Robustness of Refugee-Matching Gains to Off-Policy Evaluation Choices

TL;DR

This paper assesses the robustness of refugee-matching impact evaluations in the US, demonstrating consistent results across multiple off-policy evaluation methods and model variations.

Contribution

It provides evidence that refugee-matching impact estimates are stable and reliable across different evaluation techniques and modeling choices.

Findings

Impact estimates remain consistent in magnitude across methods.

Results are statistically significant in most scenarios.

Estimates align with previous findings by Bansak et al. (2018).

Abstract

Previous research has investigated the potential of refugee matching for boosting refugee outcomes, first considered by Bansak et al. (2018). This paper demonstrates the stability of counterfactual impact evaluation results in the context of refugee matching in the United States using a range of off-policy evaluation methods. In order to estimate counterfactual impact and test the robustness of our results, we employ several evaluation methods, including inverse probability weighting (IPW) and multiple variants of augmented inverse probability weighting (AIPW). We also consider various modifications, including alternative modeling architectures and different assignment procedures. The impact estimates remain consistent in magnitude in all scenarios as well as statistically significant in most cases. Furthermore, the estimates are also consistent with the results originally presented in Bansak et al. (2018).