Beyond R&D: What makes innovation policy?

Knowledge lies at the heart of the political rhetoric of Western economic policy. From Barack Obama to Jean-Claude Juncker, politicians regularly confirm the importance of knowledge output – or of fostering “innovation”, to use the more popular buzzword. But despite all the rhetoric around innovation policies, policymakers often remain scant on details. If it is talked about, innovation policy is mostly linked to financial expenditure. During the financial crisis, when national budgets came under pressure and austerity programmes abound, many states, such as the UK, refrained from cutting their R&D expenses. The newest national budget even pledges an additional £240 million to innovation. In a similar vein, programmes such as EU Horizon 2020 committed to foster innovation by investing €80 billion in science and innovation programmes.

Where does this belief in a purely financial investment approach to innovation policy come from? In general, it is founded on the persistent popularity of the linear model of innovation among policymakers. The linear model prescribes a simple flowchart diagram, where basic research leads to applied research, which sparks technology development and finally results in production and diffusion of a new technology or invention. In this way, the linear model is often likened to a closed pipeline, where investment in basic research leads to innovation in form of new technologies and products. Its persistent popularity can be explained by the stakes held in it by various shareholders, such as scientists, businesspeople, but also politicians. Certain industries, for example the pharmaceutical one, have long supported this model in order to justify additional public investment in basic research, which directly benefits their business. Politicians have an interest in a model that is easy to explain to their voters and yet compelling, at least superficially. Unsurprisingly, this simplistic approach to innovation has long been questioned by the academic world, for example by Nathan Rosenburg, who proclaimed the linear model “dead” as early as 1994. And once such an opinion becomes the topic of (admittedly brilliant) TED talks, it has indeed reached the mainstream of popular science.

Since others have already done such a great job in disassembling the myth of the linear model of innovation, I will focus on the implications on policy recommendations in practice. Even though most of the academic world seems to agree that the linear model of innovation is dead, it is much less clear what the consequences of its funeral should be. In recent years, and especially with the rising popularity of the buzzword “innovation” among policymakers, a range of alternative policy approaches have been proposed. This has to do with the vagueness of the term “innovation” itself, which includes the Schumpeterian “carrying out of new combinations” by entrepreneurs as well as John Kay’s conclusion that innovation is about finding new ways to meet customer needs. Innovation, in a broad sense, is about products, processes and organisation both in radical and incremental ways. American taxi startup Uber, for example, is an incremental addition to the cab industry and relies almost exclusively on a rearrangement of pre-existing technology, yet it is considered innovative. This vagueness of the term “innovation” itself translates in differing views on its purpose. Should innovation policy be mainly seen as a tool to increase productivity and stimulate economic growth? Or should it be focused on its beneficiaries, i.e. society, and aim at improving individual quality of life?

One of the most popular papers on innovation policy in recent years has been “The Entrepreneurial State”, a pamphlet written for UK think tank Demos by Mariana Mazzucato, who is RM Phillips Chair in Science and Technology Policy at the University of Sussex. Mazzucato suggests that the most successful innovation policies are those where the state goes beyond merely providing a free market framework. Rather, she claims that the state should actively engage in the creation of new markets as an “entrepreneurial state”. This is necessary because private investors are rarely willing to take on uncertainties, i.e. incalculable risks in the sense of Frank H Knight, but only accepts those risks with a measurable probability for success. Examples for such government-funded success stories are genetic modification and artificial intelligence programmes. Throughout her pamphlet, she emphasises productivity as the main goal of a successful innovation policy, and rejects the notion that support of small businesses will yield the highest productivity. Very clearly, for Mazzucato innovation policy goes beyond setting a national R&D budget, but includes the active participation of the state to foster innovation. At the same time, this approach is centred on increasing productivity as a measure of success. Other factors, such as increased quality of life appear to be more of a by-product. Another result of this focus is Mazzucato’s rejection of specific funding for small businesses on grounds of lack in productivity ­– an argument which is entirely ignoring the fact that a wide range of small businesses in different economic sectors can contribute to shielding a national economy from market volatilities by reducing reliance on single sectors. Nonetheless, this productivity-focused argument has proven to be persistent and has recently been reiterated by other contributors to Demos.

DARPA Vector Logo.eps
The American DARPA is one of the more prominent examples cited by Mazzucato to illustrate the success of agency-driven innovation success. By DARPA ( [Public domain], via Wikimedia Commons.

Patrick Cunningham, scientific adviser to the Irish government and professor at Trinity College Dublin, advances a different approach to innovation policy. In a similar vein to Demos, Patrick Cunningham goes beyond “innovation policy” as a synonym for an increased R&D budget. But instead of focusing on productivity in terms of GDP and therefore pure economic growth, Cunningham suggests a measurement of innovation policy in terms of “progress”, as through the Human Development Index (HDI) by the United Nations Development Programme (UNDP). The Human Development Index and others such as the Gini Coefficient or Gross National Happiness, include factors such as individual satisfaction, sustainability and environmental footprint in their measurement and thus create an image of progress that differs decisively from productivity measured in raw economic growth as through the GDP. This focus on human well-being entails an innovation policy with investment and frameworks focused on those technologies which are most likely to increase human development, rather than productivity or GDP. A company like Uber, although productive and innovative, in this logic might be less worthy of government support than a start-up endeavouring to increase life expectancy or to combat climate change.

So what makes a good innovation policy? Clearly, academic opinions on this matter diverge. But in any case, there is a consensus that it goes beyond mindless investment in R&D. The very vagueness entailed in our understanding of innovation enables both academics and policymakers to branch out and reach beyond R&D. How ever this “beyond” may look like in the end, almost everyone seems to suggest that innovation policy contains some form of governmental guidance through stimulation and regulation. It is now up to political parties and their think tanks to seize the matter, and give the voter an actual choice. Whether our innovation policies should be merely focused on productivity, or embrace a broader notion of human progress should be for the people to decide, not a matter of backroom negotiations between bureaucrats and government experts.


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