Optimal multi‐action treatment allocation: A two‐phase field experiment to boost immigrant naturalization

2024/09/05 by Achim Ahrens, Alessandra Stampi‐Bombelli, Selina Kurer +1 · 1 voice
Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Migration and Labor Dynamics #Names, Identity, and Discrimination Research

paper · pdf · doi:10.1002/jae.3092

openalex publication_date 2024/09/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/22

Abstract

Summary Research underscores the role of naturalization in enhancing immigrants' socio‐economic integration, yet application rates remain low. We estimate a policy rule for a letter‐based information campaign encouraging newly eligible immigrants in Zurich, Switzerland, to naturalize. The policy rule assigns one out of three treatment letters to each individual, based on their observed characteristics. We field the policy rule to one‐half of 1717 immigrants, while sending random treatment letters to the other half. Despite only moderate treatment effect heterogeneity, the policy tree yields a larger, albeit insignificant, increase in application rates compared with assigning the same letter to everyone.

Citations

• Statistical Decision Functions
• Econometric Issues in the Analysis of Regressions with Generated Regressors
• Estimating causal effects of treatments in randomized and nonrandomized studies.
• Statistical Treatment Rules for Heterogeneous Populations
• The central role of the propensity score in observational studies for causal effects
• Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice
• Citizenship and integration

Discussions

• 1 - How can we encourage eligible immigrants to naturalize? We design and field a policy tree for a letter-based information campaign. The policy tree optimally selects between alternative encourageme [bsky, 7 points, 1 comments]

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