Employers and apprenticeships in England: costs, risks and policy reforms
Bibliographic record
Abstract
That so many employers in various sectors continue to train apprentices despite incurring substantial costs can be taken as prima facie evidence of there being ways to mitigate the investment risk. The main downside in the English apprenticeship system is that the overall levels of employer engagement and training volumes are considered sub-optimal and in reality there is a fine balance which can put the performance of the firm and the wider economy at risk due to insufficient skill development. This paper draws on data from the Net Costs and Benefits of Training to Employers series of studies (carried out between 1994 and 2012). Each of the original studies consisted of case studies of employers in particular sectors in which the costs associated with training an apprentice were considered against the benefits employers obtain through this activity during the training period. Data in the original studies was collected in face-to-face interviews with the employers. The data were aligned with an accounting framework that captured the relevant costs and benefits of apprenticeship training from the perspective of the employer. Calculations were carried out to also indicate the payback period (i.e. the period after training has taken over which an employer could be expected to recoup any net cost they had incurred). We present the most comprehensive available estimates of the net costs of apprenticeship to employers in England to illustrate how costs vary across sectors and how, for most employers, engaging in apprenticeship training leaves them with a net cost at the end of the training period. The extent of this net cost at the end of the formal training period is found to vary substantially by sector and level of apprenticeship and over time, in real terms, the net costs of training have tended to increase across all sectors. Employers reported a range of rationales for training apprentices; from supply-led (e.g. where participation was prompted by training providers offering it for free) to more demand-led (e.g. where employers saw apprenticeship as necessary for acquiring needed skills) reasons. Employer sensitivity to various cost parameters depended upon where on the supply- to demand-led continuum they fell. Despite various policy changes since the mid-1990s, employers’ engagement in apprenticeships in England has not achieved a significant volume and there are still financial risks attached to investing in this form of training. Even with the latest reforms of funding and delivery embodied in the move to apprenticeship standards and the introduction of an employer levy, there is no guarantee that more employers will be willing to train apprentices or that those who already engage will train more. There is a need to consider various ways of reducing employers’ risk without inducing unintended behavioural changes that counter overall policy objectives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".