We study the effects of conviction and incarceration on recidivism using quasi-random judge assignment. We extend the typical binary-treatment framework to a setting with multiple treatments, and outline a set of assumptions under which standard 2SLS regressions recover causal and margin-specific treatment effects. Under these assumptions, 2SLS regressions applied to data on felony cases in Virginia imply that conviction leads to a large and long-lasting increase in recidivism relative to dismissal, consistent with a criminogenic effect of a criminal record. In contrast, incarceration reduces recidivism, but only in the short run. The assumptions we outline could be considered restrictive in the random judge framework, ruling out some reasonable models of judge decision-making. Indeed, a key assumption is empirically rejected in our data. Nevertheless, after deriving an expression for the resulting asymptotic bias, we argue that the failure of this assumption is unlikely to overturn our qualitative conclusions. Finally, we propose and implement alternative identification strategies. Consistent with our characterization of the bias, these analyses yield estimates qualitatively similar to those based on the 2SLS estimates. Taken together, our results suggest that conviction is an important and potentially overlooked driver of recidivism, while incarceration mainly has shorter-term incapacitation effects.


This talk is presented by the Labor Economics Seminar, sponsored in part by the Department of Economics with generous gifts given through the Abraham and Thelma Zwerdling Labor Economics Program.