Understanding the young social care workforce: An analysis of linked workforce, education and census data

Main Article Content

Katy Huxley

Abstract

Objectives
To provide a better understanding of the characteristics and pathways into social care work of Welsh educated learners. This may then inform efforts to reduce the crisis of recruitment and retention of social care workers.


Method
We have linked administrative education data to the social care workforce register and the UK Census 2011. Five cohorts of school leavers are followed through post-16 education and, where relevant, to higher education data, and into registered occupations in social care work. This allows us to interrogate the qualifications and subjects studied by young workers within the social care workforce. Linkage to the Census 2011 allows us to consider family background and social economic classifications of learner households.


Results
Our results indicate that young social care workers are still much more likely to be female. The proportion who have low socio-economic status (measured by receipt of free school meals) is similar to the population; as is their ethnicity classification. They are more likely to report Welsh fluency than the social care workforce overall. In terms of pathways to social care work, the majority undertake vocational learning at post-16, whilst a significant minority undertaking A-levels. Registration to study in higher education is lower than the average for the school cohort population.


Conclusion
Vocational rather than traditional 'academic' pathways are more common amongst young social care workers. Continued gendered patterns in occupational choices are clear, and must be tackled to ensure greater sustainability of social care. Furthermore, targeted interventions that focus on male entrants to social care could ameliorate the social care workforce crisis.

Article Details

How to Cite
Huxley, K. (2025) “Understanding the young social care workforce: An analysis of linked workforce, education and census data”, International Journal of Population Data Science, 10(4). doi: 10.23889/ijpds.v10i4.3112.

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