Social Exclusion As a State-Level Predictor for Changes in Collateral Sanctions

Background/Purpose

Collateral sanctions are civil penalties for those convicted of crimes. They impact some crucial aspects of life such as access to housing, employment, public assistance, and education. Around 700,000 U.S. citizens return from prison each year and nearly 24 million others cycle through local jails. In the meantime, two out of three released are reincarcerated within three years. Research suggests collateral sanctions contribute to recidivism. However, it is not clear to what extent. Furthermore, little research is available concerning state-level predictors of these policies. Current research shows racial threat and political conservatism are associated with harsher collateral sanctions policies. This study builds on this knowledge by testing the relationship between state-level variables from a social exclusion framework and both static collateral sanctions policies and changes in these policies over time.

Methods

We used two regression models – one cross sectional and one longitudinal – to estimate the extent to which state-level variables help predict the adoption of laws applying collateral sanctions.  

The Legal Action Center (LAC) provides comprehensive scores for each state regarding the severity of their collateral sanctions. We used the scores from their 2009 study and the difference in scores from their 2004 and 2009 studies as outcome variables in the two models. Independent variables were selected using a social exclusion framework: unemployment, affordable housing scarcity, public assistance, and state fiscal health. Violent crime, prison releases, and conservatism, along with state Black and state male populations were included as controls. State-level data was drawn from the American Community Survey, the FBI, the Bureau of Justice Statistics, and the 2004 and 2008 presidential elections.

We used a zero truncated Poisson (ZTP) regression model to assess the relationship between social exclusion and static collateral sanction policies. The model assessing the relationship of changes in policy over time incorporated OLS regression.

Results

In the ZTP model, violent crime, along with state Black and state male populations were significantly associated with more severe policies, which is consistent with previous research. The social exclusion measures of affordable housing scarcity, poor state fiscal health, and increased usage of public assistance were significantly associated with more severe policies.

Affordable housing scarcity, poor state fiscal health and increased usage of public assistance were indicative of increases in the severity of policy over time, while the state Black population was associated with a decrease in policy severity over time.  

Conclusions and Implications

This study provides preliminary evidence that indicators of social exclusion help predict state decisions about collateral sanctions. The likelihood that social welfare policies are in part motivated by scarcity and social exclusion prompts further research and policy analysis about the intended and unintended outcomes of these policies. Policymakers and analysts must meaningfully explore the extent to which policy choices represent best practices versus financially convenient practices. In addition, a critical lens is required to consider how decisions about resource allocations affect or hinder the social integration of those with criminal convictions.