We held a workshop on Monday 8th July to explore the relationship between the ‘evidence system’ for innovation policies (how and what evidence we collect, share, analyse, use and value) and the inclusiveness of innovation policies. Following provocations from Robyn Klingler-Vidra and Maria Savona, we asked participants to discuss how the goals of innovation policy are changing, and the implications of this for evidence and measurement. Researchers, policymakers and others shared insights in a showcase of different methods to support inclusive innovation policymaking.
At Nesta we have worked for many years on innovation policy. Our work has largely focused on the how of innovation policy - exploring what works in innovation support, and helping governments to design policies and programmes that are more evidence-based. But increasingly we’re also thinking about the why of innovation policy - what are its goals, and what could, or should they be?
We are not alone in questioning dominant approaches to innovation policy. Mariana Mazzucato and colleagues at the Institute for Innovation and Public Purpose argue for mission-driven policies that address societal goals. The Transformative Innovation Policy Consortium is trying to develop innovation policies to address global challenges as set out by the UN in the SDGs. The Responsible Research and Innovation movement focuses on how the governance of innovation can be carried out ethically and responsibly, and with the involvement of the public.
At Nesta our concept of ‘inclusive innovation policy’ holds that innovation policies should: more effectively direct innovation towards social challenges; encourage the benefits and the risks of innovation to be shared more equally; broaden participation in innovative jobs and sectors; involve more people in processes of priority-setting and regulation. We’ve summarised this as focusing on who benefits, who participates, and who decides.
These new framings all recognise that innovation is not a neutral force but a deeply political matter, in which the choices we make create winners and losers, and shape society for better or worse.
There are tensions between these different framings and new goals. In her opening provocation, Robyn Klingler-Vidra noted some of the choices that those promoting more ‘inclusive’ policy goals need to make, and the implications of these choices. For example, should we focus efforts on innovation promotion, expanding the group of those participating in and becoming wealthy by innovation to include a more diverse range of people? Or should we instead think about distributing the benefits more equitably across society? Questions such as these have implications for the measurement systems we develop. Whilst we would reason that we cannot protect a sector’s elite status and expect to open it up at the same time, this focus on inputs, for example promoting diversity amongst startup founders, generally poses less of a challenge for measurement than attempting to change outcomes and impacts.
In a similar vein, Maria Savona pointed out that promoting ‘disruptive’ innovation - even when this is directed towards societal goals, as in a mission-oriented approach - can support ‘frontier’ places and firms to move further ahead of the pack, potentially widening the divide with those getting left behind. She argued that we need instead to focus on creating ‘inclusive structural change’. This might mean supporting places to develop their innovative capacity more slowly, so that less innovative firms can catch up.
Evidence can support paradigm shifts. Thomas Kuhn’s work on scientific revolutions argues that as measurement becomes more precise, we start seeing anomalies in our current theories, and paving the way for new ways of understanding the world.
Evidence systems can also hinder progress, when they reinforce old ways of thinking. Almost since it was first introduced, GDP has been criticised for measuring the wrong things - in 1968 Bobby Kennedy famously described it as
“[Measuring] everything in short, except that which makes life worthwhile”
Yet it still drives economic decision-making the world over (one reason the announcement that the New Zealand government was introducing a budget to increase wellbeing, rather than GDP, made headlines).
When it comes to the evidence system for inclusive innovation policy, we are at an early stage. Scholarly research on innovation is only just starting to engage with questions around who takes part in, and who benefits from, innovation. This lack of evidence can get in the way of change. For instance, it is common to hear that there is a ‘trade-off’ between equity and excellence. We don’t actually know how far this is true and in what circumstances, but it’s a powerful argument against changing approaches to the distribution of funding.
Participants at the workshop noted that progress towards inclusive innovation policy is thwarted by current approaches to collecting, linking, and sharing data: we are hampered by our partial understanding of the system and how it works for different groups in society, and by insufficient coherence between policy areas, their theories of change and their data collection activities. While inclusion must be systematic in order to be effective, it is often the case that these concerns are confined to individual interventions within particular policy areas.
Participants argued that we need to improve the interoperability of datasets, and develop tools to enhance sharing and pooling of data. For example, the Lost Einsteins research in the US was able to produce powerful insights by linking patent data with data on individual inventors’ social backgrounds, thereby making a case for policy change; such work is yet to be done in the UK.
Similarly, others pointed out that while we can evaluate individual pilots, even when these show positive results it is hard to make the case for larger-scale investment in promoting inclusive innovation. We can show that a mentorship programme is effective in supporting innovators from minority backgrounds to access funding, but it is still difficult to make the case that overall, a more inclusive approach to innovation policy is effective.
To move beyond narrative, we must turn our attention to the technical work of developing and testing indicators, datasets and frameworks for measurement. Such work would improve our understanding of innovation activity and the effects of innovation policies, and serve to strengthen, clarify and reduce tensions between emerging theories of innovation.
There is huge potential in using new and diverse research methods to enhance our understanding of inclusive innovation policy and its impact. At the workshop we heard about four projects that are developing the evidence base for inclusive innovation policy:
Nesta’s Inclusive Innovation and Innovation Mapping teams are currently preparing a paper that outlines our thinking on this topic for publication in the autumn. If you’re doing work in this area and would like to share thoughts and/or information, do get in touch with us: [email protected]