政策收益还是家庭需求？中国低保项目的分配逻辑

中文摘要 AI 辅助

当社会救助资源稀缺时，应优先分配给需求最迫切的家庭，还是预期受益最大的家庭？利用中国家庭金融调查的面板数据，我们通过将预测的政策收益与进入中国最低生活保障（低保）相结合，来区分分配原则。我们估计了消费和教育方面异质性的预测收益，然后恢复了受助者选择所揭示的条件优先级。预测收益对分配的贡献甚微：沙普利分解将分配拟合改进的96.9%归因于家庭优先级，仅3.1%归因于预测收益。在支持的结果价值规格中，较低的收入、户主较低的教育水平以及家中老年人存在持续预测更高的优先级。在保持地方项目容量固定、去除家庭优先级差异同时保留完整模型估计的结果价值的情况下，所得排名与受助者的重叠率仅为10.9%，意味着89.1%的受助者更替。这些发现表明，低保分配与贫困和脆弱性相关的家庭状况更为紧密对齐，而非从我们数据中可观察到的结果和信息可预测的政策收益，这凸显了社会救助中分配性瞄准与影响性瞄准之间的区别。

英文摘要

When social assistance is scarce, should it prioritize households in greatest need or those expected to benefit most? Using panel data from the China Household Finance Survey, we distinguish allocation principles by combining predicted policy gains with entry into China's Minimum Living Standard Guarantee (Dibao). We estimate heterogeneous predicted gains in consumption and education and then recover the conditional priorities revealed by recipient selection. Predicted gains explain little of allocation: a Shapley decomposition attributes 96.9\% of the improvement in allocation fit to household priorities and 3.1\% to predicted gains. Lower income, lower education of the household head, and elderly presence consistently predict higher priority across supported outcome-value specifications. Holding local program capacity fixed and removing household-priority differences while retaining the full model's estimated outcome values, the resulting ranking overlaps with recipients by only 10.9\%, implying 89.1\% recipient churn. These findings show that Dibao allocation is more closely aligned with household circumstances associated with poverty and vulnerability than with policy gains predictable from the outcomes and information observed in our data, underscoring the distinction between distributional and impact targeting in social assistance.

发表机构

• University of California, Riverside（加州大学河滨分校）

机构由 AI 辅助整理，请以论文原文为准。