Key education indicators on social inclusion and ... · PDF file2.3.2 Eurostat Harmonised...

European Research Associates

Key education indicators on social inclusion and efficiency

Final Project report

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Executive summary iv Abbreviations and acronyms 2 Introduction 5

• Background 5 • The study 7

Chapter 1: Completing statistical gaps for the benchmark indicators

1.1 Introduction. 10 1.2 Overview of findings 11 1.3 Completing gaps for the benchmark indicators on MST graduates 13

1.3.1 International sources 13 1.3.2 National sources 14

1.4 Completing gaps for the benchmark indicators for Japan and the US 19 1.4.1 Percentage of those aged 20-24 who have successfully completed at

least upper secondary education (ISCED 3) 19

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

22

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

33

1.5 Comparability of data collected with the European Statistical System (ESS) 41 1.6 Conclusion 41

Appendix 44 Chapter 2: Combining data on public and private spending on education 47

2.1 Introduction 52 2.2 Data needed for calculating total spending on education 53 2.3 Data currently collected from international sources 56

2.3.1 UNESCO/OECD/Eurostat collection on education systems 59 2.3.2 Eurostat Harmonised Household Budget Survey 63 2.3.3 Continuing Vocational Training Survey (CVTS) 64 2.3.4 Labour Cost Survey (LCS) 66

2.4 Data currently collected from national sources 66 2.5 Reconciling data needed with data collected 67 2.6 Methodological aspects of combining data 68

2.6.1 Expenditure on goods and services within educational institutions 68 2.6.2 Households expenditure on goods and services outside educational

institutions 69

2.6.3 Expenditure of other private entities 69

Key education indicators on social inclusion and efficiency ii

2.6.4 Expenditure on ancillary services 70 2.6.5 Expenditure on research and development (R&D) 72 2.6.6 Harmonising households’ expenditure on educational goods and

services 72

2.6.7 Estimating expenditure of enterprises on vocational training 75 2.6.8 Double counting of expenditure 76 2.6.9 Coverage of education and training 77 2.6.10 Collection of private expenditures for non-EU countries 80

2.7 Conclusions 80 Appendix: Estimation of total private and public spending on education 83 Chapter 3: Efficiency and effectiveness of education systems

3.1 Introduction 96 3.2 Measuring efficiency and effectiveness 98

3.2.1 Parametric or regression based estimators 99 3.2.2 Non-parametric techniques 101 3.2.3 Cost benefit analysis 103 3.2.4 Cost-effectiveness analysis 103 3.2.5 Rates of return to education 104

3.3 Efficiency, effectiveness and equity indicators 106 3.4 Calculating efficiency in relation to the benchmark indicators 107

3.4.1 Defining the inputs and outputs 108 3.4.2 Relationship between inputs and outputs 109 3.4.3 Data needed to calculate efficiency 115 3.4.5 Data collected by international sources 117

3.5 Measuring internal efficiency without expenditure 125 3.5.1 Indicators of internal efficiency in education 126 3.5.2 Methods of measuring internal efficiency in education 127

3.6 Survival rates in tertiary education 130 3.7 Conclusions 132

Chapter 4: Social inclusion /exclusion and the benchmark indicators

4.1 Introduction 134 4.2 Data needs for examining social inclusion 136 4.3 Data currently collected from international sources 143

4.3.1 EU Labour Force Survey (LFS) 145 4.3.2 UNESCO/OECD/Eurostat collection on education systems 147 4.3.3 The PISA International database 147

4.4 Data collected from other international sources 151 4.4.1 European Statistical System (ESS) 151

4.5 Social inclusion/exclusion in relation to benchmark indicators 153 4.5.1 Educational attainment of 20 to 24 year olds 153 4.5.2 Early school leavers 154 4.5.3 MST graduates 156 4.5.4 Participation in lifelong learning for 25 to 64 year olds 163 4.5.5 Reading literacy of 15 year olds 170

4.6 Conclusion 180

Key education indicators on social inclusion and efficiency iii

List of figures and tables

• List of figures 183 • List of tables 185

Annexes Annex 1: Statistical sources and some methodological notes and information 190 Annex 2: Calculation of efficiency in relation to the benchmark indicators 207 Annex 3: Calculations of internal efficiency – promotion, dropout and repetition rates 219 Annex 4: Relationship between benchmark indicators and spending on education -

regressions statistics 228

Annex 5: Summary tables of expenditure on education from public and private sources (2000 to 2004) for all ISCED levels

240

Annex 6: Background data 259

I

Executive summary

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Key education indicators on social inclusion and efficiency v

EExxeeccuuttiivvee ssuummmmaarryy 11.. Introduction 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 Country Data availability China Available India Not available

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

Russia Partially available Japan Partially available Share of the population aged 18 – 24 with only lower

secondary education and not in education and training USA Available Japan Available Percentage of those aged 20-24 who have successfully

completed at least upper secondary education (ISCED 3); USA Available Japan Not available Percentage of population aged 25 – 64 participating in

education and training in four weeks prior to the survey. USA Not available

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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)

EU-25 China Russia 775.8 1093.3 436.1

Source: Eurostat, UNESCO, and National Statistical Institute of China 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 2000 2001 2002 2003 2004 2005 EU-25 76.6 76.5 76.7 76.9 77.1 77.5

US 85.5 84.8 84.5 85.6 85.9 85.9 Japan 91.8 : 94.3 : : :

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

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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.

2000 2001 2002 2003 2004 USA 12.4 13.1 12.8 12.1 12.6 Japan 5.8 : 5.7 : :

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

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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 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. 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

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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.

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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

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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.

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.

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

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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.

Executive Summary

Key education indicators on social inclusion and efficiency xiv

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.

II

Abbreviations & acronyms

Abbreviations

Key education indicators on social inclusion and efficiency 2

Abbreviations & acronyms

Some of the abbreviations and acronyms used in this report are presented below. CVT Continuing Vocational Training CVTS Continuing Vocational Training Survey COICOP Classification Of Individual Consumption by Purpose CPI Consumer Price Index EEA European Economic Area EURO PPS Euro Purchasing Power Standard ESS European Statistical System ESA 95 European System of Accounts GDP Gross Domestic Product ILO International Labour Organisation ISCED-97 International Standard Classification of Education HBS Household Budget Survey HE Higher Education ISCO-88 International Standard for the Classification of Occupations LCS Labour Cost Survey LFS Labour Force Survey LLL Lifelong Learning NACE Statistical Classification of Economic Activities in the European

Community NSIs National Statistical Institutes NUTS Nomenclature of Territorial Units for Statistics OECD Organisation for Economic Co-operation and Development PPS Purchasing Power Standard R&D Research and Development UOE UNESCO/OECD/Eurostat data collection on education UNESCO United Nations Educational, Scientific and Cultural Organisation UNESCO-UIS United Nations Educational, Scientific and Cultural Organisation

Institute for Statistics

Abbreviations


Country codes The following standard country codes were used EU-25

BE Belgium LU Luxembourg CZ Czech Republic HU Hungary DK Denmark MT Malta DE Germany NL The Netherlands EE Estonia AT Austria EL Greece PL Poland ES Spain PT Portugal FR France SI Slovenia IE Ireland SK Slovakia IT Italy FI Finland CY Cyprus SE Sweden FI Finland UK United Kingdom LV Latvia

Acceding countries

BG Bulgaria RO Romania Candidate countries

HR Croatia MK F.Y. R of Macedonia TR Turkey

Other countries

JP Japan US United States of America

Introduction


IIIIII

IInnttrroodduuccttiioonn

Introduction


IInnttrroodduuccttiioonn BBaacckkggrroouunndd 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

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 Indicator Source 1 Share of the population aged 18-24 with only

lower secondary education and not in education and training

Eurostat, EU-Labour Force Survey

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

Eurostat, UOE data collection

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


4 Percentage of pupils with reading literacy proficiency level 1 and lower in PISA reading literacy scale

OECD, PISA

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

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;

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

Chapter

1

CCoommpplleettiinngg ssttaattiissttiiccaall ggaappss ffoorr tthhee bbeenncchhmmaarrkk iinnddiiccaattoorrss



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 Country


Greece, China, India, Russia

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

Japan, USA

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

Japan, USA

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

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



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.



Table 1.2: Reconciling information required for the indicators with data currently available Indicator Country Level of

importance Data availability Observations

Greece High Partially available 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.

China Medium Available 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.

India Medium Not available 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.


Russia Medium Partially available Data is available for the year 2004 from UNESCO. Japan High Partially available 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.


USA High Available The Census Bureau collects this information through the Current Population Survey. Data is available for 2000 to 2004 and is disseminated on its’ website.

Japan High Available Data concerning this indicator is collected through the Employment Status Survey and the Population Census. Data is available for the years 2000 and 2002.

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

USA High Available The Census Bureau collects this information through the Current Population Survey. Data is available for 2000 to 2004 and is disseminated on its’ website.

Japan High Not available 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.


USA High Not available 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.



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

Unit Thousands Country 2000 2001 2002 2003 2004

Greece 422.3 478.2 528.0 561.5 597.0 China 7364.1 9398.6 12143.7 15186.2 19417.0 India 9404.5 9834.0 10576.7 11295.0 11852.9 Russia : : 8022.8* 8151.4* :

Source: UNESCO/OECD/Eurostat collection Note: * Estimated by UNESCO-UIS

Table 1.4: Enrolment in science and engineering


Greece : : 157.2 : 189.7 China : : : : : India : 1947.0 2124.1 : : Russia : : : : :

Source: UNESCO/OECD/Eurostat collection

Table 1.5: Graduates in maths, science and technology


Greece : : : : 13.2 China : : : : : India : : : : : Russia : : : : 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.



Table 1.6: Total graduates in all programs

Unit Thousands

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).

Country 2000 2001 2002 2003 2004 Greece : 39.0 43.7 : 48.1 China 1776.0 1804.7 1948.1 : : India : : : : : Russia 1190.6 1240.5 1353.8 1536.4 1706.2



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

Unit Thousands Variable 2000 2001 2002 2003 2004

Total no of MST graduates 452.5 464.9 591.3 817.1 1019.6 Graduates in MST % of all fields 47.6 44.9 41.2 43.5 42.6 Students enrolled in MST % of all students 48.3 44.6 43.6 42.4 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 2000 2001 2002 2003 2004 Science 21.7 24.9 22.2 21.2 20.3 Engineering 78.3 75.1 77.8 78.8 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



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 1999 2000 2001 2002 2003 UGC 7705 8051 8399 8821 9228 IAMR 7815 9158 9963 8564 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.



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

Unit thousands

Data Source 1999 2000 2001 2002 2003 UGC 1817.4 1866.1 2103.5 2359.7 2527 IAMR : : 2127.6 : : 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

Unit thousands

Data Source 1999 2000 2001 2002 2003 Degree 185.4 197.1 262.9 305.4 : Diploma 100.9 159.6 201.1 185.0 : Total 286.3 356.6 464.0 490.3 :

Source: IAMR – ‘Manpower profile India yearbook 2004’ Notes: The data was not broken down by subject discipline.

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

Unit thousands

Data Source 1999 2000 2001 2002 2003 2004 IAMR* 154.1 166.1 196.7 205.4 : NASSCOM : : 201.2 233.9 259.1 215.0 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



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 1999 2000 2001 2002 2003 2004 IAMR 72247 74323 94639 101914 : : COD** 75177 82107 109376 124469 141646 184347 % Difference 4.1 10.5 15.6 22.1 : : 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 1999 2000 2001 2002 2003 2004 Graduates 3655.4 3837.7 4024.9 : : : Postgraduates 730.6 767.1 805.0 : : : Total 7386 4604.8 4829.9 : : :

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 1999 2000 2001 2002 2003 2004 Degree 913.7 969.5 1024.4 1078.3 1183.2 : Diploma 1379.5 1456.0 1531.7 1606.7 1720.5 : Total 2293.2 2425.5 2556.1 2685.0 2903.7 :


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



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

Unit thousands

Discipline 1999 2000 2001 2002 2003 2004 Science (1) 218.8 225.1 : : : : Engineering (2) 132.3 130.6 128.9 218.7 : : Engineering (3) 154.1 166.1 196.7 205.4 : Total (1) +(2) 351.1 355.7 : : : : Total (1)+(3) 372.9 391.3 : : : :


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. 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.



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

2000 2001 2002 2003 2004 USA 85.5 84.8 84.5 85.6 85.9 Japan 91.8 : 94.3 : : Sources: Statistics Japan, ‘Population Census’ and ‘Employment Status Survey’, and US Census Bureau ‘

Current Population Survey’ 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

Country 25-64 25 – 34 35-44 45 –54 55- 64 Male 84 92 93 82 67 Female 84 96 95 83 63 Japan Total 84 94 94 82 65

Male 87 86 87 90 86 USA Female 89 88 89 90 87

Total 88 87 88 90 86 Source: OECD ‘Education at a Glance 2006’

UUSSAA 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)

Source: US Census Bureau, ‘Current Population Survey’

0

10

20

30

40

50

60

70

80

90

100

2000 2001 2002 2003 2004

% (2

0- 2

4) U

pper

seco

ndar

y ed



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

Source: Statistics Japan, ‘Population Census’

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

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

Total Males Females 91.8 89.8 93.7

91 92 93 94 95 96

Total

Male

Female

%



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 2001 2002 2003 2004 USA 12.4 13.1 12.3 11.8 12.1 Japan : : : : : Sources: Statistics Japan, ‘Population Census’ and ‘Employment Status Survey’, and US Census Bureau ‘

Current Population Survey’ 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



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

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

1998 1999 2000 2001 2002 No of dropouts 111372 106578 109146 104894 89409 Dropout rate (%) 2.6 2.5 2.6 2.6 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


0

20000

40000

60000

80000

100000

120000

1998 1999 2000 2001 2002

No

of d

ropo

uts



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.



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

Year GED Service1 CPS2, 3 (CPS) 1990 222,295 111,023 16,728 1991 247,767 117,371 17,197 1992 249,470 107,030 16,425 1993 241,787 107,415 16,455 1994 247,051 211,560 23,047

1995 256,441 237,876 24,424 1996 258,957 312,645 27,957 1997 244,749 286,811 26,793 1998 254,239 340,784 24,790 1999 267,932 320,187 27,331

2000 263,465 90,810 24,831 2001 342,156 107,202 28,249 2002 176,291 70,745 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 1997 1998 1999 2000 2001 2002 CPS1 1.1 1.4 1.2 0.5 0.4 0.3

GED Service2 : 1.0 1.0 1.0 1.3 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



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 Status Completion

rate

Dropout rates*

Number of completers (thousands)

Population (thousands)

2000 86.5 13.5 21,743 25,138 2001 86.5 13.5 22,084 25,543 2002 86.6 13.4 22,249 25,697 2003 87.1 12.8 22,508 25,831

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



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

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.

10.5

11

11.5

12

12.5

13

13.5

14

2000 2001 2002 2003

% D

ropu

ts

Dropout_status Droputs_EU



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



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.



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

2003 2004 2005 2006 Country General

(1) Restricted

(2) Difference(2) – (1)

General (1)

Restricted(2)

Difference(2) – (1)

General (1)

Restricted(2)


General (1)

Restricted(2)


EU25 : : : 14.9 15.6 0.7 14.6 15.4 0.8 : : : BE : : : 11.9 12.5 0.6 13.0 13.6 0.6 : : : CZ 6.0 6.1 0.1 6.1 6.2 0.0 6.4 6.5 0.1 5.5 5.7 0.1 DK 10.3 11.7 1.4 8.5 10.8 2.3 8.5 10.7 2.3 10.9 13.3 2.4 DE 12.8 13.1 0.3 12.1 12.4 0.4 13.8 14.2 0.4 13.8 14.3 0.5 EE 11.8 12.4 0.5 13.7 14.0 0.2 14.0 14.0 0.0 13.2 13.2 0.0 GR 15.5 16.0 0.6 14.9 15.1 0.2 13.3 13.5 0.1 15.9 16.2 0.3 ES 31.3 33.8 2.6 31.7 34.2 2.5 30.8 34.7 3.9 29.9 29.9 0.0 FR 13.8 15.0 1.3 13.1 14.6 1.5 12.3 13.5 1.2 13.1 14.8 1.6 IE 12.3 12.6 0.3 12.9 13.1 0.2 12.3 12.4 0.1 12.5 12.7 0.2 IT 22.3 22.7 0.4 21.9 22.3 0.5 : : : CY 17.4 17.8 0.5 20.6 21.2 0.7 18.1 18.4 0.3 16.0 16.5 0.5 LV 18.1 18.7 0.7 15.6 15.9 0.3 11.9 12.1 0.2 : : : LT 9.5 9.5 0.0 9.2 9.3 0.2 10.3 10.3 0.0 LU 12.3 12.8 0.5 12.7 15.2 2.6 13.3 14.4 1.0 : : : HU : : : 12.6 12.8 0.3 12.3 12.5 0.2 12.4 12.7 0.3

MT : : : 42.0 43.5 1.5 41.2 43.6 2.5 : : : NL 14.2 16.9 2.7 14.0 15.9 1.8 13.6 14.9 1.4 12.9 14.4 1.5 AT 9.2 9.9 0.7 8.7 9.5 0.8 9.0 9.8 0.8 9.6 11.0 1.4 PL : : : 5.7 5.8 0.1 5.5 5.6 0.1 5.6 5.6 0.1 PT : : : 39.4 40.9 1.6 38.6 39.7 1.1 40.0 40.6 0.6 SI 4.3 4.5 0.2 4.2 4.6 0.4 4.3 4.6 0.3 5.2 5.9 0.7 SK 4.9 4.9 0.0 7.1 7.1 0.0 5.8 5.9 0.1 6.4 6.4 0.0 FI 10.7 11.3 0.6 10.2 11.2 1.0 9.3 10.2 0.8 12.2 13.1 0.8 SE 9.0 10.4 1.4 8.6 10.3 1.7 11.7 13.7 2.0 12.0 14.2 2.2 UK 14.9 11.4 -3.5 14.0 14.4 0.4 13.0 13.2 0.2 IS 23.1 27.0 3.8 27.4 29.8 2.4 26.3 29.2 2.8 : : :

NO 6.6 6.8 0.3 4.5 4.7 0.2 4.6 4.9 0.3 5.9 6.5 0.6 BG 22.4 22.4 0.0 21.4 21.4 0.0 20.0 20.0 0.0 18.0 18.1 0.0 HR : : : : : : 4.8 4.8 0.1 : : : RO 23.2 23.3 0.0

23.6 22.8 -0.8

20.8 19.6 -1.2

19.0 17.7 -1.3 Source: Eurostat Labour Force Survey

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



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


(1) Restricted


General (1)

Restricted(2)


General (1)

Restricted(2)


General (1)

Restricted(2)


EU25 : : : 17.6 18.4 0.8 17.1 18.0 0.9 : : : BE : : : 15.6 16.0 0.4 15.3 16.0 0.7 : : : CZ 5.2 5.3 0.1 5.8 5.9 0.1 6.2 6.3 0.1 : : : DK 10.4 12.0 1.6 10.4 12.9 2.5 9.4 11.6 2.2 5.7 5.9 0.2 DE 12.9 13.1 0.3 12.2 12.6 0.4 13.5 13.8 0.3 12.8 15.8 3.0 EE 16.1 16.5 0.4 20.5 20.5 0.0 17.4 17.4 0.0 13.9 14.5 0.6 GR 19.9 20.4 0.5 18.3 18.5 0.2 17.5 17.6 0.1 19.6 19.6 0.0 ES 37.3 40.1 2.9 38.5 41.0 2.5 36.4 40.8 4.5 20.7 21.0 0.3 FR 15.3 16.7 1.4 15.1 16.6 1.6 14.5 15.9 1.3 35.8 35.8 0.0 IE 15.0 15.3 0.3 16.1 16.3 0.2 14.9 15.0 0.1 15.0 16.6 1.6 IT : : : 26.2 26.6 0.4 25.9 26.3 0.5 15.7 15.9 0.3 CY 24.2 24.9 0.6 27.2 27.9 0.7 26.6 27.1 0.5 : : : LV 22.7 24.1 1.4 20.5 21.0 0.5 15.5 15.5 0.0 23.5 24.6 1.1 LT : : : 11.6 11.6 0.0 12.2 12.3 0.2 : : : LU 14.3 14.7 0.4 12.6 16.2 3.5 17.0 18.2 1.2 13.3 13.3 0.0 HU : : : 13.7 13.9 0.2 13.5 13.8 0.3 : : : MT : : : 44.2 46.2 1.9 43.0 45.3 2.3 14.0 14.6 0.5 NL 15.3 18.2 2.9 16.1 17.9 1.9 15.8 17.3 1.5 : : : AT 8.7 9.4 0.7 9.5 10.1 0.6 9.4 10.3 0.8 15.1 17.1 2.0 PL : : : 7.7 7.8 0.1 6.9 7.0 0.1 9.3 11.1 1.8 PT : : : 47.9 49.7 1.8 46.7 47.9 1.2 7.2 7.3 0.1 SI 6.2 6.4 0.2 5.8 6.3 0.6 5.7 6.1 0.3 47.2 47.8 0.6 SK 5.2 5.2 0.0 7.8 7.8 0.0 6.0 6.1 0.1 6.9 7.6 0.6 FI 12.9 13.4 0.5 12.8 14.0 1.3 11.1 12.0 0.9 7.3 7.4 0.1 SE 9.8 11.3 1.4 9.3 11.1 1.8 12.4 14.9 2.5 13.8 15.1 1.3 UK 15.7 11.9 -3.8 14.7 15.2 0.5 13.3 15.9 2.6 IS 27.0 28.7 1.7 27.8 29.6 1.8 30.5 33.3 2.8 14.6 14.7 0.1

NO 7.9 8.3 0.4 5.2 5.6 0.4 5.3 5.7 0.5 : : : BG 23.3 23.3 0.0 22.1 22.1 0.0 19.5 19.5 0.1 7.4 8.3 0.9 HR : : : : : : 5.6 5.7 0.1 18.2 18.2 0.0 RO 24.7 24.8 0.0

24.9 23.7 -1.2

21.4 19.8 -1.6

: : : Source: Eurostat Labour Force Survey




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


(1) Restricted


General (1)

Restricted(2)


General (1)

Restricted(2)


General (1)

Restricted(2)


EU25 : : : 12.2 12.8 0.6 12.0 12.8 0.8 : : : BE : : : 8.3 9.0 0.7 10.6 11.2 0.6 : : : CZ 6.8 6.8 0.0 6.5 6.5 0.0 6.6 6.6 0.0 5.4 5.4 0.0 DK 10.2 11.4 1.2 6.7 8.8 2.0 7.5 9.8 2.3 9.1 10.8 1.7 DE 12.8 13.1 0.3 11.9 12.2 0.3 14.1 14.5 0.4 13.6 14.1 0.5 EE 7.3 7.9 0.6 7.0 7.5 0.4 10.7 10.7 0.0 6.5 6.5 0.0 GR 11.0 11.7 0.6 11.6 11.8 0.2 9.2 9.4 0.2 11.0 11.2 0.3 ES 25.0 27.3 2.2 24.6 27.1 2.4 25.0 28.4 3.4 23.8 23.8 0.0 FR 12.2 13.4 1.2 11.1 12.5 1.4 10.1 11.1 1.0 11.3 13.0 1.7 IE 9.5 9.9 0.3 9.7 9.9 0.2 9.6 9.7 0.1 9.2 9.4 0.2 IT : : : 18.4 18.9 0.5 17.8 18.3 0.5 : : : CY 11.8 12.1 0.3 14.9 15.6 0.7 10.6 10.6 0.0 9.2 9.2 0.0 LV 13.4 13.4 0.0 10.7 10.7 0.0 8.2 8.6 0.4 : : : LT : : : 7.4 7.4 0.0 6.2 6.3 0.1 7.0 7.0 0.0 LU 10.2 10.8 0.6 12.7 14.2 1.5 9.6 10.4 0.8 : : : HU : : : 11.4 11.7 0.3 11.1 11.2 0.1 10.7 10.9 0.1 MT : : : 39.5 40.6 1.1 39.3 41.9 2.6 : : : NL 13.0 15.5 2.4 11.9 13.7 1.8 11.2 12.5 1.3 10.7 11.7 1.0 AT 9.7 10.3 0.7 7.9 8.9 1.1 8.5 9.3 0.8 9.8 10.8 1.0 PL : : : 3.7 3.7 0.0 4.0 4.1 0.0 3.8 3.9 0.1 PT : : : 30.6 32.0 1.3 30.1 31.2 1.1 32.6 33.2 0.5 SI 2.3 2.5 0.2 2.6 2.8 0.3 2.8 3.1 0.3 3.3 4.0 0.7 SK 4.7 4.7 0.0 6.4 6.4 0.0 5.7 5.7 0.1 5.5 5.5 0.0 FI 8.6 9.3 0.7 7.8 8.6 0.8 7.6 8.4 0.8 10.9 11.3 0.4 SE 8.2 9.5 1.3 7.9 9.5 1.6 10.9 12.4 1.5 10.7 12.5 1.8 UK 14.2 10.9 -3.2 13.2 13.6 0.4 11.4 11.7 0.4 IS 18.7 25.0 6.3 27.0 30.1 3.1 22.0 24.8 2.8 : : :

NO 5.3 5.4 0.1 3.7 3.8 0.1 3.9 4.0 0.2 4.3 4.7 0.3 BG 21.6 21.6 0.0 20.7 20.7 0.0 20.6 20.6 0.0 17.9 18.0 0.1 HR : : : : : : 3.8 3.8 0.0 : : : RO 21.7 21.7 0.0

22.4 21.9 -0.5

20.1 19.4 -0.8

18.9 17.9 -0.9 Source: Eurostat Labour Force Survey




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)


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



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 Male Female 0.36 0.43 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 Male Female 50.9 47.0 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



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)


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

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.

Total Male Female 52.1 48.3 55.9

0

5

10

15

20

25

30

35

40

Adult basic education College/university,vocational/technical

programs

Personal interestcourses

Work-related courses

% o

f pop

ulat

ion

25-6

4

male female



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

Overall participation

Age group Total adults (in thousands)

(in thousands) Percent 25 to 29 18793 10915 58.1 30 to 34 19532 10718 54.9 35 to 39 22223 12190 54.9 40 to 44 21132 11463 54.2 45 to 49 20737 11558 55.7 50 to 54 17373 9015 51.9 55 to 59 12316 5451 44.3 60 to 64 11634 3632 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)


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

Less than high school diploma

High school diploma or its

equivalent

Associate’s degree Bachelor’s degree or higher

Total adults (thousands)

Number (thousands)

% Number (thousands)



%

143739 4199 5.6 33837 45.2 6228 8.3 30679 40.9



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.



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 JP US EU-25

Name of survey Survey on Time Use and Leisure activities Adult Education and Lifelong Learning Survey LFS ad hoc module on lifelong learning Periodicity Every 5 years Erratic Ad hoc Reference date of last survey

October 2001 April 2001 2003

Coverage of education Studies and researches only include 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 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

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-based tutorials, mentoring at the workplace, attending conferences or “brown-bag” presentations, or reading professional journals or magazines.

Participation in regular education. This variable only covers the regular education system (formal education including 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.

Accessibility Freely available on the website of Statistics Japan http://www.stat.go.jp/english/data/shakai/2001/kodo/zenkoku/study.htm

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

Freely available on the Eurostat website.



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

Total 2000 2001 2002 2003 2004 EU-251 : : : 42 : USA2 : 52.1 : : : Japan3 : 38.3 : : : Male EU-251 : : : 42.8 : USA2 : 48.3 : : : Japan3 : 38.3 : : : Female EU-251 : : : 41.1 : USA2 : 55.9 : : : Japan3 : 38.3 : : : 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.



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

Country Total Males Females BE 22 22 22 CZ 14 14 13 DK 46 44 49 DE 14 14 15 EL 4 3 4 ES 9 8 10 FR 23 23 24 IE 14 13 15 IT 6 5 7 LU 16 16 16 HU 5 5 7 AT 24 24 24 PL 12 12 13 PT 9 8 10 SK 24 25 22 FI 44 40 48 SE 45 42 48 UK 34 32 36 US 44 41 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.



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



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.



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.



AAppppeennddiixx



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

mathematics, science, and technology Number of graduates in Maths, Science and Technology

Unit: Thousands 2000 2001 2002 2003 2004 EU-251 635.2 680.7 704.8 754.9 775.8 China2 : : : : 1093.3 India : : : : : Russia3 : : : : 436.1 Graduates (ISCED 5-6) in Maths, Science and Technology fields - as % of all fields 2000 2001 2002 2003 2004 EU-251 24.8 24.4 24.3 24.1 23.6 China2 : : : : 43 India : : : : : Russia3 : : : : 25.6 Students at ISCED levels 5-6 enrolled in the following fields: science, mathematics, computing, engineering, manufacturing, construction - as % of all students 2000 2001 2002 2003 2004 EU-251 26.1 26.0 26.1 25.9 25.8 China2 : : : : 26.1 India : : : : : Russia3 : : : : : 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.



Indicator: Percentage of those aged 20-24 that have successfully completed at least upper secondary education (ISCED 3) Total 2000 2001 2002 2003 2004 2005 EU-251 76.6 76.5 76.7 76.9 77.1 77.5 USA2 85.5 84.8 84.5 85.6 85.9 85.9 Japan3 91.8 : 94.3 : : Males

2000 2001 2002 2003 2004 2005 EU-251 73.7 73.7 74.0 74.2 74.2 74.7 USA2 83.7 82.9 83.3 84.2 84.1 84.4 Japan3 89.8 : 93.0 : : Females 2000 2001 2002 2003 2004 2005 EU-251 79.5 79.3 79.4 79.5 80.1 80.3 USA2 87.3 86.8 85.8 86.8 87.8 87.3 Japan3 93.7 : 95.7 : : 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:



(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.



Indicator: Share of population aged 18-24 with only lower secondary

education and not in education and training. Total

2000 2001 2002 2003 2004 2005 EU-251 17.3 17.0 16.6 16.2 15.6 15.2 USA2 12.4 13.1 12.3 11.8 12.1 : Japan : : : : : : Males 2000 2001 2002 2003 2004 2005 EU-251 19.5 19.2 18.9 18.1 18.0 17.3 USA2 13.8 15.1 14.0 13.7 13.9 : Japan : : : : : Females 2000 2001 2002 2003 2004 2005 EU-251 15.2 14.8 14.4 14.2 13.1 12.8 USA2 11.1 11.1 10.6 9.9 10.4 : Japan : : : : : : 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.



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

Total

2000 2001 2002 2003 2004 EU-25 : : : : : USA1 12.4 13.1 12.8 12.1 12.6 Japan2 5.8 : 5.7 : : Males 2000 2001 2002 2003 2004 EU-25 : : : : : USA1 13.6 15.0 14.4 14.1 14.0 Japan2 6.8 : 6.9 : : Females 2000 2001 2002 2003 2004 EU-25 : : : : : USA1 11.1 11.2 11.2 10.2 11.2 Japan2 4.8 : 4.3 : : 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. 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.



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 2000 2001 2002 2003 2004 EU-251 7.9 7.8 7.9 9.2 10.3 USA : : : : : Japan : : : : Male 2000 2001 2002 2003 2004 EU-251 7.4 7.2 7.2 8.5 9.4 USA : : : : : Japan : : : : Female 2000 2001 2002 2003 2004 EU-251 8.4 8.4 8.5 10.0 11.1 USA : : : : : Japan : : : : 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.

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

Key education indicators on social inclusion and efficiency

Chapter

2

CCoommbbiinniinngg ddaattaa oonn ppuubblliiccaanndd pprriivvaattee ssppeennddiinngg oonneedduuccaattiioonn



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



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



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

Total national spending = Σi3 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. Public refers to spending on education by central (national) government; regional government

(province, state, Land, etc…); and local government (municipality, district, commune etc…) 02. Private households refer to students and their families 03. 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 = Σi3 Public + Private household + Other private entities + inter

01 02 03 04 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.



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 = Σi3 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 Component variables

Goods and services of educational institutions Goods and services of purchased outside of educational institutions

Public spending

Public subsidies to students for student living costs Goods and services of educational institutions Private household

spending Goods and services of purchased outside of educational institutions Goods and services of educational institutions Other private entity

spending 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



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 Periodicity of collection Latest available year

Continuing Vocational Training Survey (CVTS2)

The survey is carried out approximately every 6 years. For CVTS 1the reference year is 1993, and for CVTS2 it is 1999.

1999

Eurostat Harmonised Household Budget Survey

Every 5 years: 1988, 1994, 1999, and 2005 1999 (2005 is not yet available on Eurostat website)

EU-Labour Cost Survey (LCS)

Every 4 years 2000 (2004 is not yet available on Eurostat website)

UNESCO/OECD/Eurostat collection on education systems

Every year since 1998 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



Table 2.3: Geographical coverage of international data collections Name of survey Geographical coverage

Continuing Vocational Training Survey (CVTS2)

CVTS1 covered the then EU12 Member States. CVTS2 covered EU Members (except CY, MT, and SK), and NO and 2 Acceding Countries (BG, RO)

Eurostat Harmonised Household Budget Survey

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

EU-Labour Cost Survey (LCS)

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.



Table 2.4: Summary of data collected on public and private expenditure on education by international data collections Public (State, Regional, local) Private household Other private entity

Data source

Goods and services of educational institutions

Goods and services of purchased outside of educational institutions

Public subsidies to students for student living costs





UNESCO/OECD/Eurostat collection on education system n n n n n n

Eurostat Harmonised Household Budget Survey2 n

Continuing Vocational Training Survey (CVTS2)3 n n Labour Cost Survey (LCS)4

n n

Table 2.5: Coverage of education expenditure by international data collections


Eurostat Harmonised Household Budget Survey2

Continuing Vocational Training Survey (CVTS2

Labour Cost Survey (LCS)

Does not include private non-subsidised expenditure on student living costs outside of educational institutions. Data is collected by ISCED level, which means that if a programme cannot be assigned by a ISCED level it 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. It does not include vocational and technical training in enterprises with the exception of combined school and work based programmes that are explicitly deemed to be parts of the education system.

It refers only to private household expenditure on 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. It includes expenditure on education transmitted through the radio or television

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.

Refers to expenditures of enterprises on CVT and apprenticeship. 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 are deducted. It includes the costs of apprentices, however this expenditure can be separated out.



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 G5 G20 H5 H18 H20 E5 BE h h h h h h CZ h h h h h h DK h h h h h DE h h h h EE h h EL h h h h h h ES h h h h h FR h h h h h h IE h h h h h IT h h h h h h CY LV h h h h h h LT h h h h h LU h h HU h h h h MT h h h h h NL h h h h h h AT h h h h h PL h h h h h PT h h h h h SI h h h h h SK h h h h h h FI h h SE h h h UK h h h h h h BG h h h h h h RO h h IS h h h LI h h NO h h h HR h h TR h h h h h MK h h

Key G5 Direct expenditure of all levels of government on all types of institutions G20 Education expenditure for all levels of government H5 Payment to educational institutions by households H18 Payments by households for educational goods and services other than to educational

institutions H20 Educational expenditure of households E5 Payments to all types of institutions by other private entities



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.



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 that not many countries provide data on student loans to the UNESCO/OECD/Eurostat statistical exercise.

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

Country 2000 2001 2002 2003 BE h h h CZ na na na DK h h h h DE h h h h EE n na na n EL ES n N n FR na na na na IE n n IT h h h CY h LV h h h h LT h h h h LU na HU na h h na MT na n NL h h h h AT na na na na PL n na na PT n SI na n SK h h h h FI na na n SE h h h h UK n n h h BG na na na na RO na na na na IS h h h h LI h n NO h h h h HR h TR h h h h MK h h

Key na Not applicable n nil



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.



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)

Country G13 E5a BE h h CZ n DK n DE h EE h EL n ES n FR na IE h IT n CY LV n LT LU HU n MT n n NL n AT h PL h PT h SI h n SK na h FI h SE na n UK n n BG na na RO n IS h LI h NO n HR TR MK n

Key G13 All levels of government transfers to other private entities E5b Expenditure by private enterprises on the work-based element of school and work-based

training of apprentices and students n Nil a 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).



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



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’



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



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 to be combined together with the data from the UNESCO/OECD/Eurostat statistical exercise. 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.



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

Variable Component variables Sources Goods and services of educational institutions

UNESCO/OECD/Eurostat collection



Public spending

Public subsidies to students for student living costs



UNESCO/OECD/Eurostat collection /National Household Budget Surveys

Private household spending Goods and services of purchased outside of

educational institutions National Household Budget Surveys


UNESCO/OECD/Eurostat collection Other private entity spending


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.



22..66..22 HHoouusseehhoollddss eexxppeennddiittuurree oonn ggooooddss aanndd sseerrvviicceess oouuttssiiddee 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. The UNESCO/OECD/Eurostat (UOE) statistical exercise has classified household 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.



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. Table 2.10, shows that the reporting of data on ancillary services in the 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.



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

All levels of expenditure Higher Education (ISCED 5/6)

Country

G5b H5b E5b G5b H5b E5b BE h h h h CZ h h h h h DK DE EE EL h h ES h h FR h h h h h h IE IT h h h h CY LV LT LU HU h h h h MT h NL h h AT h h PL h h PT h SI h h SK h h h h h h FI SE UK BG h h h h RO IS LI NO h HR h h TR h h h MK US h h JP

Key G5b Direct expenditure of all levels of government on ancillary services H5b Expenditure of households on ancillary services E5b 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



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 Total exp Mio € PPS

Total exp per student Mio € PPS

CZ 52.1 187.8 FR 1211.1 571.5 HU 115.2 546.6 SK 51.1 477.3 BG 109.8 476.3 US 24937 2507.1

22..66..55 EExxppeennddiittuurree oonn rreesseeaarrcchh aanndd ddeevveellooppmmeenntt ((RR&&DD)) The UNESCO/OECD/Eurostat methodology includes expenditure on 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, it was decided to maintain coherence with 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. 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



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.



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 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 of non-household member

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.

Extra tutoring Payments for extra tutoring Registration fees Registration fees paid directly to educational establishments. Examination fees Payments made to an institution to register for an examination Textbooks Payments or contributions directly to the educational establishments for

textbooks. 1.1 Ancillary services

Food and board while attending school

Food and board up to ISCED 3 (upper secondary education). These expenses are paid directly to the educational establishment.

Student dormitories Payments made directly to educational establishments at the higher education level for accommodation, which may or may not include meals.

School refectories These generally relate to meals, which are paid directly to an educational establishment.

School transport Expenditure on all forms of transport, which is paid directly to the educational institution, for example: buses, trains, trams, etc…

Laboratory and library fees Library fees also includes payments for photocopies. Health and welfare services Fees for health and welfare services paid directly to educational

institutions. Membership fees This includes fees paid directly to institutions for the following: student

union fees, student council membership fee, parent and teacher association membership fee

Rental of school equipment Payments for rental of any form of equipment belonging to the institution

Extra-curricula activities Payments to institutions made for activities, which are normally conducted outside of school hours.

School trips / visits Trips / visits organised by the school / college. Other 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;

Items necessary for participation in the classroom. This includes items such as textbooks, laboratory equipment, art supplies, and stationary.

School uniform Includes items of clothing as stated in the school regulations. In some countries this may also include purchase of clothing for sport.

School trips / visits Trips / visits organised by the school / college. 3. Expenditure on goods not imposed by an institution

Purchase of educational material for self-study

Expenditures on books, CDs, videos for learning at home. For example language learning courses etc.. However it does not include personal computers (PCs).

Gifts to non-household members for educational purposes

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). 4. Payments on private tutoring

Private tutoring Lessons taken privately outside an educational establishment



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 Costs of training included CVTS2 These include: fees and payments to CVT providers and cost of external trainers,

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.

LCS 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.

. 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.



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

NACE Name CVTS2 LCS

C Mining and quarrying h h D Manufacturing h h E Electricity, gas and water supply h h F Construction h h G Wholesale and retail trade; repair of motor vehicles,

motorcycles and personal and household goods h h

H Hotels and restaurants h h I Transport, storage and communication h h J Financial intermediation h h K Real estate, renting and business activities h h L Public administration and defence; compulsory social

security h

M Education h N Health and social work h O Other community, social and personal service activities h h P Activities of households Q Extra-territorial organizations and bodies

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.



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.



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

Source of funding

Name of source Type of education / training included Type of education / training NOT included

Public UNESCO/OECD/Eurostat collection on education systems

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)

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

Private UNESCO/OECD/Eurostat collection on education systems

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)

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

Private CVTS Continuing vocational training courses All other education and training Private EU-LCS Continuing vocational training courses All other education and training Private 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 (ISCED level programmes); Adult education (ISCED level programmes)

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 some countries leisure /recreation classes (e.g. DK, EL, NL, US)

For most countries leisure /recreation classes are excluded



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

Com

bina

tion

of d

ata

sour

ces

UOE, CVTS

Key Total coverage Partial coverage Not covered Notes ISCED Education and training, which can be classified by ISCED categories (0-6); CVT 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.

IVT Initial Vocational Training. General or vocational education carried out in the initial education system, usually before entering working life.

Adult / continuing Education provided for adults, often intended for general purposes rather than vocational education

Training for unemployed

Training which can be provided by either public or private organisations to the unemployed to re-integrate them back onto the labour market.

Special education Educational activity and support designed to address special education needs



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

CVTS LCS Country 1993 1999 1996 2000

BG h h RO h h HR MK TR IS h LI NO h h JP US

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



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,



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.



Appendix



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



- Data on household spending on education from national household budget surveys;

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



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

Survey



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

Mio PPS

Mio PPS LV LT LU HU MT NL AT PL PT SI SK FI SE UK Educational institutions 1001.1 : : 5387.6 269.5 17928.1 11356.7 17504.6 8891.3 : 2084.2 6547.5 13640.2 66812.1

Of which public expenditure 876.8 1512.4 4755.3 240.8 16183.8 10696.8 17454.5 8762.8 2009.0 6414.6 13228.2 56897.9

Expenditure on ancillary services

: : : : 28.7 : : : : : : : : :

Expenditure on R&D 28.0 : : 188.6 : 1863.3 : : : : 33.2 708.9 : 5050.2

Estimates of total education spending Method 1 1025.5 : : : : : 15936 : : 374.7 : : : 81692 Method 2 1151.8 : : 6000.9 : : 16234.1 : : : 2246.9 : 17114.8 85414.8 Method 3 1013.5 : : : : 23134.6 12429.7 : : : 2277.9 : : 81131.8 Method 4 1139.8 : : 6306.8 : 22116.9 12727.8 : : : : : 17064.4 84854.6 Average 1082.6 14331.9 83273.3 Of which public expenditure 905.2 1579.1 : 4891.8 258.7 18481.7 11459.6 17504.6 8965.6 : 2016.5 6996.6 15528.2 58272.7

EU-25 BE CZ DK DE EE EL ES FR IE IT CY Educational institutions 21200 12921.0 5598.3 8992.2 95526.8 657.1 6226.1 35608.5 82175.2 4207.4 62203.2 861.8

Of which public expenditure 18106 11909.3 5037.7 8632.2 77515.7 657.1 5839.5 31138.2 75649.1 3914.1 56549.0 565.9

Expenditure on ancillary services : : : : : : : : : : : 283.9

Expenditure on R&D : 1053.1 216.3 603.2 7297.9 : 369.5 : 439.4 219.5 : 6.9 Estimates of total education spending

Method 1 : 14319.9 6070.2 12542.8 100843.5 : : 37006.4 : : : : Method 2 : 13606.9 6213.9 12845.7 105895.4 : : 40826.6 87784.4 : : : Method 3 : 14377.3 5991.5 11620.7 98299.9 : : 36826.5 : 4607.6 68478 : Method 4 : 13664.2 6135.2 11923.6 103351.8 : : 40646.7 89960.6 4713.8 67808 639.9 Average : 13992.08 6102.68 12233.2 102098 38826.5 Of which public expenditure

12329.3 5312.4 11220.2 82377.7 708.1 5930.7 31978.2 78732.4 4135.6 58958.1 613.6



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

All levels of education (Current prices)

as a % of GDP

as a % of GDP

LV LT LU HU MT NL AT PL PT SI SK FI SE UK Educational institutions 5.9 : : 5.0 4.4 4.5 5.6 4.9 5.4 : 4.0 5.6 6.4 5.0 Of which public expenditure 5.2 5.6 : 4.4 3.9 4.1 5.3 4.9 5.3 : 3.9 5.5 6.2 4.3

Expenditure on ancillary services : : : : 0.5 : : : : : : : :

Expenditure on R&D 0.2 : : 0.2 : 0.5 : : : : 0.1 0.6 : 0.4 Estimates of total education spending

Method 1 6.1 : : : : : 7.9 : : 1.3 : : : 6.2 Method 2 6.8 : : 5.5 : : 8.0 : : : 4.4 : 8.1 6.4 Method 3 6.0 : : : : : 6.1 : : : 4.4 : : 6.1 Method 4 6.8 : : 5.8 : 5.6 6.3 : : : : : 8.0 6.4 Average 6.4 7.1 6.3 Of which public expenditure 5.4 5.9 : 4.5 4.2 4.6 5.7 4.9 5.4 : 3.9 6.0 7.3 4.4

EU-25 BE CZ DK DE EE EL ES FR IE IT CY Educational institutions 4.8 5.4 4.3 6.6 5.2 5.8 3.9 4.8 5.9 4.4 4.8 7.6

Of which public expenditure 4.4 5.0 3.8 6.4 4.2 5.8 3.7 4.2 5.5 4.1 4.4 5.0

Expenditure on ancillary services : : : : : : : : : : : 2.5

Expenditure on R&D : 0.4 0.2 0.4 0.4 : 0.2 : 0.0 0.2 : 0.1

Estimates of total education spending Method 1 : 6.0 4.6 9.3 5.5 : : 5.0 : : : : Method 2 : 5.7 4.7 9.5 5.7 : : 5.5 6.3 : : : Method 3 : 6.0 4.6 8.6 5.3 : : 4.9 : : : : Method 4 : 5.7 4.7 8.8 5.6 : : 5.4 6.5 4.9 5.2 5.7 Average : 5.8 4.6 9.0 5.5 5.2 Of which public expenditure 5.1 4.0 8.3 4.5 6.3 3.7 4.3 5.7 4.3 4.5 5.4




Mio PPS

EU-25 BE CZ DK DE EE EL ES FR IE IT CY Educational institutions 19944.1 15665.8 5947.5 9832.4 97489.9 : 6685.4 37151.0 84764.3 4562.4 69104.8 :

Of which public expenditure 17809 14568.6 5451.3 9449.2 79353.7 608.7 6299.2 32636.0 78003.7 4210.6 62682.6 :

Expenditure on ancillary services : : 170.9461 5223.16 : : : : 9970.5 : : :

Expenditure on R&D : 1028.9 : 620.2 7620.6 : 322.4 : 473.1 210.8 4709.2 8.2 Estimates of total education spending

Method 1 : 17225.9 6227.6 : 103610.3 : : 38558.1 87879.7 : : : Method 2 : 16521.4 6322.8 : 108370.8 : : 42533.3 90670.8 : : Method 3 : 17287.2 6471.3 : 101008.5 : : 38371.5 90181.5 : 73766.3 : Method 4 : 16582.7 6566.5 : 105769.0 : : 42346.6 92972.6 5048.0 73802.7 : Average : 16904.3 6397.04 : 104690 : 40452.4 90426.2 Of which public expenditure 18717 15082.9 5748.8 11775.3 84733.7 658.7 6436.8 33533.2 81138.6 4421.3 64697.9 :

Mio PPS LV LT LU HU MT NL AT PL PT SI SK FI SE UK Educational institutions 1124.6 1608.9 700.9 6084.1 285.3 19955.5 11651.2 19712.7 9551.7 : 2219.9 7064.9 13678.9 73761.6

Of which public expenditure 923.1 1608.9 700.8 5413.5 255.3 18134.4 11003.4 19712.7 9409.5 : 2157.4 6911.6 13243.9 62485.8

Expenditure on ancillary services : : : : : : : : : : 241.2377 : : :

Expenditure on R&D 31.1 : 247.8 : 1985.5 835.6 750.2 : : 45.5 745.7 1910.6 3580.9

Estimates of total education spending Method 1 : : : 6435.1 : : : : : 327.9 : : : 96009.4 Method 2 : : 828.7 : : : 13041.3 : : 157.7 : : : 99413.7 Method 3 : : : 6829.9 : 23031 : : : 402.6 2440.7 : : 87043.4 Method 4 : : 833.4 7569.4 : 24184.9 12957.8 : : 232.4 2569.5 : 17069.4 90447.8 Average : : : : : : : : : 789.7 : : : : Of which public expenditure 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




as a % of GDP


Of which public expenditure 4.5 5.8 3.9 6.8 4.2 5.1 3.8 4.1 5.4 4.1 4.7 :

Expenditure on ancillary services : : 0.1 3.7 : : : : 0.7 : : :

Expenditure on R&D : 0.4 : 0.4 0.4 : 0.2 : 0.0 0.2 0.4 0.1

Estimates of total education spending Method 1 : 6.9 4.5 : 5.5 : : 4.9 6.1 : : : Method 2 : 6.6 4.6 : 5.7 : : 5.4 6.3 : 0.0 : Method 3 : 6.9 4.7 : 5.4 : : 4.9 6.2 : 5.5 : Method 4 : 6.6 4.8 : 5.6 : : 5.4 6.4 4.9 5.5 : Average : 6.7 4.6 : 5.5 : 5.1 6.2 Of which public expenditure 4.8 6.0 4.2 8.4 4.5 5.5 3.8 4.2 5.6 4.3 4.9 :

as a % of GDP

LV LT LU HU MT NL AT PL PT SI SK FI SE UK Educational institutions 6.2 5.5 3.5 5.1 4.7 4.7 5.7 5.4 5.6 : 4.1 5.8 6.4 5.3

Of which public expenditure 5.1 5.5 3.5 4.6 4.2 4.3 5.4 5.4 5.5 : 3.9 5.7 6.2 4.5


Expenditure on R&D 0.2 : 0.2 : 0.5 0.4 0.2 : : 0.1 0.6 0.9 0.3 Estimates of total education spending

Method 1 : : : 5.4 : : : : : 1.1 : : : 6.9 Method 2 : : 4.2 : : : 6.4 : : 0.5 : : : 7.1 Method 3 : : : 5.8 : 5.4 : : : 1.3 4.5 : : 6.2 Method 4 : : 4.2 6.4 : 5.7 6.3 : : 0.8 4.7 : 8.0 6.5 Average : : : : : : : 2.6 : : : : Of which public expenditure 5.5 5.9 3.7 5.1 4.6 4.8 5.7 5.4 5.6 : 4.0 6.2 7.2 4.5




Mio PPS EU-25 BE CZ DK DE EE EL ES FR IE IT CY Educational institutions 24341 16255.2 6468.248 9912.3 101090.8 : 7327.693 39689.71 86359.5 4831.978 64832.76 :

Of which public expenditure 18764 15311.7 6113.3 9523.2 84250.0 692.6 6989.9 35071.1 79534.9 4516.7 59982.0 :

Expenditure on ancillary services : : 693.7 : : : : : 9918.7 : : :

Expenditure on R&D : 1083.3 279 627.8 8177.9 : 326.8 : 482.6 : 4625.4 11.3 Estimates of total education spending

Method 1 : : 6862.2 : 109679.6 : : : : : : : Method 2 : : 6985.9 12542.7 114976.5 : : 45424.6 92550.2 : : : Method 3 : : 7127.0 12303.8 107001.0 : : : : : 70702.7 : Method 4 : : 7250.8 12610.4 112297.9 : : 45228.3 94916.0 5556.8 71555.1 : Average : : 7056.5 : 110988.8 : : : : : : : Of which public expenditure 19900.8 16001.3 6437.0 11870.9 90723.9 752.2 7131.5 36027.5 82723.1 4811.3 62658.4 :

Mio PPS

LV LT LU HU MT NL AT PL PT SI SK FI SE UK Educational institutions 1185.6 1713.3 : 7022.9 297.0 21238.4 11865.6 22627.6 9779.9 2005.6 2464.1 7492.5 14887.0 84133.1 Of which public expenditure 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


Expenditure on R&D 34.9 23.4 324.4 1.3 2089 : 725 : 75.8 37.2 826.8 : 3942.8

Estimates of total education spending Method 1 1307.2 : : 7362.7 : : : : : 2299.6 : : : 109469.7 Method 2 1401.7 : : 8175.5 : : 13396.9 : : 2125.6 : : : 112572.3 Method 3 1273.3 : : 7746.9 : 24147.1 : : : 2372.2 2844.3 : : 99999.3 Method 4 1367.9 : : 8559.7 : 25363.4 13311.1 : : 2198.2 2988.7 : : 103101.9 Average 1337.5 : : 7961.2 : : : : : 2248.9 : : : 106285.8 Of which public expenditure 1122.2 1832.9 801.8 6935.3 283.0 21199.7 11856.9 20683.3 9832.2 1915.8 2542.4 7980.4 16590.6 74806.9




as a % of GDP EU-25 BE CZ DK DE EE EL ES FR IE IT CY Educational institutions 4.8 6.2 4.4 7.0 5.2 : 4.0 4.7 5.8 4.3 4.8 : Of which public expenditure 4.3 5.9 4.2 6.8 4.4 5.2 3.8 4.1 5.4 4.0 4.4 : Expenditure on ancillary services : : 0.5 : : : : : 0.7 : : :

Expenditure on R&D : 0.4 0.2 0.4 0.4 : 0.2 : 0.0 : 0.3 0.1 Estimates of total education spending

Method 1 : : 4.7 : 5.7 : : : : : : : Method 2 : : 4.8 8.9 6.0 : : 5.4 6.2 : : : Method 3 : : 4.9 8.8 5.5 : : : : : 5.2 : Method 4 : : 5.0 9.0 5.8 : : 5.3 6.4 : 5.3 : Average : : 4.8 : 5.8 : : : : : : : Of which public expenditure 5.1 6.1 4.4 8.4 4.7 5.7 3.9 4.3 5.6 4.3 4.6 :

as a % of GDP LV LT LU HU MT NL AT PL PT SI SK FI SE UK Educational institutions 6.1 5.5 : 5.5 4.6 4.9 5.7 5.9 5.5 6.3 4.2 6.0 6.8 5.7 Of which public expenditure 5.4 5.5 3.3 : 14.1 4.1 5.0 2.5 7.1 : 0.2 5.2 0.6 6.8

Expenditure on ancillary services : : : : : : : : : : 0.5 : : : Expenditure on R&D 0.2 0.1 0.3 0.0 0.5 : 0.2 : 0.2 0.1 0.7 : 0.3

Estimates of total education spending Method 1 6.7 : : 5.8 : : : : : 7.2 : : : 7.4 Method 2 7.2 : : 6.4 : : 6.4 : : 6.6 : : : 7.6 Method 3 6.5 : : 6.1 : 5.5 : : : 7.4 4.8 : : 6.7 Method 4 7.0 : : 6.7 : 5.8 6.4 : : 6.9 5.1 : : 7.0 Average 6.9 : : 6.3 : : : : : 7.0 : : : 7.2 Of which public expenditure 5.7 5.9 3.8 5.5 4.4 4.9 5.7 5.4 5.5 6.0 4.3 6.3 7.6 5.0




Mio PPS




Expenditure on R&D : 1081.7 255.7 673.8 8253.7 570.8 2839.7 6401.4 303.6 5057.8 :

Estimates of total education spending Method 1 : : 7537.9 : : : : : : : : : Method 2 : 17464.9 8027.3 115742.6 10065.6 47765.7 99408.9 : : : Method 3 : 16770.8 7822.2 : : : : : : : 73521.1 Method 4 : 17530.0 7961.6 113001.7 47563.9 101795.2 5953.5 73575.4 Average : : 7837.2 : : : : : : : : : Of which public expenditure 20602.9 16136.2 6844.6 11746.2 91528.5 806.1 7668.0 38154.7 88463.8 5122.1 64076.3 :

Mio PPS

LV LT LU HU MT NL AT PL PT SI SK FI SE UK Educational institutions 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

Of which public expenditure 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

Expenditure on ancillary services : : : : 294.1 : : : : : 2453.1 : : :

Expenditure on R&D 33 72.6 2098.9 844.4 670 78.1 45.4 843 1956.3 3799.2 Estimates of total education spending

Method 1 1256.1 1811.6 : : : : : : : 2363.7 : : 17311.4 88675.9 Method 2 1493.9 : : 9258.4 : : 13267.7 : : 2289.1 : : 18667.6 108352.7 Method 3 1266.5 1835.1 : : : : : : : 2439.3 2959.4 : 18301.1 101858.4 Method 4 1474.7 : : 9525.8 : 26635.8 13180.7 : : 2283.7 3119.3 : 18523.8 105853.5 Average 1313.4 : : : : : : : : 2344 : : 18201 101185.1 Of which public expenditure 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



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

All levels of education (Current prices) Mio PPS




Expenditure on R&D : 0.4 0.2 0.5 0.4 : 0.3 0.3 0.4 0.3 0.4 :

Estimates of total education spending Method 1 : : 5.0 : : : : : : : : : Method 2 6.6 5.3 : 6.0 : 5.2 5.4 6.6 : : : Method 3 6.3 5.2 : : : : : : : 5.4 : Method 4 6.6 5.3 : 5.8 : : 5.4 6.8 5.1 5.5 : Average : 5.2 : : : : : : : : : Of which public expenditure 6.1 4.5 8.3 4.7 5.7 3.9 4.3 5.9 4.4 4.7 :

Mio PPS

LV LT LU HU MT NL AT PL PT SI SK FI SE UK Educational institutions 5.8 5.3 3.7 6.1 5.9 5.0 5.5 6.2 5.6 6.3 4.7 6.1 6.8 5.9

Of which public expenditure 5.0 4.9 3.7 5.5 4.5 4.5 5.2 5.6 5.5 5.5 4.2 6.0 6.5 4.9

Expenditure on ancillary services : : : : 4.6 : : : : : 4.0 : : : Expenditure on R&D 0.2 0.2 0.5 0.4 0.2 0.2 0.1 0.7 0.9 0.3

Estimates of total education spending Method 1 6.1 5.3 : : : : : : : 7.2 : : 7.7 5.9 Method 2 7.2 : : 7.1 : : 6.2 : : 6.9 : : 8.3 7.2 Method 3 6.1 5.4 : : : : : : : 7.4 4.9 : 8.1 6.8 Method 4 7.1 : : 7.3 : 6.1 6.2 : : 6.9 5.1 : 8.2 7.0 Average 6.4 : : : : : : : : 7.1 : : 8.1 6.7 Of which public expenditure 5.3 5.2 3.8 5.9 4.8 5.1 5.5 5.6 5.6 6.0 4.3 6.5 7.5 5.2



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

Mio PPS

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

Mio PPS

IS LI NO BG HR RO MK TR JP US Educational institutions 439.2 : 8466.3 : : 3581 : 13764.5 135279.1 590432.7 Expenditure on ancillary services : : : : : : : : : :

IS LI NO BG HR RO MK TR JP US Educational institutions 490.3 : 8937.1 1996.5 : : : 12879.1 138381.9 637968.3 Expenditure on ancillary services : : : : : : : : : :



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

Mio PPS

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

All levels of education (Current prices)

Mio PPS

IS LI NO BG HR RO MK TR JP US Educational institutions 538.1 : 9581.0 2012.9 1849.1 : : : 142034.3 695332.4 Expenditure on ancillary services : : : : : : : : : :

IS LI NO BG HR RO MK TR JP US Educational institutions 604.1 : 9368.9 2344.9 1993.8 4853.3 : 15124.9 145867.7 687421.7 Expenditure on ancillary services : : : : : : : : : :

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



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

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

Type Type of inputs and outputs Internal efficiency

Monetary input to non-monetary output Efficiency

External efficiency

Monetary input to monetary output

Internal effectiveness

Non-monetary input to non-monetary output Effectiveness External

effectiveness Non-monetary input to monetary output



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 inputs themselves, and between teaching/learning processes, e.g. attendance/participation, 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.

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




- 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



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).



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.



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 = Input

Output

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



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.



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.



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:

tda

dtt

d

t

tt iBiCNPV )1/()1/(

641

0+++= ∑∑

−−

−

−

=

where:

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.



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.



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;



- 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 iinn rreellaattiioonn ttoo tthhee bbeenncchhmmaarrkk iinnddiiccaattoorrss 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.



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 Total spending on education as a % of GDP h Percentage of pupils with reading literacy level 1 and lower in the PISA reading literacy scale. h


h


h

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


h



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.



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)

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.

USNO

IS

RO BGUK

SEFISK SI

PT

PL

AT

NL

MT

HU

LU

LT

LV

IT

IE FR

ES

EL EE

DE DK

CZ

BE

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6Exp as % of GDP

Ed

atta

inm

ent %



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)

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.

BE

CZ

DKDEEE

EL

ES

FRIE

IT

LV

LT

LUHU

MT

NL

ATPL

PT

SISK

FI SE

UK

BGROIS

NO

US

0

10

20

30

40

50

60

0 1 2 3 4 5 6Exp as % of GDP

% E

arly

scho

ol le

aver

s



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)

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

US NO

IS

RO BG

UK

SE

FI

SK SIPTPL AT

NL

MTHU

LT

LVIT

IE

FR

ES

EE

DE

DK

CZ

BE

0

5

10

15

20

25

30

0.0 0.5 1.0 1.5 2.0 2.5 3.0Exp as % of GDP

MST

gra

ds a

ged

20-2

9 pe

r 10

00



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)

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

USNO

IS

RO

BG

UK

SE

FI

SKSIPT

PLAT

NL

MTHU

LT

LVIT

IE

FR

ES

EEDE

DK

CZ

BE

0

5

10

15

20

25

30

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0

Exp as % of GDP

MST

gra

ds a

ged

20-2

9 pe

r 10

00



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)

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

US NO IS

SE

FI

SK

PT

PL

AT

NL

LV

IT

IE

FR

ESDE

DKCZ

0

5

10

15

20

25

30

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5Exp as % of GDP

% R

eadi

ng li

tera

cy <

=1



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.



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



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.



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

% of MSs total estimated Method Sources used 2000 2001 2002 2003

1 UNESCO/OECD/Eurostat data collection on education systems; National household budget surveys; CVTS2

36

32 24 24

2 UNESCO/OECD/Eurostat data collection on education systems; National Accounts; CVTS2

48 48 40 48

3 UNESCO/OECD/Eurostat data collection on education systems; National household budget surveys; EU Labour Cost Survey

48 44 40 36

4 UNESCO/OECD/Eurostat data collection on education systems; National Accounts; EU Labour Cost Survey

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.

Efficiency and effectiveness of education systems


Table 3.4: Availability of data for total spending on education for method of estimation by year for the EU

2000 2001 2002 2003 Country 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

BE h h h h h h h h :: :: :: :: :: h h h CZ h h h h h h h h h h h h h h h h DK h h h h :: :: :: :: :: h h h :: :: :: :: DE h h h h h h h h h h h h :: h :: h EE :: :: :: :: :: :: :: :: :: :: :: :: :: :: :: :: EL :: :: :: :: :: :: :: :: :: :: :: :: :: h :: ES h h h h h h h h :: h :: h :: h :: h FR :: h :: h h h h h :: h :: h :: h :: h IE :: :: h h :: :: :: h :: :: :: h :: :: :: h IT :: :: h h :: :: h h :: :: h h :: :: h h CY :: :: :: h :: :: :: :: :: :: :: :: :: :: :: :: LV h h h h :: :: :: :: h h h h h h h h LT :: :: :: :: :: :: :: :: :: :: :: :: h :: h :: LU :: :: :: :: :: h :: h :: :: :: :: :: :: :: :: HU :: h :: h h :: h h h h h h :: h h MT :: :: :: :: :: :: :: :: :: :: : :: :: :: :: NL :: :: h h :: :: h h :: :: h h :: :: :: h AT h h h h :: h h :: h :: h :: h :: h PL :: :: :: :: :: :: :: :: :: :: :: :: :: :: :: :: PT :: :: :: :: :: :: :: :: :: :: :: :: :: :: :: :: SI h :: :: :: h h h h h h h h h h h h SK :: h h :: :: :: h h :: :: h h :: :: h h FI :: :: :: :: :: :: :: :: :: :: :: :: :: :: :: SE :: h h :: :: :: h :: :: :: :: h h h h UK

h h h h

h h h h

h 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

household budget surveys; CVTS2 2 UNESCO/OECD/Eurostat data collection on education systems; National Accounts;

CVTS2 3 UNESCO/OECD/Eurostat data collection on education systems; National

household budget surveys; EU Labour Cost Survey 4 UNESCO/OECD/Eurostat data collection on education systems; National Accounts;

EU Labour Cost Survey



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 2000 2001 2002 2003 BE h h h h CZ h h h h DK h h h h DE h h h h EE : : : : EL h h h h ES h h h h FR h h h h IE h h h h IT h h h h CY h : : : LV h h h h LT : : : : LU : : : : HU h h h h MT h h h h NL h h h h AT h h h h PL h : : : PT : h h h SI : : : : SK h h h h FI h h h h SE h h h h UK h h h h BG : : : : RO h : : : IS h h h h LI : : : : NO h : : : HR : : : : TR h h h : MK : : : h JP h h h h US h h h h

Key h Data available : Data not available Efficiency for the country cannot be calculated



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

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

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

Eurostat, Labour Force Survey

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



UNESCO/OECD/Eurostat data collection

Percentage of pupils with reading literacy level 1 and lower in the PISA reading literacy scale.

OECD The PISA International Database



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

Eurostat, Labour Force Survey Annual 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 EU Member States not covered 2000 EE, CY, LT, MT, SI, SK 2003 EE, CY, LT, MT, SI 2006 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.



Table 3.9: Availability of data for each output indicator by year for the EU

2000 2001 2002 2003 Country

Ed attain

ESL MST Reading literacy

Ed attain


Ed attain


Ed attain


BE h h h h h h h : h h h : h h h h CZ h : h h h : h : h h h : h h h h DK h h h h h h h : h h h : h h h h DE h h h h h h h : h h h : h h h h EE h h h : h h h : h h h : h h h : EL h h h h h h : : h h : : h h : h ES h h h h h h h : h h h : h h h h FR h h h h h h h : h h : : h h : h IE h h h h h : h : h h h : h h h h IT h h h h h h h : h h h : h h h h CY h h h : h h h : h h h : h h h : LV h : h h h : h : h h h : h h h h LT h h h : h h h : h h h : h h h : LU h h h h h h : : h h : : h h : h HU h h h h h h h : h h h : h h h h MT h h h : h h h : h h h : h h h : NL h h h h h h h : h h h : h h h h AT h h h h h h h : h h h : h h h h PL h : h h h : h : h h h : h h h h PT h h h h h h h : h h h : h h h h SI h : h : h : h : h h h : h h h : SK h : h : h : h : h h h : h h h : FI h h h h h h h : h h h : h h h h SE h h h h h h h : h h h : h h h h UK

h h h h

h h h :

h h h :

h h h :

BG h : h h h : h : h h h : h h h : RO

h h h hh

h h h :

h 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



Table 3.10: Availability of data for each output indicator by year for non-EU countries

2000 2001 2002 2003 Country Ed

attain ESL MST Reading

literacy Ed


literacy Ed


literacy Ed


literacy IS h h h h h h h : h h h : h h h h LI : : : : : : : : : : : : : : : : NO h h h h h h h : h h h : h h h h HR : : : : : : : : h h : : h h : : TR h h h :: h h h : h h h : h h h : MK : : h :: : : : : : : h : : : h : JP h h h hh : h h : h h h : : : h hh US

h h h hh

h h h :

h h h :

h h h hh Key h Data available : Data not available Efficiency for all indicators cannot be calculated due to data being missing for one or more output



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.



Figure 3.6: Analysis of the flow of pupils through the education system

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

Pupils in a certain grade

Promotees

Dropouts

Repeaters



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.



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.



Table 3.11: Availability of data on enrolments and repeaters in the UNESCO/OECD/Eurostat data collection by year and by ISCED level

ISCED 1 ISCED 2 ISCED 3 2000 2001 2002 2003 2000 2001 2002 2003 2000 2001 2002 2003

Country

Enr Rep Enr Rep Enr Rep Enr Rep Enr Rep Enr Rep Enr Rep Enr Rep Enr Rep Enr Rep Enr Rep Enr Rep BE h : h : h : h : : : h : h : h : : : h : h : h : CZ h h h h h h h h h h h h h h h h h h h h h h h h DK h h h h h : h h h : h : h : h : h : h : h : h h DE h h h h h h h h h h h h h h h h h h h h h h h h EE h h h h h h h h h h h h h h h h h h h h h h h h EL : : : : : : : : : : : : : : : : : : : : : : : : ES h : h : h : h : h : h : h : h : h : h : h : : : FR h h : : : : : : h : h : : : : : h h h h h : h : IE h h h h h h h h h h h h h h h h h h h h h h : h IT h h h h h h h h h h h h h h h h h h h h h h h h CY h h h h h h h h h h h h h h h h h h h h h h h h LV h : h h h : h h h h h h h h h h h h h h h h h : LT h h h h h h h h h h h h h h h h h h h h h h h h LU h h h h h h h h h : h : h : h : h : h : h : h : HU h h h h h h h h h h h h h h h h h h h h h h h h MT h h h h h h h h h h h h h h h h h h h h h : h : NL h : h : h : h : h h h h h h h h h h h h h h h h AT : : h h h : h : : : h h h : h : h : h h h : h : PL h h h h h h h h h : h : h h h h h h h h h h h h PT : : : : : : : : : : : : : : : : : : : : : : : : SI h h h h h h h h h h h h h h h h h h h h h h h h SK h h h h h h h h h h h h h h h h h h h h h h h h FI h h h h h h h h h : h : h : h : h : h : h : h : SE h : h : h : h : h : h : h : : : h : h : h : : : UK

: : : : : : : :

: : : : : : : :

: : : : : : : : Key h Data available : Data not available



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.



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) Tertiary-type A education

(ISCED 5A) Tertiary-type B education

(ISCED 5B) Duration of programmes Duration of programmes

All 3 to less than 5 years

5 to 6 years

More than 6 years

All 2 to less

than 3 years

3 to less

than 5 years

5 years

or more

ISCED 6

Country

1 2 3 4 5 6 7 8 9 EU19 71 74 72 11 60 36 41 : 56

BE (FL) 74 75 71 82 85 a 85 na : CZ 65 74 60 na 61 66 60 na 44 DK : : : : : : : : : DE 73 92 65 na 79 87 72 na : EL 79 78 83 na 35 a 35 na 34 FR : : : : : : : na : IE 83 x(1) x(1) x(1) 69 x(5) x(5) x(5) : IT : : : : : : : : 88 LU : : : : : : : : : HU 64 64 x(2) x(2) 48 48 : na 37 NL 76 76 x(2) na na na na na : AT 65 x(1) x(1) na : : : na : PL 66 65 66 na 74 na 74 na : PT 68 62 72 na 58 na 58 na 65 SK : : : na 77 80 69 na : ES 74 71 76 na 79 79 na na : FI 71 x(1) x(1) x(1) : : na na : SE 60 x(1) x(1) na 68 x(1) na na : UK 78 78 84 53 53 x(5) x(5) x(5) 70 TR 74 74 x(2) na 79 79 na na 75 IS : : : : : : : : :

NO : : : : : : : : : JP 91 91 90 na 87 87 x(6) x(6) 89 US 54 x(1) : na : : : : :

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.



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:




- 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.

CChhaapptteerr

44

SSoocciiaall iinncclluussiioonn//eexxcclluussiioonn aannddtthhee 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



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);

ii. ‘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).

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



Figure 4.1: Low educational attainment of individuals aged 25- 64 and early school leavers aged 18-24 in percentages (2005)

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

EU-25BE

CZ

DE

EE

EL

ES

FRIE

IT

CY

LVLT

LU

HU

MT

NLAT

PL

PT

SI

SK

FI

SE

UK

0

10

20

30

40

50

60

70

80

0 5 10 15 20 25 30 35 40 45Early school leavers %

Low

edu

catio

nal a

ttai

nmen

t %



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



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 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 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/



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



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.



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

Source: Higher Education Statistical Agency, UK

Notes 1 Medicine & dentistry and veterinary science B Social studies 2 Subjects allied to medicine C Law 3 Biological sciences D Business & administrative studies 5 Agriculture & related subjects E Mass communications & documentation 6 Physical sciences F Languages 7 Mathematical sciences G Historical & philosophical studies 8 Computer sciences H Creative arts & design 9 Engineering & technology I Education A Architecture, building, and planning 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/

0

5

10

15

20

25

1 2 3 5 6 7 8 9 A B C D E F G H I JSubject of study

% o

f you

ng e

ntra

nts



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 %) Movement disabilities

Year Type of course

Total Deaf & hard of-hearing

Blind & partially sighted Walking can't walk

Other

2001 Day 0.36 0.03 0.05 0.10 0.02 0.17 2001 Evening 0.08 0.01 0.01 0.02 0.01 0.03 2001 Distance 0.1 0.004 0.008 0.018 0.010 0.029 2002 Day 0.46 0.04 0.05 0.11 0.02 0.25 2002 Evening 0.100 0.010 0.012 0.016 0.009 0.052 2002 Distance 0.093 0.006 0.008 0.022 0.011 0.046 2004 Day 0.71 0.05 0.06 0.14 0.02 0.44 2004 Evening 0.24 0.02 0.02 0.05 0.01 0.14 2004 Distance 0.003 0.0002 0.0002 0.001 0.0001 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



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

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 to the same concepts, definitions, and classifications and methodology.

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





UNESCO/OECD/Eurostat data collection



Percentage of pupils with reading literacy level 1 and lower in the PISA reading literacy scale.

OECD The PISA International Database



Table 4.3: Availability of data needed to measure social inclusion in international data sources Data source Sex1 Age Socio-

economicIncome Ed

attainment Occupation Industry Labour

force status

Geography Minority groups

Students with

special needs

ESL Eurostat, Labour Force Survey n n2 n n10

Ed attainment

Eurostat, Labour Force Survey n n2 n n10

MST graduates

UNESCO/OECD/Eurostat data collection n

LLL Eurostat, Labour Force Survey n n n3 n n n n n n n10

Reading literacy

OECD The PISA International Database n na4 n5 n6 n7 n8 n9 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



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



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. Native; ii. Non-native.



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.



Table 4.4: EU Member States participating in the OECD Programme for International Student Assessment (PISA)

EU Member State

2000 2003 2006

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



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. The highest international socio-economic index of occupational status of the father or mother;

ii. The highest level of education of the father or mother converted into years of schooling;

iii. 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,



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 clerk,

iii. Blue-collar high-skilled: skilled agricultural and fishery workers and craft and related trades workers; and

iv. 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:



i. 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;

ii. 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.



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)



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 iinncclluussiioonn//eexxcclluussiioonn iinn rreellaattiioonn ttoo bbeenncchhmmaarrkk iinnddiiccaattoorrss 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

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

0

10

20

30

40

50

60

70

80

90

2000 2001 2002 2003 2004 2005

Edu

catio

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ttai

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t %

Male Female



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

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.

0102030405060708090

100

Edu

catio

nal a

ttai

nmen

t %

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

EU25

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



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

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)

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.

0

5

10

15

20

25

2000 2001 2002 2003 2004 2005

% E

arly

scho

ol le

aver

s

Males Females

05

10152025

303540

4550

% E

arly

scho

ol le

aver

s

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

EU25




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 EL ES FR IT HU AT SI SK FI SE RO 1-2 26 20 40 26 38 33 24 10 14 13 18 47 3-4 12 8 21 17 19 9 13 8 2 15 12 14 5-6 3 11 11 6 11 3 10 : : 8 10 :

Source: Eurostat

Key ISCED 0 to 2 Pre-primary, primary and lower secondary education ISCED 3 Upper secondary education ISCED 5 to 6 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).



Figure 4.7: Graduates (ISCED 5-6) in mathematics, science and technology per 1000 of population aged 20-29 in the EU-25

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

Source: Eurostat

0

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2000 2001 2002 2003 2004

Wom

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00 m

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radu

ates

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81012

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2000 2001 2002 2003 2004

MST

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r 100

0

Total Male Female



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

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).

Indicator 2000 2001 2002 2003 2004 Female graduates (ISCED 5-6) in education and training field - as % of males and females graduates 75.9 76.2 76.5 76.9 77.3 Female graduates (ISCED 5-6) in humanities and art field - as % of males and females graduates 69 68.8 68.6 68.4 69.1 Female graduates (ISCED 5-6) in social science, business and law field - as % of males and females graduates 58.6 59.2 60 60.8 61.5 Female graduates (ISCED 5-6) in science, mathematics and computing field - as % of males and females graduates 42.2 41.9 41.7 41.8 39.8 Female graduates (ISCED 5-6) in engineering, manufacturing and construction field - as % of males and females graduates 21.1 21.8 22.3 22.7 23.6 Female graduates (ISCED 5-6) in agriculture and veterinary field - as % of males and females graduates 45 45.8 46.5 48 48.7 Female graduates (ISCED 5-6) in health and welfare field - as % of males and females graduates 74.1 75.8 76.1 76.7 77.2 Female graduates (ISCED 5-6) in services field - as % of males and females graduates 50.8 52.1 52.5 52.8 54.6



Figure 4.9: Graduates (ISCED 5-6) in mathematics, science and technology per 1000 of population aged 20-29 in 2004

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

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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

EU25




Figure 4.10: Students’ fathers compared to all men of corresponding age groups* who are economically active (2005)

Source: Eurostudent 2005

Figure 4.11: Students’ mothers compared to all men of corresponding age groups* who are economically active (2005)


Notes * Men/women aged in AT, DE, ES, NL, PT: 40-60; IE: 35- 54

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.

0102030405060708090

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0102030405060708090

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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)


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)

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).

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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)


Figure 4.15: Students’ mothers compared to all women of corresponding age groups with higher education in percentages (2005)


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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)


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)

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.

0 500 1000 1500 2000 2500 3000 3500

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Figure 4.18: Percentage of the adult population aged 25 to 64 participating in education and training in the EU-25

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)

Source: Eurostat – EU Labour Force Survey

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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

EU25




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)

Males Females Country 25-34 35-44 45-54 55-64 25-34 35-44 45-54 55-64

EU-25 50.5 45.4 41.2 31.3 49.9 44.5 39.5 27.7 BE 51.4 47.2 43.8 30.2 50.4 43.0 38.4 23.8 CZ 36.2 32.2 28.2 21.9 30.7 32.6 27.6 17.3 DK 81.4 82.2 79.5 72.2 83.2 84.7 80.2 72.1 DE 51.9 45.7 42.2 33.2 48.3 43.8 39.7 30.1 EE 41.5 31.1 25.9 17.4 40.8 40.1 32.8 14.7 EL 26.7 19.9 15.2 9.5 27.8 17.8 11.3 5.1 ES 31.7 25.9 20.1 13.7 35.1 26.0 19.1 13.6 FR 63.4 59.2 55.3 36.3 58.8 51.3 46.7 28.3 IE 46.7 47.1 42.2 37.7 55.2 56.3 52.0 46.5 IT 57.7 53.3 50.4 38.9 57.1 49.7 42.7 32.2 CY 53.6 43.5 35.6 23.6 50.1 39.3 29.6 14.8 LV 49.1 44.8 37.9 34.0 63.4 52.3 45.9 36.8 LT 30.0 23.9 22.5 13.9 38.3 38.8 27.8 18.1 LU 85.9 85.4 80.2 74.9 86.8 82.5 78.3 75.8 HU 18.2 11.0 7.6 4.9 20.8 14.9 9.0 4.1 MT 80.4 28.2 74.2 20.0 83.4 28.9 72.9 16.0 NL 53.2 47.0 40.0 33.1 47.9 40.3 38.8 26.7 AT 88.4 87.3 86.2 91.0 90.6 89.2 88.2 93.9 PL 39.4 31.2 24.7 18.2 42.3 34.9 26.8 14.5 PT 52.5 46.4 40.3 36.0 55.8 46.1 38.2 29.7 SI 84.8 80.7 79.5 77.7 87.8 84.9 80.8 78.6 SK 62.6 61.9 61.4 52.1 62.2 61.6 60.2 46.4 FI 82.4 78.1 71.6 62.0 86.8 86.6 80.5 69.3 SE 78.3 71.5 67.4 59.6 74.8 75.8 75.1 64.3 UK 43.7 40.8 36.1 23.8 44.5 43.6 41.1 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



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 ISCED 0 to 2 ISCED 3 ISCED 5 / 6 EU-25 23.1 44.2 68.7

BE 23.3 42.4 66.9 CZ 10.3 26.2 62.7 DK 61.5 77.4 93.4 DE 19.0 41.1 65.8 EE 10.1 25.0 51.8 EL 5.6 18.9 42.6 ES 12.6 30.3 47.7 FR 29.2 52.4 83.1 IE 34.5 51.4 66.4 IT 34.4 60.7 78.0 CY 8.5 34.2 76.1 LV 30.0 43.6 70.9 LT 5.8 20.9 59.6 LU 67.4 86.4 94.7 HU 3.7 11.3 27.0 MT 49.8 65.3 68.4 NL 20.1 42.6 66.2 AT 86.8 88.6 95.3 PL 9.2 26.5 73.9 PT 35.3 70.6 79.7 SI 66.8 83.2 96.7 SK 40.4 59.4 82.6 FI 60.9 76.8 90.1 SE 48.8 69.1 87.6 UK 12.2 36.9 60.8

Source: Eurostat Key ISCED 0 to 2 Pre-primary, primary and lower secondary education ISCED 3 Upper secondary education ISCED 5 to 6 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.



Table 4.9: Participation in any learning activities by occupation (2003)

Country ISCO 1 to 3 ISCO 6 to 7 ISCO 8 to 9 EU-25 64.8 34.4 27.7

BE 64.7 32.7 28.2 CZ 52.4 20.2 19.2 DK 92.7 73.8 60.8 DE 60.8 35.9 24.8 EE 63.6 20.7 13.7 EL 37.1 8.9 8.3 ES 43.5 14.8 14.7 FR 82.6 50.1 30.9 IE 59.5 39.9 35.5 IT 74.0 41.3 36.4 CY 78.3 22.6 11.8 LV 75.6 39.8 31.6 LT 66.6 16.2 12.6 LU 93.6 71.4 69.8 HU 25.8 6.8 6.5 MT 67.2 54.8 48.4 NL 60.4 26.9 19.7 AT 93.0 82.4 80.7 PL 67.2 21.3 20.2 PT 69.1 33.5 32.9 SI 94.4 76.7 73.0 SK 79.3 58.3 58.0 FI 90.2 71.0 66.9 SE 86.6 56.8 49.9 UK 57.2 26.8 22.3

Source: Eurostat

Key ISCO 1 to 3 Managers, professionals, technicians and associate professionals ISCO 4 to 5 Clerks and sales ISCO 6 to 7 Skilled agricultural and fishery workers, craft and related trades workers ISCO 8 to 9 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.



Figure 4.20: Participation in any learning activities by type of industry (2003)

Notes a Agriculture, hunting and forestry c_to_f Industry 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.

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a 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

c_to_f 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

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Figure 4.21: Participation in any learning activities by working status (2003)

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)

Source: Eurostat Key Deg1 Densely-populated area (at least 500 inhabitants/Km²) Deg2 Intermediate urbanised area (between 100 and 499 inhabitants/Km²) Deg3 Sparsely populated area (less than 100 inhabitants/Km²)

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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 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

In 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

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Deg1 44 43 32 84 44 20 27 52 54 48 44 35 80 14 44 91 50 82 75 37

Deg2 41 41 29 79 39 17 25 51 49 38 47 83 10 37 89 38 79 72 40

Deg3 41 44 26 76 40 31 10 20 49 46 48 28 46 22 82 11 46 88 42 74 69 37

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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

2000 2003 Country Male Female Male Female

BE 492 525 489 526 CZ 473 510 473 504 DK 485 510 479 505 DE 468 502 471 513 EE : : : : EL 456 493 453 490 ES 456 493 461 500 FR 481 505 476 514 IE 513 542 501 530 IT 469 507 455 495 CY : : : : LV 432 485 470 509 LT : : : : LU 429 456 463 496 HU 465 496 467 498 MT 481 505 477 516 NL : : 503 524 AT 495 520 467 514 PL 461 498 477 516 PT 458 482 459 495 SI : : : : SK : : 453 486 FI 520 571 521 565 SE 499 536 496 533 UK 512 537 492 520

Source: OECD PISA database 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



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

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.

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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 445 c 430 429 c 425 401 484 401 490 445 444 361 c 440 425 434 410 c c 403 a 404 ISCED 1 453 412 415 437 441 468 467 483 429 c 445 c 430 521 c 459 468 434 462 c 445 459 463 ISCED 2 498 459 468 487 452 502 480 505 454 461 507 430 452 523 401 495 493 426 485 511 486 464 440

ISCED 3B 487 467 480 519 458 480 491 a 496 501 475 458 491 a 439 530 503 c 494 536 497 501 a ISCED 3A 540 517 510 545 491 498 532 528 507 492 519 509 547 551 488 530 544 501 504 569 516 504 504

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)

BE CZ DK DE EL ES FR IE IT LV LU HU AT FI SK SE UK TR IS LI NO JP US

Yes 540 540 504 547 513 532 503 548 518 508 512 536 553 558 515 527 556 560 515 c 507 519 527 No 502 488 494 494 474 490 465 511 469 486 475 469 489 539 461 510 499 434 488 525 500 494 485

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) BE CZ DK DE EL ES FR IE IT LV LU HU AT FI SK SE UK TR IS LI NO JP US

Yes 537 471 517 522 485 501 471 528 478 491 506 495 507 551 452 528 526 433 507 498 521 512 500 No 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 IE IT LV LU HU AT FI SK SE UK TR IS LI NO JP US

Yes 504 513 492 528 475 a 477 532 469 488 468 484 488 550 463 a 503 c 484 c 498 a 491

No 512 495 495 495 481 a 471 507 477 493 482 481 494 543 471 a 506 c 495 523 502 a 497


Key a - The category does not apply in the country concerned. Data therefore missing c - There are too few observations to provide reliable estimates



Table 4.15: Mean performance of students per country, on the PISA reading literacy scale by father’s level of education (2003) ISCED 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 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 1 465 408 436 434 443 466 480 486 432 c 440 c 508 408 a 472 c 525 491 c 484 c 472 423 457 451 2 510 463 472 486 447 502 484 512 452 466 504 427 517 458 451 484 387 528 504 466 492 503 498 421 454 459 3B 484 478 493 516 469 472 495 a 489 498 487 464 a 488 478 500 443 a 520 498 504 528 498 392 504 a 3A 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

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 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 No 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


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 526 512 519 514 466 491 495 530 479 488 503 484 a 487 542 443 454 552 521 523 489 540 521 449 482 499 No 508 496 487 499 473 479 498 512 475 492 474 482 a 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 511 496 486 516 486 478 a 528 470 490 484 474 517 446 520 a 444 546 a 497 487 c 496 c a 496 No 511 496 495 498 470 481 a 511 477 491 479 484 518 494 494 a 474 543 a 508 496 525 502 c a 496

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



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.



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 IE IT LV LU HU NL AT PL PT SI SK FI SE UK IS LI NO TR 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 c 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

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 IE IT LV LU HU NL AT PL PT SI SK FI SE UK IS LI NO TR 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 c 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 c 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 c 467 441 500 485

Source: OECD PISA database Key

1 Working Full-time 2 Working Part-time 3 Looking for work 4 Other

a - The category does not apply in the country concerned. Data therefore missing c - There are too few observations to provide reliable estimates



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…



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)

International socio-economic index of occupational status

Performance on the PISA reading literacy scale, by national quarters of the international socio-economic index of occupational status

Country

Bottom quarter

Second quarter

Third quarter

Top quarter

Bottom quarter

Second quarter

Third quarter

Top quarter

BE 28.4 42.1 53.5 71.8 457 497 537 560 CZ 31.2 44.4 51.5 66.1 445 487 499 543 DK 29.0 44.0 54.9 71.1 465 490 511 543 DE 30.0 42.6 52.5 70.2 427 471 513 541 EL 25.6 40.2 53.0 72.3 440 460 486 519 ES 26.8 36.2 49.6 67.3 461 482 507 529 FR 27.7 41.1 53.1 71.2 469 496 520 552 IE 28.5 42.7 53.2 69.4 491 520 535 570 IT 28.5 40.6 50.3 68.9 457 481 494 525 LV 27.7 40.4 58.5 74.1 428 449 479 492 LU 25.1 37.5 50.6 66.1 394 428 473 497 HU 30.4 42.6 53.7 71.5 435 461 504 531 AT 32.9 44.7 52.2 69.1 467 500 522 547 PL 27.3 40.0 49.8 67.0 445 472 493 534 PT 26.8 34.5 48.4 65.7 431 452 485 527 FI 29.7 43.4 55.1 71.8 524 535 555 576 SE 30.4 44.1 55.7 72.1 485 509 522 558 UK 30.7 45.7 56.9 71.8 481 513 543 579 IS 31.4 47.3 58.6 73.8 487 496 513 540 LI 28.0 41.8 52.1 68.2 437 491 495 523 NO 35.6 47.1 59.0 73.9 477 494 514 547 US 30.3 47.4 59.5 72.5 466 507 528 556

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.



Figure 4.24: Percentage of 15-year-olds expecting to have a white- or blue-collar occupation (2000)

Notes 1 Blue-collar low-skilled 2 Blue-collar high-skilled 3 White-collar low-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

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

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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

BE CZ DK DE EL ES FR IE IT LV LU HU NL AT PL PT FI SE UK IS LI JP US



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


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)


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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)


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,

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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)

IV

List of Figures & Tables



LLiisstt ooff FFiigguurreess && TTaabblleess

LLiisstt ooff ffiigguurreess Chapter 1 Figure

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

20

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

21

1.3 Trends in number of upper secondary school dropouts in Japan 23 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

27

1.5 Percentage of the population aged 25-64 participating in education broken down by type of education and sex (US)

35

Chapter 2

2.1 Types of education and training included when different sources are combined 79 Chapter 3

3.1 Ordinary least squares regression analysis 99 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)

110

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)

111

3.4 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)

112

3.5 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)

114

3.6 Analysis of the flow of pupils through the education system 126 Chapter 4

4.1 Low educational attainment of individuals aged 25- 64 and early school 136



leavers aged 18-24 in percentages (2005) 4.2 Percentage of young entrants to full-time first degree courses from low

participation neighbourhoods by subject and entry qualification 2004/05 141

4.3 Percentage of the female and male population aged 20 to 24 having completed at least upper secondary education in the EU-25 153

4.4 Percentage of the female and male population aged 20 to 24 having completed at least upper secondary education in 2005 154

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-25 155

4.6 Percentage of the population aged 18-24 with at most lower secondary education and not in further education or training (2005) 155

4.7 Graduates (ISCED 5-6) in mathematics, science and technology per 1000 of population aged 20-29 in the EU-25 157

4.8 Women per 100 men graduating from ISCED levels 5-6 157 4.9 Graduates (ISCED 5-6) in mathematics, science and technology per 1000 of

population aged 20-29 in 2004 159

4.10 Students’ fathers compared to all men of corresponding age groups* who are economically active (2005) 160

4.11 Students’ mothers compared to all men of corresponding age groups* who are economically active (2005) 160

4.12 Students’ fathers compared to all men of corresponding age groups with working class status (blue collar) in percentages (2005) 161

4.13 Students’ mothers compared to all men of corresponding age groups with working class status (blue collar) in percentages (2005) 161

4.14 Students’ fathers compared to all men of corresponding age groups with higher education in percentages (2005) 162

4.15 Students’ mothers compared to all women of corresponding age groups with higher education in percentages (2005) 162

4.16 Income cut-off point between the upper and lower half of students’ parent income distribution, median in € (2005) 163

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) 163

4.18 Percentage of the adult population aged 25 to 64 participating in education and training in the EU-25 164

4.19 Percentage of the adult population aged 25 to 64 participating in education and training (2005) 164

4.20 Participation in any learning activities by type of industry (2003) 168 4.21 Participation in any learning activities by working status (2003) 169 4.22 Participation in any learning activities by type of location (2003) 169 4.23 Percentage difference in PISA reading literacy scores between males and

females in 2000 and 2003 171

4.24 Percentage of 15-year-olds expecting to have a white- or blue-collar occupation (2000) 178

4.25 Differences in reading performance by immigration status 178 4.26 Percentage of pupils with reading literacy level 1 and lower in the PISA

reading literacy scale by immigration status 179

4.27 Performance on the reading scale by immigration status if language spoken at home is different from test language (2003) 179

4.28 Performance on the reading scale by immigration status if language spoken at home is the same as test language (2003) 180



LLiisstt ooff ttaabblleess Chapter 1

1.1 List of indicators and countries with data gaps 10 1.2 Reconciling information required for the indicators with data currently

available 12

1.3 Enrolment in higher education 13 1.4 Enrolment in science and engineering 13 1.5 Graduates in maths, science and technology 13 1.6 Total graduates in all programmes 14 1.7 Graduates in mathematics, science, and technology from regular higher

education institutions 15

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)

16

1.9 Number of students enrolled in higher education in India 17 1.10 Number of students enrolled in science and engineering in India 16 1.11 Number of students admitted to engineering in India 17 1.12 Number of engineering graduates (holders of degree and diploma) in India 17 1.13 Number of engineering graduates at degree level in India 18 1.14 Estimated stock of science graduates and postgraduates in India 18 1.15 Estimated stock of engineers by type of qualification in India 19 1.16 Number of MST graduates in India 19 1.17 Overview of data collected on youth educational attainment for Japan and the

USA 20

1.18 Population that has attained at least upper secondary education in 2003, total and broken down by gender in percentages in Japan and the USA 20

1.19 Population (aged 20-24) that has attained at least upper secondary education in 2000, total and broken down by gender in Japan 21

1.20 Overview of data collected on low educational attainment for Japan and the USA for the age group 18 to 24 22

1.21 Share of the population aged 20 – 24 with only lower secondary education and not in education and training (2000) in Japan 23

1.22 Number of 18 to 24 year olds, who received a GED, by data source: 1990 through 2002 25

1.23 Percentage of 18 to 24 year olds who received a GED qualification according to the CPS, and the GED service 25

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 26

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

30

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 31



and the restricted definition 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

32

1.28 Percentage of population aged 25 –64 participating in education and training (2001) in Japan 33

1.29 Percentage of population aged 25 – 64 attending school in Japan (2000) 34 1.30 Percentage of population aged 25-64 participating in adult education in

USA(2001) 34

1.31 Percentage of population aged 25-64 participating in any form of education and training in USA (2001) 35

1.32 Number of adults and rates of participation in any type of adult education by age groups in USA (2001) 36

1.33 Number of adults and rates of participation in any type of adult education by highest level of education received in USA (2001) 36

1.34 Summary of main aspects of surveys in the EU, Japan and US collecting information concerning participation of adults in education and training 38

1.35 Percentage of population aged 25-64 participating in education and training 12 months prior to the survey 39

1.36 Participation in non-formal job-related continuing education and training for the labour force, by age and gender (2003) 40

Chapter 2

2.1 Summary of data required to calculate total expenditure on education 55 2.2 Periodicity and availability of data from international data collections 56 2.3 Geographical coverage of international data collections 57 2.4 Summary of data collected on public and private expenditure on education by

international data collections 58

2.5 Coverage of education expenditure by international data collections 58 2.6 Availability of required data in the UOE data collection for the year 2003 by

variable (all levels of education) 59

2.7 Availability of data on student loans in the UOE data collection by year (all levels of education) 61

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)

63

2.9 Breakdown of variables and the corresponding sources of data 68 2.10 Availability of data in the UOE data collection on ancillary services by

variable (2003) 71

2.11 Public and private expenditures on ancillary services in higher education (2003) 72

2.12 List of educational goods and services for the UOE data collection 74 2.13 Costs of training by enterprises included in the CVTS2 and the LCS 75 2.14 Coverage of economic activities in the CVTS2 and the LCS 76 2.15 Overview of the coverage of education and training by source of funding and

data source 78

2.16 Coverage of non-EU countries in EU data sources collecting information on continuing vocational training 80



Chapter 3

3.1 Overview on analysing efficiency and effectiveness 97 3.2 Benchmark indicators broken down by type of indicator 108 3.3 Sources used to estimate total spending on education and percentage of

Member States where spending was estimated by year 118

3.4 Availability of data for total spending on education for method of estimation by year for the EU

119

3.5 Availability of data on total spending on educational institutions by year from the UOE

120

3.6 Data sources used to provide the data for the output indicators 121 3.7 Periodicity of data collection 122 3.8 EU Member States not participating in the PISA survey by year 122 3.9 Availability of data for each output indicator by year for the EU 123

3.10 Availability of data for each output indicator by year for non-EU countries 124 3.11 Availability of data on enrolments and repeaters in the

UNESCO/OECD/Eurostat data collection by year and by ISCED level 129

3.12 Survival rates in tertiary education (2004) 131 Chapter 4

4.1 Share of disabled students in higher education by type of disability (in %) in Poland 142

4.2 Data sources used to construct the benchmark indicators 143 4.3 Availability of data needed to measure social inclusion in international data

sources 144

4.4 EU Member States participating in the OECD Programme for International Student Assessment (PISA) 148

4.5 Share of early school leavers (aged 15- 35) by parents highest educational attainments in %, (2000) 156

4.6 Female graduates by field of education as a percentage of males and female graduates 158

4.7 Participation in any learning activities by age group (2003) 165 4.8 Participation in any learning activities by educational attainment (2003) 166 4.9 Participation in any learning activities by occupation (2003) 167

4.10 Mean performance of students per country, on the PISA reading literacy scale by gender in 2000 and 2003 170

4.11 Mean performance of students per country, on the PISA reading literacy scale by mothers level of education (2003) 172

4.12 Mean performance of students per country, on the PISA reading literacy scale with mother with ISCED level 5A/6 (2003) 172

4.13 Mean performance of students per country, on the PISA reading literacy scale with mother with ISCED level 5B (2003) 172

4.14 Mean performance of students per country, on the PISA reading literacy scale with mother with ISCED level 4 (2003) 172

4.15 Mean performance of students per country, on the PISA reading literacy scale by father’s level of education (2003) 173

4.16 Mean performance of students per country, on the PISA reading literacy scale with father with ISCED level 5A/6 (2003) 173

4.17 Mean performance of students per country, on the PISA reading literacy scale with father with ISCED level 5B (2003) 173



4.18 Mean performance of students per country, on the PISA reading literacy scale with father with ISCED level 4 (2003) 173

4.19 Mean performance of students per country, on the PISA reading literacy scale by labour force status of mother (2003) 175

4.20 Mean performance of students per country, on the PISA reading literacy scale by labour force status of father (2003) 175

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)

177

4.22 Mean performance of students per country, on the PISA reading literacy scale by labour force status of father (2003) 137

4.23 International socio-economic index of occupational status (ISEI) and performance on the PISA reading literacy scale, by national quarters of the index

139

4.24 Percentage of 15-year-olds expecting to have a white- or blue-collar occupation (2000) 140

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