John Eric Humphries, Aurelie Ouss, Megan Stevenson, Kamelia Stavreva, and Winnie van Dijk

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.

[Revise and resubmit at the Quarterly Journal of Economics]

Robert Collinson, John Eric Humphries, Nicholas Mader, Davin Reed, Daniel Tannenbaum, Winnie van Dijk

More than two million U.S. households have an eviction case filed against them each year. Policymakers at the federal, state, and local levels are increasingly pursuing policies to reduce the number of evictions, citing harm to tenants and high public expenditures related to homelessness. We study the consequences of eviction for tenants using newly linked administrative data from two major urban areas: Cook County (which includes Chicago) and New York City. We document that prior to housing court, tenants experience declines in earnings and employment and increases in financial distress and hospital visits. These pre-trends pose a challenge for disentangling correlation and causation. To address this problem, we use an instrumental variables approach based on cases randomly assigned to judges of varying leniency. We find that an eviction order increases homelessness and hospital visits and reduces earnings, durable goods consumption, and access to credit in the first two years. Effects on housing and labor market outcomes are driven by impacts for female and Black tenants. In the longer-run, eviction increases indebtedness and reduces credit scores.

[Forthcoming at the Quarterly Journal of Economics]