Employers’ behavioural responses to the introduction of an apprenticeship levy in England: an ex ante assessment
Bibliographic record
Abstract
An apprenticeship levy was introduced in England in 2017 to help the government meet its target of 3 million apprenticeships between 2015 and 2020. Training levies have been, until recently, something of an anathema in public policy circles in England with most having been abolished by the mid-1980s as the government moved towards creating a flexible, de-regulated labour market. So why would an apprenticeship levy now produce better results? In this paper, we analyse in-depth employer interviews carried out in 2016 to identify possible impacts of the levy on businesses’ approaches to apprenticeships. We consider whether, ex ante, the levy was expected to result in: more apprentices being trained; reconfiguration of existing training structures into apprenticeships; employers ‘gaming’ the system, with levy funds being used but not producing real gains in training; or, employers writing off the levy. These findings are presented alongside data on apprenticeship starts since the levy’s introduction. We discuss the dramatic fall in apprenticeship starts relative to pre-levy numbers. Given the latest figures and our ex ante study of employers, it appears as though even such wide-sweeping change to the programme may do little to overcome longstanding, low levels of employer demand for apprenticeships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".