By Laurenz Langer
Evidence flow diagram
I suppose most of us might be familiar with the famous joining the gym dilemma. In the quest of keeping to one’s new year’s resolution, earlier in the year an overly ambitious past-persona decided to take out an annual gym membership. Fees were paid and a shiny new gym gear stocked in the wardrobe. The gym also kept its part of the deal being accessible for most of the time and providing a variety of opportunities to exercise. One could say demand to exercise and supply of opportunities to do so were in equilibrium, thus supposedly resulting in the event of exercising. Alas, by July I assume I might be part of a sizable share of gym members for whom the equilibrium conditions miraculously did not translate into a manifestation of the equilibrium itself – that is, actually exercising.
Even with good intentions and strong ambitions changing one’s own behaviour is tricky enough; certainly, it is even more challenging then for institutions, policies, and programmes to change the behaviour of others, and likewise for others to change the behaviours of institutions and the decision-makers within them. Fortunately for us, thanks to emerging fields such Behavioral Psychology and Behavioral Economics we now know a great deal more about how human beings change their behaviour and make decisions. For example, a cleverly designed nudge or commitment device might work wonders to change my exercise habit – at least it did for Matt!
Over the course of the last decade, the Social Sciences have started to pay greater attention to behavioral factors and what these mean for the design of social policies and programmes. The UK government, for example, established a special unit – interchangeably know as the Behavioural Insights Team or Nudge Unit – to improve the design of public policies through behavioural insights. Also, and a bit closer to UJ-BCURE’s context, this year’s World Development report – Mind, Society, and Behaviour – compiled a huge collection of behavioural insights on human decision-making and what the implications of these might be for the formulation of development policies.
Our UJ-BCURE programme in South Africa and Malawi hopes to generate effective means of building decision-makers’ capacity to use research evidence. I assume it is fair to say that on Kirsty Newman’s wonderful classification of programmes aiming to support evidence-informed decision-making (EIDM), we would be regarded as a demand-side intervention. Also, in the early stages of UJ-BCURE, we mapped out the evidence-to-policy interface in both countries (the results are published in two landscape reviews, here and here). Based on this, we were quite certain that a range of actors were working towards the supply of fit-for-purpose research evidence already, supporting our rationale to focus on improving the demand for such evidence through relation- and mentorship focused capacity-building.
Though, while I was listening to the podcast of last month’s AEN regional meeting, it struck me that the EIDM community so far seems to have overlooked most of the recent behavioral and psychological insights on human decision-making. We seem to be narrowly focused on improving EIDM’s demand and supply, neglecting the actual event or activity of using evidence during the decision-making process. Listening to the podcast I wondered: what if our programme turned out to be successful and our partner departments are left with a decent number of decision-makers well-trained and motivated to use evidence. As mentioned, a robust supply of fit-for-purpose does exist too. The conditions for evidence use are since given; however, is it enough to improve the basic conditions of the evidence-to-policy interface?; does this create an active flow of evidence through the system?; are conditions sufficient to bring about actions?
I mapped out my thinking in the above diagram and would assume that – so far – most efforts in supporting EIDM have been on improving the conditions required to use evidence, which one could understand as improving the supply and demand. The equilibrium of evidence use or flow event, though, is too often neglected. Incorporating behavioral insights on human decision-making could turn out to be the sweet spot in the quest for EIDM.
To be sure, I would position insights on the behaviour of decision-making as a supplement to the more fundamental knowledge of understanding how to get the enabling conditions for EIDM right. Nevertheless, EIDM programmes as UJ-BCURE might be able to benefit from a detailed understanding on human behaviour and how and why we take decisions. For example, evaluations of strategies to improve evidence use conditions consistently highlight the importance of interaction between decision-makers and researchers. These interactions are preferable face-to-face, in small groups, and take place in an interactive manner. But maybe there is more to it? In particular if we want to change behaviour we might want to zoom in a bit closer on the decision-makers whose behaviours we hope to influence. Might there not be virtue in looking at the communications and marketing literature on how to frame a message to stick with the customer? If we know from development studies that the marketing of social norms (the norm of evidence use in this case) can change behaviours, should we not think about incorporating such mechanisms into our interaction events? If we know that reminders are a powerful tool in a health setting to get patients to take their medication, might there not be virtue in following up the meeting with regular short messages? If we know that Tweeting can increase awareness of evidence, should we not also pay attention to the behavioral insights when using Twitter that most people read Tweets around 5PM on a Wednesday and prefer a link or a picture to accompany the tweet? These are just some examples and there is much to be gained even from a speed read of the recent World Development Report or Richard Thaler’s new book ‘Misbehaving’.
In sum, if we are serious about improving decision-making in a policy setting we might want to pay a bit more attention to the actual act of taking a decision. Creating the perfect conditions for evidence use is of limited virtue if this leads to a static system in which no one presses the ‘On’ button. Understanding better how humans make decisions could help us to tweak our programmes to have higher chances to change behaviours. It might also discover a range of micro-interventions that are needed, in addition to supply- and demand-side interventions, to complement an effective in-motion evidence-to-policy-to-decision-making interface.