Institutions increasingly use prediction to allocate scarce resources. From a design perspective, better predictions compete with other investments, such as expanding capacity or improving treatment quality. Here, the big question is not how to solve a specific allocation problem, but rather which problem to solve. In this work, we develop an empirical toolkit to help planners form principled answers to this question and quantify the bottom-line welfare impact of investments in prediction versus other policy levers such as expanding capacity and improving treatment quality. Applying our framework in two real-world case studies on German employment services and poverty targeting in Ethiopia, we illustrate how decision-makers can reliably derive context-specific conclusions about the relative value of prediction in their allocation problem. We make our software toolkit, rvp, and parts of our data available in order to enable future empirical work in this area.

翻译：机构日益依赖预测来分配稀缺资源。从设计视角看，改进预测能力需与其他投资（如扩大容量或提升处理质量）进行权衡。此时的核心问题并非如何解决特定分配问题，而是应优先解决哪类问题。本研究开发了一套实证工具包，帮助规划者对此问题形成原则性答案，并量化预测投资相较于扩大容量、提升处理质量等其他政策杠杆对福利终值的实际影响。通过将本框架应用于德国就业服务与埃塞俄比亚贫困瞄准两项现实案例研究，我们展示了决策者如何在其分配问题中可靠地得出针对具体情境的预测相对价值结论。为促进该领域未来的实证研究，我们公开了软件工具包 rvp 及部分数据。