Successful development programs rely on people to behave and choose in certain ways, and behavioural economics helps us understand why people behave and choose as they do. Approaching problems in development using behavioural economics thus leads to better diagnosis and to better-designed solutions.
This paper sketches how to use behavioural insights to design development programs. It distils key insights of behavioural economics into a simple framework about the constraints under which people make decisions. It then shows how this framework leads to a set of behavioural design principles whose use can improve the reach and effectiveness of many development programs.
Key findings:
- First, efforts to apply behavioural economics insights have to be built around the objective of achieving impact at scale. This means moving away from a focus on relatively narrowly conceived research projects and ‘boutique’ pilots that aim to pin down a specific behavioural insight or insights and towards a focus on existing programs or projects that seek to address big development problems, but whose effectiveness is constrained by behaviours.
- Second, innovation has to be embedded into the process of intervention design from the very beginning and must run all the way through it. The goal must not be to test one or two interventions but to design (possibly several) interventions based on careful problem analysis and the identification of behavioural bottlenecks. This process of design should be an iterative process that incorporates feedback from small tests carried out as part of the design itself.
- Adopting this systematic approach towards diagnosis is important not just because it leads to better solutions to the problem in question but because it provides us with diagnoses and diagnostic techniques that can carry over to other contexts. Thus, if we find good diagnostics that indicate that self-control plays a large role in understanding a particular behavior in one country, we would have good reason to explore the use of these diagnostics in a different setting. Insights and diagnoses are likely to have external validity even if particular designs do not. In that sense, we should think of the kind of policy experimentation being described here partly also as mechanism experiments.
- For donors, this means selecting projects where successes can be scaled. This affects the kinds of problems chosen for experimentation. The most useful problems to work on would affect people in countries or regions beyond the one initially chosen, for instance because they are pervasive across a number of developing countries. There must also be reason to believe, ex ante, that behavioural barriers are a critical reason for program goals not being met. It also affects the choice of partners. Potential partners should reach large numbers of people so that any successes can be scaled up in the context of an existing program. Working with governments or large aid agencies may be more impactful than working directly with individual researchers.
- Involving private-sector players with established distribution and outreach networks can also be an effective way to scale, assuming that these players benefit from the behavioral problem being solved (whether directly in terms of profits or indirectly, by creating a base of consumers for other products).
- For researchers, this focus on impact at scale means privileging projects that build on existing government or large-scale non-profit programs rather than collaborations with small, boutique NGOs or service providers. It also means 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. And it means paying close attention to the administrative burden or logistical requirements of any proposed solution, because these affect whether a solution can be scaled up.
- Finally, this means that governments need to be open to involving behavioural experts when programs are first designed as well as to experimenting on existing programs. As important is openness to exploring new (and sometimes surprising) pathways to impact that emerge in the course of the detailed problem and behavioural analysis.
- Embedding innovation into the design process itself leads to designs that have a greater chance of success than if we proceeded to testing the first feasible and reasonable set of ideas about how to solve a problem. Over time, a rigorous application of the approach to program design outlined in this paper should lead to more effective, cheaper and more easily replicable innovations. As we have seen, many policy problems can be traced in the ultimate analysis to gaps between intentions and actions. A systematic application of behavioural design should help close another, equally important gap: that between what policy seeks to achieve and what it accomplishes.
