Search and Matching for Adoption from Foster Care

A team of researchers from multiple institutions has published a groundbreaking study applying game theory and matching algorithms to the critical problem of foster care adoption. Their paper, "Search and Matching for Adoption from Foster Care," introduces a novel model that compares two dominant approaches used by U.S. child welfare agencies: family-driven search (where prospective families respond to agency announcements) versus caseworker-driven search (where caseworkers proactively contact families recommended for specific children). The research demonstrates that under their game-theoretical framework, no family-driven equilibrium can Pareto dominate any caseworker-driven outcome, while caseworker-driven equilibria can dominate all family-driven ones.

The team's empirical analysis of an agency that switched to caseworker-driven search revealed dramatic improvements in outcomes. The agency achieved a 44.9% higher three-year adoption probability compared to statewide benchmarks, along with a statistically significant 54% higher adoption hazard rate. The model shows that when families are sufficiently impatient—a realistic assumption in adoption scenarios—caseworker-driven search benefits all children. Numerical simulations further indicate that most agents (children and families) are better off under caseworker-driven search across a wide range of parameter values, suggesting this approach could help find homes for the more than 70,000 U.S. children awaiting adoption.