Youth Indicators: What exists and
what is missing?
Kari P Hadjivassiliou and Catherine Rickard (IES)
InGRID Expert Workshop
Budapest, 27-29 November 2013
the institute for employment studies
Overview
 Development of EU Youth Indicators
 Dashboard of EU Youth Indicators
 Strengths & Weaknesses of EU Youth Indicators
 Examples of Gaps in existing EU Youth Indicators
 Examples of Member States Using Youth
Indicators
 Use of Indicators in our Research
 Q&A
Development of EU Youth Indicators
 Ad-hoc Expert Group on EU Youth Indicators
● proposed a dashboard of indicators in education, employment,
social inclusion & health; &
● Provided an overview of new indicators in 'core' youth policy areas
where these did not exist, e.g. volunteering, youth participation
 2011 DG EAC Study on Assessing Practices for Using
Indicators in Fields related to Youth
 Eurostat’s Youth Section on youth & open database - latest
available Eurostat data for all youth indicators
 DG EAC’s Youth Section – info on other indicators, incl
Eurobarometer surveys)
Dashboard of EU Youth Indicators
 Context
 Education/Training
 Employment & Entrepreneurship
 Health & Well-Being
 Social Inclusion
 Culture & Creativity
 Youth Participation
 Volunteering
 Youth & The World
Contextual Indicators
 Child population
● Total No of children aged 0-14 living in a MS
 Youth population
● Total No of young people aged 15-19, 20- 24 & 25-29
living in a MS
 Ratio of young people in total population
● Young people (aged 15-19, 20-24 & 25-29) as a share of
total population living in a MS
 Mean age of young people leaving parental household
Education/Training Indicators (I)
 Early leavers from education & training
● % of population aged 18-24 with at most lower secondary
education & who are no longer in education or training
(EU2020 target: <10% by 2020)
 Low achievers in maths, reading & science
● Share of 15-year olds with a score of 1 or below in PISA
tests (EU2020 target: <15% by 2020)
 Tertiary education attainment
● % of those aged 30-34 with degrees (EU2020 target: at
least 40% by 2020)
Education/Training Indicators (II)
 Young people (20-24) having completed at least upper secondary
education
● % of young people (20-24) having completed at least upper
secondary education (ISCED level 3c)
 Learning at least two foreign languages
● Young people in upper secondary education (ISCED level 3 general
programmes, excluding pre-vocational & vocational education)
learning 2 or more foreign languages
 Type of education/training currently in
● % of population engaged in lower & upper secondary education
(general & vocational education and training incl apprenticeships;
post-secondary, non-higher & higher education)
Employment & Entrepreneurship
Indicators (I)
 Youth unemployment
● Youth unemployment rate
 % of unemployed among active population (employed &
unemployed) aged 15-24
● Long-term youth unemployment rate
 % of unemployed youth 15-24 without a job for last 12 months
or more
 Youth unemployment ratio
● % of unemployed among the total population
(employed, unemployed & inactive), aged 15-24
Employment & Entrepreneurship
Indicators (II)
 Self-employed youth
● % of self-employed among all employed aged 20-24 &
25-29
 Young people who would like to set up their own
business
● % of young people (15-30) answering Yes to Question
‘Would you like to set up your own business in the
future?’
 Young employees with a temporary contract
● % of young employees (20-29) who are on a contract of
limited duration
Health & Well-Being Indicators (I)
 Regular smokers
● % of daily cigarette smokers in 15-24 population
 Obesity
● Young people (18-24) with a BMI of 30 or above
 Alcohol use in past 30 days
● % of those turning 16 in survey year who said Yes
to Question asking if they had had any alcoholic
beverage to drink in last 30 days
Health & Well-Being Indicators (II)
 Cause of death of young people – Suicide
● Deaths caused by suicide per 100,000 inhabitants aged
15-24
 Psychological distress
● Young people (15-24) having had psychological distress
during past 4 weeks
 Injuries: Road traffic; Self-reported incidences
● % of those aged 15-24 reporting to have had a road
traffic accident, resulting in injury for which medical
treatment was sought in past 12 months
 Use of illicit drugs
● % of those aged 15-34 reporting to have used cannabis
in past 12 months
Social Inclusion Indicators (I)
 At-risk of poverty or exclusion rate
● For children (<18) & young people (18-24) compared to
total population
 % of children & young people (18-24) who are at risk of poverty
&/or severely materially deprived &/or living in a household
with very low work intensity compared to total population
 At-risk of poverty rate
● For children (<18) & young people (18-24) compared to
total population
 % of children & young people (18-24) living in families with an
equivalised disposable income below 60% of national median
equivalised disposable income (after social transfers) compared
to total population
Social Inclusion Indicators (II)
 Severe Material Deprivation Rate
● For children (<18) & young people (18-24)
compared to total population
 % of population that cannot afford at least 3 of
these 9 items: (i) pay their rent, mortgage or
utility bills; (ii) keep their home adequately warm;
(iii) face unexpected expenses; (iv) eat meat or
proteins regularly; (v) go on holiday; or cannot
afford to buy a (vi) TV; (vii) refrigerator; (viii) car;
(ix) telephone
Social Inclusion Indicators (III)
 Living in households with very low work
intensity
● For children (<18) & young people (18-24)
compared to total population
 % of children (<18) & young people (18-24) who live
in households with very low work intensity
(households where adults worked less than 20 % of
their total work potential in past year) compared
to total population
Social Inclusion Indicators (IV)
 Self-reported unmet need for medical care for
young people (18-24) compared to total
population
● Self-reported unmet need for medical care for these 3
reasons: (i) financial barriers + too far to travel +
waiting times compared to total population
 Young people not in employment, education or
training (NEET)
● Young people (15-24) not in employment, nor in any
education or training
Culture & Creativity Indicators (I)
 Performing/taking part in amateur artistic
activities
● % of young people (15-30) who declare that
they have participated in any of these amateur
artistic activities at least once in last 12
months: (i) playing a musical instrument; (ii)
singing; (iii) acting; (iv) dancing; (v) writing
poetry; (vi) photography; (vii) film-making
Culture & Creativity Indicators (II)
 Participation in cultural activities
● % of young people (15-30) reporting that they have
participated in any of these cultural activities in last
12 months: (i) visited historical monuments (palaces,
castles, churches, gardens, etc.); (ii) museums or
galleries; (iii) been to a cinema or a concert, a
theatre, a dance performance or an opera
 Participation in sports clubs, leisure time or youth
clubs/associations or cultural organisations
● % of young people (15-30) reporting that they have
participated in activities of a sports club, leisure time
or youth club, any kind of youth association or cultural
organisation in last 12 months
Youth Participation Indicators (I)
 Young people’s participation in political organisations/
party or community/environmentally-oriented
organisations
● Self-reported participation of those aged 15-30 in such
activities in last 12 months
 Participation of young people in political elections at
local, regional, national or EU level
● % of young people (18-30) who declare that they
participated in such elections in last 3 years
 Young people (18-30) who got elected into the European
Parliament
● No of young MEPs elected (2009 elections)
Youth Participation Indicators (II)
 Young people who use internet for interaction with
public authorities
● % of those aged 16-24 who have used the Internet, in last
12 months for interaction with public authorities, i.e. (i)
obtaining info from their web sites; (ii) downloading
official forms; (iii) sending filled-in forms
 Young people using the Internet for accessing or posting
opinions on websites (e.g. blogs, social networks, etc.)
for discussing civic & political issues (in last 3 months)
● % of those aged 16-24 declaring that they have used
internet for accessing or posting opinions on websites (e.g.
blogs, social networks, etc.) for discussing civic and
political issues (in last 3 months)
Volunteering Indicators (I)
 Young people’s participation in organised voluntary
activities
● Self-reported involvement of those aged 15-30 in
organised voluntary activities in last 12 months
 Share of young people participating in organised
voluntary activities aimed at improving their local
community
● % of young people (15-30) declaring that they have
taken part in such action during last 12 months
Volunteering Indicators (II)
 Share of young people who have stayed abroad for the
purpose of volunteering
● % of young people (15-30) declaring that they have
stayed abroad for the purpose of volunteering
 Formal recognition of participation in voluntary
activities
● % of young people (15-30) that declare having taken
part in voluntary activities who have received a
certificate, a diploma or other kind of formal
recognition for their participation
Youth & The World Indicators (I)
 Young people’s participation in NGOs active in domains
of global climate change/global warming,
development aid or human rights
● Self-reported participation of those aged 15-30 in
activities of NGOs active these domains in last 12 months
 Participation of young people in activities or projects
aimed at fostering cooperation with youth from other
continents
● Self-reported involvement of those aged 15-30 in such
activities or projects during the past year
Strengths/Value Added of Youth Indicators
(I)
 They provide an evidence-based approach to youth
policy
 They help identify key issues, challenges & gaps to
inform policy so as youth-related interventions are
relevant & directed at those most in need
 They provide a basis for developing & regularly
monitoring progress on policy targets
● This provides a basis for assessing policy effectiveness
& impact, e.g. BE, EE, NL, UK, IE, DE
Strengths/Value Added of Youth Indicators
(II)
 They provide a common evidence-base that can be
used by a range of youth stakeholders, incl
researchers, practitioners & policy makers.
 Their systematic & co-ordinated use & provision of
evidence across youth policy areas may help the
better co-ordination & integration across all relevant
policy areas, e.g. SE, NL, FI, EE, LU, SI etc.
 They can help stimulate debate & greater awareness
around policy objectives
Weaknesses of Youth Indicators (I)
 They enable comparisons between MS, regions
& local areas, but
● danger of comparison without attention to external
factors & national/local contexts  their use &
analysis must take into account any influencing factors
& contexts
 Determining the attribution of specific
interventions on particular outcome indicators
is rarely possible
Weaknesses of Youth Indicators (II)
 There are no commonly used definitions for some of
‘core’ youth policy areas, esp re youth participation
● danger that indicators may misrepresent policy
objectives
 No agreement across MS & even across various
Ministries in one MS of re age definition of youth
● youth = period between 'dependent’ & childhood‘ &
'independent adulthood'




age limit for child benefit(s)
end of full-time compulsory schooling
voting age
minimum age for standing for elections, etc.
● Transition to adulthood = the time when young people
become financially independent
Weaknesses of Youth Indicators (III)
 Young people represent a heterogeneous group  Indicators should
be disaggregated across several basic socio-demographic
characteristics (i.e. age, gender, disability, ethnicity, sexual
preference, etc), but
● in some countries it is illegal to document some of this info in
administrative datasets (e.g. ethnic background and sexual
preferences)
● sample sizes of national & international surveys are often not
large enough to represent the circumstances of young people for
different sub-groups & sub-areas
● limited applicability of indicators at local level has been a
particular barrier to implementing a national youth monitoring
system in Member States, e.g. AT, BE, DE
 Indicators can provide info on the situation of young people &
policy performance & enable policy review & adjustments but
cannot provide policy solutions
Gaps in Existing Youth Indicators (I)

Indicators of Youth Employment Programme Outcomes
●
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availability of existing data, & esp re long-term employment sustainability & costeffectiveness, is very patchy & uneven both between MS & youth programmes
often based on monitoring data provided by organisations running youth
employment programmes which raises issues re data reliability
where data does exist, it is rarely consistent between MS, & even within some MS
very limited data on sustainability of youth employment (Impact indicators)
high level of heterogeneity in coverage features of schemes across the EU also
makes the results of existing evaluation studies difficult to generalise

While some outcome indicators for participants of youth programmes are
comparatively well documented, e.g. completion rates, No/% of beneficiaries in
employment immediately upon completion, similar type data for a group of nonparticipants is not collected

In order to make a judgement about the effectiveness of youth programmes, it
would also be necessary to understand whether programmes – large scale
programmes like apprenticeships have an impact on non-participants.
●
If participants & non-participants compete for training opportunities & jobs, large
scale programmes would reduce opportunities for non-participants, e.g. via
displacement effects
Gaps in Existing Youth Indicators (II)
 Definitional gaps (e.g. youth participation & age)
 Data limitations (e.g. disaggregation of data across
several socio-demographic markers
 Definitions of indicators & methods for collecting data
may differ at different spatial levels
 Depiction of state of play (‘what’) but not ‘how’ or
‘why’
 Difficulty in adequately capturing complex &
multifaceted youth policy interventions
Examples of MS Using Youth Indicators (I)
 Luxembourg: Youth indicators & research on young
people, presented in National Youth Report, was used
heavily to elaborate the Youth Pact 2011-2014
 Slovenia: Indicators used by the Ministry of Labour
showed the difficult STW transitions of social science
graduates. This led to a new national programme
targeted at these graduates
 Finland: Work by 2 working groups was undertaken to
develop a set of indicators for children (<18) & young
people (18-29). Both groups prepared a list of around
50 indicators
Examples of MS Using Youth Indicators (II)
 Netherlands: Its National Youth Monitor provides a summary of info
re situation of young people (0-24) in relation to (i) young people &
families; health & welfare; (iii) education; (iv) employment; & (v)
justice. In total, 60 indicators are collected
 Sweden: The Swedish National Board for Youth Affairs regularly
produces a number of reports, incl Youth Today – an annual
compilation & analysis of 85 indicators organised across 5 youth –
related policy areas: (i) education &learning; (ii) employment &
self sufficiency; (iii) health & social exclusion; (iv) influence &
representation; & (v) culture & leisure
 Ireland: Indicators are regularly used in the biennial State of the
Nation's Children report series
Use of Indicators in our Research (I)
 Study on a Comprehensive Overview on Traineeship Arrangements
In Member States
● Youth activity & employment, youth unemployment level
● Youth at risk of exclusion from labour market
● No of young people involved in various types of traineeships
broken down by gender, educational background, type of
traineeship scheme, sector of activity; etc.
● No of EU trainees which are undertaking a traineeship as part of
international exchange programmes, e.g. Erasmus, LdV
● Sectors which employ the largest Nos of young people
● Average age of entry of young people into MS’s labour market
(broken down by gender & type of education: secondary,
vocational, tertiary)
● Average earnings of young people (broken down by gender, age
group & type of education: secondary, vocational, tertiary); etc.
Use of Indicators in our Research (II)
 Providing Targeted Advice on ESF Support to Apprenticeship & Traineeship
Schemes (2012-2014)
● No of beneficiaries of a youth training/apprenticeship/traineeship
programme (broken down by gender)
● No & % of apprentices/trainees continuing in employment immediately upon
completion of training
● No & % of apprentices/trainees continuing in employment 6 months after
completion of training
● No & % of apprentices/trainees with certified qualifications
● No & % apprentices/trainees who continue into further education/training
after completion of scheme
● No & % of apprentices/trainees who return to unemployment/job search
after completion of scheme
● No & % of apprentices/trainees who dropped out/did not complete scheme
● No & % of apprentices/trainees who successfully completed scheme
● No & % of apprentices/trainees by type of employment contract (after
completion of scheme)
 Data on all of indicators sought across all MS was lacking or inconsistent
Use of Indicators in our Research (III)
 Availability of existing effectiveness & impact
indicators, esp employment outcomes for young
people & cost-effectiveness of programmes, is very
patchy & uneven both between MS & programmes
● Quantitative/numerical info about costs, incl employer
costs & cost-effectiveness; impact & evaluation data,
incl progression outcomes over time
 Different degrees of data availability & quality of
traineeship-related data across MS
● Dearth of aggregate & comparable data across the EU
re traineeship schemes for young people
Use of Indicators in our Research (IV)
 Data is collected at different time intervals
across MS & between programmes
 Information gaps & inconsistencies in data sets
 High degree of heterogeneity in available data
across programmes &/or MS
● It is difficult to make generalisations from
evaluation results & identifying common
success factors & transferable good practice
examples
… thank you
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