Innovation Policy – From monetary incentives to behavioural and experimentation

When we think of policies, we inevitably think of taxes or fiscal stimulus. Innovation is certainly no stranger. The promotion innovation is often implemented as a tax relief for investments categorised as innovation.

I guess we are all immediately able to recognise the limitations and shortcomings of this practice. Just to follow with the line of reasoning above, once the tax relief is effective, suddenly many investments are re-labelled as innovation investments or if the implementation is costly and the elasticity of the sector is low … they fail to attract companies that want to implement it.

We all are aware that monetary incentives go as far as they go and very often backfire. This is particularly true when we deal with a complex system where solutions vary a lot in their effectiveness as a result of small changes in either their design or implementation.

Complex systems are the result of multiple factors interacting together and many agents that try to adapt to them. Markets and particularly innovation, are good examples. As a result of this interaction, excellent solutions are close to the ones that don’t work, there is not a smooth path, an incremental way to approach best solutions.

In addition to that, the new all digital world makes more necessary to put in place specific policies. We need to promote apps that use Open Data or innovation in citizenship by developing new participatory systems or the change in business models in the electrical grid allowing co-generation. All this is certainly very specific, very precise to be able to be addressed by generic monetary stimulus.

Therefore leading agencies and leading cities are promoting new types of policies where the monetary incentive takes the backseat.

A good example of this is the use of challenges – competitions – in order to promote innovation in a particular field. This is not new, the XIX century is full of them, but actual IT platforms make it straightforward and global. DARPA competitions or any Open Data or apps4x challenge such as Bigapps, apps4Amsterdam, apps4Barcelona, apps4Finland, etc…

This type of policies draw more on the behavioural aspect than on monetary incentives, and they backfire less. A good example of these policies is the use of he concept of free versus a small payment. Humans don’t react linearly between free and small amounts, free always draws more than a proportional level of attention.

Therefore the use of small payments is useful in situations such as traffic congestion. Because traffic congestion is also not linear, a proportionally small reduction of it produces a more than significant impact, the use of small payments can lead to this small reduction and therefore to solving a problem that looked insolvable. A good example of this is Stockholm, where this small tax was introduced, taken away and reintroduced, producing a kind of natural experiment. And by the way, surveys show that the population is happy about the tax (it is important that the tax doesn’t limit the behaviour of a sector of the population because of affordability … this is certainly not the case of Stockholm).

Therefore it looks like this kind of problems are better solved by using behavioural policies. However, if this is a complex system how can we be sure that they are appropriate?

The easy and complex answer is that you cannot. In complex systems is very difficult to predict the REAL outcome without trying, therefore experimentation is a must. Experimentation can we implemented in multiple forms but it is the only way to really learn about collateral effects and overall effectiveness and experimentation has to lead to getting rid of the policies that don’t work, modify and perfect the ones that are promising and strength the ones that work.

Policies have to be easily understood and known by the actors in order to be effective. Nothing more useless that an unknown policy or one that only tax-experts are aware of. This is the reason why they must be simple, limited in number and easy to understand. Attention, particularly now, is scarce.

Summarizing, the future of innovation policy is headed towards simple, limited in number, behavioural and experimentation enabled by IT and IT platforms that aim to address specific sectors and needs. This is certainly a dramatic change and we look at the reality of our legislation system and the organizations that implement policy … a long way to go. However, I suspect that it is the only effective one …

Esteve Almirall

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