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Background Notes and Methodology

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Educational Longitudinal Database (ELD)

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 further education outcomes uses as its primary data source an annual dataset on graduations, provided by QQI. 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. 

For further information on the data sources and linking procedures, see the methodological documentation. 


CSO Policy and National Data Infrastructure (NDI)

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 further education outcomes using administrative data for the QQI 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 QQI, as well as the innovative methodologies used in the report, will become a template for further collaborations with other government departments and agencies.


Cohort Definition


Due to the nature of apprenticeships, a graduate may be enrolled in apprenticeship training in a higher education institute or employed as part of their apprenticeship training in the year after graduation. Thus, to disentangle apprenticeship training from destination outcomes, two years after graduation is examined.

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 further education course. Threshold ages for young and mature graduates are shown below in Terminology.

Graduates of minor, special purpose or supplemental awards are excluded from this study.

Missing PPSN

All graduates (including graduates with missing/invalid PPSNs) are included in QQI Data. Since a valid PPSN is required for linking to administrative datasets, those graduates which do not have an associated PPSN must be excluded and hence, outcomes analysis exclude graduates with missing or invalid PPSN. The data for rates of missing PPSN is given in Table 6.1 as a guide to the reader. As outcome analysis excludes both mature graduates and apprenticeships, Table 6.1. excludes both mature graduates and apprenticeships to better reflect the total number of graduates used in outcomes analysis. Note that the numbers may not sum to the total due to rounding.

Show Table: 6.1 Breakdown of Graduates by Year

Graduates with more than one Graduation per Year

Graduates could complete more than one QQI major award in a single year and hence, recorded as having received more than one QQI Major award in a single year. It was desirable for the purposes of data matching that there be only one graduation record per year per individual. Hence, the total number of unique graduates were used throughout this report. In cases where an individual graduated from more than one major award per year, then the following criteria were applied (in the same order) to select one graduation per individual per year:

  • If any one award was an apprenticeship, then the apprenticeship took precedence over all other awards completed in that year;

  • The award with the highest NFQ Level was selected;

  • An award of any award title took precedence over awards with the award titles 'General Learning' or 'General Studies';

  • Awards of any field of study took precedence over awards in Generic Programmes & Qualifications;

  • In the case that no single award could be selected, then the latter award was kept.

Show Table: 6.2 Number of Graduates by Year


Throughout this report, individual figures have been rounded to the nearest five and thus, the sum of individual components may not add up to the totals shown.



Young/Mature Graduates

For distinction of young and mature graduates, a threshold age for young and mature graduates was defined. A graduate must be aged 25 and under at the time of graduation to be classified as 'Young'. Thus, graduates over the age of 25 at the time of graduation are classified as 'Mature'. The same criteria is used in several reports in the area of further education.

NFQ Level

The Irish National Framework for Qualifications (NFQ) [1] is a framework which classifies learning achievement based on the level of knowledge, skill and competence. The NFQ is a framework through which all learning achievements may be measured and related to each other in a coherent way. In this report, the outcomes of graduates of awards at NFQ Levels 1 to 6 are examined. In the Outcomes analysis, higher education is explored, whereby Levels 6 to 9 are mentioned. 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 honours bachelor's degrees. Level 9 awards include master's degrees and postgraduate diplomas. NFQ Level is used as an analysis variable throughout this report since it is fully standardised.

Field of Study

The fields of study referred to in this report are based on the International Standard Classification of Education (ISCED) [2] broad fields, which is the UNESCO classification system for education and training.


NACE [3] represents the Statistical Classification of Economic Activities in the European Community. The industry sectors in this report are based on the alphabetical letter of the NACE code (Revision 2). In cases where a graduate 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. Note that the NACE code is associated with the main activity of the employer, rather than that of the employee.


Outcome Definitions

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.

A. Substantial P35 employment: They fulfil the following 2 requirements:

  1. They have at least 12 weeks of insurable work within the calendar year across all employments. This can be supplemented by weeks of maternity leave and/or illness leave;

  2. The average weekly earnings from their main employer only is at least €100 per week.

B. Substantial self-employment: Their total turnover across all self-employment activities is at least €1,000 within the calendar year.

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.


An individual is regarded as being 'in education' within a given calendar year if they fulfil either of the criteria A or B below. 

A. Enrolment in further education: The graduate has a record of enrolment in further education in the year in question. Enrolment in further education was examined using QQI, SOLAS and HEA data sources. A breakdown of graduates from these data sources is provided in Table 6.3. 'Other' consists of Level 5 courses found in HEA data. As NFQ Level 5 is within the scope of further education, these awards were considered further education. A small number of individuals would be enrolled in further education courses in higher education institutes and thus, found in HEA data sources.

B. Enrolment in higher education: The graduate has a record of enrolment in higher education in the year in question, according to HEA and QQI data sources.

An individual may be enrolled in both further and higher education within the same year. However, this does not imply simultaneity. For example, an individual may be enrolled in further education in the first half of the year and be enrolled in higher education in the latter half of the year. It is noteworthy that enrolment in further education includes enrolment in all award types, namely major, minor, special purpose and supplemental awards.

Show Table: Table 6.3 Breakdown of Graduates Enrolled in Further Education One Year After Graduation by Year

Not Captured and Neither Employment nor Education

Where a graduate was neither in substantial employment nor 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 enrolled in education. The following is a list of examples of situations where a graduate would fall into this category:

  • The graduate had a total number of weeks of insurable work which was less than 12;

  • The graduate had an average weekly earnings of less than €100 per week from their main employer;

  • The graduate had a self-employment activity but had a total turnover within that calendar year of less than €1,000;

  • The graduate received some benefit e.g. disability benefit or jobseekers allowance.

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 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 'neither employment nor education'.






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