Dissimilation: Shifting the Perspective on Migrant Outcomes

Drawing on the fields of migration mechanisms, immigrant incorporation, and causal inference, this paper develops a dissimilation framework for estimating causal effects of migration on migrant outcomes. This framework quantifies how migrants become different from stay-at-homes in their countries of origin as they experience cultural change, material change, and a new set of public goods. Using flexible machine learning techniques, this paper applies this framework to academic test score data from the 2012, 2015, and 2018 Programme for Student Assessment, comparing 8,754 15-year-old migrants in 43 destination countries to 681,094 counterparts in 60 countries of origin. Overall, migrant students obtain lower scores on these exams than similar stay-at-homes, but children who migrate to wealthier countries with larger co-ethnic communities see positive effects of migrating. Despite their weaker performance than stay-at-homes, these migrants’ dissimilation results paint a rosier picture than assimilation-style comparisons to local non-migrants, where estimates are more negative.