COMMISSION STAFF WORKING DOCUMENT IMPACT ASSESSMENT REPORT Accompanying the document Proposal for a Council Recommendation on individual learning accounts
Date: 2021-12-10
Institutions: Directorate-General for Employment, Social Affairs and Inclusion, European Commission
Subjects: EU employment policy, access to education, adult education, continuing education, continuing vocational training, economic recovery, employment aid, labour force, training leave, updating of skills

EN 

  EN 

   EUROPEAN COMMISSION  Brussels, 10.12.2021  SWD(2021) 369 final  COMMISSION STAFF WORKING DOCUMENT  IMPACT ASSESSMENT REPORT  Accompanying the document Proposal for a Council Recommendation on individual learning accounts {COM(2021) 773 final} - {SEC(2021) 417 final} - {SWD(2021) 368 final} - {SWD(2021) 370 final}  
 
 
List of acronyms .................................................................................................... 0 
1. INTRODUCTION: POLITICAL AND LEGAL CONTEXT ................................................. 1 
2. PROBLEM DEFINITION ....................................................................................... 3 
2.1 What are the problems? ................................................................................ 3 
2.2 What are the problem drivers? ....................................................................... 4 
2.3 What are the consequences? ........................................................................13 
2.4 How will the problem evolve? .......................................................................15 
3. WHY SHOULD THE EU ACT?...............................................................................15 
3.1 Legal basis .................................................................................................15 
3.2 Subsidiarity: Necessity and added value of EU action .......................................16 
4. OBJECTIVES: WHAT IS TO BE ACHIEVED? .............................................................17 
4.1 General objectives ......................................................................................17 
4.2 Specific objectives .......................................................................................17 
5. WHAT ARE THE AVAILABLE POLICY MEASURES? ..................................................18 
5.1 What is the baseline from which options are assessed? ....................................18 
5.2 Measures discarded at an early stage ............................................................19 
5.3 Description of the policy measures ................................................................20 
5.4 Description of the alternative policy packages .................................................27 
6. WHAT ARE THE IMPACTS OF THE POLICY PACKAGES? ............................................28 
6.1 Analysing the main impacts ..........................................................................28 
6.2 The choice of legal instrument ......................................................................37 
7. HOW DO THE POLICY PACKAGES COMPARE? ........................................................38 
8. PREFERRED POLICY PACKAGE ...........................................................................42 
9. HOW WILL ACTUAL IMPACTS BE MONITORED AND EVALUATED? .............................43 
ANNEX 1: PROCEDURAL INFORMATION ...............................................................44 
1. Lead DG, Decide Planning/CWP references .......................................................44 
2. Organisation and timing ................................................................................44 
3. Consultation of the RSB .................................................................................44 
4. Evidence, sources and quality .........................................................................47 
ANNEX 2: STAKEHOLDER CONSULTATIONS .........................................................47 
1. Introduction .................................................................................................48 
2. Consultation strategy ....................................................................................48 
3. Inception Impact Assessment .........................................................................49 
4. Targeted consultations ..................................................................................50 
5. Public Consultation ........................................................................................51 
ANNEX 3: WHO IS AFFECTED AND HOW? ............................................................56 
1. Practical implications of the initiative ...............................................................56 
2. Summary of costs and benefits .......................................................................56 
3. Summary of the SME test ..............................................................................62 
ANNEX 4: ANALYTICAL METHODS .......................................................................65 
1. Quantification of impacts ...............................................................................65 
2. Qualitative methods ......................................................................................66 
3. Constraints ..................................................................................................67 
ANNEX 5: OVERVIEW OF THE INTERVENTION LOGIC ............................................69 
ANNEX 6: ADULT LEARNING: MEASUREMENT, KEY CHARACTERISTICS AND 

PARTICIPATION LEVELS ..............................................................................70 
1. Measurement ...............................................................................................70 
2. Key characteristics ........................................................................................71 
3. Participation levels and differences between groups ..........................................72 

 
 
 
4. Recommendations to increase participation in adult learning in the European 

Semester ..................................................................................................86 
ANNEX 7: ADDITIONAL EVIDENCE ON THE PROBLEM DRIVERS ..............................87 
1. Driver 1: Insufficient financial support .............................................................87 
2. Driver 2:  Limited incentives and motivation of individuals .................................97 
ANNEX 8: BASELINE SCENARIO ....................................................................... 111 
1. The existing EU instruments ......................................................................... 111 
2. Baseline scenario by Member State ............................................................... 119 
3. Estimating adult learning participation in 2030 under the baseline scenario ........ 140 
ANNEX 9: EVIDENCE ON THE DISCARDED POLICY MEASURES ............................. 141 
1. Tax incentives for individuals ........................................................................ 141 
2. Subsidised loans or savings schemes for adult learning ................................... 143 
ANNEX 10: EVIDENCE ON THE BENEFITS OF ADULT LEARNING ............................ 145 
1. Returns to individuals .................................................................................. 145 
2. Returns to enterprises ................................................................................. 153 
3. Returns to society ....................................................................................... 155 
4. Selection of coefficients for impact estimates ................................................. 157 
ANNEX 11: EVIDENCE ON INDIVIDUAL TRAINING ENTITLEMENTS ........................ 158 
1. Effectiveness of training entitlements ............................................................ 159 
2. Evidence on the costs of providing training entitlements .................................. 167 
ANNEX 12: QUANTIFYING IMPACTS .................................................................. 172 
A. IMPACT ON ADULT LEARNING PARTICIPATION ............................................... 173 
B. COST-BENEFIT ANALYSIS OF SHORT TO MEDIUM TERM IMPACTS .................... 185 
C. LONG TERM AND INDIRECT IMPACTS ............................................................ 216 
ANNEX 13: CASE STUDIES .............................................................................. 227 
1. Austria: Education Savings ........................................................................... 228 
2. Estonia: Unemployment Insurance Fund ........................................................ 229 
3. Greece: Targeted training voucher schemes ................................................... 230 
4. Italy: Regional individual training entitlements ............................................... 231 
5. Netherlands: The STAP - from income tax deduction towards individual training 

entitlements ............................................................................................ 233 
6. Singapore: SkillsFuture Credit - using personal accounts for a universal yet targeted 
provision of training entitlements ............................................................... 238 
7. UK:  Individual Learning Accounts ................................................................. 240 
ANNEX 14: INDIVIDUAL LEARNING ACCOUNTS – THE FRENCH EXPERIENCE .......... 242 
1. Summary ................................................................................................... 242 
2. General Description, Key Features, and Achievements ..................................... 243 
3. Performance of the CPF ............................................................................... 250 
ANNEX 15: OPERATIONAL OBJECTIVES AND MONITORING .................................. 261 

 
 
List of acronyms 

Advisory Committee for Vocational Training 
Adult Education Survey 
Adult Learning 
Compte Personnel de Formation (French individual learning account) 
Counry-specific Recommendation (European Semester) 
Continuing vocational education and training 
Continuing Vocational Training Survey 

ACVT 
AES 
AL 
CPF 
CSR 
CVET 
CVTS 
DG EMPL  Directorate-General for Employment, Social Affairs and Inclusion 
DGVT 
EC 
EMCO  
EENEE 
EPSR 
EQAVET 
EQF 
ESF 
EU 
IA 
ICT 
ILA 
ILO 
ISCED 
ISCO 
JRC 
LFS 
OECD 
PC 
PES 
PIAAC 
SME 
TEU 
TFEU 
VET 

Directors General for Vocational Training 
European Commission 
Employment Committee 
European Expert Network on Economics of Education 
European Pillar of Social Rights 
European Quality Assurance in Vocational Education and Training 
European Qualifications Framework 
European Social Fund 
European Union 
Impact Assessment 
Information and Communication technologies 
Individual Learning Account 
International Labour Organisation 
International Standard Classification of Education 
International Standard Classification of Occupations 
Joint Research Centre 
Labour Force Survey 
Organisation for Economic Co-operation and Development 
Public Consultation 
Public Employment Services 
Programme for the International Assessment of Adult Competencies 
Small and medium-sized Enterprise  
Treaty on European Union 
Treaty on the Functioning of the European Union 
Vocational education and training 

0 

 
 
 
 
1. INTRODUCTION: POLITICAL AND LEGAL CONTEXT 

On 25 June 2021, the European Council welcomed the EU headline targets of the European 

 Leaders thereby supported the 
Pillar of Social Rights Action Plan and the Porto Declaration.
ambition that at least 60% of all adults should participate in training every year by 2030.2 
This contributes to making a reality the right to quality and inclusive education, training and 
life-long learning, as set out in the Charter of Fundamental Rights of the European Union 
(Article 14: Right to education) and principle 1 of the European Pillar of Social Rights on 

1

education, training and lifelong learning.
 It also acknowledges the importance of improving 
the opportunities for developing skills throughout working life to achieve the EU’s ambitions 
for  the  coming  decade:  a  swift  economic  recovery  from  the  COVID-19  pandemic  and  a 
successful digital and green transition.  

3

Notably, the EU will only achieve its digital transition if individuals have the skills to take 
advantage of emerging technologies and jobs. Equally, the green transition “can only succeed 
if the EU has the skilled workforce it needs to stay competitive”, as set out in the “Fit for 55 - 

 As labour 
delivering the EU's 2030 Climate Target on the way to climate neutrality” package.
markets are reshaped by these transitions, the existing support systems for developing skills 
need to evolve as well to allow all individuals to thrive in the labour markets of the future. 

4

This requires additional investments as well as the engagement of individuals. The European 
Council invited Member States to “explore possible models for public and private financing 
of  lifelong  learning  and  the  development  of  skills  on  an  individual  level”,  and  asked  the 

Commission  to  support  Member  States  in  these  efforts.

5

  The  revised  Employment 

6

Guidelines
  call  on  Member  States  to  strengthen  the  provisions  on  individual  training 
entitlements and ensure their transferability during professional transitions including, where 
appropriate, through individual learning accounts.  

Following up on the Mission Letter to Commissioner Schmit and the request to “explore the 

idea of individual learning accounts”

7

, the Commission’s Communication on the European 

1

2

 European Council Conclusions, 24-25 June 2021. This follows up on the Porto Declaration of 8 May 2021. 
 A discussion of translating this target at EU level into corresponding Member State-specific targets is ongoing 
within the Employment Committee. The EU level target builds on the objective from the European Skills 
Agenda of 1 July 2020 that 50% of EU adults should participate in learning every year by 2025, with 
differentiated adult learning targets for the low qualified and unemployed and a target of at least 70% of adults 
having at least basic digital skills. “Learning” and “training” are used interchangeably across these documents 
and throughout this IA. 
3
 Charter of Fundamental Rights of the European Union and European Pillar of Social Rights. 
 COM/2021/550, adopted on 17 July 2021. 
 Council  Conclusions of 8 June 2020 on reskilling and  upskilling as a basis  for increasing sustainability and 
employability, in the context of supporting economic recovery and social cohesion. 
6
 Council Decision (EU) 2020/1512 of 13 October 2020 on guidelines for the employment policies of the Member 
States. 
7
 Mission letter of Nicolas Schmit, Commissioner for Jobs and Social Rights, December 2019. 

5

4

1 

 
 
                                                 
8
 announced its intention to  explore individual learning accounts  as a tool to 
Skills  Agenda
support the up- and reskilling of working age adults, complementing other actions targeted at 

employers and education and training providers.

9

The European Parliament shares the views of the European Council, the Council of the EU 
and the Commision and called to ensure access to skills development and life-long training 

opportunities in its Resolution on a European Pillar of Social Rights

10

strong social Europe for Just Transitions.
points  out  the  potential of  individual  learning  accounts  as  a  step  towards  universal  lifelong 
learning entitlements and a funding mechanism key to the implementation of the Skills Agenda. 

 The Resolution on the European Skills Agenda

11

 and its Resolution on a 
12

The present initiative aims to put forward recommendations to Member States to close gaps 
in existing support systems for training and to increase individuals’ motivations and incentives 
to  take  up  this  support  and  participate  in  training.  Recognising  the  complex  reasons  why 
individuals  do  not  take  up  training  (Section  2.2),  the  initiative  aims  to  outline  measures  to 
integrate  financial/demand  side  with  non-financial/supply  side  support.  It  thereby  aims  to 
support  the  implementation  of  existing  EU  Recommendations  on  education  and  training, 
notably  the  Council  Recommendations  on  VET,  Upskilling  Pathways  and  the  validation  of 

non-formal and informal learning.
working training policies, but to complement them and help close the remaining gaps.  

 The initiative does not seek to replace any currently well-

13

This proposal is related to two other Commission initiatives in preparation. Firstly, the initiative 

14

on  Micro-credentials
complements  the  present  initiative  by  promoting  transparency  and 
recognition  of  short  training  programmes  and  therefore  incentivising  uptake  of  training. 
Jointly, both initiatives aim to empower individuals to seek training by addressing challenges 
on the supply side and demand side. Secondly, the present initiative is also complementary to 
the initiative on improving the working conditions in platform work. The lack of access to 

training  opportunities  is  an  important  challenge  for  platform  workers
,  and  the  present 
initiative considers concrete policy measures to support the training of individuals that do not 
depend on the current employer or employment status.  

15

8

9

 European Skills Agenda for sustainable competitiveness, social fairness and resilience of 1 July 2020. 
 See Annex 8.1 for an overview of related EU initiatives.
on a European Pillar of Social Rights. 
10

 European Parliament resolution of 19 January 2017 

 European Parliament resolution of 19 January 2017 on a European Pillar of Social Rights. 
 European Parliament resolution of 17 December 2020 on a strong social Europe for Just Transitions. 
  European  Parliament  resolution  of  11  February  2021  on  the  European  Skills  Agenda  for  sustainable 

11

12

10

competitiveness, social fairness and resilience. 
13

 Council Recommendation of 24 November 2020 on vocational education and training (VET); Council 
Recommendation of 19 December 2016 on Upskilling Pathways: New Opportunities for Adults; Council 
Recommendation of 20 December 2012 on the validation of informal and non-formal learning. See Annex 8.1 
for an overview of relevant existing EU initiatives. 
14

 Included alongside in the Commission Work Programme for 2021. Also see the Roadmap of the initiative. 
 C(2021) 1127 final. 

15

2 

 
 
  
 
                                                 
2. PROBLEM DEFINITION 

2.1 What are the problems? 

,  only  37.4%  of  adults  in  the  EU 
Not  enough  adults  participate  in  learning.  In  2016
participated  in  learning.  This  is  22.6  percentage  points  below  the  2030  target  of  60%  and 

16

17

participation levels fell also short of the 2020 EU-level target
 in 21 of 27 EU Member States. 
Progress in increasing participation in adult learning in the EU over the last decade has been 
limited and uneven across EU Member States. The COVID-19 crisis has had a negative impact 
on  training  participation  but  already  in  2019  20  Member  States  were  below  the  target.  The 
public and targeted consultations on this initiative showed a broad consensus by stakeholders 
that  participation  in  adult  learning  is  insufficient,  even  more  so  in  light  of  the  expected 
18

acceleration of the green and digital transitions.

In addition to being low, participation in adult learning is unequal and strongly depends 
on the labour market status, employment relationship, company size, educational attainment 
and the exposure of sectors or occupations to automation. To varying degrees, this problem is 
present in all Member States and all of them received country-specific recommendations on 

skills in the context of the European Semester in either 2019 or 2020.

19

Across  the  EU,  permanent  employees  have  higher  adult  learning  participation  rates 
(45%)  compared  to  other  adults  (29%).  This  pattern  can  be  observed  across  all  Member 
States, although with different sizes of the gap (Figure 1). 83% of the respondents in the public 
consultation agreed that a lack of support for workers with no or loose links to an employer is 

an obstacle to a higher training participation.

20

Lower shares of employees in SMEs (42%) and micro-enterprises (36%) participate in 
adult learning compared to those in large companies (55%). This pattern is visible across 
 of respondents see the lack of capacity 
all EU Member States. In the public consultation, 83%
by small, medium-sized and micro-enterprises to organise training for their employees as an 
obstacle. 

21

Adults  with  lower  educational  attainment  participate  less  in  learning,  and  participation 
rates are lower in sectors, occupations and types of employment with higher shares of lower 

16

 The most recent year with available data for the 12 months reference period. The EU benchmarking framework 

for adult learning includes formal and non-formal learning, see Annex 6.1 for details. 
17

 The Education and Training 2020 framework foresaw that at least 15% of adults should participate in learning 
(Council  conclusions  of  12  May  2009  on  a  strategic  framework  for  European  cooperation  in  education  and 
training). The reference period for EU benchmarking of adult learning has since been increased from 4 week to 
12 months, which explains the considerably higher 2030 target (see Annex 6 for details). 
18

 See Annex 2 for details on the open public and targeted consultations referenced throughout this document. 
 See Annex 6.3 for details on the inequalities in adult learning participation referenced throughout this Section, 

19

and Annex 6.4 for an overview of the country-specific recommendations on skills. 
20

 Respondents agreed “fully” or “somewhat”. A majority of respondents from all stakeholder groups agreed to 
this statement, ranging from 72% for businesses to 96% for trade unions. The main stakeholders’ categories for 
the  public  consultation  (PC)  data  are citizens  (78  respondents),  public  authorities  (26),  business 
organisations/businesses (46), trade unions (26) and NGOs/other respondents (40).  
21

 Ranging from 74% for businesses to 96% for public authorities. 

3 

 
 
  
 
  
                                                 
qualified  people.  However,  also  within  groups  of  similar  educational  attainment,  the 
employment status remains a significant determinant of participation.  

There are no pronounced gender differences in the participation of adults in learning, but 
men  are  somewhat  more  likely  than  women  to  report  learning  for  job-related  reasons  (see 
Annex 6.3). 

Adults  in  occupations  and  sectors  with  a  higher  risk  of  automation  participate  less  in 
learning. This means that workers in the sectors and occupations where employment shares 
are predicted to decrease the most until 2030 participate the least in learning. Individuals facing 
a  higher  risk  of  skills  obsolescence  and  unemployment  are  hence  less  equipped  for  these 
vulnerabilities in the near future. 

Figure 1: Participation in adult learning for permanent employees vs. other adults

22

 in 2016 

Source: Adult Education Survey 2016, ages 25-64.
Participation figures presented in this IA 
exclude  guided-on-the-job  training  to  match  the  definition  of  adult  learning  used  for  EU 
benchmarking. See Annex 6 for details on the definitions used and disaggregated figures. 

2.2 What are the problem drivers? 

The  reasons  behind  low  participation  in  adult  learning  in  the  Member  States  and  Member 

23

States’ policy  responses  to  the issue are heterogeneous.
 Yet,  there are two broad  problem 
drivers which can be observed across the EU: insufficient financial support for training, and 
insufficient incentives and motivation of individuals to take up training that go beyond a 
lack  of  financial  support.  This  Section  outlines  how  they  limit  progress  in  increasing 
participation rates of adults in learning and contribute to inequalities in participation between 
different groups of adults. Additionally, external drivers, outside of the scope of this initiative, 

22

 The category of “other adults” is a heterogeneous group that includes the self and temporarily employed, the 
unemployed and inactive, with the common feature that they receive less support for learning and participate less 
in learning (Annex 6). 
23

 For a synthesis of country reports on the adult learning systems in all Member States, see European Commission 

(2019), Adult Learning policy and provision in the Member States of the EU. 

4 

 
 
  
 
                                                 
amplify the incidence or the adverse consequences of low and unequal participation of adults 
in learning (Figure 2).  

Figure 2: Problem tree 

2.2.1 Internal Drivers24 
1. Insufficient financial support 

The costs of skills development are often concentrated on a specific actor, while the benefits 
(e.g. in the form of job progression, higher wages or productivity or a successful transition to 
new employment) only become apparent later and are typically shared among the individual, 

  This  results in  insufficient  investment  overall,  a  limited 
employers  and  society  at  large.
coverage  of  existing  support  schemes  in  terms  of  groups  of  adults  and  types  of  training, 
barriers to devoting time to training and the fragmentation of the existing support schemes. 

25

Insufficient investment levels in adult learning: According to the best available evidence, the 
overall level of investments (public and private) in adult learning in the EU is around 1.7% of 

26

GDP
  with  the  largest  share  contributed  by  employers  (1%  of  GDP),  followed  by  public 
authorities  (0.5%  of  GDP)  and  the  remaining  share  contributed  by  individuals  themselves 
(0.2% of GDP). The shares of overall investments in Member States range from 0.5% of GDP 
in RO to almost 2.5% of GDP in SE and FI. The public share of investments (0.5% of GDP) 

24

25

 See Annex 7 for more detailed evidence on the problem drivers introduced in this Section. 
 See for instance Münich and Psacharopoulus (2018): Education externalities: what they are and what we know, 

European Expert Network on Economics of Education (EENEE) Analytical report No. 34. 
26

 Annex 7.1 explains in more details how these estimates were made in the absence of coherent measurement of 

adult learning expenditure in national statistical systems. 

5 

 
 
 
 
                                                 
is relatively low when compared to the 4.7% of GDP that public authorities in the Member 

States  invest  on  average  in  education.
  While  there  has  been  rapid  progress  in  increasing 
educational attainments over the recent decade (notably by reducing early school leaving an 
increasing the share of adults with tertiary education), there has been no corresponding increase 
in the public commitment to support the continuing skills development of adults after they have 

27

left initial education.

 28

 Also 23 of 27 national adult learning experts who have been consulted 

for the purpose of this impact  assessment
Member State as insufficient. 

29

  consider the current  levels  of  financing  in  their 

There  is  a  positive  correlation  between  the  participation  in  adult  learning  and  the 
estimated available funding in relation to GDP. The six Member States that scored above 
the EU participation targets for 2020 (SE, FI, DK, NL, EE and LU) spend on average 2.0% of 
GDP  on  adult  learning,  compared  to  an  average  of  1.3%  of  GDP  for  the  other  21  Member 
States. Member States with higher levels of investments in adult learning also show smaller 
differences  in  participation  between  permanent  employees  and  other  adults  (Annex  7.1), 
indicating that sufficient investments also ensure a more equal access to learning for all adults.
This suggests that raising the level of participation in adult learning will also require increasing 
the level of investment in adult learning.  

Current  investments  in  adult  learning  are  low  also  when  compared  against  available 
estimates  of their social  and economic benefits. Most evidence in  the  academic literature 
suggests  that  the  benefits  of  additional  investments  (such  as  wage  and  productivity  gains) 
30  However, even where countries 
would outweigh their costs at the current participation levels.
have  earmarked  funds  for  adult  learning,  they  are  not  always  underpinned  by  sustainable 
financing sources, limiting their impact and leading to insufficient funding during recessions, 

when it is most needed.

31

Limited coverage of  existing support schemes in  terms of  the  groups  of  adults and types of 
training  that  are  supported:  Survey  data  and  the  public  consultation  point  to  the  costs  of 

training as a key barrier for individuals

32

 leading to low and unequal adult learning participation 

(Section 2.1). As currently employers sponsor 88% of job-related non-formal training
, adults 
who would like to learn more and are not in permanent employment report costs as a barrier 
more  often  than  those  in  permanent  employment  (31%  vs.  22%).  Among  employees,  those 

33

27

30

28

31

29

 As measured by the Eurostat variable gov_10a_exp. 
 European Commission (2020): Education and Training Monitor 2020. 
 See Annex 1 for a presentation of this network, which is one of the sources used throughout this IA. 
 See Annex 10 for a review of evidence on the benefits of adult learning. 
 Baiocco (2020), The state of play of evidence about the conditions under which individual-oriented instruments 
for incentivising adult participation in learning are effective. EENEE Ad-hoc Question nº4-2019; Brunello and 
Bertoni (2021), Adult learning during recessions in Europe. EENEE Policy Brief 1/2021. 
32

 25.5% of adults who wanted to learn more mentioned costs as a barrier in the 2016 AES, increasing to 32.2% 
for adults who did not participate during the past year. 89% of respondents to the PC agreed fully or somewhat 
that the direct costs of training (for course fees etc.) prevent individuals from accessing training. 
33

 Such training constitutes the majority of adult learning, see Annex 6 for details on the definition of AL. 

6 

 
 
 
 
                                                 
employed in SMEs receive less support for training than those in large companies
the pattern of inequality in participation levels documented in the previous Section.  

34

, mirroring 

Another dimension of coverage gaps concerns the types of training undertaken. Any current 
employer is unlikely to have an incentive to provide support for training that will help a worker 
to change company or sector. However, such training is important to prevent skills gaps in light 
of  the  labour  market  transformations  ahead.  This  results  in  a  risk  of  underinvestment  of 
35

employers  in  certain  types  of  trainings  and  in  particular  for  non-company-specific  skills

, 

including transversal skills which are increasing in importance on the labour market.
 Only in 
three  Member  States  (AT,  EE,  IE),  the  available  support  for  professional  transitions  was 
assessed  as  adequate  by  national  adult  learning  experts.  In  the  public  consultation,  88%  of 
respondents agreed that additional policy efforts are needed to support training in transversal 

36

skills, and 93% agreed with respect to digital skills.

37

Existing public support schemes in the Member States are often insufficient to close these 
coverage gaps. Income tax incentives for training exist in a majority of Member States, but 
require upfront investment by individuals and a sufficiently high income. Schemes providing 
individuals with training entitlements upfront such as training voucher schemes could help and 

exist in 21 Member States, however often with small target groups
 such as the unemployed 
and  hence  not  supporting  a  broader  range  of  “preventive”  trainings  for  transversal  skills  or 
professional transitions.  

38

Barriers to devoting time to training, related to a lack of income to cover the costs of living 

  Again,  the 
during  training  periods:  Lack  of  time  for  training  also  limits  participation.
employer plays an important role in providing support by giving the opportunity to participate 
in training during working hours, or allowing individuals to make use of existing paid training 
leave  provisions.  Paid  training  leave  allows  workers  to  keep  their  salary  or  a  replacement 
income during periods of training that may range from a few days to several months. Related 
provisions of some sort already  exist in  24  Member States, of which  12 are bound by their 

39

34

  See  for  instance  Cedefop  (2019),  and  Eurofound  and  Cedefop  (2020),  ECS  2019:  Workplace  practices 

unlocking employee potential. 
35

38

37

 See Leuven (2005), The economics of private sector training: A survey of the literature, Journal of Economic 
Surveys. Accoring to the 2015 CVTS, only 13% of training by employers is focused on more transversal skills 
such as general IT skills, and less than 1% on numeracy and literacy skills. 
36

 See European Commission (2020), Facing the Digital Transformation: are Digital Skills Enough?   
 Ranging from 78% (transversal skills) and 85% (digital skills) of businesses to 98% of NGOs and others. 
 See the discussion of existing training vouchers in the Member States in Annex 8.2. Also the OECD concludes 
that  “in most cases, single adult learning  reforms  reached less than 1% of the  adult population”.  See OECD 
(2020), Increasing Adult Learning Participation- Learning from Successful Reforms. 
39

 36.6% of adults who wanted to learn more mentioned their schedule as a barrier in the 2016 AES, increasing to 
40.7% for adults who did not participate during the past year. 82% of respondents to the public consultation agreed 
fully or somewhat that the indirect costs of training (loss of income during training etc.) prevent individuals from 
accessing training. 

7 

 
 
 
                                                 
  However,  access  to  paid 
ratification  of  the  ILO  Paid  Educational  Leave  Convention.
training leave is often restricted based on features of the employment relationship. Take-up 
of existing provisions is usually below 1% of the employed population, driven by restrictive 
eligibility  criteria  (e.g.  excluding  employees  without  permanent  contracts),  a  lack  of 
information about available training opportunities and a lack of trust in the quality of training 
among employees and employers. Employees in SMEs are also at a disadvantage since smaller 

40

 Moreover, 
enterprises often face greater difficulties in handling temporary absence of staff.
the  self-employed  (including  the  growing  and  diverse  groups  of  the  “dependent  self-
employed”) are usually excluded entirely from schemes that provide income support during 

41

periods of training.

42

Fragmentation of existing support systems: The fragmentation of existing support systems
further contributes to gaps in financial support. One example of fragmentation are weak links 
between sectoral support systems. A worker may contribute to a sectoral training fund for 
most of his/her working life, but cannot use it to train for a professional transition and loses 
44

access  to  such  entitlement  the  moment  the  worker  changes  to  a  different  sector.
Fragmentation  can  also  create  or  perpetuate  support  gaps  if  responsibilities  for  support  are 
strictly  divided  by  target  group  and  the  coordination  between  different  support  schemes 

43

 An example are platform workers who often 
does not keep up with labour market changes.
neither  benefit  from  the  training  support  offers  for  employees  nor  from  those  for  the 
unemployed.  Fragmentation  can  also  prevent  individuals  from  making  use  of  available 
support, as individuals need to be well informed about the eligibility conditions to be able to 
benefit from  it when support is  linked to the current  employment or income status.  87% of 

45

40

 As of 1st of February 2020, according to a  preliminary  version of the updated  “Financing  Adult  Learning” 
database  by  Cedefop  (update  forthcoming)  presented  in  Annex  8.2.  The  last  ratification  of  the  ILO’s  Paid 
Educational Leave Convention by a Member State took place in 1993. 
41
 Cedefop (2012), Training leave. Policies and practice in Europe. 
 For an exception, see the recently reformed  Finnish  Adult Education  Allowance,  which is open to the self-

42

employed. See the mapping of recent reforms in the Member States in Annex 8.2. 
43

 Reducing fragmentation does not necessarily mean centralising of funding: for instance, although most funding 
for the French “Compte personnel de formation” comes from a central payroll levy, the scheme allows individuals 
to receive training entitlements from a large number of different national and regional stakeholders and employers. 
See Annex 14 for details. 
44

 For instance, a review of sectoral training funds by the Dutch Ministry of Social Affairs found that only in 4 

out of the 180 training funds, activities are undertaken to promote inter-sectoral mobility of workers.  
45

 This is a particular point of attention since a 2019 review concludes that “adult learning does often not align 
clearly  with  the  remit  of  a  particular  Ministry  or  Government  department  at  national  level-  see  European 
Commission (2019), Adult learning policy and provision in the member states of the EU: a synthesis of reports 
by country experts. Reducing fragmentation does not necessarily require centralisation of funding: for instance, 
the French CPF allows individuals to receive training entitlements from a large number of different national and 
regional  stakeholders  and  employers,  eliminating  fragmentation  at  the  “front  end”/  from  the  perspective  of 
individuals while maintaining flexibility at the “back end” (Annex 14). 

8 

 
 
 
 
 
                                                 
respondents in the public consultation highlight that a lack of awareness of available financial 

support for training hampers participation.

46

2. Limited incentives and motivation of individuals to participate in training 

80% of adults who did not participate in learning during the previous year say that they did not 

want to participate,
 showing that financial support is necessary but not sufficient.  This is not 
due to a lack of appreciation of adult learning, as 96% of adults agree that learning throughout 

47

48

life is important.
 The reasons why this does not always translate into action are most notably 
a  limited  awareness  of  one’s  own  skills  needs,  limited  transparency  about  available 
training  offers,  uncertainty  about  the  quality  of  training  provision  and  recognition  of 
training  outcomes  in  the  labour  market  and  an  insufficient  tailoring  of  training  offers  to 
individual needs. 

Limited  awareness  of  own  skills  needs  by  individuals:  There  is  a  large  divide  between 
employers’ and employees’ views on the adequacy of skills: Only 16% of employers report 
that their workers have all the required skills, while 76% of adults do not think that they lack 
general skills and 71% do not think they lack technical skills.49 The literature suggests that the 
truth may lie somewhere in the middle as employees may be reluctant to acknowledge missing 
skills while employer reports must be assessed in light of the wages and working conditions 

50

offered.

Limited  transparency  about  available  training  offers:  The  advent  of  digital  and  blended 
learning  opportunities  creates  an  increasing  number  of  potentially  relevant  training 

51

opportunities.
  Since  employers  play  an  important  role  also  for  signalling  skills  needs  and 
suggesting concrete training programmes, navigating the training offer is particularly difficult 

 to 
for individuals or trainings without a strong link to a single employer. 89% of respondents
the public consultation pointed to limited transparency of information on training opportunities 
as  a  reason  preventing  individuals  to  train,  consistent  with  the  90%  of  respondents  in  the 

52

46

 Ranging from 85% of citizens and NGO and other to 92% of public authorities. The case studies in Annex 13 
point to Greece as an example where fragmentation of support poses a challenge for individuals, with 20 training 
voucher schemes with distinct target groups and eligibility conditions implemented since 2015.  
47

  Data  are  taken  from  the  2016  AES.  The  remaining  20%  respond  that  they  „wanted,  but  encountered 
difficulties“. This is confirmed by PIAAC data: The OECD places 50% of adults into the group of “disengaged 
learners” who lack willingness or the opportunity to engage in available learning opportunities. See OECD (2021), 
Skills Outlook- Learning for Life. Chapter 4: Promoting interest and participation in adult learning. 
48

 Cedefop (2020). Perceptions on adult learning and continuing vocational education and training in Europe. 

Second  Opinion survey – Volume 1. Henceforth referred to as “Cedefop AL perceptions survey”. 
49

  See  Eurofound  and  Cedefop  (2020),  ECS  2019-  Workplace  practices  unlocking  employee  potential  and  

Cedefop AL perceptions survey. Differences across different groups of adults are small, see Annex 6.2. 
50

  Cedefop  (2021),  Understanding  technological  change  and  skill  needs:  skills  surveys  and  skills  forecasting. 

Cedefop practical guide 1 and Brunello and Wruuck (2019), Skill shortages and skill mismatch in Europe. 
51

 The parallel initiative on an EU approach to Micro-credentials introduced in Section 1 complements the present 

initiative from the “supply side” by developing the transparency and quality of Micro-credentials. 
52

 Ranging from 85% of NGOs and other respondents to 96% of public authorities.  

9 

 
 
 
  
                                                 
Cedefop adult learning perceptions survey agreeing that more information and guidance would 
encourage participation in learning.  

According  to  the  adult  learning  expert  network,  20  Member  States  already  have  legally 
mandated  career  guidance  offers  in  place,  which  can  help  individuals  with  increasing  the 
awareness of skills needs and training opportunities. However, they often have low effective 
outreach, as measured by the percentage of adults who have received career guidance in the 

past  year  in  the  AES.

53

  Most  national  public  authorities  are  already  conducting  awareness-
54

raising campaign on existing  adult learning opportunities.
 Survey respondents identify the 
internet as most frequently used source of information on training opportunities. This points to 
the potential of user-friendly digital registries of training opportunities that are recognised 
on the basis of their quality and labour market relevance in the provision of career guidance 
services more broadly understood. However, such registries only exist in 12 Member States, 

and are less likely to exist in Member States with lower adult learning participation rates.

55

Uncertainties about quality and recognition of a training programme: To engage in training, 
individuals  need  to  believe  that  it  will  pay  off  for  them  personally,  but  they  need  to  make 
choices among a large number of training offers based on imperfect information and with 
substantial uncertainty about individual  returns.  The lack of quality  assurance has  been 

 Almost a quarter of 
pinpointed as a reason for low participation in recent national initiatives.
respondents across the EU (24%) do not have sufficient confidence in the overall quality of 
adult learning in their country, and 87% of respondents are of the opinion that increased quality 

56

 to 
standards would encourage participation in work-related training.
the  public  consultation  agreed  that  uncertainties  about  the  quality  of  training  prevent 
individuals from accessing training. Although a number of international quality labels exist, 
the quality assurance reports underlying the certification are usually non-public, leading to little 

  80% of respondents

57

58

53

 Career guidance is strongly positively correlated with AL participation at Member State level (Annex 6.2). See 
European Commission (2021),  Adult Education and Training in Europe- Building inclusive pathways to skills 
and qualification, for a mapping of guidance services provided by PES showing that in only around half of the 
countries/regions, all adults are able to benefit from them regardless of employment status. 
54

 All Member States except CZ, EL, LT, RO between 2015-2020 according to European Commission (2021), 

Adult Education and Training in Europe- Building inclusive pathways to skills and qualification. 
55

 Based on the reports from adult learning experts, such databases already exist in 6 of the 9 Member States with 
the highest adult learning participation rates (AT, DE, DK, FI, FR, SE), as compared to 3 of the 9 for the Member 
States  with  medium  (BE,  LU,  SI)  and  low  (BG,  ES,  PL)  participation  rates  (Annex  8.2).  See  European 
Commission  (2021),  Adult  Education  and  Training  in  Europe-  Building  inclusive  pathways  to  skills  and 
qualification, for a detailed discussion of such databases, concluding that the their availability has increased since 
2015 but that many are not yet tailored specifically to the needs of adult learners. 
56
 See for instance the case study on voucher schemes in Greece in Annex 13. 
  Cedefop  AL  perceptions  survey.  AL  participation  is  significantly  negatively  correlated  with  the  share  of 
respondents  who  rate  the  quality  of  AL  in  their  country  as  bad.  AL  experts  confirm  the  need  for  significant 
improvements in quality assurance mechanisms, in particular for non-formal programmes and for Member States 
with low adult learning participation levels (Annex 8.2).  
58

 Ranging from 78% of NGOs and others to 81% of public authorities and trade unions. Agreement was somewhat 
higher among respondents from Member States with low (81%) and medium (79%) as compared to those with 
relatively high adult learning participation (63%).  

57

10 

 
 
 
                                                 
 Moreover, there is a risk that the coexistence 
visibility as to what the labels actually represent.
of  multiple  quality  labels  hampers  their  ability  to  reduce  uncertainties  about  quality  and 

59

recognition of training programmes.

60

The currently high uncertainty about the recognition of learning outcomes further reduces 
incentives  for  training.  If  learners  cannot  evidence  the  value  of  newly  acquired  skills  and 
competences, they may  not  be able to  reap the  full benefits  of training  (i.e. negotiate wage 
increases  or  improve  job  content  and  prospects).  The  2020  evaluation  of  the  Council 
Recommendation on validation of informal and non-formal learning concluded that while there 
has been some progress towards providing more validation opportunities in the Member States, 
strengthening  support  to  individuals  is  necessary  to  promote  the  take-up  of  these 

61

opportunities.
 Historical evidence suggests that in particular in times of rapidly changing skill 
requirements,  a  lack  of  recognition  of  skills  reduces  the  bargaining  power  of  workers  and 

62

reduces  wages.
  The  existence  of  digital  registries  of  recognised  training  opportunities 
could  support  the  standardisation  and  recognition  of  skills,  but  as  mentioned  above,  such 
registries do not exist in most Member States. 

Insufficient  tailoring  of  training  offers  to  individual  needs:  It  reduces  the  motivation  of 
individuals to take up training in view of the heterogeneous needs of adult learners in terms 
of the timing and the delivery mode of training. Related barriers to training participation are 
among  the  most  frequently  mentioned  in  both  the  AES  and  the  Cedefop  adult  learning 

perceptions survey

63

no or a small influence on management decisions concerning training and skills development.

, and managers in more than 40% of enterprises report that employees have 
64

65

77% of respondents
 to the public consultation agreed that insufficient tailoring of available 
training  to  individual  needs  prevents  individuals  from  accessing  training,  and  87%  of 
respondents to the Cedefop adult learning perceptions survey reported that better adaptability 
to  individual  learning  needs  would  encourage  participation  in  work-related  training.  Adult 
learning experts indicate that supporting individuals directly has been recognised as a policy 

59

 See the background paper of the EQAVET Peer Learning Activity on Quality Assurance in continuing vocational 

education and training (CVET) on 27-28 April 2021. 
60

 See OECD (2021), Improving the Quality of Non-Formal Adult Learning- Learning from European Best 

Practices on Quality Assurance.  
61

 See the Council Recommendation of 20 December 2012 on the validation of informal and non-formal learning 

and its 2020 evaluation (SWD (2020) 121 final). 
62

63

 See Bessen (2015), Learning by Doing- The Real Connection between Innovation, Wages, and Wealth. 
 In the  2016 AES, 36%  of respondents  who  wanted to participate  more in learning  mentioned schedule as a 
factor preventing them from participating, 32% family responsibilities, 16% a lack of suitable training activities 
and 13% distance to training  activities. In the Cedefop AL perceptions survey, 32% of non-participants stated 
participation  would  have  been  difficult  to  fit  with  other  commitments,  and  13%  found  no  suitable  activity. 
Inflexible scheduling of training activities magnifies the time barriers discussed under driver 1. 
64

 Cedefop and Eurofound (2019), ECS 2019- Workplace practices unlocking employee potential. 
 Ranging from 70% of businesses to 88% of public authorities and trade unions and from 63% of respondents 

65

from countries with relatively high adult learning participation to 81% from countries with low participation.  

11 

 
 
  
 
                                                 
priority  by  two  thirds  of  Member  States  in  the  top  tier  of  adult  learning  participation,  as 
compared to one third of Member States in the bottom tier (Annex 8.2). 

2.2.2 External Drivers 

In addition, a number of external drivers are relevant insofar as they increase the importance 
of training or risk reducing its provision in the absence of a policy response.  

Technological  change:  It  makes  some  jobs  increasingly  susceptible  to  substitution  by 
digitalisation and automation while at the same time creating new jobs. Estimates suggest that 
around 9% to 14% of jobs in the EU face a high risk of automation, and that a further 32% to 
40% of jobs are likely to be transformed by automation, entailing corresponding changes in 

their skill requirements.

66 

 Changes in the technology used at work are already felt by 43% of 

  This indicates 
adult employees in Europe according to the European Skills and Jobs Survey.
that  many  workers  may  require  training  to  perform  job  tasks  which  are  potentially  very 
different from those they have previously undertaken. On the upside, the digital transformation 
also  creates  many  new  online  learning  opportunities  for  adults  that  can  reduce  cost  and 
distance  barriers  and  allow  for  better  targeting  of  learning  to  individual  needs,  further 

67

accelerated by COVID-19 pandemic.
and recognition prevents adults from fully seizing these opportunities (Section 2.2). 

 However, the present lack of certainty about quality 

68

The  green  transition:  It  is  driving  a  profound  transformation  of  labour  markets,  not  only 
displacing workers but also by changing the task requirements within occupations and creating 

69

 Substantial investments are needed to provide opportunities 
new jobs that require new skills.
for  acquiring  new  skills  to  those  who  are  at  risk  of  losing  their  jobs  in  high-emissions 
71

  Skills  shortages  already  constrain  the  transition  to  a  greener  economy

industries.
, 
highlighting  the  importance  of  skills  investment  also  to  reach  the  ambitious  EU  emission 
reduction targets. 

70

Demographic ageing:  Europe has  an  ageing and shrinking labour force.  With people living 
longer, there is an increasing need for people to update their skills. Ageing is also expected to 

66

 Pouliakas (2018), Automation risk in the EU labour market: a skill-needs approach; OECD (2018), Automation, 

skills use and training. 
67

 Cedefop (2018), Insights into skill shortages and skill mismatch: Learning from Cedefop’s European skills and 

jobs survey. 
68

  OECD  (2021),  Adult  learning  and  COVID-19:  How  much  informal  and  non-formal  learning  are  workers 
missing? Also see the Education and Training Monitor 2021 (forthcoming) for evidence that participation of adults 
in online training has increased sharply in 2020. 
69

  See  for  example  Cedefop  (2019),  Skills  for  Green  Jobs:  2018  update.  European  synthesis  report  and  ILO 

(2018), World Employment and Social Outlook 2018: Greening with jobs. 
70

 European Commission (2020), Employment and social developments in Europe- Leaving no one behind and 

striving for more : fairness and solidarity in the European social market economy. 
71

ILO (2019), Skills for a greener future: A global view based on 32 country studies.  

12 

 
 
                                                 
72

  This  suggests  that  there  will  be  an  increased 
further  increase  old-age  dependency  ratios.
demand to raise employment rates of older workers as well as productivity of employed adults 
across  the  board,  whereby  training  will  play  a  key  role.  At  the  same  time,  older  people 
participate significantly less in adult learning than younger workers (see Annex  6.3), which 
highlights the urgency of creating learning opportunities that are also tailored to the  diverse 
needs of older learners. 

High atypical employment and more frequent labour market transitions: The share of workers 
in atypical forms of work is high and the frequency of professional transitions has increased. 
Around 4 in 10 adults are in atypical forms of work (defined as not having full-time permanent 
employment contracts). However, most groups of atypical workers participate less in learning 
(cf. Section 2.1). While the share of atypical workers has remained rather stable in recent years 
(cf. Section 2.4) the number of professional transitions from one labour market status to another 

73

 This increases the need for training 
throughout one’s working life has increased considerably.
to adjust to new job tasks; it is, however, less likely to be supported by the current employer 
who has fewer incentives to support such transitions into new jobs (cf. Section 2.1). A high 
share of atypical forms of work and an increasing frequency of labour market transitions both 
risk reducing the coverage of employer support for training on the labour market: individuals 
may not have a single employer to turn to for support, or this employer may be more reluctant 
to  grant  it  for  fear  of  not  being  able  to  capture  the  returns  of  their  investment  once  their 
employee moves on. 

2.3 What are the consequences? 

The  low  and  unequal  participation  in  adult  learning  across  the  EU  reduces  prosperity  and 
produces inequalities between those who benefit from learning and those who do not. Beyond 
that, socio-economic consequences are also felt by enterprises and the society as a whole (see 
Annex 10 for a literature review, and Section 6 for quantitative estimates). 

2.3.1 For individuals 

Many immediate consequences of low and uneven participation in training are observed at the 
individual level. Low-skilled individuals are more likely to face unemployment and exclusion 

74

from the labour market.
 These consequences are particularly pertinent given that workers 
facing the highest risks of displacement in the future are also the ones that currently participate 
less in learning (Section 2.1). For those in employment, less training means foregoing wage 

72

 Defined as the ratio between the number of persons aged 65 and over and the number of persons aged between 
15 and 64. The value is expressed per 100 persons of working age (15-64), and has already increased from 26 to 
32 between 2010 and 2021. See Eurostat, old-age-dependency ratio. 
73

 OECD Employment Outlook (2019), The Future of Work, Figure 3.1: Job tenure of workers decreased by on 
average 7.9 percent between 2006 and 2017 for the 22 EU Member States included in the analysis once accounting 
for population ageing, and it decreased in all EU Member States except EE, LV and ES. The largest declines in 
tenure have occurred for low-educated workers. 
74

 The skill intensity of EU labour markets is reflected in the steep gradient of EU-27 unemployment rates by 
educational attainment (4.8% for those with tertiary education vs. 13.4% for those with at most lower secondary 
education for 2020) and the even steeper gradient for employment rates (85.5% vs. 55.6%). See Eurostat variables 
UNE_EDUC_A and LFSA_ERGAED. 

13 

 
 
                                                 
75

  In  terms  of  risks  to  well-being  more  broadly,  the  literature  points  to  negative 
increases.
associations with personal and job satisfaction, health and social inclusion for individuals 

that participate less in training.

76

2.3.2 For enterprises 

73%  of  EU  employers  considered  difficulties  in  finding  workers  with  the  right  skills  as  an 

obstacle  to  investment  in  2020.

77

  Skills  shortages  reduce  productivity  and  enterprises’ 

capacity to adopt new technologies
, which is particularly costly in the face of the digital 
and green transitions. There is also evidence that companies who fail to invest sufficiently in 

78

the skills of their workforce are more likely to go out of business.

79

These consequences are particularly relevant for SMEs and micro-enterprises, whose staff 
participate  in  training  comparatively  less  than  that  of  larger  enterprises.  This  reduces  their 
opportunities  to  develop  personally  and  professionally  and  impacts  on  the  attractiveness  of 
SMEs  as  a  workplace.  It  also  reduces  the  productivity  levels  of  the  SME,  increasing  the 

competitiveness gap with larger companies.

80

2.3.3 For society as a whole 

At a macroeconomic level, income and productivity losses by individuals and enterprises and 
a less efficient reallocation of labour add up to lower GDP levels (ceteris paribus) and higher 

unemployment with associated transfer spending.
 Skills are also associated with a range of 
non-monetary externalities concerning civic participation and social cohesion, and there is 

81

growing evidence to suggest that the relationship is causal.

82

Taken together, this suggests that low and unequal levels of participation in adult learning make 
the EU less resilient in the face of economic downturns and structural change. This is of 
particular  concern  in  light  of  the  rapid  digital  and  green  transition  as  both  transitions  will 
require both an openness to adapt to change and an ability to shape the change, for instance in 
the form of innovations that advance the EU on the path to carbon neutrality. 

75

76

 Algan et al.  (2021), Boosting Social and Economic Resilience in Europe by Investing in Education. 
 Feinstein and Hammond (2007), The contribution of adult learning to health and social capital. Oxford Review 

of Education. 
77

79 

European Investment Bank (2021),  Investment Report 2020/2021- Building a smart and green Europe in the 
COVID-19 era. This makes skills shortages the second most frequently mentioned constraint, behind “Uncertainty 
about the future” (81%) but ahead of “Business regulations and taxation” (65%) and 6 other potential barriers. 
78

 See the Algan et al. (2021) review paper and Cedefop (2012), Learning and innovation in enterprises. 
Collier et al. (2005), Training and Establishment Survival. Scottish Journal of Political Economy. 
 OECD (2013), Skills Development and Training in SMEs. 
 See Cedefop (2017), Investing in skills pays off, and the estimates introduced in Section 6. 
 See Ruhose et al. (2019): The benefits of adult learning: Work-related training, social capital, and earnings. 

80

81

82

Economics of Education Review and the Algan et al. (2021) review paper. 

14 

 
 
  
  
  
  
                                                 
2.4 How will the problem evolve? 83
Extrapolating  recent  trends,  adult  learning  participation  in  the  EU  could  increase  from 
37.4% in 2016 to 48.6% in 2030, but would stay below the EU-level target of 60%. Large 
differences  in  participation  rates  between  Member  States  are  likely  to  persist,  reflecting 
differences in investments in and policy attention for adult learning. Thus, existing support 
gaps  will  not  disappear  without  further  policy  action.  External  drivers  like  the  green 
transition, digital transformation and demographic trends will persist and even partly accelerate 
in light of the ambition to reach climate neutrality by 2050.  

At the same time, labour market transitions from one job or employment status to another 
are expected to become more frequent. This is expected to result both from increasing life 
expectancy  and  extrapolating  the  significant  decrease  in  job  tenure  observed  across  EU 
Member States over recent years (Section 2.2.2). While there is no compelling evidence that 

atypical forms of work will increase further overall

84

, some forms of it such as platform work 

are expected to grow.
to training. 

85

 These are typically associated with fewer benefits in terms of support 

There is broad recognition at EU and Member State levels of the importance to increase adult 
participation in learning in order to increase the resilience of labour markets and ease necessary 
professional transitions (Section 1). The measures adopted to tackle the impact of the COVID-
19 pandemic, notably Next Generation EU, could provide a strong impetus for reform and 
some Member States have included reforms of adult learning policies in their recovery 
plans (see Annex 8.1). However, pressures on public budgets in the medium-term increase the 
importance  to  devise  sustainable  funding  arrangements  through  a  comprehensive  policy 
framework.   

3. WHY SHOULD THE EU ACT?  

3.1 Legal basis 

The  envisaged  legal  basis  is  Article  292  combined  with  Article  149  TFEU.
  Article  149 
allows for non-binding measures in the field of employment aimed at providing comparative 
analysis  and  advice  as  well  as  promoting  innovative  approaches.  It  contributes  to  the 
implementation of Article 145 TFEU, according to which, “Member States and the Union shall 
(…)  work  towards  developing  a  coordinated  strategy  for  employment  and  particularly  for 
promoting  a  skilled,  trained  and  adaptable  workforce  and  labour  markets  responsive  to 
economic change”. 

86

83

 Also see Section 5.1 for a complementary description of policy efforts under the baseline scenario, and Annex 

8.3 for details on the estimation of AL participation in 2030 under the baseline scenario. 
84

 The Impact Assessment for the Council Recommendation on Access to Social Protection for Workers and the 
Self-employed estimated that the share of traditional non-standard work in employment could rise from 25% in 
2016 to 29% by 2030, while self -employed would remain broadly constant at 14%. However, the share of atypical 
forms of work among those aged 25-64 decreased slightly from 38.5% to 36.6% between 2016 and 2020 according 
to data from the Labour Force Survey.  
85

 See the discussion paper on the recent Commission consultation on the working conditions in platform work. 
  Article  149  TFEU  could  serve  as  the  substantive  legal  basis  and  Article  292  TFEU  as  basis  for  a  Council 

86

Recommendation, since it allows the Council to adopt recommendations on a proposal from the Commission.  

15 

 
 
 
                                                 
Article 166 TFEU specifies that Union action shall aim to “facilitate adaptation to industrial 
changes, in particular through vocational training and retraining”. However, the ultimate and 
predominant purpose of the initative is to support Member States in reaching the objectives of 
employment policy (cf. also Section 4), leading to Article 149 TFEU as the legal basis (next to 
Article 292). Article 153 TFEU, which would allow for measures to “support and complement 
the Member States [on] working conditions”, was also discarded. Its relevant paragraph 1) b. 
on  working  conditions  would  only  cover  workers  whilst  leaving  out  self-employed, 
unemployed, and inactive people. In light of the problem analysis, it was essential to choose a 
legal basis which could cover the entire population on the labour market.  

3.2 Subsidiarity: Necessity and added value of EU action 

The problems described above are widespread across EU Member States and have similar 
underlying causes, as shown in Section 2. The structural transformation of labour markets in 
light of the digital and green transitions and an ageing European population greatly increase 
the stakes for all Member States. An EU initiative in this area is warranted by the EU’s interest 
in having a skilled labour force as reflected in its objectives (Section 4.1). This is particularly 
important  for  upward  socio-economic  convergence  across  the  economic  and  monetary 

87

union
 and the EU in general in a context of rapid shifts in skills demand within and across 
sectors. Increasing access to training opportunities can also improve the resilience of labour 
markets to shocks such as the one induced by COVID-19 pandemic.  

While Member States can take measures to improve the situation at national level (see baseline 
scenario  in  Section  5.1),  an  EU  initiative  can  support  and  accelerate  national  efforts  by 
sharing experiences and promoting innovative approaches (see Section 6.2 for a discussion of 
the available instruments). It can also help set expectations and create trust among Member 
States and stakeholders for an increase in public and private investment in skills, sending 
the message that all of them will contribute to, and benefit from, the shared asset of a skilled 

EU labour force. 84% of respondents
also lead to a more efficient use of EU funds for skills development. 

 to the public consultation agreed that the initiative could 

88

While the main EU added value of the initiative is to support Member State reforms to reach 
EU level objectives in the field of employment policy and the European Pillar of Social Rights, 
there are a number of additional dimensions of EU added value: The initiative can facilitate 
access  to  training  for  the  growing  number  of  EU  citizens  who  reside  in  another  EU 
Member State, contributing to labour mobility within the EU and a further integration of the 

  89

single  market.
  The  initiative  can  also  contribute  to  creating  a  level  playing  field  for 
companies  operating  on  the  single  market  by  enhancing  the  quality  and  transparency  of 

87

88

 This was highlighted in the PC on establishing a European Pillar of Social Rights, SWD(2017) 206 final. 
  Ranging  from  70%  for  businesses  to  93%  for  NGOs  and  others.  Agreement  was  13  ppt  higher  among 

respondents from MS with low (90%) compared to MS with relatively high (77%) AL participation rates. 
89

 See European Commission (2020), Annual report on intra-EU labour mobility, for trends in EU labour mobility 
and Dorn and Zweigmüller (2021),  Migration and Labor Market Integration in Europe (Journal of Economic 
Perspectives) for an argument that missing language skills limit intra-EU labour mobility. 

16 

 
 
                                                 
 agreed that the 
training markets across the EU. 75% of the public consultation respondents
initiative  can  improve  the  portability  and  recognition  of  training  outcomes  across  Member 

90

91

States,  and  71%
companies operating on the single market.   

  agreed  it  can  increase  the  transparency  of  national  training  markets  for 

Article  149  TFEU  precludes  measures  that  would  lead  to  a  harmonisation  of  the  laws  and 
regulations  of  the  Member  States  in  this  area,  respecting  subsidiarity  by  allowing  Member 
States to adapt the measures taken to national circumstances.  

4. OBJECTIVES: WHAT IS TO BE ACHIEVED?  

4.1 General objectives 

The general objective of the initiative is to support Member States with reforms to empower 
adults to participate in training in order to increase participation rates and reduce skills 
gaps. The initiative thereby contributes to the EU's objective to promote a highly competitive 
social market economy, aiming at full employment and social progress (Article 3 TEU).  

The initiative also aims to contribute to the implementation of several individual social rights 
that have been derived from this general objective in the context of the European Pillar of 
Social Rights:  the right to “quality and inclusive education, training and life-long learning” 
(first principle), the right to “active support to employment” (fourth principle), and the right to 
training “regardless of the type and duration of the employment relationship” (fifth principle). 
Alongside the other inititives from the European Skills Agenda, the initiative aims to support 
Member States in making progress towards the EU-level target of 60% adult participation in 
training every year by 2030. This target has been set by the European Pillar Social Rights 
Action  Plan  and  endorsed  by  EU  leaders  in  the  Porto  Declaration  and  in  the  European 
Council conclusions of June 2021 (see Section 1). 

Significant progress on these dimensions will be crucial in order to support the just transition 
to  a  carbon  neutral  Europe,  to  equip  citizens  with  the  skills  they  need  to  grasp  the 
opportunities  resulting  from  the  digital  transition,  and  to  adapt  to  change  and 
uncertainties such as those ensuing from the COVID-19 pandemic. 

4.2 Specific objectives 

To reach the general objective, the specific objectives of the initiative are to support Member 
State reforms to:  

-  Close gaps in existing support systems so that all adults have access to support for 
training, including for professional transitions and irrespective of their labour force or 
professional status.

92

-  Increase the incentives and motivation of individuals to seek training. 

90

 Ranging from 38% for trade unions to 93% for NGOs and others, with a majority disagreeing only among trade 

union respondents. 
91

 Ranging from 54% for businesses to 91% for citizens. 

92

 Hence including the employed independent of their contractual status, the self-employed, the unemployed and 

people outside the labour force. 

17 

 
 
 
                                                 
5. WHAT ARE THE AVAILABLE POLICY MEASURES?  

5.1 What is the baseline from which options are assessed? 

Under the baseline scenario, adult learning will continue to be an important strand of the 
EU’s policy work and funding. The most important recent Skills Agenda initiatives include 
the  2020  Council  Recommendation  on  Vocational  Education  and  Training  and  the  Pact  for 

93

Skills
 launched in November 2020. Several of the Recovery and Resilience Plans to address 
the socio-economic impact of the COVID-19 pandemic agreed between the Member States and 

the Commission include relevant initiatives
, and the European Social Fund+, the European 
Regional  Development  Fund  and  Erasmus+  remain  important  funding  sources  for  skills 
development.  

94

However,  existing  EU  policy  initiatives  focus  mostly  on  the  design  of  the  provision  of 
education and training for adults (the “supply side”), rather than on making this provision 
accessible to adults and encouraging them to use it (the “demand side”). The 2019 stocktaking 
report  on  the  implementation  of  the  Upskilling  Pathways  Recommendation  and  the  2020 
evaluation  of  the  Council  Recommendation  on  the  validation  of  non-formal  and  informal 
learning show that a key challenge is to ensure a sufficient uptake of new opportunities that are 

 95

created on the supply side.
 Moreover, existing EU policy initiatives are often limited in scope. 
For instance, the 2016 Council Recommendation on Upskilling Pathways focuses primarily on 
one level of skills (basic skills) and one target group (low-skilled adults).  Hence, under the 
baseline scenario, the EU’s policy support will not fully cover Member State policy reforms 
and programming of EU funds to reach the objectives of the initiative. 

At the national level, 24 Member States have identified increasing participation of adults in 
learning as a priority in legal acts, policies, or strategies, which can be expected to translate 
96

into further concrete measures relevant to the general and specific objectives of the initiative.
However, on the basis of past  experience, overall progress  is expected to be uneven  across 
Member States  and  groups of adults. While most Member States have for instance training 
voucher  schemes  in  place  (giving  individuals  an  entitlement  to  a  specific  type  of  training), 
target groups are usually small and the overall impact on participations levels limited. This is 
also reflected in the Recovery and Resilience Plans by Member States: among the 18 plans that 
had been endorsed by the European Commission by August 2021, 7 include schemes providing 
individuals  with  training  entitlements,  however  often  with  a  limited  number  of  expected 
beneficiaries (ranging from 3 500-30 000, with the exception of 500 000 expected beneficiaries 

93

95

 Council Recommendation of 24 November 2020 on vocational education and training (VET)  and  Pact for 
Skills. See Annex 8.1 for a more extensive discussion of relevant existing EU initiatives in the baseline scenario. 
94

 See Annex 8.1 for a summary of relevant initiatives in these plans. 
 Evaluation of the Council Recommendation of 20 December 2012 on the validation of non-formal and informal 
learning  (SWD(2020)  121)  and  Council  Recommendation  on  Upskilling  Pathways-  Taking  stock  of 
implementation measures (SWD(2019) 89). See Annex 8.1 for further analysis on the existing instruments. 
96

  In  13  Member  States,  these  initiatives  aim  to  increase  the  demand  for  adult  learning  offers  by  individuals 
directly, and this mostly concerns the group of Member States already reporting higher participation figures. See 
Annex 8.2 for details on the information summarized in this paragraph, which is based on information provided 
by the network of national adult learning experts. 

18 

 
 
   
                                                 
in EL- cf. Annex 8.1). Some of the supporting services that could address the limited incentives 
and  motivation  of  individuals  to  take  up  training  (such  as  registries  of  recognised  training 
opportunities, validation and guidance services) exist in many Member States, but they often 
97

have low effective outreach and are not systematically linked to financial support instruments.

The  baseline  scenario  for  this  impact  assessment  hence  assumes  that  adult  learning 
participation until 2030 stays on its modest trend growth observed between 2007 and 2016, 
reflecting the ongoing policy efforts at Member State and EU level and the combined impact 
of  the  external  drivers  (Section  2.2.2).  While  adult  learning  participation  dropped  in  2020 
following  COVID-19  restrictions,  direct  impacts  of  the  pandemic  on  adult  learning 
participation are are likely to be transitory (Annex 6.1). However, expected growth in adult 
learning participation under the baseline scenario is not fast enough to reach the EU-level 

98

targets
 and to address the challenges facing the EU. Moreover, the inequalities in access to 
learning opportunities observed between different groups of adults (Section 2) are not expected 
to narrow, given the inconclusive trends in participation gaps (Annex 8.3) and the systematic 
patterns of under-representation also observed in Member States with high participation rates.  

5.2 Measures discarded at an early stage 

A number of policy measures have been discarded for inclusion in this initiative, in light of the 
existing  policy  initiatives  at  EU  and  Member  State  level  in  the  baseline  scenario  and  the 

specific objectives of this initiative.

99

Tax incentives for individuals: Income tax incentives for training exist in 16 Member States.
Their potential to increase incentives and motivation of non-participating adults is limited by a 
significant delay between expenditure and reimbursement (via the income tax return), requiring 
significant forward planning by individuals. While income tax incentives have a large potential 
target group, progressive income tax regimes imply that they often favour high-income earners, 
and by design they cannot support individuals who do not pay any or little income tax.
 This 
limits their potential to close existing support gaps. This is further exacerbated by a structural 
lack of integration with measures that could provide information on training opportunities and 

100

their quality and recognition.

101

Subsidised loan or savings schemes for adult learning: Their high administrative burden makes 
them more appropriate for one-off formal education rather than shorter and more frequent non-
formal  learning  to  update  skills  throughout  working  life.  They  are  better  suited  for  already 
motivated and informed individuals, and less effective in incentivising and motivating current 

97

98

99

100

101

 Member States with more supporting services tend to have higher participation rates (Annex 8.2). 
 50% for 2025 from the European Skills Agenda and 60% for 2030 from the EPSR Action Plan. 
 Supported by the review of evidence in Annex 9. 
 According to the forthcoming update of the Cedefop financing of adult learning data base, see Annex 8.2. 
 See the NL case study in Annex 13, where income tax incentives are replaced by individual learning budgets 

after an evaluation found them to be ineffective at stimulating training.  

19 

 
 
   
 
 
 
 
                                                 
non-learners. They are also less well suited for low-income learners, who may face restricted 

access to credit and saving. Existing schemes point to generally limited outreach.

102

Financial incentives for training for employers and training funds: Both instruments are an 

important  part  of  training  support  systems  in  many  Member  States.
  They  can  increase 
training  provision  by  employers  by  internalising  the  broader  benefits  that  their  training 
investment  will  confer  to  other  employers,  individuals  and  society  at  large,  and  related 
104

initiatives are supported by the Commission.
 They are outside of the scope of the present 
initiative in light of its objective to close access gaps to training so that all adults receive support 
to training, including those not well-served by training from an employer, as well as its focus 
on individual’s incentives and motivation to take up existing training opportunities.  

103

Supply side policy guidance and funding: Public funding to education and training providers is 
used to support adult learning in many Member States, and plays an important role in particular 

for  training  for  the  unemployed  and  vulnerable  groups.
  Other  policy  initiatives  from  the 
Skills Agenda and EU funding support Member States to further develop the supply side of 
their adult learning systems. However, the evaluation of the Council Recommendation on the 
validation  of  informal  and  non-formal  learning  highlights  that  better  training  offers  on  the 
supply  side  need  to  be  complemented  with  incentives  on  the  demand  side  to  significantly 

105

 The present initiative covers policy measures to ensure that 
increase outreach to individuals.
the substance of existing initiatives on the supply side reaches individuals and empowers them 
to participate in training. 

106

5.3 Description of the policy measures  

Identified  policy  measures  need  to  contribute  to  closing  the  existing  gaps  in  financial 
support.  This  requires  particular  attention  to  groups  that  are  often  not  well  served  by  the 
current support systems as described in Section 2. Measures need to also increase individuals’ 
motivation  and  incentives  to  seek  training  as  evidence  shows  that  financial  support  is 
important but not sufficient for participation. They are grouped in three areas and combined 
into  packages  (Section  5.4).  Respecting  the  principles  of  subsidiarity  and  proportionality, 
choices on key design parameters of all measures are left to the Member States as well as 

the  Advisory  Committee  on  Vocational  Training  (ACVT)
bargaining for instance concerning the funding of training. 

107

  to  respect  national  collective 

102

103

104

 See the case study from AT in Annex 13 for an example. 
 25 Member States have financial incentives for companies and 14 have training funds, cf. Annex 8.2. 
 See for instance the non-exhaustive list of operations that can be supported with the future EU budget to deliver 

on the Skills Agenda on p. 23 of the European Skills Agenda. 
105

 European Commission (2019), Adult learning policy and provision in the member states of the EU: a synthesis 

of reports by country experts. 
106
 SWD (2020) 121 final. 
ACVT, Opinion on an EU initiative on ILAs and strenghtening training provision in Europe.  

107

20 

 
 
 
                                                 
Area 1: Individual training entitlements 

For the purpose of this impact  assessment, individual  training entitlements are defined as  a 
recurrent personal budget that is at the individual’s disposal to cover the direct costs of 
his/her  training/course  fees  within  a  set  time  period.  Also  guidance,  skills  assessment  and 
validation of offers may be eligible for funding from this budget. Training entitlements can 
close gaps in financial support by channelling funding for skills directly to the individuals in 
need  of  training.  They  can  also  increase  individuals’  incentives  and  motivation  to  seek 

training  by  providing  salient  financial  support
  and  by  allowing  for  content,  timing  and 
delivery mode of training that is more tailored to individual needs and professional ambitions. 
In  the  public  consultation,  more  then  80%  of  respondents  agreed  that  individual  training 
entitlements  are  effective  at  tackling  financial  constraints  and  at  increasing  incentives  and 

108

motivation influencing participation in training.

109

There are two main delivery modes to provide individual entitlements and they can be targeted 
at  different  groups,  as  discussed  in  the  sub-section  below.  The  precise  amount  of 
entitlements, the funding source and other key design features would be left to Member 
States. As stated in the ACVT Opinion, Member States are best placed to make these decisions 
in light of differences in national costs for training and existing arrangements for the funding 
of  training.  Member  States  would  be  asked  to  ensure  that  the  implementation  of  the 
Recommendation  does  not  reduce  existing  financial  support  for  training  by  employers  or 
training providers, but brings about a significant net increase in skills investments in order to 
satisfy the training needs not well covered by existing support systems.    

Area 1.1: Delivery mode of training entitlements 

This  impact  assessment  analyses  training  vouchers  (measure  1.1.1)  as  some  kind  of  them 
existed  in  21  EU  Member  States  in  2020.  Another  delivery  mode  are  personal  accounts 
(measure  1.1.2).  Whilst  not  widespread  in  the  EU,  personal  accounts  are  an  innovative 

approach that has been receiving increasing attention in the policy debate.

110

Measure 1.1.1: Training vouchers 

For the purpose of this impact assessment, we define training vouchers as a delivery mode for 
training  entitlements  through  which  a  specific  funder  (e.g.  PES)  funds  a  specific  type  of 
individuals’  training  (e.g.  in  digital  skills).  Thus,  the  individual’s  flexibility  in  using  the 
entitlement is  limited. There may be  also  other  modalities and conditions  depending on the 

108

 Theory and experimental evidence from behavioural economics suggests that ex ante training entitlements are 
more  effective  at  incentivising  training  than  an  ex  post  reimbursement  of  training  costs,  as  individuals  “lose” 
support  if  they  do  not  spend  their  budget.  See  for  instance  Sunstein  and  Thaler  (2008),  Nudge:  Improving 
Decisions about Health, Wealth and Happiness.  
109

 84% of PC respondents agreed that training entitlements delivered through individual learning accounts are 
effective at tackling financial constraints, and 82% agreed that they increase incentives and motivation. Figures 
are similar for individual training entitlements delivered in other forms (82%, for both).  
110

 For more information on training vouchers in the Member States, see Annex 8.2. Personal accounts in France 
are presented in Annex 14. Such accounts also exist in Singapore, see the case study in Annex 13. Recent OECD 
skills strategies have recommended their introduction for BE (Flanders), NL and SI. 

21 

 
 
  
 
                                                 
scheme  (e.g.  the  period  during  which  the  entitlement  has  to  be  used).  This  policy  measure 

addresses  concerns  voiced  by  stakeholders,  in  particular  employers
correspondence of training to labour market needs.   

111

,  to  consider  the 

Measure 1.1.2: Personal accounts  

For the purpose of this impact assessment, we define personal accounts as a delivery mode for 
training entitlements that allows individuals to accumulate training entitlements over time in 
order  to  use  them  on  whichever  training,  guidance  or  validation  opportunity  they  deem  the 

most useful and whenever they see fit within the boundaries of the scheme.
 They de-couple 
training entitlements from their original funder and give individuals full ownership over 
the entitlements. They allow for a portability of entitlements during professional transitions 
and  can  facilitate  cost-sharing  between  different  funders,  such  as  public  authorities  and 
employers,  by  allowing  different  funders  to  contribute  to  the  account.  This  delivery  mode 
addresses  concerns  voiced  by  some  stakeholders  (including  the  European  Trade  Union 
Committee  for  Education  and  civil  society  organisations  such  as  the  AGE  Platform  and 

112

) about the need for broad autonomy in how individuals can 
FEPS/Jaques Delors Institute
spend their training entitlements in order to promote a culture of developing skills throughout 
working life. The possibility to accumulate entitlements over several years was also specifically 

113

supported by some stakeholders.

114

Area 1.2: Target group for training entitlements 

Training  entitlements  could  be  provided  to  a  specific  target  group  (measure  1.2.1)  or  to  all 
working age adults (measure 1.2.2).   

Measure 1.2.1: Defined priority target groups  

This approach can ensure that those who are in most pressing need of training can access it. 
These target groups can be defined on the basis of the employment or contract status, level of 
skills or the sector of employment, as well as other circumstances (i.e. disability). This measure 
addresses  stakeholders’  concerns  about  potential  displacement  effects  of  private  skills 

investments (“deadweight loss”)
 when opting for a broader target group. Such effects can be 
reduced  by  successfully  targeting  only  those  individuals  who  would  not  have  been  able  to 
participate in  learning otherwise.  It  corresponds  to  the approach followed by most Member 

115

States to date

116

 and is in line with the view expressed by some stakeholders, such as AONTAS 

111

 BusinessEurope, SMEUnited, SGI Europe, “Employers’ input to an ACVT Opinion on a future EU initiative 

aiming to improve/boost training provision across Europe”. 
112
 See Section 5.4 for a discussion of these boundaries. 
  ETUCE  and  AGE  Platform  Europe,  response  to  PC  on  ILAs.  Fernandes  and  Kerneïs  (2021),  Towards  an 

113

individual right to adult learning for all Europeans, FEPS and Jacques Delors Institute.  
114

 For instance, the response by the Flemish Ministry of Education and Training to the PC on ILAs. 
 I.e., training entitlements could be used to pay for training individuals that would otherwise have been paid for 

115

with private funds. See Annex 11 for details. 
116

  Seen  the  mapping  and  discussion  of  training  voucher  schemes  in  Annex  8.2  as  well  as  the  IT  case  study 

presented in Annex 13, where the target groups are chosen at regional level. 

22 

 
 
 
                                                 
(Ireland’s national adult learning organisation) or SMEunited (an employers organisation), that 

attention should be focused on specific vulnerable groups.

117

Measure 1.2.2: All working age adults118 
This approach includes e.g. all those currently in employment who would like to train for new 
tasks to  be  able to keep  their job  or  prepare for  professional  transitions. It addresses issues 
related  to  the  fragmentation  of  support  schemes  for  adult  learning,  and  can  facilitate  the 
promotion  of  a  culture  of  developing  skills  throughout  working  lives.  This  is  the  approach 

119

taken by FR, NL and Singapore.
 It is in line with the view that the initiative should promote 
a  universal  right  and  be  available  to  all  learners,  expressed  in  the  public  consultation  by 
stakeholders  such  as  the  Lifelong  Learning  Platform  (LLP),  the  European  Trade  Union  
Confederation  (ETUC)  and  Eurocadres  (trade  unions)  or  WKÖ  (an  Austrian  employers’ 
organisation). In its dedicated opinion, the ACVT agrees that while the initiative should focus 
on the upskilling and reskilling of the workforce (workers/employees/self-employed), it may 
also tackle training for other target groups such as the unemployed and inactive individuals, 

retired people or young persons not in employment, education or training.

120

Area 2: Career guidance 

Career  guidance  services  support  individuals  in  their  training  and  career  choices,  direct 
individuals  to  skills  assessment  and  validation  procedures,  and  can  provide  assurance 
concerning  the  quality  of  training  opportunities  and  the  subsequent  recognition  of  training 
outcomes on the labour market. This can increase individuals’ incentives and motivation to 
seek  training  by  raising  awareness  of  training  needs  and  available  offers,  improving  the 
“match” between individuals and training opportunities and training outcomes.  

The  importance  of  guidance,  and  its  integration  into  broader  strategies  to  increase  adult 
121

 It was also underlined by resondents from all 
learning, has been emphasised by the Council.
stakeholder groups in the targeted consultations and positions papers submitted in the public 

122

123

 agreed that guidance is effective in 
consultation.
increasing incentives and motivation to participate in training, reflecting near universal support 

 92% of public consultation respondents

117

118

 AONTAS, response to the PC on ILAs and SMEunited, response to PC on ILAs and micro-credentials. 
 The focus of this measure is on all working age adults, with the precise age range to be determined by 

Member States. Since the Adult Education Survey only includes individuals aged 25-64, quantifications in this 
IA are based on this age range. 
119

 See Annexes 13 for case studies on NL and Singapore, and Annex 14 for details on the French CPF. 
ACVT, Opinion on an EU initiative on ILAs and strenghtening training provision in Europe. 

120

121

 Resolution of the Council and of the Representatives of the Governments of the Member States, meeting 
within the Council of 21 November 2008 on better integrating lifelong guidance into lifelong learning strategies. 
Also see Annex 8.1. 
122

  Inluding  in  position  papers  submitted  in  response  to  the  public  consultation  by  public  authorities  (e.g.  the 
Ministry of Labour and Social Policies of Italy), social partners (e.g. SMEunited, ETUCE and Eurocadres) and 
NGOs (e.g. the Lifelong Learning Platform/LLP and European Association for the Education of Adults/EAEA). 
123

 Ranging from 87% of businesses to 96% of public authorities. 

23 

 
 
   
  
                                                 
from  all  groups  of  stakeholders.  Also  the  ACVT’s  opinion  on  the  initiative  emphasises  the 
importance of quality, effective and inclusive guidance and counselling services are essential.  

Measure 2.1: Making career guidance available to all 

Under this measure, Member States would be recommended to ensure that career guidance is 
available to all. Career guidance services already exist in almost all Member States, though 
with  a  large  variation  in  effective  outreach  (Section  2.2).  Such  services  often  accompany 

124

initiatives that provide individual training entitlements.
 This policy measure would underline 
the importance of outreach and cooperation with all relevant stakeholders as well as up-to-date 
labour  market  and  skills  intelligence  to  inform  guidance.  The  detailed  modalities  of  career 
guidance  provision  would  be  left  to  the  Member  States  (and  may  be  made  available  to 
individuals free of charge or considered as eligible for funding from training entitlements while 
ensuring it is available to all).  

Measure 2.2: Career guidance as a pre-condition for the mobilisation of the training 
entitlement 

Under this policy measure, the use of the training entitlement would be subject to compulsory 
guidance,  with  a  view  to  improve  the  match  between  the  selected  learning  opportunity,  the 
labour  market  and  the  individual  skills  needs.  This  reflects  current  provisions  or  policy 

,  and  addresses  stakeholder  concerns  (in  particular  by 
reflections  in  some  Member  States
employers such as SMEunited) about a lower efficiency of spending on training entitlements 
without specific guidance.  

125

Area 3: Enabling framework  

Beyond  career  guidance,  additional  policy  measures  can  create  an  enabling  framework 
complementing  individual  training  entitlements  and  thereby  contributing  to  reaching  the 
objectives of this initiative. 

Measure 3.1: Public registry of recognised training, validation and career guidance 
opportunities 

A  public  registry  of  recognised  training  opportunities  can  increase  individual’s  incentives 
and motivation to seek training. It does so by centralising information on available training 
opportunities,  thus  facilitating the “match” between individual  and training opportunities.  It 
has  also  the  potential  to  reduce  uncertainty  about  the  quality,  labour  market  relevance  and 
recognition of the registered training on the labour market by delegating the relevant checks to 

a third party, tackling the problem of imperfect information by individuals.
 A comparison of 
the  experiences  of  the  English  and  Scottish  training  entitlement  schemes  also  point  to  the 
importance of having such a registry for the success of related initiatives (see Annex 13). A 

126

124

 See for instance the Singapore experience described in Annex 13, where the  “Skills And Training Advisory 
Services” are available for users for the Skills Future Credit and the French CPF described in Annex 14 where the 
CPF has been accompanied by a strengthening of the “Professional Evolution Guidance” (CEP). 
125

 See the case studyies from Estonia and Italy in Annex 13 and Annex 8.2. 
 See for instance the criteria set by the SkillsFuture Singapore to enter the Skills Future Credit course directory, 

126

presented in Annex 13. 

24 

 
 
                                                 
registry of recognised offers provides an opportunity to ensure that the substance of existing 
recommendations,  notably  the  Council  Recommendation  on  VET,  becomes  tangible  for 

individuals.

127

Comparable  registries  already  exist  in  some  Member  States  (Section  2.2),  and  their 
establishment  has  been  specifically  supported  in  the  targeted  consultations  by  some 

stakeholders  such  as  the  European  Mentoring  &  Coaching  Council.

128

  In  the  public 

129

consultation,  86%  of  respondents
  agreed  that  such  registries  are  effective  in  increasing 
incentives and motivation to participate in training. Respondents also largely agreed that skills 
intelligence should play a strong role in the selection of training opportunities included in the 
registry,  and  that  public  authorities,  employer  organisations  and  trade  unions  should  play  a 
strong role. While there was broad consensus among stakeholders that strong quality assurance 
mechanisms are essential to ensure trust in and acceptance of a scheme of individual training 
entitlements,  employer  organisations  (such  as  SMEunited)  pointed  out  that  eligibility  rules 
need to be sufficiently flexible to ensure a correspondence of the training offer to (changing) 
skills needs on the labour market.  

Under  this  policy  measure,  Member  States  would  be  recommended  to  establish  a  public 
registry of recognised training, validation and career guidance opportunities.  Member 
States would be recommended to ensure adherence to quality assurance and the labour market 
relevance of the included training offers by establishing a durable governance mechanism that 
considers evidence from skills intelligence and social partners and foresees periodic updates to 
the registry, with the view to support flexibility. Member States would be recommended to also 
make the registry easily accessible, notably by digital means, and user friendly for individuals.  

Measure 3.2 Paid training leave 

Paid  training  leave  schemes  allow  individuals  to  take  time  off  work  for  training  while  still 
receiving their salary in its entirety or in part, or alternatively an allowance to cover the costs 
of  living  from  public  or  social  partner  funds.  They  can  cover  gaps  in  financial  support 
concerning the indirect/opportunity costs of training that is not covered by regular employee 
training during working hours, and for individuals with weak or no links to an employer. They 
can therefore increase incentives and motivation for individuals to train by addressing the 
time obstacle to training. 

Paid training leave provisions of some sort already exist in 24 Member States, but with variable 
coverage and often low take-up (Section 2).  In the public consultation, 82% of respondents 
agreed  that  paid  training  leave  for  employees  is  effective  to  address  time  constraints  to 
participation  in  training,  and  85%  agreed  to  make  allowances  for  the  cost  of  living  during 

127

 Council Recommendation of 24 November 2020 on vocational education and training (VET). See Annex 8.1 

for a discussion of additional relevant EU initiatives. 
128

 European Mentoring & Coaching Council, response to PC on ILAs and micro-credentials. 
 Ranging from 65% of businesses to 95% of NGOs and others. 

129

25 

 
 
 
                                                 
  Trade unions  emphasised the need to  strengthen the 
training available to  non-employees.
existing paid training leave provisions in the Member States in position papers and the targeted 

130

consultations.

131

Member States would be invited to introduce paid training leave provisions or reassess the 
existing  provisions  to  ensure  they  cover  all  types  of  employed  individuals,  including  on 
financial support for employers whose employees make use of paid training leave (in particular 
SMEs  who  face  greater  challenges,  cf.  Section  2).  They  would  also  be  recommended  to 
strengthen  the  links  between  paid  training  leave  provisions  and  other  available  support 
measures. The detailed modalities of provisions would be left to the Member States. 

Measure 3.3 Effective governance for continuous improvements 

Effectively closing support gaps and increasing individual’s incentives and motivation requires 
an effective governance to ensure sustainable funding and efficient spending. It involves a 
constant  monitoring  and  evaluation  of  the  success  of  the  existing  support  schemes  and 
possible adjustments to them. For instance, priority target  groups may change as the labour 

  and  the 
market  evolves.  Also  systematically  integrating  relevant  EU  Recommendations
experience from evaluations and observed take-up rates among different groups of adults can 
help  a  support  scheme  reach  its  objectives  more  effectively  and  efficiently,  including 

132

133

  With  this  policy  measure,  Member 
concerning  effective  outreach  to  vulnerable  groups.
States  would  be  recommended  to  set  up  funding  and  governance  arrangements  that  pay 
particular  attention  to  the  needs  of  micro-entreprises  and  SMEs  and  allow  for  an  effective 
integration  of  financial  and  non-financial  support  for  training.  They  would  also  be 
recommended to ensure outreach and awareness-raising tailored to the needs of the potential 
beneficiaries of the scheme for individual learning accounts, jointly with social partners, civil 
society organisations, regional and local organisations and other relevant actors and networks. 

In  line  with  the  principle  of  subsidiarity,  these  governance  arrangements  would  be  set  at 
national level, with the view to evaluate and adjust as necessary, for instance, concerning the 
amount  of  training  entitlements,  priority  target  groups,  the  registry  of  recognised  training 
opportunities  or dedicated outreach and  awareness campaigns. The importance of  including 
external  stakeholders,  such  as  social  partners,  was  underlined  by  employer  representative 

organisations and trade unions.

134

  This was also underlined in the ACVT’s dedicated opinion.  

130

 Ranging from 54% for trade unions to 100% for public authorities concerning paid training leave for employees 

and 54% for trade unions and 94% for citizens concerning allowances open to non-employees.  
131

 ETUC and ETUCE, responses to the PC on ILAs and dedicated social partner hearing. 
 Notably the Council Recommendations on VET, Upskilling Pathways and the validation of non-formal and 

132

informal learning, see Annex 8.1 for an overview. 
133

 Improving outreach was one of the main points of the EAEA contribution to the PC.  According to them this 

is especially important for vulnerable groups such as the low skilled.  
134

 Stressed in the responses to public consulations on individual learning accounts by ETUC, IndustriAll and 
Eurocadres on the trade union side and BusinessEurope, SMEunited and SGI Europe, as well as the MEDEF, on 
the employer side. 

26 

 
 
  
                                                 
Annex  5  provides  an  overview  of  the  intiative’s  intervention  logic,  linking  the  policy 
measures to the identified problems and the objectives of the initiative.  

5.4 Description of the alternative policy packages 

The policy measures are grouped in two policy packages (packages A and B). Packages are 
designed to be coherent and their policy measures are mutually reinforcing in achieving 
the  specific  objectives.  For  instance,  making  career  guidance  a  pre-condition  for  the 
mobilisation  of  the  training  entitlement  is  more  feasible  when  the  provision  of  training 
entitlements is limited to defined priority target groups. At the same time, they highlight the 
main trade-offs that emerge on the basis of the available evidence and the stakeholder views 
(cf. the discussion above under Area 1).  

Package A combines measures 1.A.1 (vouchers as a delivery mode) and 1.B.1 (defined target 
groups). It hence aims to empower adults to participate in learning by closing gaps in existing 
support systems in a targeted way, focusing financial support on those most in need of training 
and on providing the skills which are the most needed on the labour market at a certain point 
in time. Vouchers are the delivery mode that fits best this approach because they enable the 
funders to decide who they give the voucher to and what type of training the voucher should 
cover.  Measure  2.2  (Career  guidance  as  a  precondition  for  the  mobilisation  of  the  training 
entitlement) is also included in package A, as it allows to ensure that the funder’s assessment 
of labour market relevance is reflected in the choice of training.  

Package B combines measures 1.A.2 (personal accounts) and 1.B.2 (covering the whole adult 
population of working age), with the view to operationalise the right to training as a universal 
right. It responds to the argument that in a context and changing skills requirements and more 
frequent labour market transitions, everyone should receive incentives to participate in training, 
no matter their  current  employment  status  or their level  of skills.  Personal  accounts are the 
delivery mode that fits best this approach as they allow for entitlements to follow individuals 
throughout their careers and they facilitate cost-sharing, which is essential when the entitlement 
covers the whole adult population of working age (employed, unemployed, atypical workers, 
platform workers, etc.). 

Policy  package  B  has  2  two  sub-options,  B.1  and  B.2:  under  package  B.1,  all  beneficiaries 
receive the same amount of entitlements (measure 1.2.2), whereas under package B.2 training 
entitlements are modulated according to the target group, with those having particular training 
needs as determined by  the Member States  receiving a higher amount of entitlements (new 
measure 1.2.3). This is due to the fact that during the analysis, the complementarity between 
some elements of package A and some elements of package B became clear. This also follows 
the OECD’s and the ILO’s recommendation for universal support that is however modulated 

based on recipient characteristics.

135

Both  packages  A  and  B  include  measure  2.1  and  measures  from  area  3  because  they  are 
considered as essential to the success of the initiative irrespective of the package chosen. Only 

135

 OECD (2019), Individual learning accounts: Panacea or Pandora’s box? The ILO position paper submitted 

in the PC also expressed support for a universal yet differentiated approach. 

27 

 
 
  
                                                 
training, validation and career guidance opportunities included in the public registry of 
recognised opportunities would be eligible for funding from training entitlements under all 

packages,  to  promote  quality,  labour  market  relevance
  and  recognition  of  the  training 
undertaken. Under package A, this condition complements the requirement of guidance as a 
condition  for  mobilising  the  training  entitlements.  However,  the  public  registry  would  be 
accessible also for those who do not receive training entitlements.  

136

Table 1: Overview of the policy packages 

Measures 

Package A  

Package B.1 

Package B.2 

Training 
entitlement 
delivery mode  

Measure 1.1.1 : 
Training voucher 

– 

Measure 1.1.2: 
Personal account 

Measure 1.1.2:  
Personal account 

Training 
entitlements 
target groups  

– 

Measure 1.2.1:  
Defined priority 
target groups 

Measure 1.2.2: 
Entire adult 
population of 
working age and 
same entitlement 

Measure 1.2.3: 
Entire adult population of 
working age, but 
modulated entitlement 
according to the target 
group 

Career guidance 

Measure 2.1 (Guidance for all) 

Measure 2.2 
(Guidance as a 
pre-condition) 

Enabling 
framework 

Measure 3.1 (Public registry) 

Measure 3.2 (Paid training leave) 

Measure 3.3 (Effective governance) 

6. WHAT ARE THE IMPACTS OF THE POLICY PACKAGES?  

6.1 Analysing the main impacts 

This section presents the main social and economic impacts of the policy packages as well as 
impacts on fundamental rights. No direct environmental impact was identified, and potential 
indirect impacts are discussed in Section 7 under “coherence with other EU objectives”.  

Significant  efforts  have  been  made  to  collect  the  necessary  data  and  provide  a  quantitative 
assessment. However, this was not always possible. A first reason are limitations concerning 
the  availability  of  evidence,  notably  on  possible  interactions  between  policy  measures.  A 
second reason is that respecting the principle of subsidiarity underlined in Article 149 TFEU, 
choices  on  key  design  parameters  of  the  policy  options  are  left  to  the  Member  States, 
including on the amount of training entitlements and priority target groups. This precludes a 

136

 The need for labour market relevance was widely mentioned during the consultation process, with a minority 

view expressed especially by NGOs (EAEA, LLP) that a broader set of life skills should also be supported. 

28 

 
 
 
 
                                                 
full measurement of their social and economic (including fiscal) impacts. Finally, impacts will 
depend on the degree of implementation of the recommendations by Member States and on 
the extent to which it can build on already existing provisions. 

Therefore, the approach taken is to outline a quantification of expected impacts for specific 
implementation scenarios on the basis of assumptions on the key design parameters derived 
from the available evidence, complemented with a qualitative assessment. Impacts are assessed 
relative to the baseline scenario (Section 5.1), based on a view of how the problem may evolve 
(Section 2.4) and taking into account differences in Member States’ estimated adult learning 
participation  levels  and  training  costs.  This  is  complemented  by  a  qualitative  mapping  of 
relevant  already  existing provisions in  the Member States in  Annex 8.2.   The methodology 
used  is  described  further  in  Annex  4,  and  details  on  these  scenario  analyses  for  the 
quantification of impacts referred to throughout this Section, including sensitivity checks on 
the key parameters, can be found in Annex 12. 

Across  impact  categories  and  target  groups,  the  horizontal  recommendation  of  effective 
governance  mechanisms  with  monitoring  and  evaluation  is  expected  to  have  positive 
impacts  by  allowing  for  an  adjustment  of  the  design  parameters  on  the  basis  of  “lessons 

learned”.

137

6.1.1 Social impacts 

On individuals 

Participation in learning: All packages are expected to stimulate reforms in Member States 
that increase the participation of adults in learning compared to the baseline scenario. The 
impact on participation rates will depend on the size of the target group that receives training 
entitlements,  on  the  number  of  potential  beneficiaries  who  spend  their  training  entitlement 
(“take up”), and the extent to which individuals purchase training they would otherwise not 
have undertaken (no “deadweight loss”, cf. Annex 11 for details). The scenario analyses below 
are  based  on  the  existing  evidence  from  related  schemes  and  from  training  voucher 
experiments.  A  limitation  is  that  they  do  not  fully  consider  interactions  between  training 
entitlement and the other policy measures that are included in packages A and B, and whose 
expected impact is discussed qualitatively below. 

Package A could result in an additional 5.8 million adult learners every year across the EU if 
training  entitlements  were  provided  to  all  low  qualified.  This  would  increase  their  annual 
participation rate in 2030 considerably (from a predicted 23.4% under the baseline scenario to 
35.0%). The overall participation rate would increase from 48.6% to 51.0% due to the relatively 
small size of the target group. Similar changes are expected for additional priority target groups 
considered in the scenario analyses on packages A and B.2 (the inactive, unemployed, workers 
not in permanent employment and workers in SMEs), with the support provided to workers in 
SMEs (the largest of these priority target groups) leading to the highest overall participation 

137

 See e.g. Annex 14 for discussion of how the recent reforms of the French CPF have increased take-up, and 

have eliminated the initial under-representation of low-qualified individuals among training participants. 

29 

 
 
  
                                                 
138

rate of 56.2%.
by 2030 for plausible choices of priority target groups.  

 Package A would, therefore, not reach the Porto target of 60% participation 

Packages B would be expected to significantly increase adult learning participation across all 
groups  of  working  age  adults.  Package  B.1  could  result  in  an  additional  33.6  million  adult 
learners across the EU and increase the participation rate to 62.7% by 2030. Under package 
B.2, the corresponding figures could be an additional 36.6 million learners and a participation 

rate  of  63.9%  when  providing  all  workers  in  SMEs  with  additional  entitlements.
  Both 
scenarios  under  package  B  would,  therefore,  achieve  the  Porto  target  of  at  least  60% 
participation by 2030. 

139

By  targeting  specific  groups,  package  A  could  also  reduce  inequalities  in  learning 
participation across groups of adults: for example, the gap in the participation rate of low 
qualified adults compared to the average in 2030 could decrease by 9.2 percentage points (from 
25.2 to 16.0 percentage points). The impacts of packages B on inequalities in participation 
across groups are ambiguous: they may increase if take-up rates among under-represented 
groups are significantly lower, or decrease them due to the lower deadweight loss for priority 
target groups. Modulating support according to the target group in package B.2 tends to reduce 
inequalities. The larger target group of packages B allow them to better address support gaps 
that cannot be easily anticipated by policy makers on the basis of individual characteristics (for 
instance, the ambition to prepare for a professional transition).  

Both  packages  could  also  reduce  inequalities  in  adult  learning  participation  across 
Member States, with stronger impacts under packages B due to its larger target group. The 
reason is that in Member States where adult learning participation rates are lower  under the 
baseline  scenario,  deadweight  loss  can  be  expected  to  be  less  prevalent,  resulting  in  larger 
increases  in  participation  rates.  Under  package  B.1,  the  increase  could  range  from  11.6 
percentage points in the Member State with the highest participation rate under the baseline 

scenario (SE) to 17.7 percentage points in the Member State with the lowest (RO).

140

The limited available evidence suggests that making prior career guidance a condition for 
the mobilisation of training entitlements in package A may have a small negative effect on the 

uptake of training.

141

 The evidence from the French CPF suggests that when individuals can 

138

 We assume an annual take-up rate of training entitlements of 13.1% for the low qualified and 22% for other 
groups, on the basis of evidence from existing schemes. We further assume a deadweight loss of 30%, adjusted 
by Member State and target group. See Annex 11 for a review of evidence underlying these assumptions, and 
Annex 12.A for further details on the quantification scenarios. 
139

 We assume training entitlement worth 30 hours of training under package B and 50 hours for priority target 
groups under package A and B2. The monetary costs depend on Member States’ training costs, and are estimated 
to be around €381 for 30 hours and €631 for 50 hours on average across the EU, similar to the amounts typically 
provided in existing schemes. For the entitlements worth 30 hours, we assume somewhat lower take-up rates of 
18.4% (9.5% for the low-qualified), cf. Table 12A.2. 
140

  The  corresponding  figures  are  12.8  and  19.4  percentage  points  for  package  B2  with  additional  support  to 

workers in SMEs, cf. Table 12A.3. 
141

 Perez-Johnson et al. (2011), Improving the Effectiveness of Individual Training Accounts: Long-Term Findings 
from an Experimental Evaluation of Three Service Delivery Models. Concerns that such a pre-condition might 

30 

 
 
  
                                                 
accumulate  their  training  entitlements  in  personal  accounts  over  multiple  years  (as 
foreseen in packages B), they use this flexibility to undertake less frequent yet longer or more 
expensive trainings, which would reduce the impacts on annual participation rates in packages 
B. However, aligning the intensity of training to the current skills needs and circumstances is 
142

expected to increase its effectiveness overall.

Registries of recognised training opportunities  can be expected to  increase the uptake of 
training by making it less time-consuming for individuals to identify a suitable training offer. 
The  review  and  strengthening  of  paid  training  leave  is  expected  to  increase  the  share  of 
beneficiaries who make use of their training entitlements as it relaxes time barriers, in particular 
among  groups often not  yet  covered by existing schemes such as atypical  workers (Section 
2.2).  

Wages and employment: The impacts on wages and employment follow from the changes in 
participation rates and are lower for package A and highest for package B.2. Studies from a 
large number of countries suggest that there are substantial positive wage returns to training. 
Return estimates also remain significant in studies that are able to account for the selection of 
high-ability adults into training, and a review of these estimates suggests returns around 1% 
for a training course of 30 hours (Annex 10). 

Regarding  the impact  on  employment,  a  review  of  more  than  200  recent  studies  of  active 
labour  market  programmes  by  Card  et  al.  (2018)  identified  asignificant  increases  in 

employment probability of the previously unemployed after training participation.
 Results 
from  macroeconomic modelling that also  takes into  account  general  equilibrium effects  via 
higher productivity and a more efficient reallocation of workers across jobs suggest that giving 
low-qualified individuals training entitlements under package A could yield an additional 30 
000 jobs across the EU by 2030, compared to 120 000 under package B.1 and 140 000 under 
package B.2 when providing additional support to all low qualified, with job gains increasing 
to 100 000 - 400 000 by 2040 (see Annex 12C for details). 

143

There  will  be  substantial  heterogeneity  in  wage  and  employment  returns  both  across 
individuals  and  training  courses.  The  conditions  on  quality  assurance  and  labour  market 
relevance  for  inclusion  in  the  registry  of  recognised  training  opportunities  and  effective 
governance provisions are expected to ensure that average returns are in line with those found 
in  the  literature.  While  giving  individuals  greater  freedom  in  their  choice  of  training 
opportunities under packages B compared to package A can be expected to lead to a higher 

deter potential participants from participating in training are also raised in the description of the STAP scheme in 
NL (see the case study in Annex 13). 
142

 For instance, the average cost of a CPF-funded training between Nov. 2019 and June 2021 was €1 263 and is 
higher for the unemployed compared to a basic annual training entitlement of €500, indicating that people make 
use of the possibility to accumulate their entitlements (see Annex 14 for details). Note that the estimated impact 
of the policy measures on annual adult learning participation rates should be considered an (imperfect) summary 
measure of effectiveness, but is distinct from the specific objectives of the initiative outlined in Section 4.2. 
143

 Card et al (2018), What works? A meta-analysis of recent active labor market program evaluations. Journal of 

the European Economic Association. See Annex 10 for a review of the evidence. 

31 

 
 
 
                                                 
share of training conferring more transversal skills, there is no evidence that it is less labour 
market relevant (Annex 11). 

All  packages  are  expected  to  have  positive  indirect  effects  on  wages  and  employment 
outcomes  by  improving  the  participation  in  guidance  and  take-up  of  skills  validation 
opportunities, which can facilitate wage negotiations or finding employment.  The evaluation 
of the Council Recommendation on validation of informal and non-formal learning concluded 
that  strengthening  the  financial  support  to  individuals  can  promote  the  uptake  of  the  new 

validation offers that have been created by the Member States.
 81% of public consultation 
respondents agreed that the European initiative on individual learning accounts can add value 
on  the  validation  of  non-formal  and  informal  learning  outcomes,  and  77%  agreed  on  the 

144

provision of guidance services.

145

Working conditions and social dialogue: All packages are expected to improve the working 
conditions  of  the  beneficiaries  of  training  entitlements  by  improving  access  to  training. 
Providing individuals with training entitlements is already used by some employers, mainly 
large companies, to improve the working conditions and motivation of their employees to take 
up training. Under package A, positive impacts would concern only some target groups selected 
by the Member State. Under packages  B, this  would  cover all  adults  on  the labour market, 
whereby the groups most in need could receive additional support under B.2. 

The  recommendation  to  revisit  paid  training  leave  provisions  is  expected  to  improve  the 
working conditions of employed adults, benefitting particularly those who are not well covered 
by the current paid training leave provisions (notably those in atypical work and employees of 
SMEs). It would also benefit employees at large by making it easier to identify quality training 
opportunities and cover the direct cost of training, strengthening the take-up of existing training 
leave offers beyond the currently low levels (Section 2.1). 

In  the  consultations,  trade  unions,  such  as  ETUC
,  have  expressed  their  concern  that  the 
initiative  could  reduce  the  training  provision  by  employers,  and  employers,  such  as 

146

147

, have argued that resulting national schemes should not regulate such training 
SMEunited
provision.  Both  packages  would  recommend  to  Member  States  to  ensure  that  the 
implementation of the Recommendation does not reduce existing financial support for training 
by employers, mitigating this concern. Moreover, the decision to spend training entitlements 
would  lie  with  the  individual,  ensuring  that  the  resulting  training  would  complement  the 
existing training provision by employers. 

Packages  can  strengthen  social  dialogue,  by  recommending  to  give  social  partners  an 
important role in the governance of the registry of recognised training opportunities.  Packages 

144

 See the Council Recommendation of 20 December 2012 on the validation of informal and non-formal learning 

and its 2020 evaluation (SWD (2020) 121 final). 
145

 Ranging from 57% of businesses to 91% of citizens for validation, and from 52% of businesses to 92% of trade 

unions for guidance.  
146

 ETUC, response to PC on ILAs and micro-credentials. 
 SMEunited, response to PC on ILAs and micro-credentials. 

147

32 

 
 
 
                                                 
B could further strengthen the role of skills in social dialogue by creating the infrastructure for 

top-ups to the personal accounts of employees in specific companies or sectors.

148

Other social impacts on individuals: Packages A and B are expected to have a range of positive 
impacts on individuals’ health and well-being (Section 2.3). The universal packages B is 
expected to create less stigma for vulnerable groups than the targeted package A in light of 
their approach that training is for everyone, no matter their current employment situation or 

skills level.

149

On education and training systems 

All  packages  are  expected  to  increase  the  size  of  the  market  for  continuing  training  by 
mobilising additional skills investments, with a larger potential impact of packages B stemming 
from their larger scale. This creates opportunities for training providers, who are also expected 
to benefit from enhanced visibility for their training offer after inclusion in the registry and 
from  a rapid  invoicing process  with  the  authority managing  the training entitlements as the 
single contact point.  

The  registry  of  recognised  training  opportunities  is  expected  to  increase  the  quality  and 
transparency of training offers, in particular for non-formal training offers where the scope 
for  improvements  is  largest  (Section  2.2).  While  small  training  providers  may  face  greater 
difficulties to meet the requirements for inclusion in the registry, the effective and inclusive 
governance  of  the  registry  recommended  to  the  Member  States  is  expected  to  remove 
unnecessary access barriers. This form of governance is also expected to improve the dialogue 
between training providers, public authorities and social partners, which can help improve the 
tailoring  of  the  training  offers  to  labour  market  needs,  including  the  skills  needs  of 

employees of SMEs.

150

The  increased  transparency  from  the  registries  of  recognised  training  opportunities  can  be 
expected to increase competition among training providers and reduce the cost of training, 
thereby  having  a  positive  impact  on  the  funder  of  the  training.  This  is  confirmed  by  first 
indicative  evidence  from  the  French  CPF,  where  the  average  price  of  more  standardised 
training offers such as English language certificates or driving licences has decreased following 
the most recent reform (Annex 14).  

On society as a whole 

Packages A and B are expected to have a range of positive impacts on civic participation and 
social  cohesion  (Section  2.3).  Since  these  benefits  are  expected  to  be  proportional  to  the 
increase in adult learning participation, they can be expected to be stronger for packages B. 

148

149

 This is possible in the French CPF since 2020. See Annex 14 for details. 
  See  Moffitt  (1983),  An  Economic  Model  of  Welfare  Stigma  (The  American  Economic  Review)  for  a 
presentation of this argument and Stuber and Schlesinger (2006), Sources of stigma for means-tested government 
programs (Social Science & Medicine) for empirical evidence. 
150

 In the consultations, representatives of SMEs pointed to a lack of training offers that are tailored to their needs 
as a significant hurdle. SMEs are less able than large companies to “create their own training offer” due to their 
smaller scale.  

33 

 
 
 
 
  
                                                 
The evidence also suggests that increasing inequality of opportunity has played an important 

role in the recent rise of anti-establishment sentiment.
of closing gaps in the access to training and thereby equalising opportunities. 

  This hints at broader positive impacts 

 151

6.1.2 Economic impacts 

Direct costs of training entitlements   

The direct costs of training entitlements depend on the size of the target group, the take up 
and  the  amount  of  entitlements.  The  scenario  analyses  introduced  above  suggest  that 
implementing package A in all Member States could result in a direct annual cost of €4.2 billion  
Packages B costs would 
(or 0.03% of 2020 GDP) for EU-27 when targeting the low-qualified.
be higher, ranging from €16.8 billion /0.13% of GDP (package B.1) to around €24.5 billion / 
0.18% of GDP (package B.2 with additional entitlements for SME workers). The amounts will 
152

depend on decisions by Member States regarding target groups and levels of entitlements.
Depending on the financing arrangement chosen by the Member States, these costs would be 

partly or fully borne by public authorities, employers or individuals.

153

Additional costs for enterprises including SMEs 

Additional costs may result from staff absence during training, which can lead to a decrease 
in productivity. Scenario analyses suggest that possible additional costs of undertaking training 
during working hours (either with informal permission from the employer, or making use of 
paid training leave provisions) would not outweigh training benefits (see Annex 12B). In the 
consultations, SME representatives have highlighted that the absence of staff during periods of 
training  is  a  particular  challenge  for  SMEs.  Therefore,  both  policy  packages  include  a 
recommendation to also include the financial support provided to employers whose employees 
make use of paid training leave in the review of the adequacy of existing paid training leave 

  They  also  include  a 
provisions,  with  particular  attention  to  the  needs  of  SMEs.
recommendation to consider the needs of micro-entreprises and SMEs in the design of funding 
arrangements. 
Additional costs for public authorities 

154

Under  all  packages,  the  set-up,  maintenance  and  governance  of  the  registry  of  recognised 
training opportunities would result in costs to public authorities. Additional costs may be 

151

152

 Sergei Guriev (2018), Economic Drivers of Populism. AEA Papers and Proceedings. 
  According  to  the  mapping  in  Annex  8.2,  21  Member  States  already  have  schemes  providing  training 
entitlements to individuals, and integrating these schemes would reduce additional funding needs. However, these 
schemes usually have a narrower target group than the scenarios considered under packages A and B. 
153

  In  the  PC,  91%  of  respondents  agreed  that  public  funding  should  be  used  for  training  entitlements,  68% 
supported the use of an employer´s levy (e.g. on payroll) and 53% supported individual contributions. Note that 
the policy measures on training entitlements (Section 5.3, Area 1) do not include a requirement of individual co-
payments when using their training entitlements, but individuals may contribute voluntarily or via social security 
contributions. Funding sources may be combined, and 94% of respondents supported a (complementary) use of 
EU funds. 
154

 Currently, public authorities already provide financial support to employers of workers on paid training leave 

in 12 Member States according to adult learning experts, see Annex 8.2. 

34 

 
 
 
 
  
                                                 
limited for the 12 Member States where similar registries already exist. Under packages B, the 
set-up  and  maintenance  of  personal  accounts  would  create  additional  costs.  Limited 
information  is  available  on  the  magnitude  of  these  costs,  as  Member  States  do  not 
systematically report them in a sufficiently disaggregated way. Estimates for the French CPF 
point to costs of running the system of personal accounts of about €100 million over the three-
year period 2020-2022. A review of the limited available evidence suggests that administrative 
costs as a share of direct costs of training entitlements decrease with the number of beneficiaries 

from a scheme.

155

Providing  career  guidance  for  free  could  result  in  additional  costs  for  the  guidance 
providers,  often  public  authorities.  However,  public  career  guidance  already  exists  in  25 
(Section  2.2).  The  simplified  cost  options  used  for 
Member  States  at  least  to  some  extent

reimbursement of guidance services under the ESF
 place the cost for an hour of in-person 
guidance at on average €18 across the Member States, ranging from €2 to €39. Thus, no or 
minimal set-up costs would arise and most additional costs would result from a higher uptake. 
Similarly, modest additional costs for public authorities can be expected due to the increase in 
the use of validation opportunities.  

156

Both packages allow for a provision of guidance services by providing orientation through the 
registry of recognised opportunities. Under package A, in-person guidance is a precondition 
for the use of training entitlements, and additional costs of this can be substantial. Therefore, 
expected additional guidance costs per beneficiary are significantly lower under packages 
B compared to A.  

The  recommendation  to  revisit  paid  training  leave  provisions  included  in  both  packages 
could also lead to additional costs for public authorities. In the Member States where public 
authorities are already involved in the funding of paid training leave, additional costs would 
be limited to those resulting from expanding coverage or higher take-up resulting from the 

other policy measures included under packages A and B.

157

Costs for education and training providers 

Compliance with the criteria required for inclusion in the registry of training offers may create 
costs for training providers, which can be a relatively higher burden for smaller providers. To 
minimise compliance costs for training providers, both packages recommend to establish these 

155

 See Annex 11.2 for an overview of the available evidence on costs, and Annexe 12B for details on how this 
has been incorporated in the cost benefit calculations. The mapping of available infrastructure in Annex 8.2 shows 
that  relevant  supporting  factors  to  limit  administrative  costs  of  the  policy  packages  (e.g.  secure  electronic 
identification) exist in many Member States. 
156

 European Commission (2018), Developing ‘Off-the-Shelf’ Simplified Cost Options (SCOs) under Article 14.1 

of the European Social Fund (ESF) regulation. 
157

  See  Annex  12B  for  an  estimate  of  additional  paid  training  leave  costs  for  a)  beneficiaries  from  training 
entitlements; and b) and among all those in employment. See the recent Impact assessment on work life balance 
for an estimation of the costs and benefits of paid training leave more generally, based on the assumption of a 
two-week paid leave period.  

35 

 
 
  
 
  
                                                 
criteria in line with existing national quality assurance frameworks and a governance of these 
registries that is responsive to these concerns. 

Other economic impacts 

On enterprises and SMEs: All packages are expected to lead to an increase in the productivity 
of  workers  and  hence  in  the  competitiveness  of  their  employers.  The  literature  review 
suggests that productivity impacts are about double the size of wage impacts, so that a 30 hour 

training increases productivity by about 2%.

158

 In the public consultation, 77% of respondents 

159

agreed
 that the initiative could increase the productivity and competitiveness of companies. 
As employees of SMEs currently participate less in training, the potential impacts of both 
packages on the productivity and competitiveness of SMEs are larger. Yet, while it is clear 
that packages B would cover SMEs, the impact of package A on SMEs would depend on each 
Member State’s choice of target groups, which could or could not cover SME employees.   

On individuals: Individuals participating in training may face indirect costs related to their 
inability  to  earn  income  during periods  of  training  or  costs  of  transport.  The  initiative 
reduces such indirect costs by including the paid training leave provisions in all packages, and 
in packages B by the flexibility given to individuals concerning the timing of training, allowing 
them to select the most convenient time (such as a period of low economic activity to minimise 
income losses).  

On public authorities: The increase in employment foreseen for both packages can be expected 
to entail in the long run higher tax returns and cost savings resulting from lower spending 
on unemployment benefits or healthcare (Section 2.3), with the smallest impacts for package 
A and the strongest impacts for package B.2. 

On  society  as  a  whole:  By  facilitating  training  in  transversal  skills  and  for  professional 
transitions, both packages are expected to improve the employability of workers and thus 
support successful labour market transitions in particular from declining to growing sectors 

of  the  economy.    81%  of  public  consultation  respondents
  agreed  that  the  initiative  could 
support the digital and green transitions by providing relevant skills. Impacts of packages 
B  are  expected  to  be  stronger  as  they  can  support  a  broader  range  of  training  to  support 
professional transitions, with the strongest impacts for package B.2 due to its additional support 
for target groups with specific needs.  

160

Both  packages  are  expected  to  increase  investment  and  business  growth,  in  light  of  the 
evidence of skills shortages as a barrier for companies (Section 2.3). They can be expected to 
161

have  indirect  positive  impacts  on  research,  innovation  and  technology  diffusion
throughout the economy. Impacts of packages B could be expected to be larger due to the fact 
that it is expected to trigger more participation in learning than package A. This is in part also 

158

159

160

161

 See Annex 10 for a literature review. 
 Ranging from 42% of trade unions to 93% of NGOs and others. 
 Ranging from from 46% of trade unions to 94% of citizens.  
 Sekmokas et al. (2020), Workforce skills and innovation diffusion- Trends and policy implications. 

36 

 
 
 
                                                 
due  to  the  fact  that  personal  accounts  can  stimulate  additional  skills  investments  by 

companies by facilitating a flexible sharing of training costs among multiple stakeholders.

162

The  estimated  macroeconomic  benefits  include  a  higher  level  of  GDP  per  capita  in  2030 
estimated at 0.23% (package A targeting the low qualified), 0.87% (package B.1) and 0.99% 
(package B.2 with additional entitlements for the low qualified) in comparison to the baseline, 
with increases between 0.33-1.4% of GDP by 2040 (cf. Annex 12C). The set-up of personal 
accounts  in  packages  B  is  expected  to  contribute  to  macroeconomic  stabilisation,  as  it 
allows individuals to accumulate training entitlements over time and spend them on training 
during economic downturns and also creates the infrastructure to quickly provide individuals 

with additional public support for skills investments during downturns.

163

6.1.3 Fundamental rights impacts 

Article 14(1) of the Charter of Fundamental Rights of the EU states that  “everyone has the 
right to education and to have access to vocational and continuing training”,  which is also 
reflected in principles 1, 4 and 5 of the European Pillar of Social Rights.  Both packages are 
expected to have a positive impact  on fundamental rights  by bridging  gaps in  the access  to 
continuous  training.  Fundamental  rights  impacts  are  stronger  under  the  universal  but 
targeted support provided under package  B.2, as it foresees  coverage of the entire  adult 
population of working age while also providing specific support to those most in need. 

6.2 The choice of legal instrument 

In  light  of  the  objective  of  the  initiative  to  support  reform  in  the  Member  States  and  the 
envisaged  legal  basis,  only  non-binding  instruments  are  under  consideration.  A  first  option 
would be a revision of the Employment Guidelines to support Member State reforms through 
the  European  Semester  process  and  country-specific  recommendations.  The  most  recent 

164

 already asks Member States to “strengthen the provisions on 
revision from October 2020
individual  training  entitlements  and  ensure  their  transferability  during  professional 
transitions, including, where appropriate, through individual learning accounts”. However, as 
they  are  general  by  nature,  they  do  not  provide  further  guidance  on  how  to  do  this.  A 
Commission  Communication  or  Recommendation  could  provide  further  guidance  to  the 
Member States and inform country-specific recommendations. However, it would not have any 
political ownership by the Member States, and would therefore be insufficient to incentivise 
the required reforms in the Member States. 

The  preferred  instrument  is  a  proposal  for  a  Council  Recommendation.  A  Council 
Recommendation would provide a common framework for action that could serve as basis for 

162

 In the PC, 69% agreed that a lack of instruments for an effective sharing of training costs between stakeholders 
is an obstacle to a higher training provision on labour markets. See Annex 14 for a discussion of cost sharing in 
the French CPF.  
163

 See Annex 14 for an illustration of the rapid increase in CPF registrations during COVID 19 and a discussion 
of the use of top-ups to the CPF as part of the FR recovery strategy. About 30% of CPF trainings can be undertaken 
remotely/online, easing possible capacity constraints during periods of higher demand. Also in Singapore, top-
ups to the personal accounts are part of the recovery strategy (Annex 13). 
164

  Council  Decision  (EU)  2020/1512  of  13  October  2020  on  guidelines  for  the  employment  policies  of  the 

Member States. 

37 

 
 
  
 
                                                 
subsequent analysis of reforms and progress. It would provide Member States with commonly 
agreed recommendations on a concrete tool that can help them to make the necessary progress 
towards  the  objectives  of  this  initiative,  with  a  view  to  implement  the  right  to  education, 
training and life-long learning from the European Pillar of Social Rights and make progress 
towards the ambitious 2030 EU headline target on training participation (Section 1). A Council 
Recommendation  would  be  complementary  to  country-specific  recommendations  in  the 
European Semester, as it would provide a commonly agreed framework for the implementation 
of  recommendations  that  are  well  suited  to  a  Member  States’  starting  point.  In  the  public 

165

consultation, 61%
 of respondents agreed that the introduction of EU legislation to be adhered 
by Member States on a voluntary basis such as a Council Recommendation would be suitable 
to reach the objectives of this initiative.  

7. HOW DO THE POLICY PACKAGES COMPARE? 

Table 2 summarises the results of comparing the baseline scenario to the alternative packages 
A, B.1 and B.2, on the basis of the analysis from the previous Section.  

Effectiveness 

All packages are expected to support reforms in Member States to increase overall participation 
in training and reduce skills gaps compared to the baseline scenario. The increase in training 
participation is expected to be smallest under package A and greatest under package B.2.  

Regarding the specific objective of closing support gaps, effectiveness is expected to be of 
medium strength for packages A and B.1.  In package A, this is because of the expected 
small  target  group  receiving  training  entitlements.  In  package  B.1,  the  positive  impacts  of 
universal coverage are reduced by a risk of insufficient support for individuals who are in need 
of more fundamental reskilling. Package B.2 is expected to have significant positive impacts 
by supporting all adults with training entitlements while also envisaging additional support for 
individuals with additional training needs. Through personal accounts, packages B also reduce 
the fragmentation of support and the portability of training entitlements during professional 
transitions, and facilitate the cost-sharing among different funders of training entitlements. 

With  regards  to  increasing  the  incentives  and  motivation  of  individuals  to  take  up 
training,  the  recommended  individual  entitlements,  strengthened  guidance,  increased 
transparency about quality assured training offers, improved access to paid training leave and 
effective governance are expected to contribute to effectiveness across all packages. Impacts 
are expected to be moderate under package A but significant under packages B. This is 
because packages B allow for a greater tailoring of training to individual needs by allowing 
individuals to freely select among eligible opportunities and at the time that suits them best. 
Hence, packages B provide incentives to train also in situations that cannot be easily anticipated 

165

 Ranging from 50% among businesses to 83% among NGOs and other respondents. Agreement was somewhat 
higher  among  respondents  from  Member  States  with  currently  low  (71%)  and  medium  (76%)  adult  learning 
participation than in those with relatively high participation (48%).  

38 

 
 
                                                 
 (e.g., a desire to learn 
by policymakers for targeted support at the level of groups of adults
new skills to prepare for a professional transition, or a sudden period of low economic activity 
and work intensity), and are expected to trigger a positive change in the overall approach of 
adults to learning. 

166

Table 2: Comparison of the packages 

Criterion 

Policy package 

Baseline 

A 

B.1 

B.2 

Effectiveness 

1. Close support gaps  

2.  Increase 
and motivation 

incentives 

Efficiency  

Coherence 

1.  European  Pillar  of 
Social Rights 

2. Other EU objectives  

0 

0 

0 

0 

0 

++ 

++ 

+++ 

+ 

+ 

++ 

+++ 

+++ 

++ 

++ 

+++ 

+++ 

+++ 

+++ 

+++ 

Note: For the purpose of comparing the impacts of the packages with the baseline scenario, a 
seven-stage  qualitative  grading  scale  is  used:  significant  positive  impact  (+++),  moderate 
(++), small (+), no impact (0), small negative impact/cost (-), moderate (--), significant (---). 

Efficiency 

Scenario analyses on the basis of the available evidence on the quantifiable impacts discussed 
above  suggest  that  the  benefits  of  packages  A  and  B  would  outweigh  their  costs.  The 
estimated  ratio  between  benefits  and  costs  is  around  3  for  most  packages  5  years  after 

implementation.

167

 This is in line with the finding of significant increases in GDP (Annex 12C) 

and the evidence from other cost effectiveness calculations in the literature.
 It highlights that 
the additional skills  investments  foreseen under all packages can support  sustainable public 
finances, as additional costs can be paid for with higher tax revenues from and lower transfer 

168

166

 See Cedefop (2021), More perceptions: opinion survey on adult learning and continuing vocational 
education and training in Europe- Volume 2: Views of adults in Europe, for analysis suggesting that the 
complexity of decisions to train limits the effectiveness of ex ante-targeting at the level of groups of adults.  
167

 These scenario analyses take into account the direct costs of training entitlements and administrative costs as 
well as subsequent benefits in terms of increased productivity and wages, see Annex 12B for details. Ratios for 
EU-27  range  from  2.6  for  when  providing  training  entitlements  to  the  unemployed  to  3.8  for  when  providing 
entitlements to the entire adult population, and additional entitlements to the low-qualified. 
168

 See for instance OECD (2019), Returns to different forms of job related training. 

39 

 
 
                                                 
spending on the beneficiaries of the training. Benefit-cost ratios are estimated to be somewhat 
higher in Member States with lower adult learning participation under the baseline scenario 
(due  to  lower  expected  deadweight  loss)  or  lower  costs  of  training,  but  above  one  for  all 

Member States.

169

No  clear  differences  between  packages  A  and  B  concerning  efficiency  is  identified  in 
comparison  to  the  baseline  scenario:  aiming  at  priority  target  groups  can  be  expected  to 
increase the efficiency of package A, as it is less likely that these groups would have been able 
to  fund their training from  private sources (lower deadweight). However,  the smaller target 
group over which administrative fixed costs are spread and making prior guidance a condition 
for the mobilisation of entitlements can be expected to reduce the ratio of benefits to costs. 
There is no conclusive evidence on heterogeneity of the returns to training across groups of 
adults or returns to scale (Annex 10), and no evidence that giving individuals greater autonomy 
in their choice among quality assured and labour market relevant training opportunities under 
packages B reduces positive impacts or the ratio of benefits to costs (Annex 11).  

Main impacts  

All main impacts  (social impacts, direct costs, other economic impacts and fundamental rights 
impacts) of the policy packages are expected to stem from resulting increases in adult learning 
participation,  and  are  hence  expected  to  be  smallest  under  package  A  and  greatest  under 
package B.2. These main impacts are identified in Section 6.1. 

Coherence 

European Pillar of Social Rights (EPSR): All packages help to make the first, fourth and 
fifth Pillar principles tangible for individuals, whereby the strength of impacts is expected 
to be roughly proportional to the expected increase in training participation. By covering the 
entire adult population of working age, packages B.1 and B.2 are most fully in line with the 
principle  1,  which  states  that  “everyone  has  the  right  to  quality  and  inclusive  education, 
training and life-long learning”, while packages A and B.2 support best the implementation of 
principle  4  on  active  support  to  employment  and  principle  5  on  equal  treatment  regarding 
working conditions. Hence, the impact of package A is the smallest and the impact of package 
B.2 is expected to be highest.  

Other EU objectives: All packages are expected to stimulate reforms in the Member States that 
170 to  promote  a  lifelong  learning 
contribute  to  the  objectives  of  European  VET  policy
culture and make continuing training more learner-centred, whereby impacts are stronger 
for  packages  B.1  and  B.2  as  these  would  recommend  more  significant  departure  from  the 
baseline scenario in most Member States. The packages build on past DG EMPL initiatives 

169

 Ranging from 1.3-1.9 for package A and 1.8-1.9 for package B in LU (depending on the choice of priority 

target group) to 3.8-6.5 for package A and 5.8-6.6 for package B in RO, cf. Annex 12B.4. 
170

 See  for instance the  Council Recommendation of 24 November 2020 on vocational education and training 
(VET)  for  sustainable  competitiveness,  social  fairness  and  resilience  (2020/C  417/01),  and  the  Osnabrück 
Declaration of 30 November 2020.  

40 

 
 
  
                                                 
and evaluation results.

171

 They complement Commission objectives such as those of the Digital 

172

Education  Action  Plan
  by  proposing  concrete  policy  measures  that  support  an  effective 
adaptation  of  education  and  training  systems  of EU  Member  States  to  the  digital  age. 
Packages  B  outline  a  tool  for  implementing  the  European  Parliament’s  calls  for  a  “Skills 
Guarantee as a new right for everyone, at every stage of life, to acquire fundamental skills for 

the 21st century”.

173

All packages are expected to contribute to the green and digital transition. The fundamental 
reorganisation of the EU economy required to reach the related EU objectives in this area such 
as climate neutrality by 2050 depends on significant increases in training participation: to equip 
the existing workforce with the necessary skills, and to ensure social fairness and continued 
public  support.  Since  the  impacts  of  the  digital  and  green  transitions  are  expected  to  be 
comprehensive yet heterogeneous across sectors, package A is likely to have small impacts due 
to the expected small target group, whereas the universal but modulated approach for support 
of package B.2 is expected to have the most significant impacts. 

Stakeholders’ views: Respondents in the public consultation expressed clear support for 

174

the  measures  included  in  packages  B  over  those  of  package  A:  77%
  agreed  that  all 
working-age adults should receive training entitlements as compared to 43%175 agreement to a 
model  corresponding  to  package  A  where  only  priority  target  groups  receive  training 
entitlements. There was also broad support for allowing individuals to select their training offer 
independently from the registry of eligible training opportunities, again implying support for 
packages B over package A.176 A majority of respondents agreed that this initiative addresses 
relevant constraints to training and expected positive impacts, with particular strong agreement 
among  Member  States  with  lower  participation  rates.177  A  majority  of  respondents  also 
expected  positive  impacts  of  the  initiative,  with  particularly  high  agreement  among 

citizens/individuals.

178

171

173

172

 See Annex 8.1 for an overview and discussion. 
 Digital Education Action Plan (2021-2027). 
 European Parliament resolution of 19 January 2017 on a European Pillar of Social Rights. Also see European 
Parliament  resolution  of  17  December  2020  on  a  strong  social  Europe  for  Just  Transitions,  stating  that  the 
European Skills Agenda “must guarantee the right to lifelong learning for all and in all areas”. 
174

 Ranging from 65% of businesses to 85% of citizens and of trade unions; a similar share (78%) stated agreement 

to the “universal, but differentiated” support model from package B.2. 
175

 The highest agreement was among public authorities (58%, consistent with the small observed target groups 

in existing voucher schemes), but only 35% of businesses and 19% of trade unions agreed.   
176

 93% agreed to a model of free selection among eligible training opportunities, as compared to 31% to a model 

of prior guidance as precondition for the mobilisation of training entitlements. 
177

91% of respondents from low participation countries agreed ILAs are effective at tackling financial constraints 
to  participation  as  compared  to  74%  from  high  participation  countries,  and  86%  of  respondents  from  low 
participation countries expected positive impacts concerning secure and adaptable employment as compared to 
63% from high participation countries.   
178

 92% of citizens agreed that the initiative could have impact on “improving access to secure and adaptable 
employment regardless of the type and duration of the employment relationship” and on “improving health and 
wellbeing”, compared to 75% of respondents overall.  

41 

 
 
 
  
                                                 
Subsidiarity and proportionality 

Both packages respect subsidiarity by leaving decisions on key design parameters to the 
Member States, notably decisions on the funding source, the amount of entitlements, priority 
target  groups  or  eligible  training  opportunities.  Packages  B  make  the  more  specific 
recommendations  to  provide  training  entitlements  to  all  working  age  adults  and  to  set  up 
personal accounts to ensure their transferability.  

These  recommendations  can  be  considered  proportionate  in  light  of  the  urgency  of  the 
challenge, reflected in Member States’ ambition to increase adult learning participation 
levels significantly by 2030 beyond levels that can reasonably be expected to be reached under 
the baseline scenario (see Section 5.1) or an initiative in a different form (such as a Commission 
Recommendation  without  buy-in  by  Member  States).  Specifically,  the  implementation 
scenarios  presented  in  Section  6.1  show  that  the  recommendation  to  provide  support  to  all 

working age adults is proportionate to the Porto Declaration target
 that at least 60% of all 
adults should participate in training every year by 2030: EU training participation would not 
be expected to reach 60% in 2030 with more narrow target groups for support. 

179

They  are also  proportionate in  view of Member  States’ stated ambition to strengthen the 
provisions  on  individual  training  entitlements  and  ensure  their  transferability  during 
professional transitions (see Section 1). The recommendation to set up personal accounts for 
training  entitlements  is  proportionate  because  such  accounts  allow  to  de-couple  training 
entitlements  from  their  original  funder  and  give  individuals  full  ownership  over  the 
entitlements, which is essential to ensure the transferability of entitlements. Personal accounts 
also facilitate flexible funding models  with  cost-sharing across different  funders of training 
entitlements and modulated support by target groups, accommodating a wide range of possible 
national funding and implementation models. 

8. PREFERRED POLICY PACKAGE  

The preferred policy package is B.2, i.e. recommending Member States reforms to have in 
place individual training entitlements for all working age adults modulated according to 
selected target groups and in the form of personal accounts.  Training entitlements and personal 
accounts are complemented by career guidance available for all, a public registry of recognised 
training opportunities, paid training leave provisions and effective governance for continuous 
improvements. It is the package that is expected to be most effective in reaching the objectives 
of the initiative with a high degree of efficiency and coherence with other EU policy objectives. 
The practical implications and expected impacts of package B.2 are summarised in Annex 3.  

The elements of package B.2 are compatible with the different starting points in terms of 
adult  learning  participation  and  support  systems  in  the  Member  States.  Most  Member 
States already have experiences with schemes providing individuals with training entitlements 
and relevant  infrastructure, career  guidance and  paid  training leave offers  (Annex 8.2). The 
implementation  of  the  recommendations  is  expected  to  increase  their  impacts  on  training 
participation  by  enhancing  their  efficiency  and  by  overcoming  problems  deriving  from 

179

 European Council Conclusions, 24-25 June 2021. This follows up on the Porto Declaration of 8 May 2021. 

42 

 
 
                                                 
fragmentation, insufficient integration of the different elements of financial and non-financial 
support, or ineffective governance.  

For  countries  where  individual  training  entitlements  are  negotiated  as  part  of  collective 
bargaining, the recommendations under package B.2 do not suggest a departure from well-
established  national  funding  models.  The  recommended  policy  measures  can  instead  be 
expected to strengthen the available non-financial support to facilitate an effective use of these 
entitlements by individuals, in addition to closing support gaps for those not benefiting from 
collective bargaining agreements.  

9. HOW WILL ACTUAL IMPACTS BE MONITORED AND EVALUATED? 

The Recommendation will invite Member States to work jointly with the Commission on fully 
exploiting and where necessary improving the scope and relevance of the collection of data 
at Union level concerning the investments in adult learning in national accounts and in public 
budgets,  as  well  as  further  evidence  on  the  factors  influencing  individuals’  incentives  and 
motivation to take up training.  

The Commission will be invited to establish a monitoring framework with agreed common 
quantitative and qualitative indicators jointly with the EMCO, to assess the implementation 
of this Recommendation and enable its review. An initial proposal of operational objectives 
and corresponding indicators can be found in Annex 15 of this impact assessment. 

Member  States  will  be  recommended  to  implement  the  principles  set  out  in  the 
Recommendation  as  soon  as  possible  and  submit  a  plan  setting  out  the  corresponding 
measures to be taken at national level. The progress made in the implementation of those 
plans  should  be  discussed  in  the  context  of  the  multilateral  surveillance  of  the  European 
Semester in the EMCO. 

The  Commission  will  be  invited  to  assess  and  evaluate  the  progress  made  in  the 
implementation of this Recommendation, in cooperation with the Member States and after 
consulting the stakeholders concerned, and report to the Council  within five  years from the 
date of its adoption.  

43 

 
 
 
 
 
 
 
 
 
 
 
 
ANNEX 1: PROCEDURAL INFORMATION 

1. Lead DG, Decide Planning/CWP references 

Lead DG: Employment, Social Affairs and Inclusion.  

Decide planning number: PLAN/2020/7916. 

Commission Work Programme reference: Promoting our European Way of Life; initiative No. 
36 b): Follow-up to the European Education Area and the updated Skills Agenda/ Individual 

learning accounts.

180

2. Organisation and timing 

The preparation of the impact assessment was supported by the Inter-services Steering Group 
(ISG) to which the following DGs were invited: CNECT, EAC, ECFIN, ESTAT, GROW, JRC, 
JUST, LS, REFORM and SG.  The ISG group met three times: on 15/06/2021, on 16/07/2021 
and on 18/08/2021. The ISG also steered an external study supporting the Impact Assessment.  

3. Consultation of the RSB 

An  “upstream”  meeting  with  the  RSB  took  place  on  23  June  2021.  The  impact  assessment 
report was submitted to the RSB on 1 September 2021, and discussed with DG EMPL in a 
meeting  on  29  September  2021.  On  1  October,  the  RSB  issued  a  positive  opinion  with 
reservations. 

The RSB reservations and the revisions introduced in response to them are summarised in the 
Table below: 

RSB reservations 

Changes done in the impact assessment 

(1)    The  report  should  discuss 
upfront  the  various  existing  EU 
legal instruments that target  adult 
learning.  It  should  map  the  gaps 
this initiative aims to fill.  

It  should  clarify  to  what  extent  it 
also  addresses  identified  supply-
side  problems, 
the 
such  as 
lack  of  quality  of 
perceived 
training or insufficient tailoring of 
training to individual needs. 

Sections  1  and  5.1  now  clarify  that  the  relevant  existing  EU  policy 
initiatives focus mostly on the design of the provision of education and 
training  for  adults  (the  “supply  side”),  rather  than  on  making  this 
provision  accessible  to  adults  and  encouraging  them  to  use  it  (the 
“demand  side”),  referring  to  Annex  8.1  for  an  overview  of  relevant 
existing EU initiatives. 

Sections 1 and 5.1 clarify that this initiative aims to outline measures to 
integrate  financial/demand  side  with  non-financial/supply  side  support, 
tackling constraints to training participation in a comprehensive way and 
supporting the implementation of existing EU  recommendations on the 
supply  side  of  education  and  training.  This  includes  issues  concerning 
quality and the tailoring of training to individual needs, see the discussion 
of  “coherence”  in  Section  7  of  this  report  and  the  2020  Council 
Recommendation on VET. 

(2)  The  report  should  clearly 
establish  the  status  of  the  60% 
target of the Porto  Declaration. It 
should  briefly  recall  the  rationale 

Section  1  indicates  that  the  60%  target  from  the  the  Porto  Social 
Commitment  reflects  a  broad  consensus  around  the  importance  of 
significantly improving the opportunities for developing skills throughout 
working life to achieve the EU’s ambitions for the coming decade, also 

180

  See the Annexes to the Communication from the Commission to the European Parliament, the Council, the 
European Economic and Social Committee and the Committee of the Regions, Commission Work Programme 
2021, A Union of vitality in a world of fragility (COM(2020) 690 final, Annex 1). 

44 

 
 
 
                                                 
and  supporting  evidence  behind 
the  target  and  explain  how  and 
why  this  target  is  used  in  the 
analysis 
(for  example,  as  a 
benchmark  across  EU,  Member 
States, sectors, etc). 

given  that  training  participation  in  the  EU  is  not  particularly  high  in 
international comparison (Annex 6.3.1).  It explains that  while the 60% 
target  is  established  at  EU  level,  a  discussion  of  translating  it  into 
corresponding  Member  State-specific  targets  is  ongoing  within  the 
Employment Committee.  

Section 4.1 clarifies that all initiatives from the European Skills Agenda 
support  Member  States  in  making  progress  towards  the  60%  target. 
Section 7 clarifies that predicted increases in adult learning participation 
serve as an indicator of effectiveness next to other considerations (such 
as  fragmentation  and  transferability  of  support,  and  the  tailoring  of 
training  to  individual  needs).  Progress  towards  the  Porto  Social 
Commitment target is one of the considerations in the discussion of the 
proportionality of the policy options. 

(3)  The 
report  should  better 
explain how the baseline takes into 
account 
the  existing  EU  and 
national policies.  

The  description  of  the  baseline  scenario  in  Section  5.1  now  includes  a 
summary  of  evidence  on  the  implementation  of  existing  EU  initiatives 
and  of  related  schemes  included  by  some  Member  States  in  their 
Recovery and Resilience Plans (also see Annex 8.1).  

assumes 

It should justify  why the baseline 
adult 
scenario 
learning  participation  until  2030 
stays on its trend growth observed 
between 2007 and 2016.  

that 

the 
It  should  better  explain 
rationale  behind  grouping 
the 
policy  measures  into  two  policy 
packages  and  clarify  whether 
alternative  packages  have  been 
explored. 

(4)  The  assessment  of  impacts 
far-reaching 
includes 
a 
analysis. 
macroeconomic 
However, 
should 
the 
acknowledge  the  high  level  of 
uncertainty  about  the  response  to 
voluntary measures from Member 
States.  

report 

It  should  identify  any  significant 
risks that may lead to the expected 
impacts not materialising, such as 
adequate 
the 
funding  for  individual  learning 
accounts.  

availability  of 

The report should better justify its 
finding that all options have equal 
benefit-to-cost ratios and how this 

Section 5.1 clarifies that the drop in adult learning participation observed 
in 2020 due to COVID-19 restrictions is expected to be transitory, leading 
to the baseline scenario assumption of continued modest progress that is 
however  not  fast  enough  to  reach  the  2030  target  and  does  not  close 
existing inequalities in the access to training. Annex 8.3 presents further 
details on the extrapolation of trend growth in adult learning participation 
underlying the baseline scenario, indicating that increases have been close 
to linear between the 2007, 2011 and 2016 AES survey waves for the EU 
Member States without any statistical break across these waves.  

The discussion in Section 5.4 clarifies why the policy measures included 
in the two policy packages are coherent and mutually reinforcing, while 
at the same time highlighting the main trade-offs that emerge on the basis 
of the available evidence and the stakeholder views. Section 5.2 explains 
why a number of other policy measures have been discarded in view of 
the objectives of this initiative. 

Section 6.1 now acknowledges that impacts will depend on the degree of 
implementation  of  the  recommendations  by  Member  States.  It  clarifies 
that the approach taken is to estimate impacts for selected implementation 
scenarios with clearly stated assumptions that have been derived from the 
available evidence.  

Section 6.1 now also highlights the importance of effective governance 
arrangements  with  constant  monitoring  and  evaluation  and  suitable 
adjustments to the scheme for guiding Member States’ implementation 
choices (cf. Section 5.3, policy measure 3.3). Annex 4 acknowledges the 
uncertainty  about  Member  State’s 
responses  and  degree  of 
implementation. It outlines the approach taken to ensure that the analyses 
in this impact assessment provide  useful  input  into decisions by policy 
makers while avoiding the risk of “circularity”, whereby a Member State 
would  not  adopt  a  recommended  measure  on  the  basis  of  evidence  of 
limited impacts from an impact assessment that already assumes this lack 
of adoption. 

45 

 
 
can  be  reconciled  with  possible 
different  returns  of  training  by 
target group or diminishing returns 
on training. 

that  some  of 

(5)  Given 
the 
proposed measures already exist in 
the 
some  Member  States  and 
variations 
in 
significant 
participation 
of 
rates, 
training and funding structures, the 
report  should  explore  impacts  by 
Member  State  or  groups  of 
Member States, and explain which 
would be impacted the most. 

costs 

Section 7 better explains the finding of similar ratios of benefits to costs 
across the policy options: on the one hand, aiming at priority target groups 
can be expected to increase the efficiency of package A, as it is less likely 
that these groups would have been able to fund their training from private 
sources (lower deadweight). On the other hand, the smaller target group 
over  which  administrative  fixed  costs  are  spread  and  making  prior 
guidance a condition for the mobilisation of entitlements can be expected 
to reduce the ratio of benefits to costs.  

Section 6.1.1 indicates that the conditions on quality assurance and labour 
market  relevance  for  inclusion  in  the  registry  of  recognised  training 
opportunities and effective governance provisions are expected to ensure 
that  average  returns  to  training  are  in  line  with  those  found  in  the 
literature. The sensitivity analyses in Annex 12B.5 shows that results are 
robust to the assumption of diminishing returns to training. There is no 
conclusive  evidence  on  heterogeneity  of  the  returns  to  training  across 
groups  of  adults  or  returns  to  scale  (Annex  10),  and  no  evidence  that 
giving  individuals  greater  autonomy  in  their  choice  among  quality 
assured and labour market relevant training opportunities under packages 
B reduces positive impacts or the ratio of benefits to costs (Annex 11). 

Section  6.1  clarifies  how  the  impact  analysis  considers  Member  State 
specific  information  quantitatively  (concerning  predicted  participation 
levels  under  the  baseline  scenario  and  training  costs)  and  qualitatively 
(concerning already existing relevant provisions). Section 7 indicates that 
benefit-cost  ratios  after  5  years  are  estimated  to  be  above  one  for  all 
Member States. Estimates are somewhat higher for Member States with 
lower  adult  learning  participation  under  the  baseline  scenario  (due  to 
lower expected deadweight loss). 

(6) The report should clarify how 
much flexibility would be given to 
Member States in deciding on the 
appropriate measures and whether 
it  is  necessary  to  specify  the 
recommended  measures  on  the 
basis  of  a  preferred  set  of 
measures.  

It should indicate which measures 
in 
will  be 
the 
recommended 
envisaged 
Council 
recommendation  and  which  key 
design  parameters  will  be  left  at 
the  discretion  of  the  Member 
States. 

(7) The report should define what 
success would look like and what 
is expected to be  achieved by the 
time  of  the  evaluation  of  this 
initiative. 

Section  7  clarifies  that  the  preferred  option  respects  subsidiarity  by 
leaving Member States flexibility on key design parameters, notably the 
funding  source,  the  amount  of  entitlements,  priority  target  groups  or 
eligible  training  opportunities.  It  clarifies  that  the  more  specific 
recommendations  under  the  preferred  policy  package  B.2  concerning 
target group (modulated support for entire adult population of  working 
age) and delivery mode (personal accounts) are considered proportionate 
in  view  of  the  Member  States’  stated  ambitions  (Section  1)  and  the 
problem analysis (Section 2). In particular, the recommendation to set up 
personal accounts for training entitlements is proportionate because such 
accounts  allow  to  de-couple  training  entitlements  from  their  original 
funder and give individuals full ownership over the entitlements, which 
is essential to ensure the transferability of entitlements.  

Section  8  now  recalls  the  specific  recommendations  to  Member  States 
envisaged under the preferred policy measure. 

Section  9  indicates  that  the  Commission  will  be  invited  to  establish  a 
monitoring framework with agreed common quantitative and qualitative 
indicators jointly  with the EMCO,  to assess the  implementation of this 
Recommendation and enable its review. An initial proposal of operational 

46 

 
 
objectives  and  corresponding  indicators  for  measuring  success  can  be 
found in Annex 15. 

4. Evidence, sources and quality 

The following evidence has fed into the Impact Assessment: 

  External  study  “Study  to  support  the  Commission  explorations  for  a  possible  EU 
initiative on Individual Learning Accounts”  by Fondazione Giacomo Brodolini. The 
Terms of Reference for the external study and the reports were shared with the ISG.  

  A validation workshop on the preliminary findings of the supporting study with national 
adult learning experts and representatives from Member States public authorities and 
social partners. 

  Country reports by the network of independent national adult learning experts located 
in the EU Member States on the “environment for providing direct financial incentives 
for adult learning to individuals”. The IA also draws on substantive prior work of this 
network that has supported DG EMPL since 2016 as referenced in the footnotes.  

  Results  from  the  forthcoming  update  of  the  Cedefop  “Financing  adult  learning 
database”, used for the analysis of existing provisions in the Member States in Annex 
8.2. 

  Literature review on “The state of play of evidence about the conditions under which 
individual-oriented  instruments  for  incentivising  adult  participation  in  learning  are 
effective” by Sarah Baiocco on behalf of the European Expert Network on Economics 
of Education. 

  Policy brief and analytical report on “Adult learning during Recessions in Europe” by 
Giorgio  Brunello  and  Marco  Bertoni  on  behalf  of  the  European  Expert  Network  on 
Economics of Education. 

  Analytical input by the European Commissions Joint Research Centre on forecasting 
adult  learning  participation  for  the  baseline  scenario  by  Federico  Biagi,  Giorgio  Di 
Pietro and Zbigniew Karpiński. 

  Study to determine the necessary basic features of ILA models for an efficient use of 

off-the-shelf tools and simplified cost options under the ESF+.  

  Ad  Hoc  Report  on  the  feasibility  of  ‘off-the-shelf’  tools  for  Individual  Learning 
Accounts in the context of the external study “‘Off-the-shelf’ solutions for post-2020: 
A study complementing the ESF+ impact assessment” by PPMI. 

  Targeted stakeholder consultations and a public consultation, as referred to in footnotes 

and summarized in Annex 2. 

  Relevant literature and data as referred to in footnotes. 

ANNEX 2: STAKEHOLDER CONSULTATIONS 

47 

 
 
 
 
1. Introduction 

This synopsis outlines the consultations that were organised as part of the work on the initiative 
on individual learning accounts (ILAs) and presents their main findings in support of the impact 
assessment. 

2. Consultation strategy 

In line with the Better Regulation Guidelines wide stakeholder consultations were carried out. 
The mapping exercise identified the following groups of individuals or organisations:  

 

those having an interest in the matter (e.g. national public authorities, social partners, 
industry/businesses, training providers,  NGOs)  

  potential beneficiaries of an ILA (adults on the labour market) 

  experts, e.g. researchers, consultancies and advisors, international organisations 

The stakeholder consultation included targeted consultation events and the public consultation.  
Stakeholders could send comments on the Commission’s inception impact assessment as well 
as  provide  written  statements,  in  response  to  the  targeted  consultations  and  the  public 
consultation. 

Table A2.1: Overview of the stakeholders reached through each consultation tool/method 

Type of stakeholder 

General public  

National public authorities  

Organisations 
regional and local authorities 

representing 

partners 

Social 
organisations 
unions) 

(business 
trade 

and 

NGOs 

Education/training providers 

International organisations  

Researchers/academics 

High  level 
forum 

Targeted 
consultations 

Public 
consultation 

Validation 
workshop 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

x 

Eleven targeted consultation events were held, with different groups of stakeholders. The list 
includes  the  high-level  forum  on  ILAs  (nearly  800  participants  from  48  countries)  and  the 
validation  workshop  focused  on  the  problem  and  impact  analysis  (around  25  participants, 
mostly NGOs and public authorities).  

48 

 
 
 
 
 
 
 
 
 
 
 
Table A2.2: Targeted Consultations 

Event  

High-level forum on ILAs 

Social  Partners  (with  presentations  from 
trade unions and employer bodies) 

Employment Committee (EMCO) 

Committee of the Regions (SEDEC) 

European Quality Assurance in vocational 
Education and training (EQAVET) 

Date 

4/5.3.21 

15.4.21 

20.4.21 

22.4.21 

28.4.21 

Public  Employment 
Network  

Service 

(PES) 

6.5.21 

Advisory  Committee 
for  Vocational 
Training  (ACVT)181/  Directors  General 
for Vocational Training (DGVT)  

19.5.21 

European Qualifications  Framework and 
Europass  Advisory  Groups,  the  National 
Europass Centres, Euroguidance Centres, 
and European Qualifications Framework  
National Coordination Points 

Validation workshop 

European 
Committee (EESC) 

Economic 

and 

Social 

20.5.21 

9.6.21 

1.7.21 

The 12-week public consultation was launched by the European Commission on 23rd April 
through  an  online  questionnaire  (including  both  open-ended  and  closed  questions)  and 
completed on 16 July.  

216 respondents replied to the consultation comprising 78 citizens, 26 public authorities, 46 
business associations and enterprises, 26 trade unions and 40 NGOs/other respondents. 
The  majority  of  answers  came  from  Belgium  (35),  of  which  23  belonged  to  EU-level 
organisations, followed by  Italy (24). In addition, 38 unique written responses to the public 
consultation were received from selected stakeholders.  

3. Inception Impact Assessment  

The Inception Impact Assessment was available online for public feedback between 23 March 
2021 and 20 April 2021. 23 contributions were received, mostly from trade unions (9), NGOs 
(5) and EU citizens (3). Almost half of the responses stressed the importance of designing ILAs 

181

 ACVT subsequently submitted a formal opinion on the EU initiative. 

49 

 
 
 
                                                 
in  conjunction  with  social  partners,  building  on  their  knowledge.  The  quality  assurance  of 
training  was  also  highlighted  as  were  guidance  and  information.  Almost  half  of  the 
contributions stressed the importance of targeting vulnerable groups. 

4. Targeted consultations 

Problem definition 

There was a general over-arching agreement on the main challenges (increasing transitions; 
automation, digitisation and decarbonisation; skill shortages etc.). Indeed, some stakeholders 
(including  employers’  organisations  and  trade  unions)  underlined  the  urgency  of  these 
challenges, from their ‘on the ground’ perspectives. There was a general view, and especially 
from  practitioners  (including  employer  organisations  and  trade  unions)  that  the  Covid-19 
pandemic was contributing to the acceleration of change (especially digitisation).  

Most trade unions and some academics and NGOs stressed that the underlying problem was 
the  insufficient  application  of  fundamental  rights  to  participate  in  training  across  Member 
States and urged the Commission to focus on ways to ensure compliance with principles 1 and 

4  of  the  European  Pillar  of  Social  Rights.
Federation set out a 12-point plan to guarantee these rights across all Member States. 

  A  response  from  the  European  Trade  Union 

182

The general opinion of stakeholders on the barriers to training was that time was as important 
as finance. Trade unions in particular stressed the importance of employees being allowed to 
pursue training during work-time (reporting mixed experiences, especially the variances in the 
availability  of  paid  training  leave).  There  was  also  a  general  opinion  across  all  stakeholder 
groups  that  a  lack  of  information  on  training  opportunities,  and  how  to  access  them,  was  a 
barrier to participation. Those stakeholders with a particular involvement in training systems 
highlighted as a barrier the uncertainties (by potential trainees) as to the quality of training and 
its value in the labour market (dependent on recognised qualifications). 

 Policy options 

The interest  of the EU in promoting  adult learning  and  placing the individual at  the centre 
through training entitlements, was welcomed. In this respect, there was a general agreement 

183

  that  the  ILA  approach  offered  more  opportunities  than  a 
amongst  adult  learning  experts
voucher scheme to encourage lifelong learning and to improve employability. The majority of 
stakeholders (including social partners), and especially those from Member States with more 
advanced training systems e.g. Nordic countries, stressed the importance of integrating new 
initiatives  with  the  existing  national  (or  regional)  training  infrastructure,  respecting  the 
collective  agreements  for  training  already  in  place,  normally  involving  employers,  trade 
unions,  and  governments.  Positive  examples  were  given  of  collective  bargaining  and 
agreements.  

182

183

 European Commission (2017), The European Pillar of Social Rights.  
 During the validation workshop. 

50 

 
 
                                                 
There was a general agreement that the training focus of a new training entitlements initiative 
should be labour market oriented.  This was stressed in particular by employer organisations, 
while some experts and NGOs saw ILAs as an opportunity to promote wider lifelong learning 
including civic responsibilities and other citizenship skills. 

The  target  selection  for  a  new  training  entitlement  initiative  drew  varied  views.  There  was 
support for a universal approach, but also a concern that such an approach might favour the 
higher  skilled  and  those  better  resourced  to  navigate  the  labour  market,  unless  there  were 
safeguards to avoid disadvantaging priority groups (e.g. low-qualified, unemployed etc.).  

There was some support from trade unions for a focus on older workers and high skilled (e.g. 
in IT sector where the technologies rapidly develop) – the argument being that these groups 
were strongly affected by the changes at the labour market and by the digital transition. 

Some trade unions felt whilst putting the individual at the centre was positive, the onus on the 
individual to anticipate and plan for employment transitions in the context of changing labour 
should not absolve employers and public authorities from their responsibilities.  

There was a general consensus amongst stakeholders on the importance of: 

  quality information, especially for low skilled groups; whilst websites and digital 
  were  useful,  they  need  to  be  accompanied  by  other  outreach  and 

184

applications
other activities 

 

free and easily accessible guidance 

  quality assurance and accredited training; they promote participation raising trust 

in training and its perceived value 

 

the validation of non-formal and informal learning 

  social dialogue and the involvement of social partners in ILA governance 

Impact 

There was a general expectation that training entitlements could be one tool as part of a wider 
package  of  measures  to  motivate  more  adults  to  participate  in  learning  and  close  gaps  in 
training systems by incentivising individuals from priority groups. Additional skills training 
would lead to economic benefits. There was general agreement that impacts would be enhanced 
where entitlements are coordinated with free guidance, quality assured systems and accredited 
training. 

5. Public Consultation  

Problem Analysis 

The direct (tuition fees) and indirect costs of training (including loss of income due to time 
taken up by training) were highlighted as barriers to accessing training respectively by 192 

184

 Including a dedicated digital application for mobile phones and other devices (an example being the CPF, the 

French individual learning account). 

51 

 
 
                                                 
185

  There  was  also  a  high  level  of  agreement  that  there  was  insufficient 
89%  and  176  82%.
awareness  of  available  financial  support  for  training  (187,  87%).  Other  barriers  mentioned 
included poor accessibility to training opportunities in rural areas and a lack of access to IT.  

Concerning  low  motivation  to  train,  175  respondents  (82%,  including  60  citizens)  fully  or 

186

somewhat
 agreed that insufficient awareness of the benefits of training was a factor whilst 
188 88% mentioned uncertainty about which skills were needed to improve employment and 
income prospects. 192 (89% – the highest response to this question) highlighted fragmented or 
insufficiently  transparent  information  on  available  training  opportunities,  with  171  80% 
highlighting  uncertainty  of  the  quality  of  the  training  available.  177  82%  agreed  that 
uncertainty about whether training outcomes will be recognised by employers was a factor and 
166 77% highlighted insufficient tailoring of training to individual needs. 189 88% agreed that 
a  lack  of  time  was  a  constraint  with  178  83%  agreeing  that  rigid  time  patterns  of  training 
provision was a factor.  

When asked about obstacles to higher level of training  provision in the labour market, 150 
69% agreed that a lack of instruments to share training costs between companies, individuals 
and public authorities was a factor, 180 83% (including 34 enterprises/business associations) 
highlighted a lack of capacity within small, medium and micro businesses to organise training 
for their employees. 179 83% (including 65 citizens) thought that a lack of support to atypical 
workers was an obstacle.  

Objectives and EU added value 

Respondents were asked whether a European initiative on ILAs could add value. Respondents 
agreed that there could be added value related to: 

 

increased transparency about national training markets – 154 71%  

  portability and recognition of training outcomes across Member States – 163 75%, 22 

fully disagreed 

  more efficient use of EU funds for skills development – 181 84%, 10 fully disagreed 

  development of registries of quality assured training opportunities at the national level 

– 168 78%, 15 fully disagreed 

 

implementation of quality assurance for non-formal training opportunities – 168 78%, 
while 20 fully disagreed 

  validation of non-formal and informal learning outcomes – 176 81%, 15 fully disagreed 

  career guidance – 166 77%, 12 fully disagreed 

  provision of paid educational leave and its take up by individuals – 167 77%, 15 fully 

disagreed  

185

 All responses are based on N= 216 unless otherwise stated: 78 citizens, 26 public authorities, 46 enterprises 

and business associations, 26 trade unions and 40 NGOs and other respondents. 
186

 Hereafter, ‘fully and somewhat agree are merged as ‘agreed’. 

52 

 
 
                                                 
There was a general plea to avoid complexity and bureaucracy (also highlighted in position 
papers e.g. by employer organisations). 

Policy efforts to support learning amongst adults were widely supported by respondents, with 
very few disagreements: 

  short job-related training - 185 86% 

  more fundamental job-related training - 195 91% 

  basic, soft and inter-personal skills - 189 88% 

  digital skills - 200 93% 

  skills for green transitions - 198 92% 

Support was less strong for non-job-related training: 158 73% 

Entitlements and individual learning accounts (ILAs) 

On  approaches  for  tackling  financial  constraints  influencing  participation  in  training,  the 
majority agreed with: 

  establishment of ILAs – 182 84% (including 21 public authorities and 22 trade 

unions)  

  establishment of training entitlements in other forms, such as vouchers -177 82%  

 

tax incentives for companies 188 87% 

  subsidies to training and education providers - 148 68% 

 

 

increased overall public funding to support training - 189 88% 

facilitate  the  sharing  of  training  costs  between  companies,  public  authorities  and 
individuals - 167 77% 

Motivation to participate in learning could be increased through:  

 

 

ILAs - 178 82%  

training entitlements in other forms, such as vouchers – 177 82%  

  public  registry  of  quality  assured  training  opportunities  -  185  86%  (including  72 

citizens and 38 NGOs) 

  one-stop  shop  digital  platform  (e.g.  smartphone  app)  linked  to  a  registry  of  training 
opportunities  -  189  88%  (including  71  citizens,  34  enterprises  and  business 
associations) 

  subsidies to education and training providers - 158 73% 

  awareness raising campaigns - 190 88% 

 

in-person advice and guidance - 198 92% 

  opportunities  for  the  validation  of  informal  and  non-formal  learning  -  192  89%, 

(notably 37 NGOs) 

 

increased  modularisation  of  training  offers  (e.g.  creating  opportunities  for  the 
certification of short courses) - 181 84% 

53 

 
 
Respondents  agreed  that  time  constraints  to  participation  in  training  could  be  addressed 
through: 

 

 

ILAs – 135 63%, 24 fully disagreed 

training entitlements in other forms – 136 63%, 22 fully disagreed 

  modularisation of training – 181 84%, 4 fully disagreed 

  paid educational leave – 177 82%, 9 fully disagreed 

  allowance to cover living costs -182 85%, 5 fully disagreed 

On  universal,  targeted  or  mixed  approaches  to  individual  training  entitlements,  166  77% 
agreed in a universal approach open to all working age adults (15 fully disagreed), with 168 
78% for a universal approach combining a greater level of entitlement to those with particular 
training needs. 92 43% agreed to targeted support only for those individuals with particular 

187

,  but  more  respondents  (111  52%)  somewhat  or  fully  disagreed.

training  needs
  The 
position  papers  also  reflected  the  debate  between  universal  and  targeted  entitlements,  with 
concerns that universal schemes could widen gaps in the labour market, benefiting those with 
higher skills, and excluding vulnerable groups without the resources and knowledge to access 
training systems, whereas others arguing that access to training was a fundamental right for the 
benefit of all citizens.  

188

On the rules for spending training entitlements, 191 88% agreed that there should be a free 
selection by individuals from the registry of eligible training offers and that training should be 
allowed to take place during working hours with the agreement of the employer. Only 83 38% 
agreed that there should be a requirement for training to take place outside of working hours, 
and only 77 35% agreed that individuals should have to follow compulsory guidance on the 
selection of training (with 118 55% somewhat or fully disagreeing). 

In respect of sources to increase funding for training, there was broad agreement on the use of 
public funding by Member States (197, 91%). 147 (68% including 24 trade unions) agreed with 
an employer’s levy (with 31 fully disagreed) and 115 53% agreed with individual contributions, 
but 85 (including 22 trade unions and 24 citizens) somewhat or fully disagreed.  

111 80% agreed that strengthening monitoring and exchange of experience would be effective 
in increasing the uptake in training. 84 61% agreed that the initiative should be promoted via 
an instrument  on a  voluntary basis (such as a Council Recommendation but  with  countries 
deciding  what  and  how  to  implement  the  recommendation),  43  31%  somewhat  or  fully 
disagreed.  61 44% agreed that there should be no additional instrument/ that the existing EU-
level  framework  is  sufficient,  but  slightly  more  respondents  (64  46%)  somewhat  or  fully 
disagreed. Position papers from employer representative organisations and trade unions argued 
for a role in the development and governance of any new initiatives, given their experience in 
developing collective agreements. 

187

 Examples given were atypical workers, the unemployed, the low qualified and those in industries undergoing 

structural change. 
188

 The remaining 6% answered ‘don’t know’. 

54 

 
 
                                                 
Impact 

The consultation asked whether impacts related to fundamental and social rights could result 
from an ILA initiative. The majority of respondents thought that ILA could: 

 

 

improve access to secure and adaptable employment –  162 75% 

improve health and well-being – 161 75% 

  promote active citizenship – 141 65%  

  make it easier for individuals to manage labour market conditions – 179 83%  

 

improve employment prospects for the unemployed – 174 81% 

  help  tackle  discrimination  in  respect  of  access  to  training,  employment  and  career 

progression - 162 75% 

A second question focused on labour market and economic impacts. Lower majorities fully or 
somewhat agreed that the initiative could: 

  support labour mobility - 143 66% with 54 in somewhat or full disagreement 

 

 

reduce skills gaps and mismatches – 172 80% 

improve productivity and company competitiveness – 167 77% 

  support efficient reallocation of labour – 164 76% 

Finally, on societal and environmental benefits 174 81% agreed that the initiative could support 
green and digital transitions by offering the relevant skills whilst 147 68% agreed that cohesion 
in  society  could  be  improved.  These  generally  positive  views  are  reflected  in  many  of  the 
position papers (from NGOs, employer organisations and trade unions) but with the caveat that 
implementation is critical, as ILAs need to be developed in conjunction with national training 
systems supporting existing arrangements.

55 

 
 
ANNEX 3: WHO IS AFFECTED AND HOW?  

1. Practical implications of the initiative 

If this initiative is adopted, its key obligations will apply for the Member States’ public authorities. Public authorities will have to consider policy 
and/or legislative changes to: 

(a)  set up governance arrangements to determine the  amount and modulation of training entitlements (for all working age adults and with 
higher amounts of entitlements for target groups with particular training needs) in the form of personal accounts, as well as corresponding 
funding arrangements; 

(b) set up the public registry of recognised training, validation and career guidance opportunities; 

(c)  ensure career guidance are available for all working age adults;  

(d) introduce or reassess paid training leave provisions and their integration with the other support measures. 

Changes should consider inclusive digital solutions taking into account needs of person with disabilities or less digitally skilled persons. Training 
providers who want to receive funding from training entitlements will need to ensure that their training offers meet the requirements to be included 
in the public registry of recognised training, validation and career guidance opportunities. For enterprises or individuals, additional obligations 
may result depending on the funding arrangements chosen by public authorities.  

2. Summary of costs and benefits 

Table A3.1: Overview of benefits - preferred option 

I. Overview of Benefits (total for all provisions) – Preferred Option 

Description 

Amount 

Comments 

Direct benefits 

56 

 
 
Higher adult learning participation.  Estimated 

of 

by 

increase 
rate 

EU-27 
participation 
14.2-15.3 
percentage  points  (ppts)  by  2030, 
corresponding  to  an  additional  34.0-
36.6  million  adult  learners  per  year 
compared to the baseline scenario and 
a participation rate of 62.8-63.9%. 

Reduced  inequalities  in  access  to 
training among groups of adults.  

inequalities/ 
in  adult 

upward 
Reduced 
convergence 
learning 
participation  across  EU  Member 
States. 

Estimated  increase  of  adult  learning 
participation  by  11.5-12.8  ppts  in  the 
Member  State  with 
the  highest 
participation  rate  under  the  baseline 
scenario  (SE),  compared  to  17.8-19.5 
ppts  in  the  Member  State  with  the 
lowest  (RO),  for  a  reduction  in  the 
participation  gap  by  6.3-6.7  ppts 
compared to the baseline scenario. 

For individuals and depending on the modulation of support. See 
Section 6.1.1 and Annex 12A for details and sensitivity checks on 
these scenario analyses. 

The reduced inequalities in access to training also translate into 
reduced inequalities in participation rates, except for groups with 
significantly  lower  take  up  rates  for  training  entitlements.    See 
Section  6.1.1  for  a  discussion  and  Annex  12A  for  details  and 
examples.  

For EU as a whole and depending on the modulation of support. 
See Section 6.1.1 and Annex 12A for details. 

Higher wages. 

1% after 30 hours of training.  

For individuals, see Section 6.1.1.  

Estimated  EU-27  increase  in  annual 
earnings  by  employed  participants  by 
€5.7-9.4 billion per year. 

See Annex 12B for the estimation of EU-27 earning increases and 
sensitivity checks. Increases depend on the modulation of support. 

57 

 
 
 
Higher employment. 

2.5 percentage points after 30 hours of 
training for those out of employment.  

For individuals, see Section 6.1.1. 

Improved  working  conditions  and 
social dialogue. 

Higher  quality  and  transparency  of 
the training market. 

Higher productivity of workers and 
competitiveness of their employer. 

Improved health and well-being. 

Lower  cost  of  training  resulting 
from  more 
and 
competition. 

transparency 

EU-27 

increase 

Estimated 
in 
employment by 0.2-0.4 million adults 
in  the  year  after  introduction  of  the 
policy package. 

See  Annex  12B  for  the  estimation  of  EU-27  employment 
increases  and  sensitivity  checks.  Increases  depend  on  the 
modulation of support. 

See  «indirect  benefits« below  for  long-run  general  equilibrium 
estimates of employment effects.  

For  individuals  and  enterprises.  Not  possible  to  quantify,  see 
Section 6.1.1 for a discussion. 

For individuals and enterprises, in particular SMEs. Not possible 
to quantify, see Section 6.1.1 for a discussion. 

2% after 30 hours of training. 

For enterprises, see Section 6.1.2. 

Estimated  EU-27  increase  in  annual 
productivity  of  €11.5-18.6  billion  per 
year. 

See Annex 12B for the estimation of EU-27 productivity increases 
and  sensitivity  checks.  Increases  depend  on  the  modulation  of 
support. 

Indirect benefits 

For individuals. Not possible to quantify, see Section 6.1.1 for a 
discussion. 

For the funder of training. Not possible to quantify, see Section 
6.1.2 for a discussion. 

58 

 
 
 
 
 
 
Higher civic participation and social 
cohesion. 

For society as a whole. Not possible to quantify, see Section 6.1.1 
for a discussion. 

Increased  tax  revenue  and  lower 
spending on unemployment benefits 
and healthcare. 

Increased  EU-27  annual  tax  revenue 
by  €7.1-9.6  billion  per  year.  Lower 
benefit expenditure by €2.5-4.5 billion 
per year. 

successful 

More 
technology 
diffusion,  business  growth  and 
labour 
transitions, 
supporting  the  digital  and  green 
transitions. 

market 

For public authorities, see Section 6.1.2. 

See  Annex  12B  for  the  estimation  of  increases  in  EU-27  tax 
revenue and unemployment benefits savings. Increases depend on 
the modulation of support. 

 Healthcare expenditure savings not possible to quantify. 

For society as a whole. Not possible to quantify, see Section 6.1.2 
for a discussion. 

Higher long-run levels of GDP and 
employment. 

Increase of EU-27 GDP by 0.99% by 
2030  and  1.4%  by  2040.  Increase  of 
employment rate by 0.06 ppts by 2030 
and 0.18 ppts by 2040 (corresponding 
to 0.14-0.4 million additional jobs). 

For society as a whole and for a low-qualified as a priority target 
group. See Section 6.1.2 and Annex 12C for details and sensitivity 
checks. 

Macroeconomic 
stabilisation  by 
facilitating skills investments during 
downturns. 

For society as a whole. Not possible to quantify, see Section 6.1.2 
for a discussion. 

59 

 
 
 
 
 
Table A3.2: Overview of costs - Preferred option 

II. Overview of costs – Preferred option 

Citizens  

Enterprises 

Public authorities 

One
-off 

Recurrent 

One-off 

Recurrent 

One-off 

Recurrent 

- 

Depends  on  funding 
arrangement. 

- 

Depends 
arrangement. 

on 

funding 

Direct 
costs 

Possible 
set-up 
costs. 

Scenario  analyses  estimate  annual 
costs for EU-27 between €17.6-24.5 
billion.  See  Section  6.1.1  and 
Annex 12B. 

(a)  

Action 
Individual 
training 
entitlements 
personal 
in 
accounts  and 
related 
governance 
arrangements 

Indirect 
costs 

- 

- 

to 

earn 
Inability 
income 
during 
periods  of  training- 
incurred 
however, 
voluntarily 
by 
individuals  making 
use  of  their  training 
and 
entitlements, 
only  for  those  not 
benefitting from paid 
leave 
training 
provisions. 

- 

during 
absence 
Staff 
training (with permission of 
employer).  Estimated  at 
around € 4.7 billion if half of 
those  using  their  training 
entitlements  do  so  during 
working  hours.  See  Annex 
12B.  

Administrative costs assumed to be 
8%  of  direct  costs  in  cost-benefit 
analyses, corresponding to €1.4-2.0 
billion. See Annex 12B.  

Action 
Public 
registry 

(b) 

of 

Direct 
costs 

- 

- 

For 
training 
providers: 

Costs  of  compliance  with 
for  quality  and 
criteria 
labour  market  relevance  of 

Possible 
set-up 
costs.  May 

Maintenance costs are  included in 
the estimate of administrative costs 
for training entitlements (a). 

60 

 
 
 
recognised 
training, 
validation 
and 
guidance 
opportunities 

career 

Indirect 
costs 

- 

- 

Action (c) 

- 

- 

Making 
career 
guidance 
available 
all 

to 

Direct 
costs 

Indirect 
costs 

Action (d)  Direct 
costs 

- 

- 

- 

- 

offers required for inclusion 
in the registry. 

Costs  of 
certificati
on 
of 
offers  for 
inclusion 
in 
registry. 

the 

- 

- 

- 

- 

- 

- 

- 

Staff 
during 
absence 
training.  If  revisiting  of 
national  paid  training  leave 

limited 
be 
in  12  MS 
where 
similar 
registries 
already 
exist.  

- 

Possible 
set-up 
costs.  May 
limited 
be 
as 
public 
career 
guidance 
already 
exist  in  25 
MS to some 
extent.  

- 

- 

Costs from increasing use of public 
guidance 
validation 
and 
opportunities. 

Costs from increasing use of career 
guidance services. 

- 

Costs  of  public 
for  
enterprises  granting  paid  training 
SMEs), 
leave 

particular 

support 

(in 

61 

 
 
Paid  training 
leave 

depending 
arrangement.  

on 

the 

funding 

arrangements  leads  to  an 
upward 
convergence  of 
annual take-up rates of paid 
training  leave  arrangements 
to 5% (corresponding to the 
highest  currently  observed 
take-up 
annual 
rates), 
estimated costs are €3.5-5.9 
billion  for  a  30-50  hour 
leave.
    See  Annex  12B. 
Costs  for  enterprises  are 
lower to the extent pulic co-
funding is available. 

  189

Indirect 
costs 

- 

- 

- 

- 

- 

- 

3. Summary of the SME test 

Table A3.3: The "SME test"- summary of results 

(1) Identification of affected enterprises 

189

 Since no comprehensive data on existing expenditure for paid training leave in the Member States are available on which a cost estimate for the baseline scenario could be 
based, the corresponding estimates in this table provide estimates of total costs. Additional costs relative to the baseline scenario are hence likely to be lower in view of existing 
costs under the baseline scenario. However, the difference is expected to be small in view of the low effective outreach of most existing schemes (Section 2). 

62 

 
 
 
 
                                                 
Workers  in  SMEs  and  in  particular  micro-enterprises  currently  participate  less  in  learning  than  those  in  large  companies  (42%  and  35.6% 
respectively vs. 55.1%). SMEs are often at a disadvantage compared to larger companies when it comes to supporting the continuing skills 
development of their staff as they have less resources available to: 

(a)  support their staff with training selection and funding; 

(b) identify or design training programmes tailored to their needs; 

(c)  compensate for temporary absence of staff during training (see Section 2 and Annex 7). 

This makes them more vulnerable to skills shortages and puts them at a competitive disadvantage compared to large enterprises. 

(2) Consultations capturing the SME angle 

In the public consultation, 83% of repondents agreed that a lack of capacity by small, medium and micro-enterprises to organize training for 
their employees is an obstacle to a higher training provision on the labour market.

190

SMEs have been also consulted directly through the public consultation and in targeted consultations.  SMEunited, the association of crafts and 
SMEs in Europe, contributed with a joint paper with Business Europe and SGI Europe
 (submitted during the targeted consultations) and a 
separate position paper to the public consultation, and additional contributions have been received by national SME respresentatives. During 
the consultations, SME representatives emphasised in particular the need to ensure the labour market relevance of training to ensure efficient 
spending. In its public consultation position paper, SME United
 stressed that the training offers need to be designed around the needs of 
companies, especially SMEs. Enabling factors need to accompany the training provision (e.g. guidance services and quality assurance). Some 
groups  (e.g  long-term  unemployed,  low  skilled)  will  require  additional  support  to  reintegrate  into  the  labour  market.  For  more  on  the 
consultations, see Annex 2 and the references throughout this report.  

191

192

(3) Measurement of the impact on SMEs 

190

191

 Ranging from 74% of businesses to 96% of public authorities.  
 SMEunited, Business Europe, SGI Europe,  Employers’ input to an  ACVT opinion on  a future  EU initiative aiming  to improve/boost training provision across Europe 

(submitted within the targeted cosultations).  
192

 SMEunited, response to PC on ILAs and micro-credentials. 

63 

 
 
  
                                                 
Overall, SMEs and micro-enterprises are expected to gain in particular from the support for training provided to individuals under the preferred 
policy option. Expected impacts are: 

(a)  improved access of SME workers to training even if there is limited capacity by their employer to fund or organize it; 

(b) increased transparency about the quality of training offers form the public registry, facilitating own staff skill development efforts by 

SMEs; 

(c)  increased public financial support for SMEs granting their staff  paid training leave, resulting from the recommendation to take into 

account SME needs when revisiting existing provisions. 

SME staff is also a possible priority target group for additional support under the preferred policy option (to be decided by Member States). 

(4) Mitigating measures 

Strengthened provisions on paid training leave or increasing uptake of it resulting form the initiative could be particularly challenging for SMEs. 
This is why the preferred policy option recommends to Member States to consider in particular SMEs in their assessment of public support for 
paid training leave, to avoid or mitigate any adverse impacts form paid training leave on SMEs. 

64 

 
 
ANNEX 4: ANALYTICAL METHODS  

This impact assessment draws on a variety of quantitative and qualitative methods and data 
sources,  (Annex  1).  Stakeholder  opinions  have  been  captured  via  extensive  targeted 
consultations and a Public Consultation, which closed on 16 July 2021 (Annex 2). Overall, care 
has  been  taken  to  validate  information  via  triangulation  from  different  sources  and  reflect 
minority opinions. 

As  set  out  in  Section  6,  the  report  assesses  social  impacts  (including  participation  in  adult 
learning, wages, employment, working conditions and social dialogue, health and well-being 
etc.), economic impacts (including the costs and benefits for individuals, education and training 
providers, enterprises, public authorities, as well as the wider impacts on the economy in terms 
of GDP for instance) and the impact on fundamental rights. This integrated approach covering 
both the micro- and macroeconomic perspectives requires a combination of quantitative and 
qualitative assessments. 

In this annex, we outline: 1) the quantification of impacts; 2) qualitative approaches and 3) 
constraints to the assessment of impacts and how they have been addressed. 

1. Quantification of impacts 

The quantitative assessment of this impact assessment consists of three parts: 

- 

- 

- 

an assessment of the impact on participation rates, which feeds into Section 6.1.1. on 
social  impacts,  and  in  turn  provides  the  basis  for  the  cost-benefit  analysis  and 
macroeconomic assessments described below; 

a  cost-benefit  analysis  which  feeds  into  Section  6.1.2  on  economic  impacts  and 
contributes to the assessment of efficiency of the packages in Section 7; 

a  macroeconomic  assessment  of  the  impacts  on  output  and  employment,  which 
feeds into Section 6.1.2 on economic impacts and also contributes to the assessment of 
efficiency of the packages in Section 7. 

First an assessment is made of the impact of providing training entitlements on levels of 
participation  in  training  (Annex  12A).  The  aim  here  is  to  estimate  the  impact  of  training 
entitlements on training participation, bearing in mind that not all people who receive training 
entitlements will use them (take up) and that some people who use their training entitlements 
might  have  trained  in  any  case  without  the  entitlement  (deadweight  loss).  The  estimates  of 
take-up  and  deadweight  loss  have  been  taken  from  experiences  with  past  schemes  giving 
training  entitlements  to  adults.  The  selection  gives  preference  to  a  small  number  of 
experimental studies (see “constraints” for an explanation), while placing them in the broader 
context of experiences from larger-scale schemes.  

The approach to estimating impacts on training participation for packages A and B.2 (where it 
is up to Member States to select priority target groups for training entitlements) is to present 
estimates for a range of plausible priority target groups, selected on the basis of the problem 
analysis (Section 2). Impacts are estimated for the reference year 2030 by applying the take-up 
and deadweight rates identified. 2030 is chosen so as to leave sufficient time for impacts to 

65 

 
 
materialise and to allow for a comparison of impacts to the 2030 EU targets on adult learning 
participation.  Impacts  are  assessed  relative  to  the  baseline  scenario,  factoring  in  expected 
baseline changes in training participation rates (Annex 8.3) and population changes (derived 
from Eurostat projections of population change to 2030). The estimates presented in the main 
report are based on “middle ground” estimates on the key parameters (take-up and deadweight 
loss),  and  Annex  12A  also  presents  sensitivity  checks  for  reasonable  “pessimistic”  and 
“optimistic” values on both parameters. 

The analysis of likely impacts on participation rates then feeds into the cost-benefit analysis 
(CBA) of the policy packages  (Annex  12B).  The CBA  estimates the  short  to  medium  term 
costs and benefits of the policy packages on individuals, employers and public authorities. Key 
costs are the direct costs of training entitlements, administrative costs and the costs of devoting 
time for training, while key benefits concern higher productivity and wages as well as higher 
tax  revenues  for  public  authorities  and  savings  on  social  benefits  resulting  from  positive 
employment impacts. Overall estimated ratios of benefits to costs are derived on the basis of 
the  participation  rate  estimates  from  Annex  12A,  relevant  estimates  from  the  literature  and 
Eurostat data. A limitation of these estimates is that they are of a “partial equilibrium” nature, 
i.e. they do not take into account some more complex impacts that are likely to affect GDP and 
employment levels in the longer run. 

Macroeconomic  modelling  using  the  BeTa  model  (see  Annex  12C)  is  hence  used  to 
complement  the  cost-benefit  analysis.    It  is  able  to  control  for  the  complex  economic 
interactions which might result from, for example, impacts of additional taxes used to fund the 
training  entitlements  and  automation  in  response  to  higher  productivity.  In  looking  at  the 
benefits of training it is also able to capture the wider impacts and is not limited to quantifying 
the  impact  on  those  directly  affected  by  the  entitlements  (i.e.  it  is  able  to  capture  various 
externalities).  Accordingly,  the  BeTa  model  is  able  to  estimate  how  increased  levels  of 
participation in training might affect output and employment by adjusting the stock of human 
capital available to an economy, and the impact this may have on the efficiency with which 
people are matched to available jobs. As a forecasting model, it is able to provide an assessment 
of medium to long-effects on output and employment, providing impact estimates for 2030 but 
also extending them to 2040. 

2. Qualitative methods  

While the quantification of impacts is primarily used to support Sections 6 and 7, qualitative 
methods were used throughout the impact assessment to support the problem definition, the 
definition  of  policy  measures  as  well  as  to  assess  the  impacts  of  the  policy  packages,  in 
particular the social impacts and the impacts on fundamental rights, in addition to the economic 
impacts, where quantitative data is limited. 

An in-depth  systematic  literature review was implemented in  order to  collect  detailed and 
comprehensive evidence about the main problems and issues as well as the effects of different 
training schemes and interventions. 

Seven case studies were conducted (see Annex 13) with the objective of (i) gaining a better 
understanding  of  the  context  (socio-economic  and  policy  framework)  in  which  financial 

66 

 
instruments  for  adult  learning  are  being  implemented  in  selected  Member  States,  (ii) 
identifying recent and relevant policy initiatives as well as “lessons learned” that could inspire 
other Member States, and (iii) explore the working mechanism for such policy instruments and 
their  potential  to  respond  to  specific  contextual  challenges  and  policy  problems.  Country 
experts  were  engaged  to  collect  the  required  information  and  institutional  stakeholders’ 
opinion. In selecting the Member States, a set of criteria was adopted to ensure geographical 
representativity  at  the  EU  level  and  reflect  other  dimensions  such  as  heterogeneity  of  adult 
learning  systems  and  good  coverage  of  the  EU  population  but  also  relevant  international 
experiences. 

In addition, an in-depth analysis of the French Compte Personnel de Formation was carried 
out  (see  Annex  14).  The  development  and  dynamic  adjustments  of  the  French  scheme  in 
response to experiences made represents an interesting example in particular to assess the costs 
and benefits of personal accounts for training entitlements. 

The  outcome  of  the  consultations  of  experts  and  stakeholders  also  contributed  to  the 
qualitative analysis in the report, as described in Annexes 1 and 2.  

3. Constraints 

Since this impact assessment supports a non-binding proposal for a Council Recommendation, 
a  first  challenge  is  uncertainty  about  Member  State’s  responses  and  degree  of 
implementation. The approach taken in the context of this impact assessment is to estimate 
impacts on the basis of clearly stated assumptions derived from the available evidence. These 
estimates hence do not attempt to estimate the output of the relevant political processes in the 
Member States after the adoption of a Council Recommendation (e.g. by scaling down impacts 
according  to  some  estimate  of  partial  non-implementation),  in  order  to  provide  useful  and 
independent input into these processes. This approach avoids the risk of “circularity”, whereby 
a Member State would not adopt a recommended measure on the basis of evidence of limited 
impacts from an impact assessment that already assumes this lack of adoption. 

The  assessment  of  impacts  faced  additional  challenges  related  to  limited  availability  of 
quantitative data on which to base estimates: 

  Experimental  data  is  needed  for  a  rigorous  identification  of  the  key  parameters  of 
interest, as observational data does not allow to observe deadweight loss (due to self-
selection  of  motivated  individuals  into  the  scheme)  and  often  also  does  not  provide 
good  evidence  on  take  up  rates  (as  existing  schemes  are  often  targeted  at  specific 
groups).  Therefore,  the  quantification  draws  on  evidence  from  a  limited  set  of  field 
experiments, in particular a voucher experiment that assigned training entitlements to a 
randomly  subset  of  the  entire  Swiss  adult  population,  which  allows  for  a  rigorous 
193

assessment of take-up and deadweight loss.

  Estimates  of  deadweight  loss  are  often  not  able  to  factor  in  impacts  of  training 
entitlements on the intensity of training in response to receiving training entitlements 

193

 See Schwerdt et al. (2012), The impact of an adult education voucher program: Evidence from a randomized 

field experiment. Journal of Public Economics. 

67 

 
  
                                                 
or future training intentions (i.e., individuals would have undertaken some training even 
in the absence of entitlements, but train longer or more frequently over a certain period 
thanks to entitlements), leading to an over-estimation of deadweight loss. 

  There is some uncertainty in the literature as to whether returns to training are constant, 
increasing or decreasing (Annex 11), and this matters in particular for the estimates of 
long-run impacts in the BeTa model (Annex 12). 

  Relatively little data is available on interactions between different policy measures, e.g. 
the impacts of training entitlements in the presence of strengthened guidance of paid 
training leave provisions and vice versa.  

  Availability of data on costs (set-up and operational) is limited, and where these data 
are available they tend to reveal wide variation across countries and there is always a 
degree of uncertainty as to whether like is being compared with like. 

  The benefits of wider benefits of adult learning (on job and life satisfaction, health etc.) 
and on some policy measures (e.g.  paid  training  leave)  are difficult to  quantify,  and 
large parts of the evidence base are of a correlational nature. 

The following steps have been taken in this impact assessment to address these constraints:  

  Deadweight  loss  estimates  have  been  adjusted  to  ensure  that  deadweight  estimates 
reflect differing levels of training participation in the Member States as explained in 
Annex 12A. 

  Annex 11 situates these assumptions in the broader literature. Sensitivity checks on the 
key  parameters  are  conducted  throughout  Annex  12,  to  show  the  robustness  of  key 
results to alternative assumptions concerning take-up rates and deadweight loss, wage 
and  employment  impacts  and  administrative  costs.  In  the  BeTa  model,  decreasing 
returns to scale have been selected as a cautious benchmark assumption for long-run 
impacts of higher training participation. 

  Wider  expected  benefits  of  increased  participation  of  adults  in  learning  are  not 

quantified but discussed qualitatively. 

More  generally,  qualitative  data  has  been  drawn  upon  in  order  to  provide  a  more  holistic 
analysis of the likely additionality resulting from the introduction of a training entitlement and 
capture some of the costs and benefits where quantitative data are limited. There are constraints 
from using these data too, including: 

  Questions  about  the representativeness  of such data (e.g.  adult learning experts may 

have a tendency to favour greater expenditure on adult learning); 

  The comparability of data across countries given the highly specific nature of some of 

the more qualitative inquiries. 

This impact assessment recognizes these constraints and addresses them to the extent possible 
by triangulating information from different sources to increase the robustness of conclusions, 
e.g.  by  providing  expert  assessments  next  to  survey  evidence  and  stakeholder  views.  The 
constraints  are  further  recognised  in  the  design  of  the  policy  options,  which  include  a 

68 

 
recommendation of constant monitoring and evaluation of the success of the policy measures 
in reaching the objectives of the initiative in order to allow for appropriate adjustments to them 
where necessary (measure 3.3). 

ANNEX 5: OVERVIEW OF THE INTERVENTION LOGIC  

Figure A5.1 – Intervention logic 

69 

 
 
   
 
 
 
 
ANNEX 6: ADULT LEARNING: MEASUREMENT, KEY 
CHARACTERISTICS AND PARTICIPATION LEVELS  

1. Measurement  

For  the  purpose  of  EU-level  benchmarking,  adult  learning  is  measured  as  participation  in 
formal or non-formal learning among adults  aged 25-64 during the past 12 months.194 
This is also the definition used for monitoring progress towards the EU’s 2030 target that at 
least 60% of adults should participate in training every year by 2030 (Section 1). 

Formal learning is characterised by an official recognition of the learning outcome by public 
authorities (such as a University diploma). Non-formal learning aims to improve knowledge 
and  skills  in  any  areas  in  an  institutionalised  setting  and  may  take  the  form  of  courses, 
workshops  or  private  lessons,  sometimes  resulting  in  credentials  and  certificates  that  are 
recognised  by  employers.  It  excludes  informal  learning  (such  as  learning  from  a  friend, 
colleague, book, guided tour or library visit).195 Non-formal learning is much more prevalent 
among adults than formal learning: in 2016 (the most recent year with available data), 34.8% 
of adults participated in non-formal learning, compared to only 5.0% for formal learning.  

The three main EU statistical data sources on adult learning are the Labour Force Survey 
(LFS), the Adult Education Survey (AES) and the Continuing Vocational Training Survey 
(CVTS) on training provision by enterprises. 

The LFS provides annual data on adult learning participation during the last 4 weeks, which 
has been used to monitor progress towards the 15% participation target by 2020 set out as part 
196

of the strategic framework for European cooperation in education and training (“ET 2020”).
However,  the  short  reference  period  is  considered  sub-optimal  for  a  comprehensive 

 Therefore, 
measurement of the short learning periods that are characteristic of adult learning.
the LFS will also measure adult learning participation during the last 12 months every second 

197

year starting in 2022
target (Section 1).  

198

, which will be available to monitor progress towards the EU’s 2030 

The AES collects more detailed information on adult learning than the LFS, however at a lower 
frequency: the most recent available data are for 2016, and further data are available for 2011 
and  2007  (with  limited  comparability  between  the  2007  “pilot  survey”  and  the  subsequent 
survey  waves).  It  uses  a  12  months  reference  period.  The  coverage  of  non-formal  learning 
activities  in  the  AES  is  slightly  broader  than  in  the  LFS  as  it  includes  “guided  on  the  job 
training”,  which  is  closer  to  informal  learning  than  the  other  types  of  non-formal  learning 

194

195

196

 See the European Skills Agenda for sustainable competitiveness, social fairness and resilience of 1 July 2020. 
 Eurostat (2016). Classification of learning activities (CLA): Manual.  
 Council conclusions of 12 May 2009 on a strategic  framework  for European cooperation in education and 

training (‘ET 2020’) (2009/C 119/02). 
197

 See European Commission (2020), Towards an improved adult learning monitoring framework

.

 Revisiting the 

available data and indicators, for a discussion. 
198

 European Commission (2020), Education and Training Monitor 2020. 

70 

 
 
                                                 
activities  and  not  included  in  the  EU  benchmarking  framework.  Most  adult  learning 
participation data presented throughout this impact assessment are hence taken from the 
2016 AES, adjusted to correspond to the definition of adult learning used for EU benchmarking 

by excluding guided on the job training.

199

The CVTS complements the individual-level survey data from LFS and AES with information 
on  enterprises’  training  needs,  planning,  provision  and  financing  from  the  perspective  of 
enterprises. The results of the fifth and latest wave of CVTS are available for the reference year 
2015. 

2. Key characteristics  

The  AES  allows  for  a  characterisation  of  adult  learning  in  terms  of  its  purpose,  duration, 

providers and funders.

200

Purpose:  Concerning  the  purpose  of  adult  learning,  AES  asks  participants  of  non-formal 
learning activities whether these activities have been “mainly job-related” or “mainly personal/ 

201

non-job related”.
 About 80% of non-formal learning in the EU was mainly job-related, 
a pattern that is consistent across EU Member States for both men and women, with somewhat 
higher shares for men (85%) as compared to women (76%). An analysis of the instruction hours 
spent by field of study reveals that about one fifth (18%) of adult learning in the EU concerned 
business, administration and law, followed by services (16%), and health and welfare (14%),  
arts  and  humanities  (14%),  engineering,  manufacturing  and  construction  (12%)  and 
information and communication technologies (6%). 

Duration:  An  average  adult  learning  activity  was  118  hours  in  2016,  with  a  considerably 
shorter average duration of non-formal (75 hours) as compared to formal (469 hours) learning 
activities (and  an average closer to the duration of non-formal learning activities due to the 
small  share  of  formal  learning  in  overall  adult  learning).  The  average  duration  noticeable 

decreased compared to 2007 when it was 134 hours.
 This  decrease may  reflect  a move 
towards  more  frequent  but  shorter  learning  activities  facilitated  by  digital  learning  offers, 
consistent also with the overall modest increase in participation rates over this period (Section 
2.4).  

202

Providers:  Formal  learning  is  provided  by  Universities,  VET  schools  or  similar  publicly 
recognised  institutions.  The  main  provider  of  non-formal  education  are  employers  (35%), 

199

 This leads to differences between the AES participation data reported in this impact assessment to the published 
data by Eurostat: for instance, excluding guided on the job training reduces EU-27 participation in 2016 from 
43.7% to 37.4%. The data excluding guided on the job training can be found here. 
200

  The  figures  in  this  section  are  based  on  European  Commission  (2020),  Adult  learning statistical  synthesis 

report and Eurostat statistics explained: Adult learning statistics - characteristics of education and training 
 and refer to the year 2016. 
201

 No corresponding question is asked for formal learning activities, or in the LFS. Starting in 2022, the LFS will 

also ask for whether non-formal learning has been undertaken for mainly personal or professional reasons. 
202

 All respondents that indicated to have participate in education or training in the last 12 months were asked to 
specify  the  total  number  of  instruction  hours  for  the  most  recent  formal  education  or  training  activity.  The 
presented average is an aggregated of all these responses at the EU level.  

71 

 
 
 
                                                 
followed  by  non-formal  education  and  training  institutions  (18%).  All  the  other  types  of 
providers, from commercial institutions to non-profit associations, from trade unions to formal 
education institutions, etc. constitute a mosaic of providers of adult education, with none of 
them amounting to more than 10%. 

Funders: It is common for adults to pay for the costs of formal learning themselves (55%). 
Non-formal learning activities are often for free (23%) or fully paid by someone else (53%). 
88% of job related non-formal learning activities were at least partially fundered by the 

employer.

203

3. Participation levels and differences between groups  

3.1 Progress in adult learning participation towards EU-level targets  

Progress in participation in adult learning over the last decade has been limited and uneven 
across EU Member States; the EU-level target of 15% (of adults participating in the 4 weeks 
reference period) was not met in 21 of 27 EU Member States in 2020 and reached 9.2% overall, 
204

compared to 7.8% in 2010 (see Figure A6.1).
even decreased over the last 10 years (DK, AT, ES, SI, CZ, CY, PL, SK, RO).   

 In nine Member States, the participation rate 

Trends in adult learning participation over time need to be interpreted with caution as they are 
influenced by statistical breaks in some Member States (with a tendency to overstate progress 
over recent years). However, the overall finding of moderate progress is confirmed by analyses 
focussing  only  on  those  Member  States  for  which  comparable  data  series  are  available  (cf. 
Annex 8.3). COVID-19 restrictions resulted in a drop in overall adult learning participation 

from 10.8% in 2019 to 9.2% in 2020 for EU-27.
likely to rebound once COVID-19 restrictions are lifted. 

205

 However, adult learning participation is 

203

 Training took either place fully or in part during working hours and/or was paid-for in part or in full by the 

employer. See Eurostat website, data code TRNG_AES_123. 
204

 Council conclusions of 12 May 2009 on a strategic  framework  for European cooperation in education and 

training (‘ET 2020’) (2009/C 119/02).  
205
 See Eurostat variable trng_lfse_01. 

72 

 
  
                                                 
Figure A6.1 – Progress to ET2020 objectives - participation of adults 2010 and 2020 

Source: Labour Force Survey, trng_lfse_01. Participation rate is calculated as the share of 
adults from 25 to 64 year in some form of learning in the last four weeks as percentage of total 
population.  

According  to  the  OECD’s  “Programme  for  the  International  Assessment  of  Adult 
Competencies” (PIAAC), adult learning participation in the EU is also not particularly high in 
international comparison, with EU average participation lagging behind the OECD average and 

countries such as the US, Canada or Singapore.

206

The  remainder  of  this  Annex  and  the  impact  assessment  in  general  will  draw  on  the  Adult 
Education  Survey,  as  LFS  data  with  a  12  month  reference  period  is  not  yet  available  (see 
Section 1 of this Annex). 

3.2 Participation differences between groups of adults  

Access to adult learning opportunities is influenced by characteristics of the employment status, 

the characteristics of the job as well as individual-level characteristics (Figure A6.2).

207

206

  See  Figure  4.1  of  OECD  (2019),  Returns  to  different  forms  of  job-related  training:  factoring  in  informal 
learning, OECD Social, Employment and Migration Working Papers and the PIAAC data on the OECD’s website. 
207

 See European Commission (2020), Education and Training Monitor 2020, for further analysis. 

73 

 
 
  
 
                                                 
Figure A6.2 – Overview of participation figures – key characteristics  

Source: AES 2016. 

Participation differences based on the employment status  

Statistics  show  consistently  lower  adult  learning  participation  for  groups  of  adulta  that  are 
likely to receive less support from an employer. For instance, permanent employees are more 
likely to participate in training than other adults across all EU Member States (45.4% vs. 28.7% 
during last 12 months, see Figure 1 in Section 2.1 of the report). Figure A6.2 above shows how 
this gap between groups has been relatively constant between 2011 and 2016. Details on the 
participation  rates  of  the  different  sub-groups  are  provided  below.  To  put  the  numbers  into 
perspective, Figure A6.3 provides an overview of the composition of the EU adult population 
by labour market status. 

74 

 
 
Figure A6.3 – Population aged 25-64 years by employment status in 2020 

Source:  Eurostat  Labour  Force  Survey  2020.  The  split  of  permanent  workers  between  SMEs  and  larger 
enterprises was conducted on the basis of the AES 2016, as the LFS uses different company size categories. 

Labour market status: The share of employed individuals that participated in adult learning 
(43.3%) is almost double that of unemployed (26%) and inactive persons (21.3%). Particularly 
large differences can be observed in BG, HU, LT, PL, PT, SI, and SK. The only exception to 
this pattern is found in RO, where the share of unemployed that participated in adult learning 
in  the  last  year  is  higher  than  the  share  of  employees.  Generally,  the  share  of  inactive  that 
participates in adult learning is lower than that of unemployed, except in DK, FI and SE.  The 
importance of labour market status-related variables is also apparent in regression analyses of 

the determinants of adult learning.

208

208

 Biagi et al. (2020), Adult learning in Europe: An analysis of the determinants and an attempt at forecasting, 

analytical input by JRC for DG EMPL. 

75 

 
 
  
 
                                                 
Figure A6.4 - Participation in adult learning - by labour market status 

Source: Adult Education Survey 2016. Participation rate in education and training – formal and non-formal, 25-
64 years old. Excluding guided on the job training (GOJT).  

Employed / self-employed: Employees (wage earners) are consistently more often engaged in 
adult learning (44.9%, group size estimated by LFS2020 as 152.1 million, or 64% of 25-64 
population) than self-employed individuals (36.9%, group size estimated by LFS2020 as 25.1 
million, or 11% of the 25-64 population). Particularly steep differences between these groups 
can  be  identified  in  RO,  MT,  and  SK.  Only  in  a  handful  of  Member  States,  self-employed 
report more often to have participated in adult learning, such as in CZ, HR, EE, DE, and IT. 
The  OECD  PIAAC  study  found  an  even  more  substantial  difference,  with  35%  of  self-
employed  workers  participating  in  adult  learning  yearly  compared  with  57%  of  full-time 

permanent employees.

209

209

 OECD (2019), Employment Outlook 2019: the future of work. Based on Survey of Adults Skills (PIAAC), 

chapter 6.  

76 

 
 
  
                                                 
Figure A6.5 - Participation rate, employed and self-employed 

Source: Adult Education Survey 2016. Participation rate in education and training of employees  – formal and 
non-formal, 25-64 years old. Excluding guided on the job training (GOJT). Member States are sorted by overall 
participation in adult learning. 

Contract  status:  Generally,  employees  with  permanent  contracts  (estimated  by  LFS2020  as 
134.7  million  or  57%  of  25-64  population)  report  more  often  to  have  participated  in  adult 
learning (45.4%) than employees with a temporary contract (41.4%, entire group estimated by 
LFS2020 as 15.8 million or 6% of 25-64 population). However, exceptions are found AT, HU, 
FI, IE, LU, BE, LV, EE, MT, ES, LT, and BG.  

Figure A6.6 - Participation in adult learning – by contract status 

Source: Adult Education Survey 2016 Participation rate in education and training – formal and non-formal, 25-
64 years old. Excluding guided on the job training (GOJT). Member States are sorted by overall participation in 
adult learning. 

Full-time / part-time: Participation patterns between employees with full-time and part-time 
contracts  are  relatively  similar  when  aggregated  at  the  EU  level.  In  most  Member  States 
however, individuals with full-time contracts (estimated by LFS2020 as 146.9 million or 62% 

77 

 
 
  
of  25-64  population)  report  more  often  to  have  participated  in  adult  learning  (43.8%)  than 
employees with part-time contracts (43.5%, group size estimated by LFS2020 as 30.3 million 
or  13%  of  25-64  population),  except  in  SE,  EE,  CZ,  LV,  SI,  EL,  BG,  and  RO.  Larger 
differences  appear  for  part-time  workers  when  comparing  those  with  a  permanent  contract 
(48.8%), and those and without (38.8%). JRC regressions analysis on AES data shows that full 
time  employees  are  significantly  less  likely  to  participate  in  formal  education  and  training 
relative  to  part-time  employees,  whereas  they  are  more  likely  to  participate  non-formal 

education and training.
affect each in their own way preferences for specific types of training activities.  

 This shows how the factors of employer support and time available 

210

Figure A6.7 - Participation in adult learning – by contract type  

Source: Adult Education Survey 2016. Participation rate in education and training of employees  – formal and 
non-formal, 25-64 years old. Excluding guided on the job training (GOJT). Member States are sorted by overall 
participation in adult learning. 

Participation differences based on the job-characteristics 

Participation  in  adult  learning  also  differs  considerably  based  on  characteristics  of  the 
employer, the sector and type of occupation as presented below.  

210

 Biagi et al. (2020), Adult learning in Europe: An analysis of the determinants and an attempt at forecasting, 

analytical input by JRC for DG EMPL. 

78 

 
 
                                                 
Figure A6.8 - Different patterns in participation for work-related characteristics – EU27 

Source: Adult Education Survey 2016. Participation rate in education and training of employees  – formal and 
non-formal, 25-64 years old. Excluding guided on the job training (GOJT). Member States are sorted by overall 
participation in adult learning. 

Enterprise  size:  The  size  of  the  employing  enterprise  is  consistently  associated  with 
participation in adult learning; Figure A6.8 shows how participation consistently increases for 
each  increase  in  size.  This  patterns  is  consistent  in  every  Member  State.  A  particularly 
substantial  difference  is  visible  when  comparing  the  participation  (42.0%)  of  employees  in 
SMEs (less than 250 employees, estimated group size 100 million, 41.7% of population aged 
25-64) with the 55.1% of workers in larger enterprises (with at least 250 employees, group size 

211

estimated  at  52.9  million  or  21.9%  of  25-64  population).
  Differences  between  smaller 
enterprises (1-49 staff, group size estimated by LFS2020 as 78.8 million, 32.8% of population 
aged  25-64)  and  micro-enterprises  (1-10  staff,  group  size  estimated  by  LFS2020  as  37.9 
million, 16.0% of population aged 25-64) are less profound (39.7% vs. 35.6%). The pattern of 
lower learning participation in smaller enterprises is confirmed by CVTS data and regression 
analysis  controlling  for  other  factors  such  as  individual  characteristics.  Specifically,  JRC 
regressions show that employees working in firms with 50 or more workers are found about 
7.2 percentage points more likely to participate in non-formal learning compared with those 
employed in firms with 10 or less workers. 

Sector: AES data shows steep differences between sectors, particularly between workers in the 
education  /  social  sector  (participation  of  59.7%,  group  size  estimated  by  LFS2020  as  34.9 
million  or 15% of 25-64 population) against agriculture  (participation of 20.7%, group size 

211

 The LFS does not distinguish between small and medium-sized enterprises when asking respondents how many 
employees work in their company. An estimated 73.9 million people (27.1%) work in companies larger than 50 
employees. Comparing an estimate from DG GROW in the Communication from the Commission to the European 
Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions  An 
SME Strategy for a sustainable and digital Europe (COM/2020/103 final) that SME employ around 100 million 
people against the LFS finding that in 2020 78.8 million work in enterprises of less than 50 employees, this would 
put the estimated group size of employees working in enterprises with over 250 staff at 52.9 million, or 21.9% of 
the 25-64 population.  

79 

 
 
                                                 
estimated  by  LFS2020  as  7.7  million,  or  3%  of  25-64  population)  and  construction  sectors 
(participation  29.7%,  group  size  estimated  by  LFS2020  as  12.7  million,  or  5%  of  25-64 
population). Industry (37.1%) and business services (43.5%) score on average in between these 
other sectors.   

Occupation:  Occupations  that  require  low  and  medium  level  skills  show  the  lowest 
participation figures in most Member States. Workers in occupations that require higher levels 
of skills (technicians, professionals and managers, estimated by LFS2020 as 80.0 million, or 
34% of 25-64 population) show considerably higher participation figures (60.1%). Especially 
groups that run the highest risk of displacement by automation show the lowest participation 
figures in most Member States (Figure A6.9). 

Figure A6.9 - Participation in adult learning and risk of automation 

Source: Cedefop estimates from the Skills Panorama and Adult Education Survey 2016. 

Estimated  employment  growth:  Based  on  the  evidence  provided  above,  using  Cedefop’s 
forecasts  of  future  skill  demand,  it  is  possible  to  assess  how  participation  is  linked  to 
occupations  for  which  shortages  are  expected.  Figure  A6.10  shows  projected  growth  in 
occupational employment (at the 2-digit ISCO level) projected to 2030 by occupation along 
with the percentage of people in that occupation who participated in adult learning. It reveals 
that there are a distinct set of occupations which are characterised by relatively low growth and 
low  level  of  participation  in  adult  learning  (agricultural  workers,  machine  operatives  and 
assemblers, and skilled-trades workers). In contrast, there are occupations where relatively high 
levels of growth are projected and where the workers participate considerably more in adult 
learning.  It may well be that the risks facing these different groups of occupations vis-à-vis 
their participation in adult learning may become even more differentiated in the future.  

80 

 
 
Figure  A6.10  -  Projected  employment  growth  by  occupation  by  participation  in  adult 

learning 

Source:  Cedefop Skill Forecasts via Skills Panorama, Adult Education Survey 2016, own calculations 

Figure A6.11 summarises the expected employment trends against participation figures at the 
sector level. It shows that there is a range of sectors where employment is expected to fall and 
where  workers  participate  comparatively  less  often  in  adult  learning  (i.e.  manufacturing, 
agriculture, mining, and water supply). The position of those currently working in such sectors 
may be a relatively precarious one: a relatively high risk of job loss with limited participation 
in  training  opportunities  compared  with  people  working  in  other  occupations.  This  is 
particularly  true  against  the  background  of  different  consequences  of  green  jobs  and 
occupational changes for different sectors per sector. Some sectors such as renewable energy 
and  environmental  goods  and  services  (including  water  and  waste  management)  have 
developed  significantly  and  show  potential  for  employment  growth.  For  other  sectors  the 
impact varies, such as the construction sector, depending on the degree to which the existing 
built environment is greened through retro-fitting or, conversely, where the focus is on ensuring 
that new construction is greener. Some parts of manufacturing, notably the automotive sector, 
are  gradually  changing  their  output  to  produce  more  energy-efficient  versions  of  the  same 
product, with limited net employment gains. Other parts of manufacturing are producing green 
products and creating jobs in the supply chains of green sectors: an example is the production 

of wind turbines.

212

212

 ILO (2018), World Employment and Social Outlook 2018: Greening with jobs. 

81 

 
 
 
                                                 
Figure A6.11 - Projected employment growth by sector and participation in adult learning

Source:  Cedefop Skill Forecasts via Skills Panorama, Adult Education Survey, own calculations 

Participation differences based on individual characteristics 

Besides the characteristic of the employment relationship, and characteristics of the occupation, 
individual characteristics play an important role explaining levels of participation as well, such 
as  level  of  qualification,  age,  and  gender.  However,  within  groups  of  similar  education 
attainment  and  age,  differences  observed  above  continue  to  be  important  determinants  of 
participation as well. 

Educational attainment: The level of education of individuals is widely regarded as one of the 

 Low-qualified adults (estimated by 
driving factors of participation in education and training.
LFS2020 as 49.7  million,  or 21% of  25-64 population)  are considerably less likely  to  have 
engaged in adult learning (18.0%) than adults with secondary (33.4%) or tertiary qualifications 
(58.1%). Across all OECD countries, participation in adult learning by low qualified adults is 

213

40  percentage  points  below  that  of  high-qualified  adults.
  This  broader  pattern  is  largely 
confirmed for the EU by the AES as well, which are presented in Figure A6.12 below. Low 
qualified workers participate considerably less in EL, HR, PL, and RO; here the participation 
rates of lower qualified adults are less than a quarter of that of the general population.  

214

213

  This  is  know  as  Matthew  effect.  Individuals  with  a  higher  level  of  education  are  especially  motivated  to 
deepening  their  learning  Boeren  and  Boeren  (2017),  Understanding  adult  lifelong  learning  participation  as  a 
layered  problem.  Studies  in  Continuing  Education;  Rubenson  (2018),  Conceptualizing  participation  in  adult 
learning and education: Equity issues. In Milana et al.(Eds.), The Palgrave international handbook on adult and 
lifelong education and learning (pp. 337-357),Palgrave 
214

 OECD (2019), Employment Outlook 2019: the future of work. Based on Survey of Adults Skills (PIAAC). 

82 

 
 
                                                 
Figure A6.12 - Participation in adult learning – by education level 

Source: Adult Education Survey 2016. Participation rate in education and training – formal and non-formal, 25-
64 years old. Excluding guided on the job training (GOJT). 

Regression analysis performed by the Joint Research Centre confirms this. It also shows that 
the size of the effect of education is stronger for non-formal education and training and informal 
learning compared with formal education and training. The JRC regression analysis shows that 
individuals  with  tertiary  education  are  about  between  28  and  30  percentage  more  likely  to 
participate in non-formal education and training and informal learning than those with lower 
secondary  education  or  less.  The  corresponding  figure  for  formal  education  and  training  is 
about 7 percentage points. 

While education level is widely considered as one of the main determinants of an individual’s 
participation in adult learning, one needs a broader perspective if one seeks to address such 
differences in participation. The barriers experienced by individuals with lower qualifications 
vary, based on other conditions, such as the support available by their employer, or the need 
for specific training on the short term for their work. The figure below for instance highlights 
the differences in adult learning participation across different education levels, but also shows 
that the differences in support for individuals continue to contribute to participation as well; 
among  each level  of  qualification, individuals with  permanent  contracts  participate more in 
adult learning than workers who are not permanent employees. The figure also highlights how 
for each qualification level, workers in larger companies consistently participated more often 
in  adult  learning  that  individuals  in  smaller  companies,  while  the  unemployed  and  inactive 
participate consistently less. This underlines the need for better understanding the barriers that 
all  individuals  face,  considering  the  extent  to  which  these  affect  individuals  with  lower 
qualifications.  

83 

 
 
Figure  A6.13  -  Participation  rate,  by  education  level,  employment  status  and  size  of 

enterprise 

Source: Adult Education Survey 2016. Participation rate in education and training. Excluding guided on the job 
training (GOJT). Note that the two categories depicting size of firm overlap with the two categories depicting the 
type of contract of salaried workers\  

Age:  Participation  tends  to  be  consistently  lower  for  higher  age  categories  (Figure  A6.14 
below). This pattern is observed across all Member States. Particularly the higher age group 
(55 to 64 years – participation of 27.4%, group size estimated by LFS2020 as 60.1 million, or 
25%  of  25-64  population)  reports  considerably  lower  participation  rates  than  other  groups. 
Nevertheless,  additional  analysis  again  points  to  substantial  differences  when  comparing 

participation in formal and non-formal learning.
 For non-formal learning the estimates point 
to an inverse-U relationship: the probability increases up to age 31-32 and declines thereafter. 
Particularly  for  job-related  non-formal  learning,  the  mid-aged  participate  most,  which  is 
consistent with a need to update skills via non-formal learning for those with some distance to 

215

216

initial education.
likelihood decreasing after age 21 and increasing again after age 61.  

 On the other hand, for formal learning, a U-shape is observed, with the 

215

 Biagi et al. (2020), Adult learning in Europe: An analysis of the determinants and an attempt at forecasting, 

analytical input by JRC for DG EMPL. 
216

 See also Eurostat (2021), Adult learning statistics.  

84 

 
 
                                                 
Figure A6.14 - Participation in adult learning – by age  

Source: Adult Education Survey 2016. Participation rate in education and training – formal and non-formal, 25-
64 years old. Excluding guided on the job training (GOJT). 

Gender: Figure A6.15 shows that women participate slightly more (38.4%) in adult learning 
than men (36.4%) at the aggregated level, although the differences are small. In some Member 
States larger differences can be observed such as in SE (considerably more women than men 
participating in adult learning) or CY (considerably more men participating in adult learning). 
While these statistics do not suggest major differences in participation, larger differences exist 
in the types of adult learning that men and women participate. A total of 83.2% of non-formal 
learning activities of men was job-related, compared to 74.6 % for women. This pattern was 
found in almost all EU. Only in CY the trend is reversed, where the share of job-related non-

formal learning activities was higher for women than for men (79.3 % against 66.3 %).

217

Figure A6.15 - Participation in adult learning – by sex  

217

 See also Eurostat (2021), Adult learning statistics.  

85 

 
  
 
  
                                                 
Source: Adult Education Survey 2016. Participation rate in education and training – formal and non-formal, 25-
64 years old. Excluding guided on the job training (GOJT 

Persons with disabilities: Data also suggest that persons with disabilities (estimated by EU-
SILC as 14%, or roughly 33.2 million of the 25-64 EU adult population) participate only half 

of the average amount of individuals without disabilities.
 While such data is not collected 
systematically in the Adult Education Survey, transposing the estimates about participation in 
adult learning from the Survey on Income and Living Conditions to the averages presented in 
this section would be equivalent to roughly the participation figures of the inactive population 

218

219

(21.73).
 The average employment rate at EU level among persons with disabilities is also 
considerably lower (52.0%) than for other adults (76.2%), as a result of which they also are 

less likely to receive support from an employed to enrol in training.

220

4. Recommendations to increase participation in adult learning in the European 
Semester 

Analysis of the Country Specific Recommendations over the years 2019/2020 shows that all 
countries received a recommendation related to skills development, mostly related to basic and 
digital skills. The contents of each recommendation  are classified and summarised in Table 
A6.1.  It  shows  how  recommendations  also  referred  to  the  need  for  strengthening  lifelong 
learning; and improving the performance, quality labour market relevance, inclusiveness and 

221

 While references 
flexibility of education and training systems addressing skills mismatches.
to the education system or inclusive education may not explicitly refer to adult learning per se, 
such recommendations still have a bearing for adult learning, which also covers formal learning 
In 19 Member States, adult learning was highlighted specifically as an area to focus system-
level reforms. The table below compares Member States on their existing level of participation 
in adult learning (the main benchmark for this study) and categorise these in three groups. The 
table  shows  that  despite  the  variety  of  different  adult  learning  systems,  Recommendations 
single out adult learning in most Member States, both in Member States where participation is 
above the EU average and below.  

Several Country Specific Recommendations refer to specific target groups like low qualified/ 
skilled, job seekers, inactive people, older workers, people with a migrant background, Roma, 
and other groups with specific needs. In these cases, the Country Specific Recommendation 
generally address the need for increasing adult learning and improve coverage of education and 

218

 Grammenos (2018), Statistics on Persons with Disabilities 2018, EU-SILC 2018. The SILC only classifies 
fulltime training, or working less than 30hours in combination with training, so is relatively insensitive for the 
types  of  short  and  nonformal  training  that  is  the  object  of  this  impact  assessment.  Its  results  with  regard  to 
measuring participation in adult learning are therefore not comparable with that of the Adult Education Survey. 
However, the trends for persons with disabilities compared to others give us some indication of the differences in 
participation.  
219

  Estimate  based  on  the  finding  in  EU-SILC  2016  that  2.4%  of  population  with  disabilities  reports  to  have 

221

stopped working (temporarily) for training purposes, against 4.1% of the population without disabilities.  
220

 Grammenos (2018), Statistics on Persons with Disabilities 2018, EU-SILC 2018. 
  An  analysis  was  made  of  all  Country  Specific  Recommendation  to  MS  for  2019  and  2020.  Where 
recommendations focused on skills and explicitly pointed to adult learning as a way to improve such skills, these 
were classified in multiple categories.  

86 

 
 
                                                 
training  systems  as  well  as  the  need  to  strengthen  quality  and  labour  market  relevance  of 
training offer, on which the ILA initiative could contribute.  

Table  A6.1:  Overview  of  Country  Specific  Recommendations  2019/2020  on  skills,  adult 

learning and performance of education and training systems 

MS 

Partici
pation 
in AL  

Attention in CSR  Attention for system-level reform 
Performance 
of 
systems 
general 

Adult 
learning  
specifically 

education 
in 

2019 

2020 

Inclusive 
education 
in general 

Attention for skills 

Skills 
general 

in 

Digital skills 

Basic skills 

IT 

LV 

EE 

BE 

CY 

AT 
High 
DK  High 
High 
FI 
FR 
High 
DE   High 
HU  High 
High 
IE 
High 
NL 
High 
SE 
Mediu
m  
Mediu
m  
Mediu
m  
Mediu
m  
Mediu
m  
Mediu
m  
Mediu
m  
Mediu
m  
Mediu
m  
Low 
Low 
Low 
Low 
Low 
Low 
Low 
Low 
Low 

BG 
HR 
CZ 
EL 
LT 
MT 
PL 
RO 
ES 
TOTAL: 

SK 

LU 

PT 

SI 

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 
26 

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 
26 

X 

X 
X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 
X 
X 
X 
X 
X 
X 
X 
X 
21 

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 

19 

10 

X 
X 
X 
X 
X 

X 
X 
X 

X 

X 

X 

X 

X 

X 

X 
X 
X 

X 
X 
X 
X 
X 
22 

X 
X 

X 

X 
X 
X 

X 

X 

X 

X 

X 

X 

X 

X 

X 
X 
X 

X 
X 
X 
20 

X 

X 

X 
X 

X 

5 

ANNEX 7: ADDITIONAL EVIDENCE ON THE PROBLEM 
DRIVERS  

1. Driver 1: Insufficient financial support 

This Section further explores how the existing gaps in financial support for adult learning is a 
first  driver  that  limits  progress  in  increasing  participation  rates  of  adults  in  learning  and 
produces inequalities. Gaps in  financial support  are identified due to  an  overall insufficient 

87 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
level of investments in adult learning, as well as limited coverage and fragmentation of existing 
support for adult learning. Each of these three elements are explored in more detail below.  

1.1 Level of investments in adult learning 

A  first  factor  that  contributes  to  gaps  in  financial  support  is  related  to  the  overall  level  of 
support available, i.e. the existing level of investments in adult learning. This refers to any type 
of  financial  support,  which  can  include  the  supply-side  coverage  of  the  formal  education 
system (if relevant), or specific public policies that subsidise individuals or firms in taking up 
adult learning courses (which may be formal or non-formal). Investments in adult learning are 
made by employers, public authorities as well as by individuals themselves. In most Member 
States, formal adult education is fully subsidised by the State until upper secondary level, while 
higher  education  and  VET  for  adults  are  usually  subject  to  fees,  which  are  often  paid  by 
individuals.  Non-formal education and trainingactivities are more widely subsidized (in most 

cases by employers) than formal education and training activities.

222

By combining information from the AES, CVTS and public investment on training as part of 
active labour market  policies, investments  in  adult learning  could  be estimated at  a total  of 

1.7% of GDP for the EU27.
 Despite the methodological caveats, this measure comes closest 
to  an  EU-wide  mapping  of  financial  investments  in  adult  learning.  The  estimates  for  each 

223

  Because  these  estimates  are 
Member  State  were  validated  by  the  adult  learning  network.
primarily based on self-reporting, it can be assumed that these tend towards the higher-end of 
estimations;  actual  total  investments  may  be  below  these  estimates,  but  are  unlikely  to  be 
higher. This is also confirmed when compared to earlier aggregated estimates of adult learning 

224

investments, which suggested a range of 0.8%-1.2% of GDP
, of which public investments 
in most Member States tends to be less than half. The maximum level of estimated financing 
in  adult  learning  across  Member  States  varies  substantially,  from  less  than  0.5%  in  RO  to 
almost 2.5% of GDP in Scandinavian countries.  

225

222

 OECD (2019), Getting Skills Right: Future-Ready Adult Learning Systems, based on Adult Education Survey 

2016. 
223

  This  builds  on  European  Comission  (2020),  Workforce  skills  and  innovation  diffusion:  trends  and  policy 
implications.  The  variety  of  different  systems,  contributions  and  actors  makes  it  difficult  to  compare  actual 
investments  over  time  and  across  Member  States.  The  collection  of  coherent  and  comparable  data  is  further 
complicated by the fact that public funding for adult learning is the responsibility of the central or state level  in 
around half of the Member States; in the other half regional or local governments also play a significant role in 
supporting adult learning European Commission (2020), Achievements under the renewed European agenda for 
adult learning (2011-2018), Report of the ET 2020 working Group on adult learning (2018-2020), page 37. No 
data is available to estimate the investments of publicly financed non-formal adult learning outside the domain of 
active labour market policies. 
224

Mapping of available instruments conducted by the adult learning expert network for the purpose of the IA on 

ILAs. See Annex 4 for more information. 
225

FiBS/DiE (2013), Financing the Adult Learning Sector.  

88 

 
  
                                                 
Figure A7.1 - Investments in adult learning as % of GDP 

Source: based on estimates suggested by European Commission (2020)
, for individuals’ and 
household expenditures on formal and non-formal learning (Adult Education Survey 2016), 
expenditure  on  employee  training  by  public  and  private  employers  (Continuing  Vocational 
Training Survey 2016), public investments based on expenditures in training as part of active 
labour market policies (Labour Market Policies database).  

 226

Data in Figure A7.1 confirms the relatively low level of the public share of investments, below 

 227

 Correspondingly, there are important private contributions to adult learning, 
0.5% of GDP.
most particularly by employers. Substantially over half of the estimated investments can be 
linked to employer contributions. This is estimated at over two-thirds of the total investments 
in CZ, DK, FR, HR, IE, LU, MT, RO, and SI.  

This  data  also  highlights  how  member  states  with  the  highest  levels  of  adult  learning 
investments also have considerably larger shares of public investments in adult learning than 
member states with lower levels of investments in adult learning.  

A  review  by  experts  pointed  that  the  current  levels  of  investments  in  adult  learning  is 
inadequate  for  ensuring  sufficient  quality  and  access  in  adult  learning  in  23  out  of  the  27 
Member States.  The research literature also  confirms  that actively  encouraging all adults to 

learn would require additional investments, both of public and private nature
. One in five 
European  firms  for  instance  also  indicate  they  have  underinvested  in  training  of  their 

228

226

 European Commission (2020), Workforce skills and innovation diffusion: trends and policy implications. See 

also European Commission (2020), Adult Learning Statistical Synthesis Report:, pp. 22-34. 
227

 These estimates for public investments in principle include EU support for active labour market policies, in 
the form of ESF/YEI or otherwise. However, the extent to which these are fully reflected in the LMP database 
figures and are reported as active labour market policies vary per Member State and per year, depending both on 
data availability and definitions used. See for a detailed discussion for instance European Commission (2020), 
Labour market policy Expenditure and participants.  
228

 Brunello and Wruuck (2020), Employer provided training in Europe: determinants and obstacles. IZA Institute 

of Labor Economics.  

89 

 
 
                                                 
workforce.

229

EE, MT, NL).

 National experts deemed investments only adequate in four Member States (AT, 
230

The importance of investing in adult learning is further highlighted; because higher investments 

move together with higher levels of participation.
 Moreover, the level of investment also 
correlates with the relative difference in participation between those in atypical employment 

231

232

 Member States with higher investments in adult learning 
and of other working-age adults.
(by  public  authorities,  employers  and  individuals  together)  have  lower  inequalities  in 
participation  between  permanent  workers  and  other  adults  aged  25-64,  as  presented  in  the 
figure below. Member States that mobilise higher overall amounts of funding for adult learning 
therefore not only see more often higher participation rates but also lower inequalities in the 
participation rates between full-time permanent employees and other adults. 

Figure A7.2: Correlation of participation in adult learning with % of GDP invested 

Source:  Adult  Education  Survey  2016  for  participation  figures,  financial  estimates  based  on  European 
Commission (2020), who estimate for individuals’ and household expenditures on formal and non-formal learning 
(Adult Education Survey 2016), expenditure on employee training by public and private employers (Continuing 
Vocational Training Survey 2016), public investments based on expenditures in training as part of active labour 

market policies (Labour Market Policies database).

233 

229

  European  Investment  Bank,  (2019),  EIB Investment  Report  2018/2019:  retooling  Europe’s  economy  -  Key 

findings..  
230

 Individual (unpublished) country reports by Adult Learning expert network 2020, supporting DG EMPL.  
 Both elements show a statistically significant correlation (Pearson’s r of 0.724 at p<.01). 
 Statistical significant correlation (Pearson’s r of -0.65 at p<.01). 
  European  Commission  (2020),  Workforce  skills  and  innovation  diffusion:  trends  and  policy  implications. 

231

232

233

Annex 8. See also European Commission (2020), Adult Learning Statistical Synthesis Report:, pp. 22-34. 

90 

 
  
  
                                                 
 
Figure A7.3 - Correlation between % GDP invested and differences in participation of key 

groups 

Source:  Adult  Education  Survey  2016  for  participation  figures,  financial  estimates  based  on  European 
Commission (2020), who estimate for individuals’ and household expenditures on formal and non-formal learning 
(Adult Education Survey 2016), expenditure on employee training by public and private employers (Continuing 
Vocational Training Survey 2016), public investments based on expenditures in training as part of active labour 

market policies (Labour Market Policies database).

234 

1.2 Limited coverage of existing support schemes in terms of groups of adults and types 
of training 

In addition to the insufficient overall level of investment available for adult learning, systematic 
gaps in coverage can be identified for specific target groups and for specific types of training. 
Both elements are discussed in more detail below.   

Limited coverage of groups of adults 

The  vast  majority  of  participants  in  non-formal  education  and  training  do  not  have  to  pay 
235

themselves for costs to participate; most often because such costs are covered by employers.
Such investments  in  the skills  of staff  are done  with  a view on possible future productivity 
gains and help explain the importance of the provision of job-related training among the overall 
share of  adult learning.  Nevertheless,  employers need to  weigh possible  future productivity 

  Such  uncertainty 
gains  against  the  risk  of  poaching  of  trained  workers  by  competitors.
increases  substantially  when  investing  in  training  of  part-time  staff  or  staff  on  temporary 

236

234

European Commission (2020), Workforce skills and innovation diffusion: trends and policy implications. Annex 
8. See also European Commission (2020), Adult Learning Statistical Synthesis Report:, pp. 22-34. 
235

 European Commission (2020), Adult learning statistical synthesis report, p. 25.  
 See for instance: Mohrenweiser et al., (2013), Poaching and Firm-sponsored Training: First Clean Evidence, 

236

Centre for European Economic Research.  

91 

 
 
 
 
                                                 
  
237

 The returns of training may not (fully) benefit the enterprise when an individual 
contracts.
works only part-time, and are more likely to be negative for staff in temporary contracts. As a 
result,  existing  support  for  training  mostly  benefits  individuals  in  permanent  employment. 
People without an employer, either because they are self-employed or unemployed or inactive, 
also  receive  considerably  less  financial  support  to  pursue  adult  learning.  As  shown  below 
(Figure  A7.4),  at  the  level  of  the  EU  individuals  without  a  permanent  contract  consistently 
mention costs as a reason for not participating in adult learning more often (30.9%) than those 
with such a contract (22.3%).   

Figure A7.4 - Share of respondents that want to train more and mention cost as a reason 

Source: authors, based on Adult Education Survey 2016. Other adults include other employees (employees with 
a temporary contract, self-employed and family workers), as well as unemployed and inactive. 

Figure A7.5 below summarises an expert judgment of the extent to which available financing 
measures are an adequate support for different target groups. It confirms that financial support 
for employees with regular contracts is more often in place than instruments for other groups, 
such  as  self-employed,  unemployed  or  inactive,  as  well  as  individuals  in  professional 
transitions.  

237

 Poulissen et al., , (2021), Employers' Willingness to Invest in the Training of Temporary Workers: A Discrete 

Choice Experiment, Institute of Labor Economics (IZA). 

92 

 
 
                                                 
Figure A7.5 - Adequacy of support per target group as assessed by experts 

Source: authors, based on mapping of available instruments conducted by Adult Learning expert network for the 
purpose of the Impact Assessment on the Individual Learning Accounts initiative. 

The size of an enterprise is also a relevant factor for the level of support for training its workers. 
Data  consistently shows  that larger enterprises offer training opportunities to  workers  more 

often than smaller enterprises.

238

 The OECD estimates that staff in SMEs participate in 50% 

 The European Company Survey showed 
fewer training activities than those of larger firms.
that  small  establishments  were  most  likely  to  train  less  than  20%  of  their  workers  during 

239

working  time,  while  large  establishments  were  least  likely  to  do  so.
  SMEs  often  find  it 
difficult to financially support learning activities and ensure replacement of staff, due to their 
small  size  and  the  relatively  high  costs  for  training.  Larger  companies  more  often  have 
explicitly developed career plans and internal growth opportunities, linked to training budgets 
and specific training programmes.  

240

Finally,  individuals  who  want  to  pursue  training  without  the  financial  support  from  an 
employer face greater difficulties to finance it. As a proxy, an analysis of Eurofound’s Survey 
on Living Conditions highlights how people at risk of poverty mention the costs of training as 

barrier  for  participation  almost  three  times  as  much  as  other  respondents.
    The  available 
public support measures insufficiently help reducing cost barriers to participation (Annex 8.2).  

241

Limited coverage of types of training 

The  dominance  of  employer-sponsored  training  has  implications  for  the  types  of  training 
supported. Employer-sponsored investments in the skills of staff can be expected to prioritise 
the  types  of  training  that  are  firm-specific  and  directly  benefit  productivity  levels  of  the 
enterprise. There is not incentive to support training on transversal skills, which could help a 

238

239

240

 Cedefop (2019), Continuing vocational training in EU enterprises: Developments and challenges ahead.  
 OECD (2013), Overview of training and skills development in SMEs.  
 Eurofound and Cedefop (2020), European Company Survey 2019: Workplace practices unlocking employee 

potential, page 92.  
241

 Based on 2016 SILC module, available at Eurostat.  

93 

 
 
                                                 
worker to maintain his/her employability in the long run, even by changing company or sector. 
This is reflected in the relative small share of employer-sponsored training dedicated to more 

transversal  skills  of  employees,  such  as  general  IT  skills.
  The  Continuing  Vocational 
Training Survey in 2015 for instance shows that only 13% of training by employers is focused 
on more transversal skills, such as general IT skills, and less than 1% on numeracy and literacy 
skills. The selective coverage of specific types of training risks insufficiently allows individuals 
to  prepare  for  future  shifts  in  skills  demands  when  job  stability  has  decreased  in  most  EU 

242

243

countries,
trend is particularly evident among workers with lower qualifications.  

 which increases in job mobility, as the evidence from the OECD suggests. This 

1.3 Barriers to devoting time to training 

Conflicting  commitments  of  individuals  and  insufficient  time  available  for  training  are 
important barriers to participation, as shown in the figure below. Time is one of the most often 
mentioned barriers by permanent  employees  (46.3%), and to  a lesser extent  by other adults 
(38.1%).    This  section  presents  evidence  on  barriers  to  devoting  time  to  training  from  the 
perspective of employers. Personal reasons that limit individuals from devoting time to training 
also affect participation and are discussed under driver 2 (motivation).  

Figure  A7.6  -  Share  of  respondents  that  want  to  train  more  and  mention  conflicting 

schedules as a barrier 

Source: authors, based on Adult Education Survey 2016.   Other adults include other employees (employees with a temporary 
contract, self-employed and family workers), as well as unemployed and inactive. 

The relatively high short-term costs of freeing an individual from the workplace to learn and 
investing in an employee’s training contributes to a possible misalignment of incentives for 
employers,  and  can  act  as  important  barrier  to  participation  in  adult  learning.  A  Cedefop 
mapping shows how access to training leave provisions was much more available to workers 

242

 European Commission (2020), Facing the Digital Transformation: are Digital Skills Enough?  The Continuing 
Vocational Training Survey in 2015 for instance shows that only 13% of training by employers is focused on 
more transversal skills, such as general IT skills, and less than 1% on numeracy and literacy skills. 
243

 Particularly when controlling for ageing of the workforce. See OECD (2019), Employment Outlook: The Future 

of Work., chapter 3.  

94 

 
  
                                                 
with open-ended employment relationship, with some minimum work experience or minimum 

244

duration of the employment relationship).
 Employees in SMEs face more often considerable 
difficulties in organising the training leave practice, i.e. in terms of organisation of the work 
and possible (temporary) replacements. This is reflected in their lower participation rates in 
learning. Respondents to the 2020 Cedefop perception survey confirm this possible relation to 
participation; 90% of respondents at the EU level think that flexible working hours or time off 
work can encourage more adults to participate in work-related learning and training.  

Figure  A7.7  -  Respondants  in  agreement  that  flexible  working  hours  would  encourage 

participation in training – by employment status 

l

e
b
i
x
e
l
f
e
r
o
m

r
o
f
f
o
e
m
T

i

l

d
u
o
w
s
r
u
o
h
g
n
i
k
r
o
w

n
o
i
t
a
p
i
c
i
t
r
a
p
e
g
a
r
u
o
c
n
e

All

Without a paid activity

Employee - manual work

Employee - non-manual work

Self- employed

58%

56%

58%

59%

57%

32%

33%

31%

32%

32%

5%

5%

6%

6%

6%

0%

20%

40%

60%

80%

100%

Totally agree

Tend to agree

Tend to disagree

% respondents (EU)
Totally disagree

No answer

Source: Cedefop Perception survey (2020). 

The  existence  of  a  right  to  training  leave  can  also  reduce  the  timing  constraints  that  act  as 
barriers to training.  

In 2020, a total of 22 of 27 EU Member States had some form of national legislation on paid 
training  leave,  of  which  twelve  are  bound  by  the  1974  ILO  convention  on  paid  training 

245

leave.
. While actual implementation of the provisions for training leave varies considerably, 
an evidence review shows how the take-up of paid education leave schemes across the EU has 
been rarely above 1%. This is confirmed by European trade unions in particular, who in the 
public  consultation  highlighted  the  limits  of  practical  implementation  of  training  leave 
provisions across most Member States. Furthermore, adult learning experts underline that the 
current way (paid) training leave provisions are organised further contributes to the existing 
differences in participation between permanent employees and individuals with other types of 
contracts or unemployed.  

1.4 Fragmentation of existing support 

Responsibilities  for  adult  learning  policy  are  often  divided  across  several  ministries  and 
agencies (e.g. education, social affairs, labour, migration, justice) and several levels of policy 

244

 The discussion in this paragraph draw on Cedefop (2012), Training leave. Policies and practice in Europe, 

and input by the adult learning expert network. 
245

 ILO (1974), C140 - Paid Educational Leave Convention, 1974 (No. 140). 

95 

 
 
                                                 
 
 
 
 
 
 
 
. This often results in a situation where adult learning 
making (municipal, regional, national)
policy is fragmented, which may have implications for the effectiveness of support measures.  

246

Figure  A7.8  -  Overview  of  adult  learning  expert  mapping  –  fragmentation  of  financial 

support 

Source:  authors,  based  on  mapping  of  available  instruments  conducted  by  Adult  Learning  expert 
network for the purpose of the Impact Assessment on the Individual Learning Accounts initiative. 

Fragmentation of support measures is a challenge in a majority of Member States, and is more 
common in those with lower participation rates. The impacts of the fragmentation that results 
from too specific support measures become obvious when individuals do not neatly fit into the 
predefined categories of that policy. Related to this are limitations to the types of training that 
are often attached to support measures; support measures for training unemployed generally do 
not  cover  the  cost  of  longer-term  and/or  formal  education  programmes,  regardless  of  the 

specific needs of the individual.

247

The issue of fragmentation is also highlighted as a specific challenge for the sub-sector of adult 
learning in the workplace. A 2016 mapping by experts in adult learning shows that only two 
Member  States  (HU  and  LU  –  both  with  participation  rates  above  the  EU  average)  have 
comprehensive  policies  in  place  supporting  adults  learning  in  the  workplace,  whereas  11 
Member  States  have  in  place  policies  that  are  not  sufficiently  coordinated  or  only  partially 

cover the area of learning in the workplace.

248

The  fragmentation  of  supporting  policies  for  adult  learning  particularly  poses  a  problem  to 
effectively encourage participation of those in more vulnerable situations. An isolated policy 
measure may address a single barrier to participation, but if it does not help lifting the multiple 
barriers at the same time, it may not effectively help improve participation. Besides coherent 
financial support for training more vulnerable groups also depend on support for other types of 
costs, such as those related to subsistence, childcare or transportation. Systems where support 
is  coherently  offered  are  more  able  to  support  individuals  reconcile  private  family 
responsibilities with work and / or training responsibilities, which leads to higher participation 

246

  European  Commission  (2018),  Promoting  adult  learning  in  the  workplace  -  Final  report  of  the  ET  2020 

Working Group 2016 – 2018 on Adult Learning.  
247

 Highlighted for instance the German and Italian country reports of Adult learning network.  
  European  Commission  (2017),  Analysis  of  self-reported  country  factsheets  from  Member  States  on  adult 

248

learning in the workplace. Produced by ET2020 working group. 

96 

 
 
  
  
                                                 
rates in training, of both women and men.249 In addition, potential beneficiaries, in particular 
vulnerable groups, neeed to be aware of the support measures.  Fragmented support policies 
make this more challenging and risk excluding target groups, as it complicates communication 
efforts when reaching out to them. 

Note that fragmentation is not necessarily the same as decentralisation. Decentralised support 
measures do not necessarily fragment support from the perspective of individuals. As long as 
individuals can apply for complementary support measures in a single place (either with the 
central authorities, or decentralised), fragmentation is not an issue.  

2. Driver 2:  Limited incentives and motivation of individuals 

A second driver that limits participation in training is the limited incentives and motivation 
to take up training. Studies consistently identify roughly 80% of non-participants that are not 
willing  to  participate  in  training  (which  corresponds  to  an  estimated  45%  of  the  adult 

250

population).
contribute to individual’s motivation.  

  This  makes  it  particularly  relevant  to  better  understand  the  factors  that 

The value of learning is largely uncontested, not only in terms of its potential to contribute to 
economic growth and social inclusion, but also at the individual level, in terms of labour market 
position,  wage  growth,  job  satisfaction  and  wellbeing.  The  various  benefits  of  learning  are 
widely recognised by European workers. As shown in Figure A7.9 below, no less than 96% of 
individuals across the EU agreed that learning throughout life is an important value, with only 
minor differences across Member States (the lowest value still reaches 88%). There are some 
minor differences when comparing the intensity with which employees doing predominantly 
manual  work  agree  (73%  totally  agree)  compared  to  employees  whose  work  is  not 
predominantly manual (81% totally agree). Self-employed appear to value the importance of 
learning throughout life the most (83% totally agree).    

249

 Massing and Gauly (2017). Training Participation and Gender: Analyzing Individual Barriers Across Different 

Welfare State Regimes. Adult Education Quarterly, 266-285. 
250

 We draw mainly on the Adult Education Survey, conducted among all EU Member States. However, similar 
shares are reported by the OECD Survey of Adult Skills (PIAAC) survey, see for instance OECD (2021), Skills 
Outlook 2021, Chapter 4.  

97 

 
                                                 
Figure A7.9 – Respondents that agree that lifelong learning is important – by type of work 

All (EU)

Without a paid activity

Employee - manual work

Employee - non-manual work

Self- employed

Totally agree
Tend to agree
Tend to disagree
Totally disagree

0%

20%

78%

77%

73%

81%

83%

40%

18%

18%

22%

17%

15%

60%

80%

100%

Source: Cedefop Perception survey (2020). 

However,  existing  survey  data  and  evidence  from  the  literature  do  not  allow  to  determine 
conclusively if disengagement from training stems from lack of interest in any form of training 
opportunity  or  rather  indicates  a  mismatch  between  the  preferences  and  interests  of  the 

individual  and  the  existing  supply  of  training.

251

  An  assessment  of  the  former  have  been  a 

traditional  focus  of  the  literature  on  psychological  barriers.
  However,  given  the  high 
importance adults give to training in general terms, it can be assumed that at least a relevant 
share of disengaged individuals could be motivated to engage in training if the various barriers 
that contribute to a mismatch and limit their participation are addressed.  

252

This section starts by reviewing in more detail the willingness to participate across Member 
States. The figure below points to considerable differences across Member States. The share 
of non-participants that are not interested in training varies substantially. These differences can 
not be explained by looking only at macro-level characteristics of Member States’ education 
and training systems. Instead, it is important to zoom in on micro-level behavioural aspects and 
how existing policies affect these.  

251

252

 OECD (2021), Skills Outlook 2021, chapter 4.  
 Cross (1981), Adults as Learners. Increasing Participation and Facilitating Learning.; Knapper and Cropley 
(2000),  Lifelong  Learning  in  Higher  Education.  3rd  ed.  Kogan  Page;  Pont(2004),  Improving  Access  to  and 
Participation in Adult Learning in OECD Countries, European journal of education, p.31-45.  

98 

 
 
                                                 
Figure A7.10 - Willingness to participate in adult learning – by Member States 

Source: Adult Education Survey 2016 

A  recent  OECD  analysis  of  the  OECD  Survey  of  Adult  Skills  (PIAAC)  survey  found  that 
workers with atypical contracts were significantly less likely to be willing to participate than 

253

workers with a permanent contract
. Other individual-level factors that influence willingness 
to train include education level, job tenure and contract status. Individuals that are not in stable 
employment  may  not  immediately  be  able  to  transform  the  benefits  training  into  wage 
increases; for them the benefits of training are more diffuse and possibly less visible and offer 
therefore less of an incentive for participation. This suggests that lack of motivation to engage 
in training also explains low participation, particularly for more vulnerable groups.  

There are a number of reasons that can help explain why the willingness to train is lower among 
individuals  in  atypical  employment  and  lower  skillednotably  the  limited  information  and 
transparency about the available support and training offer, uncertainty about their  quality 
and  recognition  in  the  labour  market,  and  insufficient  tailoring  of  training  offers  to 
individual needs.  

2.1 Limited awareness of own skills needs 

Without sufficient information about the types of training available and the support available 
to enrol in it, individuals struggle to assess what training could be relevant and how they could 
benefit it. It can be difficult for individuals to recognize their own learning needs, which makes 

it even more challenging to subsequently identify relevant training programmes.
 In a recent 
Cedefop Survey, individuals that do not want to participate in training often indicate not to see 
the benefits of possible training programmes and do not feel that their competences fall short 
for their work. Some 28% of all respondents in work say that they lack some technical skills 

254

253

254

 OECD (2021), Skills Outlook 2021, chapter 4. 
 OECD (2015), Adults with low literacy and numeracy skills: A literature review on policy intervention. OECD 

Education Working Papers.  

99 

 
 
                                                 
and 22% some general skills to carry out their job at the required level, as presented in the 
figure below. There are some minor differences between workers, depending on their type of 
work; manual workers in higher numbers indicated that they lack both technical skills (33%) 
and general skills (31%).  

Figure A7.11 - Self-reported missing skills – by type of work 

U
E
s
t
n
e
d
n
o
p
s
e
r

%

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

41%

42%

27%

20%

11%

30%

19%

8%

40%

26%

20%

13%

47%

48%

25%

17%
9%

31%

14%
6%

42%

26%

17%

14%

Self-
employed

Employee -
non-manual
work

Employee -
manual work

Self-
employed

Employee -
non-manual
work

Employee -
manual work

Missing technical skills

Missing general skills

No answer
Totally disagree
Tend to disagree
Tend to agree
Totally agree

Source: Cedefop Perception survey (2020). 

These findings are also replicated in other sources. Eurofound’s Working Conditions survey 
shows that 14% of respondents for the entire EU believe that they need further training to cope 

with daily duties in their work.
 On the employer’s side on the other hand, 77% of companies 
mentioned in 2019 the scarcity of skilled staff as the most frequent reason to limit long-term 

255

256

 When interpreting these findings, it is important to be aware of the possible 
investments.
bias of such surveys among both employees and employers. There is always the possibility that 
257

employees overstate their skill levels (either deliberately or by lack of actual comparison).
Employers,  on  the  other  hand,  may  have  an  incentive  to  overstate  skill  shortages,  or  may 
experience difficulties in identifying the required skills for other – unrelated – reasons (such as 
the terms and conditions of its employment offer, instead of the actual supply of skills in the 

258

 Still, the overall trend suggests that without adequate guidance, workers face 
labour market).
challenges determining their own training needs. An individual may not be aware of the types 
of skills needed, or could for instance review their skills needs based on a relatively short time 
horizon.259 This perceived lack of urgency can be exacerbated by the fact that it is hard to gauge 
what  impact  future  developments  will  have  on  skills  needs  in  their  own  jobs  or  in  other 

255

256

 Eurofound, European Working Conditions Survey 2016, online data viewer.  
  European  Investment  Bank  (2019),      EIB  Group  Survey  on  Investment  and  Investment  Finance  2019:  EU 

Overview , p. 19. 
257

 Cedefop (2021), Understanding technological change and skill needs: skills surveys and skills forecasting. 

Cedefop practical guide 1, page 21. 
258

  Gambin  et  al.  (2016).  Research  to  understand  the  extent,  nature  and  impact  of  skills  mismatches  in  the 

economy. Department for Business Innovation and Skills.  
259 

Grijpstra et al. (2019). Belemmeringen voor deelname aan Leven lang ontwikkelen. Panteia.  

100 

 
 
 
                                                 
 
 
 
260

 Guidance and support help influence such attitudes, and the existing 
occupations or sectors.
support structures in (larger) enterprises for employees are one explanation of the differences 
in  attitudes  between  individuals  in  permanent  employment  and  those  in  more  atypical 

employment situations.

261

Workers themselves confirm this. A total of 90% of respondents across the EU agree that more 
information and guidance would encourage more adults to participate in work-related learning 
and training, with limited differences between respondents from different Member States. 53% 
of manual workers totally agree , while the group of non-manual employees does so less often 
(45% totally agree); possibly this group already receives better guidance than manual workers 
and therefore looks for other types of support.  

Figure A7.12 – Respondants that agree that more information and guidance would increase 

participation– by type of employment situation 

All

Without a paid activity

Employee - manual work

Employee - non-manual work

Self- employed

50%

52%

53%

45%

49%

40%

37%

39%

45%

40%

6%

5%

5%

8%

6%

Totally agree

Tend to agree

0%

20%
Tend to disagree

40%

60%
Totally disagree

80%

100%

No answer

Source: Cedefop Perception survey  (2020).  

2.2 Limited transparency and information about training offers 

Another  challenge  for  participation  is  to  identify  training  programmes  that  match  training 
needs. Individuals without concrete support from employers or other forms of guidance face 
more uncertainties in doing so.  

34% of respondents in the EU (totally) disagree that they are well-informed about organised 

 Those with an 
work-related training activities, against a total of 60% that (totally) agrees.
employment relation are more positive about the available information on training activities. 
As  can  be  expected,  people  in  a  salaried  position  considerably  more  often  turn  to  their 

262

260

261

 Maslowski (2019). Grenzen aan een leven lang leren, Sociaal en Cultureel Planbureau. 
 OECD (2011), Leveraging Training Skills Development in SMEs: An Analysis of the West Midlands, England, 

UK, OECD Local Economic and Employment Development (LEED) Papers.  
262

Cedefop  (2020), Perceptions on adult learning and continuing vocational education and training in Europe. 
Second opinion survey – Volume 1. Member States. Cedefop reference series. The remaining 6% did not provide 
an answer.  

101 

 
 
 
                                                 
 This  is  also  confirmed by results 
employers for  advice  and guidance  (see  Figure  A7.13).
from the Adult Education Survey, which shows that employees slightly more often report to 

263

receive support in guidance than other types of workers. 

264

Figure A7.13 - Possible sources for advice and guidance about adult learning 

Source: Cedefop Perception survey  (2020).  

 While 
Most individuals pointed to internet as the best source about adult learning and CVET.
this highlights the potential of internet to empower individual citizens in taking control of their 
own learning trajectory, it does not always ensure that individuals actually find the types of 
training they are looking for.  

265

Figure A7.14 below shows the extent to which different groups of individuals are able to find 
a suitable training offer. In some cases, individuals may not be able to  find a suitable offer 
because  it  is  simply  not  there;  there  may  be  certain  supply-side  constraints  that  reduce  the 
availability of relevant trainingThe differences between different groups suggest that at least 
to some extent, the ability to find relevant training is related to the types of support in place. A 
total of 14.7% of individuals with a permanent contract refer to the lack of a suitable offer as 
reason for not participation in adult learning, against 18.4% of all adults without permanent 
contracts.  

263

265

  Interestingly,  guidance  by  employment  agencies  (such  as  Public  Employment  Services,  or  other  types  of 
counselling)  is  mentioned  less  often  as  a  good  source  for  guidance  by  unemployed  (29%)  than  individuals  in 
manual work (34%). 
264

 Not presented in figure here. Based on analysis of special extract of Adult Education Survey 2016.  
 Self-employed and non-manual employees most often point to the internet as the best source for information 
(71%  and  73%  respectively),  which  is  considerably  lower  for  manual  employees  and  individuals  out  of 
employment  (64%  and  53%  respectively).  Cedefop  (2020),  Perceptions  on  adult  learning  and  continuing 
vocational  education  and  training  in  Europe.  Second  Opinion  survey  –  Volume  1.  Member  States.  Cedefop 
reference series. 

102 

 
 
 
                                                 
Figure A7.14 - Share of respondents that want to train more and mention lack of suitable 

offer as a reason 

Source: authors’ analysis, based on custom extract of Adult Education Survey 2016. 

The relevance of ensuring the provision of guidance as a way to contribute to participation in 
adult  learning  is  further  underlined  in  the  figure  below,  which  compares  the  overall 
participation  rate  in  adult  learning  against  the  share  of  individuals  that  had  not  received 
information or advice on learning possibilities, also based on the Adult Education Survey. The 
result  is  a  strong  negative  correlation,  showing  that  Member  States  with  lower  shares  of 
individuals without access to advice / guidance are generally the ones where participation rates 
are higher.  

103 

 
 
Figure  A7.15  –  Rate  of  participation  (last  12  months)  and  access  to  free  guidance  on 
learning possibilities 

NOTE: Correlation presented for aggregated Member State-level values with Pearson’s r of -.752 (significant at 
P<.01). Participation in adult learning in last 12 months, 2016 (excluding guided on the job training).  

Source: Special extract from Adult Education Survey 2016

Policies and practices in the field of career guidance have undergone considerable changes over 

the last years and increasingly receive policy attention.
 A recent mapping by adult learning 
experts identified that public or legally mandated offers for career and guidance in learning 

266

were  present  in  almost  all  Member  States,  as  presented  in  the  figure  below.
  Despite  the 
broader  introduction  of  such  an  offer  for  guidance,  the  mapping  shows  that  there  is  still 
considerable room for improvement, particularly for adults not in permanent employment.  

267

A practical example of how guidance can be offered in direct response to the prominence of 
the use of the internet in looking for training offer can be an online database that lists training 
opportunities. As shown in Figure A7.16 below, such a database is available in roughly half of 
the Member States and more often in Member States with higher participation rates.  A review 
of guidance practices across shows how the offered support is fragmented and does not always 

benefit all target groups equally.

268

266 

See  for  instance  European  Commission  (2020),  Lifelong  guidance  policy  and  practice  in  the  EU:  trends, 

challenges and opportunities.  
267 

268 

DG EMPL Adult Learning network expert mapping in 2019 / 2020 in preparation for the IA.  
See  for  instance  European  Commission  (2020),  Lifelong  guidance  policy  and  practice  in  the  EU:  trends, 

challenges and opportunities.  

104 

 
 
 
 
                                                 
Figure A7.16 - Overview of adult learning expert mapping  – availability of guidance and 

database 

Source: authors, based on mapping of available instruments conducted by Adult Learning expert network for the 
purpose of the Impact Assessment on Individual Learning Accounts initiative. 

2.3 Uncertainties about quality and recognition 

To provide potential learners with information on content, quality and recognition of training 
programmes, quality assurance systems have an important role to play, and contribute at least 

 Across the EU 87% of respondents to a CEDEFOP 
indirectly to participation in learning.
survey  on  the  matter  are  of  the  opinion  that  increased  quality  standards  would  encourage 

269

participation in work-related training.

270

For this reason, attention for quality standards and quality assurance systems has been on the 

271

European agenda for years.
 Member States have increasingly implemented regulations and 
policies  to  improve  quality  assurance  in  adult  learning.  Particularly  under  the  influence  of 
EQAVET, considerable development and improvements in terms of quality assurance can be 

identified across the EU.
 However, EQAVET developments tend to be restricted to initial 
VET programmes, and a majority of EU countries has not put in place a system-level quality 

272

269

 European Commission (2019), Adult learning policy and provision in the member states of the EU: a synthesis 

of reports by country experts, p.119.  
270

 Cedefop (2020), Perceptions on adult learning and continuing vocational education and training in Europe. 

Second Opinion survey – Volume 1. Member States. Cedefop reference series. 
271 

272 

European Commission (2013), Developing the adult learning sector: Quality in the adult learning sector.  
See  the  background  paper  of  the  EQAVET  Peer  Learning  Activity  on  Quality  Assurance  in  continuing 

vocational education and training (CVET) on 27-28 April 2021. 

105 

 
 
 
 
                                                 
  Being  non-formal  (i.e.  outside  the  formal 
assurance  framework  for  non-formal  training.
education system), it typically comes with less regulations, requirements, and standards. Even 

273

so, there are differences in quality approaches across the EU.
 The recent mapping of formal 
and  non-formal  learning  by  DG  EMPL’s  Adult  learning  expert  network  highlights  these 
differences.  As  shown  in  the  figure  below,  quality  assurance  for  formal  programmes  is 
consistently  more  established  than  in  non-formal  programmes.  At  the  same  time,  quality 
assurance is also more often found for nonformal programmes in Member States with higher 
participation rates in adult learning.  

274

Figure A7.17 - Overview of adult learning expert mapping – availability of quality assurance 

mechanisms in formal and non-formal programmes 

Source: authors, based on mapping of available instruments conducted by Adult Learning expert network for the 
purpose of the Impact Assessment on Individual Learning Accounts initiative. 

The availability of quality assurance for adult learning programmes can increase transparency 
and communicate trust in the quality of the education on offer. The absence of such provisions 
is  reflected  in  individual’s  perceptions  of  the  quality  of  adult  learning  in  their  countries. 
Cedefop’s recent perception survey shows that, although people more often describe quality of 
adult learning as good (69%) rather than bad (24%), substantial number of citizens in several 
EU countries are not confident about the quality of their national adult learning system, even 
in  some  of  those  which  have  quality  assurance  provisions  for  non-formal  learning 

275

programmes.
workers with different types of contracts.  

.  No  significant  differences  appear  between  different  types  of  workers  and 

273

  EQAVET  (2020),  Peer  Learning  Activity  on  Quality  Assurance  in  continuing  vocational  education  and 

training: background paper.  
274

 OECD (2021), Improving the Quality of Non-Formal Adult Learning: Learning from European Best Practices 

on Quality Assurance, Getting Skills Right.  
275

 Cedefop (2020), Perceptions on adult learning and continuing vocational education and training in Europe. 

Second Opinion survey – Volume 1. Member States. Cedefop reference series. 

106 

 
 
                                                 
 
Figure A7.18 - Perceptions about quality of adult learning – by type of employment situation 

Source: Cedefop Perception survey  (2020).  

Member  States  with  higher  participation  rates  in  adult  learning  are  also  by  and  large  the 
Member  States  where  learners  are  more  positive  about  the  quality  of  adult  learning  system 
(Figure A7.19). Even if there are no data to determine the exact causal mechanism, it can be 
concluded from the figure below that quality is a relevant factor.  

Figure A7.19 –Participation and share of respondents that rate quality of adult learning as 

bad 

Note: correlation analysis shows Pearson’s r of -.412, significant at p<.05) 

Source: authors’ analysis, based on custom  Adult Education Survey 2016 extract (participation in the last 12 
months, excluding guided on the job training) and Cedefop Perception survey  (2020).  

In addition to the quality assurance, it is just as important that the learning outcomes of training 
programmes  are  recognised,  and  that  future  learners  know  in  advance  how  the  anticipated 
learning outcomes will be assessed by a potential future employer.  

107 

 
 
 
Particularly  in  non-formal  training  however,  uncertainties  about  the  recognition  of  learning 
outcomes are common. A recent OECD working paper points that the productivity gains for 
companies from non-formal training of employees substantially outweigh the wage effect for 
individuals, partly because individuals are not able to communicate the value of this training 

276

to other employers
. If such uncertainties about the value of learning outcomes persist, these 
can have a demotivating effect on individuals to participate in training, particularly if they have 
to  cover  (part  of)  the  costs  themselves.  Without  a  common  standard  to  signal  the  value  of 
training, individuals may not  fully be able to  recoup their investment  of time (and possibly 
money) in the form of higher wages or better career prospects more generally.  

Systems for the validation of prior learning can be a way to offer such a ‘common standard’, 
allowing individuals to  prove that they  acquired certain  competences  or  learning outcomes. 
However, like quality assurance, provisions for and access to such systems of validating prior 
learning vary considerably across and within countries. Individuals and employers are too often 

unaware of the potential value of newly acquired learning outcomes.

277

In this respect, the recent evaluation of the Council Recommendation on the validation of non-

278

formal and informal learning
 points to substantial progress, but also identified the limits of 
its support for individuals to ensure a better use of validation opportunities. Progress has been 
particularly  fragmented  in  terms  of  the  provision  of  information  on  available  validation 
opportunities. In response, the evaluation highlights how for disadvantaged groups, the costs, 
complexity and length of validation processes, service fragmentation (e.g. offered to for certain 
qualifications, or certain groups), and the perceived low value of validation in certain countries 

continue to limit opportunities for individuals to take advantage of them.
 In just over half of 
Member  States  experts  identified  relevant  policies,  with  one-third  where  these  existing 

279

initiatives for recognition of prior learning were identified as a strength.

280

2.4 Insufficient tailoring of training offers to individual needs 

The training offer may insufficiently respond to specific individual needs, for instance in terms 
of  form  and  length  and  is  unlikely  to  make  further  positive  contributions  to  individual 
enrolment  in  such  training,  even  if  they  are  actively  looking  for  that  training.  Another 
possibility  is  that  the  content  of  adult  learning  programmes  is  insufficiently  tailored  to 
individual needs. Adult learning needs to be specifically tailored to trigger adults to engage in 
learning, particularly when addressing more disadvantaged learners.  

276

 OECD (2019), Returns to different forms of job related training, OECD Social, Employment and Migration 

Working Papers. 
277

 OECD (2010), Recognising Non-Formal and Informal Learning: Outcomes, Policies and Practices. 
  European  Commission  (2020),  Study  supporting  the  evaluation  of  the  Council  Recommendation  of  20 

278

December 2012 on the validation of non-formal and informal learning. 
279

  European  Commission  (2020).  Study  supporting  the  evaluation  of  the  Council  Recommendation  of  20 

December 2012 on the validation of non-formal and informal learning.  
280

 European Commission (2019), Adult learning policy and provision in the member states of the EU: a synthesis 

of reports by country experts, p.114.  

108 

 
 
 
                                                 
Difficulties to combine training with other commitments – such as work, family responsibilities, 
or other – is consistently among the most cited reasons of individuals that do not participate in 

training.

281

  These  difficulties  are  more  commonly  found  among  atypical  workers,  who  as  a 

less 

in  adult  education  programmes.

  Conflicting  family 
consequence  participate 
responsibilities feature as an important barrier to participation in adult learning, particularly for 
part-time workers (41.1%) and to a lesser extent for self-employed (35.8%), as shown in the 
figure  below.  Self-employed  also  mention  scheduling  conflicts  often  (50.1%).  Intuitively, 
scheduling  problems  are  mentioned  considerably  less  often  by  unemployed  or  inactive 
individuals (13.4%), compared to other groups.  

282

Figure A7.20 - Share of respondents that want to train more and mention distance, family 

and/or schedule as a barrier 

Employee with permanent job - fulltime
Employee with permanent job - parttime
Employee with temporary job
Self-employed and family worker
Unemployed / inactive

60

50

40

30

20

10

0

i

d
e
n
o
n
t
e
m

t
a
h
t

s
t
n
e
d
n
o
p
s
e
r

%

e
r
o
m
g
n
n
a
r
t
o
t

i

i

)
s
(
r
e
i
r
r
a
b

Distance

Family reasons

Schedule

Source: authors’ analysis, based on Adult Education Survey 2016.  

Other practical reasons, such as the distance of the training location from home or workplace 
are also relevant,  yet are mentioned less often. Learners indicated in Cedefop’s  most recent 
perception survey on adult learning and CVET in Europe that better adaptability of training to 

  Self-
individual  learning  needs  would  encourage  participation  in  work-related  training.
employed  individuals  were  slightly  more  pronounced  about  the  potential  to  encourage 
participation  (52% totally  agree) than employees (48% of non-manual  workers  and 50% of 
manual  workers  totally  agree).  Attention  for  family  responsibilities  as  a  way  to  encourage 
participation in adult learning is highlighted most by individuals out of paid employment (57% 
totally agree), and slightly less so by non-manual employees (50% totally agree).   

283

281

 Cedefop (2020), Perceptions on adult learning and continuing vocational education and training in Europe. 

Second Opinion survey – Volume 1. Member States. Cedefop reference series. 
282
 OECD (2019), Individual Learning Accounts: Panacea or Pandora's Box?  
 Cedefop (2020). Perceptions on adult learning and continuing vocational education and training in Europe. 

283

Second Opinion survey – V. 1. Member States. Cedefop reference series. 

109 

 
 
                                                 
 
 
 
 
 
 
 
Figure A7.21 – Factors that could encourage participation – by type of work 

Source: Cedefop Perception survey  (2020).  

Difficulties in tailoring delivery mode to trigger adults to engage in learning. Tailoring the 
mode of delivery requires an understanding and responding to the practical barriers potential 
participants in learning encounter and to what makes learning easier or more difficult for some, 

284

and can make learning a desirable activity.
 To overcome this challenge, some Member States 
have already worked on increasing flexibility of training offer, for instance by encouraging the 
offer of different forms of distance learning, forms of blended learning, modular and part-time 
learning provision, or allowing providers to offer the types of training that best fit their learners’ 
needs.  Others  put  in  place  new  forms  of  entrance  exams  for  those  who  do  not  fulfil  the 
traditional  entrance  criteria  but  who  have  gone  through  VET  and  apprenticeship  routes.  A 
review of recent policy developments by the Adult Learning Expert network shows that the 
results  of  these  efforts  are  not  uniform,  and  some  Member  States  have  not  shown  much 
progress  in  supporting  the  introduction  of  more  tailored  training  programmes.  In  a  well 
functioning  market,  education  and  training  providers  would  have  a  clear  incentive  to  offer 
programmes tailored to the needs of individuals. However, in the current context, it is not the 
individual,  but  their  employer  or  Public  Employment  Services  that  choose  training 
programmes.  As  a  result,  adult  learning  programmes  are  still  primarily  offered  in  more 
traditional forms of training. For just under half of Member States, examples were identified 

of introducing innovation in the delivery of learning.

285

Also, an increase in the use of digital learning tools could further address this particular barrier, 
for instance through blending innovations with more traditional forms of adult learning. Such 
developments come in response of persisting differences between Member States in adult skill 

285

 European Commision (2019), Adult Learning policy and provision in the Member States of the EU, A synthesis 

of reports by country experts, page 118.  
285

 European Commision (2019), Adult Learning policy and provision in the Member States of the EU, A synthesis 

of reports by country experts, page 118.  

110 

 
 
 
                                                 
levels, access to ICTs, the availability of relevant content, and in the development of educators’ 

innovative learning skills and competences.
 Such differences have considerable implications 
for  the  possibilities  of  increasing  flexible  training  offer  for  adult  learning.  In  recent  years, 
attention for digital tools to increase the flexibility of adult learning has considerably increased, 
for instance reflected in the EC’s first Digital Action Plan, adopted in 2018 and its successor 

286

launched in 2020.

287

The response to COVID-19 restrictions and school closures forced a shift to emergency modes 
of digital  education  and training,  resulting in  a  large number of adult learners dropping out 
during  the  transition  to  online  learning  and  a  drop  in  overall  adult  learning  participation  as 
288

measured in the Labour Force Survey (for EU-27, from 10.8% in 2019 to 9.2% in 2020).
While it is too soon to draw firm conclusions on the impacts of this response on participation 
figures, participation is likely to rebound once COVID-19 restrictions are lifted and the induced 
push towards increasing online provision provides opportunities for an improved tailoring of 
adult learning provision to individual needs. 

ANNEX 8: BASELINE SCENARIO 

This Annex presents relevant existing or planned instruments and initiatives at EU and Member 
State levels, and discusses the extent to which they can be expected to make progress towards 
the specific objectives of this initiative in the baseline scenario, i.e. in the absence of additional 
policy efforts resulting from the present initiative. It concludes with a discussion of how adult 
learning participation rates and inequalities across groups are expected to evolve until 2030 in 
the baseline scenario, against which the expected impacts of the policy packages are assessed. 

1. The existing EU instruments 

Adult learning has always been part of the EU vocational training policy, though for a long 
time it has only meant skills development of adult workers. The 1963 Decision on a common 

289

vocational training policy
 stated that its ten “general principles must deal with the training 
of young persons and adults” (first principle) in the workforce and promoted vocational training 
“suitable  for  the  various  stages  of  working  life”  (second  principle,  paragraph  (f)).  Adult 
vocational skills development retains all its relevance today: while the concept of vocational 

, an 
education and training (VET) has evolved, the 2020 Council Recommendation on VET
action of the European Skills Agenda, “aims to equip young people and adults” with the skills 
required  on  the  labour  market.  The  Recommendation  is  likely  to  generically  contribute  to 

290

286

287

288

 European Commission (2015). Adult Learners in Digital Learning Environments - Final Report.  
 Digital Education Action Plan (2021-2027). 
  Communication from the Commission to the European Parliament, the Council, the European Economic and 
Social  Committee  and  the  Committee  of  the  Regions  Digital  Education  Action  Plan  2021-2027  Resetting 
education and training for the digital age (COM/2020/624 final) and Eurostat trng_lfse_01. 
289

 Council Decision 63/266/EEC of 2 April 1963 laying down  general principles for implementing a common 

vocational training policy.  
290

 Council Recommendation of 2 November 2020 on vocational education and training (VET) for sustainable 

competitiveness, social fairness and resilience, (2020/C 417/01), p.1. 

111 

 
  
 
                                                 
increase  participation  in  training,  including  adult  participation.  However,  while  it  invites 
Member States to make use of EU funds and programmes for reforms or investments in VET, 
it does not address the issue of financial support to individuals, which would therefore not be 
part of its implementation. 

Since 2000, several European policy initiatives have highlighted the relevance of adult learning 
in a wider sense, including, but not limited to vocational skills development: the memorandum 

on  lifelong  learning  in  2000

291

,  the  Barcelona  Council  conclusions  of  spring  2002

292

,  the 

Communication on making lifelong learning a reality in 2002

293

 and the Council Resolution of 

27  June  2002  on  lifelong  learning

294

,  the  2006  Commission  Communication  and  the  2007 

Action plan on adult learning

295

, the Council conclusions on adult learning of 2008. 

296

 In 2011, 

the Council adopted a resolution on a renewed European agenda for adult learning
, which 
aimed to bring fresh impetus to this important area of education, setting out a long-term vision 
in this field up to 2020, integrated in the ET 2020 strategic framework for policy cooperation 

297

in  education  and  training
,  and  established  several  short-term  priorities  to  be  achieved  by 
2014, then revised in 2015. The agenda has been the EU policy framework for adult learning 

298

299

in the last decade
. In these documents, the concept of adult learning is extended to the entire 
adult population, going beyond those active in the labour market. The baseline scenario hence 
covers  well  EU  policy  support  for  the  provision  of  adult  learning,  ie.  the  “supply  side”.  
However, this EU policy support does not directly address the need to support the demand for 
participation in learning. This is why the policy options presented in this report focus on the 
demand for learning.  

One EU initiative that was aimed among other to support the demand in learning is the Council 

Recommendation  on  validation  of  non-formal  and  informal  learning
,  which  was 
adopted in 2012. It called for Member States to take action by 2018 to ensure that everybody 
had  access  to  validation  opportunities.  Such  opportunities  can  support  the  demand  of  adult 
learning. On the one hand, when validation opportunities are available their main beneficiaries 

300

291

292

293

  Commission Memorandum of 30 October 2000 on lifelong learning (SEC(2000) 1832 final). 
 Presidency conclusions, Barcelona European Council 15 and 16 March 2002 (C/02/930). 
 Communication from the Commission  Making a European are of lifelong learning a reality, (COM(2001)678 

final). 
294

 Council Resolution of 27 June 2002 on lifelong learning (2002/C 163/01) 
), Communication from the Commission on adult learning: it is never too late to learn, (COM (2006) 614 final); 

295

Action Plan on Adult Learning: it is always a good time to learn, (COM (2007) 558 final). 
296

 Council conclusions of 22 May 2008 on adult learning (2008/C 140/09) 
 Council Resolution of 28 November 2011 on a renewed European agenda for adult learning, (2011/C 372/01) 

297

, p. 1. 
298

  Established  through  the  Council  Conclusions  of  12  May  2009  on  a  strategic  framework  for  European 

cooperation in education and training (ET 2020).  
299

 Cf. the Report of the ET 2020 Working Group on Adult Learning (2018-2020). 
 Council Recommendation of 20 December 2012 on the validation of non-formal and informal learning. 

300

112 

 
                                                 
 
are adult workers, who may then be more motivated to join organised learning opportunities; 
on the other hand, the lack of validation opportunities is one of the reasons why people may 
not engage in learning pathways, as the skills they would develop risk not being recognised. 
While good validation opportunities might encourage more adults to participate in learning, 
engaging  in  validation  may  be  a  burden  for  individuals,  demanding  time,  effort  and  often 
money. Indeed, the evaluation of the 2012 Recommendation on validation found that “if there 
is no active support to individuals, such as paid leave or a financial contribution, they may not 
be able to engage in validation”. The evaluation further finds that “fewer people than expected 
have  engaged  in  validation,  despite  larger  provision  of  opportunities  and  guidance”  and 
concludes that “Providing more validation opportunities is not enough. Providing support to 

301

individuals is necessary”.
 The Recommendation itself does not include any provision about 
financial support to individuals for the validation of skills or any other purpose and hence the 
baseline scenario here also differs from the policy options presented in this report, which are 
complementary to the existing provisions on validation. 

Guidance, as promoted at policy level by the 2008 Council Resolution on lifelong guidance
303

, can be a major factor in increasing demand for 
and in practice by the Euroguidance network
learning, helping individuals and organisations to  analyse their learning needs, find reliable 
information on available opportunities and choose the most appropriate pathways. Under the 
baseline scenario, the EU will continue to support Member States in their guidance provision. 
Good quality guidance may generically lead to increased participation in training and reduction 
of skill gaps, and may help individuals take advantage of financial support, if such support is 
available.  However,  both  policy  options  presented  would  enable  a  better  integration  of 
guidance into adult learning systems than the baseline scenario. 

302

305

The 2016 Council Recommendation on Upskilling Pathways304, an action of the 2016 New 
Skills Agenda for Europe
, is the major latest EU legislative action in adult learning policy. 
With  it,  Member  States  agreed  to  adopt  a  strategic  and  coordinated  approach  to  providing 
joined-up learning opportunities to the EU’s 61 million low-skilled adults. It aims to support 
adults with low levels of qualifications to enhance their basic skills (i.e. literacy, numeracy and 
digital  skills),  and/or  to  acquire  a  broader  set  of  skills  by  progressing  towards  higher 
qualifications. Upskilling Pathways provide support for low skilled and low qualified adults to 
have access to upskilling opportunities. The concept of an easily accessible pathway comprises 
three steps: (1) skills assessment; (2) tailored learning offer; and (3) validation and recognition. 
The  recommendation  also  addresses  enabling  conditions,  such  as  outreach,  guidance  and 

301

 Commission Staff Working Document, Evaluation of the Council Recommendation of 20 December 2012 on 

the validation of non-formal and informal learning, (SWD (2020) 121), pp. 41-43. 
302

 Resolution of the Council and of the Representatives of the Governments of the Member States, meeting within 

the Council of 21 November 2008 on better integrating lifelong guidance into lifelong learning strategies. 
303

 The Euroguidance network provides, since 1996, local counsellors with assistance, training and information 

resources on learning or working in another Member State. 
304

 Council Recommendation of 19 December 2016 on Upskilling Pathways: New Opportunities for Adults. 
 Communication from the Commission on a New Skills Agenda for Europe: Working together to strengthen 

305

human capital, employability and competitiveness, (COM(2016)0381 final). 

113 

 
 
                                                 
financial support should be an intrinsic part of this process. The 2019 report on implementation 
of  the  Recommendation  shows  the  variety  of  policy  responses  that  can  be  identified  at  the 
national  level.  Some  Member  States  are  integrating  the  principles  in  existing  (reviews  of) 
lifelong learning policies, employment strategies or national skill strategies, or in their existing 
policies in the field of adult education. Some Member States have put in place dedicated pilot 
projects  and  initiatives,  sometimes  funded  through  European  funds.  The  report  shows  that  
these often lack  emphasis on outreach and guidance.  However, people furthest away from the 
labour market, who face multiple barriers, would profit more from the involvement of a wider 
range of actors, including social services. The report also indicates that the three basic skills 
literacy,  numeracy  and  digital  skills  are  often  not  explicitly  addressed  by  these  initiatives. 
Instead, vocational and job specific skills for employment emerge most prominently.  

While  the  Council  Recommendation  on  Upskilling  Pathways  also  aims  to  increase 
participation in adult learning, it is focused on basic skills and has a narrow target group (low 
skilled adults). Hence, the policy options presented above offer an opportunity to extend the 
scope of action currently foreseen in the baseline scenario. Furthermore, the Recommendation 
does  not  mention  financial  support  to  individual  learners,  even  though  one  of  the 
recommendations  to  Member  States  mentions  that  support  measures  could  include  “direct 
support to learners”. The baseline scenario hence differs from the two policy options presented 
in this report, which both propose financial support to individuals.  

In  addition  to  the  instruments  presented  above,  the  baseline  scenario  foresees  a  number  of 
political documents, which call upon the EU and Member States to upscale skills policies.  

  The European Pillar of Social Rights presented by the Commission and endorsed by 

306

Member States in 2017
 includes as its very first principle the right of everybody to 
good quality and inclusive education, training and lifelong learning, enabling people 
“to  participate  fully  in  society  and  manage  successfully  transitions  in  the  labour 
market”, while its fourth principle, focusing on employment, states that everybody had 
a right to re-qualification.  

307

  The Commission Communication on a European Skills Agenda

 for Sustainable 
Competitiveness,  Social  Fairness  and  Resilience  published  in  July  2020,  after  the 
COVID-19 pandemic had started having a major impact on the European economy and 
society, was largely organised around the need for upskilling and reskilling adults to 
convert the huge societal challenges brought by the digital and green transitions into 
opportunities  for  a  prompt  recovery  and  sustainable  growth.  The  European  Skills 
Agenda specifically envisaged four targets to be achieved by 2025, all related to adult 
skills development; participation in learning in one year of adults in general (50 %), of 
low  qualified  adults  (30%)  and  of  unemployed  (20  %)  with  a  four  week  reference 
period),  as  well  as  the  share  pf  adults  with  at  least  basic  digital  skills  (70%).  The 

306

307

 Interinstitutional Proclamation on the European Pillar of Social Rights, (2017/C 428/09).  
 Commission Communication on a European Skills Agenda for Sustainable Competitiveness, Social Fairness 

and Resilience, (COM(2020) 274 final). 

114 

 
                                                 
European  Skills  Agenda  specified  that  the  targets  should  be  monitored  within  the 
framework of the European Semester process and by disaggregating data per gender.  

308

  The European Pillar of Social Rights Action Plan

, released by the Commission in 
February 2021, sets as one of its three headline targets that by 2030 that share of adults 
participating in learning in one year should be 60 % and, as a complementary target, 
80%  of  adults  should  have  basic  digital  skills.  Confirming  the  relevance  of  adult 
learning to support resilience and recovery in a time of transitions, the EU Heads of 
State and Governments on 8 May 2021 in their Porto Declaration and then the European 

309

Council on 25 June 2021
 welcome the headline targets of the Action Plan. Following 
the  crisis  generated  by  the  COVID-19  pandemic,  increasing  participation  in  adult 
learning  has  become  one  of  three  main  objectives  of  social  policy,  together  with 
increasing employment and reducing poverty. 

 

In  March  2021,  the  Commission  adopted  a  Recommendation  on  effective  active 

310

support to employment
, which invited Member States to devleop policy packages 
organised around three components. The second component aims to foster upskilling 
and reskilling opportunities and support measures. 

 

Individual  learning  accounts  feature  in  some  of  these  recent  policy  documents  as 
follows: The European Skills Agenda is the first EU policy document that suggests the 
opportunity  for  individual  learning  entitlements.  In  its  action  9  the  Commission 
declares its intention to assess how a possible European initiative on individual learning 
accounts can support participation of working age adultsin training and how this could 
be complemented by enabling factors.  

  The Council 2020 employment guidelines, under guideline 6 on access to employment 
and skills, invites Member States to “strengthen the provisions on individual training 

entitlements and ensure their transferability during professional transitions”.

311

  The Council  Recommendation on VET mentioned above (Action 4 of the European 

Skills  Agenda)  and  the  Osnabrück  Declaration
  included  among  the  short-term 
deliverables  2020-2025  at  EU  level  the  exploration  of  “financial  and  non-financial 
incentives for IVET and CVET addressing adult learners”.  

312

308

309

 European Pillar of Social Rights Action Plan, (COM(2021) 102 final). 
 “the European Council welcomes the EU headline targets of the European Pillar of Social Rights Action Plan, 
in line with the Porto Declaration”, Council of the European Union (2021), European Council conclusions, 24-25 
June 2021. 
310

 Commission Recommendation of 4.3.2021 on effective active support to employment (EASE) following the 

COVID-19 crisis, (C(2021) 1372 final). 
311

Council Decision (EU) 2020/1512 of 13 October 2020 on guidelines for the employment policies of the Member 

States,, p.22. 
312

Osnabrück Declaration, endorsed on 30 November 2020 by the Ministers in charge of vocational education and 
training of the Member States, the EU Candidate Countries and the EEA countries, the European social partners 
and the European Commission. 

115 

 
 
                                                 
  The  European  Pillar  of  Social  Rights  Action  Plan  announced  the  Commission’s 
intention  to  present  in  Q4  2021  “an  initiative  on  Individual  Learning  Accounts  to 
overcome  barriers  to  access  to  training  and  to  empower  adults  to  manage  career 
transitions”.  

 

In  March  2021  the  Commission  Recommendation  on  effective  active  support  to 
employment suggests Member States to provide adults “with entitlements for quality-
assured training and career guidance”. 

Although some of the policy options presented in this report are reflected in these initiatives, 
yet they do not offer concrete suggestions to Member States on implementation. Therefore, the 
policy options presented in this report go clearly further than the baseline scenario. 

Other  recent  EU  initiatives  in  the  area  of  education,  training,  skills  and  qualifications 
complement the policy options presented in this report:  

  The 2017 Council recommendation on the European Qualifications Framework for 

lifelong  learning
promotes the transparency and quality of qualifications.   

,  bringing  forward  the  instrument  first  established  in  2008, 

313

  The 2018 Decision on a common framework for the provision  of better services for 
skills and qualifications (Europass), supports the transparency and understanding of 
skills  and  qualifications  acquired  in  formal,  non-formal  and  informal  settings  and 
facilitates information flows among learners, learning providers, workers, employers 
and other actors.  

Both initiatives can support the effectiveness of the policy options presented in this report, by 
facilitating the understanding of the learning outcomes acquired in the learning opportunities 
foreseen  by  these  policy  options.  However,  these  initiatives  do  not  addresses  the  issue  of 
financial support to individuals for learning purposes and would not allow the baseline scenario 
to reach the specific objectives of this initiative. 

Another recent major initiative, the Pact for Skills314, launched in November 2020, aims at 
mobilising  private  and  public  stakeholders  to  take  concrete  action  for  the  upskilling  and 
reskilling of people of working age, and, when relevant, pool efforts in the partnerships. 

In  the  baseline  scenario,  the  EU  will  continue  to  support  adult  learning  though  its  funding 
instruments.  Throughout the 2021-2027 period, the European Social Fund+, with a budget of 
€88 billion, will remain an important funding source for national up- and reskilling activities. 
Other programmes such as Erasmus+, Horizon Europe, Digital Europe, the European Regional 
Development  Fund,  the  Just  Transition  Fund,  the  Brexit  Adjustment  Reserve,  the 
Modernisation Fund and InvestEU will also support this objective. These will complement the 
support  provided  under  the  Recovery  and  Resilience  Facility,  whereby  the  prominence  of 
individual training entitlements in some of the national plans reflects the common challenges 

313

 Council recommendation of 22 May 2017 on the European Qualifications Framework for lifelong learning and 
repealing  the  recommendation  of  the  European  Parliament  and  of  the  Council  of  23  April  2008  on  the 
establishment of the European Qualifications Framework for lifelong learning. 
314

 Pact for Skills.  

116 

 
                                                 
presented in this report (Table A8.1). However, they cannot promote reforms in all Member 
States  with  a  common  framework  which  would  facilitate  the  set-up  of  effective  support 
systems, maximising the positive effect on increasing participation on training.   

Table A8.1: Skills and individual training entitlements in the Recovery and Resilience Plans 

of Member States 

Skills and adult learning actions take a prominent role in the Member States' Recovery and 
Resilience  Plans.  Among  the  18  Member  States'  Plans  that  have  been  endorsed  by  the 
European  Commission  by  August  2021,  7  have  planned  actions  to  support  training 
entitlements  for  individuals,  such  as  individual  learning  accounts  or  vouchers  (Belgium, 
Croatia, France, Greece, Latvia, Lithuania, Luxembourg). Their measures are summarised 
below: 

In Belgium, at the federal level, the learning account reform consists of three measures: i) a 
pathway ensuring that from 2024, all workers are entitled to an average of 5 days of training 
per year; (ii) developing tax advantages for companies which provide employees with more 
hours of training than those already provided for by law and (iii) removing, in consultation 
with  the federated entities, obstacles to  participation  in training for workers in  temporary 
unemployment. The RRF sets a target of 25 000 workers in long-term or structural temporary 
unemployment  to  have  received  training  from  the  regional  employment  services  to 
reintegrate in  the labour market.  Further actions at  the level  of the  federated entities will 
strengthen these measures. 

Croatia plans to introduce a voucher system for adult education and upskilling, to be used 
in  quality-assured  adult  learning  programmes  that  are  aligned  with  the  Croatian 
Qualifications Framework. Among the 30 000 planned beneficiaries are both the employed 
and  the  unemployed,  of  which  at  least  12 000  people  who  are  long  term  unemployed, 
inactive, or young people not in employment, education or training. The new system aims to 
contain  a  skills  mapping  catalogue  and  an  IT  application  for  managing  and  awarding 
vouchers. 

France  plans  to  top  up  workers’  entitlements  on  the  already  existing  individual  learning 
accounts (“Compte personnel de formation”) with a €1 000 credit, which may be used for 
trainings linked to digital skills or digital careers. Around 400 trainings have been authorised 
for this use, which may be attended during working hours, provided the employer agrees. 
Once the training has been completed, the cost is paid to the training body by the Caisse des 
Dépôts et Consignations, in charge of the management of the French ILAs. The measure 
aims to train 25 000 individuals until the end of 2021. The total cost of the measure is €25 
million. 

Greece plans to utilise Lifelong Skilling Accounts (LSAs) as one of the tools for continuous 
training, based on individualised needs. It will be based on a revised quality assurance system 
through  a  new  National  Eligible  Training  Provider  List,  and  an  upgraded  labour  market 
diagnosis mechanism. The reform also plans to establish a National Skills Council for an 
annual update of the National Skills Strategy. The reform is accompanied by an investment 

117 

 
in horizontal skilling programmes for 500 000 participants and aims at providing a) baseline 
and medium-level digital skills, b) green skills and c) financial literacy skills. 

Latvia plans to develop the ILAs concept for adult learning, aiming to pilot it for around 
3 500 adults to develop digital skills. As part of the project, an online platform in Latvian 
will be established to launch the ILAs pilot, targeting both employed and unemployed who 
will have their personal ILAs set up and funded by the state. The platform will host around 
170 learning modules, including from international sources and translated into Latvian. The 
project is expected to enable access to a wider scope of learning content by making use of 
digital technologies and remote learning, in combination with personalized coaching, and 
thus resulting in a more tailor-made training.  

Lithuania plans to introduce, as a RRF reform, a one-stop-shop model for lifelong learning 
that  will  consolidate  the  currently  fragmented  framework  of  adult  skills  development, 
including  both  VET  and  higher  education.  The  new  model  aims  to  improve  quality 
assurance, skills assessment and career guidance, and to develop an electronic system for 
individuals  to  access  information  on  learning  opportunities  and  to  register  in  the 
programmes. The system will be based on the principle of an individual learning account, 
which  will  encompass  both  the  IT  service  to  access  training,  and  the  financing  of  adult 
learning (specific features will be defined in the legislative proposal). The  RRF will fund 
quality assured trainings for 21 600 people within the new framework (at least 40% of these 
in digital skills). 

Luxembourg  plans  to  further  develop  continuing  and  vocational  training  through  the 
Skillsdësch  reform,  which  will  bring  together  public  and  private  actors  in  the  field  of 
employment  and  vocational  training.  The  reform  is  part  of  a  wider  set  of  measures  – 
supported by the RRF, the national  budget  and  other funding sources  –  to  strengthen the 
adult  learning  system  in  Luxembourg  with  a  view  to  the  creation  of  a  possible  personal 
training  account  system.  In  addition,  the  RRP  will  support  the  Digital  Skills  investment, 
which will provide individual vouchers for digital skills up- and reskilling for employees 
who were put on short-time work schemes during the COVID-19 crisis. The voucher will be 
provided to 11 700 people through a new digital government service, also supported by the 
RRF (“MyGuichet”).  

Finally, under the baseline scenario, the European Semester and the EMCO, will continue 
to  provide  recommendations  for  Member  States  to  act  in  this  field.  However,  they  are  not 
specific enough to guide the action of Member States towards an integrated policy set up 
to addresses the problem drivers with sufficient impact and in an inclusive way. 

Concluding,  the  baseline  scenario  foresees  extensive  coverage  of  EU  action  to  support  the 
provision of education and training and of EU financial support in this area. Under the Baseline 
scenario, there is however no EU instrument yet that promotes financial support to individual 
adults in line with their learning needs in the same way the policy options presented in this 
report do, giving a broader choice for the type of skills and target groups supported.  

118 

 
 
2. Baseline scenario by Member State 

The baseline assumes that current national policy responses continue, as do national variations 
in policy choices. The baseline also takes into account recent reforms proposed and how these 
will evolve having an impact on the general and specific objectives of empowering adults to 
participate  in  training  and  closing  gaps  in  financial  support  and  increasing  incentives  and 
motivation of individuals to train. This section provides the following information:  

  Baselines of policies and reforms in EU Member States 

  Baseline of financial instruments used in EU Member States  

  Baseline of individual entitlement and supporting infrastructure and services  

2.1 Existing policy frameworks and recent reforms 

Several Member States have taken steps to address the general and specific objectives of the 
initiative, but overall progress is uneven across Member States and target groups (see table 
below). Most Member States identified increasing participation of adults in learning as a clear 

315

  This  is  mainly  the  case  for 
priority  in  legal  acts,  policies,  or  strategies  at  national  level.
Member  States  reporting  higher  participation  figures  and  in  several  of  these  countries  new 
policy plans are erected,  addressing the importance of adult learning  and  re- and upskilling 
throughout life. In only 3 Member States increasing participation is not or somewhat identified 
as priority, mostly in the Member States reporting lower participation figures. In 13 Member 
States, these relevant legal acts or strategies aim to increase the demand for adult learning offers 
by individuals directly. This mostly concerns the group of Member States already reporting 
higher participation figures. 

Table A8.2 – Overview of national policy (reforms) on incentivising individual participation 
in  adult  learning:  document  and  strategies  on  increasing  participation  & 
reference to demand side instruments for individuals  

Cluster 

Country  Documents and strategies on 

increasing participation 

MS  with  high 
participation 
(>45%) in AL 

MS 
medium 

with 

AT 
DE 
DK 
FI 
FR 
HU 
IE 
NL 
SE 

BE 
CY 

Yes 
To some extent 
To some extent 
Yes 
Yes 
To some extent 
Yes 
Yes 
Yes 

Yes 
Yes 

by 

Aim  to  increase  demand  for 
individuals 
learning 
directly  
Yes 
Yes 
Yes 
No 
Yes 
No 
No 
Yes 
Yes 

Yes 
No 

315

 Based on mapping done in 2020 on financial instrument by the Adult Learning Expert Network of the European 

Commission. 

119 

 
  
  
  
   
  
  
  
  
                                                 
participation 
in AL 

MS  with  low 
participation 
(<33%) in AL 

EE 
IT 
LU 
LV 
PT 
SI 
SK 

BG 
CZ 
EL 
ES 
HR 
LT 
MT 
PL 
RO 

Yes 
To some extent 
Yes 
To some extent 
Yes 
Yes 
To some extent 

Yes 
No 
No 
To some extent 
No 
To some extent 
To some extent 
Yes 
Yes 

Yes 
Yes 
No 
No 
No 
Yes 
No 

Yes 
No 
No 
No 
No 
No 
Yes 
No 
Yes 

TOTAL 

Yes = 15 
To some extent = 9 
No = 3 

Yes = 13 
To some extent = 0 
No =14  

Source: authors, based on mapping of available instruments conducted AL expert network for the purpose of the 
IA on ILAs. 

Table A8.2 is summarised in the Figure A8.1 below, showing a clear pattern. Those countries 
reporting lower participation figures are mostly also the countries that do not aim to increase 
the demand for adult learning offers by individuals directly in their legal acts and strategies, 
while countries reporting higher figures, mostly address such demand side instruments in their 
policies. Moreover, the group of countries reporting the lowest participation rates also represent 
the countries that do not give clear priority to participation of adults in learning in legal acts, 
policies, legislation, or strategies at the national level. 

120 

 
  
  
  
   
  
  
  
  
  
  
  
  
  
  
  
  
 
Figure A8.1 - Overview of national policy (reforms) on incentivising individual participation 

in adult learning per cluster of countries. 

Existance of policy (reform) addressing the objective increasing 
participation in adult learning

4

5

6

7

8

9

MS with high participation (>45%) in AL

MS with medium participation in AL

0

1

2

3

6

6

MS with low participation (<33%) in AL

3

3

Yes

To some extent

No

3

3

3

Demand side instruments for individuals discussed (direct 
support)

0

1

MS with high participation (>45%) in AL

MS with medium participation in AL

MS with low participation (<33%) in AL

3

3

6

2

4

Yes

No

4

5

7

8

9

3

5

6

6

Source: authors, based on mapping of available instruments conducted AL expert network for the purpose of the 
IA on ILAs. 

Table  A8.3  provides  an  overview  over  recent  or  planned  national  policies  and  reforms  on 
incentivising individual participation in adult learning in each of the EU Member States. 

Table A8.3 - Existing policies/instruments/policy reforms in Member State316 

Member 
State 

Austria 

Policies and reforms 

  Lifelong Learning Strategy LLL 2020 (Strategie zum lebensbegleitenden 
Lernen in Österreich LLL: 2020): aims to increase the participation rate in 
lifelong learning. It foresees several measures to help increasing participation 
rates, some of which also relate to individual support for learners. One of the 
measures proposed is the development of an individual education account as 
an incentive for private investment in education.  

316

 Based on mapping done by AL expert network on financial incentives for adult learning to individuals in 2020 
and a report on national developments in adult learning with specific reference to the Recovery and Resilience 
Facility, produced early 2021, including suggestions for reform & investment priorities with respect to flagship 7 
“Reskill & Upskill”. 

121 

 
 
 
                                                 
  The government programme 2020-2024 more concretely also follows up on 
the ambition to further develop the financing of adult learning through the 
introduction of learning accounts and training vouchers.  

  Social partners are advocating for strengthening individual learning 

entitlements. In a "Post Corona Working Group", the Federation of Austrian 
Industry (Industriellenvereinigung) recommended the establishment of 
publicly financed temporary vouchers or token systems for disadvantaged 
population groups, which can be used for example for training. The Chamber 
of Labour (Arbeiterkammer (AK)), being the legal representation of the 
employees' interests, is also advocating training vouchers and accounts. These 
are not intended to replace the existing structural subsidies for adult education, 
but to supplement them. The AK supports that every employee should have 
the right to update his or her qualification or to acquire a new qualification 
after a certain period of employment. For this purpose, the AK proposes a new 
model, namely the "qualification budget" (Qualifizierungsgeld). This bundles 
already existing instruments (educational leave, part-time training and 
scholarships for skilled workers) and advocates a training time account 
supported by a qualification budget. The Chamber of Commerce 
(Wirtschaftskammer (WKO)), as the legal representative of the interests of the 
business community, advocates that all formal training up to the Matura level 
(equivalent to the higher education entrance permission) should be free of 
charge, including for adults who are preparing for it as part of second-chance 
courses. For non-formal continuing vocational education and training, direct 
funding is favoured. Costs should be shared between learners, employers and 
the public sector. Political parties differ in their approaches.  

  Adult learning participation has been identified as a priority in the new 
policy plans introduced by the Flanders and Wallonia governments. The 
importance of re- and upskilling throughout life is recognised by the Flemish 
Vision 2050 plan and the Walloon Marshall Plan 4.0, especially in the areas of 
STEM and digital. In both regions, formal adult education is regulated in 
separate decrees. PES are regulated to guide adults towards employment, by 
focusing on training in skills in line with labour market needs. This includes 
the stimulation of the uptake of adult education opportunities. Separate plans, 
like the Flemish Literacy Plan, aim to help adults increase their levels of 
proficiency in reading and writing.  

  Legal acts and strategies in both parts of Belgium show a stronger focus 
on lifelong learning. However, policy documents make no specific reference 
to the possible role of individual entitlements yet.   

Belgium 

Bulgaria 

  The most recent national strategical document, which refers to lifelong 

learning and adult education, is the National Development Programme 
Bulgaria 2030, adopted in January 2020. The Programme outlines the 
importance of lifelong learning as a key principle in the sphere of education 

122 

 
and defines it as a national target goal for 2030 to reach 7% participation rate 
in lifelong learning of people aged 25-64 (with a four week reference period). 
The programme envisages that “[a] system of incentives and effective 
mechanisms will be put in place to improve the skills of the population 
(including the elderly) to enable the reintegration of the unemployed into the 
labour market, including structurally unemployed, long-term unemployed and 
economically inactive low-skilled workers”. The programme does not give 
priority to direct or indirect incentives and schemes.  

 

In recent years, the major policy documents related to adult learning were 
annual national employment action plans and the action plan for the 
Implementation of the National Strategy for Lifelong Learning 2014-2020. 
These plans outline several concrete measures targeting unemployed people 
with low levels of education (ISCED 2), long-term unemployed, unemployed 
without professional qualifications and inactive persons to acquire 
professional qualifications and key competences. Recently several important 
policy documents have been adopted in the sphere of digitalization and 
development of digital skills. Another important policy document is the 
national programme “Digital Bulgaria 2025” and associated Roadmap with 
more concrete policy measures. In the area of adult learning, it envisages 
improving the digital skills of the workforce, including financing of vocational 
training and key ICT competences, increasing the number of young employed 
and unemployed people trained by the ICT professions, teachers’ training in 
digital skills, raising the skills of ICT professionals in the perspective of 
lifelong learning.  

Cyprus 

  Increasing participation in adult learning has been identified as a priority 

by the national strategy on Lifelong Learning 2014-2020.   

  Besides this strategy, the remaining acts/reforms offer mainly indirect 

motivation to adults to participate in learning activities/programmes. For 
example, the Human Resources Development Authority (HRDA) offers 
basically free of charge training opportunities to the inactive population and to 
employees through subsidised training programmes. The only exceptions are 
some schemes targeting the inactive population —the unemployed or the 
unemployed Guaranteed Minimum Income (GMI) recipients— for which the 
HRDA grants the participants with a small weekly or monthly allowance (€90 
- €125) for their expenses. Moreover, the Evening High Schools and the 
Evening Technical and VET schools also offer their programmes free of 
charge to all adults (18+). Additionally, in the framework of the 
implementation of the Digital Strategy for Cyprus, since 2017, the Cyprus 
Productivity Centre (CPC) of the Ministry of Labour and Social Insurance has 
launched a series of free short-courses and workshops designed to enhance 
digital literacy and to promote the use of e-government.  

123 

 
Czech 
Republic 

  In the national policy framework no major reforms or policy initiatives 

on adult learning, can be identified.   

Germany 

  In August 2020 the Strategic Framework for Employment until 2030 was 
approved by the Cabinet. Only a draft version from 2019 was publicly 
available in January 2021 (foreseen to be published in December 2021), with 
no concrete measures for adult learning proposed. The Strategy of Education 
Policy until 2030+ was approved by the Cabinet in October 2020, but it 
addresses only initial formal education.  

  In mid-2019, the new National Strategy for Continuing Education was 
presented. It aims to improve funding opportunities, information, and a 
counselling system. It aims to support individuals in their education and 
development, but also small and medium-sized enterprises. Action goals 
include improving the transparency of continuing education offers, adapting 
and optimising public funding systems, expanding advisory services and 
increasing the quality of education. 

  The strategy also specifies its ambition to strive for a new skills culture 

which sees continuing education and training (CET) as a normal part of life. 
The strategy supports (1) the transparency of CET opportunities and 
programmes by developing a central gateway for individuals with 
information about the support available for CVET ; (2) closing the gaps  in 
support systems; (3) ensuring joined-up lifelong CET counselling nationwide 
and strengthening skills development counselling; (4) strengthening the 
responsibility of the social partners; (5) review and enhance the quality and 
quality assessment of CET; (6) increasing the visibility and recognising the 
sills acquired by worked through VET; (7) developing further training 
qualification and continuing education and training programmes; (8) 
strategically developing educational institutions into centres of excellence for 
CVET; (9) supporting CET staff and equipping them with the skills required 
for the digital transformation; and (10) strengthening strategic forecasting and 
optimising statistics on continuing education and training. 

  Various schemes and policies are already in place, such as an education 

voucher scheme within the framework of the Federal Employment Agency's 
"Förderung beruflicher Weiterbildung" (FbW) programme, and a so-called 
Bildungsprämie” (training premia), which is called the “savings voucher”. 
This savings voucher is implemented under the legal framework of a so-called 
“Capital Formation Act” (Vermögensbildungsgesetz), and allows individuals 
to accrue savings into a savings account, which are complemented by a small 
public benefit. 

Denmark  

  Recently, the government has launched two plans for upskilling the 

workforce, both based on tripartite agreements. One is a programme for 
boosting the competencies of unemployed adults during the period of crisis. 

124 

 
The focus is on educating unskilled workers to skilled level, and the main 
instruments used are economic benefits for individuals. Unemployed adults 
participating in education usually get a wage compensation amounting to 80% 
of the unemployment benefit. The upskilling programme increases this to 
100% of the benefit for all types of vocational education and 110% for types 
where labour is especially in demand. The focus of the other programme is on 
secondary vocational education. The main element here is also economic, an 
increased compensation to employers for the wages that they pay trainees. The 
source is not directly state funds but based on training levies paid by 
employers. The vocational students benefitting from this programme are 
mainly young people, with some adults. The strategies and legal acts include 
both direct and indirect measures. The direct measures include especially (1) 
economic support for individual students and (2) information and guidance 
regarding education opportunities for adults, including recognition of prior 
learning.  

  The idea of introducing an individual learning account was one of the 
recommendations from an expert group on adult and continuing 
education in 2017, established by the Danish government after consultation 
with the social partners. The reception of the proposal was generally not 
positive, neither among political parties nor among labour market actors. 
Quite recently, in the context of the Covid-19 investments in education, the 
Social Liberals have relaunched their proposal for individual learning 
accounts. The reactions from other political parties and labour market actors 
have been the same as earlier. 

Estonia 

  The Estonian education strategy for 2014-2020 already set adult 

participation in learning as one of the key indicators to monitor the 
lifelong learning system. It particularly highlights the need to support 
learning among people with low qualifications. The strategy also introduced 
an annually revised Adult Education Programme which sets out specific 
measures to promote learning among adults. Promoting participation of adults 
in learning is also prioritised in Estonia 2020 – the document outlining 
strategic aims in the European Semester framework. In addition, as described 
in the work plan for 2021, the Ministry of Education and Research has 
planned to develop the principles of the skills portal and digital story 
(täiskasvanute oskuste digilugu) for adults and the concept of micro-
credentials by the end of 2021. Furthermore, the ongoing formulation of future 
policy strategy (see for instance Smart and Active Estonia 2035) already 
considers updating the Adult Education Act. The Ministry intends to 
specify the principles of quality assurance of continuing education and make it 
compulsory for training institutions to enter data on training participants into 
the Estonian education information system. This is a prerequisite for the 
development of the web-based skills inventory database for adults. 

125 

 
  While the overall strategic or legal documents do not prioritise the provision 

of financial incentives directly to individuals, such measures are introduced in 
Estonia. Hence, the aim is not to increase the demand for adult learning offers 
directly by individuals but to increase the demand through a combination 
of measures. Providing financial incentives directly to individuals is one of a 
combination of measures that are introduced to raise participation of adults in 
learning. Several research results have pointed out the need to introduce a 
training voucher or individual learning accounts scheme in Estonia to 
motivate learning among those individuals who are in need of training but 
tend to participate less or are among particular risk group.  

Greece 

  Following consultations with relevant stakeholders, the Ministry of Education 

and Religious Affairs passed a bill in December 2020 (254/2020) to 
completely restructure the overall system of Vocational Education and 
Training and Lifelong Learning (LLL): The main changes revolve around 
three pillars.  

o  (i) Unified strategic planning of VET and LLL. It introduces a new 
framework with distinct levels of qualifications to avoid overlapping 
structures and services. In this context, a national VET system is 
established, which is developed at levels 3, 4 and 5 of the National 
Qualifications Framework, in line with those of the European Qualifications 
Framework. As part of the overall reform, the law introduces new post-
secondary training leading to EQF 3, which was previously not provided. 
Additionally, the new law allows progress from the institutes of Vocational 
Training and Vocational Lyceum to Universities (for up to 5% of 
University entrants);  

o  (ii) Direct and effective connection of VET and LLL with the labour 
market. Introduction of a new system of institutional governance at 
central/sectoral and regional level with substantial involvement of social 
partners in identifying the needs of the labour market as well as in specific 
aspects of the design, implementation and governance of the vocational 
education and training system;  

o  (iii) Upgrade of the provided education and training (initial and 

continuing) in terms of structures, procedures, curricula and certification. 
The law re-introduces some quality dimensions in the operation of LLL 
centres - for example, by introducing minimum requirements for scientific 
and administrative staff, minimum quality specifications in the 
implementation of theoretical, practical and distance learning, introducing 
certification for the educational content of co-financed programmes etc. 
Additionally, the law creates a framework of regular control/inspections by 
the General Secretariat for Vocational Education, Training, Lifelong 
Learning and Youth. 

126 

 
Spain 

  An integral reform of the professional training policies has been 

announced in December 2020. It will follow a consultation process with 
social partners and other relevant stakeholders. The focus of the reform is 
proposed to be in “permanent” training, upskilling and reskilling.  

 

It will build on the modernization Plan for Professional Training launched in 
July 2020, with a foreseen investment of €1 900 million in four years. Besides 
reinforcing VET, it aims to boost the validation of skills for up to 40% of 
active population less that 55 years old, which means over 3 million people, 
until 2023. It will also increase the number oc actions for adult learning in 
scarcely populated areas. The Plan also foresses more flexible ways for 
training enhancing cooperation between firms and the education systems.  

 

It will also benefit from the newly created coordination bodies on professional 
training with the regions, established in November 2020 and with social 
partners in December 2020.   

  Professional training as part of life-long training has been highlighted as a 

priority for the National Resilience and Recovery Plan, including the aim of 
providing digital skills to 80% of the population, with particular enphasis on 
women and vulnerable groups. 

  Simultaneously, there are plans for enhancing the tailoring of training for 

jobseekers as part of the reform of active labour market policies. While this 
initiatives do not prioritise the provision of financial incentives directly to 
individuals, they aim to increase the demand through a combination of 
measures.  

  Recent debates on lifelong learning are reflected in three documents on adult 
learning, namely the Programme of Sanna Marin’s Government 2019, the 
recent Education policy report 2030, and the upcoming parliamentary reform 
proposal of continuous learning. Education policy report 2030 is expected to 
propose a major overhaul of the lifelong learning policy and direction in 
Finland.  The upcoming parliamentary reform proposal of continuous learning 
builds in part on an outline published by The Finnish Innovation Fund 
(SITRA) on continuous learning, suggesting a frame for future development 
of the educational system of Finland.  

  The focus in Finland is currently on increasing access to paid training 

leave, guidance (targeting specified vulnerable groups) and participation 
in learning.  The adult education allowance was reformed in August 2020. 
The reform seeks to encourage students to work part-time and to make the 
adult education allowance scheme appeal to new kinds of applicants. It 
includes a new two-stage application process by using an online portal.  
Moreover, the amount of allowance will make it possible for certain 
applicants to receive an educational allowance. The scheme will continue to 
focus on those having an employment history of at least eight years, those 

127 

Finland 

 
who have been employed by their current employer in a full time capacity for 
at least one year, those that study towards a qualification that complies with 
the scheme, those who have been granted study leave, and those eligible for 
social security in Finland. Specific provisions also allow for self-employed to 
request a similar (though slightly lower) education allowance. The idea of an 
individual learning account was discussed in in 2018 as an initiative from 
9 labour unions, advocating for a model funded through employer and 
employee taxes (a sort of insurance model, like the unemployment insurance 
currently collected this way) and employee tax deductions. They call their 
model Competence accounts.  

France 

  In France, increasing adult learning participation through conferring 

funds directly to the individual is identified as a priority. This challenge is 
addressed by the “Law for the Freedom to Choose an Occupational Future” 
(Loi sur la liberté de choisir son avenir professionnel) of 2018. France 
therefore aims at providing learning opportunities to all so that all are given an 
opportunity to (re)enter the labour market, or to benefit from upward 
occupational mobility. The individual is made responsible of her/his learning 
pathway, as opposed to the employer, the State and/or the PES. The 
Individual Learning Account (Compte personnel de formation, CPF) is 
the tool for this emancipation. In Annex 13 more information is included 
about this specific measure.  

  Education and training are at the heart of the recovery plan (France 
Relance), launched by the government to address the major economic 
downturn caused by the Covid crisis (mid 2020). The plan has set aside €15 
billion for the purpose of employment and competences development. Some 
of the measures announced are: education and training for young adults, the 
National Fund for Employment earmarked for training, and facilitating 
reskilling and occupational transition. 

  Croatia still has a Law of Adult Education from 2007. In May 2020, the draft 
of the Adult Education Act was withdrawn from the parliamentary procedure. 
No new policy papers in adult education have been published since 2014. 
A small change to Ordinance on Public Documents in Adult Education 
(Official Gazette 61/14) was adopted, but it was not crucial for the general 
adult education policy.  

  The national policy debate does not aim at incentivising adult learning 

participation – neither by targeting the individual to increase their motivation, 
nor targeting the employer to increase investment in training. The adult 
education system in Croatia is not part of the regular education system, 
without a regular system of financing.   

Croatia 

Hungary 

  The need for increasing the participation of adults in learning and the 

need for competence development are reflected in recent strategies. The 

128 

 
increase in adult learning participation was mainly supported by EU resources, 
while state funding remained limited. The main overarching problem remains 
the lack of a comprehensive adult learning policy framework or strategy at 
national level. Despite this, over the past few years the Government has 
launched several projects in adult learning. However, in most cases the 
effectiveness of these projects has not been measured. This was recognised by 
the Government, that launched the 2020 Vocational Education Law that serves 
as a basis for the mid-term strategic policy plan called Vocational Training 4.0 
Strategy. The reform includes system-wide changes, which includes the 
concept of VET, its structure, finances, and curriculum content. The Adult 
Education Act was comprehensively amended in several stages, most recently 
in July 2020 and then January 2021. The new Adult Education Act and related 
implementing regulations are a significant step towards rethinking the adult 
education system: a more transparent, simpler system, more clear terms of 
reference and with fewer types and exceptions. Furthermore, in June 2021, a 
new form of Student Loan named a “training loan” will be introduced as a 
type of adult-education version of the Student Loan for higher education 
students. The training loan will be available for those who are between 18 
and 55 years of age and participate in adult education and vocational 
training. According to preliminary estimates, the training loan could reach 
more than 150 000 people: more than 50 000 in school-based VET and up to 
100 000 in adult training. 

  The ‘life-wide’ aspect of the lifelong learning concept has not been in the 
focus of the government yet, and important aspects of adult learning (e.g. 
general adult learning beyond labour market oriented learning, promoting 
active citizenship, health literacy, family learning, as well as increasing the 
social recognition of adult learning’s values) have not been covered in an 
articulated, distinguished policy approach. Nevertheless, the government 
provides all citizens with the opportunity to acquire a state-recognised 
vocational qualification on the first and second level. The latter can be 
obtained free of charge in school-based vocational education as well as in a 
so-called vocational training. Incentives for learning directly to individuals are 
not part of the current political debate. 

Ireland 

  Participation in adult learning is a policy priority in Ireland and 

increasing engagement in lifelong learning is a key objective of Ireland’s 
National Skills Strategy 2025. The strategy sets the ambitious goal to 
increase the adult learning participation rate to 15% by 2025 (with a four week 
reference period). In the national debate, there is little evidence on providing 
learning incentives directly to individuals. Individuals are nevertheless the 
focus for intervention in relation to activation. 

Italy 

  The general strategy which drives public policy in Italy aims at strengthening 

policy areas where delays persist in the formation of human capital, in 

129 

 
productivity and infrastructures, supporting them with appropriate 
macroeconomic stimuli. This is accompanied by a sort of dual strategy for 
adult learning. On the one hand, for vulnerable groups achieving adult 
learning objectives is postponed until the resumption of economic growth, and 
the negative effects that ensue are mitigated with the strengthening of social 
policies. On the other hand, developing and increasing the skills of high 
skilled workers - the individuals and companies that autonomously provide 
investment in training - are supported by the state both through opportunities 
for tax deduction on expenses, and through public interventions to support 
research and innovation. 

  In 2020 the Italian government introduced an important measure aimed 
at promoting firm-level training and workers’ reskilling. Coming into 
force in 2021, the Fondo Nuove Competenze (New skills fund) provides 
companies with monetary and organizational help to implement training and 
re-skilling programmes explicitly oriented at favouring investments in new 
technologies and reducing the employment impact of restructuring processes. 
The fund aims to homogenise regional training policies, reducing the 
asymmetries and extending the good practices developed in some regions. 
One of the key weaknesses of the Italian adult learning system concerns the 
differences between regions, with heterogenous distribution of resources and 
institutional capabilities.  

  Funding the individual learning demand has been debated in Italy in the 
early 2000s. It came on when Tuscany regional government introduced the 
first Individual Learning Account. Tuscany promoted the adoption of the 
measure among other regional territories. 

Lithuania 

  The Ministry of Education, Science and Sports delegated distribution of 

funds for non-formal adult education programs to the local municipalities 
since 2017, but these do not possess the funding necessary to make a 
substantial impact on the increase of participation in adult education. The 
lack of central coordination of funding and actions deployed by different 
ministries and programmes further decrease the impact on the rates of 
participation in adult education.   

Luxembourg    Increasing the participation rates in adult learning is given attention in 

legal acts, policies, legislation and strategies at the national level. The 
current legislation assures that a wide and accessible provision (including 
financially) is offered to individual learners. In addition, the new 
government coalition announced to further develop the lifelong learning and 
to improve the quality and flexibility in learning pathways. The relevant legal 
acts and strategies focus on supporting the provision of adult learning by 
subsidising it extensively at the level of the PES, public training providers and 
working closely with the professional chambers. All the measures aim to 

130 

 
encourage individual adult learning through a wide and accessible offer of 
opportunities for formal, non-formal and informal learning.   

Latvia 

  In the Guidelines for the Development of Education for 2014-2020, 

lifelong learning is mentioned, but rather as indirectly linked to adult 
learning. No other relevant adult education legal acts or strategies exist that 
aim to increase the demand for adult learning offers to individuals directly.  

Malta 

The 
Netherlands 

Poland 

  There is limited political debate on providing direct incentives to 
individuals to engage in adult education in Malta. Debate on direct 
incentives also did not attract much attention.  

  In 2018, the inter-ministerial programme for lifelong development was 

introduced. The approach aims to increase the demand for adult learning by 
individuals directly by, on the one hand, offering individual financial 
incentives and on the other hand, amongst others, increasing the flexibility 
of the VET and higher education offer. This is expected to lead to higher 
demand from learners for education programmes. The approach seeks to 
increase awareness of individuals, and help individuals decide for themselves 
which training they want to pursue. This development must be seen in the 
context of the existing system, in which employers and sectoral funds are 
responsible for the largest share of adult learning.  

  A key instrument in this strategy is the introduction of a new training 
allowance scheme, the STAP budget (Dutch acronym for Stimulering 
Arbeidsmarktpositie, or Incentive Labour Market Position). The STAP budget 
is introduced to replace an existing tax incentive scheme, in which individuals 
can request tax credits for costs for participating in adult learning. The 
objective of the scheme is to better empower individuals to take control of 
their learning careers more actively. To do so, it offers all adults the 
possibility of spending up to €1 000 once a year on training. The funds can be 
used on trainings that are included in a training register.   

  Poland has implemented numerous strategies aiming to increase adult 
learning participation. Funding is directed to the supply side and 
traditionally the training offer was decided by public administration and 
training institutions, rather than employers. Two national strategies have a 
perspective to 2030; the Human Capital Development Strategy (Strategia 
Rozwoju Kapitalu Ludzkiego) – aiming to raise the competences and 
qualifications of citizens – and the Strategy for Responsible Development 
2030, that links skills development to changes in the education system.  

Portugal  

  In Portugal, participation in adult learning is considered a policy priority. 
Since 2016, the Adult learning and education policy has observed changes. 
The existing offers are aimed at increasing demand indirectly. 

131 

 
Romania 

Slovenia 

  The demand-side-funding instruments of adult learning targeting 

individuals did not have a relevant discussion in recent times in public 
arenas.  

  In Romania, the low participation rate was identified as a problem and 
the Lifelong Learning Strategy 2015-2020 set the goal to increase it to 
10% by 2020. This strategy is part of a package of three strategies adopted in 
2015 on lifelong learning, on access to higher education, and on reducing 
early school leaving. The incentives to increasing participation are directed 
towards institutions and services, despite the non-functionality of the 
system has been recognised as a barrier to training. 

  Increasing the adult learning participation is considered a policy priority 
and the main document is the Adult Education Master Plan (AEMP), 
adopted by the National Assembly. In the latest AEMP, 2021-2023, 
individuals are directly addressed through the formal recognition of their skills 
and knowledge. Besides the AEMP, the Slovenian Development Strategy 
2030 sets a goal in adult learning participation of 19% by 2030 (11.6% in 
2016, according to LFS).  

Slovakia 

  In Slovakia, there has been an ongoing effort to prepare a new law and 
strategy on lifelong learning to increase adult participation in learning. 
To incentivise training, in 2018 the Ministry of Finance introduced a tax 
incentive for employers to motivate them to invest more into training.  

 

In the meantime, the Implementation Plan of the National Programme for 
Development of Education,  adopted in 2018, specifically refers to the 
piloting and possible introduction of individual learning accounts. It 
budgets with €1 955 000 per year for the period 2020-2027. 

Sweden 

  According to the government, the goal of the municipal adult education 

(komvux) is for adults to be supported and stimulated in their learning. 
They should be given the opportunity to develop their knowledge and skills in 
order to strengthen their position in work and social life and to promote their 
personal development. The starting point for education should be the 
individual's needs and conditions. Those who have received the least 
education should be given priority in komvux.  

  There is a general understanding in Swedish politics that it is good both 
for the individual and the society that komvux is free of charge and open 
to anyone who needs it. The issue of study support (in Swedish studiemedel 
or CSN) in the form of a mix of soft loans and subsidies to the learners (or 
individual training subsidies) is also not questioned by any party. 

132 

 
 
2.2 Existing financial support instruments for adult learning 

The following tables  are based on information from  the forthcoming update of the Cedefop 
“Financing  adult  learning  database”.  This  update  includes  existing  provisions  as  of  1st  of 
February 2020. These data have been complemented with information from the adult learning 
epert network, as indicated below. 

Box A8.1: Definitions used 

Grant for individuals- training vouchers or ILAs: Adults may receive public funding to 
cover  (part  of)  the  costs  related  to  their  participation  in  education  and  training.  Such  co-
funding  schemes  are  implemented  under  various  names:  grant,  training  voucher,  training 
account, individual learning account (ILA). Consistent with the terminology used in this IA, 
we  refer  to  them  as  “training  voucher”  unless  they  involve  personal  accunts  (notably,  in 
France). They are part of a shift away from simply financing training providers to a more 
demand-led approach that finances learners. 

Tax incentives for individuals: Tax incentives for the purposes of personal income tax may 
allow adults to deduct their costs for continuing vocational training or adult learning related 
to their current or future occupation from their individual income tax base or tax due. 

Subsidised  loans:  A  loan  scheme  allows  individuals  to  borrow  financial  resources  (on 
favourable conditions) from their future income to cover part of their (education and training) 
expenditure. The State may support the availability of loans and co-finance loan-related costs 
to encourage participation in adult learning 

Paid training leave: The training/educational leave is a regulatory instrument which, either 
by statutory right and/or through collective agreements, sets out the conditions under which 
employees may be granted temporary leave from work for learning purposes. Furthermore, 
the training leave allows the employee to be absent from the workplace for education and 
training purposes without losing the right to return to work later on or other social rights 
connected to a current employment. Training leave may be paid (the employer fully or partly 
covers the employee’s salary, supported by the government or not) or unpaid (the employee 
isn’t paid during the training leave period, but they are guaranteed to maintain their position 
once the period ends). The below mapping only considers paid training leave schemes. 

Tax incentives and grants for companies: Concerning tax incentives for the purposes of 
corporate  income  tax,  countries  typically  regard  company  expenditure  on  training  as  a 
business  cost  which  is  100%  deductible  from  the  taxable  income.  In  some  countries, 
companies  may  also  receive  additional  tax  incentives  related  to  their  training  activities. 
Companies may also receive public funding (grants) to cover (part of) their training costs. 

Training fund: A ‘training fund’ is a dedicated stock or flow of financing outside normal 
government budgetary channels for the purpose of developing productive skills for work. 
Training funds in the EU are very heterogeneous. The differences concern the governance 
models (bipartite or tripartite nature), the number of funds established per country, the type 
of (education and training) activities and target groups supported, and the way the money is 
collected and redistributed. 

133 

 
Table A8.4: Overview on the use of funding instruments in the EU Member States 

MS  with 
high 
participation (>45%) 
in AL 

MS  with  medium 
participation in AL 

Traini
ng 
vouch
er/ 
grants 
V 
V 
V 
V 
V 
V 
V 
V 
V 
V 

V 
V 
V 

V 

V 
V 

Countr
y 

AT 
DE 
DK 
FI 
FR 
HU 
IE 
NL 
SE 
BE 
CY 
EE 
IT 
LU 
LV 
PT 
SI 
SK 
BG 

Financial support for individuals 

Financial support for companies 

Individ
ual 
learning 
account 

Tax 
incentiv
es 

Subsidis
ed loans 

Paid 
training 
leave 

Paid training 
leave with 
public co-
funding 

Tax 
incentives / 
grants  

Training funds 

V 

V 
V 
V 
V 

V 
V 
V 

V 
V 
V 
V 
V 
V 

V 

V 
V 
V 

V 
V 

V 
V 

V 
V 
V 
V 

V 
V 
V 
V 
V 

 V 
V 
V 
V 
V 
V 
V 
V 
V 
V 
V 

V 
V 
V 
V 
V 

V 
V 
V 

V 

V 
V 
V 
V 
V 
V 
V 
V 

V 
V 
 V 
V 
V 
V 
 V 
V 
 V 
V 

V 
V 
V 

V 
V 
V 
V 

V 
V 

V 
V 

V 

134 

 
 
 
 
  
  
 
  
  
 
 
  
 
 
 
 
  
  
 
 
 
 
 
  
  
 
 
  
  
  
 
 
 
 
  
 
 
 
 
  
  
 
 
 
 
  
 
 
 
 
 
  
  
 
 
 
 
 
 
 
low 
with 
MS 
participation (<33%) 
in AL 

CZ 
EL317 
ES 
HR 
LT 
MT 
PL 
RO 
TOTA
L 

V 
V 
V 
V 
V 

V 

V 
V 

V 
V 

V 
V 
V 

21 

1 

16 

17 

V 
V 
V 
V 
V 
V 
V 
V 
24 

V 
V 
V 

12 

 V 

V 
V 
 V 
V 
 V 
V 
25 

V 
V 

14 

Source:  Forthcoming  update  of  Cedefop’s  ”Financing  Adult  Learning  Database”,  complemented  with  information  provided  by  the  national 
experts of DG Employment’s Adult Learning Network concerning the information whether paid training leave is supported with public funding. 

317

 Adult learning experts pointed to 20 voucher schemes running since 2015, co-funded by the ESF (see the case study in Annex 13). These are not included in the Cedefop 
database, since these vouchers represent a particular type of service (a type of course) provided by one or a small number of providers, so that these scheme are more similar 
to a supply-side funding arrangement. 

135 

 
  
  
  
 
 
 
 
 
  
  
 
 
 
 
 
  
 
 
 
 
 
  
  
 
 
 
 
 
 
 
 
 
                                                 
Table  A8.4  shows  that  the  most  commonly  available  financial  instruments  are  financial 
incentives for employers for employee training (available in 25 Member States), followed by paid 
training leave (24 Member States- whereby public co-funding is available in half of them according 
to the adult learning expert network, and often with low effective outreach as discussed in Annex 7) 
and training vouchers (21 Member States). The Member States that report high participation in adult 
learning all have training voucher such schemes in place, as compared to two thirds of Member States 
that report low or medium participation. 

Figure A8.2 – Existence of training voucher schemes by level of adult learning participation 

Source: Forthcoming update of the Cedefop Financing Adult Learning Database and AL expert mapping and AES 2016. 

However, only few training voucher schemes are available for all adults. An analysis of the 86 
schemes included in the Cedefop database, only 8 are open to all adults. Schemes commonly target 
the unemployed or (sub-groups among) employees, and few were available to the self-employed or 
inactive.  In the case vouchers address employees, in several cases this is restricted to employees that 
are working a minimum number of hours a month. Examples include a training voucher in BE that 
apply to persons with a working contract of less than 80 hours per month or  a continuing education 
grant in DE that applies for people that are employed 15 hours or more per week. 

Available  participation  data  also  point  to  a  limited  outreach  to  adults  as  measured  by 
participation  rates  in  the  adult  population,  limiting  their  impact  on  increasing  adult  learning 
participation rates or reducing inequalities in the access to training opportunities. For instance, the 
REPAS training voucher scheme in the Slovak Republic reported 13 398 participants in 2018, and 
the training card for employed people in Estonia reported around 5 700 participants in 2020. Effective 
outreach of most voucher schemes is hence smaller also in comparison to income tax incentives. See 
Annex 13 for a case study of the NL case, where income tax incentives are currently replaced by a 
training  voucher  scheme  with  a  comparable  outreach  (“STAP-budget”),  leading  to  a  number  of 
expected beneficiaries (200 000 adults a year) that is significant in comparison to the adult population.  

136 

339660123456789MS with high participation (>45%) in ALMS with medium participation in ALMS with low participation (<33%) in ALExistence of voucher schemesDoes not existExists 
 
2.3 Existing infrastructure and supporting services 

The  table  below  provides  an  overview  of  infrastructure  and  supporting  services  for  increasing 
incentives and motivation of adults to participate in adult learning. This includes infrastructure and 
services like: 

  Register of training providers, being a user friendly and up to date database of adult learning 

opportunities that is also including non-formal training opportunities at national level 

  Digital platform for citizens where they can identify themselves securely and access public 

services (e-government platforms)  

  Quality assurance for formal and non-formal adult learning provision that could be applied 
or built upon to safeguard the quality of the training demanded by individuals.  This could for 
instance be a list of certified providers and trainings that qualify for some already existing 
support schemes.  

  Career and adult learning guidance including public or legally mandated guidance offers 

  Skills validation mechanisms are in place to validate skills obtained through non-formal and 

informal learning, modular training etc 

It shows that existing practice to increase incentives and motivation of individuals varies across 
Member States. According to the assessment of the AL expert network almost all countries have a 
publicly or legally mandated career guidance systems (25 Member States of which 5 Member States 
to some extent), quality assurance systems for non-formal learning (21 Member States of which 11 
to  some  extent),  and  skills  validation  systems  (26  Member  States  of  which  16  to  some  extent)  in 
place. Nevertheless, these are generally not fully covering all type of learners and not always 
linked to financial instruments in a systematic and integrated approach. A concrete example in 
this respect  is  the availability of  guidance systems.  In most countries,  the PES plays a major role 
providing guidance services, limiting the services only to the unemployed and jobseekers. In other 
cases, guidance services are fragmentated, not having a centralised institution for career guidance for 
all adults but provided by different subsectors (such as PES, guidance centres in the education system, 
and youth services). Only a few countries have a national guidance system providing support to all 
adults.  Austria,  for  example,  has  a  more  elaborated  guidance  system  which  provides  educational 
guidance free of charge in all provinces, and which can be used on a voluntary basis. Another example 
is the Netherlands that recently developed a voucher system for guidance and counseling services for 
all adults (Nederland Leert Door!).  

Although some countries invested in the last years in digitalising services in portals, only 12 Member 
States have education registers in place or databases with training opportunities addressing the lack 
of transparency about available support and training offer. All Member States have digital platforms 
in  place  where  they  can  identify  themselves  securely  and  access  public  services  (e-government 
platforms). In almost all member States there is a legal right to educational leave.  

Table  A8.5  -  Existing  infrastructure  and  supporting  services  for  increasing  incentives  and 
motivation of adults to participate in adult learning (based on mapping done by AL 
expert network on financial incentives for adult learning to individuals in 2020) 

Cluster 

Coun
try 

Register 
of 
training 

Digital 
platfor
m 

Quality assurance 
in non-formal AL 

Career 
guidance 

Skills 
validation 

137 

 
with 

MS 
high 
participatio
n (>45%) in 
AL 

with 

MS 
medium 
participatio
n in AL 

MS with low 
participatio
n (<33%) in 
AL 

provide
rs 

AT  Yes 
DE  Yes 
DK  Yes 
FI  Yes 
FR  Yes 
HU  No 

IE  No 
NL  No 
SE  Yes 

BE  Yes 
CY  No 
EE  No 
IT  No 

LU  Yes 
LV  No 
PT  No 
SI  Yes 
SK  No 

BG  Yes 
CZ  No 
EL  No 

ES  Yes 
HR  No 
LT  No 
MT  No 

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 
Yes 
No 
To some extent 

To some extent 
Yes 
To some extent 

To some extent 
Yes 
Yes 
To some extent 

To some extent 
To some extent 
No 
To some extent 
To some extent 

Yes 
To some extent 
No 

Yes 
No 
No 
No 

To some extent 
To some extent 
yes 
yes 
yes 
To some extent 

To some extent 
yes 
yes 

yes 
To some extent 
To some extent 
To some extent 

yes 
To some extent 
yes 
To some extent 
To some extent 

To some extent 
To some extent 
To some extent 

yes 
No 
To some extent 
To some extent 

Yes 
Yes 
Yes 
Yes 
Yes 
To 
extent 
Yes 
Yes 
Yes 

some 

some 

Yes 
No 
Yes 
To 
extent 
Yes 
Yes 
Yes 
Yes 
To 
extent 
Yes 
No 
To 
extent 
Yes 
Yes 
Yes 
To 
extent 
Yes 
Yes 
Yes = 20 
To 
extent = 5 
No = 2 

some 

some 

some 

TOTAL 

PL  Yes 
RO  No 

Yes 
To some extent 
Yes = 10 
To  some  extent  = 
11 
No = 6  
Source: authors, based on mapping of available instruments conducted AL expert network for the purpose of the IA on 
ILAs 

To some extent 
yes 
Yes = 10 
To some extent 
= 16 
No = 1 

Yes 
Yes 
Yes  = 
27 

Yes = 12 
No = 15 

some 

By  relating  the  availability  of  infrastructure  and  supporting  services  in  Member  States  to  the 
participation statistics, a clear trend becomes visible. In Figure A8.3 below, scores are provided to 
individual  items  (availability  of  register  of  training  providers;  digital  platform;  quality  assurance; 
career  guidance;  and  skills  validation)  to  assess  the  general  availability  of  infrastructure  and 

138 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
supporting services in the EU Member States in  a comparative manner (Yes = 2 points; To some 
extent  =  1  point;  No  =0  point).  The  figure  shows  that  Member  States  that  report  the  highest 
participation figures, generally also have the infrastructure and supporting services in place 
(see figure below).  

Figure  A8.3  -  Overview  of  adult  learning  expert  mapping  –  existence  of  infrastructure  and 

supported services

318

Source: authors, based on mapping of available instruments conducted by AL expert network for the purpose of the IA 
on ILAs 

Member States refer to different good practice regarding the register of training providers, digital 
platform,  quality  assurance,  career  guidance,  and  skills  validation.  With  regards  the  educational 
register, Denmark serves as good practice having a register with all relevant information on Danish 
citizens and include records of schools and education institutions. Information on education is linked 
to the individual citizen codes, which allows linking to other types of individual information, such as 
income,  employment,  and  health.  There  are  dedicated  registers  for  adult  education  and  training, 
including information on shorter courses and part-time education. Furthermore, following the 2017 
tripartite  agreement  on  adult  education  and  training,  the  platform  ‘voksenuddannelse.dk’  was 
established in the fall of 2018. The platform integrates information from previous separate platforms 
and is designed to give a comprehensive overview of all training opportunities for adults.   

A good practice example in relation to quality assurance of non-formal adult learning is Ö-Cert 
in Austria, which is an overall framework of quality ("umbrella label") for adult education providers. 
It´s  a  nationwide  quality  trademark,  regulated  by  law  (contract  between  the  Federal  Ministry  of 
Education, Science and Research and all 9 provinces) and became effective in 2012.  

A  good  example  of  career  guidance  is  available  in  Ireland  where  guidance  counsellors  and 
coordinators provide a guidance service to numerous target groups. The Adult Educational Guidance 
and Information Services are based in the 16 Education and Training Boards and Waterford Institute 
of Technology.  

With regard to skills validation, Denmark serves as good practice, where assessment of prior learning 
and skills is a right for adults who want to enrol in some types of adult education and training. The 
assessment is done by an educational institution and regulated by the official guidelines for the chosen 
type of education. If the skills obtained through prior learning are recognized for the chosen type of 
education, the applicant gets a certificate. For persons who are unskilled or have an upper secondary 
vocational education as highest level, a prior learning assessment in relation to Labour market training 

318

 A score is calculated whether there is register of training providers, digital platform, quality assurance in non formal 

adult learning, career guidance, skills validation and right on education leave. 

139 

6321142530%10%20%30%40%50%60%70%80%90%100%MS with high participation (>45%) in ALMS with medium participation in ALMS with low participation (<33%) in ALAvailability of infrastructure and  supporting servicesTo a great extentTo some extentLimited extent 
 
 
                                                 
courses, vocational educations, general subjects and academy programmes, the assessment is free. 
For  persons  with  a  higher  education  degree,  the  schools  often  charge  a  fee  for  prior  learning 
assessment for academy and diploma programs. 

3. Estimating adult learning participation in 2030 under the baseline scenario 

Adult learning participation under the baseline scenario is influenced by numerous external drivers 
(Sections 2.2.2 and 2.4) and policy efforts at EU and Member State level (Section 5.1 and this Annex). 
Forecasting participation trends is complicated by the limited data availability (only three AES 12 
survey waves for the 12 months reference period are available for 2007, 2011 and 2016) and by survey 
revisions in some Member States that have led to statistical breaks in their data series which distort 
the comparison of participation rates over time.  

Concerning  the  projection  of  participation  levels,  this  impact  assessment  therefore  draws  on 
analytical work by the JRC, which has forecasted the evolution of adult learning participation in the 
EU until 2030 based on past trends only in those EU Member States without any statistical break 

319

between the 2007, 2011 and 2016 AES survey waves.
 This analysis shows an almost linear increase 
in  adult  learning  participation  across  the  three  survey  waves  (consistent  with  LFS  data  for 
participation with a 4 week reference period). Projecting the relative increase to 2030 and applying it 
to the 2016 participation rate for EU-27 yields a predicted increase from 37.4% in 2016 to 48.6% in 
2030. The EU-level targets for 2025 (50%, European Skills Agenda) and 2030 (60%, European Pillar 
of Social Rights Action Plan) are hence not met under the baseline scenario.  

Concerning participation gaps/inequalities, no clear trends are apparent in the data: the JRC analysis 
shows similar rates of increases for the low qualified (though at lower participation levels, see Annex 
5.3), and an analysis of participation gaps between permanent employees and other adults between 

320

2007 and 2016 also does not establish a clear trend.
 Hence, the baseline scenario of this impact 
assessment assumes a constant rate of increase in adult learning participation rates across all groups 
of  adults  until  2030,  applied  to  their  respective  participation  level  in  2016.  This  yields  a  stable 
benchmark against which expected impacts of the policy packages can be compared. It avoids the 
pitfalls of alternative sub-group specific extrapolations which would be based on small sample sizes 
and would suffer from associated high measurement error. Table A8.6 shows the resulting assumed 
changes in average participation rates of adults aged 25-64 between 2016 and 2030 under the baseline 
scenario for EU-27 and by Member State.  

Table A8.6: Projected baseline participation rate in 2030, based on AES 2016 

Country                Participation rate (2016) 

EU-27 
Belgium 

37.4 
39.7 

Projected baseline participation rate 
(2030) 
48.6 
51.6 

319

 Biagi et al. (2020), Adult learning in Europe: An analysis of the determinants and an attempt at forecasting, analytical 
input by JRC for DG EMPL. This applies to 13 Member States which account for about half of the EU-27 population and 
are broadly representative of the EU-27 in terms of their average adult learning participation rate in 2016. Participation 
rates in all but 3 Member States (BG, LT, ES) increased between 2007 and 2016. 
320

  Based  on  the  assumption  that  statistical  breaks  in  participation  levels  still  allow  for  a  comparison  of  relative 
participation levels of differengroups of adults over time, a comparison is possible for 22 Member States who participated 
in the AES in 2007 and 2016. Participation gaps decreased in 13 out of these Member States and increased in 9, with only 
a small average change. 

140 

 
                                                 
Bulgaria 
Czechia 
Denmark 
Germany 
Estonia 
Ireland 
Greece 
Spain 
France 
Croatia 
Italy 
Cyprus 
Latvia 
Lithuania 
Luxembourg 
Hungary 
Malta 
Netherlands 
Austria 
Poland 
Portugal 
Romania 
Slovenia 
Slovakia 
Finland 
Sweden 
Source: AES 2016 and author calculation. 

11.8 
22.8 
50.5 
46.4 
33.9 
46.0 
16.0 
30.5 
48.4 
26.9 
33.9 
44.8 
39.0 
25.0 
43.4 
54.8 
32.8 
57.8 
55.3 
20.9 
38.0 
5.8 
40.3 
42.6 
51.4 
58.9 

15.3 
29.6 
65.7 
60.3 
44.1 
59.8 
20.8 
39.7 
62.9 
35.0 
44.1 
58.2 
50.7 
32.5 
56.4 
71.2 
42.6 
75.1 
71.9 
27.2 
49.4 
7.5 
52.4 
55.4 
66.8 
76.6 

ANNEX 9: EVIDENCE ON THE DISCARDED POLICY 
MEASURES 

As outlined in Section 5.2, the objectives of this initiative are complementary to and not a substitute 
for the financial incentives for training for employers and training funds as well as supply side policy 
guidance  and  funding.  This  Annex  provides  further  information  on  alternative  ways  of  providing 
financial support for training to individuals –income tax incentives and subsidised loan or savings 
schemes-,  and  outlines  why  they  are  not  considered  in  the  context  of  this  initiative.  For  further 
information on the links between the approaches discarded in the context of this initiative and the 

objecitves of this initative, see the external study supporting this initative.

321

1. Tax incentives for individuals 

General description 

321

 Fondazione Giacomo Brodolini (2021), Study to support the Commission explorations for a possible EU initiative on 

Individual Learning Accounts. 

141 

 
 
                                                 
Tax incentives are the concessions in tax codes that mean a conscious loss of government budgetary 
322

revenue  because  they  reduce  either  the  tax  base  (tax  allowance)  or  the  tax  due  (tax  credit).
Concerning tax incentives for the purposes of personal income tax, they may allow adults to deduct 
the  costs  for  continuing  vocational  training  or  adult  learning  related  to  their  current  or  future 
occupation from their individual income tax base or tax due. 

It is one of the most prominent tools used by Member States to incentivise individuals to participate 
in adult learning, both in terms of the number of Member States using it (16 as of 2021, cf. Annex 
8.2) and even more in terms of the size of the target group and volumes spent on it (although more 
systematic data on the amounts of public funds spent on this instrument are lacking, see the estimate 
in the DE example below).  

Examples

323

Netherlands:  The  Dutch  example  of  tax  incentives  provides  useful  evidence  and  a  relatively  rare 
example of a scheme that has been evaluated. The evaluation of the training expenditure deduction 

in the income tax in the Netherlands by the Bureau for Economic Policy Analysis
 shows that 2.6% 
of all tax payers between 25 and 60 years of age (equivalent to 5% of adults participating in adult 
learning, made use of this deduction, for an average of €1 700 per year. The deduction possibility 
seems to be used mostly for the purchase of books and for tuition fees. Three-quarters of the users of 
the deduction for education expenses are following learning programmes that are not supported with 
public funds, or in private training institutes. Most of those who follow a government-funded course 
are enrolled in a bachelor's or master's programme in higher education.  

324

The users are often highly educated and/or employed. The evaluation estimates that the deadweight 
loss - i.e. the part of an extra euro training deduction that does not lead to extra training - amounts to 
between 73 and 100%, depending on the group and the tax rate. One of the reasons for a low uptake 
amongst lower-educated and unemployed adults can be partly explained by the fact that training still 
has to be paid out of the individual’s pocket in advance of any tax incentive with the uncertainty as 
to whether it can be actually deducted from tax payments (especially in case of low or no income). 
Even if eligible, beneficiaries typically consider future tax deductions as less attractive or valuable, 
considering the time value of money.  

As a result, the Netherlands is replacing the tax deduction to introduce a scheme based on individual 
training entitlements (“STAP – Stimulating Position on the Labour Market-budget”, see the NL case 
study in Annex 13), to improve the outreach to learners.  

Germany:  There  have  been  different  rules  for  training  and  further  training/education  with  tax 
incentives  more  generous  for  the  latter  (all  expenses  could  be  deducted  as  expenses)  whereas 
restrictions were in place for tax incentives for training. Tax relief was pegged at a maximum of 42% 
(e.g. for €1 000, €420 could be reimbursed but €580 must be privately financed). 

322

 Cedefop (2009), Using tax incentives to promote education and training.  

323

324

 See Dohmen and Timmermann (2010), Financing Adult Learning in Times of Crisis, for additional examples. 
 CPB (2016), Evaluatie aftrekpost scholingsuitgaven. See Leuven and Oosterbeek  (2004), Evaluating the effect of tax 
deductions on training (Journal of Labor Economics) for an earlier assessment of the NL income tax incentives, also 
finding limited effectiveness. 

142 

 
 
 
                                                 
An estimate for Germany suggests that 1.9 million people have made use of these tax incentives in 
Germany in 2010, or some 8.5% of all adult learning participants, which would suggest that the reach 
of this instrument – in terms of adult participation - is approximately 50% larger than that of all other 

instruments together.

325

 No evaluations have been conducted on the effectiveness of the scheme to 

increase participation in adult learning.

 326

Austria: Individuals (as well as companies) can retrospectively deduct training expenses from their 
taxable income but on the condition of advancing the employee’s occupational skills in their place of 
employment. Expenses are not limited to fees and can also cover travel, learning materials and even 
some accommodation costs. Individuals can deduct between 38.33% and 50% of expenses from their 
taxable income, depending on their rate of tax, with tax relief increasing in line with income.  

Conclusions on the effectiveness of income tax incentives for adult learning 

Income  tax  incentives  for  training  have  a  large  potential  target  group  and  the  limited  available 
evidence suggests that where they exist, this often makes them the largest incentive instruments for 
individuals in terms of public spending volumes (cf. DE example). However, they can only provide 
incentives  for  individuals  with  incomes  above  the  income  tax  exemption  limit  (cf.  AT  example). 
Evaluations of their effectiveness at increase participation among adults who would otherwise not 
have participated in  training  are scarce, and the  limited available evidence points to  high rates of 
dadweight loss (exceeding estimates for the deadweight loss of individual training entitlements, cf. 
NL example). This is consistent with a limited effectiveness of income tax incentives for training for 
reaching the objectives of this initiatives concerning the closing of coverage gaps & the increase of 
individual’s incentives and motivation to take up training.  

2. Subsidised loans or savings schemes for adult learning 

General description 

According to Cedefop’s financing adult learning database, many Member States subsidise loans for 
educational and training purposes. Most frequently, these schemes are aimed at first-time students in 
higher education, but some of them are also open for adults (25 and older) up to a certain age limit or 
without such a limit.327  Moreover, there are examples of loan schemes designed explicitly for adults 
for (specific) educational and training purposes (e.g. training loan for unemployed people, job seekers 
and employees aged 45+ in Poland). 

Loan schemes allow individuals to borrow financial resources (on favourable conditions) from their 
future income to cover part of their (education and training) expenditure. The majority of Member 
States have such loans in place, see the mapping in Annex 8.2 for an overview.  BG, DE, EE, FI, HU, 
IT, LT, LU, LV use these to support both formal and non-formal education and training programmes, 
while  other  Member  States  AT,  BE,  RO  and  SE  use  such  loans  to  support  formal  education  and 
training programmes only. Loans are provided to adult students in a number of Member States (BG, 
DE, EE, FI, HU, LT, LU, LV, MT, PT) to fund higher education programmes. 

325

326

327

 Cordes and Dohmen (2019), Verbreitung öffentlicher Förderinstrumente in Deutschland und der Blick in die Länder. 

 Leuven and Oosterbeek (2004), Evaluating the effect of tax deductions on training, Journal of Labor Economics. 

 Cedefop (2012), Loans for vocation education and training in Europe.  

143 

 
 
                                                 
 
By contrast, subsidised saving schemes for adult learning seek to promote individual saving for future 
education/training costs. The account holder is required to set aside money over time in a savings 
account. Such individual savings could be matched by contributions from the State budget and/or 
employers. There are very few saving schemes in Europe; indeed the only currently operating scheme 

we identified is used in AT, the education savings scheme.

328

Example of a subsidised loan scheme for adult learning: “Lifelong Learning Credit” in NL 

A recent study on the “Lifelong Learning Credit” scheme in the Netherlands
 shows that a total of 
6 837 users were granted a total of 7 743 loans in the academic year 2017/2018. Over half (56%) of 
the users are women and less than half (44%) are men. Almost one third (31%) are younger than 30 
years and over two thirds (69%) are 30 years or older.  

329

In the majority of cases, these are average monthly loan amounts per user of €0-249 (75%) and €250-
499 (17%). Loan amounts of €1 000 or more per month are rare. Almost two-thirds (65%) of loans 
support  part-time  courses  and  almost  a  third  (32%)  full-time  courses.  The  share  of  users  with  an 
allocation for a dual programme is very small (2%). A survey amongst participants showed that 45% 
would  not  have  participated  in  the  training  programme  without  the  loan,  pointing  on  a  positive 
contribution. Respondents indicate that they choose to apply for a loan because it provides money 
that they would not have available (74%); not willing to borrow money from others; and not able to 
earn an income while studying. Those who have taken out a loan are more positive about taking out 
a loan to follow a training programme (63% is positive), compared to non-users (26%). 

The  study  also  explored  the  possibility  to  expand  the  Lifelong  Learning  Credit  for  other  types  of 
training  for  low  qualified  participants  and  vulnerable  groups.  The  study  concludes  that  for  these 
groups,  providing  only  loans  is  not  sufficient  to  increase  participation.  These  groups  must  be 
convinced of the benefits of their financial investment in training. Moreover, these groups are less 
inclined to look for training opportunities themselves. Reaching and then stimulating these potential 
users necessitate additional provisions, including easily accessible, independent guidance and training 
advice, tailored to the needs of potential users. 

Conclusions on the effectiveness of subsidised loans and savings for adult learning 

Loans  are  particularly  useful  for  the  financing  of  longer,  more  expensive  and  formal  education 
programmes, whereas most adult learning is shorter and non-formal (cf. Annex 6.2). Moroever, they 
require significant forward planning and motivation from the side of potential adult learners, limiting 
their expected attractiveness in view of the problem analysis in Section 2 of this impact assessment. 
Similarly,  savings  schemes  require  significant  forward  planning  low-income  earners  and  other 
vulnerable groups who often face challenges to save on a regular basis. These barriers are reflected 
in generally low take-up rates of such schemes, as observed in the NL example and the AT case study 
in Annex 13. Subsidised loans or savings schemes for adult learning are hence not expected to make 
a  significant  contribution  for  reaching  the  objectives  of  this  initiative.  Nevertheless,  in  particular 
subsidised loan schemes can complement the policy measures under consideration in the context of 
this initiative by supporting the funding of longer periods of training. 

328

329

 Another identified saving scheme existed in the Netherlands, but has been closed because of its low take-up. 
 Panteia (2019), Onderzoek levenlanglerenkrediet: eindrapportage. 

144 

 
 
                                                 
ANNEX 10: EVIDENCE ON THE BENEFITS OF ADULT 
LEARNING  

This Annex provides an overview of the benefits of adult learning. It is based on a literature review 
and  serves  as  input  to  the  quantification  of  estimated  impacts  in  Annex  12,  and  the  qualitative 
discussion of impacts where quantification is not possible. There are two key challenges for deriving 
robust estimates on the benefits of adult learning: 

  Self-selection of adults into training: correlations between training participation and 

subsequent wage or employment outcomes will tend to over-estimate the benefits of training 
participation. This is because higher ability or more motivated individuals are more likely to 
take up training opportunities and also more likely to experience subsequent wage or 
employment gains, and correlations are not able to distinguish between the relative 
importance of both factors. 

  Delayed realisation of benefits: it often takes some time even for a successful training to 
translate into wage and employment increases, and very short-term effects may even be 
negative as time in training is time not spent earning income or searching for a job. This 
means that studies that are not able to observe individuals for several years after training are 

not able to assess its benefits.

330

To address the first limitation, preference has been given to evidence from randomised  controlled 
trials  or  field  experiments  that  feature  and  exogenous  assignment  of  training  and  allow  for  a 
comparison between a treated and a control group, or quasi-experimental methods that aim to exploit 
som exogenous variation in training assignment to identify causal estimates. To address the second 
limitation, preference has been given to studies that allow to follow up on individuals for some time 
after training. However, depending on the methodology, the ability of studies to address these issues 
varies, leading to the considerable dispersion of point estimates in the studies summarised below. To 
derive  reasonable  “middle  ground”  estimates,  preference  has  been  given  to  literature  reviews  by 
experts in the filed and meta-analyses, and the range of estimates observed in the literature has been 
used to inform the selection of sensitivity checks in Annex 12. 

1. Returns to individuals 

1.1 Wages 

Table A10.1 provides an overview of wage return estimates for studies that tackle the issues of self-
selection and delayed benefits to some extent. Adjustments have been made to scale impact estimates 
to a training duration of 30 hours, in order to allow for a comparison.  Even after narrowing the range 
of studies considered, this Table shows a large range of estimated wage impacts of training across 
studies. 

330

 For instance, the study by Schwerdt et al. (2012) on a Swiss training voucher experiment introduced in Annex 11 and 
used to estimate take up rates and deadweight loss for the quantifications of impact in this report only observes individuals 
at most one year after they have received their training voucher, not allowing for a comprehensive assessment of training 
impacts. 

145 

 
 
                                                 
Table A10.1: Estimated impact of training on wages  

Authors 

Volume of the training 

Dearden et al. (2006)332  Around 80 hours  

Increase in wages 

30-hour training 
entitlement 
equivalent331 

11.2% for each 
individual 

Net 

0.3% at the industry 
level per each 1 
percentage point 
increase in employees’ 
participation rates in 
training over the last 4 
weeks  

Konings and 
Vanormelingen 
(2015)333 

37 hours/€1 400  

16.1% 

About 10%334  

Leuven and Oosterbeek 
(2008)335 

40hours 

50hours 

Görlitz (2011)336  

38 hours 

Brunello et al. (2012)

337

40 hours 

Based on the review of 
the literature so far, at 
least 3% 
11% when controlling 
for observable 
characteristics, 0.9% 
(but not statistically 
significant) when 
controlling for self-
selection on observable 
and unobservable 
characteristics 
0.5% but not statistically 
significant 
1.36% 

2.25% 

0.54% 

0.4% but not statistically 
significant 
1% 

331

 Conversions to 30-hour training entitlement equivalent are based on information on the hours of training per individual 
examined in the papers under review. Where these are not available, information on the cost of training or training duration 
is considered. An hour-to-€ ratio of around €15 per hour is used, in line with evidence on training from Annex 11. In case 
both the cost and duration of the training are included in the paper and their ratio differs significantly from the average 
ratio used in this study, an adjustment is made. In any event, the estimates produced should not be considered as precise 
point estimates but as indicative values that allow defining a range of plausible increases in wages from a 30-hour training 
entitlement in the medium term.   
332

 Dearden et al. (2006): The impact of training on productivity and wages: Evidence from British panel data. Oxford 

bulletin of economics and statistics. 
333

 Konings and Vanormelingen (2015): The Impact Of Training On Productivity And Wages: Firm-Level Evidence, The 

Review of Economics and Statistics. 
334

 The exact proportion based on the hours of training would be 13%. This value is further reduced to account for the 

higher cost per hour of the training examined in the paper.  
335

  Leuvenn  and  Oosterbeek  (2008):  An alternative  approach  to  estimate  the  wage  returns  to private‐sector  training. 

Journal of Applied Econometrics. 
336

  Görlitz  (2011),  Continuous  training  and  wages:  An  empirical  analysis  using  a  comparison-group  approach. 

Economics of Education Review. 
337

 Brunello et al.   (2012), Training subsidies and the wage returns to continuing vocational training: Evidence from 

Italian regions. Labour Economics. 

146 

 
 
                                                 
Authors 

Volume of the training 

Increase in wages 

Net 

30-hour training 
entitlement 
equivalent331 

Martins (2020)338   

Approx. 35 hours339 

Rinne et al.  (2013)340  

Doerr et al. (2017)341  

Doerr and Strittmatter 
(2017)342 

Conti (2005)343 

Heinrich et al. (2013)344  

Levy-Yeyati et al. 
(2019)346 

Hours of training not 
known, average duration 
of training 6 months 
(part-time)  

Total amount of training 
not known, but hourly 
effects provided 
Hours of training not 
known. Average length 
is 6-9 months (part-
time). However, avg. 
expenditure €2 400-
2700345 
No info on hours of 
training, but €1 300 as 
median cost 

Not statistically 
significant 
€160(€110 more than 
pre-reform) per month, 
daily wage around €60, 
so around 8.8% increase 
0 
€90more than supply-
led (pre-reform) around 
5% increase 

0% 

Not available 

0% 

Not available 

0.05% per hour 

1.5% 

30% for the first 
months, 15% after 
depreciation (for men) 
slightly higher for 
women 

2.5% (long term)-
4%(short term) 

Median impact of 7.7%  Around 2.5% 

Meta-analyses  point  to  positive  wage  returns347,  and  the  literature  review  by  Algan  et  al.  (2021) 
concludes that studies which are able to control for self-selection into training often put the returns to 
a week of training at around 1%.348   

338

 Martins (2020), Employee Training and Firm Performance: Quasi-experimental evidence from the European Social 

Fund, GLO Discussion Paper Series.  
339

 Ibid. “Considering the average number of workers per firm, the approved training hours  figure amounts to a mean 
number of training per worker similar to the 35-hour figure established in labour law”. In monetary terms, this equals to 
an average of €267 per worker (€30 000 per firm, 112 workers) in PT values, 2007. 
340

 Rinne et al.. (2013) Vouchers and caseworkers in training programs for the unemployed, Empirical Economics. 
 Doerr et al.  (2017). Employment and Earnings Effects of Awarding Training Vouchers in Germany, ILR Review.  
  Doerr. and Strittmatter  (2017), Assignment  Mechanisms,  Selection  Criteria,  and  the  Effectiveness  of  Training 

341

342

Programs, Economics Working Paper Series, University of St. Gallen. 
343

344

345

 Conti (2005) Training, productivity and wages in Italy, Labour Economics. 
 Heinrich et al. (2013) Do Public Employment and Training Programs Work?. IZA J Labor Economics. 
 In current prices, considering a € to USD change of 1.24 in 2005 and inflation from 2005 to 2021. The estimates are 
based on the analysis of the adult programme, as the estimates for the dislocated workers programme are considered not 
reliable by the authors due to counfonding factors.  
346

 Levy  et. Al (2019): What works for active labor market policies? CID Working Paper Series 
 See for instance: What Works Centre for Local Economic Growth (2016), Evidence review: Employment training. 
 Algan et al. (2021): Boosting Social and Economic Resilience in Europe by Investing in Education. EENEE Analytical 
Report No. 42. European Commission. 

348

347

147 

 
  
                                                 
1.2 Employability  

Employment effects are mostly examined at the individual level and for those out of employment.   
In many countries, training is an important component of active labour market policies (ALMP). Card 
et  al.  (2018),  for  example,  in  their  review  of  over  200  recent  studies  of  active  labour  market 
programmes found that those which contained a training element might have little to no impact in the 
short term, but tend to reveal positive impact two to three years after programme completion.349 Their 
results show that the mean ALMP effects on the probability of being employed is 2 ppts in the short 
term, 6.6 ppts and 6.7 ppts in the medium to long term (so almost identical). They do not disclose 
information  on the value or length of training and only test  for possible  differences in  effects  for 
training  durations  over  nine  months  (without  finding  any).  Similarly,  Levy-Yeyati  et  al.  (2019) 
indicate significant employment effects (6.6 ppts) resulting from ALMPs where the emphasis is upon 

350

training.
  In  the  case  of  the  latter,  information  on  the  median  cost  of  the  training  is  available, 
allowing to assume what some average effect from a week of training on employment chances might 
be  (see  Section  5).  Similarly,  the  evidence  review  from  the  What  Works  Centre  (2016)  generally 
found that the evidence points to training, of one kind or another, having a positive impact on entering 

and remaining in employment.

351

1.3 Heterogeneity of returns  

The average impacts reported above hide substantial heterogeneity across individuals and types of 
training, but it proves challenging to distill clear lessons from the literature concerning specific groups 
of adults or types of training: 

Gender: Returns to schooling are estimated to be slighty higher for women352, and also Card et al. 
(2018) identify somewhat stronger impacts of training on women.  By contrast, gender is found to 
have no effect in Levi-Yeyati et al. (2019).  In conclusion, heterogeneity of returns by gender seems 
to either be absent or slightly in favour of women.  

Age: Evidence is  scarce  overall.  Card  et  al.  (2010)
 find higher short-run  employment increases 
among younger adults, but higher medium and long-term increases in employment for older adults.  
Dauth and Toomet (2016) find that subsidised training for low skilled and older workers improves 

353

the  probability  of  remaining  in  paid  employment.
(2019) find no significant effects of age. 

354

  However,  the  meta-analysis  by  Yeyati  et  al. 

Educational attainment: This aspect is not directly investigated in the meta-analysis from Card et al. 
(2018).355  However,  it  finds  stronger  effects  for  long-term  unemployed,  which  is  suggestive  of 

349

350

351

352

 Card et al (2018), What works? A meta-analysis of recent active labor market program evaluations. Journal of the 
European Economic Association.  
 Levy Yeyati et al. (2019), What works for active labor market policies? CID Working Paper Series. 
 What Works Centre (2016) ibid. 
 Psacharopoulos and Patrinos (2018), Returns to Investment in Education, A Decennial Review of the Global Literature, 

World Bank Group. 
353

 Card et al. (2010), Active labour market policy evaluations: A meta-analysis. Economic Journal. 
 Dauth and Toomet (2016), On Government-Subsidized Training Programs for Older Workers. Labour. 
 Card et al. (2018): What Works? A Meta Analysis Of Recent Active Labor Market Program Evaluations, Journal of 

354

355

the European Economic Association. 

148 

 
  
                                                 
somewhat higher returns for individuals most affected by skills obsolescence. In the meta-analysis of 
recent RCTs from Levy-Yeyati et al (2019), educational attainment appears to have no statistically 
significant  effect  on  the  impact  of  training  on  wages  or  employability.  All  in  all,  no  clear-cut 
conclusion can be drawn from the literature, with estimates that vary both in sign and intensity but 
no  clear  trend  allowing  to  reliably  assume  decreasing  or  increasing  returns  to  training  based  on 
different levels of educational attainments.  

Types of training: Most evidence on wage and employment returns is based on job-related training 
and often employer-sponsored. However, the literature also reports significant returns to basic skills 

356

 357  

) and other transversal skills including language skills.

(such as literacy
The growing importance 
of non-routine cognitive skills is also emphasises by studies analysing changes in skill demand from 
a perspective of job tasks where either machines of humans have a comparative advantage (Acemoglu 

and  Autor,  2011;  Autor  et  al.,  2013;  Handel,  2016).
359

transformation increases the value of transversal skills

358

  The  evidence  suggests  that  the  digital 

, in particular the combination of technical 

360

with  social  skills.
  These  findings  are  consistent  with  evidence  that  increasing  the  freedom  of 
individual’s choice of training leads them to select training conferring somewhat more transversal 
skills, but does not reduce impacts on wages or employment  (see Annex 11). 

1.4 Returns to scale and human capital depreciation 

One aspect of particular interest for a policy that looks at training from a lifelong perspective is that 
of the interaction between existing levels of skills and the effect of training, further distinguishing for 
intensity and recurrence of training. The specific interest here is about marginal returns. In addition, 
to discuss the cumulative effect of a policy that is repeated over time it is important to discuss if and 
to what extent the stock of human capital depreciates over time. 

This is important as, in discussing a structural measure that aims to increase levels of participation in 
adult  learning  from  a  lifelong  learning  perspective,  attention  should  be  given  to  whether  each 
additional unit of training supported every year generates the same returns, and whether the returns 
from skills acquired in the past, can produce the same additional effects of those just acquired.  

Thus, there are challenges to the possibility of drawing clear cut conclusions on whether returns to 
training should be assumed as decreasing, constant or even increasing. At the same time, there exists 
a  rich  and  consolidated  literature  on  returns  to  education,  which  could  serve  as  a  benchmark.  In 
addition, the literature has investigated with some more coherent findings, the issue of depreciation 
of  human  capital.  The  following  sub-paragraphs  will  shed  some  light  on  the  individual  questions 
listed above. Some conclusions are offered further below.  

356

357

358

359

360

 Mason et al. (2010). The Value of Skills: An Evidence Review. Wath-upon-Dearne: UK Commission for Employment 
and Skills. 
 Lang (2021): Employment effects of language training for unemployed immigrants. Journal of Population Economics. 
  Acemoglu.  and  Autor  (2011):  Skills,  Tasks  and  Technologies:  Implications  for  Employment  and  Earnings  in 
Ashenfelter  and  Card.  (eds.).  Handbook  of  Labor  Economics:  Volume  4.  Amsterdam:  Elsevier.  Autor,  Levy,  and 
Murnane  (2003): The Skill Content of Recent Technological Change: An Empirical Exploration. Quarterly Journal of 
Economics. Handel (2016): What Do People Do at Work? A Profile of U.S. Jobs from the Survey of Workplace Skills, 
Technology, and Management Practices (STAMP). Journal for Labour Market Research.  
 European Commission (2020), Facing the Digital Transformation: are Digital Skills Enough?  
 Deming (2017), The Growing Importance of Social Skills in the Labor Market. The Quarterly Journal of Economics. 

149 

 
                                                 
Differences by duration  and or frequency of  training:   In the meta-analysis of ALMP from  Card, 
Kluve and Weber (2018), no precise information is available on the cost of training. Nevertheless, 
the authors control econometrically whether the average effects they estimate differ when training 
duration exceeds 9 months. They find  no statistically significant  difference in  the wage effects  of 
training of longer duration. However:  

a)  training duration is not necessarily a perfect proxy of cost; 

b) the  authors  only  control  for  duration  of  support  over  the  full  sample  of  studies  they  have 

(including non-training programmes);  

c)  the  authors  only  control  for  duration  of  support  focusing  on  the  average  effect  of  the 

programmes (not disaggregated by short vs long term).  

Hence  it  is  not  possible  to  exclude  that  an  increasing  value  of  training  is  associated  with  higher 
benefits, as it would be in presence of constant (and even diminishing, non-negative) returns. 

A positive correlation between the intensity of support and labour market effects is confirmed by the 
large RCT carried out in the US on the individual training accounts. In the related study from Perez-
Johnson, Moore & Santilliano (2011)361 three different intensities of support were compared across 
slightly different delivery modes. The study concluded that in the model with higher average values 
of training entitlements (approx. $4 600 versus approx. $2 800), 4 ppts more of the group ended up 
in high paying jobs, and 5ppts in jobs consistent with the occupation they trained for. This led to a 3-
6% increase in the quarterly earnings during the period of observation. This study seems suggestive 
of positive returns to training, and drives the results of the Cost-Benefit analysis carried out in the 
same study where the model with higher average values of training entitlements yields a better cost-
benefit ratio. This is suggestive of constant or even increasing returns to scale for the first training 
episode.  

Insights on returns to scale can also be drawn from Brunello et al. (2012) and EIB (2020). According 
to the former, heterogeneity of returns to wages which favour enterprises below 100 employees could 
be  explained  by  the  fact  that  “[..]  Policies  that  induce  firms  and  workers  to  invest  in  additional 
training are likely to produce higher returns in smaller firms because the marginal benefits to training 
are decreasing in the quantity of training”. 

In the more recent work from the EIB (2020)362, geographical heterogeneity of returns to productivity 
(higher in Southern and Eastern Europe) is explained along similar lines, indicating that this finding 
is  consistent  with  decreasing  returns  to  training.  In  these  two  papers,  the  best  fitting  function  for 
marginal returns is the cube root function, with declining returns to scale. The same specification was 
discussed as best fitting in Frazis and Lowenstein (2006).363  

In summary, the findings  from  the literature are  mixed. Whilst  there is  a consensus  that  marginal 
returns  are  positive  once  it  is  accounted  for  lock-in  effects,  their  trend  (decreasing,  constant  or 
increasing)  cannot  be  reliably  assumed  as  it  will  likely  depend  on  multiple  interrelated  factors. 

361

 Perez-Johnson, Moore & Santilliano (2011): Improving the Effectiveness of Individual Training Accounts: Long-Term 
Findings  from  an  Experimental  Evaluation  of  Three  Service  Delivery  Models,  Final  Report,  Mathematica  Policy 
Research. 
362

 EIB (2020): Financing contraints and employers’ investment in training. Economics – Working Papers 2020/05. 
 Frazis L and Lowenstein M, (2006) On the Job Training, Now, The Essence of Knowledge. 

363

150 

 
                                                 
According to the literature on returns to schooling, returns tend to fall between the low and medium 
qualified, but remain constant for further increases. Constant returns are also accepted in influential 
literature on endogenous growth models. When it comes more specifically to adult learning, which 
may or may not follow a pattern that is similar to investment in human capital through formal / initial 
education, findings on training at firm or individual level seems to suggest overall declining marginal 
returns to training. For this reason, and to ensure a sufficiently conservative approach to the long-
term macroeconomic estimates, decreasing marginal returns to training accumulation are used in the 
simulations later described in Annex 12C.  

However, as highlighted above, no conclusive evidence is available and recent meta-analyses find 
little to no confirmation that initial educational levels matter when the effect of a training episode is 
assessed. For instance, in one of the few randomised control trials focusing on the long-term labour 
market  outcomes of a training entitlement scheme364, more generous entitlements were associated 
with comparatively higher returns to the individuals and the society. This suggests to employ linear 
rates when discussing the difference between a one-off 30 and 50 hours training entitlement given to 
different target groups (i.e. hourly effects are the same for all target groups and only vary pro-rata 
with the value/duration of the training entitlement), but consider decreasing returns for the longer-
term dynamic of recurring training episodes and their cumulative effect on productivity over time.   

Depreciation  of  human  capital:  Skills  obsolescence  and  human  capital  depreciation  are  widely 
acknowledged and investigated in  the literature.  Their existence is  part of the justification for the 
importance of lifelong learning. These aspects also have a bearing on the estimation of the medium 
to long run effects of any training policy, as the focus lies on the  cumulative effect of learning on 
productivity, and via productivity on other macroeconomic variables.  

In order to identify some general findings applicable to the current simulation it is important to start 
from an understanding of the drivers of human capital depreciation and skills obsolescence.  

Skills obsolescence might occur in (at least) two different ways: 

  a “natural” wear of skills due to the aging process, injuries, changing requirements or working 

conditions in a sector for individuals in employment; 

 

the “atrophy of skills” for individuals who fall outside of the labour market or have career 
interruptions. 

There are also different ways of measuring such depreciation both direct (mostly survey based) or 
indirect (looking at labour market outcomes). To the end of this brief review, the effect on labour 
market outcomes (wages and productivity) are the most relevant. Clearly these indirect measures are 
based  on  observational  studies  which  can  be  biased  by  imperfect  labour  markets  rewarding  e.g. 
loyalty of the workers, suffering from low job mobility etc.  

364

 Perez-Johnson, Moore & Santilliano (2011): Improving the Effectiveness of Individual Training Accounts: Long-Term 
Findings  from  an  Experimental  Evaluation  of  Three  Service  Delivery  Models,  Final  Report,  Mathematica  Policy 
Research. 

151 

 
                                                 
As recently estimated in Lentini and Gimenez (2019)365, on top of substantial additional literature on 
the theme, there is strong sectoral dimension to human capital depreciation. The intuitive correlation 
between intensity of skills in a given sector and human capital depreciation tends to be confirmed 
empirically. In high-skill sectors, the authors identify a rate of depreciation of 6%, whereas this falls 
to 1% in low-skill ones. An additional source of heterogeneity in human capital depreciation appears 
to  be  the  individual’s  educational  level  and  type.  According  to  Weber  (2014)366,  academic  and 
concept-based  education  protects  workers  from  obsolescence  better  than  vocational.  This  is  fully 

confirmed  in  the  influential  work  form  Hanushek  et  al  (2017).
highlight the relevance of human capital depreciation in the context of vocational training. 

  The  latter  finding  goes  on  to 

367

In the literature reviewing average returns to training, depreciation of human capital is factored in in 
the estimates from Conti et al. (2005), Dearden et al. (2006) and Brunello et al. (2012) at a rate of 1-
15%. In particular in Brunello et Al (2012), this entire range of possible depreciation rates is tested 
to understand which is the better fit to the data available. The results identify a 3% depreciation rate 
as the best fitting.  This value seems to fall in the middle of the range defined by Lentini and Gimenez 
(2019) taking into account the sectoral variation in the depreciation rates. Hence, this is the value 
retained for the long-term simulations described in Annex 12C.  

1.5 Wider benefits for individuals 

In  terms  of  well-being  there  is  evidence  showing  improved  well-being  of  individuals,  including 
improved health as a result of participation in learning. Jenkins (2011) shows that participation in 
evening classes by older adults has a positive effect on their perception of their general well-being.368 
Hammond and Feinstein (2005) demonstrate that adult learning can lead to increased self-confidence 
for participants369. A report for the UK’s Mental Health Foundation (2011) showed that community-
based learning courses for adults were successful in improving the subjective mental wellbeing of 
adults370.  Feinstein  et  al  (2003)  analysed  the  contribution  of  adult  learning  to  improvements  in 
physical health and showed that it can reduce the likelihood of smoking and consumption of alcohol 

371

372

and increase the likelihood of exercise
 found a positive association 
for women participation in adult learning to take up cervical screening. In addition, data analysis in a 
study of the European Commission on effectiveness of adult learning policies and their effectiveness 
in Europe (2015) demonstrates a strong positive and statistically significant correlation between an 
individual’s life satisfaction and their participation in learning. This association is stronger for people 

. Sabates and Feinstein (2006)

365

 Lentini and Gimenez (2019),  Depreciation of human capital: a sectoral analysis in OECD countries, International 

Journal of Manpower. 
366

 Weber (2014), Human capital depreciation and education level, International Journal of Manpower. 
 Hanushek et al. (2017), General Education, Vocational Education, and Labour-Market Outcomes over the Life-Cycle, 

367

Journal of Human Resources. 
368

  Jenkins,  A.  (2011):  Participation  in  learning  and  wellbeing among  older adults.  International  Journal  of  Lifelong 
Education. 
 Hammond, C. & Feinstein, L. (2005): The Effects of Adult Learning on Self-Efficacy. London Review of Education. 
 Mental Health Foundation (2011): Learning for Life. Adult learning, mental health and wellbeing. 
 Feinstein and Hammond  (2007), The contribution of adult learning to health and social capital. Oxford Review of 

369

370

371

Education. 
372

 Sabates, R., & Feinstein, L. (2006): The role of education in the uptake of preventative health care: the case of cervical 
screening in Britain. Social science & medicine, 62(12), 2998-3010. 

152 

 
                                                 
with lower qualification levels. This suggests that participation in learning is linked to an individual’s 
perception  of  their  wellbeing373.  Yamashita  et  al  (2017)  showed  that  additional  participation  in 
organised education programs was positively associated with life satisfaction.374 

In terms of civic participation, Fujiwara (2012) showed that the social value of increased community 
participation owing to participation in adult learning is worth about £130 to the learner.375 Feinstein 
et al (2003) show that there is an improvement in civic attitudes arising from participation in adult 
learning,  as  well  as  a  3%  increase  in  the  number  of  adults  who  were  likely  to  join  community 
organisations  as  a  result  of  participating  in  adult  learning.376  Moreover,  adults  with  low  literacy 
(PIAAC level 1 or below) are nearly twice as likely as adults with high literacy skills (scoring at level 
4 or 5) to say that they trust others very little. 

2. Returns to enterprises 

Beyond  individuals,  also  enterprises  benefit  from  a  more  training  workforce.  Returns  to 
enterprises/employers can be measured primarily in terms of labour productivity. There are additional 
benefits  including  contributions  to  company  innovation,  developing  skills  capacity  (to  avoid 
shortages) and business survival. 

2.1 Productivity 

Table A10.2 below provides an overview over the most relevant studies that review in detail the effect 
of training on firms’ productivity which contain information on the value of the related investment in 
training are listed below. Their “30-hour equivalent” is also calculated for comparability.  

Table A10.2:  Estimated impact of training on productivity 

Authors 

Volume of the 
training 

Net 

Increase in productivity 

30-hour training 
entitlement 
equivalent377 

Dearden et Al. 
(2006)378 

Around 80 hours  

0.6% at the industry level per 
each 1 percentage point increase 
in employees’ participation rates 
in training over the last 4 weeks  

22.4% 

373

376

374

375

  European  Commission  (2015):  An  in-depth  analysis  of  adult  learning  policies  and  their  effectiveness  in  Europe. 
Prepared by ICF Consulting Services Limited. 
 Yamashita et al. (2017): Types of Learning Activities and Life Satisfaction among Older Adults in Urban Community-
Based Lifelong Learning Programs. Activities, Adaptation, & Aging. 
 Daniel Fuijware (2012): Valuing the impact of adult learning: an analysis of the effect of adult learning on different 
domains in life. 
 Feinstein, L. et al. (2003): The contribution of adult learning to health and social capital. London: Centre for Research 
on the Wider Benefits of Learning. 
 Conversions to 30-hour training entitlement equivalent are based on information on the hours of training per individual 
examined in the papers under review. Where these are not available, information on the cost of training or training duration 
is considered. An hour-to-€ratio of around €15 per hour is used, in line with evidence on training from Annex 11, section 
4.1 “training costs”. In case both the cost and duration of the training are included in the paper and their ratio differs 
significantly from the average ratio used in this study, an adjustment is made. In any event, the estimates produced should 
not be considered as precise point estimates but as indicative values that allow defining a range of plausible increases in 
individual productivity from a 30-hour training entitlement in the medium term.   
378

 Dearden, L., Reed, H., & Van Reenen, J. (2006): The impact of training on productivity and wages: Evidence from 

377

British panel data. Oxford bulletin of economics and statistics. 

153 

 
                                                 
37 hours/€1 400 

27.9% 

Roughly 17% 

Approx. 35 hours381 

5% 

5% (the cost is slightly 
lower) 

Konings and 
Vanormelingen 
(2015)379 
Martins 
(2020)380   

Conti (2005)382 

Hourly effects 
provided 

EIB (2020)

383

€20 (10% increase in 
training per employee) 

0.1% per hour 

3% 

0.32% (This is 0.09% in northern 
Europe, 0.27% in southern 
Europe and 0.048% in central and 
eastern Europe. These results are 
considered consistent with 
decreasing returns to training)  

6-7% 

From the above, it can be seen that returns to training on firms’ productivity can be quite substantial, 

ranging  from  3%  to  22.4%.  Also  OECD  research
  points  to  substantial  productivity  returns  of 
training, shared between enterprises and their workers. Unfortunately, these studies are not always 
able  to  fully  control  for  self-selection  into  training/endogeneity,  and  may  be  subject  to  some 
overestimation of returns.   

384

In Dearden et al. (2006), Konings et al. (2015) and Conti (2005), increases in firms’ productivity are 
also  coupled  with  wage  increases  for  employees.  This  allows  identifying  a  partial  equilibrium 
relationship between the increases in wages and those in productivity. Based on these studies it is 
possible to identify a productivity to wages ratio of roughly 2:1. This is helpful as it allows to use 
increases in wages calculated in studies that control for endogeneity through instrumental variables 
as a proxy of increases in productivity, to come to robust estimates.    

2.2 Further benefits of training  

There  is  also  evidence  linking  training  provision  to  a  broader  range  of  positive  enterprise-level 
outcomes. While it is usually derived on the basis of data on enterprise-level provision of training 
(the most frequent type of adult learning, see Annex 6.2), it is likely indicative of positive impacts of 
a more trained labour force on enterprises more generally. 

Innovation Performance : Cedefop’s meta-analysis of training and workplace learning on a firm’s 
performance  on  innovation  (2012)  finds  that  the  proportion  of  companies  providing  training, 

379

 Konings and Vanormelingen (2015): The Impact Of Training On Productivity And Wages: Firm-Level Evidence, The 

Review of Economics and Statistics. 
380

 Martins, Pedro S. (2020) Employee Training and Firm Performance: Quasi-experimental evidence from the European 

Social Fund, GLO Discussion Paper Series. 
381

 Ibid. “Considering the average number of workers per firm, the approved training hours figure amounts to a mean 
number of training per worker similar to the 35-hour figure established in labour law”. In monetary terms, this equals to 
an average of €267 per worker (€30 000 per firm, 112 workers) in PT values, 2007. 
382
 Conti, G. (2005) Training, productivity and wages in Italy, Labour Economics. 
 EIB (2020): Financing contraints and employers’ investment in training. 
 OECD (2019), Returns to different forms of job related training. 

384

383

154 

 
  
 
                                                 
employee  participation  in  training,  and  the  costs  of  continuing  vocational  training  (CVT)  as  a 

percentage of total labour costs have a positive relationship with innovation performance.

385

Avoidance  of  skills  shortages:  The  other  area  where  investment  in  skills  can  have  a  pay-off  is  in 
relation to avoiding skill shortages or internal skill gaps (i.e. where the existing workforce are not as 
proficient as required to meet a company’s product market strategy). One has to be careful because 
companies at the cutting edge of technological developments often report skill shortages because of 
their cutting-edge position and the difficulties the supply-side has in keeping pace with that change.  
But overall, there is evidence that employers which train are less likely to encounter skill shortages 
and, critically, are more likely to have product market strategies which are oriented towards higher 
386 There does appear to be a symbiotic relationship between investing 

value segments of the market.
in skills via training, innovation, and product market strategies. 

There has been a longstanding interest in promoting high quality employment and high performance 
of the workforce. A central element of high performance of the workforce is not only the provision 
of skills training so that people are equipped to  meet the demands of the  enterprises, but they are 

 There is seen to be a virtuous circle 
granted a degree of autonomy of in exercising those skills.
between  employers  investing  in  the  human  capital  of  their  employees,  increasing  levels  of  job 
satisfaction,  and  reinforcing  the  bond  between  employer  and  employee  all  of  which  feeds  into 
improved organisational performance.  It is likely to be the case that high-quality employment and 
high  performance  working  are  dependent  to  some  degree  on  the  provision  of  training  (or  human 
capital development more generally) by the employer. 

387

Business survival: Research from  the UK indicates that companies which train  are more likely to 
survive  as  a  result  of  the  training  (and  by  implication  the  skills  they  invest  in).  Non-training 
companies are estimated to be nearly twice as likely to go out of business than their counterparts who 
388 The failure rate of companies not training was 27 per cent over six 

train (other things being equal).
years compared with 11 per cent for those that did so. 

3. Returns to society  

The  wider  returns  to  society  can  be  measured  in  economic,  social  and  environmental  terms,  each 
summarised below. 

3.1 Macroeconomic benefits 

An analysis of the OECD (2013) shows that countries with high rates of participation in adult learning 

are more competitive.
 This is corroborated by the statistically significant and positive correlation 
between the participation rate of employed individuals and GDP per resident. PIAAC data illustrate 

389

385

386

387 

388

389

 Cedefop (2012): Learning and innovation in enterprises.  
 Mason  (2004): Enterprise product strategies and employer demand for skills in Britain: evidence from the Employers 
Skills.  
Green et al. (2016): Skills and work organisation in Britain: a quarter century of change. Journal for Labour Market 
Research.; Findlay et al. (2017) ‘Opportunity Knocks?: The Possibilities and Levers for Improving Job Quality’, Work 
and Occupations; Gloster et al. (2016). Mapping investment in adult skills: which individuals, in what learning and 
with what returns?. 
 Collier et al.  (2005): Training and establishment survival. Scottish Journal of Political Economy.  
 OECD (2013): OECD Skills Outlook 2013: First Results from the Survey of Adult Skills,  

155 

 
 
                                                 
the central role that basic skills play in shaping economic outcomes. In the EU17, an increase of skills 
by around 40 points (slightly less than one skills level) is linked with an increase in wages ranging 

from approximately 5% in Denmark, Finland and Italy to more than 10% in the UK
. At national 
and European level, it has been estimated that if Europe achieved its current literacy benchmark, this 
391

could lead to an aggregate GDP gain of €21 trillion over the lifetime of the generation born in 2010.
These estimates are particularly relevant as they have the advantage of taking into account general 
equilibrium dynamics, although typically rely on slightly simplified assumptions to account for the 
complexity of the estimation.  

390

 has reviewed the macroeconomic returns to training low-
A more recent study (Cedefop, 2017)
skilled adults in the EU and has generated significant positive, illustrative effects through its scenario 
analysis in respect of gross earnings, tax revenues and benefits to individuals in terms of health and 
crime benefits. In the upskilling scenario (7.4% ) of the low-skilled, total net benefits over ten years 

392

393

could equal €2 013 billion
 and €3 528 billion in a zero low-skilled (0%) scenario with increases in 
annual GDP (2025-50) at €200 billion and €410 billion respectively die to the reduction or elimination 
of low skilled adults. 

3.2 Social benefits 

Social  benefits  in  general,  arising  from  adult  learning,  are  largely  found  from  evidence  showing 
relevant statistical relationships between adult learning and community. Countries having high skills 
levels in literacy and numeracy show a higher participation in volunteer and political activity as well 

as higher levels  of trust.

394

  This  is  confirmed by  a  positive and statistically  significant  correlation 

 In addition to 
between participation in learning and voter turnout in the most recent EU elections.
that,  an  increased  participation  in  learning  also  affects  a  reduction  in  economic  inequality  (as 
measured by the Gini coefficient): an increase in the participation rate in learning by 10 percentage 
points  is  associated  with  a  decrease  of  two  points  in  the  Gini  coefficient  (with  zero  representing 

395

396

equality on Gini coefficient).
 Furthermore, the increase in employment achieved through training 
generates a reduction in  welfare dependency and thus a decrease in the cost of social benefits for 
Public Authorities (see Huber et al., 2011 for a study with German data; and OECD, 2017 for the 
Netherlands). 

Targeted  health  education  allows  to  reduce  chronic  illness  and  increase  the  quality  and  years  of 
healthy life of older people. The review of roughly 600 studies found that almost 64% of the studies 
reported  positive  health  effects  on  the  learners  from  their  participation  in  a  therapeutic  education 

390

391

392

 European Commission (2013). The Survey of Adult Skills (PIAAC): Implications for education and training policies 
in Europe. DG-EAC. OECD (2013). OECD Skills Outlook 2013: First results from the Survey of Adult Skills.   
 Hanushek, Eric and Woessmann, Ludger (2011): The Cost of Low Educational Achievement in the European Union, 
EENEE Analytical Report No. 7. 
 Cedefop research paper 5560 (2017): Investing in skills pays off – the economic and social costs of low-skilled adults 

in the EU. 
393

 Of which €1 030 billion would be health and crime benefits and a further €904 billion in aggregate net income before 

taking into account opportunity costs. 
394
 OECD Skills Outlook (2013). 
 European Parliament (2014): Results of the 2014 European elections. 
 Labour Force Survey (2013): EU Statistics on Income and Living Conditions, 2012. 

395

396

156 

 
 
                                                 
programme

397

. But there also other positive effects about the expenditure on healthcare coming from 

participation in learning: for example, in the UK
 the postponement of entry into residential care by 
one month because of participation in learning might lead to potential savings worth between £18.2 
million and £36.3 million to the state per annum at that time. 

398

3.3 Environmental benefits 

Participating in adult learning programmes also leads to an improvement of environmental literacy 

and  a  better  behaviour  in  relation  to  the  environment  among  adults

399

  as  well  as  reduction  in  in 

400

reoffending rates.
  From the outset there seems to be a consensus that there overall is an increasing 
demand for green skills. For example, the employment and social impacts of climate change policies 
was recently addressed in the Commission publication “Employment and Social Developments in 

401

Europe 2019”.
 It assessed that efforts to meet the climate targets by 2050, would by 2030 lead to 
1.1% higher employment and 0.5% higher GDP compared with a baseline scenario. This amounts to 
an additional 1.2 million jobs in the EU by 2030, on top of 12 million jobs expected to be created 
under the baseline from 2015 to 2030. The expected positive impacts are largely due to the investment 
activity required to achieve such a transition, together with the impact of lower spending on the import 
of  fossil  fuels.  Furthermore,  lower  consumer  prices,  notably  of  solar  photovoltaic  electricity,  are 
expected  to  boost  disposable  incomes,  consumer  expenditure  and  consequently  the  demand  for 
consumer services, which are generally labour intensive. 

Another  recent  assessment  made  regarding  the  need  to  equip  the  existing  workforce  with  the 
necessary  green  skills  to  meet  the  requirements  of  the  green  transition  is  that  of  the  Commission 
Communication, COM(2020) 662 final, "A Renovation Wave for Europe – greening our buildings, 
creating jobs, improving lives". It concludes that one of the main developments needed to achieve the 
EU environmental and climate targets is that of increased energy efficiency renovations of buildings. 
Furthermore, it finds that renovation works are labour-intensive, create jobs and investments rooted 
in often local supply chains, can generate demand for highly energy and resource-efficient equipment 
and bring long-term value to properties. By 2030 an additional 160,000 green jobs could be created 
in the EU construction sector through a renovation wave. 

4. Selection of coefficients for impact estimates 

Table A10.3 presents the estimated  impacts of a training of 30 hours, selected on the basis of the 
literature review in this Section and used in Annex 12 to quantify the expected benefits of the policy 
packages. All selected coefficients  are considered to be “middle ground” estimates in view of the 
evidence. The uncertainty reflecting these middle ground estimates is reflected in Annex 12 in the 

397

398

399

400

401

 Lagger et al. (2010): Efficacy of therapeutic patient education in chronic diseases and obesity. Patient Education and 
Counselling. 
 Schuller and Watson  (2009): Learning Through Life: Inquiry into the Future for Lifelong Learning. NIACE, Leicester. 
 Digby (2013): The Impact of Non-formal and Informal Learning on Adult Environmental Behaviors. International 
Electronic Journal of Environmental Education. 
 Gordon and Weldon (2003): The Impact of Career and Technical Education Programs on Adult Offenders: Learning 
Behind Bars. Journal of Correctional Education. 
 https://ec.europa.eu/social/main.jsp?catId=738&langId=en&pubId=8219 

157 

 
                                                 
form of sensitivity checks, notably probing the robustness of key conclusions to more pessimistic 
assumptions on the different parameters.  

Table A10.3: Coefficients selected for the estimation of the benefits of a 30 hour training 

Variable 

Wages 

Partial Equilibrium (effects on participants/beneficiaries) 

1% increase.  
This  estimate  is  based  on widely  acknowledged literature
variable approach, hence likely to duly address self-selection issues.  

402

  employing  an  instrumental 

Productivity 

2% increase 
This  estimate  is  drawn  at  the  lower  bound  of  the  range  of  estimates  discussed  in  the 
literature review above. 403 

It  is  tied  (fixed  ratio)  to  the  increases  in  wages,  which  are,  in  turn,  calculated  using 
instrumental variables. This should increase robustness towards problems of self-selection. 

Employment 
effects 

For the longer-term macroeconomic estimations (i.e. Annex 12C), this productivity gain:  
-  Depreciates over time at a rate of 3% per year. 
-  Does  not fully  accumulate  year  on  year. Their  cumulative increases  over time  are 
factored in based on a cube root function (decreasing marginal returns), to favour a 
more conservative approach to the long-term estimates. 

2.5 ppts increase in the medium to long-term probability of being in employment (for 
those not in employment) 
This is deemed to be a middle ground estimate, as:  
-  They are based on the two influential meta-analyses discussed above. 
-  Although  in  Card  et  al.  (2018)  there  is  no  specific  information  on  the  cost  of  the 
training,  heterogeneous  effects  for  very  long  trainings  are  tested  and  rejected. 
Information on the median cost of trainings is available in Levy-Yeyati et al. (2019) 
which allows to scale down proportionally the measured average effect to a training 
duration of 30 hours. 

These effects are then scaled proportionally for target groups receiving 50-hour training support and, 
given the inconclusive evidence on heterogeneity of returns, are applied linearly to the different target 
groups addressed by the policy packages for the purpose of the short to medium-term CBA analysis 
on direct impacts (see Annex 12B).  

Estimates on productivity gains, which is the least likely to produce displacement and can thus be 
measured quite reliably also at the micro (individual or enterprise) level, are also used as an input to 
the macroeconomic simulation described in detail in  section 12C. Broader macroeconomic effects 
(including on structural increases in employment) are assessed in Section 12C. 

ANNEX 11: EVIDENCE ON INDIVIDUAL TRAINING 
ENTITLEMENTS 

Individual training entitlements can be defined as a personal budget that is at the individual’s disposal 
to  cover  the  direct  costs  of  his/her  training/course  fees  within  a  set  time  period.  As  discussed  in 

402

 See in particular Brunello et al. (2012): Training subsidies and the wage returns to continuing vocational training: 

Evidence from Italian regions. Labour Economics. 
403

 See in particular Konings and Vanormelingen (2015): The Impact Of Training On Productivity And Wages: Firm-

Level Evidence, The Review of Economics and Statistics. 

158 

 
 
 
 
                                                 
Section  5.3,  they  are  currently  most  often  provided  in  the  form  of  training  voucher  schemes  (see 
Annex  8.2  for  an  overview,  and  the  case  studies  in  Annex  13  for  examples).  They  can  also  be 
delivered via personal accounts (see the case study on Singapore in Annex 13, and Annex 14 on the 
French CPF). The purpose of this Annex is to provide an overview of evidence on the effectiveness 
of  individual  training  entitlements  at  increasing  participation  in  labour  market  relevant  training 
among recipients, and to summarise information on the costs of providing training entitlements. 

1. Effectiveness of training entitlements  

In order to assess the effectiveness and additionality of the type of entitlement two inter-related factors 
need to be considered: 

1.  the take up rate of the training entitlements; and 

2.  the extent of deadweight loss associated with the intervention – i.e. the extent to which training 
would have taken place in any case (i.e. to provide the basis for estimating the net impact or 
additionality of the intervention). 

1.1 Take up rates of training entitlements 

There are few schemes offering training entitlements with broad eligibility conditions, with no cost-
sharing requirements and for which detailed data on take up rates is available. These are the French 

404

CPF
  and  the  Singapore  SkillsFuture  Credit  (SFC)  and  a  Swiss  experiment  on  adult  learning 
vouchers. More frequently, existing training voucher schemes target vulnerable groups such as the 
low  skilled,  and  further  evidence  comes  from  a  Dutch  training  voucher  scheme.  Table  A11.1 
summarizes the available evidence on take up of these training entitlements, further discussed below.   

Table A11.1: Summary of participation levels by the various schemes 

Name 

Type 
scheme 

of 

Scope 

Expiring 
period 

Avg. value  Avg. Take up   Heterogeneity 

Adult  learning 
voucher 

Adult 
education 
vouchers 
(CH)

405

Broad 
scope 
(randomly 
selected 
sample) 

6 months  €600 

(worth 
about 
hours) 

21 

18.4%  over  6 
months 

Personal 
training 
account (yearly 
accumulation) 

Compte 
Personnel 
de 
Formation 
(FR)- 
Annex 14 

see 

Broad 
Scope  

None 

€500 or per 
year 

13-16%* over 1 
year 

for 
low 

(€800 
the 
qualified) 

in take up 

Low  take  up  at 
low  face  values 
(12%  at  €160, 
above  20%  for 
€600 and 1 200) 
and 
low 
for 
qualified 
individuals 
(9.5%). 

since 
In  data 
recent 
reform, 
no  evidence  of 
under-
representation 
of  low-qualified 
(cf. Annex 14).  

404

405

 An evaluation of the CPF is planned but not yet implemented 
 See See Schwerdt et al. (2012), The impact of an adult education voucher program: Evidence from a randomized field 

experiment. Journal of Public Economics. 

159 

 
 
                                                 
Name 

Type 
scheme 

of 

Scope 

Expiring 
period 

Avg. value  Avg. Take up   Heterogeneity 

in take up 

SkillsFuture 
Credit 
(SGP)  -  see 
case  study 
in Annex 13 

Training 
voucher  for 
low  skilled 
workers 
(NL)

407

(worth 
about  30-
50 hours) 

Broad 
Scope 

None 

€310  

23.5%  over  5 
in 
years/  7% 
2020  

Data 
available. 

not 

Personal 
training 
account 
regular 
provision 
additional 
entitlements)

(no 

of 

406

2 years 

€1 000 

41%  over  two 
years 

Adult  learning 
voucher 

low 

Targeted 
to 
skilled 
workers 
in 
selected 
industries 

(randomly 
selected 
sample) 

* Estimated values 

Take up of Swiss adult education vouchers: The Swiss experiment was a large-scale randomized field 
evaluation of a programme which issued vouchers for adult education in Switzerland in 2005-2006. 
Vouchers  were  offered  to  a  representative  sample  of  Swiss  citizens.  The  adult  learning  vouchers 
varied in value, at €160, 600 and 1 200 respectively, and could be supplemented by individuals’ own 
resources. The vouchers could be redeemed over a six-month period. The authors estimate that the 

 reviewed 
average voucher value was sufficient to pay for about 20 lessons/ 30 hours. Two papers
the  experiment  giving  information  on  take-up  rates  and  the  way  these  varied  and  found  that  the 

408

average take-up rate was 18.4% over the six-month period.

409

Take up of entitlements in French CPF: The French CPF differs from the Swiss experiment as the 
training entitlements, instead of expiring, accumulate yearly until a threshold of €5 000 (€8 000 for 
the low qualified) is reached. Since the average training hour of CPF-funded training costs about €15, 
the annual entitlement is sufficient to buy about 30-50 training hours. However, individuals do not 
have  to  use  their  training  entitlements  immediately  and  are  able  to  take  up  longer  or  more  costly 

406

407

408

 Top-ups for two specific target groups have been granted in response to COVID-19, see Annex 13. 
 See Hidalgo et al.  (2014), The Impact of Training Vouchers on Low Skilled Workers. Labour Economics. 
  Schwerdt    et  al.  (2012):  The  impact  of  an  adult  education  voucher  program:  Evidence  from  a  randomized  field 
experiment, Journal of Public Economics and Messer, D. and Wolter, S. (2009): Money matters: evidence from a large-
scale Randomized field experiment with vouchers for adult training. CESifo Working Paper, No. 2548. 
409

 With some variation depending on the voucher value: 12.4% at €160, 21% at €600 and 22% at €1 200.  

160 

 
 
 
 
 
                                                 
training spells. Data from the French CPF indicate that the median length of trainings is  about 80 

hours, confirming that individuals make use of their flexibility.

410

Precise yearly figures of the individual take-up rate of the CPF cannot be calculated. This is because 
individuals can purchase multiple trainings over time and data on unique users purchasing training 
each year is not available. A way of approximating annual take up is to compare the average number 

411

of activated profiles
 in a given period (e.g., at t= -1 considering a lag of 1 year from the activation 
of the profile and the validation of the training undertaken) and the number of trainings undertaken 
at t= 0. 

Using this approach (based on the average number of activated profiles in 2019 and the total number 
of trainings validated in 2020), the estimated take up rate is around 13%. However, it is important to 
note that take up in France has seen a steep increase in recent months after the launch of the portal 
and app Moncompteformation (Annex 14). Applying the same approach as above (taking the average 
number of activated profiles in the first half of 2020 and a yearly value of trainings validated in the 
second half of 2020), the resulting take up rate is around 16%. Even if we assume there is no lag 
between  profile  activation  and  training  validation,  the  estimated  take-up  rate  is  still  around  13%. 
These take-up rates are hence slightly below those of the Swiss experiment.  

Take up in the Singaporean SFC: Like the French CPF, the SFC in Singapore accumulated funds do 
not expire. But they do have a smaller face value (approx. €310). According to data from the Ministry 

412

of Education,
 five years after the scheme was introduced (in August 2020) 23.5 per cent of eligible 
Singaporean have used their credits. The largest level of take-up was in 2020, with around 190 000 

 However, these take up rates 
individuals taking up training (7 per cent of the eligible population).
are not easily comparable with the other ones: since SFC users could not expect regular top-ups, they 
were likely more reluctant to spend it/ more likely to “save” it than in a scheme providing entitlements 
regularly.   

413

Take  up  of  Dutch  training  vouchers:  Other  schemes  provide  further  insights  into  the  take-up  of 
individual training entitlements. In the Netherlands, vouchers were offered to low qualified workers 

in specific sectors.
this period was 41%. 

414

 Vouchers could be redeemed over a period of 2 years, and the take up rate over 

Summary on take up of training entitlements: The French CPF and especially the Singaporean SFC 
have lower take up rates than the one observed in the Swiss voucher experiment, but: (i) the absence 
of regular entitlement top-ups can be expected delay redemption behaviour in SFC, and (ii) the annual 

410

411

 The information is only available for approx. 80% of the trainings undertaken.  
 The actual take up rate should be that of target population vs trainings validated, whereas here the reference is only 
that of activated profiles. This seems however reasonable as the roll out of the system is still ongoing. The number of 
active profiles is however already over 50% of the employed and unemployed population in FR. 
412
 https://www.moe.gov.sg/news/parliamentary-replies/20210104-skillsfuture-credits-utilisation. 
https://www.straitstimes.com/singapore/540000-singaporeans-benefited-from-skillsfuture-initiatives-in-2020-

413

skillsfuture-singapore. 
414

 Vouchers were issued through the following development funds: Aequor Services in the agricultural sector, the Centre 
for  Knowledge  and  Development  of  the  Potatoes,  Vegetables  and  Fruit  Association,  the  Training  Fund  for  the  Food 
Industry, and the Natural Stone Centre. 

161 

 
  
                                                 
rate of take up in SFC and in particular CPF has increased over time, with convergence in particular 
of  CPF  take  up  rates  towards  those  observed  in  the  Swiss  voucher  scheme.  The  Swiss  voucher 
experiment has the advantage of allowing for a comparison between “treatment” and “control” group, 
which allows for an estimation of deadweight loss alongside take up (see next Section of this Annex).  

On this basis, a take up rate around the Swiss value of 18.4% for training entitlements sufficient to 
purchase around 30 hours of training can be considered a reasonable “middle ground” estimate for 
annual take up.  

Evidence on heterogeneity in  take up rates across  groups  of  adults:  In their systematic  review of 
financial incentives to increase participation rates, Vanderkooy et al. (2019) conclude that the higher 
qualified are more likely to take up training entitlements.415  This is confirmed by a data from the 
CPF during the 2015-2018 period416, and by the data from the Swiss voucher experiment:  here, take-
up was 9.5% for the low qualified, 17.1% for those with upper secondary education and 26.3% for 
those with tertiary education. By contrast, the Dutch voucher experiment suggest that higher take up 
rates are possible also among the low qualified, and recent data from the French CPF since its reform 
in November 2019 (which facilitated access, including by introducing a Smartphone app) suggests 
that  the  low-qualified  are  not  longer  under-represented  among  CPF  users  (see  Annex  14). 
Nevertheless, a conventional assumption on the basis of the evidence is to assume lower take-up rates 
for  the  low  qualified.  Training  voucher  schemes  also  often  report  somewhat  higher  take  up  rates 
among women. However, there are no significant gender differences in the use of the CPF in France 
(Annex 14). 

1.2 Controlling for deadweight loss 

Deadweight loss occurs if training entitlements are used to finance training that would have taken 
place in any case/ if they crowd out private skills investments instead of triggering additional training.  

It  is  important  to  stress  from  the  outset  that  experimental  research  designs  are  crucial  for  the 
estimation of deadweight loss and, net impacts of training entitlement schemes on participation. In 
their absence, it is impossible to disentangle the causal contribution of individual training entitlements 
to any additional training carried out from training which would have been undertaken anyways.   

The concept of deadweight in its simplest configuration (DW) can be measured as: 

  DW = Pnt/Pt 

Where  

Pt = participation rate of the treated group (redemption rate) 

Pnt = participation rate of the non-treated group 

This configuration is standard practice in the literature given the inherent difficulties to gauge within 
an experimental design differences in the quality/ extent of the training undertaken. For instance, in 
Schwerdt et. al. (2012) the variable of interest is drawn from the Swiss Labour Force Survey (SLFS). 
In particular “the selected SLFS module was the one that asked participants subsequently whether 

415

  Vanderkooy  et  al.  (2019),  Investing  in  inclusive  growth: A  systematic  review  of  the  role  of  financial  incentives  to 

promote lifelong learning. Educational Research Review. 
416

 OECD (2019): Individual Learning Accounts: Panacea or Pandora's Box?  

162 

 
                                                 
they  had  attended  fee-based  courses  within  the  past  12  months”.  Their  conclusion  is  that  “[..]the 
estimate shows that receiving a voucher increases the probability to participate in an adult education 
course in 2006 by 13 percentage points”. This can be interpreted as the causal net impact on adult 
learning  participation.  This  approach  is  common  to  other  experimental  and  quasi-experimental 
studies  discussing  the  impact  of  vouchers  on  training  participation,  including  the  previously 
introduced studies by Schwerdt et al. (2012) and Hidalgo et al. (2014). 

417

  the  mere  fact  that  individuals  would  have 
However,  as  argued  by  Bauer  et  al.  (2019),
undertaken  some  form  of  training  even  without  support  does  not  imply  the  absence  of 
additional – economically valuable – training which was followed by participants thanks to the 
voucher  (i.e.  beyond  the  net  effects  on  adult  learning  participation).  In  this  more  nuanced 
configuration, participants who would have participated in training activities, but e.g. of lower value 
/ duration or in the same reference year cannot be completely discounted from an assessment of the 
economic additionality of the voucher. One might also want to consider, in a fine-grained approach, 
whether there has been an effect in terms of training which have been followed earlier than planned 
without the voucher.  

Unfortunately, no clear experimental evidence is available that employs such a nuanced assessment 
of the deadweight, but one example that delves a little further into the issue of deadweight is the first 

418

review of the Swiss experiment from Messer and Wolter (2009)
, where a set of regressions are 
applied to  the relation  between voucher value and number of training activities followed within a 
year.  The  number  of  training  activities  is  a  discrete  non-standardised  measure  which  does  not 
necessarily inform  about the actual economic value of the training undertaken (it is  impossible to 
distinguish  between  two  20-hour  training  sessions  and  one  40-hour  one).  However,  it  might,  in 
practice, be reasonable to assume that there is a certain average duration of typical training offers and 
that therefore the number of trainings is a proxy to measure additionality of the vouchers beyond the 
net effects on adult learning participation.  

Based  on  their  findings,  multiple  participation  in  courses  is  not  widespread  (25  per  cent  of  those 
receiving  training).  Their  estimates  though  indicate  that  this  is  around  9  per  cent  higher  than  the 

control group.

419

In Bauer et al (2019), participants were posed counterfactual questions asking them to reflect about 
what  would  have  happened  to  their  training  participation  in  the  absence  of  the  educational  bonus 
(voucher). Possible answers included: 

- 

- 

- 

I  undertook  a  training  of  higher  quality/price  thanks  to  the  voucher  (24  per  cent  of 
respondents) 

I was able to participate earlier than planned thanks to the voucher (41 per cent of respondents) 

I was able to participate in additional training beyond what purchased with the voucher thanks 
to it (45 per cent of respondents) 

417

418

 Bauer et al. (2019): Evaluation des Bundesprogramms Bildungpramie (BIP) Kantar, Public Division, München. 
 Messer and Wolter (2009): Money matters: evidence from a large-scale randomized field experiment with vouchers 

for adult training. IZA Discussion Paper 4017. 
419

Such values are however only statistically significant for vouchers of 1 500 CHF (€1 200). 

163 

 
  
                                                 
The  conclusion  from  the  research  is  that  the  actual  full  deadweight  from  this  specification  of  the 
survey goes down from 41 per cent to 15 per cent. 

In addition to the effect on training in the current year, a few studies discussed the impact on 
subsequent  participation  in  adult  learning.  The  rationale  for  such  an  investigation  is  that  a 
comprehensive review of deadweight loss should also consider if there is a causal effect of public 
support to participation in adult learning which stretches beyond the year of the support.  

In Schwerdt et al. (2012) the estimates produced with the instrumental variable approach, hence those 
that  should  produce  the  more  reliable  results  in  terms  of  causality,  find  small  positive  effects  on 
subsequent private investments in adult education. A similar finding is included in Hidalgo  et. Al 
(2014) examining the Dutch experiment on vouchers offered to the low skilled workers. Results from 
the estimations show that voucher receipt affects the plans to enrol in a course over the subsequent 6 
months by 20 ppts Although this is a measure of the “intention to train further” and not actual and 
measured additional private investment  in  training, it is another confirmation that public stimulus 
might change the attitude of the individuals towards further training opportunities. Perhaps the key 
point here is that the future intention to continue to train is likely to have some reduce any tendency 
towards participation rates diminishing in the future. 

Finally,  training  entitlements  can  also  stimulate  private  investment  in  training  (i.e.  that 
individuals or their employers top-up the training entitlement in some way). In the case of the 
CPF in France the incidence of crowding-in funding was low, but in the experiment with vouchers in 
Switzerland it was as high as 50 per cent. While this will not necessarily affect the number of people 
engaged in training, it is likely to have some impact on the overall duration and / or quality of the 
training experience.  

On the basis of this discussion, it is useful to distinguish between four different ways in which the the 
action by an adult who makes use of her/his training entitlements and participates in training translates 
into additional training. These are summarized in Table A11.2. 

164 

 
Table A11.2: Classification of deadweight vs. additionality for an adult using training entitlements 

I 
I would not otherwise 
have participated in 
training-  
and on top, training  
entitlements  crowd-in 
additional private 
resources or encourage 
me to participate more 
in the future 
Negative deadweight/   
resources crowded in: 
Take-up of training 
entitlements 
underestimates net 
increase in training 
participation  

II 

III 

IV 

I would not otherwise 
have participated in 
training  

I would have 
participated in 
training anyway- but to 
less a degree/at lower 
intensity  

I would have 
participated in 
training- and to the 
same extent I do now 

No deadweight: 
Take-up of training 
entitlements 
corresponds to net 
increase in training 
participation & 
economic additionality 

Partial deadweight:  
economic additionality 
resulting from the 
higher training intensity 

Total deadweight:  
no economic 
additionality 

Increase in the amount of training undertaken 

Deadweight in the sense that the number of 
yearly participants in at least one training (as 
measured in the adult learning participation rate) 
does not increase 

The Swiss and Dutch voucher experiments reviewed in this Annex only provides estimates comparing 
a response corresponding to column II (no deadweight/full additionality) to a response corresponding 
to column IV (total deadweight/no additionality). This is because data on the duration/intensity of 
training, private co-funding or future training participation, which would allow for a more nuanced 
assessment, is not available.  

In practical terms, this means that a deadweight loss estimate of 30% (as used below) means that of 
100 additional adult learners making use of their training entitlements during a year, 7 of them would 
not otherwise have participated in learning in that year and 3 would have participated in some form 
of  learning  anyway.  This  estimate  however  overstates  deadweight  loss/  understates  the  additional 
training  induced  by  the  scheme  if  there  is  crowding-in  of  private  resources  (column  I)  and/or  an 
impact on training intensity (column III).  

However, the evidence reviewed in this Section suggests that both factors matter in practice. This 
points  to  the  need  of  a  more  nuanced  treatment  of  deadweight  loss  when  assessing  the  costs  and 
benefits of training than the one implied by the headline deadweight loss estimates often provided in 
the literature. Annex 12A explains how this is incorporated in the quantifications of impacts in this 
impact assement. 

1.3 Impacts of individual autonomy on take-up of training entitlements, training choices and 
labour market outcomes 

Autonomy on the use of individual training entitlements differ depending on the scheme, and range 
from full autonomy among eligible courses in the French CPF to limited autonomy in many training 
voucher schemes administered by PES.  

If the objective of a training entitlement schemes is to increase outreach and incentivise current non-
learners to take up training that is tailored to their individuals needs but also strengthen the labour 

165 

 
 
 
 
market position of individuals through training, a key question is hence how individuals respond to 
autonomy  in  training  selection.  I.e.,  does  autonomy  increase  take-up  rates,  what  type  of  training 
courses  do  they  choose,  and  is  there  a  link  between  the  degree  of  autonomy  individuals  have  in 
training selection and subsequent labour market outcomes?  

Impacts of autonomy on take-up rates: The limited available evidence available from a randomized-

 420

controlled trial Perez-Johnson et al. (2011)
 suggests that making prior career guidance a condition 
for the mobilisation of training entitlements in package A may have a negative effect on the take-up  
of  training  entitlements:  taking  as  a  reference  the  model  with  complete  freedom  of  choice  (no 
guidance, no veto power), the findings of the study point to a 4 percentage point reduction in the take-
up  rate  of  those  bound  to  intensive  guidance  –  despite  the  more  generous  entitlements  –  and  a  6 
percentage  point  reduction  in  the  take-up  rate  of  the  model  with  moderate  guidance  and  average 
entitlements’  value.    A  limitation  is  that  the  guidance  requirement  was  not  strictly  enforced  as 
counsellors in the experiment were hesitant to over-rule training choices by individuals. Hence, the 
observed  differential  take-up  rates  in  the  experiment  may  under-estimate  the  impacts  of  a  more 
strictly enforced compulsory guidance rule on take up rates.  

Impacts of autonomy on training choices: In the Swiss voucher scheme studied by Schwerdt et al. 
(2012),  no  restrictions  were  placed  on  the  types  of  adult  education  courses  that  could  be  chosen/ 
individuals  had  full  autonomy.  90%  of  participants  used  their  voucher  for  broadly  market  related 
courses:  40%  were  directly  job-related,  28%  were  foreign  language  courses,  and  21%  were  IT 
courses. Only one in ten were leisure courses.  

Looking at training choices in the French CPF up to 2018, Perez and Vourc'h (2020) found that some 

 Training in 
employees (just under one third) used their CPF to fund work-related English courses.
transport, handling and storage (11%) was the next most frequently identified courses followed by IT 
training (7%) and undertaking skills assessments. The training choices of jobseekers included skills 
assessments and validation; starting-up a business; and materials handling trucks. Jobseekers were 
more likely to undertake diploma-based training programmes whilst employees were more likely to 
follow unaccredited training courses. Looking at data after the 2019 reform, the share of anguages 
(mostly English) has however decreased to 12.5% of all learning activities (Annex 14).   

421

 Analysing the training choices of the low-skilled Dutch voucher recipients who equally enjoyed full 
autonomy,  Hidalgo  et  al.  (2014)  find  that  the  treated  group  was  significantly  less  likely  to  take  a 
course to improve their current job tasks, and more likely to take a course to improve their conditions 
in the labor market in general or to change sectors. 

Taken together, the  evidence suggests that if people are given  more freedom of choice, most will 
choose courses that are labour market relevant, but put more emphasis on more general/transversal 
skills and personal professional development.  

Impacts  of  autonomy  on  labour  market  outcomes:  A  further  question  is  whether  these  more 
general/transversal types of training “pay off” on the labour market/ translate into better wage and 
employment prospects. Hence the importance of analysing freedom of choice not only with respect 

420

 Perez-Johnson et al. (2011), Improving the Effectiveness of Individual Training Accounts: Long-Term Findings from 

an Experimental Evaluation of Three Service Delivery Models.  
421

 Perez  and Vourc'h (2020), Individualising training access schemes: France – the Compte Personnel de Formation 

(Personal Training Account – CPF). OECD Social, Employment and Migration Working Papers No. 245. 

166 

 
                                                 
to the types of trainings undertaken in descriptive terms, but also in connection to any differentials in 
the  labour  market  outcomes  generated  by  them.  Experimental  evidence  is  limited,  two  relevant 
examples exist. 

First, counterfactual evaluations of the Hartz reform in Germany analyze the effect of the reform’s 
movingaway  from  a  supply/PES-led  training  offer  for  the  unemployed  to  a  voucher  system  with 
increased freedom of choice (see Rinne et. al (2013)422 and Doerr and Stirttmatter (2017)423). Findings 
suggest that freedom of choice tends to increase employment chances and earnings in the medium 
and  long  term.  This  is  evident  already  in  the  short  to  medium  term  in  Rinne  et  al.  (2013),  where 
increased freedom of choice appears to increase employment chances between 6 and 15 months of 
the voucher’s assignment by approximately 7ppts. Positive effects are only found in the long term 
(after three years and until seven years) in Doerr and Stirttmatter (2017), who find small but positive 
effects on both earnings and employment chances.  

Second, a randomised control trial in the US on the Individual Training Accounts scheme offers an 
in-depth comparative assessment of three alternative delivery modes, at different levels of freedom 
of choice for the individuals (see Perez-Johnson, Moore and Santilliano (2011)).424  When comparing 
the standard model of providing individual training entitlement through the “guided choice” to the 
“maximum  choice”  model  (the  latter  features  hardly  any  guidance,  only  initial  orientation),  the 
authors find slightly higher earnings and larger shares of individuals being employed in occupations 
they had trained for in the maximum choice model.  

In conclusion, there appears to be no evidence that increasing freedom of choice affects learning 
behaviour in a way that negatively influences the labour market outcomes for the individuals. 
The available evidence suggests that effects of autonomy on earnings and employment prospects are 
either neutral or slightly positive, especially in the medium to long term, irrespective of a modest shift 
towards more general/transversal types of training. This is consistent with the finding of a growing 
importance of transversal skills on the labour market (Annex 10). 

2. Evidence on the costs of providing training entitlements 

Evidence on the direct costs of training entitlements:  A first question is how much it will cost funders 
of training entitlements to fund an hour of training for a recipient of training entitlements. Looking at 
EU-level Simplified Cost Options (SCOs) for reimbursement under the ESF calculated on the basis 
of data from the CVTS on training costs incurred by employers. Median costs per training hour are 
estimated to be €21.88 on average for EU-27 (though with large differences across Member States, 
ranging between €0.27 in RO to €58.02 in SE).   

By contrast, the average hourly cost on training funded under the French CPF is significantly lower, 
around  €15.  A  first  explanation  of  the  difference  is  that  the  training  SCO  includes  travelling  and 
subsistence costs which are not covered by CPF training entitlements. A second explanation concerns 

422

423

 Rinne et al. (2013) Vouchers and caseworkers in training programs for the unemployed. Empirical Economics 
  Doerr  and Stirttmatter  (2017), Assignment  Mechanisms,  Selection  Criteria,  and  the  Effectiveness  of  Training 
Programs,  No  1421,  Economics  Working  Paper  Series,  University  of  St.  Gallen,  School  of  Economics  and  Political 
Science 
424

 Perez-Johnson, Moore & Santilliano (2011): Improving the Effectiveness of Individual Training Accounts: Long-Term 
Findings  from  an  Experimental  Evaluation  of  Three  Service  Delivery  Models,  Final  Report,  Mathematica  Policy 
Research. 

167 

 
                                                 
the  types  of  training  chosen:  about  30%  of  training  courses  available  under  the  CPF  are  offered 

remotely/online, likely reducing average costs.

425

Evidence on the indirect/ administrative costs of training entitlements: In addition to the direct costs 
of training entitlements, one-off and recurrent indirect/administrative costs will be incurred for the 
set up and operation of a training entitlement scheme. One-off costs might comprise the design and 
set-up of the IT infrastructure (including online registries and tools to allow for the effective search 
of  training  opportunities),  training  of  the  personnel,  the  development  of  the  policy  and  related 
procedural arrangements, scientific advice, evaluation.  

Recurring costs might include the cost of personnel involved in the processing of the applications and 
the  provision  of  guidance  (which  may  be  compulsory  for  individuals  who  want  to  mobilise  their 
entitlements),  interaction  with  training  providers  and  other  relevant  stakeholders,  ongoing 
monitoring,  ongoing  IT  and  policy  developments.  Also  the  set-up  of  the  registry  of  eligible 
opportunities is included in these costs. For both one-off and recurrent costs, economies of scale are 
expected, e.g. a one-off cost of an IT system for which each additional participant will not raise the 
cost by the same amount, but also certain recurrent activities (e.g. monitoring, training of personnel, 
evaluation procedures etc.) that are likely to become more efficient for each additional participant.  

The details of such costs inevitably depend on the framework currently in place in a Member State, 
and the different existing administrative arrangements thereof. However, some insights can be drawn 
based on current or past training entitlement schemes that allow identifying some benchmark delivery 
costs for training entitlements. Unfortunately, such costs are seldomly presented in detail. However, 
the following four sources allow for an estimation of administrative costs: 

-  The education Bonus (BIP or Bildungsprämie) Programme in DE and related evaluation
-  The 2000-02’s ILA in England and related review from the Parliament 
-  Evidence from the French CPF (see Annex 14) 
-  The  new  STAP  scheme  to  be  introduced  in  the  Netherlands  in  2022,  but  currently  under 

426

development (see Annex 13, case study 5).  

These schemes differ on several dimensions. The most notable for a comparative assessment and in 
order to identify a range of proportions to estimate administrative costs are: 

-  The size of the target group: the number of beneficiaries is largest in the French CPF (as of 
early 2021, around 15 million accounts created), followed by the ILA in England (around 
2.6 million accounts created over 2000-2002) and, at a large distance, the small and targeted 
educational bonus in Germany (less than 25 thousand annual participants). For the STAP, 
200,000 beneficiaries are estimated per annum based on an entitlement of €1 000 per person. 
-  The value of entitlements: STAP will be largest, followed by the French CPF (either €500 or 

800), the German educational Bonus (between €340-380) and the UK ILA (≃ €200). 
-  The conditions for mobilising entitlements: The German educational Bonus requires an in-
person meeting with a counsellor, while the Dutch STAP, French CPF and UK ILA 
will/do/did not. 

425

 See  Annex 14 and PPMI (2021): ‘Off-the-shelf’ solutions for post-2020: A study complementing the ESF+ impact 

assessment / Ad Hoc Report on feasibility of ‘off-the-shelf’ tools for Individual Learning Accounts. 
426

 Kantar/FBB/IAW (2019), Evaluation des Bundesprogramms Bildungsprämie (BIP). 

168 

 
  
 
                                                 
-  The running time of the scheme: the CPF is the scheme running for the longest time (around 
7 years, despite several reforms and subsequent updates), followed by the German BIP (5 
years) and the English ILA (two years before being discontinued). The STAP has an initial 
budget commitment for 5 years. 

-  The type of support: the CPF entails a personal account. In the case of the educational bonus 

and STAP, training entitlements do not accumulate over time. 

Table A11.3 provides a summary of the identified administrative costs, and puts them in relation to 
the direct soending on training entitlements.  Despite some uncertainties in the estimations due to lack 
of precise figures on the administrative costs, the available data is suggestive of significant economies 
of scales: administrative costs appear to decrease in the size of target group, entitlement value and 
running time of the scheme. Moreover, scale seems to be more important than potentially higher costs 
due to the existence of personal accounts.  

The STAP scheme in the Netherlands provides particularly useful insights as such costs are currently 
very much the focus of the planners of the scheme The estimated recurrent administrative costs for 
running the STAP scheme is €21.5 million per year for manageing €200 million of training costs (or 
10.75% of the cost of training purchased). These administrative costs include €16.9 million staff costs 
(for 160 FTE jobs for a new established unit within the implementing agency UWV), €0.7 million 
housing costs, €3.9 million maintenance costs for the online platform, and €70 thousand  maintenance 
costs for the education register.  These  structural  costs relate to  running the client  contact  centres, 
enforcement, implementing procedures, maintenance of online portal, education register etc. The total 
one-off costs for setting up the scheme is estimated at €20.7 million. See the NL case study in Annex 
13 for further details. 

The CPF has a three-year goal and performance contract (2020-2022) with a budget of €100 million. 
This budget includes the development of the CPF App, the website, the online portal, the management 
of the portal, the search engine etc.

169 

 
Table A11.3: Overview of administrative cost for training entitlement schemes 

Name  of  the 
scheme 

Type 

No  of  beneficiaries 
/individual 
trainings 
purchased 

Running time  Value 

of 

the 

training 
entitlement 

of 

Value 
training 
purchased 

Total  adm  costs 
as  a  share  of 
training 
purchased 

Recurrent  adm 
costs as a share of 
training 
purchased 

French CPF 

Broad scope  ≃ 3.5 million 

≃ 7 years 

value 

yearly 
€500-800, 
of 
(avg. 
training  purchased 
over last 18 months 
≃ €2,400) 

≃  €2.33  billion 
over  the  last  18 
427

months

≃ 2%428 €100 mil 
years 
3 
over 
(recurring) 

N.a. 

English ILA 

Broad scope  ≃ 2 million 

≃ 2 years 

≃ €200 

≃ £235 mil 

German BIP 

Targeted

431

<25 
year 

thousand  per 

6 years 

€340-380 

€34.5 mil 

429

≃ 16%

432

≃ 54%

430

≃ 10%

433

≃ 20%

427

428

 Monthly and cumulative purchases through Moncompteformation, from 12/2019 to 5/2021.  
 The estimate is calculated assuming a total value of training for 36 months that slightly more than double that of the last 18 months, which showed a rapidly increasing trend in 
 This is calculated as total cost paid to the implementing body Capita (37.6 million pounds) over the total training incentives (235.1 million pounds) in 2000-02. 

expenditure. 
Capita was the entity in charge of the design and implementation of the scheme.  Source: https://publications.parliament.uk/pa/cm200203/cmselect/cmpubacc/544/54403.htm.   
429

 This is calculated as total cost paid to the implementing body Capita (37.6 million pounds) over the total training incentives (235.1 million pounds) in 2000-02. Capita was the 

429

entity in charge of the design and implementation of the scheme.  Source: https://publications.parliament.uk/pa/cm200203/cmselect/cmpubacc/544/54403.htm.   
430

 This is calculated based on the 2001 (second year) only, given the first year was likely dominated by set up costs and the third by the closing of the scheme (with much less 

trainings purchased).  
431

 Income-tested, for low income individuals, 50% cost-sharing up to €500. 
 These include: the cost of administration of the leading entity (BVA), Scientific monitoring (BIBB), the IT system, the Hotline and a Final evaluation. Counselling services and 

432

related training for counsellors are excluded from the estimate, as they cover a different policy measure. 
433

 Average cost for the “variable administrative costs”, as identified by the authors of Kantar/FBB/IAW (2019), Evaluation des Bundesprogramms Bildungsprämie (BIP). Author‘s 

calculations based on the annual variable cost weighted by the number of vouchers used every year.  

170 

 
 
 
 
 
  
 
                                                 
Dutch STAP 

Broad scope  Target  of  200,000 
p.a  but  based  on 
per 
€1,000 
entitlement 
(the 
actuall  number  of 
beneficiariers 
expected 
be 
to 
higher,  as  not  all 
will 
applicants 
request 
the 
maximum €1,000) 

Initial 
budget 
commitment for 
5 years (starting 
1 March 2022). 

 As requested, up to 
of 
a  maximum 
€1,000. 

≃ 10.75%

435

€200 million p.a   One-off  cost  of 
€20.7  million  (≃ 
10.35% of training 
entitlement budget 
for one year); plus 
running 
annual 
costs  of  €21.5 
434

million

434 The total incidental costs for setting up the STAP scheme is estimated at €20.7 million. This includes €18.2 million for setting up the administration of the scheme by UWV 
(including the €12.12 million for setting up the online platform for the STAP budget) and €2.5 million for setting up the training register (by DUO).  
435

 The total annual recurrent cost is estimated at €21.5 million. This includes €21.5 million for running the scheme (by UWV) and €70 thousand for maintenance of the training 

register (by DUO). The maintenance costs of the training register will gradually decrease to €35 thousand a year, after 2 years of implementation. 

171 

 
  
 
 
 
                                                 
ANNEX 12: QUANTIFYING IMPACTS 

This  Annex  provides  quantitative  estimates  of  the  impacts  of  the  policy  packages  introduced  in 
Section 5.4. Since key parameters of the policy measures are left to Member States, the approach 
taken  is  to  estimate  impacts  for  different  implementation  scenarios  concerning  plausible  priority 
target groups selected on the basis of the problem analysis in Section 2, for parameters chosen on the 
basis of the evidence review in Annexes 10 and 11 (also see Annex 4 for a summary of the analytical 
approach). This Annex consists of three parts: 

Annex 12A – Estimates the expected increase in training participation resulting for the different 
policy packages and implementation scenarios, driving the estimates of further impacts in parts B and 
C.  This  part  of  the  Annex  shows  how  the  impact  on  participation  rates  is  estimated,  which 
assumptions  are  made  regarding  deadweight  losses,  and  how  impacts  could  vary  across  Member 
States and for different groups.  

Annex 12B- Presents the results of the cost-benefit analysis, comparing the costs and benefits for 
the different policy packages and implementation scenarios. 

Annex 12C – Estimates of the long-run and general equilibrium effects that are expected to derive 
from the policy packages, based on macroeconomic modelling. 

The estimation of participation rates and impacts is dependent upon the particular policy package and 
implementation scenario under investigation. The following policy package-implementation scenario 
combinations are considered- for consistency with the adult learning participation data available from 
the AES and used for EU-level benchmarking, always for the age range of 25-64 years: 

Package A – Vouchers for priority groups (50 hours of training) 

A1- training entitlements for the low qualified 

A2 – training entitlements for the economically inactive 

A3 – training entitlements for the unemployed 

A4 – training entitlements for employees of SMEs (less than 250 employees) 

A5 – training entitlements for atypical workers (all workers who are not permanent employees) 

Package B1 – Training entitlements for all adults (30 hours of training) 

B1 – training entitlements for all adults  

Package B2 – training voucher to all adults, but with an enhanced package for certain priority groups 
(30 hours of training delivered to all adults, but priority groups provided with an additional 20 hours) 

B2.1 training entitlements for all adults, enhanced for the low qualified 

B2.2 training entitlements for all adults, enhanced for the economically inactive 

B2.3 training entitlements for all adults, enhanced for the unemployed 

B2.4 training entitlements for all adults, enhanced for employees of SMEs 

B2.5  training  entitlements  for  all  adults,  enhanced  for  atypical  workers  (all  workers  who  are  not 
permanent employees) 

Figure  12.1  below  provides  an  overview  of  the  predicted  2030  sizes  of  the  different  target  group 
populations (25-64 years old) at the EU-27 level, in absolute and relative values.  

172 

 
 
Figure 12.1– Predicted EU-27 population aged 25-64 in 2030 by target group  

(absolute values and as a % of the overall 25-64 population)  

Low qualified

Economically inactive

20.9%

20.3%

Unemployed

5.0%

Employees of SMEs (less than 250 employees)

47.1%

Atypical workers (all workers who are not permanent
employees)

18.1%

0

20

40

60

80

100

120

Million

Source: Authors’ elaboration based on Eurostat data (cf. Section A.5 and Annex 8.3).   

A. IMPACT ON ADULT LEARNING PARTICIPATION 

1. Estimating the impact on participation rates: a summary of the approach 

The first step in the methodology is to estimate the impact of a training entitlement intervention on 
participation in training. The steps taken to produce the estimates were as follows: 

 

Identify  the  size  of  the  target  groups,  in  other  words  the  total  number  of  people  in  the 
population,  in  employment,  unemployed,  inactive,  low-qualified,  in  atypical  employment, 
working in small and medium sized enterprises (i.e. with less than 250 employees); 

  For each target group estimate the extent to which people will be likely to redeem the training 
entitlement  and  engage  in  training  (without  considering  possible  deadweight  losses).  The 
proportions are derived from the existing evidence (reviewed in Annex 11); 

  Once the impact on take-up has been estimated, an adjustment is made for deadweight loss so 
as to derive an estimate of the extent to which training is being delivered which would not 
otherwise have taken place. Two estimates are derived: 

o  The extent to which people entered training due to the policy packages who would not 
otherwise have been in any training at all (relevant for impacts on annual participation 
rates); 

o  In addition to the above estimate, the extent to which more frequent or longer trainings 
took place due to the policy packages (relevant for the estimation of broader impacts 
beyond yearly figures of participation in training in Annexes 12B and 12C). 

Because the estimates of net and gross impacts are initially derived from common parameters for all 
EU Member States (cf. Annex 11), there is a need to make an adjustment to take into account that 
some Member States have much higher participation rates than others. In Member States with low 
levels of participation under the baseline scenario, deadweight loss is expected to be lower, as there 
is  less  scope  for  displacing  already  existing  training.  This  is  also  in  line  with  evidence  from  the 

173 

 
 
 
 
literature, which points to lower deadweight losses for individuals with lower participation rates (see 
Annex 11).  

Finally, estimates are produced of the number of people who are likely to be in training in 2030 under 
each  of  the  policy  packages.  They  are  derived  by  looking  at  the  impact  on  the  different  groups 
included in each of the policy packages. The estimates of increases in participation in training are 
produced for 2030, and compared to the predicted changes in adult learning participation until 2030 
under the baseline scenario (cf. Annex 8.3).  

2. Identification of plausible take up rates for individual training entitlements  

The first step was to identify a benchmark estimate (and a range of plausible variation) for the rate of 
the takeup (or take up) of a policy intervention providing individuals with training entitlements. As 
detailed in Annex 11, disaggregated yearly data on schemes providing individual training entitlements 
to sufficiently broad target groups is scarce. One notable exception is a Swiss experiment (randomised 
control trial) reviewed in the scientific literature (see e.g. Schwerdt et al. 2012).436 In Annex 11, take-
up  rates  from  such  experiment  are  compared  with  those  from  other  existing  schemes  for  which 
information  is  available, including  the  French CPF and the SkillsFuture  Credits in  Singapore. An 
analysis  was  done  controlling  for  relevant  features  of  the  schemes  (e.g.  cost-sharing,  expiration 
period, target group, value of the training entitlement, accumulation etc.) and of the target groups. 
The analysis concludes that the Swiss experiment represents a credible middle ground scenario for 
the take up rate of an individual training entitlement that is similar to what is outlined in the policy 
packages.  It  also  indicates  a  possible  range  of  variation  (described  below  in  section  8)  and  some 
expected differences in take up by educational attainment level and value of the training entitlement. 
These coefficients are summarised below in Table 12A.2, together with the assumptions concerning 
deadweight.  

3. The treatment of deadweight loss 

As noted above, one wants to estimate the increase in training (or additionality) which takes place as 
a  consequence  of  the  intervention  (i.e.  the  training  that  would  not  have  taken  place  without  the 
intervention). From any observed increase in training following the intervention there is a need to 
control for deadweight (in this case the volume of training which would have taken place in any case 
without the intervention). In particular, it is useful to distinguish between four different ways in which 
the  action  by  an  adult  who  makes  use  of  her/his  training  entitlements  and  participates  in  training 
translates into additional training. These are summarized in Table 12A.1 (introduced in Annex 11). 

436

  Schwerdt  et  al.  (2012),  The  impact  of  an  adult  education  voucher  program:  Evidence  from  a  randomized  field 

experiment, Journal of Public Economics. 

174 

 
 
                                                 
Table 12A.1: Classification of deadweight vs. additionality for an adult using training entitlements 

I 
I would not otherwise 
have participated in 
training-  
and on top, training  
entitlements  crowd-in 
additional private 
resources or encourage 
me to participate more 
in the future 
Negative deadweight/   
resources crowded in: 
Take-up of training 
entitlements 
underestimates net 
increase in training 
participation  

II 

III 

IV 

I would not otherwise 
have participated in 
training  

I would have 
participated in 
training anyway- but to 
less a degree/at lower 
intensity  

I would have 
participated in 
training- and to the 
same extent I do now 

No deadweight: 
Take-up of training 
entitlements 
corresponds to net 
increase in training 
participation & 
economic additionality 

Partial deadweight:  
economic additionality 
resulting from the 
higher training intensity 

Total deadweight:  
no economic 
additionality 

Increase in the amount of training undertaken 

Deadweight in the sense that the number of 
yearly participants in at least one training (as 
measured in the adult learning participation rate) 
does not increase 

As discussed in more detail in Annex 11, this conceptual overview – which is of great importance in 
assessing  the  costs  and  the  benefits  of  the  intervention  –  was  not  systematically  adopted  in  the 
literature due to issues linked to the experimental design of the studies. In particular, the literature 
typically  only  provides  estimates  comparing  a  response  corresponding  to  column  II  (no 
deadweight/full  additionality)  to  a  response  corresponding  to  column  IV  (total  deadweight/no 
additionality). This is because data on the duration/intensity of training, private co-funding or future 
training participation, which would allow for a more nuanced assessment, is often not available.  

In practical terms, this means that a deadweight loss estimate of 30% (as used below) means that of 
100 additional adult learners making use of their training entitlements during a year, 7 of them would 
not otherwise have participated in learning in that year and 3 would have participated in some form 
of  learning  anyway.  This  estimate  however  overstates  deadweight  loss/  understates  the  additional 
training  induced  by  the  scheme  if  there  is  crowding-in  of  private  resources  (column  I)  and/or  an 
impact on training intensity (column III). There is strong evidence that both factors matter in practice 
(Annex 11). This points to the need of a more nuanced treatment of deadweight loss when assessing 
the  costs  and  benefits  of  training  than  the  one  implied  by  the  headline  deadweight  loss  estimates 
provided in the literature. 

The approach taken is hence the following, summarised in Table  12A.2: for the estimation of the 
impacts of the policy packages on adult learning participation rates in this Annex, a “middle ground” 
deadweight loss estimate is taken from the literature reviewed in Annex 11. For the estimation of 
broader impacts in Annexes 12B and 12C, this deadweight estimate is reduced somewhat on the basis 
of evidence that even among those who would have also participated in training in the absence of the 
scheme, the scheme resulted in an increase in training intensity. Note that this only partially addresses 
the conceptual issues for measuring additionality highlighted by Table 12A.1. To provide a range of 

175 

 
 
 
 
 
plausible outcomes in view of the evidence, all Sections in this Annex include sensitivity checks on 
the key parameters in addition to the main/”middle ground” estimate. 

Table 12A.2: Estimates used to derive estimates of the impact on participation levels and broader 

impacts 

Gross  impact  (i.e.  the  take  up  rate  of  training 
entitlements, regardless of any deadweight) 

Middle Ground 
18.4 % for the general population and 9.5% for the 
 All the other 
low qualified (Schwerdt et al. 2012).
target  groups  use  the  average  rate  in  absence  of 
more granular and consistent info on heterogeneity. 

437

This  value  rises  to  22%  (13.1%  for  the  low 
qualified) for a 50-hour entitlement. 

See the discussion in Annex 12A.7 and the evidence 
review in Annex 11. 

Deadweight-  for  calculating  impacts  on  adult 
learning participation rates in Annex 12A 
Deadweight-  for  assessing  broader  impacts  of 
increased participation in Annexes 12B and 12C 

30% (Schwerdt et al. 2012) 

22.8% (Schwerdt et al. 2012 corrected for the share 
of  people  who  trained  more  as  per  Messer  and 
Wolter 2009). See Annex 11. 

4. Accounting for differences in Member State’s participation levels 

The  discussion  above  clarifies  that,  to  identify  the  net  increase  in  participation  in  training  that  is 
caused  by  the  training  entitlement  (the  true  effect  the  policy),  an  estimate  is  required  of  the 
deadweight  associated  with  it.  This  estimate  needs  to  be  derived  from  a  randomised  control  trial 
(RCT)  so  that  it  is  possible  to  observe  the  behaviour  of  those  who  were  provided  with  a  training 
entitlement (the treatment group) and those who were not (the control group) while ensuring that like 
is being compared with  like. This is necessary as the motivation to learn might vary substantially 
across individuals irrespective of any background feature one can observe and measure, making it 
impossible  to  account  for  self-selection  in  training  based  on  observable  characteristics  of  the 
participants.  However,  there  are  relatively  few  RCTs  which  examine  training  entitlements  or 
vouchers. Schwerdt et al. (2012) is one of the few RCTs which has been published in a peer reviewed 
journal, and the only one where an experiment is done with a voucher that is offered to a broad target 

group (i.e. a randomly selected sample of the whole population aged 20-60).
 Hence, the impact 
assessment  relies  on  this  paper  as  a  source  of  information  on  deadweight.  As  Switzerland  has  a 
relatively high training participation rate, there is a need to adjust the estimates provided by Schwerdt 
et al. (2012) so that deadweight estimates better reflect the conditions in the EU Member States. 

438

Participation rates vary substantially also between EU Member States. It seems reasonable to assume 
that where participation rates are relatively high, this might limit the scope for further net increases 
in  participation  to  take  place  (or  at  least  increases  not  subject  to  deadweight  loss).  For  instance, 

437

 Schwerdt, G. et al. (2012):  The impact of an adult education voucher program: Evidence from a randomized field 

experiment, Journal of Public Economics. 
438

 Schwerdt, G. et al. (2012):  The impact of an adult education voucher program: Evidence from a randomized field 

experiment, Journal of Public Economics. 

176 

 
 
 
 
 
 
 
                                                 
arguably there is more scope to increase rates in Romania where the AES reported that 6 per cent of 
individuals had trained in the last 12 months than in, say, Sweden where the corresponding rate was 
59 per cent. This is also in line with evidence from the literature, which points to lower deadweight 
losses for groups of individuals with lower participation rates (see Annex 11).  

To correct for this, Member State-specific deadweight losses have been estimated as follows. For a 
Member State with the same participation rate as Switzerland, we assume that the level of deadweight 
will  be  the  same  as  in  Schwerdt  et  al.  (2012)  –  i.e.  30  per  cent.  For  countries  with  different 
participation rates, deadweight losses are proportionally adjusted. To do this, the gross participation 
rate in country x is divided by that for Switzerland (62 per cent) and multiplied by the deadweight 
factor (i.e. 30 per cent). For example, if the case of Romania is taken, the calculation is: 

 

5.9 (the rate for Romania) /61.9 (the rate for Switzerland) *0.3 (the deadweight reported in the Swiss study). 
This gives an adjusted estimate for Romania of 0.03, whereas that for Sweden is 0.27. 

In essence, one is saying that the deadweight in Romania is expected to be proportionately lower in 
Switzerland to the same extent that its adult learning participation rate is lower, to account for the 
lower risk of crowding out private training investments.  

5. Producing estimates for 2030 

Estimates are produced relative to the baseline scenario and for 2030 (see Annex 8.3). Labour Force 
Survey data have been weighted by the projected change in population in the EU until 2030. 

6. Estimating the impact of additional training entitlements on the take up rate 

As part of the analysis an indication is required about what might happen if the amount of training 
entitlements/ the duration of training that can be purchased with them is increased for priority target 
groups.  The  evidence  from  the  Swiss  voucher  scheme  indicates  that  if  the  value  of  a  voucher  is 
increased from a low to middle value (i.e. from approx. €160 to approx. €600 in the Swiss case), this 

can have a substantial increase on participation rates.
to high value (i.e. approx. €600 to approx. €1 200), the resulting increase is smaller. 

 But if the value is increased from a middle 

439

On the basis of the evidence review from Annex 11, for our “middle ground” scenario we assume a 
take  up  rate  of  18.4  per  cent  within  a  year  for  training  entitlements  worth  30  hours  of  training 

(corresponding  to  approx.  €381  on  average  across  EU  Member  States
),  which  we  assume  to 
increase slightly to 22 per cent for training entitlements worth 50 hours (approx. €631 on average 
across EU Member States). Due to evidence of a lower take-up of the low qualified, the corresponding 
assumed take-up rates among the low-qualified are 9.5 and 13.1%, respectively (Annex 11).  

440

7. Estimated impacts on participation rates 

Based on the estimation process described above, Table 12A.3 provides an estimate of the additional 
number  of  people  likely  to  be  in  training  –  controlling  for  deadweight  –  in  the  EU-27  and  each 
Member State by 2030 for each of the policy scenarios outlined in the previous section.  

439

 Messer, D. and Wolter, S. (2009):  Money matters: evidence from a large-scale Randomized field experiment with 

vouchers for adult training.  
440

 The amounts required are country-specific as they depend on national training costs. See Annex 12B for a discussion 

of how these have been estimated.  

177 

 
 
                                                 
Table 12A.3 shows the potential additional participation in training by Member State and for the EU-
27  and  for  the  different  packages  and  sub-packages,  a  possible  33  million  participants  under  B1 
(entire adult population of working age receives an entitlement) representing a gain in participation 

441

 The targeted vouchers deliver lower volumes  and 
of 14.1 percentage points  (see Table 12A.4).
marginal gains as would be expected whilst the B2 sub-packages deliver higher volumes and rates 
than B1 as a result of additional financial incentives for priority groups. As increases depend on the 
size of the target group, these are comparatively larger for target groups that are overrepresented in a 
given country. For instance, the large proportion of low qualified (A1) in Italy means that, if targeted, 
they would raise overall participation levels more than in Finland, where they are underrepresented. 
Increases are also higher in countries with lower levels of participation, given the smaller estimated 
deadweight loss.  

441

 Note that sub-packages cannot be aggregated given overlaps in the target groups. 

178 

 
 
 
                                                 
Table 12A.3: Net increases in participation in training in ppts and absolute values, by Member State and Policy Package  

Policy Packages 

EU-27 

Belgium 
Bulgaria 
Czechia 
Denmark 
Germany 
Estonia 
Ireland 
Greece 
Spain 
France 
Croatia 
Italy 

Cyprus 

Latvia 

Lithuania 

Luxembourg 

Hungary 

Malta 

Netherlands 

Austria 

Poland 

Portugal 

Romania 

Slovenia 

Slovakia 

Finland 
Sweden 

A1 

5.8 

2.4 
2.4 
2.2 
0.8 
1.9 
1.6 
1.1 
1.6 
2.7 
4.4 
2.1 
1.7 
4.4 

1.9 

1.0 

0.6 

2.4 

1.4 

4.8 

1.9 

1.6 

0.9 

4.9 

2.6 

1.2 

0.9 

0.9 
1.3 

A2 

9.2 

3.9 
4.4 
4.2 
3.1 
2.7 
2.7 
2.8 
3.9 
5.0 
3.7 
4.0 
5.4 
5.6 

3.3 

2.8 

2.9 

3.8 

3.7 

3.7 

2.8 

3.4 

4.7 

3.3 

5.0 

3.5 

4.1 

2.5 
1.6 

A3 

2.2 

0.9 
0.6 
0.8 
0.4 
0.7 
0.5 
1.0 
0.6 
2.4 
2.0 
0.9 
1.0 
1.1 

1.0 

1.2 

1.5 

0.7 

0.5 

0.5 

0.3 

0.6 

0.4 

0.9 

0.7 

0.7 

1.0 

0.9 
1.0 

A4 

18.2 

7.6 
6.0 
9.5 
11.0 
7.0 
7.7 
9.6 
6.8 
7.7 
7.8 
6.5 
9.2 
7.3 

8.6 

9.3 

9.4 

5.9 

6.7 

6.9 

4.3 

6.8 

8.3 

8.7 

10.3 

5.7 

8.3 

7.9 
7.7 

A5 

7.1 

3.0 
2.2 
2.1 
3.1 
2.2 
2.6 
1.4 
2.7 
3.7 
1.9 
3.1 
3.3 
3.3 

3.2 

1.4 

1.8 

1.0 

1.8 

3.2 

3.6 

2.1 

5.1 

4.5 

2.8 

2.9 

2.7 

3.2 
2.9 

B1 

33.6 

14.1 
13.8 
17.0 
15.7 
12.5 
13.0 
14.5 
13.1 
16.5 
14.9 
12.8 
15.3 
14.5 

13.2 

13.9 

15.5 

13.4 

12.0 

14.6 

11.7 

12.0 

16.0 

14.0 

17.7 

13.7 

13.4 

12.4 
11.6 

B2.1 

35.0 

14.6 
14.4 
18.0 
17.4 
13.2 
13.9 
15.7 
14.0 
17.2 
14.5 
13.4 
16.4 
14.1 

14.0 

15.2 

17.2 

13.9 

12.8 

13.9 

12.3 

12.8 

17.6 

13.2 

18.5 

14.9 

14.8 

13.6 
12.4 

B2.2 

35.1 

14.7 
14.5 
17.7 
16.3 
13.0 
13.4 
14.9 
13.7 
17.4 
15.5 
13.4 
16.2 
15.4 

13.7 

14.3 

16.0 

14.0 

12.6 

15.2 

12.1 

12.5 

16.7 

14.5 

18.5 

14.3 

14.1 

12.8 
11.8 

B2.3 

34.0 

14.2 
13.9 
17.2 
15.8 
12.6 
13.1 
14.6 
13.1 
16.9 
15.2 
12.9 
15.4 
14.6 

13.4 

14.1 

15.7 

13.5 

12.1 

14.7 

11.7 

12.1 

16.0 

14.1 

17.8 

13.8 

13.6 

12.6 
11.7 

B2.4 

36.6 

15.3 
14.8 
18.6 
17.5 
13.7 
14.3 
16.0 
14.2 
17.8 
16.1 
13.8 
16.8 
15.6 

14.6 

15.4 

17.0 

14.3 

13.1 

15.7 

12.4 

13.1 

17.3 

15.4 

19.4 

14.7 

14.8 

13.7 
12.8 

B2.5 

34.8 

14.5 
14.1 
17.4 
16.3 
12.9 
13.4 
14.7 
13.5 
17.1 
15.2 
13.3 
15.8 
15.0 

13.7 

14.1 

15.8 

13.5 

12.3 

15.1 

12.3 

12.3 

16.8 

14.7 

18.2 

14.2 

13.9 

12.9 
12.0 

Million 
people 

Percentage 
Points 

179 

 
 
 
 
8. Sensitivity checks 

Given that there is a degree of uncertainty around the level of (i) take-up rates and (ii) deadweight 
loss, there is an interest in assessing their implications– i.e. to test the sensitivity of the overall of 
additionality to the assumptions which are made about take-up rates and deadweight.  

To this end, it was decided to estimate the impacts of relatively high (pessimistic) and low (optimistic) 
levels of deadweight and take up rates. The relatively high and low estimates were obtained from 
looking at the variation reported around the estimates contained in the scientific literature as discussed 
in Annex 11 and recalled here below. 

Table 12A.4: Sensitivity checks 

Sensitivity check 

Choice of parameters 

Deadweight (high) 

Deadweight loss (low) 

High take-up rate 

Low take-up rate 

442

60%, scaled down by target group and MS as per the procedure 
described in section 4.  
This value is drawn from Hidalgo et al. (2014), which suggest that 
deadweight can be as high as 60 per cent.
0% 
There is evidence from the CPF in France (Annex 14) and Schwerdt 
et al. (2012) that the provision of training entitlements stimulates 
additional private investment. There is also evidence of significant 
impacts of training entitlements on future training intentions from 
both Schwerdt et al. (2012) and Hidalgo et al. (2014). In order to take 
into account these impacts, a sensitivity check is considered where 
they offset deadweight losses and reduce them to 0. 
30%, scaled by target group and value of the voucher following the 
same approach of the main estimates  
10%, scaled by target group and value of the voucher following the 
same approach of the main estimates 

The data reveals that if different levels of take up or deadweight arise then the impact on levels of 
participation can be significant. The baseline data refers to the participation level which is likely to 
arise in absence of any intervention.  

The main estimate is based as in Section 7 on the the level of take-up and deadweight in the Swiss 
experiment  reported  by  Schwerdt  et  al  (2012).  This  clarifies  that  policy  packages  which  offer 
individual training entitlements to the whole population (B1 and B2.x) would suffice to reach the 
objective of at least 60% of the population in training by 2030.  

The  following  rows  refer  to  differing  levels  of  take-up  and  deadweight  as  explained  above.  They 
show  that  even  in  presence  of  high  deadweight  loss  or  low  take-up  rates,  packages  B  would  be 
expected  to  increase  learning  participation  significantly,  with  average  net  increases  in  training 
participation  of  around  nine  percentage  points  vis-à-vis  the  baseline.  Levels  in  2030  would  be 
expected to stay slightly below, but come close to, the 60% target for EU-27. 

442

  Hidalgo,  D.,  Oosterbeek,  H. and  Webbink,  D.  (2014):  The  Impact of  Training  Vouchers  on  Low  Skilled  Workers. 

Labour Economics. 

180 

 
 
 
 
                                                 
Table 12A.5: Overall participation rates at the EU-27 by policy package and target group - baseline, main estimate and sensitivity checks 

Package ID 

A1 

A2 

A3 

A4 

A5 

B1 

B2.1 

B2.2 

B2.3 

B2.4 

B2.5 

Policy Packages 

Focus of the 
package 

Low 
qualified 

Inactive  Unempl 

Predicted participation 
rate (baseline) 

Target group  

23.4 

Whole population  48.6 

Main estimate 

Middle ground take-up 
and DWL 

Target group  

35.0 

Whole population  51.0 

Sensitivity checks 

Low (0%) deadweight 
loss 

High (60%) deadweight 
loss 

High take-up rate 

Low take-up rate 

Target group  

36.5 

Whole population  51.4 

Target group  

33.4 

Whole population  50.7 

Target group  

43.4 

Whole population  52.8 

Target group  

30.4 

Whole population  50.1 

27.7 

48.6 

46.7 

52.5 

52.0 

53.1 

43.5 

51.8 

65.0 

55.7 

42.0 

51.1 

33.8 

48.6 

52.2 

49.5 

55.8 

49.7 

48.2 

49.3 

68.8 

50.4 

45.8 

49.2 

Workers 
in SMEs 

Baseline 
54.6 

48.6 

70.8 

56.2 

76.6 

59.0 

64.3 

53.2 

89.6 

65.1 

66.6 

54.3 

Workers 
not in 
permanent 
empl 

General 
population 

General 
+ low 
qualified 

General 
+ 
Inactive 

General 
+ 
Unempl. 

General 
+ 
Workers 
in SMEs 

General 
+ not in 
perm 
empl. 

52.9 

48.6 

69.3 

51.6 

74.9 

52.6 

63.0 

50.4 

87.9 

55.0 

64.9 

50.8 

48.6 

62.7 

67.0 

57.9 

67.0 

58.6 

23.4 

48.6 

35.0 

63.2 

36.5 

67.8 

33.4 

58.2 

43.4 

79.7 

30.4 

59.0 

27.7 

48.6 

46.7 

63.3 

52.0 

67.8 

43.5 

58.4 

65.0 

79.6 

42.0 

59.0 

33.8 

48.6 

52.2 

62.8 

55.8 

67.2 

48.2 

58.0 

68.8 

78.9 

45.8 

58.7 

54.6 

48.6 

70.8 

63.9 

76.6 

68.7 

64.3 

58.6 

89.6 

81.0 

66.6 

59.6 

52.9 

48.6 

69.3 

63.2 

74.9 

67.7 

63.0 

58.2 

87.9 

79.5 

64.9 

59.0 

181 

 
 
 
 
 
9. Estimates on the effects on training participation gaps across countries 

Table 12A.6: Cross-MS comparison of changes vs baseline participation rates, by group of country 

Countries with high participation 
Countries with average participation 
Countries with low participation 
Gap (high - low) 
Reduction in gap (ppts) 
Reduction in gap (%) 
Country with highest values (SE) 
Country with lowest values (RO) 

Baseline 2030  
participation rates 
67.8 
51.4 
27.8 
40.0 

76.6 
7.5 

A1 

A2 

A3 

A4 

A5 

B1  B2.1  B2.2  B2.3  B2.4  B2.5 

69.4 
53.6 
30.1 
39.3 
0.70 
1.8% 
77.9 
10.1 

68.5 
52.3 
28.9 
39.6 
0.40 

74.7 
59.1 
36.7 
37.9 
2.07 

70.5 
53.9 
30.8 
39.7 
0.31 

80.6 
70.9 
65.6 
55.1 
44.2 
32.0 
36.4 
38.9 
3.63 
1.16 
2.9%  1.0%  5.2%  0.8%  8.9%  9.0%  9.4%  9.1%  9.8%  9.1% 
88.6 
78.2 
25.7 
12.5 

80.2 
65.2 
43.7 
36.4 
3.58 

80.6 
65.8 
44.4 
36.2 
3.77 

81.0 
65.8 
44.5 
36.4 
3.59 

80.3 
65.3 
43.9 
36.4 
3.64 

81.3 
66.4 
45.2 
36.1 
3.92 

88.1 
25.3 

84.3 
17.9 

77.6 
8.2 

79.5 
10.3 

89.0 
26.1 

88.4 
26.1 

88.3 
25.4 

89.4 
27.0 

Table  12A.6  highlights  the  likely  variation  in  the  cross-MS  gaps.  The  average  predicted  participation  rate  of  the  countries  having  the  9  highest 
participation rates in 2016 (and 2030) is compared with that of the 9 countries with the lowest participation rates in the same years. This allows an 
appraisal of the comparative effect of the different policy packages in terms of the evolution of the gaps across Member States.  

As apparent from the values highlighted, all policy packages contribute to reducing the gaps in participation rates between countries. However, there 
is significant variation across the policy options. The largest reduction in gaps is observed for the policy packages B1 and B2, with a relative reduction 
of around 9%. Policy packages A1-5 ensure smaller reductions in gaps, given the smaller target groups.  

The averages impacts across country groups mask stronger predicted impacts on participation gaps between specific Member States: under package 
B.1, the increase in adult learning could for instance range from 11.5 percentage points in the Member State with the highest participation rate under 
the baseline scenario (SE) to 17.8 percentage points in the Member State with the lowest (RO). 

182 

 
 
 
  
 
 
 
 
 
10. Estimates on the effects on training participation gaps across target groups 

Table 12A.7: Comparison of changes vs baseline participation rates, absolute values and gaps, by target group and policy package 

Target group 

Indicator 

Baseline 2030  
participation 
rates 

A1 

A2 

A3 

A4 

A5 

B1 

B2.1 

B2.2 

B2.3 

B2.4 

B2.5 

Abs. Value (participation rate) 

Low qualified 

Gap with overall pop (ppts) 

Gap with overall pop (% variation)    

Abs. Value 

Inactive 

Gap with overall pop (ppts) 

Gap with overall pop (% variation)    

Abs. Value 

Unemployed 

Gap with overall pop (ppts) 

Gap with overall pop (% variation)    

Abs. Value 

Gap with large companies (ppts) 
Gap with large companies (% 
variation) 

Abs. Value 
Gap with permanent employees 
(ppts) 

Working in 
SMEs 

Workers not 
in permanent 
empl. 

Gap with permanent employees 
(% variation) 
Source: authors’ elaboration. 

35.0 

16.0 

-36% 

27.7 

23.4 

12% 

33.8 

17.2 

16% 

23.4 

29.1 

15% 

46.7 

8.0 

-62% 

33.8 

21.0 

23.4 

26.1 

4% 

27.7 

22.4 

7% 

52.2 

-2.1 

41% 

-114% 

23.4 

32.8 

30% 

27.7 

33.0 

58% 

33.8 

26.9 

82% 
70.8    
0.83    

-95%    

23.4 

25.2 

27.7 

20.9 

33.8 

14.8 

54.6    

17.0    

52.9    

6.1    

23.4 

28.2 

12% 

27.7 

25.6 

22% 

33.8 

19.5 

32% 

31.8 

30.9 

22% 

43.6 

19.1 

-9% 

49.2 

13.5 

-9% 

35.0 

28.2 

12% 

43.6 

19.6 

-6% 

49.2 

14.1 

-5% 

31.8 

31.5 

25% 

46.7 

16.6 

-21% 

49.2 

14.1 

-5% 

31.8 

31.0 

23% 

43.6 

19.2 

-8% 

52.2 

10.6 

-28% 

69.3    

-10.3    

-269%    

31.8 

32.1 

27% 

43.6 

20.3 

-3% 

49.2 

14.7 

-1% 

70.8    

1.33    

-25%    

31.8 

31.3 

24% 

43.6 

19.5 

-7% 

49.2 

14.0 

-6% 

69.3 

13.8 

-54% 

183 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Another dimension of interest is the evolution of gaps in training participation across target groups. 
Table 12A.7 investigates this issue by displaying the absolute and relative changes in the gaps for the 
five target groups discussed and across all policy packages.     

The values presented suggest the following key findings:  

 

 

if  the  focus  is  on  the  low  qualified,  due  to  their  typically  low  take-up  rates,  then  only  by 
targeting them in A.1 it is possible to reduce their participation gap relative to other adults. 
This,  of  course,  holds  for  an  average  hypothetical  scenario  that  does  not  consider  specific 
outreach strategies put in place to encourage their participation; 
for  the  inactive  and  unemployed,  although  the  main  net  gains  are  generated  by  the  policy 
packages  that  target  them  specifically,  also  B.1-B.2  are  likely  to  positively  influence  their 
participation gaps. This is due to the fact that deadweight loss is on average lower for these 
target groups; 

  whenever a single target group is provided with training entitlements, the related increases in 
participation raise the average participation rate (for the overall population) thus exacerbating 
the gaps of any group with lower-than-average participation rates that is not targeted by the 
same policy;  

  when the target of the policy package is workers in SMEs or not in permanent employment, 
the training entitlements should suffice  to significantly reduce  (workers in SMEs) or close 
(workers  not  in  permanent  employment)  their  participation  gap  vis-à-vis  workers  in  large 
companies and workers in permanent employment, respectively.   

11. Estimated impacts on participation rates for additional target groups 

Under both policy packages, it is left to Member States to specify priority target groups for training 
entitlements, leading to a large number of potential scenarios. This sub-section provides estimates on 
the impacts on 2030 participation rates of providing a 50-hour training entitlement to three additional 
groups  that  are  not  considered  further  in  the  analyses  below,  but  that  are  plausible  priority  target 
groups based on the problem analysis, notably:  

  Low and medium qualified: this extends the target group of policy package A1 to the medium 
qualified, on account for their possible need of specific support as highlighted by the literature 
on skills polarisation443;  

 

Individuals aged 45-64, who may suffer from a higher risk of skills obsolescence444 ; 

  Workers  in  occupations  with  a  high  risk  of  automation,  and  especially  Plant  and  machine 
operators,  workers  in  Craft  and  related  trades,  Skilled  Agricultural  and  Elementary 

occupations

445

 (which currently participate less in learning, see Annex 6.3) 

443

  See  for  instance  Cedefop’s  focus  on  skills  polarisation,  stressing  that  “demand  for  medium-skilled  intermediate 

occupations is falling, while demand in both high-skilled and low-skilled occupations is rising”. 
444

 See amongst others OECD (2019), Working Better with Age, OECD Publishing. In line with this, as indicated in Annex 

12, specific top-ups are granted in the SkillsFuture credit scheme in Singapore to elder individuals.  
445

 ISCO-08, occupations 6, 7, 8 and 9. 

184 

 
 
                                                 
Table 12A.8: Effects  on 2030 participation in training  for three additional  target groups under 

policy package A 

Focus of the package 

Package A -
Low and 
medium 
qualified  

Package A- 
Individuals 
aged 45-64 

Package A-
Workers in 
occupations at 
a high risk of 
automation 

Baseline 

Predicted participation rate 
(baseline) 

Target group  
Whole population 

37.1 
48.6 

Main Estimate (Middle ground take-up and DWL) 

Net increase in AL 
participation 

Overall 2030 AL 
participation rates 

Target group  
Whole population 
Target group  
Whole population 

17.4 

10.6 
52.8 

59.8 

42.0 
48.6 

17.5 

9.2 
59.5 

57.8 

33.6 
48.6 

18.4 

4.3 
52.0 

53.0 

Table 12A.8 above reveals the predicted 2030 participation rates for the three additional target groups 
of policy package A in terms of baseline values, net (marginal) increases and overall participation 
rates.  

Looking at the baseline values, individuals in the age range 45-64 show the smallest gap from the 
participation rate of the overall population, but this is still above 6 ppts. Such predicted gap is over 
11ppts for the low and medium qualified and stretches until 15 ppts for workers in occupations at a 
high risk of automation.  

The net increases in participation are highest for workers in occupations at a high risk of automation, 
given a predicted take-up rate in line with the average and lower-than-average deadweight loss. This 
translates  into  overall  participation  rates  that  would  come  very  close  to  the  population  average, 
closing the gap from 15 ppts to just 1 ppt. However, the impact on the participation rate of the overall 
population  appears  modest  and  not  enough  to  achieve  the  60%  threshold.  This  is  only  due  to  the 
comparatively small size of the target group.  

For individuals aged 45-64, net increases in adult learning participation remain substantial at 17.5 
ppts.  The  impact  on  the  overall  participation  rates  is  significant,  driving  the  population’s  2030 
participation rate to nearly 58 ppts. In this scenario, the participation rate of the target group would 
exceed that of the overall population.  

For the low and medium qualified, a comparatively low deadweight loss is offset by the modelling 
assumption  of  take-up  rates  which  are  below  average  for  the  low  qualified.  The  net  increases  in 
participation rates for the target group remain substantial (17.4 ppts). The net (marginal) impact on 
the population’s 2030 participation rates is very significant too, driving overall participation rates to 
around 60%. The gap between the population and target group’s participation rate would fall from 
over 11ppts to 7ppts.  

B. COST-BENEFIT ANALYSIS OF SHORT TO MEDIUM TERM IMPACTS 

This  sub-section presents  the costs  and benefits  for different stakeholders  that can be  expected to 
result from the policy packages in comparison to the baseline. The cost-benefit analysis (CBA) has 
been undertaken in accordance with the Commission’s Better Regulations Guidelines. It focuses on 

185 

 
 
 
those costs and benefits that can be monetised, in order to calculate an overall benefit-cost ratio. The 
CBA therefore does not take into account the full range of benefits that can be expected to arise from 
increased  participation  in  training,  for  example,  in  terms  of  increased  personal  wellbeing  and 
satisfaction.  The  CBA  is  based  on  a  number  of  assumptions,  derived  from  the  evidence  base  in 
Annexes 10 or 11 or from other evidence, as indicated. 

It  should  be  noted  that  the  policy  options  leave  it  to  Member  States  to  determine  how  to  finance 
training entitlements. For simplicity, the costs of training entitlements are presented here as falling 
on public authorities, but in practice Member States might choose to require participating individuals 
or their employers to provide co-financing.  

Moreover, the analysis in this Annex focuses on the quantification of the expected costs and benefits 
of providing individual training entitlements, in view of the scarcity of quantifiable evidence on the 
policy measures other than training  entitlements in  packages A and  B.  However, evidence on the 
costs  of  infrastructure  such  as  registries,  personal  accounts  etc.  is  considered  in  the  estimates  on 
administrative  costs  in  the  calculations  below.  While  no  quantified  evidence  is  available  on  the 
benefits of paid training leave, the analysis below estimates the costs of time spent in training to allow 
for a comparison with the expected benefits of training entitlements (Sections 12B.6 and 7 of this 
Annex).  Further  expected  impacts  of  the  policy  measures  included  in  the  packages  are  discussed 
qualitatively in Section 7, on the basis of the evidence review in Annexes 10 and 11. 

1. Costs and benefits for individuals 

Costs 

As take up of training entitlements is optional, no costs are imposed on individuals. Instead, such 
opportunity costs are taken into account in the calculation of participation rates. They hence do not 
feature in the core CBA, but are considered separately in Sections 12B.6 and 7 below. 

Whilst Member States might choose to require individuals to co-finance training entitlements (i.e., 
require individuals to pay a share of training costs out of their own pocket), this is not recommended 
under the policy packages. For the purposes of the CBA, it is therefore, assumed that schemes do not 
require individuals to co-finance. Should Member States introduce a co-financing requirement, this 
would be expected to reduce take up rates. 

Benefits 

A number of monetary benefits can be expected to arise from the Training entitlement schemes. 

First,  it  can  be  expected  that  participation  in  training  will  lead  to  an  increase  in  wages  for  some 
employed  participants.  As  noted  in  the  evidence  review  (Annex  11),  the  literature  suggests  it  is 
reasonable to assume that the increase in wages that might arise for employed participants in training 
will be 1 per cent on average after 30 hours training and thus 1.67 per cent after 50 hours (although 
it  is  likely  to  vary  widely  across  the  cohort  of  participants).  As  shown  in  Annex  10,  no  clear-cut 
conclusion  can  be  drawn  from  the  literature  as  to  whether  the  length  of  training  will  generate 
increasing  or  decreasing  returns;  estimates  varied  both  in  sign  and  intensity  and  no  clear  trend  is 
apparent.  For  that  reason,  a  “middle-ground”  approach  is  taken,  which  assumes  constant  returns 
between  30  hours  and  50  hours.  However,  the  sensitivity  of  the  findings  to  adjustments  of  these 
assumptions is shown in Section 12B.5. 

186 

 
 
For the purposes of this CBA, it is also assumed that: 

  Members of the overall working population earn 100% of average annual net earnings per person on average. 
  Low-qualified persons in employment earn 80% of average earnings. 446 Detailed data are not available on the 
earnings of low-qualified persons. However, Eurostat notes that 27% of employees with a low education level 
earn two-thirds or less of the national median gross hourly earnings.447  

  Non-low-qualified persons in employment earn 125% of average annual net earnings on average. If low-qualified 
people are assumed to earn less than the average (e.g. 80%), it follows that other workers must earn more than 
the average. Since Eurostat only provides data at 100%, 125% or more, the figure of 125% is chosen. 

  A certain proportion of participants would have participated in training in the absence of the policy packages. 
The benefits are thus based on net participation rates rather than gross participation rates, i.e. taking deadweight 
loss into account. 

Given the expected net increase in participation in training (i.e. taking into account deadweight loss), 
the expected increase in wages for different types of employed persons and for each policy package 
is as presented in the tables below. 

NB: the tables only include wage increases for those in employment prior to participation in training. 
Wage increases for unemployed of inactive people entering employment are considered later. 

Participation in training can be expected to generate non-monetary benefits above and beyond the 
monetary benefits analysed here, for example, in terms of increased skills, confidence and motivation 
of individuals. 

Table 12B.1: Annual wage impacts for persons in employment (EU27) 

Target group  Annual net 

earnings 
(€)* 

Increase 
in 
earnings 

in 

Average 
increase 
annual 
earnings  per 
person (€) 

Net  participation 
of 
in 
people 
employment (m) 

Increase  in  annual 
earnings 
for 
employed 
participants (€m) 

20 029 

1.67% 

24 005 

1.67% 

334 

401 

3.1 

18.2 

1 041.5 

7 295.9 

24 005 

1.67% 

401 

7.1 

2 836.7 

24 005 

1% 

240 

23.9 

5 731.6 

Low-qualified 
(50 hours) 

SME 
employees  (50 
hours) 

Non-permanent 
employees  (50 
hours) 

Adult 
population  of 
working 
age 
(30 hours) 

*Source: Eurostat (online data code earn_nt_net) 

446 Eurostat provides earnings figures at 50%, 67%, 80%, 100%, 125% or more. 
447 https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Earnings_statistics#Low-wage_earners  

187 

 
 
                                                 
Table 12B.2 - Annual wage impacts of policy packages (EU27) 

Package / Target groups 

Net participation of people in 
employment (m) 

Increase  in  annual  earnings 
for  employed  participants 
(€m) 

A.1 Low-qualified 

A.2 Inactive 

A.3 Unemployed 

A.4 SME employees 

A.5 Non-permanent employees 

B.1 Adult population of working age 

B.2.1 Adult population + top-up for low 
qualified 

B.2.2  Adult  population  +  top-up  for 
inactive 

B.2.3  Adult  population  +  top-up  for 
unemployed 

B.2.4  Adult  population  +  top-up  for 
SME employees 

B.2.5  Adult  population  +  top-up  for 
non-permanent employees 

3.1 

0.0 

0.0 

18.2 

7.1 

23.9 

24.4 

23.9 

23.9 

26.9 

25.0 

1 041.5 

0.0 

0.0 

7 295.9 

2 836.7 

5 731.6 

7 170.0 

5 731.6 

5 731.6 

9 373.6 

7 147.6 

Second, it can be expected that training will lead to an increase in employment amongst unemployed 
and inactive people. Based on the evidence in Annex 10, the increase in employment that might arise 
for previously unemployed or inactive participants in training entitlement schemes is assumed to be 
2.5 percentage points after 30 hours and 4.175 percentage points after 50 hours. As with impacts on 
wages, a “middle-ground” approach is taken, which assumes constant returns between 30 hours and 
50 hours, given that no clear-cut conclusion can be drawn from the literature as to whether the length 
of training will generate decreasing or increasing returns (see Annex  10). A sensitivity analysis is 
also undertaken based on decreasing returns; see section 12B.5. 

The increase in employment would also lead to increased income, as unemployed or inactive people 
move  from  benefits  to  salaries.  For  the  individuals,  the  net  increase  would  consist  only  of  the 
difference  between  wage  income  and  income  from  benefits.  Given  that  unemployed  or  inactive 
people  tend  to  enter  low-paid  rather  than  high-paid  jobs,  it  is  assumed  that  they  receive  80%  of 
average annual net earnings in their respective countries. 

The table below presents the estimates for the employment impacts and consequent income impacts 
of Training entitlement schemes after one year of operation. It shows the total incomes as well as the 
increase after accounting for the removal of benefits that would otherwise be paid to the unemployed 
188 

 
 
 
or  inactive.  Annual  benefits  for  the  unemployed  or  inactive  are  based  on  the  EU27  average  of 
€10 343.448 Income impacts in future years (for the Year 1 cohort) would most likely be lower, as 
some of the unemployed or inactive would eventually enter employment anyway, even in the absence 
of a training entitlement. This is taken into account in the calculation of the benefit-cost ratios below, 
as explained below (see “Scenario analysis” in section 12B.4 below). 

Table 12B.3: Increased annual income for those entering employment (EU27) 

Target group 

Net 
participatio
n of those 
not in 
employment 
(m) 

Number 
entering 
employmen
t (m) 

Annual 
net 
earning
s per 
person 
(€)* 

Increased 
wages for 
persons 
entering 
employmen
t (€m) 

Savings 
in 
benefit
s (€m) 

Increase 
in 
incomes 
(after 
benefit 
reduction
) (€m) 

Inactive (50 hours) 

Unemployed (50 hours) 

Inactive (30 hours) 

Unemployed (30 hours) 

9.2 

2.2 

7.7 

1.8 

0.4 

20 029 

7 722.58 

3 987.9 

3 734.6 

0.1 

20 029 

1 849.46 

955.1 

894.4 

0.2 

20 029 

3 867.6 

1 997.2 

1 870.4 

0.0 

20 029 

926.2 

478.3 

447.9 

*Single person without children earning 80% of the average earning; Source: Eurostat (online data code earn_nt_net) 

Table 12B.4: Annual income effects of packages (EU27) 

Package / Target groups 

A.1 Low-qualified 

A.2 Inactive 

A.3 Unemployed 

A.4 SME employees 

A.5 Non-permanent employees 

B.1  Adult  population  of 
working ge 

B.2.1  Adult  population  +  top-
up for low qualified 

Number 
entering 
employment 
(m) 

Total income 
of persons 
entering 
employment 
(€m) 

Savings in 
benefits (€m) 

Increase in 
incomes (after 
benefit 
reduction) 
(€m) 

0.1 

0.4 

0.1 

0.0 

0.0 

0.2 

0.3 

2 254.5 

7 722.5 

1 849.4 

0.0 

0.0 

1 164.2 

3 987.9 

955.1 

0.0 

0.0 

1 090.3 

3 734.6 

894.4 

0.0 

0.0 

4 793.8 

2 475.5 

2 318.3 

5 130.8 

2 649.6 

2 481.2 

448 Eurostat (2020): Social protection statistics - unemployment benefits. 

189 

 
 
                                                 
B.2.2  Adult  population  +  top-
up for inactive 

B.2.3  Adult  population  +  top-
up for unemployed 

B.2.4  Adult  population  +  top-
up for SME employees 

B.2.5  Adult  population  +  top-
up 
non-permanent 
for 
employees 

0.4 

0.3 

0.2 

0.2 

8 648.8 

4 466.3 

4 182.5 

5 717.0 

2 952.3 

2 764.7 

4 793.8 

2 475.5 

2 318.3 

4 793.8 

2 475.5 

2 318.3 

2. Costs and benefits for employers 

This  sub-section  lists  the  main  effects  for  employers  whose  staff  participates  in  the  training 
entitlement schemes through Packages A or Package B.  

Costs 

For the purposes of this CBA, it is assumed that the costs will be borne by public authorities and that 
there is no compulsion on employers to meet any costs. The direct costs for employers will therefore 
be zero. Whilst Member States might choose to require employers to co-finance training entitlements, 
this would reduce the costs for public authorities and considered below and hence not affect overall 
benefit-cost ratios. 

Employers may also incur costs due to staff absence during training, either because they voluntarily 
allow to participate in training funded by individual entitlements during working hours, or because 
such obligations  result from  strengthened  paid  training leave provisions. These potential costs are 
considered separately in Sections 12B.6 and 7 below. 

Benefits 

Based on the evidence in Annex 10, the total increase in productivity that would arise for employed 
participants in Training entitlement schemes is assumed to be 2 per cent on average after 30 hours 
and 3.33 per cent after 50 hours (although it is likely to vary widely across the cohort of participants). 
Net  increase  in  productivity  is  calculated  by  deducting  wage  increases  from  the  value  of  total 
productivity.  As  with  impacts  on  wages,  a  “middle-ground”  approach  is  taken,  which  assumes 
constant returns between 30 hours and 50 hours, given that no clear-cut conclusion can be drawn from 
the literature as to whether the length of training will generate decreasing or increasing returns (see 
Annex 10). A sensitivity analysis is also undertaken based on decreasing returns (see Section 12B.5). 

Comprehensive, up-to-date and accurate data on labour productivity was not available to inform the 
CBA. In order not to overstate the benefits to employers, a cautious approach is therefore taken to 
estimating the increase in value added to employers arising from increased productivity. It is assumed 
that added value prior to participation in training is equal to wages, although in practice added value 
would usually exceed wages.  

As with the benefits for individuals, it is assumed that a certain proportion of participants would have 
participated in training in the absence of Training entitlement schemes. The benefits to employers are 
190 

 
 
 
thus based on net participation rates rather than gross participation rates, i.e. taking deadweight loss 
into account. 

Given the expected net increase in participation in training amongst employed persons (i.e. taking 
into account deadweight loss), the expected increase in productivity for each type of employed person 
and for each policy package is as presented in the Tables below. 

Table 12B.5: Annual increase in productivity for persons in employment (EU27) 

Target group 

Increase in 
productivity per 
employee participating 
(€) 

Net participation 
of people in 
employment (€m) 

Total increase 
in productivity 
(€m) 

Net increase in 
productivity 
(€m) 

Package  A 
hours) 

(50 

Low-qualified 

SME employees 

Non-permanent 
employees 

Package  B 
hours) 

(30 

Adult  population 
of working age 

661 

792 

792 

3.1 

18.2 

7.1 

2 058.0 

1 016.5 

14 417.0 

7 121.1 

5 605.4 

2 768.7 

480 

23.9 

11 463.2 

5 731.6 

Table 12B.6: Annual increase in productivity for policy packages (EU27) 

Package / Target groups 

Net  participation  of 
in 
people 
employment (m) 

Total 
increase 
productivity (€m) 

in 

increase 

Net 
productivity (€m) 

in 

A.1 Low-qualified 

A.2 Inactive 

A.3 Unemployed 

A.4 SME employees 

A.5 Non-permanent employees 

B.1  Adult  population  of 
working age 

B.2.1  Adult  population  +  top-
up for low qualified 

3.1 

0.0 

0.0 

18.2 

7.1 

23.9 

24.4 

2 058.0 

1 016.5 

0.0 

0.0 

14 417.0 

5 605.4 

11 463.2 

0.0 

0.0 

7 121.1 

2 768.7 

5 731.6 

14 315.0 

7 145.0 

191 

 
 
 
 
 
 
 
 
 
 
 
B.2.2  Adult  population  +  top-
up for inactive 

B.2.3  Adult  population  +  top-
up for unemployed 

B.2.4  Adult  population  +  top-
up for SME employees 

B.2.5  Adult  population  +  top-
up 
non-permanent 
for 
employees 

23.9 

23.9 

26.9 

25.0 

11 463.2 

5 731.6 

11 463.2 

5 731.6 

18 572.4 

9 198.8 

14 227.3 

7 079.7 

3. Costs and benefits for public authorities 

This sub-section lists the main impacts for public authorities in terms of direct and administrative 
costs of financing the training entitlement schemes, increased tax revenues and savings on benefits 
expeditures. 

Costs 

First, there is the cost of the training entitlements for individuals. Since the unit cost of the training 
entitlements is fixed, the total cost is simply the unit cost multiplied by the number of participants. 

Package A: It is assumed that the training entitlement is 50 hours of training. Based on the evidence 
in Annex 10, it is assumed that the average unit price for one hour of training is €15 in France (as per 
review of evidence in Annex 11). The cost of equivalent training entitlements in other countries is 
calculated using national price deflators for the education sector.449 

Package B.1: it is assumed that all adults (aged 25-64 years) receive a training entitlement of 30 hours. 
Again, the unit price in France would be €15 per hour, whilst the cost in other countries is calculated 
using national price deflators. 

Package B.2.1 to B.2.5: the various target groups would receive a training entitlement of 50 hours, 
whilst  the rest  of the  Adult population  of working age would receive a training entitlement of 30 
hours. Costs per hour are the same as in the other packages. 

Second,  there  is  the  administrative  cost  of  operating  training  entitlement  schemes.  Based  on  the 
evidence in Annex 10, the annual costs of operating schemes are assumed to account for 15% of costs 
of  training  entitlements  within  Package  A.  Since  Package  B  is  estimated  to  have  higher  levels  of 
participation, some economies of scale can be expected in respect of administrative costs; for that 
reason, the annual costs of operating schemes in Package B are assumed to account for 8% of training 
entitlements.450 The examples of previous schemes (Annex 11) did not provide evidence of set-up 
costs separate from recurring costs. Administrative costs are therefore assumed to include both set-

449 Sánchez-Barrioluengo, M. (2016): Expenditure on education in Purchasing Power Standards: A comparison of three 
alternative deflators. €28261 EN. doi:10.2791/690227. European Commission. 
450 As shown in Annex 10, evidence from France suggested an administration cost equal to 2% of the training entitlement, 
whilst in Germany it was 30%. On that basis, a middle ground is taken here, i.e. 15% for the targeted schemes in Package 
A and 8% for the comprehensive schemes in Package B. 

192 

 
 
 
                                                 
up costs and recurring costs. After the first year, operating costs might be expected to fall slightly 
depending on the extent to which any one-off set-up costs are incurred. However, in order to avoid 
understating annual operating costs, it is assumed that they remain constant beyond Year 1. 

The table below presents the estimated cost of each package for EU27. From the table, it can be seen 
that the total cost as a percentage of GDP varies from 0.01% for Package A.3 (50 hours entitlement 
for the unemployed) to 0.20% for Package B.2.4 (30 hours entitlement for all entire Adult population 
of working age, with 20-hours top-up for employees of SMEs). 

These totals for EU27 hide significant variations between Member States in respect of the cost of 
each training entitlement (i.e. taking into account the relative costs of education and training provision 
in each country). 

  The estimated cost of a training entitlement of 30-50 hours is lowest in Bulgaria (€87-145), Romania (€99-165) 
and Lithuania (€145-241). It is highest in Luxembourg (€1 214-2 023), Sweden (€813-1 356) and Denmark (€ 
628-1 046). The average across EU27 is €381-631. 

193 

 
 
Table 12B.7: Annual cost of training entitlements (EU27) 

Package 

Target group 

Gross 
participation 
(m) 

Cost of training 
entitlements (€m) 

Administrative cost as a 
percentage of training 
entitlements 

Administrative 
cost (€m) 

Total cost 
(€m) 

Total cost 
(% of 
GDP*) 

A 

A.1 

A.2 

A.3 

A.4 

A.5 

B 

B.1 

B.2.1 

B.2.2 

B.2.3 

B.2.4 

B.2.5 

Low-qualified 

Inactive 

Unemployed 

SME employees 

Non-permanent 
employees 

low 

Adult  population  of 
working age 
Adult population  
+ 
top-up  for 
qualified 
Adult population  
+ top-up for inactive 
Adult population  
+ 
top-up 
unemployed 
Adult population  
+  top-up  for  SME 
employees 
Adult population  

for 

6.6 

10.7 

2.6 

24.8 

9.5 

44.0 

45.8 

45.8 

44.4 

48.1 

45.6 

4 229.0 

6 579.8 

1 707.5 

15 617.5 

6 044.1 

16 758.6 

19 147.5 

20 036.6 

17 609.3 

24 539.0 

19 769.6 

15% 

15% 

15% 

15% 

15% 

8% 

8% 

8% 

8% 

8% 

8% 

634.3 

4 863.3 

987.0 

7 566.8 

256.1 

1 963.7 

2 342.6 

17 960.1 

906.6 

6 950.7 

1 340.7 

18 099.3 

1 531.8 

20 679.3 

1 602.9 

21 639.5 

1 408.7 

19 018.0 

0.04 

0.06 

0.01 

0.13 

0.05 

0.14 

0.15 

0.16 

0.14 

1 963.1 

26 502.1 

0.20 

1 581.6 

21 351.2 

0.16 

194 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
+  top-up  for  non-
permanent 
employees 

*EU27 GDP at market prices, 2020 (Source: Eurostat)

195 

 
 
Benefits 

Increased participation in training can be expected to improve the public finances in two ways. 

The first monetary benefit for public authorities would consist of increased tax revenues, which would 
arise in two ways. First, the increased added value for enterprises (arising from higher productivity) 
would result in increased tax revenue (from taxes on employers or employees). Second, part of the 
new wages of unemployed or inactive people entering employment would be paid in tax.  

The level of tax revenue will vary widely depending on the structure of business and personal income 
taxes and social security contributions in each Member State, the income level of individuals (i.e. 
higher  paid  participants  would  often  face  higher  tax  rates).  For  the  CBA,  it  is  assumed  that  the 
proportion of increases in  incomes (of employers or employees) that is paid in tax is equal to the 
overall proportion of tax revenue to GDP in each country.451 On that basis, the expected increase in 
tax revenue for each policy package is as presented in the tables below. 

The  second  monetary  benefit  for  public  authorities  would  consist  of  savings  on  benefits  paid  to 
inactive  or  unemployed  people  who  enter  employment.  As  noted  above,  the  proportion  of 
unemployed or inactive participants in training entitlement schemes that would enter employment is 
assumed to be 2.5 per cent after 30 hours and 4.175% after 50 hours. Data from Eurostat suggests 
that the level of benefits paid to each unemployment person in EU27 is on average €10 343.452 Taking 
this average, an estimate of the total savings on benefits is offered in the table below. 

The table below presents the estimates for the improvements to the public finance attributable to the 
training entitlement schemes after one year of operation, the costs of training entitlements and thus 
the net effect on public finances. It should be noted this is a one-year “highest cost scenario” that 
assumes that public authorities bear the full cost, whereas in practice Member States will be free to 
decide how to finance schemes, e.g. whether to require employers or individuals to meet any of the 
costs or whether to fund schemes through levies, taxation, borrowing or cutting other forms of public 
expenditure. 

The table shows that schemes are unlikely to be self-financing within the same year. However, while 
the costs of training entitlements are incurred only in the year of operation, the benefits (e.g. improved 
productivity, more people in employment) can be expected to last into future years. This creates the 
potential for schemes to become self-financing in time. The scenario analysis in the next sub-section 
provides the detailed analysis of this question. 

In future years, the increased tax revenue from those entering employment and the savings on benefit 
(for  Year  1  participants  entering  employment)  would  most  likely  be  lower,  as  some  of  the 
unemployed or inactive might have displaced other hires and the higher productivity levels might 
imply a lower number of workers needed to produce the same output. These longer term or indirect 
effects are left to the general equilibrium analysis in 12C to assess.  

451 Source: Eurostat (Online data code: GOV_10A_TAXAG) 
452https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Social_protection_statistics_-
_unemployment_benefits    

196 

 
 
 
 
                                                 
Table 12B.8: Year 1 net effect on public finances (EU27) 

Package / Target group 

Increased 
tax revenue 
(€m) 

Savings  on 
benefit 
expenditure 
(€m) 

Total 
improvement 
to 
public 
finances (€m) 

of 

Cost 
training 
entitlements 
(€m) 

Net  effect 
on  public 
finances 
(€m) 

A.1 Low-qualified 

1 772.4 

1 164.2 

2 936.7 

4 863.3 

-1 926.6 

A.2 Inactive 

3 174.0 

3 987.9 

7 161.9 

7 566.8 

-404.9 

A.3 Unemployed 

760.1 

955.1 

1 715.2 

1 963.7 

-248.5 

A.4 SME employees 

5 925.4 

A.5 Non-permanent employees 

2 303.8 

0.0 

0.0 

5 925.4 

17 960.1 

-12 034.7 

2 303.8 

6 950.7 

-4 646.8 

B.1  Adult  population  of 
working age 

B.2.1  Adult  population  +  top-
up for low qualified 

B.2.2  Adult  population  +  top-
up for inactive 

B.2.3  Adult  population  +  top-
up for unemployed 

B.2.4  Adult  population  +  top-
up for SME employees 

B.2.5  Adult  population  +  top-
up 
non-permanent 
for 
employees 

6 681.6 

2 475.5 

9 157.2 

18 099.3 

-8 942.1 

7 992.2 

2 649.6 

10 641.8 

20 679.3 

-10 037.5 

8 266.0 

4 466.3 

12 732.3 

21 639.5 

-8 907.2 

7 061.1 

2 952.3 

10 013.4 

19 018.0 

-9 004.6 

9 603.5 

2 475.5 

12 079.1 

26 502.1 

-14 423.0 

7 817.7 

2 475.5 

10 293.2 

21 351.2 

-11 058.0 

4. Estimating overall ratios of benefits to costs 

This Section presents an overall comparison of the costs and benefits of the different packages against 
the baseline. The costs presented above would arise in Year 1, whilst the benefits would mostly arise 
in future years. The analysis presents total costs and benefits to society. It is therefore not necessary 
to introduce any assumptions about the distribution of benefits between employers, individuals and 
public authorities. 

In order to calculate the overall benefit-cost ratio, the following assumptions are made: 

  Schemes operate for one-year with all training taking place within the year; 
  Costs of training entitlements arise within Year 1; 
  One year’s administrative costs are incurred; 
  There is a lag of up to 12 months between participation and increases in productivity (for employed participants). 

All productivity impacts occur at the end of Year 1 (i.e. from the start of Year 2); 

  There is a lag of up to 12 months between participation and entry into employment (for previously unemployed 
or inactive people). All employment impacts therefore occur at the end of Year 1 (i.e. the newly-employed enter 
employment at the start of Year 2), to account for lock-in effects; 

197 

 
 
 
  Productivity impacts are sustained for 5 years, i.e. from start Year 2 to end Year 6; 
  Employment impacts are included for 18 months after the end of the year of operation (i.e. to the middle of Year 
3). This is a  cautious approach that assumes that unemployed or inactive participants  would have eventually 
found work in the absence of any training entitlement. 

  Social discount rate is 4% (as recommended by the Better Regulation Guidelines). 

The  table  below  presents  the  summary  of  the  scenario  analysis.  From  the  table,  a  number  of 
conclusions can be drawn: 

  Benefits will exceed the cost of Training entitlement schemes for all policy packages, provided that productivity 
impacts  are  sustained  for  2  years  (i.e.  end  Year  3)  and  employment  impacts  are  sustained  for  1.5  years  (i.e. 
middle of Year 3). 

  The highest benefit-cost ratios after five years (i.e. end Year 6) are offered by the various sub-packages within 
Policy  Package  B,  as  well  as  by  Policy  Packages  A.4  (SME  employees  50  hours)  and  A.5  (non-permanent 
employees 50 hours). 

If constant returns are assumed, then the costs and benefits of operating schemes in future years (and 
thus the benefit-cost ratios) would be identical to those in Year 1. 

It should be noted that the CBA is based on a partial equilibrium analysis. Some caution is therefore 
required when extrapolating the benefits over several years. Over time, it could be expected that those 
entering  employment  would  gradually  receive  further  increases  in  wages,  as  they  gain  skills, 
experience,  etc.  The  initial  participation  in  training  (funded  by  the  Training  entitlement  scheme) 
might  stimulate  some  individuals  to  participation  in  additional  training,  thus  generating  further 
positive impacts on wages and productivity. Long-term wage impacts in future years might thus be 
greater  than  estimated  here  for  Year  1,  due  to  the  progressive  accumulation  of  human  capital 
investments  and  its  cumulative  effects  on  productivity,  output  and,  in  turn,  aggregated  demand. 
Conversely, some of those entering employment might be made redundant at some point in the future. 
Moreover, some of those entering employment after participation in Year 1 might be hired instead of 
and  not  in  addition  to  other  individuals  not  taking  up  training  (displacement  effect).  Impacts  on 
incomes and public finances in future years might thus be different than estimated here for Year 1. 

Given  these  limits  to  analysis  based  on  partial  equilibrium,  a  complementary  analysis  based  on 
general equilibrium is provided in Annex 12C.

198 

 
 
Table 12B.9 - Comparison of scenarios (one year of costs, benefits sustained over 5 years)  

EU27 

A.1 

A.2 

A.3 

A.4 

A.5 

Low-
qualifie
d 

Inactive  Unempl

oyed 

SME 
employe
es 

Non-
perm 

B.1 

All 

B2.1 

B2.2 

B2.3 

B2.4 

B2.5 

+ 

All 
top-up 
for  low-
qualif 

+ 

All 
top-up 
for 
inactive 

+ 

All 
top-up 
for 
unempl 

+ 

All 
top-up 
for 
SMEs 

+ 

All 
top-up 
for  non-
perm 

Costs (€m) 

Total annual cost of Training 
entitlement schemes (Year 1) 

Benefits (€m) 

Pre-tax increase in enterprise 
higher 
revenues 
productivity (Year 2)* 

from 

Pre-tax  increase  in  income 
entering 
those 
for 
employment (Year 2)* 

4 863.3 

7 566.8 

1 963.7  17 960.1 

6 950.7  18 099.3  20 679.3  21 639.5  19 018.0  26 502.1  21 351.2 

2 058.0 

0.0 

0.0  14 417.0 

5 605.4  11 463.2  14 315.0  11 463.2  11 463.2  18 572.4  14 227.3 

2 254.5 

7 722.5 

1 849.4 

0.0 

0.0 

4 793.8 

5 130.8 

8 648.8 

5 717.0 

4 793.8 

4 793.8 

Total benefits (Yr 2) 

4 312.5 

7 722.5 

1 849.4  14 417.0 

5 605.4  16 257.0  19 445.8  20 112.0  17 180.2  23 366.2  19 021.1 

Total benefits (Yr 3) 

2 935.5 

3 558.5 

852.2  13 286.7 

5 165.9  12 773.5  15 557.0  14 549.8  13 198.9  19 325.3  15 320.9 

Total benefits (Yr 4) 

2 818.1 

3 416.2 

818.1  12 755.2 

4 959.3  12 262.5  14 934.7  13 967.8  12 670.9  18 552.3  14 708.0 

Total benefits (Yr 5) 

2 705.4 

3 279.6 

785.4  12 245.0 

4 760.9  11 772.0  14 337.3  13 409.1  12 164.1  17 810.2  14 119.7 

Total benefits (Yr 6) 

2 597.2 

3 148.4 

754.0  11 755.2 

4 570.5  11 301.1  13 763.8  12 872.8  11 677.5  17 097.8  13 554.9 

Net present value of benefits 

199 

 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
1 year (end Year 2) 

4 312.5 

7 722.5 

1 849.4  14 417.0 

5 605.4  16 257.0  19 445.8  20 112.0  17 180.2  23 366.2  19 021.1 

2 years (end Year 3) 

7 248.0  11 281.1 

2 701.7  27 703.7  10 771.4  29 030.5  35 002.8  34 661.8  30 379.1  42 691.5  34 342.0 

3 years (end Year 4) 

10 066.1  14 697.3 

3 519.8  40 459.0  15 730.7  41 293.0  49 937.6  48 629.6  43 050.0  61 243.8  49 050.0 

4 years (end Year 5) 

12 771.5  17 976.9 

4 305.2  52 704.0  20 491.6  53 065.0  64 274.9  62 038.8  55 214.1  79 054.0  63 169.7 

5 years (end Year 6) 

15 368.7  21 125.2 

5 059.2  64 459.2  25 062.1  64 366.2  78 038.7  74 911.5  66 891.6  96 151.9  76 724.6 

Benefit-cost ratios 

1 year (end Year 2) 

2 years (end Year 3) 

3 years (end Year 4) 

4 years (end Year 5) 

5 years (end Year 6) 

0.9 

1.5 

2.1 

2.6 

3.2 

1.0 

1.5 

1.9 

2.4 

2.8 

0.9 

1.4 

1.8 

2.2 

2.6 

0.8 

1.5 

2.3 

2.9 

3.6 

0.8 

1.5 

2.3 

2.9 

3.6 

0.9 

1.6 

2.3 

2.9 

3.6 

0.9 

1.7 

2.4 

3.1 

3.8 

0.9 

1.6 

2.2 

2.9 

3.5 

0.9 

1.6 

2.3 

2.9 

3.5 

0.9 

1.6 

2.3 

3.0 

3.6 

0.9 

1.6 

2.3 

3.0 

3.6 

* Some of the increases in revenues for enterprises would be paid to employees in higher wages and some would be paid in tax (either directly or indirectly via increased taxes on 
wages). Some of the pre-tax increase in income for those entering employment would accrue to public authorities through personal tax or through the removal of social security 
benefits. It is also assumed that unemployed people would not remain unemployed indefinitely in the absence of Training entitlement schemes but would return to work within 18-
30 months.

200 

 
  
  
  
  
  
  
  
  
  
  
  
Table 12B.10 below presents benefit-cost ratios for each Member State five years after the first year 
of operation (i.e. end Year 6). The table shows that after 5 years, benefit-cost ratios are positive for 
all policy packages in all Member States. 

Both tables show that there are considerable differences in Member States. Such differences reflect 
differences  in  participation  rates,  different  costs  of  training  and  different  levels  of  earnings.  In 
particular, where earnings are high relative to the cost of education and training, this tends to result 
in a higher benefit-cost ratio. 

201 

 
Table 12B.10: Benefit-cost ratios by Member State after 5 years (end Year 6) 

A.1 

A.2 

A.3 

A.4 

A.5 

 Benefit-cost 
ratios 

Low-
qualified 

Inactive 

Unempl 

SME 
employee
s 

Non-
permane
nt 

(end Year 3) 

B.1 

All 

B2.1 

B2.2 

B2.3 

B2.4 

B2.5 

All + top-
up 
low-
qualified 

All + top-
up 
inactive 

All + top-
up 
unempl 

All + top-
up SMEs 

All + top-
up  non-
permane
nt 

EU-27 

Belgium 

Bulgaria 

Czechia 

Denmark 

Germany 

Estonia 

Ireland 

Greece 

Spain 

France 

Croatia 

Italy 

Cyprus 

3.2 

2.6 

4.0 

3.5 

2.8 

3.5 

3.7 

3.8 

2.9 

3.2 

3.0 

2.6 

2.9 

2.6 

2.8 

2.3 

3.4 

2.8 

2.2 

2.7 

3.1 

3.2 

2.4 

2.5 

2.7 

2.4 

2.5 

2.3 

2.6 

2.0 

3.4 

2.8 

2.2 

2.9 

2.9 

2.9 

2.4 

2.5 

2.2 

2.3 

2.4 

2.1 

3.6 

2.9 

4.3 

3.7 

3.5 

4.0 

4.1 

4.3 

3.0 

3.5 

3.6 

2.9 

3.2 

3.1 

3.6 

2.8 

5.2 

4.1 

3.3 

3.7 

4.1 

4.2 

3.6 

3.7 

3.3 

3.0 

3.2 

3.1 

3.6 

2.8 

5.1 

4.2 

3.1 

3.8 

4.2 

3.9 

3.3 

3.6 

3.2 

3.0 

3.1 

2.8 

3.8 

2.9 

5.6 

4.8 

3.4 

4.1 

4.6 

4.2 

3.4 

3.7 

3.3 

3.1 

3.2 

3.0 

3.5 

2.7 

4.8 

4.1 

3.0 

3.6 

4.1 

3.9 

3.2 

3.4 

3.1 

2.9 

3.0 

2.8 

3.5 

2.8 

5.0 

4.2 

3.1 

3.7 

4.2 

3.9 

3.2 

3.5 

3.1 

3.0 

3.1 

2.8 

3.6 

2.9 

4.6 

3.9 

3.4 

4.0 

4.2 

4.3 

3.1 

3.5 

3.5 

3.0 

3.2 

3.1 

3.6 

2.8 

5.1 

4.2 

3.1 

3.8 

4.2 

4.0 

3.4 

3.6 

3.2 

3.0 

3.2 

2.9 

202 

 
 
 
Latvia 

Lithuania 

Luxembourg 

Hungary 

Malta 

Netherlands 

Austria 

Poland 

Portugal 

Romania 

Slovenia 

Slovakia 

Finland 

Sweden 

2.3 

3.9 

1.8 

2.3 

2.9 

3.7 

2.5 

3.4 

2.5 

4.7 

2.1 

2.4 

2.7 

1.8 

2.0 

3.5 

1.5 

2.5 

2.5 

3.1 

2.1 

2.9 

2.2 

4.0 

1.7 

2.2 

2.1 

1.3 

1.9 

3.4 

1.3 

2.3 

2.2 

2.6 

1.9 

2.9 

2.1 

3.8 

1.7 

2.1 

2.5 

1.6 

2.7 

4.2 

2.0 

3.4 

3.3 

4.6 

3.0 

3.7 

2.9 

5.0 

2.3 

2.7 

3.4 

2.4 

2.6 

4.7 

1.9 

2.9 

3.5 

3.6 

2.5 

4.3 

2.9 

6.5 

2.2 

2.7 

3.2 

2.1 

2.6 

4.6 

1.9 

2.8 

3.3 

3.7 

2.5 

4.1 

2.8 

6.1 

2.2 

2.5 

3.0 

2.0 

2.9 

5.1 

1.9 

3.0 

3.5 

4.0 

2.7 

4.6 

2.9 

6.6 

2.4 

2.8 

3.2 

2.2 

2.5 

4.5 

1.8 

2.7 

3.2 

3.7 

2.5 

3.9 

2.7 

5.8 

2.1 

2.5 

2.9 

1.9 

2.6 

4.6 

1.8 

2.8 

3.3 

3.7 

2.5 

4.1 

2.8 

6.1 

2.2 

2.5 

3.0 

2.0 

2.7 

4.5 

1.9 

3.3 

3.4 

4.2 

2.9 

3.9 

2.9 

5.4 

2.3 

2.8 

3.4 

2.3 

2.6 

4.6 

1.9 

2.8 

3.4 

3.8 

2.5 

4.2 

2.9 

6.2 

2.2 

2.6 

3.1 

2.0 

203 

 
 
5. Sensitivity analysis 

The coefficients underpinning the CBA are informed by the evidence in the literature review,  and 
should  be  considered  as  “middle  ground”  estimates  in  view  of  the  literature.  The  purpose  of  this 
section is to assess the robustness of results to alternative assumptions. 

Eight sensitivity analyses have been undertaken: 

First, a moderately higher deadweight loss (DWL). The calculations of conventional measures of 
DWL based on Schwerdt et al. (2012) are inflated by 25% across all policy packages. 

Second, a significantly higher DWL of 60%, drawn from Hidalgo et al. (2014) and scaled by target 
group and Member State as per the procedure described in Annex 12A.8. This can be considered as 
upper bound in terms of the DWL estimates found in the literature. 

Third, an adjustment of  DWL taking into account dynamic effects. Within  the figures above,  a 
conventional measure of DWL is used, which provides an indication of the number of people who 
would not have otherwise trained. This is taken from Schwerdt et al. (2012). As explained in Annex 
11 (cf. columns 3 of Table A11.2), the conventional measure ignores some dynamic effects that will 
affect rates of participation, most notably the likelihood that some people might train more often or 
for longer as a consequence of possessing a training entitlement. An adjusted measure of DWL was 
therefore  used  taking  account  of  such  effects,  as  derived  from  Messer  and  Wolter  (2009).  The 
application of this measures of DWL tends to result in higher participation rates. 

Fourth,  a  lower  productivity  coefficient.  A  cautious  approach  is  taken,  using  a  productivity 
coefficient which is three quarters of the value of the productivity coefficient used above, i.e. 1.5% 
after 30 hours training (instead of 2%) and 2.475% after 50 hours training (instead of 3.3%). 

Fifth, a lower employment coefficient. Again, a cautious approach is taken, using an employment 
coefficient which is three quarters of the value of the employment coefficient used above, i.e. 1.875% 
after 30 hours training (instead of 2.5%) and 3.13% after 50 hours training (instead of 4.175%). 

Sixth, higher training costs. A previous study for the European Commission has established EU-
level simplified cost options (SCOs) under Article 14.1 of the European Social Fund (ESF) regulation. 
The  study  estimated  hourly  costs  of  continual  vocational  training  courses  for  employees  per 

participant training hour incurred by enterprises in each Member State at 2015 values.
 The SCO 
have been updated in line with 2020 values and then applied to all training to be offered under the 

453

proposed training entitlement schemes.
 Using this method, the hourly cost of training is greater in 
every  Member  State  (except  Portugal),  compared  to  the  hourly  cost  calculating  according  to  the 
method described above. 

454

Seventh,  higher  administration  costs.  Across  all  policy  packages,  the  administration  costs  are 
increased by 50%, i.e. from 15% to 22.5% of the cost of training entitlements in Package A and from 
8% to 12% of the cost of training entitlements in Package B. 

453

 PPMI (2018), Developing ‘Off-the-Shelf’ Simplified Cost Options (SCOs) under Article 14.1 of the European 

Social Fund (ESF) regulation. 
454

 The SCO figure for Romania in the report appeared very low (€0.27 per hour at 2015 values). Instead, the next 

lowest figure has been used, i.e. the value for Bulgaria (€5.46 at 2020 values). 

204 

 
 
                                                 
Last, an assumption of decreasing returns to training between 30 hours and 50 hours. The additional 
return between 30 hours and 50 hours is reduced by half in respect of productivity and employment. 
Thus  the  productivity  coefficient  for  50  hours  is  2.67%  (instead  of  3.3%)  and  the  employment 
coefficient is 3.33 (instead of 4.175%). 

NB: it should be noted that a sensitivity analysis of the wage coefficient is unnecessary as it does not 
affect  overall  benefit-cost  ratios,  only  the  distribution  of  productivity  gains  between  employers 
(profits) and employees (wages). 

The results of the sensitivity analysis are presented below. They show that: 

  all packages feature a benefit-cost ratio above one at the latest four years after the first year 
of operation, even when using considerably more pessimistic assumptions concerning DWL, 
porductivity, employment effects, training or administrative costs; 

  using the  simplified cost options calculated under Article 14.1 of the ESF regulation, benefit-
cost ratios are not positive until three years after the year of operation (i.e. end Year 4) or four 
years in the case of packages A.1, A.2, A3 and B.2.2. However, this should be considered a 
pessimistic  scenario,  since  the  SCOs  are  based  on  training  for  employees,  whereas  the 
evidence from the French CPF points to lower hourly costs for ILA-funded training; 

  even using the “upper bound” measure of DWL (60%), benefit-cost ratios exceed or are equal 
to costs within 2 years (i.e. end Year 3) within all the packages, except A.4, A.5 and B2.5. 
However, all benefit-cost ratios are positive within 3 years (i.e. end Year 4); 
the inflated measure of DWL slightly reduces the benefit-cost ratios for all policy packages, 
however,  all  packages  still  feature  a  positive  benefit-cost  ratio  two  years  after  the  year  of 
operation (i.e. end Year 3); 

 

  a  more  dynamic  measure  of  DWL  results  in  higher  benefit-cost  ratios,  due  to  the  higher 

 

 

 

participation rates; 
the benefit-cost ratios are most sensitive to any changes in the productivity coefficient (except 
in  the  packages  that  only  serve  the  inactive  or  unemployed,  i.e.  A.2  and  A.3,  since  by 
definition these packages do not offer benefits for those already in employment); 
the  benefit-cost  ratios  are  only  slightly  sensitive  to  the  higher  estimates  for  administration 
costs; 
the benefit-cost ratios are only slightly affected by assuming decreasing returns to training 
between 30 hours and 50 hours. 

205 

 
 
Table 12B.12: Sensitivity analysis for benefit-cost ratios 

A.1 

A.2 

A.3 

A.4 

A.5 

Benefit-cost ratios  

Low-
qualified 

Inactive  Unempl
oyed 

SME 
employe
es 

Non-
permane
nt 

set 
Benchmark 
parameters (above) 

of 

1 year (end Year 2) 

2 years (end Year 3) 

3 years (end Year 4) 

4 years (end Year 5) 

5 years (end Year 6) 

Moderately 
deadweight loss 

higher 

1 year (end Year 2) 

2 years (end Year 3) 

3 years (end Year 4) 

4 years (end Year 5) 

5 years (end Year 6) 

Upper  bound  deadweight 
loss (60%)  

1 year (end Year 2) 

2 years (end Year 3) 

3 years (end Year 4) 

0.9 

1.5 

2.1 

2.6 

3.2 

0.9 

1.4 

2.0 

2.5 

3.1 

0.8 

1.3 

1.8 

1.0 

1.5 

1.9 

2.4 

2.8 

1.0 

1.4 

1.9 

2.3 

2.7 

0.8 

1.2 

1.6 

0.9 

1.4 

1.8 

2.2 

2.6 

0.9 

1.3 

1.7 

2.1 

2.4 

0.7 

1.1 

1.4 

0.8 

1.5 

2.3 

2.9 

3.6 

0.7 

1.4 

2.0 

2.7 

3.3 

0.5 

0.9 

1.3 

0.8 

1.5 

2.3 

2.9 

3.6 

0.7 

1.4 

2.1 

2.7 

3.3 

0.5 

0.9 

1.4 

206 

B.1 

All 

B2.1 

B2.2 

B2.3 

B2.4 

B2.5 

All + 
top-up 
low-
qualified 

All + 
top-up 
inactive 

All + 
top-up 
unemplo
yed 

All + 
top-up 
SMEs 

All + 
top-up 
non-
permane
nt 

0.9 

1.6 

2.3 

2.9 

3.6 

0.8 

1.5 

2.1 

2.7 

3.3 

0.6 

1.0 

1.4 

0.9 

1.7 

2.4 

3.1 

3.8 

0.9 

1.6 

2.2 

2.9 

3.5 

0.6 

1.1 

1.5 

0.9 

1.6 

2.2 

2.9 

3.5 

0.9 

1.5 

2.1 

2.6 

3.2 

0.6 

1.1 

1.5 

0.9 

1.6 

2.3 

2.9 

3.5 

0.8 

1.5 

2.1 

2.7 

3.2 

0.6 

1.0 

1.4 

0.9 

1.6 

2.3 

3.0 

3.6 

0.8 

1.5 

2.1 

2.7 

3.3 

0.6 

1.0 

1.4 

0.9 

1.6 

2.3 

3.0 

3.6 

0.8 

1.5 

2.1 

2.7 

3.2 

0.5 

0.9 

1.3 

 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A.1 

A.2 

A.3 

A.4 

A.5 

Benefit-cost ratios  

Low-
qualified 

Inactive  Unempl
oyed 

SME 
employe
es 

Non-
permane
nt 

B.1 

All 

B2.1 

B2.2 

B2.3 

B2.4 

B2.5 

All + 
top-up 
low-
qualified 

All + 
top-up 
inactive 

All + 
top-up 
unemplo
yed 

All + 
top-up 
SMEs 

All + 
top-up 
non-
permane
nt 

4 years (end Year 5) 

5 years (end Year 6) 

Dynamic deadweight loss 

1 year (end Year 2) 

2 years (end Year 3) 

3 years (end Year 4) 

4 years (end Year 5) 

5 years (end Year 6) 

Lower 
coefficient 

productivity 

1 year (end Year 2) 

2 years (end Year 3) 

3 years (end Year 4) 

4 years (end Year 5) 

5 years (end Year 6) 

Lower 
coefficient 

employment 

1 year (end Year 2) 

2 years (end Year 3) 

3 years (end Year 4) 

2.2 

2.7 

0.9 

1.5 

2.1 

2.7 

3.3 

0.8 

1.3 

1.8 

2.2 

2.7 

0.8 

1.3 

1.8 

2.0 

2.3 

1.1 

1.5 

2.0 

2.5 

2.9 

1.0 

1.5 

1.9 

2.4 

2.8 

0.8 

1.1 

1.5 

1.7 

2.0 

1.0 

1.4 

1.9 

2.3 

2.7 

0.9 

1.4 

1.8 

2.2 

2.6 

0.7 

1.0 

1.3 

1.7 

2.1 

0.9 

1.7 

2.4 

3.2 

3.9 

0.6 

1.2 

1.7 

2.2 

2.7 

0.8 

1.5 

2.3 

1.8 

2.2 

0.9 

1.7 

2.5 

3.2 

3.9 

0.6 

1.2 

1.7 

2.2 

2.7 

0.8 

1.5 

2.3 

207 

1.8 

2.2 

1.0 

1.7 

2.5 

3.2 

3.8 

0.7 

1.3 

1.8 

2.4 

2.8 

0.8 

1.5 

2.2 

2.0 

2.4 

1.0 

1.8 

2.6 

3.3 

4.1 

0.8 

1.4 

1.9 

2.5 

3.0 

0.9 

1.7 

2.4 

1.9 

2.2 

1.0 

1.7 

2.4 

3.1 

3.7 

0.7 

1.2 

1.7 

2.1 

2.6 

0.8 

1.5 

2.1 

1.8 

2.2 

1.0 

1.7 

2.4 

3.1 

3.8 

0.8 

1.3 

1.8 

2.4 

2.8 

0.8 

1.5 

2.1 

1.8 

2.2 

1.0 

1.7 

2.5 

3.2 

3.9 

0.7 

1.3 

1.8 

2.3 

2.8 

0.8 

1.5 

2.2 

1.6 

2.0 

1.0 

1.8 

2.5 

3.2 

3.9 

0.7 

1.3 

1.8 

2.3 

2.8 

0.8 

1.5 

2.2 

 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A.1 

A.2 

A.3 

A.4 

A.5 

Benefit-cost ratios  

Low-
qualified 

Inactive  Unempl
oyed 

SME 
employe
es 

Non-
permane
nt 

B.1 

All 

B2.1 

B2.2 

B2.3 

B2.4 

B2.5 

All + 
top-up 
low-
qualified 

All + 
top-up 
inactive 

All + 
top-up 
unemplo
yed 

All + 
top-up 
SMEs 

All + 
top-up 
non-
permane
nt 

4 years (end Year 5) 

5 years (end Year 6) 

Higher training costs 

1 year (end Year 2) 

2 years (end Year 3) 

3 years (end Year 4) 

4 years (end Year 5) 

5 years (end Year 6) 

Higher 
costs 

administration 

1 year (end Year 2) 

2 years (end Year 3) 

3 years (end Year 4) 

4 years (end Year 5) 

5 years (end Year 6) 

Decreasing 
training 

returns 

to 

1 year (end Year 2) 

2 years (end Year 3) 

3 years (end Year 4) 

2.4 

2.8 

0.4 

0.7 

0.9 

1.2 

1.4 

0.8 

1.4 

1.9 

2.5 

3.0 

0.7 

1.2 

1.7 

1.8 

2.1 

0.5 

0.7 

0.9 

1.0 

1.2 

1.0 

1.4 

1.8 

2.2 

2.6 

0.8 

1.2 

1.5 

1.6 

1.9 

0.4 

0.6 

0.8 

1.0 

1.2 

0.9 

1.3 

1.7 

2.1 

2.4 

0.8 

1.1 

1.4 

2.9 

3.6 

0.3 

0.7 

1.0 

1.3 

1.6 

0.8 

1.4 

2.1 

2.8 

3.4 

0.6 

1.2 

1.8 

2.9 

3.6 

0.3 

0.7 

1.0 

1.3 

1.6 

0.8 

1.5 

2.1 

2.8 

3.4 

0.7 

1.3 

1.8 

208 

2.8 

3.4 

0.4 

0.7 

1.0 

1.3 

1.6 

0.9 

1.5 

2.2 

2.8 

3.4 

0.9 

1.6 

2.3 

3.1 

3.8 

0.4 

0.8 

1.1 

1.4 

1.7 

0.9 

1.6 

2.3 

3.0 

3.6 

0.9 

1.6 

2.3 

2.6 

3.2 

0.4 

0.7 

0.9 

1.2 

1.4 

0.8 

1.4 

2.0 

2.5 

3.1 

0.9 

1.5 

2.1 

2.7 

3.3 

0.4 

0.7 

1.0 

1.3 

1.6 

0.9 

1.5 

2.2 

2.8 

3.4 

0.9 

1.6 

2.2 

2.9 

3.5 

0.4 

0.7 

1.0 

1.3 

1.6 

0.9 

1.6 

2.2 

2.9 

3.5 

0.8 

1.4 

2.0 

2.8 

3.4 

0.4 

0.7 

1.0 

1.3 

1.6 

0.9 

1.6 

2.2 

2.9 

3.5 

0.8 

1.5 

2.2 

 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A.1 

A.2 

A.3 

A.4 

A.5 

Benefit-cost ratios  

Low-
qualified 

Inactive  Unempl
oyed 

SME 
employe
es 

Non-
permane
nt 

B.1 

All 

B2.1 

B2.2 

B2.3 

B2.4 

B2.5 

All + 
top-up 
low-
qualified 

All + 
top-up 
inactive 

All + 
top-up 
unemplo
yed 

All + 
top-up 
SMEs 

All + 
top-up 
non-
permane
nt 

4 years (end Year 5) 

5 years (end Year 6) 

2.1 

2.5 

1.9 

2.2 

1.7 

2.1 

2.4 

2.9 

2.4 

2.9 

2.9 

3.6 

3.0 

3.6 

2.7 

3.3 

2.9 

3.5 

2.6 

3.2 

2.8 

3.4 

209 

 
  
6. Cost of time for training funded by individual training entitlements 

The CBA presented above does not include the cost of time taken out of work for training purposes. Since training entitlement schemes aim to 
empower individuals to undertake training in their own interest and at the request of individuals, it is expected that a significant share of training 
funded by them will be undertaken outside of working hours, hence entailing costs that are difficult to monetize. Part of the training may however 
take place during working hours-  either with the informal agreement of the employer (which is  a way of cost sharing between employee and 
employer, possible e.g. in the French CPF), or because the individual makes use of rights granted formally under paid training leave schemes. 

Estimated potential costs of lost working time are hence offered in the first table below. It should be noted that the cost of working time might fall 
on employers (should they agree to time off or be required by the rules set at Member State level) or employees (i.e. if employers decide and are 
able to reduce wages accordingly). Equally, Member States might choose to provide an entitlement to pay educational leave (see Section 12B.7 
below). 

Regardless of where the costs fall, the additional cost of training time (in comparison to the baseline scenario) will depend on net participation in 
training entitlement schemes, the number of learning hours, the level of earnings and the percentage of training undertaken in working time. The 
costs have thus been calculated as follows: 

  Gross participation figures are drawn from the earlier analysis (Annex 12A) and are consistent with the figures used in the CBA above; 
  Hours per person reflect the training entitlements within Packages A and B; 
  Median hourly earnings are sourced from Eurostat (EU27); to be consistent with the rest of the CBA, low-qualified employees are assumed to earn 80% of the median; 
  Where training takes place outside working time, it is assumed that no costs in terms of working time lost are incurred. 

Table 12B.13 therefore provides an estimation of costs based on 5%, 10%, 50% and 100% of training funded by individual training entitlements 
taking place in work time. The estimates present the net cost to society regardless of where costs fall, i.e. regardless of any decision by Member 
States to finance or co-finance paid training leave. 

Table 12B.13 - Gross costs of training time during working hours 

Target group 

Gross 
participation 
of employees 
(m) 

Median hourly 
earnings (€) 

5% of training 
in working 
hours (€m) 

10% of 
training in 
working hours 
(€m) 

50% of 
training in 
working hours 
(€m) 

100% of 
training in 
working hours 
(€m) 

210 

 
 
A.1 

Low-qualified 

A.2 

Inactive 

A.3  Unemployed 

A.4 

SME employees 

A.5  Non-permanent employees 

B.1  Working age population 

B.2.1 

Working  age  +  top-up  for  low 
qualified 

B.2.2  Working age + top-up for inactive 

B.2.3  Working 

age  + 

top-up 

for 

unemployed 

B.2.4  Working  age  +  top-up  for  SME 

employees 

B.2.5  Working  age  +  top-up  for  non-

permanent employees 

3.6 

0.0 

0.0 

24.8 

9.5 

32.9 

33.9 

32.9 

32.9 

36.9 

34.4 

10.54 

0.0 

0.0 

13.18 

13.18 

13.18 

Low-qualified: 
10.54 
Others - 13.18 
13.18 

13.18 

13.18 

13.18 

96.1 

0.0 

0.0 

816.4 

313.9 

649.9 

693.7 

649.9 

649.9 

192.1 

960.7 

1,921.4 

0.0 

0.0 

0.0 

0.0 

0.0 

0.0 

1 632.7 

8 163.7 

16 327.4 

627.8 

3 139.0 

6 278.0 

1 299.8 

6 498.9 

12 997.7 

1 387.4 

6 936.8 

13 873.7 

1 299.8 

6 498.9 

12 997.7 

1 299.8 

6 498.9 

12 997.7 

1 056.6 

2 113.2 

10 565.9 

21 131.7 

806.3 

1 612.5 

8 062.6 

16 125.3 

211 

 
 
Net cost of training time  

In order to calculate the benefit-cost ratios of schemes including costs of lost working time, it 
is necessary to use rates of net participation in training entitlement schemes and thus the net 
costs  of  training  time.  This  takes  account  of  the  fact  that  some  employers  already  provide 
training  for  employees  and  thus  already  incur  costs  in  terms  of  working  time  lost.  The 
difference between gross costs and net costs would represent a deadweight transfer from public 
authorities to employers, i.e. employers receive compensation for costs of training time that 
they would have incurred anyway. 

The net costs have thus been calculated as follows: 

  Net  participation  figures  are  drawn  from  the  earlier  analysis  (Annex  12A)  and  are 

consistent with the figures used in the CBA above; 

  Hours per person reflect the training entitlements within Packages A and B; 
  Median hourly earnings are sourced from Eurostat (EU27); to be consistent with the 
rest of the CBA, low-qualified employees are assumed to earn 80% of the median; 

Table 12B.14 therefore provides an estimation of costs based on 5%, 10%, 50% and 100% of 
training taking place in work time. The estimates present the net cost to society regardless of 
where costs fall, i.e. regardless of any decision by Member States to finance or co-finance paid 
educational leave. 

212 

 
 
 
 
 
Table 12B.14 - Net costs of training time  

Target group 

A.1 

Low-qualified 

A.2 

Inactive 

A.3  Unemployed 

A.4 

SME employees 

A.5  Non-permanent employees 

B.1  Working age population 

B.2.1 

Working  age  +  top-up  for  low 
qualified 

B.2.2  Working age + top-up for inactive 

B.2.3  Working 

age  + 

top-up 

for 

unemployed 

B.2.4  Working  age  +  top-up  for  SME 

employees 

B.2.5  Working  age  +  top-up  for  non-

permanent employees 

Net 
participation 
of employees 
(m) 

Median hourly 
earnings (€) 

5% of training 
in working 
hours (€m) 

10% of 
training in 
working hours 
(€m) 

50% of 
training in 
working hours 
(€m) 

100% of 
training in 
working hours 
(€m) 

3.1 

0.0 

0.0 

18.2 

7.1 

23.9 

24.4 

32.9 

32.9 

26.9 

25.0 

10.54 

0.0 

0.0 

13.18 

13.18 

13.18 

Low-qualified: 
10.54 
Others - 13.18 
13.18 

13.18 

13.18 

13.18 

213 

82.0 

0.0 

0.0 

599.7 

233.2 

472.0 

502.6 

649.9 

649.9 

164.1 

820.4 

1,640.9 

0.0 

0.0 

0.0 

0.0 

0.0 

0.0 

1 199.4 

5 996.8 

11 993.5 

466.3 

944.1 

2 331.6 

4 663.1 

4 720.4 

9 440.9 

1 005.2 

5 026.0 

10 052.1 

1 299.8 

6 498.9 

12 997.7 

1 299.8 

6 498.9 

12 997.7 

770.8 

1 541.6 

7 707.9 

15 415.8 

588.2 

1 176.4 

5 882.0 

11 764.0 

 
 
 
 
 
 
Benefit-cost ratios including cost of training time 

The  table  below  presents  an  estimation  of  the  benefit-cost  ratios  for  training  entitlement 
schemes including the cost of working time. Given the impossibility in determining how much 
training will take place in work time, the table below provides an estimation of the benefit-cost 
ratios in a “highest-cost scenario”, i.e. where costs are incurred for 100% of training time (i.e. 
all training takes place in working time). It should be noted that the same benefit-cost ratios 
apply both in a situation where where employers incur the cost of training time (the ratios are 
based  on  benefits  and  costs  to  society  without  taking  account  of  distribution  effects,  i.e. 
payments are made to employers to compensate them for training in working hours). 

Table 12B.15 shows that: 

 

In the core proposal (see scenario analysis in section 12B.4), which takes no account of 
the cost of training time, benefits exceed costs within 2 years (i.e. end Year 3) in all 
packages. 

  where 100% of training takes place in work time, benefits exceed or are equal to costs 
within 2 years (i.e. end Year 3) within all the packages, except those serving only SME 
employees (A.4) or only non-permanent employees (A.5). 

  where 100% of training takes place in work time, benefits exceed costs only after 3 
years (i.e. end Year 4) within the packages targeting only SME employees (A.3) or only 
non-permanent employees (A.4). 

Overall then, these estimates suggest that even where 100% of training takes place in working 
time, there is at worst only a slightly delay in the time period before benefits of schemes exceed 
costs. 

214 

 
 
 
 
 
 
Table 12B.15 - Estimated benefit-cost ratios including cost of training time  

A.1 

A.2 

A.3 

A.4 

A.5 

Benefit-cost ratios  

Low-
qualified 

Inactive  Unempl
oyed 

SME 
employe
es 

Non-
permane
nt 

B.1 

All 

B2.1 

B2.2 

B2.3 

B2.4 

B2.5 

All + 
top-up 
low-
qualified 

All + 
top-up 
inactive 

All + 
top-up 
unemplo
yed 

All + 
top-up 
SMEs 

All + 
top-up 
non-
permane
nt 

Excluding training time 

1 year (end Year 2) 

2 years (end Year 3) 

3 years (end Year 4) 

4 years (end Year 5) 

5 years (end Year 6) 

Including 
(100%) 

training 

time 

1 year (end Year 2) 

2 years (end Year 3) 

3 years (end Year 4) 

4 years (end Year 5) 

5 years (end Year 6) 

0.9 

1.5 

2.1 

2.6 

3.2 

0.7 

1.1 

1.5 

2.0 

2.4 

1.0 

1.5 

1.9 

2.4 

2.8 

1.0 

1.5 

1.9 

2.4 

2.8 

0.9 

1.4 

1.8 

2.2 

2.6 

0.9 

1.4 

1.8 

2.2 

2.6 

0.8 

1.5 

2.3 

2.9 

3.6 

0.5 

0.9 

1.4 

1.8 

2.2 

0.8 

1.5 

2.3 

2.9 

3.6 

0.5 

0.9 

1.4 

1.8 

2.2 

0.9 

1.6 

2.3 

2.9 

3.6 

0.6 

1.1 

1.5 

1.9 

2.3 

0.9 

1.7 

2.4 

3.1 

3.8 

0.6 

1.1 

1.6 

2.1 

2.5 

0.9 

1.6 

2.2 

2.9 

3.5 

0.6 

1.0 

1.4 

1.8 

2.2 

0.9 

1.6 

2.3 

2.9 

3.5 

0.6 

1.1 

1.5 

1.9 

2.4 

0.9 

1.6 

2.3 

3.0 

3.6 

0.6 

1.0 

1.5 

1.9 

2.3 

0.9 

1.6 

2.3 

3.0 

3.6 

0.6 

1.0 

1.5 

1.9 

2.3 

NB: “highest-cost scenario” assuming that 100% of training time occurs is during working hours.

215 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7. Cost of paid training leave 

In order to guide decision-making, this sub-section presents illustrative estimates of the potential costs 
of strengthening paid training leave provisions as such, as recommended under packages A and B. 
The difference to the previous sub-Section is that in this Section, we consider potential costs of a 
higher  paid  training  leave  uptake  among  all  employed  adults,  independently  of  whether  they  also 
make use of training entitlements provided under packages A or B. 

Evidence from previous schemes does not provide a reliable basis on which to determine likely take-
up rates. As noted in Annex 11, a previous report by Cedefop examined existing paid training leave 
schemes in the EU. Of the schemes which had wide or comprehensive coverage of the overall adult 
population,  only  three  offered  data  on  take-up  rates  amongst  eligible  groups:  Belgium  (2.29%), 

Estonia (5%), and Latvia (0.4%).

455

For that reason, illustrative cost estimates are offered for paid training leave take-up rates of 1%, 3% 
and 5% amongst the full population of employed adults aged 25-64 years, whereby an annual take-
up rate of 5% across EU-27 could be considered as successful “upward convergence” to the highest 
values currently observed among annual paid training leave schemes with broad coverage.  

The cost estimates in the below Table show that at €5.9 billion, the annual costs of a 5% take-up are 
significantly smaller than the net benefits of all packages previously shown in Table 12B.9 after 5 
years (with the exception of package A.3 due to its small target group, the unemployed). This suggests 
that taken together, the policy measures recommended under packages A and B can contribute to 
sustainable public finances. 

Table 12B.16: Illustrative estimates of the cost of paid training leave 

Hours 
per 
person 

Number 
employed 
adults  25-
64 
years 
(m) 

Hours 
per 
person 

Median 
hourly 
earnings 
(€) 

Cost  at  1% 
take-up 
(€m) 

Cost at 3% 
take-up 
(€m) 

Cost  at  5% 
take-up (€m) 

50 

30 

178.7 

178.7 

50 

30 

13.18 

1 177.3 

3 532.0 

5 886.6 

13.18 

706.4 

2 119.2 

3 532.0 

C. LONG TERM AND INDIRECT IMPACTS 

1. Rationale and scope of the analysis 

The goal of this Annex Section is to complement the analysis undertaken in the CBA (Annex 12B) 
with  insights  on  the  longer  term  and  general  equilibrium  effects  of  the  provision  of  training 
entitlements to different target groups, drawing on simulation estimates from the BeTa model (see 
Box 12C.1 below for a presentation of this model).  

This is relevant as, for instance: 

455

 Cedefop (2012), Training leave: Policies and practice in Europe. 

216 

 
 
 
 
                                                 
  In terms of costs, any initiative involving public resources needs to be funded either through 
additional  taxation/  employers’  levies  or  by  steering  resources  away  from  other  public 
initiatives. Rational agents within an economy are likely to anticipate such effects, adjusting 
their behaviour e.g. in terms of expenses on goods or investments. Public investment might 
displace private investments not only in the domain directly addressed by the policy, but also 
in any other sector, if it exerts upward pressure on interest rates (e.g. due to higher public 
debts), or just by affecting the composition of aggregate demand and supply (e.g. due to higher 
labour costs/taxes or lower subsidies to other sectors). This might depress, with respect to the 
baseline, several macroeconomic variables including GDP. Such effects cannot be factored in 
by studies or models focusing only on partial equilibrium effects, that is, the short- or medium-
term  effects  on  those  directly  concerned  by  the  policy.  At  the  same  time,  individuals 
increasing their employment chances as examined in partial equilibrium studies (e.g. meta-
analysis of counterfactual evaluations of training policies) might be doing so at the expense 
of other individuals not receiving support (displacement effect). This is clearly acknowledged 
in Card, Kluve and Weber (2018)456, demonstrated in Crepon et al. (2013)457, Gautier et al. 
(2014)458  and  broadly  confirmed  by  the  literature  (see  e.g.  OECD  1996,459  Calmfors  and 
Skendinger460 and Escudero, 2015461). Although not strictly speaking a cost, employment 
growth might also be stifled in the short-term due to sluggish adjustments of labour demand 
to increased output. This is intuitive if one thinks that, at first, when employees become more 
productive a fewer number of them are needed to produce the same output. Only later the 
increased individual productivity will induce employers to hire more as they retain part of the 
increased productivity through bargaining power.  

  In terms of benefits, there might be a range of positive effects such as increased productivity 
or  changes  in  the  capacity  of  economies  to  innovate  leading  to  additional  effects  such  as 
increased employment and output (GDP), which are not limited to those directly affected by 
the policy but spread across countries, firms and individuals. The stress here is again on the 
indirect,  second-order  effects  that  over  time  and  across  economies  materialise  as  a 
consequence  of  the  change  in  behaviour  from  those  targeted  by  the  policy.  The  literature 
suggests  that  in  particular  for  investments  in  human  capital,  this  longer-term  and  general 

456

 “We emphasize that the evaluations in our sample have many limitations. At best, these studies measure the partial 
equilibrium effects of ALMPs, comparing the mean outcomes in a treatment group to those of an untreated control or 
comparison group.” Card, D., Kluve, J., Weber, A. (2018): What Works? A Meta Analysis of Recent Active Labor Market 
Program Evaluations. European Economic Association, p. 898. 
457

 Crépon, B., Duflo, E. Gurgand, M. Rathelot, R., Zamora, P. (2013):  Do Labor Market Policies have Displacement 

Effects? Evidence from a Clustered Randomized Experiment, The Quarterly Journal of Economics  
458

 Gautier, P., Muller, P., Van der Klaauw, B., Rosholm, M. and Svarer, M. (2014): Estimating Equilibrium Effects of 

Job Search Assistance, Journal of Labor Economics 
459

 OCED (1996): Enhancing the effectiveness of Active Labour Market Policies: Evidence from programme evaluations 

in OECD countries, LABOUR MARKET AND SOCIAL POLICY OCCASIONAL PAPERS No. 18 
460

  Calmfors,  L.  and  Skendinger,  P.  (1995):  Does  active  labour  market  policy  increase  employment?  –  Theoretical 

considerations and some empirical evidence from Sweden, Oxford Review of Economic Policy 
461

 Escudero, V. (2015): Are active labour market policies effective in activating and integrating low-skilled individuals? 

An international comparison, International Labour Office (ILO) 

217 

 
 
                                                 
  The BeTa model estimates 
equilibrium dimension of benefits is particularly significant.
structural changes, e.g. employment created structurally in the economy due to more efficient 
matching between workers and jobs. Put differently, estimates of employment gains form the 
BeTa model are “net gains” that already take into account possible employment losses related 
to higher productivity/automation that may result form the policy packages.  

462

General equilibrium modelling therefore provides an important complementary perspective on the 
likely impacts of additional skills investment, not just at the time of the roll out of the initiative and 
with a focus on its beneficiaries, but in its longer-term implications and for the economy as a whole.  

Nevertheless, some of the limitations of this exercise should be acknowledged. First, the BeTA model 
does  not  feature  a  disaggregation  by  Member  State  and  generates  estimates  which  should  be 
interpreted  as  EU-27-level  averages.  In  addition,  the  full  range  of  differences  between  the  policy 

 Hence, this exercise does 
packages are often impossible to quantify based on existing evidence.
not have the ambition to produce a full-fledged comparative assessment of all policy packages. Its 
goal, as stated above, is rather to inform on the long-term indirect effects stemming from investment 
in training entitlements and adjusting them based on a few key parameters (size of the target groups, 
deadweight loss, administrative costs) which vary across policy packages in order to complement the 
remainder of the analytical framework. In particular, the macroeconomic effects of Policy Packages 
A1, B1 and B2.1 are examined in Section 3 below. Sensitivity checks are discussed in section 4.  

463

Box 12C.1: The BeTa model 

The BeTa model is a macroeconomic model that – in the spirit of the QUEST III-RD model and 
the above-mentioned RHOMOLO model – adopts the theoretical approach of the product variety 
semi-endogenous  growth  model  of  Jones  (1995;  2005).  It  has  a  dynamic  innovation  process, 
described by the interaction of the choices taken in three sectors (Varga et al., 2013): the R&D 
sector,  the  household  sector  and  the  monopolistically  competitive  intermediate  sector. 
Furthermore, based on the fact than in the macro model of Varga et al.  (2013) a human capital 
sector is missing and given also the spirit and the aim of the present study, a fourth sector describing 
the Human Capital sector was set up. More in the specific for the Human Capital sector is based 
on  Varga  et  al.’s  endogenous  growth  formulation.  Furthermore,  the  Diamond-Mortensen-

462

 Recent macroeconomic estimations done by the JRC on EU investments in human capital, suggest that productivity 
enhancing component of investment in human capital is expected to generate long-lasting positive effects in the medium 
to long-term, but that in the short term employment and GDP impacts are less visible. “Looking at the immediate impact 
of a policy can be misleading as it ignores the cumulated impact on the economy over time. [..] In our simulation exercise 
the  reported  cumulative  multiplier  in  2023  is  around  0.6  and  increases  further,  even  though  ESF  investment  is 
discontinued, and becomes larger than 1 in 2030. The main reason behind this result is that ESF is human capital oriented 
and as such it takes time for its effect to diffuse in the economy, a common feature of supply-side policy interventions.” 
Stylianos Sakkas, Andrea Conte, And Simone Salotti (2018):  The Impact of the European Social Fund: The Rhomolo 
Assessment. Territorial Development - JRC Policy Insights, p. 3. This is consistent with the literature on returns to training 
as presented in Annex 10.  
463

 There is, at present, no hard data or evidence on the quantitative differences between policy package A and B with 
respect to training participation, productivity and wages that are due to their delivery mode (i.e. delivery through vouchers 
or a personal account). This is due to the fact that the only broad scope ILA experience in the EU is currently ongoing in 
France and no counterfactual study could examine its effects, let alone long term effects, as yet. Therefore, the modelling 
strategy for the two packages focuses on the macroeconomic effects of training entitlements. 

218 

 
 
                                                 
Pissarides464 search and matching labour market structure allows to account for the interaction of 
ex-ante investments on HC and costly search in the labour market suggested by Acemoglu.465 SeeB 

Beqiraj et al. (2021)
was presented and used. 

466

 for a recent peer-reviewed article in which a simplified version of the model 

The model is based on a hybrid formulation structure which consists in equations partly derived 
from “hard theory”, partly from “soft theory”.  

  Hard theory: the micro foundations (i.e. formal hypotheses on preferences and technology), and inter-

temporal optimization under rational expectations (i.e. model-consistent expectations/certainty equivalence) 
are considered to derive the behavioural equations; and 

  Soft theory: general macroeconomic reasoning, supported by statistical information, is used in the 

specification of the mathematical representation of economic behaviour.  

The model inputs consist of a rich and large dataset which is required by the estimation strategy. 
The data that will be used in  the estimation stage are:  GDP, consumption, investment,  imports, 
exports, wages; the unemployment rate, the rates of change of the price deflators for consumption, 
import, export, nominal effective exchange rate, the domestic and the monetary policy short term 
interest rate, labour force, participation rates, data on R&D, and human capital. 

The model outputs consist of the provision of different socio-economic scenarios. The focus will 
lie  in  particular  on  GDP  and  employment  outcomes  as  a  result  of  the  provision  of  training 
entitlements.  

2. The modelling strategy  

Main impact channels 

Based on the current specification of the policy packages (see Section 5.4 and the scenarios developed 
in part A of this Annex) and their intervention logic, the channels below were used to simulate the 
macroeconomic effects of training entitlements. 

With respect to the main channels engendering positive effects on employment and GDP, the first 
step is an exogenous positive shock to the number of individuals in adult learning, according to the 
comparative estimations of direct impacts (see Annex 12.A). This is considered a structural change, 
i.e.  training  is  added  on  a  year-by-year  basis  and  there  is  no  expectation  that  the  policy  will  be 
discontinued in the long term. Such exogenous shifts in training participation will, in turn, generate: 

  a positive shock on productivity: this affects the efficiency of the labour factor in the production 
technology. Due to rigidities in the labour market, employers retain part of the benefits of the 
increased productivity, whilst individuals partly benefit from it in the form of a wage premium 
and increased employment opportunities in the medium to long term. Increases in productivity 
are factored in as per the review of evidence described in Annex 10 and in Section 12B. They are 
assumed  to  be  constant  across  educational  attainment  levels  and  type  of  occupation  given  no 
conclusive evidence can be drawn on heterogeneity from the literature, as described in Annex 10. 

464

465

 Pissarides, C. (2000): Equilibrium Unemployment Theory, Cambridge, MA: MIT Press. 
 Acemoglu, D., (1996): A microfoundation for social increasing returns in human capital accumulation. The Quarterly 

Journal of Economics 111(3), pp. 779-804. 
466

 Beqiraj et al. (2021), Fiscal retrenchments and the transmission mechanism of the sovereign risk channel for highly 

indebted countries, The North American Journal of Economics and Finance. 

219 

 
 
                                                 
Such  increases  are  scaled  pro-rata  to  match  the  duration/value  of  the  training  which  can  be 
purchased by the different target groups. While the BeTa model allows for accumulation of human 
capital over time, this accumulation is limited by skills obsolescence and depreciation of human 
capital (3% a year, as per the literature reviewed in Annex 10) and an assumption of decreasing 
marginal  returns.  Although  the  evidence  in  the  literature  on  the  question  of  whether  marginal 
returns  to  human  capital  are  constant  or  decreasing  is  somewhat  inconclusive  (Annex  10),  an 
assumption of decreasing returns is chosen to present more conservative estimates.   

  positive shock on job matching efficiency: another channel leading to employment and GDP 
impacts is that of matching efficiency. Training and validation activities are likely to improve the 
signal  to  employers  on  the  skills  possessed  by  individuals  and  facilitate  job  mobility.  This  is 
confirmed in the literature467 and accounted for in the BeTa through a semi-elasticity parameter 
linking the value of training investment to increases in job matching efficiency.  

  The direct costs of additional training entitlements are assumed to be funded through additional 
taxes. The specific mix of taxes used in the model aims to be neutral as it is fully a matter for the 
Member States to decide. To ensure neutrality, the shares of taxes by type (labour, capital and 
consumption) is based on the EU-27 information on taxation trends in 2019.468   

Although  the  modelling  strategy  does  not  change  across  the  different  policy  options,  the  policy 
packages address different target groups with different training entitlements. They are also expected 
to entail different operational costs (as indicated e.g. in Annex 12B above). This generates differences 
with respect to: 

  The total financial resources entailed; 

  The  intensity  of  training  support  (value  of  training  entitlements)  that  the  different  target 
groups will receive (i.e. in policy packages A and B2, individuals from priority target groups 
receive a 50-hour training entitlement per year instead of 30-hour one) and related differential 
effects on participation rates, wages, productivity and employment chances; 

  The overall effects on training participation and thereby productivity, wages and employment 

chances. 

Specific modelling assumptions  

The following detailed assumptions are used in determining the exact input data for the BeTa model:  

467

 See for instance Zhang, Y., Salm, M. & van Soest, A. (2021) The effect of training on workers’ perceived job match 
quality. Empirical Economics. They identify a 12-25 ppts increase (depending on the type of training, excluding post-
specific training) in job changing incidence, leading to a positive increase in job matching quality for those changing jobs 
one year after the training episode. The relationship between general training and job mobility is confirmed in Dekker R, 
De Grip A, Heijke H (2002) The effects of training and overeducation on career mobility in a segmented labour market. 
Int J Manpow as well as Cheng Y, Waldenberger F (2013) Does training affect individuals’ turnover intention? Evidence 
from China. J Chin Hum Resour Manag. Taking into account these effects in the simulation is consistent with the fact 
that  individual  training  entitlements  tend  to  favour  general  training  for  human  capital  accumulation  as  opposed  to 
firm/job-specific training (see Annex 11 and the discussion on freedom of choice).  
468

 Taxation Trends in the European Union, 2021 edition, Directorate-General for Taxation and Customs Union, European 

Commission, Publications Office of the European Union, Luxembourg, 2020. 

220 

 
 
                                                 
 

training is funded through additional public resources: private resources that are freed through 
deadweight loss for the public authorities, are reflected in the simulation as higher disposable 
income for individuals and lower costs of labour for employers; 

  additional  investment  in  upskilling  activities  resulting  from  voluntary  cost-sharing  is  not 

considered, in line with the assumptions used in the CBA; 

  Decreasing marginal returns over time are assumed469 ; 

 

 

 

from a long-term perspective, fixed set up costs are omitted. This is justifiable because from 
a long-term perspective, one-off costs become negligible. Hence, the focus lies on operational 
yearly costs;  

the  average  cost  of  training  for  the  EU-27  estimate  is  calculated  based  on  the  number  of 
training entitlements redeemed by each target group, in each country, using as deflators for 
the education sector as per the cost benefit analysis in Annex 12B.470 It is therefore a weighted 
average which adjusts to the amount of entitlements used by each country and target group; 

input data used for this simulation is based on the middle ground scenario for the take up rate 
of  the  training  entitlements  and  considers,  as  net  effects  on  training  participation,  all  the 
economically relevant additionality in training undertaken (i.e. all the training which would 
have not been undertaken without the training entitlements); 

  wage levels are left free to fluctuate to ensure macroeconomic coherence in combination with 
the increases in taxes, monetary transfers (training purchased with public resources that would 
have been purchased through private ones) and effects on the job matching function. This is 
necessary  as  all  these  factors  (taxes,  transfers  and  changes  to  job  finding  rates)  affect  the 
equilibrium value of wages.471  

Table 12C.1: Overview of the main coefficients used for the simulation 

30-hour training entitlement 

50-hour training entitlement 

Take up rates 

18.4% 

(13.1% 

22% 
qualified) 

for 

the  Low 

Deadweight loss 

Middle ground scenario: 22.8%, scaled down by Member State and 
Target group and then aggregated at the EU-27. It is based on a broad 
definition of deadweight loss that takes into account some impacts of 
the policy packages on training intensity (see Annex 12A). 

469

 This is done to favour more conservative estimates against a background where the rich and wide-ranging literature 
discussing  the  issue  of  returns  to  scale  is  not  fully  conclusive.  For  a  detailed  description  please  refer  to  Annex  10. 
Nevertheless, given that it is not necessarily the same pool of individuals who will undertake training every year (given 
the assumed annual take-up rates of around 20 percent), this might lead to underestimation of the benefits of the initiative.    
470
  Such  deflators  are  included  in  Sánchez-Barrioluengo,  M.  (2016):  Expenditure  on  education  in  Purchasing  Power 

Standards: A comparison of three alternative deflators. European Commission 
471

 It is worth recalling that estimates discussed in the literature and presented in Annex 10 on returns to training remain 
partial equilibrium ones (i.e. individual level or firm level estimates, in absence of spillovers). It is therefore appropriate 
to take a slightly different approach to their estimation in the context of a general equilibrium simulation.  

221 

 
 
 
                                                 
Sensitivity checks: 0% and 60%.  

Effects on individual productivity  2% 

3.34% 

Effects on wages 

Endogenous variable 

Effect on matching 

Semi-elasticity parameter borrowed from the literature on the effect of 
training on job matching efficiency. 

Cost of the training 

EU-27 weighted average: €380 

EU-27 weighted average: €645  

Composition of taxes 

Labour taxes 51.7%, consumption taxes 27.8 %, capital taxes 20.5% 

Operational Costs 

They  vary  with  the  policy  packages  depending  on  the  volume  of 
vouchers redeemed as per section  12B: 15% for A1, 8% for B1 and 
B2.1. 

Accumulation  and  persistence  of 
investment in human capital 

Depreciation of human capital is factored in at 3% per year, as per the 
review in Annex 10.  

Decreasing  marginal  returns  to  training  accumulation  over  time  are 
calculated based on a cube root function.  

3. Key results 

The key results of the analysis carried out through the BeTa model are shown below. The focus lies 
on estimating the GDP and employment effects of policy packages A1, B1 and B2.1. The aim is thus 
to capture what is the likely overall macroeconomic and structural impact of providing individuals 
with  training  entitlements  not  just  as  a  one-off  measure  but  over  a  longer  time  span  on  selected 
macroeconomic variables. 

Figure 12C.1 – GDP effects of Policy Package A.1, B1 and B2.1  

Deviations from the baseline as a % of GDP 

1,5%

1,0%

0,5%

0,0%

2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040

Policy Package A1

Policy Package B1

Policy Package B2.1

Source: authors’ elaboration based on the output from the BeTa model 

Figure 12C.1 above shows that a policy intervention providing individuals with training entitlements 
not  only  creates  positive  effects  for  those  receiving  the  entitlements,  but  also  generates 
structural, long lasting positive effects on the economy as a whole. This is consistent with recent 

222 

 
 
 
macroeconomic estimates produced in the context of the evaluation of similar public investments in 
education and training.472 

In particular, the largest positive effects vis-à-vis the baseline are measured on GDP, which is 
expected  to  raise  considerably,  e.g.  by  0.23%-0.99%  in  2030  (policy  package  A1  and  B2.1, 
respectively)  and  0.33%-1.40%  in  2040  for  the  same  policy  packages,  as  a  consequence  of  the 
cumulative productivity enhancing effects of training on the economy. Such effects are magnified by 
general equilibrium dynamics, as higher productivity leads to an expansion in supply which drives an 
increase  in  aggregated  demand  and  private  investments.  Negative  effects  of  displacement  from 
increases  in  public  expenditure  are  more  than  offset  by  the  positive  effects  from  increased 
productivity.473  This  is  also  explained  by  the  fact  that  the  increasing  output  generates  additional 
revenues for public authorities, allowing the support in subsequent years to be de facto self-financed. 
These estimates are based on rapidly decreasing returns to scale and take into account the depreciation 
of the new skills generated by the policy support and can hence be considered conservative estimates.  

In the medium to long term and in constant prices, these increases would range from over €30 billion 
of higher GDP every year (e.g. in 2030 for policy package A1), up to just below. €200 billion every 
year (e.g. in 2040 for policy package B2.1). These appear to be higher than the benefits calculated by 
the  CBA  in  the  previous  section  12B  for  the  same  policy  packages.  These  findings  place  further 
emphasis on the fact that one key strength of this policy intervention is the multiplicative effect of 
increased productivity on growth as well as the long-term positive effects linked to the accumulation 
of human capital with respect to the baseline. This happens despite the depreciation of human capital 
and the fact that the additional skills accrued are added only partially to the effects on productivity 
(via decreasing marginal returns to training accumulation).  

Focusing on the trend of the deviation of GDP from the baseline, all policy packages show a steadily 
increasing positive impact. This is explained by the cumulative effect of investment in human capital 
and  the  recursive  nature  of  the  policy  packages,  offering  additional  training  opportunities  to 
individuals every year.  Increases are steeper in the short term as the economy adjusts towards new 
equilibrium levels where output is increased via a more productive labour force. The deviation of 
GDP from the baseline continues to stretch, although at a slightly slower pace, as training is continued 
to be offered to and taken up by individuals across the EU-27. 

In terms of comparative effects between the policy packages, these tend to be proportional to the size 
of the intervention. The main reason for this is that the higher operational costs assumed for a targeted 
scheme on low qualified individuals are offset by the lower deadweight loss estimated for the same 
target group. A similar reasoning would apply to policy packages for target groups that are currently 
underrepresented in training participation.   

472 See, for instance, the recent evaluation 2014-2018 ESF support to employment and labour mobility, social inclusion 
and education and training- SWD(2021) 10 final, Evaluation of the 2014-2018 ESF support to employment and labour 
mobility, social inclusion and education and training, European Commission, p. 36.  
473 This is consistent with a macroeconomic framework based on current trends in monetary policies with low interest 
rates.  

223 

 
 
                                                 
Figure 12C.2 – Employment effects of Policy Package A.1, B1 and B2.1 

Deviations from the baseline as a % of employment rate 

0,3%

0,2%

0,1%

0,0%

-0,1%

-0,2%

-0,3%

2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040

Policy Package A1

Policy Package B1

Policy Package B2.1

Source: authors’ elaboration based on the output from the BeTa model 

In terms of employment effects, as training is a measure that boosts labour supply and employers 
retain part of the benefits of such increased supply, a lower number of workers is necessary to produce 
the same output. In general, whether employment can increase structurally as a result of training tends 
 474 see e.g. Escudero 
to depend on whether scale effects are larger or smaller than substitution effects,
(2015)475 amongst others. This is apparent in the short term, and particularly in the first year, where 
employment levels show a negative deviation from the baseline, as rational agents in the economy (i) 
suffer from increases in taxes to fund the initiative and (ii) anticipate in the productivity shock putting 
hirings on hold.476 However, the ripple of positive effects generated by increased productivity, output 
and increased demand boosts employment levels from the medium to long term. In addition, in line 
with the literature review in Annex 9 training is expected to affect employment levels also through 
changes to job matching, as it facilitates labour market transitions through both signalling effects (e.g. 
validation of skills, acquisition of certificates) and the provision of skills that are missing in the labour 
markets  (lower  skills  mismatches).  As  explained  in  the  working  assumptions,  such  effects  are 
captured  in  BeTa  and  contribute  to  generating  structural  employment  effects.  In  particular,  in  the 
simulation above, medium to long term deviations from the baseline of employment levels  range 
from 0.01%-0.06% in 2030 (approx. 30 – 140 thousand new jobs in 2030, for policy packages 
A1 and B2.1, respectively) to over 0.04%-0.18% in 2040 (approx. 100-400 thousand more jobs 

474

 Scale effects denote the expansion of production which stems from higher labour efficiency and the fact that employers 
are induced to expand their production, leading to additional hires. At the same time, substitution effects imply that as 
each individual is able to produce more, a smaller number of them is needed to produce the same output. 
475

 Escudero, V. (2015): Are active labour market policies effective in activating and integrating low-skilled individuals? 
An international comparison, International Labour Office (ILO) “Moreover, labour demand can be reduced if the scale 
effect resulting of an increase in the marginal productivity of labour (i.e. that shifts labour demand upwards because a 
fall of the relative unit cost of labour provides an incentive to expand output by using more efficient units of labour) is 
dominated by the substitution effect (i.e. arising since one unit of product can be produced by less units of labour)”. 
476

 The first year reflects the sum of a few additional effects: (i) the increase in taxation needed to finance the measure 
(our working hypothesis is that government do not opt for additional debt – this is done to highlight the self-financing 
nature of investment in human capital in the long term and in times where public finances are under significant pressure. 
Governments however may well decide otherwise and this would reduce the negative effect on employment in the first 
year); (ii) the sudden increase in productivity, that is particularly strong in the first year given that in subsequent year the 
additional training undertaken increases the productivity only marginally (decreasing marginal returns); (iii) anticipatory 
effects from rational agents.  

224 

 
 
 
                                                 
in 2040, for A1 and B2.1 respectively). These increases might seem comparatively small against 
GDP increases, but are still sizable and steadily increasing over time. This finding is consistent with 
extant literature on the macroeconomic effects of training policies.477  

As indicated in the methodological section above, it is important  to stress that these estimates are 
based on a holistic consideration of all the costs and side effects (positive and negative spillovers) 
that  are  brought  about  by  any  public  intervention.  In  addition,  these  positive  macroeconomic 
performances  come  on  top  of  several  non-quantifiable  and/  or  non-monetary  impacts  already 
described in the report, including increased social cohesion, civic participation, reduced crime rates 
etc., as per the review in Annex 10.  

Overall, the findings from the macroeconomic simulation broadly confirm and reinforce the findings 
of  the  detailed  CBA  in  Annex  12B,  i.e.  that  even  from  a  general  equilibrium,  macroeconomic 
perspective there exists a clear economic case for substantial investment in up and reskilling. The 
estimates place further emphasis on the long-term, structural effects of the accumulation of human 
capital on GDP growth, whilst confirming comparatively small but positive and structural effects on 
employment  rates,  in  line  with  the  literature  of  macroeconomic  employment  effects  of  training 
policies.  

4. Scenario analysis and sensitivity checks 

Rationale and scope of the sensitivity analysis 

As highlighted in the analysis above, the significant shock on productivity generated by the training 
entitlements drives GDP and employment increases in the medium to long run fully offsetting the 
cost of the investment. The estimates show positive returns for the economy in terms of both GDP 
and employment and such returns tend to increase over time, thanks to the accumulation of human 
capital and the ripple of positive effects brought about by higher productivity through the positive 
interaction between aggregate supply and aggregate demand.  

Differences  among  the  policy  packages  appear  proportionally  small  as  their  efficiency  is  similar 
(losses in administrative costs in A.1 tend to be compensated by lower deadweight in presence of low 
qualified individuals) and the key driving factor is number of individuals that are triggered to take 
additional training together with the duration of such training.  

To test the stability of these findings against a misspecification of the assumptions, there is an interest 
to apply some variation to: 

 

take-up rates; and 

  deadweight loss. 

477

 See e.g. Schmid, G. O’Reilly, J. and Schömann, K. (1996):  International Handbook of Labour Market Policy and 
Evaluation,  Edward  Elgar,  pp.  725–  746.  “Labour-supply-oriented  measures  (including  training,  workers’  subsidies, 
supported employment and rehabilitation and job rotation and job sharing measures), are expected to have little, if any, 
impact  on  the  level  of  unemployment”,  Calmfors  L.  (1994):  Active  Labour  Market  Policy  and  Unemployment  –  A 
Framework for the Analysis of Crucial Design Features, OECD Economic Studies, No. 22; but also the recent impact 
assessment on the ESF+ from the JRC as in Sakkas, S. (2018): The macroeconomic implications of the European Social 
Fund: An impact assessment exercise using the RHOMOLO model. JRC Working Papers on Territorial Modelling and 
Analysis. “We observe that during the whole programming period the effects on employment are small but positive and 
increasing”, p. 9. 

225 

 
 
                                                 
With respect to different assumptions on take-up rates, it can be clarified from the outset that these 
do not strongly affect the efficiency of the policy under review, but only the volume of additional 
training purchased. From this perspective, the comparison between policy package A1, B1 and B2.1 
already informs on the comparative effects of different take-up rates: the smaller the target group or 
the lower the take up, the lower the absolute gains in productivity or employment. However, there is 
no  major  deviation  appearing  as  long  as  the  share  of  administrative  costs  does  not  become 
disproportionately high (e.g. in presence of schemes with a few thousand individuals478) thanks to the 
substantial and cumulative gains from increased productivity.  

Thus, the following paragraphs will focus on the sensitivity of the estimates to different assumptions 
on levels of deadweight loss. 

Results of the sensitivity analysis 

Figure 12C.3 - GDP effects of Policy Package B1 – sensitivity checks 

1,6%

1,4%

1,2%

1,0%

0,8%

0,6%

0,4%

0,2%

0,0%

2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040

B1 - sensitivity check low deadweight

B1 - sensitivity check high deadweight

Source: authors’ elaboration based on the output from the BeTa model 

Figure 12C.3 above displays the trends in GDP effects (deviation from the baseline) for the policy 
package B1 using two different assumptions on deadweight, respectively 0%, to account for crowding 
in of private resources in the case of cost-sharing from individuals, top-ups from employers and the 
impact of training entitlements on future training intentions, and 60%, to account for the possibility 
of very high levels of deadweight (see the discussion in Annex 12A).  

The main finding of the sensitivity analysis is that impacts on GDP remain largely positive even in 
presence of high deadweight loss. In particular, the short and medium term the (comparatively) small 
direct  effect  on  productivity  due  to  the  (comparatively)  small  increase  in  training  participation  is 
partly  offset  by  significant  transfers  to  employers  and  individuals  (who  are  no  longer  paying  for 
training they would have purchased with private resources). However, such public expenditure mostly 
boosts aggregate demand in the short to medium term and the long-lasting and structural effects on 
growth are diminished.  

Figure 12C.4 - Employment effects of Policy Package B1 – sensitivity checks 

478

 However, such small volumes are not meaningfully discussed in macroeconomic terms. 

226 

 
 
 
 
                                                 
0,3%

0,2%

0,1%

0,0%

-0,1%

-0,2%

-0,3%

2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040

B1 - sensitivity check low deadweight

B1 - sensitivity check high deadweight

Source: authors’ elaboration based on the output from the BeTa model 

The key finding from the sensitivity analysis illustrated in Figure 12C.4 above, is that the medium to 
long  term  employment  effects  of  the  policies  remain  positive  even  under  more  pessimistic 
assumptions on deadweight loss. 

The  sensitivity  checks  indicate  that  employment  levels  show  a  pattern  similar  to  that  of  the  main 
estimates, with negative effects on employment levels especially the first year in conjunction with 
the strongest increase in productivity, which are progressively re-absorbed in the economy as output 
grows and matching efficiency is positively affected by the training undertaken.  

ANNEX 13: CASE STUDIES 

This Annex presents brief illustrative case studies from five EU Member States plus the UK which 
has operated training entitlement schemes, and also Singapore, which operates an individual learning 
account. Each case study highlights particular features which can provide a useful guide if Member 
States are implementing new demand-led training programmes. In most cases the training entitlement 
is part of a wider adult learning strategy with a range of measures, including resisters of approved 
training, adult guidance systems and quality assurance frameworks. Not all the examples are good 
practice – there are some which did not meet expectations – and the development periods are also 
different.  For  example,  the  STAP  voucher  in  the  Netherlands  is  not  implemented  until  2022  but 
nevertheless provides useful information on scheme set-up. Also, the levels of available data vary 
between case studies and in some cases new monitoring systems are being established and developed 
to better capture the outputs and impacts of the initiatives. The French CPF has its own dedicated 
Annex (Annex 14). 

The seven case studies are as follows: 

Table A13.1: Case studies overview 

Country 
Austria 

Featured Scheme 
Education Savings 

Estonia 

Unemployment 
Fund 

Insurance 

Summary 
The  use  of  saving  schemes  to  help  support 
individuals  to  overcome  financial  barriers  to 
training. 
A fund targeted at the unemployed and those 
at risk of unemployment to help increase rates 
of adult participation in training. 

227 

 
 
  
 
Greece 

Targeted voucher schemes 

Italy 

Regional  individual  training 
entitlements 

The 
Netherlands 

STAP 

Singapore 

SkillsFuture Credit 

UK 

Individual 
Accounts 

Learning 

Vouchers  programmes  targeted  at  different 
groups  of  unemployed  people,  different 
sectors  and  different  parts  of  Greece,  all 
designed  to  contribute  to  improving  adult 
participation in learning. 
Individual  training entitlements  developed at 
regional  level  and  subsequently  adopted  in 
other Italian regions. 
The  new  voucher  scheme  which  becomes 
operational in 2022, replacing a tax incentive 
scheme. 
Universal but modulated training entitlement 
scheme  that  has  been  developed  and  applied 
to changing labour market conditions. 
The  short-lived  ILA  for  England  and  the 
subsequent  training  entitlement  schemes  for 
Scotland and Wales. 

1. Austria: Education Savings  

Education savings are a mechanism that is based on building savings schemes, ie. a low-interest loan 
for  housing  and  house  construction  which  builds  up  over  time  (up  to  six  years)  and  where  saved 
capital can be used for education and training, amongst other priorities, like care. Some 3.8 million 

479

citizens
 are part of the building savings scheme although only some 1% release funds for education 
and training purposes. The origins of education savings dates back to the start of this century with the 

aim to create a personal provision for ‘people willing to be educated
’ and to tackle directly the 
financial barrier to training. There are guidelines for the use of funds with vocational training and 
continuing lifelong (vocational) learning. The government aimed for this scheme to be open to all 
and not targeting specific vulnerable groups, as it was the case for existing voucher schemes. The 
reality is that funds have been mostly used by more highly educated groups.  

480

Statistics on education savings are limited but the National Bank of Austria (OeNB) estimated that 
some €4 million were held in loan accounts – for education and to support care commitments – for 
the first three quarters of 2020. Another building and loan association estimated €6.8 million in 2018 
in its loan accounts, less than 0.1% of its total portfolio. Based on these figures, a total loan value of 

481

some €20 million could be estimated.
 Limited data suggests that of some 5 700 loan agreements in 
2018, only 0.75% were concerned with education and training. This innovative approach has not met 
expectations. One reason is that the state premiums for building savings, to which education savings 
are linked in Austria, are very low and therefore do not provide an incentive. Also, vulnerable groups 
are often assisted by the PES and often receive free training. A further reason could be the multiple 

 In a population of just over 9 million. 

479

480

 It could also be used by parents and grandparents to provide training funds for their offspring. The funds are therefore 
transferable. 
481

 Extrapolated  data from one of the four building and loan associations. 

228 

 
 
 
                                                 
 
array  of  training  and  lifelong  learning  initiatives  at  all  levels,  including  training  vouchers,  whose 
access is complicated to some extent by regional variations.  

2. Estonia: Unemployment Insurance Fund  

The Unemployment Insurance Fund (UIF), introduced in 2017, includes targeted training courses 
for the unemployed in line with the labour force and skills needs forecast done by Estonia’s national 

482

  It  is  funded  by  the  Estonian  government.  The  schemes  cover  the  unemployed 
agency  OSKA.
registered with the PES , those employed but at risk of unemployment due to health reasons or who 

have no skills, including languages (Estonian), or those who are 50 years and older.
 The Fund is 
implemented through training cards – a voucher scheme to a maximum value of €2 500, which can 

483

be used on more than one course although cannot be accumulated over time
 - given to individuals. 
The cards cover trainings deemed eligible by OSKA (the skills list is updated with a new focus on 

484

 Career counsellors play a key role in advising and guiding 
digital and sector specific ICT skills).
individuals  to  a  training  course,  though  the  UIF  training  cards  can  also  be  accessed  by  an  online 
platform. 

485

The number of participants has increased from 900 in 2017 (just 8 months of the year) to 3 000 in 
2018 and 5 700 in 2019. 84% of all participants in the first two years were women and 60% of all 
participants in this period were aged 25 to 49, 38% being 50 years or older. The most popular courses 
are language courses and digital/ICT skills courses. 

The UIF has yet to be formally evaluated, but a new monitoring system is being introduced to see 
how individuals have used the training card (is it in their own sector for example? Is it in a sector that 
is deemed to have a lower risk than the one they are/were in?).   In the 2017-2019 period, there had 
been an increase in participation among the groups targeted by the UIF, twice the increase for the 
people with low qualifications or educational attainment compared to those with secondary or tertiary 

level qualifications
 and whilst a direct correlation has not yet been proven, the UIF has claimed a 
contributory  role.  Awareness  raising  campaigns  encouraging  individuals  to  participate  in  adult 

486

482

484

 OSKA skills forecast is part of the OSKA programme, launched in 2015 by the Estonian Government as a measure to 
contribute to the objectives of the Estonia 2020 Strategy, in particular to enhance employment and productivity. The main 
aim  of  the  programme  was  to  reduce  the  skills’  mismatch  and  to  facilitate  stakeholders’  cooperation.  (Source: 
https://ec.europa.eu/social/main.jsp?catId=1080&langId=en&practiceId=81).  
483

 There is also a category for those working in the oil shale sector. 
There has been some debate around the implementation of individual learning accounts but this has not evolved due 
to concerns about the administrative complexity of operating an ILA. See also Järve, J.; Räis, M.-L.; Seppo, I. (2012). 
Erialase tasemehariduseta isikute osalemine elukestvas õppes. [Participation in lifelong learning of people with no 
professional qualifications]. Tallinn: Estonian Applied Research Centre CentAR. 
485

 There is a freedom for the individual to choose a course of the type and duration of their preference but it is not a fully 
free choice and is guided by careers counsellors as well as training that meets the country’s skills needs. Languages aside 
popular courses have included driving skills, hair and beauty and teaching. 
486

Some 20% compared to 10%. 

229 

 
 
                                                 
 
learning – including also specific campaigns to promote the UIF  have also played a role.

interest in adult learning has been reported across all labour market groups.

488

487

 Growing 

Further refinements to publically funded training are envisaged as the lessons from the current scheme 
are learnt. One concern is that the quality and labour market value of courses require further testing 

with mandatory guidance under consideration, which is already part of the UIF
. In addition, and as 
described in the work plan for 2021, the Ministry of Education and Research plans to develop the 
principles  of  the  skills  portal  and  digital  story  (täiskasvanute  oskuste  digilugu)  for  adults  and  the 
490

concept of micro-credentials by the end of 2021.

489

3. Greece: Targeted training voucher schemes  

The  Greek  voucher  system  was  first  introduced  in  2012  and  targets  specific  sub-groups  of 

  Vouchers were introduced to replace 
unemployed by offering vocational training in specific areas.
the  previous  system  under  which  training  was  provided  only  by  providers  who  had  individual 
agreements  with  the  Ministry  of  Labour.  That  system  was  considered  insufficient  due  to  its 
complexity, high administrative costs, delivery delays and lack of transparency. 

491

The  main  objective  of  training  vouchers  is  to  achieve  a  structured  path  of  training,  which  could 
contribute to the entry of the unemployed into the labour market. Since the implementation of the 

492

voucher system, there have been 20 voucher schemes
, all of which have been co-funded by the 
ESF, with a combined budget approaching €500 million. Though the format and length of the training 
offer  differ  in  each  scheme,  the  training  provided  by  the  voucher  schemes  generally  include 
theoretical training (typically around 120 hours but up to 600 in some cases) in life-long learning 
(LLL) centres followed by practical training in a company or in the public sector (typically around 
200 hours but up to 800 in some cases). Many schemes also include counselling offered by the LLL 
centres.  

The vouchers cover all training expenditure and provide participants with a training allowance for 
both  the  theoretical  and  practical  training;  this  helps  unemployed  participants  overcome  financial 
constraints. By allowing participants to choose the content of training and the training provider (both 
need to be certified), it is expected that a higher quality of training and services by providers will be 
achieved through competition among providers to attract participants. At the same time, although it 

487

 44% of adults rated the availability of information concerning adult learning opportunities to be good or very good. 
See Räis, M. L., et al. (2014). Põhi- ja tasemehariduseta täiskasvanute tasemeharidusse tagasitoomise toetamine. Eesti 
Rakendusuuringute Keskus CentAR.  
488

 Interview with the Ministry of Education and Research. 
A recommendation in the mid-term evaluation of the lifelong learning strategy by the Praxis Center for Policy Studies 

489

(2019). 
490

 Ministry of Education and Research, (2021). Haridus- ja Teadusministeeriumi ning Haridus- ja Noorteameti 2021. 
aasta (arendus)tööplaan. (Work plan of the Ministry of Education and Research and the Education and Youth Authority 
for 2021).  
491

 The Greek Training Vouchers are most similar to a service voucher. Moreover, they were described as mainly oriented 
towards the unemployed. For these reasons, these vouchers have not been selected for representation in the CEDEFOP 
database https://www.cedefop.europa.eu/en/publications-and-resources/tools/financing-adult-learning-db 
492

 excluding voucher schemes for the 15-24 cohort 

230 

 
 
 
 
                                                 
is not a direct objective of voucher schemes, such a programme can contribute to eliminating time 
constraints. Indeed, vouchers may help participants to obtain the needed flexibility by choosing the 
training provider and training path that best suits their needs. To improve information, details of all 

voucher schemes can be found on the dedicated website
494

public employment service.
 The presence of certification can enable unemployed individuals to 
signal their improved skill status in the labour market, emphasizing the additional competence they 
acquired and removing a prior asymmetry of skills between themselves and employers.   

493

 as well as on the website of the national 

495

Overall  voucher  schemes  can  be  separated  into  several  groups  with  similar  characteristics
:  2 
schemes targeting young NEETs aged up to 29; 8 schemes for participants in public works measures 
mainly in administrative regions with high unemployment rates; 3 schemes targeting ex-employees 
of specific enterprises; 2 schemes offering ICT training to graduates with scientific, technological or 
economic background; 2 schemes aiming to improve technical skills in certain cutting-edge sectors 
(i.e. in sectors with growth prospects like trade, logistics, tourism, information and communication 
technologies, solid and liquid waste management, food/beverages, energy, industry, agriculture etc.); 
1 scheme available only to residents of a specific administrative region (Elefsina) targeting those aged 
at least 45 and who are unemployed for at least 6 months; and 1 scheme focusing on the training and 
certification of unemployed loaders.  

The bill of December 2020 aims to upgrade the education and training provided in terms of structures, 
procedures, curricula and certification, in particular to address the fact that the issue of quality has 
emerged as one of the major impediments to participation.  

4. Italy: Regional individual training entitlements  

The Carta di Credito Formativo Individuale (CCFI) is an individual training entitlement adopted in 

 The CCFI is a prepaid credit card (worth up to €2 500) 
Tuscany and later in Umbria and Piedmont.
that allows individuals to receive a financial contribution to cover partially or totally the costs incurred 
for the implementation of a personal training project.  

496

The  key  aim  is  to  promote  and  encourage  the  training  of  individuals  throughout  their  life,  thus 
overcoming some of the limitations affecting standard training tools and practices (lack of time to 
devote to the training, problems related to work-life balance, financial barriers, lack of exhaustive 
information on the local supply of training, poor efficiency of the training-related public services, 
lack  of  community  support).  The  flexible  nature  of  the  CCFI  allows  cardholders  to  distribute  the 
resources provided by the card among the courses that they consider most suitable for their needs. 
Such flexibility goes hand in hand with a tailor-made support provided by the public employment 
centres’  personnel  in  terms  of  advisory  and  counselling  services,  which  aims  at  helping  CCFI 
beneficiaries  make  the  best  use  of  the  potential  of  the  card.  In  particular,  the  employment  centre 

493

495

494

 http://voucher.gov.gr 
 https://www.oaed.gr/ 
 Evaluation reports are not yet publically available 
 The measure was introduced in 2004 till 2015. All types of training were allowed in the initial period (between 2004 
and 2007), including informal training. Subsequently (2008-2015), training courses were listed on a regional catalogue 
of approved programmes to avoid misuse of public funds. 

496

231 

 
 
                                                 
personnel helps people choose the career path to take, set goals that are actually achievable given 
their  background  and  professional  experience,  and  ensure  an  effective  and  timely  match  between 
demand and supply of training services. 

The  CCFI  identifies  priority  groups  as  women,  non-standard  employees,  immigrants,  etc.  The 
distribution of resources among target groups is arranged at provincial level. Such approach reflects 
the attempt to combine, on the one hand, the promotion of personalised training activities and, on the 
other, the support of social groups having specific needs or facing a higher level of socio-economic 
vulnerability.  To effectively support these target groups, the card, unlike most of the existing tools 
as the standard training vouchers, has the feature of also financing services which are only indirectly 
connected to training but that might affect the decision to select and undertake a certain course. For 
example, the CCFI can be used to pay babysitters or caregivers. This CCFI feature is particularly 
important to encourage individuals bearing most of the family care burden to seize the opportunity of 
a training course which can, in turn, increase their employability and/or income. 

In addition, to attract individuals who may be reluctant to come back to the traditional classroom-
based  education,  the  CCFI  can  be  also  used  to  pay  for  ‘informal’  training  activities,  e.g.,  non-
institutionalized training which can take place almost anywhere within the family, with friends, at 
work or at facilities made available by education and training providers. With a view to ensure that 
the selected activities are consistent with the CCFI’s holder background and in line with the very aim 
of the instrument, participants are guided by employment centre counsellors in charge of validating 
their choice. 

497

 of this experimental initiative highlight that, as for the participants’ satisfaction, 71%

The results
of  card  holders  declared  that  the  training  activities  met  their  expectations  in  terms  of  quality  and 
effectiveness. The percentage of those reporting a good level of satisfaction concerning the matching 
between training contents and personal/work needs was also quite high (53%). With regards to the 
occupational  outcomes,  66%  of  the  sample  believe  that  they  have  improved  their  professional 
condition, and of this 66% almost the entire sample recognizes the usefulness of the CCFI for the 
purpose of improving their professional condition. 

498

Detailed data on the total number of CCFI holders is not available. There were approximately three 
thousand  CCFI  beneficiaries  between  2006  and  2008  in  Tuscany  (only  the  provinces  of  Pistoia, 
Arezzo, Prato), about half of the holders (51%) is aged between 25 and 35 years while about 33% is 
over 35 years old. For what concerns their educational level, the most common qualifications of the 
CCFI  holders  are  the  High  School  Degree  (32%)  and  the  Master  Degree  (36%).  After  having 
benefited  from  the  service,  the  number  of  participants  with  Professional  School  Diploma  and 
Postgraduate Education Diploma increased, going from 8% to 14% in the first case and from 4% to 
27%  in  the  second  case.  Regarding  the  employment  profile  of  the  beneficiaries,  alongside  a 
significant percentage of unemployed (about 39%), a significant share of inactive people (33%) is 
also included in addition to a 16% of atypical and 8% of workers in transition (on the move or just 

497

498

 Ministero del Lavoro et al. (2008), Carta ILA: i principali risultati della sperimentazione. 
 The survey has involved all beneficiaries who at the time of the interview had completed training activities financed 
by the CCFI. It reached a response rate of 66%, i.e. 239 beneficiaries out of a sample made up of 363 recipients initially 
contacted. 

232 

 
 
 
                                                 
laid off). The training sectors in which the largest number of CCFI funded courses are concentrated 
is  the  post-graduate  and  high  training  field  including  the  area  of  socio-educational  services,  the 
foreign language sector, office work and information technology. 

5. Netherlands: The STAP - from income tax deduction towards individual training 
entitlements  

The Netherlands introduced in 2018 an inter-ministerial programme for lifelong development, which 
sets  out  the  main  policy  orientations  for  the  coming  years.  The  programme  has  the  ambition  to 
increase the demand for adult learning by individuals directly by, on the one hand, offering individual 
financial incentives and on the other hand, increasing the flexibility of the VET and HE offer, amongst 
others.  

Initially, the previous government planned to give every citizen in the Netherlands the same skills 

development budget from the government at birth by means of an individual learning account.
 This 
budget would have been partly spent on initial education. The higher the initial education, the lower 
the learning rights that remain when entering the labour market (and vice versa). In this way, publicly 
available education and training budgets would be more equally distributed amongst citizens, assuring 
that budgets are allocated to groups in greatest need. It was planned that employers would also be 
able to provide training contributions into the development account. Already in 2001-2003 a pilot of 

499

an Individual Learning Account was undertaken in the Netherlands

500

 that reported positive effects 

on learning behaviour and attitude.

501

Finally, this individual learning account was not implemented for several reasons related to the 
technical  implementation,  legal  aspect,  and  available  budget.  The  development  of  a  system  of 
learning  rights,  in  which  for  every  citizen  the  available  budget  and  training  history  should  be 
monitored,  was  considered  a  very  demanding  and  complex  ICT  operation.  Moreover,  the  budget 
available  was  not  considered  sufficient  to  provide  every  citizen  a  reasonable  amount  to  increase 
training  take  up.  In  addition,  with  these  small  amounts,  private  banks  were  also  not  interested  to 
cooperate and invest, setting up an account scheme. Finally, there were legal concerns about the legal 
ownership of the learning right. As a result, the government has decided to introduce a new training 
allowance  scheme,  the  STAP  budget  (Dutch  acronym  for  Stimulering  Arbeidsmarktpositie,  or 
Incentive Labour Market Position), to better empower individuals to take control of their learning 
careers more actively. 

499

  24%  of  the  5.6  million  employees  that  are  part  of  the  collective  labour  agreement  have  a  personnel  learning  and 
development budget (around 1.3 million employees). These budgets are paid by the employers and employees with the 
support of the levy. 
500

 Anna Geertsema, et al. (2004): Experimenten met individuele leerrekeningen: de balans opgemaakt. ’s-Hertogenbosch: 
CINOP. In the pilot  for 2 500 low qualified employees a  learning account  was established on  which the government 
contributed €450. The employee, employers and others could contribute to this account as well. 
501

 In the Netherlands, there are also private companies that provide Individual Learning Account services to individuals, 
such as the James Learning Account, where employers could transfer budgets on the learning account, and the learner 
could select a training via de catalogue of learning providers. 

233 

 
 
 
                                                 
At the same time the Dutch government supports existing private individual learning accounts 
that are increasinly made available by private parties such as social partners, sectoral training funds 
and  employers.  According  to  recent  research,  approximately  1.3  million  (24%)  of  the  5.6  million 
employees covered by collective bargaining agreements had an individual learning and development 

502

budget in 2017.
 The government is encouraging the development of private learning budgets by 
clarifying the application of the current framework for the fiscal treatment of training costs to these 
budget,  so  employers  could  contribute  to  the  private  learning  accounts.  The  Tax  Authority  has 

developed an information tool for this purpose.

503

The STAP budget is introduced to replace an existing tax incentive scheme, in which individuals 
could request tax credits for costs for participating in adult learning. The tax incentive scheme had a 
comparatively high limit (€15 000 per individual) but did not produce a tax credit for the first €250 
spent.  An  evaluation  of  the  tax  incentive  concluded  that  the  tax  measure  did  not  substantially 
contribute  to  stimulating  participation  of  individuals  and  underperformed  particularly  for  lower 
income families. A total of 2.6% of the Adult population of working age made use of the credit, for 
an average of €1 700 per year. Applicants were more often higher educated and in permanent fulltime 
employment was mostly used by individuals who would in most likelihood have paid for the training 

504

courses themselves anyway (deadweight loss
). It was calculated that the marginal deadweight loss 
- i.e. the part of an extra euro training deduction that does not lead to extra training - amounted to 
between 73 and 100%, depending on the group and the tax rate. One of the reasons mentioned of the 
low use amongst lower educated and unemployed is the fact that trainings still need to be pre-financed 
without certainty whether it can be deducted from tax payment (in case low or no income). 

The  STAP  scheme  will  offer  all  adults  the  possibility  of  spending  up  to  €1 000  per  year  on 
training. The financial  amount of  €1 000 is  backed up by a study of the Netherlands  Institute of 
Social  Research  (2018)  Grenzen  aan  een  leven  lang  leren  (Barriers  to  Lifelong  Learning),  that 
concluded that in most cases an amount of €500 to €2 000 is sufficient to remove the threshold of the 
costs  of  following  training  activities.  Moreover,  the  average  amount  for  training  used  in  the  tax 
reduction scheme was €1 700, and in view of the personal contribution under the current tax system 
(dependent on the relevant tax bracket), the maximum amount of the STAP account is higher than 

505

the average subsidy for the fiscal deduction of training expenses.
 Moreover, 82 % of all training 
costs applied for in the tax reduction scheme is below the €2 500. Many shorter training programmes 
can be paid for in this way and for those who want to follow an extensive training programme, the 
personal contribution is reduced sufficiently so that the costs are no longer perceived as a barrier. 
Although other schemes, such as the temporary scheme for education for  occupations that have a 
shortage  of  labour  (Tijdelijke  regeling  subsidie  scholing  richting  een  kansberoep)  have  a  higher 
amount of €2 500 per person, for the STAP budget it was decided to keep it lower to increase to 
outreach of the scheme, given the available budget, to 200 000 beneficiaries per year. The actual take 
up will probably be higher since most training cost less that €1 000. Moreover, it was argued that a 

502

 Ministerie van SZW (2019) Individuele ontwikkelbudgetten in cao’s in 2017. 

503

504

505

 https://download.belastingdienst.nl/belastingdienst/docs/cao-alg-scholingkost-lh-on7251z2ed.pdf 
 CPB (2016), Evaluatie aftrekpost scholingsuitgaven. 
 CPB (2016) Evaluatie aftrekpost opleidinguitgaven.   

234 

 
 
  
                                                 
larger budget, could also have a negative distorting effect on the market prices for training (this will 
be monitored and evaluated after some years).  

Beneficiaries can apply for the STAP budget once a year during six time periods. Beneficiaries can 
apply for the STAP budget once a year during six time periods. The STAP aims to facilitate multi-
year  training  with  multiple  payment  dates,  but  this  is  not  possible  yet  in  the  first  years  of 
implementation  of the scheme in  which the STAP budget  will be implemented in  its rudimentary 
form, since it takes time by implementation partners to implement additional features. The idea for 
the  future  is  that  the  initial  application  can  indicate  that  it  concerns  multi-year  training,  with  a 
maximum budget of €1 000 for each training year.  If the initial application is granted, a follow-up 
application can be submitted for each payment moment during the multi-year training. If the budget 
for the period concerned is exhausted, the assessment of follow-up applications is postponed to the 
next period. These applications will therefore be given priority in the assessment of that next period. 

A low threshold  for  applying for the STAP budget is  an important starting point.  Individuals  can 
therefore apply for this  subsidy online (with their digital  ID) via a simple digital  form,  where the 
various conditions for the STAP budget are immediately checked. Before a participant can apply for 
the STAP account, it is important that the applicant selects the training activity he or she wants to 
follow and registers with the trainer. The list of training activities is provided in an online register. 
The  training  provider  will  then  give  the  participant  proof  of  application.  This  certificate  must  be 
enclosed  with  the  grant  application.  For  people  for  whom  it  is  not  possible  to  go  through  the 

application  process  digitally,  there  is  a  provision  at  the  UWV
  to  support  these  people  in  the 
application  process  (by  telephone  or  at  the  office).  The  budget  will  be  directly  transferred  to  the 
training provider, based on earlier experiences with training vouchers schemes in 2016-2017 where 
the budget was directly transferred to the bank account of the learner, where it appeared that in 3% 
of the cases the budget was not used for training, and 6% of the cases it was only partly used for 
training. 

506

The learner must complete the training activity with a diploma or certificate, or to have attended the 
course for at least 80% of the duration. This attendance rate must be demonstrated by the certificate 
of participation that the training institute provides within 6 months after the end date of the training 
activity. If none of the results are achieved, the subsidy can be reclaimed. 

The current STAP budget is presented as a scheme for all. No specific measures are included that 
assures a match between the skills set of the individual, individual and labour market needs, and the 
learning  offer.  Also,  no  specific  measures  are  proposed  to  strengthen  the  outreach  to  vulnerable 
groups. During the interviews, it was indicated that this is considered as one of the biggest challenges. 
Currently, the Netherlands aims to make the online application process as easy as possible, and they 
will open a call centre for questions. In the future, the government is considering integrating guidance 
and counselling services in the schemes, but this is currently left out (included in the initial design of 
the scheme). Currently the government is running a temporary voucher scheme for career guidance 
(Nederland Leert Door!) of €700, though this is not connected with the STAP budget, but can be 
combined.  Interviews  point  out  that  obliging  participants  to  undertake  guidance  and  counselling 

506

 UWV (Employee Insurance Agency) is an autonomous administrative authority and is commissioned by the Ministry 

of Social Affairs and Employment to implement employee insurances and provide labour market and data services. 

235 

 
 
                                                 
activities before starting the training could also demotivate applicants to apply, especially those not 
easy to reach. The government is also thinking about adjusting the financial amount in the future for 
training for professions that have a labour market shortage or for greening professions.  

It is to be noted here that neither the STAP, nor the register of training providers is yet operational; 
the objective is to start implementation in 2022. This also means that possible conditions may still be 
revised before it is formally launched. The STAP budget scheme will run for 5 years, but the plan is 
to make it an integral part of policies after 5 years. 

At this moment,  the financial scope of the STAP budget  is  planned to  be kept  like the budgetary 
reservation  of  the  expiring  tax  incentive  scheme,  set  around  €200  million  annually  for  training 
costs. While the STAP budget offers in theory the possibility to all individuals in the Netherlands to 
make use of the scheme, it is not expected that this will indeed be the case. There are no provisions 
to  increase  the  allocated  budget  in  case  take-up  proves  higher  than  expected.  Nevertheless,  the 
explanatory memorandum of the STAP-budget indicates that an infrastructure is created allowing for 
quicker and simpler deployment of any additional public budgets that may become available in the 
future - for certain goals or target groups. This contributes to further bundling of public resources and 
reducing fragmentation of implementation modalities in the field of lifelong learning. 

To estimate the structural annual costs for implementing the STAP scheme (in addition to the 

200 million Euro training budget) an analysis was made by the implementing bodies UWV

507

 and 

508

DUO
 (uitvoeringstoets’ or ‘implementation test’). The structural costs for UWV are estimated at 
€21.5  million  a  year.  This  includes  the  cost  for  maintaining  the  online  platform  (€3.9  million), 
housing costs (€0.7 million) and staff costs (€16.9 million). For the staff costs, UWV indicated that 
yearly 161 fte is needed for implementing this scheme (setting up a new dedicated unit within UWV). 
The anual cost for maintaining the education register are estimated at €70 thousand in the first year 
(2022), €46 thousand in the second year  (2022), and €35 thousand from 2014 onwards. The total 
estimated structural annual costs of UWV and DUO together are €21.5 million. This is more 
than the planned costs of 18 million Euro as included in the STAP regulation. It was agreed between 
the ministries and implementing agencies that in the end of the first year of implementation, based on 
actual costs, budgets will be be adjusted if needed. 

To estimate the one-off costs for setting up and running the voucher scheme the analysis of UWV 
and DUO shows that the one-off costs for setting up the scheme for the UWV are estimated at €18.2 

million (€7.2 million in 2020; €10.6 million in 2021; and €0.4 million in 2022)
. This is including 
the costs for setting up the online platform for STAP budget, which  is estimated at €12.12 million, 
but does not include the costs for setting up the the training provider register by DUO at €2,5 million 
(25,159 hours work estimated). This makes the total one-off costs for setting up the scheme €20.7 
million (€18.2 million + €2,5 million). 

509

507

 UWV (2019). Uitvoeringstoets subsidieregeling leer- en ontwikkelbudget voor stimulering van de 

arbeidsmarktpositie (STAP-regeling). 5 November 2019. 
508

 DUO (2020). Uitvoeringstoets STAP-Regeling. 

509

 UWV (2019). Uitvoeringstoets subsidieregeling leer- en ontwikkelbudget voor stimulering van de 

arbeidsmarktpositie (STAP-regeling). 5 November 2019. 

236 

 
 
                                                 
Value of training purchased  One-off costs 

Recurrent adm. costs  

€200 million p.a  

Total  annual recurrent cost is 21.5 million 

  Staff costs: €16.9 million (161fte) 

  Housing costs: €0.7 million 

  Online platform: €3.9 million 

  Education register: €70 000 in the first 
year (2022), €46 000 in the second year 
(2022), and €35 000 from 2014 onwards 

Total  one  of  costs  is  €20.7 
million 

  UWV: €18.2 million (€7.2 
million in 2020; €10.6 
million in 2021; and €0.4 
million in 2022) 

  Online platform for 

STAP budget: €12.12 
million (is part of the 
UWV budget of 18.2 
million) 

  Training provider 

register (DUO): €2.5 
million 

The  compliance  costs  for  providers  relate  to  the  registration  and  updating  of  the  necessary 
information about training in the training register and the production and sending of the certificate of 
participation  per participant  by the training provider. The  government  estimates these compliance 
costs  to  be  one-off  €90  000  (€2  000  X  1-hour  costs  for  the  administrative  assistant  at  €45  for 
registering the training register) and annual €1 965 000 (2 000 X 1-hour costs for the administrative 
assistant at €45 for periodic costs for keeping the training register up to date + €250 000 X 10 minutes 
costs for the administrative assistant at €45 per hour for the certificate of participation). Furthermore, 
it  is  estimated  that  it  takes  the  applicant  about  10  minutes  per  application  to  fill  in  the  required 
information.  Assuming  250 000  applications  (based  on  an  average  subsidy  of  €800),  the 
administrative burden comes to €625 000. In cases 40% of the applications involve training that costs 
more than €1 000, the administrative burden for the trainer is estimated at approximately €3.4 million 
(100 000 x 45 minutes of administrative assistant time at €45 per hour).  

The current conditions considered seek to strike a balance between ensuring quality and preserving 
flexibility by allowing for a certain degree of decentralisation/creating “alternative routes” into the 
registry, including, e.g., a recognition by branch or sector organisations. At the moment, the following 
conditions  are set  for training providers to  be included in  the registry:  (1)  the training provider is 
recognised by the Dutch Ministry of Education, Culture and Science, (2) the provider has a quality 
mark  (NRTO),  (3)  the  providers  offers  training  that  is  classified  in  the  national  qualifications 
framework  (NLQF),  (4)  there  is  a  sectoral/branch  recognition  of  the  provider,  (5)  the  provider  is 
recognised by the National Knowledge Centre (RPL). The quality framework for STAP is currently 
being prepared but there will be audits/studies to test compliance. In the meanwhile, a small group of 
agencies has been accredited as providers of quality assurance to STAP. Given the STAP project is 
planned to be launched in 2022, it is not possible to provide an assessment of the impact. 

237 

 
 
 
 
 
When the subsidy applicant fails to meet his/her obligations on the grounds of the subsidy rules (for 
example by not successfully completing the training and not meeting the attendance requirement), 
the participant can be excluded for a maximum period of two years from submitting an application 
for a STAP subsidy. The duration of the exclusion depends on the severity of the offence. 

6. Singapore: SkillsFuture Credit - using personal accounts for a universal yet targeted 
provision of training entitlements 

The SkillsFuture Credit (SFC) was introduced in 2015. The central features of the SFC scheme are 
threefold: (i) giving individuals the autonomy and flexibility to decide about their training needs and 
goals by not prescribing how the credits should be used; (ii) to encourage working adults to be active 
learners  and  to  invest  in  their  continued  learning  journey  -  SFC  beneficiaries  have  generally 
completed their full-time education and are in the workforce or preparing to join the workforce; and 
(iii) SFC can be used on top of existing Government course subsidies for a wide range of approved 
skills-related courses, hence training is made even more affordable with the use of SFC. 

The SkillsFuture Credit of $500 (€312) is given to all Singapore citizens aged 25 and above. It is not 
time expired and there are periodic top-ups from the government. Citizens can use their credits to 
claim from a wide range of SkillsFuture Credit eligible courses as well on top of existing government 
course subsidies to pay for the range of skills-related courses approved by SkillsFuture Singapore 
(SSG). In 2020, more than 188 000 Singapore citizens utilised their SkillsFuture Credit, an increase 
from  156 000 Singapore Citizens  in  2019, who could  choose among roughly 28  000 SkillsFuture 

510

Credit-eligible  courses  courses
  in  a  wide  range  of  training  areas  (e.g.  Information  and 
Communications, Personal Development, Pharmaceutical and Biotechnology etc.). As of end-August 
2020, about 600 000 or 23.5% of eligible Singaporeans had utilised their SkillsFuture Credit (SFC). 
The latest available data for the breakdown by age groups is as of end-2019: the utilisation rate was 
approximately 16% among Singaporeans aged 60 and above, and around 22% among Singaporeans 
511

aged 25 to 39 years old and also 40 to 59 years old.
 Since the launch of the SkillsFuture movement, 
there has been an increase in the annual training participation rate which cannot be attributed to just 
the SFC initiative but to in combination with wider skills policies. For instance, a Skills and Training 
Advisory services was set up to provide skills and training needs support for individuals at various 
stages of their career, recommending suitable courses and programmes. 

510

511

 Data provided by the Ting Sze Yun, Strategic Planning Division - SkillsFuture Singapore, July 2021. 
SkillsFuture credits utilisation data (2021). 

238 

 
 
                                                 
, Coursera
516

Courses that are eligible under SFC include open online courses offered by training providers such 

as Udemy

512

513

, Udacity

514

 and edX

515

, which allow individuals to learn at their own pace 

and convenience
. There is a wide range of SFC approved courses offered by both local and online 
providers listed on the SFC course directory, and all of them have been pre-approved by SkillsFuture 
Singapore (SSG). SSG has put in place a set of guidelines that must be met before courses can be 
approved and added to the SFC course directory. Key course eligibility criteria include: (i) courses 
must be skills-related and have clear and relevant learning outcomes, (ii) courses must be open for 
individual registration (i.e. not restricted to employer-sponsored trainees), as well as (iii) courses with 
a  total  duration  of  at  least  7  hours  (excluding  assessment  and  lunch)  may  be  broken  down  into 
modules. 

As a consequence of the pandemic, two SkillsFuture Credit top-ups were introduced in 2020. Unlike 
the  broad-based  SkillsFuture  Credit,  the  top-ups  were  designed  to  expire  in  five  years’  time  to 
encourage timely actions  by individuals in reskilling and upskilling. First, a  one-off SkillsFuture 
Credit top-up of $500 was given to all Singapore citizens aged 25 and above as at 31 Dec 2020. The 
top-up can be used on a wide range of skills-related courses, on top of existing government course 
fee  subsidies.  A  further  one-time  $500  credit  was  given  in  the  form  of  additional  SkillsFuture 
Credit (mid-career support) to all Singapore citizens aged 40 to 60 (inclusive) to target citizens 
who are most likely to require retraining or upskilling. The additional SkillsFuture Credit can only be 
used on selected training programmes that support career transition (e.g. SGUnited Skills Programme 
(SGUS),  SGUnited  Mid-Career  Pathways  Programme  –  Company  Training  (SGUP  –  Company 
Training) and Career Transition Programmes). 

Although  no  data  is  currently  available  about  the  utilisation  rates  of  these  top-ups,  the  training 
participation rate for the resident labour force aged 15 to 64 held up (49% in 2020, similar to a year 
ago) despite restrictions placed on in-person training due to the pandemic, as more people relied on 

517

online learning solutions.
  Thanks to the above mentioned top-ups and the support programmes for 
career transition, training industry rebounded and companies increased their efforts to re-train their 
workers during the pandemic period. Similar to what happened during SARS, the training uptake was 
very high whenever there was a downturn, because people went into very intensive training. 

512

 Udemy is a massive open online course (MOOC) provider aimed at professional adults and students. As of April of 
2021, the platform has more than 40 million students, 155 000 courses and 70 000 instructors teaching courses in over 65 
languages. There have been over 480 million course enrolments. Students and instructors come from 180+ countries and 
2/3 of the students are located outside of the U.S. 
513

 Coursera is a massive open online course provider (MOOC) founded in 2012, providing online courses, certifications, 
and degrees in a variety of subjects. As of May 2021, Coursera is partnered with more than 200 institutions around the 
world and offers over 3 000 courses. 
514

 Udacity is an online learning platform with over 160 000 students in more than 190 countries enrolled in 2021. 
 edX is an massive open online course (MOOC) provider created by Harvard and MIT. s of 20 July 2020, edX has 

515

around 33 million[6] students taking more than 3 000 courses online. 
516
https://www.myskillsfuture.gov.sg/content/portal/en/index.html. 
  Ministry  of  Manpower-Manpower  Research  and  Statistics  Department  (2021),  Labour  Force  in  Singapore  2020 

517

(mom.gov.sg) 

239 

 
 
                                                 
The assessment of the SkillsFuture Credit identifies some lessons learnt for future initiatives in terms 
of i) providing beneficiaries with clear evidence and detailed information in order to make informed 
decisions  about  their  training  choices,  and  ii)  helping  citizens,  enterprises  and  training  partners 
(Institutes of Higher Learning) to have the required job-skills insights and training support and iii) 
being prepared to adapt the initiative in the light of market changes, uneven take-up rates and in this 
case, the aftermath of the Covid-19 pandemic. 

7. UK:  Individual Learning Accounts 

ILAs were introduced in England in September 2000.
 Their aim was to increase adult skill levels, 
and to address inclusion and diversity issues within adult education and training. Also, and in line 
with wider skills policies, there was a desire to create a demand-led system to improve the quality 
520

519

and responsiveness of providers

, and to increase levels of personal investment in skills training

518

  Prior to their introduction, different versions of ILAs were piloted 
which were comparatively low.
in different regions including universal and targeted ILAs. The final versions combined these two 

521

elements; ILAs were universally available but marketed to specific population groups

522

:  

  young people between 19 and 30 with low qualifications; 
  self-employed people; 
  women returners to work; 
  non-professional school staff; and, 
  ethnic minorities. 

The pilots also led to a change in the financial model of ILAs.  Rather than an actual savings account, 
ILAs became virtual learning ‘tokens’.   

There were three incentives offered by the ILAs

523

: 

  an initial incentive of £150 towards the cost of eligible learning for the first million account 

users, with a small contribution of at least £25 from the account holder; 

  a discount of 20% on the cost of a broad range of learning capped at £100
  a discount of 80% on the cost of a limited list of basic IT and mathematics courses, limited to 

; and 

524

a total of £200 discount per account from October 2000. 

Individuals  and  employers  also  received  tax  incentives  if  they  ‘topped  up’  their  accounts.  Total 
funding  for  2000-2002  was  £150  million.  The  programme  was  operated  by  a  private  contractor 

518

519

 ILAs were introduced separately, and later, in Scotland and Wales (see further below).  
 Lee, B. (2010) The individual learning account experiment in the UK: A conjunctural crisis? Critical Perspectives on 

Accounting 21 (2010) 18–30.  
520

 Owens, J. (September 2001) Evaluation Of Individual Learning Accounts – Early Views Of Customers & Providers 

Technical Report. Department for Education and Skills.  
521

 Hillage, J. et. al. (2000) op. cit. 
 National Audit Office (October 2002) Individual Learning Accounts.  
 Ibid.  
 Those which enhance the learners' employability or increases their vocational skills. The learning need not necessarily 

522

523

524

lead to the learner achieving qualifications. Recreational courses were not permitted.  

240 

 
 
 
                                                 
(Capita) who registered learners and set-up their accounts. Capita was also responsible for the register 
of providers from which learners could choose. There were relatively few programme targets in terms 
of  the  types  of  learners  or  provision  they  undertook.  Programme  objectives  were  operational, 
concerned with how the scheme would work rather than what it should achieve. The primary target 
was opening 1 million accounts by April 2002 which was exceeded. By July 2002 1.5 million learners 
had been registered. In the same year, 8 910 providers were registered to deliver learning through the 
ILAs.  

English ILAs were withdrawn in 2002 because of concerns over serious misuse
, before the scheme 
could develop and deliver the anticipated impacts. An early evaluation was undertaken in 2001 and 
showed that there was a broad spread of people registering and redeeming their ILAs, but that the 
operation of the scheme by a private contractor with insufficient oversight had contributed to a lack 
of quality assurance and fraud prevention. There was a significant uptake from women (58%) and 
from employees of small firms (43%), as well as those of non-white ethnicity (20%); in social classes 

525

526

DE

 (19%); labour market returners (18%); with no qualifications (16%); and the self-employed 

 ILAs 
(10%). However, one quarter were graduates and 40% qualified to at least NVQ Level 4.
were used to fund predominantly entry level courses, ICT in particular. Most learners (57%) had little 
or no prior knowledge of the subject, 48% could not have paid for the learning without an ILA, and 
528

73% said the ILA increased their learning options.

527

It is difficult to assess the legacy of ILAs in England since the programme did not operate for long. 
However, it is interesting to look at the development of ILAs in Scotland and Wales and what they 
learned from the experience in England.  

Scottish ILAs (SILAs) began in December 2004 with similar aims to English ILAs i.e. widening 
participation, increasing participation amongst non-learners, and increasing personal investment in 
skills.  The  SILAs’s  target  group  was  low  earners.  Unlike  in  England,  SILAs  could  only  be  spent 
within  the  established  provider  base  (300  approved  providers).  Furthermore,  the  programme  was 
managed by Skills Development Scotland rather than subcontracted to a private sector organisation. 
Therefore,  whilst  the  aims  and  principles  were  similar  to  English  ILAs,  SILAs  were  much  more 
targeted, to  be used with  a narrower range of existing providers, and were directly managed by a 
Government organisation. In the first two years, SILAs funded around 60 000 learners, most of these 
undertook provision at a college (63%), and most courses were in ICTs, and leading to a qualification. 
However, 54% of learners were qualified to at least NVQ Level 4. Over half of learners would not 
529

  Levels  of  deadweight  in  Scotland  were  27% 
have  undertaken  the  course  without  the  SILAs.
compared to 44% in England. SILAs ran to 2017 when they were replaced by Individual Training 

525

 The ILA programme was closed in November 2001 due to allegations of fraud (concerning a large number of account 
numbers that had been extracted from the system and offered for sale).  
526

  This  is  a  socio-economic  classification  ranging  from  A-E.  Social  classes  DE  are  the  two  lowest  classes  in  the 

classification. 
527

 Owens, J. (September 2001) op. cit. and National Audit Office (October 2002) op. cit. 
 Ibid. 
 Cedefop (2009) Individual Learning Accounts.  

528

529

241 

 
 
  
                                                 
 
Accounts (ITAs). ITAs are also targeted at those on low income, and in receipt of income related 
benefits. Training must be within one of Scotland’s priority sectors. By 2020, 47 000 learners had 
taken up an  ITA. Over half (55%) were not in employment,  training focused on the: construction 

(29%); fitness, health and beauty (12%), and transport sectors (9%).

530

ILA Wales were introduced in 2003, with similar aims to those in Scotland and England. However, 
ILA Wales were more tightly targeted at people on income related benefits. Participants also had to 

have a qualification below NVQ Level 3.
 An independent evaluation undertaken in 2007 reported 
that ILA Wales generated 7 126 registrations and 5 274 course starts. The evaluation found that there 
were impacts on participants in terms of employment (53% got a job); earnings (58% received a pay 

531

rise); and competencies  (88% said their knowledge/skills had improved).
  The programme was 
ended in 2011 due to austerity.  However, following a pilot in 2019, a national programme of Personal 
Learning Accounts (PLAs) was introduced with a similar remit to the original ILA Wales.  By March 
2021, 6 000 people had applied for a PLA and 3 000 had entered training.   

532

ANNEX 14: INDIVIDUAL LEARNING ACCOUNTS – THE 
FRENCH EXPERIENCE  

The annex is prepared based on the available literature and information shared by the programme 
managers for the French ILA (Caisse des dépôts et consignations - CDC).  

1. Summary 

  The  French  Individual  Learning  Account  –  the  CPF  Compte  personnel  de  formation  – 
stems from an initiative from 2005 and is subject to continuous reforms, the most recent 
in  2018  (operational  in  2019),  enshrined  in  employment  laws.  The  ability  to  learn 
operational lessons and adapt as required is a key feature of the CPF. 

  The  online  training  accounts  are  accessible  to  all  adults  of  working  age,  with  recent 
reforms extending coverage to the self-employed volunteers and school leavers, provided 
they work more than half time. A time-based discount applies for part-timers below half 
time. 

  The CPF can be held and utilised until retirement. The CPF is not tied to employment 
contracts, although training leave – for employees – has to be agreed with employers, with 
no loss of wage. 

  Training has to be purchased from accredited training providers, authorised for the CPF; 
and it has to lead to a qualification or recognized certificate. There are currently 19 000 
registered  accredited  providers  (the  list  is  reviewed  periodically).  For  users,  there  is  a 
search engine to help select courses and a helpdesk. 

530

531

532

 CIPD (January 2021) Skills to grow: the case for enhanced individual learning accounts in Scotland.  
 Ibid. 
 BMG Research (September 2007) Individual Learning Account Wales (ILAW): Fourth Main Evaluation Report. Welsh 

Assembly Government.  

242 

 
 
 
 
                                                 
  The CPF is largely funded through taxes on employers and allows additional contributions 
by  individuals,  their  employers  or  public  authorities  (e.g.,  the  Public  Employment 
Service).  

  The 2018 reform was intended to strengthen the autonomy and choice of the individual, 
thus improving flexibility, strengthening employment rights and increasing the number of 
training actions and learners, as the previous measures did not have the desired effect.  

  One of the features of the reform is the monetisation of the learning accounts. Individuals 
can  now  access  credits  of  €500  year,  with  additional  financial  incentives  for  the  low-
skilled (€800), which they themselves can spend for training.  

  Another key innovation is to make the account details available to users online, via PC, 

tablet or mobile. The aim is to increase transparency and improve user access.  

  Since November 2019 (until January 2021) there have been 6.5 million activations of the 
CPF,  linked  to  a  total  of  1.77  million  registrations  for  training  activities.  Participation 
figures  for  the  revised  CPF  highlight  a  continued  bias  towards  higher  qualified 
individuals. So far, young and older people (below 19 and over 55) have also been under-
represented in participation figures.  

  Only  about  10%  of  trainings  are  complemented  with  individual  contributions,  for  an 

average value of around €485 (3.9% of the total training costs
). The possibility of top 
ups from businesses is relatively new (2020) explaining relatively low numbers (0.5% - 
see Table 13.1). 

533

  The operational budget for the CPF for 2020-2022 (3 full years) is €100 million but subject 

to monitoring and additional investment. 

  An evaluation/impact assessment of the CPF was due to be undertaken later in 2021. This 
may follow later to ensure that the results are not distorted by the Covid-19 pandemic and 
its impact on businesses and individuals. 

2. General Description, Key Features, and Achievements 

This section highlights the key points of individual learning accounts in France, by focusing on the 
portal Mon Compte Formation (MCF - my learning account). This portal combines the entitlements 
from  multiple  training  schemes  into  a  single  portal.  Most  attention  is  subsequently  given  to  the 
provisions  of  the  CPF  (Compte  Personnel  de  Formation),  the  largest  and  most  visible  training 
entitlement  scheme  included  in  the  portal  that  covers  all  workers  in  the  private  sector  and 
unemployed. 

533

 It depends on the share of total trainings where individual contributions play a role (about 10%). 3.9% refers to the 

total costs for all trainings. 

243 

 
 
                                                 
Currently there are between eight and ten thousand

534

 learning activities starting up every day that 

are partly or fully financed by the training funds accessed through the online training portal.
 This 
makes it an interesting case to further study. The organic growth and dynamic adjustments of the 
scheme  in  response  to  changing  needs  and  political  priorities  gives  a  valuable  insight  into  the 
implementation process of such a scheme for other Member States. 

535

2.1 Key Features of the Current System (The Extended CPF) 

Rationale 

The law that underpins the current shape of French Individual Learning Accounts seeks to explicitly 
address the challenge of increasing participation in adult learning (“Law for the Freedom to Choose 
a Vocational Future” (Loi sur la liberté de choisir son avenir professionnel, a.k.a. Law “Pénicaud 

”  or  Law  “Avenir”).  The  2018  Law  came  into  force  on  1  January  2019  and  was  effectively 

536

2
implemented by November 2019. 

The MCF seeks to improve the economic opportunities of citizens by giving them training rights, 
regardless  of their professional  status,  and  ensuring their accessibility in one location. The online 
training portal offers every individual information about his/her rights and responsibilities and offers 
information  about  the  amount  of  money  available  on  the  website  or  the  smartphone  application 
(respectively moncompteformation.gouv.fr and moncompteformation, CPF Platform for short in the 
rest of this Annex).  

With the MCF, individuals are made responsible for their own learning pathway, instead of depending 
on  their  employer,  the  State  and/or  the  Public  Employment  Service  (PES)  to  initiate  training.  In 
practice, the CPF allows all individuals, without intermediaries, to decide on which learning activities 
they would like to participate, from a list of training activities provided by accredited institutions. 
The  objective  is  that  this  contributes  to  a  higher  completion  rate  of  training,  increases  user 
satisfaction, and helps individuals to find learning activities that correspond to their vocational and 
occupational aspirations.  

Coverage  

The online learning portal offers a unified point of entry for the selection and purchase of training for 
all citizens. Within the account, the multiple existing entitlement schemes (that of employees in the 
private  sector;  CPF,  public  officials  and  volunteers;  early  school  leavers  with  the  Compte 

d'Engagement Citoyen (CEC
to the selection and purchase of training for all citizens.  

537

)), are presented into a single location, which offers a unified approach 

The  CPF  is  in  principle  open  to  all  salaried  workers  in  the  private  sector.  In  addition,  any  self-
employed, freelancer, liberal and non-employee professions, collaborating spouse, artists, authors can 

534

 This figure from May 2021 relates to effective enrolments in formal learning activities thanks to the CPF (the dropout 
rate  is  around  10%).  There  is  a  difference  between  creating  an  account  (or  opening  a  CPF)  and  actually  using  it  for 
engaging in formal learning. 
535

 CPF for short in the rest of this document unless the point is to differentiate the 2014 version from the 2018 version of 

the CPF. 
536

 After the name of the Minister at that time. 

537

 A Citizen Commitment Account (Compte engagement citoyen, CEC). 

244 

 
 
                                                 
sign up for the CPF by paying their Contribution à la formation professionnelle (CFP, Contribution 
to Training).  

In principle, under the CPF, all participants are eligible for €500 per year, cumulable up to €5 000 
over ten years (part-timers working between 50% and 100% of the time receive the corresponding 

538

fraction of these amounts
). Specific additional contributions are in place for lower-qualified (Below 
CAP / EQF level 3), who are eligible for €800, cumulable up to €8 000 over ten years (here too part-
timers receive the corresponding fraction). Self-employed that worked less than fulltime are eligible 
to a share of the annual €500 that is proportional to the time they worked.  

Practical implementation 

With the introduction of the portal, users know exactly at any point in time how much money they 
have  on  their  account  and  what  amount  they  may  spend  for  learning  activities.  Creating  and 
consulting one’s entry on the online training portal requires a social security number that is unique 
for each person and provided by the National Statistical Institute (INSEE in France) at birth (a.k.a 
Physical Person Registration Number, NIRPP), composed of thirteen digits.  

Users may add money on their learning account with a credit card. Only about 10% of trainings are 
complemented with individual contributions, for an average value of around €485 (3.9% of the total 
training costs). The system also allows for additional funders (e.g., employers, and public authorities 
can use the same mechanism to target specific groups) to provide extra funding to an individual CPF. 
So far, this has not been done in great numbers. Employers have contributed so far 0.5% of the total 
costs of the training actions paid for by the CPF (see Table A14.1 below). It is noted that this is only 
possible since July 2020, so it is still early to draw conclusions about this. The lockdown and other 
COVID-19 related measures may have substantially influenced this figure.  

The  CDC  also  asks  providers  to  publish  their  existing  provision  on  the  website.  The  “market”  is 
therefore fully transparent for all end users. 

The CDC describes the online portal in terms of an e-commerce site: individuals ‘shop’ for training 
opportunities, add these to their ‘cart’, and pay for these with their training credit (to which additional 
credits  can  be  added  automatically,  depending  on  specific  eligibility),  supplemented  –  where 
necessary  – by their own contribution.  Once a learner signs up, the training providers  receive the 
request for participation, validate the registration and are subsequently paid. 

If  potential  users  want  to,  they  may  receive  free  guidance  through  the  Professional  Evolution 

Guidance (Conseil en évolution professionnelle, CEP

539

).  

All in all, the system is developed to make it as easy as possible for the end users. The online platform 
has a helpdesk for users who have questions.  

Registration of training providers 

538

https://travail-emploi.gouv.fr/formation-professionnelle/droit-a-la-formation-et-orientation-professionnelle/compte-

personnel-formation. 
539

 See: https://www.service-public.fr/particuliers/vosdroits/F32457. However, awareness of the CEP remains relatively 
low.  See:  https://www.studyrama.com/pro/formation/dispositifs-de-formation-continue/cpf-et-cep-des-dispositifs-de-
formation-et-d-accompagnement-professionnels-encore-trop-meconnus-22051.html. 

245 

 
 
                                                 
The Caisse des dépôts et consignation (CDC) is responsible for the hosting of the web portal, and 
functions as the central point of coordination towards training providers. The registration of all 19 
000  providers  is  managed  by  the  CDC,  which  now  uses  a  common  contract  with  standards  and 
identical terms and conditions for every provider. This is a rather radical change compared to the past 
whereby providers drafted their own contracts and conditions. Training providers can include training 
programmes  in  the  system  that  are  either  included  on  certified  national,  regional  or  sectoral 
interprofessional lists of training provision.  

In interviews, the CDC highlighted that it wants to avoid the online portal to become a search engine 
that produces popularity-based results, i.e. that some providers would appear more often than others 
on the basis of ‘clicks’, instead of their intrinsic quality. In practice, individuals need to define and 
select  the  trade / sector  the  users  are  looking  for.  From  there,  they  find  learning  activities 
corresponding to the selected trade. The CDC standardised how information on existing providers is 
displayed. No information is available about the user-friendliness of the chosen approach.  

France  Compétences,  the  regulatory  body  for  the  registry  of  training  opportunities,  has  the 
responsibility to  add new trainings within the registry, as  long as they comply with  the necessary 
quality standards. 

2.2 A Brief History of individual training entitlements in France 

The first version of the current ILA was introduced in 2004 and was called the Individual Right to 
Undertake Formal  Learning Activities (DIF  – Droit Individuel à la Formation). Since then, it has 
been revisited several times and the last reform in 2019 led to the Extended CPF (Compte personnel 
de  formation  étendu,  CPF  étendu).  The  most  recent  reforms  brought  new  elements  such  as  the 
digitalisation and the monetisation of the ILA. In addition, individual users do not need an agreement 
from the body managing the money anymore, before buying learning activities. Previously, the CPF 
was based on the number of hours (not on EUR) and it was managed by the bodies collecting the 
money, OPCAs (Organisme paritaire collecteur agréé). During the recent reforms, OPCAs have been 
replaced by the CDC (Caisse des dépôts et consignations). 

Figure A14.1 - The development of the French ILA 

The Law of 2004 – Creation of the DIF (implemented in 2005) 

The  Law  of  the  4th  May  of  2004
  on  “Lifelong  Vocational  Education  and  Training  and  Social 
Dialogue” provides the legal basis for the establishment of the Individual Right to take part in Formal 

540

540

 LOI n° 2004-391 du 4 Mai 2004 relative à la formation professionnelle tout au long de la vie et au dialogue social. 

246 

 
 
 
 
                                                 
Learning Activities (DIF, Droit Individuel à la Formation
and the DIF existed between 2005 and 2014. 

541

). It was implemented on 7 May 2005 

  Reason for the legislation: The main objectives of the law were to reduce inequalities in 
access  to  training, with  particular  attention for lower-qualified workers  and workers in 

smaller enterprises

542

.  

  Coverage: The DIF targeted individuals in employment in the private sector (salariat)

, 
and was embedded in labour contracts, signed between the employer and the employee. 
By design it therefore excluded anyone not in an employment relation, such as freelancers, 
self-employed, or individuals that were fired from that enterprise. Young people entering 
the labour market without an employment contract could not benefit from the provisions 
either.  

543

  Value  of  entitlements:  The  law  requires  enterprises  to  pay  for  20  hours  per  year, 

cumulable to up to six years, of learning activities.  

  Financing: The law introduces a mandatory contribution for enterprises to a newly created 
national  training  fund,  effectively  introducing  a  ‘training  tax’  of  1.5-1.6%  of  the  total 
salaries paid. The tax levied on smaller enterprises was lower ranging from 0.25% to 0.4% 
of the salary base.  

  Portability: There was a “portable” version of the DIF (DIF portable in French) but the 

acquired right to learning activities was only valid for two years.  

Review of the measure: The DIF was limited in scope, focussing only on employees and companies 
(leaving  out  unemployed,  self-employed  and  young  people)  and  with  a  limited  duration  of  the 
portability of rights (two years).  

The Laws of 2013 and 2014 – Creation of the CPF (1 January 2015) 

The Law of the 14 June 2013
545

544

 on “securing employment”, further supplemented by the law of the 

, introduced an individual learning account to all persons active on the labour market. 
5 March 2014
Together these laws offer the foundation for the implementation of CPF as of the 1 January 2015, 
when it replaced the DIF. It combined multiple existing sectoral schemes into a national universal 
system.  

  Reasons  for  the  legislation:  The  law  is  part  of  a  broader  package  on  innovating 
employment  and  combating  precarious  work.  Learning  rights  are  extended  from 
employees to all individuals active on the labour market.  

541

 The term formation in French has a very broad meaning and may range from academic training to purely vocational 
training, or both. In addition, it could be formal or not. Nevertheless, in this particular case, it is formal learning that is 
meant, i.e., organised in a formal context, in particular with learning objectives (Werquin, OECD, 2010). 
542

 https://www.legifrance.gouv.fr/dossierlegislatif/JORFDOLE000017759490/  

543

 Strictly speaking, i.e., workers earning a wage paid by a regular employer (fixed term or unknown duration contract, 

full- or part-time). The DIF was linked to the status of employee. 
544

 https://www.legifrance.gouv.fr/loda/id/JORFTEXT000027546648/. 

545

 https://www.legifrance.gouv.fr/affichTexte.do?cidTexte=JORFTEXT000028683576. 

247 

 
 
                                                 
  Coverage: CPF was open to anyone aged 16 or more active on the labour market (i.e., 
workers in the private sector, job seekers, being involved in a guidance and reintegration 
project or hosted in an institution that is providing assistance to ‘at risk’ groups through 
work).  Upon  retirement,  the  individual  account  is  closed  and  no  longer  offers  training 
rights.  

  Value  of  entitlements:  The  account  defined  a  certain  number  of  hours  of  learning 
activities. The learning activities that are eligible to financing from the CPF are all those 
leading  to  a  vocational  qualification  or  those  listed  by  the  sectoral  branches,  or 
interprofessional ones. 

  Financing: The law does not alter the existing structure of financing training. The 2014 
law introduces a common contribution of 1% based on the salary base for every employer, 
to  which  the  national  government  complements  to  cover  for  the  unemployed  now 
introduced in the system.  

  Portability/Transferability: The main innovation compared to the previous system (the 
DIF)  was  that  the  training  hours  on  the  account  of  the  newly  created  CPF  was  made 
transferable:  hours  on  the  account  remain  available  in  the  event  of  a  change  in  labour 
market status or switching employers; essentially they become credits for the individual, 
instead  of  the  employer.  Someone  that  loses  their  employment  keeps  their  rights  to 
learning activities. The CPF allows to accumulate up to 150 hours, at a rate of 24 hours 
per year up to 120 hours, and then 12 hours per year. 

Review: The use of the rights acquired in the context of the CPF is on individual's initiative: in case 
individuals are employees, learning activities are generally connected to enterprise needs, but this is 
not  necessarily  the  case.  In  the  event  of  an  agreement  between  the  employer  and  the  employee 
regarding the use of the CPF, the learning activities may take place during working hours for all or 
part of the learning. The employer organises the training and registers the individual for the training, 
using the credits  in  the individual’s account.  In the absence of  an employer-employee agreement, 
employees can still use credits in their CPF the way they want, but this must be done outside working 
hours and without additional funding. To enrol in training, they depend on the infrastructure of Public 
Employment  Service  (Pôle  emploi),  or  with  the  regional  authorities  that  regulate  the  learning 
activities. 

The Law of 2016 – Opening up the CPF 

The Law of the 8th of August 2016 (a.k.a Law “Labour”, Loi “Travail”) provided the legal grounds 
for the creation of the Occupational Activity Individual Account (Compte personnel d’activité, CPA), 
which offers an approach to group different from individual accounts, including the CPF, together. 
As such, it opens learning beyond only employed and jobseekers and now comes to encompass other 
categories of individuals: e.g., public officials, volunteers and early school leavers. The former may 
open a Citizen Commitment Account (Compte d’engagement citoyen, CEC) which allowed them, for 
instance, to undertake training or a Competences Audit (Bilan de compétences, BC) or to engage into 
recognition  of prior learning (RPL), i.e., Validation  of Experiential  Learning Outcomes in  France 
(Validation des acquis de l'expérience, VAE). 

  Reasons for the legislation: While the CPF is open to all employees in the private sector 
and jobseekers, it did not include the inactive population, public servants, volunteers and 

248 

 
 
early  school-leavers.  The  2016  reform  combines  other  existing  schemes,  so  that  the 
training  entitlements  are  unified  from  the  perspective  of  citizens.  This  reinforces  the 
universal nature of the training accounts. It explicitly defines that learning is a right for 
every citizen.  

  Coverage: The CPA brings together the various existing accounts and ensures  training 

entitlements to the workforce.  

  Financing: No changes to the financing of training.  

  Portability / transferability: No changes.  

Review: The law offers yet another step towards a ‘universal learning entitlement’, by combining 
multiple  training  accounts  into  a  single  heading  (each  of  the  training  accounts  continue  to  exist 
legally, but from the perspective of the individual are all combined into a Personal Activity Account). 
Individuals’  rights  in  the  activity  accounts  are  registered  centrally  by  the  CDC.  Any  requests  to 
engage in training continue to be organized through the Public Employment Service (Pôle emploi), 
or with the regional authorities in charge of learning activities. 

The Law of 2018 – The Major Reform of the CPF 

546

The Law of 5 September 2018
 for the Freedom to Choose a Vocational Future is considered as a 
major reform of the CPF. Its major innovation is to put learners at the centre of the process, and better 
empower  these  individuals  in  selecting  training  opportunities.  This  avoids  learners  getting  stuck 
between the organisation that purchases the learning activities (e.g., the enterprise) and the providers 
that deliver it.  The reform directly transforms the training credits (previously expressed in hours) in 
money. 

  Reasons for the legislation: While all individuals had a theoretical right to training, and 
access to their activity accounts, initiative most often came from the employer. Individuals 
that  wanted  to  use  the  credits  for  training  of  their  choice  depended  on  the  Public 
Employment Service (Pôle emploi), or the regional authorities so their credits could be 
monetized and used as compensation for the selected training.  

  Coverage: The law does not change coverage. Under the CPF, workers are eligible for 
€500 per  year, cumulable up to €5 000 over ten  year. Specific additional contributions 
exist for lower-qualified (below CAP/EQF level 3), who are eligible for €800, cumulable 
up to €8 000 over ten years. Public civil servants continue to be part of the CEC training 
scheme which is expressed in hours.  

  Financing: No major changes to the financing of training. The rules for the financing of 

VET by enterprises is adjusted and is conducted on the basis of a single levy.  

  Portability / transferability: No changes.  

This reform makes it easier and better understandable how and what trainings individuals can select 
and what financial means they have to support their purchase. The responsibility of using the training 
entitlement is now fully in the hands of the individuals, who are made responsible for purchasing the 
training (compared toearlier years, where the employers or PES were the ones purchasing the training, 

546

 Loi pour la liberté de choisir son avenir professionnel. 

249 

 
 
                                                 
using the individual’s credits). This shift in responsibility also facilitates the organisation of additional 
contributions  by  the  individual,  as  it  is  immediately  clear  how  much  money  is  available  on  the 
account, and how much additional contribution would be necessary. Under this new approach, users 
are able to buy the learning activities online, directly from the providers of their choice.  

3. Performance of the CPF 

Accurate and complete data regarding the functioning of the CPF can not be provided at this point. 
The two main reasons for this are the following: 

  The  CPF  system  and  the  Platform  in  particular,  as  it  is  operating  now,  is  rather  new 
(November  2019)  and  the  CDC  is  still  developing  its  data  collection  system.  Data 
collection is based on an Oracle powered database, but CDC is not yet able to provide up-
to-date relevant statistics.

547

  The start of the online portal and changes to the CPF coincide with the outbreak of Covid-
19. As a result, any early conclusions and insights may not necessarily be representative 
for the next years.

548

3.1 Headlines 

The demand from 21 November 2019 to 30 June 2021 

In terms of demand (i.e., individual consumption), the key figures from November 2019 to June 2021 

(CDC, 2021a and CDC, 2021b

549

) are: 

  8.5  million  personal  accounts  have  been  activated/created/opened  (compared  to  29.6 

million of individuals in the labour force), 

  2.31  million  validated  registrations  for  a  learning  activity  (this  number  can  include 
multiple registrations per individual);  i.e., 7.8% of the labour force, to  be compared to 
18.8% of the population aged 25 to 64 participating in education and training (Eurostat, 

550

2018
not complete the training activities, 

). A drop-out rate of 10% was reported for these 2.31 million, i.e. learners who did 

  3.1 million downloads of the smartphone application moncompteformation, 

  232 990  users  (roughly  10%)  have  made  an  individual  top-up  to  their  accounts  to 
complement their funds available to be able to purchase training (via their credit card). 
The amount added varies from €4 to €500 (data available only until January 2021). 

  €1 263 is the average price of a learning activity purchased through the CPF. 

  €2.92 billion is the total cost (€2.31 billion x 1 263). 

547

 However, CDC and Dares have published analyses of the evolution of CPF usage between 2019 and 2020, also 

documenting a strong increase in use over this period. 
548

 Regarding the  figures  measured in 2020, they  may be affected by some bias: The period from November 2019 to 
March 2020 saw the start-up and the rise of moncompteformation; Covid-19 interrupted this rise, which only resumed 
during the  summer 2020: third parties funding  was not covered by  moncompteformation at the  opening in November 
2019. This possibility was first reopened in July 2020 for the Public Employment Service (Pôle emploi) and in September 
2020 for companies. 
549

 CDC, 2021. Presentation of the Device moncompteformation, PowerPoint presentation, CDC, Paris, 29 January. 

550

https://ec.europa.eu/eurostat/statistics-

explained/index.php?title=Archive:Statistiques_sur_l’apprentissage_des_adultes&oldid=410024. 

250 

 
 
  
 
                                                 
  13.5 million unique clicks on the CPF platform. 

  There is no significant difference in CPF use by gender. 

These figures show that most of the users so far have consulted their balance, but have not yet used 
their accounts to buy learning activities. Among the users that have actually registered for a learning 
activity with support of the CPF through the portal, the most frequently undertaken learning activities 
are: 

  Languages (mostly English): 12.5% of all learning activities, 

  Driving licence (car): 13%, 

  Entrepreneurship (for creating or buying an enterprise): 7.9%, and 

  Competence Audit (Bilan de compétences): 4.3%. 

The supply from 21 November 2019 to 30 June 2021 

In terms of supply (i.e., the providers), the key figures from November 2019 to June 2021 (CDC, 
2021b) are: 

  22 130 accredited training providers (of whom 18 670 have at least one training action 

readily available). 

  A pool of 338 940 different training actions on supply (of whom 30% relate to languages, 

and whereby about 30% of training sessions can be followed remotely/online). 

  A pool of 5 741 different qualifications on supply (of whom 40% do not mention a specific 

level in relation to the national qualifications framework). 

Demand by Unemployed People from 8 July 2020 to 30 June 2021 

For unemployed people, the key figures from July 2020 to June 2021 (CDC, 2021b) are: 

  656 741 validated registrations (of whom 601 287 are autonomous, not prescribed by the 
Public Employment Service for example). This is 34.5% of all validated registrations. 

  €777 million is the total cost (630.44 million for autonomous learners). This is 31.8% of 

all costs. 

  The financing comes from France Compétence (83.9%), the Public Employment Service 

(10.3%) and others (5.8%). 

  From 1 992 validated registrations in July 2020 to 50 911 in June 2021, (respectively €3 

million and €80 million), with a steady trend upward. 

Co-funding by Enterprises from 8 July 2021 to 30 June 2021 

Enterprises have contributed to the CPF in the following way from September 2020 to June 2021 
(CDC, 2021b): 

  3 659 enterprises have contributed. 

  These enterprises have added money to 6 108 CPF. 

  The total amount of this co-funding is €22.7 million. 

3.2 Insights in the (development of) registration and take-up of training through CPF 

The take-up rate may be seen from two points of view: 

251 

 
 
  The number of personal accounts created (or activated), and 

  The number of learning activities actually bought (and validated) thanks to the CPF. 

Monthly number and cumulative number of CPF’s profile activation (Figure A14.2) – is an indication 
of the interest in the approach among the population. It displays the number of individuals that have 
been interested in checking how much money they own for training purposes, from the inception of 
the CPF on. The graph shows a strong interest at the time of the inception of the first version of the 
CPF,  in  2015,  when  it  replaced  the  DIF;  and  then  some  sort  of  seasonality,  with  “calm’  summer 
months,  and  a  renewed  interest  when  job  seekers  –  not  necessarily  unemployed  –  are  actively 
searching the labour market; typically, at the beginning of each calendar year, and at the beginning 
of each academic year, after the summer break. 

The progress in the number of activated profiles also indicates a strong sustained renewed interest 
when the New or Extended CPF (CPF étendu) was created. The monetisation of accounts and the 
new portal (since 2019) clearly fostered interest, even if this graph is not very conclusive as checking 
the amount available on the CPF is not a good predictor of its actual use for engaging in training.  

Monthly number and cumulative number of training undertaken with CPF (Figure A14.3) confirms 
the interest for the initial CPF, in 2015, and for the New CPF, in November 2019. There is an inflexion 
point in the cumulative distribution after the inception of the New CPF, as can be seen in the previous 
graph. This appears to confirm that there is not only curiosity regarding the Extended CPF but a real 
use  for  buying  training  actions,  although  there  is  an  absence  of  longitudinal  data.  The  impact  of 
Covid-19 is also not fully measured at this stage, except in qualitative terms.  

Worthy of note is the time lag that exist on the second graph, as if potential users had checked their 
balanced on their CPF, and then took some time – nine to twelve months – to organise themselves 
and engage in learning activities.  

The seasonality is even clearer on the second graph than on the first graph, with the months after the 
summer break being the most “active”.  

The somewhat significant peak in, and shortly after, March 2017 may indicate a specific use of the 
CPF, for taking the driving licence for car, as this became possible with the CPF in March 2017. 

The most significant finding remains that the number of training activities undertaken thanks to the 
CPF nearly doubled between 2019 and 2020, which coincide with  the opening of the smartphone 
application moncompteformation. This suggests an important effect of adequate tools and portals to 
communicate individual learning rights to individuals.  

252 

 
 
Figure A14.2 - CPF activations. 

The number of training validations nearly doubled between 2019 and 2020 with the opening of 
moncompteformation.  

Figure A14.3 - Training undertaken with CPF. 

253 

 
 
 
 
3.3 Insights in the (development of) unit costs of training through CPF 

551

The monetisation of the CPF
 (in 2019) led to better visibility of the budget spent on training by the 
employees in employment (salariées). For example, at the end of October 2019, the employees in 
employment had, on average, €1 040 on their CPF. Since not all employees have transferred their 
rights  in  hours  from  the  time  of  the  DIF  to  the  Extended  CPF  in  EUR,  the  theoretical  average 

estimated for these accounts is around €2 000 (CDC, 2020

552

553

).

The average hourly cost of CPF-funded training is about €15. There is some evidence that the average 
price  of  more  standardised  training  offers  such  as  skills  assessment  (-9.5%),  English  language 
certificates (-41%) or driving licences (-29.6%) has decreased since November 2019. While this may 
reflect the impacts of more competition due to higher transparency, further evidence regarding the 
unit cost of training undertaken in the context of the CPF needs to be  collected as as the numbers 
collected during the year 2020 are atypical in view of the COVID-19 pandemic. It is hence somewhat 
early to provide conclusive pieces of evidence regarding the unit cost of training through the Extended 
CPF. 

3.4 Insights into cost-sharing 

The possibility for other stakeholders – typically employers, individuals themselves and the Public 
Employment  Service  –  to  add  money  to  individual  CPF  appeared  only  recently:  in  2019  for  the 
individuals themselves, and in July 2020 for the others: employers, the Public Employment Service 
and other stakeholders. Evidence suggests that  France compétence remain  the main  funder of the 
CPF (see the Graph and the Table below, respectively the Monthly and cumulative purchases through 
the Smartphone App moncompteformation, and the table about the cost sharing over period July 2020 
– April 2021).  

The second largest contributors are the individuals themselves and the Public Employment Service, 
on par for 3.9% each. Employers and the Regional Councils (which have some responsibility in terms 
of vocational training) are marginal contributors. 

This  global  picture  hides  some  difference  to  the  extent  that  the  Public  Employment  Service,  for 
example, has a rather intensive approach: when it does support training, it does it at a rather high level 
(€1 575  on  average).  On  the  contrary,  individuals  may  be  more  numerous  to  contribute,  but  their 
contribution is three time less: €485 on average. 

In addition, the figure shows that most of the training actions are not co-funded anyway; which makes 
these number somewhat tricky to analyse. This is the total share of the contribution that is displayed 
in the Graph and in Figure A14.4 below. It remains true that France compétence is so far the main 
overall contributor (91.5%). When the rights of the CPF owners are not sufficient to cover the cost of 

551

 Correspondence between the European Commission and the French authorities that latter stated that “hours” were not 
the most appropriate data to make comparison. With the switch in monetary value, anyone can swiftly and easily have a 
better understanding of the cost of any training and be in a position to make an informed choice. It improves clarity and 
transparency of the market’.   
552

 https://retraitesolidarite.caissedesdepots.fr/sites/default/files/QRS_29.pdf. 

553

 Note that workers had until 1 July 2021 to transfer their DIF rights to CPF accounts. Those that did not apply for a 

transfer will have lost their training rights. It will have to be seen to what extent this will have been done.  

254 

 
 
  
                                                 
the training, they may apply for a contribution to supplement their rights. Contributions can be done 
by a list of funders defined in II of Article L. 6323-4 of the Labour Code. 

In detail, co-funding may be done by: 

 

 

 

 

the individual owners of the CPF themselves (to finance targeted training only) 

employers, where the account holders are employees 

competence operators (OPCO) 

the National Health Insurance Fund 

  occupational branches 

 

 

 

the State 

the Regional Authority  

the Public Employment Service (Pôle emploi) 

  AGEFIPH (Association for the management of the fund for the professional integration of 

persons with disabilities) 

 

 

 

 

training insurance funds for self-employed  

chambers of trades and crafts 

local and regional authorities other than regions 

the National Agency for Public Health 

  The unemployment benefits body (Unedic) 

Figure A14.4 - Total volume of spending for eligible activities via the CPF Monthly data, since 

2015, including funding from all sources. 

Monthly amounts are indicated on the above graphic, as well as the monthly part of CPF funding 
(%). 

255 

 
 
 
 
The average cost breaks down as follow, on trainings bought between 8 July 2020 and 25 April 
2021, that is the period when third parties began to finance CPF trainings in moncompteformation. 

Table A14.1: Contributions. 

  Funders 

Part of cost funding 

  France Compétences (CPF) 

 

Individual contributions 

  Pôle Emploi (PES) 

  Companies 

  Others (mostly Regions) 

91.5% 

3.9% 

3.9% 

0.5% 

0.2% 

  Only 10.1% of the trainings are financed on individual contributions, but the individual contribution is on 

average €485. 

 

In the same way, only 3.3%% of the trainings are financed by Pôle Emploi, but Pôle Emploi contribution is on 
average €1 575. 

  This possibility of tops-ups is a new opportunity launched in 2020, built around social dialogue and collective 
decisions, which explains the low percentage. Given that the launch came at a time of the pandemic with 
many businesses in an emergency position there were not high expectations from the French government. It is 
indeed very recent and explain this low percentage.  

13.3.5 Insights into operational costs 

A budget of €100 million has been allocated to a goal and performance contract (contrat d’objectif et 
de performance) for the years 2020, 2021 and 2022. The budget includes the development of the CPF 
App, the website, the online portal, the management of the portal, the search engine etc. It includes 
all the needed costs. This 3 year-contract is monitored and, if necessary, adjusted every year. It can 

be renewed every three years.

554

3.5 Ensuring training for those that need it the most 

The  CDC  explicitly  states  that  one  of  the  objectives  of  the  Extended  CPF  was  to  ensure  that 
individuals that benefit most from training would be able to access it. Previous studies on early-stage 
data (2015-2017/2018) for the first version of the CPF point to mixed or even negative results in this 
respect. In particular: 

- 

In Perez and Vourc’h (2020)
 individuals with below ISCED 2 educational attainment were 
deemed to be significantly underrepresented, based on 2015-2017 data. More specifically, the 
study reports that employees with no qualification (below ISCED 2) account for only 5% of 

555

554

 The  information follows a request from the European Commission to the  French national authorities. Further cost 

breakdowns are not available and the contract with the national operators, CDC, is subject to confidentialty clauses. 
555

 Perez, C. and A. Vourc'h (2020), "Individualising training access schemes: France – the Compte Personnel de 

Formation (Personal Training Account – CPF)", OECD Social, Employment and Migration Working Papers 

256 

 
 
 
 
 
                                                 
the  validated  trainings  purchased  via  the  CPF  against  12%  of  employees  with  the  same 
educational attainment level. The breakdown by educational attainement level is however not 
available for the jobseekers.  

- 

In OECD (2019),

556

 a similar situation is described again based on 2015-2018 data, although 

the trend, as indicated in Balmat & Corazza (2020)
 pointed to an increase in the take up of 
the low qualified, representing 4% of all trainings validated in 2015 and 7% in 2018. However, 
according to the study, in 2018 56% of the trainings were still validated by inviduals with 
tertiary education, although they only represented 37% of the labour force.  

557

Based on such evidence, the underrepresentation of the low qualified in 2015-2018 would seem quite 
apparent. However, no study has yet assessed possible changes occurred after the introduction 
of the portal and mobile app Mon Compte Formation. Furthermore, according to the CDC, in the 
first version of the CPF, many users had an unknow level of educational attainment. This is a possible 
source of bias, if low qualified invididuals were overrepresented among them. It is also important to 
stress that these studies do not consider the educational attainment level of the unemployed, where 
the low qualified are likely to be overrepresented, as well as their take up.  

Hence, a more updated picture is needed to draw some conclusions. Before presenting an assessment 
that is based on data up to 2020, it is worth explaining how this new assessment is made. In general, 
the take up rate is defined as the ratio between:  

- 

- 

Individuals who sign up to the CPF, and use their funds to purchase and carry out a training; 
and 

Individuals who are offered training entitlements via the CPF (eligible population) 

Whilst data is available for the former (number of trainings validated) measuring the pool of eligible 
individuas is more complex as their eligibility might vary over time along with their condition in the 
labour market.  

For instance, inactive individuals are in principle not offered training entitlments via the CPF. So, 
one would think that it is sensible to compare CPF users who have validated trainings, with the active 

558

population. However, based on Eurostat experimental data on labour market transitions,
 about 6% 
of the inactive become unemployed every year in France and an additional 4% find employment. This 
means that around 10% of the inactive become eligible for the CPF every year. If we consider that 
the CPF run for 6 years until 2020 (latest available data), it is fair to assume that about half of the 
inactive  have  been  entitled  to  some  training  entitlements  in  their  CPF  over  time,  although  the 
accumulation is  arguably  more limited for them. As there  are labour market  transitions  also  from 
employment  and  unemployment  to  inactivity,  some  will  have  also  ceased  to  accumulate  CPF 
entitlements as they became inactive. This is to say that in comparing the background features of CPF 
users and the eligible population there is no perfect proxy for the latter, and a balanced assessment is 
needed.  

556

 OECD (2019), Individual Learning Accounts: Panacea or Pandora's Box?, OECD Publishing, 

Paris, https://doi.org/10.1787/203b21a8-en 
557

 Balmat, C., & Corazza, E. (2020). Le compte personnel de formation en 2018, DARES 

558

 Eurostat, Labour market transitions – annual data [LFSI_LONG_A] 

257 

 
 
                                                 
Below  we  review  the  extent  to  which  various  target  groups  have  made  use  of  their  training 
entitlements  via  the  CPF,  based  on  updated  2020  data  and  two  different  proxies  of  the  eligible 
population: the active population and the general population. When the distribution of CPF users by 
occupation  is  discussed  (i.e.  in  Table  A14.4),  the  comparison  is  necessarily  limited  to  those  in 
employment.  

Table A14.2: Qualification Level among the CPF Users, active and general population. 

Highest Qualification 
Level before using the 
CPF (EQF) 

% in the 
active 
population 
25--64 

% in the 
total 
population  
25-64 

% among 
CPF users 

1-2 
3-4 
5-8 
Total

559

14 
41 
45 
100 

18 
42 
40 
100 

17 
43 
40 
100 

Difference 
between 
CPF and 
overall 
population 
-1 
+1 
0 
100 

Difference 
between CPF 
and active 
population 

+3 
+2 
-5 
0 

Source: CDC, Eurostat,

560

 INSEE,

561

 preparation by the author. As of November 2020. 

Legend: In the French active population aged 25-64, 14% of the active population are EQF level 1 or 2. Among the CPF 
users, they are 17%. Individuals with a low level of qualification are slightly overrepresented among the CPF users (by 
3 percentage points with respect to the active population in France). If we consider the overall population 25-64 y.o., 
and not just the active population, there is a slight (1 percentage point) underrepresentation of the low skilled among 
CPF users.  

Table A14.3: Age Group in the Population and among the CPF Users 

Age Group 

% in the 
active 
population 15 

% in the 
overall 
population 
15+ 

% among 
CPF users 

Difference 
between CPF 
and active 
population 15+ 

Difference 
between 
CPF and 
population 
15+ 

15-19 for population, 16-
19 for CPF users 
20-29 
30-39 
40-49 
50-54 for population, 50-
55 for CPF users 
55+ 
Total

562

2 

18 
24 
25 

13 

18 
100 

8 

14 
15 
15 

8 

41 
100 

0 

20 
34 
27 

11 

8 
100 

-2 

+2 
+10 
+2 

-2 

-10 
0 

-8 

+6 
+19 
+12 

+3 

-33 
0 

Source: CDC, INSEE, preparation by the author. As of November 2020. 

Legend: In the active French population aged 15+, 18% of the people are 55 or more. Among the CPF users they are 
8%. Individuals above 55 are underrepresented among the CPF users by 10 percentage points. 

559

 May not be 100 or 0 due to rounding errors. 

560

 Author’s elaboration on Eurostat, Active population by sex, age and educational attainment level (1 000) 

[LFSA_AGAED] 
561

 https://www.insee.fr/fr/statistiques/2416872#figure1_radio2  

562

 May not be 100 or 0 due to rounding errors. 

258 

 
 
 
 
                                                 
Table A14.4: Social Category among Workers and among the CPF Users 

Social Category 

% among workers   % among CPF users 

White Collar High Skill 
White Collar Low Skill 
White Collar Medium Skill 
Blue Collar 
Craftsman, shopkeeper, and 
business owner 
Farmer 
Unknown 
Total

563

20.4 
25.8 
26 
19.2 
6.8 

1.4 
0.4 
100 

17 
49 
8 
10 
2 

0 
14 
100 

Difference between 
CPF and workers 
-3.4 
23.2 
-18 
-9.2 
-4.8 

-1.4 
+13.6 
0 

Source: CDC, INSEE, preparation by the author. As of November 2020. 

Legend: Among the workers in France, 20.4% of the people are WCHS. Among the CPF users they are 17%. WCHS are 
underrepresented among the CPF users by 3.4 percentage points. 

The figures in table A14.2 above show that after the introduction of the portal and mobile app 
Mon  Compte  Formation,  the  low  and  medium  qualified  are  no  longer  under-represented 
among  CPF  users.  In  fact,  focusing  on  the  active  population  only,  there  seem  to  be  a  slight 
overrepresentation of the low and medium qualified and a slight underrepresentation of those with 

564

tertiary education.
 This conclusion departs from previous studies indicating an underrepresentation 
of the low qualified. In terms of the trend, there is some evidence of a positive trend in this respect 
already over 2015-2018 and likely a break in 2019. However, it is not possible to conclude whether 
such break is due to a change in the profile of CPF users that is due to the introduction of the Mon 
Compte Formation. This is because changes to the monitoring system of the CPF meant that in the 
new data there no longer are trainings validated by individuals with “unknown educational attainment 
level”.  In  general,  it  should  also  be  considered  that  although  the  low  qualified  are  currently  not 
underrepresented in the scheme, they receive higher entitlements than the general population, so it is 
not  possible  to  assess  what  would  happen  had  they  received  the  same  amount  of  the  other  target 
groups.  

As per the breakdown by age, a slight overrepresentation of those aged between 20 and 50 continues 
to exist (Table A14.3).  

Blue collar workers use their CPF less than their share in the labour force (19.2% and 10%) but this 
is also the case, for instance, for some white-collar workers, especially the white collar medium skill 
workers (26% and 8%). Table A14.4 does not allow us to conclude about the use of the CPF by self-
employed workers since they are present in several, almost all, categories of the Table. However, they 
are  probably  most  present  in  the  category  “Craftsman,  shopkeeper,  and  business  owner”  which 
displays  a  rather  clear  underutilisation  of  the  CPF.  Most  likely,  this  is  related  to  the  financial 
contribution  these  individuals  have  to  make  in  order  to  be  eligible  for  the  CPF;  the  financial 

563

 May not be 100 or 0 due to rounding errors. 

564

 One small caveat to this is that the comparison is limited to those up to 64 years old. If individuals over 65 years of 
age were taken into account in the distribution of educational attainment levels for the eligible population, there would 
be some underrepresentation of the low qualified, as these are overrepresented in this age bracket. However, 
considering the low take up rates of individuals above 55 years old, this should not be the case.  

259 

 
 
 
                                                 
contribution  is  a  share  of  the  salary  base,  and  in  many  cases  is  not  much  lower  than  the  annual 
entitlement made available.  

Information  on the size of enterprise (e.g., small  and medium-sized enterprise or else) and on the 
nature of the job (e.g., regular or marginal, full time or part time) are not available either. Therefore, 
it is not possible to assess whether the CPF was also a success in terms or attracting workers in SME 
and/or having an atypical job. 

3.6 Types of training selected by CPF users 

Easy access to CPF seems to have contributed to different patterns in requests for training, possibly 
signalling  the  reaching  of  new  target  groups.  Before  the  launch  of  the  Smartphone  App, 
moncompteformation, many learning activities were related to language courses (40%). Now, more 
recent figures after the launch of the App shows that the choice of users are somewhat changing, and 
the top five areas of learning activities are given in Table A14.5. 

Table A14.5: The top-5 learning areas selected by CPF users 

Learning Area 

Logistic 
(Including driving 
license) 
Guidance for integration 
in the labour market 
Languages 
Computer Science 
Security 

% before the 
inception of the 
Smartphone App 

% since start of the 
Smartphone App 

Difference 

14 

10 

40 
11 
4 

30 

19 

18 
7 
5 

+16 

+9 

-22 
-4 
+1 

Source: CDC (as of November 2020). 

3.7 Current plans for the future  

The CDC is planning to produce additional aggregated data for the broader public (there is work to 
be done to define how monitoring will be done and how data protection issues will be resolved). For 
instance, their database contains: 

  Description  of  all  periods  worked  (thanks  to  monthly  reports  with  employment  status, 

actual working time, wage etc.), 

  Data on situation in relation to disabilities. 

By the end of 2021, the CDC is planning to launch a “competence passport” on LinkedIn. 

The CDC also implements other projects with a data collection component, such as the Agora Project: 

  Data on entry into any kind of learning activities, whether in the context of the CPF or not 
(e.g., funded by the Region, the Public Employment Service, or paid by the Operator of 
Competences – OPCO – for employees), 

  Data  on  any  qualification  achieved  at  the  end  of  the  CPF,  and  at  the  end  of  any  adult 

learning process (including qualifications achieved in the RPL/VAE system), 

  Data on the entire occupational history of individuals. 

260 

 
 
 
 
ANNEX 15: OPERATIONAL OBJECTIVES AND MONITORING 

Table A15.1: Operational objectives and monitoring  

Specific objectives  

Operational objective 

Indicators* 

Source 

a)  Number  of  Member  States  with  personal 
accounts  with  training  entitlements  for  entire 
adult population of working age. 

b) Personal accounts activated- absolute number 
and  as  share  of  the  eligible  population  group, 
with  disaggregation  by  Member  State, 
employment status (employed, unemployed and 
inactive) and characteristics of the employment 
relationship  (permanent  employee  vs.  other 
employed person and SME vs. large enterprise). 
Number  of  Member  States  that  have  specified 
priority target groups for training entitlements. 

Support  Member 
reforms to: 

State 

1.  Close support gaps  

AND 

2.  Increase incentives 
and motivation to 
seek training 

Member  States  have  in 
place  personal  accounts 
with  training  entitlements 
for entire adult population 
of working age. 

to 
Linked 
objectives (SO) 1 and 2. 

specific 

that 

Member  States  modulate 
training 
the  amount  of 
are 
entitlements 
credited  to  the  personal 
accounts  according  to  the 
target  group  (providing 
more  to  priority  target 
groups). 

Linked to SO 1 and 2. 

Specific ad-hoc study
State public authorities. 

565

 and Member 

Specific  ad-hoc  study  and  Member 
State public authorities. 

565

 Such studies can for instance draw on the independent expert network on adult learning maintained by DG EMPL since 2016. 

261 

 
 
 
 
 
 
 
                                                 
a)  Number  of  Member  State  with  a  public 
registry  of  recognized  training,  guidance  and 
validation opportunities. 

Specific  ad-hoc  study  and  Member 
State public authorities. 

Member  States  have  in 
place  a  public  registry  of 
recognized 
training, 
guidance  and  validation 
opportunities. 

Linked to SO 2. 

States 

offer 
Member 
career guidance services to 
all  entire  adult population 
of working age. 

b) Number of users of the registry as measured 
by  the  number  of  included  training,  guidance 
and validation events that have taken place per 
year.  
a)  Number  of  Member  States  with  public 
provisions  to  ensure  that  the  entire  adult 
population  of  working  age  can  access  career 
guidance. 

Linked to SO 2. 

b)  Share  of  adult  population  of  working  age 
receiving career guidance per year. 

a) Specific ad-hoc study and Member 
State public authorities. 

b) Variable “GUIDEINST” from  the 
Survey 
Education 
Adult 
(“Information  or  advice/help  on 
learning  possibilities  received  from 
institutions/organisations  in  the  last 
12  months”).  Available  for  2016, 
2022,  and  in  six  year  intervals  after 
2022. 
Specific  ad-hoc  study  and  Member 
State public authorities. 

Specific  ad-hoc  study  and  Member 
State public authorities. 

a)  Share  of  employed  adult  population  that  is 
eligible to take paid training leave. 

b) Share of employed adult population that takes 
paid training leave per year. 
Number of Member States that have conducted 
an  evaluation  of 
their  scheme  providing 
individuals with training entitlements. 

Member States make paid 
training  leave  accessible 
for all employed adults. 

Linked to SO 1 and 2. 
Member  States  ensure 
continuous  improvements 
of the scheme. 

Linked to SO 1 and 2. 
Outcome 
indicator: 
Member  States  increase  the 
share 
adults  who 
participate  in  learning  per 
year. 

of 

General objective: 
overall 
Increase 
participation  of  adults  in 
training. 

Participation of adults aged 25-64 in formal or 
non-formal learning per year. 

EU  Labour  Force  Survey,  data 
available  every  2  years  starting  in 
2022.  

Total  (EU  and  per  Member  State)  and  with 
status 
disaggregation 

employment 

by 

262 

 
 
 
 
 
 
 
 
 
 
 
 
(employed,  unemployed  and 
inactive)  and 
characteristics  of  the  employment  relationship 
(permanent employee vs. other employed person 
and SME vs. large enterprise). 

For  2022,  also  data  from  the  Adult 
Education  Survey  will  be  available, 
ensuring a direct comparability to the 
statistics  on  participation  from  the 
2016  Adult  Education  Survey 
presented in this Impact Assessment. 

*  The  above  table  provides  a  tentative  list  of  indicators.  The  feasibility  of  collecting  such  indicators  will  be  further  analyzed  (in  terms  of  data 
availability, sensitivity etc.) 

263