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.