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Filling out a medical form

Have you ever missed an important appointment because you were too busy and forgot it?

Have you ever given up on filling out a public form correctly because it was too long  and difficult to understand?

Have you ever driven above the speed limit because all the other drivers were speeding too?

These are everyday examples of how context and behavioral biases can influence decision making.

A better understanding of human behavior can lead to better policies. For this you should consider what actually drives the decisions and behaviors of citizens rather than relying on assumptions about how they should act.

Behavioral Insight (BI) can help you in this task.

Drawing on rigorous research from economics and behavioral sciences, BI can help public agencies to understand why citizens behave the way they do and test in advance which policy solutions will be most effective and then implement them on a large scale.

By integrating BI into your decision-making process, you can better anticipate the behavior, consequences of your policy, and ultimately deliver more effective policies that improve citizens' well-being.

An example 

Have you ever picked a candy bar as a snack only to regret it later? 

Have you ever filled your whole plate with pasta even if you only wanted to have a small portion? 

Have you ever decided on a side of fries instead of a salad?

It's easy to make unhealthy choices even when you're choosing to be healthy.  Today, more than one in two adults is overweight or obese in OECD countries (OECD, 2017b). Worldwide, obesity-related diseases are estimated to cost $1.2 trillion by 2025 (WOF, 2015). 

Why do people make such choices? 

As a policy maker, you can use BI to address "serious issues" like obesity to design and deliver better policy outcomes.

To integrate BI into your daily work, you can use the template  to:

to.  analyze behaviors

b.  conduct an analysis

c.  develop strategies

d.  test them with interventions and improve outcomes for policy change. 

For example, if your desired policy outcome is to lower adult obesity rates, you can start by selecting a specific and relevant behavior, e.g.  the amount of healthy items ordered from restaurant menus .

§  Behavior

Suppose you learn that 60% of your city's residents often plan to choose healthy options but end up choosing hamburgers. 

§  Analysis

Start by writing your hypotheses as to why this happens:

-  information: citizens don't know exactly how much calories hamburgers have.

-  cost: Citizens believe that hamburgers are cheaper than healthier foods.

-  Access: Citizens cannot easily access restaurants serving healthy options, they are too expensive.

At first, you can consider traditional policy tools.

§  Traditional strategy

  • require calorie labeling on restaurant menus

  • put a tax on junk food, on hamburgers, to make them less convenient

  • provide a tax credit to restaurants that provide healthy alternatives

In theory, better information, cost or access should lead to healthier eating habits. This is in line with classical economic theory which assumes that individuals will choose the rational decision that maximizes their utility. People use information to make better decisions, so you can assume that the more information they have about how unhealthy burgers are, the more likely they are to choose a healthier option that will benefit them in the long run.

Unfortunately, we know, also from our own personal experience, that this is not always the case. 

By understanding how people actually behave in different situations, you can predict in advance  the behavioral consequences of your policy and as a result, design policies that can help citizens make a better choice; in this case healthier. 

§  Strategy (BI)

  • require calorie labeling on restaurant menus by prefixing calorie counts to food because people place disproportionate weight on the first piece of information they see. Dallas (2019) found that the first view of calories resulted in a 16.31% decrease in calories

  • enforce a junk food tax, and have increased burger prices clearly indicated on the menu because a price difference is more salient to decision making. Loss aversion. Chetty (2009) found that prices with evidence of the surcharge  reduced demand by 8%

  • provide a tax credit to restaurants that provide only healthy options. The addition of healthy foods alongside hamburgers indirectly increases indulgent eating habits with the opposite effect to the intended goal. Wilcox (2009) found that adding a healthy alternative increased unhealthy orders by 230%.

§  Intervention and  change

At this point, you can choose which solutions are the most appropriate in your context and you can test which are the most effective for increasing the quantity of healthy products ordered from restaurant menus. Through testing, you will obtain evidence-based results before setting policy to lower adult obesity rates and subsequent  large-scale implementation.

This approach is not only limited to healthy eating. 

By integrating BI from the start of the policy cycle, decision makers can design behavior-informed policies around a variety of issues that align with how people actually behave and improve outcomes as a result without compromising people's autonomy. 

This guide helps you get started by breaking down a policy issue into its behavioral components and identifying potential behavioral barriers that can undermine the intended policy outcome or  suitable_factors  that can improve the effectiveness of the policy.

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Source: BASIC – the Behavioral Insights Toolkit and Ethical Guidelines for Policy Makers

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