Network Effects in Mexico-U.S. Migration: Disentangling the Underlying Social Mechanisms
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CitationGarip, F., and A. L. Asad. 2016. “Network Effects in Mexico-U.S. Migration: Disentangling the Underlying Social Mechanisms.” American Behavioral Scientist (April 19). doi:10.1177/0002764216643131.
AbstractScholars have long noted how migration streams, once initiated, obtain a self-feeding character. Studies have connected this phenomenon, called the cumulative causation of migration, to expanding social networks that link migrants in destination to individuals in origin. While extant research has established a positive association between individuals’ ties to prior migrants and their migration propensities, seldom have researchers interrogated how multiple social mechanisms—as well as exposure to common environmental factors—might account for these interdependencies. This article uses a mixed-methods strategy to identify the social mechanisms underlying the network effects in Mexico–U.S. migration. Three types of social mechanisms are identified, which all lead to network effects: (a) social facilitation, which is at work when network peers such as family or community members provide useful information or help that reduces the costs or increases the benefits of migration; (b) normative influence, which operates when network peers offer social rewards or impose sanctions to encourage or discourage migration; and (c) network externalities, which are at work when prior migrants generate a pool of common resources that increase the value or reduce the costs of migration for potential migrants. The authors first use large-sample survey data from the Mexican Migration Project to establish the presence of network effects and then rely on 138 in-depth interviews with migrants and their family members in Mexico to identify the social mechanisms underlying these network effects. The authors thus provide a deeper understanding of migration as a social process, which they argue is crucial for anticipating and responding to future flows.
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