for 
any shortfalls that arise within the system.  

legislation 

(39)  For  a  complete  description  of  pension  schemes  in  the 
Member  States,  please  consult  the  PENSREF  database, 
available  at:  https://ec.europa.eu/info/business-economy-
euro/indicators-statistics/economic-databases_en. 

Thirdly, 
the  breakdown  of  new  pension 
expenditure  for  point  systems  is  further  refined. 
The  2018  questionnaire  introduced  a  specific 

92 

                                                           
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

breakdown  for  the  earnings-related  part  of  the 
three  broad  pension  system  types:  defined  benefit 
(DB),  notional  defined  contribution  (NDC),  and 
point  system  (PS).  However,  the  PS  template 
sometimes  failed  to  reflect  the  way  these  systems 
operate.  As  a  result,  consistency  with  the  new 
earnings-related pension expenditure as reported in 
Block  1  was  lost.  Member  States  also  had  the 
choice  to  report  under  the  same  reporting  line 
either  the  average  contributory  period  or  the 
average amount of pension points accumulated per 
year. Moreover, the point value was considered as 
a  component  of  the  accrual  rate  rather  than  a 
determinant of the pension benefit in its own right.   

Therefore, the new pension questionnaire includes 
a  reshuffling  and  extension  of  the  breakdown  of 
new  pensions  for  the  point  system.  The  main 
changes concern: (i) the accrual rate – which now 
shows  the  average  number  of  points  accrued  per 
contributing  year;  and  (ii)  the  addition  of  a 
‘correction  coefficient’  to  reflect  any  additional 
correction 
is  not  a 
sustainability  factor  (i.e.  correction  of  pension 
benefit for life expectancy) or automatic balancing 
mechanism.  For  countries  like  France  in  which 
there  is  a  point  cost,  the  ‘point  value’  indicator  is 
normalised  by  this  point  cost  and  the  indicator 
‘total  points  at  retirement’  takes  into  account  the 
point  cost,  thus  reflecting  total  contributions  at 
retirement. 

that  applies  and 

that 

Fourthly,  a  reporting  block  on  pension  assets  and 
returns was reintroduced. A comparable, voluntary 
block was included in the questionnaire until 2012, 
although  few countries reported data. Considering 
the  rising  importance  of  funded  schemes  across 
Member States – and the central role such schemes 
already fulfil in a number of Member States – the 
voluntary block was reintroduced to give Member 
States the opportunity to report relevant figures. 

the 

questionnaire 

consistently 
Fifthly, 
distinguishes  between  mandatory 
individual 
schemes  and  non-mandatory  ones.  Although  the 
latter  are  more  prevalent  as  they  exist  in  all 
Member  States,  when  Member  States  report 
figures 
the  mandatory 
schemes.  The  2018  questionnaire  often  lumped 
together both types in a single reporting line. 

they  mostly  relate 

to 

Finally,  for  streamlining  reasons,  some  reporting 
lines  included  in  the  2018  framework  were  not 

all 

nearly 

because 

retained.  The  ‘total  number  of  contributors  to  all 
pension  schemes’  did  not  provide  a  meaningful 
indicator, 
employees 
contributing to any kind of supplementary scheme 
supposedly  also  contribute  to  the  public  scheme. 
Finally, the gross average replacement rate for the 
overall  public  pension  is  no  longer  included. This 
is because it is the earnings-related pension benefit 
–  including  the  flat  component  –  that  matters  for 
replacement rates. 

As  a  result,  the  2021  reporting  sheet  is  organised 
around 11 groups of indicators (see Annex 4): 

1.  gross pension expenditure; 

2. 

taxes on pensions & net pension expenditure; 

3.  benefit ratio;  

4.  gross average replacement rates; 

5.  number of pensions; 

6.  number of pensioners; 

7.  contributions;  

8.  number of contributors; 

9. 

indexation factors; 

10.  breakdown of new earnings-related pensions; 

11.  pension assets & return on assets. 

The reporting sheet consists of 195 variables (189 
if the country  has a DB system), of  which 80 can 
be  provided  on  a  voluntary  basis  and  5  are  input 
data provided by the Commission. A complete list 
of  items  covered  by  the  2021  pension-projection 
exercise is presented in Annex 4. 

Adhering  to  the  principle  of  not  changing  the 
modality  of  the  variables  that  were  classified  as 
voluntary  in  the  previous  exercise,  variables  are 
reported  on  a  voluntary  basis  for  private  schemes 
and 
(occupational, 
individual 
the 
following are classified as voluntary: (i) the break-
down  by  age  of  the  total  number  of  pensions;  (ii) 
the  total  number  of  pensioners;  (iii)  the  taxes  on 

individual  mandatory, 
non-mandatory).  Moreover, 

93 

European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

pensions(40);  and  (iv)  data  on  pension  assets  and 
returns.  

In  parallel  to  the  pension  questionnaire,  Member 
States report administrative data on the number of 
new  pensioners  by  sex  and  public  scheme.  While 
2016-2019  figures  are  reported  on  a  mandatory 
basis, 2010-2015 figures are voluntary. During the 
2018  projection  cycle,  most  Member  States 
reported administrative data for 2015. 

Building  on  the  mapping  of  special  pension 
schemes  for  the  situation  in  2016  in  the  2018 
Ageing Report, Member States have the possibility 
to  report,  on  a  voluntary  basis,  details  for  these 
schemes.  All  special  pension  schemes  should  be 
included in the overall projections. The scope and 
nature of special schemes would also be discussed 
in the country fiche. The structure of the reporting 
sheet is based on the 2018 exercise (see Annex 4). 
Reporting on special schemes makes it possible to 
monitor  changes  in  the  weight  of  these  schemes, 
which  are  often  the  subject  of  specific  reforms. 
Detailed  descriptions  of  special  schemes  are 
included in the online PENSREF database. 

1.3.  DEFINITIONS OF THE VARIABLES 

1.3.1.  Reporting norms and input data 

Member  States  will  run  projections  for  the  period 
2019-2070. For each year of the projection period, 
annual data are to be provided. Both the historical 
data  for  the  years  2000-2018  and  the  projections 
for 2019-2070 must be presented in current prices. 
The base year of the projections is 2019. 

The  GDP  projections  for  each  country  over  the 
period  2019-2070  are  those  generated  by  the 
the  production 
Commission (DG ECFIN)  using 
the  agreed 
function  model  on 
assumptions.  

the  basis  of 

Average  wages  are  calculated  as  the  ratio  of  total 
gross  wages  from  national  account  data  and 
employed  persons  (both  employees  and  self-
employed)  aged  15-74.  The  change  in  the  gross 

(40)  With the  exception  of  taxes  on pensions  for  the  base  year 
(2019).  For  those  Member  States  that  have  difficulties  in 
providing  the  requested  information  on  taxes  on  pensions 
in the base year, detailed information of the tax system are 
included in the pension country fiches. 

94 

wage  total  is  projected  for  each  country  in 
accordance  with  labour  productivity  growth  and 
changes in the number of hours worked (41).  

Figures  on  the  economy-wide  average  wage  at 
retirement must be reported by the Member States. 
The  assumptions  used  when  projecting 
this 
variable should be reported separately and  will be 
peer reviewed. 

Values are expressed in millions of euro. For non-
euro area countries, the conversion should be made 
on the basis of the average exchange rate for 2019, 
except  for  the  ERM II  countries  for  which  the 
conversion is based on the central rates. 

Member  States  should  report outturn  data  back  to 
2000  and,  in  the  country  fiche  accompanying  the 
pension-projection  data,  discuss  actual  develop-
ments  since  2000.  This  should  clarify  the  reasons 
behind:  (i)  notable  changes  in  pension  spending; 
(ii) overall trends in pension spending in the recent 
past;  and  (iii)  the  possible  implications  of  these 
changes and trends for the projections. 

the 

before 

cut-off 

legislation 

The  pension  projections  include  the  impact  of  the 
most recent pension reforms that will have entered 
into 
date 
(1 December  2020)  for  submitting  the  pension 
projections.  To  this  end,  in  their  country  fiches 
Member States provide detailed descriptions of the 
projections, 
introduced 
including:  (i)  recently 
reforms;  (ii)  the  implementation  of  these  reforms; 
and  (iii)  how  these  reforms  are  reflected  in  the 
projection results.  

1.3.2.  Variables: definitions and clarifications 

Pension expenditure 

Definition:  Pension  expenditures  should  cover 
pensions and equivalent cash benefits granted for a 
long  period  (over  1  year)  for  old  age,  early 
retirement,  disability,  survivors  (widows  and 
orphans), and other specific purposes which should 
be considered as equivalents  or substitutes for the 
above-mentioned  types  of  pensions  (i.e.  pensions 
or  pension-type  benefits  granted  because  of 
reduced work capacity or because of labour market 
conditions). 

(41)  In line with the assumption of constant labour share. Gross 
wages include employers’ social security contributions. 

                                                           
                                                           
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

include: 

Clarification:  Pension  expenditures  are  projected 
according  to  the  current  legislation  in  force. 
Pensions  should 
(i)  earnings-related 
pensions;  (ii)  flat-rate  pensions;  (iii)  means-tested 
benefits  that  aim  at  providing  a  social  minimum 
pension; and (iv) supplements that are a part of the 
pension and are granted for an indefinite period on 
the basis of fulfilling certain criteria but which are 
not directly linked to the remuneration of costs (i.e. 
supplements  aimed  at  supporting  the  purchase  of 
home  or  healthcare  services).  Pensions  and 
benefits can be paid out from specific  schemes or 
directly  from  government  budgets.  In  particular, 
social  assistance  should  be  included  if  it  is 
equivalent to a minimum pension (i.e. similar to a 
non-earnings-related  minimum  pension).  Housing 
subsidies  should  be  excluded  from  pensions  and 
considered  as  other  means-tested  social  transfers. 
To  ensure  the  consistency  of  the  projections  with 
the  underlying  assumptions, 
the  reporting  of 
pension expenditures should include pensions paid 
overseas. 

Short-term disability benefits should be considered 
as  sickness  benefits,  while  prolonged  unemploy-
ment  benefits 
to  older  workers  should  be 
considered as unemployment benefits.  

include  social  security 
Pensions  should  not 
contributions  paid  by  pension  schemes  on  behalf 
of  their  pensioners  to  other  social  protection 
schemes, notably to health schemes.  

Pension expenditure by age 

to 

Many  countries  have  introduced  pension  reforms 
that  will  increase  the  retirement  age.  To  better 
understand  the  impact  of  these  reforms,  Member 
States  have 
report  pension  expenditure 
disaggregated by age groups -54, 55-59, 60-64, 65-
69,  70-74  and  75+  for  both  public  pensions  and 
increases 
total 
transparency  and  consistency  between  population, 
labour  force  and  projections  of  pensioners.  The 
sum  of  public/total  pension  expenditures  for  all 
age groups should be equal to the overall projected 
values for public/total pension expenditures. 

pensions.  This 

breakdown 

New pension expenditure 

To  monitor  reforms  and  increase  transparency, 
Member States provide annual projections on new 
pensions  expenditure  for  each  of  the  pension 

schemes: (i) old-age and early pensions (earnings-
related,  flat  component,  and  minimum  pensions); 
(ii)  disability  pensions;  (iii)  survivors’  pensions; 
and  (iv)  other  pensions  (see  descriptions  below). 
New pension expenditures for earnings-related and 
basic  pensions  should  match  the  breakdown  of 
new pension expenditures under Block 9. 

Gross pension expenditure 

In  Block  1  of  the  reporting  framework,  pensions 
expenditure should be recorded in gross terms, i.e. 
without  deducting  taxes  and  compulsory  social 
security  contributions  paid  by  beneficiaries  on 
benefits. In countries where pensions are treated as 
non-taxable  income,  gross  pensions  equal  net 
pensions. 

Net pension expenditure 

Pensions should be recorded as net pensions, after 
deducting taxes on pensions and compulsory social 
security  contributions  paid  by  beneficiaries  from 
gross  public  pension  expenditure.  Projections 
should  be  made  for  overall  net  public  pension 
expenditures as  well as the absolute share of non-
earnings-related  minimum pensions and  minimum 
income guarantees. 

Taxes on pensions 

to 

Member  States  must  provide  data  on  taxes  on 
the  base  year.  Consistent  and 
pensions  for 
comparable  projections  of  taxes  on  all  pensions 
can  be  provided  on  a  voluntary  basis:  public, 
private  occupational,  and  private 
individual 
pensions (both mandatory and non-mandatory). In 
the 
particular,  attention  should  be  paid 
progressivity  of  the  tax  system  on  this  source  of 
public  revenue.  The  Commission  proposes  to 
project taxes in a way that tax revenues as a share 
of  pension  expenditures  stay  constant  over  time. 
This  implicitly  means  that:  (i)  ‘value’  parameters, 
such as tax allowances or tax-contribution ceilings, 
are  adjusted  annually 
line  with  pension 
expenditures;  while  (ii)  ‘rate’  parameters,  such  as 
the  implicit  average  tax  rate  on  pensions  remain 
unchanged.  These  assumptions  on  tax  projections 
should  hold  for  every  single  scheme,  both  public 
and private pillar schemes.  

in 

Countries that provide figures for taxes on private 
occupational  and  private  individual  pensions  are 

95 

European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

asked 
to  provide  all  other  data  on  private 
occupational  and  private  individual  pensions  (the 
provision  of  this  other  data  is  voluntary  for  all 
other  countries).  They  also  have  a  mandatory 
requirement  to  give  a  breakdown  of  new  pension 
expenditures  for  private  occupational  and  private 
individual  pensions.  These  requirements  make  it 
possible  to  increase  the  transparency  of  private 
pension taxation and check its consistency. 

Categories of pension expenditure 

EU  Member  States  generally  have  a  variety  of 
pension  schemes  in  place  (e.g.  for  employees  in 
different  sectors).  The  parameters  across  systems 
might  differ,  and  the  share  of  population  covered 
by  each  system  might  change  over  time.  To 
address  these  issues,  Member  States  should  fill  in 
the  questionnaire  for  each  scheme  separately 
where  possible,  in  addition  to  the  questionnaire 
that  they  fill  in  on  aggregated  public  pension 
expenditure.  

Public schemes and other non-occupational 
public pensions 

Definition:  Public  schemes  and  other  public 
pensions are the schemes that are statutory and that 
the general government sector administers (42). 

(42)  In  line  with  Eurostat  (2004):  “If  a  government  unit  is 
responsible  for  the  management  of  a  defined-contribution 
funded  scheme  for  which  no  government  guarantee  exists 
for  the  risks  of defaulting  payments covering  the  majority 
of the participants, the scheme is not treated in the national 
accounts  as  a  social  security  scheme  in  the  government 
sector.  In  such  schemes,  the  schemes  are  not  financed  by 
the government nor does the government define the level of 
pensions to be paid (the members have a say in how much 
they  contribute  and  how  their  contributions  are  invested). 
Thus,  the  contributions  and  payments  in  respect  of  such 
schemes  have  no  impact  on  the  EDP  deficit,  as  they  are 
stripped  out  of  general  government  revenue  and  general 
government expenditure, respectively.” 

Moreover,  the  same  source,  with  regards  to  funded  schemes, 
underlines  the  following:  “In  recent  years,  some  countries 
have set up defined-contributions funded pension schemes 
(or  identifiable  as  such  –  see  below)  where  a  government 
imposes or encourages participation, collects contributions 
from  employers  and  pays  pension  benefits  to  households, 
fixes the level of contributions and maybe change the rules, 
but  where  it  is  explicitly  stated  that  pension  benefits  will 
predominantly depend on accumulated assets. Under these 
conditions, it  seems  that  all  ESA95  criteria  for  classifying 
such  schemes  as  social  security  schemes  are  not  fulfilled, 
as government is not fixing the level of the pension benefit 
and  it  is  difficult  to  consider  that  it  is  ‘financing’  the 
scheme.” 

96 

Clarification:  The  aim  is  to  cover  those  pension 
schemes that affect public finances, in other words 
schemes  that  are  considered  to  belong  to  the 
general government sector in the national accounts 
system.  Usually,  there  is  a  specific  or  general 
social-security  contribution  to  the  scheme,  which 
is  defined  as  part  of  total  taxes  in  the  national 
accounting system. However, the scheme can also 
be  financed,  either  partially  or  fully,  by  general 
taxes.  Thus,  ultimately,  the  government  bears  the 
financial cost and risk attached to the scheme. The 
pensions  provided  by  the  social-security  schemes 
can be either earnings-related, flat rate, or  means-
tested.  In  addition,  this  category  should  cover 
pensions  that  are  paid  directly  by  the  government 
budget  or  other  public-sector  entity  without 
forming a specific scheme for special pensions for 
public  sector  and  defence  employees.  Cash 
benefits  equivalent  to  pensions,  notably  social 
assistance  to  older,  retired  people  should  be 
included in this category. 

the 

between 

boundaries 

On 
and 
occupational  pensions  –  and  the  identification  of 
pension  schemes  within  these  categories  –  see 
Annex 5. 

public 

The  statutorily  funded  part  of  old-age  pension 
schemes  that  are  attached  to  notional  defined 
contribution  schemes  in  some  countries  should  be 
excluded from public schemes and included in the 
private-sector  schemes  in  accordance  with  the 
Eurostat decision on the matter (43). 

Occupational private pensions 

Definition:  Pensions  provided  by  occupational 
schemes are those that, rather than being statutory 
by  law,  are  linked  to  an  employment  relationship 
with  the  scheme  provider.  They  are  based  on 
contractual  agreements  between  employers  and 
or 
at 
employees, 
sector/industry 
funds, 
insurance companies, or the sponsoring companies 
themselves run the schemes. 

level.  Private  pension 

company 

either 

the 

Further  information  can  be  found  in  Eurostat  (2004). 
‘Classification  of  funded  pension  schemes  and  impact  on 
government  finance’,  Economy  and  finance  collection: 
Methodologies and working papers, Luxemburg. 

(43)  Classification  of  funded  pension  schemes  in  case  of 
government responsibility and guarantee, Eurostat 30/2004, 
2 March 2004. 

                                                           
                                                                                   
  
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

bargaining 

Clarification:  These  schemes  can  be  quasi-
mandatory in the sense that, based on a nation- or 
industry-wide 
the 
agreement, 
to  provide  a  private 
employers  are  obliged 
occupational  pension  scheme  to  their  employees. 
However, 
individual 
the  participation  of  an 
remains  voluntary.  Private  occupational  schemes 
can  be  equivalent  to  statutory,  earnings-related 
pension  schemes  or  complementary  to  them.  In 
particular, 
the 
projections the schemes that play a role equivalent 
to  social-security  schemes  in  pension  provision. 
The AWG agreed that, for the projection of private 
pensions  in  the  2021  Ageing  Report,  the  real  rate 
of return on private funded pensions should reflect 
the real interest-rate assumptions (see Chapter 4 in 
Part 1).  

important 

include 

to 

in 

is 

it 

Private individual pensions  

For  the  most  part,  private  individual  pension 
schemes  are  non-mandatory,  but  they  can  also  be 
mandatory. The insured persons have ownership of 
the  pension  assets.  This  means  that  the  owner 
enjoys  the  rewards  and  bears  the  risks  regarding 
the  value  of  the  assets.  The  insurance  contract 
specifies  a  schedule  of  contribution  in  exchange 
for which benefits will be paid when the members 
reach  a  specific  retirement  age.  The  scheme 
provider  administers  the  scheme  by  managing  the 
pension  assets  through  a  separate  account  on 
behalf  of  its  members.  Access  to  such  a  scheme 
does not require an employment relationship, even 
though  in  some  cases  the  contribution  may  be  set 
based on a wage amount.  

Mandatory private individual pensions 

Definition: Mandatory private pension schemes are 
similar  to  public  schemes.  Transactions  occur 
between the individual and the insurance provider. 
Transactions  are  not  recorded  as  government 
revenues  or  government  expenditure  and,  there-
fore,  do  not  have  an  impact  on  the  government 
balance.  Pension  expenditure  projections  should 
cover the individual schemes that switch – at least 
in part – either voluntarily or statutorily (especially 
for new labour market entrants) – from the current 
social  security  scheme  to  private  funds.  Such 
schemes have an increasing relevance in a number 
of countries. 

Clarification: In some cases, there are government 
guarantees to these pension schemes. Nevertheless, 
such a guarantee is a contingent liability by nature, 
and these liabilities are not considered as economic 
transactions  until  they  materialise.  Thus,  the  cited 
Eurostat  decision 
a 
further 
government guarantee is not an adequate condition 
to  classify  such  schemes  as  social  security 
schemes. 

specifies 

that 

Non-mandatory individual private pensions 

Definition:  Non-mandatory  private  pensions  are 
based  on  individual  insurance  contracts  between 
the  individual  and  the  private  pension  scheme 
provider,  usually  an  insurance  company  or  a 
pension  fund.  They  include  pension  schemes  for 
which  membership  is  not  required  by  law  and  is 
independent  of  any  employment  link  (even  if 
members  are  mostly  employed  people).  However, 
the  State  may 
in  some  cases  employers  or 
contribute  to  the  plan.  Such  schemes  may  also  be 
joined through membership of an association. 

the  savings 

Clarification:  The  main  difficulty  in  analysing 
individual  provision  stems  from  the  fact  that  it  is 
difficult  to  distinguish  among  different  types  of 
savings  those  that  are  clearly  for  retirement 
that  are  not 
purposes.  Part  of 
specifically  labelled  as  pension  savings  may  be 
used  for  retirement  purposes,  whereas  part  of  the 
savings  collected  by  retirement  schemes  may  – 
depending  on  national  rules  –  in  fact  be  used  for 
purposes  other  than  providing  periodic  retirement 
income  (such  as  one-off  lump-sum  benefits  or 
early  withdrawal  options).  The  extent  to  which 
these  schemes  are  used  for  retirement  savings 
depends in particular on the conditions attached to 
them.  For  example,  the  conditions  could  include: 
(i)  tax  incentives  linked  to  the  condition  that  the 
bulk  of  such  savings  must  be  used  for  a  regular 
income (annuity) rather than for paying out a lump 
sum;  or  (ii)  the  minimum  age  at  which  a  person 
can  access  his  or  her  savings.  In  some  cases, 
pension 
investment 
tax  advantages,  for 
vehicles  with  noticeable 
instance when a number of years of participation in 
the  plan  are  required  to  benefit  from  a  lower  tax 
rate. 

instruments  are  used  as 

97 

European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Breakdown of public pensions 

Gross  public  pensions  are  split  into  four  different 
categories:  (i)  old-age  and  early  pensions;  (ii) 
(iii)  survivors’  pensions 
disability  pensions; 
(iv)  other  pensions 
(earnings-related); 
(earnings-related).  The  sum  of 
the  different 
subcategories  should  be  equal  to  the  overall 
projected  number  of  gross  public  pension 
expenditures. 

and 

Old-age and early pensions  

and 

non-earnings-related 

Old-age and early pensions include both earnings-
related 
pensions. 
Earnings-related pensions reflect all those pensions 
for  which  entitlements  are  dependent  on  personal 
earnings/contributions  to  the  old-age  and  early 
pension  scheme.  Non-earnings-related  pensions 
are  often  social-assistance  benefits  financed  by 
taxes  that  nevertheless  match  the  definition  of 
pension expenditure.  

Three sub-categories of old-age and early pensions 
are  considered:  basic  pensions,  earnings-related 
non-contributory  minimum 
pensions, 
pensions. 

and 

in 

flat 

other 

amounts 

-  Basic  pensions  (flat  component)  are  reported 
earnings-related 
from 
separately 
supplementary  public  pensions 
those 
for 
countries  where  the  concept  of  a  flat  pension 
component  exists.  These  represent  pension 
to 
benefits 
beneficiaries  meeting  certain  requirements,  for 
instance  a  minimum  number  of  contributory 
years. For some countries (i.e. DK, IE, NL), the 
is  non-contributory  with 
flat  component 
residency  being  the  qualifying  condition.  In 
such cases, the expenditure should be included 
in 
the  old-age  pension  expenditure  but 
projected  separately  from  the  earnings-related 
component. 

allocated 

-  Earnings-related  pensions  other 

on 

depend 

than  basic 
pensions  concern  all  pensions  for  which 
entitlements 
personal 
earnings/contributions to the  old-age and early 
pension  scheme.  Earnings-related  old-age  and 
early pensions should be considered as a single 
pension category, given that in many countries 
a proper distinction between the two cannot be 
made (this is either because early retirement is 

98 

built  into  the  old-age  pension  system,  or 
because  the  standard  retirement  age  varies 
between  gender  and  will  increase  or  become 
more flexible with time). Early pensions should 
include  –  in  addition  to  genuine  (actuarial) 
early 
retirement  schemes  –  other  early 
retirement  schemes  that  are  not  disability 
pensions and are granted, primarily on the basis 
of  reduced  work  capacity  or  labour-market 
reasons,  to  a  specified  (age  or  work)  group  at 
an age below the statutory retirement age. This 
category  also  includes  minimum  pensions  that 
are contributory-based. 

-  Non-contributory  minimum  pensions  and 
minimum income guarantees are reported. The 
variable ‘minimum pensions (non-contributory, 
i.e.  minimum  income  guarantees  for  retired 
people)’  in  the  reporting  framework  includes 
all pension expenditures for which entitlements 
are  not  dependent  on  contribution  require-
ments, e.g. means-tested minimum pensions. In 
line  with  the  general  definition  of  pension 
expenditure, social assistance (if it is equivalent 
to  a  minimum  pension  and  targeted  to  people 
aged  over  the  early  retirement  age,  e.g.  60) 
must be included in the projections through this 
variable.  If  a  non-contributory  benefit 
is 
granted  because  of  disability  or  other  reasons, 
this  benefit 
included  under 
should  be 
‘Disability pensions’ or ‘Other pensions’. 

Disability pensions 

earnings- 

Expenditure related to disability should be reported 
separately,  although  without  making  a  distinction 
between 
non-earnings-related 
and 
disability  pensions.  Some  countries  consider 
disability  pensions  (benefits)  as  part  of  their 
sickness  insurance  scheme,  while  in  others  they 
belong  to  the  pension  scheme (44).  While  in  some 
remains 
the  pension  classification 
countries 
unchanged between the time it is first granted and 
the  moment  payments  end,  in  most  countries  an 
early disability pension is transformed into an old-

(44)  In  general,  disability  pensions as defined above  should be 
covered  by  the  pension  projections,  even  though  some 
countries consider them as part of their sickness insurance 
scheme.  If  countries  wish  to  keep  disability  pensions  as 
part  of  health  expenditures,  it  should  be  clarified  in  the 
peer-review  process  that  those  expenditures  are  indeed 
covered by one of the two projections. 

                                                           
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

age  pension  once  the  beneficiary  reaches  the 
standard old-age retirement age. 

These  issues  and  assumptions  on  disability  rates 
should  be  made  clear  during  the  peer-review 
process.  Take-up  ratios  of  disability  pensions  are 
supposed  to  stay  broadly  constant  over  time  if 
there  are  no  reforms  affecting  retirement  ages, 
although  a  small  decreasing  variation  may  occur 
due to cohort effects.  

In  line  with  the  agreements  on  the  long-term  care 
and  healthcare  projection  methodologies  (see 
Chapters  2  and  3  in  Part  II),  two  categories  of 
expenditure  must  be  considered  as  long-term  care 
expenditure:  (i)  care  allowances  (benefits  paid  to 
disabled  people  who  need  frequent  or  constant 
assistance  to  help  them  meet  the  extra  costs  of 
attendance);  and  (ii)  expenditure  on  the  economic 
integration  of  handicapped  people  (allowances 
paid to disabled people when they undertake work 
adapted to their condition, normally in a sheltered 
workshop,  or  when 
they  undergo  vocational 
training).  These  two  categories  of  expenditure 
should not be included when calculating disability 
pensions. 

Survivors' pensions 

Survivors’  pensions,  without  any  age  limit,  must 
be  included  separately  in  the  projections.  These 
should  include  both  earnings-related  pensions  and 
similar  means-tested  minimum 
flat-rate  or 
pensions.  The  country  fiche  should  provide  a 
detailed description of the assumptions behind the 
projection  of  survivors’  pension  expenditure  in 
terms  of  household  composition,  joint  probability 
to survive, etc. 

Other pensions 

The category ‘other’ is used for pensions or social 
assistance  with  a  similar  purpose  that  do  not  fit 
under any of the above categories.  

1.3.3.  Benefit ratio and replacement rate at 

retirement 

a 

better 

For 
projected 
understanding 
expenditure,  the  following  components  of  the 
reporting framework are key.  

of 

Benefit ratio 

(including 

Definition: The benefit ratio is the average pension 
benefit 
i.e. 
contributory  and  non-contributory)  divided  by  an 
economy-wide average wage, as calculated by the 
Commission.  

components 

all 

its 

Clarification:  Changes  in  the  benefit  ratio  are 
crucial  to  analyse  and  understand  the  projection 
results,  because  they  reflect  the  legal  features  of 
the  pension  framework  as  far  as  the  rules  for 
calculating  and  indexing  the  pension  entitlement 
are concerned.  

The  benefit  ratio  captures  several  features  at  the 
same time. Firstly, it reflects the assumed increases 
in average pensions due to: (i) indexation rules; (ii) 
the  maturation  of  the  pension  system;  (iii)  longer 
contribution  periods;  and  (iv)  changes  in  the 
pension  formula.  Secondly,  it  reflects  the  changes 
in  average  wages  driven  by  the  assumptions  on 
labour-productivity  growth 
it 
captures  the  changes  in  the  structure  of  the 
respective  population  groups,  namely  the  share  of 
pensioners  and  wage  earners  in  each  year  of  the 
projection exercise. 

rates.  Thirdly, 

1.3.4.  Gross average replacement rate at 

retirement 

Definition:  The  gross  average  replacement  rate  at 
retirement  is  the  ratio  of  the  first  average  pension 
of those who retire in a given year over the average 
wage at retirement. The latter usually differs from 
the  overall  economy-wide  average  wage,  unless  a 
flat  wage profile over the entire  working career is 
assumed in the projection exercise. 

the  series  on 

As  already  underlined  earlier,  in  order  to  ensure 
the  consistency  of  the  projected  replacement  at 
the  economy-wide 
retirement, 
average  wage  at  retirement  is  included  in  the 
reporting  framework.  This  wage  series  is  the  one 
to be used for projecting the  replacement rate and 
the  adopted  assumptions  will  be  part  of  the  peer 
review. 

Clarification:  For  public  pension  schemes,  the 
gross  average  replacement  rate  (at  retirement) 
reflects  only  earnings-related  pensions,  including 
the  flat  component  if  relevant.  The  gross  average 
for  old-age 
replacement 

retirement 

rate  at 

99 

European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

earnings-related pensions should equal the average 
new pension benefit (new earnings-related old-age 
and  early  pension  expenditures  divided  by  the 
number  of  new  earnings-related  old-age  and  early 
pensions)  divided  by  the  average  wage  at  retire-
ment, as reported in the pension questionnaire.   

1.3.5.  Disaggregation of pension expenditure 

into stocks and flows 

New public earnings-related pensions 

Definition:  New  pensions  expenditure  is  to  be 
calculated  separately  for  those  who  retire  in  the 
considered  year  (males,  females, 
total).  The 
projected data concern only new entitlements, thus 
excluding  disability  benefits  that  are  transformed 
into old-age benefits upon reaching retirement age. 

Changes  in  the  flows  of  pensions  and  pension 
expenditure  over  time  should  properly  reflect  the 
impact  of  recently  legislated  reforms  on  the 
functioning  of  pension  systems.  Therefore,  the 
questionnaire  includes  a  disaggregation  of  the 
projected  annual  flow  of  earnings-related  old-age 
and early new pensions in its main drivers. 

Clarification:  Publicly  provided  earnings-related 
pension  schemes  can  be  subdivided  into  the 
following three broad types: DB, NDC, and PS. 16 
of the 28 considered countries have DB schemes, 6 
of them have NDC systems and 8 are PS schemes 
(see Table II.1.1) (45). 

(45)  Counting twice France (once in the DB group and once in 
the PS group) and Greece (once in the DB group and once 
in the NDC group). 

100 

Table II.1.1:  Pension schemes across Member States 

Country
BE
BG
CZ
DK
DE
EE
IE
EL(1)
ES
FR(2)
HR
IT
CY
LV

Type
DB
DB
DB
Flat rate + DB
PS
PS
Flat rate + DB

Country
LT
LU
HU
MT
NL
AT
PL
Flat rate + DB + NDC PT
RO
SI
SK
FI
SE
NO

DB
DB + PS
PS
NDC
PS
NDC

Type
PS
DB
DB 
Flat rate + DB
Flat rate + DB
DB
NDC 
DB
PS
DB
PS
DB
NDC 
NDC 

(1) The NDC is an auxiliary mandatory pension scheme; (2) 
PS refers to the complementary schemes AGIRC and 
ARRCO. 
Source: European Commission, EPC. 

New  pension  expenditures  for  DB  and  NDC 
systems can be disaggregated as follows: 

(cid:1842)(cid:3041)(cid:3032)(cid:3050) = (cid:1829)̅(cid:3041)(cid:3032)(cid:3050) (cid:1827)̅(cid:3041)(cid:3032)(cid:3050) (cid:1842)(cid:3364)(cid:1831)(cid:3364)(cid:3041)(cid:3032)(cid:3050) (cid:1840)(cid:3365)(cid:3041)(cid:3032)(cid:3050)  

            [1.1] 

With      total  spending  on  new  pensions;  C(cid:3364) (cid:2924)(cid:2915)(cid:2933)  the 
average  contributory  period  or  years  of  service  of 
new pensions; A(cid:3365)(cid:2924)(cid:2915)(cid:2933) the average accrual rate of the 
new  pensions;  P(cid:3365)E(cid:3365)(cid:2924)(cid:2915)(cid:2933) the  average  pensionable 
earnings over the contributory period related to the 
new  pensions;  and    N(cid:3365)(cid:2924)(cid:2915)(cid:2933)    the  number  of  new 
pensions (pensioners). 

For  some  countries,  an  additional  sustainability 
factor or adjustment factor might apply. In the case 
of DB systems, the accrual rate is predefined. For 
NDC systems, it is determined by the contribution 
rate to the notional accounts and the annuity factor.   

For PS, the disaggregation along the lines defined 
for  DB  and  NDC  systems  is  either  not  possible 
(because,  for  example,  pensionable  earnings  are 
not  explicitly  considered  but  rather  accounted  for 
through  the  pension-point  accumulation)  or  not 
meaningful  because  of  the  inherent  nature  of  a 
point  system.  For  this  reason,  an  alternative 
formula is used for the PS reporting block: 

(cid:1842)(cid:3041)(cid:3032)(cid:3050) = (cid:1840)(cid:3041)(cid:3032)(cid:3050)(cid:1842)(cid:3364)(cid:3041)(cid:3032)(cid:3050) = (cid:1840)(cid:3041)(cid:3032)(cid:3050) (cid:1874)(cid:3021) (cid:1868)(cid:1868)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3021)                  [1.2] 

Where total new pension expenditure  (cid:1842)(cid:3041)(cid:3032)(cid:3050)  is the 
product of the number of new pensioners (cid:1840)(cid:3041)(cid:3032)(cid:3050) and 
the  average  new  pension  benefit  (cid:1842)(cid:3364)(cid:3041)(cid:3032)(cid:3050).  The  latter 
equals  the  pension  point  value  at  retirement  (cid:1874)(cid:3021), 

 
                                                           
 
    
 
 
 
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

multiplied  by  the  average  number  of  accumulated 
pension  points  of  new  pensioners  (cid:1868)(cid:1868)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3021).  For  some 
countries,  an  additional  sustainability  factor  or 
adjustment factor might apply. 

The  average  number  of  pension  points  ((cid:1868)(cid:1868)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3021))  can 
be  further  disaggregated.  Under  some  social-
security regimes, pension points can be accrued in 
ways other than contributions, and those points can 
be  considerable  in  terms  of  the  final  amount. 
Accordingly,  it  is  relevant  to  have  information  on 
the time span needed to accumulate pension points, 
independently  of  how  they  were  accrued,  so  the 
formula is:  

(cid:1868)(cid:1868)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3021) = (cid:1829)̅(cid:3021) (cid:1868)(cid:1868)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3047)                                                           [1.3]

With  (cid:1829)̅(cid:3021) the  average  contributory  period  (actual 
and virtual) and (cid:1868)(cid:1868)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3047) the average yearly number of 
pension  points  (which  can  be  interpreted  as  the 
effective  accrual  rate  in  the  case  of  PS,  i.e.  the 
number  of  pension  points  at  retirement  over  the 
contributory period). 

for  PS 

thus  shows  strong 
The  breakdown 
similarities  with  that  for  DB  and  NDC  systems 
(e.g. contributory periods and accrual rates exist in 
all  three).  This  makes  it  possible  to  assess  the 
effects  of  a  broad  range  of  possible  systemic 
reforms. 

To  cater  for  the  way  DB,  NDC  and  PS  schemes 
operate,  there  are  three  different  versions  of  the 
block  collecting  data  on  Breakdown  of  new 
pension  expenditure  –  public  pensions,  earnings-
related  (see  Table  II.A4.2  –  Table  II.A4.4  in 
Annex 4). Reporting lines under the three blocks to 
some extent correspond to each other, but they also 
contain  scheme-specific  elements.  To  assure  the 
sustainability  of  their  pension  systems,  several 
Member  States  introduced  automatic  balancing 
as 
are 
mechanisms 
‘sustainability/adjustment 
the 
questionnaire. The specific way these factors apply 
needs  to  be  taken  into  account  when  dealing  with 
new projections for pension expenditure.  

referred 
factors’ 

to 
in 

that 

If the old-age pension includes a flat component or 
a basic pension (contributory or non-contributory), 
the  relevant  subcomponents  need  to  be  projected 
separately: for males, females and total (see Table 
II.A4.2 – Table II.A4.4 in Annex 4).  

to  some  distribution. 

Because  not  all  the  new  pensioners  will  retire  on 
1 January,  new  pension  benefits  are  calculated  as 
monthly  averages.  To  be  consistent  with  the  data 
on  total  expenditure  on  new  pensions  (reporting 
lines 15, 17 and 19 in the reporting sheet – Annex 
4  Table  II.A4.1),  and  to  allow  for  a  check  of  the 
reported  data,  countries  are  asked  to  provide  the 
average number of months of pension paid the first 
year.  If  there  is  no  specific  constraint  due  to 
legislation, the new pensioners are spread over the 
year  according 
If  a 
symmetrical  distribution  over  the  year  is  assumed 
(or empirically fitted the data), the average number 
of months of pension paid the first year turns out to 
be  six.  If  the  distribution  is  asymmetrical,  the 
average  should  be  calculated  according  to  the 
assumed distribution. If there is a single retirement 
date  fixed  by  law,  the  average  number  of  months 
of pension paid the first year will be the difference 
with  the  end  of  the  year.  If  more  than  one 
retirement  date  is  fixed  by  law,  the  average 
number  of  months  of  pension  paid  the  first  year 
should  be  calculated  as  an  average  of 
the 
remaining  months,  weighted  by  the  number  of 
people that retire on each specific date. 

is 

to  analyse 

An  alternative  use  of  the  data  on  new  public 
earnings-related  pensions 
the 
development and internal consistency of the stock 
of  old  pensions  (those  already  existing  at  the 
beginning  of  the  year  to  be  calculated  as  the 
difference  between 
the  ‘new’ 
pensions in the reporting sheet). At every point in 
time 
the  projection  of  average  pension 
expenditure related to ‘old pensions’ must be close 
to the value of the average pension expenditure in 
t-1 indexed by the rule applied in each country and 
scheme, and thus: 

total  and 

the 

t, 

(

NP
-
1
t
old
P
t

t

+

1)(
-
1
old
N
t

e
)

»

1

where: 

[1.6] 

1-tP   is  the  projection  of  total  public  earnings-
related  pensions  expenditure  (including  the  flat 
component) in year t-1;  

1-tN   is  the  number  of  pensioners  entitled  to  a 
public earnings-related pension in year t-1;  

101 

 
   
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

(1+ɛ)  is  the  pension  indexation  rule  applied  in 
each country and scheme; 

old

the  projection  of 

tP is 
the  ‘old’  pensions 
expenditure at time t (total public earnings-related 
pensions  expenditure  minus 
the  expenditure 
related to ‘new’ public earnings-related pensions);  

old

tN  is the number of old pensioners in year t. 
and 
It can be calculated as the difference between total 
pensioners  entitled  to  a  public  earnings-related 
pension  minus  the  new  pensioners  in  the  same 
typology of pension as reported in the last block of 
the reporting sheet. 

Such an indicator is expected to take a value close 
to 1 if projections are internally consistent and the 
distribution  of  the  retired  people  has  not  been 
selected by mortality (46).  

1.3.6.  Additional information on number of 
pensioners, contributors and 
contributions, and applied indexation 

The number of pensions 

The  number  of  pensions  reflects  the  number  of 
cases  in  which  a  pension  is  paid  to  an  individual. 
type  of  pension  should  be  considered 
Each 
separately  (earnings-related  old-age  and  early 
pensions,  minimum  pensions,  disability  pensions, 
survivors’ pensions, other public pensions, private 
individual 
occupational  pensions  and  private 
mandatory/non-mandatory  pensions).  To  ensure 
the consistency of the exercise, the reporting of the 
number of pensioners and pensions should include 
pensions paid overseas. 

The  number  of  all  pensions  and  public  pensions 
must  be  reported  by  age  groups.  This  breakdown, 
the  public  scheme, 
which 
increases  transparency  and  consistency  between 
population, labour force and pension projections.  

is  mandatory  for 

(46)  If  the  assumption  of  orthogonality  between  mortality  and 
pension  distribution  is  removed,  we  are  left  with  the 
empirical evidence that mortality rates are higher for older 
people,  and  that  these  people  receive,  on  average,  smaller 
pensions.  This  will  result  in 
tN   being  larger  than 
tP /
1-tN .  In  terms  of  the  proposed  indicator,  a  value 
1-tP /
smaller than (but still close to) 1 is to be expected. 

old

old

102 

The number of pensioners 

The  number  of  pensioners  in  each  of  the  pension 
schemes  should  be  reported  separately,  allowing 
for the fact that the same person may be a recipient 
of several types of pensions (for instance, a social-
security pension and a private mandatory pension). 
Thus,  the  detailed  lines  should  reflect  the  number 
of recipients of the specific pension. However, the 
figures on summary lines, in particular the number 
of  all  (public)  pensioners,  are  not  likely  to  match 
with  the  sum  of  the  subtotals.  Ideally,  the  total 
number  of  pensioners  (line  108)  should  be  the 
number  of  persons  who  receive  pension  benefits, 
but  calculated  only  once  for  those  receiving 
multiple  pensions.  If  an  exact  figure  is  not 
available,  an  estimate  is  preferred  instead  of 
simply  summing  up.  If  such  a  rule  is  applied,  a 
minimum requirement of the projections is that the 
number of pensioners does not exceed the number 
of pensions.  

In  the  projections,  the  ratio  between  pensions  and 
pensioners  should  be  held  constant  if  there  is  no 
reform  affecting  the  pension  take-up  ratio  or  any 
process  of  merging/closing  of  pension  schemes. 
Any  departure  from  this  hypothesis  should  be 
documented  and  will  be  part  of  the  peer-review 
process. 

The  overall  number  of  pensioners  by  age  group 
should  be  consistent  with  agreed  labour  force 
figures. The share of pensioners in each age group 
should be below – but very close to – the number 
of inactive people in the same group. 

In  particular, 

A  breakdown  of  pensioners  by  age  and  sex  must 
be provided by Member States for public pensions 
(and  a  similar  breakdown  can  be  provided  on  a 
voluntary  basis  for  all  pensions).  This  breakdown 
is needed to increase transparency and consistency 
between  projections  for  population,  labour  force 
and  pensioner  numbers. 
this 
breakdown  will  make  it  possible  to  carry  out  a 
consistency check between gender-specific labour-
force  participation  rates  and  gender-specific  data 
on  the  number  of  pensioners.  Some  form  of 
correlation should be evident, once mortality rates 
have  been  taken  into  account,  between  today's 
participation  rates  and  pensioners  projected  30-40 
years in the future. This data should be particularly 
interesting  when  analysing  the  effects  of  reforms 
on  the  effective  retirement  age.  In  addition, 

                                                           
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

comparing  the  overall  number  of  pensioners  with 
inactive/total  population  for 
the  size  of 
different  age  groups  might  result 
in  further 
insights.  

the 

The availability of data on pensioners (or pensions 
as  a  second  best)  is  particularly  relevant  when 
disaggregating  the  pension  expenditure-to-GDP 
ratio.  In  particular,  this  data  allows  for  the 
calculation of the coverage ratio.  

The  coverage  ratio  is  defined  as  the  number  of 
pensioners  of  all  ages  to  the  number  of  people 
aged 65 or older. The analysis of the coverage ratio 
provides information about how the developments 
of  the  effective  exit  age  and  the  percentage  of 
population  covered  by  a  pension  scheme  have  an 
impact  on  pension  spending.  The  coverage  ratio 
should  also  be  disaggregated  by  age  group  and 
calculated in relation to the inactive population (to 
labour  force 
check  for  consistency  with 
projections).  

the 

Contributions to pension schemes 

Contributions  to  pension  schemes  paid  by  both 
employers and employees – as well any legislated 
contribution  by  the  state  or  other  revenue  sources 
(e.g.  nuisance  charges,  people  transferring  rights 
and  savings  from  private  schemes  to  the  public 
scheme)  –  provide  information  on  whether  or  not 
there  is  a  potential  future  financial  gap  in  the 
pension system to be closed by state contributions. 
If  the  pension  contribution  is  part  of  a  broader 
social-security  contribution 
rate,  an  estimate 
should be provided, if possible, for the share of the 
pension contribution, e.g. based on the most recent 
expenditure  structure.  If  general  tax  revenues  are 
used  to  finance  pensions,  no  estimate  should  be 
provided  here.  Total  contributions  should  be 
provided,  as  well  as  employers’,  employees’  and 
state contributions individually. 

It  is  relevant  to  include  estimates  of  pension 
contributions  (in  millions  of  euro)  to:  (i)  public 
schemes;  (ii)  private  occupational  schemes;  and 
(iii)  private  individual  mandatory/non-mandatory 
schemes. It is especially relevant to include these if 
statutory  contributions  are  divided  between  a 
public  scheme  and  a  private pension  scheme.  The 
sum 
private 
individual  pension 
occupational,  and  private 

contributions 

public, 

of 

to 

schemes  provides  the  number  of  total  pension 
contributions.  

Number of contributors 

As  is  the  case  with  the  number  of  pensioners,  the 
number  of  contributors  to  each  type  of  pension 
should  be  considered  separately,  allowing  for  the 
fact  that  the  same  person  may  be  a  contributor  to 
several schemes. This is the case, for instance, for 
pension systems in  which part of a public scheme 
is  switched  to  a  private  (mandatory)  pension 
total  pension 
scheme.  However, 
contributors should count contributors only once if 
the person contributes to more than one scheme at 
the  same  time.  Thus,  the  number  of  contributors 
should be close to the number of employed persons 
or the working-age population as projected by the 
Commission and the AWG. 

line  of 

the 

The  number  of  contributors  should  correspond  to 
an  estimate  of  the  number  of  persons  covered  by 
pension  schemes  without  regard  to  the  amount  of 
the  contribution.  Thus,  a  contributor  in  a  short-
term  contract  should  count  as  a  contributor  based 
on  a  permanent  (full-time)  contract.  However,  in 
practice, a contributor in a short-term contract may 
appear as a contributor several times during a year, 
and  it  may  not  be  possible  to  disentangle  the 
number  of  contributors  during  a  year  from  the 
number of contribution periods. Therefore, a better 
proxy  for  the  number  of  persons  covered  by 
the  number  of 
pension  schemes  should  be 
contributors at a given point in time, e.g. at the end 
of the year. 

Applied indexation  

These variables concern a simple recording of the 
indexation factor used for projecting gross pension 
expenditure.  The  following  must  be  provided: 
indexation  effectively  applied  to  earnings-related 
public  pensions,  indexation  applied  to  the  flat 
components  of  old-age  pensions,  and  indexation 
applied  to  minimum  pensions.  This  reporting  is 
particularly  relevant  for  pension  components,  for 
which  the  effectively  assumed  future  indexation 
differs from the legally stipulated mechanism.  

Of  particular  concern  is  the  indexation  factor  that 
is  applied 
the 
interpretation  of  the  AWG’s  standard  no-policy-
change  assumption  in  this  regard.  Past  experience 

to  minimum  pensions  and 

103 

European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

a 

of 

also 

rule) 

applies 

shows  that  the  no-policy-change  assumption  was 
not  always  fully  applied  with  respect  to  the 
treatment 
non-earnings-related  minimum 
pensions and  was interpreted in different  ways by 
Member  States.  When 
lower-than-wage 
indexation  rule  (e.g.  a  pure  price  indexation  or  a 
Swiss 
to  minimum 
pensions/minimum  income  guarantees  according 
to legislation, the expenditure on that specific part 
of  the  pension  scheme  is  projected  to  shrink 
rapidly  over  time.  Past  practice  across  Member 
States  with  regard  to  the  application  of  such  an 
the  minimum 
ungenerous 
pension has shown that those mechanisms resulted 
in  several  ad  hoc  interventions  beyond  the  legal 
indexation.  This  occurred  in  order  to  re-align 
the  up-to-date 
minimum  pension  benefits 
standard  of  living,  which,  ultimately,  is  the  main 
objective  of  providing  a  minimum  pension 
scheme. 

indexation  rule 

to 

to 

for 

the 

During  the  2018  projection  round,  a  common 
methodology 
indexation  of  non-
contributory  minimum  pensions  was  therefore 
agreed,  to  ensure  that  the  projections  reflect  the 
‘safety  net’  role  minimum  pension  schemes  play. 
This was done by assuming adjustment in line with 
average  wage  developments.  In  practice,  all 
Member  States  should  apply  full  wage  indexation 
after  a  maximum  of  10  years.  This  concerns  all 
Member  States  with  below-wage  indexation  (pure 
price 
indexation, 
indexation to GDP, no fixed rule).  

indexation,  partial  wage 

104 

2.  HEALTHCARE 

One  of  the  components  of  public  age-related 
expenditure,  spending  on  healthcare,  in  the  EU  is 
high  and  continues  to  rise.  This  makes  public 
spending  on  healthcare  an  integral  part  of  the 
debates  on  the  long-term  sustainability  of  public 
finances.  

This  chapter  presents  a  number  of  scenarios  to 
project public expenditure on healthcare in the 27 
Member States of the EU and Norway up to 2070. 
The general methodology is explained below. 

2.1.  OVERVIEW OF THE PROJECTION 

METHODOLOGY 

The  Commission  (DG  ECFIN)  simulation  model 
will  be  used  to  project  public  health  expenditure, 
as outlined in the 2018 Ageing Report. 

This  simulation  model  assumes  that  the  whole 
population  is  divided  into  groups  which  are 
assigned  certain  characteristics  (e.g.  age,  sex,  per 
(47). 
capita  expenditure,  health  status,  etc.) 
Changes  in  these  groups  lead  to  expenditure 
changes  over  time.  These  types  of  models  are 
widely  used  when  running  long-term  expenditure 
projections,  especially  when  the  precise  micro 
information on the individuals and  their  transition 
rates  from  one  health  status  to  another  is  missing 
or unreliable. 

The  choice  of  methodology  and  various  scenarios 
is constrained by the availability, accessibility and 
quality  of  healthcare  data.  Therefore,  the  model 
may  not  include  all  the  relevant  factors  identified 
as affecting healthcare spending.  

In  general,  the  long-term  budgetary  projections 
and  certainly  the  base-case  scenario  illustrate  a 
policy-neutral situation. This is the situation where 
future  possible  changes  in  government  policy  are 
not considered. In other words, any potential future 
institutional  or  legal  changes  to  the  financing  and 
organisation of healthcare systems are not reflected 
in 
projecting 
expenditure.  Such  institutional  and  legal  changes 
would  include  for  example  changes  in  the  degree 

the  methodology 

used 

for 

(47)  For  the  most  recent  projections,  see:  "The  2018  Ageing 
Report Economic and budgetary projections for the 28 EU 
Member States (2016-2070)", European Economy, No 079, 
5/2018. 

of regulation of markets for pharmaceuticals or the 
introduction  of  referral  systems.  Instead,  the  only 
changes  modelled  in  these  projections  are  those 
deemed  automatic  and  adequate  responses  to  new 
needs resulting directly from changes in population 
structure,  health  status  or  income.  Therefore,  the 
determinants  of  expenditure  considered  in  the 
projections  can  be  seen  as  mostly  independent  of 
government activity or public policy. 

The  general  methodology  used  to  project  public 
expenditure  on  healthcare  is  the  following  (See 
Graph II.2.1): 

Step  1:  take  baseline  population  projection  (i.e. 
number of individuals) by age and sex provided by 
Eurostat for each year up to 2070; 

Step  2:  take  age/sex  specific  public  expenditure 
per  capita  on  healthcare  i.e.  the  age/sex  specific 
expenditure profiles provided by Member States; 

Step  3:  calculate  age/sex  expenditure  profiles  for 
each  projection  year  up  to  2070  based  on  various 
assumptions i.e. the projection scenarios; 

Step  4:  for  each  projection  year,  multiply  the 
projected number of people in each age/sex group 
by the respective age/sex expenditure profiles;  

Step  5:  for  each  projection  year,  sum  all  the 
groups’  expenditure  to  obtain  the  total  projected 
public expenditure on healthcare. 

There are three important aspects of the projection 
exercise to be stressed. 

First, the analysis assumes that the determinants of 
public  expenditure  on  healthcare, 
such  as 
government  health  policy  and  actions  by  any 
individual  participant  in  the  health  market  stay 
constant.  This  means  that  changes  in  the  way 
health systems are  financed and organised are not 
modelled.  The  adjustments  observed  relate  to 
healthcare  provision  adjusting  automatically  to 
needs  resulting 
in  population 
from  changes 
structure and health status, and changes in income. 
It is assumed that such changes force an automatic 
change  in  the  amount  of  goods  and  services 
provided to the population by the publicly financed 

105 

 
                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Graph II.2.1:  Schematic presentation of the projection methodology 

Source: European Commission. 

health  system.  As  such,  most  scenarios  should  be 
considered as “no-policy change” scenarios (48). 

are 

Second,  many  of  the  determinants  of  expenditure 
described  in  the  next  section,  notably  supply  side 
determinants  of 
either  not 
spending 
quantifiable or depend on ad-hoc policy decisions. 
This is why the methodology used in the previous 
2018  EPC-EC  Ageing  Report to  project  public 
healthcare expenditure and used again here reflects 
mainly  demand-side  factors  such  as  demographic 
structure, 
the 
population.  That  said,  a  regression  analysis 
impact  of  non-
attempts 
demographic  factors  such  as 
technology  and 
institutional  settings,  while controlling  for income 
and the demographic structure of the population. 

income  and  health  status  of 

to  quantify 

the 

Third,  the  analysis  tries  to  identify  the  impact  of 
each  quantifiable  determinant  separately  based  on 
hypothetical  assumptions  (estimated  guess  or  a 
“what  if”  scenario).  Therefore,  the  results  of  the 
projections  should  not  be  interpreted  as  forecasts 
of expenditure. 

for 

The  proposed  methodology 
the  coming 
projection  exercise  builds  on  the  2018  EPC-EC 
projections  exercise  and  maintains  the  existing 
scenarios  and  sensitivity  tests.  The  schematic 
methodology  to  project  healthcare  expenditure  is 
presented in Graph II.2.1above. 

(48)  Only  the  "EU27  cost  convergence  scenario"  can  be 
considered  as  a  policy  change  scenario  for  the  countries 
with below  the  EU average  public  spending  on  healthcare 
in the base year. 

106 

As  in  2018,  the  projections  on  healthcare  need  to 
be  viewed  in  the  context  of  the  overall  projection 
exercise.  Therefore,  the  common  elements  of  all 
the  2019-based  population 
scenarios  will  be 
projections  provided  by  Eurostat  and  the  baseline 
assumptions  on  labour  force  and  macroeconomic 
variables  agreed  by  the  EC  and  the  AWG-EPC. 
The  age  and  sex-specific  per  capita  public 
expenditure on healthcare profiles are provided by 
Member States. They are applied to the population 
projections  provided  by  Eurostat  to  calculate 
nominal  spending on  healthcare. In a further  step, 
the age profiles applied to the population structure 
are  adjusted  to  add  up  to  the  total  expenditure  on 
healthcare  in  the  specific  year  of  reference  (49).  It 
was  agreed  for  previous  exercises  to  do  this 
adjustment  by  keeping  the  base  year  proportions 
between  specific  age  cohorts  constant  while 
adjusting the total (calculated as sum of per capita 
weighted  by  population 
to 
correspond  to  the  aggregate  figure  as  reported  to 
the  international  databases  and  confirmed  by  the 
AWG delegates in the healthcare questionnaire.  

in  each  cohort) 

To reflect the effects of the different determinants 
on  public  expenditure  on  healthcare,  changes  are 
made  to  three  main  inputs:  1)  the  population 
projections; 2) the age-related expenditure profiles 

(49)  Total  headline  data  on  total  expenditure  may  differ  from 
the  figures  resulting  from  the  combination  of  age  profiles 
with underlying population. Discrepancies between the two 
measures on health expenditure can result from differences 
in their computation. While total expenditure is calculated 
from aggregate budgetary perspective, cost per capita is in 
many countries estimated on the basis of hospital inpatient 
data,  in  most  countries  based  on  the  diagnosis-related 
groups.  

                                                           
                                                           
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

(capturing  unit  costs);  and  3)  assumptions  on  the 
development of unit costs over time driven by the 
macroeconomic variables or assumptions on health 
status  for  example.  As  in  the  2018  projections 
exercise, the list of determinants to be modelled is 
not  exhaustive.  The  different  scenarios  are 
summarised  in  Table  II.2.1.  and  explained  in  the 
next section. 

legislated 

regarding 

according 

Information  on 

any 
information 
Country-specific 
relevant 
and/or 
reforms 
recent 
implemented  that  could  have  an  impact  on  public 
healthcare  expenditure  (e.g.  binding  spending 
ceilings,  etc.)  will  be  taken  into  account  in  the 
current  projections, 
technical 
feasibility. 
legislated  policy 
reforms and their quantification (50), both increases 
and  reductions,  are  provided  by  Member  States. 
The  annual  percentage  reduction  /  increase  is 
deducted  from  /  added  to  the  level  of  spending, 
effectively  changing  the  level  of  total  healthcare 
spending.  Further, 
the  age-cost  profiles  are 
adjusted  proportionally  to  the  change  in  the  level 
of spending in the base year. 

to 

2.2.  DIFFERENT SCENARIOS FOR PROJECTING 
PUBLIC HEALTHCARE EXPENDITURE 

the  costs  of 

The  purpose  of  the  healthcare  systems  is  to 
"improve  the  health  of  the  population  they  serve; 
respond  to  people's  expectations  and  provide 
ill-
financial  protection  against 
health"(51).  In  the  seminal  WHO  report  from 
2000 (52),  health  systems  are  attributed  four  vital 
functions:  1)  service  provision  i.e.  the  delivery  of 
personal  and  non-personal  health  services;  2) 
financing i.e. the revenue collection, the pooling of 
funds  (insurance  function)  and  purchasing  of 
services  (the  process  by  which  pooled  funds  are 
paid  to  providers  in  order  to  deliver  the  health 
interventions  to  care  users);  3)  resource  creation 
i.e. investment in equipment, buildings and people 
(training);  and  4)  stewardship  or  oversight  of  all 
the  functions  i.e.  the  careful  and  responsible 
management of the health system. 

(50)  Including COVID-19 measures in 2020 and 2021. 
(51)  World  Health  Organization  (2000),  "Health  Systems: 
Improving  Performance",  The  World  Health  Report  2000, 
p.8. 
(52)  Ibid. 

In  this  context,  public  expenditure  on  healthcare 
depends  on  a  number  of  factors  that  affect  the 
demand and supply of health services and goods.  

These include: 

(cid:129) 

the health status of the population; 

(cid:129)  economic growth and development; 

(cid:129)  new technologies and medical progress; 

(cid:129) 

the organisation and financing of the healthcare 
system; and 

(cid:129)  healthcare  resource  inputs,  both  human  and 

capital.  

The  long-term  projection  scenarios  on  public 
healthcare  expenditure,  described  below,  capture 
demand  and  supply-side  factors,  and  include 
demographic and non-demographic variables (53).  

2.2.1.  Demographic scenario 

It  assumes 

The  aim  of  a  "demographic  scenario"  is  to 
estimate  in  isolation  the  effect  of  an  ageing 
future  public  expenditure  on 
population  on 
that  age/sex  specific 
healthcare. 
morbidity  rates  and  provision  structure  of  health 
treatments do  not change over time. This, in turn, 
means  that  age/sex  specific  per  capita  public 
(on  healthcare)  profiles  can  be 
expenditure 
considered  as  proxies  for  the  morbidity  rates (54), 
remain  constant  in  real  terms  over  the  whole 
projection  period.  It  also  assumes  a  gradual 
increase 
the  basis  of 
life  expectancy  on 
underlying  population  projections.  An  increase  in 
life expectancy and no changes in health status as 
compared to today's health status mean that all the 
gains  in  life  expectancy  are  implicitly  assumed  to 
be spent in bad health. The number of years spent 
in  good  health  remains  constant.  This  is  in  line 

in 

(53)  See  also  Annex  9  for  mathematical  illustration  of  the 

healthcare scenarios. 

(54)  Strictly  speaking,  age  profiles  of  expenditure  illustrate 
exclusively  public  healthcare  spending  per  person  of  a 
given  age  cohort.  As  such,  it  is  not  a  measure  of  health 
status  or  morbidity.  However,  given  the  lack  of  a  reliable 
and  comparable  data  on  the  latter,  one  can  plausibly 
assume  that  the  shape  of  the  profile  follows  the  evolution 
of health status over the lifespan, i.e., over time, we assume 
that  the  same  segments  of  the  curve  (early  childhood,  old 
age and motherhood) follow the same pattern. 

107 

                                                           
                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table II.2.1:  Overview of scenarios to project healthcare expenditure 

Demographic 
scenario

High life 
expectancy 
scenario

Healthy 
ageing 
scenario

Death-related 
costs scenario

Income 
elasticity 
scenario

EU27 cost 
convergence 
scenario

Labour 
intensity 
scenario

I

II

III

IV

V

VI

VII

Sector-
specific 
composite 
indexation 
scenario
VIII

Non-
demographic 
determinants 
scenario

AWG 
reference 
scenario

AWG 
risk scenario

IX

X

XI

Population 
projection

Eurostat 
2019-based
 population 
projections

Alternative 
higher life 
expectancy 
scenario 
(+2 years) 

Eurostat 
2019-based
 population 
projections

Eurostat 
2019-based
 population 
projections

Eurostat 
2019-based
 population 
projections

Eurostat 
2019-based
 population 
projections

Eurostat 
2019-based
 population 
projections

Eurostat 
2019-based
 population 
projections

Eurostat 
2019-based
 population 
projections

Eurostat 
2019-based
 population 
projections

Eurostat 
2019-based
 population 
projections

Age-related 
expenditure 
profiles 

2019 profiles 
held constant 
over the 
projection 
period

2019 profiles 
held constant 
over the 
projection 
period

2019 profiles 
shift in line 
with changes 
in age-
specific life 
expectancy

2019 profiles 
split into 
profiles of 
decedents and 
survivors and 
adjust in line 
with changes 
in age-specific 
life expectancy

2019 profiles 
held constant 
over the 
projection 
period

Individual 
country 
profiles 
converging 
upwards to 
the EU27 
average 
profile over 
the projection 
period

2019 profiles 
held constant 
over the 
projection 
period

2019 profiles 
held constant 
over the 
projection 
period

2019 profiles 
held constant 
over the 
projection 
period

Intermediate 
scenarios I 
and III 
whereby 2019 
profiles shift 
by half the 
change in age-
specific life 
expectacy

Intermediate 
scenarios I 
and III 
whereby 2019 
profiles shift 
by half the 
change in age-
specific life 
expectacy

Unit cost 
development

GDP per 
capita

GDP per 
capita

GDP per 
capita

GDP per 
capita

GDP per 
capita

GDP per 
capita

GDP per 
hours worked

Input-specific 
indexation

GDP per 
capita

GDP per 
capita

GDP per 
capita

Elasticity of 
demand

1

1

1

1

Source: European Commission, EPC. 

Cost 
sensitivity of 
1.1 in 2019 
converging to 
1 by 2070

1

1

1

Cost 
sensitivity of 
1.5 in 2019 
converging to 
1 by 2070

Cost 
sensitivity of 
1.1 in 2019 
converging to 
1 by 2070

Cost 
sensitivity of 
1.5 in 2019 
converging to 
1 by 2070

with the expansion of morbidity hypothesis, which 
suggests  that  falling  mortality  is  largely  due  to  a 
decreasing fatality rate 

of  diseases  and  is  therefore  accompanied  by  an 
increase in morbidity and disability. 

To  calculate 
future  public  expenditure  on 
healthcare (55),  the  age/sex  specific  per  capita 
public  expenditure  profiles  are  multiplied  by  the 
in  each 
respective  age/sex  population  group 
projection  year.  These  age/sex  groups  change  in 
line  with  the  population  projections  up  to  2070. 
This  scenario  also  assumes  that  "unit  costs"  –  i.e. 
the healthcare expenditure per capita for each year 
of age – evolves in line with GDP per capita. Such 
cost  development  applied  to  the  baseline  age/sex-
specific  per  capita public  expenditure  profiles  can 
be considered neutral in macroeconomic terms – if 
no  change  in  the  age  structure  of  the  population 
occurred,  the  share  of  public  expenditure  on 
healthcare to GDP would remain the same over the 
projection period. 

(55)  The projected total public expenditure on healthcare do not 

include spending on LTC (health). 

108 

2.2.2.  High life expectancy scenario 

A variant of the demographic scenario is the "high 
life expectancy scenario". This is a sensitivity test 
to  measure  the  impact  of  alternative  assumptions 
on mortality rates. This scenario assumes, as in the 
sensitivity  tests  run  for  pension  projections,  that 
life  expectancy  at  birth  in  2070  exceeds  the 
projected 
the 
expectancy 
life 
two  years.  This 
"demographic  scenario"  by 
the 
to 
identical 
is  methodologically 
scenario 
"demographic 
alternative 
but 
demography and GDP data are used (56). 

scenario", 

used 

in 

2.2.3.  Healthy ageing scenario 

The  "healthy  ageing  scenario"  is  based  on  the 
relative  compression  of  morbidity  hypothesis.  It 
mimics  improving  health  status  in  line  with 
declines  in  mortality  rates  and  increasing  life 
expectancy.  It  assumes  that  the  number  of  years 
spent  in  bad  health  during  a  life  time  remains 
constant  over  the  whole  projection  period.  This 
means  that  all  future  gains  in  life  expectancy  are 
spent in  good health. Consequently, the  morbidity 
rate  and  therefore  the  age/sex  specific  per  capita 

(56)  Based  on the  approach  applied  to  assess  the  sensitivity  of 
pension  spending,  GDP  data  captures  the  life  expectancy 
change through the impact of the latter on the labour force 
projections. 

                                                           
                                                           
 
  
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

Graph II.2.2:  Stylised illustration of the constant healthy ageing scenario using age-profiles of healthcare costs 

Note: The healthy ageing scenario is identical with the constant health scenario from previous Ageing Reports. 
Source: European Commission. 

public  expenditure  profiles  are  declining  with  the 
mortality rate. 

(57). 
to 

shift 
in 

in  calculated.  For  example, 

  This  "outward" 
the  projected  gains 

Under  this  scenario,  the  country  specific  age/sex 
per  capita  expenditure  profiles  are  progressively 
shifted  outwards,  in  line  with  increasing  life 
is 
expectancy 
proportional 
life 
expectancy.  First,  for  each  projection  year  the 
change  in  life  expectancy  in  relation  to  the  base 
year 
life 
expectancy  of  a  50-year-old  man  is  expected  to 
increase  by  4  years  from  30  years  in  year  t  to  34 
years  in  year  t+20  in  a  specific  Member  State. 
Then, the scenario assumes that in t+20 a 50-year-
old man  will have a per capita public expenditure 
profile of a (50-4) = 46-year old man in year t (the 
latter  adjusted  as  usual  with  the  GDP  per  capita 
growth rate over the last 20 years). 

the 

In Graph II.2.2, the dotted line illustrates the  new 
age-specific  per  capita  public  expenditure  profile 
that would be applied in each projection year up to 
the  year  2070.  As  in  the  "demographic  scenario", 

(57) The  method  is  applied  to  those  age/gender  groups  where
expenditure  per  capita  is  growing.  For  the  young  and  the
oldest  old, 
therefore
age/gender  per  capita  public  expenditure  profile  remains
the same over the whole projection period. 

reference  age/gender  and 

the 

each  age  and  sex  group  in  each  projection  year  is 
multiplied  by  the  modified  age/sex  specific  per 
capita  public  expenditure  profiles  to  calculate  the 
future public expenditure on healthcare. 

2.2.4.  Death-related costs scenario 

The  "death-related  costs  scenario"  employs  an 
alternative method to project public expenditure on 
healthcare.  The  methodology  links  per  capita 
public expenditure on healthcare to the number of 
remaining  years  of  life.  Indeed,  there  is  empirical 
evidence that a large share of the total expenditure 
on healthcare during a person’s life is concentrated 
in  the  final  years  of  life  (58).    As  life  expectancy 
increases  and  mortality  rates  decline,  a  smaller 
share of each age cohort is  in a terminal phase of 
life  and  mortality  is  concentrated  in  very  old  age 
cohorts.  If  more  people die  at  very  old  ages  there 
may  be  a  reduction  in  public  expenditure  on 
healthcare because per capita public expenditure in 
very old ages does actually decrease. 

In practical terms, for countries which provide the 
relevant data for running the model, it is proposed 

(58) For  an  overview  of  empirical  studies,  see:  Raitano  M.
(2006),  "The  Impact  of  Death-Related  Costs  on  Health-
Care  Expenditure:  A  Survey",  ENEPRI  Research  Report
No 17. 

109 

European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

to use an average profile of death-related costs by 
age (59). 

results are confirmed by empirical evidence from a 
number of studies (60).  

Next, the age/sex  specific  mortality rates are used 
as  probabilities,  to  split  each  age  group  into  two 
sub-groups  according  to  the  number  of  remaining 
years  of  life:  1)  that  of  decedents,  i.e.  those  who 
are  expected  to  die  within  a  certain  number  of 
years,  and  2)  that  of  survivors,  i.e.  those  who  are 
not expected to die within those number of years.  

Each  of  the  two  sub-groups  within  each  age/sex 
group is assigned a specific and different per capita 
public expenditure profile – the death-related costs 
profiles, 
expenditure 
occurring  a  full  year  before  for  decedents  versus 
survivors.  The  ratio  between  the  health  costs  of 
survivors and decedents is called the k-ratio.   

differentiating 

ideally 

Then  the  number  of  individuals  in  each  subgroup 
of  decedents  and  survivors  is  multiplied  by  its 
respective  per  capita  public  expenditure  profile. 
This gives the total public expenditure of each age 
group in each year.  

Summing total expenditure of each age group in a 
given  year  corresponds 
total  public 
expenditure on healthcare in that year.  

the 

to 

Note  that  the  death-related  costs  profiles  are  as 
usual  indexed  to  GDP per  capita  growth  as  in  the 
previous scenarios.  

As in the 2018 EPC/EC Ageing Report, the k-ratio 
is  projected  according  to  a  cohort  approach.  This 
allows  capturing  changes  in  perceived  healthcare 
needs  and  therefore  treatment  expectations  of  the 
very old as life expectancy increases.  

The k-ratio decreases in the older ages,  where the 
probabilities of death increase dramatically. This is 
because  normal  and  death-related  costs  have 
different correlations with age. In particular, while 
the  former  are  likely  to  increase  along  with  age 
because  of  the  progressive  worsening  of  health 
status,  the  latter  are  likely  to  follow  an  opposite 
path  insofar  as  the  event  of  death,  for  elderly 
people, is not as costly as for younger people. Such 

(59)  Ideally,  the  death-related  cost  profiles  should  exclude 
expenditure  on  LTC  (health).  However,  not  all  countries 
providing  death-related  cost  profiles  can  separate  the 
expenditure on LTC (health), when compiling them. 

110 

Therefore, the k-ratio cost profile varies over time, 
as longevity increases. Essentially, this means that 
it is the distance to time period before death rather 
than  age  per  se  which  influences  the  k-ratio  for 
people of a specific age/sex group.  

Maintaining  the  relationship  between  the  k-ratio 
and life expectancy unchanged, as observed in the 
base  year  (cross-sectional  analysis),  implies  that 
the  age  profile  of  the  k-ratio  moves  over  time 
according  to  changes  in  longevity  (intertemporal 
analysis).  

2.2.5.  Income elasticity scenario 

The  "income  elasticity  scenario"  attempts 
to 
capture the effect of changes in national income on 
demand  for  healthcare  goods  and  services.  This 
effect  is  the  result  of  a  number  of  factors:  higher 
living  standards,  the  fulfilment  of  the  basic  needs 
and  therefore  growing  expectations  and  social 
pressure  to  catch-up  with  the  healthcare  quality, 
and  the  coverage  provided  in  richer  neighbouring 
countries (61).  

the  possible  effect  of 

income, 
To  calculate 
different  levels  of  income  elasticities  to  the  basic 
GDP per capita evolution path can be used. More 
specifically,  this  scenario  shows  the  effect  of  an 
income elasticity of demand higher than 1, i.e. ε = 
1.1 (62), on the evolution of public expenditure on 
healthcare.  An  income  elasticity  exceeding  1 
indicates  that  healthcare  is  considered  by  society 
as  a  'luxury  good'.  An  elasticity  of  1.1  at  the 
beginning of the period is chosen based on existing 

(60)  Aprile, R. (2013); Gabriele et al. (2005); Lubitz and Riley 
(1993);  Van  Vliet  and  Lamers  (1998);  Madsen  (2004); 
Raitano (2006). 

(61)  The  demand  for  higher  quality  care  may  translate  into 
demand  for  the  most  modern  medical  knowledge  and 
technologies. In this context the impact of income could to 
a  certain  extent  capture  the  impact  of  technology.  The 
impact  of  technological  development  is  assessed  in  a 
separate scenario, using econometric analysis of past trends 
in public  expenditure  on  healthcare, demographic, income 
and non-income variables. 

(62)  Recent  studies  show  that  the  income  elasticity  of  health 
services  in  advance  economies  is  lower  than  1.  However, 
the  EPC-AWG  decided  to  keep  the  income  elasticity 
scenario  with 
to  keep 
comparability  with  the  previous  report  and  to  allow  for 
comparability  with  the  non-demographic  indexation  used 
in the “AWG reference scenario”. 

income  elasticity  of  1.1 

                                                           
                                                           
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

reviews  of  empirical  evidence  gathered  over  the 
recent  decades  (63).    It  is  also  assumed  that 
economic  growth  and 
real 
convergence  between  countries  over  the  long  run 
will  drive  elasticity  down  towards  common  unity 
level, by 2070 (64).  

the  process  of 

This  scenario  is  identical  to  the  "demographic 
scenario"  except  that  the  income  elasticity  of 
demand is set equal to 1.1 in the base year (rather 
than 1 in the case of the "demographic scenario"), 
converging  in  a  linear  manner  to  1  by  the  end  of 
projection horizon in 2070.  

2.2.6.  EU27 cost convergence scenario 

(which 

The "EU27 cost convergence scenario" is a policy 
change  scenario  meant  to  capture  the  possible 
effect  of  an  upward  convergence  in  real  living 
standards 
the 
healthcare 
assumptions) 
macroeconomic 
spending. In other words, this scenario proposes to 
take  into  account  the  convergence  of  citizens' 
expectations  towards  a  similar  basket  of  health 
goods. 

emerges 

from 

on 

This  scenario  considers  the  convergence  of  all 
countries that are below the EU27 average in terms 
of percent of GDP per capita health expenditure to 
that  average.  This  is  illustrated  as  follows:  the 
relative  age/sex  per  capita  public  expenditure 
the  corresponding  (calculated) 
profiles  below 
capita  public 
age/sex  per 
average 
EU27 
expenditure  in  the  base  year  are  assumed  to 
progressively 
this  EU27  average 
to 
age/sex  specific  per  capita  public  expenditure 
profile  (as  a  percent  of  GDP  per  capita).  The 
convergence is achieved by 2070. As a result, the 
convergence  speed  for  all  the  countries  below  the 
EU27  average  would 
the 
differences in the initial situation, i.e. the extent of 
the initial gap between country-specific and EU27 
average profile.  

into  account 

increase 

take 

(63)  See  Getzen  T.  E.  (2000),  "Healthcare  is  an  individual 
necessity  and  a  national  luxury:  Applying  multilevel 
decision models to the analysis of healthcare expenditures", 
Journal of Health Economics, Vol. 19(2), pp. 259-270. 
(64)  This is also a common technical assumption in many long-
run projection models, to avoid "explosive" path of some of 
the variables used in the exercise. 

2.2.7.  Labour intensity scenario 

The  "labour  intensity  scenario"  is  an  attempt  to 
estimate  the  evolution  of  public  expenditure  on 
healthcare  taking  into  account  that  healthcare  is 
and  will  remain  a  highly  labour-intensive  sector. 
Therefore,  unit  costs  (and  therefore  the  age/sex 
specific per capita public expenditure profiles) are 
assumed  to  evolve  in  line  with  changes  in  labour 
productivity, rather than growth in GDP per capita. 
This  assumption  implies  that  the  cost  of  public 
provision of healthcare is supply-driven rather than 
demand-driven.  In  practical  terms,  the  proposed 
scenario  is  similar  to  the  "demographic  scenario" 
except that unit costs are assumed to evolve in line 
with  the  evolution  of  GDP  per  hours  worked  
(which is usually higher than GDP per capita) (65). 

As  wages  are  projected  to  grow  in  line  with 
productivity  and  generally  faster  than  GDP  per 
capita,  this  scenario  provides  an  insight  into  the 
effects of unit costs in the  healthcare sector being 
driven  mostly  by  increases  in  wages  and  salaries. 
Note that this scenario still assumes that  wages in 
the health sector grow at the same rate as wages in 
the  whole  economy,  and  that  wages  in  the  whole 
economy  generally  follow  the  trend  of  economy-
wide  productivity.  Therefore,  expenditures  per 
head  are  assumed  to  grow  at  the  same  rate  as 
productivity in the whole economy. 

2.2.8.  Sector-specific composite indexation 

scenario 

Given the special character of the healthcare sector 
(high  level  of  government  regulation,  investment 
in  new  technologies,  high  labour  intensity),  it 
might  be  preferable  to  use  sector-specific  rather 
than  economy-wide  elements  as  determinants  of 
unit  costs  in  the  model.  While  a  significant  share 
of  public  expenditure  on  health  corresponds  to 
expenditure on staff (wages), one could go further 
and  consider  other  inputs  and  therefore  sectoral 
components  of  public  expenditure  on  healthcare. 
These  components  may  have  evolved  at  a  pace 
different  from  that  of  wages.  The  "sector-specific 
composite indexation scenario" tries to capture the 
importance  and  evolution  of  various  components 
to healthcare provision. This scenario looks at each 
of  these  different  components  separately  and 

(65)  The  2009  "labour  intensity  scenario"  used  GDP  per 

worker. 

111 

                                                           
                                                           
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2021 Ageing Report: Underlying assumptions and projection methodologies 

indexes  each  of  them  in  a  separate/different  way, 
creating  a  sort  of  country-specific  composite 
indexation for "unit cost development". 

To capture the importance and evolution of various 
components,  a  set  of  such  components  is  chosen 
and their respective share in public expenditure on 
healthcare  is  calculated.  It  is  considered  that 
expenditure on healthcare can be disaggregated in 
its  different  components,  broadly  reflecting  the 
different sectors of the health system, including: 1) 
inpatient  care;  2)  outpatient  care  and  ancillary 
therapeutic 
services;  3)  pharmaceuticals  and 
appliances;  4)  preventive  care;  5)  governance  and 
administration; and 6) capital investment (66).  For 
each of these components the share in total public 
expenditure  on  healthcare  is  calculated  and  then 
applied  to  the  age-specific  per  capita  expenditure. 
In  doing 
this,  each  age-specific  per  capita 
expenditure is divided (mechanically) into six sub-
items of expenditure. 

Next,  the  past  evolution  of  public  expenditure  on 
each of those inputs is considered. In other words, 
the  average  annual  growth  of  the  expenditure 
associated  with  each  of  those  components  for  the 
past  10  years  is  calculated.   Further,  the  ratio  of 
each  of  these  growth  rates  to  the  GDP  growth 
rate (67) is calculated. 

Then  each  sub-item  of  the  age-specific  per  capita 
expenditure by this growth ratio is multiplied. This 
allows  for  different  evolution  patterns  for  each 
component of expenditure so that in the future the 
share  of  each  of  these  components  is  allowed  to 
change,  something  which  was  not  captured  by 
previous  scenarios.  It  is  then  assumed  that  the 
ratio  multiplying  each  sub-item  of 
growth 
expenditure converges to 1 in a certain year in the 
future (i.e. grows at the same pace as productivity 
or GDP) (68).  

(66)  In the 2018 and current projection exercise, they are largely 
based  on  the  SHA  2011  classification  of  healthcare 
functions (see Annex 5, Table II.A5.2). 

(67)  In  previous  Ageing  Reports,  GDP  per  capita  was  used 

instead.  

(68)  Let  us  assume  that  per  capita  public  expenditure  on 
healthcare for 20-year old men is €2000 in year t. Assume 
too, that in line with total public expenditure on healthcare, 
40%  is  inpatient  care,  30%  outpatient  care  and  ancillary 
services, 5% capital investment, 17% pharmaceuticals and 
therapeutic  appliances,  3%  preventive  care,  and  5%  other 
inputs.  Therefore,  per  capita  public  expenditure  is divided 
into 6 sub-items: €800 in for inpatient care, €600 outpatient 

Due  to  high  volatility  in  the  relative  growth  rates 
for the sub-components on prevention, governance 
and  administration,  and  capital  formation,  these 
items  are  excluded  from  the  indexation.  The 
relative growth rates of the other three components 
(hospitals,  outpatient  care  and  medical  goods),  as 
applied in previous Ageing Reports, are capped at 
their respective 25th and 75th percentiles. 

2.2.9.  Non-demographic determinants 

scenario 

the  second  half  of 

the  20th  century, 
Since 
healthcare  expenditure  has  been  growing  faster 
than 
that 
income.  Econometric  studies  show 
demographic  factors  (e.g.  ageing)  have  a  positive 
but  relatively  minor  impact  on  spending  when 
compared  with  other  drivers,  such  as  income, 
technology, 
institutional 
relative  prices  and 
settings (69).  In the 2018 EPC-EC Ageing Report, 
for  healthcare 
the  non-demographic  scenario 
expenditure  was  projected  to  have  a  substantial 
impact,  relatively  to  the  AWG  reference  scenario, 
raising  public  health  expenditure  in  the  EU  (over 
the  2016-70  period)  by  1.4  pps  of  GDP  in  the 
EU27  compared  with  only  0.7  pps  in  the  AWG 
reference scenario (70).  By ignoring the effects due 
to non-ageing drivers, the AWG reference scenario 
implicitly assumes a substantial progressive down-
ward  tilt  of  past  trends  in  healthcare  spending, 
flattening out at the end of the period (71). 

therapeutic  appliances, 

care  and  ancillary  services,  €100  capital  investment,  €340 
in  pharmaceuticals  and 
€60 
preventive care and €100 in other inputs. Then in year t+1 
we have that expenditure increases as follows (numbers are 
just  illustrative):  €800x1.2  +  €600x1.1  +  €100x1.4  + 
€340x1.3 + €60x1.1 + €100x1, where 1.2, 1.1, 1.4, 1.3, 1.1 
and  1  are  the  (past  observed)  growth  ratios  of  each 
component.  As  to  the  pattern  of  convergence,  we  can  use 
past  observations  to  determine  the  convergence  pattern  of 
the growth ratios. 

(69)  Maisonneuve  C.  and  Martins  J.O.  (2013),  "A  projection 
method of public health and long-term care expenditures", 
OECD Economic Department WP No 1048. 

(70)  European  Commission  and  Economic  Policy  Committee 
(2015), "The 2015 Ageing Report Economic and budgetary 
projections  for  the  28  EU  Member  States  (2013-2060)", 
European Economy, No. 3/2015. 

(71)  The reason for the convergence of the elasticity is that only 
a  partial  continuation  of  past  trends  related  to  non-
demographic determinants in the future is expected. In the 
past,  extensions  of  insurance  to  universal  coverage  of  the 
population were an important trigger of increases in public 
health  expenditures.  As  universal  coverage  is  nearly 
reached in the EU, this one-time shock will not occur again 
in  the  future.  Note  that  by  "coverage"  is  not  only  meant 
coverage in terms of percentage of population covered, but 

112 

                                                           
                                                                                   
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

To address this critical aspect of past exercises and 
following analytical work carried out for the 2009 
Ageing  Report (72)  and  for  the  2012  Ageing 
Report (73),  this  scenario  reassesses  the  impact  of 
non-demographic  factors  (NDF)  (e.g.  technology, 
relative  prices)  on  healthcare  expenditure.  It  uses 
the  residual  approach  to  identify  the  impact  of 
NDF on healthcare spending. In practice, the effect 
of  demographic  changes  is  subtracted  from  the 
total  increase  in  expenditure  and  the  remaining 
part  (i.e.  the  residual)  is  attributed  to  changes  in 
NDF (74).  

This  scenario  uses  panel  regression  techniques  to 
estimate  country-specific  non-demographic  cost 
(NDC)  of  healthcare.    NDC  is  defined  as  the 
excess  of  growth  in  real  per-capita  healthcare 
expenditure  over  the  growth  in  real  per-capita 
GDP 
demographic 
composition effects. Alternatively, results can also 
terms  of  country-specific 
be  expressed 
"average" 
income  elasticities  of  healthcare 
expenditure.  

controlling 

after 

for 

in 

Panel  regressions  are  run  using  data  in  growth 
rates  (75)  and  assuming  country  fixed  effects. 
Multiple model specifications were tried using the 
datasets, namely estimates including and excluding 
country-fixed effects and a period dummy. 

On  the  implementation  of  the  NDD  scenario,  and 
based  on  the  technical  work  carried  out  by 
Commission,  the  AWG  decided  to  use  a  common 

also in terms of the "depth" of the coverage, i.e. the size of 
the  benefits  basket  and  the  coverage  rates  of  benefits. 
However,  data  availability  at  the  level  of  individual 
countries to correct for coverage effects is suboptimal. 
(72)  Dybczak  K.  and  Przywara  B.  (2010),  "The  role  of 
technology in healthcare expenditure in the EU", European 
Economy, Economic Papers No 400. 

(73)  Medeiros  J.  and  Schwierz  C.  (2013),  "Estimating  the 
drivers and projecting long-term public health expenditure 
in  the  European  Union:  Baumol's  'cost  disease'  revisited", 
European Economy, Economic Papers No 507. 

(74)  Ideally,  in  order  to  identify  the  impact  of  technology  on 
healthcare  expenditure,  besides  income  one  should  also 
control  for  other  non-demographic  factors,  such  as  the 
health  status,  relative  prices,  and  institutional  variables. 
Limitations  on  data  coverage  prevent  us  from  using  a 
broader set of regressors. However, in some specifications 
a proxy variable for relative prices of healthcare goods and 
services will also be used. 

(75)  This  avoids the  difficult  and  largely  unsettled  issue  in  the 
the  co-integration  of  healthcare 

literature 
regarding 
expenditure and income variables. 

elasticity  (h)  of  1.5  (76)  throughout  the  projection 
period,  which  will  be  reduced  to  1  in  2070, 
following a non-linear path. 

2.2.10. AWG reference scenario 

The  “AWG  reference  scenario”  is  used  as  the 
central  scenario  when  calculating  the  overall 
budgetary  impact  of  ageing.  It  is  the  point  of 
reference  for  comparisons  with  the  2018  Ageing 
Report.  In  this  scenario,  healthcare  expenditures 
are driven by the assumption that half of the future 
gains  in  life  expectancy  are  spent  in  good  health 
and an income elasticity of healthcare spending is 
converging  linearly  from  1.1  in  2019  to  unity  in 
2070. 

2.2.11. AWG risk scenario 

and 

changes 

The "AWG risk scenario", as the "AWG reference 
scenario",  keeps  the  assumption  that  half  of  the 
future  gains  in  life  expectancy  are  spent  in  good 
health.  That  said,  it  attempts  to  take  into  account 
technological 
institutional 
mechanisms  which  have  stimulated  expenditure 
growth  in  recent  decades,  following  the  same 
approach  as  described  in  the  "non-demographic 
determinants  scenario".  A  proxy  for  the  non-
demographic  costs  (NDC)  with  estimated  EU 
average  elasticity  of  1.5,  based  on  Commission 
research (77) and endorsed by the Ageing Working 
Group,  is  used  in  2019,  which  then  converges 
linearly to 1 until the end of the projection period 
(78). This elasticity is added to the effect of ageing 
as modelled in the “demographic scenario”. 

2.2.12. Other sensitivity scenarios 

Alternative  sensitivity  tests  are  applied  to  the 
“Demographic 
reference 
scenario"  and  “AWG  risk  scenario”  to  show  the 
effect  of  key  demographic  and  macroeconomic 

scenario”, 

"AWG 

(76)  Corresponding to the weighted median of country-specific 

estimates. 

(77)  Medeiros  J.  and  Schwierz  C.  (2013),  "Estimating  the 
drivers and projecting long-term public health expenditure 
in  the  European  Union:  Baumol's  'cost  disease'  revisited", 
European Economy, Economic Papers No 507. 

(78)  Ideally,  in  order  to  identify  the  impact  of  NDD  on 
healthcare  expenditure  one  should  also  control  for  other 
variables,  such  as  the  health  status,  relative  prices,  and 
institutional  variables.  However,  limitations  on  data  and 
methodological concerns prevent the use of a broader set of 
regressors. 

113 

                                                                                   
                                                           
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2021 Ageing Report: Underlying assumptions and projection methodologies 

long-term 

on 
projections. 

health 
assumptions 
expenditure 
alternative 
sensitivity tests are applied to all age-related items 
and are therefore described in Part I, Chapter 5. 

public 

These 

114 

3. 

LONG-TERM CARE 

3.1. 

INTRODUCTION 

This  chapter  presents  several  different  scenarios 
and  sensitivity 
the 
tests  designed 
potential  impact  of  each  of  the  determinants  of 
long-term  care  expenditure  on  future  public 
expenditure. These are broadly in line with those 
used for the 2018 Ageing Report.  

to  assess 

3.2.  OVERVIEW OF THE PROJECTION METHO-
DOLOGY  AND MODEL STRUCTURE 

3.2.1.  Methodology 

As  in  previous  projection  exercises  conducted 
jointly by the European Commission (EC) and the 
Ageing Working Group (AWG), the methodology 
to project long-term care  expenditure is based on a 
simple  macro-simulation  model.  This  model  is 
based on the assumption that the whole population 
is  divided  into  groups  that  are  assigned  certain 
(e.g.  age,  gender,  per  capita 
characteristics 
expenditure,  health  status,  need  for  care  and  type 
of care, etc.). When over time the (relative) size or 
features of these groups change, the long-term care 
expenditure  changes  in  line  with  the  change  in 
those  characteristics.  These  types  of  models  are 
often used in long-term expenditure projections, in 
particular  in  cases  where  precise  information  at 
micro  level  on  the  individuals  and  their  transition 
from  one  status  to  the  next  are  not  available  or 
unreliable. 

The choice for the methodology to be used and the 
various  scenarios  to  be  run  is  limited  by  the 
availability,  accessibility  and  quality  of  long-term 
care  data.  For  the  projection  exercise,  System  of 
Health Accounts (SHA) expenditure data are used 
where  available  –  complemented  with  some 
proxies  calculated  on  the  basis  of  categories  from 
Integrated  Social 
the  European  System  of 
Protection 
and 
supplemented  by  national  data  sources  when 
necessary (79). Data on the number of recipients is 
however only available from national sources.  

(ESSPROS) 

Statistics 

The  approach  aims  to  examine  as  many  of  the 
factors affecting future long-term care expenditure 
as is possible. At the same time, it is necessary to 
ensure the necessary data to run the projections is 
available for a large number of Member States. A 
schematic 
projected 
methodology  can  be  found  in  Graph  II.3.1  below. 
Specifically,  the  methodology  aims  at  analysing 
the  impact  of  changes  in  the  assumptions  made 
about: 

presentation 

the 

of 

(cid:129) 

(cid:129) 

(cid:129) 

(cid:129) 

the number of elderly people (through changes 
in the population projections used); 

the  number  of  dependent  elderly  people(80) 
of 
to 
(changes 
dependency); 

prevalence 

rates 

the 

the  balance  between  formal  and  informal  care 
provision (assuming a given shift in demand or 
exogenous  changes 
the  availability  of 
in 
informal carers); 

balance 

between 

and 
the 
institutional care within the formal care system; 
and 

home 

care 

(cid:129) 

the unit costs of care per recipient. 

The methodology allows projecting the future need 
for  long-term  services  in  terms  of  number  of 
people  who  are  assumed  to  need  long-term  care 
services.  This  is  achieved  by  using  dependency 
rates,  to  estimate  the  fraction  of  the  elderly 
population  that  is  dependent,  i.e.  with  a  severe 
disability requiring care. 

the 

Firstly,  a  projection  is  made  of  the  dependent 
population, on the basis of the baseline population 
projection  and  dependency  rates.  Secondly,  the 
dependent  elderly  population  is  split,  by  age  and 
gender,  following 
type  of  care  received 
(informal,  formal  in-kind  at  home,  formal  in-kind 
in institutions and cash benefits). Thirdly, average 
expenditure (i.e. age-sex profiles) is calculated for 
each  type  of  care,  and  then  multiplied  by  the 
projected  number  of  recipients  to  obtain  the 
projected  public  expenditure.  More  specifically, 
the necessary steps are: 

(79)  For  dependency  rates,  EU-SILC  data  are  used  (EU-SILC: 
The European Statistics on Income and Living Conditions; 
see  the  Eurostat  website  at:  http://epp.eurostat.ec.europa. 
eu/ortal/page/portal/microdata/eu_silc). 

(80)  Based on a proposal by Comas-Herrera et al. (2005). 

115 

                                                           
                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

to 

them 

Step  1:  taking  the  baseline  population  projection 
(by  age  and  gender),  a  projection  is  made  of  the 
dependent  population,  who  are  assumed  to  need 
some  form  of  long-term  care,  and  the  non-
dependent population who are assumed not to be in 
need of long-term care. This projection is made by 
taking age and gender-specific dependency rates at 
the value observed in the base year estimated using 
existing  indicators  of  disability  from  comparable 
sources)  and  applying 
the  baseline 
population  projection.  Dependency  rates  refer  to 
the  concept  of  dependency  understood  as 
difficulties  in  performing  at  least  one  activity  of 
daily  living  (ADL)  (Katz  et  al.,  1963)  (81)  or  at 
least  one  instrumental  activities  of  daily  living 
(IADL) (Lawton and Brody,  1969) (82).Long-term 
care is usually defined as a set of services required 
by  people  with  a  reduced  degree  of  functional 
capacity (whether physical or cognitive) and who, 
because  of  this,  are  dependent  for  an  extended 
period  of  time  on  help  with  these  activities.  EU-
SILC  data  are  used  to  obtain  a  proxy (83)  of 
’dependency‘ rates. For these dependency rates, an 
average  over  the  last  five  years  will  be  used, 
excluding data breaks. 

The  model  has  been  set  up  so  that  the  projected 
amount  of  dependent  people  (i.e.  people  with 
disability)  will  not  decrease  due  to  increasing  life 
expectancy,  by  ensuring 
the  amount  of 
dependent people in a five-year age class cannot be 
inferior to that in the preceding one. Note that the 
practical  implications  of  this  adjustment  may  be 
rather small. 

that 

Step 2: the projected dependent elderly population 
is  split,  by  age  and  gender,  into  four  groups 
depending on the type of care they receive, namely 
(1)  informal  care,  which  is  assumed  to  have  no 
impact  on  public  spending,  (2)  formal  care  at 

(81)  Activities  of  daily  living  (ADL)  are  the  things  people 
normally do in daily living including any daily activity they 
perform  for  self-care  (such  as  feeding,  bathing,  dressing, 
grooming), work, homemaking and leisure (see: Webster's 
New  World  Medical  Dictionary,  Wiley  Publishing, 2008). 
If  a  person  has  difficulty  in  performing  at  least  one  of 
them, he is considered as ADL-dependent. 

(82)  IADL  are  shopping,  laundry,  vacuuming,  cooking  and 
performing  housework,  managing  finances,  using  the 
telephone, etc. 

(83)  Please note that this is a conservative proxy as EU-SILC is 
used to identify ‘severe’ disability and the AWG definition 
of long-term care is wider, encompassing people who have 
one  IADL  and  whose  level  of  disability  is  therefore 
relatively mild.  

116 

home, (3) formal in-kind care in institutions (both 
of  which impact on public  spending but their  unit 
costs may differ) and (4) cash benefits. The model 
implicitly  assumes  that  all  those  receiving  home 
care or institutional care have difficulties with one 
or  more  ADLs  or  IADLs,  and  that  all  persons 
deemed  -dependent  either  receive  informal  care, 
home care, institutional care and cash benefits. The 
split  by  type  of  care  received  is  performed  by 
calculating  the  ‘probability  of  receiving  different 
types of long-term care by age and gender’. This is 
calculated  for  a  base  year  using  data  on  the 
numbers  of  people  with  dependency  (projected  in 
step  1),  and  the  numbers  of  people  receiving 
formal in-kind care at home and in institutions  and 
cash  benefits  (provided  by  Member  States).  It  is 
assumed  that  the  difference  between  the  total 
number of dependent people and the total number 
of  people  receiving  formal  care  (at  home  or  in 
institutions)  is  the  number  of  people  who  rely 
exclusively on informal care.  

(i.e. 

‘age-sex 

services: 

Step  3: 
involves  calculating  average  public 
spending  for  the  three  types  of  formal  long-term 
of 
care 
expenditure’)  for  a  base  year  using  data  on  total 
public expenditure in home care, institutional care 
and  cash  benefits  and  the  numbers  of  people 
receiving  in-kind  care  at  home,  in  long-term  care 
institutions  and  receiving  cash  benefits  (provided 
by Member States). Two assumptions are required: 

profiles 

(cid:129)  current  expenditure  in  services  divided  by  the 
number of users equals the long-run  unit costs 
of services; 

(cid:129)  average expenditure per user increases with the 
age  of  the  user  (84),  in  contrast  to  the  average 
expenditure per head of population.  

Step  4:  involves  calculating  public  spending  for 
the three care services, by multiplying the number 
of people receiving each type of care (at home, in 
institutions  and  receiving  cash  benefits)  by  the 
average 
expenditure 
(respectively  at  home,  in  institutions  and  for  cash 

age-specific 

public 

(84)  In  practice,  average  expenditure  (aged  15  and  above),  for 
each  type  of  service,  is  disaggregated  into  average 
expenditure  by  age  groups,  by  assuming  the  same  rate  of 
increase 
the  age-related 
expenditure profile. This is done for each care setting. The 
model  uses  average  public  expenditure  in  formal  care  to 
project future expenditure in both types of services. 

in  spending  by  age  as 

in 

                                                           
                                                           
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

Graph II.3.1:  Schematic presentation of the projection methodology / in-kind long-term care benefits 

(1) As in 2018, the projections need to be viewed in the context of the overall projection exercise. Consequently, the 
common elements of all scenarios will be the population projections provided by Eurostat and the baseline assumptions on 
labour force and macroeconomic variables agreed by the Commission and the AWG-EPC. The age and gender-specific per 
capita public expenditure (on long-term care) profiles are provided by Member States. They are applied to the demographic 
projections provided by Eurostat to calculate nominal spending on long-term care.  
(2) This schematic representation shows the methodology for projecting long-term care benefits. Total public expenditure on 
long-term care is the sum of public expenditure on long-term care in-kind benefits plus public expenditure on long-term care 
in cash benefits.  
Source: European Commission. 

benefits)  per  year  and  per  user.  By  adding  up  the 
total  public 
expenditure  on 
expenditure on long-term care services is obtained.  

setting 

each 

3.2.2.  Estimating dependency 

Overall,  given  the  availability  of  a  numerical 
measure for disability, the projection methodology 
described above is more precise than that used for 
healthcare  expenditure  where  there  is  no  direct 
indicator  of  health  status  and  the  age-related 
expenditure  profile  is  used  as  a  proxy.  However, 
an important caveat is that while dependency rates 
are  an  indicator  of  the  need  for  care,  those  needs 
may  not  necessarily  translate  into  real  public 
expenditure, for at least two reasons. 

of 

are 

care 
involves 

First,  the  links  between  disability  levels  and 
not 
long-term 
demand/use 
straightforward.  Each 
some 
step 
uncertainty.  Many  people  with  some  form  of 
disability  can  lead  completely  independent  lives 
without  the  need  for  care  services.  Furthermore, 
dependency also depends on a person’s perception 
of their ability to perform activities associated with 
daily  living.  On  the  one  hand,  survey  data  can 
underestimate  some  forms  of  disability.  People 
may  not 
report  certain  socially  stigmatised 
conditions,  such  as  alcohol  and  drug  related 

conditions, schizophrenia and mental degeneration. 
On  the  other  hand,  disability  data  can  be  too 
inclusive  and  measure  minor  difficulties 
in 
functioning  that  do  not  require    community  care. 
To  attempt  to  minimise  these  potential  issues,  the 
focus  is  on  those  dependency  levels  reported  as 
‘severe’ (85) according to EU-SILC. 

Second,  most  long-term  care  is  still  provided  by 
informal  carers.  Expenditure  profiles 
unpaid 
contain information about the propensity to receive 
paid  formal  care,  which  depends  on  a  number  of 
factors  other  than  dependency  that  affect  demand 
for  paid  care  such  as  household  type,  availability 
of  informal  carers,  income  or  housing  situation. 
Most of these factors are also correlated with age. 

3.2.3.  Country-specific legislation on 

indexation of long-term care benefits 

Where  countries  can  demonstrate  that  they  apply 
price  indexation  for  cash  benefits,  this  is  allowed 

(85)  As  these  people  are  most  in  need  of  income  support  and 
services, such as long-term care. This minimises the chance 
to  mistakenly  capture  people  who  are  not  dependent, 
although  some  people  with  lower  levels  of  dependency 
may  be  missed.  Therefore,  this  measure  of  dependency 
may underestimate the dependency rates for those EU MS 
with  comprehensive  long-term  care  systems  that  cover 
relatively light levels of dependency. 

117 

                                                           
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Report  standard  assumptions  and  the  remaining 
one third in line with prices. For cash benefits, two 
thirds  of  expenditure  are  indexed  in  line  with 
prices  and  the  remaining  one  third  in  line  with 
standard Ageing Report assumptions. This applies 
for the entire projection period. 

For  France,  price  indexation  would  be  applied  to 
cash  benefit  expenditure,  with  the  rest  being 
indexed  according  to  standard  assumptions.  This 
applies for the entire projection period. 

For  Slovenia,  price  indexation  is  applied  to  cash 
benefit  expenditure,  with  the  rest  being  indexed 
according  to  standard  assumptions.  This  applies 
for the first 10 years of the projection. 

Any  further  exceptions  will  be  set  out  in  the 
Ageing Report.  

for 10 years from the base year of the projections. 
There  are  however  two  exceptions  where  the 
impact  of  legislation  is  modelled  for  the  whole 
projection period.  

For Germany, this relates to the impact of German 
long-term  care 
the  ceiling  of 
legislation  on 
standard 
the 
to 
expenditure.  According 
assumptions  (explained  below),  unit  costs  are 
indexed  to  GDP  per  hours  worked  or  GDP  per 
capita.  Under  current  German  rules,  both  in-kind 
and  cash,  long-term  care  benefits  are  indexed  to 
indexed  by 
prices.  With  contribution 
inflation, long-term care expenditure shares would 
be  almost  unchanged  until  2070.  The  difference 
between  the  amounts  financed  by  the  German 
government  and  the  costs  of  long-term  care  are 
either  recovered  by  private  insurance  or  are  paid 
by  the  beneficiaries  themselves.  The  German 
government is required by law to check every three 
years  the  need  and  extent  of  adjusting  long-term 
care benefits based on inflation. 

rates 

For  France,  this  relates  to  the  fact  that  the  most 
cash  benefits  are  required  by  lawto  be  indexed 
according to prices. 

For  Slovenia,  this  relates  to  the  fact  that  all  cash 
benefits  are  required  by  law  to  be  indexed 
according to prices.  

Although this legislation binds these states to these 
indexations  methodology,  there  are  limits  to  the 
extent to which it can be taken into account in the 
projection.  In  an  extreme  case, 
indexing  all 
benefits to prices for the duration of the projection 
period could lead to a noticeable reduction in long-
term care expenditure as a share of GDP and in per 
capita 
standard 
assumptions. This would in fact represent a  policy 
change  scenario  and  breach  the  no-policy  change 
scenario requirement. 

compared 

terms 

the 

to 

To  account  for  this  legislation  and  the  financial 
precaution  principle  while  preserving  the  realism 
of  the  projections,  the  following  assumptions  are 
set  to  be  used  for  the  ‘AWG  reference  scenario’ 
projections as outlined in the 2018 Ageing Report, 
if 
this 
legislation is still in place: 

the  specific  countries  confirm 

that 

For  Germany, 
in-kind  benefit 
expenditure  are  indexed  in  line  with  the  Ageing 

thirds  of 

two 

118 

Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

Table II.3.1:  Overview of the different scenarios to project long-term care expenditure 

Demographic 
scenario

Base case 
scenario

High life 
expectancy 
scenario

Healthy ageing  
scenario

Shift to formal 
care scenario

I

II

III

IV

V

Population 
projection

Eurostat 
population 
projections

Eurostat 
population 
projections

Alternative higher 
life expectancy 
scenario

Eurostat 
population 
projections

Eurostat 
population 
projections

Coverage 
convergence 
scenario

VI

Eurostat 
population 
projections

Cost convergence 
scenario

VII

Eurostat 
population 
projections

Cost and 
coverage 
convergence 
scenario
VIII

Eurostat 
population 
projections

AWG reference 
scenario

AWG risk 
scenario

IX

Eurostat 
population 
projections

X

Eurostat 
population 
projections

2016-2019 
average disability 
rates held constant 
over projection 
period

2016-2019 
average disability 
rates held constant 
over projection 
period

2016-2019 
average disability 
rates held constant 
over projection 
period

2016-2019 
average disability 
rates held constant 
over projection 
period

2016-2019 
average disability 
rates held constant 
over projection 
period

2016-2019 
average disability 
rates held constant 
over projection 
period

2016-2019 
average disability 
rates held constant 
over projection 
period

2016-2019 
average disability 
rates held constant 
over projection 
period

Half of projected 
gains in life 
expectancy are 
spent without 
disability.

Half of projected 
gains in life 
expectancy are 
spent without 
disability.

Dependency 
status

Age-related 
expenditure 
profiles 

2019 cost profiles 2019 cost profiles 2019 cost profiles

2019 profiles shift 
in line with 
changes in age-
specific life 
expectancy

Policy setting / 
Care mix

Probability of 
receiving each 
type of care held 
constant at 2019 
level

Probability of 
receiving each 
type of care held 
constant at 2019 
level

Probability of 
receiving each 
type of care held 
constant at 2019 
level

Probability of 
receiving each 
type of care held 
constant at 2019 
level

2019 cost profiles 2019 cost profiles

Gradual increase  
(1% per year 
during 10 years) of 
the share of the 
disabled 
population 
receiving formal 
care (at home or in 
an institution).

Probability of 
receiving any type 
of formal in-kind 
care converging 
until 2070 
upwards to the 
EU27 average.

Cost profiles per 
Member State 
converge upwards 
to the EU27 
average by 2070

Cost profiles per 
Member State 
converge upwards 
to the EU27 
average by 2070

2019 cost profiles

Probability of 
receiving each 
type of care held 
constant at 2019 
level

Probability of 
receiving any type 
of formal in-kind 
care converging 
until 2070 
upwards to the 
EU27 average.

Probability of 
receiving each 
type of care held 
constant at 2019 
level

Cost profiles per 
Member State 
converge upwards 
to the EU27 
average by 2070

Probability of 
receiving any type 
of formal in-kind 
care converging 
until 2070 
upwards to the 
EU27 average.

Unit cost 
development

GDP per capita

In-kind : GDP per 
hours worked;
 cash benefits : 
GDP per capita

In-kind : GDP per 
hours worked;
 cash benefits : 
GDP per capita

In-kind : GDP per 
hours worked;
 cash benefits : 
GDP per capita

In-kind : GDP per 
hours worked;
 cash benefits : 
GDP per capita

In-kind : GDP per 
hours worked;
 cash benefits : 
GDP per capita

In-kind : GDP per 
hours worked;
 cash benefits : 
GDP per capita

In-kind : GDP per 
hours worked;
 cash benefits : 
GDP per capita

In-kind : GDP per 
hours worked;
 cash benefits : 
GDP per capita

In-kind : GDP per 
hours worked;
 cash benefits : 
GDP per capita

Elasticity of 
demand

1

1

1

1

1

1

1

1

1 for MS in 
highest LTc 
expenditure 
quartile in 2019, 
for the rest 1.1 in 
2019 converging 
to 1 by 2070

1

* Alternative indexation rules for unit costs in the ‘Reference scenario’ in order to reflect the specific institutional arrangements 
of specific countries are discussed in Section 3.22 
Source: European Commission. 

3.3.  DIFFERENT SCENARIOS PROJECTING 

PUBLIC LONG-TERM CARE EXPENDITURE

the  potential 

Several scenarios and sensitivity tests are made to 
the 
assess 
determinants  of  long-term  care  expenditure  on 
future public expenditure on long-term care. 

impact  of  each  of 

The  examination  of  different  scenarios  helps 
identify  how  sensitive  the  projections  are  to 
changes  in  key  assumptions  such  as  the  evolution 
of dependency rates, unit costs and policy settings. 
Building  on  the  2018  Ageing  Report,  the  present 
exercise  maintains  most  of  the  existing  scenarios 
and  sensitivity  tests  while  adding  a  new  specific 
sensitivity scenario to account for the impact of the 
COVID  pandemic.  The  overview  of  the  scenarios 
is presented in Table II.3.1 above (86). The analysis 
identifies 
impact  of  each  quantifiable 
determinant  separately,  based  on  hypothetical 
assumptions or a ‘what if’ situation. Therefore, the 

the 

(86)  See  Annex  10,  Mathematical  illustration  of  the  long-term 

care scenarios. 

results of the projections should not be interpreted 
forecasts  of  expenditure  since  particular 
as 
policy/institutional  settings  in  Member  States  or 
future  policy  reforms  beyond 
those  already 
legislated  and  reported  by  Member  States  are  not 
taken into account. 

3.3.1.  Demographic Scenario 

is  also  kept  constant  over 

The  ‘demographic  scenario’  assumes  that  the 
shares  of  the  older  disabled  population  who 
receive  either  informal  care,  formal  care  at  home 
or  institutional  care  are  kept  constant  over  the 
projection  period.  Those  constant  shares  are  then 
applied  to  the  projected  changes  in  the  dependent 
the  prevalence  of  ADL-
population.  Since 
dependency 
the 
projection  horizon, 
the  dependent  population 
evolves  precisely  in  line  with  the  total  elderly 
population.  This  implies  that  none  of  the  gains  in 
life  expectancy  translate  into  an  improvement  in 
health.  Arguably,  it  is  a  pessimistic  scenario  with 
respect to dependency status, since it assumes that 
average  lifetime  consumption  of  long-term  care 
services  will  increase  over  time  in  line  with 
population  ageing.  It  is  a  ‘no  policy  change 

119 

 
 
                                                           
 
  
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

scenario’  as  the  probability  of  receiving  care 
(either at  home or in an institution)  is assumed to 
remain constant at the 2019 level. The scenario is 
similar  to  the  analogous  scenario  for  healthcare 
expenditure, and costs are also assumed to evolve 
in line with GDP per capita growth (for all types of 
long-term care expenditure). 

3.3.2.  Base case scenario  

in 

the  above-mentioned  elements 

While 
the 
‘demographic scenario’ is similar to the analogous 
scenario  for  healthcare  expenditure,  the  actual 
‘base case scenario’ is not exactly equivalent to the 
former, as in this case, the long-term care unit cost 
is  linked  to  GDP  per  worker,  rather  than  to  GDP 
per capita. Indeed, there exists a current imbalance 
of  care  mix,  with  a  relative  deficit  of  formal  care 
provision.  Further,  this  sector  is  highly  labour-
intensive and productivity gains can be expected to 
be  particularly  slow  in  this  sector.  Therefore, 
public  expenditure  on  long-term  care  is  expected 
to be rather more supply than demand-driven. For 
that  reason,  GDP  per  worker  (which  is  also 
assumed  to  reflect  wage  evolution  in  all  sectors, 
including in the care  sector), rather than  GDP per 
capita had been chosen as the main (but not only) 
driver of unit costs. In this sense, it is more similar 
to  the  ‘labour  intensity  scenario’  run  for  the 
healthcare expenditure projections.  

Similar  to  the  2018  exercise,  the  projections  will 
link unit cost to GDP per hours worked for in-kind 
benefits (services), while unit cost of cash benefits 
will evolve in line with GDP per capita growth (as 
cash benefits are more related to a form of income 
support).  

3.3.3.  High life expectancy scenario 

The  ‘high  life  expectancy  scenario’  presents  the 
budgetary  effects  of  an  alternative  demographic 
scenario that assumes life expectancy to be higher 
for  all  ages  than  in  the  baseline  scenario.  This 
scenario  is  methodologically  identical  to  the  base 
case  scenario,  but  alternative  demography  and 
GDP data are used (in the same way that it is used 
to  assess  the  sensitivity  of  pension  and  health 
expenditure 
life  expectancy).  The 
rationale  is  twofold.  First,  the  marked  increase  in 
public  expenditure  for  older  people  (i.e.  80+).  In 
fact,  the  overall  age  profile  for  long-term  care 
expenditure  has  a  different  shape  than  that  for 

to  higher 

120 

health expenditure, partly because the costs related 
to long-term care are very high for institutionalised 
individuals,  and  the  share  of  institutionalised 
individuals  increases  sharply  among  persons  aged 
over 80. Second, the higher age groups are also the 
part of the demographic projections that are likely 
to  be  the  most  uncertain.  The  model  ensures  that 
the  value  in  a  five-year  age  class  cannot  be  less 
than that in the preceding one. 

3.3.4.  Healthy ageing scenario 

inspired  by 

This  scenario  reflects  an  alternative  assumption 
about  trends  in  age-specific  dependency  rates. 
Being 
‘relative  compression  of 
morbidity’,  it  is  analogous  to  the  ‘constant  health 
scenario’  performed  in  healthcare  expenditure 
projections in that the number of years spent in bad 
health remains constant over the projection period. 
The  age-sex  specific  dependency  rates  are  shifted 
in line with changes in life expectancy (e.g. if life 
expectancy  for  a  50-year  old  has  increased  by  2 
years  in  year  2030,  then  the  dependency  rate  of  a 
50-year    person  in  2030  is  that  of  a  48-year  old 
today). This results in a gradual decrease over time 
in the prevalence of disability for each age cohort, 
as the increase in life expectancy adds new cohorts 
and the total number of years in bad health remains 
the same. Lower dependency rates over the whole 
population translate in lower proportional demand 
for  and  therefore  lower  expenditure  on  long-term 
care services. As in the ‘base case scenario’, public 
expenditure  on  long-term  care  in-kind  services  is 
assumed  to  evolve  in  line  with  GDP  per  hours 
worked,  while  expenditure  on  cash  benefits 
evolves in line with GDP per capita. 

3.3.5.  Shift to formal care scenario 

Ultimately, the public funding of long-term care – 
and the policy orientation – will determine whether 
future  needs  for  long-term  care  translate  into 
(direct)  public  expenditure  or  not,  as  neither 
informal care provision nor private expenditure on 
long-term  care  are  formally  part  of  public 
expenditure on long-term care. 

Indeed, pressure for increased public provision and 
financing  of  in-kind  long-term  care  services  may 
the  coming  decades, 
grow  substantially 
particularly  in  Member  States  where  the  bulk  of 
long-term  care  is  currently  provided  informally 

in 

 
 
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

(87).  To  illustrate  the  impact  of  possible  future 
policy changes, such as Member States deciding to 
provide  more  formal  in-kind  care  services  to  the 
elderly, additional scenarios have been prepared.  

test  examines 

this  sensitivity 

This  policy-change  scenario  is  run  to  assess  the 
impact  of  a  given  –  demand-driven  –  increase  in 
the  (public)  provision  of  formal  in-kind  care 
replacing  care  provided  in  an  informal  setting.  In 
particular, 
the 
budgetary  impact  of  a  progressive  shift  into  the 
formal  in-kind  sector  of  care  of  1%  per  year  of 
disabled  elderly  who  have  so  far  received  only 
informal  care.  This  extra  shift  compared  to  the 
‘base case scenario’ takes place during the first ten 
years  of  the  projection  period  only.  Therefore,  it 
adds up to about 10% shift from informal to formal 
in-kind care.  

The  shift  from  informal  to  formal  in-kind  care  is 
considered in line with the current shares of home 
care  and  institutional  care  in  total  formal  care.  In 
other  words,  if  currently  10%  of  the  dependents 
receiving  in-kind  care  receive  care  at  home,  the 
shift/increase  will  also  go  for  10%  to  home  care 
(and 90% to institutional care). 

3.3.6.  Coverage convergence scenario   

This  scenario,  similar  to  the  one  in  the  2018 
Ageing Report, assumes that the real convergence 
across  Member  States,  the  exchange  of  best 
the 
practices  and  growing  expectations  of 
populations  will  drive  an  expansion  of  publicly 
financed  formal  in-kind  care  provision  into  the 
groups of population that have not been covered by 
the  public  programmes  so  far.  Similarly  to  the 
scenarios  assessing  the  effect  of  a  shift  from 
informal  to  formal  care,  this  scenario  should  also 
be  considered  as  a  policy-change  scenario,  as  it 
assumes  a  considerable  shift  in  the  current  long-
term  care  provision  policy,  while  aiming  to  take 
into account the high diversity of country-specific 
current care mix. 

The  Member  States  where  the  formal  in-kind 
coverage  rate  is  below  the  EU27  average  in  the 

(87)  Another  reason  being  the  difficulties  of  the  private 
insurance  market  for  long-term  care  to  develop  in  most 
Member  States  (see  OECD  (2011),  “Help  Wanted?” 
www.oecd.org/els/health-systems/47884985.pdf ). 

starting year would be assumed to converge to the 
average by 2070. 

Convergence  would  be  calculated  for  each  age 
group  and  relative  proportions  of  each  type  of 
formal  in-kind  care  are  kept  constant.  As  in  the 
‘base  case  scenario’,  public  expenditure  on  long-
term care in-kind services is assumed to develop in 
line  with  GDP  per  hours  worked,  while 
expenditure  on  cash  benefits  evolves  in  line  with 
GDP  per  capita.  More  specifically,  the  Member 
States  where  the  formal  in-kind  coverage  rate  is 
below  the  EU27  average  in  the  starting  year  are 
assumed  to  converge  to  this  average  by  2070  for 
that  age  group.  By  contrast,  for  countries  with 
coverage  above  the  EU  average  in  the  base  year 
this scenario is equivalent to the base case scenario 
for that age group. 

3.3.7.  Cost convergence scenario 

This  scenario  is  proposed  in  parallel  with  the 
scenario  on  healthcare  expenditure  projections, 
similar  to  the  2018  Ageing  Report.  For  those 
Member  States  with  high  levels  of  informal  care, 
and  therefore  relatively  low  costs  for  long-term 
care,  an  increase  in  public  expectations  for  more 
formal  care  (and  therefore  an  increase  in  the 
average cost of long-term care) might be expected. 
For  example,  an  increase  in  the  costs  of  care  (as 
per  cent  of  GDP  per  capita)  towards  the  average 
for  EU  Member  States  could  be  expected.  The 
‘cost convergence scenario’ is meant to capture the 
possible  effect  of  a  convergence  in  real  living 
standards 
the 
macroeconomic  assumptions)  on  long-term  care 
spending.  It  assumes  an  upward  convergence  of 
the  age-sex  specific  per  beneficiary  expenditure 
profiles  (as  per  cent  of  GDP  per  capita)  of  all 
countries  below  the  corresponding  EU27  average 
to the EU27 average, for each type of formal care 
coverage  (i.e.  formal  care  in  institutions,  formal 
care  at  home  and  cash  benefits).  Note  that  the 
convergence  is  calculated  for  each  age  group 
separately,  based  on  the  coverage  gap  for  all 
services  in  kind.  Again,  for  countries  with  unit 
costs  above  the  EU  average  in  the  base  year,  this 
scenario is equivalent to the base case scenario. 

emerges 

(which 

from 

121 

                                                           
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

3.3.8.  Cost and coverage convergence 

scenario   

in-kind  services  and  to  GDP per  capita  in  case  of 
cash benefits (88). 

As  described  in  the  sections  above,  this  scenario 
combines  the  coverage  convergence  scenario  and 
the cost convergence scenario.  

leading 

It assumes a shift in the current in-kind long-term 
to  an  upward 
care  provision  policy 
coverage  convergence  to  the  EU27  average  by 
2070. More specifically, the Member States where 
the formal in-kind coverage rate is below the EU27 
average  in  the  starting  year  are  assumed  to 
converge to this average by 2070. In addition, this 
scenario  assumes  an  upward  convergence  of  the 
expenditure  profiles  (as  per  cent  of  GDP  per 
capita)  of  all  countries  below  the  corresponding 
EU27 average to the EU27 average.  

This  scenario 
is  a  balanced  and  plausible 
distribution  of  risks  stemming  from  future  needs 
that  lead  to  a  convergence  in  both  costs  and 
coverage. From the perspective of country-specific 
needs in these convergence processes, it is evident 
that countries are very differently affected by these 
convergence  processes.  For 
countries  with 
coverage  and  unit  costs  above  the  EU  average  in 
the  base  year  in  all  age  groups,  this  scenario  is 
equivalent to the base case scenario. 

3.3.9.  AWG reference scenario 

The  ‘AWG  reference  scenario’  is  the  ‘central 
scenario’ used by the AWG to calculate the overall 
budgetary impact of ageing. It shows the combined 
effect of a set of interrelated determinants of public 
expenditure  on 
long-term  care,  while  other 
scenarios measure the separate effect of individual 
determinants  and  therefore  provide  only  a  partial 
analysis. It is meant to provide a plausible path for 
the underlying variables, while acknowledging that 
the projection outcome is subject to uncertainty.  

The  AWG  reference  scenario  combines 
the 
assumptions  of  the  ‘base  case  scenario’  and  the 
‘constant  disability  scenario’.  It  assumes  that  half 
of  the  projected  longevity  gains  up  to  the  end  of 
the  projection  period  will  be  spent  in  good  health 
and free of disability/dependency. So, accordingly, 
age-specific  disability  rates  shift  along  the  age 
profile  by  half  of  the  projected  increase  in  life 
expectancy. Furthermore, the  unit costs are linked 
to GDP per hour worked in case of long-term care 

122 

As  countries  become  richer,  they  are  likely  to 
spend  a  larger  proportion  of  their  GDP  on  long-
term  care.  This  is  modelled  in  the  reference  and 
risk  scenarios  by  including  the  assumption  that 
income  elasticity  starts  at  1.1  in  the  base  year  of 
2019,  falling  to  1  by  the  end  of  the  projection 
period. Since the GDP projections include a degree 
of  catching-up, 
to  a  degree  of 
convergence  in  long-term  care  expenditure,  albeit 
more  moderate  than  in  the  cost  and  coverage 
convergence scenario. 

leads 

this 

Taking into account this increase in long-term care 
expenditure  may  not  affect  countries  that  already 
have  highly  developed  long-term  care  systems, 
those EU Member States in the highest quartile of 
long-term care expenditure as a proportion of GDP 
in  the  base  year  are  excluded  from  this  and 
therefore their income elasticity will be assumed to 
remain 1. 

3.3.10.  AWG risk scenario 

increases 

There  is  considerable  uncertainty  as  to  future 
developments of age-related public expenditure, in 
particular  related  to  the  challenge  to  cope  with 
trend 
in 
particular,  on  healthcare  and 
long-term  care 
expenditure.  For  this  reason  and  to  contribute  to 
the wider policy debate on fiscal challenges the EU 
will be facing in the future, an AWG risk scenario 
will be prepared for the Ageing Report.  

in  public  spending  and 

reference 

In  addition, 

The  ‘AWG  risk  scenario’  retains  the  assumption 
that  half of the  future gains in life expectancy are 
spent  without  care-demanding  disability,  as  in  the 
‘AWG 
it 
scenario’. 
combines this scenario with the ‘cost and coverage 
convergence  scenario’  by  assuming  convergence 
upwards of unit costs to the EU-average as well as 
coverage convergence upwards to the EU-average. 
In  comparison  to  the  ‘AWG  reference  scenario’, 
this scenario thus captures the impact of additional 
cost  drivers  to  demography  and  health  status.  In 
comparison 
the  ‘AWG  risk  scenario’  for 
healthcare,  this  scenario  models  the  impact  that 
increased  GDP  has  on  expenditure  in  a  different 
more  specific  way,  by  first  modelling  the  impact 

to 

(88)  With the specific exceptions set out in Section 3.2.2. 

                                                           
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

on coverage and unit costs and then deriving from 
this the increase in expenditure. 

3.3.11. Other sensitivity scenarios 

A  number  of  sensitivity  scenarios  modify  the 
‘AWG  reference  scenario’  by  making  alternative 
assumptions on factors such as migration, fertility, 
employment  rate,  Total  Factor  Productivity  and 
life expectancy (the full list and description of the 
assumptions  can  be  found  in  Part  I,  Chapter  3  of 
this report).  

123 

 
4. 

EDUCATION 

4.1. 

INTRODUCTION 

on 

general 

government 

The  projection  exercise  aims  at  quantifying  the 
impact  of  demographic  changes  over  the  long 
term 
education 
expenditure.  Therefore,  baseline  projections  are 
carried  out  under  the  assumption  of  ‘no  policy 
change’ (89).  In  addition,  projections  under  a 
scenario of higher enrolment rates are also carried 
out. 

Table II.4.1: 

Education expenditure, % of GDP 

2003

2007

2012

2017

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO
EA
EU27

5.9
4.2
5.2
6.6
4.1
6.6
4.1
4.3
4.0
5.8
5.3
4.6
6.2
5.3
5.7
5.0
6.4
5.9
5.2
5.3
6.1
6.7
3.5
6.3
4.2
6.3
6.8
6.1
4.8
5.4

5.5
3.6
4.7
5.9
3.9
5.9
4.3
3.6
4.0
5.3
4.8
4.5
5.9
5.6
5.3
4.5
5.5
5.2
5.0
4.7
5.7
6.2
3.9
5.9
3.5
5.8
6.3
4.9
4.6
5.0

6.2
3.3
5.0
7.0
4.2
6.3
4.9
4.5
4.2
5.5
4.9
4.1
6.3
5.7
5.8
5.8
4.7
5.8
5.4
5.0
5.4
5.8
3.0
6.4
4.1
6.4
6.5
4.9
4.8
5.3

6.3
3.6
4.6
6.5
4.1
5.8
3.3
3.9
4.0
5.4
4.7
3.8
5.7
5.8
4.9
4.7
5.1
4.9
5.1
4.8
4.9
5.0
2.8
5.4
3.8
5.7
6.7
5.6
4.5
4.9

avg
2003-2017
6.0
3.8
4.9
6.7
4.1
6.2
4.2
4.1
4.1
5.5
5.0
4.2
6.2
5.8
5.7
5.0
5.4
5.5
5.3
4.9
5.5
6.2
3.5
6.2
3.9
6.2
6.5
5.3
4.7
5.2

Source: Eurostat, Classification of the functions of 
government (COFOG) data. 

is 

undetermined, 

A priori, the impact of ageing on public education 
expenditure 
somewhat 
contrasting  with  the  expected  increasing  effect  of 
ageing  on  other  major  expenditure  items,  such  as 
on  pensions  and  health.  In  fact,  on  the  one  hand, 
the  expected  decline  in  the  number  of  young 
people is likely to allow  for some savings, but on 
the  other,  the  trends  of  higher  enrolment  rates, 
longer periods spent in education, and persistently 

education 

expenditure, 

(89)  Many  other  factors  have  also  an  important  bearing  on 
government 
the 
involvement  of  the  general  government  in  the  education 
system,  the  duration  of  mandatory  education,  progress  in 
enrolment rates in upper secondary and tertiary education, 
relative  wages  in  the  education  sector,  the  average  size  of 
classes, discretionary saving measures to curb expenditure 
trends, etc. 

such 

as 

rising costs of tertiary education might put upward 
pressure  on 
total  education  expenditure.  The 
methodology  used  is  highly  stylised  and,  as  such, 
it cannot fully reflect the complexities of Member 
States education systems. It has been set out to use 
harmonised datasets, secure equal treatment across 
countries,  and  be  consistent  with  wide  labour 
market developments, particularly on participation 
rates. 

Table 

On  average  in  2003-2017,  education  expenditure 
represented 5.2% of GDP in the EU (around 11.7% 
total  general  government  expenditure) (90). 
of 
Expenditure 
ratios  vary  considerably  across 
Member States  from a  minimum of 3.5% of GDP 
in  Romania  to  a  maximum  of  6.7%  in  Denmark 
education 
(see 
II.4.1). 
expenditure  requires  a  number  of 
important 
methodological issues to be considered, namely (i) 
the definition (or perimeter) of education activities; 
(ii) taking into account that studying can take place 
on  a  part-time  basis  after  compulsory  education; 
and (iii) considering that there are various outlays 
for public spending on education (91). 

Projecting 

4.2. 

SHORT OVERVIEW OF THE 
METHODOLOGY USED TO PROJECT 
EXPENDITURE ON EDUCATION  

The  methodology  uses  a  ‘quasi-demographic’ 
approach,  meaning  that  not  only  demographic 
projections  are  used  but  also  participation  rate 
projections.  A  strong  point  of  the  methodology  is 
the  use  of  the  UOE (92)  data  collection,  which 
covers  enrolment  rates,  staff  levels,  the  labour 
force  status  of  students  (i.e.  part-time  versus  full- 

(90)  Classification  of  the  functions  of  government  (COFOG) 
data.  In  the  same  period,  2003-2017,  health  expenditure 
represented  6.1%  of  GDP  (and  13.5%  of  total  general 
government 
'social  protection' 
represented 16.3% (and 35.8% of total general government 
expenditure).  'Social  protection'  includes  the  'old  age' 
(pensions) function. 

expenditure),  while 

(91)  The latter takes two main forms: (i) direct purchases by the 
government  of  educational  resources  to  be  used  by 
educational  institutions  (e.g.  direct  payments  of  teachers' 
wages  by  the  education  ministry);  or  (ii)  payments  by  the 
government  to  educational  institutions  that  have  the 
responsibility 
resources 
for  purchasing  educational 
themselves (e.g. a block grant to a university).  
(92)  UNESCO-UIS/OECD/Eurostat  Data  Collection 
on 
Education  Statistics.  The  current  version  of 
the 
classification is ISCED 2011, which replaced ISCED 1997, 
and it has already been used in the 2018 Ageing Report.  

124 

 
   
 
                                                           
                                                           
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

international 

time)(93),  and  detailed  data  on 
total  public 
expenditure.  Data  are  disaggregated  by  single  age 
and 
classification  of 
standard 
education  (ISCED)  levels.  As  in  the  2018  Ageing 
Report,  projections  should  be  run  separately  for 
four  ISCED  groupings,  representing  primary 
education  (ISCED  1),  lower  secondary  education 
(ISCED  2),  upper  secondary  education  (ISCED  3 
and 4), and tertiary education (ISCED 5 to 8).  

where (cid:1857)(cid:3036),(cid:3047) is the total enrolment rate (both full and 
part-time students) for single age cohort i in period 
t; (cid:1868)(cid:3036),(cid:3047) is the participation rate; (cid:2009)(cid:3036),(cid:3047) is the fraction of 
∗   is  the 
part-time  students  in  the  total;  and  (cid:1861)(cid:3036),(cid:3047)
fraction  of  inactive  individuals  minus  full-time 
students over the total population. 

Equation  (4.1)  will  be  implemented  in  terms  of 
differences to a base period (b): 

In order to simplify, it is assumed that enrolment in 
primary  and  lower  secondary  education  levels  is 
compulsory (94),  while 
in  upper 
secondary and tertiary education levels depends on 
labour  market 
in 
outcomes, 
participation  rates  affect  enrolment  rates  (in  the 
opposite direction).  

enrolment 

changes 

as 

Projections  are  broken  down  in  two  components: 
(1)  number  of  students;  and  (2)  per  capita 
expenditure per student (see Graph II.4.1).  

4.2.1.  Number of students  

Compulsory levels 

Enrolment  rates  per  single  age  are  assumed  to 
remain  constant  at  the  level  observed  in  a  base 
period/year  for  the  compulsory  levels  considered 
(ISCED 1 and 2). In order to obtain the projected 
number of students enrolled in ISCED levels 1 and 
2,  demographic  projections  are  multiplied  by 
enrolment rates in the base period. 

Non-compulsory levels  

Enrolment rates for ISCED groupings 3-4 and 5-8 
take  into  account  labour  market  developments 
according  to  the  formula  (see  Annex  13  for  a 
derivation):  

(cid:1857)(cid:3036),(cid:3047) =

∗
(cid:2869)(cid:2879)(cid:3043)(cid:3284),(cid:3295)(cid:2879)(cid:3036)(cid:3284),(cid:3295)
(cid:2869)(cid:2879)(cid:3080)(cid:3284),(cid:3295)

               (4.1) 

(93)  Students  are  classified  as  full-time  and  part-time  on  the 
basis  of  the  intended  study-load  of  the  student  within  the 
reference  school  or  academic  year.  A  full-time  student  is 
one  who  is  enrolled  in  an  education  programme  whose 
intended study-load amounts to at least 75% of the normal 
full-time annual study-load. A part-time student is one who 
is  enrolled  in  an  education  programme  whose  intended 
study load is less than 75% of the normal full-time annual 
study  load  (UNESCO-UIS/OECD/EUROSTAT  (UOE), 
2019).  

(94)  In  the  baseline  scenario,  enrolment  rates  for  the  two 
compulsory groupings are fixed at their historical levels.  

(cid:1857)(cid:3036),(cid:3047) − (cid:1857)(cid:3036),(cid:3029) = −

(cid:3089)(cid:3284),(cid:3277)
(cid:2869)(cid:2879)(cid:3080)(cid:3284),(cid:3277)

∗ (cid:3435)(cid:1868)(cid:3036),(cid:3047) − (cid:1868)(cid:3036),(cid:3029)(cid:3439)   

(4.2) 

where 

0 ≤ (cid:2018)(cid:3036),(cid:3029), (cid:2009)(cid:3036),(cid:3029) ≤ 1 

where  (cid:2018)(cid:3036),(cid:3029)  is  the  ratio  between  full-time  students 
and total inactive individuals; (cid:2009)(cid:3036),(cid:3029) is the fraction of 
part-time  students  over 
total  number  of 
students.  These  two  ratios  are  assumed  to  remain 
constant throughout the projection period. 

the 

According  to  equation  (4.2),  an  increase  in  the 
participation  rate  leads  to  a  decrease  in  the 
enrolment rate (95). 

Enrolment rates per age are then broken down into 
ISCED  levels  (3-4  and  5-8)  values,  based  on 
student shares in the base period/year.  

4.2.2.  Expenditure per student 

student 

Annual  expenditure  per 
in  public 
educational  institutions  varies  significantly  across 
education level and country (see Table II.4.2) (96). 
This  variability  reflects  a  number  of  factors,  such 
as labour costs of teachers and non-teaching staff, 
different  class  sizes,  differences 
in  capital 
expenditure, 
specific  national 
as  well 
circumstances (97). 

as 

(95)  To the extent that individuals entering the labour force are 
likely  to  have  been  previously  involved  in  education 
activities. The LFS variable MAINSTAT, which describes 
the  main  labour  market  status,  was  used  to  assess  the 
distribution  of  inactive  individuals  by  age,  distinguishing 
between  schooling  and  other  forms  of  inactivity,  such  as 
retirement and domestic tasks.  

(96)  For  those  countries  where  data  are  missing  for  the  base 
period,  AWG  delegates  will  be  asked  to  provide  them  to 
the Commission. 

(97)  For example, small EU Member States tend to send abroad 
a  higher  fraction  of  their  tertiary  students.  Other  things 
being equal, this tends to raise expenditure levels. 

125 

 
 
 
                                                           
                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table II.4.2: 

Total public expenditure on education per 
pupil in EUR PPS (1) in 2016 

(cid:3284)
∑ (cid:3006)(cid:3005)(cid:3022)(cid:3295)
(cid:3284)
(cid:3008)(cid:3005)(cid:3017)(cid:3295)

=

∑ (cid:4674)(cid:3024)(cid:3295)
(cid:3284)

(cid:3284)(cid:2878)(cid:3012)(cid:3295)

(cid:3284)(cid:4675)
(cid:3284)(cid:2878)(cid:3019)(cid:3295)

(cid:3284)(cid:2878)(cid:3016)(cid:3295)
(cid:3008)(cid:3005)(cid:3017)(cid:3295)

                             (4.3) 

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO

ISCED 1 
7560
3126
3555
:
6335
4561
6077
4022
4718
5312
:
5591
7812
4607
4298
12322
3725
5029
6226
8824
4658
5080
1359
5698
:
6843
8248
9180

ISCED 2
9544
3610
5978
:
7856
4849
7605
4662
5910
7291
:
6271
9476
4645
3985
15293
4004
7315
8988
11682
4900
6992
2647
6926
:
10894
8907
9932

ISCED 3-4
10153
2899
5391
:
8515
4947
8740
3811
6686
9394
:
6628
9765
5513
3899
14613
5731
10109
8826
11337
4417
6037
2481
5415
:
:
9776
12794

ISCED 5-8
12884
2774
5395
:
13049
8538
8710
:
6785
9957
:
6910
5893
3924
4183
34293
5795
15764
14653
13618
5837
5633
4720
7501
:
13960
20291
20537

Total*
9060
3309
4674
:
8381
:
:
:
5560
7230
:
6091
7293
4510
4042
14705
4769
8202
8892
10799
4778
5507
2470
5876
:
:
10607
11980

(1) Public expenditure on education per pupil/student by 
education level and programme orientation, 
'educ_uoe_fine09'. Based on full-time equivalent. The 
category 'Total' includes pre-primary education (ISCED 02). 
Source: Eurostat. 

4.2.3.  Expenditure-to-GDP ratios are 
calculated using indexes 

As a rule, expenditure data given by the average of 
the  two  last  available  years,  generally  2015  and 
2016 (or more recent data if available), are chosen. 
This  is  then  uprated  until  the  base  year  using 
COFOG  data  (98).  Total  public  expenditure  on 
education is broken down into four components: i) 
expenditure  on  staff  compensation  (i.e.  gross 
wages  and  salaries  of  teaching  and  non-teaching 
staff),  ii)  other  current  expenditure,  iii)  capital 
expenditure,  and  iv)  transfers  (e.g.  scholarships 
and  public 
to  private  education 
institutions).  

subsidies 

The  objective  is  to  project  the  total  (education) 
expenditure-to-GDP  ratio.  The  ISCED 
levels 
considered  are:  ISCED  1,  ISCED  2,  ISCED  3-4, 
and ISCED 5-8 (99). 

(98)  If data for 2018 is not available, the latest available public 
expenditure data as a share of GDP is used. See Annex 12 
for a description of data sources and methodology.  

(99)  It should be stressed that no attempt is made to project total 
expenditure  on  education,  as  ISCED  0  level  expenditure 

126 

where (cid:1831)(cid:1830)(cid:1847)(cid:3047)
level  i  and  year  t,  (cid:1849)(cid:3047)
compensation,  (cid:1841)(cid:3047)
is capital expenditure, (cid:1844)(cid:3047)
for the ISCED groups: 1, 2, 3-4, and 5-8.  

(cid:3036) is expenditure on education in ISCED 
(cid:3036)  is  expenditure  on  staff 
(cid:3036)  is  other  current  expenditure,  (cid:1837)(cid:3047)
(cid:3036) 
(cid:3036) is transfers; and i stands 

In the baseline scenario, the main assumptions are 
the following:  

in 

line  with 

labour 
Per-capita  costs  grow 
productivity. Per-capita values are defined in terms 
of  education  staff  or  students.  Specifically,  the 
average compensation is defined per staff member: 
(cid:3036)
(cid:1849)(cid:3047)
(cid:3036)(cid:3416) ),  while 

three  expenditure 

the  other 

(

(cid:1846)(cid:3047)

variables are defined in terms of student ratios: 

(cid:3436)

(cid:3036)
(cid:1841)(cid:3047)
(cid:3036)(cid:3416) ,
(cid:1845)(cid:3047)

(cid:3036)
(cid:1837)(cid:3047)
(cid:3036)(cid:3416) ,
(cid:1845)(cid:3047)

(cid:3036)
(cid:1844)(cid:3047)
(cid:3036)(cid:3416) (cid:3440) 
(cid:1845)(cid:3047)

Where  T and S are the numbers of  workers in the 
education sector and students, respectively (100). 

The  education  staff-to-student  ratio  will  remain 
constant over the projection period, which implies 
that  staff  adjusts  instantaneously  and  fully  to 
demographic and macroeconomic changes.  

Assuming  that  per  capita  variables  grow  in  line 
with labour productivity is sufficient to derive the 
following  compact  general 
the 
expenditure in education-to-GDP ratio: 

formula 

for 

i
EDU
t



i

GDP
t


i
W
0

i
GDP
0

=








*

IT

t

+

[
+
KO

i
0

i
0

+

i
R
0



i

GDP
0

]
*

IS

t








*

IP
t
IG
t

+

CE
t

  (4.4) 

(cid:3036)  are  indexes  of 
Where  (cid:1835)(cid:1846)(cid:3047)
respectively,  staff,  students,  labour  productivity, 

(cid:3036),  and  (cid:1835)(cid:1833)(cid:3047)

(cid:3036),  (cid:1835)(cid:1845)(cid:3047)

(cid:3036),  (cid:1835)(cid:1842)(cid:3047)

(pre-primary and not allocated by level) is not covered by 
the analysis.  

(100) These  modelling  assumptions 

involve  considerable 
simplifications  of  the  determinants  of  the  unit  costs  of 
education.  A  key  variable  missing  is  class  size.  Research 
suggests that costs tend to change discontinuously with the 
creation/destruction  of  classes.  Given  the  difficulty  in 
obtaining  data  on  the  relationship  between  class  size  and 
costs,  a  reasonable  approximation  may  be  that  of  using 
student-to-staff ratios.  

 
  
 
                                                                                   
 
 
                                                                                   
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

4.3. 

SENSITIVITY ANALYSIS 

In  addition  to  the  baseline  scenario  described 
above, a sensitivity test is run.  

High enrolment rates – as carried out in the 2018 
Ageing Report, a sensitivity analysis of the impact 
of a gradual upward convergence is performed (to 
be completed by 2045); namely an assumption that 
raises the enrolment rates in ISCED levels 3-4 and 
5-8 towards the average of the 3 best performers in 
the EU. 

and GDP (101). A bar over an index represents one 
calculated  over  all  ISCED  levels  considered (102). 
CEt  is  the  composition  effect,  which  is  usually  a 
total 
small 
expenditure-to-GDP ratio (103). 

compared  with 

number 

the 

Equation  (4.4)  expresses 
in 
education-to-GDP  ratio  as  a  function  of  base 
period ratios, and indexes for staff, students, labour 
productivity and GDP.  

the  expenditure 

In the baseline scenario, which assumes a constant 
(cid:3036)),  equation 
ratio  of  staff-to-students  (i.e  (cid:1835)(cid:1846)(cid:3047)
(4.4) can be further simplified to: 

(cid:3036) = (cid:1835)(cid:1845)(cid:3047)

EDU

i
t



i

GDP

t

EDU

i
0



i

GDP

0

=

IS

*

*
t
IG

IP
t

t

+

CE

t

(4.5) 

Equivalently, equation (4.5) can also be written as: 

EDU

i
t



i

GDP
t

EDU

i
0



i

GDP
0

=

*

IS
IE

t

t

+

CE

t

»

EDU

i
0



i

GDP
0

*

IS
IE

t

t

(4.6) 

where IEt is the employment index (104). 

In the baseline scenario, equation (4.5) allows  the 
following  clear-cut  interpretation:  projections  for 
the  expenditure-to-GDP  ratio  are  obtained  by 
‘inflating’  base  period  values  by  a  students  and 
labour  productivity  indexes  and  by  ‘deflating’ 
them by a GDP index (105). There are two sources 
for  the  increase  in  expenditure  (ratios):  i)  the 
(average)  number  of  students  and,  ii)  per-capita 
costs that are assumed to grow in line with labour 
productivity,  conversely  GDP  growth  ‘deflates’ 
expenditure ratios. 

(101) An index  measures the ratio between the values of variable 
X  in  the  current  period  t  and  in  the  base  period  0: 

X
t
0X
T

t

T




i

i

IX

t =

IT

t

=

(102) 
(103) The 

CE

t

=



i







i

i

IS

=

t

0  and 
composition 
{
W
IT
IT

-

*

i

i
0

t

GDP

0




i

i

t

}
+

i

t

i

S

S

0 . 
effect 
[

+
O

i
0

i

is 
R

i
0

] {
*
IS

K

i
0

+

GDP

0

i
t

given 
}

-
IS





t

by: 

*

t

IP
IG

t

(104) The approximation assumes that CEt is a small number. 
(105) The  discrepancy  being  given  by  the  composition  effect 

(CEt). 

127 

 
 
                                                           
 
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Graph II.4.1: 

Implicit breakdown of expenditure per student 

Source: European Commission, EPC. 

128 

 
ANNEX 1 

Projecting labour force developments using the cohort 
simulation model (CSM)  

Overall approach of the CSM  

The  CSM  calculates  entry  and  exit  rates  in  the 
labour  market  by  gender  and  cohort (106).  The 
dynamic cohort approach is based on the estimates 
of  exit  and  entry  rates  in  the  labour  market  of  a 
‘synthetic’  generation/cohort.  The  cohort 
is 
synthetic  because,  due  to  the  lack  of  individual 
longitudinal data on labour  market transitions, the 
same  individual  cannot  be  followed  over  time. 
Instead,  it  is  assumed  that  those  individuals  aged 
x+1  at  year  t+1  are  representative  of  the  same 
generation observed in the previous year (aged x at 
time t). Due to the lack of specific information on 
each 
this  assumption 
neglects  inflows  and  outflows  from  the  labour 
market that cancel out (107).  

individual's  behaviour, 

Participation  rate  projections  are  produced  by 
applying the average entry and exit rates observed 
over  the  period  2010-2019  by  gender  and  single 
age to the period 2020-2070. Specifically, average 
entry  rates  for  the  period  2010-2019  are  kept 
constant  over  the  entire  projection  period.  For 
example,  average  entry  rates  in  2010-2019  for 
people aged x (with x varying between 15 and 74 
years  of  age),  are  applied  to  persons  aged  x  over 
the  projection  horizon  in  order  to  calculate  future 
participation  rates.  In  this  way,  the  CSM  captures 
‘cohort  effects’,  namely  those  resulting  from  the 
stronger  attachment  of  younger  women  of  more 
recent cohorts to the labour market.  

The  CSM  is  also  able  to  incorporate  a  broad 
typology  of  pension  reforms,  inter  alia,  increases 
in the statutory retirement age, the convergence of 
lower  female  statutory  retirement  ages  to  male 
ones, the linking of the statutory retirement age to 
changes  in  life  expectancy,  changes  in  conditions 
for  early  retirement,  and  changes 
in  (price) 
incentives  affecting  the  retirement  decision.  The 
likely impact of pension reforms is incorporated in 
the  labour  force  projections  by  appropriately 
exit 
changing 

labour  market 

average 

the 

(106) See Burniaux et al. (2003) and Carone (2005). 
(107) For  example,  this  means  that  if  in  year  t  there  are  100 
persons aged x in the labour force and the next year these 
same  individuals  (aged  x+1)  leave  the  labour  force 
(because  of,  for  example,  discouragement,  death  or 
emigration) but they are replaced by 100 other individuals 
aged  x+1,  previously  not  in  the  labour  force,  we  do  not 
observe  any  change  in  the  size  of  the  ‘synthetic’  cohort. 
Therefore,  the  calculated  net  rates  of  exit  and  entry  are 
equal to zero, while the actual (gross) values are 100%. 

probabilities  for  people  aged  51-74,  as  calculated 
for the period 2010-2019.  

The calculation of entry rates 

Entry  rates  from  inactivity  to  labour  market 
participation  are  calculated  as 
follows.  The 
calculation of the number of persons that enter the 
labour  market  takes  into  account  the  size  of  each 
gender/age group. It can be expressed as: 

(cid:1840)(cid:1838)(cid:1832)(cid:3051)

(cid:3047)(cid:2878)(cid:2869) = ((cid:1842)(cid:1867)(cid:1868)(cid:1865)(cid:1853)(cid:1876)(cid:3050)(cid:3028) −   (cid:1838)(cid:1832)(cid:3051)

(cid:3047)) − ((cid:1842)(cid:1867)(cid:1868)(cid:1865)(cid:1853)(cid:1876)(cid:3050)(cid:3028) −   (cid:1838)(cid:1832)(cid:3051)(cid:2878)(cid:2869)

(cid:3047)(cid:2878)(cid:2869))  

where (cid:1838)(cid:1832)(cid:3051)

(cid:3047) +   (cid:1840)(cid:1838)(cid:1832)(cid:3051)(cid:2878)(cid:2869)

(cid:3047)(cid:2878)(cid:2869) ≤   (cid:1842)(cid:1867)(cid:1868)(cid:1865)(cid:1853)(cid:1876)(cid:3050)(cid:3028) 

where  NLF  is  the  number  of  people  expected  to 
become active between ages x and x+1; Popmaxwa 
is the maximum population at working age that can 
potentially enter the labour force (which is usually 
slightly  lower  than  the  overall  population  at 
working  age,  due  for  example  to  illness  or 
inability)  and  LF  is  the  number  of  active  persons 
(in the labour force) aged x in year t and aged x+1 
in year t+1.  

Multiplying and dividing by the population aged x 
at time t (which is supposed to be the same as the 
population  aged  x+1  at  time  t+1),  the  following 
equation is obtained: 

(cid:1840)(cid:1838)(cid:1832)(cid:3051)

(cid:3047)(cid:2878)(cid:2869) = [((cid:1842)(cid:1844)(cid:3040)(cid:3028)(cid:3051) − (cid:1842)(cid:1844)(cid:3051)

(cid:3047) ) −   ((cid:1842)(cid:1844)(cid:3040)(cid:3028)(cid:3051)− (cid:1842)(cid:1844)(cid:3051)(cid:2878)(cid:2869)

(cid:3047) 
(cid:3047)(cid:2878)(cid:2869))] ∗ (cid:1842)(cid:1867)(cid:1868)(cid:3051)

where PRmax is the upper limit to the participation 
rate (0.99 for both men and women). Next, one can 
calculate  the  rate  of  entry,  Ren  by  dividing  the 
number  of  people  expected  to  become  active  by 
the number of people inactive at time t:  

(cid:1844)(cid:3032)(cid:3041) =

(cid:3047)(cid:2878)(cid:2869)

(cid:1840)(cid:1838)(cid:1832)(cid:3051)
((cid:1842)(cid:1867)(cid:1868)(cid:1865)(cid:1853)(cid:1876)(cid:3050)(cid:3028) − (cid:1838)(cid:1832)(cid:3051)

(cid:3047))

        =   [((cid:1842)(cid:1844)(cid:3040)(cid:3028)(cid:3051) − (cid:1842)(cid:1844)(cid:3051)

(cid:3047) ) − ((cid:1842)(cid:1844)(cid:3040)(cid:3028)(cid:3051) − (cid:1842)(cid:1844)(cid:3051)(cid:2878)(cid:2869)

(cid:3047)(cid:2878)(cid:2869))] ∗

(cid:3047)
(cid:1842)(cid:1867)(cid:1868)(cid:3051)
((cid:1842)(cid:1867)(cid:1868)(cid:1865)(cid:1853)(cid:1876)(cid:3050)(cid:3028) − (cid:1838)(cid:1832)(cid:3051)

(cid:3047))

which,  taking  into  account  that  (cid:1842)(cid:1844)(cid:3051)

(cid:1842)(cid:1844)(cid:3040)(cid:3028)(cid:3051) =  

(cid:3295)
(cid:3017)(cid:3042)(cid:3043)(cid:3040)(cid:3028)(cid:3051)(cid:3298)(cid:3276)(cid:3299)
(cid:3295)
(cid:3017)(cid:3042)(cid:3043)(cid:3299)

, can be reformulated as:  

(cid:3295)

(cid:3047) =   (cid:3017)(cid:3042)(cid:3043)(cid:3299)
(cid:3013)(cid:3007)(cid:3299)

(cid:3295)   and 

 (cid:1844)(cid:3032)(cid:3041)(cid:3051)(cid:2878)(cid:2869) =   [((cid:1842)(cid:1844)(cid:3040)(cid:3028)(cid:3051) − (cid:1842)(cid:1844)(cid:3051)

(cid:3047) ) − ((cid:1842)(cid:1844)(cid:3040)(cid:3028)(cid:3051) − (cid:1842)(cid:1844)(cid:3051)(cid:2878)(cid:2869)

(cid:3047)(cid:2878)(cid:2869))] ∗

or (cid:1844)(cid:3032)(cid:3041)(cid:3051)(cid:2878)(cid:2869) = 1 −

(cid:3295)(cid:3126)(cid:3117) (cid:3439)
(cid:3435)(cid:3017)(cid:3019)(cid:3288)(cid:3276)(cid:3299) (cid:2879) (cid:3017)(cid:3019)(cid:3299)(cid:3126)(cid:3117)
(cid:3295) (cid:3439)
(cid:3435)(cid:3017)(cid:3019)(cid:3288)(cid:3276)(cid:3299) (cid:2879) (cid:3017)(cid:3019)(cid:3299)

  ≥ 0 

1
(cid:3047) )
((cid:1842)(cid:1844)(cid:3040)(cid:3028)(cid:3051) − (cid:1842)(cid:1844)(cid:3051)

129 

                                                           
 
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

or (cid:1844)(cid:3032)(cid:3041)(cid:3051)(cid:2878)(cid:2869) =

(cid:3435)(cid:3017)(cid:3019)(cid:3299)(cid:3126)(cid:3117)

(cid:3295)(cid:3126)(cid:3117)  (cid:2879) (cid:3017)(cid:3019)(cid:3299)
(cid:3295) (cid:3439)
(cid:3295) (cid:3439)
(cid:3435)(cid:2869) (cid:2879) (cid:3017)(cid:3019)(cid:3299)

≥ 0   when (cid:1842)(cid:1844)(cid:3040)(cid:3028)(cid:3051) = 1 

After  re-arranging,  we  obtain 
the  analytical 
formulation used for projecting participation rates: 

(cid:1842)(cid:1844)(cid:3051)(cid:2878)(cid:2869)

(cid:3047)(cid:2878)(cid:2869) = (cid:1844)(cid:3032)(cid:3041)(cid:3051)(cid:2878)(cid:2869) ∗ ((cid:1842)(cid:1844)(cid:3040)(cid:3028)(cid:3051) −   (cid:1842)(cid:1844)(cid:3051)

(cid:3047) ) +   (cid:1842)(cid:1844)(cid:3051)

(cid:3047)  

Thus,  projections  of  participation  rates  for  each 
single-year  cohort  (x+1)  can  be  calculated  by 
applying the entry rates observed in a given year or 
period  over  the  period  of  projections  (t = 2020-
2070).  In  practical  terms,  the  entry  rates  for  each 
age  are  calculated  based  on  the  average  of  the 
participation rates observed over 2010-2019.  

Multiplying and dividing by the population aged x 
at time t, which is supposed to be the same as the 
population aged x+1 at time t+1, result in: 

(cid:1841)(cid:1842)(cid:3051)

(cid:3047)(cid:2878)(cid:2869) = ((cid:1842)(cid:1844)(cid:3051)

(cid:3047) − (cid:1842)(cid:1844)(cid:3051)(cid:2878)(cid:2869)

(cid:3047) 
(cid:3047)(cid:2878)(cid:2869) ) ∗ (cid:1842)(cid:1867)(cid:1868)(cid:3051)

where PR are the participation rates. 

Thus,  we  can  calculate  the  (conditional)  rate  of 
exit,  Rex  by  dividing  the  number  of  people  that 
become  inactive  at  time  t+1  by  the  number  of 
people active at time t: 

(cid:1844)(cid:3032)(cid:3051) =

(cid:3047)(cid:2878)(cid:2869)
(cid:3047) = ((cid:1842)(cid:1844)(cid:3051)

(cid:1841)(cid:1842)(cid:3051)
(cid:1838)(cid:1832)(cid:3051)

(cid:3047) − (cid:1842)(cid:1844)(cid:3051)(cid:2878)(cid:2869)

(cid:3047)(cid:2878)(cid:2869) ) ∗

(cid:3047)
(cid:1842)(cid:1867)(cid:1868)(cid:3051)
(cid:3047)  
(cid:1838)(cid:1832)(cid:3051)

The calculation of exit rates 

which can also be re-arranged as follows: 

In the same  way,  when participation rates for two 
adjacent  single-year  age  groups  are  falling,  we 
calculate  an  exit  rate  (that  is  the  net  reduction  in 
the  labour  force  relative  to  the  number  of  people 
who  were initially in the labour force in the same 
cohort the year before) as follows. 

(cid:1844)(cid:3032)(cid:3051) =

(cid:3047)(cid:2878)(cid:2869)
(cid:3047) = 1 −

(cid:1841)(cid:1842)(cid:3051)
(cid:1838)(cid:1832)(cid:3051)

(cid:3047)(cid:2878)(cid:2869)
(cid:1842)(cid:1844)(cid:3051)(cid:2878)(cid:2869)
(cid:3047)  
(cid:1842)(cid:1844)(cid:3051)

Thus,  we  can  use  this  Rex  to  project  participation 
rates of older workers as: 

(cid:1842)(cid:1844)(cid:3051)(cid:2878)(cid:2869)

(cid:3047) 
(cid:3047)(cid:2878)(cid:2869) = (cid:3435)1 − (cid:1844)(cid:3032)(cid:3051)(cid:3051)(cid:2878)(cid:2869)(cid:3439) ∗ (cid:1842)(cid:1844)(cid:3051)

The  number  of  persons  that  leave  the  labour 
market at time t+1 is equivalent to: 

and 

(cid:1841)(cid:1842)(cid:3051)

(cid:3047)(cid:2878)(cid:2869) = (cid:1838)(cid:1832)(cid:3051)

(cid:3047)(cid:2878)(cid:2869) 
(cid:3047) − (cid:1838)(cid:1832)(cid:3051)(cid:2878)(cid:2869)

(cid:1842)(cid:1844)(cid:3051)(cid:2878)(cid:3041)

(cid:3047) 
(cid:3047)(cid:2878)(cid:3041) = (cid:3435)1 − (cid:1844)(cid:3032)(cid:3051)(cid:3051)(cid:2878)(cid:2869)(cid:3439) ∗ (cid:3435)1 − (cid:1844)(cid:3032)(cid:3051)(cid:3051)(cid:2878)(cid:2870)(cid:3439) ∗ … ∗ (cid:3435)1 − (cid:1844)(cid:3032)(cid:3051)(cid:3051)(cid:2878)(cid:3041)(cid:2879)(cid:2869)(cid:3439) ∗ (cid:1842)(cid:1844)(cid:3051)

where OP is the number of individuals expected to 
become  inactive between ages x and  x+1, and  LF 
is the number of active people (in the labour force) 
aged x in year t and aged x+1 in year t+1. 

130 

 
 
ANNEX 2 

Estimation of the average exit age from the labour market  

Average exit age from the labour force 

In order to estimate the ‘average exit age’ from the 
labour force, the CSM is used, which is basically a 
probabilistic  model  using  gender/single  year 
participation rates (108). The methodology is based 
on  the  comparison  of  labour  force  participation 
rates over time.   

The conditional probability for each person to stay 
in  the  labour  force  at  age  a  in  year  t  (conditional 
upon staying in the labour force in year t-1), can be 
the  observed 
the  basis  of 
calculated  on 
participation rates PR. 

Probability to stay 

(cid:1855)(cid:1842)(cid:1870)(cid:1867)(cid:1854)(cid:3028),(cid:3047)

(cid:3046)(cid:3047)(cid:3028)(cid:3052) =

(cid:3047)
(cid:1842)(cid:1844)(cid:3028)
(cid:3047)(cid:2879)(cid:2869)  
(cid:1842)(cid:1844)(cid:3028)(cid:2879)(cid:2869)

Probability of retiring  

(cid:1842)(cid:1870)(cid:1867)(cid:1854)(cid:3028),(cid:3047)

(cid:3045)(cid:3032)(cid:3047) = (cid:1842)(cid:1870)(cid:1867)(cid:1854)(cid:3028),(cid:3047)

(cid:3041)(cid:3042)(cid:3047)_(cid:3045)(cid:3032)(cid:3047) ∗ (cid:1855)(cid:1842)(cid:1870)(cid:1867)(cid:1854)(cid:3028),(cid:3047)

(cid:3032)(cid:3051)(cid:3036)(cid:3047) 

By  assuming  that  everybody  will  be  retired  at  a 
given  age  M  (e.g.  M = 75),  the  sum  of  the 
probability of retiring between the minimum age m 
and the maximum age M is equal to 1: 

(cid:3014)
(cid:3533) (cid:1842)(cid:1870)(cid:1867)(cid:1854)(cid:3028)
(cid:3028)(cid:2880)(cid:3040)

(cid:3045)(cid:3032)(cid:3047) =

 1 

the 

from 

labour  market 

The  ‘average  exit  age’  or  effective  age  of 
retirement 
then 
calculated  as  the  weighted  sum  of  the  retirement 
ages (between the minimum and the maximum age 
of  retirement,  e.g.  60-74),  where  the  weights  are 
the probability of retiring at each age a, as follows: 

is 

where 0 ≤ (cid:1855)(cid:1842)(cid:1870)(cid:1867)(cid:1854)(cid:3028),(cid:3047)

(cid:3046)(cid:3047)(cid:3028)(cid:3052) ≤ 1 

Average exit age  

Thus, at time t, the conditional probability for each 
person to exit at age a is simply equal to: 

(cid:3014)

(cid:1853)(cid:1874)(cid:1857)(cid:1870)(cid:1853)(cid:1859)(cid:1857) (cid:1857)(cid:1876)(cid:1861)(cid:1872) (cid:1853)(cid:1859)(cid:1857) = (cid:3533) (cid:1842)(cid:1870)(cid:1867)(cid:1854)(cid:3028)

(cid:3045)(cid:3032)(cid:3047) ∗ (cid:1853)

(cid:3028)(cid:2880)(cid:3040)

Probability of exit 

(cid:1855)(cid:1842)(cid:1870)(cid:1867)(cid:1854)(cid:3028),(cid:3047)

(cid:3032)(cid:3051)(cid:3036)(cid:3047) = 1 −

(cid:3047)
(cid:1842)(cid:1844)(cid:3028)
(cid:3047)(cid:2879)(cid:2869) = 1 − (cid:1855)(cid:1842)(cid:1870)(cid:1867)(cid:1854)(cid:3028),(cid:3047)
(cid:1842)(cid:1844)(cid:3028)(cid:2879)(cid:2869)

(cid:3046)(cid:3047)(cid:3028)(cid:3052) 

where 0 ≤ (cid:1855)(cid:1842)(cid:1870)(cid:1867)(cid:1854)(cid:3028),(cid:3047)

(cid:3032)(cid:3051)(cid:3036)(cid:3047) ≤ 1 

Assuming that nobody retires before the minimum 
age  m  (e.g.  before  m = 60),  the  (unconditional) 
probability  that  any  person  will  still  be  in  the 
labour  force  (or  the  probability  of  not  retiring 
before  a  given  age  a),  can  be  calculated  as  the 
product  of  all  the  conditional  probabilities  to  stay 
in the labour force from age m to age a-1.  

Probability of not retiring before  

(cid:3028)(cid:2879)(cid:2869)

(cid:1842)(cid:1870)(cid:1867)(cid:1854)(cid:3028),(cid:3047)

(cid:3041)(cid:3042)(cid:3047)_(cid:3045)(cid:3032)(cid:3047) = (cid:3537) (cid:1855)(cid:1842)(cid:1870)(cid:1867)(cid:1854)(cid:3036)

(cid:3046)(cid:3047)(cid:3028)(cid:3052)

(cid:3036)(cid:2880)(cid:3040)

Thus,  the  probability  of  retiring  at  age  a  can  be 
calculated  as  the  product  of  the  unconditional 
probability of not retiring from age m to a and the 
(conditional) probability of exit, that is:  

(108) See Carone (2005). 

131 

 
                                                           
 
 
 
 
 
ANNEX 3 

Methodology underpinning potential GDP growth projections 

A3.1.  DESCRIPTION OF THE PRODUCTION 

FUNCTION FRAMEWORK 

The  production  function  framework  used  is  based 
on the standard specification of the Cobb-Douglas 
production  with  constant  returns  to  scale,  where 
potential  GDP  can  be  expressed  formally  as  total 
output  represented  by  a  combination  of  factor 
inputs  multiplied  with  total  factor  productivity 
(TFP), which embeds the technological level (109).  

Y

=

TFP

*

-
1

b

=

b
*
KL
b

*

L

*

K

-
1

b

=






1
b

TFP





(
*
LE

b

)

*

K

-
1

b

where:  

Y is total output (GDP); 

L is the supply of labour (total hours worked);  

K is the stock of capital; 

E is the labour-augmenting technical progress (i.e. 
Harrod-neutral technical progress).  

is 

then 

interpretable  as 

E.L 
in 
efficiency  units.  TFP  and  the  labour-augmenting 
linked  with  a  simple 
technical  progress  are 

labour 

total 

relationship: 

TFP=

b)E(

β is the labour share, i.e. the share of labour costs 
in total value-added. It is set at 0.65 (110). 

As  a  result,  potential  labour  productivity  growth 
comes down to the following expression (where Y, 
L,  E  and  TFP  denote  potential  output,  potential 
labour, trend labour-augmenting technical progress 
and trend TFP). 

Thus, the projection of TFP growth and the growth 
in  capital  per  hour  worked,  so  called  capital 
deepening, are the key drivers of projected labour 
productivity over the medium run. 

In  the  long-run,  according  to  the  standard  neo-
classical  growth  model (111),  the  economy  should 
reach  its  equilibrium,  also  called  steady  state  or 
balanced  growth  path,  where  the  ratio  of  capital 
stock  to  labour  expressed  in  efficiency  unit, 
K/(L.E),  remains  constant  over  time.  As  a  result, 
the  capital  stock  per  hour  worked  grows  at  the 
same pace as labour augmenting technical progress 
E.  Therefore,  labour  productivity  growth  (i.e. 
output  per  hour  worked  growth)  coincides  with 
TFP growth divided by the labour share: 

(cid:129)
Y
L






=




(cid:129)
K
L


=


(cid:129)
E

=

(cid:129)
TFP
b

It should also be noted that, in the steady state, the 
contribution of capital deepening to output growth 
is  a  simple  function  of  TFP(112),  which  becomes 
the single driver of labour productivity (113).  

contrib

(cid:129)
K
L






=


1(

-

b
)

(cid:129)
K
L






=


1(

b
)

-
b

(cid:129)
TFP

(109) See  K.  Havik,  K.  Mc  Morrow,  F.  Orlandi,  C.  Planas,  R. 
Raciborski,  W.  Röger,  A.  Rossi,  A.  Thum-Thysen,  V. 
Vandermeulen, "The Production Function Methodology for 
Calculating  Potential  Growth  Rates  &  Output  Gaps",  
European Economy Economic Papers No. 535, 2014. 
(110) Although  there  is  some  debate  about  the  recent  and 
observed  decline  of  the  labour  share,  most  economists 
assume  that  it  will  remain  broadly  constant  in  a  long  run 
perspective,  while  allowing  for  a  variation  in  the  short-
term. This rule is uniformly applied in the projections to all 
Member  States  in  order  to  allow  for  consistent  cross-
country comparisons of the results. The assumption is also 
well-founded in economic theory. If the real wage is equal 
to the marginal productivity of labour, it follows that under 
the standard features of the production function, real wage 

growth is equal to labour productivity growth and real unit 
labour costs remain constant.  

(111) Also  known  as  the  Solow  growth  model  -  See  Solow  R. 
(1956) "A contribution to the theory of economic growth". 
Quarterly Journal of Economics. 70 (1): 65-94 

(112) With  the  assumption  of  a  long-run  TFP  growth  rate 
equivalent  to  1%  per  annum  in  the  baseline  scenario  (see 
section 3.5), this implies a long-run contribution of capital 
deepening to labour productivity growth equal to 0.5% and 
hence a labour productivity growth rate of 1.5%. 

(113) This in turn implies that, in the long run, the growth rate of 
the capital stock is set equal to the sum of the growth rate 
of  labour  and  labour-augmenting  technological  progress, 
the so-called “capital rule”. 

132 

  
 
                                                           
 
 
 
 
                                                                                   
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

Graph II.A3.1: Overview of the production function approach 

Source: European Economy Economic Papers No. 535, November 2014 

As  all  these  variables  can  be  influenced  by  the 
business  cycle  in  the  short  term,  it  is  safer  to 
project the potential output, i.e. the output adjusted 
for  cyclical  movements  in  the  economy.  This 
requires  estimating  the  trend  components  for  the 
individual production factors, except for the capital 
stock, which can only adjust in the long run.  

structural 

Estimating  potential  output  therefore  amounts  to 
removing  the  cyclical  component  from  both  TFP 
and  labour.  Trend  TFP  is  obtained  using  a 
detrending  technique. Potential labour input is the 
total labour obtained when the unemployment rate 
rate 
the 
equals 
(NAWRU). 
It  equals  LF*(1-NAWRU)*Hours, 
where  LF  stands  for  total  labour  force  and  Hours 
for  average  hours  worked  per  worker.  The 
potential  output  denoted  Yp  can  be  expressed  in 
logarithm as the sum (in logarithm) of trend TFP, 
potential labour input weighted by the labour share 
in  total  value-added  and  the  total  capital  stock 

unemployment 

multiplied  by  one  minus  the  labour  share.  More 
formally, we get:  

Log(Yp)=Log(trendTFP)+βLog(LF*(1-
Nawru)*Hours)+(1-β)logK) 

Graph II.A3.1 illustrates the building blocks of the 
production  function  used  in  the  medium-term 
potential  growth  projection  and 
the  T+10 
methodology  developed  by  the  Commission  and 
EPC (Output Gap Working Group).  

Following  the  practice  used  for  the  2018  Ageing 
Report,  the  AWG  and  EPC  decided  to  use  the 
OGWG  methodology  for  potential  growth  and  its 
components  until  T+10  (2029),  see  section  A3.2 
for details.  

133 

 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

A3.2.  POTENTIAL GDP PROJECTIONS FOR THE 
FIRST TEN YEARS ('T+10' PROJECTIONS) 

The T+10 methodology was first used for the 2015 
Ageing  Report  for  projecting  potential  GDP 
growth  for  the  initial  ten  years  of  the  forecast 
because  it  had  a  number  of  advantages  vis-à-vis 
previous approaches: 

structural 

incorporation  of  additional 

More 
information:  The  T+10 
approach  marks  an  improvement  with  respect  to 
the 
information 
regarding  the  structural  determinants  of  growth. 
This is explicitly the case with respect to the T+10 
NAWRU  anchor  and  is  implicitly  driving  the 
rationale  behind 
formation  and 
the  capital 
participation rate forecasts over the period T+6 to 
T+10. There are clear advantages from introducing 
more  structural 
the  T+10 
information 
methodology,  including  (i)  it  is  easier  to  explain 
it  permits  a 
country  differences;  and 
quantitative evaluation of structural reforms. 

into 

(ii) 

T+10  NAWRU  anchor  versus  reversion  to  a 
pre-crisis  NAWRU  level:  The  T+10  NAWRU 
anchor  represents  a  significant  methodological 
improvement  over 
the  previous  method  by 
anchoring medium term NAWRU developments to 
a  long  run  unemployment  rate  which  is  estimated 
from  the  main  structural  determinants  of  labour 
market  trends.  Alternative  approaches  that  do  not 
rely  on  economic  information  were  discussed  and 
eventually  abandoned.  In  particular,  approaches 
relying on the concept of a return to the pre-crisis 
level for the NAWRU appeared impractical.  

"Structural"  approach  to 
investment:  The 
debate in relation to the assumption to be used for 
the  T+10  capital  formation  projections  was 
initiated with a discussion on the relative merits of 
pursuing  a  structural  model  of  investment.  This 
option was not pursued however since there would 
be  only  limited  gains  relative  to  the  "capital  rule" 
approach  which  was  finally  adopted.  The  latter 
approach effectively amounts to a structural model 
of  investment  since  it  links  investment  to  its 
fundamental  long  run  drivers,  namely  labour 
supply and TFP. 

A  more  credible  evolution  for  the  path  of 
participation  rates:  The  approach  adopted  for 
projecting  participation 
to  T+10 
constitutes  a  balanced  mixture  of  the  information 

rates  up 

134 

important 

emanating  from  time  series  trends  with  the  solid 
structural  information  derived  from  the  cohort 
method.  An 
the 
introduction  of  a  technical  transition  rule  for 
smoothing the unacceptable breaks in participation 
rates which occurred in the forecasts using the T+5 
and the T+10 methodologies.  

improvement 

is 

Internally  consistent  TFP  projections  up  to 
T+10: Despite the fact that attempts to anchor the 
trend  TFP  projections  using  policy  and  structural 
variables  (which  have  been  identified  in  the 
literature as relevant determinants of long run TFP 
growth)  have  been  abandoned,  the  current  T+6  to 
T+10  TFP  projections  are  arguably  superior  to 
those used until the 2015 Ageing Report since the 
T+5  and  T+10  estimates  are  now  both  produced 
with the  same bivariate  Kalman  filter approach & 
consequently are internally consistent. 

additional 

The T+10 methodology has been changed slightly 
since  the  2018  Ageing  Report  with  respect  to  the 
estimation  of  the  NAWRU  anchor.  The  anchor 
uses 
labour  market 
structural 
information  to  anchor  the  short  and  medium-term 
NAWRU  estimates,  resulting  in  less  pro-cyclical 
NAWRU  estimates.  While  the  earlier  version  of 
the  anchor  worked  reasonably  well  in  practice,  it 
used  some  quick  fixes  to  deal  with  certain 
limitations  of  the  data.  Most  importantly,  the 
coefficients for the anchor were only estimated for 
those Member States that had joined the European 
Union before 2004. In addition, a closer analysis of 
the  residuals  suggested  that  some  variables  were 
missing in the specification of the anchor. In order 
to  address  these  limitations,  the  new  approach 
estimates  the  coefficients  for  the  anchor  using 
information  for  all  countries.  It  also  partly  solves 
the  missing  variable  problem  by  including  a 
demographic  control  variable  for  all  Member 
States.  

Following  these  changes  to  the  methodology,  the 
AWG and the EPC endorsed the use of the Spring 
2020  T+10  potential  GDP  growth  projections  for 
the 2021 Ageing Report. 

 
 
 
ANNEX 4 

Pension projection reporting sheet 

Table II.A4.1:  Pension projection reporting sheet: blocks common to all schemes 

European Commission
DG ECFIN Unit C2
2021 Ageing Report: reporting framework on pensions (expenditure, pensioners, contributions and taxes)

Country  
Scenario  
Pension system type  
Reporting of variable on voluntary basis  
Variable calculated by formula  

A. Fixed table

1 GDP (used in projections, in current prices - billion EUR)
2 GDP deflator
3 Economy-wide gross wage total (current prices - billion EUR)
4 Average gross wage (current prices - 1000 EUR)
5 Consumer price inflation

0 - AVERAGE GROSS WAGE AT RETIREMENT 

6 Average gross wage at retirement (current prices - 1000 EUR)
1 - PENSION EXPENDITURE (gross, million EUR, current prices) 

7 Public pensions scheme, gross (8+9+10+11+12+13) and (14+22+24+26)

   Of which 

2000

2019

2030

2040

2050

2060

2070

8                      aged -54
9                      aged 55-59
10                      aged 60-64
11                      aged 65-69
12                      aged 70-74
13                      aged 75+
14   Old-age and early pensions (16+18+20)
15           Of which new pensions (17+19+21)
16       Of which flat component (basic pension)
17            Of which new pensions (161*162*163)
18       Of which earnings-related pensions
19            Of which new pensions (DB/NDC: 155*156*157*158*159*160; PS: 155*176*177*159*178*160)
20      Of which  minimum pensions (non-contributory, i.e. minimum income guarantees for retired people)
21           Of which new pensions
22   Disability pensions
23
24   Survivors' pensions
25
26   Other pensions
27
28 Private occupational schemes, gross
29
30 Private individual mandatory schemes, gross
31
32 Private individual non-mandatory schemes, gross
33
34 Total pension expenditure, gross (35+36+37+38+39+40) and (7+28+30+32)

      Of which new pensions (191*192*193*194*195*196)

      Of which new pensions (179*180*181*182*183*184)

      Of which new pensions (185*186*187*188*189*190)

      Of which new pensions

      Of which new pensions

      Of which new pensions

   Of which 

35                      aged -54
36                      aged 55-59
37                      aged 60-64
38                      aged 65-69
39                      aged 70-74
40                      aged 75+

2 - TAXES ON PENSIONS & NET PENSION EXPENDITURES (million EUR, current prices) 

41 Public pension scheme, tax revenues (including compulsory social security contributions paid by pensioners)
42 Private occupational schemes, tax revenues
43 Private individual mandatory schemes, tax revenues
44 Private individual non-mandatory schemes, tax revenues
45 Total revenues from taxes on pensions (41+42+43+44)
46 Public pensions scheme, net of taxes on pensions (7-41)
47 Of which minimum pensions (non-contributory, i.e. minimum income guarantees for retired people)
48 Private occupational schemes, net of taxes on pensions (28-42)
49 Private individual mandatory schemes, net of taxes on pensions (30-43)
50 Private individual non-mandatory schemes, net of taxes on pensions (32-44)
51 Total pension expenditure, net of taxes on pensions (34-45) and (46+48+49+50)

3 - BENEFIT RATIO

52 Public pensions (7/86)/4
53     Of which old-age earnings-related pensions (including the flat component) ((16+18)/100)/4
54 Private occupational schemes (28/105)/4
55 Private individual mandatory schemes (30/106)/4
56 Private individual non-mandatory schemes (32/107)/4
57 Total benefit ratio (34/108)/4

4 - GROSS AVERAGE REPLACEMENT RATES (at retirement)

58 Public pensions: old-age earnings-related pensions (including the flat component)
59 Private occupational schemes (29/179)/6

(Continued on the next page) 

135 

 
 
   
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table (continued) 

60 Private individual mandatory schemes (31/185)/6
61 Private individual non-mandatory schemes (33/191)/6
62 Total replacement rate

5 - NUMBER OF PENSIONS (in 1000)

63 Public pensions (64+65+66+67+68+69) and (70+73+74+75)

Of which 

64                      aged -54
65                      aged 55-59
66                      aged 60-64
67                      aged 65-69
68                      aged 70-74
69                      aged 75+
70 Old-age and early pensions (71+72)
71
72
73   Disability pensions
74   Survivors' pensions 
75   Other pensions 
76 Private occupational schemes 
77 Private individual mandatory schemes
78 Private individual non-mandatory schemes
79 All pensions (63+76+77+78) and (80+81+82+83+84+85)

Of which earnings-related pensions and the flat component
Of which minimum pensions (non-contributory, i.e.minimum income guarantees for retired people)

Of which 

80                      aged -54
81                      aged 55-59
82                      aged 60-64
83                      aged 65-69
84                      aged 70-74
85                      aged 75+

6 - NUMBER OF PENSIONERS (in 1000)

86 Public pensioners (87+89+91+93+95+97) and (99+102+103+104)

Of which 

87                      aged -54
88                           Of which female
89                      aged 55-59
90                           Of which female
91                      aged 60-64
92                           Of which female
93                      aged 65-69
94                           Of which female
95                      aged 70-74
96                           Of which female
97                      aged 75+
98                           Of which female
99   Old-age and early pensions (100+101)

Of which earnings-related pensions or flat component
Of which minimum pensions (non-contributory, i.e.minimum income guarantees for retired people)

100
101
102   Disability 
103   Survivors pensions 
104   Other pensions 
105 Private occupational schemes 
106 Private individual mandatory schemes
107 Private individual non-mandatory schemes
108 All pensioners (109+111+113+115+117+119)

   Of which 

109                      aged -54
110                           Of which female
111                      aged 55-59
112                           Of which female
113                      aged 60-64
114                           Of which female
115                      aged 65-69
116                           Of which female
117                      aged 70-74
118                           Of which female
119                      aged 75+
120                           Of which female
7 - CONTRIBUTIONS (million EUR, current prices)
121 Public pensions (122+123+124+125)
122 Of which employer
123 Of which employee
124 Of which state (only if contribution is legislated)
125 Of which other revenues, i.e. private pension funds, nuisance charges (only if legislated)
126 Private occupational schemes (total contributions)
127 Private individual mandatory schemes (total contributions)
128 Private individual non-mandatory schemes (total contributions)
129 Total pension contributions (121+126+127+128)

136 

(Continued on the next page) 

 
 
   
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

Table (continued) 

8 - NUMBER OF CONTRIBUTORS (employees, in 1000) 
130 Public pensions
131 Private occupational schemes 
132 Private individual mandatory schemes
133 Private individual non-mandatory schemes
9 - INDEXATION FACTORS (percentage) 
134 Indexation factor earnings-related pensions
135 Indexation factor flat component
136 Indexation factor minimum pensions
10 - BREAKDOWN OF NEW PENSION EXPENDITURES

PUBLIC PENSIONS 
PRIVATE OCCUPATIONAL SCHEMES - TOTAL (calculated in line 29)

179 Number of new pensions (in 1000)
180 Average contributory period (in years)
181 Average accrual rate 
182 Monthly average pensionable earning
183 Adjustment factors (1 if not applicable)
184 Average number of months paid the first year

PRIVATE INDIVIDUAL MANDATORY SCHEMES - TOTAL (calculated in line 31)

185 Number of new pensions (in 1000)
186 Average contributory period (in years)
187 Average accrual rate 
188 Monthly average pensionable earning
189 Adjustment factors (1 if not applicable)
190 Average number of months paid the first year

PRIVATE INDIVIDUAL NON-MANDATORY SCHEMES - TOTAL (calculated in line 33)

191 Number of new pensions (in 1000)
192 Average contributory period (in years)
193 Average accrual rate 
194 Monthly average pensionable earning
195 Adjustment factors (1 if not applicable)
196 Average number of months paid the first year
11 - ASSETS AND RESERVES & RETURN (million EUR, current prices)
197 Public pension scheme: assets and reserves
198 Public pension scheme: average return (%)
199 Private occupational schemes: assets and reserves
200 Private occupational schemes: average return (%)
201 Private individual mandatory schemes: assets and reserves
202 Private individual mandatory schemes: average return (%)
203 Private individual non-mandatory schemes: assets and reserves
204 Private individual non-mandatory schemes: average return (%)

B. Additional information

205
206
207

Source: European Commission, EPC. 

Table II.A4.2:  Pension projections reporting sheet: disaggregation of new public pensions expenditure – earnings-related  for 

2000

2019

2030

2040

2050

2060

2070

defined benefit (DB) schemes 

10 - BREAKDOWN OF NEW PENSION EXPENDITURES

PUBLIC PENSIONS - DEFINED BENEFIT
TOTAL - Earnings-related pension (calculated in line 19)

155
156
157
158
159
160

161
162
163

Number of new pensions (in 1000)
Average contributory period (in years)
Average accrual rate (contributory only)
Monthly average pensionable earning (in 1000 EUR)
Sustainability/adjustment factors (1 if not applicable)
Average number of months paid the first year

TOTAL - Flat component (basic pension) (calculated in line 17)

Number of new pensions (in 1000)
Average monthly new pension (EUR)
Average number of months paid the first year

Data to be provided also by gender. 
Source: European Commission, EPC. 

137 

 
 
 
   
 
 
   
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table II.A4.3:  Pension projection reporting sheet: disaggregation of new public pension expenditure - earnings-related for 

notional defined contribution (NDC) schemes 

10 - BREAKDOWN OF NEW PENSION EXPENDITURES

2000

2019

2030

2040

2050

2060

2070

PUBLIC PENSIONS - NOTIONAL DEFINED CONTRIBUTION
TOTAL - Earnings-related pension (calculated in line 19)

155
156
157
168
169
158
159
160

161
162
163

Number of new pensions (in 1000)
Average contributory period (in years)
Average accrual rate (c/A) 

  Notional-accounts contribution rate (c)
  Annuity factor (A)

Monthly average pensionable earning
Sustainability/adjustment factors (1 if not applicable)
Average number of months paid the first year

TOTAL - Flat component (basic pension) (calculated in line 17)

Number of new pensions (in 1000)
Average monthly new pension (EUR)
Average number of months paid the first year

Data to be provided also by gender. 
Source: European Commission, EPC. 

Table II.A4.4:  Pension projection reporting sheet: disaggregation of new public pension expenditure - earnings-related for 

2000

2019

2030

2040

2050

2060

2070

point systems (PS) 

10 - BREAKDOWN OF NEW PENSION EXPENDITURES

PUBLIC PENSIONS - POINT SYSTEM
TOTAL - Earnings-related pension (calculated in line 19)

155
176
157
177
156
159
178
160

161
162
163

Number of new pensions (in 1000)
Point value (EUR/month)
Average accrual rate (points/year; 177/156)

Total pension points at retirement
Average contributory period (years)

Sustainability/adjustment factors (1 if not applicable)
Correction coefficient (1 if not applicable)
Average number of months paid the first year

TOTAL - Flat component (basic pension) (calculated in line 17)

Number of new pensions (in 1000)
Average monthly new pension (EUR)
Average number of months paid the first year

Data to be provided also by gender. 
Source:  European Commission, EPC. 

138 

 
 
 
   
 
 
   
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

Table II.A4.5:  Reporting sheet for special pension schemes (voluntary reporting) 

Are special pension schemes included in the projections? (SELECT)
Where they included in the previous projections?

YES/NO
YES/NO

2009

2019

2030

2040

2050

2060

2070

1 -  Special public pension schemes: expenditure (million EUR)

1 Total (4+5+6)
2 Of which new pensions
3 Total (%GDP)
4 Difficult conditions
5 Security and defence 
6 Other (7+8+9+10+11+12+13)
of which self-employed
7
of which merit and deprived
8
of which judicial staff
9
of which railway staff
10
of which teachers
11
of which civil servants (not included in the above categories)
12
of which atypical (all other)
13

2 -  Special public pension schemes: number of pensioners (in 1000)

14 Total (17+18+19)
15 Of which new pensioners
16 Total (% of public pensioners)
17 Difficult conditions
18 Security and defence 
19 Other (20+21+22+23+24+25+26)
20
21
22
23
24
25
26

of which self-employed
of which merit and deprived
of which judicial staff
of which railway staff
of which teachers
of which civil servants (not included in the above categories)
of which atypical (all other)

Source: European Commission, EPC. 

139 

 
 
 
 
  
 
ANNEX 5 

Overview of pension systems in the Member States 

Table II.A5.1:  Pension schemes in EU Member States and projection coverage 

Public pensions(3)

Private pension scheme

Pension scheme type

Minimum 
Pension(4)

Old-age 
pensions

Early 
retirement 
pensions

Disability 
pensions

Survivors' 
pensions

BE

BG
CZ

DK

DE
EE

IE

EL(1)
ES
FR(2)
HR
IT

CY

LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO

DB

DB
DB

MT - SA

MT - SA
X

Flat rate + DB

FR & MT suppl.

PS
PS

MT - SA*
MT - SA

ER

ER
ER
FR & MT 
suppl.
ER
ER

Flat rate + DB

MT - FR & SA

FR

Flat rate + DB + NDC
DB
DB + PS
PS
NDC

PS

NDC
PS
DB
DB 
Flat rate + DB
Flat rate + DB
DB
NDC 
DB
PS
DB
PS
DB
NDC 
NDC 

MT - FR
MT
MT - SA
ER
MT - SA

MT & ER

FR - SA
SA
MT - SA*
MT - SA
MT - SA
SA
MT - SA
ER
MT - SA(5)
SA
X
MT - SA
MT
MT
FR

FR - ER
ER
ER
ER
ER

ER

ER
ER
ER
ER
FR & ER
FR
ER
ER
ER
ER
ER
ER
ER
ER
ER

ER

ER
ER

V

ER
ER

X

FR - ER
ER
ER
ER
ER

ER

ER
ER
ER
ER
X
X
ER
ER
ER
ER
ER
ER
ER
ER
X

ER priv       

FR self-emp
ER
ER

FR 

ER
ER

ER

ER
ER

FR

ER
ER

FR - MT 

FR - MT

FR - ER
ER
ER
ER
ER

ER

ER
ER
ER
ER
FR & ER
ER
ER
ER
ER
ER
ER
ER
ER
ER
ER

FR - ER
ER
ER
ER
ER

ER

ER
ER
ER
ER
FR & ER
FR
ER
ER
ER
ER
ER
ER
ER
ER
ER

Occupational 
pension scheme
M* priv         

V* self-emp 
V*
X

Quasi M

V*
M*

M pub          
V* priv
V*
V
V*
X
V*

M* pub         
V* priv
X
X
V*
V*
V*
M
V*
V*
Quasi M
X
V*
X
V*
Quasi M
M*

Mandatory 
private 
individual

Voluntary 
private 
individual

X

Yes*
X

X

X
Yes

X

X
X
X
Yes
X

X

Yes
Quasi M
X
X
X
X
X
Yes*
X
Yes
X
X
X
Yes
X

Yes*

Yes*
Yes*

Yes

Yes*
Yes*

Yes*

Yes*
Yes
Yes*
Yes*
Yes*

Yes*

Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes*
Yes
Yes*
Yes*
Yes*
Yes
Yes*

(1) The public supplementary pension fund is NDC since 2015.  
(2) Point system refers to the ARRCO and AGIRC pension schemes. 
(3) Public pension expenditure include all public expenditure on pension and equivalent cash benefits granted for a long 
period, see Annex 6 for details on the coverage of the public pension expenditure projections.  
(4) Minimum pension corresponds to minimum pension and other social allowances for older people not included elsewhere.  
(5) Includes all pensions of the non-earning-related scheme such as old-age, disability and survivors’ pensions and the social 
supplement (equal to the difference between the guaranteed minimum amount and pension benefits calculated according 
to the rules) granted to the earning-related pensioners. 

DB: Defined benefit system 
NDC: Notional defined contribution system 
PS: Point system 

MT - Means-tested 
FR - Flat rate 
ER - Earnings-related 
SA - Social allowance/assistance 
V - Voluntary 
M - Mandatory 
X - Does not exist 
* Not covered in the projections 
Source: European Commission, EPC. 

140 

 
 
  
 
 
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

Table II.A5.2:  Statutory retirement ages, early retirement ages (in brackets) and incentives to postpone retirement 

Statutory retirement age (early retirement age)

Incentives**

MALE

FEMALE

BE

BG

CZ

DK*

DE

EE*

IE

EL*

ES

FR

HR

IT*

CY*

LV

LT

LU

HU

MT

NL*

AT

PL

PT*

RO

SI

SK

FI*

SE

NO

2019

65 (63)

64.2 (63.2)

63.5 (60)

65.5 (63)

65.7 (63)

2030

67 (63)

65 (64)

65 (60)

68 (65)

67 (63)

2050

67 (63)

65 (64)

65 (60)

72 (69)

67 (63)

2070

67 (63)

65 (64)

65 (60)

2019

65 (63)

2030

67 (63)

61.3 (60.3)

63.3 (62.3)

61.2 (58.2)

74.5 (71.5)

65.5 (63)

67 (63)

65.7 (63)

65 (60)

68 (65)

67 (63)

2050

67 (63)

65 (64)

65 (60)

72 (69)

67 (63)

2070

67 (63)

65 (64)

65 (60)

74.5 (71.5)

67 (63)

63.6 (60.6)

65.5 (60.5)

67.7 (62.7)

69.8 (64.8)

63.6 (60.6)

65.5 (60.5)

67.7 (62.7)

69.8 (64.8)

66 (66)

67 (62)

65.7 (63.7)

66.8 (61.8)

65 (60)

67 (64)

65 (65)

63.5 (61.5)

63.8 (58.8)

65 (57)

64 (64.3)

62.9 (61)

68 (68)

68 (68)

68 (68)

68.8 (63.8)

70.8 (65.8)

72.6 (67.6)

67 (65)

67 (62)

65 (60)

67 (65)

67 (62)

65 (60)

67.7 (64.7)

69.3 (66.3)

67 (65)

67 (62)

65 (60)

71 (68)

66.5 (66.5)

68.3 (68.3)

69.9 (69.9)

65 (63)

65 (60)

65 (57)

65 (65)

65 (61)

65 (63)

65 (60)

65 (57)

65 (65)

65 (61)

65 (63)

65 (60)

65 (57)

65 (65)

65 (61)

66 (66)

67 (62)

65.7 (63.7)

66.8 (61.8)

62.3 (57.3)

67 (64)

65 (65)

63.5 (61.5)

62.7 (57.7)

65 (57)

64 (64.3)

62.9 (61)

68 (68)

68 (68)

68 (68)

68.8 (63.8)

70.8 (65.8)

72.6 (67.6)

67 (65)

67 (62)

65 (60)

67 (65)

67 (62)

65 (60)

67.7 (64.7)

69.3 (66.3)

67 (65)

67 (62)

65 (60)

71 (68)

66.5 (66.5)

68.3 (68.3)

69.9 (69.9)

65 (63)

65 (60)

65 (57)

65 (65)

65 (61)

65 (63)

65 (60)

65 (57)

65 (65)

65 (61)

65 (63)

65 (60)

65 (57)

65 (65)

65 (61)

66.3 (66.3)

67.3 (67.3)

68.5 (68.5)

69.8 (69.8)

66.3 (66.3)

67.3 (67.3)

68.5 (68.5)

69.8 (69.8)

65 (60)

65 (65)

66.4 (60)

65 (60)

65 (60)

62.5 (60.5)

65 (60)

65 (65)

67 (60)

65 (60)

65 (60)

64 (62)

65 (60)

65 (65)

65 (60)

65 (65)

60 (58)

60 (60)

68.3 (60)

69.3 (60)

66.4 (60)

65 (60)

65 (60)

64 (62)

65 (60)

65 (60)

64 (62)

61.2 (56.2)

64.5 (60)

62.5 (60.5)

63.5 (60)

60 (60)

67 (60)

63 (58)

65 (60)

64 (62)

65 (60)

60 (60)

65 (60)

60 (60)

68.3 (60)

69.3 (60)

63 (58)

65 (60)

64 (62)

63 (58)

65 (60)

64 (62)

63.5 (61)

65.1 (62.3)

66.5 (63.7)

67.7 (64.8)

63.5 (61)

65.1 (62.3)

66.5 (63.7)

67.7 (64.8)

67 (61)

67 (62)

67 (62)

67 (62)

67 (62)

67 (62)

67 (62)

67 (62)

67 (61)

67 (62)

67 (62)

67 (62)

67 (62)

67 (62)

67 (62)

67 (62)

Penalty

Bonus

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

BG - The latest pension reform included a provision to link retirement ages to life expectancy as from 2037. This provision has 
not been implemented, though. 
CZ - Statutory retirement age depends on the number of children. Values for women with two children are reported. 
DK – Increase in the retirement age is subject to a Parliamentary decision. 
IT - Retirement is allowed with at least 20 years of contribution and a minimum pension amount of 1.5 times the old-age 
allowance in 2012. In bracket the minimum age for early retirement under the NDC system is reported (a minimum pension 
amount of 2.8 times the old-age allowance is required in addition to the minimum of 20 years of contribution). Early 
retirement is also allowed regardless of age, with a contribution requirement of 43.1 years in 2019, indexed to changes in life 
expectancy (43.4 in 2030, 45.1 in 2050 and 46.8 in 2070). Workers who reach the age of 62 with a minimum contribution 
requirement of 38 years (so-called Quota 100) may retire earlier in the period 2019-2021. 
PT - Since 2015, early retirement is possible from the age of 60 with 40 contributory years. For each year the contributory 
career exceeds 40 years, the statutory retirement age is reduced by 4 months. The pension benefit is reduced by 0.5% for 
each month of anticipation to the statutory retirement age (penalty).  
SE - Retirement age flexible from age of 61 without an upper limit. Under the Employment Protection Act, an employee is 
entitled to stay in employment until the age of 67.  
*Countries where the statutory retirement age is legislated to increase in line with life expectancy. Reported retirement ages 
are calculated on the basis of life expectancy expectation in the Eurostat population projections. 
**Actuarial equivalence is not considered as a penalty/bonus. 
Source: European Commission, EPC. 

141 

 
 
           
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table II.A5.3:  Key indexation and valorisation parameters of pension systems (old-age pensions) 

BE
BG
CZ
DK
DE
EE
IE
EL
ES
FR
HR
IT
CY
LV
LT
LU
HU
MT
NL
AT
PL
PT
RO
SI
SK
FI
SE
NO

Pensionable earnings reference
Full career
Full career
Full career
Years of residence
Full career
Full career
Flat rate
Full career
Last 25 years
25 best years (CNAVTS)
Full career
Full career 
Full career
Full career
Full career
Full career
Full career
10 best of last 41 years
Years of residence
Full career
Full career
Full career up to a limit of 40 years
Full career
Best consecutive 24 years
Full career 
Full career
Full career
Full career

General valorisation variable(s)
Prices
Wages
Wages
Not applicable
Wages
Prices and social taxes
Not applicable
Prices and wages
Prices
Prices
Prices and wages
GDP
Wages
Contribution wage sum index
Wage sum
Prices and wages
Wages
Cost of living
Not applicable
Wages
NDC 1st: Wages, NDC 2nd: GDP
Prices
Prices and wages
Wages
Wages
Prices and wages
Wages
Wages

General indexation variable(s)
Prices and living standard
Prices and wages
Prices and wages
Wages
Wages plus sustainability factor
Prices and social taxes
No fixed rule
Prices and GDP (max 100% prices)
Index for pension revaluation
Prices
Prices and wages
Prices
Prices and wages
Prices and wage sum
Wage sum
Prices and wages
Prices
Prices and wages
Wages
Prices
Prices and wages
Prices and GDP
Prices and wages
Prices and wages
Prices
Prices and wages
Wages
Wages

BG - Pensionable earnings reference is full career back to 2000. 
CZ - Pensionable earnings reference is full career back to 1986. 
IE - A price and wage indexation rule has been assumed in the projections. 
EL - Pensionable earnings reference is full career, taking into account wages/income from 2002 onwards.  
ES - Pensionable earnings reference is last 25 years as of 2022. The IPR is established annually at a level consistent with a 
balanced budget of the Social Security system over the medium run. Depending on the balance of the system, the 
indexation will be less than price (budget deficit) or price + 0.5% (budget balance). It has been suspended since 2018 and is 
expected to remain suspended during the 2021 fiscal year. 
FR - The pensionable earnings reference is full career in AGIRC (Association générale des institutions de retraite des cadres) 
and ARRCO (Association pour le régime de retraite complémentaire des salariés); CNAVTS: Caisse nationale de l'assurance 
vieillesse des travailleurs salariés. Valorisation rule and indexation of 1% in both AGIRC and ARRCO. 
LT - Pensionable earnings reference is full career back to 1994. Pensions are indexed to the seven-year average of the wage 
sum growth over the current, previous three and next three years. The index is applied in case of a balanced budget of the 
Pension Social Security System in two consecutive years and contingent on positive GDP or wage sum growth.  
LU - Indexation rule is wages if sufficient financial resources are available, otherwise only cost of living indexation. 
HU - Pensionable earnings reference is full career back to 1988. 
MT - Pensionable earnings reference rule applies to people born as of 1969. 
PT - Pensionable earnings reference is full career as of 2002. Price and wage valorisation rule applies to earnings registered 
between 2002 and 2011. 
SK - Pensionable earnings reference is full career back to 1984. 
SE - Indexation rule is wage growth minus 1.6 pps. 
NO - Indexation rule is wage growth minus 0.75 pps. 
Source:  European Commission, EPC. 

142 

 
 
                
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

Table II.A5.4:  Automatic balancing mechanisms, sustainability factors and links to life expectancy in pension systems 

Automatic balancing 
mechanism

Sustainability factor (benefit 
linked to life expectancy)(6)

Retirement age linked to 
life expectancy

CY

DE
DK(1)
FR(2)

FI
EL(3)

ES

EE

IT

LT

LV
MT(4)
NL(5)

PL
PT(5)

SE

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

X

(1) Subject to Parliamentary decision. 
(2) Pension benefits evolve in line with life expectancy through the 'proratisation' coefficient; it has been legislated until 2035. 
(3) An automatic balancing mechanism is applied in the auxiliary pension system. 
(4) Subject to Parliamentary decision. The Government is obliged to provide Parliament, at least every five years, with 
recommendations to keep a stable proportion between the contribution period and life expectancy at retirement. 
(5) Only two thirds of the increase in life expectancy is reflected in the retirement age. 
(6) In NDC systems, the benefit is linked to changes in life expectancy through the annuity factor. 
Source: European Commission, EPC. 

143 

 
 
           
 
1
4
4

Table II.A5.5:  Contribution rates to the public pension system 

Contribution rate: employer

Contribution rate: employee

Contribution rate

r

E
u
o
p
e
a
n
C
o
m
m

i
s
s
i
o
n

i

2
0
2
1
A
g
e
n
g
R
e
p
o
r
t
:

U
n
d
e

i

r
l
y
n
g
a
s
s
u
m
p

j

r

t
i
o
n
s
a
n
d
p
o
e
c
t
i
o
n
m
e
t
h
o
d
o
o
g
e
s

l

i

State contributions

Other provisions
Social security spending is also funded by State subisidies (17.7% of total 
revenue in 2019) and alternative funding (15.7% of total revenue), mainly 
VAT revenues. 

 State commitment for covering the deficit on an annual basis.

Balance of pension system is part of general governement budget
-
State subsidies with annual indexation. 'Sustainability fund' fluctuates 
between 20% and 150% of monthly pension expenditures. The 
contribution rate is set so that this requirement is met.

-

Contribution rate: self-employed

In 2020, 20.5% for revenues up to 60428 EUR and 
14.16% for revenues between 60428 EUR and 89051 
EUR.
born before 1960: 19.8% of declared covered earnings 
in the preceding year; born after 1959: 14.8% of 
declared covered earnings
28%
-

18.6%

20%

Social Insurance Fund and Social Assistance Fund (to finance other, non-
pension social benefits). Shortfalls are met by the Exchequer.

4% of covered income

National budget/other sources

Contributions are based on insurance classes. 
Corresponding insurable base is derived taking into 
account contribution rate of 20%

Pension Reserve Fund. If needed, annual funding gaps are covered 
through central government transfers.

28.3%

Pensions Reserve Fund and Old-age solidarity fund

17.75% up to the SSC.

Government is committed to cover deficits.

20% (public PAYG scheme participants only); 15% 
(participants in both public PAYG scheme and 
mandatory fully-funded DC scheme)

Residual funding by the State (pension expenditure exceeding 
contributions)
Reserve fund

24%

15.6% of insurable income

-

Contribution rate for old-age pension capital: 20% (if no 
participant of 2nd tier) or 16% (if participant of 2nd tier) 
with 4% contribution to the 2nd tier 

State provides funds from the national budget to cover the general 
pension part of public pension scheme

8.72% - based on 50% of declared earnings

Buffer fund of at least 1.5 times the amount of annual pension expenditure

16%

-

-

10% of declared monthly earnings and 11.8% of 
declared monthly earnings in the form of a social 
contribution tax
15% of the annual income, subject to the same ceiling 
as for employees

Government supplements shortfall between expenditure and funds raised 
by the 17.9% tax levy

17.9%

Federal budget covers the deficits in public pension schemes

Demographic Reserve Fund

Social Security Trust Fund

17% for farmers, 18.5% for self-employed and 20% for 
liberal professions

19.52%
Employee: 21.4% or 25.2%; employer: 10%, if economic 
dependence is higher than 80%, or 7%

State provides funds from the national budget to cover the public pension 
system deficit.

State provides funds from the national budget and other sources to cover 
shortfalls.

Government makes contributions for people insured by the state (e.g. 
maternity leave) and covers special benefits (e.g. Christmas bonus). 
Otherwise, social security system deficits are covered by state transfers.

National and guarantee pensions are fully funded by the State. Part of 
farmers’, self-employed persons’ and seafarers’ pension are funded by the 
State. 25% of private sector pension are prefunded.
Buffer funds
State Pension Fund contributes to financing government expenditures 
(pension and other)

10.5% or 26.3%

24.35%

18% if only covered in the 1st pillar; otherwise 4.75% is 
sent to the second pillar in 2019 (rising to 6% by 2024)

24.1% (18-52y and +63y); 25.6% (53-62y)

17.21%

11.4%

BE

24.92% (for all Social Security schemes)

13.07% (for all Social Security schemes)

BG

CZ
DK

DE

EE

IE

8.22% when born after 1959; 11.02% when 
born before 1960

6.58% when born after  1959; 8.78% when 
born before 1960

21.5%
-

9.3%

20% (if not participating to 2nd pillar); 16% (if 
participating to 2nd pillar)

Varies

6.5%
-

9.3%

-

Varies

EL

Main pensions 13.33%; auxiliary pensions 3% Main pensions 6.67%; auxiliary pensions 3%

Private sector: 23.6% 

Private sector: 4.7% 

Private sector (CNAV): 10.45% up to the 
Social Security Ceiling (SSC)

4.86% to 17.58% for employees in arduous 
and hazardous occupations

23.81%

8.3%

Total contribution rate for old-age pension 
capital (employer and employee): 20% (if no 
participant of 2nd tier) or 16% (if participant of 
2nd tier), with 4% contribution to the 2nd tier

0.0%

8%

15.5% in 2018, 13.0% in 2019, 11.8% in 2020 
(part of social contribution tax payed into 
Pension Insurance Fund)

10%

-

12.55%

9.76%

23.75%

Between 0% and 8%: 0% (normal working 
conditions); 4% (difficult working conditions) 
and 8% (special working conditions)

8.85%

14% of gross wage if one does not participate 
in the 2nd pillar; otherwise 4.75% is sent to the 
second pillar in 2019 (rising to 6 % by 2024)

17.35% for private sector; 21.17% for local 
government (in 2019)

Private sector (CNAV): 7.3% up to the social 
security ceiling (SSC). Reduced contribution 
rates are applied to some specific groups 
(artists, journalists and part-time medical 
workers)

20% (public PAYG scheme participants only); 
15% (participants in both public PAYG scheme 
and mandatory fully-funded DC scheme)

9.19%

8.3%

-

8.72%

8%

10%

10%

17.9%

10.25%

9.76%

11%

25%

15.5%

4 % of gross wage

ES

FR

HR

IT

CY

LV

LT

LU

HU

MT

NL

AT

PL

PT

RO

SI

SK

FI

SE

NO

-

-

-
-

-

-

-

-

-

-

-

4.9%

-

-

8%

-

10%

-

For farmers, self employed and liberal 
professions, the difference with the standard 
contribution rate of 22.8% is borne by federal 
transfers
-

-

-

-

-

6.75% (18-52y and +63y); 8.25% (53-62y)

17.1% for State pensions

10.21% (including Premium Pension)
PAYG system without earmarked tax going to 
pensions. 

7% (including Premium Pension)
PAYG system without earmarked tax going to 
pensions

Employer contribution' for social insurance
PAYG system without earmarked tax going to 
pensions

When several schemes exist, the information refers to the main (general regime) pension scheme. 
EL: Main pensions: unified rates from 2022 onwards. Auxiliary pensions: 2019-21: 3.25%. 

Source: European Commission, EPC. 

 
 
 
 
 
 
 
 
 
 
 
  
 
  
 
ANNEX 6 

Coverage and specification of pension schemes 

Table II.A6.1:  Pension schemes included in the projections 

Schemes covered in the projections 

Schemes not covered 

BE  Public pensions: old-age and early pensions 

Public pensions scheme 

Means-tested minimum benefits: 65+; 66+ as of 2025; 67+ as of 
2030. 

Wage earners: earnings-related old-age (63+ and 41 career years in 
2018 and 63+ and 42 career years as of 2019(a)), widows. 

Self-employed: earnings-related old-age (63+ and 41 career years in 
2018 and 63+ and 42 career years as of 2019(a)), widows. 

Civil servants: earnings-related old-age (63+ and 41 career years in 
2018 and 63+ and 42 career years as of 2019(a)), widows, disability. 

Unemployment with company 
allowance only includes the part paid 
from unemployment benefit scheme, 
not the allowance paid by the employer. 

Private occupational pensions scheme 

Wage earners. 

Self-employed. 

Private individual pensions scheme 

Unemployment with company allowance (wage earners): 62+ (as of 
2015) and 40 career years (for men as of 2015 and for women as of 
2024), until the age of 64 (65 as of 2025, 66 as of 2030). 

Non-mandatory. 

Unemployment with company allowance (wage earners) for 
companies undergoing restructuring or in difficulty (55+ in 2016; 
56+ in 2017 and 2018; 58+ in 2019; 59+ in 2020; 60+ as of 2021), 
until the age of 64 (65 as of 2025, 66 as of 2030). 

Public pensions: disability 

Wage earners, disability pensions: -64; -65 as of 2025; -66 as of 
2030. 

Self-employed, disability pensions: -64; -65 as of 2025; -66 as of 
2030. 

(a)Some exceptions: 61 and 43 career years, 60 and 44 career years. 

BG  Public pensions: old-age and early pensions 

Earnings-related old-age pensions (including farmers and military 
officials). 

Public pensions: other 

Earnings-related disability pensions due to general disease 
(including farmers and military officials). 

Earnings-related disability pensions due to work injury and 
professional disease (including farmers and military officials). 

Earnings-related survivors’ pensions according to relationship with 
the deceased – widows, children, parents. 

Pensions not related to employment – social pensions, special merits 
pensions, pensions by Decree. 

Supplementary mandatory pension  
schemes 

Supplementary life-long old-age 
pensions - Universal Pension Funds 
(UPF). 

Early retirement pensions for a limited 
period of time for people working in 
hazardous conditions - Professional 
Pension Funds (PPF). 

Supplementary voluntary pension 
schemes – individual private and 
occupational pensions. 

Teachers’ Pension Fund.  

(Continued on the next page) 

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2021 Ageing Report: Underlying assumptions and projection methodologies 

Table (continued) 

CZ  Public pensions: old-age and early pensions 

Individual private schemes 

Earnings-related old-age pensions (all sectors except armed forces, 
all ages). 

Voluntary fully funded scheme. 

Early pensions with permanent reductions (all sectors except armed 
forces, all ages). 

Public pensions: other 

Disability pensions (all three types of disability, all sectors except 
armed forces, all ages). 

Widows and widowers pensions (all ages). 

Orphans pensions (all ages). 

DK  Public pensions: old-age and early pensions 

Public flat-rate old-age pensions and means-tested supplements, all 
citizens 65+. 

Civil servants old-age pensions 65+, central and local government. 

Voluntary early retirement schemes, all wage earners. 

Public  pensions: other 

Disability pensions, -64. 

Occupational pensions 

Labour market pensions.  

Individual, private pensions. 

Labour market supplementary pensions, ATP. 

Employees’ capital fund (LD). 

DE  Public pensions: old-age and early pensions 

Earnings-related old-age, widows and disability schemes, all ages. 

General scheme and civil servants. 

Early pensions for long-time workers. 

Early pensions for severely handicapped. 

Public pensions: other 

(covered above; not shown separately) 

Means-tested minimum benefits to 
elderly (social assistance); 0.1% of 
GDP in 2019. 

Farmers pensions; 0.08% of GDP in 
2019. 

Occupational pensions 

Annual contributions.  

Pension expenditure of 1.2% of GDP in 
2019.  

Individual funded and state subsidised 
private pension (Riester-Rente), 
schemes at a building stage, only 
contributions to the schemes. 

(Continued on the next page) 

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Table (continued) 

EE  Public pensions: old-age and early pensions 

Minimum flat-rate pensions, all citizens. 

Earnings-related old-age pensions; length-of-service component to 
60+ for women and 63+ for men in 2007, 65+ for both sexes as of 
2026, all sectors (Pension Insurance Fund). 

Early pensions (possible to retire 3 years before the statutory 
retirement age), all sectors. 

Public pensions: other 

Disability and widows’ pensions, all ages, all sectors (Pension 
Insurance Fund). 

Private mandatory pensions 

Mandatory funded pensions, mandatory for people born as of 1983. 

IE  Public pensions: old-age and early pensions 

Occupational pensions 

Minimum flat-rate old-age non-contributory pensions, 66+ (also 
includes widow(er)s non-contributory pensions, deserted wives, 
66+), all sectors. 

Private sector schemes and public 
sector commercial bodies. 

Carers, 66+, all sectors. 

Flat-rate contributory 66+, private sector, self-employed and some 
civil servants. 

Widow(er)s contributory pensions, 66+, all sectors. 

Carers and deserted wives (scheme winded down), 66+, private 
sector, self-employed and some civil servants. 

Public pensions: others 

Widow(er)s non-contributory pensions,  65-, all sectors. 

Blind people, carers, 65-, all sectors. 

Disability pensions, 65-, and invalidity pensions 65-, private sector, 
self-employed, some civil servants. 

Carers, contributory, 65-, private sector, self-employed, some civil 
servants. 

Widow(ers) contributory pension, 65-, all sectors. 

Public sector (occupational) pensions 

Pensions, lump sums and spouses, civil service, defence, police, 
education, health and local authorities, non-commercial state 
bodies. 

(Continued on the next page) 

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2021 Ageing Report: Underlying assumptions and projection methodologies 

Table (continued) 

EL  Public pensions: old-age and early pensions  

Welfare benefits 

Occupational and private pension 
schemes. 

Main pension:  

Private sector (employees, self-employed and farmers) and public 
sector: national pension (flat-rate) and (earnings-related) 
proportionate amount on the basis of their total period of insurance 
for all insured (statutory retirement age 67+) (including transitional 
period for old system regarding age thresholds and farmers). 

Means-tested flat rate pensions of uninsured individuals 67+. 

Auxiliary pensions: NDC system (including transitional period for 
old DB system). 

Disability pensions, 15-67y. 

Survivor pensions, all ages. 

Early pensions 62+, transition period. 

Public pensions: other 

EKAS (Pensioners Social Solidarity Fund - up to 2019). 

ES  Public pensions: old-age and early pensions 

Earnings-related old-age and early retirement pensions for private 
sector employees, the self-employed, regional and local and central 
government and the military. 
Means-tested minimum pension supplements (contributory). 

Means-tested minimum pension scheme (non-contributory). 

War pensions. 
Public  pensions: other 

Disability (-64) and survivors’ pensions (all ages) for private sector 
employees, self-employed, regional, local and central government 
and the military. 
Means-tested minimum pension supplements (contributory). 
Means-tested minimum pension scheme (non-contributory). 
Private pensions   

Private (supplementary and voluntary) pension schemes: 
occupational and individual. 

(Continued on the next page) 

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Table (continued) 

FR  Public pensions scheme - earnings-related 

Earnings-related private sector pensions scheme for private sector 
wage-earners and non-civil servants public sector workers 
(CNAV). 

Earnings-related complementary pension scheme for private wage-
earners (Agirc, for executives, and Arrco, for all workers). 

Earnings-related agricultural sector pension scheme (MSA).

Earnings-related public sector pension schemes (CNRACL, for 
civil servants in local administrations or hospitals, and SRE, for 
civil servants in state administration and military). 

Earnings-related public sector complementary pension schemes 
(Ircantec, for non-civil servants public sector workers).

Earnings-related basic pension scheme for licensed workers (RSI, 
for professions such as craftsmen, tradesmen...).

Earnings-related pension scheme for law professions (CNAVPL, 
CNBF specifically for lawyers). 

Earnings-related pension schemes for other specific professions 
(railwayman, etc.).

Non-earning-related pensions 

General ‘old-age solidarity fund’ scheme (FSV). 

Disability (earnings-related and non-earnings-related) pension 
benefits covered by the health insurance scheme. 

HR  Public pension scheme 

Old-age and early retirement pensions. 

Disability pensions. 

Survivors' pensions. 

Minimum pensions (no means-tested). 

Pensions of people who could be granted benefits from PAYG 
public pension scheme under more favourable conditions (e.g. 
military officers, police officers and authosized officials, war 
veterans from the Homeland War). 

Mandatory fully funded defined-contribution (DC) scheme based 
on individual savings accounts 

Mandatory for all people born as of 1962 and for people born 
between 1953 and 1962 who voluntarily joined the private second 
pillar scheme. 

Public pensions scheme - earnings-
related 

Earnings-related public sector 
complementary pension schemes 
(RAFP, for all civil servants): < 0.02% 
of GDP in 2015. 

Earnings-related complementary 
pension scheme for licensed workers 
(RCI, for professions such as 
craftsmen, tradesmen...): 0.1% of GDP 
in 2015. 

Occupational and other private 
pension schemes (PERP, PERCO, 
PERE, PREFON): <0.3% of GDP in 
2015. 

Voluntary fully funded pension 
scheme DC or DB have not been 
covered in the pensions projections. 

(Continued on the next page) 

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2021 Ageing Report: Underlying assumptions and projection methodologies 

Table (continued) 

IT  Public pension system - Public pensions and social assistance 

benefits 

Old-age and early retirement pensions.  

Disability pensions. 

Survivors' pensions. 

Old-age allowances and social assistance additional lump sums 
(State budget). 

Occupational pensions 
They are not included in the definition 
of the ‘Public pension system’ (used for 
the analysis of the sustainability of 
public finances) insofar as: 
i) they are never mandatory; 
ii) they provide a pension supplement 
that corresponds to a minor fraction of 
the pension guaranteed by the public 
pension system and never replace it. No 
risk is taken by the State on investment 
returns. 

CY 

Public pensions: old-age and early pensions 

Occupational pensions 

General Social Insurance Scheme (GSIS) covering the following 
pension benefits: early and old-age, invalidity,  

  DB pension schemes for semi-state and 

private sector employees. 

Government Employees Pension Scheme (GEPS) covering old-age, 
widows’ and disability pensions. 

DC provident funds for private sector 
employees. 

Social pension scheme and special allowances to pensioners. 

LV  Public pensions: old-age and early pensions 

Old-age minimum pension, 63+ (65+ as of 2025). 

Earnings-related old-age DB pensions, granted before 1996. 

Earnings-related old-age NDC pensions, 63+ (65+ as of 2025), 
granted as of 1996 (included early retirement). 

Service pensions (early pensions), selected professions, public 
sector (during the transition period). 

Disability pensions, granted before 1996 and not transformed to old-
age pensions. 

Survivor’s pensions (for widows during the transition period). 

Public pensions: other 

Disability pensions -63 (-65 as of 2025). 

Survivors’ pensions -24. 

Private mandatory pensions 

Individual funded old-age, mandatory for people born after 1971. 

Social pension 

public benefit, insurance record <15 years (<20 years from 2025), 
paid from the state basic budget. 

Voluntary private funded pension 
scheme 

Specific public sector service pensions 
schemes (paid from state basic budget). 

(Continued on the next page) 

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Table (continued) 

LT  Public pensions: old-age and early pensions 

Social assistance pensions, women 62.7+, men 63.8+ (65+ as of 
2026); (State budget). 

Earnings-related old-age pensions, women 62.7+, men 63.8+ (65+ 
as of 2026), all sectors (Social Insurance scheme). 

Early retirement pensions (possible to retire 5 years before the 
statutory retirement age), all sectors (Social Insurance scheme). 

Public pensions: disability pensions 

Social assistance disability pensions (State budget). 

Earnings-related disability pensions, all sectors (Social Insurance 
scheme). 

Public pensions: survivors’ pensions 

Social assistance survivors’ pensions (State budget). 

Survivors’ pensions, all sectors (Social Insurance scheme). 

Public pensions: other 

Special public service (state) pensions for selected professions 
(scientists, judges) (State budget); state pensions of the first and 
second degree of the Republic of Lithuania (State budget); state 
pensions of deprived persons (State budget) women 62.7+, men 
63.8+ (65+ as of 2026). 

Officials and military personnel pensions (for service, disability and 
survivors), public sector (State budget); length of service pensions, 
compensation for extraordinary working conditions (Social 
Insurance scheme). 

Pension supplement to small social insurance old-age and disability 
pensions – top-up to the ceiling of the basket of minimum 
consumption needs; depends on contribution period and the total 
amount of pension benefits received by the pensioner. 

Private mandatory pensions 

Individual funded old-age pension, quasy-mandatory, all sectors. 

LU  Public pensions: old-age and early pensions 

Earnings-related old-age, early retirement, disability pensions (65+), 
private sector & self-employed (general pension scheme). 

Earnings-related old-age, early retirement, disability pensions (65+), 
public sector (special pension scheme), state budget. 

Public pensions: other 

Disability (-64 years) and survivors’ pensions, all sectors 

Voluntary private pension schemes 
(occupational and individual), social 
assistance (REVIS) 

(Continued on the next page) 

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Handicap support, political 
compensation allowances 

Voluntary private pension schemes 
(occupational and individual) 

Private pension schemes (occupational 
and individual) 

Table (continued) 

HU  Public pensions: old-age and early pensions 

Social allowances close to minimum pensions to people above 
retirement age. 

Earnings-related old-age and anticipatory old-age pensions, all 
sectors. 

Survivors pensions, above retirement age, all sectors. 

Disability pensions, above retirement age, all sectors. 

Public pensions: other 

Disability pensions, below retirement age, all sectors. 

Survivors pensions, below retirement age, all sectors. 

Pension-like regular social allowances, below retirement age. 

Private mandatory pensions 

Individual funded pensions. People who entered the labour market 
before 2010 and chose to remain in the scheme, can have some 
entitlements. 

MT  Public pensions: old-age and early pensions 

Two-thirds pension scheme (incorporating two-thirds retirement 
pension, national minimum pension, increased national minimum 
pension, and decreased national minimum pension), 63y in 2019, 
64y in 2023 and 65y in 2027. 

Public pensions: other 

Pensions other than those listed above, notably disability and 
survivors’ pensions and some pensions, including Treasury Pensions 
(a DB pension scheme open to Public Officers who joined the 
Public Service prior to 15/01/1979 and that is closed to new 
members) and increased retirement pension, which will be phased 
out over a transition period, to specific groups of pensioners.  

Public pensions: disability: decreased national invalidity pension, 
national minimum invalidity pension 

Public pensions: survivors: early survivorship pension, national 
minimum widows’ pension, survivors’ pension 

Non-contributory old-age pension 

NL  Public pensions: old-age and early pensions 

Individual private pensions 

Public flat-rate old-age pensions, 65+, all citizens (AOW).  

Widows’ pensions, women 55+, all sectors (ANW). 

Public pensions: other 

Disability benefits, all sectors (WAO; being phased out), WIA, 
WaJong).  

Occupational pensions 

Occupational old-age pensions, 65+, all sectors. 

(Continued on the next page) 

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Age-related expenditure items: coverage, projection methodologies and data sources 

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Private occupational and individual 
pensions. 

Minimum guarantee pensions: no 
legal minimum pension exists; if 
individual pension claims are lower 
than legally defined thresholds, the gap 
will be closed by federal budget 
contributions to guarantee a minimum 
income for pensioners (equalising 
allowance; ‘Ausgleichszulage’); all 
sectors. 

Prisoner of war compensation 

Table (continued) 

AT  Public pensions: old-age and early pensions 

Earnings-related regular old-age pensions: 

Private sector (including blue and white-collar workers, self-
employed and farmers): female 60y, male 65y (female retirement 
age will be gradually raised to 65 years in 2024-2033). 

Public sector: female 65y, male 65y. 

Earnings-related early retirement pensions (private sector): 

Corridor pension scheme (‘Korridorpension’): female 62y, male 
62y (for women this gets relevant only by 2028); required number 
of insurance years is 40; 5.1% deduction per year before the regular 
retirement age (for people born as of 1955). 

Early old-age pension for long-term contributors 
(‘Hacklerregelung’): female 57y (born as of 1959), male 62y (born 
as of 1954); retirement age for women will be gradually raised to 
62; required number of contribution years for men is 45, the 
required contribution years for women will be gradually raised from 
42 to 45; 4.2% deduction per year before the regular retirement age 
(for men born as of 1954 and women at the age of 62 born as of 
1966). 

Heavy worker regulation (‘Schwerarbeitspension’): female 60y, 
male 60y (for women this gets relevant only by 2024); required 
number of insurance years is 45, at least 10 years of ‘hard labour’ 
within 20 years before retirement; 1.8% deduction per year before 
the regular retirement age (for people born as of 1955). 

Early old-age pension for long-term contributors in 
combination with heavy worker regulation (‘Hackler-
Schwerarbeit’): female 55y (born 1959-1963), male 60y (born 
1954-1958); required number of insurance years is 40 for women, 
45 for men; 1.8% deduction per year before the regular retirement 
age. 

In general, there are no deductions after 45 contribution years. 

Public pensions: other 

Survivors’ pensions (widow, widower and orphans): all sectors. 

Invalidity and occupational disability pensions: only in case of 
permanent disability; the temporary invalidity pension was replaced 
by medical and job-related rehabilitation and was completely 
abolished for people born as of 1964 (therefore, the temporary 
invalidity pension will fade out); all sectors. 

(Continued on the next page) 

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Table (continued) 

PL  Public pensions: old-age and early pensions 

Private individual pensions 

Earnings-related DB old-age, women 60+, men 65+, disability, 
widows, people born before 1949, private and public sector, self-
employed (ZUS, Social Insurance Institution). 

Earnings-related NDC old-age, women 60+, men 65+, private and 
public sector, self-employed (ZUS). 

Earnings-related NDC bridging-pensions (employment in special 
conditions or character) women 55+, men 60+, expiring scheme. 

Earnings-related DB old-age, disability and widows’ pensions, all 
ages, farmers (KRUS, Farmers social insurance scheme). 

Armed forces old-age pensions (State budget). 

Public pensions: other 

Disability and survivors’ pensions, -54y, private and public sector, 
self-employed (ZUS). 

Private quasi mandatory pensions 

DC funded old-age pensions.  

Includes supplements to ensure minimum pensions. 

PT  Public pensions 

Social Security System 

- Contributory Welfare System (private sector employees and self-
employed, public employees since 2006 and special pensions): old-
age, disability and survivors’ pensions. Includes supplements to 
ensure minimum pension value;  

- Social Solidarity protection System (non-contributory and means-
tested): old-age, disability and survivors’ pensions; 

- RESSAA (special social security scheme for agricultural 
workers): old-age, disability and survivors’ pensions. 

CGA (Pension scheme of civil servants hired until 2005): old-age 
and early pensions, disability and survivors’ pensions. Includes 
supplements to ensure minimum pensions values and special 
pensions. Non-contributory CGA pensions are reported under 
‘other pensions’ in the questionnaire. 

Solidarity supplement for the elderly (non-contributory means-
tested scheme designed to help pensioners and low incomes). 

Private occupational pensions 
Banking sector DB schemes and other DB schemes and DC 
schemes financed by pension funds. 

Private individual (non-mandatory) 
pension schemes (including the 
remaining part of the former 
mandatory FDC pillar). Private (non-
mandatory) occupational pension 
schemes. 

Private individual pensions:  

Individual (non-mandatory) private 
pension schemes. 

(Continued on the next page) 

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Table (continued) 

RO  Public old-age pensions 

Private individual, voluntary pensions 

Women 60+/63y, men 65y, standard contribution period women 
30+/35y, men 35y.  

No contribution period requirements for work accidents, 
professional disease, people with neoplasms, suffering from 
schizophrenia or AIDS. For ordinary diseases and accidents not 
related to work, a minimum contribution period is required. 

Early and partial early retirement 

5 years before the statutory retirement age, provided the full 
contribution period is exceeded by at least 8 years (for early 
retirement) or by less than 8 (for partial early retirement, which is 
penalised). 

Public pensions: other 

Survivors’ pensions: children and spouse. 

Disability pension: people who lost at least half of their capacity to 
work.  

Military, farmers, special pensions, special indemnities. 

Sine 2016, military pensions are paid by the State’s Budget, instead 
of the State’s Social Insurance Budget. 

Private mandatory pension  

Compulsory for eligible people under the age of 35; voluntary for 
age group 35-45. 

SI  Public pensions: old-age and early pensions 

Old-age pension (60+ with 40 year of service; 65+ with minimum 
insurance period of 15 year). 

Early retirement (60+ with 40 year of insurance period, including 
purchased years). 

Special compulsory (occupational) pensions for workers in high-
risk occupations, private and public sector. 

Public pensions: other 

Disability pensions 

Survivors’ pensions 

Flat-rate pensions for farmers 

Pension supplements for the military personnel of the former 
Yugoslav army and retirees from other republics of former SFRY. 

Annual allowance for pensioners 

National (state) pensions: excluded 
from Pension and Disability Act in 
2012 (individuals can ask for social 
assistance). 

Mandatory collective supplementary 
pensions for public employees.  

Non-mandatory collective 
supplementary pensions (private 
sector) - based on collective 
agreements. 

Private non-mandatory individual 
supplementary pensions (private and 
public sector). 

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Voluntary individual pension: funded 
DC scheme, introduced in 1996.  

Occupational and voluntary individual 
pensions: collective and voluntary 
supplementary schemes. 

Table (continued) 

SK  Public pensions: old-age and early pensions

Statutory retirement is specified on cohort basis. Cohorts retiring in 
2019 have a retirement age of 62 years and 139 days (born in 1956) 
or 62 years and 6 months (born in 1957). The retirement age will 
rise to 64 years in 2030, lower for mothers. Early retirement is 
possible two years before the statutory retirement age.  

Public pensions: other 

Disability, widow(er), orphans, minimum pension, Christmas bonus. 

Private mandatory pensions – partly covered 

Individual funded old-age pension, covers people that chose to take 
part in the scheme and those that had been included in the scheme 
when it was mandatory (prior to 2008) and did not exit during any 
of the openings (in 2008, 2009, 2012 and 2015). 

The special pension system of the armed forces and police. 

FI  Public pensions: old-age and early pensions 

Earnings-related old-age pension, +63.5y (+65y as of 2027 and 
linked to life expectancy as of 2030), private sector (TyEL), self-
employed (YEL), farmers (MYEL) and the public sector (JuEL, 
covers central government, municipal sector and church employees). 

Earnings-related partial early old-age pension (50% or 25%), +61y 
(+62y as of 2026 and 3y lower than old-age pension as of 2027). 

National old-age pension (National pension insurance), +65y.  

Early national pension, +63y for people born in 1957 or before and 
+64y for people born in 1958-1961. 

Old-age pension for long-term unemployed, +62y for people born in 
1957 or before and +64y for people born in 1958-1961. 

Public  pensions: other 

Guarantee pension (guaranteed minimum amount) 65+ (16-64y for 
disability pensioners). 

Disability pension, 16-64y, national pension scheme. 

Disability pension, 17-62y, earnings-related, all sectors.  

Survivors’ pension, no age limit for widow(er)s, 0-18y for orphans 
(both earnings-related and national pension schemes). 

Years-of-service pension, +63y, earnings-related, all sectors. 

(Continued on the next page) 

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Table (continued) 

SE  Public pensions: old-age and early pensions 

Minimum pension, housing supplement for pensioners, maintenance 
support for the elderly (State budget), 65+. 

Earnings-related NDC old-age pensions, flexible age from 62 
(including old transitional DB system), all sectors (Social insurance 
scheme). 

Public pensions: other 

Disability pensions, 19-64y.  

Survivors benefits, all ages (State budget). 

Occupational pensions 

Occupational (supplementary) DC and DB pensions, all sectors 

Private mandatory pensions 

Individual mandatory fully funded old-age pension, flexible age 
from 62, all sectors (Social insurance scheme) 

Private non-mandatory pensions 

Tax-deductible pension savings (from 2016 only deductible for self-
employed). 

NO  Public pensions: old-age and early pensions 

Earnings-related benefits. 

Minimum income guarantee. 

Public pensions: other 

Disability pensions.  

Survivors’ pensions. 

Source: European Commission, EPC. 

Central government occupational 
pension scheme financed by employee 
contributions and transfers from State 
budget. Supplement to public old-age 
pension. 

Local government occupational pension 
schemes are funded schemes. 
Supplement to public old-age pension. 

Mandatory private sector occupational 
schemes are funded defined 
contribution schemes. Supplement to 
public old-age pension. 

Private non-mandatory defined benefits 
(and from 2001 also defined 
contribution) schemes. 

157 

 
 
 
 
 
ANNEX 7 

Long-term care model structure 

Graph II.A7.1:  Long-term care model structure 

Source: European Commission. 

158 

ANNEX 8 

Sources of data to compute healthcare and long-term care 

A8.1.  DATA SOURCES HEALTHCARE 

Computing total public expenditure on 
healthcare 

The  data  required 
expenditure 
healthcare (114) includes: 

projections 

to  run 
in 

long-term  public 
of 

field 

the 

(cid:129)  per  capita  public  expenditure  on  healthcare  by 
(age/sex-specific 

cohorts 

sex 

and 

age 
expenditure profiles); 

(cid:129)  sex-specific  per  capita  public  expenditure  on 
healthcare  borne  by  decedents  and  survivors 
broken down by the number of remaining years 
of  life  required  to  run  the  death-related  costs 
scenario;  

(cid:129)  data  on  planned  reforms  and  COVID-19-

related expenditure; and 

(cid:129) 

total public expenditure on healthcare. 

The  data  collection  procedure  takes  two  steps. 
First, the Commission (DG ECFIN) pre-filles data 
on  the  basis  of  existing  international  databases 
managed  by  international  organisations  (Eurostat, 
OECD,  AMECO).  The  questionnaire 
then 
circulated  to  the  Member  States,  to  endorse  the 
pre-filled  figures  and  complement  these  with  data 
from national sources if no data was available from 
completed  data 
international 
questionnaires  are  used 
the 
projections.  

for  conducting 

sources.  The 

is 

Note 
that  age/sex-specific  per  capita  public 
expenditure  on  healthcare  and  sex-specific  per 
capita  public  expenditure  on  healthcare  borne  by 
decedents  and  survivors  broken  down  by  the 
number of remaining years of life are not available 
in any common international databases. Therefore, 
they  are  provided  exclusively  by  AWG  delegates 
and 
and 
methodologies. 

are  based  on  national 

sources 

(114) As  explained below,  this definition  of  healthcare  excludes 
SHA  expenditure  category  HC.3,  which is included  in  the 
long-term care expenditure category. 

In  order  to  calculate  total  public  expenditure  on 
two 
healthcare, 
components is used: 

the  sum  of 

following 

the 

1)  Public  current  expenditure  on  healthcare  – 
computed  as  the  sum  of  all  "core"  healthcare 
System  of  Health  Accounts  2011  (SHA  2011) 
functions/expenditure  categories  HC.1  to  HC.9, 
excluding  HC.3  (defined  as  "Long-Term  Care 
(health)" in SHA 2011). More specifically, for the 
current  public  expenditure  on  healthcare 
the 
following SHA 2011 categories are used: Curative 
care  (HC.1);  and  Rehabilitative  care  (HC.2); 
Ancillary services (HC.4); Medical goods (HC.5); 
Preventive  care  (HC.6);  Governance,  and  health 
system and financing administration (HC.7); Other 
healthcare 
services  not  elsewhere  classified 
(HC.9). 

2) Public expenditure on gross capital formation in 
health  from  the  COFOG  GF07  "Health"  function 
excluding the GF0705 "R&D Health" category. In 
order  to  smooth  the  volatility  inherent  to  capital 
formation, the average value for the last four years 
is used. 

SHA  data  by  function/expenditure  category  and 
respective  sub-functions  is  availableon  Eurostat 
NewCronos,  OECD  Health  Data,  and  WHO  Data 
for All. Most recent data refers to 2018 on OECD 
Health  Data  and  to  2017  and  2018  on  Eurostat 
NewCronos.  Eurostat  reports  data  for  all  Member 
States  and  Norway,  while  data  for  five  EU 
Member  States  non-OECD  members  (Bulgaria, 
Croatia,  Cyprus,  Malta,  and  Romania)  is  not 
reported on OECD Health Data. 

On  top  of  these  components,  COFOG  data  on 
capital  formation  from  Eurostat  NewCronos  is 
added. Most recent data refers to year 2018.  

Data used for calculating the sector-specific 
composite indexation 

the  "sector-specific  composite 

In 
indexation 
scenario" the importance and evolution of various 
components  to  healthcare  provision  is  captured. 
inpatient  care,  (2) 
The  components  are:  (1) 
outpatient  care  and  ancillary 
(3) 
services, 
pharmaceuticals  and  therapeutic  appliances,  (4) 

159 

                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table II.A8.1:  Data sources for the sector-specific indexation components 

Inpatient care 
(curative and 
rehabilitative care)

Outpatient care 
(curative and 
rehabilitative care) 
+ Ancillary services

Medical goods 
(pharmaceuticals 
and therapeutic 
appliances)

Preventive care

Governance and 
administration

Capital formation

HC.1.1 + HC.1.2 + 
HC.2.1 + HC.2.2

(HC.1.3 + HC.1.4 + 
HC.2.3 + HC.2.4) + 
HC.4

SHA
Eurostat or OECD

SHA

HC.5

SHA

HC.6

HF.7 + HF.9

SHA

SHA

Eurostat or OECD Eurostat or OECD Eurostat or OECD Eurostat or OECD

GF07 "Health" 
function excluding 
GF0705 "R&D 
Health"
COFOG
Eurostat

Source: European Commission. 

care, 

governance 

preventive 
and 
(5) 
administration,  and  (6)  capital  investment.  They 
broadly  reflect  the  different  sectors  of  the  health 
system  and  correspond  to  the  categories  of  the 
System of Health Accounts (SHA).  

As shown in Table II.A8.1 the respective share in 
public  expenditure  on  healthcare  of  each 
component  is  calculated  with  SHA  data  for  the 
latest  year  available,  except  for 
the  capital 
formation  component,  for  which  COFOG  data  on 
gross  capital  formation  on  health  excluding  R&D 
health is used.  

These  shares  are  then  applied  to  the  age-specific 
per  capita  expenditure  and  by  so  doing  each  age-
specific  per  capita  expenditure  is  divided  into  six 
sub-items of expenditure. 

to  current  data 

Next,  the  past  evolution  of  public  expenditure  on 
each of those components is calculated as average 
annual  growth  rate  for  the  past  10  years  for  each 
country. Due 
for 
building 10-year time series from data based on the 
SHA  2011  classification,  data  from  COFOG 
categories  in  correspondence  to  the  SHA  2011 
healthcare functions are used for the calculation of 
the  average  annual  expenditure  growth  rate  for 
each component. 

limitations 

Lastly,  the  ratio  of  each  of  these  10-year  average 
growth rates to the 10-year average growth rate of 
GDP is built. Due to high volatility in the relative 
growth rates  for prevention, capital formation and 
governance  and  administration,  these  items  are 
excluded  from  the  indexation.  Moreover,  the 
relative growth rates of the other three components 
(hospitals,  outpatient  care  and  medical  goods)  are 
capped at their respective 25th and 75th percentiles. 

160 

A8.2.  DATA SOURCES LONG-TERM CARE 

In  order  to  assure  the  best  possible  comparability 
of  data,  it  was  already  agreed  in  the  previous 
projections exercises to rely, to the extent possible, 
on:  

a) common methodologies and definitions (i.e. the 
System  of  Health  Accounts  -  SHA)  agreed  by 
international  institutions  (Eurostat,  OECD  and 
WHO); 

b)  data  gathered  through  the  joint  data  collection 
exercise 
joint  OECD-Eurostat-WHO 
questionnaire)  and  reported  in  Eurostat  (Cronos) 
and OECD (Health Data) databases (115). 

(i.e. 

As  in  the  2018  exercise,  SHA  2011  data  is  now 
available for every EU Member State in the case of 
variable  HC.3,  but  only  for  a  limited  number  of 
MS  for  voluntary  variable  HCR.1,  which  requires 
the  use  of  the  'European  System  of  integrated 
Social  Protection  Statistics'  (ESSPROS)  database 
to  construct  a  proxy  for  this  missing  variable  in 
several countries. 

For the 2021 exercise, the aim is to refine the use 
of  the  data  as  compared  to  that  of  the  2018  and 
earlier  rounds  of  projections.  Nevertheless,  the 
choice  of  the  best  option  is  still  dependent  on  the 
availability  of  data  in  the  international  databases. 
When  information  is  missing  in  the  international 
databases,  it  has  to  be  provided  by  each  Member 

(115) See  the  SHA  2011  Manual  (OECD,  Eurostat,  WHO 
(2011)).  The  manual  contains  guidelines  for  reporting 
health expenditure according to an international standard. It 
proposes  a  common  boundary  of  healthcare  as  well  as  a 
comprehensive  and  detailed  structure  for  classifying  the 
components of total expenditure on health. 

                                                           
 
 
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

State individually. This is particularly the case for 
the  number  of  recipients  and  the  breakdown  of 
expenditure  by  care  setting.  The  detailed  analysis 
of  available  data  and  classifications  carried  out 
(116)  led  to  the  following  agreement.  Annex  8,  on 
sources  of  data,  gives  an  overview  of 
the 
combinations  of  data  sources  for 
the  2021 
projections exercise.  

The  data  collecting  procedure  covers  the  same 
steps  as 
for  healthcare  (see  chapter  2  on 
healthcare),  with  an  equivalent  questionnaire, 
being  used  to  report  the  data  required  for  each  of 
health and long-term care expenditure projections. 

of  Health  Accounts)  and  social  services  of 
long-term care (classified as HCR.1); 

(cid:129)  a  disaggregation  of  total  public  spending  on 
long-term  care  into  spending  on  institutional 
care,  home  care  and  long-term  care-related 
cash  benefits  derived  from  ESSPROS  and/or 
SHA; 

(cid:129)  disability  rates  by  sex  and  five-year  cohorts 

(based on EU-SILC data); and 

(cid:129)  data  on  planned  reforms  and  COVID-19-

related expenditure. 

For  the  Commission  (DG  ECFIN)  to  be  able  to 
calculate  the  proposed  scenarios  and  run  the 
relevant  sensitivity  tests,  the  AWG  delegates 
provide 
the 
framework  of  the  long-term  care  expenditure 
projections:  

information 

following 

the 

in 

(cid:129) 

total  number  of  dependent  people  receiving 
long-term care a) in institutions b) at home and 
c) cash benefits, by sex and five-year cohorts;  

(cid:129)  possible  overlapping  between  the  recipients  of 
the  recipients  of  LTC 

cash  benefits  and 
services (legal possibility + numbers); 

(cid:129) 

total  number  and  categories  of 
caregivers; 

informal 

(cid:129)  public  expenditure  per  user  (patient)  on  long-
term  care,  by  sex  and  single  age  or  five-year 
cohorts  (so-called  "age-related  expenditure 
profiles"); 

(cid:129)  and  public  expenditure  breakdown  by  care 
setting  (institutional  care,  home  care,  cash 
benefits).  

In  addition,  the  Commission  (DG  ECFIN)  pre-
filled  (according  to  the  data  availability)  the 
following items, which the AWG delegates had to 
verify/confirm: 

(cid:129) 

long-term  care, 
total  public  spending  on 
disaggregated, 
long-term 
nursing care (classified as HC.3 in the  System 

into  services  of 

(116) See the note for the attention of the Ageing Working Group 
of the EPC: European Commission–DG ECFIN (2020). 

Public expenditure on long-term care 

Data  sources  on  long-term  care  are  described  on 
Table II.A8.2. 

According  to  the  System  of  Health  Accounts 
classification,  public  expenditure  on  long-term 
care is defined as the sum of the following publicly 
financed items:  

(cid:129)  services  of  long-term  nursing  care  (HC.3  in 
SHA  2011)  (which  is  also  called  "long-term 
healthcare", and includes both nursing care and 
medical  care  related  to  the  cause  of  the 
dependency); 

(cid:129)  “social services” of long-term care (HCR. 1 in 
the 
SHA  2011),  which 
"assistance  services" part, relating primarily to 
assistance with IADL tasks. 

represents  both 

Together  these  should  represent  the  total  benefits 
to  dependent  people,  although,  as 
allocated 
explained below, this data has to be supplemented 
to  different  degrees  with  ESSPROS  data  to  fulfil 
the projection needs.  

Long-term care (health) public expenditure 

The  health  component  of  long-term  care  (HC.3) 
includes  a  range  of  services  required  by  persons 
with  a  reduced  degree  of  functional  capacity, 
physical  or  cognitive,  and  who  are  consequently 
dependent  on  help  with  basic  activities  of  daily 
living  (ADL),  such  as  bathing,  dressing,  eating, 
getting in and out of bed or chair,  moving around 
and  using  the  bathroom.  The  underlying  physical 

161 

                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Table II.A8.2:  LTC public expenditure base data requirements according to availability 

I.  Preferred solution: SHA, when data is available (all countries except those listed below) 

LTC (health) 

LTC (social) 

SHA: HC.3 

SHA: HCR.1 

LTC  
(institutional care) 

LTC  
(home care) 

LTC  
(cash benefits) 

SHA: 
(HC.3+HCR.1) 
institutional care 
share according to 
ESSPROS, SHA, 
ESSPROS+SHA 
or national data  

SHA: 
(HC.3+HCR.1) 
home care share 
according to 
ESSPROS, 
SHA, 
ESSPROS+SHA 
or national data 

SHA: 
(HC.3+HCR.1) 
cash benefits share 
according to 
ESSPROS, SHA, 
ESSPROS+SHA 
or national data 

II.  Alternative: When data on LTC (social) HCR.1 is not available from SHA, a proxy is constructed 

based on ESSPROS data (BE, BG, IE, EL, HR, IT, CY, HU, MT, AT, PL and SK) 

LTC (health) 

LTC (social) 

LTC  
(institutional care) 

LTC  
(home care) 

LTC  
(cash benefits) 

SHA:  
(HC.3+HCR.1) 
institutional care 
share according to 
ESSPROS, SHA, 
ESSPROS+SHA  
or national data  

SHA: 
(HC.3+HCR.1) 
home care share 
according to 
ESSPROS, SHA, 
ESSPROS+SHA 
or national data 

SHA: 
(HC.3+HCR.1) 
cash benefits share 
according to 
ESSPROS, SHA, 
ESSPROS+SHA 
or national data 

SHA: HC.3 

ESSPROS: proxy 
based on cash and in-
kind benefits 
according to Disability 
and Old age functions, 
inclunding 
“Accommodation”, 
“Home help”, 
“Periodic care 
allowance”, and in the 
Disability function  
“Lump sum care 
allowance”. Adjusted 
to reduce potential 
double-counting with 
expenditure already 
included within HC.3 
in SHA. 

Source: European Commission. 

frailty 

illness, 

or  mental  disability  can  be  the  consequence  of 
in  old  age,  mental 
chronic 
retardation  or  other 
limitations  of  mental 
functioning  and/or  cognitive  capacity.  It  includes 
as  well as any further  medical treatment linked to 
the  cause  of  disability  as  well  as  basic  medical 
services including help  with wound dressing, pain 
management,  medication,  health  monitoring, 
prevention,  rehabilitation  or  services  of  palliative 
care. 

Finally,  HC.3  also  includes  any  cash  benefits  that 
are  spent  on  the  services  detailed  above.  These 
cash benefits, however, cannot be identified in the 
data,  since  they  are  assigned  to  the  category  that 
they are spent on by the recipients.  

Long-term care (social) public expenditure 

LTC 
recipients  may  also  need  help  with 
instrumental activities of daily living (IADL) more 
generally,  such  as  help  with  activities  of 
housework,  meals,  shopping,  using  the  telephone 

162 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

and  managing  one’s  finances.  Although  the  need 
for this type of services does not stem from social 
causes  but  rather  from  dependency  that  is  caused 
by  medical  conditions,  old  age  or  long-term 
disability, this type of care is classified in SHA as 
a “social” long-term care category HCR.1(117). As 
in  HC.3,  this  category  also  includes  cash  benefit 
expenditure,  although  the  data  is  not  currently 
populated.  A  further  shortcoming  of  this  data  is 
that  the  SHA  classification  does  not  currently 
include  expenditure  on  those  LTC  recipients  that 
need help with IADL, but not with ADL. 

As  in  the  case  of  healthcare,  the  SHA  figures  on 
public expenditure on long-term care are available 
in  two  separate  databases:  EUROSTAT  database 
available  at  NewCronos  website  and  a  parallel 
OECD  database  "OECD  Health  Data".  SHA  data 
on  HC.3  is  available  for  all  Member  States.  Data 
on  HCR.1  is  currently  available  for  15  Member 
States and Norway. For those not reporting HCR.1, 
a proxy is calculated on the basis of the following 
ESSPROS  data  categories,  adjusted  in  order  to 
reduce  the  likelihood  of  double-counting  between 
HC.3 and the ESSPROS proxy: 

In-kind benefits in ESSPROS: 

(a)  "Disability"  function  –  "Accommodation" 
(institutional  care),  and  "Home  help/assistance  in 
carrying out daily tasks" (home care) 

Public expenditure by care setting 

The 
long-term  care  model  projects  public 
expenditure  separately  by  care  setting.  This 
requires the identification of public expenditure on 
institutional care, home care, and cash benefits. 

Public expenditure on cash benefits 

Public  spending  on  cash  benefits  include  social 
programmes  offering  care  allowances.  Care 
allowances  were 
in  a  number  of 
introduced 
countries  in  order  to  allow  households  for  more 
choice  over  care  decisions,  and  to  support  care 
provided  at  home  or  in  institutions.  They  are 
mainly  addressed  to  persons  with  long-term  care 
needs  who  live  in  their  own  homes,  but  can  also 
include  people  who  receive  care  in  an  institution. 
However,  the  design  of  these  programmes  varies 
the 
widely  across  countries,  which 
this 
comparability  between 
variety  of  systems,  it  is  noteworthy  that  cash 
benefits can be included just as well in the HC.3 as 
in the HCR.1 category.  

reduces 
them.  Illustrating 

At  least  three  types  of  cash-benefit  programmes 
and/or  consumer-choice  programmes  can  be 
distinguished: 

(cid:129)  personal 

budgets 
employment of care assistants; 

and 

consumer-directed 

(b)  "Old  age"  function  –  "Accommodation" 
(institutional  care)  and  "Home  help/assistance  in 
carrying daily tasks" (home care).  

(cid:129)  payments  to  the  person  needing  care  who  can 
spend  it  as  she/he  likes,  but  has  to  acquire 
sufficient care; and 

Benefits in cash in ESSPROS: 

(cid:129)  payments to informal caregivers. 

"Disability" 

a) 
allowance",  "Lump sum care allowance"; and  

function  – 

"Periodic 

care 

b) "Old age" function – "Periodic care allowance". 

These  proxies  are  then  validated  on  the  basis  of 
national  expertise 
to  eliminate  any 
in  order 
possible double counting or under-counting in this 
area. 

(117) Unfortunately,  the  SHA  classification  does  not  currently 
include expenditure on those LTC recipients that need help 
with IADL, but not with ADL. 

As  in  the  2018  exercise,  SHA  2011  data  is  now 
available for every EU Member State in the case of 
variable  HC.3,  but  only  for  a  limited  number  of 
Member  States  for  the  variable  HCR.1,  which 
requires the use of ESSPROS database to construct 
a  proxy  for  this  missing  variable  in  several 
countries.  However,  in  the  variable  HC.3  it  is  not 
possible  to  identify  cash  benefits,  as  they  are 
assigned  to  either  home  or  institutional  care 
according to  what they are  spent on. In HCR.1, it 
would  in  principle  be  possible  to  identify  cash 
benefits,  but  this  sub-category  is  not  currently 
populated for any country. SHA data can therefore 
be used to calculate the breakdown by care setting 

163 

                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

only  for  those  countries  where  there  are  no  cash 
benefits or where there is no alternative data.  

two  ESSPROS 

In  contrast,  LTC-related  cash  benefits  as  a  %  of 
GDP  are  available  for  the  same  year  as  of  SHA 
joint  questionnaire  data  (or  for  the  latest  year 
available)  within 
functions: 
disability and old age. Both periodic and lump-sum 
parts of care allowances in the disability function, 
as  well  as  periodic  care  allowance  in  the  old-age 
function,  are  compared 
total  LTC 
expenditure in ESSPROS in order to calculate the 
breakdown by care setting.  

the 

to 

This  represents  a  pragmatic  approach  of  using 
available  data  to  estimate  this  split  of  LTC 
expenditure.  These  proportions  are  then  validated 
taking 
the 
expenditure breakdowns and institutional set-up. 

into  account  national  data  on 

Home  care  and 
expenditure 

institutional  care  public 

Long-term care is provided in a variety of settings. 
It can be provided at home and in the community, 
or  in  various  types  of  institutions,  including 
nursing  homes  and  long-stay  hospitals.  Mixed 
forms  of  residential  care  and  (internally  or 
externally provided) care services exist in the form 
of assisted living facilities, sheltered housing, etc., 
for  which  a  wide  range  of  national  arrangements 
and national labels exist. 

Services  in  institutions  include  services  provided 
to  people  with  moderate  to  severe  functional 
restrictions  who 
live  permanently  or  for  an 
extended period of time (usually for six months or 
longer)  in  specially  designed  institutions,  or  in  a 
hospital-like setting where the predominant service 
component  is  long-term  care,  although  this  may 
frequently  be  combined  with  other  services  (basic 
medical  services,  help  with  getting  meals,  social 
activities,  etc.).  In  these  cases,  eligibility  is  often 
explicitly  assessed  and  defined  by  level  (severity) 
of dependency and level of care needs. 

Services  at  home  include  services  provided  by 
external  home  care  providers,  both  public  and 
private,  in  a  person’s  private  home  on  a  long-
lasting basis. This includes living arrangements in 
specially designed or adapted flats for persons who 
require  help  on  a  regular  basis,  but  where  this 
living  arrangement  still  guarantees  a  high  degree 

164 

of autonomy and self-control over other aspects of 
a  person’s  private  life.  Also  included  are  services 
received  on  a  day-case  basis  or  in  the  form  of 
short-term stays in institutions, for example in the 
form  of  respite  care.  During  these  stays,  persons 
are not considered as ‘institutionalised’, but rather 
receiving temporarily services, which support their 
continued stay at home. They also include tele-care 
where  the  care  is  provided  in  the  home  of  the 
patient through IT. 

some 

requires 

A  necessary step for the purpose of the long-term 
projections is therefore to calculate the amount of 
long-term  care  expenditure  associated  with 
institutional  care  and  that  associated  with  home 
care.  This 
data 
reclassification.  For  all  the  countries,  information 
on  HC.3  (services  of  long-term  nursing  care)  is 
available for: HC.3.1 (In-patient long-term nursing 
care);  HC.3.2  (day-cases  of  long-term  nursing 
care); HC.3.3 (outpatient long-term care, including 
both  regular  outpatient  visits  and  the  provision  of 
remote  monitoring  services  for  LTC  patients)  and 
HC.3.4 (long-term nursing care: home care).  

further 

According  to  the  above  definitions,  HC.3.1  and 
HC.3.2  are  types  of  care  that  are  provided  in  the 
institutions  or  in  the  community  facilities  (in  any 
case not at beneficiary's  home), while HC.3.3 and 
HC.3.4  are  provided  at  home.  However,  this 
breakdown includes not only expenditure spent on 
services  in-kind,  but  also  cash  benefits  that  are 
spent on each of these services.  

With regards to the part of HCR.1 that constitutes 
home  care  and 
the  part  which  constitutes 
institutional  care,  this  breakdown  is  not  available 
the  classification.  The  System  of  Health 
in 
Accounts  methodology  foresees  a  breakdown 
between  in-kind  benefits  and  cash  benefits,  but 
currently it is not populated for any country.  

the 

Therefore, 
shares  of  home  care  and 
institutional  care  can  be  calculated  in  ESSPROS 
or, in cases where there are no cash benefits in that 
country, in SHA as well. These shares can then be 
applied to the total expenditure on LTC calculated 
by  adding  HC.3  to  HCR.1  or  otherwise  the 
ESSPROS proxy.  

Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

Disability rates 

Similarly  to  past  Ageing  Reports  projections' 
exercises,  disability  rates  are  derived  from  EU-
SILC  data  and  more  specifically  data  reported  by 
the Global activity limitation indicator (GALI), on 
severe  "Limitations  in  activities  because  of  health 
problems [for at least the last 6 months]" (118). EU-
SILC data, used to construct the GALI indicator, is 
available for all EU Member States and Norway by 
age-sex group and has a disability  measure  which 
allows  us  to  identify  severe  (strongly  limited) 
limitations.  

This 
is  considered  an  adequate  measure  of 
dependency with a high degree of data availability 
and comparability. Indeed, it is available for 27 EU 
Member States and Norway, by age-sex group for 
people  aged  15+ (119).  A  moving  average  of  the  5 
most  recent  years  of  data  available  will  be 
constructed  and  used  for  the  projections,  albeit 
excluding any data breaks.  

(118) The person’s self-assessment of whether they are hampered 
in  their  daily  activity  by  any  ongoing  physical  or  mental 
health problem, illness or disability. An activity is defined 
as:  "the  performance  of  a  task  or action by  an  individual" 
and thus activity limitations are defined as "the difficulties 
the  individual  experience  in  performing  an  activity". 
Limitations  should  be  due  to  a  health  condition.  The 
activity 
limitations  are  assessed  against  a  generally 
accepted population standard, relative to cultural and social 
expectations  by  referring  only  to  activities  people  usually 
do.  This  is  a  self-perceived  health  question  and  gives  no 
restrictions  by  culture,  age,  sex  or  the  subject's  own 
ambition. The purpose of the instrument is to measure the 
presence of long-standing limitations, as the consequences 
of  these  limitations  (e.g.  care,  dependency)  are  more 
serious. A 6 months period is often used to define chronic 
or long-standing diseases in surveys. 

(119) For  those  aged  0-14  years,  either  national  data  is  used  if 
available or the rate is assumed to equal those aged 15-19. 
The  required  age  breakdowns  of  the  EU-SILC  disability 
rates for the Ageing Report are calculated upon request of 
the AWG by Eurostat. 

165 

 
 
 
                                                           
ANNEX 9 

Mathematical illustration of the healthcare scenarios 

The  formal  illustration  of  the  scenarios  to  project 
public  expenditure  on  healthcare  are  presented  in 
the following sections. 

I. Demographic scenario 

The  "demographic  scenario"  estimates  the  effect 
future  public 
of  an  ageing  population  on 
expenditure on healthcare. It assumes that age/sex-
specific morbidity rates and the provision structure 
of  health  treatments  remain  constant  in  real  terms 
over the whole projection period. It also assumes a 
gradual increase in life expectancy on the basis of 
underlying population projections.  

To  calculate 
future  public  expenditure  on 
healthcare,  the  age/sex-specific  per  capita  public 
the 
expenditure  profiles  are  multiplied  by 
respective  age/sex  population  group 
in  each 
projection year.  

The  age/sex  specific  public  expenditure  profiles, 
showing the average public spending on healthcare 
per  capita  for  each  year  of  age  (from  0  to  100, 
according  to  data  availability),  are  assumed  to 
grow  over  time  in  line  with  GDP  per  capita. 
Therefore,  the  per  capita  cost  (expenditure)  in  a 
projected year t is: 

Next,  in  each  year  the  respective  unit  cost  is 
multiplied by the projected population of each age 
group  (using  the  baseline  population  projections) 
to obtain the total public spending for each age/sex 
group: 

S

d
tag
,

, =

c

d
tag
,
,

p

,
tag
,

II.A9.3

where: 

g,a,t  is  public  spending  on  healthcare  for  all 

Sd
persons of sex g and age a in year t. 

the  resulting 

Last, 
total  public  spending  on 
healthcare  is  divided  by  the  projected  GDP  in 
order to obtain the public healthcare expenditure as 
a percentage of GDP: 

T =

d

t

S

d
,
,
tag

Y
t

II.A9.4

where: 

Td
t  is  the  ratio  of  total  public  spending  on 
healthcare to GDP in year t computed according to 
the pure demographic scenario. 

II.A9.1

II. High life expectancy scenario 

c

d
ag
,

0,

=

c

ag
,

0,

c

d
tag
,
,

=

c

d
tag
,

-
1,

(
1

D+

Ypc
t

)

 t

=

0

 t

>

0

where: 

d stands for demographic scenario; 

cg,a,t-1 is the cost per capita of a person of a given 
sex g and age a in period t-1; 

ΔYpct is GDP per capita growth rate in year t.  

D

Ypc
t

=





Y
t
P
t

-

Y
t
P
t

-
1

-
1









Y
t
P
t

-
1

-
1





II.A9.2

with  Yt  and  Pt  representing  GDP  and 
population in projection year t; 

total 

Hence, this "adjusted" per capita unit cost, cd
g,a,t, is 
the cost per capita of a person of sex g and age a in 
year  t  of  the  projection  period,  following  the 
adjustment to GDP per capita growth. 

166 

in 

the 

to  measure 

The "high life expectancy scenario" is a sensitivity 
impact  of  alternative 
test 
assumptions on mortality rates. It assumes that life 
expectancy  at  birth  in  2070  exceeds  the  projected 
the  "demographic 
life  expectancy  used 
is 
scenario 
scenario"  by  2  years.  This 
methodologically  identical  to  the  "demographic 
scenario",  but  alternative  demography  and  GDP 
data  are  used  (120).  Therefore,  the  mathematical 
formulation  used  in  the  previous  scenario  still 
applies,  except  that  the  number  of  individuals  in 
each  age/sex  group  up  to  2070  is  replaced  by  the 
new population and macroeconomic assumptions. 

(120) Since  GDP  data  also  captures  the  life  expectancy  change 
through  the  impact  of  the  latter  on  the  labour  force 
projections. 

        
         
          
          
 
 
 
  
 
 
 
 
 
 
 
                                                           
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

III. Healthy ageing scenario 

The  "healthy  ageing  scenario"  is  based  on  the 
relative  compression  of  morbidity  hypothesis, 
meaning  that  health  status  is  improving  in  line 
with declines in mortality rates and increasing life 
expectancy.  It  assumes  that  the  number  of  years 
spent  in  bad  health  during  a  lifetime  remains 
constant  over 
the  whole  projection  period. 
Consequently, the morbidity rate and therefore the 
age/sex-specific  per  capita  public  expenditure 
profiles are declining with the mortality rate. 

This  scenario  starts  with  calculating,  for  each 
projection  year,  the  change  in  life  expectancy  in 
relation  to  the  base  year.  The  change  in  life 
expectancy  of  a  person  of  sex  g  and  age  a  in 
relation to the base year (say, 2019) for each year 
of  the  projections,  using  the  Eurostat  population 
projections 2019 (121) is given by: 

Then, for each year t, the projected per capita cost 
equals: 

(cid:3035)(cid:3028) = (cid:1855)(cid:3034),(cid:3028),(cid:2868) 
(cid:1855)(cid:3034),(cid:3028),(cid:2868)

t=0 

II.A9.6

(cid:3035)(cid:3028) = (cid:1855)(cid:3034),(cid:3028),(cid:3047)(cid:2879)(cid:2869) ∙ (1 + ∆(cid:1851)(cid:1868)(cid:1855)(cid:3047) + ∆(cid:1855)(cid:3034),(cid:2868),(cid:3028)(cid:2879)∆(cid:3013)(cid:3006)(cid:3295)) 
(cid:1855)(cid:3034),(cid:3028),(cid:3047)

t>0 

where: 

ha stands for healthy ageing scenario; 

cha
g,a,t is the cost per capita assigned to a person of 
sex g and age a in year t of the projection period; 
and 

Δcg,0,a-ΔLEt is the growth rate in costs per capita due 
to  the  change  in  life  expectancy  between  year  0 
and projection year t.  

∆(cid:1855)(cid:3034),(cid:2868),(cid:3028)(cid:2879)∆(cid:3013)(cid:3006)(cid:3295) =

((cid:1855)(cid:3034),(cid:2868),(cid:3028)(cid:2879)∆(cid:3013)(cid:3006)(cid:3282),(cid:3276),(cid:3295),(cid:3116) − (cid:1855)(cid:3034),(cid:2868),(cid:3028))
(cid:1855)(cid:3034),(cid:2868),(cid:3028)

II.A9.7

∆(cid:1838)(cid:1831)(cid:3034),(cid:3028),(cid:3047),(cid:2868) = (cid:1838)(cid:1831)(cid:3034),(cid:3028),(cid:3047) − ∆(cid:1838)(cid:1831)(cid:3034),(cid:3028),(cid:2868) 

    II.A9.5 

where: 

where:  

ΔLEg,a,t,0  is  the  additional  life  expectancy  of  a 
person of sex g and age a in year t compared to a 
person of sex g and age a in the base year 2019; 

LEg,a,t  is  the  life  expectancy  of  a  person  of  sex  g 
and age a in year t; and  

LEg,a,0  is  the  life  expectancy  of  a  person  of  sex  g 
and age a in the base year 2019. 

(cid:1855)(cid:3034),(cid:2868),(cid:3028)(cid:2879)∆(cid:3013)(cid:3006)(cid:3282),(cid:3276),(cid:3295),(cid:3116)  is  the  cost  per  capita  assigned  to  a 
person of sex g and of age a in the base year 2019 
minus  the  years  gained  in  life  expectancy  by  a 
person of sex g and age a between year t and year 
2019, as defined in equation II.A9.5 and specified 
with a precision to a decimal part of a year in the 
base  year  2019  (122).  This  is  done  only  for  those 
sections  of  the  age-profile  where  the  cost  per 
capita is growing (123). 

The  cost  per  capita  is  further  adjusted  to  reflect 
changes in income per capita over the years using 
the  same  indexation  system  as  in  the  previous 
scenario  i.e.  cost  per  capita  grows  in  line  with 
GDP per capita growth. 

(121) In the "healthy ageing scenario" the total number of years 
spent in bad health during a person’s lifetime is assumed to 
remain  constant  while  life  expectancy  increases,  so  the 
morbidity  rate  must  evolve  in  line  with  mortality  rate  for 
each age cohort. Thus, if between time t and t+1, total life 
expectancy  increases  by  n  years  for  a  cohort  of  age  a, 
healthy life expectancy for that very same age cohort must 
also  increase  by  n  years,  as  assumed  by  the  relative 
life 
compression  of  morbidity  hypothesis.  If  healthy 
expectancy increases by n years, then the health status (and 
consequently healthcare spending) of this cohort of age a at 
time  t+1  will  be  the  same  as  the  health  status  (and 
healthcare spending) of cohort of age a-n at time t. 

(122) Changes  in  life  expectancy  and  therefore  shifts  in  the  age 
profile from one year to another are sometimes very small 
(in  a  range  of  a  tenth  part  of  a  year).  However,  the  data 
gathered  by  the  Member  States  does  not  provide  detailed 
information  on  costs  per  capita  by  single  year  of  age  (the 
most  detailed  item  available  is  a  5-year  average),  so  an 
additional calculation needs to be performed. To solve this 
problem, the intermediate values can be obtained by simple 
extrapolation/trend-smoothening  method  from  the  existing 
average  figures.  In  this  way  it  is  possible  to  assign  a 
concrete value of cost per capita to each tenth part of a year 
of age. 

(123) For the young and the oldest old the reference age remains 

the same over the whole projection period. 

167 

 
 
 
                                                           
 
 
 
 
                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

As  before,  in  each  year  the  respective  unit  cost  is 
multiplied by the projected population in each age 
group  age 
the  baseline  population 
projections) to obtain the total public spending for 
each age/sex group: 

(using 

(cid:3035)(cid:3028) = (cid:1855)(cid:3034),(cid:3028),(cid:3047)
(cid:1845)(cid:3034),(cid:3028),(cid:3047)

(cid:3035)(cid:3028) ∙ (cid:1842)(cid:3034),(cid:3028),(cid:3047) 

    II.A9.8 

subgroup. The sum of total spending borne by the 
two  subgroups  is  the  total  spending  on  healthcare 
in a given year.  

Mathematically,  we 
formulation:  

have 

the 

following 

where: 

(cid:3035)(cid:3028)  is  public  spending  on  healthcare  for  all 

(cid:1845)(cid:3034),(cid:3028),(cid:3047)
persons of sex g and age a in year t. 

Next,  the  resulting  total  public  spending  on 
healthcare  is  divided  by  the  projected  GDP  in 
order to obtain the public healthcare expenditure as 
a percentage of GDP: 

(cid:3035)(cid:3028) =

(cid:1846)(cid:3047)

(cid:3035)(cid:3028)
∑ (cid:1845)(cid:3034),(cid:3028),(cid:3047)
(cid:1851)(cid:3047)

where: 

    II.A9.9 

(cid:3035)(cid:3028)  is  the  ratio  of  total  public  spending  on 

(cid:1846)(cid:3047)
healthcare to GDP in year t. 

IV. Death-related costs scenario 

The "death-related costs scenario" links per capita 
public expenditure on healthcare to the number of 
remaining  years  of  life.  It  reflects  empirical 
evidence  which  suggests  that  a  large  share  of  the 
total  expenditure  on  healthcare  during  a  person’s 
life is concentrated in the final years of life (124).   

In  this  scenario,  the  population  of  each  sex-age 
group  is  divided  into  subgroups  according  to  the 
number  of  remaining  years  of  life  using  mortality 
rate  as  a  weighting  factor.  In  this  case  the  groups 
are:  those  supposed  to  die  within  a  year,  the 
decedents, and those who do not, the survivors. 

Each  subgroup  is  assigned  a  different  unit  cost, 
being an adjustment of the "normal" unit cost with 
the  ratio  of  healthcare  expenditure  borne  by  a 
person  of  a  given  age  and  sex  who  is  in  her 
terminal  phase  of  life  to  healthcare  expenditure 
borne by a survivor. The number of people in each 
subgroup  is  thus  multiplied  by  its  respective  cost 
per  capita  to  get  the  total  spending  of  each 

(124) For an overview of empirical studies, see Raitano (2006). 

168 

We divide people of the same age and sex into the 
groups  of  survivors  and  those  supposed  to  die 
within  a  year.  The  costs  of  the  decedents-death 
related costs – are labelled with y DR
, and the costs 
for the survivors – normal costs – are labelled with 
yNC
,
tag ,

,  where  g,  a  and  t  refer,  respectively,  to  sex, 
,m   being  the  probability  of 

age  and  year.  With 
death within a year in year t, we get: 

tag ,

,
tag
,

y

,
,
tag

=

y

NC
,
,
tag

=

y

NC
,
,
tag

(
1
(
1

-

-

m

,
,
tag

m

,
tag
,

)
DR
+
my
,
,
tag
+

,
tag
,

k

)m

,
tag
,

,
,
tag

II.A9.10

, =
tagk
,

DR
yy
,
,
tag

NC
,
,
tag

is 

the  k-ratio. 

It 
where: 
estimates,  for  a  given  sex  and  age,  how  many 
times  the  healthcare  costs  of  decedents  exceed 
those  of  a  survivor.  If  kg,a,t=1,  then  death-related 
costs  do  not  matter,  while  with  k  going  toward 
infinity means that total healthcare costs are spent 
in the last life year.  

If one assumes a constant k-ratio over time (t = 0), 
the  healthcare  costs  would  vary  along  with 
changes in the probabilities of death: 

y

tag
,
,

=

y

NC
ag
,

0,

(
1

-

m

,
tag
,

+

k

,
ag

0,

)m

,
tag
,

II.A9.11 

Taking into account that costs of survivors can be 
derived  from  the  total  one,  according  to  the 
following equation: 

y

NC
tag
,
,

=

y

NC
ag
,

=

0,

y

0,

ag
,
+

k

1

-

m

ag
,

0,

m

ag
,

0,

0,

II.A9.12 

ag
,

equation II.A9.10 becomes: 

y

tag
,
,

=

y

ag
,

0,

1

-

m

tag
,
,

1

-

m

ag
,

0,

+

+

k

k

ag
,

0,

ag
,

0,

m

tag
,
,

m

ag
,

0,

  II.A9.13

 
 
 
 
 
                                                           
    
 
 
 
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

Equation II.A9.13 shows how the age-sex specific 
healthcare cost profile evolves, keeping the k-ratio 
unchanged with respect to the base year. 

However,  as  shown  by  Aprile 
the 
empirical evidence strongly suggests a changing k-
ratio as a function of changes in life expectancy.   

(2013), 

As  stated  in  the  above  mentioned  paper,  the 
following potential function approximates well the 
empirical observations: 

k

+= 1

l LE

f

  II.A9.14 

according  to  which  k  is  positively  correlated  with 
life  expectancy  and  is  1  when  life  expectancy  is 
nil (125).  Then,  assuming  the  constant  coefficients 
of  the  function  over  time,  one  may  derive  the 
relation between the k-ratio and age conditional on 
life expectancy as follows:  

k

tag
,
,

=

k

ag
,

0,

f
f

,

(
LEtag
,,
(
ag
,

,0,

LE

t

)
)0

  II.A9.15 

where kg,a,0 is the value of k-ratio in the base year 
at the age a, and f(g,a,t,LE) is the fitted function.  

As can be seen, k-ratio is projected according to a 
cohort approach, starting from the base-year value 
at  the  age  a  being  positively  correlated  with 
changes in life expectancy. If no change occurs in 
life  expectancy,  the  age  profile  of  k-ratio  is  the 
same as in the base year. 

Combining  equations  II.A9.13  and  II.A9.15,  the 
age  profile  of  healthcare  costs 
is  projected 
according to the following equation: 

y

tag
,
,

=

y

ag
,

0,

1

+

m

tag
,
,

k

ag
,

0,

1

+

m

ag
,

0,

f
f
k

,

(
LEtag
,,
t
(
ag
,

)
)

-

,0,
-

LE
0
m

ag
,

0,

ag
,

0,

m

tag
,
,

  II.A9.16 

As  previously,  the  age-sex  specific  costs  are 
adjusted  to  the  GDP  per  capita  growth  and 
summed  up  over  the  entire  population  for  each 
respective year to arrive at total costs. 

(125) With  this  function  the  death-related  cost  profile  is  also 
spurious  volatility 

thereby  decreasing 

smoothened, 
especially in young age cohorts. 

V. Income elasticity scenario 

and 

The  "income  elasticity  scenario"  captures  the 
effect  of  changes  in  national  income  on  demand 
for  healthcare  goods 
services.  More 
specifically,  this  scenario  shows  the  effect  of  an 
income elasticity of demand higher than 1, i.e. ε = 
1.1,  on  the  evolution  of  public  expenditure  on 
healthcare.  It  assumes  that  economic  growth  and 
process  of  real  convergence  between  countries 
over  the  long  run  will  drive  elasticity  down 
towards common unity level, by 2070 (126).  

The  methodology  used 
to  project  healthcare 
spending  is  the  same  as  for  the  "demographic 
scenario",  except  in  the  way  per  capita  public 
expenditure  on  healthcare  is  evolving  over  the 
projection  period.  Income  elasticity  is  taken  into 
account  by  replacing  equation  II.A9.1  by  the 
following equation II.A9.17, so that the per capita 
cost of a person of sex g and age a in year t of the 
projection period, cie
g,a,t, is adjusted to the GDP per 
capita growth with an elasticity that goes from 1.1 
to 1 in 2070: 

(
1

D+

Ypc
t

)
e
t

t

=

0

t

>

0

 II.A9.17

c

c

ie

ag
,

0,

ie

tag
,
,

=

c

,
ag

0,

=

c

ie

tag
,

-
1,

where: 

ie stands for "income elasticity" scenario; 

cie
g,a,t-1  is  the  cost  per  capita  of  a  person  of  sex  g 
and  age  a  in  year  t-1  in  scenario  "income 
elasticity"; 

ΔYpct is GDP per capita growth rate in year t;  

εt  is  income  elasticity  of  demand,  assumed  to 
converge  from  ε2019  to  ε2070  in  2070  according  to 
the following equation: 

(cid:2013)(cid:3047) = (cid:2013)(cid:2870)(cid:2868)(cid:2869)(cid:2877) − ((cid:1872) − 2019)

∙

(cid:2013)(cid:2870)(cid:2868)(cid:2869)(cid:2877) − (cid:2013)(cid:2870)(cid:2868)(cid:2875)(cid:2868)
2070 − 2019

 II.A9.18 

(126) This is also a common technical assumption in many long-
run projection models, to avoid "explosive" path of some of 
the variables used in the exercise. 

169 

 
 
 
 
  
                                                           
 
      
 
          
          
          
 
                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

In the specific case where the income elasticity of 
demand converges from 1.1 in 2019 to 1 in 2070, 
the value will be the following: 

(cid:2013)(cid:3047) = 1.1 − ((cid:1872) − 2019) ∙

0.1
51

CCC
g,a,t,i is cost per capita of a person of sex g and 
age a in year t of the projection period, in country 
i,  adjusted  to  the  GDP  per  capita  growth  and  a 
catch-up  effect  if  country  i  is  below  the  EU27 
average; 

 II.A9.19 

ΔYpct,i  is  GDP  per  capita  rate  growth  in  year  t  of 
country i; and 

The other steps of the projections are the same as 
in equations II.A9.3 and II.A9.4. 

VI. EU27 cost convergence scenario 

The  "EU27  cost  convergence  scenario"  captures 
the  possible  effect  of  an  upward  convergence  in 
real  living  standards  on  healthcare  spending, 
resulting 
from  a  convergence  of  citizens' 
expectations  towards  a  similar  basket  of  (health) 
goods. It considers the convergence by 2070 of all 
countries  that,  in  the  base  year,  are  below  the 
EU27  average  in  terms  of  percent  of  GDP  per 
capita health expenditure to that average.  

mg,a,i is a hypothetical rate of growth of per capita 
costs, which is higher than zero for those countries 
below  the  EU27  average,  and  equal  to  zero  for 
those countries at or above  the EU27 average. To 
close  the  gap,  mg,a,i  is  assumed  to  be  constant  in 
time and equal to (127): 

(cid:1865)(cid:3034),(cid:3028),(cid:3036) = (cid:4686)(cid:4678)

(cid:1870)(cid:1855)(cid:3365) (cid:3034),(cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877)
(cid:1870)(cid:1855)(cid:3034),(cid:3028),(cid:3036),(cid:2870)(cid:2868)(cid:2869)(cid:2877)

(cid:4679)

(cid:2869)
(cid:2870)(cid:2868)(cid:2875)(cid:2868)(cid:2879)(cid:2870)(cid:2868)(cid:2869)(cid:2877)

(cid:4687) − 1 

II.A9.21 

if  (cid:1870)(cid:1855)(cid:3365) (cid:3034),(cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877) ≥ (cid:1870)(cid:1855)(cid:3034),(cid:3028),(cid:3036),(cid:2870)(cid:2868)(cid:2869)(cid:2877) 

for 
for 

used 
Indeed, 

the  methodology 
scenario". 

To project public spending on healthcare, we build 
the  
on 
"demographic 
those 
countries  whose  age/sex  per  capita  public 
expenditure as a share of GDP per capita (relative 
per capita spending) is equal to or above the EU27 
average  (relative  per  capita  spending),  equations 
II.A9.1  to  II.A9.4  from  the  demographic  scenario 
to project public spending on healthcare are used.  

For  those  countries  whose  age/sex  per  capita 
public expenditure as a share of GDP per capita is 
below  the  EU27  average  in  the  baseline  year  of 
2019, we assume a different evolution path for this 
variable. We assume it evolves over the projection 
period  so  as  to  reach  the  EU27  average  in  2070. 
The real convergence to EU27 average is assumed 
the  following  path,  based  on  an 
to  follow 
adjustment of equation II.A9.1 of the demographic 
scenario:   

c

cc
ag
,

,0,

i

=

c

ag
,

,0,

i

c

cc
itag
,
,,

=

c

cc
tag
,

-
i
,1,

(
1

D+

Ypc

it
,

+

m

iag
,
,

t

=

0

)

t

>

0

II.A9.20

where: 

(cid:1870)(cid:1855)(cid:3365) (cid:3034),(cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877)  is  the  weighted  EU27  average 
relative  cost  per  capita  of  sex  g  and  age  a 
calculated  in  the  baseline  year  of  2019;  and  
(cid:1870)(cid:1855)(cid:3034),(cid:3028),(cid:3036),(cid:2870)(cid:2868)(cid:2869)(cid:2877)  is  the  relative  cost  per  capita  of  sex  g 
and age a for country i (if below the EU27 average 
cost  per  capita)  calculated  in  the  baseline  year  of 
2019 defined as: 

(cid:1870)(cid:1855)(cid:3034),(cid:3028),(cid:3036),(cid:2870)(cid:2868)(cid:2869)(cid:2877) = (cid:4678)

(cid:3030)(cid:3282),(cid:3276),(cid:3284),(cid:3118)(cid:3116)(cid:3117)(cid:3125)
(cid:3026)(cid:3043)(cid:3030)(cid:3284),(cid:3118)(cid:3116)(cid:3117)(cid:3125)

(cid:4679)                                

and  

(cid:1870)(cid:1855)(cid:3365) (cid:3034),(cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877) = (cid:4678)

(cid:1855)̅(cid:3034),(cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877)
(cid:1851)(cid:1868)(cid:1855)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877)

(cid:4679) 

where  (cid:1855)̅(cid:3034),(cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877) is the weighted EU27 average 
cost per capita of sex g and age a calculated in the 
baseline  year  of  2019;  and  (cid:1851)(cid:1868)(cid:1855)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877)  is  the 
average GDP per capita in the EU27 calculated in 
the baseline year of 2019. 

where: 

cc stands for cost convergence; 

(127) Assumptions  for  different  convergence  paths  according  to 
the  initial  country-specific  situation  -  comparing  to  the 
EU27 average age profile - will be explored further as soon 
as data is made available to calculate the new age profiles. 

170 

 
 
   
        
          
          
          
 
 
 
                                                           
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

After  country-specific  per  capita  cost  has  been 
calculated,  corresponding  equations  II.A9.3  and 
II.A9.4  are  used  to  obtain  total  age/sex  group 
total  public  expenditure  on 
expenditure  and 
healthcare in each projection year. 

VII. Labour intensity scenario 

labour-intensive  sector. 

The  "labour  intensity  scenario"  estimates  the 
evolution  of  public  expenditure  on  healthcare 
taking  into  account  that  healthcare  is  and  will 
remain  a  highly 
In 
practical  terms,  this  scenario  is  similar  to  the 
"demographic  scenario"  except  that  unit  costs  are 
assumed  to  evolve  in  line  with  the  evolution  of 
GDP  per  hours  worked.  Therefore,  the  growth  in 
GDP per capita is replaced by the growth in GDP 
per  hours  worked,  so 
that  equation  II.A9.1 
becomes: 

c

li
ag
,

0,

=

c

ag
,

0,

c

li
tag
,
,

=

c

li
tag
,

-
1,

(
1

D+

Yphw

)

t

t

=

0

t

>

0

 II.A9.22 

where: 

li stands for "labour intensity" scenario; 

ΔYphwt  is  the  rate  of  growth  of  GDP  per  hours 
worked in year t    

D

Yphw
t

=





Y
t
HW
t

-

Y
-
1
t
HW
t

-
1









Y
-
t
1
HW
t

-
1





 II.A9.23 

where HW stands for total hours worked. 

Corresponding  equations  II.A9.3  and  II.A9.4  are 
then  used 
total  age/sex  group 
expenditure  and 
total  public  expenditure  on 
healthcare in each projection year. 

to  calculate 

reflecting 

This  scenario  considers 
that  expenditure  on 
healthcare  can  be  disaggregated  in  its  different 
components,  broadly 
the  different 
sectors  of  the  health  system:  1)  inpatient  care,  2) 
outpatient  care  and  ancillary 
services,  3) 
pharmaceuticals  and  therapeutic  appliances,  4) 
preventive care, 5) capital investment, and 6) other 
factors.  The  different  components  are  treated 
separately and indexed in a separate/different way, 
creating  a  sort  of  composite  indexation  for  "unit 
cost development". 

In  mathematical  terms,  the  different  steps  of  this 
scenario  are  as  follows:  The  share  of  each  of  the 
six  components  in  total  public  expenditure  on 
healthcare  in  each  year  t  of  available  data,  up  to 
the baseline year of 2019 is calculated as follows: 

PE
,
ti

s
,
ti

= 6


i

=
1

PE
,
ti

   II.A9.24 

where  Si,t  is  the  share  of  public  expenditure  on 
component or input i at each time t to total public 
expenditure on healthcare,  

PEi,t is total public expenditure on component i at 
each time t and  

6



tiPE
,

i

=
1

  is  total  public  expenditure  on  healthcare 
expressed as the sum of the public expenditure on 
each of the six components.   

The average share of the ten past observations, up 
to the latest available data,  is  of each component is 
calculated as 

-
9

,
s

ti

== t

0
10

   II.A9.25 

VIII.  Sector-specific 
scenario 

composite 

indexation 

s

i

composite 

"sector-specific 

indexation 
The 
scenario"  presents  the  special  character  of  the 
healthcare  sector  (high 
level  of  government 
regulation,  investment  in  new  technologies,  high 
labour intensity), and uses sector-specific elements 
as unit costs determinants in the model.  

These  average  shares  are  combined  with  the 
age/sex-specific per capita expenditure in 2019 so 
that this is the sum of the expenditure on the above 
six components 

(cid:2874)

(cid:1855)(cid:3034),(cid:3028),(cid:2868) = (cid:3533) (cid:1871)̅(cid:3036) ∙ (cid:1855)(cid:3034),(cid:3028),(cid:2868)
(cid:3036)(cid:2880)(cid:2869)

   II.A9.26 

171 

 
 
     
        
          
          
 
 
 
 
 
 
 
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

We can define the cost per capita in each subsector 
as  

ΔYt  is  the  GDP  rate  of  growth  in  year  t  for  each 
country.  

(cid:1855)(cid:3034),(cid:3028),(cid:3036),(cid:2868) = (cid:1871)̅(cid:3036) ∙ (cid:1855)(cid:3034),(cid:3028),(cid:2868) 

   II.A9.27 

To  calculate  the  annual  growth  rate  of  public 
expenditure  for  each  of  the  six  components,  the 
growth rate of public expenditure for component i 
at time t of available data up to the baseline year of 
2019 included is:  

Each of the six ratios of growth rates (the ratio of 
iPED
 to ΔY) converges linearly to 1 by a specified 
date, 2070.  

II.A9.3  and 
Again,  corresponding  equations 
II.A9.4  are  then  used  to  calculate  total  age/sex 
group expenditure and total public expenditure on 
healthcare in each projection year. 

D

PE

ti
,

=

PE






-

ti
,
PE

-
ti
1,

PE

-
ti
1,






   II.A9.28 

IX. Non-demographic determinants scenario 

and  the  average  annual  growth  rate  of  public 
expenditure  for  component  i  for  the  last  past  10 
years where available, which is:  

-

9



t

=

0

D

PE

i

=

D

PE

ti
,

10

   II.A9.29 

Next, we calculate the average annual growth rate 
of  GDP  (ΔY  )  for  the  past  ten  years  of  available 
data as: 

∆(cid:1851)(cid:3364)(cid:3364)(cid:3364)(cid:3364) =

1
10

(cid:2879)(cid:2877)
∙ (cid:3533) ∆(cid:1851)(cid:3047)
(cid:3047)(cid:2880)(cid:2868)

   II.A9.30 

The  ratio  of  average  annual  grow  rate  of 
expenditure  on  each  component  to  the  average 
annual  growth  rate  of  GDP  is  calculated  by 
dividing equation II.A9.29 by equation II.A9.30.  

Following these calculations  the per capita cost is 
assumed to evolve in the following manner: 

(cid:3031)(cid:3036) =
(cid:1855)(cid:3034),(cid:3028),(cid:3047)

(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)
∆(cid:1842)(cid:1831)(cid:3114)
∆(cid:1851)(cid:3114)(cid:3364)(cid:3364)(cid:3364)(cid:3364)

(cid:2873)

∙ (cid:3533)(cid:3435)(cid:1871)̅(cid:3036) ∙ (cid:1855)(cid:3034),(cid:3028),(cid:3047)(cid:2879)(cid:2869)(cid:3439)
(cid:3036)(cid:2880)(cid:2869)

II.A9.31 

where: 

di stands for decomposed indexation scenario; and 

172 

The  "non-demographic  determinants  scenario" 
shows  the  effect  of  other  healthcare  spending 
to  population's  ageing,  such  as 
drivers  next 
income, 
and 
institutional  settings.  These  factors  have  been 
identified  as 
the  main  drivers  of  healthcare 
expenditure growth by several econometric studies 
(128).  

technology, 

relative 

prices 

This  scenario  uses  panel  regression  techniques  to 
estimate  country-specific  non-demographic  cost 
(NDC)  of  healthcare.    NDC  is  defined  as  the 
excess  of  growth  in  real  per-capita  healthcare 
expenditure  over  the  growth  in  real  per-capita 
demographic 
GDP 
composition effects. Alternatively, results can also 
terms  of  "average"  country 
be  expressed 
specific 
healthcare 
of 
expenditure. 

in 
income 

controlling 

elasticities 

after 

for 

This scenario is identical to the "income elasticity 
scenario"  except  that  the  elasticity  of  demand  is 
set equal to 1.5 in the base year (rather than 1.1 in 
the  case  of  the  "income  elasticity  scenario"), 
converging  in  a  linear  manner  to  1  by  the  end  of 
projection horizon in 2070.  

X. AWG reference scenario 

The  “AWG  reference  scenario”  is  the  central 
scenario  used  when  calculating 
the  overall 
budgetary impact of ageing. Formally, it builds on 
the "income elasticity scenario", combining it with 
age/sex  specific  expenditure  profiles  intermediate 
between  the  "demographic  scenario"  and  the 

(128) Maisonneuve and Martins (2013), "A projection method of 
public  health  and  long-term  care  expenditures",  OECD 
Economic Department WP No. 1048. 

 
 
 
 
 
 
 
                                                           
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

"healthy  ageing 
the 
assumption  that  half  of  the  future  gains  in  life 
expectancy are spent in good health. 

scenario",  driven  by 

XI. AWG risk scenario 

The  "AWG  risk  scenario",  follows  the  same 
approach  as  described  in  the  "non-demographic 
determinants  scenario"  in  combination  with  the 
assumption  that  half  of  the  future  gains  in  life 
expectancy  are 
in  good  health,  an 
intermediate  approach  to  the  age/sex  specific 
expenditure  profiles  between  the  "demographic 
scenario" and the "healthy ageing scenario". 

spent 

173 

 
 
 
ANNEX 10 

Mathematical illustration of the long-term care scenarios  

General definitions 

Let us define Ng,a,t the population of a given sex g 
and age a in year t. Following the main steps of the 
general  methodology  process  presented  in  the 
chapter  on 
following 
definitions are derived. 

long-term  care, 

the 

STEP 1: dependent/non-dependent population 

The  ratio  of  dependent  (resp.  non-dependent) 
persons in the base year t=b (e.g. 2019) is derived 
from the EU-SILC data, for each age – actually, 5-
year age groups (15+) – and sex group: dg,a,b (resp. 
1- dg,a,b). The average dependency rates for the last 
5 years are being used, based on data availability. 
Therefore, the projected dependent population of a 
given sex g and age a in a projected year t is: 

D

tag
,
,

=

d

bag
,
,

N

 ,
tag
,

II.A10.1 

STEP 2: split into types of care 

To  be  able  to  differentiate  the  impact  of  different 
scenarios according to the respective behaviour of 
the  different  types  of  care,  one  needs  to  split  the 
projected  dependent  population  into  four  groups: 
those receiving formal in-kind care at home, those 
receiving formal in-kind care in institutions, those 
receiving cash benefits (which is also defined as a 
type  of  formal  care)  and  those  receiving  only 
informal care.  

Therefore,  one  defines  DFhg,a,t,  DFig,a,t,  DIg,a,t  the 
projected  dependent  population  of  a  given  sex  g 
and  age  a 
t  receiving 
in  a  projected  year 
respectively  in-kind  formal  care  at  home  (DFh), 
in-kind  formal  care  in  institutions  (DFi),  cash 
benefits and informal care (DI), as follows: 

DFh

tag
,
,

=

D

tag
,
,

p

Fh
ag
,

 0,

DFi

tag
,
,

=

D

tag
,
,

p

Fi
ag
,

 0,

II.A10.2 

II.A10.3 

DI

tag
,
,

=

D

tag
,
,

1(

-

p

Fh
ag
,

0,

-

p

Fi
ag
,

0,

)

II.A10.4 

174 

Where  pFh
g,a,0  is  the  probability  for  a  dependent 
person of sex g and age a to receive in-kind formal 
care  at  home,  in  the  base  year  0  (e.g.  2019). 
Similarly,  pFi
g,a,0  is  the  correspondent  probability 
of  being  taken  care  of  formally  in  institutions, 
while pI
g,a,0 – the probability of being take care of 
informally – is defined as not receiving any formal 
care service. 

STEP 3: age-sex profiles of expenditure 

Average  expenditure  is  calculated  for  a  base  year 
0,  to  define  the  long-run  unit  costs  of  services.  If 
the  data  is  available  (through  the  SHA  joint 
questionnaire and/or provided by Member States), 
unit costs  for in-kind formal  care at home and in-
kind  formal  care  in  institutions  are  calculated 
separately(129): 

(cid:3007)(cid:3035) =
(cid:1855)(cid:3034),(cid:3028),(cid:2868)

(cid:3007)(cid:3035)
(cid:1845)(cid:3034),(cid:3028),(cid:2868)
(cid:3007)(cid:3035)  
(cid:1840)(cid:3034),(cid:3028),(cid:2868)

II.A10.5 

where: SFh
g,a,0 is public spending on in-kind formal 
care  at  home  in  the  base  year  (e.g.  2019); 
and  NFh
g,a,0  is  the  number  of  recipients  of  a  given 
sex g and age a of in-kind formal care at home, for 
the same year. 

Similarly,  the  unit  cost  per  beneficiary  of  a  given 
sex  g  and  age  a  of  in-kind  formal  care  in 
institutions is:  

(cid:3007)(cid:3036) =
(cid:1855)(cid:3034),(cid:3028),(cid:2868)

(cid:3007)(cid:3036)
(cid:1845)(cid:3034),(cid:3028),(cid:2868)
(cid:3007)(cid:3036)
(cid:1840)(cid:3034),(cid:3028),(cid:2868)

II.A10.6 

Note that two adjustments are made to the derived 
unit costs. The first one applies when age profiles 
are not provided separately for the two types of in-
kind  formal  care.  The  age  profiles  provided  by 
Member  States  for  public  expenditure  on  in-kind 
formal care services are then used in order to "re-
calibrate"  the  unit  costs.  In  other  words,  the 
relative  size  of  the  amounts  provided  for  each 
sex/age  group  is  applied  to  respective  "total" 
public  expenditure  aggregates  of  in-kind  formal 
care  at  home  (SFh
0)  and  in-kind  formal  care  in 
institutions (SFi
0). 

(129) Otherwise, an average of the age-cost profiles of other EU 

Member States is used. 

 
 
 
 
 
 
 
 
 
 
                                                           
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

The  unit  costs  evolve  in  time  with  the  GDP 
growth, as will be explained in the next section of 
this annex (see equation II.A10.10). 

assumptions are being applied. These assumptions 
are illustrated in the following section. 

STEP  4:  total  public  expenditure  on  long-term 
care services 

Assumptions for the different scenarios 

I. Demographic scenario 

For  a  projected  year  t,  public  spending  on  both 
types of in-kind formal care is then computed as: 

TS

Fh
tag
,
,

=

c

AFh
tag
,
,

DFh

 ,
tag
,

II.A10.7 

g,a,t (resp. TSFi

where: TSFh
g,a,t) is public spending on 
formal  in-kind  care  at  home  (resp.  in  institution) 
for all persons of sex g and age a in year t. 

Hence, for all age and sex groups: 

Fh
TS
t

,=
TS

Fh
 ,
tag

and 

TS

Fi
t

,=
TS

Fi
 ,
tag

II.A10.8 

where: 

As mentioned above, the first assumption added to 
the  general  model  is  the  following:  for  the  time 
horizon  of  the  projection  exercise,  the  age-sex 
specific  public  expenditure  profiles  (showing  the 
average  public  spending  on  long-term  care  per 
beneficiary  for  each  year  of  age  –  or  5-year  age 
group, from 15 to 85+ or  more, according to data 
availability)  are  assumed  to  grow  in  line  with 
income, i.e. with GDP per capita (131). 

Therefore, 
(expenditure) in a projected year t is: 

the  adjusted  per  beneficiary  cost 

c

F
¢
,
ag

0,

=

c

AF
,
ag

0,

c

F
¢
tag
,
,

F
¢=
c
tag
,

-
1,

(
1

D+

Ypc

t

     t
)
     t

=

0

>

0

II.A10.10 

STEP  5:  total  public  expenditure  on  long-term 
care (services and cash) 

Therefore,  total  public  expenditure  on  both  types 
of  formal  long-term  care  services  are  added  to 
long-term care related cash benefit expenditure, so 
as to obtain TSLTC

t for a projected year t: 

TS

LTC
t

=

TS

Fh
t

+

TS

Fi
t

+

TS

C
t

II.A10.9 

Where  TSC
expenditure on in-kind benefits(130).  

t  is  projected  in  a  similar  manner  to 

These  general  definitions  apply  to  the  general, 
"basic"  model  structure.  In  order  to  run  more 
accurate  scenarios,  general  and  scenario-specific 

c'F
g,a,t  is  the  cost  per  beneficiary  of  a  given  sex  g 
and age group a in period t of formal care F – Fh 
for formal in-kind care at home, Fi  for formal in-
kind care in institution; 

ΔYpct is GDP per capita growth rate in year t, i.e.:  

D

Ypc
t

=





Y
t
P
t

-

Y
t
P
t

-
1

-
1









Y
t
P
t

-
1

-
1





II.A10.11

with  Yt  and  Pt  representing  GDP  and 
population in projection year t; 

total 

Hence, the adjusted per beneficiary cost, c' F
g,a,t, is 
the  formal  in-kind  care  cost  per  beneficiary  of  a 
person of sex g and age a in year t of the projection 
period, following the adjustment to GDP per capita 
growth. 

(130) The projection of cash benefit expenditure is illustrated in 
less detail than that for in-kind benefits due to the fact that 
the data on recipients is less readily available and therefore 
the profile is in some cases assumed to be the same as that 
for in-kind care.  

(131) Alternative  indexation  assumptions  in  order  to  reflect  the 
institutional  set-up  of  specific  EU  Member  States  are 
discussed in Chapter 3 of Section II. 

175 

 
 
 
 
 
 
 
                                                           
 
          
          
 
 
                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

Equation II.A10.7 above becomes II.A10.7' as the 
adjusted unit cost c' is considered, i.e.: 

where: 

TS

Fh
tag
,
,

Fh
¢=
c
,
tag
,

DFh

 ,
tag
,

II.A10.7' 

ΔYphwt  is  the  rate  of  growth  of  GDP  per  hours 
worked in year t,  

The  same  applies  to  formal  in-kind  care  in 
institutions: 

D

Yphw

t

=





Y
t
HW

t

-

Y
-
t
1
HW

t

-
1









Y
-
t
1
HW

t

-
1





II.A10.12 

TS

Fi
tag
,
,

Fi
¢=
c
,
tag
,

DFi

 ,
tag
,

II.A10.7'b 

where HW stands for total hours worked. 

Similarly  for  cash  benefits,  total  public  spending 
becomes  TS'C
t,  and  an  adapted  equation  II.A10.9 
gives  adjusted  total  public  spending  on  long-term 
care, i.e.: 

TS

'

LTC
t

=

TS

'

Fh
t

+

TS

'

Fi
t

+

TS

'

C
t

II.A10.9' 

Corresponding  equations  II.A10.7  and  II.A10.7'b 
are then used and coupled with TS'C
t as calculated 
in  the  "demographic  scenario"  to  calculate  total 
age/sex  group  expenditure  and 
total  public 
expenditure  on  long  term  care  in  each  projection 
year. 

ST

LTC
¢¢
t

=

ST

Fh
+¢¢
t

ST

Fi
 +¢¢
t

TS

'

C
t

II.A10.9'' 

II. Base case scenario 

For  the  "base  case  scenario",  the  assumption  on 
unit cost development is slightly different from the 
"demographic  scenario".  Indeed, 
it  has  been 
agreed to differentiate two kinds of unit costs. The 
projections  will  link  unit  cost  to  GDP  per  hours 
worked  (132)  for  in-kind  benefits  (services),  while 
unit  cost  of  cash  benefits  will  evolve  in  line  with 
GDP  per  capita  growth.  Therefore,  the  age-sex 
specific public expenditure profiles are assumed to 
grow in line with: 

1.  GDP per capita for cash benefits; 

2.  GDP per hours worked for benefits in kind. 

The  situation  is  unchanged  for  cash  benefits,  i.e. 
TS'C
t, whereas GDP per hours worked will be used 
to  adjust  total  public  spending  on  formal  care 
services. Equation II.A10.10 becomes: 

c

c

c

Fc
Fc
=¢¢
ag
ag
,
0,
,
(
Fc
Fc
¢¢=¢¢
11,
-
,
,
,
tag
tag

c

0,

D+

Yphw

)t

II.A10.10' 

(132) GDP  per  hours  worked  is  used,  similar  to  the  previous 
ageing  report,  to  stay  in  line  with  the  macroeconomic 
assumptions and the other parts of the projections. 

176 

III. High life expectancy scenario 

The  "high  life  expectancy  scenario"  presents  the 
budgetary  effects  of  an  alternative  demographic 
scenario,  which  assumes  life  expectancy  to  be 
higher for all ages than in the demographic and in 
the base case scenarios. In terms of methodology, 
the  scenario  does  not  differ  from  the  "base  case 
scenario",  apart  from  the  fact  that  the  baseline 
demographic  projections  used  as  input  data  are 
replaced with the alternative, high life expectancy, 
variant  (the  same  used  to  assess  the  sensitivity  of 
pension  spending).  Therefore,  the  mathematical 
illustration  of  the  previous  scenario  only  changes 
in  Ng,a,t,  i.e.  the  number  of  individuals  in  each 
age/sex  group  up  to  2070  (replaced  by  the  new 
population  assumptions  in  equation  II.A10.1  and 
II.A10.11). 

IV. Constant disability scenario 

This  scenario  reflects  an  alternative  assumption 
about  trends  in  age-specific  ADL-dependency 
rates.  The  profile  of  age-specific  disability  rates 
shifts  in  line  with  changes  in  life  expectancy 
(disability  rate  in  the  future  is  equal  to  that  of  a 
younger  -  by  the  same  number  of  years  as  the 
change in age-specific life expectancy - age cohort 
today), resulting in a gradual decrease over time in 

 
 
 
 
 
 
                                                           
 
 
 
 
 
 
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

disability  prevalence  for  each  age  cohort,  i.e. 
affecting the variable Dg,a,t. 

D

=¢
,
tag
,

d

ag
,

D-

LE

btag
,

,

,

N

 ,
tag
,

II.A10.1' 

In practical terms, it follows the same reasoning as 
for 
the  similar  healthcare  "constant  health 
scenario".  One  starts  by  calculating,  for  each 
projection  year,  the  change  in  life  expectancy  in 
relation  to  the  base  year.  For  example,  life 
expectancy  for  a  50-year-old  man  is  expected  to 
increase by, say, 4 years: from 30 years in year t to 
34 years in year t+20 in a specific Member State. 
Then,  the  scenario  assumes  that  in  t+20,  in  that 
same Member State, a 50-year-old man will have a 
disability prevalence of a (50-4) = 46-year old man 
in year t.  

Hence, the change in life expectancy of a person of 
sex  g  and  age  a  in  relation  to  the  base  year  (say, 
2019)  is  first  calculated  for  each  year  of  the 
projections,  using 
the  Eurostat  population 
projections (133):  

D

LE

0,
tag
,
,
II.A10.13 

=

LE

tag
,
,

-

LE

ag
,

0,

where: 

ΔLEg,a,t,0  is  the  additional  life  expectancy  of  a 
person of sex g and age a in year t compared to a 
person of sex g and age a in the base year, 

LEg,a,t  is  the  life  expectancy  of  a  person  of  sex  g 
and age a in year t and  

LEg,a,0  is  life  expectancy  of  an  average  person  of 
sex g and age a in the base year. 

For  year  t  of  the  projections,  the  "adjusted" 
disability  prevalence  for  the  cohort  of  sex  g  and 
age  a  is  then  based  on  equation  II.A10.1  adjusted 
such as: 

(133) In  the  "constant  disability  scenario"  the  total  number  of 
years  spent  with  disability  during  a  person’s  lifetime  is 
life  expectancy 
assumed  to  remain  the  same  while 
increases.  Thus,  if  between  time  t  and  t+1,  total  life 
expectancy  increases  by  n  years  for  a  cohort  of  age  a, 
"disability-free"  life  expectancy  for  that  very  same  age 
cohort  must  also  increase  by  n  years  in  order  for  the 
relative compression of morbidity hypothesis to be valid. If 
"disability-free"  life  expectancy increases by n  years, then 
the disability prevalence of this cohort of age a at time t+1 
will  be  the  same  as  the  disability  prevalence  of  cohort  of 
age a-n at time t. 

And  the  adjusted  projected  dependent  population 
D'g,a,t  will  therefore  replace  former  Dg,a,t  in  the 
subsequent equations II.A10.2 to II.A10.4 and then 
II.A10.10'  and  II.A10.9',  to  follow  the  subsequent 
steps of the "base case scenario". 

V.  Scenario  assessing  the  effect  of  a  shift  from 
informal to formal care 

Building on the "base case scenario", this policy-
change  scenario  is  a  sensitivity  test  that  examines 
the budgetary impact of a progressive shift into the 
formal  in-kind  sector  of  care  of  1%  per  year  of 
disabled  elderly  who  have  so  far  received  only 
informal  care.  This  extra  shift  takes  place  during 
the  first  ten  years  of  the  projection  period,  thus  it 
sums  up  to  about  10.5%  shift  from  informal  to 
formal care. This shift  will not have an impact on 
the relative shares of home and institutional formal 
in-kind care. The shift will thus not be 50% of the 
"new" beneficiaries to move into institutional care, 
while  the  other  50%  will  be  assumed  to  receive 
formal  care  at  home  but  a  shift  in  line  with  the 
existing shares of home and institutional care. The 
variables  DFhg,a,t,  DFig,a,t,  and  DIg,a,t  will  be 
adjusted to the new assumptions. 

The projected dependent population of a given sex 
g  and  age  a  in  a  projected  year  t  receiving 
respectively  formal  in-kind  care  at  home  (DFh), 
formal  in-kind  care  in  institutions  (DFi)  and 
informal  care 
in  equations 
(DI),  calculated 
II.A10.2  to  II.A10.4,  will  be  changed  as  follows. 
For  t  ˛   [0+1,  0+10]  –  let  us  say,  for  the  first  ten 
years of the projection period: 

ID

hDF

iDF

=¢
,
tag
,
=¢
,
tag
,
=¢
tag
,
,

DI

tag
,
,

-

1.0

DI

tag
,
,

-
1

=

9.0

DI

tag
,
,

-
1

DFh

tag
,
,

(

DFh

/

D

1.0)

DI

tag
,
,

-
1

DFi

tag
,

1,

DFi

tag
,

tag
,
,
/

-
1,

-
1
D

-
,
tag
,
1
1.0)

tag
,

-
1,

DI

tag
,

-
1,

-
1
- +
- +
(

1

These  adapted  projected  numbers  of  dependents  / 
recipients  of  formal  care  are  then  injected  in 
equations  II.A10.7',  II.A10.7b'  and  II.A10.9'  to 
calculate  the  total  public  spending  on  long-term 
care, as it was done in the "base case scenario". It 
should be noted that cash benefit recipients are not 
affected  by  this  and  this  population  remains 

177 

              
                                                           
 
 
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

constant for each age-sex group for the duration of 
the  projections.  For  the  rest  of  the  projection 
period until its end in 2070 the baseline equations 
are used as above. 

VI. Coverage convergence scenario 

This policy-change scenario assumes an expansion 
of publicly financed formal care provision into the 
groups of population that have not been covered by 
the  public  in-kind  programmes  so  far.  "Formal 
coverage"  covers  any  of  the  three  types  of  formal 
long-term  care:  in-kind  institutional  care,  in-kind 
formal home care, and cash benefits. This scenario 
assumes  that  coverage  of  in-kind  formal  care 
(institutional  and  home  care)  will  be  extended  to 
cover a proportion of those dependent people who 
do  not  receive  any  type  of  care  in  the  base  year. 
The  assumption  is  that  all  recipients  of  long-term 
care  are  dependent.  It  means  that  the  equations 
II.A10.2  to  II.A10.4  become  four  equations,  with 
probabilities  now  changing  over 
i.e. 
depending  on  t,  but  also  country-specific  (for  a 
country i).  

time, 

The scenario envisaged is a coverage convergence 
to the EU27 average for in-kind care. It is meant to 
take  into  account  the  high  diversity  of  country-
specific  current  in-kind  care-mix  between  home 
care  and  institutional  care.  The  Member  States 
where  the  in-kind  formal  coverage  rate  for  a 
specific  age-range  is  below  the  EU27  average  in 
the  starting  year  are  assumed  to  converge  to  this 
average  by  2070.  For  age  ranges  with  coverage 
above the EU average, this scenario is the same as 
the base case scenario. 

The  “base  case  scenario”  steps  are  used  for  the 
countries  whose  formal  in-kind  coverage  is  the 
same or greater than the EU27 average in the base 
year  (2019).  For  those  countries  whose  in-kind 
formal  coverage  is  below  the  EU27  average,  it  is 
assumed  to  converge  to  the  EU27  average.  It 
therefore  implies  that  each  type  of  in-kind  formal 
care  converges  at  a  different  pace,  making  up  for 
the  respective  relative  gaps  to  the  EU27  average. 
This  scenario  allows  a  country  to  grow  faster  the 
relatively  less-developed  type  of  in-kind  formal 
care. 

178 

VII. Cost convergence to EU27 average scenario 

to  capture 

This policy-change scenario is run in parallel with 
the  analogous  scenario  on  healthcare  expenditure 
projections.  The  "cost  convergence  scenario"  is 
meant 
the  possible  effect  of  a 
convergence  in  real  living  standards  on  long-term 
care spending. It assumes an  upward convergence 
of  the  relative  age-sex  specific  per  beneficiary 
expenditure profiles (as percent of GDP per capita) 
of  all  countries  below  the  corresponding  EU27 
average to the EU27 average. This is done for each 
type  of  formal  care  coverage  (i.e.  formal  care  in 
institutions, formal care at home, cash benefits).  

run 

this  scenario,  one  builds  on 

To 
the 
methodology  used  for  the  "base  case  scenario". 
For those countries whose per beneficiary costs are 
equal  to  or  above  the  EU27  average  the  steps 
illustrated above are followed. 

For  those  countries  below  the  EU27  average  per 
beneficiary costs in the base year (2019) a further 
change in the way cost per beneficiary is evolving 
over  the  projection  period  is  assumed,  so  as  to 
reach  the  EU27  average  of  per  beneficiary  costs. 
Building  on  the  equations  II.A10.10  –  for  cash 
benefits  –  and  II.A10.10'  –  for  in-kind  benefits  – 
the real convergence  to EU27 average is assumed 
to follow the adjusted equations: 

(cid:4593)(cid:3004) = (cid:1855)(cid:3028),(cid:2868),(cid:3036)
(cid:1855)(cid:3028),(cid:2868),(cid:3036)

(cid:3004)

(cid:4593)(cid:3004) = (cid:1855)(cid:3028),(cid:3047)(cid:2879)(cid:2869),(cid:3036)
(cid:1855)(cid:3028),(cid:3047),(cid:3036)

(cid:4593)(cid:3004)

∙ (cid:3435)1 + ∆(cid:1851)(cid:1868)(cid:1855)(cid:3047),(cid:3036)
+ (cid:1865)(cid:3028),(cid:3036)(cid:3439) 

t=0 

t>0 

II.A10.10 

(cid:3002)(cid:3007)  
(cid:4593)(cid:4593)(cid:3007) = (cid:1855)(cid:3028),(cid:2868),(cid:3036)
(cid:1855)(cid:3028),(cid:2868),(cid:3036)

t=0 

(cid:4593)(cid:4593)(cid:3007) = (cid:1855)(cid:3028),(cid:3047)(cid:2879)(cid:2869),(cid:3036)
(cid:1855)(cid:3028),(cid:3047),(cid:3036)

(cid:4593)(cid:4593)(cid:3007)

∙ (cid:3435)1 + ∆(cid:1851)(cid:1868)ℎ(cid:1875)(cid:3047),(cid:3036) + (cid:1866)(cid:3028),(cid:3036)(cid:3439) 

t>0 

II.A1
0.10 

where: 

c''F
a,t,i  is  the  country  i-specific  cost  of  in-kind 
benefits per beneficiary of a given age a in period t 
– Fh for formal care at home, Fi for formal care in 
institution – adjusted to the GDP per hours worked 

 
 
 
 
 
 
Age-related expenditure items: coverage, projection methodologies and data sources 

Part II 

growth and a catch-up effect  if country i is below 
the EU27 average; 

(cid:1870)(cid:1855)(cid:3365) (cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877) = (cid:4678)

(cid:1855)̅(cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877)

(cid:1851)(cid:1868)ℎ(cid:1875)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)

(cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877)

(cid:4679) 

c'C
a,t,i is the country i-specific cost of cash benefits 
per  beneficiary  of  a  given  age  a  in  period  t  –
adjusted to GDP per capita growth and a catch-up 
effect if country i is below the EU27 average; 

ΔYpct,i  is  the  GDP  per  capita  rate  growth  rate  in 
year t, for country i; 

ΔYphwt,i  is  GDP  per  hours  worked  growth  rate  in 
year t, for country i, and 

ma,i and na,i are hypothetical rates of growth of per 
beneficiary  costs.  They  are  higher  than  zero  for 
countries  whose  per  beneficiary  costs  are  below 
the  EU27  average,  and  equal  to  zero  for  those 
countries whose per beneficiary costs are equal or 
above the EU27 average. To close the gap, ma,i is 
assumed to be constant in time and equal to (134): 

(cid:1865)(cid:3028),(cid:3036) = (cid:4686)(cid:4678)

(cid:1870)(cid:1855)(cid:3365) (cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877)
(cid:1870)(cid:1855)(cid:3028),(cid:3036),(cid:2870)(cid:2868)(cid:2869)(cid:2877)

(cid:4679)

(cid:2869)
(cid:2870)(cid:2868)(cid:2875)(cid:2868)(cid:2879)(cid:2870)(cid:2868)(cid:2869)(cid:2877)

(cid:4687)

− 1 

II.A10.14 

if (cid:1870)(cid:1855)(cid:3365) (cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877) ≥ (cid:1870)(cid:1855)(cid:3028),(cid:3036),(cid:2870)(cid:2868)(cid:2869)(cid:2877) 

where: 

is 

(cid:1870)(cid:1855)(cid:3365) (cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877) 
the  weighted  EU27  average 
relative cost per beneficiary of age a calculated in 
the base year of 2019 and  

(cid:1870)(cid:1855)(cid:3365) (cid:3028),(cid:3036),(cid:2870)(cid:2868)(cid:2869)(cid:2877) is the relative cost per beneficiary of age 
a for country i calculated in the base year of 2019 
defined as: 

(cid:1870)(cid:1855)(cid:3028),(cid:3036),(cid:2870)(cid:2868)(cid:2869)(cid:2877) = (cid:4678)

(cid:4593)(cid:4593)
(cid:1855)(cid:3028),(cid:3036),(cid:2870)(cid:2868)(cid:2869)(cid:2877)
(cid:1851)(cid:1868)ℎ(cid:1875)(cid:3028),(cid:3036),(cid:2870)(cid:2868)(cid:2869)(cid:2877)

(cid:4679) 

and  

(134) Assumptions  for  different  convergence  paths  according  to 
the  initial  country-specific  situation  -  comparing  to  the 
EU27-average age profile - could be explored further when 
data is made available. 

where: 

(cid:1855)̅(cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877) is the weighted EU27 average cost per 
beneficiary  of  age  a  calculated  in  the  base  year 
(2019); and  

(cid:1851)(cid:1868)ℎ(cid:1875)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)(cid:3364)
(cid:3028),(cid:3006)(cid:3022)(cid:2870)(cid:2875),(cid:2870)(cid:2868)(cid:2869)(cid:2877)  is  the  average  GDP  per  hours 
worked  in  the  EU27  calculated  in  the  base  year 
(2019). 

The  same  type  of  reasoning  can  be  run  with  the 
corresponding equations for cash benefits, adjusted 
to  GDP  per  capita  growth  instead  of  GDP  per 
hours worked growth. 

Then  after  country-specific  per  beneficiary  cost 
has  been  calculated,  subsequent  corresponding 
equations  are  used  to  obtain  total  age-sex  group 
expenditure  and  then  total  public  expenditure  on 
long-term  care  in  each  projection  year,  as  in 
equation II.A10.9''. 

VIII. Cost and coverage convergence scenario 

This  policy-change  scenario  combines  the  two 
previous  scenarios,  the  "coverage  convergence 
scenario"  and  the  "cost  convergence  scenario"  to 
the  EU  27  average.  For  countries  with  cost  and 
coverage  above  the  EU  average,  this  scenario  is 
the same as the "base case scenario". 

IX. AWG reference scenario 

The  "AWG  reference  scenario"  combines  the 
assumptions  of  the  "base  case  scenario"  and  the 
"constant disability scenario". It assumes that half 
of  the  projected  longevity  gains  up  to  the  end  of 
the  projection  period  will  be  spent  in  good  health 
and  free  of  disability/dependency.  Accordingly, 
age-specific  disability  rates  shift  along  the  age 
profile  by  half  of  the  projected  increase  in  life 
expectancy. Furthermore, the  unit costs are linked 
to  GDP  per  hour  worked  in  case  of  LTC  services 
and  to  GDP  per  capita  in  case  of  cash  benefits 

179 

 
 
                                                           
 
 
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

(subject  to  the  relevant  exceptions  in  order  to 
reflect country-specific assumptions). 

For Member States in the highest quartile of LTC 
expenditure  as  a  proportion  of  GDP  in  the  base 
year,  income  elasticity  of  LTC  expenditure  is 
assumed  to  remain  1  over  the  projection  period. 
For the rest, income elasticity is assumed to start at 
1.1 in the base year of 2019, falling to 1 by the end 
of the projection period.  

X. AWG risk scenario 

The  "AWG  risk  scenario"  keeps  the  assumption 
that  half of the  future gains in life expectancy are 
spent  without  care-demanding  disability,  as  in  the 
"AWG 
it 
scenario". 
combines this scenario with the "cost and coverage 
convergence  scenario"  by  assuming  convergence 
upwards of unit costs to the EU-average as well as 
coverage convergence upwards to the EU-average.  

In  addition, 

reference 

180 

 
 
 
ANNEX 11 

Organisational structure of secondary education 

single 

structure; 

i)  a 
integrated 

Three  different  organisational  models  can  be 
ii)  a 
distinguished: 
compulsory 
education 
corresponding to a 'common core'; and iii) distinct 
types  of  education.  In  some  new  Member  States 
(the  Czech  Republic,  Latvia,  Lithuania,  Hungary 
and Slovakia), combinations of these three models 
coexist. (135) 

secondary 

In  all  countries  where  the  single  structure  is  the 
only  type  (Denmark,  Estonia,  Portugal,  Slovenia, 
Finland,  Sweden,  Iceland,  Norway  and  Bulgaria), 
the end of secondary education coincides with the 
end  of  compulsory  education,  except  in  Bulgaria 
where compulsory education ends one year later.  

In almost half of all European countries, all pupils 
follow  the  same  general  curriculum  "common 
core"  during  lower  secondary  education.  In  seven 
of  these  countries,  the  end  of  lower  secondary 
education  coincides  with  the  end  of  full-time 
compulsory education.  

In  Belgium,  France,  Ireland,  Italy,  Hungary, 
Austria,  Slovakia  and  Bulgaria,  the  end  of  full-
time compulsory education does not coincide with 
the end of lower secondary education. Instead, one 
or  more  final  years  of  compulsory  education  are 
part of upper secondary education. Thus, pupils in 
these  countries  -  with  the  exception  of  Ireland  - 
have  to  choose  between  general,  technical  or 
vocational  education  of  one  or  two  years  (or  four 
full-time 
in  Hungary)  before 
compulsory education. 

the  end  of 

In 
the  French  and  German-speaking  Belgian 
Communities,  Germany,  Latvia,  Lithuania, 
Luxembourg,  the  Netherlands  and  Austria,  pupils 
may  select  or  be  streamed  into  different  types  of 
provision  or  school  from  the  beginning  or  before 
the end of lower secondary education. Even though 
pupils  in  Germany  attend  different  schools,  they 
follow  entirely  compatible  curricula  for  the  first 
two years so that selection of an appropriate study 
branch can be deferred. In the Netherlands, pupils 
follow  a  common  core  curriculum  usually  for  the 
first two years at VMBO and three years at HAVO 
and VWO. While its level varies depending on the 
type  of  school  concerned,  it  specifies  minimum 
skills that should be acquired by all pupils.  

(135) Source:  Key  data  on  education  in  Europe  2005,  European 

Commission, Eurydice, Eurostat, 2005. 

181 

                                                           
 
ANNEX 12 

Sources of data for education expenditure 

-  GDP per worker; 

-  GDP. 

Data sources and information  

As in the previous projection exercise, Eurostat is 
the main provider of data, mainly through the UOE 
data  collection (136).  As  a  rule,  expenditure  data 
given  by  the  average  of  the  two  last  available 
years,  generally  2015  and  2016  (or  more  recent 
data if available), are chosen. This is then uprated 
until  the  base  year  using  COFOG  data (137).  For 
those countries where data are missing for the base 
period, AWG delegates could be asked to provide 
them to the Commission.  

Specifically,  by  country,  year,  and 
groupings 
(1,  2,  3-4,  5-8), 
information from the UOE dataset will be used: 

ISCED 
following 

the 

-  Total number of students by single age; 

-  Number of working students by single age;  

-  Number of teachers and non-teaching staff; 

-  Total expenditure in staff compensation (138); 

-  Other  current  (excluding  staff  compensation) 

and capital expenditure; 

-  Share  of  transfers  over  total  public  education 

expenditure (139);  

-  Share of publicly funded education. 

Furthermore, and to secure full consistency of the 
long-term  budgetary  exercise,  the  common  AWG 
macroeconomic  assumptions  for  the  following 
variables are used:  

-  Total population per single age; 

-  Labour force per single age; 

(136) The  objective  of  the  UNESCO-UIS/OECD/EUROSTAT 
(UOE) data collection on education statistics is to provide 
internationally  comparable  data  on  key  aspects  of 
education  systems,  specifically  on  the  participation  and 
completion  of  education  programmes,  as  well  as  the  cost 
and 
education 
(http://www.oecd.org/dataoecd/32/53/33712760.pdf). 
(137) If data for 2018 is not available, the latest available public 

dedicated 

resources 

type 

of 

to 

expenditure data as a share of GDP are used. 

(138) Current  expenditure  on  staff  compensation  is  obtained  by 
deducting  expenditure  designated  for  capital,  ancillary 
services, and R&D from direct expenditure on educational 
institutions (UOE, 2019).  

(139) From the OECD, Education at a Glance.  

182 

                                                           
 
 
ANNEX 13 

Estimating the education enrolment rate 

Starting with the labour market identity:  

E
,
ti

+

U

+

I

,
ti

”

P
,
ti

,
ti

    (1) 

where (cid:1831)(cid:3036),(cid:3047),  (cid:1847)(cid:3036),(cid:3047),  (cid:1835)(cid:3036),(cid:3047)  and  (cid:1842)(cid:3036),(cid:3047)  are  respectively 
employment,  unemployment, 
the 
population for age cohort i in period t.  

inactive  and 

After  adding  and  subtracting  the  number  of 
full-time students ((cid:1845)(cid:1832)(cid:3036),(cid:3047)), and of part-time students 
((cid:1845)(cid:1842)(cid:3036),(cid:3047)):  

SF
,
ti

+

SP
,
ti

-

SP
,
ti

-+++
IUE
,
ti
,
ti
ti
,

SF
,
ti

”

P
,
ti

   (2) 

Let  us  use  the  definition  of  total  students  (cid:1845)(cid:1846)(cid:3036),(cid:3047) ≡
(cid:1845)(cid:1832)(cid:3036),(cid:3047) + (cid:1845)(cid:1842)(cid:3036),(cid:3047),  labour  force  (cid:1838)(cid:1832)(cid:3036),(cid:3047) ≡ (cid:1831)(cid:3036),(cid:3047) + (cid:1847)(cid:3036),(cid:3047),  and 
∗ ≡ (cid:1835)(cid:3036),(cid:3047) − (cid:1845)(cid:1832)(cid:3036),(cid:3047): 
inactive minus full-time students (cid:1835)(cid:3036),(cid:3047)

ST
,
ti

-

SP
,
ti

+

LF

+

I

*

,
ti

”

P
,
ti

    (3) 

Dividing equation (1) by the population ((cid:1842)(cid:3036),(cid:3047)), and 
defining  

”a
ti
,

SP
ti
,
+
SP
,
ti

SF

,
ti

=

SP
,
ti

ST

,
ti

as  the  fraction  of  part-time  students  in  the  total 
number  of  students,  the  following  identity  is 
obtained: 

ST
ti
,

P
ti
,

-

SP
ti
,
ST
ti
,

*

ST
ti
,

P
ti
,

+

LF
ti
,

P
ti
,

+

*

I

ti
,
P
ti
,

”

1

    (4) 

Equation 4 can be rearranged as: 

e
ti
,

=

1

-

p

-

ti
,
a-
ti
,

1

*
i
ti
,

(5) 

where the enrolment rate for total students is; 

e

, ”
ti

ST

,
ti

P

,
ti

 and the participation rate is;  

p

, ”
ti

LF

,
ti

P
,
ti

i

*
, ”
ti

I

*
ti
,

P

,
ti

is the fraction of inactive minus full-time students 
over the population. 

In equation 5, enrolment rates are inversely related 
to  the  participation  and  the  (adjusted)  inactivity 
rates.  

In most EU Member States, the LFS MAINSTAT 
variable  can  be  used  to  assess  the  distribution  of 
inactivity by age, distinguishing between schooling 
and other forms of inactivity (140). 

Assume  that  the  ratio  between  full-time  students 
and the total inactive ((cid:2018)(cid:3036),(cid:3029)) is constant over time at 
the value observed in the base period (b):  

= (cid:3435)1 − (cid:2018)(cid:3036),(cid:3029)(cid:3439) ∗

⇒ (cid:1861)(cid:3036),(cid:3047)

∗ −(cid:1861)(cid:3036),(cid:3029)

∗ = (cid:3435)1 − (cid:2018)(cid:3036),(cid:3029)(cid:3439) ∗

(cid:3010)(cid:3284),(cid:3295)
(cid:3017)(cid:3284),(cid:3295)
    (6)  

(cid:3020)(cid:3007)(cid:3284),(cid:3295)
(cid:3010)(cid:3284),(cid:3295)

=

(cid:3020)(cid:3007)(cid:3284),(cid:3277)
(cid:3010)(cid:3284),(cid:3277)

= (cid:2018)(cid:3036),(cid:3029)

⇒

∗
(cid:3010)(cid:3284),(cid:3295)
(cid:3017)(cid:3284),(cid:3295)

(cid:3435)(cid:1861)(cid:3036),(cid:3047) − (cid:1861)(cid:3036),(cid:3029)(cid:3439) 

Where: 

 (cid:2018)(cid:3036),(cid:3029) ≤ 1;  

(cid:1861)(cid:3036),(cid:3047) ≡

(cid:3010)(cid:3284),(cid:3295)
(cid:3017)(cid:3284),(cid:3295)

 , 

∗ ≡
(cid:1861)(cid:3036),(cid:3047)

∗
(cid:3010)(cid:3284),(cid:3295)
(cid:3017)(cid:3284),(cid:3295)

are the inactivity and the adjusted inactivity rates, 
respectively. A bar over a variable indicates that it 
is constant (i.e. time invariant). 

Enrolment  rates  are  projected  by  expressing 
equation  5  in  terms  of  differences  to  the  base 
period,  substituting  equation  6,  and  using  the 
identity (cid:3435)(cid:1868)(cid:3036),(cid:3047) − (cid:1868)(cid:3036),(cid:3029)(cid:3439) + (cid:3435)(cid:1861)(cid:3036),(cid:3047) − (cid:1861)(cid:3036),(cid:3029)(cid:3439) ≡ 0:  

(cid:1857)(cid:3036),(cid:3047) − (cid:1857)(cid:3036),(cid:3029) = −

(cid:3089)(cid:3284),(cid:3277)
(cid:2869)(cid:2879)(cid:3080)(cid:3284),(cid:3277)

∗ (cid:3435)(cid:1868)(cid:3036),(cid:3047) − (cid:1868)(cid:3036),(cid:3029)(cid:3439)      (7) 

(140) However,  the  MAINSTAT  variable,  which  describes  the 

main labour market status, is an optional one.  

183 

 
  
     
 
 
 
  
  
                                                           
European Commission 

2021 Ageing Report: Underlying assumptions and projection methodologies 

where  (cid:2018)(cid:3036),(cid:3029) =

(cid:3020)(cid:3007)(cid:3284),(cid:3277)
(cid:3010)(cid:3284),(cid:3277)

; (cid:2009)(cid:3036),(cid:3029) ≡

(cid:3020)(cid:3017)(cid:3284),(cid:3277)
(cid:3020)(cid:3007)(cid:3284),(cid:3277)(cid:2878)(cid:3020)(cid:3017)(cid:3284),(cid:3277)

=

(cid:3020)(cid:3017)(cid:3284),(cid:3277)
(cid:3020)(cid:3021)(cid:3284),(cid:3277)

,  

and 0 ≤ (cid:2018)(cid:3036),(cid:3029), (cid:2009)(cid:3036),(cid:3029) ≤ 1 

A value for (cid:2018)(cid:3036),(cid:3029) lower than one means that changes 
in the labour force do not necessary reduce one by 
one enrolment rates, because some people coming 
from  inactivity  were  not  involved  in  education 
activities. 

184 

 
Part III 
Statistical Annex 

 
 
1. 

BELGIUM 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

6.5
6.0

4.7
4.6
-24.5
-0.2
0.4
-2.9
-4.7
-6.1
8.9
6.0
11.9
12.5
AVG 19-70

1.2
0.0
0.0
1.2
0.8
0.4
1.2
1.2
Ch 19-70

-513
-0.3
-312
1.1
0.2
3.0
-1.5
9.4
6.5
2.0
1.3
4.1
-1.4
12.0
7.5
0.3
-1.0
2.0
-1.7
6.8
5.4
0.9
1.0
0.8
0.3
-0.4
1.0
0.9
-0.3
-0.3
0.2
-5.1
2.9
2.0
Ch 19-70

2019

1.58

79.8
84.3

18.9
22.2
45.0
0.4
11.5
22.4
39.5
58.6
19.0
5.7
29.8
9.7
2019

1.5
1.0
1.1
0.4
0.1
0.3
1.0
0.5
2019

6,723
0.2
5,011
74.5
64.2
49.7
84.8
54.6
4.3
70.0
59.7
46.9
80.3
49.2
2.9
79.1
68.7
52.5
89.3
60.1
5.9
63.4
63.3
63.5
70.6
60.9
5.2
5.2
4.7
4.8
6%
76%
17%
1%
2019

2030

1.59

81.2
85.7

19.9
23.2
20.5
0.2
11.8
21.1
37.5
56.2
22.8
6.7
29.3
11.9
2030

0.9
0.2
0.2
0.7
0.5
0.2
0.7
0.7
2030

6,611
-0.2
5,047
76.4
65.0
52.4
83.8
65.9
10.5
72.7
61.6
50.7
79.7
62.5
9.7
79.9
68.5
54.1
87.8
69.1
11.3
64.3
64.3
64.3
71.6
61.1
6.2
6.1
4.7
4.9
7%
72%
19%
3%
2030

2040

1.62

82.6
87.0

20.9
24.2
19.2
0.2
11.9
20.1
37.1
54.7
25.2
8.6
34.2
15.7
2040

1.3
-0.2
-0.2
1.5
1.0
0.5
1.2
1.6
2040

6,515
-0.1
4,967
76.2
65.0
53.0
83.4
65.0
11.0
72.6
61.8
51.2
79.1
62.7
10.5
79.8
68.3
54.7
87.7
67.4
11.4
64.3
64.3
64.3
71.5
61.0
6.2
6.1
4.7
4.8
6%
72%
18%
3%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

22.4
40.5
78.0
141.4
53.6
52.0

22.5
32.5
70.8
139.3
45.0
44.5

21.9
46.0
82.6
147.9
61.2
59.4

0.8
20.8
19.8
21.1
27.0
25.2

2050

1.64

83.9
88.2

21.8
25.1
19.8
0.2
11.9
20.1
36.1
53.5
26.4
10.3
39.1
19.2
2050

1.3
-0.2
-0.2
1.5
1.0
0.5
1.3
1.5
2050

6,386
-0.2
4,850
76.0
64.8
52.7
83.3
64.3
10.9
72.2
61.4
50.9
79.0
61.4
10.6
79.7
68.2
54.4
87.6
67.1
11.3
64.3
64.3
64.3
71.2
60.9
6.2
6.1
4.5
4.7
6%
72%
19%
3%
2050

22.6
49.2
86.7
154.4
65.9
64.0

2060

1.66

85.2
89.3

22.7
26.0
20.4
0.2
11.9
19.9
35.7
52.8
27.4
10.9
39.7
20.6
2060

1.4
-0.1
-0.1
1.5
1.0
0.5
1.5
1.5
2060

6,261
-0.1
4,748
75.8
64.3
52.6
83.2
64.0
10.7
72.1
60.8
50.8
78.9
61.1
10.2
79.5
67.7
54.3
87.5
67.0
11.2
64.3
64.3
64.3
71.1
60.3
6.2
6.1
4.5
4.6
6%
72%
18%
3%
2060

22.1
51.8
89.5
158.3
69.7
67.5

2070

1.68

86.3
90.3

23.6
26.8
20.5
0.2
11.8
19.5
34.8
52.5
28.0
11.7
41.7
22.2
2070

1.4
-0.1
-0.1
1.5
1.0
0.5
1.4
1.5
2070

6,210
-0.1
4,699
75.7
64.4
52.8
83.3
64.0
10.8
71.9
61.0
51.0
78.9
61.2
10.3
79.3
67.8
54.5
87.6
66.9
11.3
64.3
64.3
64.3
70.9
60.4
6.2
6.1
4.4
4.5
6%
71%
19%
3%
2070

23.3
53.3
90.5
160.4
71.9
69.8

186 

2. 

BULGARIA 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

11.4
8.9

7.2
6.6
13.9
0.3
-1.9
-0.6
-8.5
-8.8
9.4
9.1
22.7
19.4
AVG 19-70

1.2
-0.9
-0.9
2.1
1.3
0.8
1.9
2.1
Ch 19-70

-1,600
1.1
-1,280
-0.9
-0.2
0.2
0.6
0.6
4.9
-1.1
1.0
-0.1
-0.4
2.2
6.5
-0.9
-1.7
0.6
1.5
-1.4
2.3
0.5
0.0
0.9
-1.7
-0.9
1.1
1.0
-1.2
-1.3
1.3
-4.7
1.9
1.4
Ch 19-70

2019

1.58

71.5
78.8

14.2
18.1
-3.9
-0.1
7.0
18.9
41.6
59.6
21.5
4.9
22.6
8.1
2019

2.2
0.2
0.2
1.9
1.3
0.7
2.9
2.0
2019

4,159
-1.4
3,264
78.5
66.7
44.2
85.8
67.1
11.0
73.7
60.7
38.0
81.4
62.4
7.8
83.2
72.9
49.9
90.1
72.2
15.3
63.9
64.7
63.2
75.2
63.9
4.2
4.2
3.1
3.2
4%
74%
19%
3%
2019

2030

1.65

74.3
80.9

15.9
19.6
0.8
0.0
6.4
18.7
37.2
57.0
24.3
6.6
27.3
11.7
2030

1.2
-1.3
-1.3
2.5
1.3
1.1
1.9
2.5
2030

3,660
-0.8
2,830
77.3
66.1
43.8
86.2
66.5
14.9
72.5
60.7
37.3
81.6
62.3
12.6
82.0
71.6
49.9
90.6
70.9
17.7
64.1
64.7
63.6
73.3
62.7
5.2
5.2
2.7
2.8
5%
70%
21%
4%
2030

2040

1.68

76.7
82.8

17.4
20.9
3.1
0.1
6.0
17.8
34.3
54.7
27.5
8.3
30.3
15.2
2040

1.2
-1.1
-1.1
2.3
1.5
0.8
1.9
2.3
2040

3,282
-1.4
2,516
76.7
64.3
44.5
86.1
66.5
15.3
71.9
59.2
38.0
80.8
63.7
12.9
81.2
69.4
50.6
91.1
69.3
18.1
64.4
64.7
64.1
72.6
61.0
5.3
5.2
2.4
2.5
5%
67%
23%
5%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

25.6
42.7
75.6
129.8
53.9
51.7

22.9
36.0
67.7
116.6
44.8
43.5

27.8
50.2
82.7
139.3
63.8
60.7

2.1
24.8
29.0
39.6
33.3
31.2

2050

1.70

79.0
84.6

18.8
22.3
5.5
0.1
5.6
18.2
32.5
51.0
30.8
9.7
31.5
19.1
2050

1.0
-1.2
-1.2
2.2
1.4
0.8
1.6
2.2
2050

2,874
-1.3
2,214
77.0
63.3
44.4
86.5
65.8
15.4
72.0
58.4
37.9
81.0
62.7
13.9
81.9
68.2
50.5
91.7
69.0
17.1
64.4
64.7
64.1
73.0
60.0
5.3
5.2
2.1
2.2
5%
68%
22%
6%
2050

27.0
60.5
96.2
154.0
76.8
72.5

2060

1.71

81.0
86.2

20.1
23.5
7.7
0.1
5.3
18.3
33.3
49.2
32.5
12.4
38.1
25.2
2060

1.3
-0.5
-0.5
1.8
1.2
0.6
1.9
1.9
2060

2,613
-0.3
2,042
78.1
64.5
44.2
86.6
67.8
14.9
73.2
59.6
37.7
81.3
64.7
13.4
82.9
69.2
50.3
91.6
70.9
16.6
64.4
64.7
64.1
74.0
61.1
5.3
5.2
1.9
2.0
5%
71%
18%
5%
2060

22.2
66.2
103.5
160.8
83.9
79.6

2070

1.71

82.9
87.7

21.4
24.7
10.0
0.2
5.0
18.2
33.1
50.8
30.9
14.0
45.2
27.5
2070

1.2
-0.4
-0.4
1.5
1.0
0.5
1.7
1.5
2070

2,559
-0.3
1,984
77.5
66.5
44.4
86.4
67.7
15.9
72.6
61.8
37.9
81.0
64.6
14.3
82.3
71.1
50.5
91.5
70.7
17.6
64.4
64.7
64.1
73.5
63.1
5.3
5.2
1.9
2.0
5%
69%
21%
4%
2070

25.0
60.8
96.7
156.1
78.1
74.7

187 

3.  CZECH REPUBLIC 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

8.3
6.9

6.0
5.7
-26.0
-0.2
-0.5
-0.4
-8.4
-7.8
8.2
8.5
24.3
17.3
AVG 19-70

1.6
-0.3
-0.3
2.0
1.3
0.7
1.7
2.0
Ch 19-70

-1,075
0.6
-919
-0.7
0.1
0.9
0.4
2.3
1.6
0.4
2.2
1.4
0.5
5.7
3.4
-1.9
-2.3
0.5
0.1
-1.7
-0.6
1.4
0.7
2.0
-1.9
-0.9
1.5
1.5
-1.0
-1.0
1.7
-4.9
2.9
0.3
Ch 19-70

2019

1.71

76.5
82.3

16.5
20.0
44.2
0.4
10.7
20.4
43.0
59.8
19.8
4.1
20.7
6.8
2019

2.6
0.7
0.6
2.0
1.5
0.5
2.2
1.9
2019

6,386
-0.5
5,239
82.0
70.2
52.5
89.1
68.4
10.9
74.5
62.4
44.9
81.8
60.5
8.4
89.3
77.9
59.8
95.9
76.5
13.9
62.4
63.5
61.4
80.4
68.8
2.0
1.9
5.1
5.3
5%
76%
17%
3%
2019

2030

1.75

78.4
83.9

17.8
21.3
16.3
0.2
10.8
20.5
38.2
57.3
22.1
6.6
29.6
11.4
2030

1.9
-0.3
-0.3
2.2
1.5
0.8
2.0
2.2
2030

6,168
-0.2
5,042
81.7
70.6
52.9
89.8
71.0
10.9
75.3
64.2
45.8
83.2
65.7
9.1
87.8
76.8
59.7
95.9
76.1
12.9
63.8
64.2
63.4
78.9
68.2
3.5
3.4
4.9
5.0
6%
72%
20%
2%
2030

2040

1.77

80.2
85.4

19.1
22.5
16.6
0.2
10.6
19.6
35.1
55.4
25.0
8.0
31.8
14.4
2040

1.3
-0.7
-0.7
2.1
1.3
0.7
1.5
2.1
2040

5,882
-1.0
4,721
80.3
68.0
53.3
89.3
69.0
13.6
73.7
62.0
46.2
82.1
64.4
12.6
86.4
73.8
60.1
96.0
73.4
14.6
63.8
64.2
63.4
77.5
65.7
3.5
3.4
4.6
4.7
6%
68%
22%
4%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

23.8
38.6
74.4
115.6
46.4
45.2

20.4
33.0
67.1
102.4
38.4
37.4

26.6
45.2
80.6
124.4
54.4
52.4

2.7
20.6
25.0
35.2
27.0
26.0

2050

1.78

81.8
86.7

20.3
23.6
17.5
0.2
10.5
19.9
34.0
51.7
28.3
8.7
30.7
16.8
2050

1.3
-0.6
-0.6
1.9
1.3
0.7
1.4
2.0
2050

5,444
-0.6
4,411
81.0
66.6
53.6
89.3
69.8
11.8
74.3
60.7
46.4
81.9
65.2
10.9
87.3
72.3
60.4
96.2
74.2
12.6
63.8
64.2
63.4
78.2
64.4
3.5
3.4
4.3
4.4
6%
70%
21%
4%
2050

24.7
54.8
93.4
138.0
66.2
63.7

2060

1.78

83.4
88.0

21.4
24.7
18.0
0.2
10.4
20.3
34.6
50.1
29.6
11.9
40.1
23.7
2060

1.7
0.0
0.0
1.7
1.1
0.6
1.9
1.7
2060

5,212
0.0
4,274
82.0
68.4
53.3
89.6
70.8
11.9
75.6
62.8
46.1
82.7
66.4
11.1
88.0
73.7
60.0
96.1
74.9
12.7
63.8
64.2
63.4
79.1
66.1
3.5
3.4
4.1
4.3
6%
73%
17%
3%
2060

20.6
59.2
99.6
143.5
71.1
68.7

2070

1.78

84.8
89.2

22.5
25.7
18.2
0.2
10.2
20.0
34.6
52.1
27.9
12.6
45.0
24.1
2070

1.5
0.0
0.0
1.5
1.0
0.5
1.7
1.5
2070

5,312
0.1
4,320
81.3
70.3
53.5
89.4
70.7
12.5
74.9
64.6
46.3
82.4
66.3
11.7
87.4
75.7
60.2
96.1
74.8
13.2
63.8
64.2
63.4
78.5
67.9
3.5
3.4
4.2
4.3
6%
71%
20%
3%
2070

23.1
53.7
92.1
137.6
65.3
63.4

188 

4.  DENMARK 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

6.6
6.5

4.8
5.2
12.6
0.2
0.3
-1.6
-4.3
-6.4
8.0
6.3
15.9
13.2
AVG 19-70

1.7
0.2
0.2
1.5
1.0
0.5
1.6
1.5
Ch 19-70

-191
-0.1
-113
1.4
4.2
2.4
-0.7
7.3
23.9
1.1
4.8
2.0
-1.3
7.8
27.6
1.7
3.5
2.7
-0.2
6.6
19.7
4.8
4.5
5.1
2.5
5.1
-1.4
-1.5
-0.1
0.1
-1.0
-5.8
0.9
5.9
Ch 19-70

2019

1.72

79.5
83.3

18.5
21.1
-1.6
0.0
5.8
22.4
38.8
57.9
19.7
4.6
23.4
8.0
2019

2.3
0.9
0.4
1.8
1.2
0.7
2.0
1.4
2019

3,364
0.0
2,768
82.3
71.4
72.3
86.5
74.4
14.6
78.7
66.8
70.7
82.8
70.2
7.4
85.8
75.9
73.9
90.1
78.7
22.2
64.5
65.0
64.1
78.4
68.1
4.7
4.6
2.6
2.7
9%
68%
19%
3%
2019

2030

1.74

81.0
84.8

19.5
22.2
12.4
0.2
6.0
21.6
36.6
55.4
22.9
7.2
31.5
13.1
2030

1.5
0.3
0.3
1.2
0.9
0.4
1.3
1.2
2030

3,310
-0.3
2,716
82.0
71.2
74.7
85.9
74.4
18.1
78.0
67.1
72.8
81.7
70.0
15.0
86.0
75.4
76.5
90.0
78.7
21.3
65.8
66.1
65.5
79.3
69.0
3.3
3.2
2.6
2.7
9%
66%
20%
4%
2030

2040

1.75

82.4
86.2

20.5
23.3
12.5
0.2
6.1
21.6
36.4
53.2
25.2
8.3
32.9
15.6
2040

1.6
0.1
0.1
1.5
1.0
0.5
1.5
1.5
2040

3,223
-0.2
2,666
82.7
71.8
74.7
85.7
77.0
22.6
78.8
67.8
72.8
81.4
73.2
20.0
86.6
75.8
76.5
89.9
80.7
25.4
66.9
67.2
66.7
80.0
69.6
3.3
3.1
2.6
2.7
9%
67%
19%
6%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

23.3
41.4
80.4
117.4
47.5
45.5

21.1
47.4
88.0
121.2
52.9
49.8

21.6
34.1
72.7
112.7
39.9
38.5

0.9
19.7
21.3
4.6
16.1
12.3

2050

1.76

83.7
87.5

21.5
24.4
11.3
0.2
6.1
21.0
35.4
53.4
25.6
9.9
38.9
18.6
2050

1.7
0.2
0.2
1.5
1.0
0.5
1.7
1.5
2050

3,257
0.1
2,704
83.0
73.9
74.7
85.7
79.2
27.4
79.1
69.9
72.8
81.4
75.2
24.0
86.8
77.9
76.5
89.9
83.0
30.9
67.8
68.0
67.6
80.3
71.6
3.3
3.1
2.6
2.8
9%
64%
21%
6%
2050

23.0
47.9
87.3
118.7
53.0
49.7

2060

1.77

84.9
88.7

22.4
25.3
11.0
0.2
6.1
20.8
35.0
52.4
26.8
10.6
39.5
20.2
2060

1.6
0.0
0.1
1.5
1.0
0.5
1.6
1.5
2060

3,207
-0.3
2,677
83.5
74.6
74.7
85.7
80.9
34.4
79.6
70.6
72.8
81.4
77.1
30.3
87.3
78.5
76.5
89.9
84.5
38.5
68.7
69.0
68.5
80.7
72.3
3.3
3.1
2.6
2.8
8%
63%
20%
9%
2060

22.7
51.2
90.9
116.3
53.9
49.3

2070

1.77

86.1
89.8

23.3
26.3
11.0
0.2
6.2
20.7
34.6
51.6
27.7
10.9
39.3
21.1
2070

1.6
0.0
0.0
1.5
1.0
0.5
1.5
1.5
2070

3,174
-0.1
2,655
83.7
75.5
74.7
85.7
81.7
38.4
79.8
71.6
72.8
81.5
78.0
35.0
87.5
79.4
76.5
89.9
85.3
41.9
69.3
69.5
69.2
80.9
73.2
3.3
3.1
2.6
2.8
8%
62%
20%
9%
2070

22.4
53.8
94.0
117.3
56.0
50.8

189 

5.  GERMANY 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

6.9
6.2

5.0
5.0
-63.2
-0.1
-1.4
1.1
-4.6
-7.8
6.8
5.3
11.3
11.8
AVG 19-70

1.2
-0.2
-0.2
1.4
0.9
0.5
1.3
1.4
Ch 19-70

-7,230
0.2
-5,590
1.0
-0.5
0.2
0.8
1.4
4.6
2.8
1.7
0.5
2.3
4.9
6.0
-0.8
-2.6
0.0
-0.5
-2.2
3.0
0.9
1.0
0.7
0.2
-1.1
1.0
0.9
-5.8
-5.3
0.7
-0.2
-2.2
1.8
Ch 19-70

2019

1.53

79.1
83.7

18.4
21.4
277.4
0.3
83.1
18.4
39.6
59.9
21.7
6.7
30.8
11.1
2019

1.2
0.6
0.5
0.8
0.5
0.2
1.0
0.6
2019

49,766
-0.1
41,389
83.2
73.1
71.1
88.0
74.6
13.9
78.6
68.1
68.3
83.3
70.0
10.5
87.6
78.0
73.6
92.6
79.4
17.6
64.6
64.7
64.5
80.6
70.9
3.1
3.1
40.1
41.3
8%
68%
22%
3%
2019

2030

1.57

80.6
85.1

19.5
22.5
248.2
0.3
83.4
19.1
36.6
55.2
25.6
7.4
28.9
13.4
2030

0.7
-0.7
-0.7
1.4
0.9
0.5
0.7
1.4
2030

46,080
-1.0
38,428
83.4
70.7
71.3
88.6
73.9
18.3
79.9
66.8
68.8
84.8
70.9
15.9
86.8
74.5
73.6
92.2
77.0
20.9
65.5
65.7
65.3
80.0
67.9
4.0
3.9
36.9
38.9
7%
67%
21%
5%
2030

2040

1.60

82.1
86.4

20.5
23.6
240.7
0.3
83.2
18.8
35.8
53.4
27.9
9.2
33.0
17.2
2040

1.4
-0.1
-0.1
1.5
1.0
0.5
1.5
1.5
2040

44,388
0.1
37,294
84.0
71.4
71.3
88.8
75.7
17.1
81.0
68.0
68.9
85.4
73.9
14.8
86.9
74.9
73.6
92.0
77.7
19.6
65.5
65.7
65.3
80.6
68.6
4.1
4.0
35.8
37.5
8%
68%
19%
5%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

24.5
46.4
81.1
114.6
52.4
49.7

24.6
36.1
66.9
101.3
41.9
40.8

22.5
52.2
87.4
121.7
59.8
57.1

-2.3
18.5
25.2
25.6
20.8
19.1

2050

1.63

83.5
87.7

21.5
24.6
227.0
0.3
82.6
18.8
35.2
53.1
28.1
11.9
42.4
22.4
2050

1.3
-0.2
-0.2
1.5
1.0
0.5
1.4
1.5
2050

43,883
-0.2
36,807
83.9
72.6
71.3
88.8
75.3
19.0
81.0
69.3
68.9
85.4
73.9
16.9
86.7
75.8
73.6
92.1
76.8
21.3
65.5
65.7
65.3
80.4
69.7
4.1
4.0
35.3
37.0
8%
67%
20%
5%
2050

23.7
52.8
88.3
123.1
60.7
57.8

2060

1.65

84.8
88.9

22.5
25.5
221.4
0.3
81.8
19.5
35.2
52.2
28.3
11.1
39.2
21.3
2060

1.4
-0.1
-0.1
1.5
1.0
0.5
1.5
1.5
2060

42,675
-0.1
35,902
84.1
71.9
71.3
88.9
75.6
18.3
81.3
68.8
68.8
85.5
74.4
16.3
86.9
74.9
73.6
92.2
76.9
20.5
65.5
65.7
65.3
80.7
69.1
4.1
4.0
34.4
36.2
8%
68%
19%
5%
2060

22.5
54.3
91.7
126.0
62.1
59.1

2070

1.67

86.0
89.9

23.4
26.4
214.2
0.3
81.7
19.5
35.1
52.1
28.4
11.9
42.0
22.9
2070

1.6
0.0
0.0
1.5
1.0
0.5
1.5
1.5
2070

42,536
0.1
35,800
84.2
72.6
71.3
88.8
76.0
18.5
81.4
69.8
68.8
85.5
74.9
16.5
86.8
75.4
73.6
92.1
77.2
20.6
65.5
65.7
65.3
80.7
69.7
4.1
4.0
34.3
36.0
8%
68%
19%
5%
2070

22.3
54.6
92.1
127.0
62.7
59.9

190 

6. 

ESTONIA 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.2

9.4
6.5

6.1
5.0
-4.0
-0.3
-0.1
-2.8
-7.6
-7.7
10.5
8.1
16.7
17.3
AVG 19-70

1.9
-0.3
-0.3
2.2
1.4
0.8
2.1
2.1
Ch 19-70

-171
0.0
-118
4.1
4.2
2.0
2.4
11.9
12.7
4.3
5.7
1.4
3.5
8.9
12.6
3.8
2.4
2.5
1.4
15.1
12.5
4.3
4.2
4.4
2.3
2.7
1.8
1.7
-0.1
-0.1
0.7
-7.9
3.7
3.5
Ch 19-70

2019

1.51

74.9
83.4

16.5
21.5
6.6
0.5
1.3
21.1
41.1
59.0
19.9
5.7
28.8
9.7
2019

3.9
1.0
0.3
3.5
2.1
1.5
3.5
2.9
2019

783
-0.2
657
83.8
75.5
72.3
87.8
75.7
28.1
80.3
71.0
67.8
82.7
77.6
27.5
87.4
80.2
76.6
92.5
73.6
29.2
65.1
65.2
65.0
80.2
72.3
4.4
4.3
0.6
0.7
6%
69%
19%
6%
2019

2030

1.59

76.7
84.7

17.6
22.4
1.8
0.1
1.3
19.8
38.3
56.9
23.3
6.8
29.4
12.0
2030

2.5
-0.5
-0.6
3.0
1.9
1.2
2.7
3.0
2030

744
-0.1
632
84.9
73.9
73.8
89.4
76.7
20.3
81.3
69.5
68.7
85.6
74.8
20.5
88.4
78.4
78.7
93.0
78.6
20.0
66.0
66.1
65.9
79.7
69.5
6.1
6.0
0.6
0.6
8%
68%
19%
5%
2030

2040

1.66

78.9
86.1

18.9
23.5
1.9
0.1
1.3
18.3
36.7
56.0
25.8
8.8
34.3
15.8
2040

1.6
-0.3
-0.3
1.9
1.2
0.7
1.8
1.9
2040

717
-0.6
616
86.0
75.1
74.4
89.8
80.5
23.6
82.3
70.9
69.3
85.7
78.5
22.0
89.4
79.1
79.3
93.5
82.3
25.4
66.8
67.0
66.7
80.6
70.5
6.2
6.1
0.6
0.6
7%
65%
22%
6%
2040

2050

1.68

80.8
87.5

20.2
24.6
2.2
0.2
1.3
18.8
34.3
52.8
28.4
10.0
35.3
19.0
2050

1.3
-0.5
-0.5
1.8
1.2
0.6
1.5
1.8
2050

662
-0.9
576
86.9
75.4
74.2
90.2
83.0
28.4
83.3
71.7
69.2
86.2
81.0
26.5
90.2
78.9
79.1
94.0
84.7
30.4
67.8
67.9
67.6
81.5
70.9
6.2
6.0
0.5
0.6
6%
63%
23%
8%
2050

2060

1.69

82.6
88.7

21.4
25.6
2.4
0.2
1.2
18.7
34.2
50.3
30.9
11.6
37.4
23.0
2060

1.4
-0.3
-0.3
1.7
1.1
0.6
1.7
1.7
2060

616
-0.2
541
87.8
76.3
74.0
90.2
86.5
32.9
84.5
73.3
69.0
86.5
85.3
31.6
90.9
79.2
78.9
93.8
87.5
34.1
68.7
68.8
68.7
82.4
71.8
6.2
5.9
0.5
0.6
7%
64%
20%
9%
2060

2070

1.70

84.3
89.9

22.6
26.5
2.6
0.2
1.2
18.2
33.5
51.3
30.5
13.9
45.6
27.0
2070

1.5
0.0
0.0
1.5
1.0
0.5
1.8
1.5
2070

612
-0.2
538
88.0
79.7
74.3
90.2
87.6
40.9
84.6
76.7
69.2
86.3
86.5
40.1
91.1
82.6
79.1
93.9
88.7
41.7
69.4
69.4
69.3
82.5
75.0
6.2
6.0
0.5
0.6
7%
61%
23%
9%
2070

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

22.5
40.9
75.6
109.7
46.1
43.9

24.5
46.1
78.7
109.1
50.9
48.1

22.2
33.8
69.5
99.3
35.9
33.9

2.7
25.6
25.4
15.1
25.5
21.9

26.3
53.8
89.4
114.8
57.5
53.1

22.1
61.5
98.7
118.9
64.1
58.2

24.9
59.4
94.9
114.5
61.4
55.8

191 

7. 

IRELAND 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.0

5.7
5.6

4.2
4.6
-22.2
-0.5
1.6
-6.3
-7.4
-6.9
13.2
8.1
18.1
16.4
AVG 19-70

1.8
0.3
0.3
1.5
1.1
0.5
1.3
1.5
Ch 19-70

471
-1.4
448
2.3
-0.1
0.6
2.6
6.3
7.4
3.8
1.8
0.6
3.3
10.9
12.1
0.8
-2.0
0.6
1.8
1.6
2.7
1.1
0.6
1.5
0.7
-1.3
1.8
1.6
0.4
0.5
-0.6
-7.1
4.4
3.3
Ch 19-70

2019

1.78

81.1
84.8

19.6
22.1
32.7
0.7
4.9
26.9
41.8
58.8
14.3
3.4
23.8
5.8
2019

5.6
2.1
2.1
3.4
1.6
1.8
4.1
3.5
2019

2,904
1.4
2,287
78.8
71.1
72.3
83.5
64.1
16.7
72.1
64.5
69.6
76.7
55.9
10.0
85.5
77.9
74.9
90.6
72.5
23.6
65.0
65.5
64.5
75.1
67.9
4.6
4.5
2.2
2.2
9%
73%
15%
3%
2019

2030

1.80

82.1
85.8

20.3
22.9
19.3
0.3
5.5
23.6
40.0
58.6
17.8
4.9
27.4
8.3
2030

1.5
0.4
0.4
1.1
0.9
0.3
0.7
1.1
2030

3,241
0.8
2,593
80.0
72.1
72.6
85.1
67.2
22.4
74.7
66.9
69.9
79.1
62.4
19.0
85.5
77.4
75.2
91.4
72.1
26.0
66.0
66.0
66.1
74.6
67.3
6.8
6.6
2.4
2.5
9%
70%
17%
4%
2030

2040

1.80

83.4
87.1

21.2
23.9
16.1
0.3
5.9
22.2
37.8
56.6
21.2
6.4
30.5
11.4
2040

1.8
0.2
0.2
1.5
1.0
0.5
1.1
1.5
2040

3,353
0.0
2,705
80.7
71.7
73.0
85.8
69.4
23.9
75.6
67.0
70.4
79.6
66.3
21.5
86.0
76.6
75.6
92.2
72.7
26.3
66.0
66.0
66.1
75.3
67.1
6.7
6.4
2.5
2.7
8%
67%
19%
5%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

20.5
30.3
70.5
118.4
35.8
34.2

18.6
24.2
70.0
119.5
29.1
28.3

23.2
37.4
76.7
121.7
43.7
41.3

4.8
28.7
22.7
18.5
33.8
30.8

2050

1.80

84.6
88.3

22.1
24.9
14.4
0.2
6.2
22.0
36.4
53.3
24.8
8.1
32.7
15.2
2050

1.6
0.1
0.1
1.5
1.0
0.5
1.2
1.5
2050

3,315
0.0
2,692
81.2
70.7
72.8
86.1
69.5
23.7
75.9
65.9
70.2
80.1
65.5
21.9
86.6
75.8
75.4
92.4
73.8
25.6
66.0
66.0
66.1
76.0
66.4
6.4
6.1
2.5
2.7
8%
68%
17%
6%
2050

21.5
46.5
87.8
131.3
54.3
50.9

2060

1.80

85.7
89.4

23.0
25.8
12.1
0.2
6.4
21.2
35.3
52.6
26.3
10.3
39.2
19.6
2060

1.7
0.1
0.1
1.5
1.0
0.5
1.5
1.5
2060

3,366
0.2
2,734
81.2
71.5
72.8
86.1
70.5
23.7
76.1
66.7
70.1
80.1
67.0
21.4
86.5
76.5
75.4
92.3
74.3
26.2
66.0
66.0
66.1
76.0
67.2
6.4
6.1
2.6
2.7
8%
67%
19%
6%
2060

22.3
50.0
90.3
135.6
59.4
56.0

2070

1.81

86.8
90.4

23.8
26.7
10.5
0.2
6.5
20.6
34.3
51.9
27.5
11.5
41.9
22.2
2070

1.5
-0.1
0.0
1.5
1.0
0.5
1.4
1.5
2070

3,374
-0.1
2,735
81.1
71.0
72.9
86.1
70.4
24.1
75.9
66.2
70.2
80.1
66.8
22.0
86.3
76.0
75.5
92.3
74.2
26.4
66.0
66.0
66.1
75.9
66.7
6.4
6.1
2.6
2.7
8%
66%
19%
6%
2070

23.4
53.0
92.6
138.0
63.0
59.0

192 

8.  GREECE 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.2

7.4
6.0

5.1
4.9
12.3
0.2
-2.1
-2.5
-7.4
-8.1
10.6
8.1
14.1
18.0
AVG 19-70

1.2
-0.3
-0.3
1.5
1.0
0.4
1.6
1.5
Ch 19-70

-1,939
0.2
-1,070
8.4
7.9
3.5
2.8
30.4
17.7
13.3
12.6
1.5
7.0
39.2
19.4
3.0
2.5
5.3
-1.9
19.7
15.4
4.7
4.6
4.8
15.6
14.1
-10.5
-10.6
-0.5
-0.3
0.7
-13.1
7.4
5.0
Ch 19-70

2019

1.34

79.0
84.3

18.8
21.8
13.7
0.1
10.7
19.4
40.2
58.4
22.2
7.2
32.3
12.2
2019

-0.4
-0.6
-0.6
0.2
0.3
-0.1
-0.2
0.2
2019

6,259
-0.6
4,622
73.8
63.5
42.4
85.4
50.4
8.0
65.4
55.5
40.8
77.8
38.0
5.2
82.5
71.8
43.8
93.3
64.5
11.3
62.9
63.0
62.9
60.9
52.5
17.5
17.3
3.8
3.9
4%
78%
16%
2%
2019

2030

1.39

80.8
85.7

20.0
22.9
11.6
0.1
10.3
17.5
36.2
56.5
26.0
8.4
32.1
14.8
2030

0.7
-0.1
-0.1
0.9
0.7
0.2
1.1
0.9
2030

5,810
-0.7
4,496
77.4
65.1
45.4
87.0
65.8
10.1
71.6
59.0
41.7
82.0
57.9
8.0
83.1
71.4
48.7
91.8
74.6
12.7
64.8
64.8
64.8
68.0
57.3
12.1
12.0
4.0
4.1
5%
70%
22%
3%
2030

2040

1.43

82.4
86.9

21.1
23.9
16.0
0.2
9.9
16.3
33.8
53.0
30.6
10.4
34.1
19.7
2040

1.7
-0.4
-0.4
2.2
1.4
0.8
2.1
2.2
2040

5,245
-1.1
4,170
79.5
65.8
46.1
88.0
71.2
14.8
75.2
60.9
42.6
84.3
65.9
12.8
83.6
70.7
49.3
91.5
76.7
17.1
65.8
65.8
65.8
71.9
59.6
9.6
9.4
3.8
4.0
5%
67%
23%
5%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

26.0
46.1
77.0
152.5
64.4
62.5

22.3
37.9
71.1
174.7
59.8
58.5

26.7
57.8
88.6
149.2
74.8
71.1

2.6
27.3
27.6
-33.5
17.5
13.3

2050

1.47

83.8
88.1

22.1
24.9
20.7
0.2
9.5
16.7
33.4
49.5
33.8
13.1
38.7
26.4
2050

1.6
-0.5
-0.5
2.0
1.3
0.7
2.0
2.0
2050

4,697
-0.7
3,843
81.8
67.5
45.7
88.4
77.2
16.9
78.1
63.4
42.2
84.9
72.9
15.8
85.3
71.5
49.0
91.5
81.4
18.0
66.6
66.6
66.6
76.0
62.9
7.1
6.9
3.6
3.8
5%
69%
21%
6%
2050

23.2
68.2
101.8
150.5
83.5
78.8

2060

1.50

85.2
89.3

23.0
25.8
23.8
0.3
9.0
16.8
33.1
49.8
33.5
15.4
45.9
30.9
2060

1.5
-0.3
-0.3
1.8
1.2
0.6
2.1
1.8
2060

4,486
-0.3
3,683
82.1
70.6
45.7
88.2
79.8
21.7
78.5
67.0
42.1
84.8
76.2
20.5
85.3
74.0
48.9
91.3
83.2
22.9
67.1
67.1
67.1
76.4
65.9
6.9
6.8
3.4
3.6
5%
67%
22%
6%
2060

23.7
67.3
100.9
147.3
81.5
76.6

2070

1.54

86.4
90.3

23.9
26.7
26.0
0.3
8.6
16.9
32.9
50.3
32.8
15.2
46.4
30.3
2070

1.3
-0.3
-0.2
1.5
1.0
0.5
1.7
1.5
2070

4,320
-0.4
3,552
82.2
71.4
45.8
88.2
80.8
25.7
78.7
68.1
42.3
84.8
77.3
24.6
85.5
74.3
49.1
91.4
84.1
26.7
67.6
67.6
67.6
76.5
66.5
6.9
6.7
3.3
3.6
5%
65%
23%
7%
2070

24.9
65.2
98.7
141.2
77.2
71.7

193 

9. 

SPAIN 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.2

5.9
4.6

4.2
3.8
-269.6
-0.6
-0.1
-2.9
-9.3
-9.6
12.5
8.5
14.4
18.5
AVG 19-70

1.4
0.0
0.0
1.5
1.0
0.5
1.4
1.5
Ch 19-70

-4,574
-0.9
-2,934
2.8
1.7
0.7
-0.1
16.6
16.7
5.1
4.0
0.5
0.9
22.8
17.2
0.6
-0.6
0.9
-1.1
10.1
16.1
2.7
2.8
2.6
8.2
6.5
-7.0
-7.1
-1.2
-0.2
1.1
-11.9
5.9
4.9
Ch 19-70

2019

1.27

81.2
86.8

19.9
23.9
438.5
0.9
47.1
19.7
42.8
60.8
19.5
6.1
31.2
10.0
2019

1.7
1.2
1.1
0.6
0.5
0.1
1.0
0.5
2019

28,662
0.5
22,639
79.0
68.6
55.5
87.0
61.7
4.5
73.8
63.5
52.1
82.3
54.5
3.5
84.2
73.8
58.8
91.7
69.1
5.5
63.8
63.4
64.1
68.1
59.2
13.8
13.7
19.5
19.7
5%
78%
17%
1%
2019

2030

1.33

82.4
87.7

20.7
24.6
185.4
0.4
48.8
17.2
37.8
58.7
24.0
7.3
30.5
12.5
2030

1.6
0.4
0.4
1.1
0.8
0.4
1.4
1.1
2030

28,646
-0.3
23,389
81.7
70.5
56.0
87.4
77.1
17.5
78.3
67.1
52.4
83.9
73.9
16.2
85.1
74.1
59.5
90.9
80.4
18.9
66.3
66.0
66.5
70.4
61.1
13.8
13.5
20.2
21.1
5%
66%
24%
5%
2030

2040

1.37

83.7
88.7

21.6
25.5
178.2
0.4
49.4
16.3
35.2
54.3
29.4
9.4
32.1
17.3
2040

1.4
-0.3
-0.3
1.8
1.2
0.6
1.4
1.8
2040

26,846
-1.0
22,059
82.2
69.4
56.6
87.0
79.0
20.2
79.3
66.6
52.9
83.4
78.2
19.4
85.0
72.2
60.0
90.7
79.8
21.1
66.3
66.1
66.6
73.7
62.5
10.3
10.0
19.8
21.1
5%
65%
24%
6%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

25.9
40.9
70.3
130.7
53.0
50.5

26.4
54.0
84.0
133.8
66.2
61.9

21.6
32.1
64.4
139.1
46.1
45.6

3.5
30.5
30.9
1.9
29.5
25.5

2050

1.41

84.9
89.7

22.5
26.2
178.7
0.4
49.3
16.7
34.4
50.6
32.7
12.4
38.0
24.6
2050

1.4
-0.3
-0.3
1.7
1.1
0.6
1.5
1.7
2050

24,943
-0.4
20,517
82.3
68.8
56.1
87.0
78.5
19.0
79.3
66.0
52.4
83.3
77.8
18.8
85.3
71.7
59.6
90.7
79.3
19.2
66.4
66.1
66.6
76.5
64.2
7.0
6.8
19.1
20.3
5%
68%
21%
6%
2050

23.1
64.7
97.7
142.8
77.9
73.1

2060

1.45

86.0
90.6

23.3
27.0
175.7
0.4
48.3
16.7
33.9
50.8
32.5
15.1
46.3
29.7
2060

1.5
-0.1
-0.1
1.6
1.1
0.6
1.8
1.6
2060

24,538
-0.1
20,115
82.0
70.6
56.0
86.9
78.5
20.1
79.0
67.7
52.4
83.2
77.5
19.8
85.0
73.5
59.5
90.6
79.6
20.5
66.4
66.1
66.6
76.4
65.9
6.8
6.6
18.7
19.8
6%
67%
22%
5%
2060

23.6
64.1
96.9
143.7
77.9
73.7

2070

1.49

87.1
91.4

24.1
27.7
169.0
0.4
47.0
16.8
33.5
51.2
32.0
14.6
45.6
28.5
2070

1.3
-0.3
-0.3
1.5
1.0
0.5
1.5
1.5
2070

24,088
-0.3
19,705
81.8
70.3
56.2
86.9
78.3
21.2
78.9
67.5
52.5
83.2
77.3
20.7
84.8
73.2
59.7
90.6
79.3
21.6
66.4
66.2
66.7
76.2
65.7
6.8
6.6
18.4
19.5
6%
66%
23%
6%
2070

25.1
62.5
95.3
141.1
75.6
71.1

194 

10. FRANCE 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.0

6.6
5.1

4.2
3.8
42.1
0.1
2.3
-3.3
-4.2
-5.1
8.4
6.5
13.5
13.9
AVG 19-70

1.3
0.1
0.1
1.3
0.8
0.5
1.3
1.3
Ch 19-70

-2,276
0.1
-1,103
1.9
2.0
1.0
-0.3
10.1
9.0
3.1
3.3
1.5
1.4
10.2
9.7
0.6
0.6
0.6
-2.0
10.1
8.2
2.2
2.4
2.1
2.9
2.9
-1.5
-1.6
-0.6
0.1
0.3
-5.5
2.7
2.6
Ch 19-70

2019

1.85

80.1
86.3

20.0
24.1
38.1
0.1
67.1
24.1
37.5
55.6
20.3
6.2
30.3
11.1
2019

1.1
0.5
0.4
0.7
0.3
0.4
0.9
0.6
2019

37,327
-0.2
29,127
78.0
66.2
62.6
87.4
56.9
5.5
74.1
62.3
58.1
83.1
54.6
4.3
82.1
70.3
67.0
91.9
59.3
6.9
62.3
62.3
62.2
71.6
60.8
8.2
8.2
26.7
27.1
7%
75%
17%
1%
2019

2030

1.84

81.6
87.4

20.9
24.9
68.3
0.1
68.8
22.3
35.1
53.6
24.1
7.7
32.1
14.4
2030

1.0
0.2
0.2
0.8
0.5
0.3
0.8
0.8
2030

36,906
-0.1
29,076
78.8
66.5
63.4
87.1
62.9
9.1
75.5
63.2
59.4
83.8
59.7
8.1
82.2
70.0
67.3
90.5
66.2
10.3
63.5
63.6
63.3
72.4
61.2
8.1
8.0
26.7
27.4
8%
71%
19%
3%
2030

2040

1.84

83.0
88.6

21.8
25.7
73.9
0.1
69.8
21.6
34.6
51.7
26.8
9.9
36.8
19.1
2040

1.6
0.1
0.1
1.5
1.0
0.5
1.5
1.5
2040

36,102
-0.1
28,789
79.7
67.2
63.5
87.0
65.8
11.8
76.9
64.5
59.6
84.3
62.9
11.2
82.8
70.1
67.4
89.9
68.9
12.6
64.3
64.5
64.1
73.8
62.3
7.4
7.3
26.6
27.6
7%
71%
18%
3%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

23.6
44.9
86.5
151.0
59.3
57.8

22.8
51.7
93.4
153.2
66.5
64.2

22.6
36.5
79.8
147.4
49.4
48.7

0.9
20.4
18.3
7.6
22.6
20.5

2050

1.84

84.3
89.6

22.6
26.5
75.2
0.1
70.0
21.5
34.0
50.7
27.8
11.2
40.5
22.2
2050

1.5
-0.1
-0.1
1.5
1.0
0.5
1.5
1.5
2050

35,511
-0.2
28,417
80.0
68.2
63.5
87.2
66.4
14.4
77.3
65.5
59.5
84.6
63.9
13.7
82.8
71.1
67.4
89.8
69.2
15.2
64.5
64.7
64.3
74.6
63.7
6.8
6.6
26.5
27.6
7%
71%
18%
4%
2050

22.7
54.8
97.1
153.8
69.2
66.4

2060

1.84

85.6
90.6

23.5
27.2
74.6
0.1
69.7
21.1
33.8
50.6
28.3
11.9
42.2
23.6
2060

1.5
0.0
0.0
1.5
1.0
0.5
1.6
1.5
2060

35,255
0.1
28,246
80.1
68.3
63.5
87.1
67.3
14.0
77.5
65.6
59.5
84.6
65.1
13.3
82.8
71.0
67.4
89.8
69.6
14.7
64.5
64.7
64.3
74.7
63.8
6.8
6.6
26.3
27.4
7%
70%
18%
4%
2060

22.6
55.9
97.6
154.4
70.8
68.1

2070

1.84

86.7
91.4

24.2
27.9
80.2
0.1
69.4
20.8
33.3
50.5
28.7
12.6
43.9
25.0
2070

1.4
-0.1
-0.1
1.5
1.0
0.5
1.4
1.5
2070

35,051
-0.1
28,024
80.0
68.2
63.6
87.2
67.0
14.6
77.3
65.6
59.6
84.6
64.8
14.0
82.7
70.9
67.6
89.8
69.4
15.2
64.5
64.7
64.3
74.5
63.7
6.8
6.6
26.1
27.2
7%
69%
19%
4%
2070

23.5
56.9
98.1
155.1
72.0
69.2

195 

11. CROATIA 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.2

9.0
7.2

6.3
5.9
9.8
0.3
-1.0
-2.7
-6.3
-9.2
11.9
8.1
15.4
17.7
AVG 19-70

1.1
-0.7
-0.7
1.8
1.1
0.7
1.7
1.9
Ch 19-70

-900
0.5
-593
3.2
0.2
3.1
1.4
8.6
3.1
4.5
2.3
2.7
1.5
12.8
4.0
1.7
-2.2
3.5
1.3
3.6
2.0
0.9
0.6
1.3
2.8
0.1
0.3
0.2
-0.6
-0.6
-0.5
-3.7
2.7
1.5
Ch 19-70

2019

1.43

75.3
81.6

15.8
19.4
-3.8
-0.1
4.1
19.3
39.4
59.8
20.8
5.4
26.0
9.0
2019

1.8
0.7
0.7
1.0
0.3
0.7
2.4
1.0
2019

2,433
-1.0
1,737
71.4
60.8
52.4
83.6
45.8
5.0
66.1
55.2
44.4
80.3
37.6
3.8
76.6
66.4
59.9
86.8
54.8
6.4
62.0
62.7
61.4
66.8
57.0
6.4
6.3
1.6
1.6
7%
76%
16%
1%
2019

2030

1.48

77.3
83.2

17.2
20.7
-1.2
0.0
3.8
18.0
38.0
56.7
25.3
6.5
25.7
11.4
2030

0.7
-0.7
-0.7
1.4
0.7
0.7
0.9
1.0
2030

2,165
-0.9
1,601
73.9
61.1
55.3
84.8
50.3
6.6
69.4
56.2
46.8
81.4
45.5
5.8
78.2
66.0
63.3
88.1
55.1
7.6
62.7
62.9
62.4
68.2
56.5
7.7
7.5
1.5
1.5
6%
75%
16%
2%
2030

2040

1.51

79.3
84.7

18.5
21.9
0.8
0.0
3.6
17.1
36.1
55.2
27.8
9.2
33.1
16.7
2040

1.5
-0.8
-0.8
2.3
1.5
0.8
1.8
2.0
2040

1,986
-0.9
1,479
74.5
61.9
55.5
85.0
54.2
7.8
70.2
57.5
47.0
81.6
49.8
7.3
78.4
66.2
63.4
88.1
58.4
8.4
63.0
63.2
62.7
69.1
57.6
7.2
7.1
1.4
1.4
6%
73%
19%
3%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

23.6
44.5
76.3
152.6
63.0
61.6

24.3
34.8
67.1
146.6
50.6
49.9

25.5
50.4
81.3
155.9
70.3
68.6

1.0
29.8
30.4
28.9
39.1
37.3

2050

1.54

81.1
86.2

19.7
23.1
2.6
0.1
3.4
16.9
34.9
52.9
30.3
10.7
35.4
20.2
2050

1.2
-0.9
-0.9
2.2
1.4
0.8
2.1
2.4
2050

1,788
-1.0
1,335
74.6
61.0
55.5
85.0
54.2
8.4
70.6
57.0
47.1
81.8
49.8
8.0
78.4
64.7
63.4
88.0
58.3
8.9
63.0
63.2
62.7
69.6
57.0
6.7
6.5
1.2
1.3
6%
73%
18%
3%
2050

25.2
57.2
89.1
163.4
79.0
76.7

2060

1.56

82.7
87.5

20.9
24.2
4.4
0.1
3.2
16.7
33.8
51.6
31.7
12.0
37.9
23.3
2060

1.0
-0.8
-0.8
1.9
1.2
0.7
2.0
2.3
2060

1,643
-0.8
1,223
74.4
61.0
55.5
85.0
53.8
8.3
70.5
57.5
47.0
81.7
49.8
7.9
78.1
64.3
63.4
88.0
57.6
8.8
63.0
63.2
62.7
69.5
57.0
6.7
6.5
1.1
1.2
6%
73%
18%
3%
2060

25.2
61.5
94.0
170.9
85.5
83.0

2070

1.59

84.3
88.8

22.1
25.3
6.0
0.2
3.0
16.7
33.1
50.6
32.7
13.5
41.4
26.7
2070

0.9
-0.6
-0.6
1.5
1.0
0.5
1.8
1.9
2070

1,533
-0.6
1,143
74.6
61.0
55.5
85.0
54.5
8.1
70.6
57.6
47.1
81.8
50.4
7.8
78.3
64.3
63.4
88.1
58.4
8.4
63.0
63.2
62.7
69.6
57.1
6.7
6.5
1.1
1.1
6%
72%
19%
3%
2070

25.3
64.6
97.5
175.6
89.8
87.2

196 

12. ITALY 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.2

5.7
5.2

4.3
4.3
71.8
0.2
-6.4
-2.1
-7.1
-8.3
10.4
7.2
11.8
16.2
AVG 19-70

1.0
-0.2
-0.2
1.3
0.8
0.4
1.3
1.3
Ch 19-70

-8,285
-0.1
-4,649
4.4
5.6
0.6
0.3
18.4
23.5
6.8
8.6
0.6
2.1
23.3
25.8
1.3
2.0
0.7
-2.1
12.6
20.9
3.4
3.2
3.5
6.2
7.3
-3.1
-3.4
-3.6
-1.9
0.0
-12.6
4.7
7.9
Ch 19-70

2019

1.31

81.3
85.7

19.6
22.9
134.7
0.2
60.3
17.9
40.3
59.1
23.0
7.3
31.7
12.3
2019

0.6
0.7
0.6
0.0
0.1
-0.2
0.7
-0.1
2019

35,660
-0.4
25,139
70.5
60.7
44.7
78.2
57.5
9.1
60.5
51.3
38.5
67.8
47.0
5.7
80.6
70.3
50.4
88.5
68.7
12.9
65.5
65.2
65.8
63.6
54.8
9.8
9.7
22.7
23.3
4%
74%
20%
3%
2019

2030

1.37

82.6
86.9

20.5
23.8
224.0
0.4
59.9
15.9
35.8
56.8
27.3
8.8
32.2
15.5
2030

0.3
-0.4
-0.4
0.7
0.4
0.2
0.4
0.7
2030

34,053
-0.7
24,795
72.8
62.5
45.3
78.3
69.4
18.8
64.6
54.9
39.0
69.3
62.3
16.5
80.8
70.0
51.0
86.8
76.9
21.2
66.4
66.0
66.9
66.3
57.1
8.9
8.6
22.6
24.1
4%
63%
26%
6%
2030

2040

1.41

83.8
88.0

21.4
24.7
217.2
0.4
59.3
15.4
34.9
52.4
32.2
10.5
32.6
20.0
2040

1.1
-0.6
-0.6
1.7
1.1
0.6
1.3
1.7
2040

31,117
-1.0
23,051
74.1
62.2
45.5
78.4
72.2
21.0
66.3
55.3
39.2
69.7
66.3
20.1
81.5
68.9
51.2
86.4
78.5
22.1
67.0
66.4
67.6
68.2
57.6
7.9
7.5
21.2
23.1
4%
64%
23%
8%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

27.9
48.0
76.0
149.1
65.8
61.7

23.5
38.9
69.2
159.3
58.5
56.9

25.1
61.4
90.7
157.1
81.0
74.5

2.4
26.7
27.6
-6.7
23.7
16.7

2050

1.45

84.9
89.0

22.3
25.6
214.3
0.4
58.0
15.6
34.1
50.7
33.7
13.8
41.0
27.3
2050

1.5
-0.2
-0.2
1.7
1.1
0.6
1.8
1.7
2050

29,402
-0.4
21,912
74.5
64.3
45.3
78.7
73.3
24.1
66.9
57.5
39.0
70.1
67.8
23.1
81.6
70.9
51.0
86.5
78.7
25.1
67.6
67.0
68.2
69.4
60.1
6.9
6.5
20.4
22.2
4%
65%
23%
8%
2050

24.1
66.5
97.4
161.7
87.0
80.1

2060

1.48

86.0
90.0

23.1
26.4
210.5
0.4
55.9
15.6
33.6
51.0
33.4
15.3
45.8
30.0
2060

1.4
-0.2
-0.2
1.6
1.1
0.6
1.8
1.6
2060

28,495
-0.3
21,226
74.5
65.8
45.3
78.5
74.4
29.5
67.1
59.2
39.0
70.0
69.3
28.0
81.4
72.0
51.0
86.3
79.1
31.0
68.3
67.8
68.8
69.4
61.6
6.8
6.3
19.8
21.8
4%
63%
24%
9%
2060

25.1
65.5
96.1
156.7
84.2
76.5

2070

1.52

87.0
90.9

23.9
27.2
206.6
0.4
53.9
15.8
33.1
50.8
33.3
14.5
43.5
28.5
2070

1.3
-0.2
-0.2
1.5
1.0
0.5
1.6
1.5
2070

27,375
-0.5
20,490
74.9
66.3
45.4
78.5
75.9
32.6
67.3
59.9
39.1
69.9
70.2
31.5
81.9
72.3
51.1
86.4
81.3
33.7
68.9
68.5
69.3
69.8
62.1
6.8
6.3
19.1
21.3
4%
61%
24%
10%
2070

25.8
65.6
96.8
152.6
82.2
73.6

197 

13. CYPRUS 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.2

5.8
5.1

4.3
4.3
-5.5
-0.7
0.2
-2.3
-6.5
-8.6
10.9
6.8
15.8
13.7
AVG 19-70

1.9
0.4
0.4
1.5
0.9
0.6
1.4
1.4
Ch 19-70

40
-1.3
62
4.9
3.2
4.4
2.2
14.8
11.1
6.6
4.7
2.5
3.3
21.3
14.0
3.3
1.9
6.5
1.2
8.8
8.3
3.4
3.3
3.4
5.0
3.5
-0.5
-0.6
0.1
0.1
-1.2
-6.0
3.9
3.3
Ch 19-70

2019

1.33

80.8
85.1

19.2
22.1
7.8
0.9
0.9
21.6
43.2
62.1
16.2
3.7
22.9
6.0
2019

1.9
1.6
1.4
0.5
0.0
0.5
0.6
0.3
2019

548
1.2
443
80.9
72.1
62.5
88.3
65.3
13.8
75.7
66.5
64.0
83.5
53.9
6.9
86.5
78.1
60.8
93.3
77.0
21.4
63.9
64.4
63.4
75.1
67.0
7.2
7.1
0.4
0.4
8%
74%
15%
3%
2019

2030

1.38

82.1
86.1

20.1
22.9
3.4
0.4
1.0
21.0
43.0
59.4
19.6
5.5
28.2
9.3
2030

1.7
0.9
0.9
0.8
0.4
0.4
1.0
0.8
2030

574
0.4
480
83.7
73.6
66.8
89.7
68.6
13.5
79.7
69.9
66.4
85.8
61.0
10.6
88.1
77.5
67.3
94.0
76.6
16.4
64.7
65.3
64.2
76.8
67.7
8.2
8.0
0.4
0.5
7%
76%
14%
3%
2030

2040

1.42

83.3
87.2

21.0
23.8
3.0
0.3
1.0
20.6
41.7
58.5
20.9
7.2
34.5
12.3
2040

2.4
0.5
0.5
1.9
1.2
0.7
2.0
1.9
2040

594
0.4
501
84.4
75.2
67.0
90.1
72.0
17.2
80.6
71.6
66.6
86.4
65.5
14.7
88.6
79.1
67.4
94.2
79.3
19.8
65.5
66.1
65.1
78.1
69.7
7.5
7.3
0.5
0.5
7%
74%
16%
3%
2040

2050

1.46

84.5
88.3

21.9
24.7
2.7
0.3
1.0
19.8
39.4
57.8
22.4
8.0
35.8
13.9
2050

2.1
0.3
0.3
1.8
1.2
0.6
1.8
1.8
2050

606
0.0
513
84.6
75.1
67.0
90.3
75.0
21.1
80.9
71.3
66.6
86.6
69.6
17.8
88.8
79.3
67.4
94.4
81.6
24.8
66.2
66.7
65.8
78.9
70.2
6.8
6.5
0.5
0.5
7%
70%
18%
4%
2050

2060

1.49

85.6
89.3

22.7
25.6
2.4
0.2
1.1
19.5
38.0
55.4
25.1
8.6
34.4
15.6
2060

1.6
-0.1
-0.1
1.7
1.1
0.6
1.4
1.7
2060

596
-0.2
509
85.4
74.6
67.0
90.4
77.7
23.4
81.6
70.5
66.6
86.7
72.4
19.6
89.4
79.3
67.3
94.5
83.9
28.0
66.8
67.3
66.3
79.6
69.8
6.7
6.4
0.5
0.5
7%
69%
19%
6%
2060

2070

1.53

86.6
90.2

23.5
26.4
2.3
0.2
1.1
19.4
36.7
53.5
27.1
10.5
38.7
19.6
2070

1.5
0.0
0.0
1.5
1.0
0.5
1.3
1.5
2070

588
-0.1
505
85.9
75.4
66.9
90.5
80.1
24.9
82.2
71.2
66.5
86.7
75.2
20.9
89.8
79.9
67.3
94.5
85.8
29.7
67.3
67.7
66.8
80.1
70.5
6.7
6.4
0.5
0.5
7%
68%
19%
6%
2070

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

17.3
33.0
68.4
112.9
40.0
38.9

18.9
26.2
61.0
108.6
32.0
31.2

19.0
35.6
70.9
111.7
42.2
40.8

2.4
24.6
26.0
10.8
24.8
22.3

21.6
38.8
73.0
109.5
44.4
42.4

21.4
45.3
80.5
113.7
50.8
47.9

21.3
50.7
86.9
119.4
56.9
53.5

198 

14. LATVIA 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

12.0
8.3

7.2
6.1
4.7
0.3
-0.7
-2.3
-7.8
-9.0
11.4
9.2
19.0
20.2
AVG 19-70

1.2
-1.1
-1.1
2.3
1.4
0.9
2.2
2.3
Ch 19-70

-540
1.0
-448
0.1
-2.9
3.8
1.7
-3.1
-7.1
0.9
-0.4
4.4
3.1
-3.9
-4.0
-0.9
-6.0
3.2
0.3
-2.3
-11.0
0.9
1.1
0.8
-0.2
-3.0
0.4
0.4
-0.4
-0.4
2.3
-1.3
-0.1
-0.8
Ch 19-70

2019

1.58

70.6
80.2

14.5
19.4
-3.9
-0.2
1.9
20.6
40.4
59.0
20.4
5.7
28.0
9.7
2019

2.7
-0.1
-0.6
3.3
1.9
1.4
3.4
2.8
2019

1,129
-1.4
936
82.9
73.6
66.2
88.4
72.5
20.7
80.4
69.7
61.4
85.5
72.2
19.3
85.6
78.0
70.6
91.3
73.0
22.8
63.5
63.2
63.8
77.6
69.0
6.4
6.2
0.9
0.9
6%
70%
20%
4%
2019

2030

1.64

73.3
82.1

16.0
20.7
-7.3
-0.4
1.7
20.1
35.8
54.8
25.0
7.0
27.8
12.7
2030

1.9
-1.2
-1.2
3.1
1.9
1.2
3.1
3.2
2030

934
-1.6
766
82.0
68.8
69.6
89.1
68.3
13.8
79.7
65.4
65.6
86.9
67.5
15.1
84.3
72.4
73.5
91.2
69.1
12.0
64.5
64.3
64.6
74.7
62.8
8.9
8.7
0.7
0.7
7%
69%
20%
4%
2030

2040

1.67

75.9
83.9

17.6
22.0
-4.7
-0.3
1.5
18.1
33.7
53.2
28.7
9.4
32.9
17.7
2040

1.0
-1.2
-1.2
2.2
1.4
0.8
2.0
2.2
2040

814
-1.4
667
82.0
68.4
70.4
89.5
68.8
13.7
79.8
65.4
66.2
87.9
66.4
15.0
84.2
71.5
74.2
91.0
71.2
11.9
64.5
64.3
64.6
75.5
63.2
7.9
7.7
0.6
0.6
8%
67%
21%
4%
2040

2050

1.69

78.4
85.6

19.0
23.3
-2.3
-0.2
1.4
18.3
31.8
50.3
31.3
11.5
36.7
22.9
2050

0.6
-1.5
-1.4
2.0
1.3
0.7
1.5
2.0
2050

699
-1.7
575
82.2
67.6
70.0
90.4
67.5
13.7
80.3
65.4
65.9
88.8
66.0
14.9
84.0
69.8
73.9
91.9
68.9
12.3
64.5
64.3
64.6
76.6
63.1
6.9
6.7
0.5
0.6
7%
67%
22%
5%
2050

2060

1.70

80.6
87.1

20.4
24.4
-0.6
0.0
1.3
18.6
32.6
48.0
33.4
12.9
38.6
26.8
2060

1.0
-0.8
-0.8
1.8
1.1
0.6
1.9
1.8
2060

610
-0.6
510
83.6
67.7
69.7
90.2
69.4
12.5
81.9
66.0
65.6
88.7
68.3
14.1
85.1
69.3
73.5
91.6
70.5
10.8
64.5
64.3
64.6
77.9
63.2
6.8
6.6
0.5
0.5
8%
71%
18%
4%
2060

2070

1.71

82.6
88.5

21.7
25.5
0.7
0.1
1.2
18.3
32.6
50.0
31.8
14.9
47.0
29.9
2070

1.1
-0.4
-0.4
1.5
1.0
0.5
1.8
1.5
2070

589
-0.4
489
83.0
70.7
69.9
90.1
69.4
13.6
81.2
69.3
65.8
88.6
68.2
15.3
84.7
72.0
73.8
91.6
70.6
11.8
64.5
64.3
64.6
77.4
66.0
6.8
6.7
0.5
0.5
8%
69%
20%
4%
2070

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

25.0
45.7
82.3
133.9
56.7
54.3

24.0
34.6
69.5
108.8
39.9
38.1

26.1
53.8
87.9
138.2
66.8
64.0

0.6
29.0
30.7
40.7
38.5
37.5

27.6
62.3
98.7
147.8
76.5
73.1

22.1
69.5
108.2
155.8
84.6
80.9

24.6
63.6
100.2
149.5
78.4
75.6

199 

15. LITHUANIA 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

11.6
7.7

6.9
5.7
-7.4
-0.2
-1.0
-2.6
-7.0
-10.4
13.0
8.5
14.1
19.0
AVG 19-70

1.2
-1.0
-1.0
2.2
1.3
0.9
2.1
2.2
Ch 19-70

-776
0.0
-624
2.8
-2.4
2.6
3.7
0.7
-6.7
2.6
-1.1
2.0
3.1
1.0
-6.6
2.7
-4.2
3.2
4.1
0.1
-8.3
1.4
1.0
1.6
2.2
-2.6
0.5
0.5
-0.6
-0.6
0.2
0.6
-0.3
-0.5
Ch 19-70

2019

1.61

71.3
81.1

15.0
20.0
10.1
0.4
2.8
19.9
39.9
60.2
19.8
5.8
29.4
9.7
2019

3.8
1.4
1.3
2.4
1.1
1.3
4.0
2.4
2019

1,683
-0.3
1,407
83.6
74.2
63.1
90.1
73.8
17.5
82.1
71.0
60.1
89.0
72.7
15.4
85.2
77.8
65.8
91.3
75.1
20.8
62.7
63.4
62.1
78.3
69.6
6.4
6.2
1.3
1.4
6%
69%
21%
4%
2019

2030

1.63

73.8
82.8

16.4
21.2
-9.5
-0.4
2.6
19.6
36.3
55.3
25.1
6.7
26.6
12.1
2030

1.4
-1.7
-1.7
3.0
1.8
1.3
2.4
3.1
2030

1,417
-1.6
1,196
84.4
69.5
64.6
92.6
70.2
9.9
82.7
65.3
60.9
90.9
70.6
7.8
86.1
73.9
68.1
94.1
69.6
12.9
64.1
64.4
63.8
78.5
64.8
7.0
6.9
1.1
1.1
6%
70%
21%
3%
2030

2040

1.65

76.4
84.4

17.9
22.4
-5.2
-0.2
2.3
17.5
34.5
52.9
29.6
9.4
31.9
17.8
2040

1.0
-1.1
-1.1
2.1
1.4
0.8
1.9
2.2
2040

1,232
-1.2
1,052
85.4
69.9
65.8
93.3
72.4
10.4
83.6
66.1
62.2
91.6
71.6
8.8
87.0
73.6
69.2
94.8
73.2
12.4
64.1
64.4
63.8
79.4
65.2
7.0
6.8
1.0
1.0
7%
69%
20%
3%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

25.8
45.4
80.9
123.6
54.8
53.1

24.5
32.9
66.0
104.6
38.4
37.0

25.0
55.9
89.1
130.3
67.0
64.8

0.0
33.1
34.8
37.4
40.5
39.4

2050

1.67

78.8
86.0

19.3
23.5
-1.9
-0.1
2.1
17.1
32.6
51.3
31.6
12.3
39.0
24.0
2050

0.7
-1.3
-1.3
2.0
1.3
0.7
1.6
2.0
2050

1,092
-1.4
935
85.6
70.6
65.9
93.8
73.1
11.1
83.8
67.7
62.3
92.0
72.0
9.0
87.2
73.2
69.2
95.4
74.0
13.2
64.1
64.4
63.8
79.7
65.8
6.9
6.7
0.9
0.9
6%
68%
23%
3%
2050

27.7
61.5
94.8
136.3
73.7
71.2

2060

1.69

80.9
87.4

20.6
24.6
0.7
0.0
2.0
17.5
32.7
49.0
33.5
13.1
39.2
26.8
2060

0.8
-0.9
-0.9
1.8
1.1
0.6
1.6
1.8
2060

956
-0.9
825
86.3
69.4
65.5
93.9
73.4
10.9
84.6
67.1
61.8
92.2
72.4
8.9
87.9
71.5
68.9
95.5
74.2
12.8
64.1
64.4
63.8
80.4
64.8
6.9
6.7
0.8
0.8
6%
70%
20%
4%
2060

2070

1.70

82.9
88.8

21.9
25.7
2.6
0.1
1.8
17.3
32.8
49.8
32.9
14.3
43.5
28.7
2070

1.1
-0.4
-0.4
1.5
1.0
0.5
1.7
1.5
2070

907
-0.3
783
86.4
71.8
65.7
93.8
74.5
10.7
84.7
69.8
62.0
92.1
73.7
8.8
87.9
73.6
69.0
95.4
75.2
12.5
64.1
64.4
63.8
80.4
67.0
6.9
6.7
0.7
0.8
7%
70%
20%
3%
2070

24.2
68.4
104.2
144.5
81.2
78.1

24.5
66.0
100.8
142.0
78.9
76.5

200 

16. LUXEMBOURG 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.2

6.3
5.8

4.6
4.6
-7.7
-1.3
0.2
-3.9
-10.4
-11.3
15.2
8.3
14.0
17.1
AVG 19-70

1.8
0.7
0.7
1.1
0.7
0.4
1.3
1.1
Ch 19-70

18
-2.2
17
0.8
-4.5
2.6
3.7
0.0
1.0
4.8
-0.6
4.2
7.9
5.2
1.7
-3.1
-8.2
1.1
-0.3
-4.9
0.2
0.0
0.0
0.0
1.4
-3.7
-0.8
-0.8
0.0
0.0
-0.1
-3.2
2.6
0.7
Ch 19-70

2019

1.34

80.3
85.0

19.1
22.5
10.2
1.6
0.6
21.4
45.8
64.2
14.5
4.0
27.5
6.2
2019

2.1
2.9
2.9
-0.8
-0.7
-0.2
0.1
-0.9
2019

398
2.1
305
76.8
68.7
51.9
88.5
45.2
2.9
71.8
63.9
46.5
84.0
38.4
1.7
81.5
73.4
56.9
92.9
51.6
4.1
60.2
60.4
60.1
72.7
65.1
5.3
5.3
0.3
0.3
6%
82%
11%
0%
2019

2030

1.40

81.7
86.3

20.1
23.5
4.2
0.6
0.7
19.8
43.2
61.9
18.3
4.8
26.2
7.7
2030

1.8
1.1
1.1
0.7
0.5
0.3
1.0
0.7
2030

431
0.3
335
77.7
67.4
54.4
91.3
42.9
4.1
75.7
65.4
50.6
90.1
37.9
3.2
79.6
69.3
57.9
92.5
47.5
5.0
60.2
60.4
60.1
74.2
64.3
4.5
4.5
0.3
0.3
6%
82%
12%
1%
2030

2040

1.46

83.1
87.5

21.1
24.5
3.5
0.5
0.7
18.5
40.6
59.2
22.4
6.5
29.2
11.0
2040

2.1
0.6
0.5
1.5
1.0
0.5
1.6
1.5
2040

438
0.1
342
78.1
66.2
54.6
91.9
45.1
3.8
76.8
65.0
50.9
91.3
42.8
3.1
79.3
67.3
58.2
92.5
47.3
4.4
60.2
60.4
60.1
74.6
63.2
4.5
4.5
0.3
0.3
5%
80%
13%
1%
2040

2050

1.49

84.4
88.7

22.0
25.4
3.0
0.4
0.8
17.9
37.8
56.4
25.7
9.0
34.9
15.9
2050

1.7
0.1
0.1
1.5
1.0
0.5
1.4
1.5
2050

435
-0.2
337
77.5
64.8
54.6
92.3
44.9
3.9
76.4
63.6
50.8
91.9
43.1
3.5
78.5
65.9
58.1
92.7
46.7
4.4
60.2
60.4
60.1
74.0
61.9
4.5
4.5
0.3
0.3
5%
79%
14%
1%
2050

2060

1.53

85.5
89.8

22.9
26.3
2.7
0.3
0.8
17.7
36.5
53.8
28.5
10.6
37.2
19.7
2060

1.6
0.0
0.0
1.5
1.0
0.5
1.5
1.5
2060

422
-0.2
328
77.9
64.0
54.4
92.3
45.0
3.9
77.0
62.9
50.7
91.9
43.4
3.5
78.8
65.0
57.9
92.6
46.6
4.3
60.2
60.4
60.1
74.4
61.1
4.5
4.5
0.3
0.3
6%
80%
13%
1%
2060

2070

1.56

86.6
90.8

23.7
27.1
2.5
0.3
0.8
17.5
35.4
52.9
29.7
12.3
41.5
23.3
2070

1.3
-0.2
-0.2
1.5
1.0
0.5
1.3
1.5
2070

416
-0.1
323
77.5
64.2
54.5
92.2
45.2
3.9
76.6
63.3
50.7
91.8
43.6
3.5
78.4
65.1
58.0
92.6
46.7
4.3
60.2
60.4
60.1
74.1
61.4
4.5
4.5
0.3
0.3
6%
79%
14%
1%
2070

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

21.4
29.6
61.5
115.8
38.9
38.6

19.1
22.6
55.9
113.4
30.6
30.4

22.6
37.8
69.0
124.6
49.7
49.2

4.9
33.6
33.4
39.2
44.1
43.4

24.3
45.5
77.2
136.8
60.3
59.7

23.3
52.8
85.7
146.7
69.8
69.0

24.0
56.1
89.2
152.6
74.6
73.8

201 

17. HUNGARY 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.2

10.7
8.7

7.1
6.7
-12.8
-0.1
-0.9
-0.9
-8.0
-9.2
10.0
7.7
18.3
16.2
AVG 19-70

1.8
-0.2
-0.3
2.1
1.3
0.7
2.0
2.0
Ch 19-70

-1,337
0.6
-705
7.3
4.9
3.0
2.9
25.5
4.5
10.1
8.5
3.4
4.0
33.2
5.2
4.2
0.6
2.7
1.7
15.9
3.0
2.3
2.1
2.5
6.5
4.3
0.7
0.6
-0.7
-0.7
0.0
-8.4
7.1
1.3
Ch 19-70

2019

1.51

72.9
79.8

14.8
18.7
36.3
0.4
9.8
19.6
42.4
60.8
19.6
4.5
22.7
7.3
2019

3.9
1.1
0.9
2.9
1.2
1.7
3.9
2.7
2019

5,944
-0.8
4,634
77.9
66.8
54.4
87.1
58.2
7.1
70.0
58.4
46.4
80.5
47.4
5.1
85.9
75.6
61.8
93.4
70.7
9.8
62.8
63.2
62.4
75.4
64.6
3.3
3.3
4.5
4.6
6%
77%
16%
2%
2019

2030

1.61

75.4
81.8

16.4
20.2
23.5
0.2
9.6
19.2
39.7
59.2
21.7
5.9
27.3
10.0
2030

2.4
0.1
0.1
2.3
1.5
0.8
2.6
2.3
2030

5,687
-0.2
4,813
84.6
73.2
57.5
89.4
81.5
9.6
79.3
67.1
50.0
83.7
78.5
8.1
89.8
79.4
64.6
94.9
84.7
11.6
65.1
65.3
64.8
81.2
70.3
4.0
4.0
4.6
4.7
6%
70%
23%
2%
2030

2040

1.67

77.7
83.7

17.9
21.6
23.3
0.2
9.4
18.8
36.6
56.6
24.6
7.8
31.8
13.8
2040

1.4
-0.9
-0.8
2.3
1.5
0.8
1.6
2.3
2040

5,336
-1.2
4,523
84.8
71.6
57.5
90.0
81.7
12.6
79.4
66.0
49.9
84.4
77.8
11.2
89.8
77.2
64.5
95.1
85.6
14.3
65.1
65.3
64.8
81.4
68.8
4.0
3.9
4.3
4.5
6%
67%
24%
3%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

23.7
36.6
69.0
103.8
42.9
42.1

21.0
32.2
64.4
114.4
41.0
40.3

25.8
43.5
76.8
110.0
50.0
48.4

2.5
25.1
29.2
14.8
25.9
24.5

2050

1.69

79.8
85.4

19.3
23.0
23.2
0.3
9.3
18.7
35.1
53.5
27.8
8.6
30.8
16.0
2050

1.6
-0.5
-0.5
2.1
1.4
0.7
1.8
2.1
2050

4,953
-0.4
4,224
85.3
70.3
57.4
90.0
83.5
10.6
80.1
65.0
49.9
84.5
80.2
9.2
90.1
75.5
64.5
95.2
86.6
12.0
65.1
65.3
64.8
81.9
67.6
3.9
3.9
4.1
4.2
6%
67%
24%
3%
2050

24.8
52.0
87.0
121.3
60.2
58.4

2060

1.69

81.8
87.0

20.6
24.2
23.3
0.3
9.1
18.7
34.6
51.8
29.5
11.6
39.3
22.4
2060

1.4
-0.4
-0.4
1.8
1.2
0.6
1.6
1.8
2060

4,715
-0.5
4,023
85.3
71.1
57.3
90.0
83.6
11.8
80.2
66.2
49.8
84.6
80.5
10.5
90.2
75.7
64.4
95.2
86.5
13.1
65.1
65.3
64.8
82.0
68.3
3.9
3.9
3.9
4.0
6%
68%
22%
3%
2060

23.4
57.0
93.2
127.9
66.1
63.9

2070

1.70

83.6
88.5

21.9
25.4
23.5
0.3
8.9
18.7
34.4
51.7
29.6
12.2
41.0
23.5
2070

1.3
-0.2
-0.2
1.5
1.0
0.5
1.5
1.5
2070

4,607
-0.2
3,928
85.3
71.7
57.4
90.0
83.7
11.5
80.1
67.0
49.8
84.5
80.6
10.3
90.1
76.2
64.5
95.2
86.6
12.8
65.1
65.3
64.8
81.9
68.9
3.9
3.9
3.8
3.9
6%
68%
23%
3%
2070

23.6
57.4
93.6
129.2
66.9
64.8

202 

18. MALTA 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.3

6.3
6.1

4.3
4.6
-9.0
-2.0
0.2
-2.6
-9.5
-11.1
13.7
9.0
18.0
18.7
AVG 19-70

2.2
0.5
0.4
1.8
1.2
0.6
1.5
1.7
Ch 19-70

52
-3.8
65
6.4
2.0
1.5
5.4
16.8
-1.0
13.0
8.8
0.3
11.8
27.1
1.2
0.2
-4.6
2.6
-0.3
6.0
-3.7
0.9
1.1
0.8
5.5
1.3
0.8
0.8
0.1
0.1
-2.0
-4.7
6.5
0.2
Ch 19-70

2019

1.14

80.5
84.5

19.6
22.4
12.8
2.6
0.5
18.1
44.4
63.1
18.7
4.3
22.7
6.8
2019

4.5
3.7
2.8
1.7
1.3
0.4
1.2
0.8
2019

316
3.7
251
79.7
68.8
77.7
87.5
52.3
8.7
68.8
58.4
77.3
77.1
36.4
5.2
89.4
78.4
78.0
96.7
67.8
12.4
62.4
62.8
61.9
77.3
66.8
3.0
2.9
0.2
0.2
9%
76%
13%
2%
2019

2030

1.26

82.0
85.9

20.5
23.4
6.0
1.0
0.6
17.8
45.5
61.2
21.0
6.4
30.7
10.5
2030

3.9
1.8
1.7
2.1
1.5
0.7
2.9
2.1
2030

362
0.9
311
85.9
74.3
79.2
91.6
65.1
5.7
79.1
67.6
77.4
85.5
54.2
3.7
91.5
80.2
80.7
96.6
74.7
7.7
63.3
63.9
62.7
82.5
71.5
3.9
3.8
0.3
0.3
7%
79%
14%
1%
2030

2040

1.34

83.3
87.2

21.4
24.4
5.3
0.8
0.6
16.4
42.6
61.3
22.3
7.9
35.6
12.9
2040

2.1
0.5
0.4
1.7
1.1
0.6
1.5
1.7
2040

390
0.4
336
86.2
75.2
79.4
92.7
68.1
8.1
81.1
70.2
77.8
88.3
60.1
6.3
90.4
79.4
80.9
96.3
74.9
9.7
63.3
63.9
62.7
82.8
72.3
3.9
3.8
0.3
0.3
7%
74%
18%
2%
2040

2050

1.39

84.6
88.4

22.3
25.3
4.7
0.7
0.7
15.7
38.4
58.7
25.6
8.4
32.8
14.3
2050

1.3
-0.3
-0.3
1.6
1.0
0.6
0.9
1.6
2050

393
-0.2
336
85.6
72.0
79.4
93.0
69.1
8.2
81.1
67.8
77.8
88.9
62.9
6.7
89.3
75.6
80.8
96.4
74.2
9.6
63.3
63.9
62.7
82.3
69.3
3.9
3.8
0.3
0.3
7%
70%
21%
2%
2050

2060

1.44

85.7
89.5

23.1
26.2
4.2
0.6
0.7
15.7
36.3
53.9
30.4
10.1
33.3
18.8
2060

1.2
-0.3
-0.3
1.6
1.0
0.6
0.9
1.6
2060

374
-0.4
321
85.8
69.4
79.3
93.0
68.1
8.0
81.5
65.5
77.6
88.9
62.4
6.7
89.4
72.6
80.7
96.5
72.7
9.1
63.3
63.9
62.7
82.5
66.8
3.8
3.8
0.3
0.3
7%
71%
19%
3%
2060

2070

1.47

86.8
90.6

23.9
27.0
3.8
0.5
0.7
15.6
34.9
52.0
32.4
13.2
40.7
25.4
2070

1.4
-0.1
-0.1
1.5
1.0
0.5
1.3
1.5
2070

368
-0.1
316
86.0
70.8
79.2
93.0
69.2
7.7
81.8
67.2
77.6
88.9
63.5
6.4
89.6
73.8
80.7
96.4
73.8
8.7
63.3
63.9
62.7
82.7
68.2
3.8
3.8
0.3
0.3
7%
71%
19%
2%
2070

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

19.6
29.7
58.5
101.0
36.4
35.7

18.0
34.4
63.4
95.7
40.4
40.0

22.2
36.4
63.3
94.0
42.4
41.7

4.6
32.7
33.9
26.5
36.8
35.9

26.6
43.5
70.3
102.8
50.8
49.8

24.4
56.5
85.7
119.4
65.9
64.2

24.2
62.4
92.3
127.5
73.2
71.6

203 

19. THE NETHERLANDS 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

5.9
6.3

4.5
4.9
-72.2
-0.4
0.6
-2.2
-4.3
-7.1
9.2
6.6
15.2
13.8
AVG 19-70

1.3
0.0
0.0
1.3
0.9
0.5
1.2
1.3
Ch 19-70

-893
-0.7
-559
1.9
2.2
2.8
0.1
6.5
13.5
4.0
4.0
3.1
1.8
10.5
14.3
-0.1
0.4
2.6
-1.6
2.6
12.8
2.8
2.7
3.0
0.6
1.0
1.5
1.6
-0.7
-0.4
-0.7
-3.3
0.2
3.8
Ch 19-70

2019

1.58

80.7
83.6

19.0
21.4
105.4
0.6
17.3
21.8
39.0
58.8
19.3
4.7
24.2
7.9
2019

1.8
1.2
1.2
0.6
0.4
0.2
1.2
0.6
2019

10,205
0.5
8,435
82.6
71.8
75.7
87.4
72.0
14.4
77.8
66.7
75.3
83.3
63.1
9.1
87.4
76.9
76.1
91.5
81.0
19.8
64.9
65.8
64.0
80.2
69.7
3.0
3.0
8.2
8.4
9%
68%
19%
3%
2019

2030

1.60

81.9
85.1

19.9
22.5
33.3
0.2
18.0
20.4
37.0
55.9
23.7
6.9
29.2
12.4
2030

0.7
-0.3
-0.3
1.0
0.6
0.4
0.5
1.0
2030

10,055
-0.4
8,342
83.0
71.6
78.5
87.4
72.4
19.4
79.0
67.5
78.3
84.3
64.4
14.9
86.9
75.8
78.7
90.4
80.6
24.0
65.8
66.6
65.0
79.2
68.4
4.5
4.6
8.0
8.4
9%
67%
19%
5%
2030

2040

1.62

83.2
86.4

20.9
23.5
34.0
0.2
18.2
20.3
36.9
53.4
26.3
8.8
33.4
16.5
2040

1.5
-0.1
-0.1
1.5
1.0
0.5
1.4
1.5
2040

9,709
-0.1
8,121
83.7
71.7
78.5
87.3
74.1
18.5
80.5
68.1
78.4
84.9
67.0
14.0
86.8
75.3
78.7
89.7
81.2
23.1
66.2
67.0
65.4
79.9
68.4
4.5
4.6
7.8
8.1
9%
69%
18%
5%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

23.4
42.4
78.9
115.1
48.2
45.9

23.0
32.9
69.9
105.3
37.6
36.4

20.9
49.3
87.3
123.6
56.5
53.9

-0.2
22.4
23.2
17.3
22.9
19.9

2050

1.64

84.4
87.6

21.8
24.5
33.4
0.2
18.1
19.9
35.9
53.6
26.4
10.8
40.8
20.1
2050

1.6
0.1
0.1
1.5
1.0
0.5
1.7
1.5
2050

9,729
0.0
8,156
83.8
73.8
78.5
87.4
75.6
22.6
81.1
70.5
78.3
85.0
70.8
16.9
86.6
77.2
78.7
89.8
80.5
28.5
66.7
67.6
65.9
80.1
70.4
4.5
4.6
7.8
8.2
9%
67%
19%
5%
2050

22.7
49.3
86.4
121.4
56.0
53.3

2060

1.66

85.5
88.8

22.7
25.4
32.8
0.2
18.0
19.6
35.2
53.1
27.3
10.7
39.4
20.3
2060

1.4
-0.1
-0.1
1.5
1.0
0.5
1.5
1.5
2060

9,557
-0.2
8,042
84.1
73.6
78.5
87.5
77.1
25.6
81.3
70.3
78.4
85.1
72.0
21.0
87.0
77.0
78.7
89.9
82.3
30.5
67.3
68.1
66.4
80.3
70.2
4.5
4.6
7.7
8.2
9%
65%
20%
6%
2060

23.3
51.4
88.4
120.1
57.0
53.5

2070

1.68

86.6
89.9

23.5
26.3
33.2
0.2
18.0
19.7
34.7
51.8
28.6
11.3
39.4
21.7
2070

1.4
-0.1
-0.1
1.5
1.0
0.5
1.4
1.5
2070

9,312
-0.2
7,875
84.6
74.0
78.5
87.5
78.5
27.9
81.8
70.8
78.4
85.1
73.6
23.4
87.3
77.3
78.7
89.9
83.5
32.6
67.7
68.5
67.0
80.7
70.6
4.5
4.6
7.5
8.1
8%
65%
20%
7%
2070

22.8
55.2
93.2
122.7
60.5
56.3

204 

20. AUSTRIA 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

6.5
5.9

4.8
4.8
-18.8
-0.2
0.4
-1.0
-6.9
-9.4
10.3
7.0
14.4
14.9
AVG 19-70

1.3
0.0
0.0
1.4
0.9
0.5
1.2
1.3
Ch 19-70

-640
-0.3
-388
2.6
-0.7
1.0
1.5
7.4
3.2
5.4
2.1
0.0
2.4
17.2
3.8
-0.2
-3.6
2.0
0.6
-2.6
2.3
0.9
0.0
1.8
2.7
-0.5
-0.3
-0.3
-0.4
-0.3
0.1
-3.1
1.7
1.3
Ch 19-70

2019

1.45

79.8
84.3

18.8
21.8
44.3
0.5
8.9
19.4
41.9
61.7
18.9
5.2
27.3
8.4
2019

1.3
0.7
0.6
0.7
0.3
0.4
0.9
0.6
2019

5,478
0.3
4,399
80.3
70.6
74.0
89.0
56.5
7.1
75.6
65.6
71.6
85.7
47.4
4.8
84.9
75.6
76.2
92.4
65.9
9.7
62.3
63.2
61.4
76.8
67.5
4.4
4.3
4.2
4.3
8%
74%
16%
1%
2019

2030

1.49

81.2
85.7

19.8
22.9
31.3
0.3
9.2
19.3
38.3
57.5
23.2
6.7
29.0
11.7
2030

1.2
0.1
0.1
1.1
0.7
0.4
1.0
1.1
2030

5,268
-0.6
4,256
80.8
68.1
75.1
90.0
57.6
9.3
77.5
64.4
71.6
87.2
53.5
6.1
84.0
72.0
78.3
92.7
61.9
12.7
62.9
63.2
62.6
77.5
65.4
4.1
4.0
4.1
4.2
8%
72%
17%
2%
2030

2040

1.52

82.6
86.9

20.8
23.9
29.4
0.3
9.3
18.5
37.1
55.0
26.5
8.3
31.3
15.1
2040

1.5
0.0
-0.1
1.5
1.0
0.5
1.4
1.5
2040

5,115
-0.1
4,239
82.9
69.3
75.1
90.3
64.1
9.5
80.8
66.6
71.6
87.8
64.3
7.4
84.9
72.1
78.3
92.7
63.9
11.8
63.2
63.2
63.2
79.5
66.5
4.1
4.0
4.1
4.2
9%
72%
17%
3%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

24.1
40.3
73.8
118.8
49.5
48.3

22.4
30.7
62.1
108.2
38.5
38.0

22.5
48.2
81.8
122.7
57.9
56.4

0.6
25.2
29.0
25.7
29.0
27.7

2050

1.55

83.9
88.1

21.8
24.8
27.2
0.3
9.3
18.2
35.9
54.0
27.8
11.1
40.0
20.6
2050

1.3
-0.2
-0.2
1.5
1.0
0.5
1.3
1.5
2050

5,047
-0.3
4,168
82.6
70.0
75.1
90.6
63.5
10.5
80.6
67.5
71.6
88.0
63.9
8.9
84.6
72.5
78.3
93.0
63.0
12.3
63.2
63.2
63.2
79.2
67.2
4.1
4.0
4.0
4.1
8%
71%
18%
3%
2050

23.9
51.5
85.2
127.4
62.2
60.5

2060

1.57

85.2
89.2

22.7
25.7
26.4
0.3
9.3
18.5
35.5
52.7
28.9
11.3
39.1
21.4
2060

1.4
-0.2
-0.2
1.5
1.0
0.5
1.4
1.5
2060

4,892
-0.2
4,054
82.9
69.3
75.0
90.6
63.4
10.3
80.9
66.9
71.6
88.1
64.0
8.6
84.8
71.7
78.3
93.0
62.9
12.1
63.2
63.2
63.2
79.5
66.5
4.1
4.0
3.9
4.0
8%
72%
17%
3%
2060

22.8
54.8
89.9
132.0
66.0
64.1

2070

1.60

86.3
90.2

23.6
26.6
25.5
0.3
9.2
18.4
35.1
52.3
29.3
12.2
41.7
23.3
2070

1.4
-0.1
-0.1
1.5
1.0
0.5
1.5
1.5
2070

4,838
-0.1
4,010
82.9
69.9
75.0
90.6
63.9
10.3
81.0
67.7
71.6
88.1
64.6
8.6
84.7
72.0
78.3
93.0
63.3
12.0
63.2
63.2
63.2
79.5
67.1
4.1
4.0
3.8
4.0
8%
71%
17%
3%
2070

23.0
55.9
91.1
133.9
67.5
65.7

205 

21. POLAND 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.2

10.2
7.5

6.5
5.7
69.1
0.2
-7.1
-4.3
-10.3
-11.8
16.0
11.3
21.7
24.3
AVG 19-70

1.5
-0.8
-0.8
2.3
1.5
0.9
2.0
2.3
Ch 19-70

-8,061
0.6
-6,079
0.2
-1.9
0.3
0.8
4.0
4.1
0.6
-0.9
0.9
1.4
3.7
2.8
-0.6
-3.4
-0.3
0.0
3.0
4.6
0.0
0.0
0.0
-1.2
-2.9
1.8
1.7
-6.1
-6.0
-0.1
-5.5
3.7
1.9
Ch 19-70

2019

1.36

74.1
82.0

16.1
20.5
3.3
0.0
38.0
20.1
42.9
61.9
17.9
4.4
24.5
7.1
2019

3.5
0.4
0.3
3.2
1.8
1.3
3.5
3.1
2019

23,506
-1.0
17,798
75.7
65.7
61.3
85.3
51.1
8.5
68.0
57.7
54.3
79.0
40.3
5.5
83.5
74.1
68.0
91.6
63.0
12.3
62.9
64.5
61.3
73.3
63.6
3.2
3.2
17.2
17.6
7%
77%
15%
2%
2019

2030

1.40

76.5
83.8

17.6
21.8
25.4
0.1
37.0
18.6
40.9
58.6
22.8
5.8
25.6
9.9
2030

2.3
-0.9
-0.9
3.2
2.0
1.2
2.7
3.2
2030

21,666
-0.2
16,662
76.9
66.0
60.9
85.2
56.3
11.9
69.6
58.4
54.4
79.0
46.3
8.1
84.1
73.8
67.1
91.3
66.8
16.5
62.9
64.5
61.3
73.1
62.8
4.9
4.8
15.8
16.3
6%
75%
15%
3%
2030

2040

1.45

78.7
85.4

18.9
23.0
37.5
0.1
35.6
16.5
37.0
58.1
25.5
9.2
36.3
15.9
2040

1.4
-0.9
-0.9
2.2
1.5
0.8
1.8
2.3
2040

20,665
-0.8
15,493
75.0
64.8
61.7
85.5
54.8
13.3
67.2
57.0
55.2
79.3
43.7
9.1
82.6
72.7
67.8
91.4
66.3
18.1
62.9
64.5
61.3
71.2
61.6
5.0
4.9
14.7
15.3
7%
70%
19%
4%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

21.1
38.9
70.6
126.1
49.9
48.4

22.0
29.0
61.5
116.1
37.5
36.8

27.2
43.9
72.2
133.3
57.9
55.9

3.9
38.8
38.0
49.8
52.4
49.7

2050

1.49

80.7
86.9

20.2
24.2
47.6
0.1
34.0
16.4
34.3
53.3
30.4
9.8
32.2
18.3
2050

0.9
-1.2
-1.2
2.1
1.3
0.7
1.3
2.1
2050

18,122
-1.5
13,647
75.3
61.6
61.7
86.3
53.6
12.8
67.6
53.9
55.2
80.5
41.8
8.5
82.8
69.2
67.8
91.9
65.4
17.6
62.9
64.5
61.3
71.5
58.6
5.0
4.8
13.0
13.6
6%
71%
19%
5%
2050

27.5
57.0
87.7
150.0
74.7
71.1

2060

1.53

82.6
88.3

21.4
25.2
60.4
0.2
32.4
16.3
33.5
49.8
33.9
12.5
36.8
25.1
2060

1.1
-0.7
-0.7
1.8
1.2
0.6
1.5
1.8
2060

16,150
-0.8
12,356
76.5
62.2
61.4
86.2
54.6
12.3
69.3
55.1
54.9
80.5
43.3
8.0
83.4
69.2
67.6
91.6
65.7
16.8
62.9
64.5
61.3
72.7
59.2
5.0
4.8
11.7
12.3
6%
73%
16%
5%
2060

23.8
68.2
100.9
163.8
89.0
84.9

2070

1.56

84.3
89.5

22.6
26.2
72.4
0.2
30.8
15.9
32.6
50.1
34.0
15.7
46.3
31.4
2070

1.0
-0.6
-0.6
1.5
1.0
0.5
1.5
1.6
2070

15,445
-0.4
11,719
75.9
63.9
61.6
86.1
55.1
12.6
68.5
56.8
55.2
80.4
44.0
8.4
82.9
70.7
67.8
91.6
66.1
17.0
62.9
64.5
61.3
72.1
60.8
5.0
4.9
11.1
11.6
6%
71%
18%
4%
2070

25.9
67.8
99.5
165.9
90.0
86.4

206 

22. PORTUGAL 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.2

7.1
5.6

4.8
4.5
-21.5
-0.2
-1.8
-1.2
-7.5
-9.8
11.1
8.2
15.0
19.0
AVG 19-70

1.2
-0.5
-0.5
1.7
1.1
0.6
1.6
1.7
Ch 19-70

-1,908
-0.2
-1,374
4.3
2.1
0.5
2.1
14.0
7.0
6.3
4.8
0.6
3.9
16.9
13.1
2.2
-0.8
0.4
0.2
10.6
-0.4
2.1
2.0
2.2
4.2
2.2
-0.2
-0.3
-1.3
-1.2
0.1
-5.9
3.0
2.8
Ch 19-70

2019

1.43

78.6
84.8

18.4
22.2
40.1
0.4
10.3
19.0
40.0
59.0
22.0
6.5
29.5
11.0
2019

1.8
1.4
1.3
0.5
0.8
-0.3
1.8
0.4
2019

6,070
-0.2
4,942
81.4
70.9
58.3
90.3
64.5
16.1
78.3
66.7
55.3
88.0
58.7
9.5
84.9
75.6
61.2
92.7
71.0
24.0
64.3
64.6
64.1
76.2
66.4
6.4
6.3
4.6
4.8
6%
73%
18%
4%
2019

2030

1.47

80.2
86.0

19.4
23.2
9.9
0.1
10.1
17.5
36.2
56.1
26.5
8.0
30.4
14.4
2030

0.7
-0.7
-0.7
1.4
0.9
0.5
1.0
1.4
2030

5,648
-0.9
4,685
83.0
70.1
58.8
91.7
69.9
15.4
80.7
67.6
55.9
90.5
65.4
14.2
85.4
73.0
61.6
92.9
75.2
16.9
64.8
65.1
64.6
77.8
65.9
6.2
6.1
4.4
4.6
6%
69%
21%
4%
2030

2040

1.51

81.7
87.2

20.4
24.1
12.3
0.1
9.8
17.4
33.0
51.8
30.9
10.3
33.4
19.9
2040

1.2
-0.9
-0.9
2.1
1.4
0.7
1.5
2.1
2040

5,058
-1.3
4,226
83.5
69.5
58.6
92.2
71.6
18.8
81.8
67.4
55.8
91.5
67.7
17.8
85.5
72.0
61.5
92.9
76.1
20.0
65.2
65.5
65.0
78.3
65.3
6.2
6.1
4.0
4.2
5%
67%
22%
6%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

26.3
47.2
78.4
119.4
56.0
53.6

23.2
37.3
69.4
114.0
44.9
43.2

27.1
59.6
93.1
131.7
69.5
65.3

0.9
30.0
33.9
22.2
31.5
28.1

2050

1.53

83.2
88.3

21.4
25.0
14.3
0.2
9.4
17.3
32.5
49.0
33.7
12.8
37.9
26.1
2050

1.3
-0.6
-0.6
2.0
1.3
0.7
1.8
2.0
2050

4,581
-0.6
3,884
84.8
70.2
58.7
92.3
74.5
18.9
83.5
68.4
55.8
91.8
71.5
18.0
86.2
72.2
61.5
92.9
78.0
20.0
65.6
65.8
65.4
79.5
66.0
6.2
6.0
3.6
3.9
6%
68%
20%
6%
2050

24.1
68.8
104.1
140.9
79.8
74.9

2060

1.56

84.5
89.4

22.3
25.9
16.3
0.2
8.9
17.3
32.1
49.3
33.4
15.2
45.4
30.8
2060

1.4
-0.4
-0.4
1.8
1.1
0.6
1.9
1.8
2060

4,379
-0.4
3,729
85.2
72.7
58.8
92.3
76.9
21.1
84.0
71.2
55.9
91.8
74.3
20.3
86.5
74.4
61.6
92.8
79.9
22.0
65.9
66.1
65.8
79.9
68.3
6.2
6.0
3.5
3.7
6%
67%
21%
6%
2060

25.0
67.9
103.0
139.3
78.6
74.0

2070

1.59

85.7
90.4

23.2
26.7
18.6
0.2
8.5
17.7
32.5
49.2
33.1
14.7
44.5
30.0
2070

1.2
-0.4
-0.3
1.5
1.0
0.5
1.6
1.6
2070

4,162
-0.5
3,568
85.7
73.0
58.8
92.4
78.4
23.1
84.5
71.5
55.9
91.9
75.6
22.7
87.1
74.7
61.6
92.9
81.7
23.6
66.4
66.6
66.2
80.4
68.6
6.2
6.0
3.3
3.6
6%
67%
21%
7%
2070

24.1
67.3
103.3
136.2
76.4
71.3

207 

23. ROMANIA 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

11.6
9.0

7.2
6.8
94.5
0.5
-5.7
-3.3
-9.2
-9.5
12.8
9.6
20.2
20.4
AVG 19-70

1.7
-0.9
-0.9
2.6
1.6
0.9
2.4
2.6
Ch 19-70

-4,728
0.8
-3,330
2.2
0.5
1.6
2.7
8.2
4.5
2.5
1.1
1.8
2.4
9.0
1.9
1.2
-1.3
1.1
2.4
5.2
6.6
0.0
0.0
-0.1
1.7
0.1
0.7
0.6
-3.2
-3.2
0.4
-6.8
4.3
2.1
Ch 19-70

2019

1.65

71.9
79.5

14.9
18.6
-73.5
-0.4
19.3
21.0
42.6
60.2
18.7
4.7
25.2
7.8
2019

4.6
-0.3
-0.4
4.9
3.1
1.9
5.3
5.0
2019

11,654
-1.3
8,594
73.7
64.7
48.4
84.1
49.0
13.4
63.3
54.6
38.5
74.6
37.2
12.1
83.9
74.8
57.7
93.0
61.9
15.1
63.4
64.1
62.7
71.0
62.3
3.7
3.6
8.3
8.6
5%
78%
13%
3%
2019

2030

1.66

74.7
81.6

16.5
20.1
-40.0
-0.2
17.7
19.3
38.0
58.8
21.8
5.8
26.6
9.9
2030

2.8
-0.8
-0.8
3.7
2.3
1.3
3.6
3.7
2030

10,439
-0.5
7,760
74.3
65.1
49.6
85.7
55.2
15.5
64.1
54.9
39.9
75.6
45.4
12.0
84.0
75.1
58.5
94.8
65.3
20.0
63.3
64.1
62.6
71.1
62.3
4.4
4.2
7.4
7.7
5%
72%
19%
4%
2030

2040

1.70

77.2
83.5

18.0
21.6
-20.2
-0.1
16.5
18.0
34.9
55.1
26.9
8.4
31.0
15.2
2040

1.2
-1.1
-1.1
2.3
1.5
0.8
1.9
2.3
2040

9,103
-1.5
6,762
74.3
62.6
49.8
86.4
54.9
17.4
63.6
52.4
40.2
76.2
44.3
14.0
84.1
72.3
58.8
95.2
65.7
21.4
63.3
64.1
62.6
71.0
60.0
4.4
4.1
6.5
6.9
6%
70%
19%
6%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

26.0
37.1
70.0
129.8
48.1
46.3

20.7
31.1
66.0
126.2
40.5
39.2

26.8
48.9
81.5
140.2
62.5
58.7

3.9
31.0
31.1
30.4
39.3
36.3

2050

1.72

79.5
85.3

19.5
22.9
-2.0
0.0
15.5
18.0
33.5
51.3
30.7
10.2
33.3
19.9
2050

1.1
-1.1
-1.1
2.2
1.4
0.8
1.7
2.2
2050

7,932
-1.2
5,985
75.5
62.8
49.9
87.0
55.2
16.9
65.0
52.8
40.2
77.1
43.9
13.3
84.8
72.2
58.8
95.5
65.8
20.9
63.3
64.1
62.6
72.2
60.2
4.4
4.1
5.7
6.1
5%
71%
18%
6%
2050

25.5
59.8
94.8
153.7
76.5
71.8

2060

1.73

81.6
87.0

20.8
24.2
10.4
0.1
14.5
17.8
33.4
50.0
32.2
13.2
41.0
26.4
2060

1.2
-0.6
-0.6
1.9
1.2
0.7
1.9
1.9
2060

7,252
-0.5
5,542
76.4
64.3
49.8
86.9
57.6
17.0
66.2
54.8
40.1
77.1
46.6
13.2
85.5
73.0
58.7
95.5
67.6
20.9
63.3
64.1
62.6
73.1
61.7
4.3
4.1
5.3
5.6
5%
72%
17%
6%
2060

23.5
64.3
99.9
158.1
82.0
77.4

2070

1.74

83.5
88.5

22.1
25.4
21.0
0.2
13.7
17.8
33.4
50.7
31.5
14.3
45.4
28.2
2070

1.1
-0.5
-0.5
1.5
1.0
0.5
1.6
1.5
2070

6,927
-0.5
5,264
76.0
65.1
49.9
86.8
57.2
17.9
65.8
55.8
40.3
77.0
46.3
14.1
85.1
73.5
58.8
95.4
67.1
21.6
63.3
64.1
62.6
72.7
62.4
4.4
4.1
5.0
5.3
5%
72%
18%
5%
2070

24.5
62.1
97.1
156.6
79.8
75.5

208 

24. SLOVENIA 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

7.2
5.9

5.1
4.8
-10.5
-0.5
-0.2
-1.8
-7.5
-8.6
10.4
8.4
18.6
17.8
AVG 19-70

1.6
-0.3
-0.3
1.9
1.3
0.6
1.8
1.9
Ch 19-70

-258
0.0
-175
3.1
1.5
0.3
1.1
14.3
4.8
4.4
3.9
0.4
1.4
17.6
6.4
1.8
-0.8
0.3
0.7
10.8
3.0
0.9
0.9
0.8
1.9
0.6
1.3
1.2
-0.2
-0.2
0.9
-6.5
4.1
1.5
Ch 19-70

2019

1.55

78.7
84.5

18.1
22.0
15.7
0.8
2.1
19.6
41.4
60.4
20.0
5.4
26.9
8.9
2019

2.4
1.0
0.5
1.8
1.9
-0.1
1.7
1.3
2019

1,261
-0.1
1,007
79.9
68.4
59.3
92.4
50.3
4.6
76.6
64.5
53.5
90.4
45.6
3.0
83.0
72.1
64.4
94.3
54.9
6.4
62.0
62.1
62.0
76.4
65.5
4.4
4.3
1.0
1.0
6%
79%
15%
1%
2019

2030

1.59

80.3
85.8

19.2
23.0
4.5
0.2
2.1
18.6
37.4
56.7
24.7
6.8
27.4
11.9
2030

2.2
-0.5
-0.5
2.7
1.8
0.9
2.3
2.7
2030

1,195
-0.4
986
82.5
68.9
58.8
93.0
63.6
8.0
80.0
65.7
53.3
90.9
62.0
8.0
84.8
71.9
63.9
94.7
65.1
7.9
62.9
63.0
62.8
77.9
65.1
5.6
5.5
0.9
1.0
6%
73%
18%
2%
2030

2040

1.62

81.8
87.1

20.3
24.0
4.6
0.2
2.1
17.2
35.0
54.9
28.0
9.5
33.9
17.3
2040

1.2
-0.6
-0.6
1.8
1.2
0.6
1.3
1.8
2040

1,141
-0.7
938
82.2
68.4
59.7
93.2
63.7
9.5
79.8
65.7
54.1
91.3
61.8
9.4
84.2
70.8
64.8
94.9
65.3
9.6
62.9
63.0
62.8
77.5
64.6
5.7
5.5
0.9
0.9
7%
71%
20%
3%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

24.5
43.5
76.2
121.3
53.5
52.4

23.4
33.2
65.7
114.5
42.4
41.9

26.5
51.0
82.3
128.7
62.9
61.2

1.3
25.5
27.5
25.9
30.0
28.7

2050

1.65

83.3
88.2

21.3
25.0
4.9
0.2
2.0
17.9
34.1
51.3
30.8
11.2
36.5
21.9
2050

1.1
-0.6
-0.6
1.7
1.1
0.6
1.3
1.7
2050

1,048
-0.7
871
83.1
67.5
59.5
93.7
62.9
9.3
81.0
65.5
53.9
92.0
61.0
9.1
84.8
69.3
64.7
95.1
64.5
9.4
62.9
63.0
62.8
78.4
63.8
5.7
5.5
0.8
0.8
6%
73%
18%
3%
2050

24.5
59.9
94.8
140.9
73.3
71.1

2060

1.67

84.6
89.4

22.3
25.9
5.2
0.3
2.0
18.0
34.2
50.7
31.3
12.9
41.2
25.4
2060

1.5
-0.2
-0.2
1.6
1.1
0.6
1.7
1.6
2060

1,007
-0.1
841
83.5
69.2
59.3
93.6
64.1
9.1
81.6
67.6
53.7
91.9
62.8
8.9
85.2
70.5
64.4
95.0
65.2
9.2
62.9
63.0
62.8
78.8
65.4
5.6
5.5
0.8
0.8
7%
74%
17%
3%
2060

22.6
61.7
97.3
143.6
75.6
73.5

2070

1.68

85.9
90.4

23.2
26.8
5.2
0.3
1.9
17.8
33.9
51.8
30.4
13.8
45.4
26.7
2070

1.3
-0.2
-0.2
1.5
1.0
0.5
1.6
1.5
2070

1,002
-0.1
832
83.0
69.9
59.5
93.5
64.6
9.4
81.0
68.4
53.9
91.8
63.2
9.4
84.8
71.3
64.7
95.0
65.7
9.4
62.9
63.0
62.8
78.3
66.1
5.7
5.5
0.8
0.8
7%
72%
19%
3%
2070

24.7
58.8
93.2
140.4
72.4
70.6

209 

25. SLOVAKIA 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

9.7
7.8

6.5
6.1
3.9
0.1
-0.7
-2.5
-11.3
-12.9
15.4
11.3
25.7
23.8
AVG 19-70

1.3
-0.7
-0.7
2.1
1.3
0.8
1.6
2.0
Ch 19-70

-1,073
0.5
-875
-1.6
-4.4
1.9
-0.4
-0.9
-1.2
-2.7
-4.2
2.9
-1.3
-3.9
-1.0
-0.8
-4.9
0.9
0.4
1.7
-1.8
0.5
0.7
0.3
-2.3
-4.8
1.1
1.1
-0.8
-0.9
1.0
-3.8
2.5
0.2
Ch 19-70

2019

1.56

74.4
81.2

15.6
19.6
3.4
0.1
5.5
20.6
44.4
63.1
16.3
3.3
20.4
5.3
2019

2.3
0.9
0.4
1.9
0.9
1.0
2.2
1.3
2019

3,441
-0.7
2,683
78.0
68.2
50.4
86.6
60.5
7.0
71.3
61.1
37.4
79.6
57.7
5.4
84.6
75.5
62.9
93.2
63.5
9.0
61.7
62.0
61.4
73.6
64.4
5.6
5.5
2.5
2.6
5%
77%
16%
1%
2019

2030

1.59

76.5
82.9

17.0
20.8
4.5
0.1
5.4
20.2
40.4
58.7
21.1
5.0
23.5
8.5
2030

1.7
-0.8
-0.8
2.5
1.6
0.9
1.9
2.5
2030

3,193
-0.5
2,484
77.8
66.1
51.9
86.1
62.4
5.6
71.0
59.2
39.9
79.3
58.2
4.7
84.4
72.9
63.3
92.7
66.7
6.6
62.2
62.7
61.7
71.8
61.0
7.8
7.7
2.3
2.3
5%
76%
18%
1%
2030

2040

1.61

78.6
84.6

18.4
22.1
5.0
0.1
5.3
18.4
35.7
57.0
24.6
7.7
31.4
13.5
2040

1.2
-1.0
-1.0
2.2
1.4
0.8
1.5
2.2
2040

3,025
-0.9
2,273
75.1
63.2
52.3
86.0
58.5
6.5
67.7
56.1
40.2
78.4
53.2
5.1
82.3
70.2
63.7
93.2
63.9
8.1
62.2
62.7
61.7
69.7
58.7
7.3
7.1
2.1
2.1
6%
71%
21%
2%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

22.3
35.9
70.3
133.8
48.5
47.8

20.9
25.9
58.5
112.1
33.6
33.1

27.5
43.1
75.4
146.8
59.9
58.8

3.1
37.2
40.6
62.3
53.1
52.1

2050

1.63

80.6
86.2

19.7
23.4
5.4
0.1
5.1
18.1
33.4
52.3
29.6
9.0
30.4
17.2
2050

1.0
-1.1
-1.1
2.0
1.3
0.7
1.3
2.1
2050

2,688
-1.3
2,025
75.3
60.0
52.5
86.1
57.6
5.9
67.3
52.9
40.4
77.9
51.8
4.5
83.0
67.0
63.9
93.9
63.2
7.3
62.2
62.7
61.7
70.3
56.1
6.7
6.6
1.9
1.9
6%
72%
20%
2%
2050

27.0
56.5
91.1
165.8
78.1
76.3

2060

1.65

82.4
87.6

21.0
24.6
6.3
0.1
4.9
18.3
33.3
49.1
32.6
12.2
37.4
24.8
2060

1.3
-0.5
-0.5
1.8
1.2
0.6
1.7
1.8
2060

2,427
-0.6
1,863
76.8
61.3
52.2
86.4
58.3
5.6
68.9
54.3
40.2
78.5
52.3
4.3
84.2
68.1
63.6
93.8
64.1
7.1
62.2
62.7
61.7
71.6
57.3
6.7
6.5
1.7
1.8
6%
75%
17%
2%
2060

22.4
66.3
103.6
178.2
90.4
88.5

2070

1.67

84.1
89.0

22.1
25.7
7.4
0.2
4.7
18.1
33.1
50.2
31.7
14.6
46.1
29.1
2070

1.2
-0.4
-0.4
1.5
1.0
0.5
1.7
1.5
2070

2,367
-0.2
1,809
76.4
63.8
52.3
86.2
59.6
5.8
68.6
56.9
40.3
78.3
53.8
4.4
83.9
70.5
63.7
93.6
65.3
7.3
62.2
62.7
61.7
71.3
59.6
6.7
6.6
1.7
1.7
6%
73%
19%
2%
2070

24.1
63.1
99.1
174.3
86.7
85.2

210 

26. FINLAND 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.2

6.6
5.6

4.6
4.5
-4.4
-0.1
-0.5
-4.7
-4.5
-5.3
10.0
7.8
16.5
16.2
AVG 19-70

1.2
-0.2
-0.3
1.5
0.9
0.5
1.4
1.4
Ch 19-70

-546
-0.2
-377
2.8
4.0
3.9
0.2
9.9
13.3
3.1
4.9
4.7
0.7
9.2
13.9
2.4
3.0
3.2
-0.3
10.7
12.4
3.4
3.5
3.3
2.6
3.8
0.1
-0.1
-0.4
-0.3
-0.8
-6.4
3.6
3.7
Ch 19-70

2019

1.35

79.5
84.8

18.9
22.3
17.6
0.3
5.5
21.2
37.7
56.7
22.1
5.6
25.2
9.8
2019

1.2
0.6
0.3
0.8
0.4
0.5
1.0
0.6
2019

3,131
-0.3
2,572
82.2
69.2
70.7
87.6
71.5
11.5
80.1
66.2
67.9
84.8
72.1
8.2
84.2
72.2
73.3
90.3
70.8
15.1
63.7
63.9
63.5
77.1
65.1
6.1
6.0
2.4
2.5
8%
70%
20%
3%
2019

2030

1.38

80.9
86.0

19.8
23.3
11.3
0.2
5.5
18.8
37.8
55.3
25.9
8.4
32.5
15.2
2030

1.3
0.0
0.0
1.3
0.9
0.4
1.4
1.3
2030

3,053
-0.1
2,520
82.6
69.8
74.5
87.8
69.6
11.5
80.5
66.9
72.6
85.2
70.0
8.3
84.5
72.7
76.3
90.4
69.1
15.0
64.4
64.7
64.1
77.6
65.7
6.0
5.9
2.4
2.4
9%
71%
17%
3%
2030

2040

1.42

82.3
87.3

20.8
24.2
11.5
0.2
5.4
17.4
38.0
55.7
27.0
10.4
38.4
18.6
2040

1.4
-0.2
-0.2
1.5
1.0
0.5
1.6
1.6
2040

3,018
-0.4
2,523
83.6
71.9
74.7
87.7
74.8
13.3
81.8
69.3
72.8
85.4
74.6
10.1
85.4
74.4
76.6
90.0
75.0
16.7
65.1
65.4
64.8
78.4
67.6
6.2
6.1
2.4
2.4
7%
70%
20%
3%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

21.2
46.8
80.7
125.8
57.1
55.4

22.8
48.4
79.6
121.6
58.3
56.4

23.3
38.9
76.4
121.4
47.1
45.6

2.7
23.6
18.3
6.1
23.8
20.4

2050

1.46

83.7
88.4

21.7
25.1
12.2
0.2
5.3
17.5
36.1
54.2
28.3
11.2
39.4
20.6
2050

1.2
-0.4
-0.4
1.5
1.0
0.5
1.4
1.5
2050

2,863
-0.6
2,412
84.2
71.8
74.6
87.9
77.7
16.9
82.7
69.6
72.6
85.7
78.4
13.2
85.7
74.0
76.4
90.1
76.9
20.8
65.8
66.1
65.5
79.1
67.5
6.2
6.0
2.3
2.4
7%
67%
22%
5%
2050

24.6
52.3
84.6
122.9
61.3
58.5

2060

1.50

85.0
89.4

22.7
26.0
12.7
0.2
5.1
16.9
34.9
52.5
30.6
11.6
38.0
22.1
2060

1.2
-0.4
-0.4
1.5
1.0
0.5
1.4
1.5
2060

2,701
-0.6
2,288
84.7
72.0
74.6
88.0
79.6
20.7
83.2
69.9
72.6
85.8
80.0
17.5
86.2
74.0
76.4
90.0
79.3
23.9
66.4
66.7
66.1
79.5
67.7
6.1
5.9
2.1
2.3
7%
66%
21%
6%
2060

24.2
58.2
90.5
125.3
66.8
62.8

2070

1.53

86.1
90.4

23.5
26.8
13.2
0.3
5.0
16.5
33.3
51.4
32.1
13.4
41.7
26.1
2070

1.2
-0.3
-0.3
1.5
1.0
0.5
1.4
1.5
2070

2,585
-0.5
2,196
85.0
73.2
74.6
87.8
81.4
24.8
83.2
71.1
72.6
85.5
81.3
22.2
86.6
75.3
76.5
90.0
81.5
27.5
67.1
67.4
66.8
79.7
68.9
6.2
5.9
2.1
2.2
7%
63%
23%
7%
2070

26.0
62.5
94.7
127.5
70.9
66.0

211 

27. SWEDEN 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

5.4
5.6

4.0
4.6
-36.4
-0.4
2.8
-2.4
-4.3
-4.0
6.3
5.5
14.6
11.0
AVG 19-70

1.8
0.4
0.4
1.4
0.9
0.5
1.3
1.3
Ch 19-70

1,075
-0.5
921
-0.3
-0.6
3.6
0.5
-2.8
-0.4
0.4
0.1
5.2
1.3
-3.3
-1.3
-0.9
-1.3
2.2
-0.3
-2.5
0.2
0.1
0.0
0.1
0.8
0.4
-1.2
-1.2
0.9
1.0
1.0
-2.5
1.4
0.0
Ch 19-70

2019

1.71

81.4
84.7

19.7
22.0
66.7
0.6
10.3
23.3
39.6
56.8
20.0
5.2
25.8
9.1
2019

1.8
1.1
0.8
1.0
0.5
0.5
0.8
0.7
2019

5,833
0.7
5,094
87.3
76.2
71.5
91.2
81.7
17.8
84.8
73.2
68.7
88.7
79.1
14.8
89.8
79.1
74.1
93.7
84.3
20.9
65.0
65.6
64.5
82.1
71.8
5.9
5.8
4.8
5.0
7%
70%
18%
4%
2019

2030

1.75

82.5
85.9

20.4
22.9
52.1
0.5
11.1
22.8
37.7
55.7
21.4
7.2
33.7
13.0
2030

2.2
1.1
1.1
1.1
0.8
0.3
1.6
1.0
2030

6,205
0.4
5,414
87.2
76.6
75.1
91.7
79.4
17.8
85.0
73.7
73.9
89.7
76.1
13.4
89.4
79.4
76.3
93.6
82.6
22.3
65.1
65.6
64.6
83.1
73.1
4.7
4.6
5.2
5.4
9%
69%
19%
4%
2030

2040

1.78

83.7
87.1

21.3
23.9
45.5
0.4
11.7
21.8
38.1
55.4
22.8
7.7
33.8
13.9
2040

1.9
0.4
0.4
1.5
1.0
0.5
1.4
1.5
2040

6,489
0.4
5,664
87.3
76.1
75.2
91.7
78.9
17.5
85.2
73.3
74.0
89.9
75.3
13.6
89.3
78.6
76.4
93.3
82.4
21.4
65.1
65.6
64.6
83.2
72.6
4.7
4.6
5.4
5.6
8%
70%
18%
4%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

21.3
38.4
79.4
107.9
42.3
40.8

20.2
35.2
76.2
106.2
38.7
37.2

20.3
41.2
80.7
108.9
45.5
43.8

2.3
14.6
13.2
13.1
17.2
16.5

2050

1.78

84.8
88.2

22.2
24.8
39.8
0.3
12.3
21.8
36.5
54.7
23.5
8.8
37.2
16.0
2050

1.7
0.2
0.2
1.5
1.0
0.5
1.3
1.5
2050

6,716
0.2
5,850
87.1
76.2
75.2
91.8
78.9
17.5
85.1
73.6
74.0
90.0
75.7
13.4
89.0
78.6
76.4
93.4
81.9
21.5
65.1
65.6
64.6
83.0
72.7
4.7
4.6
5.6
5.8
8%
68%
20%
4%
2050

22.8
43.0
82.8
112.1
47.9
46.2

2060

1.78

85.8
89.3

23.0
25.7
35.1
0.3
12.7
21.5
36.1
52.9
25.6
9.5
36.9
17.9
2060

1.7
0.2
0.2
1.5
1.0
0.5
1.4
1.5
2060

6,730
0.1
5,877
87.3
74.8
75.2
91.8
78.9
17.3
85.4
72.4
74.0
90.1
75.8
13.4
89.1
77.2
76.3
93.4
81.7
21.2
65.1
65.6
64.6
83.2
71.4
4.7
4.6
5.6
5.9
8%
69%
18%
4%
2060

21.0
48.4
89.1
117.5
53.6
51.3

2070

1.78

86.8
90.3

23.7
26.6
30.3
0.2
13.1
20.9
35.3
52.8
26.3
10.6
40.4
20.1
2070

1.7
0.1
0.1
1.5
1.0
0.5
1.4
1.5
2070

6,908
0.1
6,015
87.1
75.6
75.2
91.7
78.9
17.4
85.1
73.4
74.0
90.0
75.9
13.5
88.9
77.8
76.3
93.3
81.8
21.2
65.1
65.6
64.6
83.0
72.2
4.7
4.6
5.7
6.0
8%
68%
20%
4%
2070

22.5
49.8
89.4
119.3
55.9
53.7

212 

28. NORWAY 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

5.5
5.7

4.1
4.7
-1.9
-0.1
1.4
-4.3
-5.4
-6.1
10.4
6.7
14.7
13.4
AVG 19-70

1.7
0.2
0.2
1.5
0.9
0.5
1.2
1.5
Ch 19-70

398
-0.6
294
-0.9
-3.1
1.6
0.0
-3.6
-0.8
0.3
-2.0
1.7
1.7
-2.9
-1.0
-2.1
-4.2
1.5
-1.6
-4.5
-0.9
0.0
0.0
0.0
-0.7
-2.9
-0.2
-0.2
0.3
0.3
-0.6
-2.2
1.9
0.9
Ch 19-70

2019

1.53

81.4
84.6

19.7
21.9
25.3
0.5
5.3
23.4
40.9
59.2
17.4
4.3
24.5
7.2
2019

1.8
1.7
1.7
0.1
-0.2
0.3
1.1
0.1
2019

3,164
0.6
2,599
82.1
73.1
70.7
86.3
73.9
19.0
79.1
69.6
69.1
83.5
69.4
14.9
85.0
76.4
72.2
89.0
78.3
23.2
65.4
66.0
64.7
79.4
70.7
3.3
3.2
2.5
2.6
9%
70%
18%
4%
2019

2030

1.55

82.5
85.9

20.5
22.9
27.2
0.5
5.8
21.3
39.4
58.1
20.6
6.2
30.1
10.6
2030

1.6
0.5
0.2
1.4
1.0
0.4
1.0
1.2
2030

3,360
0.5
2,737
81.5
72.2
72.2
86.1
71.5
19.9
78.9
69.1
70.7
84.0
66.8
15.1
83.9
75.2
73.7
88.1
76.0
24.6
65.4
66.0
64.7
78.9
69.9
3.2
3.1
2.6
2.8
9%
69%
19%
4%
2030

2040

1.58

83.7
87.1

21.4
23.9
25.9
0.4
6.1
20.4
39.3
56.4
23.3
7.6
32.7
13.5
2040

1.8
0.2
0.2
1.5
1.0
0.5
1.3
1.5
2040

3,444
0.2
2,809
81.6
71.2
72.4
86.2
70.0
18.5
79.3
68.4
71.0
84.9
64.3
14.0
83.7
73.8
73.8
87.5
75.4
23.1
65.4
66.0
64.7
79.0
69.0
3.1
3.0
2.7
2.8
8%
71%
17%
4%
2040

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

21.7
35.4
72.1
109.0
40.4
38.7

20.0
29.4
69.0
104.6
33.0
31.7

20.5
41.3
77.4
114.7
47.6
45.5

3.2
23.0
19.6
23.7
28.6
27.0

2050

1.60

84.8
88.2

22.2
24.8
25.2
0.4
6.4
20.0
37.7
55.5
24.5
9.1
37.1
16.4
2050

1.7
0.2
0.2
1.5
1.0
0.5
1.4
1.5
2050

3,532
0.2
2,874
81.4
71.4
72.3
86.3
70.3
18.6
79.5
69.0
70.8
85.2
66.0
13.8
83.1
73.7
73.7
87.4
74.2
23.3
65.4
66.0
64.7
78.9
69.2
3.1
3.0
2.8
2.9
8%
69%
19%
4%
2050

22.5
44.1
80.1
118.7
51.4
49.3

2060

1.62

85.9
89.3

23.0
25.7
24.4
0.4
6.6
19.5
36.6
54.2
26.3
10.0
37.8
18.4
2060

1.6
0.0
0.0
1.5
1.0
0.5
1.3
1.5
2060

3,554
0.0
2,888
81.3
70.2
72.3
86.3
70.2
18.5
79.5
67.9
70.8
85.2
66.4
14.1
82.9
72.5
73.7
87.3
73.8
22.7
65.4
66.0
64.7
78.7
68.1
3.1
3.0
2.8
2.9
8%
68%
19%
5%
2060

22.7
48.5
84.4
123.2
56.6
54.0

2070

1.65

86.9
90.3

23.8
26.6
23.4
0.3
6.7
19.2
35.5
53.0
27.8
10.9
39.3
20.6
2070

1.5
0.0
0.0
1.5
1.0
0.5
1.3
1.5
2070

3,562
0.0
2,893
81.2
70.0
72.3
86.3
70.3
18.2
79.4
67.6
70.8
85.2
66.5
13.9
82.9
72.2
73.7
87.4
73.9
22.3
65.4
66.0
64.7
78.7
67.9
3.1
3.0
2.8
2.9
8%
68%
20%
5%
2070

23.3
52.4
88.6
128.3
61.6
58.7

213 

29. EUROPEAN UNION 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

7.4
6.1

5.1
4.8
-280.7
-0.1
-23.2
-1.9
-6.6
-8.0
9.9
7.3
14.7
15.8
AVG 19-70

1.3
-0.2
-0.2
1.6
1.0
0.5
1.4
1.5
Ch 19-70

-47,860
0.0
-32,100
2.5
1.7
1.7
0.8
9.6
10.1
4.4
3.9
2.2
2.2
12.9
11.0
0.5
-0.7
1.4
-0.7
5.8
8.8
1.8
1.8
1.9
3.1
2.4
-1.0
-1.1
-28.2
-23.2
0.4
-6.0
2.5
3.1
Ch 19-70

2019

1.52

78.7
84.2

18.4
22.0
1317.5
0.3
447.2
20.3
40.4
59.3
20.4
5.9
28.8
9.9
2019

1.6
0.7
0.6
1.0
0.6
0.3
1.4
0.9
2019

265,024
-0.2
207,378
78.2
67.8
60.3
85.9
62.3
9.8
72.2
61.7
55.8
80.2
55.4
7.1
84.2
74.0
64.5
91.6
69.7
12.9
63.8
64.0
63.5
73.1
63.4
6.6
6.5
193.7
198.3
6%
73%
18%
2%
2019

2030

1.55

80.4
85.6

19.5
23.0
960.0
0.2
449.1
19.2
37.2
56.5
24.4
7.3
29.9
12.9
2030

1.1
-0.3
-0.3
1.4
0.9
0.5
1.1
1.4
2030

253,521
-0.5
201,657
79.5
68.0
60.9
86.4
68.6
14.9
74.7
63.0
56.6
81.5
63.6
12.7
84.3
73.1
64.9
91.1
73.8
17.5
64.8
65.0
64.6
74.0
63.4
7.0
6.8
187.6
195.6
7%
69%
20%
4%
2030

2040

1.59

82.0
86.9

20.6
24.1
980.8
0.2
446.6
18.4
35.6
53.9
27.7
9.3
33.6
17.3
2040

1.4
-0.3
-0.4
1.8
1.2
0.6
1.5
1.8
2040

240,781
-0.5
192,693
80.0
67.8
61.8
86.5
69.8
16.5
75.6
63.3
57.6
81.9
65.6
14.6
84.4
72.4
65.7
90.8
74.2
18.5
65.1
65.3
64.9
75.0
63.7
6.3
6.1
180.6
189.8
7%
68%
20%
5%
2040

2050

1.61

83.5
88.2

21.6
25.1
1001.3
0.2
440.8
18.5
34.6
52.0
29.6
11.4
38.6
21.9
2050

1.4
-0.3
-0.4
1.8
1.1
0.6
1.6
1.7
2050

229,065
-0.4
184,197
80.4
68.3
61.9
86.7
70.3
17.4
76.1
64.0
57.9
82.3
66.5
15.6
84.6
72.6
65.8
91.0
74.2
19.3
65.3
65.5
65.1
75.9
64.6
5.6
5.4
173.9
183.2
6%
68%
20%
5%
2050

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

24.4
43.1
77.1
129.6
53.9
51.7

22.7
34.4
68.8
125.5
44.7
43.7

24.3
51.4
85.5
135.3
63.3
60.2

1.3
24.7
26.5
16.6
27.0
24.2

23.9
56.9
92.5
140.6
69.5
66.0

2060

1.63

84.8
89.3

22.6
25.9
1020.4
0.2
432.0
18.5
34.3
51.2
30.3
12.6
41.5
24.6
2060

1.4
-0.2
-0.2
1.7
1.1
0.6
1.7
1.6
2060

221,135
-0.2
178,519
80.7
68.9
61.8
86.7
71.6
18.4
76.6
64.8
57.7
82.4
68.1
16.5
84.7
72.9
65.6
90.9
75.0
20.4
65.5
65.7
65.3
76.3
65.2
5.5
5.4
168.6
178.0
7%
68%
20%
5%
2060

23.2
59.2
95.4
142.7
71.9
68.2

2070

1.65

86.1
90.3

23.5
26.8
1036.8
0.2
424.0
18.5
33.9
51.2
30.3
13.2
43.5
25.7
2070

1.4
-0.2
-0.2
1.6
1.0
0.6
1.6
1.6
2070

217,163
-0.2
175,278
80.7
69.5
62.0
86.7
71.9
19.9
76.6
65.6
58.0
82.4
68.4
18.1
84.7
73.4
65.9
90.9
75.5
21.7
65.6
65.8
65.4
76.2
65.8
5.5
5.4
165.6
175.1
7%
67%
20%
5%
2070

24.0
59.2
95.3
142.2
71.7
67.8

214 

30. EURO AREA 

Main demographic and macroeconomic assumptions

Demographic projections - EUROPOP2019 (EUROSTAT)

Ch 19-70

Fertility rate
Life expectancy at birth

Life expectancy at 65

Net migration (thousand)
Net migration as % of population
Population (million)

males
females

males
females

Young population (0-14) as % of total population
Prime-age population (25-54) as % of total population
Working-age population (20-64) as % of total population
Elderly population (65 and over) as % of total population
Very elderly population (80 and over) as % of total population
Very elderly population (80 and over) as % of elderly population
Very elderly population (80 and over) as % of working age population

Macroeconomic assumptions*

Potential GDP (growth rate)
Employment (growth rate)
Labour input: hours worked (growth rate)
Labour productivity per hour (growth rate)

TFP (growth rate)
Capital deepening (contribution to labour productivity growth)

Potential GDP per capita (growth rate)
Potential GDP per worker (growth rate)

Labour force assumptions

Working-age population (20-64) (in thousands)
Population growth (working-age: 20-64)
Labour force 20-64 (thousands)
Participation rate (20-64)
Participation rate (20-74)

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - FEMALES
Participation rate (20-74) - FEMALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Participation rate (20-64) - MALES
Participation rate (20-74) - MALES

                                                             youngest (20-24)
                                                             prime-age (25-54)
                                                             older (55-64)
                                                             very old (65-74)

Average effective exit age (TOTAL) (1)

Men
Women

Employment rate (20-64)
Employment rate (20-74)
Unemployment rate (20-64)
Unemployment rate (20-74)
Employment (20-64) (in millions)
Employment (20-74) (in millions)

                                                             share of youngest (20-24)
                                                             share of prime-age (25-54)
                                                             share of older (55-64)
                                                             share of very old (65-74)

Dependency ratios

0.1

6.6
5.6

4.6
4.5
-405.4
-0.1
-9.2
-1.7
-6.0
-7.7
9.4
6.8
13.2
14.9
AVG 19-70

1.3
-0.1
-0.1
1.4
0.9
0.5
1.3
1.4
Ch 19-70

-31,044
-0.1
-20,003
2.5
1.9
1.5
0.6
10.0
11.5
4.6
4.2
1.7
2.2
13.6
12.8
0.4
-0.5
1.3
-1.1
6.1
10.0
2.1
2.1
2.1
3.7
3.0
-1.7
-1.7
-16.2
-11.6
0.4
-5.9
2.1
3.5
Ch 19-70

2019

1.51

79.9
85.0

19.1
22.6
1249.9
0.4
342.4
20.3
39.9
58.9
20.8
6.3
30.1
10.6
2019

1.4
0.8
0.7
0.7
0.5
0.2
1.1
0.6
2019

201,743
0.0
158,252
78.4
68.0
61.0
85.8
63.7
9.5
72.8
62.3
57.2
80.3
57.5
6.8
84.1
73.8
64.5
91.4
70.2
12.5
63.9
64.0
63.9
72.6
63.0
7.5
7.4
146.4
149.8
6%
73%
19%
2%
2019

2030

1.55

81.4
86.3

20.1
23.5
870.8
0.3
346.6
19.1
36.6
56.0
24.9
7.6
30.5
13.5
2030

0.9
-0.2
-0.2
1.1
0.7
0.4
0.9
1.1
2030

194,220
-0.5
154,819
79.7
68.0
61.4
86.2
69.9
15.4
75.4
63.5
57.8
81.8
65.5
13.4
84.0
72.5
64.9
90.6
74.4
17.6
65.1
65.2
65.1
73.6
62.9
7.7
7.5
142.9
149.4
7%
68%
21%
4%
2030

2040

1.58

82.8
87.5

21.1
24.5
861.9
0.2
347.0
18.4
35.4
53.3
28.3
9.5
33.6
17.9
2040

1.4
-0.2
-0.3
1.7
1.1
0.6
1.5
1.7
2040

184,817
-0.4
148,869
80.5
68.1
62.4
86.2
72.1
16.9
76.7
64.3
58.9
82.1
69.0
15.4
84.3
72.0
65.7
90.2
75.3
18.5
65.5
65.5
65.4
75.1
63.6
6.8
6.6
138.8
146.2
7%
68%
21%
5%
2040

2050

1.60

84.1
88.6

22.0
25.4
855.9
0.2
344.2
18.5
34.6
51.7
29.8
11.9
40.1
23.1
2050

1.4
-0.2
-0.2
1.6
1.1
0.6
1.5
1.6
2050

177,979
-0.3
143,826
80.8
69.0
62.4
86.4
72.5
18.3
77.2
65.3
58.9
82.4
69.8
17.0
84.4
72.7
65.7
90.3
75.2
19.8
65.7
65.7
65.7
76.1
65.1
5.9
5.7
135.4
142.8
7%
68%
20%
5%
2050

Share of older population (55-64) (2)
Old-age dependency ratio 20-64 (3)
Total dependency ratio (4)
Total economic dependency ratio  (5)
Economic old-age dependency ratio (20-64) (6)
Economic old-age dependency ratio (20-74) (7)
LEGENDA:
* The potential GDP and its components are used to estimate the rate of potential output growth, net of normal cyclical variations
(1) Based on the calculation of the average probability of labour force entry and exit observed. The table reports the value for 2020 instead of 2019.
(2) Share of older population = Population aged 55 to 64 as a %  of the population aged 20-64
(3) Old-age dependency ratio = Population aged 65 and over as a %  of the population aged 20-64
(4) Total dependency ratio  = Population under 20 and over 64 as a %  of the population aged 20-64
(5) Total economic dependency ratio = Total population less employed as a %  of the employed population 20-74
(6) Economic old-age dependency ratio (20-64) = Inactive population aged 65+ as a %  of the employed population 20-64
(7) Economic old-age dependency ratio (20-74) = Inactive population aged 65+ as a %  of the employed population 20-74
NB: ":" = missing data

24.9
44.4
78.5
132.0
55.7
53.3

23.1
35.3
69.7
128.6
46.3
45.3

23.8
53.2
87.8
137.4
65.4
62.1

0.8
23.6
25.5
12.5
24.6
21.6

23.5
57.6
93.4
141.0
70.1
66.4

2060

1.63

85.3
89.7

22.9
26.3
850.1
0.3
338.2
18.6
34.3
51.3
30.1
12.8
42.4
24.9
2060

1.4
-0.1
-0.1
1.6
1.0
0.6
1.6
1.6
2060

173,612
-0.1
140,505
80.9
69.5
62.2
86.4
73.3
19.5
77.4
66.0
58.6
82.4
70.8
18.0
84.4
73.0
65.5
90.3
75.8
21.1
65.9
65.9
65.8
76.2
65.6
5.8
5.6
132.3
139.9
7%
68%
20%
5%
2060

23.3
58.6
94.8
141.8
71.1
67.2

2070

1.65

86.5
90.6

23.7
27.1
844.5
0.3
333.1
18.6
33.9
51.2
30.2
13.1
43.3
25.5
2070

1.4
-0.1
-0.1
1.6
1.0
0.5
1.5
1.6
2070

170,699
-0.2
138,249
81.0
69.9
62.4
86.4
73.7
21.0
77.4
66.5
58.9
82.5
71.1
19.5
84.5
73.3
65.8
90.3
76.3
22.4
66.0
66.1
66.0
76.3
66.0
5.8
5.6
130.2
138.2
7%
67%
21%
6%
2070

23.8
58.9
95.2
141.1
71.0
66.9

215 

Part IV 
Resources 

 
 
1. 

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in 
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labour 

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Clements B., Coady D., and Gupta S. (2012), ‘The 
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Comas-Herrera  A.,  Wittenberg  R.  and  Pickard, 
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218 

Cremer  H.  and  Pestieau  P.  (2009),  ‘Long-term 
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Dybczak K. and Przywara B. (2010),  ‘The role of 
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2019-based 
projections 
(EUROPOP2019’),  Technical  Note,  ESTAT/F-
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Eurostat  (2017),  ‘Summary  methodology  of  the 
2015-based  population  projections’,  Eurostat 
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Eurostat  (2017),  ‘Methodology  for  the  migration 
assumptions 
the  2015-based  population 
projections’,  Eurostat  Technical  Note,  ESTAT/F-
2/GL. 

in 

Eurostat  (2004),  ‘Classification  of  funded  pension 
schemes  and  impact  on  government  finance’, 
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