This publication is part of a series of projects that the CSO has established in collaboration with Irish public sector bodies to examine learner outcomes. The CSO has developed a statistical framework known as the 'Educational Longitudinal Database' (ELD) to act as the basis for these projects. 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 Employment Affairs and Social Protection, and data on educational participation from the Department of Education and Skills and a number of state agencies, including the Higher Education Authority (HEA), Quality and Qualifications Ireland (QQI) and SOLAS.
The ELD can be used to analyse outcomes for learners from a wide variety of educational programmes, ranging from post-primary level to adult education. The integrated approach of the ELD ensures that analysis datasets are built in a consistent and highly efficient manner. The CSO hopes that the ELD will provide the basis for a series of partnerships with other public sector bodies leading to policy-relevant insight in outcomes analysis and impact evaluation. These projects will be mutually beneficial, with high transferability not only of technical processes but also of understanding and expertise.
The ELD will be enhanced in the coming years through the availability of real-time employment data from the Revenue Commissioners as part of the PAYE modernisation programme. Data on participation in Further Education will also be improved through the collection of data by SOLAS through the Programme and Learner Support System (PLSS). This database, in full operation since 2017, provides comprehensive data on enrolments and completions at all SOLAS-funded courses.
The present project on higher education outcomes uses as its primary data source an annual dataset on graduations from Irish Higher Education Institutions which is provided by the HEA. In line with the data protocols of the CSO, all identifiable information from each of the data sources is removed, such as name, date of birth and addresses. The resulting data is then said to be 'pseudonymised' and this is what is used for all analysis. The PPSN is replaced with a 'protected identifier key' (PIK) and it is this PIK which is used to link person-based data.
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 requires the collection and storage 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 all 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 (NDI). 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.
The analysis in this report uses as its primary data source the HEA database on annual graduations. This contains details on the courses studied, including the field of study and NFQ level, as well as information about the learners themselves, such as Sex, age and Nationality. For further details, see the accompanying ELD methodology documentation.
Excluded Categories of Students
Mature graduates are excluded from this study, as their outcomes may be strongly influenced by their working experience prior to enrolling in a higher education course. Threshold ages for young and mature graduates are shown above in the section on graduate terminology.
Graduates were excluded from the study if they were recorded as being an “overseas student”. These are students who attend a campus which is associated with an Irish institution but located in a different country.
Graduates were excluded from the study if they were recorded as being involved in a number of upskilling programmes, including the Springboard programme, the Labour Market Activation programme and the ICT Skills Conversion programme.
Graduates in General/Generic courses (broad ISCED field 0) were also excluded from the study, as these are primarily courses which are aimed at helping individuals return to education.
A number of course types were excluded from the analysis, including Access/Foundation courses, FETAC Certificates, Professional Training Qualifications, undergraduate diplomas and occasional courses.
Non-HEA institutions are by definition not covered in the HEA graduate dataset. A report for the Expert Group on Future Skills Needs estimated that in 2014 there were approximately 5,000 higher education awards made to learners outside the HEA-aided sector. There are a growing number of private and independent colleges in Ireland. Some of the larger institutions include Griffith College, King’s Inns, National College of Ireland, Dublin Business School, Galway Business School, Independent College Dublin, Hibernia College, and a number of others in areas such as business, computing, music and psychotherapy/counselling.
A number of graduation records have a missing or invalid PPSN, and therefore these cannot be matched to other administrative data sources. These graduation records are included in the statistics on graduation numbers shown in Background Statistics in order to give a more complete picture of the trends in Ireland's Higher Education sector. However they are excluded from all other chapters on outcome destinations, NACE sectors and earnings. The rates of missing and invalid PPSN across a number of parameters are given in the chapter on Background Statistics. This is given as a guide to the user, and may be used to develop a clearer picture of the quality of the data and composition of the various groups.
Graduates with more than one graduation per year
Graduates were sometimes recorded as having received more than one higher education award in a single year. Typical examples include a course which was jointly hosted by more than one institute, or a graduate who received a diploma in education in combination with an award for completing a degree course. If the award types were different, it is possible for a graduate to be classified as young in the case of one award and mature in the case of another within the same year. In such cases the duplication would not have to be dealt with since the mature graduations were already excluded.
It was desirable for the purposes of data matching that there be only one graduation record per year per individual. In cases where an individual graduated from more than one course, then the course with the higher NFQ level was kept. In cases where a graduate had more than one course with equal NFQ level, then one course was kept according to a hierarchy based on Award Type (note that the relationship between NFQ level and award type is not precisely one-to-one), with the following order of preference from highest to lowest applied: Ph.D., Master’s, Honours Degree, Ordinary Degree, Postgraduate Qualification, Certificate.
A threshold age is defined for each type of award, and a graduate must be of an age equal or younger than this at the time of graduation to be classified as ‘Young’. The threshold age for each award type is: certificates - 21; ordinary degrees - 23; postgraduate qualifications - 26; master’s degrees - 26; Ph.D.s - 27. For honours degrees the threshold age is 24 for courses of up to three years in duration, increasing by 1 for each additional course year. So for example, if a graduate was aged 26 at the time of finishing an ordinary degree they would be classified as 'mature' since they are older than 23 years of age. However, a person who is aged 26 and graduating with a master’s degree would be classified as 'young' since they are not older than the threshold age for this type of award which is 26.
The Irish National Framework for Qualifications (NFQ) is a framework which classifies learning achievement based on the level of knowledge, skill and competence. 'Award type' here refers to names that are commonly given to different types of qualifications, such as certificate, higher honours bachelor’s degree, master’s Degree, etc. For the most part, NFQ level 6 awards are advanced certificates or higher certificates, level 7 awards are primarily ordinary bachelor’s degrees and level 8 awards are primarily higher honours bachelor’s degrees. Level 9 awards include master’s degrees and postgraduate diplomas. Level 10 awards are doctoral degrees (Ph.D., including higher doctorates). The relationship between award type and NFQ Level is not precisely one-to-one, however. NFQ level is used as an analysis variable throughout this report since it is fully standardised.
Graduation year and years after graduation
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).
Field of study
The fields of study referred to in this report are based on the International Standard Classification of Education (ISCED) broad fields. Due to a change in the ISCED classification framework in 2013, some mapping was used to assign equivalent broad field classifications to courses from years prior to 2014. This mapping is described in the ELD methodology documentation.
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.
Enrolled in Education
Re-enrolment of graduates in further third-level 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 re-enrolled in both of the calendar years covered by an academic year. For example, an individual enrolled in 2013/2014 was categorised as being in education in both 2013 and 2014. Certain types of course which were excluded from the graduation dataset, such as FETAC courses, professional training qualifications and courses in the General/Generic field (ISCED code 0) were not excluded from the enrolment dataset. For example, a graduate from a FETAC course is not included in the outcomes analysis, but a person who graduates with an honours bachelor’s degree and subsequently begins a FETAC course is considered to be in ‘education’' from the perspective of assigning a destination outcome.
Not Captured and Neither Employment nor Education
Where a graduate was neither 'in substantial employment' nor re-enrolled in education 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'.
The Statistical Classification of Economic Activities in the European Community, normally known simply as NACE, is a classification system used to describe industry sectors in the European Union. Employers in the P35 database are assigned a NACE code based on their main activity. Graduates who demonstrate substantial P35 employment in a particular year are assigned a NACE code based on that of their 'main employer', which is the employer that contributes the largest earnings to the graduate within that year.
A graduate who demonstrates substantial self-employment is assigned a NACE code based on their IT Form 11 data. In cases where an individual demonstrated 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.
Note that in the case of substantial P35 employment, NACE codes describe the main activity of the employer. The activity of the graduate themselves may differ. For example, an individual carrying out research at a university would be classified as 'Education' (P), and somebody working in company law for a restaurant chain would be classified as 'Accommodation and Food Service Activities' (I). No occupation code which describes the type of work carried out is currently available in the administrative data. The results may therefore differ with other forms of research but are useful nonetheless for comparison across parameters such as sex and field of study.
Go to next Chapter: Contact Details