The Quest for Parsimony in Behavioral Economics: New Methods and Evidence on Three Fronts
Behavioral economics identifies myriad deviations from classical economic assumptions about consumer decision-making, but lacks evidence on how its diverse phenomena fit together and whether they are amenable to modeling as low-dimensional constructs. We pursue such parsimony on three fronts, with success on two and instructive failure on the third. Elicitation parsimony reduces impediments to data collection by streamlining standard methods for directly measuring a person’s behavioral tendencies. We do so for 17 potentially behavioral factors per individual in a large, nationally representative sample, and several sets of results indicate that our streamlined elicitations yield low-cost, high-quality data. Behavioral sufficient statistic parsimony aggregates information across behavioral factors, within-person, to create two new lower-dimensional, consumer-level measures of behavioral tendencies. These statistics usefully capture cross-sectional variation in behavioral tendencies, strongly and negatively correlating with a rich index of financial condition even after (over-)controlling for demographics, classical risk attitudes and patience, cognitive skills including financial literacy, and survey effort. Our quest for common factor parsimony largely fails: within-consumer correlations between behavioral factors tend to be low, and the common factor contributing to all 17 behavioral factors within-individual is weakly identified and does not help explain outcomes conditional on the other covariates. Altogether our results provide many new insights into behavioral factors: their distributions, inter-relationships, distinctions from classical factors, and links to outcomes. Our findings also support the two leading approaches to modeling behavioral factors—considering them in relative isolation, and summarizing them with reduced-form sufficient statistics—and provide data and methods for honing both approaches.
Thanks to Hannah Trachtman and Sucitro Dwijayana Sidharta for outstanding research assistance, and to the Sloan/Sage Working Group on Behavioral Economics and Consumer Finance, the Roybal Center (grant # 3P30AG024962), and the National University of Singapore for funding and patience. For comments on survey design we thank Shachar Kariv and Dan Silverman for helping us implement their (with Choi and Muller) interface for measuring choice consistency, Charlie Sprenger for help with choosing the certainty premium elicitation tasks and with adapting the convex time budget tasks, Georg Weizsacker for help in adapting one of the questions we use to measure narrow bracketing, Julian Jamison for advice on measuring ambiguity aversion, Josh Schwartzstein for many conversations, and audiences at the Sage/Sloan Foundation, UCSD-Rady, DIW-Berlin, and the Aspen Consumer Decision Making Conference. For comments on the paper we thank Stefano DellaVigna, Xavier Gabaix, Michael Haliassos, Paul Heidhues, Theresa Kuchler, Gautam Rao, Doug Staiger, Johannes Stroebel, Dmitry Taubinsky, and seminar and conference participants at Berkeley/Haas, the Boulder Conference on Consumer Financial Decision Making, the CEPR Network in Household Finance, the CFPB Research Conference, Columbia GSB, Dartmouth, the Federal Reserve Bank of Philadelphia, National University of Singapore, NBER Law and Economics, NYU/Stern, the Research in Behavioral Finance Conference in Amsterdam, and UC-Davis. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.