The Long-Term Effects of Housing and Criminal Justice Policy: Evidence and Methods

Abstract

This dissertation combines research from multiple areas of applied economics and is mostly focused on estimating the long-term impacts of housing and criminal justice policy. In addition, this dissertation covers an important methodological tool that is increasingly necessary for empirical researchers when linking multiple data sets to estimate causal treatment effects.

In the first chapter, I study the effects of rent control on the long-term outcomes of children. Rent control is a common policy enacted to limit the growth of rents and allow tenants to remain in their homes for longer. Prior empirical research has mainly focused on rent control's impact on neighborhoods and housing markets while ignoring the potential long-term impacts of rent control for the people directly affected by the policy, particularly children. Using nearest neighbor matching at the census tract level, I estimate the effects of rent control on average long-term outcomes for children, measured at the childhood census tract level. I find weakly suggestive evidence that rent control can improve the long-term labor market outcomes for children while also creating negative spillovers for children who do not directly benefit from the policy.

In the second chapter, coauthored with Michael Mueller-Smith, we develop a record linkage algorithm that is trained using a large, novel data set that includes fingerprint identifiers. Record linkage is a crucial empirical tool for contemporary applied researchers who are interested in linking data sets that do not contain unique identifiers. We show that this large training data substantially improves model performance compared to the smaller training samples frequently reported in the literature. We also show evidence that training data based on human coding can be overly conservative when identifying matches on a target sample with different characteristics than the human coder. This research has major implications for empirical researchers who wish to link data sets and estimate heterogeneous treatment effects on subpopulations.

In the last chapter, coauthored with Keith Finlay, Elizabeth Luh, and Michael Mueller-Smith, we study the long-term impacts of criminal financial sanctions on labor market outcomes and criminal recidivism. The rising use of financial sanctions in the criminal justice system in the United States necessitates a rigorous test of their impacts on criminal defendants and their families. We use data that has been processed and linked together using the record linkage algorithm detailed in my second chapter and utilize the implementation of a 2003 Michigan law that sharply increased fines associated with certain driving crimes. After carefully accounting for how the long-run behavioral effects of the policy could undermine the integrity of the research design, we find null to slightly positive effects of the policy on labor outcomes, minimal deterrent effects, and suggestive evidence of a financial burden on romantic partners.