Subnational Immigration Policymaking in Gang-Controlled Zones: A Holistic Approach for Tackling MS-13 in the Washington DC Metropolitan Area

As illustrated by the recent migrant caravans and exodus of unaccompanied children from Central America to the United States (US), many young men and women from the so-called Northern Triangle (Guatemala, Honduras and El Salvador) flee their countries to escape gang-violence. Looking for a sense of belonging, many young migrants and refugees (re-) join transnational gangs, such as the Mara-Salvatrucha (MS-13) , once they arrive in the US. In this context, local governments are the first point of contact for these migrants, mainly through the school system and local law enforcement. While US federal institutions have typically pushed for ‘intervention’ and ‘suppression’ strategies to tackle gang violence (the ‘law enforcement’ approach), preliminary data gathered by the authors show that some US localities have been implementing holistic ‘preventative’ approaches, in part to reach migrant populations. This governance and policy innovation moves away from coercive interventions by delivering a variety of social services with the aim of: 1) preventing young Central American migrants from joining gangs, and 2) facilitating exit once they join a gang. Yet, preliminary data shows there are wide variations across US localities. The proposed paper discusses key issues of local immigration policy in gang-controlled zones in the US and seeks to compare and contrast governance and policy models in four localities of the Washington DC metropolitan area—Arlington, Fairfax, and Montgomery counties, as well as the City of Alexandria. In line with the objectives of the workshop, the paper seeks to gain a better understanding of the most appropriate subnational immigration policies to address gang-related violence and integrate young migrants successfully into their new communities before and after they join street-gangs. In addition, the paper draws from the welfare state literature on activation. The data will mainly come interviews in these four localities.