Social Insurance with Imperfect Eligibility Screening: Theory and Evidence from Pandemic UI
NBER Working Papers · 2026 · Adam Isen、Elira Kuka、Bryan A. Stuart
中文摘要
本文研究资格审查不完善条件下的社会保险，重点关注2020年和2021年扩张期间的失业保险（UI）。我们利用行政税收数据和失业保险政策识别异常支付，以研究审查不完善的程度，发现可能存在不当支付的款项达2140亿美元，且主要集中于疫情失业援助（PUA）计划；其中约一半本可通过改善联邦与州之间的数据共享事先识别。此类支付存在显著的地区差异，边界设计表明，这种差异部分源于各州作出的政策决策。为评估其对最优政策的启示，我们首先开展模拟，以家计调查型一次性转移支付取代PUA，结果发现，此类转移支付本可以更低的行政成本更好地保障收入损失。其次，我们构建了一个选择加入式转移支付与自动转移支付的模型，表明当不符合资格的领取者通过福利资格审查时，选择加入式项目在精准覆盖方面的优势可能发生逆转。以2020年失业保险数据校准后，模型表明，转向自动转移支付本可以提高社会福利。
Abstract
This paper studies social insurance with imperfect eligibility screening, focusing on Unemployment Insurance (UI) during its expansion in 2020 and 2021. We study the extent of imperfect screening by identifying anomalous payments using administrative tax data and UI policies, finding $214 billion in potentially-improper payments—concentrated in the Pandemic Unemployment Assistance (PUA) program—with approximately half detectable ex-ante through improved federal-state data sharing. There is substantial geographic variation, and a border design shows this is partly due to policy decisions made by states. To assess the implications for optimal policy, we first conduct simulations that replace PUA with means-tested, lump-sum transfers, finding that these transfers would have better insured against income losses at lower administrative cost. Second, we develop a model of opt-in versus automatic transfers that shows the targeting advantage of opt-in programs can reverse when ineligible recipients pass the benefit screen. Calibrated to 2020 UI, the model implies that shifting toward automatic transfers would have increased social welfare.