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The most significant challenges to better understanding judicial behavior are lack of data and the absence of plausible exogenous variation in judicial environments. The random assignment of judges to cases has admittedly been helpful in gaining traction on the effects of judicial decisions (e.g., Dobbie, Goldin, and Yang 2018). Yet developing a full empirical account of “what judges maximize” (Posner 1993) would require a setting in which judges are randomly subjected to a wide variety of (real-world) environments with different costs, constraints, and rewards. This prospect remains pie in the sky, but that does not mean that we have not made some headway on the ground. For instance, researchers have deployed the random assignment of cases to judges to back out how judges respond to differences in case attributes when the characteristics of cases (e.g., severity) can be assessed ex ante (Leibovitch 2016) and to attempt to gauge how judicial decision making evolves over the course of the day or in response to an empty stomach (Danziger, Levav, and Avnaim-Pesso 2011; WeinshallMargel and Shapard 2011). These lines of research, however, have more to say about when judges depart from the merits of cases than about which traditional institutional features (e.g., compensation, selection) enhance judicial effort and improve accuracy.


This article is available under a CC BY-NC license. © 2018 by the University of Chicago, originally published in the Supreme Court Economic Review 25 (2017). DOI:

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