The Value of Prediction in Identifying the Worst-Off

• URL: http://arxiv.org/abs/2501.19334v2
• Date: Thu, 13 Feb 2025 09:47:38 GMT
• Title: The Value of Prediction in Identifying the Worst-Off
• Authors: Unai Fischer-Abaigar, Christoph Kern, Juan Carlos Perdomo,
• Abstract summary: Machine learning is increasingly used in government programs to identify and support the most vulnerable individuals. This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers.
• Score: 3.468330970960535
• License:
• Abstract: Machine learning is increasingly used in government programs to identify and support the most vulnerable individuals, prioritizing assistance for those at greatest risk over optimizing aggregate outcomes. This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity. Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst-off. Our findings provide clear analytical frameworks and practical, data-driven tools that empower policymakers to make principled decisions when designing these systems.

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