Annexes

Question 1

1. Annex 1: Early years turnover rates

Accepted Answer

	CEEDA – Early Years Workforce Survey 2019^{[footnote 1]}	DfE – Study of Early Education and Development (SEED): Study of Quality of Early Years Provision in England (2018)^{[footnote 2]}	DfE – Childcare and Early Years Providers Survey: 2013^{[footnote 3]}
Early years workforce turnover rate	15%	11%	13%
Sample size	563 non-domestic childcare providers, together employing 8,603 staff	598 settings for three- to four-year- olds across England	10,271 providers across England
Representative sample	Yes, of non-domestic settings on the Ofsted Early Years register	Yes (excluding childminders)	Yes
Notes	No regional breakdown. Top three drivers for staff leaving: Work closer to home, Low pay and benefits, Opportunity for promotion	Higher turnover in private sector and children’s centres compared with maintained and voluntary sector. No regional breakdown	Latest turnover data released in 2013

Question 2

2. Annex 2: Methodology

Accepted Answer

In this Annex we present the conceptual framework that underlines this study and the methodology adopted.

The limitations of our methodology are presented at the end of the Annex.

2.1 Conceptual framework

Our work started with the recognition that evidence of the instability of the EY workforce already exists, but that there is no systematic analysis of all its causes. We adopted the conceptual framework of a study by Wilke and others (2018) as a starting point for organising this evidence, as well as for developing the plan for the quantitative data analysis and the topic guides for the qualitative data collection.^{[footnote 4]}

Wilke and co-authors studied the instability of the social care workforce, organising their thinking around the following domains:

individual factors – for example: demographic characteristics, education and training, and employment history
organisational factors – for example: training protocols, work demands, working conditions (including pay and benefits) and administrative leadership
contextual factors – for example: economic indicators, population density and community health

Among the conceptual frameworks we considered, this was the most comprehensive and easy to operationalise. We adapted it to the specific context of the EY sector by including elements such as access to continuous professional development (CPD) and opportunities for career progression. In addition, in recognition of the fact that the three domains are interconnected, we allowed findings to overlap across them. In the end, we analysed the evidence through the lenses of 15 elements (Annex 3).

2.2 Overview of the methodology

Our study combines three methods, namely:

a review of the literature
a quantitative analysis of the Annual Population Survey (APS)
a qualitative analysis of interviews conducted with key stakeholders in the sector

Below, we detail the methodology utilised in each strand of our analysis.

2.3 Methodology for the literature review

The literature review followed four steps:

Searches using the Web of Science and Google.
Citation analysis.
Selection of key studies for each of the 15 elements of the conceptual framework.
Synthesis and reporting.

Searches

Following the conceptual framework, we searched the Web of Science and Google to identify both peer-reviewed articles and key studies in the grey literature. We searched for a combination of the terms “early years workforce”, “stability/turnover” and one of the following terms: “barriers”, “reasons” or “motivations”. We searched first for studies that were based in the UK and published after 2010, and we left the search open to all methodologies, while noting the strength of the evidence presented. When results on a specific domain were null or very limited, we extended the search beyond the UK to the USA and Australia, where EY provision is also based on a mixed-market system, and we extended our reach to a pre-2010 publication date.

Finally, we included a few reports published recently by key sector organisations in England, as they reflect what the sector highlights as important topics. This stage resulted in a long list of relevant studies.

Citation analysis

We analysed the forward and backward citations of all studies found at this stage and added relevant studies to our list.

Selection of key studies

We selected key studies based on the inclusion/exclusion criteria, making sure we had at least one study for each element identified by the conceptual framework. This process led to the selection of the 21 most relevant studies.

Synthesis and reporting

These 21 studies were synthesised using an extraction template that allowed us to identify key findings and clear evidence gaps (Annex 4).

2.4 Methodology for the quantitative strand

Dataset

The quantitative analysis used a special request of the three-year pooled APS, which combines data for the years 2015 to 2017 and includes individual occupation codes. The APS is based on the Labour Force Survey (LFS), which collects information on a range of socio-economic variables for households in Great Britain and is useful to compare employment sectors. The APS is compiled using survey boosters, resulting in a sample of approximately 320,000 respondents and making regional-level analyses more reliable than those obtained with the LFS.

Sample

We investigated the characteristics and working conditions of the EY workforce, as represented by the following individual occupation codes: “nursery nurses and assistants”, “childminders and related occupation” and “playworkers”. The method used to categorise occupations in the APS leads to some EY practitioners being classified together with teachers from other school stages, for example reception year teachers are grouped with primary school teachers. To avoid skewing results towards non-EY workers, these occupation codes were omitted from the study.

Where appropriate, we compared EY workers with people working in the retail sector, the female working population or the total working population of England. Retail work is commonly considered a competing occupation, with similar or higher levels of pay for workers with equivalent qualification levels and/or in a role with fewer responsibilities. These characteristics pose a risk to the stability of the EY sector to the extent that more favourable working conditions and compensation rates entice workers out of the EY sector. The female workforce is used as a comparison group because, as described below, the EY workforce is predominantly female. We include the total working population to provide wider labour market context.

Analysis

The selected variables stemmed from the conceptual framework. We generated descriptive statistics for all relevant variables and then performed cross-tabulations at regional and institutional level to investigate possible variations in results.

The regional breakdown looked into differences across regions in England, population density and areas with varying levels of deprivation. Population density was derived from the urban/rural classification published by the Office for National Statistics (ONS). Levels of deprivation were calculated using the Income Deprivation Affecting Children Index decile ranks, which cluster all local authorities into 10 groups in order of deprivation. The analysis compared the bottom and top three groups to investigate differences between the 30% most deprived areas and the 30% most affluent areas.

The institutional breakdown allowed the investigation of possible variations stemming from working for different provider types. The APS data only allows differentiating between people working in the public or the private sector, where the public sector can be considered an approximation of maintained and school-based nurseries, and the private sector includes PVIs and childminders.

2.5 Methodology for the qualitative strand

Sampling of areas

Areas were selected with a view to maximising geographical coverage. We purposively sampled eight local authorities using two sampling variables:

population density, using the urban/rural classification published by ONS; we selected four areas classified as urban and four areas classified as rural and/or semi-rural
socio-economic profile, using the Index of Multiple Deprivation; we selected four areas from the upper third (most affluent) and four areas from the lower third (most deprived)

Sampling and recruitment of interviewees

Planned sample:

In each of the eight local authorities we aimed to interview five participants:

one local policy-maker (such as Directors for Children and Young People)
one setting manager (such as EY managers of group-based and schools-based settings)
one childminder (working on their own or in a setting with other childminders)
one union representative (we sought representatives from at least three different unions)
one staff representative (we aimed to include settings of different sizes)

Our recruitment plan included two steps:

Recruiting local policy-makers in sampled local authorities; this was important so that we could check whether to proceed with recruitment in that local authority or whether we needed to identify other local authorities with a similar socio-economic make-up and area profile.
recruiting sequentially in the sampled local authority and/or region, beginning with childminders and EY managers of group and school-based settings.

Achieved sample:

Recruiting practitioners based in maintained settings proved particularly challenging, and our achieved sample includes more PVI practitioners and fewer maintained-setting practitioners than planned. In addition, some EY practitioners were recruited from outside of the initial eight areas, but from regions with similar characteristics in terms of population density and deprivation.

Table 2: Recruitment plan

Number of interviews	Initial target	Interviews conducted
Service directors	8	9
Childminders	8	9
Managers	8	7
Maintained-setting practitioners	8	4
PVI practitioners	8	11
Total	40	40

Data collection

The semi-structured interviews lasted between 45 and 60 minutes and were conducted over the phone. Separate topic guides were used for all types of participants.

Data analysis

All interviews were recorded and professionally transcribed. The resulting data was managed and analysed using the framework approach developed by NatCen (Ritchie and others 2013) and embedded in Nvivo. Data was summarised and categorised systematically by theme. The final analytic stage involved drawing out the range of experiences and views from the data and identifying similarities and differences.

2.6 Limitations of the methodology

Quantitative analysis

The APS dataset consists of 12 months of survey data with a sample of approximately 320,000 respondents, versus the 90,000 individuals in the quarterly LFS. This makes it the best survey to generate statistics at a local level. Yet there are still limitations in the use of the APS to examine the EY workforce through a place-based approach.^{[footnote 5]}

First, despite the sample boosts, some more detailed analysis was not possible due to small sample sizes. For example, we could not disaggregate the analysis across all three types of occupation that form the EY professionals.

Second, the questions related to personal wellbeing are a recent addition to the APS and are still designated as experimental.^{[footnote 6]} This means that measurement errors and/or subjective interpretation of the question can affect the robustness and validity of the answers. As an example, the analysis showed a high job satisfaction level for the EY workforce, despite the relatively high turnover rates.

Finally, APS data is useful to compare the EY sector with other occupations. But because of the structure of the survey and the classification of workers, results are not comparable with those derived from other sources, such as the Childcare and Early Years Providers Survey (CEYPS). However, other sources available in England to examine the EY workforce present more serious weaknesses or limitations. For example, the CEYPS has changed too often across the last few years to allow comparison of some important variables and has recently stopped collecting more detailed information about the workforce.^{[footnote 7]}

Qualitative analysis

One of the key risks of our qualitative research was to under-recruit employed EY practitioners, especially those working in maintained and school nurseries. These practitioners are protected by gatekeepers, are busy during working hours and have little incentive to take part in research outside of working hours.

There are three limitations relating to sampling, recruitment and interview coverage.

In our sample, the views of practitioners working in PVIs are over-represented compared with those of practitioners in school nurseries. This is not trivial given that three of the four most significant barriers seem to be more severe in PVIs. Moreover, it is important to be mindful that the views expressed in this report are not necessarily representative of those of the EY professional community in England.

Recruitment of practitioners proved highly challenging, so we asked unions to facilitate recruitment. The main limitation is the risk that this might have skewed the findings towards issues that unions tend to be most concerned with, namely pay and working conditions.

Two factors affected the depth and quality of interview coverage. First, phone interviews had to be relatively short (45 minutes) to accommodate the busy working schedules of practitioners.

This meant that although key areas of the guide were covered in depth, some aspects, such as professional development and training or policy suggestions, were either not covered across the interviews or covered lightly. Second, the issue of place-based differences was difficult for participants to answer, and often tended to be speculative rather than fully informed.

Question 3

3. Annex 3: Conceptual framework elements

Accepted Answer

3.1 Individual factors

Demographic characteristics

Key demographic characteristics include age, gender, level of education (class), ethnicity, disability, relationship status (married or single) and whether individuals have children.

Education and training

Education and training describes the type of degrees individuals have obtained, what the degrees entailed (e.g. specialised training in early childhood education) and the type of qualification acquired (e.g. Level 3 NVQ in Children’s Care, Learning and Development).

Employment history and education

Employment history describes people’s employment prior to the early years sector.

Skills and commitment

Skills include key skills for early years work such as compassion, empathy, sensitivity and communication.

Commitment describes (a) commitment to the job as an early years practitioner and (b) commitment to the organisation/setting itself, i.e. the level of identification with, loyalty to and involvement with the employer.

Burnout and emotional and physical wellbeing

Burnout describes a state of physical fatigue and emotional exhaustion, which can be the result of high levels of job- related stress.

3.2 Organisational factors

Training and continuous professional development

Training and continuous professional development refers to any training individuals complete on the job to further their skill set and stay informed about developments in the early years sector or child development.

Work demands

Work demands include hours worked, administrative tasks (e.g. paperwork), assessments, liaising with external agencies (e.g. local authorities), or challenges faced by pupils in the setting (e.g. high level of abuse and neglect).

Pay and benefits

Benefits may relate to financial perks or other incentives (e.g. healthcare, pension schemes etc).

Progression opportunities

Progression opportunities refers to the prospect of climbing up the organisational ladder and related increases in pay, but may also refer to progression prospects in the overall sector.

Organisational climate and culture

Organisational climate refers to collective perceptions of the organisation as a whole, based on the clarity of roles, type of communication (e.g. open and transparent versus closed), working relationships (e.g. collaborative versus individual) and administrative support (e.g. when struggling with workload).

Organisational culture refers to shared beliefs and behavioural expectations, e.g. how workers are treated (e.g. based on hierarchy), how they are rewarded and/or penalised, how and by whom decisions are made (e.g. collectively or by senior management only), and the degree to which people in the organisation support each other on a daily basis.

Supervision

Supervision and leadership describes the availability as well as the nature of professional support and guidance from more senior colleagues (e.g. constructive versus negative feedback, regular versus irregular meetings).

Type of provider

Providers may include school-based nurseries, group-based nurseries (private/voluntary/independent providers) or childminders.

3.3 Contextual factors

Population density

Population density refers to the degree of urbanisation and rurality.

Levels of local deprivation

Local deprivation may encompass references to income, employment, health, education, housing and crime.

Public policy

Public policy may refer to changes to the curriculum, Ofsted stipulations and guidance, funding levels, policies related to funding or other policies.

Other

Other contextual factors not captured in the previous columns that might be relevant.

Question 4

4. Annex 4: Extraction template

Accepted Answer

	CEEDA – Early Years Workforce Survey 2019^{[footnote 1]}	DfE – Study of Early Education and Development (SEED): Study of Quality of Early Years Provision in England (2018)^{[footnote 2]}	DfE – Childcare and Early Years Providers Survey: 2013^{[footnote 3]}
Early years workforce turnover rate	15%	11%	13%
Sample size	563 non-domestic childcare providers, together employing 8,603 staff	598 settings for three- to four-year- olds across England	10,271 providers across England
Representative sample	Yes, of non-domestic settings on the Ofsted Early Years register	Yes (excluding childminders)	Yes
Notes	No regional breakdown. Top three drivers for staff leaving: Work closer to home, Low pay and benefits, Opportunity for promotion	Higher turnover in private sector and children’s centres compared with maintained and voluntary sector. No regional breakdown	Latest turnover data released in 2013

Ceeda (2019), Early years workforce survey, Stockton on Tees: Ceeda Research Limited. ↩
Melhuish, E. and Gardiner, J. (2018), Study of Early Education Development (SEED): study of quality of early provision in England (revised), Research Brief, London: Department for Education ↩
Department for Education (2014), Childcare and early years providers survey 2013, London: DfE. ↩
Wilke, D.J., Radey, M., King, E., Spinelli, C., Rakes, S. and Nolan, C.R. (2018), A multi-level conceptual model to examine child welfare worker turnover and retention decisions, Journal of Public Child Welfare, 12(2), 204–231. ↩
Bonetti, S. (2019), The early years workforce in England: a comparative analysis using the Labour Force Survey, London: Education Policy Institute. ↩
ONS (2018), Personal well-being in the UK QMI, London: Office for National Statistics. ↩
Bonetti, S. (2018), The early years workforce: a fragmented picture, London: Education Policy Institute. ↩

Full citation	Location	Type of publication	Demographic characteristics	Education and training	Employment history	Skills and commitment	Burnout and emotional and physical wellbeing	Other	Training and CPD	Work demands	Pay and benefits	Progression opportunities	Organisational climate and culture	Supervision	Type of provider	Population density	Levels of local deprivation	Public policy	Other	Retention: People leaving the sector	Movement between settings	Movement within the setting
Rolfe, H. (2005), Building a stable workforce: recruitment and retention in the child care and early years sector, Children & Society, 19(1), 54–65	UK	Peer reviewed				X		X	X	X	X			X	X				X
Bridges, M., Fuller, B., Huang, D.S. and Hamre, B.K. (2011), Strengthening the early childhood workforce: how wage incentives may boost training and job stability, Early Education & Development, 22(6), 1009–1029	California, USA	Peer reviewed	X					X	X		X				X	X				X	X
Cassidy, D.J., Lower, J.K., Kintner-Duffy, V.L., Hegde, A.V. and Shim, J. (2011), The day-to-day reality of teacher turnover in pre-school classrooms: an analysis of classroom context and teacher, director, and parent perspectives, Journal of Research in Childhood Education, 25(1), 1–23	USA	Peer reviewed					X			X	X		X
Jeon, L. and Wells, M.B. (2018), An organizational- level analysis of early childhood teachers’ job attitudes: workplace satisfaction affects early head start and head start teacher turnover, Child & Youth Care Forum, 47(4), 563–581	USA	Peer reviewed	X	X			X			X			X
Jovanovic, J. (2013), Retaining early childcare educators, Gender, Work & Organization, 20(5), 528-544	Australia	Peer reviewed					X		X	X	X	X			X
Manlove, E.E. and Guzell, J.R. (1997), Intention to leave, anticipated reasons for leaving, and 12-month turnover of child care center staff, Early Childhood Research Quarterly, 12(2), 145–167	Rural and semi-rural areas	Peer reviewed	X				X	X			X					X			X	X	X
McDonald, P., Thorpe, K. and Irvine, S., (2018), Low pay but still we stay: retention in early childhood education and care, Journal of Industrial Relations, 60(5), 647–668	Australia	Peer reviewed				X				X	X		X
Totenhagen, C.J., Hawkins, S.A., Casper, D.M., Bosch, L.A., Hawkey, K.R. and Borden, L.M. (2016), Retaining early childhood education workers: a review of the empirical literature, Journal of Research in Childhood Education, 30(4), 585– 599	USA	Peer reviewed	X	X			X	X			X		X		X				X	X	X
Whitebook, M. and Sakai, L., (2003), Turnover begets turnover: an examination of job and occupational instability among child care center staff, Early Childhood Research Quarterly, 18(3), 273–293	USA	Peer reviewed	X	X				X			X		X								X
Carroll, M., Smith, M., Oliver, G. and Sung, S. (2009), Recruitment and retention in front‐line services: the case of childcare, Human Resource Management Journal, 19(1), 59–74	North-west England	Peer reviewed	X	X	X	X	X	X		X	X	X			X	X	X	X	X	X	X
Preston, D. (2013), Being a manager in the English early years sector, European Early Childhood Education Research Journal, 21(3), 326–338	England	Peer reviewed		X				X	X	X				X				X	X			X
Roberts-Holmes, G. (2015), The ‘datafication’ of early years pedagogy: ‘If the teaching is good, the data should be good and if there’s bad teaching, there is bad data’, Journal of Education Policy, 30(3), 302–315	England	Peer reviewed						X					X					X
Osgood, J. Elwick, A., Robertson, L., Sakr, M. and Wilson, D. (2017), Early years teacher and early years educator: a scoping study of the impact, experiences and associated issues of recent early years qualifications and training in England, project report, London: TACYC, available at: http://eprints.mdx.ac.uk/2 2867	England	Report		X					X		X			X	X			X	X	X
Simms, M.G. (2010), Retention of early years practitioners in day nurseries, PhD thesis, Nottingham Trent University, available at: http://irep.ntu.ac.uk/id/epri nt/209	Nottingham	Doctoral thesis				X			X	X	X	X	X
Bird, P.P. (2012), Establishing a positive emotional climate in an early years setting, PhD thesis, University of Worcester, available at: http://eprints.worc.ac.uk/2 338/1/PP%20Byrd%20Ph D%20Thesis%202012%2 0- %20Establishing%20a%2 0Positive%20Emotio%20 %281%29.pdf	East midlands	Doctoral thesis	X					X			X		X	X
NDNA (2019), NDNA 2018/19 workforce survey, England, London: NDNA	England	Report	X						X	X	X							X		X	X
Crellin, N. (2017), An exploration into early years practitioners’ work experiences in private day nurseries and voluntary sector pre- schools in England, PhD thesis, University of Southampton	England	Doctoral thesis	X			X	X		X	X	X	X	X		X			X		X
Allen, L., and Kelly, B.B. (2015), Status and well- being of the workforce, in: National Research Council (ed.), Transforming the workforce for children birth through age 8: a unifying foundation, Washington, DC: The National Academies Press, pp. 461–481	USA	Book chapter		X			X				X	X									X
Preschool Learning Alliance (2018), Minds matter: the impact of working in the early years sector on practitioners’ mental health and wellbeing, London: PLA	England	Report					X		X	X	X		X	X				X		X
Kalitowski, S. (2018), Building blocks 2018: focus on the workforce, London: PACEY	England	Report	X				X	X	X	X	X							X		X	X
Ceeda (2018), Early years sector skills survey, Stockton on Tees: Ceeda Research Limited	England	Report				X		X		X	X	X								X
Counts			10	7	1	6	7	7	10	11	17	6	10	4	6	3	1	4	3	8	7	1

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