We show how to use randomized participation incentives to test and account for nonresponse bias in surveys. We first use data from a survey about labour market conditions, linked to full-population administrative data, to provide evidence of large differences in labour market outcomes between survey participants and nonparticipants, differences which would not be observable to an analyst who only has access to the survey data. These differences persist even after correcting for observable characteristics. We then use the randomized incentives in our survey to directly test for nonresponse bias and find evidence of substantial bias. Next, we apply a range of existing methods that account for nonresponse bias and find they produce bounds (or point estimates) that are either wide or far from the ground truth. We investigate the failure of these methods by taking a closer look at the determinants of participation, finding that the composition of participants changes in opposite directions in response to incentives and reminder emails. We develop a model of participation that allows for two dimensions of unobserved heterogeneity in the participation decision. Applying the model to our data produces bounds (or point estimates) that are narrower and closer to the ground truth than the other methods. Our results highlight the benefits of including randomized participation incentives in surveys. Both the testing procedure and the methods for bias adjustment may be attractive tools for researchers who are able to embed randomized incentives into their survey.

More than two million U.S. households have an eviction case filed against them each year. Policy makers 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 before 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 effects for female and Black tenants. In the longer run, eviction increases indebtedness and reduces credit scores.

Noncarceral conviction is a common outcome of criminal court cases: for every person incarcerated, there are approximately three who were recently convicted but not sentenced to prison or jail. We extend the binary-treatment judge IV framework to settings with multiple treatments and use it to study the consequences of noncarceral conviction. We outline assumptions under which widely used 2SLS regressions recover margin-specific treatment effects, relate these assumptions to models of judge decision-making, and derive an expression that provides intuition about the direction and magnitude of asymptotic bias when a key assumption on judge decision-making is not met. We find that noncarceral conviction (relative to dismissal) leads to a large and long-lasting increase in recidivism for felony defendants in Virginia. In contrast, incarceration (relative to noncarceral conviction) leads to a short-run reduction in recidivism, consistent with incapacitation. Our empirical results suggest that noncarceral felony conviction is an important and overlooked driver of recidivism.