Key education indicators on social inclusion and efficiency : final project report.
Date: 2006-01-27
Institutions: Directorate-General for Employment, Social Affairs and Inclusion
Subjects: education costs, education policy, private sector, public sector, report, social integration

European Research Associates 

Key education 
indicators on social 
inclusion and 
efficiency 

Final Project report 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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        European Research Associates 

 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
  
 
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Executive summary 

Abbreviations and acronyms 

Introduction 

•  Background 
•  The study 

Chapter 1: 
Completing statistical gaps for the benchmark indicators 

1.1 
1.2 
1.3 

1.4 

Introduction. 
Overview of findings 
Completing gaps for the benchmark indicators on MST graduates 
1.3.1 
1.3.2  National sources 
Completing gaps for the benchmark indicators for Japan and the US 
1.4.1 

International sources 

Percentage  of  those  aged  20-24  who  have  successfully  completed  at 
least upper secondary education (ISCED 3) 
Share  of  the  population  aged  18  –  24  with  only  lower  secondary 
education and not in education and training 
Percentage of population aged 25 –64 participating in education and 
training in four weeks prior to the survey 

1.4.2 

1.4.3 

1.5 
1.6 

Comparability of data collected with the European Statistical System (ESS) 
Conclusion 

Appendix 
Chapter 2: 
Combining data on public and private spending on education 

2.1 
2.2 
2.3 

Introduction 
Data needed for calculating total spending on education 
Data currently collected from international sources 
2.3.1  UNESCO/OECD/Eurostat collection on education systems 
Eurostat Harmonised Household Budget Survey 
2.3.2 
2.3.3  Continuing Vocational Training Survey (CVTS) 
2.3.4 
Labour Cost Survey (LCS) 
Data currently collected from national sources 
Reconciling data needed with data collected 

2.4 
2.5 
2.6  Methodological aspects of combining data 

Expenditure on goods and services within educational institutions 

2.6.1 
2.6.2  Households  expenditure  on  goods  and  services  outside  educational 

institutions 
Expenditure of other private entities 

2.6.3 

iv 

2 

5 
5 
7 

10 
11 
13 
13 
14 
19 
19 

22 

33 

41 
41 
44 

47 
52 
53 
56 
59 
63 
64 
66 
66 
67 
68 
68 
69 

69 

    
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Expenditure on ancillary services 
Expenditure on research and development (R&D) 

2.6.4 
2.6.5 
2.6.6  Harmonising  households’  expenditure  on  educational  goods  and 

services 
Estimating expenditure of enterprises on vocational training 

2.6.7 
2.6.8  Double counting of expenditure 
2.6.9  Coverage of education and training 
2.6.10  Collection of private expenditures for non-EU countries 
Conclusions 

2.7 

Appendix: Estimation of total private and public spending on education 
Chapter 3: 
Efficiency and effectiveness of education systems 

Introduction 

3.1 
3.2  Measuring efficiency and effectiveness 

Parametric or regression based estimators 

3.2.1 
3.2.2  Non-parametric techniques 
3.2.3  Cost benefit analysis 
3.2.4  Cost-effectiveness analysis 
3.2.5  Rates of return to education 
Efficiency, effectiveness and equity indicators 
Calculating efficiency in relation to the benchmark indicators 
3.4.1  Defining the inputs and outputs 
3.4.2  Relationship between inputs and outputs 
3.4.3  Data needed to calculate efficiency 
3.4.5  Data collected by international sources 

3.3 
3.4 

3.5  Measuring internal efficiency without expenditure 

3.5.1 
Indicators of internal efficiency in education 
3.5.2  Methods of measuring internal efficiency in education 
Survival rates in tertiary education 
Conclusions 

3.6 
3.7 

Chapter 4: 
Social inclusion /exclusion and the benchmark indicators 

4.1 
4.2 
4.3 

4.4 

4.5 

4.6 

The PISA International database 

Introduction 
Data needs for examining social inclusion   
Data currently collected from international sources 
4.3.1 
EU Labour Force Survey (LFS) 
4.3.2  UNESCO/OECD/Eurostat collection on education systems 
4.3.3 
Data collected from other international sources 
4.4.1 
Social inclusion/exclusion in relation to benchmark indicators 
Educational attainment of 20 to 24 year olds 
4.5.1 
4.5.2 
Early school leavers 
4.5.3  MST graduates 
4.5.4 
4.5.5  Reading literacy of 15 year olds 
Conclusion 

Participation in lifelong learning for 25 to 64 year olds 

European Statistical System (ESS) 

70 
72 
72 

75 
76 
77 
80 
80 
83 

96 
98 
99 
101 
103 
103 
104 
106 
107 
108 
109 
115 
117 
125 
126 
127 
130 
132 

134 
136 
143 
145 
147 
147 
151 
151 
153 
153 
154 
156 
163 
170 
180 

Key education indicators on social inclusion and efficiency 

ii

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of figures and tables 

•  List of figures 
•  List of tables 

Annexes  

Annex 1: Statistical sources and some methodological notes and information 
Annex 2: Calculation of efficiency in relation to the benchmark indicators 
Annex 3: Calculations of internal efficiency – promotion, dropout and repetition rates  
Annex  4:  Relationship  between  benchmark  indicators  and  spending  on  education  - 

regressions statistics 

Annex 5: Summary tables of expenditure on education from public and private sources 

(2000 to 2004) for all ISCED levels 

Annex 6: Background data  

183 
185 

190 
207 
219 
228 

240 

259 

Key education indicators on social inclusion and efficiency 

iii

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
I  

Executive summary 

 
 
  
 
 
 
 
 
 
 
 
  
  
  
Executive Summary 

11..  Introduction  

EExxeeccuuttiivvee  ssuummmmaarryy  

The overall objective of this study was to contribute to the programme Education & Training 2010 
by  supporting  the  statistical  and  analytical  work  linked  to  it,  regarding  the  key  education 
indicators. The specific objective was to fill statistical gaps regarding four of the indicators related 
to European education benchmarks: 
•  Share of the population aged 18 – 24 with only lower secondary education and not in education 

and training; 

•  Total number of tertiary (5A, 5B and 6) graduates from mathematics, science, and technology; 
•  Percentage  of  those  aged  22  who  have  successfully  completed  at  least  upper  secondary 

education (ISCED 3); 

•  Percentage  of  population  aged  25  –  64  participating  in  education  and  training  in  four  weeks 

prior to the survey. 

The intention of the exercise was to enable the analysis of questions related to social inclusion and 
efficiency of education spending. This involved supplementing, combining and analysing available 
Eurostat, OECD, and national data, as well as statistical information from other sources in order to 
produce comparable figures for the EU and other regions. Another objective of the study was to 
combine data on public and private spending on education from a number of European Statistical 
Sources (ESS) and other sources to produce a figure on total spending on education per country. 

22..  Completing statistical gaps for the benchmark indicators 
The  five  benchmarks  set  by  the  Council  in  May  2003  for  the  improvement  of  education  and 
training  systems  up  to  2010  are  monitored  through  5  related  indicators.  Unfortunately,  there  are 
still  a  number  of  statistical  gaps  for  a  number  of  countries  at  the  EU  level,  which  hinder  a 
calculation of the benchmark for the EU-25.  This is the case with the benchmark indicator on the 
number  of  maths,  science  and  technology  graduates,  as  data  is  missing  for  EL  for  a  number  of 
years.  Data on the number of MST graduates is already collected for Japan and the US, however it 
is known that China, India and Russia also have a high number of MST graduates. Therefore it is 
essential  to  compare  the  performance  of  the  EU  with  these  countries.  There  is  no  information 
available  on  the  performance  of  Japan  and  the  US  in  relation  to  the  indicators  on  educational 
attainment, early school leavers and participation in lifelong learning, so as to be able to compare 
the  EU’s  performance.    Overall  a  number  of  sources  were  identified  containing  data  that  could 
potentially be used to meet the data requirements needed for the four benchmark related indicators.  

Table 1: Key gaps closed for non-EU countries 

Indicator 
Total  number  of  tertiary  (5A,  5B  and  6)  graduates  from 
mathematics, science, and technology; 

Share  of  the  population  aged  18  –  24  with  only  lower 
secondary education and not in education and training 
Percentage  of  those  aged  20-24  who  have  successfully 
completed at least upper secondary education (ISCED 3); 
Percentage  of  population  aged  25  –  64  participating  in 
education and training in four weeks prior to the survey. 

Country 
China 
India 
Russia 
Japan 
USA 
Japan 
USA 
Japan 
USA 

Data availability 

Available 
Not available 
Partially available 
Partially available 
Available 
Available 
Available 
Not available 
Not available 

Key education indicators on social inclusion and efficiency 

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Executive Summary 

Indicator : Maths, Science and Technology  (MST) graduates 
No  additional  data  sources  were  found  to  complete  the  statistical  gaps  for  Greece  for  the  period 
2000 to 2003, however data for 2004 has now been supplied to Eurostat. A number of data sources 
were  identified  at  the  national  level  to  complete  the  statistical  gaps  for  China,  India  and  Russia.  
Data  on  the  number  of  MST  graduates  in  Russia  has  now  been  provided  to  the  UNESCO.  The 
National  Statistical  Institute  in  China  publishes  data  on  the  number  of  MST  graduates  annually. 
However, it is unclear how comparable the data are with the data published by Eurostat, since no 
details are given in the publication of the precise definitions of  “science” and “engineering”. For 
these non-EU countries one can make a tentative comparison with the EU-25. In 2004, the number 
of MST graduates in China was nearly 30 percent higher than that of the EU-25. On the other hand 
the number of MST graduates produced in Russia was more 50 percent less than in the EU-25 (see 
table 2). Furthermore, in 2004, MST graduates represented 43 percent of graduates in all fields in 
contrast to only 24 percent in the EU-25 and 20 percent in Russia. 

Table 2: Number of graduates in Maths, Science and Technology (2004) 

China 
1093.3 
Source: Eurostat, UNESCO, and National Statistical Institute of China 

Russia 
436.1 

EU-25 
775.8 

In  India,  administrative  data  on  the  number  of  graduates  broken  down  by  field  is  not  readily 
available.  The data that is frequently citied in various reports in India tends to be based on surveys 
of the population or on projections, and  not on actual administrative records of higher education 
institutions. In consequence there is no coherence between different sources for certain variables. 
Nevertheless, a tentative estimate of the number of MST graduates can be made based on the stock 
of graduates and postgraduates in the population. In 2000, there were in the region of 350 000 to 
400 000 MST graduates.  

Indicator: Educational attainment of 20 to 24 year olds 
The  statistical  gaps  for  the  USA  have  now  been  filled  using  data  collected  from  the  Current 
Population  Survey  conducted  annually  by  the  Census  Bureau  for  the  years  2000  to  2004.  Data 
from Japan  can be collected from two different sources: the Employment Status Survey, and the 
Population Census for the years 2000 and 2002. However, it has to be noted that both surveys are 
conducted  only  every  5  years,  which  means  that  data  is  only  available  for  2000  and  2002. 
Nevertheless, one is able to observe in table 2 that the percentage of 20 to 24 year olds attaining at 
least  upper  secondary  education  is  higher  in  Japan  than  in  the  US  for  the  years  where  data  is 
available. Furthermore, it can also be said that the EU-25 is lagging behind Japan and the US in 
terms of the educational attainment of youth. Youth educational levels have risen for the EU-25, 
Japan and the US in the period 2000 to 2005. 

Table 3: Percentage of those aged 20-24 who have successfully completed at least upper secondary 
education (ISCED 3) 

Country 
EU-25 
US 
Japan 

2000 
76.6  
85.5 
91.8 

2001 
76.5 
84.8 
: 

2002 
76.7 
84.5 
94.3 

2003 
76.9 
85.6 
: 

2004 
77.1 
85.9 
: 

2005 
77.5 
85.9 
: 

Sources: Statistics Japan, ‘Population Census’ and ‘Employment Status Survey’, and US Census Bureau ‘ 
Current Population Survey’ 

Key education indicators on social inclusion and efficiency 

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Executive Summary 

The study also revealed that the educational attainment level of female youth is higher than that of 
male youth in both Japan and the US. In 2002, 96 percent of female youth achieved at least upper 
secondary qualification, in contrast to only 93 percent of male youth. More recently in the US, 84 
percent  of  male youth  achieved  at  least  upper  secondary  education,  in  contrast  to  87  percent  for 
females.  The  study  highlighted  that  the  gender  gap  in  upper  secondary  educational  attainment 
levels is not only confined to the EU but is also there in Japan and the US.  

Indicator: Early school leavers 18 to 24 year olds 
The Employment Status  Survey  and the Population  Census conducted by Statistics  Japan can  be 
used to construct the indicator. However, both surveys are conducted only once every five years. 
Furthermore data is only disseminated for the age group 20 – 24, in contrast to the age-group 18-
24  required.    The  US  National  Center  for  Education  Statistics  normally  publishes  data  on  three 
different types of dropout rates: event dropout rates; status dropout rates; and cohort dropout rates. 
The  US  indicator  on  status  dropout  rates  is  the  closet  in  definition  to  the  EU  indicator  on  early 
school leavers, than the other indicators on dropouts. Unfortunately, the indicator refers to the age 
group 16 to 24, whilst the EU indicator on early school leavers refers to the age group 18 to 24. 
The Current Population Survey (CPS) conducted by the US Census Bureau is used to construct the 
indicators on status dropout rates, and can be used to construct the indicator for the age group 18 – 
24.   

The population in education and training, which the surveys in Japan and the US refer to, is those 
students  who  are  in  regular  or  formal  education,  that  is  senior  high  school,  junior  college,  or 
college or university including graduate school. In contrast the data collected from the EU-LFS for 
the  EU  Member  States  refers  to  participation  in  formal  and  non-formal education  (e.g.  language 
courses, computer courses, seminars etc…). A simple solution to the problem is to adjust the data 
on early school leavers from the EU-LFS by only counting those  that are  participating in formal 
education  (that  is  regular  education)  as  participating  in  education  and  training.  Persons 
participating  in  non-formal  education  will  not  be  classified  as  participating  in  education  and 
training.  This  will  make  the  statistics  comparable  with  the  US.  The  study  demonstrated  that 
adjusting  the  EU  data  to  reflect  this  change  in  definition  would  increase  the  percentage  of  early 
school  leavers  (In  2004,  the  increase  would  be  0.7  percent,  whilst  in  2005  there  would  be  a  0.8 
percent increase). 

Whilst it is not possible to construct this indicator for Japan for the age group 18-24, it is possible 
to construct the indicator for the age group 20-24. Given that data is available for the US for this 
age group a comparison can be easily made. For the EU-25 data for the age group 20 to 24 can be 
collected from Eurostat.  It was found that the percentage of early school leavers is higher in the 
US than in Japan.  

Table 4: Share of population aged 20-24 with only lower secondary education and not in education 
and training. 

USA 
Japan 

2000 
12.4 
5.8 

2001 
13.1 
: 

2002 
12.8 
5.7 

2003 
12.1 
: 

2004 
12.6 
: 

Sources: Statistics Japan, ‘Population Census’ and ‘Employment Status Survey’, and US Census Bureau ‘ 
Current Population Survey’ 

Key education indicators on social inclusion and efficiency 

vii

 
 
  
 
 
 
Executive Summary 

Indicator: Participation in lifelong learning  
Existing  surveys  in  Japan  and  the  US  that  have  been  identified  as  collecting  information  on 
participation in lifelong learning cannot be used to construct the indicator for a number of reasons. 
This includes: 

-  Surveys in Japan and the US refer to participation in adult education and training during the 

past 12 months, instead of the past four weeks, as is needed to construct the indicator; 

-  The  definition  of  lifelong  learning  used  in  the  EU  indicator  is  far  wider  than  the  data 

collected by any survey in Japan or the US. 

An alternative indicator to participation in lifelong learning that can provide a useful insight into 
differences between EU Member States and the US is the indicator C.6 ‘Participation in continuing 
education  and  training’  which  is  currently  employed  in  the  OECD’s  ‘Education  at  a  Glance’.  
However, data for this indicator is not available for Japan, and the only data that is available for 
the  US  refers  to  participation  in  non-formal  job-related  education  and  training.  In  2003, 
participation in continuing education and training for the EU-18 countries that provide data to the 
OECD was 20 percent, which was much lower than the 44 percent that participated in the US. At 
the individual Member State level, participation in continuing education and training varied from 4 
percent in Greece to 46 percent in Finland.  

33..  Combining data on public and private spending on education  

The UNESCO/OECD/Eurostat data collection should in theory be able to provide a figure for total 
spending  on  education  from  public  and  private  sources  both  inside  and  outside  educational 
institutions.  The  coverage  of  data  from  public  sources  both  inside  and  outside  educational 
institutions is fairly comprehensive. Nevertheless, the problem arises with data collected on private 
expenditure,  both  for  households  and  other  private  entities.    The  coverage  of  data  on  private 
spending  is  not  as  comprehensive  as  on  public  expenditure.  Moreover,  for  those  countries  that 
supply  data  concerning  households  expenditure  on  education,  there  are  problems  regarding 
comparability  as  there  are  differences  between  countries  in  the  goods  and  services  that  are 
the 
regarded  as  education. 
UNESCO/OECD/Eurostat data collection refer to education that can be classified by ISCED level. 
Thus if a programmes subject content is not similar to regular programmes or it does not lead to 
similar qualifications as corresponding regular programmes, then the expenditure is not included.  

the  data  collected 

to  be  noted 

It  has  also 

that 

in 

Estimating  total  expenditure  on  education  by  combining  different  sources  does  not  guarantee 
comprehensive coverage on the total amount spent on education from public and private sources 
for  all  countries.  In  2003  estimates  of  total  spending  were  only  calculated  for  three  fifths  of 
Member States. Furthermore no matter what combination of data sources are used to estimate total 
spending on education, certain types of education will either not be covered at all, as is the case 
with  informal  learning  or  only  partly  covered  as  is  the  case  with  non-formal  learning  (e.g. 
spending by enterprises on vocational training is covered) 

There  are  a  number  of  methodological  issues  that  inevitably  arise  when  combining  data  from 
different sources: 

- 

In the case of collecting data from national household budget surveys, attention needs to be 
placed  on  harmonising  households’  expenditure  on  education  on  educational  goods  and 

Key education indicators on social inclusion and efficiency 

viii

 
 
 
 
 
Executive Summary 

services, as there are differences between countries in the goods and services considered as 
education. Therefore, to exploit the data collected from national household budget surveys, 
it  is  imperative  to  make  sure  that  the  expenditure  data  collected  contains  the  same 
educational goods and services for all countries.  Consequently the list of educational goods 
and services proposed in the study  ‘Private Household spending on education and training’ 
can be used as the basis by which the expenditures for goods and services, which should not 
be regarded as education, are deducted. In this manner, a final figure of private household 
spending on education should be arrived at which should be comparable between countries. 
However, the study revealed that for some countries (e.g. DK, DE, EE) it was not possible 
to break down total household expenditure by spending on various goods and services.  
-  The treatment of expenditure on ancillary services poses particular problems, especially at 
the higher education level. It has been proposed to exclude this expenditure since there are 
problems with comparability. In 2003, only four Member States supplied data on ancillary 
services 

-  Calculating  the  resources  that  enterprises  spent  on  vocational  training  is  particularly 
problematic, given that the ESS surveys are conducted at very long intervals. In the case of 
CVTS  it  is  every  five  years,  the  last  survey  was  conducted  in  1999.  This  means  that  the 
estimates of spending will be based on projections of data from 1999. Furthermore it is not 
possible to calculate total spending for some countries, in particular the non- EU countries 
like Japan and the US for the simple reason that these countries are not covered by a ESS 
source 

-  Care needs to be taken when combining data, as there is potential risk of double-counting. 
Therefore, it can be concluded that there are a number of areas where the availability of data can 
be  improved  in  particular  in  relation  to  the  UNESCO/OECD/Eurostat  statistical  exercise.  This 
includes improving data collected concerning expenditure of private households and other private 
entities. Combining data from different sources can yield an estimate of total spending. However, 
it  should  be  noted  that  there  would  be  a  bias  in  the  results  when  combining  data  from  different 
sources to produce a total figure of spending on education. This bias arises from the fact that in the 
case  of  CVTS  and  the  LCS  surveys,  missing  data  are  imputed  using  clearly  rules  defined  by 
Eurostat.  In  the  case  of  the  UNESCO/OECD/Eurostat  statistical  exercise,  public  and  private 
household  expenditures  are  estimated  for  a  number  of  countries  using  different  methods. 
Therefore, when combining data for the same set of countries, this will inevitably lead to a bias. 

Furthermore,  estimates  can  only  be  made  subject  to  data  being  available  in  all  sources  that  are 
being combined. This is not the case in practice, since total spending could not be calculated for 
two-fifths  of  Member  States.  The  situation  with  the  non-EU  countries  is  even  worse,  since  data 
was  collected  from  data  sources  within  the  ESS,  which  do  not  always  cover  non-EU  countries. 
This is particularly the case with Japan and the US. Collecting data for the 10 new Member States 
posed  particular  problems,  since  some  ESS  sources  have  only  recently  started  collected  data  for 
these countries. For example the CVTS2 did not collect data for Malta and the Slovak Republic. 

There are a number of areas where the availability of data can be improved in particular in relation 
to  the  UNESCO/OECD/Eurostat  statistical  exercise.  This  includes  improving  data  collected 
concerning expenditure of private households and other private entities.  

Key education indicators on social inclusion and efficiency 

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Executive Summary 

It  can  be  concluded  that  combining  different  sources  to  produce  a  total  figure  for  spending  on 
education  is  far  from  the  ideal  situation  for  a  number  of  reasons  including  the  fact  that  the 
coverage of education will not be as comprehensive and most importantly of all the data that has 
been  combined  will  only  be  a  crude  estimate.  Data  on  the  total  resources  devoted  to  education 
should be collected in the framework of satellite accounts.  

44..  Efficiency and effectiveness of education systems  

Measuring  efficiency  and  effectiveness  in  education  is  of  great  importance  considering  that 
resources  are  usually  limited,  while  needs  for  such  resources  are  often  limitless.  The  underlying 
objective of making best use of resources devoted to education requires a drive to maximising both 
the efficiency and effectiveness. Thus, these concepts are applied in order to monitor and evaluate 
how well resources are used in an educational system and to prioritise the use of such resources.  

Yet, as useful as they are, these concepts can be rather abstract in the sense that they are difficult to 
quantify, and tricky to measure through the use of standard statistical techniques.  
The problems of measuring efficiency in education are considerable, and include the following: 

•  Comparability  in  the  data  collected  concerning  the  expenditure  on  education  due  to  cost 
provision  differences.  No  matter  what  transformations  are  made  to  spending  on  data, 
comparability  between  countries  cannot  be  assured.  Thus  it  can  be  strongly  argued  that 
physical  inputs  and  outputs  have  the  important  advantage  of  being  comparable  across 
countries without the need of any questionable transformation. 

•  Measuring educational output - How educational output is measured is dependent on the 
nature of the objectives of the educational system. The objectives of educational systems 
will differ between countries.  

•  Quantifying the relationship between inputs and outputs.  

The  distinction  between  the  concepts  of  efficiency and  effectiveness  can,  at  times,  appear  rather 
blurred.  There  are  many  different  perspectives  to  both  concepts.  Efficiency  and  effectiveness  in 
(higher) education were defined as follows: 

•  Efficiency  -  “An  ability  to  perform  well  or  achieve  a  result  without  wasted  resources, 
effort, time, or money (using the smallest quantity of resources possible)” …”Educational 
efficiency  can  be  measured  in  physical  terms  (technical  efficiency)  or  in  terms  of  cost 
(economic efficiency)”. 

•  Effectiveness  -  “An  output  of  specific  review/analyses  that  measure  (the  quality  of)  the 
achievement  of  a  specific  educational  goal  or  the  degree  to  which  a  higher  education 
institution can be expected to achieve specific requirements”.  

The  recent  Communication  from  the  Commission  on  “Efficiency  and  equity  in  European 
education  and  training  systems”  further  clarified  this  by  stating  that,  “Efficiency  involves  the 
relationship  between  inputs  and  outputs  in  a  process.  Systems  are  efficient  if  the  inputs  produce 
the maximum output”.  
In  distinguishing  between  efficiency  and  effectiveness,  the  following  concepts  have  often  been 
citied and used:  

i. 

Internal  efficiency    -  A  system  is  more  internally  efficient  than  another  if,  in  order  to 
produce  the  same  level  of  output,  it  is  less  costly.  It  refers  to  a  comparison  of  learning  (a 

Key education indicators on social inclusion and efficiency 

x

 
 
 
 
 
Executive Summary 

ii. 

non-monetary outcome of  education) to the costs  of  educational inputs. Internal efficiency 
addresses the question of how funds within the educational sector should be best allocated. 
It  is  concerned  with  obtaining  the  greatest  educational  outputs  for  any  given  level  of 
spending.  

External efficiency - For measuring the external efficiency of education system one tries to 
ascertain  the  impact  which  the  school,  college,  university  graduates  i.e.  the  product  of 
education system are making on the society. Items such as community gains (i.e. community 
activities), political (i.e. government activities), personal gains (i.e. return to individual and 
his/her family), students achievement at higher levels of education, the life time income as 
well as the social stability are taken into account. It refers to the ratio of monetary outcomes 
to monetary inputs. For example the analysis of returns to schooling. 

iii. 

Internal  effectiveness  -  A  system  is  more  internally  effective  (technically  efficient)  than 
another  if,  in  order  to  produce  the  same  level  of  output,  fewer  of  at  least  one  input  is/are 
used. 

iv.  External effectiveness - External effectiveness is concerned with the relationship between 
non-monetary inputs and monetary outputs, or how the overall use of money for schooling 
compares  to  other  potential  public  and  private  uses.  e.g.  comparing  the  earnings  of 
technical-vocational  graduates  with  the  earnings  of  students  graduating  from  academic 
subjects.  By  measuring  outputs  in  monetary  values,  it  is  possible  to  compare  educational 
programs directly to other potential uses of society's resources. 

There  are  a  number  of  techniques  that  are  often  employed  in  order  to  measure  the  efficiency  of 
education  systems.  These  include:  parametric  or  regression  based  estimators;  non-parametric 
analysis;  cost  benefit  analysis;  cost-effectiveness  analysis;  measurement  of  rates  of  return  to 
education.  A  number  of  conceptual  problems  are  faced  in  attempting  to  undertake  a  meaningful 
measurement of efficiency. These include: 

-  Difficulties  with  the  identification  of  the  components  of  the  education  system  that  are 

relevant to the analysis; 

-  Determining how the components will be measured,  
-  Difficulties 

in  measuring  costs  – 

this 

includes 

the  problems  of  collecting                           

data on private costs of education both inside and outside of educational institutions; a lack 
of agreement on the type of costs that should be included; 

-  Methodological issues concerning the measurement of educational outputs and outcomes. 
For  example, there are  questions  about how  to incorporate  cognitive as well  as affective 
aspects  in  a  measurement  instrument.  When  determining  the  efficiency  of  a  system  the 
outputs are normally limited to aspects such as the number of graduates in a course or the 
average grades in a course. Measurement of long-term effects or outcomes is almost never 
taken into account.  

To meaningfully measure the efficiency of an education system, the inputs and the process have to 
be related to the effects, which are based on the outputs and the outcomes. The education system 
can be viewed as consisting of four main components.  

• 

Inputs:  these  are  the  real  resources  used  in  education,  e.g.  the  characteristics  of  learners, 
educators, curricula, textbooks, facilities and equipment, and financial resources.  

Key education indicators on social inclusion and efficiency 

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Executive Summary 

•  Processes:  these are the interactions between learners and  inputs,  between  different inputs 
teaching/learning  processes,  e.g.  attendance/participation, 

themselves,  and  between 
absenteeism, etc.  

•  Outputs:  these  are  the  direct  and  more  immediate  results  or  effects  of  education,  e.g. 

learner's completion/certification.  

•  Outcomes:  these  are  the  ultimate  or  eventual  effects  of  education,  e.g.  increased  earnings, 
employment,  contribution  to  productivity,  improved  health,  and  other  non-monetary 
outcomes.  

A  fair  amount  of  data  exists  in  national  and  international  sources,  which  could  be  useful  in 
measuring efficiency and effectiveness of education. Nevertheless, most data obtained from these 
sources  are  rather  generic  in  nature,  since  very  limited  exercise  has  been  conducted  with  the 
primary aim of tailoring data sets to the objective of measuring efficiency. Attempting to combine 
data collected through different methodologies and statistical cultures is fraught with problems of 
quality  and  of  comparability.  When  combined  with  the  differences  in  the  concept  of  efficiency 
itself, results obtained need to be used with very much caution. 

Much  efforts  have  been  put  into  the  use  of  financial  input  and  learning  as  output  in  calculating 
efficiency of investment in education. The effect of various socio-economic factors which underlie 
individual countries (and which are very difficult to compare) have rendered the use of most of the 
financial variables too weak. The use of variables of learning output is a fraught with danger as it 
is the case with the input variables.  

55..  Social inclusion/exclusion and the benchmark indicators  

identified  a  number  of  variables,  which  can  be  used 

The  study 
to  examine  social 
exclusion/inclusion  in  relation  to  the  benchmark  related  indicators.  It  has  to  be  mentioned  that 
despite  the  fact  that  two  of  the  benchmark  indicators  (early  school  leavers  and  low  reading 
literacy) are defined as primary indicators in measuring social exclusion by the Commission, they 
can  be  broken  down  further  by  the  variables  listed  so  as  to  provide  a  more  detailed  analysis  of 
social exclusion.  These variables identified are as follows:  
•  Gender - At  present  all  five  of the  benchmark related indicators  are already  broken  down by 

gender. 

•  Age  - There are a number of different age groups that can be used to break down the indicators 
further.    Breaking  down  the  benchmark  indicators  by  age  is  not  relevant  to  the  indicators  on 
educational  attainment,  early  school  leavers,  and  low  reading  literacy,  given  that  in  the  latter 
case the age group that the indicator refers to is 15. In the case of the indicators on educational 
attainment and early school leavers, which already refer to narrow age groups, breaking down 
the indicators further will not bring any added value. 

•  Social-economic background - The term ‘socio-economic’ relates to or is concerned with the 
interaction of social and economic factors. The term ‘socio-economic’ is merely a descriptive 
one. It has no theoretical or analytic status whatever and so there can be no single definition of 
a ‘socio-economic classification’. As part of its statistical harmonisation programme, Eurostat 
asked  the  UK  Office  for  National  Statistics  to  convene  an  expert  group  on  a  harmonised 
European  socio-economic  classification.  The  group  reported  in  January  2001  with  an  outline 
for an E-SEC classification based on the employment relations approach. A two level, nested 

Key education indicators on social inclusion and efficiency 

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Executive Summary 

classification  was  proposed.  Level  1  has  nine  ‘classes’.  Level  2  has  44  ‘socio-economic 
groups’ (SEGs). Thirty-five of these SEGs collapse directly to one or other of the nine classes.   
•  Income - Breaking down the benchmark indicators by income can help to shed light on whether 
students from low-income households fair the same as students from high-income households.  
•  Educational attainment - The level of parental education can help to explain a child’s chances 
of access to post-secondary education. Parents with more education tend to pass down to their 
offspring their skills and beliefs, and they also tend to get more involved with their offspring’s 
education.  This  will  yield  two  separate  breakdowns  of  the  benchmark  indicators:  mother’s 
educational  attainment,  father’s  educational  attainment.  A  student’s  social  background  cannot 
only be classified by the educational attainment of one parent without taking into consideration 
the  level  of  the  other  parent.  This  is  because  one  parent  may  have  a  higher  educational  level 
than the other parent, and we may only be classifying the student by the educational attainment 
of the parent with a lower educational attainment.   A sub-variable of interest that falls directory 
under  this  heading  is  first  generation  entrants.  This  variable  is  only  relevant  to  higher 
education.  Entrants  to  higher  education  are  first  generation  entrants  if  neither  parent  has  a 
degree  level  qualification.  This  variable  is  extremely  useful  in  predicting  and  charting  under-
representation in higher education.  

•  Occupation - Data on occupation should be collected according to the ISCO-88 classification, 
in order to maintain coherence with the European Statistical System. Usually full-time students 
are given their ‘class of origin’ that of a parent/guardian, since their life chances are dependent 
on that of their parents/guardians.  

•  Industry  (branch  of  economic  activity)  -  "Industry"  (branch  of  economic  activity)  refers  to 
the kind of production or activity of the establishment or similar unit in which the job(s) of the 
economically active person (whether employed or unemployed) was located. Full-time students 
are classified according to that of a parent/guardian, since their life chances are dependent on 
that  of  their  parents/guardians.  The  indicators  on  early  school  leavers  and  educational 
attainment of 20 to 24 year olds, and MST graduates should be broken down by the industry in 
which the parents are employed.  

•  Labour  force  status  -  Breaking  down  the  indicators  on  early  school  leavers  and  educational 
attainment of 20 to 24 year olds, and MST graduates by labour force status would be focusing 
on the outcomes of education systems and not on social inclusion. To focus on whether certain 
groups are socially excluded in relation to these two indicators, the indicators should be broken 
down by the labour force status of their parents.  

•  Geographical  location  -  The  benchmark  indicators  can  be  further  broken  down  by 
geographical  location  within  a  country  (for  example  deprived  urban  and  rural  areas)  so  as  to 
elucidate whether persons living in certain areas are disadvantaged due to their location 

•  Minority  groups  -  The  definition  of  minority  groups  will  undoubtedly  differ  between 
countries.  They  can  be  defined  by  countries  in  terms  of  place  of  birth,  language  or  racial  or 
ethnic  group,  or  even  a  mixture.    For  the  purpose  of  this  study,  emphasis  was  placed  upon 
exploring  whether  the  benchmark  indicators  could  be  broken  down  by:    place  of  birth  and 
language. 

•  Students with special needs (e.g. the disabled) - Examining the benchmark related indicators 
by students with special needs, for example the disabled will help to determine whether these 
groups are excluded. 

Three  of  the  five-benchmark  indicators  (educational  attainment,  early  school  leavers  and 
participation in lifelong learning) are constructed from one source, the EU Labour Force Survey. 

Key education indicators on social inclusion and efficiency 

xiii

Executive Summary 

The  EU  Labour  Force  Survey  has  the  potential  to  breakdown  the  indicators  by  some  of  the 
variables.  However,  there  is  a  caveat  in  relation  to  the  indicators  on  educational  attainment  and 
early  school  leavers.  Breaking  down  the  indicators  by  parents’  income,  occupation,  branch  of 
industry and the labour force would mean that the results would be biased. The bias results from 
the fact that both these indicators relate to specific age groups, in the case of early school leavers it 
is 18 to 24, whilst for educational attainment it is 20 to 24. In some countries the number of young 
adults living with there parents is low, this is especially true in the North European countries.  

The study showed that males are more at risk of social exclusion than females given that a greater 
percentage  of  them  can  be  classified  as  early  school  leavers.  In  2005,  17  percent  of  male  youth 
could  be  classified  as  early  school  leavers  in  contrast  to  13  per  cent  for  females.  Examining 
participation  in  lifelong  learning  by  age  groups  revealed  that  for  most  countries  participation  in 
learning activities for both males and females is highest for the age group 25 to 34 and lowest for 
the  age  group  55  to  64.  Participation  in  any  learning  activities  in  the  EU-25  is  highest  for 
managers,  professionals,  technicians  and  associate  professionals,  and  is  lowest  for  plant  and 
machine operators and assemblers and elementary occupations. 

Unfortunately the indicator on the number of Maths, Science and Technology graduates can only 
be broken down by gender. This is because the source that is used to construct this indicator the 
UNESCO/OECD/Eurostat data collection does not collect detailed information. The percentage of 
female MST graduates is significantly lower than that of male MST graduates. The period 2000 to 
2004 saw the gap between male and female MST graduates widen from 8 per cent in 2000 to just 
over 9 per cent in 2004.  The Eurostudent survey can be used to provide breakdowns of students in 
higher  education  by  a  number  of  variables  of  interest  in  order  to  examine  social  exclusion  / 
inclusion. However, the use of this survey is limited in relation to the benchmark indicator given 
that it does not distinguish between the fields of study in which a student studies.  

It has been demonstrated with data from the LFS ad hoc module on lifelong learning that breaking 
down the indicator on participation in lifelong learning by a number of variables is possible for a 
number of variables including:  age, educational attainment, occupation, industry  of employment, 
labour force status and geographical location. This should now be taken a step further by breaking 
down the indicator by the variables identified. 

The  OECD  PISA  database  is  used  to  construct  the  benchmark  indicator  on  low  reading  literacy. 
The  database  also  collects  background  information  on  the  social  and  economic  background  of 
students, which could be used to examine social exclusion. This includes: educational attainment 
of parents, occupation of parents, labour force status of parents, and minority groups (this refers to 
whether the student and/or are immigrants). The results of the survey showed that not only that the 
reading  literacy  of  students  varies  by  labour  force  status  of  parents,  but  also  the  difference  in 
reading  literacy  score  is  marked  by  the  gender  of  parent.  The  survey  showed  that  for  almost  all 
countries  native  students  have  the  highest  reading  performance  followed  by  second-generation 
students. First-generation students have the lowest reading literacy scores. This indicates that first-
generation students are at greater risk of social exclusion followed by second-generation students. 

Key education indicators on social inclusion and efficiency 

xiv

 
 
 
 
II 

Abbreviations & acronyms

  
 
 
 
 
 
 
 
 
  
  
  
Abbreviations 

Abbreviations & acronyms  

Some of the abbreviations and acronyms used in this report are presented below. 

CVT 
CVTS 
COICOP 
CPI 
EEA 
EURO PPS 
ESS 
ESA 95 
GDP 
ILO 
ISCED-97 
HBS 
HE 
ISCO-88 
LCS 
LFS 
LLL 
NACE 

NSIs 
NUTS 
OECD 
PPS 
R&D 
UOE 
UNESCO 
UNESCO-UIS 

Continuing Vocational Training 
Continuing Vocational Training Survey 
Classification Of Individual Consumption by Purpose 
Consumer Price Index 
European Economic Area 
Euro Purchasing Power Standard 
European Statistical System 
European System of Accounts  
Gross Domestic Product 
International Labour Organisation 
International Standard Classification of Education 
Household Budget Survey 
Higher Education 
International Standard for the Classification of Occupations 
Labour Cost Survey 
Labour Force Survey 
Lifelong Learning 
Statistical Classification of Economic Activities in the European 
Community 
National Statistical Institutes 
Nomenclature of Territorial Units for Statistics 
Organisation for Economic Co-operation and Development 
Purchasing Power Standard 
Research and Development 
UNESCO/OECD/Eurostat data collection on education 
United Nations Educational, Scientific and Cultural Organisation  
United Nations Educational, Scientific and Cultural Organisation 
Institute for Statistics 

Key education indicators on social inclusion and efficiency 

2

 
 
 
  
  
  
  
 
 
 
Abbreviations 

Country codes 

The following standard country codes were used 

EU-25 
BE 
CZ 
DK 
DE 
EE 
EL 
ES 
FR 
IE 
IT 
CY 
FI 
LV 

Belgium 
Czech Republic 
Denmark 
Germany 
Estonia 
Greece 
Spain 
France 
Ireland 
Italy 
Cyprus 
Finland 
Latvia 

Acceding countries  
Bulgaria 

BG 

Candidate countries  

HR 
TR 

Croatia 
Turkey 

Other countries  
Japan 

JP 

LU 
HU 
MT 
NL 
AT 
PL 
PT 
SI 
SK 
FI 
SE 
UK 

Luxembourg 
Hungary 
Malta 
The Netherlands 
Austria 
Poland 
Portugal 
Slovenia 
Slovakia 
Finland 
Sweden 
United Kingdom 

RO 

Romania 

MK 

F.Y. R of Macedonia 

US 

United States of America 

Key education indicators on social inclusion and efficiency 

3

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Introduction 

IIIIII  

IInnttrroodduuccttiioonn  

Key education indicators on social inclusion and efficiency 

4

    
  
  
  
  
  
  
  
  
 
 
 
Introduction 

BBaacckkggrroouunndd  

IInnttrroodduuccttiioonn  

The important role of education as an integral part of achieving economic and social cohesion was 
recognised in the Lisbon European Council, March 2000, which called for concrete objectives to 
be set to encourage cooperation in addressing issues of common concern in education and training 
across the European Union. Lifelong learning for everyone is included in the conclusions among 
key elements to achieve this objective.  

In March 2001, the Stockholm European Council agreed three strategic objectives: 

•  Improving the quality and effectiveness of education and training systems in the European 

Union; 

•  Facilitating the access to lifelong learning to all Europeans; 
•  Opening up education and training systems to the wider world. 

The Council also agreed on 13 associated objectives. The Council and the Commission adopted a 
detailed  work  programme  in  February  2002.  The  work  programme  set  out  the  key  issues  that 
needed  to  be  addressed  in  order  to  achieve  the  3  strategic  objectives  and  the  13  associated 
objectives. The report addressed various elements and levels of education and training from basic 
skills  to  vocational  and  higher  education  while  considering  the  principal  of  lifelong  learning. 
Furthermore, the Council could set benchmarks by consensus within the scope of articles 149 and 
150. Thus indicators are used for monitoring progress in all objective areas, while “benchmarks” 
function  as  reference  points  for  where  the  European  Union  should  be  in  2004  and  2010.  Most 
importantly  of  all,  they  point  to  areas  where  special  policy  efforts  are  necessary  to  improve 
education and training. 

In the Communication “European benchmarks in education and training: follow-up to the Lisbon 
Council”1, the Commission proposed five European benchmarks and invited the Council to adopt 
these benchmarks by 2003. Benchmarks were proposed in five areas central to the strategic goals 
set in Lisbon: 

•  Graduates in mathematics, science and technology; 
•  Population having completed secondary education; 
•  Key competencies; 
•  Lifelong learning. 

The proposal was followed up by the Council Conclusions on European benchmark. The Council 
set five European Benchmarks for the improvement of education and training systems in Europe 
up to 2010: 

1.  By 2010, and EU average rate of no more than 10 per cent of early school leavers should 

be achieved; 

1 COM(2002) 629 

Key education indicators on social inclusion and efficiency 

5

 
 
 
 
 
 
 
 
                                                 
Introduction 

2.  The  total  number  of  graduates  in  mathematics,  science  and  technology  in  the  European 
Union should increase by at least 15 per cent by 2010 while at the same time the level of 
gender imbalance should decrease; 

3.  By  2010,  at  least  85  per  cent  of  20  to  24 year  olds  in  the  European  Union  should  have 

completed upper secondary education; 

4.  By 2010, the percentage of low-achieving 15 year olds in reading literacy in the European 

Union should have decreased by at least 20 per cent compared to the year 2000; 

5.  By 2010, the European Union average level of participation in lifelong learning should be 

at least 12.5 per cent of the adult working age population (25-64 age group). 

These  benchmarks  are  defined  by  the  Council  as  “reference  levels  of  European  average 
performance”.  It  is  not  a  target  set  for  individual  countries,  but  a  common  European  target  of 
average performance. National governments are invited to consider, these so that European Union 
reaches the targets by 2010. 

Table 1 shows the indicators that are used for measuring progress for the benchmarks: 

Table 1: Indicators for measuring progress for the EU benchmarks 

No 
1 

2 

3 

4 

5 

Indicator 
Share  of  the  population  aged  18-24  with  only 
lower secondary education and not in education 
and training 
Total  number  of  tertiary  (5A,  5B  and  6) 
graduates 
from  mathematics,  science  and 
technology 
Percentage  of  those  aged  20  –24  who  have 
successfully completed at least upper secondary 
education (ISCED 3) 
Percentage  of  pupils  with  reading  literacy 
proficiency  level  1  and  lower  in  PISA  reading 
literacy scale 
25-64 
population 
Percentage 
participating  in  education  and  training  in  four 
weeks prior to the survey 

aged 

of 

Eurostat, EU-Labour Force Survey 

Source 

Eurostat, UOE data collection 

Eurostat, EU-Labour Force Survey 

OECD, PISA 

Eurostat, EU-Labour Force Survey 

The  use  of  indicators  for  monitoring  progress  in  the  follow-up  to  the  Lisbon  conclusions  is 
inherent  in  the  process.  Indicators  and  benchmarks  are  essential  for  the  implementation  of  the 
open  method  of  co-ordination  and  for  the  success  of  the  Lisbon  Strategy.  Without  valid  and 
comparable data, Member States would lack the information on the successes, and failures of their 
actions to support the attainment of the Lisbon objectives by 2010. 

Key education indicators on social inclusion and efficiency 

6

 
 
 
 
 
 
 
Introduction 

TThhee  ssttuuddyy  

At  the  present  time,  the  coverage  of  data  for  each  of  the  benchmark  indicators  is  not 
comprehensive, since there are statistical gaps even at EU Member State level. In order to be able 
to monitor the performance of each Member State and for the EU as a whole, it is essential that 
these gaps be filled.  

It  is  also  necessary  to  monitor  the  performance  of  the  EU-25,  in  relation  to  other  non-  EU 
countries, for example, India, China, and Russia, in relation to the number of Maths, Science and 
Technology (MST) graduates, and Japan and the USA in relation to the indicators on educational 
attainment of 20 to 24 year olds and early school leavers. Data on maths, science, and technology 
(MST) graduates for China , Russia, India are needed because they are forthcoming competitors in 
the  world  market  as  regards  the  development  of  knowledge  economies.  Data  on  educational 
attainment of 20 to 24 year olds and early school leavers for Japan and the US can help to identify 
good practice. Our  knowledge  of the  performance  of  these  non-EU countries is small to say the 
least. In order to compare the performance of the EU-25 with the non-EU countries it is necessary 
to  collect  data  that  meet,  to  a  certain  extent,  the  methodological  demands  of  the  benchmark 
indicators.   

In collecting data, especially on the non-EU countries, it is important to bear in mind that there are 
methodological disparities  in the way data  is  collected  at the national level, which at times  may 
render any comparison of benchmark performance problematic.   

Furthermore, there are gaps in our knowledge concerning the total expenditure from both public 
and  private  sources  on  education  both  inside  and  outside  educational  institutions.  Currently,  the 
coverage of information on total expenditure on educational institutions is generally well covered. 
Public  expenditure  on  education  outside  educational  institutions  is  also  well  covered.  However, 
our  knowledge  of  the  resources  spent  by  the  private  sector  (e.g.  households,  enterprises,  etc…) 
outside  of  education  institutions  is  fragmentary  and  methodological  issues  surrounding  the  data 
collected means that it is non-comparable.  

The  overall  objective  of  this  project  was  to  contribute  to  the  programme  Education  &  Training 
2010  by  supporting  the  statistical  and  analytical  work  linked  to  it  as  regards  the  key  education 
indicators.  The  specific  objective  was  to  fill  statistical  gaps  to  enable  the  analysis  of  questions 
related to social inclusion and efficiency of education spending.  

This  involved  supplementing,  combining  and  analysing  available  Eurostat,  OECD,  and  national 
data, as well as statistical information from other sources in order to produce comparable figures 
for the EU and other regions. Special emphasis will be placed on the indicators on social inclusion 
in  education  and  training,  regarding  four  of  the  indicators  related  to  European  education 
benchmarks: 

•  Share  of  the  population  aged  18  –  24  with  only  lower  secondary  education  and  not  in 

education and training; 

•  Total  number  of  tertiary  (5A,  5B  and  6)  graduates  from  mathematics,  science,  and 

technology; 

Key education indicators on social inclusion and efficiency 

7

  
 
 
 
  
 
Introduction 

•  Percentage  of  those  aged  22  who  have  successfully  completed  at  least  upper  secondary 

education (ISCED 3); 

•  Percentage  of  population  aged  25  –  64  participating  in  education  and  training  in  four 

weeks prior to the survey. 

Another  objective  of  the  study  is  to  combine  data  on  public  and  private  spending  on  education 
from  a  number  of  European  Statistical  Sources  (ESS)  and  other  sources  to  produce  a  figure  on 
total spending on education per country. 

The opinions expressed in this study are those of the authors and do not necessarily reflect 
the views of the Commission 

Key education indicators on social inclusion and efficiency 

8

 
 
 
Chapter 

1 

CCoommpplleettiinngg  ssttaattiissttiiccaall  ggaappss  ffoorr  
tthhee  bbeenncchhmmaarrkk  iinnddiiccaattoorrss  

 
 
 
 
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
Chapter 1: Completing statistical gaps for the benchmark indicators 

CCoommpplleettiinngg  ssttaattiissttiiccaall  ggaappss  ffoorr  tthhee    
bbeenncchhmmaarrkk  iinnddiiccaattoorrss    

11..11    IInnttrroodduuccttiioonn 

The  five  benchmarks  set  by  the  Council  in  May  2003  for  the  improvement  of  education  and 
training  systems  up  to  2010  are  monitored  through  5  related  indicators.  Unfortunately,  there  are 
still  a  number  of  statistical  gaps  for  a  number  of  countries  at  the  EU  level,  which  hinder  a 
calculation of the benchmark for the EU-25.  This is the case with the benchmark indicator on the 
number  of  maths,  science  and  technology  graduates,  as  data  is  missing  for  EL  for  a  number  of 
years.  In order to be able to compare the performance of the EU in relation to a number of non-EU 
competitor economies, it  is essential that data  on the  non-EU economies is  collected for  each  of 
these indicators.  For example, data on the number of MST graduates is already collected for Japan 
and the US, however it is known that China, India and Russia also have a high number of MST 
graduates.  Therefore  it  is  essential  to  compare  the  performance  of  the  EU  with  these  countries. 
There is no comparable information available on the performance of Japan and the US in relation 
to  the  indicators  on  educational  attainment,  early  school  leavers  and  participation  in  lifelong 
learning, so as to be able to compare with the EU.  Table 1.1 shows the benchmark indicators and 
the countries where statistical gaps need to be filled.  

Table 1.1: List of indicators and countries with data gaps 

Indicator 
Total  number  of  tertiary  (5A,  5B  and  6)  graduates  from  mathematics, 
science, and technology; 
Share of the  population aged 18 –  24 with only lower  secondary  education 
and not in education and training 
Percentage  of  those  aged  20  -24  who  have  successfully  completed  at  least 
upper secondary education (ISCED 3); 
Percentage of population aged 25 – 64 participating in education and training 
in four weeks prior to the survey. 

Country 
Greece, China,  
India, Russia 
Japan, USA 

Japan, USA 

Japan, USA 

As  a  first  step,  an  intensive  search  of  the  internet  was  conducted  in  order  to  locate  data  sources 
which could be used to complete the statistical gaps. For countries where we found no information 
or  a  limited  amount  of  information  on  the  internet,  we  tried  to  establish  contact  with  National 
Statistical Institutes in order to procure the data needed for the indicators.  

Upon completion of the data collection, we derived some indicators from the data collected. This 
included re-aggregating the data, where necessary to match the specific requirements of the data, 
such  as  specific  age  groups  or  sex.  In  one  case  (that  is  the  US)  this  involved  extracting  the 
appropriate data from a microfile and re-weighing the data to reflect the entire population. 

It was considered important that the data collected from various sources were comparable with the 
European  Statistical  System  (ESS).  Thus  concepts,  definitions  and  classifications  used  in  these 

Key education indicators on social inclusion and efficiency 

10

  
 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

data  sources  were  carefully  examined,  and  any  deviations  from  the  ESS  including  the  impact  of 
such deviation were carefully noted.  

In  some  instances  the  indicators  derived  for  some  countries  did  not  precisely  meet  the 
requirements  of  the  benchmark  indicators.  For  such  instances,  we  mentioned  why  this  was  the 
case, and we tried to determine whether this situation could be remedied through further statistical 
work. 

11..22    OOvveerrvviieeww  ooff  ffiinnddiinnggss  

It needs to be noted that there is no data collection at the international level that collects data for 
the following three indicators: early school leavers (age 18-24), upper secondary attainment (age 
20 – 24), and participation in lifelong learning (age 25-64).  Therefore data has to be collected at 
the national level. In contrast the UNESCO/OECD/Eurostat data collection on education systems 
collects  comparable  data  on  the  number  of  maths,  science  and  technology  graduates  for  the  EU, 
OECD  member  (e.g.  Japan  and  the  US)  and  World  Education  Indicator  (WEI)  programme 
participants (e.g. China, India and Russia) 

Table  1.2  presents  the  current  situation  as  regards  the  gap-filling  exercise  for  the  benchmark 
related indicators. Overall a number of national sources were identified containing data that could 
potentially be used to meet the data requirements needed for the benchmark related indicators. In a 
few  cases  data  gaps  identified  in  table  1.1  were  filled  (e.g.  US  –  upper  secondary  attainment  of 
young people aged 20 –24).  

In order to meet the data requirements of a few indicators contact needs to be established directly 
with the relevant authority since data is not publicly disseminated1. 

1 This is the case with Japan. 

Key education indicators on social inclusion and efficiency 

11

 
 
 
 
 
 
 
                                                 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Table 1.2: Reconciling information required for the indicators with data currently available 

Indicator 

Total  number  of  tertiary  (5A,  5B  and  6) 
graduates  from  mathematics,  science,  and 
technology; 

Country 

Greece 

Level of 
importance 
High 

Data availability 

Partially available 

China 

Medium 

Available 

India 

Medium 

Not available 

Share of the population aged 18 – 24 with 
only lower secondary education and not in 
education and training 

Russia 
Japan 

Medium 
High 

Partially available 
Partially available 

USA 

High 

Available 

Percentage  of  those  aged  20-24  who  have 
successfully  completed  at 
least  upper 
secondary education (ISCED 3); 

Japan 

High 

Available 

USA 

High 

Available 

Percentage  of  population  aged  25  –  64 
participating  in  education  and  training  in 
four weeks prior to the survey. 

Japan 

High 

Not available 

USA 

High 

Not available 

Observations 

Data has now been supplied to Eurostat for 2004. However data for 
2000  to  2003  is  missing.  No  source  at  the  national  level  has  been 
identified which can fill in the missing information for these years. 
Data  is  published  in  the  China  statistical  yearbook  of  the  National 
Bureau  of  Statistics.  However  there  are  questions  regarding 
comparability with the data in the ESS. 
The Institute of Manpower Research publishes some data concerning 
estimates of stock of graduates of science and engineering. However 
there are problems with coherence with other sources at the national 
level. 
Data is available for the year 2004 from UNESCO.  
The  Employment  Status  Survey  and  the  Population  Census  of  the 
Statistics  Bureau  of  Japan  collect  this  information.  Data  is  only 
disseminated for the age group 20 to 24.  
The  Census  Bureau  collects  this  information  through  the  Current 
Population  Survey.  Data  is  available  for  2000  to  2004  and  is 
disseminated on its’ website. 
Data concerning this indicator is collected through the Employment 
Status  Survey  and  the  Population  Census.  Data  is  available  for  the 
years 2000 and 2002. 
The  Census  Bureau  collects  this  information  through  the  Current 
Population  Survey.  Data  is  available  for  2000  to  2004  and  is 
disseminated on its’ website. 
The Statistics Bureau of Japan collects information on participation 
of  adults  in  education  through  the  ‘Survey  on  time  use  and  leisure 
activities’. Differences in definitions mean that the data collected do 
not fulfil the requirements for the indicator. 
The Adult Education and Lifelong Learning Survey of the National 
Household  Education  Surveys  Program  (NHES)  collected  data  on 
adult  education.  Differences  in  definitions  mean  that  the  data 
collected do not fulfil the requirements for the indicator. 

Key education indicators on social inclusion and efficiency 

12

 
Chapter 1: Completing statistical gaps for the benchmark indicators 

11..33      CCoommpplleettiinngg   ggaappss   ffoorr   tthhee   bbeenncchhmmaarrkk   iinnddiiccaattoorrss   oonn  
MMSSTT  ggrraadduuaatteess 

11..33..11    IInntteerrnnaattiioonnaall  ssoouurrcceess  

In  theory  the  UNESCO/OECD/Eurostat  collection  on  education  systems  gathers  comparable 
data on the number of maths, science and engineering graduates from all four  of the countries 
for which statistical gaps need to be filled2. However in reality the level of reporting by Greece, 
China, India and Russia is fragmentary. Tables 1.3 to 1.6 illustrate the current level of reporting.  

Table 1.3: Enrolment in higher education  

Country 

Greece 
China 
India 
Russia 

2000 
422.3 
7364.1 
9404.5 
: 

2001 
478.2 
9398.6 
9834.0 
: 

2002 
528.0 
12143.7 
10576.7 
8022.8* 

2003 
561.5 
15186.2 
11295.0 
8151.4* 

Unit Thousands 
2004 
597.0 
19417.0 
11852.9 
: 

Source: UNESCO/OECD/Eurostat collection 

Note:  
* Estimated by UNESCO-UIS 

Table 1.4: Enrolment in science and engineering  

Country 

Greece 
China 
India 
Russia 

2000 
: 
: 
: 
: 

2001 
: 
: 
1947.0 
: 

2002 
157.2 
: 
2124.1 
: 

Source: UNESCO/OECD/Eurostat collection 

2003 
: 
: 
: 
: 

Unit Thousands 
2004 
189.7 
: 
: 
: 

Table 1.5: Graduates in maths, science and technology 

Country 

Greece 
China 
India 
Russia 

2000 
: 
: 
: 
: 

2001 
: 
: 
: 
: 

2002 
: 
: 
: 
: 

2003 
: 
: 
: 
: 

Unit Thousands 
2004 
13.2 
: 
: 
436.1 

Source: UNESCO/OECD/Eurostat collection 

Note:  
*  It  should  be  noted  that  Greece  has  now  reported  data  on  the  number  of  MST  graduates  to  the 
UNESCO/OECD/Eurostat collection on education systems. 

2 Greece is both a member of the EU and the OECD, whilst China, India and Russia are World Education 

Indicators (WEI) programme participants. 

Key education indicators on social inclusion and efficiency 

13

 
 
 
 
 
 
 
                                                 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Table 1.6: Total graduates in all programs 

Unit Thousands 

Country 

Greece 
China 
India 
Russia 

2000 
: 
1776.0 
: 
1190.6 

2001 
39.0 
1804.7 
: 
1240.5 

2002 
43.7 
1948.1 
: 
1353.8 

2003 
: 
: 
: 
1536.4 

2004 
48.1 
: 
: 
1706.2 

Source: UNESCO/OECD/Eurostat collection 

11..33..22    NNaattiioonnaall  ssoouurrcceess  

A  number  of  sources  exist  at  the  national  level  that  could  potentially  provide  the  information 
necessary  to  construct  the  indicators  in  table  1.1.  These  sources  vary  by  country  and  by 
indicator.   

In some cases it may appear that the data needs can be met with data found in existing national 
sources. However, closer inspection of these national sources raises serious questions regarding 
the  ability  of  some  of  these  sources  to  provide  comparable  information  by  which  they  can  be 
compared  with  the  EU.  This  is  mainly  due  to  the  fact  that  the  concepts,  and  definitions 
employed  differ  from  those  in  the  European  Statistical  System.  In  some  instances  there  is  no 
coherence between national sources concerning the data. Nevertheless these national sources on 
their own can provide an interesting insight into the trends at national level. 

CChhiinnaa    

Higher education consists of comprehensive universities, teacher education schools, specialised 
universities and technical institutes. In addition, China has an extensive adult-education system. 
Institutions that  come  under  this category include  universities for workers  and  staff, radio and 
television universities, correspondence schools, night schools and self-study programs (students 
enrolled in self-study programs take exams course by course, and when all required courses are 
passed, university diplomas are awarded).  

Some data on the number of maths, science and technology graduates for China3 are published 
on  the  website  of  the  National  Bureau  of  Statistics  in  the  annual  publication  ‘Statistical 
yearbook’. Data on the number of graduates from regular higher education institutions is broken 
down by field of study for regular courses and short cycle courses of regular higher education 
institutions is available for the years 2000 to 2004 (see table 1.7). A short cycle course is a three-
year degree granted by universities and is not as rigorous as a four-year.  It is not true to say that 
the data presented in table 1.7 refer to ISCED level 5A and 5B for the simple reason that regular 
and  short  cycle courses are  ‘undergraduate’ level courses. Certain  programmes that would be 
classified as ISCED 5B are not included in this data, for example postgraduate master degrees. 
Therefore  in  order  to  compare  the  data  on  MST  graduates  for  China  with  the  EU-25,  data  is 
needed that covers  both  undergraduate and postgraduate degrees in higher education in China. 

3 The data only relate to the People’s Republic of China (PRC). 

Key education indicators on social inclusion and efficiency 

14

 
 
 
 
 
 
 
 
 
                                                 
Chapter 1: Completing statistical gaps for the benchmark indicators 

This will ensure that ISCED levels 5 and 6 are covered. Data for postgraduate degrees  (master 
and  PhD)  awarded  from  regular  higher  education  institutions  by  field  of  discipline  is  only 
available for 2004.  

Furthermore,  it  is  unclear  how  comparable  the  data  are  with  the  data  published  by  Eurostat, 
since  no  details  are  given  in  the  publication  of  the  precise  definitions  of    ‘science’  and 
‘engineering’. A recent study raised the question of what qualifies as an engineering program in 
China4.  According  to  the  Ministry  of  Education,  the  number  of  engineering  graduates  were 
obtained by adding the numbers of ‘engineering’ graduates as reported  by different provinces. 
These provinces were not required to report these degrees by major and there was no standard 
definition  of  engineering  between  the  provinces.  As  a  result,  any  bachelor’s  or  short-cycle 
degree  with  ‘engineering’  in  its  title  is  included  in  these  numbers,  regardless  of  the  degree’s 
field  or  the  academic  rigor  associated  with  it.  Wadwa  et  al  suggested  that  the  number  of 
engineers  produced  by  China  may  very  well  include  the  equivalent  of  motor  mechanics  and 
industrial technicians.  

Table 1.7: Graduates in mathematics, science, and technology disciplines from undergraduate 
courses in regular higher education institutions  

Variable 

Total no of MST graduates 
Graduates in MST % of all fields 
Students  enrolled  in  MST  %  of 
all students 

2000 
452.5 
47.6 

48.3 

2001 
464.9 
44.9 

44.6 

2002 
591.3 
41.2 

43.6 

2003 
817.1 
43.5 

42.4 

Unit Thousands 
2004 
1019.6 
42.6 

41.5 

Source: National Bureau of Statistics ‘China Statistical Yearbook’ 

Table 1.8: Share of graduates in science and engineering as a percentage of all MST graduates from 
undergraduate courses in regular higher education institutions (in percentages)   

MST field 
Science 
Engineering 

2000 
21.7 
78.3 

2001 
24.9 
75.1 

2002 
22.2 
77.8 

2003 
21.2 
78.8 

2004 
20.3 
79.7 

Source: National Bureau of Statistics ‘China Statistical Yearbook’ 

IInnddiiaa    

There are three principle levels of qualifications within the higher education system in India: 

•  Bachelor / Undergraduate level 
•  Master's / Post-graduate level 
•  Doctoral / Pre-doctoral level 

Diploma courses are also available at the undergraduate and postgraduate level.  

Administrative data  on the number of graduates broken down by field is not readily available.  
The  data  that  is  frequently  citied  in  various  reports  tends  to  be  based  on  surveys  of  the 

4  Wadwa  et  al,  ’Framing  the  Engineering  Outsourcing  Debate:  Placing  the  United  States  on  a  Level 
Playing Field with China and India’, 2005 

Key education indicators on social inclusion and efficiency 

15

 
 
 
 
 
 
 
 
 
                                                 
Chapter 1: Completing statistical gaps for the benchmark indicators 

population  or  on  projections,  and  not  on  actual  administrative  records  of  higher  education 
institutions.  In  consequence  there  is  no  coherence  between  different  sources  for  certain 
variables.  The  following  three  sources  containing  information  on  MST  graduates  were 
identified: 
i.  University  Grants  Commission  (UGC)  -  The  UGC  is  the  only  grant-giving  agency  in  the 
country  vested  with  two  responsibilities:  that  of  providing  funds  and  that  of  coordination, 
determination and maintenance of standards in institutions of higher education; 

ii.  Institute  of  Manpower  Research  (IAMR)  annual  publication  ‘Manpower  profile  –  India 
yearbook  2004’-  provides  data  on  out-turn  at  diploma  and  degree  level  of  engineering 
graduates. Data presented in the publication are the latest available up to November 2004. 
Data  are  collected  from  a  number  of  sources  including  the  Ministry  of  Human  Resource 
Development,  National  Technical  Manpower  Information  System  (NTMIS),  and  All  India 
Council for Technical Education (AICTE). It also estimates the stock of various categories 
of manpower. The estimated stock figures are based on census data; 

iii.  The  National  Association  of  Software  and  Service  Companies  (NASSCOM)  -  projections 

are based on numbers from three sources: 

-  The Institute of Applied Manpower Research's annual publication, "Manpower 
-  Profile India"; 
-  The Ministry of Human Resource Department's Annual Report; 
-  IndiaStat.com. 

Together, these sources provide data with a three to four year lag. To extrapolate 2004 data, 
NASSCOM estimates labour supply numbers based on historical compound annual growth 
rates (CAGR). 

Table 1.9: Number of students enrolled in higher education in India 

Unit thousands 

Data Source 

UGC 

IAMR 

1999 

7705 

7815 

2000 

8051 

9158 

2001 

8399 

9963 

2002 

8821 

8564 

2003 

9228 

9227* 

Notes:  
*  Data taken from UGC 
It is interesting to note that the data supplied to UNESCO by India is much higher for all years 
compared  to  the  data  available  in  India  from  UGC  and  IAMR  (see  table  1.9).  This  inevitably 
raises the question of data reliability. The fact that they are sometimes not comparable does not 
necessarily reflect on their quality and reliability for the purpose for which they were compiled. 
In the case of the number of students enrolled in science and engineering the numbers reported 
by UGC and IAMR (see table 1.10) are higher than those reported to UNESCO. 

Key education indicators on social inclusion and efficiency 

16

 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Table 1.10: Number of students enrolled in science and engineering in India 

Unit thousands 

Data Source 

UGC 

IAMR 

1999 

1817.4 

: 

2000 

1866.1 

: 

2001 

2103.5 

2127.6 

2002 

2359.7 

: 

2003 

2527 

: 

Notes:  
The data was not broken down by subject discipline. 

Whilst the breakdown of enrolments in science by discipline is not provided in any source, the 
breakdown  by  discipline  is  provided  for  engineering  graduates  by  IAMR.  In  the  case  of 
graduates at diploma level of engineering and technology, the data includes disciplines such as 
‘hotel management’. The number of diploma graduates of hotel management has been removed 
from the final number of engineering graduates in table 1.12. 

There  also  appear  to  be  differences  between  institutions  concerning  the  disciplines  that  are 
classified  as  ‘science’.  The  Department  of  Science  and  Technology  in  India  publishes  data  on 
enrolments  in  science  and  engineering  based  on  data  collected  from  the  University  Grants 
Commission.  However  the  Department  of  Science  and  Technology,  classifies  ‘medicine’  and 
‘agriculture’ under “science”. 

Table 1.11: Number of students admitted to engineering in India 

Data Source 

Degree 

Diploma 

Total 

1999 

185.4 

100.9 

2000 

197.1 

159.6 

2001 

262.9 

201.1 

2002 

305.4 

185.0 

286.3 
490.3 
Source: IAMR – ‘Manpower profile India yearbook 2004’ 

356.6 

464.0 

Unit thousands 

2003 

: 

: 

: 

Notes:  
The data was not broken down by subject discipline.  

Table 1.12: Number of engineering graduates (holders of degree and diploma) in India 

Data Source 

IAMR* 

NASSCOM 

1999 

154.1 

: 

2000 

166.1 
: 

2001 

196.7 

201.2 

2002 

205.4 

233.9 

Unit thousands 

2004 

2003 

: 

259.1 

215.0 

Notes:  
*  Data for the graduates at diploma level of hotel management have been removed 

It  is  necessary  to  point  out  that  there  are  serious  discrepancies  concerning  the  number  of 
engineering graduates published in ‘Manpower profile India’ and data found in a publication by 
the  Centre  for  Organization  Development5,  (see  table  1.13).  The  Centre  for  Organization 

5 Final Report on Women in Information Technology, December 2004 

Key education indicators on social inclusion and efficiency 

17

 
 
 
 
 
 
 
 
 
                                                 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Development quotes as the source of information exactly the same sources used in ‘Manpower 
profile India’.  

Table 1.13: Number of engineering graduates at degree level in India 

Data Source 

IAMR 

COD** 

% Difference 

1999 

72247 

75177 

4.1 

2000 

74323 
82107 

10.5 

2001 

94639 

109376 

15.6 

2002 

101914 

124469 

22.1 

2003 

: 

2004 

: 

141646 

184347 

: 

: 

Notes:  
* Centre for Organization Development,  

The  Institute  of  Applied  Manpower  Research  (IAMR)  gives  estimates  of  the  stock  of  the 
population  with  a  higher  education.  It  covers  all  of  the  higher  education  system,  except  for 
professional postgraduates for which data are not available. Professional postgraduates include 
‘engineering and technology’. The estimated stock figures are based on census data. The outflow 
figures  are  calculated  by  applying  appropriate  wastage/stagnation  rates  to  the  estimated 
enrolment at different levels of education. The stock of educated persons as given by the IAMR 
is  defined  as  the  “Total  number  of  persons  within  each  of  the  educational  categories  at  any 
given  time,  either  employed  in  the  production  process  or  looking  for  work  or  not  currently 
involved in the labour force.” 

Tables 1.14 and 1.15 show the stock of graduates in science and technology respectively. A very 
rough estimate of the number of graduates can be calculated based on the stock.  

Table 1.14: Estimated stock of science graduates and postgraduates in India 

Unit thousands 

Data Source 

Graduates 

Postgraduates 

Total 

1999 

3655.4 

730.6 

7386 

2000 

3837.7 

767.1 

4604.8 

2001 

4024.9 

805.0 

4829.9 

2002 

2003 

2004 

: 

: 

: 

: 

: 

: 

: 

: 

: 

Source: IAMR – ‘Manpower profile India yearbook 2004’ 

Notes: Stock is taken at the beginning of the year 

Table 1.15: Estimated stock of engineers by type of qualification in India 

Unit thousands 

Data Source 

Degree 

Diploma 

Total 

1999 

913.7 

1379.5 

2293.2 

2000 

969.5 

1456.0 

2425.5 

2001 

1024.4 

1531.7 

2556.1 

2002 

1078.3 

1606.7 

2685.0 

2003 

1183.2 

1720.5 

2903.7 

2004 

: 

: 

: 

Source: IAMR – ‘Manpower profile India yearbook 2004’ 

Notes: Stock is taken at the beginning of the year 
Data does not include postgraduates  

Key education indicators on social inclusion and efficiency 

18

 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Table  1.16  shows  a  very  rough  estimate  of  the  number  of  MST  graduates  based  on  available 
statistics of the number of graduates in science and engineering. 

Table 1.16: Number of MST graduates in India 

Discipline 

Science (1) 

Engineering (2) 

Engineering (3) 

Total (1) +(2) 

Total (1)+(3) 

1999 

218.8 

132.3 

154.1 

351.1 

372.9 

2000 

225.1 

130.6 

166.1 

355.7 

391.3 

2001 

: 

128.9 

196.7 

: 

: 

2002 

: 

218.7 

205.4 

: 

: 

Source: IAMR – ‘Manpower profile India yearbook 2004’ 

Notes: Estimated by EU-RA based on data compiled by IAMR.  
(1) Calculated based on the stock of graduates and postgraduates in the population. 
(2) Calculated based on the stock of graduates in the population. 
(3) Calculated based on the number of graduates. 

Unit thousands 

2003 

2004 

: 

: 

: 

: 

: 

: 

: 

: 

: 

11..44      CCoommpplleettiinngg   ggaappss   ffoorr   tthhee   bbeenncchhmmaarrkk  iinnddiiccaattoorrss   ffoorr  
JJaappaann  aanndd  tthhee  UUSS 

This section examines sources at the national level that could be used to complete statistical gaps 
for Japan and the US for the three benchmark indicators: educational attainment (20 to 24); early 
school leavers (18 to 24) and participation in lifelong learning (25 to 64). 

11..44..11      PPeerrcceennttaaggee   ooff   tthhoossee   aaggeedd   2200--2244   wwhhoo   hhaavvee   ssuucccceessssffuullllyy   ccoommpplleetteedd  
aatt  lleeaasstt  uuppppeerr  sseeccoonnddaarryy  eedduuccaattiioonn  ((IISSCCEEDD  33))  

The EU indicator on youth educational level is defined as the percentage of young people aged 
20-24 years having attained at least upper secondary education, that is with an education level 
ISCED  3a,  3b  or  3c  long  minimum  (numerator).  The  denominator  consists  of  the  total 
population  of  the  same  age  group,  excluding  no  answers  to  the  questions  'highest  level  of 
education or training attained’. It needs to be noted that from 27 October 2006, this indicator is 
based  on  annual  averages  of  quarterly  data  instead  of  one  unique  reference  quarter  in  spring. 
This improves both the accuracy and reliability of the results thanks to a better coverage of all 
weeks of the year and an increased sample size. 

OOvveerrvviieeww ooff  ddaattaa  ccoolllleecctteedd 

Table 1.17 summarises the data collected from national sources in Japan and the USA that met 
the requirements for this indicator. 

Key education indicators on social inclusion and efficiency 

19

 
 
 
 
 
 
 
 
 
 
  
Chapter 1: Completing statistical gaps for the benchmark indicators 

Table 1.17: Overview of data collected on youth educational attainment for Japan and the USA 

2000 
85.5 
91.8 

2001 
84.8 
: 

USA 
Japan 
Sources: Statistics Japan, ‘Population Census’ and ‘Employment Status Survey’, and US Census Bureau ‘ 
Current Population Survey’ 

2002 
84.5 
94.3 

2003 
85.6 
: 

2004 
85.9 
: 

DDaattaa  ccoolllleecctteedd  aatt  tthhee  iinntteerrnnaattiioonnaall  lleevveell  

At  the  international  level,  the  OECD  Education  at  a  Glance  publishes  data  on  the  educational 
attainment of the population both in Japan and the USA.  The data published do not correspond 
to the age group 20 –24 (see table 1.18). 

Table 1.18: Population that has attained at least upper secondary education in 2004, total and 
broken down by gender in percentages in Japan and the USA 

Male 
Female 
Total 
Male 
Female 
Total 

Country 

Japan 

USA 

UUSSAA  

25-64 
84 
84 
84 
87 
89 
88 

25 – 34 
92 
96 
94 
86 
88 
87 
Source: OECD ‘Education at a Glance 2006’ 

35-44 
93 
95 
94 
87 
89 
88 

45 –54 
82 
83 
82 
90 
90 
90 

55- 64 
67 
63 
65 
86 
87 
86 

The  US  Census  Bureau’s  ‘Current  Population  Survey’  can  adequately  meet  the  data 
requirements  for  this  indicator.  Data  is  available  broken  down  by  sex  and  for  the  single  age 
group  22  and  is  freely  disseminated  on  its  website.  Figure  1.1  shows  the  percentage  of  those 
aged 20 to 24 who have successfully completed at least upper secondary education. 

Figure 1.1: Percentage of those aged 20 to 24 who have successfully completed at least upper 
secondary education (ISCED 3) 

100

90

80

70

60

50

40

30

20

10

0

d
e
y
r
a
d
n
o
c
e
s

r
e
p
p
U

)
4
2
-
0
2
(

%

2000

2001

2002

2003

2004

Source: US Census Bureau, ‘Current Population Survey’ 

Key education indicators on social inclusion and efficiency 

20

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

JJaappaann   

The Statistics Bureau  of  Japan  conducts  two  surveys, which  can  provide  some information  on 
the educational attainment of the population. These are: 
i.  Population Census - The ‘Population Census’ of Japan collects is conducted once every five 
years. The last survey was conducted in 2005. The survey collects background information 
on  educational  status  and  attainment  of  the  respondent.  The  latest  year  for  which  data  is 
available on the internet is 2000. The data published on the website of the Statistics Bureau 
of  Japan  do  not  distinguish  between  upper  secondary  (ISCED  3)  and  lower  secondary 
(ISCED 2) education. However, the information required in order to meet the needs of this 
indicator can be requested directly from the Statistics Bureau of Japan 

Table 1.19: Population (aged 20-24) that has attained at least upper secondary education in 2000, 
total and broken down by gender in Japan 

Total 

Males 

Females 

91.8 

93.7 
Source: Statistics Japan, ‘Population Census’ 

89.8 

Notes 
The figures presented do not include persons whose type of last school completed was not reported in the 
survey. 

ii.  Employment  Status  Survey  -  The  Statistics  Bureau  of  Japan  conducts  ‘The  Employment 
Status Survey’ every 5 years. The survey aims to obtain basic data on the actual conditions 
of the employment structure, changes in usual labour force status, wish for work, etc. at both 
national and regional levels by surveying usual labour force status in Japan.  The survey also 
collects background information on the educational status and attainment of the respondent 
(e.g.  attending  or  graduated  from  an  educational  institution).  The  Statistics  Bureau  do  not 
disseminate  this  level  of  information  on  its  website.    However  this  information  can  be 
requested  directly  from  the  Statistics  Bureau.  Figure  1.2  shows  the  percentage  of  the 
population that has attained at least upper secondary education in 2002  

Figure 1.2: Population (aged 20 to 24) that has attained at least upper secondary education in 2002, 
total and broken down by gender in Japan 

Female

Male

Total

91

92

93

%

94

95

96

Source: ‘Employment Status Survey’, Statistics Bureau of Japan as reproduced on Web Japan 

Key education indicators on social inclusion and efficiency 

21

 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

11..44..22      SShhaarree   ooff   tthhee   ppooppuullaattiioonn   aaggeedd   1188   ––   2244   wwiitthh   oonnllyy   lloowweerr   sseeccoonnddaarryy  
eedduuccaattiioonn  aanndd  nnoott  iinn  eedduuccaattiioonn  aanndd  ttrraaiinniinngg  

This EU indicator is otherwise known as the indicator on early school leavers is defined as the 
percentage  of  the  population  aged  18-24  with  at  most  lower  secondary  education  and  not  in 
further education or training. It refers to persons aged 18 to 24 in the following two conditions: 
the highest level of education or training attained is ISCED 0, 1, 2 or 3c short and respondents 
declared not having received any education or training in the four weeks preceding the survey 
(numerator). The denominator consists in the total population of the same age group, excluding 
no answers to the questions 'highest level of education or training attained’ and ‘participation to 
education and training’. 

OOvveerrvviieeww of data collected 

Data  sources  exist  in  Japan  and  the  USA,  which  can  give  an  indication  of  the  share  of  the 
population  with  a  low  level  of  educational  achievement.  Table  1.20  summarises  the  data 
collected  from  national  sources  in  Japan  and  the  USA  that  met  the  requirements  for  this 
indicator. 

Table 1.20: Overview of data collected on low educational attainment for Japan and the USA for 
the age group 18 to 24 

2000 
12.4 
: 

2001 
13.1 
: 

2002 
12.3 
: 

USA 
Japan 
Sources: Statistics Japan, ‘Population Census’ and ‘Employment Status Survey’, and US Census Bureau ‘ 
Current Population Survey’ 

2004 
12.1 
: 

2003 
11.8 
: 

JJaappaann   

The  Statistics  Bureau  of  Japan  conducts  two  surveys,  which  can  provide  information  on  the 
number of early school leavers. These are: 
i.  Population Census - The ‘Population Census’ undertaken by the Statistics Bureau of Japan 
collects  this  information.  There  are  a  number  of  points  that  need  to  be  pointed  out 
concerning this source.  
-  The survey is only conducted once every five years. 2000 is the latest available year for 

which data is available on the internet; 

-  The Statistics Bureau only disseminates data for a narrower age group, that is 20 – 24, 

as compared to the age group of 18 – 24 required by the indicator; 

-  The Statistics Bureau website publishes data which has aggregated upper secondary and 
lower secondary under one category of secondary education. Thus data is available for 
the  share  of  the  population  with  only  primary  education.  However  data  broken  down 
into lower and upper secondary education can be requested directly from the Statistics 
Bureau; 

-  The  population  which  is  in  education  and  training  refers  to  those  students  who  are  in 
regular  or  formal  education,  that  is  senior  high  school,  junior  college,  or  college  or 
university including graduate school. In contrast the data collected from the EU-LFS for 

Key education indicators on social inclusion and efficiency 

22

 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

the EU Member States refers to participation in formal and non-formal education (e.g. 
language courses, computer courses, seminars etc…). 

Table 1.21: Share of the population aged 20 – 24 with only lower secondary education and not in 
education and training (2000) in Japan 

Total 

8.2 

Males 

10.2 

Females 

6.3 

Notes: 
The figures presented here  do not include persons whose  type of school  completed is not reported. The 
2000  Population  Census  was  taken  as  of  0:00  a.m.,  1  October  2000.  The  data  refer  to  persons  who 
completed primary education and persons who never attended school.  

ii.  Employment  Status  Survey  –  This  survey  conducted  by  the  Statistics  Bureau  of  Japan 
conducted every 5 years has the potential to calculate the indicator. However as is the case 
with  the  ‘Population  Census’  data  is  only  available  for  the  age  group  20  to  24  and  the 
population which is in education and training refers to those students who are in regular or 
formal education, that is senior high school, junior college, or college or university including 
graduate school. 

The data disseminated by the Statistics Bureau of Japan does not meet the full requirements for 
this indicator since the data refer to the age group 20 to 24 instead of 18 to 24. 

‘Education at a Glance 2005’ published by the Ministry of Education publishes data concerning 
the number of upper secondary school dropouts, which can give us an indication of the number 
of early school leavers. 

Figure 1.3: Trends in number of upper secondary school dropouts in Japan 

s
t
u
o
p
o
r
d
f
o
o
N

120000

100000

80000

60000

40000

20000

0

1998

1999

2000

2001

2002

No of dropouts 
Dropout rate (%) 

1998 
111372 
2.6 

1999 
106578 
2.5 

2000 
109146 
2.6 

2001 
104894 
2.6 

2002 
89409 
2.3 

Notes: 
‘Education at a Glance’, Ministry of Education (MEXT), Japan. Data was taken from the Survey on the 
State of Dropouts in Upper Secondary Schools 

Key education indicators on social inclusion and efficiency 

23

 
 
 
 
 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

UUSSAA 

The US National Center for Education Statistics normally publishes data on three different types 
of dropout rates6: 

i.  Event  dropout  rates  estimates  the  percentage  of  both  private  and  public  high  school 
students who left high school between the beginning of one school year and the beginning 
of  the  next  without  earning  a  high  school  diploma  or  its  equivalent  (e.g.,  a  General 
Educational Development certificate, or GED). It can be used to track annual changes in 
the experiences of students in the U.S. school system. The indicator refers to the age group 
15  to  24-year-olds  who  dropped  out  of  grades  10–12  during  the  year  preceding  data 
collection.  

ii.  Status dropout rates reports the percentage of individuals in a given age range who are 
not  in  school  and  have  not  earned  a  high  school  diploma  or  equivalency  credential, 
irrespective  of  when  they  dropped  out.  The  rate  focuses  on  an  overall  age  group  as 
opposed  to  individuals  in  the  U.S.  school  system,  so  it  can  be  used  to  study  general 
population  issues.  Status  rates  are  higher  than  event  rates  because  they  include  all 
dropouts in  a given age range, regardless of when they last attended school. Status rates 
also count individuals who never attended school, and immigrants who did not complete 
the  equivalency  of  a  high  school  education  in  their  home  country  as  a  dropout.  For  the 
purpose of national indicators the status rate measures individuals ages 16 through 24 who 
are not enrolled in school and who have neither earned a high school diploma nor obtained 
an alternative high school credential, such as a General Educational Development (GED) 
certificate. The GED program, sponsored by the American Council on Education, enables 
individuals to demonstrate that they have acquired a level of learning comparable to that 
of  high  school  graduates.  High  school  equivalency  diplomas  are  considered  valid 
completion credentials in the US and they give access to post-secondary education. Thus 
they  are  classified  as  an  ISCED  3  level  qualification.  It  has  been  argued  by  some 
researchers  that  if  individuals  holding  a  GED  qualification  were  counted  as  dropouts, 
since they did not hold a high school diploma, then the status rate would be much higher. 
For example, recent studies suggest that the dropout rate may be closer to one-quarter of 
entering freshmen7. Table 1.22, shows the number of 18 to 24 year olds, who received a 
GED,  by  data  source  from  1990  to  2002.  Examination  of  the  changes  in  the  CPS  GED 
items  in  the  October  2000  and  subsequent  surveys  has  indicated  that  GED  estimates  for 
2000 and later years are not comparable with earlier data and may not be reliable estimates 
of high school equivalency completions. Even for the years 1996 to 1999, the estimate of 
18  to  24  year  olds  who  received  a  GED  qualification  was  overestimated.  Table  1.23, 
shows the percentage of 18 to 24 year olds who received a GED qualification according to 
the  CPS,  and the  GED service. It is  clear that the  percentage  of  18 to 24 year  olds who 
received a GED qualification is very small. 

6  For  further  information  see  Kaufman,P;  Alt,  M.  N;  MPR  Associates,  Inc;  Chapman,  C.  D.”Dropout 
Rates in the United States: 2001”, U.S. Department of Education, Institute of Education Sciences, 2004 
7 Sum and Harrington 2003, ‘The Hidden Crisis in the High School Dropout Problems of Young Adults in 
the U.S.: Recent Trends in Overall School Dropout Rates and Gender Differences in Dropout Behavior’ 
Greene and Forster 2003, ‘Public High School Graduation and College Readiness Rates in the United 
States’. Education  Working Paper 3. New York, N.Y.: Manhattan Institute. 

Key education indicators on social inclusion and efficiency 

24

 
 
 
                                                 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Table 1.22: Number of 18 to 24 year olds, who received a GED, by data source: 1990 through 2002 

Year  GED Service1 
1990 
1991 
1992 
1993 
1994 

222,295 
247,767 
249,470 
241,787 
247,051 

1995 
1996 
1997 
1998 
1999 

2000 
2001 
2002 

256,441 
258,957 
244,749 
254,239 
267,932 

263,465 
342,156 
176,291 

CPS2, 3 
111,023 
117,371 
107,030 
107,415 
211,560 

237,876 
312,645 
286,811 
340,784 
320,187 

90,810 
107,202 
70,745 

Standard Error 
(CPS) 
16,728 
17,197 
16,425 
16,455 
23,047 

24,424 
27,957 
26,793 
24,790 
27,331 

24,831 
28,249 
12,111 

Source: U.S. Department of Commerce, Bureau of the Census, Current Population Survey (CPS) (various 
years); and American Council on Education, GED Testing Service, GED 

Notes: 
1  These numbers represent 18 to 24 year olds who passed the GED examination in the United 
States. 
2  Estimates  of  the  number  of  GEDs  from  the  Current  Population  Survey  (CPS)  may  include 

alternative credentials other than those earned by passing the GED examination. 
3  Starting in 2000, estimates reflect changes made to questions about GED receipt. 

Table 1.23: Percentage of 18 to 24 year olds who received a GED qualification according to the CPS, 
and the GED service 

Source 
CPS1 
GED Service2 

1997 
1.1 
: 

1998 
1.4 
1.0 

1999 
1.2 
1.0 

2000 
0.5 
1.0 

2001 
0.4 
1.3 

2002 
0.3 
0.6 

Source: U.S. Department of Commerce, Bureau of the Census, Current Population Survey (CPS) (various 
years); and American Council on Education, GED Testing Service, GED 

Notes: 
1  Calculated based upon the data from the CPS of the US Census Bureau. 
2    Calculated  based  on  data  from  the  GED  service  and  projections  of  the  Total  Resident 

Population by 5 year age groups:  Middle Series – reference date July 

Nevertheless  it  should  be  pointed  out  that  the  status  rate  is  designed  to  report  the 
percentage  of  youth  and  young  adults  in  the  United  States  who  lack  what  is  now 
considered a basic level of education. The status rate should not be used as an indicator of 
the performance of U.S. schools because it counts as dropouts individuals who may have 
never attended a U.S. school8. 

iii.  Cohort dropout rates measure what happens to a group of students over a period of time. 
These rates are based on repeated measures of a particular cohort of students with shared 
experiences; they show how many students starting in a specific grade drop out over time. 

8 ‘The Condition of Education- supplemental note”, NCES 

Key education indicators on social inclusion and efficiency 

25

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Unlike event rates that measure the percentage of persons dropping out over a single time 
period  (typically  one  school  year),  cohort  rates  measure  the  percentage  of  persons 
dropping out over longer periods of time and over multiple periods of time (over 2 years, 4 
years,  etc).  Cohort  rates  require  data  from  longitudinal  collections.  The  National 
Education Longitudinal Study of 1988 provided cohort dropout data. New cohort data was 
collected in 2004 with the first follow-up to the Educational Longitudinal Study of 2002. 

The US also publishes statistics on status completion rates, which can also provide information 
regarding dropouts and covers the age group 18 to 24 years old: 

•  Status  completion  rates  indicates  the  percentage  of  individuals  in  a  given  age  range 
who are not in high school and who have earned a high school diploma or equivalency 
credential,  irrespective  of  when  the  credential  was  earned.  It  is  important  to  note  that 
those  still  in  high  school  are  excluded  from  the  calculation.  The  indicator  on  status 
completion  rates  represents  the  percentage  of  18  to  24  year  olds  who  have  left  high 
school  and  earned  a  high  school  diploma  or  the  equivalent,  including  a  General 
Educational Development (GED) credential.  

The  indicator  can  be  easily  transformed  to  calculate  the  percentage  of  dropouts.  However,  the 
data  obtained  from  this  transformation  cannot  be  compared  to  the  EU  indicator  for  the  simple 
reason that those still in high school are excluded from the base population. Table 1.24 shows 
the status completion rates and the calculation and the corresponding dropout rate calculated on 
the basis of completers. 

Table 1.24: Status completion rates, and number and distribution of completers ages 18-24 not 
currently enrolled in high school or below in the USA 

Year 

Dropout 
rates* 

Population 
(thousands) 

13.5 
13.5 
13.4 
12.8 
Source: National Center for Education Statistics, ‘Dropout rates in the United States 2002 and 2003’ 

25,138 
25,543 
25,697 
25,831 

2000 
2001 
2002 
2003 

Status 
Completion 
rate 
86.5 
86.5 
86.6 
87.1 

Number of 
completers 
(thousands) 
21,743 
22,084 
22,249 
22,508 

Notes: 
*        Calculated by EU-RA based on the status completion rates  

Whilst  conceptually  the  status  completion  and  status  dropout  rates  are  related,  they  are  not 
perfectly  complementary.  In  the  US  individuals  can  legally  drop  out  of  high  school  in  many 
states at  age 16, thus the indicator on status dropout age range starts at 16. Since most people 
graduate  from  high  school  when  they  are  18,  the  status  completion  rate  starts  at  age  18.  In 
addition, the status dropout rate includes all 16 to 24 year olds, whereas the status completion 
rate excludes those still enrolled in high school. Hence, the base populations used are different.  

It can be concluded that the US indicator on status dropout rates is the closet in definition to the 
EU  indicator  on  early  school  leavers,  than  the  other  published  indicators  on  dropouts. 
Unfortunately,  the  indicator refers to  the  age  group  16 to  24, whilst the EU  indicator on  early 
school leavers refers to the age group 18 to 24. This difference between age groups renders any 
direct comparison to the EU indicator problematic. The Current Population Survey (CPS) is the 

Key education indicators on social inclusion and efficiency 

26

 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

data source that is used to construct the indicators on status dropout and completion rates. Data 
collected  in  the  Current  Population  Survey  (CPS)  conducted  by  the  Census  Bureau,  is 
disseminated by age. Thus the share of the population with only lower secondary education and 
not in education or training for the age group 18 - 249 can be calculated.  This is in fact the US 
definition  of  status  dropout  rates,  but  calculated  for  the  age  group  18-24.  Data  for  other  age 
groups can also be easily extracted.  

Figure 1.4 shows the share of the population aged 18 – 24 with only lower secondary education 
and not in education and training based on the status completion rates and on the EU definition 
of early school leavers. The percentage of dropouts calculated using the status completion rates 
are higher than the dropouts using the EU definition because the population in the base is lower 
by excluding 18 to 24 years olds who are enrolled in high school. 

Figure 1.4: Share of the population aged 18 – 24 with only lower secondary education and not in 
education and training based on the status completion rates and on the EU definition of early school 
leavers in the US 

s
t
u
p
o
r
D
%

14

13.5

13

12.5

12

11.5

11

10.5

2000

2001

2002

2003

Dropout_status

Droputs_EU

Source: National Center for Education Statistics, ‘Dropout rates in the United States 2002 and 2003’and 
the Census Bureau 

Notes: 
Dropout_status        Calculated by EU-RA based on the status completion rates  
Dropouts_EU         Calculated by EU-RA using the EU definition of early school leavers based on data 

from the Census Bureau’s ‘Current Population Survey’ 

It should be noted that calculating the percentage of early school leavers in the US based on data 
from  the  CPS  means  that  the  population,  which  is  in  education  and  training  refers  to  those 
students  enrolled  in  formal  education,  that  is  elementary,  high  school  and  college.  The  school 
enrolment statistics from the CPS are based on replies to the interviewer’s inquiry whether the 
person  was  enrolled  in  regular  school.  Such  schools  include  nursery  schools,  kindergartens, 
elementary  schools,  high  schools,  colleges,  universities,  and  professional  schools.  Attendance 
may be on either a full-time, or part-time basis and during the day or night. Regular schooling is 

9  CPS  data  include  weights  to  help  make  estimates  from  the  data  representative  of  the  civilian,  non-
institutionalised population in the United States. These weights are based on decennial Census data that 
are adjusted for births, deaths, immigration, emigration, etc. over time. 

Key education indicators on social inclusion and efficiency 

27

 
 
 
 
 
                                                 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

that  which  may  advance  a  person  toward  an  elementary  or  high  school  diploma,  a  college, 
university,  or  professional  school  degree.  Enrolment  in  schools,  which  are  not  in  the  regular 
school  system,  such  as  trade  schools,  business  colleges,  and  schools  for  the  mentally 
handicapped, which do not advance students to regular school degrees, is not included. People 
enrolled  in  classes,  which  do  not  require  physical  presence  in  school,  such  as  correspondence 
courses or other courses of independent study, and in training courses given directly on the job, 
are  also  excluded  from  the  count  of  those  enrolled  in  school,  unless  such  courses  are  being 
counted for credit at a regular school.  

In contrast the data collected from the EU-LFS for the EU Member States refers to participation 
in formal and non-formal education (e.g. language courses, computer courses, seminars etc…).  
A simple solution to the problem is to adjust the data on early school leavers from the EU-LFS 
by only counting those that are participating in formal education10. Persons participating in non-
formal  education11  will  not  be  classified  as  participating  in  education  and  training.  This  will 
make the statistics comparable with the US.  

It can be argued that a person aged 18-24 who dropped out of school early and then participates 
in  education  of  a  non-formal  nature  will  still  be  disadvantaged.  This  disadvantage  will  most 
notably manifest itself on the labour market, as the early school leaver that participates in non-
formal  learning  will  still  not  have  the  formal  qualifications  that  an  employer  demands. 
Moreover,  for  statistical  purposes,  the  early  school  leaver  participating  in  non-formal  learning 
will still be classified as an early school leaver once the non-formal learning finishes. It should 
be  noted  that  the  Eurostat  Working  Group  on  Employment  Statistics12  proposed  that  the 
definition  of  the  indicator  should  be  restricted  to  participation  in  regular  education.  This 
proposal was later reiterated in the Eurostat Expert Group ‘Education in the EU-LFS’13. 

Table 1.25, shows the EU indicator on early school leavers according to the existing definition 
and according to a restricted definition which covers only participation in formal education for 
all EU countries and some non-EU countries. Tables 1.26 and 1.27, show the EU indicator on 
early school leavers according to the existing definition and according to a restricted definition, 
which covers only participation in formal education for males and females respectively. It can be 
observed from tables 1.24 to 1.26, that restricting the definition of participating in education and 
training  to  only  regular  education  will  mean  that  the  percentage  of  early  school  leavers  will 
increase.  However,  the  size  of  the  increase  varies  between  countries,  from  2.6  percent  in 
Luxembourg to no increase at all in Lithuania and the Slovak Republic in 2004 for both sexes. 

It  should  be  noted  that  the  population  of  the  EU  indicator  on  early  school  leavers  also  covers 
those  that  never  attended  school  (e.g.  children  of  travellers,  children  schooled  at  home,  etc), 
despite  the  fact  that  the  name  of  the  indicator  alludes  to  measuring  dropouts  from  the  school 
system. The EU-Labour Force Survey (EU-LFS) does not distinguish between those persons that 
attended school and those that did not. This is because, the EU-LFS collects information on the 
educational level attained, but does not ask any questions in order to elucidate whether a person 

10 This refers to variable EDUCSTAT (col 293) in the EU-LFS survey 
11 This refers to variable COURATT (col 298) in the EU-LFS survey 
12  ‘Working Group – Employment Statistics Minutes 2004’, Eurostat 
13  ‘Short  conclusions  –  Meeting  of  the  expert  group  Education  in  the  EU  LFS  Luxembourg,  28  June 

2004’, Eurostat 

Key education indicators on social inclusion and efficiency 

28

 
 
 
 
                                                 
Chapter 1: Completing statistical gaps for the benchmark indicators 

actually attended school or not. The indicator  does not measure the percentage of persons that 
dropped  out  of  the  school  system  or  in  other  words  the  performance  of  the  school  system. 
Therefore, in the absence of any other suitable alternative to measuring dropouts from the school 
system,  this  indicator  is  a  good  proxy  of  the  population  that  can  be  classified  as  early  school 
leavers.  This  is  also  the  case  with  data  collected  from  the  US  Census  Bureaus  ‘Current 
population survey’ and data from the ‘Population Census’ and the ‘Employment Status Survey’ 
of  the  Statistics  Bureau  of  Japan,  since  they  too  also  cover  the  population  that  did  not  attend 
school.  Thus,  data  from  the  Japan  and  the  US  can  be  compared  with  data  from  the  EU-LFS 
survey,  on  the  proviso  that  the  data  from  the  EU-LFS  survey  is  adjusted  so  that  persons 
participating in non-formal education and training are not counted as participating in education. 

Whilst  it  is  not  possible  to  construct  this  indicator  for  Japan  for  the  age  group  18-24,  it  is 
possible to construct the indicator for the age group 20-24. Given that data is available for the 
US for this age group a comparison can be easily made. For the EU-25 data for the age group 20 
to 24 can be collected from Eurostat.  

Key education indicators on social inclusion and efficiency 

29

 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Table 1.25: Share of the population aged 18 – 24 with only lower secondary education and not in education and training according to the general definition and the 
restricted definition 

Country 

EU25 
BE 
CZ 
DK 
DE 
EE 
GR 
ES 
FR 
IE 
IT 
CY 
LV 
LT 
LU 
HU 

MT 
NL 
AT 
PL 
PT 
SI 
SK 
FI 
SE 
UK 
IS 
NO 
BG 
HR 
RO 

General 
(1) 
: 
: 
6.0 
10.3 
12.8 
11.8 
15.5 
31.3 
13.8 
12.3 

2003 
Restricted 
(2) 
: 
: 
6.1 
11.7 
13.1 
12.4 
16.0 
33.8 
15.0 
12.6 

17.4 
18.1 

12.3 
: 

: 
14.2 
9.2 
: 
: 
4.3 
4.9 
10.7 
9.0 

23.1 
6.6 
22.4 
: 
23.2 

17.8 
18.7 

12.8 
: 

: 
16.9 
9.9 
: 
: 
4.5 
4.9 
11.3 
10.4 

27.0 
6.8 
22.4 
: 
23.3 

Difference
(2) – (1) 

: 
: 
0.1 
1.4 
0.3 
0.5 
0.6 
2.6 
1.3 
0.3 

0.5 
0.7 

0.5 
: 

: 
2.7 
0.7 
: 
: 
0.2 
0.0 
0.6 
1.4 

3.8 
0.3 
0.0 
: 
0.0 

General 
(1) 
14.9 
11.9 
6.1 
8.5 
12.1 
13.7 
14.9 
31.7 
13.1 
12.9 
22.3 
20.6 
15.6 
9.5 
12.7 
12.6 

2004 
Restricted
(2) 
15.6 
12.5 
6.2 
10.8 
12.4 
14.0 
15.1 
34.2 
14.6 
13.1 
22.7 
21.2 
15.9 
9.5 
15.2 
12.8 

Difference
(2) – (1) 
0.7 
0.6 
0.0 
2.3 
0.4 
0.2 
0.2 
2.5 
1.5 
0.2 
0.4 
0.7 
0.3 
0.0 
2.6 
0.3 

General 
(1) 
14.6 
13.0 
6.4 
8.5 
13.8 
14.0 
13.3 
30.8 
12.3 
12.3 
21.9 
18.1 
11.9 
9.2 
13.3 
12.3 

2005 
Restricted
(2) 
15.4 
13.6 
6.5 
10.7 
14.2 
14.0 
13.5 
34.7 
13.5 
12.4 
22.3 
18.4 
12.1 
9.3 
14.4 
12.5 

Difference
(2) – (1) 
0.8 
0.6 
0.1 
2.3 
0.4 
0.0 
0.1 
3.9 
1.2 
0.1 
0.5 
0.3 
0.2 
0.2 
1.0 
0.2 

General 
(1) 
: 
: 
5.5 
10.9 
13.8 
13.2 
15.9 
29.9 
13.1 
12.5 
: 
16.0 
: 
10.3 
: 
12.4 

2006 
Restricted
(2) 
: 
: 
5.7 
13.3 
14.3 
13.2 
16.2 
29.9 
14.8 
12.7 
: 
16.5 
: 
10.3 
: 
12.7 

42.0 
14.0 
8.7 
5.7 
39.4 
4.2 
7.1 
10.2 
8.6 
14.9 
27.4 
4.5 
21.4 
: 
23.6 

43.5 
15.9 
9.5 
5.8 
40.9 
4.6 
7.1 
11.2 
10.3 
11.4 
29.8 
4.7 
21.4 
: 
22.8 
Source: Eurostat Labour Force Survey 

41.2 
13.6 
9.0 
5.5 
38.6 
4.3 
5.8 
9.3 
11.7 
14.0 
26.3 
4.6 
20.0 
4.8 
20.8 

1.5 
1.8 
0.8 
0.1 
1.6 
0.4 
0.0 
1.0 
1.7 
-3.5 
2.4 
0.2 
0.0 
: 
-0.8 

43.6 
14.9 
9.8 
5.6 
39.7 
4.6 
5.9 
10.2 
13.7 
14.4 
29.2 
4.9 
20.0 
4.8 
19.6 

2.5 
1.4 
0.8 
0.1 
1.1 
0.3 
0.1 
0.8 
2.0 
0.4 
2.8 
0.3 
0.0 
0.1 
-1.2 

: 
12.9 
9.6 
5.6 
40.0 
5.2 
6.4 
12.2 
12.0 
13.0 
: 
5.9 
18.0 
: 
19.0 

: 
14.4 
11.0 
5.6 
40.6 
5.9 
6.4 
13.1 
14.2 
13.2 
: 
6.5 
18.1 
: 
17.7 

Difference
(2) – (1) 

: 
: 
0.1 
2.4 
0.5 
0.0 
0.3 
0.0 
1.6 
0.2 
: 
0.5 
: 
0.0 
: 
0.3 

: 
1.5 
1.4 
0.1 
0.6 
0.7 
0.0 
0.8 
2.2 
0.2 
: 
0.6 
0.0 
: 
-1.3 

Key 
General (1) – Refers to participation in formal and non-formal education and training 
Restricted (2) – Refers to participation in formal education only.  

Key education indicators on social inclusion and efficiency 

30

 
 
 
 
 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Table 1.26: Share of the male population aged 18 – 24 with only lower secondary education and not in education and training according to the general definition and 
the restricted definition 

Country 

EU25 
BE 
CZ 
DK 
DE 
EE 
GR 
ES 
FR 
IE 
IT 
CY 
LV 
LT 
LU 
HU 
MT 
NL 
AT 
PL 
PT 
SI 
SK 
FI 
SE 
UK 
IS 
NO 
BG 
HR 
RO 

General 
(1) 
: 
: 
5.2 
10.4 
12.9 
16.1 
19.9 
37.3 
15.3 
15.0 
: 
24.2 
22.7 
: 
14.3 
: 
: 
15.3 
8.7 
: 
: 
6.2 
5.2 
12.9 
9.8 

27.0 
7.9 
23.3 
: 
24.7 

2003 
Restricted 
(2) 
: 
: 
5.3 
12.0 
13.1 
16.5 
20.4 
40.1 
16.7 
15.3 
: 
24.9 
24.1 
: 
14.7 
: 
: 
18.2 
9.4 
: 
: 
6.4 
5.2 
13.4 
11.3 

28.7 
8.3 
23.3 
: 
24.8 

Difference
(2) – (1) 

: 
: 
0.1 
1.6 
0.3 
0.4 
0.5 
2.9 
1.4 
0.3 
: 
0.6 
1.4 
: 
0.4 
: 
: 
2.9 
0.7 
: 
: 
0.2 
0.0 
0.5 
1.4 

1.7 
0.4 
0.0 
: 
0.0 

General 
(1) 
17.6 
15.6 
5.8 
10.4 
12.2 
20.5 
18.3 
38.5 
15.1 
16.1 
26.2 
27.2 
20.5 
11.6 
12.6 
13.7 
44.2 
16.1 
9.5 
7.7 
47.9 
5.8 
7.8 
12.8 
9.3 
15.7 
27.8 
5.2 
22.1 
: 
24.9 

2004 
Restricted
(2) 
18.4 
16.0 
5.9 
12.9 
12.6 
20.5 
18.5 
41.0 
16.6 
16.3 
26.6 
27.9 
21.0 
11.6 
16.2 
13.9 
46.2 
17.9 
10.1 
7.8 
49.7 
6.3 
7.8 
14.0 
11.1 
11.9 
29.6 
5.6 
22.1 
: 
23.7 
Source: Eurostat Labour Force Survey 

Difference
(2) – (1) 
0.8 
0.4 
0.1 
2.5 
0.4 
0.0 
0.2 
2.5 
1.6 
0.2 
0.4 
0.7 
0.5 
0.0 
3.5 
0.2 
1.9 
1.9 
0.6 
0.1 
1.8 
0.6 
0.0 
1.3 
1.8 
-3.8 
1.8 
0.4 
0.0 
: 
-1.2 

General 
(1) 
17.1 
15.3 
6.2 
9.4 
13.5 
17.4 
17.5 
36.4 
14.5 
14.9 
25.9 
26.6 
15.5 
12.2 
17.0 
13.5 
43.0 
15.8 
9.4 
6.9 
46.7 
5.7 
6.0 
11.1 
12.4 
14.7 
30.5 
5.3 
19.5 
5.6 
21.4 

2005 
Restricted
(2) 
18.0 
16.0 
6.3 
11.6 
13.8 
17.4 
17.6 
40.8 
15.9 
15.0 
26.3 
27.1 
15.5 
12.3 
18.2 
13.8 
45.3 
17.3 
10.3 
7.0 
47.9 
6.1 
6.1 
12.0 
14.9 
15.2 
33.3 
5.7 
19.5 
5.7 
19.8 

Difference
(2) – (1) 
0.9 
0.7 
0.1 
2.2 
0.3 
0.0 
0.1 
4.5 
1.3 
0.1 
0.5 
0.5 
0.0 
0.2 
1.2 
0.3 
2.3 
1.5 
0.8 
0.1 
1.2 
0.3 
0.1 
0.9 
2.5 
0.5 
2.8 
0.5 
0.1 
0.1 
-1.6 

General 
(1) 
: 
: 
: 
5.7 
12.8 
13.9 
19.6 
20.7 
35.8 
15.0 
15.7 
: 
23.5 
: 
13.3 
: 
14.0 
: 
15.1 
9.3 
7.2 
47.2 
6.9 
7.3 
13.8 
13.3 
14.6 
: 
7.4 
18.2 
: 

2006 
Restricted
(2) 
: 
: 
: 
5.9 
15.8 
14.5 
19.6 
21.0 
35.8 
16.6 
15.9 
: 
24.6 
: 
13.3 
: 
14.6 
: 
17.1 
11.1 
7.3 
47.8 
7.6 
7.4 
15.1 
15.9 
14.7 
: 
8.3 
18.2 
: 

Difference
(2) – (1) 

: 
: 
: 
0.2 
3.0 
0.6 
0.0 
0.3 
0.0 
1.6 
0.3 
: 
1.1 
: 
0.0 
: 
0.5 
: 
2.0 
1.8 
0.1 
0.6 
0.6 
0.1 
1.3 
2.6 
0.1 
: 
0.9 
0.0 
: 

Key 
General (1) – Refers to participation in formal and non-formal education and training 
Restricted (2) – Refers to participation in formal education only.  

Key education indicators on social inclusion and efficiency 

31

 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Table 1.27: Share of the female population aged 18 – 24 with only lower secondary education and not in education and training according to the general definition 
and the restricted definition 

Country 

EU25 
BE 
CZ 
DK 
DE 
EE 
GR 
ES 
FR 
IE 
IT 
CY 
LV 
LT 
LU 
HU 
MT 
NL 
AT 
PL 
PT 
SI 
SK 
FI 
SE 
UK 
IS 
NO 
BG 
HR 
RO 

General 
(1) 
: 
: 
6.8 
10.2 
12.8 
7.3 
11.0 
25.0 
12.2 
9.5 
: 
11.8 
13.4 
: 
10.2 
: 
: 
13.0 
9.7 
: 
: 
2.3 
4.7 
8.6 
8.2 

18.7 
5.3 
21.6 
: 
21.7 

2003 
Restricted 
(2) 
: 
: 
6.8 
11.4 
13.1 
7.9 
11.7 
27.3 
13.4 
9.9 
: 
12.1 
13.4 
: 
10.8 
: 
: 
15.5 
10.3 
: 
: 
2.5 
4.7 
9.3 
9.5 

25.0 
5.4 
21.6 
: 
21.7 

Difference
(2) – (1) 

: 
: 
0.0 
1.2 
0.3 
0.6 
0.6 
2.2 
1.2 
0.3 
: 
0.3 
0.0 
: 
0.6 
: 
: 
2.4 
0.7 
: 
: 
0.2 
0.0 
0.7 
1.3 

6.3 
0.1 
0.0 
: 
0.0 

General 
(1) 
12.2 
8.3 
6.5 
6.7 
11.9 
7.0 
11.6 
24.6 
11.1 
9.7 
18.4 
14.9 
10.7 
7.4 
12.7 
11.4 
39.5 
11.9 
7.9 
3.7 
30.6 
2.6 
6.4 
7.8 
7.9 
14.2 
27.0 
3.7 
20.7 
: 
22.4 

2004 
Restricted
(2) 
12.8 
9.0 
6.5 
8.8 
12.2 
7.5 
11.8 
27.1 
12.5 
9.9 
18.9 
15.6 
10.7 
7.4 
14.2 
11.7 
40.6 
13.7 
8.9 
3.7 
32.0 
2.8 
6.4 
8.6 
9.5 
10.9 
30.1 
3.8 
20.7 
: 
21.9 
Source: Eurostat Labour Force Survey 

Difference
(2) – (1) 
0.6 
0.7 
0.0 
2.0 
0.3 
0.4 
0.2 
2.4 
1.4 
0.2 
0.5 
0.7 
0.0 
0.0 
1.5 
0.3 
1.1 
1.8 
1.1 
0.0 
1.3 
0.3 
0.0 
0.8 
1.6 
-3.2 
3.1 
0.1 
0.0 
: 
-0.5 

General 
(1) 
12.0 
10.6 
6.6 
7.5 
14.1 
10.7 
9.2 
25.0 
10.1 
9.6 
17.8 
10.6 
8.2 
6.2 
9.6 
11.1 
39.3 
11.2 
8.5 
4.0 
30.1 
2.8 
5.7 
7.6 
10.9 
13.2 
22.0 
3.9 
20.6 
3.8 
20.1 

2005 
Restricted
(2) 
12.8 
11.2 
6.6 
9.8 
14.5 
10.7 
9.4 
28.4 
11.1 
9.7 
18.3 
10.6 
8.6 
6.3 
10.4 
11.2 
41.9 
12.5 
9.3 
4.1 
31.2 
3.1 
5.7 
8.4 
12.4 
13.6 
24.8 
4.0 
20.6 
3.8 
19.4 

Key 
General (1) – Refers to participation in formal and non-formal education and training 
Restricted (2) – Refers to participation in formal education only.  

Key education indicators on social inclusion and efficiency 

Difference
(2) – (1) 
0.8 
0.6 
0.0 
2.3 
0.4 
0.0 
0.2 
3.4 
1.0 
0.1 
0.5 
0.0 
0.4 
0.1 
0.8 
0.1 
2.6 
1.3 
0.8 
0.0 
1.1 
0.3 
0.1 
0.8 
1.5 
0.4 
2.8 
0.2 
0.0 
0.0 
-0.8 

General 
(1) 
: 
: 
5.4 
9.1 
13.6 
6.5 
11.0 
23.8 
11.3 
9.2 
: 
9.2 
: 
7.0 
: 
10.7 
: 
10.7 
9.8 
3.8 
32.6 
3.3 
5.5 
10.9 
10.7 
11.4 
: 
4.3 
17.9 
: 
18.9 

2006 
Restricted
(2) 
: 
: 
5.4 
10.8 
14.1 
6.5 
11.2 
23.8 
13.0 
9.4 
: 
9.2 
: 
7.0 
: 
10.9 
: 
11.7 
10.8 
3.9 
33.2 
4.0 
5.5 
11.3 
12.5 
11.7 
: 
4.7 
18.0 
: 
17.9 

Difference
(2) – (1) 

: 
: 
0.0 
1.7 
0.5 
0.0 
0.3 
0.0 
1.7 
0.2 
: 
0.0 
: 
0.0 
: 
0.1 
: 
1.0 
1.0 
0.1 
0.5 
0.7 
0.0 
0.4 
1.8 
0.4 
: 
0.3 
0.1 
: 
-0.9 

32

 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

11..44..33      PPeerrcceennttaaggee   ooff   ppooppuullaattiioonn   aaggeedd   2255   ––6644   ppaarrttiicciippaattiinngg   iinn   eedduuccaattiioonn  
aanndd  ttrraaiinniinngg  iinn  ffoouurr  wweeeekkss  pprriioorr  ttoo  tthhee  ssuurrvveeyy  

The  EU  indicator  refers  to  persons  aged  25  to  64  who  answered  they  received  education  or 
training  in  the  four  weeks  preceding  the  survey  (numerator).  The  denominator  consists  of  the 
total  population  of  the  same  age  group,  excluding  no  answers  to  the  question  ‘participation  to 
education and training’. 

OOvveerrvviieeww  ooff  ffiinnddiinnggss  

While  data  sources  exist  both  in  Japan  and  the  USA  which  can  give  an  indication  of  the 
percentage of the adult population undertaking some form of educational activity, differences in 
concepts  and  definitions,  especially  concerning  the  content  of  education  render  a  direct 
comparison between EU sources (i.e. EU Labour Force Survey) difficult. It is worth noting that 
in Japan, the main form of adult education focuses on “hobbies” or “enjoyment of life” 

JJaappaann   
The  ‘Survey  on  Time  Use  and  Leisure  activities’  undertaken  by  Statistics  Japan  can  give  an 
indication of the share of the population undertaking studies and researches. The list of activities 
considered, as “studies and researches” is very limited.   

Table 1.28: Percentage of population aged 25 –64 participating in education and training in Japan 
(2001) 

Total 
38.3 

Males 
38.3 

Females 
38.3 

Source: Statistics Bureau of Japan, Survey on Time Use and Leisure activities 

Notes: 
Data refer to leisure activities engaged in during the past year from October 20, 2000 to October 19, 2001. 
The  survey  was  conducted  as  of  October  20,  2001.  However,  as  for  the  survey  on  time  allocation,  two 
straight  days  within  the  nine-day  period  from  October  13  to  21  were  designated  for  each  enumeration 
district. The survey was carried out in 6,440 enumeration districts that were selected from among the 1995 
Population Census Enumeration Districts.  

The data refer to participation in "Studies and researches".  This includes only those performed during free 
time  as  a  leisure  activity  or  in  preparation  for  work,  and  do  not  include  those  as  an  occupation.  For 
example, study by students at school or as homework and research done by scientists as their occupation 
are excluded. 

The survey classifies studies and researches as follows: 

•  English language  
•  Other foreign languages - French, Chinese, etc.  
•  Computing, etc. --- how to use computing software, programming, etc.  
•  Commerce  and  business  -  commerce,  management,  bookkeeping,  interpreting,  shorthand,  abacus 

use, etc.  

•  Caring - caring at home, care visitors  
•  Home economics and housework - home economics, knitting, handcrafts, etc.  
•  Humanities, social or natural science - literature, history, philosophy, psychology, political science, 
sociology,  economics,  astronomy,  chemistry,  biology,  medical  science,  measurement,  electric 
engineering, civil engineering, construction, nursing, dental work, radiology, etc.  

•  Arts and culture - art, pictures, sculpture, design, music, industrial arts, graphic design, theatrical, 

calligraphy, etc.  

•  Other 

Key education indicators on social inclusion and efficiency 

33

 
 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

The “Population Census” also collects data on the number of persons attending school. However 
the subject of interest is limited to regular schools such as elementary schools, junior and senior 
high schools, colleges and universities. 

Table 1.29: Percentage of population aged 25 – 64 attending school in Japan (2000) 

Total 
0.36 

Male 
0.43 

Female 
0.28 

Source: Statistics Bureau of Japan, Population Census 

Notes 
The data refer to persons who were attending school at the census date. Schools mentioned refer to regular 
schools such as elementary schools, junior and senior high schools, collages and universities and the like, 
irrespective  of  whether  they  are  governmental,  public  or  private  schools.  Schools  also  include both  day 
and night schools and schools of both current and old systems. However, those non-regular schools such 
as  preparatory  schools,  dress-making  schools,  cooking  schools,  English  conversation  schools,  training 
centres for the employees, etc. are not included in "school" here. 

Age refers to the age at the last birthday before 1 October 2000. For an infant who was born at 0:00 a.m. 
of 1 October 2000, his/her age is regarded as zero year old. 

UUSSAA   

The  ‘Adult  Education  and  Lifelong  Learning  Survey’  of  the  National  Household  Education 
Surveys  Program  (NHES)  collects  data  on  participation  in  adult  education.  Adult  education  is 
defined  as  all  education  activities,  except  full-time  enrolment  in  higher  education  credential 
programs.    Table  1.30  shows  the  percentage  of  the  population  participating  in  education  and 
training according to this narrow definition. 

Table 1.30: Percentage of population aged 25-64 participating in adult education  in USA(2001) 

Total 
50.9 

Male 
47.0 

Female 
54.6 

Source: U.S Department of Education, National Center for Education Statistics, Adult Education and 
Lifelong Learning Survey of the National Household Education Surveys Program (NHES). 

Notes 
The  data  refer  to  participation  in  adult  education  during  the  past  12  months  according  to  the  narrow 
definition  of  adult  education.  Adult  education  is  defined  as  all  education  activities,  except  full-time 
enrolment in higher education credential programs.  Examples of adult education activities include part-
time  college  attendance,  classes  or  seminars  given  by  employers,  and  classes  taken  for  adult  literacy 
purposes, or for recreation and enjoyment. The estimates of participation in basic education include only 
those  participating  in  courses  to  improve  "reading,  writing,  and  math  skills,"  and  do  not  count 
participation in GED or other high-school equivalency courses. Data are based upon a sample survey of 
the  civilian non-institutional population.   Data do not include  persons enrolled in high school or  below.  
Data revised from previously published figures.   

The  National  Center  for  Education  Statistics  freely  disseminates  the  results  of  the  ‘Adult 
Education and Lifelong Learning Survey’ on its’ website.  

Whilst table 1.30, shows the percentage of adults participating in education based on a narrow 
definition of adult education, data can be easily extracted from the survey which is based on a 

Key education indicators on social inclusion and efficiency 

34

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

slightly  more  wider  definition  of  adult  education.  This  wider  definition  of  adult  education 
includes English as a second language classes, basic skills education, apprenticeships, full-time 
and  part-time  college/university  or  vocational/technical  credential  programs,  work-related 
courses and personal interest courses (see table 1.31). 

Table 1.31: Percentage of population aged 25-64 participating in any form of education and training 
(2001) 

Total 
52.1 

Male 
48.3 

Female 
55.9 

Source: U.S Department of Education, National Center for Education Statistics, Adult Education and 
Lifelong Learning Survey of the National Household Education Surveys Program (NHES). 

Figure 1.5: Percentage of the population aged 25-64 participating in education broken down by type 
of education and sex 

4
6
-
5
2
n
o
i
t
a
l
u
p
o
p
f
o
%

40

35

30

25

20

15

10

5

0

Adult basic education

College/university,
vocational/technical
programs

Personal interest
courses

Work-related courses

male

female

Source:  U.S  Department  of  Education,  National  Center  for  Education  Statistics,  Adult  Education  and 
Lifelong Learning Survey of the National Household Education Surveys Program (NHES). 

Notes 
Basic  adult  education  refers  to  English  as  a  Second  Language  (ESL),  adult  basic  education  (ABE),  or 
General Education Development (GED) 

The data disseminated by the National Center for Education Statistics is very detailed. Analysis 
of  participation  in  education  can  be  cross  analysed  by  a  number  of  background  variables 
including:  sex,  age,  race/ethnicity,  educational  attainment,  marital  status,  and  employment/ 
occupation.  

Key education indicators on social inclusion and efficiency 

35

 
 
 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Table 1.32: Number of adults and rates of participation in any type of adult education by age 
groups  in USA (2001) 

Age group 

Total adults (in 
thousands) 

Overall participation 

25 to 29  
30 to 34  
35 to 39  
40 to 44  
45 to 49  
50 to 54  
55 to 59  
60 to 64  

18793 
19532 
22223 
21132 
20737 
17373 
12316 
11634 

(in thousands) 
10915 
10718 
12190 
11463 
11558 
9015 
5451 
3632 

Percent 
58.1 
54.9 
54.9 
54.2 
55.7 
51.9 
44.3 
31.2 

Source:  U.S  Department  of  Education,  National  Center  for  Education  Statistics,  Adult  Education  and 
Lifelong Learning Survey of the National Household Education Surveys Program (NHES). 
Notes 
The data refer to participation in adult education during the past 12 months.  Any type of adult education 
includes English as a second language classes, basic skills education, apprenticeships, full-time and part-
time  college/university  or  vocational/technical  credential  programs,  work-related  courses  and  personal 
interest courses. 

Table 1.33: Number of adults and rates of participation in any type of adult education by highest 
level of education received in USA (2001) 

Total adults 
(thousands) 

Less than high 
school diploma 

High school 
diploma or its 
equivalent 

Associate’s degree  Bachelor’s degree 

or higher 

Number 
(thousands) 
4199 

% 

5.6 

Number 
(thousands) 
33837 

% 

45.2 

Number 
(thousands) 
6228 

% 

8.3 

Number 
(thousands) 
30679 

% 

40.9 

143739 

Source: U.S Department of Education, National Center for Education Statistics, Adult Education and 
Lifelong Learning Survey of the National Household Education Surveys Program (NHES). 

Existing surveys in Japan and the US cannot be used to construct the indicator for a number of 
reasons. One of the most important reasons why this is so is the fact that surveys in Japan and 
the US refer to participation in adult education and training during the past 12 months, instead of 
the past four weeks, as is required by the indicator. 

Nevertheless, one can consider comparing the surveys conducted in Japan and the US with the 
2003  EU-LFS ad hoc  module on  lifelong learning. The 2003 -LFS ad hoc  module  on lifelong 
learning collected information on participation in education and training in the past 12 months.   

Table  1.34, shows the  main  aspects  of  each of the surveys.  Apart from the  main difference  of 
there being different reference periods involved in each of the three surveys (the EU-LFS ad hoc 
module  was  conducted  in  2003),  there  are  also  significant  differences  in  the  coverage  of 
education in each of the surveys. It is evident from table 1.33, that the coverage of education in 
the EU-LFS ad hoc module on lifelong learning is far wider than that of the surveys conducted 
in Japan and the US.  In contrast to the US, the 2003 EU-LFS includes a far wider definition of 
informal learning which is not restricted to the workplace.  

Thus in comparing the results of these surveys caution needs to be exercised in interpreting the 
results due to differences in the coverage of education and training, and the fact that they refer to 

Key education indicators on social inclusion and efficiency 

36

 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

different years. Despite the differences in coverage of education between the surveys, it can be 
said that the share of the population aged 25 to 64 participating in education and training in the 
US is greater than that in the EU-25.  

It  can  be  suggested  to  take  the  results  of  the  surveys  in  Japan  and  the  US  for  the  12  month 
reference period and convert them into a 4 week period as is necessary to be able to construct 
the  indicator.  There  is  no  statistically  justifiable  method  of  breaking  down  participation  in 
education and training 12 months prior to the survey down to 4 weeks. Nevertheless, one could 
take a very simplistic approach to the problem, and for this would assume a linear relationship 
regarding time and the amount.  

This simplistic approach was performed on the EU-LFS ad hoc module on lifelong learning to 
convert the data down to 4 weeks from a 12-month reference period. Breaking down formal and 
non-formal learning for the data obtained from the EU-LFS ad hoc module on lifelong learning 
did not yield the same results obtained from the core of the LFS for 4 weeks. 

Key education indicators on social inclusion and efficiency 

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Chapter 1: Completing statistical gaps for the benchmark indicators 

Table 1.34: Summary of main aspects of surveys in the EU, Japan and US collecting information concerning participation of adults in education and training 

Country 
Name of survey 
Periodicity 
Reference date of last 
survey 
Coverage of education 

Accessibility 

JP 
Survey on Time Use and Leisure activities 
Every 5 years 
October 2001 

US 
Adult Education and Lifelong Learning Survey 
Erratic 
April 2001 

EU-25 
LFS ad hoc module on lifelong learning 
Ad hoc 
2003 

include 

those 
researches  only 
Studies  and 
performed during free time as a leisure activity or 
in preparation for work, and do not include those 
as an occupation. For example, study by students 
at  school  or  as  homework  and  research  done  by 
scientists as their occupation are excluded. 
The list is as follows: 
English language  
Other foreign languages;  
Computing, etc.  
Commerce and business;  
Caring ; 
Home  economics  and  housework  Humanities, 
social or natural science; Arts and culture; 
Other 
Freely available on the website of Statistics Japan 
http://www.stat.go.jp/english/data/shakai/2001/k
odo/zenkoku/study.htm 

Formal  learning  addressed  in  the  AELL  survey 
includes  English  as  a  Second  Language  (ESL), 
basic  skills  education,  part-time  postsecondary 
degree  or  diploma  programs,  apprenticeship 
programs,  work-related  courses,  and  personal 
interest  courses.  The  survey  addresses  only  work-
related  informal  learning,  including  computer-
the  workplace, 
based 
attending 
“brown-bag” 
presentations,  or  reading  professional  journals  or 
magazines. 

tutorials,  mentoring  at 
or 
conferences 

Freely  available  on  the  website  of  the  National 
Centre for Education Statistics  
Data files (ASCII, SPSS, STATA, SAS) 
http://nces.ed.gov/nhes/dataproducts.asp#2001dp 

including 

Participation  in  regular  education.  This  variable 
only  covers the  regular  education  system (formal 
education 
schools,  colleges,  and 
universities ISCED 1 -6); 
Participation  in  courses,  seminars  or  received 
private  lessons  outside  the  regular  education 
system; 
Participation  in  informal  learning  (including  self 
studying  by  making  use  of  printed  material; 
computer based learning / training; online internet 
based  web  education; studying  by making use  of 
educational  broadcasting  or  offline  computer 
based;  visiting  facilities  aimed  at  transmitting 
educational content. 

Freely available on the Eurostat website. 

Key education indicators on social inclusion and efficiency 

38

 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Table 1.35: Percentage of population aged 25-64 participating in education and training 12 months 
prior to the survey 

Total 

EU-251 
USA2 
Japan3 

Male 
EU-251 
USA2 
Japan3 

Female 
EU-251 
USA2 
Japan3 

2000 
: 
: 
: 

: 
: 
: 

: 
: 
: 

2001 
: 
52.1 
38.3 

: 
48.3 
38.3 

: 
55.9 
38.3 

2002 
: 
: 
: 

: 
: 
: 

: 
: 
: 

2003 
42 
: 
: 

42.8 
: 
: 

41.1 
: 
: 

2004 
: 
: 
: 

: 
: 
: 

: 
: 
: 

Notes 
1 EU-LFS ad hoc module on lifelong learning. 
2   U.S Department of Education, National Center for Education Statistics, Adult Education and Lifelong 
Learning  Survey  of  the  National  Household  Education  Surveys  Program  (NHES).  The  data  refer  to 
participation in adult education during the past 12 months.  Any type of adult education includes English 
as  a  second  language  classes,  basic  skills  education,  apprenticeships,  full-time  and  part-time 
college/university or vocational/technical credential programs, work-related courses and personal interest 
courses. 
3 Statistics Bureau of Japan, Survey on Time Use and Leisure activities  
Data refer to leisure activities engaged in during the past year from October 20, 2000 to October 19, 2001. 
The data refer to participation in "Studies and researches".  This includes only those performed during free 
time  as  a  leisure  activity  or  in  preparation  for  work,  and  do  not  include  those  as  an  occupation.  For 
example, study by students at school or as homework and research done by scientists as their occupation 
are excluded. The survey classifies studies and researches as follows:·  English  language;  Other  foreign 
languages - French, Chinese, etc; Computing, etc. --- how to use computing software, programming, etc; 
Commerce and business - commerce, management, bookkeeping, interpreting, shorthand, abacus use, etc; 
Caring  -  caring  at  home,  care  visitors;  Home  economics  and  housework  -  home  economics,  knitting, 
handcrafts, etc; Humanities, social or natural science - literature, history, philosophy, psychology, political 
science,  sociology,  economics,  astronomy,  chemistry,  biology,  medical  science,  measurement,  electric 
engineering,  civil  engineering,  construction,  nursing,  dental  work,  radiology,  etc;  Arts  and  culture  -  art, 
pictures, sculpture, design, music, industrial arts, graphic design, theatrical, calligraphy, etc; Other 

Given  the  problems  of  collecting  comparable  data  for  both  Japan  and  the  US,  an  alternative 
indicator  to  participation  in  lifelong  learning  that  can  be  proposed  to  compare  JP  and  the  US 
with the EU-25 is the OECD indicator C.6 ‘Participation in continuing education and training’ 
which  is  currently  employed  in  the  OECD’s  ‘Education  at  a  Glance’.    However,  data  for  this 
indicator is not available for Japan, and the only data available for the US refers to participation 
in non-formal job-related education and training. One disadvantage in using this indicator is that 
it is very narrow with respect to the coverage of education, in that it only considers work-related 
non-formal education and training. Nevertheless it can provide a useful insight into differences 
between EU Member States and the US. 

Table 1.36 shows participation in non-formal job-related continuing education and training for a 
number of EU-OECD countries and the US. 

Key education indicators on social inclusion and efficiency 

39

 
 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Table 1.36: Participation in non-formal job-related continuing education and training for the 
labour force, by age and gender (2003) 

Country 
BE 
CZ 
DK 
DE 
EL 
ES 
FR 
IE 
IT 
LU 
HU 
AT 
PL 
PT 
SK 
FI 
SE 
UK 
US 

Total 
22 
14 
46 
14 
4 
9 
23 
14 
6 
16 
5 
24 
12 
9 
24 
44 
45 
34 
44 

Males 
22 
14 
44 
14 
3 
8 
23 
13 
5 
16 
5 
24 
12 
8 
25 
40 
42 
32 
41 

Females 
22 
13 
49 
15 
4 
10 
24 
15 
7 
16 
7 
24 
13 
10 
22 
48 
48 
36 
47 

Source: ‘Education at a Glance’, OECD 

Note: 
For  the  European  countries  the  data  are  compiled  from  the  ad  hoc  module  on  Lifelong  Learning  of  the 
2003 EU Labour Force Survey. 
US  -The  source  for  this  indicator  was  the  2003  Adult  Education  Survey  of  the  National  Household 
Education Survey 

Methodological note: 
It should be noted that the data collected from the US Census Bureau CPS refers to the civilian 
non-institutionalised population. This universe includes civilians in households, people in non-
institutional group quarters (other than military barracks) and military in households living off 
post or with their families on post (as long as at least one household member is a civilian adult). 
The universe excludes other military in households and in group quarters (barracks), and people 
living  in  institutions  like  prisoners.    The  EU-LFS  results  cover  the  total  population  usually 
residing  in  Member  States,  except  persons  living  in  collective  or  institutional  households. 
Therefore, it can be said that the two surveys cover more or less the same population. 

Key education indicators on social inclusion and efficiency 

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Chapter 1: Completing statistical gaps for the benchmark indicators 

11..55    CCoommppaarraabbiilliittyy  ooff  ddaattaa  ccoolllleecctteedd  wwiitthh  tthhee  EEuurrooppeeaann  
SSttaattiissttiiccaall  SSyysstteemm  ((EESSSS)) 

In  most  cases  the  data  collected  are  not  comparable.  This  is  not  to  say  that  they  are  of  poor 
quality,  but  rather  they  have  been  collected  under  methodologies  (including  the  use  of 
definitions, concepts, and classification), which are not in full conformance with the standards 
adopted by the ESS. (It should be borne in mind that the China, India, Japan, Russia and USA 
are not obliged to follow the ESS standards. 

For  example  in  USA,  the  ‘Adult  Education  and  Lifelong  Learning  Survey’  of  the  National 
Household Education Surveys Program (NHES) collects data on participation in adult education 
which could be used to provide information on the indicator: percentage of the population aged 
25-64  participating  in  education  and  training  in  four  weeks  prior  to  the  survey.  However,  the 
data  collected  by  the  ‘Adult  Education  and  Lifelong  Learning  Survey’  refer  to  participation  in 
adult  education  during  the  past  12  months,  whereas  the  indicator  refers  to  participation  in 
education and training 4 weeks prior to the survey. Furthermore the definition of education and 
training  employed  by  the  survey  can  be  considered  more  restrictive  than  the  definition  of 
education  and  training  employed  by  the  indicator.  The  indicator  relates  to  initial  education, 
further  education,  continuing  or  further  training,  training  within  the  company,  apprenticeship, 
on-the-job  training,  seminars,  distance  learning,  evening  classes,  self-learning  etc.  It  includes 
also courses followed for general interest and may cover all forms of education and training as 
language,  data  processing,  management,  art/culture,  and  health/medicine  courses.  In  contrast, 
the  ‘Adult  Education  and  Lifelong  Learning  Survey’  only  partly  covers  informal  learning.  It 
covers  work-related  informal  learning,  which  includes  computer-based  tutorials,  mentoring  at 
the  workplace,  attending  conferences  or  “brown-bag”  presentations,  or  reading  professional 
journals or magazines.  

What  we  have  done  with  the  data  is  to  clean  the  data  wherever  it  was  possible,  given  the 
information available.  

For example in India, the number of engineering graduates at diploma level included graduates 
of hotel management. In the data presented, we have removed the number of graduates of hotel 
management. 

11..66    CCoonncclluussiioonnss  

Overall  a  number  of  sources  were  identified  containing  data  that  could  potentially  be  used  to 
meet the data requirements needed for the four benchmark related indicators.  

Indicator: Maths, Science and Technology  (MST) graduates 
A number of data sources were identified at the national level to complete the statistical gaps for 
China, India and Russia.  No additional data sources were found to complete the statistical gaps 

Key education indicators on social inclusion and efficiency 

41

 
 
 
 
 
 
 
 
 
  
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

for Greece for the period 2000 to 2003. The National Statistical Institute in China publishes data 
on the number of MST graduates annually. However, it is unclear how comparable the data are 
with the data published by Eurostat, since no details are given in the publication of the precise 
definitions  of    “science”  and  “engineering”.  In  India,  administrative  data  on  the  number  of 
graduates  broken  down  by  field  is  not  readily  available.    The  data  that  is  frequently  citied  in 
various reports in India tends to be based on surveys of the population or on projections, and not 
on  actual  administrative  records  of  higher  education  institutions.  In  consequence  there  is  no 
coherence between different sources for certain variables. Data on the number of MST graduates 
in  Russia has now been provided to the UNESCO. 

Indicator: Educational attainment of 20 to 24 year olds 
The  statistical  gaps  for  the  USA  have  now  been  filled  using  data  collected  from  the  Current 
Population Survey conducted annually by the Census Bureau for the years 2000 to 2004. Data 
from Japan can be collected from two different sources: the Employment Status Survey, and the 
Population Census for the years 2000 and 2002. However it has to be noted that both surveys are 
conducted only every 5 years, which means that data is only available for 2000 and 2002. 

Indicator: Early school leavers 18 to 24 year olds 
The Employment Status Survey and the Population Census conducted by Statistics Japan can be 
used to construct the indicator. However, both surveys are conducted only once every five years, 
Furthermore data is only disseminated for the age group 20 – 24, in contrast to the age-group 18-
24 required.   

The US National Center for Education Statistics normally publishes data on three different types 
of  dropout  rates:  event  dropout  rates;  status  dropout  rates;  and  cohort  dropout  rates.  The  US 
indicator  on  status  dropout rates is the  closet  in  definition to the  EU indicator on early school 
leavers,  than  the  other  indicators  on  dropouts.  Unfortunately,  the  indicator  refers  to  the  age 
group 16 to 24, whilst the EU indicator on early school leavers refers to the age group 18 to 24. 
The Current Population Survey (CPS) conducted by the US Census Bureau is used to construct 
the  indicators  on  status  dropout  rates,  and  can  be  used  to  construct  the  indicator  for  the  age 
group 18 – 24.  Data for other age groups can also be easily extracted.  

The Employment Status Survey and the Population Census conducted by Statistics Japan can be 
used to construct the indicator. However, both surveys are conducted only once every five years, 
Furthermore data is only disseminated for the age group 20 – 24, in contrast to the age-group 18-
24  required.    The  population  in  education  and  training,  which  both  these  surveys  refer  to,  is 
those students who are in regular or formal education, that is senior high school, junior college, 
or college or university including graduate school.  In contrast  the  data collected from the EU-
LFS for the EU Member States refers to participation in formal and non-formal education (e.g. 
language courses, computer courses, seminars etc…). 

A simple solution to the problem is to adjust the data on early school leavers from the EU-LFS 
by  only  counting  those  that  are  participating  in  formal  education  (that  is  regular  education)  as 
participating  in education and training.  Persons  participating in non-formal education will not 
be classified as participating in education and training. This will make the statistics comparable 
with the US. 

Key education indicators on social inclusion and efficiency 

42

 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

The US National Center for Education Statistics normally publishes data on three different types 
of  dropout  rates:  Event  dropout  rates;  Status  dropout  rates;  and  cohort  dropout  rates.  The  US 
indicator on  status  dropout rates is the  closet  in  definition to the  EU indicator  on early school 
leavers,  than  the  other  published  indicators  on  dropouts.  Unfortunately,  the  indicator  refers  to 
the age group 16 to 24, whilst the EU indicator on early school leavers refers to the age group 18 
to  24.  The  Current  Population  Survey  (CPS)  conducted  by  the  US  Census  Bureau  is  used  to 
construct the indicators on status dropout rates, and can be used to construct the indicator for the 
age group 18 – 24.  Data for other age groups can also be easily extracted.  

Whilst  it  is  not  possible  to  construct  this  indicator  for  Japan  for  the  age  group  18-24,  it  is 
possible to construct the indicator for the age group 20-24. Given that data is available for the 
US for this age group a comparison can be easily made. For the EU-25 data for the age group 20 
to 24 can be collected from Eurostat.  

Indicator: Participation in lifelong learning  
Existing  surveys  in  Japan  and  the  US  that  have  been  identified  as  collecting  information  on 
participation  in  lifelong  learning  cannot  be  used  to  construct  the  indicator  for  a  number  of 
reasons. This includes: 

-  Surveys in Japan and the US refer to participation in adult education and training during 

the past 12 months, instead of the past four weeks, as is needed to construct the indicator; 

-  The  definition  of  lifelong  learning  used  in  the  EU  indicator  is  far  wider  than  the  data 

collected by any survey in Japan or the US. 

An alternative indicator to participation in lifelong learning that can provide a useful insight into 
differences  between  EU  Member  States  and  the  US  is  the  indicator  C.6  ‘Participation  in 
continuing education and training’ which is currently employed in the OECD’s ‘Education at a 
Glance’.    However,  data  for  this  indicator  is  not  available  for  Japan,  and  the  only  data  that  is 
available for the US refers to participation in non-formal job-related education and training. 

Key education indicators on social inclusion and efficiency 

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Chapter 1: Completing statistical gaps for the benchmark indicators 

AAppppeennddiixx  

Key education indicators on social inclusion and efficiency 

44

 
 
 
 
  
  
  
  
  
  
  
Chapter 1: Completing statistical gaps for the benchmark indicators 

Indicator: Total number of tertiary (5A, 5B and 6) graduates from 
mathematics, science, and technology 

Number of graduates in Maths, Science and Technology 

EU-251 
China2 
India 
Russia3 

2000 
635.2 
: 
: 
: 

2001 
680.7 
: 
: 
: 

2002 
704.8 
: 
: 
: 

2003 
754.9 
: 
: 
: 

Unit: Thousands 

2004 
775.8 
1093.3 
: 
436.1 

Graduates (ISCED 5-6) in Maths, Science and Technology fields - as % of all fields 

EU-251 
China2 
India 
Russia3 

2000 
24.8 
: 
: 
: 

2001 
24.4 
: 
: 
: 

2002 
24.3 
: 
: 
: 

2003 
24.1 
: 
: 
: 

2004 
23.6 
43 
: 
25.6 

Students at ISCED levels 5-6 enrolled in the following fields: science, mathematics, 
computing, engineering, manufacturing, construction - as % of all students 

EU-251 
China2 
India 
Russia3 

2000 
26.1 
: 
: 
: 

2001 
26.0 
: 
: 
: 

2002 
26.1 
: 
: 
: 

2003 
25.9 
: 
: 
: 

2004 
25.8 
26.1 
: 
: 

Notes 
1  DG Education & Culture, Greece not included 
2  China  Statistical  Yearbook.  The  data  refer  to  graduates  of  science  &  engineering  of  regular  higher 
education institutions and postgraduates of institutions of higher education and research organisations 

3  UNESCO database 

Caution  needs  to  be  exercised  when  interpreting  the  figures  for  China,  since  no  definition  of 
‘science’ and ‘engineering’ or breakdown by discipline is given.  

Key education indicators on social inclusion and efficiency 

45

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Indicator:  Percentage  of  those  aged  20-24  that  have  successfully 
completed at least upper secondary education (ISCED 3) 

Total 

EU-251 
USA2 
Japan3 

Males 

EU-251 
USA2 
Japan3 

Females 

EU-251 
USA2 
Japan3 

2000 
76.6  
85.5 
91.8 

2000 
73.7 
83.7 
89.8 

2000 
79.5 
87.3 
93.7 

2001 
76.5 
84.8 
: 

2001 
73.7 
82.9 
: 

2001 
79.3 
86.8 
: 

2002 
76.7 
84.5 
94.3 

2002 
74.0 
83.3 
93.0 

2002 
79.4 
85.8 
95.7 

2003 
76.9 
85.6 
: 

2003 
74.2 
84.2 
: 

2003 
79.5 
86.8 
: 

2004 
77.1 
85.9 
: 

2004 
74.2 
84.1 
: 

2004 
80.1 
87.8 
: 

2005 
77.5 
85.9 

2005 
74.7 
84.4 

2005 
80.3 
87.3 

Methodological notes 
1    Eurostat  -  The  EU-LFS  results  cover  the  total  population  usually  residing  in  Member  States,  except 
persons living in collective or institutional households.  From 27 October 2006, this indicator is based on 
annual averages of quarterly data instead of one unique reference quarter in spring. This improves both the 
accuracy and reliability of the results thanks to a better coverage of all weeks of the year and an increased 
sample  size. Annual  averages  are  used  from 2005 onwards for all  countries except CH.  Spring  data  are 
used between 2000 and 2002 for DE, FR, LU, CY, MT, SE and IS, and for 2003-2004 for DE and CY. 
The average of the two semi-annual surveys is used for LV and LT for 2000-2001 and from 2002 for HR. 
 Estimations are performed by Eurostat in case of outliers or missing information in the quarterly series. 
From 1998 data onwards ISCED 3c levels of duration shorter than 2 years do not fall any longer under the 
level  ‘upper  secondary’  but  under  ‘lower  secondary’.  This  change  implies  revised  results  in  DK  (from 
2001),  ES,  CY  and  IS  compared  to  results  published  before  December  2005.  However,  the  definition 
cannot yet be implemented in EL, IE and AT where all ISCED 3c levels are still included.  
Due to changes in the survey characteristics, data lack comparability with former years in IT (from 1993), 
DK and DE (from 1996), PT (from 1998), BE and UK (from 1999), PL (1999 – quarter 1 for that year), FI 
(from  2000),  SE  and  BG  (from  2001),  LV  and  LT  (from  2002),  DK  and  HU  (from  2003),  AT  (from 
2004),  DE  (from  2005).  In  CY,  students  usually  living  in  the  country  but  studying  abroad  are  not  yet 
covered by the survey. FR data do not cover the overseas departments (DOM). TR: national data. In case 
of missing country data, the EU aggregates are provided using the closest available year result. 
2  US Census Bureau, Current Population Survey 
Annual  sample  size  is  about  100,000  addresses.  The  data  refer  to  the  civilian  non-institutionalised 
population.  The  CPS  sample  unit’s  householder  (one  of  the  people  in  whose  name  the  unit is  rented  or 
owned) must consider the unit to be his or her place of usual residence (where he or she spends most of 
the  time  during  the  year)  to  be  counted  as  an  occupied  unit,  which  is  traditional  in  most  censuses  and 
housing surveys. If a family has more than one home, the interviewer has to determine if the sample unit 
is  its  usual  residence.  The  CPS  includes  the  civilian  non-institutionalised  population.  This  universe 
includes civilians in households, people in non-institutional group quarters (other than military barracks) 
and military in households living off post or with their families on post (as long as at least one household 
member  is  a  civilian  adult).  The  universe  excludes  other  military  in  households  and  in  group  quarters 
(barracks),  and  people  living  in  institutions.  The  weighting  is  controlled  to  population  estimates  as  of 
March 1 (e.g., March 1, 2004 for the 2004 CPS  ASEC). The ASEC, asks respondents 15 years old and 
older about their highest degree or level of school completed. (Those aged 3 to 14 are asked about school 
enrolment.) 
3  Two sources of information were used to compute this indicator: 

Key education indicators on social inclusion and efficiency 

46

  
 
 
 
 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

(i.)  Statistics Japan, ‘Population Census’ was used to calculate the data for 2000. The figures presented 
here do not include persons whose type of school completed is not reported. The 2000 Population Census 
was  taken  as  of  0:00  a.m.,  1  October  2000.  The  data  refer  to  persons  who  graduated  from  senior  high 
school and above.  
(ii.)  Statistics  Japan,  ‘Employment  Status  Survey’  was  used  to  calculate  the  data  for  2002.  The  figures 
presented here do not include persons whose type of school completed is not reported. The data refer to 
persons who graduated from senior high school and above.  

The data show that the educational attainment of the population aged 20 –24 is higher both in 
Japan and the US than in the EU-25. The educational attainment of the population aged 20 – 24 
is very high in Japan. This is also true broken down by gender. From 2000 to 2004, the share of 
the population having successfully completed at least upper secondary education in the EU-25 
has increased.  This is in contrast, in the US, where it has only increased slightly. In the EU-25, 
Japan  and  the  US  the  percentage  of  females  attaining  at  least  upper  secondary  education  is 
greater than that of males. However the difference in educational attainment between genders is 
slightly  more  pronounced  in  the  EU-25  than  in  Japan  and  the  US.  From  2000  to  2004  the 
increase in females attaining at least upper secondary education was greater than that of males in 
the EU-25. 

Key education indicators on social inclusion and efficiency 

47

 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Indicator: Share of population aged 18-24 with only lower secondary 
education and not in education and training. 

Total 

EU-251 
USA2 
Japan 

Males 

EU-251 
USA2 
Japan 

Females 

EU-251 
USA2 
Japan 

2000 
17.3 
12.4 
: 

2000 
19.5 
13.8 
: 

2000 
15.2 
11.1 
: 

2001 
17.0 
13.1 
: 

2001 
19.2 
15.1 
: 

2001 
14.8 
11.1 
: 

2002 
16.6 
12.3 
: 

2002 
18.9 
14.0 
: 

2002 
14.4 
10.6 
: 

2003 
16.2 
11.8 
: 

2003 
18.1 
13.7 
: 

2003 
14.2 
9.9 
: 

2004 
15.6 
12.1 
: 

2004 
18.0 
13.9 
: 

2004 
13.1 
10.4 
: 

2005 
15.2 
: 
: 

2005 
17.3 
: 

2005 
12.8 
: 
: 

Methodological notes 
1    Eurostat  -  The  EU-LFS  results  cover  the  total  population  usually  residing  in  Member  States,  except 
persons living in collective or institutional households. Education and training refers to formal education 
and non-formal education. Data refer to the second quarter. 
From 1998 data onwards ISCED 3c levels of duration shorter than 2 years do not fall any longer under the 
level upper secondary but under lower secondary. This change implies revised results in DK (from 2001), 
ES, CY and IS compared to results published before December 2005. However, the definition cannot yet 
be implemented in EL, IE and AT where all ISCED 3c levels are still included. Due to the implementation 
of  harmonised  concepts  and  definitions  in  the  survey,  information  on  education  and  training  lack 
comparability with former years: 
-  from 2005 in SE due to changes in the reference period (formerly 4 weeks preceding the survey  instead 
of one week from 2005), 
-   from 2003 in CZ, DK, EL, IE, CY, HU, NL, AT, SI, FI, SE, NO, CH, from 2004 in BE, LT, IT, IS, 
MT, PL, PT, UK and RO, and from 2005 in ES due to wider coverage of  taught activities  
-  from 2003 in SK  due to restrictions for self-learning  
-   in 2003 and 2004 in DE due to the exclusion of personal interest courses 
-   in 2001 and 2002 in SI due to the exclusion of certain vocational training 
-  1999 in NL, 2000 in PT, 2003 in FR and CH due to changes in the reference period (formerly one week 
preceding the survey; additionally in CH: 12 months for vocational training instead of 4 weeks), 
-   LU (1999) due to a new definition of lower secondary education level 
-  EU15, euro area, EU25, consequently. 
2  US Census Bureau, Current Population Survey 
   Annual  sample  size  is  about  100,000  addresses.  The  data  refer  to  the  civilian  non-institutionalised 
population. The CPS sample unit’s householder (one of the people in whose name the unit is rented or 
owned) must consider the unit to be his or her place of usual residence (where he or she spends most of 
the time during the year) to be counted as an occupied unit, which is traditional in most censuses and 
housing  surveys.  If  a  family  has  more  than  one  home,  the  interviewer  has  to  determine if  the  sample 
unit is its usual residence. The CPS includes the civilian non-institutionalised population. This universe 
includes  civilians  in  households,  people  in  non-institutional  group  quarters  (other  than  military 
barracks) and military in households living off post or with their families on post (as long as at least one 
household member is a civilian adult). The universe excludes other military in households and in group 
quarters (barracks), and people living in institutions. The weighting is controlled to population estimates 
as of March 1 (e.g., March 1, 2004 for the 2004 CPS ASEC). The ASEC, asks respondents 15 years old 
and older about their highest degree or level of school completed. (Those aged 3 to 14 are asked about 
school enrolment.) Participation in education refers to formal education only. 

Key education indicators on social inclusion and efficiency 

48

 
 
 
 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Indicator: Share of population aged 20-24 with only lower secondary 
education and not in education and training. 

Total 

EU-25 
USA1 
Japan2 

Males 

EU-25 
USA1 
Japan2 

Females 

EU-25 
USA1 
Japan2 

2000 
: 
12.4 
5.8 

2000 
: 
13.6 
6.8 

2000 
: 
11.1 
4.8 

2001 
: 
13.1 
: 

2001 
: 
15.0 
: 

2001 
: 
11.2 
: 

2002 
: 
12.8 
5.7 

2002 
: 
14.4 
6.9 

2002 
: 
11.2 
4.3 

2003 
: 
12.1 
: 

2003 
: 
14.1 
: 

2003 
: 
10.2 
: 

2004 
: 
12.6 
: 

2004 
: 
14.0 
: 

2004 
: 
11.2 
: 

Methodological notes 
1    Eurostat  -  The  EU-LFS  results  cover  the  total  population  usually  residing  in  Member  States,  except 
persons living in collective or institutional households. Education and training refers to formal education 
and non-formal education. Data refer to the second quarter. 
From 1998 data onwards ISCED 3c levels of duration shorter than 2 years do not fall any longer under the 
level upper secondary but under lower secondary. This change implies revised results in DK (from 2001), 
ES, CY and IS compared to results published before December 2005. However, the definition cannot yet 
be implemented in EL, IE and AT where all ISCED 3c levels are still included. Due to the implementation 
of  harmonised  concepts  and  definitions  in  the  survey,  information  on  education  and  training  lack 
comparability with former years: 
-  from 2005 in SE due to changes in the reference period (formerly 4 weeks preceding the survey  instead 
of one week from 2005), 
-   from 2003 in CZ, DK, EL, IE, CY, HU, NL, AT, SI, FI, SE, NO, CH, from 2004 in BE, LT, IT, IS, 
MT, PL, PT, UK and RO, and from 2005 in ES due to wider coverage of  taught activities  
-  from 2003 in SK  due to restrictions for self-learning  
-   in 2003 and 2004 in DE due to the exclusion of personal interest courses 
-   in 2001 and 2002 in SI due to the exclusion of certain vocational training 
-  1999 in NL, 2000 in PT, 2003 in FR and CH due to changes in the reference period (formerly one week 
preceding the survey; additionally in CH: 12 months for vocational training instead of 4 weeks), 
-   LU (1999) due to a new definition of lower secondary education level 
-  EU15, euro area, EU25, consequently. 
2  US Census Bureau, Current Population Survey 
   Annual  sample  size  is  about  100,000  addresses.  The  data  refer  to  the  civilian  non-institutionalised 
population. The CPS sample unit’s householder (one of the people in whose name the unit is rented or 
owned) must consider the unit to be his or her place of usual residence (where he or she spends most of 
the time during the year) to be counted as an occupied unit, which is traditional in most censuses and 
housing  surveys.  If  a  family  has  more  than  one  home,  the  interviewer  has  to  determine if  the  sample 
unit is its usual residence. The CPS includes the civilian non-institutionalised population. This universe 
includes  civilians  in  households,  people  in  non-institutional  group  quarters  (other  than  military 
barracks) and military in households living off post or with their families on post (as long as at least one 
household member is a civilian adult). The universe excludes other military in households and in group 
quarters (barracks), and people living in institutions. The weighting is controlled to population estimates 
as of March 1 (e.g., March 1, 2004 for the 2004 CPS ASEC). The ASEC, asks respondents 15 years old 
and older about their highest degree or level of school completed. (Those aged 3 to 14 are asked about 
school enrolment.) Participation in education refers to formal education only. 

Key education indicators on social inclusion and efficiency 

49

 
 
 
 
 
 
 
 
 
 
Chapter 1: Completing statistical gaps for the benchmark indicators 

Indicator: Percentage of population aged 25-64 participating in 
education and training 4 weeks prior to the survey 

Percentage  of  population  aged  25-64  participating  in  education  and  training  4  weeks 
prior to the survey 

Total 

EU-251 
USA 
Japan 

Male 

EU-251 
USA 
Japan 

Female 

EU-251 
USA 
Japan 

2000 
7.9  
: 

2000 
7.4  
: 

2000 
8.4  
: 

2001 
7.8  
: 
: 

2001 
7.2  
: 
: 

2001 
8.4  
: 
: 

2002 
7.9 
: 
: 

2002 
7.2 
: 
: 

2002 
8.5 
: 
: 

2003 
9.2  
: 
: 

2003 
8.5  
: 
: 

2003 
10.0  
: 
: 

2004 
10.3  
: 
: 

2004 
9.4  
: 
: 

2004 
11.1  
: 
: 

Methodological notes 
1 Eurostat Population Statistics. Life-long learning is computed on the basis of the variable ‘participation 
in  education  and  training  in  the  last  four  weeks’  from  the  EU  Labour  Force  Survey.  From  2004,  this 
variable  is  derived  from  two  variables  ‘participation  in  regular  education’  and  ‘participation  in  other 
taught  activities’.  Self-learning  activities  are  no  longer  covered.  The  EU-LFS  results  cover  the  total 
population  usually  residing  in  Member  States,  except  persons  living  in  collective  or  institutional 
households. 

Key education indicators on social inclusion and efficiency 

50

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
Chapter 2:Combining data on public and private spending on education 

Chapter 

2 

CCoommbbiinniinngg   ddaattaa   oonn   ppuubblliicc
aanndd   pprriivvaattee  
ssppeennddiinngg   oonn
eedduuccaattiioonn  

Key education indicators on social inclusion and efficiency 

 
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

CCoommbbiinniinngg  ddaattaa  oonn  ppuubblliicc  aanndd  pprriivvaattee  
ssppeennddiinngg  oonn  eedduuccaattiioonn  

22..11  IInnttrroodduuccttiioonn  

Investment  in  human  resources  is  of  great  importance  in  realising  the  strategic  goal  set  in  the 
Lisbon Council in March 2000 of becoming the most competitive and dynamic knowledge based 
economy in the world by 2010. Education and training are crucial to achieving this goal. In order 
for this goal to be fulfilled, sufficient resources must be invested in Member States’ education and 
training and these have to be well targeted and managed in the most efficient way. The European 
Council called for a “substantial annual increase in per capita investment in human resources”. 
Realising a genuine and sustained increase in investment in human resources requires action from 
all relevant actors (individuals, enterprises, social partners and public authorities).  

The  2001  Stockholm  European  Council  agreed  that  efforts  should  continue  to  develop  a  work 
programme organised around the quality and effectiveness, facilitating access to all, the openness 
to the world of education and training systems. To enable this to happen, the Stockholm European 
Council adopted thirteen concrete objectives.  The level of investment in education and training 
has implications for all 13 objectives and most of the key issues in the Detailed Work Programme 
(Education and Training 2010). One of the objectives adopted was objective 1.5, which aimed at 
“Making  best  use  of  resources”1.  Currently  investment  in  education  is  monitored  through  5 
indicators: 

•  Public expenditure on education as a percentage of GDP; 
•  Private expenditure on educational institutions as a percentage of GDP; 
•  Enterprise  expenditure  on  continuing  vocational  training  as  a  percentage  of  total  labour 

costs; 

•  Total expenditure on educational institutions per pupil/ student by level of education (PPS); 
•  Total  expenditures  on  educational  institutions  per  pupil  /student  by  level  of  education 

relative to GDP per capita. 

Despite the fact that the above indicators cover both public and private investments in education, 
including total  expenditure  on educational institutions,  there is  no indicator that covers the total 
investment  made  in  education  from  public  and  private  sources,  both  inside  and  outside  of 
educational institutions. 

The  ideal  situation  would  be  that  total  spending  on  education  could  be  calculated  from  one 
statistical  source. However,  if this is  not possible, then  data  from  a number of different  sources 

1 http://europa.eu.int/comm/education/policies/2010/objectives_en.html#making 

Key education indicators on social inclusion and efficiency 

52

 
  
  
  
  
  
 
 
 
                                                 
Chapter 2:Combining data on public and private spending on education 

needs to be combined. In this less than ideal situation,  a number of issues need to be taken into 
consideration.  These  include  amongst  other  things  data  requirements,  type  of  data  currently 
collected  from  national  and  international  data  sources,  coverage  of  education,  concepts, 
definitions  and  classifications  used  in  the  sources,  periodicity  of  data  collection  and  other 
methodological aspects.  

This chapter examines the data that is currently collected by data collections firstly at international 
level,  and  then  at  national  level,  and  methodological  aspects  that  need  to  be  taken  into 
consideration when combining data from different sources. 

22..22   DDaattaa   nneeeeddss   ffoorr   ccaallccuullaattiinngg   ttoottaall   ssppeennddiinngg   oonn  
eedduuccaattiioonn  

In order to calculate total spending on education, data on public and private spending needs to be 
combined together. In its most simplistic form, equation one summarises the data that is required 
in order to calculate total national spending on education for each country. 

Equation 1:   

Total spending on education = Public  + Private 

Public expenditure consists of spending by central, regional and local government on education.  
Private expenditure can be further broken into expenditure by: 

•  Private households and  
•  Other private entities.  

The  definitions  used  in  the  UNESCO/OECD/Eurostat  collection  on  education  systems  could  be 
used to  define  these  two  terms.  According to the  methodology the term   ‘other private  entities’ 
includes  private  businesses  and  non-profit  organisations,  including  religious  organisations,  and 
business and labour associations. It also includes expenditure by private companies on the work-
based element of school and work-based training of apprentices and students.  

The term ‘private households’ refer to students and their families. Whilst this definition is more 
than adequate to meet the needs of the UNESCO/OECD/Eurostat collection, it is too ambiguous 
when the intention is to merge data from several sources, such as household budget surveys. For 
the  purpose  of  this  exercise  we  will  adopt  the  Eurostat  proposal2  of  a  definition  of  private 
households,  which  is  based  on  two  criteria:  co-residence  and  sharing  of  expenditures.  Thus  the 
term ‘private households’ can be defined as persons sharing a common accommodation or address 
and  sharing  of  expenditures  including  joint  provision  of  essentials  of  living.  This  would 
undoubtedly include students and their families.  

2  ‘Household  Budget  Surveys  in  the  EU-  Methodology  and  recommendations  for  harmonisation  –  2003’, 

Eurostat 

Key education indicators on social inclusion and efficiency 

53

 
  
  
  
  
 
 
 
 
 
 
                                                 
Chapter 2:Combining data on public and private spending on education 

Equation 1 can be disaggregated into equation 2 which breakdowns the components further. 

Equation 2: 

Total national spending  =  Σi

3 Public   +   Private household     +   Other private entities  
      01 

          02 

   03 

Abbreviations 
where     i=1 is central government spending on education; 
i=2 is regional government spending on education; 
i=3 is local government spending on education. 

01. 

02. 
03. 

Public  refers  to  spending  on  education  by  central  (national)  government;  regional  government 
(province, state, Land, etc…); and local government (municipality, district, commune etc…) 
Private households refer to students and their families  
Other private entities include private businesses and non-profit organisations, including religious 
organisations, charitable organisations and business and labour associations. 

It is important to remember that equation 2 does not take into account other international/foreign 
sources.  If  we  incorporate  these,  the  result  will  be  the  total  spending  on  education  within  a 
country (see equation 3). 

Equation 3: 

Total spending  = Σi

3 Public +  Private household +   Other private entities +      inter 
   01 

       03                                       04 

02 

Abbreviations 

04. 

Inter refers to funds from international agencies and other foreign sources. 

Equation 2 is a subset of equation 3. The final figure for spending derived from equation 2 will be 
lower than that from equation 3.  

For  the  purpose  of  this  statistical  exercise  we  concentrated  on  collecting  data  in  order  to  solve 
equation 2. 

It is important to note that total spending on education consists of spending on: 

•  Goods  and  services  of  educational  institutions-  this  includes  all  direct  public,  private  and 
international  expenditure  whether  educational  or  non  educational  (for  example  ancillary 
services); 

•  Private  expenditure  on  educational  goods  and  services  purchased  outside  of  educational 

institutions; 

•  Public subsidies to students for student living costs regardless of where or how the student 

spends the subsidies; 

•  Transfers and payments to other private entities. 

Key education indicators on social inclusion and efficiency 

54

  
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

In  collecting  data  on  certain  categories  of  spending  (for  example:  Research  and  Development, 
ancillary  services),  a  number  of  problems  arise  which  will  inevitably  pose  problems  for 
comparability. These problems will be considered later in this chapter in slightly more depth. 

Equation 2 can be decomposed down further as follows: 

Equation 4: 

Totalexp  =  Σi

3 Pubinst  Σi

3 Pubsub  + Hdinst   +   Hdout  + Oth_privinst  + Oth_privout  

Abbreviations 
where     i=1 is central government spending on education; 
i=2 is regional government spending on education; 
i=3 is local government spending on education. 

Pubins                 Public expenditure on educational institutions 
Pubsubt               Public subsidies to the private sector (e.g. subsidies to cover student living costs 
Hdinst                  Household expenditure on educational institutions 
Hdout                   Household expenditure outside of educational institution 
Oth_privinst   Other private entity expenditure on educational institutions 
Oth_privout     Other private entity expenditure outside of educational institutions 

In  summary  table  2.1  summarises  the  data  requirements  for  calculating  total  spending  on 
education: 

Table 2.1: Summary of data required to calculate total expenditure on education 

Variable 
Public spending  

household 

Private 
spending  
Other  private  entity 
spending  

Component variables 

Goods and services of educational institutions 
Goods and services of purchased outside of educational institutions 
Public subsidies to students for student living costs 
Goods and services of educational institutions 
Goods and services of purchased outside of educational institutions 
Goods and services of educational institutions 
Goods and services of purchased outside of educational institutions 

Notes 
Goods  and  services  of  educational  institutions  includes  all  direct  public,  private  and  international 
expenditure.  Educational  institutions  include  teaching  institutions,  and  non-teaching  institutions  such  as 
ministries,  local  authorities  and  student  unions.  It  covers  all  expenditure  on  teachers,  school  buildings, 
teaching materials, books, and administration of schools. 
Goods and services of purchased outside of educational institutions  - This includes books, private tutoring, 
student living costs and student transport not provided by educational institutions. This would also include 
costs of training at the workplace excluding expenditure on educational institutions. 
Public spending includes central, regional and local government 

Key education indicators on social inclusion and efficiency 

55

 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
Chapter 2:Combining data on public and private spending on education 

22..33    DDaattaa  ccuurrrreennttllyy  ccoolllleecctteedd  ffrroomm  iinntteerrnnaattiioonnaall  ssoouurrcceess  

The following international data sources collect data  concerning  public and private  spending  on 
education: 

•  UNESCO/OECD/Eurostat collection on education systems; 
•  Eurostat Harmonised Household Budget Survey (EU-HBS) 
•  Continuing Vocational Training Survey (CVTS); 
•  Labour Cost Survey (LCS). 

Table  2.2  lists  the  periodicity  of  each  data  collection  along  with  the  year  for  which  the  latest 
available data is available on the Eurostat website3. Each of the above mentioned data collections 
varies  in  the  periodicity  in  which  they  are  conducted.  The  UNESCO/OECD/Eurostat  data 
collection  is  the  only  data  collection,  which  is  conducted  annually,  whilst  the  other  data 
collections are conducted at various regular intervals of between four to six years.  Whilst it is true 
to say that the Continuing Vocational Training Survey, Eurostat Harmomised Household Budget 
Survey, and the EU Labour Cost Survey can provide information on expenditure on education, the 
long  intervals  between  surveys  effectively  means  that  in  using  these  surveys  to  calculate  total 
spending on education there will be statistical gaps. 

Table 2.2:  Periodicity and availability of data from international data collections 

Name of survey 

Continuing 
Training Survey (CVTS2) 

Vocational 

Eurostat 
Household Budget Survey 

Harmonised 

Periodicity of collection 
The survey is carried out approximately every 6 
years.  For  CVTS  1the  reference  year  is  1993, 
and for CVTS2 it is 1999. 
Every 5 years: 1988, 1994, 1999, and 2005 

EU-Labour  Cost  Survey 
(LCS) 

Every 4 years 

UNESCO/OECD/Eurostat 
collection 
systems 

on 

education 

Every year since 1998 

Latest available year 
1999 

1999  (2005  is  not  yet 
available  on  Eurostat 
website) 
2000  (2004  is  not  yet 
available  on  Eurostat 
website) 

2004 

Another important point that needs to be taken into consideration is the geographical coverage of 
each of the international data collections. Table 2.3 shows that only the UNESCO/OECD/Eurostat 
statistical  exercise  covers  all  EU-25  countries,  and  in  addition  the  acceding  and  candidate 
countries  and  non-EU  OECD  countries.  The  other  three  surveys  are  conducted  within  the 
European Statistical System (ESS), which means that they tend to focus their collection activities 
on EU countries. However, the countries covered by each collection round varies in each of these 
data  collections.  For  example,  in  1988  Eurostat  harmonised  the  results  of  household  budget 
surveys for 10 EU countries, and in 1999 15 EU countries were covered along with Bulgaria and 
Romania. This means that there will be statistical gaps for a number of countries. 

3 www.europa.eu.int/comm/eurostat 

Key education indicators on social inclusion and efficiency 

56

  
  
 
 
 
 
                                                 
 
 
Chapter 2:Combining data on public and private spending on education 

Table 2.3: Geographical coverage of international data collections 

Name of survey 

Continuing 
Training Survey (CVTS2) 

Vocational 

Eurostat 
Household Budget Survey 

Harmonised 

EU-Labour  Cost  Survey 
(LCS) 
UNESCO/OECD/Eurostat 
collection 
systems 

education 

on 

Geographical coverage 
CVTS1  covered  the  then  EU12  Member  States.  CVTS2  covered  EU 
Members (except CY, MT,  and  SK), and NO  and 2  Acceding Countries 
(BG, RO)  
1988: EU-10 (BE, DE, EL, ES, FR, IT, LU, NL, PT and UK) 
1995: EU-15 (BE, DK, DE, EL, ES, FR, IE, IT, LU, NL, PT AT, FI, SE 
and UK) 
1999: EU-15 and BG, RO 
2000: EU-25 except MT 

All  EU-25  Member  States,  EFTA/EEA  countries  (IS,  LI,  NO,  CH), 
Acceding countries (BG, RO) 

Each source varies in the type of data that is collected, whether it is public or private expenditures. 
Table  2.4,  summarises  the  data  collected  in  each  of  the  sources.  It  shows  that  the 
UNESCO/OECD/Eurostat data collection is the only source, which covers the entire spectrum of 
expenditures, both public and private. The other data sources focus solely on private expenditure. 
Table  2.5  summarises  the  types  of  education  expenditures  that  are  included  in  each  of  the  data 
collections 

Each of the above mentioned data sources are considered in turn below in order to determine the 
type  of  information  that  they  can  provide  concerning  private  and  public  expenditures  on 
education.  

Key education indicators on social inclusion and efficiency 

57

 
 
 
Chapter 2:Combining data on public and private spending on education 

Table 2.4: Summary of data collected on public and private expenditure on education by international data collections 

Goods 
services 
educational 
institutions 

Public (State, Regional, local) 
Goods  and  services 
and 
purchased 
of 
of 
of 
outside 
educational 
institutions 

Public 
to 
subsidies 
for 
students 
student  living 
costs 

Goods 
services 
educational 
institutions 

and 
of 

Private household 
Goods 
services 
purchased 
outside 
educational 
institutions 

Other private entity 

and 
of 

of 

and 
of 

Goods 
services 
educational 
institutions 

and 
of 

of 

Goods 
services 
purchased 
outside 
educational 
institutions 

n 

n 

n 

n 

n 

n 

Table 2.5: Coverage of education expenditure by international data collections 

Continuing Vocational Training 
Survey (CVTS2 
Refers  to  expenditure  on  internally  and 
externally  managed  CVT  courses  of 
enterprises. Does not include expenditure 
on initial  vocational training. It  does not 
include  expenditure  on  other  forms  of 
CVT.  

Eurostat Harmonised Household 
Budget Survey2 

include 

It  refers  only 
to  private  household 
expenditure  on  educational  services.  It 
does  not 
expenditure  on 
educational  materials  such  as  books  and 
stationary or educational support services 
such  as  healthcare  services,  transport 
services and accommodation. It also does 
not 
include  expenditure  on  driving 
lessons, recreational lessons, and sport or 
tourist activities.  It includes expenditure 
on  education  transmitted  through  the 
radio or television 

n 

n 

n 

n 

n 

Labour Cost Survey (LCS) 

of 

buildings 
staff 

Refers  to  expenditures  of  enterprises  on 
CVT  and  apprenticeship.  These  include: 
expenditure on  vocational training services 
and  facilities,  depreciation,  small  repairs 
and 
and  maintenance 
installations, 
costs; 
excluding 
expenditure on participation in courses; the 
fees  of 
the 
enterprise;  expenditure  on  teaching  aids 
and  tools  used  for  training;  sums  paid  by 
the  enterprise 
training 
to 
organisations,  etc.  Subsidies 
vocational training are deducted. It includes 
the  costs  of  apprentices,  however  this 
expenditure can be separated out. 

to  vocational 

from  outside 

instructors 

linked 

Data source 

UNESCO/OECD/Eurostat  collection 
on education system 
Eurostat  Harmonised  Household 
Budget Survey2 
Continuing  Vocational  Training 
Survey (CVTS2)3 
Labour Cost Survey (LCS)4 

level 

ISCED 

is  not 

institutions.  Data 

UNESCO/OECD/Eurostat collection on 
education systems 
Does  not  include  private  non-subsidised 
expenditure on student living costs outside 
of  educational 
is 
collected  by  ISCED  level,  which  means 
that if a programme cannot be assigned by 
a 
included. 
it 
Consequently,  certain  types  of  adult  or 
continuing  education  are  not  included  if 
their  subject  content  is  not  similar  to 
regular  programmes  or it does  not  lead  to 
similar  qualifications  as  corresponding 
regular  programmes.  It  does  not  include 
in 
vocational  and 
training 
technical 
the 
enterprises  with 
of 
exception 
combined 
and  work  based 
school 
programmes  that  are  explicitly  deemed  to 
be parts of the education system. 

Key education indicators on social inclusion and efficiency 

58

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

22..33..11    UUNNEESSCCOO//OOEECCDD//EEuurroossttaatt  ccoolllleeccttiioonn  oonn  eedduuccaattiioonn  ssyysstteemmss  

Whilst  the  UNESCO/OECD/Eurostat  collection  on  education  systems  appears  to  meet  most 
of  the  data  requirements  outlined  out  in  table  2.1,  for  collecting  information  on  both  public 
and  private  spending  on  education,  national  statistical  offices  do  not  provide  all  the  data 
requested.  This  is  particularly  the  case  with  regard  to  private  household  spending  on 
education and expenditure of other private entities. Table  2.6, summarises the data situation 
for the year 2003 with regard to the variables of interest from the data collection.  

Table 2.6: Availability of required data in the UOE data collection for the year 2003 by variable 
(all levels of education) 

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

G5 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 

h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 

G20 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 

h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 

H5 
h 
h 
h 

h 
h 
h 
h 
h 

h 
h 

h 
h 
h 
h 
h 
h 
h 
h 

h 
h 
h 

h 

h 

h 

H18 
h 
h 
h 
h 

H20 
h 
h 
h 

h 

h 

h 

h 

h 

h 
h 

h 

h 
h 

h 
h 
h 
h 
h 

h 
h 

h 
h 
h 
h 
h 
h 
h 

h 
h 

E5 
h 
h 

h 

h 
h 
h 
h 
h 

h 
h 

h 
h 
h 
h 

h 
h 

h 
h 

h 

h 

Key 
G5 
G20 
H5 
H18 

H20 
E5 

Direct expenditure of all levels of government on all types of institutions 
Education expenditure for all levels of government 
Payment to educational institutions by households 
Payments  by  households  for  educational  goods  and  services  other  than  to  educational 
institutions 
Educational expenditure of households 
Payments to all types of institutions by other private entities 

Key education indicators on social inclusion and efficiency 

59

  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Data  concerning  public  expenditure  on  educational  institutions  and  outside  of  them  is 
available  for  all  countries,  with  the  exception  of  Cyprus.  Fewer  than  three-quarters  of 
countries  were  able  to  provide  data  on  household  spending  on  educational  institutions.  In 
contrast,  only  42  per  cent  of  countries  provided  data  on  spending  by  private  households  on 
educational  goods  and  services  other  than  to  educational  institutions.    Data  relating  to 
household payments to educational institutions can be extracted from administrative sources 
such as the accounts of educational institutions. In contrast, payments for goods and services 
outside of educational institutions can only be adequately captured by a survey of households. 
This  means  that  as  a  result  of  incomplete  coverage  of  payments  by  private  households  on 
educational  goods  purchased  outside  educational  institutions,  total  private  expenditure  on 
education is underestimated. Less than three-fifths of countries provided data on expenditure 
on  education  by  other  private  entities.  Apart  from  the  issue  of  data  availability,  there  are  a 
number of other issues that need to be taken into consideration regarding the data collected. 
These issues are as follows: 

•  Comparability of data regarding data collected on educational goods and services 

purchased by households 
The Eurostat ‘Survey on Country profiles’ found that countries consider different sets 
of  items  as  educational  goods  and  services.  Therefore  the  question  of  comparability 
arises  between  the  data  that  is  collected.  (This  is  dealt  with  in  slightly  more  detail  in 
section 2.6.6) 

•  Coverage of education  

The coverage of education is limited to programmes that can be assigned to an ISCED 
level. Thus if a programmes subject content is not similar to regular programmes or it 
does  not  lead  to  similar  qualifications  as  corresponding  regular  programmes,  then  the 
expenditure  is  not  included.  Consequently,  certain  types  of  adult  or  continuing 
education are not included if their subject content is not similar to regular programmes 
or it does not lead to similar qualifications as corresponding regular programmes.  

Courses for adults that are primarily for general interest or personal enrichment and / or 
for  leisure  are  excluded.  It  also  does  not  include  vocational  and  technical  training  in 
enterprises with the exception of combined school and work based programmes (such 
as dual-system  apprenticeship) that are explicitly deemed to  be parts  of the  education 
system. This  means that  private investment in adult training is  under-estimated in the 
UNESCO/OECD/Eurostat statistical exercise. 

•  Financial aid to students 

Tax  benefits  have  been  identified  as  an  instrument  to  subsidise  participants  in 
educational programmes at the higher education level in many European countries. The 
UNESCO/OECD/Eurostat statistical exercise excludes any tax benefits to students and 
their families in the data collected on public subsidies to households. In not taking into 
account  tax  benefits  when  comparing  financial  aid  to  students  across  Europe  leads  to 
over-estimating  financial  aid  to  students  of  countries  not  offering  tax  reductions 
compared  to  those  channelling  a  part  of  their  subsidies  to  participants  of  educational 
programmes in form of tax benefits.  

Key education indicators on social inclusion and efficiency 

60

 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Furthermore,  at  the  present  time,  countries  report  data  on  student  loans  to  the 
UNESCO/OECD/Eurostat statistical exercise on a gross basis. This means that student 
loans  are  reported  without  subtracting  or  netting  out  repayments  or  interest  payments 
from the borrowers (students or households). The expenditure reported on student loans 
represents  the  total  value  of  loans  paid  by  the  government  to  students  during  the 
reference year. The cost to government of servicing these loans in the form of interest 
rate subsidies and the cost of default payments is not included. However, government 
supported  loans  (e.g.  interest  subsidies,  costs  of  guaranteeing  loans,  cost  of  default 
payments) paid by private financial institutions are included under public subsidies to 
other  private  entities.  It  can  be  argued  that  public  student  loans  tend  to  over-estimate 
the contribution of the public authorities because student loans are reported on a gross 
basis  and  do  not  include  the  reimbursements  from  former  beneficiaries.  Table  2.7 
shows 
the 
UNESCO/OECD/Eurostat statistical exercise. 

that  not  many  countries  provide  data  on 

student 

loans 

to 

Table 2.7: Availability of data on student loans in the UOE data collection by year (all levels of 
education) 

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

2000 

h 
h 
n 

na 

h 
h 
h 

na 

h 
na 

n 

h 

h 
n 
na 
na 
h 

h 

h 

2001 
h 
na 
h 
h 
na 

2002 
h 
na 
h 
h 
na 

2003 
h 
na 
h 
h 
n 

n 
na 
n 
h 

h 
h 
na 
h 
na 
h 
na 
n 

h 
na 
h 
n 
na 
na 
h 

h 

h 

N 
na 
n 
h 

h 
h 

h 

h 
na 
na 

na 
h 
na 
h 
h 
na 
na 
h 
h 
h 

h 
h 

n 
na 

h 

h 
h 

na 
n 
h 
na 
na 

n 
h 
n 
h 
h 
na 
na 
h 
n 
h 
h 
h 
h 

Key 
na 
n 

Not applicable 
nil 

Key education indicators on social inclusion and efficiency 

61

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

In order to assess the magnitude of the problem more information needs to be reported 
to the UNESCO/OECD/Eurostat exercise to facilitate the interpretation of indicators on 
financial aid to students currently published. In the long term the concepts on financial 
aid to students (eg the inclusion of tax benefits, procedure to net student loans) need to 
be revised in order to enable the development of unbiased indicators measuring equity 
of education systems. 

• 

Inclusion of state subsidies /tax incentives under private expenditure  
Expenditure  by  enterprises  is  collected  under  expenditure  of  ‘other  private  entities’.  
This includes expenditure by private employers on the training of apprentices and other 
participants  in  mixed  school  and  work  based  educational  programmes.  This  relates  to 
the costs of apprenticeship training after the deduction of expenditure on staff costs of 
apprentices. However, state subsidies and or tax incentives to enterprises offering this 
type  of  training  have  not  been  deducted  from  the  reported  total.  Therefore,  it  can  be 
argued that expenditure by other private entities is over-estimated. 

Nevertheless,  it  should  be  mentioned  that  the  UNESCO/OECD/Eurostat  statistical 
exercise also collects data on public subsidies to other private entities separately. This 
includes  for  example  government  transfers  and  certain  other  payments  (mainly 
subsidies)  to  other  private  entities  such  as  commercial  companies  and  non-profit 
organisations. Therefore it could be argued that public subsidies could be deducted. In 
2003, one third of countries provided data on public transfers to other private entities, 
whilst  12  per  cent  were  not  able  to  supply  this  information.  For  the  other  countries 
spending was nil or the expenditure category was not applicable (see table 2.8).  

The  UNESCO/OECD/Eurostat  statistical  exercise  also  collects  separately  expenditure 
by private enterprises on the work-based element of school and work-based training of 
apprentices. However, in reality only six percent of countries supplied this information. 
This  can  lead  to  the  conclusion  that  spending  by  other  private  entities  is  under-
estimated. 

Key education indicators on social inclusion and efficiency 

62

 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Table 2.8: Availability of data on public subsidies to other private entities and enterprise 
expenditure on training of apprentices and students in 2003 by variable (all levels of education) 

E5a 
h 

n 

n 
h 

n 
n 
na 

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

G13 
h 
n 
n 
h 
h 
n 
n 
na 
h 
n 

n 

n 
n 
n 
h 
h 
h 
h 
na 
h 
na 
n 
na 
n 
h 
h 
n 

n 

Key 
G13 
E5b 

n 
a 

All levels of government transfers to other private entities 
Expenditure by private enterprises on the work-based element of school and work-based 
training of apprentices and students 
Nil 
Not applicable 

22..33..22    EEuurroossttaatt  HHaarrmmoonniisseedd  HHoouusseehhoolldd  BBuuddggeett  SSuurrvveeyy 

Essentially,  Household  Budget  Surveys  provide 
information  concerning  household 
consumption  expenditures  on  goods  and  services,  with  considerable  detail  in  the  categories 
used. Historically the prime objective of conducting HBSs in all member States was to collect 
information on household consumption expenditures for use in updating the ‘weights’ for the 
basket of goods used in the Consumer Price Indices (CPI). 

Key education indicators on social inclusion and efficiency 

63

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Data  collected  by  Eurostat  from  National  Statistical  Institutes  for  the  EU  Harmonised 
Household  Budget  Survey  has  been  output  harmonised.  Eurostat  undertakes 
the 
harmonisation exercise every 5 years.  

It is important to note that Eurostat has not harmonised the results of national HBSs for all 25 
Member States for all three years.  

The  Eurostat  Harmonised  Household  Budget  Survey  collects  data  from  national  HBSs 
according to the Classification of Individual Consumption by Purpose (COICOP-HBS). The 
data  collected  by  Eurostat  under  the  division  ‘education’  under  the  COICOP-HBS 
classification  relates  only  to  educational  services.  It  does  not  include  expenditure  on 
educational  materials  such  as  books  and  stationary  or  educational  support  services  such  as 
healthcare  services,  transport  services  and  accommodation.  It  also  does  not  include 
expenditure on driving lessons, recreational lessons, and sport or tourist activities.  However it 
includes expenditure  on education transmitted through the radio or television. Thus the data 
collected will not include a certain proportion of expenditure. 

22..33..33    CCoonnttiinnuuiinngg  VVooccaattiioonnaall  TTrraaiinniinngg  SSuurrvveeyy  ((CCVVTTSS)) 

The purpose of this survey is to obtain some key information about the training provided by 
enterprises  for  their  employees.  The  focus  of  the  survey  is  continuing  vocational  training. 
Continuing Vocational Training is defined as ‘training measures or activities which have as 
their  primary  objectives  the  acquisition  of  new  competencies  or  the  development  and 
improvement  of  existing  ones  and  which  enterprises  finance,  wholly  or  partly  for  their 
employees who either have a working contract or who benefit directly from their work for the 
enterprise such as unpaid family workers and casual workers’.  The survey concentrates on 
training that is provided for those employees who are not apprentices and trainees.  

The CVTS2 did not include expenditure on the following measures and activities: 

-  Measures  for  persons  initiated  by  and  entirely  financed  by  the  labour  market 

authorities; 

-  Any continuing vocational training financed wholly by the public authorities; 
-  Any  continuing  vocational  training  financed  wholly  by  an  individual  employee  and 

undertaken entirely in their own time; 

-  Initial  vocational  training  provided  for  apprentices  and  others  who  have  a  training 

contract that are not supported by a working contract. 

Unfortunately, the Eurostat CVTS survey is conducted only once every six years and the last 
survey  that  was  conducted  was  in  1999.  This  means  that  in  order  to  use  the  data  collected 
from this survey to calculate total spending on  education, data from  1999 would have to be 
projected  forward  for  a  number  of  years.    Furthermore,  there  will  be  gaps  for  a  number  of 
countries given the fact that the CVTS2 did not collect data for all EU Member States.  

It should also be noted that the population of interest for the CVTS2 survey was enterprises 
within each participating country with 10 or more employees belonging to a limited number 
of  sectors  of  economic  activity  as  defined  by  NACE  (International  Standard  Industrial 

Key education indicators on social inclusion and efficiency 

64

 
 
 
  
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Classification  of  All  Economic  Activities).  Since  the  data  collection  does  not  cover  micro 
enterprises and enterprises in certain sectors of economic activity, it can be concluded that the 
total  cost  of  spending  of  enterprises  on  continuing  vocational  training  is  underestimated  by 
the CVTS2. 

The  CVTS2  alsocollected  information  on  whether  enterprises  provided  other  forms  of 
continuing  vocational  training  to  their  employees.    However,  it  did  not  collect  information 
concerning  the  expenditure  of  enterprises  on  other  forms  of  CVT.  Other  forms  of  CVT 
include:  

-  Planned periods of training, instruction or practical experience, using the normal tools 

of work, either at the immediate place of work or in the work situation; 

-  Planned learning through job rotation, exchanges or secondments; 
-  Attendance at learning/quality circles; 
-  Self-learning through open and distance learning; 
-  Instruction at conferences, workshops, lectures and seminars at which the purpose is to 

learn/receive training. 

Therefore,  it  can  be  argued  that  spending  by  enterprises  on  CVT  is  underestimated. 
Expenditure  on  other  forms  of  training,  is  expected  to  be  partially  covered  in  forthcoming 
CVTS surveys. 

Vocational training costs as collected by the CVTS include: 

-  Fees  and  payments  made  to  organisations  for  the  provision  of  CVT  courses  and 

services; 

-  Travel and subsistence payments; 
-  Labour costs of internal trainers; 
-  Costs of premises; 
-  Contributions  to  collective  funding  arrangements,  that  is  levies  and  subscriptions  for 

CVT courses 

Receipts  from  collective funds, that is  grants for  CVT courses,  from  sources of revenue for 
CVT courses are deducted from total costs. 

It is important to note that Eurostat defined the total costs for training courses as ‘the sum of 
direct  costs,  personal  absence  costs  and  the  balance  form  contributions  to  national  or 
regional vocational training funds and receipts from national or other financial settlements4’  
Direct  costs  include:  fees  and  payments  made  to  organisations  for  the  provision  of  CVT 
courses and services, travel and subsistence payments, labour costs of internal trainers, costs 
of premises. Personnel absence costs can be understood as the ‘opportunity costs for training 
courses’  that  enterprises  incur  if  the  employees  are  not  working  productively  during 
participation in training courses but are giving rise to labour costs. 

The inclusion of personal absence costs of employees in the total training costs of enterprises 
is debatable. Personnel absence costs do not in all cases constitute direct expenditure by the 
enterprises. Particularly in the case of measures of short duration and measures for executives 
and some specialists, enterprises manage to transfer the cost burden to the employees. This is 

4  Eurostat  ‘Continuing  training  in  Europe-  Results  of  the  Second  European  Continuing  Vocational 

Training Survey in enterprises’ 

Key education indicators on social inclusion and efficiency 

65

 
 
 
 
                                                 
Chapter 2:Combining data on public and private spending on education 

made  possible  by  moving  training  measures  to  employees’  free  time.  Costs  are  also  shifted 
when the enterprises expect participants in training measures to make up for the working time 
lost  through  continuing  training  by  undertaking  unpaid  work  in  their  free  time  or  by  short-
term intensification of the employees’ own work or that of the team of colleagues. One of the 
advantages of the CVTS survey is the fact that it is  relatively straightforward to distinguish 
between direct and personnel absence costs given that they are collected separately.  

For the purpose of this exercise only the direct costs of enterprises on CVT were considered. 

22..33..44    LLaabboouurr  CCoosstt  SSuurrvveeyy  ((LLCCSS)) 

The  Community  statistics  on  labour  costs  provide  detailed  harmonised  data  on  wages  and 
salaries and other related costs. The surveys on labour costs are conducted every four years. 
The LCS is carried out for all employees (including apprentices) with direct contracts with the 
enterprise  or  local  unit  and  who  receive  remuneration  irrespective  of  the  type  of  work.  The 
contract duration or the hours worked. 

Vocational training costs as collected by the LCS include:  

-  Expenditure  on  vocational  training  services  and  facilities,  depreciation,  small  repairs 

and maintenance of buildings and installations, excluding staff costs;  

-  Expenditure  on  participation  in  courses;  the  fees  of  instructors  from  outside  the 

enterprise; expenditure on teaching aids and tools used for training;  
-  Sums paid by the enterprise to vocational training organisations, etc.  

Subsidies linked to vocational training are deducted. It includes the costs of apprentices (that 
is the wages and social security however this expenditure is collected separately. 

As is the case with the CVTS2, the LCS does not cover micro-enterprises, that is enterprise 
with less than 10 employees.  The LCS covers the following NACE categories C to O 

22..44    DDaattaa  ccuurrrreennttllyy  ccoolllleecctteedd  ffrroomm  nnaattiioonnaall  ssoouurrcceess  

One of the principal sources which is available in all EU Member States that can be used to 
collect  information  on  private  household  spending  on  education  are  national  household 
budget surveys.  

HBSs have the potential to collect both fees paid to institutions and payments on educational 
goods purchased outside institutions. However, at present the data collected through them is 
not comparable due to differences in the following aspects: 

i.  Concepts and definitions used; 
ii.  Goods and services, which are defined as education. 

The latter aspect is particularly important given that the intention is to use the data concerning 
household  expenditure  on  education  as  one  of  the  sources  to  produce  a  total  figure  for 
spending  on  education.  In  order  to  use  the  data  collected  by  Member  States  from  national 

Key education indicators on social inclusion and efficiency 

66

 
 
 
 
 
 
 
 
 
  
 
 
Chapter 2:Combining data on public and private spending on education 

HBSs  concerning  expenditure  on  education,  it  is  imperative  that  data  are  harmonised  by 
making sure that the only expenditure on a pre-defined list of educational goods and services 
are included in the final figures (See section for further details) 

22..55    RReeccoonncciilliinngg  ddaattaa  nneeeeddeedd  wwiitthh  ddaattaa  ccoolllleecctteedd  

It can be concluded that in order to have a fuller picture of total spending on education, data 
from  other  sources  needs 
the 
UNESCO/OECD/Eurostat statistical exercise. 

to  be  combined 

together  with 

the  data 

from 

In  view  of  the  fact  that  expenditure  on  education  both  inside  and  outside  educational 
institutions  is  extremely  well  covered  by  the  UNESCO/OECD/Eurostat  statistical  exercise, 
there is no need to search for alternate sources for public expenditure.  

The principal problem arises with private expenditure, since the data provided by countries to 
the UNESCO/OECD/Eurostat statistical exercise is not as well covered as public expenditure, 
especially  for  household  expenditure  outside  of  educational  institutions.  There  are  also 
problems with regard to the comparability of the data collected concerning private household 
expenditure. Thus data on private household spending on education has to be collected from a 
variety of sources at the international and national level. Spending by other private entities in 
the  form  of  enterprises  on  continuing  vocational  training  is  covered  by  statistical  sources 
within  the  European  Statistical  System,  whilst  expenditure  by  other  private  entities  to 
educational institutions is only covered by the UNESCO/OECD/Eurostat statistical exercise. 
Unfortunately  the  coverage  of  this  variable  in  the  UNESCO/OECD/Eurostat  statistical 
exercise is not as comprehensive as the data supplied on public expenditure. Inevitably, it will 
not be possible to calculate total spending for a number of countries. 

It  should  be  noted  that  there  would  be  a  bias  in  the  results  when  combining  data  from 
different sources to produce a total figure of spending on education. This bias arises from the 
fact  that  in  the  case  of  CVTS  and  the  LCS  surveys,  missing  data  are  imputed  using  clearly 
rules  defined  by  Eurostat.  In  the  case  of  the  UNESCO/OECD/Eurostat  statistical  exercise, 
public  and  private  household  expenditures  are  estimated  for  a  number  of  countries  using 
different  methods.  Therefore,  when  combining  data  for  the  same  set  of  countries,  this  will 
inevitably lead to a bias. 

Table 2.9, shows the variables required to calculate total expenditure on education along with 
the data sources that can be used.  

Key education indicators on social inclusion and efficiency 

67

 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Table2.9: Breakdown of variables and the corresponding sources of data 

Variable 
Public spending  

Private 
household 
spending  

private 
Other 
entity spending  

Component variables 
services  of 
and 

educational 

Goods 
institutions 
Goods and services of purchased outside of 
educational institutions 
Public  subsidies  to  students  for  student 
living costs 
Goods 
institutions 
Goods and services of purchased outside of 
educational institutions 
and 
Goods 
institutions 
Goods and services of purchased outside of 
educational institutions 

services  of 

services  of 

educational 

educational 

and 

Sources 
UNESCO/OECD/Eurostat collection 

UNESCO/OECD/Eurostat collection 

UNESCO/OECD/Eurostat collection 

UNESCO/OECD/Eurostat  collection 
/National Household Budget Surveys 
National Household Budget Surveys 

UNESCO/OECD/Eurostat collection 

CVTS, LCS 

22..66    MMeetthhooddoollooggiiccaall  aassppeeccttss  ooff  ccoommbbiinniinngg  ddaattaa  

This section considers the types of expenditure, which should be included in a total figure for 
spending  on  education  for  all  levels  of  education.  It  also  examines  some  methodological 
aspects that need to be taken into consideration when combining data from different sources.  

22..66..11   EExxppeennddiittuurree   oonn   ggooooddss   aanndd   sseerrvviicceess   wwiitthhiinn   eedduuccaattiioonnaall  
iinnssttiittuuttiioonnss 

This includes all direct public, private and international expenditure 
This includes the following: 

Instruction (in other words teaching costs), including teaching hospitals; 

• 
•  Educational goods (books, materials, etc) provided by institutions; 
•  Training  of  apprentices  and  other  participants  in  combined  school  and  work-based 

educational programmes at the workplace; 

•  Administration; 
•  Capital expenditure and rent; 
•  Special educational needs; guidance; 
•  Educational research and curriculum development (including teaching hospitals). 

Expenditure on the following items are considered in slightly more detail below: research and 
development performed at higher education institutions; and ancillary services. 

Key education indicators on social inclusion and efficiency 

68

 
 
  
  
 
 
 
 
 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

   HHoouusseehhoollddss   eexxppeennddiittuurree   oonn   ggooooddss   aanndd   sseerrvviicceess   oouuttssiiddee  

22..66..22  
eedduuccaattiioonnaall  iinnssttiittuuttiioonnss   

There are three types of expenditure related to education that occur outside of institutions that 
need to be considered: 

i.  Expenditure on educational goods and services purchased by households and students 

outside institutions in the free market; 

ii.  Expenditure by students on student living costs and  
iii.  Foregone earnings 

For  the  purpose  of  this  exercise  expenditure  by  students  and  households  on  student  living 
costs are not regarded as educational expenditures. However, subsidies for student living costs 
if  they  are  subsidise  through  financial  aid  to  students  by  public  and  private  entities  are 
designated  as  educational  expenditure.  This  is  in  order  to  maintain  coherence  with  the 
concepts  used  in  the  UNESCO/OECD/Eurostat  collection  on  education  systems.  Students 
foregone  earnings  are  totally  excluded  since  they  are  not  considered  as  educational 
expenditures.  

(UOE)  statistical  exercise  has  classified  household 
The  UNESCO/OECD/Eurostat 
expenditure on educational goods and services outside institutions according to the following 
three categories: 
1.  Expenditure  on  educational  goods  purchased  outside  institutions,  which  are  needed  to 
participate  in  the  programmes.  This  includes  school  uniforms,  books  requested  for 
instruction, athletic equipment, materials for art lessons etc…; 

2.  Expenditure by households on educational goods not requested by institutions, but bought 
by  households  with  the  intention  to  support  learning  in  UOE  type  education.  This 
includes additional books, computer, learning software to be used at home etc… ; 

3.  Fees for outside school tuition related to educational programmes.  

22..66..33    EExxppeennddiittuurree  ooff  ootthheerr  pprriivvaattee  eennttiittiieess  

Expenditure by other private entities should include the following items: 

•  Contributions  or  subsidies  to  vocational  and  technical  schools  by  business  or  labour 

organisations; 

•  Payments by private companies to universities under contracts for research, training, or 

other services; 

•  Grants  to  educational  institutions  from  non-profit  organisations,  such  as  private 

foundations; 

•  Charitable donations to educational institutions (other than from households); 
•  Rents paid by private organisations and earnings from private endowment funds;; 
•  Expenditure by private employers on the training of apprentices and other participants 

in mixed school and work based educational programmes; 

•  Expenditure by private employers on vocational and technical training in enterprises. 

Key education indicators on social inclusion and efficiency 

69

 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

In contrast to the UNESCO/OECD/Eurostat (UOE) statistical exercise, expenditure by private 
employers  on  vocational  and  technical  training  should  be  included  in  a  total  figure  of 
spending on education. 

22..66..44    EExxppeennddiittuurree  oonn  aanncciillllaarryy  sseerrvviicceess 

Ancillary  services  are  defined  in  the  UNESCO/OECD/Eurostat  methodology  on  education 
systems  as  services  provided  by  educational  institutions  that  are  peripheral  to  the  main 
educational mission. Ancillary services include: 

-  Student welfare services (e.g. at ISCED levels 0- 3, this includes meals, school health 
services,  and  transportation  to  and  from  school,  at  ISCED  level  5  and  6  this  includes 
halls of residence, refectories, and health care); 

-  Services  for  the  general  public  include  items  such  as  museums,  radio,  and  television 

broadcasting, sports, and recreational or cultural programmes. 

It should be noted that day or evening care provided by pre-primary and primary institutions 
are excluded.  

in 

the 

that 

shows 

services 

reporting  of  data  on  ancillary 

the 
Table  2.10, 
UNESCO/OECD/Eurostat  collection  is  to  say  the  least  very  patchy.  A  number  of  countries 
reported the data as nil or not applicable. 
The  Eurostat  ‘Survey  on  Country  profiles’  outlined  five  comparability  problems  associated 
with the collection of data on ancillary services. These problems are as follows: 
1.  Countries differ in the way they make each type of ancillary service available to students. 
2.  Ancillary  services  are  provided  by  a  variety  of  public  and  private  organisations  (e.g. 
education  authorities,  public  non-education  agency,  private  contractors  etc.).  In  cases 
where  the  statistical  coverage  is  incomplete  for  some  providers,  there  will  be  problems 
with comparability between countries. 

3.  The  mode  of  financing  ancillary  services  will  affect  comparisons  between  countries, 

given that the private components of spending are inadequately reported. 

4.  Some  countries  cover  the  total  costs  of  these  services,  regardless  of  the  source  of 
financing.  Whilst  other  countries  cover  only  the  net  costs  to  the  public  sector  (i.e. 
excluding the portion covered by fees), and other countries do not report expenditures for 
certain services. 

5.  The manner in which expenditures for ancillary services are reflected in international data 
submissions  varies  by  country,  and  not  always  in  the  same  way  as  in  the  country’s 
internal statistics. 

Key education indicators on social inclusion and efficiency 

70

  
 
 
  
 
Chapter 2:Combining data on public and private spending on education 

Table 2.10: Availability of data in the UOE data collection on ancillary services by variable 
(2003) 

Country 

All levels of expenditure 

G5b 
h 
h 

H5b 
h 
h 

E5b 

h 

h 
h 
h 

h 

h 

h 
h 
h 
h 
h 
h 

h 
h 

h 

h 

h 

h 

h 

h 

Higher Education (ISCED 
5/6) 
H5b 
h 

E5b 

G5b 
h 
h 

h 

h 

h 

h 

h 

h 
h 
h 

h 
h 

h 

h 

h 

h 

h 

h 

h 

h 

h 

h 

h 
h 

h 

h 

h 
h 

h 

h 

BE 
CZ 
DK 
DE 
EE 
EL 
ES 
FR 
IE 
IT 
CY 
LV 
LT 
LU 
HU 
MT 
NL 
AT 
PL 
PT 
SI 
SK 
FI 
SE 
UK 
BG 
RO 
IS 
LI 
NO 
HR 
TR 
MK 
US 
JP 

Key 
G5b 
H5b 
E5b 

Direct expenditure of all levels of government on ancillary services 
Expenditure of households on ancillary services 
Expenditure of other private entities on ancillary services 

At  the  higher  education  level,  problems  in  the  comparability  of  data  on  ancillary  services 
between countries have been reported. A reasoned argument that has been put forward is to 
exclude  these  expenditures  from  the  analysis  of  higher  education  finance5,  given  that  these 
services  are  not  directly  related  to  the  instruction  of  students  and  are  self-supporting.  Table 

5 Twenty-fourth Meeting of the INES Technical Group,’ The value and limitations of comparing U.S 
elementary,  secondary,  and  higher  education  financial  statistics  with  other  OECD  countries’, 
OECD, 2005 

Key education indicators on social inclusion and efficiency 

71

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                                 
Chapter 2:Combining data on public and private spending on education 

2.11, shows the total spending (public and private) on ancillary services for the countries that 
report this data. It may be concluded from table 2.8 that ancillary services play a major role in 
the  US  compared  to  the  other  countries.  For  instance  it  may  be  the  case  that  the  other 
countries  do  not  have  extensive  college  sports  programs  as  is  the  case  in  US  institutions.  It 
can even be assumed that countries do have extensive ancillary services, but for some reason 
they  are  unable  to  report  data  on  them.  If  this  is  indeed  the  case,  then  it  can  be  justified  to 
exclude this data from the combined total figure of spending on higher education. 

Table 2.11: Public and private expenditures on ancillary services in higher education (2003) 

Country 

CZ 
FR 
HU 
SK 
BG 
US 

Total exp 
Mio € PPS 
52.1 
1211.1 
115.2 
51.1 
109.8 
24937 

Total exp per student  
Mio € PPS 
187.8 
571.5 
546.6 
477.3 
476.3 
2507.1 

22..66..55    EExxppeennddiittuurree  oonn  rreesseeaarrcchh  aanndd  ddeevveellooppmmeenntt  ((RR&&DD)) 

includes  expenditure  on 

The  UNESCO/OECD/Eurostat  methodology 
research  and 
development  performed  at  higher  education  institutions,  but  R&  D  performed  outside  is 
excluded. Given that data on public expenditure from the collection will be used to estimate 
total  spending  on  education, 
the 
UNESCO/OECD/Eurostat  methodology.  Nevertheless,  it  can  be  argued  that  expenditure  on 
R&D  should  be  excluded  from  a  total  figure  of  spending  on  education  since  it  is  not 
education.  The  tables  presented  in  the  annex  to  this  chapter  showing  total  expenditure  on 
education show a figure for expenditure on R & D in higher education institutions separately. 

to  maintain  coherence  with 

it  was  decided 

22..66..66      HHaarrmmoonniissiinngg   hhoouusseehhoollddss’’   eexxppeennddiittuurree   oonn   eedduuccaattiioonnaall   ggooooddss  
aanndd  sseerrvviicceess 

Expenditure  on  educational  goods  and  services  purchased  outside  institutions  are  typically 
measured  by  household  expenditure  surveys.  Thus  the  definitions  of  educational  goods  and 
services tend to be determined by those used in the national survey instrument 

It  was  already  mentioned  under  the  section  ‘Data  currently  collected  from  international 
sources’ that countries consider different sets of items as educational goods and services. The 
study  ‘Private  Household  spending  on  education  and  training’6  looked  at  this  issue  more 
closely  in  relation  to  the  reporting  of  household  spending  on  education  from  national 
household budget surveys. Information collected from twenty-three EU Member States on the 
goods and services that are classified as expenditure on education showed that there are major 
differences between Member States in what constitute educational expenditures. Despite the 

6 http://ec.europa.eu/education/doc/reports/index_en.html 

Key education indicators on social inclusion and efficiency 

72

 
 
 
  
 
  
 
 
                                                 
 
Chapter 2:Combining data on public and private spending on education 

fact that there are major differences between countries in the good and services regarded  as 
educational expenditure, one finds that there are several common items, which are as follows:   

•  Tuition fees; 
•  Registration fees;  
•  Private tuition.  

Some  goods  and  services  are  regarded  as  educational  expenditures  by  only  a  handful  of 
countries,  for  example  recreational  or  leisure  lessons,  school  transport,  school  trips,  school 
uniform.  The  classification  of  these  latter  goods  and  services  as  educational  expenditure  is 
dependent  upon  a  number  of  factors,  including  the  relevance  of  the  item  to  the  specific 
peculiarities  of  the  national  education  system.  Some  countries  (i.e.  EE,  EL,  and  LV)  also 
report  courses  paid  for  by  employer  or  tuition  fees  paid  for  by  private  companies  as 
educational expenditures In the case of Estonia, these expenditures account for between 3 – 8 
per  cent  of  educational  expenditure.    This  will  undoubtedly  affect  comparability  between 
Member States on the data collected.  

As  a result of the incomparability  in the  goods  and services  regarded  as education, a list  of 
educational goods and services was proposed to be adopted in the UNESCO/OECD/Eurostat 
(UOE) statistical exercise.  

In exploiting the data collected from national household budget surveys concerning household 
expenditure on education to calculate a total figure of spending on education, it is imperative 
to  make  sure  that  the  expenditure  data  collected  contains  the  same  educational  goods  and 
services for all countries.  Consequently the list of educational goods and services proposed in 
the study  ‘Private Household spending on education and training’ can be used as the basis 
by which the expenditures for goods and services, which should not be regarded as education, 
are deducted. In this manner, a final figure of private household spending on education should 
be  arrived  at  which  should  be  comparable  between  countries.  Table  2.12,  shows  the  list  of 
educational goods and services. 

It  should  be  noted  that  Eurostat  is  currently  working  on  a  list  of  educational  goods  and 
services.  

Key education indicators on social inclusion and efficiency 

73

 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Table 2.12: List of educational goods and services for the UOE data collection 

  Educational good / service 

Explanation 

1.   Payments directly to institutions 

Tuition fees 

Tuition  fees  of  non-household 
member 

Extra tutoring 
Registration fees 
Examination fees  
Textbooks 

Food and board while attending 
school 
Student dormitories 

School refectories 

School transport 

Laboratory and library fees 
Health and welfare services 

Membership fees 

Rental of school equipment 

Extra-curricula activities 

School trips / visits 
Other 

This  includes  nurseries,  kindergartens,  school,  college,  university,  and 
adult education in regular education programmes (ISCED 0 to 6). This 
refers  only  to  fees  paid  directly  to  educational  establishments  for 
members of the household. 
Tuition  fees  paid  by  a  parent  directly  to  an  educational  institution  on 
behalf  of  their  child  in  higher  education  who  is  not  a  member  of  the 
household. 
Payments for extra tutoring  
Registration fees paid directly to educational establishments. 
Payments made to an institution to register for an examination 
Payments or contributions directly to the educational establishments for 
textbooks. 

1.1   Ancillary services 

Food  and  board  up  to  ISCED  3  (upper  secondary  education).  These 
expenses are paid directly to the educational establishment. 
Payments  made  directly  to  educational  establishments  at  the  higher 
education  level  for  accommodation,  which  may  or  may  not  include 
meals. 
These  generally  relate  to  meals,  which  are  paid  directly  to  an 
educational establishment. 
Expenditure  on  all  forms  of  transport,  which  is  paid  directly  to  the 
educational institution, for example: buses, trains, trams, etc… 
Library fees also includes payments for photocopies. 
Fees  for  health  and  welfare  services  paid  directly  to  educational 
institutions. 
This includes fees paid directly to institutions for the following: student 
union  fees,  student  council  membership  fee,  parent  and  teacher 
association membership fee 
Payments  for  rental  of  any  form  of  equipment  belonging  to  the 
institution 
Payments  to  institutions  made  for  activities,  which  are  normally 
conducted outside of school hours. 

Trips / visits organised by the school / college. 

This includes any other item not included above for which payment is 
made directly to the institution. 

2. Expenditure on goods imposed by an institution 

Purchase of textbooks, technical 
and other equipment; 
School uniform 

Items  necessary  for  participation  in  the  classroom.  This  includes  items 
such as textbooks, laboratory equipment, art supplies, and stationary. 
Includes  items  of  clothing  as  stated  in  the  school  regulations.  In  some 
countries this may also include purchase of clothing for sport.  
Trips / visits organised by the school / college. 
3. Expenditure on goods not imposed by an institution 

School trips / visits 

Purchase 
material for self-study 

of 

educational 

Gifts 
members 
purposes 

to 

non-household 
educational 

for 

Expenditures on books, CDs, videos for learning at home. For example 
language  learning  courses  etc..  However  it  does  not  include  personal 
computers (PCs).  
Gifts  either  monetary  or  purchased  items  for  the  expressed  purpose  of 
education.  (This  does  not  include  gifts  for  the  purpose  of  student 
maintenance, i.e. food and board).  

Private tutoring 

4. Payments on private tutoring 
Lessons taken privately outside an educational establishment 

Key education indicators on social inclusion and efficiency 

74

 
 
 
Chapter 2:Combining data on public and private spending on education 

22..66..77  EEssttiimmaattiinngg  eexxppeennddiittuurree  ooff  eenntteerrpprriisseess  oonn  vvooccaattiioonnaall  ttrraaiinniinngg  

The Continuing Vocational Training Survey (CVTS) and the EU Labour Cost survey (LCS) 
can  be  both  used  to  provide  an  estimate  on  the  expenditure  of  enterprises  on  vocational 
training. Table 2.13, shows the costs of training by enterprises collected by both surveys. 

Table 2.13: Costs of training by enterprises included in the CVTS2 and the LCS 

Survey 
CVTS2  These include: fees and payments to CVT providers and cost of external trainers,  

Costs of training included 

travel  and  subsistence  payments,  labour  costs  of  internal  trainers  exclusively  involved  in 
managing  and  delivering  training,  labour  costs  of  internal  trainers  partly  involved  in 
managing and delivering training, costs of premises, contributions to collective to collective 
funding agreements. The total costs are calculated by deducting the receipts from collective 
funds, that is grants from CVT courses, from sources of revenue for CVT courses. 
These include: expenditure on vocational training services and facilities, depreciation, small 
repairs and maintenance of buildings and installations, excluding staff costs; expenditure on 
participation in courses; the fees of instructors from outside the enterprise; expenditure on 
teaching aids and tools used for training; sums paid by the enterprise to vocational training 
organisations, etc.  Subsidies linked to vocational training should be deducted. 

LCS 

. 

One  of  the  major  setbacks  with  both  these  surveys  is  that  they  are  conducted  at  very  long 
regular  periods,  four  and  five years  respectively.  Therefore  in  order  to  exploit  this  data,  the 
data  from  the  year  the  survey  was  last  conducted  was  projected  forward  using  the  Labour 
Cost  Index  (For  details  see  Annex  1).    The  data  produced  was  according  classified  as 
estimates. 

Data collected in both surveys by economic activity of the enterprises, which is classified by 
NACE.  NACE Rev. 1.1 is the classification of economic activities. The coverage of NACE7 
categories is not the same for both surveys. The CVTS2 covers enterprises whose activity is 
classified by NACE categories C to K and O, whereas the LCS covers all enterprises that are 
classified  by  NACE  categories  C  to  O  (see  table  2.11).  Therefore,  in  order  to  maintain 
coherence  in  the  calculations,  and  at  the  same  time  to  avoid  double-counting  only  NACE 
categories C to K, and O were considered. 

7 NACE Rev. 1.1 is the classification of economic activities corresponding to ISIC Rev.3 at European 
level. Though more disaggregated than ISIC Rev.3.1, NACE Rev.1.1 is totally in line with it and can 
thus be regarded as its European counterpart. 

Key education indicators on social inclusion and efficiency 

75

 
 
 
 
 
                                                 
Chapter 2:Combining data on public and private spending on education 

Table 2.14: Coverage of economic activities in the CVTS2 and the LCS 

NACE 
C 
D 
E 
F 
G 

H 
I 
J 
K 
L 

M 
N 
O 
P 
Q 

Name 

Mining and quarrying 
 Manufacturing  
 Electricity, gas and water supply  
 Construction  
 Wholesale and retail trade; repair of motor vehicles, 
motorcycles and personal and household goods  
 Hotels and restaurants  
 Transport, storage and communication  
 Financial intermediation  
 Real estate, renting and business activities  
 Public administration and defence; compulsory social 
security  
 Education  
 Health and social work  
 Other community, social and personal service activities  
 Activities of households  
 Extra-territorial organizations and bodies  

CVTS2 
h 
h 
h 
h 
h 

h 
h 
h 
h 

h 

LCS 
h 
h 
h 
h 
h 

h 
h 
h 
h 
h 

h 
h 
h 

22..66..88    DDoouubbllee  ccoouunnttiinngg  ooff  eexxppeennddiittuurree 

It  is  inevitable  that  there  is  a  potential  risk  of  double  counting  when  data  from  different 
sources is combined. Double counting occurs when data from several sources containing the 
same data are combined resulting in the same expenditures being counted twice or even more. 
For example: 

•  When data on vocational training by enterprises is collected from the EU-Labour Cost 
Survey, it is important to make sure that data on apprenticeship is not included, as it is 
already collected in the UNESCO/OECD/Eurostat data collection; 

•  The  EU  Labour  Cost  Survey  collects  data  from  enterprises  according  to  NACE 
categories, this includes the NACE categories of ‘Public administration and defence, 
compulsory  social  security’,    ‘Education’,  and    ‘Health  and  social  work’.  Data 
collected  on  public  spending  on  education,  may  already  contain  these  expenditures. 
So in order to avoid any double-counting data collected for these categories was not 
included in the final figures. 

Whilst  every  effort  has  been  made  to  ensure  that  this  is  avoided,  it  is  inevitable  that  some 
instances of double counting will be unavoidable because of the nature of the data collection. 

Key education indicators on social inclusion and efficiency 

76

 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
Chapter 2:Combining data on public and private spending on education 

22..66..99  CCoovveerraaggee  ooff  eedduuccaattiioonn  aanndd  ttrraaiinniinngg 

It is essential to examine the coverage of education and training in each data source in order 
to  determine  the  types  of  education  and  training  that  will  inevitably  be  omitted  when  data 
from different sources are combined.  

Three types of education can be identified, which are as follows: 

•  Formal  education  -  refers  to  intentionally  organised  learning  events,  with  regular 
fixed  duration  and  schedule,  structured  hierarchically  with  chronological  succession 
of  levels  and  grades,  admission  requirements  and  formal  registration,  held  within 
established  educational 
institutions  and  using  pre-determined  pedagogical 
organisation, contents, methods and teaching /learning materials 

•  Non-formal education – refers to intentionally organised learning events, which take 
place  in  an  institutional  setting  but  do  not  fulfil  one  or  more  of  the  following 
conditions:  hierarchy  level-grade  structure,  admission  requirements,  registration, 
teaching/learning methods (predetermined/not flexible), and duration (regular school 
year) and scheduling. 
Informal  learning  –  is  generally  intentional,  however  it  is  less  organised  and 
structured and may include learning events that occur in the family, workplace and in 
the daily life of every person, on a self-directed, family-directed or socially directed 
basis. 

• 

For the purpose of this study informal learning will not be covered in the calculation of total 
spending on education.  

Table  2.15  presents  an  overview  of  the  coverage  of  education  and  training  in  the  different 
types of data sources. It is apparent from table 2.15, that no matter what combination of data 
sources are used to produce a total figure of spending on education, certain types of education 
will unfortunately not be included in the spending figures. This is particularly the case with 
programmes that cannot be allocated to an ISCED level.  In some countries this amount could 
be  account  for  a  significant  proportion  of  spending.  For  example  in  Denmark  private 
household spending on leisure / youth schools accounted for 20 per cent of total spending on 
education in 2002.  

Figure 2.1 shows the types of education and training that are included when different sources 
are combined. 

Thus  it  can  be  said  that  whilst  formal  education  will  be  covered  in  total  spending  on 
education, non-formal education is only partially covered. Informal education is not included 
in the expenditure figures. 

Key education indicators on social inclusion and efficiency 

77

 
 
 
 
 
 
  
Chapter 2:Combining data on public and private spending on education 

Table 2.15: Overview of the coverage of education and training by source of funding and data source 

Name of source 

Type of education / training included 

Type of education / training NOT included 

Source of 
funding 

Public 

UNESCO/OECD/Eurostat 
collection on education systems 

Private 

UNESCO/OECD/Eurostat 
collection on education systems 

Private 
Private 
Private 

CVTS 
EU-LCS 
National  Household  Budget 
Surveys 

All education programmes that can be assigned to an 
ISCED level including: Pre-primary; Primary; Lower 
secondary;  Upper  secondary;  Post-secondary  non-
tertiary;  Higher  education;  Special  education 
level  programmes);  Adult  education 
(ISCED 
(ISCED level programmes) 
All education programmes that can be assigned to an 
ISCED level including: Pre-primary; Primary; Lower 
secondary;  Upper  secondary;  Post-secondary  non-
tertiary;  Higher  education;  Special  education 
(ISCED 
level  programmes);  Adult  education 
(ISCED level programmes) 
Continuing vocational training courses 
Continuing vocational training courses 
All education programmes that can be assigned to an 
ISCED level including: Pre-primary; Primary; Lower 
secondary;  Upper  secondary;  Post-secondary  non-
tertiary;  Higher  education;  Special  education 
level  programmes);  Adult  education 
(ISCED 
(ISCED level programmes) 
For  some  countries  leisure  /recreation  classes  (e.g. 
DK, EL, NL, US) 

All programmes that can not be allocated to a ISCED level, for 
example:  Special  education;  Adult  education;  Continuing 
education;  General  interest  /  personal  enrichment  classes; 
Leisure  or  recreation  classes;  Vocational  and  technical  training 
conducted solely in enterprises 

All programmes that can not be allocated to a ISCED level, for 
example:  Special  education;  Adult  education;  Continuing 
education;  General  interest  /  personal  enrichment  classes; 
Leisure  or  recreation  classes;  Vocational  and  technical  training 
conducted solely in enterprises 

All other education and training 
All other education and training 
All programmes that can not be allocated to a ISCED level, for 
example:  Special  education;  Adult  education;  Continuing 
education;  General  interest  /  personal  enrichment  classes; 
Vocational and technical training conducted solely in enterprises 

For most countries leisure /recreation classes are excluded 

Key education indicators on social inclusion and efficiency 

78

 
 
 
Chapter 2:Combining data on public and private spending on education 

Figure 2.1: Types of education and training included when different sources are combined 

Types of education / learning 

ISCED 

programmes 

CVT 

IVT 

Adult / 

Continuing 

Training 
for 
unemployed 

Special 

education 

UOE 

UOE, 
HBS, 
CVTS 

UOE, 

HBS, 
LCS 

UOE, 
HBS 

UOE, 
CVTS 

Total coverage 

Partial coverage 

Not covered 

s
e
c
r
u
o
s
a
t
a
d
f
o
n
o
i
t
a
n
b
m
o
C

i

Key 

Notes 
ISCED 
CVT 

IVT 

Education and training, which can be classified by ISCED categories (0-6); 
Continued Vocational Training. Education or training after initial education 
or  entry  into  working  life,  aimed  at  helping  individuals  to:  improve  or 
update their knowledge and/or skills; acquire new skills for a career move or 
retraining; continue their personal or professional development. 
Initial  Vocational  Training.  General  or  vocational  education  carried  out  in 
the initial education system, usually before entering working life. 
Education  provided  for  adults,  often  intended  for  general  purposes  rather 
than vocational education 
Training which can be provided by either public or private organisations to 
the unemployed to re-integrate them back onto the labour market. 
Educational  activity  and  support  designed  to  address  special  education 
needs 

Adult / continuing 

Training 
unemployed 
Special education 

for 

Key education indicators on social inclusion and efficiency 

79

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
Chapter 2:Combining data on public and private spending on education 

2.6.10  CCoolllleeccttiioonn  ooff  pprriivvaattee  eexxppeennddiittuurreess  ffoorr  nnoonn--EEUU  ccoouunnttrriieess 

Unfortunately, a total figure for spending on education could not be estimated for a number of 
non-EU  countries.  One  of  the  reasons  why  this  is  so,  is  because  the  data  collected  in  this 
exercise  regarding  continuing  vocational  training  came  from  EU  data  collections,  that  is 
Continuing Vocational Training Survey (CVTS) and the EU Labour Cost Survey (LCS). The 
coverage of non-EU countries in these surveys is presented in table 2.16. In the case of Japan 
and the US no data collection was found at the national level that could provide this type of 
information. 

Table 2.16: Coverage of non-EU countries in EU data sources collecting information on 
continuing vocational training 

Country 

CVTS 

1993 

BG 
RO 
HR 
MK 
TR 
IS 
LI 
NO 
JP 
US 

1999 
h 
h 

h 

LCS 

1996 

2000 
h 
h 

h 

h 

22..77    CCoonncclluussiioonnss 

The UNESCO/OECD/Eurostat data collection should in theory be able to provide a figure for 
total  spending  on  education  from  public  and  private  sources  both  inside  and  outside 
educational  institutions.  The  coverage  of  data  from  public  sources  both  inside  and  outside 
educational  institutions  is  fairly  comprehensive.  Nevertheless,  the  problem  arises  with  data 
collected on private expenditure, both for households and other private entities.  The coverage 
of data on private spending is not as comprehensive as on public expenditure. Moreover, for 
those  countries  that  supply  data  concerning  households  expenditure  on  education,  there  are 
problems regarding comparability as there are differences between countries in the goods and 
services that  are regarded as education. It has also to  be noted that the data collected in the 
UNESCO/OECD/Eurostat data collection refer to education that can be classified by ISCED 
level. Thus if a programmes subject content is not similar to  regular programmes  or it does 
not lead to similar qualifications as corresponding regular programmes, then the expenditure 
is not included.  

Estimating total expenditure on education by combining different sources does not guarantee 
comprehensive  coverage  on  the  total  amount  spent  on  education  from  public  and  private 
sources  for  all  countries.  In  2003  estimates  of  total  spending  were  only  calculated  for  three 
fifths  of  Member  States.  The  situation  with  the  non-EU  countries  is  even  worse,  since  data 
was  collected  from  data  sources  within  the  ESS,  which  do  not  always  cover  non-EU 

Key education indicators on social inclusion and efficiency 

80

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

countries. This is particularly the case with Japan and the US. Collecting data for the 10 new 
Member States posed particular problems, since some ESS sources have only recently started 
collected data for these countries. For example the CVTS2 did not collect data for Malta and 
the Slovak Republic.   

Furthermore no matter what combination of data sources are used to estimate total spending 
on education, certain types of education will either not be covered at all, as is the case with 
informal learning or only partly covered as is the case with non-formal learning (e.g. spending 
by enterprises on vocational training is covered). There are a number of methodological issues 
that inevitably arise when combining data from different sources: 

- 

In the case of collecting data from national household budget surveys, attention needs 
to  be  placed  on  harmonising  households’  expenditure  on  education  on  educational 
goods and services, as there are differences between countries in the goods and services 
considered  as  education.  Therefore,  to  exploit  the  data  collected  from  national 
household  budget  surveys,  it  is  imperative  to  make  sure  that  the  expenditure  data 
collected  contains  the  same  educational  goods  and  services  for  all  countries.  
Consequently the list of educational goods and services proposed in the study  ‘Private 
Household spending on education and training’ can be used as the basis by which the 
expenditures  for  goods  and  services,  which  should  not  be  regarded  as  education,  are 
deducted.  In  this  manner,  a  final  figure  of  private  household  spending  on  education 
should  be  arrived  at  which  should  be  comparable  between  countries.  However,  the 
study revealed that for some countries (e.g. DK, DE, EE) it was not possible to break 
down total household expenditure by spending on various goods and services.  

-  The treatment of expenditure on ancillary services poses particular problems, especially 
at  the  higher  education  level.  It  has  been  proposed  to  exclude  this  expenditure  since 
there are problems with comparability. In 2003, only four Member States supplied data 
on ancillary services 

-  Calculating  the  resources  that  enterprises  spent  on  vocational  training  is  particularly 
problematic,  given  that  the  ESS  surveys  are  conducted  at  very  long  intervals.  In  the 
case  of  CVTS2  it  is  every  five  years,  the  last  survey  was  conducted  in  1999.  This 
means that the estimates  of spending will  be  based  on  projections  of data from  1999. 
Furthermore  it  is  not  possible  to  calculate  total  spending  for  some  countries,  in 
particular the non- EU countries like Japan and the US for the simple reason that these 
countries are not covered by a ESS source 

-  Care  needs  to  be  taken  when  combining  data,  as  there  is  potential  risk  of  double 

counting. 

Therefore, it can be concluded that there are a number of areas where the availability of data 
can be improved in particular in relation to the UNESCO/OECD/Eurostat statistical exercise. 
This  includes  improving  data  collected  concerning  expenditure  of  private  households  and 
other private entities.  

It  should  be  noted  that  there  would  be  a  bias  in  the  results  when  combining  data  from 
different sources to produce a total figure of spending on education. This bias arises from the 
fact  that  in  the  case  of  CVTS  and  the  LCS  surveys,  missing  data  are  imputed  using  clearly 
rules  defined  by  Eurostat.  In  the  case  of  the  UNESCO/OECD/Eurostat  statistical  exercise, 

Key education indicators on social inclusion and efficiency 

81

 
 
Chapter 2:Combining data on public and private spending on education 

public  and  private  household  expenditures  are  estimated  for  a  number  of  countries  using 
different methods.  

It can be concluded that combining different sources to produce a total figure for spending on 
education  is  far  from  the  ideal  situation  for  a  number  of  reasons  including  the  fact  that  the 
coverage  of  education  will not be as comprehensive as desired;  and  most importantly of all 
the  data  that  has  been  combined  will  only  be  a  crude  estimate.  Data  on  the  total  resources 
devoted to education should be collected in the framework of satellite accounts.  

Key education indicators on social inclusion and efficiency 

82

 
  
  
  
  
  
  
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Appendix 

Key education indicators on social inclusion and efficiency 

83

 
 
 
 
 
 
 
 
 
 
 
  
  
Chapter 2:Combining data on public and private spending on education 

EEssttiimmaattiioonn  ooff  ttoottaall  pprriivvaattee  aanndd  ppuubblliicc  ssppeennddiinngg  oonn  
eedduuccaattiioonn  

The tables presented in this appendix aim to present total spending on education (for all levels 
of education) based on combining data from a number of sources at national and international 
level.  The  tables  present  estimates  of  total  spending  on  education  for  the  following  sets  of 
countries: 

-  EU-25; 
-  Non-EU countries. 

The following data is presented in each table: 

•  Total spending on education institutions  

 Data from only the UNESCO/OECD/Eurostat data collection on education systems is 
used to calculate this row. 

•  Expenditure on ancillary services 
•  Expenditure on research and development 
•  Method 1  

The following data is combined together to produce an estimate: 

-  Total  public  spending  on  education  from  the  UNESCO/OECD/Eurostat  data 

collection on education systems; 

-  Data  on  household  spending  on  education  from  national  household  budget 

surveys; 

-  Data on enterprises spending on vocational training from the CVTS2 

•  Method 2 

The following data is combined together to produce an estimate: 

-  Total  public  spending  on  education  from  the  UNESCO/OECD/Eurostat  data 

collection on education systems; 

-  Data on household spending on education from the National Accounts; 
-  Data on enterprises spending on vocational training from the CVTS2 

•  Method 3 

The following data is combined together to produce an estimate: 

-  Total  public  spending  on  education  from  the  UNESCO/OECD/Eurostat  data 

collection on education systems; 

-  Data  on  household  spending  on  education  from  national  household  budget 

surveys; 

-  Data on enterprises spending on vocational training from the EU Labour Cost 

Survey 

•  Method 4 

The following data is combined together to produce an estimate: 

-  Total  public  spending  on  education  from  the  UNESCO/OECD/Eurostat  data 

collection on education systems; 

-  Data on household spending on education from the National Accounts; 
-  Data on enterprises spending on vocational training from the EU Labour Cost 

Survey 

Key education indicators on social inclusion and efficiency 

84

 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Calculations of total private and public spending on education for the EU-25 (2000) 
All levels of education (Current prices) 

Educational 
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary services 
Expenditure on R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of  which 
expenditure 

public 

EU-25 

BE 

CZ 

DK 

DE 

EE 

EL 

ES 

FR 

IE 

IT 

CY 

21200 

12921.0 

5598.3 

8992.2 

95526.8 

657.1 

6226.1 

35608.5 

82175.2 

4207.4 

62203.2 

861.8 

18106 

11909.3 

5037.7 

8632.2 

77515.7 

657.1 

5839.5 

31138.2 

75649.1 

3914.1 

56549.0 

565.9 

Mio PPS 

: 

: 

: 
: 
: 
: 
: 

: 

: 

: 

: 

1053.1 

216.3 

603.2 

7297.9 

: 

: 

: 

369.5 

: 

: 

: 

: 

439.4 

219.5 

14319.9 
13606.9 
14377.3 
13664.2 
13992.08 

6070.2 
6213.9 
5991.5 
6135.2 
6102.68 

Estimates of total education spending 
: 
12542.8 
: 
12845.7 
: 
11620.7 
11923.6 
: 
12233.2 

100843.5 
105895.4 
98299.9 
103351.8 
102098 

: 
: 
: 
: 

37006.4 
40826.6 
36826.5 
40646.7 
38826.5 

: 
87784.4 
: 
89960.6 

: 
: 
4607.6 
4713.8 

: 

: 

: 
: 
68478 
67808 

283.9 

6.9 

: 
: 
: 
639.9 

12329.3 

5312.4 

11220.2 

82377.7 

708.1 

5930.7 

31978.2 

78732.4 

4135.6 

58958.1 

613.6 

LV 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

5387.6 

269.5 

17928.1 

11356.7 

17504.6 

8891.3 

SI 

: 

SK 

FI 

SE 

UK 

2084.2 

6547.5 

13640.2 

66812.1 

Mio PPS 

Educational 
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary 
services 
Expenditure on 
R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average 
Of which public 
expenditure 

1001.1 

: 

876.8 

1512.4 

: 

28.0 

1025.5 
1151.8 
1013.5 
1139.8 
1082.6 

: 

: 

: 
: 
: 
: 

905.2 

1579.1 

: 

: 

: 

: 
: 
: 
: 

: 

4755.3 

240.8 

16183.8 

10696.8 

17454.5 

8762.8 

2009.0 

6414.6 

13228.2 

56897.9 

: 

28.7 

: 

: 

: 

: 

: 

188.6 

: 
6000.9 
: 
6306.8 

: 

: 
: 
: 
: 

1863.3 

Estimates of total education spending 

: 
: 
23134.6 
22116.9 

15936 
16234.1 
12429.7 
12727.8 
14331.9 

: 
: 
: 
: 

: 

: 

: 
: 
: 
: 

: 

: 

: 

: 

33.2 

708.9 

: 

: 

374.7 
: 
: 
: 

: 
2246.9 
2277.9 
: 

: 
: 
: 
: 

: 
17114.8 
: 
17064.4 

: 

5050.2 

81692 
85414.8 
81131.8 
84854.6 
83273.3 

4891.8 

258.7 

18481.7 

11459.6 

17504.6 

8965.6 

: 

2016.5 

6996.6 

15528.2 

58272.7 

Key education indicators on social inclusion and efficiency 

85

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
 
 
  
  
  
  
  
  
 
Chapter 2:Combining data on public and private spending on education 

Calculations of total private and public spending on education for the EU-25 (2000) 
All levels of education (Current prices) 

EU-25 

4.8 

4.4 

: 

: 

: 
: 
: 
: 
: 

Educational 
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary services 
Expenditure on 
R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of  which  public 
expenditure 

Educational institutions 
Of which public 
expenditure 
Expenditure on ancillary 
services 
Expenditure on R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average 
Of which public 
expenditure 

LV 
5.9 

5.2 

: 

0.2 

6.1 
6.8 
6.0 
6.8 
6.4 

5.4 

BE 

5.4 

5.0 

: 

0.4 

6.0 
5.7 
6.0 
5.7 
5.8 

5.1 

LT 
: 

5.6 

: 

: 

: 
: 
: 
: 

5.9 

CZ 

4.3 

3.8 

: 

0.2 

4.6 
4.7 
4.6 
4.7 
4.6 

4.0 

LU 
: 

: 

: 

: 

: 
: 
: 
: 

: 

Key education indicators on social inclusion and efficiency 

DK 

6.6 

6.4 

: 

0.4 

DE 

5.2 

4.2 

: 

0.4 

EE 

5.8 

5.8 

: 

: 

EL 

3.9 

3.7 

: 

0.2 

Estimates of total education spending 
: 
9.3 
: 
9.5 
: 
8.6 
8.8 
: 
9.0 

5.5 
5.7 
5.3 
5.6 
5.5 

: 
: 
: 
: 

8.3 

4.5 

6.3 

3.7 

MT 
4.4 

3.9 

0.5 

NL 
4.5 

4.1 

: 

AT 
5.6 

5.3 

: 

: 

: 

0.5 
Estimates of total education spending 
: 
: 
: 
5.6 

: 
: 
: 
: 

7.9 
8.0 
6.1 
6.3 
7.1 

HU 
5.0 

4.4 

: 

0.2 

: 
5.5 
: 
5.8 

4.5 

ES 

4.8 

4.2 

: 

: 

5.0 
5.5 
4.9 
5.4 
5.2 

4.3 

FR 

5.9 

5.5 

: 

0.0 

: 
6.3 
: 
6.5 

5.7 

PL 
4.9 

4.9 

: 

: 

: 
: 
: 
: 

PT 
5.4 

5.3 

: 

: 

: 
: 
: 
: 

SI 
: 

: 

: 

: 

1.3 
: 
: 
: 

IE 

4.4 

4.1 

: 

0.2 

: 
: 
: 
4.9 

4.3 

SK 
4.0 

3.9 

: 

0.1 

: 
4.4 
4.4 
: 

IT 

4.8 

4.4 

: 

: 

: 
: 
: 
5.2 

4.5 

FI 
5.6 

5.5 

: 

0.6 

: 
: 
: 
: 

4.2 

4.6 

5.7 

4.9 

5.4 

: 

3.9 

6.0 

as a % of GDP 
CY 

7.6 

5.0 

2.5 

0.1 

: 
: 
: 
5.7 

5.4 

as a % of GDP 
SE 
6.4 

UK 
5.0 

6.2 

4.3 

: 

: 

: 
8.1 
: 
8.0 

7.3 

0.4 

6.2 
6.4 
6.1 
6.4 
6.3 

4.4 

86

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
 
 
  
  
  
  
  
  
 
Chapter 2:Combining data on public and private spending on education 

Calculations of total private and public spending on education for the EU-25 (2001) 
All levels of education (Current prices) 

Educational 
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary services 
Expenditure on R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of which public 
expenditure 

EU-25 

BE 

CZ 

DK 

DE 

19944.1 

15665.8 

5947.5 

9832.4 

97489.9 

EE 

: 

EL 

ES 

FR 

IE 

IT 

CY 

Mio PPS 

6685.4 

37151.0 

84764.3 

4562.4 

69104.8 

17809 

14568.6 

5451.3 

9449.2 

79353.7 

608.7 

6299.2 

32636.0 

78003.7 

4210.6 

62682.6 

: 

: 

: 
: 
: 
: 
: 

: 

170.9461 

5223.16 

: 

1028.9 

: 

620.2 

7620.6 

: 

: 

: 

322.4 

: 

: 

9970.5 

: 

: 

473.1 

210.8 

4709.2 

8.2 

17225.9 
16521.4 
17287.2 
16582.7 
16904.3 

6227.6 
6322.8 
6471.3 
6566.5 
6397.04 

:  

Estimates of total education spending 
: 
: 
: 
: 

103610.3 
108370.8 
101008.5 
105769.0 
104690 

: 
: 
: 
: 

:  

: 
: 
: 
: 

38558.1 
42533.3 
38371.5 
42346.6 
40452.4 

87879.7 
90670.8 
90181.5 
92972.6 
90426.2 

: 
: 
: 
5048.0 

: 

73766.3 
73802.7 

18717 

15082.9 

5748.8 

11775.3 

84733.7 

658.7 

6436.8 

33533.2 

81138.6 

4421.3 

64697.9 

: 

: 

: 

: 
: 
: 
: 

: 

Mio PPS 

Educational  
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary services 
Expenditure on 
R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of which public 
expenditure 

LV 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

SI 

SK 

FI 

SE 

UK 

1124.6 

1608.9 

700.9 

6084.1 

285.3 

19955.5 

11651.2 

19712.7 

9551.7 

923.1 

1608.9 

700.8 

5413.5 

255.3 

18134.4 

11003.4 

19712.7 

9409.5 

: 

31.1 

: 
: 
: 
: 
: 

: 

: 

: 
: 
: 
: 
: 

: 

: 

247.8 

6435.1 
: 
6829.9 
7569.4 
: 

: 
828.7 
: 
833.4 
: 

: 

: 

: 
: 
: 
: 
: 

: 

: 

: 

1985.5 

835.6 

750.2 

Estimates of total education spending 
: 
13041.3 
: 
12957.8 
: 

: 
: 
23031 
24184.9 
: 

: 
: 
: 
: 
: 

: 

: 

: 
: 
: 
: 
: 

: 

: 

: 

: 

2219.9 

7064.9 

13678.9 

73761.6 

2157.4 

6911.6 

13243.9 

62485.8 

241.2377 

: 

: 

: 

45.5 

745.7 

1910.6 

3580.9 

327.9 
157.7 
402.6 
232.4 
789.7 

: 
: 
2440.7 
2569.5 
: 

: 
: 
: 
: 
: 

: 
: 
: 
17069.4 
: 

96009.4 
99413.7 
87043.4 
90447.8 
: 

998.6 

1718.0 

740.4 

6049.4 

280.4 

20335.7 

11665.9 

19952.5 

9611.9 

: 

2180.8 

7551.1 

15481.4 

63130.0 

Key education indicators on social inclusion and efficiency 

87

 
 
 
  
 
 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Calculations of total private and public spending on education for the EU-25 (2001) 
All levels of education (Current prices) 

as a % of GDP 

Educational 
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary services 
Expenditure on 
R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of which public 
expenditure 

EU-25 

4.6 

4.5 

: 

: 

: 
: 
: 
: 
: 

4.8 

BE 

6.2 

5.8 

: 

0.4 

6.9 
6.6 
6.9 
6.6 
6.7 

6.0 

CZ 

4.3 

3.9 

0.1 

: 

4.5 
4.6 
4.7 
4.8 
4.6 

4.2 

LV 

6.2 

LT 

5.5 

LU 

3.5 

5.1 

5.5 

3.5 

: 

0.2 

: 
: 
: 
: 

: 

: 

: 
: 
: 
: 

: 

: 
4.2 
: 
4.2 

HU 

5.1 

4.6 

: 

0.2 

5.4 
: 
5.8 
6.4 

MT 

4.7 

4.2 

: 

: 

: 
: 
: 
: 

Educational  
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary services 
Expenditure on R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of which public 
expenditure 

Key education indicators on social inclusion and efficiency 

DK 

7.0 

6.8 

3.7 

0.4 

DE 

5.2 

4.2 

: 

0.4 

EE 

: 

5.1 

: 

: 

EL 

4.0 

3.8 

: 

0.2 

Estimates of total education spending 
: 
: 
: 
: 
: 
: 
: 
: 
: 

5.5 
5.7 
5.4 
5.6 
5.5 

: 
: 
: 
: 
: 

8.4 

4.5 

5.5 

3.8 

NL 

4.7 

4.3 

: 

AT 

5.7 

5.4 

: 

PL 

5.4 

5.4 

: 

0.5 

0.2 

0.4 
Estimates of total education spending 
: 
6.4 
: 
6.3 

: 
: 
5.4 
5.7 

: 
: 
: 
: 

PT 

5.6 

5.5 

: 

: 

: 
: 
: 
: 

ES 

4.7 

4.1 

: 

: 

4.9 
5.4 
4.9 
5.4 
5.1 

4.2 

FR 

5.8 

5.4 

0.7 

0.0 

6.1 
6.3 
6.2 
6.4 
6.2 

5.6 

IE 

4.4 

4.1 

: 

0.2 

: 
: 
: 
4.9 

4.3 

IT 

5.2 

4.7 

: 

0.4 

: 
0.0 
5.5 
5.5 

4.9 

CY 

: 

: 

: 

0.1 

: 
: 
: 
: 

: 

as a % of GDP 

SI 

: 

: 

: 

: 

1.1 
0.5 
1.3 
0.8 
2.6 

: 

SK 

4.1 

3.9 

0.4 

0.1 

: 
: 
4.5 
4.7 

4.0 

: 

FI 

5.8 

5.7 

: 

0.6 

: 
: 
: 
: 

: 

: 

6.2 

SE 

6.4 

6.2 

: 

0.9 

: 
: 
: 
8.0 

7.2 

UK 

5.3 

4.5 

: 

0.3 

6.9 
7.1 
6.2 
6.5 

4.5 

: 

88

: 

: 

: 

: 

: 

: 

: 

5.5 

5.9 

3.7 

5.1 

4.6 

4.8 

5.7 

5.4 

5.6 

 
 
 
 
 
 
 
 
 
 
  
  
 
Chapter 2:Combining data on public and private spending on education 

Calculations of total private and public spending on education for the EU-25 (2002) 
All levels of education (Current prices) 

Educational  
institutions 
Of which public 
expenditure 
Expenditure on ancillary 
services 
Expenditure on R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of 
expenditure 

which 

public 

EU-25 

BE 

CZ 

DK 

DE 

24341 

16255.2 

6468.248 

9912.3 

101090.8 

EE 

: 

EL 

ES 

FR 

IE 

IT 

7327.693 

39689.71 

86359.5 

4831.978 

64832.76 

18764 

15311.7 

6113.3 

9523.2 

84250.0 

692.6 

6989.9 

35071.1 

79534.9 

4516.7 

59982.0 

: 

: 

: 
: 
: 
: 
: 

: 

1083.3 

: 
: 
: 
: 
: 

693.7 

279 

6862.2 
6985.9 
7127.0 
7250.8 
7056.5 

: 

: 

: 

: 

326.8 

: 

8177.9 

627.8 
Estimates of total education spending 
: 
: 
: 
: 
: 

109679.6 
114976.5 
107001.0 
112297.9 
110988.8 

: 
12542.7 
12303.8 
12610.4 
: 

: 
: 
: 
: 
: 

: 

: 

: 
45424.6 
: 
45228.3 
: 

9918.7 

482.6 

: 
92550.2 
: 
94916.0 
: 

: 

: 

: 
: 
: 
5556.8 
: 

: 
: 
70702.7 
71555.1 
: 

: 

4625.4 

11.3 

19900.8 

16001.3 

6437.0 

11870.9 

90723.9 

752.2 

7131.5 

36027.5 

82723.1 

4811.3 

62658.4 

Mio PPS 
CY 

: 

: 

: 

: 
: 
: 
: 
: 

: 

Mio PPS 

Educational  
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary services 
Expenditure on 
R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of  which  public 
expenditure 

LV 

LT 

1185.6 

1713.3 

LU 

: 

HU 

MT 

NL 

AT 

PL 

PT 

SI 

SK 

FI 

SE 

UK 

7022.9 

297.0 

21238.4 

11865.6 

22627.6 

9779.9 

2005.6 

2464.1 

7492.5 

14887.0 

84133.1 

1044.6 

1713.3 

785.8 

6308.9 

257.6 

19181.9 

11074.2 

20177.8 

9625.2 

1731.0 

2347.9 

7330.5 

14506.7 

71026.5 

: 

: 

34.9 

23.4 

1307.2 
1401.7 
1273.3 
1367.9 
1337.5 

: 
: 
: 
: 
: 

: 

: 
: 
: 
: 
: 

: 

: 

: 

324.4 

1.3 

2089 

: 

: 

: 

725 

7362.7 
8175.5 
7746.9 
8559.7 
7961.2 

Estimates of total education spending 

: 
: 
: 
: 
: 

: 
: 
24147.1 
25363.4 
: 

: 
13396.9 
: 
13311.1 
: 

: 
: 
: 
: 
: 

: 

: 

: 
: 
: 
: 
: 

: 

268.2 

: 

75.8 

37.2 

826.8 

2299.6 
2125.6 
2372.2 
2198.2 
2248.9 

: 
: 
2844.3 
2988.7 
: 

2542.4 

: 
: 
: 
: 
: 

: 

: 

: 
: 
: 
: 
: 

: 

3942.8 

109469.7 
112572.3 
99999.3 
103101.9 
106285.8 

1122.2 

1832.9 

801.8 

6935.3 

283.0 

21199.7 

11856.9 

20683.3 

9832.2 

1915.8 

7980.4 

16590.6 

74806.9 

Key education indicators on social inclusion and efficiency 

89

 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Calculations of total private and public spending on education for the EU-25 (2002) 
All levels of education (Current prices) 

Educational  institutions 
Of which public expenditure 
Expenditure on ancillary 
services 
Expenditure on R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of which public expenditure 

EU-25 
4.8 
4.3 

: 

: 

: 
: 
: 
: 
: 
5.1 

Educational  
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary services 
Expenditure on 
R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of  which  public 
expenditure 

LV 

6.1 

5.4 

: 

0.2 

6.7 
7.2 
6.5 
7.0 
6.9 

5.7 

LT 

5.5 

5.5 

: 

0.1 

: 
: 
: 
: 
: 

BE 
6.2 
5.9 

: 

0.4 

: 
: 
: 
: 
: 
6.1 

LU 

: 

3.3 

: 

: 
: 
: 
: 
: 

CZ 
4.4 
4.2 

0.5 

0.2 

4.7 
4.8 
4.9 
5.0 
4.8 
4.4 

HU 

5.5 

: 

: 

0.3 

5.8 
6.4 
6.1 
6.7 
6.3 

5.5 

DK 
7.0 
6.8 

: 

0.4 

DE 
5.2 
4.4 

: 

0.4 

EE 
: 
5.2 

: 

: 

Estimates of total education spending 

: 
8.9 
8.8 
9.0 
: 
8.4 

5.7 
6.0 
5.5 
5.8 
5.8 
4.7 

: 
: 
: 
: 
: 
5.7 

EL 
4.0 
3.8 

: 

0.2 

: 
: 
: 
: 
: 
3.9 

MT 

4.6 

14.1 

: 

0.0 

: 
: 
: 
: 
: 

4.4 

NL 

4.9 

4.1 

: 

AT 

5.7 

5.0 

: 

PL 

5.9 

2.5 

: 

0.5 

0.2 
Estimates of total education spending 

: 

: 
: 
5.5 
5.8 
: 

4.9 

: 
6.4 
: 
6.4 
: 

5.7 

: 
: 
: 
: 
: 

5.9 

3.8 

5.4 

5.5 

ES 
4.7 
4.1 

: 

: 

: 
5.4 
: 
5.3 
: 
4.3 

PT 

5.5 

7.1 

: 

: 

: 
: 
: 
: 
: 

FR 
5.8 
5.4 

0.7 

0.0 

: 
6.2 
: 
6.4 
: 
5.6 

SI 

6.3 

: 

: 

0.2 

7.2 
6.6 
7.4 
6.9 
7.0 

6.0 

as a % of GDP 

IE 
4.3 
4.0 

: 

: 

: 
: 
: 
: 
: 
4.3 

IT 
4.8 
4.4 

: 

0.3 

: 
: 
5.2 
5.3 
: 
4.6 

CY 
: 
: 

: 

0.1 

: 
: 
: 
: 
: 
: 

SK 

4.2 

0.2 

0.5 

0.1 

: 
: 
4.8 
5.1 
: 

4.3 

as a % of GDP 
SE 

6.8 

0.6 

: 

: 

: 
: 
: 
: 
: 

FI 

6.0 

5.2 

: 

0.7 

: 
: 
: 
: 
: 

6.3 

7.6 

UK 

5.7 

6.8 

: 

0.3 

7.4 
7.6 
6.7 
7.0 
7.2 

5.0 

Key education indicators on social inclusion and efficiency 

90

 
 
 
 
 
  
 
Chapter 2:Combining data on public and private spending on education 

Calculations of total private and public spending on education for the EU-25 (2003) 
All levels of education (Current prices) 

EU-25 

BE 

CZ 

DK 

DE 

22821.7 

16325.7 

7101.4 

9898.6 

102706.8 

EE 

: 

EL 

ES 

FR 

IE 

IT 

CY 

Mio PPS 

8066.8 

41866.8 

94462.6 

5166.4 

66566.5 

Educational  
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary services 
Expenditure on 
R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of  which  public 
expenditure 

: 

: 

: 
: 
: 
: 
: 

15776.9 

15381.4 

6538.7 

9448.9 

84885.3 

758.8 

7630.8 

37078.0 

85375.2 

4806.1 

61128.5 

: 

6468.2 

: 

: 

: 

: 

: 

86359.5 

: 

: 

1081.7 

255.7 

673.8 

8253.7 

570.8 

2839.7 

6401.4 

303.6 

5057.8 

: 
17464.9 
16770.8 
17530.0 
: 

7537.9 
8027.3 
7822.2 
7961.6 
7837.2 

6844.6 

Estimates of total education spending 

: 

: 

: 

: 
115742.6 
: 
113001.7 
: 

: 

: 

: 

: 
10065.6 
: 

: 

: 
47765.7 
: 
47563.9 
: 

: 
99408.9 
: 
101795.2 
: 

11746.2 

91528.5 

806.1 

7668.0 

38154.7 

88463.8 

: 
: 
: 
5953.5 
: 

5122.1 

: 
: 
73521.1 
73575.4 
: 

64076.3 

20602.9 

16136.2 

: 

: 

: 

: 

: 
: 

: 

: 

Mio PPS 

Educational  
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary services 
Expenditure on R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of  which 
expenditure 

public 

LV 

LT 

LU 

HU 

MT 

NL 

AT 

PL 

PT 

SI 

SK 

FI 

SE 

UK 

1195.8 

1815.9 

846.6 

7979.7 

376.7 

21959.1 

11720.3 

24321.8 

9294.2 

2083.4 

2845.1 

7728.0 

15340.7 

88051.7 

1024.2 

1658.7 

846.6 

7242.3 

286.0 

19851.6 

11079.9 

21741.9 

9139.4 

1799.3 

2568.0 

7564.3 

14779.2 

73943.6 

: 

33 

1256.1 
1493.9 
1266.5 
1474.7 
1313.4 

: 

72.6 

1811.6 
: 
1835.1 
: 
: 

: 

: 
: 
: 
: 
: 

: 

294.1 

: 

: 

2098.9 

844.4 

: 

670 

Estimates of total education spending 

: 
9258.4 
: 
9525.8 
: 

: 
: 
: 
: 
: 

: 
: 
: 
26635.8 
: 

: 
13267.7 
: 
13180.7 
: 

: 
: 
: 
: 
: 

: 

: 
: 
: 
: 
: 

: 

2453.1 

78.1 

45.4 

: 

843 

: 

: 

1956.3 

3799.2 

2363.7 
2289.1 
2439.3 
2283.7 
2344 

: 
: 
2959.4 
3119.3 
: 

: 
: 
: 
: 
: 

17311.4 
18667.6 
18301.1 
18523.8 
18201 

88675.9 
108352.7 
101858.4 
105853.5 
101185.1 

1099.2 

1761.3 

866.7 

7761.7 

304.6 

22330.1 

11686.8 

21920.6 

9279.1 

1986.8 

2636.7 

8203.7 

16849.4 

77840.3 

Key education indicators on social inclusion and efficiency 

91

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Calculations of total private and public spending on education for the EU-25 (2003) 
All levels of education (Current prices) 

EU-25 

5.5 

4.8 

: 

: 

: 

Educational  
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary services 
Expenditure on 
R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of  which  public 
expenditure 

BE 

6.1 

5.8 

: 

0.4 

: 
6.6 
6.3 
6.6 
: 

6.1 

Educational  
institutions 
Of which public 
expenditure 
Expenditure on 
ancillary services 
Expenditure on 
R&D 

Method 1 
Method 2 
Method 3 
Method 4 
Average  
Of  which  public 
expenditure 

LV 

5.8 

5.0 

: 

0.2 

6.1 
7.2 
6.1 
7.1 
6.4 

5.3 

LT 

5.3 

4.9 

: 

0.2 

5.3 
: 
5.4 
: 
: 

5.2 

LU 

3.7 

3.7 

: 

: 
: 
: 
: 
: 

3.8 

CZ 

4.7 

4.3 

4.3 

0.2 

5.0 
5.3 
5.2 
5.3 
5.2 

4.5 

DK 

7.0 

6.7 

: 

0.5 

DE 

5.3 

4.4 

: 

0.4 

EE 

: 

5.3 

: 

: 

EL 

4.2 

3.9 

: 

0.3 

Estimates of total education spending 

: 
: 
: 
: 
: 

8.3 

HU 

6.1 

5.5 

MT 

5.9 

4.5 

: 

4.6 

: 
6.0 
: 
5.8 
: 

4.7 

NL 

5.0 

4.5 

: 

0.5 

: 
: 
: 
: 
: 

5.7 

: 
5.2 
: 
: 
: 

3.9 

AT 

5.5 

5.2 

: 

0.4 

PL 

6.2 

5.6 

: 

0.2 

Estimates of total education spending 

: 
7.1 
: 
7.3 
: 

5.9 

: 
: 
: 
: 
: 

4.8 

: 
: 
: 
6.1 
: 

5.1 

: 
6.2 
: 
6.2 
: 

5.5 

: 
: 
: 
: 
: 

ES 

4.7 

4.2 

: 

0.3 

: 
5.4 
: 
5.4 
: 

4.3 

PT 

5.6 

5.5 

: 

: 
: 
: 
: 
: 

5.6 

5.6 

Key education indicators on social inclusion and efficiency 

FR 

6.3 

5.7 

5.7 

0.4 

: 
6.6 
: 
6.8 
: 

5.9 

SI 

6.3 

5.5 

: 

0.2 

7.2 
6.9 
7.4 
6.9 
7.1 

6.0 

IE 

4.4 

4.1 

: 

0.3 

: 
: 
: 
5.1 
: 

4.4 

SK 

4.7 

4.2 

4.0 

0.1 

: 
: 
4.9 
5.1 
: 

4.3 

IT 

4.9 

4.5 

: 

0.4 

: 
: 
5.4 
5.5 
: 

4.7 

FI 

6.1 

6.0 

: 

0.7 

: 
: 
: 
: 
: 

6.5 

Mio PPS 

CY 

: 

: 

: 

: 

: 
: 
: 
: 
: 

: 

SE 

6.8 

6.5 

: 

0.9 

7.7 
8.3 
8.1 
8.2 
8.1 

7.5 

Mio PPS 

UK 

5.9 

4.9 

: 

0.3 

5.9 
7.2 
6.8 
7.0 
6.7 

5.2 

92

 
 
 
 
 
 
 
 
 
 
  
  
  
  
 
Chapter 2:Combining data on public and private spending on education 

Calculations of total private and public spending on education institutions for non-EU countries (2000) 
All levels of education (Current prices) 

Mio PPS 

Educational institutions 
Expenditure on ancillary services 

IS 
439.2
: 

: 

LI 

: 

NO 
8466.3 
: 

BG 

HR 

: 

: 

: 

: 

RO 
3581 
: 

MK 

: 

: 

TR 
13764.5 
: 

JP 
135279.1
: 

US 
590432.7 
: 

Calculations of total private and public spending on education for non-EU countries (2001) 
All levels of education (Current prices) 

Mio PPS 

Educational institutions 
Expenditure on ancillary 
services 

IS 
490.3 

: 

LI 
: 

: 

NO 
8937.1 

BG 
1996.5 

: 

: 

HR 
: 

: 

RO 
: 

: 

MK 
: 

: 

TR 
12879.1 

: 

JP 
138381.9 

US 
637968.3 

: 

: 

Key education indicators on social inclusion and efficiency 

93

 
 
 
 
 
 
 
 
 
 
Chapter 2:Combining data on public and private spending on education 

Calculations of total private and public spending on education for non-EU countries (2002) 
All levels of education (Current prices) 

Mio PPS 

Educational institutions 
Expenditure on ancillary services 

IS 
538.1 
: 

LI 
: 
: 

NO 
9581.0 
: 

BG 
2012.9 
: 

HR 
1849.1 
: 

RO 
: 
: 

MK 
: 
: 

TR 
: 
: 

JP 
142034.3 
: 

US 
695332.4 
: 

Calculations of total private and public spending on education for non-EU countries (2003) 
All levels of education (Current prices) 

Mio PPS 

Educational institutions 
Expenditure on ancillary services 

IS 
604.1 
: 

LI 
: 
: 

NO 
9368.9 
: 

BG 
2344.9 
: 

HR 
1993.8 
: 

RO 
4853.3 
: 

MK 
: 
: 

TR 
15124.9 
: 

JP 
145867.7 
: 

US 
687421.7 
: 

Key education indicators on social inclusion and efficiency 

94

 
 
 
 
 
 
 
CChhaapptteerr  

33  

Efficiency & effectiveness 
of education systems 

    
 
 
 
 
 
 
 
Chapter 3:Efficiency and effectiveness of education systems 

EEffffiicciieennccyy  aanndd  eeffffeeccttiivveenneessss  ooff  eedduuccaattiioonn  
ssyysstteemmss  

33..11    IInnttrroodduuccttiioonn    

Measuring  efficiency  and  effectiveness  in  education  is  of  great  relevance  considering  that 
resources are usually limited, while needs for such resources are often limitless.  Thus, like all 
other  services,  it  is  important  to  do  some  form  of  cost-benefits  analysis,  and  be  able  to 
compare  this  over  time  within  a  particular  Member  State,  or  between  different  Member 
States.  The  underlying  objective  of  making  best  use  of  resources  devoted  to  education 
requires a drive to maximising both the efficiency and effectiveness quotients for this pursuit.  
Thus, the concepts of efficiency and effectiveness are applied in order to monitor and evaluate 
how  well  resources  are  used  in  an  educational  system  and  to  prioritise  the  use  of  such 
resources.  

As useful as they are, these concepts can be rather abstract in the sense that they are difficult 
to quantify. Other than in broad general terms, there is not one universally accepted definition 
of  efficiency  and  effectiveness.  Even  less  so  is  a  universally  accepted  method  to  quantify 
these from a statistical perspective. In other words, they are tricky to measure through the use 
of standard techniques. The reason for this in part is the fact that, there are too many socio-
economic factors, which affect the two sides of the equation to varying degrees. There are too 
many  possible  combinations  and  permutations  of  these  factors.  Attempting  to  use  statistical 
tools to compare efficiency and effectiveness of education between different Member States, 
with  differences  in  these  socio-economic  factors,  could  invariably  amount  to  little  less  than 
comparing things that are fundamentally different. 

 In  addition,  the  distinction  between  the  two  concepts  can,  at  times,  appear  rather  blurred. 
There  are  many  different  perspectives  to  the  concepts  of  efficiency  and  effectiveness  of 
education systems. For example, one definition of efficiency and effectiveness that was given 
in relation to higher education1 is: 

•  Efficiency - “An ability to perform well or achieve a result without wasted resources, 
effort,  time,  or  money  (using  the  smallest  quantity  of  resources  possible)” 
…”Educational efficiency can be measured in physical terms (technical efficiency) or 
in terms of cost (economic efficiency)”. 

•  Effectiveness  -  “An  output  of  specific  review/analyses  that  measure  (the  quality  of) 
the  achievement  of  a  specific  educational  goal  or  the  degree  to  which  a  higher 
education institution can be expected to achieve specific requirements”.  

1 Lazăr Vlăsceanu, Laura Grünberg, and Dan Pârlea “Quality Assurance and Accreditation: A Glossary 

of Basic Terms and Definitions” UNESCO, 2004 

Key education indicators on social inclusion and efficiency 

96

  
 
 
  
 
 
 
                                                 
Chapter 3:Efficiency and effectiveness of education systems 

In  contrast,  the  recent  Communication  from  the  Commission  “Efficiency  and  equity  in 
European  education  and  training  systems2”  stated,    “Efficiency  involves  the  relationship 
between  inputs  and  outputs  in  a  process.  Systems  are  efficient  if  the  inputs  produce  the 
maximum  output”.  Thus  an  education  system  is  said  to  be  efficient  if  maximum  output  is 
obtained from a given input, or if a given output is obtained with minimum possible input.  

In  distinguishing  between  efficiency  and  effectiveness,  the  following  concepts  have  often 
been citied and used3:  

i. 

Internal efficiency  - A system is  more internally efficient than another if, in  order to 
produce the same level of output, it is less costly. It refers to a comparison of learning 
(a  non-monetary  outcome  of  education)  to  the  costs  of  educational  inputs.  Internal 
efficiency addresses the question of how funds within the educational sector should be 
best  allocated.  It  is  concerned  with  obtaining  the  greatest  educational  outputs  for  any 
given level of spending.  

ii.  External  efficiency  -  For  measuring  the  external  efficiency  of  education  system  one 
tries  to  ascertain  the  impact  which  the  school,  college,  university  graduates  i.e.  the 
product of education system are making on the society. Items such as community gains 
(i.e.  community  activities),  political  (i.e.  government  activities),  personal  gains  (i.e. 
return  to  individual  and  his/her  family),  students  achievement  at  higher  levels  of 
education, the life time income as well as the social stability are taken into account. It 
refers to the ratio of monetary outcomes to monetary inputs. For example the analysis 
of returns to schooling. 

iii.  Internal effectiveness - A system is more internally effective (technically efficient) than 
another if, in order to produce the same level of output, fewer of at least one input is/are 
used. 

iv.  External  effectiveness  -  External  effectiveness  is  concerned  with  the  relationship 
between non-monetary inputs and monetary outputs, or how the overall use of money 
for  schooling  compares  to  other  potential  public  and  private  uses.  e.g.  comparing  the 
earnings  of  technical-vocational  graduates  with  the  earnings  of  students  graduating 
from  academic  subjects.  By  measuring  outputs  in  monetary  values,  it  is  possible  to 
compare educational programs directly to other potential uses of society's resources. 

Table 3.1: Overview on analysing efficiency and effectiveness  

Efficiency 

Effectiveness 

Type 

Internal 
efficiency 
External 
efficiency 
Internal 
effectiveness 
External 
effectiveness 

to 

Type of inputs and outputs 
Monetary 
non-
input 
monetary output 
Monetary  input  to  monetary 
output 
Non-monetary  input  to  non-
monetary output 
Non-monetary 
monetary output 

input 

to 

2 COM(2006) 481 final 
3 This distinction was first presented by Lockheed and Hanushek in: The International Encyclopedia of 
Education  1994.  Second  Edition.  Vol.3,  Educational  Efficiency  and  Effectiveness,  Concepts  of. 
Exeter, New York, Tokyo: Pergamon pp. 1779-1784 

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Chapter 3:Efficiency and effectiveness of education systems 

33..22    MMeeaassuurriinngg  eeffffiicciieennccyy  aanndd  eeffffeeccttiivveenneessss  

In order to measure the efficiency of an education system the inputs and the process, have to 
be related to the effects, which are based upon the outputs and the outcomes. The education 
system can be viewed as consisting of four main components4.  

• 

Inputs:  these  are  the  real  resources  used  in  education,  e.g.  the  characteristics  of 
learners,  educators,  curricula,  textbooks,  facilities  and  equipment,  and  financial 
resources.  

•  Processes:  these  are  the  interactions  between  learners  and  inputs,  between  different 
e.g. 

teaching/learning 

processes, 

inputs 
between 
attendance/participation, absenteeism, etc.  

themselves, 

and 

•  Outputs:  these  are  the  direct  and  more  immediate  results  or  effects  of  education,  e.g. 

learner's completion/certification.  

•  Outcomes:  these  are  the  ultimate  or  eventual  effects  of  education,  e.g.  increased 
earnings,  employment,  contribution  to  productivity,  improved  health,  and  other  non-
monetary outcomes.  

Measuring  efficiency  creates  many  substantial  and  conceptual  problems,  in  relation  to  the 
measurement of the constituting items. This includes the following: 

-  Difficulties with the identification of the components of the education system that are 

relevant to the analysis; 

-  Determining how the components will be measured,  
-  Difficulties 

in  measuring  costs  – 

this 

includes 

the  problems  of  collecting                           

data on private costs of education both inside and outside of educational institutions; a 
lack of agreement on the type of costs that should be included; 

-  Methodological  issues  concerning  the  measurement  of  educational  outputs  and 
outcomes. For example, there are questions about how to incorporate cognitive as well 
as affective aspects in a measurement instrument. When determining the efficiency of a 
system the outputs are normally limited to aspects such as the number of graduates in a 
course  or  the  average  grades  in  a  course.  Measurements  of  long-term  effects  or 
outcomes are almost never taken into account.  

There are a number of techniques that are often employed in order to measure the efficiency 
of education systems. These include: 

•  Parametric or regression based estimators; 
•  Non-parametric analysis; 
•  Cost benefit analysis; 
•  Cost-effectiveness analysis; 
•  Measurement of rates of return to education.  

It  should  be  noted  that  the  purpose  of  this  study  was  not  to  conduct  a  detailed  analysis  of 
efficiency using all or some of the above-mentioned methods. However, a brief description of 

4  ‘NFENFE-MIS  Handbook  -  Developing  a  subnational  Non-Formal  Education  Management  

information System’, UNLD - LIFE  Publication 

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98

 
 
 
 
 
                                                 
Chapter 3:Efficiency and effectiveness of education systems 

each of the mentioned techniques is presented below. Each method has its relative merits and 
drawbacks. 

33..22..11   PPaarraammeettrriicc  oorr  rreeggrreessssiioonn  bbaasseedd  eessttiimmaattoorrss    

There are a number of methods that fall directly under this heading, including ordinary least 
squares regression analysis and Stochastic Frontier Analysis.   

33..22..11..11    OOrrddiinnaarryy  LLeeaasstt  SSqquuaarreess  ((OOLLSS))  rreeggrreessssiioonn  aannaallyyssiiss  

Ordinary least squares (OLS) is one of a variety of techniques that fall under the heading of 
regression  analysis.  It  involves  the  identification  of  a  statistical  relationship  between 
variables. Ordinary least squares analysis fits a line of best fit to the these points such that the 
line  minimises  the  sum  of  squared  vertical  distances  to  the  observed  output.  This  can  be 
illustrated  by  a  simple  example.  If  the  costs  of  a  higher  education  institution  (C)  depended 
only  on  the  number  of  students  enrolled  in  higher  education  institutions  (L)  then  each 
country’s level of costs and students enrolled could be plotted on a graph (see figure 3.1) 

Figure 3.1: Ordinary least squares regression analysis 

OLS regression analysis fits a line of ‘best fit’ to these points, such that the line minimises the 
sum of the squared vertical distances of the observed country’s costs (represented by crosses 
in figure 3.1). The line of best fit can be written as follows: 

Ci  = α + βLi   + ui 

Where i represents the observations for different institutions, α is the fixed cost involved, β is 
the cost of educating another student, and ui is the regression residual (the difference between 
actual costs and those predicted by the line of best fit).  

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Chapter 3:Efficiency and effectiveness of education systems 

The best-fit line represents the costs that an average higher education institution would expect 
to incur with a certain number of students.  Those institutions with an observation above the 
line have costs above those of an institution of average efficiency with the same  number of 
institutions. These institutions are considered as inefficient. Conversely those institutions that 
lie below the regression line can be viewed as being relatively efficient, that is above average 
efficiency.  The  difference  between  an  institution’s  actual  costs  and  its  predicted  costs  is 
termed the residual. A positive residual indicates inefficiency relative to the sample ‘average’ 
and a negative residual indicates inefficiency relative to the same average. 

In  reality  most  cost  functions  are  likely  to  have  more  than  one  cost  driver.  OLS  regression 
analysis handles this through the use of multivariate regressions. 

33..22..11..22    SSttoocchhaassttiicc  FFrroonnttiieerr  aannaallyyssiiss  ((SSFFAA))  

Stochastic  frontier  estimators  provide  parametric  estimates  of  efficiency.  It  is  based  on 
regression analysis, however there is an important distinction between the two. In the case of 
Stochastic  Frontier  regression,  the  traditional  random  error  term  is  divided  into  two 
components:  a  normally  distributed  random  error  term  (u)  and  a  second  positive  error  term 
that  captures  inefficiency  (v).  This  means  that  in  SFA  models  the  possibility  that  some 
component  of  the  model  residual  may  result  from  errors  in  measurement  of  costs  or  the 
omission  of  explanatory  variables,  as  opposed  to  the  existence  of  genuine  inefficiencies.  In 
contrast  OLS  regression  models  implicitly  assume  that  the  whole  of  the  residual  for  a 
particular  observation  (in  this  context  a  country)  is  the  result  of  genuine  inefficiency.  This 
decomposition of residuals between ‘error’ and ‘genuine efficiency’ provides a more accurate 
reflection of the true level of inefficiency. Furthermore the regression for SFA looks not at the 
average firm but at the most efficient firm. 

Ci  = α + βLi   +  vi   + ui 

In  order  to  decompose  the  residual  into  inefficiency  and  random  error,  a  number  of 
assumptions  are  made  about  the  distributions  of  its  two  components.    Once  the  relative 
importance  of  the  genuine  efficiencies  in  the  residual  distribution  have  been  estimated,  it  is 
possible  to  estimate  the  regression  line  downward  by  this  amount  to  form  an  efficiency 
frontier.  The  distance  of  each  observation  from  the  original  OLS  regression  line  measures 
deviations from the average performance, whilst in the new line they measure deviations from 
the efficiency frontier. 

Once the efficiency frontier has been estimated it is still necessary to estimate the efficiency 
of each institution, taking into account that the vertical distance of the  measured costs from 
the  efficiency  frontier  will  still  be  composed  of  the  observation  error  and  the  genuine 
efficiency. 

The  main  disadvantage  of  SFA  is  that  strong  assumptions  need  to  be  made  so  as  to 
decompose the regression residuals. 

Key education indicators on social inclusion and efficiency 

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Chapter 3:Efficiency and effectiveness of education systems 

33..22..22  

    NNoonn--ppaarraammeettrriicc  tteecchhnniiqquueess  

33..22..22..11    DDaattaa  EEnnvveellooppmmeenntt  AAnnaallyyssiiss  ((DDEEAA))  

Data  Envelopment  Analysis  (DEA)  can  be  used  as  an  alternative  to  regression-based 
techniques.  It  does  not  involve  statistical  estimation,  but  instead  makes  use  of  linear 
programming or some other form of mathematical programming methods to characterise the                                 
set of efficient producers and then derive estimates of efficiency for inefficient observations 
based on how far they deviate from the most efficient ones 

DEA  is  commonly  used  to  evaluate  the  efficiency  of  a  number  of  producers  or  Decision 
Making  Units  (DMUs).  A  typical  statistical  approach  is  characterised  as  a  central  tendency 
approach and it  evaluates  producers relative  to an average producer. In contrast, DEA is an 
extreme point method and compares each producer with only the ‘best’ producers. 

In a general sense, education provision is efficient if its producers make the best possible use 
of available inputs. An education system not being efficient would mean either that results (or 
“outputs”) could be increased without spending more, or else that expense could actually be 
reduced without affecting the outputs, provided that more efficiency is assured.  A common 
and  simple  measure  for  calculating  relative  efficiency  that  can  be   used  to  calculate  the 
efficiency of a one input one output model is as follows:  

Efficiency = 

Output
Input

However, this simplistic measure is often inadequate due to the existence of multiple inputs 
and  outputs  related  to  different  resources,  activities  and  environmental  factors.  This  is  the 
case with the benchmark indicators since there is  more than one output. In this instance the 
above equation can be written as follows: 

Efficiency = Weighted sum of outputs 

 Weighted sum of inputs 

This can be rewritten as follows: 

                                 Efficiency of unit j  =     u1y1j + u2y2j  + …. 

                                   v1x1j + v2x2j  + 

Where:               u1 = the weight given to output i 

 y1j = amount of output 1 from unit j 
 v1 = weight given to input 1 
 x1j = amount of input 1 to unit j   

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Chapter 3:Efficiency and effectiveness of education systems 

The initial assumption is that this measure of efficiency requires a common set of weights to 
be  applied  across  all  units.  This  immediately  raises  the  problem  of  how  such  an  agreed 
common  set  of  weights  can  be  obtained.  It  may  simply  be  difficult  to  value  the  inputs  or 
outputs.  For  example,  if  an  attempt  has  been  made  to  compare  the  relative  efficiency  of 
schools with achievements at music and sport amongst the outputs. Some schools may value 
achievements  in  sport  or  music  differently  to  other  schools,  and  in  general  units  may  value 
inputs and outputs differently and thus require different weights. 

Charnes, Cooper and Rhodes5 recognised the difficulty in seeking a common set of weights to 
determine relative efficiency. They recognised the legitimacy of the proposal that units might 
value inputs and outputs differently and therefore adopt different weights, and proposed that 
each unit should be allowed to adopt a set of weights, which showed it in the most favourable 
light in comparison to the other units.  

A fundamental assumption behind an extreme point method is that if a given producer, A, is 
capable of producing Y(A) units of output with X(A) inputs, then other producers should also 
be able to do the same if they were to operate efficiently. Similarly, if producer B is capable 
of  producing  Y(B)  units  of  output  with  X(B)  inputs,  then  other  producers  should  also  be 
capable of the same production schedule. Producers A, B, and others can then be combined to 
form  a  composite  producer  with  composite  inputs  and  composite  outputs.  Since  this 
composite producer does not necessarily exist, it is sometimes called a virtual producer.  

If the virtual producer is better than the original producer by either making more output with 
the  same  input  or  making  the  same  output  with  less  input  then  the  original  producer  is 
inefficient. The  procedure  of finding  the  best virtual producer  can be formulated as a linear 
program.  Analysing  the  efficiency  of  n  producers  is  then  a  set  of  n  linear  programming 
problems.  

The  following  formulation  is  one  of  the  standard  forms  for  DEA.  lambda  is  a  vector 
describing the percentages of other producers used to construct the virtual producer. lambda 
X and lambda Y and are the input and output vectors for the analysed producer. Therefore X 
and  Y  describe  the  virtual  inputs  and  outputs  respectively.  The  value  of  theta  (Θ)  is  the 
producer's efficiency.  

min Θ, 
Subject to Y λ≥ Y0, 
Θ X0  - Xλ ≥ 0, 
Θ free,  λ ≥ 0. 

Where: 
X = m * n matrix of inputs; 
Y = k * n matrix of outputs; 
Y0 = k* 1 vector of institutions outputs; 
X0  = m * 1 vector of institutions inputs; 

5 Charnes A., Cooper W.W. and Rhodes E. (1978) ‘Measuring the efficiency of decision making units’, 

Eur. J. Opl. Res 2, 429-444. 

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102

 
 
 
 
 
 
                                                 
Chapter 3:Efficiency and effectiveness of education systems 

It should be noted that efficiency is usually constrained to the range [0,1].  
There are a number of advantages of DEA analysis, including the following: 

• 

It  allows  a  complex  non-linear  (concave  or  convex)  relationship  to  exist  between 
outputs  and  costs,  whereas  regression  usually  restricts  such  relationships  to  be  either 
linear or to have fairly simple non-linear forms; 

•  Can handle multiple input and multiple output models; 
•  Decision making units are directly compared against a peer or combination of peers; 
• 

Inputs and outputs can have very different units 

There are a number of disadvantages associated with DEA, including the following: 

•  Since DEA is an extreme point technique, noise such as measurement error can cause 

• 

significant problems; 
It is good at estimating “relative” efficiency of a decision-making unit, but it converges 
very  slowly  to  “absolute”  efficiency.  In  other  words,  it  can  tell  how  well  a  DMU  is 
doing compared to its peers but not to a theoretical maximum; 

•  Since it is a non-parametric technique, statistical hypothesis tests are difficult  
•  DEA  is  unable  to  assess  efficiency  with  outputs  on  the  edge  of  the  data  set,  unless 

restrictive assumptions (such as constant returns to scale are imposed). 

33..22..33   CCoosstt  bbeenneeffiitt  aannaallyyssiiss  

Cost-benefit analysis can be applied when both costs and effects can be measured in monetary 
terms.  Since,  it  is  almost  impossible  to  assess  the  effects  of  an  educational  process  in  a 
reliable  monetary  amount,  the  cost-benefit  analysis  is  practically  not  applicable  in  an 
educational  context.  In  a  training  situation  it  is  more  likely  that  the  benefit  of  a  training 
program  can  be  valued  in  monetary  terms  and  therefore  cost-benefit  analysis  could  be 
appropriate in such situations.  

33..22..44   CCoosstt--eeffffeeccttiivveenneessss  aannaallyyssiiss  

The  concept  of  internal  efficiency  is  normally  measured  by  cost-effectiveness  analysis.  The 
analysis  is  applied  when  the  costs  are  expressed  in  monetary  terms  and  the  effects  are 
measured in non-monetary terms (but still quantifiable, for instance the number or percentage 
of graduates in a course). In order to be able to compare two alternative educational systems, 
either the cost or the effects of both the alternatives have to be fixed. When the costs are the 
same for both alternatives, the system with the largest effects is the most efficient. When the 
effects  are  the  same  for  both  alternatives,  the  system  with  the  smallest  costs  is  the  most 
efficient.  Often  the  exact  measurement  of  the  effects  of  an  educational  system  is  very 
difficult.  

Key education indicators on social inclusion and efficiency 

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33..22..55   RRaatteess  ooff  rreettuurrnn  ttoo  eedduuccaattiioonn  

The  concept  of  external  efficiency  is  frequently  measured  through  rates  of  returns  to 
education analysis. The rate of return represents a measure of the returns obtained, over time, 
relative  to  the  cost  of  the  initial  investment  in  education.  Rates  of  return  can  be  measured 
from  the  private  individual’s point  of  view  or from  society’s point of  view. Private rates  of 
return  measure  the  future  net  economic  payoff  to  an  individual  of  increasing  the  amount  of 
education undertaken whilst social rates of return measure the benefits to society of additional 
education. The calculation formulae for both types of returns are the same, only the costs and 
benefits that are included differ between the two.  

The internal rate of return calculation is based on the actuarial method of calculating present 

value, a means for returning to a common date various streams of costs and benefits occurring 

at different moments in time.  The calculation of present value is a traditional criterion of an 

investment choice in face of a certain future.   

Net Present Value is calculated as follows: 

NPV

=

where: 

d

1
−

∑

t

=

0

C

t

1/(

+

i

)

t

+

64

−−

da
B
t

∑

dt
−

1/(

+

i

)

t

Ct: Costs at period t (t ∈ 0, d – 1) 
Bt: Benefits at period t (t ∈ d, 64 – a – d) 
i : discount rate 

d : duration of studies (in year) 

a : age at beginning of education / training 

64: age at lat year of activity in the labour market 

The internal rate of return is the discount rate at which NPV = 0. 

In estimating private internal rates of return, the benefits are computed as the present value of 

the flow of after tax lifetime earnings corresponding to a given level of education. This flow 

can be decomposed into three parts: the labour income that a person receives if employed, the 

unemployment  benefits  received  if  unemployed  and  the  retirement  benefits  at  the  end  of 

working  life.    The  costs  of  education  are  represented  by  the  opportunity  costs  of  adding  an 

additional year of schooling (i.e. foregone earnings in the labour market net of student support 

and support elements in loan schemes) and by the direct private costs incurred by individuals.   

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104

 
 
 
 
 
 
 
 
 
 
 
Chapter 3:Efficiency and effectiveness of education systems 

The  formal  framework  for  computing  social  rates  of  return  is  the  same  as  that  used  for 

computing private returns but social returns now refer to the sum of private and public costs 

and benefits. Social benefits include the economy-wide productivity increase associated with 

higher  education.  The  social  costs  comprise  the  opportunity  cost  of  having  less  people 

participating  in  production  during  the  schooling  period,  and  the  public  expenditure  on 

education, which is a proxy for the direct costs of schooling. 

Fiscal rates of returns refer to the (long-run) net budgetary gains from public intervention in 

higher  education.  The  present  value  of  the  costs  of  higher  education  is  compared  with  the 

present  value  of  the  revenues  induced  by  the  higher  wages  and  higher  employment  that  are 

associated  with  additional  years  of  education  of  the  labour  force.    de  la  Fuente  and  Jimeno 

(2005) defined the fiscal benefits as the sum of the tax revenues that are expected from adult 

workers (adjusted for the probability of employment and participation in the labour  market) 

and  retirees,  plus  the  consumption  taxes  paid  by  adult  workers.  The  costs  are  given  by  the 

sum of direct public costs of education  (including student support) and the opportunity cost, 

which is measured by the foregone net tax receipt of a potential full-time worker who decides 

instead to engage in a higher level of education. 

Comparing  social  and  private  returns  provides  information  on  the  scope  for  efficiency-

enhancing  public  intervention  in  the  tertiary  education  field.  Examining  fiscal  returns 

provides  insights  on  the  consistency  of  policies  with  the  sustainability  of  public  finances  in 

each country. In particular, fiscal returns to education allow comparing the yields from public 

investment  in  higher  education  with  those  from  other  forms  of  investment.  In  comparing 

social and fiscal returns across countries may also give insights on the relative effectiveness 

of different systems of public intervention in this field. 

In summary: 

Private costs: 

Foregone earnings  + direct private expenditures + future taxes 

State costs:     

Loss of taxes received during the training + public expenditures 

Social costs : 

Private costs + state costs (future taxes paid by individual are taken into 

account    into benefit flows) 

In  private  rates  of  return  the  private  costs  are  included  and  social  rates  of  return  the  social 

costs are included in the calculation. 

Private benefits: 

Increases in earnings + higher probability of being employed 

State benefits:     

Future taxes 

Social benefits: 

Private benefits + State benefits 

In  private  rates  of  return  the  private  benefits  are  included  and  in  social  rates  of  return  the 

social benefits are included in the calculation. 

Key education indicators on social inclusion and efficiency 

105

 
 
 
 
 
 
Chapter 3:Efficiency and effectiveness of education systems 

33..33    EEffffiicciieennccyy,,  eeffffeeccttiivveenneessss  aanndd  eeqquuiittyy  iinnddiiccaattoorrss  

Based  on  the  main  components  of  the  education  system,  indicators  are  normally  classified 
into the following four types: 

• 

Input indicators are measures of the effectiveness of resources used. These measure the 
characteristics of learners (that is the  availability of a resource, its nature and quality, 
and its manner/rate of use), educators, facilities, materials and equipment. 

•  Process  indicators  are  measures  of  the  interaction  taking  place  between  inputs.  These 
show the transition of inputs into outputs. They are of use at many different levels in an 
administrative hierarchy and are important for evaluation of a programme. 

•  Output/outcome  indicators  are  measures  of  the  immediate/long-term  effects  of  the 
educational  activity,  e.g.  attainments  effects,  achievement  effects,  attitude/behaviour 
effects, and equity effects.  

Effects  on  equity  are  also  outputs  of  an  education  system  but  of  a  different  kind.  Equity 
indicators provide interpretation of inputs, processes and other outputs as well as outcomes  

Efficiency-effectiveness  indicators  are  sub-indicators  of  the  general  indicator  system,  which 
give an indication concerning the quality of the process and the relevance of the programme.  

Efficiency-outputs  indicators:  measure  how  well,  for  example,  an  organisation  achieves  its 
shorter  term  objectives.  An  internal  efficiency  analysis  has  several  components:  inputs, 
processes,  i.e.  where  inputs  are  transformed  into  outputs,  and  educational  costs,  which  are 
determined  by  the  interaction  of  input  resources  and  process  resources.  For  example: 
completion rates (ratio completed learners /enrolled learners); rate of utilisation of educators; 
rate of utilisation of facilities. 

Effectiveness  indicators  -  outcomes  measure  the  extent  to  which  the  education  system  is 
'producing' outcomes, in keeping with its longer-term objectives. For example: the effects on 
learning achievement; the impact of skills development on the economy, health, as well as the 
economic impact of the system on society as a whole 

Equity  indicators  measure  the  extent  to  which  education  has  a  neutral,  negative,  or  positive 
effect  on  the  initial  disadvantages  of  certain  groups,  in  terms  of  access/distribution, 
participation/opportunities,  and  achievements/consequences.  'Disadvantaged  groups'  often 
overlap,  for  example  refugees,  the  poor,  minority  language  populations,  ethnic  minorities, 
nomads, and include women and girls, in general. 

It  is  important  to  note  that  equity  issues  arise  in  the  analysis  of  both  internal  and  external 
efficiency: 

•  Equity analysis of internal efficiency. Access to learning opportunities refers to getting 

into, and being able to progress in, an education system. For example: 
-  Availability of learning centres; 
-  Female versus male enrolments, by location, urban/rural, etc.;  
-  Variation in learner/educator ratios; 

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106

 
 
 
 
 
 
 
 
Chapter 3:Efficiency and effectiveness of education systems 

-  Distribution of total expenditure per learners, by gender, location, urban/rural; 
-  Variations in salaries per educator, by gender, urban/rural. 

•  Equity analysis of external efficiency - Equity is reflected in the results of participation 
in  an  education  system  by  different  groups.  Equity  in  outputs/outcomes  is  usually 
closely related to inputs. For example: 
-  Comparative learning achievement of girls versus boys, women versus men; 
-  Percentage of learners from lower-income background who completed a particular 

programme/course; 

-  Experiences  in  the  labour  force  of  females  versus  males/disadvantaged  groups 

versus others. 

33..44   CCaallccuullaattiinngg   eeffffiicciieennccyy  
bbeenncchhmmaarrkk  iinnddiiccaattoorrss  

iinn   rreellaattiioonn  

ttoo  

tthhee  

Data  was  collected  on  public and  private  spending  on  education as a  part of  this  project, to 
estimate  a  total  spending  figure  on  education  in  order  to  calculate  the  indicator  of  total 
spending on education as a percentage of GDP.   

The study in part, aimed to use the estimate total spending on education in order to examine 
the  efficiency  and  effectiveness  of  education  systems  across  Europe  in  relation  to  the 
benchmark  indicators,  and  hence  to  combine  the  benchmark  indicators  to  formulate  a 
composite indicator. 

In  calculating  efficiency  in  relation  to  the  benchmark  related  indicators  we  decided  to 

calculate efficiency, using Data Envelopment Analysis (DEA). DEA was one of the methods 

already outlined in section 3.2. The choice of the method merely reflected the fact that it was 

judged  to  be  the  most  appropriate  method  in  which  to  calculate  efficiency  based  on  the 

benchmark-related  indicators,  since  it  had  an  input  and  multiple  outputs.    In  theory,  this 

should  have  allowed  a  one  input  and  multiple  output  model.  Unfortunately,  due  to  the 

instability of the relationships between inputs and outputs a one input one output DEA model  

was adopted. This meant that the efficiency of the input in relation to each of the outputs was 

calculated.  An  average  score  was  then  calculated  based  upon  the  efficiency  scores  of  the 

individual input – output models. The calculations of efficiency are presented in Annex 2. 

Key education indicators on social inclusion and efficiency 

107

 
  
  
  
 
 
Chapter 3:Efficiency and effectiveness of education systems 

33..44..11    DDeeffiinniinngg  tthhee  iinnppuuttss  aanndd  oouuttppuuttss  

Prior to calculating efficiency, it is necessary to first present which of the indicators are input 
or  output  indicators  and  then  to  examine  the  data,  which  is  currently  available  for  each 
indicator.    

Defining the inputs and outputs in relation to the benchmark indicators and the indicator on 
total  spending  on  education  is  the  foundation  of  the  exercise.    Table  3.2  shows  each  of  the 
five  benchmark  related  indicators  examined  in  this  study  along  with  the  indicator  on  total 
spending  on  education  classified  according  to  the  four  main  components  of  the  education 
system.  The  table  clearly  shows  that  four  of  the  benchmark  indicators  are  in  fact  output 
indicators  whilst  the  indicator  on  participation  on  lifelong  learning  is  in  fact  a  process 
indicator. This has repercussions for the rest  of the exercise.  Since this indicator cannot be 
classified  as  an  output  indicator,  it  cannot  be  used  to  calculate  efficiency.  Therefore,  the 
efficiency  of  spending  on  education  can  only  be  calculated  on  the  basis  of  four  of  the 
indicators. None of the indicators were classified as outcome indicators. 

Table 3.2: Benchmark indicators broken down by type of indicator 

Indicator 

Input  Process  Output  Outcome 

h 

Total spending on education as a % of GDP 
Percentage of pupils with reading literacy level 1 and 
lower in the PISA reading literacy scale. 
Share of the population aged 18 – 24 with only lower 
secondary  education  and  not  in  education  and 
training 
Percentage of those aged 20 -24 who have successfully 
completed  at 
least  upper  secondary  education 
(ISCED 3); 
Total  number  of  tertiary  (5A,  5B  and  6)  graduates 
from mathematics, science, and technology; 
Percentage  of  population  aged  25  –  64  participating 
in  education  and  training  in  four  weeks  prior  to  the 
survey. 

h 

h 

h 

h 

h 

Key education indicators on social inclusion and efficiency 

108

    
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Chapter 3:Efficiency and effectiveness of education systems 

33..44..22  RReellaattiioonnsshhiipp  bbeettwweeeenn  iinnppuuttss  aanndd  oouuttppuuttss  

It is important to note that all the output indicators defined in table 3.2 refer to outputs from 
the  formal  education  system.  This  refers  to  education,  which  can  be  defined  by  ISCED.  In 
contrast, the input total spending refers to both formal and non-formal education. Non-formal 
education  will  take  the  form  of  seminars,  continuing  vocational  training  in  the  workplace, 
correspondence courses, leisure classes etc… Unfortunately there is no benchmark indicator 
that can be used to measure output from non-formal education. It was already mentioned in 
section  3.4.1,  that  the  benchmark  indicator  on  participation  in  lifelong  learning  is  a  process 
indicator since it is an interaction between the learner and the input, and it does not indicate 
the  direct  results  of  education.    The  lack  of  qualifications  in  the  non-formal  education  can 
partly  help  to  explain  the  inability  to  formulate  an  adequate  indicator  measuring  the  output 
from this type of learning. 

Thus, it can  be  argued that in order to examine the efficiency of spending in relation to the 
defined  outputs,  total  spending  on  education  should  refer  to  only  spending  on  educational 
institutions.  

Figure  3.2,  shows  a  clustering  of  countries  spending  between  2.5  to  5  percent  of  GDP  on 
primary and secondary education, and between 70 to 100 % of 20 to 24 year olds achieving at 
least  upper  secondary  education  (ISCED  level  3).  There  are  a  number  of  countries  that  lie 
outside  this  cluster  including  MT,  PT,  IS  and  to  a  certain  extent  ES.  For  example  Portugal 
spends the nearly the same proportion of the nation’s wealth on education as Finland, but a 
smaller percentage of the age group 20 to 24 has achieved at least upper secondary education. 
This  could  lead  one  to  interpret  that  Finland  is  more  efficient  than  Portugal.  In  contrast 
Luxembourg  spends  less  of  its’  wealth  on  education  than  Denmark  but  both  countries  have 
nearly the same percentage of 20 to 24 year olds achieving upper secondary education. There 
may be a number of reasons to explain why these countries do not exhibit the same traits as 
the other countries. This may include peculiarities of national education systems, and the fact 
that expenditure as a percentage of GDP only provides a measure of the relative proportion of 
a nation’s wealth that is invested in education.  

Two lines have been superimposed on figure 3.2, representing the average public spending on 
education  for  all  countries  on  the  x-axis,  and  the  average  percentage  of  20  to  24  year  olds 
attaining at least upper secondary level of education on the y-axis. Countries in lying in the 
lower right quadrant (DK, LU, LV, PT and IS) spent more than the average on education, but 
achieved  less  than  the  average  in  terms  of  the  percentage  of  20  to  24  year  olds  achieving 
upper secondary education. Countries lying in the upper right quadrant (EE, FR, HU, AT, PL, 
SI, FI, SE, NO, and US) also spent more than the average on education, but achieved above 
average upper secondary attainment levels. 

Countries lying in the lower left quadrant of figure 3.2, (DE, ES, IT, NL, MT, NL, BG, RO) 
spent  below  average  on  education,  and  achieved  below  average  upper  secondary  education 
attainment levels. Finally, countries lying in the upper left quadrant (CZ, EL, IE, LT and SK) 
spent below average on education, but achieved more than average upper secondary levels. 

Key education indicators on social inclusion and efficiency 

109

 
 
 
 
 
 
 
Chapter 3:Efficiency and effectiveness of education systems 

Figure 3.2: Public expenditure on primary and secondary education (ISCED 1 – 3) as a 
percentage of GDP versus percentage of 20 to 24 year olds who have at least upper secondary 
education (ISCED 3) (2003) 

CZ

SK

EL

IE

PL

US

HU

LT

EE

AT

FR

SI
FI
BE

RO

BG

DE

NL

UK

LV

LU

IT

NO

SE

DK

ES

MT

PT

IS

100

%

t
n
e
m
n
i
a
t
t
a
d
E

90

80

70

60

50

40

30

20

10

0

0

1

2

3
Exp as % of GDP

4

5

6

Source: Based on data collected from Eurostat, the OECD and US Census Bureau 

Key: 

Average 

Figure  3.3, charts  the relationship  between  public  spending  on  education as  a  percentage  of 
GDP versus the percentage of early school leavers in the age group 18 to 24. Most countries 
are very clustered  in the region of around 6 to  17  per cent  of early school leavers spending 
between  3  and  4  per  cent  of  GDP  on  education.  However  there  are  a  number  of  countries, 
which can be classified as outliers. These countries are ES, IT, MT, PT, IS, BG and RO. 

Two lines have been superimposed on figure 3.3, representing the average public spending on 
education for all countries on the x-axis, and the average percentage of early school leavers on 
the y-axis. Most countries lie in the lower right quadrant (BE, DK, EE, FR, HU, LU, AT, PL, 
FI, SI, SE, NO and the US). They spent above average on education, but had below average 
percentage  of  early  school  leavers.  Only  four  countries  (LV,  PT,  US  and  UK),  spent  more 
than average, and had an above average percentage of early school leavers. 

On the other hand, countries that spent below average in the upper left quadrant (ES, IT, EL, 
BG,  and  RO)  had  a  high  percentage  of  early  school  leavers.  Countries  in  the  lower  left 
quadrant (CZ, DE, IE, LT, NL, and SK) also spent below average, but had a low percentage 
of early school leavers. 

Key education indicators on social inclusion and efficiency 

110

 
 
 
 
 
 
Chapter 3:Efficiency and effectiveness of education systems 

Figure 3.3: Public expenditure on primary and secondary education as a percentage of GDP 
versus percentage of early school leavers 18 to 24 year olds (2003) 

60

50

40

30

20

10

s
r
e
v
a
e
l

l
o
o
h
c
s
y
l
r
a
E
%

0

0

MT

ES

RO

BG

EL

IE

DE

PT

FR

BE

EE

FI

SI

PL

IT

LV

NL

UK

HU

LU
US
AT

LT

CZ

SK

IS

DK

SE

NO

1

2

3
Exp as % of GDP

4

5

6

Source: Based on data collected from Eurostat, and US Census Bureau  

Key: 

Average 

Figure 3.4, examines  the  relationship between  public  spending  on higher  education  (ISCED 
5/6)  as  a  percentage  of  GDP  and  the  percentage  of  Maths,  Science  and  Technology  (MST) 
graduates  aged  20  to  29  per  1000  of  the  population.  There  is  a  clustering  of  countries  that 
spent between 0.5 per cent and 1.5 of GDP on education, whilst achieving between 5 to 10 per 
cent of MST graduates per 1000 of the population. For example, Hungary spent slightly more 
than  1  percent  of expenditure  as  a  percentage  of GDP  on  education  and  had  a low share  of 
MST graduates in the population (4.8 percent).  In contrast France, spent the same amount of 
public  resources  on  higher  education  and  had  a  high  proportion  of  MST  graduates  in  the 
population (22 percent).  It is interesting to note that the Nordic countries (DK, FI, SE, and 
NO)  are  clustered  together  as  they  spend  more  on  education  than  the  other  countries. 
However,  the  proportion  of  MST  graduates  in  the  population  differs  significantly  between 
them. For example, Norway spent 2.3 per cent of GDP on education, but less than 10 per cent 
of  the  population  were  MST  graduates.  In  contrast,  Finland  spent  just  over  two  percent  of 
GDP  on  education,  but  managed  to  achieve  a  very  high  proportion  of  the  population  with 
MST graduates (17 per cent). 

Some  countries  (IE,  FR,  LT,  and  the  UK),  spent  a  low  amount  of  public  resources  on 
education,  but  had  a  high  proportion  of  MST  graduates  in  the  population.    For  example, 
Lithuania and the UK spent one percent of GDP on education, however they have more MST 
graduates  in  the  population  than  both  Poland  and  Portugal  (less  than  10  per  cent  of  MST 
graduates in the population). This can lead to the interpretation that Lithuania and the UK are 
more efficient than the Poland and Portugal.  

Key education indicators on social inclusion and efficiency 

111

 
 
 
 
 
 
 
 
Chapter 3:Efficiency and effectiveness of education systems 

Two lines have been superimposed on figure 3.4, representing the average public spending on 
education  for  all  countries  on  the  x-axis,  and  the  average  percentage  of  MST  graduates  per 
1000 of the population on the y-axis. Countries situated in the lower left quadrant (CZ, EE, 
IT,  MT,  AT,  PL,  SK,  BG  and  RO)  spent  below  average  on  education,  and  achieved  below 
average percentage of MST graduates in the population. However, four countries (ES, IE, LV, 
and  UK),  spent  below  average,  but  managed  to  achieve  a  greater  percentage  of  MST 
graduates in the population. 

Countries that spent above average on higher education in the upper right quadrant (BE, DK, 
FI, SE and US) achieved a high percentage of MST graduates in the population. On the other 
hand,  countries  in  the  lower  right  quadrant  (HU,  NL,  AT,  SI,  IS  and  NO)  also  spent  above 
average  but  only  managed  to  achieve  below  average  percentage  of  MST  graduates  in  the 
population. 

Figure 3.3: Public expenditure on higher education (ISCED 5/6) as a percentage of GDP versus 
percentage MST graduates aged 20 – 29 per 1000 of the population (2003) 

30

25

20

15

10

5

0

0
0
0
1
r
e
p
9
2
-
0
2
d
e
g
a
s
d
a
r
g
T
S
M

IE

FR

UK

LT

ES

SK

PL
PT

EE

BG

CZ

MT

IT

LV

RO

FI

SE

DK

NO

BE

US

SI

AT

DE

NL

IS

HU

0.0

0.5

1.0

1.5
Exp as % of GDP

2.0

2.5

3.0

Source: Based on data collected from Eurostat, the OECD  

Key: 

Average 

It can be argued that in order to examine the relationship between spending on education and 
the number of MST graduates, public spending on the entire continuum of education (ISCED 
levels 1 to 6) needs to be considered for the simple reason that the number of graduates from 
higher  education  will  be  effected  by  the  resources  devoted  to  the  preceding  levels  of 
education (see figure 3.4). Two lines have been superimposed on figure 3.5, representing the 
average  public  spending  on  education  for  all  countries  on  the  x-axis,  and  the  average 
percentage  of  MST  graduates  per  1000  of  the  population  on  the  y-axis.    It  can  be  observed 
that the distribution of countries amongst the four quadrants has altered slightly. For example, 
Poland  and  Portugal  were  situated  in  the  lower  left  quadrant  in  figure  3.4,  spending  below 
average  on  higher  education,  and  achieving  a  low  percentage  of  MST  graduates  in  the 
population. However, in figure 3.5 these countries move to the lower right quadrant, spending 

Key education indicators on social inclusion and efficiency 

112

 
 
 
 
 
 
 
 
 
Chapter 3:Efficiency and effectiveness of education systems 

above  average,  but  still  achieving  a  below  average  proportion  of  MST  graduates  in  the 
population. 

Figure 3.5: Public expenditure on education for all levels of education as a percentage of GDP 
versus MST graduates aged 20 – 29 per 1000 of the population (2003) 

30

25

20

15

10

5

0

0
0
0
1
r
e
p
9
2
-
0
2
d
e
g
a
s
d
a
r
g
T
S
M

IE

ES

SK

IT
DE

CZ

BG

RO

FR

FI

UK

LT

US

BE

LV

EE
AT

PL

SI

PT

NL

SE

DK

NO

IS

HU

MT

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

Exp as % of GDP

Source: Based on data collected from Eurostat, the OECD 

Key: 

Average 

Figure  3.6,  shows  a  clustering  of  countries  that  spend  between  2  to  3  percent  of  GDP  on 
primary and  lower secondary  education, having  between  15 to  25  % of 15 year olds  with  a 
low reading literacy.  To a certain extent it can be said that countries spending more of their 
wealth on education have a lower percentage of 15 year olds with a reading literacy of 1 and 
lower  on  the  PISA  reading  scale.  Whereas  countries  spending  less  of  their  wealth  on 
educational institutions have a higher percentage of 15 year olds with a reading literacy of 1 
and lower.  For example, in Sweden, public expenditure on primary and secondary education 
was  slightly more  than  3  percent  of  GDP,  and  there  are  only  13  percent  of  pupils  with  low 
reading literacy, whereas the Slovak Republic spent less than 2 percent of GDP on education, 
yet one quarter of 15 year olds had a reading literacy score of 1 and lower. Nevertheless, it 
has to  be  stated there  are  a  number  of  countries (IE, NL, FI and  IS) that  do not follow this 
pattern.  For  example,  Finland  spent  more  than  2.5  per  spent  of  its  GDP  on  education  and 
more than 5 percent of 15 year olds had a low reading literacy, whilst France spent the same 
amount  and  had  a  much  higher  percentage  of  15  year  olds  with  a  low  reading  literacy  (18 
percent). 

Two lines have been superimposed on figure 3.6, representing the average public spending on 
education for all countries on the  x-axis, and  the  average  percentage of  15 year olds  with  a 
reading  literacy  of  1  and  lower  on  the  PISA  scale  on  the  y-axis.  Countries  situated  in  the 
lower  left  quadrant  (FR,  NL,  and  FI)  spent  below  average  and  had  a  low  percentage  of  15 
year olds with a low reading literacy. Countries in  the  upper left quadrant also spent  below 
average, but had a high percentage of 15 year olds with low reading literacy. Countries that 
spent  above  average  and  lay  in  the  lower  right  quadrant  (DK,  NL,  PL,  and  SE)  had  a  low 

Key education indicators on social inclusion and efficiency 

113

 
 
 
 
 
 
 
 
 
Chapter 3:Efficiency and effectiveness of education systems 

percentage  of  15  year  olds  with  low  reading  literacy.  In  contrast,  countries  that  lay  in  the 
upper right quadrant spent above average, but had a high percentage of 15 year olds with low 
reading literacy. 

Figure 3.6: Public expenditure on primary and lower secondary education (ISCED 1 & 2) as a 
percentage of GDP versus percentage of pupils with reading literacy level 1 and lower on the 
PISA reading literacy scale (2003) 

30

25

20

15

10

5

0

1
=
<
y
c
a
r
e
t
i
l
g
n
i
d
a
e
R
%

SK

IT

DE

AT

PT

ES

CZ

LV

FR

US NO
DK

PL

IE

NL

SE

FI

IS

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Exp as % of GDP

Source: Based on data collected from Eurostat, OECD  

Key: 

Average 

A  simple  two  variable  regression  was  conducted  in  order  to  determine  whether  there  is  a 
relationship  between  expenditure  on  education  as  a  percentage  of  GDP  to  each  of  four 
benchmark  indicators  (educational  attainment,  early  school  leavers,  MST  graduates,  and 
reading literacy). The regression took the following simple linear form: 

yi = α + βxi + ei 

where: 
xi = expenditure as a percentage of GDP; 
yi  =  benchmark  indicator  (educational  attainment,  early  school  leavers,  MST  graduates, 
reading literacy of 15 year olds); 
ei = stochastic error; 
i = BE, CZ, DK,………………….UK 
The  regression  results  (see  Annex  4)  showed  that  care  should  be  taken,  since  the  statistical 
results  showed  for  the  regressions  conducted,  the  independent  variable  xi,  (expenditure  as  a 
percentage of GDP) is not significant at the five percent level for the regressions conducted 
on  educational  attainment  of  20  to  24  year  olds,  reading  literacy  of  15  year  olds,  and  MST 
graduates.  This is not remedied if the variables are transformed by taking the logarithms of 
the  variables.  This  could  suggest  that  there  is  no  relationship  between  expenditure  as  a 
percentage  of  GDP  and  each  of  the  benchmark  indicators.  However,  it  is  probably  the  case 
that  there  are  other  variables,  which  can  explain  the  dependent  variable  have  been  omitted 

Key education indicators on social inclusion and efficiency 

114

 
 
 
 
 
 
 
Chapter 3:Efficiency and effectiveness of education systems 

from  the  regression.  However,  the  independent  variable  xi,  (expenditure  as  a  percentage  of 
GDP)  is  significant  at  the  five  percent  level  for  the  regression  conducted  on  early  school 
leavers.  This  indicates  a  relationship  between  expenditure  as  a  percentage  of  GDP  and  the 
percentage of early school leavers. 

33..44..33    DDaattaa  nneeeeddeedd  ttoo  ccaallccuullaattee  eeffffiicciieennccyy  

In order to calculate efficiency based on input and output indicators outlined in table 3.3, it is 
necessary  to  have  a  full  data  set.  It  is  evident  that  the  calculation  of  efficiency  will  involve 
one input and multiple outputs. This means that if one data point (this can be either the input 
or any one of the four  outputs) is missing for one country then the calculation of efficiency 
cannot  be  calculated  for  that  country  and  thus  the  calculation  of  efficiency  for  the  other 
countries  will  not  be  based  on  the  efficiency  of  that  country.    Thus,  it  can  be  said  that 
calculating efficiency based on four outputs is extremely sensitive to data availability.  

Hence,  this  section  examines  the  data  needed  for  each  of  the  input  and  outputs  outlined  in 
table 3.2. 

Inputs 

There is only one input being considered to calculate efficiency and that is the total spending 
on  education  from  public  and  private  sources.  Total  spending  on  education  consists  of 
spending  on  education  inside  and  outside  of  institutions.  It  is  also  interesting  to  examine 
efficiency in relation to spending on educational institutions only.  

There are a number of indicators that could be considered in relation to total spending, these 
are the following: 

i.  Total expenditure on education (in euros); 
ii.  Total expenditure on education per student (pps); 
iii.  Total expenditure on education as a percentage of GDP; 
iv.  Total expenditure on education per student compared to GDP per capita;  
v.  Total expenditure on education per student as a percentage of GDP; 
vi.  Total expenditure on education as a percentage of GNP; 
vii.  Cummulative spending  

Indicators (i.) to (vi.) can also be calculated for the total spending of educational institutions 
and by level of education. 

Indicator  (i.)  can  be  calculated  easily  for  non  euro-zone  countries  by  applying  the  average 
exchange  rate.  However,  it  would  be  difficult  to  compare  the  expenditure  figures  between 
countries  due  to  differences  in  price  levels.  Therefore  in  order  to  compare  expenditure  on 
education  between  countries  by  eliminating  the  differences  in  price  levels  among  countries, 
the data needs to be transformed into purchasing power standards (PPS)6.  

6 Purchasing power standards are based on comparisons of the prices of representative and comparable 

goods or services recorded in the national currency of the country in question on a specific date. 

Key education indicators on social inclusion and efficiency 

115

 
  
 
 
 
 
 
 
 
                                                 
Chapter 3:Efficiency and effectiveness of education systems 

Indicator  (ii.)  is  calculated  by  dividing  the  total  expenditure  on  education  by  the 
corresponding  full-time  equivalent  enrolment  and  is  accordingly  adjusted  for  differences  in 
price  levels.  It  usually  measures  how  countries  vary  in  the  extent  of  their  investment  in 
education  in  order  to  assess  the  effectiveness  of  different  models  of  educational  provision. 
Nevertheless,  results  could  hardly  be  deemed  comparable  given  that  they  will  reflect  both 
inefficiency and cost provision differences. For example, countries where teachers are better 
paid  would  tend  to  show  up  as  inefficient,  irrespective  of  the  intrinsic  performance  of  the 
education system.   

Alternatively one can consider relating expenditure to GDP, which will yield indicator (iii.). 
This  indicator  provides  a  measure  of  the  relative  proportion  of  a  nation’s  wealth  that  is 
invested in education and educational institutions. However, it still does not solve the problem 
of cost provision differences and a country’s ability to pay for education. 

Indicator (iv.) permits an evaluation of how countries vary in the extent of their investment in 
education.  Benchmarking  against  GDP  per  capita,  compares  a  countries’  investment  in 
education  relative  to  its  ability  to  pay.  As  with  expenditure  per  student  expressed  in  PPP 
terms, this indicator is calculated by dividing the total expenditure on educational institutions 
at a given level of education by the corresponding full-time equivalent enrolment. 

Indicator  (v.)  relates  spending  per  student  to  GDP.  It  does  not  solve  the  problem  of  cost 
provision differences between countries nor a country’s ability to pay for education. 

Indicator (vi.) relates spending on education to the total value of all final goods and services 
produced  within  a  nation  in  a  particular  year,  plus  income  earned  by  its  citizens  (including 
income of those located abroad), minus income of non-residents located in that country. This 
indicator suffers the same problems as indicator (iii.). 

Calculating  indicator  (vii.)  would  be  very  difficult  in  the  context  of  total  spending  on 
education.  The  intention  of  this  exercise  was  to  cover  spending  on  non-formal  education  as 
well  such  as  continuing  vocational  education  in  the  enterprise.  Therefore,  it  would  be  very 
difficult to calculate the average duration of studies. This indicator is usually calculated in the 
context of higher education using either the approximation formula or the chain method7.  The  
differences  between  countries’  in  annual  expenditure  per  student  (indicator  ii.)  do  not 
necessarily  reflect  the  variation  in  the  total  cost  of  educating  the  typical  tertiary  student. 
Varying enrolment patterns between countries can affect the interpretability of expenditure on 
education per student. In particular, comparatively low annual expenditure on education per 
student  can  result  in  comparatively  high  overall  costs  of  tertiary  education  if  the  typical 
duration of tertiary studies is long. Cumulative expenditure on education is calculated over the 
average  duration  of  tertiary  studies  by  multiplying  annual  expenditure  per  student  by  an 
estimate of the average duration of tertiary studies. 

It can be concluded, that using total expenditure per student will not remedy the problem of 
differences  in  costs  differences  between  countries.  The  best  option  is  to  use  as  inputs  for 

7  For  further  details  on  the  two  methods  see  ‘OECD  Handbook  for  Internationally  Comparative 

Education Statistics – Concepts, standards, definitions and classifications’, OECD, 2004 

Key education indicators on social inclusion and efficiency 

116

 
 
 
 
 
 
                                                 
Chapter 3:Efficiency and effectiveness of education systems 

spending on education per student compared to GDP per capita. However, since the intention 
of collecting data on spending on education was to cover non-formal education, data on the 
number of students enrolled in non-formal learning is not collected given its nature. 

Thus it can be argued that physical inputs and outputs have the important advantage of being 
comparable across countries without the need of any questionable transformation. 

Outputs 

The data needs for the outputs have already been defined. These are as follows: 

•  Share of the population aged 18 – 24 with only lower secondary education and not in 

education and training; 

•  Percentage  of  those  aged  20  -24  who  have  successfully  completed  at  least  upper 

secondary education (ISCED 3); 

•  Total  number  of  tertiary  (5A,  5B  and  6)  graduates  from  mathematics,  science,  and 

technology; 

•  Percentage  of  pupils  with  reading  literacy  level  1  and  lower  in  the  PISA  reading 

literacy scale. 

33..44..44    DDaattaa  ccoolllleecctteedd  bbyy  iinntteerrnnaattiioonnaall  ssoouurrcceess    

This section examines the data collected by international sources which could meet the data 
required needed to examine the efficiency of education systems in relation to the benchmark 
indicators. 

Inputs 

Obtaining  a  total  figure  for  spending  on  education  both  inside  and  outside  of  educational 
institutions  from  international  data  sources  is  a  difficult  undertaking.  In  chapter  two,  it  was 
stated  that  whilst  the  UNESCO/OECD/Eurostat  collection  on  education  systems  appears  to 
meet  most  of  the  data  requirements  needed  for  collecting  information  on  both  public  and 
private  spending  on  education,  national  statistical  offices  do  not  provide  all  the  data 
requested. Even if all national statistical offices did supply all the data requested, there are a 
number  of  questions  that  linger,  including  the  question  over  the  comparability  of  data 
collected  on  spending  by  private  sources.  Furthermore,  the  UNESCO/OECD/Eurostat 
statistical  exercise  only  collects  data  on  education  defined  by  ISCED,  which  means  that 
expenditure  on  education,  which  cannot  be  classified  by  ISCED  level  is  not  included.  For 
example containing vocational training in the enterprise. Therefore, in order to calculate total 
spending  on  education,  both  inside  and  outside  of  educational  institutions  from  public  and 
private sources, data from a number of sources needs to be combined together.   

Key education indicators on social inclusion and efficiency 

117

 
 
 
 
 
  
  
 
Chapter 3:Efficiency and effectiveness of education systems 

Total spending on education (inside and outside educational institutions) 

Based on data collected from a number of sources estimates of total spending were produced. 
The  results  presented  in  chapter  two  showed  that  it  was  not  always  possible  to  produce  an 
estimate  of total spending on education for all countries. The principal reason why this was 
the case was the fact that data was missing for at least one of the sources used to combine the 
data.  This  was  despite  the  fact  that  four  methods  were  used  to  calculate  total  spending  on 
education. Each method combined data from different sources in order to produce an estimate  

Table  3.3,  shows  the  data  sources  used  to  combine  the  data  and  the  percentage  of  Member 
States where total spending on education was estimated.  

Table 3.3: Sources used to estimate total spending on education and percentage of Member States 
where spending was estimated by year 

Method 

Sources used 

1 

2 

3 

4 

data 

data 

UNESCO/OECD/Eurostat 
education systems;  
National household budget surveys;  
CVTS2 
UNESCO/OECD/Eurostat 
education systems;  
National Accounts;  
CVTS2 
UNESCO/OECD/Eurostat 
education systems;  
National household budget surveys;  
EU Labour Cost Survey 
UNESCO/OECD/Eurostat 
education systems;  
National Accounts;  
EU Labour Cost Survey 

data 

data 

collection 

on 

collection 

on 

collection 

on 

collection 

on 

% of MSs  total estimated 

2000 

2001 

2002 

2003 

36 

32 

24 

24 

48 

48 

40 

48 

48 

44 

40 

36 

60 

60 

56 

60 

Note 
25 Member States = 100 % 
1 Member State = 4 % 

Table 3.4, presents an overview of the availability of estimates of total spending on education 
calculated for each year by method. 

It is evident that using the estimate  of total spending on education calculated by any one of 
the methods outlined in table 3.3, as the input to calculate efficiency of education will leave 
out a number of countries from the analysis. Filling in data gaps will not solve the problem 
entirely. 

An  alternative  is  to  examine  efficiency  in  relation  to  total  spending  on  educational 
institutions.  

Key education indicators on social inclusion and efficiency 

118

  
  
  
 
 
 
 
 
 
 
 
Efficiency and effectiveness of education systems 

Country 

2000 

2001 

2002 

2003 

Table 3.4: Availability of data for total spending on education for method of estimation by year for the EU 

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

1 
h 
h 
h 
h 
:: 
:: 
h 
:: 
:: 
:: 
:: 
h 
:: 
:: 
:: 
:: 
:: 
h 
:: 
:: 
h 
:: 
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:: 
h 

2 
h 
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h 
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h 
h 
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:: 
h 
:: 
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h 
:: 
:: 
h 
:: 
:: 
:: 
h 
:: 
h 
h 

3 
h 
h 
h 
h 
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h 
:: 
h 
h 
:: 
h 
:: 
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:: 
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h 
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:: 
:: 
h 
:: 

h 

4 
h 
h 
h 
h 
:: 
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h 
h 
h 
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h 
:: 
:: 
h 

h 
h 
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:: 
:: 
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h 
h 

1 
h 
h 
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h 
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h 
:: 
:: 
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:: 
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h 

2 
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3 
h 
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:: 
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:: 
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4 
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1 
:: 
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2 
:: 
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:: 
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h 

3 
:: 
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:: 
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h 
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:: 
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4 
:: 
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:: 
h 
: 
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h 
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1 
:: 
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2 
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3 
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4 
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h 
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:: 
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:: 
:: 
h 
:: 
h 
h 
:: 
:: 
h 
h 

h 
h 

Key 
h             Data available 
  :              Data not available 

Efficiency for the country cannot be calculated  

1  UNESCO/OECD/Eurostat  data  collection  on  education  systems;  National 

2  UNESCO/OECD/Eurostat  data  collection  on  education  systems;  National  Accounts; 

household budget surveys; CVTS2 

CVTS2 

3  UNESCO/OECD/Eurostat  data  collection  on  education  systems;  National 

4  UNESCO/OECD/Eurostat  data  collection  on  education  systems;  National  Accounts; 

household budget surveys; EU Labour Cost Survey 

EU Labour Cost Survey 

Key education indicators on social inclusion and efficiency 

119

 
 
 
 
 
 
 
  
 
 
 
  
 
 
 
Efficiency and effectiveness of education systems 

Total spending on educational institutions 

In contrast to the availability of data on total spending on education both inside and outside 
educational  institutions,  the  situation  regarding  the  availability  of  data  on  total  spending  on 
educational  institutions  is  a  different  matter.  Data  concerning  total  spending  on  educational 
institutions  is  available  from  the  UNESCO/OECD/Eurostat  (UOE)  data  collection  on 
education systems. Table 3.5, shows the availability of data for this indicator by country from 
the  UOE.  It  is  quite  clear  that  the  data  needed  for  this  input  is  available  for  most  Member 
States and for a number of non-EU countries including Japan and the USA. 

Table 3.5: Availability of data on total spending on educational institutions by year from the 
UOE 

Country 
BE 
CZ 
DK 
DE 
EE 
EL 
ES 
FR 
IE 
IT 
CY 
LV 
LT 
LU 
HU 
MT 
NL 
AT 
PL 
PT 
SI 
SK 
FI 
SE 
UK 
BG 
RO 
IS 
LI 
NO 
HR 
TR 
MK 
JP 
US  

2000 
h 
h 
h 
h 
: 
h 
h 
h 
h 
h 
h 
h 
: 
: 
h 
h 
h 
h 
h 
: 
: 
h 
h 
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: 
h 
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: 
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: 
h 
: 
h 
h 

2001 
h 
h 
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: 
h 
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: 
h 
: 
: 
h 
h 
h 
h 
: 
h 
: 
h 
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: 
h 
: 
: 
: 
h 
: 
h 
h 

2002 
h 
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: 
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: 
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: 
: 
h 
h 
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: 
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: 
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: 
: 
: 
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: 
h 
h 

2003 
h 
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: 
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: 
h 
: 
: 
h 
h 
h 
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: 
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: 
h 
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h 
h 
: 
: 
h 
: 
: 
: 
: 
h 
h 
h 

Key 
h             Data available 
  :              Data not available 

Efficiency for the country cannot be calculated  

Key education indicators on social inclusion and efficiency 

120

  
 
  
  
 
Efficiency and effectiveness of education systems 

The numerous methodological problems associated with gathering data on total spending on 
education  both  inside  and  outside  educational  institutions  have  been  considered  in  chapter 
two.  Nevertheless  even  if  an  estimate  is  produced  from  combining  data  from  different 
sources, data is not available for a number of countries.  

Thus  it  can  be  concluded  that  in  the  absence  of  data  for  all  countries  for  total  spending  on 
education both inside and outside educational institutions, efficiency can still be examined in 
relation to spending on educational institutions.  

In  fact,  examining  efficiency  of  spending  on  education  of  educational  institutions  is  more 
pertinent in relation to the benchmark indicators given that that all the benchmark indicators 
relate  to  outputs  of  the  formal  education  system.  In  contrast  total  spending  on  education 
covers spending on education, which may not necessarily have an impact on the benchmark 
indicators. 

Furthermore, spending on educational institutions can easily be related to students unlike the 
wider  concept  of  spending  on  education  both  inside  and  outside  of  educational  institutions. 
This  means  that  in  comparing  the  investment  in  education  between  countries,  spending  on 
educational institutions can be related to students and compared to GDP per capita. 

It is difficult to relate total spending on education to students, as this includes both formal and 
non-formal  learning.  The  number  of  students  in  formal  learning  are  collected  by  the 
UNESCO/OECD/Eurostat  data  collection  on  education  systems.  However,  only  data  on  the 
number  of  students  on  some  forms  of  non-formal  learning  are  collected.  For  example  the 
Continuing  Vocational  Training  Survey  2  collected  data  on  participants  for  a  number  of 
NACE categories.  

Outputs 

In table 3.2, we identified the output indicators, whilst table 3.6  shows the data sources used 
to construct the output indicators.  

Table 3.6: Data sources used to provide the data for the output indicators 

Indicator 
Share of the population aged 18 – 24 with only lower 
secondary  education  and  not  in  education  and 
training 
Percentage  of  those  aged  20  -  24  who  have 
successfully  completed  at  least  upper  secondary 
education (ISCED 3); 
Total  number  of  tertiary  (5A,  5B  and  6)  graduates 
from mathematics, science, and technology; 
Percentage of pupils with reading literacy level 1 and 
lower in the PISA reading literacy scale. 

Data source 

Eurostat, Labour Force Survey 

Eurostat, Labour Force Survey 

UNESCO/OECD/Eurostat data collection 

OECD The PISA International Database 

Several  points  need  to  be  considered  concerning  the  collection  of  data  from  these  data 
sources: 

i.  Periodicity  of  data  collection  –  If  a  data  collection  is  not  annual  then  the  exercise  of 
calculating  the  efficiency  of  spending  using  all  four  of  the  benchmark  indicators  will 

Key education indicators on social inclusion and efficiency 

121

 
 
 
 
 
  
  
Efficiency and effectiveness of education systems 

not  be  possible.  Table  3.7,  shows  that  the  data  sources  used  to  supply  data  on  the 
indicators  early  school  leavers,  educational  attainment  and  maths,  science  and 
technology  graduates  is  annual  but  the  PISA  survey  is  only  conducted  every  three 
years. 

Table 3.7: Periodicity of data collection  

Data source 

Periodicity of data collection 
Annual  
Eurostat, Labour Force Survey 
UNESCO/OECD/Eurostat data collection  Annual 
OECD The PISA International Database 

Every three years 

ii.  Coverage  of  countries  –  Whilst  the  EU-Labour  Force  Survey  collects  data  for  all  the 
EU Member States, the PISA survey does not (see table 3.8 for countries not covered 
for  each  year  of  the  survey).  This  means  that  some  countries  will  automatically  be 
omitted from any calculation of efficiency using the four indicators.  

Table 3.8: EU Member States not participating in the PISA survey by year  

Year 
2000 
2003 
2006 

EU Member States not covered 

EE, CY, LT, MT, SI, SK 
EE, CY, LT, MT, SI 
CY, MT 

iii.  Whether  countries  are  able  to  supply  the  information  requested  in  the  collection  - 
Tables  3.9  and  3.10  present  the  availability  of  data  for  the  output  indicators  for  each 
year,  by  country  for  the  EU  and  non-EU  countries  respectively.  The  shaded  cells 
indicate that the efficiency of a country cannot be calculated due to data being missing 
for one or more output indicators. 
It should be noted that the period refers to 2000 to 2003 because the financial data are 
only available for that period. 

Key education indicators on social inclusion and efficiency 

122

 
 
 
 
 
Efficiency and effectiveness of education systems 

Table 3.9: Availability of data for each output indicator by year for the EU 

Country 

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

BG 
RO 

Ed 
attain 
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h 
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h 
h 

2000 
ESL  MST 

h 
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Reading 
literacy 
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Key 
h             Data available 
  :              Data not available 

Ed 
attain 
h 
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2001 

ESL  MST  Reading 
literacy 
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Ed 
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h 

2002 

ESL  MST  Reading 
literacy 
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h 
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h  h 
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: 
h 
h 
: 
h 
h  h 
h 
h 
h 
h 
h 
h 
h  h 
: 
h 
h 
h 
h 
h 
h 
h 
h  h 
h  h 
h 
h 
h 
h 
h 
h 
h  h 
h  h 
h 
h 

Ed 
attain 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 

2003 

ESL  MST  Reading 
literacy 
h 
h 
h 
h 
: 
h 
h 
h 
h 
h 
: 
h 
: 
h 
h 
: 
h 
h 
h 
h 
: 
: 
h 
h 
: 

h 
h 
h 
h 
h 
: 
h 
: 
h 
h 
h 
h 
h 
: 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 

h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 
h 

h 
h 

: 
h 

h 
h 

: 
: 

h 
h 

h  h 
h 
h 

: 
: 

h 
h 

h 
h 

h 
h 

:  
:  

Efficiency for all indicators cannot be calculated due to data being missing for one or more output 

Key education indicators on social inclusion and efficiency 

123

 
 
  
 
 
 
 
 
 
 
 
 
  
Efficiency and effectiveness of education systems 

Table 3.10: Availability of data for each output indicator by year for non-EU countries 

Country 

IS 
LI 
NO 
HR 
TR 
MK 
JP 
US 

Ed 
attain 
h 
: 
h 
: 
h 
: 
h 
h 

2000 
ESL  MST 

h 
: 
h 
: 
h 
: 
h 
h 

h 
: 
h 
: 
h 
h 
h 
h 

Reading 
literacy 
h 
: 
h 
: 
:: 
:: 
hh 
hh 

Ed 
attain 
h 
: 
h 
: 
h 
: 
: 
h 

2001 

ESL  MST  Reading 
literacy 
: 
: 
: 
: 
: 
: 
: 
: 

h 
h 
: 
: 
h  h 
: 
: 
h 
h 
: 
: 
h 
h 
h 
h 

Ed 
attain 
h 
: 
h 
h 
h 
: 
h 
h 

2002 

ESL  MST  Reading 
literacy 
: 
: 
: 
: 
: 
: 
: 
: 

h 
h 
: 
: 
h  h 
h 
: 
h 
h 
h 
: 
h 
h 
h 
h 

Ed 
attain 
h 
: 
h 
h 
h 
: 
: 
h 

Key 
h             Data available 
  :              Data not available 

Efficiency for all indicators cannot be calculated due to data being missing for one or more output 

2003 

ESL  MST  Reading 
literacy 
h 
: 
h 
: 
: 
: 
hh 
hh 

h 
: 
h 
h 
h 
: 
: 
h 

h 
: 
h 
: 
h 
h 
h 
h 

Key education indicators on social inclusion and efficiency 

124

        
 
 
 
  
 
 
 
  
 
 
 
 
 
 
  
 
  
Efficiency and effectiveness of education systems 

33..55  MMeeaassuurriinngg  iinntteerrnnaall  eeffffiicciieennccyy  wwiitthhoouutt  eexxppeennddiittuurree  

A simple method of measuring output consists of considering the output of a given cycle of 
education  as  the  number  of  pupils  who  complete  a  cycle,  in  other  words  the  number  of 
graduates.  

It can be argued that the dropouts will no doubt have acquired some of the skills, which the 
system  set  out  to  teach  them.  Thus  it  can  be  argued  that  a  more  comprehensive  definition 
should also take into account, the educational attainment of the pupils dropping out, as well as 
the level of educational achievement of the graduates.  

Normally  inputs  into  the  education  system  are  defined  in  terms  of  the  buildings,  teachers, 

books, teaching-materials, etc. which are aggregated financially in terms of expenditures per 

pupil-year.  However,  the  number  of  pupil-years  used  by  a  cohort  of  pupils  to  graduate 

constitutes  an  input  indicator  appropriate  for  the  measure  of  efficiency  in  education.  One 

pupil who spends one year at school is said to have spent one pupil-year. In this way, we can 

relate efficiency to the amount of inputs expressed in monetary terms through the number of 

pupil-years used. The concept of pupil year is a convenient, non-monetary way of measuring 

inputs. One pupil represents all the resources spent to keep one pupil in school for one year. 

Thus it represents one year’s worth of education and accompanying expenditure. By dividing 

total expenditure on education by total pupil years, an estimate of unit cost that is the cost per 

pupil can be obtained. Alternatively by multiplying pupil years by unit cost, the total cost can 

be estimated.  

If  a  pupil  repeats  a  grade,  the  pupil  is  only  getting  one  years  worth  of  expenditure,  but 
consuming two year’s worth of expenditure. If it takes 5 years to obtain a qualification, but a 
pupil  dropouts  after  3  years  has  used  three  years  of  expenditure,  and  failed  to  obtain  a 
qualification.  Repeater,  and  dropouts  are  considered  as  wastage  in  this  analysis.  Figure  3.6, 
illustrates  the  analysis  of  the  flow  of  pupils  through  the  education  system.  Promotion, 
repetition  and  dropout  rates  are  the  three  paths  of  student  flow  from  grade  to  grade  and 
characterise the efficiency of the education system in producing graduates. 

Key education indicators on social inclusion and efficiency 

125

  
 
 
  
 
 
Efficiency and effectiveness of education systems 

Figure 3.6: Analysis of the flow of pupils through the education system 

Promotees

Repeaters 

Pupils in a 
certain 
grade 

Dropouts 

Promotion, repetition and dropout rates characterise the efficiency of the education system in 

producing graduates. These three rates can be used for the purpose of evaluations, monitoring 

and projecting the efficiency of student flow in an education system.   

Promotion  rate  is  the  proportion  of  pupils  who  have  successfully  completed  a  grade  and 
proceeded  to  the  next  grade  the  following  year.  It  shows  the  relative  size  of  the  group  who 
moved successfully to the next grade within a cycle. 
Repetition  rate  is  the  proportion  of  pupils  who  repeat  a  grade  once  or  twice.  The  repetition 
rate  measures  the  rate  at  which  pupils  repeat  grades.  A  high  repetition  rate  implies  a  high 
wastage ratio.  
Dropout  rate  is  the  proportion  of  pupils  who  leave  the  system  without  completing  a  given 
grade in a given school year. High dropout rates imply high input-output ratios and hence lead 
to low internal efficiency. 

33..55..11  IInnddiiccaattoorrss  ooff  iinntteerrnnaall  eeffffiicciieennccyy  iinn  eedduuccaattiioonn  

There are two indicators that are commonly used to summarise internal efficiency. These are 

the following: 

• 

Input–output  ratio  –  This  ratio  represents  the  ration  of  pupil  years  expended  per 
graduate. It is calculated by dividing the total pupil years expended by the cohort by 
the pupil years expended by those who graduated on time.  

•  Coefficient of efficiency - The most common indicator used to assess the educational 
efficiency  is  the  coefficient  of  efficiency  (or  its  reciprocal  referred  to  as  the  input-
output ratio). The efficiency that this indicator refers to is both internal and external. 
External  efficiency  in  this  context  refers  to  the  degree  to  which  the  educational 
system  meets  the  social,  cultural  and  economic  objectives.  The  coefficient  of 
efficiency is calculated by dividing the optimal (ideal) number of pupil-years (i.e. in 
absence of repetition and drop-out) by the number of pupil-years actually spent by a 

Key education indicators on social inclusion and efficiency 

126

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Efficiency and effectiveness of education systems 

cohort of pupils. In a 'perfectly efficient' system, this coefficient would equal 100%, 
and inefficiency arises when it is lesser than 100% (If the input-output ratio  is used 
instead, the perfect state would be 1, and inefficiency arises from any point which is 
greater than 1).  

33..55..22    MMeetthhooddss  ooff  mmeeaassuurriinngg  iinntteerrnnaall  eeffffiicciieennccyy  iinn  eedduuccaattiioonn 

Different people tend to apply their own different methods for analysing internal efficiency of 
education systems. Of these methods, cohort analysis seems to stand out more as it is  more 
commonly used. There are three ways of analysing educational internal efficiency by means 
of the cohort student flow analysis method, depending on the type of data that is available: 

i.  True Cohort  - This is the ideal way of obtaining a precise assessment of wastage, 
which  involves  either  longitudinal  study  in  monitoring  the  progress  of  a  selected 
cohort of pupils through the educational cycle, or through study of school records in 
order to retrace the flows of pupils through grades in past years. This method is costly 
and time-consuming and is dependent  on a good and reliable school-records system 
based on data for repeaters by grade together with enrolment by grade for at least two 
consecutive  years  using  either  the  apparent  or  reconstructed  cohort  method.  This 
method is not feasible on a national scale. 

ii.  Apparent cohort – This method is used when there is no information on repeaters. 
Data  on  enrolment  in  grade  1  in  a  particular  year  is  compared  with  enrolment  in 
successive  years  and  it  is  assumed  that  the  decrease  from  each  grade  to  the  next 
represents  wastage.  This  method  produces  very  approximate  estimates  of  dropouts. 
However  its’  main  weakness  is  that  it  assumes  that  pupils  are  either  promoted  or 
dropout of the school system, whilst repetition as a factor is completely overlooked. 
iii.  Reconstructed cohort – places less demand on the availability of detailed data over 
time. Data on enrolment by grade for two consecutive years and on repeaters by grade 
from the first to the second year are sufficient to estimate the three main flow rates: 
promotion, repetition, and dropouts.  

It  is  costly  and  difficult  to  generalise  the  school-record  system  based  on  reliable  individual 
pupil  information,  educational  internal  efficiency  is  assessed  using  the  reconstructed  cohort 
method.  At  the  international  level,  the  UNESCO/OECD/Eurostat  collection  on  education 
systems collects data on enrolments and repeaters by grade for ISCED levels 1 to 3. Thus the 
reconstructed  cohort  method  can  be  applied  to  data  collected  to  produce  estimates  of 
promotion,  repetition  and  dropout  rates.  Nevertheless,  it  should  be  pointed  out  that  the 
internal  structures  of  education  systems  vary  by  country.  This  includes  variation  in  the 
national  grade  structure  at  ISCED  levels  1  to  3.  Thus  it  is  difficult  to  compare  between 
countries  the  wastage  in  the  system  from  the  calculations  of  repeaters  and  dropouts. 
Nevertheless,  the  reconstructed  cohort  method  can  give  an  insight  into  the  efficiencies  of 
national  systems.    Table  3.11  shows  the  availability  of  data  by  ISCED  level  on  enrolments 
and repeaters by grade for the years 2000 to 2003. A number of countries such as the UK do 
not supply any data concerning enrolments and repeaters for each of the ISCED levels.  

Annex  3  contains  estimates  of  promotion,  repetition  and  dropout  rates  for  a  number  of 
countries at ISCED level 3.  

Key education indicators on social inclusion and efficiency 

127

 
 
 
 
Efficiency and effectiveness of education systems 

The  concept  of  internal  efficiency  has  two  main  advantages  measurability  and  analytical 
clarity  as  a  tool  of  educational  diagnosis.  Nevertheless,  there  are  a  number  of  limitations, 
which are related to the weaknesses of some of the key-concepts used to define efficiency in 
education. These include the following: 

-  The pupil year is a non-monetary concept that does not take into account the concepts 
and  findings  of  educational  cost  analysis.  The  concept  of  pupil-year  fails  to  grasp 
many determinants of educational costs; 

-  Since  outputs  are  equated  with  the  number  of  graduates,  the  view  of  the  education 
process can be considered as very narrow, in regard to its contribution to the economy 
and society in general; 

-  There are positive and negative effects of repetition. However in this context, only the 

negative aspects of grade repetition are taken into consideration;  

-  No  output  value  is  attributed  to  the  years  spent  by  dropouts  in  school.  This  ignores 
research on threshold of literacy retention; for secondary education, this assumption is 
particularly unrealistic; 

-  The concept of internal efficiency in education is applicable only to those educational 
processes which follow the age/grade-pattern of conventional formal schooling; 
Internal efficiency does not necessarily ensure external efficiency; in reality, the two 
concepts frequently militate against each other 

- 

The  indicators  derived  naturally  are  subject  to  the  limitations  and/or  assumptions  related  to 
this cohort analysis method. 

Annex 3, presents calculations of promotion, repetition, and dropout rates for a number of EU 

countries. 

Key education indicators on social inclusion and efficiency 

128

 
 
 
 
Efficiency and effectiveness of education systems 

Table 3.11: Availability of data on enrolments and repeaters in the UNESCO/OECD/Eurostat data collection by year and by ISCED level 

Country 

ISCED 1 

ISCED 2 

ISCED 3 

2000 

2001 

2002 

2003 

2000 

2001 

2002 

2003 

2000 

2001 

2002 

2003 

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

:  h 

:  h 

: 
: 
:  h 
: 
: 

: 
: 
:  h 
: 

: 
: 
:  h 
: 
: 

Enr  Rep  Enr  Rep  Enr  Rep  Enr  Rep 
h 
:  h 
: 
h  h  h  h  h  h  h  h 
:  h  h 
h  h  h  h  h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
: 
: 
h 
: 
h  h 
: 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
:  h  h 
h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
:  h 
:  h 
h 
: 
:  h  h  h 
: 
: 
h  h  h  h  h  h  h  h 
: 
: 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
:  h 
h 
: 
: 
: 
: 
: 

:  h  h  h 

:  h 
:  h 

:  h 
: 
: 

:  h 
: 
: 

: 

: 

: 

: 

: 

: 

:  h 

:  h 

:  h 

:  h 

: 
: 
:  h 
: 
: 

: 
: 
:  h 
: 
: 

: 
: 
:  h 
:  h 

Enr  Rep  Enr  Rep  Enr  Rep  Enr  Rep 
:  h 
: 
: 
h  h  h  h  h  h  h  h 
:  h 
h 
: 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
: 
: 
h 
: 
h 
: 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
h 
:  h 
: 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
: 
: 
:  h  h  h  h 
h 
: 
: 
: 
: 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
:  h 
h 
: 
:  h 
h 
: 
: 
: 
: 
: 

:  h  h  h 
:  h 
: 
: 

:  h 
: 
: 
: 
: 

:  h 
:  h 
: 
: 

:  h 

:  h 

:  h 

: 

: 

:  h 

:  h 

:  h 

: 
: 
:  h 

: 
: 
: 
: 
:  h 

Enr  Rep  Enr  Rep  Enr  Rep  Enr  Rep 
:  h 
: 
: 
h  h  h  h  h  h  h  h 
:  h  h 
:  h 
h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
: 
: 
: 
: 
h 
:  h 
: 
h  h  h  h  h 
: 
h  h  h  h  h  h 
:  h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h 
: 
h  h  h  h  h  h  h  h 
h 
:  h 
: 
h  h  h  h  h  h  h  h 
h  h  h  h  h 
: 
h  h  h  h  h  h  h  h 
h 
: 
h  h  h  h  h  h  h  h 
: 
: 
h  h  h  h  h  h  h  h 
h  h  h  h  h  h  h  h 
:  h 
h 
: 
:  h 
h 
: 
: 
: 
: 
: 

:  h 
: 
: 
: 
: 

:  h 
:  h 
: 
: 

:  h  h  h 

:  h 

:  h 

:  h 

:  h 

: 

: 

: 

: 

: 

: 

Key 
h             Data available 
  :              Data not available 

Key education indicators on social inclusion and efficiency 

129

 
 
 
 
Efficiency and effectiveness of education systems 

33..66    SSuurrvviivvaall  rraatteess  iinn  tteerrttiiaarryy  eedduuccaattiioonn  

Tertiary level survival rates is a useful indicator of the internal efficiency of tertiary education 
systems.  There  are  many  reasons  why  a  student  quits  a  tertiary  education  programme, 
including the following: 

-  A  student  may  realise  that  they  have  chosen  the  wrong  subject  or  educational 

programme;  

-  A student may fail to meet the standards set by their educational institution, particularly 

in tertiary systems that provide broader access;  

-  A student may find attractive employment before completing their programme.  

Dropping  out  is  not  necessarily  an  indication  of  failure  by  individual  students,  but  high 
dropout  rates  can  indicate  that  the  education  system  is  not  meeting  the  needs  of  its  clients. 
Students  may  not  find  that  the  educational  programmes  offered  meet  their  expectations  or 
their labour market needs. It may also be that students find that programmes take longer than 
the number of years which they can justify being outside the labour market. 

The tertiary survival rate is defined as the proportion of new entrants to the specified level of 
education  who  successfully  complete  a  first  qualification.  It  is  calculated  as  the  ratio  of  the 
number  of  students  who  are awarded  an initial  degree to the number  of  new entrants to the 
level ‘n’ years before, where ‘n’ is the number of years full-time study required to complete 
the degree: 

          Grad, y       *  100        

Entrants, y – n 

where: 
Grad, y       number of graduates at ISCED level 6 in year ‘y’ 
Entrants,y-n number of entrants at ISCED level 6 in year y-n where n is the typical number of 
years of full-time study required to complete the qualification  

Table  3.12,  shows  the  survival  rates  in  tertiary  education  for  2004.  In  the  EU  countries, 
tertiary survival rates for tertiary type 5A8 programmes vary from 64 per cent in Hungary to 
83  per  cent  in  Ireland.  Furthermore,  tertiary  survival  rates  for  type  5A  programmes  for  the 
EU-19 countries shown in table 3.12 are much lower than in Japan (91 per cent), but higher 
than in the United States. For tertiary type 5B9 programmes, survival rates vary from 35 per 
cent in Greece to 85 per cent in the Belgium Flemish region. However, survival rates for the 
EU-19 are much lower compared to those in Japan, 60 to 87 per cent. 

8 ISECD 5A programmes are largely theoretically based/research preparatory or which provide access 

to profession with high skills requirements. 

9 ISCED 5B programmes are practical /technical/occupationally specific. 

Key education indicators on social inclusion and efficiency 

130

  
  
 
 
 
 
  
 
                                                 
Efficiency and effectiveness of education systems 

At ISCED level 610, survival rates vary from 34 percent in Greece to 88 percent in Italy for 
those countries where data is available. Survival rates at this level of higher education is far 
higher in Japan, than in the EU (89 percent). 

Table 3.12: Survival rates in tertiary education (2004) 

Country 

Tertiary-type A education  
(ISCED 5A) 
Duration of programmes 

All 

3 to less 
than 5 
years 

5 to 6 
years 

More 
than 6 
years 

EU19 
BE (FL) 
CZ 
DK 
DE 
EL 
FR 
IE 
IT 
LU 
HU 
NL 
AT 
PL 
PT 
SK 
ES 
FI 
SE 
UK 
TR 
IS 
NO 
JP 
US 

1 
71 
74 
65 
: 
73 
79 
: 
83 
: 
: 
64 
76 
65 
66 
68 
: 
74 
71 
60 
78 
74 
: 
: 
91 
54 

2 
74 
75 
74 
: 
92 
78 
: 
x(1) 
: 
: 
64 
76 
x(1) 
65 
62 
: 
71 
x(1) 
x(1) 
78 
74 
: 
: 
91 
x(1) 

3 
72 
71 
60 
: 
65 
83 
: 
x(1) 
: 
: 
x(2) 
x(2) 
x(1) 
66 
72 
: 
76 
x(1) 
x(1) 
84 
x(2) 
: 
: 
90 
: 

4 
11 
82 
na 
: 
na 
na 
: 
x(1) 
: 
: 
x(2) 
na 
na 
na 
na 
na 
na 
x(1) 
na 
53 
na 
: 
: 
na 
na 

All 

Tertiary-type B education  
(ISCED 5B) 
Duration of programmes 
2 to 
3 to 
less 
less 
than 3 
than 5 
years 
years 
7 
6 
41 
36 
85 
a 
60 
66 
: 
: 
72 
87 
35 
a 
: 
: 
x(5) 
x(5) 
: 
: 
: 
: 
: 
48 
na 
na 
: 
: 
74 
na 
58 
na 
69 
80 
na 
79 
na 
: 
na 
x(1) 
x(5) 
x(5) 
na 
79 
: 
: 
: 
: 
x(6) 
87 
: 
: 

5 
years 
or 
more  
8 
: 
na 
na 
: 
na 
na 
na 
x(5) 
: 
: 
na 
na 
na 
na 
na 
na 
na 
na 
na 
x(5) 
na 
: 
: 
x(6) 
: 

5 
60 
85 
61 
: 
79 
35 
: 
69 
: 
: 
48 
na 
: 
74 
58 
77 
79 
: 
68 
53 
79 
: 
: 
87 
: 

ISCED 6 

9 
56 
: 
44 
: 
: 
34 
: 
: 
88 
: 
37 
: 
: 
: 
65 
: 
: 
: 
: 
70 
75 
: 
: 
89 
: 

Source: OECD ‘Education at a Glance 2006’ 

Notes 
x(1, 2, 3…) see column 1,2, 3,… 
:                   missing 
na 

     not applicable      

10 ISECD 6 level programmes lead directly to an advanced research qualification, such as a doctoral 

degree. 

Key education indicators on social inclusion and efficiency 

131

  
 
 
                                                 
Efficiency and effectiveness of education systems 

33..77    CCoonncclluussiioonnss  

Measuring  efficiency  and  effectiveness  in  education  is  of  great  relevance  considering  that 
resources  are  usually  limited,  while  needs  for  such  resources  are  often  limitless.  The 
underlying objective of making best use of resources devoted to education requires a drive to 
maximising both the efficiency and effectiveness. Thus, these concepts are applied in order to 
monitor and evaluate how well resources are used in an educational system and to prioritise 
the use of such resources.  

Yet,  as  useful  as  they  are,  these  concepts  can  be  rather  abstract  in  the  sense  that  they  are 
difficult to quantify, and tricky to measure through the use of standard statistical techniques.  
The  problems  of  measuring  efficiency  in  education  are  considerable,  and  include  the 
following: 

•  Comparability  in  the  data  collected  concerning  the  expenditure  on  education  due  to 
cost provision differences. No matter what transformations are made to spending on 
data,  comparability  between  countries  cannot  be  assured.  Thus  it  can  be  strongly 
argued  that  physical  inputs  and  outputs  have  the  important  advantage  of  being 
comparable across countries without the need of any questionable transformation. 
•  Measuring educational output - How educational output is measured is dependent on 
the nature of the objectives of the educational system. The objectives of educational 
systems will differ between countries.  

•  Quantifying the relationship between inputs and outputs.  

As  regards  efficiency,  some  of  the  conceptual  problems  faced  in  attempting  to  undertake  a 
meaningful measurement include: 

-  Difficulties with the identification of the components of the education system that are 

relevant to the analysis; 

-  Determining how the components will be measured,  
-  Difficulties 

in  measuring  costs  – 

this  includes  the  problems  of  collecting                           

data on private costs of education both inside and outside of educational institutions; 
a lack of agreement on the type of costs that should be included; 

-  Methodological  issues  concerning  the  measurement  of  educational  outputs  and 
outcomes.  For  example,  there  are  questions  about  how  to  incorporate  cognitive  as 
well  as  affective  aspects  in  a  measurement  instrument.  When  determining  the 
efficiency of a system the outputs are normally limited to aspects such as the number 
of graduates in a course or the average grades in a course. Measurement of long-term 
effects or outcomes is almost never taken into account.  

Annex  2  presents  estimates  of  efficiency,  based  on  data  calculated  using  expenditure  on 
educational  institutions  as  the  input  and  the  four  benchmark  indicators  which  have  been 
classified as outputs. 

Key education indicators on social inclusion and efficiency 

132

 
 
  
  
 
 
CChhaapptteerr  

44  

SSoocciiaall  iinncclluussiioonn//eexxcclluussiioonn  aanndd
tthhee  bbeenncchhmmaarrkk  iinnddiiccaattoorrss  

  
  
  
  
  
  
  
  
 
 
Social inclusion / exclusion and the benchmark indicators 

SSoocciiaall  iinncclluussiioonn  //  eexxcclluussiioonn  aanndd  tthhee  
bbeenncchhmmaarrkk  iinnddiiccaattoorrss  

44..11    IInnttrroodduuccttiioonn  

In an increasingly knowledge-based society and economy, the lack of basic competencies and 
qualifications is a major barrier to inclusion in society. This may translate into a society where 
there  are  those  who  have  access  to  lifelong  learning  to  enhance  their  employability  and 
adaptability and to facilitate their personal development and active citizenship, and those who 
remain excluded due to a lack of basic skills and competencies.  

Those without adequate skills are more likely to spend long periods out of work and if they do 
work they are more likely to be in low paid-jobs. Persons with more qualifications are more 
likely to benefit from training opportunities over their life course.  

The  recent  Commission  Communication  on  ‘Efficiency  and  equity  in  European  Education 
and training systems1’ stated that persons with low qualifications are at an increased risk of 
unemployment and social exclusion. In 2004, 75 million EU citizens were low skilled, which 
is nearly a third of the workforce. Low skilled is defined in this context as persons aged 25 to 
64  who  have  only  a  lower  secondary  education.  This  figure  is  declining  every  year  by  one 
million,  because  younger  age  cohorts  with  higher  levels  of  education  are  replacing  older 
cohorts who had lower levels of education. 

The  terms  social  inclusion  and  social  exclusion  were  defined  in  the  Joint  Social  Inclusion 
Report2 as follows: 

‘Social inclusion is a process which ensures that those at risk of poverty and social exclusion 
gain  the  opportunities  and  resources  necessary  to  participate  fully  in  economic,  social  and 
cultural life and to enjoy a standard of living and well-being that is considered normal in the 
society in which they live. It ensures that they have greater participation in decision making 
which affects their lives and access to their fundamental rights’ 

‘Social exclusion is a process whereby certain individuals are pushed to the edge of society 
and  prevented  from  participating  fully  by  virtue  of  their  poverty,  or  lack  of  basic 
competencies  and  lifelong  learning  opportunities,  or  as  a  result  of  discrimination.  This 
distances them from job, income and education opportunities as well as social and community 
networks and activities. They have little access to power and decision-making bodies and thus 

1 COM(2006) 481 Final 
2 http://europa.eu.int/comm/employment_social/social_inclusion/jrep_en.htm 

Key education indicators on social inclusion and efficiency 

134

  
  
 
 
 
 
 
 
                                                 
 
Social inclusion / exclusion and the benchmark indicators 

often feeling powerless and unable to take control over the decisions that affect their day to 
day lives’ 

In  December  2001,  the  Laeken  European  Council  endorsed  a  first  set  of  18  indicators  of 
social exclusion and poverty, organised in a two-level structure of: 

-  Primary  indicators  –  consisting  of  10  lead  indicators  covering  the  broad  fields 

considered to be the most important elements in leading to social exclusion 

-  Secondary  indicators  –  consisting  of  8  indicators  intended  to  support  the  lead 

indicators and describe other dimensions of the problem.  

In July 2003, the Social Protection Committee approved a revised list of common indicators, 
which were once again revised in June 20063. 

Two  of  the  primary  indicators  agreed  upon  on  social  exclusion  and  poverty  are  education 
benchmark related indicators.  These are as follows:  
i. 

‘Early  school  leavers  not  in  education  and  training’  -  Share  of  persons  aged  18  to  24 
who have only lower secondary education (their highest level of education or training 
attained  is  0,  1  or  2  according  to  the  1997  International  Standard  Classification  of 
Education – ISCED 97) and have not received education or training in the four weeks 
preceding the survey (Source: Eurostat Labour Force Survey); 
‘Low literacy performance of pupils’ - Share of 15 years old pupils who are at level 1 
or below of the PISA combined reading literacy score. (Source: PISA Survey, OECD). 

ii. 

The  secondary  indicator  ‘Persons  with  low  educational  attainment’  measures  the  proportion 
of individuals aged 25 or more whose highest level of education or training corresponds to at 
most lower secondary education is similar to the primary indicator on early school leavers. It 
was  found  that  the  two  indicators  are  highly  correlated4.  Participation  in  education  and 
training leading to a recognised qualification for those aged 25 and more and in particular for 
the  low  qualified  in  this  age  group  is  still  very  limited.  Therefore  the  skill-base  of  adults 
reflects  very  much  the  levels  of  qualification  attained  when  the  individuals  were  younger. 
Figure 4.1, shows the correlation between the primary indicator on early school leavers and 
the secondary indicator on educational attainment. 

3 http://ec.europa.eu/employment_social/social_inclusion/docs/2006/indicators_en.pdf 
4 ‘Joint Report on Social Protection and Inclusion 2006’, COM( 2006) 62 Final 

Key education indicators on social inclusion and efficiency 

135

 
 
 
                                                 
Social inclusion / exclusion and the benchmark indicators 

Figure 4.1: Low educational attainment of individuals aged 25- 64 and early school leavers aged 
18-24 in percentages (2005) 

PT

MT

IT

ES

CY

80

70

60

50

40

30

20

10

0

%

t
n
e
m
n
i
a
t
t
a
l
a
n
o
i
t
a
c
u
d
e
w
o
L

EL

LU
IE
FR
NL

BE

EU-25

UK

HU

LV

EE

FI
AT
DE

SI

PL

SE

SK

LT

CZ

0

5

10

15

20

25

30

35

40

45

Early school le ave rs %

Source: Eurostat – Quarter 2 results (except FR: Q1 for the low educational attainment indicator and FI 

2004 and 2005: Q1, AT 1999: Q1 for the early school leavers indicator 

Notes:  CY:  students  usually  living  in  the  country  but  studying  abroad  are  not  yet  covered  by  the 
survey. DE, LU, FI - 2004 data, IE-provisional 

Tackling  disadvantages  in  education  and  training  is  one  of  the  seven  key  policy  priorities 
agreed by Member States and the Commission in the 2005 Joint Report on Social Protection 
and  Social  Inclusion5.  Emphasis  is  placed  upon  preventing  early  departure  from  formal 
education and training; facilitating the transition from school to work, in particular of school 
leavers with low qualifications; increasing access to education and training for disadvantaged 
groups  and  integrating  them  into  mainstream  provision;  promoting  lifelong  learning, 
including  e-learning,  for  all.  It  is  recognised  by  many  the  need  to  invest  more,  and  more 
efficiently, in human capital at all ages. 

44..22    DDaattaa  nneeeeddss  ffoorr  eexxaammiinniinngg  ssoocciiaall  iinncclluussiioonn      

This section examines the data needed in order to examine whether certain groups in society 
have been excluded, as measured by the benchmark related indicators. During the course of 
the study a number of variables were identified by which the benchmark indicators could be 
broken down in order to establish which groups are being socially excluded.  

The following variables have been identified by which the benchmark could be broken down 
in order to establish whether certain groups in society are being excluded. 

5 http://ec.europa.eu/employment_social/social_inclusion/docs/com_en.pdf 

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Social inclusion / exclusion and the benchmark indicators 

Gender 
At present all five of the benchmark related indicators are already broken down by gender. 

Age 
There  are  a  number  of  different  age  groups  that  can  be  used  to  break  down  the  indicators 
further. 
This is not viable in relation to the indicator ‘Percentage of pupils with reading literacy level 1 
and  lower  in  the  PISA  reading  literacy  scale’,  for  the  reason  that  the  survey  collects 
information only on 15 year olds.   

Social-economic background 
The  term  ‘socio-economic’  relates  to  or  is  concerned  with  the  interaction  of  social  and 
economic  factors.  The  purpose  of  a  set  of  "socio-economic  groups"  is  to  identify  different 
groups of persons where the members of a particular group are, on the one hand, reasonably 
homogeneous  and,  on  the  other  hand,  fairly  clearly  distinguished  from  members  of  other 
groups  in  respect  of  their  social,  economic,  demographic  and/or  cultural  circumstances  and 
behaviour.   

The term ‘socio-economic’ is merely a descriptive one. It has no theoretical or analytic status 
whatever  and  so  there  can  be  no  single  definition  of  a  ‘socio-economic  classification’.  A 
recent  study  that  looked  into  under-represented  groups  in  higher  education  found  that  the 
question of whether a student in higher education belongs to a low socio-economic group is 
dependent on four factors: income, occupation, geography and the level of education6. It has 
been found that whilst one country uses one factor, other countries will use a range of factors 
to identify socio-economic groups. Social class is an important mechanism through which life 
chances are distributed. The classes themselves are seen as ‘sets of structural positions. Social 
relationships within markets, especially within labour markets, and within firms define these 
positions.  

Socio-economic  classifications  are  usually  occupationally  based  measures,  but  they  take 
different  forms.  In  particular,  some  are  continuous  multi-dimensional  measures  of  socio-
economic  position,  while  others  are  categorical  measures.  At  the  international  level  ,  the 
OECD  in  the  Programme  for  International  Student  Assessment  (PISA),  classification  of 
socio-economic  status  based  the  classification  of  social-economic  status  upon  occupation, 
education  and  possession  of  student  resources  (desks,  computers,  books).  Classifications  at 
the  national  level  exist  in  some  countries  for  example  in  the  UK  there  is  the  NS-SEC 
classification.    According  to  the  NS-SEC  classification  a  person’s  NS-SEC  position  (or 
‘class’) depends upon the combination of their current or last main job and their employment 
status (i.e. whether an employer, self-employed, a manager, a supervisor or an employee). For 
example,  a  self-employed  plumber  would  be  in  a  different  class  from  a  plumber  with  25 
employees,  and  both  would  be  in  different  classes  from  an  employee  plumber,  who  in  turn 
would be in a different class from a supervisor of plumbers. In Sweden, the classification of 

6 Thomas, Quinn, ‘International insights into widening participation: supporting the success of under-

represented groups in higher education’, 2003 

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Social inclusion / exclusion and the benchmark indicators 

socio-economic groups links parents’ level of education to occupation and ranks them into 11 
groups, which are most frequently grouped into 6 groups. 

As part of its statistical harmonisation programme, Eurostat asked the UK Office for National 
to  convene  an  expert  group  on  a  harmonised  European  socio-economic 
Statistics 
classification. The group reported in January 2001 with an outline for an E-SEC classification 
based on the employment relations approach. A two level, nested classification was proposed. 
Level  1  has  nine  ‘classes’.  Level  2  has  44  ‘socio-economic  groups’  (SEGs).  Thirty-five  of 
these SEGs collapse directly to one or other of the nine classes.  

The  proposed  classification  does  not  include  ‘educational  attainment’  for  the  simple  reason 
that  a  SEC  should  not  include  within  it  any  element  of  what  we  might  wish  to  use  it  to 
explain.  Neither  should  it  be  constructed  to  highlight  any  correlation  with  educational 
attainment.  If  the  SEC  is  measuring  education  it  cannot  be  used  to  explain  how  education 
relates to social position7.  

Building on the work of Eurostat TaskForce, as part of the Sixth Framework Programme, the 
project  entitled  “European  Socio-economic  Classification  (ESeC)”  aims  to  create  a 
conceptually clear, validated and easily operationalised socio-economic classification for use 
in  comparative  European  analyses,  key  policy  and  scientific  issues  relating  to  health  and 
socio-economic inequalities8.  

Income 
Breaking  down  the  benchmark  indicators  by  income  can  help  to  shed  light  on  whether 
students from low-income households fair the same as students from high-income households. 
In  breaking  down  the  lifelong  learning  indicator  by  income,  we  will  be  able  to  determine 
whether persons from low-income groups participate less in lifelong learning than those with 
a  high  income.    However  there  are  numerous  difficulties  in  examining  the  benchmark 
indicators in relation to income of the student or the student’s parents if they are in full-time 
education.  For example income is determined by other factors such as the level of taxes and 
transfers  from  the  government.  A  more  appropriate  concept  that  could  be  considered  is 
personal disposable income. Disposable income is the amount of an individual's total income 
left  after  taxes,  plus  any  transfer  payments  received  from  the  government  or  elsewhere. 
Nevertheless  collecting  data  on  the  income  in  general  and  the  income  situation  of  student’s 
parents are regarded as sensitive.   

The indicators on early school leavers and educational attainment of 20 to 24 year olds, and 
MST graduates should be broken down by the income of the parents. 
An alternative solution would be to proxy income by the variable occupation. In the case of 
students in full-time higher education it would be the occupation of the parent contributing the 
highest income to the household.  

7 David Rose and David Pevalin ‘Towards A European Socio-economic Classification Final Report to 

Eurostat of the Expert Group’  

8 http://www.iser.essex.ac.uk/esec/aims/ 

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Social inclusion / exclusion and the benchmark indicators 

Educational attainment 
Education  attainment  is  classified  according  to  the  International  Standard  Classification  of 
Education  (ISCED  97).  In  determining  the  highest  level  of  education,  both  general  and 
vocational education/training are taken into consideration. 

In the case of a student in full-time education, the subject of particular interest in relation to 
social inclusion is the level of educational attainment of the parents. The indicators on early 
school leavers and  educational attainment of 20 to 24 year olds, and MST graduates  should 
also  be  broken  down  by  the  educational  attainment  of  the  parents.  The  level  of  parental 
education can help to explain a child’s chances of access to post-secondary education. Parents 
with more education tend to pass down to their offspring their skills and beliefs, and they also 
tend  to  get  more  involved  with  their  offspring’s  education.  This  will  yield  two  separate 
breakdowns of the benchmark indicators: 
-  Mothers educational attainment; 
-  Fathers educational attainment; 

It is important to remember that a student’s social background cannot only be classified by the 
educational attainment of one parent without taking into consideration the level of the other 
parent. This is because one parent may have a higher educational level than the other parent, 
and we may only be classifying the student by the educational attainment of the parent with a 
lower  educational  attainment.  For  instance  a  father  may  have  an  educational  attainment  of 
ISCED level 3 whilst the mother may have an ISCED level 5 qualification. In order to avoid 
this  type  of  problem,  the  highest  level  of  education  attained  by  either  parent  is  taken  as  an 
indication of the social background. 

A  sub-variable  of  interest  that  falls  directly  under  this  heading  is  first  generation  entrants. 
This  variable  is  only  relevant  to  higher  education.  Entrants  to  higher  education  are  first 
generation  entrants  if  neither  parent  has  a  degree  level  qualification.  This  variable  is 
extremely  useful  in  predicting  and  charting  under-representation  in  higher  education. 
Researchers have looked into the question of whether the indicators first generation entrants 
into  higher  education,  or  socio-economic  background  is  a  more  sensitive  indicator  in 
determining  inequality  with  respect  to  higher  education.  It  was  concluded  that  gathering 
information on first generation entrants is more readily available. However, the ideal solution 
is to gather both sets of data9. Collecting data on first generation entrants is only relevant for 
the indicator ‘Total number of tertiary (5A, 5B and 6) graduates from mathematics, science, 
and technology’. 

Occupation 
Data  on  occupation  should be  collected according to  the  ISCO-88  classification, in  order to 
maintain coherence with the European Statistical System. Usually full-time students are given 
their ‘class of origin’ that of a parent/guardian, since their life chances are dependent on that 
of their parents/guardians. The indicators on early school leavers and educational attainment 
of 20 to 24 year olds, and MST graduates should be broken down by the occupations of the 
parents. 

9 Thomas, Quinn, ‘International insights into widening participation: supporting the success of under-

represented groups in higher education’, 2003 

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Social inclusion / exclusion and the benchmark indicators 

Industry (branch of economic activity) 
"Industry"  (branch  of  economic  activity)  refers  to  the  kind  of  production  or  activity  of  the 
establishment or similar unit in which the job(s) of the economically active person (whether 
employed or unemployed) was located. Full-time students are classified according to that of a 
parent/guardian, since their life chances are dependent on that of their parents/guardians. The 
indicators on early school leavers and educational attainment of 20 to 24 year olds, and MST 
graduates should be broken down by the industry in which the parents are employed.  

For  purposes  of  comparability  within  the  ESS,  the  branches  of  economic  activity  should  be 
classified by NACE. 

Labour force status   
The  economic  active  population  comprises  employed  and  unemployed  persons.  The  labour 
force status10 of a person can be classified as follows: 

-  Employed  persons  are  persons  aged  15 year  and  over  who  during  the  reference  week 
performed work, even for just one hour a week, for pay, profit or family gain or were 
not at work but had a job or business from which they were temporarily absent because 
of, e.g., illness, holidays, industrial dispute and education and training. 

-  Unemployed  persons  are  persons  aged  15-74  who  were  without  work  during  the 
reference  week,  were  currently  available  for  work  and  were  either  actively  seeking 
work  in  the  past  four  weeks  or  had  already  found  a  job  to  start  within  the  next  three 
months. 
Inactive persons are those who neither classified as employed nor as unemployed. 

- 

Breaking down the indicators on early school leavers and educational attainment of 20 to 24 
year olds, and MST graduates, by labour force status would be focusing on the outcomes of 
education systems and not on social exclusion. In order to focus on whether certain groups are 
socially excluded in relation to these two indicators, the indicators should be broken down by 
the labour force status of their parents.  

Breaking  down the indicators on reading literacy by labour force status  has  no  meaning for 
full-time  students  since  they  would  be  classified  as  inactive.  In  order  to  examine  social 
inclusion  in  relation  to  these  two  indicators,  the  indicators  should  be  broken  down  by  the 
labour force status of students’ parents.  

Geographical location 
The  benchmark  indicators  can  be  further  broken  down  by  geographical  location  within  a 
country (for example deprived urban and rural areas) so as to elucidate whether persons living 
in certain areas are disadvantaged due to their location 

In some countries students living in particular areas are targeted as being disadvantaged. For 
example  in  the  UK,  the  Higher  Education  Funding  Council  For  England  (HEFCE)  sets 
benchmarks  for  each  institution  on  the  percentage  intake  of  entrants  whose  home  area  (as 

10    It  is  based  on  those  contained  in  the  Recommendation  of  the  13th  International  Conference  of 

Labour Statisticians, convened in 1982 by the International Labour Organisation. 

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140

 
 
 
 
 
 
 
                                                 
Social inclusion / exclusion and the benchmark indicators 

denoted by their postcode) is known to have a low proportion of 18 and 19 year-olds in higher 
education (see Figure 4.2). The HEFCE publishes POLAR maps showing how the chances of 
young  people  entering  higher  education  vary  by  where  they  live11.  They  are  primarily 
designed as a web-based resource to aid those involved in widening participation activities. 

Figure 4.2: Percentage of young entrants to full-time first degree courses from low participation 
neighbourhoods by subject and entry qualification 2004/05 

25

20

s
t
n
a
r
t
n
e

15

g
n
u
o
y

10

f
o
%

5

0

1

2

3

5

6

7

8

9 A B C D E
S ubject of study

F G H I

J

Source: Higher Education Statistical Agency, UK 

Notes 
1 Medicine & dentistry and veterinary science 
2 Subjects allied to medicine 
3 Biological sciences 
5 Agriculture & related subjects 
6 Physical sciences 
7 Mathematical sciences 
8 Computer sciences 
9 Engineering & technology 
A Architecture, building, and planning 

                              C Law   

 B Social studies   

 D Business & administrative studies 

               E Mass communications & documentation 

                             F Languages 
                             G Historical & philosophical studies 
                             H Creative arts & design 

               I Education 

J Combined subjects 

Minority groups 
The definition of minority groups will undoubtedly differ between countries. Minority groups 
can  be  defined  in  terms  of  place  of  birth,  language  or  racial  or  ethnic  group,  or  even  a 
mixture.    For  the  purpose  of  this  study,  emphasis  was  placed  upon  exploring  whether  the 
benchmark indicators could be broken down by: 

-  Place of birth – This will help to establish whether a person is a native or non-native.  
-  Language  –Whether  the  person  speaks  another  language  at  home  as  compared  to  the 

language of the country. 

Students with special needs (e.g. the disabled) 
Examining the benchmark related indicators by students with special needs, for example the 
disabled  will  help  to  determine  whether  these  groups  are  excluded  from  society.  Collecting 

11  http://www.hefce.ac.uk/widen/polar/ 

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Social inclusion / exclusion and the benchmark indicators 

comparable  information  on  the  disabled  is  fraught  with  difficulties,  since  there  is  no 
agreement  on  the  exact  concept  of  disability.  Differences  in  national  policies  regarding  the 
disabled has led to a situation where each Member State has its own systems for defining the 
population of disabled people. A basic principal shared by all definitions is that disability has 
a medical cause and results in limitations in daily activities. Any data collected would have to 
take into account in devising a classification system of various types of disabilities.  

A  number  of  countries  publish  data  on  the  number  of  persons  with  disabilities  in  higher 
education.  For  example  the  Higher  Education  Statistical  Agency  publishes  as  part  of  its 
indicators on widening access to higher education publish data on the percentage of students 
who are in receipt of Disabled Students’ Allowance12. In Poland the Central Statistical Office 
publishes data13 on the number of disabled students in higher education (see Table 4.1). 

Table 4.1: Share of disabled students in higher education by type of disability in Poland (in %) 

Year 

Type of 
course 

Total 

Day 
2001 
2001 
Evening 
2001  Distance 
Day 
2002 
2002 
Evening 
2002  Distance 
Day 
2004 
2004 
Evening 
2004  Distance 

0.36 
0.08 
0.1 
0.46 
0.100 
0.093 
0.71 
0.24 
0.003 

Deaf  & 
hard of-
hearing 
0.03 
0.01 
0.004 
0.04 
0.010 
0.006 
0.05 
0.02 
0.0002 

Movement 
disabilities 

Blind & 
partially 
sighted  Walking  can't walk 
0.10 
0.02 
0.018 
0.11 
0.016 
0.022 
0.14 
0.05 
0.001 

0.05 
0.01 
0.008 
0.05 
0.012 
0.008 
0.06 
0.02 
0.0002 

0.02 
0.01 
0.010 
0.02 
0.009 
0.011 
0.02 
0.01 
0.0001 

Other 

0.17 
0.03 
0.029 
0.25 
0.052 
0.046 
0.44 
0.14 
0.002 

Source: ‘Higher Education and their Finances’, Central Statistical Office 

Remarks 

Some  of  the  variables  (income,  occupation,  branch  of  industry  employed  and  labour  force 
status) identified in this section could be considered as interrelating since any combination of 
them can be examined together in the context of socio-economic status. However, given the 
fact  that  it  may  not  always  be  possible  through  existing  data  sources  to  look  at  social-
economic  status  using  the  proposed  classification  of  social-economic  status,  the  next  best 
alternative will be to look at the these variables separately. 

12 The definition of disability used for the performance indicator is based on the HESA field DISALL. 
This  field  records  whether  a  student  is  in  receipt  of  the  Disabled  Students  Allowance  (DSA).  The 
indicator  is  the  proportion  of  students  in  receipt  of  DSA,  i.e.  the  proportion  of  students  with 
DISALL=4.  

13 ‘Higher education and their finances’, GUS 

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44..33    DDaattaa  ccuurrrreennttllyy  ccoolllleecctteedd  ffrroomm  iinntteerrnnaattiioonnaall  
ssoouurrcceess  

Table  4.2,  illustrates  the  3  international  data  collections  that  are  used  to  construct  the 
benchmark related indicators.  

Table 4.2: Data sources used to construct the benchmark indicators  
Data source 

Indicator 
Share of the population aged 18 – 24 with only lower 
secondary  education  and  not  in  education  and 
training 
Percentage  of 
-24  who  have 
those  aged  20 
successfully  completed  at  least  upper  secondary 
education (ISCED 3); 
Total  number  of  tertiary  (5A,  5B  and  6)  graduates 
from mathematics, science, and technology; 
Percentage of population aged 25 – 64 participating 
in education and training in four weeks prior to the 
survey. 
Percentage of pupils with reading literacy level 1 and 
lower in the PISA reading literacy scale. 

Eurostat, Labour Force Survey 

Eurostat, Labour Force Survey 

UNESCO/OECD/Eurostat data collection 

Eurostat, Labour Force Survey 

OECD The PISA International Database 

The ideal situation would be that the data collections, which are currently used to calculate the 
five  benchmark  related  indicators  could  also  be  used  to  breakdown  the  indicators  by  the 
variables identified above. 

Unfortunately this is not the case, since not all the information is collected by these sources. 
Table 4.3, summarises the availability of data needed to measure social inclusion in relation to 
the  benchmark  indicators  from  the  international  data  sources.  The  table  shows  that 
breakdowns by a number of variables are theoretically possible in the benchmark indicators 
with  the  exception  of  the  number  of  maths,  science  and  technology  graduates,  where  the 
indicator can only be broken down by gender.  

Attention  has  to  be  drawn  to  the  fact  that  no  other  data  source  either  at  national  or 
international level can breakdown the indicator on reading literacy by the variables of interest 
for looking at social inclusion / exclusion. This is because no other survey collects this type of 
information  conforming 
the  same  concepts,  definitions,  and  classifications  and 
methodology. 

to 

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143

 
 
  
  
  
 
 
Social inclusion / exclusion and the benchmark indicators 

Data source 

Table 4.3: Availability of data needed to measure social inclusion in international data sources  
Ed 
attainment 

Socio-
economic

Industry Labour 

Sex1  Age 

Occupation 

Income 

Geography  Minority 
groups 

force 
status 

Students 
with 
special 
needs 

ESL 

Ed 
attainment 
MST 
graduates 
LLL 

Reading 
literacy 

Eurostat,  Labour  Force 
Survey 
Eurostat,  Labour  Force 
Survey 
UNESCO/OECD/Eurostat 
data collection 
Eurostat,  Labour  Force 
Survey 
OECD 
International Database 

PISA 

The 

n  n2 
n  n2 

n 

n  n 

n3 

n 

n 

na4  n5 

n 

n6 

n 

n7 

n 

n8 

n 

n9 

n 

n 

n 

n10 

n10 

n10 

n11 

Notes 
1        All five of the benchmark indicators are already broken down by gender 
2      The  indicators on  educational  attainment  and  early  school  leavers  already  refer  to  a  narrow  age group  band.   Breaking  down the  indicator  on  reading  literacy  by  age   

group is not relevant because the indicator only refers to 15 year olds. 

3     Breaking down the indicators by socio-economic status is possible on the basis of the proposed ESEC classification of socio-economic status.  
4     The indicator refers to 15 year olds. Therefore it is impossible to breakdown the indicator any further. 
5    The social-economic status is measured by the PISA index of economic, social and cultural status (ESCS) 
6    Refers to the educational background of parents 
7    Refers to the occupation of parents 
8    Refers to the industry in which the parents are employed 
9   Refers to the labour force status of the parents 
10   Data collected on place of birth, and language 
11  Refers to native, first, and second-generation students 

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44..33..11    EEUU  LLaabboouurr  FFoorrccee  SSuurrvveeyy  ((LLFFSS)) 

The  EU  Labour  Force  Survey  currently  supplies  data  for  three  of  the  five  benchmark 
indicators: early school leavers, educational attainment and participation in lifelong learning. 
One of the advantages of the LFS is the fact that data can be cross-referenced by a range of 
demographic, social and economic characteristics in order to examine social exclusion. 

It  should  be  noted  that  breaking  down  the  indicators  early  school  leavers  and  educational 
attainment by the parents’ income, occupation, branch of industry and the labour force status 
would  mean  that  the  results  would  be  biased.  The  bias  results  from  the  fact  that  both  these 
indicators relate to specific age groups, in the case of early school leavers it is 18 to 24, whilst 
for  educational  attainment  it  is  20  to  24.  Establishing  the  income,  occupation,  branch  of 
industry  or  labour  force  status  of  parents  is  possible  on  the  very strong  assumption  that  the 
person aged 18 to 24 is still living with their parents.  

However, in some countries the number of young adults living with there parents is low, for 
example in Denmark three-fifths of men aged 20 still live at home, whereas only two-fifths of 
women aged 20 still live at home. In the UK the number of adults living with their parents is 
low, despite the fact that it has increased in recent years. In 2003 just over half of men aged 
20 to 24 still lived at home with their parents in contrast to only 37 per cent of women14. A 
number of studies have shown that the fraction of young adults still living at home with their 
parents is higher in Southern European countries than in Western Europe. Information on the 
number of adults still living at home with their parents can be elucidated from the LFS. 

Gender 
All  the  three  benchmark  related  indicators  that  are  constructed  from  the  EU  Labour  Force 
Survey are already broken down by gender. 

Age  
Given that the indicators on early school leavers and educational attainment already refer to a 
particular age group, breaking it down further would not bring any added value.  

Nevertheless, the indicator on participation in lifelong learning could be broken down further 
into age groups from the LFS micro data. The following age groups could be used to examine 
whether any particular age group participates less in lifelong learning: 

- 
- 
- 
- 

25 to 34; 
35 to 44; 
45 to 54; 
55 to 64. 

Data  from  the  EU-LFS  ad  hoc  module  is  already  disseminated  on  the  Eurostat  website  and 
clearly shows that this is possible 

14 ‘Social Trends – 34’, National Statistics – UK   

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Social inclusion / exclusion and the benchmark indicators 

Educational attainment 
In  the  case  of  participation  in  lifelong  learning,  educational  attainment  refers  to  that  of  the 
participant. This information can easily be extracted through record linkage in the LFS. This 
is attested by the fact that data from the EU-LFS ad hoc module on lifelong learning publishes 
data on participation in lifelong learning broken down by educational attainment. 

Occupation and industry 
Information  on  the  occupation  and  the  industry  of  employment  of  a  person  participating  in 
lifelong learning can be easily extracted through record linkage in the LFS. This is attested by 
the fact that data from the EU-LFS ad hoc module on lifelong learning publishes this data. 

Labour force status 
The LFS collects information  on the labour force status of the  population  based on the ILO 
definition. Breaking down the indicator on participation in lifelong learning for the age group 
is possible through linking of variables. 

Geographical location 
The EU-LFS collects some information on the geographical location. Information is collected 
on whether the person lives in a: 

-  Densely  populated  area  -  This  is  a  contiguous  set  of  local  areas,  each  of  which  has  a 
density superior to 500 inhabitants per square kilometre, where the total population for 
the set is at least 50,000 inhabitants; 
Intermediate area - This is a contiguous set of local areas, not belonging to a densely-
populated  area,  each  of  which  has  a  density  superior  to  100  inhabitants  per  square 
kilometre, and either with a total population for the set of at least 50,000 inhabitants or 
adjacent to a densely-populated area; 

- 

-  Thinly populated  area. - This is a contiguous set of local  areas  belonging neither to a 

densely populated nor to an intermediate area.  

Whilst  the  information  collected  may  provide  some  insight  on  whether  persons  living  in 
thinly populated areas have the same access to lifelong learning and the same opportunities to 
achieve  the  same  level  of  education,  it  does  not  entirely  reflect  the  complexities  of 
disadvantaged areas within each country. 

Minority groups 
A number of questions are asked in the EU-LFS, which can help to identify certain minority 
groups in the population. The questions asked are: 

−  Nationality; 
−  Country of birth; 
−  Number of years in the Member State; 

All three benchmark indicators can be broken down by these groups. In the case of country of 
birth, the benchmark indicators should be broken down simply into two categories: 

i. 
ii. 

Native; 
Non-native. 

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Social-economic groups background 
One  of  the  advantages  of  the  Labour  Force  Survey  is  that  it  collects  data  on  a  number  of 
variables  that  are  important  for  establishing  the  social-economic  background  of  a  person. 
These  include  occupation,  industry,  labour  force  status,  and  income.  Furthermore,  the  data 
collected on educational attainment, early school leavers and lifelong learning can be cross-
referenced  against  all  of  these  variables  in  order  to  examine  whether  persons  with  a  low 
social-economic background are for instance fairing worse in educational attainment and have 
a  greater  tendency  to  drop  out  of  school  early  than  those  from  a  higher  social-economic 
background. However, in order to perform this type of analysis with regard to the benchmark 
indicators, the data collected from the LFS needs to be classified according to SEGs proposed 
by the Eurostat harmonised European socio-economic classification. 

44..33..22    UUNNEESSCCOO//OOEECCDD//EEuurroossttaatt  ccoolllleeccttiioonn  oonn  eedduuccaattiioonn  ssyysstteemmss  

The UNESCO/OECD/Eurostat collection on education systems is used to supply data for only 
one indicator, the number of maths, science and technology graduates. Unfortunately data on 
the number of maths, science and technology graduates can only be broken down by gender. 

44..33..33    TThhee  PPIISSAA  IInntteerrnnaattiioonnaall  DDaattaabbaassee 

The PISA international database is used to supply data on the indicator on reading literacy of 
15  year  olds.  The  Programme  for  International  Student  Assessment  (PISA)  is  an 
internationally standardised assessment that was jointly developed by participating countries 
and administered to15-year-olds in schools. Tests are typically administered to between 4,500 
and  10,000  students  in  each  country.  The  statistical  exercise  takes  place  every  3  years.  The 
survey was implemented in 43 countries in the first assessment in 2000, in 41 countries in the 
second assessment in 2003 and at least 58 countries have participated in the third assessment 
in 2006. It is important to note that not all the EU-25 Member States are covered in the survey 
for all three years.  

Table  4.4,  summarises  the  EU  Member  States  that  took  place  in  each  of  the  three  PISA 
surveys. 

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Table 4.4: EU Member States participating in the OECD Programme for International Student 
Assessment (PISA) 

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

2000 

2003 

2006 

h 
h 
h 
h 

h 
h 
h 
h 

h 

h 
h 

h 
h 
h 
h 

h 
h 
h 
h 

h 
h 
h 
h 

h 
h 
h 
h 
h 

h 

h 
h 

h 
h 
h 
h 

h 
h 
h 
h 
h 

h 
h 
h 
h 
h 
h 
h 
h 
h 
h 

h 

h 
h 

h 
h 
h 
h 
h 

h 
h 
h 
h 
h 

The PISA database can be used to examine aspects of social exclusion, by breaking down the 
breaking down the benchmark related indicator on reading literacy by: 

Gender  
The indicator is already broken down by gender. 

Labour force status of parents  
Questions  on  the  current  activity  of  both  mother  and  father  were  asked  in  the  student 
questionnaire. Whilst the possible states in the labour force differ from those defined by the 
ILO, it is still possible to gauge the labour force status of both parents.  The following options 
were asked: 

-  Working full-time <for pay>  
-  Working part-time <for pay>  
-  Not working, but looking for a job  
-  Other (e.g. home duties, retired) 

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Parental educational attainment  
Students were asked to classify the highest level of education of their mother and father on the 
basis of national qualifications, which  were then coded in  accordance with the International 
Standard  Classification  of  Education  (ISCED  1997)  in  order  to  obtain  internationally 
comparable categories of educational attainment.  

It should be noted that whilst the structure and wording of the questions asked relating to the 
educational attainment of both parents changed from the surveys conducted in 2000 to 2003 
another, it is still possible to establish the level of parental education. 

Occupation of parents  
Information  on  parental  occupation  was  collected  through  open-ended  questions,  which 
ascertained  the  main  job  of  both  parents.  The  responses  were  then  coded  according  to  the 
International Standard Classification of Occupations (ISCO)). Once the responses of parents’ 
occupations were coded by ISCO they were then mapped to the international socio-economic 
index of occupational status (ISEI) (Ganzeboom et al., 1992). The ISEI provides a measure of 
the  socio-economic  status  of  occupations  comparable  across  the  countries  participating  in 
PISA.  Three  indices  were  obtained  from  these  scores:  father’s  occupational  status  (BFMJ); 
mother’s occupational status (BMMJ); and the highest occupational status of parents (HISEI) 
which corresponds to the higher ISEI score of either parent or to the only available parent’s 
ISEI  score.  For  all  three  indices,  higher  ISEI  scores  indicate  higher  levels  of  occupational 
status.  Values  on  the  index  range  from  0  to  90;  low  values  represent  low  socio-economic 
status and  high  values represent  high socio-economic status. In 2000, on average across the 
OECD countries, the value of the index was 49 and its standard deviation was 16.  

The index captures the attributes of occupations that convert parents’ education into income. 
The index was derived by the optimal scaling of occupation groups to maximise the indirect 
effect  of  education  on  income  through  occupation  and  to  minimise  the  direct  effect  of 
education  on  income,  net  of  occupation  (both  effects  being  net  of  age).  The  PISA 
International Socio-Economic Index of Occupational Status is based on either the father’s or 
mother’s occupations, whichever is the higher. 

Socio-Economic  status  –  this  was  measured  by  the  PISA  index  of  economic,  social  and 
cultural status (ESCS). The index of economic, social and cultural status was created for the 
2003 survey to capture wider aspects of a student’s family and home background in addition 
to occupational status and is a variation of the index used in PISA 2000. It was derived from 
the following variables:  

i. 

ii. 

iii. 

The highest international socio-economic index of occupational status of the father 
or mother; 
The  highest  level  of  education  of  the  father  or  mother  converted  into  years  of 
schooling; 
The number of books at home as well as access to home educational and cultural 
resources,  obtained  by  asking  students  whether  they  had  at  their  home:  a  desk  to 
study at, a room of their own, a quiet place to study, a computer they can use for 
school  work,  educational  software,  a  link  to  the  Internet,  their  own  calculator, 

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Social inclusion / exclusion and the benchmark indicators 

classic literature, books of poetry, works of art (e.g., paintings), books to help with 
their school work, and a dictionary.  

The rationale for the choice of these variables was that socio-economic status is usually seen 
as  being  determined  by  occupational  status,  education  and  wealth.  As  no  direct  measure  on 
parental  wealth was available  from  PISA,  access to  relevant  household items  was  used as a 
proxy. The student scores on the index are factor scores derived from a Principal Component 
Analysis, which are standardised to have an OECD mean of zero and a standard deviation of 
one. 

Socio-economic groups 
Students  were  asked  to  report  their  mothers’  and  fathers’  occupations,  and  to  state  whether 
each parent was: in full-time paid work; part-time paid work; not working but looking for a 
paid  job;  or  “other”.  The  open-ended  responses  were  then  coded  in  accordance  with  the 
International Standard Classification of Occupations (ISCO 1988). Students were also asked 
to report on their occupation at age 30. These three variables – mother, father and students – 
were transformed into four socio-economic categories:  

i.  White-collar high-skilled: legislators, senior officials and managers, and professional, 

technicians and associate professionals;  

ii.  White-collar  low-skilled:  service  workers  and  shop  and  market  sales  workers  and 

iii. 

iv. 

clerk,  
Blue-collar high-skilled: skilled agricultural and fishery workers and craft and related 
trades workers; and  
Blue-collar low-skilled: plant and machine operators and assemblers and elementary 
occupations. 

Minority groups – PISA collects information on place of birth of students and their parents, 
and the language spoken at home if it is different from the language, which is being assessed.  
The  data  collected  from  these  questions  can  help  to  look  at  social  inclusion  from  the 
perspective of ethnicity. 
-  Place of birth of student and parents 

Students  were  asked  if  they,  their  mother  and  their  father  were  born  in  the  country  of 
assessment or in another country. The response categories were then grouped into three 
categories:  
i)  Native  students  -  those  students  born  in  the  country  of  assessment  and  who  had  at 

least one parent born in that country;  

ii)  Second-generation  students  -  those  born  in  the  country  of  assessment  but  whose 

parents were born in another country; 

iii)  First-generation  students  -  those  born  outside  the  country  of  assessment  and  whose 

parents were also born in another country. 

-  Language spoken at home 

Students were asked if the language spoken at home most of the time was the language of 
assessment,  another  official  national  language,  another  national  dialect  or  language,  or 
another language (some countries collected more detailed information on language use). 
In order to derive an index of language spoken at home (LANG), responses were grouped 
into two categories:  

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150

 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

i. 

ii. 

Language  spoken  at  home  most  of  the  time  is  different  from  the  language  of 
assessment,  from  other  official  national  languages  and  from  other  national 
dialects or languages;  
The language spoken at home most of the time is the language of assessment, is 
another official national language, or other national dialect or language. 

44..44    DDaattaa  ccoolllleecctteedd  ffrroomm  ootthheerr  iinntteerrnnaattiioonnaall  ssoouurrcceess  

A number of data sources exist at the international level, which could potentially be used to 
breakdown  the  benchmark  indicators  by  the  variables  identified  in  section  4.2  in  order  to 
examine social inclusion/exclusion. 

44..44..11    EEuurrooppeeaann  SSttaattiissttiiccaall  SSyysstteemm  ((EESSSS)) 

Before embarking on examining other data sources which could be used to complete the gaps 
identified  in  table  4.3  in  relation  to  examining  social  inclusion/exclusion,  it  is  necessary  to 
consider other sources in the European Statistical System for the simple reason of maintaining 
coherence and comparability in the data produced.   

After examination of other sources within the ESS it was concluded that only the EU Survey 
on  Income  and  Living  Conditions  has  the  potential  to  breakdown  the  indicators  on 
educational attainment and early school leavers.  

EEUU  SSuurrvveeyy  oonn  IInnccoommee  aanndd  LLiivviinngg  CCoonnddiittiioonnss  ((EEUU--SSIILLCC)) 

EU-SILC  covers  cross-sectional  data  on  income,  poverty,  social  exclusion  and  other  living 
conditions  as  well  as  longitudinal  data  (pertaining  to  individual  level  changes  over  time, 
observed  periodically  over  a  certain  duration)  restricted  to  income,  labour  and  a  number  of 
non-monetary indicators on social exclusion.  

The  EU-SILC  in  theory  could  be  used  to  break  down  the  benchmark  indicators  on  early 
school  leavers  and  educational  attainment  by  the  following  variables  to  examine  social 
inclusion: 

i.  Sex; 
ii.  Total household disposable income; 
iii.  Educational attainment of parents; 
iv.  Labour force status; 
v.  Occupation of parents; 

However,  as  is  the  case  with  the  Labour  Force  Survey,  breaking  down  the  indicators  on 
early school leavers and educational attainment by the parents’ income, occupation, branch 
of industry and the labour force status would produce biased results. The bias results from 
the fact that both these indicators relate to specific age groups, in the case of early school 
leavers  it  is  18  to  24,  whilst  for  educational  attainment  it  is  20  to  24.  Establishing  the 
income, occupation, branch of industry or labour force status of parents is possible on the 
very strong assumption that the person aged 18 to 24 is still living with their parents.  

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Social inclusion / exclusion and the benchmark indicators 

Information on the number of adults aged 18-24 still living at home with their parents can 
be elucidated from the EU-SILC.  

44..44..22    EEuurroossttuuddeenntt  ssuurrvveeyy 

Information  collected  in  the  Eurostudent  survey  could  be  considered  in  relation  to  the 
benchmark indicator on the number of maths, science and technology graduates.  

The Eurostudent project aims to generate and present internationally comparable indicators on 
the social and economic conditions of students engaged in higher education. Thus it aims to 
provide  information  relevant  to  educational  policy  for  strengthening  and  supporting  the 
Bologna  Process  establishing  the  European  Higher  Education  Area  (EHEA).  The  data 
collected  by  Eurostudent  support  the  identification  of  obstacles  to  equal  access  and 
international mobility related to students’ economic and social background as a precondition 
removing  them.  The  main,  though  not  the  only,  instrument  of  data  collection  are  national 
surveys  of  higher  education  students.  Each  participating  country  must  survey  their  students 
using the EUROSTUDENT core questions within a specific timeframe. In many cases, these 
questions are integrated into larger national surveys 

Unfortunately, the survey does not collect information on students by field of study. Thus we 
are  unable  to  differentiate  the  parental  backgrounds  of  students  studying  in  Maths,  Science 
and Technology subjects from other fields.   

Data  is  available  for  the  following  countries:  Austria,  Finland,  France,  Germany,  Ireland, 
Italy, Latvia, Netherlands, Portugal, Spain, United Kingdom (England and Wales).  

Countries that currently have observer status and plan to join the survey in the next round are: 
Bulgaria,  Czech  Republic,  Estonia,  Greece,  Hungary,  Lithuania,  Malta,  Norway,  Poland, 
Republic of Cyprus, Romania, Slovak Republic, Slovenia, and Switzerland.  

Nevertheless, the information collected is interesting and can help to explain which groups in 
society are excluded from higher education.  Amongst the information collected of particular 
interest  in  order  to  examine  social  inclusion  is  the  social  background  of  students,  which 
includes: 

i.  Work status of students’ parents  -The work status is divided into the main categories of 

economically active, unemployed, vocationally inactive, and retired.  

ii.  Occupational status of students' parents – This refers to whether the father or mother is 

occupied in a blue or white-collar profession. 

iii.  Students' educational background (School-leaving qualification of students' parents)  

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Social inclusion / exclusion and the benchmark indicators 

iv.  Income of students' parents versus all private households  - The Eurostudent confirmed 
that  it  is  difficult  to  collect  data  on  students’  financial  background.  Nevertheless  two 
countries the Netherlands and Portugal managed to supply the data. 

44..55   SSoocciiaall  
bbeenncchhmmaarrkk  iinnddiiccaattoorrss   

iinncclluussiioonn//eexxcclluussiioonn  

iinn  

rreellaattiioonn  

ttoo  

This  section  breaks  down  each  of  the  five  benchmark  indicators  by  the  variables  identified 
above  in  section  4.2  in  order  to  examine  aspects  of  social  inclusion/exclusion.  It  should  be 
noted  

44..55..11    EEdduuccaattiioonnaall  aattttaaiinnmmeenntt  ooff  2200  ttoo  2244  yyeeaarr  oollddss  

Figure  4.3,  shows  the  percentage  of  the  female  and  male  population  aged  20  to  24  having 
completed  at  least  upper  secondary  education  in  the  EU-25.  It  can  be  observed  that  the 
percentage  of  male  youth  achieving  at  least  upper  secondary  education  is  less  than  that  of 
females.  This  can  lead  one  to  conclude  that  male  youth  are  at  greater  risk  than  females  of 
being socially excluded, since a greater percentage lack basic competences, which may act as 
a barrier in accessing the labour market and further education opportunities. Nevertheless, it 
can be seen that the percentage of youth that attained at least upper secondary education has 
increased  both  for  females  and  males  from  2000  to  2005.  The  percentage  of  males  that 
attained  at  least  upper  secondary  education  increased  by  0.9  per  cent  from  2000  to  2005  in 
comparison to a smaller increase of 0.3 per cent of females. The gap between male and female 
upper  secondary attainment  narrowed from  5.7  per cent in  2000  to just  over 5.1  per cent in 
2004.   

Figure 4.3: Percentage of the female and male population aged 20 to 24 having completed at least 
upper secondary education in the EU-25 

90

80

70

60

50

40

30

20

10

0

%

t
n
e
m
n
i
a
t
t
a
l
a
n
o
i
t
a
c
u
d
E

2000

2001

2002

Male

2003
Female

2004

2005

Source: Eurostat 

There  are  major  differences  in  upper  secondary  attainment  between  Member  States  in  2005 
(see  figure  4.4).  The  percentage  of  20  to  24  year  olds  achieving  at  least  upper  secondary 
education was greater for females than that of males for most Member States and Acceding 

Key education indicators on social inclusion and efficiency 

153

 
  
  
  
 
 
  
 
 
Social inclusion / exclusion and the benchmark indicators 

countries.  However,  in  CZ,  UK  and  BG  the  percentage  of  males  achieving  at  least  upper 
secondary education was greater than of females. In 2005, the percentage of males achieving 
at least upper secondary education varied significantly between countries from 40 per cent in 
Portugal to nearly 91 per cent in the Slovak Republic. Upper secondary attainment of females 
also  varied  significantly  between  countries,  from  52  per  cent  in  Malta  to  94  percent  in 
Slovenia. 

The gap between upper secondary attainment of females and males varied between countries. 
Examining  the  countries  where  a  lower  proportion  of  males  than  females  achieved  at  least 
upper secondary education, it can be observed that Germany and the Slovak Republic had the 
narrowest gap in upper secondary attainment between males and females (1.2 per cent). The 
largest gap in upper secondary attainment between males and females was in Cyprus (17 per 
cent).  

Figure 4.4: Percentage of the female and male population aged 20 to 24 having completed at least 
upper secondary education in 2005 

%

t
n
e
m
n

i
a
t
t
a
l
a
n
o
i
t
a
c
u
d
E

100

90

80

70

60

50

40

30

20

10

0

EU
25

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

Male

74 76 91 75 70 75 79 55 81 83 68 72 77 81 67 81 45 71 84 88 40 88 91 83 87 78 77 74

Female 80 85 90 78 72 87 89 68 84 89 78 89 87 90 76 85 52 79 88 92 57 94 92 87 89 77 76 76

Source: Eurostat  

44..55..22   EEaarrllyy  sscchhooooll  lleeaavveerrss  

The percentage of the population aged 18 to 24 with at most lower secondary education and 
not in further education or training decreased steadily for both females and males from 2000 
to 2005 in the EU-25. The percentage of males with at most lower secondary education and 
not in further education or training decreased by nearly 3 percent from 2000 to 17 percent in 
2005.   

The percentage of female early school leavers decreased by just over two percent in the same 
period. 

It can be observed in figure 4.5, that males are more at risk of social exclusion than females 
given that  a greater percentage of them can be classified as early school leavers. Whilst the 
percentage of male and female early school leavers decreased over the period 2000 to 2005, 
the gap between the percentage of male and female early school leavers only decreased by 0.2 
percent during this period. 

Key education indicators on social inclusion and efficiency 

154

 
 
  
 
 
Social inclusion / exclusion and the benchmark indicators 

Figure 4.5: Percentage of the population aged 18-24 with at most lower secondary education and 
not in further education or training in the EU-25 

25

20

15

10

5

0

s
r
e
v
a
e
l

l
o
o
h
c
s
y
l
r
a
E
%

2000

2001

2002

2003

2004

2005

Males

Females

Source: Eurostat    
There  are  big  variations  in  the  percentage  of  male  and  female  early  school  leavers  between 
countries (see figure 4.6). The percentage of male early school leavers was greater than that of 
female  early  school  leavers  for  most  Member  States.  However,  in  the  Czech  Republic, 
Germany,  and  Bulgaria  the  opposite  was  the  case.    Slovenia  had  the  lowest  percentage  of 
male  early  school  leavers  (5.7  percent),  whilst  Portugal  had  the  highest  percentage  of  male 
early  school  leavers  (47  percent).  There  are  also  big  variations  in  the  percentage  of  female 
early school leavers. Slovenia had the lowest percentage of female early school leavers, whilst 
Malta recorded the highest percentage (39 percent). 

Figure 4.6: Percentage of the population aged 18-24 with at most lower secondary education and 
not in further education or training (2005) 

50

45

40

35

30

25

20

15

10

5

0

EU
25

s
r
e
v
a
e
l

l
o
o
h
c
s
y
l
r
a
E
%

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

SE UK BG RO

Male

17

15 6.2 9.4 14

17

18 36

15 15

26

27 16

12

17 14

43 16 9.4 6.9 47 5.7

6

11 9.3 15

20 21

Female 13

11 6.6 7.5 14

11 9.2 25

11 9.6 18

11 8.2 6.2 9.6 11

39 11 8.5

4

30 2.8 5.7 7.3 7.9 13

21 20

Source: Eurostat  

There are also variations between Member States in the gap between male and female early 
school leavers. For countries where there are more male early school leavers than females, the 
gap ranges from 0.3 percent in the Slovak Republic to 17 percent in Portugal.  

Key education indicators on social inclusion and efficiency 

155

  
  
 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

The  2000  LFS  ad  hoc  module  on  transition  from  school  to  working  life15,  collected 
information on the highest level of education successfully completed by father or mother. The 
data presented in table 4.5, shows the percentage of early school leavers by parents’ highest 
educational attainment for the age group 15 to 35. Data for the age group 18 to 24 can easily 
be extracted from the module. 

Table 4.5: Share of early school leavers (aged 15- 35) by parents highest educational attainments 
in %,  (2000) 

ISCED  BE 
26 
12 
3 

1-2 
3-4 
5-6 

EL 
20 
8 
11 

ES 
40 
21 
11 

FR 
26 
17 
6 

IT 
38 
19 
11 

HU 
33 
9 
3 

AT 
24 
13 
10 

SI 
10 
8 
: 

SK 
14 
2 
: 

FI 
13 
15 
8 

SE 
18 
12 
10 

RO 
47 
14 
: 

Source: Eurostat 

Key 
ISCED 0 to 2 
ISCED 3 
ISCED 5 to 6 

Pre-primary, primary and lower secondary education 
Upper secondary education 
Tertiary education 

Table  4.5,  shows  a  relationship  between  early  school  leavers  and  their  parental  educational 
attainment. For most countries, the share of early school leavers was highest for those whose 
parents’  highest  educational  attainment  was  less  than  upper  secondary  education,  whilst  the 
lowest share of early school leavers was for those whose parents achieved a higher education 
level (ISCED 5/6).  

44..55..33    MMSSTT  ggrraadduuaatteess  

Figure  4.7,  illustrates  the  evolution  of  graduates  (ISCED  5-6)  in  mathematics,  science  and 
technology per 1000 of population aged 20-29 for the EU-25 for both sexes and broken down 
by gender  for the years 2000 to  2004.    The  percentage  of  MST  graduates in  the EU-25 has 
increased  during  from  10  per  cent  in  2000  to  13  per  cent  in  2004.  The  percentage  of  both 
female and male graduates increased from 2000 to 2004. However, the increase was less for 
females than for males. 

It  can  be  observed  that  the  percentage  of  female  MST  graduates  is  significantly  lower  than 
that of male MST graduates. The period 2000 to 2004 saw the gap between male and female 
MST graduates widen from 8 per cent in 2000 to just over 9 per cent in 2004.   

15 The module was aimed at 15 to 35 year olds who left continuous education within the past five or 10 

years. (i.e. between 1990/1995 and 2000). 

Key education indicators on social inclusion and efficiency 

156

 
  
  
  
 
                                                 
Social inclusion / exclusion and the benchmark indicators 

Figure 4.7: Graduates (ISCED 5-6) in mathematics, science and technology per 1000 of 
population aged 20-29 in the EU-25 

20

18

16

14

12

10

8

6

4

2

0

0
0
0
1
r
e
p
9
2
-
0
2
d
e
g
a

s
e
t
a
u
d
a
r
g
T
S
M

2000

2001

2002
T otal Male

2003

2004

Female

Source: Eurostat 

Caution  needs  to  be  exercised  when  interpreting  the  gap  between  male  and  female  MST 
graduates in relation to social exclusion for a number of reasons. This includes the fact that 
the  definition  of  social  exclusion  relates  to  a  lack  of  basic  competences,  which  can  be 
examined  in  relation  to  the  indicators  on  educational  attainment  and  early  school  leavers. 
However  the  normal  prerequisite  for  entering  higher  education  (ISCED  5/6)  is  an  ISCED 
level  3  qualification,  which  can  be  measured  in  terms  of  the  indicators  on  educational 
attainment  and  early  school  leavers.  Therefore,  the  person  graduating  with  an  ISCED  level 
5/6 qualification will have already acquired the basic competencies required. Nevertheless, it 
can be argued that there is a barrier for females to pursue studies in MST related disciplines. 
However, examining the number of female graduates to  men for all fields of education (see 
figure 4.8), it can be seen that there are more women graduating from higher education than 
men.  

Figure 4.8: Women per 100 men graduating from ISCED levels 5-6 

160

140

120

100

80

60

40

20

0

s
e
t
a
u
d
a
r
g
n
e
m
0
0
1
/
n
e
m
o
W

2000

2001

2002

2003

2004

Source: Eurostat 

Key education indicators on social inclusion and efficiency 

157

 
 
 
 
 
 
 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Examining  the  percentage  of  female  graduates  by  field  of  education  reveals  that  there  are 
more female graduates than men in education and training, humanities, health and welfare and 
in  services.  However,  there  are  more  male  than  female  graduates  in  science,  maths  and 
computing, and especially in engineering, manufacturing and construction. Thus, it cannot be 
argued  that  there  is  a  barrier  for  women  to  access  higher  education  studies.  However,  there 
may be other factors, which play a part in explaining why females prefer undertaking studies 
in the arts and humanities rather than maths, science and engineering disciplines. 

Table 4.6: Female graduates by field of education as a percentage of males and female graduates 

Indicator 
Female  graduates  (ISCED  5-6)  in  education  and  training 
field - as % of males and females graduates 
Female graduates (ISCED 5-6) in humanities and art field - 
as % of males and females graduates 
Female  graduates  (ISCED  5-6)  in  social  science,  business 
and law field - as % of males and females graduates 
Female graduates (ISCED 5-6) in science, mathematics and 
computing field - as % of males and females graduates 
Female 
engineering, 
(ISCED 
manufacturing  and  construction  field  -  as  %  of  males  and 
females graduates 
Female  graduates 
veterinary field - as % of males and females graduates 
Female graduates (ISCED 5-6) in health and welfare field - 
as % of males and females graduates 
Female  graduates  (ISCED  5-6)  in  services  field  -  as  %  of 
males and females graduates 

in  agriculture  and 

(ISCED  5-6) 

graduates 

5-6) 

in 

2000 

2001 

2002 

2003 

2004 

75.9 

76.2 

76.5 

76.9 

77.3 

69 

68.8 

68.6 

68.4 

69.1 

58.6 

59.2 

60 

60.8 

61.5 

42.2 

41.9 

41.7 

41.8 

39.8 

21.1 

21.8 

22.3 

22.7 

23.6 

45 

45.8 

46.5 

48 

48.7 

74.1 

75.8 

76.1 

76.7 

77.2 

50.8 

52.1 

52.5 

52.8 

54.6 

Source: Eurostat 

Examining  this  indicator  at  a  country  level  reveals  that  for  all  countries  the  percentage  of 
male  MST  graduates  is  greater  than  that  of  females.  The  lowest  percentage  of  females 
graduating  in  MST  is  in  Malta  (2.3  percent)  and  the  highest  percentage  is  in  Ireland  (15 
percent).  In  contrast,  the  variation  in  the  percentage  of  male  MST  graduates  ranges  from  5 
percent in Cyprus to 32 percent in Ireland.  

There  are  significant  differences  between  countries  in  the  gap  between  males  and  females 
MST graduates produced.  The smallest gap between  male and female MST  graduates is in 
Cyprus (2 percent) whilst the largest gap is in France and Ireland (17 percent). 

Key education indicators on social inclusion and efficiency 

158

 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Figure 4.9: Graduates (ISCED 5-6) in mathematics, science and technology per 1000 of 
population aged 20-29 in 2004 

35

30

25

20

15

10

5

0

n
o
i
t
a
l
u
p
o
p
f
o
0
0
0
1
r
e
p
9
2
-
0
2
d
e
g
a
s
d
a
r
g
T
S
M

EU
25

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

Female 7.9 5.7 4.4

9

4.3 7.4 6.8 7.7 13 15 7.6 3.2 6.3 13

2.9 2.3 3.1

4

6.4 9.1 4.8 6.6 10 11 11 7.2 7.7

Male

17 17 10 19 13 11 9.2 17 31 32 13 5.2 13 22

7.1 4.8 13 13 12 13 14 12 24 21 25 9.6 12

Source: Eurostat  

Notes 
Data for FR, MT and FI refer to 2003 

The  work  status  of  higher  education  students’  parents  can  be  examined  by  looking  at  the 
results of the Eurostudent survey (see figure 4.10 and 4.11). The share of economically active 
higher education students’ mothers in almost all countries account for at least ten percentage 
points lower than those of students’ fathers. This is not the case in Latvia or Finland. Spain is 
an  extreme  example  with  the  highest  quota  of  economically  active  fathers  (95%)  and  the 
lowest quota of economically active mothers (50%). 

The  findings  of  the  survey  did  not  confirm  the  assumption  that  the  parents  of  students  are 
more  frequently  in  employment  than  the  parents  of  the  same-aged  population.  In  Austria, 
Germany, Ireland and Portugal, students' fathers are less economically active than men of the 
corresponding age-group in the general population. This means that economically active men 
are underrepresented among students’ fathers.  

However, the reason students' fathers are actually under-represented in that category does not 
lie  in  unemployment.  Differences  can  largely  be  explained  by  the  age-related  greater 
frequency  of  entry  into  retirement.  The  situation  in  Spain,  France  and  the  Netherlands  is 
slightly  different.  In  these  countries,  employment  among  students'  fathers  is  relatively  more 
widespread compared to the same-aged male population.  

In Austria and Germany students’ mothers are less economically active than all women of the 
same  age.  In  Spain  and  Ireland  these  quotas  are  the  same,  and  in  France,  Portugal  and 
especially the Netherlands students’ mothers are more often economically active than within 
the whole female population of roughly the same age 

Key education indicators on social inclusion and efficiency 

159

 
 
 
 
 
 
 
 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Figure 4.10: Students’ fathers compared to all men of corresponding age groups* who are 
economically active (2005) 

100
90
80
70
60
50
40
30
20
10
0

e
v
i
t
c
a
y
l
l
a
c
i
m
o
n
o
c
e

%

AT DE

ES

FI

FR

IE

IT

LV NL

PT UK

Students' fathers M en of corresponding age groups

Source: Eurostudent 2005 

Figure 4.11: Students’ mothers compared to all men of corresponding age groups* who are 
economically active (2005) 

e
v
i
t
c
a
y
l
l
a
c
i
m
o
n
o
c
e

%

90

80
70

60

50
40

30

20
10

0

AT

DE

ES

FI

FR

IE

IT

LV NL

PT

Students' mothers Women of corresponding age groups

Notes 
*         Men/women aged in AT, DE, ES, NL, PT: 40-60; IE: 35- 54 

Source: Eurostudent 2005 

Figures  4.12  and  4.13,  illustrate  that  the  proportion  of  students  from  a  working-class 
background varies substantially from country to country. It is important to note that the 
possibility that the delineating definitions given to the “blue-collar worker” category in 
the individual countries do not absolutely coincide with each other. However, the lack 
of  clear  borderlines  cannot  fully  explain  the  substantial  differences  in  the  educational 
mobilisation of disadvantaged groups. 

Key education indicators on social inclusion and efficiency 

160

 
 
 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

The highest share of students whose fathers’ occupational status is working class is in 
Spain. In Finland these students account for 29% of the total student population, whilst 
in Ireland they make up less than a quarter.   

Figure 4.12: Students’ fathers compared to all men of corresponding age groups with working 
class status (blue collar) in percentages (2005)  

s
u
t
a
t
s

s
s
a
l
c
g
n

i

k
o
w
%

50
45
40
35
30
25
20
15
10
5
0

AT

DE

ES

FI

FR

IE

IT

LV NL

PT

Students' fathers Men of corresponding age groups

Source: Eurostudent 2005 

In  Austria,  Germany,  France,  and  Portugal,  students  whose  mothers’  occupational 
status is working class are under-represented in higher education. However, in Finland, 
the  Netherlands,  and  Spain  students  whose  mothers  are  working  class  are  over-
represented. 

Figure 4.13: Students’ mothers compared to all men of corresponding age groups with working 
class status (blue collar) in percentages (2005)  

s
u
t
a
t
s

s
s
a
l
c
g
n
i
k
r
o
W
%

50
45
40
35
30
25
20
15
10
5
0

AT DE

ES

FI

FR

IE

IT

LV NL

PT

Students' mothers Women of corresponding age groups

Source: Eurostudent 2005 

The proportions of students from households in which the mother and father holds a higher 
education degree varied from country to country (see figures 4.14 and 4.15). 

Key education indicators on social inclusion and efficiency 

161

 
 
 
 
 
 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Finland  has  the  highest  proportion  of  students  from  families  with  higher  education 
qualifications  with  a  value  of  48%  of  fathers  and  50%  of  mothers.  Whilst  Italy  reports  the 
lowest share  of students  whose parents hold a  higher education qualification, with 17% and 
14%, respectively for fathers and mothers respectively. 

Comparatively highly qualified are students’ parents in France, the Netherlands, Germany and 
Latvia; above all the fathers. 

Figure 4.14: Students’ fathers compared to all men of corresponding age groups with higher 
education in percentages (2005)  

n
o
i
t
a
c
u
d
e

r
e
h
g
i
h
h
t
i

w
%

60

50

40

30

20

10

0

AT DE

ES

FI

FR

IE

IT

LV NL

PT UK

Students' fathers Men of corresponding age groups

Source: Eurostudent 2005 

Figure 4.15: Students’ mothers compared to all women of corresponding age groups with higher 
education in percentages (2005)  

n
o
i
t
a
c
u
d
e

r
e
h
g
i
h
h
t
i

w
%

60

50

40

30

20

10

0

AT DE

ES

FI

FR

IE

IT

LV NL

PT

Students' mothers Women of corresponding age groups

Source: Eurostudent 2005 

Key education indicators on social inclusion and efficiency 

162

 
 
 
 
 
 
 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

The  Eurostudent  survey  confirmed  that  it  is  difficult  to  collect  data  on  students’  financial 
background. Nevertheless two countries the Netherlands and Portugal managed to supply the 
data (see figures 4.16 and 4.17). 

Figure 4.16: Income cut-off point between the upper and lower half of students’ parent income 
distribution, median in € (2005)  

PT

NL

0

500

1000

1500

2000

2500

3000

3500

€

Source: Eurostudent 2005 

Figure 4.17: Percentage of students’ parents with an income below the income cut-off point for 
the lowest income quartile of all private households (2005)  

PT

NL

0

5

10

15

20

25

€

Source: Eurostudent 2005 

44..55..44    PPaarrttiicciippaattiioonn  iinn  lliiffeelloonngg  lleeaarrnniinngg  ffoorr  2255  ttoo  6644  yyeeaarr  oollddss 

Participation in lifelong learning for 25 to 64 year olds increased for both males and females 
in  the  EU-25  from  2000  to  2004  (see  figure  4.18).  During  this  period,  the  percentage  of 
females and males participating in lifelong learning increased by nearly 4 per cent and 3 per 
cent respectively. The percentage of females undertaking lifelong learning is greater than that 
of males, which can indicate that males are at greater risk of being socially excluded.  

Key education indicators on social inclusion and efficiency 

163

 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Figure 4.18: Percentage of the adult population aged 25 to 64 participating in education and 
training in the EU-25 

L
L
L
%

14

12

10

8

6

4

2

0

2000

2001

2002

2003

2004

2005

Male

Female

Source: Eurostat – EU Labour Force Survey 

There  are  big  variations  in  participation  in  lifelong  learning  for  both  males  and  females 
between countries in 2005 (see figure 4.19).  Female participation in lifelong learning varied 
from  just  over  one  percent  in  Bulgaria  to  40  percent  in  Sweden.  Male  participation  varied 
from  just  over  one  percent  in  Bulgaria  to  30  percent  in  Sweden.  For  most  countries,  more 
females  participated  in  lifelong  learning  than  males.  The  exceptions  to  this  are  Belgium, 
Greece,  and  Malta.  In  Luxembourg  and  the  Netherlands  the  same  proportion  of  males  and 
females participated in lifelong learning. 

It  should  be  noted  that  the  gap  between  male  and  female  participation  in  lifelong  learning 
varied from 0.1 in the Netherlands to nearly 10 percent in Sweden for those countries where 
there are more females participating in lifelong learning than men. For those countries where 
there are more males participating in lifelong learning than females the gap is small. 

Figure 4.19: Percentage of the adult population aged 25 to 64 participating in education and 
training (2005) 

L
L
L
%

50

40

30

20

10

0

EU
25

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

SE UK BG RO

Male

10 10 5.5 24 8.3 4.2 1.9 11 7.4 6.6 5.7 5.1 4.9 4.9 8.5 3.5 6.7 17 13 4.3 4.5 16 4.7 21

30 24 1.1 1.5

Female 12 9.7 6.4 31

8

7.5 1.7 13 7.9 9.4 6.6 6.1 10 7.6 8.5 4.8 4.8 17 15 5.6 4.7 20 5.2 29

40 34 1.1 1.7

Source: Eurostat – EU Labour Force Survey 

Key education indicators on social inclusion and efficiency 

164

 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Examining  participation  in  lifelong  learning  by  age  groups  shows  that  for  most  countries 
participation in learning activities for both males and females is highest for the age group 25 
to 34 and participation is lowest for the age group 55 to 64 (see table 4.7). The exception is 
Austria, where participation is highest for the age group 55 to 64 and lowest in the age group 
25 to 34. This suggests that in most counties, persons of both sexes in the age group 55 to 64 
are at greater risk of social exclusion since fewer of them participate in lifelong learning.   

Table 4.7: Participation in any learning activities by age group (2003) 

Country 

Males 

Females 

EU-25 
BE 
CZ 
DK 
DE 
EE 
EL 
ES 
FR 
IE 
IT 
CY 
LV 
LT 
LU 
HU 
MT 
NL 
AT 
PL 
PT 
SI 
SK 
FI 
SE 
UK 

25-34 
50.5 
51.4 
36.2 
81.4 
51.9 
41.5 
26.7 
31.7 
63.4 
46.7 
57.7 
53.6 
49.1 
30.0 
85.9 
18.2 
80.4 
53.2 
88.4 
39.4 
52.5 
84.8 
62.6 
82.4 
78.3 
43.7 

35-44 
45.4 
47.2 
32.2 
82.2 
45.7 
31.1 
19.9 
25.9 
59.2 
47.1 
53.3 
43.5 
44.8 
23.9 
85.4 
11.0 
28.2 
47.0 
87.3 
31.2 
46.4 
80.7 
61.9 
78.1 
71.5 
40.8 

45-54 
41.2 
43.8 
28.2 
79.5 
42.2 
25.9 
15.2 
20.1 
55.3 
42.2 
50.4 
35.6 
37.9 
22.5 
80.2 
7.6 
74.2 
40.0 
86.2 
24.7 
40.3 
79.5 
61.4 
71.6 
67.4 
36.1 

55-64 
31.3 
30.2 
21.9 
72.2 
33.2 
17.4 
9.5 
13.7 
36.3 
37.7 
38.9 
23.6 
34.0 
13.9 
74.9 
4.9 
20.0 
33.1 
91.0 
18.2 
36.0 
77.7 
52.1 
62.0 
59.6 
23.8 

25-34 
49.9 
50.4 
30.7 
83.2 
48.3 
40.8 
27.8 
35.1 
58.8 
55.2 
57.1 
50.1 
63.4 
38.3 
86.8 
20.8 
83.4 
47.9 
90.6 
42.3 
55.8 
87.8 
62.2 
86.8 
74.8 
44.5 

35-44 
44.5 
43.0 
32.6 
84.7 
43.8 
40.1 
17.8 
26.0 
51.3 
56.3 
49.7 
39.3 
52.3 
38.8 
82.5 
14.9 
28.9 
40.3 
89.2 
34.9 
46.1 
84.9 
61.6 
86.6 
75.8 
43.6 

45-54 
39.5 
38.4 
27.6 
80.2 
39.7 
32.8 
11.3 
19.1 
46.7 
52.0 
42.7 
29.6 
45.9 
27.8 
78.3 
9.0 
72.9 
38.8 
88.2 
26.8 
38.2 
80.8 
60.2 
80.5 
75.1 
41.1 

55-64 
27.7 
23.8 
17.3 
72.1 
30.1 
14.7 
5.1 
13.6 
28.3 
46.5 
32.2 
14.8 
36.8 
18.1 
75.8 
4.1 
16.0 
26.7 
93.9 
14.5 
29.7 
78.6 
46.4 
69.3 
64.3 
21.3 

Source: Eurostat – EU Labour Force Survey ad hoc module on lifelong learning 

The  2003  EU  Labour  Force  Survey  ad  hoc  module  on  lifelong  learning  showed  that  in  the 
EU-25,  persons  with  a  higher  education  qualification  (ISCED  5/6)  were  more  likely  to 
participate in lifelong learning compared to persons with upper secondary education or lower 
(see table 4.8). Less than one quarter of persons with less than a upper secondary qualification 
participated in any training activities. This is in contrast to more than two-thirds for persons 
with a higher education qualification. This suggests that persons in the EU are at greater risk 
of social exclusion when they have less than upper secondary education.  

Investigating  participation  in  lifelong  learning  at  the  Member  State  level  shows  that  for  all 
countries, persons with a higher education qualification participated more in lifelong learning 
than  those  with  upper  secondary  education  or  lower.  However,  participation  in  lifelong 
learning  based  on  educational  attainment  of  persons  varies  significantly  between  countries. 
For  example,  in  Austria  participation  in  lifelong  learning  is  very  high  for  all  levels  of 
educational attainment including persons with less than upper secondary education (above 80 
percent). In contrast in Hungary participation in lifelong learning is very low for all levels of 

Key education indicators on social inclusion and efficiency 

165

 
 
 
Social inclusion / exclusion and the benchmark indicators 

educational attainment. The gap between participation in lifelong learning for persons with a 
higher  educational  qualification  and  those  with  less  than  upper  secondary  education  varies 
from 9 percent in Austria to 68 percent in Cyprus. 

Table 4.8: Participation in any learning activities by educational attainment (2003) 

Country 
EU-25 
BE 
CZ 
DK 
DE 
EE 
EL 
ES 
FR 
IE 
IT 
CY 
LV 
LT 
LU 
HU 
MT 
NL 
AT 
PL 
PT 
SI 
SK 
FI 
SE 
UK 

ISCED 0 to 2 
23.1 
23.3 
10.3 
61.5 
19.0 
10.1 
5.6 
12.6 
29.2 
34.5 
34.4 
8.5 
30.0 
5.8 
67.4 
3.7 
49.8 
20.1 
86.8 
9.2 
35.3 
66.8 
40.4 
60.9 
48.8 
12.2 

ISCED 3 
44.2 
42.4 
26.2 
77.4 
41.1 
25.0 
18.9 
30.3 
52.4 
51.4 
60.7 
34.2 
43.6 
20.9 
86.4 
11.3 
65.3 
42.6 
88.6 
26.5 
70.6 
83.2 
59.4 
76.8 
69.1 
36.9 

ISCED 5 / 6 
68.7 
66.9 
62.7 
93.4 
65.8 
51.8 
42.6 
47.7 
83.1 
66.4 
78.0 
76.1 
70.9 
59.6 
94.7 
27.0 
68.4 
66.2 
95.3 
73.9 
79.7 
96.7 
82.6 
90.1 
87.6 
60.8 

Source: Eurostat 

Key 
ISCED 0 to 2 
ISCED 3 
ISCED 5 to 6 

Pre-primary, primary and lower secondary education 
Upper secondary education 
Tertiary education 

In  the  EU-25,  participation  in  any  learning  activities  is  highest  for  managers,  professionals, 
technicians  and  associate  professionals,  and  is  lowest  for  plant  and  machine  operators  and 
assemblers and elementary occupations (see table 4.9). This is also true at the Member State 
level. This indicates that certain occupations are less likely to participate in lifelong learning.  

Nevertheless,  there  are  significant  differences  between  countries  in  participation  in  lifelong 
learning by occupation. For example, in Austria participation in lifelong learning is high for 
all occupations (above 80 percent). In contrast, in the UK, participation in lifelong learning is 
highest for managers, professionals, etc, whilst it is lowest for plant and machine operators. 

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166

 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Table 4.9: Participation in any learning activities by occupation (2003) 

Country 
EU-25 
BE 
CZ 
DK 
DE 
EE 
EL 
ES 
FR 
IE 
IT 
CY 
LV 
LT 
LU 
HU 
MT 
NL 
AT 
PL 
PT 
SI 
SK 
FI 
SE 
UK 

ISCO 1 to 3 
64.8 
64.7 
52.4 
92.7 
60.8 
63.6 
37.1 
43.5 
82.6 
59.5 
74.0 
78.3 
75.6 
66.6 
93.6 
25.8 
67.2 
60.4 
93.0 
67.2 
69.1 
94.4 
79.3 
90.2 
86.6 
57.2 

ISCO 6 to 7 
34.4 
32.7 
20.2 
73.8 
35.9 
20.7 
8.9 
14.8 
50.1 
39.9 
41.3 
22.6 
39.8 
16.2 
71.4 
6.8 
54.8 
26.9 
82.4 
21.3 
33.5 
76.7 
58.3 
71.0 
56.8 
26.8 
Source: Eurostat 

ISCO 8 to 9 
27.7 
28.2 
19.2 
60.8 
24.8 
13.7 
8.3 
14.7 
30.9 
35.5 
36.4 
11.8 
31.6 
12.6 
69.8 
6.5 
48.4 
19.7 
80.7 
20.2 
32.9 
73.0 
58.0 
66.9 
49.9 
22.3 

Key 
ISCO 1 to 3 
ISCO 4 to 5 
ISCO 6 to 7 
ISCO 8 to 9 

Managers, professionals, technicians and associate professionals 
Clerks and sales 
Skilled agricultural and fishery workers, craft and related trades workers 
Plant and machine operators and assemblers and elementary occupations 

Data  from  the  LFS  ad  hoc  module  on  lifelong  learning  shows  that  participation  lifelong 
learning in the EU-25 is highest for those employed in services, whilst it is lowest for those 
employed  in  agriculture  (see  figure  4.20).  For  most  countries,  participation  in  lifelong 
learning is highest for persons employed in services, followed by industry, whilst the lowest 
participation is for persons employed in agriculture, hunting and forestry. However, in France 
persons employed in agriculture, hunting and forestry participate the most in lifelong learning. 
In  Estonia,  Denmark,  Latvia,  Netherlands,  Finland  and  Sweden  persons  employed  in 
agriculture, hunting and forestry participate more in lifelong learning than those employed in 
industry,  but  less  than  those  employed  in  services.    In  Hungary,  persons  employed  in 
agriculture, hunting and forestry participate as much as those employed in industry, but less 
than those employed in services. 

It can be concluded that persons employed in agriculture, hunting and forestry are probably at 
greater  risk  of  social  exclusion  in  most  Member  States  given  that  they  participate  less  in 
lifelong learning. 

Key education indicators on social inclusion and efficiency 

167

  
 
 
Social inclusion / exclusion and the benchmark indicators 

Figure 4.20: Participation in any learning activities by type of industry (2003) 

%

100

80

60

40

20

0

EU
25

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

SE UK

a 

c_to_f 

38 38 25

78 39 26 6.7 15 66 33 39

13 47 14 75 9.6 53 34 85 23

27 77 55 80

69 26

39 39 25

74 40 24 14

20 50 44 47

27 41 26 75 9.4 56 33 84 30

40 80 62 74

61 33

g_to_q  52 53 40

86 50 46 26

32 63 55 60

51 58 43 87 18

61 51 88 49

55 89 72 85

76 48

Notes 
Agriculture, hunting and forestry 
a 
Industry 
c_to_f 
g_to_q  Services 

Data  from  the  EU-LFS  ad  hoc  module  on  lifelong  learning  on  participation  in  lifelong 
learning has already been broken down by labour force status (see figure 4.21). It should be 
noted that the ad hoc module refers to participating in education over a 12 month period prior 
to  the  survey,  thus  the  labour  force  status  may  not  necessarily  be  static  throughout  the  12 
months, when an individual was participating in education. Whilst the EU-LFS collects data 
on the labour force status during the reference week and the one-year prior to the survey, it 
classifies the labour force status according to the reference week. Thus an individual who was 
unemployed 11 months ago, when they participated in an educational activity may have since 
then  found  employment.  However,  it  is  also  possible  the  person  is  still  unemployed. 
Nevertheless, the indicator, can provide a good indication of whether the labour force status 
of a person can act as a barrier to participation in lifelong learning. 

In  the  EU-25,  participation  in  lifelong  learning  is  highest  for  those  that  are  employed  and 
lowest for those those are inactive. The data show that for all countries with the exception of 
Malta,  Netherlands  and  Austria,  participation  in  learning  activities  is  highest  for  those  who 
are employed, and lowest for the inactive. 

Key education indicators on social inclusion and efficiency 

168

 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Figure 4.21: Participation in any learning activities by working status (2003) 

120%

100

80

60

40

20

0

EU
25

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

SE UK

Empl 48 49 34

83 48 38 21 27

60 51 55 43 52

33 85 15 59 47 87

39 47 85 67 82

72 45

Un

In

41 46 17

77 40 23 20 31

52 47 48 33 38

16 83 8.3 61 47 94

23 47 79 42 75

63 33

28 25 14

68 31 12 9.6 17

23 44 37 18 31

11 75

6

44 25 96

13 32 75 44 62

65 19

Key 
Empl 

Employed 

Un  Unemployed 

In 

Inactive 

Figure 4.22, shows that in the EU-25 participation in lifelong learning activities is highest in 
densely populated areas. This can indicate that persons living in less urbanised areas and in 
rural  areas  have  less  access  to  lifelong  learning  opportunities.  Participation  in  lifelong 
learning  by  type  of  area  varies  between  Member  States.  However,  for  most  Member  States 
participation  in  lifelong  learning  is  highest  in  densely  populated  areas.  However  in  the 
Netherlands participation is the highest in sparsely populated areas. In the UK, participation 
in  lifelong  learning  is  the  highest  in  intermediate  urbanised  areas,  whilst  participation  in 
lifelong  learning  in  densely  urbanised  areas  is  lower  and  it  is  the  same  as  in  sparsely 
urbanised areas. 

Figure 4.22: Participation in any learning activities by type of location (2003) 

100

%

80

60

40

20

0

Deg1

Deg2

Deg3

EU
25

44

41

41

BE CZ DK DE EE EL ES FR IE IT CY LV LT LU HU NL AT PT

FI

SE UK

43

41

44

32

29

26

84

79

76

44

39

40

20

17

10

27

25

20

52

51

49

54

46

48

49

48

44

38

28

35

22

80

83

82

14

10

11

44

37

46

91

89

88

50

38

42

82

79

74

75

72

69

37

40

37

47

46

31

Source: Eurostat 

Key 
Deg1  Densely-populated area (at least 500 inhabitants/Km²) 
Deg2 
Deg3 

Intermediate urbanised area (between 100 and 499 inhabitants/Km²) 
Sparsely populated area (less than 100 inhabitants/Km²) 

Key education indicators on social inclusion and efficiency 

169

 
 
Social inclusion / exclusion and the benchmark indicators 

44..55..55    RReeaaddiinngg  lliitteerraaccyy  ooff  1155  yyeeaarr  oollddss 

Table  4.10,  shows  the  mean  performance  of  students  per  country,  on  the  PISA  reading 
literacy scale  by gender for the years  2000 and 2003.  The  mean performance of students in 
the PISA reading literacy varied by country and by gender. 

In  2000,  the  mean  performance  of  females  was  highest  in  Finland  (576)  and  lowest  in 
Luxembourg (456). For males, the mean performance was highest in Finland (520) and lowest 
in Luxembourg (429). In 2003, the mean performance of females was highest in Finland (565) 
and lowest in the Slovak Republic (486).  

It is clearly shown that in all EU countries, boys are lagging behind girls in reading literacy in 
2000 and then in 2003. 

Table 4.10: Mean performance of students per country, on the PISA reading literacy scale by 
gender in 2000 and 2003 

Country 

2000 

2003 

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

Male 
Female 
Male 
489 
525 
492 
473 
510 
473 
479 
510 
485 
471 
502 
468 
: 
: 
: 
453 
493 
456 
461 
493 
456 
476 
505 
481 
501 
542 
513 
455 
507 
469 
: 
: 
: 
470 
485 
432 
: 
: 
: 
463 
456 
429 
467 
496 
465 
477 
505 
481 
503 
: 
: 
467 
520 
495 
477 
498 
461 
459 
482 
458 
: 
: 
: 
453 
: 
: 
521 
571 
520 
496 
536 
499 
492 
537 
512 
Source: OECD PISA database 

Female 
526 
504 
505 
513 
: 
490 
500 
514 
530 
495 
: 
509 
: 
496 
498 
516 
524 
514 
516 
495 
: 
486 
565 
533 
520 

Figure  4.23,  shows  that  the  percentage  gap  in  reading  literacy  scores  between  females  and 
males  varied  by  countries  for  both  rounds  of  the  PISA  survey.  In  2000,  female  reading 
literacy  scores  were  nearly  11  per  cent  higher  than  that  of  males,  whilst  in  the  UK  the 
difference  between  reading  literacy  scores  was  less  than  5  per  cent.  In  2003,  the  largest 

Key education indicators on social inclusion and efficiency 

170

 
 
 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

difference in reading literacy scores was in Austria, where female reading literacy scores were 
9 per cent higher than that of males. In contrast, the lowest gap in reading literacy scores was 
in  the  Netherlands,  where  the  reading  literacy  of  males  was  4  per  cent  lower  than  that  of 
females.  

It  can  also  be  noted  that  in  most  countries  the  gap  between  the  reading  literacy  scores  of 
males to females actually increased from 2000 to 2003. This happened in Belgium, Denmark, 
Germany, Spain, France, Italy, Luxembourg, Malta,  Austria, Poland, Portugal, Sweden, and 
the United Kingdom. In Austria the gap between males and females increased by more than 4 
per  cent.  However,  in  the  Czech  Republic,  Hungary  and  Finland,  the  difference  in  reading 
scores between males and females actually decreased.  

Figure 4.23: Percentage difference in PISA reading literacy scores between males and females in 
2000 and 2003 

12

10

e
c
n
e
r
e
f
f
i
i
d
%

8

6

4

2

0

BE CZ DK DE EE EL ES FR IE IT CY LV LT LU HU MT NL AT PL PT SI

SK FI SE UK

2000

2003

Examining reading literacy scores  of students  by  the educational attainment  of  their  parents 
reveals  that  the  higher  the  educational  attainment  of  the  parents  achieved  the  higher  is  the 
child’s reading literacy score. This is true for both mothers and fathers.  

Key education indicators on social inclusion and efficiency 

171

 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Table 4.11 : Mean performance of students per country, on the PISA reading literacy scale by mothers level of education (2003) 

BE  CZ  DK  DE  EL  ES 

FR 

IE 

IT  LV  LU  HU  AT 

FI 

SK 

SE  UK  TR 

IS 

LI  NO 

JP  US 

None 
ISCED 1 
ISCED 2 
ISCED 3B 

ISCED 3A 

Yes 
No 

Yes 
No 

c 

430  429 

425  401  484  401  490  445  444  361 

403 
404 
a 
445 
c 
445  459  463 
c 
453  412  415  437  441  468  467  483  429 
498  459  468  487  452  502  480  505  454  461  507  430  452  523  401  495  493  426  485  511  486  464  440 
487  467  480  519  458  480  491 
540  517  510  545  491  498  532  528  507  492  519  509  547  551  488  530  544  501  504  569  516  504  504 

496  501  475  458  491 

459  468  434  462 

494  536  497  501 

440  425  434  410 

439  530  503 

430  521 

445 

c 
c 

a 

c 

c 

a 

a 

c 

c 

c 

Source: OECD PISA database 

Table 4.12: Mean performance of students per country, on the PISA reading literacy scale with mother with ISCED level 5A/6 (2003) 
LI  NO 
IT  LV  LU  HU  AT 
BE  CZ  DK  DE  EL  ES  FR 

SE  UK  TR 

SK 

IE 

FI 

IS 

JP  US 

540  540  504  547  513  532  503  548  518  508  512  536  553  558  515  527  556  560  515 
507  519  527 
502  488  494  494  474  490  465  511  469  486  475  469  489  539  461  510  499  434  488  525  500  494  485 

c 

Source: OECD PISA database 

Table 4.13 : Mean performance of students per country, on the PISA reading literacy scale with mother with ISCED level 5B (2003) 
LI  NO 
IT  LV  LU  HU  AT 
BE  CZ  DK  DE  EL  ES  FR 

SE  UK  TR 

SK 

IE 

FI 

IS 

JP  US 

537  471  517  522  485  501  471  528  478  491  506  495  507  551  452  528  526  433  507  498  521  512  500 
503  497  479  499  480  497  473  512  475  491  472  481  491  540  470  511  502  441  491  528  489  494  489 

Source: OECD PISA database 
Table 4.14: Mean performance of students per country, on the PISA reading literacy scale with mother with ISCED level 4 (2003) 

BE  CZ  DK  DE  EL  ES  FR 

Yes 
No 

504  513  492  528  475 
512  495  495  495  481 

a 
a 

IE 

SK 
IT  LV  LU  HU  AT 
477  532  469  488  468  484  488  550  463 
471  507  477  493  482  481  494  543  471 

FI 

SE  UK  TR 
c 
a 
c 
a 

503 
506 

IS 

LI  NO 
498 
484 
c 
495  523  502 

JP  US 
491 
497 

a 
a 

Key 
a - The category does not apply in the country concerned. Data therefore missing 
c - There are too few observations to provide reliable estimates  

Source: OECD PISA database 

Key education indicators on social inclusion and efficiency 

172

 
 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Table 4.15: Mean performance of students per country, on the PISA reading literacy scale by father’s level of education (2003) 

IE 

IT  LV  LU  HU  NL  AT  PL  PT  SK  FI 

SE  UK 

IS  LI  NO  TR 

JP  US 

ISCED  BE  CZ  DK  DE  EL  ES  FR 
None 

436 

c 

439  430  417  437  412  487  397  457  444  444  434 

c 

467  448  477  456  447  457  431 

c 

412  435 

a 

411 

1 

2 
3B 
3A 

472  423  457  451 
508  408 
465  408  436  434  443  466  480  486  432 
510  463  472  486  447  502  484  512  452  466  504  427  517  458  451  484  387  528  504  466  492  503  498  421  454  459 
484  478  493  516  469  472  495 
541  515  512  550  496  500  533  533  507  497  519  510  538  541  514  514  491  553  533  497  551  569  519  478  509  507 

520  498  504  528  498  392  504 

489  498  487  464 

488  478  500  443 

525  491 

472 

484 

440 

c 

c 

a 

a 

a 

a 

a 

c 

c 

c 

Source: OECD PISA database 

Table 4.16 : Mean performance of students per country, on the PISA reading literacy scale with father with ISCED level 5A/6 (2003) 

BE  CZ  DK  DE  EL  ES  FR 

IE 

IT  LV  LU  HU  NL  AT  PL  PT  SK  FI 

SE  UK 

IS  LI  NO  TR 

JP  US 

Yes 

No 

548  540  531  556  507  513  537  550  517  516  521  542  539  553  555  513  524  569  531  561  520  563  520  521  529  536 
498  486  488  489  463  473  489  510  469  486  472  470  505  486  492  473  458  536  510  497  487  518  496  430  481  483 

Source: OECD PISA database 

Table 4.17 :Mean performance of students per country, on the PISA reading literacy scale with father with ISCED level 5B (2003) 

BE  CZ  DK  DE  EL  ES  FR 

IE 

IT  LV  LU  HU  NL  AT  PL  PT  SK  FI 

SE  UK 

IS  LI  NO  TR 

JP  US 

Yes 

No 

526  512  519  514  466  491  495  530  479  488  503  484 
508  496  487  499  473  479  498  512  475  492  474  482 

a  
a  

487  542  443  454  552  521  523  489  540  521  449  482  499 
495  492  481  470  540  513  504  493  520  493  440  501  495 

Source: OECD PISA database 
Table 4.18 :Mean performance of students per country, on the PISA reading literacy scale with father with ISCED level 4 (2003) 

BE  CZ  DK  DE  EL  ES  FR 

IE 

IT  LV  LU  HU  NL  AT  PL  PT  SK  FI  SE  UK 

IS  LI  NO  TR 

JP  US 

Yes 

No 

511  496  486  516  486  478 

511  496  495  498  470  481 

a 

a 

528  470  490  484  474  517  446  520 

511  477  491  479  484  518  494  494 

Source: OECD PISA database 

Key 
a - The category does not apply in the country concerned. Data therefore missing 
c - There are too few observations to provide reliable estimates  

a 

a 

444  546 

474  543 

a 

a 

497  487 

c 

496 

508  496  525  502 

c 

c 

a 

a 

496 

496 

Key education indicators on social inclusion and efficiency 

173

 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Tables  4.19  and  4.20,  show  the  mean  performance  of  15  year  olds  on  the  PISA  reading 
literacy scale by the labour force status of the parents. The results of the survey showed that 
not  only  that  the  reading  literacy  of  students  varies  by  labour  force  status,  but  also  the 
difference in reading literacy score is marked by the gender of parent. 

The reading literacy of 15 year olds by labour force status of the mother, reveals that for 57 
per cent of EU countries that participated in the 2003 PISA survey, the reading literacy of 15 
year  olds  is  highest  for  mothers  who  are  working  full-time.  However,  in  a  number  of 
countries the reading literacy scores are highest for students whose mothers are working part-
time. These countries include: Belgium, Czech Republic, Denmark, Germany, Luxembourg, 
Netherlands, Austria and the United Kingdom. In Ireland reading literacy scores are the same 
for mothers that work full-time or part-time. For most countries, the reading literacy is lowest 
for those whose mothers are looking for work. However, in Portugal, Finland and the United 
Kingdom, the scores are lowest for those working part-time.  

In contrast the results of the 2003 PISA survey show that for all countries the reading literacy 
of  15  year  olds  is  highest  for  students  whose  fathers  are  working  full-time.  For  most 
countries,  the  reading  literacy  is  lowest  for  students  whose  fathers  are  looking  for  work. 
However, in the Czech Republic, Germany, and Austria, the reading literacy score is lowest 
for  students  whose  fathers  are  working  part-time.  In  Italy,  Poland,  and  Portugal  the  lowest 
reading  literacy  scores  are  for  students  whose  fathers  work  part-time  and  those  who  are 
looking for work. 

It  is  interesting  to  note  that  for  most  EU  countries,  the  reading  literacy  scores  of  students 
whose  mothers  are  working  full-time  are  higher  than  those  of  students  whose  fathers  are 
working  full-time.  However,  in  Germany,  Luxembourg  and  the  Netherlands  the  opposite  is 
the  case.  In  Denmark,  Latvia,  and  Sweden  the  reading  literacy  scores  are  the  same  for 
students whose mothers and fathers are working full-time.  

For  most  EU  countries,  the  reading  literacy  scores  of  students  whose  mothers  are  working 
part-time  are  higher  than  those  of  students  whose  fathers  are  working  part-time.  The 
exceptions to this is the Slovak Republic where the reading literacy scores are the same, and 
Finland where the reading literacy scores of students whose fathers work part-time are higher 
than those whose mothers work part-time.  

It  is  also  notable  that  for  most  EU  countries,  the  reading  literacy  score  of  students  whose 
mothers are looking for work is higher than those of students whose fathers are looking for 
work.  However,  in  the  Czech  Republic,  Latvia,  Hungary,  Netherlands,  Poland  and  Sweden, 
reading  literacy  scores  of  students  whose  fathers  are  looking  for  work  is  higher  than  those 
whose  mothers  are  looking  for  work.  The  exception  to  this  is  Denmark,  where  the  reading 
literacy scores are the same. 

Key education indicators on social inclusion and efficiency 

174

 
 
 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Table 4.19: Mean performance of students per country, on the PISA reading literacy scale by labour force status of mother (2003) 

  BE  CZ  DK  DE  EL  ES  FR 
JP  US 
1  525  499  497  503  486  490  507  525  496  499  469  497  508  501  515  490  490  480  550  521  511  494  511  510  495  497  500 
2  536  503  502  514  451  472  503  525  476  484  493  474  529  509  489  448  472  451  519  517  520  501  537  491  428  500  512 
3  443  462  471  488  450  463  460  480  454  452  453  442  486  471  460  453  463  423  523  472  465  455 
475  391  469  446 
4  487  494  478  487  469  479  481  504  460  489  488  452  518  473  491  466  479  460  532  503  491  486  524  489  442  506  495 

IT  LV  LU  HU  NL  AT  PL  PT 

IS  LI  NO  TR 

SE  UK 

SK  FI 

IE 

SI 

c 

Table 4.20: Mean performance of students per country, on the PISA reading literacy scale by labour force status of father (2003) 

  BE  CZ  DK  DE  EL  ES  FR 
JP  US 
1  524  500  497  512  477  484  504  522  482  499  487  490  521  499  509  484  484  479  547  521  514  494  529  507  454  505  505 
2  485  466  476  469  438  466  470  486  444  477  439  463  515  453  474  447  466  451  525  493  493  470 
477  425  439  450 
3  435  474  471  471  434  451  460  465  444  473  427  451  494  458  474  447  451  412  522  479  464  479 
484  405  476  457 
4  471  485  476  475  466  487  479  496  476  499  462  469  505  495  483  468  487  454  531  499  486  494 
467  441  500  485 

IT  LV  LU  HU  NL  AT  PL  PT 

IS  LI  NO  TR 

SE  UK 

SK  FI 

c 
c 
c 

IE 

SI 

Source: OECD PISA database 

Key 

1 
2 
3 
4 

Working Full-time   
Working Part-time                                                
Looking for work                                                  
Other                                                               

a - The category does not apply in the country concerned. Data therefore missing 
c - There are too few observations to provide reliable estimates  

Key education indicators on social inclusion and efficiency 

175

 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Table  4.21,  shows  students  performance  on  the  PISA  reading  literacy  scale  by  national 
quarters  of  the  International  Socio-Economic  Index  of  occupational  status  (ISEI).  Typical 
occupations  among  parents  of  15  year  olds  with  between  16  and  35  points  on  the  index 
include  small-scale  farming,  metalworking,  motor  mechanics,  taxi  and  lorry  driving  and 
waiting.  Between  35  and  53  points,  the  most  common  occupations  are  bookkeeping,  sales, 
small business management and nursing. Between 54 and 70 index points, typical occupations 
are marketing management, teaching, civil engineering and accountancy. Finally between 71 
and 90 points, occupations include medicine, university teaching, and law. 

Higher  parental  occupational  status  can  influence  student’s  occupational  aspirations  and 
expectations  and  in  turn  their  commitment  to  learning.  Identifying  the  characteristics  of  the 
students most likely to perform poorly can help educators and policy-makers locate areas for 
policy  intervention.  If  it  can  be  shown  that  some  countries  find  it  easier  than  others  to 
accommodate  different  background  factors,  important  policy  insights  can  be  generated  and 
used in other countries. 

The results clearly demonstrate that for all countries 15 year olds whose parents have higher 
status  jobs  have  higher  reading  literacy  performance.  However,  the  advantage  in  reading 
literacy  performance  between  the  highest  and  the  lowest  quartiles  is  greater  for  some 
countries,  that  us  more  than  100  points:  Belgium,  Germany,  and  Luxembourg.    The  Czech 
Republic, Hungary, Portugal, United Kingdom and the United States also have differences of 
90  points  and  above  for  students  in  the  highest  and  lowest  quartiles  of  the  socio-economic 
index 

Whilst  these  results  does  lead  one  to  conclude  that  students  from  a  lower  socio-economic 
background are more at risk of being disadvantaged.  It cannot be concluded that differences 
in  reading  literacy  proficiency  are  as  a  direct  result  of  the  higher  expectations  of  parents 
employed  in  higher  occupations.  There  are  many  other  factors  that  affect  students’ 
performance such as the quality of education, geographical location, etc… 

Key education indicators on social inclusion and efficiency 

176

 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Table 4.21: International socio-economic index of occupational status (ISEI) and performance on 
the PISA reading literacy scale, by national quarters of the index, based on students' self-reports 
(2000) 

Country 

International socio-economic index of 
occupational status 

Bottom 
quarter 
28.4 
31.2 
29.0 
30.0 
25.6 
26.8 
27.7 
28.5 
28.5 
27.7 
25.1 
30.4 
32.9 
27.3 
26.8 
29.7 
30.4 
30.7 
31.4 
28.0 
35.6 
30.3 

Second 
quarter 
42.1 
44.4 
44.0 
42.6 
40.2 
36.2 
41.1 
42.7 
40.6 
40.4 
37.5 
42.6 
44.7 
40.0 
34.5 
43.4 
44.1 
45.7 
47.3 
41.8 
47.1 
47.4 

Third 
quarter 
53.5 
51.5 
54.9 
52.5 
53.0 
49.6 
53.1 
53.2 
50.3 
58.5 
50.6 
53.7 
52.2 
49.8 
48.4 
55.1 
55.7 
56.9 
58.6 
52.1 
59.0 
59.5 

Top    
quarter 
71.8 
66.1 
71.1 
70.2 
72.3 
67.3 
71.2 
69.4 
68.9 
74.1 
66.1 
71.5 
69.1 
67.0 
65.7 
71.8 
72.1 
71.8 
73.8 
68.2 
73.9 
72.5 

BE 
CZ 
DK 
DE 
EL 
ES 
FR 
IE 
IT 
LV 
LU 
HU 
AT 
PL 
PT 
FI 
SE 
UK 
IS 
LI 
NO 
US 

socio-economic 

Performance  on  the  PISA  reading  literacy 
the 
scale,  by  national  quarters  of 
international 
index  of 
occupational status 
Second 
Bottom 
quarter 
quarter 
497 
457 
487 
445 
490 
465 
471 
427 
460 
440 
482 
461 
496 
469 
520 
491 
481 
457 
449 
428 
428 
394 
461 
435 
500 
467 
472 
445 
452 
431 
535 
524 
509 
485 
513 
481 
496 
487 
491 
437 
494 
477 
507 
466 

Third 
quarter 
537 
499 
511 
513 
486 
507 
520 
535 
494 
479 
473 
504 
522 
493 
485 
555 
522 
543 
513 
495 
514 
528 

Top    
quarter 
560 
543 
543 
541 
519 
529 
552 
570 
525 
492 
497 
531 
547 
534 
527 
576 
558 
579 
540 
523 
547 
556 

Source: ‘Education at a Glance 2002’, OECD 

The  PISA  survey  also  explored  students’  expected  occupations  at  the  age  of  30  in  order  to 
gain an insight into their future aspirations and expectations. These expectations are likely to 
affect  their  academic  performance,  as  well  as  the  academic  paths  followed.  The  2000  PISA 
survey  suggested  that  the  students’  expected  occupations  are  associated  with  their  parents’ 
professions. However, these correlations are only weak to moderate. PISA classified students’ 
expectations into four socio-economic categories: white-collar high skilled, white-collar low 
skilled, blue-collar high skilled, and blue-collar low skilled. 

Figure  4.24,  shows  that  for  most  countries;  15  year  olds  expect  to  have  a  white-collar  high 
skilled occupation. However, in the Czech Republic, Germany, France and Liechtenstein, less 
than one halve of students expect to have a white-collar high skilled occupation. 

Key education indicators on social inclusion and efficiency 

177

 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Figure 4.24: Percentage of 15-year-olds expecting to have a white- or blue-collar occupation 
(2000) 

100

80

%

60

40

20

0

BE CZ DK DE EL ES FR IE IT LV LU HU NL AT PL PT FI

SE UK IS

LI

JP US

1 4.9 17.3 4.3 13.2 6.6 13.1 26.5 12.1 9.9 5.5 17.4 11.7 17.1 15.8 1.7

9

11.5 18.5 19 20.3 32.4 37.4 6.2

2 15.4 16.2 19.6 17.2 9.4 8.2 9.9 11.7 5.8 13.4 8.7 16.6 12.9 11.7 14.2 5.1 12.2 8.1 7.6 7.9 14.2

4

5.1

3 14.2 22 17.5 20.9 11.7 12.2 14.7 12.2 15.2 18 14.3 19 12.7 17.2 15.4 9.5 15.8 10.3 16.3 12.6 17.1 12.9 8.2

4 65.6 44.5 58.5 48.8 72.3 66.6 48.9 64.1 69.1 63.1 59.6 52.7 57.4 55.3 68.8 76.5 60.4 63.2 57.1 59.2 36.3 45.8 80.5

Notes 
1  Blue-collar low-skilled 
3  White-collar low-skilled 

2  Blue-collar high-skilled 
4  White-collar high-skilled 

Figure  4.25,  illustrates  differences  in  the  mean  reading  performance  of  students  by 
immigration  status.  It  clearly  shows  that  for  almost  all  countries  native  students  have  the 
highest  reading  performance  followed  by  second-generation  students.  First-generation 
students  have  the  lowest  reading  literacy  scores.  However,  in  Denmark  and  Germany,  first-
generation students  have  higher reading literacy scores than  second-generation students, but 
lower than native students. 

Figure 4.25: Differences in reading performance by immigration status 

e
r
o
c
s
n
a
e
M

600

500

400

300

200

100

0

BE DK DE

FR

LU NL AT

SE NO US

Native 

Second-generation

First-generation

Source: ‘Where immigrant students succeed -  A comparative review of performance and engagement 
in PISA 2003’, OECD  

This indicates that first-generation students are at greater risk of social exclusion followed by 
second-generation students. This is reinforced when we examine the percentage of pupils with 

Key education indicators on social inclusion and efficiency 

178

 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

reading  literacy  level  1  and  lower  in  the  PISA  reading  literacy  scale  by  immigration  status 
(see figure 4.26). For all countries the percentage of pupils with reading literacy level 1 and 
lower in the PISA reading literacy scale is highest for first-generation students, followed by 
second-generation students. The percentage of native students with low reading literacy is the 
lowest of the three groups. 

Figure 4.26: Percentage of pupils with reading literacy level 1 and lower in the PISA reading 
literacy scale by immigration status 

60

50

40

30

20

10

0

1
l
e
v
e
l
y
c
a
r
e
t
i
l
g
n
i
d
a
e
r
h
t
i

w
s
l
i
p
u
p
f
o

%

r
e
w
o
l
d
n
a

BE DK

DE

FR

LU

NL

AT

SE

NO

US

Native 

Second-generation

First-generation

Source: ‘Where immigrant students succeed -  A comparative review of performance and engagement 
in PISA 2003’, OECD  

Examining the reading literacy scores of first and second-generation students if the language 
spoken at home is different from the test language reveals that the reading literacy scores are 
low and that second-generation students score higher than the second-generation (see figure 
4.27).  The  exceptions  to  this  are  Germany  and  the  Netherlands  where  first  and  second 
generation  students  score  the  same,  and  in  Austria  where  second-generation  students 
outperform the first generation. 

Figure 4.27: Performance on the reading scale by immigration status if language spoken at home 
is different from test language (2003) 

e
r
o
c
s
n
a
e
M

600

500

400

300

200

100

0

BE

DK

DE

FR

LU

NL

AT

SE

NO

Second-generation

First-generation

Source: ‘Where immigrant students succeed -  A comparative review of performance and 
engagement in PISA 2003’, OECD 

Key education indicators on social inclusion and efficiency 

179

 
 
 
 
 
 
 
 
 
 
 
Social inclusion / exclusion and the benchmark indicators 

Examining the reading literacy scores of all three groups of students if the language spoken at 
home is the same as the test language reveals that for almost all countries that native students 
score  the  highest.  The  exception  to  this  is  the  US,  where  native  students  score  the  same  as 
second-generation students.   

Figure 4.28: Performance on the reading scale by immigration status if language spoken at home 
is the same as test language (2003) 

e
r
o
c
s
n
a
e
M

600

500

400

300

200

100

0

BE DK DE

FR

LU

NL AT

SE NO US

Native 

Second-generation

First-generation

Source: ‘Where immigrant students succeed -  A comparative review of performance and engagement 
in PISA 2003’, OECD 

44..66    CCoonncclluussiioonnss  

The  study  identified  a  number  of  variables,  which  can  be  used  to  examine  social 
exclusion/inclusion in relation to the benchmark related indicators. It has to be mentioned that 
despite  the  fact  that  two  of  the  benchmark  indicators  (early  school  leavers  and  low  reading 
literacy) are defined as primary indicators in measuring social exclusion, they can be broken 
down  further  by  the  variables  listed  so  as  to  provide  a  more  detailed  analysis  of  social 
exclusion.  At  the  present  time,  all  the  benchmark  indicators  are  broken  down  by  gender. 
Breaking  down  the  benchmark  indicators  by  age  is  not  relevant  to  the  indicators  on 
educational attainment, early school leavers, and low reading literacy, given that in the latter 
case  the  age  group  that  the  indicator  refers  to  is  15.  In  the  case  of  the  indicators  on 
educational  attainment  and  early  school  leavers,  which  already  refer  to  narrow  age  groups, 
breaking down the indicators further will not bring any added value. 

Three  of  the  five-benchmark  indicators  (educational  attainment,  early  school  leavers  and 
participation  in  lifelong  learning)  are  constructed  from  one  source,  the  EU  Labour  Force 
Survey. The EU Labour Force Survey has the potential to breakdown the indicators by most 
of  the  variables.  However,  there  is  a  caveat  in  relation  to  the  indicators  on  educational 
attainment  and  early  school  leavers.  Breaking  down  the  indicators  by  parents’  income, 

Key education indicators on social inclusion and efficiency 

180

 
 
 
  
 
  
 
 
Social inclusion / exclusion and the benchmark indicators 

occupation,  branch  of  industry  and  the  labour  force  would  mean  that  the  results  would  be 
biased. The bias results from the fact that both these indicators relate to specific age groups, in 
the case of early school leavers it is 18 to 24, whilst for educational attainment it is 20 to 24. 
Establishing  the  income,  occupation,  branch  of  industry  or  labour  force  status  of  parents  is 
possible on the very strong assumption that the person aged 18 to 24 is still living with their 
parents. In some countries the number of young adults living with there parents is low, this is 
especially true in the North European countries. 

Unfortunately the indicator on the number of Maths, Science and Technology graduates can 
only  be  broken  down  by  gender.  This  is  because  the  source  that  is  used  to  construct  this 
indicator the UNESCO/OECD/Eurostat data collection does not collect detailed information. 
It has been shown that the Eurostudent survey can be used to provide breakdowns of students 
in higher education by a number of variables of interest in order to examine social exclusion / 
inclusion.  However,  the  use  of  this  survey  is  limited  in  relation  to  the  benchmark  indicator 
given that it does not distinguish between the fields of study in which a student studies. 

It  has  been  demonstrated  with  data  from  the  LFS  ad  hoc  module  on  lifelong  learning  that 
breaking down the indicator on participation in lifelong learning by a number of variables is 
possible  for  a  number  of  variables  including:  age,  educational  attainment,  occupation, 
industry  of  employment,  labour  force  status  and  geographical  location.  This  should  now  be 
taken a step further by breaking down the indicator by the variables identified. 

The  OECD  PISA  database  is  used  to  construct  the  benchmark  indicator  on  low  reading 
literacy.  The  database  also  collects  background  information  on  the  social  and  economic 
background  of  students,  which  could  be  used  to  examine  social  exclusion.  This  includes: 
educational  attainment  of  parents,  occupation  of  parents,  labour  force  status  of  parents,  and 
minority groups (this refers to whether the student and/or are immigrants) 

Key education indicators on social inclusion and efficiency 

181

 
 
 
 
IV 

List of Figures & Tables 

  
 
 
 
 
 
 
 
 
  
  
  
List of figures and tables 

LLiisstt  ooff  FFiigguurreess  &&  TTaabblleess  

LLiisstt  ooff  ffiigguurreess  

Chapter 1 

Figure 
1.1 

1.2 

1.3 
1.4 

1.5 

Chapter 2  

Percentage of those aged 20 to 24 who have successfully completed at least 
upper secondary education (ISCED 3) 
Population (aged 20 to 24) that has attained at least upper secondary education 
in 2002, total and broken down by gender in Japan 
Trends in number of upper secondary school dropouts in Japan 
Share of the population aged 18 – 24 with only lower secondary education and 
not in education and training based on the status completion rates and on the 
EU definition of early school leavers in the US 
Percentage of the population aged 25-64 participating in education broken 
down by type of education and sex (US) 

20 

21 

23 
27 

35 

2.1 

Types of education and training included when different sources are combined 

79 

Chapter 3 

3.1 
3.2 

3.3 

3.4 

3.5 

3.6 

Ordinary least squares regression analysis 
Public  expenditure  on  primary  and  secondary  education  (ISCED  1  –  3)  as  a 
percentage of GDP versus percentage of 20 to 24 year olds who have at least 
upper secondary education (ISCED 3) (2003) 
Public  expenditure  on  primary  and  secondary  education  as  a  percentage  of 
GDP versus percentage of early school leavers 18 to 24 year olds (2003) 
Public expenditure on higher education (ISCED 5/6) as a percentage  of GDP 
versus  percentage  MST  graduates  aged  20  –  29  per  1000  of  the  population 
(2003) 
Public expenditure on primary and lower secondary education (ISCED 1 & 2) 
as a percentage of GDP versus percentage of pupils with reading literacy level 
1 and lower on the PISA reading literacy scale (2003) 
Analysis of the flow of pupils through the education system 

99 
110 

111 

112 

114 

126 

Chapter 4 

4.1 

Low  educational  attainment  of  individuals  aged  25-  64  and  early  school 

136 

Key education indicators on social inclusion and efficiency 

183

 
 
 
  
  
 
 
 
 
 
 
 
 
 
 
List of figures and tables 

4.2 

4.3 

4.4 

4.5 

4.6 

4.7 

4.8 
4.9 

4.10 

4.11 

4.12 

4.13 

4.14 

4.15 

4.16 

4.17 

4.18 

4.19 

4.20 
4.21 
4.22 
4.23 

4.24 

4.25 
4.26 

4.27 

4.28 

leavers aged 18-24 in percentages (2005) 
Percentage  of  young  entrants  to  full-time  first  degree  courses  from  low 
participation neighbourhoods by subject and entry qualification 2004/05 
Percentage of the female and male population aged 20 to 24 having completed 
at least upper secondary education in the EU-25 
Percentage of the female and male population aged 20 to 24 having completed 
at least upper secondary education in 2005 
Percentage  of  the  population  aged  18-24  with  at  most  lower  secondary 
education and not in further education or training in the EU-25 
Percentage  of  the  population  aged  18-24  with  at  most  lower  secondary 
education and not in further education or training (2005) 
Graduates  (ISCED  5-6)  in  mathematics,  science  and  technology  per  1000  of 
population aged 20-29 in the EU-25 
Women per 100 men graduating from ISCED levels 5-6 
Graduates  (ISCED  5-6)  in  mathematics,  science  and  technology  per  1000  of 
population aged 20-29 in 2004 
Students’ fathers compared to all men of corresponding age groups* who are 
economically active (2005) 
Students’ mothers compared to all men of corresponding age groups* who are 
economically active (2005) 
Students’  fathers  compared  to  all  men  of  corresponding  age  groups  with 
working class status (blue collar) in percentages (2005) 
Students’  mothers  compared  to  all  men  of  corresponding  age  groups  with 
working class status (blue collar) in percentages (2005) 
Students’ fathers compared to all men of corresponding age groups with higher 
education in percentages (2005) 
Students’  mothers  compared  to  all  women  of  corresponding  age  groups  with 
higher education in percentages (2005) 
Income  cut-off  point  between  the  upper  and  lower  half  of  students’  parent 
income distribution, median in € (2005) 
Percentage of students’ parents with an income below the income cut-off point 
for the lowest income quartile of all private households (2005) 
Percentage of the adult population aged 25 to 64 participating in education and 
training in the EU-25 
Percentage of the adult population aged 25 to 64 participating in education and 
training (2005) 
Participation in any learning activities by type of industry (2003) 
Participation in any learning activities by working status (2003) 
Participation in any learning activities by type of location (2003) 
Percentage  difference  in  PISA  reading  literacy  scores  between  males  and 
females in 2000 and 2003 
Percentage  of  15-year-olds  expecting  to  have  a  white-  or  blue-collar 
occupation (2000) 
Differences in reading performance by immigration status 
Percentage  of  pupils  with  reading  literacy  level  1  and  lower  in  the  PISA 
reading literacy scale by immigration status 
Performance on the reading scale by immigration status if language spoken at 
home is different from test language (2003) 
Performance on the reading scale by immigration status if language spoken at 
home is the same as test language (2003) 

Key education indicators on social inclusion and efficiency 

141 

153 

154 

155 

155 

157 

157 

159 

160 

160 

161 

161 

162 

162 

163 

163 

164 

164 

168 
169 
169 

171 

178 

178 

179 

179 

180 

184

 
List of figures and tables 

LLiisstt  ooff  ttaabblleess  

Chapter 1 

1.1 
1.2 

1.3 
1.4 
1.5 
1.6 
1.7 

1.8 

1.9 
1.10 
1.11 
1.12 
1.13 
1.14 
1.15 
1.16 
1.17 

1.18 

1.19 

1.20 

1.21 

1.22 

1.23 

1.24 

1.25 

1.26 

List of indicators and countries with data gaps 
Reconciling information required for the indicators with data currently 
available 
Enrolment in higher education 
Enrolment in science and engineering 
Graduates in maths, science and technology 
Total graduates in all programmes 
Graduates  in  mathematics,  science,  and  technology  from  regular  higher 
education institutions 
Share  of  graduates  in  science  and  engineering  as  a  percentage  of  all  MST 
graduates from undergraduate  courses in regular  higher education institutions 
(in percentages)   
Number of students enrolled in higher education in India 
Number of students enrolled in science and engineering in India 
Number of students admitted to engineering in India 
Number of engineering graduates (holders of degree and diploma) in India 
Number of engineering graduates at degree level in India 
Estimated stock of science graduates and postgraduates in India 
Estimated stock of engineers by type of qualification in India 
Number of MST graduates in India 
Overview of data collected on youth educational attainment for Japan and the 
USA 
Population  that  has attained at  least upper  secondary  education in  2003, total 
and broken down by gender in percentages in Japan and the USA 
Population (aged 20-24) that has attained at least upper secondary education in 
2000, total and broken down by gender in Japan 
Overview  of  data  collected  on  low  educational  attainment  for  Japan  and  the 
USA for the age group 18 to 24 
Share of the population aged 20 – 24 with only lower secondary education and 
not in education and training (2000) in Japan 
Number  of  18  to  24  year  olds,  who  received  a  GED,  by  data  source:  1990 
through 2002 
Percentage of 18 to 24 year olds who received a GED qualification according 
to the CPS, and the GED service 
Status completion rates, and number and distribution of completers ages 18-24 
not currently enrolled in high school or below in the USA 
Share of the population aged 18 – 24 with only lower secondary education and 
not  in  education  and  training  according  to  the  general  definition  and  the 
restricted definition 
Share  of  the  male  population  aged  18  –  24  with  only  lower  secondary 
education and not in education and training according to the general definition 

10 

12 

13 
13 
13 
14 

15 

16 

17 
16 
17 
17 
18 
18 
19 
19 

20 

20 

21 

22 

23 

25 

25 

26 

30 

31 

Key education indicators on social inclusion and efficiency 

185

 
 
 
 
 
List of figures and tables 

1.27 

1.28 

1.29 
1.30 

1.31 

1.32 

1.33 

1.34 

1.35 

1.36 

and the restricted definition 
Share  of  the  female  population  aged  18  –  24  with  only  lower  secondary 
education and not in education and training according to the general definition 
and the restricted definition 
Percentage  of  population  aged  25  –64  participating  in  education  and  training 
(2001) in Japan 
Percentage of population aged 25 – 64 attending school in Japan (2000) 
Percentage  of  population  aged  25-64  participating  in  adult  education  in 
USA(2001) 
Percentage  of  population  aged  25-64  participating  in  any  form  of  education 
and training in USA (2001) 
Number of adults and rates of participation in any type of adult education by 
age groups  in USA (2001) 
Number of adults and rates of participation in any type of adult education by 
highest level of education received in USA (2001) 
Summary  of  main  aspects  of  surveys  in  the  EU,  Japan  and  US  collecting 
information concerning participation of adults in education and training 
Percentage of population aged 25-64 participating in education and training 12 
months prior to the survey 
Participation  in  non-formal  job-related  continuing  education  and  training  for 
the labour force, by age and gender (2003) 

Chapter 2 

2.1 
2.2 
2.3 
2.4 

2.5 
2.6 

2.7 

2.8 

2.9 
2.10 

2.11 

2.12 
2.13 
2.14 
2.15 

2.16 

Summary of data required to calculate total expenditure on education 
Periodicity and availability of data from international data collections 
Geographical coverage of international data collections 
Summary of data collected on public and private expenditure on education by 
international data collections 
Coverage of education expenditure by international data collections 
Availability of required data in the UOE data collection for the year 2003 by 
variable (all levels of education) 
Availability  of  data  on  student  loans  in  the  UOE  data  collection  by  year  (all 
levels of education) 
Availability of data on public subsidies to other private entities and enterprise 
expenditure  on  training  of  apprentices  and  students  in  2003  by  variable  (all 
levels of education) 
Breakdown of variables and the corresponding sources of data 
Availability  of  data  in  the  UOE  data  collection  on  ancillary  services  by 
variable (2003) 
Public  and  private  expenditures  on  ancillary  services  in  higher  education 
(2003) 
List of educational goods and services for the UOE data collection 
Costs of training by enterprises included in the CVTS2 and the LCS 
Coverage of economic activities in the CVTS2 and the LCS 
Overview of the coverage of education and training by source of funding and 
data source 
Coverage of non-EU countries in EU data sources collecting information on 
continuing vocational training 

32 

33 

34 

34 

35 

36 

36 

38 

39 

40 

55 
56 
57 

58 

58 

59 

61 

63 

68 

71 

72 

74 
75 
76 

78 

80 

Key education indicators on social inclusion and efficiency 

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List of figures and tables 

Chapter 3 

3.1 
3.2 
3.3 

3.4 

3.5 

3.6 
3.7 
3.8 
3.9 
3.10 
3.11 

3.12 
Chapter 4  
4.1 

4.2 
4.3 

4.4 

4.5 

4.6 

4.7 
4.8 
4.9 
4.10 

4.11 

4.12 

4.13 

4.14 

4.15 

4.16 

4.17 

Overview on analysing efficiency and effectiveness 
Benchmark indicators broken down by type of indicator 
Sources  used  to  estimate  total  spending  on  education  and  percentage  of 
Member States where spending was estimated by year 
Availability of  data for  total  spending  on education for  method  of estimation 
by year for the EU 
Availability of data on total spending on educational institutions by year from 
the UOE 
Data sources used to provide the data for the output indicators 
Periodicity of data collection 
EU Member States not participating in the PISA survey by year 
Availability of data for each output indicator by year for the EU 
Availability of data for each output indicator by year for non-EU countries 
Availability 
UNESCO/OECD/Eurostat data collection by year and by ISCED level 
Survival rates in tertiary education (2004) 

enrolments 

repeaters 

data 

and 

the 

on 

of 

in 

Share of disabled students in higher education by type of disability (in %) in 
Poland 
Data sources used to construct the benchmark indicators 
Availability of data needed to measure social inclusion in international data 
sources 
EU Member States participating in the OECD Programme for International 
Student Assessment (PISA) 
Share of early school leavers (aged 15- 35) by parents highest educational 
attainments in %,  (2000) 
Female graduates by field of education as a percentage of males and female 
graduates 
Participation in any learning activities by age group (2003) 
Participation in any learning activities by educational attainment (2003) 
Participation in any learning activities by occupation (2003) 
Mean performance of students per country, on the PISA reading literacy scale 
by gender in 2000 and 2003 
Mean performance of students per country, on the PISA reading literacy scale 
by mothers level of education (2003) 
Mean performance of students per country, on the PISA reading literacy scale 
with mother with ISCED level 5A/6 (2003) 
Mean performance of students per country, on the PISA reading literacy scale 
with mother with ISCED level 5B (2003) 
Mean performance of students per country, on the PISA reading literacy scale 
with mother with ISCED level 4 (2003) 
Mean performance of students per country, on the PISA reading literacy 
scale by father’s level of education (2003) 
Mean performance of students per country, on the PISA reading literacy scale 
with father with ISCED level 5A/6 (2003) 
Mean performance of students per country, on the PISA reading literacy scale 
with father with ISCED level 5B (2003) 

Key education indicators on social inclusion and efficiency 

97 
108 
118 

119 

120 

121 
122 
122 
123 
124 
129 

131 

142 

143 

144 

148 

156 

158 

165 
166 
167 

170 

172 

172 

172 

172 

173 

173 

173 

187

 
 
List of figures and tables 

4.18 

4.19 

4.20 

4.21 

4.22 

4.23 

4.24 

Mean performance of students per country, on the PISA reading literacy scale 
with father with ISCED level 4 (2003) 
Mean performance of students per country, on the PISA reading literacy scale 
by labour force status of mother (2003) 
Mean performance of students per country, on the PISA reading literacy scale 
by labour force status of father (2003) 
International socio-economic index of occupational status (ISEI) and 
performance on the PISA reading literacy scale, by national quarters of the 
index, based on students' self-reports (2000) 
Mean performance of students per country, on the PISA reading literacy scale 
by labour force status of father (2003) 
International  socio-economic 
index  of  occupational  status  (ISEI)  and 
performance  on  the  PISA  reading  literacy  scale,  by  national  quarters  of  the 
index 
Percentage  of  15-year-olds  expecting  to  have  a  white-  or  blue-collar 
occupation (2000) 

173 

175 

175 

177 

137 

139 

140 

Key education indicators on social inclusion and efficiency 

188