Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine
ß The Author 2014; all rights reserved. Advance Access publication 26 June 2014
Health Policy and Planning 2015;30:747–758
doi:10.1093/heapol/czu052
Applying behavioural economics to health
systems of low- and middle-income
countries: what are policymakers’ and
practitioners’ views?
Antonio J Trujillo,1 Amanda Glassman,2 Lisa K Fleisher,3 Divya Nair4* and Denizhan Duran2
1
Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, 2Center for
Global Development, Washington DC 20036, 3Department of Health Policy and Management, Bloomberg School of Public Health, Johns
Hopkins University, Baltimore, MD 21205 and 4Department of Population, Family and Reproductive Health, Bloomberg School of Public
Health, Johns Hopkins University, Baltimore, MD 21205, USA
*Corresponding author. Bloomberg School of Public Health, Johns Hopkins University, 615 N, Wolfe St., Baltimore, MD 21205, USA. E-mail:
[email protected]
Accepted
13 May 2014
Interest in behavioural economics has soared in recent years, particularly
because of its application to several areas of public policy, now including
international development, education, and health. Yet, little is known about how
the policy and political implications of behavioural economics are perceived
among stakeholders. Using an innovative vignette-based online survey, we
assessed the opinions of 520 policymakers and practitioners around the world
about health policy recommendations emanating from behavioural economics
principles that are relevant to low- and middle-income country settings. We also
determined the sources of disagreement among the respondents. The results
suggest that there is strong support for health policies based on the concepts of
framing choices to overcome present bias, providing periodic information to
form habits, and messaging to promote social norms. There is less support for
policies which use cash rewards as extrinsic motivators either to change
individual behaviour related to the management of chronic conditions or to
mitigate risky sexual behaviour. The sources of disagreement for these policy
prescriptions derive mainly from normative concerns and perceived lack of
effectiveness of such interventions. Addressing these disagreements may require
developing a broader research agenda to explore the policy and political
implications of these prescriptions.
Keywords
Behaviour change, developing countries, economics, health behaviour, health
care seeking behaviour, health policy, health systems research
747
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HEALTH POLICY AND PLANNING
KEY MESSAGES
Behavioural economics is now popular in academic and policy circles and numerous interventions drawing insights from
this field are being implemented in low- and middle-income countries. Little, however, is known about their reception
among those policymakers or practitioners working on health.
We conducted an innovative vignette-based survey of policymakers and practitioners who work on health in low- and
middle-income countries to ascertain their familiarity with some key concepts in behavioural economics and gauge their
agreement with certain policy applications that emerge from this field.
Our results indicate that there is strong consensus among respondents in the areas of framing choices to overcome
present bias, providing periodic information to form habits, and messaging to tap into social norms. There is less appetite
to use incentives in the form of cash rewards as extrinsic motivators to change individual behaviour related to the
prevention and management of chronic conditions or for the mitigation of risky sexual behaviours.
Introduction
Over the past 20 years the traditional rational choice framework
of economics has been extended to better understand individual
behaviours. By combining insights from psychology, and, more
recently, neuroscience, the field of behavioural economics has
introduced psychological realism to economics. It recognizes
that certain fundamental assumptions of the neoclassical
framework regarding an individual’s decision-making process
are in fact violated with systematic regularity. Instead of being
rational, responses to incentives are often based on biases and
heuristics (‘rules of thumb’). As a result, a wide range of new
insights has expanded our understanding of how individuals
make decisions and behave, and these have the potential to
improve public policy.
These insights, however, continue to be a collection of
isolated observations that are aggregated together informally
by different analysts (Mullainathan et al. 2011). For reviews,
see Camerer et al. (2005); Shafir (2012); Thaler and Sunstein
(2003); Wilkinson (2008). A battery of theories has thus been
tested using empirical data from well-designed, mostly smallscale, and short-term field experiments in different areas of
social policy including health (Johnson and Goldstein 2003;
Loewenstein et al. 2007a; Cohen and Dupas 2010; Basinga et al.
2011; Milkman et al. 2011), anti-poverty programmes (Bertrand
et al. 2004), education (Duflo and Hanna 2005), environment
(Allcott 2011; Ferraro and Price 2013), and more extensively, in
finance and insurance (Ashraf et al. 2006; Barberis 2013).
Insights from behavioural economics are now also being
applied in a variety of low- and middle-income settings to
improve health behaviours1.
Are these behavioural insights, their applications, and the
results of these interventions, known to policymakers and
practitioners? Subsequently, do stakeholders consider them
viable for application in different contexts or on larger scales?
Little progress has been made in understanding how the policy
advice and recommendations that emerge from behavioural
economics experiments in the lab and field can be translated
into effective policies at scale. Policymakers and influential
experts may not be aware of the behavioural economics
literature, may not find the empirical evidence intuitive or
compelling or may consider the interventions difficult to
implement. In 2010, for example, the European Union held a
meeting entitled: ‘Behavioral economics, so what?’ Some may
believe that behavioural economics adds nothing new to
traditional public health principles (Bonell et al. 2011). Others,
however, may have very high, and possibly unrealistic, expectations. Indeed, behavioural economics is sometimes viewed as
appealing to policymakers as an alternative to stricter—and
politically controversial—forms of regulation such as taxes
and bans (Oliver 2012). This is in line with an Op-ed by
Loewenstein and Ubel (2010) that makes the same argument,
namely that ‘behavioural economics is being asked to solve
problems it wasn’t meant to address’—which is important also
because Lowenstein is one of the foremost behavioural economists in the field. Further, in a recent review of the behavioural
economics literature, Berggren (2012) suggests that policymakers themselves may have cognitive limitations and
biases that may affect the feasibility and appropriateness of
implementing such policy recommendations.
If the aim of behavioural economics is to improve the
analytical framework for policy and to maximize the impact of
policy interventions, major questions relate to how these
prescriptions compare with traditional policies used to solve
market failures, the types of institutional barriers policies based
on behavioural economics will face in a democratic process, the
anticipated government failures in the implementation of these
policy prescriptions, and how policymakers will handle the
tradeoff of values—efficiency, equity, organizational constraints
and budgetary considerations—inherent in political decisions.
Behavioural economics has made slow progress in identifying
these fundamental political, institutional, cultural, and economic constraints.
The purpose of this article is to better understand how
behavioural economics might contribute to health policy and
the related political discourse in low- and middle-income
countries. We ascertain the opinions of 520 policymakers and
practitioners around the world about health policy recommendations emerging from behavioural economics principles and
empirical evidence. Similar surveys have been implemented on
other topics by economists like Fuchs (1996); Fuchs et al.
(1998); Morrisey and Cawley (2008); Gordon and Dahl (2013).
We also assess some of the determinants of consensus and
disagreement over such policies (e.g. Robert and Zeckhauser
2011).
We expect an inverse relationship between the level of
disagreement and the available empirical evidence about the
impact of policies based on behavioural economics. However,
lack of consensus may also exist because of disagreement on
VIEWS ON BEHAVIOURAL ECONOMICS IN HEALTH SYSTEMS
normative issues (Friedman 1953; Fuchs et al. 1998).
Understanding the sources of agreement or disagreement
around policy prescriptions from behavioural economics can
help to improve the policy options available to improve the
health of populations. In addition to assessing determinants of
consensus and disagreement, we test respondents’ basic knowledge of fundamental theoretical principles from behavioural
economics. Lastly, we also explore the individual characteristics
associated with an increase or decrease in disagreement with
health policies derived from behavioural economics principles.
We frame the health policy prescriptions from behavioural
economics within the control knobs framework commonly used
to describe the performance of health systems.
Health systems frameworks: where does
behavioural economics fit in?
The first task in building our survey was to understand where
policy approaches based on behavioural economics might fit
within a health systems framework. In the context of countries’
progress towards the Millennium Development Goals (MDGs),
attention to health systems has increased. A strong health
system is integral to reducing the burden of disease, achieving
the health MDGs and to reducing inefficiencies. As a result,
health system strengthening has become a priority for the
global development agenda.
Many frameworks have been developed to describe the
components and functions of health systems and illustrate
the relationships among them (World Bank 2007). Each is
based on varying definitions of what a health system is and
provides a useful lens through which health reform efforts are
directed. However, at the centre of each health system are
individual patients and providers, whose behaviours are often
poorly aligned with the goals of the health system. This can
undermine progress and efforts to improve health outcomes.
Among the health systems frameworks developed over the
past two decades, the Roberts et al. (2002) ‘control knobs’
framework is the only one to explicitly integrate the role of
human behaviour in health system performance and health
status. It thus offers a full complement of functional and
behavioural components. This is particularly useful for incorporating behavioural economics approaches.
Application of the control knobs framework
The components of the control knobs framework are: financing,
payment, organization, regulation and persuasion (Roberts et al.
2002; Hsiao 2003). The persuasion knob is where policy options
grounded in behavioural economics fits most logically to
address health behaviours. These policy tools focus especially
on influencing the choices made by providers and patients to
subtly encourage individuals to make decisions that are better
aligned with their best interests. Four categories of human
behaviour may be influenced by the persuasion control knob
(Roberts et al. 2002). These include treatment seeking behaviours, health professional behaviours, patient compliance
behaviours and lifestyle and prevention behaviours. Each of
these four categories of human behaviour is amenable to
interventions rooted in behavioural economics. Treatment
seeking, patient adherence to prescribed medications, and
lifestyle and prevention behaviours all function on the
749
demand side while health professional behaviours can be
influenced by supply side interventions. For the purposes of
analysis, we grouped the questions in our survey instrument
according to these four categories.
Methods
Survey description
The survey questions and vignettes were developed by the
authors and were reviewed by several experts in the field. The
survey was web-based. Such online surveys have been commonly used in the economics literature to explore values,
preferences, and policy positions among economists. Recent
examples are Fuchs et al. (1998), Gordon and Dahl, (2013),
Morrisey and Cawley (2008) and Stremikis et al. (2011).
Invitations to complete the survey were sent by e-mail to the
6535 subscribers of the Center for Global Development’s (CGD)
global health newsletter. Subscribers were also permitted to
forward the survey to other interested professionals working in
the field. Three follow-up e-mails were sent to the original CGD
subscriber list. We centralized the process of data collection to
minimize or eliminate the problem of duplicate responses from
one individual. The online survey was available from January
2012 until March 2012. An abridged version of the survey is
available as an online supplementary Appendix S1. The survey
is not intended to be representative of the policymaker or
practitioner community, but to begin understanding some of
sources of policy disagreements and to identify promising
avenues for future work.
Table 1 summarizes the characteristics of the sample. The
survey was completed by 520 respondents. The rate of response
was around 8% of the subscriber list. The relatively low
response rate of this online survey raises the issue of generalizability of the findings. It is possible that the type of person
who responded is different from those who did not, and that
our results reflect the opinions of this particular type of
policymaker or practitioner. However, the direction of nonresponse bias, if any, is unclear because we have limited
background information (only on employer organization) on
the sampling frame. In the full CGD subscriber pool, 13% of
subscribers identify themselves as affiliated with a university,
compared with 17% in the survey sample. Roughly 7% of CGD
subscribers are affiliated with a US or foreign government or
government agency, as compared with 8% in the survey sample.
In both the subscriber pool and the survey sample, 4% of
subscribers are affiliated with a multilateral donor. The survey
sample thus appears to be quite representative of the CGD pool,
and the latter provides a useful gauge of policy opinions and
views in the field.
Most survey respondents (62%) have an educational background in economics, law, or international development. In
terms of occupation, the largest proportion of the respondents
(25%) work at non-governmental organizations, 17% work at
universities and 16% work for government or donors (bilateral
and multilateral). About 55% of respondents are older than
35 years of age. The average time to complete the survey was
17 min with a standard deviation of 4 min.
In the first section of the survey five questions probed
respondents’ knowledge about five key concepts of behavioural
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HEALTH POLICY AND PLANNING
Table 1 Description of respondents to policy questions (sample
size ¼ 520)
Variables
Frequency (%)
Age
25 and below
115 (22)
26–35
113 (22)
36–45
92 (18)
46–55
102 (20)
56–65
57 (11)
Above 65
Missing values
35 (7)
6 (1)
Gender
Female
222 (43)
Male
195 (38)
Missing values
103 (20)
Educational background and traininga
Health policy and public health
Medicine
Economics, law, inter.development and others
207 (40)
58 (11)
322 (62)
Employer organization
Bilateral donors
21 (4)
Government
40 (8)
Medical institution
10 (2)
Multilateral donor (including development bank)
Non-governmental organization
20 (4)
131 (25)
Private company
39 (8)
Think tank or policy research unit
37 (7)
University
Other
Missing values
88 (17)
32 (6)
102 (20)
a
Totals do not add to 520 since more than one answer was allowed.
economics. These included loss aversion, reference bias, confirmation bias, social bias, and hyperbolic discounting.
Respondents were asked whether they were ‘very familiar’,
‘somewhat familiar’ or ‘not familiar’ with each of these
concepts. Tversky and Kahneman (1991) and Laibson (1997)
pioneered work particularly on hyperbolic discounting and loss
aversion. The other three concepts are somewhat broader
psychosocial themes. In the case of reference bias, social bias
and confirmation bias, influential work has been done by
Fleming et al. (2010), Wilson (2006) and Loewenstein et al.
(1989).
An extensive literature review of behavioural economics and
its applications, particularly for health policy in low- and
middle-income countries, was undertaken. The second section
of the survey consisted of 20 questions related to health policy
implications of behavioural economics. The questions were
framed as policy vignettes where a respondents’ answer
indicated his/her level of agreement with the particular application of behavioural economics to health policy. A challenge
when examining behavioural insights in health is that there
exist a range of findings and a variety of applications in
different contexts. To enable comparability on this range of
subjective and qualitative responses, we use an adapted version
of a vignette-based questionnaire to anchor the survey questions (see Chevalier and Fielding 2011; King et al. 2004; Van
Soest et al. 2011 ). The vignettes were crafted to relate to
specific health challenges in low- and middle-income settings
(e.g. HIV testing, smoking cessation, child vaccination).
Moreover, they were drawn from actual field experiments or
empirical findings reported in the literature of behavioural
economics.
As indicated earlier, we classified the vignettes according to
the four policy areas described by the control knobs framework.
Table 2 shows the response rate for each of the questions by the
four relevant categories: treatment seeking behaviour; health
professional behaviour; patient compliance; and lifestyle and
prevention behaviour. The number of total responses fluctuates
from 492 for question 1 to 421 responses for questions 16, 17,
18, 19 and 20 (question numbers from the original survey are
included in the first column of each table to allow for crossreference across tables.)
At this point, it may be illustrative to provide two vignettes
used in our online survey. Conditional cash transfer programmes, implemented in more than 40 countries over the past
decade and subject to many rigorous policy experiments, have
proven to be effective in increasing utilization of preventive
health-care services and improving nutrition and some health
outcomes (Fiszbein and Schady 2009). Yet nuances remain, for
example, over how to structure incentives, via rewards or
penalties—and these can draw on insights from behavioural
economics. Experiments in behavioural economics indicate that
cash incentives structured in small and frequent rewards can be
effective to overcome present costs (that tend to be highly
valued) to obtain long-term changes in behaviour (Fudenberg
2006; Loewenstein et al. 2007b). Basically, constant stimulation
using small rewards may help individuals overcome problems
of procrastination. The behavioural economics literature also
suggests that incentive schemes based on positive rewards
rather than penalties are more effective to change individual
behaviour (Loewenstein et al. 2007b; Volpp et al. 2008). To
explore policy analysts’ opinion about these prescriptions from
behavioural economics, we included the following scenarios.
(Vignette illustration 1) How incentive programmes are
structured matters for an individual’s response. For instance,
providing cash payments to patients with chronic conditions if they
visit their doctors, or paying patients if they take health tests may
influence their behaviour. The effect of rewards (or punishment)
increases when rewards have some level of uncertainty. In other
words, individuals engage in mental accounting that helps them
prioritize rewards.
Monetary incentives to reduce bad behaviours (e.g. drinking,
smoking, low compliance with prevention) would be more
effective if they involve some uncertainty in terms of timing of
the reward or the amount of the reward.
(Vignette illustration 2) Individual health behaviour often
involves receiving immediate benefits and delaying present costs. For
example, a diabetes patient who postpones self-management
may receive immediate gratification (e.g. not following a
VIEWS ON BEHAVIOURAL ECONOMICS IN HEALTH SYSTEMS
751
Table 2 Responses to health policy implications from behavioural economics (sample size ¼ 520)
Policy questionsc
Total responses
Missing responses
(number)
438
82
Treatment seeking behaviours
9. Cash rewards to patients for bringing family members encourages utilization
Health professional behaviours
1. Supply of informal caregivers may be responsive to social recognition
492
28
11. Reducing health workers’ pay based on absent days can discourage absenteeism
431
89
13. Bonuses to health facilities for achieving goals can improve quality of care
424
96
14. Public display of good performers can reduce health worker absenteeism
424
96
2. Bundle organizational and financing incentives in pay-for-performance
474
46
5. Programmes with small and frequent rewards can help overcome present costs
452
68
12. Cash rewards are more effective with uncertainty in timing and amounta
428
92
17. Periodic reminders can increase habit formation
421
99
Patient compliance
Lifestyle and prevention behaviours
3. Programmes to develop self-control in children can have adult health benefits
460
60
4. Changing social norms can encourage chronic diseases prevention
458
62
6. Commitment devices can change behaviour
446
74
7. Rewards for good behaviour are more effective than penaltiesb
444
76
8. Safety devices to prevent drunk driving can reduce morbidity and mortality
440
80
10. Information of relative risk of HIV can reduce teenage pregnancy
434
86
15. Introducing small charges for cost-effective products increases campaign effect
424
96
16. Change default option to opt-out to increase take up of testing and screening
421
99
18. Paying individuals not to engage in risky behaviour can reduce sexual risk taking
421
99
19. Incentives provided over the long-term can change behaviour
421
99
20. Messages to promote social norms can reduce risky sexual behaviour
421
99
Source: Data are from a survey conducted by authors on behavioural economics and health policy in low- and middle-income countries.
a
Could also be categorized under lifestyle and prevention behaviours.
b
Could also be categorized under patient compliance.
c
Numbers on the left indicate the order in which questions were asked and allow for ease of cross-reference across tables.
physician-prescribed diet) but faces ‘costs’ such as becoming sicker
in the long-run. In these circumstances, programmes that provide
small but tangible rewards and positive feedback in the immediate
or short run may help individuals to change their behaviour
more effectively than incentive programmes that incorporate future
rewards.
Programmes with small and frequent rewards can help
individuals overcome present costs that they tend to value
more highly.
Thus each policy vignette contained three parts: (1) a
statement about a public health problem; (2) a statement
about a behavioural economic principle that relates to this
public health problem and (3) a statement written in bold
italics about how behavioural economic principles could be
applied in a practical way to address the public health
problem2. For the purposes of this survey, respondents were
asked to accept the first two statements of each vignette in the
first paragraph as facts and only react to the policy statement in
bold italics by choosing the most appropriate answer from the
available options. Responses were presented in a five-level
Likert scale with options ‘strongly agree’, ‘agree’, ‘not sure’,
‘disagree’ and ‘strongly disagree’.
To explore the level of consensus among policymakers on
recommendations from behavioural economics principles, for
the analysis, we classified responses into three distinct groups.
First, we labelled as ‘strong’ consensus those questions where
65% or more of the respondents are on one side of the issue.
This implies that respondents strongly agreed or agreed, or
strongly disagreed or disagreed. Second, we identified as areas
with ‘moderate’ consensus those questions where 50–64% of
the respondents either strongly agree or agree or strongly
disagree or disagree. Lastly, we classified as areas with ‘low’
consensus those questions where less than half of the
respondents are on one side of the issue. Our approach is
similar to that used by Morrisey and Cawley (2008).
Furthermore, to understand the sources of disagreement, each
time a respondent disagreed or strongly disagreed with the
policy relevance of the statement, we asked them to identify the
reason for disagreement. When disagreement over a policy
option is over positive grounds (efficiency criteria), it is possible
to expect that further evidence may help to resolve the debate.
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HEALTH POLICY AND PLANNING
Not familiar
Somewhat familiar
Very Familiar
Vignettes that elicited strong consensus
20%
Hyperbolic discounting
35%
44%
4%
Social bias
31%
65%
12%
Confirmation bias
39%
49%
7%
Reference bias
38%
55%
15%
Loss Aversion
48%
38%
Figure 1 Respondents’ familiarity with behavioural economics concepts.
Note: Data are from a survey conducted by authors on behavioural
economics and health policy in low- and middle-income countries.
If the disagreement is over the trade-offs between different
value domains (e.g. equity, ethics), the policy’s future acceptability may depend on finding common ground or political
compromise irrespective of further research (Robert and
Zeckhauser 2011). Respondents were thus offered a menu of
seven possible reasons for disagreement: equity (would affect
the poor or sick differently than the wealthy or well, for
example); efficiency (same results could be achieved with other
interventions at less or same cost, for example); feasibility
related to organizational/institutional capacity; fiscal constraints
(intervention too expensive or unaffordable); ethics (unethical,
affects intrinsic motivations); lack of political will (policymakers would not adopt) and ineffective (would not be a
feasible incentive).
For the policy recommendations with moderate or low
consensus, we investigated these reasons behind the results.
In the last section of the results, using standard regression
analysis, we also investigate the individual characteristics that
increase the probability of disagreeing with policies. We restrict
this analysis to those policies where we found modest or low
consensus among respondents. This step allows us to investigate if the policy preferences of the respondents differ by age,
gender, type of education and training, level of knowledge and
geographic focus of their work.
Results and discussion
Familiarity with behavioural economics principles
Figure 1 shows respondents’ familiarity with key conceptual
principles in behavioural economics. Overall, an average of 50%
of respondents were ‘very familiar’ with any of the five
behavioural economics principles. Respondents reported being
most familiar with the concept of social bias (65% said they
were ‘very familiar’). They were least familiar with concepts of
present bias such as loss aversion and hyperbolic discounting:
only 38% and 44% respondents claimed that they were ‘very
familiar’ with loss aversion and hyperbolic discounting,
respectively.
Table 3 summarizes the vignettes with strong consensus for
policy prescriptions from behavioural economics. In 11 out of
20 questions, respondents show strong consensus in their
position. In general, respondents are in favour of more ‘benign’,
non-intrusive policies such as information campaigns. The most
favoured policy (85% agreed or strongly agreed) was one that
promotes social norms related to sexual behaviour such as
condom use by connecting with customers’ social bias (for
example, with messages that relate to their group identities and
refer to the behaviour of ‘growing numbers of people’ or ‘most
people’). The second and third most favoured (84% and 82%
agreed or strongly agreed) were policies that provide small and
immediate rewards to overcome present costs for healthier
behaviours, and that provide periodic reminders (via, for
example, periodic text messages to ‘nudge’ patients to take
medicines, refill prescriptions, and check vital signs) to enable
habit formation. The fourth most favoured policy involved
providing financial bonuses to health facilities for improving
quality of care, and the fifth most favoured policy was that
which makes screening and health testing the default option
(77% and 76% respectively agreed or strongly agreed,
respectively).
Among this group of policies less consensus exists regarding
the relevance of addressing self-control during childhood to
improve future health outcomes. This is despite recent empirical
evidence linking early self-control and future outcomes in life
(Heckman and Kautz 2012). Furthermore, respondents in our
sample are also somewhat less inclined to support incentive
schemes based on rewards rather than penalties that aim to
reduce bad behaviours such as smoking.
Vignettes that elicited moderate and low consensus
Table 4A and B present results related to areas of moderate and
low consensus as defined by 50–64% of respondents strongly
agreeing or disagreeing with the survey question. There is
modest agreement for seven of the survey questions. Across
these seven questions, agreement ranges from 57% to 62%. The
health policy implications with stronger agreement within this
group applies incentives over the long run—involving, for
example, a programme that redeemed a long-term bond for
daughters who were unmarried on their 18th birthday (Sinha
and Yoong 2007), and changing social norms, for instance, by
asking high-ranking officials to publically receive screening or
testing for chronic conditions. In both cases, around 62% of the
respondents strongly agree or agree with the policy implications
from behavioural economics. Respondents perceive as less
effective and seem to disagree more with the use of commitment devices to change time-inconsistent behaviour. In this
case, 42% of the respondents are not sure or disagree with a
policy statement that involves smokers losing an initial deposit
if they are found to continue smoking after 6 months (Giné
et al. 2010).
Other areas of moderate or low consensus are related to
health policy areas that are the subject of extensive research in
behavioural economics. As shown in Table 4A, 43% of the
respondents are not sure or disagree with charging small fees
for cost-effective products. Charging for cost-effective interventions (e.g. condoms and mosquito nets) has traditionally been
VIEWS ON BEHAVIOURAL ECONOMICS IN HEALTH SYSTEMS
753
Table 3 Areas of health policy based on behavioural economics with strong consensus (percentage)
Policy questions
Strongly
agree
Agree
Not sure
Disagree
Strongly
disagree
20. Messages to promote social norms can reduce risky sexual behaviour
19
66
12
3
0
5. Programmes with small and frequent rewards can help overcome present costs
27
57
13
3
0
17. Periodic reminders can increase habit formation
25
57
12
5
1
13. Bonuses to health facilities for achieving goals can improve quality of care
25
52
14
8
1
16. Change default option to opt-out to increase take up of testing and screening
30
46
16
7
1
9. Cash rewards to patients for bringing family members encourages utilization
20
55
17
7
1
8. Safety devices to prevent drunk driving can reduce morbidity and mortality
24
50
16
8
2
2. Bundle organizational and financing incentives in pay-for-performance
25
45
18
11
1
3. Programmes to develop self-control in children have adult health benefits
25
45
23
5
2
7. Rewards for good behaviour are more effective than penalties
24
43
18
13
2
1. Supply of informal caregivers responsiveness to social recognition
16
49
20
15
0
Source: Data are from a survey conducted by authors on behavioural economics and health policy in low- and middle-income countries.
Table 4 Areas of health policy based on behavioural economics with modest and low consensus (percentage)
Policy questions
Strongly
agree
Agree
Not sure
Disagree
Strongly
disagree
A. Modest consensus
19. Incentives provided over the long-term can change behaviour
17
45
22
11
5
4. Changing social norms can encourage chronic diseases prevention
19
42
22
16
1
10. Information of relative risk of HIV can reduce teenage pregnancy
15
46
22
16
1
11. Reducing health workers’ pay based on absent days can discourage absenteeism
20
39
17
20
4
14. Public display of good performers can reduce health worker absenteeism
14
45
23
14
4
6. Commitment devices can change behaviour
14
44
27
14
1
15. Introducing small charges for cost-effective products increases campaign effect
19
38
21
15
7
B. Low consensus
12. Cash rewards are more effective with uncertainty in timing and amount
8
36
36
15
5
18. Paying individuals not to engage in risky behaviour can reduce sexual risk taking
7
33
28
25
7
Source: Data are from a survey conducted by authors on behavioural economics and health policy in low- and middle-income countries.
justified on the grounds that fees might promote better use of
the goods or fees might promote the development of local
entrepreneurs. On the contrary, based on insights from behavioural economics (Shampanier et al. 2007) and from some
randomized interventions, Cohen and Dupas (2010) provide
empirical evidence in favour of free distribution of mosquito
nets for malaria prevention. Our respondents’ views are in line
with this latter strand of behavioural literature.
Furthermore, the idea of introducing uncertainty in the
rewards schemes to promote changes in individual behaviour
has been argued in the literature by Loewenstein et al. (2007).
This policy recommendation also shows low levels of support
among the respondents. As indicated in Table 4B, 56% of
respondents are not sure or disagree with this policy position.
Lastly, using monetary rewards to reduce risky sexual
behaviour has been justified by behavioural economics because
such rewards can alter present costs and enable individuals to
better link present and future actions. However, this policy
recommendation has low consensus among our respondents
(60% are not sure or disagree) in spite of a number of recent
studies and reviews of the effectiveness of these schemes (de
Walque et al. 2012; Pettifor et al. 2012).
Reasons for disagreement with applications of
behavioural economics to health policy
As noted earlier, each time a respondent disagreed or strongly
disagreed with a survey question, they were asked to indicate
whether the disagreement was related to concerns over equity,
efficiency, (lack of) feasibility related to organizational/institutional capacity, fiscal constraints, ethics, lack of political will or
ineffectiveness. Table 5A and B present the findings on the
reasons for respondents’ disagreement among those policies
where there is moderate to low consensus.
Among these nine policy statements, most of the reasons for
disagreement rely on value tradeoffs related to equity and
ethics (see Table 5A). For example, 49% of respondents who
disagree with a programme to pay parents if their daughter is
unmarried on her 18th birthday and 45% of individuals who
754
HEALTH POLICY AND PLANNING
Table 5 Reasons for modest and low consensus with areas of health policy based on behavioural economics (percentage)
Policy questions
Efficiency
Equity
Ethics
Organizational
feasibility
Fiscal
constrains
Lack of
political will
Ineffective
A. Modest consensus
19. Incentives provided over the long-term can change behaviour
3
15
32
12
0
6
32
4. Changing social norms can encourage chronic diseases prevention
3
9
3
4
3
11
67
10. Information of relative risk of HIV can reduce teenage pregnancy
10
4
18
4
0
4
60
11. Reducing health workers’ pay based on absent days can
discourage absenteeism
6
6
12
21
0
10
45
14. Public display of good performers can reduce health worker
absenteeism
8
1
43
13
4
1
30
6. Commitment devices can change behaviour
3
4
9
9
7
3
65
15. Introducing small charges for cost-effective products increases
campaign effect
7
33
12
3
17
2
26
12
3
14
7
7
1
44
5
4
27
10
6
3
44
B. Low consensus
12. Cash rewards are more effective with uncertainty in timing
and amount
18. Paying individuals not to engage in risky behaviour can reduce
sexual risk taking
Source: Data are from a survey conducted by authors on behavioural economics and health policy in low- and middle-income countries.
disagree with charging small amounts for cost-effective interventions hold these positions on grounds of equity or ethics.
As per Table 5B, the disagreements over using cash rewards
with an element of uncertainty, or paying individuals for good
behaviour relies less on equity and ethical concerns (17% and
31%, respectively) than on perceived ineffectiveness. In both
cases, 44% of respondents choose lack of effectiveness as an
argument for disagreement. This may reflect respondents’
perception that such nudges are simply not feasible, or that
they are wary about introducing concepts such as uncertainty
into rewards. It may also reflect their reservations about the
effectiveness of such nudging experiments to change behaviour
for larger populations (Marteau et al. 2011). This is despite
strong evidence—and growing use—in favour of performance
incentives in general (Eichler et al. 2009; Pettifor et al. 2012).
It is important to highlight that small percentages of
respondents perceive positive issues such as efficiency as a
main reason to oppose the policy prescriptions. Lastly, organizational feasibility and fiscal constraints are generally not
perceived as main obstacles in most of these options. Yet, in the
case of reducing health-worker pay for absent days, 21% of the
respondents cited organizational feasibility as a source of
disagreement.
Understanding disagreement over policy options
We explore which individual characteristics of the respondents
increases the probability of disagreeing with a specific policy
prescription emanating from behavioural economics principles
in Table 6. We restrict this analysis to those policies where we
found moderate or low consensus because our focus is to
provide information to help build consensus around policy
prescriptions. For instance, it is possible that individuals with
training in health policy and social sciences differ in their policy
preferences compared with individuals with a medical background. Alternatively, one may expect that individuals with
more familiarity with behavioural economics principles may
respond differently to our vignettes than individuals with low
familiarity. Likewise, individuals working for a government
organization may have policy preferences with little overlap
with the preferences reported by individuals working in the
private sector. Lastly, policy preferences may differ by age,
gender and geographic location of the work of the respondents.
To assess the relevance of these factors, we run standard
ordinary least square (OLS) regression models for each policy
prescription where we found modest or low consensus (in
Table 4). The dependent variable is a newly created dummy
indicator (labeled 0 and 1) to capture the individual’s disagreement with the policy. Responses strongly disagree, disagree or
not sure were collapsed to represent disagreement (labeled
one). And the reference category represents those individuals
who either strongly agree or agree with the policy prescription
(labeled zero). The set of explanatory variables includes age,
gender, educational background and training, employer organization, level of knowledge of behavioural economics and
geographic location where work of respondent focuses.
To analyse occupational characteristics of respondents, we
created a dummy category for those individuals working in
government positions and another for those individuals working in non-governmental organizations; therefore, the reference
category for this dummy indicator collapses the rest of the
occupations included in the survey (see Table 1 for a complete
list of categories). For familiarity with behavioural economics
principles, we computed a new continuous indicator that added
the responses in each question (very familiar ¼ 3; somewhat
familiar ¼ 2 and not familiar with the concept ¼ 1). Therefore,
the total score for this variable ranges from 5 up to 15. Lastly,
the geographic locations are USA (reference category), East
Asia and the Pacific, Eastern Europe and Central Asia, Latin
America and the Caribbean, Middle East and North Africa,
South Asia and Sub-Saharan Africa.
Overall, the linear probability models fit the data relatively
well. As shown in Table 6, with the exception of Vignette 4, the
set of covariates included explains between 10% and 25% of the
observed variability in the dependent variable. In most cases,
(0.140)
0.06
2.25
0.123
5.06
421
R square
F-test
N
SE
(0.184)
(0.008)
(0.054)
(0.082)
(0.062)
(0.053)
(0.048)
(0.018)
434
4.73
0.116
0.179
0.01
0.053
0.002
0.025
0.021
0.032
0.029
Coeff.
SE
(0.175)
(0.008)
(0.051)
(0.078)
(0.059)
(0.049)
(0.045)
(0.017)
Coeff.
431
4.49
0.111
0.117
0.004
0.031
(0.177)
(0.008)
(0.052)
(0.078)
(0.059)
0.008
(0.051)
0.113
(0.046)
(0.016)
SE
0.102
0.027
0.001
424
6.31
0.148
0.561
0.007
0.029
0.081
0.105
0.086
0.013
0.021
Coeff.
(0.173)
(0.008)
(0.051)
(0.076)
(0.058)
(0.049)
(0.045)
(0.017)
SE
446
3.92
0.098
0.423
0.011
0.044
0.126
0.072
0.018
0.056
0.038
Coeff.
SE
(0.182)
(0.009)
(0.053)
(0.081)
(0.061)
(0.052)
(0.047)
(0.017)
424
5.33
0.129
0.306
0.003
0.052
0.121
0.093
0.033
0.074
0.017
Coeff.
(0.177)
(0.009)
(0.051)
(0.079)
(0.059)
(0.051)
(0.046)
(0.017)
SE
SE
428
9.42
0.207
0.302
0.009
(0.178)
(0.009)
(0.078)
(0.052)
0.121
(0.059)
(0.050)
(0.046)
(0.017)
0.005
0.105
0.076
0.065
0.042
Coeff.
421
11.42
0.241
0.419
0.002
0.02
0.048
0.047
0.068
0.014
0.013
Coeff.
(0.173)
(0.008)
(0.051)
(0.077)
(0.058)
(0.049)
(0.045)
(0.017)
SE
Q18
Paying individuals
not to engage in
risky behaviour
can reduce sexual
risk taking
Q12
Cash rewards are
more effective
with uncertainty
in timing and
amount
Note: (1) Coefficients in bold are significant at P < 0.10. (2) All models include dummies for region where the respondent works. (3) All models include a dummy variable for missing values in age. Age was imputed
at the average value. (4) The reference group in Employer Organization included the rest of the categories. (5) Knowledge is a continuous variable which adds the score of the five questions included to assess
respondent’s familiarity with behavioural economics concept (mean ¼ 11.9; SD ¼ 2.4; min value ¼ 5; max ¼ 15). Coeff, coefficient; SE, standard of the error.
Source: Data are from a survey conducted by authors on behavioural economics and health policy in low- and middle-income countries.
458
0.234
0.298 (0.171)
Constant
0.069
0.092
0.049
0.009
(0.050)
(0.080)
(0.057)
(0.050)
(0.008)
0.01
0.014
Knowledge
0.015
Government
Non-governmental organization
0.058
0.04
Employer organization (Ref ¼ other)
Training in social sciences
Training in health policy
0.041
0.034
0.007
Coeff.
Q15
Introducing small
charges for
cost-effective
products increases
campaign effect
Q6
Commitment devices
can change
behaviour
Q14
Public display of
good performers
can reduce
health worker
absenteeism
Q11
Reducing health
workers’ pay
based on absent
days can
discourage
absenteeism
Q10
Information of
relative risk
of HIV can reduce
teenage pregnancy
Q4
Changing social
norms can
encourage chronic
diseases
prevention
Educational background and training (Ref ¼ medicine)
0.035
Male
(0.016)
SE
Variables
0.002
Coeff.
Vignette
Age
Q19
Incentives over
the long-term
can change
behaviour
Question number:
Table 6 OLS estimates of the probability of disagreement with a specific policy (1 ¼ strong disagreement, disagreement or not sure; 0 ¼ strongly agree or agree): policies with modest or low
consensus OLS estimates of the probability of disagreement with a specific policy: for policies with modest or low consensus (1 ¼ strong disagreement, disagreement or not sure; 0= strongly agree or
agree)
VIEWS ON BEHAVIOURAL ECONOMICS IN HEALTH SYSTEMS
755
756
HEALTH POLICY AND PLANNING
we were able to reject the null hypothesis that all covariates are
different from zero at the 10% significance level.
For all policies considered, older people are more likely to
disagree than younger people (though results are not always
significant). The results are statistically significant at the 10%
level for the vignettes about how information on the relative
risk of HIV can reduce teenage pregnancy (Q10) and regarding
the usefulness of commitment devices (Q6) or uncertainty
imbedded in cash rewards (Q12).
For policies with modest or low support, males tend to have
lower probability of disagreement. However, these results are
not statistically significant. For the most part, the respondent’s
level of education and training does not influence their position
on the proposed policy. Interestingly, respondents with health
policy or social science training are more likely to disagree than
fellow respondents with a background in medicine about the
use of monetary incentives to reduce absenteeism (Q11) and
the display of workers’ performance to reduce absenteeism
(Q14). The reported effects are important in size when one
compares them to the size of other dummies included in the
model.
Employer organization seems to be irrelevant to explain policy
preferences in our sample. Lastly, in all cases, higher level of
familiarity with behavioural economics principles is associated
with a reduced probability of disagreeing with a specific policy
vignette but these results are not significant.
Conclusion
The results reveal an appetite among respondents to use
insights from behavioural economics in the design and implementation of health policies. The policy prescriptions where
there is strong consensus are in the areas of framing choices to
overcome present bias, providing further periodic information
to form habits and messaging to tap into social norms.
However, in spite of the extensive research and practice on
the use of financial incentives to change both household and
provider behaviour, the results suggest that there is less
appetite among respondents to use incentives in the form of
cash rewards as extrinsic motivators to change individual
behaviour related to the prevention and management of chronic
conditions or for the mitigation of risky sexual behaviour.
Respondents seem skeptical about how effective cash rewards
are to promote habit formation or to develop enough internal
motivation to sustain changes in individual behaviour once the
incentives are removed. The results suggest that future work
focusing instead on non-financial incentives to modify behaviour may be more easily adopted and tested.
The sources of disagreement on policy recommendations do
not arise over positive, empirical issues. Instead, most of the
disagreement is captured by normative values such as equity
and ethics. Further, a large proportion of respondents who do
not support the proposed policies based on behavioural
economic principles also perceive these recommendations as
likely to be ineffective in practice. This result suggests that
gaining support for policy prescriptions from behavioural
economics might require greater discussion of the ethical and
equity dimensions of these policies, greater dissemination of
existing empirical research findings from a variety of settings,
more discussion of the political economy of enacting and
implementing behavioural economics policies and better documentation and analysis of the unintended consequences
of these policies on equity and ethics issues. For example,
common policymaker objections to the use of financial incentives for providers relate to dampening of intrinsic motivations
to act professionally or in the best interest of patients, or beliefs
that private gains from pro-healthy behaviour for consumers
should be sufficient to motivate behaviour change.
Support for policy prescription with only modest and low
consensus seems to differ according to the personal characteristics of individuals who responded to the survey. We found
that individuals with education or training in health policy and
social sciences tend to provide less support than respondents
with medical backgrounds for policies oriented to increase
health workers’ productivity. Older individuals are slightly more
likely to disagree with these vignettes (for three of nine policies
with low to moderate consensus this association is statistically
significant). This could be because older individuals have less
experience with these policies than the younger generation of
policy analysts. Similarly, level of familiarity with behavioural
economics tends to reduce disagreement towards policy
(though this association is not statistically significant). Taken
together, these results indicate individual heterogeneity towards
policies derived from behavioural economics principles.
It is important to highlight that alternatively, one may be
interested in exploring why a group supports a specific policy
action. This information may be helpful to build stronger and
sustainable political alliances. In our approach, we focus on
areas of disagreement because understanding the root of
disagreement may help policymakers reach consensus. Future
research could be devoted to explore reasons for agreement
among policymakers.
We recommend caution in generalizing these findings to all
policymakers and practitioners in health policy given that
responses were confined to the CGD subscribers who chose to
complete our survey. Yet our survey is unique in reaching out to
practitioners and it is not confined to expert-opinion (Gordon
and Dahl 2013). Further research is needed to understand—
perhaps qualitatively—the possible reasons behind respondents’
lack of support for particular policy prescriptions from experts
in behavioural economics. The finding that certain policy
prescriptions from behavioural economics are thought to be
ineffective in improving population health could imply that
respondents feel more comfortable using more traditional
(often supply side) approaches to address fundamental
market failures in the health sector, for example, via subsidies
and taxes. They may concur that ‘behavioral economics is being
used as a political expedient, allowing policymakers to avoid
painful but more effective solutions rooted in traditional
economics’ (Loewenstein and Ubel 2010). Alternatively, respondents may perceive that these prescriptions would suffer
higher bureaucratic limitations and value problems than other
options.
Certainly, while the vignettes seem most appropriate in
enabling comparability of responses for the present survey
(King et al. 2004), some of the respondents’ answers may be
driven by the context of the vignettes rather than by a general
preference towards these policies. For example, respondents
VIEWS ON BEHAVIOURAL ECONOMICS IN HEALTH SYSTEMS
may perceive as ineffective the use of cash rewards to change
the behaviours of individuals with chronic conditions, but they
may be more inclined to support their use to improve maternal
and child health outcomes.
Behavioural economists have made great strides to persuade
economists and non-economists alike that there are sufficient
anomalies in the traditional rational decision-making framework to question its validity in understanding individual
behaviour. Yet, little is known about policy implications in
the field. The results of our survey suggest that respondents are
open to incorporating some of these policy prescriptions in their
tool kit. There are still some reservations about their effectiveness to ameliorate problems in the health sectors of low- and
middle-income countries. Future research should devote effort
in translating the knowledge from experimental interventions
into a broader understanding of the political and policy
implications of behavioural economics.
Supplementary data
Supplementary data are available at HEAPOL online.
757
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Funding
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Endnote
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2
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