Article Summaries
Behavioral Change
Articles from 7 to 18 are presentation articles.
Article 1
Sanders, M., Snijders, V., & Hallsworth, M. (2018). Behavioural science and policy: where
are we now and where are we going?. Behavioural Public Policy, 2(2), 144-167.
BIT - one of the first (if not the first) government institution dedicated to applying behavioural
science to policy and public administration. They were set 3 objectives to achieve:
1. Transform at least two major areas of policy
2. Spread an understanding of behavioural approaches across Whitehall
3. Achieve at least a tenfold return on cost
In addition to that, BIT developed two main guiding principles, which are still practiced today.
➔ First principle was to have a positive social impact, inspired by Richard Thaler’s mantra
of “nudge for good”.
➔ Second principle was to robustly evaluate the impact of its interventions. Often, this
principle was realised by promoting the use of randomised controlled trials (RCTs)
wherever possible, including in public administration contexts where they had
previously been uncommon. We summarised this principle as ‘test, learn, adapt’
(Haynes et al., 2012).
Expansion of BIT has not been limited to the UK, however: many countries now explicitly use
behavioural science in policy settings. While behavioural science is much more widely used
than it was, it has yet to sit alongside economics as a discipline dominant in the thinking of
policy-makers.
Complications and challenges
Long-term effects
● Despite many field experiments testing the application of psychology or behavioural
economic interventions to practical problems, relatively little is known about the
long-term effects of many such interventions.
● 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.
● 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.
One-off behaviours
● Some interventions might have a ‘once and done’ property, and therefore have lasting
effects without requiring any follow-up. Obvious examples here are those where an
individual is being asked to complete a single action that changes their status in some
way – for example, registering as a potential organ donor or enrolling in a workplace
pension.
Resilient shifts
● Another possibility is that an intervention may succeed in creating a new behavioural
pattern that is sufficiently resilient to endure, even if the stimulus is withdrawn.
Examples include when people are paid to visit the gym multiple times within a limited
period, and then continue to show elevated attendance when payments are removed,
or when a single letter leads to a sustained shift in the prescribing practices of doctors.
The initial changes may be unintentional, rather than constituting a carefully
constructed intervention.
Environmental changes
● An intervention might introduce a sustainable change to the decision-making
environment that is likely to influence choices over the long term – for example,
changes to the design and physical properties of a hospital waiting room. In this case,
the behavioural stimulus endures, making it more likely that the behaviour will as well.
● A slight variation on this point are those interventions where a behavioural analysis
suggests that the best option is not to try to change behaviour, but rather to diminish
its consequences. In other words, the intervention does not actually require people to
do anything differently. Perhaps the best examples here are the successful
interventions to reformulate food to reduce levels of salt and sugar while not changing
consumer purchasing patterns.
Repeated exposure effects
● Another concern is that repeated exposure to behaviourally informed approaches or
interventions will lead to diminishing returns. We can distinguish between two main
cases here:
, ○ First, ‘structured repetition’, 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)
○ Second, ‘unstructured repetition’, where individuals are exposed to the same
kind of approach from different actors, at different times and in relation to
different topics (e.g. when a social norm message appears in a different
context).
● In both cases, the concern is that we may have a prior expectation that approaches
become less effective with repeated exposure. The assumption underpinning this
expectation is that the impact of an approach is driven by a novelty effect, wherein the
approach: (a) succeeds in attracting attention in the first place; and (b) provides salient
motivation to act in a particular way. Repeated exposure means the approach becomes
less salient and novel, and thus less effective. It is worth noting, therefore, that these
concerns relate mainly to approaches that require attention from the individual, rather
than those that are not immediately apparent – like default changes. One would expect
the latter to continue to be effective, since they rely more on automatic processes.
● On the other hand, we can be less sure about whether and how issues around
unstructured repetition will be addressed, mainly because they are likely to be more
complex and less tractable to analysis. Essentially, the problem of unstructured
repetition is caused by success.
○ As a result, we try to promote solutions that seem to work (as is common in
academia). To give an example, Hallsworth et al. (2017) considered the impact
of including social norm messages (“9 out of 10 people pay their tax on time”) on
tax compliance and found a significant and positive impact.
○ The problem is that if the use of social norm messages spreads to many other
policy domains, we have to start considering the effect of receiving the same or
similar interventions from different quarters at the same time. E.g., How
effective would social norm messages be if they were found on our gas bills, tax
letters?
Problems with proxy measures
● One response to the difficulties in including long-term effects is to use proxy measures.
For example, many studies in education take attendance as an outcome measure. We
might reasonably expect that school attendance would have a positive impact on
grades, as shown in Gottfried (2010)’s analysis of longitudinal data on school
attendance and attainment.
● 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. He 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.
● 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.
● This problem is likely to be particularly acute with measures that are currently
considered to be reliable proxies but where no causal chain exists.
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.
○ 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 (2017) offer a good example of the hidden tradeoffs
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, 2012). 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.
True universals and cultural variation
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, 2016). This is a useful shorthand
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. Behavioural scientists are
relatively quiet on the issues of nationhood and culture.
● As Henrich et al. (2010) 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 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. 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”.
● 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.
● 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, 1970). 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
,It is important to know what challenge 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.
Opportunities
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. 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.
● 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.
● The other reason is more tactical. Critics of using behavioural public policy often use the
fact that the government is also vulnerable to biases to justify the claim that
governments should simply refrain from nudging (or other applications) altogether.
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.
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, text messages or web
, forms. Other kinds of intervention are a feasible route to scaling up, even if they are not
as cheap or easy as message changes.
● 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.
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. 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.
● The second reason is that one could argue that the issue of scaling is one of
organisational behaviours. It is known 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. 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.
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.
Arguably, behavioural science’s focus on individual decision-making means it has neglected
relevant insights from theories about social networks and systems thinking. This is a
generalisation, and many such valuable studies do exist. But, 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’. 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.
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.
1. 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.
2. 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.
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
or to reduce calorie consumption using food labels. 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.
How far can we translate findings from individual psychology to organisational
psychology, or is this attempt hopelessly naive? After all, businesses are not people, but
they are made up of people.
1. 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 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 because many firms have created processes
to prevent single employees from making large decisions.
a. 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.
2. Question of identity: the evidence suggests that relatively weak, short-term prompts
that invoke a particular identity, such as being ‘a voter’ rather than simply ‘voting’ 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.
3. 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.
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.
Article 2
Tannenbaum, D., Fox, C. R., & Rogers, T. (2017). On the misplaced politics of behavioural
policy interventions. Nature Human Behaviour, 1(7), 1-7.
Focus of this paper: a prominent form of behavioural policy interventions that ‘nudge’ desired
behaviours without meaningfully altering material incentives or limiting freedom of choice.
➔ More precisely it is examined whether partisans from both ends of the political
spectrum fail to differentiate their feelings about the use of general purpose policy
nudges with their feelings about salient examples of specific policy objectives to which
those interventions might be applied or their feelings about the policymakers who
endorse such tools. We refer to this phenomenon as ‘partisan nudge bias’. Partisan
nudge bias can be thought of as an instance of attribute substitution, where individuals
evaluate the acceptability of a policy nudge by instead assessing how they feel about
the associated policy objective or policy sponsor.
, Study 1
➔ Study 1 was designed to examine partisan nudge bias across a range of policy nudges
and policy settings.
➔ Methodology
◆ Participants were presented with short descriptions of five empirically validated,
field-tested policy nudges. Each policy nudge was shown on a separate page
and explained in nonpartisan language. Importantly, we randomly paired each
nudge with an illustration of how the technique could be applied to either a
politically conservative or liberal policy goal. For instance, for one policy nudge,
participants learned that policymakers can often increase participation in a
programme by providing information about how other people behave in the
same situation (that is, descriptive social norms). From there, some
participants read an illustration of using descriptive social norms to increase the
uptake of tax breaks for high-income citizens (a relatively conservative policy
goal), whereas other participants read an illustration of using descriptive norms
to increase the uptake of supplemental nutrition assistance programmes for
low-income citizens (a relatively liberal policy goal). The pairing of illustrations
to nudges was randomized for each participant, with the constraint that
participants viewed each nudge and illustration only once. Afterwards,
participants rated the acceptability of the nudge, also in the context of its
general approach to public policy and reported their political orientation.
➔ Results
◆ Liberal respondents reported greater acceptance of the general use of policy
nudges when illustrated by liberal (as opposed to conservative) policy
objectives; meanwhile, conservative respondents reported greater acceptance
when nudges were illustrated by conservative (as opposed to liberal) policy
objectives.
◆ To provide a sense of the magnitude of partisan nudge bias - the degree of bias
is estimated (that is, differences in acceptance when illustrated by liberal
versus conservative policy goals) relative to the overall distribution in nudge
attitudes across all treatment conditions.
Study 2
➔ In study 2, it was examined whether partisan nudge bias emerges when both the nudge
and policy illustration are held constant, while information about the identity of the
policy sponsor (either a prominent liberal or a prominent conservative) is varied.
Such a finding would suggest that individuals are not merely responding to associations
with particular policy illustrations, but rather exhibiting more general partisan
reactions.
