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Structural Behavioral Economics NBER WORKING PAPER SERIES STRUCTURAL BEHAVIORAL ECONOMICS Stefano DellaVigna Working Paper 24797 http://www.nber.org/papers/w24797 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 July 2018 Forthcoming in the 1st Handbook of Behavioral Economics, Vol.1, edited by Douglas Bernheim, Stefano DellaVigna, and David Laibson, Elsevier. I thank Hunt Allcott, Charles Bellemare, Daniel Benjamin, Douglas Bernheim, Colin Camerer, Vincent Crawford, Thomas Dohmen, Philipp Eisenhauer, Keith Ericson, Lorenz Goette, Johannes Hermle, Lukas Kiessling, Nicola Lacetera, David Laibson, John List, Edward O'Donoghue, Gautam Rao, Alex Rees-Jones, John Rust, Jesse Shapiro, Charles Sprenger, Dmitry Taubinsky, Bertil Tungodden, Hans-Martin von Gaudecker, George Wu, and the audience of presentations at the 2016 Behavioral Summer Camp, at the SITE 2016 conference, and at the University of Bonn for their comments and suggestions. I thank Bryan Chu, Avner Shlain, Alex Steiny, and Vasco Villas-Boas for outstanding research assistance. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2018 by Stefano DellaVigna. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Structural Behavioral Economics Stefano DellaVigna NBER Working Paper No. 24797 July 2018 JEL No. C1,C9,D03,D9 ABSTRACT What is the role of structural estimation in behavioral economics? I discuss advantages, and limitations, of the work in Structural Behavioral Economics. I also cover common modeling choices and how to get started. Among the advantages, I argue that structural estimation builds on, and expands, a classical behavioral tool, simple calibrations, and that it benefits from the presence of a few parsimonious behavioral models which can be taken to the data. Estimation is also well suited for experimental work, common in behavioral economics, as it can lead to improvements in the experimental design. In addition, at a time where policy implications of behavioral work are increasingly discussed, it is important to ground these policy implications in (estimated) models. Structural work, however, has important limitations, which are relevant to its behavioral applications. Estimation takes much longer and the extra degree of complexity can make it difficult to know which of a series of assumptions is driving the results. For related reasons, it is also easy to over-reach with the welfare implications. Taking this into account, I provide a partial how-to guide to structural behavioral economics, covering: (i) the choice of estimation method; (ii) the modeling of heterogeneity; (iii) identification and sensitivity. Finally, I discuss common issues for the estimation of leading behavioral models. I illustrate this discussion with selected coverage of existing work in the literature. Stefano DellaVigna University of California, Berkeley Department of Economics 549 Evans Hall #3880 Berkeley, CA 94720-3880 and NBER [email protected] 1 Introduction Behavioral economics, with its lessons regarding non-standard preferences, beliefs, and decision- making, has important applications in most fields of economics. This Handbook is a clear illustration of this broad reach, with chapters on a variety of fields, including finance, public economics, and industrial organization. The applications in these fields employ a range of data sources|observational studies, survey collection, laboratory experiments, and field experiments, among others. The applications come with a variety of estimation methods, including simple treatment-control comparisons in experiments, correlations, instrumental variables, but also structural estimation. In this chapter I ask: Is there an important role for structural estimation in behavioral economics, or for short Structural Behavioral Economics? For our purposes, I define structural as the \estima- tion of a model on data that recovers estimates (and confidence intervals) for some key behavioral parameters".1 Further, are there special lessons for structural estimation in behavioral economics beyond the well-known advantages, such as the ability to do welfare and policy evaluations, but also the well-known pitfalls, such as the complexity of the analysis? I argue that the answer is: Yes, and Yes. In Section 2, I discuss six advantages of structural estimation, several of which have roots in key features of behavioral research. One of the most important ones is that estimation builds on a long-standing tradition in behavioral economics of calibration of models, often through simple back-of-the-envelope calculations. Taking seriously the economic magnitude of the calibrated parameters has been the foundation for important behavioral insights, such as Rabin's calibration theorem for risk (Rabin, 2000). As I argue, estimation takes the calibration one step further, including cases in which a simple calibration is not possible. Second, in behavioral economics there has always been a healthy exchange of ideas between theo- rists and applied researchers. Partly because of the focus on calibrating models, behavioral theorists have paid attention, arguably more than in some other fields, to empirical evidence. Conversely, empirical researchers, given the importance of testing the null hypothesis of the standard model, have typically followed closely the development of behavioral theory, or at least applied theory. Structural estimation builds on, and reinforces, this closeness, as it forces empirical researchers to take seriously models which they bring to the data. Third, structural estimation also benefits from the fact that behavioral economics has a small number of widely-used parsimonious models, such as beta-delta preferences (Laibson, 1997; O'Donoghue and Rabin, 1999a) and reference dependence (Kahneman and Tversky, 1979). The presence of commonly-used models makes it more useful to test for the stability of estimates across settings. Fourth, a key advantage of structural estimation is that one can estimate the out-of-sample performance of a model, a stronger test than in-sample fit. Indeed, we point to at at least one case in which a behavioral model and a standard model have similar in-sample fit, but the out-of-sample predictions clearly tell apart the models. Still, out-of-sample predictions appear under-utilized in behavioral economics. A fifth advantage relates to a key feature of the behavioral field: the importance of experimental 1For definitions of structural estimation see Reiss and Wolak (2007), Wolpin (2013), and Rust (2014). 1 evidence, both from the laboratory, source of much of the initial behavioral evidence, and from the field. In experiments, there are extra advantages to estimation: paying close attention to the models at the design stage can lead to different designs that allow for a clearer test of models. In observational studies, in contrast, the design is limited by the data and the setting (though models can of course motivate the search for the right observational design). This particular advantage of models, interestingly, so far has played a larger role in laboratory experiments than in field experiments (Card, DellaVigna, and Malmendier, 2011). There is an opportunity for more work of this type. A sixth motivation is shared by all applications of structural estimation: welfare and policy analysis. The timing for that in behavioral economics is just right. While behavioral economics has mostly shied away from policy implications until the last decade, the recent emphasis on cautious paternalism (Camerer et al., 2003), nudges (Thaler and Sunstein, 2008), and behavioral welfare economics (Bernheim and Rangel, 2009) substantially increased the policy reach of behavioral eco- nomics. Yet, many policy applications of behavioral findings do not have a fully worked out welfare or policy evaluation. Structural estimation has a role to play to ensure, for example, that we \nudge for good". Having said this, should all of behavioral economics be structural? Absolutely not. To start with, many studies do not lend themselves well to structural estimation, for example because they explore a channel for which we do not have yet a well-understood model, e.g., framing effects, or the interest is on a reduced-form finding. In addition, even in cases in which there is an obvious model-data link, an alternative strategy is to derive comparative statics from the model (e.g., Andreoni and Bernheim, 2009), including in some cases even an axiomatic characterization (e.g., Halevy, 2015), to derive empirical testable predictions. This strategy allows for clear model testing, without the extra assumptions and time involved in structural estimation. For the studies where structural estimation makes sense, in Section 3 we outline common limita- tions of structural estimation. These limitations are shared with applications of structural estimation in other fields, but I emphasize examples, and specific issues, within behavioral economics. First, and perhaps most obviously, structural estimation typically takes much more time, given the number of necessary steps: the reduced-form results, spelling out the full model, the estimation
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