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Determinants of county migrant regularization policymaking in the United States: Understanding temporal and spatial realities

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  • M.Anne Visser
  • Sheryl-Ann Simpson

Abstract

While immigration policymaking has traditionally been the sole prerogative of nation states, research has documented increased instances of migration policymaking at sub-national levels across migrant-receiving societies. This paper examines the temporally and spatially distinctive dynamics that underscore the adoption of these policies at the county level in the United States. The study considers the implementation of migrant labor market regularizations (LRs) for the time period 2004–2014. LRs are defined as discrete arenas of policymaking at the sub-national level that affect aspects of migrant workers’ status in labor markets and include laws and ordinances related to: anti-solicitation, language access, local enforcement of federal immigration law, and employment verification. Utilizing a multilevel event histories model, we analyze data from a unique dataset of over 5000 LR policies across 2959 counties in the United States, and address two research questions: (1) What are the social, economic, and political factors that influence the adoption of LRs by counties and municipalities in the United States; and (2) do policy adoption trends that occurred during 2004–2014 indicate a unique type of diffusion pattern? We find that the adoption of LRs by county governments are influenced by the racialization of immigration discourse and by policy behaviors at the municipal and state government levels, while economic characteristics of the local labor market and perceived ethnic competition from migrants have little direct impact on the probability of policy adoption.

Suggested Citation

  • M.Anne Visser & Sheryl-Ann Simpson, 2019. "Determinants of county migrant regularization policymaking in the United States: Understanding temporal and spatial realities," Environment and Planning A, , vol. 51(1), pages 91-111, February.
    • Handle: RePEc:sae:envira:v:51:y:2019:i:1:p:91-111
    DOI: 10.1177/0308518X18797134
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