Appraisal of the modelling methodology for the long-term apprenticeship model

See 'Attachments' for a word version of this description. In spring 2017 the way the government funds apprenticeships in England is changing.

Some employers will be required to contribute to a new apprenticeship levy and there will changes to the funding for apprenticeship training for all employers. Employers in all sectors will pay the apprenticeship levy if their pay bill is more than £3 million each year.

The levy will be paid to HM Revenue and Customs (HMRC) through the Pay As You Earn (PAYE) process and it is expected the levy will fund all government spending on apprenticeship training. The link below provides further details of how the levy will work and explains the principles that apprentieceship funding will operate on from 1 May 2017. https://www.gov.uk/government/publications/apprenticeship... The introduction of the levy and the changes to the funding system presents a significant challenge to the way the DfE forecasts apprenticeships starts and funding requirements.

A forecasting model has been developed in-house to project apprenticeship starts and costs in both levy and non-levy paying employers for the period 2017-18 to 2020-21. The projections will be used to understand the demands on the apprenticeship budget primarily for the purposes of the financial control of apprenticeship spending.

OBJECTIVES The aim of the project is to review the design of the model, consider if the model is fit for purpose, and whether other approaches might improve the quality and capability of the model. The project should satisfy the two core objectives below: 1.

Evaluate the model design with respect to: a. The data sources available to the DfE with information relevant to predicting future apprenticeships starts and costs at both levy and non-levy paying employers. b.

The other modelling approaches available to forecast future starts and costs. c. The other economic and statistical modelling techniques that could be employed to make best use of the data sources and other information available. 2. Advise on how any alternative or additional approaches could be implemented that would enhance the model, with an estimate/understanding of the benefits they would have on the starts and costs projections.