Date: 2024
Type: Article
Which individual-level factors explain public attitudes toward immigration? : a meta-analysis
Journal of ethnic and migration studies, 2024, Vol. 50, No. 2, pp. 317-340[Migration Policy Centre]
DRAŽANOVÁ, Lenka, GONNOT, Jerome, HEIDLAND, Tobias, FINJA, Krüger, Which individual-level factors explain public attitudes toward immigration? : a meta-analysis, Journal of ethnic and migration studies, 2024, Vol. 50, No. 2, pp. 317-340[Migration Policy Centre] - https://hdl.handle.net/1814/75964
Retrieved from Cadmus, EUI Research Repository
Public attitudes toward immigration have attracted much scholarly interest and extensive empirical research in recent years. Despite a sizeable theoretical and empirical literature, no firm conclusions have been drawn regarding the factors affecting immigration opinion. We address this gap through a formal meta-analysis derived from the literature regarding immigration attitudes from the top journals of several social science disciplines in the years 2009–2019 and based on a population of 1185 estimates derived from 144 unique analyses on individual-level factors affecting attitudes to immigration. The meta-analytical findings show that two individual-level characteristics are most significantly associated with attitudes to immigration - education (positively) and age (negatively). Our results further reveal that the same individual characteristics do not necessarily explain immigration policy attitudes and attitudes toward immigrants’ contribution. The findings challenge several conventional micro-level theories of attitudes to immigration. The meta-analysis can inform future research when planning the set of explanatory variables to avoid omitting key determinants.
Additional information:
Published online: 05 October 2023
Cadmus permanent link: https://hdl.handle.net/1814/75964
Full-text via DOI: 10.1080/1369183X.2023.2265576
ISSN: 1369-183X; 1469-9451
Series/Number: [Migration Policy Centre]
Publisher: Routledge
Sponsorship and Funder information:
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 882986.
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