Early Learning Care Graduate Outcomes 2013-2017

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 who have completed courses or programmes to other datasets which describe their outcomes in subsequent years.

The data sources used to examine learner outcomes include:

• Datasets from the Revenue Commissioners for the employees and the self-employed.
• Benefits data from the Department of Employment Affairs and Social Protection (DEASP).
• Data on educational participation from the Department of Education and Skill, the Higher Education Authority (HEA), Quality and Qualifications Ireland (QQI) and Pobal.

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 deliver a series of partnerships with public sector bodies, leading to policy-relevant insights on outcomes in education. 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. 

The primary data sources for the present project are annual datasets on Further and Higher Education graduations provided by the HEA and 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 ELD methodology 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 (NDI) - 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 Eircode, 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 Early Learning Care (ELC) outcomes using administrative data for the HEA and QQI is a good example of the type of partnership approach the CSO can adopt with public agencies using the NDI. The CSO is hopeful that this joint project between the CSO and the HEA 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

The analysis in this report uses as its primary data source the HEA and QQI databases 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.

The cohort was chosen by selecting the courses officially recognized in the Early Learning Care sector by the Department of Children, Equality, Disability, Integration and Youth. Not all of the recognized courses were included in this report, for example, the 'Bachelor of Education - Primary Teaching' course was omitted as it had no specific reference to Early Learning Care. Because of these omissions, some of the data within this report may differ from figures in other studies on ELC graduates.

Missing PPSN

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 to give a more complete picture of the trends in Ireland's ELC sector. However, they are excluded from all other chapters on outcome destinations, employment sectors and earnings. The rate of missing PPSN among ELC graduates was only 1.5%, and so there are only slight differences between graduate numbers in the Background Statistics and the remaining chapters.

Graduates with more than one graduation per year

A small number of individuals were recorded as graduating from more than one course in a single year. In such cases, the course with higher NFQ was kept.

Graduate Terminology

Institution Type

The different institution types presented in this report are:

• Education and Training Boards - Courses on ELC provided by ETBs lead to awards at NFQ levels 5 and 6.
• Institute of Technology - ELC courses are available at NFQ levels 5 through to 8. Institutes included are Athlone IT, Cork IT, Dublin Institute of Technology (DIT), Dundalk IT, IT Blanchardstown, IT Carlow, IT Sligo, IT Tralee, Letterkenny IT and Waterford IT.
• Other Providers - These include Community/Comprehensive Schools, Voluntary Secondary Schools, Solas Community/Voluntary Sector Organisations and Private Providers. Courses from these providers are at NFQ levels ranging from 5 to 8.
• University - These award qualifications from levels 6 to 9. The universities providing ELC courses which are included in this study are Maynooth University, National University of Ireland, Galway, Trinity College Dublin and University College Cork. Mary Immaculate College is also included here, as it is affiliated with the National University of Ireland.

NFQ Level

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 qualifications, such as certificate, higher honours bachelor's degree. master's degree, etc. For the most part, NFQ level 5 awards and below are certificates, 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.

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 two 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 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.

Enrolled in Education

Re-enrolment of graduates in further third-level education was analysed using datasets provided by the HEA, QQI and Solas. While the graduate cohort was restricted to graduates from ELC courses only, all courses including non-ELC courses were included for the definition of "in education" as an outcomes activity. The HEA enrolment data includes a record for each academic year in which a learner is enrolled. As the academic year spans two calendar years, a graduate was considered to be re-enrolled in both of the calendar years covered by an academic year for this report. For example, an individual enrolled in 2013/2014 was categorised as being in education in both 2013 and 2014. The further education data from QQI does not include a record for each year that a learner was enrolled in a course, only the year in which they received an award. For the purposes of defining "in education" as an outcomes activity, a person was considered to be "in education" in the year in which they were recorded by QQI as receiving an award, as well as in the preceding year. For example, a person who received an award in 2015 was categorised as being "in education in both 2014 and 2015.

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 or 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:

• The graduate had a total number of weeks of insurable work which was less than 12;
• The graduate had 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 the calendar year of less than €1,000;
• The graduate received some benefit, e.g. disability benefit or Jobseeker's 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 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.

NACE Codes

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.

Employment Sector

An employment sector variable has been developed in an attempt to analyse and describe whether graduates found substantial employment in sectors which are related to their education. For this report, the employment sector variable has been customised to categorise all employment according to its relationship to the ELC Sector. Three categories have been defined as outlined below. Note that while the definition is given in terms of "employers", self-employment is included also.

• ELC Sector - these are employers which have a NACE classification of P8510 (pre-primary education) and Q8891 (child day-care activities). Also included here is work with an employer which has been approved by Pobal for ELC.
• Non-ELC Sector Health & Education - this category covers all employment in the sectors of Education (P) and Human Health & Social Work Activities (Q) which are not already classified as being in the ELC Sector (above).
• Other Employment Sectors - any substantial employment which does not meet the criteria of the previous two categories.

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