Predictive Risk Scores in the Public Sector: Experimental Evidence from Child-Protection Investigations

E. Jason Baron, Arkadev Ghosh and Richard Lombardo

No 35540, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Many public-sector decisions require allocating scarce attention under uncertainty. We examine whether algorithmic risk assessments improve child-protection decisions, where supervisors decide which cases need closer scrutiny. In a randomized evaluation of 4,752 child referrals over 14 months in Northampton County, supervisors received an algorithmic risk score alongside standard case records. Access to the score increased foster-care placements and services for children at highest predicted risk, with little change for lower-risk cases, and it reduced subsequent maltreatment referrals. We find no evidence that the score widened racial disparities in decisions or outcomes, suggesting algorithms can improve targeting while preserving human discretion.

JEL-codes: C93 I38 J13 (search for similar items in EconPapers)
Date: 2026-07
Note: CH LS PE
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