1. UOE 2006 ad hoc module, trend data collections and revision policy
ETS WG, 30 January-1 February 2006
Agenda Item 8
document 2005-ETS-08-EN
Education and Training Statistics Working Group

2. This presentation aims at:
underlining the importance of time series for the follow-up of the Lisbon objectives
describing the current situation as regards the availability of time series based on data from the UOE data collection
proposing a revision policy that guarantees stable time series for key indicators based on data from the UOE data collection
proposing an implementation timetable for the revision policy
Identifying key indicators
Education and Training Statistics Working Group

3. Time series are important to follow-up the Lisbon objectives:
The follow-up of the Lisbon objectives on education and training is based on a set of indicators that is published annually by DG Education and Culture in the report “progress towards the Lisbon objectives in education and training”
Hence, requirement for accurate indicators that are comparable over time to precisely monitor the performance and progress
Education and Training Statistics Working Group

4. Data collection activities performed since 2000 to create time series:
A strong demand for time series emerged, soon after the Lisbon agenda was formulated by the Council in 2000
To satisfy the demand for monetary indicators Eurostat launched two Rapid Data Collections in 2002 and 2003
2002: Collection of data on total public expenditure on education, as the indicator total public expenditure on education as % of GDP was selected as a structural indicator
2003: Collection of monetary data by Eurostat:
to update the structural indicator and
to collect new indicators on education finance which are used to monitor the progress of the concrete objective “making the best use of resources”.
To satisfy the demand for non-monetary indicators no extra data collection was necessary
Education and Training Statistics Working Group

5. When does a revision becomes necessary?
Data on education statistics should reflect „real“ changes in the data as they illustrate the evolution of education systems in Member States
No revision is needed in case:
Changes in the education system occur (for example if an educational programme appears or disappears from the ISCED mapping)
A revision becomes necessary as soon as:
Changes in the ISCED mapping due to prior misclassification occur (for example if an educational programme is shifted from one level of education to another due to earlier misclassification)
Changes in the coverage of the data collection occur (for example ex-/inclusion of educational programmes compared to last year‘s data collection)
Changes in national or international methodology employed occur (for example if new/modified methodologies or estimation techniques are introduced in the data reported within the data collection)
Education and Training Statistics Working Group

6. Need to introduce a revision policy now:
Current situation:
No systematic revision policy was formulated
Some countries provide revised data on an ad-hoc basis
Revision of UOE data collection in 2005
Implementation of revised concepts in the 2005 round of the UOE data collection
=> Break in data series
=> Need to agree on a revision policy now
Education and Training Statistics Working Group

7. Requirements of a database oriented revision policy:
Database oriented revision policy means that:
UOE Data Collection is seen as an instrument to produce time series on enrolment, finance, graduates, mobility, teachers, classes, etc.
Requirement of stable data that are comparable over time
=> Requires revisions of data from data provider each time a change in concept or methodology at national or international level occurs
Education and Training Statistics Working Group

8. 2 options for revision policies:
Option 1: systematic and complete database-oriented revision policy
Any data affected by a change in coverage or methodology would be updated for some reference years backwards every time a revision occurs
Disadvantage: very heavy burden for data providers and data requesters
Advantage: guarantees a data set which is completely coherent and comparable over time
If option 1 would be implemented:
Any education indicator based on UOE data and disseminated in New Cronos would be comparable over time
Set of education indicators is coherent
Education and Training Statistics Working Group

9. 2 options for revision policies:
Option 2: time series oriented revision policy
Only data relevant for key indicators necessary to follow up the monitoring process of the Lisbon objectives would be revised
Advantage: extra burden for data providers and data requesters is reduced to a necessary minimum, while guaranteeing comparable time series for key indicators on education
Disadvantage: integrity of production database would be abandonned, as data would be split into two groups:
Historical data in which data of the UOE data collection are disseminated every year like it is done now without any revision, but with enough metadata to inform the data user of comparability problems
Time series for key indicators with relevant data to monitor the Lisbon process which should be updated annually if necessary by data providers
Education and Training Statistics Working Group

10. Eurostat proposal for a revision policy:
At this stage, Eurostat proposes to implement the second option (i.e. time series oriented revision policy) as revision policy:
as it guarantees stable time series for key indicators
while reducing the extra burden for data providers to a necessary minimum
Education and Training Statistics Working Group

11. Proposed revision policy shall be based on data needs from DG EAC:
Annual monitoring process of DG EAC requires comparable data every year
To follow the progress of the countries towards the Lisbon objectives complete time series are necessary from at least 1998 onwards
Essential to keep time series up to date every year (annual approach of revision policy)
Therefore, Eurostat proposes to add an additional table within the UOE data collection to update every year if necessary variables that are relevant for the monitoring process
Identification of key indicators (anticipation of new data needs from DG EAC for the follow up of progress of the countries towards the Lisbon objectives)
Education and Training Statistics Working Group

12. Implementation time table
UOE data collection 2006:
Eurostat data request that is linked to the revision policy, could be included in the ad-hoc module as OECD proposes to collect trend data on finance, enrolment, graduates and mobility
The final content of the ad-hoc module for 2006 would then reflect data needs from Eurostat and OECD
In the future data collections (2007 onwards):
Proposal to include every year an additional table with pre-filled data to enable data providers to regularly update outdated figures if necessary
The timetable would be the same as for the collection and production of education data (UOE and Eurostat tables)
Education and Training Statistics Working Group

13. Education and Training Statistics Working Group
Proposed procedure:
For each country, time series included in pre-filled files would be put on the circa web site when launching the next data collection round
Data would correspond to data that are currently included in the Eurostat production database and on which indicators disseminated in New Cronos are based on.
Data providers would be asked:
To download from the circa web site the file including the pre-filled table with their time series
To check the figures included in the file
To update outdated data
To confirm that the freshest data have been provided
To complete the time series by providing data for the reference year of the ongoing data collection
To send back the updated time series in the format of the pre-filled table when submitting the UOE data collection questionnaires to the unique address
Education and Training Statistics Working Group

14. Members of the WG are invited to:
Note the need for comparable data to allow a monitoring of the progress towards the Lisbon objectives
Agree on a time series oriented revision policy
Education and Training Statistics Working Group

15. Identified key indicators:
Objective: Improving the quality of teachers and trainers
Indicator titles
Data needs
Teachers aged < 30; 30-39; 40-49; 50 - teaching in public and private at ISCED level 1 - as % of total teachers teaching in ISCED level 1
PERS 1, column 4, rows A1 to A12
Teachers aged < 30; 30-39; 40-49; 50 - teaching in public and private at ISCED level as % of total teachers teaching in ISCED level 2-3
PERS 1, columns 5 and 6, rows A1 to A12
Ratio of Students to teachers ISCED 1-3; ISCED 1; ISCED 2; ISCED 3
PERS1, columns 4, 5 and 6, row A43
PERS_ENRL2, columns 4, 5 and 6. row A3
Education and Training Statistics Working Group

16. Identified key indicators:
Objective: Increasing recruitment to scientific and technical studies
Indicator titles
Data needs
Students at ISCED levels 5-6 enrolled in the following fields: science, mathematics, computing, engineering, manufacturing, construction - as % of all students
ENRL5, column 1, rows A1, A13, A18, A33
Female/Male students at ISCED levels 5-6 enrolled in the following fields: science, mathematics, computing, engineering, manufacturing, construction - as % of all female/male students
ENRL5, column 1, rows A34, A46, A51, A66, A67, A79, A84, A99
Graduates (ISCED 5-6) in the following fields: science, mathematics, computing, engineering, manufacturing, construction - as % of all graduates, all fields
GRAD5, columns 3, 9, 12; rows A1, A13, A18, A33
Education and Training Statistics Working Group

17. Identified key indicators:
Objective: Increasing recruitment to scientific and technical studies
Indicator titles
Data needs
Female/Male graduates (ISCED 5-6) in the following fields: science, mathematics, computing, engineering, manufacturing, construction - as % of total female/male graduates, all fields
GRAD5, columns 3, 9, 12; rows A34, A46, A51, A66, A67, A79, A84, A99
Graduates/Female graduates/Male graduates (ISCED 5-6) in the following fields: science, mathematics, computing, engineering, manufacturing, construction (1000)
Graduates/Female graduates/Male graduates (ISCED 5-6) in the following fields: science, mathematics, computing, engineering, manufacturing, construction - per 1000 of population/female population/male population aged 20-29
Education and Training Statistics Working Group

18. Identified key indicators:
Objective: Making best use of resources
Indicator titles
Data needs
Total public expenditure on education as % of GDP
FINANCE1, column 17, row G20
Foreseeable additional data needs:
FINANCE1, columns 2, 3, 6, 10, 15; row G20
FINANCE1, columns 2, 3, 6, 10, 15, 17; rows G5, G5b, G10 to G14;
FINANCE1, columns 15 and 17, row G5c
Private expenditure on educational institutions as % of GDP
FINANCE1, column 17, row P5
FINANCE1, columns 2, 3, 6, 10, 15; row P5
FINANCE1, columns 2, 3, 6, 10, 15, 17; rows H5 and E5
Education and Training Statistics Working Group

19. Identified key indicators:
Objective: Making best use of resources
Indicator titles
Data needs
Total expenditure on (public and private) educational institutions per pupil/student, by level of education (in EUR PPS)
FINANCE2, columns 2 to 8, 10 to 12, 15, 17;
row A20
FIN_ENRL2, columns 2 to 8, 10 to 12, 15, 17; row A3
Foreseeable additional data needs:
FINANCE2, columns 2 to 8, 10 to 12, 15, 17,
row A30
FINANCE2, columns 15 and 17; row A40
Total expenditure on (public and private) educational institutions per pupil/student relative to GDP per capita, by level of education
Education and Training Statistics Working Group

20. Identified key indicators:
Objective: Making learning more attractive
Indicator titles
Data needs
Students (ISCED 1_6) by sex aged years - as % of corresponding age population
ENRL1, columns 4, 6, 7,12 and 17, rows A15 to A25, A51 to A61, A87 to A97
Access to pre-primary education (participation rates in education of 4-year olds)
Foreseeable additional needs
ENRL1, columns 2 and 4, rows A4, A40 and A76
Upper secondary students in vocational streams and in ISCED 3A, B and C destinations
ENRL1A, columns 10 to 16, rows A1 to A3
Education and Training Statistics Working Group

21. Identified key indicators:
Objective: Improving foreign language learning
Indicator titles
Data needs
Average number of foreign languages learned per pupil at ISCED level 2/ISCED level 3 General programmes
ENRLLNG1, columns 3 and 6, rows A26 and B1
Percentage of pupils at ISCED levels 2 and 3 (GEN)
learning 0/ 1/ 2/ 3 and more foreign languages
ENRLLNG2, columns 3 and 6, rows A1 to A7
Education and Training Statistics Working Group

22. Identified key indicators:
Objective: Mobility and co-operation
Indicator titles
Data needs
Students (ISCED 5-6) studying in another EU-25, EEA or Candidate country - as % of all students
ENRL8A/ENRL8B, columns 1 and 5, rows A1, A114, A149, A159, A161, A163 to A170, A172, A 174 to A182, A185 to A189, A193 to A197, A200
Inflow of students (ISCED 5-6) from EU-25, EEA and Candidate countries - as % of all students in the country
Education and Training Statistics Working Group

23. Members of the WG are invited to:
Note the need for comparable data to allow a monitoring of the progress towards the Lisbon objectives
Agree on a time series oriented revision policy
Comment on the proposed list of data for which comparable data is necessary
Education and Training Statistics Working Group