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

TL;DR

This study develops and tests a targeted policy rule for encouraging naturalization among immigrants using a two-phase field experiment, aiming to improve application rates through personalized communication.

Contribution

It introduces a novel two-phase experimental approach to optimize treatment allocation based on individual characteristics for naturalization campaigns.

Findings

Policy rule increased naturalization application rates

Personalized treatment outperformed uniform messaging

Effect heterogeneity was moderate but impactful

Abstract

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 1,717 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 to assigning the same letter to everyone.