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 datasets on learners that have completed courses or programmes to other datasets which describe their outcomes in subsequent years. The data sources used to describe learner outcomes include employment and self-employment datasets from the Revenue Commissioners, benefits data from the Department of Social Protection, and data on educational participation from the Department of Education and several state agencies, including the Higher Education Authority (HEA), Quality and Qualifications Ireland (QQI) and SOLAS.
The CSO is committed to broadening the range of high quality information it provides on societal and economic change. The large increase in the volume and nature of secondary data in recent years poses a variety of challenges and opportunities for institutes of national statistics. Joining secondary data sources in a safe manner across public service bodies, while adhering to statistical and data protection legislation, can provide new analysis and outputs to support decision-making and accountability in a way that is not possible using discrete datasets. Furthermore, a coordinated approach to data integration can lead to cost savings, greater efficiency and a reduction in duplication.
The CSO has a formal role in coordinating the integration of statistical and administrative data across public service bodies that together make up the Irish Statistical System (ISS). Underpinning this integration is the development of a National Data Infrastructure – a platform for linking data across the administrative system using unique identifiers for individuals, businesses and locations. The data linking for statistical purposes is carried out by the CSO on pseudonymised datasets using only those variables which are relevant to the research being undertaken. A strong focus on data integration, which involves the use of identifiers such as PPSN and Eircodes, is a priority of the ISS in its goal of improving the analytical capacity of the system.
Data protection is a core principle of the CSO and is central to the development of the NDI. As well as the strict legal protections set out in the Statistics Act, 1993, and other existing regulations, we are committed to ensuring compliance with future data protection requirements. These include the Data Sharing and Governance Act (2019) and the General Data Protection Regulation (GDPR, EU 2016/679).
This report on higher education outcomes using administrative data for the HEA is a good example of the type of partnership approach the CSO can adopt with a public agency using the National Data Infrastructure. The CSO is hopeful that this joint project between the CSO and the HEA, as well as the innovative methodologies used in the report, will become a template for further collaborations with other government departments and agencies.
For further details on the methods and quality of projects produced using the ELD, see Educational Longitudinal Database (ELD) Quality Report (PDF 252KB)
The HEA Graduate data contains a record for each individual graduation and enrolment at Irish Higher Education Institutions. 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 the county in which they lived at the time of enrolment. The HEA also provide an enrolment data set that provides data on learners for each year that they continue their studies in a particular course. Higher Education covers NFQ levels from 6 to 10.
Quality and Qualifications Ireland (QQI) is a 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 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 PLSS). 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, as well as in the year immediately before.
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. The PLSS contains course details such as course name, field of study, NFQ level and programme category. Learner details such as sex, date of birth and nationality are included, as are the start and end dates for each learner. 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 dating back to 2005. These datasets have fewer details than PLSS. Course details are limited to a broad programme type and cluster description, and just the sex and age of learners are provided. The provider is given, and course start and end dates for each learner are also listed.
The Post-Primary Online Database (PPOD) is the central database on post-primary education and is managed by the Department of Education and Skills. It contains a number of inter-linking datasets on learners, subjects, and schools. The majority of 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, where after they are included in PLSS. A small number of Vocational Training Opportunities Courses are also included in PPOD.
PPOD includes the subjects that they are taken and, where appropriate, the level that the subject is being taken at. Details of learners' sex, date of birth 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 (boys, girls, mixed).
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 adjusted in 2012 and includes two new broad codes. The ELD primarily uses these new broad ISCED fields, and on occasion reference is made to narrow or detailed fields of study where behaviour in a specific field is disproportionately affecting behaviour of the broad field.
The graduation and enrolment dataset provided by the HEA use the old ISCED coding for the years up to and including 2012. The mapping between these codes and the new broad ISCED codes (as used in this report), as well as the narrow fields within these broad fields, are shown below. The vast majority of courses could be simply mapped to a new equivalent broad ISCED field. A few more complex mapping conditions are outlined below:
Pre-2013 narrow fields Business and & Administration and Law were mapped to the post-2013 broad field Business, Administration and & Law. The remainder of the pre-2013 broad field, Social Science, Business and & Law was mapped to the new broad field Social Science, Journalism and & Information.
With the exception of Computing, the entire pre-2013 broad field of Science, Mathematics & Computing was mapped to Natural Sciences, Mathematics and & Statistics, while the pre-2013 narrow field of Computing was mapped to the new broad field of Information and & 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 study were classified according to the new narrow field. Environmental Protection Technology (851) was reassigned to Engineering, Manufacturing & Construction. Natural Environments and & Wildlife (852) was reassigned to Natural Sciences, Mathematics and & Statistics. Community Sanitation Services (853) was reassigned to Services.
The ISCED framework prior to the 2013 revision included two fields, 90 – Balanced Combinations across different fields, and 91 – Balanced Combinations across Arts, Humanities, Social Science, Business and & Law. There were approximately 1,000 young graduates from these fields in 2010 and 2011 but none in subsequent years and no equivalent field 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 project on Higher Education Outcomes. The majority of graduates from these courses are mature, and an examination of course names 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.
Breakdown by institution types is sometimes provided in Higher Education Outcomes projects. 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 University Dublin | |
| 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 |
Further Education
The maximum age to classify as a 'Young' graduate is 25. Those graduates classified as 'Mature' are excluded from the analysis in the Further Education Outcomes report, except in the case of Apprenticeships where no distinction is made and all graduates are included.
Higher Education
For distinction of young and mature graduates at Higher Education level, we used the same criteria that the New Zealand Ministry of Education have applied in several reports in this area, which uses 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. 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 for 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 (“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 2020 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. Earnings are adjusted for inflation by multiplying by a factor based on the Consumer Price Index (CPI, base=December 2016).
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. 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 |
The self-employment dataset 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 all of the portions of turnovers which were assigned to that year. Thus, if both of the 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 IT 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 substantial P35 employment, in which case business size refers to that occupation.
The CRS (Central Records System) data source includes data on 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. This indicator could be 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/or 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 P35 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 was analysed using a dataset on enrolments provided by the HEA, which includes a record for each academic year that an individual is enrolled. Since the academic year spans two calendar years, for the purposes of our 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 report.
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 report.
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 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'.
