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Global Health Policy Blog


In the United Kingdom and elsewhere in high-income countries, behavioral economics informed policies are all the rage. The argument justifying behavioral approaches is well-known: traditional economics assumes that humans are rational and will make decisions that maximize their well-being, whereas in fact, many people still make decisions with harmful, self-defeating consequences. In the health sphere, for example, many people still do not use preventive health products, engage in risky behaviors, or fail to take their medicines regularly.  Limited self-control, aversion to active choices, inattention and mental models are all phenomena that may go some way to explaining and reversing these perverse behaviors, yet most global health programs ignore the implications of new behavioral models for policy design.

Last week, CGD published a policy paper by Saugato Datta and Sendhil Mullainathan that might help.   The paper distills the major insights of behavioral economics and discusses how a policy-maker could actually use insights to design better policy.  The authors write:

 “Behavioral economics affects program design in three steps. First, it changes how we diagnose problems. For example, when we see parents failing to vaccinate their child we may be tempted to conclude that they do not understand the value of vaccination. Behavioral economics forces us to consider another possibility: they want to vaccinate, they understand the benefits, but they don’t get around to doing it. Vaccination may be one of many behaviors, such as savings or going to the gym, where what we do fails to match up with what we want to do. Secondly, it changes how we design solutions to problems. In some cases it may suggest that something as simple as a reminder can have an unreasonable impact on behavior. In others it may suggest a different way to offset our tendency to plan our spending poorly. Finally, it changes how we define the scope of the problem. Problems we overlooked may suddenly become interesting ones to solve. We often focus on access (“Make sure people get the drugs they need at low cost”). Behavioral economics suggests important problems that remain even after access is solved (“Make sure that people actually take the drugs they are given”).

Datta and Mullainathan’s framework begins with defining the behavior-related problem that is limiting policy impact (see figure above). Once the problem and actionable bottlenecks have been diagnosed, an intervention to overcome these limitations can be designed. The authors suggest seven principles to guide behaviorally-informed policy or intervention design: facilitating self-control via commitment devices, reducing the need for self-control, reducing the need to make active choices, using micro-incentives, reducing inattention, maximizing the impact of messaging, and framing messages to match mental models. Once designed, the intervention(s) can be rigorously evaluated.

To run such a process, Datta and Mullainathan argue that “deep-seated changes in the way we go about applying behavioral insights to development” are needed.  Academics and behavioral experts will need to move away from “boutique pilots” and research-driven projects towards a “focus on existing programs or projects that seek to address big development problems, but whose effectiveness is constrained by behaviors.”  This will mean “being willing to evaluate an intervention that may not necessarily isolate the causal effect of a single psychology or pathway, but of a suite of linked design innovations.” Governments and donors will need to be “open to involving behavioral experts when programs are first designed as well as to experimenting on existing programs.”

In the coming months, we will publish more on how behavioral design approaches can enhance global health and development outcomes. Stay tuned.

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CGD blog posts reflect the views of the authors, drawing on prior research and experience in their areas of expertise. CGD is a nonpartisan, independent organization and does not take institutional positions.