The Educational Longitudinal Database (ELD) is a statistical framework for the compilation and analysis of learner outcomes over many years. The ELD provides the basis for a series of projects that the CSO has established in collaboration with Irish public sector bodies to examine learner outcomes across a range of educational levels and programmes. The ELD is produced by matching pseudonymised data on learners that have completed courses or programmes to other pseudonymised data that describe their outcomes in subsequent years. The data sources used to describe learner outcomes include:
The CSO is Ireland’s National Statistics Institute (NSI), which means we are uniquely placed from a legislative perspective to make use of administrative data from multiple public sector bodies and, where appropriate, to link the data together. The CSO is committed to providing more high-quality information on societal and economic change. Joining administrative data sources from public service bodies in a safe manner, while adhering to statistical and data protection legislation, can provide new analysis and outputs to support decision-making. Furthermore, data integration can lead to cost savings, greater efficiency and a reduction in duplication. Learn more about the CSO’s role in delivering better data here.
To facilitate linking data across administrative systems, the National Data Infrastructure (NDI) was established as a common platform for the collection of three key unique identifiers on public sector data holdings: Personal Public Service Number (PPSN), Eircode and Unique Business Identifier (UBI). Learn more about the National Data Infrastructure here.
In line with the CSO data protocol, data linking for statistical purposes is carried out by the CSO on pseudonymised datasets. This means that all identifiable information from each separate data source is removed, such as name, date of birth and addresses. The PPSN is replaced with a 'protected identifier key' (PIK) and it is this PIK which is used to link person-based data. Similarly, the Eircode is replaced with a PIK which is used to link location-based data. The resulting data is then said to be 'pseudonymised' and this is what is used for all analysis.
Data protection is a core principle of the CSO and is central to the development of the NDI. Confidentiality is protected by law which means that no individual, household, or enterprise can be identified from the data we publish. The Statistics Act, 1993, and particularly Section 33 of the Act, provide strong protections for the public when it comes to protecting confidentiality. EU laws are also in place to offer additional legal protection. The CSO is also subject to, and complies with, the General Data Protection Regulation (GDPR). Learn more about how the CSO protects data here.
For further details on the methods and quality of projects produced using the ELD, see Educational Longitudinal Database (ELD) Quality Report (PDF 252KB)
POD is a nationwide database of primary school pupils managed by the Department of Education and was implemented for the first time in the 2015/16 academic year. The system allows schools to make online returns and provides the Department of Education with the information required to develop and evaluate educational policy.
POD contains personal information related to each pupil including date of birth, gender, address, nationality and mother tongue. Information is also recorded, by consent of the parent/guardian, on ethnic or cultural background and religion. POD also contains enrolment data related to each pupil including standard (junior infants, first class etc), class, capitation category, details on Irish language exemptions and learning supports.
PPOD is the central database on post-primary education and is managed by the Department of Education. It contains several inter-linking datasets on learners, subjects, and schools. The ELD contains information from PPOD from the 2001/02 academic year. Most learners are in secondary school programmes, i.e. Junior Certificate, Transition Year and Leaving Certificate (including Leaving Certificate Applied and Leaving Certificate Vocational Programme). Post-Leaving Certificate courses are included up to 2015. After this they are included in data provided by SOLAS described below. A small number of Vocational Training Opportunities Courses are also included in PPOD.
PPOD includes the subjects that are taken and, where relevant, the level that the subject is being taken at. Details of learners' sex and county are recorded. There are a number of indicators for various forms of support for learners, including a traveller support indicator and a medical card indicator. School details include indicators for fee-paying schools, DEIS schools and schools in Gaeltacht areas. Other school classifications include ethos and gender (i.e. boys, girls, mixed).
The SEC is responsible for the development, assessment, accreditation and certification of the second-level examinations in Ireland. The SEC data provides details on students' examinations in the Junior Certificate, Leaving Certificate and Leaving Certificate Applied. Details include the subject, level and grade for each examination taken by students. The data includes an indicator if a student received a reasonable accommodation in their examination subject and if a student was granted a fee-exemption when registering for the examination. There is also information on the school in which the examination was taken. The ELD contains information from the SEC from the 2005/06 academic year. The SEC data does not contain information directly on Leaving Certificate points used by the Central Applications Office however, an approximation can be calculated using information on subject, level and grade.
SOLAS is Ireland's Further Education and Training authority, with responsibilities in the management, funding, promotion and monitoring of Further Education in Ireland. SOLAS manages a database for courses and participants known as the Programme and Learner Support System, or PLSS. The CSO receives the portion of the PLSS which relates to learners' enrolment and course completion since 2017. The PLSS contains course details such as course name, field of study, NFQ level, programme category and the course start and end dates. Learner details such as sex and nationality are included. The PLSS also includes a description of the outcome for each learner, specifying not only whether a learner successfully completed a course, but also their first destination after the course, or the reason for leaving the course prematurely.
Prior to the establishment of SOLAS in 2013, coordination of Further Education and Training in Ireland was carried out by FÁS, through the Further Education and Training Act. The CSO holds datasets on enrolment which were provided by FÁS from 2005 to 2015. These datasets have fewer details than PLSS. Course details are limited to a broad programme type and cluster description. The provider is given, and course start and end dates for each learner are also listed.
The National Apprenticeship Office (NAO) is a body set up jointly in 2022 by SOLAS and the Higher Education Authority and has responsibility for all aspects of the management, oversight, and development of the apprenticeship system. The National Apprenticeship Database contains information on apprenticeship registrations and this data is also provided to the CSO by SOLAS. Together with the Quality and Qualifications Ireland data on apprenticeship awards, the NAO data is used to build a complete picture of the apprentices' studies. The NAO data also contains additional information on the consortia-led apprenticeships related to course completions. Consortia-led apprenticeships are newer training programmes that are overseen and developed by industry-led groups together with education and training providers and other partners.
The HEA Graduate data provides details on annual enrolments and graduations at publicly funded Higher Education institutions in Ireland. Details include the name of the course, the NFQ (National Framework Qualification) level, the degree class, and the field of study (broad, narrow and detailed fields as classified using the ISCED framework). Details on the graduates themselves include age, sex, nationality and their home county in which they lived at the time of enrolment. The enrolment data provides details on learners for each year that they are enrolled in a particular course. Higher Education covers NFQ levels from 6 to 10.
Quality and Qualifications Ireland (QQI) is the state body with responsibility for quality assurance in Ireland's Further and Higher Education sectors and provides accreditation for a wide variety of courses and awards. The QQI dataset includes all awards made by QQI in each year. Details on the award include the name of the course, the field of study, the NFQ (National Framework Qualification) level and the award type (minor, major, supplementary or special purpose). It includes the name and type of the course provider and includes learner details such as sex and age.
The QQI dataset includes most Further Education courses (NFQ levels 1 to 6) which are provided in Ireland. It also includes several Higher Education awards (levels 6 to 10), particularly from private providers, including Hibernia College, Griffith College, and the National College of Ireland. The Universities, Institutes of Technology, Technological Universities and Colleges which are funded by the HEA are not accredited through QQI and are not included in this dataset. Apart from the QQI, there are several other bodies which provide accreditation for courses undertaken in Ireland, including City & Guilds, Accounting Technicians Ireland, and the Open University. Awards such as these are not included in the QQI dataset.
It should be noted that the QQI dataset records awards only, and not enrolment. Therefore, the starting date of a course is not known, and learners who do not complete a course are not recorded (though enrolment may be captured elsewhere, for example in the SOLAS PLSS database). This presents drawbacks from the perspective of the ELD, which aims to chart the pathways of learners through education. For the purposes of outcomes analysis, a learner is assumed to be active in further education in the year that they received an award from QQI, and in the year immediately before.
SUSI is Ireland’s national awarding authority for further and higher education grants. SUSI provides support to eligible students in approved courses at Post Leaving Certificate (PLC), undergraduate and postgraduate levels, and in some cases, to students studying outside Ireland. Support is available to all types of students, from school leavers to mature students returning to education.
The SUSI dataset includes details for each individual grant application and payment. SUSI provides two main types of support, which are maintenance grants that assist with living expenses and fee grants that cover some or all tuition fees and the student contribution. Eligibility for grants and the amount awarded depend on reckonable family income. The dataset also records each income component used in the reckonable income calculation. In addition, grant award rates are differentiated based on adjacency, which refers to the distance a student lives from their college of choice.
The ELD framework defines a student or learner as having received SUSI support if they received either a maintenance grant to help with living costs, a fee grant towards tuition fees, or both.
Fields of study are classified according to the International Standard Classification of Education (ISCED), which is the UNESCO classification system for education and training. This classification system identifies broad, narrow and detailed fields of study. The framework was revised in 2012 and includes two new broad codes.
The graduation and enrolment datasets provided by the HEA use the old ISCED coding for the years up to and including 2012. The ELD uses the new, post-2013, ISCED classification. The mapping for the narrow and broad ISCED codes are shown below. The vast majority of courses could be simply mapped to a new equivalent ISCED field. Some more complex mapping conditions are outlined below:
Pre-2013 narrow fields 'Business & Administration' and 'Law' were mapped to the post-2013 broad field 'Business, Administration & Law'. The remainder of the pre-2013 broad field, 'Social Science, Business & Law' was mapped to the new broad field 'Social Science, Journalism & Information'.
With the exception of 'Computing', the entire pre-2013 broad field of 'Science, Mathematics & Computing' was mapped to 'Natural Sciences, Mathematics & Statistics', while the pre-2013 narrow field of 'Computing' was mapped to the new broad field of 'Information & Communication Technologies'.
The pre-2013 narrow field of 'Environmental Science' (85) consisted of three detailed fields which were reassigned to new narrow fields under the ISCED revision. Graduates in this field of study were classified according to the new narrow field. 'Environmental Protection Technology' (851) was reassigned to 'Engineering, Manufacturing & Construction'. 'Natural Environments & Wildlife' (852) was reassigned to 'Natural Sciences, Mathematics & Statistics'. 'Community Sanitation Services' (853) was reassigned to 'Services'.
The ISCED framework prior to the 2013 revision included two fields, 'Balanced Combinations across Different Fields' (90), and 'Balanced Combinations across Arts, Humanities, Social Science, Business & Law' (91). There were approximately 1,000 young graduates from these fields in 2010 and 2011 but none in subsequent years and there are no equivalent fields under the new framework. These courses were assigned to fields according to the course names.
Graduations from the broad field known as General/Generic Programmes are excluded from the Higher Education Outcomes releases. Most graduates from these courses are mature, and an examination of course titles in this category reveals that the majority of courses appear to be aimed at individuals who are seeking to return to education as mature students.
Only degrees awarded at NFQ level 8 were considered where outcomes were analysed by degree class. The degree classes considered were First Class Honours (H1), Upper Second Class Honours (H21), Lower Second Class Honours (H22) and Third Class Honours (H3). Since some courses and institutions use slightly different grading classification systems, the grades awarded for each course in every institution were analysed, and certain records were re-assigned to one of the four standard classifications listed above. In cases where a course awarded a grade of Pass instead of H3, these were re-assigned to H3. Some courses break up the range normally assigned to a H3 into two classes, H3 and Pass (e.g. 40-45% = Pass, 45-50% = H3) and these were also both assigned to H3. In some courses, particularly in the area of Health, a classification consisting of H1, H2/Other Honour and Pass is used, where H2 or Other Honour corresponds to the same percentage score associated with H21, and Pass corresponds to the score associated with H22. Thus, in certain courses a Pass was equivalent to a H22, and in others it was equivalent to a H3. The distinction was made in each case based on the presence of either H2 or H21/H22 grades among all awards for that course. These were re-assigned as appropriate. Some courses use a four-tier system of Distinction, Merit 1, Merit 2 and Pass, and these were reassigned to H1, H21, H22 and H3 (respectively) as the associated percentage scores are the same. Finally, some courses have Pass or Fail grades only, and it was necessary to excluded these. This was done by identifying courses with 5 graduates or more where Pass was the only degree class awarded.
A breakdown by institution types is sometimes provided in Higher Education Outcomes releases. The specific institutions included in each category are listed below. Note that while some of the Colleges are affiliated with certain Universities, they are considered here as separate institutions. This is unless, a merger of institutions occurred.
| Universities | |
| University College Cork | Dublin City University |
| University College Dublin | National University of Ireland, Galway |
| University of Limerick | Maynooth University |
| Trinity College Dublin | |
| Institutes of Technology | |
| Athlone Institute of Technology | Institute of Technology Blanchardstown |
| Cork Institute of Technology | Institute of Technology Carlow |
| Dundalk Institute of Technology | Dún Laoghaire Institute of Art, Design and Technology |
| Dublin Institute of Technology | Galway-Mayo Institute of Technology |
| Limerick Institute of Technology | Letterkenny Institute of Technology |
| Institute of Technology Sligo | Institute of Technology Tallaght |
| Institute of Technology Tralee | Waterford Institute of Technology |
| Technological Universities | |
| Atlantic Technological University | Technological University Dublin |
| Munster Technological University | Technological University of the Shannon |
| South East Technological University | |
| Colleges | |
| National College of Art and Design | St Angela's College, Sligo |
| Mater Dei Institute of Education | Mary Immaculate College |
| St Patrick's College | Royal College of Surgeons in Ireland |
The CSO has received written consent from the Presidents of these Universities, Colleges and Institutes of Technology to identify their enterprises when disseminating statistics from the graduate outcomes data we receive from the HEA. This enables the publication of more detailed statistics and enhances the value of these statistics for the institutions themselves, policy makers and wider society.
The institutions that merged with an existing institution, or to form a new institution are listed below by the graduation cohorts that the merger occurred.
| Merged Institution | Previous Institution |
| Graduation Cohort 2017 | |
| Dublin City University | St Patrick's College |
| Dublin City University | Mater Dei Institute of Education |
| Graduation Cohort 2018 | |
| Technological University Dublin | Institute of Technology Blanchardstown |
| Technological University Dublin | Dublin Institute of Technology |
| Technological University Dublin | Institute of Technology Tallaght |
| Graduation Cohort 2020 | |
| Munster Technological University | Cork Institute of Technology |
| Munster Technological University | Institute of Technology Tralee |
| Graduation Cohort 2021 | |
| Atlantic Technological University | Galway-Mayo Institute of Technology |
| Atlantic Technological University | Letterkenny Institute of Technology |
| Atlantic Technological University | Institute of Technology Sligo |
| South East Technological University | Institute of Technology Carlow |
| South East Technological University | Waterford Institute of Technology |
| Technological University of the Shannon | Limerick Institute of Technology |
| Technological University of the Shannon | Athlone Institute of Technology |
Further Education
The maximum age to classify as a 'Young' graduate is 25. Graduates classified as 'Mature' are excluded from the analysis in the Further Education Outcomes releases. No distinction is made for Apprenticeships where all learners are included in the analysis.
Higher Education
For distinction of young and mature graduates in the Higher Education Outcomes releases, we followed the same criteria that the New Zealand Ministry of Education have applied in several reports in this area, which use age in combination with the award type. The threshold ages for each award type are shown below:
| Award Type | Maximum Age to Classify as 'Young' Graduate |
| Certificate | 21 |
| Ordinary Degree | 23 |
| Higher Degree | 24 plus one year for each additional course year beyond three years |
| Postgraduate Qualification | 26 |
| Master's | 27 |
| Ph.D. | 29 |
The year of graduation is assumed to be the latter of the two calendar years spanned by the final academic year. For example, where a graduate’s final year was in 2012/2013, the graduation year is taken as 2013. The first year after graduation then refers to the calendar year following the graduation year (2014 in the previous example).
Revenue's employee tax data contains a complete register of all employments where tax is deducted through "Pay As You Earn" or PAYE. It does not include the hourly wage or the number of hours worked. It provides details of gross annual earnings and number of weeks worked in the year for all employments. For years 2011-2018 the employee tax data used in the ELD came from employer end-of-year returns, P35, submitted to Revenue. The P35 was an annual return that was completed by all registered employers after the tax year end, up to 2018. Since 1 January 2019, Revenue have operated real-time reporting of payroll known as “PAYE Modernisation" (PMOD). Employers are required to report their employees’ pay and deductions in real-time to Revenue each time they operate payroll. Information is provided to Revenue at individual payslip level. The ELD analysis for 2019 and subsequent years is based on the employee tax data provided from Revenue’s PMOD system.
The Main Employer for each individual is the one which contributes the single largest pay to that individual over the course of the year. The Average Weekly Earnings for each individual is found using data from the main employer only, and is calculated as the gross pay divided by the number of weeks of insurable work.
The data does not contain an occupation code which may provide greater information on the type of work carried out by each employee. It does contain a NACE code which is associated with the main activity of the employer, rather than that of the employee. NACE represents the Statistical Classification of Economic Activities in the European Community. The industry sectors are based on the alphabetical letter of the NACE code under Revision 2 (see Eurostat website). In cases where an individual had more than one employment in a single year, then the NACE code associated with that individual for that year was taken from the main employer.
The Business Size was calculated using all employment data by calculating the number of unique individuals associated with each enterprise number within each calendar year. The average number of weeks worked for each enterprise was also calculated, and this was used as a weighting factor for the effective number of people employed within the year. In cases where an individual had more than one occupation in a single year, then the business size associated with that individual for that year was taken from their main employer. Each employer was assigned to a category for business size based on their effective number of employees:
| Business Size | Number of Employees |
|---|---|
| Micro | < 10 |
| Small | 10 - 49 |
| Medium | 50 - 249 |
| Large | > 250 |
Outputs have been published from the ELD since 2018. It is occasionally necessary to refine the methodology. Two notable revisions have been made to the methodology for calculating average weekly earnings.
The first change relates to adjusting earnings using the Consumer Price Index.
The second change relates to adjusting how additional employments are considered.
The impact of these methodological changes has been detailed in the Background Notes on each release affected.
Details on self-employment activity are obtained from annual Form 11 returns submitted by self-employed individuals to Revenue. The records can contain data from a number of previous years, but for the purposes of outcomes analysis it was desirable to consider outcomes within specific calendar years only. All self-employment records spanning all of the outcome years were therefore first combined into a single dataset, and the turnover and number of days associated with each activity were calculated. The turnover here includes income from sales, receipts from Government agencies and other income including tax exempt income. Each activity was then assigned to one or more calendar years depending on the start and end dates associated with that activity. The turnover associated with that activity was subdivided into each of those calendar years depending on the proportion of days associated with that activity which were in each of those calendar years.
Example A: An activity began in March 2012 and ended in August 2012, and the turnover is €6,000. In this case 100% of the turnover would be assigned to 2012.
Example B: An activity began 100 days before the end of 2012 and ended 200 days into 2013, and the turnover is €15,000. In this case the turnover would be subdivided in the ratio 1:2 into the calendar years 2012 and 2013, i.e. €5,000 in 2012 and €10,000 in 2013.
The Total Turnover for each calendar year was then calculated as the sum of the portions of turnovers which were assigned to that year. Thus, if both examples' A and B above were associated with a single individual, their total turnover for 2012 would be the €6,000 from example A plus the €5,000 portion from example B, leading to a total turnover for 2012 of €11,000. Their total turnover for 2013 would be the associated portion of example B, i.e. €10,000.
Each self-employment record had an associated NACE code. For outcomes analysis it was desirable to have a single NACE code associated with each calendar year. In cases where there was more than one self-employment record assigned to a single calendar year, then the NACE code for that individual for that year was taken from the record which contributed the single greatest portion of turnover to that calendar year. In our example where a single individual carried out both activities in examples A and B above in separate self-assessment records, their NACE code for 2012 would be that of the activity in example A, since this activity contributed €6,000 to the total turnover for 2012 while the activity in example B contributed only €5,000 to the total turnover in 2012.
The Form 11 also allows a spouse to declare self-employment income, and the appropriate PPSN is also supplied. These make up approximately 10.5% of all entries in the IT Form 11 dataset.
Business size is not available for self-employed persons, unless they are also engaged in employment (as described above), in which case business size refers to that occupation.
Benefits data are obtained from the Department of Social Protection. There are two data sources, the Central Records System (CRS) which is used for years up to and including 2014 and the Business Object Model which is used for the year 2015 and after. Both sources contain data on a wide a wide range of benefits, including Jobseekers Allowance, Jobseekers Benefit, Maternity Benefit and Disability Benefits.
A note was taken as to whether or not each graduate received any benefit in this dataset for each year. As described further below, this data is used to distinguish between the outcome categories of 'Not Captured' and 'In Neither Employment nor Education'. More specifically, a graduate could not be categorised as 'Not Captured' within a calendar year if they received any benefit on the CRS data source during that year.
The total number of weeks spent in receipt of illness and maternity benefits within each year was also calculated, as this was used as an input into the classification for 'Substantial Employment'.
An individual is regarded as being in 'Substantial Employment' within a given calendar year if they fulfil either of the criteria A or B below.
Note that a graduate may have more than 12 weeks of maternity leave or illness leave (thereby fulfilling the sub-criterion A.1), but without at least one week of insurable work they will not have a value for weekly earnings which can satisfy the sub-criterion A.2.
In cases where an individual had both 'Substantial PAYE Employment' and 'Substantial Self-Employment' within the same calendar year, and where the NACE codes from those two occupations differed, the NACE code for outcomes analysis was taken from the occupation which had the longest duration.
There are a number of databases which record participation in various types and levels of education. Due to the varied nature of these sources, there are different criteria for determining whether or not a learner is 'Active' within that database in a certain year. These criteria are outlined below for each data source. Depending on the specific statistical product, different combinations of these inputs may be used to classify an individual as being 'In Education'. Some reports also distinguish between being 'In Further Education' and being 'In Higher Education'. Each release or publication produced using the ELD contains a section on Background Notes that outlines which sources are included in the definition for being 'In Education'.
HEA Activity
Re-enrolment of graduates in higher education is analysed using an enrolments dataset provided by the HEA, which includes a record for each academic year during which an individual is enrolled. Since the academic year spans two calendar years, for the purposes of outcomes analysis a graduate was considered to be 'Active' in both of the calendar years covered by an academic year, e.g. an individual enrolled in 2013/2014 was categorised as being 'In Education' in both 2013 and 2014.
While the vast majority of records in the HEA Enrolment dataset relate to Higher Education courses, there are a small number of enrolments relating to FETAC Certificates, and these are used as an input to the classification for 'In Further Education' for the Further Education Outcomes releases.
SOLAS Activity
The PLSS dataset (and the datasets from FÁS which preceded SOLAS) contains start and end dates for courses, and learners were considered to be 'Active' for all of the years that are spanned by the course dates.
PPOD Activity
As with the database on HEA Enrolment, the PPOD database includes records based on the academic year, and learners are considered to be 'Active' in both of the calendar years covered by the academic year. E.g. a learner is 'Active' in 2013 if they appear in either the 2012/13 or 2013/14 academic years. Note that the PPOD dataset includes Post-Leaving Certificate courses up to 2015, and these are used as an input to the classification for 'In Further Education' for the Further Education Outcomes releases.
QQI Activity
As mentioned above, the QQI dataset is not an enrolment dataset but a record of awards only. Thus, the start date of courses is not precisely known. As an estimate, awardees are considered to be 'Active' for two calendar years, both during the year in which they receive their award and the year immediately before.
Where a graduate was neither in 'Substantial Employment' nor 'In Education' (according to the specific definition in use) within a specific calendar year, then they may be assigned to one of two remaining categories: 'Neither Employment nor Education' and 'Not Captured'. A graduate is assigned to 'Neither Employment nor Education' if they appear in any of the datasets for that year without being classified as being in 'Substantial Employment' or re-enrolled in education. The following is a list of examples of situations where a graduate would fall into this category.
A graduate is assigned to the category of 'Not Captured' if they do not appear in any of the datasets for that year and have no recorded activities such as those listed above. Most of these graduates categorised as 'Not Captured' are assumed to have emigrated or returned to their country of origin, but it is possible that a graduate remained in the country but was not captured by any of the administrative data. It is also possible that a graduate had emigrated but engaged in some activity which was captured by the administrative data, and therefore was categorised as being in 'Neither Employment nor Education'.