Combining Brain and Behavioral Data to Improve Econometric Policy Analysis

Abstract

For an economist, ultimate goals of neuroeconomic research include improving economic policy analysis. One path toward this goal is to use neuroeconomic data to advance economic theory, and productive efforts have been made towards that end. Equally important, though less studied, is how neuroeconomics can provide quantitative evidence on policy, and in particular the way in which it might inform structural econometric inference. This paper is a first step in that direction. We suggest here that key forms of preference (or decision strategy) heterogeneity can be identified by brain imaging studies and, consequently, linked stochastically to observable individual characteristics. Then, recognizing that brain-imaging studies are substantially costly, we derive conditions under which the probabilistic link between observable characteristics and type, a quantity critical to policy analysis, can be estimated more precisely by combining data from traditional and brain-based decision studies.