Applying behavioural sciences to EU policy-making.
Date: 2013-09-12
Institutions: Institute for Prospective Technological Studies
Subjects: EU policy, behavioural sciences, decision-making, research report, social behaviour, social sciences

JRC SCIENTIFIC AND POLICY REPORTS

Applying Behavioural Sciences
to EU Policy-making

2013

René van Bavel
Benedikt Herrmann
Gabriele Esposito 
Antonios Proestakis

EUR 26033 EN

Joint 
Research 
Centre

European Commission
Joint Research Centre
Institute for Prospective Technological Studies 

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JRC83284
EUR 26033 EN

ISBN 978-92-79-31459-9 (pdf) 
ISBN 978-92-79-31460-5 (print) 

ISSN 1831-9424 (online)
ISSN 1018-5593 (print) 

doi:10.2788/4659 

Luxembourg: Publications Office of the European Union, 2013

© European Union, 2013

Reproduction is authorised provided the source is acknowledged.

Printed in Spain

Table of Contents

Summary

Introduction

Not assuming rationality

When should behavioural science be applied to policy-making?

Issues to consider when applying 
behavioural science to policy-making

Identifying at what stage behavioural science should be applied 
to the policy-making process

Defining the role of behaviour in a policy initiative

Reviewing the available evidence

Estimating the value added of a behavioural study

Specifying the unit of analysis

Considering time constraints

Commissioning a behavioural study

Experiments 

Randomised controlled trials (RCTs) 

Surveys

Qualitative research

Recommended reading

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Applying Behavioural Science to EU Policy-making 
 
 
 
 
Acknowledgments
This report has been produced in the frame of the Behavioural 
Studies for European Policy project by the Joint Research 
Centre and the Directorate-General for Health and Consumers 
of the European Commission. The authors are grateful to 
Dan Dionisie and Emanuele Ciriolo for useful conversations 
and comments on these issues. 

Summary 
Policy-making  can  greatly  benefit  from  a  better  understanding  of  people’s 
behaviour. Often the assumption has been that people are ‘rational’. However, this 
assumption has been shown to be unrealistic, and perhaps explains the limited 
effectiveness of some policies in the past. 
Well-designed behavioural studies can offer useful insights to policy-makers by 
generating the evidence required to improve policies. Such studies are applicable 
to a wide range of EU policy areas, wherever a behavioural element exists. Whether 
policy-makers aim at changing behaviour or designing better regulations, greater 
knowledge on how people are likely to behave should serve them well. 

Introduction
People’s behaviour is relevant to EU policy. Sometimes, the objective of a policy 
will be to change behaviour for the common good, for example getting people to 
quit smoking or recycle more. In other cases, it will be to prevent citizens from 
behaving against their best interest, for example by falling victim to misleading 
marketing practices. Or it might be to encourage consumers to take better into 
account the future consequences of their current behaviour (e.g. smoke less today 
to stay healthy tomorrow). 
In all these cases, behaviour is a key element for the success of a policy initiative. 
The policy can be said to have a relevant behavioural element. Good policy-making 
should identify this element, assess its importance for the effectiveness of a policy, 
and try to understand it better. Sometimes this will be a straightforward task, 
sometimes not. 
This report is intended as a guide for policy-makers wishing to introduce a more 
refined understanding of human behaviour into the policy-making process.1 It 
begins by discussing the background to the current popularity of applied behavioural 
science and goes on to cover specific issues that should be taken into account 
when incorporating behavioural insights into the design, implementation and 
monitoring of policies.2

1  This report discusses the application of behavioural science to EU policy-making. Good accounts of the 
merits  of  behavioural  insights,  in  general  and  as  applied  to  policy-making,  can  be  found  elsewhere  in  the 
literature (see list of recommended readings at the end of this document).
2  This  report  uses  the  term  applied  behavioural  science  instead  of  behavioural  economics,  following  the 
suggestion of Daniel Kahneman in his preface to The Behavioral Foundations of Public Policy (see recommended 
readings).

2

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Applying Behavioural Science to EU Policy-makingApplying Behavioural Science to EU Policy-makingI. Not assuming rationality 
Applied behavioural science studies human behaviour and applies its findings to 
policy analysis. It questions the assumption that people act ‘rationally’, making 
choices that lead to the best possible outcome for them. However, empirically – 
and anecdotally – this assumption does not hold. People sometimes make foolish 
decisions, which are ultimately not in their self-interest. We smoke. We eat too 
much. We save too little. We get heavily into debt. 
Public policy has often relied, tacitly perhaps, on this assumption of rationality 
when accounting for human behaviour. This may have led to suboptimal policies 
in the past. For example, the amount of information passed on to consumers 
(through product labelling or mass media campaigns) has tended to increase 
continuously, under the assumption that they will be able to process it to their best 
advantage. This has not always worked, and may have even been counterproductive 
in some cases.3
In the behavioural science literature, deviations from rationality in decision-making 
are well documented.4 Moreover, these deviations tend to be consistent across 
time and populations. For example, people dislike losing something (say €20) more 
than they like gaining it, whereas this should not be the case if they were ‘rational’. 
Or, for example, when making decisions, people tend to give disproportionate 
weight to information that readily comes to mind (a recent conversation, for 
example). Again, this shouldn’t happen to ‘rational’ agents. 
The question emerges: if people’s behaviour is not rational, then how can it be 
explained? The scientific literature is full of attempts to provide explanations. Many 
factors are taken into account. However, there is no single alternative model of 
human behaviour, no unifying and all-encompassing theory which can predict it. 
Any attempt at explaining behaviour in a particular context needs to rely on specific 
empirical observations. And this is probably a good thing.
A proper understanding of human behaviour requires reality checks. In a policy 
context, this approach makes sure policy-makers rely on evidence, not assumptions. 
It results in a more grounded policy-making process, where ideas about people’s 
expected  behaviour  are  first  tested  and  then  reassessed.  The  original  policy 
initiative can then be implemented, dropped or modified.5 
Take the public health example of smoking. What policies should be put in place 
to curb smoking? Apart from higher taxes and smoking bans in public places, 

3  Some studies, for example, have shown that calorie labelling in New York restaurants have not led to a 
decrease in calorie consumption, and in some cases have even led to an increase in calorie consumption among 
certain groups of people.
4  The term commonly used to describe such deviations is biases and heuristics (mental shortcuts, or ‘rules 
of thumb’), following the work of Kahneman and Tversky in the 1970s. They are also sometimes referred to as 
anomalies.
5  See Test, Learn, Adapt (in the list of recommended readings) for a good account of this approach.

4

System 1 vs. System 2

One of the most policy-relevant lessons to come out of behavioural economics is 
the distinction between automatic and in-depth thinking. Kahneman (2011) refers 
to these as System 1 and System 2, respectively. We go about most of our daily 
lives using System 1 (e.g. recognising facial expressions, driving our cars). System 
2  requires  greater  mental  effort  and  is  used  only  on  specific  occasions  (e.g. 
calculating a tip). System 1 will make judgments subject to biases and heuristics, 
while System 2 will opt for detailed calculations relying on mathematics and 
statistics. 

Take the following example: a bat and a ball together cost 1.10 Euros. The bat costs 
1 Euro more than the ball. How much does the ball cost? System 1 will lead you to 
believe, if for a moment only, that the answer is 10 cents. System 2 will take a bit 
longer, but will eventually make you arrive at the correct answer, which is 5 cents.

By challenging the assumption of rationality and by introducing notions such as 
System  1  vs.  System  2,  applied  behavioural  science  has  immediate  policy 
implications. For one, changing behaviour does not necessarily mean changing 
minds. It is well-documented that people can think one way but act in another way 
(e.g. smokers continue to smoke even though they are aware of the health risks). 
So the focus of any attempt to change behaviour should be behaviour itself, not 
the thought process that is presumed to lead to behaviour.

Another  immediate  implication  is  that  information  is  not  enough  to  change 
behaviour.  Since  much  of  behaviour  is  automatic,  instinctive  and  habitual, 
information will have no bearing on it. The success of policy initiatives such as mass 
media campaigns and product labelling, therefore, cannot be guaranteed.

System 1 

System 2 

Relies on stereotypes 

Regulates automatic behaviour 
Thinks fast 

• 
• 
•  Uncontrolled, unconscious, effortless 
• 
•  Gives immediate responses  to 
frequent and familiar situations 
•  More prone to biases and heuristics  
• 
Examples: driving a car, brushing 
teeth 

Regulates reflective behaviour 
Thinks slow 
Controlled, self-aware and effortful 
Solves problems through calculation 
and deliberation 
Takes well-thought out decisions 
Less prone to biases and heuristics 
Example: calculating a tip, planning the 
day 

• 
• 
• 
• 

• 
• 
• 

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Applying Behavioural Science to EU Policy-makingApplying Behavioural Science to EU Policy-making 
 
 
 
warning messages on cigarette packets are an option. In Europe, such warning 
messages are regulated by the EU. However, which messages will be most effective 
in discouraging smokers? Will it be simple facts expressed in clear language, or will 
pictures be more effective? Perhaps none of these options are as good as plain 
packaging, without any kind of labels at all. 
If smokers were ‘rational’, a label with clear information about the hazards of 
smoking might be a good idea. But since they are not, alternative options need to 
be considered and tested. For example, an experiment could be conducted in which 
some subjects are exposed to traditional packages and others to new package 
types (with new messages or pictures or plain packaging). Their reactions to the 
different packages could be measured and compared.
This is only one example of how applied behaviour science could help identify the 
best policy option (more details about the different methods available for studying 
behaviour are listed below). The same approach can be applied to other EU policies 
where human behaviour is relevant.

When should behavioural science be applied to policy-making? 
Behavioural science can be applied to policies whenever there is a behavioural 
element to them. It can help design new policies, suggest improvements to existing 
ones, or provide ex-post explanations of why the target group of a policy reacted 
in a particular way. 
There is a behavioural element to a policy:
(a) When behaviour change is the main objective of the policy, for example getting 
people to select an appropriate retirement savings plan, quit smoking or waste less 
food. This is the main thrust of the argument presented in Nudge, the bestselling 
book that is largely responsible for the popularity of applied behavioural science 
in policy circles today.6
(b) When people’s behavioural response affects the effectiveness of a given policy, 
even if the aim of the policy itself may not be to change behaviour. Policies intended 
to protect the consumer are a case in point. They do not seek to change consumer 
behaviour, but to prevent abuse by industry. Knowing, for example, that consumers 
are particularly vulnerable to pre-set default options allowed EU regulators to take 
action and limit their use. 
(c) In the policy-making process itself. Decision-makers are also subject to biases, 
heuristics, and a number of other influencing factors (such as social norms and 
in-group vs. out-group hostilities). Being aware of these potential pitfalls can help 
when developing and pushing forward new policies.7 

6  See list of recommended readings for a full reference. 
7  Guided by the conviction that policy-makers can apply behavioural insights reflexively to develop better 
policies,  the  European  Commission’s  Joint  Research  Centre  established  a  ‘Summer  School’  in  behavioural 
economics for EU policy-makers in 2012. 

UK tax letter example1

In 2011, the UK Behavioural Insights Team (BIT) conducted a trial to see whether 
and how the repayment rate of overdue taxes could be increased. Every year, 
substantial public resources are taken up to chase people who haven’t paid their 
taxes on time. Moreover, the delay in payment of these taxes implies a loss in 
interest on these amounts which, added up over the entire population of the UK, 
can be quite significant.

BIT proposed harnessing the power of social norms in order to get people to pay 
their taxes on time. Humans are social animals and it has been confirmed empirically 
that they are strongly influenced by the behaviour of others. They conform to the 
norm, especially if they feel they are in a minority.

BIT tested different types of reminder letters to be sent to those who were in 
arrears. Instead of a menacing letter warning of the potential consequences of not 
paying on time (the traditional way), a tailored letter was sent explaining that most 
people paid their taxes on time. 

This trial boosted repayment rates by up to 15% in the first 6 weeks. It was 
estimated that this would advance £160 million of tax debts over the six-week 
period of the trial. This would generate £30 million of extra revenue annually by 
freeing up resources allocated to collecting debt and improving cash flow.

Moreover, the trial showed that the more specific and targeted the message in the 
tax letter, the more effective it would be. If the recipient was only told that most 
people in Britain paid their taxes on time, repayment rates increased by 5%. But if 
they were told about people in their town, the figure increased to 15%. 

1  For more information on the application of behavioural insights to taxation, see Applying Behavioural 
Insights to Reduce Fraud, Error and Debt in the list of recommended readings. 

A word of caution is necessary at this point. Applied behavioural science will help 
to better understand and anticipate behaviour, and perhaps nudge people in a 
particular direction. By showing what does (and what does not) affect behaviour, 
it  can  make  policy  interventions  more  effective.  However,  it  is  intended  to 
complement existing initiatives, not replace them. Because of its growing popularity, 
applied behavioural science may be expected to solve problems it is not meant to 
address.8
The possibility of behaviourally-inspired policy solutions should not discard options 
that could be more appropriate and direct. Take the issue of tax avoidance. The UK 
government  managed  to  accelerate  tax  declaration  with  smart  framing  of 
reminders, as in the tax letter example above. The premise is that people are fine 
with paying taxes and just need a friendly nudge to do it quicker. However, increasing 

8  See the New York Times article Economics behaving badly (in the list of recommended readings) for more 
along this line of argumentation. 

6

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Applying Behavioural Science to EU Policy-makingApplying Behavioural Science to EU Policy-makingthe rate of tax compliance is something very different, as the intervention would 
address citizens who are in principle unwilling to pay their taxes. Such incompliance 
may be due to social norms, to disappointment with the quality of public services, 
or to a lack of trust in institutions. Tackling this problem, therefore, would require 
different and bolder interventions, though behavioural science could still help.

Behavioural studies in the European Commission

Behavioural insights began to be formally applied in the European Commission in 
2009, when the Consumer Rights Directive recognised the power of default options. 
The Commission proposed limiting the use of pre-checked boxes in consumer 
contracts (the kind that made consumers purchase travel insurance even if they did 
not want it) in order to save consumers money by default.1 Also, in a landmark case 
against Microsoft, the Commission relied on behavioural insights to arrive at a 
solution  which  offered  consumers  a  sensible  choice  of  Internet  browser.  This 
solution proved effective and is still in place today, offering a limited, but expandable, 
choice of browsers upon the installation of Microsoft Windows. This was the first 
instance where a supply-side issue was tackled by acting on demand side, while 
letting the market find its own natural outcome.

In 2010, DG Health and Consumers (SANCO) conducted a pilot study entitled 
Consumer Decision-making in Retail Investment Services. Through a series of 
laboratory experiments, the study observed how consumers reacted when faced 
with a choice between different investment products. It found that people struggled 
to make optimal investment choices even in the most simplified of environments. 
It also showed that subjects were prone to biases and framing effects (i.e. the way 
in which choices were presented). One of the conclusions of this work was that 
simplifying and standardising product information would significantly improve 
investment decisions. 

Encouraged by the success of this study, and responding to expressions of interest 
from across the Commission, in 2012 DG SANCO set up the Framework Contract 
for the Provision of Behavioural Studies, open to all Commission services. Its 
purpose is to facilitate the running of behavioural studies in support of EU policy-
making. However, given the interest raised across Commission services and the 
number of studies expected to be launched in the first years, DG SANCO requested 
assistance  from  the  Joint  Research  Centre  to  provide  scientific  support.  This 
collaboration started in 2012 and will continue for the foreseeable future, covering 
a wide range of policy areas, from CO2 labelling in cars to package travel regulation. 

1  The Consumer Rights Directive was ultimately adopted by the European Parliament and the Council of 
the European Union in November 2011.

II. 

Issues to consider when applying 
behavioural science to policy-making
There are a number of key issues for policy-makers to consider when thinking 
about applying behavioural insights generally or conducting a specific behavioural 
study. 

(a) Identifying at what stage behavioural science should be applied to 
the policy-making process 
In principle, behavioural science can be applied at any stage:

• At the design stage, when having a clear, evidence-based, understanding of 
how people may respond to a policy is a valuable asset. Wrong assumptions at 
this stage may restrict the spectrum of available policy options and jeopardise 
the success of the policy.

•  Later in the process, for example when deciding how to implement a policy 
initiative. In the EU policy-making cycle, this would be the Impact Assessment 
Stage. 

•  It is also relevant once a particular option has been decided. For example, it may 
be sensible to run a small behavioural pilot study before committing to full-
scale implementation. 

•  Finally, applied behavioural science is not only relevant to the design of new 
policy initiatives; it is also relevant to the critical evaluation of existing ones.

The earlier in the policy-making process behavioural science is incorporated the 
more effective its contribution is likely to be. An early application would not only 
will ensure the soundness of the policy design (from a behavioural perspective), 
but it will also allow for more time to conduct behavioural studies in support of 
that policy.

(b) Defining the role of behaviour in a policy initiative 
Before applying behavioural science to a policy issue, the relevant behavioural 
element needs to be defined in as much detail as possible. Imagine a behavioural 
study is being considered for an EU policy initiative on effective labelling of food 
products. Since labels are intended for consumers to read, understand, and act 
upon, there is clearly a behavioural element here. However what, in particular, 
should the aim of such a study be? Discovering how consumers understand the 
label or finding out how their behaviour changes as a result of the label?9 The 
choice will depend on the objective of the policy itself. This process of narrowly 
defining the objective of the policy and the role of behaviour, therefore, is necessary 

It cannot be assumed that just because consumers understand the label, they will change their behaviour. 

9 
This is just an assumption, not supported by evidence (scientific or anecdotal). 

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Applying Behavioural Science to EU Policy-makingApplying Behavioural Science to EU Policy-makingBehavioural studies in the framework of an  
impact assessment process

The European Commission has established some practices for better regulation, 
which include an impact assessment process to improve the design of European 
policy  proposals.1  The  goal  is  to  have  more  evidence-based  European  policy 
initiatives. 

An impact assessment (IA) is a structured process, resulting in a formal document, 
which  needs  to  follow  specific  guidelines.2  By  playing  a  role  in  this  process, 
behavioural studies can effectively contribute to the push for better regulation in 
the EU. 

Behavioural insights will be most effective when placed early in the roadmap of the 
policy initiative, before an IA study is even set up. The earlier, the better: ideally when 
there are no concrete ideas yet on how to solve a problem and when new, innovative 
policy options are welcome. A behavioural study with a narrow focus which comes 
too late in the IA process may turn out not to be very useful. 

The earliest stage of the IA process where behavioural insights can be introduced 
is the problem definition section. In IA terms, individual behaviour can be one of the 
drivers of a policy problem that a new initiative tries to tackle. For example, drinking 
too many sugar-sweetened beverages (individual behaviour) could drive obesity (a 
problem that a public authority would like to tackle). This should be reflected in the 
problem  tree,  the  IA  standard  illustration  of  how  problems  develop  and  their 
potential solutions. 

When analysing the policy options for tackling a problem, actions with a behavioural 
approach can be identified. These actions could be self-standing policy initiatives 
which attempt to nudge individual behaviour. Alternatively, they could be ‘softer’ 
contributions to larger, more complex pieces of ‘hard’ legislation. 

Finally, behavioural studies have a role to play in the impact analysis, which lies at 
the core of the IA process. This is where the effects of an intended policy are 
estimated. Quantification of impacts, whenever possible, is expected. Behavioural 
studies might not provide a precise calculation of an intervention’s impact on 
variables such as Gross Domestic Product (GDP) and employment, but they will 
establish the direction of the impact and help estimate its overall magnitude.

1  See EC brochure Better Regulation – simply explained, 2006
http://ec.europa.eu/governance/better_regulation/documents/brochure/br_brochure_en.pdf

2  See the 2009 impact assessment guidelines
http://ec.europa.eu/governance/impact/commission_guidelines/commission_guidelines_en.htm 

for adequately designing a research project and allowing for effective behavioural 
support to policy.

(c) Reviewing the available evidence
The attitudes and behaviour of citizens with regard to the policy issue need to be 
well understood before any behavioural study is considered. This may imply 
carrying out a thorough literature review or consultation with stakeholders. Such 
a preliminary phase is necessary to get a first impression of people’s attitudes and 
behaviour, and of the possible policy options available. This will provide a sound 
basis on which to begin designing a behavioural study.

(d) Estimating the value added of a behavioural study 
A sound preliminary reflection of the aims of a specific study and its possible 
results is absolutely essential. Without it, the study could well fail to deliver useful 
information. Other kinds of studies (e.g. a data-gathering exercise on economic 
indicators) can generate large amounts of data, which in turn might lead to 
analyses that were not anticipated. Other types of study can afford ‘casting a wide 
net’ in the hope of landing valuable information. Not so with a behavioural study. 
An internal foresight exercise must be conducted, and a series of questions need 
to be asked. What do we expect from this study? What if the results are not what 
we expected? What if we don’t get any statistically significant results (i.e. if the 
various policy options are equivalent in terms of influence on behaviour)? 

(e) Specifying the unit of analysis
Usually the unit of analysis for a behavioural study will be the individual (not a 
firm, not a family, not a group of people). It is the individual who is subject to biases 
and heuristics in his or her thought processes, the individual who feels emotions, 
the individual who has a set of preferences, and the individual who is ultimately 
responsible for his or her actions.
However, there is a demand for behavioural studies to be applied to firms (and 
other complex bodies), since their behaviour is such a vital component of economic 
activity. This is a difficult task. Complex bodies, especially large ones, tend to 
behave ‘rationally’. They rely on the expertise of many experienced and trained 
professionals and often have structured and formalised decision-making processes. 
Complex bodies cannot be nudged, as a nudge works well when individuals take 
decisions using their System 1, the fast and spontaneous way of thinking. Complex 
bodies do not think fast or spontaneously; rather, they slowly and deliberately seek 
to maximise their utility taking reasoned decisions. 
However, small enterprises (particularly micro-enterprises, with fewer than 10 
people)  are  often  less  structured  when  it  comes  to  formal  decision-making. 
Sometimes owners of companies – human beings themselves – will make decisions 
on behalf of the company, without the benefit of a support structure, in the same 
way they make decisions about their lives. Also, small groups of people organised 

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Applying Behavioural Science to EU Policy-makingApplying Behavioural Science to EU Policy-makingPlanning a behavioural study in the European Commission

Identify policy 
objective 

Is there a 
behavioural 
element? 
est? 

Yes 

Review available 
evidence 

No 

Is the review 
exhaustive? 

Yes 

No 

Run a behavioural study 
(seek SANCO and JRC 
support) 

Incorporate 
findings into 
policy-making 
process  

No need for 
a behavioural 
study 

Define aim and research 
question precisely 

Identify appropriate 
methods of research 

Draft technical 
specifications and send 
out call for tender 

Evaluate offers and 
adjudicate study 

around a common task or objective, such as research teams applying for funding, 
will sometimes respond to incentives in a similar way to individuals. Therefore, 
while not common, there is scope for applying behavioural insights to small groups 
of people, especially if they lack formalised decision-making mechanisms.

(f) Considering time constraints 
Behavioural studies, like most studies, require time to be conducted properly. The 
exact problem to be investigated needs to be narrowly defined and translated into 
a research design. If the study is based on an experiment, the proper experimental 
treatments and controls, the appropriate size of subject pool, and the right level 
and form of incentives all need to be established. Moreover, after the collection of 
data, time is needed to analyse and interpret the results. 
The simplest version of a project of this kind, with some lab experiments or on-line 
surveys, needs at least 6 months. If a randomised control trial (RCT) is considered, 
the project can run for a year or more. In the context of the usual time horizon of 
the policy-making cycle, this would present a challenge. Hence, studies of this kind 
must be considered early in the game, if they are to be a realistic option. 

III. Commissioning a behavioural study 
Imagine a Directorate-General (DG) of the European Commission is convinced of 
the value of a behavioural approach and decides to commission a study to gather 
evidence in support of a policy initiative. While the study itself will be conducted 
by a contractor, the DG in charge will need to address some specific questions 
before issuing a call for tender. 
The main issue to be cleared regards the type of study to be carried out. The aim 
of a behavioural study is to gain a better understanding of how people act (or 
perhaps think and feel too). Following this definition, a number of different types 
of study are possible. 

(a) Experiments 
Well-conceived experimental studies provide consistently reliable results that are 
replicable in different places and different times. In an experiment, the researcher 
will manipulate one element, something resembling a policy intervention, while 
measuring people’s specific behaviour or reaction. The aim is to draw conclusions 
on the effect of the manipulated element on participants’ actual behaviour. For 
example (as mentioned earlier), people can be shown different types of warnings 
on tobacco packages to see which one provokes the strongest reaction. 
A good experimental design will include monetary incentives – actual money that 
people get to keep after the experiment – whenever appropriate. This is not done 
for the purpose of recruiting subjects, but rather to ensure that the decisions made 

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Applying Behavioural Science to EU Policy-makingApplying Behavioural Science to EU Policy-making 
 
 
in the experiment are as realistic as possible. Without such monetary incentives, 
subjects might participate carelessly, without paying too much attention to what 
they do (i.e. there is no incentive to put genuine effort into the task). 
An experiment is systematic and rigorous, as it allows for an almost clinical control 
of all the relevant elements. Moreover, by manipulating these elements and 
measuring their impact on behaviour, the investigator is able to identify cause-
and-effect relationships (and not simply correlations, which is a common limitation 
with surveys). 
In a laboratory experiment, the researcher can draw statistically significant results 
from relatively small samples. This is a particularly attractive feature for policy, as 
it saves time and money. However, whether a single experiment can be representative 
of the 28 EU Member States remains an open question at this stage. Some results 
(e.g. loss aversion) might apply equally to all Member States, while others (e.g. 
social norms on collaboration) might differ according to different cultures. 
An additional benefit of experimental methodology is that results can be applied 
to a broader range of policy issues. For instance, while a survey can only give an 
answer on which images are the most effective deterrents to include on cigarette 
packets, experimentalists in a laboratory can possibly reveal the corresponding 
emotion, feeling or reasoning (such as fear or disgust) behind this effective image. 
This result could then be applicable in other settings, different to smoking. 
Experiments have been challenged because of concerns about their internal and 
external validity, as well as the implicit trade-off between these two.10 However, 
the already expanded experimental literature, together with cautious experimental 
design, can minimise any loss of internal validity. At the same time, consistent 
results, replicated in different situations and settings, help establish experiments’ 
external validity.

(b) Randomised controlled trials (RCTs) 
Ideally, people’s behaviour should be observed in their natural environment without 
any interference from observers. This is the purest and most accurate observation 
of behaviour, unlike experiments which take place in a laboratory. Following this 
rationale, and taking inspiration from the drug industry, where the effects of new 
drugs are tested with large numbers of patients, RCTs have been established as a 
methodological option in applied behavioural science. 
In its simplest form, a RCT divides a target population in two groups: the control 
group and the treatment group. The control group receives the equivalent of a 
placebo (i.e. nothing is changed substantially), while the treatment group faces 
something new. In the UK tax letter example, mentioned earlier, the control group 
received the same letter as always. The treatment group, however, received a 

10  An experiment is said to have internal validity when the outcome of the experiment is not due to external 
factors that were not measured in the experiment. It is said to have external validity when its outcome can be 
generalised to a real-world setting, outside the laboratory.

Setting up treatment groups in experiments 

In a behavioural experiment, the main results will come from measuring and 
comparing the behaviour of different treatment groups (and a control group). 
Knowing some aspects of how these groups are set up, therefore, is useful when 
commissioning a behavioural study.

An experiment can either 

•  compare the behaviour of two sets of people, one of which was exposed to the 

variable, the other not (‘between-subjects’ design) or 

•  measure the behaviour of the same group of people at two points in time, 

before being exposed to the variable and after (‘within-subjects’ design). 

In both cases, the observed behaviour after being exposed to the relevant variable 
is a key aspect to measure. Subjects cannot be exposed twice to such variables, as 
this would lead to habituation and learning, and would bias the results . 

Regarding the size of treatment groups: the larger, the better. A larger size will make 
it more likely to identify differences between groups when these exist (i.e. greater 
statistical power) and less likely to detect differences when these do not actually 
exist (i.e. greater confidence intervals). It will also allow for detecting smaller 
differences between groups (i.e. smaller effect size). However, as sample size is 
directly proportional to invested money, a balance needs to be struck. Considering 
these  elements,  the  adequate  sample  size  can  be  computed  according  to 
mathematical formulas. 

Another consideration is the number of treatment groups to be included. Every time 
an additional element (condition or variable) is included in a behavioural experiment, 
a new treatment group must be created to test its effect. This can quickly lead to 
an exponential growth in the number of treatment groups.

Take the example of product labelling and consumers’ purchasing behaviour. Say 
one of the variables expected to have an impact is the size of the label: whether it 
is big or small. Testing the effect of label size would require two treatment groups, 
one for each size (plus one control group). Testing, in addition, the effect of two 
colours (say red or blue), would require 4 treatment groups (big-red, big-blue, small-
red, small-blue), plus one control group (no colour or size alteration). Add another 
variable (e.g. large vs. small font) and the number of treatment groups rises 
exponentially to 8 plus a control group. By making comparisons between the 
behaviour in each treatment group with the behaviour in the control group, a 
behavioural study can draw conclusions about the effect of size, colour and font 
size on consumers’ behaviour.

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Applying Behavioural Science to EU Policy-makingApplying Behavioural Science to EU Policy-makingletter with a different wording. Both groups were followed to see who paid their 
taxes earlier. The differences in repayment rates can be credited to the different 
wording of the letter in the treatment group.
The use of this methodology for EU policy support, however, needs to consider a 

Basic design of a randomised controlled trial (RCT)

Policy intervention

Control group

Measure and 
compare 
outcomes  

few issues. For one, given the complexity of social systems, the result of a RCT is 
valid for a specific point in time, location and target group. A policy initiative that 
proved valid in a RCT in Baden-Württemberg might completely fail in Andalucía. 
Even minor differences in social norms could lead to completely different outcomes 
somewhere else. Therefore, a RCT would have to be conducted at EU level, which 
can be very costly in terms of preparation, data collection and data processing. 
Moreover, as with other experimental designs, RCTs are susceptible to the usual 
false positive (showing a causal relationship where there is none) and false 
negative (failing to show one, where in fact one exists) results. To ensure that the 
results of a RCT are valid, therefore, it would need to be repeated, preferably twice, 
which would further increase the cost. In sum, while they provide robust evidence 
for  policy-making,  proper  RCTs  at  EU  level  can  be  expensive  and 
time-consuming. 

(c) Surveys
An extensively-used method of research for policy support is the survey: questioning 
a large sample of people about their attitudes, beliefs, expectations, etc. One of 
the reasons for its popularity is its external validity (i.e. it effectively captures how 

people ‘out there’ think), since it will often have a sample that is representative of 
the larger population. 
However,  the  way  a  questionnaire  is  designed  will  limit  the  richness  of  the 
information collected. In surveys, respondents express their ideas, thoughts and 
feelings through a set of pre-established questions. There is also no guarantee 
that respondents will be open, honest, and unaffected by the conditions of the 
survey itself (e.g. the particular effect of being approached for a survey, self-
censoring in responses, providing socially acceptable answers). Moreover, the 
survey is not the ideal way of gathering data about behaviour, as it collects self-
reported behaviour, amounting to respondents’ own account of what their behaviour 
has been or will be, rather than their actual behaviour. 

(d) Qualitative research
A number of research methods that are widely used in the ‘softer’ behavioural 
sciences (such as social psychology and anthropology), but which are less common 
in policy support, fall under the umbrella term of qualitative research. These include, 
but are not limited to, focus groups, semi-structured interviews and participant 
observation.
Their main advantage is that they allow for richer, more nuanced data on how 
people think, act and feel. Participants are not constrained by a set of pre-established 
questions (as in a survey) nor are they observed in an artificial setting (as in 
laboratory experiments). Rather, they are given ample room to demonstrate their 
behaviour or express their thoughts. In participant observation, for example (the 
method of choice in anthropology), participants are simply observed in their natural 
setting, going about their daily lives, with practically no intervention from the 
investigator.
The problem with such qualitative methods is that their external validity is often 
questioned. There is no guarantee that what is learned from observing a limited 
number of people will apply to the rest of the population. And this is particularly 
delicate in policy-making, as results can often be contested on these grounds. 
However, this should not lessen their value. This is especially true if the aim is to 
observe and describe behaviours or thoughts that are shared among a population. 
One need not observe how a representative sample of the population justifies their 
decision to smoke, for example, to hear most the arguments that exist (‘you have 
to die of something’ or ‘I only smoke very little’, etc.). A representative sample  
would  only  be  required  if  the  aim  was  to  establish  the  prevalence  of  these 
arguments among a population of smokers. 
Because they do not necessarily require a large sample, qualitative studies also 
have  the  advantage  of  being  potentially  very  cost-effective.  They  are  also 
appropriate for some specific cases, such as testing the user-friendliness of an 

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eGovernment website interface (software or automobile companies, for example, 
do not rely on representative samples for testing their products). And, finally, they 
are  very  useful  for  gaining  insights  ‘from  the  bottom-up’,  untainted  by 
presuppositions and prejudice, which can later be tested by other methods with 
greater external validity.

In the end, the decision on which type of behavioural study to be commissioned 
will depend on the issue to be studied and the available time horizon. If, for example, 
information is needed about European citizens’ attitudes on a particular issue, a 
survey or qualitative research might be appropriate. Behavioural experiments or 

Summary of types of behavioural studies

Type of study

Pros

Cons

Experiments

Can establish causality (not 
only correlation)
Can provide statistically 
significant results from a 
relatively small sample size
Core findings can apply to 
other contexts

Representativeness for 
EU-28 not feasible
A laboratory is an 
unrealistic and artificial 
environment

Minimum time 
horizon needed

6 months

Randomised 
control trials 
(RCTs)

Can establish causality, not 
only correlation
Allow for observations in 
natural settings

Surveys

Representativeness for EU-28 
is feasible
Relatively cost-effective 

Qualitative 
research 
methods

Provide richer, more nuanced 
data about behaviour
Often take place in realistic 
settings
Participants are given freedom 
to express themselves, with 
limited intervention by 
researcher

12 months

4 months

4 months

Very expensive to run at EU 
level (and to replicate in 
order to validate results)
Results from one location 
not generalizable to others

Respondents are limited by 
pre-established options to 
questions
Respondents might not be 
truthful
Only gather data on self-
reported behaviour
Cannot establish causality, 
only correlation 

Data collected is generally 
not representative of the 
larger population
Usually have small samples 
due to the time and cost 
involved

RCTs, on the other hand, can be used when there is a need to directly observe 
behavioural reactions to a policy intervention.

The timing also needs to be considered. Sound empirical investigation requires a 
proper preparatory phase, a design period, pilot testing, revisions to the original 
design, etc. If the aim is to better understand how European citizens would react 
to new policy, and the time horizon is greater than 18 months, a RCT is possible. If 
the time horizon is less than 9 months, then a laboratory experiment will probably 
be the best option. It will not provide a perfect picture of reality, but it will shed light 
on the fundamental elements of citizens’ reaction. 

There remain a number of issues to consider in the process of commissioning a 
study, but most of these are common to any study in support of policy. Perhaps 
the one issue which is specifically relevant to behavioural studies is the degree to 
which findings of a non-representative sample can be generalised to the entire 
population. 

On this matter it is worth noting that:

•  If the purpose of the study is to identify and describe behaviour, without 
establishing  their  prevalence  in  the  general  population,  a  representative 
sample is not required. 

•  Evidence collected from a sample which is representative of only a segment 
of the population will be sufficient if the policy is aimed at that particular 
segment (e.g. teenage pregnant women) or if success in that segment will 
automatically imply success elsewhere (e.g. vulnerable consumers).

•  The findings of a study whose sample is not representative of the general 
population can be generalised if the study can be replicated, with similar 
findings, in different contexts. 

Caveats such as these must be kept in mind when analysing results and preparing 
policy recommendations from behavioural studies. In the end, a behavioural study 
will not offer a silver bullet to solve policy issues. But it can provide robust evidence 
on how people behave – and this evidence should contribute towards more effective 
policies. 

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Applying Behavioural Science to EU Policy-makingApplying Behavioural Science to EU Policy-making•  Mindspace:  Influencing  behaviour  through  public  policy  (2010)  Institute  for 
Government. Provides a summary of factors affecting human behaviour and 
explains how this knowledge can be applied to policy-making. 

•  Test, Learn, Adapt: Developing public policy with randomised controlled trials 
(2012) by the UK Cabinet Office Behavioural Insights Team. Explains how to design 
randomised controlled trials for testing policy options. 

Articles in the press

•  ‘Successful public service design must focus on human behaviour’, The Guardian, 
17 January 2013. Provides an account of the successes of the UK Cabinet Office 
Behavioural Insights Team. 

•  ‘We  can  make  you  behave’,  The  Guardian,  28  January  2010.  Expresses  the 
willingness by the UK government to implement policies inspired by behavioural 
insights. 

•  ‘Economics behaving badly’, New York Times, 14 July 2010. Argues that behavioural 

economics is being asked to solve problems it was not meant to address. 

•  ‘Nudge nudge, think think’, The Economist, 24 March 2012. Describes achievements 

in UK, France and Denmark in applying behavioural insights.

•  ‘The nanny state’s biggest test’, The Economist, 15 December 2012. Illustrates and 
comments on policy attempts to reduce obesity, with special attention to options 
based on behavioural insights.

•  ‘Beware stubby glasses’, New York Times, 10 January 2013. Highlights some 

psychological insights with good potential to shape policy-making. 

Recommended reading 
Books 

•  Advances in Behavioral Economics (2004) by Colin Camerer, George Loewenstein 
and Matthew Rabin (eds.). Collection of articles which have shaped behavioural 
economics as a discipline.

•  The Behavioral Foundations of Public Policy (2012) by Eldar Shafir (ed.). Collection 
of interdisciplinary essays challenging the hegemony of cost-benefit analysis as 
a policy analysis tool. 

•  The Black Swan (2007) by Nassim N. Taleb. Book on the notion of ‘narrative 
fallacy’: people’s limited ability to look at sequences of facts without weaving an 
explanation into them.

•  Influence: The psychology of persuasion (2006) by Robert B. Cialdini. Classic book 
on the psychology of why people say ‘yes’ (and how to apply this understanding). 

•  Nudge: Improving decisions about health, wealth and happiness (2008) by Richard 
H. Thaler and Cass R. Sunstein. Seminal introduction to the use of nudges in 
policy-making. 

•  Thinking, Fast and Slow (2011) by Daniel Kahneman. Authoritative review of how 
people actually make choices, summarising a lifetime of Nobel prize-winning 
research.

Policy reports (all available on-line)

•  Applying Behavioural Insight to Health (2010) by the UK Cabinet Office Behavioural 
Insights Team. Presents possible policy interventions on public health inspired by 
behavioural insights.

•  Applying Behavioural Insights to Reduce Fraud, Error and Debt (2012) by the UK 
Cabinet Office Behavioural Insights Team. Builds on the UK tax letter example 
mentioned in this report. 

•  Behaviour Change and Energy Use (2011) by the UK Cabinet Office Behavioural 
Insights Team. Examines a series of policy tests which have been conducted in the 
field of energy savings. 

•  Better Choices: Better Deals (2011) by the UK Cabinet Office Behavioural Insights 

Team. Explains how consumers can be empowered through better deals. 

•  “Green  Nudges”:  New  incentives  for  ecological  behaviour  (2011)  by  Centre 
d’Analyse Strategique. Introduces efforts to encourage ecological behaviour with 
innovative tools inspired by behavioural economics.

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European Commission
EUR 26033 – Joint Research Centre – Institute for Prospective Technological Studies

Title: Applying Behavioural Sciences to EU Policy-making: Policy Brief

Authors: René van Bavel, Benedikt Herrmann, Gabriele Esposito, Antonios Proestakis

Luxembourg: Publications Office of the European Union

2013 – 21 pp. – 21.0 x 14.8 cm

EUR – Scientific and Technical Research series – ISSN 1831-9424 (online), ISSN 1018-5593 (print) 

ISBN 978-92-79-31459-9 (pdf) 
ISBN 978-92-79-31460-5 (print) 

doi:10.2788/4659 

As  the  Commission’s  in-house  science  service,  the  Joint  Research  Centre’s  mission  is  to  provide  EU  policies  with 
independent, evidence-based scientific and technical support throughout the whole policy cycle.

Working in close cooperation with policy Directorates-General, the JRC addresses key societal challenges while stimulating 
innovation through developing new standards, methods and tools, and sharing and transferring its know-how to the 
Member States and international community.

Key policy areas include: environment and climate change; energy and transport; agriculture and food security; health and 
consumer protection; information society and digital agenda; safety and security including nuclear; all supported through 
a cross-cutting and multi-disciplinary approach.

Joint 
Research 
Center