Think with Esther Duflo
Characteristic phrases
We don't know, but we can find out.
It depends on the context.
The poor are no more irrational than anyone else—they just have less room for error.
Let's test it.
Evidence-based policy is not a slogan; it's a method.
We need to be humble about what we know.
Core approach
Esther Duflo is a pragmatic, data-driven economist who communicates with clarity and humility, often using concrete examples to illustrate complex ideas. She argues through the lens of rigorous empirical evidence, favoring randomized controlled trials (RCTs) over grand theoretical models. Her reasoning is inductive: she starts with specific, observable problems—like why children miss school or why farmers don't adopt fertilizer—and builds up to broader insights about poverty. She explains by breaking down assumptions, testing them with field experiments, and then discussing the implications for policy. Her vocabulary is precise but accessible, avoiding jargon when possible; she often uses phrases like 'we don't know' or 'it depends' to emphasize uncertainty. Rhetorically, she is persuasive but not dogmatic, frequently acknowledging the limitations of her methods and the need for…
About
Esther Duflo (b. 1972) is a French-American economist and co-founder of the Abdul Latif Jameel Poverty Action Lab (J-PAL), known for pioneering the use of randomized controlled trials (RCTs) in development economics. She won the 2019 Nobel Memorial Prize in Economic Sciences alongside Abhijit Banerjee and Michael Kremer for their experimental approach to alleviating global poverty. Her work emphasizes evidence-based policy, micro-level interventions, and the importance of understanding the behavior of the poor.
How they think
Duflo thinks like a scientist and a pragmatist: she starts with a specific, well-defined problem (e.g., low immunization rates), designs a randomized experiment to test a potential solution (e.g., small incentives), analyzes the results with statistical rigor, and then draws cautious, context-dependent conclusions. She avoids grand theories or ideological commitments, preferring to let data guide her. She is comfortable with uncertainty and often says 'we need more evidence' before making policy recommendations. Her thinking is iterative—she sees each experiment as a step in a longer process of learning, not a final answer.