Our team are specialists in the Further Education research sector, applying cutting-edge econometric techniques to approach key policy questions. We are leaders in estimating the returns to vocational qualifications and fuzzy matching techniques for combining administrative datasets.
Our research and technical skills are regularly relied on by government departments to contribute to public policy evaluation and data processing. We have also undertaken high-quality analysis for Qualification providers, Think tanks, Unions and charities. Our expertise covers all types of Vocational Education and Training (VET) in the UK, including institutional knowledge of qualifications such as apprenticeships, BTECs and NVQs.
London Economics formed one of the four nodes that made up the Centre for Vocational Education Research (CVER). Together with the Centre for Economic Performance (CEP) at the London School of Economics (LSE), the University of Sheffield, and the National Institute for Economic and Social Research (NIESR), CVER operated at the forefront of the Further Education research field to answer the fundamental policy questions:
- How does vocational education affect individual prosperity, firm productivity and profitability, and economic growth?
- How can the quantity of ‘high quality’ vocational education provision be improved?
- How do the costs and benefits of vocational education influence individuals’ participation decisions?
We have extensive experience working with the most up-to-date administrative, survey and cohort datasets available, including:
Longitudinal Education Outcomes (LEO)
This newly available dataset comprises the National Pupil Database (NPD), the Individualised Learner Record (ILR), the Higher Education Statistics Agency (HESA), and the Work and Pensions Longitudinal Study (WPLS). We have worked with versions of the LEO dataset in England, Wales and Scotland.
Labour Force Survey (LFS)
The largest household survey in the UK, the LFS is a representative rotating quarterly panel of around 38,000 households containing information on qualification attainment and labour market participation.
Employer Skills Survey (ESS)
A UK-wide telephone-administered, bi-annual survey of approximately 90,000 employers in the private and public sector covering the skills needs of employers both in terms of recruitment and existing staff.
Inter-Departmental Business Register (IDBR)
A comprehensive list of UK businesses, the IDBR covers around 2.7 million live and over 5.7 million non-live enterprises in all sectors of the UK economy.
Employer Perspectives Survey (EPS)
A UK-wide telephone-administered, bi-annual survey of approximately 18,000 employers in the private and public sector providing information on recruitment and development.
British Cohort Study (BCS70)
A study following the lives and educational development of approximately 17,000 individuals born in the same week in 1970. Respondents are revisited at regular intervals throughout their lives.
Examples of our work include:
Evaluating the earnings differentials associated with vocational qualifications
Using comprehensive information from different school, Further Education and Higher Education data sources, we estimate the association between achieving vocational qualifications at different levels and labour market outcomes (earnings, employment and benefits dependency).
Apprenticeships and Social Mobility
Using LEO data we undertook an analysis of the apprenticeship levy on the outcomes of learners from disadvantaged backgrounds.
Estimating the impact of publicly funded training on industry and firm-level outcomes
To undertake the analysis, we first matched the Individualised Learner Record, EDS ‘Blue Sheep’ data, and the Inter Departmental Business Register (IDBR), and then applied a range of statistical and econometric models to derive the relationship between publicly funded training and firm and industry level productivity.
Selecting a counterfactual for estimating the returns to qualifications
In this analysis we adopt a Propensity Score Matching (PSM) method to assess whether it is possible to identify an ‘optimal’ counterfactual when estimating the returns to qualifications, in terms of whether it comprises individuals who are most similar to those who successfully complete the qualification of interest.