The use of behavioural science in policy has exploded since the publication of Nudge in 2008 and the creation of BIT in 2010. We were asked to reflect on the team’s work for a new issue of Behavioural Public Policy, and we decided to be open about some of the challenges with applying behavioural science to policy. Right now you can read the whole issue for free, so we wanted to give you some highlights.
One challenge is that we still often lack evidence for the long-term effects of interventions on behaviour. BIT has often been asked to range widely across many policy areas, which can limit the incentives to return to an intervention. Nonetheless we are beginning to address this issue in some areas – for example, we are engaged in a long-term evaluation of the economic impact of receiving growth vouchers issued in 2014, one of the largest studies of its kind.
Similarly, behavioural scientists within the policy domain may be concerned that repeated exposure to certain concepts or interventions will lead to diminishing returns. We identify two related problems here: “structured” and “unstructured” repetition.
“Structured” repetition is where the same approach is deliberately used as a direct follow-up to an initial intervention in order to reinforce its effects (e.g. reminders). If some of the impact of an intervention comes from a novelty effect, then repetition should reduce this effect. The evidence is not clear on this point: we have seen this happen in some studies, but others suggest that repeated exposure reinforced initial changes in behaviour.
“Unstructured” repetition may be more concerning. This is where people are exposed to the same kind of approach from different actors, at different times and in relation to different topics. Take social norm messages, for example. We know they are effective in some contexts. But what if they were found on our gas bills, tax letters, inducements to travel by public transport, and reminders to attend class at the local college? If the same message gets associated with different behaviours, that could mean any changes are not reinforced – and the novelty effect wears off quicker.
This hints at a final issue that’s worth mentioning in the context of applying behavioural insights to policy: interventions may be having unintended and unmeasured effects elsewhere. We may shift the dial on one outcome measure, even as problems mount elsewhere, unobserved. For example, we may successfully reduce fraud by grocers participating in a food subsidy programme, only to find they stop offering the food in response.
The challenge of unintended consequences is, of course, not specific to behavioural policy interventions. But since behavioural science itself studies how people react to changes in cues or incentives, it should be particularly sensitive to the problem. For example, the evidence on “licensing effects” shows how attempts at self-control in one domain create indulgence in another, how virtuous behaviour in one situation may increase dishonesty elsewhere, and so on.
As well as these challenges (and the others we discuss in the article), we also wanted to flag the opportunities that should be seized in the future. Some of these, like the need to influence the behaviour of policy makers themselves, we have started to address elsewhere. But other opportunities – like scaling interventions, influencing organizations (rather than individuals), and dealing with complex “wicked issues” – still need a push.
For example, many applications of behavioural science to policy have adopted a fairly crude model of influence: a public sector actor attempts to influence (usually) an individual, organisation or group. Obviously, this excludes avenues for influence that exist between individuals, organisations or groups. Arguably, the focus on individual decision-making means it has neglected relevant insights from theories about social networks and systems thinking.
In a world of limited resources, a better understanding of how to harness peer-to-peer transmission of behaviour could mean that the same or better outcomes are achieved at much less cost (since far fewer contacts from government would be needed). BIT has conducted some preliminary studies on how to create ‘network nudges’, but the potential gains here are so large that they justify more work.
This blog post has merely scratched the surface in terms of outlining some of the core challenges and opportunities that behavioural scientists face in the policy arena today. A far more in-depth and extensive exploration of the debate is covered in the journal. This includes a set of responses to our arguments – all of which we welcome. In particular, we recommend Liam Delaney’s thoughts on ethics, and Olivia Maynard and Marcus Munafo’s arguments on transparency. Anyone interested in the range of barriers and possibilities that face behavioural scientists in policy today should take a look.
