Citizenship status and requirement, culture and immigrants’ attitudes toward local immigration: A study of 21 western and non-western societies

Background: Understanding the attitudes of immigrants toward local immigration (i.e., immigration in their host societies) is increasingly important in the context of rising international migration and naturalized immigrants. However, little is known about how immigrants view local immigration, especially across societies with different cultures and naturalization requirements. Objective: This research examines two questions: 1) whether immigrants with and without host-society citizenship view local immigration differently across Western and non-Western societies, and 2) how the potential attitudinal differences between naturalized and non-naturalized immigrants vary across societies with different cultures and naturalization requirements. Methods: Drawing from World Values Survey data (WVS7, 2017–2022), this study analyzes how naturalized immigrants and non-naturalized immigrants from 21 societies accommodating around 40 % of the world's international migrants view local immigration. Results: Results indicate that naturalized immigrants exhibit less favorable attitudes toward local immigration than their non-naturalized counterparts, particularly in societies with collectivistic cultures or stringent naturalization processes or without language requirements. Conclusions: The host-society citizenship is associated with less favorable attitudes toward local immigration. Access to naturalization and its requirements are associated with local immigration attitudes, which can potentially shape the integration environment and overall social cohesion in the host society. Contribution: The study goes beyond the conventional native-versus-immigrants attitudes in Western contexts and explores the important yet underexplored attitudinal outcomes of naturalization. It analyzes the moderating impacts of the host-society culture and naturalization criteria and combines a multilevel analysis with a coarsened exact matching and machine-learning approach, offering valuable insights for future studies and integration strategies.