Long-term effects

One common concern is that we lack evidence that effects on behaviour endure for a substantial period. Despite many field experiments testing the application of psychology or behavioural economic interventions to practical problems (a good summary of which can be found in DellaVigna, Reference DellaVigna2009), relatively little is known about the long-term effects of many such interventions (Frey & Rogers, Reference Frey and Rogers2014). BIT has experienced this problem because it has been required to range widely across many policy areas, which has sometimes limited the incentives to return to an intervention in order to assess its long-term consequences. We are beginning to address this omission in some areas. We know, for example, that non-compliant Guatemala taxpayers who received either a social norm or an ‘oversight message’ were significantly more likely to comply 12 months later, despite not receiving another letter in the interim (Kettle et al., Reference Kettle, Hernandez, Ruda and Sanders2016). We are also engaged in a long-term evaluation of the economic impact of receiving a growth voucher, one of the largest RCTs of its kind.Footnote ¹

One underlying issue is that public officials and academics (particularly junior scholars) are rarely incentivised to choose studies where the main outcome measure will only be reported far in the future.Footnote ² Obviously, a longer discussion is needed to deal with the various aspects of this problem, so we only sketch some ideas here. One is that public officials applying behavioural science should plan to achieve a balance between longer-term outputs and the short-term outputs that they often need to justify continued support and funding. Another possibility is to anticipate that personnel and attention will shift as time moves on and mitigate the consequences. For example, those running trials could be required to leave them in a state that allows others to revisit them and match interventions to longer-term outcomes. Initiatives like the ‘Datalab’ created in HM Revenue and Customs could allow this to happen.Footnote ³ The Datalab works by listing the datasets available to researchers, inviting proposals for research and then allowing researchers to access and analyse the anonymised data. Thus, the data associated with any behavioural tax compliance initiative could be accessed later by other researchers.

It is reasonable to expect to see more studies of long-term effects emerge as the field matures. From BIT's perspective, the need to focus more on ‘quick wins’ has subsided, and so we have more scope to pursue projects that pay off only in the longer term. In the meantime, we should also address the problem actively. For example, Frey and Rogers (Reference Frey and Rogers2014) give a helpful overview of the different pathways that can be used to make long-term effects more likely. We can also identify at least three situations in which concerns over long-term effects may be avoided or assuaged.

Problems with proxy measures

One response to the difficulties in including long-term effects is to use proxy measures.Footnote ⁵ For example, many studies in education take attendance as an outcome measure (e.g. Kraft & Rogers, Reference Kraft and Rogers2015; Chande et al., Reference Chande, Luca, Sanders, Soon, Borcan, Barak-Corren, Linos, Kirkman and Robinson2017). We might reasonably expect that school attendance would have a positive impact on grades, as shown in Gottfried (Reference Gottfried2010)’s analysis of longitudinal data on school attendance and attainment. Similarly, one can infer long-term effects of behavioural interventions that reduce short-term unemployment (such as Altmann et al., Reference Altmann, Armin, Jäger and Zimmermann2015) on the basis that they prevent ‘employment scarring’ – the tendency for one protracted spell of unemployment to lead to others, even if the first spell can be attributed to bad luck, such as graduating into a recession (Arulampalam et al., Reference Arulampalam, Gregg and Gregory2001).

However, the causal relationship between the two types of measures may not be constant. This can be thought of as a behavioural science-specific form of the Lucas critique found in economics. Lucas (Reference Lucas, Brunner and Meltzer1976) argued that since consumption functions are not fixed and can respond to changes in circumstances, they are not ‘policy invariant’, and so may change in response to changes of policy. In economics, this critique remains important, as policies designed with a particular intended direction or magnitude of effect, based on current policy-response parameters in consumption functions, might be undermined because consumers’ consumption functions change in response to the policy itself.

The equivalent argument is that the relationship between short-term and long-term behaviours is not ‘nudge invariant’: the act of nudging someone can cause their short- and long-term behaviours to become untethered from each other. To illustrate, we can imagine that there is a well-established causal relationship between applying for jobs and leaving unemployment. It need not hold, however, that increasing job applications necessarily leads to increasing employment. For example, it is possible that marginal job applications are of a lower standard, or are less well targeted, than those that preceded them.Footnote ⁶ Or we might speculate that a quick return to work may be less insulating from the psychological aspect of employment scarring if people are aware that they were nudged into work.

This problem is likely to be particularly acute with measures that are currently considered to be reliable proxies but where no causal chain exists. Self-control in children (e.g. measured by a ‘marshmallow test’) has been shown to predict several later-life outcomes and particularly later-life self-control (Mischel et al., Reference Mischel, Shoda and Rodriguez1989). Given the existence of a problem early on in life, many would argue that it is at this stage that interventions should be targeted. Although this makes sense, we have no reason to expect (given the lack of a causal link between marshmallows now and alcohol later) that today's proxy and tomorrow's outcome are inextricably linked, or even linked that strongly. Hence, it may be possible that we can change one behaviour (self-control while young) without influencing the other(s) at all. This would not invalidate the intervention, as self-control when young may have all manner of direct positive benefits in the short and medium term. However, behavioural scientists should be cautious about extrapolating too far from their findings and should, through long-term data monitoring, attempt to identify whether relationships between variables that exist in the absence of interventions continue to do so in their presence.

Spillovers and unintended consequences

The argument about proxy measures can be taken in a more troubling direction. Rather than just questioning the relationship between immediate and proxy measures, we could point to the possibility that interventions are having unintended and unmeasured effects elsewhere. In this view, the focus in the experimental paradigm on specific, predefined outcome measures becomes a source of weakness as well as strength. Research on applying complex adaptive systems to policy emphasises how we may shift the dial on one outcome measure, even as problems mount elsewhere unobserved (Dörner, Reference Dörner1996; Dolphin & Nash, Reference Dolphin and Nash2012).

In some senses, this phenomenon may be unsurprising because governments may have a multitude of policy goals that they may be pursuing at the same time. Fisman and Golden (Reference Fisman and Golden2017) offer a good example of the hidden trade-offs between these goals that interventions may reveal. They documented a US initiative that attempted to reduce fraud committed by grocery stores participating in a government food voucher scheme intended to improve nutrition. The fraud consisted of artificially inflating the prices charged for the goods covered by the scheme and was only possible because stores were not required to reveal the prices they charged to cash customers. The new initiative made changes that revealed the discrepancy. As hoped, the fraud disappeared. But the change also had the unintended consequence that many retailers simply stopped selling the high-nutrition foods altogether, since they were no longer profitable. Those who retained the foods increased their prices by nearly 10%. The result was poorer nutrition for both those on the programme and those in the local area who used the same stores.

Again, this argument about unintended consequences is not specific to behavioural policy interventions (see Cartwright & Hardie, Reference Cartwright and Hardie2012). But since behavioural science itself studies how such spillovers are triggered, there is a case to be made that it should be particularly sensitive to them. Here we can point to the large literature on how ‘licensing effects’ may mean that attempts at self-control in one domain create indulgence in another, how virtuous behaviour in one situation may increase dishonesty elsewhere and so on (Khan & Dhar, Reference Khan and Dhar2006; Merritt et al., Reference Merritt, Effron and Monin2010).

True universals and cultural variation

As noted above, many more people and groups have been applying behavioural science to policy around the world. One description of what these people are trying to do is “to bring a more realistic model of human behaviour into … policy and regulation” (Halpern, Reference Halpern2016). This is a useful shorthand and is not strictly speaking incorrect. But it also suggests that behavioural public policy has a tendency to treat human beings as a broad class, with variation occurring primarily at the individual level. Although we might, as in the case of stereotype threat, allow for the possibility of systematic differences by gender (Spencer et al., Reference Spencer, Steele and Quinn1999) and ethnicity (Steele & Aronson, Reference Steele and Aronson1995), behavioural scientists are relatively quiet on the issues of nationhood and culture.Footnote ⁷

As Henrich et al. (Reference Henrich, Heine and Norenzayan2010) noted, most people in the world are not ‘WEIRD’ – Western, educated, industrialised, rich and developed – but a large proportion of the people who apply behavioural science are (as are the subjects of their studies). This issue may be particularly acute for undergraduate participants in the labs of American universities, but it is still true to a great extent for participants on Amazon's Mechanical Turk and for participants in many field experiments conducted by BIT, as these mostly take place in the UK, Australia and the USA.

This perspective makes it much more troubling that we lack field experimental evidence on the effectiveness of the same interventions in different contexts. We can make some general predictions, however. There is relatively strong evidence that Western societies have a stronger concept of individual, autonomous actors than East Asian societies, where a more collectivist perspective is often more prevalent (Heine, Reference Heine2008). This difference may have an impact on the relative effectiveness of certain interventions. For instance, Bond and Smith's (Reference Bond and Smith1996) study suggests that this difference means that social norm effects are larger in non-Western societies. Interestingly, Kettle et al. (Reference Kettle, Hernandez, Ruda and Sanders2016, p. 36) found that social norms in tax letters were similarly effective in Guatemala, even though they stated that “65 percent of people pay their tax on time,” a very different number from that found on UK tax letter.Footnote ⁸

The growth of teams around the world that draw on the same literature and develop similar interventions may allow us to address this challenge. If new teams are able to conduct very similar replication studies, it could be possible to establish which (if any) heuristics and biases are common to which societies and, for those that are not universal, what factors moderate or mediate their effects. This process is likely to identify issues, contexts and cultures that challenge behavioural scientists to develop and test new theories and interventions.

‘Reverse impact’

In recent years, the UK higher-education sector has been encouraged to document how its research has ‘impact’, defined as “an effect on, change or benefit to the economy, society, culture, public policy or services, health, the environment or quality of life, beyond academia” (Penfield et al., Reference Penfield, Baker, Scoble and Wykes2014, p. 21). Interestingly, behavioural scientists working on policy issues may experience the situation in reverse. Rather than publishing peer-reviewed research that may then influence government action, they may alter government actions and then attempt to publish the results in peer-reviewed journals. In other words, the impact comes first.

The issue is that there may be several barriers to academic publication, many of which BIT has experienced in its history and tried to surmount. Publication is usually entirely dependent on the approval of senior officials, who may see little reason to approve this request, particularly if there is any transfer of data involved (even anonymised data). The issues are particularly acute if the people responsible for the study are public officials, as BIT staff members were until spring 2014. In this case, there are often few resources provided to support the publication process, which is likely to be seen as a luxury. But even if resources are supplied by academic partners, there may be differences over the proposed timing, framing and conclusions of any potential publication. While BIT has always been pressing the case for publication, it has not always been successful.

Should we be concerned by the lack of ‘reverse impact’? Here, we should distinguish between initiatives that are not made public and those that are made public but do not go through the additional step of peer-reviewed publication. The former create obvious problems in terms of reducing the transparency of government, as well as potentially creating a ‘public file drawer’ problem if it is null results that are selectively held back. The latter are less serious but may mean that (depending on exactly how they have been reported) the quality and reliability of their results cannot be fully assessed. The worry is that if not all the studies have been prepared and structured carefully, this may introduce quality uncertainty, where even good studies are suspected of being potentially flawed (Akerlof, Reference Akerlof1970). One obvious solution for this second problem is for public but non-peer-reviewed reports to provide enough detail to allow a reasonable judgement of their quality.

The replication crisis

Finally, we wanted to mention the challenge that the replication crisis brings behavioural public policy. BIT takes many of its intervention ideas from existing lab studies or field experiments and is therefore dependent on their reliability. If this reliability is questionable, there are two main consequences for behavioural public policy: first, and most importantly, it would mean that we have wasted resources trying to implement concepts that are not viable – resources that could have been allocated more profitably; and second, it could damage the trust policy-makers, and the public, have in behavioural science, with the result that they become reluctant to use the approach in future.

This is a challenge that will not go away immediately, since new questionable practices are still being discovered regularly. International efforts on pre-registration, data sharing, identification of statistical irregularities and replication consortia (such as the Many Labs Replication Project; see Klein et al. Reference Klein, Ratliff, Vianello, Adams, Bahník, Bernstein and Cemalcilar2014) are all part of the emerging solution to the crisis. However, as behavioural scientists, we are all responsible for helping the field overcome this challenge. In particular, those behavioural scientists who are helping to form policy (and thus determine the allocation of limited public resources) have a special responsibility to review the relevant evidence critically and in detail, bearing in mind the problems that have recently emerged. Robustly evaluating the ensuing interventions becomes even more important. Finally, and most painfully, we need to accept with humility that some of our own trials may have been one-off findings. The implication is that we should be attempting to replicate an intervention as a matter of course before trying to scale up an intervention. This would present a significant challenge, since the public sector is often still reluctant to evaluate interventions in the first place.

Behavioural government

Those who apply behavioural science to policy have focused most of their energies on using new approaches to improve policy outcomes. Much less attention has been paid to behavioural science as a tool to improve the way government itself functions (Lodge & Wegrich, Reference Lodge and Wegrich2016). This division of resources reflects how incentives are structured: individuals and groups using behavioural science may not seek to challenge fellow public sector actors because they need them as allies for implementing interventions. Yet there are strong reasons for doing so.

The main reason is that these critiques are almost certainly correct. Government is vulnerable to biases and pathologies in the way it acts; no serious observer could argue otherwise. Moreover, many of these traits are the same ones that behavioural science has discussed extensively in relation to individuals. A minister visiting a hospital or factory may be exposed to a particular piece of information that acts as a breeding ground for confirmation bias. A finance officer will rely heavily on the previous year's budget as the default when allocating resources, even if ‘zero-based budgeting’ is applied.Footnote ⁹ There are real gains to be made here; the task should not be dismissed as too difficult or mundane. Behavioural scientists have much to add to existing studies on organisational behaviour in this regard.

The other reason is more tactical. Critics of using behavioural public policy often use the fact that government is also vulnerable to biases to justify the claim that governments should simply refrain from nudging (or other applications) altogether (Waldron, Reference Waldron2014). This does not necessarily follow: a stronger argument is that this points towards the need for more behavioural science, not less, just applied to government itself. Yet, in general terms, behavioural scientists have allowed this argument to go mostly unchallenged, partly because they have not developed the arguments or generated the examples to do so. With both these reasons in mind, BIT is currently working on a self-funded project on this issue.

Scaling interventions

Scaling interventions represents a specific opportunity for taking a behavioural approach to government itself. The application of behavioural science has often adopted a basic approach of ‘experiment and then scale’: a trial is conducted on a sample (which nonetheless might be relatively large) and the best-performing variant is adopted more widely. In many cases, interventions are constructed to ensure that they can be integrated into existing large-scale practices, so they have a clear path to wider adoption.

Perhaps the best examples are those that concern changes to messages that are already being sent out, since the change itself can require little expenditure. Hence, many of BIT's studies have concerned modification of letters (e.g. Hallsworth et al., Reference Hallsworth, List, Metcalfe and Vlaev2017), text messages (e.g. Haynes et al., Reference Haynes, Green, Gallagher, John and Torgerson2013) or web forms (Kettle et al., Reference Kettle, Hernandez, Sanders, Hauser and Ruda2017). Other kinds of intervention are a feasible route to scaling up, even if they are not as cheap or easy as message changes. Examples might be changes to the way payments are structured, such as using lotteries (SBST, 2016), different match rates (Huck & Rasul, Reference Huck and Rasul2011) or social incentives (BIT, 2016).Footnote ¹⁰

However, despite these apparent advantages, the proportion of behavioural science interventions that reach scale could be much higher. Even when interventions are scaled, this does not necessarily mean that they retain their original effectiveness. A well-known example concerns surgical checklists, a patient safety measure that requires the surgical team to check whether certain actions have been carried out. Checklists had been highly successful in improving patient outcomes in previous studies, reducing surgical complications from 11% to 7% (Haynes et al., Reference Haynes, Weiser, Berry, Lipsitz, Breizat, Dellinger and Merry2009). However, when their use was mandated across hospitals in Canada, that effect disappeared, despite self-reported compliance being over 90% (Urbach et al., Reference Urbach, Govindarajan, Saskin, Wilton and Baxter2014). Checklists also did not improve outcomes in a hospital in The Netherlands; patients for whom the checklists were used correctly had improved outcomes, but they were only used for 39% of the procedures (Van Klei et al., Reference Van Klei, Hoff, Van Aarnhem, Simmermacher, Regli, Kappen and Peelen2012). In these cases, both context and how the checklist was implemented, as well as a potential lack of intensive training or monitoring of compliance, may have affected their effectiveness (Urbach et al., Reference Urbach, Govindarajan, Saskin, Wilton and Baxter2014).

It is important to note that the failure of innovations to reach scale is often observed in the public administration literature: the issue is not specific to behavioural science interventions (Bason, Reference Bason2010). Nevertheless, there are at least two reasons why failure to scale is particularly relevant to behavioural public policy. The first is that recent efforts in this field have tended to couple behavioural science with an emphasis on the importance of experimentation (Haynes et al., Reference Haynes, Goldacre and Torgerson2012; SBST, 2015; Ames & Hiscox, Reference Ames and Hiscox2016). There are many good reasons for this approach. However, the danger is that it means too much focus is placed on trials and trial results as ends in themselves (particularly in the light of our comments about proxy measures above). Furthermore, it is arguable that academic incentives encourage innovative results and highly cited publications, but fail to encourage researchers to scale their interventions. We need to ensure that the individuals and organisations applying behavioural science to policy consider scalability when selecting projects and that they are incentivised to look beyond the successful completion of a trial. Luckily, this is something government and other organisations (e.g. the Educational Endowment Fund and the Health Foundation) are becoming increasingly interested in, providing new opportunities for trials in this area.

The second reason is that one could argue that the issue of scaling is, at heart, one of organisational behaviours – and thus behavioural science itself may have something useful to say about the problem. We are aware, of course, of the contribution that implementation science has made in this regard. But we argue that more of the attention and resources dedicated to behavioural public policy should be directed to this question (as for government activity in general). For example, the literature on diffusion of innovations has established that homophily (i.e. the tendency for individuals to associate with similar others) plays a significant role in how ideas do or do not reach scale (e.g. Yavaş & Yücel, Reference Yavaş and Yücel2014). It seems likely that behavioural science could contribute to this field by, for example, offering insights into how the source of a message affects its persuasiveness or into how social norms function. But we are still some way from using behavioural science to give policy-makers recommendations about how to use homophily to maximise the likelihood that others take up their innovation. How best to scale up and spread interventions is an area where there is a strong need for further research that uses the same robust methods that are associated with behavioural science trials.

Social diffusion

Many applications of behavioural science to policy have adopted a simple, unidirectional model of influence: a public sector actor attempts to influence (usually) an individual, organisation or group. This approach is clearly important and covers many common policy situations. However, it notably excludes avenues for influence that exist between individuals, organisations or groups. Sacerdote's (Reference Sacerdote2001) canonical study of social influence, in which undergraduates at Dartmouth College were randomly assigned to roommates, found that spatial proximity (and, presumably, the relationships that form as a consequence) significantly influences Grade Point Average (GPA) at the end of students’ first year of college. Moreover, it is likely that this estimate is a lower bound, since most friendships are formed through associative matching rather than random assignment.

Arguably, behavioural science's focus on individual decision-making means it has neglected relevant insights from theories about social networks and systems thinking (Ormerod, Reference Ormerod2010). Obviously, this is a generalisation, and many such valuable studies do exist (e.g. Drago et al., Reference Drago, Mengel and Traxler2015; Kim et al., Reference Kim, Hwong, Stafford, Hughes, O'Malley, Fowler and Christakis2015). But the potential gains to policy-makers are so large that they justify more work. 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). The same is true if governments are attempting to limit this transmission.

BIT has conducted some preliminary studies on how to create ‘network nudges’. One example compared the difference between: asking an email recipient (at an investment bank) to click on a link to donate to charity; asking the recipient to “reach out and email their friends and colleagues”; and asking them to reach out and tell their acquaintances “about the huge contribution their donation can make.” For each of these variants, the proportion of investment bankers making a donation was 12.4%, 23.6% and 38.8%, respectively (BIT, 2016). This is clearly a very simple example, however, and much more work is needed to identify the best ways to use social networks.

Nudging organisations

Most behavioural science interventions have focused on individuals, most commonly in their roles as citizens and consumers, and less frequently, but increasingly, as employees. There has been much less work that targets organisations, and there are a few likely reasons why. One is that (generally, but not exclusively) the main academic disciplines informing behavioural policy have tended to use the individual as their unit of analysis. Another is that regulators, which constitute one of the main points of contact between government and organisations, have traditionally taken a legalistic approach that is averse to experimentation and innovation. Recent developments show that this attitude is changing (Financial Conduct Authority, 2013). In addition, the drive for robust evaluation may have guided attention towards individuals. Since there are fewer organisations than people, randomised trials that use organisations as the unit of analysis may be more likely to encounter problems with statistical power.

The more important point is that many policy-relevant decisions are made by organisations. The majority of fossil fuels are consumed by organisations, through transportation of products, the powering of factories and so on. Although consumer behaviour is an important part of preventing and mitigating climate change, any strategy that does not incorporate changing business behaviour is destined to fail. This argument also applies to the products that firms make. Policy interventions may try to reduce carbon emissions by getting people to drive less (Leape, Reference Leape2006) or to reduce calorie consumption using food labels (Roberto et al., Reference Roberto, Larsen, Agnew, Baik and Brownell2010). However, in both cases, the problem could be solved upstream if firms made cars that used less petrol or reformulated their products to contain fewer calories – something the UK's new sugared drinks levy will aim to address.

How far can we translate findings from individual psychology to organisational psychology, or is this attempt hopelessly naïve? After all, businesses are not people, but they are made up of people.Footnote ¹¹ If the government were to write a letter to all grocery stores asking them to stock more low-calorie drinks and fewer high-calorie ones, that letter, if it is read at all, will be read by a person, and so to the extent that people can be nudged, we could expect businesses to be ‘nudgeable’.

This argument may not follow, however. Many firms may have created processes to prevent single employees from making large decisions – indeed, these processes may have been created explicitly to guard against cognitive biases. Two or more decision-makers may not be more biased than one, but if only one of them has been nudged, the intervention's effect may be reduced. There is also a question of identity (Akerlof & Kranton, Reference Akerlof and Kranton2000). When people are working for a firm, we do not expect them to behave the way that they do at home. The evidence suggests that relatively weak, short-term prompts that invoke a particular identity, such as being ‘a voter’ rather than simply ‘voting’ (Bryan et al., Reference Bryan, Walton, Rogers and Dweck2011), can have substantive effects on subsequent behaviour. Employees may have even more intense and sustained exposure to prompts that encourage behaviours that are in line with corporate identities (Akerlof & Kranton, Reference Akerlof and Kranton2005). Finally, we must acknowledge the great variety of organisational forms. For example, it is plausible that smaller firms may have less of a distinct identity from the individual(s) running them, which could lead them to behave more like individuals. Since organisation size is often recorded, there is a strong case for trying to answer this question by gathering together existing evidence on how size interacts with behavioural science interventions.

Governments have often approached the behaviour of organisations through the lens of regulation. At this stage, we do not know enough about how behavioural science can improve, complement or even replace regulation, despite the many valuable contributions of Cass Sunstein (Reference Sunstein2011). BIT has been considering this question, from the 2010 MINDSPACE report – which stresses that its framework “does not attempt to replace [legislation and regulation] … it extends and enhances them” (Dolan et al., Reference Dolan, Hallsworth, Halpern, King and Vlaev2010, p. 49) – to its recent report on applying behavioural insights to regulated markets (Costa et al., Reference Costa, King, Dutta and Algate2016). However, it is clear that we have made much less progress here than we have in the domain of individual decisions. Oliver (Reference Oliver2015)’s discussion of ‘budging’ rightly emphasises the potential gains that can be made in the field of behavioural regulation; clearly, more work is needed here.

Thorny problems

One criticism of behavioural policy is that the breadth of its usefulness is limited to certain domains where one-off behaviours with binary desirable decisions are prevalent, such as tax compliance, attending appointments or enrolling in a pension plan. It is certainly the case that the majority of government teams that work in behavioural science have typically focused their early work on changing these kinds of behaviours. We (Hallsworth & Sanders, Reference Hallsworth, Sanders and Spotswood2016; Sanders & Halpern, Reference Sanders and Halpern2016) have been among those advocating for pursuing so-called ‘low-hanging fruit’ early in the life of a behavioural insights team. The social value of this work has now, become easy to downplay, particularly when policy-makers have become accustomed to ‘tax letter trials’ producing effects.

However, we argue it would be disappointing if compliance were the only application of behavioural science active in policy ten years from now. Our hope is that behavioural science has as much to offer on some trickier, more complex problems. When we think of a particularly challenging area of government policy, we often think of areas where the traditional tools of regulation, incentives and information have been extensively utilised and yet the problem persists. At first, this can seem something daunting and to be avoided, but it is precisely here that new tools should be used. If we consider that behavioural economics came into existence to explain phenomena that standard economic analysis found inexplicable, perhaps behavioural science can solve problems that standard economic tools have found insoluble.

The last few years have seen the beginnings of these changes at BIT in the UK. We are making dedicated attempts to consider how behavioural science can be applied to relieve poverty (Gandy et al., Reference Gandy, King, Streeter Hurle, Bustin and Glazebrook2016); to curb recidivism, where financial and regulatory incentives are ineffective for a substantial number of people; and to prevent corruption and bribery, which may be so culturally pervasive as to evade traditional tools. Work in these areas is still in its early stages. As a community of practice, we must not become disheartened if/when we do not achieve immediate successes in these areas. Where we do start to make progress, we must guard against the temptation to overstate our successes and to declare prematurely that the battle has been won.