The Risk of Dancing in the Dark: Towards a 'Constitution' for Behavioral Policy-Making
32 Pages Posted: 1 Dec 2016
Date Written: November 30, 2016
Behavioral policy interventions aimed at redirecting individuals’ behavior toward optimal choices are characterized by a fundamental flaw which is often overlooked: the lack of an instrument to define what “optimal” means. If agents are subject to behavioral biases leading them to make “wrong” choices, the policymaker can no longer rely on the revealed preferences approach (e.g. what people choose is what people prefer) for defining a welfare criterion. In this article, we first raise awareness of the fundamental problem of choosing a suitable welfare criterion once the link between observed choices and individuals’ preferences is broken. We review the state of the art in the literature and the possible approaches proposed to overcome the problem, concluding that a solution has not yet been reached. Second, we argue that the lack of an established welfare criterion characterizing behavioral policymaking opens up a major threat to individual freedom. In the absence of any legislative constraint for the executive, stating that what individuals choose is not what they prefer in principle justifies any government intervention that restricts individual freedom, since choices can be arbitrarily labelled “sub-optimal” or “welfare-reducing”. To avoid the risk of freedom-reducing government interventions without turning down the potential of behavioral policymaking, we propose that an independent committee establishes ex-ante procedural rules and domains where behavioral policymaking can be implemented. The article suggests some possible examples of normative provisions characterizing this constitution-type document, such as the selective identification of the only sectors where behavioral policies could be effectively applied, the periodic evaluation of policy effects, and the use of sunset clauses.
Keywords: Law and Economics, Nudging, Public Policy, Revealed Preferences
JEL Classification: K30, K40
Suggested Citation: Suggested Citation