Primary Submission Category: Race/Ethnicity
Racialized Legal Status: An Intersectional Framework for Understanding Mental Health in Latino Communities
Authors: Lianeris Estremera-Rodriguez, Lorraine Dean,
Presenting Author: Lianeris Estremera-Rodriguez*
Racialized legal status (RLS) is the intersection of an individual’s legal status, race and ethnicity, reflecting a social position created by immigration laws that appear neutral but, in practice, marginalize and socially exclude people who are Black or Brown and have discredited legal status (i.e., undocumented). The current sociopolitical climate calls for intersectional approaches to understand how the enforcement of these race-neutral policies disproportionately target specific groups, affecting their health. RLS laws establish a social hierarchy in which individuals with a discredited legal status, compared to U.S. citizens or naturalized individuals, are the most excluded. They may also face exclusion based on their ethnicity. Latino individuals face higher levels of exclusion compared to Non-Hispanic White individuals. Legal and social exclusion factors create a dual exclusion that directly impacts the mental health of individuals with a discredited legal status and indirectly affects the mental health of their family members, regardless of their own legal status. There are no current frameworks for empirically assessing the mental health impacts of RLS in Latinos.
Latinos with discredited legal status are more likely to suffer from mental illness. We need a framework that explains Latino mental health disparities beyond behavioral or cultural factors to develop later measures that capture social stratification within the context of 21st-century immigration. We create a framework to conceptualize exclusion due to RLS as a multidimensional process and understand how different multiple exclusion indicators affect Latinos’ mental health. Our framework is based on a comprehensive review of literature and current policies. It will be presented in a schematic diagram and will provide applied examples using large real-world datasets in the U.S. This framework can guide the development of new measures to assess disparities and inform policies to address them.