Introduction to Behavioural Science in Public Policy

Platform: World Bank Group Institute for Economic Development
Duration: 6 hours (Self-paced)
Certification: Yes (available upon completion)
Course Fees: Free

When I first came across the term behavioural science in a public policy context, I assumed it had more to do with psychology than policymaking. But over time, working in agricultural extension and rural development in India, I repeatedly encountered cases where technically sound policies fell short—not due to a lack of evidence or resources, but because they didn’t align with how people actually make decisions.

Curious to explore this further, I enrolled in the “Introduction to Behavioural Science in Public Policy” course offered by the World Bank. The course is completely self-paced. What stood out to me immediately was how it connects everyday human behaviour to some of the biggest policy challenges—from encouraging retirement savings to promoting life-saving health practices.

The course is led by Renos Vakis, Jonathan Karver, and Dana Qarout from the World Bank’s Mind, Behaviour, and Development Unit (eMBeD). Their teaching style is rooted in real-world experience, drawing on compelling case studies and practical examples from the field. Rather than relying on theory-heavy lectures, the instructors use engaging narratives and relatable scenarios to explain key behavioural concepts.

The course is structured around core behavioural concepts such as present bias, loss aversion, choice overload, and social norms, and it illustrates how these can be harnessed (or misjudged) in policy design. Each module includes global case studies across sectors like education, health, finance, and public service delivery, which help ground abstract theory in real-world impact.

One case that stuck with me showed how simply reframing reminders for tax payments or vaccination appointments led to significantly higher compliance rates. It made me reflect: in agricultural development, how often do we overestimate the power of technical advice and underestimate the small design tweaks that could actually change behaviour? These examples weren’t just theoretical—they were often based on experimental evidence from real field interventions.

Unlike some online courses that overwhelm you with content, this one strikes a good balance. The modules are short, to the point, and include reflection prompts and quizzes. The course is divided into eight thoughtfully designed modules that progressively build the learner’s understanding—from foundational behavioural theory to applied policy design and evaluation. The table below summarises the structure and content of the course:

Module No.	Title	Key Themes & Concepts
1	Why Behavioural Science Matters in Policy	Introduction to behavioural science, limitations of rational models, policy relevance
2	How People Think and Decide	Cognitive biases, heuristics, dual-system thinking (System 1 & System 2)
3	Common Behavioural Biases in Public Decisions	Present bias, loss aversion, mental accounting, status quo bias
4	Social Norms and Peer Influence	Role of social networks, group conformity, descriptive vs injunctive norms
5	Designing Behaviourally Informed Interventions (Nudges)	Choice architecture, framing, simplification, defaults, timing interventions
6	Ethics of Behavioural Interventions	Autonomy, manipulation concerns, transparency and fairness in behavioural design
7	Behavioural Insights in Practice	Real-world applications from health, finance, education, and public services
8	Evaluating Behavioural Interventions	Experimental methods (RCTs, A/B testing), metrics for behavioural outcomes

The course was straightforward to follow. While it didn’t delve into complex elements like coding or data analysis, it made up for that with strong conceptual clarity and practical relevance. It not only helped refine my assumptions but also prompted me to include variables like trust in technology and peer influence—factors I might otherwise have overlooked—in my ongoing research. This research explores how the adoption of AI-driven agricultural technologies affects the income and resilience of smallholder dairy farmers in India.

If I had one suggestion, it would be to include more examples from agriculture and rural livelihoods, particularly from South Asia. A case or two focused on farmers, extension services, or nutrition behaviour could make the course even more relatable for extension and development practitioners in this region.

That said, I highly recommend this course to social scientists, extension/development professionals, and policy researchers—especially those working in the agriculture, health, or education sectors.

Sidharth S is a fourth-year PhD scholar at the National Dairy Research Institute (NDRI), Karnal, in the Dairy Extension Division. His academic interests lie in behavioural science, digital agriculture, and rural development. He is currently exploring how farmers’ behavioural responses influence the adoption of emerging agricultural technologies. He can be reached at sidoachira@gmail.com.