Article Gold Open Access 2026

Apprenticeships and Firm Performance an Empirical Investigation Using “Big Data” for All English Businesses

Industrial Relations Journal
Journal · Vol. 57 · Issue 1 · pp. 87-101
Abstract

There are quite a few robust estimates of the earnings effects of successful apprenticeships for individuals, but there is a shortage of research concerning the relationship between apprentices and firm performance, and most of this study is qualitative or based on surveys. This paper aims for an empirical investigation of this relationship using quantitative data available from large government registers. We analyse data for all English businesses, which—linked to Individual Learner Record data (ILR) for participants in apprenticeship programmes—provide structural information on apprenticeship firms and other firms for the years 2010 to 2015. The descriptions show that around 10%–15% of all eligible firms undertook apprenticeships and that apprenticeship firms are larger both in terms of turnover and employment than other firms. Regression analysis is used to explore the nature of the relationship between apprenticeships and the firms' turnover. In models employing a range of observable characteristics and using Inverse Probability Weighting to alleviate the selection into apprenticeships, our findings point towards a positive relationship between engaging in apprenticeships and firm growth, but not to a change in business productivity. © 2025 The Author(s). Industrial Relations Journal published by Brian Towers (BRITOW) and John Wiley & Sons Ltd.

Keywords

Author Keywords

Apprenticeship firm performance linked employer employee data

Index Keywords

Author Affiliations
University of Brighton, Brighton, East Sussex, United Kingdom
Loughborough University, Loughborough, Leicestershire, United Kingdom
Funding & Acknowledgements
Department for Education, UK Government, DfE
Grant: RE140182BIS
This work was supported by Centre for Vocational Education (CVER) work programme, with funding by the Department for Education, UK Central Government [grant number RE140182BIS]. We would like to thank an anonymous referee and Sandra McNally for helpful comments and acknowledge useful feedback after presenting work at the CVER Conference 2018. We would also like to thank the Department for Education (DfE) for their help with access to the data and for financial support under the CVER work programme. Data access in the Office for National Statistics is also gratefully acknowledged.
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