Work

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

Year: 2024

Type: article

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.

Source: Journal of Applied Econometrics

DOI: 10.1002/jae.3092

Authors: Achim Ahrens, Alessandra Stampi‐Bombelli, Selina Kurer, Dominik Hangartner

Institution: ETH Zurich

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: hybrid

Estimated APC paid: $3,840