This study investigates whether the share of immigrants in a community influences support for anti-immigration parties. It systematically reviews 20 existing studies employing causal inference methods and utilizes a novel Bayesian meta-analysis framework to address reporting bias and heterogeneity across contexts.
Data & Methods
* The analysis synthesizes results from 20 distinct studies.
* A sophisticated Bayesian meta-analysis framework is applied, specifically designed to:
* Account for variation in effect sizes between different contexts (heterogeneity)
* Investigate the potential impact of reporting bias
* Standard causal inference techniques were initially used but found limitations without adjustments.
Key Findings
* Traditional methods suggest a moderate influence of local immigration share on voting for far-right parties.
* However, after accounting for reporting bias using the Bayesian model, this effect is statistically negligible across most contexts.
* A significant finding remains: substantial heterogeneity in effects exists. The impact varies considerably depending on specific political, social, and economic settings.
Why It Matters
* These results challenge previous moderate estimates of local immigration's influence.
* They suggest that findings indicating a link may be partly driven by reporting bias rather than genuine effects.
* Crucially, the analysis highlights that anti-immigration voting is context-dependent. While local density might matter in some specific situations, its overall average impact appears minimal when accounting for potential biases.
| Local Immigration and Support for Anti-Immigration Parties: A Meta-Analysis was authored by Henning Finseraas, Sara Cools and Ole Rogeberg. It was published by Wiley in AJPS in 2021. |
