Predictive Risk Scores in the Public Sector: Experimental Evidence from Child-Protection Investigations
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.
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Copy CitationE. Jason Baron, Arkadev Ghosh, and Richard Lombardo, "Predictive Risk Scores in the Public Sector: Experimental Evidence from Child-Protection Investigations," NBER Working Paper 35540 (2026), https://doi.org/10.3386/w35540.Download Citation
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