A Dynamic Matching Framework for Faster Child Adoptions

TL;DR

This paper introduces a dynamic matching framework for child adoptions that incentivizes faster matches, improves adoption rates by at least 25%, and is robust to prediction errors, addressing limitations of existing methods.

Contribution

It proposes novel mechanisms for foster care matching that promote expedient, fair, and strategy-proof matches without costly preference elicitation, outperforming naive extensions of existing algorithms.

Findings

Mechanisms could increase adoptions by at least 25%

Proposed methods are robust to prediction errors

Naive dynamic Deferred Acceptance does not achieve these benefits

Abstract

Caseworkers in foster care systems match waiting children to adoptive homes. We use dynamic matching market design to characterize a class of mechanisms that incentivize expedient matches that homes can accept or decline. We design mechanisms satisfying fairness and limited strategy-proofness. They also avoid costly patience. Our empirically-based simulations suggest the mechanisms could increase adoptions by at least 25% versus the status quo. A naive dynamic extension of Deferred Acceptance does not attain these benefits. Our mechanisms sidestep direct preference elicitation by predicting preferences, and they are robust to prediction error.