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Linguistic Assimilation Does Not Reduce Discrimination Against Immigrants: Evidence from Germany

Published online by Cambridge University Press:  19 August 2020

Donghyun Danny Choi
University of Pittsburgh, 4600 Posvar Hall, 230 S Bouquet Street, Pittsburgh, PA15213, USA, Twitter:@dhdannychoi
Mathias Poertner
Texas A&M University, 4220 TAMU, College Station, TX77843-4220, USA, Twitter:@MathiasPoertner
Nicholas Sambanis*
Identity and Conflict Lab, University of Pennsylvania, The Ronald O. Perelman Center for Political Science and Economics, 133 S. 36th Street, Philadelphia, PA19104-6215, USA
*Corresponding author: Email:


Many western liberal democracies have witnessed increased discrimination against immigrants and opposition to multiculturalism. Prior research identifies ethno-linguistic differences between immigrant and native populations as the key source of such bias. Linguistic assimilation has therefore been proposed as an important mechanism to reduce discrimination and mitigate conflict between natives and immigrants. Using large-scale field experiments conducted in 30 cities across Germany – a country with a high influx of immigrants and refugees – we empirically test whether linguistic assimilation reduces discrimination against Muslim immigrants in everyday social interactions. We find that it does not; Muslim immigrants are no less likely to be discriminated against even if they appear to be linguistically assimilated. However, we also find that ethno-linguistic differences alone do not cause bias among natives in a country with a large immigrant population and state policies that encourage multiculturalism.

Research Article
© The Experimental Research Section of the American Political Science Association 2020

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All authors contributed equally to this work; their names are listed alphabetically.

We thank the editor and associate editor, two anonymous reviewers, Vivian Bronsoler Nurko, Peter Dinesen, Thad Dunning, Don Green, Dan Hopkins, LaShawn Jefferson, Anna Schultz, as well as participants at the University of Pennsylvania, Carnegie Mellon University, Ohio State University, and UC Berkeley for valuable comments and suggestions. We are also grateful to our excellent team of 52 confederates and enumerators for their assistance in the implementation of these experiments. The research protocol was reviewed and approved by the University of Pennsylvania Institutional Review Board (IRB Protocols #829824 and #833206).

The authors are aware of no conflicts of interest regarding this research. Support for this research was provided by the Penn Identity and Conflict Lab. The data, code, and any additional materials required to replicate all analyses in this article are available at the Journal of Experimental Political Science Dataverse within the Harvard Dataverse Network, at: doi: 10.7910/DVN/D2XPJ6.


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