Dan Poniachik presents an initial road map to imbed behavioral design in policy-making, providing examples extracted from the United States’ Social and Behavioral Sciences Team in 2015, as well as some potential apprehensions for further debate.
During the last century, society has experienced an acute decrease in several indicators of trust levels. Nobel Memorial Prize winner Kenneth Arrow (1974) called trust “a lubricant for social systems” and Francis Fukuyama (1995) argued vehemently that modern prosperous countries tend to be those where business relations between people can be conducted flexibly on the basis of trust.
According to the General Social Survey from 2018, the percentage of respondents in the US mentioning that most people can be trusted went from forty-eight percent in 1972 to thirty-one percent in 2016 and the UK shows a similar decline. In the 1950s, sixty percent of the public said that most strangers can be trusted (Wright 2015), but in 2013 this same figure was thirty-eight percent (Centre for Social Investigation 2015).
As might be expected, trust in institutions has also eroded. The percentage of the population in OECD countries reporting confidence in the national government went from forty-four percent in 2006 to thirty-eight percent in 2014 (OECD 2015). However, some countries show a higher decline. France, the US, and Australia all show declines close to ten percentage points, whereas developing countries such as Mexico and Chile show much higher declines.
Embedding behavioral science as a new approach to help restore trust
One possible answer to regaining legitimacy and strengthening the government’s relationship with citizens is better understanding these institutions’ purpose. Luckily, advances in technology and data science and a deeper knowledge of decision-making processes can provide some useful insights.
Until very recently, many of our policies were built upon assumptions of human behavior that did not hold true on the ground. The UK’s Behavioral Insight Team (BIT) created in 2010 was one of the first offices around the world to integrate these principles into policy design. In the documentary “Dis(Honesty): The Truth About Lies” (2015), BIT’s Director David Halpern said, “We’ve been kind of stumbling in the dark with naïve models of human behavior built into policy. Imagine what we can do if we put even a half decent model of how people behave into what we do and how we design our economies and societies.”
As the awareness of our flaws when designing policies became ever more clear, other countries followed suit with their own versions of the BIT, though with a more limited scope. In September 2015, former US President Barack Obama created the Social and Behavioral Sciences Team (SBST) through Executive Order 13707. This executive order included key advice for integrating behavioral design into public policies such as:
- Small barriers to program access can have a large impact on outcomes: Behavioral insights have shown that people who do not sign up for programs that benefit them may not be due to a lack of interest. Instead, low take-up may result from barriers to program access that deter eligible people from participating (Bertrand, Mullainathan & Shafir 2006). These barriers, for example, might be travel or time costs or complex enrollment requirements.
- Individuals understand and respond to information depending on its presentation: Often it is thought that people respond to information if every possible detail is offered, which would allow a rational evaluation by the individual. Behavioral insights, on the other hand, show that how the information is presented is also very relevant. This suggests that governments should present information in a manner that effectively promotes the use of that information to the intended audience.
- Excessive choices offered can lead individuals to choose inconsistently: Academic literature shows that individuals can have difficulty choosing, and choosing consistently, when choices involve numerous alternatives, vary along complex dimension, involve assessments of risk, or have consequences in the long-term future. For example, Miller & Krosnick (1998) found that individuals are more likely to choose the first item from a list or the first option they consider.
- Individuals also respond to non-financial incentives: There are design components of programs other than prices, taxes, or subsidies that can be used to encourage or discourage particular behaviors. For example, in many contexts, individuals are motivated by social comparisons, such as learning about the behavior of their peers. One study found that individuals reduced their residential energy consumption when provided with information on how their consumption compared with that of their neighbors (Allcott 2011).
Designing for humans: What exactly is the behavioral sciences approach?
According to Tantia (2017), the design process centered on people and based on behavioral sciences has the following stages:
- Definition. The first step is to carefully define the problem, avoiding biases, previous assumptions, and definitions of problems that fit already designed solutions. Ideally, the problem should be defined according to the modifiable behavior. For example, how to encourage people to consume less energy at home.
- Diagnosis. This stage seeks to understand and generate a hypothesis to determine why the previously defined problem arose. Proper diagnosis requires a minute revision of academic literature that includes the most recent findings of behavioral sciences as well as qualitative interviews and quantitative data research.
- Design. Once the behavioral barriers have been filtered and prioritized, it is time to generate ideas, bearing in mind the behavioral bottlenecks that each design aims to solve.
- Test. If possible, a randomized controlled trial (RCT) should be implemented to compare the results from groups randomly selected. While it is possible to conduct streamlined RCTs, it is necessary to maintain scientific standards of quality. There are also other innovative ways to test solutions, such as A/B testing.
- Scalability. Finally, if all of the results are positive, scaling up can be considered. The public sector is particularly suited to scale up solutions that can positively impact millions of citizens’ lives.
Apprehensions about the integration of behavioral sciences into the formulation of public policies
There are legitimate reasons why citizens and public policy formulators could be reluctant to integrate behavioral sciences into the design of public policies.
First, there are emotionally-based motives. If the approach seems paternalistic, it will not be welcomed, and some citizens might feel that their range of freedom is reduced through this approach.
Second, nudges might have a detrimental effect on the subjective well-being of individuals. Often, behaviors such as high caloric food consumption and alcohol intake are part of people’s preferences, so policies that aim to reduce their consumption could have a negative impact on people’s well-being. As a result, these policies might be seen as paternalistic and will require more justification.
Addressing the decline in trust indicators – either towards fellow citizens or governmental institutions – is critical if we want to pursue a shared path of sustainable development. One approach to tackle this issue is through re-thinking the way public policies are designed and taking advantage of the increasingly popular advancements of new technologies and of behavioral science.
Integrating behavioral design is not a panacea. But it is bringing attention to our flaws and biases, reminding us that we are designing for (and that, in fact, designers are also) homo sapiens and not homo economicus, and holding already tremendous and yet unseen potential on policy-making.
Appendix: Data on decrease of trust in national governments.
Table A: Confidence in national governments (2007-2016, percentage change), selected countries.
Percentage of people who answered “yes” to the question, “Do you have confidence in national government?”
|Country||Confidence in 2016||Confidence 2007||Percentage points change since 2007|
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