Behavioral Design: A New Approach to Development Policy

By S. Datta, Sendhil Mullainathan. The Review of Income and Wealth, 2014

295 citations recorded by the source corpus.

Successful development programs rely on people to behave and choose in certain ways, and behavioral economics helps us understand why people behave and choose as they do. This paper sketches how to design development programs and policies in ways that are cognizant of and informed by the insights behavioral economics provides into human behavior. It distills the key insights of behavioral economics into a parsimonious framework about the constraints under which people make decisions. It then shows how this framework leads to a set of design principles that can be employed to design programs in areas including health, education, productivity, agriculture, finance, and the delivery of public services. Finally, it offers some reflections on the ways in which these insights and design principles can be incorporated into existing and planned programs to improve their reach and effectiveness.

Subfields: Accounting, Gender Studies, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/d2b3cf01388dc9c56acb49eb7148034e49891b16

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Citations in the corpus

A Review and Taxonomy of Choice Architecture Techniques (2016, methodology): “Datta and Mullainathan (2012) suggest that four basic “mental constraints” serve as a starting point for choice architecture interventions: self-control, attention, cognitive capacity, and understanding.”
Norm Perception as a Vehicle for Social Change (2016, background): “This behavioral “diagnosis” (Datta & Mullainathan, 2012) may reveal, for example, that people are not yet informed about the importance of a new behavior, that they are already motivated to engage in it but have trouble acting on their intention, or that they are already motivated but feel socially…”
Beyond Good Intentions: Designing CSR Initiatives for Greater Social Impact (2020, background): ““This ‘evaluation revolution’ has made it possible to measure whether a given program or policy works, thereby turning the spotlight on the question of how to go about designing programs that are likely to work” (Datta & Mullainathan, 2014: 8).”