The standard model of economic decision making – the model of a fully rational and purely self-interested homo oeconomicus – still dominates in most fields of economics. However, the past two decades brought tremendous progress in our understanding of how real people take economic decisions. While the standard model has been confirmed in some domains, it is now clear that many aspects of economic decision making are neither fully rational nor purely selfish and cannot be understood from this perspective.
This insight is important both for our understanding of economic phenomena and for the optimal design of economic policy interventions. While early theoretical advances in the emerging field of behavioral economics were informed by evidence from laboratory experiments, more recently, theory and evidence have become somewhat disconnected. The profession now faces the challenge to formulate better theories, incorporating newly generated causal evidence on individual behavior, in order to conduct behaviorally sound evaluations of policy proposals. This goal can only be achieved if theory building, data generation and data analysis are closely intertwined, which is the research agenda of EBE.
Moreover, the research paradigm of EBE explicitly acknowledges that researchers studying human behavior face fundamental identification problems that make it hard to uncover causal effects. For example, Abhijit Banerjee and Esther Duflo from the MIT Poverty Action Lab pioneered “randomized controlled trials” (RCTs) that have revolutionized the field of development economics. Banerjee and Duflo argue forcefully that RCTs are the sole scientific way of conducting empirical research in economics, even questioning the need for doing economic theory.
Other economists disagree and emphasize the importance of economic theory for the guidance and interpretation of experimental or empirical studies. The Journal of Economic Perspectives devoted its Spring 2010 issue to a widely influential high-profile symposium on this debate. Arguably, the majority of the profession agrees on the importance of exploiting natural experiments and carefully designing laboratory and field studies, combined with solid theoretic modeling.
Angus Deaton1 convincingly demonstrates that interpreting results of natural or field experiments without a theoretical foundation becomes vacuous, and David Card, Stefano DellaVigna, and Ulrike Malmendier2 make the case that theory-driven experimental design is necessary to understand the causal effects of economic policy interventions.
This is the approach adopted by the graduate program “Evidence-Based Economics”.
1 Angus Deaton, “Instruments, Randomization, and Learning about Development”, Journal of Economic Literature, Vol. 48 (2010), pp. 424-455.
2David Card, Stefano DellaVigna, and Ulrike Malmendier, “The Role of Theory in Field Experiments”, Journal of Economic Perspectives, Vol. 25 (2011), pp. 39-62.