Evaluating the Industrial Strategy Challenge Fund
What is the issue?
Announced in 2016 and delivered by UK Research and Innovation (UKRI), the Industrial Strategy Challenge Fund (ISCF) aims to support the development of solutions to major industrial and societal challenges facing the UK through the delivery of a mission-oriented funding programme. Under the ISCF, funds have been distributed through the establishment of individual ‘Challenges’, each addressing a pressing societal or industrial issue.
Public and private organisations have been invited to bid for funding for collaborative research and innovation (R&I) projects that contribute to addressing the Challenges. The ISCF has a total government funding commitment of £2.8 billion, combined with an additional £3 billion in matched private sector funding.
How are we helping?
RAND Europe is leading a four-year independent evaluation of the ISCF, building on evaluations of each of the ISCF Challenges to examine the impact of the ISCF as a whole. The evaluation, undertaken in collaboration with Frontier Economics, has three core aims:
- To build an evidence base with which to inform ongoing and future improvements to the ISCF
- To demonstrate what the ISCF has delivered to taxpayers
- To understand the impact of mission-oriented and challenge-focussed R&I support.
The evaluation will be undertaken in four overarching phases: evaluation framework development; baseline measurement; review of Challenge-level evaluation findings; and primary data collection, analysis and reporting.
This will include consultation of a wide range of Challenge-level, Fund-level and external data sources, together with interviews, workshops, case studies and econometric analysis of business supported by the Fund. The evaluation of the ISCF adopts a theory-based approach using contribution analysis, the foundation for which is an ISCF Theory of Change. Other key features of the evaluation include a participative, formative approach designed to maximise opportunities for cross-Challenge and cross-Fund learning.